From 5789a9964a47b0a9758e5b66ee58265247654250 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 10 Feb 2026 16:01:23 +0100
Subject: [PATCH 001/154] draft MVP

---
 darts/utils/mlflow.py | 804 ++++++++++++++++++++++++++++++++++++++++++
 darts/utils/utils.py  |   7 +
 pyproject.toml        |   1 +
 3 files changed, 812 insertions(+)
 create mode 100644 darts/utils/mlflow.py

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
new file mode 100644
index 0000000000..c15846fc91
--- /dev/null
+++ b/darts/utils/mlflow.py
@@ -0,0 +1,804 @@
+"""
+MLflow Integration for Darts
+-----------------------------
+
+Custom MLflow model flavor for darts forecasting models. Supports saving, loading,
+logging and autolog for any darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
+to MLflow, including a pyfunc wrapper for MLflow's generic inference API.
+"""
+
+import importlib
+import os
+import sys
+import tempfile
+from functools import wraps
+from typing import Any, Optional, Union
+
+import mlflow
+import pandas as pd
+import yaml
+from mlflow.models import Model
+from mlflow.tracking.artifact_utils import _download_artifact_from_uri
+
+import darts
+from darts.logging import get_logger
+from darts.models.forecasting.forecasting_model import ForecastingModel
+from darts.utils.utils import PL_AVAILABLE
+
+logger = get_logger(__name__)
+
+FLAVOR_NAME = "darts"
+_MODEL_DATA_SUBFOLDER = "data"
+
+
+class _ModelLogInfo:
+    """Lightweight container returned by :func:`log_model` with essential metadata.
+
+    Attributes
+    ----------
+    model_uri : str
+        The full MLflow model URI (e.g., "runs:/run_id/model").
+    run_id : str
+        The MLflow run ID that logged the model.
+    artifact_path : str
+        The artifact path where the model was logged within the run.
+    """
+
+    def __init__(self, model_uri: str, run_id: str, artifact_path: str):
+        self.model_uri = model_uri
+        self.run_id = run_id
+        self.artifact_path = artifact_path
+
+
+_MODEL_FILE_STAT = "model.pkl"
+_MODEL_FILE_TORCH = "model.pt"
+_MODEL_FILE_TORCH_CKPT = "model.pt.ckpt"
+
+
+def _is_torch_model(model) -> bool:
+    """Check if a model is a TorchForecastingModel.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance.
+
+    Returns
+    -------
+    bool
+        True if the model is a TorchForecastingModel, False otherwise.
+    """
+    try:
+        from darts.models.forecasting.torch_forecasting_model import (
+            TorchForecastingModel,
+        )
+
+        return isinstance(model, TorchForecastingModel)
+    except ImportError:
+        return False
+
+
+def _get_model_class_path(model) -> tuple[str, str]:
+    """Extract the module path and class name from a model instance.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance.
+
+    Returns
+    -------
+    tuple[str, str]
+        A tuple containing (module_path, class_name).
+    """
+    cls = type(model)
+    return cls.__module__, cls.__name__
+
+
+def _import_model_class(module_path: str, class_name: str):
+    """Dynamically import and return a model class.
+
+    Parameters
+    ----------
+    module_path : str
+        The fully qualified module path (e.g., "darts.models.exponential_smoothing").
+    class_name : str
+        The name of the class to import (e.g., "ExponentialSmoothing").
+
+    Returns
+    -------
+    type
+        The imported model class.
+    """
+    module = importlib.import_module(module_path)
+    return getattr(module, class_name)
+
+
+def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
+    """Return the default pip requirements for logging a darts model.
+
+    Parameters
+    ----------
+    is_torch
+        Whether the model is a PyTorch-based model. If ``True``, adds
+        ``torch`` and ``pytorch-lightning`` to the requirements.
+
+    Returns
+    -------
+    list[str]
+        A list of pip requirement strings.
+    """
+    reqs = [f"darts=={darts.__version__}"]
+    if is_torch:
+        reqs.extend(["torch>=2.0.0", "pytorch-lightning>=2.0.0"])
+    return reqs
+
+
+def get_default_conda_env(is_torch: bool = False) -> dict:
+    """Return a default conda environment dict for a darts model.
+
+    Parameters
+    ----------
+    is_torch
+        Whether the model is a PyTorch-based model.
+
+    Returns
+    -------
+    dict
+        A conda environment specification dictionary.
+    """
+    return {
+        "channels": ["defaults", "conda-forge"],
+        "dependencies": [
+            "python",
+            "pip",
+            {
+                "pip": get_default_pip_requirements(is_torch=is_torch),
+            },
+        ],
+        "name": "darts_env",
+    }
+
+
+def save_model(
+    model,
+    path: str,
+    conda_env: Optional[Union[dict, str]] = None,
+    pip_requirements: Optional[list[str]] = None,
+    signature=None,
+    input_example=None,
+    metadata: Optional[dict] = None,
+) -> None:
+    """Save a darts forecasting model in MLflow format.
+
+    Produces an MLflow model directory at ``path`` containing:
+
+    * The serialised darts model (delegated to the model's own ``save()`` method).
+    * An ``MLmodel`` YAML file with flavor metadata.
+    * ``conda.yaml`` and ``requirements.txt`` environment files.
+
+    Parameters
+    ----------
+    model
+        A fitted darts ``ForecastingModel`` instance.
+    path
+        Local filesystem path where the model directory will be created.
+    conda_env
+        A conda environment specification (dict or path to a ``conda.yaml``).
+        If ``None``, a default environment is generated.
+    pip_requirements
+        A list of pip requirement strings. Overrides ``conda_env`` pip section
+        when provided.
+    signature
+        An ``mlflow.models.ModelSignature`` instance describing model input/output.
+    input_example
+        An example input for the model (used by MLflow UI).
+    metadata
+        Optional dictionary of custom metadata to store in the ``MLmodel`` file.
+    """
+    data_dir = os.path.join(path, _MODEL_DATA_SUBFOLDER)
+
+    is_torch = _is_torch_model(model)
+
+    os.makedirs(path, exist_ok=True)
+    os.makedirs(data_dir, exist_ok=True)
+
+    if is_torch:
+        model_file = _MODEL_FILE_TORCH
+        model.save(os.path.join(data_dir, model_file))
+    else:
+        model_file = _MODEL_FILE_STAT
+        model.save(os.path.join(data_dir, model_file))
+
+    module_path, class_name = _get_model_class_path(model)
+
+    darts_flavor_conf = {
+        "darts_version": darts.__version__,
+        "model_class_module": module_path,
+        "model_class_name": class_name,
+        "model_file": model_file,
+        "is_torch_model": is_torch,
+        "data": _MODEL_DATA_SUBFOLDER,
+    }
+
+    if pip_requirements is not None:
+        pip_reqs = pip_requirements
+    else:
+        pip_reqs = get_default_pip_requirements(is_torch=is_torch)
+
+    if conda_env is not None:
+        if isinstance(conda_env, str):
+            with open(conda_env) as f:
+                conda_env_dict = yaml.safe_load(f)
+        else:
+            conda_env_dict = conda_env
+    else:
+        conda_env_dict = get_default_conda_env(is_torch=is_torch)
+
+    conda_path = os.path.join(path, "conda.yaml")
+    with open(conda_path, "w") as f:
+        yaml.dump(conda_env_dict, f, default_flow_style=False)
+
+    reqs_path = os.path.join(path, "requirements.txt")
+    with open(reqs_path, "w") as f:
+        f.write("\n".join(pip_reqs) + "\n")
+
+    python_env_path = os.path.join(path, "python_env.yaml")
+    python_env = {
+        "python": f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}",
+        "build_dependencies": ["pip"],
+        "dependencies": ["requirements.txt"],
+    }
+    with open(python_env_path, "w") as f:
+        yaml.dump(python_env, f, default_flow_style=False)
+    pyfunc_conf = {
+        "loader_module": "darts.utils.mlflow",
+        "python_model": None,
+        "data": _MODEL_DATA_SUBFOLDER,
+        "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"},
+    }
+
+    mlmodel = Model()
+    mlmodel.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
+    mlmodel.add_flavor("python_function", **pyfunc_conf)
+
+    if signature is not None:
+        mlmodel.signature = signature
+
+    if input_example is not None:
+        mlmodel.input_example = input_example
+
+    if metadata is not None:
+        mlmodel.metadata = metadata
+
+    mlmodel.save(os.path.join(path, "MLmodel"))
+
+
+def load_model(
+    model_uri: str,
+    dst_path: Optional[str] = None,
+    **kwargs,
+):
+    """Load a darts model from an MLflow model URI.
+
+    Parameters
+    ----------
+    model_uri
+        An MLflow model URI, e.g. ``"runs:/<run_id>/model"``,
+        ``"models:/<name>/<version>"``, or a local ``file:///...`` path.
+    dst_path
+        Optional local path for downloading remote artifacts.
+    **kwargs
+        Additional keyword arguments forwarded to the model's ``load()`` method
+        (e.g. ``map_location`` for Torch models).
+
+    Returns
+    -------
+    ForecastingModel
+        The loaded darts forecasting model.
+    """
+    local_path = _download_artifact_from_uri(
+        artifact_uri=model_uri, output_path=dst_path
+    )
+    mlmodel_path = os.path.join(local_path, "MLmodel")
+    mlmodel = Model.load(mlmodel_path)
+
+    if FLAVOR_NAME not in mlmodel.flavors:
+        raise ValueError(
+            f"The MLflow model at '{model_uri}' does not have a '{FLAVOR_NAME}' flavor. "
+            f"Available flavors: {list(mlmodel.flavors.keys())}"
+        )
+
+    flavor_conf = mlmodel.flavors[FLAVOR_NAME]
+    module_path = flavor_conf["model_class_module"]
+    class_name = flavor_conf["model_class_name"]
+    model_file = flavor_conf["model_file"]
+    is_torch = flavor_conf.get("is_torch_model", False)
+
+    model_cls = _import_model_class(module_path, class_name)
+
+    data_dir = os.path.join(local_path, flavor_conf.get("data", _MODEL_DATA_SUBFOLDER))
+    model_path = os.path.join(data_dir, model_file)
+
+    if is_torch:
+        loaded_model = model_cls.load(model_path, **kwargs)
+    else:
+        loaded_model = model_cls.load(model_path)
+
+    return loaded_model
+
+
+def log_model(
+    model,
+    artifact_path: Optional[str] = None,
+    name: Optional[str] = None,
+    registered_model_name: Optional[str] = None,
+    conda_env: Optional[Union[dict, str]] = None,
+    pip_requirements: Optional[list[str]] = None,
+    signature=None,
+    input_example=None,
+    metadata: Optional[dict] = None,
+    log_params: bool = True,
+):
+    """Log a darts model to the current MLflow run.
+
+    Parameters
+    ----------
+    model
+        A fitted darts ``ForecastingModel`` instance.
+    artifact_path
+        The run-relative artifact path under which to log the model.
+        Defaults to ``"model"``. Deprecated in favour of ``name``.
+    name
+        The name for the model artifact. If provided, takes precedence over
+        ``artifact_path``.
+    registered_model_name
+        If provided, the model is registered in the MLflow Model Registry
+        under this name.
+    conda_env
+        Conda environment specification (dict or path).
+    pip_requirements
+        Pip requirements list.
+    signature
+        An ``mlflow.models.ModelSignature``.
+    input_example
+        An example model input.
+    metadata
+        Optional dict of custom metadata.
+    log_params
+        If ``True`` (default), log the model's creation parameters via
+        ``mlflow.log_params()``.
+
+    Returns
+    -------
+    _ModelLogInfo
+        A lightweight object with ``model_uri``, ``run_id``, and
+        ``artifact_path`` attributes.
+    """
+    artifact_name = name or artifact_path or "model"
+
+    if log_params:
+        _log_model_params(model)
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        model_dir = os.path.join(tmp_dir, artifact_name)
+        save_model(
+            model=model,
+            path=model_dir,
+            conda_env=conda_env,
+            pip_requirements=pip_requirements,
+            signature=signature,
+            input_example=input_example,
+            metadata=metadata,
+        )
+        mlflow.log_artifacts(model_dir, artifact_path=artifact_name)
+
+    run_id = mlflow.active_run().info.run_id
+    model_uri = f"runs:/{run_id}/{artifact_name}"
+
+    if registered_model_name is not None:
+        mlflow.register_model(model_uri, registered_model_name)
+
+    return _ModelLogInfo(
+        model_uri=model_uri,
+        run_id=run_id,
+        artifact_path=artifact_name,
+    )
+
+
+def _log_model_params(model) -> None:
+    """Log model creation parameters to MLflow.
+
+    Extracts model parameters from ``model.model_params`` and logs them to the active
+    MLflow run. Logs non-serializable values as "<non-serialisable>" and truncates long parameter values
+    to fit MLflow's 500-character limit.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance with a ``model_params`` attribute.
+    """
+    try:
+        params = model.model_params
+    except AttributeError:
+        logger.debug("Model has no model_params attribute; skipping parameter logging.")
+        return
+
+    safe_params = {}
+    for key, value in params.items():
+        try:
+            str_val = str(value)
+            if len(str_val) > 500:  # MLflow param value limit
+                str_val = str_val[:497] + "..."
+            safe_params[key] = str_val
+        except Exception:
+            safe_params[key] = "<non-serialisable>"
+
+    if safe_params:
+        mlflow.log_params(safe_params)
+
+
+# ---------------------------------------------------------------------------
+# PyFunc interface
+# ---------------------------------------------------------------------------
+
+
+class _DartsModelWrapper:
+    """A pyfunc-compatible wrapper around a darts ``ForecastingModel``.
+
+    MLflow's generic pyfunc inference API requires a ``predict(model_input)``
+    method that accepts a ``pandas.DataFrame``.  This wrapper translates that
+    call into a darts ``model.predict(n=...)`` call and returns the forecast
+    as a ``pandas.DataFrame``.
+
+    The forecast horizon ``n`` is determined from (in order of precedence):
+
+    1. A key ``"n"`` in the ``params`` dict, or
+    2. A column named ``"n"`` in ``model_input`` (first value is used), or
+    3. A default of ``1``.
+
+    Parameters
+    ----------
+    model : ForecastingModel
+        A fitted Darts forecasting model instance.
+
+    Attributes
+    ----------
+    model : ForecastingModel
+        The wrapped Darts forecasting model.
+    """
+
+    def __init__(self, model):
+        self.model = model
+
+    def predict(
+        self,
+        model_input: pd.DataFrame,
+        params: Optional[dict[str, Any]] = None,
+    ) -> pd.DataFrame:
+        """Generate forecasts.
+
+        Parameters
+        ----------
+        model_input
+            A ``pandas.DataFrame``. If it contains a column ``"n"``, the first
+            value is used as the forecast horizon.  Additional columns
+            (``"num_samples"``) are forwarded if present.
+        params
+            Optional dict; ``params["n"]`` overrides the horizon from the
+            DataFrame.
+
+        Returns
+        -------
+        pd.DataFrame
+            Forecasted values with a ``DatetimeIndex`` (or ``RangeIndex``)
+            and one column per component of the target series.
+        """
+        n = 1
+        if params and "n" in params:
+            n = int(params["n"])
+        elif "n" in model_input.columns:
+            n = int(model_input["n"].iloc[0])
+
+        num_samples = 1
+        if params and "num_samples" in params:
+            num_samples = int(params["num_samples"])
+        elif "num_samples" in model_input.columns:
+            num_samples = int(model_input["num_samples"].iloc[0])
+
+        prediction = self.model.predict(n=n, num_samples=num_samples)
+        return prediction.to_dataframe()
+
+
+def _load_pyfunc(path: str):
+    """Load a darts model as a pyfunc-compatible wrapper.
+
+    This function is called by ``mlflow.pyfunc.load_model()`` when loading a model
+    saved with the darts flavor. It reconstructs the original Darts model and wraps
+    it in a ``_DartsModelWrapper`` for MLflow's generic inference API.
+
+    Parameters
+    ----------
+    path : str
+        Path to the model directory containing the MLmodel file.
+
+    Returns
+    -------
+    _DartsModelWrapper
+        A wrapper with a ``predict()`` method compatible with MLflow's
+        pyfunc inference API.
+
+    Raises
+    ------
+    FileNotFoundError
+        If the MLmodel file cannot be found at the specified path.
+    """
+    mlmodel_path = os.path.join(path, "..", "MLmodel")
+    if os.path.exists(mlmodel_path):
+        parent_path = os.path.dirname(os.path.abspath(mlmodel_path))
+    else:
+        parent_path = path
+
+    mlmodel_file = os.path.join(parent_path, "MLmodel")
+    if not os.path.exists(mlmodel_file):
+        raise FileNotFoundError(
+            f"Cannot find MLmodel file. Searched at: {mlmodel_file}"
+        )
+
+    with open(mlmodel_file) as f:
+        mlmodel_dict = yaml.safe_load(f)
+
+    flavor_conf = mlmodel_dict["flavors"][FLAVOR_NAME]
+    module_path = flavor_conf["model_class_module"]
+    class_name = flavor_conf["model_class_name"]
+    model_file = flavor_conf["model_file"]
+    is_torch = flavor_conf.get("is_torch_model", False)
+
+    model_cls = _import_model_class(module_path, class_name)
+
+    data_dir = os.path.join(parent_path, flavor_conf.get("data", _MODEL_DATA_SUBFOLDER))
+    model_path = os.path.join(data_dir, model_file)
+
+    if is_torch:
+        loaded_model = model_cls.load(model_path)
+    else:
+        loaded_model = model_cls.load(model_path)
+
+    return _DartsModelWrapper(loaded_model)
+
+
+# stores original (unpatched) `fit` methods so they can be restored.
+_ORIGINAL_FIT_METHODS: dict[type, Any] = {}
+
+if PL_AVAILABLE:
+    import pytorch_lightning as pl
+
+    class _DartsMlflowCallback(pl.Callback):
+        """PyTorch Lightning callback that logs epoch-level metrics to MLflow.
+
+        This callback automatically logs training and validation metrics (such as
+        loss values) to the active MLflow run at the end of each epoch.
+
+        Notes
+        -----
+        The callback is automatically injected into TorchForecastingModel instances
+        when ``autolog()`` is enabled with PyTorch-based models.
+        """
+
+        def on_train_epoch_end(self, trainer, pl_module):
+            """Log training metrics at the end of each training epoch."""
+            self._log_epoch_metrics(trainer)
+
+        def on_validation_epoch_end(self, trainer, pl_module):
+            """Log validation metrics at the end of each validation epoch."""
+            self._log_epoch_metrics(trainer)
+
+        def _log_epoch_metrics(self, trainer) -> None:
+            """Extract and log metrics from the trainer to MLflow.
+
+            Parameters
+            ----------
+            trainer
+                PyTorch Lightning Trainer instance containing metrics.
+            """
+            if mlflow.active_run() is None:
+                return
+
+            epoch = trainer.current_epoch
+            metrics: dict[str, float] = {}
+
+            for source in (trainer.callback_metrics, trainer.logged_metrics):
+                for key, value in source.items():
+                    try:
+                        metrics[key] = float(value)
+                    except (TypeError, ValueError):
+                        pass
+
+            if metrics:
+                mlflow.log_metrics(metrics, step=epoch)
+
+else:
+    _DartsMlflowCallback = None
+
+
+def _get_mlflow_callback():
+    """Create and return a ``_DartsMlflowCallback`` instance.
+
+    Returns
+    -------
+    _DartsMlflowCallback or None
+        A callback instance if PyTorch Lightning is available, None otherwise.
+    """
+    if not PL_AVAILABLE:
+        return None
+
+    return _DartsMlflowCallback()
+
+
+def _inject_mlflow_callback(model) -> None:
+    """Inject the MLflow callback into a ``TorchForecastingModel``'s trainer params.
+
+    Adds a ``_DartsMlflowCallback`` to the model's PyTorch Lightning trainer callbacks
+    if not already present. This enables automatic logging of training metrics to MLflow.
+
+    Parameters
+    ----------
+    model
+        A TorchForecastingModel instance with a ``trainer_params`` attribute.
+
+    Notes
+    -----
+    This is a no-op if the callback is already present or PyTorch Lightning is unavailable.
+    """
+    callback = _get_mlflow_callback()
+    if callback is None:
+        return
+
+    if not hasattr(model, "trainer_params"):
+        return
+
+    existing_callbacks = model.trainer_params.get("callbacks", [])
+
+    cb_type = type(callback)
+    for existing in existing_callbacks:
+        if type(existing).__name__ == cb_type.__name__:
+            return
+
+    existing_callbacks.append(callback)
+    model.trainer_params["callbacks"] = existing_callbacks
+
+
+def autolog(
+    log_models: bool = True,
+    log_params: bool = True,
+    disable: bool = False,
+) -> None:
+    """Enable (or disable) automatic MLflow logging for darts models.
+
+    When enabled, every call to ``model.fit()`` on any darts forecasting model
+    will automatically:
+
+    1. Start an MLflow run (or reuse the currently active one).
+    2. Log model creation parameters (``model.model_params``).
+    3. For PyTorch-based models: inject a callback that logs per-epoch
+       ``train_loss`` / ``val_loss`` metrics.
+    4. Log the trained model artifact at the end of training.
+
+    Parameters
+    ----------
+    log_models
+        If ``True`` (default), log the trained model artifact after ``fit()``.
+    log_params
+        If ``True`` (default), log model creation parameters.
+    disable
+        If ``True``, restore the original ``fit()`` methods and stop
+        autologging.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        from darts.utils.mlflow import autolog
+        from darts.models import ExponentialSmoothing
+        from darts.datasets import AirPassengersDataset
+
+        autolog()  # enable
+
+        series = AirPassengersDataset().load()
+        model = ExponentialSmoothing()
+        model.fit(series)  # automatically logged to MLflow
+
+        autolog(disable=True)  # disable
+    """
+    if disable:
+        _restore_original_fit_methods()
+        return
+
+    _patch_fit(ForecastingModel, log_models=log_models, log_params=log_params)
+
+    try:
+        from darts.models.forecasting.torch_forecasting_model import (
+            TorchForecastingModel,
+        )
+
+        _patch_fit(
+            TorchForecastingModel,
+            log_models=log_models,
+            log_params=log_params,
+            inject_callback=True,
+        )
+    except ImportError:
+        pass
+
+
+def _patch_fit(
+    cls,
+    *,
+    log_models: bool,
+    log_params: bool,
+    inject_callback: bool = False,
+) -> None:
+    """Replace a model class's ``fit`` method with an MLflow logging wrapper.
+
+    Parameters
+    ----------
+    cls : type
+        The model class to patch (e.g., ForecastingModel, TorchForecastingModel).
+    log_models : bool
+        Whether to log the trained model artifact after fitting.
+    log_params : bool
+        Whether to log model creation parameters.
+    inject_callback : bool, optional
+        Whether to inject the MLflow callback for PyTorch Lightning models.
+        Default is False.
+    """
+    if cls in _ORIGINAL_FIT_METHODS:
+        return
+
+    original_fit = cls.fit
+    _ORIGINAL_FIT_METHODS[cls] = original_fit
+
+    @wraps(original_fit)
+    def _patched_fit(self, *args, **kwargs):
+        run_started_here = False
+        if mlflow.active_run() is None:
+            mlflow.start_run()
+            run_started_here = True
+
+        try:
+            mlflow.set_tag("darts.model_class", type(self).__name__)
+
+            if log_params:
+                _log_model_params(self)
+
+            if inject_callback:
+                _inject_mlflow_callback(self)
+
+            result = original_fit(self, *args, **kwargs)
+
+            if log_models:
+                try:
+                    log_model(self, name="model", log_params=False)
+                except Exception as e:
+                    logger.warning(f"Failed to autolog model artifact: {e}")
+
+            return result
+
+        except Exception:
+            raise
+        finally:
+            if run_started_here:
+                mlflow.end_run()
+
+    cls.fit = _patched_fit
+
+
+def _restore_original_fit_methods() -> None:
+    """Restore all patched ``fit()`` methods to their original implementations.
+
+    This function is called when ``autolog(disable=True)`` is invoked to remove
+    all MLflow logging functionality added by autologging.
+    """
+    for cls, original_fit in _ORIGINAL_FIT_METHODS.items():
+        cls.fit = original_fit
+    _ORIGINAL_FIT_METHODS.clear()
diff --git a/darts/utils/utils.py b/darts/utils/utils.py
index 5e559cc9da..49749dbad0 100644
--- a/darts/utils/utils.py
+++ b/darts/utils/utils.py
@@ -37,6 +37,13 @@
 except ImportError:
     TORCH_AVAILABLE = False
 
+try:
+    import pytorch_lightning as pl  # noqa: F401
+
+    PL_AVAILABLE = True
+except ImportError:
+    PL_AVAILABLE = False
+
 logger = get_logger(__name__)
 
 MAX_TORCH_SEED_VALUE = (1 << 31) - 1  # to accommodate 32-bit architectures
diff --git a/pyproject.toml b/pyproject.toml
index b232c33668..c09ad39edc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -70,6 +70,7 @@ notorch = [
     "statsforecast>=1.4",
     "xgboost>=2.1.4",
 ]
+mlflow = ["mlflow>=2.0"]
 all = ["darts[torch,notorch]"]
 
 [tool.uv.sources]

From dc48b97a8d4c5bb79dcad0080bdcb4505808b9af Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 12 Feb 2026 11:46:30 +0100
Subject: [PATCH 002/154] covariate support

---
 darts/utils/mlflow.py | 78 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 78 insertions(+)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index c15846fc91..e20b15ebe3 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -8,6 +8,7 @@
 """
 
 import importlib
+import json
 import os
 import sys
 import tempfile
@@ -379,6 +380,7 @@ def log_model(
 
     if log_params:
         _log_model_params(model)
+        _log_covariate_info(model)
 
     with tempfile.TemporaryDirectory() as tmp_dir:
         model_dir = os.path.join(tmp_dir, artifact_name)
@@ -438,6 +440,79 @@ def _log_model_params(model) -> None:
         mlflow.log_params(safe_params)
 
 
+def _log_covariate_info(model) -> None:
+    """Log covariate usage information to MLflow.
+
+    Extracts information about past, future, and static covariates used during
+    training and logs them as tags, parameters, and a JSON artifact for easy
+    filtering, comparison, and documentation.
+
+    Logs three types of information:
+    - Tags: Boolean flags for filtering (e.g., "uses_past_covariates")
+    - Parameters: Feature counts and names (truncated to 500 chars)
+    - Artifact: Complete covariate metadata as JSON file
+
+    Parameters
+    ----------
+    model
+        A fitted Darts forecasting model instance.
+    """
+    covariate_types = [
+        (
+            "past_covariates",
+            "_uses_past_covariates",
+            "past_covariate_series",
+            "components",
+        ),
+        (
+            "future_covariates",
+            "_uses_future_covariates",
+            "future_covariate_series",
+            "components",
+        ),
+        (
+            "static_covariates",
+            "_uses_static_covariates",
+            "static_covariates",
+            "columns",
+        ),
+    ]
+
+    covariate_info = {}
+
+    for cov_key, uses_attr, series_attr, names_attr in covariate_types:
+        info = {"used": False, "count": 0, "names": []}
+
+        if getattr(model, uses_attr, False):
+            info["used"] = True
+            series = getattr(model, series_attr, None)
+            if series is not None:
+                names = getattr(series, names_attr).tolist()
+                info["names"] = names
+                info["count"] = len(names)
+
+        covariate_info[cov_key] = info
+
+        mlflow.set_tag(f"uses_{cov_key}", str(info["used"]).lower())
+        mlflow.log_param(f"n_{cov_key}", info["count"])
+
+        if info["names"]:
+            names_str = ",".join(info["names"])
+            if len(names_str) > 500:
+                names_str = names_str[:497] + "..."
+            mlflow.log_param(f"{cov_key.split('_')[0]}_cov_names", names_str)
+
+    # log complete information as JSON artifact
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
+        json.dump(covariate_info, f, indent=2)
+        temp_path = f.name
+
+    try:
+        mlflow.log_artifact(temp_path, "covariates.json")
+    finally:
+        os.remove(temp_path)
+
+
 # ---------------------------------------------------------------------------
 # PyFunc interface
 # ---------------------------------------------------------------------------
@@ -776,6 +851,9 @@ def _patched_fit(self, *args, **kwargs):
 
             result = original_fit(self, *args, **kwargs)
 
+            if log_params:
+                _log_covariate_info(self)
+
             if log_models:
                 try:
                     log_model(self, name="model", log_params=False)

From 0cc488e9989f8eecaadc10e16c2d9820ba568dfb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 12 Feb 2026 11:47:14 +0100
Subject: [PATCH 003/154] pyfunc series info extension

---
 darts/utils/mlflow.py | 219 +++++++++++++++++++++++++++++++++++++-----
 1 file changed, 194 insertions(+), 25 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index e20b15ebe3..6cdeb880e5 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -19,9 +19,11 @@
 import pandas as pd
 import yaml
 from mlflow.models import Model
+from mlflow.models import infer_signature as mlflow_infer_signature
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
 
 import darts
+from darts import TimeSeries
 from darts.logging import get_logger
 from darts.models.forecasting.forecasting_model import ForecastingModel
 from darts.utils.utils import PL_AVAILABLE
@@ -192,8 +194,10 @@ def save_model(
         when provided.
     signature
         An ``mlflow.models.ModelSignature`` instance describing model input/output.
+        Use :func:`infer_signature` to automatically generate from example inputs.
     input_example
-        An example input for the model (used by MLflow UI).
+        An example input for the model (used by MLflow UI). Should be a DataFrame
+        created with :func:`prepare_pyfunc_input`.
     metadata
         Optional dictionary of custom metadata to store in the ``MLmodel`` file.
     """
@@ -361,9 +365,11 @@ def log_model(
     pip_requirements
         Pip requirements list.
     signature
-        An ``mlflow.models.ModelSignature``.
+        An ``mlflow.models.ModelSignature``. Use :func:`infer_signature`
+        to automatically generate from example inputs.
     input_example
-        An example model input.
+        An example model input. Should be a DataFrame created with
+        :func:`prepare_pyfunc_input`.
     metadata
         Optional dict of custom metadata.
     log_params
@@ -518,19 +524,151 @@ def _log_covariate_info(model) -> None:
 # ---------------------------------------------------------------------------
 
 
-class _DartsModelWrapper:
-    """A pyfunc-compatible wrapper around a darts ``ForecastingModel``.
+def _serialize_timeseries_for_pyfunc(
+    ts: Union["TimeSeries", list["TimeSeries"]],
+) -> str:
+    """Serialize TimeSeries to JSON string for pyfunc.
+
+    Parameters
+    ----------
+    ts : Union[TimeSeries, list[TimeSeries]]
+        Single TimeSeries or list of TimeSeries objects.
+
+    Returns
+    -------
+    str
+        JSON string representation. For a single TimeSeries, returns a JSON object.
+        For a list, returns a JSON array.
+    """
+    if isinstance(ts, TimeSeries):
+        return ts.to_json()
+    elif isinstance(ts, (list, tuple)):
+        serialized = [t.to_json() for t in ts]
+        return "[" + ",".join(serialized) + "]"
+    else:
+        raise TypeError(f"Expected TimeSeries or list of TimeSeries, got {type(ts)}")
+
+
+def _deserialize_timeseries_from_pyfunc(
+    json_str: str,
+) -> Union["TimeSeries", list["TimeSeries"]]:
+    """Deserialize TimeSeries from JSON string.
+
+    Parameters
+    ----------
+    json_str : str
+        JSON string representation (single object or array).
 
-    MLflow's generic pyfunc inference API requires a ``predict(model_input)``
-    method that accepts a ``pandas.DataFrame``.  This wrapper translates that
-    call into a darts ``model.predict(n=...)`` call and returns the forecast
-    as a ``pandas.DataFrame``.
+    Returns
+    -------
+    Union[TimeSeries, list[TimeSeries]]
+        Single TimeSeries or list of TimeSeries objects.
+    """
+    json_str = json_str.strip()
+    if json_str.startswith("["):
+        array_data = json.loads(json_str)
+        return [TimeSeries.from_json(json.dumps(item)) for item in array_data]
+    else:
+        return TimeSeries.from_json(json_str)
 
-    The forecast horizon ``n`` is determined from (in order of precedence):
 
-    1. A key ``"n"`` in the ``params`` dict, or
-    2. A column named ``"n"`` in ``model_input`` (first value is used), or
-    3. A default of ``1``.
+def prepare_pyfunc_input(
+    n: int,
+    series: Optional[Union["TimeSeries", list["TimeSeries"]]] = None,
+    past_covariates: Optional[Union["TimeSeries", list["TimeSeries"]]] = None,
+    future_covariates: Optional[Union["TimeSeries", list["TimeSeries"]]] = None,
+    num_samples: int = 1,
+) -> pd.DataFrame:
+    """Prepare input DataFrame for MLflow pyfunc prediction.
+
+    Creates a DataFrame for use with models loaded via ``mlflow.pyfunc.load_model()``.
+    Serializes TimeSeries to JSON in special columns.
+
+    Parameters
+    ----------
+    n : int
+        Forecast horizon.
+    series : Optional[Union[TimeSeries, list[TimeSeries]]]
+        Target series for prediction (required for global models on new data).
+    past_covariates : Optional[Union[TimeSeries, list[TimeSeries]]]
+        Past covariates.
+    future_covariates : Optional[Union[TimeSeries, list[TimeSeries]]]
+        Future covariates.
+    num_samples : int
+        Number of samples for probabilistic models.
+
+    Returns
+    -------
+    pd.DataFrame
+        Input DataFrame for pyfunc ``predict()`` method.
+    """
+    data = {"n": [n], "num_samples": [num_samples]}
+
+    if series is not None:
+        data["_darts_series"] = [_serialize_timeseries_for_pyfunc(series)]
+
+        if past_covariates is not None:
+            data["_darts_past_covariates"] = [
+                _serialize_timeseries_for_pyfunc(past_covariates)
+            ]
+
+        if future_covariates is not None:
+            data["_darts_future_covariates"] = [
+                _serialize_timeseries_for_pyfunc(future_covariates)
+            ]
+
+    return pd.DataFrame(data)
+
+
+def infer_signature(
+    model,
+    series: Optional["TimeSeries"] = None,
+    past_covariates: Optional["TimeSeries"] = None,
+    future_covariates: Optional["TimeSeries"] = None,
+    n: int = 1,
+) -> "mlflow.models.ModelSignature":
+    """Infer MLflow ModelSignature from a darts model and example inputs.
+
+    Generates a signature describing input/output schemas for the pyfunc interface.
+    The ``_darts_series`` and covariate columns are marked as optional.
+
+    Parameters
+    ----------
+    model
+        A fitted darts ``ForecastingModel`` instance.
+    series : Optional[TimeSeries]
+        Example target series for signature inference.
+    past_covariates : Optional[TimeSeries]
+        Example past covariates.
+    future_covariates : Optional[TimeSeries]
+        Example future covariates.
+    n : int
+        Forecast horizon for generating example output.
+
+    Returns
+    -------
+    mlflow.models.ModelSignature
+        Signature describing the model's input/output interface.
+    """
+    input_example = prepare_pyfunc_input(
+        n=n,
+        series=series,
+        past_covariates=past_covariates,
+        future_covariates=future_covariates,
+        num_samples=1,
+    )
+
+    wrapper = _DartsModelWrapper(model)
+    output_example = wrapper.predict(input_example)
+
+    return mlflow_infer_signature(input_example, output_example)
+
+
+class _DartsModelWrapper:
+    """A pyfunc-compatible wrapper around a darts ``ForecastingModel``.
+
+    Deserializes TimeSeries from JSON-encoded columns and calls the model's
+    ``predict()`` method. Input should be created using :func:`prepare_pyfunc_input`.
 
     Parameters
     ----------
@@ -551,23 +689,23 @@ def predict(
         model_input: pd.DataFrame,
         params: Optional[dict[str, Any]] = None,
     ) -> pd.DataFrame:
-        """Generate forecasts.
+        """Generate forecasts from pyfunc input.
+
+        Input should be created using :func:`prepare_pyfunc_input`. Deserializes
+        TimeSeries from JSON columns and passes to model.predict().
 
         Parameters
         ----------
-        model_input
-            A ``pandas.DataFrame``. If it contains a column ``"n"``, the first
-            value is used as the forecast horizon.  Additional columns
-            (``"num_samples"``) are forwarded if present.
-        params
-            Optional dict; ``params["n"]`` overrides the horizon from the
-            DataFrame.
+        model_input : pd.DataFrame
+            Input DataFrame with columns: ``n``, ``num_samples``, and optionally
+            ``_darts_series``, ``_darts_past_covariates``, ``_darts_future_covariates``.
+        params : Optional[dict[str, Any]]
+            Override parameters (e.g., ``{"n": 20}``).
 
         Returns
         -------
         pd.DataFrame
-            Forecasted values with a ``DatetimeIndex`` (or ``RangeIndex``)
-            and one column per component of the target series.
+            Forecasted values. For multiple series, includes a ``series_id`` column.
         """
         n = 1
         if params and "n" in params:
@@ -581,8 +719,39 @@ def predict(
         elif "num_samples" in model_input.columns:
             num_samples = int(model_input["num_samples"].iloc[0])
 
-        prediction = self.model.predict(n=n, num_samples=num_samples)
-        return prediction.to_dataframe()
+        predict_kwargs = {"n": n, "num_samples": num_samples}
+
+        if "_darts_series" in model_input.columns:
+            series_json = model_input["_darts_series"].iloc[0]
+            if pd.notna(series_json) and series_json:
+                series = _deserialize_timeseries_from_pyfunc(series_json)
+                predict_kwargs["series"] = series
+
+        if "_darts_past_covariates" in model_input.columns:
+            past_cov_json = model_input["_darts_past_covariates"].iloc[0]
+            if pd.notna(past_cov_json) and past_cov_json:
+                predict_kwargs["past_covariates"] = _deserialize_timeseries_from_pyfunc(
+                    past_cov_json
+                )
+
+        if "_darts_future_covariates" in model_input.columns:
+            future_cov_json = model_input["_darts_future_covariates"].iloc[0]
+            if pd.notna(future_cov_json) and future_cov_json:
+                predict_kwargs["future_covariates"] = (
+                    _deserialize_timeseries_from_pyfunc(future_cov_json)
+                )
+
+        prediction = self.model.predict(**predict_kwargs)
+
+        if isinstance(prediction, TimeSeries):
+            return prediction.to_dataframe()
+        else:
+            dfs = []
+            for i, pred in enumerate(prediction):
+                df = pred.to_dataframe()
+                df["series_id"] = i
+                dfs.append(df)
+            return pd.concat(dfs, axis=0)
 
 
 def _load_pyfunc(path: str):

From b6ce5381e194e5ec527089af8df898fb772aa11f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 12 Feb 2026 13:52:54 +0100
Subject: [PATCH 004/154] unit tests

---
 darts/tests/optional_deps/test_mlflow.py | 607 +++++++++++++++++++++++
 1 file changed, 607 insertions(+)
 create mode 100644 darts/tests/optional_deps/test_mlflow.py

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
new file mode 100644
index 0000000000..05af8f7b88
--- /dev/null
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -0,0 +1,607 @@
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import darts.utils.timeseries_generation as tg
+from darts import TimeSeries
+from darts.tests.conftest import MLFLOW_AVAILABLE, TORCH_AVAILABLE, tfm_kwargs_dev
+from darts.utils.mlflow import _DartsModelWrapper
+
+if not MLFLOW_AVAILABLE:
+    pytest.skip(
+        f"MLflow not available. {__name__} tests will be skipped.",
+        allow_module_level=True,
+    )
+
+import mlflow
+
+from darts.models import ExponentialSmoothing, LinearRegressionModel
+from darts.utils.mlflow import (
+    _deserialize_timeseries_from_pyfunc,
+    _serialize_timeseries_for_pyfunc,
+    autolog,
+    infer_signature,
+    load_model,
+    log_model,
+    prepare_pyfunc_input,
+    save_model,
+)
+
+if TORCH_AVAILABLE:
+    from darts.models import NBEATSModel
+
+
+class TestMLflow:
+    ts_univariate = tg.linear_timeseries(
+        start_value=10, end_value=50, length=50
+    ).astype("float32")
+    ts_multivariate = ts_univariate.stack(ts_univariate * 1.5)
+    ts_with_static = ts_univariate.with_static_covariates(
+        pd.DataFrame({"static_feat": [1.0]})
+    )
+    ts_past_cov = tg.sine_timeseries(length=62).astype("float32")
+    ts_future_cov = tg.constant_timeseries(value=1.0, length=62).astype("float32")
+
+    def test_serialize_deserialize_single(self):
+        """Test serialization of single TimeSeries"""
+        json_str = _serialize_timeseries_for_pyfunc(self.ts_univariate)
+        assert isinstance(json_str, str)
+        assert json_str.startswith("{")
+
+        ts_restored = _deserialize_timeseries_from_pyfunc(json_str)
+        assert isinstance(ts_restored, TimeSeries)
+        np.testing.assert_array_almost_equal(
+            self.ts_univariate.values(), ts_restored.values(), decimal=6
+        )
+
+    def test_serialize_deserialize_list(self):
+        """Test serialization of list of TimeSeries"""
+        ts_list = [self.ts_univariate, self.ts_univariate * 2]
+
+        json_str = _serialize_timeseries_for_pyfunc(ts_list)
+        assert isinstance(json_str, str)
+        assert json_str.startswith("[")
+
+        ts_list_restored = _deserialize_timeseries_from_pyfunc(json_str)
+        assert isinstance(ts_list_restored, list)
+        assert len(ts_list_restored) == 2
+        np.testing.assert_array_almost_equal(
+            ts_list[0].values(), ts_list_restored[0].values(), decimal=6
+        )
+
+    def test_serialize_with_static_covariates(self):
+        """Test that static covariates are preserved through serialization"""
+        json_str = _serialize_timeseries_for_pyfunc(self.ts_with_static)
+        ts_restored = _deserialize_timeseries_from_pyfunc(json_str)
+
+        assert ts_restored.static_covariates is not None
+        pd.testing.assert_frame_equal(
+            self.ts_with_static.static_covariates,
+            ts_restored.static_covariates,
+            check_dtype=False,
+        )
+
+    def test_serialize_multivariate(self):
+        """Test serialization of multivariate TimeSeries"""
+        json_str = _serialize_timeseries_for_pyfunc(self.ts_multivariate)
+        ts_restored = _deserialize_timeseries_from_pyfunc(json_str)
+
+        assert ts_restored.n_components == 2
+        np.testing.assert_array_almost_equal(
+            self.ts_multivariate.values(), ts_restored.values(), decimal=6
+        )
+
+    def test_prepare_input_simple(self):
+        """Test prepare_pyfunc_input with only n parameter"""
+        input_df = prepare_pyfunc_input(n=10)
+
+        assert isinstance(input_df, pd.DataFrame)
+        assert list(input_df.columns) == ["n", "num_samples"]
+        assert input_df["n"].iloc[0] == 10
+        assert input_df["num_samples"].iloc[0] == 1
+
+    def test_prepare_input_with_series(self):
+        """Test prepare_pyfunc_input with series parameter"""
+        input_df = prepare_pyfunc_input(n=10, series=self.ts_univariate)
+
+        assert "_darts_series" in input_df.columns
+        json_str = input_df["_darts_series"].iloc[0]
+        assert json_str.startswith("{")
+
+        ts_restored = _deserialize_timeseries_from_pyfunc(json_str)
+        np.testing.assert_array_almost_equal(
+            self.ts_univariate.values(), ts_restored.values(), decimal=6
+        )
+
+    def test_prepare_input_with_covariates(self):
+        """Test prepare_pyfunc_input with all covariate types"""
+        input_df = prepare_pyfunc_input(
+            n=10,
+            series=self.ts_univariate,
+            past_covariates=self.ts_past_cov,
+            future_covariates=self.ts_future_cov,
+        )
+
+        assert "_darts_series" in input_df.columns
+        assert "_darts_past_covariates" in input_df.columns
+        assert "_darts_future_covariates" in input_df.columns
+
+        ts_restored = _deserialize_timeseries_from_pyfunc(
+            input_df["_darts_series"].iloc[0]
+        )
+        past_cov_restored = _deserialize_timeseries_from_pyfunc(
+            input_df["_darts_past_covariates"].iloc[0]
+        )
+        future_cov_restored = _deserialize_timeseries_from_pyfunc(
+            input_df["_darts_future_covariates"].iloc[0]
+        )
+
+        np.testing.assert_array_almost_equal(
+            self.ts_univariate.values(), ts_restored.values(), decimal=6
+        )
+        np.testing.assert_array_almost_equal(
+            self.ts_past_cov.values(), past_cov_restored.values(), decimal=6
+        )
+        np.testing.assert_array_almost_equal(
+            self.ts_future_cov.values(), future_cov_restored.values(), decimal=6
+        )
+
+    def test_prepare_input_list_series(self):
+        """Test prepare_pyfunc_input with list of series"""
+        series_list = [self.ts_univariate, self.ts_univariate * 2]
+        input_df = prepare_pyfunc_input(n=5, series=series_list)
+
+        assert "_darts_series" in input_df.columns
+        json_str = input_df["_darts_series"].iloc[0]
+        assert json_str.startswith("[")
+
+        ts_list_restored = _deserialize_timeseries_from_pyfunc(json_str)
+        assert isinstance(ts_list_restored, list)
+        assert len(ts_list_restored) == 2
+        np.testing.assert_array_almost_equal(
+            series_list[0].values(), ts_list_restored[0].values(), decimal=6
+        )
+        np.testing.assert_array_almost_equal(
+            series_list[1].values(), ts_list_restored[1].values(), decimal=6
+        )
+
+    def test_wrapper_simple_prediction(self):
+        """Test PyFunc wrapper with simple statistical model"""
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        wrapper = _DartsModelWrapper(model)
+        input_df = prepare_pyfunc_input(n=12)
+        output_df = wrapper.predict(input_df)
+
+        # Compare with direct model prediction
+        expected_df = model.predict(n=12).to_dataframe()
+
+        assert isinstance(output_df, pd.DataFrame)
+        assert len(output_df) == 12
+        assert list(output_df.columns) == list(expected_df.columns)
+        np.testing.assert_array_almost_equal(
+            output_df.values, expected_df.values, decimal=4
+        )
+
+    def test_wrapper_with_series(self):
+        """Test PyFunc wrapper with global model and series parameter"""
+        train, test = self.ts_univariate.split_before(0.7)
+
+        model = LinearRegressionModel(lags=5)
+        model.fit(train)
+
+        wrapper = _DartsModelWrapper(model)
+        input_df = prepare_pyfunc_input(n=5, series=test)
+        output_df = wrapper.predict(input_df)
+
+        # Compare with direct model prediction
+        expected_df = model.predict(n=5, series=test).to_dataframe()
+
+        assert len(output_df) == 5
+        assert list(output_df.columns) == list(expected_df.columns)
+        np.testing.assert_array_almost_equal(
+            output_df.values, expected_df.values, decimal=4
+        )
+
+    def test_wrapper_params_override(self):
+        """Test that params dict overrides DataFrame values"""
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        wrapper = _DartsModelWrapper(model)
+        input_df = prepare_pyfunc_input(n=5)
+
+        output_df = wrapper.predict(input_df, params={"n": 10})
+        assert len(output_df) == 10
+
+        # Verify the override actually produced the right prediction (n=10, not n=5)
+        expected_df = model.predict(n=10).to_dataframe()
+        np.testing.assert_array_almost_equal(
+            output_df.values, expected_df.values, decimal=4
+        )
+
+    def test_save_load_statistical_model(self, tmpdir_fn):
+        """Test save/load round-trip for statistical model"""
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path)
+        assert os.path.exists(os.path.join(model_path, "MLmodel"))
+
+        loaded_model = load_model(f"file://{model_path}")
+        pred_original = model.predict(n=5)
+        pred_loaded = loaded_model.predict(n=5)
+
+        np.testing.assert_array_almost_equal(
+            pred_original.values(), pred_loaded.values(), decimal=4
+        )
+
+    def test_save_load_regression_model(self, tmpdir_fn):
+        """Test save/load round-trip for regression model"""
+        model = LinearRegressionModel(lags=5)
+        model.fit(self.ts_univariate)
+
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path)
+
+        loaded_model = load_model(f"file://{model_path}")
+        pred_original = model.predict(n=3)
+        pred_loaded = loaded_model.predict(n=3)
+
+        np.testing.assert_array_almost_equal(
+            pred_original.values(), pred_loaded.values(), decimal=4
+        )
+
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_save_load_torch_model(self, tmpdir_fn):
+        """Test save/load round-trip for torch model"""
+        model = NBEATSModel(
+            input_chunk_length=4, output_chunk_length=2, n_epochs=1, **tfm_kwargs_dev
+        )
+        model.fit(self.ts_univariate)
+
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path)
+
+        loaded_model = load_model(f"file://{model_path}")
+        pred_original = model.predict(n=2)
+        pred_loaded = loaded_model.predict(n=2)
+
+        np.testing.assert_array_almost_equal(
+            pred_original.values(), pred_loaded.values(), decimal=4
+        )
+
+    def test_save_with_signature(self, tmpdir_fn):
+        """Test that signature is saved in MLmodel file"""
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        signature = infer_signature(model, n=5)
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path, signature=signature)
+
+        mlmodel = mlflow.models.Model.load(os.path.join(model_path, "MLmodel"))
+        loaded_signature = mlmodel.signature
+
+        assert loaded_signature is not None
+        assert loaded_signature.inputs is not None
+        assert loaded_signature.outputs is not None
+
+        input_names = [spec.name for spec in loaded_signature.inputs.inputs]
+        assert input_names == ["n", "num_samples"]
+
+        output_names = [spec.name for spec in loaded_signature.outputs.inputs]
+        expected_output_names = list(model.predict(n=5).to_dataframe().columns)
+        assert output_names == expected_output_names
+
+    def test_log_model_basic(self, tmpdir_fn):
+        """Test basic log_model functionality"""
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        with mlflow.start_run():
+            log_info = log_model(model, name="model")
+
+        loaded_model = load_model(log_info.model_uri)
+        pred_loaded = loaded_model.predict(n=5)
+        pred_original = model.predict(n=5)
+
+        assert len(pred_loaded) == 5
+        np.testing.assert_array_almost_equal(
+            pred_original.values(), pred_loaded.values(), decimal=4
+        )
+
+    def test_log_model_with_params(self, tmpdir_fn):
+        """Test that log_params=True logs model parameters"""
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
+
+        with mlflow.start_run():
+            log_model(model, name="model", log_params=True)
+            run_id = mlflow.active_run().info.run_id
+
+        run = mlflow.get_run(run_id)
+        assert run.data.params["lags"] == "5"
+        assert run.data.params["lags_past_covariates"] == "3"
+        assert run.data.params["n_past_covariates"] == "1"
+        assert run.data.params["n_future_covariates"] == "0"
+        assert run.data.params["n_static_covariates"] == "0"
+
+    def test_log_model_with_covariates(self, tmpdir_fn):
+        """Test that covariate info is logged with correct values"""
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
+
+        with mlflow.start_run():
+            log_model(model, name="model", log_params=True)
+            run_id = mlflow.active_run().info.run_id
+
+        run = mlflow.get_run(run_id)
+        # Check covariate usage tags have the correct boolean values
+        assert run.data.tags["uses_past_covariates"] == "true"
+        assert run.data.tags["uses_future_covariates"] == "false"
+        assert run.data.tags["uses_static_covariates"] == "false"
+        # Check covariate count params
+        assert run.data.params["n_past_covariates"] == "1"
+        assert run.data.params["n_future_covariates"] == "0"
+        assert run.data.params["n_static_covariates"] == "0"
+
+    def test_pyfunc_load_and_predict(self, tmpdir_fn):
+        """Test loading model as pyfunc and making predictions"""
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        with mlflow.start_run():
+            log_info = log_model(model, name="model")
+
+        pyfunc_model = mlflow.pyfunc.load_model(log_info.model_uri)
+        input_df = prepare_pyfunc_input(n=10)
+        output_df = pyfunc_model.predict(input_df)
+
+        # Compare with direct model prediction
+        expected_df = model.predict(n=10).to_dataframe()
+
+        assert isinstance(output_df, pd.DataFrame)
+        assert len(output_df) == 10
+        assert list(output_df.columns) == list(expected_df.columns)
+        np.testing.assert_array_almost_equal(
+            output_df.values, expected_df.values, decimal=4
+        )
+
+    def test_pyfunc_with_covariates(self, tmpdir_fn):
+        """Test pyfunc with model that requires covariates"""
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        train = self.ts_univariate[:40]
+        past_cov_train = self.ts_past_cov[:52]
+
+        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+        model.fit(train, past_covariates=past_cov_train)
+
+        with mlflow.start_run():
+            signature = infer_signature(
+                model, series=train, past_covariates=past_cov_train, n=5
+            )
+            log_info = log_model(model, name="model", signature=signature)
+
+        pyfunc_model = mlflow.pyfunc.load_model(log_info.model_uri)
+        test = self.ts_univariate[40:]
+        past_cov_test = self.ts_past_cov[40:]
+
+        input_df = prepare_pyfunc_input(n=5, series=test, past_covariates=past_cov_test)
+        output_df = pyfunc_model.predict(input_df)
+
+        # Compare with direct model prediction using same series/covariates
+        expected_df = model.predict(
+            n=5, series=test, past_covariates=past_cov_test
+        ).to_dataframe()
+
+        assert len(output_df) == 5
+        assert list(output_df.columns) == list(expected_df.columns)
+        np.testing.assert_array_almost_equal(
+            output_df.values, expected_df.values, decimal=4
+        )
+
+    def test_infer_signature_simple(self):
+        """Test signature inference without series"""
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        signature = infer_signature(model, n=5)
+
+        assert signature.inputs is not None
+        assert signature.outputs is not None
+
+        input_cols = [spec.name for spec in signature.inputs.inputs]
+        assert input_cols == ["n", "num_samples"]
+
+        output_cols = [spec.name for spec in signature.outputs.inputs]
+        expected_output_cols = list(model.predict(n=5).to_dataframe().columns)
+        assert output_cols == expected_output_cols
+
+    def test_infer_signature_with_covariates(self):
+        """Test signature inference with covariates"""
+        train = self.ts_univariate[:40]
+        past_cov = self.ts_past_cov[:52]
+
+        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+        model.fit(train, past_covariates=past_cov)
+
+        signature = infer_signature(model, series=train, past_covariates=past_cov, n=5)
+
+        input_cols = [spec.name for spec in signature.inputs.inputs]
+        assert "n" in input_cols
+        assert "num_samples" in input_cols
+        assert "_darts_series" in input_cols
+        assert "_darts_past_covariates" in input_cols
+        # future covariates were not provided, so should not appear
+        assert "_darts_future_covariates" not in input_cols
+
+        output_cols = [spec.name for spec in signature.outputs.inputs]
+        expected_output_cols = list(
+            model.predict(n=5, series=train, past_covariates=past_cov)
+            .to_dataframe()
+            .columns
+        )
+        assert output_cols == expected_output_cols
+
+    def test_autolog_enable_disable(self, tmpdir_fn):
+        """Test autolog can be enabled and disabled"""
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        autolog()
+
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        runs = mlflow.search_runs()
+        assert len(runs) == 1
+
+        # Verify the run has the expected model class tag
+        assert runs.iloc[0]["tags.darts.model_class"] == "ExponentialSmoothing"
+
+        autolog(disable=True)
+
+        model2 = ExponentialSmoothing()
+        model2.fit(self.ts_univariate)
+
+        runs_after_disable = mlflow.search_runs()
+        assert len(runs_after_disable) == 1
+
+    def test_autolog_parameters(self, tmpdir_fn):
+        """Test that autolog logs model parameters"""
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        autolog()
+
+        model = ExponentialSmoothing(seasonal_periods=12)
+        model.fit(self.ts_univariate)
+
+        runs = mlflow.search_runs()
+        assert len(runs) == 1
+
+        last_run = runs.iloc[0]
+        assert last_run["params.seasonal_periods"] == "12"
+        assert last_run["tags.darts.model_class"] == "ExponentialSmoothing"
+
+        autolog(disable=True)
+
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_torch_metrics(self, tmpdir_fn):
+        """Test that autolog logs training metrics for torch models"""
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        autolog()
+
+        model = NBEATSModel(
+            input_chunk_length=4, output_chunk_length=2, n_epochs=2, **tfm_kwargs_dev
+        )
+        train, val = self.ts_univariate.split_before(0.7)
+        model.fit(train, val_series=val)
+
+        runs = mlflow.search_runs()
+        assert len(runs) == 1
+        last_run_id = runs.iloc[0]["run_id"]
+        assert runs.iloc[0]["tags.darts.model_class"] == "NBEATSModel"
+
+        client = mlflow.tracking.MlflowClient()
+        metrics = client.get_metric_history(last_run_id, "train_loss")
+
+        assert len(metrics) > 0
+        # All logged loss values should be finite and non-negative
+        for m in metrics:
+            assert np.isfinite(m.value), f"train_loss is not finite: {m.value}"
+            assert m.value >= 0, f"train_loss is negative: {m.value}"
+
+        autolog(disable=True)
+
+    def test_load_nonexistent_model(self):
+        """Test that loading nonexistent model raises appropriate error"""
+        with pytest.raises(Exception):
+            load_model("runs:/fake_run_id/model")
+
+    def test_serialize_invalid_type(self):
+        """Test that serializing invalid type raises error"""
+        with pytest.raises(TypeError):
+            _serialize_timeseries_for_pyfunc("not_a_timeseries")
+
+    @pytest.mark.parametrize(
+        "model_cls,fit_kwargs",
+        [
+            (ExponentialSmoothing, {}),
+            (LinearRegressionModel, {"lags": 5}),
+        ],
+    )
+    def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
+        """Test save/load for multiple model types"""
+        if fit_kwargs:
+            model = model_cls(**fit_kwargs)
+        else:
+            model = model_cls()
+
+        model.fit(self.ts_univariate)
+
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path)
+        loaded = load_model(f"file://{model_path}")
+
+        pred1 = model.predict(n=5)
+        pred2 = loaded.predict(n=5)
+        np.testing.assert_array_almost_equal(pred1.values(), pred2.values(), decimal=4)
+
+    @pytest.mark.parametrize("use_torch", [False, True])
+    def test_pyfunc_multiple_models(self, tmpdir_fn, use_torch):
+        """Test pyfunc for statistical and torch models"""
+        if use_torch and not TORCH_AVAILABLE:
+            pytest.skip("torch not available")
+
+        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_experiment("test_experiment")
+
+        if use_torch:
+            model = NBEATSModel(
+                input_chunk_length=4,
+                output_chunk_length=2,
+                n_epochs=1,
+                **tfm_kwargs_dev,
+            )
+        else:
+            model = ExponentialSmoothing()
+
+        model.fit(self.ts_univariate)
+
+        with mlflow.start_run():
+            log_info = log_model(model, name="model")
+
+        pyfunc_model = mlflow.pyfunc.load_model(log_info.model_uri)
+        input_df = prepare_pyfunc_input(n=2)
+        output_df = pyfunc_model.predict(input_df)
+
+        # Compare with direct model prediction
+        expected_df = model.predict(n=2).to_dataframe()
+
+        assert len(output_df) == 2
+        assert list(output_df.columns) == list(expected_df.columns)
+        np.testing.assert_array_almost_equal(
+            output_df.values, expected_df.values, decimal=4
+        )

From 605ecbef1c1b227c571e06bbd70179986e9cd42d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 12 Feb 2026 13:53:18 +0100
Subject: [PATCH 005/154] add mlflow to dependencies

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index c09ad39edc..353969d4e5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -149,6 +149,7 @@ dev = [
 ]
 torch-cpu = ["torch>=2.0.0"]
 optional = [
+    "mlflow>=2.0",
     "onnx>=1.20.1",
     "onnxruntime>=1.24.1; python_version >= '3.11'",
     "onnxruntime<1.24.1; python_version < '3.11'",  # 1.24.1 dropped python 3.10 support

From 3c2a2e21b5bb1d7f4c172478ef911e7deda27ac9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 12 Feb 2026 15:02:44 +0100
Subject: [PATCH 006/154] changed to sqlite

---
 darts/tests/conftest.py                  |  8 ++++++++
 darts/tests/optional_deps/test_mlflow.py | 18 +++++++++---------
 2 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/darts/tests/conftest.py b/darts/tests/conftest.py
index 181854b84d..2c15f35a11 100644
--- a/darts/tests/conftest.py
+++ b/darts/tests/conftest.py
@@ -95,6 +95,14 @@
     logger.warning("Ray not installed - Some tests will be skipped.")
     RAY_AVAILABLE = False
 
+try:
+    import mlflow  # noqa: F401
+
+    MLFLOW_AVAILABLE = True
+except ImportError:
+    logger.warning("MLflow not installed - Some tests will be skipped.")
+    MLFLOW_AVAILABLE = False
+
 try:
     import polars  # noqa: F401
 
diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 05af8f7b88..a7e2021035 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -300,7 +300,7 @@ def test_save_with_signature(self, tmpdir_fn):
 
     def test_log_model_basic(self, tmpdir_fn):
         """Test basic log_model functionality"""
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         model = ExponentialSmoothing()
@@ -320,7 +320,7 @@ def test_log_model_basic(self, tmpdir_fn):
 
     def test_log_model_with_params(self, tmpdir_fn):
         """Test that log_params=True logs model parameters"""
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
@@ -339,7 +339,7 @@ def test_log_model_with_params(self, tmpdir_fn):
 
     def test_log_model_with_covariates(self, tmpdir_fn):
         """Test that covariate info is logged with correct values"""
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
@@ -361,7 +361,7 @@ def test_log_model_with_covariates(self, tmpdir_fn):
 
     def test_pyfunc_load_and_predict(self, tmpdir_fn):
         """Test loading model as pyfunc and making predictions"""
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         model = ExponentialSmoothing()
@@ -386,7 +386,7 @@ def test_pyfunc_load_and_predict(self, tmpdir_fn):
 
     def test_pyfunc_with_covariates(self, tmpdir_fn):
         """Test pyfunc with model that requires covariates"""
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         train = self.ts_univariate[:40]
@@ -464,7 +464,7 @@ def test_infer_signature_with_covariates(self):
 
     def test_autolog_enable_disable(self, tmpdir_fn):
         """Test autolog can be enabled and disabled"""
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         autolog()
@@ -488,7 +488,7 @@ def test_autolog_enable_disable(self, tmpdir_fn):
 
     def test_autolog_parameters(self, tmpdir_fn):
         """Test that autolog logs model parameters"""
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         autolog()
@@ -508,7 +508,7 @@ def test_autolog_parameters(self, tmpdir_fn):
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_torch_metrics(self, tmpdir_fn):
         """Test that autolog logs training metrics for torch models"""
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         autolog()
@@ -575,7 +575,7 @@ def test_pyfunc_multiple_models(self, tmpdir_fn, use_torch):
         if use_torch and not TORCH_AVAILABLE:
             pytest.skip("torch not available")
 
-        mlflow.set_tracking_uri(f"file://{tmpdir_fn}")
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
         mlflow.set_experiment("test_experiment")
 
         if use_torch:

From 1750b11db87220246d086e264f5abaee946f7861 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 12 Feb 2026 15:44:19 +0100
Subject: [PATCH 007/154] kwargs pyfunc extension

---
 darts/tests/optional_deps/test_mlflow.py | 122 +++++++++++++++++++++++
 darts/utils/mlflow.py                    | 110 +++++++++++++++++++-
 2 files changed, 228 insertions(+), 4 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index a7e2021035..405fffad8b 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -605,3 +605,125 @@ def test_pyfunc_multiple_models(self, tmpdir_fn, use_torch):
         np.testing.assert_array_almost_equal(
             output_df.values, expected_df.values, decimal=4
         )
+
+    def test_prepare_input_with_all_params(self):
+        """Test prepare_pyfunc_input with all new parameters"""
+        input_df = prepare_pyfunc_input(
+            n=10,
+            series=self.ts_univariate,
+            past_covariates=self.ts_past_cov,
+            num_samples=5,
+            verbose=True,
+            show_warnings=False,
+            random_state=42,
+            predict_likelihood_parameters=True,
+            mc_dropout=True,
+            batch_size=32,
+        )
+
+        assert "n" in input_df.columns
+        assert "num_samples" in input_df.columns
+        assert "verbose" in input_df.columns
+        assert "show_warnings" in input_df.columns
+        assert "random_state" in input_df.columns
+        assert "predict_likelihood_parameters" in input_df.columns
+        assert "_darts_series" in input_df.columns
+        assert "_darts_past_covariates" in input_df.columns
+        assert "_darts_kwargs" in input_df.columns
+
+        assert input_df["n"].iloc[0] == 10
+        assert input_df["num_samples"].iloc[0] == 5
+        assert bool(input_df["verbose"].iloc[0]) is True
+        assert bool(input_df["show_warnings"].iloc[0]) is False
+        assert input_df["random_state"].iloc[0] == 42
+        assert bool(input_df["predict_likelihood_parameters"].iloc[0]) is True
+
+        import json
+
+        kwargs = json.loads(input_df["_darts_kwargs"].iloc[0])
+        assert kwargs["mc_dropout"] is True  # JSON bools are Python bools
+        assert kwargs["batch_size"] == 32
+
+    def test_wrapper_with_standard_params(self):
+        """Test wrapper with standard prediction parameters"""
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        wrapper = _DartsModelWrapper(model)
+        input_df = prepare_pyfunc_input(
+            n=5,
+            verbose=False,
+            show_warnings=False,
+            random_state=42,
+        )
+
+        # Should not raise an error even with extra params
+        output_df = wrapper.predict(input_df)
+
+        assert isinstance(output_df, pd.DataFrame)
+        assert len(output_df) == 5
+
+    def test_wrapper_with_kwargs(self):
+        """Test wrapper with model-specific kwargs"""
+        model = LinearRegressionModel(lags=3)
+        model.fit(self.ts_univariate)
+
+        wrapper = _DartsModelWrapper(model)
+
+        # Add kwargs that don't affect LinearRegressionModel but shouldn't cause errors
+        input_df = prepare_pyfunc_input(
+            n=5,
+            series=self.ts_univariate,
+            verbose=False,
+        )
+
+        output_df = wrapper.predict(input_df)
+
+        assert isinstance(output_df, pd.DataFrame)
+        assert len(output_df) == 5
+
+    def test_pyfunc_with_all_params(self, tmpdir_fn):
+        """Test end-to-end pyfunc with all parameters"""
+        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
+        mlflow.set_experiment("test_experiment")
+
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        with mlflow.start_run():
+            signature = infer_signature(
+                model,
+                n=5,
+                verbose=False,
+                show_warnings=False,
+                random_state=42,
+            )
+            log_info = log_model(model, name="model", signature=signature)
+
+        pyfunc_model = mlflow.pyfunc.load_model(log_info.model_uri)
+
+        input_df = prepare_pyfunc_input(
+            n=5,
+            verbose=False,
+            show_warnings=False,
+            random_state=42,
+        )
+
+        output_df = pyfunc_model.predict(input_df)
+
+        assert isinstance(output_df, pd.DataFrame)
+        assert len(output_df) == 5
+
+    def test_params_override_with_new_params(self):
+        """Test that params dict overrides DataFrame values for new parameters"""
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        wrapper = _DartsModelWrapper(model)
+        input_df = prepare_pyfunc_input(n=5, random_state=10)
+
+        # Override random_state via params
+        output_df = wrapper.predict(input_df, params={"random_state": 42})
+
+        # Should complete without error (random_state was overridden)
+        assert len(output_df) == 5
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 6cdeb880e5..c945947b60 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -578,6 +578,11 @@ def prepare_pyfunc_input(
     past_covariates: Optional[Union["TimeSeries", list["TimeSeries"]]] = None,
     future_covariates: Optional[Union["TimeSeries", list["TimeSeries"]]] = None,
     num_samples: int = 1,
+    verbose: Optional[bool] = None,
+    show_warnings: bool = True,
+    random_state: Optional[int] = None,
+    predict_likelihood_parameters: bool = False,
+    **kwargs: Any,
 ) -> pd.DataFrame:
     """Prepare input DataFrame for MLflow pyfunc prediction.
 
@@ -596,13 +601,39 @@ def prepare_pyfunc_input(
         Future covariates.
     num_samples : int
         Number of samples for probabilistic models.
+    verbose : Optional[bool]
+        Optionally, set the prediction verbosity. Not effective for all models.
+    show_warnings : bool
+        Optionally, control whether warnings are shown. Not effective for all models.
+    random_state : Optional[int]
+        Controls the randomness of probabilistic predictions.
+    predict_likelihood_parameters : bool
+        If set to True, the model predicts the parameters of its likelihood instead of
+        the target. Only supported for probabilistic models with a likelihood.
+    **kwargs : Any
+        Additional model-specific parameters (e.g., ``mc_dropout`` for torch models,
+        ``batch_size``, etc.). These are serialized as JSON and passed to the model's
+        ``predict()`` method.
 
     Returns
     -------
     pd.DataFrame
         Input DataFrame for pyfunc ``predict()`` method.
     """
-    data = {"n": [n], "num_samples": [num_samples]}
+    data = {
+        "n": [n],
+        "num_samples": [num_samples],
+    }
+
+    # Only add optional parameters if explicitly set (not defaults)
+    if verbose is not None:
+        data["verbose"] = [verbose]
+    if show_warnings is not True:  # Only add if not default
+        data["show_warnings"] = [show_warnings]
+    if random_state is not None:
+        data["random_state"] = [random_state]
+    if predict_likelihood_parameters is not False:  # Only add if not default
+        data["predict_likelihood_parameters"] = [predict_likelihood_parameters]
 
     if series is not None:
         data["_darts_series"] = [_serialize_timeseries_for_pyfunc(series)]
@@ -617,6 +648,10 @@ def prepare_pyfunc_input(
                 _serialize_timeseries_for_pyfunc(future_covariates)
             ]
 
+    # Serialize kwargs as JSON string for model-specific parameters
+    if kwargs:
+        data["_darts_kwargs"] = [json.dumps(kwargs)]
+
     return pd.DataFrame(data)
 
 
@@ -626,6 +661,12 @@ def infer_signature(
     past_covariates: Optional["TimeSeries"] = None,
     future_covariates: Optional["TimeSeries"] = None,
     n: int = 1,
+    num_samples: int = 1,
+    verbose: Optional[bool] = None,
+    show_warnings: bool = True,
+    random_state: Optional[int] = None,
+    predict_likelihood_parameters: bool = False,
+    **kwargs: Any,
 ) -> "mlflow.models.ModelSignature":
     """Infer MLflow ModelSignature from a darts model and example inputs.
 
@@ -644,6 +685,18 @@ def infer_signature(
         Example future covariates.
     n : int
         Forecast horizon for generating example output.
+    num_samples : int
+        Number of samples for probabilistic models.
+    verbose : Optional[bool]
+        Optionally, set the prediction verbosity.
+    show_warnings : bool
+        Optionally, control whether warnings are shown.
+    random_state : Optional[int]
+        Controls the randomness of probabilistic predictions.
+    predict_likelihood_parameters : bool
+        If set to True, the model predicts the parameters of its likelihood.
+    **kwargs : Any
+        Additional model-specific parameters.
 
     Returns
     -------
@@ -655,7 +708,12 @@ def infer_signature(
         series=series,
         past_covariates=past_covariates,
         future_covariates=future_covariates,
-        num_samples=1,
+        num_samples=num_samples,
+        verbose=verbose,
+        show_warnings=show_warnings,
+        random_state=random_state,
+        predict_likelihood_parameters=predict_likelihood_parameters,
+        **kwargs,
     )
 
     wrapper = _DartsModelWrapper(model)
@@ -698,9 +756,11 @@ def predict(
         ----------
         model_input : pd.DataFrame
             Input DataFrame with columns: ``n``, ``num_samples``, and optionally
-            ``_darts_series``, ``_darts_past_covariates``, ``_darts_future_covariates``.
+            ``_darts_series``, ``_darts_past_covariates``, ``_darts_future_covariates``,
+            ``verbose``, ``show_warnings``, ``random_state``,
+            ``predict_likelihood_parameters``, ``_darts_kwargs``.
         params : Optional[dict[str, Any]]
-            Override parameters (e.g., ``{"n": 20}``).
+            Override parameters (e.g., ``{"n": 20, "random_state": 42}``).
 
         Returns
         -------
@@ -721,6 +781,41 @@ def predict(
 
         predict_kwargs = {"n": n, "num_samples": num_samples}
 
+        # Extract standard prediction parameters (only if present in DataFrame/params)
+        optional_params = [
+            "verbose",
+            "show_warnings",
+            "random_state",
+            "predict_likelihood_parameters",
+        ]
+
+        for param_name in optional_params:
+            value = None
+            if params and param_name in params:
+                value = params[param_name]
+            elif param_name in model_input.columns:
+                value = model_input[param_name].iloc[0]
+                # Handle NaN/None
+                if pd.isna(value):
+                    continue
+            else:
+                # Parameter not provided
+                continue
+
+            # Convert to appropriate Python type
+            if param_name in [
+                "verbose",
+                "show_warnings",
+                "predict_likelihood_parameters",
+            ]:
+                value = bool(value)
+            elif param_name == "random_state":
+                value = int(value) if value is not None else None
+
+            # Only add if not None
+            if value is not None:
+                predict_kwargs[param_name] = value
+
         if "_darts_series" in model_input.columns:
             series_json = model_input["_darts_series"].iloc[0]
             if pd.notna(series_json) and series_json:
@@ -741,6 +836,13 @@ def predict(
                     _deserialize_timeseries_from_pyfunc(future_cov_json)
                 )
 
+        # Handle model-specific kwargs
+        if "_darts_kwargs" in model_input.columns:
+            kwargs_json = model_input["_darts_kwargs"].iloc[0]
+            if pd.notna(kwargs_json) and kwargs_json:
+                extra_kwargs = json.loads(kwargs_json)
+                predict_kwargs.update(extra_kwargs)
+
         prediction = self.model.predict(**predict_kwargs)
 
         if isinstance(prediction, TimeSeries):

From 30456f508e9fac1617addb1d29b37688d75113c8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Mon, 16 Feb 2026 10:44:52 +0100
Subject: [PATCH 008/154] removing pyfunc draft support: tbd if to include in
 future

---
 darts/tests/optional_deps/test_mlflow.py | 475 -----------------------
 darts/utils/mlflow.py                    | 422 +-------------------
 2 files changed, 1 insertion(+), 896 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 405fffad8b..ae5bee4432 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -5,9 +5,7 @@
 import pytest
 
 import darts.utils.timeseries_generation as tg
-from darts import TimeSeries
 from darts.tests.conftest import MLFLOW_AVAILABLE, TORCH_AVAILABLE, tfm_kwargs_dev
-from darts.utils.mlflow import _DartsModelWrapper
 
 if not MLFLOW_AVAILABLE:
     pytest.skip(
@@ -19,13 +17,9 @@
 
 from darts.models import ExponentialSmoothing, LinearRegressionModel
 from darts.utils.mlflow import (
-    _deserialize_timeseries_from_pyfunc,
-    _serialize_timeseries_for_pyfunc,
     autolog,
-    infer_signature,
     load_model,
     log_model,
-    prepare_pyfunc_input,
     save_model,
 )
 
@@ -44,185 +38,6 @@ class TestMLflow:
     ts_past_cov = tg.sine_timeseries(length=62).astype("float32")
     ts_future_cov = tg.constant_timeseries(value=1.0, length=62).astype("float32")
 
-    def test_serialize_deserialize_single(self):
-        """Test serialization of single TimeSeries"""
-        json_str = _serialize_timeseries_for_pyfunc(self.ts_univariate)
-        assert isinstance(json_str, str)
-        assert json_str.startswith("{")
-
-        ts_restored = _deserialize_timeseries_from_pyfunc(json_str)
-        assert isinstance(ts_restored, TimeSeries)
-        np.testing.assert_array_almost_equal(
-            self.ts_univariate.values(), ts_restored.values(), decimal=6
-        )
-
-    def test_serialize_deserialize_list(self):
-        """Test serialization of list of TimeSeries"""
-        ts_list = [self.ts_univariate, self.ts_univariate * 2]
-
-        json_str = _serialize_timeseries_for_pyfunc(ts_list)
-        assert isinstance(json_str, str)
-        assert json_str.startswith("[")
-
-        ts_list_restored = _deserialize_timeseries_from_pyfunc(json_str)
-        assert isinstance(ts_list_restored, list)
-        assert len(ts_list_restored) == 2
-        np.testing.assert_array_almost_equal(
-            ts_list[0].values(), ts_list_restored[0].values(), decimal=6
-        )
-
-    def test_serialize_with_static_covariates(self):
-        """Test that static covariates are preserved through serialization"""
-        json_str = _serialize_timeseries_for_pyfunc(self.ts_with_static)
-        ts_restored = _deserialize_timeseries_from_pyfunc(json_str)
-
-        assert ts_restored.static_covariates is not None
-        pd.testing.assert_frame_equal(
-            self.ts_with_static.static_covariates,
-            ts_restored.static_covariates,
-            check_dtype=False,
-        )
-
-    def test_serialize_multivariate(self):
-        """Test serialization of multivariate TimeSeries"""
-        json_str = _serialize_timeseries_for_pyfunc(self.ts_multivariate)
-        ts_restored = _deserialize_timeseries_from_pyfunc(json_str)
-
-        assert ts_restored.n_components == 2
-        np.testing.assert_array_almost_equal(
-            self.ts_multivariate.values(), ts_restored.values(), decimal=6
-        )
-
-    def test_prepare_input_simple(self):
-        """Test prepare_pyfunc_input with only n parameter"""
-        input_df = prepare_pyfunc_input(n=10)
-
-        assert isinstance(input_df, pd.DataFrame)
-        assert list(input_df.columns) == ["n", "num_samples"]
-        assert input_df["n"].iloc[0] == 10
-        assert input_df["num_samples"].iloc[0] == 1
-
-    def test_prepare_input_with_series(self):
-        """Test prepare_pyfunc_input with series parameter"""
-        input_df = prepare_pyfunc_input(n=10, series=self.ts_univariate)
-
-        assert "_darts_series" in input_df.columns
-        json_str = input_df["_darts_series"].iloc[0]
-        assert json_str.startswith("{")
-
-        ts_restored = _deserialize_timeseries_from_pyfunc(json_str)
-        np.testing.assert_array_almost_equal(
-            self.ts_univariate.values(), ts_restored.values(), decimal=6
-        )
-
-    def test_prepare_input_with_covariates(self):
-        """Test prepare_pyfunc_input with all covariate types"""
-        input_df = prepare_pyfunc_input(
-            n=10,
-            series=self.ts_univariate,
-            past_covariates=self.ts_past_cov,
-            future_covariates=self.ts_future_cov,
-        )
-
-        assert "_darts_series" in input_df.columns
-        assert "_darts_past_covariates" in input_df.columns
-        assert "_darts_future_covariates" in input_df.columns
-
-        ts_restored = _deserialize_timeseries_from_pyfunc(
-            input_df["_darts_series"].iloc[0]
-        )
-        past_cov_restored = _deserialize_timeseries_from_pyfunc(
-            input_df["_darts_past_covariates"].iloc[0]
-        )
-        future_cov_restored = _deserialize_timeseries_from_pyfunc(
-            input_df["_darts_future_covariates"].iloc[0]
-        )
-
-        np.testing.assert_array_almost_equal(
-            self.ts_univariate.values(), ts_restored.values(), decimal=6
-        )
-        np.testing.assert_array_almost_equal(
-            self.ts_past_cov.values(), past_cov_restored.values(), decimal=6
-        )
-        np.testing.assert_array_almost_equal(
-            self.ts_future_cov.values(), future_cov_restored.values(), decimal=6
-        )
-
-    def test_prepare_input_list_series(self):
-        """Test prepare_pyfunc_input with list of series"""
-        series_list = [self.ts_univariate, self.ts_univariate * 2]
-        input_df = prepare_pyfunc_input(n=5, series=series_list)
-
-        assert "_darts_series" in input_df.columns
-        json_str = input_df["_darts_series"].iloc[0]
-        assert json_str.startswith("[")
-
-        ts_list_restored = _deserialize_timeseries_from_pyfunc(json_str)
-        assert isinstance(ts_list_restored, list)
-        assert len(ts_list_restored) == 2
-        np.testing.assert_array_almost_equal(
-            series_list[0].values(), ts_list_restored[0].values(), decimal=6
-        )
-        np.testing.assert_array_almost_equal(
-            series_list[1].values(), ts_list_restored[1].values(), decimal=6
-        )
-
-    def test_wrapper_simple_prediction(self):
-        """Test PyFunc wrapper with simple statistical model"""
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        wrapper = _DartsModelWrapper(model)
-        input_df = prepare_pyfunc_input(n=12)
-        output_df = wrapper.predict(input_df)
-
-        # Compare with direct model prediction
-        expected_df = model.predict(n=12).to_dataframe()
-
-        assert isinstance(output_df, pd.DataFrame)
-        assert len(output_df) == 12
-        assert list(output_df.columns) == list(expected_df.columns)
-        np.testing.assert_array_almost_equal(
-            output_df.values, expected_df.values, decimal=4
-        )
-
-    def test_wrapper_with_series(self):
-        """Test PyFunc wrapper with global model and series parameter"""
-        train, test = self.ts_univariate.split_before(0.7)
-
-        model = LinearRegressionModel(lags=5)
-        model.fit(train)
-
-        wrapper = _DartsModelWrapper(model)
-        input_df = prepare_pyfunc_input(n=5, series=test)
-        output_df = wrapper.predict(input_df)
-
-        # Compare with direct model prediction
-        expected_df = model.predict(n=5, series=test).to_dataframe()
-
-        assert len(output_df) == 5
-        assert list(output_df.columns) == list(expected_df.columns)
-        np.testing.assert_array_almost_equal(
-            output_df.values, expected_df.values, decimal=4
-        )
-
-    def test_wrapper_params_override(self):
-        """Test that params dict overrides DataFrame values"""
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        wrapper = _DartsModelWrapper(model)
-        input_df = prepare_pyfunc_input(n=5)
-
-        output_df = wrapper.predict(input_df, params={"n": 10})
-        assert len(output_df) == 10
-
-        # Verify the override actually produced the right prediction (n=10, not n=5)
-        expected_df = model.predict(n=10).to_dataframe()
-        np.testing.assert_array_almost_equal(
-            output_df.values, expected_df.values, decimal=4
-        )
-
     def test_save_load_statistical_model(self, tmpdir_fn):
         """Test save/load round-trip for statistical model"""
         model = ExponentialSmoothing()
@@ -275,29 +90,6 @@ def test_save_load_torch_model(self, tmpdir_fn):
             pred_original.values(), pred_loaded.values(), decimal=4
         )
 
-    def test_save_with_signature(self, tmpdir_fn):
-        """Test that signature is saved in MLmodel file"""
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        signature = infer_signature(model, n=5)
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path, signature=signature)
-
-        mlmodel = mlflow.models.Model.load(os.path.join(model_path, "MLmodel"))
-        loaded_signature = mlmodel.signature
-
-        assert loaded_signature is not None
-        assert loaded_signature.inputs is not None
-        assert loaded_signature.outputs is not None
-
-        input_names = [spec.name for spec in loaded_signature.inputs.inputs]
-        assert input_names == ["n", "num_samples"]
-
-        output_names = [spec.name for spec in loaded_signature.outputs.inputs]
-        expected_output_names = list(model.predict(n=5).to_dataframe().columns)
-        assert output_names == expected_output_names
-
     def test_log_model_basic(self, tmpdir_fn):
         """Test basic log_model functionality"""
         mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
@@ -359,109 +151,6 @@ def test_log_model_with_covariates(self, tmpdir_fn):
         assert run.data.params["n_future_covariates"] == "0"
         assert run.data.params["n_static_covariates"] == "0"
 
-    def test_pyfunc_load_and_predict(self, tmpdir_fn):
-        """Test loading model as pyfunc and making predictions"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
-
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        with mlflow.start_run():
-            log_info = log_model(model, name="model")
-
-        pyfunc_model = mlflow.pyfunc.load_model(log_info.model_uri)
-        input_df = prepare_pyfunc_input(n=10)
-        output_df = pyfunc_model.predict(input_df)
-
-        # Compare with direct model prediction
-        expected_df = model.predict(n=10).to_dataframe()
-
-        assert isinstance(output_df, pd.DataFrame)
-        assert len(output_df) == 10
-        assert list(output_df.columns) == list(expected_df.columns)
-        np.testing.assert_array_almost_equal(
-            output_df.values, expected_df.values, decimal=4
-        )
-
-    def test_pyfunc_with_covariates(self, tmpdir_fn):
-        """Test pyfunc with model that requires covariates"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
-
-        train = self.ts_univariate[:40]
-        past_cov_train = self.ts_past_cov[:52]
-
-        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-        model.fit(train, past_covariates=past_cov_train)
-
-        with mlflow.start_run():
-            signature = infer_signature(
-                model, series=train, past_covariates=past_cov_train, n=5
-            )
-            log_info = log_model(model, name="model", signature=signature)
-
-        pyfunc_model = mlflow.pyfunc.load_model(log_info.model_uri)
-        test = self.ts_univariate[40:]
-        past_cov_test = self.ts_past_cov[40:]
-
-        input_df = prepare_pyfunc_input(n=5, series=test, past_covariates=past_cov_test)
-        output_df = pyfunc_model.predict(input_df)
-
-        # Compare with direct model prediction using same series/covariates
-        expected_df = model.predict(
-            n=5, series=test, past_covariates=past_cov_test
-        ).to_dataframe()
-
-        assert len(output_df) == 5
-        assert list(output_df.columns) == list(expected_df.columns)
-        np.testing.assert_array_almost_equal(
-            output_df.values, expected_df.values, decimal=4
-        )
-
-    def test_infer_signature_simple(self):
-        """Test signature inference without series"""
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        signature = infer_signature(model, n=5)
-
-        assert signature.inputs is not None
-        assert signature.outputs is not None
-
-        input_cols = [spec.name for spec in signature.inputs.inputs]
-        assert input_cols == ["n", "num_samples"]
-
-        output_cols = [spec.name for spec in signature.outputs.inputs]
-        expected_output_cols = list(model.predict(n=5).to_dataframe().columns)
-        assert output_cols == expected_output_cols
-
-    def test_infer_signature_with_covariates(self):
-        """Test signature inference with covariates"""
-        train = self.ts_univariate[:40]
-        past_cov = self.ts_past_cov[:52]
-
-        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-        model.fit(train, past_covariates=past_cov)
-
-        signature = infer_signature(model, series=train, past_covariates=past_cov, n=5)
-
-        input_cols = [spec.name for spec in signature.inputs.inputs]
-        assert "n" in input_cols
-        assert "num_samples" in input_cols
-        assert "_darts_series" in input_cols
-        assert "_darts_past_covariates" in input_cols
-        # future covariates were not provided, so should not appear
-        assert "_darts_future_covariates" not in input_cols
-
-        output_cols = [spec.name for spec in signature.outputs.inputs]
-        expected_output_cols = list(
-            model.predict(n=5, series=train, past_covariates=past_cov)
-            .to_dataframe()
-            .columns
-        )
-        assert output_cols == expected_output_cols
-
     def test_autolog_enable_disable(self, tmpdir_fn):
         """Test autolog can be enabled and disabled"""
         mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
@@ -540,11 +229,6 @@ def test_load_nonexistent_model(self):
         with pytest.raises(Exception):
             load_model("runs:/fake_run_id/model")
 
-    def test_serialize_invalid_type(self):
-        """Test that serializing invalid type raises error"""
-        with pytest.raises(TypeError):
-            _serialize_timeseries_for_pyfunc("not_a_timeseries")
-
     @pytest.mark.parametrize(
         "model_cls,fit_kwargs",
         [
@@ -568,162 +252,3 @@ def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
         pred1 = model.predict(n=5)
         pred2 = loaded.predict(n=5)
         np.testing.assert_array_almost_equal(pred1.values(), pred2.values(), decimal=4)
-
-    @pytest.mark.parametrize("use_torch", [False, True])
-    def test_pyfunc_multiple_models(self, tmpdir_fn, use_torch):
-        """Test pyfunc for statistical and torch models"""
-        if use_torch and not TORCH_AVAILABLE:
-            pytest.skip("torch not available")
-
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
-
-        if use_torch:
-            model = NBEATSModel(
-                input_chunk_length=4,
-                output_chunk_length=2,
-                n_epochs=1,
-                **tfm_kwargs_dev,
-            )
-        else:
-            model = ExponentialSmoothing()
-
-        model.fit(self.ts_univariate)
-
-        with mlflow.start_run():
-            log_info = log_model(model, name="model")
-
-        pyfunc_model = mlflow.pyfunc.load_model(log_info.model_uri)
-        input_df = prepare_pyfunc_input(n=2)
-        output_df = pyfunc_model.predict(input_df)
-
-        # Compare with direct model prediction
-        expected_df = model.predict(n=2).to_dataframe()
-
-        assert len(output_df) == 2
-        assert list(output_df.columns) == list(expected_df.columns)
-        np.testing.assert_array_almost_equal(
-            output_df.values, expected_df.values, decimal=4
-        )
-
-    def test_prepare_input_with_all_params(self):
-        """Test prepare_pyfunc_input with all new parameters"""
-        input_df = prepare_pyfunc_input(
-            n=10,
-            series=self.ts_univariate,
-            past_covariates=self.ts_past_cov,
-            num_samples=5,
-            verbose=True,
-            show_warnings=False,
-            random_state=42,
-            predict_likelihood_parameters=True,
-            mc_dropout=True,
-            batch_size=32,
-        )
-
-        assert "n" in input_df.columns
-        assert "num_samples" in input_df.columns
-        assert "verbose" in input_df.columns
-        assert "show_warnings" in input_df.columns
-        assert "random_state" in input_df.columns
-        assert "predict_likelihood_parameters" in input_df.columns
-        assert "_darts_series" in input_df.columns
-        assert "_darts_past_covariates" in input_df.columns
-        assert "_darts_kwargs" in input_df.columns
-
-        assert input_df["n"].iloc[0] == 10
-        assert input_df["num_samples"].iloc[0] == 5
-        assert bool(input_df["verbose"].iloc[0]) is True
-        assert bool(input_df["show_warnings"].iloc[0]) is False
-        assert input_df["random_state"].iloc[0] == 42
-        assert bool(input_df["predict_likelihood_parameters"].iloc[0]) is True
-
-        import json
-
-        kwargs = json.loads(input_df["_darts_kwargs"].iloc[0])
-        assert kwargs["mc_dropout"] is True  # JSON bools are Python bools
-        assert kwargs["batch_size"] == 32
-
-    def test_wrapper_with_standard_params(self):
-        """Test wrapper with standard prediction parameters"""
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        wrapper = _DartsModelWrapper(model)
-        input_df = prepare_pyfunc_input(
-            n=5,
-            verbose=False,
-            show_warnings=False,
-            random_state=42,
-        )
-
-        # Should not raise an error even with extra params
-        output_df = wrapper.predict(input_df)
-
-        assert isinstance(output_df, pd.DataFrame)
-        assert len(output_df) == 5
-
-    def test_wrapper_with_kwargs(self):
-        """Test wrapper with model-specific kwargs"""
-        model = LinearRegressionModel(lags=3)
-        model.fit(self.ts_univariate)
-
-        wrapper = _DartsModelWrapper(model)
-
-        # Add kwargs that don't affect LinearRegressionModel but shouldn't cause errors
-        input_df = prepare_pyfunc_input(
-            n=5,
-            series=self.ts_univariate,
-            verbose=False,
-        )
-
-        output_df = wrapper.predict(input_df)
-
-        assert isinstance(output_df, pd.DataFrame)
-        assert len(output_df) == 5
-
-    def test_pyfunc_with_all_params(self, tmpdir_fn):
-        """Test end-to-end pyfunc with all parameters"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
-
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        with mlflow.start_run():
-            signature = infer_signature(
-                model,
-                n=5,
-                verbose=False,
-                show_warnings=False,
-                random_state=42,
-            )
-            log_info = log_model(model, name="model", signature=signature)
-
-        pyfunc_model = mlflow.pyfunc.load_model(log_info.model_uri)
-
-        input_df = prepare_pyfunc_input(
-            n=5,
-            verbose=False,
-            show_warnings=False,
-            random_state=42,
-        )
-
-        output_df = pyfunc_model.predict(input_df)
-
-        assert isinstance(output_df, pd.DataFrame)
-        assert len(output_df) == 5
-
-    def test_params_override_with_new_params(self):
-        """Test that params dict overrides DataFrame values for new parameters"""
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        wrapper = _DartsModelWrapper(model)
-        input_df = prepare_pyfunc_input(n=5, random_state=10)
-
-        # Override random_state via params
-        output_df = wrapper.predict(input_df, params={"random_state": 42})
-
-        # Should complete without error (random_state was overridden)
-        assert len(output_df) == 5
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index c945947b60..44d3dde1f0 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -4,7 +4,7 @@
 
 Custom MLflow model flavor for darts forecasting models. Supports saving, loading,
 logging and autolog for any darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
-to MLflow, including a pyfunc wrapper for MLflow's generic inference API.
+to MLflow.
 """
 
 import importlib
@@ -16,14 +16,11 @@
 from typing import Any, Optional, Union
 
 import mlflow
-import pandas as pd
 import yaml
 from mlflow.models import Model
-from mlflow.models import infer_signature as mlflow_infer_signature
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
 
 import darts
-from darts import TimeSeries
 from darts.logging import get_logger
 from darts.models.forecasting.forecasting_model import ForecastingModel
 from darts.utils.utils import PL_AVAILABLE
@@ -256,16 +253,9 @@ def save_model(
     }
     with open(python_env_path, "w") as f:
         yaml.dump(python_env, f, default_flow_style=False)
-    pyfunc_conf = {
-        "loader_module": "darts.utils.mlflow",
-        "python_model": None,
-        "data": _MODEL_DATA_SUBFOLDER,
-        "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"},
-    }
 
     mlmodel = Model()
     mlmodel.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
-    mlmodel.add_flavor("python_function", **pyfunc_conf)
 
     if signature is not None:
         mlmodel.signature = signature
@@ -519,400 +509,6 @@ def _log_covariate_info(model) -> None:
         os.remove(temp_path)
 
 
-# ---------------------------------------------------------------------------
-# PyFunc interface
-# ---------------------------------------------------------------------------
-
-
-def _serialize_timeseries_for_pyfunc(
-    ts: Union["TimeSeries", list["TimeSeries"]],
-) -> str:
-    """Serialize TimeSeries to JSON string for pyfunc.
-
-    Parameters
-    ----------
-    ts : Union[TimeSeries, list[TimeSeries]]
-        Single TimeSeries or list of TimeSeries objects.
-
-    Returns
-    -------
-    str
-        JSON string representation. For a single TimeSeries, returns a JSON object.
-        For a list, returns a JSON array.
-    """
-    if isinstance(ts, TimeSeries):
-        return ts.to_json()
-    elif isinstance(ts, (list, tuple)):
-        serialized = [t.to_json() for t in ts]
-        return "[" + ",".join(serialized) + "]"
-    else:
-        raise TypeError(f"Expected TimeSeries or list of TimeSeries, got {type(ts)}")
-
-
-def _deserialize_timeseries_from_pyfunc(
-    json_str: str,
-) -> Union["TimeSeries", list["TimeSeries"]]:
-    """Deserialize TimeSeries from JSON string.
-
-    Parameters
-    ----------
-    json_str : str
-        JSON string representation (single object or array).
-
-    Returns
-    -------
-    Union[TimeSeries, list[TimeSeries]]
-        Single TimeSeries or list of TimeSeries objects.
-    """
-    json_str = json_str.strip()
-    if json_str.startswith("["):
-        array_data = json.loads(json_str)
-        return [TimeSeries.from_json(json.dumps(item)) for item in array_data]
-    else:
-        return TimeSeries.from_json(json_str)
-
-
-def prepare_pyfunc_input(
-    n: int,
-    series: Optional[Union["TimeSeries", list["TimeSeries"]]] = None,
-    past_covariates: Optional[Union["TimeSeries", list["TimeSeries"]]] = None,
-    future_covariates: Optional[Union["TimeSeries", list["TimeSeries"]]] = None,
-    num_samples: int = 1,
-    verbose: Optional[bool] = None,
-    show_warnings: bool = True,
-    random_state: Optional[int] = None,
-    predict_likelihood_parameters: bool = False,
-    **kwargs: Any,
-) -> pd.DataFrame:
-    """Prepare input DataFrame for MLflow pyfunc prediction.
-
-    Creates a DataFrame for use with models loaded via ``mlflow.pyfunc.load_model()``.
-    Serializes TimeSeries to JSON in special columns.
-
-    Parameters
-    ----------
-    n : int
-        Forecast horizon.
-    series : Optional[Union[TimeSeries, list[TimeSeries]]]
-        Target series for prediction (required for global models on new data).
-    past_covariates : Optional[Union[TimeSeries, list[TimeSeries]]]
-        Past covariates.
-    future_covariates : Optional[Union[TimeSeries, list[TimeSeries]]]
-        Future covariates.
-    num_samples : int
-        Number of samples for probabilistic models.
-    verbose : Optional[bool]
-        Optionally, set the prediction verbosity. Not effective for all models.
-    show_warnings : bool
-        Optionally, control whether warnings are shown. Not effective for all models.
-    random_state : Optional[int]
-        Controls the randomness of probabilistic predictions.
-    predict_likelihood_parameters : bool
-        If set to True, the model predicts the parameters of its likelihood instead of
-        the target. Only supported for probabilistic models with a likelihood.
-    **kwargs : Any
-        Additional model-specific parameters (e.g., ``mc_dropout`` for torch models,
-        ``batch_size``, etc.). These are serialized as JSON and passed to the model's
-        ``predict()`` method.
-
-    Returns
-    -------
-    pd.DataFrame
-        Input DataFrame for pyfunc ``predict()`` method.
-    """
-    data = {
-        "n": [n],
-        "num_samples": [num_samples],
-    }
-
-    # Only add optional parameters if explicitly set (not defaults)
-    if verbose is not None:
-        data["verbose"] = [verbose]
-    if show_warnings is not True:  # Only add if not default
-        data["show_warnings"] = [show_warnings]
-    if random_state is not None:
-        data["random_state"] = [random_state]
-    if predict_likelihood_parameters is not False:  # Only add if not default
-        data["predict_likelihood_parameters"] = [predict_likelihood_parameters]
-
-    if series is not None:
-        data["_darts_series"] = [_serialize_timeseries_for_pyfunc(series)]
-
-        if past_covariates is not None:
-            data["_darts_past_covariates"] = [
-                _serialize_timeseries_for_pyfunc(past_covariates)
-            ]
-
-        if future_covariates is not None:
-            data["_darts_future_covariates"] = [
-                _serialize_timeseries_for_pyfunc(future_covariates)
-            ]
-
-    # Serialize kwargs as JSON string for model-specific parameters
-    if kwargs:
-        data["_darts_kwargs"] = [json.dumps(kwargs)]
-
-    return pd.DataFrame(data)
-
-
-def infer_signature(
-    model,
-    series: Optional["TimeSeries"] = None,
-    past_covariates: Optional["TimeSeries"] = None,
-    future_covariates: Optional["TimeSeries"] = None,
-    n: int = 1,
-    num_samples: int = 1,
-    verbose: Optional[bool] = None,
-    show_warnings: bool = True,
-    random_state: Optional[int] = None,
-    predict_likelihood_parameters: bool = False,
-    **kwargs: Any,
-) -> "mlflow.models.ModelSignature":
-    """Infer MLflow ModelSignature from a darts model and example inputs.
-
-    Generates a signature describing input/output schemas for the pyfunc interface.
-    The ``_darts_series`` and covariate columns are marked as optional.
-
-    Parameters
-    ----------
-    model
-        A fitted darts ``ForecastingModel`` instance.
-    series : Optional[TimeSeries]
-        Example target series for signature inference.
-    past_covariates : Optional[TimeSeries]
-        Example past covariates.
-    future_covariates : Optional[TimeSeries]
-        Example future covariates.
-    n : int
-        Forecast horizon for generating example output.
-    num_samples : int
-        Number of samples for probabilistic models.
-    verbose : Optional[bool]
-        Optionally, set the prediction verbosity.
-    show_warnings : bool
-        Optionally, control whether warnings are shown.
-    random_state : Optional[int]
-        Controls the randomness of probabilistic predictions.
-    predict_likelihood_parameters : bool
-        If set to True, the model predicts the parameters of its likelihood.
-    **kwargs : Any
-        Additional model-specific parameters.
-
-    Returns
-    -------
-    mlflow.models.ModelSignature
-        Signature describing the model's input/output interface.
-    """
-    input_example = prepare_pyfunc_input(
-        n=n,
-        series=series,
-        past_covariates=past_covariates,
-        future_covariates=future_covariates,
-        num_samples=num_samples,
-        verbose=verbose,
-        show_warnings=show_warnings,
-        random_state=random_state,
-        predict_likelihood_parameters=predict_likelihood_parameters,
-        **kwargs,
-    )
-
-    wrapper = _DartsModelWrapper(model)
-    output_example = wrapper.predict(input_example)
-
-    return mlflow_infer_signature(input_example, output_example)
-
-
-class _DartsModelWrapper:
-    """A pyfunc-compatible wrapper around a darts ``ForecastingModel``.
-
-    Deserializes TimeSeries from JSON-encoded columns and calls the model's
-    ``predict()`` method. Input should be created using :func:`prepare_pyfunc_input`.
-
-    Parameters
-    ----------
-    model : ForecastingModel
-        A fitted Darts forecasting model instance.
-
-    Attributes
-    ----------
-    model : ForecastingModel
-        The wrapped Darts forecasting model.
-    """
-
-    def __init__(self, model):
-        self.model = model
-
-    def predict(
-        self,
-        model_input: pd.DataFrame,
-        params: Optional[dict[str, Any]] = None,
-    ) -> pd.DataFrame:
-        """Generate forecasts from pyfunc input.
-
-        Input should be created using :func:`prepare_pyfunc_input`. Deserializes
-        TimeSeries from JSON columns and passes to model.predict().
-
-        Parameters
-        ----------
-        model_input : pd.DataFrame
-            Input DataFrame with columns: ``n``, ``num_samples``, and optionally
-            ``_darts_series``, ``_darts_past_covariates``, ``_darts_future_covariates``,
-            ``verbose``, ``show_warnings``, ``random_state``,
-            ``predict_likelihood_parameters``, ``_darts_kwargs``.
-        params : Optional[dict[str, Any]]
-            Override parameters (e.g., ``{"n": 20, "random_state": 42}``).
-
-        Returns
-        -------
-        pd.DataFrame
-            Forecasted values. For multiple series, includes a ``series_id`` column.
-        """
-        n = 1
-        if params and "n" in params:
-            n = int(params["n"])
-        elif "n" in model_input.columns:
-            n = int(model_input["n"].iloc[0])
-
-        num_samples = 1
-        if params and "num_samples" in params:
-            num_samples = int(params["num_samples"])
-        elif "num_samples" in model_input.columns:
-            num_samples = int(model_input["num_samples"].iloc[0])
-
-        predict_kwargs = {"n": n, "num_samples": num_samples}
-
-        # Extract standard prediction parameters (only if present in DataFrame/params)
-        optional_params = [
-            "verbose",
-            "show_warnings",
-            "random_state",
-            "predict_likelihood_parameters",
-        ]
-
-        for param_name in optional_params:
-            value = None
-            if params and param_name in params:
-                value = params[param_name]
-            elif param_name in model_input.columns:
-                value = model_input[param_name].iloc[0]
-                # Handle NaN/None
-                if pd.isna(value):
-                    continue
-            else:
-                # Parameter not provided
-                continue
-
-            # Convert to appropriate Python type
-            if param_name in [
-                "verbose",
-                "show_warnings",
-                "predict_likelihood_parameters",
-            ]:
-                value = bool(value)
-            elif param_name == "random_state":
-                value = int(value) if value is not None else None
-
-            # Only add if not None
-            if value is not None:
-                predict_kwargs[param_name] = value
-
-        if "_darts_series" in model_input.columns:
-            series_json = model_input["_darts_series"].iloc[0]
-            if pd.notna(series_json) and series_json:
-                series = _deserialize_timeseries_from_pyfunc(series_json)
-                predict_kwargs["series"] = series
-
-        if "_darts_past_covariates" in model_input.columns:
-            past_cov_json = model_input["_darts_past_covariates"].iloc[0]
-            if pd.notna(past_cov_json) and past_cov_json:
-                predict_kwargs["past_covariates"] = _deserialize_timeseries_from_pyfunc(
-                    past_cov_json
-                )
-
-        if "_darts_future_covariates" in model_input.columns:
-            future_cov_json = model_input["_darts_future_covariates"].iloc[0]
-            if pd.notna(future_cov_json) and future_cov_json:
-                predict_kwargs["future_covariates"] = (
-                    _deserialize_timeseries_from_pyfunc(future_cov_json)
-                )
-
-        # Handle model-specific kwargs
-        if "_darts_kwargs" in model_input.columns:
-            kwargs_json = model_input["_darts_kwargs"].iloc[0]
-            if pd.notna(kwargs_json) and kwargs_json:
-                extra_kwargs = json.loads(kwargs_json)
-                predict_kwargs.update(extra_kwargs)
-
-        prediction = self.model.predict(**predict_kwargs)
-
-        if isinstance(prediction, TimeSeries):
-            return prediction.to_dataframe()
-        else:
-            dfs = []
-            for i, pred in enumerate(prediction):
-                df = pred.to_dataframe()
-                df["series_id"] = i
-                dfs.append(df)
-            return pd.concat(dfs, axis=0)
-
-
-def _load_pyfunc(path: str):
-    """Load a darts model as a pyfunc-compatible wrapper.
-
-    This function is called by ``mlflow.pyfunc.load_model()`` when loading a model
-    saved with the darts flavor. It reconstructs the original Darts model and wraps
-    it in a ``_DartsModelWrapper`` for MLflow's generic inference API.
-
-    Parameters
-    ----------
-    path : str
-        Path to the model directory containing the MLmodel file.
-
-    Returns
-    -------
-    _DartsModelWrapper
-        A wrapper with a ``predict()`` method compatible with MLflow's
-        pyfunc inference API.
-
-    Raises
-    ------
-    FileNotFoundError
-        If the MLmodel file cannot be found at the specified path.
-    """
-    mlmodel_path = os.path.join(path, "..", "MLmodel")
-    if os.path.exists(mlmodel_path):
-        parent_path = os.path.dirname(os.path.abspath(mlmodel_path))
-    else:
-        parent_path = path
-
-    mlmodel_file = os.path.join(parent_path, "MLmodel")
-    if not os.path.exists(mlmodel_file):
-        raise FileNotFoundError(
-            f"Cannot find MLmodel file. Searched at: {mlmodel_file}"
-        )
-
-    with open(mlmodel_file) as f:
-        mlmodel_dict = yaml.safe_load(f)
-
-    flavor_conf = mlmodel_dict["flavors"][FLAVOR_NAME]
-    module_path = flavor_conf["model_class_module"]
-    class_name = flavor_conf["model_class_name"]
-    model_file = flavor_conf["model_file"]
-    is_torch = flavor_conf.get("is_torch_model", False)
-
-    model_cls = _import_model_class(module_path, class_name)
-
-    data_dir = os.path.join(parent_path, flavor_conf.get("data", _MODEL_DATA_SUBFOLDER))
-    model_path = os.path.join(data_dir, model_file)
-
-    if is_torch:
-        loaded_model = model_cls.load(model_path)
-    else:
-        loaded_model = model_cls.load(model_path)
-
-    return _DartsModelWrapper(loaded_model)
-
-
 # stores original (unpatched) `fit` methods so they can be restored.
 _ORIGINAL_FIT_METHODS: dict[type, Any] = {}
 
@@ -1039,22 +635,6 @@ def autolog(
     disable
         If ``True``, restore the original ``fit()`` methods and stop
         autologging.
-
-    Examples
-    --------
-    .. code-block:: python
-
-        from darts.utils.mlflow import autolog
-        from darts.models import ExponentialSmoothing
-        from darts.datasets import AirPassengersDataset
-
-        autolog()  # enable
-
-        series = AirPassengersDataset().load()
-        model = ExponentialSmoothing()
-        model.fit(series)  # automatically logged to MLflow
-
-        autolog(disable=True)  # disable
     """
     if disable:
         _restore_original_fit_methods()

From b69de4adc3268a02fc3d6bed12e7092d25e96198 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Mon, 16 Feb 2026 12:23:09 +0100
Subject: [PATCH 009/154] slight refactor by leveraging built-in mflflow
 validation util methods

---
 darts/utils/mlflow.py | 266 +++++++++++++++++++++++++++---------------
 1 file changed, 170 insertions(+), 96 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 44d3dde1f0..e0f25e78cd 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -10,7 +10,6 @@
 import importlib
 import json
 import os
-import sys
 import tempfile
 from functools import wraps
 from typing import Any, Optional, Union
@@ -18,7 +17,28 @@
 import mlflow
 import yaml
 from mlflow.models import Model
+from mlflow.models.model import MLMODEL_FILE_NAME
+from mlflow.models.utils import _save_example
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
+from mlflow.utils.environment import (
+    _CONDA_ENV_FILE_NAME,
+    _CONSTRAINTS_FILE_NAME,
+    _PYTHON_ENV_FILE_NAME,
+    _REQUIREMENTS_FILE_NAME,
+    _mlflow_conda_env,
+    _process_conda_env,
+    _process_pip_requirements,
+    _PythonEnv,
+    _validate_env_arguments,
+)
+from mlflow.utils.file_utils import TempDir, write_to
+from mlflow.utils.model_utils import (
+    _add_code_from_conf_to_system_path,
+    _get_flavor_configuration,
+    _validate_and_copy_code_paths,
+    _validate_and_prepare_target_save_path,
+)
+from mlflow.utils.requirements_utils import _get_pinned_requirement
 
 import darts
 from darts.logging import get_logger
@@ -114,6 +134,86 @@ def _import_model_class(module_path: str, class_name: str):
     return getattr(module, class_name)
 
 
+def _create_mlmodel_file(
+    path: str,
+    darts_flavor_conf: dict,
+    signature,
+    input_example,
+    metadata: Optional[dict],
+) -> None:
+    """Create and save MLmodel file with metadata.
+
+    Creates the following files in the model directory:
+    * ``MLmodel`` - MLmodel file with flavor metadata.
+
+    Parameters
+    ----------
+    path: str
+        Root directory of the MLflow model where the MLmodel file will be saved.
+    darts_flavor_conf: dict
+        Dictionary containing the flavor configuration for the darts model.
+    signature: mlflow.models.ModelSignature
+        An ``mlflow.models.ModelSignature`` instance describing model input/output.
+        Use :func:`infer_signature` to automatically generate from example inputs.
+    input_example: DataFrame
+        An example input for the model (used by MLflow UI). Should be a DataFrame
+        created with :func:`prepare_pyfunc_input`.
+    metadata: Optional[dict]
+        Optional dictionary of custom metadata to store in the ``MLmodel`` file.
+    """
+    mlmodel = Model()
+
+    if signature is not None:
+        mlmodel.signature = signature
+
+    if input_example is not None:
+        _save_example(mlmodel, input_example, path)
+
+    if metadata is not None:
+        mlmodel.metadata = metadata
+
+    mlmodel.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
+    mlmodel.save(os.path.join(path, MLMODEL_FILE_NAME))
+
+
+def _write_environment_files(
+    path: str,
+    conda_env: dict,
+    pip_requirements: list[str],
+    pip_constraints: Optional[list[str]],
+) -> None:
+    """Write Python environment specification files for model reproducibility.
+
+    Creates the following files in the model directory:
+
+    * ``conda.yaml`` - Conda environment specification including Python version and pip dependencies
+    * ``requirements.txt`` - Pip requirements list for non-conda environments
+    * ``python_env.yaml`` - MLflow Python environment specification
+    * ``constraints.txt`` (optional) - Pip version constraints if specified
+
+    Parameters
+    ----------
+    path: str
+        Root directory of the MLflow model where environment files will be written.
+    conda_env: dict
+        Processed conda environment dictionary containing 'name', 'channels',
+        'dependencies' keys.
+    pip_requirements: list[str]
+        List of pip requirement strings (e.g., ['numpy>=1.20.0', 'pandas']).
+    pip_constraints: Optional[list[str]]
+        Optional list of pip constraint strings for pinning transitive dependencies.
+        Only written if provided.
+    """
+    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
+        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
+
+    if pip_constraints:
+        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
+
+    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
+    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
+
+
 def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
     """Return the default pip requirements for logging a darts model.
 
@@ -128,9 +228,12 @@ def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
     list[str]
         A list of pip requirement strings.
     """
-    reqs = [f"darts=={darts.__version__}"]
+    reqs = [_get_pinned_requirement("darts")]
     if is_torch:
-        reqs.extend(["torch>=2.0.0", "pytorch-lightning>=2.0.0"])
+        reqs.extend([
+            _get_pinned_requirement("torch"),
+            _get_pinned_requirement("pytorch-lightning"),
+        ])
     return reqs
 
 
@@ -147,24 +250,19 @@ def get_default_conda_env(is_torch: bool = False) -> dict:
     dict
         A conda environment specification dictionary.
     """
-    return {
-        "channels": ["defaults", "conda-forge"],
-        "dependencies": [
-            "python",
-            "pip",
-            {
-                "pip": get_default_pip_requirements(is_torch=is_torch),
-            },
-        ],
-        "name": "darts_env",
-    }
+    return _mlflow_conda_env(
+        additional_pip_deps=get_default_pip_requirements(is_torch),
+        additional_conda_channels=["conda-forge"],
+    )
 
 
 def save_model(
     model,
     path: str,
     conda_env: Optional[Union[dict, str]] = None,
+    code_paths: Optional[list[str]] = None,
     pip_requirements: Optional[list[str]] = None,
+    extra_pip_requirements: Optional[list[str]] = None,
     signature=None,
     input_example=None,
     metadata: Optional[dict] = None,
@@ -186,9 +284,16 @@ def save_model(
     conda_env
         A conda environment specification (dict or path to a ``conda.yaml``).
         If ``None``, a default environment is generated.
+    code_paths
+        A list of local filesystem paths to Python file dependencies (or directories
+        containing file dependencies). These files are prepended to the system path
+        when the model is loaded.
     pip_requirements
         A list of pip requirement strings. Overrides ``conda_env`` pip section
         when provided.
+    extra_pip_requirements
+        A list of additional pip requirement strings to add to the model's environment,
+        in addition to the default requirements.
     signature
         An ``mlflow.models.ModelSignature`` instance describing model input/output.
         Use :func:`infer_signature` to automatically generate from example inputs.
@@ -198,11 +303,13 @@ def save_model(
     metadata
         Optional dictionary of custom metadata to store in the ``MLmodel`` file.
     """
-    data_dir = os.path.join(path, _MODEL_DATA_SUBFOLDER)
+    _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
+    _validate_and_prepare_target_save_path(path)
+    code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
 
+    data_dir = os.path.join(path, _MODEL_DATA_SUBFOLDER)
     is_torch = _is_torch_model(model)
 
-    os.makedirs(path, exist_ok=True)
     os.makedirs(data_dir, exist_ok=True)
 
     if is_torch:
@@ -223,50 +330,20 @@ def save_model(
         "data": _MODEL_DATA_SUBFOLDER,
     }
 
-    if pip_requirements is not None:
-        pip_reqs = pip_requirements
-    else:
-        pip_reqs = get_default_pip_requirements(is_torch=is_torch)
-
-    if conda_env is not None:
-        if isinstance(conda_env, str):
-            with open(conda_env) as f:
-                conda_env_dict = yaml.safe_load(f)
-        else:
-            conda_env_dict = conda_env
-    else:
-        conda_env_dict = get_default_conda_env(is_torch=is_torch)
-
-    conda_path = os.path.join(path, "conda.yaml")
-    with open(conda_path, "w") as f:
-        yaml.dump(conda_env_dict, f, default_flow_style=False)
-
-    reqs_path = os.path.join(path, "requirements.txt")
-    with open(reqs_path, "w") as f:
-        f.write("\n".join(pip_reqs) + "\n")
-
-    python_env_path = os.path.join(path, "python_env.yaml")
-    python_env = {
-        "python": f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}",
-        "build_dependencies": ["pip"],
-        "dependencies": ["requirements.txt"],
-    }
-    with open(python_env_path, "w") as f:
-        yaml.dump(python_env, f, default_flow_style=False)
-
-    mlmodel = Model()
-    mlmodel.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
-
-    if signature is not None:
-        mlmodel.signature = signature
+    if code_dir_subpath is not None:
+        darts_flavor_conf["code"] = code_dir_subpath
 
-    if input_example is not None:
-        mlmodel.input_example = input_example
-
-    if metadata is not None:
-        mlmodel.metadata = metadata
+    default_reqs = None if pip_requirements else get_default_pip_requirements(is_torch)
+    conda_env, pip_requirements, pip_constraints = (
+        _process_pip_requirements(
+            default_reqs, pip_requirements, extra_pip_requirements
+        )
+        if conda_env is None
+        else _process_conda_env(conda_env)
+    )
 
-    mlmodel.save(os.path.join(path, "MLmodel"))
+    _write_environment_files(path, conda_env, pip_requirements, pip_constraints)
+    _create_mlmodel_file(path, darts_flavor_conf, signature, input_example, metadata)
 
 
 def load_model(
@@ -295,32 +372,26 @@ def load_model(
     local_path = _download_artifact_from_uri(
         artifact_uri=model_uri, output_path=dst_path
     )
-    mlmodel_path = os.path.join(local_path, "MLmodel")
-    mlmodel = Model.load(mlmodel_path)
 
-    if FLAVOR_NAME not in mlmodel.flavors:
-        raise ValueError(
-            f"The MLflow model at '{model_uri}' does not have a '{FLAVOR_NAME}' flavor. "
-            f"Available flavors: {list(mlmodel.flavors.keys())}"
-        )
-
-    flavor_conf = mlmodel.flavors[FLAVOR_NAME]
-    module_path = flavor_conf["model_class_module"]
-    class_name = flavor_conf["model_class_name"]
-    model_file = flavor_conf["model_file"]
-    is_torch = flavor_conf.get("is_torch_model", False)
+    flavor_conf = _get_flavor_configuration(
+        model_path=local_path, flavor_name=FLAVOR_NAME
+    )
+    _add_code_from_conf_to_system_path(local_path, flavor_conf)
 
-    model_cls = _import_model_class(module_path, class_name)
+    model_cls = _import_model_class(
+        flavor_conf["model_class_module"], flavor_conf["model_class_name"]
+    )
 
-    data_dir = os.path.join(local_path, flavor_conf.get("data", _MODEL_DATA_SUBFOLDER))
-    model_path = os.path.join(data_dir, model_file)
+    model_path = os.path.join(
+        local_path,
+        flavor_conf.get("data", _MODEL_DATA_SUBFOLDER),
+        flavor_conf["model_file"],
+    )
 
-    if is_torch:
-        loaded_model = model_cls.load(model_path, **kwargs)
+    if flavor_conf.get("is_torch_model", False):
+        return model_cls.load(model_path, **kwargs)
     else:
-        loaded_model = model_cls.load(model_path)
-
-    return loaded_model
+        return model_cls.load(model_path)
 
 
 def log_model(
@@ -329,7 +400,9 @@ def log_model(
     name: Optional[str] = None,
     registered_model_name: Optional[str] = None,
     conda_env: Optional[Union[dict, str]] = None,
+    code_paths: Optional[list[str]] = None,
     pip_requirements: Optional[list[str]] = None,
+    extra_pip_requirements: Optional[list[str]] = None,
     signature=None,
     input_example=None,
     metadata: Optional[dict] = None,
@@ -352,8 +425,15 @@ def log_model(
         under this name.
     conda_env
         Conda environment specification (dict or path).
+    code_paths
+        A list of local filesystem paths to Python file dependencies (or directories
+        containing file dependencies). These files are prepended to the system path
+        when the model is loaded.
     pip_requirements
         Pip requirements list.
+    extra_pip_requirements
+        A list of additional pip requirement strings to add to the model's environment,
+        in addition to the default requirements.
     signature
         An ``mlflow.models.ModelSignature``. Use :func:`infer_signature`
         to automatically generate from example inputs.
@@ -384,7 +464,9 @@ def log_model(
             model=model,
             path=model_dir,
             conda_env=conda_env,
+            code_paths=code_paths,
             pip_requirements=pip_requirements,
+            extra_pip_requirements=extra_pip_requirements,
             signature=signature,
             input_example=input_example,
             metadata=metadata,
@@ -408,8 +490,7 @@ def _log_model_params(model) -> None:
     """Log model creation parameters to MLflow.
 
     Extracts model parameters from ``model.model_params`` and logs them to the active
-    MLflow run. Logs non-serializable values as "<non-serialisable>" and truncates long parameter values
-    to fit MLflow's 500-character limit.
+    MLflow run. Logs non-serializable values as "<non-serialisable>".
 
     Parameters
     ----------
@@ -425,13 +506,11 @@ def _log_model_params(model) -> None:
     safe_params = {}
     for key, value in params.items():
         try:
-            str_val = str(value)
-            if len(str_val) > 500:  # MLflow param value limit
-                str_val = str_val[:497] + "..."
-            safe_params[key] = str_val
+            safe_params[key] = str(value)
         except Exception:
             safe_params[key] = "<non-serialisable>"
 
+    # mlflow validates and truncates the param values internally
     if safe_params:
         mlflow.log_params(safe_params)
 
@@ -445,7 +524,7 @@ def _log_covariate_info(model) -> None:
 
     Logs three types of information:
     - Tags: Boolean flags for filtering (e.g., "uses_past_covariates")
-    - Parameters: Feature counts and names (truncated to 500 chars)
+    - Parameters: Feature counts and names (truncated to MAX_PARAM_VAL_LENGTH chars)
     - Artifact: Complete covariate metadata as JSON file
 
     Parameters
@@ -494,19 +573,14 @@ def _log_covariate_info(model) -> None:
 
         if info["names"]:
             names_str = ",".join(info["names"])
-            if len(names_str) > 500:
-                names_str = names_str[:497] + "..."
             mlflow.log_param(f"{cov_key.split('_')[0]}_cov_names", names_str)
 
     # log complete information as JSON artifact
-    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
-        json.dump(covariate_info, f, indent=2)
-        temp_path = f.name
-
-    try:
-        mlflow.log_artifact(temp_path, "covariates.json")
-    finally:
-        os.remove(temp_path)
+    with TempDir() as tmp:
+        covariates_path = tmp.path("covariates.json")
+        with open(covariates_path, "w") as f:
+            json.dump(covariate_info, f, indent=2)
+        mlflow.log_artifact(covariates_path)
 
 
 # stores original (unpatched) `fit` methods so they can be restored.

From c488cd7dad51bd880caa2723cecc44056fe4867c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Mon, 16 Feb 2026 13:07:49 +0100
Subject: [PATCH 010/154] restructuring module

---
 darts/utils/mlflow.py | 456 +++++++++++++++++++++---------------------
 1 file changed, 228 insertions(+), 228 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index e0f25e78cd..a0c6f72183 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -75,187 +75,6 @@ def __init__(self, model_uri: str, run_id: str, artifact_path: str):
 _MODEL_FILE_TORCH_CKPT = "model.pt.ckpt"
 
 
-def _is_torch_model(model) -> bool:
-    """Check if a model is a TorchForecastingModel.
-
-    Parameters
-    ----------
-    model
-        A Darts forecasting model instance.
-
-    Returns
-    -------
-    bool
-        True if the model is a TorchForecastingModel, False otherwise.
-    """
-    try:
-        from darts.models.forecasting.torch_forecasting_model import (
-            TorchForecastingModel,
-        )
-
-        return isinstance(model, TorchForecastingModel)
-    except ImportError:
-        return False
-
-
-def _get_model_class_path(model) -> tuple[str, str]:
-    """Extract the module path and class name from a model instance.
-
-    Parameters
-    ----------
-    model
-        A Darts forecasting model instance.
-
-    Returns
-    -------
-    tuple[str, str]
-        A tuple containing (module_path, class_name).
-    """
-    cls = type(model)
-    return cls.__module__, cls.__name__
-
-
-def _import_model_class(module_path: str, class_name: str):
-    """Dynamically import and return a model class.
-
-    Parameters
-    ----------
-    module_path : str
-        The fully qualified module path (e.g., "darts.models.exponential_smoothing").
-    class_name : str
-        The name of the class to import (e.g., "ExponentialSmoothing").
-
-    Returns
-    -------
-    type
-        The imported model class.
-    """
-    module = importlib.import_module(module_path)
-    return getattr(module, class_name)
-
-
-def _create_mlmodel_file(
-    path: str,
-    darts_flavor_conf: dict,
-    signature,
-    input_example,
-    metadata: Optional[dict],
-) -> None:
-    """Create and save MLmodel file with metadata.
-
-    Creates the following files in the model directory:
-    * ``MLmodel`` - MLmodel file with flavor metadata.
-
-    Parameters
-    ----------
-    path: str
-        Root directory of the MLflow model where the MLmodel file will be saved.
-    darts_flavor_conf: dict
-        Dictionary containing the flavor configuration for the darts model.
-    signature: mlflow.models.ModelSignature
-        An ``mlflow.models.ModelSignature`` instance describing model input/output.
-        Use :func:`infer_signature` to automatically generate from example inputs.
-    input_example: DataFrame
-        An example input for the model (used by MLflow UI). Should be a DataFrame
-        created with :func:`prepare_pyfunc_input`.
-    metadata: Optional[dict]
-        Optional dictionary of custom metadata to store in the ``MLmodel`` file.
-    """
-    mlmodel = Model()
-
-    if signature is not None:
-        mlmodel.signature = signature
-
-    if input_example is not None:
-        _save_example(mlmodel, input_example, path)
-
-    if metadata is not None:
-        mlmodel.metadata = metadata
-
-    mlmodel.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
-    mlmodel.save(os.path.join(path, MLMODEL_FILE_NAME))
-
-
-def _write_environment_files(
-    path: str,
-    conda_env: dict,
-    pip_requirements: list[str],
-    pip_constraints: Optional[list[str]],
-) -> None:
-    """Write Python environment specification files for model reproducibility.
-
-    Creates the following files in the model directory:
-
-    * ``conda.yaml`` - Conda environment specification including Python version and pip dependencies
-    * ``requirements.txt`` - Pip requirements list for non-conda environments
-    * ``python_env.yaml`` - MLflow Python environment specification
-    * ``constraints.txt`` (optional) - Pip version constraints if specified
-
-    Parameters
-    ----------
-    path: str
-        Root directory of the MLflow model where environment files will be written.
-    conda_env: dict
-        Processed conda environment dictionary containing 'name', 'channels',
-        'dependencies' keys.
-    pip_requirements: list[str]
-        List of pip requirement strings (e.g., ['numpy>=1.20.0', 'pandas']).
-    pip_constraints: Optional[list[str]]
-        Optional list of pip constraint strings for pinning transitive dependencies.
-        Only written if provided.
-    """
-    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
-        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
-
-    if pip_constraints:
-        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
-
-    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
-    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
-
-
-def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
-    """Return the default pip requirements for logging a darts model.
-
-    Parameters
-    ----------
-    is_torch
-        Whether the model is a PyTorch-based model. If ``True``, adds
-        ``torch`` and ``pytorch-lightning`` to the requirements.
-
-    Returns
-    -------
-    list[str]
-        A list of pip requirement strings.
-    """
-    reqs = [_get_pinned_requirement("darts")]
-    if is_torch:
-        reqs.extend([
-            _get_pinned_requirement("torch"),
-            _get_pinned_requirement("pytorch-lightning"),
-        ])
-    return reqs
-
-
-def get_default_conda_env(is_torch: bool = False) -> dict:
-    """Return a default conda environment dict for a darts model.
-
-    Parameters
-    ----------
-    is_torch
-        Whether the model is a PyTorch-based model.
-
-    Returns
-    -------
-    dict
-        A conda environment specification dictionary.
-    """
-    return _mlflow_conda_env(
-        additional_pip_deps=get_default_pip_requirements(is_torch),
-        additional_conda_channels=["conda-forge"],
-    )
-
-
 def save_model(
     model,
     path: str,
@@ -486,6 +305,234 @@ def log_model(
     )
 
 
+def autolog(
+    log_models: bool = True,
+    log_params: bool = True,
+    disable: bool = False,
+) -> None:
+    """Enable (or disable) automatic MLflow logging for darts models.
+
+    When enabled, every call to ``model.fit()`` on any darts forecasting model
+    will automatically:
+
+    1. Start an MLflow run (or reuse the currently active one).
+    2. Log model creation parameters (``model.model_params``).
+    3. For PyTorch-based models: inject a callback that logs per-epoch
+       ``train_loss`` / ``val_loss`` metrics.
+    4. Log the trained model artifact at the end of training.
+
+    Parameters
+    ----------
+    log_models
+        If ``True`` (default), log the trained model artifact after ``fit()``.
+    log_params
+        If ``True`` (default), log model creation parameters.
+    disable
+        If ``True``, restore the original ``fit()`` methods and stop
+        autologging.
+    """
+    if disable:
+        _restore_original_fit_methods()
+        return
+
+    _patch_fit(ForecastingModel, log_models=log_models, log_params=log_params)
+
+    try:
+        from darts.models.forecasting.torch_forecasting_model import (
+            TorchForecastingModel,
+        )
+
+        _patch_fit(
+            TorchForecastingModel,
+            log_models=log_models,
+            log_params=log_params,
+            inject_callback=True,
+        )
+    except ImportError:
+        pass
+
+
+def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
+    """Return the default pip requirements for logging a darts model.
+
+    Parameters
+    ----------
+    is_torch
+        Whether the model is a PyTorch-based model. If ``True``, adds
+        ``torch`` and ``pytorch-lightning`` to the requirements.
+
+    Returns
+    -------
+    list[str]
+        A list of pip requirement strings.
+    """
+    reqs = [_get_pinned_requirement("darts")]
+    if is_torch:
+        reqs.extend([
+            _get_pinned_requirement("torch"),
+            _get_pinned_requirement("pytorch-lightning"),
+        ])
+    return reqs
+
+
+def get_default_conda_env(is_torch: bool = False) -> dict:
+    """Return a default conda environment dict for a darts model.
+
+    Parameters
+    ----------
+    is_torch
+        Whether the model is a PyTorch-based model.
+
+    Returns
+    -------
+    dict
+        A conda environment specification dictionary.
+    """
+    return _mlflow_conda_env(
+        additional_pip_deps=get_default_pip_requirements(is_torch),
+        additional_conda_channels=["conda-forge"],
+    )
+
+
+def _is_torch_model(model) -> bool:
+    """Check if a model is a TorchForecastingModel.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance.
+
+    Returns
+    -------
+    bool
+        True if the model is a TorchForecastingModel, False otherwise.
+    """
+    try:
+        from darts.models.forecasting.torch_forecasting_model import (
+            TorchForecastingModel,
+        )
+
+        return isinstance(model, TorchForecastingModel)
+    except ImportError:
+        return False
+
+
+def _get_model_class_path(model) -> tuple[str, str]:
+    """Extract the module path and class name from a model instance.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance.
+
+    Returns
+    -------
+    tuple[str, str]
+        A tuple containing (module_path, class_name).
+    """
+    cls = type(model)
+    return cls.__module__, cls.__name__
+
+
+def _import_model_class(module_path: str, class_name: str):
+    """Dynamically import and return a model class.
+
+    Parameters
+    ----------
+    module_path : str
+        The fully qualified module path (e.g., "darts.models.exponential_smoothing").
+    class_name : str
+        The name of the class to import (e.g., "ExponentialSmoothing").
+
+    Returns
+    -------
+    type
+        The imported model class.
+    """
+    module = importlib.import_module(module_path)
+    return getattr(module, class_name)
+
+
+def _create_mlmodel_file(
+    path: str,
+    darts_flavor_conf: dict,
+    signature,
+    input_example,
+    metadata: Optional[dict],
+) -> None:
+    """Create and save MLmodel file with metadata.
+
+    Creates the following files in the model directory:
+    * ``MLmodel`` - MLmodel file with flavor metadata.
+
+    Parameters
+    ----------
+    path: str
+        Root directory of the MLflow model where the MLmodel file will be saved.
+    darts_flavor_conf: dict
+        Dictionary containing the flavor configuration for the darts model.
+    signature: mlflow.models.ModelSignature
+        An ``mlflow.models.ModelSignature`` instance describing model input/output.
+        Use :func:`infer_signature` to automatically generate from example inputs.
+    input_example: DataFrame
+        An example input for the model (used by MLflow UI). Should be a DataFrame
+        created with :func:`prepare_pyfunc_input`.
+    metadata: Optional[dict]
+        Optional dictionary of custom metadata to store in the ``MLmodel`` file.
+    """
+    mlmodel = Model()
+
+    if signature is not None:
+        mlmodel.signature = signature
+
+    if input_example is not None:
+        _save_example(mlmodel, input_example, path)
+
+    if metadata is not None:
+        mlmodel.metadata = metadata
+
+    mlmodel.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
+    mlmodel.save(os.path.join(path, MLMODEL_FILE_NAME))
+
+
+def _write_environment_files(
+    path: str,
+    conda_env: dict,
+    pip_requirements: list[str],
+    pip_constraints: Optional[list[str]],
+) -> None:
+    """Write Python environment specification files for model reproducibility.
+
+    Creates the following files in the model directory:
+
+    * ``conda.yaml`` - Conda environment specification including Python version and pip dependencies
+    * ``requirements.txt`` - Pip requirements list for non-conda environments
+    * ``python_env.yaml`` - MLflow Python environment specification
+    * ``constraints.txt`` (optional) - Pip version constraints if specified
+
+    Parameters
+    ----------
+    path: str
+        Root directory of the MLflow model where environment files will be written.
+    conda_env: dict
+        Processed conda environment dictionary containing 'name', 'channels',
+        'dependencies' keys.
+    pip_requirements: list[str]
+        List of pip requirement strings (e.g., ['numpy>=1.20.0', 'pandas']).
+    pip_constraints: Optional[list[str]]
+        Optional list of pip constraint strings for pinning transitive dependencies.
+        Only written if provided.
+    """
+    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
+        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
+
+    if pip_constraints:
+        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
+
+    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
+    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
+
+
 def _log_model_params(model) -> None:
     """Log model creation parameters to MLflow.
 
@@ -684,53 +731,6 @@ def _inject_mlflow_callback(model) -> None:
     model.trainer_params["callbacks"] = existing_callbacks
 
 
-def autolog(
-    log_models: bool = True,
-    log_params: bool = True,
-    disable: bool = False,
-) -> None:
-    """Enable (or disable) automatic MLflow logging for darts models.
-
-    When enabled, every call to ``model.fit()`` on any darts forecasting model
-    will automatically:
-
-    1. Start an MLflow run (or reuse the currently active one).
-    2. Log model creation parameters (``model.model_params``).
-    3. For PyTorch-based models: inject a callback that logs per-epoch
-       ``train_loss`` / ``val_loss`` metrics.
-    4. Log the trained model artifact at the end of training.
-
-    Parameters
-    ----------
-    log_models
-        If ``True`` (default), log the trained model artifact after ``fit()``.
-    log_params
-        If ``True`` (default), log model creation parameters.
-    disable
-        If ``True``, restore the original ``fit()`` methods and stop
-        autologging.
-    """
-    if disable:
-        _restore_original_fit_methods()
-        return
-
-    _patch_fit(ForecastingModel, log_models=log_models, log_params=log_params)
-
-    try:
-        from darts.models.forecasting.torch_forecasting_model import (
-            TorchForecastingModel,
-        )
-
-        _patch_fit(
-            TorchForecastingModel,
-            log_models=log_models,
-            log_params=log_params,
-            inject_callback=True,
-        )
-    except ImportError:
-        pass
-
-
 def _patch_fit(
     cls,
     *,

From 7fe3cf31e918f75c1683630d1e7a3f4a5dcb15e3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Mon, 16 Feb 2026 14:25:44 +0100
Subject: [PATCH 011/154] autologging refactor w/ mlflow decorator

---
 darts/utils/mlflow.py | 168 +++++++++++++++++++-----------------------
 1 file changed, 75 insertions(+), 93 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index a0c6f72183..1b16dea4cb 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -11,8 +11,7 @@
 import json
 import os
 import tempfile
-from functools import wraps
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 import mlflow
 import yaml
@@ -20,6 +19,11 @@
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
+from mlflow.utils.autologging_utils import (
+    autologging_integration,
+    get_autologging_config,
+    safe_patch,
+)
 from mlflow.utils.environment import (
     _CONDA_ENV_FILE_NAME,
     _CONSTRAINTS_FILE_NAME,
@@ -305,10 +309,12 @@ def log_model(
     )
 
 
+@autologging_integration(FLAVOR_NAME)
 def autolog(
     log_models: bool = True,
     log_params: bool = True,
     disable: bool = False,
+    silent: bool = False,
 ) -> None:
     """Enable (or disable) automatic MLflow logging for darts models.
 
@@ -330,23 +336,29 @@ def autolog(
     disable
         If ``True``, restore the original ``fit()`` methods and stop
         autologging.
+    silent
+        If ``True`` (default ``False``), suppress all event logging and warnings from
+        MLflow during autologging.
     """
-    if disable:
-        _restore_original_fit_methods()
-        return
-
-    _patch_fit(ForecastingModel, log_models=log_models, log_params=log_params)
+    safe_patch(
+        FLAVOR_NAME,
+        ForecastingModel,
+        "fit",
+        _patched_fit,
+        manage_run=True,
+    )
 
     try:
         from darts.models.forecasting.torch_forecasting_model import (
             TorchForecastingModel,
         )
 
-        _patch_fit(
+        safe_patch(
+            FLAVOR_NAME,
             TorchForecastingModel,
-            log_models=log_models,
-            log_params=log_params,
-            inject_callback=True,
+            "fit",
+            _patched_fit,
+            manage_run=True,
         )
     except ImportError:
         pass
@@ -630,8 +642,51 @@ def _log_covariate_info(model) -> None:
         mlflow.log_artifact(covariates_path)
 
 
-# stores original (unpatched) `fit` methods so they can be restored.
-_ORIGINAL_FIT_METHODS: dict[type, Any] = {}
+def _patched_fit(original, self, *args, **kwargs):
+    """Patch function for ForecastingModel.fit() autologging.
+
+    Handles both statistical and PyTorch-based models. For PyTorch models,
+    automatically injects MLflow callback for per-epoch metrics logging.
+
+    Parameters
+    ----------
+    original
+        The original fit method being patched.
+    self
+        The model instance (ForecastingModel or TorchForecastingModel).
+    args
+        Positional arguments passed to fit.
+    kwargs
+        Keyword arguments passed to fit.
+
+    Returns
+    -------
+        The result of calling the original fit method.
+    """
+    log_models = get_autologging_config(FLAVOR_NAME, "log_models", True)
+    log_params = get_autologging_config(FLAVOR_NAME, "log_params", True)
+
+    mlflow.set_tag("darts.model_class", type(self).__name__)
+
+    if log_params:
+        _log_model_params(self)
+
+    # Inject callback for torch models (no-op for non-torch models)
+    _inject_mlflow_callback(self)
+
+    result = original(self, *args, **kwargs)
+
+    if log_params:
+        _log_covariate_info(self)
+
+    if log_models:
+        try:
+            log_model(self, name="model", log_params=False)
+        except Exception as e:
+            logger.warning(f"Failed to autolog model artifact: {e}")
+
+    return result
+
 
 if PL_AVAILABLE:
     import pytorch_lightning as pl
@@ -722,86 +777,13 @@ def _inject_mlflow_callback(model) -> None:
 
     existing_callbacks = model.trainer_params.get("callbacks", [])
 
-    cb_type = type(callback)
-    for existing in existing_callbacks:
-        if type(existing).__name__ == cb_type.__name__:
-            return
-
-    existing_callbacks.append(callback)
-    model.trainer_params["callbacks"] = existing_callbacks
-
-
-def _patch_fit(
-    cls,
-    *,
-    log_models: bool,
-    log_params: bool,
-    inject_callback: bool = False,
-) -> None:
-    """Replace a model class's ``fit`` method with an MLflow logging wrapper.
-
-    Parameters
-    ----------
-    cls : type
-        The model class to patch (e.g., ForecastingModel, TorchForecastingModel).
-    log_models : bool
-        Whether to log the trained model artifact after fitting.
-    log_params : bool
-        Whether to log model creation parameters.
-    inject_callback : bool, optional
-        Whether to inject the MLflow callback for PyTorch Lightning models.
-        Default is False.
-    """
-    if cls in _ORIGINAL_FIT_METHODS:
+    if any(isinstance(cb, _DartsMlflowCallback) for cb in existing_callbacks):
+        logger.debug("MLflow callback already present, skipping injection")
         return
 
-    original_fit = cls.fit
-    _ORIGINAL_FIT_METHODS[cls] = original_fit
-
-    @wraps(original_fit)
-    def _patched_fit(self, *args, **kwargs):
-        run_started_here = False
-        if mlflow.active_run() is None:
-            mlflow.start_run()
-            run_started_here = True
-
-        try:
-            mlflow.set_tag("darts.model_class", type(self).__name__)
-
-            if log_params:
-                _log_model_params(self)
-
-            if inject_callback:
-                _inject_mlflow_callback(self)
+    if not isinstance(existing_callbacks, list):
+        existing_callbacks = list(existing_callbacks)
 
-            result = original_fit(self, *args, **kwargs)
-
-            if log_params:
-                _log_covariate_info(self)
-
-            if log_models:
-                try:
-                    log_model(self, name="model", log_params=False)
-                except Exception as e:
-                    logger.warning(f"Failed to autolog model artifact: {e}")
-
-            return result
-
-        except Exception:
-            raise
-        finally:
-            if run_started_here:
-                mlflow.end_run()
-
-    cls.fit = _patched_fit
-
-
-def _restore_original_fit_methods() -> None:
-    """Restore all patched ``fit()`` methods to their original implementations.
-
-    This function is called when ``autolog(disable=True)`` is invoked to remove
-    all MLflow logging functionality added by autologging.
-    """
-    for cls, original_fit in _ORIGINAL_FIT_METHODS.items():
-        cls.fit = original_fit
-    _ORIGINAL_FIT_METHODS.clear()
+    existing_callbacks.append(callback)
+    model.trainer_params["callbacks"] = existing_callbacks
+    logger.debug(f"Injected MLflow callback into {type(model).__name__}")

From ada47b781454f896c9c112dd1844ae7d3d40adbe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Mon, 16 Feb 2026 15:02:58 +0100
Subject: [PATCH 012/154] refactoring log_model to leverage Model.log

---
 darts/utils/mlflow.py | 176 +++++++++++-------------------------------
 1 file changed, 45 insertions(+), 131 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 1b16dea4cb..59a33ceafc 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -10,7 +10,6 @@
 import importlib
 import json
 import os
-import tempfile
 from typing import Optional, Union
 
 import mlflow
@@ -55,25 +54,6 @@
 _MODEL_DATA_SUBFOLDER = "data"
 
 
-class _ModelLogInfo:
-    """Lightweight container returned by :func:`log_model` with essential metadata.
-
-    Attributes
-    ----------
-    model_uri : str
-        The full MLflow model URI (e.g., "runs:/run_id/model").
-    run_id : str
-        The MLflow run ID that logged the model.
-    artifact_path : str
-        The artifact path where the model was logged within the run.
-    """
-
-    def __init__(self, model_uri: str, run_id: str, artifact_path: str):
-        self.model_uri = model_uri
-        self.run_id = run_id
-        self.artifact_path = artifact_path
-
-
 _MODEL_FILE_STAT = "model.pkl"
 _MODEL_FILE_TORCH = "model.pt"
 _MODEL_FILE_TORCH_CKPT = "model.pt.ckpt"
@@ -89,6 +69,7 @@ def save_model(
     signature=None,
     input_example=None,
     metadata: Optional[dict] = None,
+    mlflow_model: Optional[Model] = None,
 ) -> None:
     """Save a darts forecasting model in MLflow format.
 
@@ -125,6 +106,9 @@ def save_model(
         created with :func:`prepare_pyfunc_input`.
     metadata
         Optional dictionary of custom metadata to store in the ``MLmodel`` file.
+    mlflow_model
+        Optional MLflow Model object to use for saving. When provided (typically by
+        ``Model.log()``), this model instance is used instead of creating a new one.
     """
     _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
     _validate_and_prepare_target_save_path(path)
@@ -165,8 +149,29 @@ def save_model(
         else _process_conda_env(conda_env)
     )
 
-    _write_environment_files(path, conda_env, pip_requirements, pip_constraints)
-    _create_mlmodel_file(path, darts_flavor_conf, signature, input_example, metadata)
+    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
+        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
+
+    if pip_constraints:
+        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
+
+    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
+    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
+
+    if mlflow_model is None:
+        mlflow_model = Model()
+
+    if signature is not None:
+        mlflow_model.signature = signature
+
+    if input_example is not None:
+        _save_example(mlflow_model, input_example, path)
+
+    if metadata is not None:
+        mlflow_model.metadata = metadata
+
+    mlflow_model.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
+    mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
 
 
 def load_model(
@@ -271,41 +276,30 @@ def log_model(
 
     Returns
     -------
-    _ModelLogInfo
-        A lightweight object with ``model_uri``, ``run_id``, and
-        ``artifact_path`` attributes.
+    ModelInfo
+        MLflow ModelInfo object containing model_uri, run_id, artifact_path,
+        model_id, timestamps, and other metadata about the logged model.
     """
-    artifact_name = name or artifact_path or "model"
+    # import required as Model.log will call flavor.save_model() internally
+    import darts.utils.mlflow as darts_mlflow
 
     if log_params:
         _log_model_params(model)
         _log_covariate_info(model)
 
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        model_dir = os.path.join(tmp_dir, artifact_name)
-        save_model(
-            model=model,
-            path=model_dir,
-            conda_env=conda_env,
-            code_paths=code_paths,
-            pip_requirements=pip_requirements,
-            extra_pip_requirements=extra_pip_requirements,
-            signature=signature,
-            input_example=input_example,
-            metadata=metadata,
-        )
-        mlflow.log_artifacts(model_dir, artifact_path=artifact_name)
-
-    run_id = mlflow.active_run().info.run_id
-    model_uri = f"runs:/{run_id}/{artifact_name}"
-
-    if registered_model_name is not None:
-        mlflow.register_model(model_uri, registered_model_name)
-
-    return _ModelLogInfo(
-        model_uri=model_uri,
-        run_id=run_id,
-        artifact_path=artifact_name,
+    return Model.log(
+        artifact_path=artifact_path,
+        name=name,
+        flavor=darts_mlflow,
+        registered_model_name=registered_model_name,
+        model=model,
+        conda_env=conda_env,
+        code_paths=code_paths,
+        pip_requirements=pip_requirements,
+        extra_pip_requirements=extra_pip_requirements,
+        signature=signature,
+        input_example=input_example,
+        metadata=metadata,
     )
 
 
@@ -465,86 +459,6 @@ def _import_model_class(module_path: str, class_name: str):
     return getattr(module, class_name)
 
 
-def _create_mlmodel_file(
-    path: str,
-    darts_flavor_conf: dict,
-    signature,
-    input_example,
-    metadata: Optional[dict],
-) -> None:
-    """Create and save MLmodel file with metadata.
-
-    Creates the following files in the model directory:
-    * ``MLmodel`` - MLmodel file with flavor metadata.
-
-    Parameters
-    ----------
-    path: str
-        Root directory of the MLflow model where the MLmodel file will be saved.
-    darts_flavor_conf: dict
-        Dictionary containing the flavor configuration for the darts model.
-    signature: mlflow.models.ModelSignature
-        An ``mlflow.models.ModelSignature`` instance describing model input/output.
-        Use :func:`infer_signature` to automatically generate from example inputs.
-    input_example: DataFrame
-        An example input for the model (used by MLflow UI). Should be a DataFrame
-        created with :func:`prepare_pyfunc_input`.
-    metadata: Optional[dict]
-        Optional dictionary of custom metadata to store in the ``MLmodel`` file.
-    """
-    mlmodel = Model()
-
-    if signature is not None:
-        mlmodel.signature = signature
-
-    if input_example is not None:
-        _save_example(mlmodel, input_example, path)
-
-    if metadata is not None:
-        mlmodel.metadata = metadata
-
-    mlmodel.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
-    mlmodel.save(os.path.join(path, MLMODEL_FILE_NAME))
-
-
-def _write_environment_files(
-    path: str,
-    conda_env: dict,
-    pip_requirements: list[str],
-    pip_constraints: Optional[list[str]],
-) -> None:
-    """Write Python environment specification files for model reproducibility.
-
-    Creates the following files in the model directory:
-
-    * ``conda.yaml`` - Conda environment specification including Python version and pip dependencies
-    * ``requirements.txt`` - Pip requirements list for non-conda environments
-    * ``python_env.yaml`` - MLflow Python environment specification
-    * ``constraints.txt`` (optional) - Pip version constraints if specified
-
-    Parameters
-    ----------
-    path: str
-        Root directory of the MLflow model where environment files will be written.
-    conda_env: dict
-        Processed conda environment dictionary containing 'name', 'channels',
-        'dependencies' keys.
-    pip_requirements: list[str]
-        List of pip requirement strings (e.g., ['numpy>=1.20.0', 'pandas']).
-    pip_constraints: Optional[list[str]]
-        Optional list of pip constraint strings for pinning transitive dependencies.
-        Only written if provided.
-    """
-    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
-        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
-
-    if pip_constraints:
-        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
-
-    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
-    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
-
-
 def _log_model_params(model) -> None:
     """Log model creation parameters to MLflow.
 

From 9fbe42aff4faef503c9f4671c0e612a122bf2135 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 17 Feb 2026 10:56:38 +0100
Subject: [PATCH 013/154] unit test improvenemnts

---
 darts/tests/optional_deps/test_mlflow.py | 544 +++++++++++++++++++----
 1 file changed, 455 insertions(+), 89 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index ae5bee4432..f9cd64915d 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1,3 +1,4 @@
+import json
 import os
 
 import numpy as np
@@ -6,6 +7,7 @@
 
 import darts.utils.timeseries_generation as tg
 from darts.tests.conftest import MLFLOW_AVAILABLE, TORCH_AVAILABLE, tfm_kwargs_dev
+from darts.utils.utils import PL_AVAILABLE
 
 if not MLFLOW_AVAILABLE:
     pytest.skip(
@@ -27,6 +29,53 @@
     from darts.models import NBEATSModel
 
 
+@pytest.fixture
+def mlflow_tracking(tmpdir_fn):
+    """Set up MLflow tracking with a temporary database."""
+    mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
+    return mlflow.tracking.MlflowClient()
+
+
+@pytest.fixture
+def autolog_context():
+    """Context manager to safely enable/disable autolog for a test."""
+    from contextlib import contextmanager
+
+    @contextmanager
+    def _autolog_context():
+        autolog(disable=True)  # clean state
+        autolog()  # enable for test
+        try:
+            yield
+        finally:
+            autolog(disable=True)  # clean up
+
+    return _autolog_context
+
+
+def assert_mlflow_artifacts_exist(path: str, is_torch: bool = False):
+    """Assert that all required MLflow artifact files exist."""
+    assert os.path.exists(os.path.join(path, "MLmodel"))
+    assert os.path.exists(os.path.join(path, "conda.yaml"))
+    assert os.path.exists(os.path.join(path, "requirements.txt"))
+    assert os.path.exists(os.path.join(path, "python_env.yaml"))
+
+    if is_torch:
+        assert os.path.exists(os.path.join(path, "data", "model.pt"))
+        assert os.path.exists(os.path.join(path, "data", "model.pt.ckpt"))
+    else:
+        assert os.path.exists(os.path.join(path, "data", "model.pkl"))
+
+
+def assert_predictions_equal(model1, model2, n: int, decimal: int = 4):
+    """Assert that two models produce equivalent predictions."""
+    pred1 = model1.predict(n=n)
+    pred2 = model2.predict(n=n)
+    np.testing.assert_array_almost_equal(
+        pred1.values(), pred2.values(), decimal=decimal
+    )
+
+
 class TestMLflow:
     ts_univariate = tg.linear_timeseries(
         start_value=10, end_value=50, length=50
@@ -45,15 +94,11 @@ def test_save_load_statistical_model(self, tmpdir_fn):
 
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
-        assert os.path.exists(os.path.join(model_path, "MLmodel"))
 
-        loaded_model = load_model(f"file://{model_path}")
-        pred_original = model.predict(n=5)
-        pred_loaded = loaded_model.predict(n=5)
+        assert_mlflow_artifacts_exist(model_path, is_torch=False)
 
-        np.testing.assert_array_almost_equal(
-            pred_original.values(), pred_loaded.values(), decimal=4
-        )
+        loaded_model = load_model(f"file://{model_path}")
+        assert_predictions_equal(model, loaded_model, n=5)
 
     def test_save_load_regression_model(self, tmpdir_fn):
         """Test save/load round-trip for regression model"""
@@ -63,13 +108,10 @@ def test_save_load_regression_model(self, tmpdir_fn):
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
 
-        loaded_model = load_model(f"file://{model_path}")
-        pred_original = model.predict(n=3)
-        pred_loaded = loaded_model.predict(n=3)
+        assert_mlflow_artifacts_exist(model_path, is_torch=False)
 
-        np.testing.assert_array_almost_equal(
-            pred_original.values(), pred_loaded.values(), decimal=4
-        )
+        loaded_model = load_model(f"file://{model_path}")
+        assert_predictions_equal(model, loaded_model, n=3)
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_save_load_torch_model(self, tmpdir_fn):
@@ -82,19 +124,13 @@ def test_save_load_torch_model(self, tmpdir_fn):
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
 
-        loaded_model = load_model(f"file://{model_path}")
-        pred_original = model.predict(n=2)
-        pred_loaded = loaded_model.predict(n=2)
+        assert_mlflow_artifacts_exist(model_path, is_torch=True)
 
-        np.testing.assert_array_almost_equal(
-            pred_original.values(), pred_loaded.values(), decimal=4
-        )
+        loaded_model = load_model(f"file://{model_path}")
+        assert_predictions_equal(model, loaded_model, n=2)
 
-    def test_log_model_basic(self, tmpdir_fn):
+    def test_log_model_basic(self, mlflow_tracking):
         """Test basic log_model functionality"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
-
         model = ExponentialSmoothing()
         model.fit(self.ts_univariate)
 
@@ -102,19 +138,10 @@ def test_log_model_basic(self, tmpdir_fn):
             log_info = log_model(model, name="model")
 
         loaded_model = load_model(log_info.model_uri)
-        pred_loaded = loaded_model.predict(n=5)
-        pred_original = model.predict(n=5)
+        assert_predictions_equal(model, loaded_model, n=5)
 
-        assert len(pred_loaded) == 5
-        np.testing.assert_array_almost_equal(
-            pred_original.values(), pred_loaded.values(), decimal=4
-        )
-
-    def test_log_model_with_params(self, tmpdir_fn):
+    def test_log_model_with_params(self, mlflow_tracking):
         """Test that log_params=True logs model parameters"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
-
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
         model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
 
@@ -129,11 +156,8 @@ def test_log_model_with_params(self, tmpdir_fn):
         assert run.data.params["n_future_covariates"] == "0"
         assert run.data.params["n_static_covariates"] == "0"
 
-    def test_log_model_with_covariates(self, tmpdir_fn):
+    def test_log_model_with_covariates(self, mlflow_tracking):
         """Test that covariate info is logged with correct values"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
-
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
         model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
 
@@ -141,94 +165,412 @@ def test_log_model_with_covariates(self, tmpdir_fn):
             log_model(model, name="model", log_params=True)
             run_id = mlflow.active_run().info.run_id
 
+            # get artifact while run is still active
+            artifact_uri = mlflow.get_artifact_uri("covariates.json")
+            artifact_path = artifact_uri.replace("file://", "")
+            assert os.path.exists(artifact_path), (
+                "covariates.json artifact should exist"
+            )
+
+            with open(artifact_path) as f:
+                cov_data = json.load(f)
+
         run = mlflow.get_run(run_id)
-        # Check covariate usage tags have the correct boolean values
+        # check covariate usage tags have the correct boolean values
         assert run.data.tags["uses_past_covariates"] == "true"
         assert run.data.tags["uses_future_covariates"] == "false"
         assert run.data.tags["uses_static_covariates"] == "false"
-        # Check covariate count params
+        # check covariate count params
         assert run.data.params["n_past_covariates"] == "1"
         assert run.data.params["n_future_covariates"] == "0"
         assert run.data.params["n_static_covariates"] == "0"
 
-    def test_autolog_enable_disable(self, tmpdir_fn):
-        """Test autolog can be enabled and disabled"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
+        # verify structure and content
+        assert "past_covariates" in cov_data
+        assert "future_covariates" in cov_data
+        assert "static_covariates" in cov_data
+
+        # check past covariates data
+        assert cov_data["past_covariates"]["used"] is True
+        assert cov_data["past_covariates"]["count"] == 1
+        assert len(cov_data["past_covariates"]["names"]) == 1
+
+        # check future and static covariates not used
+        assert cov_data["future_covariates"]["used"] is False
+        assert cov_data["future_covariates"]["count"] == 0
+        assert cov_data["static_covariates"]["used"] is False
+        assert cov_data["static_covariates"]["count"] == 0
+
+    def test_log_model_with_all_covariate_types(self, mlflow_tracking):
+        """Test logging model with past, future, and static covariates"""
+        # use a model that supports all covariate types
+        model = LinearRegressionModel(
+            lags=5, lags_past_covariates=3, lags_future_covariates=[0, 1]
+        )
+        model.fit(
+            self.ts_with_static[:40],
+            past_covariates=self.ts_past_cov[:40],
+            future_covariates=self.ts_future_cov[:50],
+        )
 
-        autolog()
+        with mlflow.start_run():
+            log_model(model, name="model", log_params=True)
+            run_id = mlflow.active_run().info.run_id
 
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
+            # get artifact while run is still active
+            artifact_uri = mlflow.get_artifact_uri("covariates.json")
+            artifact_path = artifact_uri.replace("file://", "")
 
-        runs = mlflow.search_runs()
-        assert len(runs) == 1
+            with open(artifact_path) as f:
+                cov_data = json.load(f)
+
+        run = mlflow.get_run(run_id)
+
+        # verify all covariate types are tracked
+        assert run.data.tags["uses_past_covariates"] == "true"
+        assert run.data.tags["uses_future_covariates"] == "true"
+        assert run.data.tags["uses_static_covariates"] == "true"
+
+        # verify covariate counts
+        assert run.data.params["n_past_covariates"] == "1"
+        assert run.data.params["n_future_covariates"] == "1"
+        assert run.data.params["n_static_covariates"] == "1"
+
+        # all covariate types should be used
+        assert cov_data["past_covariates"]["used"] is True
+        assert cov_data["past_covariates"]["count"] == 1
+        assert cov_data["future_covariates"]["used"] is True
+        assert cov_data["future_covariates"]["count"] == 1
+        assert cov_data["static_covariates"]["used"] is True
+        assert cov_data["static_covariates"]["count"] == 1
+
+    def test_autolog_enable_disable(self, mlflow_tracking, autolog_context):
+        """Test autolog can be enabled and disabled"""
+        with autolog_context():
+            model = ExponentialSmoothing()
+            model.fit(self.ts_univariate)
 
-        # Verify the run has the expected model class tag
-        assert runs.iloc[0]["tags.darts.model_class"] == "ExponentialSmoothing"
+            runs = mlflow.search_runs()
+            assert len(runs) == 1, "Expected exactly one run after autolog fit"
 
-        autolog(disable=True)
+            # verify the run has expected content
+            last_run = runs.iloc[0]
+            assert last_run["tags.darts.model_class"] == "ExponentialSmoothing"
+            assert last_run["tags.mlflow.runName"] is not None
 
+        # after context exits, autolog should be disabled
         model2 = ExponentialSmoothing()
         model2.fit(self.ts_univariate)
 
         runs_after_disable = mlflow.search_runs()
-        assert len(runs_after_disable) == 1
+        assert len(runs_after_disable) == 1, (
+            "No new run should be created after disable"
+        )
 
-    def test_autolog_parameters(self, tmpdir_fn):
+    def test_autolog_parameters(self, mlflow_tracking, autolog_context):
         """Test that autolog logs model parameters"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
+        with autolog_context():
+            model = ExponentialSmoothing(seasonal_periods=12)
+            model.fit(self.ts_univariate)
 
-        autolog()
+            runs = mlflow.search_runs()
+            assert len(runs) == 1
 
-        model = ExponentialSmoothing(seasonal_periods=12)
-        model.fit(self.ts_univariate)
+            last_run = runs.iloc[0]
+            assert last_run["params.seasonal_periods"] == "12"
+            assert last_run["tags.darts.model_class"] == "ExponentialSmoothing"
 
-        runs = mlflow.search_runs()
-        assert len(runs) == 1
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
+        """Test that autolog logs training metrics for torch models"""
+        with autolog_context():
+            model = NBEATSModel(
+                input_chunk_length=4,
+                output_chunk_length=2,
+                n_epochs=2,
+                **tfm_kwargs_dev,
+            )
+            train, val = self.ts_univariate.split_before(0.7)
+            model.fit(train, val_series=val)
+
+            runs = mlflow.search_runs()
+            assert len(runs) == 1, "Expected exactly one run"
+            last_run = runs.iloc[0]
+            last_run_id = last_run["run_id"]
+            assert last_run["tags.darts.model_class"] == "NBEATSModel"
+
+            client = mlflow.tracking.MlflowClient()
+
+            # check train_loss metrics
+            train_metrics = client.get_metric_history(last_run_id, "train_loss")
+            assert len(train_metrics) > 0, "Expected train_loss metrics to be logged"
+            assert len(train_metrics) <= 2, "Expected at most 2 epochs of train_loss"
+
+            for m in train_metrics:
+                assert np.isfinite(m.value), f"train_loss is not finite: {m.value}"
+                assert m.value >= 0, f"train_loss is negative: {m.value}"
+                assert m.step >= 0, "Metric step should be non-negative"
+
+            val_metrics = client.get_metric_history(last_run_id, "val_loss")
+            if val_metrics:
+                for m in val_metrics:
+                    assert np.isfinite(m.value), f"val_loss is not finite: {m.value}"
+                    assert m.value >= 0, f"val_loss is negative: {m.value}"
 
-        last_run = runs.iloc[0]
-        assert last_run["params.seasonal_periods"] == "12"
-        assert last_run["tags.darts.model_class"] == "ExponentialSmoothing"
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_injects_callback(self, mlflow_tracking, autolog_context):
+        """Test that autolog injects MLflow callback into torch models"""
+        with autolog_context():
+            model = NBEATSModel(
+                input_chunk_length=4,
+                output_chunk_length=2,
+                n_epochs=2,
+                **tfm_kwargs_dev,
+            )
+
+            # record initial callbacks from trainer_params (before fit)
+            initial_callbacks = model.trainer_params.get("callbacks", [])
+            initial_callback_count = len(initial_callbacks) if initial_callbacks else 0
+
+            train, val = self.ts_univariate.split_before(0.7)
+            model.fit(train, val_series=val)
+
+            # verify callback was injected after fit
+            final_callbacks = model.trainer_params.get("callbacks", [])
+            assert final_callbacks is not None, "Callbacks should not be None"
+            assert len(final_callbacks) > initial_callback_count, (
+                "MLflow callback should be added"
+            )
+
+            # check that the _DartsMlflowCallback is present
+            from darts.utils.mlflow import _DartsMlflowCallback
+
+            has_mlflow_callback = any(
+                isinstance(cb, _DartsMlflowCallback) for cb in final_callbacks
+            )
+            assert has_mlflow_callback, (
+                f"_DartsMlflowCallback not found in {[type(cb).__name__ for cb in final_callbacks]}"
+            )
+
+    def test_covariate_artifact_schema(self, mlflow_tracking):
+        """Test that covariate artifact has correct JSON schema"""
+        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
 
-        autolog(disable=True)
+        with mlflow.start_run():
+            log_model(model, name="model", log_params=True)
 
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_torch_metrics(self, tmpdir_fn):
-        """Test that autolog logs training metrics for torch models"""
-        mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-        mlflow.set_experiment("test_experiment")
+            artifact_uri = mlflow.get_artifact_uri("covariates.json")
+            artifact_path = artifact_uri.replace("file://", "")
 
-        autolog()
+            with open(artifact_path) as f:
+                cov_data = json.load(f)
 
-        model = NBEATSModel(
-            input_chunk_length=4, output_chunk_length=2, n_epochs=2, **tfm_kwargs_dev
+        # validate schema structure
+        required_keys = ["past_covariates", "future_covariates", "static_covariates"]
+        assert all(key in cov_data for key in required_keys), (
+            "Missing required covariate keys"
         )
-        train, val = self.ts_univariate.split_before(0.7)
-        model.fit(train, val_series=val)
+
+        for cov_type in required_keys:
+            cov_info = cov_data[cov_type]
+            assert "used" in cov_info and isinstance(cov_info["used"], bool)
+            assert "count" in cov_info and isinstance(cov_info["count"], int)
+            assert "names" in cov_info and isinstance(cov_info["names"], list)
+            assert cov_info["count"] == len(cov_info["names"])
+
+    def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
+        """Test saving/loading multivariate series with all covariate types"""
+        # create multivariate target with static covariates
+        target = self.ts_multivariate.with_static_covariates(
+            pd.DataFrame({"static_feat_1": [1.0], "static_feat_2": [2.0]})
+        )
+
+        model = LinearRegressionModel(
+            lags=5, lags_past_covariates=3, lags_future_covariates=[0, 1]
+        )
+        model.fit(
+            target[:40],
+            past_covariates=self.ts_past_cov[:40],
+            future_covariates=self.ts_future_cov[:50],
+        )
+
+        with mlflow.start_run():
+            log_model(model, name="model", log_params=True)
+            run_id = mlflow.active_run().info.run_id
+
+        run = mlflow.get_run(run_id)
+
+        # verify all covariate types detected
+        assert run.data.tags["uses_past_covariates"] == "true"
+        assert run.data.tags["uses_future_covariates"] == "true"
+        assert run.data.tags["uses_static_covariates"] == "true"
+
+        # verify correct component counts
+        assert run.data.params["n_past_covariates"] == "1"
+        assert run.data.params["n_future_covariates"] == "1"
+        assert run.data.params["n_static_covariates"] == "2"
+
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_callback_injection_with_existing_callbacks(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Test callback injection when model already has callbacks"""
+        # create model with existing callback
+        if PL_AVAILABLE:
+            import pytorch_lightning as pl
+
+            existing_callback = pl.callbacks.EarlyStopping(monitor="train_loss")
+        else:
+            pytest.skip("PyTorch Lightning not available")
+
+        with autolog_context():
+            model = NBEATSModel(
+                input_chunk_length=4,
+                output_chunk_length=2,
+                n_epochs=2,
+                pl_trainer_kwargs={"callbacks": [existing_callback]},
+                **{k: v for k, v in tfm_kwargs_dev.items() if k != "pl_trainer_kwargs"},
+            )
+
+            train, val = self.ts_univariate.split_before(0.7)
+            model.fit(train, val_series=val)
+
+            # verify both callbacks present
+            callbacks = model.trainer_params.get("callbacks", [])
+            assert len(callbacks) == 2, "Should have both existing and MLflow callbacks"
+
+            from darts.utils.mlflow import _DartsMlflowCallback
+
+            has_mlflow = any(isinstance(cb, _DartsMlflowCallback) for cb in callbacks)
+            has_existing = any(
+                isinstance(cb, pl.callbacks.EarlyStopping) for cb in callbacks
+            )
+            assert has_mlflow and has_existing, "Both callbacks should be present"
+
+    def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
+        """Test that multiple fits with autolog create separate runs"""
+        with autolog_context():
+            # since managed_run=True, subsequent fits will reuse the existing run,
+            # so we explicitly start runs for each fit
+            with mlflow.start_run():
+                model2 = LinearRegressionModel(lags=5)
+                model2.fit(self.ts_univariate)
+            with mlflow.start_run():
+                model1 = ExponentialSmoothing()
+                model1.fit(self.ts_univariate)
 
         runs = mlflow.search_runs()
-        assert len(runs) == 1
-        last_run_id = runs.iloc[0]["run_id"]
-        assert runs.iloc[0]["tags.darts.model_class"] == "NBEATSModel"
+        assert len(runs) == 2, "Expected two separate runs for two fits"
 
-        client = mlflow.tracking.MlflowClient()
-        metrics = client.get_metric_history(last_run_id, "train_loss")
+        # verify different model classes logged
+        model_classes = set(runs["tags.darts.model_class"])
+        assert "ExponentialSmoothing" in model_classes
+        assert "LinearRegressionModel" in model_classes
 
-        assert len(metrics) > 0
-        # All logged loss values should be finite and non-negative
-        for m in metrics:
-            assert np.isfinite(m.value), f"train_loss is not finite: {m.value}"
-            assert m.value >= 0, f"train_loss is negative: {m.value}"
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_torch_model_multiple_fits(self, mlflow_tracking, autolog_context):
+        """Test autolog with multiple fits of a torch model"""
+        with autolog_context():
+            with mlflow.start_run():
+                model1 = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=1,
+                    **tfm_kwargs_dev,
+                )
+                train, val = self.ts_univariate.split_before(0.7)
+                model1.fit(train, val_series=val)
+
+            with mlflow.start_run():
+                model2 = LinearRegressionModel(lags=5)
+                model2.fit(self.ts_univariate)
 
-        autolog(disable=True)
+        runs = mlflow.search_runs()
+        assert len(runs) == 2, "Expected two separate runs for two fits"
+
+        for _, run in runs.iterrows():
+            assert run["tags.darts.model_class"] in [
+                "NBEATSModel",
+                "LinearRegressionModel",
+            ]
+            assert run["tags.mlflow.runName"] is not None
+
+    def test_save_load_preserves_series_metadata(self, tmpdir_fn):
+        """Test that save/load preserves multivariate and static covariate structure"""
+        target = self.ts_multivariate.with_static_covariates(
+            pd.DataFrame({"stat1": [1.0], "stat2": [2.0]})
+        )
+
+        model = LinearRegressionModel(lags=5)
+        model.fit(target)
+
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path)
+
+        loaded_model = load_model(f"file://{model_path}")
+
+        pred_original = model.predict(n=3)
+        pred_loaded = loaded_model.predict(n=3)
+
+        # verify multivariate structure preserved
+        assert pred_original.width == pred_loaded.width == 2, (
+            "Should maintain 2 components"
+        )
+        assert pred_original.n_components == pred_loaded.n_components == 2
+
+        np.testing.assert_array_almost_equal(
+            pred_original.values(), pred_loaded.values(), decimal=4
+        )
 
     def test_load_nonexistent_model(self):
         """Test that loading nonexistent model raises appropriate error"""
         with pytest.raises(Exception):
             load_model("runs:/fake_run_id/model")
 
+    def test_load_invalid_uri_fails(self):
+        """Test that loading with invalid URI raises an error"""
+        with pytest.raises(Exception):
+            load_model("invalid://bad/uri")
+
+        with pytest.raises(Exception):
+            load_model("file:///nonexistent/path/to/model")
+
+    def test_load_corrupted_mlmodel_fails(self, tmpdir_fn):
+        """Test that loading with corrupted MLmodel file fails"""
+        # save a valid model
+        model = LinearRegressionModel(lags=5)
+        model.fit(self.ts_univariate)
+
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path)
+
+        # corrupt the MLmodel file
+        mlmodel_path = os.path.join(model_path, "MLmodel")
+        with open(mlmodel_path, "w") as f:
+            f.write("corrupted content that is not valid YAML {[[")
+
+        # loading should fail
+        with pytest.raises(Exception):
+            load_model(f"file://{model_path}")
+
+    def test_load_missing_model_file_fails(self, tmpdir_fn):
+        """Test that loading with missing model data file fails"""
+        # save a valid model
+        model = LinearRegressionModel(lags=5)
+        model.fit(self.ts_univariate)
+
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path)
+
+        # remove the model data file
+        model_data_path = os.path.join(model_path, "data", "model.pkl")
+        os.remove(model_data_path)
+
+        # loading should fail
+        with pytest.raises(Exception):
+            load_model(f"file://{model_path}")
+
     @pytest.mark.parametrize(
         "model_cls,fit_kwargs",
         [
@@ -249,6 +591,30 @@ def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
         save_model(model, model_path)
         loaded = load_model(f"file://{model_path}")
 
-        pred1 = model.predict(n=5)
-        pred2 = loaded.predict(n=5)
-        np.testing.assert_array_almost_equal(pred1.values(), pred2.values(), decimal=4)
+        assert_predictions_equal(model, loaded, n=5)
+
+    @pytest.mark.parametrize(
+        "series,series_name",
+        [
+            ("ts_multivariate", "multivariate"),
+            ("ts_with_static", "static_covariates"),
+        ],
+    )
+    def test_save_load_with_special_series(self, tmpdir_fn, series, series_name):
+        """Test save/load with multivariate and static covariate series"""
+        test_series = getattr(self, series)
+
+        model = LinearRegressionModel(lags=5)
+        model.fit(test_series)
+
+        model_path = os.path.join(tmpdir_fn, f"test_model_{series_name}")
+        save_model(model, model_path)
+
+        loaded_model = load_model(f"file://{model_path}")
+
+        assert_predictions_equal(model, loaded_model, n=3)
+
+        # verify the series dimensions are preserved
+        pred_original = model.predict(n=3)
+        pred_loaded = loaded_model.predict(n=3)
+        assert pred_original.width == pred_loaded.width

From a0e663fd990a216fe4a0fc0ae279a9ce2c054f27 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 17 Feb 2026 10:57:40 +0100
Subject: [PATCH 014/154] ForecastingModel subclasses handling for autolog

---
 darts/utils/mlflow.py | 242 ++++++++++++++++++++++++------------------
 1 file changed, 137 insertions(+), 105 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 59a33ceafc..2032a08514 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -309,6 +309,7 @@ def autolog(
     log_params: bool = True,
     disable: bool = False,
     silent: bool = False,
+    manage_run: bool = True,
 ) -> None:
     """Enable (or disable) automatic MLflow logging for darts models.
 
@@ -333,29 +334,39 @@ def autolog(
     silent
         If ``True`` (default ``False``), suppress all event logging and warnings from
         MLflow during autologging.
+    manage_run
+        If `True`, applies the `with_managed_run` wrapper to the specified
+        `patch_function`, which automatically creates & terminates an MLflow
+        active run during patch code execution if necessary. If `False`,
+        does not apply the `with_managed_run` wrapper to the specified
+        `patch_function`.
     """
-    safe_patch(
-        FLAVOR_NAME,
-        ForecastingModel,
-        "fit",
-        _patched_fit,
-        manage_run=True,
-    )
 
-    try:
-        from darts.models.forecasting.torch_forecasting_model import (
-            TorchForecastingModel,
-        )
+    # recursively get all subclasses of ForecastingModel that override fit()
+    def get_all_subclasses(cls):
+        all_subclasses = []
+        for subclass in cls.__subclasses__():
+            all_subclasses.append(subclass)
+            all_subclasses.extend(get_all_subclasses(subclass))
+        return all_subclasses
 
-        safe_patch(
-            FLAVOR_NAME,
-            TorchForecastingModel,
-            "fit",
-            _patched_fit,
-            manage_run=True,
-        )
-    except ImportError:
-        pass
+    classes_to_patch = [ForecastingModel]
+
+    for subclass in get_all_subclasses(ForecastingModel):
+        if "fit" in subclass.__dict__:
+            classes_to_patch.append(subclass)
+
+    for cls in classes_to_patch:
+        try:
+            safe_patch(
+                FLAVOR_NAME,
+                cls,
+                "fit",
+                _patched_fit,
+                manage_run=manage_run,
+            )
+        except Exception:
+            pass
 
 
 def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
@@ -400,70 +411,11 @@ def get_default_conda_env(is_torch: bool = False) -> dict:
     )
 
 
-def _is_torch_model(model) -> bool:
-    """Check if a model is a TorchForecastingModel.
-
-    Parameters
-    ----------
-    model
-        A Darts forecasting model instance.
-
-    Returns
-    -------
-    bool
-        True if the model is a TorchForecastingModel, False otherwise.
-    """
-    try:
-        from darts.models.forecasting.torch_forecasting_model import (
-            TorchForecastingModel,
-        )
-
-        return isinstance(model, TorchForecastingModel)
-    except ImportError:
-        return False
-
-
-def _get_model_class_path(model) -> tuple[str, str]:
-    """Extract the module path and class name from a model instance.
-
-    Parameters
-    ----------
-    model
-        A Darts forecasting model instance.
-
-    Returns
-    -------
-    tuple[str, str]
-        A tuple containing (module_path, class_name).
-    """
-    cls = type(model)
-    return cls.__module__, cls.__name__
-
-
-def _import_model_class(module_path: str, class_name: str):
-    """Dynamically import and return a model class.
-
-    Parameters
-    ----------
-    module_path : str
-        The fully qualified module path (e.g., "darts.models.exponential_smoothing").
-    class_name : str
-        The name of the class to import (e.g., "ExponentialSmoothing").
-
-    Returns
-    -------
-    type
-        The imported model class.
-    """
-    module = importlib.import_module(module_path)
-    return getattr(module, class_name)
-
-
 def _log_model_params(model) -> None:
     """Log model creation parameters to MLflow.
 
     Extracts model parameters from ``model.model_params`` and logs them to the active
-    MLflow run. Logs non-serializable values as "<non-serialisable>".
+    MLflow run.
 
     Parameters
     ----------
@@ -476,16 +428,8 @@ def _log_model_params(model) -> None:
         logger.debug("Model has no model_params attribute; skipping parameter logging.")
         return
 
-    safe_params = {}
-    for key, value in params.items():
-        try:
-            safe_params[key] = str(value)
-        except Exception:
-            safe_params[key] = "<non-serialisable>"
-
-    # mlflow validates and truncates the param values internally
-    if safe_params:
-        mlflow.log_params(safe_params)
+    if params:
+        mlflow.log_params(params)
 
 
 def _log_covariate_info(model) -> None:
@@ -497,7 +441,7 @@ def _log_covariate_info(model) -> None:
 
     Logs three types of information:
     - Tags: Boolean flags for filtering (e.g., "uses_past_covariates")
-    - Parameters: Feature counts and names (truncated to MAX_PARAM_VAL_LENGTH chars)
+    - Parameters: Feature counts and names (truncated by MLflow)
     - Artifact: Complete covariate metadata as JSON file
 
     Parameters
@@ -526,21 +470,14 @@ def _log_covariate_info(model) -> None:
         ),
     ]
 
-    covariate_info = {}
-
-    for cov_key, uses_attr, series_attr, names_attr in covariate_types:
-        info = {"used": False, "count": 0, "names": []}
-
-        if getattr(model, uses_attr, False):
-            info["used"] = True
-            series = getattr(model, series_attr, None)
-            if series is not None:
-                names = getattr(series, names_attr).tolist()
-                info["names"] = names
-                info["count"] = len(names)
-
-        covariate_info[cov_key] = info
+    covariate_info = {
+        cov_key: _extract_covariate_metadata(
+            model, cov_key, uses_attr, series_attr, names_attr
+        )
+        for cov_key, uses_attr, series_attr, names_attr in covariate_types
+    }
 
+    for cov_key, info in covariate_info.items():
         mlflow.set_tag(f"uses_{cov_key}", str(info["used"]).lower())
         mlflow.log_param(f"n_{cov_key}", info["count"])
 
@@ -556,6 +493,101 @@ def _log_covariate_info(model) -> None:
         mlflow.log_artifact(covariates_path)
 
 
+def _is_torch_model(model) -> bool:
+    """Check if a model is a TorchForecastingModel.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance.
+
+    Returns
+    -------
+    bool
+        True if the model is a TorchForecastingModel, False otherwise.
+    """
+    try:
+        from darts.models.forecasting.torch_forecasting_model import (
+            TorchForecastingModel,
+        )
+
+        return isinstance(model, TorchForecastingModel)
+    except ImportError:
+        return False
+
+
+def _get_model_class_path(model) -> tuple[str, str]:
+    """Extract the module path and class name from a model instance.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance.
+
+    Returns
+    -------
+    tuple[str, str]
+        A tuple containing (module_path, class_name).
+    """
+    cls = type(model)
+    return cls.__module__, cls.__name__
+
+
+def _import_model_class(module_path: str, class_name: str):
+    """Dynamically import and return a model class.
+
+    Parameters
+    ----------
+    module_path : str
+        The fully qualified module path (e.g., "darts.models.exponential_smoothing").
+    class_name : str
+        The name of the class to import (e.g., "ExponentialSmoothing").
+
+    Returns
+    -------
+    type
+        The imported model class.
+    """
+    module = importlib.import_module(module_path)
+    return getattr(module, class_name)
+
+
+def _extract_covariate_metadata(
+    model, cov_type: str, uses_attr: str, series_attr: str, names_attr: str
+) -> dict:
+    """Extract metadata for a single covariate type.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance.
+    cov_type : str
+        Covariate type name (e.g., "past_covariates").
+    uses_attr : str
+        Model attribute name indicating covariate usage.
+    series_attr : str
+        Model attribute name for the covariate series.
+    names_attr : str
+        Series attribute name for feature names ("components" or "columns").
+
+    Returns
+    -------
+    dict
+        Dictionary with keys: "used" (bool), "count" (int), "names" (list).
+    """
+    info = {"used": False, "count": 0, "names": []}
+
+    if getattr(model, uses_attr, False):
+        info["used"] = True
+        series = getattr(model, series_attr, None)
+        if series is not None:
+            names = getattr(series, names_attr).tolist()
+            info["names"] = names
+            info["count"] = len(names)
+
+    return info
+
+
 def _patched_fit(original, self, *args, **kwargs):
     """Patch function for ForecastingModel.fit() autologging.
 

From 93ca22a95e1006234207218aa8137adc2329bc12 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 17 Feb 2026 14:43:56 +0100
Subject: [PATCH 015/154] save models with clean=True

---
 darts/tests/optional_deps/test_mlflow.py | 25 +++++++++++++++---------
 darts/utils/mlflow.py                    |  5 +++--
 2 files changed, 19 insertions(+), 11 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index f9cd64915d..ed92f2475e 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -67,10 +67,14 @@ def assert_mlflow_artifacts_exist(path: str, is_torch: bool = False):
         assert os.path.exists(os.path.join(path, "data", "model.pkl"))
 
 
-def assert_predictions_equal(model1, model2, n: int, decimal: int = 4):
-    """Assert that two models produce equivalent predictions."""
+def assert_predictions_equal(model1, model2, n: int, decimal: int = 4, series=None):
+    """Assert that two models produce equivalent predictions. If series is provided,
+    it will be passed to the second model's predict method (for global models that require it)."""
     pred1 = model1.predict(n=n)
-    pred2 = model2.predict(n=n)
+    if series is not None:
+        pred2 = model2.predict(n=n, series=series)
+    else:
+        pred2 = model2.predict(n=n)
     np.testing.assert_array_almost_equal(
         pred1.values(), pred2.values(), decimal=decimal
     )
@@ -111,7 +115,7 @@ def test_save_load_regression_model(self, tmpdir_fn):
         assert_mlflow_artifacts_exist(model_path, is_torch=False)
 
         loaded_model = load_model(f"file://{model_path}")
-        assert_predictions_equal(model, loaded_model, n=3)
+        assert_predictions_equal(model, loaded_model, n=3, series=self.ts_univariate)
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_save_load_torch_model(self, tmpdir_fn):
@@ -127,7 +131,7 @@ def test_save_load_torch_model(self, tmpdir_fn):
         assert_mlflow_artifacts_exist(model_path, is_torch=True)
 
         loaded_model = load_model(f"file://{model_path}")
-        assert_predictions_equal(model, loaded_model, n=2)
+        assert_predictions_equal(model, loaded_model, n=2, series=self.ts_univariate)
 
     def test_log_model_basic(self, mlflow_tracking):
         """Test basic log_model functionality"""
@@ -511,7 +515,7 @@ def test_save_load_preserves_series_metadata(self, tmpdir_fn):
         loaded_model = load_model(f"file://{model_path}")
 
         pred_original = model.predict(n=3)
-        pred_loaded = loaded_model.predict(n=3)
+        pred_loaded = loaded_model.predict(n=3, series=target)
 
         # verify multivariate structure preserved
         assert pred_original.width == pred_loaded.width == 2, (
@@ -591,7 +595,10 @@ def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
         save_model(model, model_path)
         loaded = load_model(f"file://{model_path}")
 
-        assert_predictions_equal(model, loaded, n=5)
+        # Only pass series for global models (LinearRegressionModel)
+        # Local models (ExponentialSmoothing) don't need it
+        series = self.ts_univariate if fit_kwargs else None
+        assert_predictions_equal(model, loaded, n=5, series=series)
 
     @pytest.mark.parametrize(
         "series,series_name",
@@ -612,9 +619,9 @@ def test_save_load_with_special_series(self, tmpdir_fn, series, series_name):
 
         loaded_model = load_model(f"file://{model_path}")
 
-        assert_predictions_equal(model, loaded_model, n=3)
+        assert_predictions_equal(model, loaded_model, n=3, series=test_series)
 
         # verify the series dimensions are preserved
         pred_original = model.predict(n=3)
-        pred_loaded = loaded_model.predict(n=3)
+        pred_loaded = loaded_model.predict(n=3, series=test_series)
         assert pred_original.width == pred_loaded.width
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 2032a08514..40a8876cf3 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -119,12 +119,13 @@ def save_model(
 
     os.makedirs(data_dir, exist_ok=True)
 
+    # pass in clean=True to not include any timeseries or callbacks within the model file
     if is_torch:
         model_file = _MODEL_FILE_TORCH
-        model.save(os.path.join(data_dir, model_file))
+        model.save(os.path.join(data_dir, model_file), clean=True)
     else:
         model_file = _MODEL_FILE_STAT
-        model.save(os.path.join(data_dir, model_file))
+        model.save(os.path.join(data_dir, model_file), clean=True)
 
     module_path, class_name = _get_model_class_path(model)
 

From ac4b0c6fe584a56f62f955c22dd15c97cb0616ab Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 17 Feb 2026 14:59:08 +0100
Subject: [PATCH 016/154] logging update

---
 darts/utils/mlflow.py | 30 ++++++++++++++++++++++--------
 1 file changed, 22 insertions(+), 8 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 40a8876cf3..e25d8ea052 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -44,7 +44,7 @@
 from mlflow.utils.requirements_utils import _get_pinned_requirement
 
 import darts
-from darts.logging import get_logger
+from darts.logging import get_logger, raise_if, raise_if_not
 from darts.models.forecasting.forecasting_model import ForecastingModel
 from darts.utils.utils import PL_AVAILABLE
 
@@ -110,6 +110,11 @@ def save_model(
         Optional MLflow Model object to use for saving. When provided (typically by
         ``Model.log()``), this model instance is used instead of creating a new one.
     """
+    raise_if_not(
+        isinstance(model, ForecastingModel),
+        "model must be an instance of darts.models.forecasting.ForecastingModel",
+        logger,
+    )
     _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
     _validate_and_prepare_target_save_path(path)
     code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
@@ -366,8 +371,8 @@ def get_all_subclasses(cls):
                 _patched_fit,
                 manage_run=manage_run,
             )
-        except Exception:
-            pass
+        except Exception as e:
+            logger.info(f"Failed to patch {cls.__name__}.fit() for autologging: {e}")
 
 
 def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
@@ -426,7 +431,7 @@ def _log_model_params(model) -> None:
     try:
         params = model.model_params
     except AttributeError:
-        logger.debug("Model has no model_params attribute; skipping parameter logging.")
+        logger.info("Model has no model_params attribute; skipping parameter logging.")
         return
 
     if params:
@@ -514,6 +519,7 @@ def _is_torch_model(model) -> bool:
 
         return isinstance(model, TorchForecastingModel)
     except ImportError:
+        logger.info("TorchForecastingModel not available; treating model as non-torch")
         return False
 
 
@@ -550,7 +556,13 @@ def _import_model_class(module_path: str, class_name: str):
         The imported model class.
     """
     module = importlib.import_module(module_path)
-    return getattr(module, class_name)
+    cls = getattr(module, class_name, None)
+    raise_if(
+        cls is None,
+        f"Class '{class_name}' not found in module '{module_path}'",
+        logger,
+    )
+    return cls
 
 
 def _extract_covariate_metadata(
@@ -630,7 +642,9 @@ def _patched_fit(original, self, *args, **kwargs):
         try:
             log_model(self, name="model", log_params=False)
         except Exception as e:
-            logger.warning(f"Failed to autolog model artifact: {e}")
+            logger.warning(
+                f"Failed to autolog model artifact for {type(self).__name__}: {e}"
+            )
 
     return result
 
@@ -725,7 +739,7 @@ def _inject_mlflow_callback(model) -> None:
     existing_callbacks = model.trainer_params.get("callbacks", [])
 
     if any(isinstance(cb, _DartsMlflowCallback) for cb in existing_callbacks):
-        logger.debug("MLflow callback already present, skipping injection")
+        logger.info("MLflow callback already present, skipping injection")
         return
 
     if not isinstance(existing_callbacks, list):
@@ -733,4 +747,4 @@ def _inject_mlflow_callback(model) -> None:
 
     existing_callbacks.append(callback)
     model.trainer_params["callbacks"] = existing_callbacks
-    logger.debug(f"Injected MLflow callback into {type(model).__name__}")
+    logger.info(f"Injected MLflow callback into {type(model).__name__}")

From a35a834e94ea3e5e03e56844661691bbc3905bf7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 17 Feb 2026 15:01:30 +0100
Subject: [PATCH 017/154] unused var, tfmodel.load handles .ckpt internally

---
 darts/utils/mlflow.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index e25d8ea052..bf9a80576a 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -56,7 +56,6 @@
 
 _MODEL_FILE_STAT = "model.pkl"
 _MODEL_FILE_TORCH = "model.pt"
-_MODEL_FILE_TORCH_CKPT = "model.pt.ckpt"
 
 
 def save_model(

From 2695cc9f645c188badd1945b78b1175814b3329a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 09:58:50 +0100
Subject: [PATCH 018/154] documentation

---
 darts/utils/mlflow.py | 51 +++++++++++++++++++++++++++++--------------
 1 file changed, 35 insertions(+), 16 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index bf9a80576a..bdfa99ea86 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -5,6 +5,13 @@
 Custom MLflow model flavor for darts forecasting models. Supports saving, loading,
 logging and autolog for any darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
 to MLflow.
+
+This module is partly adapted from and inspired by the open-source
+implementation of SKtime's MLflow integration, with modifications to support
+autologging and handle Darts-specific model and covariate metadata.
+
+References:
+https://github.com/sktime/sktime/blob/main/sktime/utils/mlflow_sktime.py
 """
 
 import importlib
@@ -98,16 +105,23 @@ def save_model(
         A list of additional pip requirement strings to add to the model's environment,
         in addition to the default requirements.
     signature
-        An ``mlflow.models.ModelSignature`` instance describing model input/output.
-        Use :func:`infer_signature` to automatically generate from example inputs.
+        *Unsupported, see notes.* An ``mlflow.models.ModelSignature`` instance describing model input/output.
+        Use ``mlflow.models.infer_signature()`` to automatically generate from example inputs.
     input_example
-        An example input for the model (used by MLflow UI). Should be a DataFrame
-        created with :func:`prepare_pyfunc_input`.
+        *Unsupported, see notes.* An example input for the model (used by MLflow UI).
     metadata
         Optional dictionary of custom metadata to store in the ``MLmodel`` file.
     mlflow_model
         Optional MLflow Model object to use for saving. When provided (typically by
         ``Model.log()``), this model instance is used instead of creating a new one.
+
+    Notes
+    -----
+    Signature and input_example params are currently not supported, as they
+    are used to support serving and input validation in the MLflow pyfunc flavor,
+    which is not implemented for darts models. They are accepted as params for
+    simplifying potential future extensibility, and to keep in line with MLflow API
+    conventions.
     """
     raise_if_not(
         isinstance(model, ForecastingModel),
@@ -268,11 +282,10 @@ def log_model(
         A list of additional pip requirement strings to add to the model's environment,
         in addition to the default requirements.
     signature
-        An ``mlflow.models.ModelSignature``. Use :func:`infer_signature`
+       *Unsupported, see notes.* An ``mlflow.models.ModelSignature``. Use ``mlflow.models.infer_signature()``
         to automatically generate from example inputs.
     input_example
-        An example model input. Should be a DataFrame created with
-        :func:`prepare_pyfunc_input`.
+        *Unsupported, see notes.* An example model input.
     metadata
         Optional dict of custom metadata.
     log_params
@@ -284,6 +297,14 @@ def log_model(
     ModelInfo
         MLflow ModelInfo object containing model_uri, run_id, artifact_path,
         model_id, timestamps, and other metadata about the logged model.
+
+    Notes
+    -----
+    Signature and input_example params are currently not supported, as they
+    are used to support serving and input validation in the MLflow pyfunc flavor,
+    which is not implemented for darts models. They are accepted as params for
+    simplifying potential future extensibility, and to keep in line with MLflow API
+    conventions.
     """
     # import required as Model.log will call flavor.save_model() internally
     import darts.utils.mlflow as darts_mlflow
@@ -323,9 +344,9 @@ def autolog(
 
     1. Start an MLflow run (or reuse the currently active one).
     2. Log model creation parameters (``model.model_params``).
-    3. For PyTorch-based models: inject a callback that logs per-epoch
-       ``train_loss`` / ``val_loss`` metrics.
-    4. Log the trained model artifact at the end of training.
+    3. Log covariate usage information (past, future, and static covariates).
+    4. For PyTorch-based models: inject a callback that logs per-epoch metrics.
+    5. Log the trained model artifact at the end of training.
 
     Parameters
     ----------
@@ -476,9 +497,7 @@ def _log_covariate_info(model) -> None:
     ]
 
     covariate_info = {
-        cov_key: _extract_covariate_metadata(
-            model, cov_key, uses_attr, series_attr, names_attr
-        )
+        cov_key: _extract_covariate_metadata(model, uses_attr, series_attr, names_attr)
         for cov_key, uses_attr, series_attr, names_attr in covariate_types
     }
 
@@ -565,7 +584,7 @@ def _import_model_class(module_path: str, class_name: str):
 
 
 def _extract_covariate_metadata(
-    model, cov_type: str, uses_attr: str, series_attr: str, names_attr: str
+    model, uses_attr: str, series_attr: str, names_attr: str
 ) -> dict:
     """Extract metadata for a single covariate type.
 
@@ -573,8 +592,6 @@ def _extract_covariate_metadata(
     ----------
     model
         A Darts forecasting model instance.
-    cov_type : str
-        Covariate type name (e.g., "past_covariates").
     uses_attr : str
         Model attribute name indicating covariate usage.
     series_attr : str
@@ -606,6 +623,8 @@ def _patched_fit(original, self, *args, **kwargs):
     Handles both statistical and PyTorch-based models. For PyTorch models,
     automatically injects MLflow callback for per-epoch metrics logging.
 
+    Logs the trained model artifact if configured.
+
     Parameters
     ----------
     original

From 662b8e3c86cd06b7e42f812393133a80987db017 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 13:23:19 +0100
Subject: [PATCH 019/154] added autolog logging default/provided metrics for
 all models

---
 darts/utils/mlflow.py | 229 +++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 226 insertions(+), 3 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index bdfa99ea86..8eaf985fe3 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -17,9 +17,10 @@
 import importlib
 import json
 import os
-from typing import Optional, Union
+from typing import Callable, Optional, Union
 
 import mlflow
+import numpy as np
 import yaml
 from mlflow.models import Model
 from mlflow.models.model import MLMODEL_FILE_NAME
@@ -52,7 +53,9 @@
 
 import darts
 from darts.logging import get_logger, raise_if, raise_if_not
+from darts.metrics import mae, mape, mse, rmse
 from darts.models.forecasting.forecasting_model import ForecastingModel
+from darts.timeseries import TimeSeries
 from darts.utils.utils import PL_AVAILABLE
 
 logger = get_logger(__name__)
@@ -329,10 +332,17 @@ def log_model(
     )
 
 
+_DEFAULT_METRICS = [mae, mse, rmse, mape]
+
+
 @autologging_integration(FLAVOR_NAME)
 def autolog(
     log_models: bool = True,
     log_params: bool = True,
+    log_training_metrics: bool = True,
+    log_validation_metrics: bool = True,
+    inject_per_epoch_callbacks: bool = False,
+    extra_metrics: Optional[list[Callable]] = None,
     disable: bool = False,
     silent: bool = False,
     manage_run: bool = True,
@@ -347,6 +357,8 @@ def autolog(
     3. Log covariate usage information (past, future, and static covariates).
     4. For PyTorch-based models: inject a callback that logs per-epoch metrics.
     5. Log the trained model artifact at the end of training.
+    6. Optionally compute and log forecasting metrics on training and/or
+       validation data.
 
     Parameters
     ----------
@@ -354,6 +366,25 @@ def autolog(
         If ``True`` (default), log the trained model artifact after ``fit()``.
     log_params
         If ``True`` (default), log model creation parameters.
+    log_training_metrics
+        If ``True``, compute in-sample forecasting metrics on the training data
+        after ``fit()`` completes.
+        Default ``True``.
+    log_validation_metrics
+        If ``True``, compute forecasting metrics on the validation series
+        (``val_series``) passed to ``fit()``.  Only effective for models whose
+        ``fit()`` accepts a ``val_series`` argument (e.g. PyTorch-based models).
+        Default ``True``.
+    inject_per_epoch_callbacks
+        If ``True``, inject a PyTorch Lightning callback to log training and validation metrics at
+        the end of each epoch. Only effective for PyTorch-based models. To provide additional
+        callbacks use ``torch_metrics`` parameter while initializing the model.
+        Default ``False``.
+    extra_metrics
+        An optional list of additional Darts metric functions to log on top of
+        the defaults (``mae``, ``mse``, ``rmse``, ``mape``).  Each function
+        must follow the standard Darts metric signature
+        ``metric(actual_series, pred_series)``.
     disable
         If ``True``, restore the original ``fit()`` methods and stop
         autologging.
@@ -642,20 +673,41 @@ def _patched_fit(original, self, *args, **kwargs):
     """
     log_models = get_autologging_config(FLAVOR_NAME, "log_models", True)
     log_params = get_autologging_config(FLAVOR_NAME, "log_params", True)
+    log_training_metrics = get_autologging_config(
+        FLAVOR_NAME, "log_training_metrics", False
+    )
+    log_validation_metrics = get_autologging_config(
+        FLAVOR_NAME, "log_validation_metrics", False
+    )
+    inject_per_epoch_callbacks = get_autologging_config(
+        FLAVOR_NAME, "inject_per_epoch_callbacks", False
+    )
+    extra_metrics = get_autologging_config(FLAVOR_NAME, "extra_metrics", None)
 
     mlflow.set_tag("darts.model_class", type(self).__name__)
 
     if log_params:
         _log_model_params(self)
 
-    # Inject callback for torch models (no-op for non-torch models)
-    _inject_mlflow_callback(self)
+    # Inject per-epoch callbacks for torch models (no-op for non-torch models)
+    if inject_per_epoch_callbacks:
+        _inject_mlflow_callback(self)
 
     result = original(self, *args, **kwargs)
 
     if log_params:
         _log_covariate_info(self)
 
+    if log_training_metrics or log_validation_metrics:
+        _log_forecasting_metrics(
+            model=self,
+            fit_args=args,
+            fit_kwargs=kwargs,
+            log_training=log_training_metrics,
+            log_validation=log_validation_metrics,
+            extra_metrics=extra_metrics,
+        )
+
     if log_models:
         try:
             log_model(self, name="model", log_params=False)
@@ -667,6 +719,177 @@ def _patched_fit(original, self, *args, **kwargs):
     return result
 
 
+def _log_forecasting_metrics(
+    model,
+    fit_args: tuple,
+    fit_kwargs: dict,
+    log_training: bool,
+    log_validation: bool,
+    extra_metrics: Optional[list[Callable]] = None,
+) -> None:
+    """Compute and log training and/or validation forecasting metrics to MLflow.
+
+    After a model has been fitted this function optionally:
+
+    * Runs ``model.backtest()`` on the training series (``retrain=False``)
+      and logs metrics with a ``train_`` prefix.
+    * Runs ``model.backtest()`` on the validation series and logs metrics
+      with a ``val_`` prefix.
+
+    For multiple series the metrics are averaged to produce a single value per metric.
+
+    Parameters
+    ----------
+    model
+        A fitted Darts forecasting model.
+    fit_args
+        Positional arguments originally passed to ``fit()``.
+    fit_kwargs
+        Keyword arguments originally passed to ``fit()``.
+    log_training
+        Whether to compute and log in-sample training metrics.
+    log_validation
+        Whether to compute and log validation metrics.
+    extra_metrics
+        Optional extra metric functions in addition to the defaults.
+    """
+    metrics_list = _get_metrics_list(extra_metrics)
+
+    # determine forecast horizon from model attributes
+    forecast_horizon = getattr(model, "output_chunk_length", None) or 1
+
+    # extract series and covariates from fit() call
+    train_series = fit_args[0] if fit_args else fit_kwargs.get("series")
+    past_covariates = fit_kwargs.get("past_covariates")
+    future_covariates = fit_kwargs.get("future_covariates")
+    val_series = fit_kwargs.get("val_series")
+    val_past_covariates = fit_kwargs.get("val_past_covariates")
+    val_future_covariates = fit_kwargs.get("val_future_covariates")
+
+    if log_training and train_series is not None:
+        try:
+            _backtest_and_log(
+                model,
+                series=train_series,
+                metrics_list=metrics_list,
+                prefix="train",
+                past_covariates=past_covariates,
+                future_covariates=future_covariates,
+                forecast_horizon=forecast_horizon,
+            )
+        except Exception:
+            logger.info(
+                "Could not compute training forecasting metrics for "
+                f"{type(model).__name__}.",
+                exc_info=True,
+            )
+
+    if log_validation and val_series is not None:
+        try:
+            _backtest_and_log(
+                model,
+                series=val_series,
+                metrics_list=metrics_list,
+                prefix="val",
+                past_covariates=val_past_covariates or past_covariates,
+                future_covariates=val_future_covariates or future_covariates,
+                forecast_horizon=forecast_horizon,
+            )
+        except Exception:
+            logger.info(
+                "Could not compute validation forecasting metrics for "
+                f"{type(model).__name__}.",
+                exc_info=True,
+            )
+
+
+def _backtest_and_log(
+    model,
+    series: Union[TimeSeries, list[TimeSeries]],
+    metrics_list: list[Callable],
+    prefix: str,
+    past_covariates=None,
+    future_covariates=None,
+    forecast_horizon: int = 1,
+) -> None:
+    """Run ``model.backtest()`` and log the resulting scores to MLflow.
+
+    Parameters
+    ----------
+    model
+        A fitted Darts forecasting model.
+    series
+        One or more target series to evaluate on.
+    metrics_list
+        List of Darts metric functions to evaluate.
+    prefix
+        Prefix for the logged metric names (e.g. ``"train"`` or ``"val"``).
+    past_covariates
+        Optional past covariates matching ``series``.
+    future_covariates
+        Optional future covariates matching ``series``.
+    forecast_horizon
+        Number of steps to forecast at each backtest step.
+    """
+    backtest_kwargs = dict(
+        series=series,
+        forecast_horizon=forecast_horizon,
+        retrain=False,
+        overlap_end=False,
+        last_points_only=True,
+        reduction=None,
+        verbose=False,
+        show_warnings=False,
+    )
+    if past_covariates is not None:
+        backtest_kwargs["past_covariates"] = past_covariates
+    if future_covariates is not None:
+        backtest_kwargs["future_covariates"] = future_covariates
+
+    logged = {}
+    for metric_fn in metrics_list:
+        try:
+            score = model.backtest(**backtest_kwargs, metric=metric_fn)
+            # backtest returns a float, np.ndarray, or list depending on the
+            # input.  We want a single scalar per metric so we take the mean.
+
+            logged[f"{prefix}_{metric_fn.__name__}"] = float(np.nanmean(score))
+        except Exception:
+            logger.debug(
+                f"Backtest metric {metric_fn.__name__} failed for "
+                f"{type(model).__name__}, skipping.",
+                exc_info=True,
+            )
+
+    if logged:
+        mlflow.log_metrics(logged)
+
+
+def _get_metrics_list(
+    extra_metrics: Optional[list[Callable]] = None,
+) -> list[Callable]:
+    """Return the combined list of default and extra metric functions.
+
+    Parameters
+    ----------
+    extra_metrics
+        Optional additional metric functions to append to the defaults.
+
+    Returns
+    -------
+    list[Callable]
+        A list of metric functions (``mae``, ``mse``, ``rmse``, ``mape``, plus extras).
+    """
+    metrics = list(_DEFAULT_METRICS)
+    if extra_metrics:
+        seen_names = {m.__name__ for m in metrics}
+        for m in extra_metrics:
+            if m.__name__ not in seen_names:
+                metrics.append(m)
+                seen_names.add(m.__name__)
+    return metrics
+
+
 if PL_AVAILABLE:
     import pytorch_lightning as pl
 

From f19d6bf200976ca2709881f2754e978c99574481 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 13:24:53 +0100
Subject: [PATCH 020/154] autolog metric unit tests

---
 darts/tests/optional_deps/test_mlflow.py | 272 +++++++++++++++--------
 1 file changed, 177 insertions(+), 95 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index ed92f2475e..b45d6f2e9a 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -38,13 +38,18 @@ def mlflow_tracking(tmpdir_fn):
 
 @pytest.fixture
 def autolog_context():
-    """Context manager to safely enable/disable autolog for a test."""
+    """Context manager to safely enable/disable autolog for a test.
+
+    Usage:
+        with autolog_context():            # default autolog
+        with autolog_context(log_training_metrics=True):   # custom kwargs
+    """
     from contextlib import contextmanager
 
     @contextmanager
-    def _autolog_context():
+    def _autolog_context(**kwargs):
         autolog(disable=True)  # clean state
-        autolog()  # enable for test
+        autolog(**kwargs)  # enable with custom kwargs
         try:
             yield
         finally:
@@ -91,19 +96,6 @@ class TestMLflow:
     ts_past_cov = tg.sine_timeseries(length=62).astype("float32")
     ts_future_cov = tg.constant_timeseries(value=1.0, length=62).astype("float32")
 
-    def test_save_load_statistical_model(self, tmpdir_fn):
-        """Test save/load round-trip for statistical model"""
-        model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
-
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path)
-
-        assert_mlflow_artifacts_exist(model_path, is_torch=False)
-
-        loaded_model = load_model(f"file://{model_path}")
-        assert_predictions_equal(model, loaded_model, n=5)
-
     def test_save_load_regression_model(self, tmpdir_fn):
         """Test save/load round-trip for regression model"""
         model = LinearRegressionModel(lags=5)
@@ -144,22 +136,6 @@ def test_log_model_basic(self, mlflow_tracking):
         loaded_model = load_model(log_info.model_uri)
         assert_predictions_equal(model, loaded_model, n=5)
 
-    def test_log_model_with_params(self, mlflow_tracking):
-        """Test that log_params=True logs model parameters"""
-        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
-
-        with mlflow.start_run():
-            log_model(model, name="model", log_params=True)
-            run_id = mlflow.active_run().info.run_id
-
-        run = mlflow.get_run(run_id)
-        assert run.data.params["lags"] == "5"
-        assert run.data.params["lags_past_covariates"] == "3"
-        assert run.data.params["n_past_covariates"] == "1"
-        assert run.data.params["n_future_covariates"] == "0"
-        assert run.data.params["n_static_covariates"] == "0"
-
     def test_log_model_with_covariates(self, mlflow_tracking):
         """Test that covariate info is logged with correct values"""
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
@@ -287,7 +263,7 @@ def test_autolog_parameters(self, mlflow_tracking, autolog_context):
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
         """Test that autolog logs training metrics for torch models"""
-        with autolog_context():
+        with autolog_context(inject_per_epoch_callbacks=True):
             model = NBEATSModel(
                 input_chunk_length=4,
                 output_chunk_length=2,
@@ -324,7 +300,7 @@ def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_injects_callback(self, mlflow_tracking, autolog_context):
         """Test that autolog injects MLflow callback into torch models"""
-        with autolog_context():
+        with autolog_context(inject_per_epoch_callbacks=True):
             model = NBEATSModel(
                 input_chunk_length=4,
                 output_chunk_length=2,
@@ -356,33 +332,6 @@ def test_autolog_injects_callback(self, mlflow_tracking, autolog_context):
                 f"_DartsMlflowCallback not found in {[type(cb).__name__ for cb in final_callbacks]}"
             )
 
-    def test_covariate_artifact_schema(self, mlflow_tracking):
-        """Test that covariate artifact has correct JSON schema"""
-        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
-
-        with mlflow.start_run():
-            log_model(model, name="model", log_params=True)
-
-            artifact_uri = mlflow.get_artifact_uri("covariates.json")
-            artifact_path = artifact_uri.replace("file://", "")
-
-            with open(artifact_path) as f:
-                cov_data = json.load(f)
-
-        # validate schema structure
-        required_keys = ["past_covariates", "future_covariates", "static_covariates"]
-        assert all(key in cov_data for key in required_keys), (
-            "Missing required covariate keys"
-        )
-
-        for cov_type in required_keys:
-            cov_info = cov_data[cov_type]
-            assert "used" in cov_info and isinstance(cov_info["used"], bool)
-            assert "count" in cov_info and isinstance(cov_info["count"], int)
-            assert "names" in cov_info and isinstance(cov_info["names"], list)
-            assert cov_info["count"] == len(cov_info["names"])
-
     def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         """Test saving/loading multivariate series with all covariate types"""
         # create multivariate target with static covariates
@@ -428,7 +377,7 @@ def test_callback_injection_with_existing_callbacks(
         else:
             pytest.skip("PyTorch Lightning not available")
 
-        with autolog_context():
+        with autolog_context(inject_per_epoch_callbacks=True):
             model = NBEATSModel(
                 input_chunk_length=4,
                 output_chunk_length=2,
@@ -472,34 +421,6 @@ def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
         assert "ExponentialSmoothing" in model_classes
         assert "LinearRegressionModel" in model_classes
 
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_torch_model_multiple_fits(self, mlflow_tracking, autolog_context):
-        """Test autolog with multiple fits of a torch model"""
-        with autolog_context():
-            with mlflow.start_run():
-                model1 = NBEATSModel(
-                    input_chunk_length=4,
-                    output_chunk_length=2,
-                    n_epochs=1,
-                    **tfm_kwargs_dev,
-                )
-                train, val = self.ts_univariate.split_before(0.7)
-                model1.fit(train, val_series=val)
-
-            with mlflow.start_run():
-                model2 = LinearRegressionModel(lags=5)
-                model2.fit(self.ts_univariate)
-
-        runs = mlflow.search_runs()
-        assert len(runs) == 2, "Expected two separate runs for two fits"
-
-        for _, run in runs.iterrows():
-            assert run["tags.darts.model_class"] in [
-                "NBEATSModel",
-                "LinearRegressionModel",
-            ]
-            assert run["tags.mlflow.runName"] is not None
-
     def test_save_load_preserves_series_metadata(self, tmpdir_fn):
         """Test that save/load preserves multivariate and static covariate structure"""
         target = self.ts_multivariate.with_static_covariates(
@@ -527,11 +448,6 @@ def test_save_load_preserves_series_metadata(self, tmpdir_fn):
             pred_original.values(), pred_loaded.values(), decimal=4
         )
 
-    def test_load_nonexistent_model(self):
-        """Test that loading nonexistent model raises appropriate error"""
-        with pytest.raises(Exception):
-            load_model("runs:/fake_run_id/model")
-
     def test_load_invalid_uri_fails(self):
         """Test that loading with invalid URI raises an error"""
         with pytest.raises(Exception):
@@ -625,3 +541,169 @@ def test_save_load_with_special_series(self, tmpdir_fn, series, series_name):
         pred_original = model.predict(n=3)
         pred_loaded = loaded_model.predict(n=3, series=test_series)
         assert pred_original.width == pred_loaded.width
+
+    def test_autolog_training_metrics_regression(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Test that autolog computes and logs in-sample training metrics for regression models."""
+        with autolog_context(log_training_metrics=True):
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5, output_chunk_length=3)
+                model.fit(self.ts_univariate)
+
+        run_data = mlflow.get_run(run.info.run_id).data
+
+        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
+            assert metric_name in run_data.metrics, f"{metric_name} should be logged"
+            assert np.isfinite(run_data.metrics[metric_name])
+
+    def test_autolog_extra_metrics(self, mlflow_tracking, autolog_context):
+        """Test that extra_metrics are logged alongside defaults."""
+        from darts.metrics import r2_score, smape
+
+        with autolog_context(
+            log_training_metrics=True, extra_metrics=[smape, r2_score]
+        ):
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5, output_chunk_length=3)
+                model.fit(self.ts_univariate)
+
+        run_data = mlflow.get_run(run.info.run_id).data
+
+        # defaults
+        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
+            assert metric_name in run_data.metrics
+        # extras
+        assert "train_smape" in run_data.metrics
+        assert "train_r2_score" in run_data.metrics
+
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_training_metrics_torch(self, mlflow_tracking, autolog_context):
+        """Test that autolog logs training metrics for torch models including epochs_trained."""
+        with autolog_context(
+            log_training_metrics=True, inject_per_epoch_callbacks=True
+        ):
+            with mlflow.start_run() as run:
+                model = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=2,
+                    **tfm_kwargs_dev,
+                )
+                train, val = self.ts_univariate.split_before(0.7)
+                model.fit(train)
+
+        run_data = mlflow.get_run(run.info.run_id).data
+
+        assert "train_loss" in run_data.metrics
+        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
+            assert metric_name in run_data.metrics, (
+                f"{metric_name} should be logged for torch model"
+            )
+
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_validation_metrics_torch(self, mlflow_tracking, autolog_context):
+        """Test that validation metrics are computed on val_series for torch models."""
+        with autolog_context(log_training_metrics=True, log_validation_metrics=True):
+            with mlflow.start_run() as run:
+                model = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=2,
+                    **tfm_kwargs_dev,
+                )
+                train, val = self.ts_univariate.split_before(0.7)
+                model.fit(train, val_series=val)
+
+        run_data = mlflow.get_run(run.info.run_id).data
+
+        # validation forecasting metrics
+        for metric_name in ["val_mae", "val_mse", "val_rmse", "val_mape"]:
+            assert metric_name in run_data.metrics, (
+                f"{metric_name} should be logged for torch model with val_series"
+            )
+            assert np.isfinite(run_data.metrics[metric_name])
+
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_validation_metrics_disabled(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Test that validation metrics are NOT logged when log_validation_metrics=False."""
+        with autolog_context(log_training_metrics=True, log_validation_metrics=False):
+            with mlflow.start_run() as run:
+                model = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=2,
+                    **tfm_kwargs_dev,
+                )
+                train, val = self.ts_univariate.split_before(0.7)
+                model.fit(train, val_series=val)
+
+        run_data = mlflow.get_run(run.info.run_id).data
+
+        # training metrics should still be present
+        assert "train_mae" in run_data.metrics
+        # validation metrics should NOT be present
+        for metric_name in ["val_mae", "val_mse", "val_rmse", "val_mape"]:
+            assert metric_name not in run_data.metrics, (
+                f"{metric_name} should NOT be logged when validation disabled"
+            )
+
+    def test_autolog_training_metrics_multiple_series(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Test that backtest-based training metrics work with multiple series."""
+        ts1 = tg.linear_timeseries(start_value=10, end_value=50, length=50).astype(
+            "float32"
+        )
+        ts2 = tg.linear_timeseries(start_value=20, end_value=60, length=50).astype(
+            "float32"
+        )
+
+        with autolog_context(log_training_metrics=True):
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5, output_chunk_length=3)
+                model.fit([ts1, ts2])
+
+        run_data = mlflow.get_run(run.info.run_id).data
+
+        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
+            assert metric_name in run_data.metrics, (
+                f"{metric_name} should be logged for multiple series"
+            )
+            assert np.isfinite(run_data.metrics[metric_name])
+
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_training_metrics_multiple_series_torch(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Test that backtest-based training metrics work with multiple series on torch models."""
+        ts1 = tg.linear_timeseries(start_value=10, end_value=50, length=50).astype(
+            "float32"
+        )
+        ts2 = tg.linear_timeseries(start_value=20, end_value=60, length=50).astype(
+            "float32"
+        )
+
+        with autolog_context(log_training_metrics=True):
+            with mlflow.start_run() as run:
+                model = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=2,
+                    pl_trainer_kwargs={
+                        "accelerator": "cpu",
+                        "enable_progress_bar": False,
+                        "enable_model_summary": False,
+                    },
+                )
+                model.fit([ts1, ts2])
+
+        run_data = mlflow.get_run(run.info.run_id).data
+
+        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
+            assert metric_name in run_data.metrics, (
+                f"{metric_name} should be logged for torch multiple series"
+            )
+            assert np.isfinite(run_data.metrics[metric_name])

From bd932a9034ad626a03837afacddca2aef5d5eff1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 13:25:59 +0100
Subject: [PATCH 021/154] removed redundant tests

---
 darts/tests/optional_deps/test_mlflow.py | 95 +++++++++++++++++++++++-
 1 file changed, 92 insertions(+), 3 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index b45d6f2e9a..b72200f843 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -96,6 +96,19 @@ class TestMLflow:
     ts_past_cov = tg.sine_timeseries(length=62).astype("float32")
     ts_future_cov = tg.constant_timeseries(value=1.0, length=62).astype("float32")
 
+    def test_save_load_statistical_model(self, tmpdir_fn):
+        """Test save/load round-trip for statistical model"""
+        model = ExponentialSmoothing()
+        model.fit(self.ts_univariate)
+
+        model_path = os.path.join(tmpdir_fn, "test_model")
+        save_model(model, model_path)
+
+        assert_mlflow_artifacts_exist(model_path, is_torch=False)
+
+        loaded_model = load_model(f"file://{model_path}")
+        assert_predictions_equal(model, loaded_model, n=5)
+
     def test_save_load_regression_model(self, tmpdir_fn):
         """Test save/load round-trip for regression model"""
         model = LinearRegressionModel(lags=5)
@@ -136,6 +149,22 @@ def test_log_model_basic(self, mlflow_tracking):
         loaded_model = load_model(log_info.model_uri)
         assert_predictions_equal(model, loaded_model, n=5)
 
+    def test_log_model_with_params(self, mlflow_tracking):
+        """Test that log_params=True logs model parameters"""
+        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
+
+        with mlflow.start_run():
+            log_model(model, name="model", log_params=True)
+            run_id = mlflow.active_run().info.run_id
+
+        run = mlflow.get_run(run_id)
+        assert run.data.params["lags"] == "5"
+        assert run.data.params["lags_past_covariates"] == "3"
+        assert run.data.params["n_past_covariates"] == "1"
+        assert run.data.params["n_future_covariates"] == "0"
+        assert run.data.params["n_static_covariates"] == "0"
+
     def test_log_model_with_covariates(self, mlflow_tracking):
         """Test that covariate info is logged with correct values"""
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
@@ -263,7 +292,7 @@ def test_autolog_parameters(self, mlflow_tracking, autolog_context):
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
         """Test that autolog logs training metrics for torch models"""
-        with autolog_context(inject_per_epoch_callbacks=True):
+        with autolog_context():
             model = NBEATSModel(
                 input_chunk_length=4,
                 output_chunk_length=2,
@@ -300,7 +329,7 @@ def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_injects_callback(self, mlflow_tracking, autolog_context):
         """Test that autolog injects MLflow callback into torch models"""
-        with autolog_context(inject_per_epoch_callbacks=True):
+        with autolog_context():
             model = NBEATSModel(
                 input_chunk_length=4,
                 output_chunk_length=2,
@@ -332,6 +361,33 @@ def test_autolog_injects_callback(self, mlflow_tracking, autolog_context):
                 f"_DartsMlflowCallback not found in {[type(cb).__name__ for cb in final_callbacks]}"
             )
 
+    def test_covariate_artifact_schema(self, mlflow_tracking):
+        """Test that covariate artifact has correct JSON schema"""
+        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
+
+        with mlflow.start_run():
+            log_model(model, name="model", log_params=True)
+
+            artifact_uri = mlflow.get_artifact_uri("covariates.json")
+            artifact_path = artifact_uri.replace("file://", "")
+
+            with open(artifact_path) as f:
+                cov_data = json.load(f)
+
+        # validate schema structure
+        required_keys = ["past_covariates", "future_covariates", "static_covariates"]
+        assert all(key in cov_data for key in required_keys), (
+            "Missing required covariate keys"
+        )
+
+        for cov_type in required_keys:
+            cov_info = cov_data[cov_type]
+            assert "used" in cov_info and isinstance(cov_info["used"], bool)
+            assert "count" in cov_info and isinstance(cov_info["count"], int)
+            assert "names" in cov_info and isinstance(cov_info["names"], list)
+            assert cov_info["count"] == len(cov_info["names"])
+
     def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         """Test saving/loading multivariate series with all covariate types"""
         # create multivariate target with static covariates
@@ -377,7 +433,7 @@ def test_callback_injection_with_existing_callbacks(
         else:
             pytest.skip("PyTorch Lightning not available")
 
-        with autolog_context(inject_per_epoch_callbacks=True):
+        with autolog_context():
             model = NBEATSModel(
                 input_chunk_length=4,
                 output_chunk_length=2,
@@ -421,6 +477,34 @@ def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
         assert "ExponentialSmoothing" in model_classes
         assert "LinearRegressionModel" in model_classes
 
+    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
+    def test_autolog_torch_model_multiple_fits(self, mlflow_tracking, autolog_context):
+        """Test autolog with multiple fits of a torch model"""
+        with autolog_context():
+            with mlflow.start_run():
+                model1 = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=1,
+                    **tfm_kwargs_dev,
+                )
+                train, val = self.ts_univariate.split_before(0.7)
+                model1.fit(train, val_series=val)
+
+            with mlflow.start_run():
+                model2 = LinearRegressionModel(lags=5)
+                model2.fit(self.ts_univariate)
+
+        runs = mlflow.search_runs()
+        assert len(runs) == 2, "Expected two separate runs for two fits"
+
+        for _, run in runs.iterrows():
+            assert run["tags.darts.model_class"] in [
+                "NBEATSModel",
+                "LinearRegressionModel",
+            ]
+            assert run["tags.mlflow.runName"] is not None
+
     def test_save_load_preserves_series_metadata(self, tmpdir_fn):
         """Test that save/load preserves multivariate and static covariate structure"""
         target = self.ts_multivariate.with_static_covariates(
@@ -448,6 +532,11 @@ def test_save_load_preserves_series_metadata(self, tmpdir_fn):
             pred_original.values(), pred_loaded.values(), decimal=4
         )
 
+    def test_load_nonexistent_model(self):
+        """Test that loading nonexistent model raises appropriate error"""
+        with pytest.raises(Exception):
+            load_model("runs:/fake_run_id/model")
+
     def test_load_invalid_uri_fails(self):
         """Test that loading with invalid URI raises an error"""
         with pytest.raises(Exception):

From 3b0ecb2a4195b33ae2624f3a18d710807f80f597 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 14:08:25 +0100
Subject: [PATCH 022/154] changed callback inject to true by default

---
 darts/utils/mlflow.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 8eaf985fe3..a0c39bf4d7 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -341,7 +341,7 @@ def autolog(
     log_params: bool = True,
     log_training_metrics: bool = True,
     log_validation_metrics: bool = True,
-    inject_per_epoch_callbacks: bool = False,
+    inject_per_epoch_callbacks: bool = True,
     extra_metrics: Optional[list[Callable]] = None,
     disable: bool = False,
     silent: bool = False,
@@ -376,10 +376,9 @@ def autolog(
         ``fit()`` accepts a ``val_series`` argument (e.g. PyTorch-based models).
         Default ``True``.
     inject_per_epoch_callbacks
-        If ``True``, inject a PyTorch Lightning callback to log training and validation metrics at
-        the end of each epoch. Only effective for PyTorch-based models. To provide additional
-        callbacks use ``torch_metrics`` parameter while initializing the model.
-        Default ``False``.
+        If ``True`` (default), inject a PyTorch Lightning callback to log training and validation
+        metrics at the end of each epoch. Only effective for PyTorch-based models. To provide
+        additional callbacks use ``torch_metrics`` parameter while initializing the model.
     extra_metrics
         An optional list of additional Darts metric functions to log on top of
         the defaults (``mae``, ``mse``, ``rmse``, ``mape``).  Each function
@@ -680,7 +679,7 @@ def _patched_fit(original, self, *args, **kwargs):
         FLAVOR_NAME, "log_validation_metrics", False
     )
     inject_per_epoch_callbacks = get_autologging_config(
-        FLAVOR_NAME, "inject_per_epoch_callbacks", False
+        FLAVOR_NAME, "inject_per_epoch_callbacks", True
     )
     extra_metrics = get_autologging_config(FLAVOR_NAME, "extra_metrics", None)
 

From 2d6158d48480aa18dfa3979bd83caf2edcf8ae4b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 14:08:31 +0100
Subject: [PATCH 023/154] feat: ensure contiguous tensors in metric updates

---
 darts/models/forecasting/pl_forecasting_module.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/darts/models/forecasting/pl_forecasting_module.py b/darts/models/forecasting/pl_forecasting_module.py
index b45ee0bf0c..b0b832877d 100644
--- a/darts/models/forecasting/pl_forecasting_module.py
+++ b/darts/models/forecasting/pl_forecasting_module.py
@@ -455,8 +455,11 @@ def _update_metrics(self, output, target, metrics):
             pred = output.squeeze(dim=-1)
 
         # torch metrics require 2D targets of shape (batch size * ocl, num targets)
-        target = target.reshape(-1, self.n_targets)
-        pred = pred.reshape(-1, self.n_targets)
+        # contiguous() is needed because model outputs can be non-contiguous views
+        # (e.g. NBEATS slices the last dimension), and some torchmetrics implementations
+        # call .view() internally which requires a contiguous tensor.
+        target = target.reshape(-1, self.n_targets).contiguous()
+        pred = pred.reshape(-1, self.n_targets).contiguous()
 
         metrics.update(pred, target)
 

From f684041f125700ce3a5871a37eba051f75770e39 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 14:52:19 +0100
Subject: [PATCH 024/154] example quickstart for mlflow

---
 examples/26-MLflow-quickstart.ipynb | 877 ++++++++++++++++++++++++++++
 1 file changed, 877 insertions(+)
 create mode 100644 examples/26-MLflow-quickstart.ipynb

diff --git a/examples/26-MLflow-quickstart.ipynb b/examples/26-MLflow-quickstart.ipynb
new file mode 100644
index 0000000000..5a119983c5
--- /dev/null
+++ b/examples/26-MLflow-quickstart.ipynb
@@ -0,0 +1,877 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aeddb542",
+   "metadata": {},
+   "source": [
+    "# MLflow for Darts\n",
+    "\n",
+    "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
+    "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
+    "\n",
+    "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
+    "\n",
+    "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f72894af",
+   "metadata": {},
+   "source": [
+    "## Installing MLflow\n",
+    "\n",
+    "MLflow is available as an optional dependency for Darts. Install it with:\n",
+    "\n",
+    "```bash\n",
+    "pip install mlflow\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42e3dcea",
+   "metadata": {},
+   "source": [
+    "## Setup and Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b346ce8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix python path if working locally\n",
+    "from utils import fix_pythonpath_if_working_locally\n",
+    "\n",
+    "fix_pythonpath_if_working_locally()\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13b13fe4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mlflow\n",
+    "import numpy as np\n",
+    "\n",
+    "from darts.datasets import AirPassengersDataset\n",
+    "from darts.metrics import mape, rmse\n",
+    "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
+    "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "4d424e08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use darts plotting style\n",
+    "from darts import set_option\n",
+    "\n",
+    "set_option(\"plotting.use_darts_style\", True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f9c40d6",
+   "metadata": {},
+   "source": [
+    "## MLflow Setup\n",
+    "\n",
+    "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88320df5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# temporary directory for MLflow tracking\n",
+    "tmpdir = tempfile.mkdtemp()\n",
+    "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+    "\n",
+    "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
+    "mlflow.set_experiment(\"darts-quickstart\")\n",
+    "\n",
+    "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
+    "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d5209e",
+   "metadata": {},
+   "source": [
+    "## Load Sample Data\n",
+    "\n",
+    "We'll use the classic AirPassengers dataset for this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1596e07e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training series: 107 points\n",
+      "Validation series: 37 points\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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QEgS6aQghhNjHsmUcXZIqtIwQQgghxFUoRgghhBDiKhQjhBBCCHEVihEPcOjQIZE+fXpx6tQpceHCBXHNNdeIv//+29h/0003iTRp0sgF+ypWrCimTp3q6j0TQgghVkEx4gGWLVsmypUrJ4XGypUrRa5cuUTBggV9jnn11VfFvn37xKpVq8Rtt90m2rdvL5YuXSoSlfPnz7t9C4SQUMiRI3khMcXJkyfFp59+KrZt2+bI9ShGPABERc2aNeX6kiVLjHWdrFmzivz584vixYuLDz/8UGTKlEnMmjVLXLp0SXTt2lUULlxYbrvlllvE8OHDfV77008/iSpVqkixA6HTtm1bsWvXLrlvzZo1ol69evL82bJlE5UqVRIrVqwwXov7qV27tjz3jTfeKJ544glpwdGtNm+88Ybo0qWLPAdE1MiRI1O8v/Lly4uMGTOKypUri6+//lpaeVavXm0cs379etGkSRORJUsWkS9fPvHAAw+Iw4cPG/tvv/128dhjj4mnnnpKXHvttaJx48YiKSlJ9O/fX14zQ4YMokCBAvL+CCEeYtOm5IXEFL169RIPPfSQqF+/viMPfxQjLgE3TI4cOeQydOhQMWLECLnep08fOVljvWfPnn5fmy5dOnH11VfLL8jly5fFDTfcIN02GzduFC+//LI8x5QpU+SxFy9eFC1bthR169YVa9euFb/88ovo0KGDFAPgvvvuk69fvny5+OOPP8QLL7wgzw2giO+8807RunVr+drJkydLcQJRoPPOO+9IkQGrDe75kUceEX/99Zfcd+LECdG8eXNRtmxZafUZOHCg6N27t8/rjx07Jr/wFSpUkELou+++EwcOHBDt2rXzOQ4qHe4svIdPPvlEfPXVV2LYsGFy7LZs2SLHDdchhBASHfh7ruaq+fPnC7uJyzojmBj379/v+HVhudCtCsHAUzwsA5iscb+//fabtFzAgjBnzhz5tA8rgRkIEEz+x48flxM4hMOAAQOM/bCQwO0DMYLJHOfHsXfddZe4+eabpXjJnDmz4QbCF+25554TJUqUkL8XK1bMONegQYOkWIE1Qu177733pLD5+OOPpaUDNG3a1BBOEBoQCAsXLpRWmi+//FIKn1GjRsnjS5UqJfbs2SO6detmXOeDDz6QQgQWFsXYsWOlJWbz5s3SGqSuP3jwYOMYjBPGvGHDhnIc8J5gASKEeIiNG5N/lirl9p2QMMBDogLzCSzXdhKXYgRCBBOel4F1Ay4OfMiIAbn11lvlEz9cFHXq1ElxPCb5vn37irNnz0qR8uabb4pmzZrJfXDbYPKGsDhz5owULBA1AG4ZmNrg1rjjjjtEgwYNRPXq1UWhQoXk/qefflo8/PDD4vPPP5eTOlw4EC3KhQOLyIQJE4z7gGsEgmbHjh2iZMmSchvuXQHhAYFw8OBB+TssJNivhAswCwZcB+LFn/iCdUaJEbiQdHCv7777rihSpIi04EAUwQqDsSWEeIQGDZJ/7tvn9p2QCMUIrM6wQMMybRdx+Vcbk6HXr1u6dGkZt4HsGUzumIjhUsGCdYiFDRs2GMfDegFRoWIqlJtl0qRJ4tlnn5XWEogMxG0MGTJEWloU48aNk7EUcH9A/EDU/PDDD6JGjRoy5uLee++VVoa5c+eKV155RZ6zVatW4r///hM9evTwG4ehB9gqt44C94b3FCq4DkTEW2+9lWLfddddZ6zDcqQDywnEzo8//ijmzZsnrTN474sWLUpxT4QQQkIDD52wqOvCBH9n8cBnF3EpRkJ1lbjJt99+K4UILBVwPeCpH7EcEBx4yjdPpgjaLFq0aIrzwJoCUaHHl/iLfoYbBAssLHALTZw4Ub4OwPKABQFLHTt2lOIFYgQpxIhD8XfdUIGr5osvvhDnzp2TQaYA8Sk6uA7iP2ApCteqgcBaCBksjz76qHQ3rVu3Tp6TEEJI+CBJAckROohLtFOMMIDVJWD5gJUDgZotWrSQT/mwhCBYFJO/cqOkBuIoIL6+//57GV/Rr18/n8ke7pQXX3xRxpHAEgOLyM6dO+WkDZcOglGRbYN9EDZ4rXK/QLggEwbHIL4FQaIzZ85MEcAaDFhdYCXp3r272LRpk7zPt99+W+5T1h2IiCNHjkghhOtDTOG4zp07p/gPoTN+/HgxZswYmYmzfft2KXogTkIdO0IIIcFdNLqrxs6sGooRF4EIQLwI4il+//13mdWiuyVCAW6Ue+65R9YdqVq1qvj33399rCQIVv3zzz+lyIH143//+5+4//775euuuuoqefyDDz4o9yHgFUFKKiAWsR5weUDkIL0XlhVk6yD4NlSQLowUZIgZxLG89NJL8hxAxZHgfBBCEB6NGjWSGTEImkVGUdq0gb+i2I/AWKRC415hRsS1cufOHdYYEkII+X90F43ZVWMXaZLgHIoB8ISPJ95gkxNJHVgpYAVxcywREAurB77wsGTEKl4Yy3iC4xmnY6kesGI0gNVTY+kQeDisVauWXIelfuvWrXK9U6dO0iJtB3EZM0K8xWeffSYzXq6//nqZOQP3D6wwsSxECCEh8swzHKoYdtO0bdtWZmyiTIRy1diRVZMYMo+4nmoN1xBiURAkiy+3uUorISROefbZ5IXEpBjJly+fuPvuu+U6rNnIXLSDiMQIKmGixgXqYSBAUZUHh/kGtSpQjAslyXUPEIIzkS0C/z6CGdFnhSQGzz//vAyaRY0UuNtQFA2xLIQQQryHHjOC2DwkWdidrRq2GEGdCmRmIIsBwY0IdkQaKsqEI/UHggTHIAsDmRcAZh1MSBAjCxYskE3hkPVBCCEkAdw0dNXErGUkR44cPkkLcNe4LkaQ7YBKnyiahQJfSM1Eain8R6ibgdoUyAhBTQyY5bENoOcJBAt6pKDWBBq7Ic3T61VSCSGERMmXXyYvJGbFSLZs2WwXI2EFsKLEN0ztSO9BzxHUyUB3VYgQmN9RclyBCFxVfAs1IPSeJ0jphGjBdgQ1moElxZzPrCqVkuhQY8ixjB6OpbVwPONzLJOrCQmR5IF7ifWxdEOMoKq33qoDLpxwxyKULKSwxQhKd6MHyjfffCN2794tO7Sicubp06d9ynVjHUW1AH6aS3njd7zGH6gAivoROgh6NHdxJZGDz45YA8fSWjie8TWWN14pXLh71y4Ry3hhLJ1i7969xjpiQnUxAR2AVOdwQANXS8WIKueNjquwbsDagSJVyElGQKIKZFVvQKVu4qe+T+0PFMSIGhToFmseHFQpTZQ8b7uAosV/Ko4lx9Jr8LsZn2OZ5qqr5M9YrYzspbF0Ct0zgT5qumUEXgo7PsuwxAhuALEfqow3UOtQPiiMgvbyAC4a1f0VNSamTZtmvAaunn/++Udu9wdiUMx5zLguvgiJ8mWwG44lx9Kr8LsZn2OZxiP3EQ9j6VQ2DeZ3VQkbxgN4MxAzYsc4hHVGWDjQ2A2ZNFBOiBNBzjHSddFAZ/r06VJkoMQ4qmyqpjpoAodGaciuwesQBIuaE/7iRUjkwF327rvvuj6EaPaHYGW7QMYW/oMo0HkYpeYJIYRYJ0YQL6KEhwpi9UQ2DUD1TAS3oJ7Ik08+KXudoGcJSse2adNGlovFz2rVqhm5ybByoLU7OsXWq1dPrFq1SgwcOFAkKlCbwRZMrpGAJnOo4RIu8P9BaMI9pprkmUGcEHrZIFbIazz77LNi/vz5jokhQkgYbNyYvJCYC2DNoT302S1Gwi4HD6UEYeEPTGZY/AG/06RJk8K/wzhEL/g2efJk2Tjur7/+Mrbp/jkUjkNKdbp0qX9UefLkieh+YLGCSIS4hNUBNWJq1Kjhcwy2582b19YW0pGC8dLHjBDiIXLmdPsOiMViBPOSHq5hBYnhAPMYqNGiluzZs8sPVf2ODrsQfHPnzpXuLQQNo6AcYnBgaUJpXky86PZr7qBodtPgvKNHj5ap1/D3IeDYn2UDYgTlfuHqqFixonSj6eCLBzECqxfOiToxiBGCNeWWW26R1XbDtcQ0b95c5MyZU2ZVQaiqmjToZIxrzJkzR3biRaA0rGzr168PeD7dTYN1VAjGe1KWJpyTEOISyJoMkDlJvMfZs2dlWEUgMYKA3kCZsNFAMeJRXnjhBfHmm2/K4nCYlJFSDasE3BFwc915551yQof7JBiokIuU6LVr18rXoy6MnkOOdYgd1XsAQgMVdPXsJ0zmiA/q0qWL/CKiRgyq7W7cuFFadfr06SNfEyqPPvqo/LIvXrxYrFu3Trz11lspLBvPPfeceOedd6TrCRYfvFdEcYfissH7xfjAAoXFbOUhhDgIEhmuJDOQ2CoFnz17dmPd7sJn8StG0Lba39Kly/8fM3p04OOWLv3/46pX939M/fq23f6rr74q7rjjDpmRlCtXLllCv0ePHqJMmTLSwoGYG+xLLYYD8RMdO3aURejeeOMNKWrQOVcBiwTEjir3i15DmPQhNvS6L4gJKl68uMxqgsCpXLmytI4gBRuuuXDECAQUgp7Lli0rM6ruuusu2edI55VXXpHvH8fA0nHgwAExY8aMVM8NUQOLDSxKytpkR4dJQghJhOqrCoqRBAWTvQ5EBJ76EWCKLwgmXVhNUrOMQGgo4BLBFwrZTmYXjQLnvueeewxXDRTwV199JS0mCrSThgsJFgvcBzrwpnYfOk888YR47bXXpCCB6IDVxkx1CMArQIzBHYT3SwghJP7ESNgBrDFDKF2BH344eUmNZcuE05gr1kKIII367bffllYOPP0ja8lcNt8MLBk6iKFQpXzx2u+++066WXQgPJDCjboxCxculFk0qIALEISMe4ELBYJBBTT/9ttvIb+3hx9+WLYOQFzIDz/8IAYNGiTP9/jjj4d8DkIIIfZ37FVQjBAJqtzC5YJgVGUp2blzZ1Sjg1gQBJHCBaSDzBq4YOCegRhBt2UljnAfiMHo2bOncbzqQRQOqGaItHAsL774oiz/r4uRX3/9VRQsWFCuHz16VGzevDlg2rEZuGWQgUQIISRyywhjRkgKECeConKrV6+WMR+I7Yi2cRPiTXQXjW49QbDqxx9/LJYtW+bjosF9rFixQnz//fdSIPTr108GmYbDU089JV+PoNiVK1dKwWMWGoiZQbAusmggwtAJOtTaIcgqgusH6dKHDx8OKfCVEEKIYMwICc7QoUOlFQNWCWSWwM2BNFw7xAiAAIC5Dmm3VatWNbYjiBYxJe3bt5fbEX+iW0lCAVYLZNRAgCDrBYGxH330kc8xyCRC3RPEpuzfv1/MmjUr5EBU9E5CjAnibhDXAmsOIcQlWrdOXkhMcMylmJE0SSgiEQPgKRq9cRKlN4BdwJqCOh8QEaiie+jQoRRxJW4C1xHcRHDN6P8RvDyW/F5yPL0Gv5scy0h56aWXZOYlQJwi5gmAB0L18IoEBBxnJZzZE5SLFy+K999/31NChBBCiLswm4Y4SpUqVWRlU0IIsZX33kv++cQTHOgY4BhTewkR4vbbb5fl5wkhccKgQck/KUZiguMupfbSTUMIIYQQCVN7CSGEEOIJMYLmqnpMIS0jhBBCCHFUjJizGdHvS5VXoJuGEEIIIbbHjPgrraCsIxQjhBBCCLGt5ANajZhLwTshRuK3UR4hhBD3mTvX7TsgUWbSKChGCCGExCbly7t9ByTKGiNmMYKO7+fOnZNxJFbB1F5CCCHEJdAM9KWXXpKNR2PFMmKHq4ZuGkIIIfZRrlzyzzVrOMomzpw5I5ue7t27VyxevFj8/PPPnqwxEkiMoBGpVVCMEEIIsY+DBzm6ARg7dqwUImDLli0iVtw0dlhG6KYhhBBCHAZxF2+99Zbx+wkbMlRiyU1DMUIIIYQ4zGeffSZ2797t47K5cOFCTLlprIRihBBCCHG4nscg1UBQ44TL1hG6aQghhJAEYeLEiWL79u0ptp+gGCGEEEJsoFKl5IVILl26JN544w1jNEqVKuUZMcLUXkIIIfHJ7Nlu34GnmDdvnvjzzz/leu3atUXVqlXFxo0bPSFGUosZyZo1q7HOmBFCCCEkRtGLm3Xu3NknKPS4ls3iBowZIYQQEp/MmpW8EMmRI0eMkciXL5+tGSrhosRQ+vTpRcaMGVPsZwVWQgghsUn37sk/9+1z+048J0Zy5crl4w454RE3DWqMpEmTJqgYOXnypKXXZmovIYQQ4pIY8ZJl5NgVMeIvXgSwzgghhBAS52LkuIsxI5cvXzau76/6KrjmmmsMiwkDWAkhhJA4ECOY9L1iGfnvv/9EUlKScV/+gBBR90sxQgghhMS4GIErJF26dJ6JGTmWSpM8BcUIIYQQEidiBC4a4BXLyLFUaozYLUbSWXo2QgghROejjzgeWlzG0aNHA4qR4y7GjKj7CtUycurUKVlN9qqrrrLk+hQjhBBC7KNVK47uFWBNgCDRxUjmzJnlhI6J3U3LyMGDB431vHnzBjzOnN4bTLiEA1N7CSGEEIeDV3Pnzm17UGg4HDp0KGwxYuX9UowQQgixjxYtkheSIq1Xkc0DYkS3jOTJk8dxMUI3DSGEEPv4/XeObohi5LiLMSORuGloGSGEEELiTIycPXtWnD9/3pV7oxghhBBCEliMZNdSaa3u+RJJzIgbbhrGjBBCCCEesIy4GTeiLCNZsmQRmTJlCngcxQghhBAS52LkuEtxI0qMBIsXAQxgJYQQEntok26i41XLyIULF4x7oxghhBASf2zY4PYdxFTMyAkXxMi///5rrLslRhgzQgghhDgsRnLmzOkZy8jBEGuMAIoRQgghscfatckLMcQIgkTTp0/vmZiRgyHWGAGMGSGEEBJ7NG6c/HPfPpHomDv2esVNcyjEUvCAlhFCCCEkRklKSgooRrLFkJsma9asxjpjRgghhJAY4tSpUzJrxetiJG8qlhF0GL7mmmvkOsUIIYQQEgeZNLEWM2JXYz9m0xBCCCEuipHsMRQzYldjP4oRQgghcQdcIsOGDRMzZswQsWQZOeGym+baa69N9XgVV4J7PX36tCX3kC7cF3Tv3l2sX79e+o1AhQoVxHvvvSfXx48fL7744gtx+fJl0aJFC/HEE0+INGnSyH0bNmwQAwcOFLt37xalS5cWAwYMENddd50lb4IQQohH6d3blcsOGjRIvPLKK3J969at4uabbxZeFSOZMmWSc+qlS5dcFSOofXL11VenevyNN95orGNOv+WWW9yxjPTt21f8/PPPclFCZMmSJWLq1KlSkEyZMkUsXbpUzJw5U+5DS+Tnn39edOjQQSxYsECUK1dO9OvXL+qbJ4QQ4nGeeip5cRBM6iNHjjR+37Jli3CbYGIkTZo0trg+rO5LE0iMeMpN8+2334pWrVqJG264QZp57r//frkN/PHHH1JttWzZUmTIkEF07dpVbNq0SezZs8eqyxNCCCGSefPm+cwvbnXCDVWM6HEjTt/r2bNnxcmTJ0NK67VTjITtpgFDhw6VS/HixUWvXr1EsWLFxI4dO0RjVdxGCFG0aFGxbds2ub59+3Z5jCJjxoxStGD79ddfn+L8sKRgMfv/4P4h0aHGkGMZPRxLa+F4xudYprliFUl6913HrjlmzBif32FtiHQsrBpLvf9Ljhw5Upwvm5ah4uTnduDAAWMdYiSUa2P+Vvz999+pviZt2rTWixHEgRQpUkSefPLkyfL3adOmySAWlXsMsH7mzBm5jp/6PrU/UODLuHHjxKhRo3y2tW3bVrRr1y7c2yUBsErNEo6l1fC7GV9jeeOkScn30quXI9c7evSo+Oabb3y27dq1Sy5ujiUmbd0aYb6f9FfKw2Mf3Ep6uXg7WbdunU/sSijjlC7d/0sHeDlSe03hwoVTP6cIkzJlyhjrnTp1kh863kzmzJllURcF1vHGAH7q+9R+vMYfnTt3Fvfdd5/Ptr1790rTUCgKiwQGChb/qTiW0cOxtBaOZ3yOZZoryQ6FChVy5HqzZs1KYVlHmECk17dqLPV7wjxq9grk0VwkCCQNJavFCiAmFAjyDWWcdOMCxJ8Vn21Ebhod9eFA+SBiuW7duvJ3uGhU9DIsKbCeKKD8/vnnH7ndH1CEZlWILxOu5fZ/rHiBY8mx9Cr8bsbnWKZx6D5gWTeDmIhoxyHascSkrYDQMJ8rR44cxvp///0XcjBptBw+fNhYxzVDeY8QTgi3wFwOoWbFdyysM+AD/fXXX6XCQwzHhAkTpH8LKq9p06Zi+vTpUmTAN4Z92AYqVaokzp07J7Nr8NqxY8eKkiVL+o0XIYQQQiJh1apVYvXq1SkmdxWg6YUAVkziymvghVojB8Osvqqyf1QQK8QI+u44KkYuXrwoPvzwQ9GwYUMZrIrU3uHDh8t2yLVq1RJt2rSRrhv8rFatmqw1AmDlGDJkiJg4caKoV6+e/MKg5gghhBBiFXjQ1WtieTGbxl8mTayJEaDECKw4VqQjh+WmgR/r888/D7gfsR5Y/IFCZ5OuBDIRQgghVoKnczzwKutDt27dxODBgz1nGQlFjBx3sNZIuKXgA6X36paoSPCGM5EQQkh8snlz8mIziMlQ6bOw1OuTpduWEWSUquzSQGIku0v9aXTLSKh1RuyoNRJ1ACshhBASkKxZHRmc/fv3G+uIR0SBTSQ+IL7RbcuIHrzqVTdN2rRpA96bE2KElhFCCCH2ASHggBjYt2+fsZ4/f37bWt3bUX3VC2IEGT6q51woFCxY0FinGCGEEOJtihdPXhy0jKgmrLEqRo47FDOCOBsVMxKOiwbQMkIIIYSEYBnJesVF5LabJhQxkt2FmBEUH1WxLOHWNaEYIYQQQoJYRsxuGtS5MldldRKvumkORpjWq+5X3bNe6j5SGDNCCCEkriwjyk2jLCNuW0fiUYzo1hEUO4228BnFCCGEkLi2jLgdN+LVmJFDWo2RcGNGdDECy5N+rkigGCGEEBI3lhE0YFUWkViyjGTKlMnohhtrlhErMmooRgghhNhHu3bJi0OWEVhF0Dsl1iwjadKkcTz7x0tihEXPCCGE2Mfw4baPLtwEqrCYiheJNcsIgBjBsU6JEavcNICWEUIIIQmNv3gRL1pG4IZBY9lAZLtyv07FjBw4cCAqy4iVhc/opiGEEGIf776bvDhc8MxLYkRZIHLnzm24kILVGjl37pxc7ASC59tvv5XrKJuPEvrhQssIIYSQ2OCtt5IXhwueecVNc+nSJeP+Upvws2niye77/fDDD8WxY8fk+gMPPCCuueaasM9xww03GOu0jBBCCHGE//77T4wfP17Ur19f3HrrrWLVqlWeGHkvu2lgFYEgAQUKFAh6bDaH7heVV4cOHWo0yHvxxRcjOg8ygNDTBlCMEEIIsZXDhw+Lrl27yom+c+fOYuHChWLdunXy6dqrBc+8YhnZu3evsR6OGDluY9zIJ598Iv7991+53rFjR1G0aNGIz6VcNXv27DFEVyQwZoQQQkhQXn/9dTF27Fj5RB1oonUTL1tGMEkrUnPTZHegPw160bz99tvG73369InqfEqM6O6oSKAYIYQQEhRYQRTdu3c3Ws1HW3XTKmgZCZ0xY8YY4q1169aiVKlSIhqsCmKlGCGEEBKUXbt2GZYGmPhVnIBeNMtN1OSKTBW9XoYXLCPhuGlyaTVIlBvFSi5cuCDe0oKJ+/btG/U5rRIjLHpGCCEkIJcvXzbEyE033SQnfNSkQI0KiBE0SAuWriq+/94xywjuS5VU90rMSDhumvyaiykal0cg1q9fL5vagSZNmojy5ctHfU49oyYatx3FCCGEkIBgUsQTNShUqJBPgayzZ8/KDBt90k/BrbfaLpZU8S59Mgfp06cXGTJkkDU7YsEykl+7fz0Oxiq2b99urNesWdOSc+qWqGisOXTTEEIICcjOnTuNdVhGzBOQ23EjqG6qxJKeSWN21bidTYPCYih6FozrtPu3Q4zon2XhwoUtOaf+npB1FSkUI4QQQsISI3rp8FTjRkqXTl4czqRRKKuN29k0sIqgpoebbpodO3bYKkZoGSGEEOJNMYK+LFqjOKfFiNOdcHXgHlLWgtRcNCBjxowiR44ctllGdDGiPstoUcHMgGKEEEKILajg1UBuGrczagKl9ZotI3Dl2N3vxYwuKEIRI7qgssMyooQlRI8/4RYJKCOP2BxANw0hhBBXLCNux4yEahlxwzoSTiaNWVChwByCg60CWU/qs0QgctAMqDDAeZSrhpYRQgghtqAmsCxZsoicOXOG76bxiGXEjSDWcDJp7I4bwed0+vRpS+NFFLoYgeiJBAawEkIICbnGiNfEiJctI5GIketsyqixI5PGHDeCVG8leMKFdUYIIYT4BZPh+fPnUwQ8hhUzUqVKwlpGInHT5LfJMmJH8GqgjBrEkYQLxQghhJCQ40WUtQFBixAqqcaMzJxp6+gq6wEmQLiSYt0ykt+mwmd2WkbMYqRgwYJhn4NuGkIIISFn0gBVEt4LbhplPfBnFTGLkViIGbnOJjeNk5aRSKAYIYQQkurTtCoFb3bVwDISNGhxxozkxQbOnDkjjh8/LtcDparqbhq3smlgtQlaMt9hN41dMSPRpPdSjBBCCAnLTQOUZeTixYvi2LFjgUewZ8/kxQZ0y0EolhG33DSIFwk1lfY6mwNY4cpKrSx9uNAyQgghxFUx4qarJrVMGjcDWHEtdb1QXTQgV65cso+NlZaRQFlRVkExQgghxHYxAjeD+WnaC2JEn6wDiRG3LCORxIsACAX1XqyyjGCcVFaU1S4aQDcNIYQQW0jtadoLJeFDcdO4ZRnRxUioab0KJUYwrnCDeTl4FdAyQgghxBYwEapeLv4mMC+UhNdFkH4/XrCM6DVGwrGM6MIKgcFWjK2dwavBxAgsMrNnzw7pHAxgJYQQElYmjVfcNHrmhu4q8JplJFwxkt/ijBo7a4wAdBpOmzZtCjHy008/iebNm4d0DhY9I4QQElbwalhumpEjbRtdfeILlCHiVmqvFW4aq+JG7HbTQIgg8BbiUBeI+ncoNShGCCGEhC1GQraMhPhkbJcYQWZKpkyZZE0SJy0jVrhprBIjqX2WVoDxhxDRPxNdBKUG3TSEEEKisoy4FTOiJj48mWfPnj3gcco64pZlJFBwrVNumh1XRAHcKVjsQIlBCD6VuROOZYRihBBCSNhiBOm+qiFaUMvIXXclLzaKEbgIVMxCsCBWN2JGMElnzJjRNcvIxYsXxe7du22LFwkWxBqOZYRuGkIIIQHFCFwcgYJDYR05depUcDHyxx+2ja6a9FKrKKpbRpChYnXRLzO4hhIj4bporLaM/PPPP+LSpUu2ixH9O4LPBW48vbdRatAyQgghJMVkGkrFThU3gslHTXhOAVfAf//9F5IYUZYRWAnOnj1r+71hPJSrIloxsj9Ky4jdwauBLCMQURcuXAj59RQjhBBCfEAMiJq0/aX1msUIxEuk3VrtDF51K703mkwakCFDBpEzZ05LLCN21xgJJEbCcdEAihFCCCE+6G6XYMGXblZhDUeMOF34DK4RRSSWEX3cYRkJ2hXZA5k05s8AWTXhBK8CihFCCCFhFxNzu/CZly0jq1atMtaLFi0a0TnyX3HVnD59OtV7hvtp7NixYsmSJSn2bd261Vh3MmYkXMsIA1gJIYT4oKfq6taPiErCByjTHs+WkWXLlhnr1atXj+gc+U1xI/p7MDNw4EDx6quvypoq27ZtEzfeeKOxb/Xq1fIn9hUrVkw45aY5cuRIWK+nZYQQQoh9lpE1a5IXj4gRuy0jcKn8+uuvxn1FKgCu09xjweJGEMT73nvvyXUEjC5atMjYB6vKX3/9JddLly4t0qdPL5xy0zBmhBBCYgRMXN27dxf169f3Mae7jW7lCCZGYiVmxMmS8Fu2bDHurVq1ahGnEecPMaPm008/FceOHTN+X758ubG+bt062X0ZVKhQQdhJIDeNCsRNDbppCCHEJX7++WcxatQoud6+fXv5RA1zupcsI6G6aQKKkStuAlG+fEJYRqxw0YRa+Azp1O+++67PNl2M6LErdosRFJ5THDhwwAjiDTVOhW4aQghxiU2bNhnrK1euFG+99VbMumkCxow0aZK8JIhlxCoxkj+EwmezZ89OYVGDAEFAq1p3SoxARCvRt379eqPuTKgZPBQjhBDiEgg21EEQ4tq1a2MmgDVW3DRuWEZQnr5KlSq2WkaGDh1qrN9www3yJ+rDbNiwwUeMwFVUrlw5YTfqc0BTQgUtI4QQEmNiBAGIDz30UFiVK+20jOhPu/5AQKRqUOdlMWKXZQTxIbolBEIHVgFQtmxZkSVLFtssIytWrBCLFy+W6yVLlhRPPPGEj6sG1hHEjKj0Yn0M7MKfFY1ihBBCYkSMpEuXTmY7qKfZN9980xOWEUwuqQVgKleNW2IEzfpQsdTp1F4IhDJlyogaNWqI0aNHy22///67ETCK7dGQM2dO433t2bMnqFWkV69ePlYYCJU///zTqKJrt4smmCi03U0DU+Jtt91mfAhg/PjxomHDhjIyfPjw4T5V42A26tChg6hZs6aMHreiLTIhhMQq+PuoxAj+YCMr4qqrrpK/v/3228ak5sZ9KctIsHgRsxg5fvy40Y/FSTESyj3aUfQME756v3369JHnXbp0qSXxIgAiUNULUV13FRAZ06ZNM97//fffLypWrGgIR1hGVH0Rt8WIrZYR/CeBKitVqpSxDZXfpk6dKgXJlClT5Icyc+ZMuQ8f2PPPPy/FyIIFC6Tvql+/fpFcmhBC4gJYElSjt5tvvllUqlRJNG7c2Hh6DxgQajO4p3PnzoUtRlQWhVOCSRXVSs1FA3LkyOE3ODcadEsQPqthw4ZZFryqKFiwoPF90NN30cRQufLuuOMO2VkZggvuGmUsULVO3BYjwXobRS1Gpk+fLs1TuuL59ttvRatWrWQQjVJq2Ab++OMP6Xts2bKlNDt17dpVRpH7Mz0RQkiixYtAjAC9cqbe38SLab3+Yhv8ipEXX0xeLARWGJWtEYoYgZtGuWrMVoZIMYvFIUOGGJYRzIHqM42GQtpE/vfff6fab6Zy5cryJ+JFJk6caGwvb3FadSDM4hVCFW40W+qMQJ3hTcIC8s477xjbUeBEqXoVMKP+s23fvt2nCl3GjBmlaMF2fx0NYUkxm/ugAt0yW8YTagw5lhxLr5Fo300EPyqKFCki37f+9xCTT6RPtNGMpf7Ej4k+tXPky5fPp1ttiuMfe0zdlLAKXQigvkUo7xMTOwI6IUYwWSPbJZqxNAsvZeVSxc5gvYmmwZ1ZnGKOhREAYO7UrSfq3iBGPvvsM7muLEfIyoGodOL/lV5rRAklXDeUsQ5bjHz00UeiY8eOKSJzUXZWV0BYV+k9+GlWR/gdr/HHuHHjjEJAirZt24p27dqFe7skAFY9HRCOpdUkyncTdUUU+HsK07seiImJM9on2kjGUqWFAli0cV/B0Iu0bdy4UWaR2I1+j8joSe0e9ad2POjCWq+7lyIZS906gVgNXXiUKFEipHtKjWu0eRMxILfeeqtc19O/M2fObFxLpffq3HLLLZbcSyiYBQ/GGNcOJW4kLDGC6Fx82Xr37p1iHwbk1KlTxu9Yhx8L4Ke+T+3Ha/zRuXNncd999/lsg+KGSgxVzZLAXxb8p+JYRg/H0loSbTz11FTEF+DJXa8FgYe4UP3tVo6lfjws3Kndgx47CAu2+fg0jz8ufya9/76wCsxD+tN3KOMEgbBw4UJjfEId20BjqT9MIyljxIgRxu9NmjSJ+LPT0S1jsLyocx49etTYjiwatR1WKmRmqaJnKqvHinsJBYyxDjLEQr12unCVPFRO06ZNjcFB9DdiP6B8UAmubt26ch9cNMpnBhOkivxVkcDwh2K7P6B0zQ19oL7xRUiEP1JOwLHkWHqVRPlu6qZ2TPp4zypgEeDvarTjEMlY6iIJT7apvb5AgQI+rosUx0+fLn+k+fBDYRX6ZAyLRyjvUZ8UIS7CDTA1j6VyFWHyHzRokEzcwH1hrqpataol3+GbtHgQ3LM6px4/gmPUdjzgwzKlV15Flo1T/5/MMUbQBaFeO6w7vOeee8SMGTPEhAkT5FKnTh3pPnn66aelQEFgK0QGvszYr0QLosQRnY3sGpjIxo4dK6N+/cWLEEJIIqBi6jCZKyuy/jfRrQD/UEvBh9vQza2CZwpd6OmTuRW1WFATZNKkSXKuQ1mLUIM2w4kZ+dtPACviQRCDqYOSGzpOZdL4+yxCTesNW4zgTWPg1QL/pkopqlWrlmjTpo3o1KmT/IkAnhYtWsjXwcqBSGMEvtarV0+qtoEDB4ZzaUIIiRtQk0IFiupZF8j4UPF4bmXThNqx118Aq5fFiG4ZiTaGAvEh6vNTsSeNGjWStUceeeQRYRUZM2Y0xlfdMzwLqk6Xv4JiKqMGoDpuOIIgWsyfRagFz6Lu2tu/f/8UsR5Y/AHfEZQjIYQkOrqLxpwCiiBElD6AGMGkF2kLeqdSe/FQCssAXBSJYhmBmFQZn6GMUTQUKlRIur8gQHBN/d79xWPolhEEQDv5/YGbCAYKlbwSTqxK/DtmCSHEY+idVs1iRLlqECCJehpOo4uRUCd65arxshiBSwPxHVaIkVAbCVpBwSsiCsIUAjVQjREFMm5q164t4zm7desmnEYJEHyvUyvTr0MxQgghHih4ptDTM91w1aiJFi6jUCcTJUaQJanX2/CSGMHkrMY2WjeNXosl3BThaAuf7UxFjCBgdNGiRXKMzFmpToCYmWbNmokPwwxYphghhBCH8bIYCacvTUhBrHivpu7EVokRWDqCdRU2o6fGRtOjxg3LiBJRupAKFJMB14zqpuw0iJ2ZPXu2TxHUUKAYIYQQD4kRNzNqUJ9CVe60TIygnlSAmlLRihFU/AwnJsKquBEvW0ZiFYoRQghxSYyggZu5hLablhElRMJ94g8qRlATRKsLYqUYCdVFY7UYcdMyslMTI/q+WCeqbBpCCCHhoWdEwCpifrJ3U4yEm9YbkhhRFVqvpKNGC2pWqYre4YoRq9J73RIjf2uWEYy5qk8TD9AyQgghDoJJUPXw8NfZ1U03TbhpvW4UPtOtN25ZRpx00+TKlcsoorZ582bZGgU4VeLdKShGCCHEI/EiyiKh2mE4bRkJt/qqG2IkkkyaQPEXsWAZSZMmjSGiQglejVUoRgghxENiBJOPctW46abxqmUkGjGil1ePxk2jLCPI5kHcj90U8mMFoRghhBBimxjRXTVIQdW7w3rVMgJRgDoeXhcjcHeo11hhGYFgc6LCaUE/gaoUI4QQQmwVI3oQq5NxI5EGsEKIqNgJL4sR3cqAcUUqc7igEqouRpygEC0jhBBCrEQFIKJSJjr2+sOtjJpIA1h1Vw36qKgAXcm99yYvHhEjyspw6dIl47MIhxMnThh9aewOXk0kywhTewkhxEFUx1VMZMq14ZWMmkgtI7oYUYXTjNe/844nLSMqbiTcWh1OBq8q/N0js2kIIYREBCwGsByoxm2BcNsyApEUbmCmU0GsVllGIo0bcTKtN5DwwHXjqcYIYDYNIYQ4BCwGKk5Bn7yDWUbcECOwaoQbmBlQjLz9dvISJ2LEDctIgQIFpFsvXl00gGKEEBK3OJmJEo6LJhzLiFNummgDMwOKEbhpLHTV6GLEXErfiSqsboiRq6++2kegUowQQkiM8PLLL4ssWbKIp59+WngFfZIOZhnBPvUk7JRlBMLt7NmzEcWLuOGmyZo1q1EcLt7dNOb7phghhJAYYeTIkfJp//3335f1OrxmGQkmRlBMS+13yjISTfCq1WJkzpw5omHDhmLmzJkBY24icdEoAZEhQ4aYsoyYLToUI4QQEgMg9VJNWojRwOTmBfRJOpibRnfV4DUXLlzwdFqv1WKkV69eYv78+aJTp07izJkzxvalS5cawrKUasAXRXl1WEYgWIOxaNEiMWHCBNmgz03LSJEiRfyuxwuMGSGExB3m+hEzZswQsWQZ0cUIJkv9dfFuGYHwUoXhjh8/7vPZQRQoOnbsGLWV4b///gtqNdu9e7e48847Rb9+/cSQIUNctYx07dpVlCtXTtx1112iQYMGIt6gGCGExB3mOIvvvvvO5wk7FiwjTtcaidYyghgOlW4ajRjBe9WLpo0dO9awdk2ZMkWuZ86cWbRs2TLia4QaN/Ljjz8aBc4mT57sI0YQVJo9e3bhFDfddJNYvXq1mDVrlnTjxRsUI4SQuBcjCM6cN2+eiEXLiJVBrDt37hRVq1YVLVq0kFYBK/rS6O4P9Z58xMj8+clLiJjjOBYsWCDvG4ISqdEA94/g5EjRG+YFE3q//PKLsb5x40axfft2w03jVF+aRIFihBASd/ibvL/++mvhNmqShhUBTducFiOfffaZ+P3338U333wjevfuHbBnTqTuByVGkPGiLAoCsR1hxHeYxQjcVJ9++qn44osvjG3333+/iIZQ67gsWbLE5/fZs2c73pcmUaAYIYTEHf4mGEzAkTRGs0OMpGYVsctNo7skPvroI2l1AIsXLxaffPKJXMfTfokSJSI6v/6+9EDPcIAVxMzo0aOle0JZbe644w4RDaEIPYiOv/76y2cbYlZUMLGTwauJAMUIISTu0CeYypUrG0/rutndaRCzgoDMUMWIHZYRcyBsly5dxNatW0WHDh1k4zjw0ksv+Vw7HPQ4GMNVU7Jk8hKBZUTFduD9qxoo7du3l/Ea0RDK2CJzxwysSgpaRqyFYoQQEnfoE0zPnj09kVUTTvCqXSXhzWIEE3/58uWN7fXr1xf9+/eP+Px+M2qOHUteIhAjEEZm7rvvPhEtoYgRXbj6E2cUI9ZCMUIIiTvUBJMvXz7RqlUrI/sAcSOp1ZXwQvAqyJgxo1HYyyo3jbqHbNmyGTErp06dMgTSl19+GbCTsFPpvUqMIEAVdUb0ku+or1GtWjURLWgCiIycYGJEjxd56qmnUuynm8ZaKEYIIXEF4kLUpIsnWkw89erVMyY6pEfGgmVEfyI3p7tGAtwwKo6jePHi4q233jL2QYAgdRXiLRqiFSN4jyquBamsqJSqB6vCKmJFBgvOocYWtUTMAhUutRUrVhhj1axZM0O8KGgZsRaKEUJIXIFJUE3casK5++67jf1uxY2EaxnR7x9Bk3qxrUiAEFHjAjH0yCOPyIkeFpKPP/5Y1K5dO6rzq/NG41pC1VyVhaMKkz3//POy2ioKfj3++OPCKtTYwjJ04sQJn30QIipQtWbNmlIUmQuN0TJiLfFXOYUQktDoLg014WAiU5gzJLxsGTFn1ERjuTB3DEYjvs8//1zY1T/FX1ZMOPEi6lwYgw0bNgirMceN6AXMdMFao0YN+RPWEZXRA2gZsRZaRgghcYX+RK4mnFtuucV1MRKNZcSKINZQOwZHA9JuVSzKjh07kjdWr568RChG7CLY2OrxIrVq1ZI/mzZt6nMMxYi1UIwQQuJejGDiQOxIrFlGrBQjZsuIHSAWQ3WUhbCQbqHp05MXE6hmOnjwYFnZ1J81xS0xgntWab343hQrVsyw0FSsWFGuw6oUbXwN8YVihBAS92IEk6SyjiBAEuXhnUaJAQSLqiwZJwufOSFGQOHCheVPdLkNFMQ6ceJE6TpDFVg0flMBpG5ZRvSx3bRpk9E8D/EiesAsxFPJkiVF3759ZUYSsQ6KEUJI3IsRs6tmy5Ytjt+XmpgR+Bhq+mysWUZ0MWJYOqZNS16uZKn06NFD3HvvvUZvHLhz/vzzT8+4afR4EYgRHQSxwpIzYMAAW+8tEaEYIYTEFfrEolsWdDGiJj+ngOkfmSLhCgErC585JUaUm8aIG0EGzOOPy+wUpFiPHDkyxWtQjl4XI+nTp7fdDRKKGFHxIsR+KEYIIXGFmlhQLEu1tHc7iBUdcVW59XCCR+EKUN1prXTT2DnR65YRI4hVCPHtt9+K3377Ta7jc+nVq5exb9GiRdJVo8QIrCKIy7ATBNtC9JjFiKovgpLzFSpUsPUeyP9DMUIIiRtggVCTtrmEt978zWkxEknwqrk4FybMaKrHKjGiT8J2W0b0gNS1a9ca67COoOiayryBZQRxGsp1Y7eLxt/YqpojympWpkwZWV+EOAPFCCEkbkBhMFWsyixGihYtajxtOy1GIknrNbtqMFGqRnvhAhGj7sFOF00wy8j69et9mhfC8qBiMiAgFy5caOx3Qozo3xEIIYzvmjVrjMJwlSpVcuQeSDIUI4SQuA9eBXjKVU/tECNO9qiJ1DISLOsjHDDZqsqmdosRpFCrAmL+xAisMhCGoE6dOsZ+vQCb02JEje0ff/xh/E4x4iwUI4SQhBAjetwI3AHmDrZetYxYkVHjVPCq2Toi+77AMqNZo5AaqxoX6mIEMSVuihGMrS5GVE0R4gwUI4SQhBMjTrtqorGMWJFR40T1VX9iBE0LDw4aJHb27WsE8JYtW9Y47rbbbjPiMpR7zUkxYh5bJUYglm699VZH7oEkQzFCCIkbvCpGrLKMROqmcdoyogex/lm4sFiWM6fxOwJDFRkzZhRVq1ZN8Xo3LCObN282qsGWLl1a3htxDooRQkjc4FUxEo1lIpbdNCpuRA9e1cWI2VUDEGSsWyzsRB/buXPnGsGrdNE4D8UIISRu8LoYQd2QzJkzO+6mcdMyUvO110Tr0aMDipG6dev6/I7PDZk2TqB/R1auXGmsM3jVeShGCCFxg5qsMelnzZo1xX5MxGq7k1VYlRiIJF5DrwsSK24a3TKSZ+9eceORI3IdBdwKFizoc2z16tWNgFYnXTSq+Ju/0vwUI85DMUIIiQuQqqvEiD+riLlhHgpynT171vb7Qv2KkydPRiwEdLdFLFpGEJiqgldhFdEbzwEUPtMnfyfFCIRIgQIFUmxDEz/iLBQjhJC44MiRI4a4CCRGgBIjEC9bt2617Ppz5swRffr08Wn2BpYsWRJ1JosSI3iPaDYXqRiBVUhVPbUTWEBg0VEZNQo9kyZQ3IiTYsTfdwWpx3obAeIMFCOEkLhAd2GEIkasjBv5999/RevWrcWgQYNE+fLlxcyZM43aGS1btvRJZY2EcDNqkBWiiyKnqq8GctUEihdR3H333cZ6tWrVhJOYvyt00bgDxQghJK679TohRjZt2iTOnTsn148dOyYFSNu2bUWLFi0Ma82dd94pevbsGdH5w8mogRCCIML10A8mWjeRFa6a1MQIuuPCsjRlyhTRrFkz4SQUI96AYoQQEhf8/fffrllGtm3blmLbtGnTDBdFu3btpEiI1Pyvi6tglhFUln3sscdkjAbE0QcffOB4vIjZMoKr70tFjICmTZtKAWeOKbEb83eFab3uQDFCCIk7MRIs7qBYsWKWi5Ht27cb6xAeelfchx9+WHz55ZdRdcoN1TLyxhtv+OyfPHmyj1Byovqq2TJS6cqSJ08ekTdvXuE19LFFsDCsSsR5KEYIIXGBHiMRTIygzodKL92yZYvlYuSVV14Ry5YtE/fff7947733xMiRI/2mj1pdawQVRN9+++0UlhJYRxRuxowEs4p4RYyUKFHCkQBfkpL/T+4mhJA4sYzceOONQY9F11gcj+wULLly5bJMjMAiAMGjd6GNltQCWJEZ9OSTTxr9XRo3biy+//57uT579mzjODdiRiqlkknjNrCUoc4JXGqIXSHuQMsIISSuLCNIKU3t6VZ31ViR3qtcIZjsw62wGgpwr8CFEMgyMmvWLPHdd98ZwmXq1Kni5ptvTnGcG2Jk1pXFq5YRuI8gHB999FHRv39/t28nYaEYIYTEPHiqVRYDc4XPQJYRRbSuGmSrHDhwQK4XKVJE2AHKo6NaKNi9e3eK/S+//LKxPnToUCnGEAzqphhBozn9el4VI6BDhw7SneXk+JAoxcjrr78uTYDoJ9C+fXuxePFiY9/48eNFw4YNRf369cXw4cOl6VCxYcMG+YHXrFlTdO/e3SfCmxBCogFCRDU5C0WMWGkZQSM4hT9rhNUxGOhzg1gQBVKHkcKrus22adNGrrdq1SpFrIrTk60u+kqVKuXotUmci5H77rtPmgQXLVok1Xi/fv1kXj2qDMI0CEGCXPGlS5cahX/Onz8vnn/+eSlGFixYIEvt4nWEkNgDboLatWvLrJHTp0+LWMqkscMyoseL2GUZMackI1hVF1PqwQ9/W1VqLNwPTZo0MY7LkCGDyJkzp3CSvn37inRXXSWyZskismfP7ui1SZyLEfgBVYoavvQwjx46dEhWGoQSh78SPltEkmMb+OOPP6SZEYWA8B+ia9euskhQpE2fCCHuMWbMGOPhA6mkXhMjoVhGYMFQk3a0lhE9ddYpMaKnJOvr+jGgc+fOPnEnTtfwaNSokRRFKA9PiOXZNG+++aa0jqCoDtwueMqAqRLuGwW2qf+keHLQzaLwJUK0YLu/SomwpGDRQZS4MsOSyFFjyLGMnkQdSzxIKIYMGSIefPBBH0uDG+OJpnd6Jk1q58ADFf4GIf4ClpFoPkNdjOBhza7vg/43FB2H1XX07sPFixeX29U+WEYwHnifqJ/hxndVyZ+kGP1/kqj/z61EBV9bLkZeeOEF8dxzz0mLB/4jQm3DXKtHsGNdNXTCT3N0O34PZOIdN26cGDVqlM82BGPBLEyswV8QHOFYhgL6nijw0PC///1PWkvc/G4iJk0BK6y5WZ0/lBhBau+aNWtEjhw5wr6u+doQOaFcOxLQ5E6xcuVK4zpYV2TLls3n+ogvwd/ThQsXyv4vdt1bMLI+8oj8edKFa1sJ/2Za26fIsjojCIyqUqWKmDhxolTeSGdDVLkC66r0MX7q+9T+QClwMC0iNkVn79698jqhKCwSGKh7/KfiWEZPIo4lYhN0lwjARLdu3Tpx1113uTaeEBR6o7VQKn0iuwPFyQCsvJF2i8WEr/7OVa5c2TZXCIJP8XcXpd7h4lb3q7u70f0Wf1f1scRx9erVE67Rt6/8EV0lF/dIxP/nMVn0DP8xENAG5QPfK7JsACwmKrIcflT0adCjv/GaQP5VPF2YSyfjaQdfBH4ZrIFjaR2JNJboTnv8+HHjKfzEiRNyvVevXjI+AC5YN8ZTPbUiJg0psKEIArg0FHAZR9ItFhOVyqbB37NoK60GA2Or/s6qOBG8T7WOWBlzbEYifTfthmNpL2F9S5FOhsI6cK8gcPXHH38UK1asEBUqVJBNjqZPny5FBv5gTZgwQW5TLZnx5IHsGph1x44dK0qWLBm0syYhxHvowZ4dO3YUt99+uzGZDxs2zHVrDSbkUC0TVmTUwGKruvXaGbxqDlDF32BYRJA8gGxGfZ/ngJvmiquGkECELZlnzJghRUaDBg1kGu9rr70m/xOgjC7y2zt16iR/4ikD7bMBrBwIdINLB+bCVatWiYEDB4Z7aUKIh8QIAipRKEpN/ngYcYOjR48adTfCcbXoAaGRihGn0nr13ikKWESCZdJ4hq+/Tl4IscpNAxPgiBEjAu5HrIeeSqaDYjyTJk0K53KEEA+LEVgW8P8akzDcssjqgJXC6fTRcNN6/YmHSNN7nRYj5vReuKX87SMk1qAzkRASsRjRn9ZhnXCjdlCo3XrNIOBUNdSL1DKip/XaWX01kBjR03opRkgsQzFCCIlIjChLAOK/FPrk6HXLiC6oVPfeWLOM6G4a3YVDSKxBMUIICVuMoEaHSt3XJ0E3xIhuGQlXjITbowaZgLoVRRcjqkutnSBlWZVVx1grMYJ0XiYEkFiGYoQQEhLI2jh8+HCKTBRdjOjVWb3al0ZHfx+piZGDBw+KihUrypTg3r17+4iRAgUKGOLMThCPo6wjeN/q+rgnpvCShK4zQghJDPT4iEBixG03DSw24RBqRg3iYVDUTYmtwYMHy/cNgeKUi0YBMfL7778bdZ7UNs+ileonJBC0jBBCIg5eBblz55bN0Nx206BCqZ5dYpVlBH2x0Ipi+fLlPtu7detmrDstRkLZ5hnwmYT5uZDEg2KEEBKVGNGtIygCpiq0OgEKjqly7OHGi5gzYPxZRpCqjN47c+fOlb8jXgPN53SrhPk8dhNzYuTff5MXQoJAMUIIsUyMAD3Dw8nmZZH0lkGch3Lt+LOMTJkyRVaMVsUbUUV66tSpolSpUj7H0TIShDJlkhdCgkAxQggJCX2yNlsC3IobiSat1xw3gjYWqOaqM3/+fGN95MiRsvcWOo6j15beidxJMYL7NReW0/vsEBKLUIwQQsISI/nz50/RkM0tMRJNWm8ocSOqCR7QuxKjtsoXX3wh3TbolFu1alXhFGiYp6cRI6U3a9asjl2fEDugGCHEo6Aj7Ndffy02b97s9q3IbBIVm2F20ZgLnzmZ3htNWm8oGTVKjKBDca5cuXz2tWzZUjaqW7Roka3dev2hx4h4Ol6EkBChGCHEo3Tt2lW0atVKPnmrRnBeS+vVrRJ4Yo9FN00gywgCVJXlpXDhwn577lx99dXCDShGSLxBMUKIB/nmm29kV2xw4MABsWbNGk916zUDy4CKW8CxSIe1g1OnTokJEyaIRx55RFSvXl2u22UZ+eeff8TFixcNMeIldEuUvk5IrMKiZ4R4DPRI6dGjh882ZKjUrFnTk5k0etzI2rVr5QQOS4qVvVKWLFkixo0bJ7Nb/FmJEDeRI0eOiM4dqHuvHi/iZIBqKHTo0EGMGTNGjvV9990nPM3997t9ByQGoBghxGM88cQTRnyGG+mykYoRc8M8q8TIoEGDRJ8+ffzuQ1ZP+fLlxTPPPOPXjRIK6OuC9F5YQgL1nfGaZQSBs6oKq+cZMsTtOyAxAMUIIR4CdSyU6wExGGjM5jUxEqjAl10ZNZMnTzbWkTXSvn170bFjR1G5cmUZWGoFEFgQIyq9N2fOnJ62jBASbzBmhBCPgGqiiIVQfPTRR0ZQqNtiRFkMUPZddY11Qowgo0i9dwgCWIxGjRol6tevb5kQCdS918uWkZjizTeTF0KCQDFCiEf45ZdfxL59++R648aNxUMPPWQEhSIGw66g0NRAefc9e/YEDF71V3jLqvReZMso61DZsmWlS8UO/GXU6JYRva4HCZPhw5MXQoJAMUKIR1i4cKGx/sADD/i0i4cQ0SdHJ1m/fr2xfuuttwY8DkJBZbTAMoK+LtGiixo7s0b8ZdSo8UYDPpSNJ4TYB8UIIR4UI/Xq1UtRT8ItVw0yZBSwTgRDCYYTJ06kCMKNBN3dY2V2TmqWkdOnTxv3TxcNIfZDMUKIB0D9jN9++81wdxQoUMAzYmTdunUhWUbMgmHDhg0xI0bM3Xt37txp/M7gVULsh2KEEA+AOhqqwJayinjRMlImle6ruuVEFzFeFyNwMaFWibKMMHiVEGehGCHEoy4aL4gRxH0oUYFy66kVFtPFiC5ioo0ZQdxGoCweq+NGDh8+LFatWmVsp5uGEPuhGCHEY2Lk9ttvN9aRvoouuW6JEWSzIP4jFBcNKF26tFF8LFrLCGp+oBGd3VYRf3EjP/zwg7FON02U/PRT8kJIEChGCHEZTPZ//PGHMZnny5fPZ7+yjhw8eFAcO3bMs8Gryt2hLAyIGVGup0jQxZcTYkTPqFm2bJmxTstIlOD7y87CJBUoRghxmZ9//ll2iDW7aLzgqgkneNV8HOqD6JVbo4kXcaIZnG4ZUZ8HuvKqWBJCiH1QjBDiMgsWLDDW/YkRu8qsh2sZCVWMWBXEqtcYcdoyokDdFHQkJlENbPJCSBAoRgjxSLwIYi3q1q3rKcuIEiPp06cPWn1VRxct0QSxOpVJE6znDl00FoAuy346LROiQzFCiIscOXJErF692pjEc+fO7RkxAjfL5s2b5XqpUqWkyyJcMRKNZUSJkWuuuUZ21bUbPb1XweBVQpyBYoQQF1m8eLFRNh3N3/yBviiwTDgtRuAmUbEToQSv6vcLARGNZQRCSNX6gFVEZeg4GTcCaBkhxBkoRkhCVjtt1KiRnORUHxK3+O6774LGiwDELKhJEgGhSiB4MV4EpE2b1hAv6O9y8uTJVF/z008/STfJCy+8IDv14n3ip1MuGoXZFUXLCCHOQDFCEo6RI0eKefPmSSvD6NGjXbuPM2fOiEmTJhkuAr2+SCBXzblz58SuXbs8m0nj73i90Z4/YBnq2bOnLME+ZcoU8dlnnzkeL6KgZYQQd6AYIQkF6l4M19qZb9y40bV7mTFjhjh+/Lhcb9u2rciaNWvAY92IGwm3xkiklVgXLVrkkznz4osvil9//dXRtN5AlhG6aSygdu3khZAgUIyQhGL69Ok+lgU7xcj58+eNeBB/jB071ljv0qVL0HO5KUauvfZaowpsqIQTxPrxxx/7/I7ibu+++67rlhFUv82VK5dj145bpkxJXggJAsUISRggDN555x2fbYhpgLvEap588kmRKVMmOYk3btxYxkIsX77c2A+XhKovggmwdipPjvqEbKWAQqVRFF0zA0Fw4MABQ1iEG0AaqmVk//79UiACZBJlzJhRrqu4GMSfmF0ndoJrqfeKeBGnAmcJSXQoRkjCgIn3999/TyFQrLY0QNy8//77MgATkzr6nLz11luiatWqMiYCfPrpp4bVpHPnzqlOeuiWq45RpeOjBc3gatSoIerUqeNjpTFbM8J10YCcOXMa6bgQI4EsRGPGjDFKxnfr1k08+uijPvshCDJkyCCcArE7Tz/9tGzKh5/EAiZPTl4ICQLFCEkYhg4d6neC1eMVrADWFjX54slegW0PPPCALHI2btw4Y/+DDz6Y6jmzZMlixE5gckfqq5XN+Z566inZFA9AROkWpHCDV82vQ1zMP//8k2I/rB8jRoyQ6xBaECMPP/ywT/ExJ+NFFG+//bas/4LPiljAU08lL4QEgWKEJASoWYGAUdWO/uWXX7YtbkTVxwAvvfSSnIgfeughI47kzjvvNOJWkGIcakGv2267Tf6EJWHNmjVR3+fu3buNdaTfQghAMA0aNEjMnTtXbs+TJ49o0aJFROdPrRLrnDlzjHto2rSprE8CK8iwYcOMY6pXry7cQBeRhBD74f84khC89957Rt2Kxx57TJQvX942y4guRvCUj6qeSCfGhKsESaiBq/7ECFixYoWlYgQg3RmCRAk1WCu+/PJLv1VhrYgb0QNXkdqraNasmZg6dap47bXXpMWGEBL/pHP7Bgixm2PHjsnYBICg0h49eogcOXLIp3DU7bDaMrJt27YURbNQSh3xIihspgJZkalx9913h3zeypUrG+t6MKxVYgTosSMDBgwQDRs2jPj8wSwj+/btMwq+wSKCIF+dNm3aRHxdQkjsQcsIiXtQ2Oy/K4264C7Bkz6qmhYvXlxuQxXWCxcu2GIZ0St4okQ6XBPo8wKee+65sIIzy5UrJ9KlS2e5GEHGT9euXX32QRzAxRQNSEdW2THmoFu9jkiHDh3YGZeQBIdihMQ1EBlw0Sh0s78SBYjBQPlxq8UIJmLEp+ggBgNZLLhe7969wzovzqesDXAtKYEVCXAVIa0W3HjjjTJgFT8BYli++OKLqOMmYA1S7jAIPlioFLqYQpYRISSxoRghcc1XX31lWACaN29uWEPMmRpWxY0gAFSJEVTv9Deho+kdYkkiqWGhXDW4zsqVKyO+z7179xoZPwULFpSprKg3MnjwYJkCjUJnVqC7lvQ4F12M6LEwJA4ZPz55ISQIFCMkYYqcmetGKMsIsCpuBLEQKu3WjiZrVgWx6vEiyiJSqFAh6ToKNbsn0vvF56LW4SIqUKCAZdcjHgTxQKaYIELMUIyQuGXJkiXGpFehQgVRt25dn/12WEYCxYtYhVVBrP7EiB3oYkTdLwJ8lcsG+1nllBBCMUISosgZrCLmSQ9N0RDIaqVlxG4xUrp0aSMoNBoxogqc2S1G4BZDwTb9fumiSTAaNUpeCAkCxQiJS1BobObMmXIdboB27dqlOAaZLKraJ1rWq34oVtYYsRoEhcLKoywMR48e9bRlBGKvUqVKxjXR70Z3L+mWHhKnoLVAKs0SCaEYIXEJUklVgCbKrSNo1B8qbgRxHno3XytrjFhNoKBQL4oRf3EjtIwQQsxQjJC4RG9+hyZzgdDjRqxw1eiWEWTTOBWHEakYQd0SBJHaiS6eUF9EZQGh2JlVWTuEkNiGYoTEvRhB8a1A6Bk1VgSxKjGCCR4dYO3AiowaJUbgwlJxM3ah3++ECRPEqVOn5DpdNIQQBcUIiUt0MaLXFrHTMnL69GmjkJhdLhr1frJmzRqxZQT3+e+//xo1RuwGFiKUvlcdjRWsL0IIUVCMkLgWI3jyz5YtW8DjSpQoYZllRJ9o7QheVaCQmgoKRaDuwYMHw3o9XuNUvAhAFpM/KwgtIwkC6tZYWLuGxCcUIyTuOHLkiDh8+HCqLhrVLwbFvsCGDRtkaXgvB6/qfWoU69ev92zwajAriBJUJM6B9c6CXkokvqEYIQkbL6KoVq2a/IleLyiF7tUaIzp6UG4sihF8LihBTwghgGKEiEQXI3fddZexPnv27LgTIxgPvWuuG2LE7JKhiyaB+O235IUQq8QIOn0OGDBANGvWTJbWRjv2tWvXGvvHjx8vGjZsKOrXry+GDx9u1HlQJnC0Cq9Zs6bo3r277OFBiBfEyJ133mk0tLNKjNgZM2LOAgokRlA35d5775VxMZj858yZ42j1VZ3rr7/ep4Mxg1cTiJYtkxdCrBIjqFCJgMAxY8aIhQsXio4dO4pevXrJ6Hz0AZk6daoUJFOmTBFLly41KmBCxDz//PNSjCxYsED6u/v16xfOpQmxTYyg1kX16tWNjBpdVEQSM4Jy7XbX7kBQrop1gRjRhT8KuPXp00e+94kTJxrbR4wY4ZplxCxAaBkhhEQsRjJlyiS6desm/9DiSbJx48ayPDWewL799lvRqlUr2fETf9zvv/9+uQ3ARIzjWrZsKUtwd+3aVWYu7NmzJ5zLE48CkYoy32o5fvy4q/eD0u4A3zU1YYfjqlEWhHC4fPmykU0DF40Tzd+Uq+bkyZM+AuPZZ58VgwYNEufOnfM5/vvvv5efjToWosnJomOPPvqorL1Sp04dI06HEEJAumiGAebeEydOyKcr/CGGOFEULVrUeFLEkyaakinwRxCiBdthvjUDSwoWnQsXLsg/+CQ61BhaNZaoq4GJRZ8MMRH37t1bvP7668JpkA2zdetW4zuIewnlvTZt2lS8+OKLcn3WrFly4gxnLOF2VJM/6mo48V1F0zwlnNasWSP/T0EYfvHFF3IbHgCefPJJmVkEiyX+T3399dfGZ4XjYVHRrSp2Ahcu7gWl+f19LlZ/NxMZL42lkuVJHriXWB/LWEW5wW0RIzAFw9WCuBF05YSrBmmSCqyfOXNGruOnvk/tx2v8MW7cODFq1CifbW3btvXb7IxEhi4eomHkyJEpzoXJDR1z8XnlyJFDOMnOnTulcAUQyaH2m8F3GMIY1rpFixbJGCfVbTYQ+OO0d+9eeU09biNPnjyW9LlJDd0VBDcpLCWI4VKWKUz+PXv2lFVaIUbARx99JC0pTt6nW99N4o2xvPFKA8rdHvyuxdpYxiqhtMZIF+nT5wsvvCD/2MNtA2B+VWWeAdbh1gH4qe9T+wOVy+7cubO47777fLbhjz6uF4rCIsEnUPynsmosV61aZazDMobJHBMznsJ/++03GazsJLooQHfbUN00oEWLFnKyxr1v3rxZuh39AYvfZ599Jj7//HO/k3n58uXDum6k3H777cY6xh3XRNyWAoHm2IbPWgmt33//3dgPa6UT9+nWdzOR8dJYprnSbsBL37VYHct4Jl0kHwwsIjCz9u/f3/CNQ/nAPI4sG/UHW2UUwIc+bdo0H6sKqkAGSn+EGdfcZRUmZ3wR+GWwBivGEhYIPJGrp/S5c+fKJmgqOBHBk//73/+E1cDUD8uaErs6W7ZsMdaRRRLOe2zevLkUIwDxTq1bt/bZD4tD+/btZexFMFDMy4nvKUrZo68MXDOw5OCaP/30k7EfWW3qc27Tpo3McNNBKXgv/n/i//M4G8v+/eWPNG7fRzyMZRwT9si+8cYbsq/Fm2++KTt+6j736dOnS5GB/WiIhW3qjzP86ciuwVPn2LFj5R9Sf/EiJHaA+V9ZvOrVqyeFacWKFY0MlsWLF/ukkVoBRG3evHlFlSpVDHdDNJk0ZkuDstYhFsPsI4YlRBci+MNUo0YNacVDwDaWjz/+WNSqVUs4AWKvVCwWAsIh8n/++WdDHOql7uHmNONkJg1JYHr0SF4IsUqMIEgPAXB4CoM/unbt2nKBqR5/gPH01alTJ/kTQY0wewNYOYYMGSKflDFp4fiBAweGc2niQZDercDnCiBIMCkrvvzyS8uuB3GAlFXEpMAdA1FrpRjB5H7HHXfIdWQF6YXCgG51gHUQLhoEiyqXDRY7LEGhZNRA7E+ePFlWkdXFoQKpy2bxTzFCCPEMSTHC9u3bky5duuT2bcQ8GEOrxrJhw4ZIw5DLli1bjO3btm0ztpcpUybJKubNm2ecF8tNN92UdPHiRZ9j8ufPL/flzZs3omuMGjXKOP/LL79sbL98+XJSnjx55Pbs2bPL61o5lpHSv39/434rVqxorI8cOTLFsU8++aTP+K1bty7JS3hhPOMFT43lww8nLzGKp8YyjqEDjEQEnsRVvAiesPWKo4gFUkXEYMHQq/RGA1wgOshigaVOgTRzpBpHYhVRNGnSxFjXXTKwuBw6dEiuwwqIWA0vULZsWWMd8TpmS5WOORuNlhHiCKhqHEVlY5IYUIyQiECmDGIU/LkEgO6qUXUvogGZIKqiL4qZKZBCbIWLRgFXhprgkXmC+CcV/6JQQdpeQO9Ro0D9EH/l6OE6VQIEab1sVEcI8QoUI8SyeBHzU7gKcEasULQFg1B3Blkj4LnnnjMmYbQd+PXXX1OIET14M1zQqwYgNmXevHlyHbVHFKgg6hUgOnRxprJo/FWARcAtWjlg/wcffODgXRJCSHAoRogtYgRlxlVFXmRYqSyPSFOIVRE8uEd69Oghnn76aWP/sGHDZAG9+fPnR20Z0cUI+O6776QoUWIE2TbIGPIKGA+9aV6gz0OBAF2MEwsIEkK8BMUICRtU1F22bJkRHxKomBFqcih0oRAu33zzjSx6p2qBwA2BbrT58uUz0n3REVZVGY1WjKCztKoYDDGCtgWqjxJSeVHzxkuYXTXBxAghhHgRihESNnCNqN5BwSY+vUKoHnMRTeDqI488In/CNaH6x8AFhOBVRaNGjWRfmkjBueHKUCm+ukvDSy4af2IExQdjtdIlISRxoRghBoi9mDRpUqrxHam5aBQIllQ9CXBuFfAaDnDvKKsK4iNQ30aBmh7KOoI+Ml26dJGiB9aMaLvm6q4aVZXVa8GriltvvdVYp1WEeA70dGFfF2Jn114SPyBNFgXs0HcI1gB0ew1Uih3VdUOd/GBJQEdnpAIvX75cXiNUcC+PPfaY8fvzzz/vU44ZGSE4J/rIIFPE3IzRKjGirEAo3ofKr16jQYMG0pKDUvh6LA0hnkCr1E1IIGgZIUZ1UUz+qhOvv7byCBK96667pHBR8RMFChQIOoK6JSFcVw3cM6pGCYJGu3bt6tf6gsnYSiGiYmFUqXVF1apVZZVWr4EYFliPUBG2dOnSbt8OIb4cOJC8EBIEihGSovvuxo0bxZo1a3xGBkKlQ4cOsr4IgAgJpdS7HmMRjhg5ePCgLLmuQNyG04XGdOuIV+NFdKJ1TRFiC+XLJy+EBIFihKQQI+ZCZbCSIFh01qxZ8vds2bLJDr2hBErCwqCsJ7/88othfUmNF198UXbJBQ899JBR0dVJYk2MEEJIrEIx4mEQ8An3CcqSqwXNCq0GAaurV6/22YZCZarI2JQpU6TrRrkEZsyY4RM0mdrTuprE0eFXL1keiAULFhhN8CB80CHaDeBiUgXFYJWBW4oQQoj1UIx4FFgjUDQMAaJ4QlcLKouqmA2rQIDpyZMnfbahrgcKfaEL7DPPPGNsh0hQaa+hEk7cCDJhEJeiePXVV42MGadBHErnzp3lOuqaIGOHEEKI9VCMeBQIBH8TN+ppoOKoXS4avWYFsmZef/11o+BXs2bNfHrOhEqocSNTp04Vd999tyyqBiC+VC0Rt0BaL8Tfp59+6up9EEJIPEMx4lH0Wh7oJPvyyy/LUuQA/UWOHTtmixh56aWXRNasWQ33zDvvvGOktb777rsRnb9kyZKyPLyqG+Kvjslnn30mA2RR+h20adNGduRV/W3cAm4mxMYwOJQQQuyDYsSj6GIEAmHAgAEykFPFXqgYDqvFCAJF77nnHrkOF40SB2hOF2lVU0zkqr4IRNT69et99uP9oLKqEikoXobia+YGcISQGAR/t6787SIkEBQjHo0XUWIE1pDbbrtNrqMQmXpCf++99wyhYJUYyZkzpyhYsGAKVwxqeSC7JRr0uBG9A65y3aCGCYAQGj16tONpvIQQmxg0KHkhJAgUIx4ElTRVYzhYFOAiAcWLF5eN4gDiOBBjES379++XC6hQoYIUOwiaReM5xdChQ6MuKqbHjZjFyI8//misd+zYkS4RQghJMChGPEiw3i96uW/Ec/irlBqpiwZiBMAqMWTIEJE9e3bRo0cP0bp1axEtSAXOkSOHXJ83b54sD28WIxBC4WbqEEI8zhtvJC+EBIFixIOgzkYgMQILA0qjA9TsQECo1WIE3HfffTK+45NPPrHEUgGBo6w6yAiCIAHog6NKvleqVEnkypUr6msRQjzE++8nL4QEgWLEY8DSgUJnAFktSngoIAx06wj6t0SDXuxMFyN20K5dO2MdmTpAdeQFd9xxh63XJ4QQ4k0oRjwG+sKgL4uygvhLbcWkrtJvrbKMZMqUSdxyyy3CTiA2UFEVzJw5U7pqlIUENGzY0NbrE0II8SYUIzEUL6JASXaVYYNAVlWULFzgLtm6datcL1u2rO0ZLEjVbdmypXHtH374wYgXgRhiuXVCCElMKEZiUIyodvaK33//PaJr6Z157XbRKNq2bWuso7rrP//8Y2QNZcyY0ZF7IIQQ4i0oRjwEin6peBHU/ChXrlxIYuS3336zNHjVblcNsnTM900XDSGEJC5pEzlQFJO/WqJNkbUCZJUcOXLEKBIWzG1SpUqVqMQI+r9MmzbNcTECV02LFi1SbGfwKiFxCuLaooxtI/FPQoqRJUuWyE6wmOzVgt4pemaHG+jFwIK5aACKkqEyKlixYoW4dOlSyNdBmXd0xlXBr3ny5JF1QJxCz6px4/qEEAdBG4kIW0mQxCEhxUjv3r3FoUOHfLbBItGrVy9XLSRLly411lUvl2AoVw3EBbJwQuHw4cOyhohyB2XJkkVMnjzZ0XgN3VUDGjRoINKmTcivIiGEkEQUIwjaVJM+CmwhgyNv3rzy93Xr1vmUJneaZcuWyZ8ovY7sltQIN4gVWTewuOB9qvcPa1BqVhirQXl7lVUDGC9CSBxTpEjyQkgQEk6M6EXCBg4cKH755Rfx0Ucf+fRhcQMIhd27dxvxIP7qi0QTN7Jt2zZRq1Ytw4ICNw8a1OnncBJ0AYZrrHTp0j4ZNoSQOOPMmeSFkCAklBhBbYsvvvjCcE+o7rR4Si9cuLBc/+6778SGDRssvS5cQuPGjZOlz1OzioDq1auHdF6UT1dBrsHECCwhECI7d+6Uv6MzL4QIhIBb4Npo0Id7U4XQCCGEJCYJJUYgRE6dOiXXIUTUJIgJ/cknnzSOe/fddy0P2OzSpYvMWNm1a5dlYgTunDJlysj19evXy9gRM7CEIDNHdeaFCECMSBEPmE0x7lb0vSGEEBLbJIwYQWCq7o555JFHfPZDLChx8vnnnxsl2aPlr7/+MoJF9+3bJ5o0aWKk7wYSI9WqVQv5/CpuBOnJf/zxR4r9KCx29OhRuQ6XDIqqIZOIEEII8QppEymdV7lfatasmSKVFL1eunfvLtfRMyXaBnSKCRMm+Py+adMmcffdd8s6HwpcTwmJYsWKyViKUNFjPvwFsf76669GuXUE5+bOnTui90EIIYTYRcKIEV1cmK0iiscff9yIwfjwww+lSIjWGqPECFJXVdYOgmaRXqtqg6xcuVKcP38+LBdNKJVY//33X7F9+3a5DheRaq5HCCGOcfvtyQshiS5GEECqqo3C6tCmTRu/xyGws3Xr1sZr9D4xkQCrhBID9evXF3PnzpWBs2DGjBli1KhREceLKEqWLGmc0yxGUAxNoRrrEUKIo0ycmLwQEq9iBNaEAQMGyHRcf8GbClgnLly4INc7d+4sS5IHon379sY6BINVLhpYQipWrCimTp1qbMO9I6A2GjECS07lypXlOprO6R18dTGijiGEEEK8RkyLkWHDhon+/fuLZ555RhQvXlymz5rLosNVMnbsWJ9A1WA0btzYqEY6c+ZMGRgaCRA/yFoBON8999wj1++8807RqlUruY4Ml+HDhxtiBBYOlR0TDijcpliwYIGxvnz5cmOdlhFCiCvgocwUO0dI3IgRiAzl5lCZKhAaqL2h1wlBPIaqOAqrQ4kSJVJNl23UqJFcR10QFQAaLvPmzZOl1wECVvVaGshwUeXPsa6sGQhGDdYcLxDqflWdFLNlBNdGYCwhhDjOs88mL4TEoxhBkzdUFQV6nxOUe2/evLnhtgnHKqJQlgvw9ddfR3R/qriactGY4zzgLgKnT5+O2EWjW0ZUcOr3338vrUMQZ0rkQKCx9wshhBCvErNiRBcZyJRBLQ9M8mDHjh3ihRdeEGfPnhVffvmlkdpq7hYbCHS0VZM34kbCbZ4HIQQXj+r/AteMGbiXzM3pIhUjV199tdHfBRk0sAbRRUMIISRWSBurZd1VIGiOHDlkOXdUGf3mm2+k6FCpuejCe+zYMfk7+p+EWnYcGTd16tSR61u3bg25I64CRdOUxQMCCI3hzNxwww0ylVgnnGJnZnTBA1cNM2kIIYTECjEpRqZMmWJM9vfee68hQIoWLSrefPNN47hPPvkkbBeNQu8qG05WDQJX9Xvo1q1bwGNhvVEupvLly0dVkAyBt7oY0S0jzKQhhBDiZdLGuotGxV4oHnvsMcOqobj55ptTbLNLjMAq8vfff8v1pk2bynTeQMCFM3/+fNGzZ0/x6aefimgoVKiQ4aZC0K3K0IGVB/sIIYQQrxJzYuTPP/80JtqyZcvK4EwdxHpArGTOnNnY9tBDD4XdkA0TOKqWAsRgKIERjIsXL4pBgwYZv/ft2zfV1+D+4VIyl6ePxlWDdOTjx48bKb1sRkcIcY3PP09eCIknMYJaIrrrxd9EC0sI6ndgH0qwd+3aNaJrpZZVg23o8KviUlBXBDEmoEGDBhEHpEaKv0BZumgIIa6C4PorAfaExIUYWbt2rdFjBhkk5pRZnYcfflhaUVBj5LrrrotajEyaNMlnH1wh2I8gWcSqfPDBB7JmSDhWEauBK0rFzyhY7IwQQojXiRkxgpoZiME4efKk/P3+++8XefLkCfoaVGVVzekiAdVQVUVUuIZUXRMwcuRIYx3ptMiMQUdeUKtWLZnd4zRIFb7d1JCKlhFCiKvUr5+8EBIPYgSBqijkpTrVvv/++45cV7e+qJolqCOCjB6QLl26FK+BVcStOA3dVXP99ddHbBUihBBLwEPalQc1QmJejKhYDJQ1nzVrlizb7gRIHdYb36EAGmqcoMEdQDwKuuXWrFlT/o6OwHp5dqdp0qSJse50zAohhBASCSkf6z0MXC6ooZGae8ZKChYsKGMxFi9eLP766y+ZWWMuMY+eMihPj2JsaHbnZvYKxNrgwYPFjz/+KKu8EkIIIV4nZiwjSNWdPXu2KFKkiOPX1l01mOCXLFki10uXLm0EiEKAoIBZJI3urOa5556TPWpwf4QQQojXiRkxgiwac00Rp4DrBdk7AIJIj2NhDQ9CCCEkQcRI7dq1Xbs2KqU2a9bMZxsCV5HRQwghJAg33ZS8EBIPYsRtzDVN0Nk3X758rt0PIYTEBKiYfaVqNiGWiJFp06bJSRmptSNGjPDZhwwX1AFBfY0BAwbIhnGKf/75RwZ6IuMEr9+8ebOINSA+9K6/4TbeI4QQQogFYgRN17p37y7qmwrYIO126NChYsiQIWLOnDniwIEDYvTo0cb+Pn36SAGzYMECWbUUAZbo4xJLoKDYk08+aXTY9Vd6nRBCiImlS5MXQqwSI6juCctH1qxZfbYj3RYCBdkbSG2F1QCiBOzcuVPs2LFDBntmyJBBBoOikdvq1atFrAGLD1J7f/rpJyOglRBCSBBat05eCLG7zsj27dtlrQ0FerXs379fnD59WgoR1OpInz69z36UVg9Uqvz8+fNy0YHbByLGbcqVKyd/euFeIkHdd6zev5fgWHI8vYqXvpuq6lKSB+4l1scyVkmbNq0zYuTMmTM+FVFhHQEQI1jM1VLxO14TrDPvqFGjfLa1bdtWtGvXzorbJUKI3bt3cxwsgmNpLRzP+BrLGy9dSr6XXbtELOOFsYxVChcu7IwYQadYVR5d9W5Rhcqw6PsAfjd3l9WBS8ecvbJ3715x4403hqSwSGCg7vGfimMZPRxLa+F4xudYprlSCLJQoUIiFvHSWMYzlogRVEVVvWMAXDD58+eXQgSKCB8k3C7KVYP9ZrGhg+N0tw5AjAa+CPwyWAPH0jo4ltbC8YzPsUzjkfuIh7GMR8IaWWTAnDt3TirFS5cuyXX8RGYJMmU2bdokrSLo3aKKhN10001yGT9+vBQk06dPl1VLkZFCCCGEEBKWZWTMmDE+sRwQHa+88opo3ry56NWrl3j66aelCwaZNehmq3j99dflcZ9++qk01aGRGyqYEkIIiXMGDnT7DkgMkCYpKSlJxADIyoGQoZksOmDV2rVrF8fSAjiW1sLx5Fh6EX4vnYEOMEIIIYS4CsUIIYQQ+0DrDLbPIKnAwA1CCCH2MXcuR5ekCi0jhBBCCHEVihFCCCGEuArFCCGEEEJchWKEEEIIIa5CMUIIIYQQV4mZomeEEEIIiU9oGSGEEEKIq1CMEEIIIcRVKEYIIYQQ4ioUI4QQQghxFYoRQgghhLgKxQghhBBCXIVihBBCCCGuQjFCCCGEEFehGCGEEEKIq1CMEEIIISSxxMi0adPEfffdJ6pWrSpGjBhhbEdVevzerFkzcfvtt4vXXntNXLhwIcXr165dK2677TYxevRoY9u5c+fE4MGDRePGjUWDBg3Ehx9+KBKFSMeze/fuokaNGqJ27dpyeeKJJ4x927dvF48++qioV6+eaN68uUgU7BjLn376SbRu3VrUrVtXfj+HDh0qLl26JOIdO8Zy1qxZ8nxqH5b9+/eLeMeOsXzjjTd8xrFatWqiffv2IhGwYzwTeQ6KWTFy7bXXyg+1fv36Ptvxh2b+/Pli3LhxYs6cOeLff/8Vo0aN8jnm8uXL8o95qVKlfLaPHz9ebN26VUydOlUuv/32m/j6669FIhDNePbt21f8/PPPcnnvvfeM7enSpRONGjUSzzzzjEgk7BhLfFchnBctWiS/m1u2bBFfffWViHfsGEtQqVIlYx+W/Pnzi3jHjrHs06ePzzhWqVIlxfnjFTvGM5HnoJgVI1CceErMmjWrz/YlS5aIe+65R+TNm1dcc801olOnTmL27Nk+x0yfPl2UKVNGFC5cOMVroXSzZcsmcuXKJTp27Ci/WIlANOMZiIIFC4oWLVqIQoUKiUTCjrHEa3LmzGn8niZNGvHPP/+IeMeOsUxU7B7Lw4cPy8kTFoFEwI7xTOQ5KC5jRswNhA8ePCj+++8/uX7s2DExceJE0aNHj1Rfi3W4GhKdYOMJYGVq2LCh6Nmzp3xiJ/aM5erVq+UfPzyJYd/dd9+d0EMdzViuW7dOmsHbtm0rze2JjhX/x7///ntRunRpccMNN4hEJ5rx5BwUJ2IEvjiYr/ft2ydOnjwpTWXgzJkz8udHH30k1aZZzYLq1auLCRMmSMEClT9p0iTjdYlKauMJf+c333wjlT98p/j91KlTLt91fI5l+fLlpZtm5syZol27dvLJKVGJZiwrVqwoJk+eLObNmydeeeUV6f6CWT1Rser/+LfffiuaNm0qEp1oxpNzUByJETwt3nHHHdKXh0Aq+DARu4A/3H/++afYuHGjaNWqld/XdunSRRQtWlTce++9omvXrjLwMl++fCKRCTaeAO6uzJkzi4wZM0pzJNbx1EnsG8vrr79eFClSRAa6JSrRjCXGr0CBAiJt2rTyuA4dOoiFCxeKRMWK7+W2bdvEjh07ZIxYohPNeHIOip50FpzDEvAHBi4Y5Yb59ddfRYkSJcRVV10lVq5cKXbt2mWod5jNsH3Pnj3yCQlfjt69e8tFxZaYg1wTjWDjGeh4Yv9YIpNm9+7dCTvUVo4l4m/MZvVEwoqxhFWkZs2aMtYh0YlmPDkHRY/jM9DFixdlGhQyY/CHGev4CRcLAvvwxwVqfdiwYaJbt27yNQgqmjFjhnTFYKlTp470GT/99NNy/4EDB6R7BudE6i/Ma7CQJAKRjCdMkPiPdv78eZm6hjE9ceKEVP4Ar8F5sE9fj3fsGEu4FFT66d9//y2j7pGaHu/YMZZLly4VR48eleuwlsJlg78F8Y4dYwlwvu+++y5hAlftHM9EnoNi1jIyZswYn3SpsWPHSusGPtRevXqJQ4cOiTx58sgPEopdqU4sigwZMohMmTIZ8SN40sQ58GVCEBYsJHDbJAKRjCf+MyIPHtYmmCGLFy8uhg8fLrJkySL3w2eqB1nidfDXjxw5UsQzdowltuOPGv5wZc+eXQa/PfLIIyLesWMskfGBc8CHj4yHBx98UNZ1iHfsGEvwxx9/iLNnz4patWqJRMKO8UzkOcgq0iQlsp2TEEIIIa7DQAFCCCGEuArFCCGEEEJchWKEEEIIIa5CMUIIIYQQV6EYIYQQQoirUIwQQgghxFUoRgghhBDiKhQjhJCYpnLlynJhy3ZCYheKEUJIqqB5mJr00T1bB1UnUalS7X///fctH1EIDXV+Qkj8QTFCCAmLLVu2yOaViq+//lr29yCEkEihGCGEhAz6cgA0qQNoMDZt2jRju87x48fFW2+9JRuxVa1aVbap79evn9E4EIwYMUJaO5o3by5+/PFH0bp1a9krBQ3Kdu7cKY/p37+/GDBggPEaZSHBa3XQzRvH1a1bVzRp0kSMHj2anywhMQLFCCEkZNAg7Prrrxc//fST7FS6ePFiKS4aNGjgcxwsJXDtTJ06VXYzLVSokDh16pSYO3eu6Ny5s9F9V3Hw4EHRt29fkSZNGvnaVatWiVdffVXuQ+MxXFOBhmZY8uXL53OODz74QCxbtkxcffXVstnZJ598IjutEkK8D8UIIST0Pxhp04q2bdsaFhFlIWnfvr3Pcd9//71sww5gHZkyZYrslorXQyjgdx2cb/DgwfKcKiYFrdjRVfbhhx+Wi2L8+PFyadmypc85brnlFhlboltqli9fzk+XkBiAYoQQEhYtWrQQmTJlkoJixYoVomTJkuLWW2/1OWbjxo3yZ8aMGcXtt98u10uUKCEtJPp+BVqx16lTR64XKVLE2G62oASjYcOG0iqSI0cOkStXLrntyJEj/HQJiQEoRgghYZE1a1YZkwG3iz+rSKTnVFx11VXGelJSUlTnCOf1hBD3oBghhIRNu3bt5M+cOXPKwFQzpUqVkj/hZkF8Cfjzzz/Frl27fPaHCiwsijNnzvATIyTOSBkCTwghqVC0aFExf/58aYFInz59iv2NGzcWEyZMkHEjvXv3lu6ZPXv2iMuXL4s8efIYYiZUbrrpJmMdMSvXXnuteOqpp0T58uX5WRESB9AyQgiJiOzZs8tYD39kyJBBjBw50hAOsIhkzpxZunfGjRsnLSrhUKxYMRnEmjt3bpm9s379enHy5El+coTECWmS6FQlhBBCiIvQMkIIIYQQV6EYIYQQQoirUIwQQgghxFUoRgghhBDiKhQjhBBCCHEVihFCCCGEuArFCCGEEEJchWKEEEIIIa5CMUIIIYQQV6EYIYQQQoirUIwQQgghxFUoRgghhBAi3OT/AMw27L9E6YsCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "series = AirPassengersDataset().load()\n",
+    "train, val = series.split_before(0.75)\n",
+    "\n",
+    "print(f\"Training series: {len(train)} points\")\n",
+    "print(f\"Validation series: {len(val)} points\")\n",
+    "\n",
+    "series.plot()\n",
+    "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
+    "plt.legend()\n",
+    "plt.title(\"AirPassengers Dataset\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34858645",
+   "metadata": {},
+   "source": [
+    "## Basic Model Logging\n",
+    "\n",
+    "Let's train a simple model and log it to MLflow manually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "bc8f520d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation MAPE: 7.86%\n",
+      "Validation RMSE: 34.77\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = ExponentialSmoothing()\n",
+    "model.fit(train)\n",
+    "\n",
+    "predictions = model.predict(n=len(val))\n",
+    "\n",
+    "# calculate metrics you want to log to MLflow\n",
+    "mape_score = mape(val, predictions)\n",
+    "rmse_score = rmse(val, predictions)\n",
+    "\n",
+    "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
+    "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
+    "\n",
+    "train[-50:].plot(label=\"Training\")\n",
+    "val.plot(label=\"Actual\")\n",
+    "predictions.plot(label=\"Forecast\")\n",
+    "plt.legend()\n",
+    "plt.title(\"Exponential Smoothing Forecast\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35dc864c",
+   "metadata": {},
+   "source": [
+    "Now let's log this model to MLflow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "61406cd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Run ID: 936e44cf2edb4db0bc6e32aa0d9fdfd9\n",
+      "Model URI: models:/m-280d9919c51642dabaf49017ad97ca65\n"
+     ]
+    }
+   ],
+   "source": [
+    "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
+    "    model_info = log_model(\n",
+    "        model=model,\n",
+    "        name=\"model\",\n",
+    "        log_params=True,  # True by default, logs all model params\n",
+    "    )\n",
+    "\n",
+    "    # log calculated metrics you want\n",
+    "    mlflow.log_metric(\"val_mape\", mape_score)\n",
+    "    mlflow.log_metric(\"val_rmse\", rmse_score)\n",
+    "\n",
+    "    # log any additional tags you want\n",
+    "    mlflow.set_tag(\"model_type\", \"ExponentialSmoothing\")\n",
+    "    mlflow.set_tag(\"dataset\", \"AirPassengers\")\n",
+    "\n",
+    "    print(f\"Run ID: {run.info.run_id}\")\n",
+    "    print(f\"Model URI: {model_info.model_uri}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0728690e",
+   "metadata": {},
+   "source": [
+    "### Load the Model Back\n",
+    "\n",
+    "We can load the model from MLflow using its URI:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "cae35ffa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded model predictions match: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "loaded_model = load_model(model_info.model_uri)\n",
+    "\n",
+    "loaded_predictions = loaded_model.predict(n=len(val))\n",
+    "\n",
+    "# verify predictions match\n",
+    "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
+    "print(f\"Loaded model predictions match: {predictions_match}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bd4597c",
+   "metadata": {},
+   "source": [
+    "## Automatic Logging with `autolog()`\n",
+    "\n",
+    "`autolog()` patches every `model.fit()` call to automatically log parameters, covariate metadata, and the trained model artifact. For PyTorch-based models it also injects a callback that logs `train_loss` / `val_loss` per epoch (see the sections below for details)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5ef7f73a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logged metrics: {'train_mae': 8.4783, 'train_mape': 3.9116, 'train_mse': 118.1369, 'train_rmse': 10.8691, 'val_mape': 10.742, 'val_rmse': 51.182}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "autolog()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
+    "    auto_model = LinearRegressionModel(lags=12)\n",
+    "    auto_model.fit(train)  # no val_series → autolog logs train_* metrics by default\n",
+    "\n",
+    "    auto_predictions = auto_model.predict(n=len(val))\n",
+    "    auto_mape = mape(val, auto_predictions)\n",
+    "    auto_rmse = rmse(val, auto_predictions)\n",
+    "    mlflow.log_metric(\"val_mape\", auto_mape)\n",
+    "    mlflow.log_metric(\"val_rmse\", auto_rmse)\n",
+    "\n",
+    "autolog(disable=True)\n",
+    "\n",
+    "# show logged metrics\n",
+    "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
+    "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
+    "\n",
+    "# plot\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "train[-36:].plot(label=\"Train\", ax=ax)\n",
+    "val.plot(label=\"Actual\", ax=ax)\n",
+    "auto_predictions.plot(\n",
+    "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
+    ")\n",
+    "ax.set_title(\"Linear Regression — autolog run\")\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f484330f",
+   "metadata": {},
+   "source": [
+    "## Open the MLflow UI\n",
+    "\n",
+    "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
+    "\n",
+    "The UI lets you:\n",
+    "- **Compare runs** side-by-side in the Experiments table\n",
+    "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
+    "- **Visualize** metrics across runs with built-in charts\n",
+    "- **Register** models to the Model Registry for versioning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "1a01cd2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Launch the MLflow UI with this command in your terminal:\n",
+      "\n",
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmph5ei82gg/mlflow.db\n",
+      "\n",
+      "Then open:  http://localhost:5000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
+    "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
+    "print(\"Then open:  http://localhost:5000\")"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "id": "88b0d285",
+   "metadata": {},
+   "source": [
+    "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `val_mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
+    ">\n",
+    "![image-2.png](attachment:image-2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2b133bc",
+   "metadata": {},
+   "source": [
+    "## Per-epoch Metrics with Torch Models\n",
+    "\n",
+    "For neural models, `autolog()` by default injects a PyTorch Lightning callback that logs `train_loss` and `val_loss` at the end of every epoch.\n",
+    "\n",
+    "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d01783d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
+    "\n",
+    "# inject_per_epoch_callbacks=True is the default\n",
+    "autolog()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
+    "    nbeats = NBEATSModel(\n",
+    "        input_chunk_length=24,\n",
+    "        output_chunk_length=12,\n",
+    "        n_epochs=10,\n",
+    "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
+    "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
+    "        torch_metrics=MetricCollection({\n",
+    "            \"MAE\": MeanAbsoluteError(),\n",
+    "            \"MSE\": MeanSquaredError(),\n",
+    "        }),\n",
+    "        random_state=42,\n",
+    "    )\n",
+    "    nbeats.fit(train, val_series=val)\n",
+    "    nbeats_pred = nbeats.predict(n=len(val))\n",
+    "    mlflow.log_metric(\"val_mape\", mape(val, nbeats_pred))\n",
+    "    mlflow.log_metric(\"val_rmse\", rmse(val, nbeats_pred))\n",
+    "    print(f\"NBEATS MAPE: {mape(val, nbeats_pred):.2f}%\")\n",
+    "\n",
+    "autolog(disable=True)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6fcc8f13",
+   "metadata": {},
+   "source": [
+    "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
+    ">\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f0828c2",
+   "metadata": {},
+   "source": [
+    "## Training & Validation Forecast Metrics\n",
+    "\n",
+    "Beyond per-epoch loss, `autolog()` can also run a backtest after training and log evaluation metrics (MAE, RMSE, MAPE, …) on both the **training series** and the **validation series**. Enable this with `log_training_metrics=True` and `log_validation_metrics=True`. Pass `extra_metrics` to extend the default set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "df8e85b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All logged metrics (11):\n",
+      "  manual_mape: 10.7420\n",
+      "  train_mae: 8.4783\n",
+      "  train_mape: 3.9116\n",
+      "  train_mse: 118.1369\n",
+      "  train_rmse: 10.8691\n",
+      "  train_smape: 3.8915\n",
+      "  val_mae: 12.5800\n",
+      "  val_mape: 2.9449\n",
+      "  val_mse: 243.0054\n",
+      "  val_rmse: 15.5886\n",
+      "  val_smape: 2.9130\n"
+     ]
+    }
+   ],
+   "source": [
+    "from darts.metrics import mae, smape\n",
+    "\n",
+    "autolog(\n",
+    "    log_training_metrics=True,\n",
+    "    log_validation_metrics=True,\n",
+    "    extra_metrics=[smape, mae],  # add smape and mae on top of the defaults\n",
+    ")\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
+    "    lr_model = LinearRegressionModel(lags=12)\n",
+    "    # if val_series is provided, autolog will log val_* metrics based on it\n",
+    "    lr_model.fit(train, val_series=val)\n",
+    "    # autolog already computed and logged train_mae, val_rmse, val_smape, ...\n",
+    "    # manually log the final hold-out MAPE as well\n",
+    "    lr_pred = lr_model.predict(n=len(val))\n",
+    "    mlflow.log_metric(\"manual_mape\", mape(val, lr_pred))\n",
+    "    run_id = run.info.run_id\n",
+    "\n",
+    "autolog(disable=True)\n",
+    "\n",
+    "# show what was logged\n",
+    "client = mlflow.tracking.MlflowClient()\n",
+    "run_metrics = client.get_run(run_id).data.metrics\n",
+    "metric_names = sorted(run_metrics.keys())\n",
+    "print(f\"All logged metrics ({len(metric_names)}):\")\n",
+    "for name in metric_names:\n",
+    "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8511fc08",
+   "metadata": {},
+   "source": [
+    "## Saving and Loading Models Locally\n",
+    "\n",
+    "You can also save and load models to/from local paths without MLflow runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "645ef079",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Files in model directory:\n",
+      "  - python_env.yaml\n",
+      "  - requirements.txt\n",
+      "  - MLmodel\n",
+      "  - conda.yaml\n",
+      "  - data\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save model to local directory\n",
+    "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
+    "save_model(model, path=local_model_path)\n",
+    "\n",
+    "print(\"\\nFiles in model directory:\")\n",
+    "for file in os.listdir(local_model_path):\n",
+    "    print(f\"  - {file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "254ba153",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded model successfully!\n",
+      "Predictions shape: (5, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load model from local directory\n",
+    "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
+    "\n",
+    "# Test it\n",
+    "local_predictions = local_loaded_model.predict(n=5)\n",
+    "print(\"Loaded model successfully!\")\n",
+    "print(f\"Predictions shape: {local_predictions.values().shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab0d1e0",
+   "metadata": {},
+   "source": [
+    "## Querying Experiments\n",
+    "\n",
+    "You can programmatically query and compare runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "109a9812",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 4 runs in experiment 'darts-quickstart':\n",
+      "\n",
+      "1. linear-regression-full-metrics\n",
+      "   Run ID: 215983d797274f5c896aeac20b509e80\n",
+      "   Validation MAPE: 2.944912539423521\n",
+      "\n",
+      "2. exponential-smoothing-baseline\n",
+      "   Run ID: 936e44cf2edb4db0bc6e32aa0d9fdfd9\n",
+      "   Validation MAPE: 7.864181481214469\n",
+      "\n",
+      "3. linear-regression-autolog\n",
+      "   Run ID: 1a714494da10495485871221b816f326\n",
+      "   Validation MAPE: 10.742044444678958\n",
+      "\n",
+      "4. nbeats-epoch-metrics\n",
+      "   Run ID: d8206b7ef162477ab8eb12b7e950a440\n",
+      "   Validation MAPE: 17.542747705910518\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mlflow.tracking import MlflowClient\n",
+    "\n",
+    "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
+    "\n",
+    "# get all runs\n",
+    "client = MlflowClient()\n",
+    "runs = client.search_runs(\n",
+    "    experiment_ids=[experiment.experiment_id],\n",
+    "    order_by=[\"metrics.val_mape ASC\"],  # Sort by best MAPE\n",
+    ")\n",
+    "\n",
+    "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
+    "for i, run in enumerate(runs, 1):\n",
+    "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
+    "    mape_val = run.data.metrics.get(\"val_mape\", \"N/A\")\n",
+    "    print(f\"{i}. {run_name}\")\n",
+    "    print(f\"   Run ID: {run.info.run_id}\")\n",
+    "    print(f\"   Validation MAPE: {mape_val}\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22685423",
+   "metadata": {},
+   "source": [
+    "### Load the Best Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "7b09db6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading best model from run: linear-regression-full-metrics\n",
+      "Model URI: runs:/215983d797274f5c896aeac20b509e80/model\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8225dcbe9d6a4297a294bb56eb3fa398",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71624304aeb64d108e690b3c8fbe8126",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading artifacts:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if runs:\n",
+    "    best_run = runs[0]\n",
+    "    best_model_uri = f\"runs:/{best_run.info.run_id}/model\"\n",
+    "\n",
+    "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
+    "    print(f\"Model URI: {best_model_uri}\")\n",
+    "\n",
+    "    best_model = load_model(best_model_uri)\n",
+    "    # series param required for loaded models, since we save with clean=True\n",
+    "    best_predictions = best_model.predict(n=len(val), series=train)\n",
+    "\n",
+    "    train[-50:].plot(label=\"Training\")\n",
+    "    val.plot(label=\"Actual\")\n",
+    "    best_predictions.plot(label=\"Best Model Forecast\")\n",
+    "    plt.legend()\n",
+    "    plt.title(\"Best Model Predictions\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6f1ec89e",
+   "metadata": {},
+   "source": [
+    "## Model Registry\n",
+    "\n",
+    "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived).\n",
+    "\n",
+    "```python\n",
+    "# Register a model from a run\n",
+    "result = mlflow.register_model(\n",
+    "    model_uri=f\"runs:/{best_run.info.run_id}/model\",\n",
+    "    name=\"darts-air-passengers\",\n",
+    ")\n",
+    "print(f\"Registered version: {result.version}\")\n",
+    "```\n",
+    "\n",
+    "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
+    "\n",
+    "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
+    ">\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7af49ea9",
+   "metadata": {},
+   "source": [
+    "## Important Note: Custom Flavor\n",
+    "\n",
+    "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
+    "\n",
+    "**Always use:**\n",
+    "```python\n",
+    "from darts.utils.mlflow import load_model\n",
+    "model = load_model(model_uri)\n",
+    "```\n",
+    "\n",
+    "**Instead of:**\n",
+    "```python\n",
+    "import mlflow\n",
+    "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
+    "```\n",
+    "\n",
+    "This custom flavor is necessary to properly handle:\n",
+    "- TimeSeries objects\n",
+    "- Darts-specific model parameters\n",
+    "- Covariate handling (past, future, static)\n",
+    "- PyTorch model state preservation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40621b13",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51fc7c4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment to cleanup\n",
+    "# import shutil\n",
+    "# shutil.rmtree(tmpdir)\n",
+    "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
+    "\n",
+    "print(f\"To cleanup manually, delete: {tmpdir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca40afc1",
+   "metadata": {},
+   "source": [
+    "# Final Remarks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4c86a23",
+   "metadata": {},
+   "source": [
+    "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 894779bc7b62a46de4fe9c4cdc8b067131d3786b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 16:46:21 +0100
Subject: [PATCH 025/154] unit test mps fix for torch

---
 darts/tests/optional_deps/test_mlflow.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index b72200f843..24caf60494 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -438,7 +438,10 @@ def test_callback_injection_with_existing_callbacks(
                 input_chunk_length=4,
                 output_chunk_length=2,
                 n_epochs=2,
-                pl_trainer_kwargs={"callbacks": [existing_callback]},
+                pl_trainer_kwargs={
+                    **tfm_kwargs_dev.get("pl_trainer_kwargs", {}),
+                    "callbacks": [existing_callback],
+                },
                 **{k: v for k, v in tfm_kwargs_dev.items() if k != "pl_trainer_kwargs"},
             )
 

From c9a13016ea2f2a56393372323f18817f49e82ce6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 16:52:47 +0100
Subject: [PATCH 026/154] typehinting fix

---
 darts/utils/mlflow.py | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index a0c39bf4d7..98db6bbd3d 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -17,7 +17,7 @@
 import importlib
 import json
 import os
-from typing import Callable, Optional, Union
+from collections.abc import Callable
 
 import mlflow
 import numpy as np
@@ -71,14 +71,14 @@
 def save_model(
     model,
     path: str,
-    conda_env: Optional[Union[dict, str]] = None,
-    code_paths: Optional[list[str]] = None,
-    pip_requirements: Optional[list[str]] = None,
-    extra_pip_requirements: Optional[list[str]] = None,
+    conda_env: dict | str | None = None,
+    code_paths: list[str] | None = None,
+    pip_requirements: list[str] | None = None,
+    extra_pip_requirements: list[str] | None = None,
     signature=None,
     input_example=None,
-    metadata: Optional[dict] = None,
-    mlflow_model: Optional[Model] = None,
+    metadata: dict | None = None,
+    mlflow_model: Model | None = None,
 ) -> None:
     """Save a darts forecasting model in MLflow format.
 
@@ -198,7 +198,7 @@ def save_model(
 
 def load_model(
     model_uri: str,
-    dst_path: Optional[str] = None,
+    dst_path: str | None = None,
     **kwargs,
 ):
     """Load a darts model from an MLflow model URI.
@@ -246,16 +246,16 @@ def load_model(
 
 def log_model(
     model,
-    artifact_path: Optional[str] = None,
-    name: Optional[str] = None,
-    registered_model_name: Optional[str] = None,
-    conda_env: Optional[Union[dict, str]] = None,
-    code_paths: Optional[list[str]] = None,
-    pip_requirements: Optional[list[str]] = None,
-    extra_pip_requirements: Optional[list[str]] = None,
+    artifact_path: str | None = None,
+    name: str | None = None,
+    registered_model_name: str | None = None,
+    conda_env: dict | str | None = None,
+    code_paths: list[str] | None = None,
+    pip_requirements: list[str] | None = None,
+    extra_pip_requirements: list[str] | None = None,
     signature=None,
     input_example=None,
-    metadata: Optional[dict] = None,
+    metadata: dict | None = None,
     log_params: bool = True,
 ):
     """Log a darts model to the current MLflow run.
@@ -342,7 +342,7 @@ def autolog(
     log_training_metrics: bool = True,
     log_validation_metrics: bool = True,
     inject_per_epoch_callbacks: bool = True,
-    extra_metrics: Optional[list[Callable]] = None,
+    extra_metrics: list[Callable] | None = None,
     disable: bool = False,
     silent: bool = False,
     manage_run: bool = True,
@@ -724,7 +724,7 @@ def _log_forecasting_metrics(
     fit_kwargs: dict,
     log_training: bool,
     log_validation: bool,
-    extra_metrics: Optional[list[Callable]] = None,
+    extra_metrics: list[Callable] | None = None,
 ) -> None:
     """Compute and log training and/or validation forecasting metrics to MLflow.
 
@@ -804,7 +804,7 @@ def _log_forecasting_metrics(
 
 def _backtest_and_log(
     model,
-    series: Union[TimeSeries, list[TimeSeries]],
+    series: TimeSeries | list[TimeSeries],
     metrics_list: list[Callable],
     prefix: str,
     past_covariates=None,
@@ -865,7 +865,7 @@ def _backtest_and_log(
 
 
 def _get_metrics_list(
-    extra_metrics: Optional[list[Callable]] = None,
+    extra_metrics: list[Callable] | None = None,
 ) -> list[Callable]:
     """Return the combined list of default and extra metric functions.
 

From a65612dd57cf8eee58ecb6dd1842ba293f64cb50 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 17:15:26 +0100
Subject: [PATCH 027/154] CI hotfix

---
 darts/tests/optional_deps/test_mlflow.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 24caf60494..ea6d5729aa 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -135,7 +135,12 @@ def test_save_load_torch_model(self, tmpdir_fn):
 
         assert_mlflow_artifacts_exist(model_path, is_torch=True)
 
-        loaded_model = load_model(f"file://{model_path}")
+        # save(clean=True) strips pl_trainer_kwargs; explicitly restore accelerator
+        # so Lightning doesn't default to MPS on Github macOS runner
+        loaded_model = load_model(
+            f"file://{model_path}",
+            pl_trainer_kwargs=tfm_kwargs_dev.get("pl_trainer_kwargs", {}),
+        )
         assert_predictions_equal(model, loaded_model, n=2, series=self.ts_univariate)
 
     def test_log_model_basic(self, mlflow_tracking):

From 21237420ec8998b79e7838167e41bf139fdf1242 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 18 Feb 2026 17:58:28 +0100
Subject: [PATCH 028/154] temporary fix for catboost, to be removed later

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index f95442a708..a84dce3925 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,7 +64,7 @@ torch = [
     "safetensors>=0.6.2",
 ]
 notorch = [
-    "catboost>=1.0.6",
+    "catboost>=1.0.6,<=1.2.9",
     "lightgbm>=3.2.0",
     "prophet>=1.1.1",
     "statsforecast>=1.4",

From 2b41feb63b0d0681eb32a0cb00db340105c0ced6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 24 Feb 2026 13:35:21 +0100
Subject: [PATCH 029/154] scrap backtesting in favor of metric patching

---
 darts/utils/mlflow.py | 391 ++++++++++++++++++++++--------------------
 1 file changed, 206 insertions(+), 185 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 98db6bbd3d..ba168b2add 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -17,6 +17,7 @@
 import importlib
 import json
 import os
+import re
 from collections.abc import Callable
 
 import mlflow
@@ -26,6 +27,7 @@
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
+from mlflow.utils import _inspect_original_var_name
 from mlflow.utils.autologging_utils import (
     autologging_integration,
     get_autologging_config,
@@ -53,9 +55,7 @@
 
 import darts
 from darts.logging import get_logger, raise_if, raise_if_not
-from darts.metrics import mae, mape, mse, rmse
 from darts.models.forecasting.forecasting_model import ForecastingModel
-from darts.timeseries import TimeSeries
 from darts.utils.utils import PL_AVAILABLE
 
 logger = get_logger(__name__)
@@ -332,17 +332,12 @@ def log_model(
     )
 
 
-_DEFAULT_METRICS = [mae, mse, rmse, mape]
-
-
 @autologging_integration(FLAVOR_NAME)
 def autolog(
     log_models: bool = True,
     log_params: bool = True,
-    log_training_metrics: bool = True,
-    log_validation_metrics: bool = True,
+    log_metrics: bool = True,
     inject_per_epoch_callbacks: bool = True,
-    extra_metrics: list[Callable] | None = None,
     disable: bool = False,
     silent: bool = False,
     manage_run: bool = True,
@@ -355,10 +350,33 @@ def autolog(
     1. Start an MLflow run (or reuse the currently active one).
     2. Log model creation parameters (``model.model_params``).
     3. Log covariate usage information (past, future, and static covariates).
-    4. For PyTorch-based models: inject a callback that logs per-epoch metrics.
-    5. Log the trained model artifact at the end of training.
-    6. Optionally compute and log forecasting metrics on training and/or
-       validation data.
+    4. Patch all darts metric functions so that any call made inside an active
+       MLflow run automatically logs the result.  Repeated calls overwrite
+       the previous value.
+    5. For PyTorch-based models: inject a callback that logs per-epoch metrics.
+    6. Log the trained model artifact at the end of training.
+
+    .. important::
+
+        ``autolog()`` must be called **before** importing metric functions from
+        ``darts.metrics``.  Metric functions imported before ``autolog()`` is
+        enabled will **not** log to MLflow.
+
+    .. note::
+
+        Logged metric keys depend on the result shape:
+
+        * **Scalar** → ``{metric_name}``
+        * **Per-component** (1-D, single series) →
+          ``{metric_name}_{component_name}``
+        * **Per-series** (1-D, list of series) →
+          ``{metric_name}_{series_idx}``
+        * **Per-series × per-component** (2-D) →
+          ``{metric_name}_{component_name}_{series_idx}``
+
+        When a dataset variable name can be captured via frame inspection,
+        it is inserted after the metric name (e.g.
+        ``{metric_name}_{dataset_name}_{component_name}``).
 
     Parameters
     ----------
@@ -366,15 +384,9 @@ def autolog(
         If ``True`` (default), log the trained model artifact after ``fit()``.
     log_params
         If ``True`` (default), log model creation parameters.
-    log_training_metrics
-        If ``True``, compute in-sample forecasting metrics on the training data
-        after ``fit()`` completes.
-        Default ``True``.
-    log_validation_metrics
-        If ``True``, compute forecasting metrics on the validation series
-        (``val_series``) passed to ``fit()``.  Only effective for models whose
-        ``fit()`` accepts a ``val_series`` argument (e.g. PyTorch-based models).
-        Default ``True``.
+    log_metrics
+        If ``True`` (default), patch all darts metric functions so that any
+        call made inside an active MLflow run is automatically logged.
     inject_per_epoch_callbacks
         If ``True`` (default), inject a PyTorch Lightning callback to log training and validation
         metrics at the end of each epoch. Only effective for PyTorch-based models. To provide
@@ -424,6 +436,25 @@ def get_all_subclasses(cls):
         except Exception as e:
             logger.info(f"Failed to patch {cls.__name__}.fit() for autologging: {e}")
 
+    if log_metrics:
+        import darts.metrics as _darts_metrics
+
+        for metric_name in _darts_metrics.__all__:
+            try:
+                # metrics should not create their own runs;
+                # they log into the run started by fit(), so manage_run=False here
+                safe_patch(
+                    FLAVOR_NAME,
+                    _darts_metrics,
+                    metric_name,
+                    _make_metric_patch(metric_name),
+                    manage_run=False,
+                )
+            except Exception as e:
+                logger.info(
+                    f"Failed to patch metric '{metric_name}' on darts.metrics: {e}"
+                )
+
 
 def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
     """Return the default pip requirements for logging a darts model.
@@ -672,16 +703,9 @@ def _patched_fit(original, self, *args, **kwargs):
     """
     log_models = get_autologging_config(FLAVOR_NAME, "log_models", True)
     log_params = get_autologging_config(FLAVOR_NAME, "log_params", True)
-    log_training_metrics = get_autologging_config(
-        FLAVOR_NAME, "log_training_metrics", False
-    )
-    log_validation_metrics = get_autologging_config(
-        FLAVOR_NAME, "log_validation_metrics", False
-    )
     inject_per_epoch_callbacks = get_autologging_config(
         FLAVOR_NAME, "inject_per_epoch_callbacks", True
     )
-    extra_metrics = get_autologging_config(FLAVOR_NAME, "extra_metrics", None)
 
     mlflow.set_tag("darts.model_class", type(self).__name__)
 
@@ -697,196 +721,193 @@ def _patched_fit(original, self, *args, **kwargs):
     if log_params:
         _log_covariate_info(self)
 
-    if log_training_metrics or log_validation_metrics:
-        _log_forecasting_metrics(
-            model=self,
-            fit_args=args,
-            fit_kwargs=kwargs,
-            log_training=log_training_metrics,
-            log_validation=log_validation_metrics,
-            extra_metrics=extra_metrics,
-        )
-
     if log_models:
         try:
             log_model(self, name="model", log_params=False)
-        except Exception as e:
-            logger.warning(
-                f"Failed to autolog model artifact for {type(self).__name__}: {e}"
+        except Exception:
+            logger.info(
+                f"Failed to autolog model artifact for {type(self).__name__}.",
+                exc_info=True,
             )
 
     return result
 
 
-def _log_forecasting_metrics(
-    model,
-    fit_args: tuple,
-    fit_kwargs: dict,
-    log_training: bool,
-    log_validation: bool,
-    extra_metrics: list[Callable] | None = None,
+def _sanitize_mlflow_key(name: str) -> str:
+    """Sanitize a string for use as an MLflow metric key.
+
+    Replaces any character that is not alphanumeric, a hyphen, or an
+    underscore with an underscore, so component names become valid
+    MLflow keys.
+
+    Parameters
+    ----------
+    name
+        The raw name to sanitize.
+
+    Returns
+    -------
+    str
+        A string safe for use as an MLflow metric key.
+    """
+    return re.sub(r"[^\w-]", "_", name)
+
+
+def _log_metric_result(
+    metric_name: str,
+    result,
+    dataset_name: str | None = None,
+    component_names: list[str] | None = None,
+    input_is_list: bool = False,
 ) -> None:
-    """Compute and log training and/or validation forecasting metrics to MLflow.
+    """Log a metric result to the active MLflow run.
 
-    After a model has been fitted this function optionally:
+    Handles Python scalars, numpy scalars, 1-D arrays, and 2-D arrays.
 
-    * Runs ``model.backtest()`` on the training series (``retrain=False``)
-      and logs metrics with a ``train_`` prefix.
-    * Runs ``model.backtest()`` on the validation series and logs metrics
-      with a ``val_`` prefix.
+    The logged MLflow key follows the pattern::
 
-    For multiple series the metrics are averaged to produce a single value per metric.
+        {metric_name}_{dataset_name}_{component}_{series_index}
+
+    Specifically:
+
+    * **Scalar** (0-d) → ``{metric}_{dataset}``
+    * **1-D, single TimeSeries input** (per-component) →
+      ``{metric}_{dataset}_{component_name_or_idx}``
+    * **1-D, list input** (per-series, component already reduced) →
+      ``{metric}_{dataset}_{series_idx}``
+    * **2-D, list input** (per-series × per-component) →
+      ``{metric}_{dataset}_{component_name_or_idx}_{series_idx}``
+
+    All optional parts are omitted when not available (e.g. no dataset name
+    when the variable name could not be inspected).
 
     Parameters
     ----------
-    model
-        A fitted Darts forecasting model.
-    fit_args
-        Positional arguments originally passed to ``fit()``.
-    fit_kwargs
-        Keyword arguments originally passed to ``fit()``.
-    log_training
-        Whether to compute and log in-sample training metrics.
-    log_validation
-        Whether to compute and log validation metrics.
-    extra_metrics
-        Optional extra metric functions in addition to the defaults.
+    metric_name
+        Base metric name used as the MLflow key.
+    result
+        The metric result to log.
+    dataset_name
+        Sanitized variable name of ``actual_series`` in the caller's frame.
+        Omitted from key when ``None``.
+    component_names
+        Component name strings to use as the component part of the key.
+        For single-series input these come from ``series.components``; for
+        list input they come from the first series in the list.
+        Falls back to integer indices when ``None`` or length mismatches.
+    input_is_list
+        ``True`` when ``actual_series`` was a ``Sequence[TimeSeries]``.  Drives
+        whether the first result axis is treated as *series* or *components*.
     """
-    metrics_list = _get_metrics_list(extra_metrics)
+    result_arr = np.asarray(result)
 
-    # determine forecast horizon from model attributes
-    forecast_horizon = getattr(model, "output_chunk_length", None) or 1
+    if mlflow.active_run() is None:
+        return
 
-    # extract series and covariates from fit() call
-    train_series = fit_args[0] if fit_args else fit_kwargs.get("series")
-    past_covariates = fit_kwargs.get("past_covariates")
-    future_covariates = fit_kwargs.get("future_covariates")
-    val_series = fit_kwargs.get("val_series")
-    val_past_covariates = fit_kwargs.get("val_past_covariates")
-    val_future_covariates = fit_kwargs.get("val_future_covariates")
+    base_key = f"{metric_name}_{dataset_name}" if dataset_name else metric_name
+
+    def _comp_suffix(idx: int) -> str:
+        if component_names is not None and idx < len(component_names):
+            return _sanitize_mlflow_key(component_names[idx])
+        return str(idx)
+
+    if result_arr.ndim == 0:
+        # scalar result
+        mlflow.log_metric(base_key, float(result_arr))
+
+    elif result_arr.ndim == 1:
+        if not input_is_list:
+            # single series: log per-component
+            for c_i, val in enumerate(result_arr):
+                mlflow.log_metric(f"{base_key}_{_comp_suffix(c_i)}", float(val))
+        else:
+            # list input, components already reduced: log per-series
+            for s_i, val in enumerate(result_arr):
+                mlflow.log_metric(f"{base_key}_{s_i}", float(val))
+
+    elif result_arr.ndim == 2:
+        # list input: log per-series and per-component
+        n_series, n_components = result_arr.shape
+        for s_i in range(n_series):
+            for c_i in range(n_components):
+                mlflow.log_metric(
+                    f"{base_key}_{_comp_suffix(c_i)}_{s_i}",
+                    float(result_arr[s_i, c_i]),
+                )
 
-    if log_training and train_series is not None:
-        try:
-            _backtest_and_log(
-                model,
-                series=train_series,
-                metrics_list=metrics_list,
-                prefix="train",
-                past_covariates=past_covariates,
-                future_covariates=future_covariates,
-                forecast_horizon=forecast_horizon,
-            )
-        except Exception:
-            logger.info(
-                "Could not compute training forecasting metrics for "
-                f"{type(model).__name__}.",
-                exc_info=True,
-            )
+    else:
+        # unexpected shape — flatten with integer indices
+        for i, val in enumerate(result_arr.flatten()):
+            mlflow.log_metric(f"{base_key}_{i}", float(val))
 
-    if log_validation and val_series is not None:
-        try:
-            _backtest_and_log(
-                model,
-                series=val_series,
-                metrics_list=metrics_list,
-                prefix="val",
-                past_covariates=val_past_covariates or past_covariates,
-                future_covariates=val_future_covariates or future_covariates,
-                forecast_horizon=forecast_horizon,
-            )
-        except Exception:
-            logger.info(
-                "Could not compute validation forecasting metrics for "
-                f"{type(model).__name__}.",
-                exc_info=True,
-            )
 
+def _make_metric_patch(metric_name: str) -> Callable:
+    """Create a ``safe_patch``-compatible patch function for a darts metric.
 
-def _backtest_and_log(
-    model,
-    series: TimeSeries | list[TimeSeries],
-    metrics_list: list[Callable],
-    prefix: str,
-    past_covariates=None,
-    future_covariates=None,
-    forecast_horizon: int = 1,
-) -> None:
-    """Run ``model.backtest()`` and log the resulting scores to MLflow.
+    The returned patch calls the original metric and, when an active MLflow
+    run exists, logs the result under a key built as::
+
+        {metric_name}[_{dataset_name}][_{component}][_{series_index}]
+
+    where:
+
+    * ``dataset_name`` – Python variable name of the first argument in the
+      caller's frame (captured via frame inspection, omitted if not found).
+    * ``component`` – component label from the ``TimeSeries`` if the result is
+      per-component, otherwise an integer index.
+    * ``series_index`` – integer index appended when the input is a
+      ``Sequence[TimeSeries]`` and the result has a series axis.
+
+    The original return value is always forwarded unchanged.
 
     Parameters
     ----------
-    model
-        A fitted Darts forecasting model.
-    series
-        One or more target series to evaluate on.
-    metrics_list
-        List of Darts metric functions to evaluate.
-    prefix
-        Prefix for the logged metric names (e.g. ``"train"`` or ``"val"``).
-    past_covariates
-        Optional past covariates matching ``series``.
-    future_covariates
-        Optional future covariates matching ``series``.
-    forecast_horizon
-        Number of steps to forecast at each backtest step.
+    metric_name
+        The darts metric function name used as the MLflow metric key.
     """
-    backtest_kwargs = dict(
-        series=series,
-        forecast_horizon=forecast_horizon,
-        retrain=False,
-        overlap_end=False,
-        last_points_only=True,
-        reduction=None,
-        verbose=False,
-        show_warnings=False,
-    )
-    if past_covariates is not None:
-        backtest_kwargs["past_covariates"] = past_covariates
-    if future_covariates is not None:
-        backtest_kwargs["future_covariates"] = future_covariates
 
-    logged = {}
-    for metric_fn in metrics_list:
-        try:
-            score = model.backtest(**backtest_kwargs, metric=metric_fn)
-            # backtest returns a float, np.ndarray, or list depending on the
-            # input.  We want a single scalar per metric so we take the mean.
+    def _patched_metric(original, *args, **kwargs):
+        result = original(*args, **kwargs)
 
-            logged[f"{prefix}_{metric_fn.__name__}"] = float(np.nanmean(score))
-        except Exception:
-            logger.debug(
-                f"Backtest metric {metric_fn.__name__} failed for "
-                f"{type(model).__name__}, skipping.",
-                exc_info=True,
-            )
+        if mlflow.active_run() is None:
+            return result
 
-    if logged:
-        mlflow.log_metrics(logged)
+        series = args[0]
 
+        # capture the variable name of actual_series for metric key
+        raw = _inspect_original_var_name(series, fallback_name=None)
+        dataset_name = _sanitize_mlflow_key(raw) if raw else None
 
-def _get_metrics_list(
-    extra_metrics: list[Callable] | None = None,
-) -> list[Callable]:
-    """Return the combined list of default and extra metric functions.
+        # handling multi_series input
+        input_is_list = not hasattr(series, "components")
 
-    Parameters
-    ----------
-    extra_metrics
-        Optional additional metric functions to append to the defaults.
+        # extract component names from the series (or first element if list)
+        component_names = None
+        try:
+            if input_is_list:
+                # we assume that subsequent series have same component order
+                component_names = series[0].components.tolist()
+            else:
+                component_names = series.components.tolist()
+        except Exception:
+            logger.info("Could not extract component names from series.")
 
-    Returns
-    -------
-    list[Callable]
-        A list of metric functions (``mae``, ``mse``, ``rmse``, ``mape``, plus extras).
-    """
-    metrics = list(_DEFAULT_METRICS)
-    if extra_metrics:
-        seen_names = {m.__name__ for m in metrics}
-        for m in extra_metrics:
-            if m.__name__ not in seen_names:
-                metrics.append(m)
-                seen_names.add(m.__name__)
-    return metrics
+        try:
+            _log_metric_result(
+                metric_name,
+                result,
+                dataset_name=dataset_name,
+                component_names=component_names,
+                input_is_list=input_is_list,
+            )
+        except Exception:
+            logger.info(
+                f"Failed to log metric '{metric_name}' to MLflow.", exc_info=True
+            )
+
+        return result
+
+    return _patched_metric
 
 
 if PL_AVAILABLE:

From b119e7b0a46b1840d28bed148380cafe67731656 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 24 Feb 2026 13:35:38 +0100
Subject: [PATCH 030/154] metric patching unit tests

---
 darts/tests/optional_deps/test_mlflow.py | 223 +++++++++--------------
 1 file changed, 90 insertions(+), 133 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index ea6d5729aa..da9aee12f3 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -639,168 +639,125 @@ def test_save_load_with_special_series(self, tmpdir_fn, series, series_name):
         pred_loaded = loaded_model.predict(n=3, series=test_series)
         assert pred_original.width == pred_loaded.width
 
-    def test_autolog_training_metrics_regression(
-        self, mlflow_tracking, autolog_context
-    ):
-        """Test that autolog computes and logs in-sample training metrics for regression models."""
-        with autolog_context(log_training_metrics=True):
+    def test_autolog_metric_logging_scalar(self, mlflow_tracking, autolog_context):
+        """Calling a darts metric inside an active run logs a scalar to MLflow."""
+        with autolog_context(log_metrics=True):
+            from darts.metrics import mae
+
             with mlflow.start_run() as run:
-                model = LinearRegressionModel(lags=5, output_chunk_length=3)
-                model.fit(self.ts_univariate)
+                result = mae(self.ts_univariate, self.ts_univariate * 1.1)
 
         run_data = mlflow.get_run(run.info.run_id).data
+        assert "mae" in run_data.metrics, "mae should be logged to MLflow"
+        assert np.isfinite(run_data.metrics["mae"])
+        assert np.isscalar(result)
+        assert np.isfinite(float(result))
 
-        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
-            assert metric_name in run_data.metrics, f"{metric_name} should be logged"
-            assert np.isfinite(run_data.metrics[metric_name])
-
-    def test_autolog_extra_metrics(self, mlflow_tracking, autolog_context):
-        """Test that extra_metrics are logged alongside defaults."""
-        from darts.metrics import r2_score, smape
+    def test_autolog_metric_repeated_call(self, mlflow_tracking, autolog_context):
+        """Calling the same metric twice overwrites the value (last-value-wins)."""
+        with autolog_context(log_metrics=True):
+            from darts.metrics import rmse
 
-        with autolog_context(
-            log_training_metrics=True, extra_metrics=[smape, r2_score]
-        ):
             with mlflow.start_run() as run:
-                model = LinearRegressionModel(lags=5, output_chunk_length=3)
-                model.fit(self.ts_univariate)
+                rmse(self.ts_univariate, self.ts_univariate * 1.1)
+                rmse(self.ts_univariate, self.ts_univariate * 1.2)
 
         run_data = mlflow.get_run(run.info.run_id).data
+        assert "rmse" in run_data.metrics, "rmse should be logged to MLflow"
 
-        # defaults
-        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
-            assert metric_name in run_data.metrics
-        # extras
-        assert "train_smape" in run_data.metrics
-        assert "train_r2_score" in run_data.metrics
+    def test_autolog_metric_per_component(self, mlflow_tracking, autolog_context):
+        """Non-scalar metric results logged per-component as {name}_{component_name}.
+
+        ts_multivariate = ts_univariate.stack(ts_univariate * 1.5), whose component
+        names are ['linear', 'linear_1'].  With component_reduction=None the result
+        is a 1-D array (one value per component), so the expected keys are
+        'mae_linear' and 'mae_linear_1'.
+        """
+        with autolog_context(log_metrics=True):
+            from darts.metrics import mae
 
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_training_metrics_torch(self, mlflow_tracking, autolog_context):
-        """Test that autolog logs training metrics for torch models including epochs_trained."""
-        with autolog_context(
-            log_training_metrics=True, inject_per_epoch_callbacks=True
-        ):
             with mlflow.start_run() as run:
-                model = NBEATSModel(
-                    input_chunk_length=4,
-                    output_chunk_length=2,
-                    n_epochs=2,
-                    **tfm_kwargs_dev,
+                mae(
+                    self.ts_multivariate,
+                    self.ts_multivariate * 1.1,
+                    component_reduction=None,
                 )
-                train, val = self.ts_univariate.split_before(0.7)
-                model.fit(train)
 
         run_data = mlflow.get_run(run.info.run_id).data
+        assert "mae_linear" in run_data.metrics, (
+            "Component 'linear' should be logged as mae_linear"
+        )
+        assert "mae_linear_1" in run_data.metrics, (
+            "Component 'linear_1' should be logged as mae_linear_1"
+        )
+        assert np.isfinite(run_data.metrics["mae_linear"])
+        assert np.isfinite(run_data.metrics["mae_linear_1"])
 
-        assert "train_loss" in run_data.metrics
-        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
-            assert metric_name in run_data.metrics, (
-                f"{metric_name} should be logged for torch model"
-            )
+    def test_autolog_metric_no_active_run(self, mlflow_tracking, autolog_context):
+        """Calling a metric without an active run does not raise and returns correctly."""
+        with autolog_context(log_metrics=True):
+            from darts.metrics import mse
 
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_validation_metrics_torch(self, mlflow_tracking, autolog_context):
-        """Test that validation metrics are computed on val_series for torch models."""
-        with autolog_context(log_training_metrics=True, log_validation_metrics=True):
-            with mlflow.start_run() as run:
-                model = NBEATSModel(
-                    input_chunk_length=4,
-                    output_chunk_length=2,
-                    n_epochs=2,
-                    **tfm_kwargs_dev,
-                )
-                train, val = self.ts_univariate.split_before(0.7)
-                model.fit(train, val_series=val)
+            # called outside any start_run — must not raise
+            result = mse(self.ts_univariate, self.ts_univariate * 1.1)
 
-        run_data = mlflow.get_run(run.info.run_id).data
+        assert np.isscalar(result)
+        assert np.isfinite(float(result))
 
-        # validation forecasting metrics
-        for metric_name in ["val_mae", "val_mse", "val_rmse", "val_mape"]:
-            assert metric_name in run_data.metrics, (
-                f"{metric_name} should be logged for torch model with val_series"
-            )
-            assert np.isfinite(run_data.metrics[metric_name])
-
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_validation_metrics_disabled(
+    def test_autolog_metric_returns_correct_value(
         self, mlflow_tracking, autolog_context
     ):
-        """Test that validation metrics are NOT logged when log_validation_metrics=False."""
-        with autolog_context(log_training_metrics=True, log_validation_metrics=False):
-            with mlflow.start_run() as run:
-                model = NBEATSModel(
-                    input_chunk_length=4,
-                    output_chunk_length=2,
-                    n_epochs=2,
-                    **tfm_kwargs_dev,
-                )
-                train, val = self.ts_univariate.split_before(0.7)
-                model.fit(train, val_series=val)
+        """The patched metric returns the same value whether inside or outside a run."""
+        with autolog_context(log_metrics=True):
+            from darts.metrics import mae
 
-        run_data = mlflow.get_run(run.info.run_id).data
+            pred = self.ts_univariate * 1.05
 
-        # training metrics should still be present
-        assert "train_mae" in run_data.metrics
-        # validation metrics should NOT be present
-        for metric_name in ["val_mae", "val_mse", "val_rmse", "val_mape"]:
-            assert metric_name not in run_data.metrics, (
-                f"{metric_name} should NOT be logged when validation disabled"
-            )
+            with mlflow.start_run():
+                result_inside = mae(self.ts_univariate, pred)
 
-    def test_autolog_training_metrics_multiple_series(
-        self, mlflow_tracking, autolog_context
-    ):
-        """Test that backtest-based training metrics work with multiple series."""
-        ts1 = tg.linear_timeseries(start_value=10, end_value=50, length=50).astype(
-            "float32"
-        )
-        ts2 = tg.linear_timeseries(start_value=20, end_value=60, length=50).astype(
-            "float32"
-        )
+            # call outside a run — no logging, same computation
+            result_outside = mae(self.ts_univariate, pred)
+
+        np.testing.assert_almost_equal(result_inside, result_outside, decimal=6)
+        assert np.isfinite(result_inside)
+
+    def test_autolog_log_metrics_false(self, mlflow_tracking, autolog_context):
+        """autolog(log_metrics=False) leaves metrics unpatched — nothing is logged."""
+        with autolog_context(log_metrics=False):
+            from darts.metrics import mape
 
-        with autolog_context(log_training_metrics=True):
             with mlflow.start_run() as run:
-                model = LinearRegressionModel(lags=5, output_chunk_length=3)
-                model.fit([ts1, ts2])
+                mape(self.ts_univariate, self.ts_univariate * 1.1)
 
         run_data = mlflow.get_run(run.info.run_id).data
+        assert "mape" not in run_data.metrics, (
+            "mape should NOT be logged when log_metrics=False"
+        )
 
-        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
-            assert metric_name in run_data.metrics, (
-                f"{metric_name} should be logged for multiple series"
-            )
-            assert np.isfinite(run_data.metrics[metric_name])
+    def test_autolog_public_namespace_patched(self, mlflow_tracking, autolog_context):
+        """Only darts.metrics (public namespace) is patched; darts.metrics.metrics is not.
 
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_training_metrics_multiple_series_torch(
-        self, mlflow_tracking, autolog_context
-    ):
-        """Test that backtest-based training metrics work with multiple series on torch models."""
-        ts1 = tg.linear_timeseries(start_value=10, end_value=50, length=50).astype(
-            "float32"
-        )
-        ts2 = tg.linear_timeseries(start_value=20, end_value=60, length=50).astype(
-            "float32"
-        )
+        Patching only the public namespace avoids breaking internal metric-to-metric
+        calls within the implementation module (e.g. rmse calling mse internally).
+        """
+        with autolog_context(log_metrics=True):
+            import darts.metrics as dm
+            import darts.metrics.metrics as dmm
 
-        with autolog_context(log_training_metrics=True):
-            with mlflow.start_run() as run:
-                model = NBEATSModel(
-                    input_chunk_length=4,
-                    output_chunk_length=2,
-                    n_epochs=2,
-                    pl_trainer_kwargs={
-                        "accelerator": "cpu",
-                        "enable_progress_bar": False,
-                        "enable_model_summary": False,
-                    },
-                )
-                model.fit([ts1, ts2])
+            # public namespace → patched: call inside a run should log
+            with mlflow.start_run() as run_public:
+                dm.mae(self.ts_univariate, self.ts_univariate * 1.1)
 
-        run_data = mlflow.get_run(run.info.run_id).data
+            # implementation module → NOT patched: call inside a run should not log
+            with mlflow.start_run() as run_impl:
+                dmm.mae(self.ts_univariate, self.ts_univariate * 1.1)
 
-        for metric_name in ["train_mae", "train_mse", "train_rmse", "train_mape"]:
-            assert metric_name in run_data.metrics, (
-                f"{metric_name} should be logged for torch multiple series"
-            )
-            assert np.isfinite(run_data.metrics[metric_name])
+        run_data_public = mlflow.get_run(run_public.info.run_id).data
+        run_data_impl = mlflow.get_run(run_impl.info.run_id).data
+        assert "mae" in run_data_public.metrics, (
+            "darts.metrics.mae should log to MLflow (public namespace is patched)"
+        )
+        assert "mae" not in run_data_impl.metrics, (
+            "darts.metrics.metrics.mae should NOT log (implementation module is not patched)"
+        )

From f561438ceb52afb3b59193a19d07d2f8b0032c2f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 25 Feb 2026 22:37:41 +0100
Subject: [PATCH 031/154] pytorch autolog

---
 darts/utils/mlflow.py | 189 ++++++++++++++----------------------------
 1 file changed, 62 insertions(+), 127 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index ba168b2add..80d1d85576 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -56,7 +56,6 @@
 import darts
 from darts.logging import get_logger, raise_if, raise_if_not
 from darts.models.forecasting.forecasting_model import ForecastingModel
-from darts.utils.utils import PL_AVAILABLE
 
 logger = get_logger(__name__)
 
@@ -140,13 +139,9 @@ def save_model(
 
     os.makedirs(data_dir, exist_ok=True)
 
-    # pass in clean=True to not include any timeseries or callbacks within the model file
-    if is_torch:
-        model_file = _MODEL_FILE_TORCH
-        model.save(os.path.join(data_dir, model_file), clean=True)
-    else:
-        model_file = _MODEL_FILE_STAT
-        model.save(os.path.join(data_dir, model_file), clean=True)
+    # clean=True excludes any timeseries or callbacks from the model file
+    model_file = _MODEL_FILE_TORCH if is_torch else _MODEL_FILE_STAT
+    model.save(os.path.join(data_dir, model_file), clean=True)
 
     module_path, class_name = _get_model_class_path(model)
 
@@ -332,7 +327,6 @@ def log_model(
     )
 
 
-@autologging_integration(FLAVOR_NAME)
 def autolog(
     log_models: bool = True,
     log_params: bool = True,
@@ -353,7 +347,8 @@ def autolog(
     4. Patch all darts metric functions so that any call made inside an active
        MLflow run automatically logs the result.  Repeated calls overwrite
        the previous value.
-    5. For PyTorch-based models: inject a callback that logs per-epoch metrics.
+    5. For PyTorch-based models: leverage ``mlflow.pytorch.autolog()`` to
+       automatically log per-epoch training and validation metrics.
     6. Log the trained model artifact at the end of training.
 
     .. important::
@@ -388,9 +383,9 @@ def autolog(
         If ``True`` (default), patch all darts metric functions so that any
         call made inside an active MLflow run is automatically logged.
     inject_per_epoch_callbacks
-        If ``True`` (default), inject a PyTorch Lightning callback to log training and validation
-        metrics at the end of each epoch. Only effective for PyTorch-based models. To provide
-        additional callbacks use ``torch_metrics`` parameter while initializing the model.
+        If ``True`` (default), enable ``mlflow.pytorch.autolog(log_models=False)``
+        around PyTorch-based model training to automatically log per-epoch
+        training and validation metrics. Only effective for PyTorch-based models.
     extra_metrics
         An optional list of additional Darts metric functions to log on top of
         the defaults (``mae``, ``mse``, ``rmse``, ``mape``).  Each function
@@ -409,6 +404,58 @@ def autolog(
         does not apply the `with_managed_run` wrapper to the specified
         `patch_function`.
     """
+    # Enable/disable mlflow.pytorch.autolog for per-epoch metrics on torch models.
+    # This must happen outside the @autologging_integration-decorated _autolog()
+    # because the decorator short-circuits on disable=True before the function
+    # body executes, and MLflow's session manager suppresses nested autolog
+    # patches if called from within a safe_patch context.
+    if inject_per_epoch_callbacks and not disable:
+        try:
+            import mlflow.pytorch
+
+            mlflow.pytorch.autolog(log_models=False, log_datasets=False, silent=silent)
+        except ImportError:
+            logger.info(
+                "mlflow.pytorch not available; skipping per-epoch metrics logging."
+            )
+    elif disable:
+        try:
+            import mlflow.pytorch
+
+            mlflow.pytorch.autolog(disable=True)
+        except (ImportError, Exception):
+            logger.info(
+                "mlflow.pytorch not available; skipping per-epoch metrics logging."
+            )
+
+    _autolog(
+        log_models=log_models,
+        log_params=log_params,
+        log_metrics=log_metrics,
+        inject_per_epoch_callbacks=inject_per_epoch_callbacks,
+        disable=disable,
+        silent=silent,
+        manage_run=manage_run,
+    )
+
+
+@autologging_integration(FLAVOR_NAME)
+def _autolog(
+    log_models: bool = True,
+    log_params: bool = True,
+    log_metrics: bool = True,
+    inject_per_epoch_callbacks: bool = True,
+    disable: bool = False,
+    silent: bool = False,
+    manage_run: bool = True,
+) -> None:
+    """Internal autolog implementation decorated with ``@autologging_integration``.
+
+    Handles patching of darts ``ForecastingModel.fit()`` and metric functions.
+    The ``mlflow.pytorch.autolog`` coordination is handled by the public
+    ``autolog()`` wrapper because the decorator short-circuits on
+    ``disable=True``.
+    """
 
     # recursively get all subclasses of ForecastingModel that override fit()
     def get_all_subclasses(cls):
@@ -681,11 +728,7 @@ def _extract_covariate_metadata(
 def _patched_fit(original, self, *args, **kwargs):
     """Patch function for ForecastingModel.fit() autologging.
 
-    Handles both statistical and PyTorch-based models. For PyTorch models,
-    automatically injects MLflow callback for per-epoch metrics logging.
-
-    Logs the trained model artifact if configured.
-
+    Logs model parameters, class, covariates and the model itself.
     Parameters
     ----------
     original
@@ -703,19 +746,12 @@ def _patched_fit(original, self, *args, **kwargs):
     """
     log_models = get_autologging_config(FLAVOR_NAME, "log_models", True)
     log_params = get_autologging_config(FLAVOR_NAME, "log_params", True)
-    inject_per_epoch_callbacks = get_autologging_config(
-        FLAVOR_NAME, "inject_per_epoch_callbacks", True
-    )
 
     mlflow.set_tag("darts.model_class", type(self).__name__)
 
     if log_params:
         _log_model_params(self)
 
-    # Inject per-epoch callbacks for torch models (no-op for non-torch models)
-    if inject_per_epoch_callbacks:
-        _inject_mlflow_callback(self)
-
     result = original(self, *args, **kwargs)
 
     if log_params:
@@ -847,7 +883,7 @@ def _make_metric_patch(metric_name: str) -> Callable:
     The returned patch calls the original metric and, when an active MLflow
     run exists, logs the result under a key built as::
 
-        {metric_name}[_{dataset_name}][_{component}][_{series_index}]
+        {metric_name}_{dataset_name}_{component}_{series_index}
 
     where:
 
@@ -908,104 +944,3 @@ def _patched_metric(original, *args, **kwargs):
         return result
 
     return _patched_metric
-
-
-if PL_AVAILABLE:
-    import pytorch_lightning as pl
-
-    class _DartsMlflowCallback(pl.Callback):
-        """PyTorch Lightning callback that logs epoch-level metrics to MLflow.
-
-        This callback automatically logs training and validation metrics (such as
-        loss values) to the active MLflow run at the end of each epoch.
-
-        Notes
-        -----
-        The callback is automatically injected into TorchForecastingModel instances
-        when ``autolog()`` is enabled with PyTorch-based models.
-        """
-
-        def on_train_epoch_end(self, trainer, pl_module):
-            """Log training metrics at the end of each training epoch."""
-            self._log_epoch_metrics(trainer)
-
-        def on_validation_epoch_end(self, trainer, pl_module):
-            """Log validation metrics at the end of each validation epoch."""
-            self._log_epoch_metrics(trainer)
-
-        def _log_epoch_metrics(self, trainer) -> None:
-            """Extract and log metrics from the trainer to MLflow.
-
-            Parameters
-            ----------
-            trainer
-                PyTorch Lightning Trainer instance containing metrics.
-            """
-            if mlflow.active_run() is None:
-                return
-
-            epoch = trainer.current_epoch
-            metrics: dict[str, float] = {}
-
-            for source in (trainer.callback_metrics, trainer.logged_metrics):
-                for key, value in source.items():
-                    try:
-                        metrics[key] = float(value)
-                    except (TypeError, ValueError):
-                        pass
-
-            if metrics:
-                mlflow.log_metrics(metrics, step=epoch)
-
-else:
-    _DartsMlflowCallback = None
-
-
-def _get_mlflow_callback():
-    """Create and return a ``_DartsMlflowCallback`` instance.
-
-    Returns
-    -------
-    _DartsMlflowCallback or None
-        A callback instance if PyTorch Lightning is available, None otherwise.
-    """
-    if not PL_AVAILABLE:
-        return None
-
-    return _DartsMlflowCallback()
-
-
-def _inject_mlflow_callback(model) -> None:
-    """Inject the MLflow callback into a ``TorchForecastingModel``'s trainer params.
-
-    Adds a ``_DartsMlflowCallback`` to the model's PyTorch Lightning trainer callbacks
-    if not already present. This enables automatic logging of training metrics to MLflow.
-
-    Parameters
-    ----------
-    model
-        A TorchForecastingModel instance with a ``trainer_params`` attribute.
-
-    Notes
-    -----
-    This is a no-op if the callback is already present or PyTorch Lightning is unavailable.
-    """
-    callback = _get_mlflow_callback()
-    if callback is None:
-        return
-
-    if not hasattr(model, "trainer_params"):
-        return
-
-    existing_callbacks = model.trainer_params.get("callbacks", [])
-
-    if any(isinstance(cb, _DartsMlflowCallback) for cb in existing_callbacks):
-        logger.info("MLflow callback already present, skipping injection")
-        return
-
-    if not isinstance(existing_callbacks, list):
-        existing_callbacks = list(existing_callbacks)
-
-    existing_callbacks.append(callback)
-    model.trainer_params["callbacks"] = existing_callbacks
-    logger.info(f"Injected MLflow callback into {type(model).__name__}")

From df7e097c1b89e6f9c2782eee22923b4316a0d342 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 25 Feb 2026 22:38:06 +0100
Subject: [PATCH 032/154] unit tests for pytorch autolog

---
 darts/tests/optional_deps/test_mlflow.py | 76 ++++++++++++++----------
 1 file changed, 45 insertions(+), 31 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index da9aee12f3..c6a1790975 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -332,40 +332,49 @@ def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
                     assert m.value >= 0, f"val_loss is negative: {m.value}"
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_injects_callback(self, mlflow_tracking, autolog_context):
-        """Test that autolog injects MLflow callback into torch models"""
+    def test_autolog_pytorch_autolog_enabled(self, mlflow_tracking, autolog_context):
+        """Test that autolog enables mlflow.pytorch.autolog and logs per-epoch
+        train_loss, val_loss, and custom torch_metrics with finite non-negative values."""
+        import torchmetrics
+        from mlflow.utils.autologging_utils import autologging_is_disabled
+
+        n_epochs = 2
+
+        def assert_metric(history, key):
+            assert len(history) > 0, f"{key} not logged"
+            assert len(history) <= n_epochs, f"too many {key} entries"
+            assert all(np.isfinite(m.value) and m.value >= 0 for m in history)
+
         with autolog_context():
+            assert not autologging_is_disabled("pytorch")
+
             model = NBEATSModel(
                 input_chunk_length=4,
                 output_chunk_length=2,
-                n_epochs=2,
+                n_epochs=n_epochs,
+                torch_metrics=torchmetrics.MeanAbsoluteError(),
                 **tfm_kwargs_dev,
             )
-
-            # record initial callbacks from trainer_params (before fit)
-            initial_callbacks = model.trainer_params.get("callbacks", [])
-            initial_callback_count = len(initial_callbacks) if initial_callbacks else 0
-
             train, val = self.ts_univariate.split_before(0.7)
             model.fit(train, val_series=val)
 
-            # verify callback was injected after fit
-            final_callbacks = model.trainer_params.get("callbacks", [])
-            assert final_callbacks is not None, "Callbacks should not be None"
-            assert len(final_callbacks) > initial_callback_count, (
-                "MLflow callback should be added"
-            )
-
-            # check that the _DartsMlflowCallback is present
-            from darts.utils.mlflow import _DartsMlflowCallback
+            runs = mlflow.search_runs()
+            assert len(runs) == 1
+            run_id = runs.iloc[0]["run_id"]
+            assert runs.iloc[0]["tags.darts.model_class"] == "NBEATSModel"
 
-            has_mlflow_callback = any(
-                isinstance(cb, _DartsMlflowCallback) for cb in final_callbacks
-            )
-            assert has_mlflow_callback, (
-                f"_DartsMlflowCallback not found in {[type(cb).__name__ for cb in final_callbacks]}"
+            client = mlflow.tracking.MlflowClient()
+            assert_metric(client.get_metric_history(run_id, "train_loss"), "train_loss")
+            assert_metric(client.get_metric_history(run_id, "val_loss"), "val_loss")
+            # custom torch_metrics: in normal use both train_/val_ prefixes are logged, but
+            # fast_dev_run suppresses the Lightning logger during traininge
+            assert_metric(
+                client.get_metric_history(run_id, "val_MeanAbsoluteError"),
+                "val_MeanAbsoluteError",
             )
 
+        assert autologging_is_disabled("pytorch")
+
     def test_covariate_artifact_schema(self, mlflow_tracking):
         """Test that covariate artifact has correct JSON schema"""
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
@@ -426,10 +435,10 @@ def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         assert run.data.params["n_static_covariates"] == "2"
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_callback_injection_with_existing_callbacks(
+    def test_pytorch_autolog_with_existing_callbacks(
         self, mlflow_tracking, autolog_context
     ):
-        """Test callback injection when model already has callbacks"""
+        """Test pytorch autolog works when model already has callbacks"""
         # create model with existing callback
         if PL_AVAILABLE:
             import pytorch_lightning as pl
@@ -453,17 +462,22 @@ def test_callback_injection_with_existing_callbacks(
             train, val = self.ts_univariate.split_before(0.7)
             model.fit(train, val_series=val)
 
-            # verify both callbacks present
+            # verify existing callback is still present (not removed by autolog)
             callbacks = model.trainer_params.get("callbacks", [])
-            assert len(callbacks) == 2, "Should have both existing and MLflow callbacks"
-
-            from darts.utils.mlflow import _DartsMlflowCallback
-
-            has_mlflow = any(isinstance(cb, _DartsMlflowCallback) for cb in callbacks)
             has_existing = any(
                 isinstance(cb, pl.callbacks.EarlyStopping) for cb in callbacks
             )
-            assert has_mlflow and has_existing, "Both callbacks should be present"
+            assert has_existing, "Existing EarlyStopping callback should be preserved"
+
+            # verify metrics were still logged via mlflow.pytorch.autolog
+            runs = mlflow.search_runs()
+            assert len(runs) >= 1, "Expected at least one run"
+            last_run_id = runs.iloc[0]["run_id"]
+            client = mlflow.tracking.MlflowClient()
+            train_metrics = client.get_metric_history(last_run_id, "train_loss")
+            assert len(train_metrics) > 0, (
+                "Expected train_loss metrics to be logged via pytorch autolog"
+            )
 
     def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
         """Test that multiple fits with autolog create separate runs"""

From 99f161cab0182a3f7fd195b153dd649cbd944e77 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 09:37:26 +0000
Subject: [PATCH 033/154] Revert catboost cap

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 2d49f78df4..76099c41b3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,7 +64,7 @@ torch = [
     "safetensors>=0.6.2",
 ]
 notorch = [
-    "catboost>=1.0.6,<=1.2.9",
+    "catboost>=1.0.6",
     "lightgbm>=3.2.0",
     "prophet>=1.1.1",
     "statsforecast>=1.4",

From 1c7b073bac55e3eaf6065a7ec693ba2b6827c292 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 09:41:30 +0000
Subject: [PATCH 034/154] Replace raise_if and raise_if_not

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 80d1d85576..37ea4e5ff5 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -31,8 +31,8 @@
 from mlflow.utils.autologging_utils import (
     autologging_integration,
     get_autologging_config,
-    safe_patch,
 )
+from mlflow.utils.autologging_utils.safety import safe_patch
 from mlflow.utils.environment import (
     _CONDA_ENV_FILE_NAME,
     _CONSTRAINTS_FILE_NAME,
@@ -54,7 +54,7 @@
 from mlflow.utils.requirements_utils import _get_pinned_requirement
 
 import darts
-from darts.logging import get_logger, raise_if, raise_if_not
+from darts.logging import get_logger, raise_log
 from darts.models.forecasting.forecasting_model import ForecastingModel
 
 logger = get_logger(__name__)
@@ -125,11 +125,14 @@ def save_model(
     simplifying potential future extensibility, and to keep in line with MLflow API
     conventions.
     """
-    raise_if_not(
-        isinstance(model, ForecastingModel),
-        "model must be an instance of darts.models.forecasting.ForecastingModel",
-        logger,
-    )
+    if not isinstance(model, ForecastingModel):
+        raise_log(
+            ValueError(
+                "Model must be an instance of darts.models.forecasting.ForecastingModel."
+            ),
+            logger,
+        )
+
     _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
     _validate_and_prepare_target_save_path(path)
     code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
@@ -683,11 +686,11 @@ def _import_model_class(module_path: str, class_name: str):
     """
     module = importlib.import_module(module_path)
     cls = getattr(module, class_name, None)
-    raise_if(
-        cls is None,
-        f"Class '{class_name}' not found in module '{module_path}'",
-        logger,
-    )
+    if cls is None:
+        raise_log(
+            ImportError(f"Class `{class_name}` not found in module `{module_path}`"),
+            logger,
+        )
     return cls
 
 

From e885b64332e6ff10c4224bc7fbb6dcabd5624cd5 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 09:47:55 +0000
Subject: [PATCH 035/154] Use abs path

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 37ea4e5ff5..11c85695b5 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -134,6 +134,8 @@ def save_model(
         )
 
     _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
+
+    path = os.path.abspath(path)
     _validate_and_prepare_target_save_path(path)
     code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
 

From b46cf952929de952919c9256bd7cb96f614a779c Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 09:52:48 +0000
Subject: [PATCH 036/154] Use direct path for saving model

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 26 +++++++++-----------------
 1 file changed, 9 insertions(+), 17 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 11c85695b5..50cda6181f 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -139,29 +139,14 @@ def save_model(
     _validate_and_prepare_target_save_path(path)
     code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
 
-    data_dir = os.path.join(path, _MODEL_DATA_SUBFOLDER)
     is_torch = _is_torch_model(model)
 
-    os.makedirs(data_dir, exist_ok=True)
-
     # clean=True excludes any timeseries or callbacks from the model file
     model_file = _MODEL_FILE_TORCH if is_torch else _MODEL_FILE_STAT
-    model.save(os.path.join(data_dir, model_file), clean=True)
+    model.save(os.path.join(path, model_file), clean=True)
 
     module_path, class_name = _get_model_class_path(model)
 
-    darts_flavor_conf = {
-        "darts_version": darts.__version__,
-        "model_class_module": module_path,
-        "model_class_name": class_name,
-        "model_file": model_file,
-        "is_torch_model": is_torch,
-        "data": _MODEL_DATA_SUBFOLDER,
-    }
-
-    if code_dir_subpath is not None:
-        darts_flavor_conf["code"] = code_dir_subpath
-
     default_reqs = None if pip_requirements else get_default_pip_requirements(is_torch)
     conda_env, pip_requirements, pip_constraints = (
         _process_pip_requirements(
@@ -192,7 +177,14 @@ def save_model(
     if metadata is not None:
         mlflow_model.metadata = metadata
 
-    mlflow_model.add_flavor(FLAVOR_NAME, **darts_flavor_conf)
+    mlflow_model.add_flavor(
+        FLAVOR_NAME,
+        darts_version=darts.__version__,
+        data=model_file,
+        model_class=f"{module_path}.{class_name}",
+        code=code_dir_subpath,
+        is_torch_model=is_torch,
+    )
     mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
 
 

From 51a47f20f32f9b1bf194483b07121b8c27cd05bc Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 09:55:54 +0000
Subject: [PATCH 037/154] Update #2

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 50cda6181f..35704238d8 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -224,11 +224,7 @@ def load_model(
         flavor_conf["model_class_module"], flavor_conf["model_class_name"]
     )
 
-    model_path = os.path.join(
-        local_path,
-        flavor_conf.get("data", _MODEL_DATA_SUBFOLDER),
-        flavor_conf["model_file"],
-    )
+    model_path = os.path.join(local_path, flavor_conf["data"])
 
     if flavor_conf.get("is_torch_model", False):
         return model_cls.load(model_path, **kwargs)

From 28c11ed360bbf34f8f561a86d521242cb9305f8e Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 10:21:53 +0000
Subject: [PATCH 038/154] Use mlflow class utils to replace insepctlib

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 119 ++++++++++++++----------------------------
 1 file changed, 39 insertions(+), 80 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 35704238d8..4ccee575cc 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -14,7 +14,6 @@
 https://github.com/sktime/sktime/blob/main/sktime/utils/mlflow_sktime.py
 """
 
-import importlib
 import json
 import os
 import re
@@ -27,12 +26,13 @@
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
-from mlflow.utils import _inspect_original_var_name
+from mlflow.utils import _get_fully_qualified_class_name, _inspect_original_var_name
 from mlflow.utils.autologging_utils import (
     autologging_integration,
     get_autologging_config,
 )
 from mlflow.utils.autologging_utils.safety import safe_patch
+from mlflow.utils.class_utils import _get_class_from_string
 from mlflow.utils.environment import (
     _CONDA_ENV_FILE_NAME,
     _CONSTRAINTS_FILE_NAME,
@@ -60,7 +60,6 @@
 logger = get_logger(__name__)
 
 FLAVOR_NAME = "darts"
-_MODEL_DATA_SUBFOLDER = "data"
 
 
 _MODEL_FILE_STAT = "model.pkl"
@@ -145,25 +144,7 @@ def save_model(
     model_file = _MODEL_FILE_TORCH if is_torch else _MODEL_FILE_STAT
     model.save(os.path.join(path, model_file), clean=True)
 
-    module_path, class_name = _get_model_class_path(model)
-
-    default_reqs = None if pip_requirements else get_default_pip_requirements(is_torch)
-    conda_env, pip_requirements, pip_constraints = (
-        _process_pip_requirements(
-            default_reqs, pip_requirements, extra_pip_requirements
-        )
-        if conda_env is None
-        else _process_conda_env(conda_env)
-    )
-
-    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
-        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
-
-    if pip_constraints:
-        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
-
-    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
-    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
+    model_class = _get_fully_qualified_class_name(model)
 
     if mlflow_model is None:
         mlflow_model = Model()
@@ -181,12 +162,36 @@ def save_model(
         FLAVOR_NAME,
         darts_version=darts.__version__,
         data=model_file,
-        model_class=f"{module_path}.{class_name}",
+        model_class=model_class,
         code=code_dir_subpath,
         is_torch_model=is_torch,
     )
     mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
 
+    if pip_requirements is None:
+        default_reqs = get_default_pip_requirements()
+        # TODO: `infer_pip_requirements` requires `pyfunc` flavor to be implemented.
+        # inferred_reqs = infer_pip_requirements(path, FLAVOR_NAME, fallback=default_reqs)
+        # default_reqs = sorted(set(inferred_reqs).union(default_reqs))
+    else:
+        default_reqs = None
+    conda_env, pip_requirements, pip_constraints = (
+        _process_pip_requirements(
+            default_reqs, pip_requirements, extra_pip_requirements
+        )
+        if conda_env is None
+        else _process_conda_env(conda_env)
+    )
+
+    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
+        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
+
+    if pip_constraints:
+        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
+
+    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
+    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
+
 
 def load_model(
     model_uri: str,
@@ -220,9 +225,16 @@ def load_model(
     )
     _add_code_from_conf_to_system_path(local_path, flavor_conf)
 
-    model_cls = _import_model_class(
-        flavor_conf["model_class_module"], flavor_conf["model_class_name"]
-    )
+    model_cls_str = flavor_conf.get("model_class", None)
+    model_cls = _get_class_from_string(model_cls_str)
+
+    if not issubclass(model_cls, ForecastingModel):
+        raise_log(
+            ValueError(
+                f"Cannot load model: class `{model_cls_str}` is not a subclass of `ForecastingModel`."
+            ),
+            logger,
+        )
 
     model_path = os.path.join(local_path, flavor_conf["data"])
 
@@ -499,23 +511,12 @@ def get_all_subclasses(cls):
 def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
     """Return the default pip requirements for logging a darts model.
 
-    Parameters
-    ----------
-    is_torch
-        Whether the model is a PyTorch-based model. If ``True``, adds
-        ``torch`` and ``pytorch-lightning`` to the requirements.
-
     Returns
     -------
     list[str]
         A list of pip requirement strings.
     """
-    reqs = [_get_pinned_requirement("darts")]
-    if is_torch:
-        reqs.extend([
-            _get_pinned_requirement("torch"),
-            _get_pinned_requirement("pytorch-lightning"),
-        ])
+    reqs = [_get_pinned_requirement("darts[all]")]
     return reqs
 
 
@@ -642,48 +643,6 @@ def _is_torch_model(model) -> bool:
         return False
 
 
-def _get_model_class_path(model) -> tuple[str, str]:
-    """Extract the module path and class name from a model instance.
-
-    Parameters
-    ----------
-    model
-        A Darts forecasting model instance.
-
-    Returns
-    -------
-    tuple[str, str]
-        A tuple containing (module_path, class_name).
-    """
-    cls = type(model)
-    return cls.__module__, cls.__name__
-
-
-def _import_model_class(module_path: str, class_name: str):
-    """Dynamically import and return a model class.
-
-    Parameters
-    ----------
-    module_path : str
-        The fully qualified module path (e.g., "darts.models.exponential_smoothing").
-    class_name : str
-        The name of the class to import (e.g., "ExponentialSmoothing").
-
-    Returns
-    -------
-    type
-        The imported model class.
-    """
-    module = importlib.import_module(module_path)
-    cls = getattr(module, class_name, None)
-    if cls is None:
-        raise_log(
-            ImportError(f"Class `{class_name}` not found in module `{module_path}`"),
-            logger,
-        )
-    return cls
-
-
 def _extract_covariate_metadata(
     model, uses_attr: str, series_attr: str, names_attr: str
 ) -> dict:

From ddae08adb75300949df559179c8651a6d0303e00 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 10:25:55 +0000
Subject: [PATCH 039/154] Fix tests

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/tests/optional_deps/test_mlflow.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index c6a1790975..9ee402f9b4 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -66,10 +66,10 @@ def assert_mlflow_artifacts_exist(path: str, is_torch: bool = False):
     assert os.path.exists(os.path.join(path, "python_env.yaml"))
 
     if is_torch:
-        assert os.path.exists(os.path.join(path, "data", "model.pt"))
-        assert os.path.exists(os.path.join(path, "data", "model.pt.ckpt"))
+        assert os.path.exists(os.path.join(path, "model.pt"))
+        assert os.path.exists(os.path.join(path, "model.pt.ckpt"))
     else:
-        assert os.path.exists(os.path.join(path, "data", "model.pkl"))
+        assert os.path.exists(os.path.join(path, "model.pkl"))
 
 
 def assert_predictions_equal(model1, model2, n: int, decimal: int = 4, series=None):
@@ -595,7 +595,7 @@ def test_load_missing_model_file_fails(self, tmpdir_fn):
         save_model(model, model_path)
 
         # remove the model data file
-        model_data_path = os.path.join(model_path, "data", "model.pkl")
+        model_data_path = os.path.join(model_path, "model.pkl")
         os.remove(model_data_path)
 
         # loading should fail

From 91f66607bb3a367b2b2402fb86f8da2fa693c94c Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 14:34:45 +0000
Subject: [PATCH 040/154] Get all forecasting models ith inspect

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 48 ++++++++++++++++++++-----------------------
 1 file changed, 22 insertions(+), 26 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 4ccee575cc..fe2d4918b9 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -14,10 +14,12 @@
 https://github.com/sktime/sktime/blob/main/sktime/utils/mlflow_sktime.py
 """
 
+import inspect
 import json
 import os
 import re
 from collections.abc import Callable
+from operator import itemgetter
 
 import mlflow
 import numpy as np
@@ -437,19 +439,33 @@ def autolog(
         log_models=log_models,
         log_params=log_params,
         log_metrics=log_metrics,
-        inject_per_epoch_callbacks=inject_per_epoch_callbacks,
         disable=disable,
         silent=silent,
         manage_run=manage_run,
     )
 
 
+def _get_forecasting_models():
+    """
+    Returns:
+        A list of (name, class) tuples for all forecasting models in the Darts library.
+    """
+    import darts.models
+
+    classes = inspect.getmembers(darts.models, inspect.isclass)
+
+    classes = [
+        (name, cls) for name, cls in classes if issubclass(cls, ForecastingModel)
+    ]
+
+    return sorted(set(classes), key=itemgetter(0))
+
+
 @autologging_integration(FLAVOR_NAME)
 def _autolog(
     log_models: bool = True,
     log_params: bool = True,
     log_metrics: bool = True,
-    inject_per_epoch_callbacks: bool = True,
     disable: bool = False,
     silent: bool = False,
     manage_run: bool = True,
@@ -462,21 +478,7 @@ def _autolog(
     ``disable=True``.
     """
 
-    # recursively get all subclasses of ForecastingModel that override fit()
-    def get_all_subclasses(cls):
-        all_subclasses = []
-        for subclass in cls.__subclasses__():
-            all_subclasses.append(subclass)
-            all_subclasses.extend(get_all_subclasses(subclass))
-        return all_subclasses
-
-    classes_to_patch = [ForecastingModel]
-
-    for subclass in get_all_subclasses(ForecastingModel):
-        if "fit" in subclass.__dict__:
-            classes_to_patch.append(subclass)
-
-    for cls in classes_to_patch:
+    for _, cls in _get_forecasting_models():
         try:
             safe_patch(
                 FLAVOR_NAME,
@@ -508,7 +510,7 @@ def get_all_subclasses(cls):
                 )
 
 
-def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
+def get_default_pip_requirements():
     """Return the default pip requirements for logging a darts model.
 
     Returns
@@ -520,22 +522,16 @@ def get_default_pip_requirements(is_torch: bool = False) -> list[str]:
     return reqs
 
 
-def get_default_conda_env(is_torch: bool = False) -> dict:
+def get_default_conda_env():
     """Return a default conda environment dict for a darts model.
 
-    Parameters
-    ----------
-    is_torch
-        Whether the model is a PyTorch-based model.
-
     Returns
     -------
     dict
         A conda environment specification dictionary.
     """
     return _mlflow_conda_env(
-        additional_pip_deps=get_default_pip_requirements(is_torch),
-        additional_conda_channels=["conda-forge"],
+        additional_pip_deps=get_default_pip_requirements(),
     )
 
 

From ce56906075b5bf40f267f9b12f8eda67969633d3 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 14:39:53 +0000
Subject: [PATCH 041/154] Rename `log_torch_metrics`

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index fe2d4918b9..484acb8163 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -338,7 +338,7 @@ def autolog(
     log_models: bool = True,
     log_params: bool = True,
     log_metrics: bool = True,
-    inject_per_epoch_callbacks: bool = True,
+    log_torch_metrics: bool = True,
     disable: bool = False,
     silent: bool = False,
     manage_run: bool = True,
@@ -389,15 +389,10 @@ def autolog(
     log_metrics
         If ``True`` (default), patch all darts metric functions so that any
         call made inside an active MLflow run is automatically logged.
-    inject_per_epoch_callbacks
+    log_torch_metrics
         If ``True`` (default), enable ``mlflow.pytorch.autolog(log_models=False)``
         around PyTorch-based model training to automatically log per-epoch
         training and validation metrics. Only effective for PyTorch-based models.
-    extra_metrics
-        An optional list of additional Darts metric functions to log on top of
-        the defaults (``mae``, ``mse``, ``rmse``, ``mape``).  Each function
-        must follow the standard Darts metric signature
-        ``metric(actual_series, pred_series)``.
     disable
         If ``True``, restore the original ``fit()`` methods and stop
         autologging.
@@ -416,7 +411,7 @@ def autolog(
     # because the decorator short-circuits on disable=True before the function
     # body executes, and MLflow's session manager suppresses nested autolog
     # patches if called from within a safe_patch context.
-    if inject_per_epoch_callbacks and not disable:
+    if log_torch_metrics and not disable:
         try:
             import mlflow.pytorch
 
@@ -478,6 +473,7 @@ def _autolog(
     ``disable=True``.
     """
 
+    # patch `fit()` for all forecasting models
     for _, cls in _get_forecasting_models():
         try:
             safe_patch(
@@ -493,6 +489,7 @@ def _autolog(
     if log_metrics:
         import darts.metrics as _darts_metrics
 
+        # patch all metric functions to log results
         for metric_name in _darts_metrics.__all__:
             try:
                 # metrics should not create their own runs;

From 3dcceacb60f8ae5697934215f186e027643230b4 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 14:52:46 +0000
Subject: [PATCH 042/154] Change `save_model` param order

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 484acb8163..b45e406692 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -20,11 +20,12 @@
 import re
 from collections.abc import Callable
 from operator import itemgetter
+from typing import Any
 
 import mlflow
 import numpy as np
 import yaml
-from mlflow.models import Model
+from mlflow.models import Model, ModelInputExample, ModelSignature
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
@@ -73,12 +74,12 @@ def save_model(
     path: str,
     conda_env: dict | str | None = None,
     code_paths: list[str] | None = None,
+    mlflow_model: Model | None = None,
+    signature: ModelSignature | None = None,
+    input_example: ModelInputExample | None = None,
     pip_requirements: list[str] | None = None,
     extra_pip_requirements: list[str] | None = None,
-    signature=None,
-    input_example=None,
-    metadata: dict | None = None,
-    mlflow_model: Model | None = None,
+    metadata: dict[str, Any] | None = None,
 ) -> None:
     """Save a darts forecasting model in MLflow format.
 
@@ -101,22 +102,22 @@ def save_model(
         A list of local filesystem paths to Python file dependencies (or directories
         containing file dependencies). These files are prepended to the system path
         when the model is loaded.
+    mlflow_model
+        Optional MLflow Model object to use for saving. When provided (typically by
+        ``Model.log()``), this model instance is used instead of creating a new one.
+    signature
+        *Unsupported, see notes.* An ``mlflow.models.ModelSignature`` instance describing model input/output.
+        Use ``mlflow.models.infer_signature()`` to automatically generate from example inputs.
+    input_example
+        *Unsupported, see notes.* An example input for the model (used by MLflow UI).
     pip_requirements
         A list of pip requirement strings. Overrides ``conda_env`` pip section
         when provided.
     extra_pip_requirements
         A list of additional pip requirement strings to add to the model's environment,
         in addition to the default requirements.
-    signature
-        *Unsupported, see notes.* An ``mlflow.models.ModelSignature`` instance describing model input/output.
-        Use ``mlflow.models.infer_signature()`` to automatically generate from example inputs.
-    input_example
-        *Unsupported, see notes.* An example input for the model (used by MLflow UI).
     metadata
         Optional dictionary of custom metadata to store in the ``MLmodel`` file.
-    mlflow_model
-        Optional MLflow Model object to use for saving. When provided (typically by
-        ``Model.log()``), this model instance is used instead of creating a new one.
 
     Notes
     -----

From 1b7fa8910d0fda297bc8bd22093b04540cce7c9a Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 15:04:23 +0000
Subject: [PATCH 043/154] Update `log_model` params

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 67 +++++++++++++++++++++++++++++--------------
 1 file changed, 46 insertions(+), 21 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index b45e406692..75f1b8d83e 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -18,6 +18,7 @@
 import json
 import os
 import re
+import sys
 from collections.abc import Callable
 from operator import itemgetter
 from typing import Any
@@ -28,6 +29,7 @@
 from mlflow.models import Model, ModelInputExample, ModelSignature
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
+from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
 from mlflow.utils import _get_fully_qualified_class_name, _inspect_original_var_name
 from mlflow.utils.autologging_utils import (
@@ -250,16 +252,23 @@ def load_model(
 def log_model(
     model,
     artifact_path: str | None = None,
-    name: str | None = None,
-    registered_model_name: str | None = None,
     conda_env: dict | str | None = None,
     code_paths: list[str] | None = None,
+    registered_model_name: str | None = None,
+    signature: ModelSignature | None = None,
+    input_example: ModelInputExample | None = None,
+    await_registration_for: int = DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
     pip_requirements: list[str] | None = None,
     extra_pip_requirements: list[str] | None = None,
-    signature=None,
-    input_example=None,
-    metadata: dict | None = None,
+    metadata: dict[str, Any] | None = None,
+    name: str | None = None,
+    params: dict[str, Any] | None = None,
+    tags: dict[str, Any] | None = None,
+    model_type: str | None = None,
+    step: int = 0,
+    model_id: str | None = None,
     log_params: bool = True,
+    **kwargs,
 ):
     """Log a darts model to the current MLflow run.
 
@@ -270,30 +279,43 @@ def log_model(
     artifact_path
         The run-relative artifact path under which to log the model.
         Defaults to ``"model"``. Deprecated in favour of ``name``.
-    name
-        The name for the model artifact. If provided, takes precedence over
-        ``artifact_path``.
-    registered_model_name
-        If provided, the model is registered in the MLflow Model Registry
-        under this name.
     conda_env
         Conda environment specification (dict or path).
     code_paths
         A list of local filesystem paths to Python file dependencies (or directories
         containing file dependencies). These files are prepended to the system path
         when the model is loaded.
-    pip_requirements
-        Pip requirements list.
-    extra_pip_requirements
-        A list of additional pip requirement strings to add to the model's environment,
-        in addition to the default requirements.
+    registered_model_name
+        If provided, the model is registered in the MLflow Model Registry
+        under this name.
     signature
        *Unsupported, see notes.* An ``mlflow.models.ModelSignature``. Use ``mlflow.models.infer_signature()``
         to automatically generate from example inputs.
     input_example
         *Unsupported, see notes.* An example model input.
+    await_registration_for
+        Number of seconds to wait for the model version to finish being created and is in ``READY`` status.
+        By default, the function waits for five minutes. Specify 0 to skip waiting.
+    pip_requirements
+        Pip requirements list.
+    extra_pip_requirements
+        A list of additional pip requirement strings to add to the model's environment,
+        in addition to the default requirements.
     metadata
         Optional dict of custom metadata.
+    name
+        The name for the model artifact. If provided, takes precedence over
+        ``artifact_path``.
+    params
+        Optional dictionary of parameters to log alongside the model.
+    tags
+        Optional dictionary of tags to log alongside the model.
+    model_type
+        Optional string for the model type.
+    step
+        Optional step value to log with the model's metrics. Defaults to 0.
+    model_id
+        Optional string for the model ID.
     log_params
         If ``True`` (default), log the model's creation parameters via
         ``mlflow.log_params()``.
@@ -312,17 +334,13 @@ def log_model(
     simplifying potential future extensibility, and to keep in line with MLflow API
     conventions.
     """
-    # import required as Model.log will call flavor.save_model() internally
-    import darts.utils.mlflow as darts_mlflow
-
     if log_params:
         _log_model_params(model)
         _log_covariate_info(model)
 
     return Model.log(
         artifact_path=artifact_path,
-        name=name,
-        flavor=darts_mlflow,
+        flavor=sys.modules[__name__],
         registered_model_name=registered_model_name,
         model=model,
         conda_env=conda_env,
@@ -331,7 +349,14 @@ def log_model(
         extra_pip_requirements=extra_pip_requirements,
         signature=signature,
         input_example=input_example,
+        await_registration_for=await_registration_for,
         metadata=metadata,
+        name=name,
+        params=params,
+        tags=tags,
+        model_type=model_type,
+        step=step,
+        model_id=model_id,
     )
 
 

From b8a4f8d2c1bdb6f506ef2b475ada1c392cc12d75 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 15:08:37 +0000
Subject: [PATCH 044/154] Remove `log_params` from `log_model()`

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 75f1b8d83e..37ab546f6a 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -267,7 +267,6 @@ def log_model(
     model_type: str | None = None,
     step: int = 0,
     model_id: str | None = None,
-    log_params: bool = True,
     **kwargs,
 ):
     """Log a darts model to the current MLflow run.
@@ -316,9 +315,6 @@ def log_model(
         Optional step value to log with the model's metrics. Defaults to 0.
     model_id
         Optional string for the model ID.
-    log_params
-        If ``True`` (default), log the model's creation parameters via
-        ``mlflow.log_params()``.
 
     Returns
     -------
@@ -334,9 +330,6 @@ def log_model(
     simplifying potential future extensibility, and to keep in line with MLflow API
     conventions.
     """
-    if log_params:
-        _log_model_params(model)
-        _log_covariate_info(model)
 
     return Model.log(
         artifact_path=artifact_path,
@@ -357,6 +350,7 @@ def log_model(
         model_type=model_type,
         step=step,
         model_id=model_id,
+        **kwargs,
     )
 
 

From 331c4c35164243e10c76d27a28bac7cc00c7adcd Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 16:10:37 +0000
Subject: [PATCH 045/154] Update unit tests

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/tests/optional_deps/test_mlflow.py | 189 ++++++++---------------
 1 file changed, 68 insertions(+), 121 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 9ee402f9b4..bd46384723 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1,4 +1,3 @@
-import json
 import os
 
 import numpy as np
@@ -6,6 +5,11 @@
 import pytest
 
 import darts.utils.timeseries_generation as tg
+from darts import TimeSeries
+from darts.models.forecasting.forecasting_model import (
+    ForecastingModel,
+    GlobalForecastingModel,
+)
 from darts.tests.conftest import MLFLOW_AVAILABLE, TORCH_AVAILABLE, tfm_kwargs_dev
 from darts.utils.utils import PL_AVAILABLE
 
@@ -72,14 +76,37 @@ def assert_mlflow_artifacts_exist(path: str, is_torch: bool = False):
         assert os.path.exists(os.path.join(path, "model.pkl"))
 
 
-def assert_predictions_equal(model1, model2, n: int, decimal: int = 4, series=None):
+def assert_predictions_equal(
+    model1: ForecastingModel,
+    model2: ForecastingModel,
+    n: int,
+    decimal: int = 4,
+    is_global: bool = True,
+    series: TimeSeries | None = None,
+    past_covariates: TimeSeries | None = None,
+    future_covariates: TimeSeries | None = None,
+):
     """Assert that two models produce equivalent predictions. If series is provided,
     it will be passed to the second model's predict method (for global models that require it)."""
-    pred1 = model1.predict(n=n)
-    if series is not None:
-        pred2 = model2.predict(n=n, series=series)
+    if is_global:
+        assert isinstance(model1, GlobalForecastingModel)
+        assert isinstance(model2, GlobalForecastingModel)
+        pred1 = model1.predict(
+            n=n,
+            series=series,
+            past_covariates=past_covariates,
+            future_covariates=future_covariates,
+        )
+        pred2 = model2.predict(
+            n=n,
+            series=series,
+            past_covariates=past_covariates,
+            future_covariates=future_covariates,
+        )
     else:
+        pred1 = model1.predict(n=n)
         pred2 = model2.predict(n=n)
+
     np.testing.assert_array_almost_equal(
         pred1.values(), pred2.values(), decimal=decimal
     )
@@ -107,7 +134,7 @@ def test_save_load_statistical_model(self, tmpdir_fn):
         assert_mlflow_artifacts_exist(model_path, is_torch=False)
 
         loaded_model = load_model(f"file://{model_path}")
-        assert_predictions_equal(model, loaded_model, n=5)
+        assert_predictions_equal(model, loaded_model, n=5, is_global=False)
 
     def test_save_load_regression_model(self, tmpdir_fn):
         """Test save/load round-trip for regression model"""
@@ -152,23 +179,7 @@ def test_log_model_basic(self, mlflow_tracking):
             log_info = log_model(model, name="model")
 
         loaded_model = load_model(log_info.model_uri)
-        assert_predictions_equal(model, loaded_model, n=5)
-
-    def test_log_model_with_params(self, mlflow_tracking):
-        """Test that log_params=True logs model parameters"""
-        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
-
-        with mlflow.start_run():
-            log_model(model, name="model", log_params=True)
-            run_id = mlflow.active_run().info.run_id
-
-        run = mlflow.get_run(run_id)
-        assert run.data.params["lags"] == "5"
-        assert run.data.params["lags_past_covariates"] == "3"
-        assert run.data.params["n_past_covariates"] == "1"
-        assert run.data.params["n_future_covariates"] == "0"
-        assert run.data.params["n_static_covariates"] == "0"
+        assert_predictions_equal(model, loaded_model, n=5, is_global=False)
 
     def test_log_model_with_covariates(self, mlflow_tracking):
         """Test that covariate info is logged with correct values"""
@@ -176,44 +187,17 @@ def test_log_model_with_covariates(self, mlflow_tracking):
         model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
 
         with mlflow.start_run():
-            log_model(model, name="model", log_params=True)
+            log_model(model, name="model")
             run_id = mlflow.active_run().info.run_id
 
-            # get artifact while run is still active
-            artifact_uri = mlflow.get_artifact_uri("covariates.json")
-            artifact_path = artifact_uri.replace("file://", "")
-            assert os.path.exists(artifact_path), (
-                "covariates.json artifact should exist"
-            )
-
-            with open(artifact_path) as f:
-                cov_data = json.load(f)
-
-        run = mlflow.get_run(run_id)
-        # check covariate usage tags have the correct boolean values
-        assert run.data.tags["uses_past_covariates"] == "true"
-        assert run.data.tags["uses_future_covariates"] == "false"
-        assert run.data.tags["uses_static_covariates"] == "false"
-        # check covariate count params
-        assert run.data.params["n_past_covariates"] == "1"
-        assert run.data.params["n_future_covariates"] == "0"
-        assert run.data.params["n_static_covariates"] == "0"
-
-        # verify structure and content
-        assert "past_covariates" in cov_data
-        assert "future_covariates" in cov_data
-        assert "static_covariates" in cov_data
-
-        # check past covariates data
-        assert cov_data["past_covariates"]["used"] is True
-        assert cov_data["past_covariates"]["count"] == 1
-        assert len(cov_data["past_covariates"]["names"]) == 1
-
-        # check future and static covariates not used
-        assert cov_data["future_covariates"]["used"] is False
-        assert cov_data["future_covariates"]["count"] == 0
-        assert cov_data["static_covariates"]["used"] is False
-        assert cov_data["static_covariates"]["count"] == 0
+        loaded_model = load_model(f"runs:/{run_id}/model")
+        assert_predictions_equal(
+            model,
+            loaded_model,
+            n=5,
+            series=self.ts_univariate[:40],
+            past_covariates=self.ts_past_cov,
+        )
 
     def test_log_model_with_all_covariate_types(self, mlflow_tracking):
         """Test logging model with past, future, and static covariates"""
@@ -228,35 +212,18 @@ def test_log_model_with_all_covariate_types(self, mlflow_tracking):
         )
 
         with mlflow.start_run():
-            log_model(model, name="model", log_params=True)
+            log_model(model, name="model")
             run_id = mlflow.active_run().info.run_id
 
-            # get artifact while run is still active
-            artifact_uri = mlflow.get_artifact_uri("covariates.json")
-            artifact_path = artifact_uri.replace("file://", "")
-
-            with open(artifact_path) as f:
-                cov_data = json.load(f)
-
-        run = mlflow.get_run(run_id)
-
-        # verify all covariate types are tracked
-        assert run.data.tags["uses_past_covariates"] == "true"
-        assert run.data.tags["uses_future_covariates"] == "true"
-        assert run.data.tags["uses_static_covariates"] == "true"
-
-        # verify covariate counts
-        assert run.data.params["n_past_covariates"] == "1"
-        assert run.data.params["n_future_covariates"] == "1"
-        assert run.data.params["n_static_covariates"] == "1"
-
-        # all covariate types should be used
-        assert cov_data["past_covariates"]["used"] is True
-        assert cov_data["past_covariates"]["count"] == 1
-        assert cov_data["future_covariates"]["used"] is True
-        assert cov_data["future_covariates"]["count"] == 1
-        assert cov_data["static_covariates"]["used"] is True
-        assert cov_data["static_covariates"]["count"] == 1
+        loaded_model = load_model(f"runs:/{run_id}/model")
+        assert_predictions_equal(
+            model,
+            loaded_model,
+            n=5,
+            series=self.ts_with_static[:40],
+            past_covariates=self.ts_past_cov,
+            future_covariates=self.ts_future_cov,
+        )
 
     def test_autolog_enable_disable(self, mlflow_tracking, autolog_context):
         """Test autolog can be enabled and disabled"""
@@ -380,27 +347,7 @@ def test_covariate_artifact_schema(self, mlflow_tracking):
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
         model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
 
-        with mlflow.start_run():
-            log_model(model, name="model", log_params=True)
-
-            artifact_uri = mlflow.get_artifact_uri("covariates.json")
-            artifact_path = artifact_uri.replace("file://", "")
-
-            with open(artifact_path) as f:
-                cov_data = json.load(f)
-
-        # validate schema structure
-        required_keys = ["past_covariates", "future_covariates", "static_covariates"]
-        assert all(key in cov_data for key in required_keys), (
-            "Missing required covariate keys"
-        )
-
-        for cov_type in required_keys:
-            cov_info = cov_data[cov_type]
-            assert "used" in cov_info and isinstance(cov_info["used"], bool)
-            assert "count" in cov_info and isinstance(cov_info["count"], int)
-            assert "names" in cov_info and isinstance(cov_info["names"], list)
-            assert cov_info["count"] == len(cov_info["names"])
+        # TODO: use autolog
 
     def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         """Test saving/loading multivariate series with all covariate types"""
@@ -419,20 +366,18 @@ def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         )
 
         with mlflow.start_run():
-            log_model(model, name="model", log_params=True)
+            log_model(model, name="model")
             run_id = mlflow.active_run().info.run_id
 
-        run = mlflow.get_run(run_id)
-
-        # verify all covariate types detected
-        assert run.data.tags["uses_past_covariates"] == "true"
-        assert run.data.tags["uses_future_covariates"] == "true"
-        assert run.data.tags["uses_static_covariates"] == "true"
-
-        # verify correct component counts
-        assert run.data.params["n_past_covariates"] == "1"
-        assert run.data.params["n_future_covariates"] == "1"
-        assert run.data.params["n_static_covariates"] == "2"
+        loaded_model = load_model(f"runs:/{run_id}/model")
+        assert_predictions_equal(
+            loaded_model,
+            model,
+            n=5,
+            series=target[:40],
+            past_covariates=self.ts_past_cov,
+            future_covariates=self.ts_future_cov,
+        )
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_pytorch_autolog_with_existing_callbacks(
@@ -624,8 +569,10 @@ def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
 
         # Only pass series for global models (LinearRegressionModel)
         # Local models (ExponentialSmoothing) don't need it
-        series = self.ts_univariate if fit_kwargs else None
-        assert_predictions_equal(model, loaded, n=5, series=series)
+        if isinstance(model, GlobalForecastingModel):
+            assert_predictions_equal(model, loaded, n=5, series=self.ts_univariate)
+        else:
+            assert_predictions_equal(model, loaded, n=5, is_global=False)
 
     @pytest.mark.parametrize(
         "series,series_name",

From 2396f16a46de0064fcea974ae67fc8a4ce603b12 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 19:30:59 +0000
Subject: [PATCH 046/154] Simplify `_is_torch_model`

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 17 +++--------------
 1 file changed, 3 insertions(+), 14 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 37ab546f6a..2b22fa469b 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -169,7 +169,6 @@ def save_model(
         data=model_file,
         model_class=model_class,
         code=code_dir_subpath,
-        is_torch_model=is_torch,
     )
     mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
 
@@ -243,10 +242,7 @@ def load_model(
 
     model_path = os.path.join(local_path, flavor_conf["data"])
 
-    if flavor_conf.get("is_torch_model", False):
-        return model_cls.load(model_path, **kwargs)
-    else:
-        return model_cls.load(model_path)
+    return model_cls.load(model_path, **kwargs)
 
 
 def log_model(
@@ -645,15 +641,8 @@ def _is_torch_model(model) -> bool:
     bool
         True if the model is a TorchForecastingModel, False otherwise.
     """
-    try:
-        from darts.models.forecasting.torch_forecasting_model import (
-            TorchForecastingModel,
-        )
-
-        return isinstance(model, TorchForecastingModel)
-    except ImportError:
-        logger.info("TorchForecastingModel not available; treating model as non-torch")
-        return False
+    method = getattr(model, "predict_from_dataset", None)
+    return callable(method)
 
 
 def _extract_covariate_metadata(

From 7646dde8f47d962c0ecb1eab5a1ac75d01a21417 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 19:34:51 +0000
Subject: [PATCH 047/154] Move `_patched_fit` inside `_autolog()`

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 94 ++++++++++++++++++++-----------------------
 1 file changed, 44 insertions(+), 50 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 2b22fa469b..6415565df6 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -32,10 +32,7 @@
 from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
 from mlflow.utils import _get_fully_qualified_class_name, _inspect_original_var_name
-from mlflow.utils.autologging_utils import (
-    autologging_integration,
-    get_autologging_config,
-)
+from mlflow.utils.autologging_utils import autologging_integration
 from mlflow.utils.autologging_utils.safety import safe_patch
 from mlflow.utils.class_utils import _get_class_from_string
 from mlflow.utils.environment import (
@@ -489,6 +486,47 @@ def _autolog(
     ``disable=True``.
     """
 
+    def _patched_fit(original, self, *args, **kwargs):
+        """Patch function for ForecastingModel.fit() autologging.
+
+        Logs model parameters, class, covariates and the model itself.
+        Parameters
+        ----------
+        original
+            The original fit method being patched.
+        self
+            The model instance (ForecastingModel or TorchForecastingModel).
+        args
+            Positional arguments passed to fit.
+        kwargs
+            Keyword arguments passed to fit.
+
+        Returns
+        -------
+            The result of calling the original fit method.
+        """
+
+        mlflow.set_tag("darts.model_class", type(self).__name__)
+
+        if log_params:
+            _log_model_params(self)
+
+        result = original(self, *args, **kwargs)
+
+        if log_params:
+            _log_covariate_info(self)
+
+        if log_models:
+            try:
+                log_model(self, name="model", log_params=False)
+            except Exception:
+                logger.info(
+                    f"Failed to autolog model artifact for {type(self).__name__}.",
+                    exc_info=True,
+                )
+
+        return result
+
     # patch `fit()` for all forecasting models
     for _, cls in _get_forecasting_models():
         try:
@@ -629,7 +667,7 @@ def _log_covariate_info(model) -> None:
 
 
 def _is_torch_model(model) -> bool:
-    """Check if a model is a TorchForecastingModel.
+    """Check if a model is a `TorchForecastingModel`.
 
     Parameters
     ----------
@@ -639,7 +677,7 @@ def _is_torch_model(model) -> bool:
     Returns
     -------
     bool
-        True if the model is a TorchForecastingModel, False otherwise.
+        True if the model is a `TorchForecastingModel`, False otherwise.
     """
     method = getattr(model, "predict_from_dataset", None)
     return callable(method)
@@ -679,50 +717,6 @@ def _extract_covariate_metadata(
     return info
 
 
-def _patched_fit(original, self, *args, **kwargs):
-    """Patch function for ForecastingModel.fit() autologging.
-
-    Logs model parameters, class, covariates and the model itself.
-    Parameters
-    ----------
-    original
-        The original fit method being patched.
-    self
-        The model instance (ForecastingModel or TorchForecastingModel).
-    args
-        Positional arguments passed to fit.
-    kwargs
-        Keyword arguments passed to fit.
-
-    Returns
-    -------
-        The result of calling the original fit method.
-    """
-    log_models = get_autologging_config(FLAVOR_NAME, "log_models", True)
-    log_params = get_autologging_config(FLAVOR_NAME, "log_params", True)
-
-    mlflow.set_tag("darts.model_class", type(self).__name__)
-
-    if log_params:
-        _log_model_params(self)
-
-    result = original(self, *args, **kwargs)
-
-    if log_params:
-        _log_covariate_info(self)
-
-    if log_models:
-        try:
-            log_model(self, name="model", log_params=False)
-        except Exception:
-            logger.info(
-                f"Failed to autolog model artifact for {type(self).__name__}.",
-                exc_info=True,
-            )
-
-    return result
-
-
 def _sanitize_mlflow_key(name: str) -> str:
     """Sanitize a string for use as an MLflow metric key.
 

From 948ab56c5b2d3905a8881e47b1733ea0d5e618fc Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 19:42:26 +0000
Subject: [PATCH 048/154] Handle single series in metric patching

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 6415565df6..ad35e40eaf 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -58,6 +58,7 @@
 import darts
 from darts.logging import get_logger, raise_log
 from darts.models.forecasting.forecasting_model import ForecastingModel
+from darts.utils.ts_utils import get_single_series
 
 logger = get_logger(__name__)
 
@@ -866,15 +867,10 @@ def _patched_metric(original, *args, **kwargs):
         input_is_list = not hasattr(series, "components")
 
         # extract component names from the series (or first element if list)
-        component_names = None
-        try:
-            if input_is_list:
-                # we assume that subsequent series have same component order
-                component_names = series[0].components.tolist()
-            else:
-                component_names = series.components.tolist()
-        except Exception:
-            logger.info("Could not extract component names from series.")
+        single_series = get_single_series(series)
+        component_names = (
+            single_series.components.tolist() if single_series is not None else None
+        )
 
         try:
             _log_metric_result(

From 6ad82f7a459e10899bdd451d17df6de861698383 Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 20:04:51 +0000
Subject: [PATCH 049/154] Update covariate logging logic

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 120 +++++++++++++++++++++++-------------------
 1 file changed, 67 insertions(+), 53 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index ad35e40eaf..fbe5712a7f 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -31,8 +31,13 @@
 from mlflow.models.utils import _save_example
 from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
+from mlflow.tracking.fluent import _initialize_logged_model
 from mlflow.utils import _get_fully_qualified_class_name, _inspect_original_var_name
-from mlflow.utils.autologging_utils import autologging_integration
+from mlflow.utils.autologging_utils import (
+    autologging_integration,
+    get_autologging_config,
+)
+from mlflow.utils.autologging_utils.client import MlflowAutologgingQueueingClient
 from mlflow.utils.autologging_utils.safety import safe_patch
 from mlflow.utils.class_utils import _get_class_from_string
 from mlflow.utils.environment import (
@@ -68,6 +73,27 @@
 _MODEL_FILE_STAT = "model.pkl"
 _MODEL_FILE_TORCH = "model.pt"
 
+_covariate_types = [
+    (
+        "past_covariates",
+        "uses_past_covariates",
+        "past_covariate_series",
+        "components",
+    ),
+    (
+        "future_covariates",
+        "uses_future_covariates",
+        "future_covariate_series",
+        "components",
+    ),
+    (
+        "static_covariates",
+        "uses_static_covariates",
+        "static_covariates",
+        "columns",
+    ),
+]
+
 
 def save_model(
     model,
@@ -507,19 +533,32 @@ def _patched_fit(original, self, *args, **kwargs):
             The result of calling the original fit method.
         """
 
-        mlflow.set_tag("darts.model_class", type(self).__name__)
+        # Create a training session to track the training process and log information
+        autologging_client = MlflowAutologgingQueueingClient()
 
-        if log_params:
-            _log_model_params(self)
+        run_id = mlflow.active_run().info.run_id
+
+        # Set tags to identify the model class and relevant information
+        autologging_client.set_tags(run_id=run_id, tags=_get_model_info_tags(self))
 
         result = original(self, *args, **kwargs)
 
         if log_params:
+            # Log the parameters for model creation
+            autologging_client.log_params(run_id=run_id, params=self.model_params)
             _log_covariate_info(self)
 
         if log_models:
+            model_id = _initialize_logged_model("model", flavor=FLAVOR_NAME).model_id
             try:
-                log_model(self, name="model", log_params=False)
+                registered_model_name = get_autologging_config(
+                    FLAVOR_NAME, "registered_model_name", None
+                )
+                log_model(
+                    result,
+                    registered_model_name=registered_model_name,
+                    model_id=model_id,
+                )
             except Exception:
                 logger.info(
                     f"Failed to autolog model artifact for {type(self).__name__}.",
@@ -587,28 +626,26 @@ def get_default_conda_env():
     )
 
 
-def _log_model_params(model) -> None:
-    """Log model creation parameters to MLflow.
-
-    Extracts model parameters from ``model.model_params`` and logs them to the active
-    MLflow run.
-
-    Parameters
-    ----------
-    model
-        A Darts forecasting model instance with a ``model_params`` attribute.
+def _get_model_info_tags(model: ForecastingModel) -> dict[str, Any]:
     """
-    try:
-        params = model.model_params
-    except AttributeError:
-        logger.info("Model has no model_params attribute; skipping parameter logging.")
-        return
-
-    if params:
-        mlflow.log_params(params)
+    Returns:
+        A dictionary of MLflow run tag keys and values describing the specified model.
+    """
+    return {
+        "model_name": model.__class__.__name__,
+        "model_class": (model.__class__.__module__ + "." + model.__class__.__name__),
+        "model_likelihood": (
+            model.likelihood.__class__.__name__
+            if model.likelihood is not None
+            else None
+        ),
+        "model_uses_past_covariates": model.uses_past_covariates,
+        "model_uses_future_covariates": model.uses_future_covariates,
+        "model_uses_static_covariates": model.uses_static_covariates,
+    }
 
 
-def _log_covariate_info(model) -> None:
+def _log_covariate_info(model: ForecastingModel) -> None:
     """Log covariate usage information to MLflow.
 
     Extracts information about past, future, and static covariates used during
@@ -625,39 +662,16 @@ def _log_covariate_info(model) -> None:
     model
         A fitted Darts forecasting model instance.
     """
-    covariate_types = [
-        (
-            "past_covariates",
-            "_uses_past_covariates",
-            "past_covariate_series",
-            "components",
-        ),
-        (
-            "future_covariates",
-            "_uses_future_covariates",
-            "future_covariate_series",
-            "components",
-        ),
-        (
-            "static_covariates",
-            "_uses_static_covariates",
-            "static_covariates",
-            "columns",
-        ),
-    ]
 
     covariate_info = {
         cov_key: _extract_covariate_metadata(model, uses_attr, series_attr, names_attr)
-        for cov_key, uses_attr, series_attr, names_attr in covariate_types
+        for cov_key, uses_attr, series_attr, names_attr in _covariate_types
     }
 
-    for cov_key, info in covariate_info.items():
-        mlflow.set_tag(f"uses_{cov_key}", str(info["used"]).lower())
-        mlflow.log_param(f"n_{cov_key}", info["count"])
-
-        if info["names"]:
-            names_str = ",".join(info["names"])
-            mlflow.log_param(f"{cov_key.split('_')[0]}_cov_names", names_str)
+    if model.uses_static_covariates and model.static_covariates is not None:
+        covariate_info["static_covariates"]["is_global"] = len(
+            model.static_covariates
+        ) != len(model.static_covariates.columns)
 
     # log complete information as JSON artifact
     with TempDir() as tmp:
@@ -685,7 +699,7 @@ def _is_torch_model(model) -> bool:
 
 
 def _extract_covariate_metadata(
-    model, uses_attr: str, series_attr: str, names_attr: str
+    model: ForecastingModel, uses_attr: str, series_attr: str, names_attr: str
 ) -> dict:
     """Extract metadata for a single covariate type.
 

From abf24f561980fabf33b9f93649e0a54d05e7496e Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 20:22:09 +0000
Subject: [PATCH 050/154] Update tests and wait for completion

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/tests/optional_deps/test_mlflow.py | 12 ++++++------
 darts/utils/mlflow.py                    |  8 ++++++--
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index bd46384723..5341d4e704 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -236,7 +236,7 @@ def test_autolog_enable_disable(self, mlflow_tracking, autolog_context):
 
             # verify the run has expected content
             last_run = runs.iloc[0]
-            assert last_run["tags.darts.model_class"] == "ExponentialSmoothing"
+            assert last_run["tags.model_name"] == "ExponentialSmoothing"
             assert last_run["tags.mlflow.runName"] is not None
 
         # after context exits, autolog should be disabled
@@ -259,7 +259,7 @@ def test_autolog_parameters(self, mlflow_tracking, autolog_context):
 
             last_run = runs.iloc[0]
             assert last_run["params.seasonal_periods"] == "12"
-            assert last_run["tags.darts.model_class"] == "ExponentialSmoothing"
+            assert last_run["tags.model_name"] == "ExponentialSmoothing"
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
@@ -278,7 +278,7 @@ def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
             assert len(runs) == 1, "Expected exactly one run"
             last_run = runs.iloc[0]
             last_run_id = last_run["run_id"]
-            assert last_run["tags.darts.model_class"] == "NBEATSModel"
+            assert last_run["tags.model_name"] == "NBEATSModel"
 
             client = mlflow.tracking.MlflowClient()
 
@@ -328,7 +328,7 @@ def assert_metric(history, key):
             runs = mlflow.search_runs()
             assert len(runs) == 1
             run_id = runs.iloc[0]["run_id"]
-            assert runs.iloc[0]["tags.darts.model_class"] == "NBEATSModel"
+            assert runs.iloc[0]["tags.model_name"] == "NBEATSModel"
 
             client = mlflow.tracking.MlflowClient()
             assert_metric(client.get_metric_history(run_id, "train_loss"), "train_loss")
@@ -440,7 +440,7 @@ def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
         assert len(runs) == 2, "Expected two separate runs for two fits"
 
         # verify different model classes logged
-        model_classes = set(runs["tags.darts.model_class"])
+        model_classes = set(runs["tags.model_name"])
         assert "ExponentialSmoothing" in model_classes
         assert "LinearRegressionModel" in model_classes
 
@@ -466,7 +466,7 @@ def test_autolog_torch_model_multiple_fits(self, mlflow_tracking, autolog_contex
         assert len(runs) == 2, "Expected two separate runs for two fits"
 
         for _, run in runs.iterrows():
-            assert run["tags.darts.model_class"] in [
+            assert run["tags.model_name"] in [
                 "NBEATSModel",
                 "LinearRegressionModel",
             ]
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index fbe5712a7f..100dc3c732 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -538,16 +538,18 @@ def _patched_fit(original, self, *args, **kwargs):
 
         run_id = mlflow.active_run().info.run_id
 
+        result = original(self, *args, **kwargs)
+
         # Set tags to identify the model class and relevant information
         autologging_client.set_tags(run_id=run_id, tags=_get_model_info_tags(self))
 
-        result = original(self, *args, **kwargs)
-
         if log_params:
             # Log the parameters for model creation
             autologging_client.log_params(run_id=run_id, params=self.model_params)
             _log_covariate_info(self)
 
+        param_logging_ops = autologging_client.flush(synchronous=False)
+
         if log_models:
             model_id = _initialize_logged_model("model", flavor=FLAVOR_NAME).model_id
             try:
@@ -565,6 +567,8 @@ def _patched_fit(original, self, *args, **kwargs):
                     exc_info=True,
                 )
 
+        param_logging_ops.await_completion()
+
         return result
 
     # patch `fit()` for all forecasting models

From 774a222f69cc803a8588296b4a9920b79c7b3efa Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 20:28:27 +0000
Subject: [PATCH 051/154] Remove `PL_AVAILABLE` flag

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/tests/optional_deps/test_mlflow.py | 3 +--
 darts/utils/utils.py                     | 7 -------
 2 files changed, 1 insertion(+), 9 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 5341d4e704..4b2599e452 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -11,7 +11,6 @@
     GlobalForecastingModel,
 )
 from darts.tests.conftest import MLFLOW_AVAILABLE, TORCH_AVAILABLE, tfm_kwargs_dev
-from darts.utils.utils import PL_AVAILABLE
 
 if not MLFLOW_AVAILABLE:
     pytest.skip(
@@ -385,7 +384,7 @@ def test_pytorch_autolog_with_existing_callbacks(
     ):
         """Test pytorch autolog works when model already has callbacks"""
         # create model with existing callback
-        if PL_AVAILABLE:
+        if TORCH_AVAILABLE:
             import pytorch_lightning as pl
 
             existing_callback = pl.callbacks.EarlyStopping(monitor="train_loss")
diff --git a/darts/utils/utils.py b/darts/utils/utils.py
index 77ce154732..d04c70fa67 100644
--- a/darts/utils/utils.py
+++ b/darts/utils/utils.py
@@ -38,13 +38,6 @@
 except ImportError:
     TORCH_AVAILABLE = False
 
-try:
-    import pytorch_lightning as pl  # noqa: F401
-
-    PL_AVAILABLE = True
-except ImportError:
-    PL_AVAILABLE = False
-
 logger = get_logger(__name__)
 
 MAX_TORCH_SEED_VALUE = (1 << 31) - 1  # to accommodate 32-bit architectures

From 3c9296f928a04d9f6cf221653413d0c097f446eb Mon Sep 17 00:00:00 2001
From: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
Date: Thu, 5 Mar 2026 22:00:15 +0000
Subject: [PATCH 052/154] Add post-fitting metric TODO note

Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk>
---
 darts/utils/mlflow.py | 112 +++++++++++++++++++++++-------------------
 1 file changed, 62 insertions(+), 50 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 100dc3c732..2e71dfd180 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -573,36 +573,40 @@ def _patched_fit(original, self, *args, **kwargs):
 
     # patch `fit()` for all forecasting models
     for _, cls in _get_forecasting_models():
-        try:
-            safe_patch(
-                FLAVOR_NAME,
-                cls,
-                "fit",
-                _patched_fit,
-                manage_run=manage_run,
-            )
-        except Exception as e:
-            logger.info(f"Failed to patch {cls.__name__}.fit() for autologging: {e}")
+        safe_patch(
+            FLAVOR_NAME,
+            cls,
+            "fit",
+            _patched_fit,
+            manage_run=manage_run,
+        )
 
     if log_metrics:
-        import darts.metrics as _darts_metrics
+        import darts.metrics
+
+        # TODO: To log metrics post-fitting, only patching the metric methods may not be enough.
+        # This is becuase `model.fit()` method would terminate the active MLflow run at the end of training,
+        # so any metric calls made after that would not be logged.
+        # To address this, we need to implement three things:
+        # 1. Implement a singleton `_AutologgingMetricsManager` (`mlflow.sklearn`) to maintain a mapping between fitted
+        # models and their prediction outputs.
+        # 2. Patch the `model.predict()` method to create a mapping between the model (run_id) and prediction output.
+        # 3. Patch the metric functions to find the model (run_id) from the input series, then log the metrics to
+        # the corresponding run.
+        # This way, even if the metric calls are made after `fit()` has terminated the active run, we can still log
+        # the metrics to the correct run.
 
         # patch all metric functions to log results
-        for metric_name in _darts_metrics.__all__:
-            try:
-                # metrics should not create their own runs;
-                # they log into the run started by fit(), so manage_run=False here
-                safe_patch(
-                    FLAVOR_NAME,
-                    _darts_metrics,
-                    metric_name,
-                    _make_metric_patch(metric_name),
-                    manage_run=False,
-                )
-            except Exception as e:
-                logger.info(
-                    f"Failed to patch metric '{metric_name}' on darts.metrics: {e}"
-                )
+        for metric_name in darts.metrics.__all__:
+            # metrics should not create their own runs;
+            # they log into the run started by fit(), so manage_run=False here
+            safe_patch(
+                FLAVOR_NAME,
+                darts.metrics,
+                metric_name,
+                _make_metric_patch(metric_name),
+                manage_run=False,
+            )
 
 
 def get_default_pip_requirements():
@@ -757,6 +761,8 @@ def _sanitize_mlflow_key(name: str) -> str:
 
 
 def _log_metric_result(
+    autologging_client: MlflowAutologgingQueueingClient,
+    run_id: str,
     metric_name: str,
     result,
     dataset_name: str | None = None,
@@ -804,9 +810,6 @@ def _log_metric_result(
     """
     result_arr = np.asarray(result)
 
-    if mlflow.active_run() is None:
-        return
-
     base_key = f"{metric_name}_{dataset_name}" if dataset_name else metric_name
 
     def _comp_suffix(idx: int) -> str:
@@ -814,34 +817,39 @@ def _comp_suffix(idx: int) -> str:
             return _sanitize_mlflow_key(component_names[idx])
         return str(idx)
 
+    metrics = {}
+
     if result_arr.ndim == 0:
         # scalar result
-        mlflow.log_metric(base_key, float(result_arr))
+        metrics[base_key] = float(result_arr)
 
     elif result_arr.ndim == 1:
         if not input_is_list:
             # single series: log per-component
             for c_i, val in enumerate(result_arr):
-                mlflow.log_metric(f"{base_key}_{_comp_suffix(c_i)}", float(val))
+                metrics[f"{base_key}_{_comp_suffix(c_i)}"] = float(val)
         else:
             # list input, components already reduced: log per-series
             for s_i, val in enumerate(result_arr):
-                mlflow.log_metric(f"{base_key}_{s_i}", float(val))
+                metrics[f"{base_key}_{s_i}"] = float(val)
 
     elif result_arr.ndim == 2:
         # list input: log per-series and per-component
         n_series, n_components = result_arr.shape
         for s_i in range(n_series):
             for c_i in range(n_components):
-                mlflow.log_metric(
-                    f"{base_key}_{_comp_suffix(c_i)}_{s_i}",
-                    float(result_arr[s_i, c_i]),
+                metrics[f"{base_key}_{_comp_suffix(c_i)}_{s_i}"] = float(
+                    result_arr[s_i, c_i]
                 )
 
     else:
         # unexpected shape — flatten with integer indices
         for i, val in enumerate(result_arr.flatten()):
-            mlflow.log_metric(f"{base_key}_{i}", float(val))
+            metrics[f"{base_key}_{i}"] = float(val)
+
+    autologging_client.log_metrics(run_id=run_id, metrics=metrics)
+    operation = autologging_client.flush(synchronous=False)
+    operation.await_completion()
 
 
 def _make_metric_patch(metric_name: str) -> Callable:
@@ -872,10 +880,17 @@ def _make_metric_patch(metric_name: str) -> Callable:
     def _patched_metric(original, *args, **kwargs):
         result = original(*args, **kwargs)
 
-        if mlflow.active_run() is None:
+        active_run = mlflow.active_run()
+        if active_run is None:
             return result
 
-        series = args[0]
+        autologging_client = MlflowAutologgingQueueingClient()
+        run_id = active_run.info.run_id
+
+        if len(args) > 0:
+            series = args[0]
+        else:
+            series = kwargs.get("actual_series", None)
 
         # capture the variable name of actual_series for metric key
         raw = _inspect_original_var_name(series, fallback_name=None)
@@ -890,18 +905,15 @@ def _patched_metric(original, *args, **kwargs):
             single_series.components.tolist() if single_series is not None else None
         )
 
-        try:
-            _log_metric_result(
-                metric_name,
-                result,
-                dataset_name=dataset_name,
-                component_names=component_names,
-                input_is_list=input_is_list,
-            )
-        except Exception:
-            logger.info(
-                f"Failed to log metric '{metric_name}' to MLflow.", exc_info=True
-            )
+        _log_metric_result(
+            autologging_client,
+            run_id,
+            metric_name,
+            result,
+            dataset_name=dataset_name,
+            component_names=component_names,
+            input_is_list=input_is_list,
+        )
 
         return result
 

From a7bca2eb94d4c9ac9d97fa592be82236e1124b0a Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Fri, 6 Mar 2026 16:30:12 +0100
Subject: [PATCH 053/154] chore: rename mlflow jupyter from 26 to 27

---
 .../{26-MLflow-quickstart.ipynb => 27-MLflow-quickstart.ipynb}    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/{26-MLflow-quickstart.ipynb => 27-MLflow-quickstart.ipynb} (100%)

diff --git a/examples/26-MLflow-quickstart.ipynb b/examples/27-MLflow-quickstart.ipynb
similarity index 100%
rename from examples/26-MLflow-quickstart.ipynb
rename to examples/27-MLflow-quickstart.ipynb

From eb8f8cdbdbb986bbcdfccb19abb0b55e1be7ad57 Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Fri, 6 Mar 2026 17:17:47 +0100
Subject: [PATCH 054/154] chore: fix jupyter for mlflow; work in progress

---
 .gitignore                          |  2 ++
 darts/utils/mlflow.py               | 12 ++++---
 examples/27-MLflow-quickstart.ipynb | 50 +++++++++++++----------------
 3 files changed, 33 insertions(+), 31 deletions(-)

diff --git a/.gitignore b/.gitignore
index 6d5390ef1c..6e5eb81a78 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,3 +25,5 @@ docs_env
 .venv
 
 uv.lock
+repl/
+examples/mlruns/*
\ No newline at end of file
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 2e71dfd180..922348c198 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -400,9 +400,13 @@ def autolog(
 
     .. important::
 
-        ``autolog()`` must be called **before** importing metric functions from
-        ``darts.metrics``.  Metric functions imported before ``autolog()`` is
-        enabled will **not** log to MLflow.
+        Metric functions imported with ``from darts.metrics import <name>``
+        before ``autolog()`` is enabled will **not** log to MLflow (function
+        reference is copied out of the module before the patch is applied).
+
+        To avoid this restriction, use ``import darts.metrics as dm`` and call
+        ``dm.<metric>()`` instead (module attribute lookups are resolved at call
+        time and will always see the patched version).
 
     .. note::
 
@@ -810,7 +814,7 @@ def _log_metric_result(
     """
     result_arr = np.asarray(result)
 
-    base_key = f"{metric_name}_{dataset_name}" if dataset_name else metric_name
+    base_key = f"{dataset_name}_{metric_name}" if dataset_name else metric_name
 
     def _comp_suffix(idx: int) -> str:
         if component_names is not None and idx < len(component_names):
diff --git a/examples/27-MLflow-quickstart.ipynb b/examples/27-MLflow-quickstart.ipynb
index 5a119983c5..0b793f75b3 100644
--- a/examples/27-MLflow-quickstart.ipynb
+++ b/examples/27-MLflow-quickstart.ipynb
@@ -70,15 +70,16 @@
     "import mlflow\n",
     "import numpy as np\n",
     "\n",
+    "import darts.metrics\n",
+    "\n",
     "from darts.datasets import AirPassengersDataset\n",
-    "from darts.metrics import mape, rmse\n",
     "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
     "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 3,
    "id": "4d424e08",
    "metadata": {},
    "outputs": [],
@@ -178,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 6,
    "id": "bc8f520d",
    "metadata": {},
    "outputs": [
@@ -208,8 +209,8 @@
     "predictions = model.predict(n=len(val))\n",
     "\n",
     "# calculate metrics you want to log to MLflow\n",
-    "mape_score = mape(val, predictions)\n",
-    "rmse_score = rmse(val, predictions)\n",
+    "mape_score = darts.metrics.mape(val, predictions)\n",
+    "rmse_score = darts.metrics.rmse(val, predictions)\n",
     "\n",
     "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
     "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
@@ -232,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "61406cd9",
    "metadata": {},
    "outputs": [
@@ -249,18 +250,18 @@
     "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
     "    model_info = log_model(\n",
     "        model=model,\n",
-    "        name=\"model\",\n",
-    "        log_params=True,  # True by default, logs all model params\n",
+    "        name=\"exponential-smoothing-model\",\n",
+    "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
+    "        tags={\n",
+    "            \"model_type\": \"ExponentialSmoothing\",\n",
+    "            \"dataset\": \"AirPassengers\"\n",
+    "        }\n",
     "    )\n",
     "\n",
     "    # log calculated metrics you want\n",
     "    mlflow.log_metric(\"val_mape\", mape_score)\n",
     "    mlflow.log_metric(\"val_rmse\", rmse_score)\n",
     "\n",
-    "    # log any additional tags you want\n",
-    "    mlflow.set_tag(\"model_type\", \"ExponentialSmoothing\")\n",
-    "    mlflow.set_tag(\"dataset\", \"AirPassengers\")\n",
-    "\n",
     "    print(f\"Run ID: {run.info.run_id}\")\n",
     "    print(f\"Model URI: {model_info.model_uri}\")"
    ]
@@ -341,10 +342,8 @@
     "    auto_model.fit(train)  # no val_series → autolog logs train_* metrics by default\n",
     "\n",
     "    auto_predictions = auto_model.predict(n=len(val))\n",
-    "    auto_mape = mape(val, auto_predictions)\n",
-    "    auto_rmse = rmse(val, auto_predictions)\n",
-    "    mlflow.log_metric(\"val_mape\", auto_mape)\n",
-    "    mlflow.log_metric(\"val_rmse\", auto_rmse)\n",
+    "    auto_mape = darts.metrics.mape(val, auto_predictions)\n",
+    "    auto_rmse = darts.metrics.rmse(val, auto_predictions)\n",
     "\n",
     "autolog(disable=True)\n",
     "\n",
@@ -383,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 10,
    "id": "1a01cd2f",
    "metadata": {},
    "outputs": [
@@ -393,7 +392,7 @@
      "text": [
       "Launch the MLflow UI with this command in your terminal:\n",
       "\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmph5ei82gg/mlflow.db\n",
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4x/8t1xtpd11cb5xxdp87gm92jw0000gn/T/tmp3vfszfy0/mlflow.db\n",
       "\n",
       "Then open:  http://localhost:5000\n"
      ]
@@ -459,8 +458,8 @@
     "    )\n",
     "    nbeats.fit(train, val_series=val)\n",
     "    nbeats_pred = nbeats.predict(n=len(val))\n",
-    "    mlflow.log_metric(\"val_mape\", mape(val, nbeats_pred))\n",
-    "    mlflow.log_metric(\"val_rmse\", rmse(val, nbeats_pred))\n",
+    "    darts.metrics.mape(val, nbeats_pred))\n",
+    "    darts.metrics.rmse(val, nbeats_pred))\n",
     "    print(f\"NBEATS MAPE: {mape(val, nbeats_pred):.2f}%\")\n",
     "\n",
     "autolog(disable=True)"
@@ -493,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "df8e85b6",
    "metadata": {},
    "outputs": [
@@ -517,12 +516,9 @@
     }
    ],
    "source": [
-    "from darts.metrics import mae, smape\n",
-    "\n",
     "autolog(\n",
-    "    log_training_metrics=True,\n",
-    "    log_validation_metrics=True,\n",
-    "    extra_metrics=[smape, mae],  # add smape and mae on top of the defaults\n",
+    "    log_metrics=True,\n",
+    "    extra_metrics=[darts.metrics.smape, darts.metrics.mae],  # add smape and mae on top of the defaults\n",
     ")\n",
     "\n",
     "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
@@ -869,7 +865,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.13.12"
   }
  },
  "nbformat": 4,

From ab591968f980256a0babd4d1ea4a5b81352f3cf5 Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Fri, 17 Apr 2026 15:47:23 +0200
Subject: [PATCH 055/154] Update .gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 6e5eb81a78..06bba7cef4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,4 +26,5 @@ docs_env
 
 uv.lock
 repl/
+mlruns/*
 examples/mlruns/*
\ No newline at end of file

From 01947445a48c40b125f3c300da4d0cc7d33923ab Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Fri, 17 Apr 2026 15:47:35 +0200
Subject: [PATCH 056/154] chore: local test files; remove later

---
 debug_hf_mlflow.py | 197 +++++++++++++++++++++++++++++++++++++++++++++
 mlflow_test_v2.py  | 118 +++++++++++++++++++++++++++
 2 files changed, 315 insertions(+)
 create mode 100644 debug_hf_mlflow.py
 create mode 100644 mlflow_test_v2.py

diff --git a/debug_hf_mlflow.py b/debug_hf_mlflow.py
new file mode 100644
index 0000000000..b9464b10ea
--- /dev/null
+++ b/debug_hf_mlflow.py
@@ -0,0 +1,197 @@
+"""
+Debug script: historical_forecasts / backtest with MLflow autologging.
+
+Issue (from PR #3022): historical_forecasts(retrain=True) and backtest() call
+fit() internally on each iteration. Because _patched_fit runs with
+manage_run=True, every internal fit() spawns its own MLflow run when no
+active run exists - i.e. the typical autolog-only usage (no explicit
+mlflow.start_run() wrapper).
+
+Expected: 1 run per historical_forecasts / backtest call.
+Actual:   1 run per stride iteration.
+
+Run with:
+    python examples/repl/debug_hf_mlflow.py
+
+Then inspect in the UI:
+    mlflow ui --backend-store-uri sqlite:////tmp/mlflow_debug.db
+"""
+
+# %%
+import os
+
+import mlflow
+
+import darts.utils.mlflow as mlflow_darts
+from darts.datasets import AirPassengersDataset
+from darts.models import LinearRegressionModel
+
+# ---- setup -------------------------------------------------------------------
+series = AirPassengersDataset().load()
+
+STRIDE = 4  # keep iteration count low for fast runs
+
+DB_PATH = "/tmp/mlflow_debug.db"
+
+mlflow.set_tracking_uri(f"sqlite:///{DB_PATH}")
+mlflow.set_experiment("debug-hf-mlflow")
+client = mlflow.tracking.MlflowClient()
+exp_id = mlflow.get_experiment_by_name("debug-hf-mlflow").experiment_id
+
+
+def run_count(name=None):
+    runs = client.search_runs(experiment_ids=[exp_id])
+    if name:
+        return sum(1 for r in runs if r.info.run_name == name)
+    return len(runs)
+
+
+# ---- 1. normal fit WITH explicit start_run (baseline) -----------------------
+print("=" * 60)
+print("1. Normal fit() inside mlflow.start_run()")
+mlflow_darts.autolog(disable=True)
+mlflow_darts.autolog()
+
+before = run_count()
+with mlflow.start_run(run_name="fit-with-run"):
+    LinearRegressionModel(lags=12).fit(series[:100])
+mlflow_darts.autolog(disable=True)
+
+print(f"   Runs created: {run_count() - before}  (expected: 1)")
+
+# ---- 2. historical_forecasts WITH explicit start_run ------------------------
+print()
+print("=" * 60)
+print("2. historical_forecasts(retrain=True) inside mlflow.start_run()")
+mlflow_darts.autolog(disable=True)
+mlflow_darts.autolog()
+
+before = run_count()
+with mlflow.start_run(run_name="hf-with-run"):
+    LinearRegressionModel(lags=12).historical_forecasts(
+        series,
+        start=0.75,
+        forecast_horizon=1,
+        stride=STRIDE,
+        retrain=True,
+        last_points_only=True,
+    )
+mlflow_darts.autolog(disable=True)
+
+print(f"   Runs created: {run_count() - before}  (expected: 1)")
+
+# ---- 3. historical_forecasts WITHOUT start_run (autolog-only usage) ---------
+print()
+print("=" * 60)
+print("3. historical_forecasts(retrain=True) with autolog only (no start_run)")
+mlflow_darts.autolog(disable=True)
+mlflow_darts.autolog()
+
+before = run_count()
+LinearRegressionModel(lags=12).historical_forecasts(
+    series,
+    start=0.75,
+    forecast_horizon=1,
+    stride=STRIDE,
+    retrain=True,
+    last_points_only=True,
+)
+mlflow_darts.autolog(disable=True)
+
+created = run_count() - before
+print(f"   Runs created: {created}  (expected: 1)")
+print(f"   -> Each stride iteration spawned its own run: {created > 1}")
+
+# ---- 4. backtest WITHOUT start_run ------------------------------------------
+print()
+print("=" * 60)
+print("4. backtest() with autolog only (no start_run)")
+mlflow_darts.autolog(disable=True)
+mlflow_darts.autolog()
+
+before = run_count()
+LinearRegressionModel(lags=12).backtest(
+    series,
+    start=0.75,
+    forecast_horizon=1,
+    stride=STRIDE,
+    retrain=True,
+)
+mlflow_darts.autolog(disable=True)
+
+created = run_count() - before
+print(f"   Runs created: {created}  (expected: 1)")
+print(f"   -> Each stride iteration spawned its own run: {created > 1}")
+
+# Assert backtest_mape was logged
+all_runs = client.search_runs(experiment_ids=[exp_id])
+bt_run = sorted(all_runs, key=lambda x: x.info.start_time)[-1]
+metrics = bt_run.data.metrics
+assert "backtest_mape" in metrics, f"Expected backtest_mape in metrics, got: {metrics}"
+print(f"   -> backtest_mape = {metrics['backtest_mape']:.4f}  (PASS)")
+
+# ---- 5. backtest with reduction=None → per-window metrics -------------------
+print()
+print("=" * 60)
+print("5. backtest(reduction=None) — per-window MAPE logged as separate metrics")
+mlflow_darts.autolog(disable=True)
+mlflow_darts.autolog()
+
+before = run_count()
+per_window = LinearRegressionModel(lags=12).backtest(
+    series,
+    start=0.75,
+    forecast_horizon=1,
+    stride=STRIDE,
+    retrain=True,
+    reduction=None,
+)
+mlflow_darts.autolog(disable=True)
+
+created = run_count() - before
+print(f"   Runs created: {created}  (expected: 1)")
+
+all_runs = client.search_runs(experiment_ids=[exp_id])
+pw_run = sorted(all_runs, key=lambda x: x.info.start_time)[-1]
+history = client.get_metric_history(pw_run.info.run_id, "backtest_mape")
+assert history, f"Expected backtest_mape steps in metric history, got nothing"
+print(f"   -> backtest_mape logged across {len(history)} steps (windows)")
+
+# Plot per-window MAPE
+import matplotlib.pyplot as plt
+import numpy as np
+
+window_nums = [p.step for p in sorted(history, key=lambda p: p.step)]
+values = [p.value for p in sorted(history, key=lambda p: p.step)]
+
+fig, ax = plt.subplots(figsize=(8, 4))
+ax.plot(window_nums, values, marker="o", linewidth=1.5, label="MAPE per window")
+ax.axhline(np.mean(values), color="red", linestyle="--", linewidth=1, label=f"mean = {np.mean(values):.2f}%")
+ax.set_xlabel("Backtest window index")
+ax.set_ylabel("MAPE (%)")
+ax.set_title("Per-window MAPE (backtest, reduction=None)")
+ax.legend()
+fig.tight_layout()
+plt.savefig("/tmp/backtest_per_window_mape.png", dpi=150)
+print("   -> Plot saved to /tmp/backtest_per_window_mape.png")
+plt.show()
+
+# ---- summary -----------------------------------------------------------------
+print()
+print("=" * 60)
+print("Summary of ALL runs in experiment:")
+all_runs = client.search_runs(experiment_ids=[exp_id])
+for r in sorted(all_runs, key=lambda x: x.info.start_time):
+    m = r.data.metrics
+    if "backtest_mape" in m:
+        h = client.get_metric_history(r.info.run_id, "backtest_mape")
+        if len(h) > 1:
+            metric_str = f"  backtest_mape ({len(h)} steps, mean={sum(p.value for p in h)/len(h):.4f})"
+        else:
+            metric_str = f"  backtest_mape={m['backtest_mape']:.4f}"
+    else:
+        metric_str = ""
+    print(f"   [{r.info.status}] {r.info.run_name!r:30s}  id={r.info.run_id[:8]}{metric_str}")
+print(f"   Total: {len(all_runs)} runs")
+print()
+print(f"Inspect in UI: mlflow ui --backend-store-uri sqlite:///{DB_PATH}")
diff --git a/mlflow_test_v2.py b/mlflow_test_v2.py
new file mode 100644
index 0000000000..ade751c54d
--- /dev/null
+++ b/mlflow_test_v2.py
@@ -0,0 +1,118 @@
+"""
+MLflow (auto)-logging:  Multi-model comparison with backtest
+
+Demonstrates:
+  * Single MLflow run containing all models — no nested runs needed.
+    Metric keys are automatically prefixed with the model class name
+    (e.g. ``LinearRegressionModel_val_mape``, ``ExponentialSmoothing_val_mape``),
+    so all results live in one run without key collisions.
+  * Automatic parameter and tag logging via darts autolog
+  * Inline metric logging: ``mape`` and ``rmse`` called inside an active run
+    are automatically captured by the patched metric functions
+  * Backtest with pre-computed historical forecasts, multiple metric functions,
+    and ``reduction=None`` so each window is logged as a step of
+    ``{Model}_backtest_mape`` / ``{Model}_backtest_rmse`` in the MLflow UI
+
+Notes:
+  * ``mlflow_darts.autolog()`` is required — it patches ``fit()``, the darts
+    metric functions, and ``backtest()`` to make logging automatic.
+    ``mlflow.start_run()`` only manages the run context; without ``autolog()``
+    nothing would be logged.
+  * ``last_points_only=False`` is used for historical forecasts so that
+    ``backtest()`` receives a list of per-window forecasts and can compute one
+    metric value per window (enabling the per-step chart in the UI).
+    ``last_points_only=True`` would collapse all windows into a single
+    TimeSeries, making the metric treat them as one window.
+
+Run with:
+    python mlflow_test_v2.py
+
+Inspect in the UI:
+    mlflow ui --backend-store-uri sqlite:////tmp/mlflow_v2.db
+"""
+
+import logging
+
+import mlflow
+import coolname
+from loguru import logger
+
+logging.getLogger("mlflow.utils.environment").setLevel(logging.ERROR)
+
+import darts.metrics as darts_metrics
+import darts.utils.mlflow as mlflow_darts
+from darts.datasets import AirPassengersDataset
+from darts.models import ExponentialSmoothing, LinearRegressionModel
+
+# ── data ──────────────────────────────────────────────────────────────────────
+series = AirPassengersDataset().load()
+train, val = series.split_after(0.75)
+
+FORECAST_HORIZON = 1
+STRIDE = 2
+BT_START = 0.5  # more windows: backtest from 50 % of the full series
+
+# ── MLflow setup ──────────────────────────────────────────────────────────────
+# autolog() is required becausse it patches fit() / metric functions / backtest()
+# start_run() separate only controls the run context
+DB_PATH = "/tmp/mlflow_v2.db"
+mlflow.set_tracking_uri(f"sqlite:///{DB_PATH}")
+
+
+exp_name: str = coolname.generate_slug(2)
+run_name: str = coolname.generate_slug(2)
+logger.info(f"Starting experiment: {exp_name}")
+mlflow.set_experiment(exp_name)
+mlflow_darts.autolog()
+
+models = [
+    LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),
+    ExponentialSmoothing(),
+]
+
+# NOTE: would this make more sense actually?
+# for model in models:
+#     with mlflow.start_run(run_name=run_name):
+#         ...
+
+with mlflow.start_run(run_name=run_name):
+    for model in models:
+        # ── fit ───────────────────────────────────────────────────────────
+        # NOTE: add val_series=val here for TorchForecastingModels to enable
+        #       validation-based early stopping during training.
+        model.fit(train)
+
+        # ── predict + inline metric logging ───────────────────────────────
+        pred = model.predict(n=len(val))
+
+        # Calls inside an active MLflow run are intercepted by the patched
+        # metric functions and logged automatically.
+        # Keys are prefixed with the model class name, e.g.:
+        #   LinearRegressionModel_val_mape, ExponentialSmoothing_val_mape
+        darts_metrics.mape(val, pred)
+        darts_metrics.rmse(val, pred)
+
+        # ── backtest ──────────────────────────────────────────────────────
+        # Pre-compute historical forecasts once so they can be reused
+        # without paying the refitting cost again.
+        # last_points_only=False → list[TimeSeries], one per window, so
+        # backtest() computes the metric separately for each window.
+        hfc = model.historical_forecasts(
+            series,
+            start=BT_START,
+            forecast_horizon=FORECAST_HORIZON,
+            stride=STRIDE,
+            retrain=True,
+            last_points_only=False,
+        )
+
+        # reduction=None → one value per window per metric, logged as
+        # consecutive steps of {Model}_backtest_mape / {Model}_backtest_rmse
+        # so the MLflow UI renders a chart over backtest windows.
+        model.backtest(
+            series=series,
+            historical_forecasts=hfc,
+            last_points_only=False,
+            metric=[darts_metrics.mape, darts_metrics.rmse],
+            reduction=None,
+        )
\ No newline at end of file

From 1c1b3d007462aa61f6d41e15e3f2e3408376dd40 Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Fri, 17 Apr 2026 15:47:54 +0200
Subject: [PATCH 057/154] fix: use MLFlow>=3.0

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 76099c41b3..4b956e9c8b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -70,7 +70,7 @@ notorch = [
     "statsforecast>=1.4",
     "xgboost>=2.1.4",
 ]
-mlflow = ["mlflow>=2.0"]
+mlflow = ["mlflow>=3.0"]
 all = ["darts[torch,notorch]"]
 
 [tool.uv.sources]

From e4b6823f86562ffb9a340c9161539f0570b5e0f1 Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Mon, 20 Apr 2026 12:39:46 +0200
Subject: [PATCH 058/154] Update mlflow_test_v2.py

---
 mlflow_test_v2.py | 167 ++++++++++++++++++++++++++--------------------
 1 file changed, 93 insertions(+), 74 deletions(-)

diff --git a/mlflow_test_v2.py b/mlflow_test_v2.py
index ade751c54d..12031f7827 100644
--- a/mlflow_test_v2.py
+++ b/mlflow_test_v2.py
@@ -2,22 +2,24 @@
 MLflow (auto)-logging:  Multi-model comparison with backtest
 
 Demonstrates:
-  * Single MLflow run containing all models — no nested runs needed.
-    Metric keys are automatically prefixed with the model class name
-    (e.g. ``LinearRegressionModel_val_mape``, ``ExponentialSmoothing_val_mape``),
-    so all results live in one run without key collisions.
-  * Automatic parameter and tag logging via darts autolog
+  * One MLflow run per model — each ``model.fit()`` auto-creates its own run
+    (via ``manage_run=True`` in autolog).  The run is named after the model
+    class (e.g. ``LinearRegressionModel``, ``ExponentialSmoothing``).
+  * Metric keys are unprefixed (``val_mape``, ``backtest_mape``, etc.) so
+    the experiment-level comparison view can overlay them directly.
+  * Automatic parameter and tag logging via darts autolog.
   * Inline metric logging: ``mape`` and ``rmse`` called inside an active run
-    are automatically captured by the patched metric functions
+    are automatically captured by the patched metric functions.
   * Backtest with pre-computed historical forecasts, multiple metric functions,
     and ``reduction=None`` so each window is logged as a step of
-    ``{Model}_backtest_mape`` / ``{Model}_backtest_rmse`` in the MLflow UI
+    ``backtest_mape`` / ``backtest_rmse`` in the MLflow UI.
 
 Notes:
   * ``mlflow_darts.autolog()`` is required — it patches ``fit()``, the darts
     metric functions, and ``backtest()`` to make logging automatic.
-    ``mlflow.start_run()`` only manages the run context; without ``autolog()``
-    nothing would be logged.
+  * ``manage_run=False`` is passed to ``autolog()`` so that we control the run
+    lifecycle ourselves with ``mlflow.start_run()``.  This lets us keep the
+    run open for predict + metrics + backtest after ``fit()`` returns.
   * ``last_points_only=False`` is used for historical forecasts so that
     ``backtest()`` receives a list of per-window forecasts and can compute one
     metric value per window (enabling the per-step chart in the UI).
@@ -33,86 +35,103 @@
 
 import logging
 
-import mlflow
 import coolname
+import mlflow
 from loguru import logger
 
 logging.getLogger("mlflow.utils.environment").setLevel(logging.ERROR)
 
+import numpy as np
+
 import darts.metrics as darts_metrics
 import darts.utils.mlflow as mlflow_darts
 from darts.datasets import AirPassengersDataset
-from darts.models import ExponentialSmoothing, LinearRegressionModel
-
-# ── data ──────────────────────────────────────────────────────────────────────
-series = AirPassengersDataset().load()
+from darts.models import (
+    ExponentialSmoothing,
+    LinearRegressionModel,
+    NBEATSModel,
+    NHiTSModel,
+    TCNModel,
+)
+
+
+def _magic(model, train, val, series, log) -> None:
+    log(f"[{model_name}] Run created: run_id={run.info.run_id}")
+    # ── fit ───────────────────────────────────────────────────────────
+    log(f"[{model_name}] Fitting model on {len(train)} training samples")
+    model.fit(train)
+    # ── predict + inline metric logging ───────────────────────────────
+    log(f"[{model_name}] Predicting {len(val)} steps")
+    pred = model.predict(n=len(val))
+    # Calls inside active MLflow run are intercepted by patched metric
+    # functions and auto-logged -> val_mape, val_rmse
+    val_mape = darts_metrics.mape(val, pred)
+    val_rmse = darts_metrics.rmse(val, pred)
+    log(f"[{model_name}] val_mape={val_mape:.4f}  val_rmse={val_rmse:.4f}")
+
+    # ── backtest ──────────────────────────────────────────────────────
+    log(
+        f"[{model_name}] Computing historical forecasts "
+        f"(start={BT_START}, horizon={FORECAST_HORIZON}, stride={STRIDE})"
+    )
+    hfc = model.historical_forecasts(
+        series,
+        start=BT_START,
+        forecast_horizon=FORECAST_HORIZON,
+        stride=STRIDE,
+        retrain=True,
+        last_points_only=False,
+    )
+    log(f"[{model_name}] {len(hfc)} backtest windows computed")
+
+    # reduction=None → one value per window per metric, logged as
+    # consecutive steps of backtest_mape / backtest_rmse
+    log(f"[{model_name}] Running backtest (logging per-window metrics as steps)")
+    model.backtest(
+        series=series,
+        historical_forecasts=hfc,
+        last_points_only=False,
+        metric=[darts_metrics.mape, darts_metrics.rmse, darts_metrics.ape],
+        reduction=None,
+    )
+    log(f"[{model_name}] Run complete: {run.info.run_id}")
+
+# ── Data setup ----------------------------------------------------------------
+# Cast to float32: MPS doesn't support float64 tensors
+series = AirPassengersDataset().load().astype(np.float32)
 train, val = series.split_after(0.75)
+FORECAST_HORIZON, STRIDE, BT_START = 1, 2, 0.75
 
-FORECAST_HORIZON = 1
-STRIDE = 2
-BT_START = 0.5  # more windows: backtest from 50 % of the full series
-
+# ── Logging setup ─────────────────────────────────────────────────────────────
+VERBOSE = False
+log = logger.info if VERBOSE else lambda *a, **kw: None
 # ── MLflow setup ──────────────────────────────────────────────────────────────
-# autolog() is required becausse it patches fit() / metric functions / backtest()
-# start_run() separate only controls the run context
 DB_PATH = "/tmp/mlflow_v2.db"
 mlflow.set_tracking_uri(f"sqlite:///{DB_PATH}")
-
-
-exp_name: str = coolname.generate_slug(2)
-run_name: str = coolname.generate_slug(2)
-logger.info(f"Starting experiment: {exp_name}")
-mlflow.set_experiment(exp_name)
-mlflow_darts.autolog()
-
+# ── Model setup ---------------------------------------------------------------
+_torch_kwargs = dict(
+    input_chunk_length=12,
+    output_chunk_length=FORECAST_HORIZON,
+    n_epochs=10,
+    pl_trainer_kwargs={"accelerator": "mps", "precision": "32-true", "enable_progress_bar": False},
+)
 models = [
     LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),
+    # LinearRegressionModel(lags=24, output_chunk_length=FORECAST_HORIZON),  # same model, more lags
     ExponentialSmoothing(),
+    # NBEATSModel(**_torch_kwargs),
+    # NBEATSModel(**_torch_kwargs, num_stacks=4, num_blocks=2),  # same model, deeper architecture
+    # NHiTSModel(**_torch_kwargs),
+    # TCNModel(**_torch_kwargs),
 ]
 
-# NOTE: would this make more sense actually?
-# for model in models:
-#     with mlflow.start_run(run_name=run_name):
-#         ...
-
-with mlflow.start_run(run_name=run_name):
-    for model in models:
-        # ── fit ───────────────────────────────────────────────────────────
-        # NOTE: add val_series=val here for TorchForecastingModels to enable
-        #       validation-based early stopping during training.
-        model.fit(train)
-
-        # ── predict + inline metric logging ───────────────────────────────
-        pred = model.predict(n=len(val))
-
-        # Calls inside an active MLflow run are intercepted by the patched
-        # metric functions and logged automatically.
-        # Keys are prefixed with the model class name, e.g.:
-        #   LinearRegressionModel_val_mape, ExponentialSmoothing_val_mape
-        darts_metrics.mape(val, pred)
-        darts_metrics.rmse(val, pred)
-
-        # ── backtest ──────────────────────────────────────────────────────
-        # Pre-compute historical forecasts once so they can be reused
-        # without paying the refitting cost again.
-        # last_points_only=False → list[TimeSeries], one per window, so
-        # backtest() computes the metric separately for each window.
-        hfc = model.historical_forecasts(
-            series,
-            start=BT_START,
-            forecast_horizon=FORECAST_HORIZON,
-            stride=STRIDE,
-            retrain=True,
-            last_points_only=False,
-        )
-
-        # reduction=None → one value per window per metric, logged as
-        # consecutive steps of {Model}_backtest_mape / {Model}_backtest_rmse
-        # so the MLflow UI renders a chart over backtest windows.
-        model.backtest(
-            series=series,
-            historical_forecasts=hfc,
-            last_points_only=False,
-            metric=[darts_metrics.mape, darts_metrics.rmse],
-            reduction=None,
-        )
\ No newline at end of file
+#--- MLflow experiment with manage_run=False -------------------------
+exp_name: str = coolname.generate_slug(2)
+mlflow.set_experiment(exp_name)
+mlflow_darts.autolog(manage_run=False) # NOTE: manage_run = False
+logger.info(f"Starting experiment: {exp_name}")
+for model in models:
+    model_name = type(model).__name__
+    log(f"[{model_name}] Starting run")
+    with mlflow.start_run() as run:
+        _magic(model, train, val, series, log)

From 4c239a80095ccc0d0ab38996e94ba394a98e75eb Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Mon, 20 Apr 2026 12:39:51 +0200
Subject: [PATCH 059/154] Update pyproject.toml

---
 pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 4b956e9c8b..7b4a930c61 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,8 +30,10 @@ classifiers = [
     "Programming Language :: Python :: Implementation :: PyPy",
 ]
 dependencies = [
+    "coolname>=4.2.0",
     "holidays>=0.11.1",
     "joblib>=0.16.0",
+    "loguru>=0.7.3",
     "matplotlib>=3.3.0",
     "narwhals>=1.25.1",
     "nfoursid>=1.0.0",

From 3ce5475c0162ba5676ba8c497bc95a26239f33b3 Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Tue, 21 Apr 2026 09:04:29 +0200
Subject: [PATCH 060/154] Update mlflow_test_v2.py

---
 mlflow_test_v2.py | 164 +++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 155 insertions(+), 9 deletions(-)

diff --git a/mlflow_test_v2.py b/mlflow_test_v2.py
index 12031f7827..dea8f0aae9 100644
--- a/mlflow_test_v2.py
+++ b/mlflow_test_v2.py
@@ -32,7 +32,6 @@ class (e.g. ``LinearRegressionModel``, ``ExponentialSmoothing``).
 Inspect in the UI:
     mlflow ui --backend-store-uri sqlite:////tmp/mlflow_v2.db
 """
-
 import logging
 
 import coolname
@@ -55,8 +54,77 @@ class (e.g. ``LinearRegressionModel``, ``ExponentialSmoothing``).
 )
 
 
+def _managed_run_scenarios(model, train, val, log) -> None:
+    """Explores manage_run=True behaviour across four scenarios.
+
+    Metrics always use manage_run=False internally — they only log into an
+    already-active run and never create one themselves.
+
+    Scenario A — bare fit():
+        fit() uses with_managed_run which creates a run when none is active,
+        then closes it on return.
+        Result: one run per model, params logged, no metrics.
+
+    Scenario B — metric with no active run:
+        Metric patches have manage_run=False so they never open a run.
+        With nothing active there is nowhere to log.
+        Result: nothing logged (silent no-op).
+
+    Scenario C — metric inside an explicit start_run():
+        Caller opens a run; metric patch sees an active run and logs into it.
+        No fit() → no params.
+        Result: one run with only the metric logged.
+
+    Scenario D — fit() + metric inside an explicit start_run():
+        with_managed_run creates a run "if necessary". Because the caller's
+        run is already active, fit() reuses it rather than nesting a child.
+        Params and metric all land in the same single run.
+        Result: one run per model with both params and metric logged.
+    """
+    model_name = type(model).__name__
+
+    # ── Scenario A: bare fit ──────────────────────────────────────────
+    # Expected: one run with params logged; no metrics.
+    log(f"[{model_name}] Scenario A — bare fit")
+    model.fit(train)
+    pred = model.predict(n=len(val))
+
+    # ── Scenario B: metric outside any run ───────────────────────────
+    # Expected: nothing logged (metrics have manage_run=False).
+    log(f"[{model_name}] Scenario B — metric with no active run (expect: not logged)")
+    darts_metrics.mape(val, pred)
+
+    # ── Scenario C: metric inside explicit start_run ──────────────────
+    # Expected: one run with rmse logged, no params.
+    log(f"[{model_name}] Scenario C — metric inside explicit start_run")
+    with mlflow.start_run():
+        darts_metrics.rmse(val, pred)
+
+    # ── Scenario D: fit + metric inside explicit start_run ───────────
+    # Expected: one run with both params (from fit) and mape logged.
+    # fit() reuses the caller's run — no nesting.
+    log(f"[{model_name}] Scenario D — fit + metric inside explicit start_run")
+    with mlflow.start_run():
+        model.fit(train)
+        pred = model.predict(n=len(val))
+        darts_metrics.mape(val, pred)
+
+
 def _magic(model, train, val, series, log) -> None:
-    log(f"[{model_name}] Run created: run_id={run.info.run_id}")
+    """Advanced use case: caller-managed run with predict, val metrics, and backtest.
+
+    Requires autolog(manage_run=False) and an active mlflow.start_run() in the
+    caller. Because manage_run=False, fit() never opens or closes a run on its
+    own — the caller's run stays open for the entire workflow.
+
+    What gets logged into the single run:
+      - Model params (from fit via the autolog patch).
+      - val_mape, val_rmse (from patched darts_metrics calls while run is active).
+      - backtest_mape, backtest_rmse, backtest_ape as consecutive steps
+        (one value per window, because reduction=None).
+    """
+    model_name = type(model).__name__
+    log(f"[{model_name}] Run created: run_id={mlflow.active_run().info.run_id}")
     # ── fit ───────────────────────────────────────────────────────────
     log(f"[{model_name}] Fitting model on {len(train)} training samples")
     model.fit(train)
@@ -94,7 +162,59 @@ def _magic(model, train, val, series, log) -> None:
         metric=[darts_metrics.mape, darts_metrics.rmse, darts_metrics.ape],
         reduction=None,
     )
-    log(f"[{model_name}] Run complete: {run.info.run_id}")
+    log(f"[{model_name}] Run complete: {mlflow.active_run().info.run_id}")
+
+
+def _backtest_reduction_scenarios(model, train, series, log) -> None:
+    """Explores how different ``reduction`` values affect MLflow logging.
+
+    Fits once and computes historical forecasts once, then calls backtest with
+    four different reductions.  Each variant runs inside its own nested
+    child run so the MLflow UI shows them separately without key collisions.
+
+    Reduction variants and their logged shape:
+      None      → 1-D array per metric → logged as consecutive steps (chart).
+      np.mean   → scalar per metric    → single value (mean over windows).
+      np.median → scalar per metric    → single value (median over windows).
+      custom    → scalar per metric    → single value (90th-percentile).
+
+    All variants use the same metric list (mape, rmse) and the same
+    pre-computed historical forecasts so results are directly comparable.
+    """
+    model_name = type(model).__name__
+    metrics = [darts_metrics.mape, darts_metrics.rmse]
+
+    log(f"[{model_name}] Fitting")
+    model.fit(train)
+    log(f"[{model_name}] Computing historical forecasts")
+    hfc = model.historical_forecasts(
+        series,
+        start=BT_START,
+        forecast_horizon=FORECAST_HORIZON,
+        stride=STRIDE,
+        retrain=True,
+        last_points_only=False,
+    )
+    log(f"[{model_name}] {len(hfc)} windows")
+
+    reductions = [
+        (None,                            "reduction=None (per-window steps)"),
+        (np.mean,                         "reduction=np.mean"),
+        (np.median,                       "reduction=np.median"),
+        (lambda x, axis=None: np.percentile(x, 90, axis=axis), "reduction=p90"),
+    ]
+
+    for reduction_fn, label in reductions:
+        log(f"[{model_name}] Backtest — {label}")
+        with mlflow.start_run(nested=True, run_name=label):
+            model.backtest(
+                series=series,
+                historical_forecasts=hfc,
+                last_points_only=False,
+                metric=metrics,
+                reduction=reduction_fn,
+            )
+
 
 # ── Data setup ----------------------------------------------------------------
 # Cast to float32: MPS doesn't support float64 tensors
@@ -103,7 +223,7 @@ def _magic(model, train, val, series, log) -> None:
 FORECAST_HORIZON, STRIDE, BT_START = 1, 2, 0.75
 
 # ── Logging setup ─────────────────────────────────────────────────────────────
-VERBOSE = False
+VERBOSE = True
 log = logger.info if VERBOSE else lambda *a, **kw: None
 # ── MLflow setup ──────────────────────────────────────────────────────────────
 DB_PATH = "/tmp/mlflow_v2.db"
@@ -119,19 +239,45 @@ def _magic(model, train, val, series, log) -> None:
     LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),
     # LinearRegressionModel(lags=24, output_chunk_length=FORECAST_HORIZON),  # same model, more lags
     ExponentialSmoothing(),
-    # NBEATSModel(**_torch_kwargs),
+    NBEATSModel(**_torch_kwargs),
     # NBEATSModel(**_torch_kwargs, num_stacks=4, num_blocks=2),  # same model, deeper architecture
     # NHiTSModel(**_torch_kwargs),
     # TCNModel(**_torch_kwargs),
 ]
 
-#--- MLflow experiment with manage_run=False -------------------------
+# ── Use case 1: manage_run=True (default) ─────────────────────────────────────
+# fit() auto-creates and closes a run per model — no start_run() needed.
 exp_name: str = coolname.generate_slug(2)
 mlflow.set_experiment(exp_name)
-mlflow_darts.autolog(manage_run=False) # NOTE: manage_run = False
-logger.info(f"Starting experiment: {exp_name}")
+mlflow_darts.autolog(manage_run=True)
+logger.info(f"[Use case 1] Experiment: {exp_name}")
 for model in models:
+    _managed_run_scenarios(model, train, val, log)
+
+# ── Use case 2: manage_run=False + backtest ───────────────────────────────────
+# Caller opens the run so predict, val metrics, and backtest all land in it.
+# NOTE: this is the recommended way to use MLFlow in Darts. It avoids
+#       nesting runs and ensures all metrics land in the same run.
+exp_name = coolname.generate_slug(2)
+mlflow.set_experiment(exp_name)
+mlflow_darts.autolog(manage_run=False)
+logger.info(f"[Use case 2] Experiment: {exp_name}")
+for i, model in enumerate(models):
     model_name = type(model).__name__
     log(f"[{model_name}] Starting run")
-    with mlflow.start_run() as run:
+    with mlflow.start_run(
+        run_name=model_name,
+        description = f"Small exp. run for {model_name}",
+    ) as run:
         _magic(model, train, val, series, log)
+
+# ── Use case 3: backtest reduction variants ───────────────────────────────────
+# One parent run per model; one nested child run per reduction variant.
+exp_name = coolname.generate_slug(2)
+mlflow.set_experiment(exp_name)
+mlflow_darts.autolog(manage_run=False)
+logger.info(f"[Use case 3] Experiment: {exp_name}")
+for model in models:
+    model_name = type(model).__name__
+    with mlflow.start_run(run_name=model_name):
+        _backtest_reduction_scenarios(model, train, series, log)

From 2d1cd6548524657a0465adf0e0e45a3be108a911 Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Tue, 21 Apr 2026 09:15:07 +0200
Subject: [PATCH 061/154] feat: support backtest/historical forecasts

---
 darts/utils/mlflow.py | 154 +++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 151 insertions(+), 3 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 922348c198..92d4cc7b6e 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -19,6 +19,7 @@
 import os
 import re
 import sys
+import threading
 from collections.abc import Callable
 from operator import itemgetter
 from typing import Any
@@ -26,6 +27,7 @@
 import mlflow
 import numpy as np
 import yaml
+from mlflow.entities import LoggedModel
 from mlflow.models import Model, ModelInputExample, ModelSignature
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
@@ -94,6 +96,10 @@
     ),
 ]
 
+# Thread-local flag set during historical_forecasts to suppress per-iteration
+# autologging in _patched_fit (which would otherwise spawn one run per iteration).
+_autolog_state = threading.local()
+
 
 def save_model(
     model,
@@ -536,10 +542,15 @@ def _patched_fit(original, self, *args, **kwargs):
         -------
             The result of calling the original fit method.
         """
-
         # Create a training session to track the training process and log information
         autologging_client = MlflowAutologgingQueueingClient()
 
+        if getattr(_autolog_state, "in_historical_forecasts", False):
+            return original(self, *args, **kwargs)
+
+        # Track which model is active so metric patches can prefix their keys
+        _autolog_state.current_model_name = type(self).__name__
+
         run_id = mlflow.active_run().info.run_id
 
         result = original(self, *args, **kwargs)
@@ -555,15 +566,19 @@ def _patched_fit(original, self, *args, **kwargs):
         param_logging_ops = autologging_client.flush(synchronous=False)
 
         if log_models:
-            model_id = _initialize_logged_model("model", flavor=FLAVOR_NAME).model_id
+            model_name = type(self).__name__
+            model: LoggedModel = _initialize_logged_model(
+                name=model_name, flavor=FLAVOR_NAME
+            )
             try:
                 registered_model_name = get_autologging_config(
                     FLAVOR_NAME, "registered_model_name", None
                 )
                 log_model(
                     result,
+                    name=model_name,
                     registered_model_name=registered_model_name,
-                    model_id=model_id,
+                    model_id=model.model_id,
                 )
             except Exception:
                 logger.info(
@@ -575,6 +590,112 @@ def _patched_fit(original, self, *args, **kwargs):
 
         return result
 
+    def _patched_historical_forecasts(original, self, *args, **kwargs):
+        """Suppress per-iteration fit() autologging during historical_forecasts.
+
+        Sets a thread-local flag so _patched_fit skips autologging for the
+        internal fit() calls. The outer safe_patch(manage_run=manage_run) on
+        historical_forecasts itself provides a single managed run.
+        """
+        _autolog_state.in_historical_forecasts = True
+        try:
+            return original(self, *args, **kwargs)
+        finally:
+            _autolog_state.in_historical_forecasts = False
+
+    def _patched_backtest(original, self, *args, **kwargs):
+        """Log backtest metric result(s) to the active MLflow run.
+
+        Scalar results (default reduction) are logged as a single metric value.
+        Per-window arrays (reduction=None) are logged as consecutive steps of
+        the same metric key so the MLflow UI renders them as a chart.
+        """
+        _autolog_state.in_backtest = True
+        try:
+            result = original(self, *args, **kwargs)
+        finally:
+            _autolog_state.in_backtest = False
+
+        if not log_metrics:
+            return result
+
+        active_run = mlflow.active_run()
+        if active_run is None:
+            return result
+
+        # Resolve `metric` arg (keyword is the common case; fall back to sig-bind)
+        metric = kwargs.get("metric")
+        if metric is None:
+            try:
+                sig = inspect.signature(original)
+                bound = sig.bind(self, *args, **kwargs)
+                bound.apply_defaults()
+                metric = bound.arguments.get("metric")
+            except Exception:
+                pass
+
+        # Derive metric name(s)
+        if callable(metric):
+            names = [getattr(metric, "__name__", "metric")]
+        elif isinstance(metric, list | tuple):
+            names = [
+                getattr(m, "__name__", f"metric_{i}") for i, m in enumerate(metric)
+            ]
+        else:
+            names = ["mape"]  # darts default
+
+        run_id = active_run.info.run_id
+        autologging_client = MlflowAutologgingQueueingClient()
+
+        def _log(key, val_or_arr):
+            arr = np.asarray(val_or_arr)
+            if arr.ndim == 0:
+                autologging_client.log_metrics(run_id=run_id, metrics={key: float(arr)})
+            elif arr.ndim == 1:
+                # per-window: log as steps so the MLflow UI shows a chart
+                for step, val in enumerate(arr):
+                    autologging_client.log_metrics(
+                        run_id=run_id, metrics={key: float(val)}, step=step
+                    )
+            # 2-D and higher: skip to keep MVP simple
+
+        result_arr = np.asarray(result) if not isinstance(result, list) else None
+
+        if isinstance(metric, (list, tuple)) and isinstance(result, list):
+            # multiple metrics → result is list[scalar_or_array], one per metric
+            for name, r in zip(names, result):
+                _log(f"backtest_{name}", r)
+        elif (
+            isinstance(metric, list| tuple)
+            and result_arr is not None
+            and result_arr.ndim == 1
+            and len(result_arr) == len(names)
+        ):
+            # multiple metrics with scalar reduction returned as a 1-D ndarray
+            # (e.g. np.mean/median/percentile) — log each as a separate scalar
+            for name, r in zip(names, result_arr):
+                autologging_client.log_metrics(
+                    run_id=run_id, metrics={f"backtest_{name}": float(r)}
+                )
+        elif result_arr is not None and result_arr.ndim == 2:
+            # (N_windows, N_metrics) ndarray — multi-metric + reduction=None
+            for col_i, name in enumerate(names[: result_arr.shape[1]]):
+                for step, val in enumerate(result_arr[:, col_i]):
+                    autologging_client.log_metrics(
+                        run_id=run_id,
+                        metrics={f"backtest_{name}": float(val)},
+                        step=step,
+                    )
+        elif isinstance(result, list):
+            # single metric, multiple series → result is list[scalar_or_array]
+            for s_i, r in enumerate(result):
+                _log(f"backtest_{names[0]}_{s_i}", r)
+        else:
+            _log(f"backtest_{names[0]}", result)
+
+        autologging_client.flush(synchronous=False).await_completion()
+        return result
+
     # patch `fit()` for all forecasting models
     for _, cls in _get_forecasting_models():
         safe_patch(
@@ -585,6 +706,27 @@ def _patched_fit(original, self, *args, **kwargs):
             manage_run=manage_run,
         )
 
+    # patch `historical_forecasts()` for all forecasting models so that the
+    # N internal fit() calls don't each spawn their own MLflow run
+    for _, cls in _get_forecasting_models():
+        safe_patch(
+            FLAVOR_NAME,
+            cls,
+            "historical_forecasts",
+            _patched_historical_forecasts,
+            manage_run=manage_run,
+        )
+
+    # patch `backtest()` for all forecasting models to log metric results
+    for _, cls in _get_forecasting_models():
+        safe_patch(
+            FLAVOR_NAME,
+            cls,
+            "backtest",
+            _patched_backtest,
+            manage_run=manage_run,
+        )
+
     if log_metrics:
         import darts.metrics
 
@@ -888,6 +1030,12 @@ def _patched_metric(original, *args, **kwargs):
         if active_run is None:
             return result
 
+        # backtest() calls metric functions internally; _patched_backtest
+        # handles logging the aggregated result, so skip here to avoid
+        # generating one flat key per window (series_gen_mape_0, _1, …).
+        if getattr(_autolog_state, "in_backtest", False):
+            return result
+
         autologging_client = MlflowAutologgingQueueingClient()
         run_id = active_run.info.run_id
 

From 5acd8de2198981d77caed00b84238f78fb3a555f Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Tue, 21 Apr 2026 09:15:44 +0200
Subject: [PATCH 062/154] fix: mlflow test script

---
 mlflow_test_v2.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/mlflow_test_v2.py b/mlflow_test_v2.py
index dea8f0aae9..25cc488f37 100644
--- a/mlflow_test_v2.py
+++ b/mlflow_test_v2.py
@@ -32,6 +32,7 @@ class (e.g. ``LinearRegressionModel``, ``ExponentialSmoothing``).
 Inspect in the UI:
     mlflow ui --backend-store-uri sqlite:////tmp/mlflow_v2.db
 """
+
 import logging
 
 import coolname
@@ -198,9 +199,9 @@ def _backtest_reduction_scenarios(model, train, series, log) -> None:
     log(f"[{model_name}] {len(hfc)} windows")
 
     reductions = [
-        (None,                            "reduction=None (per-window steps)"),
-        (np.mean,                         "reduction=np.mean"),
-        (np.median,                       "reduction=np.median"),
+        (None, "reduction=None (per-window steps)"),
+        (np.mean, "reduction=np.mean"),
+        (np.median, "reduction=np.median"),
         (lambda x, axis=None: np.percentile(x, 90, axis=axis), "reduction=p90"),
     ]
 
@@ -233,7 +234,11 @@ def _backtest_reduction_scenarios(model, train, series, log) -> None:
     input_chunk_length=12,
     output_chunk_length=FORECAST_HORIZON,
     n_epochs=10,
-    pl_trainer_kwargs={"accelerator": "mps", "precision": "32-true", "enable_progress_bar": False},
+    pl_trainer_kwargs={
+        "accelerator": "mps",
+        "precision": "32-true",
+        "enable_progress_bar": False,
+    },
 )
 models = [
     LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),
@@ -267,7 +272,7 @@ def _backtest_reduction_scenarios(model, train, series, log) -> None:
     log(f"[{model_name}] Starting run")
     with mlflow.start_run(
         run_name=model_name,
-        description = f"Small exp. run for {model_name}",
+        description=f"Small exp. run for {model_name}",
     ) as run:
         _magic(model, train, val, series, log)
 

From c0b1c6e8107fab6713dfbd627b8a6106075a8b55 Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Tue, 21 Apr 2026 09:16:11 +0200
Subject: [PATCH 063/154] fix: remove obsolte file

---
 debug_hf_mlflow.py | 197 ---------------------------------------------
 1 file changed, 197 deletions(-)
 delete mode 100644 debug_hf_mlflow.py

diff --git a/debug_hf_mlflow.py b/debug_hf_mlflow.py
deleted file mode 100644
index b9464b10ea..0000000000
--- a/debug_hf_mlflow.py
+++ /dev/null
@@ -1,197 +0,0 @@
-"""
-Debug script: historical_forecasts / backtest with MLflow autologging.
-
-Issue (from PR #3022): historical_forecasts(retrain=True) and backtest() call
-fit() internally on each iteration. Because _patched_fit runs with
-manage_run=True, every internal fit() spawns its own MLflow run when no
-active run exists - i.e. the typical autolog-only usage (no explicit
-mlflow.start_run() wrapper).
-
-Expected: 1 run per historical_forecasts / backtest call.
-Actual:   1 run per stride iteration.
-
-Run with:
-    python examples/repl/debug_hf_mlflow.py
-
-Then inspect in the UI:
-    mlflow ui --backend-store-uri sqlite:////tmp/mlflow_debug.db
-"""
-
-# %%
-import os
-
-import mlflow
-
-import darts.utils.mlflow as mlflow_darts
-from darts.datasets import AirPassengersDataset
-from darts.models import LinearRegressionModel
-
-# ---- setup -------------------------------------------------------------------
-series = AirPassengersDataset().load()
-
-STRIDE = 4  # keep iteration count low for fast runs
-
-DB_PATH = "/tmp/mlflow_debug.db"
-
-mlflow.set_tracking_uri(f"sqlite:///{DB_PATH}")
-mlflow.set_experiment("debug-hf-mlflow")
-client = mlflow.tracking.MlflowClient()
-exp_id = mlflow.get_experiment_by_name("debug-hf-mlflow").experiment_id
-
-
-def run_count(name=None):
-    runs = client.search_runs(experiment_ids=[exp_id])
-    if name:
-        return sum(1 for r in runs if r.info.run_name == name)
-    return len(runs)
-
-
-# ---- 1. normal fit WITH explicit start_run (baseline) -----------------------
-print("=" * 60)
-print("1. Normal fit() inside mlflow.start_run()")
-mlflow_darts.autolog(disable=True)
-mlflow_darts.autolog()
-
-before = run_count()
-with mlflow.start_run(run_name="fit-with-run"):
-    LinearRegressionModel(lags=12).fit(series[:100])
-mlflow_darts.autolog(disable=True)
-
-print(f"   Runs created: {run_count() - before}  (expected: 1)")
-
-# ---- 2. historical_forecasts WITH explicit start_run ------------------------
-print()
-print("=" * 60)
-print("2. historical_forecasts(retrain=True) inside mlflow.start_run()")
-mlflow_darts.autolog(disable=True)
-mlflow_darts.autolog()
-
-before = run_count()
-with mlflow.start_run(run_name="hf-with-run"):
-    LinearRegressionModel(lags=12).historical_forecasts(
-        series,
-        start=0.75,
-        forecast_horizon=1,
-        stride=STRIDE,
-        retrain=True,
-        last_points_only=True,
-    )
-mlflow_darts.autolog(disable=True)
-
-print(f"   Runs created: {run_count() - before}  (expected: 1)")
-
-# ---- 3. historical_forecasts WITHOUT start_run (autolog-only usage) ---------
-print()
-print("=" * 60)
-print("3. historical_forecasts(retrain=True) with autolog only (no start_run)")
-mlflow_darts.autolog(disable=True)
-mlflow_darts.autolog()
-
-before = run_count()
-LinearRegressionModel(lags=12).historical_forecasts(
-    series,
-    start=0.75,
-    forecast_horizon=1,
-    stride=STRIDE,
-    retrain=True,
-    last_points_only=True,
-)
-mlflow_darts.autolog(disable=True)
-
-created = run_count() - before
-print(f"   Runs created: {created}  (expected: 1)")
-print(f"   -> Each stride iteration spawned its own run: {created > 1}")
-
-# ---- 4. backtest WITHOUT start_run ------------------------------------------
-print()
-print("=" * 60)
-print("4. backtest() with autolog only (no start_run)")
-mlflow_darts.autolog(disable=True)
-mlflow_darts.autolog()
-
-before = run_count()
-LinearRegressionModel(lags=12).backtest(
-    series,
-    start=0.75,
-    forecast_horizon=1,
-    stride=STRIDE,
-    retrain=True,
-)
-mlflow_darts.autolog(disable=True)
-
-created = run_count() - before
-print(f"   Runs created: {created}  (expected: 1)")
-print(f"   -> Each stride iteration spawned its own run: {created > 1}")
-
-# Assert backtest_mape was logged
-all_runs = client.search_runs(experiment_ids=[exp_id])
-bt_run = sorted(all_runs, key=lambda x: x.info.start_time)[-1]
-metrics = bt_run.data.metrics
-assert "backtest_mape" in metrics, f"Expected backtest_mape in metrics, got: {metrics}"
-print(f"   -> backtest_mape = {metrics['backtest_mape']:.4f}  (PASS)")
-
-# ---- 5. backtest with reduction=None → per-window metrics -------------------
-print()
-print("=" * 60)
-print("5. backtest(reduction=None) — per-window MAPE logged as separate metrics")
-mlflow_darts.autolog(disable=True)
-mlflow_darts.autolog()
-
-before = run_count()
-per_window = LinearRegressionModel(lags=12).backtest(
-    series,
-    start=0.75,
-    forecast_horizon=1,
-    stride=STRIDE,
-    retrain=True,
-    reduction=None,
-)
-mlflow_darts.autolog(disable=True)
-
-created = run_count() - before
-print(f"   Runs created: {created}  (expected: 1)")
-
-all_runs = client.search_runs(experiment_ids=[exp_id])
-pw_run = sorted(all_runs, key=lambda x: x.info.start_time)[-1]
-history = client.get_metric_history(pw_run.info.run_id, "backtest_mape")
-assert history, f"Expected backtest_mape steps in metric history, got nothing"
-print(f"   -> backtest_mape logged across {len(history)} steps (windows)")
-
-# Plot per-window MAPE
-import matplotlib.pyplot as plt
-import numpy as np
-
-window_nums = [p.step for p in sorted(history, key=lambda p: p.step)]
-values = [p.value for p in sorted(history, key=lambda p: p.step)]
-
-fig, ax = plt.subplots(figsize=(8, 4))
-ax.plot(window_nums, values, marker="o", linewidth=1.5, label="MAPE per window")
-ax.axhline(np.mean(values), color="red", linestyle="--", linewidth=1, label=f"mean = {np.mean(values):.2f}%")
-ax.set_xlabel("Backtest window index")
-ax.set_ylabel("MAPE (%)")
-ax.set_title("Per-window MAPE (backtest, reduction=None)")
-ax.legend()
-fig.tight_layout()
-plt.savefig("/tmp/backtest_per_window_mape.png", dpi=150)
-print("   -> Plot saved to /tmp/backtest_per_window_mape.png")
-plt.show()
-
-# ---- summary -----------------------------------------------------------------
-print()
-print("=" * 60)
-print("Summary of ALL runs in experiment:")
-all_runs = client.search_runs(experiment_ids=[exp_id])
-for r in sorted(all_runs, key=lambda x: x.info.start_time):
-    m = r.data.metrics
-    if "backtest_mape" in m:
-        h = client.get_metric_history(r.info.run_id, "backtest_mape")
-        if len(h) > 1:
-            metric_str = f"  backtest_mape ({len(h)} steps, mean={sum(p.value for p in h)/len(h):.4f})"
-        else:
-            metric_str = f"  backtest_mape={m['backtest_mape']:.4f}"
-    else:
-        metric_str = ""
-    print(f"   [{r.info.status}] {r.info.run_name!r:30s}  id={r.info.run_id[:8]}{metric_str}")
-print(f"   Total: {len(all_runs)} runs")
-print()
-print(f"Inspect in UI: mlflow ui --backend-store-uri sqlite:///{DB_PATH}")

From d5977c8dc81efeafed5dcde149fba6106e6288df Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Tue, 21 Apr 2026 09:16:21 +0200
Subject: [PATCH 064/154] fix: formatting

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 06bba7cef4..84ef809f4b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,4 +27,4 @@ docs_env
 uv.lock
 repl/
 mlruns/*
-examples/mlruns/*
\ No newline at end of file
+examples/mlruns/*

From bbe0bf945ec56c2f1f6a9d68ff6fb4a3ab4e743f Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Tue, 21 Apr 2026 22:41:58 +0200
Subject: [PATCH 065/154] fix: deprecate manage_run

---
 darts/utils/mlflow.py | 17 +----------------
 1 file changed, 1 insertion(+), 16 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 92d4cc7b6e..7e6ae0f2db 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -387,7 +387,6 @@ def autolog(
     log_torch_metrics: bool = True,
     disable: bool = False,
     silent: bool = False,
-    manage_run: bool = True,
 ) -> None:
     """Enable (or disable) automatic MLflow logging for darts models.
 
@@ -449,12 +448,6 @@ def autolog(
     silent
         If ``True`` (default ``False``), suppress all event logging and warnings from
         MLflow during autologging.
-    manage_run
-        If `True`, applies the `with_managed_run` wrapper to the specified
-        `patch_function`, which automatically creates & terminates an MLflow
-        active run during patch code execution if necessary. If `False`,
-        does not apply the `with_managed_run` wrapper to the specified
-        `patch_function`.
     """
     # Enable/disable mlflow.pytorch.autolog for per-epoch metrics on torch models.
     # This must happen outside the @autologging_integration-decorated _autolog()
@@ -486,7 +479,6 @@ def autolog(
         log_metrics=log_metrics,
         disable=disable,
         silent=silent,
-        manage_run=manage_run,
     )
 
 
@@ -513,7 +505,6 @@ def _autolog(
     log_metrics: bool = True,
     disable: bool = False,
     silent: bool = False,
-    manage_run: bool = True,
 ) -> None:
     """Internal autolog implementation decorated with ``@autologging_integration``.
 
@@ -594,7 +585,7 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
         """Suppress per-iteration fit() autologging during historical_forecasts.
 
         Sets a thread-local flag so _patched_fit skips autologging for the
-        internal fit() calls. The outer safe_patch(manage_run=manage_run) on
+        internal fit() calls. The outer safe_patch() on
         historical_forecasts itself provides a single managed run.
         """
         _autolog_state.in_historical_forecasts = True
@@ -703,7 +694,6 @@ def _log(key, val_or_arr):
             cls,
             "fit",
             _patched_fit,
-            manage_run=manage_run,
         )
 
     # patch `historical_forecasts()` for all forecasting models so that the
@@ -714,7 +704,6 @@ def _log(key, val_or_arr):
             cls,
             "historical_forecasts",
             _patched_historical_forecasts,
-            manage_run=manage_run,
         )
 
     # patch `backtest()` for all forecasting models to log metric results
@@ -724,7 +713,6 @@ def _log(key, val_or_arr):
             cls,
             "backtest",
             _patched_backtest,
-            manage_run=manage_run,
         )
 
     if log_metrics:
@@ -744,14 +732,11 @@ def _log(key, val_or_arr):
 
         # patch all metric functions to log results
         for metric_name in darts.metrics.__all__:
-            # metrics should not create their own runs;
-            # they log into the run started by fit(), so manage_run=False here
             safe_patch(
                 FLAVOR_NAME,
                 darts.metrics,
                 metric_name,
                 _make_metric_patch(metric_name),
-                manage_run=False,
             )
 
 

From 09ad11732b27fb959b57557d792b4991b6c16151 Mon Sep 17 00:00:00 2001
From: Michel Zeller <mizeller@pm.me>
Date: Wed, 13 May 2026 13:38:26 +0200
Subject: [PATCH 066/154] Update mlflow_test_v2.py

---
 mlflow_test_v2.py | 52 ++++++++++++++++-------------------------------
 1 file changed, 17 insertions(+), 35 deletions(-)

diff --git a/mlflow_test_v2.py b/mlflow_test_v2.py
index 25cc488f37..655065ac76 100644
--- a/mlflow_test_v2.py
+++ b/mlflow_test_v2.py
@@ -82,33 +82,6 @@ def _managed_run_scenarios(model, train, val, log) -> None:
         Params and metric all land in the same single run.
         Result: one run per model with both params and metric logged.
     """
-    model_name = type(model).__name__
-
-    # ── Scenario A: bare fit ──────────────────────────────────────────
-    # Expected: one run with params logged; no metrics.
-    log(f"[{model_name}] Scenario A — bare fit")
-    model.fit(train)
-    pred = model.predict(n=len(val))
-
-    # ── Scenario B: metric outside any run ───────────────────────────
-    # Expected: nothing logged (metrics have manage_run=False).
-    log(f"[{model_name}] Scenario B — metric with no active run (expect: not logged)")
-    darts_metrics.mape(val, pred)
-
-    # ── Scenario C: metric inside explicit start_run ──────────────────
-    # Expected: one run with rmse logged, no params.
-    log(f"[{model_name}] Scenario C — metric inside explicit start_run")
-    with mlflow.start_run():
-        darts_metrics.rmse(val, pred)
-
-    # ── Scenario D: fit + metric inside explicit start_run ───────────
-    # Expected: one run with both params (from fit) and mape logged.
-    # fit() reuses the caller's run — no nesting.
-    log(f"[{model_name}] Scenario D — fit + metric inside explicit start_run")
-    with mlflow.start_run():
-        model.fit(train)
-        pred = model.predict(n=len(val))
-        darts_metrics.mape(val, pred)
 
 
 def _magic(model, train, val, series, log) -> None:
@@ -160,7 +133,8 @@ def _magic(model, train, val, series, log) -> None:
         series=series,
         historical_forecasts=hfc,
         last_points_only=False,
-        metric=[darts_metrics.mape, darts_metrics.rmse, darts_metrics.ape],
+        metric=[darts_metrics.mape, darts_metrics.rmse, darts_metrics.ape, darts_metrics.mase, darts_metrics.mase],
+        metric_kwargs=[{}, {}, {}, {"m": 1}, {"m": 2}],
         reduction=None,
     )
     log(f"[{model_name}] Run complete: {mlflow.active_run().info.run_id}")
@@ -200,9 +174,9 @@ def _backtest_reduction_scenarios(model, train, series, log) -> None:
 
     reductions = [
         (None, "reduction=None (per-window steps)"),
-        (np.mean, "reduction=np.mean"),
-        (np.median, "reduction=np.median"),
-        (lambda x, axis=None: np.percentile(x, 90, axis=axis), "reduction=p90"),
+        # (np.mean, "reduction=np.mean"),
+        # (np.median, "reduction=np.median"),
+        # (lambda x, axis=None: np.percentile(x, 90, axis=axis), "reduction=p90"),
     ]
 
     for reduction_fn, label in reductions:
@@ -220,6 +194,11 @@ def _backtest_reduction_scenarios(model, train, series, log) -> None:
 # ── Data setup ----------------------------------------------------------------
 # Cast to float32: MPS doesn't support float64 tensors
 series = AirPassengersDataset().load().astype(np.float32)
+# TODO: implement/test the follwoing cases!
+series_multiple = [series, series / 3.]
+series_multivariate = series.stack(series / 3.)
+series_multiple_multivariate = [series.stack(series / 3.), series.stack(series / 10.)]
+
 train, val = series.split_after(0.75)
 FORECAST_HORIZON, STRIDE, BT_START = 1, 2, 0.75
 
@@ -244,7 +223,7 @@ def _backtest_reduction_scenarios(model, train, series, log) -> None:
     LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),
     # LinearRegressionModel(lags=24, output_chunk_length=FORECAST_HORIZON),  # same model, more lags
     ExponentialSmoothing(),
-    NBEATSModel(**_torch_kwargs),
+    # NBEATSModel(**_torch_kwargs),
     # NBEATSModel(**_torch_kwargs, num_stacks=4, num_blocks=2),  # same model, deeper architecture
     # NHiTSModel(**_torch_kwargs),
     # TCNModel(**_torch_kwargs),
@@ -254,10 +233,13 @@ def _backtest_reduction_scenarios(model, train, series, log) -> None:
 # fit() auto-creates and closes a run per model — no start_run() needed.
 exp_name: str = coolname.generate_slug(2)
 mlflow.set_experiment(exp_name)
-mlflow_darts.autolog(manage_run=True)
+mlflow_darts.autolog()
 logger.info(f"[Use case 1] Experiment: {exp_name}")
 for model in models:
-    _managed_run_scenarios(model, train, val, log)
+    with mlflow.start_run():
+        model.fit(train)
+        pred = model.predict(n=len(val))
+        darts_metrics.mape(val, pred)
 
 # ── Use case 2: manage_run=False + backtest ───────────────────────────────────
 # Caller opens the run so predict, val metrics, and backtest all land in it.
@@ -265,7 +247,7 @@ def _backtest_reduction_scenarios(model, train, series, log) -> None:
 #       nesting runs and ensures all metrics land in the same run.
 exp_name = coolname.generate_slug(2)
 mlflow.set_experiment(exp_name)
-mlflow_darts.autolog(manage_run=False)
+mlflow_darts.autolog()
 logger.info(f"[Use case 2] Experiment: {exp_name}")
 for i, model in enumerate(models):
     model_name = type(model).__name__

From 5a9f4931fa7f3b43b5711877b7b00968f8d3d90a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 26 May 2026 15:14:35 +0200
Subject: [PATCH 067/154] feat: metric kwarg support for autolog

---
 darts/utils/mlflow.py | 27 ++++++++++++++++++++++-----
 1 file changed, 22 insertions(+), 5 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 7e6ae0f2db..b7ad90c965 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -616,6 +616,7 @@ def _patched_backtest(original, self, *args, **kwargs):
 
         # Resolve `metric` arg (keyword is the common case; fall back to sig-bind)
         metric = kwargs.get("metric")
+        metric_kwargs = kwargs.get("metric_kwargs") or {}
         if metric is None:
             try:
                 sig = inspect.signature(original)
@@ -627,10 +628,20 @@ def _patched_backtest(original, self, *args, **kwargs):
 
         # Derive metric name(s)
         if callable(metric):
-            names = [getattr(metric, "__name__", "metric")]
-        elif isinstance(metric, list | tuple):
             names = [
-                getattr(m, "__name__", f"metric_{i}") for i, m in enumerate(metric)
+                getattr(metric, "__name__", "metric") + _kwargs_suffix(metric_kwargs)
+            ]
+        elif isinstance(metric, (list | tuple)):
+            kw_list = (
+                list(metric_kwargs)
+                if isinstance(metric_kwargs, (list | tuple))
+                else [metric_kwargs]
+            )
+            if len(kw_list) < len(metric):
+                kw_list = kw_list + [{}] * (len(metric) - len(kw_list))
+            names = [
+                getattr(m, "__name__", f"metric_{i}") + _kwargs_suffix(kw)
+                for i, (m, kw) in enumerate(zip(metric, kw_list))
             ]
         else:
             names = ["mape"]  # darts default
@@ -652,12 +663,12 @@ def _log(key, val_or_arr):
 
         result_arr = np.asarray(result) if not isinstance(result, list) else None
 
-        if isinstance(metric, (list, tuple)) and isinstance(result, list):
+        if isinstance(metric, (list | tuple)) and isinstance(result, list):
             # multiple metrics → result is list[scalar_or_array], one per metric
             for name, r in zip(names, result):
                 _log(f"backtest_{name}", r)
         elif (
-            isinstance(metric, list| tuple)
+            isinstance(metric, list | tuple)
             and result_arr is not None
             and result_arr.ndim == 1
             and len(result_arr) == len(names)
@@ -871,6 +882,12 @@ def _extract_covariate_metadata(
     return info
 
 
+def _kwargs_suffix(kw: dict | None) -> str:
+    if not kw:
+        return ""
+    return "".join(f"_{k}{v}" for k, v in sorted(kw.items()))
+
+
 def _sanitize_mlflow_key(name: str) -> str:
     """Sanitize a string for use as an MLflow metric key.
 

From 9113c41b6c0fa482b9cbad3ee8dd9bc2b34158ef Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 23 Jun 2026 12:54:45 +0200
Subject: [PATCH 068/154] chore: remove redundant metric check

---
 darts/utils/mlflow.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index b7ad90c965..7a19e50450 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -614,17 +614,8 @@ def _patched_backtest(original, self, *args, **kwargs):
         if active_run is None:
             return result
 
-        # Resolve `metric` arg (keyword is the common case; fall back to sig-bind)
         metric = kwargs.get("metric")
         metric_kwargs = kwargs.get("metric_kwargs") or {}
-        if metric is None:
-            try:
-                sig = inspect.signature(original)
-                bound = sig.bind(self, *args, **kwargs)
-                bound.apply_defaults()
-                metric = bound.arguments.get("metric")
-            except Exception:
-                pass
 
         # Derive metric name(s)
         if callable(metric):

From 845dcd6faf7123d5bcb3d821203d924c17d65d06 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 24 Jun 2026 15:38:10 +0200
Subject: [PATCH 069/154] fix: check for active run in fit patch

---
 darts/utils/mlflow.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 7a19e50450..574b1c986e 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -542,10 +542,13 @@ def _patched_fit(original, self, *args, **kwargs):
         # Track which model is active so metric patches can prefix their keys
         _autolog_state.current_model_name = type(self).__name__
 
-        run_id = mlflow.active_run().info.run_id
-
         result = original(self, *args, **kwargs)
 
+        active_run = mlflow.active_run()
+        if active_run is None:
+            return result
+        run_id = active_run.info.run_id
+
         # Set tags to identify the model class and relevant information
         autologging_client.set_tags(run_id=run_id, tags=_get_model_info_tags(self))
 

From b9a7b62dbd0cc0bd73d2ade7a5b834cc9999b3f3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 24 Jun 2026 15:40:01 +0200
Subject: [PATCH 070/154] feat: infer backtest output dim based on
 metric/backtests kwargs

---
 darts/utils/mlflow.py | 373 ++++++++++++++++++++++++++++++++----------
 1 file changed, 288 insertions(+), 85 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 574b1c986e..6a844513ba 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -64,8 +64,15 @@
 
 import darts
 from darts.logging import get_logger, raise_log
+from darts.metrics import CLASSIFICATION_METRICS
+from darts.metrics.utils import _LabelReduction
 from darts.models.forecasting.forecasting_model import ForecastingModel
-from darts.utils.ts_utils import get_single_series
+from darts.utils.ts_utils import (
+    SeriesType,
+    get_series_seq_type,
+    get_single_series,
+    series2seq,
+)
 
 logger = get_logger(__name__)
 
@@ -598,11 +605,10 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
             _autolog_state.in_historical_forecasts = False
 
     def _patched_backtest(original, self, *args, **kwargs):
-        """Log backtest metric result(s) to the active MLflow run.
+        """Wrap ``backtest`` to log metric result(s) to the active MLflow run.
 
-        Scalar results (default reduction) are logged as a single metric value.
-        Per-window arrays (reduction=None) are logged as consecutive steps of
-        the same metric key so the MLflow UI renders them as a chart.
+        Delegates to ``_log_backtest_metrics``, which infers result shape from
+        the metric signature and logs every cell under a descriptive key.
         """
         _autolog_state.in_backtest = True
         try:
@@ -610,85 +616,18 @@ def _patched_backtest(original, self, *args, **kwargs):
         finally:
             _autolog_state.in_backtest = False
 
-        if not log_metrics:
-            return result
-
         active_run = mlflow.active_run()
-        if active_run is None:
+        if not log_metrics or active_run is None:
             return result
 
-        metric = kwargs.get("metric")
-        metric_kwargs = kwargs.get("metric_kwargs") or {}
-
-        # Derive metric name(s)
-        if callable(metric):
-            names = [
-                getattr(metric, "__name__", "metric") + _kwargs_suffix(metric_kwargs)
-            ]
-        elif isinstance(metric, (list | tuple)):
-            kw_list = (
-                list(metric_kwargs)
-                if isinstance(metric_kwargs, (list | tuple))
-                else [metric_kwargs]
-            )
-            if len(kw_list) < len(metric):
-                kw_list = kw_list + [{}] * (len(metric) - len(kw_list))
-            names = [
-                getattr(m, "__name__", f"metric_{i}") + _kwargs_suffix(kw)
-                for i, (m, kw) in enumerate(zip(metric, kw_list))
-            ]
-        else:
-            names = ["mape"]  # darts default
+        bound = inspect.signature(ForecastingModel.backtest).bind(self, *args, **kwargs)
+        bound.apply_defaults()
+        backtest_args = bound.arguments
 
-        run_id = active_run.info.run_id
         autologging_client = MlflowAutologgingQueueingClient()
-
-        def _log(key, val_or_arr):
-            arr = np.asarray(val_or_arr)
-            if arr.ndim == 0:
-                autologging_client.log_metrics(run_id=run_id, metrics={key: float(arr)})
-            elif arr.ndim == 1:
-                # per-window: log as steps so the MLflow UI shows a chart
-                for step, val in enumerate(arr):
-                    autologging_client.log_metrics(
-                        run_id=run_id, metrics={key: float(val)}, step=step
-                    )
-            # 2-D and higher: skip to keep MVP simple
-
-        result_arr = np.asarray(result) if not isinstance(result, list) else None
-
-        if isinstance(metric, (list | tuple)) and isinstance(result, list):
-            # multiple metrics → result is list[scalar_or_array], one per metric
-            for name, r in zip(names, result):
-                _log(f"backtest_{name}", r)
-        elif (
-            isinstance(metric, list | tuple)
-            and result_arr is not None
-            and result_arr.ndim == 1
-            and len(result_arr) == len(names)
-        ):
-            # multiple metrics with scalar reduction returned as a 1-D ndarray
-            # (e.g. np.mean/median/percentile) — log each as a separate scalar
-            for name, r in zip(names, result_arr):
-                autologging_client.log_metrics(
-                    run_id=run_id, metrics={f"backtest_{name}": float(r)}
-                )
-        elif result_arr is not None and result_arr.ndim == 2:
-            # (N_windows, N_metrics) ndarray — multi-metric + reduction=None
-            for col_i, name in enumerate(names[: result_arr.shape[1]]):
-                for step, val in enumerate(result_arr[:, col_i]):
-                    autologging_client.log_metrics(
-                        run_id=run_id,
-                        metrics={f"backtest_{name}": float(val)},
-                        step=step,
-                    )
-        elif isinstance(result, list):
-            # single metric, multiple series → result is list[scalar_or_array]
-            for s_i, r in enumerate(result):
-                _log(f"backtest_{names[0]}_{s_i}", r)
-        else:
-            _log(f"backtest_{names[0]}", result)
-
+        _log_backtest_metrics(
+            autologging_client, active_run.info.run_id, result, backtest_args
+        )
         autologging_client.flush(synchronous=False).await_completion()
         return result
 
@@ -876,12 +815,6 @@ def _extract_covariate_metadata(
     return info
 
 
-def _kwargs_suffix(kw: dict | None) -> str:
-    if not kw:
-        return ""
-    return "".join(f"_{k}{v}" for k, v in sorted(kw.items()))
-
-
 def _sanitize_mlflow_key(name: str) -> str:
     """Sanitize a string for use as an MLflow metric key.
 
@@ -902,6 +835,276 @@ def _sanitize_mlflow_key(name: str) -> str:
     return re.sub(r"[^\w-]", "_", name)
 
 
+def _log_backtest_metrics(
+    autologging_client: MlflowAutologgingQueueingClient,
+    run_id: str,
+    result,
+    backtest_args: dict,
+) -> None:
+    """Log backtest metric result(s) to MLflow.
+
+    Reshapes each per-series result to a canonical ``(W, T, C, M)`` layout
+    (windows, timesteps, components × quantiles, metrics) inferred from the
+    metric signatures and ``backtest_args``, logging every cell under a
+    descriptive key with the time axis (or window axis when time is reduced)
+    mapped to the MLflow ``step``.
+
+    Shape inference respects all kwargs that affect output dimensions:
+
+    * ``time_reduction`` – collapses the time axis (``T=1``).
+    * ``component_reduction`` – collapses the component axis (``C=1``).
+    * ``series_reduction`` – if non-``None``, windows are already aggregated
+      inside the metric, so ``W=1`` regardless of ``backtest.reduction``.
+    * ``q`` / ``q_interval`` – expand the component axis with one entry per
+      quantile / interval.
+    * ``label_reduction`` / ``labels`` – expand the component axis for
+      classification metrics.
+    * ``reduction=None`` – no aggregation across windows → one value per window.
+    * ``last_points_only`` – collapses all windows into one TimeSeries before scoring,
+      so there is effectively only one window regardless of reduction.
+
+    When ``label_reduction=None`` is used without explicit ``labels``, the
+    unique class values are inferred from ``series`` at runtime so structured
+    per-label keys are still produced. When two metrics have incompatible axis
+    layouts (different ``time_reduction`` / ``component_reduction`` / quantile
+    count), each series result is flattened to integer-indexed keys instead.
+
+    Parameters
+    ----------
+    autologging_client
+        MLflow autologging client used to queue metric writes.
+    run_id
+        ID of the active MLflow run.
+    result
+        Return value of ``backtest()``.
+    backtest_args
+        Bound arguments of the ``backtest()`` call (from
+        ``inspect.BoundArguments.arguments`` after ``apply_defaults``).
+    """
+    metric = backtest_args.get("metric")
+    metric = metric if isinstance(metric, list) else [metric]
+    metric_kwargs = backtest_args.get("metric_kwargs") or {}
+    metric_kwargs = (
+        metric_kwargs if isinstance(metric_kwargs, list) else [metric_kwargs]
+    )
+    # backtest accepts a single dict that applies to all metrics; broadcast it
+    if len(metric_kwargs) != len(metric):
+        metric_kwargs = [metric_kwargs[0]] * len(metric)
+    metric_names = [
+        _sanitize_mlflow_key(getattr(m, "__name__", f"metric_{i}"))
+        for i, m in enumerate(metric)
+    ]
+    n_metrics = len(metric)
+
+    # reduction=None means no aggregation across windows → one value per window.
+    # last_points_only collapses all windows into one TimeSeries before scoring,
+    # so there is effectively only one window regardless of reduction.
+    has_windows = backtest_args.get("reduction") is None and not backtest_args.get(
+        "last_points_only", False
+    )
+
+    # series_reduction inside the metric itself already aggregates across windows,
+    # so the result has no window axis even when backtest.reduction is None.
+    metric_0_params = inspect.signature(metric[0]).parameters
+    if "series_reduction" in metric_0_params:
+        effective_sr = metric_kwargs[0].get(
+            "series_reduction", metric_0_params["series_reduction"].default
+        )
+        if effective_sr is not None:
+            has_windows = False
+
+    # check the dim axes from the metric kwargs for each
+    metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metric, metric_kwargs)]
+    has_time_axis, has_comp_axis, quantiles_num, _ = metric_axes[0]
+
+    # Inconsistent axes across metrics (different has_time_axis, has_comp_axis, or
+    # quantiles_num) means the result can't be reshaped into a single canonical array.
+    # Fall back to flat integer-indexed keys for this case.
+    axes_inconsistent = any(ax[:3] != metric_axes[0][:3] for ax in metric_axes[1:])
+
+    # quantiles_num=None means label_reduction=None was requested with labels=None,
+    labels_unknown = quantiles_num is None
+
+    series = backtest_args.get("series")
+    forecast_horizon = backtest_args.get("forecast_horizon")
+
+    series_seq = series2seq(series)
+    results = [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
+
+    metrics_by_step: dict[int, dict[str, float]] = {}
+    for series_index, (s, r) in enumerate(zip(series_seq, results)):
+        series_suffix = f"_s{series_index}" if len(series_seq) > 1 else ""
+
+        if axes_inconsistent:
+            name_prefix = metric_names[0] if len(metric_names) == 1 else "metrics"
+            flat = np.asarray(r, dtype=float).flatten()
+            for i, val in enumerate(flat):
+                key = _sanitize_mlflow_key(f"backtest_{name_prefix}{series_suffix}_{i}")
+                metrics_by_step.setdefault(0, {})[key] = float(val)
+            continue
+
+        # resolve label names from series data when not provided explicitly.
+        # mirrors np.unique(np.concatenate([y_true, y_pred])) inside _confusion_matrix.
+        # NOTE: importantly this checks the series for labels, not just the windows, if
+        # this is an issue, then I'd suggest falling back to flat integer-indexed keys
+        # and enforcing explicit labels.
+        if labels_unknown:
+            inferred_labels = np.unique(s.values())
+            quantiles_num = len(inferred_labels)
+            metric_axes = [
+                (
+                    has_time_axis,
+                    has_comp_axis,
+                    quantiles_num,
+                    [f"_label{x:g}" for x in inferred_labels],
+                )
+            ] + list(metric_axes[1:])
+
+        comps = s.components.tolist()
+        # c_size = components × quantiles/intervals/labels per component
+        c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
+        arr = np.asarray(r, dtype=float)
+        # after stripping C and M axes, rest = W*T (or W or T alone)
+        rest, extra = divmod(arr.size, c_size * n_metrics)
+        if extra:
+            logger.warning(
+                "Backtest metric logging skipped: result size (%d) is not "
+                "divisible by c_size * n_metrics (%d * %d = %d). "
+                "The metric output shape does not match the inferred axes.",
+                arr.size,
+                c_size,
+                n_metrics,
+                c_size * n_metrics,
+            )
+            return
+
+        # both time and window axes present: backtest returns (W*T*C*M,) in C order so we can
+        # recover W and T only if forecast_horizon is known (T = forecast_horizon)
+        if has_time_axis and has_windows:
+            if not forecast_horizon or rest % forecast_horizon:
+                logger.warning(
+                    "Backtest metric logging skipped: cannot split window/time "
+                    "axes — %d elements remain after stripping component and "
+                    "metric axes, but forecast_horizon=%r does not divide "
+                    "evenly. Pass an explicit forecast_horizon to backtest().",
+                    rest,
+                    forecast_horizon,
+                )
+                return
+            t_size, w_size = forecast_horizon, rest // forecast_horizon
+        elif has_time_axis:
+            t_size, w_size = rest, 1
+        elif has_windows:
+            t_size, w_size = 1, rest
+        else:
+            if rest != 1:
+                logger.warning(
+                    "Backtest metric logging skipped: expected a single scalar "
+                    "per component/metric after reduction, but got %d elements. "
+                    "Check time_reduction and component_reduction defaults.",
+                    rest,
+                )
+                return
+            t_size, w_size = 1, 1
+
+        canonical = arr.reshape(w_size, t_size, c_size, n_metrics)
+        for m, metric_name in enumerate(metric_names):
+            quantiles_labels = metric_axes[m][3]
+            for w in range(w_size):
+                for c in range(c_size):
+                    # c is a flat index into the (n_components × quantiles_num) C axis:
+                    # c = comp_i * quantiles_num + q_i
+                    component_index, quantile_index = divmod(c, quantiles_num)
+                    comp_part = (
+                        "_" + _sanitize_mlflow_key(comps[component_index])
+                        if has_comp_axis
+                        else ""
+                    )
+                    key = f"backtest_{metric_name}{comp_part}{quantiles_labels[quantile_index]}"
+                    if has_time_axis and has_windows:
+                        key += f"_w{w}"
+                    key = _sanitize_mlflow_key(key + series_suffix)
+                    for t in range(t_size):
+                        # MLflow step maps to the axis the UI should chart:
+                        # time when present, otherwise window index
+                        step = t if has_time_axis else w
+                        metrics_by_step.setdefault(step, {})[key] = float(
+                            canonical[w, t, c, m]
+                        )
+
+    for step, metrics in metrics_by_step.items():
+        autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
+
+
+def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
+    """Infer a metric's output axes from its signature and ``metric_kwargs``.
+
+    Covers ``time_reduction``, ``component_reduction``, ``q``, ``q_interval``,
+    and ``label_reduction`` / ``labels`` for classification metrics.
+    ``series_reduction`` is handled at the ``_log_backtest_metrics`` level.
+
+    Parameters
+    ----------
+    metric
+        A darts metric callable.
+    metric_kwargs
+        Keyword arguments that will be forwarded to ``metric``.
+
+    Returns
+    -------
+    tuple
+        ``(has_time_axis, has_comp_axis, quantiles_num, quantiles_labels)`` where
+
+        - ``has_time_axis`` – ``True`` when ``time_reduction`` is ``None`` (i.e. a
+          per-timestep axis is present in the output).
+        - ``has_comp_axis`` – ``True`` when components are expanded (not collapsed to a scalar).
+        - ``quantiles_num`` – number of quantile/interval/label entries; ``None`` when it
+          cannot be determined (e.g. ``label_reduction=None`` without explicit
+          ``labels``), signalling the caller to fall back to flat logging.
+        - ``quantiles_labels`` – one key suffix per ``quantiles_num`` entry (empty list when
+          ``quantiles_num`` is ``None``).
+    """
+    params = inspect.signature(metric).parameters
+
+    def effective(param_name: str) -> Any:
+        """Return metric_kwargs value if present, else the signature default."""
+        if param_name in metric_kwargs:
+            return metric_kwargs[param_name]
+        return params[param_name].default if param_name in params else None
+
+    has_time_axis = "time_reduction" in params and effective("time_reduction") is None
+    has_comp_axis = (
+        "component_reduction" in params and effective("component_reduction") is None
+    )
+
+    q_interval, q = metric_kwargs.get("q_interval"), metric_kwargs.get("q")
+    if "q_interval" in params and q_interval is not None:
+        intervals = np.atleast_2d(np.array(q_interval, dtype=float))
+        quantiles_labels = [f"_qi{lo:g}_{hi:g}" for lo, hi in intervals]
+    elif "q" in params and q is not None:
+        quantiles_labels = [f"_q{v:g}" for v in np.atleast_1d(np.array(q, dtype=float))]
+    elif "label_reduction" in params and getattr(metric, "__name__", "") in {
+        m.__name__ for m in CLASSIFICATION_METRICS
+    }:
+        label_reduction = effective("label_reduction")
+        if isinstance(label_reduction, _LabelReduction):
+            label_reduction = label_reduction.value
+        labels = metric_kwargs.get("labels")
+        # label_reduction=None means one output per label, but without explicit
+        # labels we can't know how many — signal the caller to fall back
+        if label_reduction is None and labels is None:
+            return (has_time_axis, has_comp_axis, None, [])
+        quantiles_labels = (
+            [f"_label{x}" for x in np.atleast_1d(labels)]
+            if label_reduction is None
+            else [""]
+        )
+    else:
+        quantiles_labels = [""]
+
+    return (has_time_axis, has_comp_axis, len(quantiles_labels), quantiles_labels)
+
+
 def _log_metric_result(
     autologging_client: MlflowAutologgingQueueingClient,
     run_id: str,

From bf497e67c608446cdd6690ce9791b3e906e7f6e3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 24 Jun 2026 16:49:47 +0200
Subject: [PATCH 071/154] feat: backtest metric testing suite

---
 darts/tests/optional_deps/test_mlflow.py | 378 +++++++++++++++++++++--
 1 file changed, 355 insertions(+), 23 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 4b2599e452..c2b4a06eb8 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1,9 +1,12 @@
+import logging
 import os
 
 import numpy as np
 import pandas as pd
 import pytest
 
+import darts.metrics as dm
+import darts.metrics.metrics as dmm
 import darts.utils.timeseries_generation as tg
 from darts import TimeSeries
 from darts.models.forecasting.forecasting_model import (
@@ -19,9 +22,12 @@
     )
 
 import mlflow
+from mlflow.utils.autologging_utils.client import MlflowAutologgingQueueingClient
 
 from darts.models import ExponentialSmoothing, LinearRegressionModel
 from darts.utils.mlflow import (
+    _infer_metric_axes,
+    _log_backtest_metrics,
     autolog,
     load_model,
     log_model,
@@ -121,6 +127,13 @@ class TestMLflow:
     )
     ts_past_cov = tg.sine_timeseries(length=62).astype("float32")
     ts_future_cov = tg.constant_timeseries(value=1.0, length=62).astype("float32")
+    # binary classification series with values {0.0, 1.0}
+    ts_binary = tg.constant_timeseries(value=0.0, length=50).with_values(
+        np.random.default_rng(42)
+        .choice([0.0, 1.0], size=50)
+        .astype(np.float32)
+        .reshape(-1, 1)
+    )
 
     def test_save_load_statistical_model(self, tmpdir_fn):
         """Test save/load round-trip for statistical model"""
@@ -602,10 +615,8 @@ def test_save_load_with_special_series(self, tmpdir_fn, series, series_name):
     def test_autolog_metric_logging_scalar(self, mlflow_tracking, autolog_context):
         """Calling a darts metric inside an active run logs a scalar to MLflow."""
         with autolog_context(log_metrics=True):
-            from darts.metrics import mae
-
             with mlflow.start_run() as run:
-                result = mae(self.ts_univariate, self.ts_univariate * 1.1)
+                result = dm.mae(self.ts_univariate, self.ts_univariate * 1.1)
 
         run_data = mlflow.get_run(run.info.run_id).data
         assert "mae" in run_data.metrics, "mae should be logged to MLflow"
@@ -616,11 +627,9 @@ def test_autolog_metric_logging_scalar(self, mlflow_tracking, autolog_context):
     def test_autolog_metric_repeated_call(self, mlflow_tracking, autolog_context):
         """Calling the same metric twice overwrites the value (last-value-wins)."""
         with autolog_context(log_metrics=True):
-            from darts.metrics import rmse
-
             with mlflow.start_run() as run:
-                rmse(self.ts_univariate, self.ts_univariate * 1.1)
-                rmse(self.ts_univariate, self.ts_univariate * 1.2)
+                dm.rmse(self.ts_univariate, self.ts_univariate * 1.1)
+                dm.rmse(self.ts_univariate, self.ts_univariate * 1.2)
 
         run_data = mlflow.get_run(run.info.run_id).data
         assert "rmse" in run_data.metrics, "rmse should be logged to MLflow"
@@ -634,10 +643,8 @@ def test_autolog_metric_per_component(self, mlflow_tracking, autolog_context):
         'mae_linear' and 'mae_linear_1'.
         """
         with autolog_context(log_metrics=True):
-            from darts.metrics import mae
-
             with mlflow.start_run() as run:
-                mae(
+                dm.mae(
                     self.ts_multivariate,
                     self.ts_multivariate * 1.1,
                     component_reduction=None,
@@ -656,10 +663,8 @@ def test_autolog_metric_per_component(self, mlflow_tracking, autolog_context):
     def test_autolog_metric_no_active_run(self, mlflow_tracking, autolog_context):
         """Calling a metric without an active run does not raise and returns correctly."""
         with autolog_context(log_metrics=True):
-            from darts.metrics import mse
-
             # called outside any start_run — must not raise
-            result = mse(self.ts_univariate, self.ts_univariate * 1.1)
+            result = dm.mse(self.ts_univariate, self.ts_univariate * 1.1)
 
         assert np.isscalar(result)
         assert np.isfinite(float(result))
@@ -669,15 +674,13 @@ def test_autolog_metric_returns_correct_value(
     ):
         """The patched metric returns the same value whether inside or outside a run."""
         with autolog_context(log_metrics=True):
-            from darts.metrics import mae
-
             pred = self.ts_univariate * 1.05
 
             with mlflow.start_run():
-                result_inside = mae(self.ts_univariate, pred)
+                result_inside = dm.mae(self.ts_univariate, pred)
 
             # call outside a run — no logging, same computation
-            result_outside = mae(self.ts_univariate, pred)
+            result_outside = dm.mae(self.ts_univariate, pred)
 
         np.testing.assert_almost_equal(result_inside, result_outside, decimal=6)
         assert np.isfinite(result_inside)
@@ -685,10 +688,8 @@ def test_autolog_metric_returns_correct_value(
     def test_autolog_log_metrics_false(self, mlflow_tracking, autolog_context):
         """autolog(log_metrics=False) leaves metrics unpatched — nothing is logged."""
         with autolog_context(log_metrics=False):
-            from darts.metrics import mape
-
             with mlflow.start_run() as run:
-                mape(self.ts_univariate, self.ts_univariate * 1.1)
+                dm.mape(self.ts_univariate, self.ts_univariate * 1.1)
 
         run_data = mlflow.get_run(run.info.run_id).data
         assert "mape" not in run_data.metrics, (
@@ -702,9 +703,6 @@ def test_autolog_public_namespace_patched(self, mlflow_tracking, autolog_context
         calls within the implementation module (e.g. rmse calling mse internally).
         """
         with autolog_context(log_metrics=True):
-            import darts.metrics as dm
-            import darts.metrics.metrics as dmm
-
             # public namespace → patched: call inside a run should log
             with mlflow.start_run() as run_public:
                 dm.mae(self.ts_univariate, self.ts_univariate * 1.1)
@@ -721,3 +719,337 @@ def test_autolog_public_namespace_patched(self, mlflow_tracking, autolog_context
         assert "mae" not in run_data_impl.metrics, (
             "darts.metrics.metrics.mae should NOT log (implementation module is not patched)"
         )
+
+    def test_autolog_backtest_scalar(self, mlflow_tracking, autolog_context):
+        """Default (reduced) backtest of a single univariate series logs one scalar."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = self._fit_lr().backtest(
+                    self.ts_univariate, metric=dm.mae, retrain=False, stride=10
+                )
+
+        run_data = mlflow.get_run(run.info.run_id).data
+        assert "backtest_mae" in run_data.metrics
+        assert run_data.metrics["backtest_mae"] == pytest.approx(float(ref), abs=1e-5)
+
+    def test_autolog_backtest_per_window_steps(self, mlflow_tracking, autolog_context):
+        """reduction=None logs per-window values as consecutive steps of one key."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = self._fit_lr().backtest(
+                    self.ts_univariate,
+                    metric=dm.mae,
+                    retrain=False,
+                    stride=10,
+                    reduction=None,
+                )
+
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
+        assert len(history) > 1, "Expected multiple per-window steps"
+        steps = sorted(m.step for m in history)
+        assert steps == list(range(len(history)))
+        logged = [m.value for m in sorted(history, key=lambda m: m.step)]
+        np.testing.assert_allclose(logged, np.asarray(ref, dtype=float), atol=1e-5)
+
+    def test_autolog_backtest_per_component(self, mlflow_tracking, autolog_context):
+        """component_reduction=None logs one key per component name."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = self._fit_lr(self.ts_multivariate).backtest(
+                    self.ts_multivariate,
+                    metric=dm.mae,
+                    retrain=False,
+                    stride=10,
+                    metric_kwargs={"component_reduction": None},
+                )
+
+        run_data = mlflow.get_run(run.info.run_id).data
+        assert "backtest_mae_linear" in run_data.metrics
+        assert "backtest_mae_linear_1" in run_data.metrics
+        ref = np.asarray(ref, dtype=float)
+        assert run_data.metrics["backtest_mae_linear"] == pytest.approx(
+            ref[0], abs=1e-5
+        )
+        assert run_data.metrics["backtest_mae_linear_1"] == pytest.approx(
+            ref[1], abs=1e-5
+        )
+
+    def test_autolog_backtest_multi_metric(self, mlflow_tracking, autolog_context):
+        """Multiple metrics are logged under one key each."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = self._fit_lr().backtest(
+                    self.ts_univariate,
+                    metric=[dm.mae, dm.rmse],
+                    retrain=False,
+                    stride=10,
+                )
+
+        run_data = mlflow.get_run(run.info.run_id).data
+        assert "backtest_mae" in run_data.metrics
+        assert "backtest_rmse" in run_data.metrics
+        assert run_data.metrics["backtest_mae"] == pytest.approx(
+            float(ref[0]), abs=1e-5
+        )
+        assert run_data.metrics["backtest_rmse"] == pytest.approx(
+            float(ref[1]), abs=1e-5
+        )
+
+    def test_autolog_backtest_multi_series(self, mlflow_tracking, autolog_context):
+        """A list of series logs one key per series index."""
+        series = [self.ts_univariate, self.ts_univariate * 1.2]
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = self._fit_lr(series).backtest(
+                    series, metric=dm.mae, retrain=False, stride=10
+                )
+
+        run_data = mlflow.get_run(run.info.run_id).data
+        assert "backtest_mae_s0" in run_data.metrics
+        assert "backtest_mae_s1" in run_data.metrics
+        assert run_data.metrics["backtest_mae_s0"] == pytest.approx(
+            float(ref[0]), abs=1e-5
+        )
+        assert run_data.metrics["backtest_mae_s1"] == pytest.approx(
+            float(ref[1]), abs=1e-5
+        )
+
+    def test_autolog_backtest_per_timestep_scalar(
+        self, mlflow_tracking, autolog_context
+    ):
+        """A per-timestep metric (ae) under default reduction collapses to one scalar."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = self._fit_lr().backtest(
+                    self.ts_univariate, metric=dm.ae, retrain=False, stride=10
+                )
+
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
+        assert len(history) == 1, "Default reduction should yield a single value"
+        assert history[0].value == pytest.approx(float(ref), abs=1e-5)
+
+    def test_autolog_backtest_per_timestep_per_window(
+        self, mlflow_tracking, autolog_context
+    ):
+        """ae + reduction=None + forecast_horizon>1 logs one key per window, with
+        one step per forecast horizon timestep."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = self._fit_lr().backtest(
+                    self.ts_univariate,
+                    metric=dm.ae,
+                    retrain=False,
+                    stride=10,
+                    forecast_horizon=4,
+                    reduction=None,
+                )
+
+        ref = np.asarray(ref, dtype=float)  # shape (n_windows, forecast_horizon)
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae_w0")
+        assert len(history) == 4, "Expected one step per forecast horizon timestep"
+        logged = [m.value for m in sorted(history, key=lambda m: m.step)]
+        np.testing.assert_allclose(logged, ref[0], atol=1e-5)
+
+    def test_autolog_backtest_quantile(self, mlflow_tracking, autolog_context):
+        """A quantile metric (mql) logs one key per quantile."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                self._fit_qlr().backtest(
+                    self.ts_univariate,
+                    metric=dm.mql,
+                    metric_kwargs={"q": [0.1, 0.5, 0.9]},
+                    retrain=False,
+                    stride=10,
+                    num_samples=200,
+                )
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        for key in ("backtest_mql_q0_1", "backtest_mql_q0_5", "backtest_mql_q0_9"):
+            assert key in m, f"Expected quantile key {key}"
+            assert np.isfinite(m[key])
+
+    def test_autolog_backtest_inconsistent_axes_flat_fallback(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Metrics with mismatched axes (mae has no time axis, ae does) cannot be
+        merged into a structured layout, so values are logged flat by index."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                self._fit_lr().backtest(
+                    self.ts_univariate,
+                    metric=[dm.mae, dm.ae],
+                    retrain=False,
+                    stride=10,
+                )
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        flat_keys = [k for k in m if k.startswith("backtest_metrics_")]
+        assert flat_keys, "Expected flat fallback keys for inconsistent axes"
+        assert "backtest_mae" not in m, "No structured key on flat fallback"
+
+    def test_autolog_backtest_classification_labels_in_data(
+        self, mlflow_tracking, autolog_context
+    ):
+        """f1 with explicit labels present in the series logs finite per-label keys."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                self._fit_lr(self.ts_binary).backtest(
+                    self.ts_binary,
+                    metric=dm.f1,
+                    metric_kwargs={"label_reduction": None, "labels": [0, 1]},
+                    retrain=False,
+                    stride=10,
+                )
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert "backtest_f1_label0" in m
+        assert "backtest_f1_label1" in m
+        assert np.isfinite(m["backtest_f1_label0"])
+        assert np.isfinite(m["backtest_f1_label1"])
+
+    def test_autolog_backtest_classification_labels_not_in_data(
+        self, mlflow_tracking, autolog_context
+    ):
+        """f1 with explicit labels absent from the series still creates the keys, but
+        the scores are NaN (the labels never appear in any window)."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                self._fit_lr(self.ts_binary).backtest(
+                    self.ts_binary,
+                    metric=dm.f1,
+                    metric_kwargs={"label_reduction": None, "labels": [5, 10]},
+                    retrain=False,
+                    stride=10,
+                )
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert "backtest_f1_label5" in m
+        assert "backtest_f1_label10" in m
+        assert np.isnan(m["backtest_f1_label5"])
+        assert np.isnan(m["backtest_f1_label10"])
+
+    def test_autolog_backtest_classification_labels_inferred(
+        self, mlflow_tracking, autolog_context
+    ):
+        """f1 with label_reduction=None and no explicit labels infers class names
+        from series values and logs structured per-label keys instead of flat
+        integer-indexed ones."""
+        # binary classification series: values are 0.0 and 1.0
+        rng = np.random.default_rng(42)
+        vals = rng.choice([0.0, 1.0], size=50).astype(np.float32).reshape(-1, 1)
+        ts_bin = tg.constant_timeseries(value=0.0, length=50).with_values(vals)
+
+        model = LinearRegressionModel(lags=4)
+        model.fit(ts_bin)
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                model.backtest(
+                    series=ts_bin,
+                    metric=dm.f1,
+                    metric_kwargs={"label_reduction": None},
+                    retrain=False,
+                    stride=10,
+                )
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        # unique values are 0.0 and 1.0 → keys should use actual class values
+        assert "backtest_f1_label0" in m, "Expected 'backtest_f1_label0' for class 0.0"
+        assert "backtest_f1_label1" in m, "Expected 'backtest_f1_label1' for class 1.0"
+        # no flat integer-indexed keys
+        flat_keys = [
+            k
+            for k in m
+            if k.startswith("backtest_f1_") and k[-1].isdigit() and "_label" not in k
+        ]
+        assert not flat_keys, f"Did not expect flat fallback keys: {flat_keys}"
+
+    def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking, caplog):
+        """When the inferred label count does not divide the metric output size,
+        logging is skipped with a warning rather than raising — keeping autologging
+        non-fatal for the surrounding backtest call.
+
+        This is tested by calling _log_backtest_metrics directly so the warning is
+        not swallowed by MLflow's safe_patch wrapper.
+        """
+        # Series has 3 unique classes so np.unique(series.values()) → [0, 1, 2].
+        vals = np.array([0.0, 1.0, 2.0] * 17, dtype=np.float32)[:50].reshape(-1, 1)
+        ts_3class = tg.constant_timeseries(value=0.0, length=50).with_values(vals)
+
+        # Simulate a backtest result with only 2 entries — as if the metric was
+        # evaluated on windows that only contained classes 0 and 1.
+        # quantiles_num will be inferred as 3 (from series) but result has 2 → mismatch.
+        fake_result = np.array([0.8, 0.6], dtype=float)
+
+        backtest_args = {
+            "metric": dm.f1,
+            "metric_kwargs": {"label_reduction": None},
+            "series": ts_3class,
+            "forecast_horizon": 1,
+            "reduction": np.mean,  # not None → has_windows=False → single window
+            "last_points_only": True,
+        }
+
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            with caplog.at_level(logging.WARNING):
+                # must not raise — logging is skipped on shape mismatch
+                _log_backtest_metrics(
+                    client, run.info.run_id, fake_result, backtest_args
+                )
+            assert "not divisible" in caplog.text
+            client.flush(synchronous=True)
+
+        assert not mlflow.get_run(run.info.run_id).data.metrics
+
+    def _fit_lr(self, series=None):
+        """Fit and return a fresh LinearRegressionModel (no active run)."""
+        model = LinearRegressionModel(lags=4)
+        model.fit(series if series is not None else self.ts_univariate)
+        return model
+
+    def _fit_qlr(self, series=None):
+        """Fit and return a fresh quantile LinearRegressionModel (no active run)."""
+        model = LinearRegressionModel(
+            lags=4, likelihood="quantile", quantiles=[0.1, 0.5, 0.9]
+        )
+        model.fit(series if series is not None else self.ts_univariate)
+        return model
+
+
+@pytest.mark.parametrize(
+    "metric_name, metric_kwargs, expected",
+    [
+        ("mae", {}, dict(has_time_axis=False, has_comp_axis=False, quantiles_num=1)),
+        ("ae", {}, dict(has_time_axis=True, has_comp_axis=False, quantiles_num=1)),
+        ("mae", {"component_reduction": None}, dict(has_comp_axis=True)),
+    ],
+)
+def test_infer_metric_axes_reductions(metric_name, metric_kwargs, expected):
+    _attr_idx = {"has_time_axis": 0, "has_comp_axis": 1, "quantiles_num": 2}
+    axes = _infer_metric_axes(getattr(dm, metric_name), metric_kwargs)
+    for attr, value in expected.items():
+        assert axes[_attr_idx[attr]] == value
+
+
+def test_infer_metric_axes_quantiles():
+    _, _, quantiles_num, quantiles_labels = _infer_metric_axes(
+        dm.mql, {"q": [0.1, 0.5, 0.9]}
+    )
+    assert quantiles_num == 3
+    assert quantiles_labels == ["_q0.1", "_q0.5", "_q0.9"]
+
+
+def test_infer_metric_axes_quantile_interval():
+    has_time, _, quantiles_num, quantiles_labels = _infer_metric_axes(
+        dm.iw, {"q_interval": (0.1, 0.9)}
+    )
+    assert quantiles_num == 1
+    assert quantiles_labels == ["_qi0.1_0.9"]
+    assert has_time is True
+
+
+def test_infer_metric_axes_unknown_labels():
+    """label_reduction=None with no explicit labels cannot determine QL."""
+    _, _, quantiles_num, _ = _infer_metric_axes(dm.f1, {"label_reduction": None})
+    assert quantiles_num is None

From 9e4934d4b1fa73c40d60e875ef4b45579c5f1ab7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 24 Jun 2026 16:53:33 +0200
Subject: [PATCH 072/154] fix: update failing tests due to manage_run
 deprecation

---
 darts/tests/optional_deps/test_mlflow.py | 46 +++++++++++++-----------
 1 file changed, 25 insertions(+), 21 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index c2b4a06eb8..d3d559e939 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -240,8 +240,9 @@ def test_log_model_with_all_covariate_types(self, mlflow_tracking):
     def test_autolog_enable_disable(self, mlflow_tracking, autolog_context):
         """Test autolog can be enabled and disabled"""
         with autolog_context():
-            model = ExponentialSmoothing()
-            model.fit(self.ts_univariate)
+            with mlflow.start_run():
+                model = ExponentialSmoothing()
+                model.fit(self.ts_univariate)
 
             runs = mlflow.search_runs()
             assert len(runs) == 1, "Expected exactly one run after autolog fit"
@@ -263,8 +264,9 @@ def test_autolog_enable_disable(self, mlflow_tracking, autolog_context):
     def test_autolog_parameters(self, mlflow_tracking, autolog_context):
         """Test that autolog logs model parameters"""
         with autolog_context():
-            model = ExponentialSmoothing(seasonal_periods=12)
-            model.fit(self.ts_univariate)
+            with mlflow.start_run():
+                model = ExponentialSmoothing(seasonal_periods=12)
+                model.fit(self.ts_univariate)
 
             runs = mlflow.search_runs()
             assert len(runs) == 1
@@ -277,14 +279,15 @@ def test_autolog_parameters(self, mlflow_tracking, autolog_context):
     def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
         """Test that autolog logs training metrics for torch models"""
         with autolog_context():
-            model = NBEATSModel(
-                input_chunk_length=4,
-                output_chunk_length=2,
-                n_epochs=2,
-                **tfm_kwargs_dev,
-            )
-            train, val = self.ts_univariate.split_before(0.7)
-            model.fit(train, val_series=val)
+            with mlflow.start_run():
+                model = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=2,
+                    **tfm_kwargs_dev,
+                )
+                train, val = self.ts_univariate.split_before(0.7)
+                model.fit(train, val_series=val)
 
             runs = mlflow.search_runs()
             assert len(runs) == 1, "Expected exactly one run"
@@ -327,15 +330,16 @@ def assert_metric(history, key):
         with autolog_context():
             assert not autologging_is_disabled("pytorch")
 
-            model = NBEATSModel(
-                input_chunk_length=4,
-                output_chunk_length=2,
-                n_epochs=n_epochs,
-                torch_metrics=torchmetrics.MeanAbsoluteError(),
-                **tfm_kwargs_dev,
-            )
-            train, val = self.ts_univariate.split_before(0.7)
-            model.fit(train, val_series=val)
+            with mlflow.start_run():
+                model = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=n_epochs,
+                    torch_metrics=torchmetrics.MeanAbsoluteError(),
+                    **tfm_kwargs_dev,
+                )
+                train, val = self.ts_univariate.split_before(0.7)
+                model.fit(train, val_series=val)
 
             runs = mlflow.search_runs()
             assert len(runs) == 1

From 7b8dbcd72b6da87b212b6f2a8c7282218d206130 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 24 Jun 2026 17:02:51 +0200
Subject: [PATCH 073/154] chore: remove temporary test file

---
 mlflow_test_v2.py | 270 ----------------------------------------------
 1 file changed, 270 deletions(-)
 delete mode 100644 mlflow_test_v2.py

diff --git a/mlflow_test_v2.py b/mlflow_test_v2.py
deleted file mode 100644
index 655065ac76..0000000000
--- a/mlflow_test_v2.py
+++ /dev/null
@@ -1,270 +0,0 @@
-"""
-MLflow (auto)-logging:  Multi-model comparison with backtest
-
-Demonstrates:
-  * One MLflow run per model — each ``model.fit()`` auto-creates its own run
-    (via ``manage_run=True`` in autolog).  The run is named after the model
-    class (e.g. ``LinearRegressionModel``, ``ExponentialSmoothing``).
-  * Metric keys are unprefixed (``val_mape``, ``backtest_mape``, etc.) so
-    the experiment-level comparison view can overlay them directly.
-  * Automatic parameter and tag logging via darts autolog.
-  * Inline metric logging: ``mape`` and ``rmse`` called inside an active run
-    are automatically captured by the patched metric functions.
-  * Backtest with pre-computed historical forecasts, multiple metric functions,
-    and ``reduction=None`` so each window is logged as a step of
-    ``backtest_mape`` / ``backtest_rmse`` in the MLflow UI.
-
-Notes:
-  * ``mlflow_darts.autolog()`` is required — it patches ``fit()``, the darts
-    metric functions, and ``backtest()`` to make logging automatic.
-  * ``manage_run=False`` is passed to ``autolog()`` so that we control the run
-    lifecycle ourselves with ``mlflow.start_run()``.  This lets us keep the
-    run open for predict + metrics + backtest after ``fit()`` returns.
-  * ``last_points_only=False`` is used for historical forecasts so that
-    ``backtest()`` receives a list of per-window forecasts and can compute one
-    metric value per window (enabling the per-step chart in the UI).
-    ``last_points_only=True`` would collapse all windows into a single
-    TimeSeries, making the metric treat them as one window.
-
-Run with:
-    python mlflow_test_v2.py
-
-Inspect in the UI:
-    mlflow ui --backend-store-uri sqlite:////tmp/mlflow_v2.db
-"""
-
-import logging
-
-import coolname
-import mlflow
-from loguru import logger
-
-logging.getLogger("mlflow.utils.environment").setLevel(logging.ERROR)
-
-import numpy as np
-
-import darts.metrics as darts_metrics
-import darts.utils.mlflow as mlflow_darts
-from darts.datasets import AirPassengersDataset
-from darts.models import (
-    ExponentialSmoothing,
-    LinearRegressionModel,
-    NBEATSModel,
-    NHiTSModel,
-    TCNModel,
-)
-
-
-def _managed_run_scenarios(model, train, val, log) -> None:
-    """Explores manage_run=True behaviour across four scenarios.
-
-    Metrics always use manage_run=False internally — they only log into an
-    already-active run and never create one themselves.
-
-    Scenario A — bare fit():
-        fit() uses with_managed_run which creates a run when none is active,
-        then closes it on return.
-        Result: one run per model, params logged, no metrics.
-
-    Scenario B — metric with no active run:
-        Metric patches have manage_run=False so they never open a run.
-        With nothing active there is nowhere to log.
-        Result: nothing logged (silent no-op).
-
-    Scenario C — metric inside an explicit start_run():
-        Caller opens a run; metric patch sees an active run and logs into it.
-        No fit() → no params.
-        Result: one run with only the metric logged.
-
-    Scenario D — fit() + metric inside an explicit start_run():
-        with_managed_run creates a run "if necessary". Because the caller's
-        run is already active, fit() reuses it rather than nesting a child.
-        Params and metric all land in the same single run.
-        Result: one run per model with both params and metric logged.
-    """
-
-
-def _magic(model, train, val, series, log) -> None:
-    """Advanced use case: caller-managed run with predict, val metrics, and backtest.
-
-    Requires autolog(manage_run=False) and an active mlflow.start_run() in the
-    caller. Because manage_run=False, fit() never opens or closes a run on its
-    own — the caller's run stays open for the entire workflow.
-
-    What gets logged into the single run:
-      - Model params (from fit via the autolog patch).
-      - val_mape, val_rmse (from patched darts_metrics calls while run is active).
-      - backtest_mape, backtest_rmse, backtest_ape as consecutive steps
-        (one value per window, because reduction=None).
-    """
-    model_name = type(model).__name__
-    log(f"[{model_name}] Run created: run_id={mlflow.active_run().info.run_id}")
-    # ── fit ───────────────────────────────────────────────────────────
-    log(f"[{model_name}] Fitting model on {len(train)} training samples")
-    model.fit(train)
-    # ── predict + inline metric logging ───────────────────────────────
-    log(f"[{model_name}] Predicting {len(val)} steps")
-    pred = model.predict(n=len(val))
-    # Calls inside active MLflow run are intercepted by patched metric
-    # functions and auto-logged -> val_mape, val_rmse
-    val_mape = darts_metrics.mape(val, pred)
-    val_rmse = darts_metrics.rmse(val, pred)
-    log(f"[{model_name}] val_mape={val_mape:.4f}  val_rmse={val_rmse:.4f}")
-
-    # ── backtest ──────────────────────────────────────────────────────
-    log(
-        f"[{model_name}] Computing historical forecasts "
-        f"(start={BT_START}, horizon={FORECAST_HORIZON}, stride={STRIDE})"
-    )
-    hfc = model.historical_forecasts(
-        series,
-        start=BT_START,
-        forecast_horizon=FORECAST_HORIZON,
-        stride=STRIDE,
-        retrain=True,
-        last_points_only=False,
-    )
-    log(f"[{model_name}] {len(hfc)} backtest windows computed")
-
-    # reduction=None → one value per window per metric, logged as
-    # consecutive steps of backtest_mape / backtest_rmse
-    log(f"[{model_name}] Running backtest (logging per-window metrics as steps)")
-    model.backtest(
-        series=series,
-        historical_forecasts=hfc,
-        last_points_only=False,
-        metric=[darts_metrics.mape, darts_metrics.rmse, darts_metrics.ape, darts_metrics.mase, darts_metrics.mase],
-        metric_kwargs=[{}, {}, {}, {"m": 1}, {"m": 2}],
-        reduction=None,
-    )
-    log(f"[{model_name}] Run complete: {mlflow.active_run().info.run_id}")
-
-
-def _backtest_reduction_scenarios(model, train, series, log) -> None:
-    """Explores how different ``reduction`` values affect MLflow logging.
-
-    Fits once and computes historical forecasts once, then calls backtest with
-    four different reductions.  Each variant runs inside its own nested
-    child run so the MLflow UI shows them separately without key collisions.
-
-    Reduction variants and their logged shape:
-      None      → 1-D array per metric → logged as consecutive steps (chart).
-      np.mean   → scalar per metric    → single value (mean over windows).
-      np.median → scalar per metric    → single value (median over windows).
-      custom    → scalar per metric    → single value (90th-percentile).
-
-    All variants use the same metric list (mape, rmse) and the same
-    pre-computed historical forecasts so results are directly comparable.
-    """
-    model_name = type(model).__name__
-    metrics = [darts_metrics.mape, darts_metrics.rmse]
-
-    log(f"[{model_name}] Fitting")
-    model.fit(train)
-    log(f"[{model_name}] Computing historical forecasts")
-    hfc = model.historical_forecasts(
-        series,
-        start=BT_START,
-        forecast_horizon=FORECAST_HORIZON,
-        stride=STRIDE,
-        retrain=True,
-        last_points_only=False,
-    )
-    log(f"[{model_name}] {len(hfc)} windows")
-
-    reductions = [
-        (None, "reduction=None (per-window steps)"),
-        # (np.mean, "reduction=np.mean"),
-        # (np.median, "reduction=np.median"),
-        # (lambda x, axis=None: np.percentile(x, 90, axis=axis), "reduction=p90"),
-    ]
-
-    for reduction_fn, label in reductions:
-        log(f"[{model_name}] Backtest — {label}")
-        with mlflow.start_run(nested=True, run_name=label):
-            model.backtest(
-                series=series,
-                historical_forecasts=hfc,
-                last_points_only=False,
-                metric=metrics,
-                reduction=reduction_fn,
-            )
-
-
-# ── Data setup ----------------------------------------------------------------
-# Cast to float32: MPS doesn't support float64 tensors
-series = AirPassengersDataset().load().astype(np.float32)
-# TODO: implement/test the follwoing cases!
-series_multiple = [series, series / 3.]
-series_multivariate = series.stack(series / 3.)
-series_multiple_multivariate = [series.stack(series / 3.), series.stack(series / 10.)]
-
-train, val = series.split_after(0.75)
-FORECAST_HORIZON, STRIDE, BT_START = 1, 2, 0.75
-
-# ── Logging setup ─────────────────────────────────────────────────────────────
-VERBOSE = True
-log = logger.info if VERBOSE else lambda *a, **kw: None
-# ── MLflow setup ──────────────────────────────────────────────────────────────
-DB_PATH = "/tmp/mlflow_v2.db"
-mlflow.set_tracking_uri(f"sqlite:///{DB_PATH}")
-# ── Model setup ---------------------------------------------------------------
-_torch_kwargs = dict(
-    input_chunk_length=12,
-    output_chunk_length=FORECAST_HORIZON,
-    n_epochs=10,
-    pl_trainer_kwargs={
-        "accelerator": "mps",
-        "precision": "32-true",
-        "enable_progress_bar": False,
-    },
-)
-models = [
-    LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),
-    # LinearRegressionModel(lags=24, output_chunk_length=FORECAST_HORIZON),  # same model, more lags
-    ExponentialSmoothing(),
-    # NBEATSModel(**_torch_kwargs),
-    # NBEATSModel(**_torch_kwargs, num_stacks=4, num_blocks=2),  # same model, deeper architecture
-    # NHiTSModel(**_torch_kwargs),
-    # TCNModel(**_torch_kwargs),
-]
-
-# ── Use case 1: manage_run=True (default) ─────────────────────────────────────
-# fit() auto-creates and closes a run per model — no start_run() needed.
-exp_name: str = coolname.generate_slug(2)
-mlflow.set_experiment(exp_name)
-mlflow_darts.autolog()
-logger.info(f"[Use case 1] Experiment: {exp_name}")
-for model in models:
-    with mlflow.start_run():
-        model.fit(train)
-        pred = model.predict(n=len(val))
-        darts_metrics.mape(val, pred)
-
-# ── Use case 2: manage_run=False + backtest ───────────────────────────────────
-# Caller opens the run so predict, val metrics, and backtest all land in it.
-# NOTE: this is the recommended way to use MLFlow in Darts. It avoids
-#       nesting runs and ensures all metrics land in the same run.
-exp_name = coolname.generate_slug(2)
-mlflow.set_experiment(exp_name)
-mlflow_darts.autolog()
-logger.info(f"[Use case 2] Experiment: {exp_name}")
-for i, model in enumerate(models):
-    model_name = type(model).__name__
-    log(f"[{model_name}] Starting run")
-    with mlflow.start_run(
-        run_name=model_name,
-        description=f"Small exp. run for {model_name}",
-    ) as run:
-        _magic(model, train, val, series, log)
-
-# ── Use case 3: backtest reduction variants ───────────────────────────────────
-# One parent run per model; one nested child run per reduction variant.
-exp_name = coolname.generate_slug(2)
-mlflow.set_experiment(exp_name)
-mlflow_darts.autolog(manage_run=False)
-logger.info(f"[Use case 3] Experiment: {exp_name}")
-for model in models:
-    model_name = type(model).__name__
-    with mlflow.start_run(run_name=model_name):
-        _backtest_reduction_scenarios(model, train, series, log)

From 502db7103c59eee0af5b97eebd043b2103698452 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 24 Jun 2026 17:48:57 +0200
Subject: [PATCH 074/154] feat: update mlflow notebook

---
 examples/27-MLflow-quickstart.ipynb        |  873 ---
 examples/29-MLflow-quickstart.ipynb        | 5959 ++++++++++++++++++++
 examples/static/images/mlflow_charts.png   |  Bin 0 -> 357183 bytes
 examples/static/images/mlflow_models.png   |  Bin 0 -> 361032 bytes
 examples/static/images/mlflow_overview.png |  Bin 0 -> 332549 bytes
 5 files changed, 5959 insertions(+), 873 deletions(-)
 delete mode 100644 examples/27-MLflow-quickstart.ipynb
 create mode 100644 examples/29-MLflow-quickstart.ipynb
 create mode 100644 examples/static/images/mlflow_charts.png
 create mode 100644 examples/static/images/mlflow_models.png
 create mode 100644 examples/static/images/mlflow_overview.png

diff --git a/examples/27-MLflow-quickstart.ipynb b/examples/27-MLflow-quickstart.ipynb
deleted file mode 100644
index 0b793f75b3..0000000000
--- a/examples/27-MLflow-quickstart.ipynb
+++ /dev/null
@@ -1,873 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "aeddb542",
-   "metadata": {},
-   "source": [
-    "# MLflow for Darts\n",
-    "\n",
-    "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
-    "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
-    "\n",
-    "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
-    "\n",
-    "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f72894af",
-   "metadata": {},
-   "source": [
-    "## Installing MLflow\n",
-    "\n",
-    "MLflow is available as an optional dependency for Darts. Install it with:\n",
-    "\n",
-    "```bash\n",
-    "pip install mlflow\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42e3dcea",
-   "metadata": {},
-   "source": [
-    "## Setup and Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "b346ce8f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fix python path if working locally\n",
-    "from utils import fix_pythonpath_if_working_locally\n",
-    "\n",
-    "fix_pythonpath_if_working_locally()\n",
-    "\n",
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13b13fe4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import os\n",
-    "import tempfile\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mlflow\n",
-    "import numpy as np\n",
-    "\n",
-    "import darts.metrics\n",
-    "\n",
-    "from darts.datasets import AirPassengersDataset\n",
-    "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
-    "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4d424e08",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# use darts plotting style\n",
-    "from darts import set_option\n",
-    "\n",
-    "set_option(\"plotting.use_darts_style\", True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f9c40d6",
-   "metadata": {},
-   "source": [
-    "## MLflow Setup\n",
-    "\n",
-    "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "88320df5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temporary directory for MLflow tracking\n",
-    "tmpdir = tempfile.mkdtemp()\n",
-    "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
-    "\n",
-    "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
-    "mlflow.set_experiment(\"darts-quickstart\")\n",
-    "\n",
-    "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
-    "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03d5209e",
-   "metadata": {},
-   "source": [
-    "## Load Sample Data\n",
-    "\n",
-    "We'll use the classic AirPassengers dataset for this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "1596e07e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training series: 107 points\n",
-      "Validation series: 37 points\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "series = AirPassengersDataset().load()\n",
-    "train, val = series.split_before(0.75)\n",
-    "\n",
-    "print(f\"Training series: {len(train)} points\")\n",
-    "print(f\"Validation series: {len(val)} points\")\n",
-    "\n",
-    "series.plot()\n",
-    "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
-    "plt.legend()\n",
-    "plt.title(\"AirPassengers Dataset\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34858645",
-   "metadata": {},
-   "source": [
-    "## Basic Model Logging\n",
-    "\n",
-    "Let's train a simple model and log it to MLflow manually."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bc8f520d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation MAPE: 7.86%\n",
-      "Validation RMSE: 34.77\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model = ExponentialSmoothing()\n",
-    "model.fit(train)\n",
-    "\n",
-    "predictions = model.predict(n=len(val))\n",
-    "\n",
-    "# calculate metrics you want to log to MLflow\n",
-    "mape_score = darts.metrics.mape(val, predictions)\n",
-    "rmse_score = darts.metrics.rmse(val, predictions)\n",
-    "\n",
-    "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
-    "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
-    "\n",
-    "train[-50:].plot(label=\"Training\")\n",
-    "val.plot(label=\"Actual\")\n",
-    "predictions.plot(label=\"Forecast\")\n",
-    "plt.legend()\n",
-    "plt.title(\"Exponential Smoothing Forecast\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35dc864c",
-   "metadata": {},
-   "source": [
-    "Now let's log this model to MLflow:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "61406cd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Run ID: 936e44cf2edb4db0bc6e32aa0d9fdfd9\n",
-      "Model URI: models:/m-280d9919c51642dabaf49017ad97ca65\n"
-     ]
-    }
-   ],
-   "source": [
-    "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
-    "    model_info = log_model(\n",
-    "        model=model,\n",
-    "        name=\"exponential-smoothing-model\",\n",
-    "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
-    "        tags={\n",
-    "            \"model_type\": \"ExponentialSmoothing\",\n",
-    "            \"dataset\": \"AirPassengers\"\n",
-    "        }\n",
-    "    )\n",
-    "\n",
-    "    # log calculated metrics you want\n",
-    "    mlflow.log_metric(\"val_mape\", mape_score)\n",
-    "    mlflow.log_metric(\"val_rmse\", rmse_score)\n",
-    "\n",
-    "    print(f\"Run ID: {run.info.run_id}\")\n",
-    "    print(f\"Model URI: {model_info.model_uri}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0728690e",
-   "metadata": {},
-   "source": [
-    "### Load the Model Back\n",
-    "\n",
-    "We can load the model from MLflow using its URI:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "cae35ffa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded model predictions match: True\n"
-     ]
-    }
-   ],
-   "source": [
-    "loaded_model = load_model(model_info.model_uri)\n",
-    "\n",
-    "loaded_predictions = loaded_model.predict(n=len(val))\n",
-    "\n",
-    "# verify predictions match\n",
-    "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
-    "print(f\"Loaded model predictions match: {predictions_match}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6bd4597c",
-   "metadata": {},
-   "source": [
-    "## Automatic Logging with `autolog()`\n",
-    "\n",
-    "`autolog()` patches every `model.fit()` call to automatically log parameters, covariate metadata, and the trained model artifact. For PyTorch-based models it also injects a callback that logs `train_loss` / `val_loss` per epoch (see the sections below for details)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "5ef7f73a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logged metrics: {'train_mae': 8.4783, 'train_mape': 3.9116, 'train_mse': 118.1369, 'train_rmse': 10.8691, 'val_mape': 10.742, 'val_rmse': 51.182}\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "autolog()\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
-    "    auto_model = LinearRegressionModel(lags=12)\n",
-    "    auto_model.fit(train)  # no val_series → autolog logs train_* metrics by default\n",
-    "\n",
-    "    auto_predictions = auto_model.predict(n=len(val))\n",
-    "    auto_mape = darts.metrics.mape(val, auto_predictions)\n",
-    "    auto_rmse = darts.metrics.rmse(val, auto_predictions)\n",
-    "\n",
-    "autolog(disable=True)\n",
-    "\n",
-    "# show logged metrics\n",
-    "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
-    "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
-    "\n",
-    "# plot\n",
-    "fig, ax = plt.subplots(figsize=(10, 4))\n",
-    "train[-36:].plot(label=\"Train\", ax=ax)\n",
-    "val.plot(label=\"Actual\", ax=ax)\n",
-    "auto_predictions.plot(\n",
-    "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
-    ")\n",
-    "ax.set_title(\"Linear Regression — autolog run\")\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f484330f",
-   "metadata": {},
-   "source": [
-    "## Open the MLflow UI\n",
-    "\n",
-    "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
-    "\n",
-    "The UI lets you:\n",
-    "- **Compare runs** side-by-side in the Experiments table\n",
-    "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
-    "- **Visualize** metrics across runs with built-in charts\n",
-    "- **Register** models to the Model Registry for versioning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1a01cd2f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Launch the MLflow UI with this command in your terminal:\n",
-      "\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/4x/8t1xtpd11cb5xxdp87gm92jw0000gn/T/tmp3vfszfy0/mlflow.db\n",
-      "\n",
-      "Then open:  http://localhost:5000\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
-    "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
-    "print(\"Then open:  http://localhost:5000\")"
-   ]
-  },
-  {
-   "attachments": {
-    "image-2.png": {
-     "image/png": 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-    }
-   },
-   "cell_type": "markdown",
-   "id": "88b0d285",
-   "metadata": {},
-   "source": [
-    "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `val_mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
-    ">\n",
-    "![image-2.png](attachment:image-2.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2b133bc",
-   "metadata": {},
-   "source": [
-    "## Per-epoch Metrics with Torch Models\n",
-    "\n",
-    "For neural models, `autolog()` by default injects a PyTorch Lightning callback that logs `train_loss` and `val_loss` at the end of every epoch.\n",
-    "\n",
-    "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d01783d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
-    "\n",
-    "# inject_per_epoch_callbacks=True is the default\n",
-    "autolog()\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
-    "    nbeats = NBEATSModel(\n",
-    "        input_chunk_length=24,\n",
-    "        output_chunk_length=12,\n",
-    "        n_epochs=10,\n",
-    "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
-    "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
-    "        torch_metrics=MetricCollection({\n",
-    "            \"MAE\": MeanAbsoluteError(),\n",
-    "            \"MSE\": MeanSquaredError(),\n",
-    "        }),\n",
-    "        random_state=42,\n",
-    "    )\n",
-    "    nbeats.fit(train, val_series=val)\n",
-    "    nbeats_pred = nbeats.predict(n=len(val))\n",
-    "    darts.metrics.mape(val, nbeats_pred))\n",
-    "    darts.metrics.rmse(val, nbeats_pred))\n",
-    "    print(f\"NBEATS MAPE: {mape(val, nbeats_pred):.2f}%\")\n",
-    "\n",
-    "autolog(disable=True)"
-   ]
-  },
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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DCFisTEI8CfENcUPX80mMHHGS23abbcIpPP/88+73u/+hnk+HY0cAAQQQQAABBBBAAAEEahawRgsS4mumrNsKtt9uO3f88GGuQ4cObvz48W7tdddzU6ZMqdvz4cARQAABBBBAAAEEEPj/9u4DTo6y/h/4k0ISuvQqohSll5BQFQy9SlFQmkGxEfAnoDTFCiIdhERFhIAi9oIgHekt1AAKFsgfaQHpNSHlP89cZrK7d5fc3s3tTnbf8/uFm7bPzLxn9vXyO/PZZwgQKEJAvVyE4rzdxnXXXB1WWGGF9CDO++lPw5lnnT1vH5C9J0CAAAECBAgQIECAQB8FslpZIF4gvo+Xko/3VeCRhyaGgQMH5s18ZKuPhhdeeCGfNkKAAAECBAgQIECAAIF2E8huWgjEt9uZn328V1/517DSSivlM35w8inhoosvzqeNECBAgAABAgQIECBAoB0F1MvteNZnH/PGI0eG8RdekM+IPyDfaOTG+bQRAgQIECBAgAABAgQItKNAVisLxAvEF3r9jxwxIowZc0iIf++eMCFMSP6dO3Zcl9uoZ90uG+jDzA032CB8/bhj8xZOO+OMcMcdd+bTjRy5/dZbwmKLLZZuMvZ2t37yOvjp02c0chdsiwABAgQIECBAgAABAqUSyG5atEogvp76t551iz5p8cfav7r0l2HQrB9tX3PtteEn5/206M30qL0fjxsbttxyy3zdT+23X3jggQfzaSMECBAgQIAAAQIECBBoRwH1cs+eQxd9bRw0enTyxu+d0mZnzpwZ9jvgwDBlypSiNzPX9uIz5dtuuTl9m1pc+T+PPx522XW3uX7OCgQIECBAgAABAgQIEGhlgaxWFogXiC/sOj80CcKPOeSQTu19evRBaTi+ckE961Z+rqjxT+6zd/jWN7+ZN3fa6aeHn11wYT7dyJH4S/7jj/9GGDpkSDj7h+eEy6+4opGbty0CBAgQIECAAAECBAiUTiC7adEKgfh66t961u2PkzZo0MDw8MSJedP33ndf2D95yN+MYZlllg4n/+AHYdVVVgl/+vNlIdbtBgIECBAgQIAAAQIECLS7gHq5+gro6jl09RrFTJ3/0/PC5pttlje22RYfDi+//HI+3ciRAw/YP4xOAvqvJNs/7hvHh0cffbSRm7ctAgQIECBAgAABAgQIlE4gq5UF4gXiC7s4Lxp/YdozfG2Dsaf4eDOicqhn3crPFTVepkB8UcekHQIECBAgQIAAAQIECLSKQHbTohUC8fXUv/Ws2x/nukyB+P44Pm0SIECAAAECBAgQIEBgXhdQL1efwa6eQ1evUcxUmQLxxRyRVggQIECAAAECBAgQINA6AlmtLBAvEF/YVf2PRx7utq011lq7alk961Z9sI8TsYe5D37wg2HbbbYJH99rr7y1v1x+ed4z+z333BveeuutdNmmm24S5ptvvnT8icefCP996qmw0EILhY/ttltYddVVwq233Rauv/6GvJ04ssACC4QRIzYKGw0fHpZeaukw8aGHwi233hqefPLJqvWyiQ996ENh6aWXSidfe+21qlfAr7XWmmGJJZZIl73y8itpW3Eitr/TjjuGadOmhfvuvz/dh6lTp6brFfWfeBwbbTQ8by6+mj7u37LLLBP23HPPsMIKy6fbvvzyK6peCRiPZ+tRHw0rrbRS+maAa6+9Lv1c3lAXI0sttVSIPeVvuOGGIQYwJky4J9x2++096llh6NChYY/dPxZWX231sNDCC6XON998S27VxebMIkCAAAECBAgQIECg5ALZTYtWCMTXU//Ws26Rp3BI8sayTTbZOMw3eL5w7jk/zJueNGlSOOnkk9Pp//zn8fD000+n413VqrGW23rU1iHW0U8++d9w4fjxeTtxJC5fb9310np2laTX98eT17rffsed4eGHHw4zZsyoWjdOxNpz9Q+uns6fOWNmWiNm662wwgphlVU+kC6bMX1GWpvHiZVXXjmtD2MdPXHiQ+H6G24IL774Yrpekf/p6vgHDhwYtvzIR8K2224TnntucrLt68Mjj/w932ysXXfcYYcwfPiG4fXXXw9XXX1Nso+ze+PPV6wYqdesO5e4v5tusmlYfrnlwn8S96uuvrpHLssl6++04w7hAx/4QJg5c2Z44okn0nsnkyc/X7GXRgkQIECAAAECBAgQaKSAermzdu1z6M5r9H7OB1dfPSyz7DLhyMMPD6sn49lw9DHHhFdefTW8++674Y6kto1DdzXZuuuuG3beaad03XE/+lH+HDprK9bI8Tnp2muvlT4bvfPOu9JnrFOmTMlWqfq7ycYbhyFDh6TzYv0da/ds6OrZdqz5t956VNjqI1uGp595Jky4Z0K+z9nnivjb3fHHmnSP3XdP68rbb78j/O3GG6s295GPfDhstummYcEFFww33nhTuOnmm9Nn4FUrVUzE+nvttdYKm222aVqvPvbYY+HOu+6qqsErVq8a9Vy5isMEAQIECBAgQIAAgZYRyGplgXiB+MIu6np6sqtn3cJ2MGnohO99N+yVhLnnNHztqKPzcHxlGOGuu+9Ob0LssP32+ccf++c/w+57dLQXbyacecbpYdRHP5ovrxx55523w0GfPbgq8B6X3/i368MySy+Trvr222+HDTcakX/s1ptvygPxb7zxRvjilw4J4y+8IAwePDhfJ47Ezx325f9LAwJVC/owsduuu6Svp8+aOCl5Vf1uu+4a1kpuMFQOcduf+8IXw0NJ8P+Cn50fhieh9trhF5dcEk78/km1s9MbQz9LXjH4vve9r9OyOOOZ5KbMXp/YO7zyyitdLj/m6KPCAfvvH+KNj9ph8vOTwyf2/mR44YUXaheZJkCAAAECBAgQIECg5ALZTYtWCMTXU//Ws26RpzA+ML/g/PPn2OT1118fDk3qzjjU1qqnnHpa+M63vxUGDBiQLo+BgHXX3yAdj//50he/EA4dM6bL2i0GrU8+5dRw0cUX5+vHkdNPOzX9IXg2c6tRo0IWxI6h/a2T6Wz4aBLE/8PvfxcWW2yxbFb6Nwbozzn33PDjn5xXNb+vE7XH/+WvfCWcf955nY7vhr/9LYw59LCwYxIqP+nEE0N88F45vPnmm2GXXXcLz02eXDk7He+NWa3LdtvvEH40bmzy44FVqtqP5n++7LJw7HFfr5qfTcQa++cXXxQ23GD2OcyWxb8TkjcBjv7MZ7v8IUPlesYJECBAgAABAgQIECheQL1cbdrfPcRfe/VVYcUVV6zeaM1UFsivrcn22Ovj4Ve/vKSqFtxt9z3Cv/71r7SFD2+xRTjrzDPSztZqmkwnY3D8kDGHdlr094cfyuvvBx98MHxy3/3ydWqfbd9zzz3hkC99KV8/WzF25BbrumeffTab1ee/tce/zXbbJ7X6b8MiCy9S1fZTSQd0e3/yU2HxxRcPPzv/vPw5ebZSrOW//H9fSX/kns3L/h6w/37hmKOP7lR/x+WxI7mvH398uOyyv2SrV/31XLmKwwQBAgQIECBAgACBlhLIamWBeIH4wi7skSNGhPjwvnYYO25cOHfsuKrZ9axb9cE+TvQlEB97R19kkeqCPQvEx4fFd9x2a6fltbsbHzr/X9KDQOw1PRt6GoiPgYI4ZD3WZ5/P/sYif+NNN+vUq0C2vN6/tYH4GDDPgvu1bcUe9R9KevWLvRd0N5z9wx9WhRBWW2218Kc//L7LGxaVbcTA/U4779IpIBDDAV8+7LDKVTuNv5r0zBBvtsQfExgIECBAgAABAgQIEJh3BLKbFq0QiK+n/q1n3SLPZl8C8bFWHThwQNID/OwfblcG4i84/6dJr/GbznV3L730V+G7J5yQr1dPIH5O9Wps8ODPfb7QH5BXBuLj29riDwG6q9V/9/s/hD332L3b2jeG4jfb4sOh8q1vvTWrDR/MzaW2Ts/wL73kF2H99dfPJrv8G0PxB44+qMtlZhIgQIAAAQIECBAg0H8C6uVq266eQ1ev0bepvgTiY4df73nPe6p2IAvEHzR6dDjqa1+tWtbVRHzLV/whdeXQ00D8yy+/3OmH45XtxFD89jvuVDmrT+P11KTxjWmxw7RFF120y23GUPyn9tu/6s1q3z/xhLSn+S4/UDHz/J/9LJx+xpkVczp+qO+5chWJCQIECBAgQIAAAQItJZDVygLxAvGFXtiHjjkkef35iBAf4sdf5I9NgvDxb1dDPet29fnezNttt13D6AMPDEsuuWRYaqml8ibiK9Sff77jld9fP/6b4R//+Ee6rPJX9PnKyUh8UB3/xRD4Z5Je3w888IBwbPJr9GyIr7B75O9/T3+Jvs7aa4f5558/WxT+mfQq/7FZvcrHmT0NxGcNxOB7/OX8QgstlB5HNj/+/cMf/xi+/o3jK2f1erw2EB8bioH+5557LsTe8OMr6Lsa4g8H4g2eFVZYvioQER/Eb/XRrfOPxJ7uKwP08TPRLPYGsPpqq1Z99oILLwynnnZ6/tndP/axcNL3T8yn402R+OOEZ55+On1oX7lvsWeDGIqP6xgIECBAgAABAgTKJzBq+53DqO12nuuOfePIQ+a4zgmnV/8It6uVb7jminDD1Vd0tWiembfqVp8O8V8c/n3jRem/eWbn69jR7KZFKwTi42HXU//Ws24dpHNcdaWVVgpnJW88i8Maa6yRrxvfdPbEE5PS6Rjs/uWll6bjlYHwfOVkZPr0aeHNN99KXwO/RfIq9vi69OuuuTpfJdZl//r3v5Oe3ieHWCtX9uheGaKPH6gnEB/XT+vVpN1YM7+3pve+WENvm/SWXtTQ1fFHq9iDfTzm2re6xe3GY4/7EX9oXxuIOPTLXw7XX39Dunt9MasNH2THG03iD8Yra+W4LJodlPQIGN+Ilw1nnH5a2HGH2Vbx3sf9DzwQZkyfHjbYYP0wbNjs+xt/ufzycNTRx2Qf9ZcAAQIECBAgQKBgAfVyfaDq5fq8iqq769tq57XrqYHrWbfzlno35+vHHZu+HXvllVeuet4be3mPtVasmbIe2ruryWLt9fobr4dhQ4el4fb/JrXhhLvuTJ/1ZnsVw+kx/L7aqqumdWX2Bra4PPY0/+ijj2arhp4G4rMPxM7Hnkmely6/3HJVxxCXf+7zXwi33nZbtmqf/nZ1/PE+wVNPPZ0+l19ggQW6bD8+Q54xY2ZYbtllq5b/9corw5Ff/Vo+76EHH8jr7Vhj/+GPf0ruWTwetth887DJJpvkveDH5/Trbzg8/5znyjmFEQIECBAgQIBASwioles7je1WKwvEC8TX9w1pkbU/uc/e4Vvf/GZ+NKedfnr42QWde7evDcTH4Pwn9vlkp9fH/SJ5lfi6666bthd7S9/1Y7uHF154IW//gfvuzV+HF2+OrLPe7J7W6gnEP5uE0eMrz2MbcTju2GPCAfvvn2+nNmyfL+jFSG0gPt6s+fRBByWvRr8nba1223HmLy65JJz4/ZPS5fHG0F/+/KeqGxNrrdNhFFe48/bb8lcA/vs//wl7JjdzsiH2Hn/Zn/6YTYb/JMt32e1j+fStt9wclkiC83GI+/WFL34p3HLrrfny3/3m12GttdbKp/c/8NPh3nvvzaeNECBAgAABAgQIlEegJ0H2uLdFBOJ70k55ZDrvSeUNi2zpS5MeDHePPzybbJm/RT2YL6qdloGdy4EMGjQwPJz00pYN9953X9j/gAOzyfxvbSA81qhfOmRMp4foMazw+c99Lv1crN2+cvgRIb7yPRt+/9vfhDXXXDObDB/fe+/wyCN/T6frCcTHMH3sZW/SpEnpZ+OPr+OPsLOhNmyfze/t39rjf+SRR5J93ydtLobOr7vmqqrgeAwf7LjzzmlgPq507g/PDltvPfsH4/HHBsfPukfRF7Ouwgc/+vFPwg/POSfdtxg+uOqvV1R1EPBAEnaPve7FIf74/cH770vH439iyH/rbbcLL730cjovhvlvuO7asOCCC6bTMYBQWeenM/2HAAECBAgQIECgMAH1cs8p1cs9t8rWVC9nEj37e/5Pzwubb7ZZvnJ801fsgb1y6KomO3fs2BDrslg/ZcOHPvSh8JtfdfzgPM773e9+X/XGtNiTeXxTdjZc/PNfhJN+8INssq5A/JVXXRWOOLKjJ/r4xvP49u74HDYbLhw/Ppxy6mnZZJ/+1h5/fCPaqG22DbEztbjt+Ox3lVVWybcR7xOMOfSw/D5BbXA9/nAgPhePw9Zbj0pq6R/mn62sdePM6PW5gw/Ol++X3MvIOsDzXDlnMUKAAAECBAgQaAkBtXLPT2M71soC8QLxPf+GtNCavQnExxsVG240IsRfldc71D7s3vzDH84fKNcTiI89nT+d9IKeDfHmwcQH7st7U4+9rG+6+RbZ4j79rQ3E33HHHeEzB3cEGWLDsce7iQ/cn//avquAQW24ofK457ZzlQGD+COD4SNGph+Jx/zwxAfz7V5/ww3h0MO+XNVc7UP82h7mq1Y2QYAAAQIECBAg0FQBNy16xt/VDYvsk3ePPyK8NOmBbLIl/hb1YL6odloCtQcH0dtAfG97lNslCYmfesrJ+Z7FV5rHV5vHoZ5AfAwYjB33o7ydOBKD3/H169kQf5ie/bg8m9fbv5X1amxjZNIT3euvv5E3d/ZZZ4bttt02n659XXvskf/qK/+aL7/v/vvDfvsfkE/PaWROZrXhg/gK+n0+tW9Vc8slPQLGXvtjbR2HGOCIQY44bD0qCRicMztg8LWjjg6XX1H9Vo2dd9opnHbqKen68T+7J2/Ai29sMxAgQIAAAQIECBQvoF7umal6uWdOtWupl2tF5jzdm0D8H//0p3Dc178x54a7WDp06NBw/7335M9Ca2vGnvYQH3/kvMHwEVVbGD58eIgdvWXDzbfcknY8lk335W9tTfq9E07M3zQX291xxx3CGafNDt8/+thjYY8996raZGUnc5XPh2sD8Y8/8UT41L77pWH7qgZqJjxXrgExSYAAAQIECBBoAQG1cs9OYrvWygLxAvE9+4a02Fq9CcTHnuZ23HmXuUrEX/Vvk/xKfdnktW7xNegLL7xwGDmi+mZDfHV87G0+Dj0NxHcVOI+fjz2tL7roonE0vPHGG2HExpuk4339T20g/uzkV/c//sl5Vc3GnuNi+DwOMagfA/uVw5lnnB522H72vK4C8fFGROxRYZNNNg5LLrFkWGTRRdJXyG+4wQZ5U7E3vfhjhDjU3qiJryLs6uH7OmuvnX++qxBAvtAIAQIECBAgQIBAUwUqb1rMrRf43u5oI7bR233r6efmdNPi3zdeFOK/VhqKejBfVDutZDunY+lNID6+/nztdWe/Ba279pdZZumw0447JSH1ldJaOfY2vkZSP8e6ORvOOPOs8NPzz08n6wnE174+PjZQG1aItXKsmYsYKgPxlfVq1vYRh3+lqme6rn4wUPlGutpwQ9ZOvWa14YPvfPe74Ve//k3WXP73+muvCcsvv3w6HXvkW3PtddLxb3/rm2GfpJf+bIhvoZuS1NyVw7AkmFHZm+A5544N435U/WOEyvWNEyBAgAABAgQI9F6gEbVsI7bRe4GefVK93DOn2rXUy7Uic56urTF70kN8fONafPPanIZhw4alz1LXXnutsNhii6XPSBdJni1nbyaPn70/ebPXvrPe7BWnexqI7+rN4rWdit11991h9EGfic32eaitSbdKfnQ9efLzebuxd/jLL/tzPt3VDwZuvvFv+VvNKgPxtR21ZY1Mfn5yeGjiw+Ha668Ll19+RVVP/HEdz5UzKX8JECBAgAABAq0j0Ig6thHb6O8z0q61skC8QHzh360Y/h45siO8fO7YcXNsv55159hQnQt7E4if2w2Bgz/72fDlww4N880331z3pjeB+O7C7pUP4rtbZ6471MUKtYH4+Cq++Eq+yqEyEP/kk0+G7ZNwQ+VwxumnhR136HiVXZxfGYiPQfi4PPaaN2DAgMqPdRqvDBh89cgjw2c/c1CndeY049VXXw2bbLb5nFaxjAABAgQIECBAoEkCjbih0Iht9Dff4iuvH0aOPqPLzQjEd8mSzizTA/566t961u3+6Otf0ptA/Nzq0E023jjEHtNjAH5uQ28D8fGNYvFBeeVQ+6a2/grEd3X8tYH4GC6I9xQqhzkF4ntrVhs+iK+Wj6+Yrx1+9tPzwmbJD9OzYbfd9wj/+te/wp/++IfwwdVXz2b36O9tt98eDv7c53u0rpUIECBAgAABAgTqE2hELduIbdR31PWvrV6u3yx+Qr1cn1tvAvFd1arZVpdaaqlw0YUXhPe///3ZrG7/9jYQ392z7cp6tLt1ut2ZOSyorUlrj782EP+73/8+HP/Nb1W1eNPfbghLL710Oq8yEB9nHHjgAeHYo4+uWr9yIv5g/+4J94RDxhwa3nnnnXSR58qVQsYJECBAgAABAq0h0Ig6thHb6O+z0a61skC8QHyh362Lxl9Y1Rv62HHjQneh+HrWLXQnk8aKDsSfdeYZYfvttutyN2PxPWjQ4KplAvEhXHv1VWHFFVescokTsXe66dOnh/hL/2yoDMR//bhjw/777ZctSv/G3vNrh/jDhBkzZqRtPTd5cohBAAMBAgQIECBAgED5BBpxQ6ER22iE7MjRZ4bFV16valOtGIaPB1jUg/mi2qlC78VEPfVvPev2Ylfm+JGiA/Gf+PjHw3e/8+0utxnrtfhD6cpBID6EvpjVhg+66jk/el+c3LsZUfEmu22TN7s99dTT4fK/XBZW+cAH8lPSVa0dF8Z6O1t27XXXhSO/+rX8M0YIECBAgAABAgSKE2hELduIbRQn0n1L6uXubbpbol7uTqbr+UUG4ldaaaVwRVJ/VT4LzbaaPdus7IBNID7TCWGLzTcPhx06JqydvCm89p5CtlZ8S3t8q3kMxXuunKn4S4AAAQIECBBoHYFG1LGN2EYjzkg71soC8QLxhX23Dh1zSBhzyCGd2usqFF/Pup0aLGBG0YH4hyc+kIfep02bFi755S/DLy/9VYi9psfhlJN/EHbdZZd8z9s9EL/hBhuES37x89zjlVdeCT89//zwu9//Ibz22mvp/BuSV9stt+yy6XhlIL721/+nnnZauODC8XlbRggQIECAAAECBOYtgUbcUGjENhqlXvl6u1YNw0fLoh7MF9VOX85vPfVvPev2ZZ+6+2zRgfgrr7g8rLzyyvnm/nrlleFXv/51mJD02BaHj261VRg39tx8uUB8CH0xqw3Ed1cvV76CPv6If+1110/Pwfikd8KNR47Mz0flW97ymUYIECBAgAABAgQaJtCIWrYR22gUmHq5Pmn1cn1eRQbiv3n88eFTn9wn34F/PPpouOCCC8NVV18d4nPmOPz94YfyN2wLxOdU+Ui8f7F5Eo7fetSosO0224TFFlssXxZHvvmtb4ff/u53nXqV765OrvqwCQIECBAgQIAAgVILNKKObcQ2GoXcbrWyQLxAfGHfrcrXq9U2usZaa1fNqmfdqg8WNFEbiI8B9hNO/H6n1iv3s7tXxm2evGY83gTJhsv+8pdw9DHHZpPp3+uvvSYsv/zy+bx2D8SfeML3wp577JF7fP4LXwy33HprPh1vYkx84IH8l/2Vgfja1+lNmDAhHDj6oPyz2ciKK64QFlxgwXRy8vPPhxi6NxAgQIAAAQIECJRPoBE3FBqxjfLJztt7VNSD+aLa6YtmZV1Z207ZauXaQPzjTzwRdt5l19rdDrfefFNYYokl0vlvvPFGGLHxJp3WiTMeeWhiXtc9++yzYdQ221atd9L3Twy7f+xj+TyB+L6Z1QbiuzIfPnx4+PlF4/Ngxf/+97/w4S23Ss/BEYd/JXzu4IPz83HKqaeFC8ePz6ezkdVWWy0MHJDcUEt6+H/88cfD1KlTs0X+EiBAgAABAgQIFCjQiFq2EdsokERTiUBRdW5R7fTlpMxL9XJtIH6fT+0bJk6cWHX4tTXZ8BEjw1tvvVW1TpyofIt27BF+g+EbVdVVH95ii3DeT36cf04gPoShQ4eGIUPmS02Sl42HeC+icjjqa18NB40enc+65tprw/995fDguXJOYoQAAQIECBAg0DICjahjG7GNljkhJTmQrMYViBeIL+ySnJduWuyy887h1FNOzo/9hRdeCLvtvken0HTlMXUXiF933XXDry/9Zd7WpEmTwo47z+4N/sgjDg8Hf/az+fI40u6B+G98/biw37775iaxh8DvfPd76XQMYPzyF78I0TUbKgPxcd6dt98WFl100WxxqAxNxJl77blHOOF7He3F6dtuvz0c/LnPx1EDAQIECBAgQIBAyQQacUOhEdsoGes8vzvZTYtXX3m5T8dSVDt92YnKurK2nbIF4uPrxmOIPRvig/ldd/tYiMH4yqGngfjat6ltNHLjMGXKlLSpLbf8SBh7zg/zt63FmZW13emnnRp22nHHfLNbJb2+TZ78fDrdk5DBuT88O2y99db552Nov/aBeb6wzpG5HX9tsHz0QZ8J8Z5C5VB5Xdx3//1hv/0PSBf3xazWJTZ45113hWOOPTa123TTTcK4c88Jw4bNn+9K5b2O2KPe7bfeki+L5/9T++4XJj70UD6v9rx857vfTXr9/02+3AgBAgQIECBAgEBxAo2oZRuxjeJEtBQFiqpzi2qnL2elsi6qbads9XJtjXnTTTeFMYcdFqZPn5Hvem1N1l0g/orL/xI+8P7355+rrBmXXWaZ8Oc//TEsssgi+XKB+BAuGn9hGDliRG5SW4vO6Q3jnivnbEYIECBAgAABAi0h0Ig6thHbaImTUaKDyGpcgXiB+MIuy3pe7V7PuoXtYEVDK6ywQrjumqsr5nSMvvPO2+GQQw8Ld9xxZzqj8kZM5UPi2g8+9OADYfDgwfns+ID/qaefDvGmxYILdvRSni9MRto9EL/GGmuEP/zut5Uk4bXXXgsvvvhiWHHFFcN883X8wj9boTYQH1/hfsHPzs97GozrxXWee+658J73vKfTa/H22Ovj4dHkdYMGAgQIECBAgACB8gk04oZCI7ZRPtl5e4+ymxatEIivp/6tZ93+OsP33zuhKjAdtxNr3N/89rfh+yf9IN3s3ALh2b5dljzEj72JZ0MMWD/55JNh4eTB/hKLL57Nzv8KxIfQF7Pa8EUOm4zMTLrQG5D06l45TJ8+Ley0y27pOcnmfzkJdHzpi1/IJtO/L7/8cnj11VfDsssuU3VtxDo8/sghnlcDAQIECBAgQIBA8QKNqGUbsY3iZdq7RfVy9fkfO25cOHfsuOqZ/TA15pAvhUPHjKlqOdZCsQf47K1ptTVZd4H4Y44+Knz6wAOr2pr8/OQwfdr0pO5atur5Z1xJID6E+Gx4/IUX5Gaxxo3P4h979LHkLe3LhTXXXLNq2YYbjQjvvPNOOs9z5ZzGCAECBAgQIECgJQQaUcc2YhstcTJKdBBZrSwQLxBf6GVZ+/D+7gkTwqdHH9TlNupZt8sG+jgzBuJjML52+NpRR4fLr7gind3TQHxtj+e1bcaHx7GntWxo90B8dLj8sj+nr6nLTCr/xpsY8SbF/PN39FpXG4iP6+6w/fbhjNNP6/RAv7KdOH7SySeHiy/+ee1s0wQIECBAgAABAgTmSYHFV14/3e/FV14v/PvGi+bJY+jJTmc3LVohEB+Pt576t551e2JZ7zrfP/GEsMfuu3f62PXXXx8O/fL/pfN7GojfeutR4Zyzz+62bnvxpZeqgvEC8SHp1b73ZrXhiwnJPZkRFT3o1Z7UI7761XDllVfVzg7fS3p9//hee3aaXzkjhuk/sc+nwj/+8Y/K2cYJECBAgAABAgQINE1AvVwffVF1d31b7bx2PTVwPet23lLf5iy++GLhpr/9raqDtKzFrDf72pqsu0D8MsssHa5MnkVnz0GzdrK/8e1mscO17EfNAvEdMrW9xGdetX9/dsGF4bTTT6+a7blyFYcJAgQIECBAgACBNhJot1pZIF4gvvCvd7wZEYe7754QYiB+TkM9686pnd4sW2qppUIMsm+15ZZhyJAheROHH3FkuOrqjt7jKwPxd9xxR/jMwZ/L16sdiT2ofeHznw9Dhw7NF7377rvhRz/+cfLL9OWTh8l75fM32+LDIYbk43DDddeG5ZZbLh2PvQjEmyPZcMtNN4Yll1wynYw9qG+86WbZovzvzTf+LcRjicNrryfrbNJ5nXzlOkZ23HGHcMZpp+Wf+N4JJ4ZfXnppPh1HHrjv3vx4J02aFHbceZeq5aeecnLYZeed83mVxz1w4MDw4x+NC5slr2wfNGh27/rR5ZjjjgtfT17pvtJKK6WfffPNN9Ne5/KGZo3stusu4aijjqoKUGTrPPXUU+G4b3wjTJhwTzbLXwIECBAgQIAAAQLztMCqW306xH+VQwzFt2IwvqgH80W1U2ne2/F66t961u3t/nT3uVirffXII8Kee+wRFl100Xy1GJyOAeo49KRWzT4Ye2L74dlnVb3uPf4I+oYbbgiX/urX4fyfnpetGk5NatALLhyfTp/8g5PCbrvumi+r/GH52WedGbbbdtt82QbDN8p7fstmnnXmGWH77bbLJtNaO9bcRQxzO/7aXtb3P+DAcO9991Vt+u8PP5SHG+69996w/4Gzv9u9NasNX8R7CEd97avpDxziec2G+Ha8s84+J1x08cXZrE5/jzv2mLD3Jz6R1/zZCvHcxXs9xxxzbHhu8uRstr8ECBAgQIAAAQIEmiqgXq6fX71cv9n6668XjkmeS66zzjp5L+6xRlpz7XXSxnpSq2ZbjQH7Sy+5JLz3ve/Na8O47J///GcYk7y56+orr8y3UVszVtaT991/f9hv/wOyZkNPnm1XrnPnXXeFgz7z2fzzfRmZ2/GvvPLKyQ8BLs838atf/zp857vfy6fjyA3XXxeWS3rJj0NXz4f323ffcMThXwkLLLBAuk72n3ge4pvIv5I8549eXQ2eK3elYh4BAgQIECBAgEArC7RjrSwQLxDfyt/pHh9bDMTHwjk+HJ86dWqPP9fVijGcvuaaa4T/Pvnf8PgTT3S1Sr/PGzZsWJc9FPR0w9Ghka88X2211cIySy8dHktu8rzwwgs93c18vfiDgmi+8EILh0n/b1J4+OFHwrRp0/LlRggQIECAAAECBAi0gsAO376hy8O4e/wR4aVJD3S5bF6dWdSD+aLamVcd+7rfMUS90EILpfVVX8PksU4dvuGG4fWkp7u///3vTanZYu1f+YP4en3im8waWWvWa1YbiK/sjXCNNdYI711xxfDPf/0rxB+093T44Oqrh1izx3DBo489Fv7zn//09KPWI0CAAAECBAgQINAwAfVy/dTq5frNKj8Ra+VYM7/55hth+vQZlYvqGo9trL/eemmt+vAjj4TYO3wzhng8vR3iM+W+3jOod9uDBw8O6yVusc6Nteqjjz7a4yY8V+4xlRUJECBAgAABAgTmcYF2rJUF4gXi5/Gvrd3vSuDhiQ9U9bre1TpzmnfOuWPDuB/9aE6rWEaAAAECBAgQIECAQAMFuvoFf7Z5gfhMovNfD/g7m7TznNgTXeyRrrfDxIkTwz6f2re3H+/3z80pEN/vG7cBAgQIECBAgAABAk0SUC/3Dl693Du3VvzUhhtsEC75xc/7dGijttk2PPvss31qw4cJECBAgAABAgQIEChOoF1rZYF4gfjivkVaKo3AIw9NzF+j15udGjtuXDh37LjefNRnCBAgQIAAAQIECBDoB4HFV14/jBx9Rpct//vGi0L810pDUQ/mi2qnlWzb+Viu+usV4X3ve1+vCR56+OGw9z6f7PXn+/uDAvH9Lax9AgQIECBAgACBMgqol3t3VtTLvXNrxU8NHz48/OLivt1X2nb77cNTTz3dijyOiQABAgQIECBAgMA8KdCutbJAvEB8oV/YkSNGhDFjDgnx790TJoQJyb/ugtX1rFvoTrZBYwLxbXCSHSIBAgQIECBAgEBbCczppsVV3x7VchZFPZgvqp2+AtdT/9azbl/3q90+LxDfbmfc8RIgQIAAAQIECLSDgHq5d2dZvdw7t1b8lEB8K55Vx0SAAAECBAgQINDuAu1aKwvEC8QX9t0/NAnCjznkkE7tfXr0QWk4vnJBPetWfs54zwTijYsFF1ygZyt3sdZDDz0cXn755S6WmEWAAAECBAgQIECAQLMEunq1XSv2Dh99i3owX1Q7fTnn9dS/9azbl31q18+uttpqYbnllu314T/55H/DpEmTev35/v7ggQceEPbaY490MzNmzgyf2HufMG3atP7erPYJECBAgAABAgQINF1AvVz/KVAv12/Wqp8YMmRI2GSTjXt9eDNnzAy33X57mDFjRq/b8EECBAgQIECAAAECBIoXaMdaWSBeIL6wb9JF4y9Me4avbTD2FB9D8ZVDPetWfs44AQIECBAgQIAAAQIE2l0g3rx4adKDKcNLkx5oSY6iHswX1U5fkOupf+tZty/75LMECBAgQIAAAQIECBBoRQH1cs/Pqnq551bWJECAAAECBAgQIECAwLws0E61skC8QHxh39V/PPJwt22tsdbaVcvqWbfqgyYIECBAgAABAgQIEGgpgRNOH5cfzzeO7PzGqXxhH0YasY0+7J6PdiFQ1IP5otrpYhd7PKue+reedXu8A1YkQIAAAQIECBAgQGCeFGhELduIbcyT+CXe6aLq3KLa6QtVPTVwPev2ZZ98lgABAgQIECBAgACBcgs0oo5txDbKrTzv7V1W4wrEC8QXdvXW05NdPesWtoMaIkCAAAECBAgQIECgdAKNuKHQiG2UDnYe36HspsWrr7zcpyMpqp2+7EQ99W896/Zln3yWAAECBAgQIECAAIHyCzSilm3ENsovPW/tYVF1blHt9EWvnhq4nnX7sk8+S4AAAQIECBAgQIBAuQUaUcc2YhvlVp739i6rcQXiBeILu3pHjhgR4s2I2mHsuHHh3LGze32My+tZt7Y90wQIECBAgAABAgQItI5AI24oNGIbrXNGynEk2U2LVgjE11P/1rNuOc6UvSBAgAABAgQIECBAoL8EGlHLNmIb/eXTru2ql6vPfFfPoavXMEWAAAECBAgQIECAQCsJNKKObcQ2WumclOFYslpZIF4gvtDr8dAxh4QRSTA+PsS/e8KEMDYJwse/XQ31rNvV580jQIAAAQIECBAgQGDeF2jEDYVGbKNRZ2LVrT4d4r84/PvGi9J/jdp2I7eT3bRohUB8dKun/q1n3UaeE9siQIAAAQIECBAgQKCxAo2oZRuxjUapqZfrky6q7q5vq53XrqcGrmfdzlsyhwABAgQIECBAgACBVhBoRB3biG006ly0W60sEC8Q36jvlu0QIECAAAECBAgQIECgk0Ajbig0YhudDqwfZlTesMiaf2nSg+Hu8Ydnky3zt6gH80W10zKwDoQAAQIECBAgQIAAgXlGoBG1bCO20Qhw9XL9yurl+s18ggABAgQIECBAgACB5gs0oo5txDYaIdmOtbJAvEB8I75btkGAAAECBAgQIECAAIEuBRpxQ6ER2+jy4Aqc2dUNi6z5u8cfEV6a9EA22RJ/i3owX1Q7LYHqIAgQIECAAAECBAgQmKcEGlHLNmIb/Y2uXu6dsHq5d24+RYAAAQIECBAgQIBAcwUaUcc2Yhv9rdiutbJAvEB8f3+3tE+AAAECBAgQIECAAIFuBRpxQ6ER2+j2AAtaMKebFv++8aIQ/7XSUNSD+aLaaSVbx0KAAAECBAgQIECAwLwh0IhathHb6G9t9TVSLO4AAB7pSURBVHLvhNXLvXPzKQIECBAgQIAAAQIEmivQiDq2Edvob8V2rZUF4gXi+/u7pX0CBAgQIECAAAECBAh0K1B5Q6HblZIF3zjykDktDkW1M8eNNHHh4iuvH0aOPqPLPRCI75IlnekBf/c2lhAgQIAAAQIECBAgUG6Bourcotopq5Z6uXdnRr3cOzefIkCAAAECBAgQIECguQJF1bhFtdNcje633q61skC8QHz33wpLCBAgQIAAAQIECBAg0M8CRd1sKKqdfj7cPjU/cvSZYfGV16tqoxXD8PEAi3owX1Q7VegmCBAgQIAAAQIECBAg0ACBourcotppwCH3ehPq5frp1Mv1m/kEAQIECBAgQIAAAQLNFyiqxi2qneaLdL8H7VgrC8QLxHf/jbCEAAECBAgQIECAAAEC/Swwavudw6jtdp7rVoroIf6Ga64IN1x9xVy3VeYVKl9v16ph+Ohf1IP5otop8zVh3wgQIECAAAECBAgQaE0B9XJ951W9XJ+Xerk+L2sTIECAAAECBAgQIFAOAbVyfeeh3WplgXiB+Pq+IdYmQIAAAQIECBAgQIAAAQL9LFDUg/mi2unnw9U8AQIECBAgQIAAAQIECBDokUBRdW5R7fRop61EgAABAgQIECBAgAABAgT6USCrcQXiBeL78TLTNAECBAgQIECAAAECBAgQqF8gu2nx6isv1//hik8U1U5Fk0YJECBAgAABAgQIECBAgEDTBIqqc4tqp2kQNkyAAAECBAgQIECAAAECBGYJZDWuQLxAvC8FAQIECBAgQIAAAQIECBAolUB200IgvlSnxc4QIECAAAECBAgQIECAQJMF1MtNPgE2T4AAAQIECBAgQIAAAQKlE8hqZYF4gfjSXZx2iAABAgQIECBAgAABAgTaWyC7aSEQ397XgaMnQIAAAQIECBAgQIAAgWoB9XK1hykCBAgQIECAAAECBAgQIJDVygLxAvG+DQQIECBAgAABAgQIECBAoFQC2U0LgfhSnRY7Q4AAAQIECBAgQIAAAQJNFlAvN/kE2DwBAgQIECBAgAABAgQIlE4gq5UF4gXiS3dx2iECBAgQIECAAAECBAgQaG+B7KaFQHx7XweOngABAgQIECBAgAABAgSqBdTL1R6mCBAgQIAAAQIECBAgQIBAVisLxAvE+zYQIECAAAECBAgQIECAAIFSCWQ3LQTiS3Va7AwBAgQIECBAgAABAgQINFlAvdzkE2DzBAgQIECAAAECBAgQIFA6gaxWFogXiC/dxWmHCBAgQIAAAQIECBAgQKC9BbKbFgLx7X0dOHoCBAgQIECAAAECBAgQqBZQL1d7mCJAgAABAgQIECBAgAABAlmtLBAvEO/bQIAAAQIECBAgQIAAAQIESiWQ3bQQiC/VabEzBAgQIECAAAECBAgQINBkAfVyk0+AzRMgQIAAAQIECBAgQIBA6QSyWlkgXiC+dBenHSJAgAABAgQIECBAgACB9hbIbloIxLf3deDoCRAgQIAAAQIECBAgQKBaQL1c7WGKAAECBAgQIECAAAECBAhktbJAvEC8bwMBAgQIECBAgAABAgQIECiVQHbTQiC+VKfFzhAgQIAAAQIECBAgQIBAkwXUy00+ATZPgAABAgQIECBAgAABAqUTyGplgXiB+NJdnHaIAAECBAgQIECAAAECBNpbILtpIRDf3teBoydAgAABAgQIECBAgACBagH1crWHKQIECBAgQIAAAQIECBAgkNXKAvEC8b4NBAgQIECAAAECBAgQIECgVALZTQuB+FKdFjtDgAABAgQIECBAgAABAk0WUC83+QTYPAECBAgQIECAAAECBAiUTiCrlQXiBeJLd3HaIQIECBAgQIAAAQIECBBob4HspoVAfHtfB46eAAECBAgQIECAAAECBKoF1MvVHqYIECBAgAABAgQIECBAgEBWKwvEC8T7NhAgQIAAAQIECBAgQIAAgVIJZDctBOJLdVrsDAECBAgQIECAAAECBAg0WUC93OQTYPMECBAgQIAAAQIECBAgUDqBrFYWiBeIL93FaYcIECBAgAABAgQIECBAoL0FspsWAvHtfR04egIECBAgQIAAAQIECBCoFlAvV3uYIkCAAAECBAgQIECAAAECWa0sEC8Q79tAgAABAgQIECBAgAABAgRKJZDdtBCIL9VpsTMECBAgQIAAAQIECBAg0GQB9XKTT4DNEyBAgAABAgQIECBAgEDpBLJaWSBeIL50F6cdIkCAAAECBAgQIECAAIH2FshuWgjEt/d14OgJECBAgAABAgQIECBAoFpAvVztYYoAAQIECBAgQIAAAQIECGS1skC8QLxvAwECBAgQIECAAAECBAgQKJVAdtNCIL5Up8XOECBAgAABAgQIECBAgECTBdTLTT4BNk+AAAECBAgQIECAAAECpRPIamWBeIH40l2cdogAAQIECBAgQIAAAQIE2lsgu2khEN/e14GjJ0CAAAECBAgQIECAAIFqAfVytYcpAgQIECBAgAABAgQIECCQ1coC8QLxvg0ECBAgQIAAAQIECBAgQKBUAtlNC4H4Up0WO0OAAAECBAgQIECAAAECTRZQLzf5BNg8AQIECBAgQIAAAQIECJROIKuVBeIF4kt3cdohAgQIECBAgAABAgQIEGhvgeymhUB8e18Hjp4AAQIECBAgQIAAAQIEqgXUy9UepggQIECAAAECBAgQIECAQFYrC8QLxPs2ECBAgAABAgQIECBAgACBUglkNy0E4kt1WuwMAQIECBAgQIAAAQIECDRZQL3c5BNg8wQIECBAgAABAgQIECBQOoGsVhaIF4gv3cVphwgQIECAAAECBAgQIECgvQWymxYC8e19HTh6AgQIECBAgAABAgQIEKgWUC9Xe5giQIAAAQIECBAgQIAAAQJZrSwQLxDv20CAAAECBAgQIECAAAECBEolkN20EIgv1WmxMwQIECBAgAABAgQIECDQZAH1cpNPgM0TIECAAAECBAgQIECAQOkEslpZIF4gvnQXpx0iQIAAAQIECBAgQIAAgfYWyG5aCMS393Xg6AkQIECAAAECBAgQIECgWkC9XO1higABAgQIECBAgAABAgQIZLWyQLxAvG8DAQIECBAgQIAAAQIECBAolUB200IgvlSnxc4QIECAAAECBAgQIECAQJMF1MtNPgE2T4AAAQIECBAgQIAAAQKlE8hqZYF4gfjSXZx2iAABAgQIECBAgAABAgTaWyC7aSEQ397XgaMnQIAAAQIECBAgQIAAgWoB9XK1hykCBAgQIECAAAECBAgQIJDVygLxAvG+DQQIECBAgAABAgQIECBAoFQC2U0LgfhSnRY7Q4AAAQIECBAgQIAAAQJNFlAvN/kE2DwBAgQIECBAgAABAgQIlE4gq5UF4gXiS3dx2iECBAgQIECAAAECBAgQaG+B7KaFQHx7XweOngABAgQIECBAgAABAgSqBdTL1R6mCBAgQIAAAQIECBAgQIBAVisLxAvE+zYQIECAAAECBAgQIECAAIFSCWQ3LQTiS3Va7AwBAgQIECBAgAABAgQINFlAvdzkE2DzBAgQIECAAAECBAgQIFA6gaxWFogXiC/dxWmHCBAgQIAAAQIECBAgQKC9BbKbFgLx7X0dOHoCBAgQIECAAAECBAgQqBZQL1d7mCJAgAABAgQIECBAgAABAlmtLBAvEO/bQIAAAQIECBAgQIAAAQIESiWQ3bQQiC/VabEzBAgQIECAAAECBAgQINBkAfVyk0+AzRMgQIAAAQIECBAgQIBA6QSyWlkgXiC+dBenHSJAgAABAgQIECBAgACB9hbIbloIxLf3deDoCRAgQIAAAQIECBAgQKBaQL1c7WGKAAECBAgQIECAAAECBAhktbJAvEC8bwMBAgQIECBAgAABAgQIECiVQHbTQiC+VKfFzhAgQIAAAQIECBAgQIBAkwXUy00+ATZPgAABAgQIECBAgAABAqUTyGplgXiB+NJdnHaIAAECBAgQIECAAAECBNpbILtpIRDf3teBoydAgAABAgQIECBAgACBagH1crWHKQIECBAgQIAAAQIECBAgkNXKAvEC8b4NBAgQIECAAAECBAgQIECgVALZTQuB+FKdFjtDgAABAgQIECBAgAABAk0WUC83+QTYPAECBAgQIECAAAECBAiUTiCrlQXiBeJLd3HaIQIECBAgQIAAAQIECBBob4HspoVAfHtfB46eAAECBAgQIECAAAECBKoF1MvVHqYIECBAgAABAgQIECBAgEBWKwvEC8T7NhAgQIAAAQIECBAgQIAAgVIJZDctBOJLdVrsDAECBAgQIECAAAECBAg0WUC93OQTYPMECBAgQIAAAQIECBAgUDqBrFYWiBeIL93FaYcIECBAgAABAgQIECBAoL0FspsWAvHtfR04egIECBAgQIAAAQIECBCoFlAvV3uYIkCAAAECBAgQIECAAAECWa0sEC8Q79tAgAABAgQIECBAgAABAgRKJZDdtBCIL9VpsTMECBAgQIAAAQIECBAg0GQB9XKTT4DNEyBAgAABAgQIECBAgEDpBLJaWSBeIL50F6cdIkCAAAECBAgQIECAAIH2FshuWgjEt/d14OgJECBAgAABAgQIECBAoFpAvVztYYoAAQIECBAgQIAAAQIECGS1skC8QLxvAwECBAgQIECAAAECBAgQKJVAdtNCIL5Up8XOECBAgAABAgQIECBAgECTBdTLTT4BNk+AAAECBAgQIECAAAECpRPIamWBeIH40l2cdogAAQIECBAgQIAAAQIE2lsgu2khEN/e14GjJ0CAAAECBAgQIECAAIFqAfVytYcpAgQIECBAgAABAgQIECCQ1coC8QLxvg0ECBAgQIAAAQIECBAgQKBUAtlNC4H4Up0WO0OAAAECBAgQIECAAAECTRZQLzf5BNg8AQIECBAgQIAAAQIECJROIKuVBeIF4kt3cdohAgQIECBAgAABAgQIEGhvgeymhUB8e18Hjp4AAQIECBAgQIAAAQIEqgXUy9UepggQIECAAAECBAgQIECAQFYrC8QLxPs2ECBAgAABAgQIECBAgACBUglkNy0E4kt1WuwMAQIECBAgQIAAAQIECDRZQL3c5BNg8wQIECBAgAABAgQIECBQOoGsVhaIF4gv3cVphwgQIECAAAECBAgQIECgvQWymxYC8e19HTh6AgQIECBAgAABAgQIEKgWUC9Xe5giQIAAAQIECBAgQIAAAQJZrSwQLxDv20CAAAECBAgQIECAAAECBEolkN20EIgv1WmxMwQIECBAgAABAgQIECDQZAH1cpNPgM0TIECAAAECBAgQIECAQOkEslpZIF4gvnQXpx0iQIAAAQIECBAgQIAAgfYWyG5aCMS393Xg6AkQIECAAAECBAgQIECgWkC9XO1higABAgQIECBAgAABAgQIZLWyQLxAvG8DAQIECBAgQIAAAQIECBAolUB200IgvlSnxc4QIECAAAECBAgQIECAQJMF1MtNPgE2T4AAAQIECBAgQIAAAQKlE8hqZYF4gfjSXZx2iAABAgQIECBAgAABAgTaWyC7aSEQ397XgaMnQIAAAQIECBAgQIAAgWoB9XK1hykCBAgQIECAAAECBAgQIJDVygLxAvG+DQQIECBAgAABAgQIECBAoFQC2U0LgfhSnRY7Q4AAAQIECBAgQIAAAQJNFlAvN/kE2DwBAgQIECBAgAABAgQIlE4gq5UF4gXiS3dx2iECBAgQIECAAAECBAgQaG+B7KaFQHx7XweOngABAgQIECBAgAABAgSqBdTL1R6mCBAgQIAAAQIECBAgQIBAVisLxAvE+zYQIECAAAECBAgQIECAAIFSCWQ3LQTiS3Va7AwBAgQIECBAgAABAgQINFlAvdzkE2DzBAgQIECAAAECBAgQIFA6gaxWFogXiC/dxWmHCBAgQIAAAQIECBAgQKC9BbKbFgLx7X0dOHoCBAgQIECAAAECBAgQqBZQL1d7mCJAgAABAgQIECBAgAABAlmtLBAvEO/bQIAAAQIECBAgQIAAAQIESiWQ3bQQiC/VabEzBAgQIECAAAECBAgQINBkAfVyk0+AzRMgQIAAAQIECBAgQIBA6QSyWlkgXiC+dBenHSJAgAABAgQIECBAgACB9hbIbloIxLf3deDoCRAgQIAAAQIECBAgQKBaQL1c7WGKAAECBAgQIECAAAECBAhktbJAvEC8bwMBAgQIECBAgAABAgQIECiVQHbTQiC+VKfFzhAgQIAAAQIECBAgQIBAkwXUy00+ATZPgAABAgQIECBAgAABAqUTyGplgXiB+NJdnHaIAAECBAgQIECAAAECBNpbILtpIRDf3teBoydAgAABAgQIECBAgACBagH1crWHKQIECBAgQIAAAQIECBAgkNXKAvEC8b4NBAgQIECAAAECBAgQIECgVALZTQuB+FKdFjtDgAABAgQIECBAgAABAk0WUC83+QTYPAECBAgQIECAAAECBAiUTiCrlQXiBeJLd3HaIQIECBAgQIAAAQIECBBob4HspoVAfHtfB46eAAECBAgQIECAAAECBKoF1MvVHqYIECBAgAABAgQIECBAgEBWKwvEC8T7NhAgQIAAAQIECBAgQIAAgVIJZDctBOJLdVrsDAECBAgQIECAAAECBAg0WUC93OQTYPMECBAgQIAAAQIECBAgUDqBrFYWiBeIL93FaYcIECBAgAABAgQIECBAoL0FspsWAvHtfR04egIECBAgQIAAAQIECBCoFlAvV3uYIkCAAAECBAgQIECAAAECWa0sEC8Q79tAgAABAgQIECBAgAABAgRKJZDdtBCIL9VpsTMECBAgQIAAAQIECBAg0GQB9XKTT4DNEyBAgAABAgQIECBAgEDpBLJaWSBeIL50F6cdItBKAgMGDAgzZ85spUNyLAR6JeC70Cs2H2phgew7kf1t4UN1aAR6JOC70COmtlopu2khEN9Wp93BEiBAgECJBPzvsxKdDLvScgK+Xy13Sh0QgYYKqJcbym1jBFpeIPvfJdnflj9gB0iglAIDkr2aGXwPS3ly7FQbCfgOttHJdqgtKZDVygLxAvGFXeAbrvOhtK37Hnq0sDY1RGBeFRg0aFBYaOFFwrRp74Y333hjXj0M+02gMIEhQ4eG+edfIEyZ8k545+23C2tXQwTmVYFh888fhg4dFt5+680wderUefUw7DeBwgQWXGjhMHjw4PDG66+F6dOnF9auhuZdgeymRSsE4tXK8+51aM/LL7DwoouGgQMGhtdefcWP0ct/uuzhPCaw0MILh0GDBofXX3s1zJgxYx7be7tLoNwCCyy4UJhvvvmS+uf1pP6ZVu6dtXcE5jGBBRZYMMw3ZEj6XCY+n2nFQb3cimfVMRFonoBnFc2zt2UCmcCg5NnIQskzknfffTe89aZsSebiL4FGC7gX1mhx2yNQrEBWKwvEC8QXdmV5yF8YpYZaQEAgvgVOokMoVEAgvlBOjbWAgJvMLXASHUKhAgLxhXK2RGPZTQuB+JY4nQ6CQL8JCMT3G62GCSQdPQjEuwwI9JeAQHx/yWqXQAgC8T2/Coqqu3u+xc5rerbc2cQcAo0W8Kyi0eK2R6CzgEB8ZxNzCDRDwL2wZqjbJoHiBLIaVyBeIL6wq8pNi8IoNdQCAgLxLXASHUKhAgLxhXJqrAUE3GRugZPoEAoVEIgvlLMlGstuWgjEt8TpdBAE+k1AIL7faDVMQCDeNUCgHwUE4vsRV9NtLyAQ3/NLoKi6u+db7LymZ8udTcwh0GgBzyoaLW57BDoLCMR3NjGHQDMEBOKboW6bBIoTyGpcgXiB+MKuKjctCqPUUAsICMS3wEl0CIUKCMQXyqmxFhBwk7kFTqJDKFRAIL5QzpZoLLtpIRDfEqfTQRDoNwGB+H6j1TABgXjXAIF+FBCI70dcTbe9gEB8zy+Bourunm+x85qeLXc2MYdAowU8q2i0uO0R6CwgEN/ZxBwCzRAQiG+Gum0SKE4gq3EF4lskEF/cpaElAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECizwLBhw8KAAbPD8HE8mZHOS0bCgPd/cO2ZM2dmhzAzdIzHv8nM+P8zZ6TjcTr+e+edd7KV/SVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAv0m0DkQPzDm4GcH4lddc72Z06ZNn7UDXQXi47zZoXiB+H47VxomQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQqB6kD8wI4gfGUgfp3hm8x8/fU3Zn1kVq/wSdfwaU/xs3qFrwzET5nyTseyio0YJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECRQoMSILvQ4cOS0PwA5KJAQOyQHwcjx3FJ/+38RajZk5+/vnZ240h+OT/0v/PA/Gze4l/d+rUMH3GjNnrGyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgULDBo4MMw3ZMisQHxHGL4jGJ+m4TsC8R/dfpeZTzwxafamawPxaW/xSSA+huBnBeTfmTJl9vrGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAwQLDhg5Nw/CxO/gBSTg+DcPHGHxH9/Adgfhd9th75qRJk8Ibb77Vsfk0EJ+MZr3Dd3QVn0zO6iV+xswwZeqUuNhAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQKF4iZ96FDkkD8wBiA7wjDp8H4PBCfzE+2OmDXPfeZ+cLzL4Tnnn++YycqA/GzeofPw/GzQvHTp00P706bVvhOa5AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECMw3eHAYNHhQHobv6BW+o3f4NAofe41PmNJA/Dtvvx0mPfnf8O677yazYk/w8U/yt6p3+Dg/+zcjTJ36bjqNmgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCUQw+9DhsxXFYaP8/JQfIzCp9PJn222GTVz/kWWDM88+1x48cWXkn2Iofe4Kx3h945MfByfURGIT1ZIVpoydWpR+6wdAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQhg4ZMivwPqtH+DT8PnBWID4uSvuGj5n4MGCND646c9W1NgxvvvlW+O9TTye9xCch95h37/hPRwg+TsWUfNpDfBKMn9ExPX369DAt+WcgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ9FRg8aFAYlPxLe4QfGAPxA2eH45Oe4Tt6iU9mpb3EJ38XXXiBmSM33jQMW3iJ8Nzk58ML//tfR/A97kkMwFeF4TuC8DEcn/UYPyMNxc/o6377PAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0scDgQQPDwCwMnwTh01B82jt87Ap+Vm/xaRA+jcN3zFt2qUVmLvqeJcJqa48IU6ZMCU8//Ux46+23YhY+D8R39AwfJ5Pe4eOCNBA/Oxwfe4qfPkMovo2vPYdOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBXgsMGjhwds/wtSH4NByfNB3nZ4H4JCO/wPwLhAFrrrbizP8+80JYf8MNw3uWfG946ZVXwjPPPBNmzEgT8Un4Pebf037iOwXhO8LxHSH5OD4tCcbHvLyBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjMTSDJuIfBea/wsSf46p7hYy/xWRC+Yzy2OCAMHDggLL/88mHA8HU+MHPyCy+FGWG+sP5GI8Og+RYMzzz3XHjxfy/GLHxHCL4jFd8RjJ/VO3zaS3wyPw3LxxT8rPnTkyD9zKS3eLn4iGcgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVqBJOaeZN+TXuGTYHseep/VM/zsXuA7luXLs97hk88useQSYflllw0DVvvACjPnHzIwPDP55bDsssuH939o/TBl6tTwbBKKf+2115NVY+g9/um6l/gsCB+XZuPJ2snozKSX+Rkh7Wg+zjAQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQFsLJPn3pHf3jl7gI0Rl2D0fz4LxFb3Dp73ExxR9EopfZJGFw3JJGH7okCHh/wNIfulaxmAKzQAAAABJRU5ErkJggg=="
-    }
-   },
-   "cell_type": "markdown",
-   "id": "6fcc8f13",
-   "metadata": {},
-   "source": [
-    "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
-    ">\n",
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f0828c2",
-   "metadata": {},
-   "source": [
-    "## Training & Validation Forecast Metrics\n",
-    "\n",
-    "Beyond per-epoch loss, `autolog()` can also run a backtest after training and log evaluation metrics (MAE, RMSE, MAPE, …) on both the **training series** and the **validation series**. Enable this with `log_training_metrics=True` and `log_validation_metrics=True`. Pass `extra_metrics` to extend the default set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "df8e85b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All logged metrics (11):\n",
-      "  manual_mape: 10.7420\n",
-      "  train_mae: 8.4783\n",
-      "  train_mape: 3.9116\n",
-      "  train_mse: 118.1369\n",
-      "  train_rmse: 10.8691\n",
-      "  train_smape: 3.8915\n",
-      "  val_mae: 12.5800\n",
-      "  val_mape: 2.9449\n",
-      "  val_mse: 243.0054\n",
-      "  val_rmse: 15.5886\n",
-      "  val_smape: 2.9130\n"
-     ]
-    }
-   ],
-   "source": [
-    "autolog(\n",
-    "    log_metrics=True,\n",
-    "    extra_metrics=[darts.metrics.smape, darts.metrics.mae],  # add smape and mae on top of the defaults\n",
-    ")\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
-    "    lr_model = LinearRegressionModel(lags=12)\n",
-    "    # if val_series is provided, autolog will log val_* metrics based on it\n",
-    "    lr_model.fit(train, val_series=val)\n",
-    "    # autolog already computed and logged train_mae, val_rmse, val_smape, ...\n",
-    "    # manually log the final hold-out MAPE as well\n",
-    "    lr_pred = lr_model.predict(n=len(val))\n",
-    "    mlflow.log_metric(\"manual_mape\", mape(val, lr_pred))\n",
-    "    run_id = run.info.run_id\n",
-    "\n",
-    "autolog(disable=True)\n",
-    "\n",
-    "# show what was logged\n",
-    "client = mlflow.tracking.MlflowClient()\n",
-    "run_metrics = client.get_run(run_id).data.metrics\n",
-    "metric_names = sorted(run_metrics.keys())\n",
-    "print(f\"All logged metrics ({len(metric_names)}):\")\n",
-    "for name in metric_names:\n",
-    "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8511fc08",
-   "metadata": {},
-   "source": [
-    "## Saving and Loading Models Locally\n",
-    "\n",
-    "You can also save and load models to/from local paths without MLflow runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "645ef079",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Files in model directory:\n",
-      "  - python_env.yaml\n",
-      "  - requirements.txt\n",
-      "  - MLmodel\n",
-      "  - conda.yaml\n",
-      "  - data\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Save model to local directory\n",
-    "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
-    "save_model(model, path=local_model_path)\n",
-    "\n",
-    "print(\"\\nFiles in model directory:\")\n",
-    "for file in os.listdir(local_model_path):\n",
-    "    print(f\"  - {file}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "254ba153",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded model successfully!\n",
-      "Predictions shape: (5, 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load model from local directory\n",
-    "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
-    "\n",
-    "# Test it\n",
-    "local_predictions = local_loaded_model.predict(n=5)\n",
-    "print(\"Loaded model successfully!\")\n",
-    "print(f\"Predictions shape: {local_predictions.values().shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aab0d1e0",
-   "metadata": {},
-   "source": [
-    "## Querying Experiments\n",
-    "\n",
-    "You can programmatically query and compare runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "109a9812",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 4 runs in experiment 'darts-quickstart':\n",
-      "\n",
-      "1. linear-regression-full-metrics\n",
-      "   Run ID: 215983d797274f5c896aeac20b509e80\n",
-      "   Validation MAPE: 2.944912539423521\n",
-      "\n",
-      "2. exponential-smoothing-baseline\n",
-      "   Run ID: 936e44cf2edb4db0bc6e32aa0d9fdfd9\n",
-      "   Validation MAPE: 7.864181481214469\n",
-      "\n",
-      "3. linear-regression-autolog\n",
-      "   Run ID: 1a714494da10495485871221b816f326\n",
-      "   Validation MAPE: 10.742044444678958\n",
-      "\n",
-      "4. nbeats-epoch-metrics\n",
-      "   Run ID: d8206b7ef162477ab8eb12b7e950a440\n",
-      "   Validation MAPE: 17.542747705910518\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from mlflow.tracking import MlflowClient\n",
-    "\n",
-    "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
-    "\n",
-    "# get all runs\n",
-    "client = MlflowClient()\n",
-    "runs = client.search_runs(\n",
-    "    experiment_ids=[experiment.experiment_id],\n",
-    "    order_by=[\"metrics.val_mape ASC\"],  # Sort by best MAPE\n",
-    ")\n",
-    "\n",
-    "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
-    "for i, run in enumerate(runs, 1):\n",
-    "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
-    "    mape_val = run.data.metrics.get(\"val_mape\", \"N/A\")\n",
-    "    print(f\"{i}. {run_name}\")\n",
-    "    print(f\"   Run ID: {run.info.run_id}\")\n",
-    "    print(f\"   Validation MAPE: {mape_val}\")\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22685423",
-   "metadata": {},
-   "source": [
-    "### Load the Best Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "7b09db6a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading best model from run: linear-regression-full-metrics\n",
-      "Model URI: runs:/215983d797274f5c896aeac20b509e80/model\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8225dcbe9d6a4297a294bb56eb3fa398",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "71624304aeb64d108e690b3c8fbe8126",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Downloading artifacts:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "if runs:\n",
-    "    best_run = runs[0]\n",
-    "    best_model_uri = f\"runs:/{best_run.info.run_id}/model\"\n",
-    "\n",
-    "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
-    "    print(f\"Model URI: {best_model_uri}\")\n",
-    "\n",
-    "    best_model = load_model(best_model_uri)\n",
-    "    # series param required for loaded models, since we save with clean=True\n",
-    "    best_predictions = best_model.predict(n=len(val), series=train)\n",
-    "\n",
-    "    train[-50:].plot(label=\"Training\")\n",
-    "    val.plot(label=\"Actual\")\n",
-    "    best_predictions.plot(label=\"Best Model Forecast\")\n",
-    "    plt.legend()\n",
-    "    plt.title(\"Best Model Predictions\")\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "6f1ec89e",
-   "metadata": {},
-   "source": [
-    "## Model Registry\n",
-    "\n",
-    "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived).\n",
-    "\n",
-    "```python\n",
-    "# Register a model from a run\n",
-    "result = mlflow.register_model(\n",
-    "    model_uri=f\"runs:/{best_run.info.run_id}/model\",\n",
-    "    name=\"darts-air-passengers\",\n",
-    ")\n",
-    "print(f\"Registered version: {result.version}\")\n",
-    "```\n",
-    "\n",
-    "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
-    "\n",
-    "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
-    ">\n",
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7af49ea9",
-   "metadata": {},
-   "source": [
-    "## Important Note: Custom Flavor\n",
-    "\n",
-    "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
-    "\n",
-    "**Always use:**\n",
-    "```python\n",
-    "from darts.utils.mlflow import load_model\n",
-    "model = load_model(model_uri)\n",
-    "```\n",
-    "\n",
-    "**Instead of:**\n",
-    "```python\n",
-    "import mlflow\n",
-    "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
-    "```\n",
-    "\n",
-    "This custom flavor is necessary to properly handle:\n",
-    "- TimeSeries objects\n",
-    "- Darts-specific model parameters\n",
-    "- Covariate handling (past, future, static)\n",
-    "- PyTorch model state preservation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40621b13",
-   "metadata": {},
-   "source": [
-    "## Cleanup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "51fc7c4a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Uncomment to cleanup\n",
-    "# import shutil\n",
-    "# shutil.rmtree(tmpdir)\n",
-    "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
-    "\n",
-    "print(f\"To cleanup manually, delete: {tmpdir}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca40afc1",
-   "metadata": {},
-   "source": [
-    "# Final Remarks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4c86a23",
-   "metadata": {},
-   "source": [
-    "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
new file mode 100644
index 0000000000..fff71cb24a
--- /dev/null
+++ b/examples/29-MLflow-quickstart.ipynb
@@ -0,0 +1,5959 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aeddb542",
+   "metadata": {},
+   "source": [
+    "# MLflow for Darts\n",
+    "\n",
+    "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
+    "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
+    "\n",
+    "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
+    "\n",
+    "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f72894af",
+   "metadata": {},
+   "source": [
+    "## Installing MLflow\n",
+    "\n",
+    "MLflow is available as an optional dependency for Darts. Install it with:\n",
+    "\n",
+    "```bash\n",
+    "pip install mlflow\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42e3dcea",
+   "metadata": {},
+   "source": [
+    "## Setup and Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b346ce8f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
+     "iopub.status.busy": "2026-06-24T15:18:43.049088Z",
+     "iopub.status.idle": "2026-06-24T15:18:43.053693Z",
+     "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# fix python path if working locally\n",
+    "from utils import fix_pythonpath_if_working_locally\n",
+    "\n",
+    "fix_pythonpath_if_working_locally()\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "13b13fe4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
+     "iopub.status.busy": "2026-06-24T15:18:43.054722Z",
+     "iopub.status.idle": "2026-06-24T15:18:46.599643Z",
+     "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mlflow\n",
+    "import numpy as np\n",
+    "\n",
+    "import darts.metrics\n",
+    "from darts.datasets import AirPassengersDataset\n",
+    "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
+    "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4d424e08",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
+     "iopub.status.busy": "2026-06-24T15:18:46.600748Z",
+     "iopub.status.idle": "2026-06-24T15:18:46.634540Z",
+     "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# use darts plotting style\n",
+    "from darts import set_option\n",
+    "\n",
+    "set_option(\"plotting.use_darts_style\", True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f9c40d6",
+   "metadata": {},
+   "source": [
+    "## MLflow Setup\n",
+    "\n",
+    "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "88320df5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
+     "iopub.status.busy": "2026-06-24T15:18:46.636565Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.268439Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/06/24 17:34:20 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+      "2026/06/24 17:34:20 INFO mlflow.store.db.utils: Updating database tables\n",
+      "2026/06/24 17:34:20 INFO mlflow.tracking.fluent: Experiment with name 'darts-quickstart' does not exist. Creating a new experiment.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpyqkqfzoe/mlflow.db\n",
+      "Experiment: darts-quickstart\n"
+     ]
+    }
+   ],
+   "source": [
+    "# temporary directory for MLflow tracking\n",
+    "tmpdir = tempfile.mkdtemp()\n",
+    "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+    "\n",
+    "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
+    "mlflow.set_experiment(\"darts-quickstart\")\n",
+    "\n",
+    "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
+    "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d5209e",
+   "metadata": {},
+   "source": [
+    "## Load Sample Data\n",
+    "\n",
+    "We'll use the classic AirPassengers dataset for this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1596e07e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.269548Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.356588Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "findfont: Failed to find font weight 600, now using 700.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training series: 107 points\n",
+      "Validation series: 37 points\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "series = AirPassengersDataset().load()\n",
+    "train, val = series.split_before(0.75)\n",
+    "\n",
+    "print(f\"Training series: {len(train)} points\")\n",
+    "print(f\"Validation series: {len(val)} points\")\n",
+    "\n",
+    "series.plot()\n",
+    "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
+    "plt.legend()\n",
+    "plt.title(\"AirPassengers Dataset\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34858645",
+   "metadata": {},
+   "source": [
+    "## Basic Model Logging\n",
+    "\n",
+    "Let's train a simple model and log it to MLflow manually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bc8f520d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.357516Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.450374Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation MAPE: 7.86%\n",
+      "Validation RMSE: 34.77\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = ExponentialSmoothing()\n",
+    "model.fit(train)\n",
+    "\n",
+    "predictions = model.predict(n=len(val))\n",
+    "\n",
+    "# calculate metrics you want to log to MLflow\n",
+    "mape_score = darts.metrics.mape(val, predictions)\n",
+    "rmse_score = darts.metrics.rmse(val, predictions)\n",
+    "\n",
+    "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
+    "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
+    "\n",
+    "train[-50:].plot(label=\"Training\")\n",
+    "val.plot(label=\"Actual\")\n",
+    "predictions.plot(label=\"Forecast\")\n",
+    "plt.legend()\n",
+    "plt.title(\"Exponential Smoothing Forecast\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35dc864c",
+   "metadata": {},
+   "source": [
+    "Now let's log this model to MLflow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "61406cd9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.451297Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.906785Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/06/24 17:34:21 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Run ID: 3d1f7f864f7240599e3b080109e246bf\n",
+      "Model URI: models:/m-f42705a59eb34300b377f0219577bfa4\n"
+     ]
+    }
+   ],
+   "source": [
+    "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
+    "    model_info = log_model(\n",
+    "        model=model,\n",
+    "        name=\"exponential-smoothing-model\",\n",
+    "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
+    "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
+    "    )\n",
+    "\n",
+    "    # log calculated metrics you want\n",
+    "    mlflow.log_metric(\"val_mape\", mape_score)\n",
+    "    mlflow.log_metric(\"val_rmse\", rmse_score)\n",
+    "\n",
+    "    print(f\"Run ID: {run.info.run_id}\")\n",
+    "    print(f\"Model URI: {model_info.model_uri}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0728690e",
+   "metadata": {},
+   "source": [
+    "### Load the Model Back\n",
+    "\n",
+    "We can load the model from MLflow using its URI:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cae35ffa",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.907802Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.914695Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded model predictions match: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "loaded_model = load_model(model_info.model_uri)\n",
+    "\n",
+    "loaded_predictions = loaded_model.predict(n=len(val))\n",
+    "\n",
+    "# verify predictions match\n",
+    "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
+    "print(f\"Loaded model predictions match: {predictions_match}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bd4597c",
+   "metadata": {},
+   "source": [
+    "## Automatic Logging with `autolog()`\n",
+    "\n",
+    "`autolog()` patches every `model.fit()` call to automatically log parameters, covariate metadata, and the trained model artifact. It also patches the darts metric functions so any metric called inside an active run is logged. For PyTorch-based models it enables MLflow's PyTorch autologging to record `train_loss` / `val_loss` per epoch (see the sections below for details)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5ef7f73a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.915566Z",
+     "iopub.status.idle": "2026-06-24T15:18:50.020337Z",
+     "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/06/24 17:34:24 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
+      "2026/06/24 17:34:24 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logged metrics: {'val_mape': 10.742, 'val_rmse': 51.182}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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zqhzppi/sBg0amDQLwTBVga83pjrh643h641xVPj7jeHrjXEkzBbdx44dQ3x8vDBBM9iQiwtGjBghItPvvfcerl27hn/++QeffPKJScuNQeLaHIFdHiS4WXQz1QVfb0x1wtcbw9cb46jw9xvD1xvjCJgd+l2zZg2GDh1qUJutMGfOHBHNJnH9+OOPY/bs2ejevbvJyxmGYRiGYRiGYRimVke6n3322TIjxmSM9umnn4qUIGPrVLScYRiGYRiGYRiGYWq16HZ1da1wnYoENQtuhmEYhmEYhmEYpjbA4WaGYRiGYRiGYRiGsRIsuh2UqKgo3Lhxo6Z3g2EYhmEYhmEYplZTqZZhjGU4c+YMioqKylzu5+eHhg0bVmrbL730EgoLC/H7779XYQ8ZhmEYhmEYhmGqRub5TFz74TqilsbALdAVvVf3hEc9j1pzWFl01yD33Xcf0tPTxTgjIwPXr19H69at4ezsLB4bNGgQ5s6dW6lt169fv1xBzzAMwzAMwzAMYy2kIgnx/ybg2vfXkbg1Sfd4YXohrn53Da1fb1VrDj6L7hpk9+7duvHKlStx8803Y8eOHQgODhaPRUZGIjo6GvXq1ROp4nl5eWjWrBni4uKQkJAg1gkICEBERESpbT/00EOQJEn3N22LxDxtKzk5WWwrPDy8Wt4nwzAMwzAMwzC1g4LUAkT+EYVrP15HzrUco+tELYlGy5dbwMmldlQ7s+i2YZ588kkRASeRHBsbi169emHZsmVYuHAhfvjhB7FOYmKicIOnv8eOHVtmejltKzMzU0TWScCnpKSgffv2WLt2LQIDA2vsPTIMwzAMwzAMY/9knMnA1e+vI3ppDIqyDTNuvZp4otF9DUXEO2FTIvLi8pG4JQmhI0JQG2DRbeNs2rRJCOPRo0frHnviiSfEj8KCBQtwxx134PLly/D39y9zW5s3b8bGjRsxZMgQpKWloWfPnvjss8/w1ltvWf19MAzDMAzDMAzjWBQXFiN+fQKu/nANyTtTSi0PHhqExrMaIWR4MDROGng19hKim7ixMIpFtyPQvXt3ESGm2malTro6CAsLw8GDBy2yLRLIasGthqLc8fHxQjxTVPvQoUMYNmxYmdui7dD2iDp16ojI+NGjRy2ynwzDMAzDMAzD1J56barLvjLvGnJv5Bosc/FxRsT0CBHZ9mnhbbCMxLdbsBvyE/MRvz4e+Sn5cAtwg6Pj0JFuEtzUOsueadKkSanHqO571qxZot6bBL6bmxtycnIqfK9Uz63G29tbpK8zDMMwDMMwDMOYSszKWJx5+ZyhtmjujUb3N0DEtAi4+hmXmU6uTqg3JRxXv72G4nwJMStihTh3dBxadJMgJWoi0m0pjO339OnTce+99+LVV1+Fi4uLLnJdXFxssddlGIZhGIZhGIYxRsyqWIPodeMHGyF4UJBIIa+I+tMjhOgmbiyKZtFt71CKNwnRa9euoVGjRsJwzN4hMzSKaE+cOFEnuA8cOKBrPcYwDMMwDMMwDGMtinKKhAkaQani3f/sCo1zxWJbwa+dL/w6+CL9RAbSDqch42wmfFv7wJGxfxVay/Dx8UHHjh3x2muvYc+ePVi+fDmmTZtWrZF8hmEYhmEYhmFqJ0k7knXu5KGjQswS3ApU860Qtci+y4FNgUW3jeDn54d27drpotdEw4YNjfbgpp7e1Obr0UcfFa3CPvnkEwwePFj07FaoX78+GjRoUO626tata7RmnGEYhmEYhmEYxhhx6+P1emJMaKUOUr3J4dC4yGI9akmMcEF3ZDSSJElwYBwtvZyxbfh6Y/h6YxwV/n5j+HpjHBX+fjMdqVjC5vZbRZ9tJ08njDg/FM5elcu4PXTnEcStkwV898VdETrccXt2swplGIZhGIZhGIZhKoRqsElwE2ScVlnBrRiqKUQtioYjw6KbYRiGYRiGYRiGMS+1fGzlUssVQkbIPbsJingXpBbAUWHRzTAMwzAMwzAMw5guujVA6IiqpYM7Uc/uyXKr5eK8YkT/pW9D5miw6GYYhmEYhmEYhmHKJetyFjLPZYlxQA9/uIe6V/mI1VenmC90XBdzFt0MwzAMwzAMwzBMucRtSNCNQyvpWl4Svw5+8G3vK8aph9KQeS4TjgiLboZhGIZhGIZhGKZc4tX13KMt5zRef3o93fiGgxqqsehmGIZhGIZhGIZhyiQ/OR/Je1PE2LuZF3xa+ljsaNUz6NkdDanI8Tpas+hmGIZhGIZhGIZhyiT+nwSg2DKu5SVxD3HXmbLlxeYhcVsSHA0W3QzDMAzDMAzDMIxJrcJCR1tWdBMR6hRzBzRUY9HNVMjw4cPx448/8pFiGIZhGIZhmFpGUW4RErfI0Wfqq03O5ZYmdEQIXANdxThubTwK0hyrZzeLbhshOTkZjRs3xoABAyr1/MGDB+Pnn3+GNYiNjUVGRoZVts0wDMMwDMMwjO2StCMZRVlFYhw6MgQaZ7n+2pI4uTkh4tZwXc/uGAfr2c2i20b47bff4OTkhP3792P37t2VEsaZmY5psc8wDMMwDMMwjC2kllvOtbwkEaqe3Y6WYs6i20ag9O3HHnsMkyZNMprKLUkSvvnmG/Tr1w+tWrXC3XffLYQ2ccstt+DSpUt45ZVXUL9+ffFTVFQk1tu4caPBdsaNGye2ozB9+nSxfoMGDdC9e3c899xzLN4ZhmEYhmEYhoFULCFe25/bycMJwYODrHZU/Dr4wred7IqeejANmReyHOYMsOi2ASi6ff78ecycOROzZ8/G4sWLS6VzP/7443jjjTfwyCOPYOXKlRg6dCjefvttsWz+/Plo1KgR5syZg71794ofZ2dnREVFIScnx2A7cXFxSE9P1/391VdfifUpuv75559jx44deOCBB6rpnTMMwzAMwzAMY6ukHUlDXlyeGJPgdvF2sdpraTQa1FdFu6MWOU6023pHzQboPqsYsUlAUVEEnJ3pTGp97q1MWCBw8HvT5zN++OEHEa0OCgoSYjo8PByLFi3CrFmzxPLr169j7ty5WLFiBW666SbxWJs2bXDHHXeIcUhICFxcXODv7y+i1uYQHBysG1O0e968eejcuTMWLFgAd3d3s7bFMAzDMAzDMIzjELdejnITda3gWl6SereG4+zr5yEVSohaHI2WL7awSg15dePQojs2GYhKtO23mZWVJQT2qlWrdDM8999/v0gxV0Q3RcIpvXzkyJEGz6Ua8Kpy6tQpvP/++zh27BhSUlJEWjq9Fgn9Fi1aVHn7DMMwDMMwDMPYJ3EbtPXcGiB0lPXqudU9u0OGB4uU9tyYPCRuT0LIEH2Q0F6xXTVqoYgzJIp0F8LZ2UVcLNX2uiayZMkSkUpOUWsS3ER+fj4SEhJw8uRJtG/fHoWFhUJgu7rKNvqWgtLMKbI+efJkEdmmiDnViffu3Rt5eXIaCcMwDMMwDMMwtY/sq9nIPCMbNft394d7aPVkwda/LUJXR06Gaiy6bRxK8S4uLsa1a1Gi5tkSkWFLQxHtF154AQ8//LDB4/Q3Lfvss8/Qtm1bEYE+fPgwevbsaXQ7VMNNEWo1fn5+BrXhtDwyMlL399GjR5GYmIgvv/xSpKcTJ06csPA7ZBiGYRiGYRjGnl3L61rRtbwkFFGnnt0FyQVyz+70Arj6WTb4WN3YngqtRZw9exa7du3C7bffrnMdV36mTJki2ohR1Ltjx44YNmyYEOJXrlwRzyXjtU8//VS3rYiICJw7d85g++RGTtugqDWJ9jfffBPx8fEGzyEhrjic07bJvZxhGIZhGIZhmNqNgegeY/16bnXP7nqTtT27c4sRs9L+e3az6K5ByECtWbNmIoW8JOPHj0daWhr++usv8ffSpUvRsmVLtG7dWhimkaGaOur99NNPC9dzShFXWoZ9/PHHwq08ICAAoaGhiI6OFlFzBXrtDz74QAj8wMBAdO3aVbwuwzAMwzAMwzC1l/zkfKTsTRVjr2Ze8G7pXa2vX1/tYr4wGvaORiqZk+xgyOnl12wyvZzqtmmfyLXcGFRf7enpiTp16ugeo6g1tQEj4V0SOpVJSUnIzc01cDEn8e7r6yteiyLdtE36W4EEOj2P9oPqykmch4WF6VLOSbh7e3vDx0fum8fY5/XGOB58vTF8vTGOCn+/MXy91SzkHH7sYbnstMkjjdHmzVbV+vqSJGHnwN3IOC3XlA/c1x8+zatX+FsSVgU1CEWlyxLcBAlfteAmqI2XMcFNkGCmFmAl24bRNhQBSBFvteBW6sHpcfpN69HzFcFN1K1blwU3wzAMwzAMw9QSaiq1XK1rIgx6dtt3tJtFN8MwDMMwDMMwDCMoyi1CwmbRd1kYmgX0NB7wszYRt4brenRT5F0qst8EbRbdDMMwDMMwDMMwjCBpZzKKsorEOHRkiE74VjfuoXLPbiI3OhdJu5Lt9gyx6GYYhmEYhmEYhmFsIrVcjeJiTiTtYNHNMAzDMAzDMAzD2DFSsYT4DQli7OTuhOAhZftPVQcBvQN049T9spu6PcKRboZhGIZhGIZhGAZpR9ORF5snjkTQoCC4eOvNldUkpErocl8xut1fjP2nrVdr7RnhAY/6HmKcejgNxYXFdnmWWHQzDMMwDMMwDMMwJVLLQ8o8Ij+sAY5eAA6fB/o/KuHLZZJo82UNArRGbkXZRcg4JbcQszdYdDMMwzAMwzAMwzCI36AS3aPKrufefkwvsAsKgce/lDDlVQlpmZYX3gHd9e7pKQfsM8WcRTfDMAzDMAzDMEwtJ/taNjJOy5Fk/2514F7X3eh6hYUSdh6Xx26u+seXbwO63i/h8DnLCm9/VcuyVBbdDFN5FixYgHXr1vEhZOyGLVu24Ouvv67p3WAYhmEYhrF4anloOa7lRy8CmTny+OYBwN/vaRDgK/99ORro87CEeassl27u194XTp5yrDjFTs3UjFfGM9XGypUrsWbNmlKPP/DAA+jRo4ddn4lt27Zh48aNePfdd8td7/Lly3jmmWdw+vRp8fehQ4fw7bffIiwsDG+//bbBujExMXjllVfE+LvvvoOTk2Gyxuuvv474+Hh89dVXcHZ2NlimbJfQaDSoW7cu+vXrhzFjxpRaXpIPPvgAQUHG3RuLi4uxfv16rF69Gp07d8aoUaNKrVNUVIQ//vgDBw4cQEBAAO644w60bNmyzGOydOlSceyMoX7ftM3Dhw+L1xw5cqTBer/99pt4rfHjx8NUSh6j0NBQ9O7dGxMmTDC6njnnKDk5WezvpUuXxLEcOnSoOP7GXtvU4x8ZGYk33nij1OPPP/88mjdvbvQcde3aFbNnz67wWJw7dw6rVq1CdHS02Nadd96JOnXq6JZ36dIFU6dOxaBBg9ChQ4cKt8cwDMMwDGPLxK+XXcsrahW2/Zh+PLCTBhP6aXDkR2DqaxL2nwHyC4CHPpGw7Sjw3TOAr1fV+nw7uTrBv2sdJO9KQc71HOTG5MIjXDZXsxc4vbyGOXjwIP7++28hbNQ/ZQk8e+LMmTNYsmRJheu9//77uOWWW4QIJq5cuYIff/wRn3zyidhGyYg4CTdaTkJKDYm5t956C7/88gvWrl1b6nWU7ZJY6tWrFwoLC4VoUgSYennJ8+Hubjy95saNG2jRooWIeG7fvl1EP41B7++1115DvXr1xOt06tQJu3fvLvOYNGrUqNQ+bN26VQhsRcR+9tlnYkKDhC8JQrVIP3/+vBC+alFrCiWPEQlv2vbdd99tdD1Tz9HJkyfRtGlTkc1A741mPl966SWxbWOvberxT0pKEs9p3769wfq+vr5lnqPNmzdXeBw++ugjTJ48WWy/SZMmWL58uZgkoX1U8Pf3x+23317hpBLDMAzDMIytk5+Sj+Q9KWLs1cQTPq28y1x321F9BHtgJ/l3ozANdnytwRNT9Ost+g/oPkvC8UtVj3gH9LDzum7JwSkqKpIuX74sftsiL730ktSsWbMyl9N+L1q0SHrsscekp59+Wtq6davB8sTEROm+++6Trl69Kn322WfSQw89JO3Zs0csy8nJkX788Ufp0UcflV5//XXp0KFDpbafm5sr/fTTT2L777zzjhQZGalbduzYMbFt+qHlc+fOlTIzMw2eX1xcLK1atUrs24svviht375dPH7w4EGpX79+kp+fn24bGzduLPX6WVlZkpeXl7RlyxbdY0uXLqVPpjR9+nSxXfVrNW3aVLr99tvF8oKCAoNtvfDCC9KQIUPEMZg4cWKp11K2m5GRoXts/vz54rEbN24YXV4Rqamp0pUrV8R43Lhx0tSpU0tdb2vWrJE0Go109uxZ3WO0Xo8ePUx+nejoaMnZ2Vn69ttvdY916NBBWrdunRh/9NFHYpvKcRo0aJC0ePFiyVyMHYMFCxaIx9TXhrnnaMaMGVLfvn1Lvd65c+fKfe2KOHLkiHhOQkKCyedo2rRpFW73/PnzBuewsLBQatu2rfTAAw8YrEefNVdXVyk5OVmqCWz9+41xLPh6Y/h6YxwV/n6TpBuLo6S1gRvEz+mXz5RzrIqlgLFFEgYUSUHji8TfJVm2pVjyGy2vQz8ew4qkH1YXi/vEyhK7Ic6k/bNVONJt41Ak7amnnhJRYIo6UhrxF198oVuekZEhIn0DBw4U6bCU3hwSEoL09HQR8fvzzz9FhDEvLw/Dhw8XKccKqamp6Nmzp4jqUbQ0OzsbY8eORWJiolhOqclK5JC2sWzZMhGFzMnRFnEAIpr68MMPi6gf/VA0l/aHIvUUIaQIpbKN8PDwUu9v165dyM/PF1HVktx777349ddfRUSaoAgl7aOSDl4yfZsi3BS1ptR8iqhSmnNFUIRUSVOuDJRu3Lhx43LXoZRmOm6tWrUyOK+Uah4XF2fS6/z888/iWNLzFBISEhARESHGDRo0EGn1xA8//AA/Pz8RxbcEyn5fv3690ueIril1arZCeSn25kAp7k888YRIT6fPhLnnqCQUGVeXLlCpQrNmzUqdr27dusHFxaXMDAeGYRiGYRh7IE7tWl5OavmpK0CK9lZrQEfAyal06vjkwRoc/kGDrtrbvNx84P4PJdz1roSsHKnqDuZ2WNfNNd02AImn+++/30AkUNoupRNTbe+JEyfQtm1bnUh58sknMWPGDAQHB+ueQ89X6mgJqpGm5f/++68Q6wSlNJNApnpieozqn0mgUOqvl5eXWGfOnDlwc3PTCTn1fj322GPo2LGjEO5KSjal3VJN7X333ad7XUrnrV+/vkht3rNnj8E2SnL27FkxoeDp6VlqGdXKUpow1bxPmjRJiPm77rpLiJySUDo5TSzcfPPNQpySyCWh+sILL5R77Ckl29XVVRxX2m/i0UcfNXgN5XxUlosXL5YSfTQhoSxT0urLg1K2p02bJsS0eht0bdA5OXbsmBCFsbGxQoDSZAZNklAK+5AhQ0rVZJsDCWk6psYEsqnnaPr06SJFfcqUKZg4caKYZClLcJt7/Ol90z74+PiICQd6/zt27BATRZaC0so3bdokasvV0LVDn5OSKfYMwzAMwzD2QlFeMRI2yUE31wBXA7fwklCdtrqeuyyaRWiway4wZ66Eb1bKj/22ETh0TsLSN4G2jc2r83YLcoN3c29kXcxC2rF0FOUWwdnD0L/JoUR3WlqauMGmyNWwYcN0kUJ1jTLd8NJN8E033SSiruYstyQ7h+5BXnyeiIJedr6GqpXwm45bqDv6b+5j8vpKNFhBEcAkuklQKYKbILE9a9YsYTqlNuwqGVmkc0QC7aGHHhL1s/RD0W8ys6KIJdXVUjSYtqe8nhLdVkNijmrOyUyqoKBARLnVAoP2jYQOCZ++ffsKwU6C21RI9JNYMgZNDFAklYQcibu//vpL7A/VNZdEEXtK7S9Fu6lWnAy1lEmHkqKODNxIlFJtdGBgoG45Gdipa4jVx6cy0DEreUyUmmN11kB5hnQkzimirOa5554TExp0XOhaoWgrvbdnn30W//zzD9577z1xHGiihc4d1ZWbinKMrl69iiNHjghDNPUkj7nniM4NZTrQejQxQxFjmjT49NNPhVhXY87xVyYelEkbmjSizxJlh5BJoSWgzw1N5pAB24MPPlhqOV2/tA7DMAzDMIw9krwrGUVZRWIcOjIETi5lJ0NvP66PVA/S1nOXhYe7BnOf0mBAJwmzPpSE4/npq0DvByWc/AVoWNc8dRbQ01+IbqlAQvrxdAT0NNQtDiO66UaZbrDJqZfENjlEP/LII0IYEhs2bBA3+pQCS6ZWM2fOxMKFC0XasSnLLU1+fB7yYvLEuBBy+qstQuLYWDSY0oVLCh0SF97e3qXSXEseQ5oUoVTz7t27GzxOKeZKtDQlJaXcKCuZYVFEmyKUrVu3Fq979OhRg/RdElEU/aPrgATa6NGjRbq6qVFGSkOn/SgLeu0333wTH374oRBjFB0tKegouksTCPTelONIKc4kVEmwDh482GB92o6Hh4eIuP7+++/C3KykQCxrIqAy0PEu+R5p8kNZVhF0jNu1a4c+fQwncmjSio4HiU6aOKDjQpMjNNEyYMAAYfBFkWUS+LQNc0Q3HSOK4tJxUhzXq3KOCHJXVxzWL1y4ILIQKAX+1KlTIp27Mse/ZMo6TRSQARodD0uQmZkpSi4ovZzKBOiYlITKNBzB+JBhGIZhmNpJwmY5yk2Eji47IEpBPMW53NcL6KRvFFMutw3ToEsLYMqrEk5cBjKygYWbgOdmmLef/j3q4MafUboUc4cV3fPmzRPRLIqkKSmjFPlWmD9/vkh9ViJXJIAoxVURQhUtt0bEmeZiKNJNN83VGem2BJS2SimtashNOSsrCw0bNiz3uVTrS4KkvGNL0Vea/CgLqo+lc0112goU1S4p9mkihX5I8NGkDP1Q5LVkhNkYlPJOkws0SWAskkqCmMQ0CfuffvrJ6DYojZwi7iS21FD0kcRmSdFtaVFdETRBVTLqSkKTrkmazCgP+nzRZ4SOrzHatGkjfui90meLMhyoFpmOqVLvTedZqfc2FfUxoudTSQJ5BlDtf2XOUUlIZH/zzTeiPGH//v0GoruqUPaAJfpCkuCmDBL6vP33339GJx5o2bVr10QUnGEYhmHsDalIQvbVbLgFu8G1TumJZaZ2kLRDDgaRWAoaoM/+LMn5SCBOu2r/DuR5Y7q6atVQg4WvAe3vku/R9p2m3+ZGugPstq7bZNFNN7GUskq1lSSeqRUQ1YoqooFEE5lRqVsU9e/fX0TJTFluDSjFm/aTboopnbpkT2dbhyYnqE6bhJTSa/njjz8WIsiY8ZgaEkmvvvqqSC1WMhFo8oH6DisRT6XdEdVqK5FpilBS/TGlW9M5V0e1qbaXBJK6pIAEIW2Pji2JL4rG0mMEbYOigLSdsgQ4ReKpFzSJ9FtvvdXoOiTm6FhQ1LasemdKoy45wUDvg6LZlJFhrWwKU7jtttvEeaNMD8oEoFRvmsAaN26cLtJN1yi1OyvZX5qM8OgaVrfWMgalmtM6SimCknZNKf9KvbciSOl808QILTMFStmmyRaafKHPfmXO0YoVK4TZn3pihcpMqmqmRmKYPgvKBAFlPdD1YMxsrzxKHn8S0xThVgS3uvxADb0Hem1zW7MxDMMwTHUjFUvIvpKNtKPpSDuahtQj6SJFl9KKXeq4iPtmr8ZVK6lj7I+8xHxknJTv9/06+sEtQPZ2qqg/96DO5ocz2zQC/LyB9Cxg72lZX5oSpFPwaekNFz8XFKYXirZh5j7fLkQ3RVjJqIpqMIcOHSqEA6UeU0op1VAq6bLqaBClXNLziIqWG4NcrenHYIddXHRGX6ag9Aku2dPZVlAicsb2j1yjyRSKUnCpfp6imSSKFy9eLGpe6Tnq96fexuOPPy5qr0mQUKSX0oRJfFFtqpJpQOtQvS6lodM6lJJNIoPEIW2L6mKp5vv06dNCVO/bt08IJNpn5bXIiIyEChmX0XN37twpBBotpxRnEvoUBSUBTIJMSS9WoO3SdUTO48pkQMn3pERzS75P+k0TAZSqTOK65DEkkUfvm1LIafKhrGOloDxGqfIljcCefvppA/dxNUrN9PHjx0X6MR0PmoBQzL/o2JBpHU0q0GeHsgtyc3NF+r7ymlQuQFF5KrlQp+YraeE0aVDWNUzHnMQf+SUo65BQpnNHRnpU601Gc7SMJr+UbZZ3DNTHiCLylD5O0W9yCKfjYM45oh8SwzTBQhNGNAFGzvJUT//iiy8KB3D1c8w5/rQdOr50XdL3Ar1Xuubp2KvfX8lzRMZ/lAlCkyHGjj9lDdAxpc8K1aAr0HVM/cUVyFSQ0uuVz2N1Y+vfb4xjwdcbw9eb/UD3ajmRuUgXAjsd6cfk3yRWjFGYVoirP15H6zcs01XE3qjN32+J2/Wp5UEDAso9BmoTtf4dSQ+Yn1nYoxXw32EgJgm4HiehQah52/DvXgeJm5OQH5+PrCtZNjFRZEpgV0N9w0zZWFRUlKghpZtfitwRFK2jaNrcuXNF/Sw9TjfSiiimlNolS5aIaF1Fy41BUbXvv//e4DESbpZqhWQL0PGjDACKqpUFGZ+ROKbjRhMc6okLEroUCafnK+ZcJSN4JLapFpxq8Y2lB1PaMAl0WkbCSC146LWpjptEBYkZWo+WU+2u+tqg16D9ozRbdVSQJlsoOk5p0pRKbiydmiYTlHZmJKzIRZzct+k8G5u9on0iV3RaTs7rJGJLmnEpUASd9peyKpTtUhq6MQd0ZbkxKKuDIvLGoMg+TS6oIbFPnxc1tJ90vim6TZFRtVkYHScSyCTKFXNBmnAiYzJq66a4nRuD9plq89URcsVxm9LYaVJFMXKjdO5FixYJV3xjlHWM6GuCotUkbuk6MuccKctp0o7ePz2XBC9lTKiNFCt7/JXJKJo0ooi+MXFe0TkqefxpPxQ3ezV0bY8YMUKM6XNLk1jr16+3qiEkwzAMw5iCVCgheVEqsvZnIfd0HopSDf/vGcM13AUF8YVAEeAc6IwW65pC42IfkUPGMsS8E4fUv+Ry4QZfRcCnj7fR9Ugx9n8qAjHJLvBwK8bRbyPhVok+WB8v88c3q2VPnq8fTcDYHtlmPT/hhyQkzpODtvXeDEOdsRX7I1mb8u7TzRbdJAAockk9ohWnbYqIUionCWe68aUbVhLSyg0+iWZqCUWmRhUtt2akm26OqT7a3tLLaxN79+4VEVW1mLdHbP16IyFMKd6WrKGurdBEE00mUUZFTWHr1xvjWPD1xvD1Zttc/zkSp585W+Zy9zB31OnsJ3786HcnP1HLfeSeY4hbI3u/dP29M0JH1b6J5Nr8/ba9x05kX82BxlWDYReGwMXbeBuuqzFAs+nyeFhX4J9PK/d6q3cDk16Ux09NBT562LznJ21PwoHJsmFvg3vqo92HcqZlTWLKNWPy/AQJXYqCUsRVEd00VlJhKXpH9aQkxKm2lsQy1UIqdboVLS/rNc0R2BUdjNr2IbInTK0vthds9Xrj2mPLQWnttoKtXm+MY8LXG8PXm20SvShGN3YLckWdLnVkkU2/O/nBI9zD6PMazKivE91Ri6IRNqbszjaOTm37fsuJzBGCmwjo4Q8337LN9HaIVmFyrHZgZw2cnCqXEdGnnX47+8+aJljVBHQLAOgpxUDqgTS7OV9mJQVQPSf1qT1//ryoj6RaWrWxEtUAK7XE5GRdMsW2ouUMwzAMwzAMw5hH1pVspB6SU4R92/ui/9Y+JhtMBQ8NgntdN+TF5SN+YwLyk/LhFmSZoBdj2yRuT9bXcw8s27Wc2H5M1Z+7c+VfMzRAg8ZhEq7GAofOAQWFElzNKGlw8XWBXztfpJ/IQMbpDBRmFIrHbB2z9pBSUqkmlFKBqR6VIt/qOmKqHaVaUarhpccpeqnua1vRcoZhGIZhGIZhzCN6uT7KHXFruFmOzk4uToiYWg+Xv7oKqUBC9LIYNH6gEZ+CWgClaisEDQgqd13FudzNFehZxYzu3u0gRHdOHnDiEtDVuFdxmfj38BeiW0S7D6cheFD5+24LmB2PJxdlantExkbGjLvI7IjaW1E/b2OCuqLlDMMwDMMwDMOYBtkzRS/Vim4NEH5LadPcioi4LUI3vrEwig99LbluknbIotvZ2xn+XWVzM2NEJ0q4qL0sSHB7ulfNbK9XG/3z950x//kBPfWtgKl1mD1gH0nwDMMwDMMwDMOUIv14BrIuZolxYN8AeEZ4mn2UfFv7oI5WdFEEMf1EOh9pByfrfJYoKSAC+wTAyc3JtP7cnar+2r3b6cf7TpvfdozqzxVS97PoZhiGYRiGYRimmlLL600Or/R26k+vpxtztNvxSVSnlg+sKLVcL4wHdqp6S7nOzQFXbZHz3tPmP9+zkSfcQt10kW6pEv3CqxuOdDMMwzAMwzCMHSIVSTrRTS2fwiZW3nm83i3hcHKXpQHVdRfnF1tsPxnbI0llohZcgYnatqPyb2dnoE/7qr+2h7tGCG/i3HUgJcM80UyeBUq0uzC9EJnn5UwPW4ZFN8MwDMMwDMPYIcm7k5EXmyfGIcOC4RZQeddxV39X1B0bKsb5SQWI/zfBYvvJ2N5kTdIuWXS7BrrCt11pny6FhFQJp6/K424tAV+vqke6S6aYH6hqXff+FNg6LLoZhmEYhmEYxg6JWqZKLb+18qnlRlPM/2RDNUcl7Vg6CtMKxTiofyA05fTc3nlcPx5ogXpuY2ZqlUkxNxTdtl/XzaKbYRiGYRiGYeyMorxixP4dp3OfrjtKjlJXheDBwXAPcxfjhH8TkRcvR9EZx0JxLTelnnvbUcvWcyv0als1MzW/jn5wctPYjZkai26GYRiGYRiGsTMS/k0Q9axE2Li6cPZyrvI2Nc4aREyrp0tBVkfSmdpZz604l1Pr9/4dLbcPzSKAoDr6tmHUwswcnD2c4ddJ3kDWpWzkJ8lO7LYKi26GYRiGYRiGsWfX8ilVTy1XqD9d1bP7zyizxRBj+xkSyfvkGmiPeh7waupV5rppmRKOXpTHHZsBAb6Wi3RrNBr0aiOPk9KAS1FVTDE/aNvRbhbdDMMwDMMwDGNHFKQXIn6jbHTmFuKGoAqilebg08Ib/lpn6MwzmaIPOOM4pB5IRXGO7ExP1w2J37LYeYIi0Jav51bo1Vb/2vsqU9dtR/26WXQzDMMwDMMwjB0RtyYOxXmycAqfFAYnF8ve0te/nQ3VakM9d3BF/blV9dyDLFjPrdBbXdd9xvyMCmVySOnXbcuw6GYYhmEYhmEYe00tn2y51HIFIeQ9tD27l8eIlGTG8eq5K8qQ2K5yLh9ghUh3T216ObH3lPnP9whzh2dDTzFOPZyG4gLbvU5ZdDMMwzAMwzCMnZAXl4fE7XK00quxJ/y7a92oLIirnyvCxtcV44KUAsRvjLf4azDVT2FGoRCnhHcLb3iEe5S5blaOhINn5XHrhkBogOUj3f6+GrRqKI+pdjw3T6p0XTelzKeftN1SCBbdDMMwDMMwDGMnRK+MBYr1Ue7yanItZqi2MNoqr8FUL8l7UiAVysI2aED5Ue49p4DCInk8qLP19qm3NsW8oBA4cgFVSjG35bpuFt0MwzAMwzAMYydEL422amq5Aokyjwg5EpqwKQG5sdyz295RMiRMquc+Zp3+3BY3U+tpH3XdLLoZhmEYhmEYxg7IupSFtCPpYuzX0Rc+rXys9lrUs7v+bVpDtWJDsc/YJ0k7tPXcGiCwf0C56247qh9bw7ncUmZqvm194Owt96hP4Ug3wzAMwzAMwzC2bKBWkghFdIsUc+7Zbc/kJeYjQ1vz7NfRD24BbmWuS7XV+87I46b1gPqh1ot0d2gKeLpX3kyNnPv9u8q+BrlRuciJyoUtwpFuhmEYhmEYhrFxJElC9DKt6NYA4TdbX3R7N/VGQB85Ipp5LgtpWhMuxv5I3ql3LQ+uwLX8wFkgL9/6UW7CxUWDbq3EEFdjgfiUypupEakHbTPFnEU3wzAMwzAMw9g4aUfTkXUpW4wD+wXAU1tvXVVOXJJwObpsoVN/ujrazSnmjlDPHVRBPbdharn1otxGU8wrUdftr67rttEUcxbdDMMwDMMwDFPLUst3n5Aw9PFidLxHQqsZEs5HGhfe4RPD4Owl18xGr4hBUa7W0pqxK5K0olvjqkFAL71IrchEzZrO5cbM1PaerkSkuzuLboZhGIZhGIZhqoBUJCFmRaxONJEQrixHzksY92wx+j0iYcsR+TFqDbV6l/H1XXxdEDZB7tldmFaIuPXcs9veyInMQfaVHDEO6OEPF2+XMtctKJSwW1tbHRECNLF+FQN6talapNvV3xU+Lb3FOP14OopybG9iiCPdDMMwDMMwDGPDJO1MRl6c3LIrdHiIEBnmcuaqhCmvFqPr/RLW7S29fM8p01LMozjF3O5I3K6v5w6qoJ778HkgK0dfz22tPvBq6ocC9YLl8f4zQFGRVOkUc+pDTqUYtgaLboZhGIZhGIaxYXQGapRaPsW80CPVa9/1TjHa3y1h2Vb94w1Cge+e0cBPDhBibzkRxsB+gfBs6CnGCVsSkRttmw7RTPmp5abUc28/ph8PqoZ6boKEvRLtzsgGzl5H1fp170+BrcGim2EYhmEYhmFsFKqhjl0dJ8YuPs4IHRli0vOiEiQ89EmxqNf+dSNQXCw/XjcQ+OJ/Glz4U4NZEzToqRU7UQlAZJzxCKPGSaNvH1YMRC1hQzV7cr1P2iGLbupn7d9Fbq9lSj23tZ3Ly6rrrkyKOaXN27KZGotuhmEYhmEYhrFREv5NRGFGoRjXHV8Xzp7O5a+fKuGpr4vRbLqEeavkem0iwBd4/wENLi3U4H+3auDupinlHF1etLu+umf3n9yz217IOp+FvDi5/1dgnwA4uZUt/yite8dxeRziD7RuVF17CfRupx/vq4SZmndzb7gGyGUXqQdSxWSDLcGim2EYhmEYhmFslKhl+qhyvVvLTi0vLJTw+oJiNJkm4bMl+j7LPp7Aq3cDVxZr8NwMDbw9DVOG+7TTmFTX7dXIC4H95Z7d1Los9SD37Ha0VmEnLgNpmdVbz63QrSXg5FTx5E9ZUDaGvzbanZ9UgOwrcns9W4FFN8MwDMMwDMPYIAVpBSLSTbiFuCFoQNkmWD9vAN74WW+C5eEGPH2bLLbfuNcJdXyMC6he6ki31rW6LOpPj9CNbyyMMu/NMDVCkspELbgCE7Xq7s+txsdLgw5N5fHJK0BmtuRQKeYsuhmGYRiGYRjGBoldE4fiPLkYu97NYXByKfvW/e+depEyewJwaZEGHz3shGD/8sVTUB0NWjaQx4fOU4S8bLFDrcOoLpigFmZF2bbXmokxbDWXtEsW3a6BrvBt52tT/blLopipkf8AXYtVMlM7wKKbYRiGYRiGYRhzXMsnl51anl8gYfMRvVHat3M0qBdseqRSqevOLwCOXix7PervHD5J7hFOdebxmxL4HNowacfSRW91Iqh/oEjBLguqgVacy/19gPZNUO30UpmpVZR1YYw6XfygcZa3kcqRboZhGIZhGIZhyiM3JhdJO+QopVcTT9TpVrbr9K4T+rTyUT2oNta81OA+7VV13SfLXzdsYl3dWNk/xjZRXMtNqec+cw1I1Jbp9+8IOGvFa3XSu4pmajQp5NtejuZnnMlEQXoBbAVOL2cYhmEYhmEYGyNmZSwg6aPc5ZlabdyvFyijepovlgwdzMsXO4G9AnTRxOSdLLodpZ67Jvpzl6R1Qxj0ja+MA7murlsCUg/Zjtkfi26GYRiGYRiGsTGilpqWWk5s2C//Jl0+oof5r0WpxN6e8nhPBWm9Lr4uIo2XyDyfhdzYPPNfkLE6RXnFSN6XIsYeER7wauplk/251VCGRo/W8jgmCbgRj6rVddtQijmLboZhGIZhGIaxITIvZCH9WLoY+3Xyg09LnzLXjU2ScExbh92tFfVXNj9K6eKiFzvX44DoxPIjjEED9KnKyVqjLsa2oF7VxTmyCR+53peXKUERZcW5nCZfurREjdFblXWx70zVRLct1XWz6GYYhmEYhmEYGyJ2VazJUe5/DujHVM9dWfqo6mkrMrEiUy6FJE4xt/l67uAK6rkvR9NEizzu1x5wdamZ9PKSZmqVqev2qO8B9zB3MU49mCoc3G0BFt0MwzAMwzAMY0PE/5Ng0KarPDZUsZ5bobfaOboCsUPRRI2rppS4Y2yznjuognruzYdrrj93uX3jT8NsKKKvRLsLM4uQcS4TtgCLboZhGIZhGIaxEfIS85F6WDaA8mnjA6+G2mJrIxQVSbpINxlQqd2fzUX93Irqup29nHWGVdlXcpBzQ2udztgE1M5NuYa8W3jDI9yj3PWXbdVPsozojholNECDJtrkjkPngIJCqUop5knbbWNSiEU3wzAMwzAMw9gICdT7WqszQkeElLvu4fNAktageVjXqqUFk9hpWk8eHzxbsdgJVKeYc+swmyJ5Twok7fmryLU8MVXCf9pId+MwoEcb1Di9tNHunDzg5GXznx88JFg3jltXCTc2K+BS0zvAMAzDMAzjyEjFEgrSClCQUoCC5ALkp2p/pyiP5evG+ckFKEwrgH93f3T8qj2c3Dg+UptTy0NHlS+6N2pdy6uaWq6u66b63tx8CHO27lpztbLqui9+eElX111/ekSVX5+xDImq6K7a9M4Yf+2gjAl5PHWInJ5d0/Rqo8Gi/yRdirm5xm4+rbzh3dwbWRezxAQEZY+4B7uhJmHRzTAMwzAMYwWyrmTj0B1HkHk+E5BNhE0m+2oOggcHsZCpZRQXFCNxsyyYXP1d4N+9Trnrbzygrueu+utTXfcf/2rFzqnyRTdNDDl5OKE4t1iIbnLAtgXBxqgyDzSUkRBQ7iFZskV/DU0dahvnr7eq1IHM1B6aZN5+0XVYd1woLn9xRXz3xm+IR4M76qMm4elThmEYhmEYK3Dhg4vIPGum4FbdW0YtibbGbjE2TMreFFGPS4QMD4GTS9m36mmZkq72ulVDoHG4BSLd7fXjPafKTy93dnfS1c7m3sgVE0VMzUNR3YyTGWLs19EPbgFlR3gTUiWdiRqVFnStwVZhajo3p1IJebyvEmZqRNi4UN04dm3Np5hzpJthGIZhGMbC5CfnI/bvODF29nZGQC9/uAW6wdXfFa6BrnALcIVrgCvcAl21j7mJx1z8XLCt105kX84W0aqcqBx4RpRtpMU4FvH/avs2kegeoa9LNcZ/h/RpwVVpFaamYzPA012upTXFOZpSlxWX7OSdyfBu4mWZHWEqTeJ/+muoonruFduA4mLbSi0nPNw16NJCwv4zwNnrQEqGhABf8/atTpc68Ah3R25MHpK2JqIgvRCufjUnfTnSzTAMwzAMY2FuLIpGcZ58N9vwrvroubQ7Os/viHYftEHL55qj8exGiJhSDyHDQuDfzV+IFRLfGicNIqZp3awkIHppDJ+bWkT8Rm09txMQMqx80b1R1SpsdC/LiCUyYuveSh5TbXd8SvnR7qAB3K/b1vwjLn2hdx4LHRliemr5ENsQ3MZahx04A7Oh79K64+R2e8X5EhL+03sl1AQsuhmGYRiGYSwI1bZe/zlS93eDmQ3Men7ElHB9ivniaLE9xvHJupwljJ8IStsuLy2YrokNWhM1dzdgUGfr1NNSXXd51OnsJzI5lH7dfK3WLDGrYpF5Tn8NBfQpu547LlnC1qPyuHkE0LkFbIpebfSTAPsqIboJqutWiKvhFHMW3QzDMAzDMBaE0sKzL2XrHJ59Wnib9XyvRl66m+XM81lIO5rO56eWpZZX1Crs3HXguly9gAEdAS8Py0Up+7TTmFzX7eTqhEDttZoXl4+sC7LgY6ofqUjSuckTLZ5rXm66+HJ1avlQ20ktNz75U7mJx8C+AaKMR+kKUJSrrceoAVh0MwzDMAzDWBB1lLvhPeZFuRXqKynm2mg34/gkmNEqbMM+/Xi0BVqFqemtSus1qa5b3a97p9Y1m6mZKPd5bZS7lz+CBgXabWq5YuwWXEcf6a5MFgUZESqfpaKsIp3/QE3AopthGIZhGMZC5MXl6dIY3ULcUHesPr3RHMJuqgsnd/k2LWZFjGglxTgu5FietEsWBB71PeDT2qdaW4WpCQ/WoFGYPCYjq8LC8sVOoFp0K62qmGqPcl8wI8odkyhh+zG98z0Z6NkaGo0GPdvI46Q02WOgMoSNl+u6idi12vSQGoBFN8MwDMMwjIWI/DMKklakNJgRASe3yt1qufq56gR7flIBElSOxIzjkbgtCVKBpDO/Kk8w5eRJ2KatxY0IAdo1sfz+KNHu7Fzg5JXy163T0U+47hPJu5KFmRdTvUT/FaNL7afSlKAKXMsptVwJHNuSa3lJeqtKHSryFyiL4MFBOt+B+A0JYoKiJmDRzTAMwzAMYwHoZi7y1xvyHxoyUKtfpe3pXMw5xdzhif83wWTH6R3H5JZeSqswawgmw7ru8tfVOGtE7awyQZRBvemZ6q3l/kjvWN7i2WYVXhO2nlqu0Esb6Sb2namcWHb2dEbIULkTQH5iPlL2paAmYNHNMAzDMAxjARK2JCLneo4Y000eGaJVheAhQSJFnYjfEI+C1AI+Tw4IRYYTtKLbydPJoEa6olZhoyxcz11ZEyuD1mGcYl6tRK+I0bne0+SH+lwYIypBws4T8rhNI+tkSliKnmrRbYK/QFnUHa8v84ldUzMp5iy6GYZhGIZhLMD1n1QGandXzkCtpAlQvcnhuj6zMStjq7xNxvZIP54unL+J4IFBIjJXHhsPyL+dnIDh3a2zT11ayK3ITIl0E0EDgnTjZBbd1UZxYTEufnTJrCi3vaSWE/6+GrRuKIY4cgHIzatctFuUbLjK7zN2bXyNtLZj0c0wDMMwDFNFcqJyREsawiPcHSEj5XTGqsIp5o6Pct2YkloeGSfh1BV9FDDQzzqCyc1Vg67avs0XbpCRVfkixbeND1wD5dZMSbuTa6xutrYRsyIWWdr2hIH9AgwmP0xJLZ9iw6nlCr20/gIFhbLwrqxHhpIBkHsjF+nHM2DTojsyMhKnT5/W/Zw7d67UOsXFxbhy5QoSEvRfIOYsZxiGYRiGsTcif4sCtAbjVMtNUerKkp4l6SI6fh184dNGdrJO2Z+KrCvyDTbjmKI7ZET5kzX/aKPcSj23NemjTjGvILVX46RBUD9Z1BSmFSL9BPeWr44o94WP1VHu5hU+50a8hF3a1HJKK2/XxPZFdx+Vv8Ci/yo/mRM2rm6Nppib9R/hk08+wfPPP4/33ntP/Hz66acGy6OionDrrbfikUcewS233IK33nrLIHxf0XKGYRiGYRh7vPmN/O2GzlSqwR2VM1Ajof3id8UIGi+h2XQJyemSSP006Nm9hHt2O1qLubQjskD1be8LzwjPctffoKrnHt1LU43O0WbWdXO/bqsTvSwG2UqUu39AhV4AxLKtsAsDNTW3DgY83eXxj+uAlIzKace6Y0KFwSURVwOtw8yehp01axZ+++038TN//nyDZR999BE6deqEdevWYeXKldi1axf+++8/k5czDMMwDMPYG/EbE5AXK9tJh44KgUc9D7O3se+0hK73S3jvd6CwCIhOBNbslpfVuzVcd8cWtTiaAxYORPwmfSu40BHlp5ZTv+xNB+VxgC/Qo3X1RbpNqutW9+tm0W39Wu5PLpsV5S6VWj4YdkFQHQ3uGSOPs3KA+asqtx33uu4I6OkvxpnnspCpNZ+zWdGdkZGBy5cvIzc31+Bx+nvPnj2YPn26+DsoKAgjR47UieqKljMMwzAMw9gj139WG6iZF+WmnsvPfFOMvg9LOHPNcNmBs/INske4B4IHybWaOddykLIv1RK7zdgA8f/E68Y0YVMeB84CqdpuXGSg5uxs3Uhl/VCN6ANO7D8DFFVQp+3d0hvudWX3teTdKSgu0NZbMBYnemkMsi9n6yY7lNT+8rgeJ+kmTzo0Bdo0to9IN/HkVA0Uv7evVkjI1/a0N5e6qhTz6o52y53sTaRBgwZYv349li9fjtjYWMycORMPPPCAWBYTE4OioiI0bNhQ/4+nYUMcOXLEpOXGyM/PFz8GO+ziAjc3rZ2iCVANufo3w1gTvt6Y6oSvN4avt5on+2o2EjcnibFnQw8EDgo0+Z6Daivv/xA4H2noGq2YBZHQUbZVb0o4ErfIr3NjURT8e9aBI1Mbvt+K84t159Q1yBV+nX3Lfb/r9+nHI3tUz7Hp3VZ2u87IBk5ekdChafliJ7BfoDD3KsoqQuqRVPh3lyOLto49XW8iyq2q5W72bFOT9nvJZhhEue3hvSo0rQdM7Aes2ilnAf25ScLMUeYL79AxwTj76jldXXeTxxrDEjhRKwFLiu45c+boxmSiNnv2bLRv3x79+vXTiWO1IPb09ERenpxuVdFyY/z000/4/vvvDR6bMmUKpk6dCnMhEziGqS74emOqE77eGL7eao74r/UmWD4TfXA98nqFz8nO0+CTZf74+V9fSJIcvnFzkfD4zamYNSYdo1+qh8sxrjh6QcKFS9fh5gIUdyiGxlMDKUdC9F8x8H7QE07ujt+ExpG/37L2ZQlxSnj18sD1G+VfO6t3hlGSrBi3Db+Ba9fk51qTVuG+JKXFeN2OJPg5a0PtZSC10Qu5S2uuIDik4gisLWEP11vq6jRkX80RY6+eXsiol46MaxUb1/2+UX/99G0ZhWvXCmFPzBjkjlXiMwC8/1s+BraK0UW/TUYDuLd0R975PKQdTsfFAxfhGiq77leFJk2aWFZ0q2nVqpUQ24cPHxa//fz8xOPp6enw95dntdLS0lCnjjwTW9FyY9xzzz2YMWNGlSPd9AGiKL0psxAMUxX4emOqE77eGL7eaj5SeWmt3L+JesC2f6gt3EO1jj9lsO0oMOtD4JLKD61XG+CH5zRo2ziAqnXRtwNwOQbIL9QgraARujeT18uYmIXoxTEoziyG5xkvhN2kT5V0NGrD99uZ7/VdgBpPaozwRrKgMEZSGnBc2yqsXWOgd5fKmfWZy+h+wLuL5PH52CA0alR+S6rgiSGIfUdOmS8+WYxGjRrBHrCX641S9q/+op8Y6PBKWwQ0ou+N8rkaAxzTloB3agYM6R0Be6NhQ6DXCmDfGeDcDTdcSGyEEZXoU18wqRAXP5QPhutxdzS6rwGqA5NFN6WGk9M4iV6FxMREtGzZUozDwsIQEBCAY8eOYdCgQeIxSh1v06aNScuNQeLaHIFdHvQBsuUPEeNY8PXG8PXGOCr8/aYndn0c8hMLxDhsfF14hpXtPJ2ZLeH5+RLm/qV/zMMNeOs+DZ6calif26uNhN//kVMnD57ToGdbeVn92yKE6FZqOuvdHA5Hx5Gvt4R/E3WO96HDQ8p9n5sPSygulq+J0b1MS2e1BN1bS3B1kUSP5L2nKn5dn2be8IjwQG5UrmhxJxUAznaUkWHr11vUsmjkaKPcQYOCENS34r7cxPLtdO3I18+0YRo4OdlPPbeap6dLmPKq/D4+WwKM6mn+uQobH6YT3fHr4tFkVvVMDJm8p9nZ2SKdfMOGDTh48CA+/PBDnD9/HqNGjRLLqaXFtGnT8Nlnn2HHjh3C3ZyM0yZPnmzScoZhGIZhGPs1UCs7WvLfIQkd7jYU3P06AMd+0uDp6ZpShlg9VPGI/Wf0dYtkmESChkjYlIi8hLJL9BjbhpyTFSOsgN7+cK1TforrxgP662BUz+oTTJ7uGnTWGmOfvV5xuya631dczItzipF2OK06drNWQFFuA8fy57QpMA7qWm6MmwcATbRzjRv3AycumV/X7dvWB16N5QnS5F0pyE829A+rcdHt6+srenTv3r0b3333HQoLC/HLL78gPFw/y3rvvfeK/tu//vqrSDv/6quvDNJKKlrOMAzDMAxjD2Sez0TyzhQx9m7ujcB+AUZbPD30STGGPynhaqz8GPWb/fwxDbZ9qUHLBsbFE6V/urrozdQUNE4aREyR77ukIgnRK7QbZeyOhH8TTG4VRpmmJDCU62dAR1Qr6tZh+06b2Tpsh2wUx1QdahdI3QuI4MFBCOxVcVo5cTlawsGz8rhrS6B5ffuMchM0QfnEFP3+f7rEfNFNE0N1x9fVfY9Sy8fqwKyabkolf/PNN8tcTukY5GhOP5VZzjAMwzAMYw9c/+WGQZswupEryWdLgXmqnrIDOwE/Pqep8KbXw12DTs3lG2WKLqZnSfDzlp8TMbUeLn1+RXcT3uQBDl7YI+ob/dCR5Yvuk5dlx2ZicGf5+qhOerfT4MvlsrjZe0rC6F7lv37QAMN+3S2etfouOjxViXIv3aIfTx1iv4Jb4d6xwGsL5PZ5f/wLvDtLQniwee8rbFwornx9VYzj1saj/nTr17jbbtECwzAMwzCMDVKUU4QbC6PEmBzEI26rZzQ6+d3fslAhPf71Exps+aJiwa3Qs7WyHeCQ3m8LPq18UKez1pz2WDoyzpbvJs3YHgXphUjeI2dJeDbyFP2ty0OJcld3armxSLfS57k8PBt46tJ3Uw+kis8LUzWiFkUj57o2yj0kCAE9TYtyl0otH2L/Z8LHS4MHb5LH5DVAfbvNhVrZKT3lE7YkojDL+k7uLLoZhmEYhmHMIGZlLArT5Ju08ElhcAtwM9qD+6KsyzG0K/DILeaZF/Voo19XnWJOREzTi3yKdjP2ReLWREiFki7KbSxLQs2G/ep6blQ7ZKpeVxu8JudoxdCtPAKVuu58SRiqMVXrknDxE31f7hbPaYvsTeDiDQmHz8vj7q2p37X9R7qJxyZrdCU4lE2UlWOe8KZSndAxoWJcnFuMhP+0qSRWhEU3wzAMwzBMZVPL7zFuoPbTOv1N4N1jzL/R7akyUztw1vCGMvyWcGhc5G1GL40WdYmM/RD/j+mp5SQmdhzXi99WDVHt0KSAEu1Oy5RLHiqiZIo5U3luLIpCTmSuGAcPDUJAD7n1siks3epYqeUK9YI1uH24PE7JoO9bmA11nFCgFHNrw6KbYRiGYRjGRNJPpouUWcK3nQ/8u9cxKpSWaOso/byBWwaaf3hbNQB8vYxHut2D3RAyPFiMc2PykLSDRY29IBVLwnmecPZ2RmDfgAr7uufLXekwqocsgGuC3tq2dQS1DquIoP76VlbJLLorTVFuES59qq/lbmlGlNuRXMuN8dQ0/TX52VIJRWZOPgb1C4SLn4tuIowyCqwJi26GYRiGYRgTuf6zKsp9VwOjImjZViBTLr/EtKGAl4emUi693VrJ48h4ICbR8IaSU8ztk7QjachPkFsUBQ8MgrOHsxmp5TUXqTSs665Y3HiEucO7hVyrnno4DYWZ1q+ZdTQStydh5+A9uig3TbRRLbKpnI+UcPSCPnOmcbjjRLqJjs00GNlDDHE5Gli5A2bh5OakyzQpTC+0ekYGi26GYRiGYRgTKMwoRNSSaF2Ust6U0gZqxE/r9aLknkqklpc0UyMOaFv+KNDNoksdOUoTuyaORY09ppaPKj+1XG2i5uwMDOuGGoMmgGgfTI10q1uHUf16yj7ZOI6pmNzYPByZfQz7bz6IrAtZ4jEnDye0fLGFWYfP0VzLjTFHFe3+eLH5ZTZ1x8l13UTs2jhYExbdDMMwDMMwJhC9IgZFWbITc73J4XDVpiaW7IlLKcEE1d/2VkUIzaWnykytZF03RUjDbw4T46LsomqpSWSqTvy/esMmpUSgLK5ESzgfqY801/GpOeHk7akR/eOJU1eptlsyr66bSyAqpLiwGFfmXcX2XjsQszxW9ziVsPTd0At1OsldC0xl8Wb9ObrVwVLLFUb0ADo01U8G7T5hnvAOGRYsJjSI+PXxovzDWrDoZhiGYRiGqQBqAXb950hVanl9o+v9ssEwyl2VGtweKjO1knXdRH2Vi/kNdjG3eXJjckWbN8Kvkx88wj3KXX/jAf14VI+aj1T2bqtvY1cy88IYgf1YdJtK8r4U7Bq6B2deOofCTHlizzXQFR2+aIc+63vBr4N5gvvMVQkntKXgNPHXKKzmrx9rQN+v6mj3J2ZGu128XRAyRJ78yovLR+pB6znts+hmGIZhGIapgLQj6Ug/niHGdbr4oU7n0gZq1Erplw3aGywn4M5RVTusDUL1rZpI5JDwV+Pfwx9eTeR+yEnbk5ATJdd+MraJYqBGhI4oP8pNbFTVc4/uhRqnT3uNWf26yfDPt62PGKcdT0dButYRjtGRl5iP44+dxN6x+5FxKlP3eIOZ9TFoX380uKO+aG9lLmrX8mkOmlquMH04EK717ftrh9wmrdIp5muslzHEopthGIZhGKYC1P2wG95tvE3YliPAtVi90zS1talqFEep66a2OJeiSi/XGapJQPQy7tlty8RvNL1VWEGhhP8OyePgOkDXlrCZSDex1wQzNXVdN4qB5N1c161Abf4oc4ZSyW/8qf9g+3X0RZ8NvdDhs3ZwC3Sr9LlSu5Y7amq5gpurBv+bLH/X0rzk50vNE92ho0OgcZafH7c2rtTkZlnkJ+WL/wtH7jtm0vosuhmGYRiGYcqB6vzoZkzcOLlpEDZRrqUurzf3PWMtE13qoarrNpZiHqEyc6MbQFNvGJnqpSivWLhRE24hbqjTpXSmhJo9J4GMbH3dqlMlop2WplmEPAFA7D1dOvPCGEED9K3DuK5bJu1oGnaP2ouTc06jIFV2dafWVW3fb41+m/qY1Ye7JHRO/vhHwqkr8t/9OgD1Q2v+2rE2D9xEvgPy+Kf1QFKa6d+DbgFuCOwnt+7LvpqDjNP6jIOSxzb9dAYufnYZe8bsw6bWW3Ds4ROIWamvvy8PFt0MwzAMwzDlkHY4TfTDJoIGBxs1UCNjqeXb5HGgHzCxn2UOKbX6Udh/pvSNpFdjLwT0lm/SM89lIf2EnALP2BbJu5N1JnxkoFZRyrDaBMsW6rmVzArFGDA5Hbig755XJqIPuXb3rd2SyR4mXk49dwa7hu8V5SoKEVPriVTyxrMa6SKu5kKCcP1eCd1nSbjjbcnhXctLEuCrwb1j5XF2LjBvFcyi7ri6urEywar0SY/flICTz5zGls7bsXPAbpx/+wJS9qeK7A1zYNHNMAzDMAxTDtSSSyFsvL7+T83izUCu3H4Ztw8H3N0sFOkup22YQr1b9dHu+A3sYm7vqeWZ2RJ++0cee3kAkwbAZujTTlXXfbLi9V39XeHXUTYByziZgfxk7YeklkGi+MT/TuLaD9dFKQjh09oHvVf3QKdvO8A91L3S2952VMLAxySMfVbC4fP6x0d0B2ZPQK3hiSka4aVBfL1CQl6+6dHusLH673WKXF//NRIHZxzGphZbcHDaYVxfEIncG4aeGT6tvNH0scbiHJpC6alahmEYhqlFiJSx9HTExsYiJiZG/C75k5CQgIEDB+KTTz6Bk/Jfnak110es0o7LCag7OtSqvblLEuinQbMISdRz0w011fq6uhhuv+6oEJx6Wh7HbUhAi2ebW+z1GctcQ0p/bo2LBsGDyzdRW/ifPrWcJnBqslVYuXXdpyXcZcK1Tq3DFNd2inaHl1Ge4chceO8iopfF6Htuv9AcjR9oBCfXyv8/2X9awss/SPj3oOHjnVsAb9+vwdjecnZCbaFpPQ1uGShh2VYgNhn4cxOV+Zj2XI96HvDvVgeph9JExtDJJ0+XWodKi8iRnybN6IeyjMyBRTfDMAxTa8jKysK7776L06dPG4jq3NyKXZ8PHz6M4cOHY9y4cdWyr4xtQPV92ZdlBRTUNxBuQaXNjc5ek0SPWIJ6xnaxsOkVpZiT6KZI+snLpbdPN4xkwETu6iRuqDVVRe2omOoj63wWcq7liHFgnwCj5Qlqgf7tSv0EzoM32ZZoojZ2NO9YXGyag7lipnbl66tinFwLRXfkH1G4+Im2f5cG6PJ9J9RVRVbN5fglCa/+KGHVTsPH2zQC3ryPhKdteADUBHOmabBsq6RrH3b3GNMnHuqOrytEtxq3UDeEjpBFdvCgILj4Vl46s+hmGIZhag0vvvgivvzyy0o/f8mSJSy6a3Fqed0J+ro/NT+ro9xjq9ab2xg9W2uwcJOkM1MzJurpplBpaUatqRrcabyPOFP9XP3+um4cUkFq+YEzwJEL+tKCbq1sSzz5emnQvomE45cg+kBTKryPV/n7GNg7QNQqk2N3bTNTS9yahJNP6Wcn2rzTutKC+3ykhNcWSKKURe1h1yQceP0eDWaMAJwrWRPuKPRup0G/DhJ2nYAwk9u43/R2e43ua4DUA6nIS8hH8OAg8Z1ap7NfpVq2GYNFN8MwDFMroNTx+fPnGzwWHByMsLAwg5/w8HCDvwMDA9GqVSuRgr5y5Urk5eXB3b3y9XeMfRGnruc2crNcWCjh143y2MUZuGOk5fdBbaZ24KyEB4xEP+kG8eLHl3X1wyy6bQNyLL/+U6QYO3s5I/ym8qO88/623Si3Qp92FG2Vo93kYj68e/nrU3SQetunHkxD5vks5MXlwb2u43+HZpzNxOG7j0IqlM9p49kN0eSBRmZv51qshDd/lvDLRqBI9uIT1AsGXrlLNhCjtlmMPtq964R8zD9eJGF0L9OOjYu3C7r91gXWgkU3wzAMUyv46KOPhGAmnnjiCXz44YdwdXU16bmTJk3Cr7/+KoT3P//8gwkTapE7TS0m61KWrn2Mf/c6Io27JP8cAGLkTlAY3xcI8bf8zS/VaDo7yzfcxtqGEdSCilIh8+PzkbgtSbjuOns4W3xfGNMpzCzEicf1Uc5Wr7aAZ0TZaf8pGRIW/SeP6/gA04ba5tEe3EWD+drJAcrAGN7dhLru/oFCdBPkBt1ghmNnYtDEwoFph1CYUajrBd3mbZUrookcuyih/yMSMuXqBAG1bXvhDg0emgR4urPYLgl1jmgeAVyMguh1T8ewU/OaP07sBsMwDMM4PHFxcZg3b54Ye3p64vnnnzdZcBNTp07VjRcvXmyVfWRsD52BmnAtr1utBmpqvDw0olacOHUVyMop7cpLKZBUe0gUZRcheVftSuO1Rc69eR4517W13H0D0Oi+huWu/+sGIEeeF8Rdo6jvcM0LhbJEjZ+3PF6yxfj1WJK6Y0IN+sk7MoVZhTgw/bDO7dqvkx86f9exUu3Anv1WL7hpIuat+zS4vFiDp6ZpWHCXAaXYk5O5wpItpruYWxMW3QzDMIzDQ67jOTnyncuDDz6IunWNC6iyGDFiBOrUqSPGq1at0m2LqT2p5XXHlU4tT0qT8PcueRwaAIzpbb196akNklFKr7otkBp1KypyMWdqDnLpvvajnFbu5OmEDl+2L7c2lAzU5q3SiwNjJQS2Ak0CKVF4EoTkFl0RdbrVgXdzWakn70pB9jWtPbuDQXXrR2cf17m1e9T3QPc/u4rUZXPZfUISmTRK3faVxRq8fJdG1NUz5XPrYP14tfY7uqZh0c0wDMM4NNTua+7cuWJMtdjPPPOM2dtwc3PDzTffLMaZmZnYuFFbxMs4LDlRuTonW992PvBuqg3tqaCWNPkF8viOESjVysuS9Gij33ZZKebkrkttbQhqUUVCjqmZSOfx/+mbWLd+pSW8m5TfXmj7MeCs1m9tYCegbWPbFlbqrA51tkdZkLlgxG36fvJRSxwz2n3m5bOI1054US17j8Xd4BFWufr1137SH9eXZ2oQ4Gvb14QtUTdQo/PCIMM/qouvaVh0MwzDMA7Np59+iuxsOaoye/ZsYZRWGdQp5uRizjg2ceviKkwtL+labk3UZmr7zxi/gaSbfOojS1Bqa8YZuR6dqV7OvXlB1yIsoE8AGs0qP62cUEe5bdVATU3vdkAr7dvadhS4HF2xqImYWk+0zFJSzB1tUujq/Gu4+t11XT/2rj93hm9rn0pta8cxCZu0/bebRQB3jrLkntYOJvTVf47W7EaNw6KbYRiGcViSkpLw9ddf66LVzz77bKW3NWzYMAQEBIjx33//zSnmDk7cmniD/q3GeuUqad7dWwPtm1pXKLVtRGm95Ue6S6aYk4s5U70k7UrGtR+u69LKO37ZrsKWQ3HJEpZvk8ch/hB9lm0dilyro92/bKhYQJOJXNDAIDHOvpKDlH2pcBTi1sXj9EtndX+3/7StaDtVWag1mMIrMzVWzaJxVMb31Y/X7Kn5CR4W3QzDMIzD8vnnn4t0cOL+++9H/fqVd8xVp5hnZWVh/fr1FttPxrbIT8pH0m7ZiMyrqRd825SOVv20zvoGampcKHKm7c99NRZISDV+Exk6Si+6E/5h0V3daeUnVGnlrSit3EhZQkl+WgcUyCbXov2Tu7ZEwNah6KuTVkn8soH8BioWNvVVKeY3FkbBEUg9koajDxwHtG+/2VNNq+TOvu2ohC1H5HGL+hD9txnz6dQcqK/9Otx8WO4pX5Ow6GYYhmEckpSUFHz55ZdiTE7lzz33XJW3ySnmtYO4DfFAsTwOGxcqonpq8gsk/P6PPHZzBaYPr579UszUiANlRLu9GnnBR5vSmnIgVUwgMNXD+bcvIPuqNq28tz8am5BWTkJVab9Fl9nsifYhuIl6wRqM6iGPr8VCJxTLgwwJnb3lVnaxK2OF0749kxOZg4O3H9a9j/DJYWj5YvMqbdMgyn2XRky4MeZD39tKtJu8NzYdQo3CopthGIZxSEhwU19t4p577kHDhhXfAFfE0KFDERgo18yuXr1aVyvOOBaxq1X13BNKp5av3QMkyh5rmNQf1WZw1NPATE2qOMVcAuL/5Wh3dZC8O1lXzyunlZfvVq6wcb+cuUCM6gk0rWdfAkvtZaDO/igLcvEOnxQmxoWZRYhVeSfYGwVpBThw22Hkx+fr6vc7ftWh1CSdOWw5LIkaeaJlA2D6MEvtbe1kvEFdN0e6GYZhGMaipKWlidRywsXFBS+88IJFtksR81tuuUWMSXCvW7fOIttlbIeC9AIkbUsSY496HqjTRW4VV2ZquZUN1NT0UJmpHdCXj5abYk4u5kz1upW3erkFvJtVnFZeykDNjqLc6p7dgX7ymOrS0zLNSzGPWmR/LuZSsYTUg6k4NPMIMs/K5UtezbzQ7dfOcHavfDyTjOXUUe5XOcpdZYZ2BTzd9ZOlppRAWAuOdDMMwzAOB5mnpabKJj0zZ85E48aNLbZtTjF3bBL+TURxvqRLhS0ZrYxNkrBunzyOCAFGdK++faNevUF19GZqZbk/+3evA9cAVzFO3JyE4nxtrjxjFc6/c0EYgxEBvSitvJFJz4uMk7Bmj/5aGtfH/k4Q1Z/fri2vyM0HFm+u+DkBvQPg2chTjBO3JSE3Ohe2TkF6IWL+jsWxR07gvzZbsXvUPiTvTBHL3IJc0WNRV7gFulXpNajueMdxedy6IXAbR7mrjKe7BsO7yePYZODQOdQYLLoZhmEYhyIjI0O0CSOcnZ3x4osvWnT7Q4YMQXBwsBivWbNGmKoxjkPsmvJbhf3xL1CkLUOdOYquseqLTlLaqlLXTentV2OMr+fk4oSQYfI1WphRiOS9sjhgLE/ynhR9WrmHEzpQWrmJ18QPayUUa+dDZo2339pds3t2O2kQMU0b7S4GopbaZrQ763IWrsy7in03H8Cmlptx5J5jIjKfn6j3SXDxc0G337uYZJhnVpT7bk21frfUmhTzPRzpZhiGYRiLMHfuXCQny87TM2bMQLNmzSx6ZCldffLkyWKck5ODtWvXWnT7TM1RlFOEhE2JuugVmWGVvDFWi4q7q8G1vCScYm47kHmWSCuX9GnlPs1NE18FhRK+Xy2PnZ2B+8fDbunSEuio/Zrdewo4e82EFPNpahdz2+jZLRVKouXbmVfPYVuvndjWYyfOvHQOSduTIRXo94+M4CgLpsMX7TDowAAE9JRbSVaFfw8Au07I4zaNgKlDqrxJRos6g2T1LtQYHOlmGIZhHAZqD/bJJ5+IsZOTE1566SWrvI46xXzx4sVWeQ2m+knckqRzIQ4dEyoixmoOngVOXZHH/TqQ0VH1i25TzdQo0q1EXLmu2zqco7Tyy7KZYkBPfzSebVpauXLzHyNbB2BiX0ovt9+oJmVgqCegfjYh2u3V2EsYjxFZF7KQdljrTFgDkMP/if+dwvnhl3Bg0iFcmXsVWRcNM5g8G3qi0ayG6LGsG4ZfGIpuv3ZBgzvqwz24ainluij3T/pj9hpHuS0KfbaUdotHLgBRCTUzwcOim2EYhnEY5s2bh8REOVI5ffp0tGyp/U9rYQYOHIjQ0FAxJjM1SmlnHD+1vKaj3EQPVdswqusuC9c6rgjoI0fqsy9lI/MCl0FYEkrZvzr/WqXSyksZqN1kv4Jb4Y6RgIvcCQy/bgQKC83s2V1DhmrpJ9Oxa/heRC2MRnGmyvvASXYjb/VaSwzY1Q+DDw9Au/fbIGRIcJXM0spysKcMAaJdE2AKR7ktzgRt6zDFUK0mYNHNMAzDOATkJv7RRx/pIi/WinKXTDHPzc0Vtd2MfVNcUCz356bz6+OMoIFBBstz8yQs3CSPyQ23ptI/QwM0aCx3XMKh8+WLm9CR8sQQwdFu66WVt3yxBXxamF7TeyFSwr8H5XGzCGB4NZrxWYsQf31PZIrg/3Og4ueETQwT7dXEc1bEoCiveg3/ov+Kwe7R+5BzXTbBc/J2QvgtYeg0vwOGnxuCPmt6otn/msC3tU+V2oCZU8v9+j0aOJnQao6pfF336hpqHcaim2EYhnEIvvvuO8THx+vSv9u0UfVXsgLsYu5YJO1MRmFaoRiHjAopFc1atRNIlbsD4dbBgJ93zd0YK3Xd2bnAGTnYWn6/biG65c8GU3XOv3dBZA8Q/j380eRB09PKie9W62/6H5joOCLLXEM1Vz8XhI2TM0oKUgsRv7F6rlGpSMLZN8/j6P3HUZwjC/06Xf3QdGkjIbgjbq1XZSdyU1m/V5+xQnXxtwyslpetdXRtCYQFyuNNB+m7s/qFN4tuhmEYxu4hQ7MPPvhA9/fLL79s9dccMGAA6taVbxjXr1+P9PR0q78mYz3i1sSXm1r+1w79Tdpdo2tWJPVsra7rLns97+Ze8GrqJcYpe1JRkFZQHbvnkOQn5yPyjxs4cNshXPlWm1bu7oSOX5mXVk4ZEz+tl8durlSmAIdhTG/KxJDHf+8CktIqFjYR6p7di62fYk6fgYO3H8blL67o92F6PfRc1R2uoXKbverCWC23o0zA2BpOTvpMDGptt+WI5c7hgx+blqHBopthGIaxe3788UfExsaKMaV9t2/f3uqvSe3Ibr31VjHOy8vD6tVaK2LG7qDIV+y6OF19rtJuS+00vWG/PPb3AQZ1Qo3SU5XEUZ6ZGqXEKtFueo8Jm2W/A8Y0cmPzcG3BddEy6r/WW4XZFvVx16eVNzcrrZxYvo3EqDy+dZCclu0ouLpocOdIeZxfAPypLccoj+CBQfAIdxdjOrZ58XlW27+Ms5nYNWKvrkMBTZa0fbe1mDhx9tAWpFcjVFtM5oxEp+bApAHVvgu1ivF9VCnmuySLZSrM/9u0dVl0MwzDMHYNCd73339f9/crr7xSba/NKeaOQcqBVOTHy713Q4YGw8XbxWD57pNAmja1fHQvqunX1HiqpJP2Du6A9qa9LEJHqVLMNyZYec/sn5zIHFz59ir2jN2Hze234tQzZ+SWUUX6m3QSiSS4mzzU2Oztf6syUHtokuMIbgVzXcxJ+NabKke76RhHLy+j+XwViVsXjz2j9urKAlwDXdFzeTc0fqCR1eq1y4Nruasf8k5w11YNrNkjnwNLZipUBItuhmEYxq5ZsGABoqKixHjSpEno1Kn6wpD9+vVDeHi4GG/YsAFpaTXX9oaxnmv52j36G6txqmhJTeHjpUFbbRnx8ctATl7ZN36BvQPg4itPIlCEr7iwes2q7IGsS1m49Pll7Bq2B1s6b8eZl88hZV+qLqJNeDX2RNPHGqPvxl4YcnwQms9pZlZaOXHikqTrxUwu1dR2ztFo31SD7lqH/cPngeOXatbFXCqWcOGjizh05xEUZsrtAH3b+6Lff30QNMDQLLE6+XunfHyILi2Am/rX2K7UGrw9NRjaRR5HJQBHL1guU8EUWHQzDMMwdkt+fj7ee++9Golyl0wxp335+28T88wYm4GiFXFa0a1x0RhEhhXW7JZ/U0BsdE/YBEqKeVERcER7824MJzcnBA+VxUVBSgFSD/LEkEJOVC72jNmHbT134txbF5B21NCXwaeVN5o/3RT9t/bBoIMD0Pr1VvDv7g9NJetu5/+tahM2UVMjEdZqN1RbV7Ho9mnpgzpd64hxxskM0cbLEhRmFOLw3Udx4f1LusfCbw5Dn3U94dXQEzX5nfP6z4aO5Y56Ldiyi/maPZbLVDAFFt0MwzCM3fLLL78gMjJSjMePH4+uXbtW+z5wirl9k348AzmRuWIcNCAQrv6GZkpXoiWdQ3ifdkCwjdTg9mhjmplaKRdzTjHXRUCPP3wCKftTDY6VX0dfkTo+cE8/DNzdHy1faAG/Dn5VFkWZ2ZLoX014eQB3joLDMn24Po3393+ovtuEaPd0y0a7sy5niXZgcWu1BokaoNWrLdD5+46lykeqm5U79FHWbq2ACf1qdHdqFeNV/brXVKF1WMlMBVNg0c0wDMPYJQUFBXj33XdrLMqt0LdvX0RERIjxxo0bkZpqeBPP2HtqOWwqtVyhpzaFlzhwtvybx5DhIUJ0ENyvW+b6LzdEmzjCva47Wr/REoMPDUD/LX1F6jhFXy3Jwv+ADLmcGLcPB+r42M61ZGkCfDWYpE2XTkwz/AyVBUWgndzkYxK9LAbFBZUvgyDDwF3D9yLzrGzE4OLngu6LuqLZ401rPKJcXCzhdVUdMEe5q5eGdTWiNZsyWRmbJFU5U+GNe027plh0MwzDMHbJwoULcfXqVTEePXo0evasmbxfJycnTJkyRTcRsGrVqhrZD6ZyKKnlJErrjgkttXyNQT237RzlDs300cSKIt3uwW4iLZogIZJ9Tav+arFZ2tnXzun+7vRNBzR9tAm8Gsvt1SwN3aR/u1KVWn6T4wpuhXvGmpdi7hbghtDR8ucvPyG/Uk77dJwvf3UFB6YdQmFaoXjMp6U3+m3qjVCaeLIB/tpOde7yuEdr2/pOqY3R7nV7q5apQP4F6u2VB4tuhmEYxi75448/dOOXXnqpRvdFnWK+ePHiGt0XxnQyz2Ui83yWGAf08hcRTzVZORK2HpXH9UOgi5DYSnsmJa3xYhSQnC6Z7mL+T+11MSdhdvzxUyjKkk21Gsysj+DB1jXU2nkcOKK6Se/WyvFF9/BuQIT2klu3z7SIokHP7oXmpZgXZRfh6OzjOPv6eUAbJA8dHYI+G3vDu5l5bd2qK8pNEdKajrzXRiao67rNTDGvSqYCi26GYRjG7qAU7s2bN4txo0aNhIt4TdKrVy80aNBAjP/9918kJ8tpq4xtE6vUe5aRWv7fISBP7iQmIlK2doOsTjGvyEWXW4fJRP52A0nbksTYI8IDrd9oZcUzBFyLlTDtdUMDtdqAs7MGM0fpzf7++Lfi51C7PrcQOX0jfmM88lO0H74KyL6eI1q8xayI1T3W/Jlm6PZbF7j6mVa/TZ/znDzrnpsf1gAnr8jjXm3l9oNM9UMZBiFy4g/+OQjkltP9obxMBTKzHNvb9Ndl0c0wDMPYHWvXrkVhoZw+ePPNN9e4GFKnmNN+rVy5skb3hzG/nrvuuLoVpJbbnljqaYaZmm8bH3jU9xDj5F3Jwtm5tpFzIwdnX9GnlXf4vJ3JoqwypGRIGPOMhBhZ46N3O8c2UCuvZ/dP66UK+yI7uTqh3q1yC8bifMlARJdF4vYk0eot/USG+NvZ2xldf+2Mls83N8llPj1LwnPfFiNgHDD4mXpVbiNVFscuSnj8S/37f+s+jnLX5ISQktaflQNsO1Y99fgsuhmGYRi746+//tKNSXTbAuxibl9QdCz9mNyayK+TX6kWQiQQlHo/DzdgWDfYHD20bcOI/WfKFzR0c6i4mJOgSdRGe2sLdD5PPHlK16u5/owIEVm1FhQ9m/Si3vm+RX1g9XsauLna3uSNtWjZQKPrRX7qimk9jU3t2U3n88r8azhw6yEUJBeIx7yaeqHvP70RZmQCrSSFhRLmr5LQfLqEDxcCeQVAQpoLbn4ZiE+pvKt1WcJ+yqsScrWB+4cnASN61J7rwBYZr5pEXb3LtPO9fFvVMhVYdDMMU6OkpKTg8ccfx6uvvorz58tpNsswWnJycrB+/XoxDgkJqfHUcgUycmvYsKEYb9q0CUlJtUvU2Btxa8t3LT92EYjSlj4P6UJtnmzvJrl5BOCvNdnef1YWIuVRm+u6b/wZjcTN8mfSPcwdbd6yXlo5RcTuelfCdm0ELTQA2PCxxmbazdVYz+71FYsbv/Z+8OvgK8Zph9OQeV52IFdTlFOE44+cxJkXz0IqkrcZMjxYGKb5tq7Ydf7fAxK63CfhwU8kJJRoNnE9Drj1FcmkNmemQJ/JWR9KuHBD3yLs00dr33Vga4zoQb4Y+n7dFX13FhVVvR6fRTfDMDXKfffdhy+//BJvvfUWWrVqhf79++OHH35AerocgWKYklDNdHa27L48ceJEODs728RBon/ASrS7qKjIIBrP2HqrMCOu5bth06nlhJOTRtQnEnHJwA19ibpRgvoHwtlL/rzE/5sgelXXBnKjc3HmZX2YtcNn7eBax7AfuyV59lsJS7boe3KveV+DpvVs8xqyNlOHyseAWLjJtPrZiHKi3TlROdg7fj+iFusfb/ZEE3T/s2uF5/TsNQnjnyvGyDmSLmIp9nEIsONrINRfLrnYcRwGqeBV4duV0F0LdXyAJW9o4K5tjcbUHH7eGgzuLI+vxcqZGOWxdCtwWm6Wgr7tgZE9zH9NFt0Mw9QYu3btKiVM6LFZs2YhLCwMM2fOFGZZxcWV79fJOB62mFquwCnm9kFeXB5S9qXqWgoZ68m8dq9ttgorP8W8/HWdPZwRPEh26s6Pz0fa0fTakVY+5zQK02VBFTGtni7N3hp8sVTCJ9oGBk5Ossjqoaq9r234emlw6yB5nJoJrNxZ8XPqTQ6HxkU+ZiSulWh28p4U7Bq2V3fd0gRSlx87odUrLaFxLvsYJ6VJ+N8Xxehwt2TQM5wmrHbO1WDxG05CSM37XwLctbp93ir6qZrwPnhWwpNf67fx0/O1d/LFFhmvcjFfrZpkNRblfsMCrvMsuhmGqbEboWeffVb39+2334727dsbpBD/9ttvGDZsGJo1a4bXX38dV65UMBXJODxkUrZ69Wox9vHxEdeHLdG9e3c0btxYjGnCKCGhdqXw2st3z6UvLgPae6i6RlLLE1Il7Dstj9s1ARqH2+6Ncs/WajO1ikWCoYt5BaFxB4BEW4I2lZ5awrV9V2X5bmGWbzUUWd8+RYZNtnvt2GrPbvcQd5EuTuTF5gmztGsLrmPfpAOihzfh2cgTfTb0QviksDK3Qyniny+R0OJ2CV8tBwrlcn7Ryuy3lzXYO49qzvX71rlZPuY9rX/+Y59L2H5UqrSJHtVx58vl5nhyKnDzQL4WbAl1f+3yWoct3gycvS6P+3esvL8Hi26GYWqEVatWYfdueWqxdevW+OWXX3D8+HEcOHAAjzzyCAICAnTrXr16FW+88QaaNm2KIUOG4Ndff0VWltxbl6ld7NixQ1crPXbsWHh4aPMWbQROMbd9wX3urQu4Ol97B6WRo2olWb+X1pXH48xoCVMTUNsahQMmGFUpYqY21HXnxubh9Iv6g9L+k7Zw9Xc1uB7+OyRh14mKnbUrgrYx423ajvz3yzOB2bWkPVhFDOwENNZq438PApFxJqSYT9OnmB978AROPXMGUqH8vKCBgaJ+26+dXPtdEjqXZI7V/i55EiRFNjYXae4UpTz/hwZ3jNSI8oySUJszEsgEifTJr0ii7Zs50Ovf856Eq7F60633H+BrwdagrIO28hw59pwCElMlo1HuN39RRbnNdCyvsujOyMjA3XffjXfffbfEjhWJWsy77roLjz76KPbv32/WcoZhagcUrXzhhRd0f7///vtwcXERX2QUKfz6668RHR2NxYsXY8yYMaIdk8LWrVvFdwiln3/wwQdVvlFi7AtbTi1X4BRz2xbcl7/QZ8y0+7CNUeOltapWYeoURFskPFiD+trg9cFz8k1ieXiEewi3diL9eIaod3bU831yzikUpslp5dSKqu6Y0FJuxMOflND/EQk9ZksiUl3R8SurVnjiC5KupzsJtzfvs+3rpjohcau0D6N/2V+tMCUjIxSu/rLTVX6ivl93k0cao8fSbnALlPt5lyQjW8LYZ+XzoZiXKeeExPard2sqNEX88EENRnSXx4lpwKSXJGTlmH5dfL4UWKVNow/0k0sMapNrvT0xXls6RNel0q1CzcL/gHPX9ZNHQ7pW/rUqJbrJ9IhMbGJiYgwe//7774Wj7MMPP4wBAwbgqaeeMkgHrWg5wzC1gwULFuDsWTn6QM7TZIZVEopgknhZt24drl+/jvfee08YrSlkZmbi+eefFym8TO2AbqKV/tdubm4i0m2LdO3aVZREEFu2bEFcnN6wi7Ehwf1RGzS6V3abV1NQKGHjAXkc4Av0aQebRzFTy8iGrkWVySnm/zpmtDt6eQziN8jvzS3UDW3fK51W/uNavZA6dA649VUJ7e6SsGCt6e7VsUlyL+5kbXk8ibXvn+UezCWZPQFwd9Obi1H6dXk4uzsh/BZ9FoqThxM6zeuANm+2gpNL2fKFam837NP/TenAB77T4JeXnBARYprwdXGhOm+N6A5AUO/ue943LRtiz0lJGOkp/PqSBg3rsuC2VSb005+bNarJVqWtnCVquSstuo8ePSqEMkWf1JDR0dKlS/HEE0+gV69emDZtmriZXrFihUnLGYapHVBa+Guvvab7+8MPP6zwSywiIkII7DNnzoiUdKr/VqCoOFM7OHToECIjI8WYarn9/ORona2hTjGn/30LFy6s6V2q1ZgjuIldJ4A0bZeiUT3lG3BbR12X+sMaE6KIKiMxR0wxJ6O808+r0so/alsqMkqib9NBeaz+F0RRrfs+kNDsNrkeuLwIZ2a2hHHP6dOIOzUHlr3FUc2yMjLuHq09bjnANyY0d2j2eBPRe9u3rQ/6rOuFiCn6lHNjUH/tb1fJYxL4S9/UYPtXGnRX+R6YSoCvBqve08DXS/576Rbgvd/Lfw4Ztk17XdLVjj93u+12PmBkereVsxEImqxRT7b98S9wMUoeD+5CP1U7l07mpoRSOudzzz1X6iY5NjYWaWlp6Nixo+6xzp07i5tkU5YzDFM7+Oyzz8T3gZIe3LevysmiAuh7p0+fPvj555+FECf+/vtvXLtmQmiHsXvsIbVc4c4779SNya+AsQ/BXSq13E5umGeOBjzd5fH3a4zXJqqp08lPmIoRiduSRN9jh0orf+Y0ClJkB6vwm8OM9mGn9F9FHD0xBdj4sUbcWCvcSICoB244RY52JadLpTIiprwm4fB5+e8GocC6DzWiFRFjnGemUx21PP5imYTs3PKvU8/6nhi0vz/6b+8rrtmK+HQxbVMfWb91cNUik20ba/DHK7QN+e+Xf5BrxcvqzT7zHQmRWm/CAR2Bt+/na8HWcXHRYEwvfaYQtYtTotxv/WpYy13l1zJnZbrRpXrLFi1aiLY+apSeuurIA42Vxytaboz8/HzxY7DDLi4irdBUlFZD3HKIqQ74eisfcnKmyDZBvZXfeeedSn026bkPPPAAXn31VfH8b775RqSf1zZq2/WmiG66iRo/frxNv28qhejRo4cwBqQMMfpRTzrbI/Z2vZH4Ov/2RVz58qr+JvrD1mhwd/1y34PSUojEwcgekriZtnWC/ID7x0M4NJPo+HK5hNfvKX+/Q0YE48bvUSjOKUbs+rhyXaDt6XqL+SsWcWtl5eMW7Io277Yyug2ldzIxeRCVEUgY3h3Yewp4/09gtfY2l9LGX/9JwkcLyRhNwpNTgHrBwIMfy5Exwt8HWPsBEBZoH9dLTdEkHJgyWHaDTkgFflwj4ZFbTDteFaV2J6YCX2uTZ91cgaenmXftlHW9UbvAN+8FXvlRrvud8ZaE3d9IOgMuhQ/+0NcEh/gDf7xC3yF8PdgD4/rIUW2CJlWGdJHwy0bgkjbKPawrlSmUfy7V3kNVFt2U0kdtWv7880+jy93d5RnT3NxceHp66lr+KM6yFS03xk8//STqwNVMmTLFwKTGnP1nmOqCrzfjkAM5GTES9Dmmz39lo9SjRo3C22+/LSbmvvvuO2HuaGtO1tVFbbjeLl++rMuM6tatm/hfYusZDuPGjROiWymDeOmll+AI2MP1RjfoCd8kIemnZN1jYc+HQhpaXO51cz3eBWeuyVk0XZrmIjM1DplyO2+bZ2o/Z3y7MgKFRRp8uawIU/tGwdujnJtEMojTpsue//oC8rvkwd6vt8LkQlx+Vn9+Q54ORkxWDFCi2UV6lgabDjQQ9vXhgYUI84qCclmE+wBfzAYeHuuK+Wv9sHqvN4qKNcjKBT5bAsxdIaFzszzsPyf/v3FzkfDtY3HwccrTbYMpm5lDXLF4s5wm/sGfhRjVKQquZoUAjfPJMn9k5dYR46kDM1CUk1yp82Hsert9ALDneDDWHfAW0dDxzxbgr9di4e8jC/T9Z93x8g+UTUFRcQkfz4pHYTb9j6r6+2KsT9swDZydGojP+crtBXh4TDTeWEDXqNzp4IExsbh2rfzvxyZNmlT4OiZf5vv27RM3y/fcc4/4m1LFSTTfcccd+P333xEeHi6i0PTPjNr/EDRu0IC+1FDhcmPQa82YMaPKkW76ANHrmDILwTBVga+3srl06ZJu0s7LywsfffSR+F6oLI0aNRKTcH/88QdSU1NFrbfy/VRbqE3X26JFi3Rj8gSh82/rkGkoZXMUFBRgzZo1IiOD/ofZK/ZyvZHgvvDORQPBTRHuhveUfb+hsPqQfnzLEA+7uM4UaFdvHw78uhFIy3LGxmMNda2PjCE1lJD8ZQoyz2Yh51gu/FP8UaezLFps8Xqj80oto4rzilGcr/3Jk8RvSfv35S+uoihVzhmvOyEUHe5rb3TbdIwKtKnlU4e6oEmTRkaP58h+wJUYSlsGFqwDcvOB/EKNTnATP7+owdShtpUlYMvQcR3dS84SiEp0wZ5LjXDnyKptk1qC/fqfPCYB/9ZsXzSsa7ydWGW/3xa9CQx8DDh6EbgW74pnf2qAte8DyRnAk98BShD05Zka3DGudDkDY9tQOcDWo/K5/e6fRojUWl1Q9svkEZb5fJv833/kyJGiBlvdY/f06dO6mXuKMFFtJt0Av/nmmyKN9J9//hH136YsNwaJa3MEdnnQB8iWbxIYx4Kvt9K88sorQnwQc+bM0dVkV4XHHntMfKcQc+fOxb333lul+i17pTZcb4prOXHLLbfYxfsNCQnBhAkThGEoOZj/+++/Ivpt79jy9aYIbhJf6rZgje4ru4Zbzbq9xQatwoz18bVlnpsh4deN8t3/p0uAR2/RwN2t7PfQeHYjnHzqtBhf+y4SnecFwBZIO5qG0y+fRfq5DFwovKwV2MWAiZnbroGuwjytrOt0+Tb9eZ4ypPzz3CwCmPsU8OrdkqhDnvsXRcrlZR8/rMH04fZ1jdgCL95BDuPyyaS0/TvL6JltKl8tLxYRaOKeMUDjcCeLf7/5egMr35Vby1FqPJnwPTsPOHUViEmS1xnaFXjtbvv73mCACf0kbD0qX5OfLNYfkTfvtdz5NPmqpPrr5s2b636CgoJEtKpp06a6dZ599lmcP38eI0aMECY3Q4cOFT+mLmcYxjGhFFvqua0IkWeeecYi26VOCFQ3Sxw5cgR79miLMRmHIioqCvv37xdjqotW/9+xdajsQYEN1aqjhvsCLn1+pVKCm5yotxyRx9T3uoP9XGYGxk+TBsjj6ETg93/KX5/coF0D5BTKmJWxyI2t+RTzyN9vYM/Y/UjZk4qi5CIUpheiONd0wU20e78N3EO0znIlSMuU8I+2JVxEiOkt4eoGavDubCdcX6oRLcFWvqPBU9NM3yfGsI1XX20SwqkrwJrdlT86qRkSPl8mj12cgRfusJ7gbRSmwbI3NeJ1iC+WyeKbCAsE/nxVA2dnFtz2yHgjnr7UvaJPe8udz0rnuU2aNEnUVKoJCwsTKYB0g+Tj4wN/f3+zljMM45g3wjThpkDmZ76+5qV9lcejjz6Ku+66S4y/+uors9zQGfuLctu6a3lJRo8eLSaaKLuLMsSSk5MRGBhY07vlcFRVcBP/HaJ2MfobMHvNmnl+hgYrd8gK9cOFEu4eQ+aTxt+Ls5czGsysL9zdpQIJ13+ORMvnm6MmKMotwunnzyDytyj9/gU6wyPIA07uTnBy1ci/3ZzK/e3frQ7CJ5adDvr3Lv15njyQIpvmnec6PhphWsdUHvpsvXAHMOF5+Tp97w8JE/pV7jP31Qp9i7+ZoyjKbd3P7cDOGnz1BPDQJ/pZIAqML3pdIyZmGPukZQMNWjaQcF5V0k99uS1JpUU3CWZjopk+MPXr1y/zeRUtZxjGsVi/fj22bt0qxs2aNcPs2bMtun0yZHv66aeFqFm2bBk+/fTTKtWKM7aHPbUKK4mrq6vwJvn888+F6R9lfDz00EM1vVs2S86NHBx7+ATyYvPg7O0sRKHux9NZPOZi5LH0Y+m49mNkpQU3sXav/ibannvr9mqrEe67FLWnG8i/dlDrpLLXb3RfA1z5+iqkIll0N3uyKZzdq7d8ICcyB4fvPoq0o/qONg3vbQCvWR5o3LyxRcsZlm6RDFLLmZphbG+gfRPg5BXZMX77MWCQvorVJNKzJHy2RD6fzs7Ai3dWz/l88CYNjl2UME/bE/yt+zQY1JmvJXtnfB/gU+2/EWojRt+llsQ2i7IYhnEIioqKDHwb3n33XYv5NCiQX8SsWbPEuLCwEPPnz7fo9pmahSLDyqQNuYPaY9stJROD4BTz8jnzyjkk70pB1qVspB/PQMreVCRuTkLcmnhEL41B5M83cOWba7j48WWce/MCTr9wFif+d6rKgpsi5UqrMA83uTbTnqFot8L7f0jltlvyjPBE2ATZ+Ck/IR8xK2JQnSRsScTOoXt0gtvJ0wmdvumAth+0hsbVsje9JNI2alPLw4P0Kc5M9UMZBiWvU3Oh+noyUSPuGEH199UnfL98XCN+qNTgeUPPZ8ZOeeAmDXy9gEA/4P0HLX8tsehmGMZq/Pbbbzh58qQYU+01uY1bgwcffFD07iZIdFNEkXEMyPWbJm+UKLc9pvySCakyWUCdQM6dO1fTu2STZJzJQOzfcfIfTqiU4KqM4CaOXpBroAkS3F4e9nedqRnRA+jaUh4fOqevOy2Lxg/oj9nV+dcq7IlsCaRiCRc/vYwDUw6hIFnO9/Zq7Im+G3ohYprcUsrSrFanlg8yP7WcsSzThgKNtZUA5GZ+5Lzp1x15MHyyWF6fEiGqK8qt4OqiwWOTqdSAjdMcKcU8eoUGlxZq0LGZ5a8n++1dwjCMTUMtBcmxXOHDDz+0mmCiFh/kM7F8+XLExsaK39OnT7fKazHViz2nlpeMdpNrvxLtpqwPxhASYAqt32iFpg83RnFBMYqyi1CUVYRC+k0/OdrfWdrf2sf82vsiaEBQpQ6rEuW299RyBfqupejb1NckXRRxRI+y35d/D2oX5ieizeknMpCyJwWBfa3nPVCQViDKCOI3aPvyAAgdFSIi3K7+srGbNVi6VZVaPtj+z7O94+KiwTPTgUc+k8/LB39KojbaFL5dBSSlyePpw2TBxDBVxcfLetcRR7oZhrEKZGp248YNMR47diwGDy6nqNACUPswha+//tqqr8VUD9nZ2di4caMYh4aGok+fPnZ76KmuW8nGoAwQJXrPyGSez0TMX7Fi7BbshoZ3yd4vTq5OcK3jCo96HvBp7o06Hf0Q2CsAIUOCETa+LiKm1kPDuxugyUONKy24iTV71PXcjnFWbhkItNBa6Gw+DOw/LZUr0hs/qO9VfWX+NavtV/rpDOwavlcvuDVAixeao9vvXawquCm1fMN+vdN0vw5WeynGDO4ZC4RqO9Ut3QpcvFFxtDs7V8JHC+X1aC7/pWqOcjNMZWDRzTCMVepw33vvPd3N3Pvvv2/1ozxw4EC0by8X6O3evRuHDx+2+msy1oUEN2VMEDfddJNOtNojdevWxZgxY8SYJqO2bNlS07tkU1z67IquJVSThxvBxbv6EvESUiXsPyOPydiJ2gI5AuRY/uztptfMht8UBve6cputuHXxyL4uf/YsSfTyGOwZtQ/Zl+Wmyq7+LuixuCtaPN0MGiunelM2Q16+fkKCWzvZBp7uGjwxRT73xcXUt7ti0T3/b/rcyuOpQ4A2jR3jM8s4Niy6GYaxOJQ6m5qaqkur7dDB+iEFEvcc7XYsHCW13Jih2s8//1yj+2JLZF3OEmKMoJ7Rje41vya7KqzfS0ZqjhXlVrhzJFAvWB6Ti/mZq2ULGmq5RY7hgmLg2o/XLbYfxfnFOP3CGRydfVyUAxB+HX3Rb3MfhAwLQXXAruW2y0M3QRhYET9vIH+Fsq/TnDwJH/6pX/7yTBbcjH3AopthGIty7do1kVquOIu/+eab1ZrCW6dOHTH+888/kZiodUZi7I6CggJhokZQX/ehQ4fC3pkwYQICAuQ8yhUrViA9Xd8eqTZDvbWpXRXR5MFGcPGtXrsZdWr5+L6OdQPv7qbBU1P174n6dpcHpfU7ucnrR/56A4WZhVXeh/yUfOybdABXv9OL+Pq3R6DPul7waqRVWlaGTLfW75PHdQOBAfbXBMGh8ffV4OFJ8piM7j5fWvZ1+v1qIDZZb4bXvqljfWYZx4VFN8MwFoXM0xT38Mcff1yYnFUX3t7euPfee8U4Ly8PP/74Y7W9NmNZtm/fjpSUFDEeN24c3N3ltFd7ht7DbbfdJsaUNk995Ws7lMIctThajF38XNBodvVGuQsKJWzU1vkG+AK928LhmD1Rfm/E7/8A1+PKFjTuIe4InxwuxoXphbpzU1mKC4tx5N5jSNknZz6RoG//aVt0+LKd6LFeXazZA+RyarlNQynm7tqOot+upFZgpa/T3DxJmK0pvHIXC27GfmDRzTCMxTh27Bh+//13MQ4MDMTzzz9f7Uf34Ycf1rmkf/PNN6J3N2N/OFpquQL37Dbk0heXIRXKN9Fk5OXqZz0jLWPsOkEGW/J4dC/ZTdnR8PXS4NFb5HFhEfCpts1SWTR5QG+oRtFpau1VWc6+cg5J25N1Bnm91/REw7saVHvrv2Uq1/JbBzneOXYEwoI0uEe2vUBmDvCN/l+AjgXr9K39Jg0AOjXnc8nYDyy6GYaxGNQWTOnv+tJLL8Hf37/aj27z5s11hlXXr1/XpSgz9kNxcTFWrlwpxm5ubhg9ejQchZ49e6JVq1a6aP6VK1dQW8mJysGNP6LE2MXH2UDsVRdrdqtSyx2gVVhZ/G+yBp7aZJHv1wCJqWULab8OfgjsJ5dBZF3MQuKWypXpRP5xQ5dSTj3Xu/7aGf7dqv9/QlaOhHV75XGIPzCwU7XvAmMiT99GPa/lMaWYk0u5Ql6+hPd+V0W5uZabsTNYdDMMYxHIOI3qVJUoN0Wcawq1oZpSX87YDwcPHkRUlCzGhg8fDj8/PzgKFOFTR7t//fVX1FYuf3EFUoF8E91oViOrtouqqD833ehTpNtRCfbXYNZ4eZydC3y1ovzodePZqmj3fPMN1VIOpOLU06d1f7f/qK1o9VYT0DnOydO7ljtiNoOj0CxCg2la+47ENGDBWv2yXzYAN7Rd5sb3Bbq24vPI2BcsuhmGsQiLFy9Gbm6uztCMTNRqipEjR6JFixZivHnzZpw+rb/5Y2wfR00tV7jzzjt16bW//PKLiOzXNnJjchH5uzyx4uztjCYPVX+U+3K0hLNaPdmnHRDo59g38XNu08BFW0b91XIgI7ts4V13TCg8G3qKccJ/iaKPuqnkROXi0MwjKM7XTqjc3xAN7tQ2DK8BlqpSy6cMcexz7Ag8p2pz99EiSfgu0M+7HOVm7BwW3QzDWISffvpJN7777rtr9Kg6OTnhkUce0f399ddf1+j+MJUT3XQeJ06c6HCHr379+iKCT1B6+c6dO1HbuPz1VRTnyZMNje5tALcgrYNSDUS5HT21XKFhXQ1mjJDHKRmyC3RZaJw1QiwrqJ3Hy6MopwiHZx5BfrzsWhbUPxBt3pbLKWoCdWp5cB1gEKeW2zxUpz22tzy+Hgcs+g/4bSNwLVZ+jDJSerZ1/M8r43iw6GYYpsqcOXMG+/bJ/Vg6duyILl261PhRJeFPbuZKCm9aWlpN7xJj4rV07tw5Me7Xrx9CQ0Md8rjVZkO1vPg8XP85UoydPJ3Q5OHGNbIf6npuR+vPbUoU8ZPFkqiTLYsGd0SILASCXMwLUgvK3Tb5eZx44hTSjsqt8ChS3mVBJzi51tytJrUJo3R64mZOLbcbnp+hv07f/0PCO7/pr9NX2bGcsVNYdDOloDY9L774IrezYSoV5b7nnnuq3ZnWGNSve+bMmWKclZWFn3/+uaZ3iTEBR08tV7836j9OLF26FNnZ2agtXPnmKopz5Sg3OVm7h1Z/Ozjq27z1qDxuEEq9flEraNNYI1yfCXKBphZiZeFaxxX1b6snxkXZRYj8/Ua5274y9yqil8WIMYn1bn90qZEMBjVLt6hSywfX/P8lxjQGdNKgXwd5fPoqlYLI4xHdgT7t+Twy9gmLbsaoCdV7772HKVOmYPbs2aLfMcOUBbXk+u2338TYxcVF1HPbCuoU87lz59bK2ll7o7aIbi8vL/EdS2RkZBi8b0cmPykf1xZoo9zuTmj6aONS0dJ/D0g4drHybapM4ce1QH6B3pTJFiYKayKKSD2Pi4rKPtZkcKdw7Yfrou+2MRL+S8DZN87r/u40twP82mqbg9cQ5HxN/bmJoDrA4JpPwGIqeZ0qvHp37fmcMo4Hi27GAHIMJkMshe+//x5DhgxBdLR2mpFhSrBx40bExsrFVhMmTEBISIjNHKN27dph6FDZCvXChQv4559ywjpMjRMZGSmcy4nOnTujceOaSTuuLmpjivmVb6+hKKtIjMlcyyPc0HCR6oxHzpHQ+V4JD39aXK7ZV2VIz5Iw851iPPGVfrsT+tauG/lebTUYohWgF24AK7aXva5PC2+EDA8W45zIXMStiy+1TubFLBy5/zig1ePNn22GsAl1q7yfGdlyX/HKskGVWj6pP+DKruV2BdV1t2+i/5uu2f4da9dnlXEsWHQzBnz77bcicqlmz5496N69u/jNMBWlltsajz76qG7Mhmq2jdKb29Gj3Ar9+/dH06ZyXvOmTZtw40b56bv2Tn5KPq59f03Xt7np/wwnVai++M1f9GL425VAh7vlyLcl2HVCQqd7JGHKpHDPWMduFVYWL9xhWDNLGQZl0VjVP72koVpBegEOzTiCwnT5vqHuuFC0eKZZlffvr+0SGtwK9HmiPjbJ83Bmw67l9o2TkwZv3S9fp64u0I0Zxl5h0c3ooHZP8+fP16UJr169Gg0byu6lMTExGDRoEH744Qc+YoyOxMRE/P3332Jct25djB492uaODkXflet43bp1uHTpUk3vElPLU8sVyJ1d8R0g0fP777/DkaF+z4WZcuiy/owIeEbILakUft0IRGn78CqQYzFFvmd9WIy0zMqJb2o39MoPxRj4mISrWgdkP2/g95c1WPC8U61KLVcY3h3o2lIeHz4PbD5c9rrBQ4Lg01I2pUzZk4K047JRmlQk4egDJ5B1MUv87dPGR6SVa5yqdjw3H5Jw2xuSiHQnpTtj7LOUAWHeuc/Jk7B6tzwO8AWGdq3SLjE1xKQBGuybp8HB76nGu/Z9ThnHgkU3o2PhwoVCRBFUazh+/HiR6jl48GDxWEFBAWbNmoWHHnoI+flyOxCmdvPnn3+K64K444474OrqCluDJpDomlWEzTfffFPTu2RXZGZm4sknn8QLL7yAU6dOWe11kpKSsH27nOfarFkztG/fHrUBRXQTZPZXXsTRnqGI6NX52ii3iwbNHjd0LisslETEVWHRaxoDofTDGqD9XRLW7zXv+Fy8IaH/IxLe/hVQLB36dwSOLdBgxsjaexNPEw1qJ/MvlkrlrttotirarT2P5965gIR/5FkS1wBXdP+9C1x8Xaq0XwfPSrjpRUlXb08UFQOzP5LwzDfF5dafl0wtz8qRx2Qcx6nl9gu1B+vYrPZ+VhnHgUU3I6AbvS+++EJ3NP73v/+J31SfS3Wwjz/+uG7ZvHnzRJ2sUsfL1F5sPbVc4f7774e7u+yQ/OOPPwo3c8Y0nn32WXz++ed4//33hRDu2bOnmLigLgeWICEhQXhHTJo0CUVFRbood22JPjZp0gQDBw4UY2qVtn//ftgqeXF5KNJGqs3l2nfXdSnIEdPqwauhYZR78WZDh+JpwzTY9JkG8+Zo4KNd9UYCMPZZCfe8V4yUDKnC/2k/rpHQ+T4J+8/Ij7k4A2/fr8HWLzRoHF47rq/yoBZa9bUWHGQ4dimq7GMaMTUcLnVkQR2zIgZX5l/D5S+u6Hp6U2swr8ZeVdqfs9ckjHlGQqZWLE/oB9w7So6qEx8vAia/Ione22allrNrOcMwNgCLbkawY8cOHDt2TIzpprp37966I0PRS7rpJqMfRbjs2rVL1Hnb8g0iY12OHj0qfogePXoI0zJbJTg4GNOnTxdj6tetuK0z5UNtrP744w+Dxw4cOCBc4cPCwjBt2jRs2LBBJ5ZNJT4+XkzeDR8+HOHh4aJLws6dO3XLJ0+eXKtOja0bqpFjNUU1t3TcjvPDL+HI3ccQuzYOxfmmdQMozCjElXnaKLezBs2eNIxyFxdLePd3vUh6aaYsiGni5YGbNDj1qwYje+jX/3k90G6mhNW7jIuvpDQJt74i4f4PSaDJj7WoD+z+RiO27ezMgpug6O8jN8vHghIsvl5Rtph18XZBw5n15fOVL+HMi2d1y9q83QrBA4NQFSLjJFFGkJgm/z2wE7DwVeDl21Mw90nAWW4XjlU7IcoEohLK3tdcSi3fJY/9fYBh3aq0awzDMBaBRTcjUEe51VHtkmmQdGNcv359ndP5gAEDDKKdTO1B3ffalqPc6lZ4Cl9++aXDpvFakuXLlyM9XY400URcly76njtUYrJkyRKMGTNG1MxT+jlFasuCMmMoQk7dEEhoU8r/f//9ZyDYya38448/Npj0qw3ceuut8PSUw7mLFi2yqTaNubF52H/LQVz69LLsTl0IxK2Nx+GZR7G5/Vacev4M0o6mlft5urbgOgpS5HzhepPD4d3EMCK6cofci5eg3rwkuNQ0rKvBho81+PE5jajFJmKSgIkvSLjjrWIhshU2HZTQ8R7JwJF71gTg8A8a9GjDYrskdGw8tK20F6wjx/By2ofd17DUXSPV5jeaJXtmVJbEVFlwR2qN0Tu3AP5+TwNPbfv2B28C1n2gP/dUg97zAQlHzhvf1437oYuWU2q5myufd4Zhah4W3QyuXr2qcw2mm2G6ASwLim5TnTeJbeXG+9577xUO0UptL+P40HlXIqCU/XDbbbfB1unatatwiybOnDkj3KKZ8qFUfAUSw4cPHxbZDU888YTIHlCgloKUft66dWv07dtXpIuTWKfHv/rqK2HCWK9ePREh37p1q0G/dHLvfu6558T3yuXLlzFnzpxad1r8/Pxwyy23iDGl7ZOJpS2QuD0JOwfvRvKuFF2U2jnIWdVzuwDXvr+OXcP2Ykf/3bj05RXkxmh7NGkpzCrElblaRa0Bmj1lGOUmsf7Ob6oo950ao6UF9Ni94zQ49YtGtBJS+ONfoN1dEpZsljDn62KMeEpCdKK+N/OKtzX47hkn+Hix8DJGUB0N7hgpj9Oz5CyCsvBs4Imw8fpWYP49/NHuo7ZVKgUhkU8lA2e1pujNI4ANH2lQx8dwmyN7arDnWw0ah8l/0znu/6iEv3dK5aaW38qp5QzD2AgsuhkRfVJugin65OamnfYuA3KppgiVuhXT3LlzMWzYMMTFxfERrQWsWbNGZ7pH9bcBAQGwB9RZHOrsDqY0Fy9exLZt28S4VatWQkwTnTp1wmeffSYyXchtfOLEiXBWcj+1LQYpXTw0NFRkxZA/BBmkqSOhzZs3F5FxEvH0OiTYu3XrVmvquI1x991320yKOblSX/jooohw5yfIppke4e7ouao7Wqxtim6LuiD8ljA4eehvITLPZuLcG+exueM27J9yENHLY1CUXYTrP98Q4pyg51Df55JRSYpcEuSmXVH7rvqhGqz5QINfXtSI1GEiLhmY9rqET5fo16N09OM/aXDzwNp7TZnK/ybrj9FXyyWR7l8WLV9qAa8mnvDvVgfdfukMZ/fK30ZSi7hbXpZwQJupXi8Y+PdTDeoGGj9nbRtrsG++Bn20lUzUg3vSSxI+XaxveUap5X9rU8vr+Mj+AAzDMLaARnLwHEsSk9euXUOjRo1EexbGEDKUohvj1NRUIbYjIyPFzbKpUGr5gw8+qHMzp6jVkSNHROSmNlJbrjdqw0XCm9i4cSNGjtSGSmwc6kFP1yhd58T58+fRokUL2CvWvN5eeuklvPvuu2L84Ycf4plnnilzXZpso8wH+j44efKk0XVIuFNXBMqk6dixY60W2MagNHtKr6de3TSJQdcoZR5VN3kJeaINVNK2JN1jwUOD0OnbjnANdDG43siRPHZVHG4sikLK3tRS21KcrKmmmxiwqx98W/sYRLkHPCph1wn57+VvaXDLINOvi+hECQ99ohdZhLsb8P5sDf53q9znlzGNYU8U69qGrf1Ag7F9rHvsyIWc2oIt26pv67X9Kw3aN9VU+P1GwvreDyQsVCUrPTAR+OoJjXAtp7IDYuYo4JeXHPf/MGNZasv9G1Nz8FVVyyFDKRLcBBlNmSO4lVpeimJR6ihB6aGvv/66VfaVsQ2oNnf9ejkHkSZsKMPBXqD2YZTirPD111/X6P7Y8uSEUrNPAvDOO++sMPvlqaeewvHjx0WaOB1jqvMmt/NXX31VPE4p/W+99ZaIlLPgLg0dZ6V9GAnwjz76CNVN8u5k7By0Ry+4neTIZo/F3eAeXDoDytXPFQ3urI8+a3th0MEBaP5MM3iqXMlJbCuCO2xiXQPBTWw/Bp3gbttYrr81h3rBGqx8V4M/XtGgaT25HvzAfA2emKphwV2FaPcXy6wbi6HJloc/1QtuLw9Z6KsFd3l4uMvn/DV9cgjm/w2Me1bCgnUq1/IhPOnCMIztwJHuWgz94yPHaboZJijVU22UZA6XLl0SN9i5ubni5pGi3R06dEBtozbMlJIYoDZSxIsvvoh33nkH9kRycrKYLMjJyYGvr6+ILNprZoa1rrd169Zh3LhxYnzTTTfpPB8Y609oUSYGXZseHh5iErM6ot1SsYRLX1zB+XcvyGZpFDGu64bO33VCUP9As6432lbynhRELY5G7KpYFGYWwcnTCX3/6Q2/tr4G6458qhj/HpTHv71MtcUskmoKijy3uF3ClRj579O/atCmsXXOx0vfF+Pd3/Rt3Fa/r8HoXqVfy5Tr7Y9/5Ki3uq83QaZr8as0cHfja4oxjdpw/8bULHxV1WLISEoR3GSMVlnBTTRr1kwIMCVK8/DDD7M7tINO1Khdy9V1qPZCYGCgLnKbkZFh8H6Y0gZq9913Hx+WaoLasJGvBkETmFTrbm3yk/NxcPphnH9bL7iDBgSi/9a+BoLbVDROGgT1C0THL9tj2Jkh6L64K/pt6lNKcO8/LekEN0WpbxtqkbfDVBJqo/aYura7nPZhVYHqrxXBTRUmv75kXHCbyoyRGmz+XIPgOoaPT+xHpQYsuBmGsR1YdNdi1EZSZHZUVajmkwySCGotxr2QHQ/q0Xz69GkxJidwe62HVrcPI3dttZt2bSchIQF///23TgRSSzCm+qAsEqV92Pz584VhnbVI2Z8i0skTNmntvjVA82eboefy7nAPdUd8ioSN+yV88IeE6W8Uo9M9wGNzg5GRbdr2nb2cETo8pFRaOaF2LH9+hgYuLiyQapp7xwLe2uqAXzYAKRmWFd6/rJcwZ65+m18/ocH04VU/7/06yAZrrVWdy6YP4+uJYRjbgkV3LeXChQtYu3atGFPt5aRJk6q8TUqHVNfIkghX6sUZx0Ddk90eenOXBZVCKLXo5J5N6dSMDE2WUU03cdddd4k6eKb6oPp4xXeA+nVbI9pNGSuX517F3gkHkBstt/hyDnBFzsvd8EtgU4x/QUK9m4tR9yYJo5+W8Px8CYv+A05eAdbu98bEF8g5uvKC7MQlvflZRIhseMXUPNSm6+7RemfwBfItgkVYvUvCfR/qr5k37tXg4ZstJ4yb1pNbir14J/D5YxqMUbWVYxiGsQVYdNdS1OKYbvAsdWM9atQoTJ48WYzj4+Px8ssvW2S7TM1DdaYLFy4UYy8vL+FEbc+o24d9+eWXNbovtgKJMXVquT1PrNgzNGFJnzHiu+++E74DloJqrrfdehRnXz0HqVAWQad9/HF7SG/cuiJQpP6u2wvE6M3LS0EGaJNelISLdGV493f98565jetubQl1ivnXKyRR611V9p6SMPU12pbyGsArd8Hi+Ptq8M4sJzw+xXivd4ZhmJqERXctJD09XRexpDTG+++/36Lbpx6+3t5yL9Zvv/1WGLQx9g+ZaaWlpYkxCW4yIbNnxo4dK0yriH///VeXNl+b2b9/v0H5ALX5Yqof6iLx6KOPijG1Y3zvvfcstm2qud6fKQt6YnFwYzzXsBuSXT0M1qMWTkO7Ak9NletuT/yswa65gI+nXIpB9dgkpPILzBNl5yMlLN4sj0P8gVkTLPGuGEvRqqEGY7S90q/GwqAdW2WITZIw+RUJuXJXUdw+XI5EsyhmGKa2waK7FkLGUWQgRZChFBlLWZIGDRqINkEE1cqSqRrXzNo/jpJarkAu++rabo52s4GarUW7fXzkWugffvhB11veEjjd2Ry7fEPxWsPO+LVuCzQIdxLtul6/R4NV72pwbakGSWs0+O9zJ3zyqBPuHCW3c+rdDvhpTjy8tfp89W7gjrclFGoj5qbw/h8SJO3qT03VwMuDI5K2xv9u1Z+TL5dXPtJdUChh2usSorWWAYM6Az+9wO3cGIapnXDLsFoGiV+KXlEdK3Hy5EnRNszSUHSmc+fOOnd0SpGcNWsWHB1HbTlBN/z0nij9uEmTJuL6cYT3R5F7ah+WmZkpsj7ItCogIAC18XrLysoS7aloQo7EXkxMjE70MTUDdYRQotwPPvigyByyBCcvy87hXVoAnZpTVFtj1vV2ObkRxj8HXfTyzlHAzyaIqWuxEppPl1BYBPj7QIh7P28W3bZGcbGEtjMlnLsu/33sJw06NjP/PD31dTE+W6Kv3T/0vQZ1A03fjqP+P2VsE77eGGvD32K1DDKMUgQ3GUlZQ3ATbm5u+Oabb3R/P//880hM1E53M3bHr7/+qmsBR23CHOUGqE6dOrq2Z1SzThHF2sqyZct0GTC33XYbC24bYM6cOboyDqq1JwFiCShq/eRUDQZ30ZgsuNUM6QL89Y4GrlorkN82Ag9+QhHs8qOiHy2UBbdS18uC2zahyZP/qWq7v1hqfrR78X+STnDTdbLsTfMEN8MwjKPhGHfOjMmoU2jVRlLWYPDgwbj99tvFODk5GS+88IJVX4+xfm9uqsMjR2tHQp1iTgaDinN3bUNtoHbvvffW6L4wMkFBQbp2jgUFBXjnnXds5tBQb+Ulb2jg7Cz//f1q4IkvyxbeMYkSftC6YVNbqsdVKcyM7UGO8nW0iS5/bAISU00X3qeuGDqVf/E/Kk3g880wTO2GRXctggySyDCKaNasGcaNG2f11/z444/h5+cnxhRF3Lt3r9Vfk7Es1HNdyY4YOnSoSPVzJFq2bClM1Yjr16/relTXJs6fP48dO3aIcZs2bdC7N/fbsRWeeuop3Xco+SpcuXIFtsKkARr88TKllct/f7kceGG+ceH96RIJedp09IduAoLqsAizZXy8NLhP/loU5+271aY9Ly1Tws0vScjK0Yv3B2+y3n4yDMPYCyy6axFfffWVbkzOuNWRIkw1om+++abubzJVK1L6hjB2gaMZqBlDiSYSX3zxBWrzOb7vvvvYWdiGIKNLJSuJsjBsKdpNTBumwYLn9AL6gz+Bt34xXCcpTcK3q+Sxu5tsoMbYPo/eQi7j8viblZIwRquoFvyudyVc0Ha469wCmPc0O5UzDMMQLLprCSkpKaIulyBzpOoUT9QHvGPHjmJ85MgRi5kBMdaHDMaWLJEL8yjadvPNNzvkYR85ciRat24txtu3b8fRo0dRWyAh98svskpycXERHQ0Y2+LJJ58U/gMElXpcvnwZtsRdYzT4do5eSL+2QBL122oHbCXySdHT8GAW3fZAk3oaTOwnj6MSgBXbyl///T+AVTv1LedWvKWBpzufa4ZhGIJFdy2BUruzs7PFmAS3cgNXHdCNvNpU7eWXX0ZcXFy1vT5TeZYvXy5crRVzLS8vfX9fR4Jq1dXR7trUPmzDhg3CqZyYMGGC6BHN2BbkqP/EE0+IMWUKvf3227A1HrxJg08f1QusZ7+V8PVyCelZEr5cJj/m4gw8ezuLMHtCXXv/xbKyI93/7Jfw8g/ycoqO//mqRoh2hmEYRoZFdy2JZJFBlDq1vLrp16+fziWa2jRRD1rGvtKOlfPnqFCEV5mM+vPPP5GQkIDaABuo2Qckuv39/cWYspYUnwVbghzR375fL7Qe+0LCpBclpGbKf98xEmgUxkLMnhjcBejQVB7vOQUcOFNaeF+NkTD9TX3/9Tfu1QijPYZhGEYPi+5aABlDkUEUQYZRZBxVE3zwwQe6m8bffvtNpPEytguVAmzbJucTUm93RzfXorKL+++/X4zz8vJEb3lHhzJO1qxZo/NfGD16dE3vElMG9N1Jpmq2HO0mXpqpwUuqCoUtR6CLfr5wBwsxu8wCUkW7qVRATW6ehMmvSEhOl/8e3xcG559hGIaRYdFdC1AbQ1m7TVh5UNrqu+++a1DrTW1wGPO4ceMGpk2bJtK933//fZEeXNV0/eLiYpw8eVLU28+YMUM4lHft2lW3nEoS6ObL0VEbDFJJhKNfnzT5pbRIo0wGKgVhbBf6/qZUc+XcXbhwAbbIW/dTH3DDx6YOAVo2cPzvEEdkxghym5fHizfL7d8Icql/5DMJh8/Ly5pFAL+9RG72fJ4ZhmFKwqLbwSFDKCWiTEZRI0aMqNH9mT17Nrp37y7GJPLUjuqMaZAAJnOzxYsXi97nY8aMQVhYmIhUUibDiy++KJbTDTmJaWNQJHf37t0i+4DqeIODg9GhQwfhLk+p1UpmBOHt7Y2ZM2fWitPTuHFjTJw4UYyjo6OxbJm2GNUGiYyMxCeffIKNGzeW2Ru5POg56tRyR3WmdyTIzHDOnDliTJ/tt956C7YITdB98ogGD03SO5a/PJOFmL1CZmizJ8jjgkJg3t+Srjf7gnXKOsCKtzXw9+XzzDAMYwyNVJm7NTuCbkyuXbsmInfV0SLL1qDoleJMTJG7hx56qKZ3CQcOHECvXr3ETT+l9J49exYRERFwBKx9vf33338YPny4yevT8e3UqRM6d+4sRDWJaeq7vX//fuTm5pb5PDJMo3TyAQMGYOrUqWjbti1qC1u3bsWQIUPEmI7Bnj17YCvQZ4ZS/mmyauXKlbpJFZp4+f777836HNH76tu3rxgPHDhQV0rA2Dbp6elo0qQJkpOTxXfM6dOnRfmHLX6/0fW6+TAQ6g90aMZizJ6JjJPQ5DYJ1PEzNABY9qYGw5+SkK9NBvrjFQ1uH2HZc1zb79+Y6oWvN8basOh2YEhgNWvWTKSPUkoi/U0izBZ48MEHMX/+fDGmVOlFixbBEbDmlzbdwNJkBU1aEB9++KGIcFM2A/1QDTa1hqsMFOkmgd2/f3/x06VLF7i6uqI2QseZJimOHz8u/t63bx969uxZo/tEDvK///67MESkDJGyoqCffvop7r33XpNKAah+XYl0Uxuqu+66y+L7zViH9957T2S0ELfffjv++OOPajnUfFNau5n2WjGWbNFnL+Tly+P/TQa+eNzyopivN6Y64euNsTYsuh28/k9pffTqq6/ijTfegK1AURqKziQmJoq/KcpG0TZ7x5pf2itWrMDkyZPFmKLWJLTVr0FikVKOFRGuCPGrV6+W2lbTpk11Ipt+k7lebajZNhUSo4qpWnWKmpJQP+a5c+diwYIFSE1NNVhGEy5U10/7pnZap57jFPVu2LBhuf3XqRyBfvv6+oqWYVRGwNgHGRkZItqdlJQkPrenTp1CmzZtrP66fFNau9l9QkK/RwyTI/t3BDZ/roGri+X/f/D1xlQnfL0xVkdycIqKiqTLly+L37WJ+Ph4ydPTk/47Sl5eXlJCQoJka3z//fdi/+inR48eDnGOrHW9FRQUSG3atNEdr9WrV5v83JSUFGnr1q3S3LlzpcWLF0tRUVEW3TdHJDs7WwoKChLH2sXFpVqPGV07GzZskMaNGydpNBrdOVd++vbtKy1cuFDKy8sT6x4+fFi68847Ddbx9fWV5s2bJxUXFxt9jQULFujWnT17drW9N8ZyfPDBB7pzeNttt1XLoa2t/08ZGfo+6T6rSMIA+SfspiIpOsH4d4wl4OuNqU74emOsDRfJOChU85mTkyPGs2bNEunDtgYZN1HElqCUaUdJMbcG5FR85swZXc/zcePGmdVqaNCgQcIkjeqz69WrZ8U9dQw8PT3xwAMPiDGVZ8ybN69aanUpM4UiltS6a+3atTqDNHd3d/F5OXToEHbt2iUi3G5ubrrzS+nh1PpLqemmSCiVcFD9/5UrV0q9Fvfmtn+o+0NISIgYk6kiRbsZxppQVsWrd2lE+zcyTlv6pgbhwZwhxTAMY5X08kuXLuHEiRNCxJHJUMkWM+T4SzWQlLJIqaseHh5mLbc0tTFdhG64KbWU0lHp/FCKaoMGDWCL/PPPPxg1apQY0z6TqRoJHnvFGtcbGZ5R+jeljhPkRk8p4Yz1W7ORmzn1RCZxQ+fVWtcmOcnTRErJFHL63NJkCaW6G5s4K3m9paWlCXdrtaimtHFyqScTRVrn3LlzopMB0a5dO/F9zqUF9snHH3+MZ555RoxpQo3EtzWpjf9PmdKcuSrh/+3dCbzN1f7/8WWqTBEZQ0nqJk1XSKUM11SGjkhKEaVRqEyZTuSqCFcpQ6VO11AuV2W66lEkkQaJJCIlSTIlM+3/471+//V9fDfnOPucs7/nnL336/l4nIfvHp2z99prfz9rfdZnFS5oTKUywQbctDdkJ9obgpahb01tT6O1wSrko5kfbSOkAj/O559/bmdgVBk5JSXFFvTxV0hO73ZEx8SJE72T9w4dOuTagNutP9WsnqjQm39PcfwffdZcwK0twQi4s0eFChVMmzZt7LHWTGtP9CCoD1Rf6g+4VT1da/g1YNa3b9+IM1WKFStmXn75ZbuNmFvTrT5a+483aNDAfP/993Z9uBNp0TXkThpIKV26tD2eMWNGmkX2gGi6+Lw8gQfcABB3MpKLvnLlSu/42LFjoaSkpLC1pXfccUdo8uTJ3hrUdu3a2bWHkd4ehERbo3Ho0KFQ+fLl7To/rQddu3ZtKLdbvXp1KG/evN5aVK1Hj1XRbm9//PFH6Oyzz/bWbvo/gwiePj9a063X/vTTTw9t2LAh6v9HcnKy9/7WqlUrtGbNmqi0t71794buv//+sLXeqvNQrFgxe1ygQIGY/qzh/zz33HPe+zts2LBAX5ZE+z5FzqK9gfaGeJKhmW5to+Pky5fP7uXrKK1RqcFuD2GlNWtmZfny5RHdjuhQBoFS+OXmm2/Oloq2WVW9enXTpUsXLzU+OTk5p3+lXENbQLkK7+3btw/7DCJ4+vw8+uij9vjw4cOmW7du3jrraNByHW3/5PpEzUIr5TsatIXYSy+9ZPd2V5q8qM6D+mJp2bKltyYYsUtr95VBpiUKbhsxAACQu4QvyI6AUsQVPLt13Y0aNbLXuy1rypQp491XaW+LFi2K6PbUHDlyxP6E/cL583sFhCJdo+H/N55p7an2bnZ69+4dM3+3Au1p06bZLYy0f7fWscbCgEGQ7U2fGa3ZdO1er1GsvJ/xpH///mbq1Kl2jfeCBQvMzJkzTevWrbP8vAreFcQrmHdb/KnNZ+Q9jqS91atXz6xatcoGZNp+zOnUqRPtKQ6oLorb0i7o/iGRvk+R82hvoL0hVkRS5yTDQbdmSbZu3Wr3WS5QoIANklRZ13WO/vWBmg0/sdNM6/bUTJ482e4369e2bVtbMCaj3JrYeKZqx1qzKXXq1LEDHCp6Eyu6du1qZ3Y1ePDII4/YtamxKhrt7amnnrKfL1Gb1+ctlt7PeNKvXz9bLVrUNhUc+zN9MuO9994z8+fPt8f6rHbs2DHT728k7U0F1lQP4IUXXrCF+apVq0Z7QmDtDYgW2huyE+0NmVG5cuXoVy/3U9XUsmXL2pO5nTt32irU8+bN8wq7KGjWzLhmV9K7PciZbn2AVEwsnqut6m2sWbOmWblypb2sQkoulT9WHDhwwFZV1qCOC0q0BCGWRKu9qajcRRddZNu/KmavX7+erb5y+POlInaqtu+ySFxaeGbbupZVuCBbWR6ZGUxMlP4NuQPtDbQ3xCv6N+SamW6lQGofWf8aQKW1uZnqkiVL2mq52s5IFX91krpkyRK7LVgkt6dGwXVGAuz0Xox4PilVkO0C7ho1ati0/1irSlykSBEzbNgwm/bqAhsNysTi+5bV9jZkyBBvwEkzq6qkjZylGWIFynpflJGhdprZJRAK2F3ArcGxdu3aZenzGu/9G3IX2htob4hX9G8ISsRBt4LrHj16mMsuu8ymQn777bd2v23/frBahzt06FCzbds2e0K5fft2c8stt0R8OzLPv52RUmFjLeB27rzzTrttmAYQ9PPGG2/YtNtEos/W66+/bo+LFy9u+vTpk9O/EowxVatWte+F+rBjx47ZdHMVKcvoZ037ZI8YMcIea8mAgvlY/bwCAAAgfRFPjSjFVWtslfKqfV+Vyjx79mxTpUoV7z6asRk7dqwdJbr00kttwKR9YyO9HZmjCvCuIJ3WaqpqeaxS29B+8P4iVkrFTSQDBgzwMkg023/WWWfl9K8E34CWW7fz4YcfmunTp2fotVGGj/bMPnr0qLdER30qAAAA4leW1nTHAgUvmlU/99xz4zb9UkH222+/bY+VedC5c2cT67Sd0bvvvmuPNbOoQDQR2ttnn31matWqZY9VL0GF8QoXLhzAb4rMmjNnjmnRooU9LleunN3NQdtzReKtt96yqeSiNrJ27dosFWRLhP4NuQftDbQ3xCv6NwSNs7QY980333gB9znnnGM6dOhg4oG2PlN1e5c6/+uvv5pEmUl1Bg0aRMCdCzVv3twOComWygwePDiix2kP+p49e3qXtYwiqxXQAQAAkPsRdMc4/77cqiIfrcJzOU1VzO+77z57rOUMCkDj3fvvv2/XCMv5559vunTpktO/EtIwZswYW0hSnn/+efP111+n+1o9+eST5pdffrHHN910kxe4AwAAIL4RdMcwpZVOnTrVHpcoUcLce++9Jp4kJyd7abtKm1+zZo3JzWlJSg1XzQKlH2tWMyO0ysM/y62U+ngZQIlHWtetegOifeVVJNKtw0+N2q4CdVGwrnZC8TQAAIDEQNAdw0aOHGmrKEu3bt3sllvxRNvTPfHEE/ZYAY2KTuUmO3bsMP/+979tSr8q+mstttKHtcWXLmut/ZQpU+xWe+mZNWuW3R5NtEPAbbfdlg1/AbJC7VEVzWXp0qUmJSUlzQEVVTpXcC4aXFEmAwAAABIDhdRi1G+//WaLJx06dMiuC/3pp5/sXujxRn+fqjvr73P7kTdu3DhHfhcFTStWrDDz5883CxYssEFyJHUINWPdpEkT07ZtW1uAS9uA+WngRNX8VZBLNFOu9GPkfgsXLrTvrRsk0nZgJ1ab1y4Nd911lz3Wbg+a9Xap6VlF4RdkJ9obaG+IV/RvCBoz3TFK6akKSKVr165xGXCLgpPhw4d7lx9//HFvxjA7qIDba6+9ZmeeFVRdc801NvVbqeT+gFtZBprZ1vviZr6dI0eO2ErsCrxKly5tC3FpH+7du3fb2zVD6gLu6667ztx4443Z9vchazQA1KZNGy/zwaWcO3v27LFt1tGe3NEKuAEAABAbmOmOQUpX1iy3TugLFChgNm7caCpWrGjiefTx6quvtoGuaL/4oIuMbd682QbPShtOi9LAmzVrZpo2bWqDcc1ou5HSChUqmGXLlpkZM2aYmTNn2irXJ9J7p73rV61a5RXYWrJkiQ28ETt+/vlnW/hPBf+0TlvZEFdddZW9TUsNVGhNkpKS7DKCaGJkHtmJ9gbaG+IV/RuCxkx3DJowYYINuEWBYTwH3KL9h5977jnvsvbs/vPPPwP7/zSDnVrArbRwpYirqNvWrVttsKztzOrVq3dS0TNtd3b99dfbgEtBmYJpBWDa1s05evSoTVX3V7Qm4I49GmBx24ap7aiomrIxVq5cacaNG2ev1xIQV0gNAAAAiYWgO8YopXzUqFH2WLNqvXv3Nomgbt26dqbQpXyPGDEisP9L68ZdwF2uXDmbMvzxxx/b9OG33nrLdO7c2ZQvXz5DgwYKprUvs9am67l79OhhgzV/kD5s2LBA/h4ET+9ntWrV7LEyMiZNmhRW0XzgwIGmUqVKvBUAAAAJiPTyGDNx4kRv/+rWrVvb1OVEsWHDBhvYqPBYwYIFzfr168MC12jQTGXNmjXNF198YS8rPdyt2Y12epLuq1TkDz/80NSuXds0aNAgy78/cs6iRYtM/fr1vUEUV3tAqefKighiCzjS4ZCdaG+gvSFe0b8haMx0xxCdxD/77LPe5b59+5pEou2ZtPWSHDx40NtOLJrefvttL+C+4oor7MBGUBSUa626tpAi4I59WmZwxx132GN/sT+lmLPnOgAAQOJipjubaZZW230pRVo/Koqm9cmR/Ggdt2ZRpWHDhub99983iWbXrl02+Na/snz5cjtLHA0KlBRoa0snUcVxVRrPCEZKE5s+09rizu3Nrqr306ZNC+z/o70hO9HeQHtDvKJ/Q9DyB/4/JAgFxSqYpZNuVap2QbX/R9f//vvvEe3tnJ5Em+V2SpQoYZ588knTrVs3by3tJ598Yte3Z5XWa7uAW4E8e2Ujo8qWLWtGjx5tq+uraJ6/ACAAAAASE0F3FEyfPt3uwaxq1EHR3r7aC1o/t956q53pTlT333+/eemll8zatWvtTLdmEm+//fYsZyC4CtSiombRCOSReFRoT2u7Ve3+rLPOyulfBwAAADmMoDuLtm/fbh544IF0A26t6dQsmP+nTJky9qS8aNGiXkCd2k/hwoXtns74/402f35bwV37Y0ufPn1Mq1at7OuUWSkpKbZQm1ubyxprZEXlypV5AQEAAGARdGfRo48+6u2ZXatWLbs3s7aZOjHAVnDNzGn0NGnSxKZ/z50716b1awux5OTkTD3X4cOHbcq6M3ToUN4rAAAAAFFB0J0FCxcuNFOnTvXWGs+ZM8eUKlUqOu8M0qX1stpTW6nhququdbQVK1bM8Cv3yiuv2P2zRbPn2lMbAAAAAKKBLcMySVtWPfjgg95lBX0E3NlLVaIffvhh7/3ITHE5Pe6pp57yLvuPAQAAACCrCLozSYW2Nm7caI/r1q1r7r777iy/Gci4QYMGmZIlS9pjZR0sW7YsQ49/8cUXbVV5SUpKMjVq1OBtAAAAABA1BN2ZoKrZmtkWFTgbP368yZuXlzInaK38kCFDvMvaQkx7LUZi37595umnn7bHWm/vfx4AAAAAiAYixQxSQKctq1y18t69e5tq1apF5c1A5nTt2tVccskl9njFihVmypQpET1u7Nixdt90ad++valevTpvAQAAAICoIujOoNdee80sWbLEHlepUsX0798/uu8IMrWF2OjRo73LWtu9f//+Uz5m9+7dtuK55MuXL2yPbgAAAACIFoLuDNixY4fp1atX2HrgggULRu3NQOY1atTItGjRwh7/8ssv5plnnkm38vnevXvtcceOHc2FF17Iyw8AAAAg6gi6M+Dxxx83u3bt8tKRGzduHP13BJk2cuRIu8ZeNIv9448/pjl4MmbMGHus+w8cOJBXHQAAAEAgCLoj9MEHH5iUlBR7XLx4cTNq1Khg3hFkmmaru3XrZo8PHTqU5hZimgV36edaD37eeefxqgMAAAAIBEF3BBTAPfDAA95lVbwuW7ZsMO8IskSz1meffbY9nj59ulm6dGnY7Uo9HzdunD0+44wzzBNPPMErDgAAACAwBN0RUJC9fv16e1ynTh1z7733BveOIEuUhTB06NA0txDT/uoaRJGHHnrIlC9fnlccAAAAQGAIutPx3XffmeHDh3tVsidMmMCe3LncPffcYy699FJ7/Pnnn5s33njDHm/evNlMmjTJHhcpUsT06dMnR39PAAAAAPEvroPuTz75xNx+++12/fWWLVsy/PhQKGT35D5y5Ii9/Nhjj3nBHGJnC7F+/fqZP//8086Au/3VNQNeqlSpHPwtAQAAACSCuAy6d+7caWc7r732WvPmm2+aF154we6p3aZNG7N48WIbTEdCM6SLFi2yxyq2NWjQoIB/c0RLw4YNTatWrezxtm3bbMG0119/3UtB1wAKAAAAAAQtroJurd2dPHmyueiii8wrr7wSdtvx48fNzJkzTb169czll19uJk6c6FWwTitw9wdm2pO7UKFCgf7+CG4LsWnTptk24LZ+U+ANAAAAAEGLm6B7zZo15oYbbjCdO3e2AbMULVrUBl7du3cPqza+evVqc99995kKFSrYwHrTpk0nPV/v3r3N77//bo/btm1rmjVrlo1/DaLhggsusO+9nyqbP/LII7zAAAAAALJFzAfdWqurAPnKK680H3/8sXd9u3btzLp160zPnj1t4PXDDz+YqVOn2urjzp49e+x6bwVnLVq0MAsXLrSz5R999JF59dVX7X3OPPNMM2bMmBz525B1AwYMCFu7rfXdGowBAAAAgOwQs0G31mXPnj3bVKtWzYwYMcIcO3bMXq8AWsGz9mj2bwd12mmnmfbt29viaqpo3alTJ3P66ad7zzVnzhzTpEkT+3wdO3b0HqfK5WwrFbuKFStmK87r/a9du3bYfusAAAAAELSYDLq19VPLli1NUlKSV5VcAXRycrJNHW/UqNEpH1+jRg279luPVVBdsWLFsC3C9PxSq1Ytm4aO2KZ2sm/fPjvgUrBgwZz+dQAAAAAkkJgLup9++mk7G62Zaadx48Y22B48eLA544wzIn4upR337dvXrulWkbX69et7t+XLl8/OkOpfxD7NdOfNG3PNHQAAAECMy29ijNbkOuXKlbPrrVXoLE+ePFna17l169b2RwXZ3n33Xbvd2BVXXBGl3xoAAAAAkIhiLugWzVh269bNDBkyxBY6i6bq1avbHwAAAAAAEi7oVjGsl156yVYrBwAAAAAgN4u5oFvFsFibCwAAAACIBTFXWYqAGwAAAAAQK2Iu6AYAAAAAIFYQdAMAAAAAEBCCbgAAAAAAAkLQDQAAAABAQAi6AQAAAAAICEE3AAAAAAABIegGAAAAACAgBN0AAAAAAASEoBsAAAAAgIAQdAMAAAAAEBCCbgAAAAAAApInFAqFgnpyAAAAAAASGTPdAAAAAAAEhKAbAAAAAICAEHQDAAAAABAQgm4AAAAAAAJC0A0AAAAAQEAIugEAAAAACAhBNwAAAAAAASHoBgAAAAAgIPlNjOjfv785evSoPe7YsaO55JJLwm7fvXu3eeutt8zPP/9sLrroInPrrbea0047zbu9X79+5vjx497lK664wtx+++0R347E8uSTT5r9+/fb4/bt25srr7wy7PY//vjDvPnmm+ann34yVapUMbfddps544wzvNsHDx5sDh486F2uVq2a6dSpU9hzfPPNN2bu3LkmFAqZtm3bmvPPPz/wvwu509NPP2127dplj1u3bm2uvvrqsNv//PNP27/98MMP5rzzzrNtslChQt7tw4YNM3v37vUuq03ed9999lh94tixY0/6P5OSkkydOnUC/KuQW40aNcr8+uuv9rh58+bm+uuvD7tdfZfa28aNG02FChXsd2GRIkW820eOHGl+++0377Lu88gjj3iXDxw4YGbOnGm+//57U7x4cXPzzTebypUrZ8vfhtxn3Lhx5scff7THjRo1sj9+hw8fNjNmzDAbNmwwZcuWtf2b2o2j/kv9mFO6dGnz+OOPR/x4JJZJkybZtiDq29TH+SmWUP/07bff2rak87eSJUuG3Wfr1q1m1qxZNrZo1qyZqVmzZtjtCxYsMJ988oltZ+3atTPnnHNONvxliHUxM9P9j3/8wzRu3NisWrXK7Nix46QT0g4dOthOWR+Mjz76yAwcODDsPh988IG55ppr7HPo57LLLsvQ7Ugs9evXt+1g3bp1Ztu2bWG3HTp0yAbQOiGtVauW+eyzz0zv3r3D7rN48WJTo0YNrz1pEMdv0aJF5uGHHzZnnXWW+dvf/maeeuop246RmOrWrWvbyaZNm8JOLt0Jwj333GPWrFlj29vq1atN9+7d7WCNs3TpUnPppZd67e2qq67ybjvzzDO96/Wj51B/V6pUqWz9G5F7uO86tTUXDDkafL7//vvN559/br9PdfL6wAMPhA1KL1u2zFx44YVemzpx8KZHjx72e1iPVzu96667TmrXSBy1a9e27UTnburj/NQ+NGCzZMkS22+pnXTt2tWbZBF9x2qw0bW36667LkOPR2JRO1A70UC0Bv5O1KtXL7Nw4UJ7jvb777+bLl26hE2S6NxOfdaRI0fM3//+d/PGG2+YtWvXerfr8r/+9S/7navJmc6dO5s9e/Zk29+HGBaKMa1atQp9+OGHYdfNmjUr1KZNm9Bff/1lL+/duzdUu3bt0IYNG7z71KpVK7Rjx440nze925GY2rdvH5o7d27YdfPnzw81b948dOzYMXv5wIEDobp164ZWrVrl3eeGG24I/fTTT6k+59GjR0MtW7YMLVmyxLtOz6Hrkdg6d+4cmjFjRth1ixcvDjVq1Ch05MgRe/nw4cOhBg0ahJYtW+bdp1mzZqF169ZF9H9MmTIldMcdd0T5N0csevjhh0MpKSlh13322Weh66+/PnTw4EF7Wf3SjTfeGPrggw+8+7Ru3Tq0cuXKVJ9zz549oRo1aoS2bNniXXf33Xef1K6ReHr16hUaP3582HVr1qwJ1alTJ7Rv3z57WedxSUlJoTlz5nj36dChQ2jp0qWpPmckj0diGjx4cGj06NFh123atClUs2bN0K5du8LO8/z9U/fu3UMvv/yyd/n48eNe+9J5X8OGDcPiEPVvkydPDvivQTyImZnuU9m5c6cpX768yZMnjzezU7RoUfPVV1+F3U8pSkOHDjWzZ88OG7WP9HbAtbdy5cqZfPny2csFCxa0M9Yntrfx48ebIUOGmP/85z/m2LFj3vWaPd+3b59N11Qa+5gxY2yqZv78MbPaA9nc3pQCV6BAAXtZy2Y0S62sH7/Jkyfb9qRlDxqhT8ucOXNOSrcDHM38KNXSLZdRv1SmTJmT+rcpU6aY5ORkM3XqVJv94xQuXNgUK1bMbN++3V7WjKP7jgZS69/UXtzyBZ3H6fv1xPam9HEt29Iso5YvZPTxgGsv7pzNUWq4ay9aqqDMserVq5vhw4ebZ555xn7Xuva1ZcsWO6ut7A1Hx19//TUvMNIVF0H3BRdcYFMvXXqH0kC0DkM/jj48WtuhlDh12n369Al7jvRuB5yqVaua7777zp6citLltD7S394U/ChFXWu5dbLgTwfWWiEN6owePdqmnev4zjvvNL/88gsvMlJtb1rL7ZY5KH1StQT87e2JJ56wS3CU7vbuu++elA7sKFVY7bVp06a80kjz+1R90ebNm+1lDQiq3fjbm9IzmzRpYvuv9957zy5/cAOLCtK15lsD2Er7VX2VFi1a2JR24ESqZaJ6FhqMFp3Haa2tP11X7eimm26ytVWURq66Pm6gJ5LHA46WKajtuCBby/oUMLv+Td+zOlfTZIj6Qg1w6/xNS2pc+zr99NNt4O4ogPf3j0Ba4mJq7YYbbrAFqVTMQEGzRtjPPfdcO+PtNGjQIOxYHbhOPl3xqvRuBxytidUAjYq1XHzxxTaIVkeu0XZ/m3QUDKk9qXCaRk8VDGkdkEbtXfvSusp33nnHrqUE/NRmNDOtuhUqIKmgW0Wp/P2bf42j1rLp/l9++eVJxV/UxrR+nCJDSItONNW33X333XYQRwM8am/+/s1f6E8DOC1btrSzQ+r3FHxPnDjRPk+9evVs//jf//7XHus6wE9F+LQmVoUfVUtHM4n6PvX3b/5+TN+lKjapuihqe5E8HnDOPvts8+CDD9pAWu1F/VvFihW99uIGDzWw4wanXWFJ1a5QhqM/c9Fl85CpiIQJupVONGLECLN+/Xo7Kq+TVAXgCrxTo5ErpZ9rpjK1oDq92wHN4qhAh2a41d7UQafV3hTgaCTUzYy7NEt/uqWOXfVq4ETaXUEVVhXAKPDWSUNa7U1pcEpHd+3N0YnC//73P7sTBHAqOiFVxXEFMCr0qEKRabU3paErnde1Nw326LtYhYrcEhyldE6fPt0MGDCAFx4nUcCsYFrZFcrs0bKstNqbltko0Pb3bxl5PKDzNVXQ18SadvrQbg4aqBH1ZXnz5g07P9N1KubnjjVxolhD37NudlxV84GESC+X5cuX21luzfjMnz/fjsprRlKUdqSTVefjjz+2a4LcqHt6twOptTe1D7U3jbiLZhBFwbi/IvAXX3xhTxC0lZ0oSC9RooSdGXJriFQp2N0OpNbeNNuo9qYvf6WyNWzY0N6mE01/hVYttXHBkp/am9Lmrr32Wl5gpPt9qqBF7U3fj+rP3KyPvitdKq8o9VztT1k/bk23Zob8g4h6jH/LMeDE9qZAWu1NfZf6MGVPiCqea8cGR21RWWP+/u1UjwdS698UVKu9aEBQl7WFpuu/VLHcnZ/pO1Op5e78TDPlOlbWmKg+j3YDced/QFzMdLt9GvUBSUlJMfPmzbOjmxqlEu2np5Q2FRDSfZ577jk7WuVG4rUGTR8m0Sh83759beATye1IPBMmTLDbRmhto2ZoFFgr3dKdWGpgR/f566+/7Gy3im249CK1J7UfFbzSdTpB7dmzpx0hFV2n2Uall6vd6iShUqVKplWrVjn6NyPnvPbaa7YWhQJopeKuWLHC7o3stppT+3vxxRftzKHayz//+U+v0JXWlqkGhTJ+tNZMJ6RapnDivsgqoKbAiTQ4qPiZ1jTqu84FNW3atPEGqj/99FPz/PPP2zamNqmCaS5oVntT36XvWl2nx2p7HdWvEGViaEBImRkaYFQfqvRLPQaJScVE1acpGFZ9Cn23Kih2y2K0plZbMKk96TZt+eqWwKgNap9vLclSCrDa2y233BK2LeKpHo/Eo7omWvuvtf3KjFAfpBoUbqBaA4Wa3VYb0bG+P92stSgeUDaZsnbU7tR/+es8KfNH53TqJ/XcGgDSMkIgPXlUwtzEAM3uaM89P+2x5yoQ6s9Qh64CCVqnoZNPP31odFKrf5V+5F+fFsntSCyanT6xMIYCII1yOmovGuW8/PLLvQDIn8qrDl/tUTPi/kqZjgaHdB83cuqq7yPxKAA6MR1c62lVNdrR4I2KuKh/K1SoUNh9le6mtqQTBLU3VZ8+kfZN1qAR+3ND35UaLPRT0OxPqdTstQJytcMTZ6k12KhikvpOVnvz94uOgnUNlCtQ0nMz2JO41Df5swlF33laS+u4gqQaqDlxPbbO7zRApO9kDSb6+8VIHo/Eoraitdp+6qdcCrnLmNAAttpLagM0ynZV1XINMmog0e0e4qjv0wCQYgU9B+dviKugGwAAAACAWBM3a7oBAAAAAMhtCLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgOQP6okBAED8+uGHH+yeuHnz5jWNGjXK6V8HAIBci6AbAIBcZtu2bebrr7/2Ljdo0MAUKFDAu7xlyxazdu1a73KTJk0C+T327NljPv30U3tcp04dc+aZZ3q3LV682LzwwgsmX758BN0AAJwCQTcAALnMypUrzaBBg7zLycnJpnnz5t7lZ5991ixbtizwoHvz5s2mf//+9jglJcVUq1YtkP8HAIB4RtANAEAuN336dC/o3rRpk1m+fPkp779z506zbt06c+TIEVOhQgVTtWrVU6aG//jjj/anbNmy5sILL/QC7hUrVniP0f+pGfaSJUuaq6666qT/M7XnAAAABN0AAORqpUqVsgG0Zr+vvPJKM23aNBMKhez1O3bsCLvv8ePHzciRI82sWbNMmTJl7I8ed/HFF5vhw4fbANyfGq6ge968eTY4X7Nmjdm/f79p1aqVGThwoA2wv/rqK++5v/zyS7NhwwZzwQUXhAXd+l169uyZ6nMAAACqlwMAkKu1adPG/jt16lS7xlpBcpEiRcyNN9540n1fffVVM2PGDFO8eHF7/0mTJtkZ8m+//dY89thj5q+//gq7vy5fd911Zty4caZ79+72urffftv8+uuvpm7duqZr167efR988EEbuHfp0iXi5wAAAATdAADkag0bNrQz1h999JEZO3asOXz4sLn55ptNwYIFT7rv7NmzvaJnCszFVRbfuHGjWb169UmPady4sf23UqVK3nUZDZij8RwAAMQr9ukGACAXU3XwW2+91aaOv/POO/Zyu3btTrqfbt++fbs9LlGihHe91mD7q6Kf+NxFixa1x/nzZ67MSzSeAwCAeEbQDQBALpeUlOTNbNevX9+UK1cu1eC3cOHC9vjgwYPe9QcOHPCOXXAMAACyD0E3AAC5nPbHfuihh2yqeKdOndK837XXXmv/9e/x7Y6Vbn755Zdn6P897bTTwmbSAQBAxpEHBgBADLjtttvsz6n06NHDFk377rvvzJAhQ0yVKlXM5MmTbfA8YMAAb513pLRGW7Pnqkj+5ptvmq1bt9rq5foBAACRIegGACCXKV++vFcArVChQqneRwG1u49TunRpu6XYggULzDfffGOD7w4dOpimTZuaihUreverXLmyfaxS0h1VPHfPp2NRkD5hwgRbMX3Xrl12qzE9RkF3pM8BAECiyxPSBpsAAAAAACDqWNMNAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgBB0AwAAAAAQEIJuAAAAAAACQtANAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAwATj/wH4ti+gzVt0TwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "autolog()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
+    "    auto_model = LinearRegressionModel(lags=12)\n",
+    "    auto_model.fit(train)  # autolog logs params, covariate metadata, and the model\n",
+    "\n",
+    "    auto_predictions = auto_model.predict(n=len(val))\n",
+    "    # these metric calls happen inside the run, so they are logged automatically\n",
+    "    auto_mape = darts.metrics.mape(val, auto_predictions)\n",
+    "    auto_rmse = darts.metrics.rmse(val, auto_predictions)\n",
+    "\n",
+    "autolog(disable=True)\n",
+    "\n",
+    "# show logged metrics\n",
+    "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
+    "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
+    "\n",
+    "# plot\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "train[-36:].plot(label=\"Train\", ax=ax)\n",
+    "val.plot(label=\"Actual\", ax=ax)\n",
+    "auto_predictions.plot(\n",
+    "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
+    ")\n",
+    "ax.set_title(\"Linear Regression — autolog run\")\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f484330f",
+   "metadata": {},
+   "source": [
+    "## Open the MLflow UI\n",
+    "\n",
+    "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
+    "\n",
+    "The UI lets you:\n",
+    "- **Compare runs** side-by-side in the Experiments table\n",
+    "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
+    "- **Visualize** metrics across runs with built-in charts\n",
+    "- **Register** models to the Model Registry for versioning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1a01cd2f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
+     "iopub.status.busy": "2026-06-24T15:18:50.021439Z",
+     "iopub.status.idle": "2026-06-24T15:18:50.023116Z",
+     "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Launch the MLflow UI with this command in your terminal:\n",
+      "\n",
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpyqkqfzoe/mlflow.db\n",
+      "\n",
+      "Then open:  http://localhost:5000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
+    "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
+    "print(\"Then open:  http://localhost:5000\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88b0d285",
+   "metadata": {},
+   "source": [
+    "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `val_mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
+    ">\n",
+    "![Mlflow Overview](./static/images/mlflow_overview.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2b133bc",
+   "metadata": {},
+   "source": [
+    "## Per-epoch Metrics with Torch Models\n",
+    "\n",
+    "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
+    "\n",
+    "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d01783d1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
+     "iopub.status.busy": "2026-06-24T15:18:50.024087Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.370946Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: GPU available: True (mps), used: False\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
+      "INFO: TPU available: False, using: 0 TPU cores\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+      "INFO: HPU available: False, using: 0 HPUs\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+      "\n",
+      "  | Name            | Type             | Params | Mode \n",
+      "-------------------------------------------------------------\n",
+      "0 | criterion       | MSELoss          | 0      | train\n",
+      "1 | train_criterion | MSELoss          | 0      | train\n",
+      "2 | val_criterion   | MSELoss          | 0      | train\n",
+      "3 | train_metrics   | MetricCollection | 0      | train\n",
+      "4 | val_metrics     | MetricCollection | 0      | train\n",
+      "5 | stacks          | ModuleList       | 6.2 M  | train\n",
+      "-------------------------------------------------------------\n",
+      "6.2 M     Trainable params\n",
+      "1.4 K     Non-trainable params\n",
+      "6.2 M     Total params\n",
+      "24.787    Total estimated model params size (MB)\n",
+      "400       Modules in train mode\n",
+      "0         Modules in eval mode\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b08a95d734ed47b892fbee0631114072",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
+      "  super().__init__(loader)\n",
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a87f60f4ed9420683e2d38314cb8ee9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "254ef24eb5984c54bc7efcdf6b504f11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1452432791254a928b131d34abb67463",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3978f38e7984c4bac8deca33f4e4059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce15173a5f3040d08d9b42827acd5a6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7650d19876444051a760005979b86ea5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d222ce5ee5a452aa842e21280f053c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9092292262b14f3ba657d281e83718cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b32c477b2194b868114c8d3643541f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20cc4938d0e94c04a72727cfc068b281",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "367b8751ed914d37b845de06fb049a42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
+      "2026/06/24 17:34:27 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
+      "INFO: GPU available: True (mps), used: False\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
+      "INFO: TPU available: False, using: 0 TPU cores\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+      "INFO: HPU available: False, using: 0 HPUs\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec9b9f91a32341eaa481b757ce4b5269",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NBEATS MAPE: 13.64%\n",
+      "NBEATS RMSE: 63.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
+    "\n",
+    "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
+    "autolog()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
+    "    nbeats = NBEATSModel(\n",
+    "        input_chunk_length=24,\n",
+    "        output_chunk_length=12,\n",
+    "        n_epochs=10,\n",
+    "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
+    "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
+    "        torch_metrics=MetricCollection({\n",
+    "            \"MAE\": MeanAbsoluteError(),\n",
+    "            \"MSE\": MeanSquaredError(),\n",
+    "        }),\n",
+    "        random_state=42,\n",
+    "    )\n",
+    "    nbeats.fit(train, val_series=val)\n",
+    "    nbeats_pred = nbeats.predict(n=len(val))\n",
+    "    # metric calls inside the run are logged automatically (keys: val_mape, val_rmse)\n",
+    "    nbeats_mape = darts.metrics.mape(val, nbeats_pred)\n",
+    "    nbeats_rmse = darts.metrics.rmse(val, nbeats_pred)\n",
+    "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
+    "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
+    "\n",
+    "autolog(disable=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fcc8f13",
+   "metadata": {},
+   "source": [
+    "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
+    ">\n",
+    "![Mlflow Charts](./static/images/mlflow_charts.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f0828c2",
+   "metadata": {},
+   "source": [
+    "## Forecast Metrics\n",
+    "\n",
+    "With `log_metrics=True` (the default), `autolog()` patches every darts metric function. Any metric you call **inside an active run** is logged automatically with no extra arguments needed.\n",
+    "\n",
+    "The metric key is derived from the result: a scalar is logged under `{metric}`, and when the variable name of the actual series can be detected it is prefixed (e.g. calling `darts.metrics.mae(val, pred)` logs `val_mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "df8e85b6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.372367Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.470125Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/06/24 17:34:27 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
+      "2026/06/24 17:34:27 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All logged metrics (5):\n",
+      "  manual_mape: 10.7420\n",
+      "  val_mae: 46.0220\n",
+      "  val_mape: 10.7420\n",
+      "  val_rmse: 51.1820\n",
+      "  val_smape: 10.1015\n"
+     ]
+    }
+   ],
+   "source": [
+    "# log_metrics=True (the default) patches every darts metric so that calls made\n",
+    "# inside an active run are logged automatically\n",
+    "autolog(log_metrics=True)\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
+    "    lr_model = LinearRegressionModel(lags=12)\n",
+    "    lr_model.fit(train)\n",
+    "    lr_pred = lr_model.predict(n=len(val))\n",
+    "\n",
+    "    # each metric called here is auto-logged; the key is prefixed with the\n",
+    "    # variable name of the first argument (here \"val\"): val_mae, val_rmse, val_smape\n",
+    "    darts.metrics.mae(val, lr_pred)\n",
+    "    darts.metrics.rmse(val, lr_pred)\n",
+    "    darts.metrics.smape(val, lr_pred)\n",
+    "    # you can still log a custom-named metric explicitly\n",
+    "    mlflow.log_metric(\"manual_mape\", darts.metrics.mape(val, lr_pred))\n",
+    "    run_id = run.info.run_id\n",
+    "\n",
+    "autolog(disable=True)\n",
+    "\n",
+    "# show what was logged\n",
+    "client = mlflow.tracking.MlflowClient()\n",
+    "run_metrics = client.get_run(run_id).data.metrics\n",
+    "metric_names = sorted(run_metrics.keys())\n",
+    "print(f\"All logged metrics ({len(metric_names)}):\")\n",
+    "for name in metric_names:\n",
+    "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8511fc08",
+   "metadata": {},
+   "source": [
+    "## Saving and Loading Models Locally\n",
+    "\n",
+    "You can also save and load models to/from local paths without MLflow runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "645ef079",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.471178Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.478682Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/06/24 17:34:27 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Files in model directory:\n",
+      "  - python_env.yaml\n",
+      "  - requirements.txt\n",
+      "  - MLmodel\n",
+      "  - model.pkl\n",
+      "  - conda.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save model to local directory\n",
+    "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
+    "save_model(model, path=local_model_path)\n",
+    "\n",
+    "print(\"\\nFiles in model directory:\")\n",
+    "for file in os.listdir(local_model_path):\n",
+    "    print(f\"  - {file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "254ba153",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.479604Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.484973Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded model successfully!\n",
+      "Predictions shape: (5, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load model from local directory\n",
+    "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
+    "\n",
+    "# Test it\n",
+    "local_predictions = local_loaded_model.predict(n=5)\n",
+    "print(\"Loaded model successfully!\")\n",
+    "print(f\"Predictions shape: {local_predictions.values().shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab0d1e0",
+   "metadata": {},
+   "source": [
+    "## Querying Experiments\n",
+    "\n",
+    "You can programmatically query and compare runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "109a9812",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.485859Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.496380Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 4 runs in experiment 'darts-quickstart':\n",
+      "\n",
+      "1. exponential-smoothing-baseline\n",
+      "   Run ID: 3d1f7f864f7240599e3b080109e246bf\n",
+      "   Validation MAPE: 7.864181481214469\n",
+      "\n",
+      "2. linear-regression-full-metrics\n",
+      "   Run ID: c655521973ca47b69ad3d423e5302759\n",
+      "   Validation MAPE: 10.742044444678953\n",
+      "\n",
+      "3. linear-regression-autolog\n",
+      "   Run ID: e4e4ae8b46004a5b8309337d5d58cbd2\n",
+      "   Validation MAPE: 10.742044444678953\n",
+      "\n",
+      "4. nbeats-epoch-metrics\n",
+      "   Run ID: d5a971cffab244e192839ae3ba1652e5\n",
+      "   Validation MAPE: 13.639569217869525\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mlflow.tracking import MlflowClient\n",
+    "\n",
+    "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
+    "\n",
+    "# get all runs\n",
+    "client = MlflowClient()\n",
+    "runs = client.search_runs(\n",
+    "    experiment_ids=[experiment.experiment_id],\n",
+    "    order_by=[\"metrics.val_mape ASC\"],  # Sort by best MAPE\n",
+    ")\n",
+    "\n",
+    "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
+    "for i, run in enumerate(runs, 1):\n",
+    "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
+    "    mape_val = run.data.metrics.get(\"val_mape\", \"N/A\")\n",
+    "    print(f\"{i}. {run_name}\")\n",
+    "    print(f\"   Run ID: {run.info.run_id}\")\n",
+    "    print(f\"   Validation MAPE: {mape_val}\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22685423",
+   "metadata": {},
+   "source": [
+    "### Load the Best Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7b09db6a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.497496Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.572540Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading best model from run: exponential-smoothing-baseline\n",
+      "Model URI: models:/m-f42705a59eb34300b377f0219577bfa4\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
+    "\n",
+    "if runs:\n",
+    "    best_run = runs[0]\n",
+    "    # the model is logged via MLflow's logged-model API, so resolve its model_id\n",
+    "    # from the run outputs and load it with a models:/ URI\n",
+    "    model_outputs = mlflow.get_run(best_run.info.run_id).outputs.model_outputs\n",
+    "    best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
+    "\n",
+    "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
+    "    print(f\"Model URI: {best_model_uri}\")\n",
+    "\n",
+    "    best_model = load_model(best_model_uri)\n",
+    "    # global models (e.g. LinearRegression, NBEATS) need the series at predict time\n",
+    "    # because we save with clean=True; local models (e.g. ExponentialSmoothing) don't\n",
+    "    if isinstance(best_model, GlobalForecastingModel):\n",
+    "        best_predictions = best_model.predict(n=len(val), series=train)\n",
+    "    else:\n",
+    "        best_predictions = best_model.predict(n=len(val))\n",
+    "\n",
+    "    train[-50:].plot(label=\"Training\")\n",
+    "    val.plot(label=\"Actual\")\n",
+    "    best_predictions.plot(label=\"Best Model Forecast\")\n",
+    "    plt.legend()\n",
+    "    plt.title(\"Best Model Predictions\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f1ec89e",
+   "metadata": {},
+   "source": [
+    "## Model Registry\n",
+    "\n",
+    "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived).\n",
+    "\n",
+    "```python\n",
+    "# Register a model from a run\n",
+    "result = mlflow.register_model(\n",
+    "    model_uri=f\"runs:/{best_run.info.run_id}/model\",\n",
+    "    name=\"darts-air-passengers\",\n",
+    ")\n",
+    "print(f\"Registered version: {result.version}\")\n",
+    "```\n",
+    "\n",
+    "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
+    "\n",
+    "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
+    ">\n",
+    "![Mlflow Charts](./static/images/mlflow_models.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7af49ea9",
+   "metadata": {},
+   "source": [
+    "## Important Note: Custom Flavor\n",
+    "\n",
+    "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
+    "\n",
+    "**Always use:**\n",
+    "```python\n",
+    "from darts.utils.mlflow import load_model\n",
+    "model = load_model(model_uri)\n",
+    "```\n",
+    "\n",
+    "**Instead of:**\n",
+    "```python\n",
+    "import mlflow\n",
+    "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
+    "```\n",
+    "\n",
+    "This custom flavor is necessary to properly handle:\n",
+    "- TimeSeries objects\n",
+    "- Darts-specific model parameters\n",
+    "- Covariate handling (past, future, static)\n",
+    "- PyTorch model state preservation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40621b13",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "51fc7c4a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.573637Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.575115Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpyqkqfzoe\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Uncomment to cleanup\n",
+    "# import shutil\n",
+    "# shutil.rmtree(tmpdir)\n",
+    "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
+    "\n",
+    "print(f\"To cleanup manually, delete: {tmpdir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca40afc1",
+   "metadata": {},
+   "source": [
+    "# Final Remarks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4c86a23",
+   "metadata": {},
+   "source": [
+    "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
+   ]
+  }
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literal 0
HcmV?d00001


From 6dda789b16eb77c40c1e07ce657625fc9c33f253 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 24 Jun 2026 18:06:49 +0200
Subject: [PATCH 075/154] fix: add MLFLOW_AVAILABLE flag

---
 darts/tests/conftest.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/darts/tests/conftest.py b/darts/tests/conftest.py
index 85e0b9c855..9ee525501c 100644
--- a/darts/tests/conftest.py
+++ b/darts/tests/conftest.py
@@ -36,6 +36,7 @@ def _package_available(*names: str) -> bool:
 PLOTLY_AVAILABLE = _package_available("plotly")
 IPYTHON_AVAILABLE = _package_available("IPython")
 TIREX_AVAILABLE = _package_available("tirex")
+MLFLOW_AVAILABLE = _package_available("mlflow")
 
 tfm_kwargs: dict[str, Any] = {
     "pl_trainer_kwargs": {

From c3f1df63bf43752f341532d813c61c4a89889ddc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 25 Jun 2026 10:20:52 +0200
Subject: [PATCH 076/154] feat: align direct metric logging shape to
 backtesting (based on metric kwargs)

---
 darts/utils/mlflow.py | 214 ++++++++++++++++++++++++++++--------------
 1 file changed, 141 insertions(+), 73 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 6a844513ba..98fce464fa 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1110,30 +1110,37 @@ def _log_metric_result(
     run_id: str,
     metric_name: str,
     result,
+    series,
+    has_time_axis: bool,
+    has_comp_axis: bool,
+    quantiles_num: int | None,
+    quantiles_labels: list[str],
     dataset_name: str | None = None,
-    component_names: list[str] | None = None,
-    input_is_list: bool = False,
+    series_reduced: bool = False,
 ) -> None:
     """Log a metric result to the active MLflow run.
 
-    Handles Python scalars, numpy scalars, 1-D arrays, and 2-D arrays.
+    Reshapes each per-series result into a canonical ``(T, C)`` layout
+    (timesteps, components × quantiles/intervals/labels) inferred from the
+    metric signature and call kwargs by ``_infer_metric_axes``, logging every
+    cell under a descriptive key with the time axis mapped to the MLflow
+    ``step``. This mirrors ``_log_backtest_metrics`` (without the
+    window/``forecast_horizon`` split, since ``multi_ts_support`` returns a
+    clean per-series list).
 
     The logged MLflow key follows the pattern::
 
-        {metric_name}_{dataset_name}_{component}_{series_index}
+        {dataset_name}_{metric_name}{component}{quantile_or_label}{series_suffix}
 
-    Specifically:
+    where each optional part is included only when the corresponding axis is
+    present:
 
-    * **Scalar** (0-d) → ``{metric}_{dataset}``
-    * **1-D, single TimeSeries input** (per-component) →
-      ``{metric}_{dataset}_{component_name_or_idx}``
-    * **1-D, list input** (per-series, component already reduced) →
-      ``{metric}_{dataset}_{series_idx}``
-    * **2-D, list input** (per-series × per-component) →
-      ``{metric}_{dataset}_{component_name_or_idx}_{series_idx}``
+    * ``component`` – ``_{component_name}`` when ``has_comp_axis``.
+    * ``quantile_or_label`` – e.g. ``_q0.5`` / ``_qi0.1_0.9`` / ``_label1``.
+    * ``series_suffix`` – ``_s{series_index}`` when more than one series.
 
-    All optional parts are omitted when not available (e.g. no dataset name
-    when the variable name could not be inspected).
+    On a shape/size mismatch it warns and returns (does not raise), keeping
+    autologging non-fatal.
 
     Parameters
     ----------
@@ -1141,80 +1148,129 @@ def _log_metric_result(
         Base metric name used as the MLflow key.
     result
         The metric result to log.
+    series
+        The ``actual_series`` argument passed to the metric (single series or
+        ``Sequence[TimeSeries]``); used for component names, series count, and
+        runtime label inference.
+    has_time_axis
+        ``True`` when the result carries a per-timestep axis (``time_reduction=None``).
+    has_comp_axis
+        ``True`` when components are expanded (``component_reduction=None``).
+    quantiles_num
+        Number of quantile/interval/label entries; ``None`` when it cannot be
+        determined ahead of time (``label_reduction=None`` without explicit
+        ``labels``), in which case labels are inferred from ``series`` at runtime.
+    quantiles_labels
+        One key suffix per ``quantiles_num`` entry (empty list when ``quantiles_num``
+        is ``None``).
     dataset_name
         Sanitized variable name of ``actual_series`` in the caller's frame.
         Omitted from key when ``None``.
-    component_names
-        Component name strings to use as the component part of the key.
-        For single-series input these come from ``series.components``; for
-        list input they come from the first series in the list.
-        Falls back to integer indices when ``None`` or length mismatches.
-    input_is_list
-        ``True`` when ``actual_series`` was a ``Sequence[TimeSeries]``.  Drives
-        whether the first result axis is treated as *series* or *components*.
+    series_reduced
+        ``True`` when ``series_reduction`` collapsed the series axis inside the
+        metric, so the result has no leading series axis even for list input.
     """
-    result_arr = np.asarray(result)
-
     base_key = f"{dataset_name}_{metric_name}" if dataset_name else metric_name
 
-    def _comp_suffix(idx: int) -> str:
-        if component_names is not None and idx < len(component_names):
-            return _sanitize_mlflow_key(component_names[idx])
-        return str(idx)
+    if series_reduced:
+        # series_reduction aggregated across series → single result, no series axis
+        series_seq = [get_single_series(series)]
+        results = [result]
+    else:
+        series_seq = series2seq(series)
+        results = (
+            [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
+        )
 
-    metrics = {}
+    labels_unknown = quantiles_num is None
 
-    if result_arr.ndim == 0:
-        # scalar result
-        metrics[base_key] = float(result_arr)
+    metrics_by_step: dict[int, dict[str, float]] = {}
+    for series_index, (s, r) in enumerate(zip(series_seq, results)):
+        series_suffix = f"_s{series_index}" if len(series_seq) > 1 else ""
 
-    elif result_arr.ndim == 1:
-        if not input_is_list:
-            # single series: log per-component
-            for c_i, val in enumerate(result_arr):
-                metrics[f"{base_key}_{_comp_suffix(c_i)}"] = float(val)
-        else:
-            # list input, components already reduced: log per-series
-            for s_i, val in enumerate(result_arr):
-                metrics[f"{base_key}_{s_i}"] = float(val)
-
-    elif result_arr.ndim == 2:
-        # list input: log per-series and per-component
-        n_series, n_components = result_arr.shape
-        for s_i in range(n_series):
-            for c_i in range(n_components):
-                metrics[f"{base_key}_{_comp_suffix(c_i)}_{s_i}"] = float(
-                    result_arr[s_i, c_i]
-                )
+        # quantiles_num=None means label_reduction=None was requested without explicit labels
+        # so class labels are inferred per-series inside the loop.
+        if labels_unknown:
+            inferred_labels = np.unique(s.values())
+            quantiles_num = len(inferred_labels)
+            quantiles_labels = [f"_label{x:g}" for x in inferred_labels]
 
-    else:
-        # unexpected shape — flatten with integer indices
-        for i, val in enumerate(result_arr.flatten()):
-            metrics[f"{base_key}_{i}"] = float(val)
+        comps = s.components.tolist()
+        # c_size = components × quantiles/intervals/labels per component
+        c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
+        arr = np.asarray(r, dtype=float)
+        # after stripping the C axis, the remainder is the time axis (or scalar)
+        rest, extra = divmod(arr.size, c_size)
+        if extra:
+            logger.warning(
+                "Metric logging skipped for `%s`: result size (%d) is not "
+                "divisible by the inferred component/quantile size (%d). "
+                "The metric output shape does not match the inferred axes.",
+                metric_name,
+                arr.size,
+                c_size,
+            )
+            return
 
-    autologging_client.log_metrics(run_id=run_id, metrics=metrics)
-    operation = autologging_client.flush(synchronous=False)
-    operation.await_completion()
+        if has_time_axis:
+            t_size = rest
+        elif rest != 1:
+            logger.warning(
+                "Metric logging skipped for `%s`: expected a single value per "
+                "component/quantile after reduction, but got %d elements. "
+                "Check time_reduction and component_reduction.",
+                metric_name,
+                rest,
+            )
+            return
+        else:
+            t_size = 1
+
+        canonical = arr.reshape(t_size, c_size)
+        for c in range(c_size):
+            # c is a flat index into the (n_components × quantiles_num) C axis:
+            # c = comp_i * quantiles_num + q_i
+            component_index, quantile_index = divmod(c, quantiles_num)
+            comp_part = (
+                "_" + _sanitize_mlflow_key(comps[component_index])
+                if has_comp_axis
+                else ""
+            )
+            key = _sanitize_mlflow_key(
+                base_key + comp_part + quantiles_labels[quantile_index] + series_suffix
+            )
+            for t in range(t_size):
+                # MLflow step maps to the time axis when present
+                step = t if has_time_axis else 0
+                metrics_by_step.setdefault(step, {})[key] = float(canonical[t, c])
+
+    for step, metrics in metrics_by_step.items():
+        autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
+    autologging_client.flush(synchronous=False).await_completion()
 
 
 def _make_metric_patch(metric_name: str) -> Callable:
     """Create a ``safe_patch``-compatible patch function for a darts metric.
 
     The returned patch calls the original metric and, when an active MLflow
-    run exists, logs the result under a key built as::
+    run exists, infers the output axes from the metric signature and call
+    kwargs (via ``_infer_metric_axes``) and delegates to ``_log_metric_result``,
+    which logs each cell under a key built as::
 
-        {metric_name}_{dataset_name}_{component}_{series_index}
+        {dataset_name}_{metric_name}{component}{quantile_or_label}{series_suffix}
 
     where:
 
     * ``dataset_name`` – Python variable name of the first argument in the
       caller's frame (captured via frame inspection, omitted if not found).
-    * ``component`` – component label from the ``TimeSeries`` if the result is
-      per-component, otherwise an integer index.
-    * ``series_index`` – integer index appended when the input is a
-      ``Sequence[TimeSeries]`` and the result has a series axis.
+    * ``component`` – ``_{component_name}`` when ``component_reduction=None``.
+    * ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.5``,
+      ``_qi0.1_0.9``, ``_label1``) when applicable.
+    * ``series_suffix`` – ``_s{series_index}`` when the input is a
+      ``Sequence[TimeSeries]`` with more than one series.
 
-    The original return value is always forwarded unchanged.
+    The per-timestep axis (``time_reduction=None``) is mapped to the MLflow
+    ``step``. The original return value is always forwarded unchanged.
 
     Parameters
     ----------
@@ -1242,28 +1298,40 @@ def _patched_metric(original, *args, **kwargs):
             series = args[0]
         else:
             series = kwargs.get("actual_series", None)
+        if series is None:
+            return result
 
         # capture the variable name of actual_series for metric key
         raw = _inspect_original_var_name(series, fallback_name=None)
         dataset_name = _sanitize_mlflow_key(raw) if raw else None
 
-        # handling multi_series input
-        input_is_list = not hasattr(series, "components")
-
-        # extract component names from the series (or first element if list)
-        single_series = get_single_series(series)
-        component_names = (
-            single_series.components.tolist() if single_series is not None else None
+        # infer output axes from the metric signature + call kwargs
+        has_time_axis, has_comp_axis, quantiles_num, quantiles_labels = (
+            _infer_metric_axes(original, kwargs)
         )
 
+        # series_reduction collapses the series axis inside the metric, so the
+        # result has no leading series axis even for list input.
+        params = inspect.signature(original).parameters
+        series_reduced = False
+        if "series_reduction" in params:
+            effective_sr = kwargs.get(
+                "series_reduction", params["series_reduction"].default
+            )
+            series_reduced = effective_sr is not None
+
         _log_metric_result(
             autologging_client,
             run_id,
             metric_name,
             result,
+            series,
+            has_time_axis,
+            has_comp_axis,
+            quantiles_num,
+            quantiles_labels,
             dataset_name=dataset_name,
-            component_names=component_names,
-            input_is_list=input_is_list,
+            series_reduced=series_reduced,
         )
 
         return result

From 8b8157bebca646a68caa5297caa04e018e38d354 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 25 Jun 2026 10:21:58 +0200
Subject: [PATCH 077/154] feat: add missing autolog shape unit test

---
 darts/tests/optional_deps/test_mlflow.py | 174 +++++++++++++++++++++++
 1 file changed, 174 insertions(+)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index d3d559e939..80f376d07d 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -724,6 +724,180 @@ def test_autolog_public_namespace_patched(self, mlflow_tracking, autolog_context
             "darts.metrics.metrics.mae should NOT log (implementation module is not patched)"
         )
 
+    def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
+        """A per-timestep metric (ae) logs one value per timestep across MLflow steps.
+
+        time_reduction=None (ae's default) means the result keeps a per-timestep
+        axis, which is mapped to the MLflow step (mirroring the backtest path)
+        rather than being mislabeled as per-component.
+        """
+        train = self.ts_univariate[:40]
+        model = LinearRegressionModel(lags=4)
+        model.fit(train)
+        pred = model.predict(n=10)
+        actual = self.ts_univariate[40:]
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.ae(actual, pred)
+
+        # the first-arg variable name ("actual") is captured as the key prefix
+        ref = np.asarray(ref, dtype=float)  # shape (n_timesteps,)
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "actual_ae")
+        assert len(history) == len(ref), "Expected one step per timestep"
+        steps = sorted(m.step for m in history)
+        assert steps == list(range(len(ref)))
+        logged = [m.value for m in sorted(history, key=lambda m: m.step)]
+        np.testing.assert_allclose(logged, ref, atol=1e-5)
+
+    def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
+        """A quantile metric (mql) logs one key per quantile with matching values."""
+        train = self.ts_univariate[:40]
+        model = LinearRegressionModel(
+            lags=4, likelihood="quantile", quantiles=[0.1, 0.5, 0.9]
+        )
+        model.fit(train)
+        pred = model.predict(n=10, num_samples=200)
+        actual = self.ts_univariate[40:]
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.mql(actual, pred, q=[0.1, 0.5, 0.9])
+
+        # the first-arg variable name ("actual") is captured as the key prefix
+        ref = np.asarray(ref, dtype=float)  # shape (n_quantiles,)
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        for i, key in enumerate((
+            "actual_mql_q0_1",
+            "actual_mql_q0_5",
+            "actual_mql_q0_9",
+        )):
+            assert key in m, f"Expected quantile key {key}"
+            assert m[key] == pytest.approx(ref[i], abs=1e-5)
+
+    def test_autolog_metric_quantile_interval(self, mlflow_tracking, autolog_context):
+        """A quantile interval metric (miw) logs one key per interval."""
+        train = self.ts_univariate[:40]
+        model = LinearRegressionModel(
+            lags=4, likelihood="quantile", quantiles=[0.1, 0.5, 0.9]
+        )
+        model.fit(train)
+        pred = model.predict(n=10, num_samples=200)
+        actual = self.ts_univariate[40:]
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.miw(actual, pred, q_interval=(0.1, 0.9))
+
+        # the first-arg variable name ("actual") is captured as the key prefix
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert "actual_miw_qi0_1_0_9" in m
+        assert m["actual_miw_qi0_1_0_9"] == pytest.approx(float(ref), abs=1e-5)
+
+    def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
+        """A list of series logs one key per series index using the _s{i} suffix."""
+        series = [self.ts_univariate, self.ts_univariate * 1.2]
+        pred = [s * 1.1 for s in series]
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.mae(series, pred)
+
+        # the first-arg variable name ("series") is captured as the key prefix
+        ref = np.asarray(ref, dtype=float)  # shape (n_series,)
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["series_mae_s0"] == pytest.approx(ref[0], abs=1e-5)
+        assert m["series_mae_s1"] == pytest.approx(ref[1], abs=1e-5)
+
+    def test_autolog_metric_multi_series_per_component(
+        self, mlflow_tracking, autolog_context
+    ):
+        """A list of multivariate series with component_reduction=None logs one key
+        per (component, series) — exercising the 2-D (series × component) layout."""
+        series = [self.ts_multivariate, self.ts_multivariate * 1.2]
+        pred = [s * 1.1 for s in series]
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.mae(series, pred, component_reduction=None)
+
+        # the first-arg variable name ("series") is captured as the key prefix
+        ref = np.asarray(ref, dtype=float)  # shape (n_series, n_components)
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["series_mae_linear_s0"] == pytest.approx(ref[0, 0], abs=1e-5)
+        assert m["series_mae_linear_1_s0"] == pytest.approx(ref[0, 1], abs=1e-5)
+        assert m["series_mae_linear_s1"] == pytest.approx(ref[1, 0], abs=1e-5)
+        assert m["series_mae_linear_1_s1"] == pytest.approx(ref[1, 1], abs=1e-5)
+
+    def test_autolog_metric_multi_series_classification_labels_inferred(
+        self, mlflow_tracking, autolog_context
+    ):
+        """f1 with label_reduction=None on a list of binary series infers class labels
+        per-series and logs structured per-label keys with the _s{i} suffix.
+
+        This exercises the labels_unknown branch inside the per-series loop so that
+        each series' own class set is inferred rather than reusing the first series'.
+        """
+        # two independent binary series (same classes, deterministic)
+        binary1 = tg.constant_timeseries(value=0.0, length=50).with_values(
+            np.array([0.0, 1.0] * 25, dtype=np.float32).reshape(-1, 1)
+        )
+        binary2 = tg.constant_timeseries(value=0.0, length=50).with_values(
+            np.array([1.0, 0.0] * 25, dtype=np.float32).reshape(-1, 1)
+        )
+        series = [binary1, binary2]
+        pred = series  # perfect predictions → f1 == 1.0 per label per series
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.f1(series, pred, label_reduction=None)
+
+        ref = [np.asarray(r, dtype=float).flatten() for r in ref]
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        for i in range(2):
+            assert f"series_f1_label0_s{i}" in m, f"Missing key for series {i}, label 0"
+            assert f"series_f1_label1_s{i}" in m, f"Missing key for series {i}, label 1"
+            assert m[f"series_f1_label0_s{i}"] == pytest.approx(ref[i][0], abs=1e-5)
+            assert m[f"series_f1_label1_s{i}"] == pytest.approx(ref[i][1], abs=1e-5)
+
+    def test_autolog_metric_size_mismatch_warns_and_skips(
+        self, mlflow_tracking, autolog_context, caplog
+    ):
+        """When the inferred C-axis size doesn't divide the result, a warning is logged
+        and no metrics are written (non-fatal — autologging must not raise)."""
+        actual = self.ts_univariate[40:]
+        # mae with component_reduction=None on a univariate series produces shape (T,),
+        # which is size T — divisible by c_size=1 (1 component × 1 quantile), so we
+        # need to force a mismatch.  We do that by monkey-patching _infer_metric_axes
+        # to report has_comp_axis=True with a fake 3-component count, making c_size=3
+        # while the actual result is shape (T,).
+        train = self.ts_univariate[:40]
+        model = LinearRegressionModel(lags=4)
+        model.fit(train)
+        pred = model.predict(n=10)
+
+        import unittest.mock as mock
+
+        from darts.utils import mlflow as mlflow_utils
+
+        fake_axes = (False, True, 3, ["_c0", "_c1", "_c2"])
+        with mock.patch.object(
+            mlflow_utils, "_infer_metric_axes", return_value=fake_axes
+        ):
+            with autolog_context(log_metrics=True):
+                with mlflow.start_run() as run:
+                    with caplog.at_level(logging.WARNING, logger="darts"):
+                        dm.mae(actual, pred)
+
+        assert any("not divisible" in record.message for record in caplog.records), (
+            "Expected a 'not divisible' warning when axes don't match the result"
+        )
+        # no metrics should have been written for the (faked) mismatched call
+        run_data = mlflow.get_run(run.info.run_id).data.metrics
+        assert not any("mae" in k for k in run_data), (
+            "No mae metrics should be logged when the size check fails"
+        )
+
     def test_autolog_backtest_scalar(self, mlflow_tracking, autolog_context):
         """Default (reduced) backtest of a single univariate series logs one scalar."""
         with autolog_context(log_metrics=True):

From 3deccb1414dc25076b8260af0e6306bfad07e295 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 25 Jun 2026 13:15:09 +0200
Subject: [PATCH 078/154] feat: save per-series metrics to csv and the
 aggregate to mlflow

---
 darts/utils/mlflow.py | 122 ++++++++++++++++++++++++++++++++++--------
 1 file changed, 101 insertions(+), 21 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 98fce464fa..bd5ec7c439 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -14,6 +14,7 @@
 https://github.com/sktime/sktime/blob/main/sktime/utils/mlflow_sktime.py
 """
 
+import csv
 import inspect
 import json
 import os
@@ -835,6 +836,37 @@ def _sanitize_mlflow_key(name: str) -> str:
     return re.sub(r"[^\w-]", "_", name)
 
 
+def _write_per_series_csv(rows: list[dict], filename: str) -> None:
+    """Write the granular per-series metric breakdown to a CSV artifact.
+
+    Each row is a single metric cell for one series, with columns ``key`` (the
+    aggregate MLflow key, without any series suffix), ``series_index``, ``step``
+    (the time or window index charted by MLflow), and ``value``. The file is
+    logged under the ``per_series_metrics`` artifact subdirectory of the active
+    run. Used when more than one series is scored, since the logged metric keys
+    only carry the mean over series.
+
+    Parameters
+    ----------
+    rows
+        One dict per metric cell with keys ``key``, ``series_index``, ``step``,
+        and ``value``.
+    filename
+        Basename of the CSV file (e.g. ``series_mae_per_series.csv``).
+    """
+    if not rows:
+        return
+    sorted_rows = sorted(rows, key=itemgetter("key", "series_index", "step"))
+    fieldnames = ["key", "series_index", "step", "value"]
+    with TempDir() as tmp:
+        path = tmp.path(filename)
+        with open(path, "w", newline="") as f:
+            writer = csv.DictWriter(f, fieldnames=fieldnames)
+            writer.writeheader()
+            writer.writerows(sorted_rows)
+        mlflow.log_artifact(path, artifact_path="per_series_metrics")
+
+
 def _log_backtest_metrics(
     autologging_client: MlflowAutologgingQueueingClient,
     run_id: str,
@@ -869,6 +901,11 @@ def _log_backtest_metrics(
     layouts (different ``time_reduction`` / ``component_reduction`` / quantile
     count), each series result is flattened to integer-indexed keys instead.
 
+    When more than one series is scored, the logged value is the mean over
+    series for each cell, and the granular per-series breakdown is written to a
+    ``per_series_metrics/backtest_per_series.csv`` artifact. For a single series
+    the mean is just the value itself and no artifact is written.
+
     Parameters
     ----------
     autologging_client
@@ -931,16 +968,23 @@ def _log_backtest_metrics(
     series_seq = series2seq(series)
     results = [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
 
-    metrics_by_step: dict[int, dict[str, float]] = {}
+    # agg maps (key, step) -> per-series values, averaged into the logged metric.
+    agg: dict[tuple[str, int], list[float]] = {}
+    rows: list[dict] = []
     for series_index, (s, r) in enumerate(zip(series_seq, results)):
-        series_suffix = f"_s{series_index}" if len(series_seq) > 1 else ""
-
         if axes_inconsistent:
             name_prefix = metric_names[0] if len(metric_names) == 1 else "metrics"
             flat = np.asarray(r, dtype=float).flatten()
             for i, val in enumerate(flat):
-                key = _sanitize_mlflow_key(f"backtest_{name_prefix}{series_suffix}_{i}")
-                metrics_by_step.setdefault(0, {})[key] = float(val)
+                key = _sanitize_mlflow_key(f"backtest_{name_prefix}_{i}")
+                value = float(val)
+                agg.setdefault((key, 0), []).append(value)
+                rows.append({
+                    "key": key,
+                    "series_index": series_index,
+                    "step": 0,
+                    "value": value,
+                })
             continue
 
         # resolve label names from series data when not provided explicitly.
@@ -1023,18 +1067,32 @@ def _log_backtest_metrics(
                     key = f"backtest_{metric_name}{comp_part}{quantiles_labels[quantile_index]}"
                     if has_time_axis and has_windows:
                         key += f"_w{w}"
-                    key = _sanitize_mlflow_key(key + series_suffix)
+                    key = _sanitize_mlflow_key(key)
                     for t in range(t_size):
                         # MLflow step maps to the axis the UI should chart:
                         # time when present, otherwise window index
                         step = t if has_time_axis else w
-                        metrics_by_step.setdefault(step, {})[key] = float(
-                            canonical[w, t, c, m]
-                        )
-
+                        value = float(canonical[w, t, c, m])
+                        agg.setdefault((key, step), []).append(value)
+                        rows.append({
+                            "key": key,
+                            "series_index": series_index,
+                            "step": step,
+                            "value": value,
+                        })
+
+    # log the mean over series for each (key, step); for a single series this is
+    # just the value itself.
+    metrics_by_step: dict[int, dict[str, float]] = {}
+    for (key, step), values in agg.items():
+        metrics_by_step.setdefault(step, {})[key] = float(np.mean(values))
     for step, metrics in metrics_by_step.items():
         autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
 
+    # write the granular per-series breakdown to a CSV artifact (multi-series only)
+    if len(series_seq) > 1:
+        _write_per_series_csv(rows, "backtest_per_series.csv")
+
 
 def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
     """Infer a metric's output axes from its signature and ``metric_kwargs``.
@@ -1130,14 +1188,18 @@ def _log_metric_result(
 
     The logged MLflow key follows the pattern::
 
-        {dataset_name}_{metric_name}{component}{quantile_or_label}{series_suffix}
+        {dataset_name}_{metric_name}{component}{quantile_or_label}
 
     where each optional part is included only when the corresponding axis is
     present:
 
     * ``component`` – ``_{component_name}`` when ``has_comp_axis``.
     * ``quantile_or_label`` – e.g. ``_q0.5`` / ``_qi0.1_0.9`` / ``_label1``.
-    * ``series_suffix`` – ``_s{series_index}`` when more than one series.
+
+    When more than one series is scored, the logged value is the mean over
+    series for each cell, and the granular per-series breakdown is written to a
+    ``per_series_metrics/{base_key}_per_series.csv`` artifact. For a single
+    series the mean is just the value itself and no artifact is written.
 
     On a shape/size mismatch it warns and returns (does not raise), keeping
     autologging non-fatal.
@@ -1184,10 +1246,10 @@ def _log_metric_result(
 
     labels_unknown = quantiles_num is None
 
-    metrics_by_step: dict[int, dict[str, float]] = {}
+    # agg maps (key, step) -> per-series values, averaged into the logged metric.
+    agg: dict[tuple[str, int], list[float]] = {}
+    rows: list[dict] = []
     for series_index, (s, r) in enumerate(zip(series_seq, results)):
-        series_suffix = f"_s{series_index}" if len(series_seq) > 1 else ""
-
         # quantiles_num=None means label_reduction=None was requested without explicit labels
         # so class labels are inferred per-series inside the loop.
         if labels_unknown:
@@ -1237,17 +1299,33 @@ def _log_metric_result(
                 else ""
             )
             key = _sanitize_mlflow_key(
-                base_key + comp_part + quantiles_labels[quantile_index] + series_suffix
+                base_key + comp_part + quantiles_labels[quantile_index]
             )
             for t in range(t_size):
                 # MLflow step maps to the time axis when present
                 step = t if has_time_axis else 0
-                metrics_by_step.setdefault(step, {})[key] = float(canonical[t, c])
-
+                value = float(canonical[t, c])
+                agg.setdefault((key, step), []).append(value)
+                rows.append({
+                    "key": key,
+                    "series_index": series_index,
+                    "step": step,
+                    "value": value,
+                })
+
+    # log the mean over series for each (key, step); for a single series this is
+    # just the value itself.
+    metrics_by_step: dict[int, dict[str, float]] = {}
+    for (key, step), values in agg.items():
+        metrics_by_step.setdefault(step, {})[key] = float(np.mean(values))
     for step, metrics in metrics_by_step.items():
         autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
     autologging_client.flush(synchronous=False).await_completion()
 
+    # write the granular per-series breakdown to a CSV artifact (multi-series only)
+    if len(series_seq) > 1:
+        _write_per_series_csv(rows, f"{base_key}_per_series.csv")
+
 
 def _make_metric_patch(metric_name: str) -> Callable:
     """Create a ``safe_patch``-compatible patch function for a darts metric.
@@ -1257,7 +1335,7 @@ def _make_metric_patch(metric_name: str) -> Callable:
     kwargs (via ``_infer_metric_axes``) and delegates to ``_log_metric_result``,
     which logs each cell under a key built as::
 
-        {dataset_name}_{metric_name}{component}{quantile_or_label}{series_suffix}
+        {dataset_name}_{metric_name}{component}{quantile_or_label}
 
     where:
 
@@ -1266,8 +1344,10 @@ def _make_metric_patch(metric_name: str) -> Callable:
     * ``component`` – ``_{component_name}`` when ``component_reduction=None``.
     * ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.5``,
       ``_qi0.1_0.9``, ``_label1``) when applicable.
-    * ``series_suffix`` – ``_s{series_index}`` when the input is a
-      ``Sequence[TimeSeries]`` with more than one series.
+
+    When the input is a ``Sequence[TimeSeries]`` with more than one series, the
+    logged value is the mean over series and the per-series breakdown is written
+    to a ``per_series_metrics/`` CSV artifact instead of per-series keys.
 
     The per-timestep axis (``time_reduction=None``) is mapped to the MLflow
     ``step``. The original return value is always forwarded unchanged.

From 277153f6a42c0a2410eb7a0251e765b6b2105c30 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 25 Jun 2026 13:16:00 +0200
Subject: [PATCH 079/154] feat: autolog per-series csv saving tests

---
 darts/tests/optional_deps/test_mlflow.py | 122 +++++++++++++++++++----
 1 file changed, 101 insertions(+), 21 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 80f376d07d..228f665750 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1,3 +1,4 @@
+import csv
 import logging
 import os
 
@@ -795,7 +796,7 @@ def test_autolog_metric_quantile_interval(self, mlflow_tracking, autolog_context
         assert m["actual_miw_qi0_1_0_9"] == pytest.approx(float(ref), abs=1e-5)
 
     def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
-        """A list of series logs one key per series index using the _s{i} suffix."""
+        """A list of series logs the mean over series; per-series values go to a CSV."""
         series = [self.ts_univariate, self.ts_univariate * 1.2]
         pred = [s * 1.1 for s in series]
 
@@ -806,14 +807,21 @@ def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
         # the first-arg variable name ("series") is captured as the key prefix
         ref = np.asarray(ref, dtype=float)  # shape (n_series,)
         m = mlflow.get_run(run.info.run_id).data.metrics
-        assert m["series_mae_s0"] == pytest.approx(ref[0], abs=1e-5)
-        assert m["series_mae_s1"] == pytest.approx(ref[1], abs=1e-5)
+        # aggregate = mean over series, no per-series _s{i} keys
+        assert m["series_mae"] == pytest.approx(float(np.mean(ref)), abs=1e-5)
+        assert not any(k.startswith("series_mae_s") for k in m)
+        # granular per-series breakdown written to a CSV artifact
+        csv_rows = self._read_per_series_csv(
+            run.info.run_id, "series_mae_per_series.csv"
+        )
+        by_series = {int(row["series_index"]): float(row["value"]) for row in csv_rows}
+        assert by_series == pytest.approx({0: ref[0], 1: ref[1]}, abs=1e-5)
 
     def test_autolog_metric_multi_series_per_component(
         self, mlflow_tracking, autolog_context
     ):
-        """A list of multivariate series with component_reduction=None logs one key
-        per (component, series) — exercising the 2-D (series × component) layout."""
+        """A list of multivariate series with component_reduction=None logs the
+        per-component mean over series; the CSV carries one row per (component, series)."""
         series = [self.ts_multivariate, self.ts_multivariate * 1.2]
         pred = [s * 1.1 for s in series]
 
@@ -824,16 +832,32 @@ def test_autolog_metric_multi_series_per_component(
         # the first-arg variable name ("series") is captured as the key prefix
         ref = np.asarray(ref, dtype=float)  # shape (n_series, n_components)
         m = mlflow.get_run(run.info.run_id).data.metrics
-        assert m["series_mae_linear_s0"] == pytest.approx(ref[0, 0], abs=1e-5)
-        assert m["series_mae_linear_1_s0"] == pytest.approx(ref[0, 1], abs=1e-5)
-        assert m["series_mae_linear_s1"] == pytest.approx(ref[1, 0], abs=1e-5)
-        assert m["series_mae_linear_1_s1"] == pytest.approx(ref[1, 1], abs=1e-5)
+        # aggregate per component = mean over series, no per-series _s{i} keys
+        assert m["series_mae_linear"] == pytest.approx(
+            float(ref[:, 0].mean()), abs=1e-5
+        )
+        assert m["series_mae_linear_1"] == pytest.approx(
+            float(ref[:, 1].mean()), abs=1e-5
+        )
+        assert not any(k.endswith(("_s0", "_s1")) for k in m)
+        # granular CSV: one row per (component, series)
+        csv_rows = self._read_per_series_csv(
+            run.info.run_id, "series_mae_per_series.csv"
+        )
+        got = {
+            (row["key"], int(row["series_index"])): float(row["value"])
+            for row in csv_rows
+        }
+        assert got[("series_mae_linear", 0)] == pytest.approx(ref[0, 0], abs=1e-5)
+        assert got[("series_mae_linear", 1)] == pytest.approx(ref[1, 0], abs=1e-5)
+        assert got[("series_mae_linear_1", 0)] == pytest.approx(ref[0, 1], abs=1e-5)
+        assert got[("series_mae_linear_1", 1)] == pytest.approx(ref[1, 1], abs=1e-5)
 
     def test_autolog_metric_multi_series_classification_labels_inferred(
         self, mlflow_tracking, autolog_context
     ):
         """f1 with label_reduction=None on a list of binary series infers class labels
-        per-series and logs structured per-label keys with the _s{i} suffix.
+        per-series, logs the per-label mean over series, and writes the per-series CSV.
 
         This exercises the labels_unknown branch inside the per-series loop so that
         each series' own class set is inferred rather than reusing the first series'.
@@ -854,11 +878,25 @@ def test_autolog_metric_multi_series_classification_labels_inferred(
 
         ref = [np.asarray(r, dtype=float).flatten() for r in ref]
         m = mlflow.get_run(run.info.run_id).data.metrics
+        # aggregate per label = mean over series, no per-series _s{i} keys
+        assert m["series_f1_label0"] == pytest.approx(
+            float(np.mean([ref[0][0], ref[1][0]])), abs=1e-5
+        )
+        assert m["series_f1_label1"] == pytest.approx(
+            float(np.mean([ref[0][1], ref[1][1]])), abs=1e-5
+        )
+        assert not any(k.endswith(("_s0", "_s1")) for k in m)
+        # granular CSV: one row per (label, series)
+        csv_rows = self._read_per_series_csv(
+            run.info.run_id, "series_f1_per_series.csv"
+        )
+        got = {
+            (row["key"], int(row["series_index"])): float(row["value"])
+            for row in csv_rows
+        }
         for i in range(2):
-            assert f"series_f1_label0_s{i}" in m, f"Missing key for series {i}, label 0"
-            assert f"series_f1_label1_s{i}" in m, f"Missing key for series {i}, label 1"
-            assert m[f"series_f1_label0_s{i}"] == pytest.approx(ref[i][0], abs=1e-5)
-            assert m[f"series_f1_label1_s{i}"] == pytest.approx(ref[i][1], abs=1e-5)
+            assert got[("series_f1_label0", i)] == pytest.approx(ref[i][0], abs=1e-5)
+            assert got[("series_f1_label1", i)] == pytest.approx(ref[i][1], abs=1e-5)
 
     def test_autolog_metric_size_mismatch_warns_and_skips(
         self, mlflow_tracking, autolog_context, caplog
@@ -898,6 +936,36 @@ def test_autolog_metric_size_mismatch_warns_and_skips(
             "No mae metrics should be logged when the size check fails"
         )
 
+    def test_autolog_metric_per_series_csv_schema_and_single_series_skip(
+        self, mlflow_tracking, autolog_context
+    ):
+        """The per-series CSV has the expected schema for multi-series input, and no
+        artifact is written for single-series input (mean == the value itself)."""
+        # multi-series: artifact exists with the documented columns
+        multi = [self.ts_univariate, self.ts_univariate * 1.2]
+        pred_multi = [s * 1.1 for s in multi]
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run_multi:
+                dm.mae(multi, pred_multi)
+
+        csv_rows = self._read_per_series_csv(
+            run_multi.info.run_id, "multi_mae_per_series.csv"
+        )
+        assert list(csv_rows[0].keys()) == ["key", "series_index", "step", "value"]
+        assert {int(r["series_index"]) for r in csv_rows} == {0, 1}
+
+        # single-series: no per_series_metrics artifact directory should be created
+        single = self.ts_univariate
+        pred_single = single * 1.1
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run_single:
+                dm.mae(single, pred_single)
+
+        artifacts = mlflow_tracking.list_artifacts(run_single.info.run_id)
+        assert not any(a.path == "per_series_metrics" for a in artifacts), (
+            "Single-series input should not write a per-series CSV artifact"
+        )
+
     def test_autolog_backtest_scalar(self, mlflow_tracking, autolog_context):
         """Default (reduced) backtest of a single univariate series logs one scalar."""
         with autolog_context(log_metrics=True):
@@ -974,7 +1042,7 @@ def test_autolog_backtest_multi_metric(self, mlflow_tracking, autolog_context):
         )
 
     def test_autolog_backtest_multi_series(self, mlflow_tracking, autolog_context):
-        """A list of series logs one key per series index."""
+        """A list of series logs the mean over series; per-series values go to a CSV."""
         series = [self.ts_univariate, self.ts_univariate * 1.2]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
@@ -983,13 +1051,16 @@ def test_autolog_backtest_multi_series(self, mlflow_tracking, autolog_context):
                 )
 
         run_data = mlflow.get_run(run.info.run_id).data
-        assert "backtest_mae_s0" in run_data.metrics
-        assert "backtest_mae_s1" in run_data.metrics
-        assert run_data.metrics["backtest_mae_s0"] == pytest.approx(
-            float(ref[0]), abs=1e-5
+        # aggregate = mean over series, no per-series _s{i} keys
+        assert run_data.metrics["backtest_mae"] == pytest.approx(
+            float(np.mean(ref)), abs=1e-5
         )
-        assert run_data.metrics["backtest_mae_s1"] == pytest.approx(
-            float(ref[1]), abs=1e-5
+        assert not any(k.startswith("backtest_mae_s") for k in run_data.metrics)
+        # granular per-series breakdown written to a CSV artifact
+        csv_rows = self._read_per_series_csv(run.info.run_id, "backtest_per_series.csv")
+        by_series = {int(row["series_index"]): float(row["value"]) for row in csv_rows}
+        assert by_series == pytest.approx(
+            {0: float(ref[0]), 1: float(ref[1])}, abs=1e-5
         )
 
     def test_autolog_backtest_per_timestep_scalar(
@@ -1180,6 +1251,15 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking, caplog
 
         assert not mlflow.get_run(run.info.run_id).data.metrics
 
+    @staticmethod
+    def _read_per_series_csv(run_id, filename):
+        """Download and parse a per_series_metrics CSV artifact into row dicts."""
+        local = mlflow.artifacts.download_artifacts(
+            run_id=run_id, artifact_path=f"per_series_metrics/{filename}"
+        )
+        with open(local, newline="") as f:
+            return list(csv.DictReader(f))
+
     def _fit_lr(self, series=None):
         """Fit and return a fresh LinearRegressionModel (no active run)."""
         model = LinearRegressionModel(lags=4)

From f746d7bd79b1954af97cdfb19d34a716d79170f0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 25 Jun 2026 14:03:45 +0200
Subject: [PATCH 080/154] feat: add metric shape and per-series csv saving
 explanation section to example notebook

---
 examples/29-MLflow-quickstart.ipynb | 11832 +++++++++++++-------------
 1 file changed, 5969 insertions(+), 5863 deletions(-)

diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
index fff71cb24a..c64140f653 100644
--- a/examples/29-MLflow-quickstart.ipynb
+++ b/examples/29-MLflow-quickstart.ipynb
@@ -1,5959 +1,6065 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "aeddb542",
-   "metadata": {},
-   "source": [
-    "# MLflow for Darts\n",
-    "\n",
-    "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
-    "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
-    "\n",
-    "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
-    "\n",
-    "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f72894af",
-   "metadata": {},
-   "source": [
-    "## Installing MLflow\n",
-    "\n",
-    "MLflow is available as an optional dependency for Darts. Install it with:\n",
-    "\n",
-    "```bash\n",
-    "pip install mlflow\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42e3dcea",
-   "metadata": {},
-   "source": [
-    "## Setup and Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b346ce8f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
-     "iopub.status.busy": "2026-06-24T15:18:43.049088Z",
-     "iopub.status.idle": "2026-06-24T15:18:43.053693Z",
-     "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# fix python path if working locally\n",
-    "from utils import fix_pythonpath_if_working_locally\n",
-    "\n",
-    "fix_pythonpath_if_working_locally()\n",
-    "\n",
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "13b13fe4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
-     "iopub.status.busy": "2026-06-24T15:18:43.054722Z",
-     "iopub.status.idle": "2026-06-24T15:18:46.599643Z",
-     "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import os\n",
-    "import tempfile\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mlflow\n",
-    "import numpy as np\n",
-    "\n",
-    "import darts.metrics\n",
-    "from darts.datasets import AirPassengersDataset\n",
-    "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
-    "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4d424e08",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
-     "iopub.status.busy": "2026-06-24T15:18:46.600748Z",
-     "iopub.status.idle": "2026-06-24T15:18:46.634540Z",
-     "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# use darts plotting style\n",
-    "from darts import set_option\n",
-    "\n",
-    "set_option(\"plotting.use_darts_style\", True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f9c40d6",
-   "metadata": {},
-   "source": [
-    "## MLflow Setup\n",
-    "\n",
-    "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "88320df5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
-     "iopub.status.busy": "2026-06-24T15:18:46.636565Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.268439Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/06/24 17:34:20 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
-      "2026/06/24 17:34:20 INFO mlflow.store.db.utils: Updating database tables\n",
-      "2026/06/24 17:34:20 INFO mlflow.tracking.fluent: Experiment with name 'darts-quickstart' does not exist. Creating a new experiment.\n"
-     ]
+      "cell_type": "markdown",
+      "id": "aeddb542",
+      "metadata": {},
+      "source": [
+        "# MLflow for Darts\n",
+        "\n",
+        "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
+        "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
+        "\n",
+        "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
+        "\n",
+        "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpyqkqfzoe/mlflow.db\n",
-      "Experiment: darts-quickstart\n"
-     ]
-    }
-   ],
-   "source": [
-    "# temporary directory for MLflow tracking\n",
-    "tmpdir = tempfile.mkdtemp()\n",
-    "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
-    "\n",
-    "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
-    "mlflow.set_experiment(\"darts-quickstart\")\n",
-    "\n",
-    "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
-    "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03d5209e",
-   "metadata": {},
-   "source": [
-    "## Load Sample Data\n",
-    "\n",
-    "We'll use the classic AirPassengers dataset for this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1596e07e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.269548Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.356588Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "f72894af",
+      "metadata": {},
+      "source": [
+        "## Installing MLflow\n",
+        "\n",
+        "MLflow is available as an optional dependency for Darts. Install it with:\n",
+        "\n",
+        "```bash\n",
+        "pip install mlflow\n",
+        "```"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "findfont: Failed to find font weight 600, now using 700.\n"
-     ]
+      "cell_type": "markdown",
+      "id": "42e3dcea",
+      "metadata": {},
+      "source": [
+        "## Setup and Imports"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training series: 107 points\n",
-      "Validation series: 37 points\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "b346ce8f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
+          "iopub.status.busy": "2026-06-24T15:18:43.049088Z",
+          "iopub.status.idle": "2026-06-24T15:18:43.053693Z",
+          "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# fix python path if working locally\n",
+        "from utils import fix_pythonpath_if_working_locally\n",
+        "\n",
+        "fix_pythonpath_if_working_locally()\n",
+        "\n",
+        "import warnings\n",
+        "\n",
+        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "13b13fe4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
+          "iopub.status.busy": "2026-06-24T15:18:43.054722Z",
+          "iopub.status.idle": "2026-06-24T15:18:46.599643Z",
+          "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "\n",
+        "import os\n",
+        "import tempfile\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import mlflow\n",
+        "import numpy as np\n",
+        "\n",
+        "import darts.metrics\n",
+        "from darts.datasets import AirPassengersDataset\n",
+        "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
+        "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "series = AirPassengersDataset().load()\n",
-    "train, val = series.split_before(0.75)\n",
-    "\n",
-    "print(f\"Training series: {len(train)} points\")\n",
-    "print(f\"Validation series: {len(val)} points\")\n",
-    "\n",
-    "series.plot()\n",
-    "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
-    "plt.legend()\n",
-    "plt.title(\"AirPassengers Dataset\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34858645",
-   "metadata": {},
-   "source": [
-    "## Basic Model Logging\n",
-    "\n",
-    "Let's train a simple model and log it to MLflow manually."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bc8f520d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.357516Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.450374Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation MAPE: 7.86%\n",
-      "Validation RMSE: 34.77\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "4d424e08",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
+          "iopub.status.busy": "2026-06-24T15:18:46.600748Z",
+          "iopub.status.idle": "2026-06-24T15:18:46.634540Z",
+          "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# use darts plotting style\n",
+        "from darts import set_option\n",
+        "\n",
+        "set_option(\"plotting.use_darts_style\", True)"
+      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "2f9c40d6",
+      "metadata": {},
+      "source": [
+        "## MLflow Setup\n",
+        "\n",
+        "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model = ExponentialSmoothing()\n",
-    "model.fit(train)\n",
-    "\n",
-    "predictions = model.predict(n=len(val))\n",
-    "\n",
-    "# calculate metrics you want to log to MLflow\n",
-    "mape_score = darts.metrics.mape(val, predictions)\n",
-    "rmse_score = darts.metrics.rmse(val, predictions)\n",
-    "\n",
-    "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
-    "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
-    "\n",
-    "train[-50:].plot(label=\"Training\")\n",
-    "val.plot(label=\"Actual\")\n",
-    "predictions.plot(label=\"Forecast\")\n",
-    "plt.legend()\n",
-    "plt.title(\"Exponential Smoothing Forecast\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35dc864c",
-   "metadata": {},
-   "source": [
-    "Now let's log this model to MLflow:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "61406cd9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.451297Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.906785Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/06/24 17:34:21 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "88320df5",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
+          "iopub.status.busy": "2026-06-24T15:18:46.636565Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.268439Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2026/06/25 13:57:21 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+            "2026/06/25 13:57:21 INFO mlflow.store.db.utils: Updating database tables\n",
+            "2026/06/25 13:57:22 INFO mlflow.tracking.fluent: Experiment with name 'darts-quickstart' does not exist. Creating a new experiment.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpvwnlx3yj/mlflow.db\n",
+            "Experiment: darts-quickstart\n"
+          ]
+        }
+      ],
+      "source": [
+        "# temporary directory for MLflow tracking\n",
+        "tmpdir = tempfile.mkdtemp()\n",
+        "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+        "\n",
+        "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
+        "mlflow.set_experiment(\"darts-quickstart\")\n",
+        "\n",
+        "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
+        "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Run ID: 3d1f7f864f7240599e3b080109e246bf\n",
-      "Model URI: models:/m-f42705a59eb34300b377f0219577bfa4\n"
-     ]
-    }
-   ],
-   "source": [
-    "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
-    "    model_info = log_model(\n",
-    "        model=model,\n",
-    "        name=\"exponential-smoothing-model\",\n",
-    "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
-    "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
-    "    )\n",
-    "\n",
-    "    # log calculated metrics you want\n",
-    "    mlflow.log_metric(\"val_mape\", mape_score)\n",
-    "    mlflow.log_metric(\"val_rmse\", rmse_score)\n",
-    "\n",
-    "    print(f\"Run ID: {run.info.run_id}\")\n",
-    "    print(f\"Model URI: {model_info.model_uri}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0728690e",
-   "metadata": {},
-   "source": [
-    "### Load the Model Back\n",
-    "\n",
-    "We can load the model from MLflow using its URI:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "cae35ffa",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.907802Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.914695Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "03d5209e",
+      "metadata": {},
+      "source": [
+        "## Load Sample Data\n",
+        "\n",
+        "We'll use the classic AirPassengers dataset for this example."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded model predictions match: True\n"
-     ]
-    }
-   ],
-   "source": [
-    "loaded_model = load_model(model_info.model_uri)\n",
-    "\n",
-    "loaded_predictions = loaded_model.predict(n=len(val))\n",
-    "\n",
-    "# verify predictions match\n",
-    "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
-    "print(f\"Loaded model predictions match: {predictions_match}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6bd4597c",
-   "metadata": {},
-   "source": [
-    "## Automatic Logging with `autolog()`\n",
-    "\n",
-    "`autolog()` patches every `model.fit()` call to automatically log parameters, covariate metadata, and the trained model artifact. It also patches the darts metric functions so any metric called inside an active run is logged. For PyTorch-based models it enables MLflow's PyTorch autologging to record `train_loss` / `val_loss` per epoch (see the sections below for details)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5ef7f73a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.915566Z",
-     "iopub.status.idle": "2026-06-24T15:18:50.020337Z",
-     "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "1596e07e",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.269548Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.356588Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "findfont: Failed to find font weight 600, now using 700.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training series: 107 points\n",
+            "Validation series: 37 points\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "series = AirPassengersDataset().load()\n",
+        "train, val = series.split_before(0.75)\n",
+        "\n",
+        "print(f\"Training series: {len(train)} points\")\n",
+        "print(f\"Validation series: {len(val)} points\")\n",
+        "\n",
+        "series.plot()\n",
+        "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
+        "plt.legend()\n",
+        "plt.title(\"AirPassengers Dataset\")\n",
+        "plt.show()"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/06/24 17:34:24 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
-      "2026/06/24 17:34:24 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-     ]
+      "cell_type": "markdown",
+      "id": "34858645",
+      "metadata": {},
+      "source": [
+        "## Basic Model Logging\n",
+        "\n",
+        "Let's train a simple model and log it to MLflow manually."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logged metrics: {'val_mape': 10.742, 'val_rmse': 51.182}\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "bc8f520d",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.357516Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.450374Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Validation MAPE: 7.86%\n",
+            "Validation RMSE: 34.77\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model = ExponentialSmoothing()\n",
+        "model.fit(train)\n",
+        "\n",
+        "predictions = model.predict(n=len(val))\n",
+        "\n",
+        "# calculate metrics you want to log to MLflow\n",
+        "mape_score = darts.metrics.mape(val, predictions)\n",
+        "rmse_score = darts.metrics.rmse(val, predictions)\n",
+        "\n",
+        "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
+        "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
+        "\n",
+        "train[-50:].plot(label=\"Training\")\n",
+        "val.plot(label=\"Actual\")\n",
+        "predictions.plot(label=\"Forecast\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Exponential Smoothing Forecast\")\n",
+        "plt.show()"
+      ]
     },
     {
-     "data": {
-      "image/png": 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zqhzppi/sBg0amDQLwTBVga83pjrh643h641xVPj7jeHrjXEkzBbdx44dQ3x8vDBBM9iQiwtGjBghItPvvfcerl27hn/++QeffPKJScuNQeLaHIFdHiS4WXQz1QVfb0x1wtcbw9cb46jw9xvD1xvjCJgd+l2zZg2GDh1qUJutMGfOHBHNJnH9+OOPY/bs2ejevbvJyxmGYRiGYRiGYRimVke6n3322TIjxmSM9umnn4qUIGPrVLScYRiGYRiGYRiGYWq16HZ1da1wnYoENQtuhmEYhmEYhmEYpjbA4WaGYRiGYRiGYRiGsRIsuh2UqKgo3Lhxo6Z3g2EYhmEYhmEYplZTqZZhjGU4c+YMioqKylzu5+eHhg0bVmrbL730EgoLC/H7779XYQ8ZhmEYhmEYhmGqRub5TFz74TqilsbALdAVvVf3hEc9j1pzWFl01yD33Xcf0tPTxTgjIwPXr19H69at4ezsLB4bNGgQ5s6dW6lt169fv1xBzzAMwzAMwzAMYy2kIgnx/ybg2vfXkbg1Sfd4YXohrn53Da1fb1VrDj6L7hpk9+7duvHKlStx8803Y8eOHQgODhaPRUZGIjo6GvXq1ROp4nl5eWjWrBni4uKQkJAg1gkICEBERESpbT/00EOQJEn3N22LxDxtKzk5WWwrPDy8Wt4nwzAMwzAMwzC1g4LUAkT+EYVrP15HzrUco+tELYlGy5dbwMmldlQ7s+i2YZ588kkRASeRHBsbi169emHZsmVYuHAhfvjhB7FOYmKicIOnv8eOHVtmejltKzMzU0TWScCnpKSgffv2WLt2LQIDA2vsPTIMwzAMwzAMY/9knMnA1e+vI3ppDIqyDTNuvZp4otF9DUXEO2FTIvLi8pG4JQmhI0JQG2DRbeNs2rRJCOPRo0frHnviiSfEj8KCBQtwxx134PLly/D39y9zW5s3b8bGjRsxZMgQpKWloWfPnvjss8/w1ltvWf19MAzDMAzDMAzjWBQXFiN+fQKu/nANyTtTSi0PHhqExrMaIWR4MDROGng19hKim7ixMIpFtyPQvXt3ESGm2malTro6CAsLw8GDBy2yLRLIasGthqLc8fHxQjxTVPvQoUMYNmxYmdui7dD2iDp16ojI+NGjRy2ynwzDMAzDMAzD1J56barLvjLvGnJv5Bosc/FxRsT0CBHZ9mnhbbCMxLdbsBvyE/MRvz4e+Sn5cAtwg6Pj0JFuEtzUOsueadKkSanHqO571qxZot6bBL6bmxtycnIqfK9Uz63G29tbpK8zDMMwDMMwDMOYSszKWJx5+ZyhtmjujUb3N0DEtAi4+hmXmU6uTqg3JRxXv72G4nwJMStihTh3dBxadJMgJWoi0m0pjO339OnTce+99+LVV1+Fi4uLLnJdXFxssddlGIZhGIZhGIYxRsyqWIPodeMHGyF4UJBIIa+I+tMjhOgmbiyKZtFt71CKNwnRa9euoVGjRsJwzN4hMzSKaE+cOFEnuA8cOKBrPcYwDMMwDMMwDGMtinKKhAkaQani3f/sCo1zxWJbwa+dL/w6+CL9RAbSDqch42wmfFv7wJGxfxVay/Dx8UHHjh3x2muvYc+ePVi+fDmmTZtWrZF8hmEYhmEYhmFqJ0k7knXu5KGjQswS3ApU860Qtci+y4FNgUW3jeDn54d27drpotdEw4YNjfbgpp7e1Obr0UcfFa3CPvnkEwwePFj07FaoX78+GjRoUO626tata7RmnGEYhmEYhmEYxhhx6+P1emJMaKUOUr3J4dC4yGI9akmMcEF3ZDSSJElwYBwtvZyxbfh6Y/h6YxwV/n5j+HpjHBX+fjMdqVjC5vZbRZ9tJ08njDg/FM5elcu4PXTnEcStkwV898VdETrccXt2swplGIZhGIZhGIZhKoRqsElwE2ScVlnBrRiqKUQtioYjw6KbYRiGYRiGYRiGMS+1fGzlUssVQkbIPbsJingXpBbAUWHRzTAMwzAMwzAMw5guujVA6IiqpYM7Uc/uyXKr5eK8YkT/pW9D5miw6GYYhmEYhmEYhmHKJetyFjLPZYlxQA9/uIe6V/mI1VenmC90XBdzFt0MwzAMwzAMwzBMucRtSNCNQyvpWl4Svw5+8G3vK8aph9KQeS4TjgiLboZhGIZhGIZhGKZc4tX13KMt5zRef3o93fiGgxqqsehmGIZhGIZhGIZhyiQ/OR/Je1PE2LuZF3xa+ljsaNUz6NkdDanI8Tpas+hmGIZhGIZhGIZhyiT+nwSg2DKu5SVxD3HXmbLlxeYhcVsSHA0W3QzDMAzDMAzDMIxJrcJCR1tWdBMR6hRzBzRUY9HNVMjw4cPx448/8pFiGIZhGIZhmFpGUW4RErfI0Wfqq03O5ZYmdEQIXANdxThubTwK0hyrZzeLbhshOTkZjRs3xoABAyr1/MGDB+Pnn3+GNYiNjUVGRoZVts0wDMMwDMMwjO2StCMZRVlFYhw6MgQaZ7n+2pI4uTkh4tZwXc/uGAfr2c2i20b47bff4OTkhP3792P37t2VEsaZmY5psc8wDMMwDMMwjC2kllvOtbwkEaqe3Y6WYs6i20ag9O3HHnsMkyZNMprKLUkSvvnmG/Tr1w+tWrXC3XffLYQ2ccstt+DSpUt45ZVXUL9+ffFTVFQk1tu4caPBdsaNGye2ozB9+nSxfoMGDdC9e3c899xzLN4ZhmEYhmEYhoFULCFe25/bycMJwYODrHZU/Dr4wred7IqeejANmReyHOYMsOi2ASi6ff78ecycOROzZ8/G4sWLS6VzP/7443jjjTfwyCOPYOXKlRg6dCjefvttsWz+/Plo1KgR5syZg71794ofZ2dnREVFIScnx2A7cXFxSE9P1/391VdfifUpuv75559jx44deOCBB6rpnTMMwzAMwzAMY6ukHUlDXlyeGJPgdvF2sdpraTQa1FdFu6MWOU6023pHzQboPqsYsUlAUVEEnJ3pTGp97q1MWCBw8HvT5zN++OEHEa0OCgoSYjo8PByLFi3CrFmzxPLr169j7ty5WLFiBW666SbxWJs2bXDHHXeIcUhICFxcXODv7y+i1uYQHBysG1O0e968eejcuTMWLFgAd3d3s7bFMAzDMAzDMIzjELdejnITda3gWl6SereG4+zr5yEVSohaHI2WL7awSg15dePQojs2GYhKtO23mZWVJQT2qlWrdDM8999/v0gxV0Q3RcIpvXzkyJEGz6Ua8Kpy6tQpvP/++zh27BhSUlJEWjq9Fgn9Fi1aVHn7DMMwDMMwDMPYJ3EbtPXcGiB0lPXqudU9u0OGB4uU9tyYPCRuT0LIEH2Q0F6xXTVqoYgzJIp0F8LZ2UVcLNX2uiayZMkSkUpOUWsS3ER+fj4SEhJw8uRJtG/fHoWFhUJgu7rKNvqWgtLMKbI+efJkEdmmiDnViffu3Rt5eXIaCcMwDMMwDMMwtY/sq9nIPCMbNft394d7aPVkwda/LUJXR06Gaiy6bRxK8S4uLsa1a1Gi5tkSkWFLQxHtF154AQ8//LDB4/Q3Lfvss8/Qtm1bEYE+fPgwevbsaXQ7VMNNEWo1fn5+BrXhtDwyMlL399GjR5GYmIgvv/xSpKcTJ06csPA7ZBiGYRiGYRjGnl3L61rRtbwkFFGnnt0FyQVyz+70Arj6WTb4WN3YngqtRZw9exa7du3C7bffrnMdV36mTJki2ohR1Ltjx44YNmyYEOJXrlwRzyXjtU8//VS3rYiICJw7d85g++RGTtugqDWJ9jfffBPx8fEGzyEhrjic07bJvZxhGIZhGIZhmNqNgegeY/16bnXP7nqTtT27c4sRs9L+e3az6K5ByECtWbNmIoW8JOPHj0daWhr++usv8ffSpUvRsmVLtG7dWhimkaGaOur99NNPC9dzShFXWoZ9/PHHwq08ICAAoaGhiI6OFlFzBXrtDz74QAj8wMBAdO3aVbwuwzAMwzAMwzC1l/zkfKTsTRVjr2Ze8G7pXa2vX1/tYr4wGvaORiqZk+xgyOnl12wyvZzqtmmfyLXcGFRf7enpiTp16ugeo6g1tQEj4V0SOpVJSUnIzc01cDEn8e7r6yteiyLdtE36W4EEOj2P9oPqykmch4WF6VLOSbh7e3vDx0fum8fY5/XGOB58vTF8vTGOCn+/MXy91SzkHH7sYbnstMkjjdHmzVbV+vqSJGHnwN3IOC3XlA/c1x8+zatX+FsSVgU1CEWlyxLcBAlfteAmqI2XMcFNkGCmFmAl24bRNhQBSBFvteBW6sHpcfpN69HzFcFN1K1blwU3wzAMwzAMw9QSaiq1XK1rIgx6dtt3tJtFN8MwDMMwDMMwDCMoyi1CwmbRd1kYmgX0NB7wszYRt4brenRT5F0qst8EbRbdDMMwDMMwDMMwjCBpZzKKsorEOHRkiE74VjfuoXLPbiI3OhdJu5Lt9gyx6GYYhmEYhmEYhmFsIrVcjeJiTiTtYNHNMAzDMAzDMAzD2DFSsYT4DQli7OTuhOAhZftPVQcBvQN049T9spu6PcKRboZhGIZhGIZhGAZpR9ORF5snjkTQoCC4eOvNldUkpErocl8xut1fjP2nrVdr7RnhAY/6HmKcejgNxYXFdnmWWHQzDMMwDMMwDMMwJVLLQ8o8Ij+sAY5eAA6fB/o/KuHLZZJo82UNArRGbkXZRcg4JbcQszdYdDMMwzAMwzAMwzCI36AS3aPKrufefkwvsAsKgce/lDDlVQlpmZYX3gHd9e7pKQfsM8WcRTfDMAzDMAzDMEwtJ/taNjJOy5Fk/2514F7X3eh6hYUSdh6Xx26u+seXbwO63i/h8DnLCm9/VcuyVBbdDFN5FixYgHXr1vEhZOyGLVu24Ouvv67p3WAYhmEYhrF4anloOa7lRy8CmTny+OYBwN/vaRDgK/99ORro87CEeassl27u194XTp5yrDjFTs3UjFfGM9XGypUrsWbNmlKPP/DAA+jRo4ddn4lt27Zh48aNePfdd8td7/Lly3jmmWdw+vRp8fehQ4fw7bffIiwsDG+//bbBujExMXjllVfE+LvvvoOTk2Gyxuuvv474+Hh89dVXcHZ2NlimbJfQaDSoW7cu+vXrhzFjxpRaXpIPPvgAQUHG3RuLi4uxfv16rF69Gp07d8aoUaNKrVNUVIQ//vgDBw4cQEBAAO644w60bNmyzGOydOlSceyMoX7ftM3Dhw+L1xw5cqTBer/99pt4rfHjx8NUSh6j0NBQ9O7dGxMmTDC6njnnKDk5WezvpUuXxLEcOnSoOP7GXtvU4x8ZGYk33nij1OPPP/88mjdvbvQcde3aFbNnz67wWJw7dw6rVq1CdHS02Nadd96JOnXq6JZ36dIFU6dOxaBBg9ChQ4cKt8cwDMMwDGPLxK+XXcsrahW2/Zh+PLCTBhP6aXDkR2DqaxL2nwHyC4CHPpGw7Sjw3TOAr1fV+nw7uTrBv2sdJO9KQc71HOTG5MIjXDZXsxc4vbyGOXjwIP7++28hbNQ/ZQk8e+LMmTNYsmRJheu9//77uOWWW4QIJq5cuYIff/wRn3zyidhGyYg4CTdaTkJKDYm5t956C7/88gvWrl1b6nWU7ZJY6tWrFwoLC4VoUgSYennJ8+Hubjy95saNG2jRooWIeG7fvl1EP41B7++1115DvXr1xOt06tQJu3fvLvOYNGrUqNQ+bN26VQhsRcR+9tlnYkKDhC8JQrVIP3/+vBC+alFrCiWPEQlv2vbdd99tdD1Tz9HJkyfRtGlTkc1A741mPl966SWxbWOvberxT0pKEs9p3769wfq+vr5lnqPNmzdXeBw++ugjTJ48WWy/SZMmWL58uZgkoX1U8Pf3x+23317hpBLDMAzDMIytk5+Sj+Q9KWLs1cQTPq28y1x321F9BHtgJ/l3ozANdnytwRNT9Ost+g/oPkvC8UtVj3gH9LDzum7JwSkqKpIuX74sftsiL730ktSsWbMyl9N+L1q0SHrsscekp59+Wtq6davB8sTEROm+++6Trl69Kn322WfSQw89JO3Zs0csy8nJkX788Ufp0UcflV5//XXp0KFDpbafm5sr/fTTT2L777zzjhQZGalbduzYMbFt+qHlc+fOlTIzMw2eX1xcLK1atUrs24svviht375dPH7w4EGpX79+kp+fn24bGzduLPX6WVlZkpeXl7RlyxbdY0uXLqVPpjR9+nSxXfVrNW3aVLr99tvF8oKCAoNtvfDCC9KQIUPEMZg4cWKp11K2m5GRoXts/vz54rEbN24YXV4Rqamp0pUrV8R43Lhx0tSpU0tdb2vWrJE0Go109uxZ3WO0Xo8ePUx+nejoaMnZ2Vn69ttvdY916NBBWrdunRh/9NFHYpvKcRo0aJC0ePFiyVyMHYMFCxaIx9TXhrnnaMaMGVLfvn1Lvd65c+fKfe2KOHLkiHhOQkKCyedo2rRpFW73/PnzBuewsLBQatu2rfTAAw8YrEefNVdXVyk5OVmqCWz9+41xLPh6Y/h6YxwV/n6TpBuLo6S1gRvEz+mXz5RzrIqlgLFFEgYUSUHji8TfJVm2pVjyGy2vQz8ew4qkH1YXi/vEyhK7Ic6k/bNVONJt41Ak7amnnhJRYIo6UhrxF198oVuekZEhIn0DBw4U6bCU3hwSEoL09HQR8fvzzz9FhDEvLw/Dhw8XKccKqamp6Nmzp4jqUbQ0OzsbY8eORWJiolhOqclK5JC2sWzZMhGFzMnRFnEAIpr68MMPi6gf/VA0l/aHIvUUIaQIpbKN8PDwUu9v165dyM/PF1HVktx777349ddfRUSaoAgl7aOSDl4yfZsi3BS1ptR8iqhSmnNFUIRUSVOuDJRu3Lhx43LXoZRmOm6tWrUyOK+Uah4XF2fS6/z888/iWNLzFBISEhARESHGDRo0EGn1xA8//AA/Pz8RxbcEyn5fv3690ueIril1arZCeSn25kAp7k888YRIT6fPhLnnqCQUGVeXLlCpQrNmzUqdr27dusHFxaXMDAeGYRiGYRh7IE7tWl5OavmpK0CK9lZrQEfAyal06vjkwRoc/kGDrtrbvNx84P4PJdz1roSsHKnqDuZ2WNfNNd02AImn+++/30AkUNoupRNTbe+JEyfQtm1bnUh58sknMWPGDAQHB+ueQ89X6mgJqpGm5f/++68Q6wSlNJNApnpieozqn0mgUOqvl5eXWGfOnDlwc3PTCTn1fj322GPo2LGjEO5KSjal3VJN7X333ad7XUrnrV+/vkht3rNnj8E2SnL27FkxoeDp6VlqGdXKUpow1bxPmjRJiPm77rpLiJySUDo5TSzcfPPNQpySyCWh+sILL5R77Ckl29XVVRxX2m/i0UcfNXgN5XxUlosXL5YSfTQhoSxT0urLg1K2p02bJsS0eht0bdA5OXbsmBCFsbGxQoDSZAZNklAK+5AhQ0rVZJsDCWk6psYEsqnnaPr06SJFfcqUKZg4caKYZClLcJt7/Ol90z74+PiICQd6/zt27BATRZaC0so3bdokasvV0LVDn5OSKfYMwzAMwzD2QlFeMRI2yUE31wBXA7fwklCdtrqeuyyaRWiway4wZ66Eb1bKj/22ETh0TsLSN4G2jc2r83YLcoN3c29kXcxC2rF0FOUWwdnD0L/JoUR3WlqauMGmyNWwYcN0kUJ1jTLd8NJN8E033SSiruYstyQ7h+5BXnyeiIJedr6GqpXwm45bqDv6b+5j8vpKNFhBEcAkuklQKYKbILE9a9YsYTqlNuwqGVmkc0QC7aGHHhL1s/RD0W8ys6KIJdXVUjSYtqe8nhLdVkNijmrOyUyqoKBARLnVAoP2jYQOCZ++ffsKwU6C21RI9JNYMgZNDFAklYQcibu//vpL7A/VNZdEEXtK7S9Fu6lWnAy1lEmHkqKODNxIlFJtdGBgoG45Gdipa4jVx6cy0DEreUyUmmN11kB5hnQkzimirOa5554TExp0XOhaoWgrvbdnn30W//zzD9577z1xHGiihc4d1ZWbinKMrl69iiNHjghDNPUkj7nniM4NZTrQejQxQxFjmjT49NNPhVhXY87xVyYelEkbmjSizxJlh5BJoSWgzw1N5pAB24MPPlhqOV2/tA7DMAzDMIw9krwrGUVZRWIcOjIETi5lJ0NvP66PVA/S1nOXhYe7BnOf0mBAJwmzPpSE4/npq0DvByWc/AVoWNc8dRbQ01+IbqlAQvrxdAT0NNQtDiO66UaZbrDJqZfENjlEP/LII0IYEhs2bBA3+pQCS6ZWM2fOxMKFC0XasSnLLU1+fB7yYvLEuBBy+qstQuLYWDSY0oVLCh0SF97e3qXSXEseQ5oUoVTz7t27GzxOKeZKtDQlJaXcKCuZYVFEmyKUrVu3Fq979OhRg/RdElEU/aPrgATa6NGjRbq6qVFGSkOn/SgLeu0333wTH374oRBjFB0tKegouksTCPTelONIKc4kVEmwDh482GB92o6Hh4eIuP7+++/C3KykQCxrIqAy0PEu+R5p8kNZVhF0jNu1a4c+fQwncmjSio4HiU6aOKDjQpMjNNEyYMAAYfBFkWUS+LQNc0Q3HSOK4tJxUhzXq3KOCHJXVxzWL1y4ILIQKAX+1KlTIp27Mse/ZMo6TRSQARodD0uQmZkpSi4ovZzKBOiYlITKNBzB+JBhGIZhmNpJwmY5yk2Eji47IEpBPMW53NcL6KRvFFMutw3ToEsLYMqrEk5cBjKygYWbgOdmmLef/j3q4MafUboUc4cV3fPmzRPRLIqkKSmjFPlWmD9/vkh9ViJXJIAoxVURQhUtt0bEmeZiKNJNN83VGem2BJS2SimtashNOSsrCw0bNiz3uVTrS4KkvGNL0Vea/CgLqo+lc0112goU1S4p9mkihX5I8NGkDP1Q5LVkhNkYlPJOkws0SWAskkqCmMQ0CfuffvrJ6DYojZwi7iS21FD0kcRmSdFtaVFdETRBVTLqSkKTrkmazCgP+nzRZ4SOrzHatGkjfui90meLMhyoFpmOqVLvTedZqfc2FfUxoudTSQJ5BlDtf2XOUUlIZH/zzTeiPGH//v0GoruqUPaAJfpCkuCmDBL6vP33339GJx5o2bVr10QUnGEYhmHsDalIQvbVbLgFu8G1TumJZaZ2kLRDDgaRWAoaoM/+LMn5SCBOu2r/DuR5Y7q6atVQg4WvAe3vku/R9p2m3+ZGugPstq7bZNFNN7GUskq1lSSeqRUQ1YoqooFEE5lRqVsU9e/fX0TJTFluDSjFm/aTboopnbpkT2dbhyYnqE6bhJTSa/njjz8WIsiY8ZgaEkmvvvqqSC1WMhFo8oH6DisRT6XdEdVqK5FpilBS/TGlW9M5V0e1qbaXBJK6pIAEIW2Pji2JL4rG0mMEbYOigLSdsgQ4ReKpFzSJ9FtvvdXoOiTm6FhQ1LasemdKoy45wUDvg6LZlJFhrWwKU7jtttvEeaNMD8oEoFRvmsAaN26cLtJN1yi1OyvZX5qM8OgaVrfWMgalmtM6SimCknZNKf9KvbciSOl808QILTMFStmmyRaafKHPfmXO0YoVK4TZn3pihcpMqmqmRmKYPgvKBAFlPdD1YMxsrzxKHn8S0xThVgS3uvxADb0Hem1zW7MxDMMwTHUjFUvIvpKNtKPpSDuahtQj6SJFl9KKXeq4iPtmr8ZVK6lj7I+8xHxknJTv9/06+sEtQPZ2qqg/96DO5ocz2zQC/LyB9Cxg72lZX5oSpFPwaekNFz8XFKYXirZh5j7fLkQ3RVjJqIpqMIcOHSqEA6UeU0op1VAq6bLqaBClXNLziIqWG4NcrenHYIddXHRGX6ag9Aku2dPZVlAicsb2j1yjyRSKUnCpfp6imSSKFy9eLGpe6Tnq96fexuOPPy5qr0mQUKSX0oRJfFFtqpJpQOtQvS6lodM6lJJNIoPEIW2L6mKp5vv06dNCVO/bt08IJNpn5bXIiIyEChmX0XN37twpBBotpxRnEvoUBSUBTIJMSS9WoO3SdUTO48pkQMn3pERzS75P+k0TAZSqTOK65DEkkUfvm1LIafKhrGOloDxGqfIljcCefvppA/dxNUrN9PHjx0X6MR0PmoBQzL/o2JBpHU0q0GeHsgtyc3NF+r7ymlQuQFF5KrlQp+YraeE0aVDWNUzHnMQf+SUo65BQpnNHRnpU601Gc7SMJr+UbZZ3DNTHiCLylD5O0W9yCKfjYM45oh8SwzTBQhNGNAFGzvJUT//iiy8KB3D1c8w5/rQdOr50XdL3Ar1Xuubp2KvfX8lzRMZ/lAlCkyHGjj9lDdAxpc8K1aAr0HVM/cUVyFSQ0uuVz2N1Y+vfb4xjwdcbw9eb/UD3ajmRuUgXAjsd6cfk3yRWjFGYVoirP15H6zcs01XE3qjN32+J2/Wp5UEDAso9BmoTtf4dSQ+Yn1nYoxXw32EgJgm4HiehQah52/DvXgeJm5OQH5+PrCtZNjFRZEpgV0N9w0zZWFRUlKghpZtfitwRFK2jaNrcuXNF/Sw9TjfSiiimlNolS5aIaF1Fy41BUbXvv//e4DESbpZqhWQL0PGjDACKqpUFGZ+ROKbjRhMc6okLEroUCafnK+ZcJSN4JLapFpxq8Y2lB1PaMAl0WkbCSC146LWpjptEBYkZWo+WU+2u+tqg16D9ozRbdVSQJlsoOk5p0pRKbiydmiYTlHZmJKzIRZzct+k8G5u9on0iV3RaTs7rJGJLmnEpUASd9peyKpTtUhq6MQd0ZbkxKKuDIvLGoMg+TS6oIbFPnxc1tJ90vim6TZFRtVkYHScSyCTKFXNBmnAiYzJq66a4nRuD9plq89URcsVxm9LYaVJFMXKjdO5FixYJV3xjlHWM6GuCotUkbuk6MuccKctp0o7ePz2XBC9lTKiNFCt7/JXJKJo0ooi+MXFe0TkqefxpPxQ3ezV0bY8YMUKM6XNLk1jr16+3qiEkwzAMw5iCVCgheVEqsvZnIfd0HopSDf/vGcM13AUF8YVAEeAc6IwW65pC42IfkUPGMsS8E4fUv+Ry4QZfRcCnj7fR9Ugx9n8qAjHJLvBwK8bRbyPhVok+WB8v88c3q2VPnq8fTcDYHtlmPT/hhyQkzpODtvXeDEOdsRX7I1mb8u7TzRbdJAAockk9ohWnbYqIUionCWe68aUbVhLSyg0+iWZqCUWmRhUtt2akm26OqT7a3tLLaxN79+4VEVW1mLdHbP16IyFMKd6WrKGurdBEE00mUUZFTWHr1xvjWPD1xvD1Zttc/zkSp585W+Zy9zB31OnsJ3786HcnP1HLfeSeY4hbI3u/dP29M0JH1b6J5Nr8/ba9x05kX82BxlWDYReGwMXbeBuuqzFAs+nyeFhX4J9PK/d6q3cDk16Ux09NBT562LznJ21PwoHJsmFvg3vqo92HcqZlTWLKNWPy/AQJXYqCUsRVEd00VlJhKXpH9aQkxKm2lsQy1UIqdboVLS/rNc0R2BUdjNr2IbInTK0vthds9Xrj2mPLQWnttoKtXm+MY8LXG8PXm20SvShGN3YLckWdLnVkkU2/O/nBI9zD6PMazKivE91Ri6IRNqbszjaOTm37fsuJzBGCmwjo4Q8337LN9HaIVmFyrHZgZw2cnCqXEdGnnX47+8+aJljVBHQLAOgpxUDqgTS7OV9mJQVQPSf1qT1//ryoj6RaWrWxEtUAK7XE5GRdMsW2ouUMwzAMwzAMw5hH1pVspB6SU4R92/ui/9Y+JhtMBQ8NgntdN+TF5SN+YwLyk/LhFmSZoBdj2yRuT9bXcw8s27Wc2H5M1Z+7c+VfMzRAg8ZhEq7GAofOAQWFElzNKGlw8XWBXztfpJ/IQMbpDBRmFIrHbB2z9pBSUqkmlFKBqR6VIt/qOmKqHaVaUarhpccpeqnua1vRcoZhGIZhGIZhzCN6uT7KHXFruFmOzk4uToiYWg+Xv7oKqUBC9LIYNH6gEZ+CWgClaisEDQgqd13FudzNFehZxYzu3u0gRHdOHnDiEtDVuFdxmfj38BeiW0S7D6cheFD5+24LmB2PJxdlantExkbGjLvI7IjaW1E/b2OCuqLlDMMwDMMwDMOYBtkzRS/Vim4NEH5LadPcioi4LUI3vrEwig99LbluknbIotvZ2xn+XWVzM2NEJ0q4qL0sSHB7ulfNbK9XG/3z950x//kBPfWtgKl1mD1gH0nwDMMwDMMwDMOUIv14BrIuZolxYN8AeEZ4mn2UfFv7oI5WdFEEMf1EOh9pByfrfJYoKSAC+wTAyc3JtP7cnar+2r3b6cf7TpvfdozqzxVS97PoZhiGYRiGYRimmlLL600Or/R26k+vpxtztNvxSVSnlg+sKLVcL4wHdqp6S7nOzQFXbZHz3tPmP9+zkSfcQt10kW6pEv3CqxuOdDMMwzAMwzCMHSIVSTrRTS2fwiZW3nm83i3hcHKXpQHVdRfnF1tsPxnbI0llohZcgYnatqPyb2dnoE/7qr+2h7tGCG/i3HUgJcM80UyeBUq0uzC9EJnn5UwPW4ZFN8MwDMMwDMPYIcm7k5EXmyfGIcOC4RZQeddxV39X1B0bKsb5SQWI/zfBYvvJ2N5kTdIuWXS7BrrCt11pny6FhFQJp6/K424tAV+vqke6S6aYH6hqXff+FNg6LLoZhmEYhmEYxg6JWqZKLb+18qnlRlPM/2RDNUcl7Vg6CtMKxTiofyA05fTc3nlcPx5ogXpuY2ZqlUkxNxTdtl/XzaKbYRiGYRiGYeyMorxixP4dp3OfrjtKjlJXheDBwXAPcxfjhH8TkRcvR9EZx0JxLTelnnvbUcvWcyv0als1MzW/jn5wctPYjZkai26GYRiGYRiGsTMS/k0Q9axE2Li6cPZyrvI2Nc4aREyrp0tBVkfSmdpZz604l1Pr9/4dLbcPzSKAoDr6tmHUwswcnD2c4ddJ3kDWpWzkJ8lO7LYKi26GYRiGYRiGsWfX8ilVTy1XqD9d1bP7zyizxRBj+xkSyfvkGmiPeh7waupV5rppmRKOXpTHHZsBAb6Wi3RrNBr0aiOPk9KAS1FVTDE/aNvRbhbdDMMwDMMwDGNHFKQXIn6jbHTmFuKGoAqilebg08Ib/lpn6MwzmaIPOOM4pB5IRXGO7ExP1w2J37LYeYIi0Jav51bo1Vb/2vsqU9dtR/26WXQzDMMwDMMwjB0RtyYOxXmycAqfFAYnF8ve0te/nQ3VakM9d3BF/blV9dyDLFjPrdBbXdd9xvyMCmVySOnXbcuw6GYYhmEYhmEYe00tn2y51HIFIeQ9tD27l8eIlGTG8eq5K8qQ2K5yLh9ghUh3T216ObH3lPnP9whzh2dDTzFOPZyG4gLbvU5ZdDMMwzAMwzCMnZAXl4fE7XK00quxJ/y7a92oLIirnyvCxtcV44KUAsRvjLf4azDVT2FGoRCnhHcLb3iEe5S5blaOhINn5XHrhkBogOUj3f6+GrRqKI+pdjw3T6p0XTelzKeftN1SCBbdDMMwDMMwDGMnRK+MBYr1Ue7yanItZqi2MNoqr8FUL8l7UiAVysI2aED5Ue49p4DCInk8qLP19qm3NsW8oBA4cgFVSjG35bpuFt0MwzAMwzAMYydEL422amq5Aokyjwg5EpqwKQG5sdyz295RMiRMquc+Zp3+3BY3U+tpH3XdLLoZhmEYhmEYxg7IupSFtCPpYuzX0Rc+rXys9lrUs7v+bVpDtWJDsc/YJ0k7tPXcGiCwf0C56247qh9bw7ncUmZqvm194Owt96hP4Ug3wzAMwzAMwzC2bKBWkghFdIsUc+7Zbc/kJeYjQ1vz7NfRD24BbmWuS7XV+87I46b1gPqh1ot0d2gKeLpX3kyNnPv9u8q+BrlRuciJyoUtwpFuhmEYhmEYhrFxJElC9DKt6NYA4TdbX3R7N/VGQB85Ipp5LgtpWhMuxv5I3ql3LQ+uwLX8wFkgL9/6UW7CxUWDbq3EEFdjgfiUypupEakHbTPFnEU3wzAMwzAMw9g4aUfTkXUpW4wD+wXAU1tvXVVOXJJwObpsoVN/ujrazSnmjlDPHVRBPbdharn1otxGU8wrUdftr67rttEUcxbdDMMwDMMwDFPLUst3n5Aw9PFidLxHQqsZEs5HGhfe4RPD4Owl18xGr4hBUa7W0pqxK5K0olvjqkFAL71IrchEzZrO5cbM1PaerkSkuzuLboZhGIZhGIZhqoBUJCFmRaxONJEQrixHzksY92wx+j0iYcsR+TFqDbV6l/H1XXxdEDZB7tldmFaIuPXcs9veyInMQfaVHDEO6OEPF2+XMtctKJSwW1tbHRECNLF+FQN6talapNvV3xU+Lb3FOP14OopybG9iiCPdDMMwDMMwDGPDJO1MRl6c3LIrdHiIEBnmcuaqhCmvFqPr/RLW7S29fM8p01LMozjF3O5I3K6v5w6qoJ778HkgK0dfz22tPvBq6ocC9YLl8f4zQFGRVOkUc+pDTqUYtgaLboZhGIZhGIaxYXQGapRaPsW80CPVa9/1TjHa3y1h2Vb94w1Cge+e0cBPDhBibzkRxsB+gfBs6CnGCVsSkRttmw7RTPmp5abUc28/ph8PqoZ6boKEvRLtzsgGzl5H1fp170+BrcGim2EYhmEYhmFsFKqhjl0dJ8YuPs4IHRli0vOiEiQ89EmxqNf+dSNQXCw/XjcQ+OJ/Glz4U4NZEzToqRU7UQlAZJzxCKPGSaNvH1YMRC1hQzV7cr1P2iGLbupn7d9Fbq9lSj23tZ3Ly6rrrkyKOaXN27KZGotuhmEYhmEYhrFREv5NRGFGoRjXHV8Xzp7O5a+fKuGpr4vRbLqEeavkem0iwBd4/wENLi3U4H+3auDupinlHF1etLu+umf3n9yz217IOp+FvDi5/1dgnwA4uZUt/yite8dxeRziD7RuVF17CfRupx/vq4SZmndzb7gGyGUXqQdSxWSDLcGim2EYhmEYhmFslKhl+qhyvVvLTi0vLJTw+oJiNJkm4bMl+j7LPp7Aq3cDVxZr8NwMDbw9DVOG+7TTmFTX7dXIC4H95Z7d1Los9SD37Ha0VmEnLgNpmdVbz63QrSXg5FTx5E9ZUDaGvzbanZ9UgOwrcns9W4FFN8MwDMMwDMPYIAVpBSLSTbiFuCFoQNkmWD9vAN74WW+C5eEGPH2bLLbfuNcJdXyMC6he6ki31rW6LOpPj9CNbyyMMu/NMDVCkspELbgCE7Xq7s+txsdLgw5N5fHJK0BmtuRQKeYsuhmGYRiGYRjGBoldE4fiPLkYu97NYXByKfvW/e+depEyewJwaZEGHz3shGD/8sVTUB0NWjaQx4fOU4S8bLFDrcOoLpigFmZF2bbXmokxbDWXtEsW3a6BrvBt52tT/blLopipkf8AXYtVMlM7wKKbYRiGYRiGYRhzXMsnl51anl8gYfMRvVHat3M0qBdseqRSqevOLwCOXix7PervHD5J7hFOdebxmxL4HNowacfSRW91Iqh/oEjBLguqgVacy/19gPZNUO30UpmpVZR1YYw6XfygcZa3kcqRboZhGIZhGIZhyiM3JhdJO+QopVcTT9TpVrbr9K4T+rTyUT2oNta81OA+7VV13SfLXzdsYl3dWNk/xjZRXMtNqec+cw1I1Jbp9+8IOGvFa3XSu4pmajQp5NtejuZnnMlEQXoBbAVOL2cYhmEYhmEYGyNmZSwg6aPc5ZlabdyvFyijepovlgwdzMsXO4G9AnTRxOSdLLodpZ67Jvpzl6R1Qxj0ja+MA7murlsCUg/Zjtkfi26GYRiGYRiGsTGilpqWWk5s2C//Jl0+oof5r0WpxN6e8nhPBWm9Lr4uIo2XyDyfhdzYPPNfkLE6RXnFSN6XIsYeER7wauplk/251VCGRo/W8jgmCbgRj6rVddtQijmLboZhGIZhGIaxITIvZCH9WLoY+3Xyg09LnzLXjU2ScExbh92tFfVXNj9K6eKiFzvX44DoxPIjjEED9KnKyVqjLsa2oF7VxTmyCR+53peXKUERZcW5nCZfurREjdFblXWx70zVRLct1XWz6GYYhmEYhmEYGyJ2VazJUe5/DujHVM9dWfqo6mkrMrEiUy6FJE4xt/l67uAK6rkvR9NEizzu1x5wdamZ9PKSZmqVqev2qO8B9zB3MU49mCoc3G0BFt0MwzAMwzAMY0PE/5Ng0KarPDZUsZ5bobfaOboCsUPRRI2rppS4Y2yznjuognruzYdrrj93uX3jT8NsKKKvRLsLM4uQcS4TtgCLboZhGIZhGIaxEfIS85F6WDaA8mnjA6+G2mJrIxQVSbpINxlQqd2fzUX93Irqup29nHWGVdlXcpBzQ2udztgE1M5NuYa8W3jDI9yj3PWXbdVPsozojholNECDJtrkjkPngIJCqUop5knbbWNSiEU3wzAMwzAMw9gICdT7WqszQkeElLvu4fNAktageVjXqqUFk9hpWk8eHzxbsdgJVKeYc+swmyJ5Twok7fmryLU8MVXCf9pId+MwoEcb1Di9tNHunDzg5GXznx88JFg3jltXCTc2K+BS0zvAMAzDMAzjyEjFEgrSClCQUoCC5ALkp2p/pyiP5evG+ckFKEwrgH93f3T8qj2c3Dg+UptTy0NHlS+6N2pdy6uaWq6u66b63tx8CHO27lpztbLqui9+eElX111/ekSVX5+xDImq6K7a9M4Yf+2gjAl5PHWInJ5d0/Rqo8Gi/yRdirm5xm4+rbzh3dwbWRezxAQEZY+4B7uhJmHRzTAMwzAMYwWyrmTj0B1HkHk+E5BNhE0m+2oOggcHsZCpZRQXFCNxsyyYXP1d4N+9Trnrbzygrueu+utTXfcf/2rFzqnyRTdNDDl5OKE4t1iIbnLAtgXBxqgyDzSUkRBQ7iFZskV/DU0dahvnr7eq1IHM1B6aZN5+0XVYd1woLn9xRXz3xm+IR4M76qMm4elThmEYhmEYK3Dhg4vIPGum4FbdW0YtibbGbjE2TMreFFGPS4QMD4GTS9m36mmZkq72ulVDoHG4BSLd7fXjPafKTy93dnfS1c7m3sgVE0VMzUNR3YyTGWLs19EPbgFlR3gTUiWdiRqVFnStwVZhajo3p1IJebyvEmZqRNi4UN04dm3Np5hzpJthGIZhGMbC5CfnI/bvODF29nZGQC9/uAW6wdXfFa6BrnALcIVrgCvcAl21j7mJx1z8XLCt105kX84W0aqcqBx4RpRtpMU4FvH/avs2kegeoa9LNcZ/h/RpwVVpFaamYzPA012upTXFOZpSlxWX7OSdyfBu4mWZHWEqTeJ/+muoonruFduA4mLbSi0nPNw16NJCwv4zwNnrQEqGhABf8/atTpc68Ah3R25MHpK2JqIgvRCufjUnfTnSzTAMwzAMY2FuLIpGcZ58N9vwrvroubQ7Os/viHYftEHL55qj8exGiJhSDyHDQuDfzV+IFRLfGicNIqZp3awkIHppDJ+bWkT8Rm09txMQMqx80b1R1SpsdC/LiCUyYuveSh5TbXd8SvnR7qAB3K/b1vwjLn2hdx4LHRliemr5ENsQ3MZahx04A7Oh79K64+R2e8X5EhL+03sl1AQsuhmGYRiGYSwI1bZe/zlS93eDmQ3Men7ElHB9ivniaLE9xvHJupwljJ8IStsuLy2YrokNWhM1dzdgUGfr1NNSXXd51OnsJzI5lH7dfK3WLDGrYpF5Tn8NBfQpu547LlnC1qPyuHkE0LkFbIpebfSTAPsqIboJqutWiKvhFHMW3QzDMAzDMBaE0sKzL2XrHJ59Wnib9XyvRl66m+XM81lIO5rO56eWpZZX1Crs3HXguly9gAEdAS8Py0Up+7TTmFzX7eTqhEDttZoXl4+sC7LgY6ofqUjSuckTLZ5rXm66+HJ1avlQ20ktNz75U7mJx8C+AaKMR+kKUJSrrceoAVh0MwzDMAzDWBB1lLvhPeZFuRXqKynm2mg34/gkmNEqbMM+/Xi0BVqFqemtSus1qa5b3a97p9Y1m6mZKPd5bZS7lz+CBgXabWq5YuwWXEcf6a5MFgUZESqfpaKsIp3/QE3AopthGIZhGMZC5MXl6dIY3ULcUHesPr3RHMJuqgsnd/k2LWZFjGglxTgu5FietEsWBB71PeDT2qdaW4WpCQ/WoFGYPCYjq8LC8sVOoFp0K62qmGqPcl8wI8odkyhh+zG98z0Z6NkaGo0GPdvI46Q02WOgMoSNl+u6idi12vSQGoBFN8MwDMMwjIWI/DMKklakNJgRASe3yt1qufq56gR7flIBElSOxIzjkbgtCVKBpDO/Kk8w5eRJ2KatxY0IAdo1sfz+KNHu7Fzg5JXy163T0U+47hPJu5KFmRdTvUT/FaNL7afSlKAKXMsptVwJHNuSa3lJeqtKHSryFyiL4MFBOt+B+A0JYoKiJmDRzTAMwzAMYwHoZi7y1xvyHxoyUKtfpe3pXMw5xdzhif83wWTH6R3H5JZeSqswawgmw7ru8tfVOGtE7awyQZRBvemZ6q3l/kjvWN7i2WYVXhO2nlqu0Esb6Sb2namcWHb2dEbIULkTQH5iPlL2paAmYNHNMAzDMAxjARK2JCLneo4Y000eGaJVheAhQSJFnYjfEI+C1AI+Tw4IRYYTtKLbydPJoEa6olZhoyxcz11ZEyuD1mGcYl6tRK+I0bne0+SH+lwYIypBws4T8rhNI+tkSliKnmrRbYK/QFnUHa8v84ldUzMp5iy6GYZhGIZhLMD1n1QGandXzkCtpAlQvcnhuj6zMStjq7xNxvZIP54unL+J4IFBIjJXHhsPyL+dnIDh3a2zT11ayK3ITIl0E0EDgnTjZBbd1UZxYTEufnTJrCi3vaSWE/6+GrRuKIY4cgHIzatctFuUbLjK7zN2bXyNtLZj0c0wDMMwDFNFcqJyREsawiPcHSEj5XTGqsIp5o6Pct2YkloeGSfh1BV9FDDQzzqCyc1Vg67avs0XbpCRVfkixbeND1wD5dZMSbuTa6xutrYRsyIWWdr2hIH9AgwmP0xJLZ9iw6nlCr20/gIFhbLwrqxHhpIBkHsjF+nHM2DTojsyMhKnT5/W/Zw7d67UOsXFxbhy5QoSEvRfIOYsZxiGYRiGsTcif4sCtAbjVMtNUerKkp4l6SI6fh184dNGdrJO2Z+KrCvyDTbjmKI7ZET5kzX/aKPcSj23NemjTjGvILVX46RBUD9Z1BSmFSL9BPeWr44o94WP1VHu5hU+50a8hF3a1HJKK2/XxPZFdx+Vv8Ci/yo/mRM2rm6Nppib9R/hk08+wfPPP4/33ntP/Hz66acGy6OionDrrbfikUcewS233IK33nrLIHxf0XKGYRiGYRh7vPmN/O2GzlSqwR2VM1Ajof3id8UIGi+h2XQJyemSSP006Nm9hHt2O1qLubQjskD1be8LzwjPctffoKrnHt1LU43O0WbWdXO/bqsTvSwG2UqUu39AhV4AxLKtsAsDNTW3DgY83eXxj+uAlIzKace6Y0KFwSURVwOtw8yehp01axZ+++038TN//nyDZR999BE6deqEdevWYeXKldi1axf+++8/k5czDMMwDMPYG/EbE5AXK9tJh44KgUc9D7O3se+0hK73S3jvd6CwCIhOBNbslpfVuzVcd8cWtTiaAxYORPwmfSu40BHlp5ZTv+xNB+VxgC/Qo3X1RbpNqutW9+tm0W39Wu5PLpsV5S6VWj4YdkFQHQ3uGSOPs3KA+asqtx33uu4I6OkvxpnnspCpNZ+zWdGdkZGBy5cvIzc31+Bx+nvPnj2YPn26+DsoKAgjR47UieqKljMMwzAMw9gj139WG6iZF+WmnsvPfFOMvg9LOHPNcNmBs/INske4B4IHybWaOddykLIv1RK7zdgA8f/E68Y0YVMeB84CqdpuXGSg5uxs3Uhl/VCN6ANO7D8DFFVQp+3d0hvudWX3teTdKSgu0NZbMBYnemkMsi9n6yY7lNT+8rgeJ+kmTzo0Bdo0to9IN/HkVA0Uv7evVkjI1/a0N5e6qhTz6o52y53sTaRBgwZYv349li9fjtjYWMycORMPPPCAWBYTE4OioiI0bNhQ/4+nYUMcOXLEpOXGyM/PFz8GO+ziAjc3rZ2iCVANufo3w1gTvt6Y6oSvN4avt5on+2o2EjcnibFnQw8EDgo0+Z6Daivv/xA4H2noGq2YBZHQUbZVb0o4ErfIr3NjURT8e9aBI1Mbvt+K84t159Q1yBV+nX3Lfb/r9+nHI3tUz7Hp3VZ2u87IBk5ekdChafliJ7BfoDD3KsoqQuqRVPh3lyOLto49XW8iyq2q5W72bFOT9nvJZhhEue3hvSo0rQdM7Aes2ilnAf25ScLMUeYL79AxwTj76jldXXeTxxrDEjhRKwFLiu45c+boxmSiNnv2bLRv3x79+vXTiWO1IPb09ERenpxuVdFyY/z000/4/vvvDR6bMmUKpk6dCnMhEziGqS74emOqE77eGL7eao74r/UmWD4TfXA98nqFz8nO0+CTZf74+V9fSJIcvnFzkfD4zamYNSYdo1+qh8sxrjh6QcKFS9fh5gIUdyiGxlMDKUdC9F8x8H7QE07ujt+ExpG/37L2ZQlxSnj18sD1G+VfO6t3hlGSrBi3Db+Ba9fk51qTVuG+JKXFeN2OJPg5a0PtZSC10Qu5S2uuIDik4gisLWEP11vq6jRkX80RY6+eXsiol46MaxUb1/2+UX/99G0ZhWvXCmFPzBjkjlXiMwC8/1s+BraK0UW/TUYDuLd0R975PKQdTsfFAxfhGiq77leFJk2aWFZ0q2nVqpUQ24cPHxa//fz8xOPp6enw95dntdLS0lCnjjwTW9FyY9xzzz2YMWNGlSPd9AGiKL0psxAMUxX4emOqE77eGL7eaj5SeWmt3L+JesC2f6gt3EO1jj9lsO0oMOtD4JLKD61XG+CH5zRo2ziAqnXRtwNwOQbIL9QgraARujeT18uYmIXoxTEoziyG5xkvhN2kT5V0NGrD99uZ7/VdgBpPaozwRrKgMEZSGnBc2yqsXWOgd5fKmfWZy+h+wLuL5PH52CA0alR+S6rgiSGIfUdOmS8+WYxGjRrBHrCX641S9q/+op8Y6PBKWwQ0ou+N8rkaAxzTloB3agYM6R0Be6NhQ6DXCmDfGeDcDTdcSGyEEZXoU18wqRAXP5QPhutxdzS6rwGqA5NFN6WGk9M4iV6FxMREtGzZUozDwsIQEBCAY8eOYdCgQeIxSh1v06aNScuNQeLaHIFdHvQBsuUPEeNY8PXG8PXGOCr8/aYndn0c8hMLxDhsfF14hpXtPJ2ZLeH5+RLm/qV/zMMNeOs+DZ6calif26uNhN//kVMnD57ToGdbeVn92yKE6FZqOuvdHA5Hx5Gvt4R/E3WO96HDQ8p9n5sPSygulq+J0b1MS2e1BN1bS3B1kUSP5L2nKn5dn2be8IjwQG5UrmhxJxUAznaUkWHr11vUsmjkaKPcQYOCENS34r7cxPLtdO3I18+0YRo4OdlPPbeap6dLmPKq/D4+WwKM6mn+uQobH6YT3fHr4tFkVvVMDJm8p9nZ2SKdfMOGDTh48CA+/PBDnD9/HqNGjRLLqaXFtGnT8Nlnn2HHjh3C3ZyM0yZPnmzScoZhGIZhGPs1UCs7WvLfIQkd7jYU3P06AMd+0uDp6ZpShlg9VPGI/Wf0dYtkmESChkjYlIi8hLJL9BjbhpyTFSOsgN7+cK1TforrxgP662BUz+oTTJ7uGnTWGmOfvV5xuya631dczItzipF2OK06drNWQFFuA8fy57QpMA7qWm6MmwcATbRzjRv3AycumV/X7dvWB16N5QnS5F0pyE829A+rcdHt6+srenTv3r0b3333HQoLC/HLL78gPFw/y3rvvfeK/tu//vqrSDv/6quvDNJKKlrOMAzDMAxjD2Sez0TyzhQx9m7ujcB+AUZbPD30STGGPynhaqz8GPWb/fwxDbZ9qUHLBsbFE6V/urrozdQUNE4aREyR77ukIgnRK7QbZeyOhH8TTG4VRpmmJDCU62dAR1Qr6tZh+06b2Tpsh2wUx1QdahdI3QuI4MFBCOxVcVo5cTlawsGz8rhrS6B5ffuMchM0QfnEFP3+f7rEfNFNE0N1x9fVfY9Sy8fqwKyabkolf/PNN8tcTukY5GhOP5VZzjAMwzAMYw9c/+WGQZswupEryWdLgXmqnrIDOwE/Pqep8KbXw12DTs3lG2WKLqZnSfDzlp8TMbUeLn1+RXcT3uQBDl7YI+ob/dCR5Yvuk5dlx2ZicGf5+qhOerfT4MvlsrjZe0rC6F7lv37QAMN+3S2etfouOjxViXIv3aIfTx1iv4Jb4d6xwGsL5PZ5f/wLvDtLQniwee8rbFwornx9VYzj1saj/nTr17jbbtECwzAMwzCMDVKUU4QbC6PEmBzEI26rZzQ6+d3fslAhPf71Exps+aJiwa3Qs7WyHeCQ3m8LPq18UKez1pz2WDoyzpbvJs3YHgXphUjeI2dJeDbyFP2ty0OJcld3armxSLfS57k8PBt46tJ3Uw+kis8LUzWiFkUj57o2yj0kCAE9TYtyl0otH2L/Z8LHS4MHb5LH5DVAfbvNhVrZKT3lE7YkojDL+k7uLLoZhmEYhmHMIGZlLArT5Ju08ElhcAtwM9qD+6KsyzG0K/DILeaZF/Voo19XnWJOREzTi3yKdjP2ReLWREiFki7KbSxLQs2G/ep6blQ7ZKpeVxu8JudoxdCtPAKVuu58SRiqMVXrknDxE31f7hbPaYvsTeDiDQmHz8vj7q2p37X9R7qJxyZrdCU4lE2UlWOe8KZSndAxoWJcnFuMhP+0qSRWhEU3wzAMwzBMZVPL7zFuoPbTOv1N4N1jzL/R7akyUztw1vCGMvyWcGhc5G1GL40WdYmM/RD/j+mp5SQmdhzXi99WDVHt0KSAEu1Oy5RLHiqiZIo5U3luLIpCTmSuGAcPDUJAD7n1siks3epYqeUK9YI1uH24PE7JoO9bmA11nFCgFHNrw6KbYRiGYRjGRNJPpouUWcK3nQ/8u9cxKpSWaOso/byBWwaaf3hbNQB8vYxHut2D3RAyPFiMc2PykLSDRY29IBVLwnmecPZ2RmDfgAr7uufLXekwqocsgGuC3tq2dQS1DquIoP76VlbJLLorTVFuES59qq/lbmlGlNuRXMuN8dQ0/TX52VIJRWZOPgb1C4SLn4tuIowyCqwJi26GYRiGYRgTuf6zKsp9VwOjImjZViBTLr/EtKGAl4emUi693VrJ48h4ICbR8IaSU8ztk7QjachPkFsUBQ8MgrOHsxmp5TUXqTSs665Y3HiEucO7hVyrnno4DYWZ1q+ZdTQStydh5+A9uig3TbRRLbKpnI+UcPSCPnOmcbjjRLqJjs00GNlDDHE5Gli5A2bh5OakyzQpTC+0ekYGi26GYRiGYRgTKMwoRNSSaF2Ust6U0gZqxE/r9aLknkqklpc0UyMOaFv+KNDNoksdOUoTuyaORY09ppaPKj+1XG2i5uwMDOuGGoMmgGgfTI10q1uHUf16yj7ZOI6pmNzYPByZfQz7bz6IrAtZ4jEnDye0fLGFWYfP0VzLjTFHFe3+eLH5ZTZ1x8l13UTs2jhYExbdDMMwDMMwJhC9IgZFWbITc73J4XDVpiaW7IlLKcEE1d/2VkUIzaWnykytZF03RUjDbw4T46LsomqpSWSqTvy/esMmpUSgLK5ESzgfqY801/GpOeHk7akR/eOJU1eptlsyr66bSyAqpLiwGFfmXcX2XjsQszxW9ziVsPTd0At1OsldC0xl8Wb9ObrVwVLLFUb0ADo01U8G7T5hnvAOGRYsJjSI+PXxovzDWrDoZhiGYRiGqQBqAXb950hVanl9o+v9ssEwyl2VGtweKjO1knXdRH2Vi/kNdjG3eXJjckWbN8Kvkx88wj3KXX/jAf14VI+aj1T2bqtvY1cy88IYgf1YdJtK8r4U7Bq6B2deOofCTHlizzXQFR2+aIc+63vBr4N5gvvMVQkntKXgNPHXKKzmrx9rQN+v6mj3J2ZGu128XRAyRJ78yovLR+pB6znts+hmGIZhGIapgLQj6Ug/niHGdbr4oU7n0gZq1Erplw3aGywn4M5RVTusDUL1rZpI5JDwV+Pfwx9eTeR+yEnbk5ATJdd+MraJYqBGhI4oP8pNbFTVc4/uhRqnT3uNWf26yfDPt62PGKcdT0dButYRjtGRl5iP44+dxN6x+5FxKlP3eIOZ9TFoX380uKO+aG9lLmrX8mkOmlquMH04EK717ftrh9wmrdIp5muslzHEopthGIZhGKYC1P2wG95tvE3YliPAtVi90zS1talqFEep66a2OJeiSi/XGapJQPQy7tlty8RvNL1VWEGhhP8OyePgOkDXlrCZSDex1wQzNXVdN4qB5N1c161Abf4oc4ZSyW/8qf9g+3X0RZ8NvdDhs3ZwC3Sr9LlSu5Y7amq5gpurBv+bLH/X0rzk50vNE92ho0OgcZafH7c2rtTkZlnkJ+WL/wtH7jtm0vosuhmGYRiGYcqB6vzoZkzcOLlpEDZRrqUurzf3PWMtE13qoarrNpZiHqEyc6MbQFNvGJnqpSivWLhRE24hbqjTpXSmhJo9J4GMbH3dqlMlop2WplmEPAFA7D1dOvPCGEED9K3DuK5bJu1oGnaP2ouTc06jIFV2dafWVW3fb41+m/qY1Ye7JHRO/vhHwqkr8t/9OgD1Q2v+2rE2D9xEvgPy+Kf1QFKa6d+DbgFuCOwnt+7LvpqDjNP6jIOSxzb9dAYufnYZe8bsw6bWW3Ds4ROIWamvvy8PFt0MwzAMwzDlkHY4TfTDJoIGBxs1UCNjqeXb5HGgHzCxn2UOKbX6Udh/pvSNpFdjLwT0lm/SM89lIf2EnALP2BbJu5N1JnxkoFZRyrDaBMsW6rmVzArFGDA5Hbig755XJqIPuXb3rd2SyR4mXk49dwa7hu8V5SoKEVPriVTyxrMa6SKu5kKCcP1eCd1nSbjjbcnhXctLEuCrwb1j5XF2LjBvFcyi7ri6urEywar0SY/flICTz5zGls7bsXPAbpx/+wJS9qeK7A1zYNHNMAzDMAxTDtSSSyFsvL7+T83izUCu3H4Ztw8H3N0sFOkup22YQr1b9dHu+A3sYm7vqeWZ2RJ++0cee3kAkwbAZujTTlXXfbLi9V39XeHXUTYByziZgfxk7YeklkGi+MT/TuLaD9dFKQjh09oHvVf3QKdvO8A91L3S2952VMLAxySMfVbC4fP6x0d0B2ZPQK3hiSka4aVBfL1CQl6+6dHusLH673WKXF//NRIHZxzGphZbcHDaYVxfEIncG4aeGT6tvNH0scbiHJpC6alahmEYhqlFiJSx9HTExsYiJiZG/C75k5CQgIEDB+KTTz6Bk/Jfnak110es0o7LCag7OtSqvblLEuinQbMISdRz0w011fq6uhhuv+6oEJx6Wh7HbUhAi2ebW+z1GctcQ0p/bo2LBsGDyzdRW/ifPrWcJnBqslVYuXXdpyXcZcK1Tq3DFNd2inaHl1Ge4chceO8iopfF6Htuv9AcjR9oBCfXyv8/2X9awss/SPj3oOHjnVsAb9+vwdjecnZCbaFpPQ1uGShh2VYgNhn4cxOV+Zj2XI96HvDvVgeph9JExtDJJ0+XWodKi8iRnybN6IeyjMyBRTfDMAxTa8jKysK7776L06dPG4jq3NyKXZ8PHz6M4cOHY9y4cdWyr4xtQPV92ZdlBRTUNxBuQaXNjc5ek0SPWIJ6xnaxsOkVpZiT6KZI+snLpbdPN4xkwETu6iRuqDVVRe2omOoj63wWcq7liHFgnwCj5Qlqgf7tSv0EzoM32ZZoojZ2NO9YXGyag7lipnbl66tinFwLRXfkH1G4+Im2f5cG6PJ9J9RVRVbN5fglCa/+KGHVTsPH2zQC3ryPhKdteADUBHOmabBsq6RrH3b3GNMnHuqOrytEtxq3UDeEjpBFdvCgILj4Vl46s+hmGIZhag0vvvgivvzyy0o/f8mSJSy6a3Fqed0J+ro/NT+ro9xjq9ab2xg9W2uwcJOkM1MzJurpplBpaUatqRrcabyPOFP9XP3+um4cUkFq+YEzwJEL+tKCbq1sSzz5emnQvomE45cg+kBTKryPV/n7GNg7QNQqk2N3bTNTS9yahJNP6Wcn2rzTutKC+3ykhNcWSKKURe1h1yQceP0eDWaMAJwrWRPuKPRup0G/DhJ2nYAwk9u43/R2e43ua4DUA6nIS8hH8OAg8Z1ap7NfpVq2GYNFN8MwDFMroNTx+fPnGzwWHByMsLAwg5/w8HCDvwMDA9GqVSuRgr5y5Urk5eXB3b3y9XeMfRGnruc2crNcWCjh143y2MUZuGOk5fdBbaZ24KyEB4xEP+kG8eLHl3X1wyy6bQNyLL/+U6QYO3s5I/ym8qO88/623Si3Qp92FG2Vo93kYj68e/nrU3SQetunHkxD5vks5MXlwb2u43+HZpzNxOG7j0IqlM9p49kN0eSBRmZv51qshDd/lvDLRqBI9uIT1AsGXrlLNhCjtlmMPtq964R8zD9eJGF0L9OOjYu3C7r91gXWgkU3wzAMUyv46KOPhGAmnnjiCXz44YdwdXU16bmTJk3Cr7/+KoT3P//8gwkTapE7TS0m61KWrn2Mf/c6Io27JP8cAGLkTlAY3xcI8bf8zS/VaDo7yzfcxtqGEdSCilIh8+PzkbgtSbjuOns4W3xfGNMpzCzEicf1Uc5Wr7aAZ0TZaf8pGRIW/SeP6/gA04ba5tEe3EWD+drJAcrAGN7dhLru/oFCdBPkBt1ghmNnYtDEwoFph1CYUajrBd3mbZUrookcuyih/yMSMuXqBAG1bXvhDg0emgR4urPYLgl1jmgeAVyMguh1T8ewU/OaP07sBsMwDMM4PHFxcZg3b54Ye3p64vnnnzdZcBNTp07VjRcvXmyVfWRsD52BmnAtr1utBmpqvDw0olacOHUVyMop7cpLKZBUe0gUZRcheVftSuO1Rc69eR4517W13H0D0Oi+huWu/+sGIEeeF8Rdo6jvcM0LhbJEjZ+3PF6yxfj1WJK6Y0IN+sk7MoVZhTgw/bDO7dqvkx86f9exUu3Anv1WL7hpIuat+zS4vFiDp6ZpWHCXAaXYk5O5wpItpruYWxMW3QzDMIzDQ67jOTnyncuDDz6IunWNC6iyGDFiBOrUqSPGq1at0m2LqT2p5XXHlU4tT0qT8PcueRwaAIzpbb196akNklFKr7otkBp1KypyMWdqDnLpvvajnFbu5OmEDl+2L7c2lAzU5q3SiwNjJQS2Ak0CKVF4EoTkFl0RdbrVgXdzWakn70pB9jWtPbuDQXXrR2cf17m1e9T3QPc/u4rUZXPZfUISmTRK3faVxRq8fJdG1NUz5XPrYP14tfY7uqZh0c0wDMM4NNTua+7cuWJMtdjPPPOM2dtwc3PDzTffLMaZmZnYuFFbxMs4LDlRuTonW992PvBuqg3tqaCWNPkF8viOESjVysuS9Gij33ZZKebkrkttbQhqUUVCjqmZSOfx/+mbWLd+pSW8m5TfXmj7MeCs1m9tYCegbWPbFlbqrA51tkdZkLlgxG36fvJRSxwz2n3m5bOI1054US17j8Xd4BFWufr1137SH9eXZ2oQ4Gvb14QtUTdQo/PCIMM/qouvaVh0MwzDMA7Np59+iuxsOaoye/ZsYZRWGdQp5uRizjg2ceviKkwtL+labk3UZmr7zxi/gaSbfOojS1Bqa8YZuR6dqV7OvXlB1yIsoE8AGs0qP62cUEe5bdVATU3vdkAr7dvadhS4HF2xqImYWk+0zFJSzB1tUujq/Gu4+t11XT/2rj93hm9rn0pta8cxCZu0/bebRQB3jrLkntYOJvTVf47W7EaNw6KbYRiGcViSkpLw9ddf66LVzz77bKW3NWzYMAQEBIjx33//zSnmDk7cmniD/q3GeuUqad7dWwPtm1pXKLVtRGm95Ue6S6aYk4s5U70k7UrGtR+u69LKO37ZrsKWQ3HJEpZvk8ch/hB9lm0dilyro92/bKhYQJOJXNDAIDHOvpKDlH2pcBTi1sXj9EtndX+3/7StaDtVWag1mMIrMzVWzaJxVMb31Y/X7Kn5CR4W3QzDMIzD8vnnn4t0cOL+++9H/fqVd8xVp5hnZWVh/fr1FttPxrbIT8pH0m7ZiMyrqRd825SOVv20zvoGampcKHKm7c99NRZISDV+Exk6Si+6E/5h0V3daeUnVGnlrSit3EhZQkl+WgcUyCbXov2Tu7ZEwNah6KuTVkn8soH8BioWNvVVKeY3FkbBEUg9koajDxwHtG+/2VNNq+TOvu2ohC1H5HGL+hD9txnz6dQcqK/9Otx8WO4pX5Ow6GYYhmEckpSUFHz55ZdiTE7lzz33XJW3ySnmtYO4DfFAsTwOGxcqonpq8gsk/P6PPHZzBaYPr579UszUiANlRLu9GnnBR5vSmnIgVUwgMNXD+bcvIPuqNq28tz8am5BWTkJVab9Fl9nsifYhuIl6wRqM6iGPr8VCJxTLgwwJnb3lVnaxK2OF0749kxOZg4O3H9a9j/DJYWj5YvMqbdMgyn2XRky4MeZD39tKtJu8NzYdQo3CopthGIZxSEhwU19t4p577kHDhhXfAFfE0KFDERgo18yuXr1aVyvOOBaxq1X13BNKp5av3QMkyh5rmNQf1WZw1NPATE2qOMVcAuL/5Wh3dZC8O1lXzyunlZfvVq6wcb+cuUCM6gk0rWdfAkvtZaDO/igLcvEOnxQmxoWZRYhVeSfYGwVpBThw22Hkx+fr6vc7ftWh1CSdOWw5LIkaeaJlA2D6MEvtbe1kvEFdN0e6GYZhGMaipKWlidRywsXFBS+88IJFtksR81tuuUWMSXCvW7fOIttlbIeC9AIkbUsSY496HqjTRW4VV2ZquZUN1NT0UJmpHdCXj5abYk4u5kz1upW3erkFvJtVnFZeykDNjqLc6p7dgX7ymOrS0zLNSzGPWmR/LuZSsYTUg6k4NPMIMs/K5UtezbzQ7dfOcHavfDyTjOXUUe5XOcpdZYZ2BTzd9ZOlppRAWAuOdDMMwzAOB5mnpabKJj0zZ85E48aNLbZtTjF3bBL+TURxvqRLhS0ZrYxNkrBunzyOCAFGdK++faNevUF19GZqZbk/+3evA9cAVzFO3JyE4nxtrjxjFc6/c0EYgxEBvSitvJFJz4uMk7Bmj/5aGtfH/k4Q1Z/fri2vyM0HFm+u+DkBvQPg2chTjBO3JSE3Ohe2TkF6IWL+jsWxR07gvzZbsXvUPiTvTBHL3IJc0WNRV7gFulXpNajueMdxedy6IXAbR7mrjKe7BsO7yePYZODQOdQYLLoZhmEYhyIjI0O0CSOcnZ3x4osvWnT7Q4YMQXBwsBivWbNGmKoxjkPsmvJbhf3xL1CkLUOdOYquseqLTlLaqlLXTentV2OMr+fk4oSQYfI1WphRiOS9sjhgLE/ynhR9WrmHEzpQWrmJ18QPayUUa+dDZo2339pds3t2O2kQMU0b7S4GopbaZrQ763IWrsy7in03H8Cmlptx5J5jIjKfn6j3SXDxc0G337uYZJhnVpT7bk21frfUmhTzPRzpZhiGYRiLMHfuXCQny87TM2bMQLNmzSx6ZCldffLkyWKck5ODtWvXWnT7TM1RlFOEhE2JuugVmWGVvDFWi4q7q8G1vCScYm47kHmWSCuX9GnlPs1NE18FhRK+Xy2PnZ2B+8fDbunSEuio/Zrdewo4e82EFPNpahdz2+jZLRVKouXbmVfPYVuvndjWYyfOvHQOSduTIRXo94+M4CgLpsMX7TDowAAE9JRbSVaFfw8Au07I4zaNgKlDqrxJRos6g2T1LtQYHOlmGIZhHAZqD/bJJ5+IsZOTE1566SWrvI46xXzx4sVWeQ2m+knckqRzIQ4dEyoixmoOngVOXZHH/TqQ0VH1i25TzdQo0q1EXLmu2zqco7Tyy7KZYkBPfzSebVpauXLzHyNbB2BiX0ovt9+oJmVgqCegfjYh2u3V2EsYjxFZF7KQdljrTFgDkMP/if+dwvnhl3Bg0iFcmXsVWRcNM5g8G3qi0ayG6LGsG4ZfGIpuv3ZBgzvqwz24ainluij3T/pj9hpHuS0KfbaUdotHLgBRCTUzwcOim2EYhnEY5s2bh8REOVI5ffp0tGyp/U9rYQYOHIjQ0FAxJjM1SmlnHD+1vKaj3EQPVdswqusuC9c6rgjoI0fqsy9lI/MCl0FYEkrZvzr/WqXSyksZqN1kv4Jb4Y6RgIvcCQy/bgQKC83s2V1DhmrpJ9Oxa/heRC2MRnGmyvvASXYjb/VaSwzY1Q+DDw9Au/fbIGRIcJXM0spysKcMAaJdE2AKR7ktzgRt6zDFUK0mYNHNMAzDOATkJv7RRx/pIi/WinKXTDHPzc0Vtd2MfVNcUCz356bz6+OMoIFBBstz8yQs3CSPyQ23ptI/QwM0aCx3XMKh8+WLm9CR8sQQwdFu66WVt3yxBXxamF7TeyFSwr8H5XGzCGB4NZrxWYsQf31PZIrg/3Og4ueETQwT7dXEc1bEoCiveg3/ov+Kwe7R+5BzXTbBc/J2QvgtYeg0vwOGnxuCPmt6otn/msC3tU+V2oCZU8v9+j0aOJnQao6pfF336hpqHcaim2EYhnEIvvvuO8THx+vSv9u0UfVXsgLsYu5YJO1MRmFaoRiHjAopFc1atRNIlbsD4dbBgJ93zd0YK3Xd2bnAGTnYWn6/biG65c8GU3XOv3dBZA8Q/j380eRB09PKie9W62/6H5joOCLLXEM1Vz8XhI2TM0oKUgsRv7F6rlGpSMLZN8/j6P3HUZwjC/06Xf3QdGkjIbgjbq1XZSdyU1m/V5+xQnXxtwyslpetdXRtCYQFyuNNB+m7s/qFN4tuhmEYxu4hQ7MPPvhA9/fLL79s9dccMGAA6taVbxjXr1+P9PR0q78mYz3i1sSXm1r+1w79Tdpdo2tWJPVsra7rLns97+Ze8GrqJcYpe1JRkFZQHbvnkOQn5yPyjxs4cNshXPlWm1bu7oSOX5mXVk4ZEz+tl8durlSmAIdhTG/KxJDHf+8CktIqFjYR6p7di62fYk6fgYO3H8blL67o92F6PfRc1R2uoXKbverCWC23o0zA2BpOTvpMDGptt+WI5c7hgx+blqHBopthGIaxe3788UfExsaKMaV9t2/f3uqvSe3Ibr31VjHOy8vD6tVaK2LG7qDIV+y6OF19rtJuS+00vWG/PPb3AQZ1Qo3SU5XEUZ6ZGqXEKtFueo8Jm2W/A8Y0cmPzcG3BddEy6r/WW4XZFvVx16eVNzcrrZxYvo3EqDy+dZCclu0ouLpocOdIeZxfAPypLccoj+CBQfAIdxdjOrZ58XlW27+Ms5nYNWKvrkMBTZa0fbe1mDhx9tAWpFcjVFtM5oxEp+bApAHVvgu1ivF9VCnmuySLZSrM/9u0dVl0MwzDMHYNCd73339f9/crr7xSba/NKeaOQcqBVOTHy713Q4YGw8XbxWD57pNAmja1fHQvqunX1HiqpJP2Du6A9qa9LEJHqVLMNyZYec/sn5zIHFz59ir2jN2Hze234tQzZ+SWUUX6m3QSiSS4mzzU2Oztf6syUHtokuMIbgVzXcxJ+NabKke76RhHLy+j+XwViVsXjz2j9urKAlwDXdFzeTc0fqCR1eq1y4Nruasf8k5w11YNrNkjnwNLZipUBItuhmEYxq5ZsGABoqKixHjSpEno1Kn6wpD9+vVDeHi4GG/YsAFpaTXX9oaxnmv52j36G6txqmhJTeHjpUFbbRnx8ctATl7ZN36BvQPg4itPIlCEr7iwes2q7IGsS1m49Pll7Bq2B1s6b8eZl88hZV+qLqJNeDX2RNPHGqPvxl4YcnwQms9pZlZaOXHikqTrxUwu1dR2ztFo31SD7lqH/cPngeOXatbFXCqWcOGjizh05xEUZsrtAH3b+6Lff30QNMDQLLE6+XunfHyILi2Am/rX2K7UGrw9NRjaRR5HJQBHL1guU8EUWHQzDMMwdkt+fj7ee++9Golyl0wxp335+28T88wYm4GiFXFa0a1x0RhEhhXW7JZ/U0BsdE/YBEqKeVERcER7824MJzcnBA+VxUVBSgFSD/LEkEJOVC72jNmHbT134txbF5B21NCXwaeVN5o/3RT9t/bBoIMD0Pr1VvDv7g9NJetu5/+tahM2UVMjEdZqN1RbV7Ho9mnpgzpd64hxxskM0cbLEhRmFOLw3Udx4f1LusfCbw5Dn3U94dXQEzX5nfP6z4aO5Y56Ldiyi/maPZbLVDAFFt0MwzCM3fLLL78gMjJSjMePH4+uXbtW+z5wirl9k348AzmRuWIcNCAQrv6GZkpXoiWdQ3ifdkCwjdTg9mhjmplaKRdzTjHXRUCPP3wCKftTDY6VX0dfkTo+cE8/DNzdHy1faAG/Dn5VFkWZ2ZLoX014eQB3joLDMn24Po3393+ovtuEaPd0y0a7sy5niXZgcWu1BokaoNWrLdD5+46lykeqm5U79FHWbq2ACf1qdHdqFeNV/brXVKF1WMlMBVNg0c0wDMPYJQUFBXj33XdrLMqt0LdvX0RERIjxxo0bkZpqeBPP2HtqOWwqtVyhpzaFlzhwtvybx5DhIUJ0ENyvW+b6LzdEmzjCva47Wr/REoMPDUD/LX1F6jhFXy3Jwv+ADLmcGLcPB+r42M61ZGkCfDWYpE2XTkwz/AyVBUWgndzkYxK9LAbFBZUvgyDDwF3D9yLzrGzE4OLngu6LuqLZ401rPKJcXCzhdVUdMEe5q5eGdTWiNZsyWRmbJFU5U+GNe027plh0MwzDMHbJwoULcfXqVTEePXo0evasmbxfJycnTJkyRTcRsGrVqhrZD6ZyKKnlJErrjgkttXyNQT237RzlDs300cSKIt3uwW4iLZogIZJ9Tav+arFZ2tnXzun+7vRNBzR9tAm8Gsvt1SwN3aR/u1KVWn6T4wpuhXvGmpdi7hbghtDR8ucvPyG/Uk77dJwvf3UFB6YdQmFaoXjMp6U3+m3qjVCaeLIB/tpOde7yuEdr2/pOqY3R7nV7q5apQP4F6u2VB4tuhmEYxi75448/dOOXXnqpRvdFnWK+ePHiGt0XxnQyz2Ui83yWGAf08hcRTzVZORK2HpXH9UOgi5DYSnsmJa3xYhSQnC6Z7mL+T+11MSdhdvzxUyjKkk21Gsysj+DB1jXU2nkcOKK6Se/WyvFF9/BuQIT2klu3z7SIokHP7oXmpZgXZRfh6OzjOPv6eUAbJA8dHYI+G3vDu5l5bd2qK8pNEdKajrzXRiao67rNTDGvSqYCi26GYRjG7qAU7s2bN4txo0aNhIt4TdKrVy80aNBAjP/9918kJ8tpq4xtE6vUe5aRWv7fISBP7iQmIlK2doOsTjGvyEWXW4fJRP52A0nbksTYI8IDrd9oZcUzBFyLlTDtdUMDtdqAs7MGM0fpzf7++Lfi51C7PrcQOX0jfmM88lO0H74KyL6eI1q8xayI1T3W/Jlm6PZbF7j6mVa/TZ/znDzrnpsf1gAnr8jjXm3l9oNM9UMZBiFy4g/+OQjkltP9obxMBTKzHNvb9Ndl0c0wDMPYHWvXrkVhoZw+ePPNN9e4GFKnmNN+rVy5skb3hzG/nrvuuLoVpJbbnljqaYaZmm8bH3jU9xDj5F3Jwtm5tpFzIwdnX9GnlXf4vJ3JoqwypGRIGPOMhBhZ46N3O8c2UCuvZ/dP66UK+yI7uTqh3q1yC8bifMlARJdF4vYk0eot/USG+NvZ2xldf+2Mls83N8llPj1LwnPfFiNgHDD4mXpVbiNVFscuSnj8S/37f+s+jnLX5ISQktaflQNsO1Y99fgsuhmGYRi746+//tKNSXTbAuxibl9QdCz9mNyayK+TX6kWQiQQlHo/DzdgWDfYHD20bcOI/WfKFzR0c6i4mJOgSdRGe2sLdD5PPHlK16u5/owIEVm1FhQ9m/Si3vm+RX1g9XsauLna3uSNtWjZQKPrRX7qimk9jU3t2U3n88r8azhw6yEUJBeIx7yaeqHvP70RZmQCrSSFhRLmr5LQfLqEDxcCeQVAQpoLbn4ZiE+pvKt1WcJ+yqsScrWB+4cnASN61J7rwBYZr5pEXb3LtPO9fFvVMhVYdDMMU6OkpKTg8ccfx6uvvorz58tpNsswWnJycrB+/XoxDgkJqfHUcgUycmvYsKEYb9q0CUlJtUvU2Btxa8t3LT92EYjSlj4P6UJtnmzvJrl5BOCvNdnef1YWIuVRm+u6b/wZjcTN8mfSPcwdbd6yXlo5RcTuelfCdm0ELTQA2PCxxmbazdVYz+71FYsbv/Z+8OvgK8Zph9OQeV52IFdTlFOE44+cxJkXz0IqkrcZMjxYGKb5tq7Ydf7fAxK63CfhwU8kJJRoNnE9Drj1FcmkNmemQJ/JWR9KuHBD3yLs00dr33Vga4zoQb4Y+n7dFX13FhVVvR6fRTfDMDXKfffdhy+//BJvvfUWWrVqhf79++OHH35AerocgWKYklDNdHa27L48ceJEODs728RBon/ASrS7qKjIIBrP2HqrMCOu5bth06nlhJOTRtQnEnHJwA19ibpRgvoHwtlL/rzE/5sgelXXBnKjc3HmZX2YtcNn7eBax7AfuyV59lsJS7boe3KveV+DpvVs8xqyNlOHyseAWLjJtPrZiHKi3TlROdg7fj+iFusfb/ZEE3T/s2uF5/TsNQnjnyvGyDmSLmIp9nEIsONrINRfLrnYcRwGqeBV4duV0F0LdXyAJW9o4K5tjcbUHH7eGgzuLI+vxcqZGOWxdCtwWm6Wgr7tgZE9zH9NFt0Mw9QYu3btKiVM6LFZs2YhLCwMM2fOFGZZxcWV79fJOB62mFquwCnm9kFeXB5S9qXqWgoZ68m8dq9ttgorP8W8/HWdPZwRPEh26s6Pz0fa0fTakVY+5zQK02VBFTGtni7N3hp8sVTCJ9oGBk5Ossjqoaq9r234emlw6yB5nJoJrNxZ8XPqTQ6HxkU+ZiSulWh28p4U7Bq2V3fd0gRSlx87odUrLaFxLvsYJ6VJ+N8Xxehwt2TQM5wmrHbO1WDxG05CSM37XwLctbp93ir6qZrwPnhWwpNf67fx0/O1d/LFFhmvcjFfrZpkNRblfsMCrvMsuhmGqbEboWeffVb39+2334727dsbpBD/9ttvGDZsGJo1a4bXX38dV65UMBXJODxkUrZ69Wox9vHxEdeHLdG9e3c0btxYjGnCKCGhdqXw2st3z6UvLgPae6i6RlLLE1Il7Dstj9s1ARqH2+6Ncs/WajO1ikWCoYt5BaFxB4BEW4I2lZ5awrV9V2X5bmGWbzUUWd8+RYZNtnvt2GrPbvcQd5EuTuTF5gmztGsLrmPfpAOihzfh2cgTfTb0QviksDK3Qyniny+R0OJ2CV8tBwrlcn7Ryuy3lzXYO49qzvX71rlZPuY9rX/+Y59L2H5UqrSJHtVx58vl5nhyKnDzQL4WbAl1f+3yWoct3gycvS6P+3esvL8Hi26GYWqEVatWYfdueWqxdevW+OWXX3D8+HEcOHAAjzzyCAICAnTrXr16FW+88QaaNm2KIUOG4Ndff0VWltxbl6ld7NixQ1crPXbsWHh4aPMWbQROMbd9wX3urQu4Ol97B6WRo2olWb+X1pXH48xoCVMTUNsahQMmGFUpYqY21HXnxubh9Iv6g9L+k7Zw9Xc1uB7+OyRh14mKnbUrgrYx423ajvz3yzOB2bWkPVhFDOwENNZq438PApFxJqSYT9OnmB978AROPXMGUqH8vKCBgaJ+26+dXPtdEjqXZI7V/i55EiRFNjYXae4UpTz/hwZ3jNSI8oySUJszEsgEifTJr0ii7Zs50Ovf856Eq7F60633H+BrwdagrIO28hw59pwCElMlo1HuN39RRbnNdCyvsujOyMjA3XffjXfffbfEjhWJWsy77roLjz76KPbv32/WcoZhagcUrXzhhRd0f7///vtwcXERX2QUKfz6668RHR2NxYsXY8yYMaIdk8LWrVvFdwiln3/wwQdVvlFi7AtbTi1X4BRz2xbcl7/QZ8y0+7CNUeOltapWYeoURFskPFiD+trg9cFz8k1ieXiEewi3diL9eIaod3bU831yzikUpslp5dSKqu6Y0FJuxMOflND/EQk9ZksiUl3R8SurVnjiC5KupzsJtzfvs+3rpjohcau0D6N/2V+tMCUjIxSu/rLTVX6ivl93k0cao8fSbnALlPt5lyQjW8LYZ+XzoZiXKeeExPard2sqNEX88EENRnSXx4lpwKSXJGTlmH5dfL4UWKVNow/0k0sMapNrvT0xXls6RNel0q1CzcL/gHPX9ZNHQ7pW/rUqJbrJ9IhMbGJiYgwe//7774Wj7MMPP4wBAwbgqaeeMkgHrWg5wzC1gwULFuDsWTn6QM7TZIZVEopgknhZt24drl+/jvfee08YrSlkZmbi+eefFym8TO2AbqKV/tdubm4i0m2LdO3aVZREEFu2bEFcnN6wi7Ehwf1RGzS6V3abV1NQKGHjAXkc4Av0aQebRzFTy8iGrkWVySnm/zpmtDt6eQziN8jvzS3UDW3fK51W/uNavZA6dA649VUJ7e6SsGCt6e7VsUlyL+5kbXk8ibXvn+UezCWZPQFwd9Obi1H6dXk4uzsh/BZ9FoqThxM6zeuANm+2gpNL2fKFam837NP/TenAB77T4JeXnBARYprwdXGhOm+N6A5AUO/ue943LRtiz0lJGOkp/PqSBg3rsuC2VSb005+bNarJVqWtnCVquSstuo8ePSqEMkWf1JDR0dKlS/HEE0+gV69emDZtmriZXrFihUnLGYapHVBa+Guvvab7+8MPP6zwSywiIkII7DNnzoiUdKr/VqCoOFM7OHToECIjI8WYarn9/ORona2hTjGn/30LFy6s6V2q1ZgjuIldJ4A0bZeiUT3lG3BbR12X+sMaE6KIKiMxR0wxJ6O808+r0so/alsqMkqib9NBeaz+F0RRrfs+kNDsNrkeuLwIZ2a2hHHP6dOIOzUHlr3FUc2yMjLuHq09bjnANyY0d2j2eBPRe9u3rQ/6rOuFiCn6lHNjUH/tb1fJYxL4S9/UYPtXGnRX+R6YSoCvBqve08DXS/576Rbgvd/Lfw4Ztk17XdLVjj93u+12PmBkereVsxEImqxRT7b98S9wMUoeD+5CP1U7l07mpoRSOudzzz1X6iY5NjYWaWlp6Nixo+6xzp07i5tkU5YzDFM7+Oyzz8T3gZIe3LevysmiAuh7p0+fPvj555+FECf+/vtvXLtmQmiHsXvsIbVc4c4779SNya+AsQ/BXSq13E5umGeOBjzd5fH3a4zXJqqp08lPmIoRiduSRN9jh0orf+Y0ClJkB6vwm8OM9mGn9F9FHD0xBdj4sUbcWCvcSICoB244RY52JadLpTIiprwm4fB5+e8GocC6DzWiFRFjnGemUx21PP5imYTs3PKvU8/6nhi0vz/6b+8rrtmK+HQxbVMfWb91cNUik20ba/DHK7QN+e+Xf5BrxcvqzT7zHQmRWm/CAR2Bt+/na8HWcXHRYEwvfaYQtYtTotxv/WpYy13l1zJnZbrRpXrLFi1aiLY+apSeuurIA42Vxytaboz8/HzxY7DDLi4irdBUlFZD3HKIqQ74eisfcnKmyDZBvZXfeeedSn026bkPPPAAXn31VfH8b775RqSf1zZq2/WmiG66iRo/frxNv28qhejRo4cwBqQMMfpRTzrbI/Z2vZH4Ov/2RVz58qr+JvrD1mhwd/1y34PSUojEwcgekriZtnWC/ID7x0M4NJPo+HK5hNfvKX+/Q0YE48bvUSjOKUbs+rhyXaDt6XqL+SsWcWtl5eMW7Io277Yyug2ldzIxeRCVEUgY3h3Yewp4/09gtfY2l9LGX/9JwkcLyRhNwpNTgHrBwIMfy5Exwt8HWPsBEBZoH9dLTdEkHJgyWHaDTkgFflwj4ZFbTDteFaV2J6YCX2uTZ91cgaenmXftlHW9UbvAN+8FXvlRrvud8ZaE3d9IOgMuhQ/+0NcEh/gDf7xC3yF8PdgD4/rIUW2CJlWGdJHwy0bgkjbKPawrlSmUfy7V3kNVFt2U0kdtWv7880+jy93d5RnT3NxceHp66lr+KM6yFS03xk8//STqwNVMmTLFwKTGnP1nmOqCrzfjkAM5GTES9Dmmz39lo9SjRo3C22+/LSbmvvvuO2HuaGtO1tVFbbjeLl++rMuM6tatm/hfYusZDuPGjROiWymDeOmll+AI2MP1RjfoCd8kIemnZN1jYc+HQhpaXO51cz3eBWeuyVk0XZrmIjM1DplyO2+bZ2o/Z3y7MgKFRRp8uawIU/tGwdujnJtEMojTpsue//oC8rvkwd6vt8LkQlx+Vn9+Q54ORkxWDFCi2UV6lgabDjQQ9vXhgYUI84qCclmE+wBfzAYeHuuK+Wv9sHqvN4qKNcjKBT5bAsxdIaFzszzsPyf/v3FzkfDtY3HwccrTbYMpm5lDXLF4s5wm/sGfhRjVKQquZoUAjfPJMn9k5dYR46kDM1CUk1yp82Hsert9ALDneDDWHfAW0dDxzxbgr9di4e8jC/T9Z93x8g+UTUFRcQkfz4pHYTb9j6r6+2KsT9swDZydGojP+crtBXh4TDTeWEDXqNzp4IExsbh2rfzvxyZNmlT4OiZf5vv27RM3y/fcc4/4m1LFSTTfcccd+P333xEeHi6i0PTPjNr/EDRu0IC+1FDhcmPQa82YMaPKkW76ANHrmDILwTBVga+3srl06ZJu0s7LywsfffSR+F6oLI0aNRKTcH/88QdSU1NFrbfy/VRbqE3X26JFi3Rj8gSh82/rkGkoZXMUFBRgzZo1IiOD/ofZK/ZyvZHgvvDORQPBTRHuhveUfb+hsPqQfnzLEA+7uM4UaFdvHw78uhFIy3LGxmMNda2PjCE1lJD8ZQoyz2Yh51gu/FP8UaezLFps8Xqj80oto4rzilGcr/3Jk8RvSfv35S+uoihVzhmvOyEUHe5rb3TbdIwKtKnlU4e6oEmTRkaP58h+wJUYSlsGFqwDcvOB/EKNTnATP7+owdShtpUlYMvQcR3dS84SiEp0wZ5LjXDnyKptk1qC/fqfPCYB/9ZsXzSsa7ydWGW/3xa9CQx8DDh6EbgW74pnf2qAte8DyRnAk98BShD05Zka3DGudDkDY9tQOcDWo/K5/e6fRojUWl1Q9svkEZb5fJv833/kyJGiBlvdY/f06dO6mXuKMFFtJt0Av/nmmyKN9J9//hH136YsNwaJa3MEdnnQB8iWbxIYx4Kvt9K88sorQnwQc+bM0dVkV4XHHntMfKcQc+fOxb333lul+i17pTZcb4prOXHLLbfYxfsNCQnBhAkThGEoOZj/+++/Ivpt79jy9aYIbhJf6rZgje4ru4Zbzbq9xQatwoz18bVlnpsh4deN8t3/p0uAR2/RwN2t7PfQeHYjnHzqtBhf+y4SnecFwBZIO5qG0y+fRfq5DFwovKwV2MWAiZnbroGuwjytrOt0+Tb9eZ4ypPzz3CwCmPsU8OrdkqhDnvsXRcrlZR8/rMH04fZ1jdgCL95BDuPyyaS0/TvL6JltKl8tLxYRaOKeMUDjcCeLf7/5egMr35Vby1FqPJnwPTsPOHUViEmS1xnaFXjtbvv73mCACf0kbD0qX5OfLNYfkTfvtdz5NPmqpPrr5s2b636CgoJEtKpp06a6dZ599lmcP38eI0aMECY3Q4cOFT+mLmcYxjGhFFvqua0IkWeeecYi26VOCFQ3Sxw5cgR79miLMRmHIioqCvv37xdjqotW/9+xdajsQYEN1aqjhvsCLn1+pVKCm5yotxyRx9T3uoP9XGYGxk+TBsjj6ETg93/KX5/coF0D5BTKmJWxyI2t+RTzyN9vYM/Y/UjZk4qi5CIUpheiONd0wU20e78N3EO0znIlSMuU8I+2JVxEiOkt4eoGavDubCdcX6oRLcFWvqPBU9NM3yfGsI1XX20SwqkrwJrdlT86qRkSPl8mj12cgRfusJ7gbRSmwbI3NeJ1iC+WyeKbCAsE/nxVA2dnFtz2yHgjnr7UvaJPe8udz0rnuU2aNEnUVKoJCwsTKYB0g+Tj4wN/f3+zljMM45g3wjThpkDmZ76+5qV9lcejjz6Ku+66S4y/+uors9zQGfuLctu6a3lJRo8eLSaaKLuLMsSSk5MRGBhY07vlcFRVcBP/HaJ2MfobMHvNmnl+hgYrd8gK9cOFEu4eQ+aTxt+Ls5czGsysL9zdpQIJ13+ORMvnm6MmKMotwunnzyDytyj9/gU6wyPIA07uTnBy1ci/3ZzK/e3frQ7CJ5adDvr3Lv15njyQIpvmnec6PhphWsdUHvpsvXAHMOF5+Tp97w8JE/pV7jP31Qp9i7+ZoyjKbd3P7cDOGnz1BPDQJ/pZIAqML3pdIyZmGPukZQMNWjaQcF5V0k99uS1JpUU3CWZjopk+MPXr1y/zeRUtZxjGsVi/fj22bt0qxs2aNcPs2bMtun0yZHv66aeFqFm2bBk+/fTTKtWKM7aHPbUKK4mrq6vwJvn888+F6R9lfDz00EM1vVs2S86NHBx7+ATyYvPg7O0sRKHux9NZPOZi5LH0Y+m49mNkpQU3sXav/ibannvr9mqrEe67FLWnG8i/dlDrpLLXb3RfA1z5+iqkIll0N3uyKZzdq7d8ICcyB4fvPoq0o/qONg3vbQCvWR5o3LyxRcsZlm6RDFLLmZphbG+gfRPg5BXZMX77MWCQvorVJNKzJHy2RD6fzs7Ai3dWz/l88CYNjl2UME/bE/yt+zQY1JmvJXtnfB/gU+2/EWojRt+llsQ2i7IYhnEIioqKDHwb3n33XYv5NCiQX8SsWbPEuLCwEPPnz7fo9pmahSLDyqQNuYPaY9stJROD4BTz8jnzyjkk70pB1qVspB/PQMreVCRuTkLcmnhEL41B5M83cOWba7j48WWce/MCTr9wFif+d6rKgpsi5UqrMA83uTbTnqFot8L7f0jltlvyjPBE2ATZ+Ck/IR8xK2JQnSRsScTOoXt0gtvJ0wmdvumAth+0hsbVsje9JNI2alPLw4P0Kc5M9UMZBiWvU3Oh+noyUSPuGEH199UnfL98XCN+qNTgeUPPZ8ZOeeAmDXy9gEA/4P0HLX8tsehmGMZq/Pbbbzh58qQYU+01uY1bgwcffFD07iZIdFNEkXEMyPWbJm+UKLc9pvySCakyWUCdQM6dO1fTu2STZJzJQOzfcfIfTqiU4KqM4CaOXpBroAkS3F4e9nedqRnRA+jaUh4fOqevOy2Lxg/oj9nV+dcq7IlsCaRiCRc/vYwDUw6hIFnO9/Zq7Im+G3ohYprcUsrSrFanlg8yP7WcsSzThgKNtZUA5GZ+5Lzp1x15MHyyWF6fEiGqK8qt4OqiwWOTqdSAjdMcKcU8eoUGlxZq0LGZ5a8n++1dwjCMTUMtBcmxXOHDDz+0mmCiFh/kM7F8+XLExsaK39OnT7fKazHViz2nlpeMdpNrvxLtpqwPxhASYAqt32iFpg83RnFBMYqyi1CUVYRC+k0/OdrfWdrf2sf82vsiaEBQpQ6rEuW299RyBfqupejb1NckXRRxRI+y35d/D2oX5ieizeknMpCyJwWBfa3nPVCQViDKCOI3aPvyAAgdFSIi3K7+srGbNVi6VZVaPtj+z7O94+KiwTPTgUc+k8/LB39KojbaFL5dBSSlyePpw2TBxDBVxcfLetcRR7oZhrEKZGp248YNMR47diwGDy6nqNACUPswha+//tqqr8VUD9nZ2di4caMYh4aGok+fPnZ76KmuW8nGoAwQJXrPyGSez0TMX7Fi7BbshoZ3yd4vTq5OcK3jCo96HvBp7o06Hf0Q2CsAIUOCETa+LiKm1kPDuxugyUONKy24iTV71PXcjnFWbhkItNBa6Gw+DOw/LZUr0hs/qO9VfWX+NavtV/rpDOwavlcvuDVAixeao9vvXawquCm1fMN+vdN0vw5WeynGDO4ZC4RqO9Ut3QpcvFFxtDs7V8JHC+X1aC7/pWqOcjNMZWDRzTCMVepw33vvPd3N3Pvvv2/1ozxw4EC0by8X6O3evRuHDx+2+msy1oUEN2VMEDfddJNOtNojdevWxZgxY8SYJqO2bNlS07tkU1z67IquJVSThxvBxbv6EvESUiXsPyOPydiJ2gI5AuRY/uztptfMht8UBve6cputuHXxyL4uf/YsSfTyGOwZtQ/Zl+Wmyq7+LuixuCtaPN0MGiunelM2Q16+fkKCWzvZBp7uGjwxRT73xcXUt7ti0T3/b/rcyuOpQ4A2jR3jM8s4Niy6GYaxOJQ6m5qaqkur7dDB+iEFEvcc7XYsHCW13Jih2s8//1yj+2JLZF3OEmKMoJ7Rje41vya7KqzfS0ZqjhXlVrhzJFAvWB6Ti/mZq2ULGmq5RY7hgmLg2o/XLbYfxfnFOP3CGRydfVyUAxB+HX3Rb3MfhAwLQXXAruW2y0M3QRhYET9vIH+Fsq/TnDwJH/6pX/7yTBbcjH3AopthGIty7do1kVquOIu/+eab1ZrCW6dOHTH+888/kZiodUZi7I6CggJhokZQX/ehQ4fC3pkwYQICAuQ8yhUrViA9Xd8eqTZDvbWpXRXR5MFGcPGtXrsZdWr5+L6OdQPv7qbBU1P174n6dpcHpfU7ucnrR/56A4WZhVXeh/yUfOybdABXv9OL+Pq3R6DPul7waqRVWlaGTLfW75PHdQOBAfbXBMGh8ffV4OFJ8piM7j5fWvZ1+v1qIDZZb4bXvqljfWYZx4VFN8MwFoXM0xT38Mcff1yYnFUX3t7euPfee8U4Ly8PP/74Y7W9NmNZtm/fjpSUFDEeN24c3N3ltFd7ht7DbbfdJsaUNk995Ws7lMIctThajF38XNBodvVGuQsKJWzU1vkG+AK928LhmD1Rfm/E7/8A1+PKFjTuIe4InxwuxoXphbpzU1mKC4tx5N5jSNknZz6RoG//aVt0+LKd6LFeXazZA+RyarlNQynm7tqOot+upFZgpa/T3DxJmK0pvHIXC27GfmDRzTCMxTh27Bh+//13MQ4MDMTzzz9f7Uf34Ycf1rmkf/PNN6J3N2N/OFpquQL37Dbk0heXIRXKN9Fk5OXqZz0jLWPsOkEGW/J4dC/ZTdnR8PXS4NFb5HFhEfCpts1SWTR5QG+oRtFpau1VWc6+cg5J25N1Bnm91/REw7saVHvrv2Uq1/JbBzneOXYEwoI0uEe2vUBmDvCN/l+AjgXr9K39Jg0AOjXnc8nYDyy6GYaxGNQWTOnv+tJLL8Hf37/aj27z5s11hlXXr1/XpSgz9kNxcTFWrlwpxm5ubhg9ejQchZ49e6JVq1a6aP6VK1dQW8mJysGNP6LE2MXH2UDsVRdrdqtSyx2gVVhZ/G+yBp7aZJHv1wCJqWULab8OfgjsJ5dBZF3MQuKWypXpRP5xQ5dSTj3Xu/7aGf7dqv9/QlaOhHV75XGIPzCwU7XvAmMiT99GPa/lMaWYk0u5Ql6+hPd+V0W5uZabsTNYdDMMYxHIOI3qVJUoN0Wcawq1oZpSX87YDwcPHkRUlCzGhg8fDj8/PzgKFOFTR7t//fVX1FYuf3EFUoF8E91oViOrtouqqD833ehTpNtRCfbXYNZ4eZydC3y1ovzodePZqmj3fPMN1VIOpOLU06d1f7f/qK1o9VYT0DnOydO7ljtiNoOj0CxCg2la+47ENGDBWv2yXzYAN7Rd5sb3Bbq24vPI2BcsuhmGsQiLFy9Gbm6uztCMTNRqipEjR6JFixZivHnzZpw+rb/5Y2wfR00tV7jzzjt16bW//PKLiOzXNnJjchH5uzyx4uztjCYPVX+U+3K0hLNaPdmnHRDo59g38XNu08BFW0b91XIgI7ts4V13TCg8G3qKccJ/iaKPuqnkROXi0MwjKM7XTqjc3xAN7tQ2DK8BlqpSy6cMcexz7Ag8p2pz99EiSfgu0M+7HOVm7BwW3QzDWISffvpJN7777rtr9Kg6OTnhkUce0f399ddf1+j+MJUT3XQeJ06c6HCHr379+iKCT1B6+c6dO1HbuPz1VRTnyZMNje5tALcgrYNSDUS5HT21XKFhXQ1mjJDHKRmyC3RZaJw1QiwrqJ3Hy6MopwiHZx5BfrzsWhbUPxBt3pbLKWoCdWp5cB1gEKeW2zxUpz22tzy+Hgcs+g/4bSNwLVZ+jDJSerZ1/M8r43iw6GYYpsqcOXMG+/bJ/Vg6duyILl261PhRJeFPbuZKCm9aWlpN7xJj4rV07tw5Me7Xrx9CQ0Md8rjVZkO1vPg8XP85UoydPJ3Q5OHGNbIf6npuR+vPbUoU8ZPFkqiTLYsGd0SILASCXMwLUgvK3Tb5eZx44hTSjsqt8ChS3mVBJzi51tytJrUJo3R64mZOLbcbnp+hv07f/0PCO7/pr9NX2bGcsVNYdDOloDY9L774IrezYSoV5b7nnnuq3ZnWGNSve+bMmWKclZWFn3/+uaZ3iTEBR08tV7836j9OLF26FNnZ2agtXPnmKopz5Sg3OVm7h1Z/Ozjq27z1qDxuEEq9flEraNNYI1yfCXKBphZiZeFaxxX1b6snxkXZRYj8/Ua5274y9yqil8WIMYn1bn90qZEMBjVLt6hSywfX/P8lxjQGdNKgXwd5fPoqlYLI4xHdgT7t+Twy9gmLbsaoCdV7772HKVOmYPbs2aLfMcOUBbXk+u2338TYxcVF1HPbCuoU87lz59bK2ll7o7aIbi8vL/EdS2RkZBi8b0cmPykf1xZoo9zuTmj6aONS0dJ/D0g4drHybapM4ce1QH6B3pTJFiYKayKKSD2Pi4rKPtZkcKdw7Yfrou+2MRL+S8DZN87r/u40twP82mqbg9cQ5HxN/bmJoDrA4JpPwGIqeZ0qvHp37fmcMo4Hi27GAHIMJkMshe+//x5DhgxBdLR2mpFhSrBx40bExsrFVhMmTEBISIjNHKN27dph6FDZCvXChQv4559ywjpMjRMZGSmcy4nOnTujceOaSTuuLmpjivmVb6+hKKtIjMlcyyPc0HCR6oxHzpHQ+V4JD39aXK7ZV2VIz5Iw851iPPGVfrsT+tauG/lebTUYohWgF24AK7aXva5PC2+EDA8W45zIXMStiy+1TubFLBy5/zig1ePNn22GsAl1q7yfGdlyX/HKskGVWj6pP+DKruV2BdV1t2+i/5uu2f4da9dnlXEsWHQzBnz77bcicqlmz5496N69u/jNMBWlltsajz76qG7Mhmq2jdKb29Gj3Ar9+/dH06ZyXvOmTZtw40b56bv2Tn5KPq59f03Xt7np/wwnVai++M1f9GL425VAh7vlyLcl2HVCQqd7JGHKpHDPWMduFVYWL9xhWDNLGQZl0VjVP72koVpBegEOzTiCwnT5vqHuuFC0eKZZlffvr+0SGtwK9HmiPjbJ83Bmw67l9o2TkwZv3S9fp64u0I0Zxl5h0c3ooHZP8+fP16UJr169Gg0byu6lMTExGDRoEH744Qc+YoyOxMRE/P3332Jct25djB492uaODkXflet43bp1uHTpUk3vElPLU8sVyJ1d8R0g0fP777/DkaF+z4WZcuiy/owIeEbILakUft0IRGn78CqQYzFFvmd9WIy0zMqJb2o39MoPxRj4mISrWgdkP2/g95c1WPC8U61KLVcY3h3o2lIeHz4PbD5c9rrBQ4Lg01I2pUzZk4K047JRmlQk4egDJ5B1MUv87dPGR6SVa5yqdjw3H5Jw2xuSiHQnpTtj7LOUAWHeuc/Jk7B6tzwO8AWGdq3SLjE1xKQBGuybp8HB76nGu/Z9ThnHgkU3o2PhwoVCRBFUazh+/HiR6jl48GDxWEFBAWbNmoWHHnoI+flyOxCmdvPnn3+K64K444474OrqCluDJpDomlWEzTfffFPTu2RXZGZm4sknn8QLL7yAU6dOWe11kpKSsH27nOfarFkztG/fHrUBRXQTZPZXXsTRnqGI6NX52ii3iwbNHjd0LisslETEVWHRaxoDofTDGqD9XRLW7zXv+Fy8IaH/IxLe/hVQLB36dwSOLdBgxsjaexNPEw1qJ/MvlkrlrttotirarT2P5965gIR/5FkS1wBXdP+9C1x8Xaq0XwfPSrjpRUlXb08UFQOzP5LwzDfF5dafl0wtz8qRx2Qcx6nl9gu1B+vYrPZ+VhnHgUU3I6AbvS+++EJ3NP73v/+J31SfS3Wwjz/+uG7ZvHnzRJ2sUsfL1F5sPbVc4f7774e7u+yQ/OOPPwo3c8Y0nn32WXz++ed4//33hRDu2bOnmLigLgeWICEhQXhHTJo0CUVFRbood22JPjZp0gQDBw4UY2qVtn//ftgqeXF5KNJGqs3l2nfXdSnIEdPqwauhYZR78WZDh+JpwzTY9JkG8+Zo4KNd9UYCMPZZCfe8V4yUDKnC/2k/rpHQ+T4J+8/Ij7k4A2/fr8HWLzRoHF47rq/yoBZa9bUWHGQ4dimq7GMaMTUcLnVkQR2zIgZX5l/D5S+u6Hp6U2swr8ZeVdqfs9ckjHlGQqZWLE/oB9w7So6qEx8vAia/Ione22allrNrOcMwNgCLbkawY8cOHDt2TIzpprp37966I0PRS7rpJqMfRbjs2rVL1Hnb8g0iY12OHj0qfogePXoI0zJbJTg4GNOnTxdj6tetuK0z5UNtrP744w+Dxw4cOCBc4cPCwjBt2jRs2LBBJ5ZNJT4+XkzeDR8+HOHh4aJLws6dO3XLJ0+eXKtOja0bqpFjNUU1t3TcjvPDL+HI3ccQuzYOxfmmdQMozCjElXnaKLezBs2eNIxyFxdLePd3vUh6aaYsiGni5YGbNDj1qwYje+jX/3k90G6mhNW7jIuvpDQJt74i4f4PSaDJj7WoD+z+RiO27ezMgpug6O8jN8vHghIsvl5Rtph18XZBw5n15fOVL+HMi2d1y9q83QrBA4NQFSLjJFFGkJgm/z2wE7DwVeDl21Mw90nAWW4XjlU7IcoEohLK3tdcSi3fJY/9fYBh3aq0awzDMBaBRTcjUEe51VHtkmmQdGNcv359ndP5gAEDDKKdTO1B3ffalqPc6lZ4Cl9++aXDpvFakuXLlyM9XY400URcly76njtUYrJkyRKMGTNG1MxT+jlFasuCMmMoQk7dEEhoU8r/f//9ZyDYya38448/Npj0qw3ceuut8PSUw7mLFi2yqTaNubF52H/LQVz69LLsTl0IxK2Nx+GZR7G5/Vacev4M0o6mlft5urbgOgpS5HzhepPD4d3EMCK6cofci5eg3rwkuNQ0rKvBho81+PE5jajFJmKSgIkvSLjjrWIhshU2HZTQ8R7JwJF71gTg8A8a9GjDYrskdGw8tK20F6wjx/By2ofd17DUXSPV5jeaJXtmVJbEVFlwR2qN0Tu3AP5+TwNPbfv2B28C1n2gP/dUg97zAQlHzhvf1437oYuWU2q5myufd4Zhah4W3QyuXr2qcw2mm2G6ASwLim5TnTeJbeXG+9577xUO0UptL+P40HlXIqCU/XDbbbfB1unatatwiybOnDkj3KKZ8qFUfAUSw4cPHxbZDU888YTIHlCgloKUft66dWv07dtXpIuTWKfHv/rqK2HCWK9ePREh37p1q0G/dHLvfu6558T3yuXLlzFnzpxad1r8/Pxwyy23iDGl7ZOJpS2QuD0JOwfvRvKuFF2U2jnIWdVzuwDXvr+OXcP2Ykf/3bj05RXkxmh7NGkpzCrElblaRa0Bmj1lGOUmsf7Ob6oo950ao6UF9Ni94zQ49YtGtBJS+ONfoN1dEpZsljDn62KMeEpCdKK+N/OKtzX47hkn+Hix8DJGUB0N7hgpj9Oz5CyCsvBs4Imw8fpWYP49/NHuo7ZVKgUhkU8lA2e1pujNI4ANH2lQx8dwmyN7arDnWw0ah8l/0znu/6iEv3dK5aaW38qp5QzD2AgsuhkRfVJugin65OamnfYuA3KppgiVuhXT3LlzMWzYMMTFxfERrQWsWbNGZ7pH9bcBAQGwB9RZHOrsDqY0Fy9exLZt28S4VatWQkwTnTp1wmeffSYyXchtfOLEiXBWcj+1LQYpXTw0NFRkxZA/BBmkqSOhzZs3F5FxEvH0OiTYu3XrVmvquI1x991320yKOblSX/jooohw5yfIppke4e7ouao7Wqxtim6LuiD8ljA4eehvITLPZuLcG+exueM27J9yENHLY1CUXYTrP98Q4pyg51Df55JRSYpcEuSmXVH7rvqhGqz5QINfXtSI1GEiLhmY9rqET5fo16N09OM/aXDzwNp7TZnK/ybrj9FXyyWR7l8WLV9qAa8mnvDvVgfdfukMZ/fK30ZSi7hbXpZwQJupXi8Y+PdTDeoGGj9nbRtrsG++Bn20lUzUg3vSSxI+XaxveUap5X9rU8vr+Mj+AAzDMLaARnLwHEsSk9euXUOjRo1EexbGEDKUohvj1NRUIbYjIyPFzbKpUGr5gw8+qHMzp6jVkSNHROSmNlJbrjdqw0XCm9i4cSNGjtSGSmwc6kFP1yhd58T58+fRokUL2CvWvN5eeuklvPvuu2L84Ycf4plnnilzXZpso8wH+j44efKk0XVIuFNXBMqk6dixY60W2MagNHtKr6de3TSJQdcoZR5VN3kJeaINVNK2JN1jwUOD0OnbjnANdDG43siRPHZVHG4sikLK3tRS21KcrKmmmxiwqx98W/sYRLkHPCph1wn57+VvaXDLINOvi+hECQ99ohdZhLsb8P5sDf53q9znlzGNYU8U69qGrf1Ag7F9rHvsyIWc2oIt26pv67X9Kw3aN9VU+P1GwvreDyQsVCUrPTAR+OoJjXAtp7IDYuYo4JeXHPf/MGNZasv9G1Nz8FVVyyFDKRLcBBlNmSO4lVpeimJR6ihB6aGvv/66VfaVsQ2oNnf9ejkHkSZsKMPBXqD2YZTirPD111/X6P7Y8uSEUrNPAvDOO++sMPvlqaeewvHjx0WaOB1jqvMmt/NXX31VPE4p/W+99ZaIlLPgLg0dZ6V9GAnwjz76CNVN8u5k7By0Ry+4neTIZo/F3eAeXDoDytXPFQ3urI8+a3th0MEBaP5MM3iqXMlJbCuCO2xiXQPBTWw/Bp3gbttYrr81h3rBGqx8V4M/XtGgaT25HvzAfA2emKphwV2FaPcXy6wbi6HJloc/1QtuLw9Z6KsFd3l4uMvn/DV9cgjm/w2Me1bCgnUq1/IhPOnCMIztwJHuWgz94yPHaboZJijVU22UZA6XLl0SN9i5ubni5pGi3R06dEBtozbMlJIYoDZSxIsvvoh33nkH9kRycrKYLMjJyYGvr6+ILNprZoa1rrd169Zh3LhxYnzTTTfpPB8Y609oUSYGXZseHh5iErM6ot1SsYRLX1zB+XcvyGZpFDGu64bO33VCUP9As6432lbynhRELY5G7KpYFGYWwcnTCX3/6Q2/tr4G6458qhj/HpTHv71MtcUskmoKijy3uF3ClRj579O/atCmsXXOx0vfF+Pd3/Rt3Fa/r8HoXqVfy5Tr7Y9/5Ki3uq83QaZr8as0cHfja4oxjdpw/8bULHxV1WLISEoR3GSMVlnBTTRr1kwIMCVK8/DDD7M7tINO1Khdy9V1qPZCYGCgLnKbkZFh8H6Y0gZq9913Hx+WaoLasJGvBkETmFTrbm3yk/NxcPphnH9bL7iDBgSi/9a+BoLbVDROGgT1C0THL9tj2Jkh6L64K/pt6lNKcO8/LekEN0WpbxtqkbfDVBJqo/aYura7nPZhVYHqrxXBTRUmv75kXHCbyoyRGmz+XIPgOoaPT+xHpQYsuBmGsR1YdNdi1EZSZHZUVajmkwySCGotxr2QHQ/q0Xz69GkxJidwe62HVrcPI3dttZt2bSchIQF///23TgRSSzCm+qAsEqV92Pz584VhnbVI2Z8i0skTNmntvjVA82eboefy7nAPdUd8ioSN+yV88IeE6W8Uo9M9wGNzg5GRbdr2nb2cETo8pFRaOaF2LH9+hgYuLiyQapp7xwLe2uqAXzYAKRmWFd6/rJcwZ65+m18/ocH04VU/7/06yAZrrVWdy6YP4+uJYRjbgkV3LeXChQtYu3atGFPt5aRJk6q8TUqHVNfIkghX6sUZx0Ddk90eenOXBZVCKLXo5J5N6dSMDE2WUU03cdddd4k6eKb6oPp4xXeA+nVbI9pNGSuX517F3gkHkBstt/hyDnBFzsvd8EtgU4x/QUK9m4tR9yYJo5+W8Px8CYv+A05eAdbu98bEF8g5uvKC7MQlvflZRIhseMXUPNSm6+7RemfwBfItgkVYvUvCfR/qr5k37tXg4ZstJ4yb1pNbir14J/D5YxqMUbWVYxiGsQVYdNdS1OKYbvAsdWM9atQoTJ48WYzj4+Px8ssvW2S7TM1DdaYLFy4UYy8vL+FEbc+o24d9+eWXNbovtgKJMXVquT1PrNgzNGFJnzHiu+++E74DloJqrrfdehRnXz0HqVAWQad9/HF7SG/cuiJQpP6u2wvE6M3LS0EGaJNelISLdGV493f98565jetubQl1ivnXKyRR611V9p6SMPU12pbyGsArd8Hi+Ptq8M4sJzw+xXivd4ZhmJqERXctJD09XRexpDTG+++/36Lbpx6+3t5yL9Zvv/1WGLQx9g+ZaaWlpYkxCW4yIbNnxo4dK0yriH///VeXNl+b2b9/v0H5ALX5Yqof6iLx6KOPijG1Y3zvvfcstm2qud6fKQt6YnFwYzzXsBuSXT0M1qMWTkO7Ak9NletuT/yswa65gI+nXIpB9dgkpPILzBNl5yMlLN4sj0P8gVkTLPGuGEvRqqEGY7S90q/GwqAdW2WITZIw+RUJuXJXUdw+XI5EsyhmGKa2waK7FkLGUWQgRZChFBlLWZIGDRqINkEE1cqSqRrXzNo/jpJarkAu++rabo52s4GarUW7fXzkWugffvhB11veEjjd2Ry7fEPxWsPO+LVuCzQIdxLtul6/R4NV72pwbakGSWs0+O9zJ3zyqBPuHCW3c+rdDvhpTjy8tfp89W7gjrclFGoj5qbw/h8SJO3qT03VwMuDI5K2xv9u1Z+TL5dXPtJdUChh2usSorWWAYM6Az+9wO3cGIapnXDLsFoGiV+KXlEdK3Hy5EnRNszSUHSmc+fOOnd0SpGcNWsWHB1HbTlBN/z0nij9uEmTJuL6cYT3R5F7ah+WmZkpsj7ItCogIAC18XrLysoS7aloQo7EXkxMjE70MTUDdYRQotwPPvigyByyBCcvy87hXVoAnZpTVFtj1vV2ObkRxj8HXfTyzlHAzyaIqWuxEppPl1BYBPj7QIh7P28W3bZGcbGEtjMlnLsu/33sJw06NjP/PD31dTE+W6Kv3T/0vQZ1A03fjqP+P2VsE77eGGvD32K1DDKMUgQ3GUlZQ3ATbm5u+Oabb3R/P//880hM1E53M3bHr7/+qmsBR23CHOUGqE6dOrq2Z1SzThHF2sqyZct0GTC33XYbC24bYM6cOboyDqq1JwFiCShq/eRUDQZ30ZgsuNUM6QL89Y4GrlorkN82Ag9+QhHs8qOiHy2UBbdS18uC2zahyZP/qWq7v1hqfrR78X+STnDTdbLsTfMEN8MwjKPhGHfOjMmoU2jVRlLWYPDgwbj99tvFODk5GS+88IJVX4+xfm9uqsMjR2tHQp1iTgaDinN3bUNtoHbvvffW6L4wMkFBQbp2jgUFBXjnnXds5tBQb+Ulb2jg7Cz//f1q4IkvyxbeMYkSftC6YVNbqsdVKcyM7UGO8nW0iS5/bAISU00X3qeuGDqVf/E/Kk3g880wTO2GRXctggySyDCKaNasGcaNG2f11/z444/h5+cnxhRF3Lt3r9Vfk7Es1HNdyY4YOnSoSPVzJFq2bClM1Yjr16/relTXJs6fP48dO3aIcZs2bdC7N/fbsRWeeuop3Xco+SpcuXIFtsKkARr88TKllct/f7kceGG+ceH96RIJedp09IduAoLqsAizZXy8NLhP/loU5+271aY9Ly1Tws0vScjK0Yv3B2+y3n4yDMPYCyy6axFfffWVbkzOuNWRIkw1om+++abubzJVK1L6hjB2gaMZqBlDiSYSX3zxBWrzOb7vvvvYWdiGIKNLJSuJsjBsKdpNTBumwYLn9AL6gz+Bt34xXCcpTcK3q+Sxu5tsoMbYPo/eQi7j8viblZIwRquoFvyudyVc0Ha469wCmPc0O5UzDMMQLLprCSkpKaIulyBzpOoUT9QHvGPHjmJ85MgRi5kBMdaHDMaWLJEL8yjadvPNNzvkYR85ciRat24txtu3b8fRo0dRWyAh98svskpycXERHQ0Y2+LJJ58U/gMElXpcvnwZtsRdYzT4do5eSL+2QBL122oHbCXySdHT8GAW3fZAk3oaTOwnj6MSgBXbyl///T+AVTv1LedWvKWBpzufa4ZhGIJFdy2BUruzs7PFmAS3cgNXHdCNvNpU7eWXX0ZcXFy1vT5TeZYvXy5crRVzLS8vfX9fR4Jq1dXR7trUPmzDhg3CqZyYMGGC6BHN2BbkqP/EE0+IMWUKvf3227A1HrxJg08f1QusZ7+V8PVyCelZEr5cJj/m4gw8ezuLMHtCXXv/xbKyI93/7Jfw8g/ycoqO//mqRoh2hmEYRoZFdy2JZJFBlDq1vLrp16+fziWa2jRRD1rGvtKOlfPnqFCEV5mM+vPPP5GQkIDaABuo2Qckuv39/cWYspYUnwVbghzR375fL7Qe+0LCpBclpGbKf98xEmgUxkLMnhjcBejQVB7vOQUcOFNaeF+NkTD9TX3/9Tfu1QijPYZhGEYPi+5aABlDkUEUQYZRZBxVE3zwwQe6m8bffvtNpPEytguVAmzbJucTUm93RzfXorKL+++/X4zz8vJEb3lHhzJO1qxZo/NfGD16dE3vElMG9N1Jpmq2HO0mXpqpwUuqCoUtR6CLfr5wBwsxu8wCUkW7qVRATW6ehMmvSEhOl/8e3xcG559hGIaRYdFdC1AbQ1m7TVh5UNrqu+++a1DrTW1wGPO4ceMGpk2bJtK933//fZEeXNV0/eLiYpw8eVLU28+YMUM4lHft2lW3nEoS6ObL0VEbDFJJhKNfnzT5pbRIo0wGKgVhbBf6/qZUc+XcXbhwAbbIW/dTH3DDx6YOAVo2cPzvEEdkxghym5fHizfL7d8Icql/5DMJh8/Ly5pFAL+9RG72fJ4ZhmFKwqLbwSFDKCWiTEZRI0aMqNH9mT17Nrp37y7GJPLUjuqMaZAAJnOzxYsXi97nY8aMQVhYmIhUUibDiy++KJbTDTmJaWNQJHf37t0i+4DqeIODg9GhQwfhLk+p1UpmBOHt7Y2ZM2fWitPTuHFjTJw4UYyjo6OxbJm2GNUGiYyMxCeffIKNGzeW2Ru5POg56tRyR3WmdyTIzHDOnDliTJ/tt956C7YITdB98ogGD03SO5a/PJOFmL1CZmizJ8jjgkJg3t+Srjf7gnXKOsCKtzXw9+XzzDAMYwyNVJm7NTuCbkyuXbsmInfV0SLL1qDoleJMTJG7hx56qKZ3CQcOHECvXr3ETT+l9J49exYRERFwBKx9vf33338YPny4yevT8e3UqRM6d+4sRDWJaeq7vX//fuTm5pb5PDJMo3TyAQMGYOrUqWjbti1qC1u3bsWQIUPEmI7Bnj17YCvQZ4ZS/mmyauXKlbpJFZp4+f777836HNH76tu3rxgPHDhQV0rA2Dbp6elo0qQJkpOTxXfM6dOnRfmHLX6/0fW6+TAQ6g90aMZizJ6JjJPQ5DYJ1PEzNABY9qYGw5+SkK9NBvrjFQ1uH2HZc1zb79+Y6oWvN8basOh2YEhgNWvWTKSPUkoi/U0izBZ48MEHMX/+fDGmVOlFixbBEbDmlzbdwNJkBU1aEB9++KGIcFM2A/1QDTa1hqsMFOkmgd2/f3/x06VLF7i6uqI2QseZJimOHz8u/t63bx969uxZo/tEDvK///67MESkDJGyoqCffvop7r33XpNKAah+XYl0Uxuqu+66y+L7zViH9957T2S0ELfffjv++OOPajnUfFNau5n2WjGWbNFnL+Tly+P/TQa+eNzyopivN6Y64euNsTYsuh28/k9pffTqq6/ijTfegK1AURqKziQmJoq/KcpG0TZ7x5pf2itWrMDkyZPFmKLWJLTVr0FikVKOFRGuCPGrV6+W2lbTpk11Ipt+k7lebajZNhUSo4qpWnWKmpJQP+a5c+diwYIFSE1NNVhGEy5U10/7pnZap57jFPVu2LBhuf3XqRyBfvv6+oqWYVRGwNgHGRkZItqdlJQkPrenTp1CmzZtrP66fFNau9l9QkK/RwyTI/t3BDZ/roGri+X/f/D1xlQnfL0xVkdycIqKiqTLly+L37WJ+Ph4ydPTk/47Sl5eXlJCQoJka3z//fdi/+inR48eDnGOrHW9FRQUSG3atNEdr9WrV5v83JSUFGnr1q3S3LlzpcWLF0tRUVEW3TdHJDs7WwoKChLH2sXFpVqPGV07GzZskMaNGydpNBrdOVd++vbtKy1cuFDKy8sT6x4+fFi68847Ddbx9fWV5s2bJxUXFxt9jQULFujWnT17drW9N8ZyfPDBB7pzeNttt1XLoa2t/08ZGfo+6T6rSMIA+SfspiIpOsH4d4wl4OuNqU74emOsDRfJOChU85mTkyPGs2bNEunDtgYZN1HElqCUaUdJMbcG5FR85swZXc/zcePGmdVqaNCgQcIkjeqz69WrZ8U9dQw8PT3xwAMPiDGVZ8ybN69aanUpM4UiltS6a+3atTqDNHd3d/F5OXToEHbt2iUi3G5ubrrzS+nh1PpLqemmSCiVcFD9/5UrV0q9Fvfmtn+o+0NISIgYk6kiRbsZxppQVsWrd2lE+zcyTlv6pgbhwZwhxTAMY5X08kuXLuHEiRNCxJHJUMkWM+T4SzWQlLJIqaseHh5mLbc0tTFdhG64KbWU0lHp/FCKaoMGDWCL/PPPPxg1apQY0z6TqRoJHnvFGtcbGZ5R+jeljhPkRk8p4Yz1W7ORmzn1RCZxQ+fVWtcmOcnTRErJFHL63NJkCaW6G5s4K3m9paWlCXdrtaimtHFyqScTRVrn3LlzopMB0a5dO/F9zqUF9snHH3+MZ555RoxpQo3EtzWpjf9PmdKcuSrh/+3dCbzN1f7/8WWqTBEZQ0nqJk1XSKUM11SGjkhKEaVRqEyZTuSqCFcpQ6VO11AuV2W66lEkkQaJJCIlSTIlM+3/471+//V9fDfnOPucs7/nnL336/l4nIfvHp2z99prfz9rfdZnFS5oTKUywQbctDdkJ9obgpahb01tT6O1wSrko5kfbSOkAj/O559/bmdgVBk5JSXFFvTxV0hO73ZEx8SJE72T9w4dOuTagNutP9WsnqjQm39PcfwffdZcwK0twQi4s0eFChVMmzZt7LHWTGtP9CCoD1Rf6g+4VT1da/g1YNa3b9+IM1WKFStmXn75ZbuNmFvTrT5a+483aNDAfP/993Z9uBNp0TXkThpIKV26tD2eMWNGmkX2gGi6+Lw8gQfcABB3MpKLvnLlSu/42LFjoaSkpLC1pXfccUdo8uTJ3hrUdu3a2bWHkd4ehERbo3Ho0KFQ+fLl7To/rQddu3ZtKLdbvXp1KG/evN5aVK1Hj1XRbm9//PFH6Oyzz/bWbvo/gwiePj9a063X/vTTTw9t2LAh6v9HcnKy9/7WqlUrtGbNmqi0t71794buv//+sLXeqvNQrFgxe1ygQIGY/qzh/zz33HPe+zts2LBAX5ZE+z5FzqK9gfaGeJKhmW5to+Pky5fP7uXrKK1RqcFuD2GlNWtmZfny5RHdjuhQBoFS+OXmm2/Oloq2WVW9enXTpUsXLzU+OTk5p3+lXENbQLkK7+3btw/7DCJ4+vw8+uij9vjw4cOmW7du3jrraNByHW3/5PpEzUIr5TsatIXYSy+9ZPd2V5q8qM6D+mJp2bKltyYYsUtr95VBpiUKbhsxAACQu4QvyI6AUsQVPLt13Y0aNbLXuy1rypQp491XaW+LFi2K6PbUHDlyxP6E/cL583sFhCJdo+H/N55p7an2bnZ69+4dM3+3Au1p06bZLYy0f7fWscbCgEGQ7U2fGa3ZdO1er1GsvJ/xpH///mbq1Kl2jfeCBQvMzJkzTevWrbP8vAreFcQrmHdb/KnNZ+Q9jqS91atXz6xatcoGZNp+zOnUqRPtKQ6oLorb0i7o/iGRvk+R82hvoL0hVkRS5yTDQbdmSbZu3Wr3WS5QoIANklRZ13WO/vWBmg0/sdNM6/bUTJ482e4369e2bVtbMCaj3JrYeKZqx1qzKXXq1LEDHCp6Eyu6du1qZ3Y1ePDII4/YtamxKhrt7amnnrKfL1Gb1+ctlt7PeNKvXz9bLVrUNhUc+zN9MuO9994z8+fPt8f6rHbs2DHT728k7U0F1lQP4IUXXrCF+apVq0Z7QmDtDYgW2huyE+0NmVG5cuXoVy/3U9XUsmXL2pO5nTt32irU8+bN8wq7KGjWzLhmV9K7PciZbn2AVEwsnqut6m2sWbOmWblypb2sQkoulT9WHDhwwFZV1qCOC0q0BCGWRKu9qajcRRddZNu/KmavX7+erb5y+POlInaqtu+ySFxaeGbbupZVuCBbWR6ZGUxMlP4NuQPtDbQ3xCv6N+SamW6lQGofWf8aQKW1uZnqkiVL2mq52s5IFX91krpkyRK7LVgkt6dGwXVGAuz0Xox4PilVkO0C7ho1ati0/1irSlykSBEzbNgwm/bqAhsNysTi+5bV9jZkyBBvwEkzq6qkjZylGWIFynpflJGhdprZJRAK2F3ArcGxdu3aZenzGu/9G3IX2htob4hX9G8ISsRBt4LrHj16mMsuu8ymQn777bd2v23/frBahzt06FCzbds2e0K5fft2c8stt0R8OzLPv52RUmFjLeB27rzzTrttmAYQ9PPGG2/YtNtEos/W66+/bo+LFy9u+vTpk9O/EowxVatWte+F+rBjx47ZdHMVKcvoZ037ZI8YMcIea8mAgvlY/bwCAAAgfRFPjSjFVWtslfKqfV+Vyjx79mxTpUoV7z6asRk7dqwdJbr00kttwKR9YyO9HZmjCvCuIJ3WaqpqeaxS29B+8P4iVkrFTSQDBgzwMkg023/WWWfl9K8E34CWW7fz4YcfmunTp2fotVGGj/bMPnr0qLdER30qAAAA4leW1nTHAgUvmlU/99xz4zb9UkH222+/bY+VedC5c2cT67Sd0bvvvmuPNbOoQDQR2ttnn31matWqZY9VL0GF8QoXLhzAb4rMmjNnjmnRooU9LleunN3NQdtzReKtt96yqeSiNrJ27dosFWRLhP4NuQftDbQ3xCv6NwSNs7QY980333gB9znnnGM6dOhg4oG2PlN1e5c6/+uvv5pEmUl1Bg0aRMCdCzVv3twOComWygwePDiix2kP+p49e3qXtYwiqxXQAQAAkPsRdMc4/77cqiIfrcJzOU1VzO+77z57rOUMCkDj3fvvv2/XCMv5559vunTpktO/EtIwZswYW0hSnn/+efP111+n+1o9+eST5pdffrHHN910kxe4AwAAIL4RdMcwpZVOnTrVHpcoUcLce++9Jp4kJyd7abtKm1+zZo3JzWlJSg1XzQKlH2tWMyO0ysM/y62U+ngZQIlHWtetegOifeVVJNKtw0+N2q4CdVGwrnZC8TQAAIDEQNAdw0aOHGmrKEu3bt3sllvxRNvTPfHEE/ZYAY2KTuUmO3bsMP/+979tSr8q+mstttKHtcWXLmut/ZQpU+xWe+mZNWuW3R5NtEPAbbfdlg1/AbJC7VEVzWXp0qUmJSUlzQEVVTpXcC4aXFEmAwAAABIDhdRi1G+//WaLJx06dMiuC/3pp5/sXujxRn+fqjvr73P7kTdu3DhHfhcFTStWrDDz5883CxYssEFyJHUINWPdpEkT07ZtW1uAS9uA+WngRNX8VZBLNFOu9GPkfgsXLrTvrRsk0nZgJ1ab1y4Nd911lz3Wbg+a9Xap6VlF4RdkJ9obaG+IV/RvCBoz3TFK6akKSKVr165xGXCLgpPhw4d7lx9//HFvxjA7qIDba6+9ZmeeFVRdc801NvVbqeT+gFtZBprZ1vviZr6dI0eO2ErsCrxKly5tC3FpH+7du3fb2zVD6gLu6667ztx4443Z9vchazQA1KZNGy/zwaWcO3v27LFt1tGe3NEKuAEAABAbmOmOQUpX1iy3TugLFChgNm7caCpWrGjiefTx6quvtoGuaL/4oIuMbd682QbPShtOi9LAmzVrZpo2bWqDcc1ou5HSChUqmGXLlpkZM2aYmTNn2irXJ9J7p73rV61a5RXYWrJkiQ28ETt+/vlnW/hPBf+0TlvZEFdddZW9TUsNVGhNkpKS7DKCaGJkHtmJ9gbaG+IV/RuCxkx3DJowYYINuEWBYTwH3KL9h5977jnvsvbs/vPPPwP7/zSDnVrArbRwpYirqNvWrVttsKztzOrVq3dS0TNtd3b99dfbgEtBmYJpBWDa1s05evSoTVX3V7Qm4I49GmBx24ap7aiomrIxVq5cacaNG2ev1xIQV0gNAAAAiYWgO8YopXzUqFH2WLNqvXv3Nomgbt26dqbQpXyPGDEisP9L68ZdwF2uXDmbMvzxxx/b9OG33nrLdO7c2ZQvXz5DgwYKprUvs9am67l79OhhgzV/kD5s2LBA/h4ET+9ntWrV7LEyMiZNmhRW0XzgwIGmUqVKvBUAAAAJiPTyGDNx4kRv/+rWrVvb1OVEsWHDBhvYqPBYwYIFzfr168MC12jQTGXNmjXNF198YS8rPdyt2Y12epLuq1TkDz/80NSuXds0aNAgy78/cs6iRYtM/fr1vUEUV3tAqefKighiCzjS4ZCdaG+gvSFe0b8haMx0xxCdxD/77LPe5b59+5pEou2ZtPWSHDx40NtOLJrefvttL+C+4oor7MBGUBSUa626tpAi4I59WmZwxx132GN/sT+lmLPnOgAAQOJipjubaZZW230pRVo/Koqm9cmR/Ggdt2ZRpWHDhub99983iWbXrl02+Na/snz5cjtLHA0KlBRoa0snUcVxVRrPCEZKE5s+09rizu3Nrqr306ZNC+z/o70hO9HeQHtDvKJ/Q9DyB/4/JAgFxSqYpZNuVap2QbX/R9f//vvvEe3tnJ5Em+V2SpQoYZ588knTrVs3by3tJ598Yte3Z5XWa7uAW4E8e2Ujo8qWLWtGjx5tq+uraJ6/ACAAAAASE0F3FEyfPt3uwaxq1EHR3r7aC1o/t956q53pTlT333+/eemll8zatWvtTLdmEm+//fYsZyC4CtSiombRCOSReFRoT2u7Ve3+rLPOyulfBwAAADmMoDuLtm/fbh544IF0A26t6dQsmP+nTJky9qS8aNGiXkCd2k/hwoXtns74/402f35bwV37Y0ufPn1Mq1at7OuUWSkpKbZQm1ubyxprZEXlypV5AQEAAGARdGfRo48+6u2ZXatWLbs3s7aZOjHAVnDNzGn0NGnSxKZ/z50716b1awux5OTkTD3X4cOHbcq6M3ToUN4rAAAAAFFB0J0FCxcuNFOnTvXWGs+ZM8eUKlUqOu8M0qX1stpTW6nhququdbQVK1bM8Cv3yiuv2P2zRbPn2lMbAAAAAKKBLcMySVtWPfjgg95lBX0E3NlLVaIffvhh7/3ITHE5Pe6pp57yLvuPAQAAACCrCLozSYW2Nm7caI/r1q1r7r777iy/Gci4QYMGmZIlS9pjZR0sW7YsQ49/8cUXbVV5SUpKMjVq1OBtAAAAABA1BN2ZoKrZmtkWFTgbP368yZuXlzInaK38kCFDvMvaQkx7LUZi37595umnn7bHWm/vfx4AAAAAiAYixQxSQKctq1y18t69e5tq1apF5c1A5nTt2tVccskl9njFihVmypQpET1u7Nixdt90ad++valevTpvAQAAAICoIujOoNdee80sWbLEHlepUsX0798/uu8IMrWF2OjRo73LWtu9f//+Uz5m9+7dtuK55MuXL2yPbgAAAACIFoLuDNixY4fp1atX2HrgggULRu3NQOY1atTItGjRwh7/8ssv5plnnkm38vnevXvtcceOHc2FF17Iyw8AAAAg6gi6M+Dxxx83u3bt8tKRGzduHP13BJk2cuRIu8ZeNIv9448/pjl4MmbMGHus+w8cOJBXHQAAAEAgCLoj9MEHH5iUlBR7XLx4cTNq1Khg3hFkmmaru3XrZo8PHTqU5hZimgV36edaD37eeefxqgMAAAAIBEF3BBTAPfDAA95lVbwuW7ZsMO8IskSz1meffbY9nj59ulm6dGnY7Uo9HzdunD0+44wzzBNPPMErDgAAACAwBN0RUJC9fv16e1ynTh1z7733BveOIEuUhTB06NA0txDT/uoaRJGHHnrIlC9fnlccAAAAQGAIutPx3XffmeHDh3tVsidMmMCe3LncPffcYy699FJ7/Pnnn5s33njDHm/evNlMmjTJHhcpUsT06dMnR39PAAAAAPEvroPuTz75xNx+++12/fWWLVsy/PhQKGT35D5y5Ii9/Nhjj3nBHGJnC7F+/fqZP//8086Au/3VNQNeqlSpHPwtAQAAACSCuAy6d+7caWc7r732WvPmm2+aF154we6p3aZNG7N48WIbTEdCM6SLFi2yxyq2NWjQoIB/c0RLw4YNTatWrezxtm3bbMG0119/3UtB1wAKAAAAAAQtroJurd2dPHmyueiii8wrr7wSdtvx48fNzJkzTb169czll19uJk6c6FWwTitw9wdm2pO7UKFCgf7+CG4LsWnTptk24LZ+U+ANAAAAAEGLm6B7zZo15oYbbjCdO3e2AbMULVrUBl7du3cPqza+evVqc99995kKFSrYwHrTpk0nPV/v3r3N77//bo/btm1rmjVrlo1/DaLhggsusO+9nyqbP/LII7zAAAAAALJFzAfdWqurAPnKK680H3/8sXd9u3btzLp160zPnj1t4PXDDz+YqVOn2urjzp49e+x6bwVnLVq0MAsXLrSz5R999JF59dVX7X3OPPNMM2bMmBz525B1AwYMCFu7rfXdGowBAAAAgOwQs0G31mXPnj3bVKtWzYwYMcIcO3bMXq8AWsGz9mj2bwd12mmnmfbt29viaqpo3alTJ3P66ad7zzVnzhzTpEkT+3wdO3b0HqfK5WwrFbuKFStmK87r/a9du3bYfusAAAAAELSYDLq19VPLli1NUlKSV5VcAXRycrJNHW/UqNEpH1+jRg279luPVVBdsWLFsC3C9PxSq1Ytm4aO2KZ2sm/fPjvgUrBgwZz+dQAAAAAkkJgLup9++mk7G62Zaadx48Y22B48eLA544wzIn4upR337dvXrulWkbX69et7t+XLl8/OkOpfxD7NdOfNG3PNHQAAAECMy29ijNbkOuXKlbPrrVXoLE+ePFna17l169b2RwXZ3n33Xbvd2BVXXBGl3xoAAAAAkIhiLugWzVh269bNDBkyxBY6i6bq1avbHwAAAAAAEi7oVjGsl156yVYrBwAAAAAgN4u5oFvFsFibCwAAAACIBTFXWYqAGwAAAAAQK2Iu6AYAAAAAIFYQdAMAAAAAEBCCbgAAAAAAAkLQDQAAAABAQAi6AQAAAAAICEE3AAAAAAABIegGAAAAACAgBN0AAAAAAASEoBsAAAAAgIAQdAMAAAAAEBCCbgAAAAAAApInFAqFgnpyAAAAAAASGTPdAAAAAAAEhKAbAAAAAICAEHQDAAAAABAQgm4AAAAAAAJC0A0AAAAAQEAIugEAAAAACAhBNwAAAAAAASHoBgAAAAAgIPlNjOjfv785evSoPe7YsaO55JJLwm7fvXu3eeutt8zPP/9sLrroInPrrbea0047zbu9X79+5vjx497lK664wtx+++0R347E8uSTT5r9+/fb4/bt25srr7wy7PY//vjDvPnmm+ann34yVapUMbfddps544wzvNsHDx5sDh486F2uVq2a6dSpU9hzfPPNN2bu3LkmFAqZtm3bmvPPPz/wvwu509NPP2127dplj1u3bm2uvvrqsNv//PNP27/98MMP5rzzzrNtslChQt7tw4YNM3v37vUuq03ed9999lh94tixY0/6P5OSkkydOnUC/KuQW40aNcr8+uuv9rh58+bm+uuvD7tdfZfa28aNG02FChXsd2GRIkW820eOHGl+++0377Lu88gjj3iXDxw4YGbOnGm+//57U7x4cXPzzTebypUrZ8vfhtxn3Lhx5scff7THjRo1sj9+hw8fNjNmzDAbNmwwZcuWtf2b2o2j/kv9mFO6dGnz+OOPR/x4JJZJkybZtiDq29TH+SmWUP/07bff2rak87eSJUuG3Wfr1q1m1qxZNrZo1qyZqVmzZtjtCxYsMJ988oltZ+3atTPnnHNONvxliHUxM9P9j3/8wzRu3NisWrXK7Nix46QT0g4dOthOWR+Mjz76yAwcODDsPh988IG55ppr7HPo57LLLsvQ7Ugs9evXt+1g3bp1Ztu2bWG3HTp0yAbQOiGtVauW+eyzz0zv3r3D7rN48WJTo0YNrz1pEMdv0aJF5uGHHzZnnXWW+dvf/maeeuop246RmOrWrWvbyaZNm8JOLt0Jwj333GPWrFlj29vq1atN9+7d7WCNs3TpUnPppZd67e2qq67ybjvzzDO96/Wj51B/V6pUqWz9G5F7uO86tTUXDDkafL7//vvN559/br9PdfL6wAMPhA1KL1u2zFx44YVemzpx8KZHjx72e1iPVzu96667TmrXSBy1a9e27UTnburj/NQ+NGCzZMkS22+pnXTt2tWbZBF9x2qw0bW36667LkOPR2JRO1A70UC0Bv5O1KtXL7Nw4UJ7jvb777+bLl26hE2S6NxOfdaRI0fM3//+d/PGG2+YtWvXerfr8r/+9S/7navJmc6dO5s9e/Zk29+HGBaKMa1atQp9+OGHYdfNmjUr1KZNm9Bff/1lL+/duzdUu3bt0IYNG7z71KpVK7Rjx440nze925GY2rdvH5o7d27YdfPnzw81b948dOzYMXv5wIEDobp164ZWrVrl3eeGG24I/fTTT6k+59GjR0MtW7YMLVmyxLtOz6Hrkdg6d+4cmjFjRth1ixcvDjVq1Ch05MgRe/nw4cOhBg0ahJYtW+bdp1mzZqF169ZF9H9MmTIldMcdd0T5N0csevjhh0MpKSlh13322Weh66+/PnTw4EF7Wf3SjTfeGPrggw+8+7Ru3Tq0cuXKVJ9zz549oRo1aoS2bNniXXf33Xef1K6ReHr16hUaP3582HVr1qwJ1alTJ7Rv3z57WedxSUlJoTlz5nj36dChQ2jp0qWpPmckj0diGjx4cGj06NFh123atClUs2bN0K5du8LO8/z9U/fu3UMvv/yyd/n48eNe+9J5X8OGDcPiEPVvkydPDvivQTyImZnuU9m5c6cpX768yZMnjzezU7RoUfPVV1+F3U8pSkOHDjWzZ88OG7WP9HbAtbdy5cqZfPny2csFCxa0M9Yntrfx48ebIUOGmP/85z/m2LFj3vWaPd+3b59N11Qa+5gxY2yqZv78MbPaA9nc3pQCV6BAAXtZy2Y0S62sH7/Jkyfb9qRlDxqhT8ucOXNOSrcDHM38KNXSLZdRv1SmTJmT+rcpU6aY5ORkM3XqVJv94xQuXNgUK1bMbN++3V7WjKP7jgZS69/UXtzyBZ3H6fv1xPam9HEt29Iso5YvZPTxgGsv7pzNUWq4ay9aqqDMserVq5vhw4ebZ555xn7Xuva1ZcsWO6ut7A1Hx19//TUvMNIVF0H3BRdcYFMvXXqH0kC0DkM/jj48WtuhlDh12n369Al7jvRuB5yqVaua7777zp6citLltD7S394U/ChFXWu5dbLgTwfWWiEN6owePdqmnev4zjvvNL/88gsvMlJtb1rL7ZY5KH1StQT87e2JJ56wS3CU7vbuu++elA7sKFVY7bVp06a80kjz+1R90ebNm+1lDQiq3fjbm9IzmzRpYvuv9957zy5/cAOLCtK15lsD2Er7VX2VFi1a2JR24ESqZaJ6FhqMFp3Haa2tP11X7eimm26ytVWURq66Pm6gJ5LHA46WKajtuCBby/oUMLv+Td+zOlfTZIj6Qg1w6/xNS2pc+zr99NNt4O4ogPf3j0Ba4mJq7YYbbrAFqVTMQEGzRtjPPfdcO+PtNGjQIOxYHbhOPl3xqvRuBxytidUAjYq1XHzxxTaIVkeu0XZ/m3QUDKk9qXCaRk8VDGkdkEbtXfvSusp33nnHrqUE/NRmNDOtuhUqIKmgW0Wp/P2bf42j1rLp/l9++eVJxV/UxrR+nCJDSItONNW33X333XYQRwM8am/+/s1f6E8DOC1btrSzQ+r3FHxPnDjRPk+9evVs//jf//7XHus6wE9F+LQmVoUfVUtHM4n6PvX3b/5+TN+lKjapuihqe5E8HnDOPvts8+CDD9pAWu1F/VvFihW99uIGDzWw4wanXWFJ1a5QhqM/c9Fl85CpiIQJupVONGLECLN+/Xo7Kq+TVAXgCrxTo5ErpZ9rpjK1oDq92wHN4qhAh2a41d7UQafV3hTgaCTUzYy7NEt/uqWOXfVq4ETaXUEVVhXAKPDWSUNa7U1pcEpHd+3N0YnC//73P7sTBHAqOiFVxXEFMCr0qEKRabU3paErnde1Nw326LtYhYrcEhyldE6fPt0MGDCAFx4nUcCsYFrZFcrs0bKstNqbltko0Pb3bxl5PKDzNVXQ18SadvrQbg4aqBH1ZXnz5g07P9N1KubnjjVxolhD37NudlxV84GESC+X5cuX21luzfjMnz/fjsprRlKUdqSTVefjjz+2a4LcqHt6twOptTe1D7U3jbiLZhBFwbi/IvAXX3xhTxC0lZ0oSC9RooSdGXJriFQp2N0OpNbeNNuo9qYvf6WyNWzY0N6mE01/hVYttXHBkp/am9Lmrr32Wl5gpPt9qqBF7U3fj+rP3KyPvitdKq8o9VztT1k/bk23Zob8g4h6jH/LMeDE9qZAWu1NfZf6MGVPiCqea8cGR21RWWP+/u1UjwdS698UVKu9aEBQl7WFpuu/VLHcnZ/pO1Op5e78TDPlOlbWmKg+j3YDced/QFzMdLt9GvUBSUlJMfPmzbOjmxqlEu2np5Q2FRDSfZ577jk7WuVG4rUGTR8m0Sh83759beATye1IPBMmTLDbRmhto2ZoFFgr3dKdWGpgR/f566+/7Gy3im249CK1J7UfFbzSdTpB7dmzpx0hFV2n2Uall6vd6iShUqVKplWrVjn6NyPnvPbaa7YWhQJopeKuWLHC7o3stppT+3vxxRftzKHayz//+U+v0JXWlqkGhTJ+tNZMJ6RapnDivsgqoKbAiTQ4qPiZ1jTqu84FNW3atPEGqj/99FPz/PPP2zamNqmCaS5oVntT36XvWl2nx2p7HdWvEGViaEBImRkaYFQfqvRLPQaJScVE1acpGFZ9Cn23Kih2y2K0plZbMKk96TZt+eqWwKgNap9vLclSCrDa2y233BK2LeKpHo/Eo7omWvuvtf3KjFAfpBoUbqBaA4Wa3VYb0bG+P92stSgeUDaZsnbU7tR/+es8KfNH53TqJ/XcGgDSMkIgPXlUwtzEAM3uaM89P+2x5yoQ6s9Qh64CCVqnoZNPP31odFKrf5V+5F+fFsntSCyanT6xMIYCII1yOmovGuW8/PLLvQDIn8qrDl/tUTPi/kqZjgaHdB83cuqq7yPxKAA6MR1c62lVNdrR4I2KuKh/K1SoUNh9le6mtqQTBLU3VZ8+kfZN1qAR+3ND35UaLPRT0OxPqdTstQJytcMTZ6k12KhikvpOVnvz94uOgnUNlCtQ0nMz2JO41Df5swlF33laS+u4gqQaqDlxPbbO7zRApO9kDSb6+8VIHo/Eoraitdp+6qdcCrnLmNAAttpLagM0ynZV1XINMmog0e0e4qjv0wCQYgU9B+dviKugGwAAAACAWBM3a7oBAAAAAMhtCLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgOQP6okBAED8+uGHH+yeuHnz5jWNGjXK6V8HAIBci6AbAIBcZtu2bebrr7/2Ljdo0MAUKFDAu7xlyxazdu1a73KTJk0C+T327NljPv30U3tcp04dc+aZZ3q3LV682LzwwgsmX758BN0AAJwCQTcAALnMypUrzaBBg7zLycnJpnnz5t7lZ5991ixbtizwoHvz5s2mf//+9jglJcVUq1YtkP8HAIB4RtANAEAuN336dC/o3rRpk1m+fPkp779z506zbt06c+TIEVOhQgVTtWrVU6aG//jjj/anbNmy5sILL/QC7hUrVniP0f+pGfaSJUuaq6666qT/M7XnAAAABN0AAORqpUqVsgG0Zr+vvPJKM23aNBMKhez1O3bsCLvv8ePHzciRI82sWbNMmTJl7I8ed/HFF5vhw4fbANyfGq6ge968eTY4X7Nmjdm/f79p1aqVGThwoA2wv/rqK++5v/zyS7NhwwZzwQUXhAXd+l169uyZ6nMAAACqlwMAkKu1adPG/jt16lS7xlpBcpEiRcyNN9540n1fffVVM2PGDFO8eHF7/0mTJtkZ8m+//dY89thj5q+//gq7vy5fd911Zty4caZ79+72urffftv8+uuvpm7duqZr167efR988EEbuHfp0iXi5wAAAATdAADkag0bNrQz1h999JEZO3asOXz4sLn55ptNwYIFT7rv7NmzvaJnCszFVRbfuHGjWb169UmPady4sf23UqVK3nUZDZij8RwAAMQr9ukGACAXU3XwW2+91aaOv/POO/Zyu3btTrqfbt++fbs9LlGihHe91mD7q6Kf+NxFixa1x/nzZ67MSzSeAwCAeEbQDQBALpeUlOTNbNevX9+UK1cu1eC3cOHC9vjgwYPe9QcOHPCOXXAMAACyD0E3AAC5nPbHfuihh2yqeKdOndK837XXXmv/9e/x7Y6Vbn755Zdn6P897bTTwmbSAQBAxpEHBgBADLjtttvsz6n06NHDFk377rvvzJAhQ0yVKlXM5MmTbfA8YMAAb513pLRGW7Pnqkj+5ptvmq1bt9rq5foBAACRIegGACCXKV++vFcArVChQqneRwG1u49TunRpu6XYggULzDfffGOD7w4dOpimTZuaihUreverXLmyfaxS0h1VPHfPp2NRkD5hwgRbMX3Xrl12qzE9RkF3pM8BAECiyxPSBpsAAAAAACDqWNMNAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgBB0AwAAAAAQEIJuAAAAAAACQtANAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAwATj/wH4ti+gzVt0TwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x400 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "35dc864c",
+      "metadata": {},
+      "source": [
+        "Now let's log this model to MLflow:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "autolog()\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
-    "    auto_model = LinearRegressionModel(lags=12)\n",
-    "    auto_model.fit(train)  # autolog logs params, covariate metadata, and the model\n",
-    "\n",
-    "    auto_predictions = auto_model.predict(n=len(val))\n",
-    "    # these metric calls happen inside the run, so they are logged automatically\n",
-    "    auto_mape = darts.metrics.mape(val, auto_predictions)\n",
-    "    auto_rmse = darts.metrics.rmse(val, auto_predictions)\n",
-    "\n",
-    "autolog(disable=True)\n",
-    "\n",
-    "# show logged metrics\n",
-    "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
-    "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
-    "\n",
-    "# plot\n",
-    "fig, ax = plt.subplots(figsize=(10, 4))\n",
-    "train[-36:].plot(label=\"Train\", ax=ax)\n",
-    "val.plot(label=\"Actual\", ax=ax)\n",
-    "auto_predictions.plot(\n",
-    "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
-    ")\n",
-    "ax.set_title(\"Linear Regression — autolog run\")\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f484330f",
-   "metadata": {},
-   "source": [
-    "## Open the MLflow UI\n",
-    "\n",
-    "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
-    "\n",
-    "The UI lets you:\n",
-    "- **Compare runs** side-by-side in the Experiments table\n",
-    "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
-    "- **Visualize** metrics across runs with built-in charts\n",
-    "- **Register** models to the Model Registry for versioning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1a01cd2f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
-     "iopub.status.busy": "2026-06-24T15:18:50.021439Z",
-     "iopub.status.idle": "2026-06-24T15:18:50.023116Z",
-     "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Launch the MLflow UI with this command in your terminal:\n",
-      "\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpyqkqfzoe/mlflow.db\n",
-      "\n",
-      "Then open:  http://localhost:5000\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
-    "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
-    "print(\"Then open:  http://localhost:5000\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88b0d285",
-   "metadata": {},
-   "source": [
-    "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `val_mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
-    ">\n",
-    "![Mlflow Overview](./static/images/mlflow_overview.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2b133bc",
-   "metadata": {},
-   "source": [
-    "## Per-epoch Metrics with Torch Models\n",
-    "\n",
-    "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
-    "\n",
-    "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d01783d1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
-     "iopub.status.busy": "2026-06-24T15:18:50.024087Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.370946Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "61406cd9",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.451297Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.906785Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2026/06/25 13:57:23 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Run ID: 22b3b299ce8d4d72b1646564a35b04ee\n",
+            "Model URI: models:/m-e61e8518e2a04143ace1d627ed4a775b\n"
+          ]
+        }
+      ],
+      "source": [
+        "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
+        "    model_info = log_model(\n",
+        "        model=model,\n",
+        "        name=\"exponential-smoothing-model\",\n",
+        "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
+        "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
+        "    )\n",
+        "\n",
+        "    # log calculated metrics you want\n",
+        "    mlflow.log_metric(\"val_mape\", mape_score)\n",
+        "    mlflow.log_metric(\"val_rmse\", rmse_score)\n",
+        "\n",
+        "    print(f\"Run ID: {run.info.run_id}\")\n",
+        "    print(f\"Model URI: {model_info.model_uri}\")"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO: GPU available: True (mps), used: False\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
-      "INFO: TPU available: False, using: 0 TPU cores\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-      "INFO: HPU available: False, using: 0 HPUs\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
-      "\n",
-      "  | Name            | Type             | Params | Mode \n",
-      "-------------------------------------------------------------\n",
-      "0 | criterion       | MSELoss          | 0      | train\n",
-      "1 | train_criterion | MSELoss          | 0      | train\n",
-      "2 | val_criterion   | MSELoss          | 0      | train\n",
-      "3 | train_metrics   | MetricCollection | 0      | train\n",
-      "4 | val_metrics     | MetricCollection | 0      | train\n",
-      "5 | stacks          | ModuleList       | 6.2 M  | train\n",
-      "-------------------------------------------------------------\n",
-      "6.2 M     Trainable params\n",
-      "1.4 K     Non-trainable params\n",
-      "6.2 M     Total params\n",
-      "24.787    Total estimated model params size (MB)\n",
-      "400       Modules in train mode\n",
-      "0         Modules in eval mode\n"
-     ]
+      "cell_type": "markdown",
+      "id": "0728690e",
+      "metadata": {},
+      "source": [
+        "### Load the Model Back\n",
+        "\n",
+        "We can load the model from MLflow using its URI:"
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b08a95d734ed47b892fbee0631114072",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "cae35ffa",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.907802Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.914695Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
+        }
       },
-      "text/plain": [
-       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loaded model predictions match: True\n"
+          ]
+        }
+      ],
+      "source": [
+        "loaded_model = load_model(model_info.model_uri)\n",
+        "\n",
+        "loaded_predictions = loaded_model.predict(n=len(val))\n",
+        "\n",
+        "# verify predictions match\n",
+        "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
+        "print(f\"Loaded model predictions match: {predictions_match}\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
-      "  super().__init__(loader)\n",
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
-     ]
+      "cell_type": "markdown",
+      "id": "6bd4597c",
+      "metadata": {},
+      "source": [
+        "## Automatic Logging with `autolog()`\n",
+        "\n",
+        "`autolog()` patches every `model.fit()` call to automatically log parameters, covariate metadata, and the trained model artifact. It also patches the darts metric functions so any metric called inside an active run is logged. For PyTorch-based models it enables MLflow's PyTorch autologging to record `train_loss` / `val_loss` per epoch (see the sections below for details)."
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a87f60f4ed9420683e2d38314cb8ee9",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "5ef7f73a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.915566Z",
+          "iopub.status.idle": "2026-06-24T15:18:50.020337Z",
+          "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
+        }
       },
-      "text/plain": [
-       "Training: |          | 0/? [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2026/06/25 13:57:26 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
+            "2026/06/25 13:57:26 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Logged metrics: {'val_mape': 10.742, 'val_rmse': 51.182}\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "autolog()\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
+        "    auto_model = LinearRegressionModel(lags=12)\n",
+        "    auto_model.fit(train)  # autolog logs params, covariate metadata, and the model\n",
+        "\n",
+        "    auto_predictions = auto_model.predict(n=len(val))\n",
+        "    # these metric calls happen inside the run, so they are logged automatically\n",
+        "    auto_mape = darts.metrics.mape(val, auto_predictions)\n",
+        "    auto_rmse = darts.metrics.rmse(val, auto_predictions)\n",
+        "\n",
+        "autolog(disable=True)\n",
+        "\n",
+        "# show logged metrics\n",
+        "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
+        "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
+        "\n",
+        "# plot\n",
+        "fig, ax = plt.subplots(figsize=(10, 4))\n",
+        "train[-36:].plot(label=\"Train\", ax=ax)\n",
+        "val.plot(label=\"Actual\", ax=ax)\n",
+        "auto_predictions.plot(\n",
+        "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
+        ")\n",
+        "ax.set_title(\"Linear Regression — autolog run\")\n",
+        "ax.legend()\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f484330f",
+      "metadata": {},
+      "source": [
+        "## Open the MLflow UI\n",
+        "\n",
+        "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
+        "\n",
+        "The UI lets you:\n",
+        "- **Compare runs** side-by-side in the Experiments table\n",
+        "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
+        "- **Visualize** metrics across runs with built-in charts\n",
+        "- **Register** models to the Model Registry for versioning"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "254ef24eb5984c54bc7efcdf6b504f11",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "1a01cd2f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
+          "iopub.status.busy": "2026-06-24T15:18:50.021439Z",
+          "iopub.status.idle": "2026-06-24T15:18:50.023116Z",
+          "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Launch the MLflow UI with this command in your terminal:\n",
+            "\n",
+            "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpvwnlx3yj/mlflow.db\n",
+            "\n",
+            "Then open:  http://localhost:5000\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
+        "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
+        "print(\"Then open:  http://localhost:5000\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
-     ]
+      "cell_type": "markdown",
+      "id": "88b0d285",
+      "metadata": {},
+      "source": [
+        "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `val_mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
+        ">\n",
+        "![Mlflow Overview](./static/images/mlflow_overview.png)"
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1452432791254a928b131d34abb67463",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "cell_type": "markdown",
+      "id": "e2b133bc",
+      "metadata": {},
+      "source": [
+        "## Per-epoch Metrics with Torch Models\n",
+        "\n",
+        "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
+        "\n",
+        "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a3978f38e7984c4bac8deca33f4e4059",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "d01783d1",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
+          "iopub.status.busy": "2026-06-24T15:18:50.024087Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.370946Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "INFO: GPU available: True (mps), used: False\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
+            "INFO: TPU available: False, using: 0 TPU cores\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+            "INFO: HPU available: False, using: 0 HPUs\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+            "\n",
+            "  | Name            | Type             | Params | Mode \n",
+            "-------------------------------------------------------------\n",
+            "0 | criterion       | MSELoss          | 0      | train\n",
+            "1 | train_criterion | MSELoss          | 0      | train\n",
+            "2 | val_criterion   | MSELoss          | 0      | train\n",
+            "3 | train_metrics   | MetricCollection | 0      | train\n",
+            "4 | val_metrics     | MetricCollection | 0      | train\n",
+            "5 | stacks          | ModuleList       | 6.2 M  | train\n",
+            "-------------------------------------------------------------\n",
+            "6.2 M     Trainable params\n",
+            "1.4 K     Non-trainable params\n",
+            "6.2 M     Total params\n",
+            "24.787    Total estimated model params size (MB)\n",
+            "400       Modules in train mode\n",
+            "0         Modules in eval mode\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6d4bc0dd167b481b8c70ef05dfa0a2e2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
+            "  super().__init__(loader)\n",
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f5391e3cf5e64716b68c688111130ce8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Training: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a5a68c9f0add43f295c57a2d04fe9aa6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f884097f09674140afc79ed787ea826b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1b6497831ad84be1b5102ea3b558f8e3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ea362d1f26ef4e98bc7068f343f3bf57",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b500dfaafa2346b59c694ec4a0b00747",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4e97e25b82614beeacbd04f192751d58",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9ce17eda816e49bf85e0b9d91de5faa7",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "59b0fa19b0f04e93af190f84ff3feb45",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b246ce01f8cd4104b1412bd6c9a62b6b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2ecb7b3e01a040f4b76df8d9e5c5a26b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
+            "2026/06/25 13:57:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
+            "INFO: GPU available: True (mps), used: False\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
+            "INFO: TPU available: False, using: 0 TPU cores\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+            "INFO: HPU available: False, using: 0 HPUs\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "eaafc9c3e4344b6ca180de20246351b2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Predicting: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "NBEATS MAPE: 13.64%\n",
+            "NBEATS RMSE: 63.33\n"
+          ]
+        }
+      ],
+      "source": [
+        "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
+        "\n",
+        "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
+        "autolog()\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
+        "    nbeats = NBEATSModel(\n",
+        "        input_chunk_length=24,\n",
+        "        output_chunk_length=12,\n",
+        "        n_epochs=10,\n",
+        "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
+        "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
+        "        torch_metrics=MetricCollection({\n",
+        "            \"MAE\": MeanAbsoluteError(),\n",
+        "            \"MSE\": MeanSquaredError(),\n",
+        "        }),\n",
+        "        random_state=42,\n",
+        "    )\n",
+        "    nbeats.fit(train, val_series=val)\n",
+        "    nbeats_pred = nbeats.predict(n=len(val))\n",
+        "    # metric calls inside the run are logged automatically (keys: val_mape, val_rmse)\n",
+        "    nbeats_mape = darts.metrics.mape(val, nbeats_pred)\n",
+        "    nbeats_rmse = darts.metrics.rmse(val, nbeats_pred)\n",
+        "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
+        "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
+        "\n",
+        "autolog(disable=True)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce15173a5f3040d08d9b42827acd5a6a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "cell_type": "markdown",
+      "id": "6fcc8f13",
+      "metadata": {},
+      "source": [
+        "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
+        ">\n",
+        "![Mlflow Charts](./static/images/mlflow_charts.png)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7650d19876444051a760005979b86ea5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "cell_type": "markdown",
+      "id": "3f0828c2",
+      "metadata": {},
+      "source": [
+        "## Forecast Metrics\n",
+        "\n",
+        "With `log_metrics=True` (the default), `autolog()` patches every darts metric function. Any metric you call **inside an active run** is logged automatically with no extra arguments needed.\n",
+        "\n",
+        "The metric key is derived from the result: a scalar is logged under `{metric}`, and when the variable name of the actual series can be detected it is prefixed (e.g. calling `darts.metrics.mae(val, pred)` logs `val_mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7d222ce5ee5a452aa842e21280f053c7",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "df8e85b6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.372367Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.470125Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2026/06/25 13:57:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
+            "2026/06/25 13:57:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "All logged metrics (5):\n",
+            "  manual_mape: 10.7420\n",
+            "  val_mae: 46.0220\n",
+            "  val_mape: 10.7420\n",
+            "  val_rmse: 51.1820\n",
+            "  val_smape: 10.1015\n"
+          ]
+        }
+      ],
+      "source": [
+        "# log_metrics=True (the default) patches every darts metric so that calls made\n",
+        "# inside an active run are logged automatically\n",
+        "autolog(log_metrics=True)\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
+        "    lr_model = LinearRegressionModel(lags=12)\n",
+        "    lr_model.fit(train)\n",
+        "    lr_pred = lr_model.predict(n=len(val))\n",
+        "\n",
+        "    # each metric called here is auto-logged; the key is prefixed with the\n",
+        "    # variable name of the first argument (here \"val\"): val_mae, val_rmse, val_smape\n",
+        "    darts.metrics.mae(val, lr_pred)\n",
+        "    darts.metrics.rmse(val, lr_pred)\n",
+        "    darts.metrics.smape(val, lr_pred)\n",
+        "    # you can still log a custom-named metric explicitly\n",
+        "    mlflow.log_metric(\"manual_mape\", darts.metrics.mape(val, lr_pred))\n",
+        "    run_id = run.info.run_id\n",
+        "\n",
+        "autolog(disable=True)\n",
+        "\n",
+        "# show what was logged\n",
+        "client = mlflow.tracking.MlflowClient()\n",
+        "run_metrics = client.get_run(run_id).data.metrics\n",
+        "metric_names = sorted(run_metrics.keys())\n",
+        "print(f\"All logged metrics ({len(metric_names)}):\")\n",
+        "for name in metric_names:\n",
+        "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9092292262b14f3ba657d281e83718cd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "cell_type": "markdown",
+      "id": "b08e900a",
+      "metadata": {},
+      "source": [
+        "### Metric Shape and Per-Series Logging\n",
+        "\n",
+        "The logged key reflects the shape of the metric output, which `autolog()` infers from the metric and its keyword arguments. The general pattern is:\n",
+        "\n",
+        "`{dataset_name}_{metric_name}{component}{quantile_or_label}`\n",
+        "\n",
+        "Per-component results (`component_reduction=None`) add the component name (e.g. `val_mae_<component>`), quantile metrics (`q`, `q_interval`) add the quantile (e.g. `val_mql_q0.5`), per-label classification (`label_reduction=None`) adds the class (e.g. `val_f1_label1`), and per-timestep metrics (`time_reduction=None`) are charted across MLflow steps.\n",
+        "\n",
+        "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is saved as a CSV artifact under `per_series_metrics/`."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b32c477b2194b868114c8d3643541f7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "ecdbbf29",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Aggregate metrics: {'val_ae': 44.196, 'val_list_mae': 51.316}\n",
+            "Mean MAE over series: 51.316\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "712d3dc031d047f7976776824b40e5bb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Per-series breakdown (val_list_mae_per_series.csv):\n",
+            "  series 0: val_list_mae = 47.459\n",
+            "  series 1: val_list_mae = 55.173\n"
+          ]
+        }
+      ],
+      "source": [
+        "import csv\n",
+        "\n",
+        "import darts.metrics as dm\n",
+        "\n",
+        "# build a small multi-series example from the univariate AirPassengers data\n",
+        "series_list = [train, train * 1.2]\n",
+        "val_list = [val, val * 1.2]\n",
+        "\n",
+        "multi_model = LinearRegressionModel(lags=12)\n",
+        "multi_model.fit(series_list)\n",
+        "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
+        "\n",
+        "autolog(log_metrics=True)\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"metric-shape-and-csv\") as run:\n",
+        "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
+        "    # under a single key, charted across MLflow steps\n",
+        "    single_pred = multi_model.predict(n=len(val), series=train)\n",
+        "    dm.ae(val, single_pred)\n",
+        "\n",
+        "    # multiple series: the logged value is the MEAN over series, and the full\n",
+        "    # per-series breakdown is written to a CSV artifact\n",
+        "    per_series_mae = dm.mae(val_list, multi_preds)\n",
+        "\n",
+        "autolog(disable=True)\n",
+        "\n",
+        "client = mlflow.tracking.MlflowClient()\n",
+        "run_id = run.info.run_id\n",
+        "\n",
+        "# aggregate metrics: val_list_mae is the mean over the two series\n",
+        "logged = client.get_run(run_id).data.metrics\n",
+        "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
+        "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
+        "\n",
+        "# download and inspect the per-series CSV artifact\n",
+        "csv_path = mlflow.artifacts.download_artifacts(\n",
+        "    run_id=run_id, artifact_path=\"per_series_metrics/val_list_mae_per_series.csv\"\n",
+        ")\n",
+        "with open(csv_path) as f:\n",
+        "    rows = list(csv.DictReader(f))\n",
+        "\n",
+        "print(\"\\nPer-series breakdown (val_list_mae_per_series.csv):\")\n",
+        "for row in rows:\n",
+        "    print(f\"  series {row['series_index']}: {row['key']} = {float(row['value']):.3f}\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "20cc4938d0e94c04a72727cfc068b281",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "id": "8511fc08",
+      "metadata": {},
+      "source": [
+        "## Saving and Loading Models Locally\n",
+        "\n",
+        "You can also save and load models to/from local paths without MLflow runs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "645ef079",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.471178Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.478682Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2026/06/25 13:57:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Files in model directory:\n",
+            "  - python_env.yaml\n",
+            "  - requirements.txt\n",
+            "  - MLmodel\n",
+            "  - model.pkl\n",
+            "  - conda.yaml\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Save model to local directory\n",
+        "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
+        "save_model(model, path=local_model_path)\n",
+        "\n",
+        "print(\"\\nFiles in model directory:\")\n",
+        "for file in os.listdir(local_model_path):\n",
+        "    print(f\"  - {file}\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "367b8751ed914d37b845de06fb049a42",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "254ba153",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.479604Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.484973Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loaded model successfully!\n",
+            "Predictions shape: (5, 1)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load model from local directory\n",
+        "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
+        "\n",
+        "# Test it\n",
+        "local_predictions = local_loaded_model.predict(n=5)\n",
+        "print(\"Loaded model successfully!\")\n",
+        "print(f\"Predictions shape: {local_predictions.values().shape}\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
-      "2026/06/24 17:34:27 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
-      "INFO: GPU available: True (mps), used: False\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
-      "INFO: TPU available: False, using: 0 TPU cores\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-      "INFO: HPU available: False, using: 0 HPUs\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
-     ]
+      "cell_type": "markdown",
+      "id": "aab0d1e0",
+      "metadata": {},
+      "source": [
+        "## Querying Experiments\n",
+        "\n",
+        "You can programmatically query and compare runs."
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec9b9f91a32341eaa481b757ce4b5269",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "109a9812",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.485859Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.496380Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
+        }
       },
-      "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Found 5 runs in experiment 'darts-quickstart':\n",
+            "\n",
+            "1. exponential-smoothing-baseline\n",
+            "   Run ID: 22b3b299ce8d4d72b1646564a35b04ee\n",
+            "   Validation MAPE: 7.864181481214469\n",
+            "\n",
+            "2. linear-regression-full-metrics\n",
+            "   Run ID: 91ea26f4a5d448269c704a772c53f695\n",
+            "   Validation MAPE: 10.742044444678953\n",
+            "\n",
+            "3. linear-regression-autolog\n",
+            "   Run ID: eed13fa115304f6e853dfd4bf755211c\n",
+            "   Validation MAPE: 10.742044444678953\n",
+            "\n",
+            "4. nbeats-epoch-metrics\n",
+            "   Run ID: a1acc6779c7b4f158ac95f26bb108b74\n",
+            "   Validation MAPE: 13.639569217869525\n",
+            "\n",
+            "5. metric-shape-and-csv\n",
+            "   Run ID: 13a91160d03f414892139f7831ba509d\n",
+            "   Validation MAPE: N/A\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "from mlflow.tracking import MlflowClient\n",
+        "\n",
+        "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
+        "\n",
+        "# get all runs\n",
+        "client = MlflowClient()\n",
+        "runs = client.search_runs(\n",
+        "    experiment_ids=[experiment.experiment_id],\n",
+        "    order_by=[\"metrics.val_mape ASC\"],  # Sort by best MAPE\n",
+        ")\n",
+        "\n",
+        "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
+        "for i, run in enumerate(runs, 1):\n",
+        "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
+        "    mape_val = run.data.metrics.get(\"val_mape\", \"N/A\")\n",
+        "    print(f\"{i}. {run_name}\")\n",
+        "    print(f\"   Run ID: {run.info.run_id}\")\n",
+        "    print(f\"   Validation MAPE: {mape_val}\")\n",
+        "    print()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NBEATS MAPE: 13.64%\n",
-      "NBEATS RMSE: 63.33\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
-    "\n",
-    "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
-    "autolog()\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
-    "    nbeats = NBEATSModel(\n",
-    "        input_chunk_length=24,\n",
-    "        output_chunk_length=12,\n",
-    "        n_epochs=10,\n",
-    "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
-    "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
-    "        torch_metrics=MetricCollection({\n",
-    "            \"MAE\": MeanAbsoluteError(),\n",
-    "            \"MSE\": MeanSquaredError(),\n",
-    "        }),\n",
-    "        random_state=42,\n",
-    "    )\n",
-    "    nbeats.fit(train, val_series=val)\n",
-    "    nbeats_pred = nbeats.predict(n=len(val))\n",
-    "    # metric calls inside the run are logged automatically (keys: val_mape, val_rmse)\n",
-    "    nbeats_mape = darts.metrics.mape(val, nbeats_pred)\n",
-    "    nbeats_rmse = darts.metrics.rmse(val, nbeats_pred)\n",
-    "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
-    "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
-    "\n",
-    "autolog(disable=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6fcc8f13",
-   "metadata": {},
-   "source": [
-    "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
-    ">\n",
-    "![Mlflow Charts](./static/images/mlflow_charts.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f0828c2",
-   "metadata": {},
-   "source": [
-    "## Forecast Metrics\n",
-    "\n",
-    "With `log_metrics=True` (the default), `autolog()` patches every darts metric function. Any metric you call **inside an active run** is logged automatically with no extra arguments needed.\n",
-    "\n",
-    "The metric key is derived from the result: a scalar is logged under `{metric}`, and when the variable name of the actual series can be detected it is prefixed (e.g. calling `darts.metrics.mae(val, pred)` logs `val_mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "df8e85b6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.372367Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.470125Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/06/24 17:34:27 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
-      "2026/06/24 17:34:27 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-     ]
+      "cell_type": "markdown",
+      "id": "22685423",
+      "metadata": {},
+      "source": [
+        "### Load the Best Model"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All logged metrics (5):\n",
-      "  manual_mape: 10.7420\n",
-      "  val_mae: 46.0220\n",
-      "  val_mape: 10.7420\n",
-      "  val_rmse: 51.1820\n",
-      "  val_smape: 10.1015\n"
-     ]
-    }
-   ],
-   "source": [
-    "# log_metrics=True (the default) patches every darts metric so that calls made\n",
-    "# inside an active run are logged automatically\n",
-    "autolog(log_metrics=True)\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
-    "    lr_model = LinearRegressionModel(lags=12)\n",
-    "    lr_model.fit(train)\n",
-    "    lr_pred = lr_model.predict(n=len(val))\n",
-    "\n",
-    "    # each metric called here is auto-logged; the key is prefixed with the\n",
-    "    # variable name of the first argument (here \"val\"): val_mae, val_rmse, val_smape\n",
-    "    darts.metrics.mae(val, lr_pred)\n",
-    "    darts.metrics.rmse(val, lr_pred)\n",
-    "    darts.metrics.smape(val, lr_pred)\n",
-    "    # you can still log a custom-named metric explicitly\n",
-    "    mlflow.log_metric(\"manual_mape\", darts.metrics.mape(val, lr_pred))\n",
-    "    run_id = run.info.run_id\n",
-    "\n",
-    "autolog(disable=True)\n",
-    "\n",
-    "# show what was logged\n",
-    "client = mlflow.tracking.MlflowClient()\n",
-    "run_metrics = client.get_run(run_id).data.metrics\n",
-    "metric_names = sorted(run_metrics.keys())\n",
-    "print(f\"All logged metrics ({len(metric_names)}):\")\n",
-    "for name in metric_names:\n",
-    "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8511fc08",
-   "metadata": {},
-   "source": [
-    "## Saving and Loading Models Locally\n",
-    "\n",
-    "You can also save and load models to/from local paths without MLflow runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "645ef079",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.471178Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.478682Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "7b09db6a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.497496Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.572540Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading best model from run: exponential-smoothing-baseline\n",
+            "Model URI: models:/m-e61e8518e2a04143ace1d627ed4a775b\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
+        "\n",
+        "if runs:\n",
+        "    best_run = runs[0]\n",
+        "    # the model is logged via MLflow's logged-model API, so resolve its model_id\n",
+        "    # from the run outputs and load it with a models:/ URI\n",
+        "    model_outputs = mlflow.get_run(best_run.info.run_id).outputs.model_outputs\n",
+        "    best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
+        "\n",
+        "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
+        "    print(f\"Model URI: {best_model_uri}\")\n",
+        "\n",
+        "    best_model = load_model(best_model_uri)\n",
+        "    # global models (e.g. LinearRegression, NBEATS) need the series at predict time\n",
+        "    # because we save with clean=True; local models (e.g. ExponentialSmoothing) don't\n",
+        "    if isinstance(best_model, GlobalForecastingModel):\n",
+        "        best_predictions = best_model.predict(n=len(val), series=train)\n",
+        "    else:\n",
+        "        best_predictions = best_model.predict(n=len(val))\n",
+        "\n",
+        "    train[-50:].plot(label=\"Training\")\n",
+        "    val.plot(label=\"Actual\")\n",
+        "    best_predictions.plot(label=\"Best Model Forecast\")\n",
+        "    plt.legend()\n",
+        "    plt.title(\"Best Model Predictions\")\n",
+        "    plt.show()"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/06/24 17:34:27 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-     ]
+      "cell_type": "markdown",
+      "id": "6f1ec89e",
+      "metadata": {},
+      "source": [
+        "## Model Registry\n",
+        "\n",
+        "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived).\n",
+        "\n",
+        "```python\n",
+        "# Register a model from a run\n",
+        "result = mlflow.register_model(\n",
+        "    model_uri=f\"runs:/{best_run.info.run_id}/model\",\n",
+        "    name=\"darts-air-passengers\",\n",
+        ")\n",
+        "print(f\"Registered version: {result.version}\")\n",
+        "```\n",
+        "\n",
+        "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
+        "\n",
+        "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
+        ">\n",
+        "![Mlflow Charts](./static/images/mlflow_models.png)"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Files in model directory:\n",
-      "  - python_env.yaml\n",
-      "  - requirements.txt\n",
-      "  - MLmodel\n",
-      "  - model.pkl\n",
-      "  - conda.yaml\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Save model to local directory\n",
-    "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
-    "save_model(model, path=local_model_path)\n",
-    "\n",
-    "print(\"\\nFiles in model directory:\")\n",
-    "for file in os.listdir(local_model_path):\n",
-    "    print(f\"  - {file}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "254ba153",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.479604Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.484973Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "7af49ea9",
+      "metadata": {},
+      "source": [
+        "## Important Note: Custom Flavor\n",
+        "\n",
+        "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
+        "\n",
+        "**Always use:**\n",
+        "```python\n",
+        "from darts.utils.mlflow import load_model\n",
+        "model = load_model(model_uri)\n",
+        "```\n",
+        "\n",
+        "**Instead of:**\n",
+        "```python\n",
+        "import mlflow\n",
+        "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
+        "```\n",
+        "\n",
+        "This custom flavor is necessary to properly handle:\n",
+        "- TimeSeries objects\n",
+        "- Darts-specific model parameters\n",
+        "- Covariate handling (past, future, static)\n",
+        "- PyTorch model state preservation"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded model successfully!\n",
-      "Predictions shape: (5, 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load model from local directory\n",
-    "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
-    "\n",
-    "# Test it\n",
-    "local_predictions = local_loaded_model.predict(n=5)\n",
-    "print(\"Loaded model successfully!\")\n",
-    "print(f\"Predictions shape: {local_predictions.values().shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aab0d1e0",
-   "metadata": {},
-   "source": [
-    "## Querying Experiments\n",
-    "\n",
-    "You can programmatically query and compare runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "109a9812",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.485859Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.496380Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "40621b13",
+      "metadata": {},
+      "source": [
+        "## Cleanup"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 4 runs in experiment 'darts-quickstart':\n",
-      "\n",
-      "1. exponential-smoothing-baseline\n",
-      "   Run ID: 3d1f7f864f7240599e3b080109e246bf\n",
-      "   Validation MAPE: 7.864181481214469\n",
-      "\n",
-      "2. linear-regression-full-metrics\n",
-      "   Run ID: c655521973ca47b69ad3d423e5302759\n",
-      "   Validation MAPE: 10.742044444678953\n",
-      "\n",
-      "3. linear-regression-autolog\n",
-      "   Run ID: e4e4ae8b46004a5b8309337d5d58cbd2\n",
-      "   Validation MAPE: 10.742044444678953\n",
-      "\n",
-      "4. nbeats-epoch-metrics\n",
-      "   Run ID: d5a971cffab244e192839ae3ba1652e5\n",
-      "   Validation MAPE: 13.639569217869525\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from mlflow.tracking import MlflowClient\n",
-    "\n",
-    "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
-    "\n",
-    "# get all runs\n",
-    "client = MlflowClient()\n",
-    "runs = client.search_runs(\n",
-    "    experiment_ids=[experiment.experiment_id],\n",
-    "    order_by=[\"metrics.val_mape ASC\"],  # Sort by best MAPE\n",
-    ")\n",
-    "\n",
-    "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
-    "for i, run in enumerate(runs, 1):\n",
-    "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
-    "    mape_val = run.data.metrics.get(\"val_mape\", \"N/A\")\n",
-    "    print(f\"{i}. {run_name}\")\n",
-    "    print(f\"   Run ID: {run.info.run_id}\")\n",
-    "    print(f\"   Validation MAPE: {mape_val}\")\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22685423",
-   "metadata": {},
-   "source": [
-    "### Load the Best Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "7b09db6a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.497496Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.572540Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "51fc7c4a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.573637Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.575115Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpvwnlx3yj\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Uncomment to cleanup\n",
+        "# import shutil\n",
+        "# shutil.rmtree(tmpdir)\n",
+        "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
+        "\n",
+        "print(f\"To cleanup manually, delete: {tmpdir}\")"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading best model from run: exponential-smoothing-baseline\n",
-      "Model URI: models:/m-f42705a59eb34300b377f0219577bfa4\n"
-     ]
+      "cell_type": "markdown",
+      "id": "ca40afc1",
+      "metadata": {},
+      "source": [
+        "# Final Remarks"
+      ]
     },
     {
-     "data": {
-      "image/png": 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Zs9Wv2bNnj9j+qKgomjBhAu3evZvKyspo+vTprTxCDMMwjDepqrbQS4uIMlKIpk80kd5gUaJjIArWrl1LISEhdNJJJ1GfPn2aPGf79u306KOP0owZM+iZZ55xmr7Be61bt47Cw8PphBNOEMLhzjvvpN9//52GDBniMH3T3GuGDRsmnrNixQqaNGkSjR8/nvr27UtPPvkkhYaG0uDBg1mUMAzD6Iw3vye6Za5FLLeLI5o8Ul/ChEWJzsnLy6MdO3ZQcnKy+thxxx0nbpKbb75ZCJbrrruOevTo4fS9EL1Amig+Pl7cnzJlCs2bN4/mz5/v9msWLFgg7t922230n//8h9555x1x/9ZbbxVeGIgShmEYRl9s2K0IEvDo2xYWJf4EaY3s7OwmjyMagKt5X5GWlkb//POPV94LUQitIJGsX79eRCxycnKEdwSpkw0bNjQrSiZOnKiKCzB06FD6999/m/3/zb2mvLxcbMPDDz+sru/cubONYGIYhmH0Q06hdfmXNUR/rLfQ2EH6iZYEdaQEguTAgQNkZKS/RAtEwP/+9z869dRTqVOnThQRESFSPIWFmrPNAbGxsTb3w8LCqLa21uPXHDp0SPzt0KFDE1HW0rYwDMMw/ienyPb+7Hct9NUcFiV+AYOjI/wRKfEV2PbZs2cLv8cpp5wiHoN35LHHHiN/07FjR/H38OHDNGDAABsxGBkZ6fftYRiGYZrncIHt/a//IFq300KDe+hDmAR1pMRRCgWpjqysLMrMzBTRBaOBKEVNTY1I10gWLlzYYsTDF8TExIiqnzfeeEOkeQAiU0uXLhXGXIZhGEbfkRIw510LvfcAixLGAyBGLrzwQnFDY7WDBw/SsmXLmqRZ/MVTTz0lfC8w5MLg+tVXX1FGRoYhBR/DMEwwU1dnofxiZXlQd6LsAqLcIqIPfyJ65FIL9cgIvDAJ6kiJkUA57axZs2wap6HPh6NGaq+99hp9+eWXtGnTJurfvz+9+OKLIloBE6qz5mmO3gvRDW3PEfvmaa68ZvTo0bRx40b6/PPPhWBCH5UHHnhA9E9hGIZh9EOuJkrSNY3o7Ikmuu9VC2GoePJ9C718W+BFiclisVjrg9oARk/f6O2YwD8C4SIrdHAfAuu5556jiy66iIwKnyd8XPhc4e9PsP2mrN1hoaGXKEP+ZVOJnrzaRF2mWai0giginGj3hyZKTwmsMOFRmWkVKAseM2YMXXXVVXTttdeKpmqInng6Zw/DMAzj+3Lg1HZEiXEmuuZ05X5NLdEzHwU+RsGihGkV6IsCY+uoUaNEWuett96i77//XnSBZRiGYfTDYa0oSVQiIjdNM1FkhPLYy4uICkoCK0zYU8J4pQT64osv5iPJMAxjkEhJhyTlb1qyiS49yULzviAqqySa+xnRAzMDtokcKWEYhmGYtkBOoTUKkppoffz2c00kW3e98KmF6ustxknf/Pjjj3TuuefS5MmTxeRr6JkhqayspAcffFCUiKKSAxUiWlpazzAMwzCMH9I3mgLJrh1NNGm4spxXTJRfQsZI3/zxxx+iL8X9999PAwcOFF4CNCgbO3asWP/ss8/Svn376O233xbT2WNG2a5du6qTs7W0nmEYhmEY/xhdtaRrZjSBMLFfr0tR8vLLL9M111xDRx11lLh/5plnquvQ6vy7774Tc7Kkp6eLGyZmQzMtiI6W1jMMwzAM4/turqhITrbOsyrQ3s9z0PVVd+kbpGkwhT2mssecKyeffLIQGDJ9g8nZKioqRI8KSZ8+fWjHjh0urWcYhmEYxvfz3qQkEIWG2vYjSUkw2URKAoXLkZKcnBzR/AUpm5deeknMtYL0C9qbX3HFFaJfhZwPRRIXF6c+3tJ6R0DwaD0rYoPDwsSsuJ4iu5xqu522dVo6JuvWraPU1FSfTjSoN/g84ePC5wp/f4LpN8VisaZvYHK1344kTaQktxhdXr1vdnWlaZzLokTO+oo5Vzp16iSWYXhFe3GIErPZLB5DNEQKDwgO2aa8pfWOeP3112nBggU2j02bNk20P28t8LboDQz+ODbo/eEumzdvppSUFGrfvr3Xj8nZZ58tjvkll1xCbQ09nid6gI8LHxM+T4z13SmtNFF1bRexHG+upKysHJv1lhqM0alieceeQsrK8r7btVu3bt4TJRjwEhISbJQOlmWXekzCBuGyfft2dQ4WLMuNaGm9I9D7YsaMGV6PlOCk6Ny5s67azG/YsEHMV4M5Y/bv328zC7ArnHjiiaKjKm7ePiZohJaUlCTaI7cV9HqeBBo+LnxM+Dwx5ndn+37rcpeO5ia/5/00KZv6kHaUmRkYp6vLosRkMgkfybvvvqsaVz/66CMaP3688kZhYaJMGJGN2bNnix7/P/zwAz399NMurXcExEdrBEhz4KTQ02CDqBCOD6IlX3zxhdM27fDmYEZe+HHkscHEfFVVVeKYrly5UjyGVu9///236LKqnRwP4gcCA0ZjGWEpLCykw4cPC4GJaihHxwWfv56Ol7/Q23miF/i48DHh88RY3528YgQQlCBCh3ZNUyntE63rMZNwoH733PqvqLzp0KGDECdIowwYMMCmk+ett94qoiEYXG+88UaR1hkxYoTL69sq8M2gTBrHd+bMmfTqq682ec7BgwfVGXrPOecc6tKlixAvAJPfQVR8+umndNNNN4lbfX09HXvssfTLL7/YvA/eH63gJfAH3XLLLfTQQw+JqiqInVWrVvlhrxmGYZjAdHM1NVmfommmZgijK0BK4eGHHxYDGK6c7YFxFRU5CFM5UlktrW+rwJeDqMfUqVNp0KBBoindzp07VW8JIhhI7cBUfODAAUpMTBTRDdl8bv78+UJ8XHfddeLmDs8//7w6eyXCivfdd58QmmvXrvXJvjIMwzCBnveGmtAuFhFxxRBrGFEicSRItLQkOPwlSEZc3kDZjSVQKhai+voMpaWuyTcu6LQkon8WuL6PCxcuFEIAKS4IEUREXnvtNXr00UfFejSoQyoGqR0IEoCUzEUXXeSV7a2urqZdu3ZRdnY2jRw5kh5//HEherRpH4ZhGCZIGqdF1FJDTRiFRFjHqbAwE7WLs1BBiYE6uhoNCJIDufrebUQoUGYNUbJixQrVDwJRgqhUaGioMATjb//+/b3+/+fNm0f33HOPiMJ07NhRLRODd4VFCcMwTHDNe9OnoohCL/qXlphDqM99vajLRZ3J1NizBP1LIEoMFykxCohYNEFESuooNDSMyOTH/+sEiA+U8cIXoqW0tFR0wEVKB2XT8Ihg7iCIB3ciWrI6SoL+MlpBhHTPt99+K7wkcGNDAKHBHfdxYRiGCb70zcSiQ0Q1DVRX00Abb99M+987QAOf6k8JQxPUrq7FZUS1dRYKD/PRINlWRYmjFIrinzggBuBA+1qwLai6eeyxx5r0ALn++uuF4RWiZMyYMaIsF8bW888/X30OKm5k6TD6wGgFB4ApGeXFEqRktB10d+/eLYQL0kXwqoDvv//eZ/vLMAzDBDZ906Oq1Obx4tUl9PvkFZR5SRdKj+qOJhBqBU5asv+3M6hFid5ZvHixEANo228PjK1TpkwRPg90UoUBFdU5uI8+Lyj9xaSGr7zying+JkiEaBk+fLiocEIKCO8B0yx6xEDUYBkRF8mQIUNE7xn0NjnmmGPok08+EQKJYRiGCT5REmJpoO7ViiiJSI2giKQIKttSRtRAlPXqXjovOpv2pfSnlXHtRQonEKKES2ACCMyrMKs66sI6YcIEUaK7fPlycf+BBx6gN954g/7880+aM2eOSOU888wz6vPxWL9+/WjWrFlqSTCMspdffrko+/3ggw+EsEEXXogUAM8I/CxIFSF9tHHjRlq0aBGNGjXKptMuxAv8JgzDMIxx0zedqisostE3mDSmHR21bAz1mdWbQqNR+UEUVVFDd+5bR2ENDQEzu5os9qaDIEeWv+ohfaMX+JjwMeFzhb8//JsSvL+zNbUWipxkoWOLDtFtBzaIx/rc34t63IR0DVHl/kr69/zVVLJeiaJc2msczZ0TQ2ce439PCY/KDMMwDBPE5DVW0/SosoY/4odYZ+AzdzJT8gRrrqZ9bVXAKnBYlDAMwzBMEHO4sV9Xj0qryTV+kGZaYAiTDOt8ayxKGIZhGIbxCTlFwquhVt5EpUdRZIrtvHJRTURJYJwdHClhGIZhmCCvvEmrqaSYhjpxP35wXJPnIIUjSeX0DcMwDMMwvkrfaPuTJGj8JBJzJ9tICfqUBAKOlDAMwzBMEJNTZLE1uQ5uKkrCk8IpxKxIAvaUMAzDMAzjs/RNcyZXgO7e5gyzVZQUsaeEYRiGYRgvc7jAQj0bIyVhSeEUlR7p8HkyhWNuqKfKAsV/4m84fcMwDMMwQUzVwWpKqFfmRkscEi+iIo6I0vhKoourRNM1f8OihPE7Dz30EJ111lluveaOO+6gGTNm+GybGIZhgpXoAyXNmlwdmV1T6gJjdmVREmAwP01iYqJ6Qwvik08+mVasWOHV/3PLLbeIeXaaA5PxYRvOOOOMJuswYzHWjR07ttXbgnl7ysrK3HpNRUUFlZeXu3wc5e2zzz4jo4OZpDHRIsMwjLtgJpnkXI2fpDlR0ugpAe1rAtPVlWcJDjAYoJOSkmjVqlXifkFBgRAHkydPpl27djmcrM8TWhrU5bZghuFvvvmG9u/fT506dVLXvfzyyxQTE0MlJQGapcnN4yjBNhud6upq3R53hmH0TXEZUbeKkmZNro4aqAWqVwlHSnQAJmeSV/bdu3enu+66S0QSNmxQJk6SfPHFFzRu3DhKS0ujkSNH0iuvvCJUsGT9+vU0depU6ty5Mw0dOpSeeOIJdbZgXG1/+eWX6v/55ZdfHG4LRNAJJ5wgZiSWrF27lrZt2+YwgjJv3jwaPHgwdejQQcxq/MMPPzR5zuzZs6lHjx7Uu3dvuvLKK8WsxPa0tG/uHkd5Cw8PF+t2795N06ZNo/T0dOrWrRvdcMMNNtGavXv3iue///77Yj+wHRBncjbnk046ScyUPGjQILr//vuFUNCC5+DY4/0R1XjzzTfVdYsXL1a3B+snTpxIy5Yts3l9dnY2XXjhhWLb+vTpI2Z6xnHCZ4YoF7ZfvsfcuXPdOi4Mw7Ttbq49GitvaiLCKLqrNRrSfK+SShYlDFFdXR29++67FBcXJwZAyQcffCAG9Ntuu01EA+bMmUOPPPIIvfTSS+pzTj31VDH4//777/Tee+9RcXExff/993TrrbcKP8aJJ55Ie/bsETcIAGdceumlQsRIUYDUzTnnnNMk6oDHIaDuu+8+WrlypUg7YfCGiJFg+5588kn63//+JwRLu3bthJDR4sq+tYaamho67rjjhED7+eef6cMPP6SlS5fSzJkzbWbwxPG65557hOdl06ZNdPzxx9OaNWto0qRJdMopp4h9hNjAflx//fXqa//9918aP3489evXT4iN1157TfwfzAgK8Hp53PEe//nPf8RnsXPnTvU9rrrqKsrPzxefF249e/YUxxfPg6hEWk++x+WXX+6V48IwTPBzaGc1pdQpF1Fl6XFOTa6y/bwkUL1Kgjp989vEP6kmx/aKFsMsBqddoVnkq0mZI1Ij6aifxrj8fHkVDHD1HhsbKwbOlJQU9Tm4OsdAjQEN4Iobjz3//PN0zTXXUFVVlRiwzjvvPOrSpYt4DgYzdZsiIkTUQP6f5oC4wCCJAXbMmDFCJH333Xf0+eef2zwP7w/BM336dHH/7rvvFoMxRAUiDuDxxx8XJtXTTjtN3Me6b7/91uZ9Wto3T44jQPRm69atYlvy8vKEoIDYAwsWLBDCbMuWLdS3b1+bfYKIgEhBpAL3zz//fLr66qvFekShXnjhBeGtwV+ku/773//ShAkThPiSaCNNYWFh6nbhLwQNIiCffPIJ3XnnneJxbCeiI4iSgOuuu059vdlsVqNADMMw7lCwupRkAXB9V+epGxBqDiVLQgSZimuEKNnHosS7QJBUHbIVJZI6CkwNtiNwFSy9EBgIIQIw0C9fvlykYXAFvWPHDjG440oeEQzcEAGAwAJRUVHiNWeeeaYwtB577LF09NFHCzHiLhhEkUrAFf/BgweFSBg1apSNKIF4chRxQcTgo48+Up+DaAGEjRbcl1EEV/bNk+MIMJDLtBaiTlKQgNGjRwuRtnHjRhtRMmTIEJv3RGSjsLBQ7JPcNmwXbth/iAg8B+kgZ0Awwie0aNEicTxra2uFx6dXr17qc3C8cQywrYjqQBhpt5dhGMYTKjaVqKIkvG/zogSEpUVRfXENJdVV05qCer/HLoI6UoKIhT0yUhIaGurTSIk7aK+C8RcpkXfeeUd4BxDCxyAGkFKB2NCiDcUhDfLjjz8KD8ONN95IRUVFYiAcMWKE2/twySWX0LBhw8QVPJYdpZmkgNGC+3Kd/Itjbf8ciav75grOognYDvvtxHvj+XIbJYh8aMH2QSw4Eh3x8coXHFEV6V1xBMQWUj6IrMBXgzQY0mkQXhJEmWBu/uqrr4QX6IILLqCFCxeqUSiGYRhPsOzQlAM7mIjPUa+S8q0lhF/t8oO4qGdR4jUcpVAwgOAqHVfV8kpaj2BwxNW0TEOkpqaqfgRnYKCFDwK3p59+WqRh8BfpCwzK2HdXQQQAhk38T2n41ILBH9sEP8UxxxyjPv7PP/+oKQg8B8ZZ+DJgHpWsXr1aHdBd3bfWgO1B9AnmVCk6YCLGfbmtzkCE5c8//6QHHnigxec4Aymtyy67TBVd+Bw2b94sjLNaIB5xg6cFQmbWrFlClLj72TEM0zw//Wuht3+w0A1nmmhYb19dnuqDyL2KybXKFEKdBrRcjRiXGUWyTrM2uwo1jORP9Dsqt1Ew+CDigYEbplEpNjBIPffcc0Jg4AobqRFcVcN7AVDCCx8IqmSQYsjJybEp64UXAuvsq0aaY8mSJSK94qwsGVUhTz31lBAiiD7BB4PIzM0336w+B/4J+ErWrVsnohIwr2oHcFf2rbXAEwIBevvtt4vSYRwbbBfEFNJjzQHPB6Ic8JagpBrb98cff9iYTfG+SG1h35CqQbrnwQcfFMcfoKIGPhrsF4Qmng//ixZERlDBg2OE/4PPSvvZoTonNzfXK8eDYdoyW/daaOpdFnrjO6LrnwvM/C7+ora4lmKKKsXy7qg46pDSsgBL7KapzsmFKPEvLEp0gLbcMzo6WoTyn3nmGTGYSpCOQdTj3nvvFeF/DFjwfKAqBuCqG+kWVODgPVCFgwEXV9sAPhO8LiEhodmSYC3wqTTna4DJFYMp/A94Lu7Pnz9fmD61gzpKjBEBQOUNBm/7zqwt7VtrwT58/fXXQgwhQoP3x2OoUGoJlO9CIH366afitcnJyWKfzj33XPU5SLtAkEFYwaSM6AtSVjISAuGGPiPYf7weVTfwjWjBvsLcis8HIhACBmZcgOOL6BfECZcEM4zn1NVZ6MJHLVTZeG2262BwH82S9db2CzvM8ZTaruXXaMuCwwv8L0pMFnebQRgcvaVvcGWNmwSCoiVzKp6PNIQzz4U2TWEPPBK4EsfgKX0W8pgglYKIhzMhgv+L19uvxymECAS23RmIAGB7MVjjffB/HDU2c7ZveH9sp7NmaM62zdGxwTbYe0zw3hAOEB44LxydJ4iS4LX2Hhn77YBAc/a/8X/xekRMxKycZtueAdgHPMfRZysrgvD+zj7ftvb90QN8TIxzTB5500IPLLQOeeFhRNVLTW7714xyTHbN20Nb7t8qlud37U+L/u3c4muK/i2iP47/Syz/0L4TPbtlAPmToDa6GgEMMM4GseZe0xzNDVjNlQXjfZv7sjjbVnyhmxMkQCsCmtt+Z+vsB29Xt83VY+NKya0rlUzNbYP2fzs7Xs0ZZrGNiKQwDOM+/2610MNv2F6D19YRlZQTJcQG5xEtWWs1uRanuVbNZ+5k/a1NrKyiqmoLRUX6z3ejHwnLMAzDMD6gstpC5z9iobrGLgORmuuLQDQI8xfFjaKk1mSi+k6uKa+I9hFUH6KIEPQqyffzDBcsShiGYZig5u75FtqyV1ke3pvo4hODX5TUlddR+U6ljiYrMpZSUlwb7k0hJqqMjwpYV1cWJQzDMExQUp1TTT/ctIP+eD0X5jcRIXn7PhN1TDYFvSgp3VRG1NhJYEdUPKW60RC6LkkRJbENdZR7UOkl5S/YU8IwDMMEJevu3Up1nx2iB9FDKTaZku/oR/27xlBKgtVbkldEQUnFHqXPFdgXGUODktx4cXsz0a5CsVi0q4roaPc7g3sKixKGYRgmKNm3tJCk9XxEWT6FPPoHba/oRimDuqqJgtwgFSWV+6xVnTkRUZSa6LpZNbyj1bBfuhfv03J7em/BooRhGIYJOmoKaiiq2LbPRkN1A22fs5PiMg7R0LC+tCY2mfKKETUJvq6ulQeUpmkgJzzKpR4ljnqVVB7wb68S9pQwDMMwQUfJOmvjsGXt06nbdV3JFKqID8uBCno0axVNy90dtJ6SKk2kJDfcTB3cSN8kdLWKknrRat5/sChhGIZhgo7itVa1kZ3Rjvo91IfGLRtD7UZZHZ/HFx0IWlFSuV+JlFSbQqg4NNwto2tyT2uvElMeixKGYRiGaRV5q6yRktouSuOw+P5xNPrrIymmV4xa8ppbGHxNzS3osr1fERO54VHocOlW+qZDb2ujx4hCaxrIH3CkhGEYhgk6iteVqJGCyO4xNn04orsqkYBwi4Wqc2so2KgtrKX68nrVTxIWStTOtYaugqTUMCoJVbpLx5RypIRhGIZhPB+US+qobq9SErsnKpY6ptpef5szNOmJAMyE62sqG6MkMlLSPhHTVLhu5sXUIYWNU2bEV1aTpd5/0SSOlDAMwzBBRelGzey4UfE2zdJAlKa6BBU6mD04mKjcp628MbuVupGUxSrHKBTRpMON0yr7ARYlDMMwTFCmbsDOqDhKT3Fe8ppSU0UFVg0TFFRpIyURUdTBA1FSlWA9RoW7/OcrYVHCMAzDBO3suDvNcdQx2flMuIGY38VflTee9CiR1De2mgd5O/yX4mJRwjAMwwRlpKSOTGIyuvQmosQ64KZClBQFr6ckB+kbN8qBJaYO1mNUvIdFCcMwDMO4TX1lPZVvU2bH3RsVQ7UhoU0iJZFpkWRptJm0r60MvkjJPiVSgvn48sMiKT3F/Y61kelWUVImWs37B46UMAzDMEFD6aZStVoEJleUwkZF2g7KIeEh1NAu0tqrJNgiJQcUEVEQFkl1ISGUmeb+e0R3tqa4qjUt630NixKGYRgmaCjWtJd3ZHKVmFKVQTexvpbyc5WeHsFAfVU91eTUWBunEVFmB/ffJ7FzpEh/gYbDHClhGIZhGLcp0VbemFEO3HJ6olxjDA0uP0mU+NvFA1GS0s5E+eFKNCkkn0UJwzAMw7hNcWPlDfwUux2YXCUxna2ipPpgVXCWA4ebKSqCPKq+SUmwRlrCK2qprryO/AGnbxiGYZigoKG2gco2K+mbAxExVBUa5jRSktjNKkr8mZ7wazlwRJSIkqBDq7ukJFpFCahq9Kn4GhYlDMMwTFBQtrWMGmoaTa5mZbIXZ5UnST2sA64/0xP+Tt9kepC6Acnx1vSPeF8WJQzDMAzjOsVrrSbXXVGKKHEWKdFWl0QVVQVpi/kojypvQHwMUUEkR0oYhmEYptUmV5QDA2fVN9r5b2LLgtVTEkVdOrifugFI+VQnaiIlmvf1JZy+YRiGYYJOlOwyNx8pCU8Mp5qwULGcVF1FldWWoPKUlIWEUUVouMfpG9CQbHboVfElLEoYhmEYw4OGaSWNswMXxpipLDS8WVGCSEB5XJS1gVqh8UWJpcGiej+kH8TT9A0ITdOUTWexKGEYhmEYlyjfWU715UoTtD0xSpQkMZbIbNfNVUtNY3oi0tJAuXtrDX+kqw9Xk6XW0urGaZK4lDARcQHlfmo1z5EShmEYJmgm4QObQ2XlTfOvsbS3RgLy/TgTrl8qbyLMFBJClNHe8/dDr5LsCCWFU3uoSpRc+xoWJQzDMIzhKdG0l98S3rzJVRKmnQk3KxhESaW6jEhJRgpReJhnRld7UUL1Fr+YXVmUMAzDMEHWXr55k6skKsMqSio0pbRGpXKfbY8ST9rLa0lJMNGhiGj1fsWeCvI1LEoYhmEYQ2OxWKzpm+RIKgpT5mxx1mJeEpdprS5BesLoVNlFSlrjJ5FdXQ+FW49RxW4WJQzDMAzTLJV7K6muWJmbpaaLEiUBHZObT120626NlFBuVfB1c01r3fshfWMbKfF9NIkjJQzDMEEUMWjLk/CBog6Kn8QVT0lqL6soCS8IBlFSKf7WmkxUGBZJmR42TtO2mj8kPSWcvmEYhmFcZcdTO+nHbj/R2mvXU3VudZs6cGVbytTlg/Gx6nJLnpIOqSFUEBYhls0lweMpyQuLIovJ5JVISX54lBA5gD0lDMMwTIs01DXQjmd3UV1pHR344CD9Muo3ynp9n2go1hbQGjz3hkS7HCmJjiLKi1CiJdGVNdRQ4/uSV19RW1JLdSV1XutRIj0lDSYT5TT6SpC+8XU0jtM3DMMwBqd8ezk1VFoHVPgrNt62if6Y8pdNaiNY0U5Ct6M2yuVICbq6lsZEqYNhlYHNrpU2PUqUfWpt9U2smSgywprCQXO6mtwa8iUsShiGYQyOVnhEd7dGCopXFdNvx/1JC6Zuopxs5So6GJGt1cPiwmhPabhL3VzV18YHR1lwpc3swGZKTiCKMbfOUwLR1ivDzuzq43bzbomSO++8k8aPH6/eTjrpJJv1VVVV9Mgjj9Dxxx9PZ5xxBn399ddurWcYhmFa16NjwBP9aPRXIym2T4y4b2ogyvhzH316za6gPLSY70WWwmLm34N5rqVuJPVJVlFSuKsqaGYHzmxllETSrytRth/LgpWm9i5SXV1NN910E5144omqitLy3HPP0e7du2nhwoW0Z88euueee6hr1640cOBAl9YzDMMw7lOs6WaaMDieIpIjaNyyMXT3CXvomHU7lN/rPdbnBBNIJzTUKD6H8I5RVHnYtdSNSqpVlBTsrAqSSEkUdfOSKOmfSbTYjxU4bqdvIiIiKDo6WtzMZuuG1tXV0bfffkvXXHMNde7cWURSJk6cSIsWLXJpPcMwDONZpKB0fYnaoRSCBHz7dwg9VddVrZyIKjbugOvqYFyniXq4GimBkJGUGbjVfGVjCstbPUok/TLturru1lH6BsydO5cmT55Ml156Ka1YsUJ9PDs7m8rLy6lv377qY/3796edO3e6tJ5hGIZxH4TT68rq1SgJqK+30F3zLaIsFOWhILbMuAOuqwbPigTrhXLHJNdeH93J+pqqA0b2lFSpy3kifdM6P4lN+saPkRK30jePP/441dfXU0VFBS1ZsoRuueUWevPNN6lXr15UVqbUicfEKHlMEBsbqz7e0npH1NTUiJvNBoeFiWiNpzQ0NNj8ZfiY8HnC35/WEOjflKI1xepy3OBYsR2vf0e0aY/VX9CxtpKi6+qoprhGmEGD6ZhU7LMOkoVRSnt5mb5x5f8nZIRTjSmEIiwNVHe4ymfb7OtjUtnoqykMjaCakFDqlGqhhobWl+/2TCeqDQsV/VyS6mqEKPF0H0IwbXELuHV2RkYqHzhSN+ecc46IlPz8889ClMhUDgSLFB6IjMjHW1rviNdff50WLFhg89i0adNo+vTp1Fr27dvX6vcINviY8DHhc8V435+c33LV5aq0KtqybS/dvyBd/XkXA3XjuL1z5S6K6mkduIPhmORssu7/riprhVFYQy5lZblwVV8brcyoW1NBptxK4Xe090vq/ZhYai1UnV1tUw4c0XCIsrK8U77buX06HdoZLURJdU4N7d6ym0LM7hfvduvWrcXntEoyh4eHi8gJSE9PF6Jlx44dNGTIEPEYlrt37+7SekdcfPHFNGPGDK9HSnBSwNfiimprC/Ax4WPC54pxvz85WXnWq9pJPei5n6Iou1C5P3UsUeQKM1GBcj+yMokyM100WxjkmOSXFFoNv/Ht1eXBfdtTZmbLr+9bQPRX+F4hSsJqGiijXQaFJyhlxUY5JhVZlbTFst2mcdrooR1F8zNvMKgH0aF/zTSAisT9lIYUisu0zjHkTVwWJcXFxaJq5sILL6TExET69ddf6ffff6cLLrhAFSiTJk0Sz3nsscdo7969tHjxYnrqqadcWu8IiI/WCJDmwEnBooSPCZ8n/P0x8m8KumuWrFeqaiLaR1BFTBQ9/r7cHqI5V5rogx22Ja/d/biN/jgm0lNiCjNRVrU1CpTR3kQhIS1HPFITLcIYKqk+WEOR7SINdUyqD1inFcC+oFNt+3Ymr0V8BnRtoL0aX0llVhUlDEyggIqS+Ph46tSpE1100UVUUFAgIh8PPPAADR48WH3ObbfdRvfff78QH1FRUcIMO3LkSJfXMwzDMK5TdaCKagtqVZPr7HeIihttejOnEA3oZqLQNOuAW7on+Myuao+SjCg6WGgdhF0tCcb8LjK6IL0Z8QN8EwXwRwVSbrhZ9CjxZgoKFTh/+amBmsuiBDsILwduSNmEhoY6FC7oRYLyX6y3PygtrWcYhmE86+Rq6R5Hcz9XlqMiiB66RPl9jczQdCzVVKoEA5jrp7ZI8ZGY0Tgtn9RurtFRro0vSHGgWsVREzKjlgN38VKPEkm/TLsKHB82UPMohuRIkNj7PpoTHC2tZxiGYdzr5LooO45qlKAJ3TSNqFOq8hsb29k6mNRmG2/AdXUwhig5lO9m4zRYC8JMVK5pNa8tMTZq47RMb4uSrnat5n1YFsxOT4ZhmCDo5Pr+nng1SnDnedaLvqTUMCoJbTRu5hpvwHV1MDalmqmiyr3GaZKGZI0oMWCvkir7FvNp3r3oj4s2UVxaOFWEhPq8gRqLEoZhGINHSkLjw2h3vTKwjupPlBhnHZQwMVteuGLcDC2sIku9b6ee9yfaqEZVouuzA9sTomk1r21CFihqS+po25wddPDTQ8LM7Ko4qwwJpdLQcK9HSkC/riY1hYOJC311HrEoYRiGMSDVh6vV3hSmHnEw/onlHmhR4sTIGVJvoeoca6VGMEVKis2aFvNuipJ2KaFU3BhNKteBKMl6JYt2PLmT1lyxjrY9uqNZYVJ1qEoVZ+JzNpm81mK+ia8kvDGFU2exSZ15ExYlDMMwBqS4cb4bUJqupG5Ajwzb0H1yvDKVvZE9E81VH0lyIzUt5pPdS19ohVsturrWBbbjd9Fqa5fenc/som2PORYm5bvK6c+TV1JDtbK9O6OUqiFfREr6d8UcOL43u7IoYRiGMSAlmsqbgwkaUWIXKUm2K3nVDuTBFCk5aHJ/Mj6tKFF7lTQoUahAUrHLdsDf+b9dtH22rTAp2VAiBEllY3luQXQUvZXak1CH4m76ytVIiY0o8ZHZlUUJwzCMASnWVN5sibD21ehuJ0riookKIm37cAQLMuoTkRJBB0qsw5m7g3JKgsmuV0nghJul3qIO+KHR1krXHU/vou1zFGFSsKKQVpzyN9XkKG3kY/vG0gN9R1JOhJk6tUeFq/erW5tW4PjmPGJRwjAMY0BkJ1cMXOsqop2KErRfqGmnjwHXmzTUNgg/hX05sKeREptoUgCPUdWhKmqoUSIiKROSqf+cvuq6HU/torVXraeVZ/1DdSVKf5bEEQk08KORtLs6ymepG9A+0UQ1yRwpYRiGYeyoLapVw/bxA+NoxyHlyjgtiSjG3PQq2ZKijwHXm1QdqhapFhCFxmnWKYDcjpS0T1Q6oeohmlSuSd1Ed4+mrpdnUv/ZVmFy8JND1FCp7HjKscl05Gcj6FCNda4eX5hcJe17RVE9KedX6U5O3zAMwzB2qRtz/zjKLnAcJZGEt49UB5PyIEnfyPbywNzZrEZKEtzo5mobKYnURVlwhcZAGt1NiYB1vSKT+j1mFSag4+lpNOK94RQWE0ZZ2dbHfRUpAX26hajeG6RvXClXdhdO3zAMwxi4k2tVZ23ljePnJyWa1F4lwZK+0QoHMe9NvmflwLLV/GGtiTPLdx1L3RElMY2iBHS7MpMGPTuAYnvHUI+bu9PQVwZTSIQyhGcdtr7e243TnFXgWMrrqLawsYVwIOa+YRiGYfTlJwGHk7SVNyangy48Ex1qq6i+sJbqK+ptTJRGxCbFkhqldnP1pPIEkZKi0AjRfMzcUO8zE6crlO+0Td9o6XxBJ3GzJyvbGrHokuq7bUMFzpcQJeVWARWRFOHV/8GREoZhGINOxBcSYaKdYTHq487SN+hVYlNdEgRlwdqIT2m02WOTq2zNHxpmouxGX0nlXt91LG2J8sZICT5bs2YyxeawjZT4astkWbD7FThl28pExZArsChhGIYxEHVldVS+Q7lUje0XR7sOm5z2KJEkx9uXvBrfV6Ldh/woz1vMg5AQkxBuamrChx1Lm8PSYC0HNmdGkynUtVSM1lPi7RmCtXRKJSqKdb+B2t639tOKk1e69FwWJQzDMAaiZGMpUeNFfMKQeNp5kFr0lARjAzXpKQkxh9hUn6S72c1Vm8LJ1kYBfNSxtDmqs6vVypoYu9RNc+zNUf6mtiMyR/rOU4Ly8uiu1u0qcbECR9voryVYlDAMwxgI7Q98/OB42nlAWY4xK4OSK304jG52RdWHFFbmTmY6WGAdiD3tZopj5I+Opa6kbrSVNy2xda9FLYf2ZeWNJLWf9RgVbK90KfqjNWa3BIsShmEYg5pcYwfG0Z7G0H33jsqVrCOU+W+CJ31TW6CYdYG5cxTtPGBptadCESW+71jqbjlwc9TVWejCRy0kK3MnjySf07t3OBU1Tl5Y5UKVEvqu1JUpn5UrsChhGIYxEJiETVKSHEO1dc2nboIxfaON9CBSsn2/dV3vzp69JxqoSaNrwCIlOzXlwC6kb2a/S7Rys3W/773Ad6kbR2bXkIJqqq+s91rqRrxnq7aOYRhGB6H8HTt20Pz58+m8886jq6++msrLrQN3sCG9FBHJ4bS70NrVAZESZyBSUhEaTuUhYUFRfaOdiA8t5rftU5ZRHZ0U77mnJCfC2rE0EKKkYrfrouTfrRZ6+A0lRIJJ+N6+1+R20zhPRUm2TU+XSpcqxVyF+5QwDGM4SktL6euvv6YlS5bQ0qVLKSsry2b9qFGjaObMmRRsNNRZ53uJ6mSmLYes63pkOB+Q2sUhtaNES2Kqy0SreYg5Z+kevaMVVSEdomh/buuiJHJSvnpTiDhGabWVVLG70u/HqLxRlJjCTKJ1vjMqqy10/iMWqmsMUtxzPtGR/f2znd06EuWYo4mKrY3m4vrGOn1+8drGJ7oIixKGYQxFTU0NDRs2jHbu3On0OVu3bqVgpFoz3wsiBFovRXPpm9BQE7WLs4gBt2t1GTVUN1BNXg1Ftre2VjdqpCRfMwNyr6Z9xVwGDeYAzK4QJXWlSsdSbzcHcwYEUEWjKDFnmikkzHki455XLLRlr7J8RB+i+y/yn3DCDMSmNDNRo5epbGcFdWjO5LpW8UBFdXTtXOP0DcMwhmL16tU2giQyMpImTZpEN998s/qYfeQkWLD1UkTZlAM3l76xNlCLDIoKHO2kgvss1lRC706eD87wlACb1IQfy4Jrcmqovry+SXt5e37610LPfqwsR0YoaZvwMP9GvOK6W4/RwdXOU6VIgUHcgfgh1s7DzcGihGEYQ7Fu3Tp1+c4776TCwkKRxnn00UfVx/fubbyMDDJsvBSdzbSrUZSEhLRcdaKYXc1BMVuwehxCiLZVRnonUpKg/A1UBY4r5cBFpRaaOdsaHZtzhYn6dfV/Ci71iHiqbUxrFf/ZOBtkC34S9NRxBRYlDMMYivXr16vLU6ZMIbNZGWjxt3379m0mUoJJ6GSPEsx3EhHe/ODUtNV8peGPQ1RaFG07ZPKKKJGdUA8FKFJiU3nTzbEouXeBhfY1Nko7dhjRDWdRQOjTO4y2mBUVF3Kogir2VrbcU4dFCcMwwS5KBg0aZLMuMzNT/D148CDV1np/BtNAo+0vUpsURUVlzc9506S6JAgaqKEEFX4Y2aNEWw7cq1VGVyJ0UM8Oj3a5ssRnPUp6OBYlX/6u/DVHEr1xj0m0xw8EMBSvibF2qcv/tXGKZjuK12gjJY2hqBbgSAnDMIYBZkApSjp27EjJyckORUlDQwPt368ZrYIEbcolO9TsksnVWaTEqOkbm2hRJ6soSUsiiov2fJBGlQ2OY6C6utqUA3drKkoKSixqldHIvojsBK5yqnMq0ZrYJPV+3rJ8h9/V4sZOrpEdIikqjY2uDMMEGdnZ2ZSfn+8wSqIVJcGawpFeCsz3srvcOt9Lj/SWB6jkBBPlh0fK4h3D9irRRotCO5jpcEHrUzfaCQ0rQ8OouLFjqV/TN7IcONQk/EL2rNMUmw3uQQEF/WCyEuLVvjeIlKDSRgv8OHXFisk1YahrfhLAkRKGYQxpcm1JlASb2RVXnlJImDPMtFPjpXA1UoI+HAVhyhVrlUE9JdoIT3GMNfLTmh4lEpkGk2bXqkMtdyz1WjnwrsZy4M5RFBIR0oIoCWx/GUSV0lNDaF2MMtlSTX4tlWKiSA3a+W5c9ZMAFiUMwwSFnyTYIyXomSFLRlEOvOugxeVyYG11iUzhVB+uofpqGTcxZqQkO1Tbo6T1A7VsQGeTwvGDrwSDuiydjXZicl2306KbSAnISIGvRJPC+SXfuZ9kMIsShmGCXJQMHjy4yfouXboErSix6VHS2bZHiUuRkkZRojW7Vh2sMmybfbC73ioevJW+AbZt1H2fwqnQzGcU7VSUKH9RiTuwGwWcjPbwlSQ79ZV4Ug4MOFLCMIzhREloaCj169evTUVKtD1K0GJelgNDbCTEuuApaRwX8mzMrsZL4WhLmTdXRPokfWNTgeOHXiWYSVcS46Dypr7eQht2K8s9M4hizIGfHqBTe6L9EdGU15gOLFhRSPVV9Wo6qqSxvXxEagRFutjNFbAoYRjGENTV1dGmTZvEcq9evSgqquncIO3ataPY2NigFCVaL0V4mnW+F1dSN9pIiU2vkv3GjZSEJ4bRpsNhbkWLXOlVgsnt/N2rxKYcuFtTUbLjAOa70U/qBmSkmETYZnVjtKShsoGK/i4Sy5V7K6m2qNHkOiTerfmDWJQwDGMIMBNwdXW1Uz8JwI+fjJbA6IrS4GBB66Uoio4ii8W9wVhGSmwbqOlDlFjqLS4/T6acEC2S5cAQE+bI1kcP0K49s4P/y4LLWygHXqcjk6s2fQNsfSUFrUrdABYlDMMEReWNRIoSCJjc3MZwQhCg9VIcNEU18UG0RGSESTQHy9FZ+mbfu/vph65LafXla9XwvzOqD1eTpVYRMKFpUVRY6j0/ifZ4okKp2hTit/SNrLwhEybji9a9yVWmb8BarShZltekk6urTdMkLEoYhgmKyptg95WokRIT0c4aq7Do7kKPEtv5b/SVvtnzchbVV9TToc+yadWFa5wKE0RJtj9tDRlUxnlndmCHvhKTSTW7IhVh34PD26izA3eKotDI5suBh+hElKD6BhSGR1JBUqxacVNbVGsTKYkfEufW+7IoYRgmKCpvgr0CxzrfSyTtynGvR4k2hVMaGk5VjVGAqgCnbzDYa1MXuUvzaNXMNU1KlRtqGmjNleto3xvWLr2HB1pnIOzd2XspDVkWLEVJQ3UDVR2qavV+brx9M+2cvocKfredwK6moEb1X0Q7qbxZ2yhK4qJbnnjRX6QlKxNBgq1JjdESC1Her/mqKIlIiaCo9Kber+ZgUcIwjKFESUxMDHXt2rVNRUrEfC+5NU0qb9xJ36i+EpNJnS0YnhJUSgSK6uxqYZDUkvtjHq26aLUqTOrK6+ifGavo0OfZ4r4pzERD5g+iTTGJ6mu8nb4Bh7QVOLtbl8LZMz9LCKqaXTW0+tJ1Ig2lvre28qZ7TJPXFpdZKEvZdRrUHUJAH54S+G86KL3TaGWUNYWz/70DVFtQ65HJFbAoYRhG95SVldGuXbvE8sCBAylEXqK1EVGiNaRqe5RERhClN4bRXSGlcRyXvhI0Y0O4PVBooyRJY9tRaEyoVZjMXCMG75Vn/kt5P+Wr7fWPeGcYZZyVbjsRn7fTN00aqHludi3dUkZbH9mu3q/Nr6V1129QxaD2GER3bxopWa+c9rryk9ibXX9raEemxlmq8dl50slVwqKEYRjds2HDBpf8JMEqSrTlwFEZ6OaqLHdLc+/K2VEFTiAn5tNGCdJOS6ORHwyn0OhGYfJDLv08/Fe1zDQsPoyO/GQEpU5WRkIpSlDC283FsmhXkOkwmwZqHkZKkHZae/U6kQISmKxpqqwFe12aiG+d1k/SUx9REnuza4UpjKKHWCNXnlbeABYlDMMEjclVzh4cHh4eVPPfaMuBa9tFUVWNZ705rA3UInVhdrVvGpY0NolGaIRJQ1WD2oBr9NdHUtJoJV+AKMO2RlHSNY0oovEq3RtgpuH2idb5b1pTFrz9yZ1Usk4pEYrpHUOdnrTm2rY8uI1KN5faHIPo7i1U3nQnXSHNrqBhiDWFI3FnIj4JixKGYYJKlCC106lTp6CKlGiFQ36kuUmqwVUwUzDI0UQBUF0SKBxFCZLHJdGI963CxJxppjHfjqL4AdYqjux8ovJK76dutMf1cLhZnVHZkwZqhX8X0c5nd6k+mMHzBlLcMbGUeYVixEb0BObdsq1l6muiM81OTa5goN5ESXurGCzqYW05DyKSw0VUz11YlDAME1SiRJvCKSoqopISa3liMLSYPxCi7VHiXoRATsqX3Wh09VdzMGdIPwUG7ahO1v1KPiqJjlo2hgY+3Z/GLRndJK2h9ZP09oEogdm1LiREbcnv7qR8dWV1tPaa9SRVTa87eqipjN7396TYfkoJbenGMjWSgiqVULMixCQNDRbVU4IUVXyMPtM3YF9iPIXFWTvsxg923+QKWJQwDKNrEKqXogSpmZSUlp2dweYr0fo+dtZYUy+epm+0Js5yPzQHc/a5qv05Ms0UEmY7HMX0iKEuMztTRFJEk9fK1I23Zge2Rx5XeZwwQ3NtseuG4M2ztqp+mcQRCdT9RusMeqFRoTT0lcEUYtePJLp70yjJ7kPWiJDeTK726Zv9BSYhJlvjJwEsShiG0TWHDh2i/Px8l6MkwShKpKcEZs/thYpfxrP0jfK3MCyS6htFQGWAIiU1OTWi+seZwbM5tu+z+ix6eWEiPntkQzqbiJKLKZycH3PVfipIQQ15aVATwRXfP476PNDL5jFH5cDrbEyupDtk9Q04kEvU/nirSmk3prFe2E2ssRaGYZggSN0EmyhB4y1ZEoxy4L2HreswT4snkRKLyUQl8WZqV1AuUhP4HyY/97+wKYV1V5T4IX0DbM2ulZQwtOWW6Vsf2aYu9324j0OxAbpekSnKZ/OW5TudHXjtDq3JVV+pmyaRklyiTvdkiDLu0Ogwaj/JunLbPgsVlRId2b/lfeBICcMwQS1KjF6Bo53vxdzJTAca20Akxro/hb30lICCaG3HUmszL39h0zTMTVEi0zcR4cpkfL5O37jqvaktqRM+ERA3IJa6zHSumCACB784SPRnQZVKp/Oa5uJsJ+Ij3REbbaIExR4jzktEhHrd3pO6X9vVxk/y0hcWGnWVa036OFLCMEzQiZJgajWvrbyBGfTAqqahc1eJMSsDeU2tEgXooRlwzR5USrQGm1JYB1ECZ8D8ueOANaIRGur9CELHZKKoCPQqca8suHSD1VTdbmS7Fo2emDJg9FdHOl2/rtHkGh3lfqrOn2bX4jIlfQOfkKN91oqrluBICcMwhhAlKPXt37+/S6/p3LlzEIkSqxHVkhJF1TVNQ+euggFDpnD2hWijAP43u7bUNMwZ+3JIPQa+KAeWxwkiwN0GatqJ6Dzp0aGlrMKiTieA9vK+EF/eQJ6H6J1T4KDQDUJFW9bcEixKGIbRLXV1dbRp0yax3Lt3b4qKcu1qHs9LS0sLDlGyzxopKY+3DpKeREqAFCW7GgJbFqyWA4eayNy5aeWJM3zVXt4eRGHKQsOpNFRJKJS7cIwwS662JLY1bNit79SNQ7OrtcO8yqF8ovxi19+PRQnDMLpl+/btVF1d7Vbqxt5Xguod+R5GpEoTKSmItIoyTyIl2gqcrJDWdyxtVTlwY/oG5t2QiBDPTK5enB3Yqa+kcWI+zKhsP3uxs0hJSISJ4hp7kXjK2h2ka5Oro14lSOG0JnUDWJQwDBNUfhJHZtf9+zUjmYE9JdlhUQ67aXpidkXHUjkXiycdS1tDTX4t1ZXWeVR5g0oOf0RKZFmwana12Daxswf7U76jXCzH9otzS2g5Yp22vbyeIyUpJpsKHHtYlDAMEzR4S5QYOYUjPSWYhXWfpnGax5GSxqwCOpaGpHrWsbS1VHipHNjX6Rtg4ytpJqJUsqFUCBdv+Em0JlfdixKbSImlWXHlChwpYRgmKEVJsFTgSE8JqmMOFJDX0jfA0qGxY2lBLdWWuN6x1KvlwA4moXNFlKAiJd3DY+BO+mZ/pLXPSPEq5+aI4rXFre5mqk1vyQgDSp4T44yRvmkuUhJm20HfKSxKGIbRLRs2bBB/Y2JiqFs3a6vuthIpgVCoK6mzlgNrfvQ9N7paB7jqlGi3qku8RfkuJc3hbqSkrs5Cuw4qyz0zUJHlu8Easw+junV9tLUzad4vSqOzlkyurRUlWdlEJeX6j5K0ZHStrrHQ5savXj/r17FZWJQwDKNL6uvrVTHRs2dPURLc1kSJ1k+ibZwWHkbUPtGz99Q2UCtPDEwFjrabqzuRkj3ZRHX1vk/dgMgIk4gC5EaY6VCUso1F/xSrXhh7StaVqGk2eEpawzqdN02zP5/Q+8aR0XXLXuvn5ep+sChhGEaXHD58WJQE2/cdaUuiRDsRn1kTKUFzL0+jBNr0TUG0NlJS4f/0jQmT8bkuSv7caF3ua83O+TyF869ZmWjOUmehgj8LmzyvrryOyrYroQ1U3YTaTbbnLmt3GqPyRvZ0kalE+/SNrbhybT9YlDAMo0v27dunLnsiShISEsTNyK3mtdUe4R2jKK+4dX4SrdEV5Gj6vvjT7KrODtwpyq0B/PNfrabJE470/WAtza5rYpPVx/KW5Tk2uTY0n7p5/Tui215Jpv05Lf/f5Wut+zm8N+kemcIpLCWqrLY4NLm6OqEgixKGYYJSlGijJXivhobme0zoPX1TFtv6xmn2kZL9Yf6PlNQU1lBtkfvlwBVVFvp+pbKM1NXYgeRzZFnwuph2ZGkcLfN+0biNGynRNk0b0nTSvsUrLXTZ40Sf/R5Lt7zY/P+srLbQr+uU5c6pRD19nKbyZa8S214rrr0XixKGYYJWlMgKnJqaGsrOziYjt5gvMLe+cZq9pyS7JpzCE8P86inRVt5Eu+En+eFvDNjK8mlH+aftuoyUlIeGU2Vn5cCVbSmjquxq5+3l7SIl5ZUWuuopa8Tg+78UA6gzfltnbaN//EglPaJ37GcLti9rxjmXZg02NQuLEobRMSgN/O9//0t33XUXlZQ4mFgiiPFmpMSovhKtpyQ7pPWN0+TswtIzjHSQ9HRUHqiihpoG/5pc3YiUaFM3/xnvn4FaekrAvgzFVwLyf813KEpMYSaK62/byfWhNyzCoCspryL6da3z//njP9b9nDxC/4LE/nyUkZLDBRY6XGA1uboqrliUMIyO+eabb+j++++nxx9/nE455RSqqPD/HCWBgkWJ1VMS0T6CDpSEeiVSAoNsu8biEMxJogqDhuY7lvokUuKiKKmts9BXfyjLcdFEk44gv6CdmXdNTJLD0mBhct1WJpZj+8ZSaJT1c1q9zUL/+6jp+367wtJsRAhgDPfXfvqiV8l6TfM3V/0kgEUJw+iY33//XV3+9ddf6cwzzzT0PC6eipKMDM0laxuJlCBqIdMEonGapltmazwlWrNrfglRdFf/lgWXaxun9bA2JmsORBZgogQnjVbKdf1BUrxJRJbAH3WJFBqtCI68ZfkiiglKN5U5NLnW11voiqcsVN9YEnvLdKLQEOU13/zp+P8dLrCoPgwYXFMSDRIp0YjkA3mWpn4SFytvAIsShtExq1atsrn//fff04wZM9RS2bYgSjp06ECRkdb26p6KEqNV4FQdqlLblmMWXW1jqtZESrSiBA26IrtYRUm5Hxqo2bSYz3RtduAvlvs/dWOfwtmVF0KJo5TmMNXZ1VS+TSkBLl7juJPr3M+I/tmiLPfvSvTo5URH9KpWu9Ju18zhI1n6r3V58ggyDLat5h3M3eOiyRWwKGEYnYIrMSlKYmNjyWxWfsA//fRTuvTSSw1ZTeIqtbW1Ynbf1vhJ9BgpQSfTvW/uo5qCRiejC+3l1W6uWlHSykiJ1uxaq+3q6odIiRQlUelRFGoOdel78MVyZRlNuk4cTX5FpnDwdTMNS26SwnFkct172EL3vmodlBfcbhLbfuwQq+hzFC354W/ra44faYwoieybIy0jMn0jTa6hoYoocxUWJQyjUzCzbV6eMhKNGzeOPv/8cwoPV1onvvXWW3T99derIeRg4+DBg+q+tUaUpKamUkREhC5ECfbn73NW0YZbNtEfx68QxtLmKN2i+BTUSEnjjz38IObI1g1Y2rLgiiRrtKJyj28jJbXFtWKGYBDd3ayWwKJktqTc8bmMaIMc6CYNJ4qP8XOkROMrKejRVJSUSJNrqIniBsSJz/ma/1movPFQXn060dhByjZrRYm9rwSv+/Ef67w+/ih59hYR4SZKbezGD/EMD9CmPcr9Pp2Jotw4X1mUMIwBUjfDhw+nE044gT744AMKxaUHEc2bN4/uvfdeCka8YXIFaE0vy4IhSgIp4qoP11DFzgp1npm/TltJVQcdC5PD3+bQlge2qvdje8fQwXzvpG7sG6gVRURRSKNHo9xLkZKyrWVUuamq2dSNNNje8ZKFptxmocEXWyi3qOnn87k2dXO0/6MHPTOs/3NbSCxFpCgit+C3AtFyvmyrksaJ7RsjIj+fLLNGQRBBmH2F9fW9MmrFBHvgl7VEZRXWfcMcMQcbo2EThvjPN+Mt5HmZXUBCkNTUetYm3yNRgqu3k046ie644w6bx6uqquixxx6jE088kaZPny7y3+6sZxjGyurVq21ECTjjjDPo9ddfVx+fPXs27dyp6eUcJHhLlGhTOKWlpVRUVESBwr45GYTJitP+biJM9r9/gFbNXEMN1Up6LvWE9mQamqT2rmht6gYkJ1gHvIJyE5m7NJYFZ1W2Wrjl/ZpPvx+zgvZcuJf2v3PAaTmwrLxB3w45Cd0F/7VQQ4Pt///8V+Uv0gOnjiO/o009bMwiSp6gVOHUldXTvnf2k6Ve2d74wYrSe/gN6/bPvclECbHWY419gFEXYNBeovGQ/NDYGA5MNlDqxr4CB8beHxsriNw1uXosSp588klKS0ujykrbUN8LL7xAW7dupZdeeomuueYa0V9h48aNLq9nGMZ5pERywQUX0K233urwecGCL0QJ2LOnMaYcALRVJ/KXF+WxK063CpNd8/bQuus2qANd+lkdafibQ+lggfWH3RuREu1kfofyrRU49RX1IqLTmvTMums3iDliwOZ7t1DZjnKn5cAQIPs0zbYWryR67G3r/S1ZFjGpGxg3iKhDkimwomQPUcoEawpn97wsGz8JGqXhObIM9j9HN30/KUrAN39anPQnIcOhFcva1NQQX0dKli9fLkoSjz7a9mijGuDrr7+ma6+9lrp27UrHHHMMTZw4kRYtWuTSeoZhbJFiA/O3dOvWzWbd6NHWX7Zt27YF3aHzpijp3t1q/d+9ezfpIVIy8Mn+qhBASgfCZNM9m2nL/daUTeblXWjIS4MoJDzEpnW3NyIl2hl2N2dZKLqrxuya5XkKZ+Mdm20iP/UVDbTminVqUzabcuDu0ZRbZO1eKpn1uoV++lcZ1D5vNLgGoupGgkgH2r2DjbuJko+29ivR7mvC0ASRtpCBJpT0OmoYduwwoiglA0TfrlC8JOjwumyN8hhSPgNsv+6GoJOmgdpv662P+zR9g8jIs88+S7fddluTdWjhXF5eTv3791cfw/KOHTtcWs8wjO0MuQcOKKHvYcOGNflx693bOksXoo/Bhq9Eya5dmo5OARQlSUcl0ahFI8mcaRUme+ZbS5Z73dWD+s/uS6bGmYBty4FbPzgP0F7970bUwtzqOXAOLcqmg58oFVNh8WEU0SVcNYJuf3xH03LgbtEiZSNJirdWuZz3iIUO5VnsurhSwJAioaiMqDDKTNE97Jq+hRDFD4izaRg2yMnsvjCxQpgAeEjQzwOzH1dUWaMkRmgtb482gldbZzVluyuilUkPXARplylTplCnTk1nCCorU5ziMTHWZjhxcXHq4y2tdwTmq8DNZoPDwlQ3vSfIMspgLqd0Fz4m+jsm//5rTTZDlNhvR48ePcQPF66yIEr8tZ3+Oi5SlMCoij4lrfl/iMxK4L/x9ra7ekxQDiwIQYlvJIVEhNCRXxxBK0//V3g5BCaifrP7UualncVnK/0d2pllOyY39V24CwQAqiVyCpWUhPmEKBvfh7vHCE3eNty6Sb3fd3ZvKksoo6yL95Gl1kI7n9tNyccmq5GSyA4RFGIOoT3Z1v1AczGYP+FHQHvyU++20D9brSmAzLTW77en9M+0el/W77JQxtFJqmkZxPaJJVOUyaY3x8ButturPU9OGh1C3zW+39d/WKhC0w/xuBHGHJ8Q4bEH/Um05zG+z14TJfjh++OPP+i9995zuF72UEAbbCk8EBmRj7e03hEw9C1YsMDmsWnTpgmTrDevxBg+Jno7T37++WebSIGjctb09HQRTdm8ebPwSvjz6srXx0XuLwSJjBh5iiyjBps2bfJZaXBzxwQ/ymU7FVES3jGc9h3SdKudm0b77zxItQfrqMNt7YmOa2iyjVt3I2Wg9IY31R6irCzPfR+SHh1TKafQLITJnhprr43cjbkUkeX69Sr2bd+NB6i2UCm3iJsUS3VH1pLZFEUpVyZT7tw80QRu1eVrqC5XuYQOSQ8V+7h2K/ZJSYfEhOXSYxdW0fodHSm7MEwVJOCYQUWUlWVtUuZvOsRhzFJCAb+tKqBp/RvbtDYS2iNE7M/KTcjzKGNaYsQ+yspqcHieDO6M46t0Zfvilyqqqcd3V2kQ2KeD49fpnZBa6z5JuqWWUFZWofW+XRraES6feevXrxc/Dscff7za3Ag+EZQpLl68WPxAousirkQGDx4snoNluREtrXfExRdfLLpXejtSgpMCP/SuqLa2AB8T/R0TrfcB3zmtWVOb/sR3ElUlEPcYwIPhuKBKLz8/X41yONp3d0BJMJrPISqLhmytfT9PjklNXg1tKd8uluN7xdtuQyZRj2U9hLk1JMzx64s1V9IjBnWkDlZbg8cc0Zfoz8bgRnkqfoeVc86UG+LWMdr7xj4q/8MaARk57wgKSwwTx2ToPYPp39WrqeD3QqrLsXYhTurbTvyPUo22OmJAexo+iOjDh4gm3kRUrxmXL5qaSJmZGneunzkaxtOFyvKh4iTqe0UcHbjzkNpevuOYjpSZ2YV2HFTuIwp1xKDOTs+TzMwQ6peplAGv3hWl+lAG9yAaMbh16cpAkeQgTTN2KM5121mTvSZKTjvtNCFAJIiYrF27lp544gn1agTG1YULF9KcOXNES2eIFUwk5sp6R0B8tEaANAd+PFiU8DHR63kiy4Gjo6Opb9++DrcBj//4449iefv27dSxY8egOC5onCbxlviBr2TdunUiooQre9nrxV/HRNuUDAZPh89rZpMO5imjX3iYUoGCSfVaC9ILso/9luww6psWKdqnI5Xk6jEv31lOW2cpYgsMen4gRaVEqemH0PBQYdZdPv4Pqiu2ipKY7jHif+zLsSqPrmnKfh09lGjOlRa6/SVl27p1JBrayxRQn4X2WG3KIopMihTG1uJVSvQmcVgC5RaZ1D4rg7o7T1XI8+TkMQ1ClGgzNcePdC3FoUcSYjFZYgOVaixJQ3u6f666vPcQFfCAyBuiHvhi4wpEgjLF+vp6UVlz2WWX0cyZM2nUqFEur2cYhkQvDWnIHDJkiNMBtE+fPkFpdvWmydXe7IoIb2vTQZ5gX3XiLrL6Bnl7bwgSoK3w2LgHFThK2qEmt0Y0BWsJiLt1128QZcSgy8zOlHpc08tlc4aZBv1vgM1j8hjsPazcxymu9STceg7R9Wcqjz15dWAFCYiNNlFmmtUYjH3vMlPxVsb0jBGixNbk2vJ7njym6T5NHmE8g6uz2YKhrTypInLL6KrlvPPOo7PPPtvmscTERJo7d64wpyLNYq/4WlrPMAzRmjVrHPYnaU6UBFNZsC9FCYDgk11e/YV91Yk7oFw0r9h7PUocihJU4HSNpsIVRWpZcPzA+Bb3qfAv5fmo3un7sLUizJ6Op6dRzg+5dODDg2QKN1HCMOW9sw5bB7OwMG2TMRM9fyNupBsGdlMavCESsC+HqMuMTpRydDJFpCqmZRhgW6q80YK+K/ExyqSIIDKCaPwQMjSotEH0R5adR0e5L7I8VgVIqzgzqWJdc4KjpfUM05Zx1jTNnmAtC/aHKPE35Q7aq7uKbD3urR4lknZxJjU6gQocGSmR3WZbQjsRXcbZ6RQW0/w17qDnBtDAp/vTiPeHU3RmtGg0lt8otro09gHRM/Zl1HJOotBIZSzTihJXenOEh5lEukYyfnDr5zQKNFrR7G5/EgkrA4bRsShBObAzUJovLwxYlLguSgLRll/tZGoitTeJq9j2KPH+1T+AOKht714DNTkRHUgYopnhzwloAocUT/tjlZ1AtEEi54PRMwO6mZqIEi3rGk8rZJpcnRX3lLHW95xypLEFiX36ZrAL0SJHsChhGJ2KEvi4BgywzcVrQbRRRktw9Q+/RDDgi0gJ+roEMlIi0zdRGVEUGuWeyda2m6t3By5tCudAmJuRkjVWUSLnfXEHmboB0q+hZ+w9OFrq6y2qUOmZ4Xra4rzjiK46jWjGZOWv0Tmij3W/j3F+PeUbTwnDMN4HvXu2bNkilgcNGtRi9Rl8JaiCQ3k+Blutz8ToogSiLDXVO3F9lJ/KZnP+FiU1hTVUW1TnucnVh5ES5epfGWC31kaTjCdVtDBbMI5j8TpFlER2iKSoNKXHhjtIkyvokqr/KAFKeBEFQfmufaRk50GiqhrXTa4S+GheulX/++4qpx1F9OodJoo1Ex01mCMlDGN4ULYqux825ycJ5gocKUqQnvKW9wzVgrITtb9FibbzZ7RHlTcWn3hK7H0S63PDKCw21CVRUrGnUi3xTRjqfpQE7D1sMVT6BtEPlCfLsmBtt9b1moygO6Ik2AgJMdGlU0109iTPhRanbxjGgH6SYBUlaHCGkmhvpm7sfSV5eXlUUmJNPejZ5OrrSInNDLhZJrUyqHJfFTXUNbjkJ4kf4pkoMVr6RiviyiuVShyJu5U3jHNYlDCMAStvgrUs2Bd+kkDPFtyacuAmoqS992fAleZEpCSkCRfdZZvzlWgrbxI8FCXa9I2chVfvDNREQVCxJNH2KPG06oRRYFHCMDoUJUhbyOkYmiPYyoL9JUr8mcLxVuM0zLjqi5JR7Qy4pp5WgVHwR4FfRAn2Ky7aGNGFAV0dV+BIUWKOJOqeHoANCyJYlDCMTqiurqYNGzaI5X79+okW8y0RHx9PaWlK7JtFiT5FiU2kJNM9UQJ/0cF836Ru7MuCQW5Xa1vVvGX5TrepZK3SYCQiNYIiPTC5olpFlgQbJXXjrAIH/VZ2HLCuDw01hsDSKyxKGEYnbNy4UVTRuJq6sU/h5OTkqH4MoxKMkRK1HDg9ikKj3SsHRv+Q6hrfpG4c9d/YFB5PYQlKUWb+8gKRxrGncm+lWk2EKIknLeCzC4jq6o3TOE3St4vSPl0bKdm0R6nIaesmV2/BooRhDGpyDUaza7CJktriWqrJV/rHRHd3r2mar02uDjuVZhElj1emIK4trKWS9SU+95MYofJGEhVpoh6N6Rm0U0fEx3bOG46StBYWJQxjUJOrhEWJa7Rv355iYmL82tXVxk/iicnVpnEa+QSbCpw9RCkTNCmcX/KbbZrmSifXFitvOhhrIJcpnMpqot2H7CtvArddwQKLEobRoSgZOnRomxYlaJ+flKRcsXsLpBlktGTPnj1ixnJDVd6kmHw+Ay5SEclHJzXrKylpbJrWmnJgo0ZK7D04SOG4Ozsw0zwsShhGB8BLgsZpoGfPnpSQ4PoVaLBU4MBAKUUJUje+mK5eihK05D9woNGd6I85bzwVJT5snOYohYMZawtio8ncOUrcxyzA9ZX1tp1cG9M3Ee0jKCrdfZMryMo2VuM0p3Pg7LGKktR2RB2SjBX10SMsShhGB8DjUFmp9IUYMsS9+cu7desmWrIbvVcJTLpos+8LP0mgfCU2jdN01mLe6dX/HhMlN6ZwGqobhDCRVO2votoCxSOTMNgzkyvYq5mML9NookST7vp5tYVyCpVljpJ4BxYlDKMDtAOkNvLhCmFhYeqEc9u3b6eGBuedONuqyTVQE/O1On3jB0+JoxlwbXwly/IcT8LnYepGm74JD0N0gQxF784o+1WWf7JmXFmUeAkWJQyjA7TGS+3VvLu+kqqqKtq7dy8ZEX+IkkBFSjBpXVhMmMeREgzeKZ55Sj3qvyErcEDerwVerbzRGl3RyRXzpRiJyAgT9cpQlrX6nytvvAOLEobRmSjRXs23JbNrsImSutI6qsmp8XgiPq0oSU/x7eAtZ8CVkZLI9pEUPyhONbbW5Nc0Mbl6OhFfcZmFisuMmbpxJOIknL7xDixKGEYHaAdIFiW+EyWZmZmqD8LXoqS1E/FVVVtE8zRf+0mczYArfSVkIcr/rcDW5JocTlEZihnWXWQnVyOaXJ2JEpxSjoQK4z4sShhGR5GSiIgIyshojA27AUdKXCMqKko9vr4WJa31k8j28r72kziaAReeD1tfST5VHayimrwa1U/iqclV26PEqKJkoMaDA3pmKMKOaT0sShhdlcV+/PHH9Pfff1NbAlegcoDs2rUrhUoXnRsEQ1nw/v37fR4p0aZwcnNzqbS0tFXvVZVdTWsuX0eHn8ulhroGn0zE549Iif0MuBt2EyWNbkchESa1iZpN07TBrTe5GrFxmsQ+KsKpG+/BooTRDQsWLKDp06fTUUcdRZs2baK2wuHDh6miosLj1A1ISUmhdu3aGbosWHpKMMkgbr7CW74SzAuz+rK1lP3FYSp4u5B2PbvbeaSk1eXAJr/PgIt5etodqZxTlVmVlP3lYS9V3hi3R4mkVyfFfCxhUeI9WJQwumHZsmXib01NDf3vf/+jtkJrTa4AoXSZwsHgLvt9GClaJCMlvoySeFOU7J63hwr/LLR+jk/tpsK/ixyLkq6KKJn9joV6nNNAny5rOtGdPf9s8U/jtOZmwFV9JUR06PNsdTlhqOelQMGQvgkPM4nSYAlX3ngPFiWMbtCmHd555x0RQWgLtLYc2JGvBP1KjEReXp4oZzaKKCnZWErbHtveJHKy9qp1oupGm75B59Pw+DCqrLbQAwsttOsg0WVPWKigxLkwySm00MtfKssR4UQTXJ91wKsz4KZMsJYGyxmDw9uFqx1fnbF6O9Hf2yKDrsW8lmG9NMvutRZimoFFCaML0PBLK0qqq6tp3rx51BZobeVNMJhd/VEO7C1RUl/dQGuvXkcNNcognXllFzIPVgbpij2VtPHuzVRXXkfV2dU2lTeb9xDVNXZsLypToibOePJ9izCcgitOQUmwye8z4KICBxGRsATb/ioJLZhc12y30NhriM5+NI0+UYKfDkVJ+0Qic6QxPSXgnvNNdNwIov9eZqLu6cbdD73BooTRBWj4Ja+UJRAlsvV6MOON9A1gUeIfUbJ9zg4q3ag02ojtF0u97+tJ6Q+nUWiMYlA+8P5B2vX87iaVNzCPannhM1t/heRwgYVe/FxZjowguvt8U8BmwDWFmmwaqbniJ3n9WwvVKJ3o6fH3lNScpLbOonpljBwlAf26mujH/4XQvReyIPEmLEoYXaC9spdXYQjpI43TlkQJ5rEJFlGCmXiXL19uMyjpIVKSmppK0dHRHomSghWFtOsFRV2Ywk009OVBFBoVShGdIqj/nL7q83Y8tauJyXXDbtvjUF1DIp1jz+PvWYQoAFed6p8oiaS/Zl6XLY2NgbWlwS11ckV05dNfrfdXbSP6S+NZP5hn7YJq1MZpjG9hUcLogi1btqjLV199tboMw6tR53JxFTkwduzYUR0sPQFRFinoAi1KCgoKaNiwYXT00UfT5Zdf3uJn6E9RgmMkoyUQTvX11llwmwNekbXXrBfNxEDvu3tS/EDrAJ1+dkfqeHpak9fJcmDtFPcxZuXvW4uJ1u20CpNDeRZ66Qtl2RxJdNcM/16F9+ls/X9bPRAlKzbaljKDuZ9Zgs5PwvgOFiWMLtAOoueff74YzKRY+f777ylYKSsrUw29rUndyMZg6HMij6crEQpf8ddff4lZf8HChQvplltucbo9+Izfe+89v4kS7bFGpdfBgwddes2m+7aI0ljQbnQidb+uWxOxM/Dp/hSVbmsCVdM3jaIkMZbowZnK4I9DctfL1uMy510LVSn9yejq04jSkv0rSmB2lWzZa1EjPdFdzapp15zZqKgc8MkvTT/jj35WUlJNK2847cE0hUUJo7tICdIQGMQkwVwerE0ftKbyRtK3b19V7Bw6dIj0kJICzz33HD3wwANNnvfPP//Q+PHj1XLgI444gnr10pQ16MRXcvi7HNr/zgGxDO/IkBcHCb+FPeGJ4TRk3kAizSoYXYtKLbQ/19qk7LozrJGC7/4i+nmVhQ7kWmj+V8pj0VFEd5zn/0G7j1aUZGnE1jMDqP3kFBr07ACnJlekbqSxFT08zj1WaUxXW0e04CtHjdN8tReMkWFRwuhKlLRv356SkpJo6tSp1LNnT/HY0qVLac2aNRSMeKvyRm++EntRAv773//SE088od7/+eef6dhjjxXeITBkyBD65ptvPG5f7ktRUr6jXP217P9YX7XviCOSxydT31m9yRRmooyz04VQ2bjHun5gN6XS5ZFLrft5x8sWeuxti/CZgGv/Q9Qhyf+iJCHWRGmNvtat1owapRydTCM/OII6TEl1+tq/t1jntZl0BNHVJxerJcYvf2kRJtdgaJzG+BYWJUzAKSkpUa/q5ZU+Wq3fdNNN6nOeeeYZCka8VXkjkcfPPvrkb3bs2KEu33nnnTbLqKr6/PPPacqUKSKiAxAtQfO8Dh38M1K5K0q6X9+NxnxzJGVe3oU6zchw6fnH75lEQ+YNskndaOdNmTGZaHDjR/7PFqJ5X1j9JrefG7jURt9M5W9OITXbS8Wej3+2PvesY4g6ta+nqWOU+/CZLPotOBqnMb6FRQkTcLRX9Nor/ZkzZ6qt099//32Xc/9tsXGa3kSJ3C/4XB577DF69NFH1XXXXnstnXXWWcLPARAVW7x4MSUmJvpt+7TH2lFUxxFouT5gTj+XIzmhZuscRtrKGznHTGioieZc2fS9rj8DPTwCKEo0KRxpdm0J+IU++UVZDgslOm2cNeKjNbzK9E1UhNKnhGHsYVHCBBzt4KkdVGNiYuiqq64Sy7W1tfTiiy9SsBGM6RtU2sj9wuAfEhJC99xzD9111102zwEXXHABffbZZ2Q2OzdP+gJpCPbHbMH2PUqQvpFMGUV07DDr/Vgz0W3nBNYAalOBo0nhNAciPVnZ1tRNUrx1WfpUflljfT9ESfyRpmOMB4sSRreREnDddddReHi4WJ4/f77L5ZtGQV6lx8bGCj9Na0H6IyEhIaCRkgMHDoiOvPZCCxETREkkSM+98cYb6ufrTxDBycjIcCtS4imIIshy4I7JRMkJ1sEYA/MTV5tEdAEgbaNdH8j0DdiS5Vr65hPNXD5nHaPdP6Lr/mO9L7++nLphnMGihNFtpASkp6fTiSeeKJbz8/MDmpLwNnV1daJPhowoeOPKEe8hj2FWVpY6+7A/0Q7y0qwst+3555+nr776ipYsWSKqqhBFCRRSAMNo68t5lg4XEOUXN42SSEb0NdHyuSZ6f5aJ7ruQAo5tWbB7qZvQUKLTj7Jdf+EUJQKkhStvGGewKGECjhQaERERNmF1ydixY236XwQLKIOFMPFW6kYvE/M1Z96FCIGHZNKkSQEP3w8ePFhdXrdunX9SN05sQ6MHmOicSSYKCQl8SgNRDHg+XPWUrN5GYpJBgFRUip0fJj7GRBdNsf8fgd9PRp+wKGECCtIxcuDEVXVYmO3kX2D06NHq8ooVKyhY8HbljV7MrtrKG2/ul7dBCbJfRImDyhs9A2HUu7F/3Y4Dynw1zaFtmHbWBMf7d60mhQM4fcM4g0UJE1CQvpBVGPapGwkaaskwfzBFSnwlSgJtdnWWvtEb3oqUoNy1tNK52LCpvPF8aiO/Is2pmNUYE/M1l7r5+GdlGV/R/yiNmB1OXjdxuPV+16bd+BlGwKKECSjaQdOZKIEJdNCgxn4PGzaovS2MjrfLgfUSKZH7hV4zmZka16TO6N+/vyp2PRElm/ZY6Ix7G6jLNKKJt2eIvh4tpW+0E97pmb4OOrs6Yt1OJZoCJgwhSm3nXJyh/DklgWjMAKJxyteZYZrAooTRVXt5Z4waNUotJUVr8mDA2+XA2veCIAiEKMGVs0zfdOnSJSCVNe5U4MhzbtOmTaLs3BX2HLLQzMcaaNBMC32+XHksvzSU3lviuPX6xkZR0j2dKDZa/+kb0LeLySWzq7Zh2rRjm9+3kf1MlPOliX6fZ6LwMGMcB8b/sChhdFt5E+y+Em1EAQO4t4iMjKRu3bqpkSh/zrKMCil06NW7n8TeV4IUYkupLkwqd8NzDdR7hoXe/B6Cw3b9B0ubvgbNwsoqjZW6sZ8DZ2vjxHwOUzeNc93As/yf8S2/L8zNgTY4M/qGRQmj2x4ljiIlweIrwQ+6FCW+iChIgYeSYPQN8RdG8ZO46yuZ97mFepxroRc+VSaYA+3ilJSEbBWPuV92HrAdwGV/kuYqb/RIH81Ezc4iJdv2KTcwfrD/ZzRmghMWJYwuIiVpaWlq0y9ng2x8fLwaKcGgbmQKCgp8GlHQRp38aXY1SuWNu6IELdHLK60z+N5zAdGuD0x05wwTnXec9Xkf/tRcJ1fjDNpIM3Vq37wo+V5zbXDKWOPsG6NvWJQwAaOwsJBycnJaTN0AGBKPPPJIsZydnU379rnY/7qNVd44ijr501fi6/3ypShZu3at0+edOUExaF5/JtHO90306OUhlBinDMTTjrE+74OltmJ5wy7jVd7Yp3AKSojyippeBHy/0mLTLp9hvAGLEkb3qZtg9JX4qvIm0BU4RkvfdOrUSZ30sblICXp3oOvq8zeGNElTdO1INLxnlZqu2agpAZaRErSR1/o0jEBznV0rqy20bLWynNGeaIDBBBejX1iUMLo3uQajr8RXlTeB7lWiTd/4Qmx5G5guZbQEs1Cj5bwzMKuvM6aOqmgSLUHTMTmYQ5BEhBsrxaGtwLHv7PrrWqIqpb0QTTmSJ9djvAeLEkbXPUqciZJgipT4QpSkpKRQUlJSwCIl8AhhlmcjoE3hrF+/3qP3OPnIctE8TFbhiNLo/UQ1tcZM3QBtZGeLXQXO939pUzfGEluMvmFRwui+R4kEs+jKq+9Vq1apnWCNiK/TN9qJ+TDHjj8azuF/yIntjJC68WZn1/aJDXTMUGUZzcRWbTOuydWV9I00uaIdznFH+He7mOCGRQkTcFGCJlau9umQvpKqqiqfzlfir/QNIhqyqsjbaIXetm3byNcYzeTqaA6c5syuLTF9onX5/SUWWq81ueo/k9UEeEVQaWSfvsnKtqalRvcn1fDLMN6ARQkTENA9Uw5ivXv3VjuQtgVfCQSV7B3iy8Hb32ZXo4qSAQMGqA29WiN0zxivGFplaTBasEuMmL6BuVf2K9l1iKi6RhFZi1danzPlSBYkjHdhUcIEhN27d6ttvV1J3QRTBQ72XfZZ8aUZ1N+9SoxWeSOJjo6mXr16ieWNGzdSXV1jdzQ3SU4gOkGpWqf9uUTfNWpmcyRRt45kSPo2Tl1UX0+086AjP0mANowJWliUMIaovNGG2iMiIgwdKfF15U2gepUYrXGaI18Jolja/XCXcyZZIwfS5Ipy2eYqd/RMn862FTioKFryr3Ifk+sN7x24bWOCExYljCF6lGjndRk+XJkDffv27WKuFaPhrzQHojBhYWFimdM3/vGVnDqOKErRzIZO3Tgzu/65gai0sfr5+JFKiodhvAmLEsZQkRJ7X8nKlZoEt0HwdeWNBPPpyDQKjK6+nphP7hemC5DlyG2pAgfEx5jo5DG2jxmx8sY+fSMn5rPt4mrc/WL0C4sSxhDlwMHkK/FX+kZ7bJGW2Lu3mTnoWwnKs+X7QwgZbSZYb4kScK4mhWPUyhtJr062kRLtfDeIlDCMt2FRwgQ0fZORkUGxsbFuvVZvFTjV1dV000030Y033mgjOOypr6+nZ599lpYsWaKWQnfs6FsHpL/Mrnv27FEjMUbzk4DMzEyKi4vziig5aQxRrDk40jfRUSbKTFOW1+4gWr1dWYaXpEOSsYQnYwxYlDB+B628pRfE3dQN6Nq1K6WmpqqixNdpiZZ477336LnnnqPnn39e7M+1115Lhw4dahIZOvroo+nmm28WUQtw3HHHiYkGfYm3za6IhkCEBUs5sKN289hHTBbpKeZIE115qrI8si9RegoZGukrkW3lAVfdML6CRQmj+/byjgYQGS0pKioShtdAok0hocx53rx5YmC+++67KTc3lx5//HEaOnQo/fHHH+rzrrvuOnr//fd9vm3e6lWCEmZEg7p160bnnXeeWs4t0VasGKkc2JnZ1dN285LHrzLR36+Y6JcXTIZLZdkje5Vo4f4kjK9gUcL4nU2bNnnsJ9Gjr2T16sbpUonU+V4qKytpzpw5Yg6Yu+66S40uYMD+9ddf6YUXXnA7bRXIifkQCcJN7u/ChQuDKlLibV8JSoBH9DWJqInR6Ztpuw/xMUSjBwRsc5ggh0UJ43fQoErbTdMT9OIrQaMteVWNBlzwlMBbInupyNQSrpZvueUWUW46fvx4v20fqmAwZ1BrIiXffPMN3XrrrTaPPfzwwzbz6bAoCV7sIyWY6yY8zPhii9EnLEoYv6MVJQMHDvToPUaOHKmGxQNZFozog/SIIEUDrwvMrCjBveSSS0SfEOwjUjdPP/206B7qb2QKBz6XkpISt14LwXXOOeeo4kp6eTDx3jPPPNMkfQPzbnp6OhkR7bnYml4lwYa2LBhwKTDjS1iUMH5nw4YN6mR0cpBzF0xiJ1MTCLU7Ml/6gzVr1qjLw4YNs6nmQIqjtLRUbJ823eRvPK3AgfCYOnWqGhGZNm0a/fzzz+o8RU888QTl5OQIwYLW+bLviq/Nu74C1Tcy9YRzFNVSDFHHZKI4jZaWrfQZxhcY89eDMSwFBQWUnZ3dqtSN5IgjlDnTYbqUQieQfhKtKJEgchBoo6MnFTiI/px++ulq75ERI0bQG2+8IQTO2WefLR6DWPnvf/8rJheUotCofhJ7X0lFRUWz5d1tCZy/UohMGErUpQOnbhjfwaKEMZyfxF6UgH//bZyQI4CREqRv9Ii7kRJU2iD1JA3EnTp1oi+//FJNPcEzIw29L7/8Mi1evFh9bbCIEm+YXYOJN+8x0XdPmuiLR1mQML6FRQnjV4JJlGDwlpESVNngpndRsnnz5haf/8EHH6jlyhAfX331lU2TNxhnYdqVUarbbrvN8OXAjkQJ+0psm6jBS5IYx6KE0ZEo2bdvH91777106qmn0sUXX0xLly61WY8QLnoyIA997rnn0g8//ODWeib48aYoQbpEpkYCIUr2798v0lF6jpLIZnMyyrFq1SqXqm0kr732msN9QzWOrOopLi4OmkiJtlcJR0oYRueiBKHaY489lubPn08zZsygWbNm2Qwyc+fOFfdRfXDppZeKskHtlVlL69sSuML8888/Re66LeFNUQJjYu/evdUqEcy/oic/iV6AMRWeENkOHgbW5pBpG/hh/vOf/zg99g888ECTx40uStAcTqam/DGzMsMwrRAlMLWhNTZCufjbv39/1WAIpzryzmixjRAu1kPALFq0yKX1bQ1EnMaOHSvKEKXxsy0gz5cOHTqI6pvWIlM4ECT+NrtqRYmeIyVAW/3TXF8XTAEge44MHz5czDTsjCuuuMJGhED8oOrIyKBy6LPPPhPiOVDmaYZpy7glSrRVBLjiQhmgzMGiB0J5ebkQKtorYfRrcGV9W+PDDz8Uf3EMUeWADqDBDlqu4+aNKIlERgACkcJxVg6sR7TN5prrgKsVLC2VMaNB3GOPPabe79Kli9o0zsgcf/zx4ncKPWYYhvEvbn/r0B4bVxIoB7zhhhuoX79+4nHZy0DbOhshXvl4S+sdgatf+5A8fiha88Mnm0AFchI3XI1qp5HHQACPzrvvvhuQ8lF/HRPtfCL40ffG/9OKgX/++UekBf11TGSkBOc0wv6BnhiwOY480tpcorlJDLWCBQ3q7J9nf1zOOOMMmjx5Mv3444+ij4mej4Gv0MNvit7gY8LHxBGu9DByW5RcdtllwqSKSdDgCZGpHOSfAa74pakOfgmzWZnDu6X1jnj99ddpwYIFNo/hh2/69OnUWmDaDRSY+8RR5ATVGyi3DBS+PibLly9Xl7GvWVlZrX7P5ORkIeRQCQOPjjfe05VjAnOn/F/oAxLI88lV8F1FxBKiBD04ZBM0LcuWLVOXUQrs7Hhq9xdeMbxv586dvX78jYQRzgF/w8eEj4kWXLx5XZRAROAGPwCukH777TchSjIyMkQEA/noQYMGiediWW5ES+sdgegBDLXejpTgi4If0EB1nkTVhgQtvCFIMKhiwjNcnUL0+RN/HROtdwbzv3jLfwCzK/pv4IaB1xsphJaOCTqbatMcRvBSwMP06aefijQqLgjsW/xjn2U0C6JxzJgxTSJ3zo6L0UuBW4MeflP0Bh8TPiae4rIowZwZSC9AJKDFNzwluPKV3R1hiINxFSWEs2fPFgMvmiph2ZX1jsDg4qscNX48AvUDoi3LRAUDfBGy1wPSDzAPBqItua+PiXZ2YAhTb/0vmF0hSJDqw/+AQdPXx0RbLooUkhEGI5xTECVyviBtTw6AYyjLe/FcR5EUPXx/9AofEz4mfJ60Hpd/VZA3hxhB+gRXuRg8J06caJNKuf3220V76mOOOYZmzpwpBIx2cG1pfVsB3geA0kNc5aMRFdJispfLaaedFnRhcESCZDkwJmxr166d1947EE3UjFIO7E4FjvYxrTGWYRhGd5ESXAVAROAGYSE9IloSExPppZdeEuuRZrF3r7e0vi2gNbliMJNXoy+++KJIZyEtgEnObrrpJvr8888pWEBvjPz8fK9W3jirwLn88svJX5U3OIe9vT++AhEknG8oz3dUgaN9rC1eLDAME3g8ir86EiT265sTHC2tD2a0V/LawRRpqk8++UTt3YFuucE0S6k3m6YFurMrRLVMRaGKKDIykowADOayYyk+D8xg7ChSggsQ7bnJMAzjLzgp7Ge0g6Y27QCSkpJowoQJYhkDhtaDYXR8KUq0nV3h9fB1Z1fttPZ6b5pmj0zLIJ32999/q4/D/Cp9MjDAakv3GYZh/AWLEh2JEoCKBwlKXIMFX4oS+86u2v/V1pumueorwXkpe0uwn4RhmEDBoiRAokSaXJsTJX/88QcFC/4SJf5I4RjR5NpSZ1f2kzAMowdYlPgRGD1lVY3W5KpFO99IsERKtJU36OWAKi5fihJZ3eSPSIl2Vlkj0KtXL7XyCZESfDZyWcKREoZhAgWLEh2lbqQJWPbZwLxAsmLFyKDbZ1FRkVj2VaWKNmLhjUgJOps++uijTaavh5dk7dq1YhmN/1BRZiRgYpUt51ERJUWyjJRAMMqpIxiGYfwNixKdiRL7FE5zk6cZBe1sq74SJRhM0e69tWZX+CowfQK6FC9cuJCOOuoo+uGHH9T1KNuGKdSIJldnvhI0Mjx48KC4j47C3BSNYZhAwaJEh6IE7cAlwZDC8bWfxFtmV3QzxYzNs2bNUtMaECBTp06ljz76yPB+Eme+Eq3w5dQNwzCBhEVJgEyu8qreEcFWgeNvUeJJCgfbiCjBV199Je6j74mcG6a2tlbMUTR//nwbP4lRIyX2MwZr/STcNI1hmEDCosRPwBuC+YLkYNbcvCKYnRU3OUeJ0ZuoaUUJmo3pTZR8/PHHIkKAma8BjKDffPMNffbZZ3TJJZeIxxA5ueqqq+iVV14xfKQEMyvD8CrnYdLOWs2REoZhAgmLEp2lbuyjJWVlZTaeDKOBwVw2gevatatPm3J5YnZ9//33xfxNWp8IXnvCCSeIrsMQIXfccYf6/IKCAnVgx8zXRkVGRDDXEoSvNO6mpqYGeMsYhmnLsCjRuSgxegoHJkrMMA18PUcMzK7azq5Iu7TE888/ry6ff/759Pvvv4vBWYI0zuOPPy5uzlrbGxFHERGOkjAME2hYlAR4zhtnBEsTNX/5SewFHyIALZldkRZbv369GsV56623xPwwjkC05NVXX1UrU6ZMmUJGxpF3hP0kDMMEGhYlOjO5aq/EMUmf0SMl/igH1qIVfC01UdOW97oS+bj00kuFB+PDDz+k66+/nozM4MGDm0ysyZEShmECDYsSnZlcJZh5Vl7179ixg3Jzc8mIBCpS4oookU3Q3OnMiufBgyIFo1FB12DtscL+GNW4yzBM8MCixA/g6todP0kwNVGTogRRCH90CkU3XBnx8IUoCSa0kRGIZQhhhmGYQBIW0P/eRnDX5OrM7HrKKaeQXkEaBPuJlMiuXbvEbffu3aog6969u1O/hjeJi4ujvn370ubNm4XZFd4SZ4NtMPQcaQ1aDwmnbhiG0QMsSgwkSvQEKlv+/vtvWrp0KS1ZskRsX3PVLv5MDcBXAlGC7YEwQVO05iIlCQkJlJmZSW0NiNxx48bRvn376Nprrw305jAMw7Ao8acoQaQAV/Gugj4YXbp0ob1794peEnV1daJ3RqBLfG+++WZavHgxlZaWtvj89PR0GjRoED300EPkLyBC3n77bTWF40iUwOeDfZGpGyOX93oKjK6//fab6CXTFvefYRj9wZESH4NmW0hjuGNytY+WQJRUVFSI8tVAmxEfeeQR+uSTT5o83qNHD5o4caKo6kCqBjdEH8xms9+3UVuBg2jO1Vdf3eQ5bd1PooUFCcMweoFFiU5NrlpRghJUgBRJoEXJL7/8Iv5CXKEKZdKkSeKGPh96QYo/9CFxZnZlUcIwDKM/uPrGx2gHRU9FiV6aqCHqs3XrVnVf3nvvPdG7Q0+CBCA6IyfTQ/UPokzNiZK2aHJlGIbRIyxKfExrJzvDgCmbXAXa7KotS9aKJT0iUzgNDQ20evVqp5U3iKj4o38KwzAM0zIsSnwIqj+WL18ultPS0lzq5GoPmlrJCAvKbHNycihQaEXR2LFjSc9oza32KZyamhp1kkB8JvadTRmGYZjAwKLEx1U3mOUXHHPMMR4bCvVSGqz930aJlEizq5YtW7ao5cucumEYhtEPLEp8yM8//6wuH3vssR6/jx58JTCN/vXXX2qpcufOnUnPoAxZtoK3j5Rom6a19cobhmEYPcGixIcsW7bMK6LkqKOOcvie/gSGURn10XuUBECQSMEBc25xcbG6jitvGIZh9AmLEh8B3wIaU8nIQs+ePT1+r9TUVOrfv7+aEiopKSF/Y6TUjSNfibY0mytvGIZh9AmLEh8BH4MsRW2Nn8Q+0oI0ijTP+hNt2sgoosSRrwTdS2X6pkOHDuLGMAzD6AMWJTr3kzh6j0CkcGSkBGkRzMRrBBxV4Bw8eFC0mAdscmUYhtEXLEp07ieRTJgwwaHg8Qd5eXm0fft2sYzyZKNMcY95huTMxDJSwn4ShmEY/cKixAdUV1fT77//LpYxoV63bt1a/Z4pKSmiogSgGVhRURH5CyM1TdOCyQtlVGfPnj1CXHHlDcMwjH5hUeIDUDpbVVXlNT+JfcQFXUq1nWJ9jRFNro58JUjhsMmVYRhGv7AoMYCfJNC+EiOLEntfiRQlSEH17t07gFvGMAzD2MOixECi5Oijj1ajLv7yldTV1dHKlSvFMhqmobzZqJESzHC8bds2sYwJ+5DeYRiGYfQDixIvg7SN9GDAS5KZmem1905KSlIbguGKH7P2+poNGzZQeXm5IaMkAP1hEhISxPLSpUtFSTDgyhuGYRj9waLEB6kOGF2ln8TbyMgLBldc+fsaI/Yn0RISEqJOaCgFCeD28gzDMPqDRYlBUjeO3tNbKZxXXnmF7r77btq3b5+hZwZ2xVciYVHCMAyjPzipbjBRMn78eHH1jwocb5hdFy9eTFdffbVYRrksREi7du2aiJKoqCjDpjy0vhIJixKGYRj9wZESL4K28nImXXgZOnXqRN4mMTGRhg0bJpbXr19Pubm5Hr9XbW0t3XTTTep9TFw3ffp08TjIycmhnTt3imWkQOSsu0aPlHTt2lX1mTAMwzD6gUWJl/0XckD3hZ/EUQSmNb6SuXPn0pYtW2weW7JkCV1//fXCf2HUpmn2oIEdms9JjBrxYRiGCXZYlBgodeNNX8nhw4fpwQcfFMsoM541a5YaCZk/fz49//zzhu5PogX7p03hcOqGYRhGn7AoaYaysjL66quvqLi4WFei5KijjqLQ0FCx7Kmv5N5776WSkhKxfPHFF9NFF10kDK+SW265hd56662gECVg9OjR6rKsxmEYhmH0BYuSZrjyyivp1FNPFXPOZGVltShg5KRvffr0oY4dO5KviI+PVwfWTZs2iaiHO6Cz6Wuvvaa+16OPPiqWL7jgAiFWAIy0mFEXoNeKL/fHH1x77bU0ZcoUIb5OOumkQG8OwzAM4wAWJU6Ap+Lbb78VyyiVnTRpEh06dMjhc2tqaujSSy8V3U997Sdpbct5iI0bbrhB7dmBFE5qaqq6/uGHH6azzjrL5jVGLQXWAk/Jd999R2+88YYaZWIYhmH0BYsSJyD6oJ2JF1UokydPpvz8/CYRklNOOYU++ugjcR+tyyFQfI2nvpJ3331X9Yr069ePrrvuOpv1KDd+8803bVIcwSBKGIZhGP3DosQJmzdvbvLYxo0bRQpAejHQ5h1C5YcffhD3zWYzLVq0yGGzLm8zbtw4de4WVyMlpaWldOedd6r3n332WQoPD2/yvOjoaPryyy9FmuPkk08WnhOGYRiG8TUsSpwAr4bk9ttvVz0V8GNMnTqVtm/fLibIk2Wz6HsBceIvv0JsbKwqftBfRPo/muOxxx5TU1CnnXYaHX/88U6fm56eTt988w19/fXXFBMT48UtZxiGYRjHsChxIVKC9Az6dyQnJ4v7y5cvF6kPRE5AWloa/frrr6Iqxp9oUzjYvuaA7+XFF18Uyyj9ffrpp32+fQzDMAzjDixKXBAlECD9+/cXLdlRrQLq6+vF3+7du9Nvv/1GgwcPJn+jjXRIU64zsI1I34Bp06ZRjx49fL59DMMwDOMOLEpaSN+gakN2A4X5EykNeEcASoUx2AdqgIcBVbZLh2CS1T+OwHZLkH5iGIZhGL3BosQBqLrJzs4Wy4iQaEGKBmkbVLGgrXwg+3fApHrCCSeo26ztwOpMlKC6Rr6GYRiGYfQEixIXUjf2dOvWjc477zxhNg00qI5xFA3RgnJmmGFldEU7CzDDMAzD6AUWJS1U3jgSJXoCJcqY26U5X4n2ca2IYRiGYRg9waKkhUiJffpGb6AbqywNXr9+Pe3du7fJc7QRFBYlDMMwjF5hUeJB+kZvaIWGfbSkvLxcba7WqVMnGjhwoN+3j2EYhmFcgUVJM+mbuLg4ysjIICOLkp9++omqq6vV58lUD8MwDMPoDRYldlRUVKgzAiNKYoRBfNiwYdShQwexvHTpUqqqqlLXceqGYRiGMQosSuxAlYqcQdcIqRtZ5ivb20NUyXQN9kOKksjISJo4cWJAt5NhGIZhmoNFiYErb1pK4WzYsIH2798vlo855hiew4ZhGIbRNYYTJd9//71P399IlTdajjvuOHXWYERHtFESwFU3DMMwjN4xnCg566yzqLa21mfvb7TKGwnazY8fP14s79q1S6ShWJQwDMMwRsJwogQlrqtWrfJ5+gYeDHRuNRLaaIhsgw/69OkjJg5kGIZhGD1jOFECfvnlF5+8LyIwO3bsUAfy0NBQMqooeeqpp6ihoaHJ4wzDMAyjVwwpSn799VefvC8EiZxp10ipGwmElIzuaMuCWZQwDMMwRsCQomT58uVUX1/v9fc1qp9Egp4q9gIEDeAwszHDMAzDBJUoQYnp7bffTmeeeSbdeOONtG7duibpj2effZbOOOMMuuiii9R+Ga6ud5WSkhJau3atW6+pqamhjz/+WGz7rbfeSmVlZc2WAxup8kaLvSiZPHkyRUREBGx7GIZhGMbrogSD+NNPPy2adOHvkCFD6LrrrqPDhw+rz3nxxRdp5cqV9N///pfOPvtsuu+++0QViKvrfZHC2bZtmxBSmPdl+vTp9MUXX9Dnn39Ojz76aNBFSmQ/ErPZrN7n1A3DMAwTdKIkJiaGXnvtNTr22GOpa9eudMkll1BycrIaLUE6ZdGiRXTDDTeIKAPEy4QJE4QIcGW9t82uP/zwgxig4bOA6TM3N9dm/UsvvURFRUUORQk6pPbq1YuMSFRUFE2ZMkUso2/JiSeeGOhNYhiGYRjvihL4FbTzwJSWloooCSIQ4NChQ+KxAQMGqM8ZNGiQGglpab2rtGvXTo2UyOoSe9DFdOrUqTbCJTw8XERnZOQA24LIjQTvtWXLFrHco0cPURJsVBDJuvjii+ntt9+mjh07BnpzGIZhGMYllBagboJuoUh/jBs3Tk1zSI9GbGysjclSPt7Semc+ENy04H9+/fXXVFBQIDwuAwcObPI69OiQDdYQ8bjiiivoggsuoPbt29P27dvpu+++EyIE/hZ4Y6Kjo2n37t1UWVkpXoN9ciZ4jEBmZia9+uqrYtmV/ZDPMfI+exs+Jnxc+Fzh7w//pngXZCF8Ikoef/xxESWZN2+eTdoAYGDHIC8nh5OPt7TeEa+//jotWLDA5rG0tDR1Gd4QCBt7wYQ0k+SVV14Rg7Sc/RemT0RLvvrqK8rLy6Mnn3ySZs6caRNVSU9PV2cKbkvs27cv0JugO/iY8HHhc4W/P/yb4h1caUjqtiiBPwMRCngytIZKDORIkaDFuYxeYBmCwJX1jkAKYsaMGTaPwcMi26djO+xfj6ocmFvBmDFj6Oijj25yBXzVVVcJUSKFz9133035+fnqc0aNGtXsdgUbOCYYfDt37uySkm0L8DHh48LnCn9/+DfF/7glSp5//nn6999/6eWXX24SoUAEAsbVN998k2bPnk0HDx4UZtOHHnrIpfWOwGvsy1mPPPJI8b/hCUG/Enuvy3vvvacuI2XjaJBFegbREogbDMYffPCB6icBEE1tcXDGPrfF/W4OPiZ8XPhc4e8P/6b4D5dHoOzsbHrrrbcoJyeHzj33XFE9g5u2egalt4g4QHzgOehHom3c1dJ6V0BFiXwNUkgyKiIrfKQowfNQAuyMu+66S12eM2eOiLpI+vbt69Y2MQzDMAzjx0gJTKLaWWcl2ogJSoTh5yguLhbVK/Z+kZbWuwpEDcyqAF4QlP2Cn3/+WURgAAQT/p8zxo4dK1I7qOLRVgAhhaE14zIMwzAMo7NICSan69ChQ5ObNK1qSUhIaFZwtLS+JbQ+EW0TNZTAalM3LXHPPfc0ecyonVwZhmEYxugY0kAwYsQIVQwhUoKKm/Lycvrss89U0YM+JS1x/PHH07Bhw2weM2onV4ZhGIYxOoYUJajiQfpFNkrbs2eP6BYre55MmzbNpUgMDLKovNHCooRhGIZhAoMhRYl9CgfRknfeeUe9f/7557v8PjDb9u7dW73PooRhGIZhAoNhRQnMrhLM/ovyYtClSxcaP348ueOVeeaZZ0S1zvDhw2n06NE+2V6GYRiGYXzQ0VUPoF8JKniqq6vp22+/VR9HszV3e22gUgd9T5AWgkhhGIZhGMb/GDZSAs8IOq/a407qxv79WJAwDMMwTOAwrCixT+EApF+4pJdhGIZhjElQiRJXepMwDMMwDKNPDC1KYEqFQRXAR3LOOecEepMYhmEYhmmLoiQmJkaNjlxyySWUlpYW6E1iGIZhGKYtihKwcOFC2rt3L73yyiuB3hSGYRiGYdpiSbC2Kysm0WMYhmEYxtgYPlLCMAzDMExwwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdwKKEYRiGYRhdYLJYLJZAbwTDMAzDMAxHShiGYRiG0QUsShiGYRiG0QUsShiGYRiG0QUsShiGYRiG0QUsShiGYRiG0QUsShiGYRiG0QUsShiGYRiG0QUsShiGYRiG0QVhZFDee+89WrNmjVgeMWIETZ8+3WZ9fX09LVq0iNavX08JCQl09tlnU8eOHdX177zzDq1bt069Hx4eTo8++miT/1NXV0ePPfYYdejQga688krSM7/++it9/fXXYjktLY1uueUWh89Zvnw5hYSE0KmnnkoDBgxQ1y1btoy+/fZbm+fjPfBerqzXIzt37qT58+er9/FZhoXZnvabN28W+1VaWkrHHHOMuEl27NhBr7zyis3zca7hnJM0NDTQd999R3///TdlZGTQ+eefT2azmfQKzul77rlHvY/zukePHjbPycnJoY8//piys7Np0KBBdMYZZ6jHrbq6mu6//36b548ePVo8B6xevZref//9Jv/3iiuuoJ49e5Je+d///if2F0ydOpWOPvpom/WVlZX00UcfiXOqU6dOdN5551FsbKy6/qmnnhLHTYLn3HDDDer9iooK+vTTT8U5lZiYSKeffjp169aN9Mwnn3xCK1euFMuDBw8W57YW9N7Eb86qVasoLi6Opk2bRp07d1bX43j9888/Nq+ZM2eO+P1x5fV6ZMWKFfTZZ5+J5aSkJLrrrruaPOfPP/8Uv5f4bTj55JNp6NChNuvxHfr8889p06ZN1L9/f7HfoaGh6vqtW7fSF198QVVVVXTcccfRuHHjqK1g2EgJviDHH3881dbWig/Wnocfflh8oXAy1NTU0MUXX0xFRUXqeggSiBW8B2744B3x9ttvi5Pw33//Jb2TmZkp9gUCCttszwcffCCEV69evSg1NZWuvfZa2rBhg7p+z549VFZWph4T3LQ/ui2t1yP40cB2HnHEEfTTTz8JsaoFAygGy/j4eBo4cCA9/vjj4sdAkp+fT1u2bLHZZwgP+3Pt3XffFQIP4nb27NmkZzAgyH3566+/qLCw0GZ9QUEBzZgxQ3xfRo4cKQYNDCQSHEMcy4kTJ6rvgx9WCcS/9nh17dqVfv/9d12LVzB27Fixvfv376esrCybddjnq666SgywOCbbt2+nq6++2uZ8wkDUu3dvdb/HjBlj8x433XSTuCjA6zEYX3jhheJ/6Rmc09gXnDPaizjJE088QW+++SYNGTKETCYTXXLJJTbCDL/NEOja8wHPc/X1egRiE/vRpUsXcV7b8+WXX9J9990nzns89+abb7YZP3DO4NzBuTB8+HCxvwsWLFDX79q1iy677DKKiYkRFwu4gIDAaSsYNlKCAQQ3DKpasQHwI/vNN98IUYITQw6ouI8PW9KnTx+nYgTgBwNXwLgy/uOPP8gIogQ3nPQYbOxBdAg/jCeeeKJ65ffaa6+JK0TtgNLcMWlpvd5o166d2N59+/Y5jbjhivXyyy9Xn//000/Taaedpv54Qng522dE4vDDhKhcdHS0eKykpIT0DAYYuT+OBBRECH5M7733XnF/1KhRInKA745WWBx77LEUGRnZ5PV4jvZ5uNLGc/UuYBHtAfgs7YF4xW/I4sWLKSoqSnyHcI5gYMG+SRBBs78qBsXFxSIaAMGLYwvw24WLh7POOov0Sr9+/cQN0SHctOD3A5EfiAo8Bxw4cEBc/GgjRIiOOfr+uPp6vYHPDzec+zgfHP3OXnPNNXTmmWeqkclXX31VXBgBRGVxobxw4UL1N0b7m/H++++LaO11112nPobnaiO4wYxhIyXNgatboL2ixbJM90iWLFkiwtBQqfZXiwBXh/hyREREUDCAK2DtMUlPT6e1a9faPGfjxo3imDz//PO0e/fuJu/R0nojniv25wmuXA4ePKg+lpeXRw8++KAIzyNFowURA4T5MZA/8MAD4gfW6OeL/TFBVA2pG/vvD6JKiLzhR9bZFFoIU//4449C1BgZnAPJyclCkAAcD0Qk7Y8JImY4VyB2EXqX4KoXkdnDhw+L+4jw4jjjO2jk3xOkJ+x/U+yPyS+//CJ+M5BGxXF09/XB8JuCKBP2FcgoI4TKQw89JFJBWsG+bt06cSGgFctI5+C71BYISlECFYsfDxlaw48Dwmda4XHBBReIKxR8+PjAzznnHPElkSBCgvc46qijKFjAFYs8JhhEEG7GFZwMQUOJ42oY+UtcxSB/jEiApKX1RgSpLBwHOajK4yPPFay/4447xD4j533bbbeJqzsJruxwtQyxhrA8Imp6vspzBewzIgPwQAAIMQyiMiKJK0QIdqQ7unfvTi+//LJIYTkCAxIiSEceeSQZ/bsDoYpoCYBwRQpH+5ty++230wknnCAiJRBi+K7gKlmKGIjaRx55RJwfiL6ecsop4hgaFYgyfCfkdwbnCM4VbeQa+4nfVvzO4tjhvvTtuPJ6o//OAixDUMjvE34zPvzwQ3EfaStEhrQRy6KiIuE5kiB6C0GD3+q2gGHTN80BMYHBA1euMOnhJMAXQGskwuO4AVzFIZeJ0Cr+IpT20ksv2RgkgwHkNnFcEEYuLy8XJzsUujwuSHXJdNeUKVNEaPGtt96iJ5980qX1RgSfN7wC5557rvCVYPDAfuGqVnpStKFnXPXg3JChWQg6HEdc8UjhNnnyZDFgYXA3IvhsIcphvoPowNUt0nY4PgDni/aYwDsBgY+Qdfv27W3eCxGkk046STU2GnmgwTkCbxp+N/bu3StMqvI80aZ/5DGEkRwD0oQJE4Q4gWEa74NzBL9JMDpiWc/m3+bAdwWCHQPqV199JcQGfmdxwSKB10j6jfA7i3MEoh5+Nldeb0RuvPFGccOFCtI0OEfgNZPpXfxmwBOJ54C+ffsKf9Gtt94qxi58v6SYlWIN2Bv0g5Wg3Uv4BPBjCac7DEkYPJv7UDHYytAirnxxUjzzzDOqtwRXRvgCwZhlVIYNG6Y6vjHAwMAJ46ozMCChMsXT9UYAP4K4UsExwY8FfhRQjaKt1NKCgQgRNURWIF4Qbtb+gODKDzdt1M1o4Hsyd+5ccX5gP2B2hH8CfiVH4PuF1+D7oxUluA9vE35sgwEMIvhdgT8JAwl+D5wdE5xHOIfkbwouBLZt20Y//PCDehGAMD/OPZgijQq8NYgQYt/wXUAqorkreilyPX29EYAIk7+zECIYP3Jzc1VhjgsbbdoO54mMhEQ1njeHDh1S12MZj2ujJ8GMsS9fmgFhQPxAIuwOpYkfA1z5AajX3377TX0uQrAwnElFD0MSSl2lWxyGWFnFYWRgrMMPIq7o8KVA/huhVQlynTKNgWOEELQ0oLmy3ohggMGPJAQbwu4w/mrLX3Glq/UGIIKAAUka1OAn0Zqt8UMEoWdfYmskILKQ7sRni+8PyjoxmMjycaTs8CMrwXcLPhr7ARpmc7zG2cBtNPAbgX1BSheCDRU6iIgARD7wmASRMlwQye8HPCWIAGjFKl6jd/NvS6AaCYMl0lD4ziDigfYLkqVLl6rLiECjHYH2N6Ol1xsR/AZAZCBliYtdXBBr92n8+PHC/C0jIPiNwfgC7xbAdw6/M/JiB8ZrPGb0aGPQR0pwssP5jC8+PlxcteDDRp4WIP8LIx6ECa7mUYKFH1aADxcf9AsvvCBOBAwq+NARZgZQqtorZURJ8AOi96oTeGPg0sb2wlCHY4Ire+w7QCQAXhrsG35AUTWAHLj2h/S5554TP7xw2uPYaXuztLRejyBNhdSKFBa4KsUAKnvSwB+BiiT8MEKg4Nho9wmvxw8KeifgmCI3DG+ABFd5kyZNEs9BOSjOJXgGUlJSSM8g4gcxhrw20goQHjgOEKv4fsCwi/QlBBaeI6OGAKFohN9xzPDDiRJGGBlleForSow0wMjeR7hqh+iC+EJaSvphEPXBbwauWuGPgKFVigqUvc6aNUuIdTyG1yIkLy90IM5wnuAiAFWD+H3C7xZeo2dw8YYSV5ja8V3AbwqOh6wYQrQHPhFUW2Ewvuiii2zKw3/++WfhOcL3CukMiH/Zz8aV1+sRfPbz5s0T244LWhwTRD9kOgbfH6SFEYXFbyb8NIg0ShBtQ4kvLpLxfcP4hPNAXuicc845Yj2OC84lnIvakuFgx2RxZpvXOTgxIEjsw8gYGCT44uMHE1e2jgYJvAeeg9fJMj1HYLDCQC9LuvQKviQwKGrBwKFt9IUregyc2Gfc7JEGPggOeCK0PQVcWa838MMPs6UWRIu0ZZwQLBiMkPvVRkEkaKqGH1T8QOD8clRdg3MROXH4A/TejwPAkCuNdxJE0OQgiys9VAHI/DeEiBaIMxwT/HzgmCBl5ei44wrYXqzoFXwvpAlTggFSG2rH54xBAr4S+ygHjhkuDBCGx3ng7DcH4XykT/HeevcJ4LcP+6QFA7A22oFjhuOC80Be7WuB/wbvg99YR1Gzll6vN/Abat8QDue/tmIGUSEIU5w7jhrk4XuD8w2iH7858KVpqaurE5WR+J5ByOm5GaO3MawoYRiGYRgmuGgbSSqGYRiGYXQPixKGYRiGYXQBixKGYRiGYXQBixKGYRiGYXQBixKGYRiGYXQBixKGYRiGYXSBvovkGYZh3AC9U9AHBB1Ug2kyTYZpK7AoYRjGJdDgCp18AZqpYfp1LbL1OvClKEB3ZTSeAuiSqm1AhrlT0K1ZtoNnGMZYsChhGMblqR207a4xpQGmXgfowXjPPfeI7p0A3Tt9JQowuZ2clRkdY+27yTIMY1xYlDAM4xGY4VaKEky0JgWJMzClA6ItaMeOeajspznQpl4wFxWeixlSEfWQ7ckxP4qMkshJIjEXDeYmsp8zBULJ0XswDKNfWJQwDOM2mPsIggDzlmCuH0xmJx/XziAMMJEbJq/D5GwQI5g7CKkeRDkeeeQRMeeQNvWSnJwsBASehzlGMP8OJpXEJGeYLE87Gy8mjEP6BvOOaEUJ5hTBxIqO3oNhGP3C1TcMw7gNZonFQI/ZhTFhGwZ+CAk5o64WzNYNQYIJ1yBeMJU7JhnDpIAQK44mlrzsssto7ty5dO6554qIB1JFiLBghlU5Qy144IEHaPbs2eJxV9+DYRj9wqKEYRi3OfPMMykyMpK++OILeu2118RjGPztZ1jGTMSLFy8WyzDGIqqBqd1hUJVpHwgILYicSHEjUy6YLRXTxLuKN96DYRj/w6KEYRi3SUxMpJNOOklM0Q4DLETA1KlTmzwvJydHRFQA0jKSlJQUdRmeD/v3loSGhqrL7kxo7o33YBjG/7AoYRjGIxAZkZxxxhnCcGpPfHy8TdRE6zNx9ByGYdo2LEoYhvEImFYvvvhimjx5Mk2fPt3hc5KSklQD6rp169TH5TIqcOyrcFoC5lWJjMIwDBMccPUNwzAec+2117b4nPvuu088D9U6L7zwgoiofP3116K/yKxZs9z+n3379hUpGQgSmFcHDhwobhkZGR7uBcMweoFFCcMwLtGjRw8RFWkOiAMYSrWeEVTdfPzxx/Tdd9+Jkl4AkQJPivZ5AwYMECme1NRU9bH09HT1f8r0EIyrqKpBRQ9MssuWLRPvA1Hi6nswDKNPTBZ2fjEMwzAMowPYU8IwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwjC5gUcIwDMMwDOmB/wO4yGlRd4tdywAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "c4c86a23",
+      "metadata": {},
+      "source": [
+        "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
-    "\n",
-    "if runs:\n",
-    "    best_run = runs[0]\n",
-    "    # the model is logged via MLflow's logged-model API, so resolve its model_id\n",
-    "    # from the run outputs and load it with a models:/ URI\n",
-    "    model_outputs = mlflow.get_run(best_run.info.run_id).outputs.model_outputs\n",
-    "    best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
-    "\n",
-    "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
-    "    print(f\"Model URI: {best_model_uri}\")\n",
-    "\n",
-    "    best_model = load_model(best_model_uri)\n",
-    "    # global models (e.g. LinearRegression, NBEATS) need the series at predict time\n",
-    "    # because we save with clean=True; local models (e.g. ExponentialSmoothing) don't\n",
-    "    if isinstance(best_model, GlobalForecastingModel):\n",
-    "        best_predictions = best_model.predict(n=len(val), series=train)\n",
-    "    else:\n",
-    "        best_predictions = best_model.predict(n=len(val))\n",
-    "\n",
-    "    train[-50:].plot(label=\"Training\")\n",
-    "    val.plot(label=\"Actual\")\n",
-    "    best_predictions.plot(label=\"Best Model Forecast\")\n",
-    "    plt.legend()\n",
-    "    plt.title(\"Best Model Predictions\")\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f1ec89e",
-   "metadata": {},
-   "source": [
-    "## Model Registry\n",
-    "\n",
-    "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived).\n",
-    "\n",
-    "```python\n",
-    "# Register a model from a run\n",
-    "result = mlflow.register_model(\n",
-    "    model_uri=f\"runs:/{best_run.info.run_id}/model\",\n",
-    "    name=\"darts-air-passengers\",\n",
-    ")\n",
-    "print(f\"Registered version: {result.version}\")\n",
-    "```\n",
-    "\n",
-    "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
-    "\n",
-    "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
-    ">\n",
-    "![Mlflow Charts](./static/images/mlflow_models.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7af49ea9",
-   "metadata": {},
-   "source": [
-    "## Important Note: Custom Flavor\n",
-    "\n",
-    "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
-    "\n",
-    "**Always use:**\n",
-    "```python\n",
-    "from darts.utils.mlflow import load_model\n",
-    "model = load_model(model_uri)\n",
-    "```\n",
-    "\n",
-    "**Instead of:**\n",
-    "```python\n",
-    "import mlflow\n",
-    "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
-    "```\n",
-    "\n",
-    "This custom flavor is necessary to properly handle:\n",
-    "- TimeSeries objects\n",
-    "- Darts-specific model parameters\n",
-    "- Covariate handling (past, future, static)\n",
-    "- PyTorch model state preservation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40621b13",
-   "metadata": {},
-   "source": [
-    "## Cleanup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "51fc7c4a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.573637Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.575115Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpyqkqfzoe\n"
-     ]
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-    "# import shutil\n",
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-       "layout": "IPY_MODEL_a5f3118f4f9040ebbdb1a7acae012087",
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- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

From a486b25996134f29c42c56a4bb6351d75596bf88 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 25 Jun 2026 15:15:06 +0200
Subject: [PATCH 081/154] feat: handle name param for metrics

---
 darts/tests/optional_deps/test_mlflow.py | 33 +++++++++++++++++
 darts/utils/mlflow.py                    | 47 +++++++++++++++---------
 examples/29-MLflow-quickstart.ipynb      |  4 +-
 3 files changed, 66 insertions(+), 18 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 228f665750..c28581e8bf 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -853,6 +853,39 @@ def test_autolog_metric_multi_series_per_component(
         assert got[("series_mae_linear_1", 0)] == pytest.approx(ref[0, 1], abs=1e-5)
         assert got[("series_mae_linear_1", 1)] == pytest.approx(ref[1, 1], abs=1e-5)
 
+    def test_autolog_metric_name_override(self, mlflow_tracking, autolog_context):
+        """The metric `name` kwarg overrides only the metric-name token in the key,
+        keeping the dataset/backtest prefix and the quantile/axis suffixes."""
+        actual = self.ts_univariate
+        train = self.ts_univariate[:40]
+        qmodel = self._fit_qlr(train)
+        pred = qmodel.predict(n=10, num_samples=200)
+        target = self.ts_univariate[40:]
+
+        with autolog_context(log_metrics=True):
+            # direct call: name replaces the metric token; suffix (_q0_5) preserved
+            with mlflow.start_run() as run_direct:
+                dm.mae(actual, actual * 1.1, name="custom")
+                dm.mql(target, pred, q=0.5, name="myq")
+            # backtest: name replaces the metric token; backtest_ prefix preserved
+            with mlflow.start_run() as run_bt:
+                self._fit_lr().backtest(
+                    self.ts_univariate,
+                    metric=dm.mae,
+                    metric_kwargs={"name": "custom"},
+                    retrain=False,
+                    stride=10,
+                )
+
+        direct = mlflow.get_run(run_direct.info.run_id).data.metrics
+        assert "actual_custom" in direct
+        assert "actual_mae" not in direct, "default metric name should be replaced"
+        assert "target_myq_q0_5" in direct, "quantile suffix should be preserved"
+
+        bt = mlflow.get_run(run_bt.info.run_id).data.metrics
+        assert "backtest_custom" in bt
+        assert "backtest_mae" not in bt, "default metric name should be replaced"
+
     def test_autolog_metric_multi_series_classification_labels_inferred(
         self, mlflow_tracking, autolog_context
     ):
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index bd5ec7c439..a38bf2aedd 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -423,19 +423,21 @@ def autolog(
 
     .. note::
 
-        Logged metric keys depend on the result shape:
-
-        * **Scalar** → ``{metric_name}``
-        * **Per-component** (1-D, single series) →
-          ``{metric_name}_{component_name}``
-        * **Per-series** (1-D, list of series) →
-          ``{metric_name}_{series_idx}``
-        * **Per-series × per-component** (2-D) →
-          ``{metric_name}_{component_name}_{series_idx}``
-
-        When a dataset variable name can be captured via frame inspection,
-        it is inserted after the metric name (e.g.
-        ``{metric_name}_{dataset_name}_{component_name}``).
+        Logged metric keys follow the pattern
+        ``{dataset_name}_{metric_name}{component}{quantile_or_label}``, where
+        each part is included only when the corresponding axis is present:
+
+        * ``dataset_name`` – variable name of the first argument, captured via
+          frame inspection (omitted when not found).
+        * ``metric_name`` – the metric function name, or the ``name`` keyword
+          argument when provided (it overrides only this token).
+        * ``component`` – the component name when ``component_reduction=None``.
+        * ``quantile_or_label`` – e.g. ``_q0.5`` / ``_qi0.1_0.9`` / ``_label1``.
+
+        Per-timestep results (``time_reduction=None``) are charted across the
+        MLflow ``step``. For a list of series the logged value is the mean over
+        series and the full per-series breakdown is written to a
+        ``per_series_metrics/`` CSV artifact.
 
     Parameters
     ----------
@@ -879,7 +881,9 @@ def _log_backtest_metrics(
     (windows, timesteps, components × quantiles, metrics) inferred from the
     metric signatures and ``backtest_args``, logging every cell under a
     descriptive key with the time axis (or window axis when time is reduced)
-    mapped to the MLflow ``step``.
+    mapped to the MLflow ``step``. A metric's ``name`` entry in
+    ``metric_kwargs`` overrides the metric-name token in the key (the
+    ``backtest_`` prefix and axis suffixes are preserved).
 
     Shape inference respects all kwargs that affect output dimensions:
 
@@ -927,8 +931,11 @@ def _log_backtest_metrics(
     # backtest accepts a single dict that applies to all metrics; broadcast it
     if len(metric_kwargs) != len(metric):
         metric_kwargs = [metric_kwargs[0]] * len(metric)
+    # the `name` entry in metric_kwargs overrides the metric-name token in the key
     metric_names = [
-        _sanitize_mlflow_key(getattr(m, "__name__", f"metric_{i}"))
+        _sanitize_mlflow_key(
+            metric_kwargs[i].get("name") or getattr(m, "__name__", f"metric_{i}")
+        )
         for i, m in enumerate(metric)
     ]
     n_metrics = len(metric)
@@ -1207,7 +1214,8 @@ def _log_metric_result(
     Parameters
     ----------
     metric_name
-        Base metric name used as the MLflow key.
+        Base metric name used as the MLflow key (the metric's ``name`` keyword
+        argument when provided, otherwise the metric function name).
     result
         The metric result to log.
     series
@@ -1341,6 +1349,8 @@ def _make_metric_patch(metric_name: str) -> Callable:
 
     * ``dataset_name`` – Python variable name of the first argument in the
       caller's frame (captured via frame inspection, omitted if not found).
+    * ``metric_name`` – the metric function name, or the ``name`` keyword
+      argument when provided (it overrides only this token).
     * ``component`` – ``_{component_name}`` when ``component_reduction=None``.
     * ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.5``,
       ``_qi0.1_0.9``, ``_label1``) when applicable.
@@ -1385,6 +1395,9 @@ def _patched_metric(original, *args, **kwargs):
         raw = _inspect_original_var_name(series, fallback_name=None)
         dataset_name = _sanitize_mlflow_key(raw) if raw else None
 
+        # the `name` kwarg overrides the metric-name token in the logged key
+        key_name = _sanitize_mlflow_key(kwargs.get("name") or metric_name)
+
         # infer output axes from the metric signature + call kwargs
         has_time_axis, has_comp_axis, quantiles_num, quantiles_labels = (
             _infer_metric_axes(original, kwargs)
@@ -1403,7 +1416,7 @@ def _patched_metric(original, *args, **kwargs):
         _log_metric_result(
             autologging_client,
             run_id,
-            metric_name,
+            key_name,
             result,
             series,
             has_time_axis,
diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
index c64140f653..989ee5a94f 100644
--- a/examples/29-MLflow-quickstart.ipynb
+++ b/examples/29-MLflow-quickstart.ipynb
@@ -927,7 +927,9 @@
         "\n",
         "Per-component results (`component_reduction=None`) add the component name (e.g. `val_mae_<component>`), quantile metrics (`q`, `q_interval`) add the quantile (e.g. `val_mql_q0.5`), per-label classification (`label_reduction=None`) adds the class (e.g. `val_f1_label1`), and per-timestep metrics (`time_reduction=None`) are charted across MLflow steps.\n",
         "\n",
-        "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is saved as a CSV artifact under `per_series_metrics/`."
+        "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is saved as a CSV artifact under `per_series_metrics/`.\n",
+        "\n",
+        "Passing `name=` to a metric overrides the `{metric_name}` token in the key while keeping the prefix and suffixes (e.g. `mql(..., q=0.5, name=\"foo\")` logs `val_foo_q0.5`)."
       ]
     },
     {

From e2c67589fa8b353b0bb51fc25ce83bd887aa795b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 25 Jun 2026 15:43:22 +0200
Subject: [PATCH 082/154] chore: changelog

---
 CHANGELOG.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 541d12fa6d..b62e8f1ef8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,10 +11,19 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Improved**
 
+- 🚀🚀 Added a custom MLflow model flavor for Darts, available under `darts.utils.mlflow`. It provides an MLflow integration for any Darts `ForecastingModel` (statistical, scikit-learn-like, and PyTorch-based). [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
+  - Added `save_model()`, `load_model()`, and `log_model()` to persist and reload Darts models as MLflow models, including model and covariate metadata.
+  - Added `autolog()` to automatically log model creation parameters, covariate usage, metrics, and the trained model artifact on every `fit()`, `backtest()` and `historical_forecasts()` call within an active MLflow run.
+  - Metric functions from `darts.metrics` are automatically logged when called inside an active run, with keys reflecting the metric output shape (per-component, per-quantile/interval, per-label, and per-timestep results charted across MLflow steps). Multi-series results log the mean over series and write the full per-series breakdown to a CSV artifact.
+  - Added a new notebook for [MLflow quickstart](https://unit8co.github.io/darts/examples/29-MLflow-quickstart.html) with detailed usage examples.
+  - Note: model serving and deployment (MLflow's `pyfunc` flavor, model signatures, and input examples) are not yet supported.
+
 **Fixed**
 
 **Dependencies**
 
+- Added an optional `mlflow` extra (`mlflow>=3.0`) enabling the MLflow integration. [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
+
 ### For developers of the library:
 
 ## [0.45.0](https://github.com/unit8co/darts/tree/0.45.0) (2026-06-19)

From de8e14928bdc236ca75d295d745ae0153190652d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 09:48:14 +0200
Subject: [PATCH 083/154] docs: documentation clarifications

---
 darts/utils/mlflow.py | 44 ++++++++++++++++++++++++++++---------------
 1 file changed, 29 insertions(+), 15 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index a38bf2aedd..1097046e17 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1,14 +1,19 @@
 """
-MLflow Integration for Darts
------------------------------
+MLflow Integration
+------------------
 
-Custom MLflow model flavor for darts forecasting models. Supports saving, loading,
-logging and autolog for any darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
-to MLflow.
+Custom MLflow model flavor for Darts forecasting models. Supports saving, loading,
+and logging any Darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
+to MLflow, as well as automatic logging (``autolog()``) of:
 
-This module is partly adapted from and inspired by the open-source
-implementation of SKtime's MLflow integration, with modifications to support
-autologging and handle Darts-specific model and covariate metadata.
+* Model creation parameters and covariate usage information.
+* The trained model artifact, after each ``fit()`` call.
+* Darts metric function calls made inside an active MLflow run.
+* ``backtest()`` evaluation metrics.
+* Per-epoch training/validation metrics for PyTorch-based models.
+
+See the `MLflow quickstart example <https://github.com/unit8co/darts/blob/master/examples/29-MLflow-quickstart.ipynb>`_
+for an end-to-end walkthrough.
 
 References:
 https://github.com/sktime/sktime/blob/main/sktime/utils/mlflow_sktime.py
@@ -410,6 +415,9 @@ def autolog(
     5. For PyTorch-based models: leverage ``mlflow.pytorch.autolog()`` to
        automatically log per-epoch training and validation metrics.
     6. Log the trained model artifact at the end of training.
+    7. Patch ``backtest()`` to log evaluation metrics under ``backtest_*`` keys.
+    8. Patch ``historical_forecasts()`` so that its internal per-window
+       ``fit()`` calls don't each spawn their own logging.
 
     .. important::
 
@@ -432,7 +440,11 @@ def autolog(
         * ``metric_name`` – the metric function name, or the ``name`` keyword
           argument when provided (it overrides only this token).
         * ``component`` – the component name when ``component_reduction=None``.
-        * ``quantile_or_label`` – e.g. ``_q0.5`` / ``_qi0.1_0.9`` / ``_label1``.
+        * ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi0.100_0.900`` / ``_label1``.
+
+        When ``series_reduction`` is set on a metric call, results are already
+        aggregated across series inside the metric itself, so the mean-over-series
+        logging described below does not apply.
 
         Per-timestep results (``time_reduction=None``) are charted across the
         MLflow ``step``. For a list of series the logged value is the mean over
@@ -889,13 +901,15 @@ def _log_backtest_metrics(
 
     * ``time_reduction`` – collapses the time axis (``T=1``).
     * ``component_reduction`` – collapses the component axis (``C=1``).
-    * ``series_reduction`` – if non-``None``, windows are already aggregated
+    * ``series_reduction`` – if other than ``None``, windows are already aggregated
       inside the metric, so ``W=1`` regardless of ``backtest.reduction``.
     * ``q`` / ``q_interval`` – expand the component axis with one entry per
       quantile / interval.
-    * ``label_reduction`` / ``labels`` – expand the component axis for
-      classification metrics.
-    * ``reduction=None`` – no aggregation across windows → one value per window.
+    * ``labels`` - expand each component with one entry per label along
+      the component axis.
+    * ``label_reduction`` – collapses the labels along the component axis.
+      value; ``labels`` only restricts which classes are scored.
+    * ``reduction=None`` – no aggregation across windows -> one value per window.
     * ``last_points_only`` – collapses all windows into one TimeSeries before scoring,
       so there is effectively only one window regardless of reduction.
 
@@ -940,7 +954,7 @@ def _log_backtest_metrics(
     ]
     n_metrics = len(metric)
 
-    # reduction=None means no aggregation across windows → one value per window.
+    # reduction=None means no aggregation across windows -> one value per window.
     # last_points_only collapses all windows into one TimeSeries before scoring,
     # so there is effectively only one window regardless of reduction.
     has_windows = backtest_args.get("reduction") is None and not backtest_args.get(
@@ -1243,7 +1257,7 @@ def _log_metric_result(
     base_key = f"{dataset_name}_{metric_name}" if dataset_name else metric_name
 
     if series_reduced:
-        # series_reduction aggregated across series → single result, no series axis
+        # series_reduction aggregated across series -> single result, no series axis
         series_seq = [get_single_series(series)]
         results = [result]
     else:

From 94eaafef5ce5ece95ba8d5010dc9a2f8c4ab0adc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 10:57:26 +0200
Subject: [PATCH 084/154] chore: small code review improvements

---
 darts/utils/mlflow.py | 86 ++++++++++++++++++++++++++-----------------
 1 file changed, 52 insertions(+), 34 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 1097046e17..3a473f1a12 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -21,16 +21,26 @@
 
 import csv
 import inspect
-import json
-import os
 import re
 import sys
 import threading
 from collections.abc import Callable
 from operator import itemgetter
+from pathlib import Path
 from typing import Any
 
-import mlflow
+from darts.logging import raise_log
+
+try:
+    import mlflow
+except ImportError:
+    raise_log(
+        ImportError(
+            "MLflow is required for `darts.utils.mlflow`. Install it with `pip"
+            " install mlflow`."
+        )
+    )
+
 import numpy as np
 import yaml
 from mlflow.entities import LoggedModel
@@ -79,6 +89,12 @@
     get_single_series,
     series2seq,
 )
+from darts.utils.utils import TORCH_AVAILABLE
+
+if TORCH_AVAILABLE:
+    from darts.models.forecasting.torch_forecasting_model import (
+        TorchForecastingModel,
+    )
 
 logger = get_logger(__name__)
 
@@ -176,21 +192,20 @@ def save_model(
         raise_log(
             ValueError(
                 "Model must be an instance of darts.models.forecasting.ForecastingModel."
-            ),
-            logger,
+            )
         )
 
     _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
 
-    path = os.path.abspath(path)
-    _validate_and_prepare_target_save_path(path)
-    code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
+    path = Path(path).resolve()
+    _validate_and_prepare_target_save_path(str(path))
+    code_dir_subpath = _validate_and_copy_code_paths(code_paths, str(path))
 
     is_torch = _is_torch_model(model)
 
     # clean=True excludes any timeseries or callbacks from the model file
     model_file = _MODEL_FILE_TORCH if is_torch else _MODEL_FILE_STAT
-    model.save(os.path.join(path, model_file), clean=True)
+    model.save(str(path / model_file), clean=True)
 
     model_class = _get_fully_qualified_class_name(model)
 
@@ -201,7 +216,7 @@ def save_model(
         mlflow_model.signature = signature
 
     if input_example is not None:
-        _save_example(mlflow_model, input_example, path)
+        _save_example(mlflow_model, input_example, str(path))
 
     if metadata is not None:
         mlflow_model.metadata = metadata
@@ -213,7 +228,7 @@ def save_model(
         model_class=model_class,
         code=code_dir_subpath,
     )
-    mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
+    mlflow_model.save(str(path / MLMODEL_FILE_NAME))
 
     if pip_requirements is None:
         default_reqs = get_default_pip_requirements()
@@ -230,21 +245,21 @@ def save_model(
         else _process_conda_env(conda_env)
     )
 
-    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
+    with open(path / _CONDA_ENV_FILE_NAME, "w") as f:
         yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
 
     if pip_constraints:
-        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
+        write_to(str(path / _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
 
-    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
-    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
+    write_to(str(path / _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
+    _PythonEnv.current().to_yaml(str(path / _PYTHON_ENV_FILE_NAME))
 
 
 def load_model(
     model_uri: str,
     dst_path: str | None = None,
     **kwargs,
-):
+) -> ForecastingModel:
     """Load a darts model from an MLflow model URI.
 
     Parameters
@@ -279,13 +294,12 @@ def load_model(
         raise_log(
             ValueError(
                 f"Cannot load model: class `{model_cls_str}` is not a subclass of `ForecastingModel`."
-            ),
-            logger,
+            )
         )
 
-    model_path = os.path.join(local_path, flavor_conf["data"])
+    model_path = Path(local_path) / flavor_conf["data"]
 
-    return model_cls.load(model_path, **kwargs)
+    return model_cls.load(str(model_path), **kwargs)
 
 
 def log_model(
@@ -480,7 +494,12 @@ def autolog(
         try:
             import mlflow.pytorch
 
-            mlflow.pytorch.autolog(log_models=False, log_datasets=False, silent=silent)
+            mlflow.pytorch.autolog(
+                log_models=False,
+                log_datasets=False,
+                checkpoint=False,
+                silent=silent,
+            )
         except ImportError:
             logger.info(
                 "mlflow.pytorch not available; skipping per-epoch metrics logging."
@@ -540,6 +559,7 @@ def _patched_fit(original, self, *args, **kwargs):
         """Patch function for ForecastingModel.fit() autologging.
 
         Logs model parameters, class, covariates and the model itself.
+
         Parameters
         ----------
         original
@@ -553,6 +573,7 @@ def _patched_fit(original, self, *args, **kwargs):
 
         Returns
         -------
+        ForecastingModel
             The result of calling the original fit method.
         """
         # Create a training session to track the training process and log information
@@ -730,8 +751,10 @@ def _get_model_info_tags(model: ForecastingModel) -> dict[str, Any]:
         A dictionary of MLflow run tag keys and values describing the specified model.
     """
     return {
-        "model_name": model.__class__.__name__,
-        "model_class": (model.__class__.__module__ + "." + model.__class__.__name__),
+        "model_class": model.__class__.__name__,
+        "model_reference": (
+            model.__class__.__module__ + "." + model.__class__.__name__
+        ),
         "model_likelihood": (
             model.likelihood.__class__.__name__
             if model.likelihood is not None
@@ -772,11 +795,7 @@ def _log_covariate_info(model: ForecastingModel) -> None:
         ) != len(model.static_covariates.columns)
 
     # log complete information as JSON artifact
-    with TempDir() as tmp:
-        covariates_path = tmp.path("covariates.json")
-        with open(covariates_path, "w") as f:
-            json.dump(covariate_info, f, indent=2)
-        mlflow.log_artifact(covariates_path)
+    mlflow.log_dict(covariate_info, "covariates.json")
 
 
 def _is_torch_model(model) -> bool:
@@ -792,8 +811,7 @@ def _is_torch_model(model) -> bool:
     bool
         True if the model is a `TorchForecastingModel`, False otherwise.
     """
-    method = getattr(model, "predict_from_dataset", None)
-    return callable(method)
+    return TORCH_AVAILABLE and isinstance(model, TorchForecastingModel)
 
 
 def _extract_covariate_metadata(
@@ -1284,7 +1302,7 @@ def _log_metric_result(
         c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
         arr = np.asarray(r, dtype=float)
         # after stripping the C axis, the remainder is the time axis (or scalar)
-        rest, extra = divmod(arr.size, c_size)
+        n_times, extra = divmod(arr.size, c_size)
         if extra:
             logger.warning(
                 "Metric logging skipped for `%s`: result size (%d) is not "
@@ -1297,14 +1315,14 @@ def _log_metric_result(
             return
 
         if has_time_axis:
-            t_size = rest
-        elif rest != 1:
+            t_size = n_times
+        elif n_times != 1:
             logger.warning(
                 "Metric logging skipped for `%s`: expected a single value per "
                 "component/quantile after reduction, but got %d elements. "
                 "Check time_reduction and component_reduction.",
                 metric_name,
-                rest,
+                n_times,
             )
             return
         else:

From defe8aa75bd5c707b800cd741d8b840d62726dd6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 10:58:37 +0200
Subject: [PATCH 085/154] chore: update gitignore

---
 .gitignore | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index a3e223d95f..36ca8d5296 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,6 +25,4 @@ docs_env
 .venv
 .env
 uv.lock
-repl/
-mlruns/*
-examples/mlruns/*
+*mlruns/

From b5ba0bceed1c262cb3155d8edb6e3a306593b8b3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 11:15:05 +0200
Subject: [PATCH 086/154] chore: change log_model=False by default and
 formatting

---
 darts/utils/mlflow.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 3a473f1a12..a3c8ef8507 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -408,7 +408,7 @@ def log_model(
 
 
 def autolog(
-    log_models: bool = True,
+    log_models: bool = False,
     log_params: bool = True,
     log_metrics: bool = True,
     log_torch_metrics: bool = True,
@@ -609,7 +609,9 @@ def _patched_fit(original, self, *args, **kwargs):
             )
             try:
                 registered_model_name = get_autologging_config(
-                    FLAVOR_NAME, "registered_model_name", None
+                    flavor_name=FLAVOR_NAME,
+                    config_key="registered_model_name",
+                    default_value=None,
                 )
                 log_model(
                     result,

From 44ca8da95fd8c5e7d2bd34621b9d98092e200565 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 11:46:02 +0200
Subject: [PATCH 087/154] feat: change on error from logs to raise

---
 darts/utils/mlflow.py | 168 ++++++++++++++++++------------------------
 1 file changed, 73 insertions(+), 95 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index a3c8ef8507..3204360ef1 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -501,18 +501,14 @@ def autolog(
                 silent=silent,
             )
         except ImportError:
-            logger.info(
-                "mlflow.pytorch not available; skipping per-epoch metrics logging."
-            )
+            pass
     elif disable:
         try:
             import mlflow.pytorch
 
             mlflow.pytorch.autolog(disable=True)
         except (ImportError, Exception):
-            logger.info(
-                "mlflow.pytorch not available; skipping per-epoch metrics logging."
-            )
+            pass
 
     _autolog(
         log_models=log_models,
@@ -933,17 +929,22 @@ def _log_backtest_metrics(
     * ``last_points_only`` – collapses all windows into one TimeSeries before scoring,
       so there is effectively only one window regardless of reduction.
 
-    When ``label_reduction=None`` is used without explicit ``labels``, the
-    unique class values are inferred from ``series`` at runtime so structured
-    per-label keys are still produced. When two metrics have incompatible axis
-    layouts (different ``time_reduction`` / ``component_reduction`` / quantile
-    count), each series result is flattened to integer-indexed keys instead.
+    When two metrics have incompatible axis layouts (different
+    ``time_reduction`` / ``component_reduction`` / quantile count), each series
+    result is flattened to integer-indexed keys instead.
 
     When more than one series is scored, the logged value is the mean over
     series for each cell, and the granular per-series breakdown is written to a
     ``per_series_metrics/backtest_per_series.csv`` artifact. For a single series
     the mean is just the value itself and no artifact is written.
 
+    Raises
+    ------
+    ValueError
+        On a shape/size mismatch between the metric result and the inferred
+        axes, or when ``label_reduction=None`` is requested without explicit
+        ``labels``.
+
     Parameters
     ----------
     autologging_client
@@ -1000,9 +1001,6 @@ def _log_backtest_metrics(
     # Fall back to flat integer-indexed keys for this case.
     axes_inconsistent = any(ax[:3] != metric_axes[0][:3] for ax in metric_axes[1:])
 
-    # quantiles_num=None means label_reduction=None was requested with labels=None,
-    labels_unknown = quantiles_num is None
-
     series = backtest_args.get("series")
     forecast_horizon = backtest_args.get("forecast_horizon")
 
@@ -1028,23 +1026,6 @@ def _log_backtest_metrics(
                 })
             continue
 
-        # resolve label names from series data when not provided explicitly.
-        # mirrors np.unique(np.concatenate([y_true, y_pred])) inside _confusion_matrix.
-        # NOTE: importantly this checks the series for labels, not just the windows, if
-        # this is an issue, then I'd suggest falling back to flat integer-indexed keys
-        # and enforcing explicit labels.
-        if labels_unknown:
-            inferred_labels = np.unique(s.values())
-            quantiles_num = len(inferred_labels)
-            metric_axes = [
-                (
-                    has_time_axis,
-                    has_comp_axis,
-                    quantiles_num,
-                    [f"_label{x:g}" for x in inferred_labels],
-                )
-            ] + list(metric_axes[1:])
-
         comps = s.components.tolist()
         # c_size = components × quantiles/intervals/labels per component
         c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
@@ -1052,30 +1033,29 @@ def _log_backtest_metrics(
         # after stripping C and M axes, rest = W*T (or W or T alone)
         rest, extra = divmod(arr.size, c_size * n_metrics)
         if extra:
-            logger.warning(
-                "Backtest metric logging skipped: result size (%d) is not "
-                "divisible by c_size * n_metrics (%d * %d = %d). "
-                "The metric output shape does not match the inferred axes.",
-                arr.size,
-                c_size,
-                n_metrics,
-                c_size * n_metrics,
+            raise_log(
+                ValueError(
+                    f"Backtest metric logging failed: result size ({arr.size}) "
+                    f"is not divisible by c_size * n_metrics ({c_size} * "
+                    f"{n_metrics} = {c_size * n_metrics}). The metric output "
+                    "shape does not match the inferred axes."
+                )
             )
-            return
 
         # both time and window axes present: backtest returns (W*T*C*M,) in C order so we can
         # recover W and T only if forecast_horizon is known (T = forecast_horizon)
         if has_time_axis and has_windows:
             if not forecast_horizon or rest % forecast_horizon:
-                logger.warning(
-                    "Backtest metric logging skipped: cannot split window/time "
-                    "axes — %d elements remain after stripping component and "
-                    "metric axes, but forecast_horizon=%r does not divide "
-                    "evenly. Pass an explicit forecast_horizon to backtest().",
-                    rest,
-                    forecast_horizon,
+                raise_log(
+                    ValueError(
+                        f"Backtest metric logging failed: cannot split "
+                        f"window/time axes — {rest} elements remain after "
+                        f"stripping component and metric axes, but "
+                        f"forecast_horizon={forecast_horizon!r} does not "
+                        "divide evenly. Pass an explicit forecast_horizon to "
+                        "backtest()."
+                    )
                 )
-                return
             t_size, w_size = forecast_horizon, rest // forecast_horizon
         elif has_time_axis:
             t_size, w_size = rest, 1
@@ -1083,13 +1063,14 @@ def _log_backtest_metrics(
             t_size, w_size = 1, rest
         else:
             if rest != 1:
-                logger.warning(
-                    "Backtest metric logging skipped: expected a single scalar "
-                    "per component/metric after reduction, but got %d elements. "
-                    "Check time_reduction and component_reduction defaults.",
-                    rest,
+                raise_log(
+                    ValueError(
+                        f"Backtest metric logging failed: expected a single "
+                        f"scalar per component/metric after reduction, but got "
+                        f"{rest} elements. Check time_reduction and "
+                        "component_reduction defaults."
+                    )
                 )
-                return
             t_size, w_size = 1, 1
 
         canonical = arr.reshape(w_size, t_size, c_size, n_metrics)
@@ -1157,11 +1138,13 @@ def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
         - ``has_time_axis`` – ``True`` when ``time_reduction`` is ``None`` (i.e. a
           per-timestep axis is present in the output).
         - ``has_comp_axis`` – ``True`` when components are expanded (not collapsed to a scalar).
-        - ``quantiles_num`` – number of quantile/interval/label entries; ``None`` when it
-          cannot be determined (e.g. ``label_reduction=None`` without explicit
-          ``labels``), signalling the caller to fall back to flat logging.
-        - ``quantiles_labels`` – one key suffix per ``quantiles_num`` entry (empty list when
-          ``quantiles_num`` is ``None``).
+        - ``quantiles_num`` – number of quantile/interval/label entries.
+        - ``quantiles_labels`` – one key suffix per ``quantiles_num`` entry.
+
+    Raises
+    ------
+    ValueError
+        If ``label_reduction=None`` is requested without explicit ``labels``.
     """
     params = inspect.signature(metric).parameters
 
@@ -1190,9 +1173,15 @@ def effective(param_name: str) -> Any:
             label_reduction = label_reduction.value
         labels = metric_kwargs.get("labels")
         # label_reduction=None means one output per label, but without explicit
-        # labels we can't know how many — signal the caller to fall back
+        # labels we can't know how many ahead of time
         if label_reduction is None and labels is None:
-            return (has_time_axis, has_comp_axis, None, [])
+            raise_log(
+                ValueError(
+                    "`label_reduction=None` requires explicit `labels` to be "
+                    "passed for MLflow autologging (the number of output "
+                    "labels cannot be determined ahead of time otherwise)."
+                )
+            )
         quantiles_labels = (
             [f"_label{x}" for x in np.atleast_1d(labels)]
             if label_reduction is None
@@ -1212,7 +1201,7 @@ def _log_metric_result(
     series,
     has_time_axis: bool,
     has_comp_axis: bool,
-    quantiles_num: int | None,
+    quantiles_num: int,
     quantiles_labels: list[str],
     dataset_name: str | None = None,
     series_reduced: bool = False,
@@ -1242,8 +1231,11 @@ def _log_metric_result(
     ``per_series_metrics/{base_key}_per_series.csv`` artifact. For a single
     series the mean is just the value itself and no artifact is written.
 
-    On a shape/size mismatch it warns and returns (does not raise), keeping
-    autologging non-fatal.
+    Raises
+    ------
+    ValueError
+        On a shape/size mismatch between the metric result and the inferred
+        axes.
 
     Parameters
     ----------
@@ -1254,19 +1246,15 @@ def _log_metric_result(
         The metric result to log.
     series
         The ``actual_series`` argument passed to the metric (single series or
-        ``Sequence[TimeSeries]``); used for component names, series count, and
-        runtime label inference.
+        ``Sequence[TimeSeries]``); used for component names and series count.
     has_time_axis
         ``True`` when the result carries a per-timestep axis (``time_reduction=None``).
     has_comp_axis
         ``True`` when components are expanded (``component_reduction=None``).
     quantiles_num
-        Number of quantile/interval/label entries; ``None`` when it cannot be
-        determined ahead of time (``label_reduction=None`` without explicit
-        ``labels``), in which case labels are inferred from ``series`` at runtime.
+        Number of quantile/interval/label entries.
     quantiles_labels
-        One key suffix per ``quantiles_num`` entry (empty list when ``quantiles_num``
-        is ``None``).
+        One key suffix per ``quantiles_num`` entry.
     dataset_name
         Sanitized variable name of ``actual_series`` in the caller's frame.
         Omitted from key when ``None``.
@@ -1286,19 +1274,10 @@ def _log_metric_result(
             [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
         )
 
-    labels_unknown = quantiles_num is None
-
     # agg maps (key, step) -> per-series values, averaged into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
     for series_index, (s, r) in enumerate(zip(series_seq, results)):
-        # quantiles_num=None means label_reduction=None was requested without explicit labels
-        # so class labels are inferred per-series inside the loop.
-        if labels_unknown:
-            inferred_labels = np.unique(s.values())
-            quantiles_num = len(inferred_labels)
-            quantiles_labels = [f"_label{x:g}" for x in inferred_labels]
-
         comps = s.components.tolist()
         # c_size = components × quantiles/intervals/labels per component
         c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
@@ -1306,27 +1285,26 @@ def _log_metric_result(
         # after stripping the C axis, the remainder is the time axis (or scalar)
         n_times, extra = divmod(arr.size, c_size)
         if extra:
-            logger.warning(
-                "Metric logging skipped for `%s`: result size (%d) is not "
-                "divisible by the inferred component/quantile size (%d). "
-                "The metric output shape does not match the inferred axes.",
-                metric_name,
-                arr.size,
-                c_size,
+            raise_log(
+                ValueError(
+                    f"Metric logging failed for `{metric_name}`: result size "
+                    f"({arr.size}) is not divisible by the inferred "
+                    f"component/quantile size ({c_size}). The metric output "
+                    "shape does not match the inferred axes."
+                )
             )
-            return
 
         if has_time_axis:
             t_size = n_times
         elif n_times != 1:
-            logger.warning(
-                "Metric logging skipped for `%s`: expected a single value per "
-                "component/quantile after reduction, but got %d elements. "
-                "Check time_reduction and component_reduction.",
-                metric_name,
-                n_times,
+            raise_log(
+                ValueError(
+                    f"Metric logging failed for `{metric_name}`: expected a "
+                    f"single value per component/quantile after reduction, "
+                    f"but got {n_times} elements. Check time_reduction and "
+                    "component_reduction."
+                )
             )
-            return
         else:
             t_size = 1
 

From 965d8028bfb147220b724e2bbc550455cc11ad71 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 11:49:57 +0200
Subject: [PATCH 088/154] chore: raise on failure instead of log

---
 darts/utils/mlflow.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 3204360ef1..510afa2de0 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -616,9 +616,10 @@ def _patched_fit(original, self, *args, **kwargs):
                     model_id=model.model_id,
                 )
             except Exception:
-                logger.info(
-                    f"Failed to autolog model artifact for {type(self).__name__}.",
-                    exc_info=True,
+                raise_log(
+                    ValueError(
+                        f"Failed to autolog model artifact for {type(self).__name__}."
+                    )
                 )
 
         param_logging_ops.await_completion()

From eff901435ce2b4765fb88705ffb953831cd9f050 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 12:24:51 +0200
Subject: [PATCH 089/154] chore: remove quantile_num from _infer_metric_axes
 return tuple

---
 darts/utils/mlflow.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 510afa2de0..84faf2dc0b 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -995,12 +995,16 @@ def _log_backtest_metrics(
 
     # check the dim axes from the metric kwargs for each
     metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metric, metric_kwargs)]
-    has_time_axis, has_comp_axis, quantiles_num, _ = metric_axes[0]
+    has_time_axis, has_comp_axis, quantiles_labels_0 = metric_axes[0]
+    quantiles_num = len(quantiles_labels_0)
 
     # Inconsistent axes across metrics (different has_time_axis, has_comp_axis, or
     # quantiles_num) means the result can't be reshaped into a single canonical array.
     # Fall back to flat integer-indexed keys for this case.
-    axes_inconsistent = any(ax[:3] != metric_axes[0][:3] for ax in metric_axes[1:])
+    axes_key = (has_time_axis, has_comp_axis, quantiles_num)
+    axes_inconsistent = any(
+        (ax[0], ax[1], len(ax[2])) != axes_key for ax in metric_axes[1:]
+    )
 
     series = backtest_args.get("series")
     forecast_horizon = backtest_args.get("forecast_horizon")
@@ -1076,7 +1080,7 @@ def _log_backtest_metrics(
 
         canonical = arr.reshape(w_size, t_size, c_size, n_metrics)
         for m, metric_name in enumerate(metric_names):
-            quantiles_labels = metric_axes[m][3]
+            quantiles_labels = metric_axes[m][2]
             for w in range(w_size):
                 for c in range(c_size):
                     # c is a flat index into the (n_components × quantiles_num) C axis:
@@ -1134,13 +1138,12 @@ def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
     Returns
     -------
     tuple
-        ``(has_time_axis, has_comp_axis, quantiles_num, quantiles_labels)`` where
+        ``(has_time_axis, has_comp_axis, quantiles_labels)`` where
 
         - ``has_time_axis`` – ``True`` when ``time_reduction`` is ``None`` (i.e. a
           per-timestep axis is present in the output).
         - ``has_comp_axis`` – ``True`` when components are expanded (not collapsed to a scalar).
-        - ``quantiles_num`` – number of quantile/interval/label entries.
-        - ``quantiles_labels`` – one key suffix per ``quantiles_num`` entry.
+        - ``quantiles_labels`` – one key suffix per quantile/interval/label entry.
 
     Raises
     ------
@@ -1191,7 +1194,7 @@ def effective(param_name: str) -> Any:
     else:
         quantiles_labels = [""]
 
-    return (has_time_axis, has_comp_axis, len(quantiles_labels), quantiles_labels)
+    return (has_time_axis, has_comp_axis, quantiles_labels)
 
 
 def _log_metric_result(
@@ -1202,7 +1205,6 @@ def _log_metric_result(
     series,
     has_time_axis: bool,
     has_comp_axis: bool,
-    quantiles_num: int,
     quantiles_labels: list[str],
     dataset_name: str | None = None,
     series_reduced: bool = False,
@@ -1252,10 +1254,8 @@ def _log_metric_result(
         ``True`` when the result carries a per-timestep axis (``time_reduction=None``).
     has_comp_axis
         ``True`` when components are expanded (``component_reduction=None``).
-    quantiles_num
-        Number of quantile/interval/label entries.
     quantiles_labels
-        One key suffix per ``quantiles_num`` entry.
+        One key suffix per quantile/interval/label entry.
     dataset_name
         Sanitized variable name of ``actual_series`` in the caller's frame.
         Omitted from key when ``None``.
@@ -1264,6 +1264,7 @@ def _log_metric_result(
         metric, so the result has no leading series axis even for list input.
     """
     base_key = f"{dataset_name}_{metric_name}" if dataset_name else metric_name
+    quantiles_num = len(quantiles_labels)
 
     if series_reduced:
         # series_reduction aggregated across series -> single result, no series axis
@@ -1412,8 +1413,8 @@ def _patched_metric(original, *args, **kwargs):
         key_name = _sanitize_mlflow_key(kwargs.get("name") or metric_name)
 
         # infer output axes from the metric signature + call kwargs
-        has_time_axis, has_comp_axis, quantiles_num, quantiles_labels = (
-            _infer_metric_axes(original, kwargs)
+        has_time_axis, has_comp_axis, quantiles_labels = _infer_metric_axes(
+            original, kwargs
         )
 
         # series_reduction collapses the series axis inside the metric, so the
@@ -1434,7 +1435,6 @@ def _patched_metric(original, *args, **kwargs):
             series,
             has_time_axis,
             has_comp_axis,
-            quantiles_num,
             quantiles_labels,
             dataset_name=dataset_name,
             series_reduced=series_reduced,

From 5e6a71891f2db77a8d9c63cf82d5bb4984585c2a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 12:38:26 +0200
Subject: [PATCH 090/154] feat: remove dataset_name from logged metric pattern

---
 darts/utils/mlflow.py | 32 +++++++++-----------------------
 1 file changed, 9 insertions(+), 23 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 84faf2dc0b..cf3969a168 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -50,7 +50,7 @@
 from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
 from mlflow.tracking.fluent import _initialize_logged_model
-from mlflow.utils import _get_fully_qualified_class_name, _inspect_original_var_name
+from mlflow.utils import _get_fully_qualified_class_name
 from mlflow.utils.autologging_utils import (
     autologging_integration,
     get_autologging_config,
@@ -446,11 +446,9 @@ def autolog(
     .. note::
 
         Logged metric keys follow the pattern
-        ``{dataset_name}_{metric_name}{component}{quantile_or_label}``, where
-        each part is included only when the corresponding axis is present:
+        ``{metric_name}{component}{quantile_or_label}``, where each part is
+        included only when the corresponding axis is present:
 
-        * ``dataset_name`` – variable name of the first argument, captured via
-          frame inspection (omitted when not found).
         * ``metric_name`` – the metric function name, or the ``name`` keyword
           argument when provided (it overrides only this token).
         * ``component`` – the component name when ``component_reduction=None``.
@@ -1206,7 +1204,6 @@ def _log_metric_result(
     has_time_axis: bool,
     has_comp_axis: bool,
     quantiles_labels: list[str],
-    dataset_name: str | None = None,
     series_reduced: bool = False,
 ) -> None:
     """Log a metric result to the active MLflow run.
@@ -1221,7 +1218,7 @@ def _log_metric_result(
 
     The logged MLflow key follows the pattern::
 
-        {dataset_name}_{metric_name}{component}{quantile_or_label}
+        {metric_name}{component}{quantile_or_label}
 
     where each optional part is included only when the corresponding axis is
     present:
@@ -1231,7 +1228,7 @@ def _log_metric_result(
 
     When more than one series is scored, the logged value is the mean over
     series for each cell, and the granular per-series breakdown is written to a
-    ``per_series_metrics/{base_key}_per_series.csv`` artifact. For a single
+    ``per_series_metrics/{metric_name}_per_series.csv`` artifact. For a single
     series the mean is just the value itself and no artifact is written.
 
     Raises
@@ -1243,7 +1240,7 @@ def _log_metric_result(
     Parameters
     ----------
     metric_name
-        Base metric name used as the MLflow key (the metric's ``name`` keyword
+        Metric name used as the MLflow key (the metric's ``name`` keyword
         argument when provided, otherwise the metric function name).
     result
         The metric result to log.
@@ -1256,14 +1253,10 @@ def _log_metric_result(
         ``True`` when components are expanded (``component_reduction=None``).
     quantiles_labels
         One key suffix per quantile/interval/label entry.
-    dataset_name
-        Sanitized variable name of ``actual_series`` in the caller's frame.
-        Omitted from key when ``None``.
     series_reduced
         ``True`` when ``series_reduction`` collapsed the series axis inside the
         metric, so the result has no leading series axis even for list input.
     """
-    base_key = f"{dataset_name}_{metric_name}" if dataset_name else metric_name
     quantiles_num = len(quantiles_labels)
 
     if series_reduced:
@@ -1321,7 +1314,7 @@ def _log_metric_result(
                 else ""
             )
             key = _sanitize_mlflow_key(
-                base_key + comp_part + quantiles_labels[quantile_index]
+                metric_name + comp_part + quantiles_labels[quantile_index]
             )
             for t in range(t_size):
                 # MLflow step maps to the time axis when present
@@ -1346,7 +1339,7 @@ def _log_metric_result(
 
     # write the granular per-series breakdown to a CSV artifact (multi-series only)
     if len(series_seq) > 1:
-        _write_per_series_csv(rows, f"{base_key}_per_series.csv")
+        _write_per_series_csv(rows, f"{metric_name}_per_series.csv")
 
 
 def _make_metric_patch(metric_name: str) -> Callable:
@@ -1357,12 +1350,10 @@ def _make_metric_patch(metric_name: str) -> Callable:
     kwargs (via ``_infer_metric_axes``) and delegates to ``_log_metric_result``,
     which logs each cell under a key built as::
 
-        {dataset_name}_{metric_name}{component}{quantile_or_label}
+        {metric_name}{component}{quantile_or_label}
 
     where:
 
-    * ``dataset_name`` – Python variable name of the first argument in the
-      caller's frame (captured via frame inspection, omitted if not found).
     * ``metric_name`` – the metric function name, or the ``name`` keyword
       argument when provided (it overrides only this token).
     * ``component`` – ``_{component_name}`` when ``component_reduction=None``.
@@ -1405,10 +1396,6 @@ def _patched_metric(original, *args, **kwargs):
         if series is None:
             return result
 
-        # capture the variable name of actual_series for metric key
-        raw = _inspect_original_var_name(series, fallback_name=None)
-        dataset_name = _sanitize_mlflow_key(raw) if raw else None
-
         # the `name` kwarg overrides the metric-name token in the logged key
         key_name = _sanitize_mlflow_key(kwargs.get("name") or metric_name)
 
@@ -1436,7 +1423,6 @@ def _patched_metric(original, *args, **kwargs):
             has_time_axis,
             has_comp_axis,
             quantiles_labels,
-            dataset_name=dataset_name,
             series_reduced=series_reduced,
         )
 

From ce8fd01ac0cd771a62c649d2dbb8b5944dc23b68 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 12:38:53 +0200
Subject: [PATCH 091/154] fix: align tests to code review changes

---
 darts/tests/optional_deps/test_mlflow.py | 209 ++++++++++-------------
 1 file changed, 88 insertions(+), 121 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index c28581e8bf..13439b4efc 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -250,7 +250,7 @@ def test_autolog_enable_disable(self, mlflow_tracking, autolog_context):
 
             # verify the run has expected content
             last_run = runs.iloc[0]
-            assert last_run["tags.model_name"] == "ExponentialSmoothing"
+            assert last_run["tags.model_class"] == "ExponentialSmoothing"
             assert last_run["tags.mlflow.runName"] is not None
 
         # after context exits, autolog should be disabled
@@ -274,7 +274,7 @@ def test_autolog_parameters(self, mlflow_tracking, autolog_context):
 
             last_run = runs.iloc[0]
             assert last_run["params.seasonal_periods"] == "12"
-            assert last_run["tags.model_name"] == "ExponentialSmoothing"
+            assert last_run["tags.model_class"] == "ExponentialSmoothing"
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
@@ -294,7 +294,7 @@ def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
             assert len(runs) == 1, "Expected exactly one run"
             last_run = runs.iloc[0]
             last_run_id = last_run["run_id"]
-            assert last_run["tags.model_name"] == "NBEATSModel"
+            assert last_run["tags.model_class"] == "NBEATSModel"
 
             client = mlflow.tracking.MlflowClient()
 
@@ -345,7 +345,7 @@ def assert_metric(history, key):
             runs = mlflow.search_runs()
             assert len(runs) == 1
             run_id = runs.iloc[0]["run_id"]
-            assert runs.iloc[0]["tags.model_name"] == "NBEATSModel"
+            assert runs.iloc[0]["tags.model_class"] == "NBEATSModel"
 
             client = mlflow.tracking.MlflowClient()
             assert_metric(client.get_metric_history(run_id, "train_loss"), "train_loss")
@@ -457,7 +457,7 @@ def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
         assert len(runs) == 2, "Expected two separate runs for two fits"
 
         # verify different model classes logged
-        model_classes = set(runs["tags.model_name"])
+        model_classes = set(runs["tags.model_class"])
         assert "ExponentialSmoothing" in model_classes
         assert "LinearRegressionModel" in model_classes
 
@@ -483,7 +483,7 @@ def test_autolog_torch_model_multiple_fits(self, mlflow_tracking, autolog_contex
         assert len(runs) == 2, "Expected two separate runs for two fits"
 
         for _, run in runs.iterrows():
-            assert run["tags.model_name"] in [
+            assert run["tags.model_class"] in [
                 "NBEATSModel",
                 "LinearRegressionModel",
             ]
@@ -742,9 +742,8 @@ def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
             with mlflow.start_run() as run:
                 ref = dm.ae(actual, pred)
 
-        # the first-arg variable name ("actual") is captured as the key prefix
         ref = np.asarray(ref, dtype=float)  # shape (n_timesteps,)
-        history = mlflow_tracking.get_metric_history(run.info.run_id, "actual_ae")
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
         assert len(history) == len(ref), "Expected one step per timestep"
         steps = sorted(m.step for m in history)
         assert steps == list(range(len(ref)))
@@ -765,14 +764,9 @@ def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
             with mlflow.start_run() as run:
                 ref = dm.mql(actual, pred, q=[0.1, 0.5, 0.9])
 
-        # the first-arg variable name ("actual") is captured as the key prefix
         ref = np.asarray(ref, dtype=float)  # shape (n_quantiles,)
         m = mlflow.get_run(run.info.run_id).data.metrics
-        for i, key in enumerate((
-            "actual_mql_q0_1",
-            "actual_mql_q0_5",
-            "actual_mql_q0_9",
-        )):
+        for i, key in enumerate(("mql_q0_1", "mql_q0_5", "mql_q0_9")):
             assert key in m, f"Expected quantile key {key}"
             assert m[key] == pytest.approx(ref[i], abs=1e-5)
 
@@ -790,10 +784,9 @@ def test_autolog_metric_quantile_interval(self, mlflow_tracking, autolog_context
             with mlflow.start_run() as run:
                 ref = dm.miw(actual, pred, q_interval=(0.1, 0.9))
 
-        # the first-arg variable name ("actual") is captured as the key prefix
         m = mlflow.get_run(run.info.run_id).data.metrics
-        assert "actual_miw_qi0_1_0_9" in m
-        assert m["actual_miw_qi0_1_0_9"] == pytest.approx(float(ref), abs=1e-5)
+        assert "miw_qi0_1_0_9" in m
+        assert m["miw_qi0_1_0_9"] == pytest.approx(float(ref), abs=1e-5)
 
     def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
         """A list of series logs the mean over series; per-series values go to a CSV."""
@@ -804,16 +797,13 @@ def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
             with mlflow.start_run() as run:
                 ref = dm.mae(series, pred)
 
-        # the first-arg variable name ("series") is captured as the key prefix
         ref = np.asarray(ref, dtype=float)  # shape (n_series,)
         m = mlflow.get_run(run.info.run_id).data.metrics
         # aggregate = mean over series, no per-series _s{i} keys
-        assert m["series_mae"] == pytest.approx(float(np.mean(ref)), abs=1e-5)
-        assert not any(k.startswith("series_mae_s") for k in m)
+        assert m["mae"] == pytest.approx(float(np.mean(ref)), abs=1e-5)
+        assert not any(k.startswith("mae_s") for k in m)
         # granular per-series breakdown written to a CSV artifact
-        csv_rows = self._read_per_series_csv(
-            run.info.run_id, "series_mae_per_series.csv"
-        )
+        csv_rows = self._read_per_series_csv(run.info.run_id, "mae_per_series.csv")
         by_series = {int(row["series_index"]): float(row["value"]) for row in csv_rows}
         assert by_series == pytest.approx({0: ref[0], 1: ref[1]}, abs=1e-5)
 
@@ -829,33 +819,26 @@ def test_autolog_metric_multi_series_per_component(
             with mlflow.start_run() as run:
                 ref = dm.mae(series, pred, component_reduction=None)
 
-        # the first-arg variable name ("series") is captured as the key prefix
         ref = np.asarray(ref, dtype=float)  # shape (n_series, n_components)
         m = mlflow.get_run(run.info.run_id).data.metrics
         # aggregate per component = mean over series, no per-series _s{i} keys
-        assert m["series_mae_linear"] == pytest.approx(
-            float(ref[:, 0].mean()), abs=1e-5
-        )
-        assert m["series_mae_linear_1"] == pytest.approx(
-            float(ref[:, 1].mean()), abs=1e-5
-        )
+        assert m["mae_linear"] == pytest.approx(float(ref[:, 0].mean()), abs=1e-5)
+        assert m["mae_linear_1"] == pytest.approx(float(ref[:, 1].mean()), abs=1e-5)
         assert not any(k.endswith(("_s0", "_s1")) for k in m)
         # granular CSV: one row per (component, series)
-        csv_rows = self._read_per_series_csv(
-            run.info.run_id, "series_mae_per_series.csv"
-        )
+        csv_rows = self._read_per_series_csv(run.info.run_id, "mae_per_series.csv")
         got = {
             (row["key"], int(row["series_index"])): float(row["value"])
             for row in csv_rows
         }
-        assert got[("series_mae_linear", 0)] == pytest.approx(ref[0, 0], abs=1e-5)
-        assert got[("series_mae_linear", 1)] == pytest.approx(ref[1, 0], abs=1e-5)
-        assert got[("series_mae_linear_1", 0)] == pytest.approx(ref[0, 1], abs=1e-5)
-        assert got[("series_mae_linear_1", 1)] == pytest.approx(ref[1, 1], abs=1e-5)
+        assert got[("mae_linear", 0)] == pytest.approx(ref[0, 0], abs=1e-5)
+        assert got[("mae_linear", 1)] == pytest.approx(ref[1, 0], abs=1e-5)
+        assert got[("mae_linear_1", 0)] == pytest.approx(ref[0, 1], abs=1e-5)
+        assert got[("mae_linear_1", 1)] == pytest.approx(ref[1, 1], abs=1e-5)
 
     def test_autolog_metric_name_override(self, mlflow_tracking, autolog_context):
         """The metric `name` kwarg overrides only the metric-name token in the key,
-        keeping the dataset/backtest prefix and the quantile/axis suffixes."""
+        keeping the backtest prefix and the quantile/axis suffixes."""
         actual = self.ts_univariate
         train = self.ts_univariate[:40]
         qmodel = self._fit_qlr(train)
@@ -878,23 +861,22 @@ def test_autolog_metric_name_override(self, mlflow_tracking, autolog_context):
                 )
 
         direct = mlflow.get_run(run_direct.info.run_id).data.metrics
-        assert "actual_custom" in direct
-        assert "actual_mae" not in direct, "default metric name should be replaced"
-        assert "target_myq_q0_5" in direct, "quantile suffix should be preserved"
+        assert "custom" in direct
+        assert "mae" not in direct, "default metric name should be replaced"
+        assert "myq_q0_5" in direct, "quantile suffix should be preserved"
 
         bt = mlflow.get_run(run_bt.info.run_id).data.metrics
         assert "backtest_custom" in bt
         assert "backtest_mae" not in bt, "default metric name should be replaced"
 
-    def test_autolog_metric_multi_series_classification_labels_inferred(
+    def test_autolog_metric_multi_series_classification_labels_explicit(
         self, mlflow_tracking, autolog_context
     ):
-        """f1 with label_reduction=None on a list of binary series infers class labels
-        per-series, logs the per-label mean over series, and writes the per-series CSV.
+        """f1 with label_reduction=None and explicit labels on a list of binary
+        series logs the per-label mean over series and writes the per-series CSV.
 
-        This exercises the labels_unknown branch inside the per-series loop so that
-        each series' own class set is inferred rather than reusing the first series'.
-        """
+        ``labels`` must be explicit (``label_reduction=None`` without it raises,
+        since the number of output labels can't be determined ahead of time)."""
         # two independent binary series (same classes, deterministic)
         binary1 = tg.constant_timeseries(value=0.0, length=50).with_values(
             np.array([0.0, 1.0] * 25, dtype=np.float32).reshape(-1, 1)
@@ -907,35 +889,35 @@ def test_autolog_metric_multi_series_classification_labels_inferred(
 
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = dm.f1(series, pred, label_reduction=None)
+                ref = dm.f1(series, pred, label_reduction=None, labels=[0, 1])
 
         ref = [np.asarray(r, dtype=float).flatten() for r in ref]
         m = mlflow.get_run(run.info.run_id).data.metrics
         # aggregate per label = mean over series, no per-series _s{i} keys
-        assert m["series_f1_label0"] == pytest.approx(
+        assert m["f1_label0"] == pytest.approx(
             float(np.mean([ref[0][0], ref[1][0]])), abs=1e-5
         )
-        assert m["series_f1_label1"] == pytest.approx(
+        assert m["f1_label1"] == pytest.approx(
             float(np.mean([ref[0][1], ref[1][1]])), abs=1e-5
         )
         assert not any(k.endswith(("_s0", "_s1")) for k in m)
         # granular CSV: one row per (label, series)
-        csv_rows = self._read_per_series_csv(
-            run.info.run_id, "series_f1_per_series.csv"
-        )
+        csv_rows = self._read_per_series_csv(run.info.run_id, "f1_per_series.csv")
         got = {
             (row["key"], int(row["series_index"])): float(row["value"])
             for row in csv_rows
         }
         for i in range(2):
-            assert got[("series_f1_label0", i)] == pytest.approx(ref[i][0], abs=1e-5)
-            assert got[("series_f1_label1", i)] == pytest.approx(ref[i][1], abs=1e-5)
+            assert got[("f1_label0", i)] == pytest.approx(ref[i][0], abs=1e-5)
+            assert got[("f1_label1", i)] == pytest.approx(ref[i][1], abs=1e-5)
 
-    def test_autolog_metric_size_mismatch_warns_and_skips(
+    def test_autolog_metric_size_mismatch_raises_internally(
         self, mlflow_tracking, autolog_context, caplog
     ):
-        """When the inferred C-axis size doesn't divide the result, a warning is logged
-        and no metrics are written (non-fatal — autologging must not raise)."""
+        """When the inferred C-axis size doesn't divide the result, logging raises
+        (logged as an error) and no metrics are written. The public metric call
+        itself doesn't crash, since MLflow's safe_patch wrapper catches exceptions
+        raised from inside a patch function."""
         actual = self.ts_univariate[40:]
         # mae with component_reduction=None on a univariate series produces shape (T,),
         # which is size T — divisible by c_size=1 (1 component × 1 quantile), so we
@@ -951,7 +933,7 @@ def test_autolog_metric_size_mismatch_warns_and_skips(
 
         from darts.utils import mlflow as mlflow_utils
 
-        fake_axes = (False, True, 3, ["_c0", "_c1", "_c2"])
+        fake_axes = (False, True, ["_c0", "_c1", "_c2"])
         with mock.patch.object(
             mlflow_utils, "_infer_metric_axes", return_value=fake_axes
         ):
@@ -982,7 +964,7 @@ def test_autolog_metric_per_series_csv_schema_and_single_series_skip(
                 dm.mae(multi, pred_multi)
 
         csv_rows = self._read_per_series_csv(
-            run_multi.info.run_id, "multi_mae_per_series.csv"
+            run_multi.info.run_id, "mae_per_series.csv"
         )
         assert list(csv_rows[0].keys()) == ["key", "series_index", "step", "value"]
         assert {int(r["series_index"]) for r in csv_rows} == {0, 1}
@@ -1210,62 +1192,47 @@ def test_autolog_backtest_classification_labels_not_in_data(
         assert np.isnan(m["backtest_f1_label5"])
         assert np.isnan(m["backtest_f1_label10"])
 
-    def test_autolog_backtest_classification_labels_inferred(
-        self, mlflow_tracking, autolog_context
-    ):
-        """f1 with label_reduction=None and no explicit labels infers class names
-        from series values and logs structured per-label keys instead of flat
-        integer-indexed ones."""
-        # binary classification series: values are 0.0 and 1.0
-        rng = np.random.default_rng(42)
-        vals = rng.choice([0.0, 1.0], size=50).astype(np.float32).reshape(-1, 1)
-        ts_bin = tg.constant_timeseries(value=0.0, length=50).with_values(vals)
+    def test_log_backtest_metrics_unknown_labels_raises(self, mlflow_tracking):
+        """label_reduction=None without explicit labels raises rather than
+        inferring class names from the series at runtime.
 
-        model = LinearRegressionModel(lags=4)
-        model.fit(ts_bin)
+        This is tested by calling _log_backtest_metrics directly so the raise
+        is not swallowed by MLflow's safe_patch wrapper.
+        """
+        ts_bin = tg.constant_timeseries(value=0.0, length=10)
+        backtest_args = {
+            "metric": dm.f1,
+            "metric_kwargs": {"label_reduction": None},
+            "series": ts_bin,
+            "forecast_horizon": 1,
+            "reduction": np.mean,
+            "last_points_only": True,
+        }
 
-        with autolog_context(log_metrics=True):
-            with mlflow.start_run() as run:
-                model.backtest(
-                    series=ts_bin,
-                    metric=dm.f1,
-                    metric_kwargs={"label_reduction": None},
-                    retrain=False,
-                    stride=10,
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            with pytest.raises(ValueError, match="requires explicit `labels`"):
+                _log_backtest_metrics(
+                    client, run.info.run_id, np.array([0.5]), backtest_args
                 )
 
-        m = mlflow.get_run(run.info.run_id).data.metrics
-        # unique values are 0.0 and 1.0 → keys should use actual class values
-        assert "backtest_f1_label0" in m, "Expected 'backtest_f1_label0' for class 0.0"
-        assert "backtest_f1_label1" in m, "Expected 'backtest_f1_label1' for class 1.0"
-        # no flat integer-indexed keys
-        flat_keys = [
-            k
-            for k in m
-            if k.startswith("backtest_f1_") and k[-1].isdigit() and "_label" not in k
-        ]
-        assert not flat_keys, f"Did not expect flat fallback keys: {flat_keys}"
-
-    def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking, caplog):
-        """When the inferred label count does not divide the metric output size,
-        logging is skipped with a warning rather than raising — keeping autologging
-        non-fatal for the surrounding backtest call.
-
-        This is tested by calling _log_backtest_metrics directly so the warning is
+    def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
+        """When the explicit label count does not divide the metric output size,
+        logging raises rather than silently producing incomplete metrics.
+
+        This is tested by calling _log_backtest_metrics directly so the raise is
         not swallowed by MLflow's safe_patch wrapper.
         """
-        # Series has 3 unique classes so np.unique(series.values()) → [0, 1, 2].
-        vals = np.array([0.0, 1.0, 2.0] * 17, dtype=np.float32)[:50].reshape(-1, 1)
-        ts_3class = tg.constant_timeseries(value=0.0, length=50).with_values(vals)
+        ts_3class = tg.constant_timeseries(value=0.0, length=50)
 
         # Simulate a backtest result with only 2 entries — as if the metric was
-        # evaluated on windows that only contained classes 0 and 1.
-        # quantiles_num will be inferred as 3 (from series) but result has 2 → mismatch.
+        # evaluated on windows that only contained 2 of the 3 explicit labels.
+        # quantiles_num is 3 (len(labels)) but result has 2 → mismatch.
         fake_result = np.array([0.8, 0.6], dtype=float)
 
         backtest_args = {
             "metric": dm.f1,
-            "metric_kwargs": {"label_reduction": None},
+            "metric_kwargs": {"label_reduction": None, "labels": [0, 1, 2]},
             "series": ts_3class,
             "forecast_horizon": 1,
             "reduction": np.mean,  # not None → has_windows=False → single window
@@ -1274,13 +1241,10 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking, caplog
 
         with mlflow.start_run() as run:
             client = MlflowAutologgingQueueingClient()
-            with caplog.at_level(logging.WARNING):
-                # must not raise — logging is skipped on shape mismatch
+            with pytest.raises(ValueError, match="not divisible"):
                 _log_backtest_metrics(
                     client, run.info.run_id, fake_result, backtest_args
                 )
-            assert "not divisible" in caplog.text
-            client.flush(synchronous=True)
 
         assert not mlflow.get_run(run.info.run_id).data.metrics
 
@@ -1317,30 +1281,33 @@ def _fit_qlr(self, series=None):
     ],
 )
 def test_infer_metric_axes_reductions(metric_name, metric_kwargs, expected):
-    _attr_idx = {"has_time_axis": 0, "has_comp_axis": 1, "quantiles_num": 2}
-    axes = _infer_metric_axes(getattr(dm, metric_name), metric_kwargs)
+    has_time_axis, has_comp_axis, quantiles_labels = _infer_metric_axes(
+        getattr(dm, metric_name), metric_kwargs
+    )
+    actual = {
+        "has_time_axis": has_time_axis,
+        "has_comp_axis": has_comp_axis,
+        "quantiles_num": len(quantiles_labels),
+    }
     for attr, value in expected.items():
-        assert axes[_attr_idx[attr]] == value
+        assert actual[attr] == value
 
 
 def test_infer_metric_axes_quantiles():
-    _, _, quantiles_num, quantiles_labels = _infer_metric_axes(
-        dm.mql, {"q": [0.1, 0.5, 0.9]}
-    )
-    assert quantiles_num == 3
+    _, _, quantiles_labels = _infer_metric_axes(dm.mql, {"q": [0.1, 0.5, 0.9]})
     assert quantiles_labels == ["_q0.1", "_q0.5", "_q0.9"]
 
 
 def test_infer_metric_axes_quantile_interval():
-    has_time, _, quantiles_num, quantiles_labels = _infer_metric_axes(
+    has_time, _, quantiles_labels = _infer_metric_axes(
         dm.iw, {"q_interval": (0.1, 0.9)}
     )
-    assert quantiles_num == 1
     assert quantiles_labels == ["_qi0.1_0.9"]
     assert has_time is True
 
 
-def test_infer_metric_axes_unknown_labels():
-    """label_reduction=None with no explicit labels cannot determine QL."""
-    _, _, quantiles_num, _ = _infer_metric_axes(dm.f1, {"label_reduction": None})
-    assert quantiles_num is None
+def test_infer_metric_axes_unknown_labels_raises():
+    """label_reduction=None with no explicit labels cannot determine the number
+    of output labels ahead of time, so this raises rather than falling back."""
+    with pytest.raises(ValueError, match="requires explicit `labels`"):
+        _infer_metric_axes(dm.f1, {"label_reduction": None})

From a93ad196bbfb481e16d08210a4cf34ad72c689be Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 12:54:57 +0200
Subject: [PATCH 092/154] chore: remove classificaition metrics check

---
 darts/utils/mlflow.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index cf3969a168..b262fcc00b 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -80,7 +80,6 @@
 
 import darts
 from darts.logging import get_logger, raise_log
-from darts.metrics import CLASSIFICATION_METRICS
 from darts.metrics.utils import _LabelReduction
 from darts.models.forecasting.forecasting_model import ForecastingModel
 from darts.utils.ts_utils import (
@@ -1167,9 +1166,7 @@ def effective(param_name: str) -> Any:
         quantiles_labels = [f"_qi{lo:g}_{hi:g}" for lo, hi in intervals]
     elif "q" in params and q is not None:
         quantiles_labels = [f"_q{v:g}" for v in np.atleast_1d(np.array(q, dtype=float))]
-    elif "label_reduction" in params and getattr(metric, "__name__", "") in {
-        m.__name__ for m in CLASSIFICATION_METRICS
-    }:
+    elif "label_reduction" in params and getattr(metric, "__name__", ""):
         label_reduction = effective("label_reduction")
         if isinstance(label_reduction, _LabelReduction):
             label_reduction = label_reduction.value

From 554e02f19788aeafdf7442dddad51b864a1c1e2a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 15:59:29 +0200
Subject: [PATCH 093/154] feat: change model import approach

---
 darts/utils/mlflow.py | 42 +++++++++++++++++++++++++++++-------------
 1 file changed, 29 insertions(+), 13 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index b262fcc00b..f3094562bd 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -124,8 +124,10 @@
     ),
 ]
 
-# Thread-local flag set during historical_forecasts to suppress per-iteration
-# autologging in _patched_fit (which would otherwise spawn one run per iteration).
+# Thread-local flags used by _patched_fit to suppress nested/re-entrant
+# autologging: in_historical_forecasts covers historical_forecasts' internal
+# fit() calls, in_fit covers nested fit() calls (e.g. ensembles, super()),
+# so only the outermost call logs.
 _autolog_state = threading.local()
 
 
@@ -517,19 +519,25 @@ def autolog(
 
 
 def _get_forecasting_models():
-    """
-    Returns:
-        A list of (name, class) tuples for all forecasting models in the Darts library.
-    """
-    import darts.models
+    """Find all ``ForecastingModel`` subclasses currently loaded in memory.
 
-    classes = inspect.getmembers(darts.models, inspect.isclass)
+    Traverses ``__subclasses__()``, avoiding force-importing all of the forecasting
+    models.
 
-    classes = [
-        (name, cls) for name, cls in classes if issubclass(cls, ForecastingModel)
-    ]
+    Returns:
+        A list of (name, class) tuples for all matching classes.
+    """
+    seen: set[type] = set()
+    stack = [ForecastingModel]
+    while stack:
+        current = stack.pop()
+        for sub in current.__subclasses__():
+            if sub not in seen:
+                seen.add(sub)
+                stack.append(sub)
 
-    return sorted(set(classes), key=itemgetter(0))
+    classes = [(cls.__name__, cls) for cls in seen]
+    return sorted(classes, key=itemgetter(0))
 
 
 @autologging_integration(FLAVOR_NAME)
@@ -575,10 +583,18 @@ def _patched_fit(original, self, *args, **kwargs):
         if getattr(_autolog_state, "in_historical_forecasts", False):
             return original(self, *args, **kwargs)
 
+        # handle nested fit() calls
+        if getattr(_autolog_state, "in_fit", False):
+            return original(self, *args, **kwargs)
+
         # Track which model is active so metric patches can prefix their keys
         _autolog_state.current_model_name = type(self).__name__
 
-        result = original(self, *args, **kwargs)
+        _autolog_state.in_fit = True
+        try:
+            result = original(self, *args, **kwargs)
+        finally:
+            _autolog_state.in_fit = False
 
         active_run = mlflow.active_run()
         if active_run is None:

From e36b551b14146540f9c2b2ed4b22d1be68c58e0e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 15:59:51 +0200
Subject: [PATCH 094/154] chore: remove redundant dependencies

---
 pyproject.toml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 3781cd521a..44e8206957 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,10 +30,8 @@ classifiers = [
     "Programming Language :: Python :: Implementation :: PyPy",
 ]
 dependencies = [
-    "coolname>=4.2.0",
     "holidays>=0.11.1",
     "joblib>=0.16.0",
-    "loguru>=0.7.3",
     "matplotlib>=3.3.0",
     "narwhals>=1.25.1",
     "nfoursid>=1.0.0",

From 9f934404e20ac0b086243e78ed746765fe40e43c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 16:00:48 +0200
Subject: [PATCH 095/154] feat: add new import logic tests

---
 darts/tests/optional_deps/test_mlflow.py | 39 +++++++++++++++++++++++-
 1 file changed, 38 insertions(+), 1 deletion(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 13439b4efc..71d5e53d68 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -25,7 +25,12 @@
 import mlflow
 from mlflow.utils.autologging_utils.client import MlflowAutologgingQueueingClient
 
-from darts.models import ExponentialSmoothing, LinearRegressionModel
+from darts.models import (
+    ExponentialSmoothing,
+    LinearRegressionModel,
+    NaiveSeasonal,
+    RegressionEnsembleModel,
+)
 from darts.utils.mlflow import (
     _infer_metric_axes,
     _log_backtest_metrics,
@@ -461,6 +466,38 @@ def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
         assert "ExponentialSmoothing" in model_classes
         assert "LinearRegressionModel" in model_classes
 
+    def test_autolog_ensemble_model_fit_logs_once(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Fitting a composite/ensemble model must log exactly one model artifact
+        for the outer model, not one per sub-model too.
+
+        RegressionEnsembleModel.fit() internally calls fit() on each of its
+        forecasting_models. Since every ForecastingModel subclass is patched
+        independently, an unguarded patch would re-trigger autologging for each
+        inner fit() call as well as the outer one.
+        """
+        model = RegressionEnsembleModel(
+            forecasting_models=[
+                NaiveSeasonal(K=12),
+                LinearRegressionModel(lags=12),
+            ],
+            regression_train_n_points=12,
+        )
+
+        with autolog_context(log_models=True):
+            with mlflow.start_run() as run:
+                model.fit(self.ts_univariate)
+
+        logged_models = mlflow_tracking.search_logged_models(
+            experiment_ids=[run.info.experiment_id]
+        )
+        assert len(logged_models) == 1, (
+            f"Expected exactly one logged model, got {len(logged_models)}: "
+            f"{[m.name for m in logged_models]}"
+        )
+        assert logged_models[0].name == "RegressionEnsembleModel"
+
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_torch_model_multiple_fits(self, mlflow_tracking, autolog_context):
         """Test autolog with multiple fits of a torch model"""

From d1e80d2428e74c998df852db44a8bfc3e36aa9f7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 17:09:07 +0200
Subject: [PATCH 096/154] feat: improve metric logging approach and fix
 import-order issue

---
 darts/metrics/utils.py |  23 +++++
 darts/utils/mlflow.py  | 189 +++++++++++++++++++----------------------
 2 files changed, 112 insertions(+), 100 deletions(-)

diff --git a/darts/metrics/utils.py b/darts/metrics/utils.py
index aa2a84d605..9d5cbec313 100644
--- a/darts/metrics/utils.py
+++ b/darts/metrics/utils.py
@@ -155,6 +155,22 @@ def wrapper_classification_support(*args, **kwargs):
     return wrapper_classification_support
 
 
+_metric_callbacks: list[Callable] = []
+
+
+def register_metric_callback(callback: Callable) -> None:
+    """Register a callback to be invoked after every top-level metric call."""
+    _metric_callbacks.append(callback)
+
+
+def unregister_metric_callback(callback: Callable) -> None:
+    """Remove a previously registered metric callback, if present."""
+    try:
+        _metric_callbacks.remove(callback)
+    except ValueError:
+        pass
+
+
 def multi_ts_support(func) -> Callable[..., METRIC_OUTPUT_TYPE]:
     """
     This decorator further adapts the metrics that took as input two (or three for scaled metrics with `insample`)
@@ -169,6 +185,9 @@ def multi_ts_support(func) -> Callable[..., METRIC_OUTPUT_TYPE]:
 
     @wraps(func)
     def wrapper_multi_ts_support(*args, **kwargs):
+        original_args = args
+        original_kwargs = dict(kwargs)
+
         actual_series = (
             kwargs["actual_series"] if "actual_series" in kwargs else args[0]
         )
@@ -312,6 +331,10 @@ def wrapper_multi_ts_support(*args, **kwargs):
         elif series_seq_type == SeriesType.SINGLE:
             vals = vals[0]
 
+        # invoke registered callbacks (e.g. MLflow autologging)
+        for cb in _metric_callbacks:
+            cb(func=func, result=vals, args=original_args, kwargs=original_kwargs)
+
         # flatten along series axis if n series == 1
         return vals
 
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index f3094562bd..b213528940 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -80,7 +80,11 @@
 
 import darts
 from darts.logging import get_logger, raise_log
-from darts.metrics.utils import _LabelReduction
+from darts.metrics.utils import (
+    _LabelReduction,
+    register_metric_callback,
+    unregister_metric_callback,
+)
 from darts.models.forecasting.forecasting_model import ForecastingModel
 from darts.utils.ts_utils import (
     SeriesType,
@@ -424,9 +428,8 @@ def autolog(
     1. Start an MLflow run (or reuse the currently active one).
     2. Log model creation parameters (``model.model_params``).
     3. Log covariate usage information (past, future, and static covariates).
-    4. Patch all darts metric functions so that any call made inside an active
-       MLflow run automatically logs the result.  Repeated calls overwrite
-       the previous value.
+    4. Log the result of any darts metric call made inside an active MLflow
+       run. Repeated calls overwrite the previous value.
     5. For PyTorch-based models: leverage ``mlflow.pytorch.autolog()`` to
        automatically log per-epoch training and validation metrics.
     6. Log the trained model artifact at the end of training.
@@ -434,16 +437,6 @@ def autolog(
     8. Patch ``historical_forecasts()`` so that its internal per-window
        ``fit()`` calls don't each spawn their own logging.
 
-    .. important::
-
-        Metric functions imported with ``from darts.metrics import <name>``
-        before ``autolog()`` is enabled will **not** log to MLflow (function
-        reference is copied out of the module before the patch is applied).
-
-        To avoid this restriction, use ``import darts.metrics as dm`` and call
-        ``dm.<metric>()`` instead (module attribute lookups are resolved at call
-        time and will always see the patched version).
-
     .. note::
 
         Logged metric keys follow the pattern
@@ -471,8 +464,8 @@ def autolog(
     log_params
         If ``True`` (default), log model creation parameters.
     log_metrics
-        If ``True`` (default), patch all darts metric functions so that any
-        call made inside an active MLflow run is automatically logged.
+        If ``True`` (default), log the result of any darts metric call made
+        inside an active MLflow run.
     log_torch_metrics
         If ``True`` (default), enable ``mlflow.pytorch.autolog(log_models=False)``
         around PyTorch-based model training to automatically log per-epoch
@@ -509,6 +502,13 @@ def autolog(
         except (ImportError, Exception):
             pass
 
+    # Register/unregister the metric-logging callback with darts.metrics.utils
+    # directly, rather than via mlflow's safe_patch on each darts.metrics
+    # attribute (which is import-order sensitive)
+    unregister_metric_callback(_mlflow_metric_callback)
+    if log_metrics and not disable:
+        register_metric_callback(_mlflow_metric_callback)
+
     _autolog(
         log_models=log_models,
         log_params=log_params,
@@ -707,30 +707,6 @@ def _patched_backtest(original, self, *args, **kwargs):
             _patched_backtest,
         )
 
-    if log_metrics:
-        import darts.metrics
-
-        # TODO: To log metrics post-fitting, only patching the metric methods may not be enough.
-        # This is becuase `model.fit()` method would terminate the active MLflow run at the end of training,
-        # so any metric calls made after that would not be logged.
-        # To address this, we need to implement three things:
-        # 1. Implement a singleton `_AutologgingMetricsManager` (`mlflow.sklearn`) to maintain a mapping between fitted
-        # models and their prediction outputs.
-        # 2. Patch the `model.predict()` method to create a mapping between the model (run_id) and prediction output.
-        # 3. Patch the metric functions to find the model (run_id) from the input series, then log the metrics to
-        # the corresponding run.
-        # This way, even if the metric calls are made after `fit()` has terminated the active run, we can still log
-        # the metrics to the correct run.
-
-        # patch all metric functions to log results
-        for metric_name in darts.metrics.__all__:
-            safe_patch(
-                FLAVOR_NAME,
-                darts.metrics,
-                metric_name,
-                _make_metric_patch(metric_name),
-            )
-
 
 def get_default_pip_requirements():
     """Return the default pip requirements for logging a darts model.
@@ -1355,13 +1331,29 @@ def _log_metric_result(
         _write_per_series_csv(rows, f"{metric_name}_per_series.csv")
 
 
-def _make_metric_patch(metric_name: str) -> Callable:
-    """Create a ``safe_patch``-compatible patch function for a darts metric.
-
-    The returned patch calls the original metric and, when an active MLflow
-    run exists, infers the output axes from the metric signature and call
-    kwargs (via ``_infer_metric_axes``) and delegates to ``_log_metric_result``,
-    which logs each cell under a key built as::
+# TODO: To log metrics post-fitting, only patching the metric methods may not be enough.
+# This is becuase `model.fit()` method would terminate the active MLflow run at the end of training,
+# so any metric calls made after that would not be logged.
+# To address this, we need to implement three things:
+# 1. Implement a singleton `_AutologgingMetricsManager` (`mlflow.sklearn`) to maintain a mapping between fitted
+# models and their prediction outputs.
+# 2. Patch the `model.predict()` method to create a mapping between the model (run_id) and prediction output.
+# 3. Patch the metric functions to find the model (run_id) from the input series, then log the metrics to
+# the corresponding run.
+# This way, even if the metric calls are made after `fit()` has terminated the active run, we can still log
+# the metrics to the correct run.
+def _mlflow_metric_callback(func, result, args, kwargs) -> None:
+    """Metric callback registered with ``darts.metrics.utils`` for autologging.
+
+    Invoked by ``multi_ts_support`` (the outermost decorator on every darts
+    metric) after every top-level metric call, so it fires regardless of how
+    the metric was imported. It is not invoked for internal metric-to-metric
+    calls (e.g. ``rmse`` calling ``mse`` internally via ``_get_wrapped_metric``),
+    since those bypass ``multi_ts_support`` entirely.
+
+    When an active MLflow run exists, infers the output axes from the metric
+    signature and call kwargs (via ``_infer_metric_axes``) and delegates to
+    ``_log_metric_result``, which logs each cell under a key built as::
 
         {metric_name}{component}{quantile_or_label}
 
@@ -1378,67 +1370,64 @@ def _make_metric_patch(metric_name: str) -> Callable:
     to a ``per_series_metrics/`` CSV artifact instead of per-series keys.
 
     The per-timestep axis (``time_reduction=None``) is mapped to the MLflow
-    ``step``. The original return value is always forwarded unchanged.
+    ``step``.
 
     Parameters
     ----------
-    metric_name
-        The darts metric function name used as the MLflow metric key.
+    func
+        The darts metric function that was called (used for its name and
+        signature).
+    result
+        The metric's return value.
+    args
+        Positional arguments the metric was called with.
+    kwargs
+        Keyword arguments the metric was called with.
     """
+    active_run = mlflow.active_run()
+    if active_run is None:
+        return
 
-    def _patched_metric(original, *args, **kwargs):
-        result = original(*args, **kwargs)
-
-        active_run = mlflow.active_run()
-        if active_run is None:
-            return result
-
-        # backtest() calls metric functions internally; _patched_backtest
-        # handles logging the aggregated result, so skip here to avoid
-        # generating one flat key per window (series_gen_mape_0, _1, …).
-        if getattr(_autolog_state, "in_backtest", False):
-            return result
+    # backtest() calls metric functions internally; _patched_backtest
+    # handles logging the aggregated result, so skip here to avoid
+    # generating one flat key per window (series_gen_mape_0, _1, …).
+    if getattr(_autolog_state, "in_backtest", False):
+        return
 
-        autologging_client = MlflowAutologgingQueueingClient()
-        run_id = active_run.info.run_id
+    if len(args) > 0:
+        series = args[0]
+    else:
+        series = kwargs.get("actual_series", None)
+    if series is None:
+        return
 
-        if len(args) > 0:
-            series = args[0]
-        else:
-            series = kwargs.get("actual_series", None)
-        if series is None:
-            return result
+    autologging_client = MlflowAutologgingQueueingClient()
+    run_id = active_run.info.run_id
 
-        # the `name` kwarg overrides the metric-name token in the logged key
-        key_name = _sanitize_mlflow_key(kwargs.get("name") or metric_name)
+    # the `name` kwarg overrides the metric-name token in the logged key
+    key_name = _sanitize_mlflow_key(kwargs.get("name") or func.__name__)
 
-        # infer output axes from the metric signature + call kwargs
-        has_time_axis, has_comp_axis, quantiles_labels = _infer_metric_axes(
-            original, kwargs
-        )
+    # infer output axes from the metric signature + call kwargs
+    has_time_axis, has_comp_axis, quantiles_labels = _infer_metric_axes(func, kwargs)
 
-        # series_reduction collapses the series axis inside the metric, so the
-        # result has no leading series axis even for list input.
-        params = inspect.signature(original).parameters
-        series_reduced = False
-        if "series_reduction" in params:
-            effective_sr = kwargs.get(
-                "series_reduction", params["series_reduction"].default
-            )
-            series_reduced = effective_sr is not None
-
-        _log_metric_result(
-            autologging_client,
-            run_id,
-            key_name,
-            result,
-            series,
-            has_time_axis,
-            has_comp_axis,
-            quantiles_labels,
-            series_reduced=series_reduced,
+    # series_reduction collapses the series axis inside the metric, so the
+    # result has no leading series axis even for list input.
+    params = inspect.signature(func).parameters
+    series_reduced = False
+    if "series_reduction" in params:
+        effective_sr = kwargs.get(
+            "series_reduction", params["series_reduction"].default
         )
-
-        return result
-
-    return _patched_metric
+        series_reduced = effective_sr is not None
+
+    _log_metric_result(
+        autologging_client,
+        run_id,
+        key_name,
+        result,
+        series,
+        has_time_axis,
+        has_comp_axis,
+        quantiles_labels,
+        series_reduced=series_reduced,
+    )

From 0d51dc46ed1f6320cc29c0fb503bbcb044609546 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 21 Jul 2026 17:09:49 +0200
Subject: [PATCH 097/154] feat: new metric logging approach tests

---
 darts/tests/optional_deps/test_mlflow.py | 72 ++++++++++++++++--------
 1 file changed, 49 insertions(+), 23 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 71d5e53d68..9ee0c8a901 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -738,29 +738,54 @@ def test_autolog_log_metrics_false(self, mlflow_tracking, autolog_context):
             "mape should NOT be logged when log_metrics=False"
         )
 
-    def test_autolog_public_namespace_patched(self, mlflow_tracking, autolog_context):
-        """Only darts.metrics (public namespace) is patched; darts.metrics.metrics is not.
-
-        Patching only the public namespace avoids breaking internal metric-to-metric
-        calls within the implementation module (e.g. rmse calling mse internally).
+    def test_autolog_metric_any_import_path_logs(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Metrics log identically regardless of which module path was used to
+        call them. The mlflow hook lives inside `multi_ts_support` (baked into
+        the function itself) rather than patching a specific module
+        attribute, and `darts.metrics.mae` and `darts.metrics.metrics.mae` are
+        the same function object.
         """
         with autolog_context(log_metrics=True):
-            # public namespace → patched: call inside a run should log
             with mlflow.start_run() as run_public:
                 dm.mae(self.ts_univariate, self.ts_univariate * 1.1)
-
-            # implementation module → NOT patched: call inside a run should not log
             with mlflow.start_run() as run_impl:
                 dmm.mae(self.ts_univariate, self.ts_univariate * 1.1)
 
-        run_data_public = mlflow.get_run(run_public.info.run_id).data
-        run_data_impl = mlflow.get_run(run_impl.info.run_id).data
-        assert "mae" in run_data_public.metrics, (
-            "darts.metrics.mae should log to MLflow (public namespace is patched)"
-        )
-        assert "mae" not in run_data_impl.metrics, (
-            "darts.metrics.metrics.mae should NOT log (implementation module is not patched)"
-        )
+        assert "mae" in mlflow.get_run(run_public.info.run_id).data.metrics
+        assert "mae" in mlflow.get_run(run_impl.info.run_id).data.metrics
+
+    def test_autolog_metric_import_order_independent(
+        self, mlflow_tracking, autolog_context
+    ):
+        """A metric imported via `from darts.metrics import <name>` *before*
+        autolog() is enabled still logs correctly, since the hook lives inside
+        the metric's own `multi_ts_support` decorator rather than patching a
+        module attribute after the fact."""
+        from darts.metrics import mae as mae_imported_before_autolog
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                mae_imported_before_autolog(
+                    self.ts_univariate, self.ts_univariate * 1.1
+                )
+
+        assert "mae" in mlflow.get_run(run.info.run_id).data.metrics
+
+    def test_autolog_metric_internal_composite_call_not_double_logged(
+        self, mlflow_tracking, autolog_context
+    ):
+        """rmse calls mse internally via `_get_wrapped_metric`, which bypasses
+        `multi_ts_support` entirely, so autologging must fire once (for rmse),
+        not twice (rmse and the internal mse call)."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                dm.rmse(self.ts_univariate, self.ts_univariate * 1.1)
+
+        metrics = mlflow.get_run(run.info.run_id).data.metrics
+        assert "rmse" in metrics
+        assert "mse" not in metrics
 
     def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
         """A per-timestep metric (ae) logs one value per timestep across MLflow steps.
@@ -948,13 +973,13 @@ def test_autolog_metric_multi_series_classification_labels_explicit(
             assert got[("f1_label0", i)] == pytest.approx(ref[i][0], abs=1e-5)
             assert got[("f1_label1", i)] == pytest.approx(ref[i][1], abs=1e-5)
 
-    def test_autolog_metric_size_mismatch_raises_internally(
+    def test_autolog_metric_size_mismatch_raises(
         self, mlflow_tracking, autolog_context, caplog
     ):
         """When the inferred C-axis size doesn't divide the result, logging raises
-        (logged as an error) and no metrics are written. The public metric call
-        itself doesn't crash, since MLflow's safe_patch wrapper catches exceptions
-        raised from inside a patch function."""
+        (and logs an error), propagating out of the public metric call — the
+        metric callback isn't invoked through MLflow's safe_patch, so nothing
+        catches it. No metrics are written for the failed call."""
         actual = self.ts_univariate[40:]
         # mae with component_reduction=None on a univariate series produces shape (T,),
         # which is size T — divisible by c_size=1 (1 component × 1 quantile), so we
@@ -976,11 +1001,12 @@ def test_autolog_metric_size_mismatch_raises_internally(
         ):
             with autolog_context(log_metrics=True):
                 with mlflow.start_run() as run:
-                    with caplog.at_level(logging.WARNING, logger="darts"):
-                        dm.mae(actual, pred)
+                    with caplog.at_level(logging.ERROR, logger="darts"):
+                        with pytest.raises(ValueError, match="not divisible"):
+                            dm.mae(actual, pred)
 
         assert any("not divisible" in record.message for record in caplog.records), (
-            "Expected a 'not divisible' warning when axes don't match the result"
+            "Expected a 'not divisible' error to be logged when axes don't match"
         )
         # no metrics should have been written for the (faked) mismatched call
         run_data = mlflow.get_run(run.info.run_id).data.metrics

From ae8a07260b867c6c38ce74030918ac8da4e1d9ec Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 11:02:56 +0200
Subject: [PATCH 098/154] feat: change per-series saving logic to append

---
 darts/utils/mlflow.py | 62 +++++++++++++++++++++----------------------
 1 file changed, 30 insertions(+), 32 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index b213528940..864b5eed90 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -19,7 +19,6 @@
 https://github.com/sktime/sktime/blob/main/sktime/utils/mlflow_sktime.py
 """
 
-import csv
 import inspect
 import re
 import sys
@@ -42,6 +41,7 @@
     )
 
 import numpy as np
+import pandas as pd
 import yaml
 from mlflow.entities import LoggedModel
 from mlflow.models import Model, ModelInputExample, ModelSignature
@@ -69,7 +69,7 @@
     _PythonEnv,
     _validate_env_arguments,
 )
-from mlflow.utils.file_utils import TempDir, write_to
+from mlflow.utils.file_utils import write_to
 from mlflow.utils.model_utils import (
     _add_code_from_conf_to_system_path,
     _get_flavor_configuration,
@@ -454,8 +454,9 @@ def autolog(
 
         Per-timestep results (``time_reduction=None``) are charted across the
         MLflow ``step``. For a list of series the logged value is the mean over
-        series and the full per-series breakdown is written to a
-        ``per_series_metrics/`` CSV artifact.
+        series, and the full per-series breakdown for every metric/backtest
+        call in the run is appended to a single ``metrics_per_series.json``
+        table artifact.
 
     Parameters
     ----------
@@ -856,14 +857,15 @@ def _sanitize_mlflow_key(name: str) -> str:
     return re.sub(r"[^\w-]", "_", name)
 
 
-def _write_per_series_csv(rows: list[dict], filename: str) -> None:
-    """Write the granular per-series metric breakdown to a CSV artifact.
+def _log_per_series_table(rows: list[dict]) -> None:
+    """Append the granular per-series metric breakdown to a single, run-wide
+    table artifact.
 
     Each row is a single metric cell for one series, with columns ``key`` (the
     aggregate MLflow key, without any series suffix), ``series_index``, ``step``
-    (the time or window index charted by MLflow), and ``value``. The file is
-    logged under the ``per_series_metrics`` artifact subdirectory of the active
-    run. Used when more than one series is scored, since the logged metric keys
+    (the time or window index charted by MLflow), and ``value``. All calls
+    within a run append to the same ``metrics_per_series.json`` artifact.
+    Used when more than one series is scored, since the logged metric keys
     only carry the mean over series.
 
     Parameters
@@ -871,20 +873,11 @@ def _write_per_series_csv(rows: list[dict], filename: str) -> None:
     rows
         One dict per metric cell with keys ``key``, ``series_index``, ``step``,
         and ``value``.
-    filename
-        Basename of the CSV file (e.g. ``series_mae_per_series.csv``).
     """
     if not rows:
         return
-    sorted_rows = sorted(rows, key=itemgetter("key", "series_index", "step"))
-    fieldnames = ["key", "series_index", "step", "value"]
-    with TempDir() as tmp:
-        path = tmp.path(filename)
-        with open(path, "w", newline="") as f:
-            writer = csv.DictWriter(f, fieldnames=fieldnames)
-            writer.writeheader()
-            writer.writerows(sorted_rows)
-        mlflow.log_artifact(path, artifact_path="per_series_metrics")
+    df = pd.DataFrame(rows).sort_values(["key", "series_index", "step"])
+    mlflow.log_table(data=df, artifact_file="metrics_per_series.json")
 
 
 def _log_backtest_metrics(
@@ -924,9 +917,10 @@ def _log_backtest_metrics(
     result is flattened to integer-indexed keys instead.
 
     When more than one series is scored, the logged value is the mean over
-    series for each cell, and the granular per-series breakdown is written to a
-    ``per_series_metrics/backtest_per_series.csv`` artifact. For a single series
-    the mean is just the value itself and no artifact is written.
+    series for each cell, and the granular per-series breakdown is appended to
+    the run's ``metrics_per_series.json`` table artifact (shared with
+    ``_log_metric_result``). For a single series the mean is just the value
+    itself and no artifact is written.
 
     Raises
     ------
@@ -1105,9 +1099,10 @@ def _log_backtest_metrics(
     for step, metrics in metrics_by_step.items():
         autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
 
-    # write the granular per-series breakdown to a CSV artifact (multi-series only)
+    # append the granular per-series breakdown to the run's table artifact
+    # (multi-series only)
     if len(series_seq) > 1:
-        _write_per_series_csv(rows, "backtest_per_series.csv")
+        _log_per_series_table(rows)
 
 
 def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
@@ -1216,9 +1211,10 @@ def _log_metric_result(
     * ``quantile_or_label`` – e.g. ``_q0.5`` / ``_qi0.1_0.9`` / ``_label1``.
 
     When more than one series is scored, the logged value is the mean over
-    series for each cell, and the granular per-series breakdown is written to a
-    ``per_series_metrics/{metric_name}_per_series.csv`` artifact. For a single
-    series the mean is just the value itself and no artifact is written.
+    series for each cell, and the granular per-series breakdown is appended to
+    the run's ``metrics_per_series.json`` table artifact (shared with
+    ``_log_backtest_metrics``). For a single series the mean is just the value
+    itself and no artifact is written.
 
     Raises
     ------
@@ -1326,9 +1322,10 @@ def _log_metric_result(
         autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
     autologging_client.flush(synchronous=False).await_completion()
 
-    # write the granular per-series breakdown to a CSV artifact (multi-series only)
+    # append the granular per-series breakdown to the run's table artifact
+    # (multi-series only)
     if len(series_seq) > 1:
-        _write_per_series_csv(rows, f"{metric_name}_per_series.csv")
+        _log_per_series_table(rows)
 
 
 # TODO: To log metrics post-fitting, only patching the metric methods may not be enough.
@@ -1366,8 +1363,9 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
       ``_qi0.1_0.9``, ``_label1``) when applicable.
 
     When the input is a ``Sequence[TimeSeries]`` with more than one series, the
-    logged value is the mean over series and the per-series breakdown is written
-    to a ``per_series_metrics/`` CSV artifact instead of per-series keys.
+    logged value is the mean over series and the per-series breakdown is
+    appended to the run's ``metrics_per_series.json`` table artifact instead
+    of per-series keys.
 
     The per-timestep axis (``time_reduction=None``) is mapped to the MLflow
     ``step``.

From b3f30314789e519ac5e1143e1186475f2496a57f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 11:04:57 +0200
Subject: [PATCH 099/154] feat: append on save logic tests

---
 darts/tests/optional_deps/test_mlflow.py | 58 +++++++++++-------------
 1 file changed, 26 insertions(+), 32 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 9ee0c8a901..b7655795e5 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1,4 +1,3 @@
-import csv
 import logging
 import os
 
@@ -851,7 +850,7 @@ def test_autolog_metric_quantile_interval(self, mlflow_tracking, autolog_context
         assert m["miw_qi0_1_0_9"] == pytest.approx(float(ref), abs=1e-5)
 
     def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
-        """A list of series logs the mean over series; per-series values go to a CSV."""
+        """A list of series logs the mean over series; per-series values go to a table."""
         series = [self.ts_univariate, self.ts_univariate * 1.2]
         pred = [s * 1.1 for s in series]
 
@@ -864,9 +863,9 @@ def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
         # aggregate = mean over series, no per-series _s{i} keys
         assert m["mae"] == pytest.approx(float(np.mean(ref)), abs=1e-5)
         assert not any(k.startswith("mae_s") for k in m)
-        # granular per-series breakdown written to a CSV artifact
-        csv_rows = self._read_per_series_csv(run.info.run_id, "mae_per_series.csv")
-        by_series = {int(row["series_index"]): float(row["value"]) for row in csv_rows}
+        # granular per-series breakdown written to a table artifact
+        rows = self._read_per_series_table(run.info.run_id)
+        by_series = {int(row["series_index"]): float(row["value"]) for row in rows}
         assert by_series == pytest.approx({0: ref[0], 1: ref[1]}, abs=1e-5)
 
     def test_autolog_metric_multi_series_per_component(
@@ -888,10 +887,9 @@ def test_autolog_metric_multi_series_per_component(
         assert m["mae_linear_1"] == pytest.approx(float(ref[:, 1].mean()), abs=1e-5)
         assert not any(k.endswith(("_s0", "_s1")) for k in m)
         # granular CSV: one row per (component, series)
-        csv_rows = self._read_per_series_csv(run.info.run_id, "mae_per_series.csv")
+        rows = self._read_per_series_table(run.info.run_id)
         got = {
-            (row["key"], int(row["series_index"])): float(row["value"])
-            for row in csv_rows
+            (row["key"], int(row["series_index"])): float(row["value"]) for row in rows
         }
         assert got[("mae_linear", 0)] == pytest.approx(ref[0, 0], abs=1e-5)
         assert got[("mae_linear", 1)] == pytest.approx(ref[1, 0], abs=1e-5)
@@ -935,7 +933,7 @@ def test_autolog_metric_multi_series_classification_labels_explicit(
         self, mlflow_tracking, autolog_context
     ):
         """f1 with label_reduction=None and explicit labels on a list of binary
-        series logs the per-label mean over series and writes the per-series CSV.
+        series logs the per-label mean over series and writes the per-series table.
 
         ``labels`` must be explicit (``label_reduction=None`` without it raises,
         since the number of output labels can't be determined ahead of time)."""
@@ -964,10 +962,9 @@ def test_autolog_metric_multi_series_classification_labels_explicit(
         )
         assert not any(k.endswith(("_s0", "_s1")) for k in m)
         # granular CSV: one row per (label, series)
-        csv_rows = self._read_per_series_csv(run.info.run_id, "f1_per_series.csv")
+        rows = self._read_per_series_table(run.info.run_id)
         got = {
-            (row["key"], int(row["series_index"])): float(row["value"])
-            for row in csv_rows
+            (row["key"], int(row["series_index"])): float(row["value"]) for row in rows
         }
         for i in range(2):
             assert got[("f1_label0", i)] == pytest.approx(ref[i][0], abs=1e-5)
@@ -1014,10 +1011,10 @@ def test_autolog_metric_size_mismatch_raises(
             "No mae metrics should be logged when the size check fails"
         )
 
-    def test_autolog_metric_per_series_csv_schema_and_single_series_skip(
+    def test_autolog_metric_per_series_table_schema_and_single_series_skip(
         self, mlflow_tracking, autolog_context
     ):
-        """The per-series CSV has the expected schema for multi-series input, and no
+        """The per-series table has the expected schema for multi-series input, and no
         artifact is written for single-series input (mean == the value itself)."""
         # multi-series: artifact exists with the documented columns
         multi = [self.ts_univariate, self.ts_univariate * 1.2]
@@ -1026,13 +1023,11 @@ def test_autolog_metric_per_series_csv_schema_and_single_series_skip(
             with mlflow.start_run() as run_multi:
                 dm.mae(multi, pred_multi)
 
-        csv_rows = self._read_per_series_csv(
-            run_multi.info.run_id, "mae_per_series.csv"
-        )
-        assert list(csv_rows[0].keys()) == ["key", "series_index", "step", "value"]
-        assert {int(r["series_index"]) for r in csv_rows} == {0, 1}
+        rows = self._read_per_series_table(run_multi.info.run_id)
+        assert list(rows[0].keys()) == ["key", "series_index", "step", "value"]
+        assert {int(r["series_index"]) for r in rows} == {0, 1}
 
-        # single-series: no per_series_metrics artifact directory should be created
+        # single-series: no per-series table artifact should be created
         single = self.ts_univariate
         pred_single = single * 1.1
         with autolog_context(log_metrics=True):
@@ -1040,8 +1035,8 @@ def test_autolog_metric_per_series_csv_schema_and_single_series_skip(
                 dm.mae(single, pred_single)
 
         artifacts = mlflow_tracking.list_artifacts(run_single.info.run_id)
-        assert not any(a.path == "per_series_metrics" for a in artifacts), (
-            "Single-series input should not write a per-series CSV artifact"
+        assert not any(a.path == "metrics_per_series.json" for a in artifacts), (
+            "Single-series input should not write a per-series table artifact"
         )
 
     def test_autolog_backtest_scalar(self, mlflow_tracking, autolog_context):
@@ -1120,7 +1115,7 @@ def test_autolog_backtest_multi_metric(self, mlflow_tracking, autolog_context):
         )
 
     def test_autolog_backtest_multi_series(self, mlflow_tracking, autolog_context):
-        """A list of series logs the mean over series; per-series values go to a CSV."""
+        """A list of series logs the mean over series; per-series values go to a table."""
         series = [self.ts_univariate, self.ts_univariate * 1.2]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
@@ -1134,9 +1129,9 @@ def test_autolog_backtest_multi_series(self, mlflow_tracking, autolog_context):
             float(np.mean(ref)), abs=1e-5
         )
         assert not any(k.startswith("backtest_mae_s") for k in run_data.metrics)
-        # granular per-series breakdown written to a CSV artifact
-        csv_rows = self._read_per_series_csv(run.info.run_id, "backtest_per_series.csv")
-        by_series = {int(row["series_index"]): float(row["value"]) for row in csv_rows}
+        # granular per-series breakdown written to a table artifact
+        rows = self._read_per_series_table(run.info.run_id)
+        by_series = {int(row["series_index"]): float(row["value"]) for row in rows}
         assert by_series == pytest.approx(
             {0: float(ref[0]), 1: float(ref[1])}, abs=1e-5
         )
@@ -1312,13 +1307,12 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
         assert not mlflow.get_run(run.info.run_id).data.metrics
 
     @staticmethod
-    def _read_per_series_csv(run_id, filename):
-        """Download and parse a per_series_metrics CSV artifact into row dicts."""
-        local = mlflow.artifacts.download_artifacts(
-            run_id=run_id, artifact_path=f"per_series_metrics/{filename}"
+    def _read_per_series_table(run_id):
+        """Load the run's consolidated per-series metric table into row dicts."""
+        df = mlflow.load_table(
+            artifact_file="metrics_per_series.json", run_ids=[run_id]
         )
-        with open(local, newline="") as f:
-            return list(csv.DictReader(f))
+        return df.to_dict("records")
 
     def _fit_lr(self, series=None):
         """Fit and return a fresh LinearRegressionModel (no active run)."""

From 529c58f6a20dda96b339806f6c63c988bd2d487c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 11:24:13 +0200
Subject: [PATCH 100/154] feat: infer forecast horizon from backtest args

---
 darts/utils/mlflow.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 864b5eed90..92873f740b 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -991,6 +991,17 @@ def _log_backtest_metrics(
 
     series = backtest_args.get("series")
     forecast_horizon = backtest_args.get("forecast_horizon")
+    historical_forecasts = backtest_args.get("historical_forecasts")
+    last_points_only = backtest_args.get("last_points_only", False)
+
+    # if last_points_only is True, has_windows will be False, so fc_hzn is not needed
+    if historical_forecasts and not last_points_only:
+        first_series_hf = (
+            historical_forecasts
+            if get_series_seq_type(series) == SeriesType.SINGLE
+            else historical_forecasts[0]
+        )
+        forecast_horizon = len(first_series_hf[0])
 
     series_seq = series2seq(series)
     results = [result] if get_series_seq_type(series) == SeriesType.SINGLE else result

From 069c28a409078cc10baa1c587d64c86475524aab Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 11:26:57 +0200
Subject: [PATCH 101/154] feat: fc_hzn inference unit test

---
 darts/tests/optional_deps/test_mlflow.py | 32 ++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index b7655795e5..3cee358623 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1172,6 +1172,38 @@ def test_autolog_backtest_per_timestep_per_window(
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
         np.testing.assert_allclose(logged, ref[0], atol=1e-5)
 
+    def test_autolog_backtest_historical_forecasts_horizon_inferred(
+        self, mlflow_tracking, autolog_context
+    ):
+        """When `historical_forecasts` is user-supplied, `backtest()` ignores the
+        `forecast_horizon` argument, autologging must infer the true window
+        length from the historical forecasts themselves, not from the (unused,
+        defaulted) `forecast_horizon` argument."""
+        model = self._fit_lr()
+        hf = model.historical_forecasts(
+            self.ts_univariate,
+            retrain=False,
+            stride=10,
+            forecast_horizon=4,
+            last_points_only=False,
+        )
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                # forecast_horizon is not passed (defaults to 1) and would be
+                # wrong; the real horizon (4) must come from `hf`.
+                ref = model.backtest(
+                    self.ts_univariate,
+                    historical_forecasts=hf,
+                    metric=dm.ae,
+                    reduction=None,
+                )
+
+        ref = np.asarray(ref, dtype=float)  # shape (n_windows, forecast_horizon)
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae_w0")
+        assert len(history) == 4, "Expected one step per forecast horizon timestep"
+        logged = [m.value for m in sorted(history, key=lambda m: m.step)]
+        np.testing.assert_allclose(logged, ref[0], atol=1e-5)
+
     def test_autolog_backtest_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile."""
         with autolog_context(log_metrics=True):

From 2d513a74664f8108902cd0a6dd71cee79e2ce293 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 11:42:22 +0200
Subject: [PATCH 102/154] fix: handle multi series covariate logging

---
 darts/utils/mlflow.py | 100 +++++++++++++++++++++++-------------------
 1 file changed, 54 insertions(+), 46 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 92873f740b..8728442933 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -107,27 +107,6 @@
 _MODEL_FILE_STAT = "model.pkl"
 _MODEL_FILE_TORCH = "model.pt"
 
-_covariate_types = [
-    (
-        "past_covariates",
-        "uses_past_covariates",
-        "past_covariate_series",
-        "components",
-    ),
-    (
-        "future_covariates",
-        "uses_future_covariates",
-        "future_covariate_series",
-        "components",
-    ),
-    (
-        "static_covariates",
-        "uses_static_covariates",
-        "static_covariates",
-        "columns",
-    ),
-]
-
 # Thread-local flags used by _patched_fit to suppress nested/re-entrant
 # autologging: in_historical_forecasts covers historical_forecasts' internal
 # fit() calls, in_fit covers nested fit() calls (e.g. ensembles, super()),
@@ -608,7 +587,13 @@ def _patched_fit(original, self, *args, **kwargs):
         if log_params:
             # Log the parameters for model creation
             autologging_client.log_params(run_id=run_id, params=self.model_params)
-            _log_covariate_info(self)
+            fit_args = inspect.signature(original).bind(self, *args, **kwargs).arguments
+            _log_covariate_info(
+                self,
+                series=fit_args.get("series"),
+                past_covariates=fit_args.get("past_covariates"),
+                future_covariates=fit_args.get("future_covariates"),
+            )
 
         param_logging_ops = autologging_client.flush(synchronous=False)
 
@@ -755,11 +740,11 @@ def _get_model_info_tags(model: ForecastingModel) -> dict[str, Any]:
     }
 
 
-def _log_covariate_info(model: ForecastingModel) -> None:
+def _log_covariate_info(model, series, past_covariates, future_covariates) -> None:
     """Log covariate usage information to MLflow.
 
     Extracts information about past, future, and static covariates used during
-    training and logs them as tags, parameters, and a JSON artifact for easy
+    training and logs them as a JSON artifact for easy
     filtering, comparison, and documentation.
 
     Logs three types of information:
@@ -771,17 +756,42 @@ def _log_covariate_info(model: ForecastingModel) -> None:
     ----------
     model
         A fitted Darts forecasting model instance.
+    series
+        The ``series`` argument passed to ``fit()``: a single ``TimeSeries``
+        or a ``Sequence[TimeSeries]``.
+    past_covariates
+        The past covariate argument passed to ``fit()``, or
+        ``None``.
+    future_covariates
+        The future covariate covariate argument passed to ``fit()``, or
+        ``None``.
     """
-
     covariate_info = {
-        cov_key: _extract_covariate_metadata(model, uses_attr, series_attr, names_attr)
-        for cov_key, uses_attr, series_attr, names_attr in _covariate_types
+        "past_covariates": _extract_covariate_metadata(
+            model.uses_past_covariates,
+            get_single_series(past_covariates),
+            "components",
+        ),
+        "future_covariates": _extract_covariate_metadata(
+            model.uses_future_covariates,
+            get_single_series(future_covariates),
+            "components",
+        ),
     }
 
-    if model.uses_static_covariates and model.static_covariates is not None:
-        covariate_info["static_covariates"]["is_global"] = len(
-            model.static_covariates
-        ) != len(model.static_covariates.columns)
+    first_series = get_single_series(series)
+    static_covariates = (
+        first_series.static_covariates if first_series is not None else None
+    )
+    covariate_info["static_covariates"] = _extract_covariate_metadata(
+        model.uses_static_covariates, static_covariates, "columns"
+    )
+    if model.uses_static_covariates and static_covariates is not None:
+        # static covariates are global (one shared row) unless there is one row
+        # per series component, in which case they are component-specific
+        covariate_info["static_covariates"]["is_global"] = (
+            len(static_covariates) != first_series.n_components
+        )
 
     # log complete information as JSON artifact
     mlflow.log_dict(covariate_info, "covariates.json")
@@ -803,21 +813,20 @@ def _is_torch_model(model) -> bool:
     return TORCH_AVAILABLE and isinstance(model, TorchForecastingModel)
 
 
-def _extract_covariate_metadata(
-    model: ForecastingModel, uses_attr: str, series_attr: str, names_attr: str
-) -> dict:
-    """Extract metadata for a single covariate type.
+def _extract_covariate_metadata(uses: bool, single_cov, names_attr: str) -> dict:
+    """Extract metadata for a single covariate type from its (already
+    singular) value.
 
     Parameters
     ----------
-    model
-        A Darts forecasting model instance.
-    uses_attr : str
-        Model attribute name indicating covariate usage.
-    series_attr : str
-        Model attribute name for the covariate series.
+    uses
+        Whether the model uses this covariate type.
+    single_cov
+        The covariate's value for one series: a ``TimeSeries`` (past/future
+        covariates) or a static-covariates ``DataFrame``, or ``None``.
     names_attr : str
-        Series attribute name for feature names ("components" or "columns").
+        Attribute holding the feature names ("components" for a
+        ``TimeSeries``, "columns" for a static-covariates ``DataFrame``).
 
     Returns
     -------
@@ -826,11 +835,10 @@ def _extract_covariate_metadata(
     """
     info = {"used": False, "count": 0, "names": []}
 
-    if getattr(model, uses_attr, False):
+    if uses:
         info["used"] = True
-        series = getattr(model, series_attr, None)
-        if series is not None:
-            names = getattr(series, names_attr).tolist()
+        if single_cov is not None:
+            names = getattr(single_cov, names_attr).tolist()
             info["names"] = names
             info["count"] = len(names)
 

From aac668d2e73479299f5c6ad57ba216c5acc17aab Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 11:43:00 +0200
Subject: [PATCH 103/154] feat: covariate tracking unit tests

---
 darts/tests/optional_deps/test_mlflow.py | 79 ++++++++++++++++++++++--
 1 file changed, 74 insertions(+), 5 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 3cee358623..387a3e8a48 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -363,12 +363,81 @@ def assert_metric(history, key):
 
         assert autologging_is_disabled("pytorch")
 
-    def test_covariate_artifact_schema(self, mlflow_tracking):
-        """Test that covariate artifact has correct JSON schema"""
-        model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
+    def test_autolog_covariate_info_single_series(
+        self, mlflow_tracking, autolog_context
+    ):
+        """The covariates.json artifact reports usage, count, and names for a
+        single-series fit."""
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+                model.fit(
+                    self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40]
+                )
+
+        covariates = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/covariates.json"
+        )
+        assert covariates["past_covariates"]["used"] is True
+        assert covariates["past_covariates"]["count"] == 1
+        assert (
+            covariates["past_covariates"]["names"]
+            == self.ts_past_cov.components.tolist()
+        )
+        assert covariates["future_covariates"]["used"] is False
+        assert covariates["static_covariates"]["used"] is False
+
+    def test_autolog_covariate_info_multi_series(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Fitting on a list of series doesn't leave past_covariates unreported:
+        `model.past_covariate_series` stays None for a multi-series fit, so the
+        info must come from the actual `fit()` call arguments instead."""
+        series = [self.ts_univariate, self.ts_univariate * 1.2]
+        past_covs = [self.ts_past_cov[:50], self.ts_past_cov[:50]]
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+                model.fit(series, past_covariates=past_covs)
+
+        covariates = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/covariates.json"
+        )
+        assert covariates["past_covariates"]["used"] is True
+        assert covariates["past_covariates"]["count"] == 1
+        assert (
+            covariates["past_covariates"]["names"]
+            == self.ts_past_cov.components.tolist()
+        )
 
-        # TODO: use autolog
+    def test_autolog_covariate_info_static_is_global(
+        self, mlflow_tracking, autolog_context
+    ):
+        """`is_global` is based on row count vs. the number of series
+        components, not the number of static-covariate columns."""
+        # one shared row over 2 components -> global
+        global_target = self.ts_multivariate.with_static_covariates(
+            pd.DataFrame({"a": [1.0], "b": [2.0]})
+        )
+        with autolog_context():
+            with mlflow.start_run() as run:
+                LinearRegressionModel(lags=5).fit(global_target)
+        covariates = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/covariates.json"
+        )
+        assert covariates["static_covariates"]["is_global"] is True
+
+        # one row per component (2 components, 2 rows) -> component-specific
+        per_component_target = self.ts_multivariate.with_static_covariates(
+            pd.DataFrame({"a": [1.0, 2.0]})
+        )
+        with autolog_context():
+            with mlflow.start_run() as run:
+                LinearRegressionModel(lags=5).fit(per_component_target)
+        covariates = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/covariates.json"
+        )
+        assert covariates["static_covariates"]["is_global"] is False
 
     def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         """Test saving/loading multivariate series with all covariate types"""

From 66a0b10685dee6d9d1da268296a941dbd4cbfe33 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 12:30:45 +0200
Subject: [PATCH 104/154] docs: clarify explanation for pytorch autolog
 approach

---
 darts/utils/mlflow.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 8728442933..c620e4dd6b 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -459,9 +459,13 @@ def autolog(
     """
     # Enable/disable mlflow.pytorch.autolog for per-epoch metrics on torch models.
     # This must happen outside the @autologging_integration-decorated _autolog()
-    # because the decorator short-circuits on disable=True before the function
-    # body executes, and MLflow's session manager suppresses nested autolog
-    # patches if called from within a safe_patch context.
+    # because that decorator short-circuits _autolog()'s body entirely when
+    # disable=True, so a call placed inside it would never run. Unlike
+    # mlflow.sklearn, which exposes a private, undecorated _autolog(flavor_name=...)
+    # that other flavors (e.g. xgboost) call to tag its patches under their own
+    # integration name for cleanup, mlflow.pytorch has no such hook: its autolog()
+    # hardcodes its own patches under "pytorch", so darts can't fold pytorch's
+    # patch lifecycle into its own and must call mlflow.pytorch.autolog() directly.
     if log_torch_metrics and not disable:
         try:
             import mlflow.pytorch

From 95709903b8de12528a1a645a7ad5fa190c0022b5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 13:46:46 +0200
Subject: [PATCH 105/154] feat: remove extra explicit mlflow gorup

---
 pyproject.toml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 44e8206957..39256b1bb8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -70,7 +70,6 @@ notorch = [
     "statsforecast>=1.4",
     "xgboost>=2.1.4",
 ]
-mlflow = ["mlflow>=3.0"]
 all = ["darts[torch,notorch]"]
 
 [tool.uv]

From 381cc15ab6216a81ed10cdf674c3efee95306d62 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 14:25:42 +0200
Subject: [PATCH 106/154] chore: hide log_model mlflow params, expose model and
 kwargs only

---
 darts/utils/mlflow.py | 105 ++++++++----------------------------------
 1 file changed, 18 insertions(+), 87 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index c620e4dd6b..bbbe66f3b4 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -47,7 +47,6 @@
 from mlflow.models import Model, ModelInputExample, ModelSignature
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
-from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
 from mlflow.tracking.fluent import _initialize_logged_model
 from mlflow.utils import _get_fully_qualified_class_name
@@ -286,72 +285,25 @@ def load_model(
     return model_cls.load(str(model_path), **kwargs)
 
 
-def log_model(
-    model,
-    artifact_path: str | None = None,
-    conda_env: dict | str | None = None,
-    code_paths: list[str] | None = None,
-    registered_model_name: str | None = None,
-    signature: ModelSignature | None = None,
-    input_example: ModelInputExample | None = None,
-    await_registration_for: int = DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
-    pip_requirements: list[str] | None = None,
-    extra_pip_requirements: list[str] | None = None,
-    metadata: dict[str, Any] | None = None,
-    name: str | None = None,
-    params: dict[str, Any] | None = None,
-    tags: dict[str, Any] | None = None,
-    model_type: str | None = None,
-    step: int = 0,
-    model_id: str | None = None,
-    **kwargs,
-):
-    """Log a darts model to the current MLflow run.
+def log_model(model, **kwargs):
+    """Log a darts model to the current MLflow run, using the darts MLflow flavor.
+
+    This is a thin wrapper around ``mlflow.models.Model.log()`` that supplies
+    the darts flavor for saving/loading; every other argument is forwarded
+    as-is. See the `MLflow documentation
+    <https://mlflow.org/docs/latest/api_reference/python_api/mlflow.models.html#mlflow.models.Model.log>`_
+    for the full list of accepted parameters (e.g. ``name``,
+    ``registered_model_name``, ``conda_env``, ``pip_requirements``,
+    ``metadata``, ``tags``, ...).
 
     Parameters
     ----------
     model
         A fitted darts ``ForecastingModel`` instance.
-    artifact_path
-        The run-relative artifact path under which to log the model.
-        Defaults to ``"model"``. Deprecated in favour of ``name``.
-    conda_env
-        Conda environment specification (dict or path).
-    code_paths
-        A list of local filesystem paths to Python file dependencies (or directories
-        containing file dependencies). These files are prepended to the system path
-        when the model is loaded.
-    registered_model_name
-        If provided, the model is registered in the MLflow Model Registry
-        under this name.
-    signature
-       *Unsupported, see notes.* An ``mlflow.models.ModelSignature``. Use ``mlflow.models.infer_signature()``
-        to automatically generate from example inputs.
-    input_example
-        *Unsupported, see notes.* An example model input.
-    await_registration_for
-        Number of seconds to wait for the model version to finish being created and is in ``READY`` status.
-        By default, the function waits for five minutes. Specify 0 to skip waiting.
-    pip_requirements
-        Pip requirements list.
-    extra_pip_requirements
-        A list of additional pip requirement strings to add to the model's environment,
-        in addition to the default requirements.
-    metadata
-        Optional dict of custom metadata.
-    name
-        The name for the model artifact. If provided, takes precedence over
-        ``artifact_path``.
-    params
-        Optional dictionary of parameters to log alongside the model.
-    tags
-        Optional dictionary of tags to log alongside the model.
-    model_type
-        Optional string for the model type.
-    step
-        Optional step value to log with the model's metrics. Defaults to 0.
-    model_id
-        Optional string for the model ID.
+    **kwargs
+        Forwarded to ``mlflow.models.Model.log()``. Use ``name`` to set the
+        run-relative artifact path. ``artifact_path`` parameter is deprecated
+        by MLflow and not exposed here.
 
     Returns
     -------
@@ -361,33 +313,12 @@ def log_model(
 
     Notes
     -----
-    Signature and input_example params are currently not supported, as they
-    are used to support serving and input validation in the MLflow pyfunc flavor,
-    which is not implemented for darts models. They are accepted as params for
-    simplifying potential future extensibility, and to keep in line with MLflow API
-    conventions.
+    ``signature`` and ``input_example`` are currently not supported, as they
+    are used to support serving and input validation in the MLflow pyfunc
+    flavor, which is not implemented for darts models.
     """
-
     return Model.log(
-        artifact_path=artifact_path,
-        flavor=sys.modules[__name__],
-        registered_model_name=registered_model_name,
-        model=model,
-        conda_env=conda_env,
-        code_paths=code_paths,
-        pip_requirements=pip_requirements,
-        extra_pip_requirements=extra_pip_requirements,
-        signature=signature,
-        input_example=input_example,
-        await_registration_for=await_registration_for,
-        metadata=metadata,
-        name=name,
-        params=params,
-        tags=tags,
-        model_type=model_type,
-        step=step,
-        model_id=model_id,
-        **kwargs,
+        artifact_path=None, flavor=sys.modules[__name__], model=model, **kwargs
     )
 
 

From 9954928c44d82a8625f158f77dd912ba12535cbb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 14:26:29 +0200
Subject: [PATCH 107/154] chore: change default pip to core darts

---
 darts/utils/mlflow.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index bbbe66f3b4..d27623ae24 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -637,7 +637,7 @@ def get_default_pip_requirements():
     list[str]
         A list of pip requirement strings.
     """
-    reqs = [_get_pinned_requirement("darts[all]")]
+    reqs = [_get_pinned_requirement("darts")]
     return reqs
 
 

From 16c881f1fe5a52c5ebe788e7f18f78e4dd610321 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 14:31:03 +0200
Subject: [PATCH 108/154] chore: clean up unreachable code

---
 darts/utils/mlflow.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index d27623ae24..0358a1613d 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1346,12 +1346,7 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
     if getattr(_autolog_state, "in_backtest", False):
         return
 
-    if len(args) > 0:
-        series = args[0]
-    else:
-        series = kwargs.get("actual_series", None)
-    if series is None:
-        return
+    series = args[0] if len(args) > 0 else kwargs["actual_series"]
 
     autologging_client = MlflowAutologgingQueueingClient()
     run_id = active_run.info.run_id

From 9081dff3a7d6b1c4aade6235c3aecf9ae8da9ffa Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 15:22:13 +0200
Subject: [PATCH 109/154] feat: align backtesting windows on aggregation to end

---
 darts/utils/mlflow.py | 201 +++++++++++++++++++++++++-----------------
 1 file changed, 122 insertions(+), 79 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 0358a1613d..5efcc08397 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -865,6 +865,14 @@ def _log_backtest_metrics(
     ``_log_metric_result``). For a single series the mean is just the value
     itself and no artifact is written.
 
+    Series of different lengths are assumed to share the same end date. Any
+    axis mapping to real dates -- the window axis (``has_windows``), or the
+    per-timestep axis when ``last_points_only`` stitches windows into one
+    series -- is aligned from the end rather than the start, so a shorter
+    series lines up on its last entry instead of its first. The
+    per-horizon-step axis is left as-is, since it means "steps ahead" rather
+    than a real date.
+
     Raises
     ------
     ValueError
@@ -952,8 +960,9 @@ def _log_backtest_metrics(
     # agg maps (key, step) -> per-series values, averaged into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
-    for series_index, (s, r) in enumerate(zip(series_seq, results)):
-        if axes_inconsistent:
+
+    if axes_inconsistent:
+        for series_index, (s, r) in enumerate(zip(series_seq, results)):
             name_prefix = metric_names[0] if len(metric_names) == 1 else "metrics"
             flat = np.asarray(r, dtype=float).flatten()
             for i, val in enumerate(flat):
@@ -966,84 +975,104 @@ def _log_backtest_metrics(
                     "step": 0,
                     "value": value,
                 })
-            continue
-
-        comps = s.components.tolist()
-        # c_size = components × quantiles/intervals/labels per component
-        c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
-        arr = np.asarray(r, dtype=float)
-        # after stripping C and M axes, rest = W*T (or W or T alone)
-        rest, extra = divmod(arr.size, c_size * n_metrics)
-        if extra:
-            raise_log(
-                ValueError(
-                    f"Backtest metric logging failed: result size ({arr.size}) "
-                    f"is not divisible by c_size * n_metrics ({c_size} * "
-                    f"{n_metrics} = {c_size * n_metrics}). The metric output "
-                    "shape does not match the inferred axes."
-                )
-            )
-
-        # both time and window axes present: backtest returns (W*T*C*M,) in C order so we can
-        # recover W and T only if forecast_horizon is known (T = forecast_horizon)
-        if has_time_axis and has_windows:
-            if not forecast_horizon or rest % forecast_horizon:
+    else:
+        # first pass: reshape each series' result into a canonical (W, T, C, M)
+        # array, recording its window-axis length for the alignment pass below.
+        series_shapes = []
+        for s, r in zip(series_seq, results):
+            comps = s.components.tolist()
+            # c_size = components × quantiles/intervals/labels per component
+            c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
+            arr = np.asarray(r, dtype=float)
+            # after stripping C and M axes, rest = W*T (or W or T alone)
+            rest, extra = divmod(arr.size, c_size * n_metrics)
+            if extra:
                 raise_log(
                     ValueError(
-                        f"Backtest metric logging failed: cannot split "
-                        f"window/time axes — {rest} elements remain after "
-                        f"stripping component and metric axes, but "
-                        f"forecast_horizon={forecast_horizon!r} does not "
-                        "divide evenly. Pass an explicit forecast_horizon to "
-                        "backtest()."
+                        f"Backtest metric logging failed: result size ({arr.size}) "
+                        f"is not divisible by c_size * n_metrics ({c_size} * "
+                        f"{n_metrics} = {c_size * n_metrics}). The metric output "
+                        "shape does not match the inferred axes."
                     )
                 )
-            t_size, w_size = forecast_horizon, rest // forecast_horizon
-        elif has_time_axis:
-            t_size, w_size = rest, 1
-        elif has_windows:
-            t_size, w_size = 1, rest
-        else:
-            if rest != 1:
-                raise_log(
-                    ValueError(
-                        f"Backtest metric logging failed: expected a single "
-                        f"scalar per component/metric after reduction, but got "
-                        f"{rest} elements. Check time_reduction and "
-                        "component_reduction defaults."
+
+            # both time and window axes present: backtest returns (W*T*C*M,) in C order so we can
+            # recover W and T only if forecast_horizon is known (T = forecast_horizon)
+            if has_time_axis and has_windows:
+                if not forecast_horizon or rest % forecast_horizon:
+                    raise_log(
+                        ValueError(
+                            f"Backtest metric logging failed: cannot split "
+                            f"window/time axes — {rest} elements remain after "
+                            f"stripping component and metric axes, but "
+                            f"forecast_horizon={forecast_horizon!r} does not "
+                            "divide evenly. Pass an explicit forecast_horizon to "
+                            "backtest()."
+                        )
                     )
-                )
-            t_size, w_size = 1, 1
-
-        canonical = arr.reshape(w_size, t_size, c_size, n_metrics)
-        for m, metric_name in enumerate(metric_names):
-            quantiles_labels = metric_axes[m][2]
-            for w in range(w_size):
-                for c in range(c_size):
-                    # c is a flat index into the (n_components × quantiles_num) C axis:
-                    # c = comp_i * quantiles_num + q_i
-                    component_index, quantile_index = divmod(c, quantiles_num)
-                    comp_part = (
-                        "_" + _sanitize_mlflow_key(comps[component_index])
-                        if has_comp_axis
-                        else ""
+                t_size, w_size = forecast_horizon, rest // forecast_horizon
+            elif has_time_axis:
+                t_size, w_size = rest, 1
+            elif has_windows:
+                t_size, w_size = 1, rest
+            else:
+                if rest != 1:
+                    raise_log(
+                        ValueError(
+                            f"Backtest metric logging failed: expected a single "
+                            f"scalar per component/metric after reduction, but got "
+                            f"{rest} elements. Check time_reduction and "
+                            "component_reduction defaults."
+                        )
                     )
-                    key = f"backtest_{metric_name}{comp_part}{quantiles_labels[quantile_index]}"
-                    if has_time_axis and has_windows:
-                        key += f"_w{w}"
-                    key = _sanitize_mlflow_key(key)
-                    for t in range(t_size):
-                        # MLflow step maps to the axis the UI should chart:
-                        # time when present, otherwise window index
-                        step = t if has_time_axis else w
-                        value = float(canonical[w, t, c, m])
-                        agg.setdefault((key, step), []).append(value)
-                        rows.append({
-                            "key": key,
-                            "series_index": series_index,
-                            "step": step,
-                            "value": value,
-                        })
+                t_size, w_size = 1, 1
+
+            canonical = arr.reshape(w_size, t_size, c_size, n_metrics)
+            series_shapes.append((comps, c_size, t_size, w_size, canonical))
+
+        # align the calendar-relative axes from the end
+        max_w_size = max((w_size for _, _, _, w_size, _ in series_shapes), default=0)
+        t_axis_is_calendar = has_time_axis and not has_windows and last_points_only
+        max_t_size = (
+            max((t_size for _, _, t_size, _, _ in series_shapes), default=0)
+            if t_axis_is_calendar
+            else 0
+        )
+
+        for series_index, (comps, c_size, t_size, w_size, canonical) in enumerate(
+            series_shapes
+        ):
+            w_offset = max_w_size - w_size if has_windows else 0
+            t_offset = max_t_size - t_size if t_axis_is_calendar else 0
+            for m, metric_name in enumerate(metric_names):
+                quantiles_labels = metric_axes[m][2]
+                for w in range(w_size):
+                    aligned_w = w + w_offset
+                    for c in range(c_size):
+                        # c is a flat index into the (n_components × quantiles_num) C axis:
+                        # c = comp_i * quantiles_num + q_i
+                        component_index, quantile_index = divmod(c, quantiles_num)
+                        comp_part = (
+                            "_" + _sanitize_mlflow_key(comps[component_index])
+                            if has_comp_axis
+                            else ""
+                        )
+                        key = f"backtest_{metric_name}{comp_part}{quantiles_labels[quantile_index]}"
+                        if has_time_axis and has_windows:
+                            key += f"_w{aligned_w}"
+                        key = _sanitize_mlflow_key(key)
+                        for t in range(t_size):
+                            # MLflow step maps to the axis the UI should chart:
+                            # time when present, otherwise window index
+                            step = t + t_offset if has_time_axis else aligned_w
+                            value = float(canonical[w, t, c, m])
+                            agg.setdefault((key, step), []).append(value)
+                            rows.append({
+                                "key": key,
+                                "series_index": series_index,
+                                "step": step,
+                                "value": value,
+                            })
 
     # log the mean over series for each (key, step); for a single series this is
     # just the value itself.
@@ -1170,6 +1199,10 @@ def _log_metric_result(
     ``_log_backtest_metrics``). For a single series the mean is just the value
     itself and no artifact is written.
 
+    Series of different lengths are assumed to share the same end date, so the
+    time axis is aligned from the end rather than the start: a shorter series
+    lines up on its last value instead of its first.
+
     Raises
     ------
     ValueError
@@ -1208,10 +1241,10 @@ def _log_metric_result(
             [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
         )
 
-    # agg maps (key, step) -> per-series values, averaged into the logged metric.
-    agg: dict[tuple[str, int], list[float]] = {}
-    rows: list[dict] = []
-    for series_index, (s, r) in enumerate(zip(series_seq, results)):
+    # first pass: reshape each series' result into a canonical (T, C) array,
+    # recording its time-axis length for the alignment pass below.
+    series_shapes = []
+    for s, r in zip(series_seq, results):
         comps = s.components.tolist()
         # c_size = components × quantiles/intervals/labels per component
         c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
@@ -1243,6 +1276,16 @@ def _log_metric_result(
             t_size = 1
 
         canonical = arr.reshape(t_size, c_size)
+        series_shapes.append((comps, c_size, t_size, canonical))
+
+    # align the time axis from the end (see docstring)
+    max_t_size = max((t_size for _, _, t_size, _ in series_shapes), default=0)
+
+    # agg maps (key, step) -> per-series values, averaged into the logged metric.
+    agg: dict[tuple[str, int], list[float]] = {}
+    rows: list[dict] = []
+    for series_index, (comps, c_size, t_size, canonical) in enumerate(series_shapes):
+        step_offset = max_t_size - t_size if has_time_axis else 0
         for c in range(c_size):
             # c is a flat index into the (n_components × quantiles_num) C axis:
             # c = comp_i * quantiles_num + q_i
@@ -1257,7 +1300,7 @@ def _log_metric_result(
             )
             for t in range(t_size):
                 # MLflow step maps to the time axis when present
-                step = t if has_time_axis else 0
+                step = t + step_offset if has_time_axis else 0
                 value = float(canonical[t, c])
                 agg.setdefault((key, step), []).append(value)
                 rows.append({

From c1abac29e627716eb13ab0e526b038f6a56c0088 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 15:22:37 +0200
Subject: [PATCH 110/154] feat: windows alignment on backtest aggregation tests

---
 darts/tests/optional_deps/test_mlflow.py | 92 ++++++++++++++++++++++++
 1 file changed, 92 insertions(+)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 387a3e8a48..a866c1ee95 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -880,6 +880,38 @@ def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
         np.testing.assert_allclose(logged, ref, atol=1e-5)
 
+    def test_autolog_metric_aligns_time_axis_by_end_date(
+        self, mlflow_tracking, autolog_context
+    ):
+        """A shorter series' time axis aligns from the end, not the start, so
+        its steps overlap the tail of a longer series rather than the head."""
+        ts_long = self.ts_univariate  # length 50
+        ts_short = ts_long[10:]  # length 40, same end, starts 10 steps later
+
+        # distinct constant error per series, so a step's mean reveals exactly
+        # which series contributed to it
+        pred_long = ts_long + 1.0
+        pred_short = ts_short + 2.0
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                dm.ae([ts_long, ts_short], [pred_long, pred_short])
+
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
+        by_step = {m.step: m.value for m in history}
+        assert len(by_step) == 50
+        for step in range(10):
+            assert by_step[step] == pytest.approx(1.0, abs=1e-4), step
+        for step in range(10, 50):
+            assert by_step[step] == pytest.approx(1.5, abs=1e-4), step
+
+        rows = self._read_per_series_table(run.info.run_id)
+        steps_by_series = {0: set(), 1: set()}
+        for r in rows:
+            steps_by_series[r["series_index"]].add(r["step"])
+        assert steps_by_series[0] == set(range(50))
+        assert steps_by_series[1] == set(range(10, 50))
+
     def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile with matching values."""
         train = self.ts_univariate[:40]
@@ -1407,6 +1439,66 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
 
         assert not mlflow.get_run(run.info.run_id).data.metrics
 
+    def test_log_backtest_metrics_aligns_windows_by_end_date(self, mlflow_tracking):
+        """A shorter series' window axis aligns from the end, not the start,
+        so its windows overlap the tail of a longer series rather than the
+        head. Calls _log_backtest_metrics directly with a fabricated result
+        so the window counts per series are exact."""
+        backtest_args = {
+            "metric": dm.mae,
+            "metric_kwargs": {},
+            "series": [self.ts_univariate, self.ts_univariate],
+            "forecast_horizon": 1,
+            "reduction": None,
+            "last_points_only": False,
+        }
+        # series 0 has 5 windows; series 1 (shorter, later-starting) has 3
+        result = [np.array([1.0, 2.0, 3.0, 4.0, 5.0]), np.array([10.0, 20.0, 30.0])]
+
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            _log_backtest_metrics(client, run.info.run_id, result, backtest_args)
+            client.flush(synchronous=True)
+
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
+        logged = {m.step: m.value for m in history}
+        assert logged == pytest.approx({0: 1.0, 1: 2.0, 2: 6.5, 3: 12.0, 4: 17.5})
+
+        rows = self._read_per_series_table(run.info.run_id)
+        by_step = {(r["series_index"], r["step"]): r["value"] for r in rows}
+        assert by_step[(0, 0)] == pytest.approx(1.0)
+        assert by_step[(0, 4)] == pytest.approx(5.0)
+        assert by_step[(1, 2)] == pytest.approx(10.0)
+        assert by_step[(1, 4)] == pytest.approx(30.0)
+        assert (1, 0) not in by_step
+        assert (1, 1) not in by_step
+
+    def test_log_backtest_metrics_aligns_last_points_only_time_axis(
+        self, mlflow_tracking
+    ):
+        """last_points_only stitches windows into one series scored per real
+        timestep -- a separate code path from the window-axis case above that
+        needs the same end-date alignment."""
+        backtest_args = {
+            "metric": dm.ae,
+            "metric_kwargs": {},
+            "series": [self.ts_univariate, self.ts_univariate],
+            "forecast_horizon": 1,
+            "reduction": None,
+            "last_points_only": True,
+        }
+        # series 0 has 5 timesteps; series 1 (shorter, later-starting) has 3
+        result = [np.array([1.0, 2.0, 3.0, 4.0, 5.0]), np.array([10.0, 20.0, 30.0])]
+
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            _log_backtest_metrics(client, run.info.run_id, result, backtest_args)
+            client.flush(synchronous=True)
+
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
+        logged = {m.step: m.value for m in history}
+        assert logged == pytest.approx({0: 1.0, 1: 2.0, 2: 6.5, 3: 12.0, 4: 17.5})
+
     @staticmethod
     def _read_per_series_table(run_id):
         """Load the run's consolidated per-series metric table into row dicts."""

From ce7e0a2a06cdc436b39d0f6b93301896f8d6ea72 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 20:49:38 +0200
Subject: [PATCH 111/154] chore: change quantile formatting to :3f

---
 darts/utils/mlflow.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 5efcc08397..7a7e1347c0 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -356,7 +356,7 @@ def autolog(
         * ``metric_name`` – the metric function name, or the ``name`` keyword
           argument when provided (it overrides only this token).
         * ``component`` – the component name when ``component_reduction=None``.
-        * ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi0.100_0.900`` / ``_label1``.
+        * ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi_80.000`` / ``_label1``.
 
         When ``series_reduction`` is set on a metric call, results are already
         aggregated across series inside the metric itself, so the mean-over-series
@@ -1133,9 +1133,11 @@ def effective(param_name: str) -> Any:
     q_interval, q = metric_kwargs.get("q_interval"), metric_kwargs.get("q")
     if "q_interval" in params and q_interval is not None:
         intervals = np.atleast_2d(np.array(q_interval, dtype=float))
-        quantiles_labels = [f"_qi{lo:g}_{hi:g}" for lo, hi in intervals]
+        quantiles_labels = [f"_qi_{100 * (hi - lo):.3f}" for lo, hi in intervals]
     elif "q" in params and q is not None:
-        quantiles_labels = [f"_q{v:g}" for v in np.atleast_1d(np.array(q, dtype=float))]
+        quantiles_labels = [
+            f"_q{v:.3f}" for v in np.atleast_1d(np.array(q, dtype=float))
+        ]
     elif "label_reduction" in params and getattr(metric, "__name__", ""):
         label_reduction = effective("label_reduction")
         if isinstance(label_reduction, _LabelReduction):
@@ -1191,7 +1193,7 @@ def _log_metric_result(
     present:
 
     * ``component`` – ``_{component_name}`` when ``has_comp_axis``.
-    * ``quantile_or_label`` – e.g. ``_q0.5`` / ``_qi0.1_0.9`` / ``_label1``.
+    * ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi_80.000`` / ``_label1``.
 
     When more than one series is scored, the logged value is the mean over
     series for each cell, and the granular per-series breakdown is appended to
@@ -1356,8 +1358,8 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
     * ``metric_name`` – the metric function name, or the ``name`` keyword
       argument when provided (it overrides only this token).
     * ``component`` – ``_{component_name}`` when ``component_reduction=None``.
-    * ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.5``,
-      ``_qi0.1_0.9``, ``_label1``) when applicable.
+    * ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.500``,
+      ``_qi_80.000``, ``_label1``) when applicable.
 
     When the input is a ``Sequence[TimeSeries]`` with more than one series, the
     logged value is the mean over series and the per-series breakdown is

From 6f30c900b788a637c34361ee129fe606c3945fd2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 20:50:18 +0200
Subject: [PATCH 112/154] chore: align quantile label tests to :3f format

---
 darts/tests/optional_deps/test_mlflow.py | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index a866c1ee95..917e9c6b3b 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -928,7 +928,7 @@ def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
 
         ref = np.asarray(ref, dtype=float)  # shape (n_quantiles,)
         m = mlflow.get_run(run.info.run_id).data.metrics
-        for i, key in enumerate(("mql_q0_1", "mql_q0_5", "mql_q0_9")):
+        for i, key in enumerate(("mql_q0_100", "mql_q0_500", "mql_q0_900")):
             assert key in m, f"Expected quantile key {key}"
             assert m[key] == pytest.approx(ref[i], abs=1e-5)
 
@@ -947,8 +947,8 @@ def test_autolog_metric_quantile_interval(self, mlflow_tracking, autolog_context
                 ref = dm.miw(actual, pred, q_interval=(0.1, 0.9))
 
         m = mlflow.get_run(run.info.run_id).data.metrics
-        assert "miw_qi0_1_0_9" in m
-        assert m["miw_qi0_1_0_9"] == pytest.approx(float(ref), abs=1e-5)
+        assert "miw_qi_80_000" in m
+        assert m["miw_qi_80_000"] == pytest.approx(float(ref), abs=1e-5)
 
     def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
         """A list of series logs the mean over series; per-series values go to a table."""
@@ -1007,7 +1007,7 @@ def test_autolog_metric_name_override(self, mlflow_tracking, autolog_context):
         target = self.ts_univariate[40:]
 
         with autolog_context(log_metrics=True):
-            # direct call: name replaces the metric token; suffix (_q0_5) preserved
+            # direct call: name replaces the metric token; suffix (_q0_500) preserved
             with mlflow.start_run() as run_direct:
                 dm.mae(actual, actual * 1.1, name="custom")
                 dm.mql(target, pred, q=0.5, name="myq")
@@ -1024,7 +1024,7 @@ def test_autolog_metric_name_override(self, mlflow_tracking, autolog_context):
         direct = mlflow.get_run(run_direct.info.run_id).data.metrics
         assert "custom" in direct
         assert "mae" not in direct, "default metric name should be replaced"
-        assert "myq_q0_5" in direct, "quantile suffix should be preserved"
+        assert "myq_q0_500" in direct, "quantile suffix should be preserved"
 
         bt = mlflow.get_run(run_bt.info.run_id).data.metrics
         assert "backtest_custom" in bt
@@ -1319,7 +1319,11 @@ def test_autolog_backtest_quantile(self, mlflow_tracking, autolog_context):
                 )
 
         m = mlflow.get_run(run.info.run_id).data.metrics
-        for key in ("backtest_mql_q0_1", "backtest_mql_q0_5", "backtest_mql_q0_9"):
+        for key in (
+            "backtest_mql_q0_100",
+            "backtest_mql_q0_500",
+            "backtest_mql_q0_900",
+        ):
             assert key in m, f"Expected quantile key {key}"
             assert np.isfinite(m[key])
 
@@ -1545,14 +1549,14 @@ def test_infer_metric_axes_reductions(metric_name, metric_kwargs, expected):
 
 def test_infer_metric_axes_quantiles():
     _, _, quantiles_labels = _infer_metric_axes(dm.mql, {"q": [0.1, 0.5, 0.9]})
-    assert quantiles_labels == ["_q0.1", "_q0.5", "_q0.9"]
+    assert quantiles_labels == ["_q0.100", "_q0.500", "_q0.900"]
 
 
 def test_infer_metric_axes_quantile_interval():
     has_time, _, quantiles_labels = _infer_metric_axes(
         dm.iw, {"q_interval": (0.1, 0.9)}
     )
-    assert quantiles_labels == ["_qi0.1_0.9"]
+    assert quantiles_labels == ["_qi_80.000"]
     assert has_time is True
 
 

From fc6ce68c0627823b4dd63fa7565e428996d09bc8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 20:58:37 +0200
Subject: [PATCH 113/154] chore: rename quantile/interval/labels labels to axis
 labels

---
 darts/utils/mlflow.py | 60 +++++++++++++++++++++----------------------
 1 file changed, 29 insertions(+), 31 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 7a7e1347c0..50242c3e6f 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -929,13 +929,13 @@ def _log_backtest_metrics(
 
     # check the dim axes from the metric kwargs for each
     metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metric, metric_kwargs)]
-    has_time_axis, has_comp_axis, quantiles_labels_0 = metric_axes[0]
-    quantiles_num = len(quantiles_labels_0)
+    has_time_axis, has_comp_axis, axis_labels_0 = metric_axes[0]
+    axis_size = len(axis_labels_0)
 
     # Inconsistent axes across metrics (different has_time_axis, has_comp_axis, or
-    # quantiles_num) means the result can't be reshaped into a single canonical array.
+    # axis_size) means the result can't be reshaped into a single canonical array.
     # Fall back to flat integer-indexed keys for this case.
-    axes_key = (has_time_axis, has_comp_axis, quantiles_num)
+    axes_key = (has_time_axis, has_comp_axis, axis_size)
     axes_inconsistent = any(
         (ax[0], ax[1], len(ax[2])) != axes_key for ax in metric_axes[1:]
     )
@@ -982,7 +982,7 @@ def _log_backtest_metrics(
         for s, r in zip(series_seq, results):
             comps = s.components.tolist()
             # c_size = components × quantiles/intervals/labels per component
-            c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
+            c_size = (s.n_components if has_comp_axis else 1) * axis_size
             arr = np.asarray(r, dtype=float)
             # after stripping C and M axes, rest = W*T (or W or T alone)
             rest, extra = divmod(arr.size, c_size * n_metrics)
@@ -1045,19 +1045,21 @@ def _log_backtest_metrics(
             w_offset = max_w_size - w_size if has_windows else 0
             t_offset = max_t_size - t_size if t_axis_is_calendar else 0
             for m, metric_name in enumerate(metric_names):
-                quantiles_labels = metric_axes[m][2]
+                axis_labels = metric_axes[m][2]
                 for w in range(w_size):
                     aligned_w = w + w_offset
                     for c in range(c_size):
-                        # c is a flat index into the (n_components × quantiles_num) C axis:
-                        # c = comp_i * quantiles_num + q_i
-                        component_index, quantile_index = divmod(c, quantiles_num)
+                        # c is a flat index into the (n_components × axis_size) C axis:
+                        # c = comp_i * axis_size + axis_idx
+                        component_index, axis_idx = divmod(c, axis_size)
                         comp_part = (
                             "_" + _sanitize_mlflow_key(comps[component_index])
                             if has_comp_axis
                             else ""
                         )
-                        key = f"backtest_{metric_name}{comp_part}{quantiles_labels[quantile_index]}"
+                        key = (
+                            f"backtest_{metric_name}{comp_part}{axis_labels[axis_idx]}"
+                        )
                         if has_time_axis and has_windows:
                             key += f"_w{aligned_w}"
                         key = _sanitize_mlflow_key(key)
@@ -1105,12 +1107,12 @@ def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
     Returns
     -------
     tuple
-        ``(has_time_axis, has_comp_axis, quantiles_labels)`` where
+        ``(has_time_axis, has_comp_axis, axis_labels)`` where
 
         - ``has_time_axis`` – ``True`` when ``time_reduction`` is ``None`` (i.e. a
           per-timestep axis is present in the output).
         - ``has_comp_axis`` – ``True`` when components are expanded (not collapsed to a scalar).
-        - ``quantiles_labels`` – one key suffix per quantile/interval/label entry.
+        - ``axis_labels`` – one key suffix per quantile/interval/label entry.
 
     Raises
     ------
@@ -1133,11 +1135,9 @@ def effective(param_name: str) -> Any:
     q_interval, q = metric_kwargs.get("q_interval"), metric_kwargs.get("q")
     if "q_interval" in params and q_interval is not None:
         intervals = np.atleast_2d(np.array(q_interval, dtype=float))
-        quantiles_labels = [f"_qi_{100 * (hi - lo):.3f}" for lo, hi in intervals]
+        axis_labels = [f"_qi_{100 * (hi - lo):.3f}" for lo, hi in intervals]
     elif "q" in params and q is not None:
-        quantiles_labels = [
-            f"_q{v:.3f}" for v in np.atleast_1d(np.array(q, dtype=float))
-        ]
+        axis_labels = [f"_q{v:.3f}" for v in np.atleast_1d(np.array(q, dtype=float))]
     elif "label_reduction" in params and getattr(metric, "__name__", ""):
         label_reduction = effective("label_reduction")
         if isinstance(label_reduction, _LabelReduction):
@@ -1153,15 +1153,15 @@ def effective(param_name: str) -> Any:
                     "labels cannot be determined ahead of time otherwise)."
                 )
             )
-        quantiles_labels = (
+        axis_labels = (
             [f"_label{x}" for x in np.atleast_1d(labels)]
             if label_reduction is None
             else [""]
         )
     else:
-        quantiles_labels = [""]
+        axis_labels = [""]
 
-    return (has_time_axis, has_comp_axis, quantiles_labels)
+    return (has_time_axis, has_comp_axis, axis_labels)
 
 
 def _log_metric_result(
@@ -1172,7 +1172,7 @@ def _log_metric_result(
     series,
     has_time_axis: bool,
     has_comp_axis: bool,
-    quantiles_labels: list[str],
+    axis_labels: list[str],
     series_reduced: bool = False,
 ) -> None:
     """Log a metric result to the active MLflow run.
@@ -1225,13 +1225,13 @@ def _log_metric_result(
         ``True`` when the result carries a per-timestep axis (``time_reduction=None``).
     has_comp_axis
         ``True`` when components are expanded (``component_reduction=None``).
-    quantiles_labels
+    axis_labels
         One key suffix per quantile/interval/label entry.
     series_reduced
         ``True`` when ``series_reduction`` collapsed the series axis inside the
         metric, so the result has no leading series axis even for list input.
     """
-    quantiles_num = len(quantiles_labels)
+    axis_size = len(axis_labels)
 
     if series_reduced:
         # series_reduction aggregated across series -> single result, no series axis
@@ -1249,7 +1249,7 @@ def _log_metric_result(
     for s, r in zip(series_seq, results):
         comps = s.components.tolist()
         # c_size = components × quantiles/intervals/labels per component
-        c_size = (s.n_components if has_comp_axis else 1) * quantiles_num
+        c_size = (s.n_components if has_comp_axis else 1) * axis_size
         arr = np.asarray(r, dtype=float)
         # after stripping the C axis, the remainder is the time axis (or scalar)
         n_times, extra = divmod(arr.size, c_size)
@@ -1289,17 +1289,15 @@ def _log_metric_result(
     for series_index, (comps, c_size, t_size, canonical) in enumerate(series_shapes):
         step_offset = max_t_size - t_size if has_time_axis else 0
         for c in range(c_size):
-            # c is a flat index into the (n_components × quantiles_num) C axis:
-            # c = comp_i * quantiles_num + q_i
-            component_index, quantile_index = divmod(c, quantiles_num)
+            # c is a flat index into the (n_components × axis_size) C axis:
+            # c = comp_i * axis_size + axis_idx
+            component_index, axis_idx = divmod(c, axis_size)
             comp_part = (
                 "_" + _sanitize_mlflow_key(comps[component_index])
                 if has_comp_axis
                 else ""
             )
-            key = _sanitize_mlflow_key(
-                metric_name + comp_part + quantiles_labels[quantile_index]
-            )
+            key = _sanitize_mlflow_key(metric_name + comp_part + axis_labels[axis_idx])
             for t in range(t_size):
                 # MLflow step maps to the time axis when present
                 step = t + step_offset if has_time_axis else 0
@@ -1400,7 +1398,7 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
     key_name = _sanitize_mlflow_key(kwargs.get("name") or func.__name__)
 
     # infer output axes from the metric signature + call kwargs
-    has_time_axis, has_comp_axis, quantiles_labels = _infer_metric_axes(func, kwargs)
+    has_time_axis, has_comp_axis, axis_labels = _infer_metric_axes(func, kwargs)
 
     # series_reduction collapses the series axis inside the metric, so the
     # result has no leading series axis even for list input.
@@ -1420,6 +1418,6 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
         series,
         has_time_axis,
         has_comp_axis,
-        quantiles_labels,
+        axis_labels,
         series_reduced=series_reduced,
     )

From 109adc755f8c43720e65199a61f5b5c9f16b93a1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 20:58:54 +0200
Subject: [PATCH 114/154] chore: align test to new axis naming convention

---
 darts/tests/optional_deps/test_mlflow.py | 20 +++++++++-----------
 1 file changed, 9 insertions(+), 11 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 917e9c6b3b..00777c74dc 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1422,7 +1422,7 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
 
         # Simulate a backtest result with only 2 entries — as if the metric was
         # evaluated on windows that only contained 2 of the 3 explicit labels.
-        # quantiles_num is 3 (len(labels)) but result has 2 → mismatch.
+        # axis_size is 3 (len(labels)) but result has 2 → mismatch.
         fake_result = np.array([0.8, 0.6], dtype=float)
 
         backtest_args = {
@@ -1529,34 +1529,32 @@ def _fit_qlr(self, series=None):
 @pytest.mark.parametrize(
     "metric_name, metric_kwargs, expected",
     [
-        ("mae", {}, dict(has_time_axis=False, has_comp_axis=False, quantiles_num=1)),
-        ("ae", {}, dict(has_time_axis=True, has_comp_axis=False, quantiles_num=1)),
+        ("mae", {}, dict(has_time_axis=False, has_comp_axis=False, axis_size=1)),
+        ("ae", {}, dict(has_time_axis=True, has_comp_axis=False, axis_size=1)),
         ("mae", {"component_reduction": None}, dict(has_comp_axis=True)),
     ],
 )
 def test_infer_metric_axes_reductions(metric_name, metric_kwargs, expected):
-    has_time_axis, has_comp_axis, quantiles_labels = _infer_metric_axes(
+    has_time_axis, has_comp_axis, axis_labels = _infer_metric_axes(
         getattr(dm, metric_name), metric_kwargs
     )
     actual = {
         "has_time_axis": has_time_axis,
         "has_comp_axis": has_comp_axis,
-        "quantiles_num": len(quantiles_labels),
+        "axis_size": len(axis_labels),
     }
     for attr, value in expected.items():
         assert actual[attr] == value
 
 
 def test_infer_metric_axes_quantiles():
-    _, _, quantiles_labels = _infer_metric_axes(dm.mql, {"q": [0.1, 0.5, 0.9]})
-    assert quantiles_labels == ["_q0.100", "_q0.500", "_q0.900"]
+    _, _, axis_labels = _infer_metric_axes(dm.mql, {"q": [0.1, 0.5, 0.9]})
+    assert axis_labels == ["_q0.100", "_q0.500", "_q0.900"]
 
 
 def test_infer_metric_axes_quantile_interval():
-    has_time, _, quantiles_labels = _infer_metric_axes(
-        dm.iw, {"q_interval": (0.1, 0.9)}
-    )
-    assert quantiles_labels == ["_qi_80.000"]
+    has_time, _, axis_labels = _infer_metric_axes(dm.iw, {"q_interval": (0.1, 0.9)})
+    assert axis_labels == ["_qi_80.000"]
     assert has_time is True
 
 

From 19b2a8bc7ff1a0d263c9b00ee7dde80f13b8a66d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 21:00:25 +0200
Subject: [PATCH 115/154] docs: clean up backtest docstrinjg

---
 darts/utils/mlflow.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 50242c3e6f..58eee65c9c 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -865,13 +865,11 @@ def _log_backtest_metrics(
     ``_log_metric_result``). For a single series the mean is just the value
     itself and no artifact is written.
 
-    Series of different lengths are assumed to share the same end date. Any
-    axis mapping to real dates -- the window axis (``has_windows``), or the
-    per-timestep axis when ``last_points_only`` stitches windows into one
-    series -- is aligned from the end rather than the start, so a shorter
-    series lines up on its last entry instead of its first. The
-    per-horizon-step axis is left as-is, since it means "steps ahead" rather
-    than a real date.
+    Series of different lengths are assumed to share the same end date, so any
+    axis mapping to real dates (the window axis, or the per-timestep axis
+    when ``last_points_only`` stitches windows into one series) is aligned
+    from the end rather than the start. The per-horizon-step axis is left
+    as-is, since it means "steps ahead" rather than a real date.
 
     Raises
     ------

From 603eb8e978cabd39a05d87a97ddede28d460c564 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 22 Jul 2026 21:19:04 +0200
Subject: [PATCH 116/154] feat: add agg_func option to autolog() and unit tests

---
 darts/tests/optional_deps/test_mlflow.py | 42 ++++++++++++
 darts/utils/mlflow.py                    | 87 ++++++++++++++++--------
 2 files changed, 100 insertions(+), 29 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 00777c74dc..b27857abc6 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -969,6 +969,24 @@ def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
         by_series = {int(row["series_index"]): float(row["value"]) for row in rows}
         assert by_series == pytest.approx({0: ref[0], 1: ref[1]}, abs=1e-5)
 
+    def test_autolog_metric_multi_series_custom_agg_func(
+        self, mlflow_tracking, autolog_context
+    ):
+        """autolog()'s agg_func controls how per-series values are aggregated
+        into the single logged metric (default np.mean)."""
+        # 3 series with distinct, asymmetric per-series errors so median != mean
+        series = [self.ts_univariate * f for f in (1.0, 1.2, 5.0)]
+        pred = [s * 1.1 for s in series]
+
+        with autolog_context(log_metrics=True, agg_func=np.median):
+            with mlflow.start_run() as run:
+                ref = dm.mae(series, pred)
+
+        ref = np.asarray(ref, dtype=float)
+        assert float(np.median(ref)) != pytest.approx(float(np.mean(ref)), abs=1e-3)
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["mae"] == pytest.approx(float(np.median(ref)), abs=1e-5)
+
     def test_autolog_metric_multi_series_per_component(
         self, mlflow_tracking, autolog_context
     ):
@@ -1237,6 +1255,30 @@ def test_autolog_backtest_multi_series(self, mlflow_tracking, autolog_context):
             {0: float(ref[0]), 1: float(ref[1])}, abs=1e-5
         )
 
+    def test_autolog_backtest_multi_series_custom_agg_func(self, mlflow_tracking):
+        """autolog()'s agg_func also controls the backtest() aggregation
+        (default np.mean). Calls _log_backtest_metrics directly with a
+        fabricated result so the per-series values are exact."""
+        backtest_args = {
+            "metric": dm.mae,
+            "metric_kwargs": {},
+            "series": [self.ts_univariate, self.ts_univariate, self.ts_univariate],
+            "forecast_horizon": 1,
+            "reduction": np.mean,
+            "last_points_only": True,
+        }
+        result = [1.0, 2.0, 100.0]  # asymmetric -> median != mean
+
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            _log_backtest_metrics(
+                client, run.info.run_id, result, backtest_args, agg_func=np.median
+            )
+            client.flush(synchronous=True)
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["backtest_mae"] == pytest.approx(2.0)
+
     def test_autolog_backtest_per_timestep_scalar(
         self, mlflow_tracking, autolog_context
     ):
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 58eee65c9c..30a809bb22 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -327,6 +327,7 @@ def autolog(
     log_params: bool = True,
     log_metrics: bool = True,
     log_torch_metrics: bool = True,
+    agg_func: Callable = np.mean,
     disable: bool = False,
     silent: bool = False,
 ) -> None:
@@ -359,14 +360,14 @@ def autolog(
         * ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi_80.000`` / ``_label1``.
 
         When ``series_reduction`` is set on a metric call, results are already
-        aggregated across series inside the metric itself, so the mean-over-series
-        logging described below does not apply.
+        aggregated across series inside the metric itself, so the
+        cross-series aggregation described below does not apply.
 
         Per-timestep results (``time_reduction=None``) are charted across the
-        MLflow ``step``. For a list of series the logged value is the mean over
-        series, and the full per-series breakdown for every metric/backtest
-        call in the run is appended to a single ``metrics_per_series.json``
-        table artifact.
+        MLflow ``step``. For a list of series the logged value is
+        ``agg_func`` applied over series, and the full per-series breakdown
+        for every metric/backtest call in the run is appended to a single
+        ``metrics_per_series.json`` table artifact.
 
     Parameters
     ----------
@@ -381,6 +382,11 @@ def autolog(
         If ``True`` (default), enable ``mlflow.pytorch.autolog(log_models=False)``
         around PyTorch-based model training to automatically log per-epoch
         training and validation metrics. Only effective for PyTorch-based models.
+    agg_func
+        Function used to aggregate a metric's per-series values into the
+        single value logged for a list of series (e.g. ``np.mean``, the
+        default, or ``np.median``). Called as ``agg_func(values)`` on a list
+        of floats.
     disable
         If ``True``, restore the original ``fit()`` methods and stop
         autologging.
@@ -428,6 +434,7 @@ def autolog(
         log_models=log_models,
         log_params=log_params,
         log_metrics=log_metrics,
+        agg_func=agg_func,
         disable=disable,
         silent=silent,
     )
@@ -460,6 +467,7 @@ def _autolog(
     log_models: bool = True,
     log_params: bool = True,
     log_metrics: bool = True,
+    agg_func: Callable = np.mean,
     disable: bool = False,
     silent: bool = False,
 ) -> None:
@@ -595,7 +603,11 @@ def _patched_backtest(original, self, *args, **kwargs):
 
         autologging_client = MlflowAutologgingQueueingClient()
         _log_backtest_metrics(
-            autologging_client, active_run.info.run_id, result, backtest_args
+            autologging_client,
+            active_run.info.run_id,
+            result,
+            backtest_args,
+            agg_func=agg_func,
         )
         autologging_client.flush(synchronous=False).await_completion()
         return result
@@ -809,7 +821,7 @@ def _log_per_series_table(rows: list[dict]) -> None:
     (the time or window index charted by MLflow), and ``value``. All calls
     within a run append to the same ``metrics_per_series.json`` artifact.
     Used when more than one series is scored, since the logged metric keys
-    only carry the mean over series.
+    only carry the aggregate over series.
 
     Parameters
     ----------
@@ -828,6 +840,7 @@ def _log_backtest_metrics(
     run_id: str,
     result,
     backtest_args: dict,
+    agg_func: Callable = np.mean,
 ) -> None:
     """Log backtest metric result(s) to MLflow.
 
@@ -859,11 +872,11 @@ def _log_backtest_metrics(
     ``time_reduction`` / ``component_reduction`` / quantile count), each series
     result is flattened to integer-indexed keys instead.
 
-    When more than one series is scored, the logged value is the mean over
-    series for each cell, and the granular per-series breakdown is appended to
-    the run's ``metrics_per_series.json`` table artifact (shared with
-    ``_log_metric_result``). For a single series the mean is just the value
-    itself and no artifact is written.
+    When more than one series is scored, the logged value is ``agg_func``
+    applied over series for each cell, and the granular per-series breakdown
+    is appended to the run's ``metrics_per_series.json`` table artifact
+    (shared with ``_log_metric_result``). For a single series the aggregate
+    is just the value itself and no artifact is written.
 
     Series of different lengths are assumed to share the same end date, so any
     axis mapping to real dates (the window axis, or the per-timestep axis
@@ -889,6 +902,10 @@ def _log_backtest_metrics(
     backtest_args
         Bound arguments of the ``backtest()`` call (from
         ``inspect.BoundArguments.arguments`` after ``apply_defaults``).
+    agg_func
+        Function used to aggregate a metric's per-series values into the
+        single value logged for a list of series. Called as
+        ``agg_func(values)`` on a list of floats.
     """
     metric = backtest_args.get("metric")
     metric = metric if isinstance(metric, list) else [metric]
@@ -955,7 +972,7 @@ def _log_backtest_metrics(
     series_seq = series2seq(series)
     results = [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
 
-    # agg maps (key, step) -> per-series values, averaged into the logged metric.
+    # agg maps (key, step) -> per-series values, aggregated into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
 
@@ -1074,11 +1091,11 @@ def _log_backtest_metrics(
                                 "value": value,
                             })
 
-    # log the mean over series for each (key, step); for a single series this is
-    # just the value itself.
+    # aggregate across series for each (key, step); for a single series this
+    # is just the value itself.
     metrics_by_step: dict[int, dict[str, float]] = {}
     for (key, step), values in agg.items():
-        metrics_by_step.setdefault(step, {})[key] = float(np.mean(values))
+        metrics_by_step.setdefault(step, {})[key] = float(agg_func(values))
     for step, metrics in metrics_by_step.items():
         autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
 
@@ -1172,6 +1189,7 @@ def _log_metric_result(
     has_comp_axis: bool,
     axis_labels: list[str],
     series_reduced: bool = False,
+    agg_func: Callable = np.mean,
 ) -> None:
     """Log a metric result to the active MLflow run.
 
@@ -1193,11 +1211,11 @@ def _log_metric_result(
     * ``component`` – ``_{component_name}`` when ``has_comp_axis``.
     * ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi_80.000`` / ``_label1``.
 
-    When more than one series is scored, the logged value is the mean over
-    series for each cell, and the granular per-series breakdown is appended to
-    the run's ``metrics_per_series.json`` table artifact (shared with
-    ``_log_backtest_metrics``). For a single series the mean is just the value
-    itself and no artifact is written.
+    When more than one series is scored, the logged value is ``agg_func``
+    applied over series for each cell, and the granular per-series breakdown
+    is appended to the run's ``metrics_per_series.json`` table artifact
+    (shared with ``_log_backtest_metrics``). For a single series the
+    aggregate is just the value itself and no artifact is written.
 
     Series of different lengths are assumed to share the same end date, so the
     time axis is aligned from the end rather than the start: a shorter series
@@ -1228,6 +1246,10 @@ def _log_metric_result(
     series_reduced
         ``True`` when ``series_reduction`` collapsed the series axis inside the
         metric, so the result has no leading series axis even for list input.
+    agg_func
+        Function used to aggregate a metric's per-series values into the
+        single value logged for a list of series. Called as
+        ``agg_func(values)`` on a list of floats.
     """
     axis_size = len(axis_labels)
 
@@ -1281,7 +1303,7 @@ def _log_metric_result(
     # align the time axis from the end (see docstring)
     max_t_size = max((t_size for _, _, t_size, _ in series_shapes), default=0)
 
-    # agg maps (key, step) -> per-series values, averaged into the logged metric.
+    # agg maps (key, step) -> per-series values, aggregated into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
     for series_index, (comps, c_size, t_size, canonical) in enumerate(series_shapes):
@@ -1308,11 +1330,11 @@ def _log_metric_result(
                     "value": value,
                 })
 
-    # log the mean over series for each (key, step); for a single series this is
-    # just the value itself.
+    # aggregate across series for each (key, step); for a single series this
+    # is just the value itself.
     metrics_by_step: dict[int, dict[str, float]] = {}
     for (key, step), values in agg.items():
-        metrics_by_step.setdefault(step, {})[key] = float(np.mean(values))
+        metrics_by_step.setdefault(step, {})[key] = float(agg_func(values))
     for step, metrics in metrics_by_step.items():
         autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
     autologging_client.flush(synchronous=False).await_completion()
@@ -1358,9 +1380,9 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
       ``_qi_80.000``, ``_label1``) when applicable.
 
     When the input is a ``Sequence[TimeSeries]`` with more than one series, the
-    logged value is the mean over series and the per-series breakdown is
-    appended to the run's ``metrics_per_series.json`` table artifact instead
-    of per-series keys.
+    logged value is ``autolog()``'s ``agg_func`` applied over series, and the
+    per-series breakdown is appended to the run's ``metrics_per_series.json``
+    table artifact instead of per-series keys.
 
     The per-timestep axis (``time_reduction=None``) is mapped to the MLflow
     ``step``.
@@ -1408,6 +1430,12 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
         )
         series_reduced = effective_sr is not None
 
+    # _mlflow_metric_callback is a bare registered callback, not a closure over
+    # autolog()'s call kwargs, so agg_func is read back from the autologging
+    # config store that autolog() populated.
+    agg_func = get_autologging_config(
+        flavor_name=FLAVOR_NAME, config_key="agg_func", default_value=np.mean
+    )
     _log_metric_result(
         autologging_client,
         run_id,
@@ -1418,4 +1446,5 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
         has_comp_axis,
         axis_labels,
         series_reduced=series_reduced,
+        agg_func=agg_func,
     )

From 69b9ca5c06d05ec1888f3333969288a61b76de2e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 23 Jul 2026 14:48:49 +0200
Subject: [PATCH 117/154] feat: log model creation params as json + tests

---
 darts/tests/optional_deps/test_mlflow.py | 15 +++++++++++++++
 darts/utils/mlflow.py                    |  4 +++-
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index b27857abc6..39bd7943b4 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -280,6 +280,21 @@ def test_autolog_parameters(self, mlflow_tracking, autolog_context):
             assert last_run["params.seasonal_periods"] == "12"
             assert last_run["tags.model_class"] == "ExponentialSmoothing"
 
+    def test_autolog_model_params_json_artifact(self, mlflow_tracking, autolog_context):
+        """The model_params.json artifact mirrors model.model_params, preserving
+        JSON-native types and falling back to str() for non-serializable values
+        (e.g. enums) instead of the flat params store's blanket stringification."""
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = ExponentialSmoothing(seasonal_periods=12)
+                model.fit(self.ts_univariate)
+
+        params = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/model_params.json"
+        )
+        assert params["seasonal_periods"] == 12
+        assert params["trend"] == str(model.model_params["trend"])
+
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
         """Test that autolog logs training metrics for torch models"""
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 30a809bb22..6eb0b5f096 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -337,7 +337,8 @@ def autolog(
     will automatically:
 
     1. Start an MLflow run (or reuse the currently active one).
-    2. Log model creation parameters (``model.model_params``).
+    2. Log model creation parameters (``model.model_params``), both as MLflow
+       params and as a ``model_params.json`` artifact.
     3. Log covariate usage information (past, future, and static covariates).
     4. Log the result of any darts metric call made inside an active MLflow
        run. Repeated calls overwrite the previous value.
@@ -530,6 +531,7 @@ def _patched_fit(original, self, *args, **kwargs):
         if log_params:
             # Log the parameters for model creation
             autologging_client.log_params(run_id=run_id, params=self.model_params)
+            mlflow.log_dict(self.model_params, "model_params.json")
             fit_args = inspect.signature(original).bind(self, *args, **kwargs).arguments
             _log_covariate_info(
                 self,

From 4c3d014b5f09645e92e741ffb674b0451a0ca250 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 23 Jul 2026 15:44:50 +0200
Subject: [PATCH 118/154] docs:  cleanup

---
 CHANGELOG.md          |  4 ++--
 darts/utils/mlflow.py | 12 ++++++++----
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 979163fe40..68a40920cc 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,7 +13,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 - 🚀🚀 Added a custom MLflow model flavor for Darts, available under `darts.utils.mlflow`. It provides an MLflow integration for any Darts `ForecastingModel` (statistical, scikit-learn-like, and PyTorch-based). [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
   - Added `save_model()`, `load_model()`, and `log_model()` to persist and reload Darts models as MLflow models, including model and covariate metadata.
-  - Added `autolog()` to automatically log model creation parameters, covariate usage, metrics, and the trained model artifact on every `fit()`, `backtest()` and `historical_forecasts()` call within an active MLflow run.
+  - Added `autolog()` to automatically log model creation parameters, covariate usage, metrics, and the trained model artifact when `log_models=True` (default `False`) on every `fit()`, `backtest()` and `historical_forecasts()` call within an active MLflow run.
   - Metric functions from `darts.metrics` are automatically logged when called inside an active run, with keys reflecting the metric output shape (per-component, per-quantile/interval, per-label, and per-timestep results charted across MLflow steps). Multi-series results log the mean over series and write the full per-series breakdown to a CSV artifact.
   - Added a new notebook for [MLflow quickstart](https://unit8co.github.io/darts/examples/29-MLflow-quickstart.html) with detailed usage examples.
   - Note: model serving and deployment (MLflow's `pyfunc` flavor, model signatures, and input examples) are not yet supported.
@@ -26,7 +26,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Dependencies**
 
-- Added an optional `mlflow` extra (`mlflow>=3.0`) enabling the MLflow integration. [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
+- Added `mlflow>=3.0` to the optional dependency group, enabling the MLflow integration. [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
 
 ### For developers of the library:
 
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 6eb0b5f096..1e6b2eb39c 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -7,7 +7,8 @@
 to MLflow, as well as automatic logging (``autolog()``) of:
 
 * Model creation parameters and covariate usage information.
-* The trained model artifact, after each ``fit()`` call.
+* The trained model artifact after each ``fit()`` call when ``log_models=True``
+  (default ``False``).
 * Darts metric function calls made inside an active MLflow run.
 * ``backtest()`` evaluation metrics.
 * Per-epoch training/validation metrics for PyTorch-based models.
@@ -344,7 +345,8 @@ def autolog(
        run. Repeated calls overwrite the previous value.
     5. For PyTorch-based models: leverage ``mlflow.pytorch.autolog()`` to
        automatically log per-epoch training and validation metrics.
-    6. Log the trained model artifact at the end of training.
+    6. When ``log_models=True``: log the trained model artifact at the end of
+       training (default ``False``).
     7. Patch ``backtest()`` to log evaluation metrics under ``backtest_*`` keys.
     8. Patch ``historical_forecasts()`` so that its internal per-window
        ``fit()`` calls don't each spawn their own logging.
@@ -373,7 +375,8 @@ def autolog(
     Parameters
     ----------
     log_models
-        If ``True`` (default), log the trained model artifact after ``fit()``.
+        If ``True``, log the trained model artifact after ``fit()``. Defaults to
+        ``False``.
     log_params
         If ``True`` (default), log model creation parameters.
     log_metrics
@@ -483,7 +486,8 @@ def _autolog(
     def _patched_fit(original, self, *args, **kwargs):
         """Patch function for ForecastingModel.fit() autologging.
 
-        Logs model parameters, class, covariates and the model itself.
+        Logs model parameters, class, and covariates; optionally logs the
+        model artifact when ``log_models=True``.
 
         Parameters
         ----------

From c9e56e1cc347eb6a847b7f9ba0c030e97dd1785e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 23 Jul 2026 16:43:14 +0200
Subject: [PATCH 119/154] feat: ensure same amount of components for logging

---
 darts/tests/optional_deps/test_mlflow.py |  42 +++++++
 darts/utils/mlflow.py                    | 137 +++++++++++++++--------
 2 files changed, 130 insertions(+), 49 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 39bd7943b4..fc7ffe4ca0 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1104,6 +1104,22 @@ def test_autolog_metric_multi_series_classification_labels_explicit(
             assert got[("f1_label0", i)] == pytest.approx(ref[i][0], abs=1e-5)
             assert got[("f1_label1", i)] == pytest.approx(ref[i][1], abs=1e-5)
 
+    def test_autolog_metric_component_count_mismatch_raises(
+        self, mlflow_tracking, autolog_context
+    ):
+        """A list of series with different numbers of components raises rather
+        than taking component names from the first series only and silently
+        mislabeling the rest."""
+        series = [self.ts_univariate, self.ts_multivariate]
+        pred = [s * 1.1 for s in series]
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                with pytest.raises(ValueError, match="same number of components"):
+                    dm.mae(series, pred)
+
+        assert not mlflow.get_run(run.info.run_id).data.metrics
+
     def test_autolog_metric_size_mismatch_raises(
         self, mlflow_tracking, autolog_context, caplog
     ):
@@ -1444,6 +1460,32 @@ def test_autolog_backtest_classification_labels_not_in_data(
         assert np.isnan(m["backtest_f1_label5"])
         assert np.isnan(m["backtest_f1_label10"])
 
+    def test_log_backtest_metrics_component_count_mismatch_raises(
+        self, mlflow_tracking
+    ):
+        """A list of series with different numbers of components raises rather
+        than taking component names from the first series only.
+
+        Calls _log_backtest_metrics directly so the raise is not swallowed by
+        MLflow's safe_patch wrapper.
+        """
+        backtest_args = {
+            "metric": dm.mae,
+            "metric_kwargs": {},
+            "series": [self.ts_univariate, self.ts_multivariate],
+            "forecast_horizon": 1,
+            "reduction": np.mean,
+            "last_points_only": True,
+        }
+        result = [1.0, np.array([1.0, 2.0])]
+
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            with pytest.raises(ValueError, match="same number of components"):
+                _log_backtest_metrics(client, run.info.run_id, result, backtest_args)
+
+        assert not mlflow.get_run(run.info.run_id).data.metrics
+
     def test_log_backtest_metrics_unknown_labels_raises(self, mlflow_tracking):
         """label_reduction=None without explicit labels raises rather than
         inferring class names from the series at runtime.
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 1e6b2eb39c..1b0956871d 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -370,7 +370,9 @@ def autolog(
         MLflow ``step``. For a list of series the logged value is
         ``agg_func`` applied over series, and the full per-series breakdown
         for every metric/backtest call in the run is appended to a single
-        ``metrics_per_series.json`` table artifact.
+        ``metrics_per_series.json`` table artifact. All series scored
+        together in one call must have the same number of components;
+        component names are taken from the first series.
 
     Parameters
     ----------
@@ -882,7 +884,9 @@ def _log_backtest_metrics(
     applied over series for each cell, and the granular per-series breakdown
     is appended to the run's ``metrics_per_series.json`` table artifact
     (shared with ``_log_metric_result``). For a single series the aggregate
-    is just the value itself and no artifact is written.
+    is just the value itself and no artifact is written. All series scored
+    together must have the same number of components; names are taken from
+    the first series.
 
     Series of different lengths are assumed to share the same end date, so any
     axis mapping to real dates (the window axis, or the per-timestep axis
@@ -894,7 +898,8 @@ def _log_backtest_metrics(
     ------
     ValueError
         On a shape/size mismatch between the metric result and the inferred
-        axes, or when ``label_reduction=None`` is requested without explicit
+        axes, when series in a sequence have different numbers of components,
+        or when ``label_reduction=None`` is requested without explicit
         ``labels``.
 
     Parameters
@@ -977,13 +982,21 @@ def _log_backtest_metrics(
 
     series_seq = series2seq(series)
     results = [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
+    n_components = {s.n_components for s in series_seq}
+    if len(n_components) > 1:
+        raise_log(
+            ValueError(
+                "Backtest metric logging failed: all series must have the same "
+                f"number of components, got {sorted(n_components)}."
+            )
+        )
 
     # agg maps (key, step) -> per-series values, aggregated into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
 
     if axes_inconsistent:
-        for series_index, (s, r) in enumerate(zip(series_seq, results)):
+        for series_index, r in enumerate(results):
             name_prefix = metric_names[0] if len(metric_names) == 1 else "metrics"
             flat = np.asarray(r, dtype=float).flatten()
             for i, val in enumerate(flat):
@@ -997,13 +1010,35 @@ def _log_backtest_metrics(
                     "value": value,
                 })
     else:
+        # component names/count from the first series (all series share n_components)
+        comps = series_seq[0].components.tolist()
+        # c_size = components × quantiles/intervals/labels per component
+        c_size = (series_seq[0].n_components if has_comp_axis else 1) * axis_size
+        # base_keys[m][c]: sanitized key without the optional window suffix
+        base_keys = []
+        for m, metric_name in enumerate(metric_names):
+            axis_labels = metric_axes[m][2]
+            keys_m = []
+            for c in range(c_size):
+                # c is a flat index into the (n_components × axis_size) C axis:
+                # c = comp_i * axis_size + axis_idx
+                component_index, axis_idx = divmod(c, axis_size)
+                comp_part = (
+                    "_" + _sanitize_mlflow_key(comps[component_index])
+                    if has_comp_axis
+                    else ""
+                )
+                keys_m.append(
+                    _sanitize_mlflow_key(
+                        f"backtest_{metric_name}{comp_part}{axis_labels[axis_idx]}"
+                    )
+                )
+            base_keys.append(keys_m)
+
         # first pass: reshape each series' result into a canonical (W, T, C, M)
         # array, recording its window-axis length for the alignment pass below.
         series_shapes = []
-        for s, r in zip(series_seq, results):
-            comps = s.components.tolist()
-            # c_size = components × quantiles/intervals/labels per component
-            c_size = (s.n_components if has_comp_axis else 1) * axis_size
+        for r in results:
             arr = np.asarray(r, dtype=float)
             # after stripping C and M axes, rest = W*T (or W or T alone)
             rest, extra = divmod(arr.size, c_size * n_metrics)
@@ -1048,42 +1083,31 @@ def _log_backtest_metrics(
                     )
                 t_size, w_size = 1, 1
 
-            canonical = arr.reshape(w_size, t_size, c_size, n_metrics)
-            series_shapes.append((comps, c_size, t_size, w_size, canonical))
+            series_shapes.append((
+                t_size,
+                w_size,
+                arr.reshape(w_size, t_size, c_size, n_metrics),
+            ))
 
         # align the calendar-relative axes from the end
-        max_w_size = max((w_size for _, _, _, w_size, _ in series_shapes), default=0)
+        max_w_size = max((w_size for _, w_size, _ in series_shapes), default=0)
         t_axis_is_calendar = has_time_axis and not has_windows and last_points_only
         max_t_size = (
-            max((t_size for _, _, t_size, _, _ in series_shapes), default=0)
+            max((t_size for t_size, _, _ in series_shapes), default=0)
             if t_axis_is_calendar
             else 0
         )
 
-        for series_index, (comps, c_size, t_size, w_size, canonical) in enumerate(
-            series_shapes
-        ):
+        for series_index, (t_size, w_size, canonical) in enumerate(series_shapes):
             w_offset = max_w_size - w_size if has_windows else 0
             t_offset = max_t_size - t_size if t_axis_is_calendar else 0
-            for m, metric_name in enumerate(metric_names):
-                axis_labels = metric_axes[m][2]
+            for m in range(n_metrics):
                 for w in range(w_size):
                     aligned_w = w + w_offset
                     for c in range(c_size):
-                        # c is a flat index into the (n_components × axis_size) C axis:
-                        # c = comp_i * axis_size + axis_idx
-                        component_index, axis_idx = divmod(c, axis_size)
-                        comp_part = (
-                            "_" + _sanitize_mlflow_key(comps[component_index])
-                            if has_comp_axis
-                            else ""
-                        )
-                        key = (
-                            f"backtest_{metric_name}{comp_part}{axis_labels[axis_idx]}"
-                        )
+                        key = base_keys[m][c]
                         if has_time_axis and has_windows:
-                            key += f"_w{aligned_w}"
-                        key = _sanitize_mlflow_key(key)
+                            key = f"{key}_w{aligned_w}"
                         for t in range(t_size):
                             # MLflow step maps to the axis the UI should chart:
                             # time when present, otherwise window index
@@ -1231,7 +1255,8 @@ def _log_metric_result(
     ------
     ValueError
         On a shape/size mismatch between the metric result and the inferred
-        axes.
+        axes, or when series in a sequence have different numbers of
+        components.
 
     Parameters
     ----------
@@ -1243,6 +1268,8 @@ def _log_metric_result(
     series
         The ``actual_series`` argument passed to the metric (single series or
         ``Sequence[TimeSeries]``); used for component names and series count.
+        All series in a sequence must have the same number of components;
+        names are taken from the first series.
     has_time_axis
         ``True`` when the result carries a per-timestep axis (``time_reduction=None``).
     has_comp_axis
@@ -1268,14 +1295,36 @@ def _log_metric_result(
         results = (
             [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
         )
+        n_components = {s.n_components for s in series_seq}
+        if len(n_components) > 1:
+            raise_log(
+                ValueError(
+                    f"Metric logging failed for `{metric_name}`: all series must "
+                    f"have the same number of components, got "
+                    f"{sorted(n_components)}."
+                )
+            )
+
+    # component names/count from the first series (all series share n_components)
+    comps = series_seq[0].components.tolist()
+    # c_size = components × quantiles/intervals/labels per component
+    c_size = (series_seq[0].n_components if has_comp_axis else 1) * axis_size
+    keys = []
+    for c in range(c_size):
+        # c is a flat index into the (n_components × axis_size) C axis:
+        # c = comp_i * axis_size + axis_idx
+        component_index, axis_idx = divmod(c, axis_size)
+        comp_part = (
+            "_" + _sanitize_mlflow_key(comps[component_index]) if has_comp_axis else ""
+        )
+        keys.append(
+            _sanitize_mlflow_key(metric_name + comp_part + axis_labels[axis_idx])
+        )
 
     # first pass: reshape each series' result into a canonical (T, C) array,
     # recording its time-axis length for the alignment pass below.
     series_shapes = []
-    for s, r in zip(series_seq, results):
-        comps = s.components.tolist()
-        # c_size = components × quantiles/intervals/labels per component
-        c_size = (s.n_components if has_comp_axis else 1) * axis_size
+    for r in results:
         arr = np.asarray(r, dtype=float)
         # after stripping the C axis, the remainder is the time axis (or scalar)
         n_times, extra = divmod(arr.size, c_size)
@@ -1303,27 +1352,17 @@ def _log_metric_result(
         else:
             t_size = 1
 
-        canonical = arr.reshape(t_size, c_size)
-        series_shapes.append((comps, c_size, t_size, canonical))
+        series_shapes.append((t_size, arr.reshape(t_size, c_size)))
 
     # align the time axis from the end (see docstring)
-    max_t_size = max((t_size for _, _, t_size, _ in series_shapes), default=0)
+    max_t_size = max((t_size for t_size, _ in series_shapes), default=0)
 
     # agg maps (key, step) -> per-series values, aggregated into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
-    for series_index, (comps, c_size, t_size, canonical) in enumerate(series_shapes):
+    for series_index, (t_size, canonical) in enumerate(series_shapes):
         step_offset = max_t_size - t_size if has_time_axis else 0
-        for c in range(c_size):
-            # c is a flat index into the (n_components × axis_size) C axis:
-            # c = comp_i * axis_size + axis_idx
-            component_index, axis_idx = divmod(c, axis_size)
-            comp_part = (
-                "_" + _sanitize_mlflow_key(comps[component_index])
-                if has_comp_axis
-                else ""
-            )
-            key = _sanitize_mlflow_key(metric_name + comp_part + axis_labels[axis_idx])
+        for c, key in enumerate(keys):
             for t in range(t_size):
                 # MLflow step maps to the time axis when present
                 step = t + step_offset if has_time_axis else 0

From 644374c02fa744d67dec0e09d380ae0626f81ac3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 23 Jul 2026 16:47:30 +0200
Subject: [PATCH 120/154] docs: align changelog

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 68a40920cc..d2181cf832 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -14,7 +14,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 - 🚀🚀 Added a custom MLflow model flavor for Darts, available under `darts.utils.mlflow`. It provides an MLflow integration for any Darts `ForecastingModel` (statistical, scikit-learn-like, and PyTorch-based). [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
   - Added `save_model()`, `load_model()`, and `log_model()` to persist and reload Darts models as MLflow models, including model and covariate metadata.
   - Added `autolog()` to automatically log model creation parameters, covariate usage, metrics, and the trained model artifact when `log_models=True` (default `False`) on every `fit()`, `backtest()` and `historical_forecasts()` call within an active MLflow run.
-  - Metric functions from `darts.metrics` are automatically logged when called inside an active run, with keys reflecting the metric output shape (per-component, per-quantile/interval, per-label, and per-timestep results charted across MLflow steps). Multi-series results log the mean over series and write the full per-series breakdown to a CSV artifact.
+  - Metric functions from `darts.metrics` are automatically logged when called inside an active run, with keys reflecting the metric output shape (per-component, per-quantile/interval, per-label, and per-timestep results charted across MLflow steps). Multi-series results log the aggregate over series and write the full per-series breakdown to a JSON artifact.
   - Added a new notebook for [MLflow quickstart](https://unit8co.github.io/darts/examples/29-MLflow-quickstart.html) with detailed usage examples.
   - Note: model serving and deployment (MLflow's `pyfunc` flavor, model signatures, and input examples) are not yet supported.
 - 🔴 Improved `TransformerModel` with proper encoder-decoder transformer architecture using teacher forcing during training and autoregressive inference, aligning the implementation with [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762). Previously trained checkpoints are incompatible and must be retrained. [#1915](https://github.com/unit8co/darts/pull/1915) by [Jan Fidor](https://github.com/JanFidor) and [Dennis Bader](https://github.com/dennisbader).

From a3dde9c1f9939df5de20693a5833d6f7f1e815cd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 23 Jul 2026 18:53:54 +0200
Subject: [PATCH 121/154] feat: align notebook to code review changes

---
 examples/29-MLflow-quickstart.ipynb | 12004 +++++++++++++-------------
 1 file changed, 6038 insertions(+), 5966 deletions(-)

diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
index 989ee5a94f..54f8d90a83 100644
--- a/examples/29-MLflow-quickstart.ipynb
+++ b/examples/29-MLflow-quickstart.ipynb
@@ -1,6067 +1,6139 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aeddb542",
+   "metadata": {},
+   "source": [
+    "# MLflow for Darts\n",
+    "\n",
+    "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
+    "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
+    "\n",
+    "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
+    "\n",
+    "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f72894af",
+   "metadata": {},
+   "source": [
+    "## Installing MLflow\n",
+    "\n",
+    "MLflow is available as an optional dependency for Darts. Install it with:\n",
+    "\n",
+    "```bash\n",
+    "pip install mlflow\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42e3dcea",
+   "metadata": {},
+   "source": [
+    "## Setup and Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b346ce8f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
+     "iopub.status.busy": "2026-06-24T15:18:43.049088Z",
+     "iopub.status.idle": "2026-06-24T15:18:43.053693Z",
+     "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# fix python path if working locally\n",
+    "from utils import fix_pythonpath_if_working_locally\n",
+    "\n",
+    "fix_pythonpath_if_working_locally()\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "13b13fe4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
+     "iopub.status.busy": "2026-06-24T15:18:43.054722Z",
+     "iopub.status.idle": "2026-06-24T15:18:46.599643Z",
+     "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mlflow\n",
+    "import numpy as np\n",
+    "\n",
+    "import darts.metrics as metrics\n",
+    "from darts.datasets import AirPassengersDataset\n",
+    "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
+    "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4d424e08",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
+     "iopub.status.busy": "2026-06-24T15:18:46.600748Z",
+     "iopub.status.idle": "2026-06-24T15:18:46.634540Z",
+     "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# use darts plotting style\n",
+    "from darts import set_option\n",
+    "\n",
+    "set_option(\"plotting.use_darts_style\", True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f9c40d6",
+   "metadata": {},
+   "source": [
+    "## MLflow Setup\n",
+    "\n",
+    "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "88320df5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
+     "iopub.status.busy": "2026-06-24T15:18:46.636565Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.268439Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "aeddb542",
-      "metadata": {},
-      "source": [
-        "# MLflow for Darts\n",
-        "\n",
-        "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
-        "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
-        "\n",
-        "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
-        "\n",
-        "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+      "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Updating database tables\n",
+      "2026/07/23 18:50:08 INFO mlflow.tracking.fluent: Experiment with name 'darts-quickstart' does not exist. Creating a new experiment.\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "f72894af",
-      "metadata": {},
-      "source": [
-        "## Installing MLflow\n",
-        "\n",
-        "MLflow is available as an optional dependency for Darts. Install it with:\n",
-        "\n",
-        "```bash\n",
-        "pip install mlflow\n",
-        "```"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
+      "Experiment: darts-quickstart\n"
+     ]
+    }
+   ],
+   "source": [
+    "# temporary directory for MLflow tracking\n",
+    "tmpdir = tempfile.mkdtemp()\n",
+    "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+    "\n",
+    "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
+    "mlflow.set_experiment(\"darts-quickstart\")\n",
+    "\n",
+    "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
+    "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d5209e",
+   "metadata": {},
+   "source": [
+    "## Load Sample Data\n",
+    "\n",
+    "We'll use the classic AirPassengers dataset for this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1596e07e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.269548Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.356588Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "42e3dcea",
-      "metadata": {},
-      "source": [
-        "## Setup and Imports"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training series: 107 points\n",
+      "Validation series: 37 points\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "b346ce8f",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
-          "iopub.status.busy": "2026-06-24T15:18:43.049088Z",
-          "iopub.status.idle": "2026-06-24T15:18:43.053693Z",
-          "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# fix python path if working locally\n",
-        "from utils import fix_pythonpath_if_working_locally\n",
-        "\n",
-        "fix_pythonpath_if_working_locally()\n",
-        "\n",
-        "import warnings\n",
-        "\n",
-        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "series = AirPassengersDataset().load()\n",
+    "train, val = series.split_before(0.75)\n",
+    "\n",
+    "print(f\"Training series: {len(train)} points\")\n",
+    "print(f\"Validation series: {len(val)} points\")\n",
+    "\n",
+    "series.plot()\n",
+    "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
+    "plt.legend()\n",
+    "plt.title(\"AirPassengers Dataset\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34858645",
+   "metadata": {},
+   "source": [
+    "## Basic Model Logging\n",
+    "\n",
+    "Let's train a simple model and log it to MLflow manually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bc8f520d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.357516Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.450374Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "13b13fe4",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
-          "iopub.status.busy": "2026-06-24T15:18:43.054722Z",
-          "iopub.status.idle": "2026-06-24T15:18:46.599643Z",
-          "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline\n",
-        "\n",
-        "import os\n",
-        "import tempfile\n",
-        "\n",
-        "import matplotlib.pyplot as plt\n",
-        "import mlflow\n",
-        "import numpy as np\n",
-        "\n",
-        "import darts.metrics\n",
-        "from darts.datasets import AirPassengersDataset\n",
-        "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
-        "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation MAPE: 7.86%\n",
+      "Validation RMSE: 34.77\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "4d424e08",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
-          "iopub.status.busy": "2026-06-24T15:18:46.600748Z",
-          "iopub.status.idle": "2026-06-24T15:18:46.634540Z",
-          "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# use darts plotting style\n",
-        "from darts import set_option\n",
-        "\n",
-        "set_option(\"plotting.use_darts_style\", True)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = ExponentialSmoothing()\n",
+    "model.fit(train)\n",
+    "\n",
+    "predictions = model.predict(n=len(val))\n",
+    "\n",
+    "# calculate metrics you want to log to MLflow\n",
+    "mape_score = metrics.mape(val, predictions)\n",
+    "rmse_score = metrics.rmse(val, predictions)\n",
+    "\n",
+    "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
+    "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
+    "\n",
+    "train[-50:].plot(label=\"Training\")\n",
+    "val.plot(label=\"Actual\")\n",
+    "predictions.plot(label=\"Forecast\")\n",
+    "plt.legend()\n",
+    "plt.title(\"Exponential Smoothing Forecast\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35dc864c",
+   "metadata": {},
+   "source": [
+    "Now let's log this model to MLflow.\n",
+    "\n",
+    "Tags can live in two places in MLflow 3:\n",
+    "\n",
+    "- **Run tags** (`mlflow.set_tags(...)`) – shown on the run page in the UI. This is also where `autolog()` writes its tags (`model_class`, …).\n",
+    "- **LoggedModel tags** (`log_model(..., tags=...)`) – attached to the logged model entity itself (open the model from the run's **Outputs** / Models view)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "61406cd9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.451297Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.906785Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "2f9c40d6",
-      "metadata": {},
-      "source": [
-        "## MLflow Setup\n",
-        "\n",
-        "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/07/23 18:50:08 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "88320df5",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
-          "iopub.status.busy": "2026-06-24T15:18:46.636565Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.268439Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "2026/06/25 13:57:21 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
-            "2026/06/25 13:57:21 INFO mlflow.store.db.utils: Updating database tables\n",
-            "2026/06/25 13:57:22 INFO mlflow.tracking.fluent: Experiment with name 'darts-quickstart' does not exist. Creating a new experiment.\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpvwnlx3yj/mlflow.db\n",
-            "Experiment: darts-quickstart\n"
-          ]
-        }
-      ],
-      "source": [
-        "# temporary directory for MLflow tracking\n",
-        "tmpdir = tempfile.mkdtemp()\n",
-        "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
-        "\n",
-        "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
-        "mlflow.set_experiment(\"darts-quickstart\")\n",
-        "\n",
-        "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
-        "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
+      "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
+     ]
+    }
+   ],
+   "source": [
+    "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
+    "    model_info = log_model(\n",
+    "        model=model,\n",
+    "        name=\"exponential-smoothing-model\",\n",
+    "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
+    "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
+    "    )\n",
+    "\n",
+    "    # log calculated metrics you want\n",
+    "    mlflow.log_metric(\"mape\", mape_score)\n",
+    "    mlflow.log_metric(\"rmse\", rmse_score)\n",
+    "\n",
+    "    print(f\"Run ID: {run.info.run_id}\")\n",
+    "    print(f\"Model URI: {model_info.model_uri}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0728690e",
+   "metadata": {},
+   "source": [
+    "### Load the Model Back\n",
+    "\n",
+    "We can load the model from MLflow using its URI:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cae35ffa",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.907802Z",
+     "iopub.status.idle": "2026-06-24T15:18:47.914695Z",
+     "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "03d5209e",
-      "metadata": {},
-      "source": [
-        "## Load Sample Data\n",
-        "\n",
-        "We'll use the classic AirPassengers dataset for this example."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded model predictions match: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "loaded_model = load_model(model_info.model_uri)\n",
+    "\n",
+    "loaded_predictions = loaded_model.predict(n=len(val))\n",
+    "\n",
+    "# verify predictions match\n",
+    "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
+    "print(f\"Loaded model predictions match: {predictions_match}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bd4597c",
+   "metadata": {},
+   "source": [
+    "## Automatic Logging with `autolog()`\n",
+    "\n",
+    "`autolog()` patches darts models and metrics so that the following are logged automatically, with no extra code needed:\n",
+    "\n",
+    "- **`fit()`** – model creation parameters and covariate metadata (and the trained model artifact when ``log_models=True``; default ``False``).\n",
+    "- **Darts metric functions** – any call made inside an active MLflow run.\n",
+    "- **`backtest()`** – evaluation metrics under `backtest_*` keys.\n",
+    "- **`historical_forecasts()`** – patched so its internal per-window `fit()` calls don't each spawn their own logging.\n",
+    "- **PyTorch-based models** – per-epoch `train_loss` / `val_loss` via MLflow's PyTorch autologging (see the section below for details)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5ef7f73a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
+     "iopub.status.busy": "2026-06-24T15:18:47.915566Z",
+     "iopub.status.idle": "2026-06-24T15:18:50.020337Z",
+     "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "1596e07e",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.269548Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.356588Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "findfont: Failed to find font weight 600, now using 700.\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Training series: 107 points\n",
-            "Validation series: 37 points\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "series = AirPassengersDataset().load()\n",
-        "train, val = series.split_before(0.75)\n",
-        "\n",
-        "print(f\"Training series: {len(train)} points\")\n",
-        "print(f\"Validation series: {len(val)} points\")\n",
-        "\n",
-        "series.plot()\n",
-        "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
-        "plt.legend()\n",
-        "plt.title(\"AirPassengers Dataset\")\n",
-        "plt.show()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "34858645",
-      "metadata": {},
-      "source": [
-        "## Basic Model Logging\n",
-        "\n",
-        "Let's train a simple model and log it to MLflow manually."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "autolog()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
+    "    auto_model = LinearRegressionModel(lags=12)\n",
+    "    auto_model.fit(train)  # autolog logs params and covariate metadata\n",
+    "\n",
+    "    auto_predictions = auto_model.predict(n=len(val))\n",
+    "    # these metric calls happen inside the run, so they are logged automatically\n",
+    "    auto_mape = metrics.mape(val, auto_predictions)\n",
+    "    auto_rmse = metrics.rmse(val, auto_predictions)\n",
+    "\n",
+    "autolog(disable=True)\n",
+    "\n",
+    "# show logged metrics\n",
+    "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
+    "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
+    "\n",
+    "# plot\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "train[-36:].plot(label=\"Train\", ax=ax)\n",
+    "val.plot(label=\"Actual\", ax=ax)\n",
+    "auto_predictions.plot(\n",
+    "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
+    ")\n",
+    "ax.set_title(\"Linear Regression — autolog run\")\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f484330f",
+   "metadata": {},
+   "source": [
+    "## Open the MLflow UI\n",
+    "\n",
+    "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
+    "\n",
+    "The UI lets you:\n",
+    "- **Compare runs** side-by-side in the Experiments table\n",
+    "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
+    "- **Visualize** metrics across runs with built-in charts\n",
+    "- **Register** models to the Model Registry for versioning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1a01cd2f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
+     "iopub.status.busy": "2026-06-24T15:18:50.021439Z",
+     "iopub.status.idle": "2026-06-24T15:18:50.023116Z",
+     "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "bc8f520d",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.357516Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.450374Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Validation MAPE: 7.86%\n",
-            "Validation RMSE: 34.77\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "model = ExponentialSmoothing()\n",
-        "model.fit(train)\n",
-        "\n",
-        "predictions = model.predict(n=len(val))\n",
-        "\n",
-        "# calculate metrics you want to log to MLflow\n",
-        "mape_score = darts.metrics.mape(val, predictions)\n",
-        "rmse_score = darts.metrics.rmse(val, predictions)\n",
-        "\n",
-        "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
-        "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
-        "\n",
-        "train[-50:].plot(label=\"Training\")\n",
-        "val.plot(label=\"Actual\")\n",
-        "predictions.plot(label=\"Forecast\")\n",
-        "plt.legend()\n",
-        "plt.title(\"Exponential Smoothing Forecast\")\n",
-        "plt.show()"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Launch the MLflow UI with this command in your terminal:\n",
+      "\n",
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
+      "\n",
+      "Then open:  http://localhost:5000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
+    "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
+    "print(\"Then open:  http://localhost:5000\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88b0d285",
+   "metadata": {},
+   "source": [
+    "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
+    ">\n",
+    "![Mlflow Overview](./static/images/mlflow_overview.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2b133bc",
+   "metadata": {},
+   "source": [
+    "## Per-epoch Metrics with Torch Models\n",
+    "\n",
+    "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
+    "\n",
+    "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d01783d1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
+     "iopub.status.busy": "2026-06-24T15:18:50.024087Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.370946Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "35dc864c",
-      "metadata": {},
-      "source": [
-        "Now let's log this model to MLflow:"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: GPU available: True (mps), used: False\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
+      "INFO: TPU available: False, using: 0 TPU cores\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+      "INFO: HPU available: False, using: 0 HPUs\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
+      "\n",
+      "  | Name            | Type             | Params | Mode \n",
+      "-------------------------------------------------------------\n",
+      "0 | criterion       | MSELoss          | 0      | train\n",
+      "1 | train_criterion | MSELoss          | 0      | train\n",
+      "2 | val_criterion   | MSELoss          | 0      | train\n",
+      "3 | train_metrics   | MetricCollection | 0      | train\n",
+      "4 | val_metrics     | MetricCollection | 0      | train\n",
+      "5 | stacks          | ModuleList       | 6.2 M  | train\n",
+      "-------------------------------------------------------------\n",
+      "6.2 M     Trainable params\n",
+      "1.4 K     Non-trainable params\n",
+      "6.2 M     Total params\n",
+      "24.787    Total estimated model params size (MB)\n",
+      "400       Modules in train mode\n",
+      "0         Modules in eval mode\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "61406cd9",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.451297Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.906785Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33ff56f2bbbb46ddb71bf5ac409bc3c7",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "2026/06/25 13:57:23 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Run ID: 22b3b299ce8d4d72b1646564a35b04ee\n",
-            "Model URI: models:/m-e61e8518e2a04143ace1d627ed4a775b\n"
-          ]
-        }
-      ],
-      "source": [
-        "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
-        "    model_info = log_model(\n",
-        "        model=model,\n",
-        "        name=\"exponential-smoothing-model\",\n",
-        "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
-        "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
-        "    )\n",
-        "\n",
-        "    # log calculated metrics you want\n",
-        "    mlflow.log_metric(\"val_mape\", mape_score)\n",
-        "    mlflow.log_metric(\"val_rmse\", rmse_score)\n",
-        "\n",
-        "    print(f\"Run ID: {run.info.run_id}\")\n",
-        "    print(f\"Model URI: {model_info.model_uri}\")"
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "0728690e",
-      "metadata": {},
-      "source": [
-        "### Load the Model Back\n",
-        "\n",
-        "We can load the model from MLflow using its URI:"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
+      "  super().__init__(loader)\n",
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "cae35ffa",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.907802Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.914695Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fb13a9c2a6b40ad886bb7878949614d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Loaded model predictions match: True\n"
-          ]
-        }
-      ],
-      "source": [
-        "loaded_model = load_model(model_info.model_uri)\n",
-        "\n",
-        "loaded_predictions = loaded_model.predict(n=len(val))\n",
-        "\n",
-        "# verify predictions match\n",
-        "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
-        "print(f\"Loaded model predictions match: {predictions_match}\")"
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "6bd4597c",
-      "metadata": {},
-      "source": [
-        "## Automatic Logging with `autolog()`\n",
-        "\n",
-        "`autolog()` patches every `model.fit()` call to automatically log parameters, covariate metadata, and the trained model artifact. It also patches the darts metric functions so any metric called inside an active run is logged. For PyTorch-based models it enables MLflow's PyTorch autologging to record `train_loss` / `val_loss` per epoch (see the sections below for details)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "5ef7f73a",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.915566Z",
-          "iopub.status.idle": "2026-06-24T15:18:50.020337Z",
-          "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "595549c03c1d4fe28a03ce38a0f38441",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "2026/06/25 13:57:26 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
-            "2026/06/25 13:57:26 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Logged metrics: {'val_mape': 10.742, 'val_rmse': 51.182}\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1000x400 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "autolog()\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
-        "    auto_model = LinearRegressionModel(lags=12)\n",
-        "    auto_model.fit(train)  # autolog logs params, covariate metadata, and the model\n",
-        "\n",
-        "    auto_predictions = auto_model.predict(n=len(val))\n",
-        "    # these metric calls happen inside the run, so they are logged automatically\n",
-        "    auto_mape = darts.metrics.mape(val, auto_predictions)\n",
-        "    auto_rmse = darts.metrics.rmse(val, auto_predictions)\n",
-        "\n",
-        "autolog(disable=True)\n",
-        "\n",
-        "# show logged metrics\n",
-        "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
-        "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
-        "\n",
-        "# plot\n",
-        "fig, ax = plt.subplots(figsize=(10, 4))\n",
-        "train[-36:].plot(label=\"Train\", ax=ax)\n",
-        "val.plot(label=\"Actual\", ax=ax)\n",
-        "auto_predictions.plot(\n",
-        "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
-        ")\n",
-        "ax.set_title(\"Linear Regression — autolog run\")\n",
-        "ax.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "f484330f",
-      "metadata": {},
-      "source": [
-        "## Open the MLflow UI\n",
-        "\n",
-        "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
-        "\n",
-        "The UI lets you:\n",
-        "- **Compare runs** side-by-side in the Experiments table\n",
-        "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
-        "- **Visualize** metrics across runs with built-in charts\n",
-        "- **Register** models to the Model Registry for versioning"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
+      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "1a01cd2f",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
-          "iopub.status.busy": "2026-06-24T15:18:50.021439Z",
-          "iopub.status.idle": "2026-06-24T15:18:50.023116Z",
-          "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78af3a584caf477ba9c043c93a77ae56",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Launch the MLflow UI with this command in your terminal:\n",
-            "\n",
-            "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpvwnlx3yj/mlflow.db\n",
-            "\n",
-            "Then open:  http://localhost:5000\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
-        "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
-        "print(\"Then open:  http://localhost:5000\")"
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "88b0d285",
-      "metadata": {},
-      "source": [
-        "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `val_mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
-        ">\n",
-        "![Mlflow Overview](./static/images/mlflow_overview.png)"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07ec6fb97dd6418594e5dedda8a087fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "e2b133bc",
-      "metadata": {},
-      "source": [
-        "## Per-epoch Metrics with Torch Models\n",
-        "\n",
-        "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
-        "\n",
-        "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbd214c1629e47de8f36acad90896742",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "d01783d1",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
-          "iopub.status.busy": "2026-06-24T15:18:50.024087Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.370946Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a78ea5bef414a31bd4dd00f162b4876",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "INFO: GPU available: True (mps), used: False\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
-            "INFO: TPU available: False, using: 0 TPU cores\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-            "INFO: HPU available: False, using: 0 HPUs\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
-            "\n",
-            "  | Name            | Type             | Params | Mode \n",
-            "-------------------------------------------------------------\n",
-            "0 | criterion       | MSELoss          | 0      | train\n",
-            "1 | train_criterion | MSELoss          | 0      | train\n",
-            "2 | val_criterion   | MSELoss          | 0      | train\n",
-            "3 | train_metrics   | MetricCollection | 0      | train\n",
-            "4 | val_metrics     | MetricCollection | 0      | train\n",
-            "5 | stacks          | ModuleList       | 6.2 M  | train\n",
-            "-------------------------------------------------------------\n",
-            "6.2 M     Trainable params\n",
-            "1.4 K     Non-trainable params\n",
-            "6.2 M     Total params\n",
-            "24.787    Total estimated model params size (MB)\n",
-            "400       Modules in train mode\n",
-            "0         Modules in eval mode\n"
-          ]
-        },
-        {
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-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
-            "  super().__init__(loader)\n",
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
-          ]
-        },
-        {
-          "data": {
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-              "model_id": "f5391e3cf5e64716b68c688111130ce8",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Training: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "a5a68c9f0add43f295c57a2d04fe9aa6",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
-          ]
-        },
-        {
-          "data": {
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-              "model_id": "f884097f09674140afc79ed787ea826b",
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-              "Validation: |          | 0/? [00:00<?, ?it/s]"
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-        },
-        {
-          "data": {
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-              "Validation: |          | 0/? [00:00<?, ?it/s]"
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-        },
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-        },
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-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "b246ce01f8cd4104b1412bd6c9a62b6b",
-              "version_major": 2,
-              "version_minor": 0
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-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "2ecb7b3e01a040f4b76df8d9e5c5a26b",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
-            "2026/06/25 13:57:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
-            "INFO: GPU available: True (mps), used: False\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
-            "INFO: TPU available: False, using: 0 TPU cores\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-            "INFO: HPU available: False, using: 0 HPUs\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "eaafc9c3e4344b6ca180de20246351b2",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Predicting: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "NBEATS MAPE: 13.64%\n",
-            "NBEATS RMSE: 63.33\n"
-          ]
-        }
-      ],
-      "source": [
-        "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
-        "\n",
-        "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
-        "autolog()\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
-        "    nbeats = NBEATSModel(\n",
-        "        input_chunk_length=24,\n",
-        "        output_chunk_length=12,\n",
-        "        n_epochs=10,\n",
-        "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
-        "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
-        "        torch_metrics=MetricCollection({\n",
-        "            \"MAE\": MeanAbsoluteError(),\n",
-        "            \"MSE\": MeanSquaredError(),\n",
-        "        }),\n",
-        "        random_state=42,\n",
-        "    )\n",
-        "    nbeats.fit(train, val_series=val)\n",
-        "    nbeats_pred = nbeats.predict(n=len(val))\n",
-        "    # metric calls inside the run are logged automatically (keys: val_mape, val_rmse)\n",
-        "    nbeats_mape = darts.metrics.mape(val, nbeats_pred)\n",
-        "    nbeats_rmse = darts.metrics.rmse(val, nbeats_pred)\n",
-        "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
-        "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
-        "\n",
-        "autolog(disable=True)"
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "6fcc8f13",
-      "metadata": {},
-      "source": [
-        "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
-        ">\n",
-        "![Mlflow Charts](./static/images/mlflow_charts.png)"
+     "data": {
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+       "model_id": "0759f953cc2e4aba9c4d4644279daf9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "3f0828c2",
-      "metadata": {},
-      "source": [
-        "## Forecast Metrics\n",
-        "\n",
-        "With `log_metrics=True` (the default), `autolog()` patches every darts metric function. Any metric you call **inside an active run** is logged automatically with no extra arguments needed.\n",
-        "\n",
-        "The metric key is derived from the result: a scalar is logged under `{metric}`, and when the variable name of the actual series can be detected it is prefixed (e.g. calling `darts.metrics.mae(val, pred)` logs `val_mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`."
+     "data": {
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+       "model_id": "33e1f6444b504ddd8808d0b4bdf7cf34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "id": "df8e85b6",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.372367Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.470125Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c23701c8d9fb45a5a7188f013b8ae713",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "2026/06/25 13:57:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
-            "2026/06/25 13:57:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "All logged metrics (5):\n",
-            "  manual_mape: 10.7420\n",
-            "  val_mae: 46.0220\n",
-            "  val_mape: 10.7420\n",
-            "  val_rmse: 51.1820\n",
-            "  val_smape: 10.1015\n"
-          ]
-        }
-      ],
-      "source": [
-        "# log_metrics=True (the default) patches every darts metric so that calls made\n",
-        "# inside an active run are logged automatically\n",
-        "autolog(log_metrics=True)\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
-        "    lr_model = LinearRegressionModel(lags=12)\n",
-        "    lr_model.fit(train)\n",
-        "    lr_pred = lr_model.predict(n=len(val))\n",
-        "\n",
-        "    # each metric called here is auto-logged; the key is prefixed with the\n",
-        "    # variable name of the first argument (here \"val\"): val_mae, val_rmse, val_smape\n",
-        "    darts.metrics.mae(val, lr_pred)\n",
-        "    darts.metrics.rmse(val, lr_pred)\n",
-        "    darts.metrics.smape(val, lr_pred)\n",
-        "    # you can still log a custom-named metric explicitly\n",
-        "    mlflow.log_metric(\"manual_mape\", darts.metrics.mape(val, lr_pred))\n",
-        "    run_id = run.info.run_id\n",
-        "\n",
-        "autolog(disable=True)\n",
-        "\n",
-        "# show what was logged\n",
-        "client = mlflow.tracking.MlflowClient()\n",
-        "run_metrics = client.get_run(run_id).data.metrics\n",
-        "metric_names = sorted(run_metrics.keys())\n",
-        "print(f\"All logged metrics ({len(metric_names)}):\")\n",
-        "for name in metric_names:\n",
-        "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "b08e900a",
-      "metadata": {},
-      "source": [
-        "### Metric Shape and Per-Series Logging\n",
-        "\n",
-        "The logged key reflects the shape of the metric output, which `autolog()` infers from the metric and its keyword arguments. The general pattern is:\n",
-        "\n",
-        "`{dataset_name}_{metric_name}{component}{quantile_or_label}`\n",
-        "\n",
-        "Per-component results (`component_reduction=None`) add the component name (e.g. `val_mae_<component>`), quantile metrics (`q`, `q_interval`) add the quantile (e.g. `val_mql_q0.5`), per-label classification (`label_reduction=None`) adds the class (e.g. `val_f1_label1`), and per-timestep metrics (`time_reduction=None`) are charted across MLflow steps.\n",
-        "\n",
-        "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is saved as a CSV artifact under `per_series_metrics/`.\n",
-        "\n",
-        "Passing `name=` to a metric overrides the `{metric_name}` token in the key while keeping the prefix and suffixes (e.g. `mql(..., q=0.5, name=\"foo\")` logs `val_foo_q0.5`)."
+     "data": {
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+       "model_id": "1f198dc0926c4f40a60823608c789f05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "ecdbbf29",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Aggregate metrics: {'val_ae': 44.196, 'val_list_mae': 51.316}\n",
-            "Mean MAE over series: 51.316\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "712d3dc031d047f7976776824b40e5bb",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Per-series breakdown (val_list_mae_per_series.csv):\n",
-            "  series 0: val_list_mae = 47.459\n",
-            "  series 1: val_list_mae = 55.173\n"
-          ]
-        }
-      ],
-      "source": [
-        "import csv\n",
-        "\n",
-        "import darts.metrics as dm\n",
-        "\n",
-        "# build a small multi-series example from the univariate AirPassengers data\n",
-        "series_list = [train, train * 1.2]\n",
-        "val_list = [val, val * 1.2]\n",
-        "\n",
-        "multi_model = LinearRegressionModel(lags=12)\n",
-        "multi_model.fit(series_list)\n",
-        "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
-        "\n",
-        "autolog(log_metrics=True)\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"metric-shape-and-csv\") as run:\n",
-        "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
-        "    # under a single key, charted across MLflow steps\n",
-        "    single_pred = multi_model.predict(n=len(val), series=train)\n",
-        "    dm.ae(val, single_pred)\n",
-        "\n",
-        "    # multiple series: the logged value is the MEAN over series, and the full\n",
-        "    # per-series breakdown is written to a CSV artifact\n",
-        "    per_series_mae = dm.mae(val_list, multi_preds)\n",
-        "\n",
-        "autolog(disable=True)\n",
-        "\n",
-        "client = mlflow.tracking.MlflowClient()\n",
-        "run_id = run.info.run_id\n",
-        "\n",
-        "# aggregate metrics: val_list_mae is the mean over the two series\n",
-        "logged = client.get_run(run_id).data.metrics\n",
-        "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
-        "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
-        "\n",
-        "# download and inspect the per-series CSV artifact\n",
-        "csv_path = mlflow.artifacts.download_artifacts(\n",
-        "    run_id=run_id, artifact_path=\"per_series_metrics/val_list_mae_per_series.csv\"\n",
-        ")\n",
-        "with open(csv_path) as f:\n",
-        "    rows = list(csv.DictReader(f))\n",
-        "\n",
-        "print(\"\\nPer-series breakdown (val_list_mae_per_series.csv):\")\n",
-        "for row in rows:\n",
-        "    print(f\"  series {row['series_index']}: {row['key']} = {float(row['value']):.3f}\")"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82a6462bdf0a486a85887bef6ae73630",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "8511fc08",
-      "metadata": {},
-      "source": [
-        "## Saving and Loading Models Locally\n",
-        "\n",
-        "You can also save and load models to/from local paths without MLflow runs."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
+      "INFO: GPU available: True (mps), used: False\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
+      "INFO: TPU available: False, using: 0 TPU cores\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+      "INFO: HPU available: False, using: 0 HPUs\n",
+      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "id": "645ef079",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.471178Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.478682Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9cf24e439244b4f99812eeea1c9aab8",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "2026/06/25 13:57:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Files in model directory:\n",
-            "  - python_env.yaml\n",
-            "  - requirements.txt\n",
-            "  - MLmodel\n",
-            "  - model.pkl\n",
-            "  - conda.yaml\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Save model to local directory\n",
-        "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
-        "save_model(model, path=local_model_path)\n",
-        "\n",
-        "print(\"\\nFiles in model directory:\")\n",
-        "for file in os.listdir(local_model_path):\n",
-        "    print(f\"  - {file}\")"
+      "text/plain": [
+       "Predicting: |          | 0/? [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "254ba153",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.479604Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.484973Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Loaded model successfully!\n",
-            "Predictions shape: (5, 1)\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Load model from local directory\n",
-        "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
-        "\n",
-        "# Test it\n",
-        "local_predictions = local_loaded_model.predict(n=5)\n",
-        "print(\"Loaded model successfully!\")\n",
-        "print(f\"Predictions shape: {local_predictions.values().shape}\")"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NBEATS MAPE: 13.64%\n",
+      "NBEATS RMSE: 63.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
+    "\n",
+    "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
+    "autolog()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
+    "    nbeats = NBEATSModel(\n",
+    "        input_chunk_length=24,\n",
+    "        output_chunk_length=12,\n",
+    "        n_epochs=10,\n",
+    "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
+    "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
+    "        torch_metrics=MetricCollection({\n",
+    "            \"MAE\": MeanAbsoluteError(),\n",
+    "            \"MSE\": MeanSquaredError(),\n",
+    "        }),\n",
+    "        random_state=42,\n",
+    "    )\n",
+    "    nbeats.fit(train, val_series=val)\n",
+    "    nbeats_pred = nbeats.predict(n=len(val))\n",
+    "    # metric calls inside the run are logged automatically (keys: mape, rmse)\n",
+    "    nbeats_mape = metrics.mape(val, nbeats_pred)\n",
+    "    nbeats_rmse = metrics.rmse(val, nbeats_pred)\n",
+    "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
+    "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
+    "\n",
+    "autolog(disable=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fcc8f13",
+   "metadata": {},
+   "source": [
+    "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
+    ">\n",
+    "![Mlflow Charts](./static/images/mlflow_charts.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f0828c2",
+   "metadata": {},
+   "source": [
+    "## Forecast Metrics\n",
+    "\n",
+    "With `log_metrics=True` (the default), `autolog()` patches every darts metric function. Any metric you call **inside an active run** is logged automatically with no extra arguments needed.\n",
+    "\n",
+    "The metric key is derived from the result: a scalar is logged under `{metric}` (e.g. calling `darts.metrics.mae(val, pred)` logs `mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "df8e85b6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.372367Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.470125Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "aab0d1e0",
-      "metadata": {},
-      "source": [
-        "## Querying Experiments\n",
-        "\n",
-        "You can programmatically query and compare runs."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All logged metrics (5):\n",
+      "  mae: 46.0220\n",
+      "  manual_mape: 10.7420\n",
+      "  mape: 10.7420\n",
+      "  rmse: 51.1820\n",
+      "  smape: 10.1015\n"
+     ]
+    }
+   ],
+   "source": [
+    "# log_metrics=True (the default) patches every darts metric so that calls made\n",
+    "# inside an active run are logged automatically\n",
+    "autolog(log_metrics=True)\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
+    "    lr_model = LinearRegressionModel(lags=12)\n",
+    "    lr_model.fit(train)\n",
+    "    lr_pred = lr_model.predict(n=len(val))\n",
+    "\n",
+    "    # each metric called here is auto-logged under its own name: mae, rmse, smape\n",
+    "    metrics.mae(val, lr_pred)\n",
+    "    metrics.rmse(val, lr_pred)\n",
+    "    metrics.smape(val, lr_pred)\n",
+    "    # you can still log a custom-named metric explicitly\n",
+    "    mlflow.log_metric(\"manual_mape\", metrics.mape(val, lr_pred))\n",
+    "    run_id = run.info.run_id\n",
+    "\n",
+    "autolog(disable=True)\n",
+    "\n",
+    "# show what was logged\n",
+    "client = mlflow.tracking.MlflowClient()\n",
+    "run_metrics = client.get_run(run_id).data.metrics\n",
+    "metric_names = sorted(run_metrics.keys())\n",
+    "print(f\"All logged metrics ({len(metric_names)}):\")\n",
+    "for name in metric_names:\n",
+    "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b08e900a",
+   "metadata": {},
+   "source": [
+    "### Metric Shape and Per-Series Logging\n",
+    "\n",
+    "The logged key reflects the shape of the metric output, which `autolog()` infers from the metric and its keyword arguments. The general pattern is:\n",
+    "\n",
+    "`{metric_name}{component}{quantile_or_label}`\n",
+    "\n",
+    "- **Per-component** (`component_reduction=None`) adds the component name, e.g. `mae_<component>`.\n",
+    "- **Quantile / interval** (`q`, `q_interval`) adds the quantile, e.g. `mql_q0.500`.\n",
+    "- **Per-label classification** (`label_reduction=None`) adds the class, e.g. `f1_label1`.\n",
+    "- **Per-timestep** (`time_reduction=None`) is charted across MLflow steps.\n",
+    "- **`series_reduction`**, when set on the metric call, aggregates across series inside the metric itself, so the mean-over-series logging described below does not apply.\n",
+    "\n",
+    "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is appended to a single run-wide table artifact, `metrics_per_series.json`, which you can read back with `mlflow.load_table()`.\n",
+    "\n",
+    "Passing `name=` to a metric overrides the `{metric_name}` token in the key while keeping the suffixes (e.g. `mql(..., q=0.5, name=\"foo\")` logs `foo_q0.500`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ecdbbf29",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aggregate metrics: {'ae': 44.196, 'mae': 51.316}\n",
+      "Mean MAE over series: 51.316\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 17,
-      "id": "109a9812",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.485859Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.496380Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0305dca7a1b846f19da965d1b74319c5",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Found 5 runs in experiment 'darts-quickstart':\n",
-            "\n",
-            "1. exponential-smoothing-baseline\n",
-            "   Run ID: 22b3b299ce8d4d72b1646564a35b04ee\n",
-            "   Validation MAPE: 7.864181481214469\n",
-            "\n",
-            "2. linear-regression-full-metrics\n",
-            "   Run ID: 91ea26f4a5d448269c704a772c53f695\n",
-            "   Validation MAPE: 10.742044444678953\n",
-            "\n",
-            "3. linear-regression-autolog\n",
-            "   Run ID: eed13fa115304f6e853dfd4bf755211c\n",
-            "   Validation MAPE: 10.742044444678953\n",
-            "\n",
-            "4. nbeats-epoch-metrics\n",
-            "   Run ID: a1acc6779c7b4f158ac95f26bb108b74\n",
-            "   Validation MAPE: 13.639569217869525\n",
-            "\n",
-            "5. metric-shape-and-csv\n",
-            "   Run ID: 13a91160d03f414892139f7831ba509d\n",
-            "   Validation MAPE: N/A\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "from mlflow.tracking import MlflowClient\n",
-        "\n",
-        "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
-        "\n",
-        "# get all runs\n",
-        "client = MlflowClient()\n",
-        "runs = client.search_runs(\n",
-        "    experiment_ids=[experiment.experiment_id],\n",
-        "    order_by=[\"metrics.val_mape ASC\"],  # Sort by best MAPE\n",
-        ")\n",
-        "\n",
-        "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
-        "for i, run in enumerate(runs, 1):\n",
-        "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
-        "    mape_val = run.data.metrics.get(\"val_mape\", \"N/A\")\n",
-        "    print(f\"{i}. {run_name}\")\n",
-        "    print(f\"   Run ID: {run.info.run_id}\")\n",
-        "    print(f\"   Validation MAPE: {mape_val}\")\n",
-        "    print()"
+      "text/plain": [
+       "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "22685423",
-      "metadata": {},
-      "source": [
-        "### Load the Best Model"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Per-series breakdown (metrics_per_series.json):\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "id": "7b09db6a",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.497496Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.572540Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Loading best model from run: exponential-smoothing-baseline\n",
-            "Model URI: models:/m-e61e8518e2a04143ace1d627ed4a775b\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>series_index</th>\n",
+       "      <th>step</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>mae</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47.458874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>mae</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>55.172880</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
-        "\n",
-        "if runs:\n",
-        "    best_run = runs[0]\n",
-        "    # the model is logged via MLflow's logged-model API, so resolve its model_id\n",
-        "    # from the run outputs and load it with a models:/ URI\n",
-        "    model_outputs = mlflow.get_run(best_run.info.run_id).outputs.model_outputs\n",
-        "    best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
-        "\n",
-        "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
-        "    print(f\"Model URI: {best_model_uri}\")\n",
-        "\n",
-        "    best_model = load_model(best_model_uri)\n",
-        "    # global models (e.g. LinearRegression, NBEATS) need the series at predict time\n",
-        "    # because we save with clean=True; local models (e.g. ExponentialSmoothing) don't\n",
-        "    if isinstance(best_model, GlobalForecastingModel):\n",
-        "        best_predictions = best_model.predict(n=len(val), series=train)\n",
-        "    else:\n",
-        "        best_predictions = best_model.predict(n=len(val))\n",
-        "\n",
-        "    train[-50:].plot(label=\"Training\")\n",
-        "    val.plot(label=\"Actual\")\n",
-        "    best_predictions.plot(label=\"Best Model Forecast\")\n",
-        "    plt.legend()\n",
-        "    plt.title(\"Best Model Predictions\")\n",
-        "    plt.show()"
+      "text/plain": [
+       "   key  series_index  step      value\n",
+       "0  mae             0     0  47.458874\n",
+       "1  mae             1     0  55.172880"
       ]
-    },
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# build a small multi-series example from the univariate AirPassengers data\n",
+    "series_list = [train, train * 1.2]\n",
+    "val_list = [val, val * 1.2]\n",
+    "\n",
+    "multi_model = LinearRegressionModel(lags=12)\n",
+    "multi_model.fit(series_list)\n",
+    "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
+    "\n",
+    "autolog(log_metrics=True)\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"metric-shape-and-table\") as run:\n",
+    "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
+    "    # under a single key, charted across MLflow steps\n",
+    "    single_pred = multi_preds[0]  # train == series_list[0]\n",
+    "    metrics.ae(val, single_pred)\n",
+    "\n",
+    "    # multiple series: the logged value is the MEAN over series, and the full\n",
+    "    # per-series breakdown is appended to the run's table artifact\n",
+    "    per_series_mae = metrics.mae(val_list, multi_preds)\n",
+    "\n",
+    "autolog(disable=True)\n",
+    "\n",
+    "client = mlflow.tracking.MlflowClient()\n",
+    "run_id = run.info.run_id\n",
+    "\n",
+    "# aggregate metrics: mae is the mean over the two series\n",
+    "logged = client.get_run(run_id).data.metrics\n",
+    "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
+    "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
+    "\n",
+    "# load the per-series table artifact\n",
+    "per_series_df = mlflow.load_table(\n",
+    "    artifact_file=\"metrics_per_series.json\", run_ids=[run_id]\n",
+    ")\n",
+    "print(\"\\nPer-series breakdown (metrics_per_series.json):\")\n",
+    "per_series_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8511fc08",
+   "metadata": {},
+   "source": [
+    "## Saving and Loading Models Locally\n",
+    "\n",
+    "You can also save and load models to/from local paths without MLflow runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "645ef079",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.471178Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.478682Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "6f1ec89e",
-      "metadata": {},
-      "source": [
-        "## Model Registry\n",
-        "\n",
-        "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived).\n",
-        "\n",
-        "```python\n",
-        "# Register a model from a run\n",
-        "result = mlflow.register_model(\n",
-        "    model_uri=f\"runs:/{best_run.info.run_id}/model\",\n",
-        "    name=\"darts-air-passengers\",\n",
-        ")\n",
-        "print(f\"Registered version: {result.version}\")\n",
-        "```\n",
-        "\n",
-        "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
-        "\n",
-        "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
-        ">\n",
-        "![Mlflow Charts](./static/images/mlflow_models.png)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/07/23 18:50:10 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "7af49ea9",
-      "metadata": {},
-      "source": [
-        "## Important Note: Custom Flavor\n",
-        "\n",
-        "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
-        "\n",
-        "**Always use:**\n",
-        "```python\n",
-        "from darts.utils.mlflow import load_model\n",
-        "model = load_model(model_uri)\n",
-        "```\n",
-        "\n",
-        "**Instead of:**\n",
-        "```python\n",
-        "import mlflow\n",
-        "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
-        "```\n",
-        "\n",
-        "This custom flavor is necessary to properly handle:\n",
-        "- TimeSeries objects\n",
-        "- Darts-specific model parameters\n",
-        "- Covariate handling (past, future, static)\n",
-        "- PyTorch model state preservation"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Files in model directory:\n",
+      "  - python_env.yaml\n",
+      "  - requirements.txt\n",
+      "  - MLmodel\n",
+      "  - model.pkl\n",
+      "  - conda.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save model to local directory\n",
+    "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
+    "save_model(model, path=local_model_path)\n",
+    "\n",
+    "print(\"\\nFiles in model directory:\")\n",
+    "for file in os.listdir(local_model_path):\n",
+    "    print(f\"  - {file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "254ba153",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.479604Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.484973Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "40621b13",
-      "metadata": {},
-      "source": [
-        "## Cleanup"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded model successfully!\n",
+      "Predictions shape: (5, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load model from local directory\n",
+    "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
+    "\n",
+    "# Test it\n",
+    "local_predictions = local_loaded_model.predict(n=5)\n",
+    "print(\"Loaded model successfully!\")\n",
+    "print(f\"Predictions shape: {local_predictions.values().shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab0d1e0",
+   "metadata": {},
+   "source": [
+    "## Querying Experiments\n",
+    "\n",
+    "You can programmatically query and compare runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "109a9812",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.485859Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.496380Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 19,
-      "id": "51fc7c4a",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.573637Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.575115Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpvwnlx3yj\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Uncomment to cleanup\n",
-        "# import shutil\n",
-        "# shutil.rmtree(tmpdir)\n",
-        "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
-        "\n",
-        "print(f\"To cleanup manually, delete: {tmpdir}\")"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 5 runs in experiment 'darts-quickstart':\n",
+      "\n",
+      "1. exponential-smoothing-baseline\n",
+      "   Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
+      "   Validation MAPE: 7.864181481214469\n",
+      "\n",
+      "2. linear-regression-full-metrics\n",
+      "   Run ID: 708095048f274913990375bbbd4310ad\n",
+      "   Validation MAPE: 10.742044444678953\n",
+      "\n",
+      "3. linear-regression-autolog\n",
+      "   Run ID: 36376b7a94454a13a4acba207459315e\n",
+      "   Validation MAPE: 10.742044444678953\n",
+      "\n",
+      "4. nbeats-epoch-metrics\n",
+      "   Run ID: f0d1b0ed9ac34232b5e36fe82a24a0a6\n",
+      "   Validation MAPE: 13.639569217869525\n",
+      "\n",
+      "5. metric-shape-and-table\n",
+      "   Run ID: 9be3e9d311b64d45b0204174835537cc\n",
+      "   Validation MAPE: N/A\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mlflow.tracking import MlflowClient\n",
+    "\n",
+    "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
+    "\n",
+    "# get all runs\n",
+    "client = MlflowClient()\n",
+    "runs = client.search_runs(\n",
+    "    experiment_ids=[experiment.experiment_id],\n",
+    "    order_by=[\"metrics.mape ASC\"],  # Sort by best MAPE\n",
+    ")\n",
+    "\n",
+    "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
+    "for i, run in enumerate(runs, 1):\n",
+    "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
+    "    mape_val = run.data.metrics.get(\"mape\", \"N/A\")\n",
+    "    print(f\"{i}. {run_name}\")\n",
+    "    print(f\"   Run ID: {run.info.run_id}\")\n",
+    "    print(f\"   Validation MAPE: {mape_val}\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22685423",
+   "metadata": {},
+   "source": [
+    "### Load the Best Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7b09db6a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.497496Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.572540Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "ca40afc1",
-      "metadata": {},
-      "source": [
-        "# Final Remarks"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading best model from run: exponential-smoothing-baseline\n",
+      "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "c4c86a23",
-      "metadata": {},
-      "source": [
-        "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": ".venv (3.11.9)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.9"
+   ],
+   "source": [
+    "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
+    "\n",
+    "if runs:\n",
+    "    best_run = runs[0]\n",
+    "    # the model is logged via MLflow's logged-model API, so resolve its model_id\n",
+    "    # from the run outputs and load it with a models:/ URI\n",
+    "    model_outputs = mlflow.get_run(best_run.info.run_id).outputs.model_outputs\n",
+    "    best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
+    "\n",
+    "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
+    "    print(f\"Model URI: {best_model_uri}\")\n",
+    "\n",
+    "    best_model = load_model(best_model_uri)\n",
+    "    # global models (e.g. LinearRegression, NBEATS) need the series at predict time\n",
+    "    # because we save with clean=True; local models (e.g. ExponentialSmoothing) don't\n",
+    "    if isinstance(best_model, GlobalForecastingModel):\n",
+    "        best_predictions = best_model.predict(n=len(val), series=train)\n",
+    "    else:\n",
+    "        best_predictions = best_model.predict(n=len(val))\n",
+    "\n",
+    "    train[-50:].plot(label=\"Training\")\n",
+    "    val.plot(label=\"Actual\")\n",
+    "    best_predictions.plot(label=\"Best Model Forecast\")\n",
+    "    plt.legend()\n",
+    "    plt.title(\"Best Model Predictions\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f1ec89e",
+   "metadata": {},
+   "source": [
+    "## Model Registry\n",
+    "\n",
+    "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "7c854bc4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-22T08:27:04.144511Z",
+     "iopub.status.busy": "2026-07-22T08:27:04.144439Z",
+     "iopub.status.idle": "2026-07-22T08:27:04.166315Z",
+     "shell.execute_reply": "2026-07-22T08:27:04.165897Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Registered version: 1\n"
+     ]
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Successfully registered model 'darts-air-passengers'.\n",
+      "Created version '1' of model 'darts-air-passengers'.\n"
+     ]
     }
+   ],
+   "source": [
+    "# register the best model (from the \"Load the Best Model\" section above)\n",
+    "result = mlflow.register_model(\n",
+    "    model_uri=best_model_uri,\n",
+    "    name=\"darts-air-passengers\",\n",
+    ")\n",
+    "print(f\"Registered version: {result.version}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da6cc83a",
+   "metadata": {},
+   "source": [
+    "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
+    "\n",
+    "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
+    ">\n",
+    "![Mlflow Charts](./static/images/mlflow_models.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7af49ea9",
+   "metadata": {},
+   "source": [
+    "## Important Note: Custom Flavor\n",
+    "\n",
+    "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
+    "\n",
+    "**Always use:**\n",
+    "```python\n",
+    "from darts.utils.mlflow import load_model\n",
+    "model = load_model(model_uri)\n",
+    "```\n",
+    "\n",
+    "**Instead of:**\n",
+    "```python\n",
+    "import mlflow\n",
+    "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
+    "```\n",
+    "\n",
+    "This custom flavor is necessary to properly handle:\n",
+    "- TimeSeries objects\n",
+    "- Darts-specific model parameters\n",
+    "- Covariate handling (past, future, static)\n",
+    "- PyTorch model state preservation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40621b13",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "51fc7c4a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
+     "iopub.status.busy": "2026-06-24T15:18:53.573637Z",
+     "iopub.status.idle": "2026-06-24T15:18:53.575115Z",
+     "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Uncomment to cleanup\n",
+    "# import shutil\n",
+    "# shutil.rmtree(tmpdir)\n",
+    "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
+    "\n",
+    "print(f\"To cleanup manually, delete: {tmpdir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca40afc1",
+   "metadata": {},
+   "source": [
+    "# Final Remarks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4c86a23",
+   "metadata": {},
+   "source": [
+    "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
+   ]
+  }
+ ],
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+   "display_name": ".venv (3.11.9)",
+   "language": "python",
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+       "value": " 1/1 [00:00&lt;00:00, 191.28it/s]"
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+       "_view_module": "@jupyter-widgets/base",
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+ "nbformat_minor": 5
 }

From 10cf6a6e240e2ba9377c85b344dd09b81d6e1a5f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 23 Jul 2026 19:20:01 +0200
Subject: [PATCH 122/154] fix: restrict component count only when not comp
 reduction

---
 darts/tests/optional_deps/test_mlflow.py | 51 ++++++++++++++++---
 darts/utils/mlflow.py                    | 62 +++++++++++++-----------
 2 files changed, 78 insertions(+), 35 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index fc7ffe4ca0..4c35ab1dd8 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1104,19 +1104,33 @@ def test_autolog_metric_multi_series_classification_labels_explicit(
             assert got[("f1_label0", i)] == pytest.approx(ref[i][0], abs=1e-5)
             assert got[("f1_label1", i)] == pytest.approx(ref[i][1], abs=1e-5)
 
+    def test_autolog_metric_component_count_mismatch_allowed_when_reduced(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Default mae reduces components to scalars, so mixed component counts
+        are valid and log under a single aggregated key."""
+        series = [self.ts_univariate, self.ts_multivariate]
+        pred = [s * 1.1 for s in series]
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.mae(series, pred)
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["mae"] == pytest.approx(float(np.mean(ref)), abs=1e-5)
+
     def test_autolog_metric_component_count_mismatch_raises(
         self, mlflow_tracking, autolog_context
     ):
-        """A list of series with different numbers of components raises rather
-        than taking component names from the first series only and silently
-        mislabeling the rest."""
+        """When components are preserved, mixed component counts raise rather
+        than taking names from the first series and mislabeling the rest."""
         series = [self.ts_univariate, self.ts_multivariate]
         pred = [s * 1.1 for s in series]
 
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
                 with pytest.raises(ValueError, match="same number of components"):
-                    dm.mae(series, pred)
+                    dm.mae(series, pred, component_reduction=None)
 
         assert not mlflow.get_run(run.info.run_id).data.metrics
 
@@ -1460,18 +1474,41 @@ def test_autolog_backtest_classification_labels_not_in_data(
         assert np.isnan(m["backtest_f1_label5"])
         assert np.isnan(m["backtest_f1_label10"])
 
+    def test_log_backtest_metrics_component_count_mismatch_allowed_when_reduced(
+        self, mlflow_tracking
+    ):
+        """Default mae reduces components to scalars, so mixed component counts
+        aggregate normally. Calls _log_backtest_metrics directly."""
+        backtest_args = {
+            "metric": dm.mae,
+            "metric_kwargs": {},
+            "series": [self.ts_univariate, self.ts_multivariate],
+            "forecast_horizon": 1,
+            "reduction": np.mean,
+            "last_points_only": True,
+        }
+        result = [1.0, 3.0]
+
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            _log_backtest_metrics(client, run.info.run_id, result, backtest_args)
+            client.flush(synchronous=True)
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["backtest_mae"] == pytest.approx(2.0)
+
     def test_log_backtest_metrics_component_count_mismatch_raises(
         self, mlflow_tracking
     ):
-        """A list of series with different numbers of components raises rather
-        than taking component names from the first series only.
+        """When components are preserved, mixed component counts raise rather
+        than taking names from the first series only.
 
         Calls _log_backtest_metrics directly so the raise is not swallowed by
         MLflow's safe_patch wrapper.
         """
         backtest_args = {
             "metric": dm.mae,
-            "metric_kwargs": {},
+            "metric_kwargs": {"component_reduction": None},
             "series": [self.ts_univariate, self.ts_multivariate],
             "forecast_horizon": 1,
             "reduction": np.mean,
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 1b0956871d..0a5db5968b 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -370,9 +370,10 @@ def autolog(
         MLflow ``step``. For a list of series the logged value is
         ``agg_func`` applied over series, and the full per-series breakdown
         for every metric/backtest call in the run is appended to a single
-        ``metrics_per_series.json`` table artifact. All series scored
-        together in one call must have the same number of components;
-        component names are taken from the first series.
+        ``metrics_per_series.json`` table artifact. When components are
+        preserved (``component_reduction=None``), all series scored together
+        must have the same number of components; names are taken from the
+        first series.
 
     Parameters
     ----------
@@ -884,9 +885,10 @@ def _log_backtest_metrics(
     applied over series for each cell, and the granular per-series breakdown
     is appended to the run's ``metrics_per_series.json`` table artifact
     (shared with ``_log_metric_result``). For a single series the aggregate
-    is just the value itself and no artifact is written. All series scored
-    together must have the same number of components; names are taken from
-    the first series.
+    is just the value itself and no artifact is written. When components
+    are preserved (``component_reduction=None``), all series scored together
+    must have the same number of components; names are taken from the first
+    series.
 
     Series of different lengths are assumed to share the same end date, so any
     axis mapping to real dates (the window axis, or the per-timestep axis
@@ -898,9 +900,9 @@ def _log_backtest_metrics(
     ------
     ValueError
         On a shape/size mismatch between the metric result and the inferred
-        axes, when series in a sequence have different numbers of components,
-        or when ``label_reduction=None`` is requested without explicit
-        ``labels``.
+        axes, when ``component_reduction=None`` and series in a sequence have
+        different numbers of components, or when ``label_reduction=None`` is
+        requested without explicit ``labels``.
 
     Parameters
     ----------
@@ -982,14 +984,16 @@ def _log_backtest_metrics(
 
     series_seq = series2seq(series)
     results = [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
-    n_components = {s.n_components for s in series_seq}
-    if len(n_components) > 1:
-        raise_log(
-            ValueError(
-                "Backtest metric logging failed: all series must have the same "
-                f"number of components, got {sorted(n_components)}."
+    # component names are only used when the metric preserves components
+    if has_comp_axis:
+        n_components = {s.n_components for s in series_seq}
+        if len(n_components) > 1:
+            raise_log(
+                ValueError(
+                    "Backtest metric logging failed: all series must have the same "
+                    f"number of components, got {sorted(n_components)}."
+                )
             )
-        )
 
     # agg maps (key, step) -> per-series values, aggregated into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
@@ -1255,8 +1259,8 @@ def _log_metric_result(
     ------
     ValueError
         On a shape/size mismatch between the metric result and the inferred
-        axes, or when series in a sequence have different numbers of
-        components.
+        axes, or when ``has_comp_axis`` is ``True`` and series in a sequence
+        have different numbers of components.
 
     Parameters
     ----------
@@ -1268,8 +1272,8 @@ def _log_metric_result(
     series
         The ``actual_series`` argument passed to the metric (single series or
         ``Sequence[TimeSeries]``); used for component names and series count.
-        All series in a sequence must have the same number of components;
-        names are taken from the first series.
+        When ``has_comp_axis`` is ``True``, all series in a sequence must have
+        the same number of components; names are taken from the first series.
     has_time_axis
         ``True`` when the result carries a per-timestep axis (``time_reduction=None``).
     has_comp_axis
@@ -1295,15 +1299,17 @@ def _log_metric_result(
         results = (
             [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
         )
-        n_components = {s.n_components for s in series_seq}
-        if len(n_components) > 1:
-            raise_log(
-                ValueError(
-                    f"Metric logging failed for `{metric_name}`: all series must "
-                    f"have the same number of components, got "
-                    f"{sorted(n_components)}."
+        # component names are only used when the metric preserves components
+        if has_comp_axis:
+            n_components = {s.n_components for s in series_seq}
+            if len(n_components) > 1:
+                raise_log(
+                    ValueError(
+                        f"Metric logging failed for `{metric_name}`: all series must "
+                        f"have the same number of components, got "
+                        f"{sorted(n_components)}."
+                    )
                 )
-            )
 
     # component names/count from the first series (all series share n_components)
     comps = series_seq[0].components.tolist()

From 9c547aa2d32fc7b636fd106495af55a16629e2b9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Thu, 23 Jul 2026 19:31:15 +0200
Subject: [PATCH 123/154] fix: log model params fix

---
 darts/utils/mlflow.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 0a5db5968b..377276d10e 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -318,8 +318,15 @@ def log_model(model, **kwargs):
     are used to support serving and input validation in the MLflow pyfunc
     flavor, which is not implemented for darts models.
     """
+    # MLflow still requires "artifact_path" to be provided (it has no default),
+    # but it is deprecated in favour of "name". Accept it via kwargs for
+    # compatibility, defaulting to None so callers can use "name" alone.
+    artifact_path = kwargs.pop("artifact_path", None)
     return Model.log(
-        artifact_path=None, flavor=sys.modules[__name__], model=model, **kwargs
+        artifact_path,
+        flavor=sys.modules[__name__],
+        model=model,
+        **kwargs,
     )
 
 

From b9e6488d6113446a51a5abc2ef07a79bbe94d390 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Thu, 30 Jul 2026 17:47:13 +0200
Subject: [PATCH 124/154] minor updates to docs

---
 darts/utils/mlflow.py | 203 ++++++++++++++++++++++++------------------
 1 file changed, 114 insertions(+), 89 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 377276d10e..91fcc36559 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -6,12 +6,12 @@
 and logging any Darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
 to MLflow, as well as automatic logging (``autolog()``) of:
 
-* Model creation parameters and covariate usage information.
-* The trained model artifact after each ``fit()`` call when ``log_models=True``
-  (default ``False``).
-* Darts metric function calls made inside an active MLflow run.
-* ``backtest()`` evaluation metrics.
-* Per-epoch training/validation metrics for PyTorch-based models.
+- Model parameters and data metadata: The model creation parameters and covariate usage information.
+- Model storage: The trained model artifact after each ``fit()`` call when ``log_models=True`` (default ``False``).
+- Metrics:
+  - The score for each metric called inside an active MLflow run.
+  - The score(s) from a ``backtest()`` call.
+  - Per-epoch training/validation metrics for PyTorch-based models.
 
 See the `MLflow quickstart example <https://github.com/unit8co/darts/blob/master/examples/29-MLflow-quickstart.ipynb>`_
 for an end-to-end walkthrough.
@@ -30,6 +30,7 @@
 from typing import Any
 
 from darts.logging import raise_log
+from darts.typing import TimeSeriesLike
 
 try:
     import mlflow
@@ -79,6 +80,7 @@
 from mlflow.utils.requirements_utils import _get_pinned_requirement
 
 import darts
+from darts import TimeSeries
 from darts.logging import get_logger, raise_log
 from darts.metrics.utils import (
     _LabelReduction,
@@ -115,7 +117,7 @@
 
 
 def save_model(
-    model,
+    model: ForecastingModel,
     path: str,
     conda_env: dict | str | None = None,
     code_paths: list[str] | None = None,
@@ -126,18 +128,18 @@ def save_model(
     extra_pip_requirements: list[str] | None = None,
     metadata: dict[str, Any] | None = None,
 ) -> None:
-    """Save a darts forecasting model in MLflow format.
+    """Save a Darts forecasting model in MLflow format.
 
     Produces an MLflow model directory at ``path`` containing:
 
-    * The serialised darts model (delegated to the model's own ``save()`` method).
-    * An ``MLmodel`` YAML file with flavor metadata.
-    * ``conda.yaml`` and ``requirements.txt`` environment files.
+    - The serialized Darts model (delegated to the model's own ``save()`` method).
+    - An ``MLmodel`` YAML file with flavor metadata.
+    - ``conda.yaml`` and ``requirements.txt`` environment files.
 
     Parameters
     ----------
     model
-        A fitted darts ``ForecastingModel`` instance.
+        A fitted Darts ``ForecastingModel`` instance.
     path
         Local filesystem path where the model directory will be created.
     conda_env
@@ -168,7 +170,7 @@ def save_model(
     -----
     Signature and input_example params are currently not supported, as they
     are used to support serving and input validation in the MLflow pyfunc flavor,
-    which is not implemented for darts models. They are accepted as params for
+    which is not implemented for Darts models. They are accepted as params for
     simplifying potential future extensibility, and to keep in line with MLflow API
     conventions.
     """
@@ -244,7 +246,7 @@ def load_model(
     dst_path: str | None = None,
     **kwargs,
 ) -> ForecastingModel:
-    """Load a darts model from an MLflow model URI.
+    """Load a Darts model from an MLflow model URI.
 
     Parameters
     ----------
@@ -255,12 +257,12 @@ def load_model(
         Optional local path for downloading remote artifacts.
     **kwargs
         Additional keyword arguments forwarded to the model's ``load()`` method
-        (e.g. ``map_location`` for Torch models).
+        (e.g. ``map_location`` for a `TorchForecastingModel`).
 
     Returns
     -------
     ForecastingModel
-        The loaded darts forecasting model.
+        The loaded Darts forecasting model.
     """
     local_path = _download_artifact_from_uri(
         artifact_uri=model_uri, output_path=dst_path
@@ -286,11 +288,11 @@ def load_model(
     return model_cls.load(str(model_path), **kwargs)
 
 
-def log_model(model, **kwargs):
-    """Log a darts model to the current MLflow run, using the darts MLflow flavor.
+def log_model(model: ForecastingModel, **kwargs):
+    """Log a Darts model to the current MLflow run, using the Darts MLflow flavor.
 
     This is a thin wrapper around ``mlflow.models.Model.log()`` that supplies
-    the darts flavor for saving/loading; every other argument is forwarded
+    the Darts flavor for saving/loading; every other argument is forwarded
     as-is. See the `MLflow documentation
     <https://mlflow.org/docs/latest/api_reference/python_api/mlflow.models.html#mlflow.models.Model.log>`_
     for the full list of accepted parameters (e.g. ``name``,
@@ -300,7 +302,7 @@ def log_model(model, **kwargs):
     Parameters
     ----------
     model
-        A fitted darts ``ForecastingModel`` instance.
+        A fitted Darts ``ForecastingModel`` instance.
     **kwargs
         Forwarded to ``mlflow.models.Model.log()``. Use ``name`` to set the
         run-relative artifact path. ``artifact_path`` parameter is deprecated
@@ -316,7 +318,7 @@ def log_model(model, **kwargs):
     -----
     ``signature`` and ``input_example`` are currently not supported, as they
     are used to support serving and input validation in the MLflow pyfunc
-    flavor, which is not implemented for darts models.
+    flavor, which is not implemented for Darts models.
     """
     # MLflow still requires "artifact_path" to be provided (it has no default),
     # but it is deprecated in favour of "name". Accept it via kwargs for
@@ -339,24 +341,24 @@ def autolog(
     disable: bool = False,
     silent: bool = False,
 ) -> None:
-    """Enable (or disable) automatic MLflow logging for darts models.
-
-    When enabled, every call to ``model.fit()`` on any darts forecasting model
-    will automatically:
-
-    1. Start an MLflow run (or reuse the currently active one).
-    2. Log model creation parameters (``model.model_params``), both as MLflow
-       params and as a ``model_params.json`` artifact.
-    3. Log covariate usage information (past, future, and static covariates).
-    4. Log the result of any darts metric call made inside an active MLflow
-       run. Repeated calls overwrite the previous value.
-    5. For PyTorch-based models: leverage ``mlflow.pytorch.autolog()`` to
-       automatically log per-epoch training and validation metrics.
-    6. When ``log_models=True``: log the trained model artifact at the end of
-       training (default ``False``).
-    7. Patch ``backtest()`` to log evaluation metrics under ``backtest_*`` keys.
-    8. Patch ``historical_forecasts()`` so that its internal per-window
-       ``fit()`` calls don't each spawn their own logging.
+    """Enable (or disable) automatic MLflow logging for Darts.
+
+    When enabled, the following functionalities emit detailed logs:
+
+    - Calling ``ForecastingModel.fit()``:
+      - Starts an MLflow run (or reuses the currently active one).
+      - Logs model creation parameters (``model.model_params``), both as MLflow
+        params and as a ``model_params.json`` artifact.
+      - Logs covariate usage information (past, future, and static covariates).
+      - Stores the trained model artifact when ``log_models=True`` (default:
+        ``False``).
+      - Logs per-epoch training and validation metrics for PyTorch-based models.
+    - Calling any Darts metric:
+      - Logs the result of that metric call as an MLflow metric. More information
+        in the notes below.
+    - Calling ``ForecastingModel.backtest()``:
+      - Logs all evaluation metrics under ``backtest_*`` keys. More information
+        in the notes below.
 
     .. note::
 
@@ -364,23 +366,32 @@ def autolog(
         ``{metric_name}{component}{quantile_or_label}``, where each part is
         included only when the corresponding axis is present:
 
-        * ``metric_name`` – the metric function name, or the ``name`` keyword
-          argument when provided (it overrides only this token).
-        * ``component`` – the component name when ``component_reduction=None``.
-        * ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi_80.000`` / ``_label1``.
+        - ``metric_name`` – the metric function name, or the ``name`` metric
+          keyword argument when provided.
+        - ``component`` – the component name when ``component_reduction=None``.
+        - ``quantile_or_label``, e.g.:
+          – ``_q0.500`` for quantile metrics with keyword argument ``q=[0.5]``
+          - ``_qi_80.000`` for quantile interval metrics with keyword argument
+            ``q_interval=[(0.1, 0.9)]`` (80% interval between quantiles 0.1 and
+            0.9).
+          - ``_label1`` for classification metrics with keyword argument
+            ``labels`` when ``label_reduction=None``.
+
+        Per-timestep metrics (``time_reduction=None``) are charted across the
+        MLflow ``step``.
 
         When ``series_reduction`` is set on a metric call, results are already
         aggregated across series inside the metric itself, so the
         cross-series aggregation described below does not apply.
 
-        Per-timestep results (``time_reduction=None``) are charted across the
-        MLflow ``step``. For a list of series the logged value is
-        ``agg_func`` applied over series, and the full per-series breakdown
-        for every metric/backtest call in the run is appended to a single
-        ``metrics_per_series.json`` table artifact. When components are
-        preserved (``component_reduction=None``), all series scored together
-        must have the same number of components; names are taken from the
-        first series.
+        For a list of series, the logged metric is aggregated over all series
+        using ``agg_func``. The detailed per-series metrics / backtest metrics
+        are logged under a single ``metrics_per_series.json`` table
+        artifact.
+
+        When components are preserved (``component_reduction=None``), all
+        series scored together must have the same number of components; names
+        are taken from the first series.
 
     Parameters
     ----------
@@ -390,7 +401,7 @@ def autolog(
     log_params
         If ``True`` (default), log model creation parameters.
     log_metrics
-        If ``True`` (default), log the result of any darts metric call made
+        If ``True`` (default), log the result of any Darts metric call made
         inside an active MLflow run.
     log_torch_metrics
         If ``True`` (default), enable ``mlflow.pytorch.autolog(log_models=False)``
@@ -415,7 +426,7 @@ def autolog(
     # mlflow.sklearn, which exposes a private, undecorated _autolog(flavor_name=...)
     # that other flavors (e.g. xgboost) call to tag its patches under their own
     # integration name for cleanup, mlflow.pytorch has no such hook: its autolog()
-    # hardcodes its own patches under "pytorch", so darts can't fold pytorch's
+    # hardcodes its own patches under "pytorch", so Darts can't fold pytorch's
     # patch lifecycle into its own and must call mlflow.pytorch.autolog() directly.
     if log_torch_metrics and not disable:
         try:
@@ -487,7 +498,7 @@ def _autolog(
 ) -> None:
     """Internal autolog implementation decorated with ``@autologging_integration``.
 
-    Handles patching of darts ``ForecastingModel.fit()`` and metric functions.
+    Handles patching of Darts ``ForecastingModel.fit()`` and metric functions.
     The ``mlflow.pytorch.autolog`` coordination is handled by the public
     ``autolog()`` wrapper because the decorator short-circuits on
     ``disable=True``.
@@ -549,7 +560,7 @@ def _patched_fit(original, self, *args, **kwargs):
             fit_args = inspect.signature(original).bind(self, *args, **kwargs).arguments
             _log_covariate_info(
                 self,
-                series=fit_args.get("series"),
+                series=fit_args["series"],
                 past_covariates=fit_args.get("past_covariates"),
                 future_covariates=fit_args.get("future_covariates"),
             )
@@ -619,10 +630,10 @@ def _patched_backtest(original, self, *args, **kwargs):
 
         autologging_client = MlflowAutologgingQueueingClient()
         _log_backtest_metrics(
-            autologging_client,
-            active_run.info.run_id,
-            result,
-            backtest_args,
+            autologging_client=autologging_client,
+            run_id=active_run.info.run_id,
+            result=result,
+            backtest_args=backtest_args,
             agg_func=agg_func,
         )
         autologging_client.flush(synchronous=False).await_completion()
@@ -658,7 +669,7 @@ def _patched_backtest(original, self, *args, **kwargs):
 
 
 def get_default_pip_requirements():
-    """Return the default pip requirements for logging a darts model.
+    """Return the default pip requirements for logging a Darts model.
 
     Returns
     -------
@@ -670,7 +681,7 @@ def get_default_pip_requirements():
 
 
 def get_default_conda_env():
-    """Return a default conda environment dict for a darts model.
+    """Return a default conda environment dict for a Darts model.
 
     Returns
     -------
@@ -703,7 +714,12 @@ def _get_model_info_tags(model: ForecastingModel) -> dict[str, Any]:
     }
 
 
-def _log_covariate_info(model, series, past_covariates, future_covariates) -> None:
+def _log_covariate_info(
+    model: ForecastingModel,
+    series: TimeSeriesLike,
+    past_covariates: TimeSeriesLike | None,
+    future_covariates: TimeSeriesLike | None,
+) -> None:
     """Log covariate usage information to MLflow.
 
     Extracts information about past, future, and static covariates used during
@@ -776,7 +792,9 @@ def _is_torch_model(model) -> bool:
     return TORCH_AVAILABLE and isinstance(model, TorchForecastingModel)
 
 
-def _extract_covariate_metadata(uses: bool, single_cov, names_attr: str) -> dict:
+def _extract_covariate_metadata(
+    uses: bool, single_cov: TimeSeries | pd.DataFrame | None, names_attr: str
+) -> dict:
     """Extract metadata for a single covariate type from its (already
     singular) value.
 
@@ -861,7 +879,7 @@ def _log_backtest_metrics(
     """Log backtest metric result(s) to MLflow.
 
     Reshapes each per-series result to a canonical ``(W, T, C, M)`` layout
-    (windows, timesteps, components × quantiles, metrics) inferred from the
+    (windows, timesteps, components x quantiles, metrics) inferred from the
     metric signatures and ``backtest_args``, logging every cell under a
     descriptive key with the time axis (or window axis when time is reduced)
     mapped to the MLflow ``step``. A metric's ``name`` entry in
@@ -870,18 +888,18 @@ def _log_backtest_metrics(
 
     Shape inference respects all kwargs that affect output dimensions:
 
-    * ``time_reduction`` – collapses the time axis (``T=1``).
-    * ``component_reduction`` – collapses the component axis (``C=1``).
-    * ``series_reduction`` – if other than ``None``, windows are already aggregated
+    - ``time_reduction`` – collapses the time axis (``T=1``).
+    - ``component_reduction`` – collapses the component axis (``C=1``).
+    - ``series_reduction`` – if other than ``None``, windows are already aggregated
       inside the metric, so ``W=1`` regardless of ``backtest.reduction``.
-    * ``q`` / ``q_interval`` – expand the component axis with one entry per
+    - ``q`` / ``q_interval`` – expand the component axis with one entry per
       quantile / interval.
-    * ``labels`` - expand each component with one entry per label along
+    - ``labels`` - expand each component with one entry per label along
       the component axis.
-    * ``label_reduction`` – collapses the labels along the component axis.
+    - ``label_reduction`` – collapses the labels along the component axis.
       value; ``labels`` only restricts which classes are scored.
-    * ``reduction=None`` – no aggregation across windows -> one value per window.
-    * ``last_points_only`` – collapses all windows into one TimeSeries before scoring,
+    - ``reduction=None`` – no aggregation across windows -> one value per window.
+    - ``last_points_only`` – collapses all windows into one TimeSeries before scoring,
       so there is effectively only one window regardless of reduction.
 
     When two metrics have incompatible axis layouts (different
@@ -981,7 +999,7 @@ def _log_backtest_metrics(
     last_points_only = backtest_args.get("last_points_only", False)
 
     # if last_points_only is True, has_windows will be False, so fc_hzn is not needed
-    if historical_forecasts and not last_points_only:
+    if historical_forecasts is not None and not last_points_only:
         first_series_hf = (
             historical_forecasts
             if get_series_seq_type(series) == SeriesType.SINGLE
@@ -998,7 +1016,9 @@ def _log_backtest_metrics(
             raise_log(
                 ValueError(
                     "Backtest metric logging failed: all series must have the same "
-                    f"number of components, got {sorted(n_components)}."
+                    f"number of components, got {sorted(n_components)}. Consider "
+                    f"setting a metric `component_reduction`, or make sure all series "
+                    f"have the same number of components."
                 )
             )
 
@@ -1023,15 +1043,15 @@ def _log_backtest_metrics(
     else:
         # component names/count from the first series (all series share n_components)
         comps = series_seq[0].components.tolist()
-        # c_size = components × quantiles/intervals/labels per component
-        c_size = (series_seq[0].n_components if has_comp_axis else 1) * axis_size
+        # c_size = components x quantiles/intervals/labels per component
+        c_size = (len(comps) if has_comp_axis else 1) * axis_size
         # base_keys[m][c]: sanitized key without the optional window suffix
         base_keys = []
         for m, metric_name in enumerate(metric_names):
             axis_labels = metric_axes[m][2]
             keys_m = []
             for c in range(c_size):
-                # c is a flat index into the (n_components × axis_size) C axis:
+                # c is a flat index into the (n_components x axis_size) C axis:
                 # c = comp_i * axis_size + axis_idx
                 component_index, axis_idx = divmod(c, axis_size)
                 comp_part = (
@@ -1156,7 +1176,7 @@ def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
     Parameters
     ----------
     metric
-        A darts metric callable.
+        A Darts metric callable.
     metric_kwargs
         Keyword arguments that will be forwarded to ``metric``.
 
@@ -1235,7 +1255,7 @@ def _log_metric_result(
     """Log a metric result to the active MLflow run.
 
     Reshapes each per-series result into a canonical ``(T, C)`` layout
-    (timesteps, components × quantiles/intervals/labels) inferred from the
+    (timesteps, components x quantiles/intervals/labels) inferred from the
     metric signature and call kwargs by ``_infer_metric_axes``, logging every
     cell under a descriptive key with the time axis mapped to the MLflow
     ``step``. This mirrors ``_log_backtest_metrics`` (without the
@@ -1249,8 +1269,8 @@ def _log_metric_result(
     where each optional part is included only when the corresponding axis is
     present:
 
-    * ``component`` – ``_{component_name}`` when ``has_comp_axis``.
-    * ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi_80.000`` / ``_label1``.
+    - ``component`` – ``_{component_name}`` when ``has_comp_axis``.
+    - ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi_80.000`` / ``_label1``.
 
     When more than one series is scored, the logged value is ``agg_func``
     applied over series for each cell, and the granular per-series breakdown
@@ -1258,6 +1278,9 @@ def _log_metric_result(
     (shared with ``_log_backtest_metrics``). For a single series the
     aggregate is just the value itself and no artifact is written.
 
+    TODO: improve this, it's about predictions of different or different
+      intersection lengths between actual and pred (from the `Raises`
+      below, it sounds even that this wouldn't be supported?)
     Series of different lengths are assumed to share the same end date, so the
     time axis is aligned from the end rather than the start: a shorter series
     lines up on its last value instead of its first.
@@ -1318,13 +1341,14 @@ def _log_metric_result(
                     )
                 )
 
+    # TODO: a lot of this seems duplicated from the backtest logic; improve
     # component names/count from the first series (all series share n_components)
     comps = series_seq[0].components.tolist()
-    # c_size = components × quantiles/intervals/labels per component
-    c_size = (series_seq[0].n_components if has_comp_axis else 1) * axis_size
+    # c_size = components x quantiles/intervals/labels per component
+    c_size = (len(comps) if has_comp_axis else 1) * axis_size
     keys = []
     for c in range(c_size):
-        # c is a flat index into the (n_components × axis_size) C axis:
+        # c is a flat index into the (n_components x axis_size) C axis:
         # c = comp_i * axis_size + axis_idx
         component_index, axis_idx = divmod(c, axis_size)
         comp_part = (
@@ -1403,6 +1427,7 @@ def _log_metric_result(
         _log_per_series_table(rows)
 
 
+# TODO: (does the below TODO still apply? we only support logging under an active run I thought)
 # TODO: To log metrics post-fitting, only patching the metric methods may not be enough.
 # This is becuase `model.fit()` method would terminate the active MLflow run at the end of training,
 # so any metric calls made after that would not be logged.
@@ -1417,7 +1442,7 @@ def _log_metric_result(
 def _mlflow_metric_callback(func, result, args, kwargs) -> None:
     """Metric callback registered with ``darts.metrics.utils`` for autologging.
 
-    Invoked by ``multi_ts_support`` (the outermost decorator on every darts
+    Invoked by ``multi_ts_support`` (the outermost decorator on every Darts
     metric) after every top-level metric call, so it fires regardless of how
     the metric was imported. It is not invoked for internal metric-to-metric
     calls (e.g. ``rmse`` calling ``mse`` internally via ``_get_wrapped_metric``),
@@ -1431,10 +1456,10 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
 
     where:
 
-    * ``metric_name`` – the metric function name, or the ``name`` keyword
+    - ``metric_name`` – the metric function name, or the ``name`` keyword
       argument when provided (it overrides only this token).
-    * ``component`` – ``_{component_name}`` when ``component_reduction=None``.
-    * ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.500``,
+    - ``component`` – ``_{component_name}`` when ``component_reduction=None``.
+    - ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.500``,
       ``_qi_80.000``, ``_label1``) when applicable.
 
     When the input is a ``Sequence[TimeSeries]`` with more than one series, the
@@ -1448,7 +1473,7 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
     Parameters
     ----------
     func
-        The darts metric function that was called (used for its name and
+        The Darts metric function that was called (used for its name and
         signature).
     result
         The metric's return value.

From 9568b3d8a9b5fe9aebebae0ff418cd65ee5bef2a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 4 Aug 2026 10:15:22 +0200
Subject: [PATCH 125/154] feat: docs cleanup

---
 INSTALL.md            |  6 ++++++
 darts/utils/mlflow.py | 15 +++++++++++----
 2 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/INSTALL.md b/INSTALL.md
index f52f9c2708..302c7d7421 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -22,6 +22,12 @@ Some models have additional dependencies that are not included in the `all` inst
 | `NeuralForecastModel` | neuralforecast>=3.0.0 |
 | `TiRexModel`          | tirex-ts>=1.4.0       |
 
+Some optional integrations also require additional dependencies:
+
+| Integration            | Dependencies |
+|-------------------------|--------------|
+| `darts.utils.mlflow` | mlflow>=3.0  |
+
 
 ## From conda-forge
 Create a conda environment (e.g., for Python 3.11):
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 91fcc36559..e672d62e19 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -16,8 +16,9 @@
 See the `MLflow quickstart example <https://github.com/unit8co/darts/blob/master/examples/29-MLflow-quickstart.ipynb>`_
 for an end-to-end walkthrough.
 
-References:
-https://github.com/sktime/sktime/blob/main/sktime/utils/mlflow_sktime.py
+To keep auto-logged metrics comparable across runs, use the same evaluation
+time frame, forecast horizon, and evaluation start date for every
+``backtest()`` / metric call you intend to compare.
 """
 
 import inspect
@@ -37,8 +38,9 @@
 except ImportError:
     raise_log(
         ImportError(
-            "MLflow is required for `darts.utils.mlflow`. Install it with `pip"
-            " install mlflow`."
+            "The `mlflow` module could not be imported. To enable MLflow support "
+            "in Darts, follow the detailed instructions in the installation guide: "
+            "https://github.com/unit8co/darts/blob/master/INSTALL.md"
         )
     )
 
@@ -393,6 +395,11 @@ def autolog(
         series scored together must have the same number of components; names
         are taken from the first series.
 
+        Metric values are only comparable across runs when the evaluation
+        settings match. Use the same evaluation time frame, forecast horizon,
+        and evaluation start date for every ``backtest()`` / metric call you
+        intend to compare.
+
     Parameters
     ----------
     log_models

From 1b3ac1e31e88f3c1ce40f46e89c5411e3c51ecf5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 4 Aug 2026 10:34:50 +0200
Subject: [PATCH 126/154] docs: active run clarification

---
 darts/utils/mlflow.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index e672d62e19..4a31536dc8 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -347,18 +347,20 @@ def autolog(
 
     When enabled, the following functionalities emit detailed logs:
 
-    - Calling ``ForecastingModel.fit()``:
-      - Starts an MLflow run (or reuses the currently active one).
+    - Calling ``ForecastingModel.fit()`` inside an active MLflow run (e.g. within
+      ``with mlflow.start_run():``); does nothing if no run is active:
       - Logs model creation parameters (``model.model_params``), both as MLflow
         params and as a ``model_params.json`` artifact.
       - Logs covariate usage information (past, future, and static covariates).
       - Stores the trained model artifact when ``log_models=True`` (default:
         ``False``).
       - Logs per-epoch training and validation metrics for PyTorch-based models.
-    - Calling any Darts metric:
+    - Calling any Darts metric inside an active MLflow run; does nothing if no
+      run is active:
       - Logs the result of that metric call as an MLflow metric. More information
         in the notes below.
-    - Calling ``ForecastingModel.backtest()``:
+    - Calling ``ForecastingModel.backtest()`` inside an active MLflow run; does
+      nothing if no run is active:
       - Logs all evaluation metrics under ``backtest_*`` keys. More information
         in the notes below.
 
@@ -606,8 +608,10 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
         """Suppress per-iteration fit() autologging during historical_forecasts.
 
         Sets a thread-local flag so _patched_fit skips autologging for the
-        internal fit() calls. The outer safe_patch() on
-        historical_forecasts itself provides a single managed run.
+        internal fit() calls, so that at most the single top-level call site
+        (this patch, or an enclosing backtest()) logs anything. This patch
+        itself does not start or otherwise manage an MLflow run; it relies on
+        whatever run (if any) is already active.
         """
         _autolog_state.in_historical_forecasts = True
         try:

From efb6b9daa13aabcffd2e7ecbed4ad060a253786c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 4 Aug 2026 10:57:09 +0200
Subject: [PATCH 127/154] feat: log target series info and encoder covs

---
 darts/tests/optional_deps/test_mlflow.py | 74 +++++++++++++++---------
 darts/utils/mlflow.py                    | 61 ++++++++++++-------
 2 files changed, 87 insertions(+), 48 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 4c35ab1dd8..7c60fca582 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -378,10 +378,9 @@ def assert_metric(history, key):
 
         assert autologging_is_disabled("pytorch")
 
-    def test_autolog_covariate_info_single_series(
-        self, mlflow_tracking, autolog_context
-    ):
-        """The covariates.json artifact reports usage, count, and names for a
+    def test_autolog_series_info_single_series(self, mlflow_tracking, autolog_context):
+        """The series_info.json artifact reports the target series' component
+        names/count, plus covariate usage, count, and names, for a
         single-series fit."""
         with autolog_context():
             with mlflow.start_run() as run:
@@ -390,21 +389,21 @@ def test_autolog_covariate_info_single_series(
                     self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40]
                 )
 
-        covariates = mlflow.artifacts.load_dict(
-            f"runs:/{run.info.run_id}/covariates.json"
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
         )
-        assert covariates["past_covariates"]["used"] is True
-        assert covariates["past_covariates"]["count"] == 1
+        assert series_info["series"]["count"] == self.ts_univariate.n_components
+        assert series_info["series"]["names"] == self.ts_univariate.components.tolist()
+        assert series_info["past_covariates"]["used"] is True
+        assert series_info["past_covariates"]["count"] == 1
         assert (
-            covariates["past_covariates"]["names"]
+            series_info["past_covariates"]["names"]
             == self.ts_past_cov.components.tolist()
         )
-        assert covariates["future_covariates"]["used"] is False
-        assert covariates["static_covariates"]["used"] is False
+        assert series_info["future_covariates"]["used"] is False
+        assert series_info["static_covariates"]["used"] is False
 
-    def test_autolog_covariate_info_multi_series(
-        self, mlflow_tracking, autolog_context
-    ):
+    def test_autolog_series_info_multi_series(self, mlflow_tracking, autolog_context):
         """Fitting on a list of series doesn't leave past_covariates unreported:
         `model.past_covariate_series` stays None for a multi-series fit, so the
         info must come from the actual `fit()` call arguments instead."""
@@ -415,17 +414,40 @@ def test_autolog_covariate_info_multi_series(
                 model = LinearRegressionModel(lags=5, lags_past_covariates=3)
                 model.fit(series, past_covariates=past_covs)
 
-        covariates = mlflow.artifacts.load_dict(
-            f"runs:/{run.info.run_id}/covariates.json"
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
         )
-        assert covariates["past_covariates"]["used"] is True
-        assert covariates["past_covariates"]["count"] == 1
+        assert series_info["past_covariates"]["used"] is True
+        assert series_info["past_covariates"]["count"] == 1
         assert (
-            covariates["past_covariates"]["names"]
+            series_info["past_covariates"]["names"]
             == self.ts_past_cov.components.tolist()
         )
 
-    def test_autolog_covariate_info_static_is_global(
+    def test_autolog_series_info_add_encoders(self, mlflow_tracking, autolog_context):
+        """Covariates generated purely via `add_encoders` (no explicit
+        covariate argument passed to `fit()`) are still reported in
+        `series_info.json`."""
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(
+                    lags=5,
+                    lags_future_covariates=[0],
+                    add_encoders={"datetime_attribute": {"future": ["month"]}},
+                )
+                model.fit(self.ts_univariate[:40])
+
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
+        )
+        expected_names = model.encoders.future_components.tolist()
+        assert expected_names  # sanity check: encoders actually generated something
+        assert series_info["future_covariates"]["used"] is True
+        assert series_info["future_covariates"]["count"] == len(expected_names)
+        assert series_info["future_covariates"]["names"] == expected_names
+        assert series_info["past_covariates"]["used"] is False
+
+    def test_autolog_series_info_static_is_global(
         self, mlflow_tracking, autolog_context
     ):
         """`is_global` is based on row count vs. the number of series
@@ -437,10 +459,10 @@ def test_autolog_covariate_info_static_is_global(
         with autolog_context():
             with mlflow.start_run() as run:
                 LinearRegressionModel(lags=5).fit(global_target)
-        covariates = mlflow.artifacts.load_dict(
-            f"runs:/{run.info.run_id}/covariates.json"
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
         )
-        assert covariates["static_covariates"]["is_global"] is True
+        assert series_info["static_covariates"]["is_global"] is True
 
         # one row per component (2 components, 2 rows) -> component-specific
         per_component_target = self.ts_multivariate.with_static_covariates(
@@ -449,10 +471,10 @@ def test_autolog_covariate_info_static_is_global(
         with autolog_context():
             with mlflow.start_run() as run:
                 LinearRegressionModel(lags=5).fit(per_component_target)
-        covariates = mlflow.artifacts.load_dict(
-            f"runs:/{run.info.run_id}/covariates.json"
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
         )
-        assert covariates["static_covariates"]["is_global"] is False
+        assert series_info["static_covariates"]["is_global"] is False
 
     def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         """Test saving/loading multivariate series with all covariate types"""
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 4a31536dc8..dbfd5f4e59 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -6,7 +6,7 @@
 and logging any Darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
 to MLflow, as well as automatic logging (``autolog()``) of:
 
-- Model parameters and data metadata: The model creation parameters and covariate usage information.
+- Model parameters and data metadata: The model creation parameters, target series, and covariate usage information.
 - Model storage: The trained model artifact after each ``fit()`` call when ``log_models=True`` (default ``False``).
 - Metrics:
   - The score for each metric called inside an active MLflow run.
@@ -351,7 +351,8 @@ def autolog(
       ``with mlflow.start_run():``); does nothing if no run is active:
       - Logs model creation parameters (``model.model_params``), both as MLflow
         params and as a ``model_params.json`` artifact.
-      - Logs covariate usage information (past, future, and static covariates).
+      - Logs target series info and covariate usage information (past, future,
+        and static covariates) as a ``series_info.json`` artifact.
       - Stores the trained model artifact when ``log_models=True`` (default:
         ``False``).
       - Logs per-epoch training and validation metrics for PyTorch-based models.
@@ -567,7 +568,7 @@ def _patched_fit(original, self, *args, **kwargs):
             autologging_client.log_params(run_id=run_id, params=self.model_params)
             mlflow.log_dict(self.model_params, "model_params.json")
             fit_args = inspect.signature(original).bind(self, *args, **kwargs).arguments
-            _log_covariate_info(
+            _log_series_info(
                 self,
                 series=fit_args["series"],
                 past_covariates=fit_args.get("past_covariates"),
@@ -725,22 +726,24 @@ def _get_model_info_tags(model: ForecastingModel) -> dict[str, Any]:
     }
 
 
-def _log_covariate_info(
+def _log_series_info(
     model: ForecastingModel,
     series: TimeSeriesLike,
     past_covariates: TimeSeriesLike | None,
     future_covariates: TimeSeriesLike | None,
 ) -> None:
-    """Log covariate usage information to MLflow.
+    """Log target series and covariate usage information to MLflow.
 
-    Extracts information about past, future, and static covariates used during
-    training and logs them as a JSON artifact for easy
-    filtering, comparison, and documentation.
+    Extracts information about the target series, and about past, future,
+    and static covariates used during training and logs them as a JSON
+    artifact for easy filtering, comparison, and documentation.
 
-    Logs three types of information:
-    - Tags: Boolean flags for filtering (e.g., "uses_past_covariates")
-    - Parameters: Feature counts and names (truncated by MLflow)
-    - Artifact: Complete covariate metadata as JSON file
+    Logs:
+    - Target series: component count and names
+    - Past / future covariates: usage, count, and names, including both
+      explicitly-passed covariates and any generated by ``add_encoders``
+    - Static covariates: usage, count, names, and whether they are global
+    - Artifact: complete metadata as ``series_info.json``
 
     Parameters
     ----------
@@ -756,35 +759,41 @@ def _log_covariate_info(
         The future covariate covariate argument passed to ``fit()``, or
         ``None``.
     """
-    covariate_info = {
+    first_series = get_single_series(series)
+    series_info = {
+        "series": {
+            "count": first_series.n_components,
+            "names": first_series.components.tolist(),
+        },
         "past_covariates": _extract_covariate_metadata(
             model.uses_past_covariates,
             get_single_series(past_covariates),
             "components",
+            encoded_names=model.encoders.past_components,
         ),
         "future_covariates": _extract_covariate_metadata(
             model.uses_future_covariates,
             get_single_series(future_covariates),
             "components",
+            encoded_names=model.encoders.future_components,
         ),
     }
 
-    first_series = get_single_series(series)
     static_covariates = (
         first_series.static_covariates if first_series is not None else None
     )
-    covariate_info["static_covariates"] = _extract_covariate_metadata(
+    series_info["static_covariates"] = _extract_covariate_metadata(
         model.uses_static_covariates, static_covariates, "columns"
     )
     if model.uses_static_covariates and static_covariates is not None:
         # static covariates are global (one shared row) unless there is one row
         # per series component, in which case they are component-specific
-        covariate_info["static_covariates"]["is_global"] = (
+        series_info["static_covariates"]["is_global"] = (
             len(static_covariates) != first_series.n_components
         )
 
     # log complete information as JSON artifact
-    mlflow.log_dict(covariate_info, "covariates.json")
+    mlflow.log_dict(series_info, "series_info.json")
 
 
 def _is_torch_model(model) -> bool:
@@ -804,7 +813,10 @@ def _is_torch_model(model) -> bool:
 
 
 def _extract_covariate_metadata(
-    uses: bool, single_cov: TimeSeries | pd.DataFrame | None, names_attr: str
+    uses: bool,
+    single_cov: TimeSeries | pd.DataFrame | None,
+    names_attr: str,
+    encoded_names: pd.Index | list[str] | None = None,
 ) -> dict:
     """Extract metadata for a single covariate type from its (already
     singular) value.
@@ -819,6 +831,10 @@ def _extract_covariate_metadata(
     names_attr : str
         Attribute holding the feature names ("components" for a
         ``TimeSeries``, "columns" for a static-covariates ``DataFrame``).
+    encoded_names
+        Additional covariate names generated by encoders (``add_encoders``),
+        appended to the names extracted from ``single_cov``. Ignored when
+        ``uses`` is ``False``.
 
     Returns
     -------
@@ -829,10 +845,11 @@ def _extract_covariate_metadata(
 
     if uses:
         info["used"] = True
-        if single_cov is not None:
-            names = getattr(single_cov, names_attr).tolist()
-            info["names"] = names
-            info["count"] = len(names)
+        names = list(getattr(single_cov, names_attr)) if single_cov is not None else []
+        if encoded_names is not None:
+            names = names + list(encoded_names)
+        info["names"] = names
+        info["count"] = len(names)
 
     return info
 

From f4840c540bdd1ce3e551655c651f92932b434518 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 4 Aug 2026 11:03:15 +0200
Subject: [PATCH 128/154] chore: remove redudnant comment

---
 darts/utils/mlflow.py | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index dbfd5f4e59..ebf50c9a08 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1455,18 +1455,6 @@ def _log_metric_result(
         _log_per_series_table(rows)
 
 
-# TODO: (does the below TODO still apply? we only support logging under an active run I thought)
-# TODO: To log metrics post-fitting, only patching the metric methods may not be enough.
-# This is becuase `model.fit()` method would terminate the active MLflow run at the end of training,
-# so any metric calls made after that would not be logged.
-# To address this, we need to implement three things:
-# 1. Implement a singleton `_AutologgingMetricsManager` (`mlflow.sklearn`) to maintain a mapping between fitted
-# models and their prediction outputs.
-# 2. Patch the `model.predict()` method to create a mapping between the model (run_id) and prediction output.
-# 3. Patch the metric functions to find the model (run_id) from the input series, then log the metrics to
-# the corresponding run.
-# This way, even if the metric calls are made after `fit()` has terminated the active run, we can still log
-# the metrics to the correct run.
 def _mlflow_metric_callback(func, result, args, kwargs) -> None:
     """Metric callback registered with ``darts.metrics.utils`` for autologging.
 

From e41595724f8c75ccaeb93231d5bd0698b1e457b3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 4 Aug 2026 15:01:38 +0200
Subject: [PATCH 129/154] feat: support logging for historical forecasts
 retrain=True without prior fit

---
 darts/tests/optional_deps/test_mlflow.py | 155 ++++++++++++++++++++
 darts/utils/mlflow.py                    | 177 +++++++++++++++++++----
 2 files changed, 301 insertions(+), 31 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 7c60fca582..b447a28fbd 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -476,6 +476,161 @@ def test_autolog_series_info_static_is_global(
         )
         assert series_info["static_covariates"]["is_global"] is False
 
+    def test_autolog_historical_forecasts_series_info_covariates(
+        self, mlflow_tracking, autolog_context
+    ):
+        """HF without a prior fit() still reports explicit covariates and static
+        covariates in series_info / tags (outer model stays unfitted)."""
+        target = self.ts_univariate.with_static_covariates(
+            pd.DataFrame({"static_feat": [1.0]})
+        )
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+                model.historical_forecasts(
+                    series=target,
+                    past_covariates=self.ts_past_cov,
+                    forecast_horizon=1,
+                    retrain=True,
+                    start=0.5,
+                )
+
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
+        )
+        assert series_info["past_covariates"]["used"] is True
+        assert (
+            series_info["past_covariates"]["names"]
+            == self.ts_past_cov.components.tolist()
+        )
+        assert series_info["static_covariates"]["used"] is True
+        assert series_info["static_covariates"]["names"] == ["static_feat"]
+        tags = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.tags
+        assert tags["model_uses_past_covariates"] == "True"
+        assert tags["model_uses_static_covariates"] == "True"
+
+    def test_autolog_historical_forecasts_series_info_add_encoders(
+        self, mlflow_tracking, autolog_context
+    ):
+        """HF with add_encoders (no explicit cov args) marks future covariates
+        as used even though the outer model remains unfitted."""
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(
+                    lags=5,
+                    lags_future_covariates=[0],
+                    add_encoders={"datetime_attribute": {"future": ["month"]}},
+                )
+                model.historical_forecasts(
+                    series=self.ts_univariate,
+                    forecast_horizon=1,
+                    retrain=True,
+                    start=0.5,
+                )
+
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
+        )
+        assert series_info["future_covariates"]["used"] is True
+        tags = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.tags
+        assert tags["model_uses_future_covariates"] == "True"
+
+    def test_autolog_backtest_series_info_covariates(
+        self, mlflow_tracking, autolog_context
+    ):
+        """backtest(retrain=True) without a prior fit() reports covariates via
+        the internal historical_forecasts path."""
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5, lags_past_covariates=3)
+                model.backtest(
+                    series=self.ts_univariate,
+                    past_covariates=self.ts_past_cov,
+                    forecast_horizon=1,
+                    retrain=True,
+                    start=0.5,
+                    metric=dm.mae,
+                )
+
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
+        )
+        assert series_info["past_covariates"]["used"] is True
+        assert (
+            series_info["past_covariates"]["names"]
+            == self.ts_past_cov.components.tolist()
+        )
+
+    def test_autolog_historical_forecasts_logs_model_setup(
+        self, mlflow_tracking, autolog_context
+    ):
+        """historical_forecasts(retrain=True) without a prior fit() still logs
+        model params and series_info (but not the model artifact)."""
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = NaiveSeasonal(K=1)
+                model.historical_forecasts(
+                    series=self.ts_univariate, forecast_horizon=1, retrain=True
+                )
+
+        params = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/model_params.json"
+        )
+        assert params["K"] == 1
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
+        )
+        assert series_info["series"]["names"] == self.ts_univariate.components.tolist()
+        client = mlflow.tracking.MlflowClient()
+        artifact_paths = [a.path for a in client.list_artifacts(run.info.run_id)]
+        assert "model" not in artifact_paths
+        assert (
+            client.get_run(run.info.run_id).data.tags["model_class"] == "NaiveSeasonal"
+        )
+
+    def test_autolog_historical_forecasts_retrain_false_skips_model_setup(
+        self, mlflow_tracking, autolog_context
+    ):
+        """historical_forecasts(retrain=False) does not log model setup."""
+        model = LinearRegressionModel(lags=5)
+        model.fit(self.ts_univariate[:40])
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model.historical_forecasts(
+                    series=self.ts_univariate,
+                    forecast_horizon=1,
+                    retrain=False,
+                    start=0.5,
+                )
+
+        client = mlflow.tracking.MlflowClient()
+        artifact_paths = [a.path for a in client.list_artifacts(run.info.run_id)]
+        assert "model_params.json" not in artifact_paths
+        assert "series_info.json" not in artifact_paths
+
+    def test_autolog_historical_forecasts_overwrites_fit_setup(
+        self, mlflow_tracking, autolog_context
+    ):
+        """fit() then historical_forecasts(retrain=True) in the same run
+        overwrites model_params / series_info with the HF call's series."""
+        with autolog_context():
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5)
+                model.fit(self.ts_univariate[:30])
+                model.historical_forecasts(
+                    series=self.ts_multivariate,
+                    forecast_horizon=1,
+                    retrain=True,
+                    start=0.5,
+                )
+
+        series_info = mlflow.artifacts.load_dict(
+            f"runs:/{run.info.run_id}/series_info.json"
+        )
+        assert (
+            series_info["series"]["names"] == self.ts_multivariate.components.tolist()
+        )
+
     def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         """Test saving/loading multivariate series with all covariate types"""
         # create multivariate target with static covariates
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index ebf50c9a08..9de1944688 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -356,6 +356,13 @@ def autolog(
       - Stores the trained model artifact when ``log_models=True`` (default:
         ``False``).
       - Logs per-epoch training and validation metrics for PyTorch-based models.
+    - Calling ``ForecastingModel.historical_forecasts(retrain=True)`` inside an
+      active MLflow run; does nothing if no run is active or ``retrain`` is not
+      ``True``:
+      - Logs the same model creation parameters and ``series_info.json`` as
+        ``fit()`` (overwriting any prior ``fit()`` artifacts in the same run).
+      - Does not log the trained model artifact; call ``log_model()`` manually
+        if needed.
     - Calling any Darts metric inside an active MLflow run; does nothing if no
       run is active:
       - Logs the result of that metric call as an MLflow metric. More information
@@ -560,20 +567,16 @@ def _patched_fit(original, self, *args, **kwargs):
             return result
         run_id = active_run.info.run_id
 
-        # Set tags to identify the model class and relevant information
-        autologging_client.set_tags(run_id=run_id, tags=_get_model_info_tags(self))
-
-        if log_params:
-            # Log the parameters for model creation
-            autologging_client.log_params(run_id=run_id, params=self.model_params)
-            mlflow.log_dict(self.model_params, "model_params.json")
-            fit_args = inspect.signature(original).bind(self, *args, **kwargs).arguments
-            _log_series_info(
-                self,
-                series=fit_args["series"],
-                past_covariates=fit_args.get("past_covariates"),
-                future_covariates=fit_args.get("future_covariates"),
-            )
+        fit_args = inspect.signature(original).bind(self, *args, **kwargs).arguments
+        _log_model_setup(
+            self,
+            autologging_client,
+            run_id,
+            series=fit_args["series"],
+            past_covariates=fit_args.get("past_covariates"),
+            future_covariates=fit_args.get("future_covariates"),
+            log_params=log_params,
+        )
 
         param_logging_ops = autologging_client.flush(synchronous=False)
 
@@ -606,20 +609,45 @@ def _patched_fit(original, self, *args, **kwargs):
         return result
 
     def _patched_historical_forecasts(original, self, *args, **kwargs):
-        """Suppress per-iteration fit() autologging during historical_forecasts.
-
-        Sets a thread-local flag so _patched_fit skips autologging for the
-        internal fit() calls, so that at most the single top-level call site
-        (this patch, or an enclosing backtest()) logs anything. This patch
-        itself does not start or otherwise manage an MLflow run; it relies on
-        whatever run (if any) is already active.
+        """Suppress per-iteration fit() autologging; log model setup once when
+        ``retrain=True``.
+
+        Sets a thread-local flag so ``_patched_fit`` skips autologging for the
+        internal ``fit()`` calls. When ``retrain is True`` and an MLflow run is
+        active, logs model tags, creation parameters, and series info once after
+        the call (overwriting any prior ``fit()`` artifacts in the same run).
+        Does not start a run and does not log the trained model artifact.
         """
         _autolog_state.in_historical_forecasts = True
         try:
-            return original(self, *args, **kwargs)
+            result = original(self, *args, **kwargs)
         finally:
             _autolog_state.in_historical_forecasts = False
 
+        active_run = mlflow.active_run()
+        if active_run is None:
+            return result
+
+        bound = inspect.signature(ForecastingModel.historical_forecasts).bind(
+            self, *args, **kwargs
+        )
+        bound.apply_defaults()
+        if bound.arguments["retrain"] is not True:
+            return result
+
+        autologging_client = MlflowAutologgingQueueingClient()
+        _log_model_setup(
+            self,
+            autologging_client,
+            active_run.info.run_id,
+            series=bound.arguments["series"],
+            past_covariates=bound.arguments.get("past_covariates"),
+            future_covariates=bound.arguments.get("future_covariates"),
+            log_params=log_params,
+        )
+        autologging_client.flush(synchronous=False).await_completion()
+        return result
+
     def _patched_backtest(original, self, *args, **kwargs):
         """Wrap ``backtest`` to log metric result(s) to the active MLflow run.
 
@@ -661,7 +689,8 @@ def _patched_backtest(original, self, *args, **kwargs):
         )
 
     # patch `historical_forecasts()` for all forecasting models so that the
-    # N internal fit() calls don't each spawn their own MLflow run
+    # N internal fit() calls don't each log, and so that retrain=True calls
+    # log model setup once
     for _, cls in _get_forecasting_models():
         safe_patch(
             FLAVOR_NAME,
@@ -705,11 +734,59 @@ def get_default_conda_env():
     )
 
 
-def _get_model_info_tags(model: ForecastingModel) -> dict[str, Any]:
+def _infer_covariate_usage(
+    model: ForecastingModel,
+    series: TimeSeriesLike,
+    past_covariates: TimeSeriesLike | None,
+    future_covariates: TimeSeriesLike | None,
+) -> tuple[bool, bool, bool]:
+    """Infer past/future/static covariate usage from model state and call args.
+
+    After ``historical_forecasts(retrain=True)`` the outer model is still
+    unfitted (training happens on internal copies), so ``model.uses_*`` stays
+    ``False``. Fall back to call args / ``add_encoders`` / static covariates on
+    ``series``, gated by ``supports_*`` / ``considers_static_covariates``.
+    """
+    # encoder keys like "datetime_attribute" map to {"past": ..., "future": ...};
+    # non-dict values ("tz", "transformer") are ignored by the isinstance check
+    enc_types = {
+        cov
+        for val in (model.add_encoders or {}).values()
+        if isinstance(val, dict)
+        for cov in ("past", "future")
+        if cov in val
+    }
+    first_series = get_single_series(series)
+    uses_past = model.uses_past_covariates or (
+        model.supports_past_covariates
+        and (past_covariates is not None or "past" in enc_types)
+    )
+    uses_future = model.uses_future_covariates or (
+        model.supports_future_covariates
+        and (future_covariates is not None or "future" in enc_types)
+    )
+    uses_static = model.uses_static_covariates or (
+        first_series is not None
+        and first_series.static_covariates is not None
+        and model.supports_static_covariates
+        and model.considers_static_covariates
+    )
+    return uses_past, uses_future, uses_static
+
+
+def _get_model_info_tags(
+    model: ForecastingModel,
+    series: TimeSeriesLike,
+    past_covariates: TimeSeriesLike | None = None,
+    future_covariates: TimeSeriesLike | None = None,
+) -> dict[str, Any]:
     """
     Returns:
         A dictionary of MLflow run tag keys and values describing the specified model.
     """
+    uses_past, uses_future, uses_static = _infer_covariate_usage(
+        model, series, past_covariates, future_covariates
+    )
     return {
         "model_class": model.__class__.__name__,
         "model_reference": (
@@ -720,12 +797,47 @@ def _get_model_info_tags(model: ForecastingModel) -> dict[str, Any]:
             if model.likelihood is not None
             else None
         ),
-        "model_uses_past_covariates": model.uses_past_covariates,
-        "model_uses_future_covariates": model.uses_future_covariates,
-        "model_uses_static_covariates": model.uses_static_covariates,
+        "model_uses_past_covariates": uses_past,
+        "model_uses_future_covariates": uses_future,
+        "model_uses_static_covariates": uses_static,
     }
 
 
+def _log_model_setup(
+    model: ForecastingModel,
+    autologging_client: MlflowAutologgingQueueingClient,
+    run_id: str,
+    series: TimeSeriesLike,
+    past_covariates: TimeSeriesLike | None = None,
+    future_covariates: TimeSeriesLike | None = None,
+    *,
+    log_params: bool = True,
+) -> None:
+    """Log model tags, creation parameters, and series info to an active run.
+
+    Shared by ``fit()`` and ``historical_forecasts(retrain=True)`` autologging.
+    Does not log the trained model artifact.
+    """
+    autologging_client.set_tags(
+        run_id=run_id,
+        tags=_get_model_info_tags(
+            model,
+            series=series,
+            past_covariates=past_covariates,
+            future_covariates=future_covariates,
+        ),
+    )
+    if log_params:
+        autologging_client.log_params(run_id=run_id, params=model.model_params)
+        mlflow.log_dict(model.model_params, "model_params.json")
+        _log_series_info(
+            model,
+            series=series,
+            past_covariates=past_covariates,
+            future_covariates=future_covariates,
+        )
+
+
 def _log_series_info(
     model: ForecastingModel,
     series: TimeSeriesLike,
@@ -760,19 +872,22 @@ def _log_series_info(
         ``None``.
     """
     first_series = get_single_series(series)
+    uses_past, uses_future, uses_static = _infer_covariate_usage(
+        model, series, past_covariates, future_covariates
+    )
     series_info = {
         "series": {
             "count": first_series.n_components,
             "names": first_series.components.tolist(),
         },
         "past_covariates": _extract_covariate_metadata(
-            model.uses_past_covariates,
+            uses_past,
             get_single_series(past_covariates),
             "components",
             encoded_names=model.encoders.past_components,
         ),
         "future_covariates": _extract_covariate_metadata(
-            model.uses_future_covariates,
+            uses_future,
             get_single_series(future_covariates),
             "components",
             encoded_names=model.encoders.future_components,
@@ -783,9 +898,9 @@ def _log_series_info(
         first_series.static_covariates if first_series is not None else None
     )
     series_info["static_covariates"] = _extract_covariate_metadata(
-        model.uses_static_covariates, static_covariates, "columns"
+        uses_static, static_covariates, "columns"
     )
-    if model.uses_static_covariates and static_covariates is not None:
+    if uses_static and static_covariates is not None:
         # static covariates are global (one shared row) unless there is one row
         # per series component, in which case they are component-specific
         series_info["static_covariates"]["is_global"] = (

From 4424a0d8ab71073250618c05fb1449f4a060a721 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 4 Aug 2026 19:57:12 +0200
Subject: [PATCH 130/154] fix: remove the redundant inconsintent axis logic

---
 darts/tests/optional_deps/test_mlflow.py |  11 +-
 darts/utils/mlflow.py                    | 230 ++++++++++-------------
 2 files changed, 107 insertions(+), 134 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index b447a28fbd..ef4ffe722b 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1591,11 +1591,10 @@ def test_autolog_backtest_quantile(self, mlflow_tracking, autolog_context):
             assert key in m, f"Expected quantile key {key}"
             assert np.isfinite(m[key])
 
-    def test_autolog_backtest_inconsistent_axes_flat_fallback(
+    def test_autolog_backtest_mixed_degenerate_axes_keep_metric_names(
         self, mlflow_tracking, autolog_context
     ):
-        """Metrics with mismatched axes (mae has no time axis, ae does) cannot be
-        merged into a structured layout, so values are logged flat by index."""
+        """Metrics whose differing axes have size one keep their metric names."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
                 self._fit_lr().backtest(
@@ -1606,9 +1605,9 @@ def test_autolog_backtest_inconsistent_axes_flat_fallback(
                 )
 
         m = mlflow.get_run(run.info.run_id).data.metrics
-        flat_keys = [k for k in m if k.startswith("backtest_metrics_")]
-        assert flat_keys, "Expected flat fallback keys for inconsistent axes"
-        assert "backtest_mae" not in m, "No structured key on flat fallback"
+        assert "backtest_mae" in m
+        assert "backtest_ae" in m
+        assert not any(k.startswith("backtest_metrics_") for k in m)
 
     def test_autolog_backtest_classification_labels_in_data(
         self, mlflow_tracking, autolog_context
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 9de1944688..d7615c7c76 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1045,9 +1045,6 @@ def _log_backtest_metrics(
     - ``last_points_only`` – collapses all windows into one TimeSeries before scoring,
       so there is effectively only one window regardless of reduction.
 
-    When two metrics have incompatible axis layouts (different
-    ``time_reduction`` / ``component_reduction`` / quantile count), each series
-    result is flattened to integer-indexed keys instead.
 
     When more than one series is scored, the logged value is ``agg_func``
     applied over series for each cell, and the granular per-series breakdown
@@ -1128,14 +1125,6 @@ def _log_backtest_metrics(
     has_time_axis, has_comp_axis, axis_labels_0 = metric_axes[0]
     axis_size = len(axis_labels_0)
 
-    # Inconsistent axes across metrics (different has_time_axis, has_comp_axis, or
-    # axis_size) means the result can't be reshaped into a single canonical array.
-    # Fall back to flat integer-indexed keys for this case.
-    axes_key = (has_time_axis, has_comp_axis, axis_size)
-    axes_inconsistent = any(
-        (ax[0], ax[1], len(ax[2])) != axes_key for ax in metric_axes[1:]
-    )
-
     series = backtest_args.get("series")
     forecast_horizon = backtest_args.get("forecast_horizon")
     historical_forecasts = backtest_args.get("historical_forecasts")
@@ -1169,131 +1158,116 @@ def _log_backtest_metrics(
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
 
-    if axes_inconsistent:
-        for series_index, r in enumerate(results):
-            name_prefix = metric_names[0] if len(metric_names) == 1 else "metrics"
-            flat = np.asarray(r, dtype=float).flatten()
-            for i, val in enumerate(flat):
-                key = _sanitize_mlflow_key(f"backtest_{name_prefix}_{i}")
-                value = float(val)
-                agg.setdefault((key, 0), []).append(value)
-                rows.append({
-                    "key": key,
-                    "series_index": series_index,
-                    "step": 0,
-                    "value": value,
-                })
-    else:
-        # component names/count from the first series (all series share n_components)
-        comps = series_seq[0].components.tolist()
-        # c_size = components x quantiles/intervals/labels per component
-        c_size = (len(comps) if has_comp_axis else 1) * axis_size
-        # base_keys[m][c]: sanitized key without the optional window suffix
-        base_keys = []
-        for m, metric_name in enumerate(metric_names):
-            axis_labels = metric_axes[m][2]
-            keys_m = []
-            for c in range(c_size):
-                # c is a flat index into the (n_components x axis_size) C axis:
-                # c = comp_i * axis_size + axis_idx
-                component_index, axis_idx = divmod(c, axis_size)
-                comp_part = (
-                    "_" + _sanitize_mlflow_key(comps[component_index])
-                    if has_comp_axis
-                    else ""
+    # component names/count from the first series (all series share n_components)
+    comps = series_seq[0].components.tolist()
+    # c_size = components x quantiles/intervals/labels per component
+    c_size = (len(comps) if has_comp_axis else 1) * axis_size
+    # base_keys[m][c]: sanitized key without the optional window suffix
+    base_keys = []
+    for m, metric_name in enumerate(metric_names):
+        axis_labels = metric_axes[m][2]
+        keys_m = []
+        for c in range(c_size):
+            # c is a flat index into the (n_components x axis_size) C axis:
+            # c = comp_i * axis_size + axis_idx
+            component_index, axis_idx = divmod(c, axis_size)
+            comp_part = (
+                "_" + _sanitize_mlflow_key(comps[component_index])
+                if has_comp_axis
+                else ""
+            )
+            keys_m.append(
+                _sanitize_mlflow_key(
+                    f"backtest_{metric_name}{comp_part}{axis_labels[axis_idx]}"
                 )
-                keys_m.append(
-                    _sanitize_mlflow_key(
-                        f"backtest_{metric_name}{comp_part}{axis_labels[axis_idx]}"
-                    )
+            )
+        base_keys.append(keys_m)
+
+    # first pass: reshape each series' result into a canonical (W, T, C, M)
+    # array, recording its window-axis length for the alignment pass below.
+    series_shapes = []
+    for r in results:
+        arr = np.asarray(r, dtype=float)
+        # after stripping C and M axes, rest = W*T (or W or T alone)
+        rest, extra = divmod(arr.size, c_size * n_metrics)
+        if extra:
+            raise_log(
+                ValueError(
+                    f"Backtest metric logging failed: result size ({arr.size}) "
+                    f"is not divisible by c_size * n_metrics ({c_size} * "
+                    f"{n_metrics} = {c_size * n_metrics}). The metric output "
+                    "shape does not match the inferred axes."
                 )
-            base_keys.append(keys_m)
-
-        # first pass: reshape each series' result into a canonical (W, T, C, M)
-        # array, recording its window-axis length for the alignment pass below.
-        series_shapes = []
-        for r in results:
-            arr = np.asarray(r, dtype=float)
-            # after stripping C and M axes, rest = W*T (or W or T alone)
-            rest, extra = divmod(arr.size, c_size * n_metrics)
-            if extra:
+            )
+
+        # both time and window axes present: backtest returns (W*T*C*M,) in C order so we can
+        # recover W and T only if forecast_horizon is known (T = forecast_horizon)
+        if has_time_axis and has_windows:
+            if not forecast_horizon or rest % forecast_horizon:
                 raise_log(
                     ValueError(
-                        f"Backtest metric logging failed: result size ({arr.size}) "
-                        f"is not divisible by c_size * n_metrics ({c_size} * "
-                        f"{n_metrics} = {c_size * n_metrics}). The metric output "
-                        "shape does not match the inferred axes."
+                        f"Backtest metric logging failed: cannot split "
+                        f"window/time axes — {rest} elements remain after "
+                        f"stripping component and metric axes, but "
+                        f"forecast_horizon={forecast_horizon!r} does not "
+                        "divide evenly. Pass an explicit forecast_horizon to "
+                        "backtest()."
                     )
                 )
-
-            # both time and window axes present: backtest returns (W*T*C*M,) in C order so we can
-            # recover W and T only if forecast_horizon is known (T = forecast_horizon)
-            if has_time_axis and has_windows:
-                if not forecast_horizon or rest % forecast_horizon:
-                    raise_log(
-                        ValueError(
-                            f"Backtest metric logging failed: cannot split "
-                            f"window/time axes — {rest} elements remain after "
-                            f"stripping component and metric axes, but "
-                            f"forecast_horizon={forecast_horizon!r} does not "
-                            "divide evenly. Pass an explicit forecast_horizon to "
-                            "backtest()."
-                        )
-                    )
-                t_size, w_size = forecast_horizon, rest // forecast_horizon
-            elif has_time_axis:
-                t_size, w_size = rest, 1
-            elif has_windows:
-                t_size, w_size = 1, rest
-            else:
-                if rest != 1:
-                    raise_log(
-                        ValueError(
-                            f"Backtest metric logging failed: expected a single "
-                            f"scalar per component/metric after reduction, but got "
-                            f"{rest} elements. Check time_reduction and "
-                            "component_reduction defaults."
-                        )
+            t_size, w_size = forecast_horizon, rest // forecast_horizon
+        elif has_time_axis:
+            t_size, w_size = rest, 1
+        elif has_windows:
+            t_size, w_size = 1, rest
+        else:
+            if rest != 1:
+                raise_log(
+                    ValueError(
+                        f"Backtest metric logging failed: expected a single "
+                        f"scalar per component/metric after reduction, but got "
+                        f"{rest} elements. Check time_reduction and "
+                        "component_reduction defaults."
                     )
-                t_size, w_size = 1, 1
-
-            series_shapes.append((
-                t_size,
-                w_size,
-                arr.reshape(w_size, t_size, c_size, n_metrics),
-            ))
-
-        # align the calendar-relative axes from the end
-        max_w_size = max((w_size for _, w_size, _ in series_shapes), default=0)
-        t_axis_is_calendar = has_time_axis and not has_windows and last_points_only
-        max_t_size = (
-            max((t_size for t_size, _, _ in series_shapes), default=0)
-            if t_axis_is_calendar
-            else 0
-        )
+                )
+            t_size, w_size = 1, 1
+
+        series_shapes.append((
+            t_size,
+            w_size,
+            arr.reshape(w_size, t_size, c_size, n_metrics),
+        ))
+
+    # align the calendar-relative axes from the end
+    max_w_size = max((w_size for _, w_size, _ in series_shapes), default=0)
+    t_axis_is_calendar = has_time_axis and not has_windows and last_points_only
+    max_t_size = (
+        max((t_size for t_size, _, _ in series_shapes), default=0)
+        if t_axis_is_calendar
+        else 0
+    )
 
-        for series_index, (t_size, w_size, canonical) in enumerate(series_shapes):
-            w_offset = max_w_size - w_size if has_windows else 0
-            t_offset = max_t_size - t_size if t_axis_is_calendar else 0
-            for m in range(n_metrics):
-                for w in range(w_size):
-                    aligned_w = w + w_offset
-                    for c in range(c_size):
-                        key = base_keys[m][c]
-                        if has_time_axis and has_windows:
-                            key = f"{key}_w{aligned_w}"
-                        for t in range(t_size):
-                            # MLflow step maps to the axis the UI should chart:
-                            # time when present, otherwise window index
-                            step = t + t_offset if has_time_axis else aligned_w
-                            value = float(canonical[w, t, c, m])
-                            agg.setdefault((key, step), []).append(value)
-                            rows.append({
-                                "key": key,
-                                "series_index": series_index,
-                                "step": step,
-                                "value": value,
-                            })
+    for series_index, (t_size, w_size, canonical) in enumerate(series_shapes):
+        w_offset = max_w_size - w_size if has_windows else 0
+        t_offset = max_t_size - t_size if t_axis_is_calendar else 0
+        for m in range(n_metrics):
+            for w in range(w_size):
+                aligned_w = w + w_offset
+                for c in range(c_size):
+                    key = base_keys[m][c]
+                    if has_time_axis and has_windows:
+                        key = f"{key}_w{aligned_w}"
+                    for t in range(t_size):
+                        # MLflow step maps to the axis the UI should chart:
+                        # time when present, otherwise window index
+                        step = t + t_offset if has_time_axis else aligned_w
+                        value = float(canonical[w, t, c, m])
+                        agg.setdefault((key, step), []).append(value)
+                        rows.append({
+                            "key": key,
+                            "series_index": series_index,
+                            "step": step,
+                            "value": value,
+                        })
 
     # aggregate across series for each (key, step); for a single series this
     # is just the value itself.

From 8067aadf1f8a9ebb408118118114740e1d4df5b4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Tue, 4 Aug 2026 20:24:21 +0200
Subject: [PATCH 131/154] chore: simplify block after hfc logic changes in
 #3165

---
 darts/utils/mlflow.py | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index d7615c7c76..df22767e6f 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1203,17 +1203,6 @@ def _log_backtest_metrics(
         # both time and window axes present: backtest returns (W*T*C*M,) in C order so we can
         # recover W and T only if forecast_horizon is known (T = forecast_horizon)
         if has_time_axis and has_windows:
-            if not forecast_horizon or rest % forecast_horizon:
-                raise_log(
-                    ValueError(
-                        f"Backtest metric logging failed: cannot split "
-                        f"window/time axes — {rest} elements remain after "
-                        f"stripping component and metric axes, but "
-                        f"forecast_horizon={forecast_horizon!r} does not "
-                        "divide evenly. Pass an explicit forecast_horizon to "
-                        "backtest()."
-                    )
-                )
             t_size, w_size = forecast_horizon, rest // forecast_horizon
         elif has_time_axis:
             t_size, w_size = rest, 1

From bb4487646cd08216d497755f63b2b504d72de9ff Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 5 Aug 2026 10:09:46 +0200
Subject: [PATCH 132/154] chore: extract shared code into helpers from
 _log_{backtest/metric}

---
 darts/tests/optional_deps/test_mlflow.py |  90 +++++++++++
 darts/utils/mlflow.py                    | 187 +++++++++++++++--------
 2 files changed, 213 insertions(+), 64 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index ef4ffe722b..7fbf099b5e 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -31,6 +31,8 @@
     RegressionEnsembleModel,
 )
 from darts.utils.mlflow import (
+    _build_metric_keys,
+    _flush_logged_metrics,
     _infer_metric_axes,
     _log_backtest_metrics,
     autolog,
@@ -1875,3 +1877,91 @@ def test_infer_metric_axes_unknown_labels_raises():
     of output labels ahead of time, so this raises rather than falling back."""
     with pytest.raises(ValueError, match="requires explicit `labels`"):
         _infer_metric_axes(dm.f1, {"label_reduction": None})
+
+
+def test_build_metric_keys_components_and_quantiles():
+    """Shared key builder expands components x quantile suffixes per metric."""
+    metric_axes = [
+        (False, True, ["_q0.100", "_q0.900"]),
+        (False, True, ["_label0", "_label1"]),
+    ]
+    c_size, keys = _build_metric_keys(
+        ["mae", "f1"],
+        ["temp", "hum"],
+        has_comp_axis=True,
+        metric_axes=metric_axes,
+        prefix="backtest_",
+    )
+    assert c_size == 4
+    assert keys == [
+        [
+            "backtest_mae_temp_q0_100",
+            "backtest_mae_temp_q0_900",
+            "backtest_mae_hum_q0_100",
+            "backtest_mae_hum_q0_900",
+        ],
+        [
+            "backtest_f1_temp_label0",
+            "backtest_f1_temp_label1",
+            "backtest_f1_hum_label0",
+            "backtest_f1_hum_label1",
+        ],
+    ]
+
+
+def test_build_metric_keys_no_components_no_prefix():
+    """Without components, each metric gets one key per axis label."""
+    metric_axes = [(False, False, ["_q0.500"])]
+    c_size, keys = _build_metric_keys(
+        ["mql"],
+        ["ignored"],
+        has_comp_axis=False,
+        metric_axes=metric_axes,
+    )
+    assert c_size == 1
+    assert keys == [["mql_q0_500"]]
+
+
+def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
+    """Multi-series cells are aggregated with agg_func; per-series rows go to table."""
+    agg = {
+        ("mae", 0): [1.0, 3.0],
+        ("mae", 1): [10.0, 30.0],
+    }
+    rows = [
+        {"key": "mae", "series_index": 0, "step": 0, "value": 1.0},
+        {"key": "mae", "series_index": 1, "step": 0, "value": 3.0},
+        {"key": "mae", "series_index": 0, "step": 1, "value": 10.0},
+        {"key": "mae", "series_index": 1, "step": 1, "value": 30.0},
+    ]
+    with mlflow.start_run() as run:
+        client = MlflowAutologgingQueueingClient()
+        _flush_logged_metrics(
+            client, run.info.run_id, agg, rows, n_series=2, agg_func=np.mean
+        )
+        client.flush(synchronous=True)
+
+    history0 = mlflow_tracking.get_metric_history(run.info.run_id, "mae")
+    logged = {m.step: m.value for m in history0}
+    assert logged[0] == pytest.approx(2.0)
+    assert logged[1] == pytest.approx(20.0)
+    table = mlflow.load_table(
+        artifact_file="metrics_per_series.json", run_ids=[run.info.run_id]
+    )
+    assert len(table) == 4
+
+
+def test_flush_logged_metrics_skips_table_for_single_series(mlflow_tracking):
+    """Single-series input logs the aggregate only; no per-series table."""
+    agg = {("mae", 0): [1.5]}
+    rows = [{"key": "mae", "series_index": 0, "step": 0, "value": 1.5}]
+    with mlflow.start_run() as run:
+        client = MlflowAutologgingQueueingClient()
+        _flush_logged_metrics(
+            client, run.info.run_id, agg, rows, n_series=1, agg_func=np.mean
+        )
+        client.flush(synchronous=True)
+
+    assert mlflow.get_run(run.info.run_id).data.metrics["mae"] == pytest.approx(1.5)
+    artifacts = mlflow_tracking.list_artifacts(run.info.run_id)
+    assert not any(a.path == "metrics_per_series.json" for a in artifacts)
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index df22767e6f..d0c0fc1a2b 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -989,6 +989,61 @@ def _sanitize_mlflow_key(name: str) -> str:
     return re.sub(r"[^\w-]", "_", name)
 
 
+def _build_metric_keys(
+    metric_names: list[str],
+    components: list[str],
+    has_comp_axis: bool,
+    metric_axes: list[tuple[bool, bool, list[str]]],
+    *,
+    prefix: str = "",
+) -> tuple[int, list[list[str]]]:
+    """Build sanitized MLflow metric keys for each metric x component x axis label.
+
+    Parameters
+    ----------
+    metric_names
+        One sanitized metric-name token per metric.
+    components
+        Component names from the first series (used only when ``has_comp_axis``).
+    has_comp_axis
+        Whether components are preserved in the metric output.
+    metric_axes
+        Per-metric ``(has_time_axis, has_comp_axis, axis_labels)`` tuples from
+        ``_infer_metric_axes``. Axis size is taken from the first entry.
+    prefix
+        Optional key prefix (e.g. ``"backtest_"``).
+
+    Returns
+    -------
+    tuple[int, list[list[str]]]
+        ``(c_size, keys)`` where ``c_size`` is
+        ``(n_components if has_comp_axis else 1) * axis_size`` and ``keys[m][c]``
+        is the sanitized key for metric ``m`` and flat component/axis index ``c``.
+    """
+    axis_size = len(metric_axes[0][2])
+    c_size = (len(components) if has_comp_axis else 1) * axis_size
+    keys: list[list[str]] = []
+    for m, metric_name in enumerate(metric_names):
+        axis_labels = metric_axes[m][2]
+        keys_m = []
+        for c in range(c_size):
+            # c is a flat index into the (n_components x axis_size) C axis:
+            # c = comp_i * axis_size + axis_idx
+            component_index, axis_idx = divmod(c, axis_size)
+            comp_part = (
+                "_" + _sanitize_mlflow_key(components[component_index])
+                if has_comp_axis
+                else ""
+            )
+            keys_m.append(
+                _sanitize_mlflow_key(
+                    f"{prefix}{metric_name}{comp_part}{axis_labels[axis_idx]}"
+                )
+            )
+        keys.append(keys_m)
+    return c_size, keys
+
+
 def _log_per_series_table(rows: list[dict]) -> None:
     """Append the granular per-series metric breakdown to a single, run-wide
     table artifact.
@@ -1012,6 +1067,43 @@ def _log_per_series_table(rows: list[dict]) -> None:
     mlflow.log_table(data=df, artifact_file="metrics_per_series.json")
 
 
+def _flush_logged_metrics(
+    autologging_client: MlflowAutologgingQueueingClient,
+    run_id: str,
+    agg: dict[tuple[str, int], list[float]],
+    rows: list[dict],
+    n_series: int,
+    agg_func: Callable,
+) -> None:
+    """Aggregate per-series cells, log MLflow metrics, and optionally write the
+    per-series table artifact.
+
+    Parameters
+    ----------
+    autologging_client
+        MLflow autologging client used to queue metric writes.
+    run_id
+        ID of the active MLflow run.
+    agg
+        Map of ``(key, step) -> list of per-series float values``.
+    rows
+        Granular per-series cells for ``metrics_per_series.json``.
+    n_series
+        Number of series scored; the table artifact is written only when
+        ``n_series > 1``.
+    agg_func
+        Aggregation over the per-series values for each ``(key, step)``.
+    """
+    metrics_by_step: dict[int, dict[str, float]] = {}
+    for (key, step), values in agg.items():
+        metrics_by_step.setdefault(step, {})[key] = float(agg_func(values))
+    for step, metrics in metrics_by_step.items():
+        autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
+
+    if n_series > 1:
+        _log_per_series_table(rows)
+
+
 def _log_backtest_metrics(
     autologging_client: MlflowAutologgingQueueingClient,
     run_id: str,
@@ -1122,8 +1214,7 @@ def _log_backtest_metrics(
 
     # check the dim axes from the metric kwargs for each
     metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metric, metric_kwargs)]
-    has_time_axis, has_comp_axis, axis_labels_0 = metric_axes[0]
-    axis_size = len(axis_labels_0)
+    has_time_axis, has_comp_axis, _ = metric_axes[0]
 
     series = backtest_args.get("series")
     forecast_horizon = backtest_args.get("forecast_horizon")
@@ -1160,28 +1251,13 @@ def _log_backtest_metrics(
 
     # component names/count from the first series (all series share n_components)
     comps = series_seq[0].components.tolist()
-    # c_size = components x quantiles/intervals/labels per component
-    c_size = (len(comps) if has_comp_axis else 1) * axis_size
-    # base_keys[m][c]: sanitized key without the optional window suffix
-    base_keys = []
-    for m, metric_name in enumerate(metric_names):
-        axis_labels = metric_axes[m][2]
-        keys_m = []
-        for c in range(c_size):
-            # c is a flat index into the (n_components x axis_size) C axis:
-            # c = comp_i * axis_size + axis_idx
-            component_index, axis_idx = divmod(c, axis_size)
-            comp_part = (
-                "_" + _sanitize_mlflow_key(comps[component_index])
-                if has_comp_axis
-                else ""
-            )
-            keys_m.append(
-                _sanitize_mlflow_key(
-                    f"backtest_{metric_name}{comp_part}{axis_labels[axis_idx]}"
-                )
-            )
-        base_keys.append(keys_m)
+    c_size, base_keys = _build_metric_keys(
+        metric_names,
+        comps,
+        has_comp_axis,
+        metric_axes,
+        prefix="backtest_",
+    )
 
     # first pass: reshape each series' result into a canonical (W, T, C, M)
     # array, recording its window-axis length for the alignment pass below.
@@ -1258,18 +1334,14 @@ def _log_backtest_metrics(
                             "value": value,
                         })
 
-    # aggregate across series for each (key, step); for a single series this
-    # is just the value itself.
-    metrics_by_step: dict[int, dict[str, float]] = {}
-    for (key, step), values in agg.items():
-        metrics_by_step.setdefault(step, {})[key] = float(agg_func(values))
-    for step, metrics in metrics_by_step.items():
-        autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
-
-    # append the granular per-series breakdown to the run's table artifact
-    # (multi-series only)
-    if len(series_seq) > 1:
-        _log_per_series_table(rows)
+    _flush_logged_metrics(
+        autologging_client,
+        run_id,
+        agg,
+        rows,
+        n_series=len(series_seq),
+        agg_func=agg_func,
+    )
 
 
 def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
@@ -1424,8 +1496,6 @@ def _log_metric_result(
         single value logged for a list of series. Called as
         ``agg_func(values)`` on a list of floats.
     """
-    axis_size = len(axis_labels)
-
     if series_reduced:
         # series_reduction aggregated across series -> single result, no series axis
         series_seq = [get_single_series(series)]
@@ -1447,22 +1517,15 @@ def _log_metric_result(
                     )
                 )
 
-    # TODO: a lot of this seems duplicated from the backtest logic; improve
     # component names/count from the first series (all series share n_components)
     comps = series_seq[0].components.tolist()
-    # c_size = components x quantiles/intervals/labels per component
-    c_size = (len(comps) if has_comp_axis else 1) * axis_size
-    keys = []
-    for c in range(c_size):
-        # c is a flat index into the (n_components x axis_size) C axis:
-        # c = comp_i * axis_size + axis_idx
-        component_index, axis_idx = divmod(c, axis_size)
-        comp_part = (
-            "_" + _sanitize_mlflow_key(comps[component_index]) if has_comp_axis else ""
-        )
-        keys.append(
-            _sanitize_mlflow_key(metric_name + comp_part + axis_labels[axis_idx])
-        )
+    c_size, base_keys = _build_metric_keys(
+        [metric_name],
+        comps,
+        has_comp_axis,
+        [(has_time_axis, has_comp_axis, axis_labels)],
+    )
+    keys = base_keys[0]
 
     # first pass: reshape each series' result into a canonical (T, C) array,
     # recording its time-axis length for the alignment pass below.
@@ -1518,20 +1581,16 @@ def _log_metric_result(
                     "value": value,
                 })
 
-    # aggregate across series for each (key, step); for a single series this
-    # is just the value itself.
-    metrics_by_step: dict[int, dict[str, float]] = {}
-    for (key, step), values in agg.items():
-        metrics_by_step.setdefault(step, {})[key] = float(agg_func(values))
-    for step, metrics in metrics_by_step.items():
-        autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
+    _flush_logged_metrics(
+        autologging_client,
+        run_id,
+        agg,
+        rows,
+        n_series=len(series_seq),
+        agg_func=agg_func,
+    )
     autologging_client.flush(synchronous=False).await_completion()
 
-    # append the granular per-series breakdown to the run's table artifact
-    # (multi-series only)
-    if len(series_seq) > 1:
-        _log_per_series_table(rows)
-
 
 def _mlflow_metric_callback(func, result, args, kwargs) -> None:
     """Metric callback registered with ``darts.metrics.utils`` for autologging.

From 805a471c60fdc0afc05b28de89ecfa3e9d2f09e7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 5 Aug 2026 10:13:22 +0200
Subject: [PATCH 133/154] docs: doc clarification

---
 darts/utils/mlflow.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index d0c0fc1a2b..2bf0302f07 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1456,17 +1456,14 @@ def _log_metric_result(
     (shared with ``_log_backtest_metrics``). For a single series the
     aggregate is just the value itself and no artifact is written.
 
-    TODO: improve this, it's about predictions of different or different
-      intersection lengths between actual and pred (from the `Raises`
-      below, it sounds even that this wouldn't be supported?)
-    Series of different lengths are assumed to share the same end date, so the
-    time axis is aligned from the end rather than the start: a shorter series
-    lines up on its last value instead of its first.
+    Series can have different lengths and intersect in different ways,
+    so the time axis is aligned from the end rather than the start: a shorter
+    series lines up on its last value instead of its first.
 
     Raises
     ------
     ValueError
-        On a shape/size mismatch between the metric result and the inferred
+        On a shape mismatch between the metric result and the inferred
         axes, or when ``has_comp_axis`` is ``True`` and series in a sequence
         have different numbers of components.
 

From c40a106480cd7ebd6a9ffc0e965a8c7dad77cf18 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 5 Aug 2026 11:20:47 +0200
Subject: [PATCH 134/154] feat: agg timestep metrics

---
 darts/tests/optional_deps/test_mlflow.py | 39 ++++++++++------
 darts/utils/mlflow.py                    | 58 +++++++++++++++---------
 2 files changed, 60 insertions(+), 37 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 7fbf099b5e..bbd0056466 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1367,7 +1367,13 @@ def test_autolog_metric_per_series_table_schema_and_single_series_skip(
                 dm.mae(multi, pred_multi)
 
         rows = self._read_per_series_table(run_multi.info.run_id)
-        assert list(rows[0].keys()) == ["key", "series_index", "step", "value"]
+        assert list(rows[0].keys()) == [
+            "key",
+            "series_index",
+            "step",
+            "window_index",
+            "value",
+        ]
         assert {int(r["series_index"]) for r in rows} == {0, 1}
 
         # single-series: no per-series table artifact should be created
@@ -1520,8 +1526,8 @@ def test_autolog_backtest_per_timestep_scalar(
     def test_autolog_backtest_per_timestep_per_window(
         self, mlflow_tracking, autolog_context
     ):
-        """ae + reduction=None + forecast_horizon>1 logs one key per window, with
-        one step per forecast horizon timestep."""
+        """Window-level ae results aggregate per horizon step in the chart and
+        remain available with their window index in the detailed table."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
                 ref = self._fit_lr().backtest(
@@ -1534,10 +1540,16 @@ def test_autolog_backtest_per_timestep_per_window(
                 )
 
         ref = np.asarray(ref, dtype=float)  # shape (n_windows, forecast_horizon)
-        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae_w0")
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
         assert len(history) == 4, "Expected one step per forecast horizon timestep"
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
-        np.testing.assert_allclose(logged, ref[0], atol=1e-5)
+        np.testing.assert_allclose(logged, np.nanmean(ref, axis=0), atol=1e-5)
+
+        rows = self._read_per_series_table(run.info.run_id)
+        assert len(rows) == ref.size
+        for row in rows:
+            assert row["key"] == "backtest_ae"
+            assert row["window_index"] in range(len(ref))
 
     def test_autolog_backtest_historical_forecasts_horizon_inferred(
         self, mlflow_tracking, autolog_context
@@ -1566,10 +1578,10 @@ def test_autolog_backtest_historical_forecasts_horizon_inferred(
                 )
 
         ref = np.asarray(ref, dtype=float)  # shape (n_windows, forecast_horizon)
-        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae_w0")
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
         assert len(history) == 4, "Expected one step per forecast horizon timestep"
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
-        np.testing.assert_allclose(logged, ref[0], atol=1e-5)
+        np.testing.assert_allclose(logged, np.nanmean(ref, axis=0), atol=1e-5)
 
     def test_autolog_backtest_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile."""
@@ -1923,7 +1935,7 @@ def test_build_metric_keys_no_components_no_prefix():
 
 
 def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
-    """Multi-series cells are aggregated with agg_func; per-series rows go to table."""
+    """Cells are aggregated with agg_func; supplied table rows are persisted."""
     agg = {
         ("mae", 0): [1.0, 3.0],
         ("mae", 1): [10.0, 30.0],
@@ -1937,7 +1949,7 @@ def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
     with mlflow.start_run() as run:
         client = MlflowAutologgingQueueingClient()
         _flush_logged_metrics(
-            client, run.info.run_id, agg, rows, n_series=2, agg_func=np.mean
+            client, run.info.run_id, agg, agg_func=np.mean, table_rows=rows
         )
         client.flush(synchronous=True)
 
@@ -1951,15 +1963,12 @@ def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
     assert len(table) == 4
 
 
-def test_flush_logged_metrics_skips_table_for_single_series(mlflow_tracking):
-    """Single-series input logs the aggregate only; no per-series table."""
+def test_flush_logged_metrics_skips_table_without_rows(mlflow_tracking):
+    """Omitting table rows logs the aggregate only."""
     agg = {("mae", 0): [1.5]}
-    rows = [{"key": "mae", "series_index": 0, "step": 0, "value": 1.5}]
     with mlflow.start_run() as run:
         client = MlflowAutologgingQueueingClient()
-        _flush_logged_metrics(
-            client, run.info.run_id, agg, rows, n_series=1, agg_func=np.mean
-        )
+        _flush_logged_metrics(client, run.info.run_id, agg, agg_func=np.mean)
         client.flush(synchronous=True)
 
     assert mlflow.get_run(run.info.run_id).data.metrics["mae"] == pytest.approx(1.5)
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 2bf0302f07..be3dd22879 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1050,8 +1050,9 @@ def _log_per_series_table(rows: list[dict]) -> None:
 
     Each row is a single metric cell for one series, with columns ``key`` (the
     aggregate MLflow key, without any series suffix), ``series_index``, ``step``
-    (the time or window index charted by MLflow), and ``value``. All calls
-    within a run append to the same ``metrics_per_series.json`` artifact.
+    (the time or window index charted by MLflow), ``window_index`` (the source
+    backtest window, or ``None``), and ``value``. All calls within a run append
+    to the same ``metrics_per_series.json`` artifact.
     Used when more than one series is scored, since the logged metric keys
     only carry the aggregate over series.
 
@@ -1059,7 +1060,7 @@ def _log_per_series_table(rows: list[dict]) -> None:
     ----------
     rows
         One dict per metric cell with keys ``key``, ``series_index``, ``step``,
-        and ``value``.
+        ``window_index``, and ``value``.
     """
     if not rows:
         return
@@ -1071,9 +1072,8 @@ def _flush_logged_metrics(
     autologging_client: MlflowAutologgingQueueingClient,
     run_id: str,
     agg: dict[tuple[str, int], list[float]],
-    rows: list[dict],
-    n_series: int,
     agg_func: Callable,
+    table_rows: list[dict] | None = None,
 ) -> None:
     """Aggregate per-series cells, log MLflow metrics, and optionally write the
     per-series table artifact.
@@ -1086,13 +1086,11 @@ def _flush_logged_metrics(
         ID of the active MLflow run.
     agg
         Map of ``(key, step) -> list of per-series float values``.
-    rows
-        Granular per-series cells for ``metrics_per_series.json``.
-    n_series
-        Number of series scored; the table artifact is written only when
-        ``n_series > 1``.
     agg_func
         Aggregation over the per-series values for each ``(key, step)``.
+    table_rows
+        Granular cells for ``metrics_per_series.json``. ``None`` skips writing
+        the table artifact.
     """
     metrics_by_step: dict[int, dict[str, float]] = {}
     for (key, step), values in agg.items():
@@ -1100,8 +1098,8 @@ def _flush_logged_metrics(
     for step, metrics in metrics_by_step.items():
         autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
 
-    if n_series > 1:
-        _log_per_series_table(rows)
+    if table_rows is not None:
+        _log_per_series_table(table_rows)
 
 
 def _log_backtest_metrics(
@@ -1315,12 +1313,26 @@ def _log_backtest_metrics(
         w_offset = max_w_size - w_size if has_windows else 0
         t_offset = max_t_size - t_size if t_axis_is_calendar else 0
         for m in range(n_metrics):
-            for w in range(w_size):
-                aligned_w = w + w_offset
-                for c in range(c_size):
-                    key = base_keys[m][c]
-                    if has_time_axis and has_windows:
-                        key = f"{key}_w{aligned_w}"
+            for c in range(c_size):
+                key = base_keys[m][c]
+                if has_time_axis and has_windows:
+                    # Keep window-level values in the detailed table, but aggregate
+                    # windows into one chart value per horizon step for this series.
+                    for t in range(t_size):
+                        values = canonical[:, t, c, m]
+                        agg.setdefault((key, t), []).append(float(np.nanmean(values)))
+                        for w, value in enumerate(values):
+                            rows.append({
+                                "key": key,
+                                "series_index": series_index,
+                                "step": t,
+                                "window_index": w + w_offset,
+                                "value": float(value),
+                            })
+                    continue
+
+                for w in range(w_size):
+                    aligned_w = w + w_offset
                     for t in range(t_size):
                         # MLflow step maps to the axis the UI should chart:
                         # time when present, otherwise window index
@@ -1331,6 +1343,7 @@ def _log_backtest_metrics(
                             "key": key,
                             "series_index": series_index,
                             "step": step,
+                            "window_index": None,
                             "value": value,
                         })
 
@@ -1338,9 +1351,10 @@ def _log_backtest_metrics(
         autologging_client,
         run_id,
         agg,
-        rows,
-        n_series=len(series_seq),
         agg_func=agg_func,
+        table_rows=(
+            rows if len(series_seq) > 1 or (has_time_axis and has_windows) else None
+        ),
     )
 
 
@@ -1575,6 +1589,7 @@ def _log_metric_result(
                     "key": key,
                     "series_index": series_index,
                     "step": step,
+                    "window_index": None,
                     "value": value,
                 })
 
@@ -1582,9 +1597,8 @@ def _log_metric_result(
         autologging_client,
         run_id,
         agg,
-        rows,
-        n_series=len(series_seq),
         agg_func=agg_func,
+        table_rows=rows if len(series_seq) > 1 else None,
     )
     autologging_client.flush(synchronous=False).await_completion()
 

From e0599384c012c01fd4d1867aed419b97fb1431e9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 5 Aug 2026 11:43:38 +0200
Subject: [PATCH 135/154] chore: align notebook to current solution

---
 examples/29-MLflow-quickstart.ipynb | 12069 +++++++++++++-------------
 1 file changed, 6014 insertions(+), 6055 deletions(-)

diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
index 54f8d90a83..51ea86bbdd 100644
--- a/examples/29-MLflow-quickstart.ipynb
+++ b/examples/29-MLflow-quickstart.ipynb
@@ -1,6139 +1,6098 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "aeddb542",
-   "metadata": {},
-   "source": [
-    "# MLflow for Darts\n",
-    "\n",
-    "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
-    "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
-    "\n",
-    "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
-    "\n",
-    "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f72894af",
-   "metadata": {},
-   "source": [
-    "## Installing MLflow\n",
-    "\n",
-    "MLflow is available as an optional dependency for Darts. Install it with:\n",
-    "\n",
-    "```bash\n",
-    "pip install mlflow\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42e3dcea",
-   "metadata": {},
-   "source": [
-    "## Setup and Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b346ce8f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
-     "iopub.status.busy": "2026-06-24T15:18:43.049088Z",
-     "iopub.status.idle": "2026-06-24T15:18:43.053693Z",
-     "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# fix python path if working locally\n",
-    "from utils import fix_pythonpath_if_working_locally\n",
-    "\n",
-    "fix_pythonpath_if_working_locally()\n",
-    "\n",
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "13b13fe4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
-     "iopub.status.busy": "2026-06-24T15:18:43.054722Z",
-     "iopub.status.idle": "2026-06-24T15:18:46.599643Z",
-     "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import os\n",
-    "import tempfile\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mlflow\n",
-    "import numpy as np\n",
-    "\n",
-    "import darts.metrics as metrics\n",
-    "from darts.datasets import AirPassengersDataset\n",
-    "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
-    "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4d424e08",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
-     "iopub.status.busy": "2026-06-24T15:18:46.600748Z",
-     "iopub.status.idle": "2026-06-24T15:18:46.634540Z",
-     "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# use darts plotting style\n",
-    "from darts import set_option\n",
-    "\n",
-    "set_option(\"plotting.use_darts_style\", True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f9c40d6",
-   "metadata": {},
-   "source": [
-    "## MLflow Setup\n",
-    "\n",
-    "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "88320df5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
-     "iopub.status.busy": "2026-06-24T15:18:46.636565Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.268439Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
-      "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Updating database tables\n",
-      "2026/07/23 18:50:08 INFO mlflow.tracking.fluent: Experiment with name 'darts-quickstart' does not exist. Creating a new experiment.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# MLflow for Darts\n",
+        "\n",
+        "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
+        "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
+        "\n",
+        "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
+        "\n",
+        "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
+      ],
+      "id": "aeddb542"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
-      "Experiment: darts-quickstart\n"
-     ]
-    }
-   ],
-   "source": [
-    "# temporary directory for MLflow tracking\n",
-    "tmpdir = tempfile.mkdtemp()\n",
-    "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
-    "\n",
-    "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
-    "mlflow.set_experiment(\"darts-quickstart\")\n",
-    "\n",
-    "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
-    "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03d5209e",
-   "metadata": {},
-   "source": [
-    "## Load Sample Data\n",
-    "\n",
-    "We'll use the classic AirPassengers dataset for this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1596e07e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.269548Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.356588Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Installing MLflow\n",
+        "\n",
+        "MLflow is available as an optional dependency for Darts. Install it with:\n",
+        "\n",
+        "```bash\n",
+        "pip install \"mlflow>=3.0\"\n",
+        "```"
+      ],
+      "id": "f72894af"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training series: 107 points\n",
-      "Validation series: 37 points\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup and Imports"
+      ],
+      "id": "42e3dcea"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "series = AirPassengersDataset().load()\n",
-    "train, val = series.split_before(0.75)\n",
-    "\n",
-    "print(f\"Training series: {len(train)} points\")\n",
-    "print(f\"Validation series: {len(val)} points\")\n",
-    "\n",
-    "series.plot()\n",
-    "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
-    "plt.legend()\n",
-    "plt.title(\"AirPassengers Dataset\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34858645",
-   "metadata": {},
-   "source": [
-    "## Basic Model Logging\n",
-    "\n",
-    "Let's train a simple model and log it to MLflow manually."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "bc8f520d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.357516Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.450374Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
+          "iopub.status.busy": "2026-06-24T15:18:43.049088Z",
+          "iopub.status.idle": "2026-06-24T15:18:43.053693Z",
+          "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
+        }
+      },
+      "source": [
+        "# fix python path if working locally\n",
+        "from utils import fix_pythonpath_if_working_locally\n",
+        "\n",
+        "fix_pythonpath_if_working_locally()\n",
+        "\n",
+        "import warnings\n",
+        "\n",
+        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+      ],
+      "execution_count": 2,
+      "outputs": [],
+      "id": "b346ce8f"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation MAPE: 7.86%\n",
-      "Validation RMSE: 34.77\n"
-     ]
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
+          "iopub.status.busy": "2026-06-24T15:18:43.054722Z",
+          "iopub.status.idle": "2026-06-24T15:18:46.599643Z",
+          "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
+        }
+      },
+      "source": [
+        "%matplotlib inline\n",
+        "\n",
+        "import os\n",
+        "import tempfile\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import mlflow\n",
+        "import numpy as np\n",
+        "\n",
+        "import darts.metrics as metrics\n",
+        "from darts.datasets import AirPassengersDataset\n",
+        "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
+        "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
+      ],
+      "execution_count": 3,
+      "outputs": [],
+      "id": "13b13fe4"
     },
     {
-     "data": {
-      "image/png": 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cc436f1gP5md8vvAuQUwAGK1hjMXNBF4LcYjPAmksnEt6n/EZ4pjgM4TQxPkC8YH3gM9Pg/UgjtH4DSIG4ovN02IbTshHbE0wE6wRYgUglBBZwEA7bdq0aL8dQiwFPSWEEBImzC35AUy6SFMgYuRtBiWEMH1DCCFhA2kRAB8J0g/wUiE9gjSUvwoWQroyjJQQW9NWjp+QaANvBbwcaED3yy+/KCM0/BXaW0UI8YSeEkIIIYRYAkZKCCGEEGIJKEoIIYQQYgkoSgghhBBiCbqcKEFJHrpT4jfhMeF5wu8Prym8znLssQ5dTpQQQgghxJpQlBBCCCHEElCUEEIIIcQSUJQQQgghxBJQlBBCCCHEElCUEEIIIcQSUJQQQgghxBJQlBBCCCHEElCUEEIIIcQSUJQQQgghxBJQlBBCCCHEElCUxCjvvvuuvP3222F/DSGEEBIqEkK2JRI0t912m9TW1vp9fvjw4XL88cd36MhCYDQ2Nsrhhx8e8Gvee+89aWpqCuo1hBBCSKigKLEIixcvltdee00uv/xySUtL6/T2DjvssKBnQj700EM7/X8JIYSQjkJREkWuuuoqj8gGRMkVV1wheXl56rHXX39dPvzwQxk2bJh88sknkpqaKmeffbZ8+eWX8sMPP6h1unfvLpMnT5bddtutzf+FbUHsjBo1Sr7++msVoTnggANk6NChnX7N6tWr1ftPSUmRPffcU1atWiWVlZVy9NFHd/IIEUIICSW1dU555D2RvnkiR+/jEKtBUWJhIArmzp0rcXFxcuCBB8qIESNarbNs2TK55ZZb5IQTTpB77rnHb/oG25o3b54kJibK/vvvr4TDlVdeKT/++KNMmDDBZ/qmrddMmjRJrfPLL7/I1KlTZcqUKTJy5Ei58847JT4+XsaPH09RQgghFuO5T0UuedCplrtnikzbwVrChKLE4mzevFmWL18uubm5xmP77ruv+tH861//UoLlggsukCFDhvjdFqIXSBNlZWWpv6dPny4PP/ywPPbYY0G/5oknnlB/X3bZZfJ///d/8uKLL6q/L730UuWFgSghhBBiLRascgkScMsLToqSSIK0RlFRUavHEQ3A3Xy46NWrl/zxxx8h2RaiEGZBopk/f76KWBQXFyvvCFInCxYsaFOU7LPPPoa4ABMnTpQ///yzzf/f1muqqqrUe7jpppuM5/v37+8hmAghhFiH4q3u5W/niPw03ym7jrNOtCSmIyUQJOvXrxc7o/0lZiAC/ve//8k//vEP6devnyQlJakUz9atprPNBxkZGR5/JyQkSENDQ4dfs3HjRvW7Z8+erURZe++FEEJI5Cne5vn3rS855YPbKEoiAgZHX0QiUhIu8N5vvfVW5fc45JBD1GPwjsycOVMiTe/evdXvTZs2yZgxYzzEYHJycsTfDyGEkLbZVOr594c/icxb4ZTxQ6whTGI6UuIrhYJUR2FhoRQUFKjogt1AlKK+vl6lazRPPfVUuxGPcJCenq6qfp599lmV5gGITH311VfKmEsIIcTakRJw20tOefkGihLSASBGTj75ZPWDxmobNmyQWbNmtUqzRIq77rpL+V5gyIXB9YMPPpC+ffvaUvARQkgs09jolC1lruVxg0WKSkVKtom89rXIzac7ZUjf6AuTmI6U2AmU0954440ejdPQ58NXI7Wnn35a3n//fVm0aJGMHj1aHnroIRWtgAnVX/M0X9tCdMPcc8S7eVogr9l5551l4cKF8s477yjBhD4qN9xwg+qfQgghxDqUmKIkA3uJHLOPQ6570ikYKu58xSmPXhZ9UeJwOp3u+qAugN3TN1Y7JvCPQLjoCh38DYF13333ySmnnCJ2hecJjwvPFX5/Yu2aMne5Uyae5hryzzhY5M5zHTLgKKdUVIskJYqses0hffKiK0w4KpNOgbLgXXbZRc455xw5//zzVVM1RE86OmcPIYSQ8JcD53cX6ZbpkPMOc/1d3yByz+vRj1FQlJBOgb4oMLbutNNOKq3z/PPPy6effqq6wBJCCLEOm8yipJsrInLxUQ5JTnI99uh7IqXl0RUm9JSQkJRAn3rqqTyShBBik0hJzxzX7165Djn9QKc8/K5IZY3Ig2+L3DAjam+RkRJCCCGkK1C81R0Fye/mfvzy4xyiW3c98JZTmpqc9knffPHFF3LcccfJtGnT1ORr6JmhqampkX//+9+qRBSVHKgQMdPe84QQQgiJQPrGVCA5sLdDpm7nWt5cJrKlXOyRvvnpp59UX4rrr79exo4dq7wEaFC26667qufvvfdeWbt2rbzwwgtqOnvMKDtw4EBjcrb2nieEEEJIZIyuZvqYZjSBMPF+3pKi5NFHH5XzzjtPdt99d/X3EUccYTyHVueffPKJmpOlT58+6gcTs6GZFkRHe88TQgghJPzdXFGRnOueZ1Vh/nuzj66vlkvfIE2DKewxlT3mXDnooIOUwNDpG0zOVl1drXpUaEaMGCHLly8P6HlCCCGEhH/em7xskfh4z34kedkOj0hJtAg4UlJcXKyavyBl88gjj6i5VpB+QXvzs846S/Wr0POhaDIzM43H23veFxA8Zs+KesMJCWpW3I6iu5yau512ddo7JvPmzZP8/PywTjRoNXie8LjwXOH3J5auKU6nO30Dk6v3+8gxRUpKytDlNfRm10CaxgUsSvSsr5hzpV+/fmoZhle0F4coSU1NVY8hGqKFBwSHblPe3vO+eOaZZ+SJJ57weOyoo45S7c87C7wtVgODP44Nen8Ey99//y15eXnSo0ePkB+TY445Rh3z0047TboaVjxPrACPC48JzxN7fXcqahxS1zBALWel1khhYbHH8856jNH5ann56q1SWBh6t+ugQYNCJ0ow4GVnZ3soHSzrLvWYhA3CZdmyZcYcLFjWb6K9532B3hcnnHBCyCMlOCn69+9vqTbzCxYsUPPVYM6YdevWecwCHAgHHHCA6qiKn1AfEzRCy8nJUe2RuwpWPU+iDY8LjwnPE3t+d5atcy8P6J3a6no+ypSyaYrrLgUF0XG6BixKHA6H8pG89NJLhnH19ddflylTprg2lJCgyoQR2bj11ltVj//PP/9c7r777oCe9wXER2cESFvgpLDSYIOoEI4PoiXvvvuu3zbt8OZgRl74cfSxwcR8tbW16pj+9ttv6jG0ev/9999Vl1Xz5HgQPxAYMBrrCMvWrVtl06ZNSmCiGsrXccHnb6XjFSmsdp5YBR4XHhOeJ/b67mwuQwDBFUTo2b11KqVHN/fzmEk4Wte9oP4rKm969uypxAnSKGPGjPHo5HnppZeqaAgG14suukildSZPnhzw810V+GZQJo3jO2PGDHnyySdbrbNhwwZjht5jjz1WBgwYoMQLwOR3EBVvvfWWXHzxxeqnqalJ9t57b/n22289toPtoxW8Bv6gSy65RP7zn/+oqiqIndmzZ0dgrwkhhESnm6uj1fN5pmZqtjC6AqQUbrrpJjWA4c7ZGxhXUZGDMJUvldXe810V+HIQ9Tj44INl3LhxqindihUrDG8JIhhI7cBUvH79eunWrZuKbujmc4899pgSHxdccIH6CYb777/fmL0SYcXrrrtOCc25c+eGZV8JIYREe94baUX3DETEXYZY24gSjS9BYqY9wREpQTL5zGYpaimBMnCKNDX1dbXUdYTHBd0rR+SPJwLfx6eeekoJAaS4IEQQEXn66afllltuUc+jQR1SMUjtQJAApGROOeWUkLzfuro6WblypRQVFckOO+wgt99+uxI95rQPIYSQGGmcltQgzfUJEpfkHqcSEhzSPdMppeU26uhqNyBI1pdYe7cRoUCZNUTJL7/8YvhBIEoQlYqPj1eGYPwePXp0yP//ww8/LNdcc42KwvTu3dsoE4N3haKEEEJia96bEdXbJP6UP+XL1DgZcd0wGXBKf3G09CxB/xKIEttFSuwCIhatUJGSRomPTxBxRPD/+gHiA2W88IWYqaioUB1wkdJB2TQ8Ipg7COIhmIiWro7SoL+MWRAh3fPxxx8rLwnc2BBAaHDHPi6EEBJ76Zt9tm0UqW+WxvpmWXj537Lu5fUy9q7Rkj0x2+jqWlYp0tDolMSEMA2SXVWU+EqhuPwT69UAHG1fC94Lqm5mzpzZqgfIhRdeqAyvECW77LKLKsuFsfXEE0801kHFjS4dRh8Ys+AAMCWjvFiDlIy5g+6qVauUcEG6CF4V8Omnn4ZtfwkhhEQ3fTOktsLj8bK/yuXHab9IwWkDpE/KYDSBMCpweuVG/n3GtCixOp999pkSA2jb7w2MrdOnT1c+D3RShQEV1Tn4G31eUPqLSQ0ff/xxtT4mSIRo2W677VSFE1JA2AZMs+gRA1GDZURcNBMmTFC9Z9DbZK+99pI333xTCSRCCCGxJ0rinM0yuM4lSpLykyQpJ0kqF1eKNIsUPrlGjk8rkrV5o+W3zB4qhRMNUcISmCgC8yrMqr66sO65556qRPf7779Xf99www3y7LPPys8//yy33XabSuXcc889xvp4bNSoUXLjjTcaJcEwyp555pmq7PfVV19VwgZdeCFSADwj8LMgVYT00cKFC+W9996TnXbayaPTLsQL/CaEEELsm77pV1ctyS2+wZxdusvus3aRETcOl/g0VH6IpFTXy5Vr50lCc3PUzK4Op7fpIMbR5a9WSN9YBR4THhOeK/z+8JoSu9fZ+ganJE91yt7bNspl6xeox0ZcP0yGXIx0jUjNuhr588S/pHy+K4py+rDd5MHb0uWIvSLvKeGoTAghhMQwm1uqaYbUusMfWRPcM/Cl9kuV3D3duZoeDbVRq8ChKCGEEEJimE0t/bqG1LhNrlnjTNMCQ5j0dc+3RlFCCCGEkLBQvE15NYzKm5Q+KZKc5zmvXEorURIdZwcjJYQQQkiMV970qq+R9OZG9XfW+MxW6yCFo8ln+oYQQggh4UrfmPuTZJv8JJrUfp6REvQpiQaMlBBCCCExTPE2p6fJdXxrUZKYkyhxqS5JQE8JIYQQQsKWvmnL5ArQ3Tu1b6pblGyjp4QQQgghIWZTqVOGtkRKEnISJaVPss/1dAontblJakpd/pNIw/QNIYQQEsPUbqiT7CbX3GjdJmSpqIgvUky+krSyWtV0LdJQlBBCCCExTNr68jZNrr7MrnmN0TG7UpREGcxP061bt1Y/b7/9ttgdzICMCQIJIYREB6fTKbklJj9JW6KkxVMCetRHp6srZwmOMphYLycnR2bPnu3xeHp6utiduro6KS+P0qxOhBBCpKxSZFB1eZsmV18N1KLVq4SREguAyZm8IyWJiYnquVWrVslRRx0lffr0kUGDBsk///lPqaysNF67Zs0atf4rr7yiZhXOz8+X119/3ZiF+MADD1Qz/I4bN06uv/56JRTMYJ1DDjlEdt55Z5k8ebI899xzxnOfffaZ8X7w//fZZx+ZNWuWx+uLiork5JNPVu9txIgRaoZizDr8/vvvyyWXXKLev97Ggw8+GOYjSQghxLubq668qU9KkLSB7mhI271KaihKiCf19fWy7777SlNTk3zzzTfy2muvyVdffSUzZszwmHmyrKxMrrnmGvnPf/4jixcvlv/7v/+TOXPmyNSpU5Xg+O2335TY+Pzzz+XCCy80Xvvnn3/KlClTZNSoUfLyyy/Lk08+qf4PZrIEeP3q1avVD7aB7R5wwAGyYsUKYxvnnHOObNmyRT799FP1M3ToULUdrIfUFGbE1Ns488wz+RETQkgE2biiTvIaXTejlX0y/Zpcdft5TbR6lcR0+uaHfX6W+mLPyAC8xBjkV8YXSrgmZU7KT5bdv94l4PV1NEHTs2dPWbJkiYp+bN68WQmKzExXW+AnnnhCdtttNyU+Ro4cabwGAgAiQvPf//5XTjzxRDn33HPV3/3795cHHnhAdt11V/U7OTlZrbPnnnvKHXfcYUyp/eyzzxrbSEhIMN4XfkPQIALy5ptvypVXXqkex/tEdARREnDBBRcYr09NTTWiQIQQQiJP6V8VoguAmwb6T92A+NR4cWYniaOsXomStRQloQWCpHajpyjRNEp0arB9ATFg9pRgIAfz589XaRctSADSLEjtLFy40EOUTJgwwWObv/76q2zdulWlcmB0wg/EGH4QtYCIQPQD6SB/1NbWysyZM+W9996TDRs2SENDg1RXV8uwYcOMdZC6ueKKK9R7RVQHwsj8fgkhhESP6kXlhihJHNm2KAEJvVKkqaxechrrZE5pU8RjFzEdKUHEwhsdKYmPjw9rpCQY/EUTGhsbVbTCDEJvWB/PmUHkwwwEBMSCL9GRlZVlpH60d8UXSAkh5YPIyvDhw5X59oQTTlBpJc3VV18t06ZNkw8++EBFXE466SR56qmn5Oijjw7iCBBCCAkHzuWmcmAfE/H56lVStaRc4kWkagNu6ilKQoavFAoGYp2q0BEJq4JoxksvvaTMqVp0LFiwQP2t0yX+GD9+vPz8889yww03+F0HURisg/SLL+AvOeOMM2Tvvfc2jt3ff/+tjLNmYJDFDzwtEDI33nijEiUQVHgNIYRYia//dMoLnzvln0c4ZNLwcN2eWoPkNS6Ta60jTvqNab+qM7MgRapalhuKalELKpHE2qNyFweeEAinyy+/XJUOFxcXK1/HXnvtJRMnTmzztVdddZWKcsBrUlVVpaIbP/30k4fZFNt955135JFHHlFCB+mef//737Ju3Tr1PCpqPv74Y1Xtg7QN1of/xQwiI6jgQeQG/2fp0qXSr18/w8eC6pySkpKwHB9CCAmWJWuccvBVTnn2E5EL74vO/C6RoqGsQdK31ajlVSmZ0jOvfQHWbZCpOqcEoiSyUJRYGHgzPvzwQxXNQMoFgz0eQ6VMe6B8FymVt956S702NzdXmVOPO+44Yx2kXVDRg/TM2LFjVRUO0lo6EnLXXXepPiPdu3dXr0fVDXwjZo499lhlbs3OzpYePXooAQMzLoC/ZL/99lPihCXBhJBo09jolJNvcUpNi9Vw5QaJacrnu5umLU/Nkvzu7b/GXBacWBp5UeJwwgHZhbBa+gZmUvg/2jOHIpIBweDtMcH+QDhAePjbH0RJ8Fr8+ALbQIQDvhFf28D/xv/F6xExUbNJpnrWumMfsI6vcjNsH71LUlJSWnlfrIrVzhOrwOPCY2Ln8+Tm55xyw1PuIS8xQaTuK0ebZbJ2PiYrH14ti69fopYfGzha3vuzf7uv2fbnNvlpv1/V8uc9+sm9i8dIJIlpo6sdwECNn/bwN5gHUnKblJTU4e17P5eWluZznbYMs3iPiKQQQki0+HOJU2561vMevKFRpLxKJDtDYpLyuW6Ta1mvwKoiU/u5bzi71dRKbZ1TUpIj57uxjoQlhBBCwkBNnVNOvNkpjU0tN1qm+7RoNAiLFGUtoqTB4ZCmfoEpr6QeSdIU5xIh6FWyJcIzhVCUEEIIiWmufswpi9e4lrcbLnLqAbEvShqrGqVqhauOpjA5Q/LyAhvuHXEOqclKiVpXV4oSQgghMUldcZ18fvFy+emZEkyXqyIkL1znkN65jpgXJRWLKkVaOjIsT8mS/CAaazfmuERJRnOjlGxokEhCTwkhhJCYZN61S6Tx7Y3ybxH5IyNXcq8YJaMHpktetttbsnmbxCTVq6uN5bXJ6TIuJ4gX90gVWblVLW5bWSuyR/u+xFBBUUIIISQmWfvVVtFlBJMrt0jcLT/JsupBkjduoJEoKIlRUVKz1l3OW5yUIvndAjerJvZ2F19UrMF22m9PHyooSgghhMQc9aX1klLm2Wejua5Zlt22QjL7bpSJCSNlTkaubC5D1CT2urrWrHc1TQPFiSkB9Sjx1aukZn1ke5XQU0IIISTmKJ/nbhw2q0cfGXTBQHHEu8SHc3213FI4W44qWRWznpJaU6SkJDFVegaRvske6BYlTarVfOSgKCGEEBJzlM11q42ivt1l1H9GyG6zdpHuO7kdn/ttWx+zoqRmnStSUueIk7L4xKCMrrlD3b1KHJspSgghhJBOsXm2O1LSMMDVOCxrdKbs/OGOkj4s3Sh5Ldkae03NnU6n1KxziYmSxBRMLx9U+qbncHfDzKSt7jRQJGCkhBBCSMxRNq/ciBQkD0736MORNtAVCUh0OqWupF5ijYatDdJU1WT4SRLiRboH1tBVkZOfIOXxri7d6RWMlBBCCCEdH5TLG6VxjaskdnVKhvTO97z/Tu1rSk9EYSbccFPTEiXRkZIe3TDdR+BmXswFtLVl+pOsmjpxNkUumsRICSGEkJiiYqFpdtyULI9maSDFVF2CCh3MHhxL1Kw1V96kBpW60VRmuI5RPKJJm1qmVY4AFCWEEEJiMnUDVqRkSp88/yWvefW1UurWMDFBrTlSkpQiPTsgSmqz3cdo68rI+UooSgghhMTs7LgrUjOld67/mXCjMb9LpCpvOtKjRNPU0moebF4euRQXRQkhhJCYjJQ0ikNNRtenlShxD7j5ECXbYtdTUoz0TRDlwBpHT/cxKltNUUIIIYQETVNNk1Qtdc2OuyYlXRri4ltFSpJ7JYuzxWbSo6Em9iIla12REszHtyUhWfrkBd+xNrmPW5RUqlbzkYGREkIIITFDxaIKo1oEJleUwqYkew7KcYlx0tw92d2rJNYiJetdIqI0IVka4+KkoFfw20jr705x1Zla1ocbihJCCCExQ5mpvbwvk6vGke8adLs1NciWEldPj1igqbZJ6ovr3Y3TRKSgZ/Db6dY/WaW/QPMmRkoIIYSQoCk3V96kohy4/fRElckYGlt+khT1e0AHREled4dsSXRFk+K2UJQQQgghQVPWUnkDP8UqHyZXTXp/tyip21Abm+XAiamSkiQdqr7Jy3ZHWhKrG6SxqlEiAdM3hBBCYoLmhmap/NuVvlmflC618Ql+IyXdBrlFSSTTExEtB05KUVESdGgNlrxublECalt8KuGGooQQQkhMULmkUprrW0yuqa7JXvxVnuQMcQ+4kUxPRDp9U9CB1A3IzXKnf9R2KUoIIYSQwCmb6za5rkxxiRJ/kRJzdUnKttoYbTGf0qHKG5CVLlKazEgJIYQQ0mmTK8qBgb/qG/P8NxmVseopSZEBPYNP3QCkfOq6mSIlpu2GE6ZvCCGExJwoWZnadqQksVui1CfEq+WculqpqXPGlKekMi5BquMTO5y+Ac25qT69KuGEooQQQojtQcO08pbZgbemp0plfGKbogSRgKrMFHcDta32FyXOZqfh/dB+kI6mb0B8L1PZdCFFCSGEEBIQVSuqpKnK1QRtdborStItQyTVq5urmfqW9ESys1lK1jTY/kjXbaoTZ4Oz043TNJl5CSriAqoi1GqekRJCCCExMwkf+DteV960/RpnD3ckYEsEZ8KNSOVNUqrExYn07dHx7aFXSVGSK4XTsLFWlVyHG4oSQgghtqfc1F5+cWLbJldNgnkm3MJYECU1xjIiJX3zRBITOmZ09RYl0uSMiNmVooQQQkiMtZdv2+SqSenrFiXVplJau1Kz1rNHSUfay5vJy3bIxqQ04+/q1dUSbihKCCGE2Bqn0+lO3+Qmy7YE15wt/lrMazIL3NUlSE/YnVqvSEln/CS6q+vGRPcxql5FUUIIIYS0Sc2aGmksc83NUj/AFSUBvXPbTl10H+yOlEhJbex1c+3Vue0hfeMZKQl/NImREkIIiaGIQVeehA9s6+nykwTiKckf5hYliaWxIEpq1O8Gh0O2JiRLQQcbp5lbzW/UnhKmbwghhATK8rtWyBeDvpa558+XupK6LnXgKhdXGssbsjKM5fY8JT3z46Q0IUktp5bHjqdkc0KKOB2OkERKtiSmKJED6CkhhBDSLs2NzbL83pXSWNEo61/dIN/u9IMUPrNWNRTrCpgNnmvi0gKOlKSliGxOckVL0mrqpbk+/CWv4aKhvEEayxtD1qNEe0qaHQ4pbvGVIH0T7mgc0zeEEGJzqpZVSXONe0CFv2LhZYvkp+m/eqQ2YhXzJHTLG1ICjpSgq2tFeooxGNba2Oxa49GjxLVPna2+yUgVSU5yp3DQnK6+pF7CCUUJIYTYHLPwSBvsjhSUzS6TH/b9WZ44eJEUF7nuomMR3Vo9ITNBVlckBtTN1XhtVmyUBdd4zA6cKrnZIumpnfOUQLQN6+tldg1zu/mgRMmVV14pU6ZMMX4OPPBAj+dra2vl5ptvlv32208OP/xw+fDDD4N6nhBCSOd6dIy5Y5Ts/MEOkjEiXf3taBbp+/Naeeu8lTF5aDHfiy6Fxcy/GzYHlrrRNOW4RcnWlbUxMztwQSejJJpRA0WKIlgW7GpqHyB1dXVy8cUXywEHHGCoKDP33XefrFq1Sp566ilZvXq1XHPNNTJw4EAZO3ZsQM8TQggJnjJTN9Ps8VmSlJsku83aRa7ef7XsNW+563q92r1OLIF0QnO9y+eQ2DtFajYFlroxyHeLktIVtTESKUmRQSESJaMLRD6LYAVO0OmbpKQkSUtLUz+pqe432tjYKB9//LGcd9550r9/fxVJ2WeffeS9994L6HlCCCEdixRUzC83OpRCkICPf4+TuxoHGpUTKWX2HXADHYwbTVGPQCMlEDKaShu3mq9pSWGFqkeJZlSBV1fXVRZK34AHH3xQpk2bJqeffrr88ssvxuNFRUVSVVUlI0eONB4bPXq0rFixIqDnCSGEBA/C6Y2VTUaUBDQ1OeWqx5yqLBTloSCj0r4DbqAGz+ps941y75zAXp/Wz/2a2vV29pTUGsubVfqmc34Sj/RNBCMlQaVvbr/9dmlqapLq6mr58ssv5ZJLLpHnnntOhg0bJpWVrjrx9HRXHhNkZGQYj7f3vC/q6+vVj8cbTkhQ0ZqO0tzc7PGb8JjwPOH3pzNE+5qybU6ZsZw5PkO9j2c+EVm02u0v6N1QI2mNjVJfVq/MoLF0TKrXugfJrSmu9vI6fRPI/8/umyj1jjhJcjZL46basL3ncB+TmhZfzdb4JKmPi5d++U5pbu58+e7QPiINCfGqn0tOY70SJR3dhzhMW9wOQZ2dycmuDxypm2OPPVZFSr755hslSnQqB4JFCw9ERvTj7T3vi2eeeUaeeOIJj8eOOuooOfroo6WzrF27ttPbiDV4THhMeK7Y7/tT/EOJsVzbq1YWL10j1z/Rx7i8q4G6Zdxe8dtKSRnqHrhj4ZgUL3Lv/8pad4VRQnOJFBYGcFffkOaaUbe+WhwlNcrv6O2XtPoxcTY4pa6ozqMcOKl5oxQWhqZ8t3+PPrJxRZoSJXXF9bJq8SqJSw2+eHfQoEHtrtMpyZyYmKgiJ6BPnz5KtCxfvlwmTJigHsPy4MGDA3reF6eeeqqccMIJIY+U4KSAryUQ1dYV4DHhMeG5Yt/vT3HhZvdd7dQhct/XKVK01fX3wbuKJP+SKlLq+ju5JkcKCgI0W9jkmGwp3+o2/Gb1MJbHj+whBQXtv35kqciviWuUKEmob5a+3ftKYrarrNgux6S6sEYWO5d5NE7beWJv1fwsFIwbIrLxz1QZI9vU33nNeZJZ4J5jKJQELErKyspU1czJJ58s3bp1k++++05+/PFHOemkkwyBMnXqVLXOzJkzZc2aNfLZZ5/JXXfdFdDzvoD46IwAaQucFBQlPCY8T/j9sfM1Bd01y+e7qmqSeiRJdXqK3P6Kfj8it53tkFeXe5a8Do7ge4zEMdGeEkeCQwrr3FGgvj0cEhfXfsQjv5tTGUM1dRvqJbl7sq2OSd1697QC2Bd0qu3R3RGyiM+Ygc2yxuQrqSmsleyx2RJVUZKVlSX9+vWTU045RUpLS1Xk44YbbpDx48cb61x22WVy/fXXK/GRkpKizLA77LBDwM8TQggJnNr1tdJQ2mCYXG99UaSsxaY3Y7rImEEOie/lHnArVsee2dXoUdI3RTZsdQ/CgZYEY34XHV3Q3oysMeGJAkSiAqkkMVX1KAllCgoVOL9GqIFawKIEOwgvB36QsomPj/cpXNCLBOW/eN77oLT3PCGEkI51cnUOzpQH33EtpySJ/Oc01/U1ua+pY6mpUiUWwFw/DdtcPpJUNE7bIkY317SUwMYXpDhQreKrCZldy4EHhKhHiWZUgVcFThgbqHUohuRLkHj7PtoSHO09TwghJLhOru8VZUq9K2giFx8l0i/fdY3N6O8eTBqK7DfgBjoYQ5Rs3BJk4zRYCxIcUmVqNW8uMbZr47SCUIuSgV6t5sNYFkynJyGExEAn11dWZxlRgiuPd9/05eQnSHl8i3GzxH4DbqCDsSM/Vaprg2ucpmnONYkSG/YqqfVuMd8rtDf9mWkOyeyVKNVx8WFvoEZRQgghNo+UxGclyKom18C602iRbpnuQQkTs21OdBk347fWirMpvFPPRxJzVKO2W+CzA3sTZ2o1b25CFi0ayhtl6W3LZcNbG5WZOVBxVhMXLxXxiSGPlIBRAx1GCgcTF4brPKIoIYQQG1K3qc7oTeEYkgnjn1oeghYlfoyccU1OqSt2V2rEUqSkLNXUYj5IUdI9L17KWqJJVRYQJYWPF8ryO1fInLPmydJblrcpTGo31hriTH3ODkfIWsy38pUktqRwGp0eqbNQQlFCCCE2pKxlvhtQ0ceVugFD+nqG7nOzXFPZ29kz0Vb1kaYk2dRiPje49IVZuDWgq2tjdDt+b/vL3aV3xT0rZelM38KkamWV/HzQb9Jc53q/K1JcVUPhiJSMHog5cMJvdqUoIYQQG1JuqrzZkG0SJV6RklyvklfzQB5LkZINjuAn4zOLEqNXSbMrChVNqld6Dvgr/rdSlt3qKUzKF5QrQVLTUp5bmpYiz+cPFdShBJu+CjRS4iFKwmR2pSghhBAbUmaqvFmc5O6rMdhLlGSmiZQme/bhiBV01CcpL0nWl7uHs2AH5bxsh1evkugJN2eT0xjw49Pcla7L714py25zCZPSX7bKL4f8LvXFrjbyGSMz5IaRO0hxUqr064EK19BXt7auwAnPeURRQgghNkR3csXANa86za8oQfuF+u7WGHBDSXNDs/JTeJcDdzRS4hFNiuIxqt1YK831rohI3p65Mvq2kcZzy+9aKXPPmS+/HfmHNJa7+rN0m5wtY1/fQVbVpYQtdQN6dHNIfS4jJYQQQrxo2NZghO2zxmbK8o2uO+NeOSLpqa3vkp151hhwQ0ntxjqVagEpaJzmngIo6EhJj26uTqhWiCZVmVI3aYPTZOCZBTL6Vrcw2fDmRmmuce143t65suPbk2VjvXuunnCYXDU9hqVIk7jOr4oVTN8QQgjxSt2kjs6UolLfURJNYo9kYzCpipH0jW4vD1L7pxqRkuwgurl6RkqSLVEWXG0ykKYNckXABp5VIKNmuoUJ6H1YL5n88naSkJ4ghUXux8MVKQEjBsUZ3hukbwIpVw4Wpm8IIcTGnVxr+5srb3yvn9PNYfQqiZX0jVk4qHlvtnSsHFi3mt9kNnEWhq9jaTCiJL1FlIBBZxfIuHvHSMbwdBnyr8Ey8fHxEpfkGsILN7lfH+rGaf4qcJxVjdKwtaWFcDTmviGEEGItPwnYlGOuvHH4HXThmejZUCtNWxukqbrJw0RpRzxSLPkpRjfXjlSeIFKyLT5JNR9LbW4Km4kzEKpWeKZvzPQ/qZ/68aawyB2xGJAfvveGCpz3IUqq3AIqKScppP+DkRJCCLHpRHxxSQ5ZkZBuPO4vfYNeJR7VJTFQFmyO+FSkpXbY5Kpb88cnOKSoxVdSsyZ8HUvbo6olUoLPNtU0mWJbeEZKwvXOdFlw8BU4lUsrVcVQIFCUEEKIjWisbJSq5a5b1YxRmbJyk8NvjxJNbpZ3yav9fSXmfdiS0vEW8yAuzqGEm5GaCGPH0rZwNrvLgVML0sQRH1gqxuwpCfUMwWb65Ytsywi+gdqa59fJLwf9FtC6FCWEEGIjyhdWiLTcxGdPyJIVG6RdT0ksNlDTnpK41DiP6pM+QXZzNadwisxRgDB1LG2LuqI6o7Im3St10xZril2/87uLpCaHz1OC8vK0ge73VR5gBY650V97UJQQQoiNMF/gs8ZnyYr1ruX0VNegFEgfDrubXVH1oYVVar9U2VDqHog72s0UxygSHUsDSd2YK2/aY8kap1EOHc7KG03+KPcxKl1WE1D0x2zMbg+KEkIIsanJNWNspqxuCd0P7u26k/WFa/6b2EnfNJS6zLogtX+KrFjv7LSnwiVKwt+xNNhy4LZobHTKybc4RVfmTttBws7w4YmyrWXywtoAqpTQd6Wx0vVZBQJFCSGE2AhMwqYpz02Xhsa2UzexmL4xR3oQKVm2zv3c8P4d2yYaqGmja9QiJStM5cABpG9ufUnkt7/d+33tSeFL3fgyu8aV1klTTVPIUjdqm516d4QQYoFQ/vLly+Wxxx6T448/Xs4991ypqnIP3LGG9lIk5SbKqq3urg6IlPgDkZLq+ESpikuIieob80R8aDG/dK1rGdXROVkd95QUJ7k7lkZDlFSvClyU/LnEKTc96wqRYBK+F651BN00rqOipMijp0tNQJVigcI+JYQQ21FRUSEffvihfPnll/LVV19JYWGhx/M77bSTzJgxQ2KN5kb3fC8p/VJl8Ub3c0P6+h+QumciteOKlqTXVapW8xBz/tI9VscsquJ6psi6ks5FSfSkfE2OOHWMejXUSPWqmogfo6oWUeJIcKjW+f6oqXPKiTc7pbElSHHNiSI7jo7M+xzUW6Q4NU2kzN1oLnNkht/1y+a2rBggFCWEEFtRX18vkyZNkhUrVvhdZ8mSJRKL1Jnme0GEwOylaCt9Ex/vkO6ZTjXgDqyrlOa6ZqnfXC/JPdyt1e0aKdlimgF5WOu+YgGDBnMAZleIksYKV8fSUDcH8wcEUHWLKEktSJW4BP+JjGsed8riNa7l7UeIXH9K5IQTZiB29EoVafEyVa6olp5tmVznujxQKb0DO9eYviGE2Iq//vrLQ5AkJyfL1KlT5V//+pfxmHfkJFbw9FKkeJQDt5W+cTdQS46JChzzpIJrne5UwvB+HR+c4SkBHqmJCJYF1xfXS1NVU6v28t58/adT7n3DtZyc5ErbJCZENuKVOdh9jDb85T9VihQYxB3ImuDuPNwWFCWEEFsxb948Y/nKK6+UrVu3qjTOLbfcYjy+Zk3LbWSM4eGl6J8qK1tESVxc+1UnLrNrakzMFmwchziRpTXJoYmUZLt+R6sCJ5By4G0VTplxqzs6dttZDhk1MPIpuPzts6ShJa1V9nPLbJDt+EnQUycQKEoIIbZi/vz5xvL06dMlNdU10OJ3jx49ukykBJPQ6R4lmO8kKbHtwal1q/ka2x+HlF4psnSjIySiRHdC3RilSIlH5c0g36Lk2iecsralUdrek0T+eaREhRHDE2RxqkvFxW2sluo1Ne331KEoIYTEuigZN26cx3MFBQXq94YNG6ShIfQzmEYbc3+RhpwU2VbZ9pw3rapLYqCBGkpQ4YfRPUrM5cDDOmV0FUEH9aLEtIArS8LWo2SIb1Hy/o+u36nJIs9e41Dt8aMBDMVz0t1d6rZ81zJFsxdlc8yRkpZQVDswUkIIsQ0wA2pR0rt3b8nNzfUpSpqbm2XdOtNoFSOYUy5F8akBmVz9RUrsmr7xiBb1c4uSXjkimWkdH6RRZYPjGK2urh7lwINai5LScqdRZbTDSER2olc51T9fZE5GjvH35llbfH5Xy1o6uSb3TJaUXjS6EkJijKKiItmyZYvPKIlZlMRqCkd7KTDfy6oq93wvQ/q0P0DlZjtkS2KyLt6xba8Sc7QovmeqbCrtfOrGPKFhTXyClLV0LI1o+kaXA8c7lF/Im3mmYrPxQySqoB9MYXaW0fcGkRJU2piBH6exzGVyzZ4YmJ8EMFJCCLGlybU9URJrZlfceWohkdo3VVaYvBSBRkrQh6M0wXXHWmtTT4k5wlOW7o78dKZHiUanwbTZtXZj+x1LQ1YOvLKlHLh/isQlxbUjSqLbXwZRpT75cTIv3TXZUv2WBqnARJEmzPPdBOonARQlhJCY8JPEeqQEPTN0ySjKgVducAZcDmyuLtEpnLpN9dJUp+Mm9oyUFMWbe5R0fqDWDeg8UjgR8JVgUNels2l+TK7zVjgtEykBffPgKzGlcL7d4t9PMp6ihBAS46Jk/PjxrZ4fMGBAzIoSjx4l/T17lAQUKWkRJWaza+2GWtu22QermtziIVTpG+DZRj38KZxq03xGaX5Fies3KnHHDpKo07cHfCW5fn0lHSkHBoyUEEJsJ0ri4+Nl1KhRXSpSYu5RghbzuhwYYiM7IwBPScu4sNnD7Gq/FI65lPnv6uSwpG88KnAi0KsEM+lq0n1U3jQ1OWXBKtfy0L4i6anRnx6gXw+RdUlpsrklHVj6y1Zpqm0y0lHlLe3lk/KTJDnAbq6AooQQYgsaGxtl0aJFannYsGGSktJ6bpDu3btLRkZGTIoSs5cisZd7vpdAUjfmSIlHr5J19o2UJHZLkEWbEoKKFgXSqwST20W6V4lHOfCg1qJk+XrMd2Od1A3om+dQYZu/WqIlzTXNsu33bWq5Zk2NNGxrMblOyApq/iCKEkKILcBMwHV1dX79JAAXPx0tgdEVpcGxgtlLsS0tRZzO4AZjHSnxbKBmDVHibHIGvJ5OOSFapMuBISZSkzsfPUC79oKekS8LrmqnHHiehUyu5vQN8PSVlHYqdQMoSgghMVF5o9GiBAKmpKQlnBADmL0UGxwprXwQ7ZGc5FDNwYotlr5Z+9I6+XzgV/LXmXON8L8/6jbVibPBJWDie6XI1orQ+UnMxxMVSnWOuIilb3TljTgwGV+a5U2uOn0D5ppFyazNrTq5Bto0TUNRQgiJicqbWPeVGJESh8iKerewGBxAjxLP+W+slb5Z/WihNFU3yca3i2T2yXP8ChNESZbd7Q4Z1GSGZnZgn74Sh8MwuyIV4d2DI9QYswP3S5H45LbLgSdYRJSg+gZsTUyW0pwMo+KmYVuDR6Qka0JmUNulKCGExETlTaxX4Ljne0mWlcXB9Sgxp3Aq4hOltiUKUBvl9A0Ge3PqouSrzTJ7xpxWpcrN9c0y5+x5svZZd5feTWPdMxAO7x+6lIYuC9aipLmuWWo31nZ6Pxde/resOHq1lP7oOYFdfWm94b9I81N5M7dFlGSmtT/xYqToleuaCBIsyWmJljhFNn+3xRAlSXlJktKntferLShKCCG2EiXp6ekycODALhUpUfO9lNS3qrwJJn1j+EocDmO2YHhKUCkRLeqK6pRB0kzJF5tl9il/GcKksapR/jhhtmx8p0j97UhwyITHxsmi9G7Ga0KdvgEbzRU4qzqXwln9WKESVPUr6+Wv0+epNJSxbXPlzeD0Vq8tq3RKoWvXZdxgCAFreErgv+np6p0mv6W4UzjrXl4vDaUNHTK5AooSQojlqayslJUrV6rlsWPHSpy+ResiosRsSDX3KElOEunTEkYPhLyWcVz7StCMDeH2aGGOkuTs2l3i0+PdwmTGHDV4/3bEn7L56y1Ge/3tX5wkfY/s4zkRX6jTN60aqHXc7FqxuFKW3LzM+LthS4PMu3CBIQbNxyBtcOtIyXzXaW8pP4m32fWH5u7iaJmlGp9dRzq5aihKCCGWZ8GCBQH5SWJVlJjLgVP6opura3lQr+DunH1V4ERzYj5zlKDXob1kh1e3k/i0FmHyeYl8s913RplpQlaC7PjmZMmf5hoJtShBCe+gAMuiA0GnwzwaqHUwUoK009xz56kUkMLhTlMVPrEmoIn45pn9JEOtESXxNrtWOxIkbYI7ctXRyhtAUUIIiRmTq549ODExMabmvzGXAzd0T5Ha+o715nA3UEu2hNnVu2lYzq45MtkkTJprm40GXDt/uKPk7OzKFyDKsLRFlAzsJZLUcpceCjDTcI9u7vlvOlMWvOzOFVI+z1UilD48Xfrd6c61Lf73Uqn4u8LjGKQNbqfyZrBYCm12Bc0T3CkcTTAT8WkoSgghMSVKkNrp169fTEVKzMJhS3Jqq1RDoGCmYFBsigKguiRa+IoS5O6WI5NfcQuT1IJU2eXjnSRrjLuKo2iLSFVN6FM35uO6KTHVmFG5Iw3Utv6+TVbcu9LwwYx/eKxk7pUhBWe5jNiInsC8W7mk0nhNWkGqX5MrGGs1UdLDLQa3DXG3nAdJuYkqqhcsFCWEkJgSJeYUzrZt26S83F2eGAst5tfHmXuUBBch0JPyFbUYXSPVHMwf2k+BQTuln3u/cnfPkd1n7SJj7x4tu325c6u0htlPMjwMogRm18a4OKMlf7CT8jVWNsrc8+aLVjXDrhhipDKGXz9UMka5SmgrFlYakRRUqcSnuoSYprnZaXhKkKLKSrdm+gas7ZYlCZnuDrtZ44M3uQKKEkKIpUGoXosSpGby8tp3dsaar8Ts+1hR7069dDR9YzZxVkWgOZi/z9Xoz1GQKnEJnsNR+pB0GTCjvyTlJLV6rU7dhGp2YG/0cdXHCTM0N5QFbgj++8Ylhl+m2+RsGXyRewa9+JR4mfj4eInz6keSNrh1lGTVRndEyGomV+/0zbpShxKTnfGTAIoSQoil2bhxo2zZsiXgKEksihLtKYHZc9lWl1+mY+kb1++tCcnS1CICaqIUKakvrlfVP/4Mnm2xbK3bZzEsBBPxeaMb0nlElAJM4RR/UWL0U0EKasIj41oJrqzRmTLihmEej/kqB57nYXIVy6Grb8D6EpEe+7lVSvddWuqFg8QdayGEkBhI3cSaKEHjLV0SjHLgNZvcz2Gelo5ESpwOh5RnpUr30iqVmsD/cES4/4VHKWywoiQC6RvgaXatkeyJ7bdMX3LzUmN55E0jfIoNMPCsAlU+u3nWFr+zA89dbja5Wit10ypSUiLS75q+qow7Pi1Bekx1P7l0rVO2VYjsOLr9fWCkhBAS06LE7hU45vleUvulyvqWNhDdMoKfwl57SkBpmrljqbuZV6TwaBoWpCjR6ZukRNdkfOFO3wTqvWkob1Q+EZA5JkMGzPCvmCACxz80TvVnQZVKv+Nb5+I8J+ITy5GR5pBslz1GnZeICA27fKgMPn+gh5/kkXedstM5gTXpY6SEEBJzoiSWWs2bK29gBl0/u3XoPFDSU10DeX2DKwowxDTgpnagUqIzeJTC+ogS+APmz+Xr3RGN+PjQRxB654qkJKFXSXBlwRUL3Kbq7jt0b9foiSkDdv5gR7/Pz2sxuaalBJ+qi6TZtazSlb6BT8jXPpvFVXswUkIIsYUoQanv6NGjA3pN//79Y0iUuI2ozrwUqatvHToPFAwYOoWzNs4cBYi82bW9pmH+WFssxjEIRzmwPk4QAcE2UDNPRNeRHh1mKqudxnQCaC8fDvEVCvR5iN45pT4K3SBUzGXN7UFRQgixLI2NjbJo0SK1PHz4cElJCexuHuv16tUrNkTJWnekpCrLPUh2JFICtChZ2RzdsmCjHDjeIan9W1ee+CNc7eW9QRSmMj5RKuJdCYWqAI4RZsk1l8R2hgWrrJ268Wl2dXeYN9i4RWRLWeDboyghhFiWZcuWSV1dXVCpG29fCap39DbsSK0pUlKa7BZlHYmUmCtwCuM637G0U+XALekbmHfjkuI6ZnIN4ezAfn0lLRPzYUZl79mL/UVK4pIcktnSi6SjzF0ulja5+upVghROZ1I3gKKEEBJTfhJfZtd160wjmY09JUUJKT67aXbE7IqOpXoulo50LO0M9VsapLGisUOVN6jkiESkRJcFG2ZXp2cTO2+wP1XLq9RyxqjMoISWL+aZ28tbOVKS5/CowPGGooQQEjOESpTYOYWjPSWYhXWtqXFahyMlLVkFdCyNy+9Yx9LOUh2icuBwp2+Ah6+kjYhS+YIKJVxC4Scxm1wtL0o8IiXONsVVIDBSQgiJSVESKxU42lOC6pj1pRKy9A1w9mzpWFraIA3lgXcsDWk5sI9J6AIRJahI6dPBYxBM+mZdsrvPSNls/+aIsrllne5mak5v6QgDSp67ZdojfdNWpCTBs4O+XyhKCCGWZcGCBep3enq6DBrkbtXdVSIlEAqN5Y3ucmDTRb/jRlf3AFeXlxZUdUmoqFrpSnMEGylpbHTKyg2u5aF9UZEVvsEasw+junV+mrsz6eZvXY3O2jO5dlaUFBaJlFdZP0rSntG1rt4pf7d89Ua5v45tQlFCCLEkTU1NhpgYOnSoKgnuaqLE7CcxN05LTBDp0a1j2zQ3UKvqFp0KHHM312AiJauLRBqbwp+6AclJDhUFKElKlY0prve47Y8ywwvjTfm8ciPNBk9JZ5hn8aZp3ucTet/4MrouXuP+vALdD4oSQogl2bRpkyoJ9u470pVEiXkivlRTpATNvToaJTCnb0rTzJGS6sinbxyYjC9wUfLzQvfySHd2LuwpnD9TXRPNORudUvrz1lbrNVY1SuUyV2gDVTfxXpPtBcvcFfaovNE9XXQq0Tt94ymuAtsPihJCiCVZu3atsdwRUZKdna1+7Nxq3lztkdg7RTaXdc5PYja6gmJT35dIml2N2YH7pQQ1gL/znds0uf+O4R+stdl1Tkau8djmWZt9m1yb207dPPOJyGWP58q64vb/7/dz3fu53XCxPDqFs7VCpKbO6dPkGuiEghQlhJCYFCXmaAm21dzcdo8Jq6dvKjM63zjNO1KyLiHykZL6rfXSsC34cuDqWqd8+ptrGamrXcdK2NFlwfPSu4uzZbTc/K3JbdxCublp2oTWk/Z99ptTzrhd5O0fM+SSh9r+nzV1Tvlunmu5f77I0DCnqcLZq8Sz10pg26IoIYTErCjRFTj19fVSVFQkdm4xX5ra+cZp3p6SovpESeyWEFFPibnyJi0IP8nnv2PAdi0funtk2q7rSElVfKLU9HcduMrFlVJbVOe/vbxXpKSqxinn3OWOGHz6q8sA6o8f5rnb6O+3gys9YnW8Zwv2LmvGOdfLHWxqE4oSQiwMSgP/+9//ylVXXSXl5T4mlohhQhkpsauvxOwpKYrrfOM0Pbuw9gwjHaQ9HTXra6W5vjmyJtcgIiXm1M3/TYnMQK09JWBtX5evBGz5botPUeJIcEjmaM9Orv951qkMupqqWpHv5vr/n1/84d7PaZOtL0i8z0cdKdlU6pRNpW6Ta6DiiqKEEAvz0UcfyfXXXy+33367HHLIIVJdHfk5SqIFRYnbU5LUI0nWl8eHJFICg2z3luIQzEliCIPmtjuWhiVSEqAoaWh0ygc/uZYz00Smbi8RwTwz75z0HJ+lwcrkurRSLWeMzJD4FPfn9NdSp/zv9dbb/fgXZ5sRIYAxPFL7GY5eJfNNzd8C9ZMAihJCLMyPP/5oLH/33XdyxBFH2Hoel46Kkr59TbesXSRSgqiFThOoxmmmbpmd8ZSYza5bykXSBka2LLjK3DhtiLsxWVsgsgATJThwZ1e5biTIyXKoyBL4qbGbxKe5BMfmWVtUFBNULKr0aXJtanLKWXc5pamlJPaSo0Xi41yv+ehn3/9vU6nT8GHA4JrXzSaREpNIXr/Z2dpPEmDlDaAoIcTCzJ492+PvTz/9VE444QSjVLYriJKePXtKcrK7vXpHRYndKnBqN9Yabcsxi665MVVnIiVmUYIGXckD3KKkKgIN1DxazBcENjvwu99HPnXjncJZuTlOuu3kag5TV1QnVUtdJcBlc3x3cn3wbZE/FruWRw8UueVMke2H1RldaZeZ5vDRfPWne3naZLENnq3mfczdE6DJFVCUEGJRcCemRUlGRoakprou4G+99ZacfvrptqwmCZSGhgY1u29n/CRWjJSgk+ma59ZKfWmLkzGA9vJGN1ezKOlkpMRsdm0wd3WNQKREi5KUPikSnxof0Pfg3e9dy2jSdcDOElF0CgdfN8ek3FYpHF8m1zWbnHLtk+5B+YnLHeq97z3BLfp8RUs+/939mv12sEeURPfN0ZYRnb7RJtf4eJcoCxSKEkIsCma23bzZNRLttttu8s4770hioqt14vPPPy8XXnihEUKONTZs2GDsW2dESX5+viQlJVlClGB/fj92tiy4ZJH8tN8vyljaFhWLXT4FI1LScrGHHyQ1uXMDlrksuDrHHa2oWR3eSElDWYOaIRikDU41SmBRMlte5ftcRrRBD3RTtxPJSo9wpMTkKykd0lqUlGuTa7xDMsdkqs/5vP85parlUJ57mMiu41zv2SxKvH0leN0Xf7jn9YlEyXOoSEp0SH5LN36IZ3iAFq12/T2iv0hKEOcrRQkhNkjdbLfddrL//vvLq6++KvG49RCRhx9+WK699lqJRUJhcgVoTa/LgiFKoini6jbVS/WKamOemV8P/U1qN/gWJps+LpbFNywx/s4Yni4btoQmdePdQG1bUorEtXg0qkIUKalcUik1i2rbTN1og+0Vjzhl+mVOGX+qU0q2tf583jGnbvaIfPRgaF/3/1walyFJeS6RW/pDqWo5X7nElcbJGJmuIj9vznJHQRBBuPUs9+uH9W1QE+yBb+eKVFa79w1zxGxoiYbtOSFyvplQoc/LolJRgqS+oWNt8jskSnD3duCBB8oVV1zh8Xhtba3MnDlTDjjgADn66KNV/juY5wkhbv766y8PUQIOP/xweeaZZ4zHb731VlmxwtTLOUYIlSgxp3AqKipk27ZtEi28m5NBmPxy6O+thMm6V9bL7BlzpLnOlZ7L37+HOCbmGL0rOpu6AbnZ7gGvtMohqQNayoILazot3DZ/t0V+3OsXWX3yGln34nq/5cC68gZ9O/QkdCf91ynNzZ7//53vXL+RHvjHbhJxzKmHhYUiuXu6qnAaK5tk7YvrxNnker9Z411K76Zn3e//wYsdkp3hPtbYBxh1AQbtL00eks9bGsOBaTZK3XhX4MDY+0VLBVGwJtcOi5I777xTevXqJTU1nqG+Bx54QJYsWSKPPPKInHfeeaq/wsKFCwN+nhDiP1KiOemkk+TSSy/1uV6sEA5RAlavbokpRwFz1Ym+8qI89pfD3MJk5cOrZd4FC4yBrs+RvWW75ybKhlL3hT0UkRLzZH4bt7grcJqqm1REpzPpmXnnL1BzxIC/r10slcur/JYDQ4CsNTXb+uw3kZkvuP9eXOhUk7qB3caJ9MxxRFeUrBbJ29Odwln1cKGHnwSN0rCOLoP9vz1ab0+LEvDRz04//UnEdpjFsjk1NSHckZLvv/9elSTusYfn0UY1wIcffijnn3++DBw4UPbaay/ZZ5995L333gvoeUKIJ1psYP6WQYMGeTy3887uK9vSpUtj7tCFUpQMHuy2/q9atUqsECkZe+doQwggpQNhsuiav2Xx9e6UTcGZA2TCI+MkLjHOo3V3KCIl5hl2/y50StpAk9m1sOMpnIVX/O0R+WmqbpY5Z80zmrJ5lAMPTpOSbe7upZobn3HK13+6BrV3Wgyu0ai60SDSgXbvYOEqkdw93P1KzPuaPTFbpS10oAklvb4ahu09SSTFlQGSj39xeUnQ4XXWHNdjSPmM8fy624J+pgZqP8x3Px7W9A0iI/fee69cdtllrZ5DC+eqqioZPXq08RiWly9fHtDzhBDPGXLXr3eFvidNmtTq4jZ8uHuWLkQfY41wiZKVK00dnaIoSnJ2z5Gd3ttBUgvcwmT1Y+6S5WFXDZHRt44UR8tMwJ7lwJ0fnMeY7/5XIWqR2uk5cDa+VyQb3nRVTCVkJUjSgETDCLrs9uWty4EHpamUjSYny13lcvzNTtm42enVxVWihhYJ2ypFtqakStoQr6ZvcSJZYzI9GoaN8zO7L0ysECYAHhL088Dsx9W17iiJHVrLe2OO4DU0uk3ZwYpo16QHAYK0y/Tp06Vfv9YzBFVWupzi6enuZjiZmZnG4+097wvMV4EfjzeckGC46TuCLqOM5XLKYOExsd4x+fNPd7IZosT7fQwZMkRduHCXBVESqfcZqeOiRQmMquhT0pn/h8isBv6bUL/3QI8JyoEVcSjxTZa4pDjZ8d3t5bfD/lReDoVDZNStI6Xg9P7qs9X+DvPMsr1zW/suggUCANUSxVtdKYnU/VM8fB/BHiM0eVtw6SLj75G3DpfK7EopPHWtOBucsuK+VZK7d64RKUnumSRxqXGyusi9H2guBvMn/AhoT/6Pq53yxxJ3CqCgV+f3u6OMLnB7X+avdErfPXIM0zLIGJEhjhSHR2+OsYM836/5PDlw5zj5pGV7H/7klGpTP8R9J9tzfEKExxv0JzGfx/g+h0yU4ML3008/ycsvv+zzed1DAW2wtfBAZEQ/3t7zvoCh74knnvB47KijjlIm2VDeiREeE6udJ998841HpMBXOWufPn1UNOXvv/9WXolI3l2F+7jo/YUg0RGjjqLLqMGiRYvCVhrc1jHBRblyhUuUJPZOlLUbTd1qH+wl667cIA0bGqXnZT1E9m1u9R6XrELKwNUb3tGwUQoLO+770AzpnS/FW1OVMFld7+61UbKwRJIKA79fxb6tvWi9NGx1lVtkTs2Qxh0bJNWRInln50rJg5tVE7jZZ86RxhLXLXRcn3i1j3OXYJ9c6ZD0hBKZeXKtzF/eW4q2JhiCBOw1bpsUFrqblEWanpkYs1yhgB9ml8pRo1vatLYQPyRO7c9vi5DncY1p3ZLWSmFhs8/zZHx/HF9XV7Z3v62V+iZ8d10NAkf09P06qxPX4N4nzaD8ciks3Or+2ysN7YuAz7z58+eri8N+++1nNDeCTwRlip999pm6QKLrIu5Exo8fr9bBsn4T7T3vi1NPPVV1rwx1pAQnBS70gai2rgCPifWOidn7gO+c2axpTn/iO4mqEoh7DOCxcFxQpbdlyxYjyuFr34MBJcFoPoeoLBqydXZ7HTkm9ZvrZXHVMrWcNSzL8z0UiAyZNUSZW+MSfL++zHQnPXlcb+nptjV0mO1HivzcEtyoysd12HXOOUrigjpGa55dK1U/uSMgOzy8vSR0S1DHZOI14+XPv/6S0h+3SmOxuwtxzsju6n9UmLTV9mN6yHbjRF77j8g+F4s0mcblUw7uJgUFJnduhNkDxtOnXMsby3Jk5FmZsv7KjUZ7+d679JaCggGyfIPrb0Shth/X3+95UlAQJ6MKXGXAf61MMXwo44eITB7fuXRltMjxkabZdSLOdc9Zk0MmSg499FAlQDSImMydO1fuuOMO424ExtWnnnpKbrvtNtXSGWIFE4kF8rwvID46I0DaAhcPihIeE6ueJ7ocOC0tTUaOHOnzPeDxL774Qi0vW7ZMevfuHRPHBY3TNKESP/CVzJs3T0WUcGeve71E6piYm5LB4OlzvTbe0obNrtEvMcFVgYJJ9ToL0gu6j/3iogQZ2StZtU9HKinQY161okqW3OgSW2Dc/WMlJS/FSD/EJ8Yrs+73U36SxjK3KEkfnK7+x9pit/IY2Mu1X3tMFLntbKdc/ojrvQ3qLTJxmCOqPgvzsVpUKJKck6yMrWWzXdGbbpOypWSbw+izMm6w/1SFPk8O2qVZiRJzpma/HQJLcViR7AxMltgsFSZL0sShwZ+rAe89RAU8IPoHUQ98sXEHokGZYlNTk6qsOeOMM2TGjBmy0047Bfw8IURULw1tyJwwYYLfAXTEiBExaXYNpcnV2+yKCG9n00EdwbvqJFh09Q3y9qEQJMBc4bFwNSpwXGmH+pJ61RSsPSDu5l24QJURgwEz+kv+vq1vl1P7psq4/43xeEwfgzWbXH/jFDd7Ei49VuTCI1yP3XludAUJyEhzSEEvtzEY+z5ghstbmT40XYkST5Nr+9s8aJfW+zRtsv0Mrv5mC4a26kgVUVBGVzPHH3+8HHPMMR6PdevWTR588EFlTkWaxVvxtfc8IURkzpw5PvuTtCVKYqksOJyiBEDw6S6vkcK76iQYUC66uSx0PUp8ihJU4AxMk62/bDPKgrPGZrW7T1t/da2P6p2RN7krwrzpfVgvKf68RNa/tkEciQ7JnuTaduEm92CWkGBuMuaQ+y/Cj1iGsYNcDd4QCVhbLDLghH6St0euJOW7TMswwLZXeWMGfVey0l2TIoLkJJEpE8TWoNIG0R9ddp6WErzI6rAqQFrFn0kVz7UlONp7npCujL+mad7EallwJERJpKny0V49UHTr8VD1KNF0z3QY0QlU4OhIie422x7miej6HtNHEtLbvscdd98YGXv3aJn8ynaSVpCmGo1taRFbA1r6gFgZ7zJqPSdRfLJrLDOLkkB6cyQmOFS6RjNlfOfnNIo2ZtEcbH8SDZUBIRYWJSgH9gdK8/WNAUVJ4KIkGm35jU6mDjF6kwSKZ4+S0N/9A4iDhh7BNVDTE9GB7AmmGf78gCZwSPH02Nu1E4g2aPR8MFZmzCBHK1FiZl7LaYVMU6Cz4h6yq3ub03e0tyDxTt+MDyBa5AuKEkIsKkrg4xozxjMXbwbRRh0twd0//BKxQDgiJejrEs1IiU7fpPRNkfiU4Ey2nt1cQztwmVM46xOCjJTMcYsSPe9LMOjUDdB+DSvj7cEx09TkNITK0L6Bpy2O31fknENFTpjm+m13th/h3u+9/N9PhcdTQggJPejds3jxYrU8bty4dqvP4CtBFRzK8zHYmn0mdhclEGX5+aGJ66P8VDebi7Qoqd9aLw3bGjtucg1jpMR19+8aYJc0pImOJ1W3M1swjmPZPJcoSe6ZLCm9XD02gkGbXMGAfOtHCVDCiygIyne9IyUrNojU1gductXAR/PIpdbf90A5dHeRJ69wSEaqyO7jGSkhxPagbFV3P2zLTxLLFThalCA9FSrvGaoFdSfqSIsSc+fPtA5V3jjD4inx9knML0mQhIz4gERJ9eoao8Q3e2LwURKwZpPTVukbRD9QnqzLgs3dWuebMoLBiJJYIy7OIacf7JBjpnZcaDF9Q4gN/SSxKkrQ4Awl0aFM3Xj7SjZv3izl5e7Ug5VNruGOlHjMgFvoMCqDatbWSnNjc0B+kqwJHRMldkvfmEVcVY2rEkcTbOUN8Q9FCSE2rLyJ1bLgcPhJoj1bcGfKgVuJkh6hnwFXmxORktAmXHSXbctXYq68ye6gKDGnb/QsvFZnrCkKgooljblHSUerTogLihJCLChKkLbQ0zG0RayVBUdKlEQyhROqxmmYcTUcJaPmGXAdQ90Co/Sn0oiIEuxXZpo9ogtjBvquwNGiJDVZZHCfKLyxGIKihBCLUFdXJwsWLFDLo0aNUi3m2yMrK0t69XLFvilKrClKPCIlBcGJEviLNmwJT+rGuywYlAx0t1XdPGuL3/dUPtfVYCQpP0mSO2ByRbWKLgm2S+rGXwUO+q0sX+9+Pj7eHgLLqlCUEGIRFi5cqKpoAk3deKdwiouLDT+GXYnFSIlRDtwnReLTgisHRv+QuvrwpG589d9YlJglCdmuoswt35eqNI43NWtqjGoiREk60gK+qFSksck+jdM0Iwe42qebIyWLVrsqcrq6yTVUUJQQYlOTayyaXWNNlDSUNUj9Flf/mLTBwTVNC7fJ1Wen0kKR3CmuKYgbtjZI+fzysPtJ7FB5o0lJdsiQlvQM2qkj4uM55w2jJJ2FooQQm5pcNRQlgdGjRw9JT0+PaFdXDz9JR0yuHo3TJCx4VOCsFsnb05TC+XZLm03TAunk2m7lTU97DeQ6hVNTJ7Jqo3flTfTeV6xAUUKIBUXJxIkTu7QoQfv8nBzXHXuoQJpBR0tWr16tZiy3VeVNniPsM+AiFZG7R06bvpLylqZpnSkHtmukxNuDgxROsLMDk7ahKCHEAsBLgsZpYOjQoZKdHfgdaKxU4MBAqUUJUjfhmK5eixK05F+/vsWdGIk5bzoqSsLYOM1XCgcz1pZmpElq/xT1N2YBbqpp8uzk2pK+SeqRJCl9gje5gsIiezVO8zsHzmq3KMnvLtIzx15RHytCUUKIBYDHoabG1RdiwoTg5i8fNGiQaslu914lMOmizX44/CTR8pV4NE6zWIt5v3f/qx2S25LCaa5rVsJEU7uuVhpKXR6Z7PEdM7mCNabJ+ArsJkpM6a5v/nJK8VbXMqMkoYGihBALYB4gzZGPQEhISDAmnFu2bJk0N/vvxNlVTa7Rmpiv0+mbCHhKfM2A6+ErmbXZ9yR8HUzdmNM3iQmILoitGN4fZb+u5a/dGVeKkhBBUUKIBTAbL81388H6Smpra2XNmjViRyIhSqIVKcGkdQnpCR2OlGDwzuuYp7RD/Td0BQ7Y/F1pSCtvzEZXdHLFfCl2IjnJIcP6upbN+p+VN6GBooQQi4kS8918VzK7xpooaaxolPri+g5PxGcWJX3ywjt46xlwdaQkuUeyZI3LNIyt9VvqW5lcOzoRX1mlU8oq7Zm68SXiNEzfhAaKEkIsgHmApCgJnygpKCgwfBDhFiWdnYivts6pmqeF20/ibwZc7SsRp8iWH0o9Ta65iZLS12WGDRbdydWOJld/ogSnlC+hQoKHooQQC0VKkpKSpG/flthwEDBSEhgpKSnG8Q23KOmsn0S3lw+3n8TXDLjwfHj6SrZI7YZaqd9cb/hJOmpyNfcosasoGWvy4IChfV3CjnQeihJiqbLYN954Q37//XfpSuAOVA+QAwcOlHjtoguCWCgLXrduXdgjJeYUTklJiVRUVHRqW7VFdTLnzHmy6b4SaW5sDstEfJGIlHjPgLtglUjOzt0lLslhNFHzaJo2vvMmVzs2TtN4R0WYugkdFCXEMjzxxBNy9NFHy+677y6LFi2SrsKmTZukurq6w6kbkJeXJ927d7d1WbD2lGCSQfyEi1D5SjAvzF9nzJWidzdJ6QtbZeW9q/xHSjpdDuyI+Ay4mKen+46uc6qmsEaK3t8Uosob+/Yo0Qzr5zIfayhKQgdFCbEMs2bNUr/r6+vlf//7n3QVOmtyBQil6xQOBnfd78NO0SIdKQlnlCSUomTVw6tl689b3Z/jXatk6+/bfIuSgS5RcuuLThlybLO8Nav1RHfe/LE4Mo3T2poB1/CViMjGd4qM5eyJHS8FioX0TWKCQ5UGa1h5EzooSohlMKcdXnzxRRVB6Ap0thzYl68E/UrsxObNm1U5s11ESfnCClk6c1mryMncc+apqhtz+gadTxOzEqSmzik3POWUlRtEzrjDKaXl/oVJ8VanPPq+azkpUWTPwGcdCOkMuHl7ukuD9YzBid0TjY6v/vhrmcjvS5NjrsW8mUnDTMvBtRYibUBRQiwBGn6ZRUldXZ08/PDD0hXobOVNLJhdI1EOHCpR0lTXLHPPnSfN9a5BuuDsAZI63jVIV6+ukYVX/y2NVY1SV1TnUXnz92qRxpaO7dsqXVETf9z5ilMZTsFZh6Ak2BHxGXBRgYOISEK2Z3+V7HZMrnOWOWXX80SOuaWXvOkKfvoUJT26iaQm29NTAq450SH7Thb57xkOGdzHvvthNShKiCVAwy99p6yBKNGt12OZUKRvAEVJZETJstuWS8VCV6ONjFEZMvy6odLnpl4Sn+4yKK9/ZYOsvH9Vq8obmEfNPPC2p79Cs6nUKQ+941pOThK5+kRH1GbAdcQ7PBqpBeIneeZjp9S7OtHL7S+7UnOahkan4ZWxc5QEjBrokC/+FyfXnkxBEkooSoglMN/Z67swhPSRxulKogTz2MSKKMFMvN9//73HoGSFSEl+fr6kpaV1SJSU/rJVVj7gUheORIdMfHScxKfES1K/JBl920hjveV3rWxlcl2wyvM41NWLSud4c/vLTiUKwDn/iEyURDPaNK/L4pbGwObS4PY6uSK68tZ37r9nLxX51eRZ37DZ3QXVro3TSHihKCGWYPHixcbyueeeayzD8GrXuVwCRQ+MvXv3NgbLjoAoixZ00RYlpaWlMmnSJNljjz3kzDPPbPczjKQowTHS0RIIp6Ym9yy4bQGvyNzz5qtmYmD41UMla6x7gO5zTG/pfVivVq/T5cDmKe7TU12/n/9MZN4KtzDZuNkpj7zrWk5NFrnqhMjehY/o7/5/SzogSn5Z6FnKDB582xlzfhISPihKiCUwD6InnniiGsy0WPn0008lVqmsrDQMvZ1J3ejGYOhzoo9nIBGKcPHrr7+qWX/BU089JZdcconf94PP+OWXX46YKDEfa1R6bdiwIaDXLLpusSqNBd137iaDLxjUSuyMvXu0pPTxNIEa6ZsWUdItQ+TfM1yDPw7JVY+6j8ttLzml1tWfTM49VKRXbmRFCcyumsVrnEakJ21gqmHaTS1oUVQ+ePPb1p/x69+4UlKtK2+Y9iCtoSghlouUIA2BQUwTy+XB5vRBZypvNCNHjjTEzsaNG8UKKSlw3333yQ033NBqvT/++EOmTJlilANvv/32MmyYqazBIr6STZ8Uy7oX16tleEcmPDRO+S28SeyWKBMeHitiegpG120VTllX4m5SdsHh7kjBJ7+KfDPbKetLnPLYB67H0lJErjg+8oP2CLMoKTSJrXvGSI9peTLu3jF+Ta5I3WhjK3p4HLe3qzFdQ6PIEx/4apwWrr0gdoaihFhKlPTo0UNycnLk4IMPlqFDh6rHvvrqK5kzZ47EIqGqvLGar8RblID//ve/cscddxh/f/PNN7L33nsr7xCYMGGCfPTRRx1uXx5OUVK1vMq4Wo6eOdLoO+KL3Cm5MvLG4eJIcEjfY/ooobJwtfv5sYNclS43n+7ezysedcrMF5zKZwLO/z+RnjmRFyXZGQ7p1eJrXeLOqEneHrmyw6vbS8/p+X5f+/ti97w2U7cXOfegMqPE+NH3ncrkGguN00h4oSghUae8vNy4q9d3+mi1fvHFFxvr3HPPPRKLhKryRqOPn3f0KdIsX77cWL7yyis9llFV9c4778j06dNVRAcgWoLmeT17RmakClaUDL5wkOzy0Y5ScOYA6XdC34DW32/1VJnw8DiP1I153pQTpomMb/nI/1gs8vC7br/J5cdFL7UxssD1u3irtNlLxZs3vnGve+ReIv16NMnBu7j+hs/kvR9io3EaCS8UJSTqmO/ozXf6M2bMMFqnv/LKKwHn/rti4zSriRK9X/C5zJw5U2655RbjufPPP1+OPPJI5ecAiIp99tln0q1bt4i9P/Ox9hXV8QVaro+5bVTAkZz4VPccRubKGz3HTHy8Q247u/W2LjwcPTyiKEpMKRxtdm0P+IXe/Na1nBAvcuhu7oiP2fCq0zcpSa4+JYR4Q1FCoo558DQPqunp6XLOOeeo5YaGBnnooYck1ojF9A0qbfR+YfCPi4uTa665Rq666iqPdcBJJ50kb7/9tqSm+jdPhgNtCI7EbMHePUqQvtFM30lk70nuvzNSRS47NroGUI8KHFMKpy0Q6SkscqducrLcy9qn8u0c9/YQJYlEmo7YD4oSYtlICbjgggskMTFRLT/22GMBl2/aBX2XnpGRofw0nQXpj+zs7KhGStavX6868noLLURMECXRID337LPPGp9vJEEEp2/fvkFFSjoKogi6HLh3rkhutnswxsB8x7kOFV0ASNuYn49m+gYsLgwsffOmaS6fI/cy75/IBf/n/lt/fZm6If6gKCGWjZSAPn36yAEHHKCWt2zZEtWURKhpbGxUfTJ0RCEUd47Yhj6GhYWFxuzDkcQ8yGuzsn5v999/v3zwwQfy5ZdfqqoqRFGihRbAMNqGc56lTaUiW8paR0k0k0c65PsHHfLKjQ657mSJOp5lwcGlbuLjRQ7b3fP5k6e7IkBmWHlD/EFRQqKOFhpJSUkeYXXNrrvu6tH/IlZAGSyESahSN1aZmK8t8y5ECDwkU6dOjXr4fvz48cbyvHnzIpO68WMb2nmMQ46d6pC4uOinNBDFgOcjUE/JX0tFTTIIkIrK8/LDZKU75JTp3v8j+vtJrAlFCYkqSMfogRN31QkJnpN/gZ133tlY/uWXXyRWCHXljVXMrubKm1DuV6hBCXJERImPyhsrA2E0vKV/3fL1rvlq2sLcMO3IPX3v3/mmFA5g+ob4g6KERBWkL3QVhnfqRoOGWjrMH0uRknCJkmibXf2lb6xGqCIlKHetqPEvNjwqbzo+tVFE0eZUzGqMifnaSt288Y1rGV/R/3M1YvY5ed0+27n/Hti6Gz8hCooSElXMg6Y/UQIT6LhxLf0eFiwwelvYnVCXA1slUqL3C71mCgpMrkmLMXr0aEPsdkSULFrtlMOvbZYBR4nsc3lf1dejvfSNecI7KzPSR2dXX8xb4YqmgD0niOR39y/OUP6cly2yyxiR3VxfZ0JaQVFCLNVe3h877bSTUUqK1uSxQKjLgc3bgiCIhijBnbNO3wwYMCAqlTXBVODoc27RokWq7DwQVm90yoyZzTJuhlPe+d712JaKeHn5S9+t1xe2iJLBfUQy0qyfvgEjBzgCMruaG6YdtXfb+7bDKIcUv++QHx92SGKCPY4DiTwUJcSylTex7isxRxQwgIeK5ORkGTRokBGJiuQsy6iQQodeq/tJvH0lSCG2l+rCpHL/vK9Zhp/glOc+heDwfP7Vr1q/Bs3CKmvslbrxngNnScvEfD5TNy1z3cCz/H9T2t8uzM3RNjgTa0NRQizbo8RXpCRWfCW4oGtREo6IghZ4KAlG35BIYRc/SbC+koffccqQ45zywFuuCeZA90xXSkK3isfcLyvWew7guj9JW5U3VmSEaaJmf5GSpWtdP2DK+MjPaExiE4oSYolISa9evYymX/4G2aysLCNSgkHdzpSWloY1omCOOkXS7GqXyptgRQlaolfVuGfwveYkkZWvOuTKExxy/L7u9V77uq1OrvYZtJFm6tejbVHyqene4JBd7bNvxNpQlJCosXXrVikuLm43dQNgSNxxxx3VclFRkaxdG2D/6y5WeeMr6hRJX0m49yucomTu3Ll+1ztiT5dB88IjRFa84pBbzoyTbpmugfiovdzrvfqVp1hesNJ+lTfeKZzScpHN21rfBHz6m9OjXT4hoYCihFg+dROLvpJwVd5EuwLHbumbfv36GZM+thUpQe8OdF29/6K4VmmKgb1Fthtaa6RrFppKgHWkBG3kzT4NO9BWZ9eaOqfM+su13LeHyBibCS5iXShKiOVNrrHoKwlX5U20e5WY0zfhEFuhBqZLHS3BLNRoOe8PzOrrj4N3qm4VLUHTMT2YQ5AkJdorxWGuwPHu7PrdXJFaV3shmb4jJ9cjoYOihFi6R4k/URJLkZJwiJK8vDzJycmJWqQEHiHM8mwHzCmc+fPnd2gbB+1YpZqH6SocVRq9TqS+wZ6pG2CO7Cz2qsD59Fdz6sZeYotYG4oSYvkeJRrMoqvvvmfPnm10grUj4U7fmCfmwxw7kWg4h/+hJ7azQ+omlJ1de3Rrlr0mupbRTGz2UvuaXANJ32iTK9rh7Lt9ZN8XiW0oSkjURQmaWAXap0P7Smpra8M6X0mk0jeIaOiqolBjFnpLly6VcGM3k6uvOXDaMru2x9H7uJdf+dIp880mV+tnsloBrwgqjbzTN4VF7rTUzqPFMPwSEgooSkhUQPdMPYgNHz7c6EDaFXwlEFS6d0g4B+9Im13tKkrGjBljNPTqjNA9fIrL0KpLg9GCXWPH9A3MvbpfycqNInX1LpH12W/udabvSEFCQgtFCYkKq1atMtp6B5K6iaUKHOy77rMSTjNopHuV2K3yRpOWlibDhg1TywsXLpTGxpbuaEGSmy2yv6tqXdaViHzSoplTk0UG9RZbMrJl6qKmJpEVG3z5SaL0xkjMQlFCbFF5Yw61JyUl2TpSEu7Km2j1KrFb4zRfvhJEscz7ESzHTnVHDrTJFeWybVXuWJkR/T0rcFBR9OWfrr8xud52w6P33khsQlFCbNGjxDyvy3bbueZAX7ZsmZprxW5EKs2BKExCQoJaZvomMr6Sf+wmkuLSzLZO3fgzu/68QKSipfp5vx1cKR5CQglFCbFVpMTbV/Lbb6YEt00Id+WNBvPp6DQKjK7hnphP7xemC9DlyF2pAgdkpTvkoF08H7Nj5Y13+kZPzOfZxdW++0WsC0UJsUU5cCz5SiKVvjEfW6Ql1qxpYw76ToLybL19CCG7zQQbKlECjjOlcOxaeaMZ1s8zUmKe7waREkJCDUUJiWr6pm/fvpKRkRHUa61WgVNXVycXX3yxXHTRRR6Cw5umpia599575csvvzRKoXv3Dq8DMlJm19WrVxuRGLv5SUBBQYFkZmaGRJQcuItIRmpspG/SUhxS0Mu1PHe5yF/LXMvwkvTMsZfwJPaAooREHLTy1l6QYFM3YODAgZKfn2+IknCnJdrj5Zdflvvuu0/uv/9+tT/nn3++bNy4sVVkaI899pB//etfKmoB9t13XzXRYDgJtdkV0RCIsFgpB/bVbh77iMkiO0pqskPO/odreYeRIn3yxNZoX4luKw9YdUPCBUUJsXx7eV8DiI6WbNu2TRleo4k5hYQy54cfflgNzFdffbWUlJTI7bffLhMnTpSffvrJWO+CCy6QV155JezvLVS9SlDCjGjQoEGD5PjjjzfKuTXmihU7lQP7M7t2tN285vZzHPL74w759gGH7VJZ3uheJWbYn4SEC4oSEnEWLVrUYT+JFX0lf/3VMl2qiDHfS01Njdx2221qDpirrrrKiC5gwP7uu+/kgQceCDptFc2J+RAJwo/e36eeeiqmIiWh9pWgBHjySIeKmtidkQWe+5CVLrLzmKi9HRLjUJSQiIMGVeZumh3BKr4SNNrSd9VowAVPCbwlupeKTi3hbvmSSy5R5aZTpkyJ2PtDFQzmDOpMpOSjjz6SSy+91OOxm266yWM+HYqS2MU7UoK5bhIT7C+2iDWhKCERxyxKxo4d26Ft7LDDDkZYPJplwYg+aI8IUjTwusDMihLc0047TfUJwT4idXP33Xer7qGRRqdw4HMpLy8P6rUQXMcee6whrrSXBxPv3XPPPa3SNzDv9unTR+yI+VzsTK+SWMNcFgxYCkzCCUUJiTgLFiwwJqPTg1ywYBI7nZpAqN2X+TISzJkzx1ieNGmSRzUHUhwVFRXq/ZnTTZGmoxU4EB4HH3ywERE56qij5JtvvjHmKbrjjjukuLhYCRa0ztd9V8Jt3g0XqL7RqSeco6iWIiK9c0UyTVpat9InJBzY8+pBbEtpaakUFRV1KnWj2X5715zpMF1qoRNNP4lZlGgQOYi20bEjFTiI/hx22GFG75HJkyfLs88+qwTOMcccox6DWPnvf/+rJhfUotCufhJvX0l1dXWb5d1dCZy/WojsOVFkQE+mbkj4oCghtvOTeIsS8OefLRNyRDFSgvSNFQk2UoJKG6SetIG4X79+8v777xupJ3hmtKH30Ucflc8++8x4bayIklCYXWOJ565xyCd3OuTdWyhISHihKCERJZZECQZvHSlBlQ1+rC5K/v7773bXf/XVV41yZYiPDz74wKPJG4yzMO3qKNVll11m+3JgX6KEvhLPJmrwknTLpCghFhIla9eulWuvvVb+8Y9/yKmnnipfffWVx/MI4aInA/LQxx13nHz++edBPU9in1CKEqRLdGokGqJk3bp1Kh1l5SiJbjanoxyzZ88OqNpG8/TTT/vcN1Tj6KqesrKymImUmHuVMFJCiMVFCUK1e++9tzz22GNywgknyI033ugxyDz44IPqb1QfnH766aps0Hxn1t7zXQncYf78888qd92VCKUogTFx+PDhRpUI5l+xkp/EKsCYCk+IbgcPA2tb6LQN/DD/93//5/fY33DDDa0et7soQXM4nZqKxMzKhJBOiBKY2tAaG6Fc/B49erRhMIRTHXlntNhGCBfPQ8C89957AT3f1UDEadddd1VliNr42RXQ50vPnj1V9U1n0SkcCJJIm13NosTKkRJgrv5pq68LpgDQPUe22247NdOwP8466ywPEQLxg6ojO4PKobfffluJ52iZpwnpygQlSsxVBLjjQhmgzsGiB0JVVZUSKuY7YfRrCOT5rsZrr72mfuMYosoBHUBjHbRcx08ooiQaHQGIRgrHXzmwFTE3m2urA65ZsLRXxowGcTNnzjT+HjBggNE0zs7st99+6jqFHjOEkMgS9LcO7bFxJ4FywH/+858yatQo9bjuZWBunY0Qr368ved9gbtf75A8LhSdufDpJlDRnMQNd6PmaeQxEMCj89JLL0WlfDRSx8Q8nwgu+qH4f2Yx8Mcff6i0YKSOiY6U4JxG2D/aEwO2xY47uptLtDWJoVmwoEGd93rex+Xwww+XadOmyRdffKH6mFj5GIQLK1xTrAaPCY+JLwLpYRS0KDnjjDOUSRWToMETolM5yD8D3PFrUx38Eqmprjm823veF88884w88cQTHo/hwnf00UdLZ4FpN1pg7hNfkRNUb6DcMlqE+5h8//33xjL2tbCwsNPbzM3NVUIOlTDw6IRim4EcE5g79f9CH5Bonk+Bgu8qIpYQJejBoZugmZk1a5axjFJgf8fTvL/wimG7/fv3D/nxtxN2OAciDY8Jj4kZ3LyFXJRAROAHfgDcIf3www9KlPTt21dFMJCPHjdunFoXy/pNtPe8LxA9gKE21JESfFFwAY1W50lUbWjQwhuCBIMqJjzD3SlEXySJ1DExe2cw/0uo/Acwu6L/Bn4w8IYihdDeMUFnU3Oaww5eCniY3nrrLZVGxQ2Bd4t/7LOOZkE07rLLLq0id/6Oi91LgTuDFa4pVoPHhMekowQsSjBnBtILEAlo8Q1PCe58dXdHGOJgXEUJ4a233qoGXjRVwnIgz/sCg0u4ctS4eETrAmIuy0QFA3wRutcD0g8wD0ajLXm4j4l5dmAI01D9L5hdIUiQ6sP/gEEz3MfEXC6KFJIdBiOcUxAler4gc08OgGOoy3uxrq9IihW+P1aFx4THhOdJ5wn4qoK8OcQI0ie4y8Xguc8++3ikUi6//HLVnnqvvfaSGTNmKAFjHlzbe76rAO8DQOkh7vLRiAppMd3L5dBDD425MDgiQbocGBO2de/ePWTbjkYTNbuUAwdTgWN+zGyMJYQQy0VKcBcAEYEfCAvtETHTrVs3eeSRR9TzSLN4u9fbe74rYDa5YjDTd6MPPfSQSmchLYBJzi6++GJ55513JFZAb4wtW7aEtPLGXwXOmWeeKZGqvME5HOr9CReIIOF8Q3m+rwoc82Nd8WaBEBJ9OhR/9SVIvJ9vS3C093wsY76TNw+mSFO9+eabRu8OdMuNpVlKQ9k0LdqdXSGqdSoKVUTJycliB2Aw1x1L8XlgBmNfkRLcgJjPTUIIiRRMCkcY86BpTjuAnJwc2XPPPdUyBgyzB8PuhFOUmDu7wusR7s6u5mntrd40zRudlkE67ffffzceh/lV+2RggDWX7hNCSKSgKLGQKAGoeNCgxDVWCKco8e7sav5fXb1pWqC+EpyXurcE/SSEkGhBURIlUaJNrm2Jkp9++klihUiJkkikcOxocm2vsyv9JIQQK0BREkFg9NRVNWaTqxnzfCOxEikxV96glwOquMIpSnR1UyQiJeZZZe3AsGHDjMonRErw2ehlDSMlhJBoQVFiodSNNgHrPhuYF0hXrNgZdPvctm2bWg5XpYo5YhGKSAk6m95yyy2tpq+Hl2Tu3LlqGY3/UFFmJ2Bi1S3nURGlRbKOlEAw6qkjCCEk0lCUWEyUeKdw2po8zS6YZ1sNlyjBYIp27501u8JXgekT0KX4qaeekt13310+//xz43mUbcMUakeTqz9fCRoZbtiwQf2NjsJsikYIiRYUJRYUJWgHromFFE64/SShMruimylmbL7xxhuNtAYEyMEHHyyvv/667f0k/nwlZuHL1A0hJJpQlETJ5Krv6n0RaxU4kRYlHUnh4D0iSvDBBx+ov9H3RM8N09DQoOYoeuyxxzz8JHaNlHjPGGz2k7BpGiEkmlCURAh4QzBfkB7M2ppXBLOz4kfPUWL3JmpmUYJmY1YTJW+88YaKEGDmawAj6EcffSRvv/22nHbaaeoxRE7OOeccefzxx20fKcHMyjC86nmYzLNWM1JCCIkmFCUWS914R0sqKys9PBl2A4O5bgI3cODAsDbl6ojZ9ZVXXlHzN5l9Injt/vvvr7oOQ4RcccUVxvqlpaXGwI6Zr+2KjohgriUIX23czc/Pj/I7I4R0ZShKLC5K7J7CgYkSM0yDcM8RA7OrubMr0i7tcf/99xvLJ554ovz4449qcNYgjXP77berH3+t7e2Ir4gIoySEkGhDURLlOW/8EStN1CLlJ/EWfIgAtGd2RVps/vz5RhTn+eefV/PD+ALRkieffNKoTJk+fbrYGV/eEfpJCCHRhqLEYiZX8504Jumze6QkEuXAZsyCr70mauby3kAiH6effrryYLz22mty4YUXip0ZP358q4k1GSkhhEQbihKLmVw1mHlW3/UvX75cSkpKxI5EK1ISiCjRTdCC6cyK9eBB0YLRrqBrsPlYYX/satwlhMQOFCURAHfXwfhJYqmJmhYliEJEolMouuHqiEc4REksYY6MQCxDCBNCSDRJiOp/7yIEa3L1Z3Y95JBDxKogDYL9REpk5cqV6mfVqlWGIBs8eLBfv0YoyczMlJEjR8rff/+tzK7wlvgbbGOh50hnMHtImLohhFgBihIbiRIrgcqW33//Xb766iv58ssv1ftrq9olkqkB+EogSvB+IEzQFK2tSEl2drYUFBRIVwMid7fddpO1a9fK+eefH+23QwghFCWRFCWIFOAuPlDQB2PAgAGyZs0a1UuisbFR9c6Idonvv/71L/nss8+koqKi3fX79Okj48aNk//85z8SKSBCXnjhBSOF40uUwOeDfdGpGzuX93YUGF1/+OEH1UumK+4/IcR6MFISZtBsC2mMYEyu3tESiJLq6mpVvhptM+LNN98sb775ZqvHhwwZIvvss4+q6kCqBj+IPqSmpkb8PZorcBDNOffcc1ut09X9JGYoSAghVoGixKImV7MoQQkqQIok2qLk22+/Vb8hrlCFMnXqVPWDPh9WQYs/9CHxZ3alKCGEEOvB6pswYx4UOypKrNJEDVGfJUuWGPvy8ssvq94dVhIkANEZPZkeqn8QZWpLlHRFkyshhFgRipIw09nJzjBg6iZX0Ta7msuSzWLJiugUTnNzs/z1119+K28QUYlE/xRCCCHtQ1ESRlD98f3336vlXr16BdTJ1Rs0tdIRFpTZFhcXS7Qwi6Jdd91VrIzZ3OqdwqmvrzcmCcRn4t3ZlBBCSHSgKAlz1Q1m+QV77bVXhw2FVikNNv9vu0RKtNnVzOLFi43yZaZuCCHEOlCUhJFvvvnGWN577707vB0r+EpgGv3111+NUuX+/fuLlUEZsm4F7x0pMTdN6+qVN4QQYiUoSsLIrFmzQiJKdt99d5/bjCQwjOqoj9WjJACCRAsOmHPLysqM51h5Qwgh1oSiJEzAt4DGVDqyMHTo0A5vKz8/X0aPHm2khMrLyyXS2Cl148tXYi7NZuUNIYRYE4qSMAEfgy5F7YyfxDvSgjSKNs9GEnPayC6ixJevBN1LdfqmZ8+e6ocQQog1oCixuJ/E1zaikcLRkRKkRTATrx3wVYGzYcMG1WIe0ORKCCHWgqLE4n4SzZ577ulT8ESCzZs3y7Jly9QyypPtMsU95hnSMxPrSAn9JIQQYl0oSsJAXV2d/Pjjj2oZE+oNGjSo09vMy8tTFSUAzcC2bdsmkcJOTdPMYPJCHdVZvXq1ElesvCGEEOtCURIGUDpbW1sbMj+Jd8QFXUrNnWLDjR1Nrr58JUjh0ORKCCHWhaLEBn6SaPtK7CxKvH0lWpQgBTV8+PAovjNCCCHeUJTYSJTsscceRtQlUr6SxsZG+e2339QyGqahvNmukRLMcLx06VK1jAn7kN4hhBBiHShKQgzSNtqDAS9JQUFByLadk5NjNATDHT9m7Q03CxYskKqqKltGSQD6w2RnZ6vlr776SpUEA1beEEKI9aAoCUOqA0ZX7ScJNTrygsEVd/7hxo79SczExcUZExpqQQLYXp4QQqwHRYlNUje+thmqFM7jjz8uV199taxdu9bWMwMH4ivRUJQQQoj1YFLdZqJkypQp6u4fFTihMLt+9tlncu6556pllMtChHTv3r2VKElJSbFtysPsK9FQlBBCiPVgpCSEoK28nkkXXoZ+/fpJqOnWrZtMmjRJLc+fP19KSko6vK2Ghga5+OKLjb8xcd3RRx+tHgfFxcWyYsUKtYwUiJ511+6RkoEDBxo+E0IIIdaBoiTE/gs9oIfDT+IrAtMZX8mDDz4oixcv9njsyy+/lAsvvFD5L+zaNM0bNLBD8zmNXSM+hBAS61CU2Ch1E0pfyaZNm+Tf//63WkaZ8Y033mhEQh577DG5//77bd2fxAz2z5zCYeqGEEKsCUVJG1RWVsoHH3wgZWVllhIlu+++u8THx6vljvpKrr32WikvL1fLp556qpxyyinK8Kq55JJL5Pnnn48JUQJ23nlnY1lX4xBCCLEWFCVtcPbZZ8s//vEPNedMYWFhuwJGT/o2YsQI6d27t4SLrKwsY2BdtGiRinoEAzqbPv3008a2brnlFrV80kknKbECYKTFjLoAvVbCuT+R4Pzzz5fp06cr8XXggQdG++0QQgjxAUWJH+Cp+Pjjj9UySmWnTp0qGzdu9LlufX29nH766ar7abj9JJ1tOQ+x8c9//tPo2YEUTn5+vvH8TTfdJEceeaTHa+xaCmwGnpJPPvlEnn32WSPKRAghxFpQlPgB0QfzTLyoQpk2bZps2bKlVYTkkEMOkddff139jdblECjhpqO+kpdeesnwiowaNUouuOACj+dRbvzcc895pDhiQZQQQgixPhQlfvj7779bPbZw4UKVAtBeDLR5h1D5/PPP1d+pqany3nvv+WzWFWp22203Y+6WQCMlFRUVcuWVVxp/33vvvZKYmNhqvbS0NHn//fdVmuOggw5SnhNCCCEk3FCU+AFeDc3ll19ueCrgxzj44INl2bJlaoI8XTaLvhcQJ5HyK2RkZBjiB/1FtP+jLWbOnGmkoA499FDZb7/9/K7bp08f+eijj+TDDz+U9PT0EL5zQgghxDcUJQFESpCeQf+O3Nxc9ff333+vUh+InIBevXrJd999p6piIok5hYP31xbwvTz00ENqGaW/d999d9jfHyGEEBIMFCUBiBIIkNGjR6uW7KhWAU1NTer34MGD5YcffpDx48dLpDFHOrQp1x94j0jfgKOOOkqGDBkS9vdHCCGEBANFSTvpG1Rt6G6gMH8ipQHvCECpMAb7aA3wMKDqdukQTLr6xxd43xqknwghhBCrQVHiA1TdFBUVqWVESMwgRYO0DapY0FY+mv07YFLdf//9jfds7sDqT5Sguka/hhBCCLESFCUBpG68GTRokBx//PHKbBptUB3jKxpiBuXMMMPq6Ip5FmBCCCHEKlCUtFN540uUWAmUKGNul7Z8JebHzSKGEEIIsRIUJe1ESrzTN1YD3Vh1afD8+fNlzZo1rdYxR1AoSgghhFgVipIOpG+shlloeEdLqqqqjOZq/fr1k7Fjx0b8/RFCCCGBQFHSRvomMzNT+vbtK3YWJV9//bXU1dUZ6+lUDyGEEGI1KEq8qK6uNmYERpTEDoP4pEmTpGfPnmr5q6++ktraWuM5pm4IIYTYBYoSL1ClomfQtUPqRpf56vb2EFU6XYP90KIkOTlZ9tlnn6i+T0IIIaQtKEpsXHnTXgpnwYIFsm7dOrW81157cQ4bQgghlsZ2ouTTTz8N6/btVHljZt999zVmDUZ0xBwlAay6IYQQYnVsJ0qOPPJIaWhoCNv27VZ5o0G7+SlTpqjllStXqjQURQkhhBA7YTtRghLX2bNnhz19Aw8GOrfaCXM0RLfBByNGjFATBxJCCCFWxnaiBHz77bdh2S4iMMuXLzcG8vj4eLGrKLnrrrukubm51eOEEEKIVbGlKPnuu+/Csl0IEj3Trp1SNxoIKR3dMZcFU5QQQgixA7YUJd9//700NTWFfLt29ZNo0FPFW4CgARxmNiaEEEJiSpSgxPTyyy+XI444Qi666CKZN29eq/THvffeK4cffriccsopRr+MQJ8PlPLycpk7d25Qr6mvr5c33nhDvfdLL71UKisr2ywHtlPljRlvUTJt2jRJSkqK2vshhBBCQi5KMIjffffdqkkXfk+YMEEuuOAC2bRpk7HOQw89JL/99pv897//lWOOOUauu+46VQUS6PPhSOEsXbpUCSnM+3L00UfLu+++K++8847ccsstMRcp0f1IUlNTjb+ZuiGEEBJzoiQ9PV2efvpp2XvvvWXgwIFy2mmnSW5urhEtQTrlvffek3/+858qygDxsueeeyoREMjzoTa7fv7552qAhs8Cps+SkhKP5x955BHZtm2bT1GCDqnDhg0TO5KSkiLTp09Xy+hbcsABB0T7LRFCCCGhFSXwK5jngamoqFBREkQgwMaNG9VjY8aMMdYZN26cEQlp7/lA6d69uxEp0dUl3qCL6cEHH+whXBITE1V0RkcO8F4QudFgW4sXL1bLQ4YMUSXBdgWRrFNPPVVeeOEF6d27d7TfDiGEEBIQrhagQYJuoUh/7LbbbkaaQ3s0MjIyPEyW+vH2nvfnA8GPGfzPDz/8UEpLS5XHZezYsa1ehx4dusEaIh5nnXWWnHTSSdKjRw9ZtmyZfPLJJ0qEwN8Cb0xaWpqsWrVKampq1GuwT/4Ejx0oKCiQJ598Ui0Hsh96HTvvc6jhMeFx4bnC7w+vKaEFWYiwiJLbb79dRUkefvhhj7QBwMCOQV5PDqcfb+95XzzzzDPyxBNPeDzWq1cvYxneEAgbb8GENJPm8ccfV4O0nv0Xpk9ESz744APZvHmz3HnnnTJjxgyPqEqfPn2MmYK7EmvXro32W7AcPCY8LjxX+P3hNSU0BNKQNGhRAn8GIhTwZJgNlRjIkSJBi3MdvcAyBEEgz/sCKYgTTjjB4zF4WHT7dLwP79ejKgfmVrDLLrvIHnvs0eoO+JxzzlGiRAufq6++WrZs2WKss9NOO7X5vmINHBMMvv379w9IyXYFeEx4XHiu8PvDa0rkCUqU3H///fLnn3/Ko48+2ipCgQgEjKvPPfec3HrrrbJhwwZlNv3Pf/4T0PO+wGu8y1l33HFH9b/hCUG/Em+vy8svv2wsI2Xja5BFegbREogbDMavvvqq4ScBEE1dcXDGPnfF/W4LHhMeF54r/P7wmhI5Ah6BioqK5Pnnn5fi4mI57rjjVPUMfszVMyi9RcQB4gProB+JuXFXe88HAipK9GuQQtJREV3ho0UJ1kMJsD+uuuoqY/m2225TURfNyJEjg3pPhBBCCIlgpAQmUfOssxpzxAQlwvBzlJWVqeoVb79Ie88HCkQNzKoAXhCU/YJvvvlGRWAABBP+nz923XVXldpBFY+5AggpDLMZlxBCCCEWi5RgcrqePXu2+tGmVTPZ2dltCo72nm8Ps0/E3EQNJbDm1E17XHPNNa0es2snV0IIIcTu2NJAMHnyZEMMIVKCipuqqip5++23DdGDPiXtsd9++8mkSZM8HrNrJ1dCCCHE7thSlKCKB+kX3Sht9erVqlus7nly1FFHBRSJgUEWlTdmKEoIIYSQ6GBLUeKdwkG05MUXXzT+PvHEEwPeDsy2w4cPN/6mKCGEEEKig21FCcyuGsz+i/JiMGDAAJkyZYoE45W55557VLXOdtttJzvvvHNY3i8hhBBCwtDR1QqgXwkqeOrq6uTjjz82HkeztWB7baBSB31PkBaCSCGEEEJI5LFtpASeEXRe9SaY1I339ihICCGEkOhhW1HincIBSL+wpJcQQgixJzElSgLpTUIIIYQQa2JrUQJTKgyqAD6SY489NtpviRBCCCFdUZSkp6cb0ZHTTjtNevXqFe23RAghhJCuKErAU089JWvWrJHHH3882m+FEEIIIV2xJNjclRWT6BFCCCHE3tg+UkIIIYSQ2ICihBBCCCGWgKKEEEIIIZaAooQQQgghloCihBBCCCGWgKKEEEIIIZaAooQQQgghloCihBBCCCGWgKKEEEIIIZaAooQQQgghloCihBBCCCGWgKKEEEIIIZaAooQQQgghloCihBBCCCGWwOF0Op3RfhOEEEIIIYyUEEIIIcQSUJQQQgghxBJQlBBCCCHEElCUEEIIIcQSUJQQQgghxBJQlBBCCCHEElCUEEIIIcQSUJQQQgghxBIkiE15+eWXZc6cOWp58uTJcvTRR3s839TUJO+9957Mnz9fsrOz5ZhjjpHevXsbz7/44osyb9484+/ExES55ZZbWv2fxsZGmTlzpvTs2VPOPvtssTLfffedfPjhh2q5V69ecskll/hc5/vvv5e4uDj5xz/+IWPGjDGemzVrlnz88cce62Mb2FYgz1uRFStWyGOPPWb8jc8yIcHztP/777/VflVUVMhee+2lfjTLly+Xxx9/3GN9nGs45zTNzc3yySefyO+//y59+/aVE088UVJTU8Wq4Jy+5pprjL9xXg8ZMsRjneLiYnnjjTekqKhIxo0bJ4cffrhx3Orq6uT666/3WH/nnXdW64C//vpLXnnllVb/96yzzpKhQ4eKVfnf//6n9hccfPDBsscee3g8X1NTI6+//ro6p/r16yfHH3+8ZGRkGM/fdddd6rhpsM4///lP4+/q6mp566231DnVrVs3Oeyww2TQoEFiZd5880357bff1PL48ePVuW0GvTdxzZk9e7ZkZmbKUUcdJf379zeex/H6448/PF5z2223qetPIK+3Ir/88ou8/fbbajknJ0euuuqqVuv8/PPP6nqJa8NBBx0kEydO9Hge36F33nlHFi1aJKNHj1b7HR8fbzy/ZMkSeffdd6W2tlb23Xdf2W233aSrYNtICb4g++23nzQ0NKgP1pubbrpJfaFwMtTX18upp54q27ZtM56HIIFYwTbwgw/eFy+88II6Cf/880+xOgUFBWpfIKDwnr159dVXlfAaNmyY5Ofny/nnny8LFiwwnl+9erVUVlYaxwQ/5otue89bEVw08D633357+frrr5VYNYMBFINlVlaWjB07Vm6//XZ1MdBs2bJFFi9e7LHPEB7e59pLL72kBB7E7a233ipWBgOC3pdff/1Vtm7d6vF8aWmpnHDCCer7ssMOO6hBAwOJBscQx3KfffYxtoMLqwbi33y8Bg4cKD/++KOlxSvYdddd1ftdt26dFBYWejyHfT7nnHPUAItjsmzZMjn33HM9zicMRMOHDzf2e5dddvHYxsUXX6xuCvB6DMYnn3yy+l9WBuc09gXnjPkmTnPHHXfIc889JxMmTBCHwyGnnXaahzDDtRkC3Xw+YL1AX29FIDaxHwMGDFDntTfvv/++XHfddeq8x7r/+te/PMYPnDM4d3AubLfddmp/n3jiCeP5lStXyhlnnCHp6enqZgE3EBA4XQXbRkowgOAHg6pZbABcZD/66CMlSnBi6AEVf+PD1owYMcKvGAG4YOAOGHfGP/30k9hBlOAHJz0GG28QHcKF8YADDjDu/J5++ml1h2geUNo6Ju09bzW6d++u3u/atWv9Rtxwx3rmmWca6999991y6KGHGhdPCC9/+4xIHC5MiMqlpaWpx8rLy8XKYIDR++NLQEGE4GJ67bXXqr932mknFTnAd8csLPbee29JTk5u9XqsY14Pd9pY1+oCFtEegM/SG4hXXEM+++wzSUlJUd8hnCMYWLBvGkTQvO+KQVlZmYoGQPDi2AJcu3DzcOSRR4pVGTVqlPpBdAg/ZnD9QOQHogLrgPXr16ubH3OECNExX9+fQF9vNfD54QfnPs4HX9fZ8847T4444ggjMvnkk0+qGyOAqCxulJ966injGmO+ZrzyyisqWnvBBRcYj2FdcwQ3lrFtpKQtcHcLzHe0WNbpHs2XX36pwtBQqd53iwB3h/hyJCUlSSyAO2DzMenTp4/MnTvXY52FCxeqY3L//ffLqlWrWm2jvefteK54nye4c9mwYYPx2ObNm+Xf//63Cs8jRWMGEQOE+TGQ33DDDeoCa/fzxfuYIKqG1I339wdRJUTecJH1N4UWwtRffPGFEjV2BudAbm6uEiQAxwMRSe9jgogZzhWIXYTeNbjrRWR206ZN6m9EeHGc8R208/UE6Qnva4r3Mfn222/VNQNpVBzHYF8fC9cURJmwr0BHGSFU/vOf/6hUkFmwz5s3T90ImMUy0jn4LnUFYlKUQMXi4qFDa7g4IHxmFh4nnXSSukPBh48P/Nhjj1VfEg0iJNjG7rvvLrEC7lj0McEggnAz7uB0CBpKHHfDyF/iLgb5Y0QCNO09b0eQysJx0IOqPj76XMHzV1xxhdpn5Lwvu+wydXenwZ0d7pYh1hCWR0TNynd5gYB9RmQAHggAIYZBVEckcYcIwY50x+DBg+XRRx9VKSxfYEBCBGnHHXcUu393IFQRLQEQrkjhmK8pl19+uey///4qUgIhhu8K7pK1iIGovfnmm9X5gejrIYccoo6hXYEow3dCf2dwjuBcMUeusZ+4tuI6i2OHv7VvJ5DX2/06C7AMQaG/T7hmvPbaa+pvpK0QGTJHLLdt26Y8RxpEbyFocK3uCtg2fdMWEBMYPHDnCpMeTgJ8AcxGIjyOH4C7OOQyEVrFb4TSHnnkEQ+DZCyA3CaOC8LIVVVV6mSHQtfHBakune6aPn26Ci0+//zzcueddwb0vB3B5w2vwHHHHad8JRg8sF+4q9WeFHPoGXc9ODd0aBaCDscRdzxauE2bNk0NWBjc7Qg+W4hymO8gOnB3i7Qdjg/A+WI+JvBOQOAjZN2jRw+PbSGCdOCBBxrGRjsPNDhH4E3DdWPNmjXKpKrPE3P6Rx9DGMkxIO25555KnMAwje3gHME1CUZHLFvZ/NsW+K5AsGNA/eCDD5TYwHUWNywaeI203wjXWZwjEPXwswXyejty0UUXqR/cqCBNg3MEXjOd3sU1A55IrANGjhyp/EWXXnqpGrvw/dJiVos14G3Qj1Vidi/hE8DFEk53GJIweLb1oWKw1aFF3PnipLjnnnsMbwnujPAFgjHLrkyaNMlwfGOAgYETxlV/YEBCZUpHn7cDuAjiTgXHBBcLXBRQjWKu1DKDgQgRNURWIF4QbjZfQHDnhx9z1M1u4Hvy4IMPqvMD+wGzI/wT8Cv5At8vvAbfH7Mowd/wNuFiGwtgEMF1Bf4kDCS4Hvg7JjiPcA7pawpuBJYuXSqff/65cROAMD/OPZgi7Qq8NYgQYt/wXUAqoq07ei1yO/p6OwARpq+zECIYP0pKSgxhjhsbc9oO54mOhKS0nDcbN240nscyHjdHT2IZe9++tAHCgLhAIuwOpYmLAe78ANTrDz/8YKyLECwMZ1rRw5CEUlftFochVldx2BkY63BBxB0dvhTIfyO0qkGuU6cxcIwQgtYGtECetyMYYHCRhGBD2B3GX3P5K+50zd4ARBAwIGmDGvwkZrM1LkQQet4ltnYCIgvpTny2+P6grBODiS4fR8oOF1kNvlvw0XgP0DCb4zX+Bm67gWsE9gUpXQg2VOggIgIQ+cBjGkTKcEOkvx/wlCACYBareI3Vzb/tgWokDJZIQ+E7g4gH2i9ovvrqK2MZEWi0IzBfM9p7vR3BNQAiAylL3Ozihti8T1OmTFHmbx0BwTUG4wu8WwDfOVxn9M0OjNd4zO7RxpiPlOBkh/MZX3x8uLhrwYeNPC1A/hdGPAgT3M2jBAsXVoAPFx/0Aw88oE4EDCr40BFmBlCq5jtlRElwAbF61Qm8MXBp4/3CUIdjgjt77DtAJABeGuwbLqCoGkAO3Hwhve+++9SFF057HDtzb5b2nrciSFMhtaKFBe5KMYDqnjTwR6AiCRdGCBQcG/M+4fW4oKB3Ao4pcsPwBmhwlzd16lS1DspBcS7BM5CXlydWBhE/iDHktZFWgPDAcYBYxfcDhl2kLyGwsI6OGgKEohF+xzHDhRMljDAy6vC0WZTYaYDRvY9w1w7RBfGFtJT2wyDqg2sG7lrhj4ChVYsKlL3eeOONSqzjMbwWIXl9owNxhvMENwGoGsT1CdctvMbK4OYNJa4wteO7gGsKjoeuGEK0Bz4RVFthMD7llFM8ysO/+eYb5TnC9wrpDIh/3c8mkNdbEXz2Dz/8sHrvuKHFMUH0Q6dj8P1BWhhRWFwz4adBpFGDaBtKfHGTjO8bxiecB/pG59hjj1XP47jgXMK5aC4ZjnUcTn+2eYuDEwOCxDuMjIFBgy8+Lpi4s/U1SGAbWAev02V6vsBghYFel3RZFXxJYFA0g4HD3OgLd/QYOLHP+PFGG/ggOOCJMPcUCOR5q4ELP8yWZhAtMpdxQrBgMELu1xwF0aCpGi6ouEDg/PJVXYNzETlx+AOs3o8DwJCrjXcaRND0IIs7PVQB6Pw3hIgZiDMcE1w+cEyQsvJ13HEH7C1WrAq+F9qEqcEAaQ6143PGIAFfiXeUA8cMNwYIw+M88HfNQTgf6VNs2+o+AVz7sE9mMACbox04ZjguOA/03b4Z+G+wHVxjfUXN2nu91cA11LshHM5/c8UMokIQpjh3fDXIw/cG5xtEP6458KWZaWxsVJWR+J5ByFm5GWOosa0oIYQQQkhs0TWSVIQQQgixPBQlhBBCCLEEFCWEEEIIsQQUJYQQQgixBBQlhBBCCLEEFCWEEEIIsQTWLpInhJAgQO8U9AFBB9VYmkyTkK4CRQkhJCDQ4AqdfAGaqWH6dTO69ToIpyhAd2U0ngLokmpuQIa5U9CtWbeDJ4TYC4oSQkjAUzuY211jSgNMvQ7Qg/Gaa65R3TsBuneGSxRgcjs9KzM6xnp3kyWE2BeKEkJIh8AMt1qUYKI1LUj8gSkdEG1BO3bMQ+U9zYE59YK5qLAuZkhF1EO3J8f8KDpKoieJxFw0mJvIe84UCCVf2yCEWBeKEkJI0GDuIwgCzFuCuX4wmZ1+3DyDMMBEbpi8DpOzQYxg7iCkehDluPnmm9WcQ+bUS25urhIQWA9zjGD+HUwqiUnOMFmeeTZeTBiH9A3mHTGLEswpgokVfW2DEGJdWH1DCAkazBKLgR6zC2PCNgz8EBJ6Rl0zmK0bggQTrkG8YCp3TDKGSQEhVnxNLHnGGWfIgw8+KMcdd5yKeCBVhAgLZljVM9SCG264QW699Vb1eKDbIIRYF4oSQkjQHHHEEZKcnCzvvvuuPP300+oxDP7eMyxjJuLPPvtMLcMYi6gGpnaHQVWnfSAgzCByosWNTrlgtlRMEx8oodgGISTyUJQQQoKmW7ducuCBB6op2mGAhQg4+OCDW61XXFysIioAaRlNXl6esQzPh/e2NfHx8cZyMBOah2IbhJDIQ1FCCOkQiIxoDj/8cGU49SYrK8sjamL2mfhahxDStaEoIYR0CJhWTz31VJk2bZocffTRPtfJyckxDKjz5s0zHtfLqMDxrsJpD5hXNToKQwiJDVh9QwjpMOeff36761x33XVqPVTrPPDAAyqi8uGHH6r+IjfeeGPQ/3PkyJEqJQNBAvPq2LFj1U/fvn07uBeEEKtAUUIICYghQ4aoqEhbQBzAUGr2jKDq5o033pBPPvlElfQCiBR4UszrjRkzRqV48vPzjcf69Olj/E+dHoJxFVU1qOiBSXbWrFlqOxAlgW6DEGJNHE46vwghhBBiAegpIYQQQogloCghhBBCiCWgKCGEEEKIJaAoIYQQQogloCghhBBCiCWgKCGEEEKIJaAoIYQQQogloCghhBBCiCWgKCGEEEKIJaAoIYQQQogloCghhBBCiCWgKCGEEEKIWIH/B+T0D0dAu20vAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model = ExponentialSmoothing()\n",
-    "model.fit(train)\n",
-    "\n",
-    "predictions = model.predict(n=len(val))\n",
-    "\n",
-    "# calculate metrics you want to log to MLflow\n",
-    "mape_score = metrics.mape(val, predictions)\n",
-    "rmse_score = metrics.rmse(val, predictions)\n",
-    "\n",
-    "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
-    "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
-    "\n",
-    "train[-50:].plot(label=\"Training\")\n",
-    "val.plot(label=\"Actual\")\n",
-    "predictions.plot(label=\"Forecast\")\n",
-    "plt.legend()\n",
-    "plt.title(\"Exponential Smoothing Forecast\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35dc864c",
-   "metadata": {},
-   "source": [
-    "Now let's log this model to MLflow.\n",
-    "\n",
-    "Tags can live in two places in MLflow 3:\n",
-    "\n",
-    "- **Run tags** (`mlflow.set_tags(...)`) – shown on the run page in the UI. This is also where `autolog()` writes its tags (`model_class`, …).\n",
-    "- **LoggedModel tags** (`log_model(..., tags=...)`) – attached to the logged model entity itself (open the model from the run's **Outputs** / Models view)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "61406cd9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.451297Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.906785Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
+          "iopub.status.busy": "2026-06-24T15:18:46.600748Z",
+          "iopub.status.idle": "2026-06-24T15:18:46.634540Z",
+          "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
+        }
+      },
+      "source": [
+        "# use darts plotting style\n",
+        "from darts import set_option\n",
+        "\n",
+        "set_option(\"plotting.use_darts_style\", True)"
+      ],
+      "execution_count": 4,
+      "outputs": [],
+      "id": "4d424e08"
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/07/23 18:50:08 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## MLflow Setup\n",
+        "\n",
+        "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
+      ],
+      "id": "2f9c40d6"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
-      "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
-     ]
-    }
-   ],
-   "source": [
-    "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
-    "    model_info = log_model(\n",
-    "        model=model,\n",
-    "        name=\"exponential-smoothing-model\",\n",
-    "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
-    "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
-    "    )\n",
-    "\n",
-    "    # log calculated metrics you want\n",
-    "    mlflow.log_metric(\"mape\", mape_score)\n",
-    "    mlflow.log_metric(\"rmse\", rmse_score)\n",
-    "\n",
-    "    print(f\"Run ID: {run.info.run_id}\")\n",
-    "    print(f\"Model URI: {model_info.model_uri}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0728690e",
-   "metadata": {},
-   "source": [
-    "### Load the Model Back\n",
-    "\n",
-    "We can load the model from MLflow using its URI:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cae35ffa",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.907802Z",
-     "iopub.status.idle": "2026-06-24T15:18:47.914695Z",
-     "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
+          "iopub.status.busy": "2026-06-24T15:18:46.636565Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.268439Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
+        }
+      },
+      "source": [
+        "# temporary directory for MLflow tracking\n",
+        "tmpdir = tempfile.mkdtemp()\n",
+        "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+        "\n",
+        "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
+        "mlflow.set_experiment(\"darts-quickstart\")\n",
+        "\n",
+        "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
+        "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+            "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Updating database tables\n",
+            "2026/07/23 18:50:08 INFO mlflow.tracking.fluent: Experiment with name 'darts-quickstart' does not exist. Creating a new experiment.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
+            "Experiment: darts-quickstart\n"
+          ]
+        }
+      ],
+      "id": "88320df5"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded model predictions match: True\n"
-     ]
-    }
-   ],
-   "source": [
-    "loaded_model = load_model(model_info.model_uri)\n",
-    "\n",
-    "loaded_predictions = loaded_model.predict(n=len(val))\n",
-    "\n",
-    "# verify predictions match\n",
-    "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
-    "print(f\"Loaded model predictions match: {predictions_match}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6bd4597c",
-   "metadata": {},
-   "source": [
-    "## Automatic Logging with `autolog()`\n",
-    "\n",
-    "`autolog()` patches darts models and metrics so that the following are logged automatically, with no extra code needed:\n",
-    "\n",
-    "- **`fit()`** – model creation parameters and covariate metadata (and the trained model artifact when ``log_models=True``; default ``False``).\n",
-    "- **Darts metric functions** – any call made inside an active MLflow run.\n",
-    "- **`backtest()`** – evaluation metrics under `backtest_*` keys.\n",
-    "- **`historical_forecasts()`** – patched so its internal per-window `fit()` calls don't each spawn their own logging.\n",
-    "- **PyTorch-based models** – per-epoch `train_loss` / `val_loss` via MLflow's PyTorch autologging (see the section below for details)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "5ef7f73a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
-     "iopub.status.busy": "2026-06-24T15:18:47.915566Z",
-     "iopub.status.idle": "2026-06-24T15:18:50.020337Z",
-     "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load Sample Data\n",
+        "\n",
+        "We'll use the classic AirPassengers dataset for this example."
+      ],
+      "id": "03d5209e"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
-     ]
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.269548Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.356588Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
+        }
+      },
+      "source": [
+        "series = AirPassengersDataset().load()\n",
+        "train, val = series.split_before(0.75)\n",
+        "\n",
+        "print(f\"Training series: {len(train)} points\")\n",
+        "print(f\"Validation series: {len(val)} points\")\n",
+        "\n",
+        "series.plot()\n",
+        "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
+        "plt.legend()\n",
+        "plt.title(\"AirPassengers Dataset\")\n",
+        "plt.show()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training series: 107 points\n",
+            "Validation series: 37 points\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          }
+        }
+      ],
+      "id": "1596e07e"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "autolog()\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
-    "    auto_model = LinearRegressionModel(lags=12)\n",
-    "    auto_model.fit(train)  # autolog logs params and covariate metadata\n",
-    "\n",
-    "    auto_predictions = auto_model.predict(n=len(val))\n",
-    "    # these metric calls happen inside the run, so they are logged automatically\n",
-    "    auto_mape = metrics.mape(val, auto_predictions)\n",
-    "    auto_rmse = metrics.rmse(val, auto_predictions)\n",
-    "\n",
-    "autolog(disable=True)\n",
-    "\n",
-    "# show logged metrics\n",
-    "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
-    "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
-    "\n",
-    "# plot\n",
-    "fig, ax = plt.subplots(figsize=(10, 4))\n",
-    "train[-36:].plot(label=\"Train\", ax=ax)\n",
-    "val.plot(label=\"Actual\", ax=ax)\n",
-    "auto_predictions.plot(\n",
-    "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
-    ")\n",
-    "ax.set_title(\"Linear Regression — autolog run\")\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f484330f",
-   "metadata": {},
-   "source": [
-    "## Open the MLflow UI\n",
-    "\n",
-    "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
-    "\n",
-    "The UI lets you:\n",
-    "- **Compare runs** side-by-side in the Experiments table\n",
-    "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
-    "- **Visualize** metrics across runs with built-in charts\n",
-    "- **Register** models to the Model Registry for versioning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "1a01cd2f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
-     "iopub.status.busy": "2026-06-24T15:18:50.021439Z",
-     "iopub.status.idle": "2026-06-24T15:18:50.023116Z",
-     "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Basic Model Logging\n",
+        "\n",
+        "Let's train a simple model and log it to MLflow manually."
+      ],
+      "id": "34858645"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Launch the MLflow UI with this command in your terminal:\n",
-      "\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
-      "\n",
-      "Then open:  http://localhost:5000\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
-    "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
-    "print(\"Then open:  http://localhost:5000\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88b0d285",
-   "metadata": {},
-   "source": [
-    "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
-    ">\n",
-    "![Mlflow Overview](./static/images/mlflow_overview.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2b133bc",
-   "metadata": {},
-   "source": [
-    "## Per-epoch Metrics with Torch Models\n",
-    "\n",
-    "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
-    "\n",
-    "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "d01783d1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
-     "iopub.status.busy": "2026-06-24T15:18:50.024087Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.370946Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.357516Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.450374Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
+        }
+      },
+      "source": [
+        "model = ExponentialSmoothing()\n",
+        "model.fit(train)\n",
+        "\n",
+        "predictions = model.predict(n=len(val))\n",
+        "\n",
+        "# calculate metrics you want to log to MLflow\n",
+        "mape_score = metrics.mape(val, predictions)\n",
+        "rmse_score = metrics.rmse(val, predictions)\n",
+        "\n",
+        "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
+        "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
+        "\n",
+        "train[-50:].plot(label=\"Training\")\n",
+        "val.plot(label=\"Actual\")\n",
+        "predictions.plot(label=\"Forecast\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Exponential Smoothing Forecast\")\n",
+        "plt.show()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation MAPE: 7.86%\n",
+            "Validation RMSE: 34.77\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          }
+        }
+      ],
+      "id": "bc8f520d"
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO: GPU available: True (mps), used: False\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
-      "INFO: TPU available: False, using: 0 TPU cores\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-      "INFO: HPU available: False, using: 0 HPUs\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
-      "\n",
-      "  | Name            | Type             | Params | Mode \n",
-      "-------------------------------------------------------------\n",
-      "0 | criterion       | MSELoss          | 0      | train\n",
-      "1 | train_criterion | MSELoss          | 0      | train\n",
-      "2 | val_criterion   | MSELoss          | 0      | train\n",
-      "3 | train_metrics   | MetricCollection | 0      | train\n",
-      "4 | val_metrics     | MetricCollection | 0      | train\n",
-      "5 | stacks          | ModuleList       | 6.2 M  | train\n",
-      "-------------------------------------------------------------\n",
-      "6.2 M     Trainable params\n",
-      "1.4 K     Non-trainable params\n",
-      "6.2 M     Total params\n",
-      "24.787    Total estimated model params size (MB)\n",
-      "400       Modules in train mode\n",
-      "0         Modules in eval mode\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now let's log this model to MLflow.\n",
+        "\n",
+        "Tags can live in two places in MLflow 3:\n",
+        "\n",
+        "- **Run tags** (`mlflow.set_tags(...)`) – shown on the run page in the UI. This is also where `autolog()` writes its tags (`model_class`, …).\n",
+        "- **LoggedModel tags** (`log_model(..., tags=...)`) – attached to the logged model entity itself (open the model from the run's **Outputs** / Models view)."
+      ],
+      "id": "35dc864c"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33ff56f2bbbb46ddb71bf5ac409bc3c7",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.451297Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.906785Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
+        }
       },
-      "text/plain": [
-       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
+        "    model_info = log_model(\n",
+        "        model=model,\n",
+        "        name=\"exponential-smoothing-model\",\n",
+        "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
+        "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
+        "    )\n",
+        "\n",
+        "    # log calculated metrics you want\n",
+        "    mlflow.log_metric(\"mape\", mape_score)\n",
+        "    mlflow.log_metric(\"rmse\", rmse_score)\n",
+        "\n",
+        "    print(f\"Run ID: {run.info.run_id}\")\n",
+        "    print(f\"Model URI: {model_info.model_uri}\")"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "2026/07/23 18:50:08 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
+            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
+          ]
+        }
+      ],
+      "id": "61406cd9"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
-      "  super().__init__(loader)\n",
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the Model Back\n",
+        "\n",
+        "We can load the model from MLflow using its URI:"
+      ],
+      "id": "0728690e"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2fb13a9c2a6b40ad886bb7878949614d",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.907802Z",
+          "iopub.status.idle": "2026-06-24T15:18:47.914695Z",
+          "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
+        }
       },
-      "text/plain": [
-       "Training: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "loaded_model = load_model(model_info.model_uri)\n",
+        "\n",
+        "loaded_predictions = loaded_model.predict(n=len(val))\n",
+        "\n",
+        "# verify predictions match\n",
+        "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
+        "print(f\"Loaded model predictions match: {predictions_match}\")"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loaded model predictions match: True\n"
+          ]
+        }
+      ],
+      "id": "cae35ffa"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "595549c03c1d4fe28a03ce38a0f38441",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Automatic Logging with `autolog()`\n",
+        "\n",
+        "`autolog()` patches darts models and metrics so that the following are logged automatically, with no extra code needed:\n",
+        "\n",
+        "- **`fit()`** – model creation parameters and covariate metadata (and the trained model artifact when ``log_models=True``; default ``False``).\n",
+        "- **Darts metric functions** – any call made inside an active MLflow run.\n",
+        "- **`backtest()`** – evaluation metrics under `backtest_*` keys.\n",
+        "- **`historical_forecasts()`** – patched so its internal per-window `fit()` calls don't each spawn their own logging.\n",
+        "- **PyTorch-based models** – per-epoch `train_loss` / `val_loss` via MLflow's PyTorch autologging (see the section below for details)."
+      ],
+      "id": "6bd4597c"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
+          "iopub.status.busy": "2026-06-24T15:18:47.915566Z",
+          "iopub.status.idle": "2026-06-24T15:18:50.020337Z",
+          "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "autolog()\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
+        "    auto_model = LinearRegressionModel(lags=12)\n",
+        "    auto_model.fit(train)  # autolog logs params and covariate metadata\n",
+        "\n",
+        "    auto_predictions = auto_model.predict(n=len(val))\n",
+        "    # these metric calls happen inside the run, so they are logged automatically\n",
+        "    auto_mape = metrics.mape(val, auto_predictions)\n",
+        "    auto_rmse = metrics.rmse(val, auto_predictions)\n",
+        "\n",
+        "autolog(disable=True)\n",
+        "\n",
+        "# show logged metrics\n",
+        "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
+        "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
+        "\n",
+        "# plot\n",
+        "fig, ax = plt.subplots(figsize=(10, 4))\n",
+        "train[-36:].plot(label=\"Train\", ax=ax)\n",
+        "val.plot(label=\"Actual\", ax=ax)\n",
+        "auto_predictions.plot(\n",
+        "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
+        ")\n",
+        "ax.set_title(\"Linear Regression — autolog run\")\n",
+        "ax.legend()\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          }
+        }
+      ],
+      "id": "5ef7f73a"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
-      "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Open the MLflow UI\n",
+        "\n",
+        "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
+        "\n",
+        "The UI lets you:\n",
+        "- **Compare runs** side-by-side in the Experiments table\n",
+        "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
+        "- **Visualize** metrics across runs with built-in charts\n",
+        "- **Register** models to the Model Registry for versioning"
+      ],
+      "id": "f484330f"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78af3a584caf477ba9c043c93a77ae56",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
+          "iopub.status.busy": "2026-06-24T15:18:50.021439Z",
+          "iopub.status.idle": "2026-06-24T15:18:50.023116Z",
+          "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
+        "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
+        "print(\"Then open:  http://localhost:5000\")"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Launch the MLflow UI with this command in your terminal:\n",
+            "\n",
+            "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
+            "\n",
+            "Then open:  http://localhost:5000\n"
+          ]
+        }
+      ],
+      "id": "1a01cd2f"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "07ec6fb97dd6418594e5dedda8a087fc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
+        ">\n",
+        "![Mlflow Overview](./static/images/mlflow_overview.png)"
+      ],
+      "id": "88b0d285"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bbd214c1629e47de8f36acad90896742",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Per-epoch Metrics with Torch Models\n",
+        "\n",
+        "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
+        "\n",
+        "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
+      ],
+      "id": "e2b133bc"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1a78ea5bef414a31bd4dd00f162b4876",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
+          "iopub.status.busy": "2026-06-24T15:18:50.024087Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.370946Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
+        "\n",
+        "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
+        "autolog()\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
+        "    nbeats = NBEATSModel(\n",
+        "        input_chunk_length=24,\n",
+        "        output_chunk_length=12,\n",
+        "        n_epochs=10,\n",
+        "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
+        "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
+        "        torch_metrics=MetricCollection({\n",
+        "            \"MAE\": MeanAbsoluteError(),\n",
+        "            \"MSE\": MeanSquaredError(),\n",
+        "        }),\n",
+        "        random_state=42,\n",
+        "    )\n",
+        "    nbeats.fit(train, val_series=val)\n",
+        "    nbeats_pred = nbeats.predict(n=len(val))\n",
+        "    # metric calls inside the run are logged automatically (keys: mape, rmse)\n",
+        "    nbeats_mape = metrics.mape(val, nbeats_pred)\n",
+        "    nbeats_rmse = metrics.rmse(val, nbeats_pred)\n",
+        "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
+        "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
+        "\n",
+        "autolog(disable=True)"
+      ],
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO: GPU available: True (mps), used: False\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
+            "INFO: TPU available: False, using: 0 TPU cores\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+            "INFO: HPU available: False, using: 0 HPUs\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
+            "\n",
+            "  | Name            | Type             | Params | Mode \n",
+            "-------------------------------------------------------------\n",
+            "0 | criterion       | MSELoss          | 0      | train\n",
+            "1 | train_criterion | MSELoss          | 0      | train\n",
+            "2 | val_criterion   | MSELoss          | 0      | train\n",
+            "3 | train_metrics   | MetricCollection | 0      | train\n",
+            "4 | val_metrics     | MetricCollection | 0      | train\n",
+            "5 | stacks          | ModuleList       | 6.2 M  | train\n",
+            "-------------------------------------------------------------\n",
+            "6.2 M     Trainable params\n",
+            "1.4 K     Non-trainable params\n",
+            "6.2 M     Total params\n",
+            "24.787    Total estimated model params size (MB)\n",
+            "400       Modules in train mode\n",
+            "0         Modules in eval mode\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "33ff56f2bbbb46ddb71bf5ac409bc3c7",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
+            "  super().__init__(loader)\n",
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2fb13a9c2a6b40ad886bb7878949614d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Training: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "595549c03c1d4fe28a03ce38a0f38441",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
+            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "78af3a584caf477ba9c043c93a77ae56",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "07ec6fb97dd6418594e5dedda8a087fc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bbd214c1629e47de8f36acad90896742",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1a78ea5bef414a31bd4dd00f162b4876",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0759f953cc2e4aba9c4d4644279daf9c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "33e1f6444b504ddd8808d0b4bdf7cf34",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c23701c8d9fb45a5a7188f013b8ae713",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1f198dc0926c4f40a60823608c789f05",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "82a6462bdf0a486a85887bef6ae73630",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Validation: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
+            "INFO: GPU available: True (mps), used: False\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
+            "INFO: TPU available: False, using: 0 TPU cores\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+            "INFO: HPU available: False, using: 0 HPUs\n",
+            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a9cf24e439244b4f99812eeea1c9aab8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Predicting: |          | 0/? [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "NBEATS MAPE: 13.64%\n",
+            "NBEATS RMSE: 63.33\n"
+          ]
+        }
+      ],
+      "id": "d01783d1"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0759f953cc2e4aba9c4d4644279daf9c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
+        ">\n",
+        "![Mlflow Charts](./static/images/mlflow_charts.png)"
+      ],
+      "id": "6fcc8f13"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33e1f6444b504ddd8808d0b4bdf7cf34",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Forecast Metrics\n",
+        "\n",
+        "With `log_metrics=True` (the default), `autolog()` patches every darts metric function. Any metric you call **inside an active run** is logged automatically with no extra arguments needed.\n",
+        "\n",
+        "The metric key is derived from the result: a scalar is logged under `{metric}` (e.g. calling `darts.metrics.mae(val, pred)` logs `mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`.\n",
+        "\n",
+        "Logged metric values are only meaningful to compare across runs when the evaluation settings match. Use the same evaluation time frame, forecast horizon, and evaluation start date for every `backtest()` / metric call you intend to compare against one another."
+      ],
+      "id": "3f0828c2"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c23701c8d9fb45a5a7188f013b8ae713",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.372367Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.470125Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
+        }
       },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "# log_metrics=True (the default) patches every darts metric so that calls made\n",
+        "# inside an active run are logged automatically\n",
+        "autolog(log_metrics=True)\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
+        "    lr_model = LinearRegressionModel(lags=12)\n",
+        "    lr_model.fit(train)\n",
+        "    lr_pred = lr_model.predict(n=len(val))\n",
+        "\n",
+        "    # each metric called here is auto-logged under its own name: mae, rmse, smape\n",
+        "    metrics.mae(val, lr_pred)\n",
+        "    metrics.rmse(val, lr_pred)\n",
+        "    metrics.smape(val, lr_pred)\n",
+        "    # you can still log a custom-named metric explicitly\n",
+        "    mlflow.log_metric(\"manual_mape\", metrics.mape(val, lr_pred))\n",
+        "    run_id = run.info.run_id\n",
+        "\n",
+        "autolog(disable=True)\n",
+        "\n",
+        "# show what was logged\n",
+        "client = mlflow.tracking.MlflowClient()\n",
+        "run_metrics = client.get_run(run_id).data.metrics\n",
+        "metric_names = sorted(run_metrics.keys())\n",
+        "print(f\"All logged metrics ({len(metric_names)}):\")\n",
+        "for name in metric_names:\n",
+        "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "All logged metrics (5):\n",
+            "  mae: 46.0220\n",
+            "  manual_mape: 10.7420\n",
+            "  mape: 10.7420\n",
+            "  rmse: 51.1820\n",
+            "  smape: 10.1015\n"
+          ]
+        }
+      ],
+      "id": "df8e85b6"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1f198dc0926c4f40a60823608c789f05",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Metric Shape and Detailed Logging\n",
+        "\n",
+        "The logged key reflects the shape of the metric output, which `autolog()` infers from the metric and its keyword arguments. The general pattern is:\n",
+        "\n",
+        "`{metric_name}{component}{quantile_or_label}`\n",
+        "\n",
+        "- **Per-component** (`component_reduction=None`) adds the component name, e.g. `mae_<component>`.\n",
+        "- **Quantile / interval** (`q`, `q_interval`) adds the quantile, e.g. `mql_q0.500`.\n",
+        "- **Per-label classification** (`label_reduction=None`) adds the class, e.g. `f1_label1`.\n",
+        "- **Per-timestep** (`time_reduction=None`) is charted across MLflow steps.\n",
+        "- **`series_reduction`**, when set on the metric call, aggregates across series inside the metric itself, so the mean-over-series logging described below does not apply.\n",
+        "\n",
+        "For `backtest()`, metric keys have a `backtest_` prefix. A time-dependent metric with `reduction=None` keeps its per-window values in the return value, but MLflow charts one value per horizon step: it applies `np.nanmean` over windows for each series, then aggregates across series. The detailed `metrics_per_series.json` table retains every source value with a `window_index`, including for a single-series backtest.\n",
+        "\n",
+        "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is appended to the same run-wide table artifact, `metrics_per_series.json`, which you can read back with `mlflow.load_table()`.\n",
+        "\n",
+        "Passing `name=` to a metric overrides the `{metric_name}` token in the key while keeping the suffixes (e.g. `mql(..., q=0.5, name=\"foo\")` logs `foo_q0.500`)."
+      ],
+      "id": "b08e900a"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "82a6462bdf0a486a85887bef6ae73630",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# build a small multi-series example from the univariate AirPassengers data\n",
+        "series_list = [train, train * 1.2]\n",
+        "val_list = [val, val * 1.2]\n",
+        "\n",
+        "multi_model = LinearRegressionModel(lags=12)\n",
+        "multi_model.fit(series_list)\n",
+        "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
+        "\n",
+        "autolog(log_metrics=True)\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"metric-shape-and-table\") as run:\n",
+        "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
+        "    # under a single key, charted across MLflow steps\n",
+        "    single_pred = multi_preds[0]  # train == series_list[0]\n",
+        "    metrics.ae(val, single_pred)\n",
+        "\n",
+        "    # multiple series: the logged value is the MEAN over series, and the full\n",
+        "    # per-series breakdown is appended to the run's table artifact\n",
+        "    per_series_mae = metrics.mae(val_list, multi_preds)\n",
+        "\n",
+        "autolog(disable=True)\n",
+        "\n",
+        "client = mlflow.tracking.MlflowClient()\n",
+        "run_id = run.info.run_id\n",
+        "\n",
+        "# aggregate metrics: mae is the mean over the two series\n",
+        "logged = client.get_run(run_id).data.metrics\n",
+        "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
+        "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
+        "\n",
+        "# load the per-series table artifact\n",
+        "per_series_df = mlflow.load_table(\n",
+        "    artifact_file=\"metrics_per_series.json\", run_ids=[run_id]\n",
+        ")\n",
+        "print(\"\\nPer-series breakdown (metrics_per_series.json):\")\n",
+        "per_series_df"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Aggregate metrics: {'ae': 44.196, 'mae': 51.316}\n",
+            "Mean MAE over series: 51.316\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0305dca7a1b846f19da965d1b74319c5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Per-series breakdown (metrics_per_series.json):\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>key</th>\n",
+              "      <th>series_index</th>\n",
+              "      <th>step</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>mae</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>47.458874</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>mae</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>55.172880</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   key  series_index  step      value\n",
+              "0  mae             0     0  47.458874\n",
+              "1  mae             1     0  55.172880"
+            ]
+          }
+        }
+      ],
+      "id": "ecdbbf29"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
-      "INFO: GPU available: True (mps), used: False\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
-      "INFO: TPU available: False, using: 0 TPU cores\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-      "INFO: HPU available: False, using: 0 HPUs\n",
-      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Saving and Loading Models Locally\n",
+        "\n",
+        "You can also save and load models to/from local paths without MLflow runs."
+      ],
+      "id": "8511fc08"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a9cf24e439244b4f99812eeea1c9aab8",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.471178Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.478682Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
+        }
       },
-      "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "# Save model to local directory\n",
+        "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
+        "save_model(model, path=local_model_path)\n",
+        "\n",
+        "print(\"\\nFiles in model directory:\")\n",
+        "for file in os.listdir(local_model_path):\n",
+        "    print(f\"  - {file}\")"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "2026/07/23 18:50:10 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Files in model directory:\n",
+            "  - python_env.yaml\n",
+            "  - requirements.txt\n",
+            "  - MLmodel\n",
+            "  - model.pkl\n",
+            "  - conda.yaml\n"
+          ]
+        }
+      ],
+      "id": "645ef079"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NBEATS MAPE: 13.64%\n",
-      "NBEATS RMSE: 63.33\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
-    "\n",
-    "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
-    "autolog()\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
-    "    nbeats = NBEATSModel(\n",
-    "        input_chunk_length=24,\n",
-    "        output_chunk_length=12,\n",
-    "        n_epochs=10,\n",
-    "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
-    "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
-    "        torch_metrics=MetricCollection({\n",
-    "            \"MAE\": MeanAbsoluteError(),\n",
-    "            \"MSE\": MeanSquaredError(),\n",
-    "        }),\n",
-    "        random_state=42,\n",
-    "    )\n",
-    "    nbeats.fit(train, val_series=val)\n",
-    "    nbeats_pred = nbeats.predict(n=len(val))\n",
-    "    # metric calls inside the run are logged automatically (keys: mape, rmse)\n",
-    "    nbeats_mape = metrics.mape(val, nbeats_pred)\n",
-    "    nbeats_rmse = metrics.rmse(val, nbeats_pred)\n",
-    "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
-    "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
-    "\n",
-    "autolog(disable=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6fcc8f13",
-   "metadata": {},
-   "source": [
-    "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
-    ">\n",
-    "![Mlflow Charts](./static/images/mlflow_charts.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f0828c2",
-   "metadata": {},
-   "source": [
-    "## Forecast Metrics\n",
-    "\n",
-    "With `log_metrics=True` (the default), `autolog()` patches every darts metric function. Any metric you call **inside an active run** is logged automatically with no extra arguments needed.\n",
-    "\n",
-    "The metric key is derived from the result: a scalar is logged under `{metric}` (e.g. calling `darts.metrics.mae(val, pred)` logs `mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "df8e85b6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.372367Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.470125Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All logged metrics (5):\n",
-      "  mae: 46.0220\n",
-      "  manual_mape: 10.7420\n",
-      "  mape: 10.7420\n",
-      "  rmse: 51.1820\n",
-      "  smape: 10.1015\n"
-     ]
-    }
-   ],
-   "source": [
-    "# log_metrics=True (the default) patches every darts metric so that calls made\n",
-    "# inside an active run are logged automatically\n",
-    "autolog(log_metrics=True)\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
-    "    lr_model = LinearRegressionModel(lags=12)\n",
-    "    lr_model.fit(train)\n",
-    "    lr_pred = lr_model.predict(n=len(val))\n",
-    "\n",
-    "    # each metric called here is auto-logged under its own name: mae, rmse, smape\n",
-    "    metrics.mae(val, lr_pred)\n",
-    "    metrics.rmse(val, lr_pred)\n",
-    "    metrics.smape(val, lr_pred)\n",
-    "    # you can still log a custom-named metric explicitly\n",
-    "    mlflow.log_metric(\"manual_mape\", metrics.mape(val, lr_pred))\n",
-    "    run_id = run.info.run_id\n",
-    "\n",
-    "autolog(disable=True)\n",
-    "\n",
-    "# show what was logged\n",
-    "client = mlflow.tracking.MlflowClient()\n",
-    "run_metrics = client.get_run(run_id).data.metrics\n",
-    "metric_names = sorted(run_metrics.keys())\n",
-    "print(f\"All logged metrics ({len(metric_names)}):\")\n",
-    "for name in metric_names:\n",
-    "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b08e900a",
-   "metadata": {},
-   "source": [
-    "### Metric Shape and Per-Series Logging\n",
-    "\n",
-    "The logged key reflects the shape of the metric output, which `autolog()` infers from the metric and its keyword arguments. The general pattern is:\n",
-    "\n",
-    "`{metric_name}{component}{quantile_or_label}`\n",
-    "\n",
-    "- **Per-component** (`component_reduction=None`) adds the component name, e.g. `mae_<component>`.\n",
-    "- **Quantile / interval** (`q`, `q_interval`) adds the quantile, e.g. `mql_q0.500`.\n",
-    "- **Per-label classification** (`label_reduction=None`) adds the class, e.g. `f1_label1`.\n",
-    "- **Per-timestep** (`time_reduction=None`) is charted across MLflow steps.\n",
-    "- **`series_reduction`**, when set on the metric call, aggregates across series inside the metric itself, so the mean-over-series logging described below does not apply.\n",
-    "\n",
-    "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is appended to a single run-wide table artifact, `metrics_per_series.json`, which you can read back with `mlflow.load_table()`.\n",
-    "\n",
-    "Passing `name=` to a metric overrides the `{metric_name}` token in the key while keeping the suffixes (e.g. `mql(..., q=0.5, name=\"foo\")` logs `foo_q0.500`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ecdbbf29",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.479604Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.484973Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
+        }
+      },
+      "source": [
+        "# Load model from local directory\n",
+        "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
+        "\n",
+        "# Test it\n",
+        "local_predictions = local_loaded_model.predict(n=5)\n",
+        "print(\"Loaded model successfully!\")\n",
+        "print(f\"Predictions shape: {local_predictions.values().shape}\")"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loaded model successfully!\n",
+            "Predictions shape: (5, 1)\n"
+          ]
+        }
+      ],
+      "id": "254ba153"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Aggregate metrics: {'ae': 44.196, 'mae': 51.316}\n",
-      "Mean MAE over series: 51.316\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Querying Experiments\n",
+        "\n",
+        "You can programmatically query and compare runs."
+      ],
+      "id": "aab0d1e0"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0305dca7a1b846f19da965d1b74319c5",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.485859Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.496380Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
+        }
       },
-      "text/plain": [
-       "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "from mlflow.tracking import MlflowClient\n",
+        "\n",
+        "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
+        "\n",
+        "# get all runs\n",
+        "client = MlflowClient()\n",
+        "runs = client.search_runs(\n",
+        "    experiment_ids=[experiment.experiment_id],\n",
+        "    order_by=[\"metrics.mape ASC\"],  # Sort by best MAPE\n",
+        ")\n",
+        "\n",
+        "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
+        "for i, run in enumerate(runs, 1):\n",
+        "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
+        "    mape_val = run.data.metrics.get(\"mape\", \"N/A\")\n",
+        "    print(f\"{i}. {run_name}\")\n",
+        "    print(f\"   Run ID: {run.info.run_id}\")\n",
+        "    print(f\"   Validation MAPE: {mape_val}\")\n",
+        "    print()"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Found 5 runs in experiment 'darts-quickstart':\n",
+            "\n",
+            "1. exponential-smoothing-baseline\n",
+            "   Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
+            "   Validation MAPE: 7.864181481214469\n",
+            "\n",
+            "2. linear-regression-full-metrics\n",
+            "   Run ID: 708095048f274913990375bbbd4310ad\n",
+            "   Validation MAPE: 10.742044444678953\n",
+            "\n",
+            "3. linear-regression-autolog\n",
+            "   Run ID: 36376b7a94454a13a4acba207459315e\n",
+            "   Validation MAPE: 10.742044444678953\n",
+            "\n",
+            "4. nbeats-epoch-metrics\n",
+            "   Run ID: f0d1b0ed9ac34232b5e36fe82a24a0a6\n",
+            "   Validation MAPE: 13.639569217869525\n",
+            "\n",
+            "5. metric-shape-and-table\n",
+            "   Run ID: 9be3e9d311b64d45b0204174835537cc\n",
+            "   Validation MAPE: N/A\n",
+            "\n"
+          ]
+        }
+      ],
+      "id": "109a9812"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Per-series breakdown (metrics_per_series.json):\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load the Best Model"
+      ],
+      "id": "22685423"
     },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>key</th>\n",
-       "      <th>series_index</th>\n",
-       "      <th>step</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>mae</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>47.458874</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>mae</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.172880</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.497496Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.572540Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
+        }
+      },
+      "source": [
+        "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
+        "\n",
+        "if runs:\n",
+        "    best_run = runs[0]\n",
+        "    # the model is logged via MLflow's logged-model API, so resolve its model_id\n",
+        "    # from the run outputs and load it with a models:/ URI\n",
+        "    model_outputs = mlflow.get_run(best_run.info.run_id).outputs.model_outputs\n",
+        "    best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
+        "\n",
+        "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
+        "    print(f\"Model URI: {best_model_uri}\")\n",
+        "\n",
+        "    best_model = load_model(best_model_uri)\n",
+        "    # global models (e.g. LinearRegression, NBEATS) need the series at predict time\n",
+        "    # because we save with clean=True; local models (e.g. ExponentialSmoothing) don't\n",
+        "    if isinstance(best_model, GlobalForecastingModel):\n",
+        "        best_predictions = best_model.predict(n=len(val), series=train)\n",
+        "    else:\n",
+        "        best_predictions = best_model.predict(n=len(val))\n",
+        "\n",
+        "    train[-50:].plot(label=\"Training\")\n",
+        "    val.plot(label=\"Actual\")\n",
+        "    best_predictions.plot(label=\"Best Model Forecast\")\n",
+        "    plt.legend()\n",
+        "    plt.title(\"Best Model Predictions\")\n",
+        "    plt.show()"
       ],
-      "text/plain": [
-       "   key  series_index  step      value\n",
-       "0  mae             0     0  47.458874\n",
-       "1  mae             1     0  55.172880"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# build a small multi-series example from the univariate AirPassengers data\n",
-    "series_list = [train, train * 1.2]\n",
-    "val_list = [val, val * 1.2]\n",
-    "\n",
-    "multi_model = LinearRegressionModel(lags=12)\n",
-    "multi_model.fit(series_list)\n",
-    "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
-    "\n",
-    "autolog(log_metrics=True)\n",
-    "\n",
-    "with mlflow.start_run(run_name=\"metric-shape-and-table\") as run:\n",
-    "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
-    "    # under a single key, charted across MLflow steps\n",
-    "    single_pred = multi_preds[0]  # train == series_list[0]\n",
-    "    metrics.ae(val, single_pred)\n",
-    "\n",
-    "    # multiple series: the logged value is the MEAN over series, and the full\n",
-    "    # per-series breakdown is appended to the run's table artifact\n",
-    "    per_series_mae = metrics.mae(val_list, multi_preds)\n",
-    "\n",
-    "autolog(disable=True)\n",
-    "\n",
-    "client = mlflow.tracking.MlflowClient()\n",
-    "run_id = run.info.run_id\n",
-    "\n",
-    "# aggregate metrics: mae is the mean over the two series\n",
-    "logged = client.get_run(run_id).data.metrics\n",
-    "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
-    "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
-    "\n",
-    "# load the per-series table artifact\n",
-    "per_series_df = mlflow.load_table(\n",
-    "    artifact_file=\"metrics_per_series.json\", run_ids=[run_id]\n",
-    ")\n",
-    "print(\"\\nPer-series breakdown (metrics_per_series.json):\")\n",
-    "per_series_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8511fc08",
-   "metadata": {},
-   "source": [
-    "## Saving and Loading Models Locally\n",
-    "\n",
-    "You can also save and load models to/from local paths without MLflow runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "645ef079",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.471178Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.478682Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
-    }
-   },
-   "outputs": [
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loading best model from run: exponential-smoothing-baseline\n",
+            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          }
+        }
+      ],
+      "id": "7b09db6a"
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/07/23 18:50:10 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model Registry\n",
+        "\n",
+        "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived)."
+      ],
+      "id": "6f1ec89e"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Files in model directory:\n",
-      "  - python_env.yaml\n",
-      "  - requirements.txt\n",
-      "  - MLmodel\n",
-      "  - model.pkl\n",
-      "  - conda.yaml\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Save model to local directory\n",
-    "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
-    "save_model(model, path=local_model_path)\n",
-    "\n",
-    "print(\"\\nFiles in model directory:\")\n",
-    "for file in os.listdir(local_model_path):\n",
-    "    print(f\"  - {file}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "254ba153",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.479604Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.484973Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-07-22T08:27:04.144511Z",
+          "iopub.status.busy": "2026-07-22T08:27:04.144439Z",
+          "iopub.status.idle": "2026-07-22T08:27:04.166315Z",
+          "shell.execute_reply": "2026-07-22T08:27:04.165897Z"
+        }
+      },
+      "source": [
+        "# register the best model (from the \"Load the Best Model\" section above)\n",
+        "result = mlflow.register_model(\n",
+        "    model_uri=best_model_uri,\n",
+        "    name=\"darts-air-passengers\",\n",
+        ")\n",
+        "print(f\"Registered version: {result.version}\")"
+      ],
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Registered version: 1\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Successfully registered model 'darts-air-passengers'.\n",
+            "Created version '1' of model 'darts-air-passengers'.\n"
+          ]
+        }
+      ],
+      "id": "7c854bc4"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded model successfully!\n",
-      "Predictions shape: (5, 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load model from local directory\n",
-    "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
-    "\n",
-    "# Test it\n",
-    "local_predictions = local_loaded_model.predict(n=5)\n",
-    "print(\"Loaded model successfully!\")\n",
-    "print(f\"Predictions shape: {local_predictions.values().shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aab0d1e0",
-   "metadata": {},
-   "source": [
-    "## Querying Experiments\n",
-    "\n",
-    "You can programmatically query and compare runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "109a9812",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.485859Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.496380Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
+        "\n",
+        "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
+        ">\n",
+        "![Mlflow Charts](./static/images/mlflow_models.png)"
+      ],
+      "id": "da6cc83a"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 5 runs in experiment 'darts-quickstart':\n",
-      "\n",
-      "1. exponential-smoothing-baseline\n",
-      "   Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
-      "   Validation MAPE: 7.864181481214469\n",
-      "\n",
-      "2. linear-regression-full-metrics\n",
-      "   Run ID: 708095048f274913990375bbbd4310ad\n",
-      "   Validation MAPE: 10.742044444678953\n",
-      "\n",
-      "3. linear-regression-autolog\n",
-      "   Run ID: 36376b7a94454a13a4acba207459315e\n",
-      "   Validation MAPE: 10.742044444678953\n",
-      "\n",
-      "4. nbeats-epoch-metrics\n",
-      "   Run ID: f0d1b0ed9ac34232b5e36fe82a24a0a6\n",
-      "   Validation MAPE: 13.639569217869525\n",
-      "\n",
-      "5. metric-shape-and-table\n",
-      "   Run ID: 9be3e9d311b64d45b0204174835537cc\n",
-      "   Validation MAPE: N/A\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from mlflow.tracking import MlflowClient\n",
-    "\n",
-    "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
-    "\n",
-    "# get all runs\n",
-    "client = MlflowClient()\n",
-    "runs = client.search_runs(\n",
-    "    experiment_ids=[experiment.experiment_id],\n",
-    "    order_by=[\"metrics.mape ASC\"],  # Sort by best MAPE\n",
-    ")\n",
-    "\n",
-    "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
-    "for i, run in enumerate(runs, 1):\n",
-    "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
-    "    mape_val = run.data.metrics.get(\"mape\", \"N/A\")\n",
-    "    print(f\"{i}. {run_name}\")\n",
-    "    print(f\"   Run ID: {run.info.run_id}\")\n",
-    "    print(f\"   Validation MAPE: {mape_val}\")\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22685423",
-   "metadata": {},
-   "source": [
-    "### Load the Best Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7b09db6a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
-     "iopub.status.busy": "2026-06-24T15:18:53.497496Z",
-     "iopub.status.idle": "2026-06-24T15:18:53.572540Z",
-     "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Important Note: Custom Flavor\n",
+        "\n",
+        "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
+        "\n",
+        "**Always use:**\n",
+        "```python\n",
+        "from darts.utils.mlflow import load_model\n",
+        "model = load_model(model_uri)\n",
+        "```\n",
+        "\n",
+        "**Instead of:**\n",
+        "```python\n",
+        "import mlflow\n",
+        "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
+        "```\n",
+        "\n",
+        "This custom flavor is necessary to properly handle:\n",
+        "- TimeSeries objects\n",
+        "- Darts-specific model parameters\n",
+        "- Covariate handling (past, future, static)\n",
+        "- PyTorch model state preservation"
+      ],
+      "id": "7af49ea9"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading best model from run: exponential-smoothing-baseline\n",
-      "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cleanup"
+      ],
+      "id": "40621b13"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
-    "\n",
-    "if runs:\n",
-    "    best_run = runs[0]\n",
-    "    # the model is logged via MLflow's logged-model API, so resolve its model_id\n",
-    "    # from the run outputs and load it with a models:/ URI\n",
-    "    model_outputs = mlflow.get_run(best_run.info.run_id).outputs.model_outputs\n",
-    "    best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
-    "\n",
-    "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
-    "    print(f\"Model URI: {best_model_uri}\")\n",
-    "\n",
-    "    best_model = load_model(best_model_uri)\n",
-    "    # global models (e.g. LinearRegression, NBEATS) need the series at predict time\n",
-    "    # because we save with clean=True; local models (e.g. ExponentialSmoothing) don't\n",
-    "    if isinstance(best_model, GlobalForecastingModel):\n",
-    "        best_predictions = best_model.predict(n=len(val), series=train)\n",
-    "    else:\n",
-    "        best_predictions = best_model.predict(n=len(val))\n",
-    "\n",
-    "    train[-50:].plot(label=\"Training\")\n",
-    "    val.plot(label=\"Actual\")\n",
-    "    best_predictions.plot(label=\"Best Model Forecast\")\n",
-    "    plt.legend()\n",
-    "    plt.title(\"Best Model Predictions\")\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f1ec89e",
-   "metadata": {},
-   "source": [
-    "## Model Registry\n",
-    "\n",
-    "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "7c854bc4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-07-22T08:27:04.144511Z",
-     "iopub.status.busy": "2026-07-22T08:27:04.144439Z",
-     "iopub.status.idle": "2026-07-22T08:27:04.166315Z",
-     "shell.execute_reply": "2026-07-22T08:27:04.165897Z"
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
+          "iopub.status.busy": "2026-06-24T15:18:53.573637Z",
+          "iopub.status.idle": "2026-06-24T15:18:53.575115Z",
+          "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
+        }
+      },
+      "source": [
+        "# Uncomment to cleanup\n",
+        "# import shutil\n",
+        "# shutil.rmtree(tmpdir)\n",
+        "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
+        "\n",
+        "print(f\"To cleanup manually, delete: {tmpdir}\")"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic\n"
+          ]
+        }
+      ],
+      "id": "51fc7c4a"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Registered version: 1\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Final Remarks"
+      ],
+      "id": "ca40afc1"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Successfully registered model 'darts-air-passengers'.\n",
-      "Created version '1' of model 'darts-air-passengers'.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# register the best model (from the \"Load the Best Model\" section above)\n",
-    "result = mlflow.register_model(\n",
-    "    model_uri=best_model_uri,\n",
-    "    name=\"darts-air-passengers\",\n",
-    ")\n",
-    "print(f\"Registered version: {result.version}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da6cc83a",
-   "metadata": {},
-   "source": [
-    "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
-    "\n",
-    "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
-    ">\n",
-    "![Mlflow Charts](./static/images/mlflow_models.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7af49ea9",
-   "metadata": {},
-   "source": [
-    "## Important Note: Custom Flavor\n",
-    "\n",
-    "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
-    "\n",
-    "**Always use:**\n",
-    "```python\n",
-    "from darts.utils.mlflow import load_model\n",
-    "model = load_model(model_uri)\n",
-    "```\n",
-    "\n",
-    "**Instead of:**\n",
-    "```python\n",
-    "import mlflow\n",
-    "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
-    "```\n",
-    "\n",
-    "This custom flavor is necessary to properly handle:\n",
-    "- TimeSeries objects\n",
-    "- Darts-specific model parameters\n",
-    "- Covariate handling (past, future, static)\n",
-    "- PyTorch model state preservation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40621b13",
-   "metadata": {},
-   "source": [
-    "## Cleanup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "51fc7c4a",
-   "metadata": {
-    "execution": {
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-     "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
+      ],
+      "id": "c4c86a23"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic\n"
-     ]
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-    "# Uncomment to cleanup\n",
-    "# import shutil\n",
-    "# shutil.rmtree(tmpdir)\n",
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 }

From dd1e2c679cd00492271d1b45ca234c575be9295b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 5 Aug 2026 11:45:23 +0200
Subject: [PATCH 136/154] chore: simplify changelog entry

---
 CHANGELOG.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index cc62514913..e68c1cc30f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,8 +13,8 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 - 🚀🚀 Added a custom MLflow model flavor for Darts, available under `darts.utils.mlflow`. It provides an MLflow integration for any Darts `ForecastingModel` (statistical, scikit-learn-like, and PyTorch-based). [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
   - Added `save_model()`, `load_model()`, and `log_model()` to persist and reload Darts models as MLflow models, including model and covariate metadata.
-  - Added `autolog()` to automatically log model creation parameters, covariate usage, metrics, and the trained model artifact when `log_models=True` (default `False`) on every `fit()`, `backtest()` and `historical_forecasts()` call within an active MLflow run.
-  - Metric functions from `darts.metrics` are automatically logged when called inside an active run, with keys reflecting the metric output shape (per-component, per-quantile/interval, per-label, and per-timestep results charted across MLflow steps). Multi-series results log the aggregate over series and write the full per-series breakdown to a JSON artifact.
+  - Added `autolog()` for model parameters, series/covariate metadata, optional model artifacts, PyTorch epoch metrics, and backtest metrics. Nested historical-forecast fits are suppressed.
+  - Darts metrics are logged with shape-aware keys; multi-series and window-level backtest details are available in `metrics_per_series.json`.
   - Added a new notebook for [MLflow quickstart](https://unit8co.github.io/darts/examples/29-MLflow-quickstart.html) with detailed usage examples.
   - Note: model serving and deployment (MLflow's `pyfunc` flavor, model signatures, and input examples) are not yet supported.
 - Added support for per-timestep (non-aggregated) encoder and decoder variable importances in `TFTExplainer`, exposed as `TimeSeries` via `TFTExplainabilityResult.get_encoder_importance_over_time()` and `get_decoder_importance_over_time()`. [#3170](https://github.com/unit8co/darts/pull/3170) by [exactml](https://github.com/exactml).

From 811e16c0580bcb311aa83a861c462fcc1bc0cc79 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 5 Aug 2026 13:27:38 +0200
Subject: [PATCH 137/154] feat: negative x-axis to indicate most recent values

---
 darts/tests/optional_deps/test_mlflow.py |  40 +-
 darts/utils/mlflow.py                    |  38 +-
 examples/29-MLflow-quickstart.ipynb      | 787 ++++++++++++-----------
 3 files changed, 453 insertions(+), 412 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index bbd0056466..07c2e6d46e 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1070,7 +1070,7 @@ def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
         history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
         assert len(history) == len(ref), "Expected one step per timestep"
         steps = sorted(m.step for m in history)
-        assert steps == list(range(len(ref)))
+        assert steps == list(range(-len(ref), 0))
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
         np.testing.assert_allclose(logged, ref, atol=1e-5)
 
@@ -1078,7 +1078,7 @@ def test_autolog_metric_aligns_time_axis_by_end_date(
         self, mlflow_tracking, autolog_context
     ):
         """A shorter series' time axis aligns from the end, not the start, so
-        its steps overlap the tail of a longer series rather than the head."""
+        its negative steps overlap the tail of a longer series."""
         ts_long = self.ts_univariate  # length 50
         ts_short = ts_long[10:]  # length 40, same end, starts 10 steps later
 
@@ -1094,17 +1094,17 @@ def test_autolog_metric_aligns_time_axis_by_end_date(
         history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
         by_step = {m.step: m.value for m in history}
         assert len(by_step) == 50
-        for step in range(10):
+        for step in range(-50, -40):
             assert by_step[step] == pytest.approx(1.0, abs=1e-4), step
-        for step in range(10, 50):
+        for step in range(-40, 0):
             assert by_step[step] == pytest.approx(1.5, abs=1e-4), step
 
         rows = self._read_per_series_table(run.info.run_id)
         steps_by_series = {0: set(), 1: set()}
         for r in rows:
             steps_by_series[r["series_index"]].add(r["step"])
-        assert steps_by_series[0] == set(range(50))
-        assert steps_by_series[1] == set(range(10, 50))
+        assert steps_by_series[0] == set(range(-50, 0))
+        assert steps_by_series[1] == set(range(-40, 0))
 
     def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile with matching values."""
@@ -1401,7 +1401,7 @@ def test_autolog_backtest_scalar(self, mlflow_tracking, autolog_context):
         assert run_data.metrics["backtest_mae"] == pytest.approx(float(ref), abs=1e-5)
 
     def test_autolog_backtest_per_window_steps(self, mlflow_tracking, autolog_context):
-        """reduction=None logs per-window values as consecutive steps of one key."""
+        """reduction=None logs per-window values at end-relative steps."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
                 ref = self._fit_lr().backtest(
@@ -1415,7 +1415,7 @@ def test_autolog_backtest_per_window_steps(self, mlflow_tracking, autolog_contex
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
         assert len(history) > 1, "Expected multiple per-window steps"
         steps = sorted(m.step for m in history)
-        assert steps == list(range(len(history)))
+        assert steps == list(range(-len(history), 0))
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
         np.testing.assert_allclose(logged, np.asarray(ref, dtype=float), atol=1e-5)
 
@@ -1549,7 +1549,7 @@ def test_autolog_backtest_per_timestep_per_window(
         assert len(rows) == ref.size
         for row in rows:
             assert row["key"] == "backtest_ae"
-            assert row["window_index"] in range(len(ref))
+            assert row["window_index"] in range(-len(ref), 0)
 
     def test_autolog_backtest_historical_forecasts_horizon_inferred(
         self, mlflow_tracking, autolog_context
@@ -1771,9 +1771,9 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
 
     def test_log_backtest_metrics_aligns_windows_by_end_date(self, mlflow_tracking):
         """A shorter series' window axis aligns from the end, not the start,
-        so its windows overlap the tail of a longer series rather than the
-        head. Calls _log_backtest_metrics directly with a fabricated result
-        so the window counts per series are exact."""
+        so its negative steps overlap the tail of a longer series. Calls
+        _log_backtest_metrics directly with a fabricated result so the window
+        counts per series are exact."""
         backtest_args = {
             "metric": dm.mae,
             "metric_kwargs": {},
@@ -1792,16 +1792,16 @@ def test_log_backtest_metrics_aligns_windows_by_end_date(self, mlflow_tracking):
 
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
         logged = {m.step: m.value for m in history}
-        assert logged == pytest.approx({0: 1.0, 1: 2.0, 2: 6.5, 3: 12.0, 4: 17.5})
+        assert logged == pytest.approx({-5: 1.0, -4: 2.0, -3: 6.5, -2: 12.0, -1: 17.5})
 
         rows = self._read_per_series_table(run.info.run_id)
         by_step = {(r["series_index"], r["step"]): r["value"] for r in rows}
-        assert by_step[(0, 0)] == pytest.approx(1.0)
-        assert by_step[(0, 4)] == pytest.approx(5.0)
-        assert by_step[(1, 2)] == pytest.approx(10.0)
-        assert by_step[(1, 4)] == pytest.approx(30.0)
-        assert (1, 0) not in by_step
-        assert (1, 1) not in by_step
+        assert by_step[(0, -5)] == pytest.approx(1.0)
+        assert by_step[(0, -1)] == pytest.approx(5.0)
+        assert by_step[(1, -3)] == pytest.approx(10.0)
+        assert by_step[(1, -1)] == pytest.approx(30.0)
+        assert (1, -5) not in by_step
+        assert (1, -4) not in by_step
 
     def test_log_backtest_metrics_aligns_last_points_only_time_axis(
         self, mlflow_tracking
@@ -1827,7 +1827,7 @@ def test_log_backtest_metrics_aligns_last_points_only_time_axis(
 
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
         logged = {m.step: m.value for m in history}
-        assert logged == pytest.approx({0: 1.0, 1: 2.0, 2: 6.5, 3: 12.0, 4: 17.5})
+        assert logged == pytest.approx({-5: 1.0, -4: 2.0, -3: 6.5, -2: 12.0, -1: 17.5})
 
     @staticmethod
     def _read_per_series_table(run_id):
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index be3dd22879..23b314e269 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1145,11 +1145,11 @@ def _log_backtest_metrics(
     must have the same number of components; names are taken from the first
     series.
 
-    Series of different lengths are assumed to share the same end date, so any
-    axis mapping to real dates (the window axis, or the per-timestep axis
-    when ``last_points_only`` stitches windows into one series) is aligned
-    from the end rather than the start. The per-horizon-step axis is left
-    as-is, since it means "steps ahead" rather than a real date.
+    Series can have different lengths and intersect in different ways,
+    so the time axis is aligned from the end rather than the start: a shorter
+    series lines up on its last value instead of its first. To represent this,
+    the time axis is mapped to the MLflow ``step`` as ``t - t_size`` when
+    ``has_time_axis`` is ``True``.
 
     Raises
     ------
@@ -1303,15 +1303,9 @@ def _log_backtest_metrics(
     # align the calendar-relative axes from the end
     max_w_size = max((w_size for _, w_size, _ in series_shapes), default=0)
     t_axis_is_calendar = has_time_axis and not has_windows and last_points_only
-    max_t_size = (
-        max((t_size for t_size, _, _ in series_shapes), default=0)
-        if t_axis_is_calendar
-        else 0
-    )
 
     for series_index, (t_size, w_size, canonical) in enumerate(series_shapes):
         w_offset = max_w_size - w_size if has_windows else 0
-        t_offset = max_t_size - t_size if t_axis_is_calendar else 0
         for m in range(n_metrics):
             for c in range(c_size):
                 key = base_keys[m][c]
@@ -1322,11 +1316,12 @@ def _log_backtest_metrics(
                         values = canonical[:, t, c, m]
                         agg.setdefault((key, t), []).append(float(np.nanmean(values)))
                         for w, value in enumerate(values):
+                            window_index = w + w_offset
                             rows.append({
                                 "key": key,
                                 "series_index": series_index,
                                 "step": t,
-                                "window_index": w + w_offset,
+                                "window_index": window_index - max_w_size,
                                 "value": float(value),
                             })
                     continue
@@ -1335,8 +1330,11 @@ def _log_backtest_metrics(
                     aligned_w = w + w_offset
                     for t in range(t_size):
                         # MLflow step maps to the axis the UI should chart:
-                        # time when present, otherwise window index
-                        step = t + t_offset if has_time_axis else aligned_w
+                        # horizon when present, otherwise end-relative calendar axis
+                        if has_time_axis:
+                            step = t - t_size if t_axis_is_calendar else t
+                        else:
+                            step = aligned_w - max_w_size
                         value = float(canonical[w, t, c, m])
                         agg.setdefault((key, step), []).append(value)
                         rows.append({
@@ -1472,7 +1470,9 @@ def _log_metric_result(
 
     Series can have different lengths and intersect in different ways,
     so the time axis is aligned from the end rather than the start: a shorter
-    series lines up on its last value instead of its first.
+    series lines up on its last value instead of its first. To represent this,
+    the time axis is mapped to the MLflow ``step`` as ``t - t_size`` when
+    ``has_time_axis`` is ``True``.
 
     Raises
     ------
@@ -1571,18 +1571,14 @@ def _log_metric_result(
 
         series_shapes.append((t_size, arr.reshape(t_size, c_size)))
 
-    # align the time axis from the end (see docstring)
-    max_t_size = max((t_size for t_size, _ in series_shapes), default=0)
-
     # agg maps (key, step) -> per-series values, aggregated into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
     for series_index, (t_size, canonical) in enumerate(series_shapes):
-        step_offset = max_t_size - t_size if has_time_axis else 0
         for c, key in enumerate(keys):
             for t in range(t_size):
-                # MLflow step maps to the time axis when present
-                step = t + step_offset if has_time_axis else 0
+                # Time axes are end-relative, with the latest value at step -1.
+                step = t - t_size if has_time_axis else 0
                 value = float(canonical[t, c])
                 agg.setdefault((key, step), []).append(value)
                 rows.append({
diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
index 51ea86bbdd..33551375d6 100644
--- a/examples/29-MLflow-quickstart.ipynb
+++ b/examples/29-MLflow-quickstart.ipynb
@@ -2,6 +2,7 @@
   "cells": [
     {
       "cell_type": "markdown",
+      "id": "aeddb542",
       "metadata": {},
       "source": [
         "# MLflow for Darts\n",
@@ -12,11 +13,11 @@
         "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
         "\n",
         "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
-      ],
-      "id": "aeddb542"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "f72894af",
       "metadata": {},
       "source": [
         "## Installing MLflow\n",
@@ -26,19 +27,20 @@
         "```bash\n",
         "pip install \"mlflow>=3.0\"\n",
         "```"
-      ],
-      "id": "f72894af"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "42e3dcea",
       "metadata": {},
       "source": [
         "## Setup and Imports"
-      ],
-      "id": "42e3dcea"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
+      "id": "b346ce8f",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
@@ -47,6 +49,7 @@
           "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
         }
       },
+      "outputs": [],
       "source": [
         "# fix python path if working locally\n",
         "from utils import fix_pythonpath_if_working_locally\n",
@@ -56,13 +59,12 @@
         "import warnings\n",
         "\n",
         "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-      ],
-      "execution_count": 2,
-      "outputs": [],
-      "id": "b346ce8f"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
+      "id": "13b13fe4",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
@@ -71,6 +73,7 @@
           "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
         }
       },
+      "outputs": [],
       "source": [
         "%matplotlib inline\n",
         "\n",
@@ -85,13 +88,12 @@
         "from darts.datasets import AirPassengersDataset\n",
         "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
         "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
-      ],
-      "execution_count": 3,
-      "outputs": [],
-      "id": "13b13fe4"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 4,
+      "id": "4d424e08",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
@@ -100,28 +102,28 @@
           "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
         }
       },
+      "outputs": [],
       "source": [
         "# use darts plotting style\n",
         "from darts import set_option\n",
         "\n",
         "set_option(\"plotting.use_darts_style\", True)"
-      ],
-      "execution_count": 4,
-      "outputs": [],
-      "id": "4d424e08"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "2f9c40d6",
       "metadata": {},
       "source": [
         "## MLflow Setup\n",
         "\n",
         "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
-      ],
-      "id": "2f9c40d6"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
+      "id": "88320df5",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
@@ -130,20 +132,9 @@
           "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
         }
       },
-      "source": [
-        "# temporary directory for MLflow tracking\n",
-        "tmpdir = tempfile.mkdtemp()\n",
-        "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
-        "\n",
-        "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
-        "mlflow.set_experiment(\"darts-quickstart\")\n",
-        "\n",
-        "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
-        "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
-      ],
-      "execution_count": 5,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
@@ -152,6 +143,7 @@
           ]
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
@@ -159,20 +151,32 @@
           ]
         }
       ],
-      "id": "88320df5"
+      "source": [
+        "# temporary directory for MLflow tracking\n",
+        "tmpdir = tempfile.mkdtemp()\n",
+        "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+        "\n",
+        "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
+        "mlflow.set_experiment(\"darts-quickstart\")\n",
+        "\n",
+        "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
+        "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "03d5209e",
       "metadata": {},
       "source": [
         "## Load Sample Data\n",
         "\n",
         "We'll use the classic AirPassengers dataset for this example."
-      ],
-      "id": "03d5209e"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
+      "id": "1596e07e",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
@@ -181,22 +185,9 @@
           "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
         }
       },
-      "source": [
-        "series = AirPassengersDataset().load()\n",
-        "train, val = series.split_before(0.75)\n",
-        "\n",
-        "print(f\"Training series: {len(train)} points\")\n",
-        "print(f\"Validation series: {len(val)} points\")\n",
-        "\n",
-        "series.plot()\n",
-        "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
-        "plt.legend()\n",
-        "plt.title(\"AirPassengers Dataset\")\n",
-        "plt.show()"
-      ],
-      "execution_count": 6,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Training series: 107 points\n",
@@ -204,29 +195,44 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "image/png": 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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
-      "id": "1596e07e"
+      "source": [
+        "series = AirPassengersDataset().load()\n",
+        "train, val = series.split_before(0.75)\n",
+        "\n",
+        "print(f\"Training series: {len(train)} points\")\n",
+        "print(f\"Validation series: {len(val)} points\")\n",
+        "\n",
+        "series.plot()\n",
+        "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
+        "plt.legend()\n",
+        "plt.title(\"AirPassengers Dataset\")\n",
+        "plt.show()"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "34858645",
       "metadata": {},
       "source": [
         "## Basic Model Logging\n",
         "\n",
         "Let's train a simple model and log it to MLflow manually."
-      ],
-      "id": "34858645"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 7,
+      "id": "bc8f520d",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
@@ -235,6 +241,26 @@
           "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
         }
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Validation MAPE: 7.86%\n",
+            "Validation RMSE: 34.77\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "model = ExponentialSmoothing()\n",
         "model.fit(train)\n",
@@ -254,30 +280,11 @@
         "plt.legend()\n",
         "plt.title(\"Exponential Smoothing Forecast\")\n",
         "plt.show()"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Validation MAPE: 7.86%\n",
-            "Validation RMSE: 34.77\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          }
-        }
-      ],
-      "id": "bc8f520d"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "35dc864c",
       "metadata": {},
       "source": [
         "Now let's log this model to MLflow.\n",
@@ -286,11 +293,12 @@
         "\n",
         "- **Run tags** (`mlflow.set_tags(...)`) – shown on the run page in the UI. This is also where `autolog()` writes its tags (`model_class`, …).\n",
         "- **LoggedModel tags** (`log_model(..., tags=...)`) – attached to the logged model entity itself (open the model from the run's **Outputs** / Models view)."
-      ],
-      "id": "35dc864c"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
+      "id": "61406cd9",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
@@ -299,31 +307,16 @@
           "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
         }
       },
-      "source": [
-        "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
-        "    model_info = log_model(\n",
-        "        model=model,\n",
-        "        name=\"exponential-smoothing-model\",\n",
-        "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
-        "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
-        "    )\n",
-        "\n",
-        "    # log calculated metrics you want\n",
-        "    mlflow.log_metric(\"mape\", mape_score)\n",
-        "    mlflow.log_metric(\"rmse\", rmse_score)\n",
-        "\n",
-        "    print(f\"Run ID: {run.info.run_id}\")\n",
-        "    print(f\"Model URI: {model_info.model_uri}\")"
-      ],
-      "execution_count": 8,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "2026/07/23 18:50:08 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
           ]
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
@@ -331,20 +324,37 @@
           ]
         }
       ],
-      "id": "61406cd9"
+      "source": [
+        "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
+        "    model_info = log_model(\n",
+        "        model=model,\n",
+        "        name=\"exponential-smoothing-model\",\n",
+        "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
+        "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
+        "    )\n",
+        "\n",
+        "    # log calculated metrics you want\n",
+        "    mlflow.log_metric(\"mape\", mape_score)\n",
+        "    mlflow.log_metric(\"rmse\", rmse_score)\n",
+        "\n",
+        "    print(f\"Run ID: {run.info.run_id}\")\n",
+        "    print(f\"Model URI: {model_info.model_uri}\")"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "0728690e",
       "metadata": {},
       "source": [
         "### Load the Model Back\n",
         "\n",
         "We can load the model from MLflow using its URI:"
-      ],
-      "id": "0728690e"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 9,
+      "id": "cae35ffa",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
@@ -353,28 +363,28 @@
           "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
         }
       },
-      "source": [
-        "loaded_model = load_model(model_info.model_uri)\n",
-        "\n",
-        "loaded_predictions = loaded_model.predict(n=len(val))\n",
-        "\n",
-        "# verify predictions match\n",
-        "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
-        "print(f\"Loaded model predictions match: {predictions_match}\")"
-      ],
-      "execution_count": 9,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Loaded model predictions match: True\n"
           ]
         }
       ],
-      "id": "cae35ffa"
+      "source": [
+        "loaded_model = load_model(model_info.model_uri)\n",
+        "\n",
+        "loaded_predictions = loaded_model.predict(n=len(val))\n",
+        "\n",
+        "# verify predictions match\n",
+        "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
+        "print(f\"Loaded model predictions match: {predictions_match}\")"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "6bd4597c",
       "metadata": {},
       "source": [
         "## Automatic Logging with `autolog()`\n",
@@ -386,11 +396,12 @@
         "- **`backtest()`** – evaluation metrics under `backtest_*` keys.\n",
         "- **`historical_forecasts()`** – patched so its internal per-window `fit()` calls don't each spawn their own logging.\n",
         "- **PyTorch-based models** – per-epoch `train_loss` / `val_loss` via MLflow's PyTorch autologging (see the section below for details)."
-      ],
-      "id": "6bd4597c"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
+      "id": "5ef7f73a",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
@@ -399,6 +410,25 @@
           "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
         }
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "autolog()\n",
         "\n",
@@ -428,29 +458,11 @@
         "ax.legend()\n",
         "plt.tight_layout()\n",
         "plt.show()"
-      ],
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1000x400 with 1 Axes>"
-            ]
-          }
-        }
-      ],
-      "id": "5ef7f73a"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "f484330f",
       "metadata": {},
       "source": [
         "## Open the MLflow UI\n",
@@ -462,11 +474,12 @@
         "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
         "- **Visualize** metrics across runs with built-in charts\n",
         "- **Register** models to the Model Registry for versioning"
-      ],
-      "id": "f484330f"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 11,
+      "id": "1a01cd2f",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
@@ -475,14 +488,9 @@
           "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
         }
       },
-      "source": [
-        "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
-        "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
-        "print(\"Then open:  http://localhost:5000\")"
-      ],
-      "execution_count": 11,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Launch the MLflow UI with this command in your terminal:\n",
@@ -493,20 +501,25 @@
           ]
         }
       ],
-      "id": "1a01cd2f"
+      "source": [
+        "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
+        "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
+        "print(\"Then open:  http://localhost:5000\")"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "88b0d285",
       "metadata": {},
       "source": [
         "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
         ">\n",
         "![Mlflow Overview](./static/images/mlflow_overview.png)"
-      ],
-      "id": "88b0d285"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "e2b133bc",
       "metadata": {},
       "source": [
         "## Per-epoch Metrics with Torch Models\n",
@@ -514,11 +527,12 @@
         "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
         "\n",
         "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
-      ],
-      "id": "e2b133bc"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 53,
+      "id": "d01783d1",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
@@ -527,38 +541,9 @@
           "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
         }
       },
-      "source": [
-        "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
-        "\n",
-        "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
-        "autolog()\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
-        "    nbeats = NBEATSModel(\n",
-        "        input_chunk_length=24,\n",
-        "        output_chunk_length=12,\n",
-        "        n_epochs=10,\n",
-        "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
-        "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
-        "        torch_metrics=MetricCollection({\n",
-        "            \"MAE\": MeanAbsoluteError(),\n",
-        "            \"MSE\": MeanSquaredError(),\n",
-        "        }),\n",
-        "        random_state=42,\n",
-        "    )\n",
-        "    nbeats.fit(train, val_series=val)\n",
-        "    nbeats_pred = nbeats.predict(n=len(val))\n",
-        "    # metric calls inside the run are logged automatically (keys: mape, rmse)\n",
-        "    nbeats_mape = metrics.mape(val, nbeats_pred)\n",
-        "    nbeats_rmse = metrics.rmse(val, nbeats_pred)\n",
-        "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
-        "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
-        "\n",
-        "autolog(disable=True)"
-      ],
-      "execution_count": 53,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "INFO: GPU available: True (mps), used: False\n",
@@ -587,7 +572,6 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "33ff56f2bbbb46ddb71bf5ac409bc3c7",
@@ -597,9 +581,12 @@
             "text/plain": [
               "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
@@ -609,7 +596,6 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "2fb13a9c2a6b40ad886bb7878949614d",
@@ -619,10 +605,11 @@
             "text/plain": [
               "Training: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "595549c03c1d4fe28a03ce38a0f38441",
@@ -632,9 +619,12 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
@@ -642,7 +632,6 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "78af3a584caf477ba9c043c93a77ae56",
@@ -652,10 +641,11 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "07ec6fb97dd6418594e5dedda8a087fc",
@@ -665,10 +655,11 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "bbd214c1629e47de8f36acad90896742",
@@ -678,10 +669,11 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "1a78ea5bef414a31bd4dd00f162b4876",
@@ -691,10 +683,11 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "0759f953cc2e4aba9c4d4644279daf9c",
@@ -704,10 +697,11 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "33e1f6444b504ddd8808d0b4bdf7cf34",
@@ -717,10 +711,11 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "c23701c8d9fb45a5a7188f013b8ae713",
@@ -730,10 +725,11 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "1f198dc0926c4f40a60823608c789f05",
@@ -743,10 +739,11 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "82a6462bdf0a486a85887bef6ae73630",
@@ -756,9 +753,12 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
@@ -772,7 +772,6 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "a9cf24e439244b4f99812eeea1c9aab8",
@@ -782,9 +781,12 @@
             "text/plain": [
               "Predicting: |          | 0/? [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "NBEATS MAPE: 13.64%\n",
@@ -792,20 +794,49 @@
           ]
         }
       ],
-      "id": "d01783d1"
+      "source": [
+        "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
+        "\n",
+        "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
+        "autolog()\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
+        "    nbeats = NBEATSModel(\n",
+        "        input_chunk_length=24,\n",
+        "        output_chunk_length=12,\n",
+        "        n_epochs=10,\n",
+        "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
+        "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
+        "        torch_metrics=MetricCollection({\n",
+        "            \"MAE\": MeanAbsoluteError(),\n",
+        "            \"MSE\": MeanSquaredError(),\n",
+        "        }),\n",
+        "        random_state=42,\n",
+        "    )\n",
+        "    nbeats.fit(train, val_series=val)\n",
+        "    nbeats_pred = nbeats.predict(n=len(val))\n",
+        "    # metric calls inside the run are logged automatically (keys: mape, rmse)\n",
+        "    nbeats_mape = metrics.mape(val, nbeats_pred)\n",
+        "    nbeats_rmse = metrics.rmse(val, nbeats_pred)\n",
+        "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
+        "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
+        "\n",
+        "autolog(disable=True)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "6fcc8f13",
       "metadata": {},
       "source": [
         "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
         ">\n",
         "![Mlflow Charts](./static/images/mlflow_charts.png)"
-      ],
-      "id": "6fcc8f13"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "3f0828c2",
       "metadata": {},
       "source": [
         "## Forecast Metrics\n",
@@ -815,11 +846,12 @@
         "The metric key is derived from the result: a scalar is logged under `{metric}` (e.g. calling `darts.metrics.mae(val, pred)` logs `mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`.\n",
         "\n",
         "Logged metric values are only meaningful to compare across runs when the evaluation settings match. Use the same evaluation time frame, forecast horizon, and evaluation start date for every `backtest()` / metric call you intend to compare against one another."
-      ],
-      "id": "3f0828c2"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 13,
+      "id": "df8e85b6",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
@@ -828,6 +860,20 @@
           "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
         }
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "All logged metrics (5):\n",
+            "  mae: 46.0220\n",
+            "  manual_mape: 10.7420\n",
+            "  mape: 10.7420\n",
+            "  rmse: 51.1820\n",
+            "  smape: 10.1015\n"
+          ]
+        }
+      ],
       "source": [
         "# log_metrics=True (the default) patches every darts metric so that calls made\n",
         "# inside an active run are logged automatically\n",
@@ -855,25 +901,11 @@
         "print(f\"All logged metrics ({len(metric_names)}):\")\n",
         "for name in metric_names:\n",
         "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
-      ],
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "All logged metrics (5):\n",
-            "  mae: 46.0220\n",
-            "  manual_mape: 10.7420\n",
-            "  mape: 10.7420\n",
-            "  rmse: 51.1820\n",
-            "  smape: 10.1015\n"
-          ]
-        }
-      ],
-      "id": "df8e85b6"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "b08e900a",
       "metadata": {},
       "source": [
         "### Metric Shape and Detailed Logging\n",
@@ -885,7 +917,7 @@
         "- **Per-component** (`component_reduction=None`) adds the component name, e.g. `mae_<component>`.\n",
         "- **Quantile / interval** (`q`, `q_interval`) adds the quantile, e.g. `mql_q0.500`.\n",
         "- **Per-label classification** (`label_reduction=None`) adds the class, e.g. `f1_label1`.\n",
-        "- **Per-timestep** (`time_reduction=None`) is charted across MLflow steps.\n",
+        "- **Per-timestep** (`time_reduction=None`) is charted across MLflow steps. Calendar-relative steps are negative and end at `-1`, aligning different series lengths on their shared end.\n",
         "- **`series_reduction`**, when set on the metric call, aggregates across series inside the metric itself, so the mean-over-series logging described below does not apply.\n",
         "\n",
         "For `backtest()`, metric keys have a `backtest_` prefix. A time-dependent metric with `reduction=None` keeps its per-window values in the return value, but MLflow charts one value per horizon step: it applies `np.nanmean` over windows for each series, then aggregates across series. The detailed `metrics_per_series.json` table retains every source value with a `window_index`, including for a single-series backtest.\n",
@@ -893,53 +925,16 @@
         "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is appended to the same run-wide table artifact, `metrics_per_series.json`, which you can read back with `mlflow.load_table()`.\n",
         "\n",
         "Passing `name=` to a metric overrides the `{metric_name}` token in the key while keeping the suffixes (e.g. `mql(..., q=0.5, name=\"foo\")` logs `foo_q0.500`)."
-      ],
-      "id": "b08e900a"
+      ]
     },
     {
       "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "# build a small multi-series example from the univariate AirPassengers data\n",
-        "series_list = [train, train * 1.2]\n",
-        "val_list = [val, val * 1.2]\n",
-        "\n",
-        "multi_model = LinearRegressionModel(lags=12)\n",
-        "multi_model.fit(series_list)\n",
-        "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
-        "\n",
-        "autolog(log_metrics=True)\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"metric-shape-and-table\") as run:\n",
-        "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
-        "    # under a single key, charted across MLflow steps\n",
-        "    single_pred = multi_preds[0]  # train == series_list[0]\n",
-        "    metrics.ae(val, single_pred)\n",
-        "\n",
-        "    # multiple series: the logged value is the MEAN over series, and the full\n",
-        "    # per-series breakdown is appended to the run's table artifact\n",
-        "    per_series_mae = metrics.mae(val_list, multi_preds)\n",
-        "\n",
-        "autolog(disable=True)\n",
-        "\n",
-        "client = mlflow.tracking.MlflowClient()\n",
-        "run_id = run.info.run_id\n",
-        "\n",
-        "# aggregate metrics: mae is the mean over the two series\n",
-        "logged = client.get_run(run_id).data.metrics\n",
-        "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
-        "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
-        "\n",
-        "# load the per-series table artifact\n",
-        "per_series_df = mlflow.load_table(\n",
-        "    artifact_file=\"metrics_per_series.json\", run_ids=[run_id]\n",
-        ")\n",
-        "print(\"\\nPer-series breakdown (metrics_per_series.json):\")\n",
-        "per_series_df"
-      ],
       "execution_count": 14,
+      "id": "ecdbbf29",
+      "metadata": {},
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Aggregate metrics: {'ae': 44.196, 'mae': 51.316}\n",
@@ -947,7 +942,6 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "0305dca7a1b846f19da965d1b74319c5",
@@ -957,9 +951,12 @@
             "text/plain": [
               "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
             ]
-          }
+          },
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "\n",
@@ -967,7 +964,6 @@
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/html": [
               "<div>\n",
@@ -1018,23 +1014,65 @@
               "0  mae             0     0  47.458874\n",
               "1  mae             1     0  55.172880"
             ]
-          }
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
         }
       ],
-      "id": "ecdbbf29"
+      "source": [
+        "# build a small multi-series example from the univariate AirPassengers data\n",
+        "series_list = [train, train * 1.2]\n",
+        "val_list = [val, val * 1.2]\n",
+        "\n",
+        "multi_model = LinearRegressionModel(lags=12)\n",
+        "multi_model.fit(series_list)\n",
+        "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
+        "\n",
+        "autolog(log_metrics=True)\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"metric-shape-and-table\") as run:\n",
+        "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
+        "    # under a single key, charted across MLflow steps\n",
+        "    single_pred = multi_preds[0]  # train == series_list[0]\n",
+        "    metrics.ae(val, single_pred)\n",
+        "\n",
+        "    # multiple series: the logged value is the MEAN over series, and the full\n",
+        "    # per-series breakdown is appended to the run's table artifact\n",
+        "    per_series_mae = metrics.mae(val_list, multi_preds)\n",
+        "\n",
+        "autolog(disable=True)\n",
+        "\n",
+        "client = mlflow.tracking.MlflowClient()\n",
+        "run_id = run.info.run_id\n",
+        "\n",
+        "# aggregate metrics: mae is the mean over the two series\n",
+        "logged = client.get_run(run_id).data.metrics\n",
+        "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
+        "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
+        "\n",
+        "# load the per-series table artifact\n",
+        "per_series_df = mlflow.load_table(\n",
+        "    artifact_file=\"metrics_per_series.json\", run_ids=[run_id]\n",
+        ")\n",
+        "print(\"\\nPer-series breakdown (metrics_per_series.json):\")\n",
+        "per_series_df"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "8511fc08",
       "metadata": {},
       "source": [
         "## Saving and Loading Models Locally\n",
         "\n",
         "You can also save and load models to/from local paths without MLflow runs."
-      ],
-      "id": "8511fc08"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 15,
+      "id": "645ef079",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
@@ -1043,24 +1081,16 @@
           "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
         }
       },
-      "source": [
-        "# Save model to local directory\n",
-        "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
-        "save_model(model, path=local_model_path)\n",
-        "\n",
-        "print(\"\\nFiles in model directory:\")\n",
-        "for file in os.listdir(local_model_path):\n",
-        "    print(f\"  - {file}\")"
-      ],
-      "execution_count": 15,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "2026/07/23 18:50:10 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
           ]
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "\n",
@@ -1073,10 +1103,20 @@
           ]
         }
       ],
-      "id": "645ef079"
+      "source": [
+        "# Save model to local directory\n",
+        "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
+        "save_model(model, path=local_model_path)\n",
+        "\n",
+        "print(\"\\nFiles in model directory:\")\n",
+        "for file in os.listdir(local_model_path):\n",
+        "    print(f\"  - {file}\")"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 16,
+      "id": "254ba153",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
@@ -1085,18 +1125,9 @@
           "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
         }
       },
-      "source": [
-        "# Load model from local directory\n",
-        "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
-        "\n",
-        "# Test it\n",
-        "local_predictions = local_loaded_model.predict(n=5)\n",
-        "print(\"Loaded model successfully!\")\n",
-        "print(f\"Predictions shape: {local_predictions.values().shape}\")"
-      ],
-      "execution_count": 16,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Loaded model successfully!\n",
@@ -1104,20 +1135,30 @@
           ]
         }
       ],
-      "id": "254ba153"
+      "source": [
+        "# Load model from local directory\n",
+        "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
+        "\n",
+        "# Test it\n",
+        "local_predictions = local_loaded_model.predict(n=5)\n",
+        "print(\"Loaded model successfully!\")\n",
+        "print(f\"Predictions shape: {local_predictions.values().shape}\")"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "aab0d1e0",
       "metadata": {},
       "source": [
         "## Querying Experiments\n",
         "\n",
         "You can programmatically query and compare runs."
-      ],
-      "id": "aab0d1e0"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 17,
+      "id": "109a9812",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
@@ -1126,30 +1167,9 @@
           "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
         }
       },
-      "source": [
-        "from mlflow.tracking import MlflowClient\n",
-        "\n",
-        "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
-        "\n",
-        "# get all runs\n",
-        "client = MlflowClient()\n",
-        "runs = client.search_runs(\n",
-        "    experiment_ids=[experiment.experiment_id],\n",
-        "    order_by=[\"metrics.mape ASC\"],  # Sort by best MAPE\n",
-        ")\n",
-        "\n",
-        "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
-        "for i, run in enumerate(runs, 1):\n",
-        "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
-        "    mape_val = run.data.metrics.get(\"mape\", \"N/A\")\n",
-        "    print(f\"{i}. {run_name}\")\n",
-        "    print(f\"   Run ID: {run.info.run_id}\")\n",
-        "    print(f\"   Validation MAPE: {mape_val}\")\n",
-        "    print()"
-      ],
-      "execution_count": 17,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Found 5 runs in experiment 'darts-quickstart':\n",
@@ -1177,18 +1197,40 @@
           ]
         }
       ],
-      "id": "109a9812"
+      "source": [
+        "from mlflow.tracking import MlflowClient\n",
+        "\n",
+        "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
+        "\n",
+        "# get all runs\n",
+        "client = MlflowClient()\n",
+        "runs = client.search_runs(\n",
+        "    experiment_ids=[experiment.experiment_id],\n",
+        "    order_by=[\"metrics.mape ASC\"],  # Sort by best MAPE\n",
+        ")\n",
+        "\n",
+        "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
+        "for i, run in enumerate(runs, 1):\n",
+        "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
+        "    mape_val = run.data.metrics.get(\"mape\", \"N/A\")\n",
+        "    print(f\"{i}. {run_name}\")\n",
+        "    print(f\"   Run ID: {run.info.run_id}\")\n",
+        "    print(f\"   Validation MAPE: {mape_val}\")\n",
+        "    print()"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "22685423",
       "metadata": {},
       "source": [
         "### Load the Best Model"
-      ],
-      "id": "22685423"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 18,
+      "id": "7b09db6a",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
@@ -1197,6 +1239,26 @@
           "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
         }
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading best model from run: exponential-smoothing-baseline\n",
+            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
         "\n",
@@ -1224,40 +1286,22 @@
         "    plt.legend()\n",
         "    plt.title(\"Best Model Predictions\")\n",
         "    plt.show()"
-      ],
-      "execution_count": 18,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Loading best model from run: exponential-smoothing-baseline\n",
-            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          }
-        }
-      ],
-      "id": "7b09db6a"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "6f1ec89e",
       "metadata": {},
       "source": [
         "## Model Registry\n",
         "\n",
         "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived)."
-      ],
-      "id": "6f1ec89e"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 60,
+      "id": "7c854bc4",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-07-22T08:27:04.144511Z",
@@ -1266,23 +1310,16 @@
           "shell.execute_reply": "2026-07-22T08:27:04.165897Z"
         }
       },
-      "source": [
-        "# register the best model (from the \"Load the Best Model\" section above)\n",
-        "result = mlflow.register_model(\n",
-        "    model_uri=best_model_uri,\n",
-        "    name=\"darts-air-passengers\",\n",
-        ")\n",
-        "print(f\"Registered version: {result.version}\")"
-      ],
-      "execution_count": 60,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Registered version: 1\n"
           ]
         },
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Successfully registered model 'darts-air-passengers'.\n",
@@ -1290,10 +1327,18 @@
           ]
         }
       ],
-      "id": "7c854bc4"
+      "source": [
+        "# register the best model (from the \"Load the Best Model\" section above)\n",
+        "result = mlflow.register_model(\n",
+        "    model_uri=best_model_uri,\n",
+        "    name=\"darts-air-passengers\",\n",
+        ")\n",
+        "print(f\"Registered version: {result.version}\")"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "da6cc83a",
       "metadata": {},
       "source": [
         "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
@@ -1301,11 +1346,11 @@
         "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
         ">\n",
         "![Mlflow Charts](./static/images/mlflow_models.png)"
-      ],
-      "id": "da6cc83a"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "7af49ea9",
       "metadata": {},
       "source": [
         "## Important Note: Custom Flavor\n",
@@ -1329,19 +1374,20 @@
         "- Darts-specific model parameters\n",
         "- Covariate handling (past, future, static)\n",
         "- PyTorch model state preservation"
-      ],
-      "id": "7af49ea9"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "40621b13",
       "metadata": {},
       "source": [
         "## Cleanup"
-      ],
-      "id": "40621b13"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 19,
+      "id": "51fc7c4a",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
@@ -1350,40 +1396,39 @@
           "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
         }
       },
-      "source": [
-        "# Uncomment to cleanup\n",
-        "# import shutil\n",
-        "# shutil.rmtree(tmpdir)\n",
-        "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
-        "\n",
-        "print(f\"To cleanup manually, delete: {tmpdir}\")"
-      ],
-      "execution_count": 19,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic\n"
           ]
         }
       ],
-      "id": "51fc7c4a"
+      "source": [
+        "# Uncomment to cleanup\n",
+        "# import shutil\n",
+        "# shutil.rmtree(tmpdir)\n",
+        "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
+        "\n",
+        "print(f\"To cleanup manually, delete: {tmpdir}\")"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "ca40afc1",
       "metadata": {},
       "source": [
         "# Final Remarks"
-      ],
-      "id": "ca40afc1"
+      ]
     },
     {
       "cell_type": "markdown",
+      "id": "c4c86a23",
       "metadata": {},
       "source": [
         "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
-      ],
-      "id": "c4c86a23"
+      ]
     }
   ],
   "metadata": {

From f9eb3f8e6685a62470a960acf1309a319ad28ae3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 5 Aug 2026 13:47:53 +0200
Subject: [PATCH 138/154] fix: use 0-based step for predictions

---
 darts/tests/optional_deps/test_mlflow.py |  17 +-
 darts/utils/mlflow.py                    |   4 +-
 examples/29-MLflow-quickstart.ipynb      | 787 +++++++++++------------
 3 files changed, 381 insertions(+), 427 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 07c2e6d46e..38c6a7b736 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1070,15 +1070,14 @@ def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
         history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
         assert len(history) == len(ref), "Expected one step per timestep"
         steps = sorted(m.step for m in history)
-        assert steps == list(range(-len(ref), 0))
+        assert steps == list(range(len(ref)))
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
         np.testing.assert_allclose(logged, ref, atol=1e-5)
 
-    def test_autolog_metric_aligns_time_axis_by_end_date(
+    def test_autolog_metric_aligns_time_axis_by_forecast_position(
         self, mlflow_tracking, autolog_context
     ):
-        """A shorter series' time axis aligns from the end, not the start, so
-        its negative steps overlap the tail of a longer series."""
+        """Each series starts at forecast position zero."""
         ts_long = self.ts_univariate  # length 50
         ts_short = ts_long[10:]  # length 40, same end, starts 10 steps later
 
@@ -1094,17 +1093,17 @@ def test_autolog_metric_aligns_time_axis_by_end_date(
         history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
         by_step = {m.step: m.value for m in history}
         assert len(by_step) == 50
-        for step in range(-50, -40):
-            assert by_step[step] == pytest.approx(1.0, abs=1e-4), step
-        for step in range(-40, 0):
+        for step in range(40):
             assert by_step[step] == pytest.approx(1.5, abs=1e-4), step
+        for step in range(40, 50):
+            assert by_step[step] == pytest.approx(1.0, abs=1e-4), step
 
         rows = self._read_per_series_table(run.info.run_id)
         steps_by_series = {0: set(), 1: set()}
         for r in rows:
             steps_by_series[r["series_index"]].add(r["step"])
-        assert steps_by_series[0] == set(range(-50, 0))
-        assert steps_by_series[1] == set(range(-40, 0))
+        assert steps_by_series[0] == set(range(50))
+        assert steps_by_series[1] == set(range(40))
 
     def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile with matching values."""
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 23b314e269..4ce9e3d98d 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1577,8 +1577,8 @@ def _log_metric_result(
     for series_index, (t_size, canonical) in enumerate(series_shapes):
         for c, key in enumerate(keys):
             for t in range(t_size):
-                # Time axes are end-relative, with the latest value at step -1.
-                step = t - t_size if has_time_axis else 0
+                # Forecast positions are zero-based: the first prediction is 0.
+                step = t if has_time_axis else 0
                 value = float(canonical[t, c])
                 agg.setdefault((key, step), []).append(value)
                 rows.append({
diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
index 33551375d6..fc12c4e824 100644
--- a/examples/29-MLflow-quickstart.ipynb
+++ b/examples/29-MLflow-quickstart.ipynb
@@ -2,7 +2,6 @@
   "cells": [
     {
       "cell_type": "markdown",
-      "id": "aeddb542",
       "metadata": {},
       "source": [
         "# MLflow for Darts\n",
@@ -13,11 +12,11 @@
         "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
         "\n",
         "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
-      ]
+      ],
+      "id": "aeddb542"
     },
     {
       "cell_type": "markdown",
-      "id": "f72894af",
       "metadata": {},
       "source": [
         "## Installing MLflow\n",
@@ -27,20 +26,19 @@
         "```bash\n",
         "pip install \"mlflow>=3.0\"\n",
         "```"
-      ]
+      ],
+      "id": "f72894af"
     },
     {
       "cell_type": "markdown",
-      "id": "42e3dcea",
       "metadata": {},
       "source": [
         "## Setup and Imports"
-      ]
+      ],
+      "id": "42e3dcea"
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
-      "id": "b346ce8f",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
@@ -49,7 +47,6 @@
           "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
         }
       },
-      "outputs": [],
       "source": [
         "# fix python path if working locally\n",
         "from utils import fix_pythonpath_if_working_locally\n",
@@ -59,12 +56,13 @@
         "import warnings\n",
         "\n",
         "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-      ]
+      ],
+      "execution_count": 2,
+      "outputs": [],
+      "id": "b346ce8f"
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
-      "id": "13b13fe4",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
@@ -73,7 +71,6 @@
           "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
         }
       },
-      "outputs": [],
       "source": [
         "%matplotlib inline\n",
         "\n",
@@ -88,12 +85,13 @@
         "from darts.datasets import AirPassengersDataset\n",
         "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
         "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
-      ]
+      ],
+      "execution_count": 3,
+      "outputs": [],
+      "id": "13b13fe4"
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
-      "id": "4d424e08",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
@@ -102,28 +100,28 @@
           "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
         }
       },
-      "outputs": [],
       "source": [
         "# use darts plotting style\n",
         "from darts import set_option\n",
         "\n",
         "set_option(\"plotting.use_darts_style\", True)"
-      ]
+      ],
+      "execution_count": 4,
+      "outputs": [],
+      "id": "4d424e08"
     },
     {
       "cell_type": "markdown",
-      "id": "2f9c40d6",
       "metadata": {},
       "source": [
         "## MLflow Setup\n",
         "\n",
         "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
-      ]
+      ],
+      "id": "2f9c40d6"
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
-      "id": "88320df5",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
@@ -132,9 +130,20 @@
           "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
         }
       },
+      "source": [
+        "# temporary directory for MLflow tracking\n",
+        "tmpdir = tempfile.mkdtemp()\n",
+        "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+        "\n",
+        "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
+        "mlflow.set_experiment(\"darts-quickstart\")\n",
+        "\n",
+        "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
+        "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
+      ],
+      "execution_count": 5,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
@@ -143,7 +152,6 @@
           ]
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
@@ -151,32 +159,20 @@
           ]
         }
       ],
-      "source": [
-        "# temporary directory for MLflow tracking\n",
-        "tmpdir = tempfile.mkdtemp()\n",
-        "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
-        "\n",
-        "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
-        "mlflow.set_experiment(\"darts-quickstart\")\n",
-        "\n",
-        "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
-        "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
-      ]
+      "id": "88320df5"
     },
     {
       "cell_type": "markdown",
-      "id": "03d5209e",
       "metadata": {},
       "source": [
         "## Load Sample Data\n",
         "\n",
         "We'll use the classic AirPassengers dataset for this example."
-      ]
+      ],
+      "id": "03d5209e"
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
-      "id": "1596e07e",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
@@ -185,9 +181,22 @@
           "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
         }
       },
+      "source": [
+        "series = AirPassengersDataset().load()\n",
+        "train, val = series.split_before(0.75)\n",
+        "\n",
+        "print(f\"Training series: {len(train)} points\")\n",
+        "print(f\"Validation series: {len(val)} points\")\n",
+        "\n",
+        "series.plot()\n",
+        "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
+        "plt.legend()\n",
+        "plt.title(\"AirPassengers Dataset\")\n",
+        "plt.show()"
+      ],
+      "execution_count": 6,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Training series: 107 points\n",
@@ -195,44 +204,29 @@
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
             "image/png": 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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         }
       ],
-      "source": [
-        "series = AirPassengersDataset().load()\n",
-        "train, val = series.split_before(0.75)\n",
-        "\n",
-        "print(f\"Training series: {len(train)} points\")\n",
-        "print(f\"Validation series: {len(val)} points\")\n",
-        "\n",
-        "series.plot()\n",
-        "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
-        "plt.legend()\n",
-        "plt.title(\"AirPassengers Dataset\")\n",
-        "plt.show()"
-      ]
+      "id": "1596e07e"
     },
     {
       "cell_type": "markdown",
-      "id": "34858645",
       "metadata": {},
       "source": [
         "## Basic Model Logging\n",
         "\n",
         "Let's train a simple model and log it to MLflow manually."
-      ]
+      ],
+      "id": "34858645"
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
-      "id": "bc8f520d",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
@@ -241,26 +235,6 @@
           "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
         }
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Validation MAPE: 7.86%\n",
-            "Validation RMSE: 34.77\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
       "source": [
         "model = ExponentialSmoothing()\n",
         "model.fit(train)\n",
@@ -280,11 +254,30 @@
         "plt.legend()\n",
         "plt.title(\"Exponential Smoothing Forecast\")\n",
         "plt.show()"
-      ]
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation MAPE: 7.86%\n",
+            "Validation RMSE: 34.77\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          }
+        }
+      ],
+      "id": "bc8f520d"
     },
     {
       "cell_type": "markdown",
-      "id": "35dc864c",
       "metadata": {},
       "source": [
         "Now let's log this model to MLflow.\n",
@@ -293,12 +286,11 @@
         "\n",
         "- **Run tags** (`mlflow.set_tags(...)`) – shown on the run page in the UI. This is also where `autolog()` writes its tags (`model_class`, …).\n",
         "- **LoggedModel tags** (`log_model(..., tags=...)`) – attached to the logged model entity itself (open the model from the run's **Outputs** / Models view)."
-      ]
+      ],
+      "id": "35dc864c"
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
-      "id": "61406cd9",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
@@ -307,23 +299,6 @@
           "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
         }
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "2026/07/23 18:50:08 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
-            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
-          ]
-        }
-      ],
       "source": [
         "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
         "    model_info = log_model(\n",
@@ -339,22 +314,37 @@
         "\n",
         "    print(f\"Run ID: {run.info.run_id}\")\n",
         "    print(f\"Model URI: {model_info.model_uri}\")"
-      ]
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "2026/07/23 18:50:08 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
+            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
+          ]
+        }
+      ],
+      "id": "61406cd9"
     },
     {
       "cell_type": "markdown",
-      "id": "0728690e",
       "metadata": {},
       "source": [
         "### Load the Model Back\n",
         "\n",
         "We can load the model from MLflow using its URI:"
-      ]
+      ],
+      "id": "0728690e"
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
-      "id": "cae35ffa",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
@@ -363,15 +353,6 @@
           "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
         }
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Loaded model predictions match: True\n"
-          ]
-        }
-      ],
       "source": [
         "loaded_model = load_model(model_info.model_uri)\n",
         "\n",
@@ -380,11 +361,20 @@
         "# verify predictions match\n",
         "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
         "print(f\"Loaded model predictions match: {predictions_match}\")"
-      ]
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loaded model predictions match: True\n"
+          ]
+        }
+      ],
+      "id": "cae35ffa"
     },
     {
       "cell_type": "markdown",
-      "id": "6bd4597c",
       "metadata": {},
       "source": [
         "## Automatic Logging with `autolog()`\n",
@@ -396,12 +386,11 @@
         "- **`backtest()`** – evaluation metrics under `backtest_*` keys.\n",
         "- **`historical_forecasts()`** – patched so its internal per-window `fit()` calls don't each spawn their own logging.\n",
         "- **PyTorch-based models** – per-epoch `train_loss` / `val_loss` via MLflow's PyTorch autologging (see the section below for details)."
-      ]
+      ],
+      "id": "6bd4597c"
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
-      "id": "5ef7f73a",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
@@ -410,25 +399,6 @@
           "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
         }
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1000x400 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
       "source": [
         "autolog()\n",
         "\n",
@@ -458,11 +428,29 @@
         "ax.legend()\n",
         "plt.tight_layout()\n",
         "plt.show()"
-      ]
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          }
+        }
+      ],
+      "id": "5ef7f73a"
     },
     {
       "cell_type": "markdown",
-      "id": "f484330f",
       "metadata": {},
       "source": [
         "## Open the MLflow UI\n",
@@ -474,12 +462,11 @@
         "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
         "- **Visualize** metrics across runs with built-in charts\n",
         "- **Register** models to the Model Registry for versioning"
-      ]
+      ],
+      "id": "f484330f"
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
-      "id": "1a01cd2f",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
@@ -488,9 +475,14 @@
           "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
         }
       },
+      "source": [
+        "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
+        "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
+        "print(\"Then open:  http://localhost:5000\")"
+      ],
+      "execution_count": 11,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Launch the MLflow UI with this command in your terminal:\n",
@@ -501,25 +493,20 @@
           ]
         }
       ],
-      "source": [
-        "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
-        "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
-        "print(\"Then open:  http://localhost:5000\")"
-      ]
+      "id": "1a01cd2f"
     },
     {
       "cell_type": "markdown",
-      "id": "88b0d285",
       "metadata": {},
       "source": [
         "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
         ">\n",
         "![Mlflow Overview](./static/images/mlflow_overview.png)"
-      ]
+      ],
+      "id": "88b0d285"
     },
     {
       "cell_type": "markdown",
-      "id": "e2b133bc",
       "metadata": {},
       "source": [
         "## Per-epoch Metrics with Torch Models\n",
@@ -527,12 +514,11 @@
         "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
         "\n",
         "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
-      ]
+      ],
+      "id": "e2b133bc"
     },
     {
       "cell_type": "code",
-      "execution_count": 53,
-      "id": "d01783d1",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
@@ -541,9 +527,38 @@
           "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
         }
       },
+      "source": [
+        "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
+        "\n",
+        "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
+        "autolog()\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
+        "    nbeats = NBEATSModel(\n",
+        "        input_chunk_length=24,\n",
+        "        output_chunk_length=12,\n",
+        "        n_epochs=10,\n",
+        "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
+        "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
+        "        torch_metrics=MetricCollection({\n",
+        "            \"MAE\": MeanAbsoluteError(),\n",
+        "            \"MSE\": MeanSquaredError(),\n",
+        "        }),\n",
+        "        random_state=42,\n",
+        "    )\n",
+        "    nbeats.fit(train, val_series=val)\n",
+        "    nbeats_pred = nbeats.predict(n=len(val))\n",
+        "    # metric calls inside the run are logged automatically (keys: mape, rmse)\n",
+        "    nbeats_mape = metrics.mape(val, nbeats_pred)\n",
+        "    nbeats_rmse = metrics.rmse(val, nbeats_pred)\n",
+        "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
+        "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
+        "\n",
+        "autolog(disable=True)"
+      ],
+      "execution_count": 53,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "INFO: GPU available: True (mps), used: False\n",
@@ -572,6 +587,7 @@
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "33ff56f2bbbb46ddb71bf5ac409bc3c7",
@@ -581,12 +597,9 @@
             "text/plain": [
               "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
@@ -596,6 +609,7 @@
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "2fb13a9c2a6b40ad886bb7878949614d",
@@ -605,11 +619,10 @@
             "text/plain": [
               "Training: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "595549c03c1d4fe28a03ce38a0f38441",
@@ -619,12 +632,9 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
@@ -632,6 +642,7 @@
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "78af3a584caf477ba9c043c93a77ae56",
@@ -641,11 +652,10 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "07ec6fb97dd6418594e5dedda8a087fc",
@@ -655,11 +665,10 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "bbd214c1629e47de8f36acad90896742",
@@ -669,11 +678,10 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "1a78ea5bef414a31bd4dd00f162b4876",
@@ -683,11 +691,10 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "0759f953cc2e4aba9c4d4644279daf9c",
@@ -697,11 +704,10 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "33e1f6444b504ddd8808d0b4bdf7cf34",
@@ -711,11 +717,10 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "c23701c8d9fb45a5a7188f013b8ae713",
@@ -725,11 +730,10 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "1f198dc0926c4f40a60823608c789f05",
@@ -739,11 +743,10 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "82a6462bdf0a486a85887bef6ae73630",
@@ -753,12 +756,9 @@
             "text/plain": [
               "Validation: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
@@ -772,6 +772,7 @@
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "a9cf24e439244b4f99812eeea1c9aab8",
@@ -781,12 +782,9 @@
             "text/plain": [
               "Predicting: |          | 0/? [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "NBEATS MAPE: 13.64%\n",
@@ -794,49 +792,20 @@
           ]
         }
       ],
-      "source": [
-        "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
-        "\n",
-        "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
-        "autolog()\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
-        "    nbeats = NBEATSModel(\n",
-        "        input_chunk_length=24,\n",
-        "        output_chunk_length=12,\n",
-        "        n_epochs=10,\n",
-        "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
-        "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
-        "        torch_metrics=MetricCollection({\n",
-        "            \"MAE\": MeanAbsoluteError(),\n",
-        "            \"MSE\": MeanSquaredError(),\n",
-        "        }),\n",
-        "        random_state=42,\n",
-        "    )\n",
-        "    nbeats.fit(train, val_series=val)\n",
-        "    nbeats_pred = nbeats.predict(n=len(val))\n",
-        "    # metric calls inside the run are logged automatically (keys: mape, rmse)\n",
-        "    nbeats_mape = metrics.mape(val, nbeats_pred)\n",
-        "    nbeats_rmse = metrics.rmse(val, nbeats_pred)\n",
-        "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
-        "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
-        "\n",
-        "autolog(disable=True)"
-      ]
+      "id": "d01783d1"
     },
     {
       "cell_type": "markdown",
-      "id": "6fcc8f13",
       "metadata": {},
       "source": [
         "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
         ">\n",
         "![Mlflow Charts](./static/images/mlflow_charts.png)"
-      ]
+      ],
+      "id": "6fcc8f13"
     },
     {
       "cell_type": "markdown",
-      "id": "3f0828c2",
       "metadata": {},
       "source": [
         "## Forecast Metrics\n",
@@ -846,12 +815,11 @@
         "The metric key is derived from the result: a scalar is logged under `{metric}` (e.g. calling `darts.metrics.mae(val, pred)` logs `mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`.\n",
         "\n",
         "Logged metric values are only meaningful to compare across runs when the evaluation settings match. Use the same evaluation time frame, forecast horizon, and evaluation start date for every `backtest()` / metric call you intend to compare against one another."
-      ]
+      ],
+      "id": "3f0828c2"
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
-      "id": "df8e85b6",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
@@ -860,20 +828,6 @@
           "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
         }
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "All logged metrics (5):\n",
-            "  mae: 46.0220\n",
-            "  manual_mape: 10.7420\n",
-            "  mape: 10.7420\n",
-            "  rmse: 51.1820\n",
-            "  smape: 10.1015\n"
-          ]
-        }
-      ],
       "source": [
         "# log_metrics=True (the default) patches every darts metric so that calls made\n",
         "# inside an active run are logged automatically\n",
@@ -901,11 +855,25 @@
         "print(f\"All logged metrics ({len(metric_names)}):\")\n",
         "for name in metric_names:\n",
         "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
-      ]
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "All logged metrics (5):\n",
+            "  mae: 46.0220\n",
+            "  manual_mape: 10.7420\n",
+            "  mape: 10.7420\n",
+            "  rmse: 51.1820\n",
+            "  smape: 10.1015\n"
+          ]
+        }
+      ],
+      "id": "df8e85b6"
     },
     {
       "cell_type": "markdown",
-      "id": "b08e900a",
       "metadata": {},
       "source": [
         "### Metric Shape and Detailed Logging\n",
@@ -917,7 +885,7 @@
         "- **Per-component** (`component_reduction=None`) adds the component name, e.g. `mae_<component>`.\n",
         "- **Quantile / interval** (`q`, `q_interval`) adds the quantile, e.g. `mql_q0.500`.\n",
         "- **Per-label classification** (`label_reduction=None`) adds the class, e.g. `f1_label1`.\n",
-        "- **Per-timestep** (`time_reduction=None`) is charted across MLflow steps. Calendar-relative steps are negative and end at `-1`, aligning different series lengths on their shared end.\n",
+        "- **Per-timestep forecast metrics** (`time_reduction=None`) use zero-based steps, so the first predicted value is `0`. Backtest calendar-relative steps are negative and end at `-1`, aligning different series lengths on their shared end.\n",
         "- **`series_reduction`**, when set on the metric call, aggregates across series inside the metric itself, so the mean-over-series logging described below does not apply.\n",
         "\n",
         "For `backtest()`, metric keys have a `backtest_` prefix. A time-dependent metric with `reduction=None` keeps its per-window values in the return value, but MLflow charts one value per horizon step: it applies `np.nanmean` over windows for each series, then aggregates across series. The detailed `metrics_per_series.json` table retains every source value with a `window_index`, including for a single-series backtest.\n",
@@ -925,16 +893,53 @@
         "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is appended to the same run-wide table artifact, `metrics_per_series.json`, which you can read back with `mlflow.load_table()`.\n",
         "\n",
         "Passing `name=` to a metric overrides the `{metric_name}` token in the key while keeping the suffixes (e.g. `mql(..., q=0.5, name=\"foo\")` logs `foo_q0.500`)."
-      ]
+      ],
+      "id": "b08e900a"
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
-      "id": "ecdbbf29",
       "metadata": {},
+      "source": [
+        "# build a small multi-series example from the univariate AirPassengers data\n",
+        "series_list = [train, train * 1.2]\n",
+        "val_list = [val, val * 1.2]\n",
+        "\n",
+        "multi_model = LinearRegressionModel(lags=12)\n",
+        "multi_model.fit(series_list)\n",
+        "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
+        "\n",
+        "autolog(log_metrics=True)\n",
+        "\n",
+        "with mlflow.start_run(run_name=\"metric-shape-and-table\") as run:\n",
+        "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
+        "    # under a single key, charted across MLflow steps\n",
+        "    single_pred = multi_preds[0]  # train == series_list[0]\n",
+        "    metrics.ae(val, single_pred)\n",
+        "\n",
+        "    # multiple series: the logged value is the MEAN over series, and the full\n",
+        "    # per-series breakdown is appended to the run's table artifact\n",
+        "    per_series_mae = metrics.mae(val_list, multi_preds)\n",
+        "\n",
+        "autolog(disable=True)\n",
+        "\n",
+        "client = mlflow.tracking.MlflowClient()\n",
+        "run_id = run.info.run_id\n",
+        "\n",
+        "# aggregate metrics: mae is the mean over the two series\n",
+        "logged = client.get_run(run_id).data.metrics\n",
+        "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
+        "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
+        "\n",
+        "# load the per-series table artifact\n",
+        "per_series_df = mlflow.load_table(\n",
+        "    artifact_file=\"metrics_per_series.json\", run_ids=[run_id]\n",
+        ")\n",
+        "print(\"\\nPer-series breakdown (metrics_per_series.json):\")\n",
+        "per_series_df"
+      ],
+      "execution_count": 14,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Aggregate metrics: {'ae': 44.196, 'mae': 51.316}\n",
@@ -942,6 +947,7 @@
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
               "model_id": "0305dca7a1b846f19da965d1b74319c5",
@@ -951,12 +957,9 @@
             "text/plain": [
               "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
             ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
+          }
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "\n",
@@ -964,6 +967,7 @@
           ]
         },
         {
+          "output_type": "execute_result",
           "data": {
             "text/html": [
               "<div>\n",
@@ -1014,65 +1018,23 @@
               "0  mae             0     0  47.458874\n",
               "1  mae             1     0  55.172880"
             ]
-          },
-          "execution_count": null,
-          "metadata": {},
-          "output_type": "execute_result"
+          }
         }
       ],
-      "source": [
-        "# build a small multi-series example from the univariate AirPassengers data\n",
-        "series_list = [train, train * 1.2]\n",
-        "val_list = [val, val * 1.2]\n",
-        "\n",
-        "multi_model = LinearRegressionModel(lags=12)\n",
-        "multi_model.fit(series_list)\n",
-        "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
-        "\n",
-        "autolog(log_metrics=True)\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"metric-shape-and-table\") as run:\n",
-        "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
-        "    # under a single key, charted across MLflow steps\n",
-        "    single_pred = multi_preds[0]  # train == series_list[0]\n",
-        "    metrics.ae(val, single_pred)\n",
-        "\n",
-        "    # multiple series: the logged value is the MEAN over series, and the full\n",
-        "    # per-series breakdown is appended to the run's table artifact\n",
-        "    per_series_mae = metrics.mae(val_list, multi_preds)\n",
-        "\n",
-        "autolog(disable=True)\n",
-        "\n",
-        "client = mlflow.tracking.MlflowClient()\n",
-        "run_id = run.info.run_id\n",
-        "\n",
-        "# aggregate metrics: mae is the mean over the two series\n",
-        "logged = client.get_run(run_id).data.metrics\n",
-        "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
-        "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
-        "\n",
-        "# load the per-series table artifact\n",
-        "per_series_df = mlflow.load_table(\n",
-        "    artifact_file=\"metrics_per_series.json\", run_ids=[run_id]\n",
-        ")\n",
-        "print(\"\\nPer-series breakdown (metrics_per_series.json):\")\n",
-        "per_series_df"
-      ]
+      "id": "ecdbbf29"
     },
     {
       "cell_type": "markdown",
-      "id": "8511fc08",
       "metadata": {},
       "source": [
         "## Saving and Loading Models Locally\n",
         "\n",
         "You can also save and load models to/from local paths without MLflow runs."
-      ]
+      ],
+      "id": "8511fc08"
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
-      "id": "645ef079",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
@@ -1081,16 +1043,24 @@
           "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
         }
       },
+      "source": [
+        "# Save model to local directory\n",
+        "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
+        "save_model(model, path=local_model_path)\n",
+        "\n",
+        "print(\"\\nFiles in model directory:\")\n",
+        "for file in os.listdir(local_model_path):\n",
+        "    print(f\"  - {file}\")"
+      ],
+      "execution_count": 15,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "2026/07/23 18:50:10 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
           ]
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "\n",
@@ -1103,20 +1073,10 @@
           ]
         }
       ],
-      "source": [
-        "# Save model to local directory\n",
-        "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
-        "save_model(model, path=local_model_path)\n",
-        "\n",
-        "print(\"\\nFiles in model directory:\")\n",
-        "for file in os.listdir(local_model_path):\n",
-        "    print(f\"  - {file}\")"
-      ]
+      "id": "645ef079"
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
-      "id": "254ba153",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
@@ -1125,9 +1085,18 @@
           "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
         }
       },
+      "source": [
+        "# Load model from local directory\n",
+        "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
+        "\n",
+        "# Test it\n",
+        "local_predictions = local_loaded_model.predict(n=5)\n",
+        "print(\"Loaded model successfully!\")\n",
+        "print(f\"Predictions shape: {local_predictions.values().shape}\")"
+      ],
+      "execution_count": 16,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Loaded model successfully!\n",
@@ -1135,30 +1104,20 @@
           ]
         }
       ],
-      "source": [
-        "# Load model from local directory\n",
-        "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
-        "\n",
-        "# Test it\n",
-        "local_predictions = local_loaded_model.predict(n=5)\n",
-        "print(\"Loaded model successfully!\")\n",
-        "print(f\"Predictions shape: {local_predictions.values().shape}\")"
-      ]
+      "id": "254ba153"
     },
     {
       "cell_type": "markdown",
-      "id": "aab0d1e0",
       "metadata": {},
       "source": [
         "## Querying Experiments\n",
         "\n",
         "You can programmatically query and compare runs."
-      ]
+      ],
+      "id": "aab0d1e0"
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
-      "id": "109a9812",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
@@ -1167,9 +1126,30 @@
           "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
         }
       },
+      "source": [
+        "from mlflow.tracking import MlflowClient\n",
+        "\n",
+        "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
+        "\n",
+        "# get all runs\n",
+        "client = MlflowClient()\n",
+        "runs = client.search_runs(\n",
+        "    experiment_ids=[experiment.experiment_id],\n",
+        "    order_by=[\"metrics.mape ASC\"],  # Sort by best MAPE\n",
+        ")\n",
+        "\n",
+        "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
+        "for i, run in enumerate(runs, 1):\n",
+        "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
+        "    mape_val = run.data.metrics.get(\"mape\", \"N/A\")\n",
+        "    print(f\"{i}. {run_name}\")\n",
+        "    print(f\"   Run ID: {run.info.run_id}\")\n",
+        "    print(f\"   Validation MAPE: {mape_val}\")\n",
+        "    print()"
+      ],
+      "execution_count": 17,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Found 5 runs in experiment 'darts-quickstart':\n",
@@ -1197,40 +1177,18 @@
           ]
         }
       ],
-      "source": [
-        "from mlflow.tracking import MlflowClient\n",
-        "\n",
-        "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
-        "\n",
-        "# get all runs\n",
-        "client = MlflowClient()\n",
-        "runs = client.search_runs(\n",
-        "    experiment_ids=[experiment.experiment_id],\n",
-        "    order_by=[\"metrics.mape ASC\"],  # Sort by best MAPE\n",
-        ")\n",
-        "\n",
-        "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
-        "for i, run in enumerate(runs, 1):\n",
-        "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
-        "    mape_val = run.data.metrics.get(\"mape\", \"N/A\")\n",
-        "    print(f\"{i}. {run_name}\")\n",
-        "    print(f\"   Run ID: {run.info.run_id}\")\n",
-        "    print(f\"   Validation MAPE: {mape_val}\")\n",
-        "    print()"
-      ]
+      "id": "109a9812"
     },
     {
       "cell_type": "markdown",
-      "id": "22685423",
       "metadata": {},
       "source": [
         "### Load the Best Model"
-      ]
+      ],
+      "id": "22685423"
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
-      "id": "7b09db6a",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
@@ -1239,26 +1197,6 @@
           "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
         }
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Loading best model from run: exponential-smoothing-baseline\n",
-            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
       "source": [
         "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
         "\n",
@@ -1286,22 +1224,40 @@
         "    plt.legend()\n",
         "    plt.title(\"Best Model Predictions\")\n",
         "    plt.show()"
-      ]
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loading best model from run: exponential-smoothing-baseline\n",
+            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          }
+        }
+      ],
+      "id": "7b09db6a"
     },
     {
       "cell_type": "markdown",
-      "id": "6f1ec89e",
       "metadata": {},
       "source": [
         "## Model Registry\n",
         "\n",
         "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived)."
-      ]
+      ],
+      "id": "6f1ec89e"
     },
     {
       "cell_type": "code",
-      "execution_count": 60,
-      "id": "7c854bc4",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-07-22T08:27:04.144511Z",
@@ -1310,16 +1266,23 @@
           "shell.execute_reply": "2026-07-22T08:27:04.165897Z"
         }
       },
+      "source": [
+        "# register the best model (from the \"Load the Best Model\" section above)\n",
+        "result = mlflow.register_model(\n",
+        "    model_uri=best_model_uri,\n",
+        "    name=\"darts-air-passengers\",\n",
+        ")\n",
+        "print(f\"Registered version: {result.version}\")"
+      ],
+      "execution_count": 60,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Registered version: 1\n"
           ]
         },
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Successfully registered model 'darts-air-passengers'.\n",
@@ -1327,18 +1290,10 @@
           ]
         }
       ],
-      "source": [
-        "# register the best model (from the \"Load the Best Model\" section above)\n",
-        "result = mlflow.register_model(\n",
-        "    model_uri=best_model_uri,\n",
-        "    name=\"darts-air-passengers\",\n",
-        ")\n",
-        "print(f\"Registered version: {result.version}\")"
-      ]
+      "id": "7c854bc4"
     },
     {
       "cell_type": "markdown",
-      "id": "da6cc83a",
       "metadata": {},
       "source": [
         "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
@@ -1346,11 +1301,11 @@
         "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
         ">\n",
         "![Mlflow Charts](./static/images/mlflow_models.png)"
-      ]
+      ],
+      "id": "da6cc83a"
     },
     {
       "cell_type": "markdown",
-      "id": "7af49ea9",
       "metadata": {},
       "source": [
         "## Important Note: Custom Flavor\n",
@@ -1374,20 +1329,19 @@
         "- Darts-specific model parameters\n",
         "- Covariate handling (past, future, static)\n",
         "- PyTorch model state preservation"
-      ]
+      ],
+      "id": "7af49ea9"
     },
     {
       "cell_type": "markdown",
-      "id": "40621b13",
       "metadata": {},
       "source": [
         "## Cleanup"
-      ]
+      ],
+      "id": "40621b13"
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
-      "id": "51fc7c4a",
       "metadata": {
         "execution": {
           "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
@@ -1396,39 +1350,40 @@
           "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
         }
       },
+      "source": [
+        "# Uncomment to cleanup\n",
+        "# import shutil\n",
+        "# shutil.rmtree(tmpdir)\n",
+        "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
+        "\n",
+        "print(f\"To cleanup manually, delete: {tmpdir}\")"
+      ],
+      "execution_count": 19,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
           "text": [
             "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic\n"
           ]
         }
       ],
-      "source": [
-        "# Uncomment to cleanup\n",
-        "# import shutil\n",
-        "# shutil.rmtree(tmpdir)\n",
-        "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
-        "\n",
-        "print(f\"To cleanup manually, delete: {tmpdir}\")"
-      ]
+      "id": "51fc7c4a"
     },
     {
       "cell_type": "markdown",
-      "id": "ca40afc1",
       "metadata": {},
       "source": [
         "# Final Remarks"
-      ]
+      ],
+      "id": "ca40afc1"
     },
     {
       "cell_type": "markdown",
-      "id": "c4c86a23",
       "metadata": {},
       "source": [
         "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
-      ]
+      ],
+      "id": "c4c86a23"
     }
   ],
   "metadata": {

From 6f73130ae3265541dcc8fed50862108083d216fc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20Ch=C5=82apek?= <jakub.chlapekk@gmail.com>
Date: Wed, 5 Aug 2026 14:41:34 +0200
Subject: [PATCH 139/154] fix: docs fix for CI

---
 darts/utils/mlflow.py               |  7 ++-
 examples/29-MLflow-quickstart.ipynb | 95 +++++++++++++++++++++--------
 2 files changed, 76 insertions(+), 26 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 4ce9e3d98d..d59aecd524 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -349,6 +349,7 @@ def autolog(
 
     - Calling ``ForecastingModel.fit()`` inside an active MLflow run (e.g. within
       ``with mlflow.start_run():``); does nothing if no run is active:
+
       - Logs model creation parameters (``model.model_params``), both as MLflow
         params and as a ``model_params.json`` artifact.
       - Logs target series info and covariate usage information (past, future,
@@ -359,16 +360,19 @@ def autolog(
     - Calling ``ForecastingModel.historical_forecasts(retrain=True)`` inside an
       active MLflow run; does nothing if no run is active or ``retrain`` is not
       ``True``:
+
       - Logs the same model creation parameters and ``series_info.json`` as
         ``fit()`` (overwriting any prior ``fit()`` artifacts in the same run).
       - Does not log the trained model artifact; call ``log_model()`` manually
         if needed.
     - Calling any Darts metric inside an active MLflow run; does nothing if no
       run is active:
+
       - Logs the result of that metric call as an MLflow metric. More information
         in the notes below.
     - Calling ``ForecastingModel.backtest()`` inside an active MLflow run; does
       nothing if no run is active:
+
       - Logs all evaluation metrics under ``backtest_*`` keys. More information
         in the notes below.
 
@@ -382,7 +386,8 @@ def autolog(
           keyword argument when provided.
         - ``component`` – the component name when ``component_reduction=None``.
         - ``quantile_or_label``, e.g.:
-          – ``_q0.500`` for quantile metrics with keyword argument ``q=[0.5]``
+
+          - ``_q0.500`` for quantile metrics with keyword argument ``q=[0.5]``
           - ``_qi_80.000`` for quantile interval metrics with keyword argument
             ``q_interval=[(0.1, 0.9)]`` (80% interval between quantiles 0.1 and
             0.9).
diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
index fc12c4e824..b5a76dbb92 100644
--- a/examples/29-MLflow-quickstart.ipynb
+++ b/examples/29-MLflow-quickstart.ipynb
@@ -144,7 +144,8 @@
       "execution_count": 5,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
             "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Updating database tables\n",
@@ -152,7 +153,8 @@
           ]
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
             "Experiment: darts-quickstart\n"
@@ -197,13 +199,15 @@
       "execution_count": 6,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Training series: 107 points\n",
             "Validation series: 37 points\n"
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "image/png": 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",
@@ -258,13 +262,15 @@
       "execution_count": 7,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Validation MAPE: 7.86%\n",
             "Validation RMSE: 34.77\n"
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "image/png": 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",
@@ -318,13 +324,15 @@
       "execution_count": 8,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "2026/07/23 18:50:08 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
           ]
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
             "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
@@ -365,7 +373,8 @@
       "execution_count": 9,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Loaded model predictions match: True\n"
           ]
@@ -432,12 +441,14 @@
       "execution_count": 10,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "image/png": 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",
@@ -483,7 +494,8 @@
       "execution_count": 11,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Launch the MLflow UI with this command in your terminal:\n",
             "\n",
@@ -559,7 +571,8 @@
       "execution_count": 53,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "INFO: GPU available: True (mps), used: False\n",
             "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
@@ -587,6 +600,7 @@
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -600,7 +614,8 @@
           }
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
             "  super().__init__(loader)\n",
@@ -609,6 +624,7 @@
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -622,6 +638,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -635,13 +652,15 @@
           }
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
             "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -655,6 +674,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -668,6 +688,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -681,6 +702,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -694,6 +716,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -707,6 +730,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -720,6 +744,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -733,6 +758,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -746,6 +772,7 @@
           }
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -759,7 +786,8 @@
           }
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
             "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
@@ -772,6 +800,7 @@
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -785,7 +814,8 @@
           }
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "NBEATS MAPE: 13.64%\n",
             "NBEATS RMSE: 63.33\n"
@@ -859,7 +889,8 @@
       "execution_count": 13,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "All logged metrics (5):\n",
             "  mae: 46.0220\n",
@@ -940,13 +971,15 @@
       "execution_count": 14,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Aggregate metrics: {'ae': 44.196, 'mae': 51.316}\n",
             "Mean MAE over series: 51.316\n"
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
@@ -960,13 +993,16 @@
           }
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "\n",
             "Per-series breakdown (metrics_per_series.json):\n"
           ]
         },
         {
+          "metadata": {},
+          "execution_count": null,
           "output_type": "execute_result",
           "data": {
             "text/html": [
@@ -1055,13 +1091,15 @@
       "execution_count": 15,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "2026/07/23 18:50:10 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
           ]
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "\n",
             "Files in model directory:\n",
@@ -1097,7 +1135,8 @@
       "execution_count": 16,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Loaded model successfully!\n",
             "Predictions shape: (5, 1)\n"
@@ -1150,7 +1189,8 @@
       "execution_count": 17,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Found 5 runs in experiment 'darts-quickstart':\n",
             "\n",
@@ -1228,13 +1268,15 @@
       "execution_count": 18,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Loading best model from run: exponential-smoothing-baseline\n",
             "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
           ]
         },
         {
+          "metadata": {},
           "output_type": "display_data",
           "data": {
             "image/png": 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",
@@ -1277,13 +1319,15 @@
       "execution_count": 60,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Registered version: 1\n"
           ]
         },
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "Successfully registered model 'darts-air-passengers'.\n",
             "Created version '1' of model 'darts-air-passengers'.\n"
@@ -1361,7 +1405,8 @@
       "execution_count": 19,
       "outputs": [
         {
-          "output_type": "stream",
+                    "name": "stdout",
+"output_type": "stream",
           "text": [
             "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic\n"
           ]

From c035d150a8920e271402fd305bf5b8f405f83b98 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 14 Aug 2026 14:02:38 +0200
Subject: [PATCH 140/154] improve docs

---
 INSTALL.md                               |   4 +-
 darts/tests/optional_deps/test_mlflow.py |  16 +-
 darts/utils/mlflow.py                    | 254 +++++++++++++++--------
 3 files changed, 176 insertions(+), 98 deletions(-)

diff --git a/INSTALL.md b/INSTALL.md
index 302c7d7421..262aa692f2 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -24,8 +24,8 @@ Some models have additional dependencies that are not included in the `all` inst
 
 Some optional integrations also require additional dependencies:
 
-| Integration            | Dependencies |
-|-------------------------|--------------|
+| Integration          | Dependencies |
+|----------------------|--------------|
 | `darts.utils.mlflow` | mlflow>=3.0  |
 
 
diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 38c6a7b736..402ed656fd 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1414,7 +1414,7 @@ def test_autolog_backtest_per_window_steps(self, mlflow_tracking, autolog_contex
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
         assert len(history) > 1, "Expected multiple per-window steps"
         steps = sorted(m.step for m in history)
-        assert steps == list(range(-len(history), 0))
+        assert steps == list(range(len(history)))
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
         np.testing.assert_allclose(logged, np.asarray(ref, dtype=float), atol=1e-5)
 
@@ -1791,16 +1791,16 @@ def test_log_backtest_metrics_aligns_windows_by_end_date(self, mlflow_tracking):
 
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
         logged = {m.step: m.value for m in history}
-        assert logged == pytest.approx({-5: 1.0, -4: 2.0, -3: 6.5, -2: 12.0, -1: 17.5})
+        assert logged == pytest.approx({0: 1.0, 1: 2.0, 2: 6.5, 3: 12.0, 4: 17.5})
 
         rows = self._read_per_series_table(run.info.run_id)
         by_step = {(r["series_index"], r["step"]): r["value"] for r in rows}
-        assert by_step[(0, -5)] == pytest.approx(1.0)
-        assert by_step[(0, -1)] == pytest.approx(5.0)
-        assert by_step[(1, -3)] == pytest.approx(10.0)
-        assert by_step[(1, -1)] == pytest.approx(30.0)
-        assert (1, -5) not in by_step
-        assert (1, -4) not in by_step
+        assert by_step[(0, 0)] == pytest.approx(1.0)
+        assert by_step[(0, 4)] == pytest.approx(5.0)
+        assert by_step[(1, 2)] == pytest.approx(10.0)
+        assert by_step[(1, 4)] == pytest.approx(30.0)
+        assert (1, 0) not in by_step
+        assert (1, 1) not in by_step
 
     def test_log_backtest_metrics_aligns_last_points_only_time_axis(
         self, mlflow_tracking
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index d59aecd524..6455b905d5 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -2,23 +2,168 @@
 MLflow Integration
 ------------------
 
+.. _darts-mlflow-autolog-logging:
+
 Custom MLflow model flavor for Darts forecasting models. Supports saving, loading,
 and logging any Darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
-to MLflow, as well as automatic logging (``autolog()``) of:
-
-- Model parameters and data metadata: The model creation parameters, target series, and covariate usage information.
-- Model storage: The trained model artifact after each ``fit()`` call when ``log_models=True`` (default ``False``).
-- Metrics:
-  - The score for each metric called inside an active MLflow run.
-  - The score(s) from a ``backtest()`` call.
-  - Per-epoch training/validation metrics for PyTorch-based models.
+to MLflow, as well as automatic logging  via ``autolog()``.
 
 See the `MLflow quickstart example <https://github.com/unit8co/darts/blob/master/examples/29-MLflow-quickstart.ipynb>`_
 for an end-to-end walkthrough.
 
-To keep auto-logged metrics comparable across runs, use the same evaluation
-time frame, forecast horizon, and evaluation start date for every
-``backtest()`` / metric call you intend to compare.
+.. dropdown:: Here's a quick start example
+
+    .. highlight:: python
+    .. code-block:: python
+
+        import os
+        import tempfile
+
+        import mlflow
+
+        import darts.metrics as metrics
+        from darts.datasets import AirPassengersDataset
+        from darts.models import LinearRegressionModel
+        from darts.utils.mlflow import autolog
+
+        # dummy temporary directory for local MLflow tracking;
+        # use a permanent location for real use cases
+        tmpdir = tempfile.mkdtemp()
+        mlflow_db = os.path.join(tmpdir, "mlflow.db")
+
+        mlflow.set_tracking_uri(f"sqlite:///{mlflow_db}")
+        mlflow.set_experiment("darts-quickstart")
+
+        # load series and create train and val splits
+        series = AirPassengersDataset().load()
+        train, val = series[:-36], series[-36:]
+
+        # two models to compare with different lookback windows
+        model_lags, names = [12, 24], ["one-year", "two-years"]
+        horizon = 12
+
+        # activate autologging and try out the models
+        autolog()
+        for lags, name in zip(model_lags, names):
+            with mlflow.start_run(run_name=name) as run:
+                model = LinearRegressionModel(lags=lags)
+                model.fit(train)  # autolog logs params and covariate metadata
+
+                # log standalone metrics from manual predictions
+                pred = model.predict(n=horizon)
+                metrics.mae(val, pred)  # time-aggregated logged as scaler metric
+                metrics.ae(val, pred)  # time-dependent logged as stepped metric
+
+                # log backtest metrics from historical forecasts over val set
+                bt = model.backtest(
+                    series=series,
+                    start=val.start_time(),
+                    retrain=False,
+                    forecast_horizon=horizon,
+                    reduction=None,  # `None` logs as stepped metric, scalar otherwise
+                    metric=[metrics.mae, metrics.rmse],
+                )
+        autolog(disable=True)
+
+        # you can launch the MLflow UI with the command below from your terminal;
+        # then open the returned address for example in a browser (similar to http://localhost:5000)
+        print(f"mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}")
+
+When ``autolog()`` is enabled, the following functionalities emit detailed logs when inside
+an active MLflow run (e.g. within ``with mlflow.start_run():``):
+
+- Calling ``ForecastingModel.fit()``:
+
+  - Logs model creation parameters (``model.model_params``), both as MLflow
+    params and as a ``model_params.json`` artifact.
+  - Logs target series info and covariate usage information (past, future,
+    and static covariates) as a ``series_info.json`` artifact.
+  - Stores the trained model artifact when ``log_models=True`` (default:
+    ``False``).
+  - Logs per-epoch training and validation metrics for PyTorch-based models.
+- Calling ``ForecastingModel.historical_forecasts(retrain=True)``:
+
+  - Logs the same model creation parameters and ``series_info.json`` as
+    ``fit()`` (overwriting any prior ``fit()`` artifacts in the same run).
+  - Does not log the trained model artifact; call ``fit()``
+    or ``log_model()`` manually if needed.
+- Calling any Darts metric:
+
+  - Logs the result of that metric call as an MLflow metric. More information
+    in the notes below.
+- Calling ``ForecastingModel.backtest()``:
+
+  - Logs all evaluation metrics under ``backtest_*`` keys. More information
+    in the notes below.
+
+.. important::
+
+    **Cross-Model Run Comparability**: Metric values are only comparable
+    across runs when the evaluation settings match. Use the same evaluation
+    time frame, forecast horizon, and evaluation start date for every
+    ``backtest()`` / metric call you intend to compare.
+
+.. note::
+
+    **Metric Naming Convention**: Logged metric keys follow the pattern
+    ``{metric_name}{component}{quantile_or_label}`` (e.g.,
+    ``"mae_target0_q0_500"``), where each part is included only when the
+    corresponding axis is present:
+
+    - ``metric_name`` – the metric function name, or the ``name`` metric
+      keyword argument when provided (e.g., ``"mae"``).
+    - ``component`` – the component name: e.g., ``"_target0"`` when
+      ``component_reduction=None``.
+    - ``quantile_or_label``:
+
+      - the quantile label: e.g., ``"_q0_500"`` for quantile metrics with
+        keyword argument ``q=[0.5]``
+      - the quantile interval label: e.g., ``"_qi_80_000"`` for quantile
+        interval metrics with keyword argument ``q_interval=[(0.1, 0.9)]``
+        (80% interval between quantiles 0.1 and 0.9).
+      - the class label: e.g., ``"_label1"`` for classification metrics with
+        keyword argument ``labels`` when ``label_reduction=None``.
+
+    **Backtest metrics** are prefixed by ``"backtest_"``.
+
+.. note::
+
+    **Metric Logging and Display**: Darts offers many ways of evaluating
+    forecasting models. To offer the highest value, we adapt what is logged
+    to MLflow based on the use case scenario.
+
+    - Metric type:
+
+      - Time-aggregated metrics (e.g. ``mae()``): Logged as
+        scaler values.
+      - Per-time step metrics (e.g. ``ae()`` where ``time_reduction=None``):
+        Logged as stepped metrics (one value per step in the forecast horizon).
+    - Single or Multi-series:
+
+      - Single series: Logged as explained above.
+      - Multiple (a list of) series: When the metric's
+        ``series_reduction=None``, the logged metric is aggregated over all
+        series using autolog's ``agg_func``. The detailed per-series metrics /
+        backtest metrics are logged under a single ``metrics_per_series.json``
+        table run artifact.
+
+    - Standalone or backtest metric:
+
+      - Standalone metric: Logged as explained above.
+      - Backtest metric: If backtest's ``reduction`` is other than ``None``,
+        the windowed forecast metrics are aggregated and logged as explained
+        above. If ``None``, metrics are logged as stepped MLflow metrics.
+
+        - Time-aggregated metrics: each step represents a specifc forecast
+          (window) metric. Steps represent the historical forecast windows
+          (0, 1, ..., n_windows - 1).
+        - Per-time step metrics: each step represents a step in the forecast
+          horizon aggregated over all windows. Steps represent the steps in
+          the forecast horizon (0, 1, ..., horizon - 1).
+
+    When components are preserved (``component_reduction=None``), all
+    series scored together must have the same number of components; names
+    are taken from the first series.
 """
 
 import inspect
@@ -345,86 +490,21 @@ def autolog(
 ) -> None:
     """Enable (or disable) automatic MLflow logging for Darts.
 
-    When enabled, the following functionalities emit detailed logs:
-
-    - Calling ``ForecastingModel.fit()`` inside an active MLflow run (e.g. within
-      ``with mlflow.start_run():``); does nothing if no run is active:
-
-      - Logs model creation parameters (``model.model_params``), both as MLflow
-        params and as a ``model_params.json`` artifact.
-      - Logs target series info and covariate usage information (past, future,
-        and static covariates) as a ``series_info.json`` artifact.
-      - Stores the trained model artifact when ``log_models=True`` (default:
-        ``False``).
-      - Logs per-epoch training and validation metrics for PyTorch-based models.
-    - Calling ``ForecastingModel.historical_forecasts(retrain=True)`` inside an
-      active MLflow run; does nothing if no run is active or ``retrain`` is not
-      ``True``:
-
-      - Logs the same model creation parameters and ``series_info.json`` as
-        ``fit()`` (overwriting any prior ``fit()`` artifacts in the same run).
-      - Does not log the trained model artifact; call ``log_model()`` manually
-        if needed.
-    - Calling any Darts metric inside an active MLflow run; does nothing if no
-      run is active:
-
-      - Logs the result of that metric call as an MLflow metric. More information
-        in the notes below.
-    - Calling ``ForecastingModel.backtest()`` inside an active MLflow run; does
-      nothing if no run is active:
-
-      - Logs all evaluation metrics under ``backtest_*`` keys. More information
-        in the notes below.
-
-    .. note::
-
-        Logged metric keys follow the pattern
-        ``{metric_name}{component}{quantile_or_label}``, where each part is
-        included only when the corresponding axis is present:
-
-        - ``metric_name`` – the metric function name, or the ``name`` metric
-          keyword argument when provided.
-        - ``component`` – the component name when ``component_reduction=None``.
-        - ``quantile_or_label``, e.g.:
-
-          - ``_q0.500`` for quantile metrics with keyword argument ``q=[0.5]``
-          - ``_qi_80.000`` for quantile interval metrics with keyword argument
-            ``q_interval=[(0.1, 0.9)]`` (80% interval between quantiles 0.1 and
-            0.9).
-          - ``_label1`` for classification metrics with keyword argument
-            ``labels`` when ``label_reduction=None``.
-
-        Per-timestep metrics (``time_reduction=None``) are charted across the
-        MLflow ``step``.
-
-        When ``series_reduction`` is set on a metric call, results are already
-        aggregated across series inside the metric itself, so the
-        cross-series aggregation described below does not apply.
-
-        For a list of series, the logged metric is aggregated over all series
-        using ``agg_func``. The detailed per-series metrics / backtest metrics
-        are logged under a single ``metrics_per_series.json`` table
-        artifact.
-
-        When components are preserved (``component_reduction=None``), all
-        series scored together must have the same number of components; names
-        are taken from the first series.
-
-        Metric values are only comparable across runs when the evaluation
-        settings match. Use the same evaluation time frame, forecast horizon,
-        and evaluation start date for every ``backtest()`` / metric call you
-        intend to compare.
+    For a detailed overview of logged params, metrics, and artifacts, see
+    :ref:`the detailed documentation <darts-mlflow-autolog-logging>`.
 
     Parameters
     ----------
     log_models
-        If ``True``, log the trained model artifact after ``fit()``. Defaults to
-        ``False``.
+        If ``True``, log the trained model artifact when calling ``fit()``.
+        Defaults to ``False``.
     log_params
-        If ``True`` (default), log model creation parameters.
+        If ``True`` (default), log model creation parameters when calling
+        ``fit()`` or ``historical_forecasts()``.
     log_metrics
         If ``True`` (default), log the result of any Darts metric call made
-        inside an active MLflow run.
+        inside an active MLflow run, including standalone metric calls or via
+        backtest.
     log_torch_metrics
         If ``True`` (default), enable ``mlflow.pytorch.autolog(log_models=False)``
         around PyTorch-based model training to automatically log per-epoch
@@ -633,9 +713,7 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
         if active_run is None:
             return result
 
-        bound = inspect.signature(ForecastingModel.historical_forecasts).bind(
-            self, *args, **kwargs
-        )
+        bound = inspect.signature(original).bind(self, *args, **kwargs)
         bound.apply_defaults()
         if bound.arguments["retrain"] is not True:
             return result
@@ -1339,7 +1417,7 @@ def _log_backtest_metrics(
                         if has_time_axis:
                             step = t - t_size if t_axis_is_calendar else t
                         else:
-                            step = aligned_w - max_w_size
+                            step = aligned_w
                         value = float(canonical[w, t, c, m])
                         agg.setdefault((key, step), []).append(value)
                         rows.append({

From 1e47e3419bc97323e00f4170417447770d37a019 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 14 Aug 2026 16:21:09 +0200
Subject: [PATCH 141/154] improve encoder handling for covariates metadata

---
 darts/utils/mlflow.py | 125 +++++++++++++++++++++++++-----------------
 1 file changed, 74 insertions(+), 51 deletions(-)

diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 6455b905d5..2d83136972 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -228,6 +228,7 @@
 
 import darts
 from darts import TimeSeries
+from darts.dataprocessing.encoders.encoders import SequentialEncoder
 from darts.logging import get_logger, raise_log
 from darts.metrics.utils import (
     _LabelReduction,
@@ -654,9 +655,9 @@ def _patched_fit(original, self, *args, **kwargs):
 
         fit_args = inspect.signature(original).bind(self, *args, **kwargs).arguments
         _log_model_setup(
-            self,
-            autologging_client,
-            run_id,
+            model=self,
+            autologging_client=autologging_client,
+            run_id=run_id,
             series=fit_args["series"],
             past_covariates=fit_args.get("past_covariates"),
             future_covariates=fit_args.get("future_covariates"),
@@ -720,9 +721,9 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
 
         autologging_client = MlflowAutologgingQueueingClient()
         _log_model_setup(
-            self,
-            autologging_client,
-            active_run.info.run_id,
+            model=self,
+            autologging_client=autologging_client,
+            run_id=active_run.info.run_id,
             series=bound.arguments["series"],
             past_covariates=bound.arguments.get("past_covariates"),
             future_covariates=bound.arguments.get("future_covariates"),
@@ -747,7 +748,7 @@ def _patched_backtest(original, self, *args, **kwargs):
         if not log_metrics or active_run is None:
             return result
 
-        bound = inspect.signature(ForecastingModel.backtest).bind(self, *args, **kwargs)
+        bound = inspect.signature(original).bind(self, *args, **kwargs)
         bound.apply_defaults()
         backtest_args = bound.arguments
 
@@ -765,30 +766,30 @@ def _patched_backtest(original, self, *args, **kwargs):
     # patch `fit()` for all forecasting models
     for _, cls in _get_forecasting_models():
         safe_patch(
-            FLAVOR_NAME,
-            cls,
-            "fit",
-            _patched_fit,
+            autologging_integration=FLAVOR_NAME,
+            destination=cls,
+            function_name="fit",
+            patch_function=_patched_fit,
         )
 
     # patch `historical_forecasts()` for all forecasting models so that the
-    # N internal fit() calls don't each log, and so that retrain=True calls
+    # N internal fit() calls do not log themselves, and so that retrain=True calls
     # log model setup once
     for _, cls in _get_forecasting_models():
         safe_patch(
-            FLAVOR_NAME,
-            cls,
-            "historical_forecasts",
-            _patched_historical_forecasts,
+            autologging_integration=FLAVOR_NAME,
+            destination=cls,
+            function_name="historical_forecasts",
+            patch_function=_patched_historical_forecasts,
         )
 
     # patch `backtest()` for all forecasting models to log metric results
     for _, cls in _get_forecasting_models():
         safe_patch(
-            FLAVOR_NAME,
-            cls,
-            "backtest",
-            _patched_backtest,
+            autologging_integration=FLAVOR_NAME,
+            destination=cls,
+            function_name="backtest",
+            patch_function=_patched_backtest,
         )
 
 
@@ -830,23 +831,16 @@ def _infer_covariate_usage(
     ``False``. Fall back to call args / ``add_encoders`` / static covariates on
     ``series``, gated by ``supports_*`` / ``considers_static_covariates``.
     """
-    # encoder keys like "datetime_attribute" map to {"past": ..., "future": ...};
-    # non-dict values ("tz", "transformer") are ignored by the isinstance check
-    enc_types = {
-        cov
-        for val in (model.add_encoders or {}).values()
-        if isinstance(val, dict)
-        for cov in ("past", "future")
-        if cov in val
-    }
+    # encoders can add past and future covariates
+    encoders = _get_model_encoders(model)
     first_series = get_single_series(series)
     uses_past = model.uses_past_covariates or (
         model.supports_past_covariates
-        and (past_covariates is not None or "past" in enc_types)
+        and (past_covariates is not None or len(encoders.past_encoders) > 0)
     )
     uses_future = model.uses_future_covariates or (
         model.supports_future_covariates
-        and (future_covariates is not None or "future" in enc_types)
+        and (future_covariates is not None or len(encoders.future_encoders) > 0)
     )
     uses_static = model.uses_static_covariates or (
         first_series is not None
@@ -904,7 +898,7 @@ def _log_model_setup(
     autologging_client.set_tags(
         run_id=run_id,
         tags=_get_model_info_tags(
-            model,
+            model=model,
             series=series,
             past_covariates=past_covariates,
             future_covariates=future_covariates,
@@ -914,7 +908,7 @@ def _log_model_setup(
         autologging_client.log_params(run_id=run_id, params=model.model_params)
         mlflow.log_dict(model.model_params, "model_params.json")
         _log_series_info(
-            model,
+            model=model,
             series=series,
             past_covariates=past_covariates,
             future_covariates=future_covariates,
@@ -955,33 +949,46 @@ def _log_series_info(
         ``None``.
     """
     first_series = get_single_series(series)
+    first_past_covariates = get_single_series(past_covariates)
+    first_future_covariates = get_single_series(future_covariates)
     uses_past, uses_future, uses_static = _infer_covariate_usage(
-        model, series, past_covariates, future_covariates
+        model=model,
+        series=series,
+        past_covariates=past_covariates,
+        future_covariates=future_covariates,
     )
+
+    encoders = _get_model_encoders(model)
+    if encoders.encoding_available:
+        first_past_covariates, first_future_covariates = encoders.encode_train(
+            target=first_series,
+            past_covariates=first_past_covariates,
+            future_covariates=first_future_covariates,
+        )
+
     series_info = {
         "series": {
             "count": first_series.n_components,
             "names": first_series.components.tolist(),
         },
         "past_covariates": _extract_covariate_metadata(
-            uses_past,
-            get_single_series(past_covariates),
-            "components",
-            encoded_names=model.encoders.past_components,
+            uses=uses_past,
+            single_cov=first_past_covariates,
+            names_attr="components",
         ),
         "future_covariates": _extract_covariate_metadata(
-            uses_future,
-            get_single_series(future_covariates),
-            "components",
-            encoded_names=model.encoders.future_components,
+            uses=uses_future,
+            single_cov=first_future_covariates,
+            names_attr="components",
         ),
     }
-
     static_covariates = (
         first_series.static_covariates if first_series is not None else None
     )
     series_info["static_covariates"] = _extract_covariate_metadata(
-        uses_static, static_covariates, "columns"
+        uses=uses_static,
+        single_cov=static_covariates,
+        names_attr="columns",
     )
     if uses_static and static_covariates is not None:
         # static covariates are global (one shared row) unless there is one row
@@ -1014,7 +1021,6 @@ def _extract_covariate_metadata(
     uses: bool,
     single_cov: TimeSeries | pd.DataFrame | None,
     names_attr: str,
-    encoded_names: pd.Index | list[str] | None = None,
 ) -> dict:
     """Extract metadata for a single covariate type from its (already
     singular) value.
@@ -1029,10 +1035,6 @@ def _extract_covariate_metadata(
     names_attr : str
         Attribute holding the feature names ("components" for a
         ``TimeSeries``, "columns" for a static-covariates ``DataFrame``).
-    encoded_names
-        Additional covariate names generated by encoders (``add_encoders``),
-        appended to the names extracted from ``single_cov``. Ignored when
-        ``uses`` is ``False``.
 
     Returns
     -------
@@ -1044,8 +1046,6 @@ def _extract_covariate_metadata(
     if uses:
         info["used"] = True
         names = list(getattr(single_cov, names_attr)) if single_cov is not None else []
-        if encoded_names is not None:
-            names = names + list(encoded_names)
         info["names"] = names
         info["count"] = len(names)
 
@@ -1072,6 +1072,29 @@ def _sanitize_mlflow_key(name: str) -> str:
     return re.sub(r"[^\w-]", "_", name)
 
 
+def _get_model_encoders(model: ForecastingModel) -> SequentialEncoder:
+    """Get the encoders for a model.
+
+    Gets either the trained encoder of a fitted model or the
+    fresh encoders of a model that has not been fitted yet.
+
+    Parameters
+    ----------
+    model
+        A Darts forecasting model instance.
+
+    Returns
+    -------
+    SequentialEncoder
+        The encoders for the model.
+    """
+    if model.add_encoders and not model.encoders.encoding_available:
+        # model has encoders but it was not fitted yet
+        return model.initialize_encoders()
+    # model has been fitted, or does not have encodings available
+    return model.encoders
+
+
 def _build_metric_keys(
     metric_names: list[str],
     components: list[str],

From 92580e1c1d9c1650559b05448983e26a31ed5a6d Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Sun, 16 Aug 2026 13:32:45 +0200
Subject: [PATCH 142/154] improve metric keys and series metadata logging

---
 darts/tests/optional_deps/test_mlflow.py |  36 ++--
 darts/utils/mlflow.py                    | 233 +++++++++++++----------
 2 files changed, 147 insertions(+), 122 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 402ed656fd..dc0fb796e9 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -445,8 +445,8 @@ def test_autolog_series_info_add_encoders(self, mlflow_tracking, autolog_context
         expected_names = model.encoders.future_components.tolist()
         assert expected_names  # sanity check: encoders actually generated something
         assert series_info["future_covariates"]["used"] is True
-        assert series_info["future_covariates"]["count"] == len(expected_names)
-        assert series_info["future_covariates"]["names"] == expected_names
+        assert series_info["future_covariates"]["count"] == 0
+        assert series_info["future_covariates"]["encodings"] == expected_names
         assert series_info["past_covariates"]["used"] is False
 
     def test_autolog_series_info_static_is_global(
@@ -464,7 +464,8 @@ def test_autolog_series_info_static_is_global(
         series_info = mlflow.artifacts.load_dict(
             f"runs:/{run.info.run_id}/series_info.json"
         )
-        assert series_info["static_covariates"]["is_global"] is True
+        assert series_info["static_covariates"]["count"] == 2
+        assert series_info["static_covariates"]["names"] == ["a", "b"]
 
         # one row per component (2 components, 2 rows) -> component-specific
         per_component_target = self.ts_multivariate.with_static_covariates(
@@ -476,7 +477,8 @@ def test_autolog_series_info_static_is_global(
         series_info = mlflow.artifacts.load_dict(
             f"runs:/{run.info.run_id}/series_info.json"
         )
-        assert series_info["static_covariates"]["is_global"] is False
+        assert series_info["static_covariates"]["count"] == 1
+        assert series_info["static_covariates"]["names"] == ["a"]
 
     def test_autolog_historical_forecasts_series_info_covariates(
         self, mlflow_tracking, autolog_context
@@ -1121,7 +1123,7 @@ def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
 
         ref = np.asarray(ref, dtype=float)  # shape (n_quantiles,)
         m = mlflow.get_run(run.info.run_id).data.metrics
-        for i, key in enumerate(("mql_q0_100", "mql_q0_500", "mql_q0_900")):
+        for i, key in enumerate(("mql_q0.100", "mql_q0.500", "mql_q0.900")):
             assert key in m, f"Expected quantile key {key}"
             assert m[key] == pytest.approx(ref[i], abs=1e-5)
 
@@ -1140,8 +1142,8 @@ def test_autolog_metric_quantile_interval(self, mlflow_tracking, autolog_context
                 ref = dm.miw(actual, pred, q_interval=(0.1, 0.9))
 
         m = mlflow.get_run(run.info.run_id).data.metrics
-        assert "miw_qi_80_000" in m
-        assert m["miw_qi_80_000"] == pytest.approx(float(ref), abs=1e-5)
+        assert "miw_qi0.800" in m
+        assert m["miw_qi0.800"] == pytest.approx(float(ref), abs=1e-5)
 
     def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
         """A list of series logs the mean over series; per-series values go to a table."""
@@ -1235,7 +1237,7 @@ def test_autolog_metric_name_override(self, mlflow_tracking, autolog_context):
         direct = mlflow.get_run(run_direct.info.run_id).data.metrics
         assert "custom" in direct
         assert "mae" not in direct, "default metric name should be replaced"
-        assert "myq_q0_500" in direct, "quantile suffix should be preserved"
+        assert "myq_q0.500" in direct, "quantile suffix should be preserved"
 
         bt = mlflow.get_run(run_bt.info.run_id).data.metrics
         assert "backtest_custom" in bt
@@ -1597,9 +1599,9 @@ def test_autolog_backtest_quantile(self, mlflow_tracking, autolog_context):
 
         m = mlflow.get_run(run.info.run_id).data.metrics
         for key in (
-            "backtest_mql_q0_100",
-            "backtest_mql_q0_500",
-            "backtest_mql_q0_900",
+            "backtest_mql_q0.100",
+            "backtest_mql_q0.500",
+            "backtest_mql_q0.900",
         ):
             assert key in m, f"Expected quantile key {key}"
             assert np.isfinite(m[key])
@@ -1879,7 +1881,7 @@ def test_infer_metric_axes_quantiles():
 
 def test_infer_metric_axes_quantile_interval():
     has_time, _, axis_labels = _infer_metric_axes(dm.iw, {"q_interval": (0.1, 0.9)})
-    assert axis_labels == ["_qi_80.000"]
+    assert axis_labels == ["_qi0.800"]
     assert has_time is True
 
 
@@ -1906,10 +1908,10 @@ def test_build_metric_keys_components_and_quantiles():
     assert c_size == 4
     assert keys == [
         [
-            "backtest_mae_temp_q0_100",
-            "backtest_mae_temp_q0_900",
-            "backtest_mae_hum_q0_100",
-            "backtest_mae_hum_q0_900",
+            "backtest_mae_temp_q0.100",
+            "backtest_mae_temp_q0.900",
+            "backtest_mae_hum_q0.100",
+            "backtest_mae_hum_q0.900",
         ],
         [
             "backtest_f1_temp_label0",
@@ -1930,7 +1932,7 @@ def test_build_metric_keys_no_components_no_prefix():
         metric_axes=metric_axes,
     )
     assert c_size == 1
-    assert keys == [["mql_q0_500"]]
+    assert keys == [["mql_q0.500"]]
 
 
 def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 2d83136972..0efc8bf454 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -116,9 +116,9 @@
       ``component_reduction=None``.
     - ``quantile_or_label``:
 
-      - the quantile label: e.g., ``"_q0_500"`` for quantile metrics with
+      - the quantile label: e.g., ``"_q0.500"`` for quantile metrics with
         keyword argument ``q=[0.5]``
-      - the quantile interval label: e.g., ``"_qi_80_000"`` for quantile
+      - the quantile interval label: e.g., ``"_qi0.800"`` for quantile
         interval metrics with keyword argument ``q_interval=[(0.1, 0.9)]``
         (80% interval between quantiles 0.1 and 0.9).
       - the class label: e.g., ``"_label1"`` for classification metrics with
@@ -683,6 +683,7 @@ def _patched_fit(original, self, *args, **kwargs):
                     registered_model_name=registered_model_name,
                     model_id=model.model_id,
                 )
+            # TODO dennis: maybe let it fail naturally?
             except Exception:
                 raise_log(
                     ValueError(
@@ -716,7 +717,7 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
 
         bound = inspect.signature(original).bind(self, *args, **kwargs)
         bound.apply_defaults()
-        if bound.arguments["retrain"] is not True:
+        if bound.arguments["retrain"] is False:
             return result
 
         autologging_client = MlflowAutologgingQueueingClient()
@@ -735,8 +736,9 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
     def _patched_backtest(original, self, *args, **kwargs):
         """Wrap ``backtest`` to log metric result(s) to the active MLflow run.
 
-        Delegates to ``_log_backtest_metrics``, which infers result shape from
-        the metric signature and logs every cell under a descriptive key.
+        Suppresses per-window metric logging. Delegates to ``_log_backtest_metrics``,
+        which infers result shape from the metric signature and logs every cell under
+        a descriptive key.
         """
         _autolog_state.in_backtest = True
         try:
@@ -763,7 +765,7 @@ def _patched_backtest(original, self, *args, **kwargs):
         autologging_client.flush(synchronous=False).await_completion()
         return result
 
-    # patch `fit()` for all forecasting models
+    # patch `fit()` to log model setup and model artifact
     for _, cls in _get_forecasting_models():
         safe_patch(
             autologging_integration=FLAVOR_NAME,
@@ -772,9 +774,8 @@ def _patched_backtest(original, self, *args, **kwargs):
             patch_function=_patched_fit,
         )
 
-    # patch `historical_forecasts()` for all forecasting models so that the
-    # N internal fit() calls do not log themselves, and so that retrain=True calls
-    # log model setup once
+    # patch `historical_forecasts()` to log model setup only once (when retrain=True);
+    # suppresses internal fit() call logging
     for _, cls in _get_forecasting_models():
         safe_patch(
             autologging_integration=FLAVOR_NAME,
@@ -783,7 +784,8 @@ def _patched_backtest(original, self, *args, **kwargs):
             patch_function=_patched_historical_forecasts,
         )
 
-    # patch `backtest()` for all forecasting models to log metric results
+    # patch `backtest()` to log backtest metrics once while suppressing per-window
+    # metric logging
     for _, cls in _get_forecasting_models():
         safe_patch(
             autologging_integration=FLAVOR_NAME,
@@ -833,6 +835,7 @@ def _infer_covariate_usage(
     """
     # encoders can add past and future covariates
     encoders = _get_model_encoders(model)
+
     first_series = get_single_series(series)
     uses_past = model.uses_past_covariates or (
         model.supports_past_covariates
@@ -923,8 +926,8 @@ def _log_series_info(
 ) -> None:
     """Log target series and covariate usage information to MLflow.
 
-    Extracts information about the target series, and about past, future,
-    and static covariates used during training and logs them as a JSON
+    Extracts information about the target series, past, future, and
+    static covariates used during training and logs them as a JSON
     artifact for easy filtering, comparison, and documentation.
 
     Logs:
@@ -951,6 +954,9 @@ def _log_series_info(
     first_series = get_single_series(series)
     first_past_covariates = get_single_series(past_covariates)
     first_future_covariates = get_single_series(future_covariates)
+    first_static_covariates = (
+        first_series.static_covariates if first_series is not None else None
+    )
     uses_past, uses_future, uses_static = _infer_covariate_usage(
         model=model,
         series=series,
@@ -958,44 +964,39 @@ def _log_series_info(
         future_covariates=future_covariates,
     )
 
+    # if available, train encoders to get past and future encodings
     encoders = _get_model_encoders(model)
     if encoders.encoding_available:
-        first_past_covariates, first_future_covariates = encoders.encode_train(
+        _ = encoders.encode_train(
             target=first_series,
             past_covariates=first_past_covariates,
             future_covariates=first_future_covariates,
         )
 
     series_info = {
-        "series": {
-            "count": first_series.n_components,
-            "names": first_series.components.tolist(),
-        },
-        "past_covariates": _extract_covariate_metadata(
+        "series": _extract_data_metadata(
+            uses=True,
+            data=first_series,
+            names_attr="components",
+        ),
+        "past_covariates": _extract_data_metadata(
             uses=uses_past,
-            single_cov=first_past_covariates,
+            data=first_past_covariates,
             names_attr="components",
+            encodings=encoders.past_components,
         ),
-        "future_covariates": _extract_covariate_metadata(
+        "future_covariates": _extract_data_metadata(
             uses=uses_future,
-            single_cov=first_future_covariates,
+            data=first_future_covariates,
             names_attr="components",
+            encodings=encoders.future_components,
+        ),
+        "static_covariates": _extract_data_metadata(
+            uses=uses_static,
+            data=first_static_covariates,
+            names_attr="columns",
         ),
     }
-    static_covariates = (
-        first_series.static_covariates if first_series is not None else None
-    )
-    series_info["static_covariates"] = _extract_covariate_metadata(
-        uses=uses_static,
-        single_cov=static_covariates,
-        names_attr="columns",
-    )
-    if uses_static and static_covariates is not None:
-        # static covariates are global (one shared row) unless there is one row
-        # per series component, in which case they are component-specific
-        series_info["static_covariates"]["is_global"] = (
-            len(static_covariates) != first_series.n_components
-        )
 
     # log complete information as JSON artifact
     mlflow.log_dict(series_info, "series_info.json")
@@ -1017,37 +1018,44 @@ def _is_torch_model(model) -> bool:
     return TORCH_AVAILABLE and isinstance(model, TorchForecastingModel)
 
 
-def _extract_covariate_metadata(
+def _extract_data_metadata(
     uses: bool,
-    single_cov: TimeSeries | pd.DataFrame | None,
+    data: TimeSeries | pd.DataFrame | None,
     names_attr: str,
+    encodings: pd.Index | None = None,
 ) -> dict:
-    """Extract metadata for a single covariate type from its (already
-    singular) value.
+    """Extract data metadata.
+
+    Encodings are logged under the "encodings" key since they are not part
+    of the input series.
 
     Parameters
     ----------
     uses
-        Whether the model uses this covariate type.
-    single_cov
-        The covariate's value for one series: a ``TimeSeries`` (past/future
-        covariates) or a static-covariates ``DataFrame``, or ``None``.
-    names_attr : str
+        Whether the model uses this data type.
+    data
+        The data from a single series: a ``TimeSeries`` (target or past/future covariates)
+        or a static-covariates ``DataFrame``, or ``None``.
+    names_attr
         Attribute holding the feature names ("components" for a
         ``TimeSeries``, "columns" for a static-covariates ``DataFrame``).
+    encodings
+        The encodings for the data: a ``pd.Index`` of encoding names, or ``None``.
 
     Returns
     -------
     dict
         Dictionary with keys: "used" (bool), "count" (int), "names" (list).
     """
-    info = {"used": False, "count": 0, "names": []}
+    info = {"used": False, "count": 0, "names": [], "encodings": []}
 
     if uses:
         info["used"] = True
-        names = list(getattr(single_cov, names_attr)) if single_cov is not None else []
+        names = list(getattr(data, names_attr)) if data is not None else []
         info["names"] = names
         info["count"] = len(names)
+        if encodings is not None:
+            info["encodings"] = list(encodings)
 
     return info
 
@@ -1055,9 +1063,9 @@ def _extract_covariate_metadata(
 def _sanitize_mlflow_key(name: str) -> str:
     """Sanitize a string for use as an MLflow metric key.
 
-    Replaces any character that is not alphanumeric, a hyphen, or an
-    underscore with an underscore, so component names become valid
-    MLflow keys.
+    Replaces any invalid character from the metric key. Valid keys may
+    only contain slashes, alphanumerics, underscores, periods, dashes,
+    and spaces.
 
     Parameters
     ----------
@@ -1069,7 +1077,7 @@ def _sanitize_mlflow_key(name: str) -> str:
     str
         A string safe for use as an MLflow metric key.
     """
-    return re.sub(r"[^\w-]", "_", name)
+    return re.sub(r"[^/\w.\- ]", "_", name)
 
 
 def _get_model_encoders(model: ForecastingModel) -> SequentialEncoder:
@@ -1122,32 +1130,31 @@ def _build_metric_keys(
     Returns
     -------
     tuple[int, list[list[str]]]
-        ``(c_size, keys)`` where ``c_size`` is
-        ``(n_components if has_comp_axis else 1) * axis_size`` and ``keys[m][c]``
-        is the sanitized key for metric ``m`` and flat component/axis index ``c``.
+        ``(n_scores, keys)`` where ``n_scores`` is
+        ``(n_components if has_comp_axis else 1) * n_labels`` and ``keys[m][c]``
+        is the sanitized key for metric ``m`` and flat index ``c``. Flat index
+        ``c`` walks axis labels innermost, then components.
     """
-    axis_size = len(metric_axes[0][2])
-    c_size = (len(components) if has_comp_axis else 1) * axis_size
+    component_names = components if has_comp_axis else [None]
+    n_labels = len(metric_axes[0][2])
+    n_scores = len(component_names) * n_labels
+
     keys: list[list[str]] = []
-    for m, metric_name in enumerate(metric_names):
-        axis_labels = metric_axes[m][2]
-        keys_m = []
-        for c in range(c_size):
-            # c is a flat index into the (n_components x axis_size) C axis:
-            # c = comp_i * axis_size + axis_idx
-            component_index, axis_idx = divmod(c, axis_size)
-            comp_part = (
-                "_" + _sanitize_mlflow_key(components[component_index])
-                if has_comp_axis
-                else ""
+    for metric_idx, metric_name in enumerate(metric_names):
+        labels = metric_axes[metric_idx][2]
+        metric_keys: list[str] = []
+        for component in component_names:
+            component_suffix = (
+                f"_{_sanitize_mlflow_key(component)}" if component is not None else ""
             )
-            keys_m.append(
-                _sanitize_mlflow_key(
-                    f"{prefix}{metric_name}{comp_part}{axis_labels[axis_idx]}"
+            for label in labels:
+                metric_keys.append(
+                    _sanitize_mlflow_key(
+                        f"{prefix}{metric_name}{component_suffix}{label}"
+                    )
                 )
-            )
-        keys.append(keys_m)
-    return c_size, keys
+        keys.append(metric_keys)
+    return n_scores, keys
 
 
 def _log_per_series_table(rows: list[dict]) -> None:
@@ -1170,7 +1177,12 @@ def _log_per_series_table(rows: list[dict]) -> None:
     """
     if not rows:
         return
-    df = pd.DataFrame(rows).sort_values(["key", "series_index", "step"])
+
+    df = pd.DataFrame(rows)
+    sort_by = ["key", "series_index", "step"]
+    if "window_index" in df.columns:
+        sort_by.append("window_index")
+    df = df.sort_values(sort_by)
     mlflow.log_table(data=df, artifact_file="metrics_per_series.json")
 
 
@@ -1251,11 +1263,21 @@ def _log_backtest_metrics(
     must have the same number of components; names are taken from the first
     series.
 
-    Series can have different lengths and intersect in different ways,
-    so the time axis is aligned from the end rather than the start: a shorter
-    series lines up on its last value instead of its first. To represent this,
-    the time axis is mapped to the MLflow ``step`` as ``t - t_size`` when
-    ``has_time_axis`` is ``True``.
+    Series can have different lengths and intersect in different ways.
+    We align the time axis from the end rather than the start: a shorter
+    series lines up on its last value instead of its first. This is an
+    assumption which does not hold true for all cases (series can also end
+    at different times).
+
+    The last step logged to MLflow represents the last window of each series.
+    There will be ``max_n_windows`` steps logged. Shorter series will not
+    contribute to the first steps. In the multi-series case, the values at the
+    same step are aggregated over all series.
+
+    For time-dependent metrics (e.g. ``ae()``), the steps represent the
+    forecast horizon rather than backteset windows. There will be
+    ``forecast_horizon`` steps logged. In the multi-series case, the values at
+    the same step are aggregated over all series.
 
     Raises
     ------
@@ -1321,24 +1343,25 @@ def _log_backtest_metrics(
     has_time_axis, has_comp_axis, _ = metric_axes[0]
 
     series = backtest_args.get("series")
+    is_single_series = get_series_seq_type(series) == SeriesType.SINGLE
+    series = series2seq(series)
+
     forecast_horizon = backtest_args.get("forecast_horizon")
     historical_forecasts = backtest_args.get("historical_forecasts")
     last_points_only = backtest_args.get("last_points_only", False)
 
-    # if last_points_only is True, has_windows will be False, so fc_hzn is not needed
+    # if last_points_only is True, has_windows will be False, so fc_hzn is not needed;
+    # otherwise, the forecast horizon is the length of one historical forecast
     if historical_forecasts is not None and not last_points_only:
         first_series_hf = (
-            historical_forecasts
-            if get_series_seq_type(series) == SeriesType.SINGLE
-            else historical_forecasts[0]
+            historical_forecasts[0] if is_single_series else historical_forecasts[0][0]
         )
-        forecast_horizon = len(first_series_hf[0])
+        forecast_horizon = len(first_series_hf)
 
-    series_seq = series2seq(series)
-    results = [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
+    results = [result] if is_single_series else result
     # component names are only used when the metric preserves components
     if has_comp_axis:
-        n_components = {s.n_components for s in series_seq}
+        n_components = {s.n_components for s in series}
         if len(n_components) > 1:
             raise_log(
                 ValueError(
@@ -1354,12 +1377,12 @@ def _log_backtest_metrics(
     rows: list[dict] = []
 
     # component names/count from the first series (all series share n_components)
-    comps = series_seq[0].components.tolist()
-    c_size, base_keys = _build_metric_keys(
-        metric_names,
-        comps,
-        has_comp_axis,
-        metric_axes,
+    comps = series[0].components.tolist()
+    n_scores, base_keys = _build_metric_keys(
+        metric_names=metric_names,
+        components=comps,
+        has_comp_axis=has_comp_axis,
+        metric_axes=metric_axes,
         prefix="backtest_",
     )
 
@@ -1369,13 +1392,13 @@ def _log_backtest_metrics(
     for r in results:
         arr = np.asarray(r, dtype=float)
         # after stripping C and M axes, rest = W*T (or W or T alone)
-        rest, extra = divmod(arr.size, c_size * n_metrics)
+        rest, extra = divmod(arr.size, n_scores * n_metrics)
         if extra:
             raise_log(
                 ValueError(
                     f"Backtest metric logging failed: result size ({arr.size}) "
-                    f"is not divisible by c_size * n_metrics ({c_size} * "
-                    f"{n_metrics} = {c_size * n_metrics}). The metric output "
+                    f"is not divisible by n_scores * n_metrics ({n_scores} * "
+                    f"{n_metrics} = {n_scores * n_metrics}). The metric output "
                     "shape does not match the inferred axes."
                 )
             )
@@ -1403,7 +1426,7 @@ def _log_backtest_metrics(
         series_shapes.append((
             t_size,
             w_size,
-            arr.reshape(w_size, t_size, c_size, n_metrics),
+            arr.reshape(w_size, t_size, n_scores, n_metrics),
         ))
 
     # align the calendar-relative axes from the end
@@ -1413,7 +1436,7 @@ def _log_backtest_metrics(
     for series_index, (t_size, w_size, canonical) in enumerate(series_shapes):
         w_offset = max_w_size - w_size if has_windows else 0
         for m in range(n_metrics):
-            for c in range(c_size):
+            for c in range(n_scores):
                 key = base_keys[m][c]
                 if has_time_axis and has_windows:
                     # Keep window-level values in the detailed table, but aggregate
@@ -1457,7 +1480,7 @@ def _log_backtest_metrics(
         agg,
         agg_func=agg_func,
         table_rows=(
-            rows if len(series_seq) > 1 or (has_time_axis and has_windows) else None
+            rows if len(series) > 1 or (has_time_axis and has_windows) else None
         ),
     )
 
@@ -1507,7 +1530,7 @@ def effective(param_name: str) -> Any:
     q_interval, q = metric_kwargs.get("q_interval"), metric_kwargs.get("q")
     if "q_interval" in params and q_interval is not None:
         intervals = np.atleast_2d(np.array(q_interval, dtype=float))
-        axis_labels = [f"_qi_{100 * (hi - lo):.3f}" for lo, hi in intervals]
+        axis_labels = [f"_qi{(hi - lo):.3f}" for lo, hi in intervals]
     elif "q" in params and q is not None:
         axis_labels = [f"_q{v:.3f}" for v in np.atleast_1d(np.array(q, dtype=float))]
     elif "label_reduction" in params and getattr(metric, "__name__", ""):
@@ -1566,7 +1589,7 @@ def _log_metric_result(
     present:
 
     - ``component`` – ``_{component_name}`` when ``has_comp_axis``.
-    - ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi_80.000`` / ``_label1``.
+    - ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi0.800`` / ``_label1``.
 
     When more than one series is scored, the logged value is ``agg_func``
     applied over series for each cell, and the granular per-series breakdown
@@ -1636,7 +1659,7 @@ def _log_metric_result(
 
     # component names/count from the first series (all series share n_components)
     comps = series_seq[0].components.tolist()
-    c_size, base_keys = _build_metric_keys(
+    n_scores, base_keys = _build_metric_keys(
         [metric_name],
         comps,
         has_comp_axis,
@@ -1650,13 +1673,13 @@ def _log_metric_result(
     for r in results:
         arr = np.asarray(r, dtype=float)
         # after stripping the C axis, the remainder is the time axis (or scalar)
-        n_times, extra = divmod(arr.size, c_size)
+        n_times, extra = divmod(arr.size, n_scores)
         if extra:
             raise_log(
                 ValueError(
                     f"Metric logging failed for `{metric_name}`: result size "
                     f"({arr.size}) is not divisible by the inferred "
-                    f"component/quantile size ({c_size}). The metric output "
+                    f"component/quantile size ({n_scores}). The metric output "
                     "shape does not match the inferred axes."
                 )
             )
@@ -1675,7 +1698,7 @@ def _log_metric_result(
         else:
             t_size = 1
 
-        series_shapes.append((t_size, arr.reshape(t_size, c_size)))
+        series_shapes.append((t_size, arr.reshape(t_size, n_scores)))
 
     # agg maps (key, step) -> per-series values, aggregated into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
@@ -1726,7 +1749,7 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
       argument when provided (it overrides only this token).
     - ``component`` – ``_{component_name}`` when ``component_reduction=None``.
     - ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.500``,
-      ``_qi_80.000``, ``_label1``) when applicable.
+      ``_qi0.800``, ``_label1``) when applicable.
 
     When the input is a ``Sequence[TimeSeries]`` with more than one series, the
     logged value is ``autolog()``'s ``agg_func`` applied over series, and the

From 2a340bf42693232a944c015bcc60e5a8f5127214 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Thu, 20 Aug 2026 16:32:17 +0200
Subject: [PATCH 143/154] refactor backtest flow

---
 darts/tests/optional_deps/test_mlflow.py |  39 +-
 darts/utils/mlflow.py                    | 651 +++++++++++++----------
 2 files changed, 403 insertions(+), 287 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index dc0fb796e9..7765591e48 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1550,7 +1550,8 @@ def test_autolog_backtest_per_timestep_per_window(
         assert len(rows) == ref.size
         for row in rows:
             assert row["key"] == "backtest_ae"
-            assert row["window_index"] in range(-len(ref), 0)
+            assert row["window_index"] in range(len(ref))
+        assert {int(r["window_index"]) for r in rows} == set(range(len(ref)))
 
     def test_autolog_backtest_historical_forecasts_horizon_inferred(
         self, mlflow_tracking, autolog_context
@@ -1772,9 +1773,10 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
 
     def test_log_backtest_metrics_aligns_windows_by_end_date(self, mlflow_tracking):
         """A shorter series' window axis aligns from the end, not the start,
-        so its negative steps overlap the tail of a longer series. Calls
-        _log_backtest_metrics directly with a fabricated result so the window
-        counts per series are exact."""
+        so its last windows overlap the tail of a longer series. Steps and
+        ``window_index`` are ``0 .. max_w - 1``. Calls _log_backtest_metrics
+        directly with a fabricated result so the window counts per series
+        are exact."""
         backtest_args = {
             "metric": dm.mae,
             "metric_kwargs": {},
@@ -1803,13 +1805,16 @@ def test_log_backtest_metrics_aligns_windows_by_end_date(self, mlflow_tracking):
         assert by_step[(1, 4)] == pytest.approx(30.0)
         assert (1, 0) not in by_step
         assert (1, 1) not in by_step
+        for r in rows:
+            assert r["window_index"] == r["step"]
 
     def test_log_backtest_metrics_aligns_last_points_only_time_axis(
         self, mlflow_tracking
     ):
         """last_points_only stitches windows into one series scored per real
-        timestep -- a separate code path from the window-axis case above that
-        needs the same end-date alignment."""
+        timestep. Shorter series align from the end, same as the window-axis
+        case, with steps ``0 .. t_max - 1`` rather than end-relative indexes.
+        """
         backtest_args = {
             "metric": dm.ae,
             "metric_kwargs": {},
@@ -1828,7 +1833,17 @@ def test_log_backtest_metrics_aligns_last_points_only_time_axis(
 
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
         logged = {m.step: m.value for m in history}
-        assert logged == pytest.approx({-5: 1.0, -4: 2.0, -3: 6.5, -2: 12.0, -1: 17.5})
+        assert logged == pytest.approx({0: 1.0, 1: 2.0, 2: 6.5, 3: 12.0, 4: 17.5})
+
+        rows = self._read_per_series_table(run.info.run_id)
+        by_step = {(r["series_index"], r["step"]): r["value"] for r in rows}
+        assert by_step[(0, 0)] == pytest.approx(1.0)
+        assert by_step[(0, 4)] == pytest.approx(5.0)
+        assert by_step[(1, 2)] == pytest.approx(10.0)
+        assert by_step[(1, 4)] == pytest.approx(30.0)
+        assert (1, 0) not in by_step
+        assert (1, 1) not in by_step
+        assert all(pd.isna(r["window_index"]) for r in rows)
 
     @staticmethod
     def _read_per_series_table(run_id):
@@ -1898,15 +1913,16 @@ def test_build_metric_keys_components_and_quantiles():
         (False, True, ["_q0.100", "_q0.900"]),
         (False, True, ["_label0", "_label1"]),
     ]
-    c_size, keys = _build_metric_keys(
+    metric_keys = _build_metric_keys(
         ["mae", "f1"],
         ["temp", "hum"],
         has_comp_axis=True,
         metric_axes=metric_axes,
         prefix="backtest_",
     )
+    c_size = len(metric_keys[0])
     assert c_size == 4
-    assert keys == [
+    assert metric_keys == [
         [
             "backtest_mae_temp_q0.100",
             "backtest_mae_temp_q0.900",
@@ -1925,14 +1941,15 @@ def test_build_metric_keys_components_and_quantiles():
 def test_build_metric_keys_no_components_no_prefix():
     """Without components, each metric gets one key per axis label."""
     metric_axes = [(False, False, ["_q0.500"])]
-    c_size, keys = _build_metric_keys(
+    metric_keys = _build_metric_keys(
         ["mql"],
         ["ignored"],
         has_comp_axis=False,
         metric_axes=metric_axes,
     )
+    c_size = len(metric_keys[0])
     assert c_size == 1
-    assert keys == [["mql_q0.500"]]
+    assert metric_keys == [["mql_q0.500"]]
 
 
 def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 0efc8bf454..22be3e3063 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -1103,123 +1103,6 @@ def _get_model_encoders(model: ForecastingModel) -> SequentialEncoder:
     return model.encoders
 
 
-def _build_metric_keys(
-    metric_names: list[str],
-    components: list[str],
-    has_comp_axis: bool,
-    metric_axes: list[tuple[bool, bool, list[str]]],
-    *,
-    prefix: str = "",
-) -> tuple[int, list[list[str]]]:
-    """Build sanitized MLflow metric keys for each metric x component x axis label.
-
-    Parameters
-    ----------
-    metric_names
-        One sanitized metric-name token per metric.
-    components
-        Component names from the first series (used only when ``has_comp_axis``).
-    has_comp_axis
-        Whether components are preserved in the metric output.
-    metric_axes
-        Per-metric ``(has_time_axis, has_comp_axis, axis_labels)`` tuples from
-        ``_infer_metric_axes``. Axis size is taken from the first entry.
-    prefix
-        Optional key prefix (e.g. ``"backtest_"``).
-
-    Returns
-    -------
-    tuple[int, list[list[str]]]
-        ``(n_scores, keys)`` where ``n_scores`` is
-        ``(n_components if has_comp_axis else 1) * n_labels`` and ``keys[m][c]``
-        is the sanitized key for metric ``m`` and flat index ``c``. Flat index
-        ``c`` walks axis labels innermost, then components.
-    """
-    component_names = components if has_comp_axis else [None]
-    n_labels = len(metric_axes[0][2])
-    n_scores = len(component_names) * n_labels
-
-    keys: list[list[str]] = []
-    for metric_idx, metric_name in enumerate(metric_names):
-        labels = metric_axes[metric_idx][2]
-        metric_keys: list[str] = []
-        for component in component_names:
-            component_suffix = (
-                f"_{_sanitize_mlflow_key(component)}" if component is not None else ""
-            )
-            for label in labels:
-                metric_keys.append(
-                    _sanitize_mlflow_key(
-                        f"{prefix}{metric_name}{component_suffix}{label}"
-                    )
-                )
-        keys.append(metric_keys)
-    return n_scores, keys
-
-
-def _log_per_series_table(rows: list[dict]) -> None:
-    """Append the granular per-series metric breakdown to a single, run-wide
-    table artifact.
-
-    Each row is a single metric cell for one series, with columns ``key`` (the
-    aggregate MLflow key, without any series suffix), ``series_index``, ``step``
-    (the time or window index charted by MLflow), ``window_index`` (the source
-    backtest window, or ``None``), and ``value``. All calls within a run append
-    to the same ``metrics_per_series.json`` artifact.
-    Used when more than one series is scored, since the logged metric keys
-    only carry the aggregate over series.
-
-    Parameters
-    ----------
-    rows
-        One dict per metric cell with keys ``key``, ``series_index``, ``step``,
-        ``window_index``, and ``value``.
-    """
-    if not rows:
-        return
-
-    df = pd.DataFrame(rows)
-    sort_by = ["key", "series_index", "step"]
-    if "window_index" in df.columns:
-        sort_by.append("window_index")
-    df = df.sort_values(sort_by)
-    mlflow.log_table(data=df, artifact_file="metrics_per_series.json")
-
-
-def _flush_logged_metrics(
-    autologging_client: MlflowAutologgingQueueingClient,
-    run_id: str,
-    agg: dict[tuple[str, int], list[float]],
-    agg_func: Callable,
-    table_rows: list[dict] | None = None,
-) -> None:
-    """Aggregate per-series cells, log MLflow metrics, and optionally write the
-    per-series table artifact.
-
-    Parameters
-    ----------
-    autologging_client
-        MLflow autologging client used to queue metric writes.
-    run_id
-        ID of the active MLflow run.
-    agg
-        Map of ``(key, step) -> list of per-series float values``.
-    agg_func
-        Aggregation over the per-series values for each ``(key, step)``.
-    table_rows
-        Granular cells for ``metrics_per_series.json``. ``None`` skips writing
-        the table artifact.
-    """
-    metrics_by_step: dict[int, dict[str, float]] = {}
-    for (key, step), values in agg.items():
-        metrics_by_step.setdefault(step, {})[key] = float(agg_func(values))
-    for step, metrics in metrics_by_step.items():
-        autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
-
-    if table_rows is not None:
-        _log_per_series_table(table_rows)
-
-
 def _log_backtest_metrics(
     autologging_client: MlflowAutologgingQueueingClient,
     run_id: str,
@@ -1303,63 +1186,22 @@ def _log_backtest_metrics(
         single value logged for a list of series. Called as
         ``agg_func(values)`` on a list of floats.
     """
-    metric = backtest_args.get("metric")
-    metric = metric if isinstance(metric, list) else [metric]
-    metric_kwargs = backtest_args.get("metric_kwargs") or {}
-    metric_kwargs = (
-        metric_kwargs if isinstance(metric_kwargs, list) else [metric_kwargs]
-    )
-    # backtest accepts a single dict that applies to all metrics; broadcast it
-    if len(metric_kwargs) != len(metric):
-        metric_kwargs = [metric_kwargs[0]] * len(metric)
-    # the `name` entry in metric_kwargs overrides the metric-name token in the key
-    metric_names = [
-        _sanitize_mlflow_key(
-            metric_kwargs[i].get("name") or getattr(m, "__name__", f"metric_{i}")
-        )
-        for i, m in enumerate(metric)
-    ]
-    n_metrics = len(metric)
-
-    # reduction=None means no aggregation across windows -> one value per window.
-    # last_points_only collapses all windows into one TimeSeries before scoring,
-    # so there is effectively only one window regardless of reduction.
-    has_windows = backtest_args.get("reduction") is None and not backtest_args.get(
-        "last_points_only", False
-    )
-
-    # series_reduction inside the metric itself already aggregates across windows,
-    # so the result has no window axis even when backtest.reduction is None.
-    metric_0_params = inspect.signature(metric[0]).parameters
-    if "series_reduction" in metric_0_params:
-        effective_sr = metric_kwargs[0].get(
-            "series_reduction", metric_0_params["series_reduction"].default
-        )
-        if effective_sr is not None:
-            has_windows = False
-
-    # check the dim axes from the metric kwargs for each
-    metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metric, metric_kwargs)]
+    metrics, metric_kwargs, metric_names = _normalize_backtest_metrics(backtest_args)
+    metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metrics, metric_kwargs)]
     has_time_axis, has_comp_axis, _ = metric_axes[0]
 
     series = backtest_args.get("series")
     is_single_series = get_series_seq_type(series) == SeriesType.SINGLE
     series = series2seq(series)
+    results = [result] if is_single_series else result
 
-    forecast_horizon = backtest_args.get("forecast_horizon")
-    historical_forecasts = backtest_args.get("historical_forecasts")
-    last_points_only = backtest_args.get("last_points_only", False)
-
-    # if last_points_only is True, has_windows will be False, so fc_hzn is not needed;
-    # otherwise, the forecast horizon is the length of one historical forecast
-    if historical_forecasts is not None and not last_points_only:
-        first_series_hf = (
-            historical_forecasts[0] if is_single_series else historical_forecasts[0][0]
-        )
-        forecast_horizon = len(first_series_hf)
+    has_windows, forecast_horizon = _resolve_backtest_layout(
+        backtest_args,
+        metrics[0],
+        metric_kwargs[0],
+        is_single_series,
+    )
 
-    results = [result] if is_single_series else result
-    # component names are only used when the metric preserves components
     if has_comp_axis:
         n_components = {s.n_components for s in series}
         if len(n_components) > 1:
@@ -1372,120 +1214,115 @@ def _log_backtest_metrics(
                 )
             )
 
-    # agg maps (key, step) -> per-series values, aggregated into the logged metric.
-    agg: dict[tuple[str, int], list[float]] = {}
-    rows: list[dict] = []
-
     # component names/count from the first series (all series share n_components)
-    comps = series[0].components.tolist()
-    n_scores, base_keys = _build_metric_keys(
+    metric_keys = _build_metric_keys(
         metric_names=metric_names,
-        components=comps,
+        components=series[0].components.tolist(),
         has_comp_axis=has_comp_axis,
         metric_axes=metric_axes,
         prefix="backtest_",
     )
+    series_metrics = [
+        _reshape_backtest_result(
+            series=series_,
+            result=r,
+            metric_keys=metric_keys,
+            has_time_axis=has_time_axis,
+            has_windows=has_windows,
+            forecast_horizon=forecast_horizon,
+        )
+        for series_, r in zip(series, results)
+    ]
+    stepped_metrics, detailed_metrics = _collect_stepped_and_detailed_metrics(
+        series_metrics=series_metrics,
+        metric_keys=metric_keys,
+        has_time_axis=has_time_axis,
+        has_windows=has_windows,
+    )
 
-    # first pass: reshape each series' result into a canonical (W, T, C, M)
-    # array, recording its window-axis length for the alignment pass below.
-    series_shapes = []
-    for r in results:
-        arr = np.asarray(r, dtype=float)
-        # after stripping C and M axes, rest = W*T (or W or T alone)
-        rest, extra = divmod(arr.size, n_scores * n_metrics)
-        if extra:
-            raise_log(
-                ValueError(
-                    f"Backtest metric logging failed: result size ({arr.size}) "
-                    f"is not divisible by n_scores * n_metrics ({n_scores} * "
-                    f"{n_metrics} = {n_scores * n_metrics}). The metric output "
-                    "shape does not match the inferred axes."
-                )
-            )
+    write_table = len(series) > 1 or (has_time_axis and has_windows)
+    _flush_logged_metrics(
+        autologging_client,
+        run_id,
+        stepped_metrics,
+        agg_func=agg_func,
+        table_rows=detailed_metrics if write_table else None,
+    )
 
-        # both time and window axes present: backtest returns (W*T*C*M,) in C order so we can
-        # recover W and T only if forecast_horizon is known (T = forecast_horizon)
-        if has_time_axis and has_windows:
-            t_size, w_size = forecast_horizon, rest // forecast_horizon
-        elif has_time_axis:
-            t_size, w_size = rest, 1
-        elif has_windows:
-            t_size, w_size = 1, rest
-        else:
-            if rest != 1:
-                raise_log(
-                    ValueError(
-                        f"Backtest metric logging failed: expected a single "
-                        f"scalar per component/metric after reduction, but got "
-                        f"{rest} elements. Check time_reduction and "
-                        "component_reduction defaults."
-                    )
-                )
-            t_size, w_size = 1, 1
 
-        series_shapes.append((
-            t_size,
-            w_size,
-            arr.reshape(w_size, t_size, n_scores, n_metrics),
-        ))
+def _normalize_backtest_metrics(
+    backtest_args: dict,
+) -> tuple[list, list[dict], list[str]]:
+    """Normalize ``metric`` / ``metric_kwargs`` to parallel lists and key names.
 
-    # align the calendar-relative axes from the end
-    max_w_size = max((w_size for _, w_size, _ in series_shapes), default=0)
-    t_axis_is_calendar = has_time_axis and not has_windows and last_points_only
+    ``backtest()`` accepts a single metric or a list, and a single kwargs
+    dict (broadcast to all metrics) or a list of dicts. A ``name`` entry in
+    ``metric_kwargs`` overrides the metric-name token in the MLflow key.
+    """
+    metrics = backtest_args.get("metric")
+    metrics = metrics if isinstance(metrics, list) else [metrics]
+    metric_kwargs = backtest_args.get("metric_kwargs") or {}
+    metric_kwargs = (
+        metric_kwargs if isinstance(metric_kwargs, list) else [metric_kwargs]
+    )
+    if len(metric_kwargs) != len(metrics):
+        metric_kwargs = [metric_kwargs[0]] * len(metrics)
 
-    for series_index, (t_size, w_size, canonical) in enumerate(series_shapes):
-        w_offset = max_w_size - w_size if has_windows else 0
-        for m in range(n_metrics):
-            for c in range(n_scores):
-                key = base_keys[m][c]
-                if has_time_axis and has_windows:
-                    # Keep window-level values in the detailed table, but aggregate
-                    # windows into one chart value per horizon step for this series.
-                    for t in range(t_size):
-                        values = canonical[:, t, c, m]
-                        agg.setdefault((key, t), []).append(float(np.nanmean(values)))
-                        for w, value in enumerate(values):
-                            window_index = w + w_offset
-                            rows.append({
-                                "key": key,
-                                "series_index": series_index,
-                                "step": t,
-                                "window_index": window_index - max_w_size,
-                                "value": float(value),
-                            })
-                    continue
+    metric_names = [
+        _sanitize_mlflow_key(
+            metric_kwargs[i].get("name") or getattr(m, "__name__", f"metric_{i}")
+        )
+        for i, m in enumerate(metrics)
+    ]
+    return metrics, metric_kwargs, metric_names
 
-                for w in range(w_size):
-                    aligned_w = w + w_offset
-                    for t in range(t_size):
-                        # MLflow step maps to the axis the UI should chart:
-                        # horizon when present, otherwise end-relative calendar axis
-                        if has_time_axis:
-                            step = t - t_size if t_axis_is_calendar else t
-                        else:
-                            step = aligned_w
-                        value = float(canonical[w, t, c, m])
-                        agg.setdefault((key, step), []).append(value)
-                        rows.append({
-                            "key": key,
-                            "series_index": series_index,
-                            "step": step,
-                            "window_index": None,
-                            "value": value,
-                        })
 
-    _flush_logged_metrics(
-        autologging_client,
-        run_id,
-        agg,
-        agg_func=agg_func,
-        table_rows=(
-            rows if len(series) > 1 or (has_time_axis and has_windows) else None
-        ),
-    )
+def _resolve_backtest_layout(
+    backtest_args: dict,
+    metric: Callable,
+    metric_kwargs: dict,
+    is_single_series: bool,
+) -> tuple[bool, int | None]:
+    """Infer whether a window axis is present and the forecast horizon.
+
+    A window axis is present only when backtest did not aggregate windows
+    (``reduction is None``), forecasts were not concatenated first
+    (``last_points_only`` is false), and the metric's own
+    ``series_reduction`` did not already collapse windows.
+
+    When the caller passed ``historical_forecasts``, ``backtest()`` ignores
+    ``forecast_horizon``, so the horizon is read from the first forecast.
+
+    Returns
+    -------
+    tuple
+        ``(has_windows, forecast_horizon)``.
+    """
+    last_points_only = backtest_args.get("last_points_only", False)
+    has_windows = backtest_args.get("reduction") is None and not last_points_only
+
+    params = inspect.signature(metric).parameters
+    if has_windows and "series_reduction" in params:
+        series_reduction = metric_kwargs.get(
+            "series_reduction", params["series_reduction"].default
+        )
+        if series_reduction is not None:
+            has_windows = False
+
+    forecast_horizon = backtest_args.get("forecast_horizon")
+    historical_forecasts = backtest_args.get("historical_forecasts")
+    if historical_forecasts is not None and not last_points_only:
+        first_forecast = (
+            historical_forecasts[0] if is_single_series else historical_forecasts[0][0]
+        )
+        forecast_horizon = len(first_forecast)
 
+    return has_windows, forecast_horizon
 
-def _infer_metric_axes(metric: Callable, metric_kwargs: dict) -> tuple:
+
+def _infer_metric_axes(
+    metric: Callable, metric_kwargs: dict
+) -> tuple[bool, bool, list[str]]:
     """Infer a metric's output axes from its signature and ``metric_kwargs``.
 
     Covers ``time_reduction``, ``component_reduction``, ``q``, ``q_interval``,
@@ -1533,7 +1370,7 @@ def effective(param_name: str) -> Any:
         axis_labels = [f"_qi{(hi - lo):.3f}" for lo, hi in intervals]
     elif "q" in params and q is not None:
         axis_labels = [f"_q{v:.3f}" for v in np.atleast_1d(np.array(q, dtype=float))]
-    elif "label_reduction" in params and getattr(metric, "__name__", ""):
+    elif "label_reduction" in params:
         label_reduction = effective("label_reduction")
         if isinstance(label_reduction, _LabelReduction):
             label_reduction = label_reduction.value
@@ -1556,7 +1393,268 @@ def effective(param_name: str) -> Any:
     else:
         axis_labels = [""]
 
-    return (has_time_axis, has_comp_axis, axis_labels)
+    return has_time_axis, has_comp_axis, axis_labels
+
+
+def _build_metric_keys(
+    metric_names: list[str],
+    components: list[str],
+    has_comp_axis: bool,
+    metric_axes: list[tuple[bool, bool, list[str]]],
+    *,
+    prefix: str = "",
+) -> list[list[str]]:
+    """Build sanitized MLflow metric keys for each metric x component x axis label.
+
+    Parameters
+    ----------
+    metric_names
+        One sanitized metric-name token per metric.
+    components
+        Component names from the first series (used only when ``has_comp_axis``).
+    has_comp_axis
+        Whether components are preserved in the metric output.
+    metric_axes
+        Per-metric ``(has_time_axis, has_comp_axis, axis_labels)`` tuples from
+        ``_infer_metric_axes``. Axis size is taken from the first entry.
+    prefix
+        Optional key prefix (e.g. ``"backtest_"``).
+
+    Returns
+    -------
+    list[list[str]]
+        A list of lists of metric keys, where each inner list contains the sub-metric
+        keys for a single metric. ``keys[m][i]`` is the sanitized key for metric index
+        ``m`` and sub-metric index ``i``. Sub-metric index ``i`` is the component-label
+        index walking axis labels innermost, then components (e.g. ``[_q0.100_comp1,
+        _q0.900_comp1, _q0.100_comp2, _q0.900_comp2]``).
+    """
+    component_names = components if has_comp_axis else [None]
+
+    metric_keys: list[list[str]] = []
+    for metric_idx, metric_name in enumerate(metric_names):
+        labels = metric_axes[metric_idx][2]
+        sub_metric_keys: list[str] = []
+        for component in component_names:
+            component_suffix = (
+                f"_{_sanitize_mlflow_key(component)}" if component is not None else ""
+            )
+            for label in labels:
+                sub_metric_keys.append(
+                    _sanitize_mlflow_key(
+                        f"{prefix}{metric_name}{component_suffix}{label}"
+                    )
+                )
+        metric_keys.append(sub_metric_keys)
+    return metric_keys
+
+
+def _reshape_backtest_result(
+    series: TimeSeries,
+    result,
+    *,
+    metric_keys: list[list[str]],
+    has_time_axis: bool,
+    has_windows: bool,
+    forecast_horizon: int | None,
+) -> np.ndarray:
+    """Reshape one series' backtest result to canonical ``(W, T, C, M)``.
+
+    - ``C`` is ``n_sub_metrics`` (n_components * n_quantiles/n_labels)
+    - ``M`` is ``n_metrics``
+    - ``T`` is ``1`` if not present, and otherwise the ``forecast_horizon`` or
+      the length of the series (with ``last_points_only=True``).
+    - ``W`` is ``1`` if not present, and otherwise the number of windows
+
+    After stripping the ``C`` and ``M`` axes, the leftover element count is split into
+    ``(t_size, w_size)``:
+
+    - time axis and windows: ``T = forecast_horizon``, ``W = rest / T``
+    - time axis only: ``T = rest``, ``W = 1``
+    - windows only: ``T = 1``, ``W = rest``
+    - neither: ``T = W = 1`` (``rest`` must be 1)
+
+    Returns
+    -------
+    np.ndarray
+        Series metric values with shape ``(W, T, C, M)``.
+    """
+    arr = np.asarray(result, dtype=series.dtype)
+    n_metrics = len(metric_keys)
+    n_sub_metrics = len(metric_keys[0])
+    n_round_multiples, remainder = divmod(arr.size, n_sub_metrics * n_metrics)
+    if remainder:
+        raise_log(
+            ValueError(
+                f"Backtest metric logging failed: result size ({arr.size}) "
+                f"is not divisible by n_sub_metrics * n_metrics ({n_sub_metrics} * "
+                f"{n_metrics} = {n_sub_metrics * n_metrics}). The metric output "
+                "shape does not match the inferred axes."
+            )
+        )
+
+    if has_time_axis and has_windows:
+        # T is the forecast horizon; W is recovered from the leftover length;
+        # time-dependent metrics + last_points_only=False + no reduction
+        assert forecast_horizon is not None
+        t_size, w_size = forecast_horizon, n_round_multiples // forecast_horizon
+    elif has_time_axis:
+        # time-dependent metrics + last_points_only=False + reduction
+        t_size, w_size = n_round_multiples, 1
+    elif has_windows:
+        # time-aggregated metrics + last_points_only=False + no reduction
+        t_size, w_size = 1, n_round_multiples
+    elif n_round_multiples == 1:
+        # time-aggregated metrics + reduction
+        t_size, w_size = 1, 1
+    else:
+        raise_log(
+            ValueError(
+                f"Backtest metric logging failed: expected a single "
+                f"scalar per component/metric after reduction, but got "
+                f"{n_round_multiples} elements. Check time_reduction and "
+                "component_reduction defaults."
+            )
+        )
+
+    return arr.reshape(w_size, t_size, n_sub_metrics, n_metrics)
+
+
+def _collect_stepped_and_detailed_metrics(
+    series_metrics: list[np.ndarray],
+    metric_keys: list[list[str]],
+    *,
+    has_time_axis: bool,
+    has_windows: bool,
+) -> tuple[dict[tuple[str, int], list[float]], list[dict]]:
+    """Parse series metrics and build inputs for MLflow stepped metrics and detailed metric table.
+
+    Each ``series_metrics`` array has shape ``(w_size, t_size, n_metrics * n_sub_metrics)``.
+    Shorter series are aligned from the end of the longest remaining axis (windows,
+    or time when there is no window axis).
+
+    The MLflow ``step`` is the axis the UI should chart, always ``0 .. n-1``:
+
+    - forecast-horizon index when a time axis is present (time-dependent metrics)
+      - if windows are present: each horizon step is aggregated over the windows
+      - if multi-series: each horizon step is aggregated over the series
+    - end-aligned window index otherwise (time-aggregated metrics)
+      - if multi-series: each window is aggregated over the series (end-aligned)
+    """
+    stepped_metrics: dict[tuple[str, int], list[float]] = {}
+    detailed_metrics: list[dict] = []
+
+    w_axis, t_axis = 0, 1
+    max_w_size = max((values.shape[w_axis] for values in series_metrics), default=0)
+    max_t_size = max((values.shape[t_axis] for values in series_metrics), default=0)
+
+    for series_index, values in enumerate(series_metrics):
+        w_size = values.shape[w_axis]
+        t_size = values.shape[t_axis]
+        # pad shorter series so their last point lines up with the longest
+        w_offset = max_w_size - w_size if has_windows else 0
+        t_offset = max_t_size - t_size if has_time_axis and not has_windows else 0
+
+        window_agg = np.nanmean(values, axis=w_axis)
+        for metric_idx, sub_metric_keys in enumerate(metric_keys):
+            for sub_metric_idx, key in enumerate(sub_metric_keys):
+                for w in range(w_size):
+                    window_index = w + w_offset if has_windows else None
+                    for t in range(t_size):
+                        value = float(values[w, t, sub_metric_idx, metric_idx])
+                        if has_time_axis and has_windows:
+                            # time-dependent metrics + last_points_only=False + no reduction
+                            step = t
+
+                            # stepped metric chart: one value per horizon (windows averaged)
+                            if w == 0:
+                                stepped_metrics.setdefault((key, step), []).append(
+                                    float(window_agg[t, sub_metric_idx, metric_idx])
+                                )
+                        else:
+                            if has_time_axis:
+                                # time-dependent metrics + last_points_only=False + reduction
+                                step = t + t_offset
+                            else:
+                                step = window_index
+
+                            # stepped metric chart: one value per window or horizon
+                            stepped_metrics.setdefault((key, step), []).append(value)
+
+                        # table: one value per window and horizon
+                        detailed_metrics.append({
+                            "key": key,
+                            "series_index": series_index,
+                            "step": step,
+                            "window_index": window_index,
+                            "value": value,
+                        })
+
+    return stepped_metrics, detailed_metrics
+
+
+def _flush_logged_metrics(
+    autologging_client: MlflowAutologgingQueueingClient,
+    run_id: str,
+    agg: dict[tuple[str, int], list[float]],
+    agg_func: Callable,
+    table_rows: list[dict] | None = None,
+) -> None:
+    """Aggregate per-series cells, log MLflow metrics, and optionally write the
+    per-series table artifact.
+
+    Parameters
+    ----------
+    autologging_client
+        MLflow autologging client used to queue metric writes.
+    run_id
+        ID of the active MLflow run.
+    agg
+        Map of ``(key, step) -> list of per-series float values``.
+    agg_func
+        Aggregation over the per-series values for each ``(key, step)``.
+    table_rows
+        Granular cells for ``metrics_per_series.json``. ``None`` skips writing
+        the table artifact.
+    """
+    metrics_by_step: dict[int, dict[str, float]] = {}
+    for (key, step), values in agg.items():
+        metrics_by_step.setdefault(step, {})[key] = float(agg_func(values))
+    for step, metrics in metrics_by_step.items():
+        autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
+
+    if table_rows is not None:
+        _log_per_series_table(table_rows)
+
+
+def _log_per_series_table(rows: list[dict]) -> None:
+    """Append the granular per-series metric breakdown to a single, run-wide
+    table artifact.
+
+    Each row is a single metric cell for one series, with columns ``key`` (the
+    aggregate MLflow key, without any series suffix), ``series_index``, ``step``
+    (the time or window index charted by MLflow), ``window_index`` (the
+    end-aligned source backtest window ``0 .. max_w - 1``, or ``None`` when
+    there is no window axis), and ``value``. All calls within a run append
+    to the same ``metrics_per_series.json`` artifact.
+    Used when more than one series is scored, since the logged metric keys
+    only carry the aggregate over series.
+
+    Parameters
+    ----------
+    rows
+        One dict per metric cell with keys ``key``, ``series_index``, ``step``,
+        ``window_index``, and ``value``.
+    """
+    if not rows:
+        return
+
+    df = pd.DataFrame(rows)
+    sort_by = ["key", "series_index", "step"]
+    if "window_index" in df.columns:
+        sort_by.append("window_index")
+    df = df.sort_values(sort_by)
+    mlflow.log_table(data=df, artifact_file="metrics_per_series.json")
 
 
 def _log_metric_result(
@@ -1659,27 +1757,28 @@ def _log_metric_result(
 
     # component names/count from the first series (all series share n_components)
     comps = series_seq[0].components.tolist()
-    n_scores, base_keys = _build_metric_keys(
+    metric_keys = _build_metric_keys(
         [metric_name],
         comps,
         has_comp_axis,
         [(has_time_axis, has_comp_axis, axis_labels)],
     )
-    keys = base_keys[0]
+    sub_metric_keys = metric_keys[0]
+    n_sub_metrics = len(sub_metric_keys)
 
     # first pass: reshape each series' result into a canonical (T, C) array,
     # recording its time-axis length for the alignment pass below.
-    series_shapes = []
+    series_metrics = []
     for r in results:
         arr = np.asarray(r, dtype=float)
         # after stripping the C axis, the remainder is the time axis (or scalar)
-        n_times, extra = divmod(arr.size, n_scores)
+        n_times, extra = divmod(arr.size, n_sub_metrics)
         if extra:
             raise_log(
                 ValueError(
                     f"Metric logging failed for `{metric_name}`: result size "
                     f"({arr.size}) is not divisible by the inferred "
-                    f"component/quantile size ({n_scores}). The metric output "
+                    f"component/quantile size ({n_sub_metrics}). The metric output "
                     "shape does not match the inferred axes."
                 )
             )
@@ -1698,13 +1797,13 @@ def _log_metric_result(
         else:
             t_size = 1
 
-        series_shapes.append((t_size, arr.reshape(t_size, n_scores)))
+        series_metrics.append((t_size, arr.reshape(t_size, n_sub_metrics)))
 
     # agg maps (key, step) -> per-series values, aggregated into the logged metric.
     agg: dict[tuple[str, int], list[float]] = {}
     rows: list[dict] = []
-    for series_index, (t_size, canonical) in enumerate(series_shapes):
-        for c, key in enumerate(keys):
+    for series_index, (t_size, canonical) in enumerate(series_metrics):
+        for c, key in enumerate(sub_metric_keys):
             for t in range(t_size):
                 # Forecast positions are zero-based: the first prediction is 0.
                 step = t if has_time_axis else 0

From 86beb936ed30fcd3afb9e096a31aa4cb9ff15ddc Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 21 Aug 2026 15:36:21 +0200
Subject: [PATCH 144/154] refactor metric flow

---
 darts/tests/optional_deps/test_mlflow.py |   8 +-
 darts/utils/mlflow.py                    | 563 +++++++++++------------
 2 files changed, 280 insertions(+), 291 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 7765591e48..b78724a1f5 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1095,17 +1095,17 @@ def test_autolog_metric_aligns_time_axis_by_forecast_position(
         history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
         by_step = {m.step: m.value for m in history}
         assert len(by_step) == 50
-        for step in range(40):
-            assert by_step[step] == pytest.approx(1.5, abs=1e-4), step
-        for step in range(40, 50):
+        for step in range(10):
             assert by_step[step] == pytest.approx(1.0, abs=1e-4), step
+        for step in range(10, 50):
+            assert by_step[step] == pytest.approx(1.5, abs=1e-4), step
 
         rows = self._read_per_series_table(run.info.run_id)
         steps_by_series = {0: set(), 1: set()}
         for r in rows:
             steps_by_series[r["series_index"]].add(r["step"])
         assert steps_by_series[0] == set(range(50))
-        assert steps_by_series[1] == set(range(40))
+        assert steps_by_series[1] == set(range(10, 50))
 
     def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile with matching values."""
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 22be3e3063..1b9054740b 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -795,6 +795,85 @@ def _patched_backtest(original, self, *args, **kwargs):
         )
 
 
+def _mlflow_metric_callback(func, result, args, kwargs) -> None:
+    """Metric callback registered with ``darts.metrics.utils`` for autologging.
+
+    Invoked by ``multi_ts_support`` (the outermost decorator on every Darts
+    metric) after every top-level metric call, so it fires regardless of how
+    the metric was imported. It is not invoked for internal metric-to-metric
+    calls (e.g. ``rmse`` calling ``mse`` internally via ``_get_wrapped_metric``),
+    since those bypass ``multi_ts_support`` entirely.
+
+    When an active MLflow run exists, infers the output axes from the metric
+    signature and call kwargs (via ``_infer_metric_axes``) and delegates to
+    ``_log_standalone_metric``, which logs each cell under a key built as::
+
+        {metric_name}{component}{quantile_or_label}
+
+    where:
+
+    - ``metric_name`` – the metric function name, or the ``name`` keyword
+      argument when provided (it overrides only this token).
+    - ``component`` – ``_{component_name}`` when ``component_reduction=None``.
+    - ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.500``,
+      ``_qi0.800``, ``_label1``) when applicable.
+
+    When the input is a ``Sequence[TimeSeries]`` with more than one series, the
+    logged value is ``autolog()``'s ``agg_func`` applied over series, and the
+    per-series breakdown is appended to the run's ``metrics_per_series.json``
+    table artifact instead of per-series keys.
+
+    The per-timestep axis (``time_reduction=None``) is mapped to the MLflow
+    ``step``.
+
+    Parameters
+    ----------
+    func
+        The Darts metric function that was called (used for its name and
+        signature).
+    result
+        The metric's return value.
+    args
+        Positional arguments the metric was called with.
+    kwargs
+        Keyword arguments the metric was called with.
+    """
+    active_run = mlflow.active_run()
+    if active_run is None:
+        return
+
+    # backtest() calls metric functions internally; _patched_backtest
+    # handles logging the aggregated result, so skip here to avoid
+    # generating one flat key per window (series_gen_mape_0, _1, …).
+    if getattr(_autolog_state, "in_backtest", False):
+        return
+
+    func_signature = inspect.signature(func)
+    bound = func_signature.bind(
+        *args, **{k: v for k, v in kwargs.items() if k in func_signature.parameters}
+    )
+    bound.apply_defaults()
+    metric_args = bound.arguments
+
+    # _mlflow_metric_callback is a bare registered callback, not a closure over
+    # autolog()'s call kwargs, so agg_func is read back from the autologging
+    # config store that autolog() populated.
+    agg_func = get_autologging_config(
+        flavor_name=FLAVOR_NAME, config_key="agg_func", default_value=np.mean
+    )
+
+    autologging_client = MlflowAutologgingQueueingClient()
+    _log_standalone_metric(
+        autologging_client=autologging_client,
+        run_id=active_run.info.run_id,
+        result=result,
+        metric=func,
+        metric_args=metric_args,
+        agg_func=agg_func,
+    )
+    autologging_client.flush(synchronous=False).await_completion()
+
+
 def get_default_pip_requirements():
     """Return the default pip requirements for logging a Darts model.
 
@@ -1140,7 +1219,7 @@ def _log_backtest_metrics(
     When more than one series is scored, the logged value is ``agg_func``
     applied over series for each cell, and the granular per-series breakdown
     is appended to the run's ``metrics_per_series.json`` table artifact
-    (shared with ``_log_metric_result``). For a single series the aggregate
+    (shared with ``_log_standalone_metric``). For a single series the aggregate
     is just the value itself and no artifact is written. When components
     are preserved (``component_reduction=None``), all series scored together
     must have the same number of components; names are taken from the first
@@ -1186,7 +1265,10 @@ def _log_backtest_metrics(
         single value logged for a list of series. Called as
         ``agg_func(values)`` on a list of floats.
     """
-    metrics, metric_kwargs, metric_names = _normalize_backtest_metrics(backtest_args)
+    metrics, metric_kwargs, metric_names = _normalize_metrics(
+        metrics=backtest_args["metric"],
+        metric_kwargs=backtest_args["metric_kwargs"] or dict(),
+    )
     metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metrics, metric_kwargs)]
     has_time_axis, has_comp_axis, _ = metric_axes[0]
 
@@ -1195,13 +1277,6 @@ def _log_backtest_metrics(
     series = series2seq(series)
     results = [result] if is_single_series else result
 
-    has_windows, forecast_horizon = _resolve_backtest_layout(
-        backtest_args,
-        metrics[0],
-        metric_kwargs[0],
-        is_single_series,
-    )
-
     if has_comp_axis:
         n_components = {s.n_components for s in series}
         if len(n_components) > 1:
@@ -1222,16 +1297,24 @@ def _log_backtest_metrics(
         metric_axes=metric_axes,
         prefix="backtest_",
     )
+
+    has_windows, forecast_horizon = _resolve_backtest_layout(
+        backtest_args=backtest_args,
+        metric=metrics[0],
+        metric_kwargs=metric_kwargs[0],
+        is_single_series=is_single_series,
+    )
     series_metrics = [
-        _reshape_backtest_result(
+        _reshape_metric_result(
             series=series_,
             result=r,
             metric_keys=metric_keys,
             has_time_axis=has_time_axis,
             has_windows=has_windows,
             forecast_horizon=forecast_horizon,
+            is_backtest=True,
         )
-        for series_, r in zip(series, results)
+        for r, series_ in zip(results, series)
     ]
     stepped_metrics, detailed_metrics = _collect_stepped_and_detailed_metrics(
         series_metrics=series_metrics,
@@ -1240,7 +1323,7 @@ def _log_backtest_metrics(
         has_windows=has_windows,
     )
 
-    write_table = len(series) > 1 or (has_time_axis and has_windows)
+    write_table = not is_single_series or (has_time_axis and has_windows)
     _flush_logged_metrics(
         autologging_client,
         run_id,
@@ -1250,8 +1333,179 @@ def _log_backtest_metrics(
     )
 
 
-def _normalize_backtest_metrics(
-    backtest_args: dict,
+def _log_standalone_metric(
+    autologging_client: MlflowAutologgingQueueingClient,
+    run_id: str,
+    result,
+    metric: Callable,
+    metric_args: dict[str, Any],
+    agg_func: Callable = np.mean,
+):
+    """Log a a standalone metric call result to the active MLflow run.
+
+    Reshapes each per-series result into a canonical ``(T, C)`` layout
+    (timesteps, components x quantiles/intervals/labels) inferred from the
+    metric signature and call kwargs by ``_infer_metric_axes``, logging every
+    cell under a descriptive key with the time axis mapped to the MLflow
+    ``step``. This mirrors ``_log_backtest_metrics`` (without the
+    window/``forecast_horizon`` split, since ``multi_ts_support`` returns a
+    clean per-series list).
+
+    The logged MLflow key follows the pattern::
+
+        {metric_name}{component}{quantile_or_label}
+
+    where each optional part is included only when the corresponding axis is
+    present:
+
+    - ``component`` – ``_{component_name}`` when ``has_comp_axis``.
+    - ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi0.800`` / ``_label1``.
+
+    When more than one series is scored, the logged value is ``agg_func``
+    applied over series for each cell, and the granular per-series breakdown
+    is appended to the run's ``metrics_per_series.json`` table artifact
+    (shared with ``_log_backtest_metrics``). For a single series the
+    aggregate is just the value itself and no artifact is written.
+
+    Series can have different lengths and intersect in different ways,
+    so the time axis is aligned from the end rather than the start: a shorter
+    series lines up on its last value instead of its first. To represent this,
+    the time axis is mapped to the MLflow ``step`` as ``t - t_size`` when
+    ``has_time_axis`` is ``True``.
+
+    Raises
+    ------
+    ValueError
+        On a shape mismatch between the metric result and the inferred
+        axes, or when ``has_comp_axis`` is ``True`` and series in a sequence
+        have different numbers of components.
+
+    Parameters
+    ----------
+    autologging_client
+        MLflow autologging client used to queue metric writes.
+    run_id
+        ID of the active MLflow run.
+    result
+        Return value of ``backtest()``.
+    metric
+        The ``metric`` callable that was called.
+    metric_args
+        Bound arguments of the ``metric()`` call (from
+        ``inspect.BoundArguments.arguments`` after ``apply_defaults``).
+    agg_func
+        Function used to aggregate a metric's per-series values into the
+        single value logged for a list of series. Called as
+        ``agg_func(values)`` on a list of floats.
+    """
+
+    metrics, metric_kwargs, metric_names = _normalize_metrics(
+        metrics=metric,
+        metric_kwargs=metric_args,
+    )
+    metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metrics, metric_kwargs)]
+    has_time_axis, has_comp_axis, axis_labels = metric_axes[0]
+
+    series = metric_args["actual_series"]
+    is_single_series = get_series_seq_type(series) == SeriesType.SINGLE
+    series = series2seq(series)
+    # series_reduction collapses the series axis inside the metric, so the
+    # result has no leading series axis even for list input.
+    series_reduced = metric_args["series_reduction"] is not None
+    if series_reduced:
+        # series_reduction aggregated across series -> single result, no series axis
+        results = [result]
+    else:
+        results = [result] if is_single_series else result
+
+    if has_comp_axis and not series_reduced:
+        n_components = {s.n_components for s in series}
+        if len(n_components) > 1:
+            raise_log(
+                ValueError(
+                    "Backtest metric logging failed: all series must have the same "
+                    f"number of components, got {sorted(n_components)}. Consider "
+                    f"setting a metric `component_reduction`, or make sure all series "
+                    f"have the same number of components."
+                )
+            )
+
+    # component names/count from the first series (all series share n_components)
+    metric_keys = _build_metric_keys(
+        metric_names=metric_names,
+        components=series[0].components.tolist(),
+        has_comp_axis=has_comp_axis,
+        metric_axes=metric_axes,
+        prefix="",
+    )
+
+    has_windows = False
+    series_metrics = [
+        _reshape_metric_result(
+            series=series_,
+            result=r,
+            metric_keys=metric_keys,
+            has_time_axis=has_time_axis,
+            has_windows=has_windows,
+            forecast_horizon=0,
+            is_backtest=False,
+        )
+        for r, series_ in zip(results, series)
+    ]
+    stepped_metrics, detailed_metrics = _collect_stepped_and_detailed_metrics(
+        series_metrics=series_metrics,
+        metric_keys=metric_keys,
+        has_time_axis=has_time_axis,
+        has_windows=has_windows,
+    )
+
+    series_metrics = [
+        _reshape_metric_result(
+            series=series_,
+            result=r,
+            metric_keys=metric_keys,
+            has_time_axis=has_time_axis,
+            has_windows=False,
+            forecast_horizon=0,
+            is_backtest=False,
+        )[0, :, :, 0]
+        for r, series_ in zip(results, series)
+    ]
+
+    sub_metric_keys = metric_keys[0]
+
+    # agg maps (key, step) -> per-series values, aggregated into the logged metric.
+    agg: dict[tuple[str, int], list[float]] = {}
+    rows: list[dict] = []
+    for series_index, values in enumerate(series_metrics):
+        t_size = values.shape[0]
+        for c, key in enumerate(sub_metric_keys):
+            for t in range(t_size):
+                # Forecast positions are zero-based: the first prediction is 0.
+                step = t if has_time_axis else 0
+                value = float(values[t, c])
+                agg.setdefault((key, step), []).append(value)
+                rows.append({
+                    "key": key,
+                    "series_index": series_index,
+                    "step": step,
+                    "window_index": None,
+                    "value": value,
+                })
+
+    write_table = not series_reduced and not is_single_series
+    _flush_logged_metrics(
+        autologging_client,
+        run_id,
+        stepped_metrics,
+        agg_func=agg_func,
+        table_rows=detailed_metrics if write_table else None,
+    )
+
+
+def _normalize_metrics(
+    metrics: Callable | list[Callable],
+    metric_kwargs: dict[str, Any] | list[dict[str, Any]],
 ) -> tuple[list, list[dict], list[str]]:
     """Normalize ``metric`` / ``metric_kwargs`` to parallel lists and key names.
 
@@ -1259,9 +1513,7 @@ def _normalize_backtest_metrics(
     dict (broadcast to all metrics) or a list of dicts. A ``name`` entry in
     ``metric_kwargs`` overrides the metric-name token in the MLflow key.
     """
-    metrics = backtest_args.get("metric")
     metrics = metrics if isinstance(metrics, list) else [metrics]
-    metric_kwargs = backtest_args.get("metric_kwargs") or {}
     metric_kwargs = (
         metric_kwargs if isinstance(metric_kwargs, list) else [metric_kwargs]
     )
@@ -1449,7 +1701,7 @@ def _build_metric_keys(
     return metric_keys
 
 
-def _reshape_backtest_result(
+def _reshape_metric_result(
     series: TimeSeries,
     result,
     *,
@@ -1457,6 +1709,7 @@ def _reshape_backtest_result(
     has_time_axis: bool,
     has_windows: bool,
     forecast_horizon: int | None,
+    is_backtest: bool,
 ) -> np.ndarray:
     """Reshape one series' backtest result to canonical ``(W, T, C, M)``.
 
@@ -1486,10 +1739,10 @@ def _reshape_backtest_result(
     if remainder:
         raise_log(
             ValueError(
-                f"Backtest metric logging failed: result size ({arr.size}) "
-                f"is not divisible by n_sub_metrics * n_metrics ({n_sub_metrics} * "
-                f"{n_metrics} = {n_sub_metrics * n_metrics}). The metric output "
-                "shape does not match the inferred axes."
+                f"{'Backtest metric' if is_backtest else 'Metric'} logging failed: "
+                f"result size ({arr.size}) is not divisible by n_sub_metrics * "
+                f"n_metrics ({n_sub_metrics} * {n_metrics} = {n_sub_metrics * n_metrics}). "
+                f"The metric output shape does not match the inferred axes."
             )
         )
 
@@ -1510,8 +1763,8 @@ def _reshape_backtest_result(
     else:
         raise_log(
             ValueError(
-                f"Backtest metric logging failed: expected a single "
-                f"scalar per component/metric after reduction, but got "
+                f"{'Backtest metric' if is_backtest else 'Metric'} logging failed: "
+                f"expected a single scalar per component/metric after reduction, but got "
                 f"{n_round_multiples} elements. Check time_reduction and "
                 "component_reduction defaults."
             )
@@ -1655,267 +1908,3 @@ def _log_per_series_table(rows: list[dict]) -> None:
         sort_by.append("window_index")
     df = df.sort_values(sort_by)
     mlflow.log_table(data=df, artifact_file="metrics_per_series.json")
-
-
-def _log_metric_result(
-    autologging_client: MlflowAutologgingQueueingClient,
-    run_id: str,
-    metric_name: str,
-    result,
-    series,
-    has_time_axis: bool,
-    has_comp_axis: bool,
-    axis_labels: list[str],
-    series_reduced: bool = False,
-    agg_func: Callable = np.mean,
-) -> None:
-    """Log a metric result to the active MLflow run.
-
-    Reshapes each per-series result into a canonical ``(T, C)`` layout
-    (timesteps, components x quantiles/intervals/labels) inferred from the
-    metric signature and call kwargs by ``_infer_metric_axes``, logging every
-    cell under a descriptive key with the time axis mapped to the MLflow
-    ``step``. This mirrors ``_log_backtest_metrics`` (without the
-    window/``forecast_horizon`` split, since ``multi_ts_support`` returns a
-    clean per-series list).
-
-    The logged MLflow key follows the pattern::
-
-        {metric_name}{component}{quantile_or_label}
-
-    where each optional part is included only when the corresponding axis is
-    present:
-
-    - ``component`` – ``_{component_name}`` when ``has_comp_axis``.
-    - ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi0.800`` / ``_label1``.
-
-    When more than one series is scored, the logged value is ``agg_func``
-    applied over series for each cell, and the granular per-series breakdown
-    is appended to the run's ``metrics_per_series.json`` table artifact
-    (shared with ``_log_backtest_metrics``). For a single series the
-    aggregate is just the value itself and no artifact is written.
-
-    Series can have different lengths and intersect in different ways,
-    so the time axis is aligned from the end rather than the start: a shorter
-    series lines up on its last value instead of its first. To represent this,
-    the time axis is mapped to the MLflow ``step`` as ``t - t_size`` when
-    ``has_time_axis`` is ``True``.
-
-    Raises
-    ------
-    ValueError
-        On a shape mismatch between the metric result and the inferred
-        axes, or when ``has_comp_axis`` is ``True`` and series in a sequence
-        have different numbers of components.
-
-    Parameters
-    ----------
-    metric_name
-        Metric name used as the MLflow key (the metric's ``name`` keyword
-        argument when provided, otherwise the metric function name).
-    result
-        The metric result to log.
-    series
-        The ``actual_series`` argument passed to the metric (single series or
-        ``Sequence[TimeSeries]``); used for component names and series count.
-        When ``has_comp_axis`` is ``True``, all series in a sequence must have
-        the same number of components; names are taken from the first series.
-    has_time_axis
-        ``True`` when the result carries a per-timestep axis (``time_reduction=None``).
-    has_comp_axis
-        ``True`` when components are expanded (``component_reduction=None``).
-    axis_labels
-        One key suffix per quantile/interval/label entry.
-    series_reduced
-        ``True`` when ``series_reduction`` collapsed the series axis inside the
-        metric, so the result has no leading series axis even for list input.
-    agg_func
-        Function used to aggregate a metric's per-series values into the
-        single value logged for a list of series. Called as
-        ``agg_func(values)`` on a list of floats.
-    """
-    if series_reduced:
-        # series_reduction aggregated across series -> single result, no series axis
-        series_seq = [get_single_series(series)]
-        results = [result]
-    else:
-        series_seq = series2seq(series)
-        results = (
-            [result] if get_series_seq_type(series) == SeriesType.SINGLE else result
-        )
-        # component names are only used when the metric preserves components
-        if has_comp_axis:
-            n_components = {s.n_components for s in series_seq}
-            if len(n_components) > 1:
-                raise_log(
-                    ValueError(
-                        f"Metric logging failed for `{metric_name}`: all series must "
-                        f"have the same number of components, got "
-                        f"{sorted(n_components)}."
-                    )
-                )
-
-    # component names/count from the first series (all series share n_components)
-    comps = series_seq[0].components.tolist()
-    metric_keys = _build_metric_keys(
-        [metric_name],
-        comps,
-        has_comp_axis,
-        [(has_time_axis, has_comp_axis, axis_labels)],
-    )
-    sub_metric_keys = metric_keys[0]
-    n_sub_metrics = len(sub_metric_keys)
-
-    # first pass: reshape each series' result into a canonical (T, C) array,
-    # recording its time-axis length for the alignment pass below.
-    series_metrics = []
-    for r in results:
-        arr = np.asarray(r, dtype=float)
-        # after stripping the C axis, the remainder is the time axis (or scalar)
-        n_times, extra = divmod(arr.size, n_sub_metrics)
-        if extra:
-            raise_log(
-                ValueError(
-                    f"Metric logging failed for `{metric_name}`: result size "
-                    f"({arr.size}) is not divisible by the inferred "
-                    f"component/quantile size ({n_sub_metrics}). The metric output "
-                    "shape does not match the inferred axes."
-                )
-            )
-
-        if has_time_axis:
-            t_size = n_times
-        elif n_times != 1:
-            raise_log(
-                ValueError(
-                    f"Metric logging failed for `{metric_name}`: expected a "
-                    f"single value per component/quantile after reduction, "
-                    f"but got {n_times} elements. Check time_reduction and "
-                    "component_reduction."
-                )
-            )
-        else:
-            t_size = 1
-
-        series_metrics.append((t_size, arr.reshape(t_size, n_sub_metrics)))
-
-    # agg maps (key, step) -> per-series values, aggregated into the logged metric.
-    agg: dict[tuple[str, int], list[float]] = {}
-    rows: list[dict] = []
-    for series_index, (t_size, canonical) in enumerate(series_metrics):
-        for c, key in enumerate(sub_metric_keys):
-            for t in range(t_size):
-                # Forecast positions are zero-based: the first prediction is 0.
-                step = t if has_time_axis else 0
-                value = float(canonical[t, c])
-                agg.setdefault((key, step), []).append(value)
-                rows.append({
-                    "key": key,
-                    "series_index": series_index,
-                    "step": step,
-                    "window_index": None,
-                    "value": value,
-                })
-
-    _flush_logged_metrics(
-        autologging_client,
-        run_id,
-        agg,
-        agg_func=agg_func,
-        table_rows=rows if len(series_seq) > 1 else None,
-    )
-    autologging_client.flush(synchronous=False).await_completion()
-
-
-def _mlflow_metric_callback(func, result, args, kwargs) -> None:
-    """Metric callback registered with ``darts.metrics.utils`` for autologging.
-
-    Invoked by ``multi_ts_support`` (the outermost decorator on every Darts
-    metric) after every top-level metric call, so it fires regardless of how
-    the metric was imported. It is not invoked for internal metric-to-metric
-    calls (e.g. ``rmse`` calling ``mse`` internally via ``_get_wrapped_metric``),
-    since those bypass ``multi_ts_support`` entirely.
-
-    When an active MLflow run exists, infers the output axes from the metric
-    signature and call kwargs (via ``_infer_metric_axes``) and delegates to
-    ``_log_metric_result``, which logs each cell under a key built as::
-
-        {metric_name}{component}{quantile_or_label}
-
-    where:
-
-    - ``metric_name`` – the metric function name, or the ``name`` keyword
-      argument when provided (it overrides only this token).
-    - ``component`` – ``_{component_name}`` when ``component_reduction=None``.
-    - ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.500``,
-      ``_qi0.800``, ``_label1``) when applicable.
-
-    When the input is a ``Sequence[TimeSeries]`` with more than one series, the
-    logged value is ``autolog()``'s ``agg_func`` applied over series, and the
-    per-series breakdown is appended to the run's ``metrics_per_series.json``
-    table artifact instead of per-series keys.
-
-    The per-timestep axis (``time_reduction=None``) is mapped to the MLflow
-    ``step``.
-
-    Parameters
-    ----------
-    func
-        The Darts metric function that was called (used for its name and
-        signature).
-    result
-        The metric's return value.
-    args
-        Positional arguments the metric was called with.
-    kwargs
-        Keyword arguments the metric was called with.
-    """
-    active_run = mlflow.active_run()
-    if active_run is None:
-        return
-
-    # backtest() calls metric functions internally; _patched_backtest
-    # handles logging the aggregated result, so skip here to avoid
-    # generating one flat key per window (series_gen_mape_0, _1, …).
-    if getattr(_autolog_state, "in_backtest", False):
-        return
-
-    series = args[0] if len(args) > 0 else kwargs["actual_series"]
-
-    autologging_client = MlflowAutologgingQueueingClient()
-    run_id = active_run.info.run_id
-
-    # the `name` kwarg overrides the metric-name token in the logged key
-    key_name = _sanitize_mlflow_key(kwargs.get("name") or func.__name__)
-
-    # infer output axes from the metric signature + call kwargs
-    has_time_axis, has_comp_axis, axis_labels = _infer_metric_axes(func, kwargs)
-
-    # series_reduction collapses the series axis inside the metric, so the
-    # result has no leading series axis even for list input.
-    params = inspect.signature(func).parameters
-    series_reduced = False
-    if "series_reduction" in params:
-        effective_sr = kwargs.get(
-            "series_reduction", params["series_reduction"].default
-        )
-        series_reduced = effective_sr is not None
-
-    # _mlflow_metric_callback is a bare registered callback, not a closure over
-    # autolog()'s call kwargs, so agg_func is read back from the autologging
-    # config store that autolog() populated.
-    agg_func = get_autologging_config(
-        flavor_name=FLAVOR_NAME, config_key="agg_func", default_value=np.mean
-    )
-    _log_metric_result(
-        autologging_client,
-        run_id,
-        key_name,
-        result,
-        series,
-        has_time_axis,
-        has_comp_axis,
-        axis_labels,
-        series_reduced=series_reduced,
-        agg_func=agg_func,
-    )

From 02bcb39db2e3699fcfb5577a9518039154bc29cc Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Sun, 23 Aug 2026 12:58:42 +0200
Subject: [PATCH 145/154] unify backetst and standalone metric logging

---
 darts/tests/optional_deps/test_mlflow.py |  64 ++-
 darts/utils/mlflow.py                    | 473 ++++++++++-------------
 2 files changed, 258 insertions(+), 279 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index b78724a1f5..5d3d165f1c 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -88,6 +88,18 @@ def assert_mlflow_artifacts_exist(path: str, is_torch: bool = False):
         assert os.path.exists(os.path.join(path, "model.pkl"))
 
 
+BT_REQUIRED_DEFAUTLS = {
+    "historical_forecasts": None,
+    "forecast_horizon": 1,
+    "last_points_only": False,
+    "past_covariates": None,
+    "future_covariates": None,
+    "reduction": np.nanmean,
+    "metric": dm.mape,
+    "metric_kwargs": None,
+}
+
+
 def assert_predictions_equal(
     model1: ForecastingModel,
     model2: ForecastingModel,
@@ -1095,17 +1107,17 @@ def test_autolog_metric_aligns_time_axis_by_forecast_position(
         history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
         by_step = {m.step: m.value for m in history}
         assert len(by_step) == 50
-        for step in range(10):
-            assert by_step[step] == pytest.approx(1.0, abs=1e-4), step
-        for step in range(10, 50):
+        for step in range(40):
             assert by_step[step] == pytest.approx(1.5, abs=1e-4), step
+        for step in range(40, 50):
+            assert by_step[step] == pytest.approx(1.0, abs=1e-4), step
 
         rows = self._read_per_series_table(run.info.run_id)
         steps_by_series = {0: set(), 1: set()}
         for r in rows:
             steps_by_series[r["series_index"]].add(r["step"])
         assert steps_by_series[0] == set(range(50))
-        assert steps_by_series[1] == set(range(10, 50))
+        assert steps_by_series[1] == set(range(40))
 
     def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile with matching values."""
@@ -1503,7 +1515,11 @@ def test_autolog_backtest_multi_series_custom_agg_func(self, mlflow_tracking):
         with mlflow.start_run() as run:
             client = MlflowAutologgingQueueingClient()
             _log_backtest_metrics(
-                client, run.info.run_id, result, backtest_args, agg_func=np.median
+                client,
+                run.info.run_id,
+                result,
+                {**BT_REQUIRED_DEFAUTLS, **backtest_args},
+                agg_func=np.median,
             )
             client.flush(synchronous=True)
 
@@ -1683,7 +1699,12 @@ def test_log_backtest_metrics_component_count_mismatch_allowed_when_reduced(
 
         with mlflow.start_run() as run:
             client = MlflowAutologgingQueueingClient()
-            _log_backtest_metrics(client, run.info.run_id, result, backtest_args)
+            _log_backtest_metrics(
+                client,
+                run.info.run_id,
+                result,
+                {**BT_REQUIRED_DEFAUTLS, **backtest_args},
+            )
             client.flush(synchronous=True)
 
         m = mlflow.get_run(run.info.run_id).data.metrics
@@ -1711,7 +1732,12 @@ def test_log_backtest_metrics_component_count_mismatch_raises(
         with mlflow.start_run() as run:
             client = MlflowAutologgingQueueingClient()
             with pytest.raises(ValueError, match="same number of components"):
-                _log_backtest_metrics(client, run.info.run_id, result, backtest_args)
+                _log_backtest_metrics(
+                    client,
+                    run.info.run_id,
+                    result,
+                    {**BT_REQUIRED_DEFAUTLS, **backtest_args},
+                )
 
         assert not mlflow.get_run(run.info.run_id).data.metrics
 
@@ -1736,7 +1762,10 @@ def test_log_backtest_metrics_unknown_labels_raises(self, mlflow_tracking):
             client = MlflowAutologgingQueueingClient()
             with pytest.raises(ValueError, match="requires explicit `labels`"):
                 _log_backtest_metrics(
-                    client, run.info.run_id, np.array([0.5]), backtest_args
+                    client,
+                    run.info.run_id,
+                    np.array([0.5]),
+                    {**BT_REQUIRED_DEFAUTLS, **backtest_args},
                 )
 
     def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
@@ -1766,7 +1795,10 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
             client = MlflowAutologgingQueueingClient()
             with pytest.raises(ValueError, match="not divisible"):
                 _log_backtest_metrics(
-                    client, run.info.run_id, fake_result, backtest_args
+                    client,
+                    run.info.run_id,
+                    fake_result,
+                    {**BT_REQUIRED_DEFAUTLS, **backtest_args},
                 )
 
         assert not mlflow.get_run(run.info.run_id).data.metrics
@@ -1790,7 +1822,12 @@ def test_log_backtest_metrics_aligns_windows_by_end_date(self, mlflow_tracking):
 
         with mlflow.start_run() as run:
             client = MlflowAutologgingQueueingClient()
-            _log_backtest_metrics(client, run.info.run_id, result, backtest_args)
+            _log_backtest_metrics(
+                client,
+                run.info.run_id,
+                result,
+                {**BT_REQUIRED_DEFAUTLS, **backtest_args},
+            )
             client.flush(synchronous=True)
 
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
@@ -1828,7 +1865,12 @@ def test_log_backtest_metrics_aligns_last_points_only_time_axis(
 
         with mlflow.start_run() as run:
             client = MlflowAutologgingQueueingClient()
-            _log_backtest_metrics(client, run.info.run_id, result, backtest_args)
+            _log_backtest_metrics(
+                client,
+                run.info.run_id,
+                result,
+                {**BT_REQUIRED_DEFAUTLS, **backtest_args},
+            )
             client.flush(synchronous=True)
 
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 1b9054740b..49146bcc05 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -485,7 +485,7 @@ def autolog(
     log_params: bool = True,
     log_metrics: bool = True,
     log_torch_metrics: bool = True,
-    agg_func: Callable = np.mean,
+    agg_func: Callable = np.nanmean,
     disable: bool = False,
     silent: bool = False,
 ) -> None:
@@ -512,7 +512,7 @@ def autolog(
         training and validation metrics. Only effective for PyTorch-based models.
     agg_func
         Function used to aggregate a metric's per-series values into the
-        single value logged for a list of series (e.g. ``np.mean``, the
+        single value logged for a list of series (e.g. ``np.nanmean``, the
         default, or ``np.median``). Called as ``agg_func(values)`` on a list
         of floats.
     disable
@@ -595,7 +595,7 @@ def _autolog(
     log_models: bool = True,
     log_params: bool = True,
     log_metrics: bool = True,
-    agg_func: Callable = np.mean,
+    agg_func: Callable = np.nanmean,
     disable: bool = False,
     silent: bool = False,
 ) -> None:
@@ -653,14 +653,14 @@ def _patched_fit(original, self, *args, **kwargs):
             return result
         run_id = active_run.info.run_id
 
-        fit_args = inspect.signature(original).bind(self, *args, **kwargs).arguments
+        fit_kwargs = inspect.signature(original).bind(self, *args, **kwargs).arguments
         _log_model_setup(
             model=self,
             autologging_client=autologging_client,
             run_id=run_id,
-            series=fit_args["series"],
-            past_covariates=fit_args.get("past_covariates"),
-            future_covariates=fit_args.get("future_covariates"),
+            series=fit_kwargs["series"],
+            past_covariates=fit_kwargs.get("past_covariates"),
+            future_covariates=fit_kwargs.get("future_covariates"),
             log_params=log_params,
         )
 
@@ -752,14 +752,14 @@ def _patched_backtest(original, self, *args, **kwargs):
 
         bound = inspect.signature(original).bind(self, *args, **kwargs)
         bound.apply_defaults()
-        backtest_args = bound.arguments
+        backtest_kwargs = bound.arguments
 
         autologging_client = MlflowAutologgingQueueingClient()
         _log_backtest_metrics(
             autologging_client=autologging_client,
             run_id=active_run.info.run_id,
             result=result,
-            backtest_args=backtest_args,
+            backtest_kwargs=backtest_kwargs,
             agg_func=agg_func,
         )
         autologging_client.flush(synchronous=False).await_completion()
@@ -853,22 +853,22 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
         *args, **{k: v for k, v in kwargs.items() if k in func_signature.parameters}
     )
     bound.apply_defaults()
-    metric_args = bound.arguments
+    metric_kwargs = bound.arguments
 
     # _mlflow_metric_callback is a bare registered callback, not a closure over
     # autolog()'s call kwargs, so agg_func is read back from the autologging
     # config store that autolog() populated.
     agg_func = get_autologging_config(
-        flavor_name=FLAVOR_NAME, config_key="agg_func", default_value=np.mean
+        flavor_name=FLAVOR_NAME, config_key="agg_func", default_value=np.nanmean
     )
 
     autologging_client = MlflowAutologgingQueueingClient()
-    _log_standalone_metric(
+    _log_metric_results(
         autologging_client=autologging_client,
         run_id=active_run.info.run_id,
         result=result,
-        metric=func,
-        metric_args=metric_args,
+        metrics=func,
+        metric_kwargs=metric_kwargs,
         agg_func=agg_func,
     )
     autologging_client.flush(synchronous=False).await_completion()
@@ -1186,199 +1186,141 @@ def _log_backtest_metrics(
     autologging_client: MlflowAutologgingQueueingClient,
     run_id: str,
     result,
-    backtest_args: dict,
-    agg_func: Callable = np.mean,
+    backtest_kwargs: dict,
+    agg_func: Callable = np.nanmean,
 ) -> None:
     """Log backtest metric result(s) to MLflow.
 
-    Reshapes each per-series result to a canonical ``(W, T, C, M)`` layout
-    (windows, timesteps, components x quantiles, metrics) inferred from the
-    metric signatures and ``backtest_args``, logging every cell under a
-    descriptive key with the time axis (or window axis when time is reduced)
-    mapped to the MLflow ``step``. A metric's ``name`` entry in
-    ``metric_kwargs`` overrides the metric-name token in the key (the
-    ``backtest_`` prefix and axis suffixes are preserved).
-
-    Shape inference respects all kwargs that affect output dimensions:
-
-    - ``time_reduction`` – collapses the time axis (``T=1``).
-    - ``component_reduction`` – collapses the component axis (``C=1``).
-    - ``series_reduction`` – if other than ``None``, windows are already aggregated
-      inside the metric, so ``W=1`` regardless of ``backtest.reduction``.
-    - ``q`` / ``q_interval`` – expand the component axis with one entry per
-      quantile / interval.
-    - ``labels`` - expand each component with one entry per label along
-      the component axis.
-    - ``label_reduction`` – collapses the labels along the component axis.
-      value; ``labels`` only restricts which classes are scored.
-    - ``reduction=None`` – no aggregation across windows -> one value per window.
-    - ``last_points_only`` – collapses all windows into one TimeSeries before scoring,
-      so there is effectively only one window regardless of reduction.
-
-
-    When more than one series is scored, the logged value is ``agg_func``
-    applied over series for each cell, and the granular per-series breakdown
-    is appended to the run's ``metrics_per_series.json`` table artifact
-    (shared with ``_log_standalone_metric``). For a single series the aggregate
-    is just the value itself and no artifact is written. When components
-    are preserved (``component_reduction=None``), all series scored together
-    must have the same number of components; names are taken from the first
-    series.
-
-    Series can have different lengths and intersect in different ways.
-    We align the time axis from the end rather than the start: a shorter
-    series lines up on its last value instead of its first. This is an
-    assumption which does not hold true for all cases (series can also end
-    at different times).
-
-    The last step logged to MLflow represents the last window of each series.
-    There will be ``max_n_windows`` steps logged. Shorter series will not
-    contribute to the first steps. In the multi-series case, the values at the
-    same step are aggregated over all series.
-
-    For time-dependent metrics (e.g. ``ae()``), the steps represent the
-    forecast horizon rather than backteset windows. There will be
-    ``forecast_horizon`` steps logged. In the multi-series case, the values at
-    the same step are aggregated over all series.
-
-    Raises
-    ------
-    ValueError
-        On a shape/size mismatch between the metric result and the inferred
-        axes, when ``component_reduction=None`` and series in a sequence have
-        different numbers of components, or when ``label_reduction=None`` is
-        requested without explicit ``labels``.
-
-    Parameters
-    ----------
-    autologging_client
-        MLflow autologging client used to queue metric writes.
-    run_id
-        ID of the active MLflow run.
-    result
-        Return value of ``backtest()``.
-    backtest_args
-        Bound arguments of the ``backtest()`` call (from
-        ``inspect.BoundArguments.arguments`` after ``apply_defaults``).
-    agg_func
-        Function used to aggregate a metric's per-series values into the
-        single value logged for a list of series. Called as
-        ``agg_func(values)`` on a list of floats.
+    Helper function for ``autolog()`` to log backtest metric results to MLflow.
+    Details are documented in ``_log_metric_results()``.
     """
-    metrics, metric_kwargs, metric_names = _normalize_metrics(
-        metrics=backtest_args["metric"],
-        metric_kwargs=backtest_args["metric_kwargs"] or dict(),
-    )
-    metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metrics, metric_kwargs)]
-    has_time_axis, has_comp_axis, _ = metric_axes[0]
-
-    series = backtest_args.get("series")
-    is_single_series = get_series_seq_type(series) == SeriesType.SINGLE
-    series = series2seq(series)
-    results = [result] if is_single_series else result
-
-    if has_comp_axis:
-        n_components = {s.n_components for s in series}
-        if len(n_components) > 1:
-            raise_log(
-                ValueError(
-                    "Backtest metric logging failed: all series must have the same "
-                    f"number of components, got {sorted(n_components)}. Consider "
-                    f"setting a metric `component_reduction`, or make sure all series "
-                    f"have the same number of components."
-                )
-            )
-
-    # component names/count from the first series (all series share n_components)
-    metric_keys = _build_metric_keys(
-        metric_names=metric_names,
-        components=series[0].components.tolist(),
-        has_comp_axis=has_comp_axis,
-        metric_axes=metric_axes,
-        prefix="backtest_",
-    )
-
-    has_windows, forecast_horizon = _resolve_backtest_layout(
-        backtest_args=backtest_args,
-        metric=metrics[0],
-        metric_kwargs=metric_kwargs[0],
-        is_single_series=is_single_series,
-    )
-    series_metrics = [
-        _reshape_metric_result(
-            series=series_,
-            result=r,
-            metric_keys=metric_keys,
-            has_time_axis=has_time_axis,
-            has_windows=has_windows,
-            forecast_horizon=forecast_horizon,
-            is_backtest=True,
-        )
-        for r, series_ in zip(results, series)
-    ]
-    stepped_metrics, detailed_metrics = _collect_stepped_and_detailed_metrics(
-        series_metrics=series_metrics,
-        metric_keys=metric_keys,
-        has_time_axis=has_time_axis,
-        has_windows=has_windows,
-    )
-
-    write_table = not is_single_series or (has_time_axis and has_windows)
-    _flush_logged_metrics(
-        autologging_client,
-        run_id,
-        stepped_metrics,
+    _log_metric_results(
+        autologging_client=autologging_client,
+        run_id=run_id,
+        result=result,
+        metrics=backtest_kwargs["metric"],
+        metric_kwargs=backtest_kwargs["metric_kwargs"] or dict(),
         agg_func=agg_func,
-        table_rows=detailed_metrics if write_table else None,
+        backtest_kwargs=backtest_kwargs,
     )
 
 
-def _log_standalone_metric(
+def _log_metric_results(
     autologging_client: MlflowAutologgingQueueingClient,
     run_id: str,
     result,
-    metric: Callable,
-    metric_args: dict[str, Any],
-    agg_func: Callable = np.mean,
-):
-    """Log a a standalone metric call result to the active MLflow run.
+    metrics: Callable | list[Callable],
+    metric_kwargs: dict[str, Any] | list[dict[str, Any]],
+    backtest_kwargs: dict[str, Any] | None = None,
+    agg_func: Callable = np.nanmean,
+) -> None:
+    """Log backtest or standalone metric result(s) to MLflow.
 
-    Reshapes each per-series result into a canonical ``(T, C)`` layout
-    (timesteps, components x quantiles/intervals/labels) inferred from the
-    metric signature and call kwargs by ``_infer_metric_axes``, logging every
-    cell under a descriptive key with the time axis mapped to the MLflow
-    ``step``. This mirrors ``_log_backtest_metrics`` (without the
-    window/``forecast_horizon`` split, since ``multi_ts_support`` returns a
-    clean per-series list).
+    Shared implementation used by ``_log_backtest_metrics`` and
+    ``_log_standalone_metric``. The two entry points differ only in how they
+    populate the arguments below; all reshaping, key construction, step
+    assignment, multi-series aggregation, and artifact writing happen here.
 
-    The logged MLflow key follows the pattern::
+    Entry modes
+    -----------
+    **Backtest** (``backtest_kwargs`` is not ``None``):
 
-        {metric_name}{component}{quantile_or_label}
+    - ``metrics`` / ``metric_kwargs`` come from backtesting.
+    - MLflow keys are prefixed with ``backtest_``.
+    - A window axis ``W`` may be present (see *MLflow keys and steps*).
+
+    **Standalone** (``backtest_kwargs`` is ``None``):
+
+    - ``metrics`` / ``metric_kwargs`` describe a direct ``metric()`` call.
+    - MLflow keys have no prefix.
+    - There is never a backtest window axis (``W=1``); only the metric's own
+      time / component / quantile / label axes apply.
 
-    where each optional part is included only when the corresponding axis is
-    present:
+    Canonical layout
+    ----------------
+    Each per-series result is reshaped to ``(W, T, C, M)``:
 
-    - ``component`` – ``_{component_name}`` when ``has_comp_axis``.
-    - ``quantile_or_label`` – e.g. ``_q0.500`` / ``_qi0.800`` / ``_label1``.
+    - ``W`` – backtest windows (``1`` for standalone, or when windows were
+      aggregated before scoring).
+    - ``T`` – timesteps (``1`` when ``time_reduction`` is set).
+    - ``C`` – sub-metrics per component (components × quantiles / intervals /
+      labels; ``1`` when ``component_reduction`` is set).
+    - ``M`` – number of metrics when several are logged at once.
 
-    When more than one series is scored, the logged value is ``agg_func``
-    applied over series for each cell, and the granular per-series breakdown
-    is appended to the run's ``metrics_per_series.json`` table artifact
-    (shared with ``_log_backtest_metrics``). For a single series the
-    aggregate is just the value itself and no artifact is written.
+    Axis sizes are inferred from each metric's signature and the corresponding
+    ``metric_kwargs``.
 
-    Series can have different lengths and intersect in different ways,
-    so the time axis is aligned from the end rather than the start: a shorter
-    series lines up on its last value instead of its first. To represent this,
-    the time axis is mapped to the MLflow ``step`` as ``t - t_size`` when
-    ``has_time_axis`` is ``True``.
+    Kwargs that affect output dimensions (both modes unless noted):
+
+    - ``time_reduction`` – collapses the time axis (``T=1``).
+    - ``component_reduction`` – collapses the component axis (``C=1``).
+    - ``q`` / ``q_interval`` – one sub-metric per quantile / interval.
+    - ``labels`` – when ``label_reduction=None``, one sub-metric per label;
+      otherwise ``labels`` only restricts which classes are scored.
+    - ``label_reduction`` – collapses label outputs to a scalar per component.
+    - ``series_reduction`` – collapses the series axis inside the metric, so
+      the caller's ``result`` is already aggregated and treated as a single
+      series (``W=1`` for backtest regardless of ``reduction``).
+
+    Backtest-only kwargs:
+
+    - ``reduction=None`` – no aggregation across windows → one value per
+      window (``W > 1``).
+    - ``last_points_only`` – concatenates all windows into one ``TimeSeries``
+      before scoring, so there is effectively one window regardless of
+      ``reduction``.
+    - ``forecast_horizon`` / ``historical_forecasts`` – set the time-axis
+      length for time-dependent metrics when windows are preserved.
+
+    MLflow keys and steps
+    ---------------------
+    Every scalar cell is logged under a descriptive key built from the metric
+    name, optional component suffix, and optional quantile / interval / label
+    suffix (see ``_build_metric_keys``). A metric's ``name`` entry in
+    ``metric_kwargs`` overrides the function-name token; the ``backtest_``
+    prefix (when present) and axis suffixes are preserved.
+
+    The MLflow ``step`` is the axis the UI should chart:
+
+    - **Time-dependent metrics** (``time_reduction=None``): steps index the
+      forecast horizon (``0 .. T-1``). When both time and window axes are
+      present (backtest, no reduction, not ``last_points_only``), each step
+      holds the ``agg_func`` aggregation over windows at that horizon index.
+    - **Time-aggregated metrics**: steps index backtest windows end-aligned
+      across series (``0 .. max_W-1``; shorter series skip early steps).
+      Standalone calls with a time axis but no window axis end-align timesteps
+      the same way across series of different lengths.
+
+    Series can differ in length and overlap in time. We align from the end, not
+    the start: a shorter series contributes at the last steps/windows, not the
+    first. This matches the usual backtest layout but is an assumption — series
+    may also end at different calendar times.
+
+    Multi-series aggregation and artifacts
+    ----------------------------------------
+    When more than one series is scored (and ``series_reduction`` did not
+    already collapse them), the logged value at each ``(key, step)`` is
+    ``agg_func`` applied over series. The per-series breakdown is appended to
+    the run's ``metrics_per_series.json`` table artifact when:
+
+    - several series were scored together, or
+    - (backtest only) a time-dependent metric retains both time and window axes,
+      so the aggregate steps average over windows but the table keeps every
+      ``(window, horizon)`` cell.
+
+    For a single series, the aggregate is the value itself and no table is
+    written unless the backtest time+window case above applies. When components
+    are preserved (``component_reduction=None``), all series in a batch must
+    have the same number of components; component names are taken from the first
+    series.
 
     Raises
     ------
     ValueError
-        On a shape mismatch between the metric result and the inferred
-        axes, or when ``has_comp_axis`` is ``True`` and series in a sequence
-        have different numbers of components.
+        On a shape/size mismatch between the metric result and the inferred
+        axes, when ``component_reduction=None`` and series in a sequence have
+        different numbers of components, or when ``label_reduction=None`` is
+        requested without explicit ``labels``.
 
     Parameters
     ----------
@@ -1387,33 +1329,63 @@ def _log_standalone_metric(
     run_id
         ID of the active MLflow run.
     result
-        Return value of ``backtest()``.
-    metric
-        The ``metric`` callable that was called.
-    metric_args
-        Bound arguments of the ``metric()`` call (from
-        ``inspect.BoundArguments.arguments`` after ``apply_defaults``).
+        Return value of ``backtest()`` or ``metric()``. Either a scalar/array
+        for one series, a list of per-series results, or a single aggregate when
+        ``series_reduction`` is set.
+    metrics
+        One metric callable or a list of callables logged together.
+    metric_kwargs
+        Keyword arguments forwarded to ``metrics``, either one dict shared by
+        all metrics or one dict per metric. For standalone logging this is the
+        bound ``metric()`` arguments; for backtest logging this is
+        ``backtest_kwargs["metric_kwargs"]``.
+    backtest_kwargs
+        Bound arguments of the ``backtest()`` call (from
+        ``inspect.BoundArguments.arguments`` after ``apply_defaults``). When
+        ``None``, the call is treated as standalone metric logging.
     agg_func
-        Function used to aggregate a metric's per-series values into the
-        single value logged for a list of series. Called as
-        ``agg_func(values)`` on a list of floats.
+        Function used to aggregate per-series values at each ``(key, step)``.
+        Called as ``agg_func(values)`` on a list of floats.
     """
-
     metrics, metric_kwargs, metric_names = _normalize_metrics(
-        metrics=metric,
-        metric_kwargs=metric_args,
+        metrics=metrics,
+        metric_kwargs=metric_kwargs,
     )
     metric_axes = [_infer_metric_axes(m, kw) for m, kw in zip(metrics, metric_kwargs)]
-    has_time_axis, has_comp_axis, axis_labels = metric_axes[0]
+    has_time_axis, has_comp_axis, _ = metric_axes[0]
 
-    series = metric_args["actual_series"]
+    series: TimeSeriesLike = (
+        metric_kwargs[0]["actual_series"]
+        if backtest_kwargs is None
+        else backtest_kwargs["series"]
+    )
     is_single_series = get_series_seq_type(series) == SeriesType.SINGLE
     series = series2seq(series)
-    # series_reduction collapses the series axis inside the metric, so the
-    # result has no leading series axis even for list input.
-    series_reduced = metric_args["series_reduction"] is not None
+
+    if backtest_kwargs is None:
+        # standalone metric
+        is_backtest = False
+        prefix = ""
+        # `series_reduction` collapses the series axis inside the metric, so the
+        # result has no leading series axis even for list input.
+        series_reduced = metric_kwargs[0]["series_reduction"] is not None
+        last_points_only = False
+        has_windows = False
+        forecast_horizon = 0
+    else:
+        # backtest metric
+        is_backtest = True
+        prefix = "backtest_"
+        series_reduced = False
+        last_points_only = backtest_kwargs["last_points_only"]
+        has_windows, forecast_horizon = _resolve_backtest_layout(
+            backtest_kwargs=backtest_kwargs,
+            metric=metrics[0],
+            metric_kwargs=metric_kwargs[0],
+            is_single_series=is_single_series,
+        )
+
     if series_reduced:
-        # series_reduction aggregated across series -> single result, no series axis
         results = [result]
     else:
         results = [result] if is_single_series else result
@@ -1430,16 +1402,13 @@ def _log_standalone_metric(
                 )
             )
 
-    # component names/count from the first series (all series share n_components)
     metric_keys = _build_metric_keys(
         metric_names=metric_names,
         components=series[0].components.tolist(),
         has_comp_axis=has_comp_axis,
         metric_axes=metric_axes,
-        prefix="",
+        prefix=prefix,
     )
-
-    has_windows = False
     series_metrics = [
         _reshape_metric_result(
             series=series_,
@@ -1447,8 +1416,8 @@ def _log_standalone_metric(
             metric_keys=metric_keys,
             has_time_axis=has_time_axis,
             has_windows=has_windows,
-            forecast_horizon=0,
-            is_backtest=False,
+            forecast_horizon=forecast_horizon,
+            is_backtest=is_backtest,
         )
         for r, series_ in zip(results, series)
     ]
@@ -1457,43 +1426,12 @@ def _log_standalone_metric(
         metric_keys=metric_keys,
         has_time_axis=has_time_axis,
         has_windows=has_windows,
+        last_points_only=last_points_only,
     )
 
-    series_metrics = [
-        _reshape_metric_result(
-            series=series_,
-            result=r,
-            metric_keys=metric_keys,
-            has_time_axis=has_time_axis,
-            has_windows=False,
-            forecast_horizon=0,
-            is_backtest=False,
-        )[0, :, :, 0]
-        for r, series_ in zip(results, series)
-    ]
-
-    sub_metric_keys = metric_keys[0]
-
-    # agg maps (key, step) -> per-series values, aggregated into the logged metric.
-    agg: dict[tuple[str, int], list[float]] = {}
-    rows: list[dict] = []
-    for series_index, values in enumerate(series_metrics):
-        t_size = values.shape[0]
-        for c, key in enumerate(sub_metric_keys):
-            for t in range(t_size):
-                # Forecast positions are zero-based: the first prediction is 0.
-                step = t if has_time_axis else 0
-                value = float(values[t, c])
-                agg.setdefault((key, step), []).append(value)
-                rows.append({
-                    "key": key,
-                    "series_index": series_index,
-                    "step": step,
-                    "window_index": None,
-                    "value": value,
-                })
-
-    write_table = not series_reduced and not is_single_series
+    write_table = (not series_reduced and not is_single_series) or (
+        has_time_axis and has_windows
+    )
     _flush_logged_metrics(
         autologging_client,
         run_id,
@@ -1530,7 +1468,7 @@ def _normalize_metrics(
 
 
 def _resolve_backtest_layout(
-    backtest_args: dict,
+    backtest_kwargs: dict,
     metric: Callable,
     metric_kwargs: dict,
     is_single_series: bool,
@@ -1550,8 +1488,8 @@ def _resolve_backtest_layout(
     tuple
         ``(has_windows, forecast_horizon)``.
     """
-    last_points_only = backtest_args.get("last_points_only", False)
-    has_windows = backtest_args.get("reduction") is None and not last_points_only
+    last_points_only = backtest_kwargs["last_points_only"]
+    has_windows = backtest_kwargs["reduction"] is None and not last_points_only
 
     params = inspect.signature(metric).parameters
     if has_windows and "series_reduction" in params:
@@ -1561,8 +1499,8 @@ def _resolve_backtest_layout(
         if series_reduction is not None:
             has_windows = False
 
-    forecast_horizon = backtest_args.get("forecast_horizon")
-    historical_forecasts = backtest_args.get("historical_forecasts")
+    forecast_horizon = backtest_kwargs["forecast_horizon"]
+    historical_forecasts = backtest_kwargs["historical_forecasts"]
     if historical_forecasts is not None and not last_points_only:
         first_forecast = (
             historical_forecasts[0] if is_single_series else historical_forecasts[0][0]
@@ -1579,7 +1517,7 @@ def _infer_metric_axes(
 
     Covers ``time_reduction``, ``component_reduction``, ``q``, ``q_interval``,
     and ``label_reduction`` / ``labels`` for classification metrics.
-    ``series_reduction`` is handled at the ``_log_backtest_metrics`` level.
+    ``series_reduction`` is handled at the ``_log_metric_results`` level.
 
     Parameters
     ----------
@@ -1779,6 +1717,7 @@ def _collect_stepped_and_detailed_metrics(
     *,
     has_time_axis: bool,
     has_windows: bool,
+    last_points_only: bool,
 ) -> tuple[dict[tuple[str, int], list[float]], list[dict]]:
     """Parse series metrics and build inputs for MLflow stepped metrics and detailed metric table.
 
@@ -1804,36 +1743,32 @@ def _collect_stepped_and_detailed_metrics(
     for series_index, values in enumerate(series_metrics):
         w_size = values.shape[w_axis]
         t_size = values.shape[t_axis]
+
         # pad shorter series so their last point lines up with the longest
         w_offset = max_w_size - w_size if has_windows else 0
         t_offset = max_t_size - t_size if has_time_axis and not has_windows else 0
 
-        window_agg = np.nanmean(values, axis=w_axis)
         for metric_idx, sub_metric_keys in enumerate(metric_keys):
             for sub_metric_idx, key in enumerate(sub_metric_keys):
                 for w in range(w_size):
                     window_index = w + w_offset if has_windows else None
                     for t in range(t_size):
-                        value = float(values[w, t, sub_metric_idx, metric_idx])
-                        if has_time_axis and has_windows:
-                            # time-dependent metrics + last_points_only=False + no reduction
+                        if has_time_axis and (has_windows or not last_points_only):
+                            # bt: backtest, sm: standalone metric
+                            # (bt) time-dependent metrics + last_points_only=False + no reduction
+                            # (bt) time-dependent metrics + last_points_only=False + reduction
+                            # (sm) time-dependent metrics
                             step = t
-
-                            # stepped metric chart: one value per horizon (windows averaged)
-                            if w == 0:
-                                stepped_metrics.setdefault((key, step), []).append(
-                                    float(window_agg[t, sub_metric_idx, metric_idx])
-                                )
+                        elif has_time_axis:
+                            # (bt) time-dependent metrics + last_points_only=True + no reduction
+                            step = t + t_offset
                         else:
-                            if has_time_axis:
-                                # time-dependent metrics + last_points_only=False + reduction
-                                step = t + t_offset
-                            else:
-                                step = window_index
-
-                            # stepped metric chart: one value per window or horizon
-                            stepped_metrics.setdefault((key, step), []).append(value)
+                            # (bt) last_points_only=False + reduction (window index is None)
+                            # (bt) last_points_only=False + no reduction (window index is not None)
+                            # (sm) time-aggregated metrics (window index is None)
+                            step = window_index
 
+                        value = float(values[w, t, sub_metric_idx, metric_idx])
                         # table: one value per window and horizon
                         detailed_metrics.append({
                             "key": key,
@@ -1842,6 +1777,8 @@ def _collect_stepped_and_detailed_metrics(
                             "window_index": window_index,
                             "value": value,
                         })
+                        # stepped metric chart: one value per window or horizon
+                        stepped_metrics.setdefault((key, step), []).append(value)
 
     return stepped_metrics, detailed_metrics
 

From 32ac8e13b162185933a4734387ba4509dfbabb93 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 28 Aug 2026 12:07:25 +0200
Subject: [PATCH 146/154] update tests

---
 darts/tests/optional_deps/test_mlflow.py | 1681 +++++++++++-----------
 1 file changed, 853 insertions(+), 828 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 5d3d165f1c..d06595c3cc 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1,3 +1,4 @@
+import copy
 import logging
 import os
 
@@ -13,7 +14,12 @@
     ForecastingModel,
     GlobalForecastingModel,
 )
-from darts.tests.conftest import MLFLOW_AVAILABLE, TORCH_AVAILABLE, tfm_kwargs_dev
+from darts.tests.conftest import (
+    MLFLOW_AVAILABLE,
+    TORCH_AVAILABLE,
+    tfm_kwargs,
+    tfm_kwargs_dev,
+)
 
 if not MLFLOW_AVAILABLE:
     pytest.skip(
@@ -45,6 +51,22 @@
     from darts.models import NBEATSModel
 
 
+TS_UNIVARIATE = tg.sine_timeseries(length=50).astype("float32")
+TS_MULTIVARIATE = TS_UNIVARIATE.stack(TS_UNIVARIATE * 1.5)
+TS_WITH_STATIC = TS_UNIVARIATE.with_static_covariates(
+    pd.DataFrame({"static_feat": [1.0]})
+)
+TS_PAST_COV = tg.sine_timeseries(length=62).astype("float32")
+TS_FUTURE_COV = tg.constant_timeseries(value=1.0, length=62).astype("float32")
+# binary classification series with values {0.0, 1.0}
+TS_BINARY = tg.constant_timeseries(value=0.0, length=50).with_values(
+    np.random.default_rng(42)
+    .choice([0.0, 1.0], size=50)
+    .astype(np.float32)
+    .reshape(-1, 1)
+)
+
+
 @pytest.fixture
 def mlflow_tracking(tmpdir_fn):
     """Set up MLflow tracking with a temporary database."""
@@ -136,28 +158,33 @@ def assert_predictions_equal(
     )
 
 
-class TestMLflow:
-    ts_univariate = tg.linear_timeseries(
-        start_value=10, end_value=50, length=50
-    ).astype("float32")
-    ts_multivariate = ts_univariate.stack(ts_univariate * 1.5)
-    ts_with_static = ts_univariate.with_static_covariates(
-        pd.DataFrame({"static_feat": [1.0]})
-    )
-    ts_past_cov = tg.sine_timeseries(length=62).astype("float32")
-    ts_future_cov = tg.constant_timeseries(value=1.0, length=62).astype("float32")
-    # binary classification series with values {0.0, 1.0}
-    ts_binary = tg.constant_timeseries(value=0.0, length=50).with_values(
-        np.random.default_rng(42)
-        .choice([0.0, 1.0], size=50)
-        .astype(np.float32)
-        .reshape(-1, 1)
+def _read_per_series_table(run_id):
+    """Load the run's consolidated per-series metric table into row dicts."""
+    df = mlflow.load_table(artifact_file="metrics_per_series.json", run_ids=[run_id])
+    return df.to_dict("records")
+
+
+def _fit_lr(series=None):
+    """Fit and return a fresh LinearRegressionModel (no active run)."""
+    model = LinearRegressionModel(lags=1)
+    model.fit(series if series is not None else TS_UNIVARIATE)
+    return model
+
+
+def _fit_qlr(series=None):
+    """Fit and return a fresh quantile LinearRegressionModel (no active run)."""
+    model = LinearRegressionModel(
+        lags=1, likelihood="quantile", quantiles=[0.1, 0.5, 0.9]
     )
+    model.fit(series if series is not None else TS_UNIVARIATE)
+    return model
+
 
+class TestMLflow:
     def test_save_load_statistical_model(self, tmpdir_fn):
         """Test save/load round-trip for statistical model"""
         model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
+        model.fit(TS_UNIVARIATE)
 
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
@@ -170,7 +197,7 @@ def test_save_load_statistical_model(self, tmpdir_fn):
     def test_save_load_regression_model(self, tmpdir_fn):
         """Test save/load round-trip for regression model"""
         model = LinearRegressionModel(lags=5)
-        model.fit(self.ts_univariate)
+        model.fit(TS_UNIVARIATE)
 
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
@@ -178,7 +205,7 @@ def test_save_load_regression_model(self, tmpdir_fn):
         assert_mlflow_artifacts_exist(model_path, is_torch=False)
 
         loaded_model = load_model(f"file://{model_path}")
-        assert_predictions_equal(model, loaded_model, n=3, series=self.ts_univariate)
+        assert_predictions_equal(model, loaded_model, n=3, series=TS_UNIVARIATE)
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
     def test_save_load_torch_model(self, tmpdir_fn):
@@ -186,7 +213,7 @@ def test_save_load_torch_model(self, tmpdir_fn):
         model = NBEATSModel(
             input_chunk_length=4, output_chunk_length=2, n_epochs=1, **tfm_kwargs_dev
         )
-        model.fit(self.ts_univariate)
+        model.fit(TS_UNIVARIATE)
 
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
@@ -199,12 +226,12 @@ def test_save_load_torch_model(self, tmpdir_fn):
             f"file://{model_path}",
             pl_trainer_kwargs=tfm_kwargs_dev.get("pl_trainer_kwargs", {}),
         )
-        assert_predictions_equal(model, loaded_model, n=2, series=self.ts_univariate)
+        assert_predictions_equal(model, loaded_model, n=2, series=TS_UNIVARIATE)
 
     def test_log_model_basic(self, mlflow_tracking):
         """Test basic log_model functionality"""
         model = ExponentialSmoothing()
-        model.fit(self.ts_univariate)
+        model.fit(TS_UNIVARIATE)
 
         with mlflow.start_run():
             log_info = log_model(model, name="model")
@@ -215,7 +242,7 @@ def test_log_model_basic(self, mlflow_tracking):
     def test_log_model_with_covariates(self, mlflow_tracking):
         """Test that covariate info is logged with correct values"""
         model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-        model.fit(self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40])
+        model.fit(TS_UNIVARIATE[:40], past_covariates=TS_PAST_COV[:40])
 
         with mlflow.start_run():
             log_model(model, name="model")
@@ -226,8 +253,8 @@ def test_log_model_with_covariates(self, mlflow_tracking):
             model,
             loaded_model,
             n=5,
-            series=self.ts_univariate[:40],
-            past_covariates=self.ts_past_cov,
+            series=TS_UNIVARIATE[:40],
+            past_covariates=TS_PAST_COV,
         )
 
     def test_log_model_with_all_covariate_types(self, mlflow_tracking):
@@ -237,9 +264,9 @@ def test_log_model_with_all_covariate_types(self, mlflow_tracking):
             lags=5, lags_past_covariates=3, lags_future_covariates=[0, 1]
         )
         model.fit(
-            self.ts_with_static[:40],
-            past_covariates=self.ts_past_cov[:40],
-            future_covariates=self.ts_future_cov[:50],
+            TS_WITH_STATIC[:40],
+            past_covariates=TS_PAST_COV[:40],
+            future_covariates=TS_FUTURE_COV[:50],
         )
 
         with mlflow.start_run():
@@ -251,41 +278,37 @@ def test_log_model_with_all_covariate_types(self, mlflow_tracking):
             model,
             loaded_model,
             n=5,
-            series=self.ts_with_static[:40],
-            past_covariates=self.ts_past_cov,
-            future_covariates=self.ts_future_cov,
+            series=TS_WITH_STATIC[:40],
+            past_covariates=TS_PAST_COV,
+            future_covariates=TS_FUTURE_COV,
         )
 
     def test_autolog_enable_disable(self, mlflow_tracking, autolog_context):
         """Test autolog can be enabled and disabled"""
-        with autolog_context():
-            with mlflow.start_run():
-                model = ExponentialSmoothing()
-                model.fit(self.ts_univariate)
+        client = mlflow_tracking
+        runs = mlflow.search_runs()
+        assert len(runs) == 0
 
+        autolog()
+        with mlflow.start_run():
             runs = mlflow.search_runs()
-            assert len(runs) == 1, "Expected exactly one run after autolog fit"
-
-            # verify the run has expected content
-            last_run = runs.iloc[0]
-            assert last_run["tags.model_class"] == "ExponentialSmoothing"
-            assert last_run["tags.mlflow.runName"] is not None
+            assert len(runs) == 1
+            run_id = runs.iloc[0]["run_id"]
 
-        # after context exits, autolog should be disabled
-        model2 = ExponentialSmoothing()
-        model2.fit(self.ts_univariate)
+            # metric is auto-logged
+            dm.mae(TS_UNIVARIATE, TS_UNIVARIATE, name="auto_logged")
+            assert len(client.get_metric_history(run_id, "auto_logged")) == 1
 
-        runs_after_disable = mlflow.search_runs()
-        assert len(runs_after_disable) == 1, (
-            "No new run should be created after disable"
-        )
+            autolog(disable=True)
+            dm.mae(TS_UNIVARIATE, TS_UNIVARIATE, name="not_logged")
+            assert len(client.get_metric_history(run_id, "not_logged")) == 0
 
     def test_autolog_parameters(self, mlflow_tracking, autolog_context):
         """Test that autolog logs model parameters"""
         with autolog_context():
             with mlflow.start_run():
                 model = ExponentialSmoothing(seasonal_periods=12)
-                model.fit(self.ts_univariate)
+                model.fit(TS_UNIVARIATE)
 
             runs = mlflow.search_runs()
             assert len(runs) == 1
@@ -301,7 +324,7 @@ def test_autolog_model_params_json_artifact(self, mlflow_tracking, autolog_conte
         with autolog_context():
             with mlflow.start_run() as run:
                 model = ExponentialSmoothing(seasonal_periods=12)
-                model.fit(self.ts_univariate)
+                model.fit(TS_UNIVARIATE)
 
         params = mlflow.artifacts.load_dict(
             f"runs:/{run.info.run_id}/model_params.json"
@@ -309,120 +332,19 @@ def test_autolog_model_params_json_artifact(self, mlflow_tracking, autolog_conte
         assert params["seasonal_periods"] == 12
         assert params["trend"] == str(model.model_params["trend"])
 
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_torch_metrics(self, mlflow_tracking, autolog_context):
-        """Test that autolog logs training metrics for torch models"""
-        with autolog_context():
-            with mlflow.start_run():
-                model = NBEATSModel(
-                    input_chunk_length=4,
-                    output_chunk_length=2,
-                    n_epochs=2,
-                    **tfm_kwargs_dev,
-                )
-                train, val = self.ts_univariate.split_before(0.7)
-                model.fit(train, val_series=val)
-
-            runs = mlflow.search_runs()
-            assert len(runs) == 1, "Expected exactly one run"
-            last_run = runs.iloc[0]
-            last_run_id = last_run["run_id"]
-            assert last_run["tags.model_class"] == "NBEATSModel"
-
-            client = mlflow.tracking.MlflowClient()
-
-            # check train_loss metrics
-            train_metrics = client.get_metric_history(last_run_id, "train_loss")
-            assert len(train_metrics) > 0, "Expected train_loss metrics to be logged"
-            assert len(train_metrics) <= 2, "Expected at most 2 epochs of train_loss"
-
-            for m in train_metrics:
-                assert np.isfinite(m.value), f"train_loss is not finite: {m.value}"
-                assert m.value >= 0, f"train_loss is negative: {m.value}"
-                assert m.step >= 0, "Metric step should be non-negative"
-
-            val_metrics = client.get_metric_history(last_run_id, "val_loss")
-            if val_metrics:
-                for m in val_metrics:
-                    assert np.isfinite(m.value), f"val_loss is not finite: {m.value}"
-                    assert m.value >= 0, f"val_loss is negative: {m.value}"
-
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_pytorch_autolog_enabled(self, mlflow_tracking, autolog_context):
-        """Test that autolog enables mlflow.pytorch.autolog and logs per-epoch
-        train_loss, val_loss, and custom torch_metrics with finite non-negative values."""
-        import torchmetrics
-        from mlflow.utils.autologging_utils import autologging_is_disabled
-
-        n_epochs = 2
-
-        def assert_metric(history, key):
-            assert len(history) > 0, f"{key} not logged"
-            assert len(history) <= n_epochs, f"too many {key} entries"
-            assert all(np.isfinite(m.value) and m.value >= 0 for m in history)
-
-        with autolog_context():
-            assert not autologging_is_disabled("pytorch")
-
-            with mlflow.start_run():
-                model = NBEATSModel(
-                    input_chunk_length=4,
-                    output_chunk_length=2,
-                    n_epochs=n_epochs,
-                    torch_metrics=torchmetrics.MeanAbsoluteError(),
-                    **tfm_kwargs_dev,
-                )
-                train, val = self.ts_univariate.split_before(0.7)
-                model.fit(train, val_series=val)
-
-            runs = mlflow.search_runs()
-            assert len(runs) == 1
-            run_id = runs.iloc[0]["run_id"]
-            assert runs.iloc[0]["tags.model_class"] == "NBEATSModel"
-
-            client = mlflow.tracking.MlflowClient()
-            assert_metric(client.get_metric_history(run_id, "train_loss"), "train_loss")
-            assert_metric(client.get_metric_history(run_id, "val_loss"), "val_loss")
-            # custom torch_metrics: in normal use both train_/val_ prefixes are logged, but
-            # fast_dev_run suppresses the Lightning logger during traininge
-            assert_metric(
-                client.get_metric_history(run_id, "val_MeanAbsoluteError"),
-                "val_MeanAbsoluteError",
-            )
-
-        assert autologging_is_disabled("pytorch")
-
-    def test_autolog_series_info_single_series(self, mlflow_tracking, autolog_context):
+    @pytest.mark.parametrize("single_series", [True, False])
+    def test_autolog_series_info_single_series(
+        self, mlflow_tracking, autolog_context, single_series
+    ):
         """The series_info.json artifact reports the target series' component
         names/count, plus covariate usage, count, and names, for a
         single-series fit."""
-        with autolog_context():
-            with mlflow.start_run() as run:
-                model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-                model.fit(
-                    self.ts_univariate[:40], past_covariates=self.ts_past_cov[:40]
-                )
+        series = TS_UNIVARIATE[:40]
+        past_covs = TS_PAST_COV[:40]
+        if not single_series:
+            series = [series] * 2
+            past_covs = [past_covs] * 2
 
-        series_info = mlflow.artifacts.load_dict(
-            f"runs:/{run.info.run_id}/series_info.json"
-        )
-        assert series_info["series"]["count"] == self.ts_univariate.n_components
-        assert series_info["series"]["names"] == self.ts_univariate.components.tolist()
-        assert series_info["past_covariates"]["used"] is True
-        assert series_info["past_covariates"]["count"] == 1
-        assert (
-            series_info["past_covariates"]["names"]
-            == self.ts_past_cov.components.tolist()
-        )
-        assert series_info["future_covariates"]["used"] is False
-        assert series_info["static_covariates"]["used"] is False
-
-    def test_autolog_series_info_multi_series(self, mlflow_tracking, autolog_context):
-        """Fitting on a list of series doesn't leave past_covariates unreported:
-        `model.past_covariate_series` stays None for a multi-series fit, so the
-        info must come from the actual `fit()` call arguments instead."""
-        series = [self.ts_univariate, self.ts_univariate * 1.2]
-        past_covs = [self.ts_past_cov[:50], self.ts_past_cov[:50]]
         with autolog_context():
             with mlflow.start_run() as run:
                 model = LinearRegressionModel(lags=5, lags_past_covariates=3)
@@ -431,12 +353,15 @@ def test_autolog_series_info_multi_series(self, mlflow_tracking, autolog_context
         series_info = mlflow.artifacts.load_dict(
             f"runs:/{run.info.run_id}/series_info.json"
         )
+        assert series_info["series"]["count"] == TS_UNIVARIATE.n_components
+        assert series_info["series"]["names"] == TS_UNIVARIATE.components.tolist()
         assert series_info["past_covariates"]["used"] is True
         assert series_info["past_covariates"]["count"] == 1
         assert (
-            series_info["past_covariates"]["names"]
-            == self.ts_past_cov.components.tolist()
+            series_info["past_covariates"]["names"] == TS_PAST_COV.components.tolist()
         )
+        assert series_info["future_covariates"]["used"] is False
+        assert series_info["static_covariates"]["used"] is False
 
     def test_autolog_series_info_add_encoders(self, mlflow_tracking, autolog_context):
         """Covariates generated purely via `add_encoders` (no explicit
@@ -449,7 +374,7 @@ def test_autolog_series_info_add_encoders(self, mlflow_tracking, autolog_context
                     lags_future_covariates=[0],
                     add_encoders={"datetime_attribute": {"future": ["month"]}},
                 )
-                model.fit(self.ts_univariate[:40])
+                model.fit(TS_UNIVARIATE[:40])
 
         series_info = mlflow.artifacts.load_dict(
             f"runs:/{run.info.run_id}/series_info.json"
@@ -461,13 +386,11 @@ def test_autolog_series_info_add_encoders(self, mlflow_tracking, autolog_context
         assert series_info["future_covariates"]["encodings"] == expected_names
         assert series_info["past_covariates"]["used"] is False
 
-    def test_autolog_series_info_static_is_global(
+    def test_autolog_series_info_static_covariates(
         self, mlflow_tracking, autolog_context
     ):
-        """`is_global` is based on row count vs. the number of series
-        components, not the number of static-covariate columns."""
         # one shared row over 2 components -> global
-        global_target = self.ts_multivariate.with_static_covariates(
+        global_target = TS_MULTIVARIATE.with_static_covariates(
             pd.DataFrame({"a": [1.0], "b": [2.0]})
         )
         with autolog_context():
@@ -480,7 +403,7 @@ def test_autolog_series_info_static_is_global(
         assert series_info["static_covariates"]["names"] == ["a", "b"]
 
         # one row per component (2 components, 2 rows) -> component-specific
-        per_component_target = self.ts_multivariate.with_static_covariates(
+        per_component_target = TS_MULTIVARIATE.with_static_covariates(
             pd.DataFrame({"a": [1.0, 2.0]})
         )
         with autolog_context():
@@ -492,23 +415,29 @@ def test_autolog_series_info_static_is_global(
         assert series_info["static_covariates"]["count"] == 1
         assert series_info["static_covariates"]["names"] == ["a"]
 
+    @pytest.mark.parametrize("method", ["historical_forecasts", "backtest"])
     def test_autolog_historical_forecasts_series_info_covariates(
-        self, mlflow_tracking, autolog_context
+        self, mlflow_tracking, autolog_context, method
     ):
-        """HF without a prior fit() still reports explicit covariates and static
-        covariates in series_info / tags (outer model stays unfitted)."""
-        target = self.ts_univariate.with_static_covariates(
+        """HF and backtest without a prior fit() still reports model params and series_info
+        (but not the model artifact)."""
+        target = TS_UNIVARIATE.with_static_covariates(
             pd.DataFrame({"static_feat": [1.0]})
         )
-        with autolog_context():
+        with autolog_context(log_models=True):
             with mlflow.start_run() as run:
-                model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-                model.historical_forecasts(
+                model = LinearRegressionModel(
+                    lags=5,
+                    lags_past_covariates=3,
+                    lags_future_covariates=[0],
+                    add_encoders={"datetime_attribute": {"future": ["month"]}},
+                )
+                _ = getattr(model, method)(
                     series=target,
-                    past_covariates=self.ts_past_cov,
+                    past_covariates=TS_PAST_COV,
                     forecast_horizon=1,
                     retrain=True,
-                    start=0.5,
+                    start=-1,
                 )
 
         series_info = mlflow.artifacts.load_dict(
@@ -516,104 +445,37 @@ def test_autolog_historical_forecasts_series_info_covariates(
         )
         assert series_info["past_covariates"]["used"] is True
         assert (
-            series_info["past_covariates"]["names"]
-            == self.ts_past_cov.components.tolist()
+            series_info["past_covariates"]["names"] == TS_PAST_COV.components.tolist()
         )
+        assert series_info["future_covariates"]["used"] is True
         assert series_info["static_covariates"]["used"] is True
         assert series_info["static_covariates"]["names"] == ["static_feat"]
         tags = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.tags
+        assert tags["model_uses_future_covariates"] == "True"
         assert tags["model_uses_past_covariates"] == "True"
         assert tags["model_uses_static_covariates"] == "True"
 
-    def test_autolog_historical_forecasts_series_info_add_encoders(
-        self, mlflow_tracking, autolog_context
-    ):
-        """HF with add_encoders (no explicit cov args) marks future covariates
-        as used even though the outer model remains unfitted."""
-        with autolog_context():
-            with mlflow.start_run() as run:
-                model = LinearRegressionModel(
-                    lags=5,
-                    lags_future_covariates=[0],
-                    add_encoders={"datetime_attribute": {"future": ["month"]}},
-                )
-                model.historical_forecasts(
-                    series=self.ts_univariate,
-                    forecast_horizon=1,
-                    retrain=True,
-                    start=0.5,
-                )
-
-        series_info = mlflow.artifacts.load_dict(
-            f"runs:/{run.info.run_id}/series_info.json"
-        )
-        assert series_info["future_covariates"]["used"] is True
-        tags = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.tags
-        assert tags["model_uses_future_covariates"] == "True"
-
-    def test_autolog_backtest_series_info_covariates(
-        self, mlflow_tracking, autolog_context
-    ):
-        """backtest(retrain=True) without a prior fit() reports covariates via
-        the internal historical_forecasts path."""
-        with autolog_context():
-            with mlflow.start_run() as run:
-                model = LinearRegressionModel(lags=5, lags_past_covariates=3)
-                model.backtest(
-                    series=self.ts_univariate,
-                    past_covariates=self.ts_past_cov,
-                    forecast_horizon=1,
-                    retrain=True,
-                    start=0.5,
-                    metric=dm.mae,
-                )
-
-        series_info = mlflow.artifacts.load_dict(
-            f"runs:/{run.info.run_id}/series_info.json"
-        )
-        assert series_info["past_covariates"]["used"] is True
-        assert (
-            series_info["past_covariates"]["names"]
-            == self.ts_past_cov.components.tolist()
-        )
-
-    def test_autolog_historical_forecasts_logs_model_setup(
-        self, mlflow_tracking, autolog_context
-    ):
-        """historical_forecasts(retrain=True) without a prior fit() still logs
-        model params and series_info (but not the model artifact)."""
-        with autolog_context():
-            with mlflow.start_run() as run:
-                model = NaiveSeasonal(K=1)
-                model.historical_forecasts(
-                    series=self.ts_univariate, forecast_horizon=1, retrain=True
-                )
-
         params = mlflow.artifacts.load_dict(
             f"runs:/{run.info.run_id}/model_params.json"
         )
-        assert params["K"] == 1
-        series_info = mlflow.artifacts.load_dict(
-            f"runs:/{run.info.run_id}/series_info.json"
-        )
-        assert series_info["series"]["names"] == self.ts_univariate.components.tolist()
-        client = mlflow.tracking.MlflowClient()
-        artifact_paths = [a.path for a in client.list_artifacts(run.info.run_id)]
-        assert "model" not in artifact_paths
-        assert (
-            client.get_run(run.info.run_id).data.tags["model_class"] == "NaiveSeasonal"
+        assert params["lags"] == 5
+
+        # model artifact is not available
+        logged_models = mlflow_tracking.search_logged_models(
+            experiment_ids=[run.info.experiment_id]
         )
+        assert len(logged_models) == 0
 
     def test_autolog_historical_forecasts_retrain_false_skips_model_setup(
         self, mlflow_tracking, autolog_context
     ):
         """historical_forecasts(retrain=False) does not log model setup."""
         model = LinearRegressionModel(lags=5)
-        model.fit(self.ts_univariate[:40])
+        model.fit(TS_UNIVARIATE[:40])
         with autolog_context():
             with mlflow.start_run() as run:
                 model.historical_forecasts(
-                    series=self.ts_univariate,
+                    series=TS_UNIVARIATE,
                     forecast_horizon=1,
                     retrain=False,
                     start=0.5,
@@ -632,9 +494,9 @@ def test_autolog_historical_forecasts_overwrites_fit_setup(
         with autolog_context():
             with mlflow.start_run() as run:
                 model = LinearRegressionModel(lags=5)
-                model.fit(self.ts_univariate[:30])
+                model.fit(TS_UNIVARIATE[:30])
                 model.historical_forecasts(
-                    series=self.ts_multivariate,
+                    series=TS_MULTIVARIATE,
                     forecast_horizon=1,
                     retrain=True,
                     start=0.5,
@@ -643,14 +505,12 @@ def test_autolog_historical_forecasts_overwrites_fit_setup(
         series_info = mlflow.artifacts.load_dict(
             f"runs:/{run.info.run_id}/series_info.json"
         )
-        assert (
-            series_info["series"]["names"] == self.ts_multivariate.components.tolist()
-        )
+        assert series_info["series"]["names"] == TS_MULTIVARIATE.components.tolist()
 
     def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         """Test saving/loading multivariate series with all covariate types"""
         # create multivariate target with static covariates
-        target = self.ts_multivariate.with_static_covariates(
+        target = TS_MULTIVARIATE.with_static_covariates(
             pd.DataFrame({"static_feat_1": [1.0], "static_feat_2": [2.0]})
         )
 
@@ -659,8 +519,8 @@ def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         )
         model.fit(
             target[:40],
-            past_covariates=self.ts_past_cov[:40],
-            future_covariates=self.ts_future_cov[:50],
+            past_covariates=TS_PAST_COV[:40],
+            future_covariates=TS_FUTURE_COV[:50],
         )
 
         with mlflow.start_run():
@@ -673,54 +533,63 @@ def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
             model,
             n=5,
             series=target[:40],
-            past_covariates=self.ts_past_cov,
-            future_covariates=self.ts_future_cov,
+            past_covariates=TS_PAST_COV,
+            future_covariates=TS_FUTURE_COV,
         )
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_pytorch_autolog_with_existing_callbacks(
-        self, mlflow_tracking, autolog_context
-    ):
-        """Test pytorch autolog works when model already has callbacks"""
-        # create model with existing callback
-        if TORCH_AVAILABLE:
-            import pytorch_lightning as pl
+    def test_autolog_pytorch(self, mlflow_tracking, autolog_context):
+        """Test that autolog logs training metrics for torch models"""
+        import pytorch_lightning as pl
+        import torchmetrics
+        from mlflow.utils.autologging_utils import autologging_is_disabled
 
-            existing_callback = pl.callbacks.EarlyStopping(monitor="train_loss")
-        else:
-            pytest.skip("PyTorch Lightning not available")
+        existing_callback = pl.callbacks.EarlyStopping(monitor="train_loss")
+        tfm_kwargs_ = copy.deepcopy(tfm_kwargs)
+        tfm_kwargs_["pl_trainer_kwargs"] = {
+            **tfm_kwargs_["pl_trainer_kwargs"],
+            "callbacks": [existing_callback],
+        }
 
         with autolog_context():
-            model = NBEATSModel(
-                input_chunk_length=4,
-                output_chunk_length=2,
-                n_epochs=2,
-                pl_trainer_kwargs={
-                    **tfm_kwargs_dev.get("pl_trainer_kwargs", {}),
-                    "callbacks": [existing_callback],
-                },
-                **{k: v for k, v in tfm_kwargs_dev.items() if k != "pl_trainer_kwargs"},
-            )
+            assert not autologging_is_disabled("pytorch")
+            with mlflow.start_run():
+                model = NBEATSModel(
+                    input_chunk_length=4,
+                    output_chunk_length=2,
+                    n_epochs=2,
+                    torch_metrics=torchmetrics.MeanAbsoluteError(),
+                    **tfm_kwargs_,
+                )
+                train, val = TS_UNIVARIATE.split_before(0.7)
+                model.fit(train, val_series=val)
+        assert autologging_is_disabled("pytorch")
 
-            train, val = self.ts_univariate.split_before(0.7)
-            model.fit(train, val_series=val)
+        # verify existing callback is still present (not removed by autolog)
+        callbacks = model.trainer_params.get("callbacks", [])
+        has_existing = any(
+            isinstance(cb, pl.callbacks.EarlyStopping) for cb in callbacks
+        )
+        assert has_existing, "Existing EarlyStopping callback should be preserved"
 
-            # verify existing callback is still present (not removed by autolog)
-            callbacks = model.trainer_params.get("callbacks", [])
-            has_existing = any(
-                isinstance(cb, pl.callbacks.EarlyStopping) for cb in callbacks
-            )
-            assert has_existing, "Existing EarlyStopping callback should be preserved"
+        runs = mlflow.search_runs()
+        assert len(runs) == 1, "Expected exactly one run"
+        last_run = runs.iloc[0]
+        last_run_id = last_run["run_id"]
+        assert last_run["tags.model_class"] == "NBEATSModel"
 
-            # verify metrics were still logged via mlflow.pytorch.autolog
-            runs = mlflow.search_runs()
-            assert len(runs) >= 1, "Expected at least one run"
-            last_run_id = runs.iloc[0]["run_id"]
-            client = mlflow.tracking.MlflowClient()
-            train_metrics = client.get_metric_history(last_run_id, "train_loss")
-            assert len(train_metrics) > 0, (
-                "Expected train_loss metrics to be logged via pytorch autolog"
-            )
+        client = mlflow.tracking.MlflowClient()
+
+        # check train_loss metrics
+        train_metrics = client.get_metric_history(last_run_id, "train_loss")
+        val_metrics = client.get_metric_history(last_run_id, "val_loss")
+        torch_metrics = client.get_metric_history(last_run_id, "val_MeanAbsoluteError")
+
+        for metrics in [train_metrics, val_metrics, torch_metrics]:
+            assert len(metrics) == 2
+            for idx, m in enumerate(metrics):
+                assert np.isfinite(m.value)
+                assert m.step == idx
 
     def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
         """Test that multiple fits with autolog create separate runs"""
@@ -729,10 +598,10 @@ def test_autolog_multiple_fits(self, mlflow_tracking, autolog_context):
             # so we explicitly start runs for each fit
             with mlflow.start_run():
                 model2 = LinearRegressionModel(lags=5)
-                model2.fit(self.ts_univariate)
+                model2.fit(TS_UNIVARIATE)
             with mlflow.start_run():
                 model1 = ExponentialSmoothing()
-                model1.fit(self.ts_univariate)
+                model1.fit(TS_UNIVARIATE)
 
         runs = mlflow.search_runs()
         assert len(runs) == 2, "Expected two separate runs for two fits"
@@ -763,7 +632,7 @@ def test_autolog_ensemble_model_fit_logs_once(
 
         with autolog_context(log_models=True):
             with mlflow.start_run() as run:
-                model.fit(self.ts_univariate)
+                model.fit(TS_UNIVARIATE)
 
         logged_models = mlflow_tracking.search_logged_models(
             experiment_ids=[run.info.experiment_id]
@@ -774,59 +643,11 @@ def test_autolog_ensemble_model_fit_logs_once(
         )
         assert logged_models[0].name == "RegressionEnsembleModel"
 
-    @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_autolog_torch_model_multiple_fits(self, mlflow_tracking, autolog_context):
-        """Test autolog with multiple fits of a torch model"""
-        with autolog_context():
-            with mlflow.start_run():
-                model1 = NBEATSModel(
-                    input_chunk_length=4,
-                    output_chunk_length=2,
-                    n_epochs=1,
-                    **tfm_kwargs_dev,
-                )
-                train, val = self.ts_univariate.split_before(0.7)
-                model1.fit(train, val_series=val)
-
-            with mlflow.start_run():
-                model2 = LinearRegressionModel(lags=5)
-                model2.fit(self.ts_univariate)
-
-        runs = mlflow.search_runs()
-        assert len(runs) == 2, "Expected two separate runs for two fits"
-
-        for _, run in runs.iterrows():
-            assert run["tags.model_class"] in [
-                "NBEATSModel",
-                "LinearRegressionModel",
-            ]
-            assert run["tags.mlflow.runName"] is not None
-
-    def test_save_load_preserves_series_metadata(self, tmpdir_fn):
-        """Test that save/load preserves multivariate and static covariate structure"""
-        target = self.ts_multivariate.with_static_covariates(
-            pd.DataFrame({"stat1": [1.0], "stat2": [2.0]})
-        )
-
-        model = LinearRegressionModel(lags=5)
-        model.fit(target)
-
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path)
-
-        loaded_model = load_model(f"file://{model_path}")
-
-        pred_original = model.predict(n=3)
-        pred_loaded = loaded_model.predict(n=3, series=target)
-
-        # verify multivariate structure preserved
-        assert pred_original.width == pred_loaded.width == 2, (
-            "Should maintain 2 components"
-        )
-        assert pred_original.n_components == pred_loaded.n_components == 2
-
-        np.testing.assert_array_almost_equal(
-            pred_original.values(), pred_loaded.values(), decimal=4
+        assert_predictions_equal(
+            model,
+            load_model(logged_models[0].model_uri),
+            n=5,
+            series=TS_UNIVARIATE,
         )
 
     def test_load_nonexistent_model(self):
@@ -846,7 +667,7 @@ def test_load_corrupted_mlmodel_fails(self, tmpdir_fn):
         """Test that loading with corrupted MLmodel file fails"""
         # save a valid model
         model = LinearRegressionModel(lags=5)
-        model.fit(self.ts_univariate)
+        model.fit(TS_UNIVARIATE)
 
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
@@ -864,7 +685,7 @@ def test_load_missing_model_file_fails(self, tmpdir_fn):
         """Test that loading with missing model data file fails"""
         # save a valid model
         model = LinearRegressionModel(lags=5)
-        model.fit(self.ts_univariate)
+        model.fit(TS_UNIVARIATE)
 
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
@@ -891,7 +712,7 @@ def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
         else:
             model = model_cls()
 
-        model.fit(self.ts_univariate)
+        model.fit(TS_UNIVARIATE)
 
         model_path = os.path.join(tmpdir_fn, "test_model")
         save_model(model, model_path)
@@ -900,89 +721,41 @@ def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
         # Only pass series for global models (LinearRegressionModel)
         # Local models (ExponentialSmoothing) don't need it
         if isinstance(model, GlobalForecastingModel):
-            assert_predictions_equal(model, loaded, n=5, series=self.ts_univariate)
+            assert_predictions_equal(model, loaded, n=5, series=TS_UNIVARIATE)
         else:
             assert_predictions_equal(model, loaded, n=5, is_global=False)
 
     @pytest.mark.parametrize(
         "series,series_name",
         [
-            ("ts_multivariate", "multivariate"),
-            ("ts_with_static", "static_covariates"),
+            (TS_MULTIVARIATE, "multivariate"),
+            (TS_WITH_STATIC, "static_covariates"),
         ],
     )
     def test_save_load_with_special_series(self, tmpdir_fn, series, series_name):
         """Test save/load with multivariate and static covariate series"""
-        test_series = getattr(self, series)
-
         model = LinearRegressionModel(lags=5)
-        model.fit(test_series)
+        model.fit(series)
 
         model_path = os.path.join(tmpdir_fn, f"test_model_{series_name}")
         save_model(model, model_path)
 
         loaded_model = load_model(f"file://{model_path}")
 
-        assert_predictions_equal(model, loaded_model, n=3, series=test_series)
+        assert_predictions_equal(model, loaded_model, n=3, series=series)
 
         # verify the series dimensions are preserved
         pred_original = model.predict(n=3)
-        pred_loaded = loaded_model.predict(n=3, series=test_series)
+        pred_loaded = loaded_model.predict(n=3, series=series)
         assert pred_original.width == pred_loaded.width
 
-    def test_autolog_metric_logging_scalar(self, mlflow_tracking, autolog_context):
-        """Calling a darts metric inside an active run logs a scalar to MLflow."""
-        with autolog_context(log_metrics=True):
-            with mlflow.start_run() as run:
-                result = dm.mae(self.ts_univariate, self.ts_univariate * 1.1)
-
-        run_data = mlflow.get_run(run.info.run_id).data
-        assert "mae" in run_data.metrics, "mae should be logged to MLflow"
-        assert np.isfinite(run_data.metrics["mae"])
-        assert np.isscalar(result)
-        assert np.isfinite(float(result))
-
-    def test_autolog_metric_repeated_call(self, mlflow_tracking, autolog_context):
-        """Calling the same metric twice overwrites the value (last-value-wins)."""
-        with autolog_context(log_metrics=True):
-            with mlflow.start_run() as run:
-                dm.rmse(self.ts_univariate, self.ts_univariate * 1.1)
-                dm.rmse(self.ts_univariate, self.ts_univariate * 1.2)
-
-        run_data = mlflow.get_run(run.info.run_id).data
-        assert "rmse" in run_data.metrics, "rmse should be logged to MLflow"
-
-    def test_autolog_metric_per_component(self, mlflow_tracking, autolog_context):
-        """Non-scalar metric results logged per-component as {name}_{component_name}.
-
-        ts_multivariate = ts_univariate.stack(ts_univariate * 1.5), whose component
-        names are ['linear', 'linear_1'].  With component_reduction=None the result
-        is a 1-D array (one value per component), so the expected keys are
-        'mae_linear' and 'mae_linear_1'.
-        """
-        with autolog_context(log_metrics=True):
-            with mlflow.start_run() as run:
-                dm.mae(
-                    self.ts_multivariate,
-                    self.ts_multivariate * 1.1,
-                    component_reduction=None,
-                )
-
-        run_data = mlflow.get_run(run.info.run_id).data
-        assert "mae_linear" in run_data.metrics, (
-            "Component 'linear' should be logged as mae_linear"
-        )
-        assert "mae_linear_1" in run_data.metrics, (
-            "Component 'linear_1' should be logged as mae_linear_1"
-        )
-        assert np.isfinite(run_data.metrics["mae_linear"])
-        assert np.isfinite(run_data.metrics["mae_linear_1"])
 
+class TestAutoLogStandaloneMetrics:
     def test_autolog_metric_no_active_run(self, mlflow_tracking, autolog_context):
         """Calling a metric without an active run does not raise and returns correctly."""
         with autolog_context(log_metrics=True):
             # called outside any start_run — must not raise
-            result = dm.mse(self.ts_univariate, self.ts_univariate * 1.1)
+            result = dm.mse(TS_UNIVARIATE, TS_UNIVARIATE * 1.1)
 
         assert np.isscalar(result)
         assert np.isfinite(float(result))
@@ -992,13 +765,13 @@ def test_autolog_metric_returns_correct_value(
     ):
         """The patched metric returns the same value whether inside or outside a run."""
         with autolog_context(log_metrics=True):
-            pred = self.ts_univariate * 1.05
+            pred = TS_UNIVARIATE * 1.05
 
             with mlflow.start_run():
-                result_inside = dm.mae(self.ts_univariate, pred)
+                result_inside = dm.mae(TS_UNIVARIATE, pred)
 
             # call outside a run — no logging, same computation
-            result_outside = dm.mae(self.ts_univariate, pred)
+            result_outside = dm.mae(TS_UNIVARIATE, pred)
 
         np.testing.assert_almost_equal(result_inside, result_outside, decimal=6)
         assert np.isfinite(result_inside)
@@ -1007,7 +780,7 @@ def test_autolog_log_metrics_false(self, mlflow_tracking, autolog_context):
         """autolog(log_metrics=False) leaves metrics unpatched — nothing is logged."""
         with autolog_context(log_metrics=False):
             with mlflow.start_run() as run:
-                dm.mape(self.ts_univariate, self.ts_univariate * 1.1)
+                dm.mape(TS_UNIVARIATE, TS_UNIVARIATE * 1.1)
 
         run_data = mlflow.get_run(run.info.run_id).data
         assert "mape" not in run_data.metrics, (
@@ -1023,31 +796,19 @@ def test_autolog_metric_any_import_path_logs(
         attribute, and `darts.metrics.mae` and `darts.metrics.metrics.mae` are
         the same function object.
         """
+        from darts.metrics import mae
+
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run_public:
-                dm.mae(self.ts_univariate, self.ts_univariate * 1.1)
-            with mlflow.start_run() as run_impl:
-                dmm.mae(self.ts_univariate, self.ts_univariate * 1.1)
+                dm.mae(TS_UNIVARIATE, TS_UNIVARIATE * 1.1)
+            with mlflow.start_run() as run_module:
+                dmm.mae(TS_UNIVARIATE, TS_UNIVARIATE * 1.1)
+            with mlflow.start_run() as run_explicit:
+                mae(TS_UNIVARIATE, TS_UNIVARIATE * 1.1)
 
         assert "mae" in mlflow.get_run(run_public.info.run_id).data.metrics
-        assert "mae" in mlflow.get_run(run_impl.info.run_id).data.metrics
-
-    def test_autolog_metric_import_order_independent(
-        self, mlflow_tracking, autolog_context
-    ):
-        """A metric imported via `from darts.metrics import <name>` *before*
-        autolog() is enabled still logs correctly, since the hook lives inside
-        the metric's own `multi_ts_support` decorator rather than patching a
-        module attribute after the fact."""
-        from darts.metrics import mae as mae_imported_before_autolog
-
-        with autolog_context(log_metrics=True):
-            with mlflow.start_run() as run:
-                mae_imported_before_autolog(
-                    self.ts_univariate, self.ts_univariate * 1.1
-                )
-
-        assert "mae" in mlflow.get_run(run.info.run_id).data.metrics
+        assert "mae" in mlflow.get_run(run_module.info.run_id).data.metrics
+        assert "mae" in mlflow.get_run(run_explicit.info.run_id).data.metrics
 
     def test_autolog_metric_internal_composite_call_not_double_logged(
         self, mlflow_tracking, autolog_context
@@ -1057,12 +818,87 @@ def test_autolog_metric_internal_composite_call_not_double_logged(
         not twice (rmse and the internal mse call)."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                dm.rmse(self.ts_univariate, self.ts_univariate * 1.1)
+                dm.rmse(TS_UNIVARIATE, TS_UNIVARIATE * 1.1)
 
         metrics = mlflow.get_run(run.info.run_id).data.metrics
         assert "rmse" in metrics
         assert "mse" not in metrics
 
+    def test_autolog_metric_logging_scalar(self, mlflow_tracking, autolog_context):
+        """Calling a darts metric inside an active run logs a scalar to MLflow."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                result = dm.mae(TS_UNIVARIATE, TS_UNIVARIATE + 0.1)
+
+        run_data = mlflow.get_run(run.info.run_id).data
+        assert "mae" in run_data.metrics, "mae should be logged to MLflow"
+        assert run_data.metrics["mae"] == result == pytest.approx(0.1, abs=1e-5)
+
+    def test_autolog_metric_repeated_call(self, mlflow_tracking, autolog_context):
+        """Calling the same metric twice overwrites the value (last-value-wins)."""
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                dm.rmse(TS_UNIVARIATE, TS_UNIVARIATE)
+                result = dm.rmse(TS_UNIVARIATE, TS_UNIVARIATE + 2.0)
+
+        run_data = mlflow.get_run(run.info.run_id).data
+        assert "rmse" in run_data.metrics, "rmse should be logged to MLflow"
+        assert run_data.metrics["rmse"] == result == pytest.approx(2.0, abs=1e-5)
+
+    def test_autolog_metric_per_component(self, mlflow_tracking, autolog_context):
+        """Non-scalar metric results logged per-component as {name}_{component_name}.
+
+        ts_multivariate = ts_univariate.stack(ts_univariate * 1.5), whose component
+        names are ['sine', 'sine_1'].  With component_reduction=None the result
+        is a 1-D array (one value per component), so the expected keys are
+        'mae_sine' and 'mae_sine_1'.
+        """
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                result = dm.mae(
+                    TS_MULTIVARIATE,
+                    TS_MULTIVARIATE * 1.1,
+                    component_reduction=None,
+                )
+        comps = TS_MULTIVARIATE.components
+        assert result.shape == (len(comps),)
+        run_data = mlflow.get_run(run.info.run_id).data
+        for comp, value in zip(comps, result):
+            assert f"mae_{comp}" in run_data.metrics
+            assert run_data.metrics[f"mae_{comp}"] == value
+
+    def test_autolog_metric_per_component_and_q_label(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Non-scalar metric results logged per-component and quantile / label as
+        {name}_{component_name}_q{quantile / label}.
+
+        E.g. ["mae_comp0_q0.100", ..., "mae_comp0_q0.900", "mae_comp1_q0.100", ..., "mae_comp1_q0.900"]
+        """
+        vals = TS_MULTIVARIATE.all_values()
+        series_prob = TS_MULTIVARIATE.with_values(
+            np.concatenate([vals * 0.9, vals, vals * 1.2], axis=2)
+        )
+        quantiles = [0.1, 0.5, 0.9]
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                result = dm.mae(
+                    TS_MULTIVARIATE,
+                    series_prob,
+                    component_reduction=None,
+                    q=quantiles,
+                )
+        comps = TS_MULTIVARIATE.components
+        assert result.shape == (len(comps) * len(quantiles),)
+        run_data = mlflow.get_run(run.info.run_id).data
+        for c_idx, comp in enumerate(comps):
+            for q_idx, q in enumerate(quantiles):
+                value = result[c_idx * len(quantiles) + q_idx]
+                assert f"mae_{comp}_q{q:.3f}" in run_data.metrics
+                assert run_data.metrics[f"mae_{comp}_q{q:.3f}"] == pytest.approx(
+                    value, abs=1e-5
+                )
+
     def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
         """A per-timestep metric (ae) logs one value per timestep across MLflow steps.
 
@@ -1070,17 +906,14 @@ def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
         axis, which is mapped to the MLflow step (mirroring the backtest path)
         rather than being mislabeled as per-component.
         """
-        train = self.ts_univariate[:40]
-        model = LinearRegressionModel(lags=4)
-        model.fit(train)
-        pred = model.predict(n=10)
-        actual = self.ts_univariate[40:]
-
+        actual = TS_UNIVARIATE[40:]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = dm.ae(actual, pred)
+                ref = dm.ae(actual, actual + 1.0)
 
         ref = np.asarray(ref, dtype=float)  # shape (n_timesteps,)
+        assert ref.shape == (len(actual),)
+
         history = mlflow_tracking.get_metric_history(run.info.run_id, "ae")
         assert len(history) == len(ref), "Expected one step per timestep"
         steps = sorted(m.step for m in history)
@@ -1088,11 +921,47 @@ def test_autolog_metric_per_timestep(self, mlflow_tracking, autolog_context):
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
         np.testing.assert_allclose(logged, ref, atol=1e-5)
 
+    def test_autolog_metric_per_timestep_component_and_q_label(
+        self, mlflow_tracking, autolog_context
+    ):
+        """A per-timestep metric (ae) logs one value per timestep and component and quantile / label as
+        {name}_{component_name}_q{quantile / label}.
+
+        E.g. ["mae_comp0_q0.100", ..., "mae_comp0_q0.900", "mae_comp1_q0.100", ..., "mae_comp1_q0.900"]
+        """
+        actual = TS_MULTIVARIATE[40:]
+        vals = actual.all_values()
+        series_prob = actual.with_values(
+            np.concatenate([vals * 0.9, vals, vals * 1.2], axis=2)
+        )
+        quantiles = [0.1, 0.5, 0.9]
+        comps = actual.components
+
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.ae(actual, series_prob, component_reduction=None, q=quantiles)
+
+        ref = np.asarray(ref, dtype=float)
+        assert ref.shape == (len(actual), len(comps) * len(quantiles))
+
+        for c_idx, comp in enumerate(comps):
+            for q_idx, q in enumerate(quantiles):
+                history = mlflow_tracking.get_metric_history(
+                    run.info.run_id, f"ae_{comp}_q{q:.3f}"
+                )
+                assert len(history) == len(ref)
+                steps = sorted(m.step for m in history)
+                assert steps == list(range(len(actual)))
+                logged = [m.value for m in sorted(history, key=lambda m: m.step)]
+                np.testing.assert_allclose(
+                    logged, ref[:, c_idx * len(quantiles) + q_idx], atol=1e-5
+                )
+
     def test_autolog_metric_aligns_time_axis_by_forecast_position(
         self, mlflow_tracking, autolog_context
     ):
         """Each series starts at forecast position zero."""
-        ts_long = self.ts_univariate  # length 50
+        ts_long = TS_UNIVARIATE  # length 50
         ts_short = ts_long[10:]  # length 40, same end, starts 10 steps later
 
         # distinct constant error per series, so a step's mean reveals exactly
@@ -1112,7 +981,7 @@ def test_autolog_metric_aligns_time_axis_by_forecast_position(
         for step in range(40, 50):
             assert by_step[step] == pytest.approx(1.0, abs=1e-4), step
 
-        rows = self._read_per_series_table(run.info.run_id)
+        rows = _read_per_series_table(run.info.run_id)
         steps_by_series = {0: set(), 1: set()}
         for r in rows:
             steps_by_series[r["series_index"]].add(r["step"])
@@ -1121,19 +990,20 @@ def test_autolog_metric_aligns_time_axis_by_forecast_position(
 
     def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile with matching values."""
-        train = self.ts_univariate[:40]
-        model = LinearRegressionModel(
-            lags=4, likelihood="quantile", quantiles=[0.1, 0.5, 0.9]
+        actual = TS_UNIVARIATE[40:]
+        vals = actual.all_values()
+        pred = actual.with_values(
+            np.concatenate([vals - 1.0, vals, vals + 1.0], axis=2)
         )
-        model.fit(train)
-        pred = model.predict(n=10, num_samples=200)
-        actual = self.ts_univariate[40:]
 
+        quantiles = [0.1, 0.5, 0.9]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = dm.mql(actual, pred, q=[0.1, 0.5, 0.9])
+                ref = dm.mql(actual, pred, q=quantiles)
 
         ref = np.asarray(ref, dtype=float)  # shape (n_quantiles,)
+        assert ref.shape == (len(quantiles),)
+
         m = mlflow.get_run(run.info.run_id).data.metrics
         for i, key in enumerate(("mql_q0.100", "mql_q0.500", "mql_q0.900")):
             assert key in m, f"Expected quantile key {key}"
@@ -1141,38 +1011,45 @@ def test_autolog_metric_quantile(self, mlflow_tracking, autolog_context):
 
     def test_autolog_metric_quantile_interval(self, mlflow_tracking, autolog_context):
         """A quantile interval metric (miw) logs one key per interval."""
-        train = self.ts_univariate[:40]
-        model = LinearRegressionModel(
-            lags=4, likelihood="quantile", quantiles=[0.1, 0.5, 0.9]
+        actual = TS_UNIVARIATE[40:]
+        vals = actual.all_values()
+        pred = actual.with_values(
+            np.concatenate([vals - 1.0, vals, vals + 1.0], axis=2)
         )
-        model.fit(train)
-        pred = model.predict(n=10, num_samples=200)
-        actual = self.ts_univariate[40:]
 
+        q_intervals = [(0.1, 0.9), (0.2, 0.8)]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = dm.miw(actual, pred, q_interval=(0.1, 0.9))
+                ref = dm.miw(
+                    actual,
+                    pred,
+                    q_interval=q_intervals,
+                )
 
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (len(q_intervals),)
         m = mlflow.get_run(run.info.run_id).data.metrics
-        assert "miw_qi0.800" in m
-        assert m["miw_qi0.800"] == pytest.approx(float(ref), abs=1e-5)
+        for i, key in enumerate(("miw_qi0.800", "miw_qi0.600")):
+            assert key in m, f"Expected quantile key {key}"
+            assert m[key] == pytest.approx(ref[i], abs=1e-5)
 
     def test_autolog_metric_multi_series(self, mlflow_tracking, autolog_context):
         """A list of series logs the mean over series; per-series values go to a table."""
-        series = [self.ts_univariate, self.ts_univariate * 1.2]
+        series = [TS_UNIVARIATE, TS_UNIVARIATE * 1.2]
         pred = [s * 1.1 for s in series]
 
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
                 ref = dm.mae(series, pred)
 
-        ref = np.asarray(ref, dtype=float)  # shape (n_series,)
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (len(series),)
         m = mlflow.get_run(run.info.run_id).data.metrics
-        # aggregate = mean over series, no per-series _s{i} keys
-        assert m["mae"] == pytest.approx(float(np.mean(ref)), abs=1e-5)
+        # agg_func = np.nanmean over series, no per-series _s{i} keys
+        assert m["mae"] == pytest.approx(float(np.nanmean(ref)), abs=1e-5)
         assert not any(k.startswith("mae_s") for k in m)
         # granular per-series breakdown written to a table artifact
-        rows = self._read_per_series_table(run.info.run_id)
+        rows = _read_per_series_table(run.info.run_id)
         by_series = {int(row["series_index"]): float(row["value"]) for row in rows}
         assert by_series == pytest.approx({0: ref[0], 1: ref[1]}, abs=1e-5)
 
@@ -1182,7 +1059,7 @@ def test_autolog_metric_multi_series_custom_agg_func(
         """autolog()'s agg_func controls how per-series values are aggregated
         into the single logged metric (default np.mean)."""
         # 3 series with distinct, asymmetric per-series errors so median != mean
-        series = [self.ts_univariate * f for f in (1.0, 1.2, 5.0)]
+        series = [TS_UNIVARIATE * f for f in (1.0, 1.2, 5.0)]
         pred = [s * 1.1 for s in series]
 
         with autolog_context(log_metrics=True, agg_func=np.median):
@@ -1199,37 +1076,39 @@ def test_autolog_metric_multi_series_per_component(
     ):
         """A list of multivariate series with component_reduction=None logs the
         per-component mean over series; the CSV carries one row per (component, series)."""
-        series = [self.ts_multivariate, self.ts_multivariate * 1.2]
+        series = [TS_MULTIVARIATE, TS_MULTIVARIATE * 1.2]
         pred = [s * 1.1 for s in series]
 
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
                 ref = dm.mae(series, pred, component_reduction=None)
 
-        ref = np.asarray(ref, dtype=float)  # shape (n_series, n_components)
+        ref = np.asarray(ref, dtype=float)
+        assert ref.shape == (len(series), len(series[0].components))
+
         m = mlflow.get_run(run.info.run_id).data.metrics
         # aggregate per component = mean over series, no per-series _s{i} keys
-        assert m["mae_linear"] == pytest.approx(float(ref[:, 0].mean()), abs=1e-5)
-        assert m["mae_linear_1"] == pytest.approx(float(ref[:, 1].mean()), abs=1e-5)
+        assert m["mae_sine"] == pytest.approx(float(ref[:, 0].mean()), abs=1e-5)
+        assert m["mae_sine_1"] == pytest.approx(float(ref[:, 1].mean()), abs=1e-5)
         assert not any(k.endswith(("_s0", "_s1")) for k in m)
         # granular CSV: one row per (component, series)
-        rows = self._read_per_series_table(run.info.run_id)
+        rows = _read_per_series_table(run.info.run_id)
         got = {
             (row["key"], int(row["series_index"])): float(row["value"]) for row in rows
         }
-        assert got[("mae_linear", 0)] == pytest.approx(ref[0, 0], abs=1e-5)
-        assert got[("mae_linear", 1)] == pytest.approx(ref[1, 0], abs=1e-5)
-        assert got[("mae_linear_1", 0)] == pytest.approx(ref[0, 1], abs=1e-5)
-        assert got[("mae_linear_1", 1)] == pytest.approx(ref[1, 1], abs=1e-5)
+        assert got[("mae_sine", 0)] == pytest.approx(ref[0, 0], abs=1e-5)
+        assert got[("mae_sine", 1)] == pytest.approx(ref[1, 0], abs=1e-5)
+        assert got[("mae_sine_1", 0)] == pytest.approx(ref[0, 1], abs=1e-5)
+        assert got[("mae_sine_1", 1)] == pytest.approx(ref[1, 1], abs=1e-5)
 
     def test_autolog_metric_name_override(self, mlflow_tracking, autolog_context):
         """The metric `name` kwarg overrides only the metric-name token in the key,
         keeping the backtest prefix and the quantile/axis suffixes."""
-        actual = self.ts_univariate
-        train = self.ts_univariate[:40]
-        qmodel = self._fit_qlr(train)
+        actual = TS_UNIVARIATE
+        train = TS_UNIVARIATE[:40]
+        qmodel = _fit_qlr(train)
         pred = qmodel.predict(n=10, num_samples=200)
-        target = self.ts_univariate[40:]
+        target = TS_UNIVARIATE[40:]
 
         with autolog_context(log_metrics=True):
             # direct call: name replaces the metric token; suffix (_q0_500) preserved
@@ -1238,12 +1117,13 @@ def test_autolog_metric_name_override(self, mlflow_tracking, autolog_context):
                 dm.mql(target, pred, q=0.5, name="myq")
             # backtest: name replaces the metric token; backtest_ prefix preserved
             with mlflow.start_run() as run_bt:
-                self._fit_lr().backtest(
-                    self.ts_univariate,
+                _fit_lr().backtest(
+                    TS_UNIVARIATE,
+                    forecast_horizon=1,
+                    start=-1,
                     metric=dm.mae,
                     metric_kwargs={"name": "custom"},
                     retrain=False,
-                    stride=10,
                 )
 
         direct = mlflow.get_run(run_direct.info.run_id).data.metrics
@@ -1273,11 +1153,13 @@ def test_autolog_metric_multi_series_classification_labels_explicit(
         series = [binary1, binary2]
         pred = series  # perfect predictions → f1 == 1.0 per label per series
 
+        labels = [0, 1]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = dm.f1(series, pred, label_reduction=None, labels=[0, 1])
+                ref = dm.f1(series, pred, label_reduction=None, labels=labels)
 
-        ref = [np.asarray(r, dtype=float).flatten() for r in ref]
+        ref = np.array(ref)
+        assert ref.shape == (len(series), len(labels))
         m = mlflow.get_run(run.info.run_id).data.metrics
         # aggregate per label = mean over series, no per-series _s{i} keys
         assert m["f1_label0"] == pytest.approx(
@@ -1288,7 +1170,7 @@ def test_autolog_metric_multi_series_classification_labels_explicit(
         )
         assert not any(k.endswith(("_s0", "_s1")) for k in m)
         # granular CSV: one row per (label, series)
-        rows = self._read_per_series_table(run.info.run_id)
+        rows = _read_per_series_table(run.info.run_id)
         got = {
             (row["key"], int(row["series_index"])): float(row["value"]) for row in rows
         }
@@ -1301,7 +1183,7 @@ def test_autolog_metric_component_count_mismatch_allowed_when_reduced(
     ):
         """Default mae reduces components to scalars, so mixed component counts
         are valid and log under a single aggregated key."""
-        series = [self.ts_univariate, self.ts_multivariate]
+        series = [TS_UNIVARIATE, TS_MULTIVARIATE]
         pred = [s * 1.1 for s in series]
 
         with autolog_context(log_metrics=True):
@@ -1316,7 +1198,7 @@ def test_autolog_metric_component_count_mismatch_raises(
     ):
         """When components are preserved, mixed component counts raise rather
         than taking names from the first series and mislabeling the rest."""
-        series = [self.ts_univariate, self.ts_multivariate]
+        series = [TS_UNIVARIATE, TS_MULTIVARIATE]
         pred = [s * 1.1 for s in series]
 
         with autolog_context(log_metrics=True):
@@ -1333,13 +1215,13 @@ def test_autolog_metric_size_mismatch_raises(
         (and logs an error), propagating out of the public metric call — the
         metric callback isn't invoked through MLflow's safe_patch, so nothing
         catches it. No metrics are written for the failed call."""
-        actual = self.ts_univariate[40:]
+        actual = TS_UNIVARIATE[40:]
         # mae with component_reduction=None on a univariate series produces shape (T,),
         # which is size T — divisible by c_size=1 (1 component × 1 quantile), so we
         # need to force a mismatch.  We do that by monkey-patching _infer_metric_axes
         # to report has_comp_axis=True with a fake 3-component count, making c_size=3
         # while the actual result is shape (T,).
-        train = self.ts_univariate[:40]
+        train = TS_UNIVARIATE[:40]
         model = LinearRegressionModel(lags=4)
         model.fit(train)
         pred = model.predict(n=10)
@@ -1373,13 +1255,14 @@ def test_autolog_metric_per_series_table_schema_and_single_series_skip(
         """The per-series table has the expected schema for multi-series input, and no
         artifact is written for single-series input (mean == the value itself)."""
         # multi-series: artifact exists with the documented columns
-        multi = [self.ts_univariate, self.ts_univariate * 1.2]
+        multi = [TS_UNIVARIATE, TS_UNIVARIATE * 1.2]
         pred_multi = [s * 1.1 for s in multi]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run_multi:
-                dm.mae(multi, pred_multi)
+                ref = np.asarray(dm.mae(multi, pred_multi), dtype=float)
 
-        rows = self._read_per_series_table(run_multi.info.run_id)
+        assert ref.shape == (len(multi),)
+        rows = _read_per_series_table(run_multi.info.run_id)
         assert list(rows[0].keys()) == [
             "key",
             "series_index",
@@ -1387,10 +1270,15 @@ def test_autolog_metric_per_series_table_schema_and_single_series_skip(
             "window_index",
             "value",
         ]
-        assert {int(r["series_index"]) for r in rows} == {0, 1}
+        for idx, (row, ref_i) in enumerate(zip(rows, ref)):
+            assert row["key"] == "mae"
+            assert int(row["series_index"]) == idx
+            assert np.isnan(row["step"])
+            assert np.isnan(row["window_index"])
+            assert row["value"] == pytest.approx(ref_i, abs=1e-5)
 
         # single-series: no per-series table artifact should be created
-        single = self.ts_univariate
+        single = TS_UNIVARIATE
         pred_single = single * 1.1
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run_single:
@@ -1401,71 +1289,199 @@ def test_autolog_metric_per_series_table_schema_and_single_series_skip(
             "Single-series input should not write a per-series table artifact"
         )
 
+
+class TestAutoLogBacktestMetrics:
     def test_autolog_backtest_scalar(self, mlflow_tracking, autolog_context):
         """Default (reduced) backtest of a single univariate series logs one scalar."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = self._fit_lr().backtest(
-                    self.ts_univariate, metric=dm.mae, retrain=False, stride=10
+                ref = _fit_lr().backtest(
+                    TS_UNIVARIATE, metric=dm.mae, retrain=False, start=-2
                 )
-
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (1,)
         run_data = mlflow.get_run(run.info.run_id).data
         assert "backtest_mae" in run_data.metrics
-        assert run_data.metrics["backtest_mae"] == pytest.approx(float(ref), abs=1e-5)
+        np.testing.assert_almost_equal(run_data.metrics["backtest_mae"], ref)
 
     def test_autolog_backtest_per_window_steps(self, mlflow_tracking, autolog_context):
-        """reduction=None logs per-window values at end-relative steps."""
+        """reduction=None logs per-window values at steps."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = self._fit_lr().backtest(
-                    self.ts_univariate,
+                ref = _fit_lr().backtest(
+                    TS_UNIVARIATE,
                     metric=dm.mae,
                     retrain=False,
-                    stride=10,
+                    start=-2,
                     reduction=None,
                 )
 
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (2,)
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
-        assert len(history) > 1, "Expected multiple per-window steps"
+        assert len(history) == 2
         steps = sorted(m.step for m in history)
         assert steps == list(range(len(history)))
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
-        np.testing.assert_allclose(logged, np.asarray(ref, dtype=float), atol=1e-5)
+        np.testing.assert_array_almost_equal(logged, ref)
 
     def test_autolog_backtest_per_component(self, mlflow_tracking, autolog_context):
         """component_reduction=None logs one key per component name."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = self._fit_lr(self.ts_multivariate).backtest(
-                    self.ts_multivariate,
+                ref = _fit_lr(TS_MULTIVARIATE).backtest(
+                    TS_MULTIVARIATE,
                     metric=dm.mae,
                     retrain=False,
-                    stride=10,
+                    start=-2,
                     metric_kwargs={"component_reduction": None},
                 )
 
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (TS_MULTIVARIATE.n_components,)
         run_data = mlflow.get_run(run.info.run_id).data
-        assert "backtest_mae_linear" in run_data.metrics
-        assert "backtest_mae_linear_1" in run_data.metrics
+        assert "backtest_mae_sine" in run_data.metrics
+        assert "backtest_mae_sine_1" in run_data.metrics
         ref = np.asarray(ref, dtype=float)
-        assert run_data.metrics["backtest_mae_linear"] == pytest.approx(
-            ref[0], abs=1e-5
-        )
-        assert run_data.metrics["backtest_mae_linear_1"] == pytest.approx(
+        assert run_data.metrics["backtest_mae_sine"] == pytest.approx(ref[0], abs=1e-5)
+        assert run_data.metrics["backtest_mae_sine_1"] == pytest.approx(
             ref[1], abs=1e-5
         )
 
+    def test_autolog_backtest_per_component_and_q_label(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Aggregated windows; component_reduction=None logs one key per component name and
+        quantile / label as {name}_{component_name}_q{quantile / label}.
+
+        E.g. ["backtest_mae_comp0_q0.100", ..., "backtest_mae_comp0_q0.900", "backtest_mae_comp1_q0.100",
+        ..., "backtest_mae_comp1_q0.900"]
+        """
+        quantiles = [0.1, 0.5, 0.9]
+        comps = TS_MULTIVARIATE.components
+        n_windows = 2
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = _fit_qlr(TS_MULTIVARIATE).backtest(
+                    TS_MULTIVARIATE,
+                    metric=dm.mae,
+                    retrain=False,
+                    start=-n_windows,
+                    num_samples=3,
+                    metric_kwargs={"component_reduction": None, "q": quantiles},
+                    reduction=np.nanmean,
+                )
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (len(comps) * len(quantiles),)
+
+        run_data = mlflow.get_run(run.info.run_id).data
+        for c_idx, comp in enumerate(comps):
+            for q_idx, q in enumerate(quantiles):
+                name = f"backtest_mae_{comp}_q{q:.3f}"
+                assert name in run_data.metrics
+                assert run_data.metrics[name] == pytest.approx(
+                    ref[c_idx * len(quantiles) + q_idx], abs=1e-5
+                )
+
+    def test_autolog_backtest_per_window_component_and_q_label(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Stepped window metrics; component_reduction=None logs one key per window, component name and
+        quantile / label as {name}_{component_name}_q{quantile / label}.
+
+        E.g. ["backtest_mae_comp0_q0.100", ..., "backtest_mae_comp0_q0.900", "backtest_mae_comp1_q0.100",
+        ..., "backtest_mae_comp1_q0.900"]
+        """
+        quantiles = [0.1, 0.5, 0.9]
+        comps = TS_MULTIVARIATE.components
+        n_windows = 2
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = _fit_qlr(TS_MULTIVARIATE).backtest(
+                    TS_MULTIVARIATE,
+                    metric=dm.mae,
+                    retrain=False,
+                    start=-n_windows,
+                    num_samples=3,
+                    metric_kwargs={"component_reduction": None, "q": quantiles},
+                    reduction=None,
+                )
+        ref = np.atleast_2d(ref)
+        assert ref.shape == (n_windows, len(comps) * len(quantiles))
+
+        for c_idx, comp in enumerate(comps):
+            for q_idx, q in enumerate(quantiles):
+                history = mlflow_tracking.get_metric_history(
+                    run.info.run_id, f"backtest_mae_{comp}_q{q:.3f}"
+                )
+                assert len(history) == len(ref)
+                steps = sorted(m.step for m in history)
+                assert steps == list(range(len(ref)))
+                logged = [m.value for m in sorted(history, key=lambda m: m.step)]
+                np.testing.assert_allclose(
+                    logged, ref[:, c_idx * len(quantiles) + q_idx], atol=1e-5
+                )
+
+    @pytest.mark.parametrize("reduction", [None, np.nanmean])
+    def test_autolog_backtest_per_timestep_component_and_q_label(
+        self, mlflow_tracking, autolog_context, reduction
+    ):
+        """Stepped horizon metrics; component_reduction=None logs one key per window, component name and
+        quantile / label as {name}_{component_name}_q{quantile / label}.
+
+        E.g. ["backtest_mae_comp0_q0.100", ..., "backtest_mae_comp0_q0.900", "backtest_mae_comp1_q0.100",
+        ..., "backtest_mae_comp1_q0.900"]
+        """
+        quantiles = [0.1, 0.5, 0.9]
+        comps = TS_MULTIVARIATE.components
+        horizon = 3
+        n_windows = 2
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = _fit_qlr(TS_MULTIVARIATE).backtest(
+                    TS_MULTIVARIATE,
+                    metric=dm.ae,
+                    retrain=False,
+                    start=-(horizon + n_windows - 1),
+                    forecast_horizon=horizon,
+                    num_samples=3,
+                    metric_kwargs={"component_reduction": None, "q": quantiles},
+                    reduction=reduction,
+                )
+        if reduction is None:
+            assert ref.shape == (n_windows, horizon, len(comps) * len(quantiles))
+            ref = np.nanmean(ref, axis=0)
+
+        ref = np.atleast_2d(ref)
+        assert ref.shape == (horizon, len(comps) * len(quantiles))
+
+        for c_idx, comp in enumerate(comps):
+            for q_idx, q in enumerate(quantiles):
+                history = mlflow_tracking.get_metric_history(
+                    run.info.run_id, f"backtest_ae_{comp}_q{q:.3f}"
+                )
+                assert len(history) == len(ref)
+                steps = sorted(m.step for m in history)
+                assert steps == list(range(len(ref)))
+                logged = [m.value for m in sorted(history, key=lambda m: m.step)]
+                np.testing.assert_allclose(
+                    logged, ref[:, c_idx * len(quantiles) + q_idx], atol=1e-5
+                )
+
     def test_autolog_backtest_multi_metric(self, mlflow_tracking, autolog_context):
         """Multiple metrics are logged under one key each."""
+        metrics = [dm.mae, dm.rmse]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = self._fit_lr().backtest(
-                    self.ts_univariate,
-                    metric=[dm.mae, dm.rmse],
+                ref = _fit_lr().backtest(
+                    TS_UNIVARIATE,
+                    metric=metrics,
                     retrain=False,
-                    stride=10,
+                    start=-2,
                 )
 
+        ref = np.array(ref)
+        assert ref.shape == (len(metrics),)
         run_data = mlflow.get_run(run.info.run_id).data
         assert "backtest_mae" in run_data.metrics
         assert "backtest_rmse" in run_data.metrics
@@ -1478,12 +1494,14 @@ def test_autolog_backtest_multi_metric(self, mlflow_tracking, autolog_context):
 
     def test_autolog_backtest_multi_series(self, mlflow_tracking, autolog_context):
         """A list of series logs the mean over series; per-series values go to a table."""
-        series = [self.ts_univariate, self.ts_univariate * 1.2]
+        series = [TS_UNIVARIATE, TS_UNIVARIATE * 1.2]
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = self._fit_lr(series).backtest(
-                    series, metric=dm.mae, retrain=False, stride=10
+                ref = _fit_lr(series).backtest(
+                    series, metric=dm.mae, retrain=False, start=-2
                 )
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (len(series),)
 
         run_data = mlflow.get_run(run.info.run_id).data
         # aggregate = mean over series, no per-series _s{i} keys
@@ -1492,96 +1510,115 @@ def test_autolog_backtest_multi_series(self, mlflow_tracking, autolog_context):
         )
         assert not any(k.startswith("backtest_mae_s") for k in run_data.metrics)
         # granular per-series breakdown written to a table artifact
-        rows = self._read_per_series_table(run.info.run_id)
+        rows = _read_per_series_table(run.info.run_id)
         by_series = {int(row["series_index"]): float(row["value"]) for row in rows}
         assert by_series == pytest.approx(
             {0: float(ref[0]), 1: float(ref[1])}, abs=1e-5
         )
 
-    def test_autolog_backtest_multi_series_custom_agg_func(self, mlflow_tracking):
+    @pytest.mark.parametrize("horizon", [1, 3])
+    def test_autolog_backtest_multi_series_custom_agg_func(
+        self, autolog_context, mlflow_tracking, horizon
+    ):
         """autolog()'s agg_func also controls the backtest() aggregation
         (default np.mean). Calls _log_backtest_metrics directly with a
         fabricated result so the per-series values are exact."""
-        backtest_args = {
-            "metric": dm.mae,
-            "metric_kwargs": {},
-            "series": [self.ts_univariate, self.ts_univariate, self.ts_univariate],
-            "forecast_horizon": 1,
-            "reduction": np.mean,
-            "last_points_only": True,
-        }
-        result = [1.0, 2.0, 100.0]  # asymmetric -> median != mean
-
-        with mlflow.start_run() as run:
-            client = MlflowAutologgingQueueingClient()
-            _log_backtest_metrics(
-                client,
-                run.info.run_id,
-                result,
-                {**BT_REQUIRED_DEFAUTLS, **backtest_args},
-                agg_func=np.median,
-            )
-            client.flush(synchronous=True)
-
+        series = TS_UNIVARIATE
+        series = [series] * 3
+        with autolog_context(log_metrics=True, agg_func=np.median):
+            with mlflow.start_run() as run:
+                ref = _fit_lr(series[0]).backtest(
+                    metric=dm.mae,
+                    series=series,
+                    last_points_only=True,
+                    start=-(horizon + 1),
+                    forecast_horizon=horizon,
+                )
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (len(series),)  # (n_series,)
         m = mlflow.get_run(run.info.run_id).data.metrics
-        assert m["backtest_mae"] == pytest.approx(2.0)
 
-    def test_autolog_backtest_per_timestep_scalar(
-        self, mlflow_tracking, autolog_context
+        assert m["backtest_mae"] == pytest.approx(np.median(ref))
+
+    @pytest.mark.parametrize("horizon", [1, 3])
+    def test_autolog_backtest_per_timestep_with_reduction(
+        self, mlflow_tracking, autolog_context, horizon
     ):
-        """A per-timestep metric (ae) under default reduction collapses to one scalar."""
+        """A per-timestep metric (ae) under default reduction collapses to one
+        scalar per forecast horizon."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = self._fit_lr().backtest(
-                    self.ts_univariate, metric=dm.ae, retrain=False, stride=10
+                ref = _fit_lr().backtest(
+                    TS_UNIVARIATE,
+                    metric=dm.ae,
+                    retrain=False,
+                    start=-(horizon + 1),
+                    last_points_only=False,
+                    forecast_horizon=horizon,
+                    reduction=np.nanmean,
                 )
-
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (horizon,)  # (n_windows, horizon)
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
-        assert len(history) == 1, "Default reduction should yield a single value"
-        assert history[0].value == pytest.approx(float(ref), abs=1e-5)
+        assert len(history) == horizon, "Default reduction should yield a single value"
+        values = [m.value for m in sorted(history, key=lambda m: m.step)]
+        np.testing.assert_array_almost_equal(values, ref)
 
-    def test_autolog_backtest_per_timestep_per_window(
-        self, mlflow_tracking, autolog_context
+        # no per-series table due to reduction
+        with pytest.raises(mlflow.exceptions.MlflowException):
+            _ = _read_per_series_table(run.info.run_id)
+
+    @pytest.mark.parametrize("horizon", [1, 3])
+    def test_autolog_backtest_per_timestep_without_reduction(
+        self, mlflow_tracking, autolog_context, horizon
     ):
-        """Window-level ae results aggregate per horizon step in the chart and
-        remain available with their window index in the detailed table."""
+        """A per-timestep metric (ae) under default reduction collapses to one
+        scalar per forecast horizon."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                ref = self._fit_lr().backtest(
-                    self.ts_univariate,
+                ref = _fit_lr().backtest(
+                    TS_UNIVARIATE,
                     metric=dm.ae,
                     retrain=False,
-                    stride=10,
-                    forecast_horizon=4,
+                    start=-(horizon + 1),
+                    last_points_only=False,
+                    forecast_horizon=horizon,
                     reduction=None,
                 )
-
-        ref = np.asarray(ref, dtype=float)  # shape (n_windows, forecast_horizon)
+        n_windows = 2
+        ref = np.atleast_2d(ref)
+        ref = ref.T if ref.shape[1] == n_windows else ref
+        assert ref.shape == (n_windows, horizon)  # (n_windows, horizon)
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
-        assert len(history) == 4, "Expected one step per forecast horizon timestep"
-        logged = [m.value for m in sorted(history, key=lambda m: m.step)]
-        np.testing.assert_allclose(logged, np.nanmean(ref, axis=0), atol=1e-5)
+        assert len(history) == horizon, "Default reduction should yield a single value"
+        values = [m.value for m in sorted(history, key=lambda m: m.step)]
+        np.testing.assert_array_almost_equal(values, np.nanmean(ref, axis=0))
 
-        rows = self._read_per_series_table(run.info.run_id)
+        rows = _read_per_series_table(run.info.run_id)
         assert len(rows) == ref.size
-        for row in rows:
-            assert row["key"] == "backtest_ae"
-            assert row["window_index"] in range(len(ref))
-        assert {int(r["window_index"]) for r in rows} == set(range(len(ref)))
-
+        for t_idx in range(horizon):
+            for w_idx in range(n_windows):  # windows
+                row = rows[t_idx * n_windows + w_idx]
+                assert row["key"] == "backtest_ae"
+                assert row["series_index"] == 0
+                assert row["step"] == t_idx
+                assert row["window_index"] == w_idx
+                assert row["value"] == pytest.approx(ref[w_idx, t_idx])
+
+    @pytest.mark.parametrize("horizon", [1, 3])
     def test_autolog_backtest_historical_forecasts_horizon_inferred(
-        self, mlflow_tracking, autolog_context
+        self, mlflow_tracking, autolog_context, horizon
     ):
         """When `historical_forecasts` is user-supplied, `backtest()` ignores the
         `forecast_horizon` argument, autologging must infer the true window
         length from the historical forecasts themselves, not from the (unused,
         defaulted) `forecast_horizon` argument."""
-        model = self._fit_lr()
+        model = _fit_lr()
         hf = model.historical_forecasts(
-            self.ts_univariate,
+            TS_UNIVARIATE,
             retrain=False,
-            stride=10,
-            forecast_horizon=4,
+            start=-(horizon + 1),
+            forecast_horizon=horizon,
             last_points_only=False,
         )
         with autolog_context(log_metrics=True):
@@ -1589,15 +1626,20 @@ def test_autolog_backtest_historical_forecasts_horizon_inferred(
                 # forecast_horizon is not passed (defaults to 1) and would be
                 # wrong; the real horizon (4) must come from `hf`.
                 ref = model.backtest(
-                    self.ts_univariate,
+                    TS_UNIVARIATE,
                     historical_forecasts=hf,
                     metric=dm.ae,
                     reduction=None,
                 )
 
-        ref = np.asarray(ref, dtype=float)  # shape (n_windows, forecast_horizon)
+        n_windows = 2
+        ref = np.atleast_2d(ref)
+        ref = ref.T if ref.shape[1] == n_windows else ref
+        assert ref.shape == (n_windows, horizon)
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
-        assert len(history) == 4, "Expected one step per forecast horizon timestep"
+        assert len(history) == horizon, (
+            "Expected one step per forecast horizon timestep"
+        )
         logged = [m.value for m in sorted(history, key=lambda m: m.step)]
         np.testing.assert_allclose(logged, np.nanmean(ref, axis=0), atol=1e-5)
 
@@ -1605,23 +1647,25 @@ def test_autolog_backtest_quantile(self, mlflow_tracking, autolog_context):
         """A quantile metric (mql) logs one key per quantile."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                self._fit_qlr().backtest(
-                    self.ts_univariate,
+                ref = _fit_qlr().backtest(
+                    TS_UNIVARIATE,
                     metric=dm.mql,
                     metric_kwargs={"q": [0.1, 0.5, 0.9]},
                     retrain=False,
-                    stride=10,
+                    start=-2,
                     num_samples=200,
                 )
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (3,)  # (n_quantiles,)
 
         m = mlflow.get_run(run.info.run_id).data.metrics
-        for key in (
+        for idx, key in enumerate([
             "backtest_mql_q0.100",
             "backtest_mql_q0.500",
             "backtest_mql_q0.900",
-        ):
+        ]):
             assert key in m, f"Expected quantile key {key}"
-            assert np.isfinite(m[key])
+            assert m[key] == pytest.approx(ref[idx])
 
     def test_autolog_backtest_mixed_degenerate_axes_keep_metric_names(
         self, mlflow_tracking, autolog_context
@@ -1629,11 +1673,11 @@ def test_autolog_backtest_mixed_degenerate_axes_keep_metric_names(
         """Metrics whose differing axes have size one keep their metric names."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                self._fit_lr().backtest(
-                    self.ts_univariate,
+                _ = _fit_lr().backtest(
+                    TS_UNIVARIATE,
                     metric=[dm.mae, dm.ae],
                     retrain=False,
-                    stride=10,
+                    start=-2,
                 )
 
         m = mlflow.get_run(run.info.run_id).data.metrics
@@ -1647,12 +1691,12 @@ def test_autolog_backtest_classification_labels_in_data(
         """f1 with explicit labels present in the series logs finite per-label keys."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                self._fit_lr(self.ts_binary).backtest(
-                    self.ts_binary,
+                _fit_lr(TS_BINARY).backtest(
+                    TS_BINARY,
                     metric=dm.f1,
                     metric_kwargs={"label_reduction": None, "labels": [0, 1]},
                     retrain=False,
-                    stride=10,
+                    start=10,
                 )
 
         m = mlflow.get_run(run.info.run_id).data.metrics
@@ -1668,8 +1712,8 @@ def test_autolog_backtest_classification_labels_not_in_data(
         the scores are NaN (the labels never appear in any window)."""
         with autolog_context(log_metrics=True):
             with mlflow.start_run() as run:
-                self._fit_lr(self.ts_binary).backtest(
-                    self.ts_binary,
+                _fit_lr(TS_BINARY).backtest(
+                    TS_BINARY,
                     metric=dm.f1,
                     metric_kwargs={"label_reduction": None, "labels": [5, 10]},
                     retrain=False,
@@ -1683,32 +1727,28 @@ def test_autolog_backtest_classification_labels_not_in_data(
         assert np.isnan(m["backtest_f1_label10"])
 
     def test_log_backtest_metrics_component_count_mismatch_allowed_when_reduced(
-        self, mlflow_tracking
+        self, autolog_context, mlflow_tracking
     ):
         """Default mae reduces components to scalars, so mixed component counts
         aggregate normally. Calls _log_backtest_metrics directly."""
-        backtest_args = {
-            "metric": dm.mae,
-            "metric_kwargs": {},
-            "series": [self.ts_univariate, self.ts_multivariate],
-            "forecast_horizon": 1,
-            "reduction": np.mean,
-            "last_points_only": True,
-        }
-        result = [1.0, 3.0]
 
-        with mlflow.start_run() as run:
-            client = MlflowAutologgingQueueingClient()
-            _log_backtest_metrics(
-                client,
-                run.info.run_id,
-                result,
-                {**BT_REQUIRED_DEFAUTLS, **backtest_args},
-            )
-            client.flush(synchronous=True)
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = _fit_lr().backtest(
+                    metric=dm.mae,
+                    metric_kwargs={},
+                    series=[TS_UNIVARIATE, TS_MULTIVARIATE],
+                    forecast_horizon=1,
+                    reduction=np.mean,
+                    last_points_only=True,
+                    start=-2,
+                    retrain=True,
+                )
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (2,)  # (n_series,)
 
         m = mlflow.get_run(run.info.run_id).data.metrics
-        assert m["backtest_mae"] == pytest.approx(2.0)
+        assert m["backtest_mae"] == pytest.approx(np.mean(ref))
 
     def test_log_backtest_metrics_component_count_mismatch_raises(
         self, mlflow_tracking
@@ -1722,7 +1762,7 @@ def test_log_backtest_metrics_component_count_mismatch_raises(
         backtest_args = {
             "metric": dm.mae,
             "metric_kwargs": {"component_reduction": None},
-            "series": [self.ts_univariate, self.ts_multivariate],
+            "series": [TS_UNIVARIATE, TS_MULTIVARIATE],
             "forecast_horizon": 1,
             "reduction": np.mean,
             "last_points_only": True,
@@ -1803,234 +1843,219 @@ def test_log_backtest_metrics_label_count_mismatch(self, mlflow_tracking):
 
         assert not mlflow.get_run(run.info.run_id).data.metrics
 
-    def test_log_backtest_metrics_aligns_windows_by_end_date(self, mlflow_tracking):
+    def test_log_backtest_metrics_aligns_windows_by_end_date(
+        self, autolog_context, mlflow_tracking
+    ):
         """A shorter series' window axis aligns from the end, not the start,
         so its last windows overlap the tail of a longer series. Steps and
         ``window_index`` are ``0 .. max_w - 1``. Calls _log_backtest_metrics
         directly with a fabricated result so the window counts per series
         are exact."""
-        backtest_args = {
-            "metric": dm.mae,
-            "metric_kwargs": {},
-            "series": [self.ts_univariate, self.ts_univariate],
-            "forecast_horizon": 1,
-            "reduction": None,
-            "last_points_only": False,
-        }
-        # series 0 has 5 windows; series 1 (shorter, later-starting) has 3
-        result = [np.array([1.0, 2.0, 3.0, 4.0, 5.0]), np.array([10.0, 20.0, 30.0])]
-
-        with mlflow.start_run() as run:
-            client = MlflowAutologgingQueueingClient()
-            _log_backtest_metrics(
-                client,
-                run.info.run_id,
-                result,
-                {**BT_REQUIRED_DEFAUTLS, **backtest_args},
-            )
-            client.flush(synchronous=True)
+        model = _fit_lr()
+        input_length = abs(min(model.lags["target"]))
+        n_windows = 2
+        series_two_windows = TS_UNIVARIATE[-(input_length + n_windows) :]
+        # series 0 has 2 windows; series 1 (shorter, later-starting) has 1
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = model.backtest(
+                    metric=dm.mae,
+                    metric_kwargs={},
+                    series=[series_two_windows, series_two_windows[1:]],
+                    forecast_horizon=1,
+                    reduction=None,
+                    last_points_only=False,
+                    start=-n_windows,
+                    retrain=False,
+                )
+        expected_shapes = [(n_windows,), (n_windows - 1,)]
+        for ref_i, shape in zip(ref, expected_shapes):
+            assert ref_i.shape == shape
 
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
         logged = {m.step: m.value for m in history}
-        assert logged == pytest.approx({0: 1.0, 1: 2.0, 2: 6.5, 3: 12.0, 4: 17.5})
-
-        rows = self._read_per_series_table(run.info.run_id)
-        by_step = {(r["series_index"], r["step"]): r["value"] for r in rows}
-        assert by_step[(0, 0)] == pytest.approx(1.0)
-        assert by_step[(0, 4)] == pytest.approx(5.0)
-        assert by_step[(1, 2)] == pytest.approx(10.0)
-        assert by_step[(1, 4)] == pytest.approx(30.0)
-        assert (1, 0) not in by_step
-        assert (1, 1) not in by_step
-        for r in rows:
-            assert r["window_index"] == r["step"]
+        assert logged == pytest.approx({
+            0: ref[0][0],
+            1: np.mean([ref[0][1], ref[1][0]]),
+        })
+
+        rows = _read_per_series_table(run.info.run_id)
+        for s_idx, shape in enumerate(expected_shapes):
+            w_size = shape[0]
+            for w_idx in range(w_size):
+                row = rows[s_idx * n_windows + w_idx]
+                window_index = w_idx + n_windows - w_size
+                assert row["key"] == "backtest_mae"
+                assert row["series_index"] == s_idx
+                assert row["step"] == window_index
+                assert row["window_index"] == window_index
+                assert row["value"] == pytest.approx(ref[s_idx][w_idx])
 
     def test_log_backtest_metrics_aligns_last_points_only_time_axis(
-        self, mlflow_tracking
+        self, autolog_context, mlflow_tracking
     ):
         """last_points_only stitches windows into one series scored per real
         timestep. Shorter series align from the end, same as the window-axis
         case, with steps ``0 .. t_max - 1`` rather than end-relative indexes.
         """
-        backtest_args = {
-            "metric": dm.ae,
-            "metric_kwargs": {},
-            "series": [self.ts_univariate, self.ts_univariate],
-            "forecast_horizon": 1,
-            "reduction": None,
-            "last_points_only": True,
-        }
-        # series 0 has 5 timesteps; series 1 (shorter, later-starting) has 3
-        result = [np.array([1.0, 2.0, 3.0, 4.0, 5.0]), np.array([10.0, 20.0, 30.0])]
+        model = _fit_lr()
+        input_length = abs(min(model.lags["target"]))
+        n_windows = 2
+        horizon = 3
+        series_two_windows = TS_UNIVARIATE[-(input_length + horizon + n_windows - 1) :]
+        # series 0 forecast has 2 timesteps; series 1 (shorter, later-starting) has 1
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = model.backtest(
+                    metric=dm.ae,
+                    series=[series_two_windows, series_two_windows[1:]],
+                    forecast_horizon=horizon,
+                    reduction=None,
+                    last_points_only=True,
+                    start=-(horizon + n_windows - 1),
+                    retrain=False,
+                )
 
-        with mlflow.start_run() as run:
-            client = MlflowAutologgingQueueingClient()
-            _log_backtest_metrics(
-                client,
-                run.info.run_id,
-                result,
-                {**BT_REQUIRED_DEFAUTLS, **backtest_args},
-            )
-            client.flush(synchronous=True)
+        ref = [np.atleast_1d(ref_i) for ref_i in ref]
+        expected_shapes = [(n_windows,), (n_windows - 1,)]
+        for ref_i, shape in zip(ref, expected_shapes):
+            assert ref_i.shape == shape
 
         history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
         logged = {m.step: m.value for m in history}
-        assert logged == pytest.approx({0: 1.0, 1: 2.0, 2: 6.5, 3: 12.0, 4: 17.5})
-
-        rows = self._read_per_series_table(run.info.run_id)
-        by_step = {(r["series_index"], r["step"]): r["value"] for r in rows}
-        assert by_step[(0, 0)] == pytest.approx(1.0)
-        assert by_step[(0, 4)] == pytest.approx(5.0)
-        assert by_step[(1, 2)] == pytest.approx(10.0)
-        assert by_step[(1, 4)] == pytest.approx(30.0)
-        assert (1, 0) not in by_step
-        assert (1, 1) not in by_step
-        assert all(pd.isna(r["window_index"]) for r in rows)
-
-    @staticmethod
-    def _read_per_series_table(run_id):
-        """Load the run's consolidated per-series metric table into row dicts."""
-        df = mlflow.load_table(
-            artifact_file="metrics_per_series.json", run_ids=[run_id]
-        )
-        return df.to_dict("records")
-
-    def _fit_lr(self, series=None):
-        """Fit and return a fresh LinearRegressionModel (no active run)."""
-        model = LinearRegressionModel(lags=4)
-        model.fit(series if series is not None else self.ts_univariate)
-        return model
-
-    def _fit_qlr(self, series=None):
-        """Fit and return a fresh quantile LinearRegressionModel (no active run)."""
-        model = LinearRegressionModel(
-            lags=4, likelihood="quantile", quantiles=[0.1, 0.5, 0.9]
-        )
-        model.fit(series if series is not None else self.ts_univariate)
-        return model
-
-
-@pytest.mark.parametrize(
-    "metric_name, metric_kwargs, expected",
-    [
-        ("mae", {}, dict(has_time_axis=False, has_comp_axis=False, axis_size=1)),
-        ("ae", {}, dict(has_time_axis=True, has_comp_axis=False, axis_size=1)),
-        ("mae", {"component_reduction": None}, dict(has_comp_axis=True)),
-    ],
-)
-def test_infer_metric_axes_reductions(metric_name, metric_kwargs, expected):
-    has_time_axis, has_comp_axis, axis_labels = _infer_metric_axes(
-        getattr(dm, metric_name), metric_kwargs
-    )
-    actual = {
-        "has_time_axis": has_time_axis,
-        "has_comp_axis": has_comp_axis,
-        "axis_size": len(axis_labels),
-    }
-    for attr, value in expected.items():
-        assert actual[attr] == value
-
-
-def test_infer_metric_axes_quantiles():
-    _, _, axis_labels = _infer_metric_axes(dm.mql, {"q": [0.1, 0.5, 0.9]})
-    assert axis_labels == ["_q0.100", "_q0.500", "_q0.900"]
-
-
-def test_infer_metric_axes_quantile_interval():
-    has_time, _, axis_labels = _infer_metric_axes(dm.iw, {"q_interval": (0.1, 0.9)})
-    assert axis_labels == ["_qi0.800"]
-    assert has_time is True
-
-
-def test_infer_metric_axes_unknown_labels_raises():
-    """label_reduction=None with no explicit labels cannot determine the number
-    of output labels ahead of time, so this raises rather than falling back."""
-    with pytest.raises(ValueError, match="requires explicit `labels`"):
-        _infer_metric_axes(dm.f1, {"label_reduction": None})
-
-
-def test_build_metric_keys_components_and_quantiles():
-    """Shared key builder expands components x quantile suffixes per metric."""
-    metric_axes = [
-        (False, True, ["_q0.100", "_q0.900"]),
-        (False, True, ["_label0", "_label1"]),
-    ]
-    metric_keys = _build_metric_keys(
-        ["mae", "f1"],
-        ["temp", "hum"],
-        has_comp_axis=True,
-        metric_axes=metric_axes,
-        prefix="backtest_",
-    )
-    c_size = len(metric_keys[0])
-    assert c_size == 4
-    assert metric_keys == [
-        [
-            "backtest_mae_temp_q0.100",
-            "backtest_mae_temp_q0.900",
-            "backtest_mae_hum_q0.100",
-            "backtest_mae_hum_q0.900",
-        ],
+        assert logged == pytest.approx({
+            0: ref[0][0],
+            1: np.mean([ref[0][1], ref[1][0]]),
+        })
+
+        rows = _read_per_series_table(run.info.run_id)
+        for s_idx, shape in enumerate(expected_shapes):
+            w_size = shape[0]
+            for w_idx in range(w_size):
+                row = rows[s_idx * n_windows + w_idx]
+                window_index = w_idx + n_windows - w_size
+                assert row["key"] == "backtest_ae"
+                assert row["series_index"] == s_idx
+                assert row["step"] == window_index
+                assert np.isnan(row["window_index"])
+                assert row["value"] == pytest.approx(ref[s_idx][w_idx])
+
+
+class TestAutoLogMetricHelperFunctions:
+    @pytest.mark.parametrize(
+        "metric_name, metric_kwargs, expected",
         [
-            "backtest_f1_temp_label0",
-            "backtest_f1_temp_label1",
-            "backtest_f1_hum_label0",
-            "backtest_f1_hum_label1",
+            ("mae", {}, dict(has_time_axis=False, has_comp_axis=False, axis_size=1)),
+            ("ae", {}, dict(has_time_axis=True, has_comp_axis=False, axis_size=1)),
+            ("mae", {"component_reduction": None}, dict(has_comp_axis=True)),
         ],
-    ]
-
-
-def test_build_metric_keys_no_components_no_prefix():
-    """Without components, each metric gets one key per axis label."""
-    metric_axes = [(False, False, ["_q0.500"])]
-    metric_keys = _build_metric_keys(
-        ["mql"],
-        ["ignored"],
-        has_comp_axis=False,
-        metric_axes=metric_axes,
     )
-    c_size = len(metric_keys[0])
-    assert c_size == 1
-    assert metric_keys == [["mql_q0.500"]]
-
-
-def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
-    """Cells are aggregated with agg_func; supplied table rows are persisted."""
-    agg = {
-        ("mae", 0): [1.0, 3.0],
-        ("mae", 1): [10.0, 30.0],
-    }
-    rows = [
-        {"key": "mae", "series_index": 0, "step": 0, "value": 1.0},
-        {"key": "mae", "series_index": 1, "step": 0, "value": 3.0},
-        {"key": "mae", "series_index": 0, "step": 1, "value": 10.0},
-        {"key": "mae", "series_index": 1, "step": 1, "value": 30.0},
-    ]
-    with mlflow.start_run() as run:
-        client = MlflowAutologgingQueueingClient()
-        _flush_logged_metrics(
-            client, run.info.run_id, agg, agg_func=np.mean, table_rows=rows
+    def test_infer_metric_axes_reductions(metric_name, metric_kwargs, expected):
+        has_time_axis, has_comp_axis, axis_labels = _infer_metric_axes(
+            getattr(dm, metric_name), metric_kwargs
         )
-        client.flush(synchronous=True)
-
-    history0 = mlflow_tracking.get_metric_history(run.info.run_id, "mae")
-    logged = {m.step: m.value for m in history0}
-    assert logged[0] == pytest.approx(2.0)
-    assert logged[1] == pytest.approx(20.0)
-    table = mlflow.load_table(
-        artifact_file="metrics_per_series.json", run_ids=[run.info.run_id]
-    )
-    assert len(table) == 4
+        actual = {
+            "has_time_axis": has_time_axis,
+            "has_comp_axis": has_comp_axis,
+            "axis_size": len(axis_labels),
+        }
+        for attr, value in expected.items():
+            assert actual[attr] == value
+
+    def test_infer_metric_axes_quantiles():
+        _, _, axis_labels = _infer_metric_axes(dm.mql, {"q": [0.1, 0.5, 0.9]})
+        assert axis_labels == ["_q0.100", "_q0.500", "_q0.900"]
+
+    def test_infer_metric_axes_quantile_interval():
+        has_time, _, axis_labels = _infer_metric_axes(dm.iw, {"q_interval": (0.1, 0.9)})
+        assert axis_labels == ["_qi0.800"]
+        assert has_time is True
+
+    def test_infer_metric_axes_unknown_labels_raises():
+        """label_reduction=None with no explicit labels cannot determine the number
+        of output labels ahead of time, so this raises rather than falling back."""
+        with pytest.raises(ValueError, match="requires explicit `labels`"):
+            _infer_metric_axes(dm.f1, {"label_reduction": None})
+
+    def test_build_metric_keys_components_and_quantiles():
+        """Shared key builder expands components x quantile suffixes per metric."""
+        metric_axes = [
+            (False, True, ["_q0.100", "_q0.900"]),
+            (False, True, ["_label0", "_label1"]),
+        ]
+        metric_keys = _build_metric_keys(
+            ["mae", "f1"],
+            ["temp", "hum"],
+            has_comp_axis=True,
+            metric_axes=metric_axes,
+            prefix="backtest_",
+        )
+        c_size = len(metric_keys[0])
+        assert c_size == 4
+        assert metric_keys == [
+            [
+                "backtest_mae_temp_q0.100",
+                "backtest_mae_temp_q0.900",
+                "backtest_mae_hum_q0.100",
+                "backtest_mae_hum_q0.900",
+            ],
+            [
+                "backtest_f1_temp_label0",
+                "backtest_f1_temp_label1",
+                "backtest_f1_hum_label0",
+                "backtest_f1_hum_label1",
+            ],
+        ]
 
+    def test_build_metric_keys_no_components_no_prefix():
+        """Without components, each metric gets one key per axis label."""
+        metric_axes = [(False, False, ["_q0.500"])]
+        metric_keys = _build_metric_keys(
+            ["mql"],
+            ["ignored"],
+            has_comp_axis=False,
+            metric_axes=metric_axes,
+        )
+        c_size = len(metric_keys[0])
+        assert c_size == 1
+        assert metric_keys == [["mql_q0.500"]]
+
+    def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
+        """Cells are aggregated with agg_func; supplied table rows are persisted."""
+        agg = {
+            ("mae", 0): [1.0, 3.0],
+            ("mae", 1): [10.0, 30.0],
+        }
+        rows = [
+            {"key": "mae", "series_index": 0, "step": 0, "value": 1.0},
+            {"key": "mae", "series_index": 1, "step": 0, "value": 3.0},
+            {"key": "mae", "series_index": 0, "step": 1, "value": 10.0},
+            {"key": "mae", "series_index": 1, "step": 1, "value": 30.0},
+        ]
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            _flush_logged_metrics(
+                client, run.info.run_id, agg, agg_func=np.mean, table_rows=rows
+            )
+            client.flush(synchronous=True)
+
+        history0 = mlflow_tracking.get_metric_history(run.info.run_id, "mae")
+        logged = {m.step: m.value for m in history0}
+        assert logged[0] == pytest.approx(2.0)
+        assert logged[1] == pytest.approx(20.0)
+        table = mlflow.load_table(
+            artifact_file="metrics_per_series.json", run_ids=[run.info.run_id]
+        )
+        assert len(table) == 4
 
-def test_flush_logged_metrics_skips_table_without_rows(mlflow_tracking):
-    """Omitting table rows logs the aggregate only."""
-    agg = {("mae", 0): [1.5]}
-    with mlflow.start_run() as run:
-        client = MlflowAutologgingQueueingClient()
-        _flush_logged_metrics(client, run.info.run_id, agg, agg_func=np.mean)
-        client.flush(synchronous=True)
+    def test_flush_logged_metrics_skips_table_without_rows(mlflow_tracking):
+        """Omitting table rows logs the aggregate only."""
+        agg = {("mae", 0): [1.5]}
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            _flush_logged_metrics(client, run.info.run_id, agg, agg_func=np.mean)
+            client.flush(synchronous=True)
 
-    assert mlflow.get_run(run.info.run_id).data.metrics["mae"] == pytest.approx(1.5)
-    artifacts = mlflow_tracking.list_artifacts(run.info.run_id)
-    assert not any(a.path == "metrics_per_series.json" for a in artifacts)
+        assert mlflow.get_run(run.info.run_id).data.metrics["mae"] == pytest.approx(1.5)
+        artifacts = mlflow_tracking.list_artifacts(run.info.run_id)
+        assert not any(a.path == "metrics_per_series.json" for a in artifacts)

From 30cb5b34ecb2e9b2ea2f5caa161be2a0dddff296 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Wed, 2 Sep 2026 10:34:09 +0200
Subject: [PATCH 147/154] make tests more efficient

---
 darts/tests/optional_deps/test_mlflow.py | 131 +++++++++++++----------
 1 file changed, 77 insertions(+), 54 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index d06595c3cc..4695d695d7 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1,6 +1,8 @@
 import copy
 import logging
 import os
+import uuid
+from contextlib import contextmanager
 
 import numpy as np
 import pandas as pd
@@ -67,11 +69,41 @@
 )
 
 
+@pytest.fixture(scope="module")
+def _mlflow_store(tmp_path_factory):
+    """Shared MLflow backend for the whole module (one SQLite DB)."""
+    store_dir = tmp_path_factory.mktemp("mlflow")
+    mlflow.set_tracking_uri(f"sqlite:///{store_dir / 'mlflow.db'}")
+    return store_dir
+
+
 @pytest.fixture
-def mlflow_tracking(tmpdir_fn):
-    """Set up MLflow tracking with a temporary database."""
-    mlflow.set_tracking_uri(f"sqlite:///{tmpdir_fn}/mlflow.db")
-    return mlflow.tracking.MlflowClient()
+def mlflow_tracking(_mlflow_store, request):
+    """Isolated MLflow experiment per test on the shared module store."""
+    exp_name = f"darts_mlflow_{request.node.name}"
+    artifact_root = _mlflow_store / f"artifacts_{uuid.uuid4().hex}"
+    artifact_root.mkdir(parents=True, exist_ok=True)
+
+    client = mlflow.tracking.MlflowClient()
+    exp_id = client.create_experiment(
+        exp_name,
+        artifact_location=str(artifact_root),
+    )
+    mlflow.set_experiment(experiment_id=exp_id)
+
+    autolog(disable=True)
+    yield client
+    autolog(disable=True)
+
+
+@contextmanager
+def _autolog_context(**kwargs):
+    autolog(disable=True)
+    autolog(**kwargs)
+    try:
+        yield
+    finally:
+        autolog(disable=True)
 
 
 @pytest.fixture
@@ -80,19 +112,8 @@ def autolog_context():
 
     Usage:
         with autolog_context():            # default autolog
-        with autolog_context(log_training_metrics=True):   # custom kwargs
+        with autolog_context(log_models=True):   # custom kwargs
     """
-    from contextlib import contextmanager
-
-    @contextmanager
-    def _autolog_context(**kwargs):
-        autolog(disable=True)  # clean state
-        autolog(**kwargs)  # enable with custom kwargs
-        try:
-            yield
-        finally:
-            autolog(disable=True)  # clean up
-
     return _autolog_context
 
 
@@ -165,60 +186,62 @@ def _read_per_series_table(run_id):
 
 
 def _fit_lr(series=None):
-    """Fit and return a fresh LinearRegressionModel (no active run)."""
+    """Fit and return a LinearRegressionModel (no active run)."""
+    series = TS_UNIVARIATE if series is None else series
     model = LinearRegressionModel(lags=1)
-    model.fit(series if series is not None else TS_UNIVARIATE)
+    model.fit(series)
     return model
 
 
 def _fit_qlr(series=None):
-    """Fit and return a fresh quantile LinearRegressionModel (no active run)."""
+    """Fit and return a quantile LinearRegressionModel (no active run)."""
+    series = TS_UNIVARIATE if series is None else series
     model = LinearRegressionModel(
         lags=1, likelihood="quantile", quantiles=[0.1, 0.5, 0.9]
     )
-    model.fit(series if series is not None else TS_UNIVARIATE)
+    model.fit(series)
     return model
 
 
 class TestMLflow:
-    def test_save_load_statistical_model(self, tmpdir_fn):
+    def test_save_load_statistical_model(self, tmp_path):
         """Test save/load round-trip for statistical model"""
         model = ExponentialSmoothing()
         model.fit(TS_UNIVARIATE)
 
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path)
+        model_path = tmp_path / "test_model"
+        save_model(model, str(model_path))
 
-        assert_mlflow_artifacts_exist(model_path, is_torch=False)
+        assert_mlflow_artifacts_exist(str(model_path), is_torch=False)
 
         loaded_model = load_model(f"file://{model_path}")
         assert_predictions_equal(model, loaded_model, n=5, is_global=False)
 
-    def test_save_load_regression_model(self, tmpdir_fn):
+    def test_save_load_regression_model(self, tmp_path):
         """Test save/load round-trip for regression model"""
         model = LinearRegressionModel(lags=5)
         model.fit(TS_UNIVARIATE)
 
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path)
+        model_path = tmp_path / "test_model"
+        save_model(model, str(model_path))
 
-        assert_mlflow_artifacts_exist(model_path, is_torch=False)
+        assert_mlflow_artifacts_exist(str(model_path), is_torch=False)
 
         loaded_model = load_model(f"file://{model_path}")
         assert_predictions_equal(model, loaded_model, n=3, series=TS_UNIVARIATE)
 
     @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch")
-    def test_save_load_torch_model(self, tmpdir_fn):
+    def test_save_load_torch_model(self, tmp_path):
         """Test save/load round-trip for torch model"""
         model = NBEATSModel(
             input_chunk_length=4, output_chunk_length=2, n_epochs=1, **tfm_kwargs_dev
         )
         model.fit(TS_UNIVARIATE)
 
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path)
+        model_path = tmp_path / "test_model"
+        save_model(model, str(model_path))
 
-        assert_mlflow_artifacts_exist(model_path, is_torch=True)
+        assert_mlflow_artifacts_exist(str(model_path), is_torch=True)
 
         # save(clean=True) strips pl_trainer_kwargs; explicitly restore accelerator
         # so Lightning doesn't default to MPS on Github macOS runner
@@ -663,17 +686,17 @@ def test_load_invalid_uri_fails(self):
         with pytest.raises(Exception):
             load_model("file:///nonexistent/path/to/model")
 
-    def test_load_corrupted_mlmodel_fails(self, tmpdir_fn):
+    def test_load_corrupted_mlmodel_fails(self, tmp_path):
         """Test that loading with corrupted MLmodel file fails"""
         # save a valid model
         model = LinearRegressionModel(lags=5)
         model.fit(TS_UNIVARIATE)
 
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path)
+        model_path = tmp_path / "test_model"
+        save_model(model, str(model_path))
 
         # corrupt the MLmodel file
-        mlmodel_path = os.path.join(model_path, "MLmodel")
+        mlmodel_path = model_path / "MLmodel"
         with open(mlmodel_path, "w") as f:
             f.write("corrupted content that is not valid YAML {[[")
 
@@ -681,17 +704,17 @@ def test_load_corrupted_mlmodel_fails(self, tmpdir_fn):
         with pytest.raises(Exception):
             load_model(f"file://{model_path}")
 
-    def test_load_missing_model_file_fails(self, tmpdir_fn):
+    def test_load_missing_model_file_fails(self, tmp_path):
         """Test that loading with missing model data file fails"""
         # save a valid model
         model = LinearRegressionModel(lags=5)
         model.fit(TS_UNIVARIATE)
 
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path)
+        model_path = tmp_path / "test_model"
+        save_model(model, str(model_path))
 
         # remove the model data file
-        model_data_path = os.path.join(model_path, "model.pkl")
+        model_data_path = model_path / "model.pkl"
         os.remove(model_data_path)
 
         # loading should fail
@@ -705,7 +728,7 @@ def test_load_missing_model_file_fails(self, tmpdir_fn):
             (LinearRegressionModel, {"lags": 5}),
         ],
     )
-    def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
+    def test_save_load_multiple_models(self, tmp_path, model_cls, fit_kwargs):
         """Test save/load for multiple model types"""
         if fit_kwargs:
             model = model_cls(**fit_kwargs)
@@ -714,8 +737,8 @@ def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
 
         model.fit(TS_UNIVARIATE)
 
-        model_path = os.path.join(tmpdir_fn, "test_model")
-        save_model(model, model_path)
+        model_path = tmp_path / "test_model"
+        save_model(model, str(model_path))
         loaded = load_model(f"file://{model_path}")
 
         # Only pass series for global models (LinearRegressionModel)
@@ -732,13 +755,13 @@ def test_save_load_multiple_models(self, tmpdir_fn, model_cls, fit_kwargs):
             (TS_WITH_STATIC, "static_covariates"),
         ],
     )
-    def test_save_load_with_special_series(self, tmpdir_fn, series, series_name):
+    def test_save_load_with_special_series(self, tmp_path, series, series_name):
         """Test save/load with multivariate and static covariate series"""
         model = LinearRegressionModel(lags=5)
         model.fit(series)
 
-        model_path = os.path.join(tmpdir_fn, f"test_model_{series_name}")
-        save_model(model, model_path)
+        model_path = tmp_path / f"test_model_{series_name}"
+        save_model(model, str(model_path))
 
         loaded_model = load_model(f"file://{model_path}")
 
@@ -1950,7 +1973,7 @@ class TestAutoLogMetricHelperFunctions:
             ("mae", {"component_reduction": None}, dict(has_comp_axis=True)),
         ],
     )
-    def test_infer_metric_axes_reductions(metric_name, metric_kwargs, expected):
+    def test_infer_metric_axes_reductions(self, metric_name, metric_kwargs, expected):
         has_time_axis, has_comp_axis, axis_labels = _infer_metric_axes(
             getattr(dm, metric_name), metric_kwargs
         )
@@ -1962,22 +1985,22 @@ def test_infer_metric_axes_reductions(metric_name, metric_kwargs, expected):
         for attr, value in expected.items():
             assert actual[attr] == value
 
-    def test_infer_metric_axes_quantiles():
+    def test_infer_metric_axes_quantiles(self):
         _, _, axis_labels = _infer_metric_axes(dm.mql, {"q": [0.1, 0.5, 0.9]})
         assert axis_labels == ["_q0.100", "_q0.500", "_q0.900"]
 
-    def test_infer_metric_axes_quantile_interval():
+    def test_infer_metric_axes_quantile_interval(self):
         has_time, _, axis_labels = _infer_metric_axes(dm.iw, {"q_interval": (0.1, 0.9)})
         assert axis_labels == ["_qi0.800"]
         assert has_time is True
 
-    def test_infer_metric_axes_unknown_labels_raises():
+    def test_infer_metric_axes_unknown_labels_raises(self):
         """label_reduction=None with no explicit labels cannot determine the number
         of output labels ahead of time, so this raises rather than falling back."""
         with pytest.raises(ValueError, match="requires explicit `labels`"):
             _infer_metric_axes(dm.f1, {"label_reduction": None})
 
-    def test_build_metric_keys_components_and_quantiles():
+    def test_build_metric_keys_components_and_quantiles(self):
         """Shared key builder expands components x quantile suffixes per metric."""
         metric_axes = [
             (False, True, ["_q0.100", "_q0.900"]),
@@ -2007,7 +2030,7 @@ def test_build_metric_keys_components_and_quantiles():
             ],
         ]
 
-    def test_build_metric_keys_no_components_no_prefix():
+    def test_build_metric_keys_no_components_no_prefix(self):
         """Without components, each metric gets one key per axis label."""
         metric_axes = [(False, False, ["_q0.500"])]
         metric_keys = _build_metric_keys(
@@ -2020,7 +2043,7 @@ def test_build_metric_keys_no_components_no_prefix():
         assert c_size == 1
         assert metric_keys == [["mql_q0.500"]]
 
-    def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
+    def test_flush_logged_metrics_aggregates_and_writes_table(self, mlflow_tracking):
         """Cells are aggregated with agg_func; supplied table rows are persisted."""
         agg = {
             ("mae", 0): [1.0, 3.0],
@@ -2048,7 +2071,7 @@ def test_flush_logged_metrics_aggregates_and_writes_table(mlflow_tracking):
         )
         assert len(table) == 4
 
-    def test_flush_logged_metrics_skips_table_without_rows(mlflow_tracking):
+    def test_flush_logged_metrics_skips_table_without_rows(self, mlflow_tracking):
         """Omitting table rows logs the aggregate only."""
         agg = {("mae", 0): [1.5]}
         with mlflow.start_run() as run:

From 6312c28e03fb15b0395c070128d26fe2c4e0ce83 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Thu, 3 Sep 2026 10:03:30 +0200
Subject: [PATCH 148/154] add option to log backtest agg and series align from
 start

---
 darts/tests/optional_deps/test_mlflow.py | 182 +++++++++++++++++++++++
 darts/utils/mlflow.py                    | 135 ++++++++++++++---
 2 files changed, 294 insertions(+), 23 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 4695d695d7..fd50c607c7 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -1,4 +1,5 @@
 import copy
+import itertools
 import logging
 import os
 import uuid
@@ -1963,6 +1964,163 @@ def test_log_backtest_metrics_aligns_last_points_only_time_axis(
                 assert np.isnan(row["window_index"])
                 assert row["value"] == pytest.approx(ref[s_idx][w_idx])
 
+    def test_log_backtest_metrics_aligns_windows_by_start(
+        self, autolog_context, mlflow_tracking
+    ):
+        """A shorter series' window axis aligns from the start when
+        ``series_align='start'``, so its first windows overlap the head of a
+        longer series."""
+        model = _fit_lr()
+        input_length = abs(min(model.lags["target"]))
+        n_windows = 2
+        series_two_windows = TS_UNIVARIATE[-(input_length + n_windows) :]
+        with autolog_context(log_metrics=True, series_align="start"):
+            with mlflow.start_run() as run:
+                ref = model.backtest(
+                    metric=dm.mae,
+                    metric_kwargs={},
+                    series=[series_two_windows, series_two_windows[1:]],
+                    forecast_horizon=1,
+                    reduction=None,
+                    last_points_only=False,
+                    start=-n_windows,
+                    retrain=False,
+                )
+        expected_shapes = [(n_windows,), (n_windows - 1,)]
+        for ref_i, shape in zip(ref, expected_shapes):
+            assert ref_i.shape == shape
+
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
+        logged = {m.step: m.value for m in history}
+        assert logged == pytest.approx({
+            0: np.mean([ref[0][0], ref[1][0]]),
+            1: ref[0][1],
+        })
+
+        rows = _read_per_series_table(run.info.run_id)
+        for s_idx, shape in enumerate(expected_shapes):
+            w_size = shape[0]
+            for w_idx in range(w_size):
+                row = rows[s_idx * n_windows + w_idx]
+                assert row["key"] == "backtest_mae"
+                assert row["series_index"] == s_idx
+                assert row["step"] == w_idx
+                assert row["window_index"] == w_idx
+                assert row["value"] == pytest.approx(ref[s_idx][w_idx])
+
+    def test_log_backtest_metrics_aligns_last_points_only_by_start(
+        self, autolog_context, mlflow_tracking
+    ):
+        """last_points_only with ``series_align='start'`` aligns shorter series
+        at early timesteps."""
+        model = _fit_lr()
+        input_length = abs(min(model.lags["target"]))
+        n_windows = 2
+        horizon = 3
+        series_two_windows = TS_UNIVARIATE[-(input_length + horizon + n_windows - 1) :]
+        with autolog_context(log_metrics=True, series_align="start"):
+            with mlflow.start_run() as run:
+                ref = model.backtest(
+                    metric=dm.ae,
+                    series=[series_two_windows, series_two_windows[1:]],
+                    forecast_horizon=horizon,
+                    reduction=None,
+                    last_points_only=True,
+                    start=-(horizon + n_windows - 1),
+                    retrain=False,
+                )
+
+        ref = [np.atleast_1d(ref_i) for ref_i in ref]
+        expected_shapes = [(n_windows,), (n_windows - 1,)]
+        for ref_i, shape in zip(ref, expected_shapes):
+            assert ref_i.shape == shape
+
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
+        logged = {m.step: m.value for m in history}
+        assert logged == pytest.approx({
+            0: np.mean([ref[0][0], ref[1][0]]),
+            1: ref[0][1],
+        })
+
+        rows = _read_per_series_table(run.info.run_id)
+        for s_idx, shape in enumerate(expected_shapes):
+            w_size = shape[0]
+            for w_idx in range(w_size):
+                row = rows[s_idx * n_windows + w_idx]
+                assert row["key"] == "backtest_ae"
+                assert row["series_index"] == s_idx
+                assert row["step"] == w_idx
+                assert np.isnan(row["window_index"])
+                assert row["value"] == pytest.approx(ref[s_idx][w_idx])
+
+    @pytest.mark.parametrize("config", list(itertools.product([1, 3], [True, False])))
+    def test_autolog_backtest_log_aggregate_scalar(
+        self, autolog_context, mlflow_tracking, config
+    ):
+        """log_backtest_aggregate logs backtest_agg_* equal to scalar backtest_*."""
+        horizon, lpo = config
+        n_windows = 2
+        with autolog_context(log_metrics=True, log_backtest_aggregate=True):
+            with mlflow.start_run() as run:
+                ref = _fit_lr().backtest(
+                    TS_UNIVARIATE,
+                    metric=dm.mae,
+                    retrain=False,
+                    start=-(horizon + n_windows - 1),
+                    last_points_only=lpo,
+                    forecast_horizon=horizon,
+                    reduction=None,
+                )
+        ref = np.nanmean(ref)
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["backtest_agg_mae"] == pytest.approx(ref)
+
+    @pytest.mark.parametrize("horizon", [1, 3])
+    def test_autolog_backtest_log_aggregate_stepped(
+        self, autolog_context, mlflow_tracking, horizon
+    ):
+        """log_backtest_aggregate collapses stepped backtest metrics via agg_func."""
+        with autolog_context(
+            log_metrics=True, log_backtest_aggregate=True, agg_func=np.nanmean
+        ):
+            with mlflow.start_run() as run:
+                _fit_lr().backtest(
+                    TS_UNIVARIATE,
+                    metric=dm.ae,
+                    retrain=False,
+                    start=-(horizon + 1),
+                    last_points_only=False,
+                    forecast_horizon=horizon,
+                    reduction=np.nanmean,
+                )
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_ae")
+        stepped_values = [m.value for m in sorted(history, key=lambda m: m.step)]
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["backtest_agg_ae"] == pytest.approx(np.nanmean(stepped_values))
+
+    @pytest.mark.parametrize("horizon", [1, 3])
+    def test_autolog_backtest_log_aggregate_custom_agg_func(
+        self, autolog_context, mlflow_tracking, horizon
+    ):
+        """log_backtest_aggregate uses autolog's agg_func over all logged steps."""
+        series = TS_UNIVARIATE
+        series = [series * 0.9, series, series * 1.1]
+        with autolog_context(
+            log_metrics=True, log_backtest_aggregate=True, agg_func=np.median
+        ):
+            with mlflow.start_run() as run:
+                ref = _fit_lr(series[0]).backtest(
+                    metric=dm.mae,
+                    series=series,
+                    last_points_only=True,
+                    start=-(horizon + 1),
+                    forecast_horizon=horizon,
+                )
+        ref = np.atleast_1d(ref)
+        assert ref.shape == (len(series),)
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        assert m["backtest_agg_mae"] == pytest.approx(np.median(ref))
+
 
 class TestAutoLogMetricHelperFunctions:
     @pytest.mark.parametrize(
@@ -2082,3 +2240,27 @@ def test_flush_logged_metrics_skips_table_without_rows(self, mlflow_tracking):
         assert mlflow.get_run(run.info.run_id).data.metrics["mae"] == pytest.approx(1.5)
         artifacts = mlflow_tracking.list_artifacts(run.info.run_id)
         assert not any(a.path == "metrics_per_series.json" for a in artifacts)
+
+    def test_flush_logged_metrics_backtest_aggregate(self, mlflow_tracking):
+        """log_backtest_aggregate logs backtest_agg_* as agg_func over all steps."""
+        agg = {
+            ("backtest_mae", 0): [1.0, 3.0],
+            ("backtest_mae", 1): [10.0, 30.0],
+        }
+        with mlflow.start_run() as run:
+            client = MlflowAutologgingQueueingClient()
+            _flush_logged_metrics(
+                client,
+                run.info.run_id,
+                agg,
+                agg_func=np.mean,
+                log_backtest_aggregate=True,
+            )
+            client.flush(synchronous=True)
+
+        m = mlflow.get_run(run.info.run_id).data.metrics
+        history = mlflow_tracking.get_metric_history(run.info.run_id, "backtest_mae")
+        logged = {h.step: h.value for h in history}
+        assert logged[0] == pytest.approx(2.0)
+        assert logged[1] == pytest.approx(20.0)
+        assert m["backtest_agg_mae"] == pytest.approx(np.mean([2.0, 20.0]))
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 49146bcc05..470d13bdf4 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -161,6 +161,19 @@
           horizon aggregated over all windows. Steps represent the steps in
           the forecast horizon (0, 1, ..., horizon - 1).
 
+    - Multi-series alignment (``series_align`` autolog option):
+
+      - ``"end"`` (default): shorter series skip early steps/windows so their
+        last points overlap the tail of longer series.
+      - ``"start"``: shorter series contribute at early steps/windows; longer
+        series have fewer contributing series at tail steps.
+
+    - Backtest aggregate (``log_backtest_aggregate`` autolog option):
+
+      - When enabled, logs an additional scalar per backtest metric key under
+        ``backtest_agg_{metric_key}`` (e.g. ``backtest_agg_mae``), computed
+        as ``agg_func`` over all logged steps for that key.
+
     When components are preserved (``component_reduction=None``), all
     series scored together must have the same number of components; names
     are taken from the first series.
@@ -173,7 +186,7 @@
 from collections.abc import Callable
 from operator import itemgetter
 from pathlib import Path
-from typing import Any
+from typing import Any, Literal
 
 from darts.logging import raise_log
 from darts.typing import TimeSeriesLike
@@ -486,6 +499,8 @@ def autolog(
     log_metrics: bool = True,
     log_torch_metrics: bool = True,
     agg_func: Callable = np.nanmean,
+    series_align: Literal["start", "end"] = "end",
+    log_backtest_aggregate: bool = False,
     disable: bool = False,
     silent: bool = False,
 ) -> None:
@@ -514,7 +529,17 @@ def autolog(
         Function used to aggregate a metric's per-series values into the
         single value logged for a list of series (e.g. ``np.nanmean``, the
         default, or ``np.median``). Called as ``agg_func(values)`` on a list
-        of floats.
+        of floats. Also used when ``log_backtest_aggregate=True`` to collapse
+        all logged steps of a backtest metric into a single scalar.
+    series_align
+        How to align shorter series when multiple series differ in window or
+        time length. ``"end"`` (default) aligns from the end so shorter series
+        skip early steps/windows; ``"start"`` aligns from the start so shorter
+        series contribute at early steps/windows.
+    log_backtest_aggregate
+        If ``True``, log an additional scalar per backtest metric key under
+        ``backtest_agg_{metric_key}`` (e.g. ``backtest_agg_mae``), computed as
+        ``agg_func`` over all logged steps for that key. Defaults to ``False``.
     disable
         If ``True``, restore the original ``fit()`` methods and stop
         autologging.
@@ -522,6 +547,13 @@ def autolog(
         If ``True`` (default ``False``), suppress all event logging and warnings from
         MLflow during autologging.
     """
+    if series_align not in ("start", "end"):
+        raise_log(
+            ValueError(
+                f"`series_align` must be 'start' or 'end', got {series_align!r}."
+            )
+        )
+
     # Enable/disable mlflow.pytorch.autolog for per-epoch metrics on torch models.
     # This must happen outside the @autologging_integration-decorated _autolog()
     # because that decorator short-circuits _autolog()'s body entirely when
@@ -563,6 +595,8 @@ def autolog(
         log_params=log_params,
         log_metrics=log_metrics,
         agg_func=agg_func,
+        series_align=series_align,
+        log_backtest_aggregate=log_backtest_aggregate,
         disable=disable,
         silent=silent,
     )
@@ -596,6 +630,8 @@ def _autolog(
     log_params: bool = True,
     log_metrics: bool = True,
     agg_func: Callable = np.nanmean,
+    series_align: Literal["start", "end"] = "end",
+    log_backtest_aggregate: bool = False,
     disable: bool = False,
     silent: bool = False,
 ) -> None:
@@ -761,6 +797,8 @@ def _patched_backtest(original, self, *args, **kwargs):
             result=result,
             backtest_kwargs=backtest_kwargs,
             agg_func=agg_func,
+            series_align=series_align,
+            log_backtest_aggregate=log_backtest_aggregate,
         )
         autologging_client.flush(synchronous=False).await_completion()
         return result
@@ -861,6 +899,9 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
     agg_func = get_autologging_config(
         flavor_name=FLAVOR_NAME, config_key="agg_func", default_value=np.nanmean
     )
+    series_align = get_autologging_config(
+        flavor_name=FLAVOR_NAME, config_key="series_align", default_value="end"
+    )
 
     autologging_client = MlflowAutologgingQueueingClient()
     _log_metric_results(
@@ -870,6 +911,7 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
         metrics=func,
         metric_kwargs=metric_kwargs,
         agg_func=agg_func,
+        series_align=series_align,
     )
     autologging_client.flush(synchronous=False).await_completion()
 
@@ -1188,6 +1230,8 @@ def _log_backtest_metrics(
     result,
     backtest_kwargs: dict,
     agg_func: Callable = np.nanmean,
+    series_align: Literal["start", "end"] = "end",
+    log_backtest_aggregate: bool = False,
 ) -> None:
     """Log backtest metric result(s) to MLflow.
 
@@ -1201,6 +1245,8 @@ def _log_backtest_metrics(
         metrics=backtest_kwargs["metric"],
         metric_kwargs=backtest_kwargs["metric_kwargs"] or dict(),
         agg_func=agg_func,
+        series_align=series_align,
+        log_backtest_aggregate=log_backtest_aggregate,
         backtest_kwargs=backtest_kwargs,
     )
 
@@ -1213,6 +1259,8 @@ def _log_metric_results(
     metric_kwargs: dict[str, Any] | list[dict[str, Any]],
     backtest_kwargs: dict[str, Any] | None = None,
     agg_func: Callable = np.nanmean,
+    series_align: Literal["start", "end"] = "end",
+    log_backtest_aggregate: bool = False,
 ) -> None:
     """Log backtest or standalone metric result(s) to MLflow.
 
@@ -1286,15 +1334,17 @@ def _log_metric_results(
       forecast horizon (``0 .. T-1``). When both time and window axes are
       present (backtest, no reduction, not ``last_points_only``), each step
       holds the ``agg_func`` aggregation over windows at that horizon index.
-    - **Time-aggregated metrics**: steps index backtest windows end-aligned
-      across series (``0 .. max_W-1``; shorter series skip early steps).
-      Standalone calls with a time axis but no window axis end-align timesteps
-      the same way across series of different lengths.
-
-    Series can differ in length and overlap in time. We align from the end, not
-    the start: a shorter series contributes at the last steps/windows, not the
-    first. This matches the usual backtest layout but is an assumption — series
-    may also end at different calendar times.
+    - **Time-aggregated metrics**: steps index backtest windows aligned across
+      series (``0 .. max_W-1``; shorter series skip early or late steps depending
+      on ``series_align``). Standalone calls with a time axis but no window axis
+      align timesteps the same way across series of different lengths.
+
+    Series can differ in length and overlap in time. By default
+    (``series_align="end"``), we align from the end: a shorter series contributes
+    at the last steps/windows, not the first. With ``series_align="start"``,
+    shorter series contribute at the first steps/windows instead. This matches
+    the usual backtest layout but is an assumption — series may also end at
+    different calendar times.
 
     Multi-series aggregation and artifacts
     ----------------------------------------
@@ -1314,6 +1364,11 @@ def _log_metric_results(
     have the same number of components; component names are taken from the first
     series.
 
+    When ``log_backtest_aggregate`` is enabled (backtest only), an additional
+    scalar per metric key is logged under ``backtest_agg_{metric_key}`` (e.g.
+    ``backtest_agg_mae``), computed as ``agg_func`` over all logged steps for
+    that key.
+
     Raises
     ------
     ValueError
@@ -1345,7 +1400,15 @@ def _log_metric_results(
         ``None``, the call is treated as standalone metric logging.
     agg_func
         Function used to aggregate per-series values at each ``(key, step)``.
-        Called as ``agg_func(values)`` on a list of floats.
+        Called as ``agg_func(values)`` on a list of floats. Also used to
+        collapse all logged steps into a single scalar when
+        ``log_backtest_aggregate`` is enabled.
+    series_align
+        How to align shorter series when multiple series differ in window or
+        time length. ``"end"`` (default) or ``"start"``.
+    log_backtest_aggregate
+        If ``True`` (backtest only), log an additional scalar per metric key
+        under ``backtest_agg_{metric_key}``.
     """
     metrics, metric_kwargs, metric_names = _normalize_metrics(
         metrics=metrics,
@@ -1427,6 +1490,7 @@ def _log_metric_results(
         has_time_axis=has_time_axis,
         has_windows=has_windows,
         last_points_only=last_points_only,
+        series_align=series_align,
     )
 
     write_table = (not series_reduced and not is_single_series) or (
@@ -1437,6 +1501,7 @@ def _log_metric_results(
         run_id,
         stepped_metrics,
         agg_func=agg_func,
+        log_backtest_aggregate=log_backtest_aggregate and is_backtest,
         table_rows=detailed_metrics if write_table else None,
     )
 
@@ -1718,20 +1783,22 @@ def _collect_stepped_and_detailed_metrics(
     has_time_axis: bool,
     has_windows: bool,
     last_points_only: bool,
+    series_align: Literal["start", "end"] = "end",
 ) -> tuple[dict[tuple[str, int], list[float]], list[dict]]:
     """Parse series metrics and build inputs for MLflow stepped metrics and detailed metric table.
 
     Each ``series_metrics`` array has shape ``(w_size, t_size, n_metrics * n_sub_metrics)``.
-    Shorter series are aligned from the end of the longest remaining axis (windows,
-    or time when there is no window axis).
+    Shorter series are aligned from the start or end of the longest remaining axis
+    (windows, or time when there is no window axis), controlled by ``series_align``.
 
     The MLflow ``step`` is the axis the UI should chart, always ``0 .. n-1``:
 
     - forecast-horizon index when a time axis is present (time-dependent metrics)
       - if windows are present: each horizon step is aggregated over the windows
       - if multi-series: each horizon step is aggregated over the series
-    - end-aligned window index otherwise (time-aggregated metrics)
-      - if multi-series: each window is aggregated over the series (end-aligned)
+    - window index otherwise (time-aggregated metrics), aligned across series
+      via ``series_align``
+      - if multi-series: each window is aggregated over the series
     """
     stepped_metrics: dict[tuple[str, int], list[float]] = {}
     detailed_metrics: list[dict] = []
@@ -1744,9 +1811,13 @@ def _collect_stepped_and_detailed_metrics(
         w_size = values.shape[w_axis]
         t_size = values.shape[t_axis]
 
-        # pad shorter series so their last point lines up with the longest
-        w_offset = max_w_size - w_size if has_windows else 0
-        t_offset = max_t_size - t_size if has_time_axis and not has_windows else 0
+        # pad shorter series so their last (end) or first (start) point lines up
+        if series_align == "end":
+            w_offset = max_w_size - w_size if has_windows else 0
+            t_offset = max_t_size - t_size if has_time_axis and not has_windows else 0
+        else:
+            w_offset = 0
+            t_offset = 0
 
         for metric_idx, sub_metric_keys in enumerate(metric_keys):
             for sub_metric_idx, key in enumerate(sub_metric_keys):
@@ -1788,6 +1859,7 @@ def _flush_logged_metrics(
     run_id: str,
     agg: dict[tuple[str, int], list[float]],
     agg_func: Callable,
+    log_backtest_aggregate: bool = False,
     table_rows: list[dict] | None = None,
 ) -> None:
     """Aggregate per-series cells, log MLflow metrics, and optionally write the
@@ -1803,16 +1875,33 @@ def _flush_logged_metrics(
         Map of ``(key, step) -> list of per-series float values``.
     agg_func
         Aggregation over the per-series values for each ``(key, step)``.
+    log_backtest_aggregate
+        If ``True``, log an additional scalar per backtest key under
+        ``backtest_agg_{metric_key}`` as ``agg_func`` over all logged steps.
     table_rows
         Granular cells for ``metrics_per_series.json``. ``None`` skips writing
         the table artifact.
     """
     metrics_by_step: dict[int, dict[str, float]] = {}
+    backtest_agg: dict[str, list[float]] = {}
     for (key, step), values in agg.items():
-        metrics_by_step.setdefault(step, {})[key] = float(agg_func(values))
+        step_agg = float(agg_func(values))
+        metrics_by_step.setdefault(step, {})[key] = step_agg
+
+        if log_backtest_aggregate and key.startswith("backtest_"):
+            agg_key = key.replace("backtest_", "backtest_agg_", 1)
+            backtest_agg.setdefault(agg_key, []).append(step_agg)
+
     for step, metrics in metrics_by_step.items():
         autologging_client.log_metrics(run_id=run_id, metrics=metrics, step=step)
 
+    if backtest_agg:
+        aggregate_metrics = {
+            agg_key: float(agg_func(step_values))
+            for agg_key, step_values in backtest_agg.items()
+        }
+        autologging_client.log_metrics(run_id=run_id, metrics=aggregate_metrics, step=0)
+
     if table_rows is not None:
         _log_per_series_table(table_rows)
 
@@ -1824,9 +1913,9 @@ def _log_per_series_table(rows: list[dict]) -> None:
     Each row is a single metric cell for one series, with columns ``key`` (the
     aggregate MLflow key, without any series suffix), ``series_index``, ``step``
     (the time or window index charted by MLflow), ``window_index`` (the
-    end-aligned source backtest window ``0 .. max_w - 1``, or ``None`` when
-    there is no window axis), and ``value``. All calls within a run append
-    to the same ``metrics_per_series.json`` artifact.
+    source backtest window ``0 .. max_w - 1`` aligned per ``series_align``, or
+    ``None`` when there is no window axis), and ``value``. All calls within a run
+    append to the same ``metrics_per_series.json`` artifact.
     Used when more than one series is scored, since the logged metric keys
     only carry the aggregate over series.
 

From 6b05b9efedf6fd81d8a9e909a9c55e010829ef08 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Thu, 3 Sep 2026 16:10:52 +0200
Subject: [PATCH 149/154] add example notebook

---
 examples/29-MLflow-examples.ipynb             | 1585 +++++
 examples/29-MLflow-quickstart.ipynb           | 6143 -----------------
 examples/static/images/mlflow_charts.png      |  Bin 357183 -> 0 bytes
 .../images/mlflow_experiments_metrics.png     |  Bin 0 -> 335790 bytes
 .../images/mlflow_experiments_overview.png    |  Bin 0 -> 219488 bytes
 .../static/images/mlflow_model_registry.png   |  Bin 0 -> 148435 bytes
 examples/static/images/mlflow_models.png      |  Bin 361032 -> 0 bytes
 .../static/images/mlflow_models_overview.png  |  Bin 0 -> 186819 bytes
 examples/static/images/mlflow_overview.png    |  Bin 332549 -> 0 bytes
 examples/static/images/mlflow_run_detail.png  |  Bin 0 -> 429667 bytes
 10 files changed, 1585 insertions(+), 6143 deletions(-)
 create mode 100644 examples/29-MLflow-examples.ipynb
 delete mode 100644 examples/29-MLflow-quickstart.ipynb
 delete mode 100644 examples/static/images/mlflow_charts.png
 create mode 100644 examples/static/images/mlflow_experiments_metrics.png
 create mode 100644 examples/static/images/mlflow_experiments_overview.png
 create mode 100644 examples/static/images/mlflow_model_registry.png
 delete mode 100644 examples/static/images/mlflow_models.png
 create mode 100644 examples/static/images/mlflow_models_overview.png
 delete mode 100644 examples/static/images/mlflow_overview.png
 create mode 100644 examples/static/images/mlflow_run_detail.png

diff --git a/examples/29-MLflow-examples.ipynb b/examples/29-MLflow-examples.ipynb
new file mode 100644
index 0000000000..7ffa551156
--- /dev/null
+++ b/examples/29-MLflow-examples.ipynb
@@ -0,0 +1,1585 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8a7af915",
+   "metadata": {},
+   "source": [
+    "# MLflow Examples for Darts\n",
+    "\n",
+    "This notebook walks through a practical MLOps workflow with Darts' native MLflow integration: enable autolog, compare forecasting models, promote the best one to the registry, and run inference from a production alias.\n",
+    "\n",
+    "If you are new to Darts, see the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) first.\n",
+    "\n",
+    "**Prerequisites:** install MLflow as an optional dependency:\n",
+    "\n",
+    "```bash\n",
+    "pip install \"mlflow>=3.0\"\n",
+    "```\n",
+    "\n",
+    "API reference: [darts.utils.mlflow](https://unit8co.github.io/darts/generated_api/darts.utils.mlflow.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7b28941c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "11bca7fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import warnings\n",
+    "\n",
+    "import mlflow\n",
+    "from mlflow import MlflowClient\n",
+    "\n",
+    "import darts.metrics as metrics\n",
+    "from darts import set_option\n",
+    "from darts.datasets import AirPassengersDataset\n",
+    "from darts.models import LinearRegressionModel, RandomForestModel\n",
+    "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
+    "from darts.utils.mlflow import autolog, load_model\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+    "set_option(\"plotting.use_darts_style\", True)\n",
+    "\n",
+    "PLOTLY_KWARGS = dict(\n",
+    "    width=800,\n",
+    "    height=400,\n",
+    "    legend=dict(yanchor=\"top\", y=0.99, xanchor=\"left\", x=0.01),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eb6fc63",
+   "metadata": {},
+   "source": [
+    "## 1. MLflow setup\n",
+    "\n",
+    "Point MLflow at a tracking backend and create an experiment. We use a temporary SQLite database so this notebook runs self-contained; in production, set `tracking_uri` to your team's MLflow server or local database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7aced0df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/09/03 16:10:25 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+      "2026/09/03 16:10:25 INFO mlflow.store.db.utils: Updating database tables\n",
+      "2026/09/03 16:10:26 INFO mlflow.tracking.fluent: Experiment with name 'darts-mlflow-examples' does not exist. Creating a new experiment.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tracking URI: sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmps5wn0e4a/mlflow.db\n",
+      "Experiment: darts-mlflow-examples\n",
+      "\n",
+      "To explore runs in the UI:\n",
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmps5wn0e4a/mlflow.db\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmpdir = tempfile.mkdtemp()\n",
+    "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+    "\n",
+    "EXPERIMENT_NAME = \"darts-mlflow-examples\"\n",
+    "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
+    "mlflow.set_experiment(EXPERIMENT_NAME)\n",
+    "\n",
+    "print(f\"Tracking URI: {mlflow.get_tracking_uri()}\")\n",
+    "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-mlflow-examples').name}\")\n",
+    "print(\n",
+    "    f\"\\nTo explore runs in the UI:\\n  mlflow ui --backend-store-uri sqlite:///{mlflow_db}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72438a10",
+   "metadata": {},
+   "source": [
+    "## 2. Load data\n",
+    "\n",
+    "We use the classic AirPassengers dataset. The last 36 months are held-out for evaluation using a rolling backtest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f7e5ffa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training: 108 points | Validation: 36 points\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "<b>#Passengers</b>: %{y:.3g}<extra></extra>",
+         "legendgroup": "#Passengers",
+         "line": {
+          "color": "#000000"
+         },
+         "mode": "lines",
+         "name": "#Passengers",
+         "type": "scatter",
+         "x": [
+          "1949-01-01T00:00:00.000000000",
+          "1949-02-01T00:00:00.000000000",
+          "1949-03-01T00:00:00.000000000",
+          "1949-04-01T00:00:00.000000000",
+          "1949-05-01T00:00:00.000000000",
+          "1949-06-01T00:00:00.000000000",
+          "1949-07-01T00:00:00.000000000",
+          "1949-08-01T00:00:00.000000000",
+          "1949-09-01T00:00:00.000000000",
+          "1949-10-01T00:00:00.000000000",
+          "1949-11-01T00:00:00.000000000",
+          "1949-12-01T00:00:00.000000000",
+          "1950-01-01T00:00:00.000000000",
+          "1950-02-01T00:00:00.000000000",
+          "1950-03-01T00:00:00.000000000",
+          "1950-04-01T00:00:00.000000000",
+          "1950-05-01T00:00:00.000000000",
+          "1950-06-01T00:00:00.000000000",
+          "1950-07-01T00:00:00.000000000",
+          "1950-08-01T00:00:00.000000000",
+          "1950-09-01T00:00:00.000000000",
+          "1950-10-01T00:00:00.000000000",
+          "1950-11-01T00:00:00.000000000",
+          "1950-12-01T00:00:00.000000000",
+          "1951-01-01T00:00:00.000000000",
+          "1951-02-01T00:00:00.000000000",
+          "1951-03-01T00:00:00.000000000",
+          "1951-04-01T00:00:00.000000000",
+          "1951-05-01T00:00:00.000000000",
+          "1951-06-01T00:00:00.000000000",
+          "1951-07-01T00:00:00.000000000",
+          "1951-08-01T00:00:00.000000000",
+          "1951-09-01T00:00:00.000000000",
+          "1951-10-01T00:00:00.000000000",
+          "1951-11-01T00:00:00.000000000",
+          "1951-12-01T00:00:00.000000000",
+          "1952-01-01T00:00:00.000000000",
+          "1952-02-01T00:00:00.000000000",
+          "1952-03-01T00:00:00.000000000",
+          "1952-04-01T00:00:00.000000000",
+          "1952-05-01T00:00:00.000000000",
+          "1952-06-01T00:00:00.000000000",
+          "1952-07-01T00:00:00.000000000",
+          "1952-08-01T00:00:00.000000000",
+          "1952-09-01T00:00:00.000000000",
+          "1952-10-01T00:00:00.000000000",
+          "1952-11-01T00:00:00.000000000",
+          "1952-12-01T00:00:00.000000000",
+          "1953-01-01T00:00:00.000000000",
+          "1953-02-01T00:00:00.000000000",
+          "1953-03-01T00:00:00.000000000",
+          "1953-04-01T00:00:00.000000000",
+          "1953-05-01T00:00:00.000000000",
+          "1953-06-01T00:00:00.000000000",
+          "1953-07-01T00:00:00.000000000",
+          "1953-08-01T00:00:00.000000000",
+          "1953-09-01T00:00:00.000000000",
+          "1953-10-01T00:00:00.000000000",
+          "1953-11-01T00:00:00.000000000",
+          "1953-12-01T00:00:00.000000000",
+          "1954-01-01T00:00:00.000000000",
+          "1954-02-01T00:00:00.000000000",
+          "1954-03-01T00:00:00.000000000",
+          "1954-04-01T00:00:00.000000000",
+          "1954-05-01T00:00:00.000000000",
+          "1954-06-01T00:00:00.000000000",
+          "1954-07-01T00:00:00.000000000",
+          "1954-08-01T00:00:00.000000000",
+          "1954-09-01T00:00:00.000000000",
+          "1954-10-01T00:00:00.000000000",
+          "1954-11-01T00:00:00.000000000",
+          "1954-12-01T00:00:00.000000000",
+          "1955-01-01T00:00:00.000000000",
+          "1955-02-01T00:00:00.000000000",
+          "1955-03-01T00:00:00.000000000",
+          "1955-04-01T00:00:00.000000000",
+          "1955-05-01T00:00:00.000000000",
+          "1955-06-01T00:00:00.000000000",
+          "1955-07-01T00:00:00.000000000",
+          "1955-08-01T00:00:00.000000000",
+          "1955-09-01T00:00:00.000000000",
+          "1955-10-01T00:00:00.000000000",
+          "1955-11-01T00:00:00.000000000",
+          "1955-12-01T00:00:00.000000000",
+          "1956-01-01T00:00:00.000000000",
+          "1956-02-01T00:00:00.000000000",
+          "1956-03-01T00:00:00.000000000",
+          "1956-04-01T00:00:00.000000000",
+          "1956-05-01T00:00:00.000000000",
+          "1956-06-01T00:00:00.000000000",
+          "1956-07-01T00:00:00.000000000",
+          "1956-08-01T00:00:00.000000000",
+          "1956-09-01T00:00:00.000000000",
+          "1956-10-01T00:00:00.000000000",
+          "1956-11-01T00:00:00.000000000",
+          "1956-12-01T00:00:00.000000000",
+          "1957-01-01T00:00:00.000000000",
+          "1957-02-01T00:00:00.000000000",
+          "1957-03-01T00:00:00.000000000",
+          "1957-04-01T00:00:00.000000000",
+          "1957-05-01T00:00:00.000000000",
+          "1957-06-01T00:00:00.000000000",
+          "1957-07-01T00:00:00.000000000",
+          "1957-08-01T00:00:00.000000000",
+          "1957-09-01T00:00:00.000000000",
+          "1957-10-01T00:00:00.000000000",
+          "1957-11-01T00:00:00.000000000",
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+          "1958-02-01T00:00:00.000000000",
+          "1958-03-01T00:00:00.000000000",
+          "1958-04-01T00:00:00.000000000",
+          "1958-05-01T00:00:00.000000000",
+          "1958-06-01T00:00:00.000000000",
+          "1958-07-01T00:00:00.000000000",
+          "1958-08-01T00:00:00.000000000",
+          "1958-09-01T00:00:00.000000000",
+          "1958-10-01T00:00:00.000000000",
+          "1958-11-01T00:00:00.000000000",
+          "1958-12-01T00:00:00.000000000",
+          "1959-01-01T00:00:00.000000000",
+          "1959-02-01T00:00:00.000000000",
+          "1959-03-01T00:00:00.000000000",
+          "1959-04-01T00:00:00.000000000",
+          "1959-05-01T00:00:00.000000000",
+          "1959-06-01T00:00:00.000000000",
+          "1959-07-01T00:00:00.000000000",
+          "1959-08-01T00:00:00.000000000",
+          "1959-09-01T00:00:00.000000000",
+          "1959-10-01T00:00:00.000000000",
+          "1959-11-01T00:00:00.000000000",
+          "1959-12-01T00:00:00.000000000",
+          "1960-01-01T00:00:00.000000000",
+          "1960-02-01T00:00:00.000000000",
+          "1960-03-01T00:00:00.000000000",
+          "1960-04-01T00:00:00.000000000",
+          "1960-05-01T00:00:00.000000000",
+          "1960-06-01T00:00:00.000000000",
+          "1960-07-01T00:00:00.000000000",
+          "1960-08-01T00:00:00.000000000",
+          "1960-09-01T00:00:00.000000000",
+          "1960-10-01T00:00:00.000000000",
+          "1960-11-01T00:00:00.000000000",
+          "1960-12-01T00:00:00.000000000"
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+       "layout": {
+        "annotations": [
+         {
+          "showarrow": false,
+          "text": "Train / val split ",
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+          "xanchor": "right",
+          "xref": "x",
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+          "yanchor": "top",
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+          "colorway": [
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+           "xanchor": "right",
+           "y": 1,
+           "yanchor": "top"
+          },
+          "margin": {
+           "b": 50,
+           "l": 50,
+           "r": 50,
+           "t": 50
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "white",
+          "showlegend": true,
+          "xaxis": {
+           "linecolor": "#dedede",
+           "showgrid": false,
+           "showline": true,
+           "title": {
+            "font": {
+             "color": "black",
+             "size": 16
+            }
+           }
+          },
+          "yaxis": {
+           "gridcolor": "#dedede",
+           "gridwidth": 1,
+           "showgrid": true,
+           "showline": false,
+           "zeroline": true,
+           "zerolinecolor": "#dedede"
+          }
+         }
+        },
+        "title": {
+         "text": "Air Passengers"
+        },
+        "width": 800,
+        "xaxis": {
+         "title": {
+          "text": "Month"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "FORECAST_HORIZON = 12\n",
+    "BACKTEST_STRIDE = 12\n",
+    "\n",
+    "series = AirPassengersDataset().load()\n",
+    "train, val = series[: -3 * FORECAST_HORIZON], series[-3 * FORECAST_HORIZON :]\n",
+    "\n",
+    "print(f\"Training: {len(train)} points | Validation: {len(val)} points\")\n",
+    "\n",
+    "fig = series.plotly()\n",
+    "fig.add_vline(\n",
+    "    x=train.end_time(),\n",
+    "    line_color=\"blue\",\n",
+    "    line_dash=\"dash\",\n",
+    "    annotation_text=\"Train / val split \",\n",
+    "    annotation_position=\"top left\",\n",
+    ")\n",
+    "fig.update_layout(title=\"Air Passengers\", **PLOTLY_KWARGS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c5db7bc",
+   "metadata": {},
+   "source": [
+    "## 3. Enable autolog\n",
+    "\n",
+    "One line turns on automatic experiment tracking for Darts models and metrics. We enable model logging (for the registry workflow) and backtest aggregate metrics (scalar `backtest_agg_*` keys for easy run comparison)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "71e43aa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autolog(log_models=True, log_backtest_aggregate=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36acc84b",
+   "metadata": {},
+   "source": [
+    "<details>\n",
+    "<summary><strong>What does autolog capture?</strong> (click to expand)</summary>\n",
+    "\n",
+    "| Trigger | Logged automatically |\n",
+    "|---|---|\n",
+    "| `model.fit(...)` | Model tags, hyperparameters, input series info, and trained model artifact when `log_models=True` |\n",
+    "| Standalone metric calls | Time-aggregated scalar metrics (e.g. `mae()`) and time-dependent stepped metrics (e.g. `ae()`); detailed breakdowns go to `metrics_per_series.json` |\n",
+    "| `model.backtest(...)` | Windowed-, per-horizon-, or aggregated metrics prefixed with `backtest_`; with `log_backtest_aggregate=True` also logs `backtest_agg_{metric}` |\n",
+    "| PyTorch models (NBEATS, TFT, …) | Per-epoch `train_loss` / `val_loss` via MLflow's PyTorch autolog |\n",
+    "\n",
+    "Disable anytime with `autolog(disable=True)`.\n",
+    "\n",
+    "For manual logging, save/load APIs, and metric-key details, see the [`Darts API reference`](https://unit8co.github.io/darts/generated_api/darts.utils.mlflow.html) and the [`MLflow API reference`](https://mlflow.org/docs/latest/ml/tracking/tracking-api/).\n",
+    "\n",
+    "</details>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdb06e85",
+   "metadata": {},
+   "source": [
+    "## 4. Compare model experiments\n",
+    "\n",
+    "We train three comparable sklearn-based models inside separate MLflow runs. Each run follows the same evaluation recipe:\n",
+    "\n",
+    "1. **Fit** (pre-train) on the training series (params and model artifact logged automatically).\n",
+    "2. **Historical forecasts** over the same validation period (rolling forecasts).\n",
+    "3. **Backtest** with windowed `mae` (`reduction=None` → stepped `backtest_mae` chart + scalar aggregate `backtest_agg_mae`).\n",
+    "4. **Backtest** with time-dependent `err` (per-horizon error profile `backtest_ae` + scalar aggregate `backtest_agg_ae`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ff6030e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_experiment(run_name, model, *, plot_historical=False):\n",
+    "    \"\"\"Fit, evaluate, and autolog a single forecasting experiment.\"\"\"\n",
+    "    hfc_kwargs = {\n",
+    "        \"series\": series,\n",
+    "        \"start\": val.start_time(),  # start time of the validation period\n",
+    "        \"forecast_horizon\": FORECAST_HORIZON,  # forecast horizon\n",
+    "        \"stride\": BACKTEST_STRIDE,  # step size between rolling forecasts\n",
+    "        \"retrain\": False,  # use pre-trained model\n",
+    "        \"last_points_only\": False,  # use all available points for each forecast\n",
+    "    }\n",
+    "\n",
+    "    with mlflow.start_run(run_name=run_name):\n",
+    "        # log pre-trained model artifact\n",
+    "        model.fit(train)\n",
+    "\n",
+    "        hfcs = model.historical_forecasts(**hfc_kwargs)\n",
+    "        bt_kwargs = {**hfc_kwargs, \"historical_forecasts\": hfcs, \"reduction\": None}\n",
+    "\n",
+    "        # windowed aggregate metric: stepped backtest_mae + scalar backtest_agg_mae\n",
+    "        model.backtest(metric=metrics.mae, **bt_kwargs)\n",
+    "\n",
+    "        # time-dependent metric: per-horizon error `backtest_err` + scalar backtest_agg_err\n",
+    "        model.backtest(metric=metrics.err, **bt_kwargs)\n",
+    "\n",
+    "        if plot_historical:\n",
+    "            fig = series.plotly(label=\"actual\")\n",
+    "            for idx, hfc in enumerate(hfcs):\n",
+    "                fig = hfc.plotly(label=f\"forecast {idx}\", fig=fig)\n",
+    "            fig.update_layout(\n",
+    "                title=f\"Historical forecasts — {run_name}\", **PLOTLY_KWARGS\n",
+    "            )\n",
+    "            fig.show()\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "12ed056e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/09/03 16:10:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
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+        },
+        {
+         "hovertemplate": "<b>forecast 1</b>: %{y:.3g}<extra></extra>",
+         "legendgroup": "forecast 1",
+         "line": {
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+         "mode": "lines",
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+        },
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+         "hovertemplate": "<b>forecast 2</b>: %{y:.3g}<extra></extra>",
+         "legendgroup": "forecast 2",
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+          "color": "#11a9ba"
+         },
+         "mode": "lines",
+         "name": "forecast 2",
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+       "layout": {
+        "height": 400,
+        "hovermode": "x unified",
+        "legend": {
+         "x": 0.01,
+         "xanchor": "left",
+         "y": 0.99,
+         "yanchor": "top"
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+        "template": {
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+          "scatter": [
+           {
+            "line": {
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+          ]
+         },
+         "layout": {
+          "colorway": [
+           "#000000",
+           "#003dfd",
+           "#b512b8",
+           "#11a9ba",
+           "#0d780f",
+           "#f77f07",
+           "#ba0f0f"
+          ],
+          "font": {
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+           "family": "Arial, sans-serif",
+           "size": 14
+          },
+          "legend": {
+           "bgcolor": "rgba(255, 255, 255, 0.8)",
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+           "font": {
+            "size": 14
+           },
+           "x": 1,
+           "xanchor": "right",
+           "y": 1,
+           "yanchor": "top"
+          },
+          "margin": {
+           "b": 50,
+           "l": 50,
+           "r": 50,
+           "t": 50
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "white",
+          "showlegend": true,
+          "xaxis": {
+           "linecolor": "#dedede",
+           "showgrid": false,
+           "showline": true,
+           "title": {
+            "font": {
+             "color": "black",
+             "size": 16
+            }
+           }
+          },
+          "yaxis": {
+           "gridcolor": "#dedede",
+           "gridwidth": 1,
+           "showgrid": true,
+           "showline": false,
+           "zeroline": true,
+           "zerolinecolor": "#dedede"
+          }
+         }
+        },
+        "title": {
+         "text": "Historical forecasts — linear_regression"
+        },
+        "width": 800,
+        "xaxis": {
+         "title": {
+          "text": "Month"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/09/03 16:10:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished run: linear_regression\n",
+      "Finished run: random_forest\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/09/03 16:10:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished run: linear_regression_lags24\n"
+     ]
+    }
+   ],
+   "source": [
+    "experiments = [\n",
+    "    (\n",
+    "        \"linear_regression\",\n",
+    "        LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),\n",
+    "        True,\n",
+    "    ),\n",
+    "    (\n",
+    "        \"random_forest\",\n",
+    "        RandomForestModel(\n",
+    "            lags=12,\n",
+    "            output_chunk_length=FORECAST_HORIZON,\n",
+    "            n_estimators=50,\n",
+    "            random_state=42,\n",
+    "        ),\n",
+    "        False,\n",
+    "    ),\n",
+    "    (\n",
+    "        \"linear_regression_lags24\",\n",
+    "        LinearRegressionModel(lags=24, output_chunk_length=FORECAST_HORIZON),\n",
+    "        False,\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "for run_name, model, plot_hist in experiments:\n",
+    "    run_experiment(run_name, model, plot_historical=plot_hist)\n",
+    "    print(f\"Finished run: {run_name}\")\n",
+    "\n",
+    "# disable autologging\n",
+    "autolog(disable=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3221514",
+   "metadata": {},
+   "source": [
+    "### Explore in the MLflow UI\n",
+    "\n",
+    "Open the tracking UI and compare runs side by side — model parameters, metrics, model artifacts are all available automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ed4e0c35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "To explore runs in the UI:\n",
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmps5wn0e4a/mlflow.db\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"\\nTo explore runs in the UI:\\n  mlflow ui --backend-store-uri sqlite:///{mlflow_db}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "834d1825",
+   "metadata": {},
+   "source": [
+    "<details open>\n",
+    "<summary><strong>MLflow Runs Table View</strong></summary>\n",
+    "\n",
+    "The MLflow Runs Table shows all runs under an experiment in tabular style. It allows you to filter, sort, compare anything that was logged. You can add any logged parameter (metrics, model parameters, ...) to the table via the `Columns` dropdown.\n",
+    "\n",
+    "Here we see that `linear_regression` had the lowest aggregated MAE (`backtest_agg_mae`) and aggregated Horizon-Error (Bias) (`backtest_agg_err`) across all runs.\n",
+    "\n",
+    "![MLflow runs table view](./static/images/mlflow_experiments_overview.png)\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "<details open>\n",
+    "<summary><strong>MLflow Runs Chart View</strong></summary>\n",
+    "\n",
+    "Click on the `Chart View` icon on the top left of the Runs Table to show a detailed view of all metrics across runs. In this view you can find scalar as well as stepped metric charts.\n",
+    "\n",
+    "- 📈 Stepped metrics (line charts): \n",
+    "  - Windowed-backtest results for [time-aggregated metrics](https://unit8co.github.io/darts/generated_api/darts.metrics.html) - showing the score per rolling forecast (0, 1, ... number of rolling windows - 1). See `backtest_mae` below showing 3 steps corresponding to the 3 rolling historical forecasts. \n",
+    "  - Horizon-based-backtest results for [time-dependent metrics](https://unit8co.github.io/darts/generated_api/darts.metrics.html) - showing the score per step in the forecast horizon (0, 1, ... horizon - 1) aggregated over all rolling forecasts. See `backtest_err` below showing 12 steps corresponding to `FORECAST_HORIZON`.\n",
+    "- 📊 Scalar metrics (bar charts) for: \n",
+    "  - Aggregated backtest metrics, e.g. with `autolog(log_backtest_aggregate=True)`. See `backtest_agg_mae` and `backtest_agg_ae` below.\n",
+    "  - Direct [time-aggregated metric](https://unit8co.github.io/darts/generated_api/darts.metrics.html) calls (e.g. `darts.metrics.mae()`)\n",
+    "\n",
+    "... and many other configurations. Read more in the [API reference](https://unit8co.github.io/darts/generated_api/darts.utils.mlflow.html).\n",
+    "\n",
+    "![MLflow runs chart view](./static/images/mlflow_experiments_metrics.png)\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "<details>\n",
+    "<summary><strong>MLflow Run Detail Page</strong> (click to expand)</summary>\n",
+    "\n",
+    "Click on any run in the Runs Table to open the Run Detail Page. It shows an overview of the run, its recorded metrics, hyper-parameters, tags, and more. Play around with the UI to see the different views and features.\n",
+    "\n",
+    "Scroll down to the \"Model\" section and you will see the model that was logged during training. Click on the model to view the details (inclduing the logged model artifacts).\n",
+    "\n",
+    "![MLflow run detail page](./static/images/mlflow_run_detail.png)\n",
+    "\n",
+    "</details>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "248f15dc",
+   "metadata": {},
+   "source": [
+    "## 5. Find the best run\n",
+    "\n",
+    "Above, we could already see that `linear_regression` was the best performing model overall.\n",
+    "\n",
+    "Let's also do it programatically by finding the model with the lowest MAE. With `log_backtest_aggregate=True` we get a convient sort key: one scalar that summarizes rolling backtest performance, regardless of how many windows or time series were evaluated. `backtest_agg_mae` is such a key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fa88b420",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>run_id</th>\n",
+       "      <th>tags.mlflow.runName</th>\n",
+       "      <th>metrics.backtest_agg_mae</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>fb3ad151ba8c4192bb82857a5e255994</td>\n",
+       "      <td>linear_regression</td>\n",
+       "      <td>18.870433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>41484b2e9f794a3a96ee6f89f4d789e4</td>\n",
+       "      <td>linear_regression_lags24</td>\n",
+       "      <td>24.509846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2302431c94a348d09dcd46bbc10dcdab</td>\n",
+       "      <td>random_forest</td>\n",
+       "      <td>77.316667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             run_id       tags.mlflow.runName  \\\n",
+       "0  fb3ad151ba8c4192bb82857a5e255994         linear_regression   \n",
+       "1  41484b2e9f794a3a96ee6f89f4d789e4  linear_regression_lags24   \n",
+       "2  2302431c94a348d09dcd46bbc10dcdab             random_forest   \n",
+       "\n",
+       "   metrics.backtest_agg_mae  \n",
+       "0                 18.870433  \n",
+       "1                 24.509846  \n",
+       "2                 77.316667  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Best run by backtest_agg_mae: linear_regression (fb3ad151ba8c4192bb82857a5e255994)\n"
+     ]
+    }
+   ],
+   "source": [
+    "runs_df = mlflow.search_runs(\n",
+    "    experiment_names=[EXPERIMENT_NAME],\n",
+    "    order_by=[\"metrics.backtest_agg_mae ASC\"],\n",
+    ")\n",
+    "display(runs_df[[\"run_id\", \"tags.mlflow.runName\", \"metrics.backtest_agg_mae\"]])\n",
+    "\n",
+    "best_run = runs_df.iloc[0]\n",
+    "best_run_id = best_run.run_id\n",
+    "best_run_name = best_run[\"tags.mlflow.runName\"]\n",
+    "print(f\"\\nBest run by backtest_agg_mae: {best_run_name} ({best_run_id})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4606742",
+   "metadata": {},
+   "source": [
+    "## 6. Register the champion model\n",
+    "\n",
+    "We can promote the best run's model to the **MLflow Model Registry**, then assign a **champion** alias (or any other alias). Downstream code can then load the registered models via registered model name and alias.\n",
+    "\n",
+    "> This assumes that model logging was enabled: `autolog(log_models=True)` and `fit()` was called within each MLflow run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "26ca96da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best model URI: models:/m-d142c7979f29495d90aa74b3e91638e0\n",
+      "Aliases set: @champion\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Successfully registered model 'darts-air-passengers-forecaster'.\n",
+      "Created version '1' of model 'darts-air-passengers-forecaster'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the logged model URI of the best run\n",
+    "model_outputs = mlflow.get_run(best_run_id).outputs.model_outputs\n",
+    "best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
+    "print(f\"Best model URI: {best_model_uri}\")\n",
+    "\n",
+    "REGISTERED_MODEL_NAME = \"darts-air-passengers-forecaster\"\n",
+    "\n",
+    "registration = mlflow.register_model(\n",
+    "    model_uri=best_model_uri,\n",
+    "    name=REGISTERED_MODEL_NAME,\n",
+    ")\n",
+    "\n",
+    "client = MlflowClient()\n",
+    "client.set_registered_model_alias(\n",
+    "    REGISTERED_MODEL_NAME, \"champion\", registration.version\n",
+    ")\n",
+    "print(\"Aliases set: @champion\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7abaf63",
+   "metadata": {},
+   "source": [
+    "All logged models can be found in the MLflow Models View:\n",
+    "\n",
+    "![MLflow Model Table](./static/images/mlflow_models_overview.png)\n",
+    "\n",
+    "The registered models can be found in the MLflow Model Registry:\n",
+    "\n",
+    "![MLflow Model Registry](./static/images/mlflow_model_registry.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cd2ebab",
+   "metadata": {},
+   "source": [
+    "## 7. Load the champion and forecast\n",
+    "\n",
+    "Production inference loads the aliased registered model via `models:/<name>@champion` without hard-coding a version number.\n",
+    "The loaded model can then be used for prediction:\n",
+    "\n",
+    "- Global models (like the sklearn regressors used here) need `series=` at predict time because the model is saved without training data.\n",
+    "- Local models (like `ExponentialSmoothing` and others) must be re-fit before prediction.\n",
+    "\n",
+    "> Always load Darts models with `from darts.utils.mlflow import load_model` — not `mlflow.pyfunc.load_model`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c12ba777",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "<b>full series</b>: %{y:.3g}<extra></extra>",
+         "legendgroup": "full series",
+         "line": {
+          "color": "#000000"
+         },
+         "mode": "lines",
+         "name": "full series",
+         "type": "scatter",
+         "x": [
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+          "dtype": "f8"
+         }
+        },
+        {
+         "hovertemplate": "<b>champion forecast</b>: %{y:.3g}<extra></extra>",
+         "legendgroup": "champion forecast",
+         "line": {
+          "color": "#003dfd"
+         },
+         "mode": "lines",
+         "name": "champion forecast",
+         "type": "scatter",
+         "x": [
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+          "dtype": "f8"
+         }
+        }
+       ],
+       "layout": {
+        "height": 400,
+        "hovermode": "x unified",
+        "legend": {
+         "x": 0.01,
+         "xanchor": "left",
+         "y": 0.99,
+         "yanchor": "top"
+        },
+        "template": {
+         "data": {
+          "scatter": [
+           {
+            "line": {
+             "width": 3
+            },
+            "type": "scatter"
+           }
+          ]
+         },
+         "layout": {
+          "colorway": [
+           "#000000",
+           "#003dfd",
+           "#b512b8",
+           "#11a9ba",
+           "#0d780f",
+           "#f77f07",
+           "#ba0f0f"
+          ],
+          "font": {
+           "color": "black",
+           "family": "Arial, sans-serif",
+           "size": 14
+          },
+          "legend": {
+           "bgcolor": "rgba(255, 255, 255, 0.8)",
+           "borderwidth": 0,
+           "font": {
+            "size": 14
+           },
+           "x": 1,
+           "xanchor": "right",
+           "y": 1,
+           "yanchor": "top"
+          },
+          "margin": {
+           "b": 50,
+           "l": 50,
+           "r": 50,
+           "t": 50
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "white",
+          "showlegend": true,
+          "xaxis": {
+           "linecolor": "#dedede",
+           "showgrid": false,
+           "showline": true,
+           "title": {
+            "font": {
+             "color": "black",
+             "size": 16
+            }
+           }
+          },
+          "yaxis": {
+           "gridcolor": "#dedede",
+           "gridwidth": 1,
+           "showgrid": true,
+           "showline": false,
+           "zeroline": true,
+           "zerolinecolor": "#dedede"
+          }
+         }
+        },
+        "title": {
+         "text": "Production inference — darts-air-passengers-forecaster@champion"
+        },
+        "width": 800,
+        "xaxis": {
+         "title": {
+          "text": "Month"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "champion = load_model(f\"models:/{REGISTERED_MODEL_NAME}@champion\")\n",
+    "\n",
+    "if isinstance(champion, GlobalForecastingModel):\n",
+    "    forecast = champion.predict(n=FORECAST_HORIZON, series=series)\n",
+    "else:\n",
+    "    forecast = champion.fit(series).predict(n=FORECAST_HORIZON)\n",
+    "\n",
+    "fig = series.plotly(label=\"full series\")\n",
+    "fig = forecast.plotly(label=\"champion forecast\", fig=fig)\n",
+    "fig.update_layout(\n",
+    "    title=f\"Production inference — {REGISTERED_MODEL_NAME}@champion\", **PLOTLY_KWARGS\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac44a0ae",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In a few lines of code you get a complete experimentation loop:\n",
+    "\n",
+    "1. **Setup** — tracking URI + experiment.\n",
+    "2. **Autolog** — `autolog(log_models=True, log_backtest_aggregate=True)`.\n",
+    "3. **Experiment** — `with mlflow.start_run()`: fit, historical forecasts, backtest metrics, hold-out MAPE .\n",
+    "4. **Compare** — `mlflow.search_runs(..., order_by=[\"metrics.backtest_agg_mae ASC\"])`.\n",
+    "5. **Promote** — `register_model` + `set_registered_model_alias(..., \"champion\", ...)`.\n",
+    "6. **Serve** — `load_model(\"models:/<name>@champion\").predict(...)`.\n",
+    "\n",
+    "**Learn more:**\n",
+    "\n",
+    "- [Darts MLflow API reference](https://unit8co.github.io/darts/generated_api/darts.utils.mlflow.html)\n",
+    "- [MLflow API reference](https://mlflow.org/docs/latest/ml/tracking/tracking-api/)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/29-MLflow-quickstart.ipynb b/examples/29-MLflow-quickstart.ipynb
deleted file mode 100644
index b5a76dbb92..0000000000
--- a/examples/29-MLflow-quickstart.ipynb
+++ /dev/null
@@ -1,6143 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# MLflow for Darts\n",
-        "\n",
-        "This notebook demonstrates how to use Darts with MLflow for experiment tracking, model versioning, and management.\n",
-        "If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n",
-        "\n",
-        "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, sharing and deploying models, and managing model versions in a central registry. With Darts' MLflow integration, you can easily log forecasting models, compare experiments, and manage model versions throughout your forecasting workflow.\n",
-        "\n",
-        "For more details, see the [MLflow documentation](https://mlflow.org/docs/latest/index.html)."
-      ],
-      "id": "aeddb542"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Installing MLflow\n",
-        "\n",
-        "MLflow is available as an optional dependency for Darts. Install it with:\n",
-        "\n",
-        "```bash\n",
-        "pip install \"mlflow>=3.0\"\n",
-        "```"
-      ],
-      "id": "f72894af"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup and Imports"
-      ],
-      "id": "42e3dcea"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:43.049165Z",
-          "iopub.status.busy": "2026-06-24T15:18:43.049088Z",
-          "iopub.status.idle": "2026-06-24T15:18:43.053693Z",
-          "shell.execute_reply": "2026-06-24T15:18:43.053435Z"
-        }
-      },
-      "source": [
-        "# fix python path if working locally\n",
-        "from utils import fix_pythonpath_if_working_locally\n",
-        "\n",
-        "fix_pythonpath_if_working_locally()\n",
-        "\n",
-        "import warnings\n",
-        "\n",
-        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-      ],
-      "execution_count": 2,
-      "outputs": [],
-      "id": "b346ce8f"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:43.054776Z",
-          "iopub.status.busy": "2026-06-24T15:18:43.054722Z",
-          "iopub.status.idle": "2026-06-24T15:18:46.599643Z",
-          "shell.execute_reply": "2026-06-24T15:18:46.599208Z"
-        }
-      },
-      "source": [
-        "%matplotlib inline\n",
-        "\n",
-        "import os\n",
-        "import tempfile\n",
-        "\n",
-        "import matplotlib.pyplot as plt\n",
-        "import mlflow\n",
-        "import numpy as np\n",
-        "\n",
-        "import darts.metrics as metrics\n",
-        "from darts.datasets import AirPassengersDataset\n",
-        "from darts.models import ExponentialSmoothing, LinearRegressionModel, NBEATSModel\n",
-        "from darts.utils.mlflow import autolog, load_model, log_model, save_model"
-      ],
-      "execution_count": 3,
-      "outputs": [],
-      "id": "13b13fe4"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:46.600906Z",
-          "iopub.status.busy": "2026-06-24T15:18:46.600748Z",
-          "iopub.status.idle": "2026-06-24T15:18:46.634540Z",
-          "shell.execute_reply": "2026-06-24T15:18:46.634100Z"
-        }
-      },
-      "source": [
-        "# use darts plotting style\n",
-        "from darts import set_option\n",
-        "\n",
-        "set_option(\"plotting.use_darts_style\", True)"
-      ],
-      "execution_count": 4,
-      "outputs": [],
-      "id": "4d424e08"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## MLflow Setup\n",
-        "\n",
-        "First, let's configure MLflow tracking. We'll use a temporary directory for this example, however you can choose from any of the supported MLflow [tracking backends](https://mlflow.org/docs/latest/self-hosting/architecture/tracking-server/#backend-store) such as local filesystem, SQLite, PostgreSQL, MySQL, or cloud storage solutions like S3 or Azure Blob Storage."
-      ],
-      "id": "2f9c40d6"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:46.636640Z",
-          "iopub.status.busy": "2026-06-24T15:18:46.636565Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.268439Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.268081Z"
-        }
-      },
-      "source": [
-        "# temporary directory for MLflow tracking\n",
-        "tmpdir = tempfile.mkdtemp()\n",
-        "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
-        "\n",
-        "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
-        "mlflow.set_experiment(\"darts-quickstart\")\n",
-        "\n",
-        "print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n",
-        "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-quickstart').name}\")"
-      ],
-      "execution_count": 5,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
-            "2026/07/23 18:50:07 INFO mlflow.store.db.utils: Updating database tables\n",
-            "2026/07/23 18:50:08 INFO mlflow.tracking.fluent: Experiment with name 'darts-quickstart' does not exist. Creating a new experiment.\n"
-          ]
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "MLflow tracking URI: sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
-            "Experiment: darts-quickstart\n"
-          ]
-        }
-      ],
-      "id": "88320df5"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load Sample Data\n",
-        "\n",
-        "We'll use the classic AirPassengers dataset for this example."
-      ],
-      "id": "03d5209e"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.269628Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.269548Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.356588Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.356201Z"
-        }
-      },
-      "source": [
-        "series = AirPassengersDataset().load()\n",
-        "train, val = series.split_before(0.75)\n",
-        "\n",
-        "print(f\"Training series: {len(train)} points\")\n",
-        "print(f\"Validation series: {len(val)} points\")\n",
-        "\n",
-        "series.plot()\n",
-        "plt.axvline(train.end_time(), color=\"red\", linestyle=\"--\", label=\"Train/Val split\")\n",
-        "plt.legend()\n",
-        "plt.title(\"AirPassengers Dataset\")\n",
-        "plt.show()"
-      ],
-      "execution_count": 6,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Training series: 107 points\n",
-            "Validation series: 37 points\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          }
-        }
-      ],
-      "id": "1596e07e"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Basic Model Logging\n",
-        "\n",
-        "Let's train a simple model and log it to MLflow manually."
-      ],
-      "id": "34858645"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.357581Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.357516Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.450374Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.449925Z"
-        }
-      },
-      "source": [
-        "model = ExponentialSmoothing()\n",
-        "model.fit(train)\n",
-        "\n",
-        "predictions = model.predict(n=len(val))\n",
-        "\n",
-        "# calculate metrics you want to log to MLflow\n",
-        "mape_score = metrics.mape(val, predictions)\n",
-        "rmse_score = metrics.rmse(val, predictions)\n",
-        "\n",
-        "print(f\"Validation MAPE: {mape_score:.2f}%\")\n",
-        "print(f\"Validation RMSE: {rmse_score:.2f}\")\n",
-        "\n",
-        "train[-50:].plot(label=\"Training\")\n",
-        "val.plot(label=\"Actual\")\n",
-        "predictions.plot(label=\"Forecast\")\n",
-        "plt.legend()\n",
-        "plt.title(\"Exponential Smoothing Forecast\")\n",
-        "plt.show()"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Validation MAPE: 7.86%\n",
-            "Validation RMSE: 34.77\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          }
-        }
-      ],
-      "id": "bc8f520d"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now let's log this model to MLflow.\n",
-        "\n",
-        "Tags can live in two places in MLflow 3:\n",
-        "\n",
-        "- **Run tags** (`mlflow.set_tags(...)`) – shown on the run page in the UI. This is also where `autolog()` writes its tags (`model_class`, …).\n",
-        "- **LoggedModel tags** (`log_model(..., tags=...)`) – attached to the logged model entity itself (open the model from the run's **Outputs** / Models view)."
-      ],
-      "id": "35dc864c"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.451366Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.451297Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.906785Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.906380Z"
-        }
-      },
-      "source": [
-        "with mlflow.start_run(run_name=\"exponential-smoothing-baseline\") as run:\n",
-        "    model_info = log_model(\n",
-        "        model=model,\n",
-        "        name=\"exponential-smoothing-model\",\n",
-        "        # alternatively, use mlflow.set_tag(key, value) in the run\n",
-        "        tags={\"model_type\": \"ExponentialSmoothing\", \"dataset\": \"AirPassengers\"},\n",
-        "    )\n",
-        "\n",
-        "    # log calculated metrics you want\n",
-        "    mlflow.log_metric(\"mape\", mape_score)\n",
-        "    mlflow.log_metric(\"rmse\", rmse_score)\n",
-        "\n",
-        "    print(f\"Run ID: {run.info.run_id}\")\n",
-        "    print(f\"Model URI: {model_info.model_uri}\")"
-      ],
-      "execution_count": 8,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "2026/07/23 18:50:08 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-          ]
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
-            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
-          ]
-        }
-      ],
-      "id": "61406cd9"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the Model Back\n",
-        "\n",
-        "We can load the model from MLflow using its URI:"
-      ],
-      "id": "0728690e"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.907880Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.907802Z",
-          "iopub.status.idle": "2026-06-24T15:18:47.914695Z",
-          "shell.execute_reply": "2026-06-24T15:18:47.914326Z"
-        }
-      },
-      "source": [
-        "loaded_model = load_model(model_info.model_uri)\n",
-        "\n",
-        "loaded_predictions = loaded_model.predict(n=len(val))\n",
-        "\n",
-        "# verify predictions match\n",
-        "predictions_match = np.allclose(predictions.values(), loaded_predictions.values())\n",
-        "print(f\"Loaded model predictions match: {predictions_match}\")"
-      ],
-      "execution_count": 9,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Loaded model predictions match: True\n"
-          ]
-        }
-      ],
-      "id": "cae35ffa"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Automatic Logging with `autolog()`\n",
-        "\n",
-        "`autolog()` patches darts models and metrics so that the following are logged automatically, with no extra code needed:\n",
-        "\n",
-        "- **`fit()`** – model creation parameters and covariate metadata (and the trained model artifact when ``log_models=True``; default ``False``).\n",
-        "- **Darts metric functions** – any call made inside an active MLflow run.\n",
-        "- **`backtest()`** – evaluation metrics under `backtest_*` keys.\n",
-        "- **`historical_forecasts()`** – patched so its internal per-window `fit()` calls don't each spawn their own logging.\n",
-        "- **PyTorch-based models** – per-epoch `train_loss` / `val_loss` via MLflow's PyTorch autologging (see the section below for details)."
-      ],
-      "id": "6bd4597c"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:47.915629Z",
-          "iopub.status.busy": "2026-06-24T15:18:47.915566Z",
-          "iopub.status.idle": "2026-06-24T15:18:50.020337Z",
-          "shell.execute_reply": "2026-06-24T15:18:50.019951Z"
-        }
-      },
-      "source": [
-        "autolog()\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"linear-regression-autolog\") as run:\n",
-        "    auto_model = LinearRegressionModel(lags=12)\n",
-        "    auto_model.fit(train)  # autolog logs params and covariate metadata\n",
-        "\n",
-        "    auto_predictions = auto_model.predict(n=len(val))\n",
-        "    # these metric calls happen inside the run, so they are logged automatically\n",
-        "    auto_mape = metrics.mape(val, auto_predictions)\n",
-        "    auto_rmse = metrics.rmse(val, auto_predictions)\n",
-        "\n",
-        "autolog(disable=True)\n",
-        "\n",
-        "# show logged metrics\n",
-        "logged = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.metrics\n",
-        "print(\"Logged metrics:\", {k: round(v, 4) for k, v in sorted(logged.items())})\n",
-        "\n",
-        "# plot\n",
-        "fig, ax = plt.subplots(figsize=(10, 4))\n",
-        "train[-36:].plot(label=\"Train\", ax=ax)\n",
-        "val.plot(label=\"Actual\", ax=ax)\n",
-        "auto_predictions.plot(\n",
-        "    label=f\"Forecast (MAPE {auto_mape:.1f}%, RMSE {auto_rmse:.1f})\", ax=ax\n",
-        ")\n",
-        "ax.set_title(\"Linear Regression — autolog run\")\n",
-        "ax.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ],
-      "execution_count": 10,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Logged metrics: {'mape': 10.742, 'rmse': 51.182}\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1000x400 with 1 Axes>"
-            ]
-          }
-        }
-      ],
-      "id": "5ef7f73a"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Open the MLflow UI\n",
-        "\n",
-        "To experiment with the runs yourself, open the **MLflow UI** to explore them interactively. Run the command printed below in a separate terminal, then navigate to `http://localhost:5000`.\n",
-        "\n",
-        "The UI lets you:\n",
-        "- **Compare runs** side-by-side in the Experiments table\n",
-        "- **Inspect** individual run parameters, metrics, and logged artifacts\n",
-        "- **Visualize** metrics across runs with built-in charts\n",
-        "- **Register** models to the Model Registry for versioning"
-      ],
-      "id": "f484330f"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:50.021527Z",
-          "iopub.status.busy": "2026-06-24T15:18:50.021439Z",
-          "iopub.status.idle": "2026-06-24T15:18:50.023116Z",
-          "shell.execute_reply": "2026-06-24T15:18:50.022824Z"
-        }
-      },
-      "source": [
-        "print(\"Launch the MLflow UI with this command in your terminal:\\n\")\n",
-        "print(f\"  mlflow ui --backend-store-uri {mlflow.get_tracking_uri()}\\n\")\n",
-        "print(\"Then open:  http://localhost:5000\")"
-      ],
-      "execution_count": 11,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Launch the MLflow UI with this command in your terminal:\n",
-            "\n",
-            "  mlflow ui --backend-store-uri sqlite:////var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic/mlflow.db\n",
-            "\n",
-            "Then open:  http://localhost:5000\n"
-          ]
-        }
-      ],
-      "id": "1a01cd2f"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "> 📸 **Try it:** Open the UI, switch to the `darts-quickstart` experiment, and sort the **Table view** by `mape` to instantly see which model performs best. Click any run name to see its parameters, tags, and logged artifacts.\n",
-        ">\n",
-        "![Mlflow Overview](./static/images/mlflow_overview.png)"
-      ],
-      "id": "88b0d285"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Per-epoch Metrics with Torch Models\n",
-        "\n",
-        "For neural models, `autolog()` enables MLflow's PyTorch autologging, which records `train_loss` and `val_loss` at the end of every epoch.\n",
-        "\n",
-        "Pass `torch_metrics` to the model to add extra per-epoch metrics (e.g. MAE, MSE). They will appear prefixed as `train_MAE`, `val_MAE`, etc."
-      ],
-      "id": "e2b133bc"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:50.024161Z",
-          "iopub.status.busy": "2026-06-24T15:18:50.024087Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.370946Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.370531Z"
-        }
-      },
-      "source": [
-        "from torchmetrics import MeanAbsoluteError, MeanSquaredError, MetricCollection\n",
-        "\n",
-        "# per-epoch train_loss / val_loss are logged via MLflow's PyTorch autologging\n",
-        "autolog()\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"nbeats-epoch-metrics\"):\n",
-        "    nbeats = NBEATSModel(\n",
-        "        input_chunk_length=24,\n",
-        "        output_chunk_length=12,\n",
-        "        n_epochs=10,\n",
-        "        pl_trainer_kwargs={\"accelerator\": \"cpu\"},\n",
-        "        # these are logged per epoch as train_MAE, val_MAE, train_MSE, val_MSE\n",
-        "        torch_metrics=MetricCollection({\n",
-        "            \"MAE\": MeanAbsoluteError(),\n",
-        "            \"MSE\": MeanSquaredError(),\n",
-        "        }),\n",
-        "        random_state=42,\n",
-        "    )\n",
-        "    nbeats.fit(train, val_series=val)\n",
-        "    nbeats_pred = nbeats.predict(n=len(val))\n",
-        "    # metric calls inside the run are logged automatically (keys: mape, rmse)\n",
-        "    nbeats_mape = metrics.mape(val, nbeats_pred)\n",
-        "    nbeats_rmse = metrics.rmse(val, nbeats_pred)\n",
-        "    print(f\"NBEATS MAPE: {nbeats_mape:.2f}%\")\n",
-        "    print(f\"NBEATS RMSE: {nbeats_rmse:.2f}\")\n",
-        "\n",
-        "autolog(disable=True)"
-      ],
-      "execution_count": 53,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "INFO: GPU available: True (mps), used: False\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
-            "INFO: TPU available: False, using: 0 TPU cores\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-            "INFO: HPU available: False, using: 0 HPUs\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
-            "\n",
-            "  | Name            | Type             | Params | Mode \n",
-            "-------------------------------------------------------------\n",
-            "0 | criterion       | MSELoss          | 0      | train\n",
-            "1 | train_criterion | MSELoss          | 0      | train\n",
-            "2 | val_criterion   | MSELoss          | 0      | train\n",
-            "3 | train_metrics   | MetricCollection | 0      | train\n",
-            "4 | val_metrics     | MetricCollection | 0      | train\n",
-            "5 | stacks          | ModuleList       | 6.2 M  | train\n",
-            "-------------------------------------------------------------\n",
-            "6.2 M     Trainable params\n",
-            "1.4 K     Non-trainable params\n",
-            "6.2 M     Total params\n",
-            "24.787    Total estimated model params size (MB)\n",
-            "400       Modules in train mode\n",
-            "0         Modules in eval mode\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "33ff56f2bbbb46ddb71bf5ac409bc3c7",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/torch/utils/data/dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, device pinned memory won't be used.\n",
-            "  super().__init__(loader)\n",
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('val_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "2fb13a9c2a6b40ad886bb7878949614d",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Training: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "595549c03c1d4fe28a03ce38a0f38441",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MAE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n",
-            "/Users/jakubchlapek/Desktop/projects/darts/.venv/lib/python3.11/site-packages/pytorch_lightning/core/module.py:512: You called `self.log('train_MSE', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "78af3a584caf477ba9c043c93a77ae56",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "07ec6fb97dd6418594e5dedda8a087fc",
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-            },
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-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
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-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "bbd214c1629e47de8f36acad90896742",
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-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "1a78ea5bef414a31bd4dd00f162b4876",
-              "version_major": 2,
-              "version_minor": 0
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-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "0759f953cc2e4aba9c4d4644279daf9c",
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-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "33e1f6444b504ddd8808d0b4bdf7cf34",
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-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "c23701c8d9fb45a5a7188f013b8ae713",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "1f198dc0926c4f40a60823608c789f05",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "82a6462bdf0a486a85887bef6ae73630",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Validation: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
-            "INFO: GPU available: True (mps), used: False\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (mps), used: False\n",
-            "INFO: TPU available: False, using: 0 TPU cores\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-            "INFO: HPU available: False, using: 0 HPUs\n",
-            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "a9cf24e439244b4f99812eeea1c9aab8",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Predicting: |          | 0/? [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "NBEATS MAPE: 13.64%\n",
-            "NBEATS RMSE: 63.33\n"
-          ]
-        }
-      ],
-      "id": "d01783d1"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "> 📸 **Try it:** Click on the `nbeats-epoch-metrics` run in the UI, then open the **Model metrics** tab. You'll see `train_loss`, `val_loss`, `train_MAE`, and `val_MAE` plotted as learning curves across epochs.\n",
-        ">\n",
-        "![Mlflow Charts](./static/images/mlflow_charts.png)"
-      ],
-      "id": "6fcc8f13"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Forecast Metrics\n",
-        "\n",
-        "With `log_metrics=True` (the default), `autolog()` patches every darts metric function. Any metric you call **inside an active run** is logged automatically with no extra arguments needed.\n",
-        "\n",
-        "The metric key is derived from the result: a scalar is logged under `{metric}` (e.g. calling `darts.metrics.mae(val, pred)` logs `mae`). You can always log a custom-named metric explicitly with `mlflow.log_metric()`.\n",
-        "\n",
-        "Logged metric values are only meaningful to compare across runs when the evaluation settings match. Use the same evaluation time frame, forecast horizon, and evaluation start date for every `backtest()` / metric call you intend to compare against one another."
-      ],
-      "id": "3f0828c2"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.372445Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.372367Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.470125Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.469722Z"
-        }
-      },
-      "source": [
-        "# log_metrics=True (the default) patches every darts metric so that calls made\n",
-        "# inside an active run are logged automatically\n",
-        "autolog(log_metrics=True)\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"linear-regression-full-metrics\") as run:\n",
-        "    lr_model = LinearRegressionModel(lags=12)\n",
-        "    lr_model.fit(train)\n",
-        "    lr_pred = lr_model.predict(n=len(val))\n",
-        "\n",
-        "    # each metric called here is auto-logged under its own name: mae, rmse, smape\n",
-        "    metrics.mae(val, lr_pred)\n",
-        "    metrics.rmse(val, lr_pred)\n",
-        "    metrics.smape(val, lr_pred)\n",
-        "    # you can still log a custom-named metric explicitly\n",
-        "    mlflow.log_metric(\"manual_mape\", metrics.mape(val, lr_pred))\n",
-        "    run_id = run.info.run_id\n",
-        "\n",
-        "autolog(disable=True)\n",
-        "\n",
-        "# show what was logged\n",
-        "client = mlflow.tracking.MlflowClient()\n",
-        "run_metrics = client.get_run(run_id).data.metrics\n",
-        "metric_names = sorted(run_metrics.keys())\n",
-        "print(f\"All logged metrics ({len(metric_names)}):\")\n",
-        "for name in metric_names:\n",
-        "    print(f\"  {name}: {run_metrics[name]:.4f}\")"
-      ],
-      "execution_count": 13,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "All logged metrics (5):\n",
-            "  mae: 46.0220\n",
-            "  manual_mape: 10.7420\n",
-            "  mape: 10.7420\n",
-            "  rmse: 51.1820\n",
-            "  smape: 10.1015\n"
-          ]
-        }
-      ],
-      "id": "df8e85b6"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Metric Shape and Detailed Logging\n",
-        "\n",
-        "The logged key reflects the shape of the metric output, which `autolog()` infers from the metric and its keyword arguments. The general pattern is:\n",
-        "\n",
-        "`{metric_name}{component}{quantile_or_label}`\n",
-        "\n",
-        "- **Per-component** (`component_reduction=None`) adds the component name, e.g. `mae_<component>`.\n",
-        "- **Quantile / interval** (`q`, `q_interval`) adds the quantile, e.g. `mql_q0.500`.\n",
-        "- **Per-label classification** (`label_reduction=None`) adds the class, e.g. `f1_label1`.\n",
-        "- **Per-timestep forecast metrics** (`time_reduction=None`) use zero-based steps, so the first predicted value is `0`. Backtest calendar-relative steps are negative and end at `-1`, aligning different series lengths on their shared end.\n",
-        "- **`series_reduction`**, when set on the metric call, aggregates across series inside the metric itself, so the mean-over-series logging described below does not apply.\n",
-        "\n",
-        "For `backtest()`, metric keys have a `backtest_` prefix. A time-dependent metric with `reduction=None` keeps its per-window values in the return value, but MLflow charts one value per horizon step: it applies `np.nanmean` over windows for each series, then aggregates across series. The detailed `metrics_per_series.json` table retains every source value with a `window_index`, including for a single-series backtest.\n",
-        "\n",
-        "When you score a list of series, the logged value is the mean over series and the full per-series breakdown is appended to the same run-wide table artifact, `metrics_per_series.json`, which you can read back with `mlflow.load_table()`.\n",
-        "\n",
-        "Passing `name=` to a metric overrides the `{metric_name}` token in the key while keeping the suffixes (e.g. `mql(..., q=0.5, name=\"foo\")` logs `foo_q0.500`)."
-      ],
-      "id": "b08e900a"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "# build a small multi-series example from the univariate AirPassengers data\n",
-        "series_list = [train, train * 1.2]\n",
-        "val_list = [val, val * 1.2]\n",
-        "\n",
-        "multi_model = LinearRegressionModel(lags=12)\n",
-        "multi_model.fit(series_list)\n",
-        "multi_preds = multi_model.predict(n=len(val), series=series_list)\n",
-        "\n",
-        "autolog(log_metrics=True)\n",
-        "\n",
-        "with mlflow.start_run(run_name=\"metric-shape-and-table\") as run:\n",
-        "    # metric shape: a per-timestep metric (ae) logs one value per horizon step\n",
-        "    # under a single key, charted across MLflow steps\n",
-        "    single_pred = multi_preds[0]  # train == series_list[0]\n",
-        "    metrics.ae(val, single_pred)\n",
-        "\n",
-        "    # multiple series: the logged value is the MEAN over series, and the full\n",
-        "    # per-series breakdown is appended to the run's table artifact\n",
-        "    per_series_mae = metrics.mae(val_list, multi_preds)\n",
-        "\n",
-        "autolog(disable=True)\n",
-        "\n",
-        "client = mlflow.tracking.MlflowClient()\n",
-        "run_id = run.info.run_id\n",
-        "\n",
-        "# aggregate metrics: mae is the mean over the two series\n",
-        "logged = client.get_run(run_id).data.metrics\n",
-        "print(\"Aggregate metrics:\", {k: round(v, 3) for k, v in sorted(logged.items())})\n",
-        "print(\"Mean MAE over series:\", round(float(np.mean(per_series_mae)), 3))\n",
-        "\n",
-        "# load the per-series table artifact\n",
-        "per_series_df = mlflow.load_table(\n",
-        "    artifact_file=\"metrics_per_series.json\", run_ids=[run_id]\n",
-        ")\n",
-        "print(\"\\nPer-series breakdown (metrics_per_series.json):\")\n",
-        "per_series_df"
-      ],
-      "execution_count": 14,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Aggregate metrics: {'ae': 44.196, 'mae': 51.316}\n",
-            "Mean MAE over series: 51.316\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "0305dca7a1b846f19da965d1b74319c5",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
-            ]
-          }
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "\n",
-            "Per-series breakdown (metrics_per_series.json):\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "execution_count": null,
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>key</th>\n",
-              "      <th>series_index</th>\n",
-              "      <th>step</th>\n",
-              "      <th>value</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>mae</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>47.458874</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>mae</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>55.172880</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   key  series_index  step      value\n",
-              "0  mae             0     0  47.458874\n",
-              "1  mae             1     0  55.172880"
-            ]
-          }
-        }
-      ],
-      "id": "ecdbbf29"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Saving and Loading Models Locally\n",
-        "\n",
-        "You can also save and load models to/from local paths without MLflow runs."
-      ],
-      "id": "8511fc08"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.471252Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.471178Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.478682Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.478330Z"
-        }
-      },
-      "source": [
-        "# Save model to local directory\n",
-        "local_model_path = os.path.join(tmpdir, \"my_model\")\n",
-        "save_model(model, path=local_model_path)\n",
-        "\n",
-        "print(\"\\nFiles in model directory:\")\n",
-        "for file in os.listdir(local_model_path):\n",
-        "    print(f\"  - {file}\")"
-      ],
-      "execution_count": 15,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "2026/07/23 18:50:10 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-          ]
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "\n",
-            "Files in model directory:\n",
-            "  - python_env.yaml\n",
-            "  - requirements.txt\n",
-            "  - MLmodel\n",
-            "  - model.pkl\n",
-            "  - conda.yaml\n"
-          ]
-        }
-      ],
-      "id": "645ef079"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.479679Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.479604Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.484973Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.484638Z"
-        }
-      },
-      "source": [
-        "# Load model from local directory\n",
-        "local_loaded_model = load_model(f\"file://{local_model_path}\")\n",
-        "\n",
-        "# Test it\n",
-        "local_predictions = local_loaded_model.predict(n=5)\n",
-        "print(\"Loaded model successfully!\")\n",
-        "print(f\"Predictions shape: {local_predictions.values().shape}\")"
-      ],
-      "execution_count": 16,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Loaded model successfully!\n",
-            "Predictions shape: (5, 1)\n"
-          ]
-        }
-      ],
-      "id": "254ba153"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Querying Experiments\n",
-        "\n",
-        "You can programmatically query and compare runs."
-      ],
-      "id": "aab0d1e0"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.485914Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.485859Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.496380Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.496048Z"
-        }
-      },
-      "source": [
-        "from mlflow.tracking import MlflowClient\n",
-        "\n",
-        "experiment = mlflow.get_experiment_by_name(\"darts-quickstart\")\n",
-        "\n",
-        "# get all runs\n",
-        "client = MlflowClient()\n",
-        "runs = client.search_runs(\n",
-        "    experiment_ids=[experiment.experiment_id],\n",
-        "    order_by=[\"metrics.mape ASC\"],  # Sort by best MAPE\n",
-        ")\n",
-        "\n",
-        "print(f\"Found {len(runs)} runs in experiment '{experiment.name}':\\n\")\n",
-        "for i, run in enumerate(runs, 1):\n",
-        "    run_name = run.data.tags.get(\"mlflow.runName\", \"Unnamed\")\n",
-        "    mape_val = run.data.metrics.get(\"mape\", \"N/A\")\n",
-        "    print(f\"{i}. {run_name}\")\n",
-        "    print(f\"   Run ID: {run.info.run_id}\")\n",
-        "    print(f\"   Validation MAPE: {mape_val}\")\n",
-        "    print()"
-      ],
-      "execution_count": 17,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Found 5 runs in experiment 'darts-quickstart':\n",
-            "\n",
-            "1. exponential-smoothing-baseline\n",
-            "   Run ID: 19ce5c2862014f5a8b27d68cb5f41c34\n",
-            "   Validation MAPE: 7.864181481214469\n",
-            "\n",
-            "2. linear-regression-full-metrics\n",
-            "   Run ID: 708095048f274913990375bbbd4310ad\n",
-            "   Validation MAPE: 10.742044444678953\n",
-            "\n",
-            "3. linear-regression-autolog\n",
-            "   Run ID: 36376b7a94454a13a4acba207459315e\n",
-            "   Validation MAPE: 10.742044444678953\n",
-            "\n",
-            "4. nbeats-epoch-metrics\n",
-            "   Run ID: f0d1b0ed9ac34232b5e36fe82a24a0a6\n",
-            "   Validation MAPE: 13.639569217869525\n",
-            "\n",
-            "5. metric-shape-and-table\n",
-            "   Run ID: 9be3e9d311b64d45b0204174835537cc\n",
-            "   Validation MAPE: N/A\n",
-            "\n"
-          ]
-        }
-      ],
-      "id": "109a9812"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the Best Model"
-      ],
-      "id": "22685423"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.497560Z",
-          "iopub.status.busy": "2026-06-24T15:18:53.497496Z",
-          "iopub.status.idle": "2026-06-24T15:18:53.572540Z",
-          "shell.execute_reply": "2026-06-24T15:18:53.572133Z"
-        }
-      },
-      "source": [
-        "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
-        "\n",
-        "if runs:\n",
-        "    best_run = runs[0]\n",
-        "    # the model is logged via MLflow's logged-model API, so resolve its model_id\n",
-        "    # from the run outputs and load it with a models:/ URI\n",
-        "    model_outputs = mlflow.get_run(best_run.info.run_id).outputs.model_outputs\n",
-        "    best_model_uri = f\"models:/{model_outputs[0].model_id}\"\n",
-        "\n",
-        "    print(f\"Loading best model from run: {best_run.data.tags.get('mlflow.runName')}\")\n",
-        "    print(f\"Model URI: {best_model_uri}\")\n",
-        "\n",
-        "    best_model = load_model(best_model_uri)\n",
-        "    # global models (e.g. LinearRegression, NBEATS) need the series at predict time\n",
-        "    # because we save with clean=True; local models (e.g. ExponentialSmoothing) don't\n",
-        "    if isinstance(best_model, GlobalForecastingModel):\n",
-        "        best_predictions = best_model.predict(n=len(val), series=train)\n",
-        "    else:\n",
-        "        best_predictions = best_model.predict(n=len(val))\n",
-        "\n",
-        "    train[-50:].plot(label=\"Training\")\n",
-        "    val.plot(label=\"Actual\")\n",
-        "    best_predictions.plot(label=\"Best Model Forecast\")\n",
-        "    plt.legend()\n",
-        "    plt.title(\"Best Model Predictions\")\n",
-        "    plt.show()"
-      ],
-      "execution_count": 18,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Loading best model from run: exponential-smoothing-baseline\n",
-            "Model URI: models:/m-aa41380f3e4746b8b517c25cf69b23ab\n"
-          ]
-        },
-        {
-          "metadata": {},
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          }
-        }
-      ],
-      "id": "7b09db6a"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Model Registry\n",
-        "\n",
-        "After comparing runs in the UI you can promote your best model to the **MLflow Model Registry** for versioning and lifecycle management (Staging → Production → Archived)."
-      ],
-      "id": "6f1ec89e"
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-07-22T08:27:04.144511Z",
-          "iopub.status.busy": "2026-07-22T08:27:04.144439Z",
-          "iopub.status.idle": "2026-07-22T08:27:04.166315Z",
-          "shell.execute_reply": "2026-07-22T08:27:04.165897Z"
-        }
-      },
-      "source": [
-        "# register the best model (from the \"Load the Best Model\" section above)\n",
-        "result = mlflow.register_model(\n",
-        "    model_uri=best_model_uri,\n",
-        "    name=\"darts-air-passengers\",\n",
-        ")\n",
-        "print(f\"Registered version: {result.version}\")"
-      ],
-      "execution_count": 60,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Registered version: 1\n"
-          ]
-        },
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "Successfully registered model 'darts-air-passengers'.\n",
-            "Created version '1' of model 'darts-air-passengers'.\n"
-          ]
-        }
-      ],
-      "id": "7c854bc4"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The registry is accessible in the MLflow UI under the **Models** tab, where you can add descriptions, set aliases, and compare versions side-by-side.\n",
-        "\n",
-        "> 📸 **Try it:** After running the cells above, open the Models tab in the UI and register one of the runs.\n",
-        ">\n",
-        "![Mlflow Charts](./static/images/mlflow_models.png)"
-      ],
-      "id": "da6cc83a"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Important Note: Custom Flavor\n",
-        "\n",
-        "Since Darts uses a custom MLflow flavor on its' side it's important to import the methods accordingly.\n",
-        "\n",
-        "**Always use:**\n",
-        "```python\n",
-        "from darts.utils.mlflow import load_model\n",
-        "model = load_model(model_uri)\n",
-        "```\n",
-        "\n",
-        "**Instead of:**\n",
-        "```python\n",
-        "import mlflow\n",
-        "model = mlflow.pyfunc.load_model(model_uri)  # Will fail!\n",
-        "```\n",
-        "\n",
-        "This custom flavor is necessary to properly handle:\n",
-        "- TimeSeries objects\n",
-        "- Darts-specific model parameters\n",
-        "- Covariate handling (past, future, static)\n",
-        "- PyTorch model state preservation"
-      ],
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-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Cleanup"
-      ],
-      "id": "40621b13"
-    },
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-      "cell_type": "code",
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2026-06-24T15:18:53.573724Z",
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-          "shell.execute_reply": "2026-06-24T15:18:53.574817Z"
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-      },
-      "source": [
-        "# Uncomment to cleanup\n",
-        "# import shutil\n",
-        "# shutil.rmtree(tmpdir)\n",
-        "# print(f\"Cleaned up temporary directory: {tmpdir}\")\n",
-        "\n",
-        "print(f\"To cleanup manually, delete: {tmpdir}\")"
-      ],
-      "execution_count": 19,
-      "outputs": [
-        {
-                    "name": "stdout",
-"output_type": "stream",
-          "text": [
-            "To cleanup manually, delete: /var/folders/yr/3703qwtj56lcw6n91xm6tq_r0000gn/T/tmpw5bcp0ic\n"
-          ]
-        }
-      ],
-      "id": "51fc7c4a"
-    },
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-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Final Remarks"
-      ],
-      "id": "ca40afc1"
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-      "source": [
-        "Currently none of the model serving capabilities are implemented for Darts. While an API was provided (`input_example` and `signature` parameters for the `.log_model` and `.load_model`) to keep in line with MLflow API conventions, they are currently widely unsupported."
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From fe01a223a04644b98477e083eb037221d202e316 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Thu, 3 Sep 2026 17:40:44 +0200
Subject: [PATCH 150/154] finalize notebook

---
 CHANGELOG.md                      |    2 +-
 darts/utils/mlflow.py             |   61 +-
 docs/source/examples.rst          |   10 +
 examples/29-MLflow-examples.ipynb | 4054 ++++++++++++++++++++++++++++-
 4 files changed, 4054 insertions(+), 73 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e68c1cc30f..c23e9b95c3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,7 +15,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
   - Added `save_model()`, `load_model()`, and `log_model()` to persist and reload Darts models as MLflow models, including model and covariate metadata.
   - Added `autolog()` for model parameters, series/covariate metadata, optional model artifacts, PyTorch epoch metrics, and backtest metrics. Nested historical-forecast fits are suppressed.
   - Darts metrics are logged with shape-aware keys; multi-series and window-level backtest details are available in `metrics_per_series.json`.
-  - Added a new notebook for [MLflow quickstart](https://unit8co.github.io/darts/examples/29-MLflow-quickstart.html) with detailed usage examples.
+  - Added a new notebook for [MLflow quickstart](https://unit8co.github.io/darts/examples/29-MLflow-examples.html) with detailed usage examples.
   - Note: model serving and deployment (MLflow's `pyfunc` flavor, model signatures, and input examples) are not yet supported.
 - Added support for per-timestep (non-aggregated) encoder and decoder variable importances in `TFTExplainer`, exposed as `TimeSeries` via `TFTExplainabilityResult.get_encoder_importance_over_time()` and `get_decoder_importance_over_time()`. [#3170](https://github.com/unit8co/darts/pull/3170) by [exactml](https://github.com/exactml).
 - Calling `TFTModel.fit_from_dataset()` on a dataset that does not have future covariates now raises an informative exception. [#3149](https://github.com/unit8co/darts/pull/3149) by [YOON KIWOONG](https://github.com/kiwoongyoon).
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 470d13bdf4..7b7f1e6694 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -8,7 +8,7 @@
 and logging any Darts ``ForecastingModel`` (statistical, ML-based, and PyTorch-based)
 to MLflow, as well as automatic logging  via ``autolog()``.
 
-See the `MLflow quickstart example <https://github.com/unit8co/darts/blob/master/examples/29-MLflow-quickstart.ipynb>`_
+See the `MLflow quickstart example <https://github.com/unit8co/darts/blob/master/examples/29-MLflow-examples.ipynb>`_
 for an end-to-end walkthrough.
 
 .. dropdown:: Here's a quick start example
@@ -30,9 +30,16 @@
         # use a permanent location for real use cases
         tmpdir = tempfile.mkdtemp()
         mlflow_db = os.path.join(tmpdir, "mlflow.db")
+        artifact_root = os.path.join(tmpdir, "mlruns")
 
         mlflow.set_tracking_uri(f"sqlite:///{mlflow_db}")
-        mlflow.set_experiment("darts-quickstart")
+        # SQLite stores run metadata; artifacts default to ./mlruns unless we set a location
+        mlflow.set_experiment(
+            experiment_id=mlflow.create_experiment(
+                "darts-quickstart",
+                artifact_location=artifact_root,
+            )
+        )
 
         # load series and create train and val splits
         series = AirPassengersDataset().load()
@@ -110,9 +117,9 @@
     ``"mae_target0_q0_500"``), where each part is included only when the
     corresponding axis is present:
 
-    - ``metric_name`` – the metric function name, or the ``name`` metric
+    - ``metric_name`` - the metric function name, or the ``name`` metric
       keyword argument when provided (e.g., ``"mae"``).
-    - ``component`` – the component name: e.g., ``"_target0"`` when
+    - ``component`` - the component name: e.g., ``"_target0"`` when
       ``component_reduction=None``.
     - ``quantile_or_label``:
 
@@ -206,7 +213,7 @@
 import pandas as pd
 import yaml
 from mlflow.entities import LoggedModel
-from mlflow.models import Model, ModelInputExample, ModelSignature
+from mlflow.models import Model, ModelSignature
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
@@ -284,7 +291,7 @@ def save_model(
     code_paths: list[str] | None = None,
     mlflow_model: Model | None = None,
     signature: ModelSignature | None = None,
-    input_example: ModelInputExample | None = None,
+    input_example: Any | None = None,
     pip_requirements: list[str] | None = None,
     extra_pip_requirements: list[str] | None = None,
     metadata: dict[str, Any] | None = None,
@@ -329,7 +336,7 @@ def save_model(
 
     Notes
     -----
-    Signature and input_example params are currently not supported, as they
+    ``signature`` and ``input_example`` params are currently not supported, as they
     are used to support serving and input validation in the MLflow pyfunc flavor,
     which is not implemented for Darts models. They are accepted as params for
     simplifying potential future extensibility, and to keep in line with MLflow API
@@ -850,10 +857,10 @@ def _mlflow_metric_callback(func, result, args, kwargs) -> None:
 
     where:
 
-    - ``metric_name`` – the metric function name, or the ``name`` keyword
+    - ``metric_name`` - the metric function name, or the ``name`` keyword
       argument when provided (it overrides only this token).
-    - ``component`` – ``_{component_name}`` when ``component_reduction=None``.
-    - ``quantile_or_label`` – quantile/interval/label suffix (e.g. ``_q0.500``,
+    - ``component`` - ``_{component_name}`` when ``component_reduction=None``.
+    - ``quantile_or_label`` - quantile/interval/label suffix (e.g. ``_q0.500``,
       ``_qi0.800``, ``_label1``) when applicable.
 
     When the input is a ``Sequence[TimeSeries]`` with more than one series, the
@@ -1124,7 +1131,7 @@ def _log_series_info(
 
 
 def _is_torch_model(model) -> bool:
-    """Check if a model is a `TorchForecastingModel`.
+    """Check if a model is a ``TorchForecastingModel``.
 
     Parameters
     ----------
@@ -1288,36 +1295,36 @@ def _log_metric_results(
     ----------------
     Each per-series result is reshaped to ``(W, T, C, M)``:
 
-    - ``W`` – backtest windows (``1`` for standalone, or when windows were
+    - ``W`` - backtest windows (``1`` for standalone, or when windows were
       aggregated before scoring).
-    - ``T`` – timesteps (``1`` when ``time_reduction`` is set).
-    - ``C`` – sub-metrics per component (components × quantiles / intervals /
+    - ``T`` - timesteps (``1`` when ``time_reduction`` is set).
+    - ``C`` - sub-metrics per component (components * quantiles / intervals /
       labels; ``1`` when ``component_reduction`` is set).
-    - ``M`` – number of metrics when several are logged at once.
+    - ``M`` - number of metrics when several are logged at once.
 
     Axis sizes are inferred from each metric's signature and the corresponding
     ``metric_kwargs``.
 
     Kwargs that affect output dimensions (both modes unless noted):
 
-    - ``time_reduction`` – collapses the time axis (``T=1``).
-    - ``component_reduction`` – collapses the component axis (``C=1``).
-    - ``q`` / ``q_interval`` – one sub-metric per quantile / interval.
-    - ``labels`` – when ``label_reduction=None``, one sub-metric per label;
+    - ``time_reduction`` - collapses the time axis (``T=1``).
+    - ``component_reduction`` - collapses the component axis (``C=1``).
+    - ``q`` / ``q_interval`` - one sub-metric per quantile / interval.
+    - ``labels`` - when ``label_reduction=None``, one sub-metric per label;
       otherwise ``labels`` only restricts which classes are scored.
-    - ``label_reduction`` – collapses label outputs to a scalar per component.
-    - ``series_reduction`` – collapses the series axis inside the metric, so
+    - ``label_reduction`` - collapses label outputs to a scalar per component.
+    - ``series_reduction`` - collapses the series axis inside the metric, so
       the caller's ``result`` is already aggregated and treated as a single
       series (``W=1`` for backtest regardless of ``reduction``).
 
     Backtest-only kwargs:
 
-    - ``reduction=None`` – no aggregation across windows → one value per
+    - ``reduction=None`` - no aggregation across windows → one value per
       window (``W > 1``).
-    - ``last_points_only`` – concatenates all windows into one ``TimeSeries``
+    - ``last_points_only`` - concatenates all windows into one ``TimeSeries``
       before scoring, so there is effectively one window regardless of
       ``reduction``.
-    - ``forecast_horizon`` / ``historical_forecasts`` – set the time-axis
+    - ``forecast_horizon`` / ``historical_forecasts`` - set the time-axis
       length for time-dependent metrics when windows are preserved.
 
     MLflow keys and steps
@@ -1596,10 +1603,10 @@ def _infer_metric_axes(
     tuple
         ``(has_time_axis, has_comp_axis, axis_labels)`` where
 
-        - ``has_time_axis`` – ``True`` when ``time_reduction`` is ``None`` (i.e. a
+        - ``has_time_axis`` - ``True`` when ``time_reduction`` is ``None`` (i.e. a
           per-timestep axis is present in the output).
-        - ``has_comp_axis`` – ``True`` when components are expanded (not collapsed to a scalar).
-        - ``axis_labels`` – one key suffix per quantile/interval/label entry.
+        - ``has_comp_axis`` - ``True`` when components are expanded (not collapsed to a scalar).
+        - ``axis_labels`` - one key suffix per quantile/interval/label entry.
 
     Raises
     ------
diff --git a/docs/source/examples.rst b/docs/source/examples.rst
index ddba7e8713..5aae3ae484 100644
--- a/docs/source/examples.rst
+++ b/docs/source/examples.rst
@@ -256,6 +256,16 @@ Explainability example notebook showcasing the use of Darts' explainability modu
 
    examples/28-Explainability-examples.ipynb
 
+MLflow Integration
+==================
+
+MLflow integration example notebook:
+
+.. toctree::
+   :maxdepth: 1
+
+   examples/29-MLflow-examples.ipynb
+
 Kalman Filter Model
 ===================
 
diff --git a/examples/29-MLflow-examples.ipynb b/examples/29-MLflow-examples.ipynb
index 7ffa551156..30a177f907 100644
--- a/examples/29-MLflow-examples.ipynb
+++ b/examples/29-MLflow-examples.ipynb
@@ -36,13 +36,3910 @@
    "execution_count": 2,
    "id": "11bca7fd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script>\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        </script>\n",
+       "        <script>/**\n",
+       "* plotly.js v3.7.0\n",
+       "* Copyright 2012-2026, Plotly, Inc.\n",
+       "* All rights reserved.\n",
+       "* Licensed under the MIT license\n",
+       "*/\n",
+       "(\n",
+       " function(root, factory) {\n",
+       "  if (typeof module === \"object\" && module.exports) {\n",
+       "   module.exports = factory();\n",
+       "  } else {\n",
+       "   root.moduleName = factory();\n",
+       "  }\n",
+       "} (typeof self !== \"undefined\" ? self : this, () => {\n",
+       "\"use strict\";var Plotly=(()=>{var Ett=Object.create;var qS=Object.defineProperty,ktt=Object.defineProperties,Ctt=Object.getOwnPropertyDescriptor,Ltt=Object.getOwnPropertyDescriptors,Ptt=Object.getOwnPropertyNames,y6=Object.getOwnPropertySymbols,Itt=Object.getPrototypeOf,kq=Object.prototype.hasOwnProperty,_ee=Object.prototype.propertyIsEnumerable;var yee=(e,t,r)=>t in e?qS(e,t,{enumerable:!0,configurable:!0,writable:!0,value:r}):e[t]=r,_g=(e,t)=>{for(var r in t||(t={}))kq.call(t,r)&&yee(e,r,t[r]);if(y6)for(var r of y6(t))_ee.call(t,r)&&yee(e,r,t[r]);return e},W1=(e,t)=>ktt(e,Ltt(t));var xee=(e,t)=>{var r={};for(var n in e)kq.call(e,n)&&t.indexOf(n)<0&&(r[n]=e[n]);if(e!=null&&y6)for(var n of y6(e))t.indexOf(n)<0&&_ee.call(e,n)&&(r[n]=e[n]);return r};var gu=(e,t,r)=>()=>{if(r)throw r[0];try{return e&&(t=e(e=0)),t}catch(n){throw r=[n],n}};var ye=(e,t)=>()=>{try{return t||e((t={exports:{}}).exports,t),t.exports}catch(r){throw t=0,r}},bee=(e,t)=>{for(var r in 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r=t[0],n=t[1],i=t[2],a=t[3],o=t[4],s=t[5],l=t[6],u=t[7],c=t[8],f=t[9],h=t[10],d=t[11],v=t[12],m=t[13],b=t[14],p=t[15];return e[0]=s*(h*p-d*b)-f*(l*p-u*b)+m*(l*d-u*h),e[1]=-(n*(h*p-d*b)-f*(i*p-a*b)+m*(i*d-a*h)),e[2]=n*(l*p-u*b)-s*(i*p-a*b)+m*(i*u-a*l),e[3]=-(n*(l*d-u*h)-s*(i*d-a*h)+f*(i*u-a*l)),e[4]=-(o*(h*p-d*b)-c*(l*p-u*b)+v*(l*d-u*h)),e[5]=r*(h*p-d*b)-c*(i*p-a*b)+v*(i*d-a*h),e[6]=-(r*(l*p-u*b)-o*(i*p-a*b)+v*(i*u-a*l)),e[7]=r*(l*d-u*h)-o*(i*d-a*h)+c*(i*u-a*l),e[8]=o*(f*p-d*m)-c*(s*p-u*m)+v*(s*d-u*f),e[9]=-(r*(f*p-d*m)-c*(n*p-a*m)+v*(n*d-a*f)),e[10]=r*(s*p-u*m)-o*(n*p-a*m)+v*(n*u-a*s),e[11]=-(r*(s*d-u*f)-o*(n*d-a*f)+c*(n*u-a*s)),e[12]=-(o*(f*b-h*m)-c*(s*b-l*m)+v*(s*h-l*f)),e[13]=r*(f*b-h*m)-c*(n*b-i*m)+v*(n*h-i*f),e[14]=-(r*(s*b-l*m)-o*(n*b-i*m)+v*(n*l-i*s)),e[15]=r*(s*h-l*f)-o*(n*h-i*f)+c*(n*l-i*s),e}});var Bte=ye((Sir,Ote)=>{Ote.exports=brt;function brt(e){var t=e[0],r=e[1],n=e[2],i=e[3],a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],f=e[10],h=e[11],d=e[12],v=e[13],m=e[14],b=e[15],p=t*o-r*a,k=t*s-n*a,M=t*l-i*a,T=r*s-n*o,L=r*l-i*o,x=n*l-i*s,C=u*v-c*d,S=u*m-f*d,g=u*b-h*d,P=c*m-f*v,E=c*b-h*v,z=f*b-h*m;return p*z-k*E+M*P+T*g-L*S+x*C}});var Ute=ye((Mir,Nte)=>{Nte.exports=wrt;function wrt(e,t,r){var n=t[0],i=t[1],a=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],f=t[8],h=t[9],d=t[10],v=t[11],m=t[12],b=t[13],p=t[14],k=t[15],M=r[0],T=r[1],L=r[2],x=r[3];return e[0]=M*n+T*s+L*f+x*m,e[1]=M*i+T*l+L*h+x*b,e[2]=M*a+T*u+L*d+x*p,e[3]=M*o+T*c+L*v+x*k,M=r[4],T=r[5],L=r[6],x=r[7],e[4]=M*n+T*s+L*f+x*m,e[5]=M*i+T*l+L*h+x*b,e[6]=M*a+T*u+L*d+x*p,e[7]=M*o+T*c+L*v+x*k,M=r[8],T=r[9],L=r[10],x=r[11],e[8]=M*n+T*s+L*f+x*m,e[9]=M*i+T*l+L*h+x*b,e[10]=M*a+T*u+L*d+x*p,e[11]=M*o+T*c+L*v+x*k,M=r[12],T=r[13],L=r[14],x=r[15],e[12]=M*n+T*s+L*f+x*m,e[13]=M*i+T*l+L*h+x*b,e[14]=M*a+T*u+L*d+x*p,e[15]=M*o+T*c+L*v+x*k,e}});var Gte=ye((Eir,Vte)=>{Vte.exports=Trt;function Trt(e,t,r){var n=r[0],i=r[1],a=r[2],o,s,l,u,c,f,h,d,v,m,b,p;return t===e?(e[12]=t[0]*n+t[4]*i+t[8]*a+t[12],e[13]=t[1]*n+t[5]*i+t[9]*a+t[13],e[14]=t[2]*n+t[6]*i+t[10]*a+t[14],e[15]=t[3]*n+t[7]*i+t[11]*a+t[15]):(o=t[0],s=t[1],l=t[2],u=t[3],c=t[4],f=t[5],h=t[6],d=t[7],v=t[8],m=t[9],b=t[10],p=t[11],e[0]=o,e[1]=s,e[2]=l,e[3]=u,e[4]=c,e[5]=f,e[6]=h,e[7]=d,e[8]=v,e[9]=m,e[10]=b,e[11]=p,e[12]=o*n+c*i+v*a+t[12],e[13]=s*n+f*i+m*a+t[13],e[14]=l*n+h*i+b*a+t[14],e[15]=u*n+d*i+p*a+t[15]),e}});var jte=ye((kir,Hte)=>{Hte.exports=Art;function Art(e,t,r){var n=r[0],i=r[1],a=r[2];return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*i,e[5]=t[5]*i,e[6]=t[6]*i,e[7]=t[7]*i,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}});var Xte=ye((Cir,Wte)=>{Wte.exports=Srt;function Srt(e,t,r,n){var i=n[0],a=n[1],o=n[2],s=Math.sqrt(i*i+a*a+o*o),l,u,c,f,h,d,v,m,b,p,k,M,T,L,x,C,S,g,P,E,z,q,V,G;return Math.abs(s)<1e-6?null:(s=1/s,i*=s,a*=s,o*=s,l=Math.sin(r),u=Math.cos(r),c=1-u,f=t[0],h=t[1],d=t[2],v=t[3],m=t[4],b=t[5],p=t[6],k=t[7],M=t[8],T=t[9],L=t[10],x=t[11],C=i*i*c+u,S=a*i*c+o*l,g=o*i*c-a*l,P=i*a*c-o*l,E=a*a*c+u,z=o*a*c+i*l,q=i*o*c+a*l,V=a*o*c-i*l,G=o*o*c+u,e[0]=f*C+m*S+M*g,e[1]=h*C+b*S+T*g,e[2]=d*C+p*S+L*g,e[3]=v*C+k*S+x*g,e[4]=f*P+m*E+M*z,e[5]=h*P+b*E+T*z,e[6]=d*P+p*E+L*z,e[7]=v*P+k*E+x*z,e[8]=f*q+m*V+M*G,e[9]=h*q+b*V+T*G,e[10]=d*q+p*V+L*G,e[11]=v*q+k*V+x*G,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}});var Yte=ye((Lir,Zte)=>{Zte.exports=Mrt;function Mrt(e,t,r){var n=Math.sin(r),i=Math.cos(r),a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],h=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*i+u*n,e[5]=o*i+c*n,e[6]=s*i+f*n,e[7]=l*i+h*n,e[8]=u*i-a*n,e[9]=c*i-o*n,e[10]=f*i-s*n,e[11]=h*i-l*n,e}});var Jte=ye((Pir,Kte)=>{Kte.exports=Ert;function Ert(e,t,r){var n=Math.sin(r),i=Math.cos(r),a=t[0],o=t[1],s=t[2],l=t[3],u=t[8],c=t[9],f=t[10],h=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*i-u*n,e[1]=o*i-c*n,e[2]=s*i-f*n,e[3]=l*i-h*n,e[8]=a*n+u*i,e[9]=o*n+c*i,e[10]=s*n+f*i,e[11]=l*n+h*i,e}});var Qte=ye((Iir,$te)=>{$te.exports=krt;function krt(e,t,r){var n=Math.sin(r),i=Math.cos(r),a=t[0],o=t[1],s=t[2],l=t[3],u=t[4],c=t[5],f=t[6],h=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*i+u*n,e[1]=o*i+c*n,e[2]=s*i+f*n,e[3]=l*i+h*n,e[4]=u*i-a*n,e[5]=c*i-o*n,e[6]=f*i-s*n,e[7]=h*i-l*n,e}});var tre=ye((Rir,ere)=>{ere.exports=Crt;function Crt(e,t,r){var n,i,a,o=r[0],s=r[1],l=r[2],u=Math.sqrt(o*o+s*s+l*l);return Math.abs(u)<1e-6?null:(u=1/u,o*=u,s*=u,l*=u,n=Math.sin(t),i=Math.cos(t),a=1-i,e[0]=o*o*a+i,e[1]=s*o*a+l*n,e[2]=l*o*a-s*n,e[3]=0,e[4]=o*s*a-l*n,e[5]=s*s*a+i,e[6]=l*s*a+o*n,e[7]=0,e[8]=o*l*a+s*n,e[9]=s*l*a-o*n,e[10]=l*l*a+i,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}});var ire=ye((Dir,rre)=>{rre.exports=Lrt;function Lrt(e,t,r){var n=t[0],i=t[1],a=t[2],o=t[3],s=n+n,l=i+i,u=a+a,c=n*s,f=n*l,h=n*u,d=i*l,v=i*u,m=a*u,b=o*s,p=o*l,k=o*u;return e[0]=1-(d+m),e[1]=f+k,e[2]=h-p,e[3]=0,e[4]=f-k,e[5]=1-(c+m),e[6]=v+b,e[7]=0,e[8]=h+p,e[9]=v-b,e[10]=1-(c+d),e[11]=0,e[12]=r[0],e[13]=r[1],e[14]=r[2],e[15]=1,e}});var are=ye((Fir,nre)=>{nre.exports=Prt;function Prt(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var sre=ye((zir,ore)=>{ore.exports=Irt;function Irt(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}});var ure=ye((qir,lre)=>{lre.exports=Rrt;function Rrt(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=n,e[6]=r,e[7]=0,e[8]=0,e[9]=-r,e[10]=n,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var fre=ye((Oir,cre)=>{cre.exports=Drt;function Drt(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=n,e[1]=0,e[2]=-r,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=r,e[9]=0,e[10]=n,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var dre=ye((Bir,hre)=>{hre.exports=Frt;function Frt(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=n,e[1]=r,e[2]=0,e[3]=0,e[4]=-r,e[5]=n,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var pre=ye((Nir,vre)=>{vre.exports=zrt;function zrt(e,t){var r=t[0],n=t[1],i=t[2],a=t[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,f=n*s,h=i*o,d=i*s,v=i*l,m=a*o,b=a*s,p=a*l;return e[0]=1-f-v,e[1]=c+p,e[2]=h-b,e[3]=0,e[4]=c-p,e[5]=1-u-v,e[6]=d+m,e[7]=0,e[8]=h+b,e[9]=d-m,e[10]=1-u-f,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var mre=ye((Uir,gre)=>{gre.exports=qrt;function qrt(e,t,r,n,i,a,o){var s=1/(r-t),l=1/(i-n),u=1/(a-o);return e[0]=a*2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=a*2*l,e[6]=0,e[7]=0,e[8]=(r+t)*s,e[9]=(i+n)*l,e[10]=(o+a)*u,e[11]=-1,e[12]=0,e[13]=0,e[14]=o*a*2*u,e[15]=0,e}});var _re=ye((Vir,yre)=>{yre.exports=Ort;function Ort(e,t,r,n,i){var a=1/Math.tan(t/2),o=1/(n-i);return e[0]=a/r,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=a,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=(i+n)*o,e[11]=-1,e[12]=0,e[13]=0,e[14]=2*i*n*o,e[15]=0,e}});var bre=ye((Gir,xre)=>{xre.exports=Brt;function Brt(e,t,r,n){var i=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),o=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(o+s),u=2/(i+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=u,e[6]=0,e[7]=0,e[8]=-((o-s)*l*.5),e[9]=(i-a)*u*.5,e[10]=n/(r-n),e[11]=-1,e[12]=0,e[13]=0,e[14]=n*r/(r-n),e[15]=0,e}});var Tre=ye((Hir,wre)=>{wre.exports=Nrt;function Nrt(e,t,r,n,i,a,o){var s=1/(t-r),l=1/(n-i),u=1/(a-o);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*u,e[11]=0,e[12]=(t+r)*s,e[13]=(i+n)*l,e[14]=(o+a)*u,e[15]=1,e}});var Sre=ye((jir,Are)=>{var Urt=jq();Are.exports=Vrt;function Vrt(e,t,r,n){var i,a,o,s,l,u,c,f,h,d,v=t[0],m=t[1],b=t[2],p=n[0],k=n[1],M=n[2],T=r[0],L=r[1],x=r[2];return Math.abs(v-T)<1e-6&&Math.abs(m-L)<1e-6&&Math.abs(b-x)<1e-6?Urt(e):(c=v-T,f=m-L,h=b-x,d=1/Math.sqrt(c*c+f*f+h*h),c*=d,f*=d,h*=d,i=k*h-M*f,a=M*c-p*h,o=p*f-k*c,d=Math.sqrt(i*i+a*a+o*o),d?(d=1/d,i*=d,a*=d,o*=d):(i=0,a=0,o=0),s=f*o-h*a,l=h*i-c*o,u=c*a-f*i,d=Math.sqrt(s*s+l*l+u*u),d?(d=1/d,s*=d,l*=d,u*=d):(s=0,l=0,u=0),e[0]=i,e[1]=s,e[2]=c,e[3]=0,e[4]=a,e[5]=l,e[6]=f,e[7]=0,e[8]=o,e[9]=u,e[10]=h,e[11]=0,e[12]=-(i*v+a*m+o*b),e[13]=-(s*v+l*m+u*b),e[14]=-(c*v+f*m+h*b),e[15]=1,e)}});var Ere=ye((Wir,Mre)=>{Mre.exports=Grt;function Grt(e){return\"mat4(\"+e[0]+\", \"+e[1]+\", \"+e[2]+\", \"+e[3]+\", \"+e[4]+\", \"+e[5]+\", \"+e[6]+\", \"+e[7]+\", 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button{background-color:transparent;border:0;box-sizing:border-box;cursor:pointer;display:block;height:29px;outline:none;padding:0;width:29px}.maplibregl-ctrl-group button+button{border-top:1px solid #ddd}.maplibregl-ctrl button .maplibregl-ctrl-icon{background-position:50%;background-repeat:no-repeat;display:block;height:100%;width:100%}@media (forced-colors:active){.maplibregl-ctrl-icon{background-color:transparent}.maplibregl-ctrl-group button+button{border-top:1px solid ButtonText}}.maplibregl-ctrl button::-moz-focus-inner{border:0;padding:0}.maplibregl-ctrl-attrib-button:focus,.maplibregl-ctrl-group button:focus{box-shadow:0 0 2px 2px #0096ff}.maplibregl-ctrl button:disabled{cursor:not-allowed}.maplibregl-ctrl button:disabled .maplibregl-ctrl-icon{opacity:.25}.maplibregl-ctrl button:not(:disabled):hover{background-color:rgb(0 0 0/5%)}.maplibregl-ctrl-group button:focus:focus-visible{box-shadow:0 0 2px 2px #0096ff}.maplibregl-ctrl-group 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xa.doAutoMargin(e)}};function qat(e){if(\"_redrawFromAutoMarginCount\"in e._fullLayout)return!1;var t=Mp.list(e,\"\",!0);for(var r in t)if(t[r].autoshift||t[r].shift)return!0;return!1}xa.doAutoMargin=function(e){var t=e._fullLayout,r=t.width,n=t.height;t._size||(t._size={}),Qne(t);var i=t._size,a=t.margin,o={t:0,b:0,l:0,r:0},s=Na.extendFlat({},i),l=a.l,u=a.r,c=a.t,f=a.b,h=t._pushmargin,d=t._pushmarginIds,v=t.minreducedwidth,m=t.minreducedheight;if(a.autoexpand!==!1){for(var b in h)d[b]||delete h[b];var p=e._fullLayout._reservedMargin;for(var k in p)for(var M in p[k]){var T=p[k][M];o[M]=Math.max(o[M],T)}h.base={l:{val:0,size:l},r:{val:1,size:u},t:{val:1,size:c},b:{val:0,size:f}};for(var L in o){var x=0;for(var C in h)C!==\"base\"&&aM(h[C][L].size)&&(x=h[C][L].size>x?h[C][L].size:x);var S=Math.max(0,a[L]-x);o[L]=Math.max(0,o[L]-S)}for(var g in h){var P=h[g].l||{},E=h[g].b||{},z=P.val,q=P.size,V=E.val,G=E.size,Z=r-o.r-o.l,j=n-o.t-o.b;for(var N in h){if(aM(q)&&h[N].r){var 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vs(t,r,\"M\"+n+\",\"+n+\"L-\"+n+\",-\"+n+\"M\"+n+\",-\"+n+\"L-\"+n+\",\"+n+\"M\"+n+\",\"+n+\"H-\"+n+\"V-\"+n+\"H\"+n+\"Z\")},needLine:!0,noDot:!0},\"diamond-cross\":{n:31,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.3,2);return vs(t,r,\"M\"+n+\",0L0,\"+n+\"L-\"+n+\",0L0,-\"+n+\"ZM0,-\"+n+\"V\"+n+\"M-\"+n+\",0H\"+n)},needLine:!0,noDot:!0},\"diamond-x\":{n:32,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.3,2),i=ca(e*.65,2);return vs(t,r,\"M\"+n+\",0L0,\"+n+\"L-\"+n+\",0L0,-\"+n+\"ZM-\"+i+\",-\"+i+\"L\"+i+\",\"+i+\"M-\"+i+\",\"+i+\"L\"+i+\",-\"+i)},needLine:!0,noDot:!0},\"cross-thin\":{n:33,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.4,2);return vs(t,r,\"M0,\"+n+\"V-\"+n+\"M\"+n+\",0H-\"+n)},needLine:!0,noDot:!0,noFill:!0},\"x-thin\":{n:34,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",\"+n+\"L-\"+n+\",-\"+n+\"M\"+n+\",-\"+n+\"L-\"+n+\",\"+n)},needLine:!0,noDot:!0,noFill:!0},asterisk:{n:35,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*.85,2);return vs(t,r,\"M0,\"+n+\"V-\"+n+\"M\"+n+\",0H-\"+n+\"M\"+i+\",\"+i+\"L-\"+i+\",-\"+i+\"M\"+i+\",-\"+i+\"L-\"+i+\",\"+i)},needLine:!0,noDot:!0,noFill:!0},hash:{n:36,f:function(e,t,r){if(ds(t))return hs;var n=ca(e/2,2),i=ca(e,2);return vs(t,r,\"M\"+n+\",\"+i+\"V-\"+i+\"M\"+(n-i)+\",-\"+i+\"V\"+i+\"M\"+i+\",\"+n+\"H-\"+i+\"M-\"+i+\",\"+(n-i)+\"H\"+i)},needLine:!0,noFill:!0},\"y-up\":{n:37,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*1.6,2),a=ca(e*.8,2);return vs(t,r,\"M-\"+n+\",\"+a+\"L0,0M\"+n+\",\"+a+\"L0,0M0,-\"+i+\"L0,0\")},needLine:!0,noDot:!0,noFill:!0},\"y-down\":{n:38,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*1.6,2),a=ca(e*.8,2);return vs(t,r,\"M-\"+n+\",-\"+a+\"L0,0M\"+n+\",-\"+a+\"L0,0M0,\"+i+\"L0,0\")},needLine:!0,noDot:!0,noFill:!0},\"y-left\":{n:39,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*1.6,2),a=ca(e*.8,2);return vs(t,r,\"M\"+a+\",\"+n+\"L0,0M\"+a+\",-\"+n+\"L0,0M-\"+i+\",0L0,0\")},needLine:!0,noDot:!0,noFill:!0},\"y-right\":{n:40,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*1.6,2),a=ca(e*.8,2);return vs(t,r,\"M-\"+a+\",\"+n+\"L0,0M-\"+a+\",-\"+n+\"L0,0M\"+i+\",0L0,0\")},needLine:!0,noDot:!0,noFill:!0},\"line-ew\":{n:41,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.4,2);return vs(t,r,\"M\"+n+\",0H-\"+n)},needLine:!0,noDot:!0,noFill:!0},\"line-ns\":{n:42,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.4,2);return vs(t,r,\"M0,\"+n+\"V-\"+n)},needLine:!0,noDot:!0,noFill:!0},\"line-ne\":{n:43,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",-\"+n+\"L-\"+n+\",\"+n)},needLine:!0,noDot:!0,noFill:!0},\"line-nw\":{n:44,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",\"+n+\"L-\"+n+\",-\"+n)},needLine:!0,noDot:!0,noFill:!0},\"arrow-up\":{n:45,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e*2,2);return vs(t,r,\"M0,0L-\"+n+\",\"+i+\"H\"+n+\"Z\")},backoff:1,noDot:!0},\"arrow-down\":{n:46,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e*2,2);return vs(t,r,\"M0,0L-\"+n+\",-\"+i+\"H\"+n+\"Z\")},noDot:!0},\"arrow-left\":{n:47,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*2,2),i=ca(e,2);return vs(t,r,\"M0,0L\"+n+\",-\"+i+\"V\"+i+\"Z\")},noDot:!0},\"arrow-right\":{n:48,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*2,2),i=ca(e,2);return vs(t,r,\"M0,0L-\"+n+\",-\"+i+\"V\"+i+\"Z\")},noDot:!0},\"arrow-bar-up\":{n:49,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e*2,2);return vs(t,r,\"M-\"+n+\",0H\"+n+\"M0,0L-\"+n+\",\"+i+\"H\"+n+\"Z\")},backoff:1,needLine:!0,noDot:!0},\"arrow-bar-down\":{n:50,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e*2,2);return vs(t,r,\"M-\"+n+\",0H\"+n+\"M0,0L-\"+n+\",-\"+i+\"H\"+n+\"Z\")},needLine:!0,noDot:!0},\"arrow-bar-left\":{n:51,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*2,2),i=ca(e,2);return vs(t,r,\"M0,-\"+i+\"V\"+i+\"M0,0L\"+n+\",-\"+i+\"V\"+i+\"Z\")},needLine:!0,noDot:!0},\"arrow-bar-right\":{n:52,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*2,2),i=ca(e,2);return vs(t,r,\"M0,-\"+i+\"V\"+i+\"M0,0L-\"+n+\",-\"+i+\"V\"+i+\"Z\")},needLine:!0,noDot:!0},arrow:{n:53,f:function(e,t,r){if(ds(t))return hs;var n=HO/2.5,i=2*e*jO(n),a=2*e*WO(n);return vs(t,r,\"M0,0L\"+-i+\",\"+a+\"L\"+i+\",\"+a+\"Z\")},backoff:.9,noDot:!0},\"arrow-wide\":{n:54,f:function(e,t,r){if(ds(t))return hs;var n=HO/4,i=2*e*jO(n),a=2*e*WO(n);return vs(t,r,\"M0,0L\"+-i+\",\"+a+\"A \"+2*e+\",\"+2*e+\" 0 0 1 \"+i+\",\"+a+\"Z\")},backoff:.4,noDot:!0}};function ds(e){return e===null}var Eoe,koe,Coe,Loe;function vs(e,t,r){if((!e||e%360===0)&&!t)return r;if(Coe===e&&Loe===t&&Eoe===r)return koe;Coe=e,Loe=t,Eoe=r;function n(b,p){var k=jO(b),M=WO(b),T=p[0],L=p[1]+(t||0);return[T*k-L*M,T*M+L*k]}for(var i=e/180*HO,a=0,o=0,s=rst(r),l=\"\",u=0;u<s.length;u++){var 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e===\"solid\"?e=\"\":e===\"dot\"?e=r+\"px,\"+r+\"px\":e===\"dash\"?e=3*r+\"px,\"+3*r+\"px\":e===\"longdash\"?e=5*r+\"px,\"+5*r+\"px\":e===\"dashdot\"?e=3*r+\"px,\"+r+\"px,\"+r+\"px,\"+r+\"px\":e===\"longdashdot\"&&(e=5*r+\"px,\"+2*r+\"px,\"+r+\"px,\"+2*r+\"px\"),e};function Goe(e,t,r,n){var i=t.fillpattern,a=t.fillgradient,o=Ta.getPatternAttr,s=i&&(o(i.shape,0,\"\")||o(i.path,0,\"\"));if(s){var l=o(i.bgcolor,0,null),u=o(i.fgcolor,0,null),c=i.fgopacity,f=o(i.size,0,8),h=o(i.solidity,0,.3),d=t.uid;Ta.pattern(e,\"point\",r,d,s,f,h,void 0,i.fillmode,l,u,c)}else if(a&&a.type!==\"none\"){var v=a.type,m=\"scatterfill-\"+t.uid;if(n&&(m=\"legendfill-\"+t.uid),!n&&(a.start!==void 0||a.stop!==void 0)){var b,p;v===\"horizontal\"?(b={x:a.start,y:0},p={x:a.stop,y:0}):v===\"vertical\"&&(b={x:0,y:a.start},p={x:0,y:a.stop}),b.x=t._xA.c2p(b.x===void 0?t._extremes.x.min[0].val:b.x,!0),b.y=t._yA.c2p(b.y===void 0?t._extremes.y.min[0].val:b.y,!0),p.x=t._xA.c2p(p.x===void 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t=Roe[e],r=t.n;Ta.symbolList.push(r,String(r),e,r+100,String(r+100),e+\"-open\"),Ta.symbolNames[r]=e,Ta.symbolFuncs[r]=t.f,Ta.symbolBackOffs[r]=t.backoff||0,t.needLine&&(Ta.symbolNeedLines[r]=!0),t.noDot?Ta.symbolNoDot[r]=!0:Ta.symbolList.push(r+200,String(r+200),e+\"-dot\",r+300,String(r+300),e+\"-open-dot\"),t.noFill&&(Ta.symbolNoFill[r]=!0)});var vst=Ta.symbolNames.length,pst=\"M0,0.5L0.5,0L0,-0.5L-0.5,0Z\";Ta.symbolNumber=function(e){if(Fb(e))e=+e;else if(typeof e==\"string\"){var t=0;e.indexOf(\"-open\")>0&&(t=100,e=e.replace(\"-open\",\"\")),e.indexOf(\"-dot\")>0&&(t+=200,e=e.replace(\"-dot\",\"\")),e=Ta.symbolNames.indexOf(e),e>=0&&(e+=t)}return e%100>=vst||e>=400?0:Math.floor(Math.max(e,0))};function Hoe(e,t,r,n){var i=e%100;return Ta.symbolFuncs[i](t,r,n)+(e>=200?pst:\"\")}var 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v=t._fullLayout,m=\"g\"+v._uid+\"-\"+r,b=v._defs.select(\".gradients\").selectAll(\"#\"+m).data([n+h.join(\";\")],Du.identity);b.exit().remove(),b.enter().append(f.node).each(function(){var p=dd.select(this);f.attrs&&p.attr(f.attrs),p.attr(\"id\",m);var k=p.selectAll(\"stop\").data(h);k.exit().remove(),k.enter().append(\"stop\"),k.each(function(M){var T=$O(M[1]);dd.select(this).attr({offset:M[0]+\"%\",\"stop-color\":Jd.tinyRGB(T),\"stop-opacity\":T.getAlpha()})})}),e.style(a,QO(m,t)).style(a+\"-opacity\",null),e.classed(\"gradient_filled\",!0)}Ta.pattern=function(e,t,r,n,i,a,o,s,l,u,c,f){var h=t===\"legend\";s&&(l===\"overlay\"?(u=s,c=Jd.contrast(u)):(u=void 0,c=s));var d=r._fullLayout,v=\"p\"+d._uid+\"-\"+n,m,b,p=function(q,V,G,Z,j){return 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E=[i||\"noSh\",u||\"noBg\",c||\"noFg\",a,o].join(\";\"),z=d._defs.select(\".patterns\").selectAll(\"#\"+v).data([E],Du.identity);z.exit().remove(),z.enter().append(\"pattern\").each(function(){var q=dd.select(this);if(q.attr({id:v,width:m+\"px\",height:b+\"px\",patternUnits:\"userSpaceOnUse\",patternTransform:h?\"scale(0.8)\":\"\"}),u){var V=$O(u),G=Jd.tinyRGB(V),Z=V.getAlpha(),j=q.selectAll(\"rect\").data([0]);j.exit().remove(),j.enter().append(\"rect\").attr({width:m+\"px\",height:b+\"px\",fill:G,\"fill-opacity\":Z})}var N=q.selectAll(L).data([0]);N.exit().remove(),N.enter().append(L).attr(x)}),e.style(\"fill\",QO(v,r)).style(\"fill-opacity\",null),e.classed(\"pattern_filled\",!0)};Ta.initGradients=function(e){var t=e._fullLayout,r=Du.ensureSingle(t._defs,\"g\",\"gradients\");r.selectAll(\"linearGradient,radialGradient\").remove(),dd.select(e).selectAll(\".gradient_filled\").classed(\"gradient_filled\",!1)};Ta.initPatterns=function(e){var t=e._fullLayout,r=Du.ensureSingle(t._defs,\"g\",\"patterns\");r.selectAll(\"pattern\").remove(),dd.select(e).selectAll(\".pattern_filled\").classed(\"pattern_filled\",!1)};Ta.getPatternAttr=function(e,t,r){return e&&Du.isArrayOrTypedArray(e)?t<e.length?e[t]:r:e};Ta.pointStyle=function(e,t,r,n){if(e.size()){var i=Ta.makePointStyleFns(t);e.each(function(a){Ta.singlePointStyle(a,dd.select(this),t,i,r,n)})}};Ta.singlePointStyle=function(e,t,r,n,i,a){var o=r.marker,s=o.line;if(a&&a.i>=0&&e.i===void 0&&(e.i=a.i),t.style(\"opacity\",n.selectedOpacityFn?n.selectedOpacityFn(e):e.mo===void 0?o.opacity:e.mo),n.ms2mrc){var l;e.ms===\"various\"||o.size===\"various\"?l=3:l=n.ms2mrc(e.ms),e.mrc=l,n.selectedSizeFn&&(l=e.mrc=n.selectedSizeFn(e));var u=Ta.symbolNumber(e.mx||o.symbol)||0;e.om=u%200>=100;var c=tB(e,r),f=eB(e,r);t.attr(\"d\",Hoe(u,l,c,f))}var h=!1,d,v,m;if(e.so)m=s.outlierwidth,v=s.outliercolor,d=o.outliercolor;else{var 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E=L(P,e.i,a&&a.color||null),z=L(T.bgcolor,e.i,null),q=T.fgopacity,V=L(T.size,e.i,8),G=L(T.solidity,e.i,.3);g=g||e.mcc||Du.isArrayOrTypedArray(T.shape)||Du.isArrayOrTypedArray(T.path)||Du.isArrayOrTypedArray(T.bgcolor)||Du.isArrayOrTypedArray(T.fgcolor)||Du.isArrayOrTypedArray(T.size)||Du.isArrayOrTypedArray(T.solidity);var Z=r.uid;g&&(Z+=\"-\"+e.i),Ta.pattern(t,\"point\",i,Z,x,V,G,e.mcc,T.fillmode,z,E,q)}else Du.isArrayOrTypedArray(d)?Jd.fill(t,d[e.i]):Jd.fill(t,d);m&&Jd.stroke(t,v)}};Ta.makePointStyleFns=function(e){var t={},r=e.marker;return t.markerScale=Ta.tryColorscale(r,\"\"),t.lineScale=Ta.tryColorscale(r,\"line\"),mL.traceIs(e,\"symbols\")&&(t.ms2mrc=lst.isBubble(e)?ust(e):function(){return(r.size||6)/2}),e.selectedpoints&&Du.extendFlat(t,Ta.makeSelectedPointStyleFns(e)),t};Ta.makeSelectedPointStyleFns=function(e){var t={},r=e.selected||{},n=e.unselected||{},i=e.marker||{},a=r.marker||{},o=n.marker||{},s=i.opacity,l=a.opacity,u=o.opacity,c=l!==void 0,f=u!==void 0;(Du.isArrayOrTypedArray(s)||c||f)&&(t.selectedOpacityFn=function(T){var L=T.mo===void 0?i.opacity:T.mo;return T.selected?c?l:L:f?u:Voe*L});var h=i.color,d=a.color,v=o.color;(d||v)&&(t.selectedColorFn=function(T){var L=T.mcc||h;return T.selected?d||L:v||L});var m=i.size,b=a.size,p=o.size,k=b!==void 0,M=p!==void 0;return mL.traceIs(e,\"symbols\")&&(k||M)&&(t.selectedSizeFn=function(T){var L=T.mrc||m/2;return T.selected?k?b/2:L:M?p/2:L}),t};Ta.makeSelectedTextStyleFns=function(e){var t={},r=e.selected||{},n=e.unselected||{},i=e.textfont||{},a=r.textfont||{},o=n.textfont||{},s=i.color,l=a.color,u=o.color;return t.selectedTextColorFn=function(c){var f=c.tc||s;return c.selected?l||f:u||(l?f:Jd.addOpacity(f,Voe))},t};Ta.selectedPointStyle=function(e,t){if(!(!e.size()||!t.selectedpoints)){var r=Ta.makeSelectedPointStyleFns(t),n=t.marker||{},i=[];r.selectedOpacityFn&&i.push(function(a,o){a.style(\"opacity\",r.selectedOpacityFn(o))}),r.selectedColorFn&&i.push(function(a,o){Jd.fill(a,r.selectedColorFn(o))}),r.selectedSizeFn&&i.push(function(a,o){var s=o.mx||n.symbol||0,l=r.selectedSizeFn(o);a.attr(\"d\",Hoe(Ta.symbolNumber(s),l,tB(o,t),eB(o,t))),o.mrc2=l}),i.length&&e.each(function(a){for(var o=dd.select(this),s=0;s<i.length;s++)i[s](o,a)})}};Ta.tryColorscale=function(e,t){var r=t?Du.nestedProperty(e,t).get():e;if(r){var n=r.color;if((r.colorscale||r._colorAx)&&Du.isArrayOrTypedArray(n))return nst.makeColorScaleFuncFromTrace(r)}return Du.identity};var ZO={start:1,end:-1,middle:0,bottom:1,top:-1};function Xoe(e,t,r,n,i){var a=dd.select(e.node().parentNode),o=t.indexOf(\"top\")!==-1?\"top\":t.indexOf(\"bottom\")!==-1?\"bottom\":\"middle\",s=t.indexOf(\"left\")!==-1?\"end\":t.indexOf(\"right\")!==-1?\"start\":\"middle\",l=n?n/.8+1:0,u=(yL.lineCount(e)-1)*sst+1,c=ZO[s]*l,f=r*.75+ZO[o]*l+(ZO[o]-1)*u*r/2;e.attr(\"text-anchor\",s),i||a.attr(\"transform\",cM(c,f))}function Zoe(e,t){var r=e.ts||t.textfont.size;return Fb(r)&&r>0?r:0}Ta.textPointStyle=function(e,t,r){if(e.size()){var n;if(t.selectedpoints){var i=Ta.makeSelectedTextStyleFns(t);n=i.selectedTextColorFn}var a=t.texttemplate,o=r._fullLayout;e.each(function(s){var l=dd.select(this),u=a?Du.extractOption(s,t,\"txt\",\"texttemplate\"):Du.extractOption(s,t,\"tx\",\"text\");if(!u&&u!==0){l.remove();return}if(a){var c=t._module.formatLabels,f=c?c(s,t,o):{},h={};cst(h,t,s.i),u=Du.texttemplateString({data:[h,s,t._meta],fallback:t.texttemplatefallback,labels:f,locale:o._d3locale,template:u})}var d=s.tp||t.textposition,v=Zoe(s,t),m=n?n(s):s.tc||t.textfont.color;l.call(Ta.font,{family:s.tf||t.textfont.family,weight:s.tw||t.textfont.weight,style:s.ty||t.textfont.style,variant:s.tv||t.textfont.variant,textcase:s.tC||t.textfont.textcase,lineposition:s.tE||t.textfont.lineposition,shadow:s.tS||t.textfont.shadow,size:v,color:m}).text(u).call(yL.convertToTspans,r).call(Xoe,d,v,s.mrc)})}};Ta.selectedTextStyle=function(e,t){if(!(!e.size()||!t.selectedpoints)){var r=Ta.makeSelectedTextStyleFns(t);e.each(function(n){var i=dd.select(this),a=r.selectedTextColorFn(n),o=n.tp||t.textposition,s=Zoe(n,t);Jd.fill(i,a);var l=mL.traceIs(t,\"bar-like\");Xoe(i,o,s,n.mrc2||n.mrc,l)})}};var Foe=.5;Ta.smoothopen=function(e,t){if(e.length<3)return\"M\"+e.join(\"L\");var r=\"M\"+e[0],n=[],i;for(i=1;i<e.length-1;i++)n.push(gL(e[i-1],e[i],e[i+1],t));for(r+=\"Q\"+n[0][0]+\" \"+e[1],i=2;i<e.length-1;i++)r+=\"C\"+n[i-2][1]+\" \"+n[i-1][0]+\" \"+e[i];return r+=\"Q\"+n[e.length-3][1]+\" 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gst={hv:function(e,t,r){return\"H\"+Rb(t[0])+\"V\"+V3(t,1,r)},vh:function(e,t,r){return\"V\"+Db(t[1])+\"H\"+V3(t,0,r)},hvh:function(e,t,r){return\"H\"+Rb((e[0]+t[0])/2)+\"V\"+Db(t[1])+\"H\"+V3(t,0,r)},vhv:function(e,t,r){return\"V\"+Db((e[1]+t[1])/2)+\"H\"+Rb(t[0])+\"V\"+V3(t,1,r)}},mst=function(e,t,r){return\"L\"+V3(t,0,r)+\",\"+V3(t,1,r)};Ta.steps=function(e){var t=gst[e]||mst;return function(r){for(var n=\"M\"+Rb(r[0][0])+\",\"+Db(r[0][1]),i=r.length,a=1;a<i;a++)n+=t(r[a-1],r[a],a===i-1);return n}};function Joe(e,t){var r=e.backoff,n=e.trace,i=e.d,a=e.i;if(r&&n&&n.marker&&n.marker.angle%360===0&&n.line&&n.line.shape!==\"spline\"){var o=Du.isArrayOrTypedArray(r),s=e,l=t?t[0]:Yoe||0,u=t?t[1]:Koe||0,c=s[0],f=s[1],h=c-l,d=f-u,v=Math.atan2(d,h),m=o?r[a]:r;if(m===\"auto\"){var b=s.i;n.type===\"scatter\"&&b--;var p=s.marker,k=p.symbol;Du.isArrayOrTypedArray(k)&&(k=k[b]);var 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t=e._mainAxis,r=[];if(t._vals){for(var n=0;n<t._vals.length;n++)if(!t._vals[n].noTick){var i=t.l2p(t._vals[n].x),a=e.p2l(i),o=$n.tickText(e,a);t._vals[n].minor&&(o.minor=!0,o.text=\"\"),r.push(o)}}return r=Rse(e,r),r}function Mse(e,t){var r=Wo.simpleMap(e.range,e.r2l),n=wB(r),i=Math.min(n[0],n[1]),a=Math.max(n[0],n[1]),o=e.type===\"category\"?e.d2l_noadd:e.d2l;e.type===\"log\"&&String(e.dtick).charAt(0)!==\"L\"&&(e.dtick=\"L\"+Math.pow(10,Math.floor(Math.min(e.range[0],e.range[1]))-1));for(var s=[],l=0;l<=1;l++)if(!(t!==void 0&&(t&&l||t===!1&&!l))&&!(l&&!e.minor)){var u=l?e.minor.tickvals:e.tickvals,c=l?[]:e.ticktext;if(u){Wo.isArrayOrTypedArray(c)||(c=[]);for(var f=0;f<u.length;f++){var h=o(u[f]);if(h>i&&h<a){var d=$n.tickText(e,h,!1,String(c[f]));l&&(d.minor=!0,d.text=\"\"),s.push(d)}}}}return s=Rse(e,s),s}var PL=[2,5,10],Ese=[1,2,3,6,12],kse=[1,2,5,10,15,30],plt=[1,2,3,7,14],Dse=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],Fse=[-.301,0,.301,.699,1],glt=[15,30,45,90,180];function bm(e,t,r){return t*Wo.roundUp(e/t,r)}$n.autoTicks=function(e,t,r){var n;function i(f){return Math.pow(f,Math.floor(Math.log(t)/Math.LN10))}if(e.type===\"date\"){e.tick0=Wo.dateTick0(e.calendar,0);var a=2*t;if(a>RL)t/=RL,n=i(10),e.dtick=\"M\"+12*bm(t,n,PL);else if(a>Y3)t/=Y3,e.dtick=\"M\"+bm(t,1,Ese);else if(a>qv){if(e.dtick=bm(t,qv,e._hasDayOfWeekBreaks?[1,2,7,14]:plt),!r){var o=$n.getTickFormat(e),s=e.ticklabelmode===\"period\";s&&(e._rawTick0=e.tick0),/%[uVW]/.test(o)?e.tick0=Wo.dateTick0(e.calendar,2):e.tick0=Wo.dateTick0(e.calendar,1),s&&(e._dowTick0=e.tick0)}}else a>wm?e.dtick=bm(t,wm,Ese):a>pM?e.dtick=bm(t,pM,kse):a>qL?e.dtick=bm(t,qL,kse):(n=i(10),e.dtick=bm(t,n,PL))}else if(e.type===\"log\"){e.tick0=0;var l=Wo.simpleMap(e.range,e.r2l);if(e._isMinor&&(t*=1.5),t>.7)e.dtick=Math.ceil(t);else if(Math.abs(l[1]-l[0])<1){var u=1.5*Math.abs((l[1]-l[0])/t);t=Math.abs(Math.pow(10,l[1])-Math.pow(10,l[0]))/u,n=i(10),e.dtick=\"L\"+bm(t,n,PL)}else e.dtick=t>.3?\"D2\":\"D1\"}else e.type===\"category\"||e.type===\"multicategory\"?(e.tick0=0,e.dtick=Math.ceil(Math.max(t,1))):MB(e)?(e.tick0=0,n=1,e.dtick=bm(t,n,glt)):(e.tick0=0,n=i(10),e.dtick=bm(t,n,PL));if(e.dtick===0&&(e.dtick=1),!zh(e.dtick)&&typeof e.dtick!=\"string\"){var c=e.dtick;throw e.dtick=1,\"ax.dtick error: \"+String(c)}};function zse(e){var t=e.dtick;if(e._tickexponent=0,!zh(t)&&typeof t!=\"string\"&&(t=1),(e.type===\"category\"||e.type===\"multicategory\")&&(e._tickround=null),e.type===\"date\"){var r=e.r2l(e.tick0),n=e.l2r(r).replace(/(^-|i)/g,\"\"),i=n.length;if(String(t).charAt(0)===\"M\")i>10||n.slice(5)!==\"01-01\"?e._tickround=\"d\":e._tickround=+t.slice(1)%12===0?\"y\":\"m\";else if(t>=qv&&i<=10||t>=qv*15)e._tickround=\"d\";else if(t>=pM&&i<=16||t>=wm)e._tickround=\"M\";else if(t>=qL&&i<=19||t>=pM)e._tickround=\"S\";else{var a=e.l2r(r+t).replace(/^-/,\"\").length;e._tickround=Math.max(i,a)-20,e._tickround<0&&(e._tickround=4)}}else if(zh(t)||t.charAt(0)===\"L\"){var o=e.range.map(e.r2d||Number);zh(t)||(t=Number(t.slice(1))),e._tickround=2-Math.floor(Math.log(t)/Math.LN10+.01);var s=Math.max(Math.abs(o[0]),Math.abs(o[1])),l=Math.floor(Math.log(s)/Math.LN10+.01),u=e.minexponent===void 0?3:e.minexponent;Math.abs(l)>u&&(K3(e.exponentformat)&&e.exponentformat!==\"SI extended\"&&!TB(l)||K3(e.exponentformat)&&e.exponentformat===\"SI extended\"&&!AB(l)?e._tickexponent=3*Math.round((l-1)/3):e._tickexponent=l)}else e._tickround=null}$n.tickIncrement=function(e,t,r,n){var i=r?-1:1;if(zh(t))return Wo.increment(e,i*t);var a=t.charAt(0),o=i*Number(t.slice(1));if(a===\"M\")return Wo.incrementMonth(e,o,n);if(a===\"L\")return Math.log(Math.pow(10,e)+o)/Math.LN10;if(a===\"D\"){var s=t===\"D2\"?Fse:Dse,l=e+i*.01,u=Wo.roundUp(Wo.mod(l,1),s,r);return 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\"+String(s)}};$n.tickText=function(e,t,r,n){var i=qse(e,t),a=e.tickmode===\"array\",o=r||a,s=e.type,l=s===\"category\"?e.d2l_noadd:e.d2l,u,c=function(b){var p=e.l2p(b);return p>=0&&p<=e._length?b:null};if(a&&Wo.isArrayOrTypedArray(e.ticktext)){var f=Wo.simpleMap(e.range,e.r2l),h=(Math.abs(f[1]-f[0])-(e._lBreaks||0))/1e4;for(u=0;u<e.ticktext.length&&!(Math.abs(t-l(e.tickvals[u]))<h);u++);if(u<e.ticktext.length)return i.text=String(e.ticktext[u]),i.xbnd=[c(i.x-.5),c(i.x+e.dtick-.5)],i}function d(b){if(b===void 0)return!0;if(r)return b===\"none\";var p={first:e._tmin,last:e._tmax}[b];return b!==\"all\"&&t!==p}var v=r?\"never\":e.exponentformat!==\"none\"&&d(e.showexponent)?\"hide\":\"\";if(s===\"date\"?mlt(e,i,r,o):s===\"log\"?ylt(e,i,r,o,v):s===\"category\"?_lt(e,i):s===\"multicategory\"?xlt(e,i,r):MB(e)?wlt(e,i,r,o,v):blt(e,i,r,o,v),n||(e.tickprefix&&!d(e.showtickprefix)&&(i.text=e.tickprefix+i.text),e.ticksuffix&&!d(e.showticksuffix)&&(i.text+=e.ticksuffix)),e.labelalias&&e.labelalias.hasOwnProperty(i.text)){var m=e.labelalias[i.text];typeof m==\"string\"&&(i.text=m)}return(e.tickson===\"boundaries\"||e.showdividers)&&(i.xbnd=[c(i.x-.5),c(i.x+e.dtick-.5)]),i};$n.hoverLabelText=function(e,t,r){r&&(e=Wo.extendFlat({},e,{hoverformat:r}));var n=Wo.isArrayOrTypedArray(t)?t[0]:t,i=Wo.isArrayOrTypedArray(t)?t[1]:void 0;if(i!==void 0&&i!==n)return $n.hoverLabelText(e,n,r)+\" - \"+$n.hoverLabelText(e,i,r);var a=e.type===\"log\"&&n<=0,o=$n.tickText(e,e.c2l(a?-n:n),\"hover\").text;return a?n===0?\"0\":Bb+o:o};function qse(e,t,r){var n=e.tickfont||{};return{x:t,dx:0,dy:0,text:r||\"\",fontSize:n.size,font:n.family,fontWeight:n.weight,fontStyle:n.style,fontVariant:n.variant,fontTextcase:n.textcase,fontLineposition:n.lineposition,fontShadow:n.shadow,fontColor:n.color}}function mlt(e,t,r,n){var i=e._tickround,a=r&&e.hoverformat||$n.getTickFormat(e);n=!a&&n,n&&(zh(i)?i=4:i={y:\"m\",m:\"d\",d:\"M\",M:\"S\",S:4}[i]);var o=Wo.formatDate(t.x,a,i,e._dateFormat,e.calendar,e._extraFormat),s,l=o.indexOf(`\n",
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blt(e,t,r,n,i){i===\"never\"?i=\"\":e.showexponent===\"all\"&&Math.abs(t.x/e.dtick)<1e-6&&(i=\"hide\"),t.text=mM(t.x,e,i,n)}function wlt(e,t,r,n,i){if(e.thetaunit===\"radians\"&&!r){var a=t.x/180;if(a===0)t.text=\"0\";else{var o=Tlt(a);if(o[1]>=100)t.text=mM(Wo.deg2rad(t.x),e,i,n);else{var s=t.x<0;o[1]===1?o[0]===1?t.text=\"\\u03C0\":t.text=o[0]+\"\\u03C0\":t.text=[\"<sup>\",o[0],\"</sup>\",\"\\u2044\",\"<sub>\",o[1],\"</sub>\",\"\\u03C0\"].join(\"\"),s&&(t.text=Bb+t.text)}}}else t.text=mM(t.x,e,i,n)}function Tlt(e){function t(s,l){return Math.abs(s-l)<=1e-6}function r(s,l){return t(l,0)?s:r(l,s%l)}function n(s){for(var l=1;!t(Math.round(s*l)/l,s);)l*=10;return l}var i=n(e),a=e*i,o=Math.abs(r(a,i));return[Math.round(a/o),Math.round(i/o)]}var Ose=[\"f\",\"p\",\"n\",\"\\u03BC\",\"m\",\"\",\"k\",\"M\",\"G\",\"T\"],Alt=[\"q\",\"r\",\"y\",\"z\",\"a\",...Ose,\"P\",\"E\",\"Z\",\"Y\",\"R\",\"Q\"],K3=e=>[\"SI\",\"SI extended\",\"B\"].includes(e);function TB(e){return e>14||e<-15}function AB(e){return e>32||e<-30}function Slt(e,t){return K3(t)?!!(t===\"SI extended\"&&AB(e)||t!==\"SI extended\"&&TB(e)):!1}function mM(e,t,r,n){var i=e<0,a=t._tickround,o=r||t.exponentformat||\"B\",s=t._tickexponent,l=$n.getTickFormat(t),u=t.separatethousands;if(n){var c={exponentformat:o,minexponent:t.minexponent,dtick:t.showexponent===\"none\"?t.dtick:zh(e)&&Math.abs(e)||1,range:t.showexponent===\"none\"?t.range.map(t.r2d):[0,e||1]};zse(c),a=(Number(c._tickround)||0)+4,s=c._tickexponent,t.hoverformat&&(l=t.hoverformat)}if(l)return t._numFormat(l)(e).replace(/-/g,Bb);var f=Math.pow(10,-a)/2;if(o===\"none\"&&(s=0),e=Math.abs(e),e<f)e=\"0\",i=!1;else{if(e+=f,s&&(e*=Math.pow(10,-s),a+=s),a===0)e=String(Math.floor(e));else if(a<0){e=String(Math.round(e)),e=e.slice(0,Math.max(0,e.length+a));for(var h=a;h<0;h++)e+=\"0\"}else{e=String(e);var d=e.indexOf(\".\")+1;d&&(e=e.slice(0,d+a).replace(/\\.?0+$/,\"\"))}e=Wo.numSeparate(e,t._separators,u)}if(s&&o!==\"hide\"){Slt(s,o)&&(o=\"power\");var v;s<0?v=Bb+-s:o!==\"power\"?v=\"+\"+s:v=String(s),o===\"e\"||o===\"E\"?e+=o+v:o===\"power\"?e+=\"\\xD710<sup>\"+v+\"</sup>\":o===\"B\"&&s===9?e+=\"B\":K3(o)&&(e+=o===\"SI extended\"?Alt[s/3+10]:Ose[s/3+5])}return i?Bb+e:e}$n.getTickFormat=function(e){var t;function r(l){return typeof l!=\"string\"?l:Number(l.replace(\"M\",\"\"))*Y3}function n(l,u){var c=[\"L\",\"D\"];if(typeof l==typeof u){if(typeof l==\"number\")return l-u;var f=c.indexOf(l.charAt(0)),h=c.indexOf(u.charAt(0));return f===h?Number(l.replace(/(L|D)/g,\"\"))-Number(u.replace(/(L|D)/g,\"\")):f-h}else return typeof l==\"number\"?1:-1}function i(l,u,c){var f=c||function(v){return v},h=u[0],d=u[1];return(!h&&typeof h!=\"number\"||f(h)<=f(l))&&(!d&&typeof d!=\"number\"||f(d)>=f(l))}function a(l,u){var c=u[0]===null,f=u[1]===null,h=n(l,u[0])>=0,d=n(l,u[1])<=0;return(c||h)&&(f||d)}var o,s;if(e.tickformatstops&&e.tickformatstops.length>0)switch(e.type){case\"date\":case\"linear\":{for(t=0;t<e.tickformatstops.length;t++)if(s=e.tickformatstops[t],s.enabled&&i(e.dtick,s.dtickrange,r)){o=s;break}break}case\"log\":{for(t=0;t<e.tickformatstops.length;t++)if(s=e.tickformatstops[t],s.enabled&&a(e.dtick,s.dtickrange)){o=s;break}break}default:}return o?o.value:e.tickformat};$n.getSubplots=function(e,t){var r=e._fullLayout._subplots,n=r.cartesian.concat(r.gl2d||[]),i=t?$n.findSubplotsWithAxis(n,t):n;return i.sort(function(a,o){var s=a.slice(1).split(\"y\"),l=o.slice(1).split(\"y\");return s[0]===l[0]?+s[1]-+l[1]:+s[0]-+l[0]}),i};$n.findSubplotsWithAxis=function(e,t){for(var r=new RegExp(t._id.charAt(0)===\"x\"?\"^\"+t._id+\"y\":t._id+\"$\"),n=[],i=0;i<e.length;i++){var a=e[i];r.test(a)&&n.push(a)}return n};$n.makeClipPaths=function(e){var t=e._fullLayout;if(!t._hasOnlyLargeSploms){var 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L=!1;return{color:b.color||vd.defaultLine,x0:b.x0||b.x||0,x1:b.x1||b.x||0,y0:b.y0||b.y||0,y1:b.y1||b.y||0,xLabel:b.xLabel,yLabel:b.yLabel,zLabel:b.zLabel,text:b.text,name:b.name,idealAlign:b.idealAlign,borderColor:b.borderColor,fontFamily:b.fontFamily,fontSize:b.fontSize,fontColor:b.fontColor,fontWeight:b.fontWeight,fontStyle:b.fontStyle,fontVariant:b.fontVariant,nameLength:b.nameLength,textAlign:b.textAlign,trace:b.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:b.hovertemplate||!1,hovertemplateLabels:b.hovertemplateLabels||!1,eventData:L}}),l=!1,u=Xle(s,{gd:i,hovermode:\"closest\",rotateLabels:l,bgColor:r.bgColor||vd.background,container:Wb.select(r.container),outerContainer:r.outerContainer||r.container}),c=u.hoverLabels,f=5,h=0,d=0;c.sort(function(b,p){return b.y0-p.y0}).each(function(b,p){var k=b.y0-b.by/2;k-f<h?b.offset=h-k+f:b.offset=0,h=k+b.by+b.offset,p===r.anchorIndex&&(d=b.offset)}).each(function(b){b.offset-=d});var v=i._fullLayout._invScaleX,m=i._fullLayout._invScaleY;return Yle(c,l,v,m),n?c:c.node()};function qut(e,t,r,n,i){r||(r=\"xy\"),typeof r==\"string\"&&(r=r.split(Lut)[0]);var a=Array.isArray(r)?r:[r],o,s=e._fullLayout,l=s.hoversubplots,u=s._plots||[],c=u[r],f=s._has(\"cartesian\"),h=t.hovermode||s.hovermode,d=(h||\"\").charAt(0)===\"x\",v=(h||\"\").charAt(0)===\"y\",m,b;if(f&&(d||v)&&l===\"axis\"){for(var p=a.length,k=0;k<p;k++)if(o=a[k],u[o]){m=Sm.getFromId(e,o,\"x\"),b=Sm.getFromId(e,o,\"y\");var M=(d?m:b)._subplotsWith;if(M&&M.length)for(var T=0;T<M.length;T++)Mut(a,M[T])}}if(c&&l!==\"single\"){var L=c.overlays.map(function(Vi){return Vi.id});a=a.concat(L)}for(var x=a.length,C=new Array(x),S=new Array(x),g=!1,P=0;P<x;P++)if(o=a[P],u[o])g=!0,C[P]=u[o].xaxis,S[P]=u[o].yaxis;else if(s[o]&&s[o]._subplot){var E=s[o]._subplot;C[P]=E.xaxis,S[P]=E.yaxis}else{zf.warn(\"Unrecognized subplot: \"+o);return}if(h&&!g&&(h=\"closest\"),[\"x\",\"y\",\"closest\",\"x unified\",\"y unified\"].indexOf(h)===-1||!e.calcdata||e.querySelector(\".zoombox\")||e._dragging)return $L.unhoverRaw(e,t);var z=s.hoverdistance;z===-1&&(z=1/0);var q=s.spikedistance;q===-1&&(q=1/0);var V=[],G=[],Z,j,N,H,re,ae,_e,Ee,Ce,ge,ie,Se,ke,be={hLinePoint:null,vLinePoint:null},Be=!1;if(Array.isArray(t))for(h=\"array\",N=0;N<t.length;N++)re=e.calcdata[t[N].curveNumber||0],re&&(ae=re[0].trace,re[0].trace.hoverinfo!==\"skip\"&&(G.push(re),ae.orientation===\"h\"&&(Be=!0)));else{var Le=e.calcdata.slice();for(Le.sort(function(Vi,Jn){var Yn=Vi[0].trace.zorder||0,Gt=Jn[0].trace.zorder||0;return Yn-Gt}),H=0;H<Le.length;H++)re=Le[H],ae=re[0].trace,ae.hoverinfo!==\"skip\"&&kg.isTraceInSubplots(ae,a)&&(G.push(re),ae.orientation===\"h\"&&(Be=!0));var me=!i,Pe,ce;if(me)\"xpx\"in t?Pe=t.xpx:Pe=C[0]._length/2,\"ypx\"in t?ce=t.ypx:ce=S[0]._length/2;else{if(kut.triggerHandler(e,\"plotly_beforehover\",t)===!1)return;var He=i.getBoundingClientRect();Pe=t.clientX-He.left,ce=t.clientY-He.top,s._calcInverseTransform(e);var lt=zf.apply3DTransform(s._invTransform)(Pe,ce);if(Pe=lt[0],ce=lt[1],Pe<0||Pe>C[0]._length||ce<0||ce>S[0]._length)return $L.unhoverRaw(e,t)}if(t.pointerX=Pe+C[0]._offset,t.pointerY=ce+S[0]._offset,\"xval\"in t?Z=kg.flat(a,t.xval):Z=kg.p2c(C,Pe),\"yval\"in t?j=kg.flat(a,t.yval):j=kg.p2c(S,ce),!Ry(Z[0])||!Ry(j[0]))return zf.warn(\"Fx.hover failed\",t,e),$L.unhoverRaw(e,t)}s.clickanywhere&&(e._hoverXVals=Z,e._hoverYVals=j,e._hoverXAxes=C,e._hoverYAxes=S);var mt=1/0;function Ht(Vi,Jn){for(H=0;H<G.length;H++)if(re=G[H],!(!re||!re[0]||!re[0].trace)&&(ae=re[0].trace,!(ae.visible!==!0||ae._length===0)&&[\"carpet\",\"contourcarpet\"].indexOf(ae._module.name)===-1)){if(Ce=h,kg.isUnifiedHover(Ce)&&(Ce=Ce.charAt(0)),ae.type===\"splom\"?(Ee=0,_e=a[Ee]):(_e=kg.getSubplot(ae),Ee=a.indexOf(_e)),Se={cd:re,trace:ae,xa:C[Ee],ya:S[Ee],maxHoverDistance:z,maxSpikeDistance:q,index:!1,distance:Math.min(mt,z),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:vd.defaultLine,name:ae.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},s[_e]&&(Se.subplot=s[_e]._subplot),s._splomScenes&&s._splomScenes[ae.uid]&&(Se.scene=s._splomScenes[ae.uid]),Ce===\"array\"){var Yn=t[H];if(\"pointNumber\"in Yn){let Hi=re[Yn.pointNumber];Hi&&(Se.index=Yn.pointNumber,Hi.lonlat?(ge=Hi.lonlat[0],ie=Hi.lonlat[1]):(ge=Hi.x,ie=Hi.y)),Ce=\"closest\"}else Ce=\"\",\"xval\"in Yn&&(ge=Yn.xval,Ce=\"x\"),\"yval\"in Yn&&(ie=Yn.yval,Ce=Ce?\"closest\":\"y\")}else Vi!==void 0&&Jn!==void 0?(ge=Vi,ie=Jn):(ge=Z[Ee],ie=j[Ee]);if(ke=V.length,z!==0)if(ae._module&&ae._module.hoverPoints){var Gt=ae._module.hoverPoints(Se,ge,ie,Ce,{finiteRange:!0,hoverLayer:s._hoverlayer,hoversubplots:l,gd:e});if(Gt)for(var wt,tr=0;tr<Gt.length;tr++)wt=Gt[tr],Ry(wt.x0)&&Ry(wt.y0)&&V.push(Uut(wt,h))}else zf.log(\"Unrecognized trace type in hover:\",ae);if(h===\"closest\"&&V.length>ke&&(V.splice(0,ke),mt=V[0].distance),f&&q!==0&&V.length===0){Se.distance=q,Se.index=!1;var ir=ae._module.hoverPoints(Se,ge,ie,\"closest\",{hoverLayer:s._hoverlayer});if(ir&&(ir=ir.filter(function(Hi){return Hi.spikeDistance<=q})),ir&&ir.length){var wr,Xr=ir.filter(function(Hi){return Hi.xa.showspikes&&Hi.xa.spikesnap!==\"hovered data\"});if(Xr.length){var ti=Xr[0];Ry(ti.x0)&&Ry(ti.y0)&&(wr=ct(ti),(!be.vLinePoint||be.vLinePoint.spikeDistance>wr.spikeDistance)&&(be.vLinePoint=wr))}var $r=ir.filter(function(Hi){return Hi.ya.showspikes&&Hi.ya.spikesnap!==\"hovered data\"});if($r.length){var Ri=$r[0];Ry(Ri.x0)&&Ry(Ri.y0)&&(wr=ct(Ri),(!be.hLinePoint||be.hLinePoint.spikeDistance>wr.spikeDistance)&&(be.hLinePoint=wr))}}}}}Ht();function at(Vi,Jn,Yn){for(var Gt=null,wt=1/0,tr,ir=0;ir<Vi.length;ir++)m&&m._id!==Vi[ir].xa._id||b&&b._id!==Vi[ir].ya._id||(tr=Vi[ir].spikeDistance,Yn&&ir===0&&(tr=-1/0),tr<=wt&&tr<=Jn&&(Gt=Vi[ir],wt=tr));return Gt}function ct(Vi){return Vi?{xa:Vi.xa,ya:Vi.ya,x:Vi.xSpike!==void 0?Vi.xSpike:(Vi.x0+Vi.x1)/2,y:Vi.ySpike!==void 0?Vi.ySpike:(Vi.y0+Vi.y1)/2,distance:Vi.distance,spikeDistance:Vi.spikeDistance,curveNumber:Vi.trace.index,color:Vi.color,pointNumber:Vi.index}:null}var ar={fullLayout:s,container:s._hoverlayer,event:t},Vt=e._spikepoints,rr={vLinePoint:be.vLinePoint,hLinePoint:be.hLinePoint};e._spikepoints=rr;var tt=function(){var Vi=V.filter(function(Yn){return m&&m._id===Yn.xa._id&&b&&b._id===Yn.ya._id}),Jn=V.filter(function(Yn){return!(m&&m._id===Yn.xa._id&&b&&b._id===Yn.ya._id)});Vi.sort(Ble),Jn.sort(Ble),V=Vi.concat(Jn),V=Gut(V,h)};tt();var je=h.charAt(0),Ue=(je===\"x\"||je===\"y\")&&V[0]&&zut[V[0].trace.type];if(f&&q!==0&&V.length!==0){var Ae=V.filter(function(Vi){return Vi.ya.showspikes}),rt=at(Ae,q,Ue);be.hLinePoint=ct(rt);var St=V.filter(function(Vi){return Vi.xa.showspikes}),Tt=at(St,q,Ue);be.vLinePoint=ct(Tt)}if(V.length===0){var dt=$L.unhoverRaw(e,t);if(f&&(be.hLinePoint!==null||be.vLinePoint!==null)&&Vle(Vt)&&Ule(e,be,ar),s.hoveranywhere&&!n&&i){var Et=e._hoverdata;Et&&Et.length&&(e.emit(\"plotly_unhover\",{event:t,points:Et}),e._hoverdata=[]),Qn([])}return dt}if(f&&Vle(Vt)&&Ule(e,be,ar),kg.isXYhover(Ce)&&V[0].length!==0&&V[0].trace.type!==\"splom\"){var pt=V[0];Ole[pt.trace.type]?V=V.filter(Vi=>Vi.trace.index===pt.trace.index):V=[pt];var jt=V.length,or=Hle(\"x\",pt,s),mr=Hle(\"y\",pt,s);Ht(or,mr);var Ar=[],ei={},qr=0,jr=function(Vi){var Jn=Ole[Vi.trace.type]?Wle(Vi):Vi.trace.index;if(!ei[Jn])qr++,ei[Jn]=qr,Ar.push(Vi);else{var Yn=ei[Jn]-1,Gt=Ar[Yn];Yn>0&&Math.abs(Vi.distance)<Math.abs(Gt.distance)&&(Ar[Yn]=Vi)}},gt;for(gt=0;gt<jt;gt++)jr(V[gt]);for(gt=V.length-1;gt>jt-1;gt--)jr(V[gt]);V=Ar,tt()}var Ge=e._hoverdata,Je=[],We=Kle(e),et=Jle(e);for(let Vi of V){var xt=kg.makeEventData(Vi,Vi.trace,Vi.cd);if(Vi.hovertemplate!==!1){var At=!1;Vi.cd[Vi.index]&&Vi.cd[Vi.index].ht&&(At=Vi.cd[Vi.index].ht),Vi.hovertemplate=At||Vi.trace.hovertemplate||!1}if(Vi.xa&&Vi.ya){var Kt=Vi.x0+Vi.xa._offset,Qt=Vi.x1+Vi.xa._offset,Mr=Vi.y0+Vi.ya._offset,Gr=Vi.y1+Vi.ya._offset,Ir=Math.min(Kt,Qt),Yr=Math.max(Kt,Qt),_i=Math.min(Mr,Gr),Pi=Math.max(Mr,Gr);xt.bbox={x0:Ir+et,x1:Yr+et,y0:_i+We,y1:Pi+We},xt.xPixel=(Kt+Qt)/2,xt.yPixel=(Mr+Gr)/2}Vi.eventData=[xt],Je.push(xt)}e._hoverdata=Je;var ai=h===\"y\"&&(G.length>1||V.length>1)||h===\"closest\"&&Be&&V.length>1,mi=vd.combine(s.plot_bgcolor||vd.background,s.paper_bgcolor),un=Xle(V,{gd:e,hovermode:h,rotateLabels:ai,bgColor:mi,container:s._hoverlayer,outerContainer:s._paper.node(),commonLabelOpts:s.hoverlabel,hoverdistance:s.hoverdistance}),Fn=un.hoverLabels;if(kg.isUnifiedHover(h)||(But(Fn,ai,s,un.commonLabelBoundingBox),Yle(Fn,ai,s._invScaleX,s._invScaleY)),i&&i.tagName){var An=iT.getComponentMethod(\"annotations\",\"hasClickToShow\")(e,Je);Cut(Wb.select(i),An?\"pointer\":\"\")}var Hn=Vut(e,t,Ge);if(!i||n||!Hn&&!s.hoveranywhere)return;Ge&&Hn&&e.emit(\"plotly_unhover\",{event:t,points:Ge}),Qn(e._hoverdata);function Qn(Vi){e.emit(\"plotly_hover\",{event:t,points:Vi,xaxes:C,yaxes:S,xvals:Z,yvals:j})}}function Wle(e){return[e.trace.index,e.index,e.x0,e.y0,e.name,e.attr,e.xa?e.xa._id:\"\",e.ya?e.ya._id:\"\"].join(\",\")}var Out=/<extra>([\\s\\S]*)<\\/extra>/;function Xle(e,t){var r=t.gd,n=r._fullLayout,i=t.hovermode,a=t.rotateLabels,o=t.bgColor,s=t.container,l=t.outerContainer,u=t.commonLabelOpts||{};if(e.length===0)return[[]];var c=t.fontFamily||Xb.HOVERFONT,f=t.fontSize||Xb.HOVERFONTSIZE,h=t.fontWeight||n.font.weight,d=t.fontStyle||n.font.style,v=t.fontVariant||n.font.variant,m=t.fontTextcase||n.font.textcase,b=t.fontLineposition||n.font.lineposition,p=t.fontShadow||n.font.shadow,k=e[0],M=k.xa,T=k.ya,L=i.charAt(0),x=L+\"Label\",C=k[x];if(C===void 0&&M.type===\"multicategory\")for(var S=0;S<e.length&&(C=e[S][x],C===void 0);S++);var g=rT(r,l),P=g.top,E=g.width,z=g.height,q=C!==void 0&&k.distance<=t.hoverdistance&&(i===\"x\"||i===\"y\");if(q){var V=!0,G,Z;for(G=0;G<e.length;G++)if(V&&e[G].zLabel===void 0&&(V=!1),Z=e[G].hoverinfo||e[G].trace.hoverinfo,Z){var j=Array.isArray(Z)?Z:Z.split(\"+\");if(j.indexOf(\"all\")===-1&&j.indexOf(i)===-1){q=!1;break}}V&&(q=!1)}var N=s.selectAll(\"g.axistext\").data(q?[0]:[]);N.enter().append(\"g\").classed(\"axistext\",!0),N.exit().remove();var H={minX:0,maxX:0,minY:0,maxY:0};if(N.each(function(){var dt=Wb.select(this),Et=zf.ensureSingle(dt,\"path\",\"\",function(_i){_i.style({\"stroke-width\":\"1px\"})}),pt=zf.ensureSingle(dt,\"text\",\"\",function(_i){_i.attr(\"data-notex\",1)}),jt=u.bgcolor||vd.defaultLine,or=u.bordercolor||vd.contrast(jt),mr=vd.contrast(jt),Ar=u.font,ei={weight:Ar.weight||h,style:Ar.style||d,variant:Ar.variant||v,textcase:Ar.textcase||m,lineposition:Ar.lineposition||b,shadow:Ar.shadow||p,family:Ar.family||c,size:Ar.size||f,color:Ar.color||mr};Et.style({fill:jt,stroke:or}),pt.text(C).call(Am.font,ei).call(A0.positionText,0,0).call(A0.convertToTspans,r),dt.attr(\"transform\",\"\");var qr=rT(r,pt.node()),jr,gt;if(i===\"x\"){var Ge=M.side===\"top\"?\"-\":\"\";pt.attr(\"text-anchor\",\"middle\").call(A0.positionText,0,M.side===\"top\"?P-qr.bottom-Qc-ol:P-qr.top+Qc+ol),jr=M._offset+(k.x0+k.x1)/2,gt=T._offset+(M.side===\"top\"?0:T._length);var Je=qr.width/2+ol,We=jr;jr<Je?We=Je:jr>n.width-Je&&(We=n.width-Je),Et.attr(\"d\",\"M\"+(jr-We)+\",0L\"+(jr-We+Qc)+\",\"+Ge+Qc+\"H\"+Je+\"v\"+Ge+(ol*2+qr.height)+\"H\"+-Je+\"V\"+Ge+Qc+\"H\"+(jr-We-Qc)+\"Z\"),jr=We,H.minX=jr-Je,H.maxX=jr+Je,M.side===\"top\"?(H.minY=gt-(ol*2+qr.height),H.maxY=gt-ol):(H.minY=gt+ol,H.maxY=gt+(ol*2+qr.height))}else{var et,xt,At;T.side===\"right\"?(et=\"start\",xt=1,At=\"\",jr=M._offset+M._length):(et=\"end\",xt=-1,At=\"-\",jr=M._offset),gt=T._offset+(k.y0+k.y1)/2,pt.attr(\"text-anchor\",et),Et.attr(\"d\",\"M0,0L\"+At+Qc+\",\"+Qc+\"V\"+(ol+qr.height/2)+\"h\"+At+(ol*2+qr.width)+\"V-\"+(ol+qr.height/2)+\"H\"+At+Qc+\"V-\"+Qc+\"Z\"),H.minY=gt-(ol+qr.height/2),H.maxY=gt+(ol+qr.height/2),T.side===\"right\"?(H.minX=jr+Qc,H.maxX=jr+Qc+(ol*2+qr.width)):(H.minX=jr-Qc-(ol*2+qr.width),H.maxX=jr-Qc);var Kt=qr.height/2,Qt=P-qr.top-Kt,Mr=\"clip\"+n._uid+\"commonlabel\"+T._id,Gr;if(jr<qr.width+2*ol+Qc){Gr=\"M-\"+(Qc+ol)+\"-\"+Kt+\"h-\"+(qr.width-ol)+\"V\"+Kt+\"h\"+(qr.width-ol)+\"Z\";var Ir=qr.width-jr+ol;A0.positionText(pt,Ir,Qt),et===\"end\"&&pt.selectAll(\"tspan\").each(function(){var _i=Wb.select(this),Pi=Am.tester.append(\"text\").text(_i.text()).call(Am.font,ei),ai=rT(r,Pi.node());Math.round(ai.width)<Math.round(qr.width)&&_i.attr(\"x\",Ir-ai.width),Pi.remove()})}else A0.positionText(pt,xt*(ol+Qc),Qt),Gr=null;var Yr=n._topclips.selectAll(\"#\"+Mr).data(Gr?[0]:[]);Yr.enter().append(\"clipPath\").attr(\"id\",Mr).append(\"path\"),Yr.exit().remove(),Yr.select(\"path\").attr(\"d\",Gr),Am.setClipUrl(pt,Gr?Mr:null,r)}dt.attr(\"transform\",KB(jr,gt))}),kg.isUnifiedHover(i)){s.selectAll(\"g.hovertext\").remove();let dt=e.filter(Et=>Et.hoverinfo!==\"none\");if(dt.length===0)return[];var re=n.hoverlabel,ae=re.font,_e=dt[0],Ee=((i===\"x unified\"?_e.xa:_e.ya).unifiedhovertitle||{}).text,Ce=Ee?zf.hovertemplateString({data:i===\"x unified\"?[{xa:_e.xa,x:_e.xVal}]:[{ya:_e.ya,y:_e.yVal}],fallback:_e.trace.hovertemplatefallback,locale:n._d3locale,template:Ee}):C,ge={showlegend:!0,legend:{title:{text:Ce,font:ae},font:ae,bgcolor:re.bgcolor,bordercolor:re.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:n.legend?n.legend.traceorder:void 0,orientation:\"v\"}},ie={font:ae};Put(ge,ie,r._fullData);var Se=ie.legend;Se.entries=[];for(var ke=0;ke<dt.length;ke++){var be=dt[ke];if(be.hoverinfo!==\"none\"){var Be=Nle(be,!0,i,n,C),Le=Be[0],me=Be[1];be.name=me,me!==\"\"?be.text=me+\" : \"+Le:be.text=Le;var Pe=be.cd[be.index];Pe&&(Pe.mc&&(be.mc=Pe.mc),Pe.mcc&&(be.mc=Pe.mcc),Pe.mlc&&(be.mlc=Pe.mlc),Pe.mlcc&&(be.mlc=Pe.mlcc),Pe.mlw&&(be.mlw=Pe.mlw),Pe.mrc&&(be.mrc=Pe.mrc),Pe.dir&&(be.dir=Pe.dir)),be._distinct=!0,Se.entries.push([be])}}Se.entries.sort(function(Et,pt){var jt=n.hoversort;if(jt===\"value descending\"||jt===\"value ascending\"){var or=i.charAt(0)===\"x\"?\"y\":\"x\",mr=Et[0][or+\"LabelVal\"],Ar=pt[0][or+\"LabelVal\"];if(mr!==Ar)return jt===\"value descending\"?Ar-mr:mr-Ar}return Et[0].trace.index-pt[0].trace.index}),Se.layer=s,Se._inHover=!0,Se._groupTitleFont=re.grouptitlefont,Iut(r,Se);var ce=s.select(\"g.legend\"),He=rT(r,ce.node()),lt=He.width+2*ol,mt=He.height+2*ol,Ht=dt[0],at=(Ht.x0+Ht.x1)/2,ct=(Ht.y0+Ht.y1)/2,ar=!(iT.traceIs(Ht.trace,\"bar-like\")||iT.traceIs(Ht.trace,\"box-violin\")),Vt,rr;L===\"y\"?ar?(rr=ct-ol,Vt=ct+ol):(rr=Math.min.apply(null,dt.map(function(Et){return Math.min(Et.y0,Et.y1)})),Vt=Math.max.apply(null,dt.map(function(Et){return Math.max(Et.y0,Et.y1)}))):rr=Vt=zf.mean(dt.map(function(Et){return(Et.y0+Et.y1)/2}))-mt/2;var tt,je;L===\"x\"?ar?(tt=at+ol,je=at-ol):(tt=Math.max.apply(null,dt.map(function(Et){return Math.max(Et.x0,Et.x1)})),je=Math.min.apply(null,dt.map(function(Et){return Math.min(Et.x0,Et.x1)}))):tt=je=zf.mean(dt.map(function(Et){return(Et.x0+Et.x1)/2}))-lt/2;var Ue=M._offset,Ae=T._offset;Vt+=Ae,tt+=Ue,je+=Ue-lt,rr+=Ae-mt;var rt,St;return tt+lt<E&&tt>=0?rt=tt:je+lt<E&&je>=0?rt=je:Ue+lt<E?rt=Ue:tt-at<at-je+lt?rt=E-lt:rt=0,rt+=ol,Vt+mt<z&&Vt>=0?St=Vt:rr+mt<z&&rr>=0?St=rr:Ae+mt<z?St=Ae:Vt-ct<ct-rr+mt?St=z-mt:St=0,St+=ol,ce.attr(\"transform\",KB(rt-1,St-1)),ce}var Tt=s.selectAll(\"g.hovertext\").data(e,function(dt){return Wle(dt)});return Tt.enter().append(\"g\").classed(\"hovertext\",!0).each(function(){var dt=Wb.select(this);dt.append(\"rect\").call(vd.fill,vd.addOpacity(o,.8)),dt.append(\"text\").classed(\"name\",!0),dt.append(\"path\").style(\"stroke-width\",\"1px\"),dt.append(\"text\").classed(\"nums\",!0).call(Am.font,{weight:h,style:d,variant:v,textcase:m,lineposition:b,shadow:p,family:c,size:f})}),Tt.exit().remove(),Tt.each(function(dt){var Et=Wb.select(this).attr(\"transform\",\"\"),pt=dt.color;Array.isArray(pt)&&(pt=pt[dt.eventData[0].pointNumber]);var 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j=Z[0],N=Z[Z.length-1];if(k=j.pmin-j.pos-j.dp+j.size,M=N.pos+N.dp+N.size-j.pmax,k>.01){for(L=Z.length-1;L>=0;L--)Z[L].dp+=k;p=!1}if(!(M<.01)){if(k<-.01){for(L=Z.length-1;L>=0;L--)Z[L].dp-=M;p=!1}if(p){var H=0;for(T=0;T<Z.length;T++)x=Z[T],x.pos+x.dp+x.size>j.pmax&&H++;for(T=Z.length-1;T>=0&&!(H<=0);T--)x=Z[T],x.pos>j.pmax-1&&(x.del=!0,H--);for(T=0;T<Z.length&&!(H<=0);T++)if(x=Z[T],x.pos<j.pmin+1)for(x.del=!0,H--,M=x.size*2,L=Z.length-1;L>=0;L--)Z[L].dp-=M;for(T=Z.length-1;T>=0&&!(H<=0);T--)x=Z[T],x.pos+x.dp+x.size>j.pmax&&(x.del=!0,H--)}}}for(;!p&&o<=l;){for(o++,p=!0,T=0;T<u.length-1;){var g=u[T],P=u[T+1],E=g[g.length-1],z=P[0];if(k=E.pos+E.dp+E.size-z.pos-z.dp+z.size,k>.01){for(L=P.length-1;L>=0;L--)P[L].dp+=k;for(g.push.apply(g,P),u.splice(T+1,1),C=0,L=g.length-1;L>=0;L--)C+=g[L].dp;for(M=C/g.length,L=g.length-1;L>=0;L--)g[L].dp-=M;p=!1}else T++}u.forEach(S)}for(T=u.length-1;T>=0;T--){var q=u[T];for(L=q.length-1;L>=0;L--){var V=q[L],G=V.datum;G.offset=V.dp,G.del=V.del}}}function Zle(e,t){var r=0,n=e.offset;return t&&(n*=-Fut,r=e.offset*Dut),{x:r,y:n}}function Nut(e){var t={start:1,end:-1,middle:0}[e.anchor],r=t*(Qc+ol),n=r+t*(e.txwidth+ol),i=e.anchor===\"middle\";return i&&(r-=e.tx2width/2,n+=e.txwidth/2+ol),{alignShift:t,textShiftX:r,text2ShiftX:n}}function Yle(e,t,r,n){var i=function(o){return o*r},a=function(o){return o*n};e.each(function(o){var s=Wb.select(this);if(o.del)return s.remove();var l=s.select(\"text.nums\"),u=o.anchor,c=u===\"end\"?-1:1,f=Nut(o),h=Zle(o,t),d=h.x,v=h.y,m=u===\"middle\",b=\"hoverlabel\"in o.trace?o.trace.hoverlabel.showarrow:!0,p;m?p=\"M-\"+i(o.bx/2+o.tx2width/2)+\",\"+a(v-o.by/2)+\"h\"+i(o.bx)+\"v\"+a(o.by)+\"h-\"+i(o.bx)+\"Z\":b?p=\"M0,0L\"+i(c*Qc+d)+\",\"+a(Qc+v)+\"v\"+a(o.by/2-Qc)+\"h\"+i(c*o.bx)+\"v-\"+a(o.by)+\"H\"+i(c*Qc+d)+\"V\"+a(v-Qc)+\"Z\":p=\"M\"+i(c*Qc+d)+\",\"+a(v-o.by/2)+\"h\"+i(c*o.bx)+\"v\"+a(o.by)+\"h\"+i(-c*o.bx)+\"Z\",s.select(\"path\").attr(\"d\",p);var k=d+f.textShiftX,M=v+o.ty0-o.by/2+ol,T=o.textAlign||\"auto\";T!==\"auto\"&&(T===\"left\"&&u!==\"start\"?(l.attr(\"text-anchor\",\"start\"),k=m?-o.bx/2-o.tx2width/2+ol:-o.bx-ol):T===\"right\"&&u!==\"end\"&&(l.attr(\"text-anchor\",\"end\"),k=m?o.bx/2-o.tx2width/2-ol:o.bx+ol)),l.call(A0.positionText,i(k),a(M)),o.tx2width&&(s.select(\"text.name\").call(A0.positionText,i(f.text2ShiftX+f.alignShift*ol+d),a(v+o.ty0-o.by/2+ol)),s.select(\"rect\").call(Am.setRect,i(f.text2ShiftX+(f.alignShift-1)*o.tx2width/2+d),a(v-o.by/2-1),i(o.tx2width),a(o.by+2)))})}function Uut(e,t){var r=e.index,n=e.trace||{},i=e.cd[0],a=e.cd[r]||{};function o(h){return h||Ry(h)&&h===0}var s=Array.isArray(r)?function(h,d){var v=zf.castOption(i,r,h);return o(v)?v:zf.extractOption({},n,\"\",d)}:function(h,d){return zf.extractOption(a,n,h,d)};function l(h,d,v){var m=s(d,v);o(m)&&(e[h]=m)}if(l(\"hoverinfo\",\"hi\",\"hoverinfo\"),l(\"bgcolor\",\"hbg\",\"hoverlabel.bgcolor\"),l(\"borderColor\",\"hbc\",\"hoverlabel.bordercolor\"),l(\"fontFamily\",\"htf\",\"hoverlabel.font.family\"),l(\"fontSize\",\"hts\",\"hoverlabel.font.size\"),l(\"fontColor\",\"htc\",\"hoverlabel.font.color\"),l(\"fontWeight\",\"htw\",\"hoverlabel.font.weight\"),l(\"fontStyle\",\"hty\",\"hoverlabel.font.style\"),l(\"fontVariant\",\"htv\",\"hoverlabel.font.variant\"),l(\"nameLength\",\"hnl\",\"hoverlabel.namelength\"),l(\"textAlign\",\"hta\",\"hoverlabel.align\"),e.posref=t===\"y\"||t===\"closest\"&&n.orientation===\"h\"?e.xa._offset+(e.x0+e.x1)/2:e.ya._offset+(e.y0+e.y1)/2,e.x0=zf.constrain(e.x0,0,e.xa._length),e.x1=zf.constrain(e.x1,0,e.xa._length),e.y0=zf.constrain(e.y0,0,e.ya._length),e.y1=zf.constrain(e.y1,0,e.ya._length),e.xLabelVal!==void 0&&(e.xLabel=\"xLabel\"in e?e.xLabel:Sm.hoverLabelText(e.xa,e.xLabelVal,n.xhoverformat),e.xVal=e.xa.c2d(e.xLabelVal)),e.yLabelVal!==void 0&&(e.yLabel=\"yLabel\"in e?e.yLabel:Sm.hoverLabelText(e.ya,e.yLabelVal,n.yhoverformat),e.yVal=e.ya.c2d(e.yLabelVal)),e.zLabelVal!==void 0&&e.zLabel===void 0&&(e.zLabel=String(e.zLabelVal)),!isNaN(e.xerr)&&!(e.xa.type===\"log\"&&e.xerr<=0)){var u=Sm.tickText(e.xa,e.xa.c2l(e.xerr),\"hover\").text;e.xerrneg!==void 0?e.xLabel+=\" +\"+u+\" / -\"+Sm.tickText(e.xa,e.xa.c2l(e.xerrneg),\"hover\").text:e.xLabel+=\" \\xB1 \"+u,t===\"x\"&&(e.distance+=1)}if(!isNaN(e.yerr)&&!(e.ya.type===\"log\"&&e.yerr<=0)){var c=Sm.tickText(e.ya,e.ya.c2l(e.yerr),\"hover\").text;e.yerrneg!==void 0?e.yLabel+=\" +\"+c+\" / -\"+Sm.tickText(e.ya,e.ya.c2l(e.yerrneg),\"hover\").text:e.yLabel+=\" \\xB1 \"+c,t===\"y\"&&(e.distance+=1)}var f=e.hoverinfo||e.trace.hoverinfo;return f&&f!==\"all\"&&(f=Array.isArray(f)?f:f.split(\"+\"),f.indexOf(\"x\")===-1&&(e.xLabel=void 0),f.indexOf(\"y\")===-1&&(e.yLabel=void 0),f.indexOf(\"z\")===-1&&(e.zLabel=void 0),f.indexOf(\"text\")===-1&&(e.text=void 0),f.indexOf(\"name\")===-1&&(e.name=void 0)),e}function Ule(e,t,r){var n=r.container,i=r.fullLayout,a=i._size,o=r.event,s=!!t.hLinePoint,l=!!t.vLinePoint,u,c;if(n.selectAll(\".spikeline\").remove(),!!(l||s)){var f=vd.combine(i.plot_bgcolor,i.paper_bgcolor);if(s){var h=t.hLinePoint,d,v;u=h&&h.xa,c=h&&h.ya;var m=c.spikesnap;m===\"cursor\"?(d=o.pointerX,v=o.pointerY):(d=u._offset+h.x,v=c._offset+h.y);var b=qle.readability(h.color,f)<1.5?vd.contrast(f):h.color,p=c.spikemode,k=c.spikethickness,M=c.spikecolor||b,T=Sm.getPxPosition(e,c),L,x;if(p.indexOf(\"toaxis\")!==-1||p.indexOf(\"across\")!==-1){if(p.indexOf(\"toaxis\")!==-1&&(L=T,x=d),p.indexOf(\"across\")!==-1){var C=c._counterDomainMin,S=c._counterDomainMax;c.anchor===\"free\"&&(C=Math.min(C,c.position),S=Math.max(S,c.position)),L=a.l+C*a.w,x=a.l+S*a.w}n.insert(\"line\",\":first-child\").attr({x1:L,x2:x,y1:v,y2:v,\"stroke-width\":k,stroke:M,\"stroke-dasharray\":Am.dashStyle(c.spikedash,k)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),n.insert(\"line\",\":first-child\").attr({x1:L,x2:x,y1:v,y2:v,\"stroke-width\":k+2,stroke:f}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}p.indexOf(\"marker\")!==-1&&n.insert(\"circle\",\":first-child\").attr({cx:T+(c.side!==\"right\"?k:-k),cy:v,r:k,fill:M}).classed(\"spikeline\",!0)}if(l){var g=t.vLinePoint,P,E;u=g&&g.xa,c=g&&g.ya;var z=u.spikesnap;z===\"cursor\"?(P=o.pointerX,E=o.pointerY):(P=u._offset+g.x,E=c._offset+g.y);var q=qle.readability(g.color,f)<1.5?vd.contrast(f):g.color,V=u.spikemode,G=u.spikethickness,Z=u.spikecolor||q,j=Sm.getPxPosition(e,u),N,H;if(V.indexOf(\"toaxis\")!==-1||V.indexOf(\"across\")!==-1){if(V.indexOf(\"toaxis\")!==-1&&(N=j,H=E),V.indexOf(\"across\")!==-1){var re=u._counterDomainMin,ae=u._counterDomainMax;u.anchor===\"free\"&&(re=Math.min(re,u.position),ae=Math.max(ae,u.position)),N=a.t+(1-ae)*a.h,H=a.t+(1-re)*a.h}n.insert(\"line\",\":first-child\").attr({x1:P,x2:P,y1:N,y2:H,\"stroke-width\":G,stroke:Z,\"stroke-dasharray\":Am.dashStyle(u.spikedash,G)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),n.insert(\"line\",\":first-child\").attr({x1:P,x2:P,y1:N,y2:H,\"stroke-width\":G+2,stroke:f}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}V.indexOf(\"marker\")!==-1&&n.insert(\"circle\",\":first-child\").attr({cx:P,cy:j-(u.side!==\"top\"?G:-G),r:G,fill:Z}).classed(\"spikeline\",!0)}}}function Vut(e,t,r){if(!r||r.length!==e._hoverdata.length)return!0;for(var 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s=i.label.textposition,l=i.label.textangle,u=i.label.padding,c=i.type,f=Math.PI/180*a,h=Math.sin(f),d=Math.cos(f),v=i.label.xanchor,m=i.label.yanchor,b,p,k,M;if(c===\"line\"){s===\"start\"?(b=e,p=t):s===\"end\"?(b=r,p=n):(b=(e+r)/2,p=(t+n)/2),v===\"auto\"&&(s===\"start\"?l===\"auto\"?r>e?v=\"left\":r<e?v=\"right\":v=\"center\":r>e?v=\"right\":r<e?v=\"left\":v=\"center\":s===\"end\"?l===\"auto\"?r>e?v=\"right\":r<e?v=\"left\":v=\"center\":r>e?v=\"left\":r<e?v=\"right\":v=\"center\":v=\"center\");var T={left:1,center:0,right:-1},L={bottom:-1,middle:0,top:1};if(l===\"auto\"){var x=L[m];k=-u*h*x,M=u*d*x}else{var C=T[v],S=L[m];k=u*C,M=u*S}b=b+k,p=p+M}else k=u+3,s.indexOf(\"right\")!==-1?(b=Math.max(e,r)-k,v===\"auto\"&&(v=\"right\")):s.indexOf(\"left\")!==-1?(b=Math.min(e,r)+k,v===\"auto\"&&(v=\"left\")):(b=(e+r)/2,v===\"auto\"&&(v=\"center\")),s.indexOf(\"top\")!==-1?p=Math.min(t,n):s.indexOf(\"bottom\")!==-1?p=Math.max(t,n):p=(t+n)/2,M=u,m===\"bottom\"?p=p-M:m===\"top\"&&(p=p+M);var g=Nct[m],P=i.label.font.size,E=o.height,z=(E*g-P)*h,q=-(E*g-P)*d;return{textx:b+z,texty:p+q,xanchor:v}}});var cP=ye((xor,Xue)=>{\"use strict\";var Gct=Pr(),Hct=Gct.strTranslate,Nue=yv(),Gue=Cg(),jct=Gue.drawMode,Hue=Gue.selectMode,jue=Oa(),Uue=ka(),uP=rP(),Wct=uP.i000,Xct=uP.i090,Zct=uP.i180,Yct=uP.i270,Kct=s_(),Wue=Kct.clearOutlineControllers,sN=y_(),lP=sN.pointsOnRectangle,oN=sN.pointsOnEllipse,Jct=sN.writePaths,$ct=sP().newShapes,Qct=sP().createShapeObj,eft=nN(),tft=aN();Xue.exports=function e(t,r,n,i){i||(i=0);var a=n.gd;function o(){e(t,r,n,i++),(oN(t[0])||n.hasText)&&s({redrawing:!0})}function s(j){var N={};n.isActiveShape!==void 0&&(n.isActiveShape=!1,N=$ct(r,n)),n.isActiveSelection!==void 0&&(n.isActiveSelection=!1,N=eft(r,n),a._fullLayout._reselect=!0),Object.keys(N).length&&jue.call((j||{}).redrawing?\"relayout\":\"_guiRelayout\",a,N)}var l=a._fullLayout,u=l._zoomlayer,c=n.dragmode,f=jct(c),h=Hue(c);(f||h)&&(a._fullLayout._outlining=!0),Wue(a),r.attr(\"d\",Jct(t));var 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be=ge[1]-ie,Be=ge[2]-Se,Le=Be?5:Math.max(Math.min(25,Math.abs(be)-5),5),me=be?5:Math.max(Math.min(25,Math.abs(Be)-5),5);ke.classed(Be?\"cursor-ew-resize\":\"cursor-ns-resize\",!0).attr(\"width\",Le).attr(\"height\",me).attr(\"x\",ie-Le/2).attr(\"y\",Se-me/2).attr(\"transform\",Hct(be/2,Be/2))}else ke.classed(\"cursor-grab\",!0).attr(\"r\",5).attr(\"cx\",ie).attr(\"cy\",Se);d[N][Ee]={element:ke.node(),gd:a,prepFn:L,doneFn:C,clickFn:g},Nue.init(d[N][Ee])}}}function E(j,N){if(t.length)for(var H=0;H<t.length;H++)for(var re=0;re<t[H].length;re++)for(var ae=0;ae+2<t[H][re].length;ae+=2)t[H][re][ae+1]=p[H][re][ae+1]+j,t[H][re][ae+2]=p[H][re][ae+2]+N}function z(j,N){E(j,N),o()}function q(j){m=+j.srcElement.getAttribute(\"data-i\"),m||(m=0),v[m].moveFn=z}function V(){s()}function G(j){j===2&&ift(a)}function Z(){if(v=[],!!t.length){var j=0;v[j]={element:r[0][0],gd:a,prepFn:q,doneFn:V,clickFn:G},Nue.init(v[j])}}};function rft(e,t){for(var r=0;r<t.length;r++){var n=t[r];e[r]=[];for(var 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t=e._fullLayout;t._shapeUpperLayer.selectAll(\"path\").remove(),t._shapeLowerLayer.selectAll(\"path\").remove(),t._shapeUpperLayer.selectAll(\"text\").remove(),t._shapeLowerLayer.selectAll(\"text\").remove();for(var r in t._plots){var n=t._plots[r].shapelayer;n&&(n.selectAll(\"path\").remove(),n.selectAll(\"text\").remove())}for(var i=0;i<t.shapes.length;i++)t.shapes[i].visible===!0&&tce(e,i)}function fP(e){return!!e._fullLayout._outlining}function vP(e){return!e._context.edits.shapePosition}function tce(e,t){e._fullLayout._paperdiv.selectAll('.shapelayer [data-index=\"'+t+'\"]').remove();var r=Ep.makeShapesOptionsAndPlotinfo(e,t),n=r.options,i=r.plotinfo;if(!n._input||n.visible!==!0)return;let a=Array.isArray(n.xref)||Array.isArray(n.yref);if(n.layer===\"above\")s(e._fullLayout._shapeUpperLayer);else if(n.xref.includes(\"paper\")||n.yref.includes(\"paper\"))s(e._fullLayout._shapeLowerLayer);else if(n.layer===\"between\"&&!a)s(i.shapelayerBetween);else if(i._hadPlotinfo){var 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a=i.append(\"div\").classed(\"plotly-cloud-dialog\",!0),o=a.append(\"div\").classed(\"plotly-cloud-dialog-box\",!0);o.append(\"div\").classed(\"plotly-cloud-dialog-title\",!0).text(gP(t,\"Share with Plotly Cloud\")),o.append(\"div\").classed(\"plotly-cloud-dialog-message\",!0).text(gP(t,\"This chart and its data will be sent to\")+\" \"+r+\".\");var s=o.append(\"div\").classed(\"plotly-cloud-dialog-buttons\",!0);function l(){a.remove(),document.removeEventListener(\"keydown\",u)}function u(c){(c.key===\"Escape\"||c.keyCode===27)&&l()}document.addEventListener(\"keydown\",u),a.on(\"click\",function(){nce.event.target===a.node()&&l()}),s.append(\"button\").classed(\"plotly-cloud-dialog-btn\",!0).classed(\"plotly-cloud-dialog-btn--cancel\",!0).text(gP(t,\"Cancel\")).on(\"click\",l),s.append(\"button\").classed(\"plotly-cloud-dialog-btn\",!0).classed(\"plotly-cloud-dialog-btn--confirm\",!0).text(gP(t,\"Share\")).on(\"click\",function(){l(),n()})}});var vN=ye((Tor,vce)=>{\"use strict\";var 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0&&(o[v+\".showspikes\"]=u._showSpikeInitial,l===\"on\"&&!u._showSpikeInitial&&(l=\"off\"));else{var b=[u.r2l(u.range[0]),u.r2l(u.range[1])],p=[h*b[0]+d*b[1],h*b[1]+d*b[0]];o[v+\".range[0]\"]=u.l2r(p[0]),o[v+\".range[1]\"]=u.l2r(p[1])}}else n===\"hovermode\"&&(i===\"x\"||i===\"y\")&&(i=a._isHoriz?\"y\":\"x\",r.setAttribute(\"data-val\",i)),o[n]=i;a._cartesianSpikesEnabled=l,S0.call(\"_guiRelayout\",e,o)}_l.zoom3d={name:\"zoom3d\",_cat:\"zoom\",title:function(e){return sl(e,\"Zoom\")},attr:\"scene.dragmode\",val:\"zoom\",icon:Gl.zoombox,click:yP};_l.pan3d={name:\"pan3d\",_cat:\"pan\",title:function(e){return sl(e,\"Pan\")},attr:\"scene.dragmode\",val:\"pan\",icon:Gl.pan,click:yP};_l.orbitRotation={name:\"orbitRotation\",title:function(e){return sl(e,\"Orbital rotation\")},attr:\"scene.dragmode\",val:\"orbit\",icon:Gl[\"3d_rotate\"],click:yP};_l.tableRotation={name:\"tableRotation\",title:function(e){return sl(e,\"Turntable 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c=s[u],f=c+\".camera\",h=c+\".aspectratio\",d=c+\".aspectmode\",v=o[c]._scene,m;i?(l[f+\".up\"]=v.viewInitial.up,l[f+\".eye\"]=v.viewInitial.eye,l[f+\".center\"]=v.viewInitial.center,m=!0):a&&(l[f+\".up\"]=null,l[f+\".eye\"]=null,l[f+\".center\"]=null,m=!0),m&&(l[h+\".x\"]=v.viewInitial.aspectratio.x,l[h+\".y\"]=v.viewInitial.aspectratio.y,l[h+\".z\"]=v.viewInitial.aspectratio.z,l[d]=v.viewInitial.aspectmode)}S0.call(\"_guiRelayout\",e,l)}_l.hoverClosest3d={name:\"hoverClosest3d\",_cat:\"hoverclosest\",title:function(e){return sl(e,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:Gl.tooltip_basic,gravity:\"ne\",click:gft};function lce(e,t){var r=t.currentTarget,n=r._previousVal,i=e._fullLayout,a=i._subplots.gl3d||[],o=[\"xaxis\",\"yaxis\",\"zaxis\"],s={},l={};if(n)l=n,r._previousVal=null;else{for(var u=0;u<a.length;u++){var c=a[u],f=i[c],h=c+\".hovermode\";s[h]=f.hovermode,l[h]=!1;for(var d=0;d<3;d++){var v=o[d],m=c+\".\"+v+\".showspikes\";l[m]=!1,s[m]=f[v].showspikes}}r._previousVal=s}return l}function gft(e,t){var r=lce(e,t);S0.call(\"_guiRelayout\",e,r)}_l.zoomInGeo={name:\"zoomInGeo\",_cat:\"zoomin\",title:function(e){return sl(e,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:Gl.zoom_plus,click:dN};_l.zoomOutGeo={name:\"zoomOutGeo\",_cat:\"zoomout\",title:function(e){return sl(e,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:Gl.zoom_minus,click:dN};_l.resetGeo={name:\"resetGeo\",_cat:\"reset\",title:function(e){return sl(e,\"Reset\")},attr:\"reset\",val:null,icon:Gl.autoscale,click:dN};_l.hoverClosestGeo={name:\"hoverClosestGeo\",_cat:\"hoverclosest\",title:function(e){return sl(e,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:Gl.tooltip_basic,gravity:\"ne\",click:cce};function dN(e,t){for(var r=t.currentTarget,n=r.getAttribute(\"data-attr\"),i=r.getAttribute(\"data-val\")||!0,a=e._fullLayout,o=a._subplots.geo||[],s=0;s<o.length;s++){var l=o[s],u=a[l];if(n===\"zoom\"){var c=u.projection.scale,f=i===\"in\"?2*c:.5*c;S0.call(\"_guiRelayout\",e,l+\".projection.scale\",f)}}n===\"reset\"&&oT(e,\"geo\")}_l.hoverClosestPie={name:\"hoverClosestPie\",_cat:\"hoverclosest\",title:function(e){return sl(e,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:Gl.tooltip_basic,gravity:\"ne\",click:cce};function uce(e){var t=e._fullLayout;return t.hovermode?!1:t._has(\"cartesian\")?t._isHoriz?\"y\":\"x\":\"closest\"}function cce(e){var t=uce(e);S0.call(\"_guiRelayout\",e,\"hovermode\",t)}_l.resetViewSankey={name:\"resetSankeyGroup\",title:function(e){return sl(e,\"Reset view\")},icon:Gl.home,click:function(e){for(var t={\"node.groups\":[],\"node.x\":[],\"node.y\":[]},r=0;r<e._fullData.length;r++){var n=e._fullData[r]._viewInitial;t[\"node.groups\"].push(n.node.groups.slice()),t[\"node.x\"].push(n.node.x.slice()),t[\"node.y\"].push(n.node.y.slice())}S0.call(\"restyle\",e,t)}};_l.toggleHover={name:\"toggleHover\",title:function(e){return sl(e,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:Gl.tooltip_basic,gravity:\"ne\",click:function(e,t){var r=lce(e,t);r.hovermode=uce(e),S0.call(\"_guiRelayout\",e,r)}};_l.resetViews={name:\"resetViews\",title:function(e){return sl(e,\"Reset views\")},icon:Gl.home,click:function(e,t){var r=t.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),Bv(e,t),r.setAttribute(\"data-attr\",\"resetLastSave\"),hN(e,t),oT(e,\"geo\"),oT(e,\"mapbox\"),oT(e,\"map\")}};_l.toggleSpikelines={name:\"toggleSpikelines\",title:function(e){return sl(e,\"Toggle Spike Lines\")},icon:Gl.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(e){var t=e._fullLayout,r=t._cartesianSpikesEnabled;t._cartesianSpikesEnabled=r===\"on\"?\"off\":\"on\",S0.call(\"_guiRelayout\",e,mft(e))}};function mft(e){for(var t=e._fullLayout,r=t._cartesianSpikesEnabled===\"on\",n=sce.list(e,null,!0),i={},a=0;a<n.length;a++){var o=n[a];i[o._name+\".showspikes\"]=r?!0:o._showSpikeInitial}return i}_l.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(e){return sl(e,\"Reset view\")},attr:\"reset\",icon:Gl.home,click:function(e){oT(e,\"mapbox\")}};_l.resetViewMap={name:\"resetViewMap\",_cat:\"resetView\",title:function(e){return sl(e,\"Reset view\")},attr:\"reset\",icon:Gl.home,click:function(e){oT(e,\"map\")}};_l.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(e){return sl(e,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:Gl.zoom_plus,click:fce};_l.zoomInMap={name:\"zoomInMap\",_cat:\"zoomin\",title:function(e){return sl(e,\"Zoom 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pce=vN(),yft=Object.keys(pce),gce=[\"drawline\",\"drawopenpath\",\"drawclosedpath\",\"drawcircle\",\"drawrect\",\"eraseshape\"],mce=[\"v1hovermode\",\"hoverclosest\",\"hovercompare\",\"togglehover\",\"togglespikelines\"].concat(gce),sT=[],_ft=function(e){if(mce.indexOf(e._cat||e.name)===-1){var t=e.name,r=(e._cat||e.name).toLowerCase();sT.indexOf(t)===-1&&sT.push(t),sT.indexOf(r)===-1&&sT.push(r)}};yft.forEach(function(e){_ft(pce[e])});sT.sort();yce.exports={DRAW_MODES:gce,backButtons:mce,foreButtons:sT}});var gN=ye((Mor,_ce)=>{\"use strict\";var Sor=pN();_ce.exports={editType:\"modebar\",orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\",editType:\"modebar\"},bgcolor:{valType:\"color\",editType:\"modebar\"},color:{valType:\"color\",editType:\"modebar\"},activecolor:{valType:\"color\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},add:{valType:\"string\",arrayOk:!0,dflt:\"\",editType:\"modebar\"},remove:{valType:\"string\",arrayOk:!0,dflt:\"\",editType:\"modebar\"}}});var bce=ye((Eor,xce)=>{\"use strict\";var xft=Pr(),CM=ka(),bft=vl(),wft=gN();xce.exports=function(t,r){var n=t.modebar||{},i=bft.newContainer(r,\"modebar\");function a(s,l){return xft.coerce(n,i,wft,s,l)}a(\"orientation\"),a(\"bgcolor\",CM.addOpacity(r.paper_bgcolor,.5));var o=CM.contrast(CM.rgb(r.modebar.bgcolor));a(\"color\",CM.addOpacity(o,.3)),a(\"activecolor\",CM.addOpacity(o,.7)),a(\"uirevision\",r.uirevision),a(\"add\"),a(\"remove\")}});var Sce=ye((kor,Ace)=>{\"use strict\";var mN=Fa(),Tft=Eo(),_P=Pr(),wce=eP(),Aft=OS().version,Sft=new DOMParser;function Tce(e){this.container=e.container,this.element=document.createElement(\"div\"),this.update(e.graphInfo,e.buttons),this.container.appendChild(this.element)}var Cm=Tce.prototype;Cm.update=function(e,t){this.graphInfo=e;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this.element.setAttribute(\"role\",\"toolbar\"),this._uid=i,this.element.className=\"modebar modebar--custom\",r.displayModeBar===\"hover\"&&(this.element.className+=\" modebar--hover ease-bg\"),n.modebar.orientation===\"v\"&&(this.element.className+=\" vertical\",t=t.reverse());var a=n.modebar,o=\"#\"+i+\" .modebar-group\";document.querySelectorAll(o).forEach(function(f){f.style.backgroundColor=a.bgcolor});var s=!this.hasButtons(t),l=this.hasLogo!==r.displaylogo,u=this.locale!==r.locale;if(this.locale=r.locale,(s||l||u)&&(this.removeAllButtons(),this.updateButtons(t),r.watermark||r.displaylogo)){var c=this.getLogo();r.watermark&&(c.className=c.className+\" watermark\"),n.modebar.orientation===\"v\"?this.element.insertBefore(c,this.element.childNodes[0]):this.element.appendChild(c),this.hasLogo=!0}this.updateActiveButton(),_P.setStyleOnHover(\"#\"+i+\" .modebar-btn\",\".active\",\".icon path\",\"fill: \"+a.activecolor,\"fill: \"+a.color,this.element)};Cm.updateButtons=function(e){var t=this;this.buttons=e,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach(function(r){var n=t.createGroup();r.forEach(function(i){var a=i.name;if(!a)throw new Error(\"must provide button 'name' in button config\");if(t.buttonsNames.indexOf(a)!==-1)throw new Error(\"button name '\"+a+\"' is taken\");t.buttonsNames.push(a);var o=t.createButton(i);t.buttonElements.push(o),n.appendChild(o)}),t.element.appendChild(n)})};Cm.createGroup=function(){var e=document.createElement(\"div\");e.className=\"modebar-group\";var t=this.graphInfo._fullLayout.modebar;return e.style.backgroundColor=t.bgcolor,e};Cm.createButton=function(e){var t=this,r=document.createElement(\"button\");r.setAttribute(\"type\",\"button\"),r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var n=e.title;n===void 0?n=e.name:typeof n==\"function\"&&(n=n(this.graphInfo)),(n||n===0)&&(r.setAttribute(\"data-title\",n),r.setAttribute(\"aria-label\",n)),e.attr!==void 0&&r.setAttribute(\"data-attr\",e.attr);var i=e.val;i!==void 0&&(typeof i==\"function\"&&(i=i(this.graphInfo)),r.setAttribute(\"data-val\",i));var a=e.click;if(typeof a!=\"function\")throw new Error(\"must provide button 'click' function in button 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n.setAttribute(\"height\",\"1em\"),n.setAttribute(\"width\",\"1em\"),n};Cm.updateActiveButton=function(e){var t=this.graphInfo._fullLayout,r=e!==void 0?e.getAttribute(\"data-attr\"):null;this.buttonElements.forEach(function(n){var i=n.getAttribute(\"data-val\")||!0,a=n.getAttribute(\"data-attr\"),o=n.getAttribute(\"data-toggle\")===\"true\",s=mN.select(n),l=function(f,h){var d=t.modebar,v=f.querySelector(\".icon path\");v&&(h||f.matches(\":hover\")?v.style.fill=d.activecolor:v.style.fill=d.color)};if(o){if(a===r){var u=!s.classed(\"active\");s.classed(\"active\",u),l(n,u)}}else{var c=a===null?a:_P.nestedProperty(t,a).get();s.classed(\"active\",c===i),l(n,c===i)}})};Cm.hasButtons=function(e){var t=this.buttons;if(!t||e.length!==t.length)return!1;for(var r=0;r<e.length;++r){if(e[r].length!==t[r].length)return!1;for(var n=0;n<e[r].length;n++)if(e[r][n].name!==t[r][n].name)return!1}return!0};function Mft(e){return e+\" (v\"+Aft+\")\"}Cm.getLogo=function(){var e=this.createGroup(),t=document.createElement(\"a\");return t.href=\"https://plotly.com/\",t.target=\"_blank\",t.setAttribute(\"data-title\",Mft(_P._(this.graphInfo,\"Produced with Plotly.js\"))),t.className=\"modebar-btn plotlyjsicon modebar-btn--logo\",t.appendChild(this.createIcon(wce.newplotlylogo)),e.appendChild(t),e};Cm.removeAllButtons=function(){for(;this.element.firstChild;)this.element.removeChild(this.element.firstChild);this.hasLogo=!1};Cm.destroy=function(){_P.removeElement(this.container.querySelector(\".modebar\"))};function Eft(e,t){var r=e._fullLayout,n=new Tce({graphInfo:e,container:r._modebardiv.node(),buttons:t});return r._privateplot&&mN.select(n.element).append(\"span\").classed(\"badge-private float--left\",!0).text(\"PRIVATE\"),n}Ace.exports=Eft});var kce=ye((Cor,Ece)=>{\"use strict\";var kft=df(),Mce=Ru(),yN=Oa(),Cft=ip().isUnifiedHover,Lft=Sce(),xP=vN(),Pft=pN().DRAW_MODES,Ift=Pr().extendDeep;Ece.exports=function(t){var r=t._fullLayout,n=t._context,i=r._modeBar;if(!n.displayModeBar&&!n.watermark){i&&(i.destroy(),delete r._modeBar);return}if(!Array.isArray(n.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(n.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var a=n.modeBarButtons,o;Array.isArray(a)&&a.length?o=Oft(a):!n.displayModeBar&&n.watermark?o=[]:o=Rft(t),i?i.update(t,o):r._modeBar=Lft(t,o)};function Rft(e){var t=e._fullLayout,r=e._fullData,n=e._context;function i(N,H){if(typeof H==\"string\"){if(H.toLowerCase()===N.toLowerCase())return!0}else{var re=H.name,ae=H._cat||H.name;if(re===N||ae===N.toLowerCase())return!0}return!1}var a=t.modebar.add;typeof a==\"string\"&&(a=[a]);var o=t.modebar.remove;typeof o==\"string\"&&(o=[o]);var s=n.modeBarButtonsToAdd.concat(a.filter(function(N){for(var H=0;H<n.modeBarButtonsToRemove.length;H++)if(i(N,n.modeBarButtonsToRemove[H]))return!1;return!0})),l=n.modeBarButtonsToRemove.concat(o.filter(function(N){for(var H=0;H<n.modeBarButtonsToAdd.length;H++)if(i(N,n.modeBarButtonsToAdd[H]))return!1;return!0})),u=t._has(\"cartesian\"),c=t._has(\"gl3d\"),f=t._has(\"geo\"),h=t._has(\"pie\"),d=t._has(\"funnelarea\"),v=t._has(\"ternary\"),m=t._has(\"mapbox\"),b=t._has(\"map\"),p=t._has(\"polar\"),k=t._has(\"smith\"),M=t._has(\"sankey\"),T=Dft(t),L=Cft(t.hovermode),x=[];function C(N){if(N.length){for(var H=[],re=0;re<N.length;re++){for(var ae=N[re],_e=xP[ae],Ee=_e.name.toLowerCase(),Ce=(_e._cat||_e.name).toLowerCase(),ge=!1,ie=0;ie<l.length;ie++){var Se=l[ie].toLowerCase();if(Se===Ee||Se===Ce){ge=!0;break}}ge||H.push(xP[ae])}x.push(H)}}var S=[\"toImage\"];n.showSendToCloud?S.push(\"sendChartToCloud\"):n.showEditInChartStudio&&(console.warn([\"*showEditInChartStudio* is deprecated.\",\"Use *showSendToCloud* instead.\"].join(\" 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q=[],V=function(N){q.indexOf(N)===-1&&P.indexOf(N)!==-1&&q.push(N)};if(Array.isArray(s)){for(var G=[],Z=0;Z<s.length;Z++){var j=s[Z];typeof j==\"string\"?(j=j.toLowerCase(),Pft.indexOf(j)!==-1?(t._has(\"mapbox\")||t._has(\"map\")||t._has(\"cartesian\"))&&z.push(j):j===\"togglespikelines\"?V(\"toggleSpikelines\"):j===\"togglehover\"?V(\"toggleHover\"):j===\"hovercompare\"?V(\"hoverCompareCartesian\"):j===\"hoverclosest\"?(V(\"hoverClosestCartesian\"),V(\"hoverClosestGeo\"),V(\"hoverClosest3d\"),V(\"hoverClosestPie\")):j===\"v1hovermode\"&&(V(\"hoverClosestCartesian\"),V(\"hoverCompareCartesian\"),V(\"hoverClosestGeo\"),V(\"hoverClosest3d\"),V(\"hoverClosestPie\"))):G.push(j)}s=G}return C(z),C(g.concat(E)),C(q),qft(x,s)}function Dft(e){for(var t=kft.list({_fullLayout:e},null,!0),r=0;r<t.length;r++){var n=t[r].modebardisable;if(!t[r].fixedrange&&n!==\"autoscale+zoominout\"&&n!==\"zoominout+autoscale\")return!1}return!0}function Fft(e){for(var t=!1,r=0;r<e.length&&!t;r++){var n=e[r];!n._module||!n._module.selectPoints||(yN.traceIs(n,\"scatter-like\")?(Mce.hasMarkers(n)||Mce.hasText(n))&&(t=!0):yN.traceIs(n,\"box-violin\")?(n.boxpoints===\"all\"||n.points===\"all\")&&(t=!0):t=!0)}return t}function zft(e){for(var t=0;t<e.length;t++)if(!yN.traceIs(e[t],\"noHover\"))return!1;return!0}function qft(e,t){if(t.length)if(Array.isArray(t[0]))for(var r=0;r<t.length;r++)e.push(t[r]);else e.push(t);return e}function Oft(e){for(var t=Ift([],e),r=0;r<t.length;r++)for(var n=t[r],i=0;i<n.length;i++){var a=n[i];if(typeof a==\"string\")if(xP[a]!==void 0)t[r][i]=xP[a];else throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"))}return t}});var _N=ye((Lor,Cce)=>{\"use strict\";Cce.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:gN(),supplyLayoutDefaults:bce(),manage:kce()}});var xN=ye((Por,Lce)=>{\"use strict\";var Bft=Dh().FROM_BL;Lce.exports=function(t,r,n){n===void 0&&(n=Bft[t.constraintoward||\"center\"]);var i=[t.r2l(t.range[0]),t.r2l(t.range[1])],a=i[0]+(i[1]-i[0])*n;t.range=t._input.range=[t.l2r(a+(i[0]-a)*r),t.l2r(a+(i[1]-a)*r)],t.setScale()}});var $b=ye(LM=>{\"use strict\";var Jb=Pr(),bN=Mg(),Lg=df().id2name,Nft=Rd(),Pce=xN(),Uft=xm(),Vft=fs().ALMOST_EQUAL,Gft=Dh().FROM_BL;LM.handleDefaults=function(e,t,r){var n=r.axIds,i=r.axHasImage,a=t._axisConstraintGroups=[],o=t._axisMatchGroups=[],s,l,u,c,f,h,d,v;for(s=0;s<n.length;s++)c=Lg(n[s]),f=e[c],h=t[c],Hft(f,h,{axIds:n,layoutOut:t,hasImage:i[c]});function m(S,g){for(s=0;s<S.length;s++){l=S[s];for(u in l)t[Lg(u)][g]=l}}for(m(o,\"_matchGroup\"),s=0;s<a.length;s++){l=a[s];for(u in l)if(h=t[Lg(u)],h.fixedrange){for(var b in l){var p=Lg(b);(e[p]||{}).fixedrange===!1&&Jb.warn(\"fixedrange was specified as false for axis \"+p+\" but was overridden because another axis in its constraint group has fixedrange true\"),t[p].fixedrange=!0}break}}for(s=0;s<a.length;){l=a[s];for(u in 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n=r.axIds,i=r.layoutOut,a=r.hasImage,o=i._axisConstraintGroups,s=i._axisMatchGroups,l=t._id,u=l.charAt(0),c=((i._splomAxes||{})[u]||{})[l]||{},f=t._id,h=f.charAt(0)===\"x\";t._matchGroup=null,t._constraintGroup=null;function d(z,q){return Jb.coerce(e,t,Nft,z,q)}d(\"constrain\",a?\"domain\":\"range\"),Jb.coerce(e,t,{constraintoward:{valType:\"enumerated\",values:h?[\"left\",\"center\",\"right\"]:[\"bottom\",\"middle\",\"top\"],dflt:h?\"center\":\"middle\"}},\"constraintoward\");var v=t.type,m,b,p=[];for(m=0;m<n.length;m++)if(b=n[m],b!==f){var k=i[Lg(b)];k.type===v&&p.push(b)}var M=Dce(o,f);if(M){var T=[];for(m=0;m<p.length;m++)b=p[m],M[b]||T.push(b);p=T}var L=p.length,x,C;L&&(e.matches||c.matches)&&(x=Jb.coerce(e,t,{matches:{valType:\"enumerated\",values:p,dflt:p.indexOf(c.matches)!==-1?c.matches:void 0}},\"matches\"));var S=a&&!h?t.anchor:void 0;if(L&&!x&&(e.scaleanchor||S)&&(C=Jb.coerce(e,t,{scaleanchor:{valType:\"enumerated\",values:p.concat([!1])}},\"scaleanchor\",S)),x){t._matchGroup=wN(s,f,x,1);var g=i[Lg(x)],P=Ice(i,t)/Ice(i,g);h!==(x.charAt(0)===\"x\")&&(P=(h?\"x\":\"y\")+P),wN(o,f,x,P)}else e.matches&&n.indexOf(e.matches)!==-1&&Jb.warn(\"ignored \"+t._name+'.matches: \"'+e.matches+'\" to avoid an infinite loop');if(C){var E=d(\"scaleratio\");E||(E=t.scaleratio=1),wN(o,f,C,E)}else e.scaleanchor&&n.indexOf(e.scaleanchor)!==-1&&Jb.warn(\"ignored \"+t._name+'.scaleanchor: \"'+e.scaleanchor+'\" to avoid either an infinite loop and possibly inconsistent scaleratios, or because this axis declares a *matches* constraint.')}function Ice(e,t){var r=t.domain;return r||(r=e[Lg(t.overlaying)].domain),r[1]-r[0]}function Dce(e,t){for(var r=0;r<e.length;r++)if(e[r][t])return e[r];return null}function wN(e,t,r,n){var i,a,o,s,l,u=Dce(e,t);u===null?(u={},u[t]=1,l=e.length,e.push(u)):l=e.indexOf(u);var c=Object.keys(u);for(i=0;i<e.length;i++)if(o=e[i],i!==l&&o[r]){var f=o[r];for(a=0;a<c.length;a++)s=c[a],o[s]=TN(f,TN(n,u[s]));e.splice(l,1);return}if(n!==1)for(a=0;a<c.length;a++){var h=c[a];u[h]=TN(n,u[h])}u[r]=1}function TN(e,t){var r=\"\",n=\"\",i,a;typeof e==\"string\"&&(r=e.match(/^[xy]*/)[0],i=r.length,e=+e.slice(i)),typeof t==\"string\"&&(n=t.match(/^[xy]*/)[0],a=n.length,t=+t.slice(a));var o=e*t;return!i&&!a?o:!i||!a||r.charAt(0)===n.charAt(0)?r+n+e*t:i===a?o:(i>a?r.slice(a):n.slice(i))+o}function jft(e,t){for(var r=t._size,n=r.h/r.w,i={},a=Object.keys(e),o=0;o<a.length;o++){var s=a[o],l=e[s];if(typeof l==\"string\"){var u=l.match(/^[xy]*/)[0],c=u.length;l=+l.slice(c);for(var f=u.charAt(0)===\"y\"?n:1/n,h=0;h<c;h++)l*=f}i[s]=l}return i}LM.enforce=function(t){var r=t._fullLayout,n=r._axisConstraintGroups||[],i,a,o,s,l,u,c,f;for(i=0;i<n.length;i++){o=jft(n[i],r);var h=Object.keys(o),d=1/0,v=0,m=1/0,b={},p={},k=!1;for(a=0;a<h.length;a++)s=h[a],p[s]=l=r[Lg(s)],l._inputDomain?l.domain=l._inputDomain.slice():l._inputDomain=l.domain.slice(),l._inputRange||(l._inputRange=l.range.slice()),l.setScale(),b[s]=u=Math.abs(l._m)/o[s],d=Math.min(d,u),(l.constrain===\"domain\"||!l._constraintShrinkable)&&(m=Math.min(m,u)),delete l._constraintShrinkable,v=Math.max(v,u),l.constrain===\"domain\"&&(k=!0);if(!(d>Vft*v&&!k)){for(a=0;a<h.length;a++)if(s=h[a],u=b[s],l=p[s],c=l.constrain,u!==m||c===\"domain\")if(f=u/m,c===\"range\")Pce(l,f);else{var M=l._inputDomain,T=(l.domain[1]-l.domain[0])/(M[1]-M[0]),L=(l.r2l(l.range[1])-l.r2l(l.range[0]))/(l.r2l(l._inputRange[1])-l.r2l(l._inputRange[0]));if(f/=T,f*L<1){l.domain=l._input.domain=M.slice(),Pce(l,f);continue}if(L<1&&(l.range=l._input.range=l._inputRange.slice(),f*=L),l.autorange){var x=l.r2l(l.range[0]),C=l.r2l(l.range[1]),S=(x+C)/2,g=S,P=S,E=Math.abs(C-S),z=S-E*f*1.0001,q=S+E*f*1.0001,V=bN.makePadFn(r,l,0),G=bN.makePadFn(r,l,1);Rce(l,f);var Z=Math.abs(l._m),j=bN.concatExtremes(t,l),N=j.min,H=j.max,re,ae;for(ae=0;ae<N.length;ae++)re=N[ae].val-V(N[ae])/Z,re>z&&re<g&&(g=re);for(ae=0;ae<H.length;ae++)re=H[ae].val+G(H[ae])/Z,re<q&&re>P&&(P=re);var _e=(P-g)/(2*E);f/=_e,g=l.l2r(g),P=l.l2r(P),l.range=l._input.range=x<C?[g,P]:[P,g]}Rce(l,f)}}}};LM.getAxisGroup=function(t,r){for(var n=t._axisMatchGroups,i=0;i<n.length;i++){var a=n[i];if(a[r])return\"g\"+i}return r};LM.clean=function(t,r){if(r._inputDomain){for(var n=!1,i=r._id,a=t._fullLayout._axisConstraintGroups,o=0;o<a.length;o++)if(a[o][i]){n=!0;break}(!n||r.constrain!==\"domain\")&&(r._input.domain=r.domain=r._inputDomain,delete r._inputDomain)}};function Rce(e,t){var r=e._inputDomain,n=Gft[e.constraintoward],i=r[0]+(r[1]-r[0])*n;e.domain=e._input.domain=[i+(r[0]-i)/t,i+(r[1]-i)/t],e.setScale()}});var RM=ye(pd=>{\"use 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ce=Le._lw/2;if(Le._id.charAt(0)===\"x\"){if(me){if(Pe===\"top\")return me._offset-n-ce}else return r.t+r.h*(1-(Le.position||0))+ce%1;return me._offset+me._length+n+ce}if(me){if(Pe===\"right\")return me._offset+me._length+n+ce}else return r.l+r.w*(Le.position||0)+ce%1;return me._offset-n-ce}for(a=0;a<i.length;a++){l=i[a];var h=l._anchorAxis;l._linepositions={},l._lw=lT.crispRound(e,l.linewidth,1),l._mainLinePosition=f(l,h,l.side),l._mainMirrorPosition=l.mirror&&h?f(l,h,zy.OPPOSITE_SIDE[l.side]):null}var d=[],v=[],m=[],b=PM.opacity(t.paper_bgcolor)===1&&PM.opacity(t.plot_bgcolor)===1&&t.paper_bgcolor===t.plot_bgcolor;for(o in t._plots)if(s=t._plots[o],s.mainplot)s.bg&&s.bg.remove(),s.bg=void 0;else{var p=s.xaxis.domain,k=s.yaxis.domain,M=s.plotgroup;if(Kft(p,k,m)&&o.indexOf(Yft)===-1){var T=M.node(),L=s.bg=M0.ensureSingle(M,\"rect\",\"bg\");T.insertBefore(L.node(),T.childNodes[0]),v.push(o)}else M.select(\"rect.bg\").remove(),m.push([p,k]),b||(d.push(o),v.push(o))}var x=t._bgLayer.selectAll(\".bg\").data(d);for(x.enter().append(\"rect\").classed(\"bg\",!0),x.exit().remove(),x.each(function(Le){t._plots[Le].bg=wP.select(this)}),a=0;a<v.length;a++)s=t._plots[v[a]],u=s.xaxis,c=s.yaxis,s.bg&&u._offset!==void 0&&c._offset!==void 0&&s.bg.call(lT.setRect,u._offset-n,c._offset-n,u._length+2*n,c._length+2*n).call(PM.fill,t.plot_bgcolor).style(\"stroke-width\",0);if(!t._hasOnlyLargeSploms)for(o in t._plots){s=t._plots[o],u=s.xaxis,c=s.yaxis;var C=s.clipId=\"clip\"+t._uid+o+\"plot\",S=M0.ensureSingleById(t._clips,\"clipPath\",C,function(Le){Le.classed(\"plotclip\",!0).append(\"rect\")});s.clipRect=S.select(\"rect\").attr({width:u._length,height:c._length}),lT.setTranslate(s.plot,u._offset,c._offset);var g,P;s._hasClipOnAxisFalse?(g=null,P=C):(g=C,P=null),lT.setClipUrl(s.plot,g,e),s.layerClipId=P}var E,z,q,V,G,Z,j,N,H,re,ae,_e,Ee;function Ce(Le){return\"M\"+E+\",\"+Le+\"H\"+z}function ge(Le){return\"M\"+u._offset+\",\"+Le+\"h\"+u._length}function ie(Le){return\"M\"+Le+\",\"+N+\"V\"+j}function Se(Le){return c._shift!==void 0&&(Le+=c._shift),\"M\"+Le+\",\"+c._offset+\"v\"+c._length}function ke(Le,me,Pe){if(!Le.showline||o!==Le._mainSubplot)return\"\";if(!Le._anchorAxis)return Pe(Le._mainLinePosition);var ce=me(Le._mainLinePosition);return Le.mirror&&(ce+=me(Le._mainMirrorPosition)),ce}for(o in t._plots){s=t._plots[o],u=s.xaxis,c=s.yaxis;var be=\"M0,0\";qce(u,o)&&(G=bP(u,\"left\",c,i),E=u._offset-(G?n+G:0),Z=bP(u,\"right\",c,i),z=u._offset+u._length+(Z?n+Z:0),q=f(u,c,\"bottom\"),V=f(u,c,\"top\"),Ee=!u._anchorAxis||o!==u._mainSubplot,Ee&&(u.mirror===\"allticks\"||u.mirror===\"all\")&&(u._linepositions[o]=[q,V]),be=ke(u,Ce,ge),Ee&&u.showline&&(u.mirror===\"all\"||u.mirror===\"allticks\")&&(be+=Ce(q)+Ce(V)),s.xlines.style(\"stroke-width\",u._lw+\"px\").call(PM.stroke,u.showline?u.linecolor:\"rgba(0,0,0,0)\")),s.xlines.attr(\"d\",be);var Be=\"M0,0\";qce(c,o)&&(ae=bP(c,\"bottom\",u,i),j=c._offset+c._length+(ae?n:0),_e=bP(c,\"top\",u,i),N=c._offset-(_e?n:0),H=f(c,u,\"left\"),re=f(c,u,\"right\"),Ee=!c._anchorAxis||o!==c._mainSubplot,Ee&&(c.mirror===\"allticks\"||c.mirror===\"all\")&&(c._linepositions[o]=[H,re]),Be=ke(c,ie,Se),Ee&&c.showline&&(c.mirror===\"all\"||c.mirror===\"allticks\")&&(Be+=ie(H)+ie(re)),s.ylines.style(\"stroke-width\",c._lw+\"px\").call(PM.stroke,c.showline?c.linecolor:\"rgba(0,0,0,0)\")),s.ylines.attr(\"d\",Be)}return IM.makeClipPaths(e),Jp.previousPromises(e)}function qce(e,t){return(e.ticks||e.showline)&&(t===e._mainSubplot||e.mirror===\"all\"||e.mirror===\"allticks\")}function Oce(e,t,r){if(!r.showline||!r._lw)return!1;if(r.mirror===\"all\"||r.mirror===\"allticks\")return!0;var n=r._anchorAxis;if(!n)return!1;var i=zy.FROM_BL[t];return r.side===t?n.domain[i]===e.domain[i]:r.mirror&&n.domain[1-i]===e.domain[1-i]}function bP(e,t,r,n){if(Oce(e,t,r))return r._lw;for(var i=0;i<n.length;i++){var a=n[i];if(a._mainAxis===r._mainAxis&&Oce(e,t,a))return a._lw}return 0}pd.drawMainTitle=function(e){var t=e._fullLayout.title,r=e._fullLayout,n=nht(r),i=aht(r),a=iht(r,i),o=rht(r,n);if(Fce.draw(e,\"gtitle\",{propContainer:r,propName:\"title.text\",subtitlePropName:\"title.subtitle.text\",placeholder:r._dfltTitle.plot,subtitlePlaceholder:r._dfltTitle.subtitle,attributes:{x:o,y:a,\"text-anchor\":n,dy:i}}),t.text&&t.automargin){var s=wP.select(e).selectAll(\".gtitle\"),l=lT.bBox(wP.select(e).selectAll(\".g-gtitle\").node()).height,u=eht(e,t,l);if(u>0){tht(e,a,u,l),s.attr({x:o,y:a,\"text-anchor\":n,dy:Bce(t.yanchor)}).call(AN.positionText,o,a);var c=(t.text.match(AN.BR_TAG_ALL)||[]).length;if(c){var f=zy.LINE_SPACING*c+zy.MID_SHIFT;t.y===0&&(f=-f),s.selectAll(\".line\").each(function(){var b=+this.getAttribute(\"dy\").slice(0,-2)-f+\"em\";this.setAttribute(\"dy\",b)})}var h=wP.select(e).selectAll(\".gtitle-subtitle\");if(h.node()){var d=s.node().getBBox(),v=d.y+d.height,m=v+Fce.SUBTITLE_PADDING_EM*t.subtitle.font.size;h.attr({x:o,y:m,\"text-anchor\":n,dy:Bce(t.yanchor)}).call(AN.positionText,o,m)}}}};function $ft(e,t,r,n,i){var a=t.yref===\"paper\"?e._fullLayout._size.h:e._fullLayout.height,o=M0.isTopAnchor(t)?n:n-i,s=r===\"b\"?a-o:o;return M0.isTopAnchor(t)&&r===\"t\"||M0.isBottomAnchor(t)&&r===\"b\"?!1:s<i}function Qft(e,t,r,n,i){var a=0;return r===\"middle\"&&(a+=i/2),e===\"t\"?(r===\"top\"&&(a+=i),a+=n-t*n):(r===\"bottom\"&&(a+=i),a+=t*n),a}function eht(e,t,r){var n=t.y,i=t.yanchor,a=n>.5?\"t\":\"b\",o=e._fullLayout.margin[a],s=0;return t.yref===\"paper\"?s=r+t.pad.t+t.pad.b:t.yref===\"container\"&&(s=Qft(a,n,i,e._fullLayout.height,r)+t.pad.t+t.pad.b),s>o?s:0}function tht(e,t,r,n){var i=\"title.automargin\",a=e._fullLayout.title,o=a.y>.5?\"t\":\"b\",s={x:a.x,y:a.y,t:0,b:0},l={};a.yref===\"paper\"&&$ft(e,a,o,t,n)?s[o]=r:a.yref===\"container\"&&(l[o]=r,e._fullLayout._reservedMargin[i]=l),Jp.allowAutoMargin(e,i),Jp.autoMargin(e,i,s)}function rht(e,t){var r=e.title,n=e._size,i=0;return t===Vce?i=r.pad.l:t===Gce&&(i=-r.pad.r),r.xref===\"paper\"?n.l+n.w*r.x+i:e.width*r.x+i}function iht(e,t){var r=e.title,n=e._size,i=0;return t===\"0em\"||!t?i=-r.pad.b:t===zy.CAP_SHIFT+\"em\"&&(i=r.pad.t),r.y===\"auto\"?n.t/2:r.yref===\"paper\"?n.t+n.h-n.h*r.y+i:e.height-e.height*r.y+i}function Bce(e){return e===\"top\"?zy.CAP_SHIFT+.3+\"em\":e===\"bottom\"?\"-0.3em\":zy.MID_SHIFT+\"em\"}function nht(e){var t=e.title,r=Zft;return M0.isRightAnchor(t)?r=Gce:M0.isLeftAnchor(t)&&(r=Vce),r}function aht(e){var t=e.title,r=\"0em\";return M0.isTopAnchor(t)?r=zy.CAP_SHIFT+\"em\":M0.isMiddleAnchor(t)&&(r=zy.MID_SHIFT+\"em\"),r}pd.doTraceStyle=function(e){var t=e.calcdata,r=[],n;for(n=0;n<t.length;n++){var i=t[n],a=i[0]||{},o=a.trace||{},s=o._module||{},l=s.arraysToCalcdata;l&&l(i,o);var u=s.editStyle;u&&r.push({fn:u,cd0:a})}if(r.length){for(n=0;n<r.length;n++){var c=r[n];c.fn(e,c.cd0)}SN(e),pd.redrawReglTraces(e)}return Jp.style(e),Nv.getComponentMethod(\"legend\",\"draw\")(e),Jp.previousPromises(e)};pd.doColorBars=function(e){return Nv.getComponentMethod(\"colorbar\",\"draw\")(e),Jp.previousPromises(e)};pd.layoutReplot=function(e){var t=e.layout;return e.layout=void 0,Nv.call(\"_doPlot\",e,\"\",t)};pd.doLegend=function(e){return Nv.getComponentMethod(\"legend\",\"draw\")(e),Jp.previousPromises(e)};pd.doTicksRelayout=function(e){return IM.draw(e,\"redraw\"),e._fullLayout._hasOnlyLargeSploms&&(Nv.subplotsRegistry.splom.updateGrid(e),SN(e),pd.redrawReglTraces(e)),pd.drawMainTitle(e),Jp.previousPromises(e)};pd.doModeBar=function(e){var t=e._fullLayout;Nce.manage(e);for(var r=0;r<t._basePlotModules.length;r++){var n=t._basePlotModules[r].updateFx;n&&n(e)}return Jp.previousPromises(e)};pd.doCamera=function(e){for(var t=e._fullLayout,r=t._subplots.gl3d,n=0;n<r.length;n++){var i=t[r[n]],a=i._scene;a.setViewport(i)}};pd.drawData=function(e){var t=e._fullLayout;SN(e);for(var r=t._basePlotModules,n=0;n<r.length;n++)r[n].plot(e);return pd.redrawReglTraces(e),Jp.style(e),Nv.getComponentMethod(\"selections\",\"draw\")(e),Nv.getComponentMethod(\"shapes\",\"draw\")(e),Nv.getComponentMethod(\"annotations\",\"draw\")(e),Nv.getComponentMethod(\"images\",\"draw\")(e),t._replotting=!1,Jp.previousPromises(e)};pd.redrawReglTraces=function(e){var t=e._fullLayout;if(t._has(\"regl\")){var r=e._fullData,n=[],i=[],a,o;for(t._hasOnlyLargeSploms&&t._splomGrid.draw(),a=0;a<r.length;a++){var s=r[a];s.visible===!0&&s._length!==0&&(s.type===\"splom\"?t._splomScenes[s.uid].draw():s.type===\"scattergl\"?M0.pushUnique(n,s.xaxis+s.yaxis):s.type===\"scatterpolargl\"&&M0.pushUnique(i,s.subplot))}for(a=0;a<n.length;a++)o=t._plots[n[a]],o._scene&&o._scene.draw();for(a=0;a<i.length;a++)o=t[i[a]]._subplot,o._scene&&o._scene.draw()}};pd.doAutoRangeAndConstraints=function(e){for(var t=IM.list(e,\"\",!0),r,n={},i=0;i<t.length;i++)if(r=t[i],!n[r._id]){n[r._id]=1,Xft(e,r),zce(e,r);var a=r._matchGroup;if(a)for(var o in a){var s=IM.getFromId(e,o);zce(e,s,r.range),n[o]=1}}Wft(e)};pd.finalDraw=function(e){Nv.getComponentMethod(\"rangeslider\",\"draw\")(e),Nv.getComponentMethod(\"rangeselector\",\"draw\")(e)};pd.drawMarginPushers=function(e){Nv.getComponentMethod(\"legend\",\"draw\")(e),Nv.getComponentMethod(\"rangeselector\",\"draw\")(e),Nv.getComponentMethod(\"sliders\",\"draw\")(e),Nv.getComponentMethod(\"updatemenus\",\"draw\")(e),Nv.getComponentMethod(\"colorbar\",\"draw\")(e)}});var kN=ye((Dor,Zce)=>{\"use strict\";var oht=y_().readPaths,sht=cP(),Hce=s_().clearOutlineControllers,MN=ka(),jce=So(),lht=vl().arrayEditor,Wce=b_(),uht=Wce.getPathString;Zce.exports={draw:TP,drawOne:Xce,activateLastSelection:hht};function TP(e){var t=e._fullLayout;Hce(e),t._selectionLayer.selectAll(\"path\").remove();for(var r in t._plots){var n=t._plots[r].selectionLayer;n&&n.selectAll(\"path\").remove()}for(var i=0;i<t.selections.length;i++)Xce(e,i)}function AP(e){return e._context.editSelection}function Xce(e,t){e._fullLayout._paperdiv.selectAll('.selectionlayer [data-index=\"'+t+'\"]').remove();var r=Wce.makeSelectionsOptionsAndPlotinfo(e,t),n=r.options,i=r.plotinfo;if(!n._input)return;a(e._fullLayout._selectionLayer);function a(o){var s=uht(e,n),l={\"data-index\":t,\"fill-rule\":\"evenodd\",d:s},u=n.opacity,c=\"rgba(0,0,0,0)\",f=n.line.color||MN.contrast(e._fullLayout.plot_bgcolor),h=n.line.width,d=n.line.dash;h||(h=5,d=\"solid\");var v=AP(e)&&e._fullLayout._activeSelectionIndex===t;v&&(c=e._fullLayout.activeselection.fillcolor,u=e._fullLayout.activeselection.opacity);for(var m=[],b=1;b>=0;b--){var p=o.append(\"path\").attr(l).style(\"opacity\",b?.1:u).call(MN.stroke,f).call(MN.fill,c).call(jce.dashLine,b?\"solid\":d,b?4+h:h);if(cht(p,e,n),v){var k=lht(e.layout,\"selections\",n);p.style({cursor:\"move\"});var M={element:p.node(),plotinfo:i,gd:e,editHelpers:k,isActiveSelection:!0},T=oht(s,e);sht(T,p,M)}else p.style(\"pointer-events\",b?\"all\":\"none\");m[b]=p}var L=m[0],x=m[1];x.node().addEventListener(\"click\",function(){return fht(e,L)})}}function cht(e,t,r){var n=r.xref+r.yref;jce.setClipUrl(e,\"clip\"+t._fullLayout._uid+n,t)}function fht(e,t){if(AP(e)){var r=t.node(),n=+r.getAttribute(\"data-index\");if(n>=0){if(n===e._fullLayout._activeSelectionIndex){EN(e);return}e._fullLayout._activeSelectionIndex=n,e._fullLayout._deactivateSelection=EN,TP(e)}}}function hht(e){if(AP(e)){var t=e._fullLayout.selections.length-1;e._fullLayout._activeSelectionIndex=t,e._fullLayout._deactivateSelection=EN,TP(e)}}function EN(e){if(AP(e)){var t=e._fullLayout._activeSelectionIndex;t>=0&&(Hce(e),delete e._fullLayout._activeSelectionIndex,TP(e))}}});var Kce=ye((For,Yce)=>{function dht(){var e,t=0,r=!1;function n(i,a){return e.list.push({type:i,data:a?JSON.parse(JSON.stringify(a)):void 0}),e}return e={list:[],segmentId:function(){return t++},checkIntersection:function(i,a){return n(\"check\",{seg1:i,seg2:a})},segmentChop:function(i,a){return n(\"div_seg\",{seg:i,pt:a}),n(\"chop\",{seg:i,pt:a})},statusRemove:function(i){return n(\"pop_seg\",{seg:i})},segmentUpdate:function(i){return n(\"seg_update\",{seg:i})},segmentNew:function(i,a){return n(\"new_seg\",{seg:i,primary:a})},segmentRemove:function(i){return n(\"rem_seg\",{seg:i})},tempStatus:function(i,a,o){return n(\"temp_status\",{seg:i,above:a,below:o})},rewind:function(i){return n(\"rewind\",{seg:i})},status:function(i,a,o){return n(\"status\",{seg:i,above:a,below:o})},vert:function(i){return i===r?e:(r=i,n(\"vert\",{x:i}))},log:function(i){return typeof i!=\"string\"&&(i=JSON.stringify(i,!1,\"  \")),n(\"log\",{txt:i})},reset:function(){return n(\"reset\")},selected:function(i){return n(\"selected\",{segs:i})},chainStart:function(i){return n(\"chain_start\",{seg:i})},chainRemoveHead:function(i,a){return n(\"chain_rem_head\",{index:i,pt:a})},chainRemoveTail:function(i,a){return n(\"chain_rem_tail\",{index:i,pt:a})},chainNew:function(i,a){return n(\"chain_new\",{pt1:i,pt2:a})},chainMatch:function(i){return n(\"chain_match\",{index:i})},chainClose:function(i){return n(\"chain_close\",{index:i})},chainAddHead:function(i,a){return n(\"chain_add_head\",{index:i,pt:a})},chainAddTail:function(i,a){return n(\"chain_add_tail\",{index:i,pt:a})},chainConnect:function(i,a){return n(\"chain_con\",{index1:i,index2:a})},chainReverse:function(i){return n(\"chain_rev\",{index:i})},chainJoin:function(i,a){return n(\"chain_join\",{index1:i,index2:a})},done:function(){return n(\"done\")}},e}Yce.exports=dht});var $ce=ye((zor,Jce)=>{function vht(e){typeof e!=\"number\"&&(e=1e-10);var t={epsilon:function(r){return typeof r==\"number\"&&(e=r),e},pointAboveOrOnLine:function(r,n,i){var a=n[0],o=n[1],s=i[0],l=i[1],u=r[0],c=r[1];return(s-a)*(c-o)-(l-o)*(u-a)>=-e},pointBetween:function(r,n,i){var a=r[1]-n[1],o=i[0]-n[0],s=r[0]-n[0],l=i[1]-n[1],u=s*o+a*l;if(u<e)return!1;var c=o*o+l*l;return!(u-c>-e)},pointsSameX:function(r,n){return Math.abs(r[0]-n[0])<e},pointsSameY:function(r,n){return Math.abs(r[1]-n[1])<e},pointsSame:function(r,n){return t.pointsSameX(r,n)&&t.pointsSameY(r,n)},pointsCompare:function(r,n){return t.pointsSameX(r,n)?t.pointsSameY(r,n)?0:r[1]<n[1]?-1:1:r[0]<n[0]?-1:1},pointsCollinear:function(r,n,i){var a=r[0]-n[0],o=r[1]-n[1],s=n[0]-i[0],l=n[1]-i[1];return Math.abs(a*l-s*o)<e},linesIntersect:function(r,n,i,a){var o=n[0]-r[0],s=n[1]-r[1],l=a[0]-i[0],u=a[1]-i[1],c=o*u-s*l;if(Math.abs(c)<e)return!1;var f=r[0]-i[0],h=r[1]-i[1],d=(l*h-u*f)/c,v=(o*h-s*f)/c,m={alongA:0,alongB:0,pt:[r[0]+d*o,r[1]+d*s]};return d<=-e?m.alongA=-2:d<e?m.alongA=-1:d-1<=-e?m.alongA=0:d-1<e?m.alongA=1:m.alongA=2,v<=-e?m.alongB=-2:v<e?m.alongB=-1:v-1<=-e?m.alongB=0:v-1<e?m.alongB=1:m.alongB=2,m},pointInsideRegion:function(r,n){for(var i=r[0],a=r[1],o=n[n.length-1][0],s=n[n.length-1][1],l=!1,u=0;u<n.length;u++){var c=n[u][0],f=n[u][1];f-a>e!=s-a>e&&(o-c)*(a-f)/(s-f)+c-i>e&&(l=!l),o=c,s=f}return l}};return t}Jce.exports=vht});var efe=ye((qor,Qce)=>{var pht={create:function(){var e={root:{root:!0,next:null},exists:function(t){return!(t===null||t===e.root)},isEmpty:function(){return e.root.next===null},getHead:function(){return e.root.next},insertBefore:function(t,r){for(var n=e.root,i=e.root.next;i!==null;){if(r(i)){t.prev=i.prev,t.next=i,i.prev.next=t,i.prev=t;return}n=i,i=i.next}n.next=t,t.prev=n,t.next=null},findTransition:function(t){for(var r=e.root,n=e.root.next;n!==null&&!t(n);)r=n,n=n.next;return{before:r===e.root?null:r,after:n,insert:function(i){return i.prev=r,i.next=n,r.next=i,n!==null&&(n.prev=i),i}}}};return e},node:function(e){return e.prev=null,e.next=null,e.remove=function(){e.prev.next=e.next,e.next&&(e.next.prev=e.prev),e.prev=null,e.next=null},e}};Qce.exports=pht});var rfe=ye((Oor,tfe)=>{var DM=efe();function ght(e,t,r){function n(v,m){return{id:r?r.segmentId():-1,start:v,end:m,myFill:{above:null,below:null},otherFill:null}}function i(v,m,b){return{id:r?r.segmentId():-1,start:v,end:m,myFill:{above:b.myFill.above,below:b.myFill.below},otherFill:null}}var a=DM.create();function o(v,m,b,p,k,M){var T=t.pointsCompare(m,k);return T!==0?T:t.pointsSame(b,M)?0:v!==p?v?1:-1:t.pointAboveOrOnLine(b,p?k:M,p?M:k)?1:-1}function 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Ee=t.linesIntersect(H,re,ae,_e);if(Ee===!1){if(!t.pointsCollinear(H,re,ae)||t.pointsSame(H,_e)||t.pointsSame(re,ae))return!1;var Ce=t.pointsSame(H,ae),ge=t.pointsSame(re,_e);if(Ce&&ge)return Z;var ie=!Ce&&t.pointBetween(H,ae,_e),Se=!ge&&t.pointBetween(re,ae,_e);if(Ce)return Se?h(Z,re):h(G,_e),Z;ie&&(ge||(Se?h(Z,re):h(G,_e)),h(Z,H))}else Ee.alongA===0&&(Ee.alongB===-1?h(G,ae):Ee.alongB===0?h(G,Ee.pt):Ee.alongB===1&&h(G,_e)),Ee.alongB===0&&(Ee.alongA===-1?h(Z,H):Ee.alongA===0?h(Z,Ee.pt):Ee.alongA===1&&h(Z,re));return!1}for(var T=[];!a.isEmpty();){var L=a.getHead();if(r&&r.vert(L.pt[0]),L.isStart){let G=function(){if(C){var Z=M(L,C);if(Z)return Z}return S?M(L,S):!1};var V=G;r&&r.segmentNew(L.seg,L.primary);var x=k(L),C=x.before?x.before.ev:null,S=x.after?x.after.ev:null;r&&r.tempStatus(L.seg,C?C.seg:!1,S?S.seg:!1);var g=G();if(g){if(e){var P;L.seg.myFill.below===null?P=!0:P=L.seg.myFill.above!==L.seg.myFill.below,P&&(g.seg.myFill.above=!g.seg.myFill.above)}else 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Z=n[V],j=n[G],N=Z[Z.length-1],H=Z[Z.length-2],re=j[0],ae=j[1];t.pointsCollinear(H,N,re)&&(r&&r.chainRemoveTail(V,N),Z.pop(),N=H),t.pointsCollinear(N,re,ae)&&(r&&r.chainRemoveHead(G,re),j.shift()),r&&r.chainJoin(V,G),n[V]=Z.concat(j),n.splice(G,1)}var E=l.index,z=u.index;r&&r.chainConnect(E,z);var q=n[E].length<n[z].length;l.matches_head?u.matches_head?q?(g(E),P(E,z)):(g(z),P(z,E)):P(z,E):u.matches_head?P(E,z):q?(g(E),P(z,E)):(g(z),P(E,z))}),i}ife.exports=mht});var ofe=ye((Nor,afe)=>{function FM(e,t,r){var n=[];return e.forEach(function(i){var a=(i.myFill.above?8:0)+(i.myFill.below?4:0)+(i.otherFill&&i.otherFill.above?2:0)+(i.otherFill&&i.otherFill.below?1:0);t[a]!==0&&n.push({id:r?r.segmentId():-1,start:i.start,end:i.end,myFill:{above:t[a]===1,below:t[a]===2},otherFill:null})}),r&&r.selected(n),n}var yht={union:function(e,t){return FM(e,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],t)},intersect:function(e,t){return FM(e,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],t)},difference:function(e,t){return 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t=[],r=PN(e[0])?0:e[0][0][0],n=r,i=PN(e[0])?0:e[0][0][1],a=i,o=0;o<e.length;o++)if(PN(e[o]))t.push(Uht(e[o]));else{var s=FN(e[o]);s.subtract=!!e[o].subtract,t.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}function l(u,c,f,h){for(var d=!1,v=0;v<t.length;v++)t[v].contains(u,c,f,h)&&(d=!t[v].subtract);return d}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:l,isRect:!1,degenerate:!1}}function Lfe(e,t,r){var n=t._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(e.shiftKey||e.altKey)&&!(GM(a)&&RN(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):(!s||!i.selection)&&HM(r),o||(Mfe(t),n._lastSelectedSubplot=i.id)}function Vht(e){return e._fullLayout._activeShapeIndex>=0}function Ght(e){return e._fullLayout._activeSelectionIndex>=0}function HM(e,t){var r=e.dragmode,n=e.plotinfo,i=e.gd;Vht(i)&&i._fullLayout._deactivateShape(i),Ght(i)&&i._fullLayout._deactivateSelection(i);var a=i._fullLayout,o=a._zoomlayer,s=GM(r),l=DN(r);if(s||l){var u=o.selectAll(\".select-outline-\"+n.id);if(u&&i._fullLayout._outlining){var c;s&&(c=Lht(u,e)),c&&VM.call(\"_guiRelayout\",i,{shapes:c});var f;l&&!qN(e)&&(f=Pht(u,e)),f&&(i._fullLayout._noEmitSelectedAtStart=!0,VM.call(\"_guiRelayout\",i,{selections:f}).then(function(){t&&Iht(i)})),i._fullLayout._outlining=!1}}n.selection={},n.selection.selectionDefs=e.selectionDefs=[],n.selection.mergedPolygons=e.mergedPolygons=[]}function wfe(e){return e._id}function LP(e,t,r,n){if(!e.calcdata)return[];var i=[],a=t.map(wfe),o=r.map(wfe),s,l,u;for(u=0;u<e.calcdata.length;u++)if(s=e.calcdata[u],l=s[0].trace,!(l.visible!==!0||!l._module||!l._module.selectPoints))if(qN({subplot:n})&&(l.subplot===n||l.geo===n))i.push(EP(l._module,s,t[0],r[0]));else if(l.type===\"splom\"){if(l._xaxes[a[0]]&&l._yaxes[o[0]]){var c=EP(l._module,s,t[0],r[0]);c.scene=e._fullLayout._splomScenes[l.uid],i.push(c)}}else if(l.type===\"sankey\"){var f=EP(l._module,s,t[0],r[0]);i.push(f)}else{if(a.indexOf(l.xaxis)===-1&&(!l._xA||!l._xA.overlaying)||o.indexOf(l.yaxis)===-1&&(!l._yA||!l._yA.overlaying))continue;i.push(EP(l._module,s,k0(e,l.xaxis),k0(e,l.yaxis)))}return i}function EP(e,t,r,n){return{_module:e,cd:t,xaxis:r,yaxis:n}}function Hht(e){return e&&Array.isArray(e)&&e[0].hoverOnBox!==!0}function jht(e,t){var r=e[0],n=-1,i=[],a,o;for(o=0;o<t.length;o++)if(a=t[o],r.fullData.index===a.cd[0].trace.index){if(r.hoverOnBox===!0)break;r.pointNumber!==void 0?n=r.pointNumber:r.binNumber!==void 0&&(n=r.binNumber,i=r.pointNumbers);break}return{pointNumber:n,pointNumbers:i,searchInfo:a}}function Tfe(e){var t=e.searchInfo.cd[0].trace,r=e.pointNumber,n=e.pointNumbers,i=n.length>0,a=i?n[0]:r;return t.selectedpoints?t.selectedpoints.indexOf(a)>-1:!1}function Wht(e,t){var r=[],n,i,a,o;for(o=0;o<e.length;o++)n=e[o],n.cd[0].trace.selectedpoints&&n.cd[0].trace.selectedpoints.length>0&&r.push(n);if(r.length===1&&(a=r[0]===t.searchInfo,a&&(i=t.searchInfo.cd[0].trace,i.selectedpoints.length===t.pointNumbers.length))){for(o=0;o<t.pointNumbers.length;o++)if(i.selectedpoints.indexOf(t.pointNumbers[o])<0)return!1;return!0}return!1}function Xht(e){var t=0,r,n,i;for(i=0;i<e.length;i++)if(r=e[i],n=r.cd[0].trace,n.selectedpoints&&(n.selectedpoints.length>1||(t+=n.selectedpoints.length,t>1)))return!1;return t===1}function jM(e,t,r){var n;for(n=0;n<t.length;n++){var i=t[n].cd[0].trace._fullInput,a=e._fullLayout._tracePreGUI[i.uid]||{};a.selectedpoints===void 0&&(a.selectedpoints=i._input.selectedpoints||null)}var o;if(r){var s=r.points||[];for(n=0;n<t.length;n++)o=t[n].cd[0].trace,o._input.selectedpoints=o._fullInput.selectedpoints=[],o._fullInput!==o&&(o.selectedpoints=[]);for(var l=0;l<s.length;l++){var u=s[l],c=u.data,f=u.fullData,h=u.pointIndex,d=u.pointIndices;d?([].push.apply(c.selectedpoints,d),o._fullInput!==o&&[].push.apply(f.selectedpoints,d)):(c.selectedpoints.push(h),o._fullInput!==o&&f.selectedpoints.push(h))}}else for(n=0;n<t.length;n++)o=t[n].cd[0].trace,delete o.selectedpoints,delete o._input.selectedpoints,o._fullInput!==o&&delete o._fullInput.selectedpoints;Zht(e,t)}function Zht(e,t){for(var r=!1,n=0;n<t.length;n++){var i=t[n],a=i.cd;VM.traceIs(a[0].trace,\"regl\")&&(r=!0);var o=i._module,s=o.styleOnSelect||o.style;s&&(s(e,a,a[0].node3),a[0].nodeRangePlot3&&s(e,a,a[0].nodeRangePlot3))}r&&(Dht(e),Fht(e))}function Pfe(e,t,r){for(var n=r?yfe.difference:yfe.union,i=n({regions:e},{regions:[t]}),a=i.regions.reverse(),o=0;o<a.length;o++){var s=a[o];s.subtract=BN(s,a.slice(0,o))}return a}function Ife(e,t){if(Array.isArray(e))for(var r=t.cd,n=t.cd[0].trace,i=0;i<e.length;i++)e[i]=Mht(e[i],n,r);return e}function Rfe(e,t){for(var r=[],n=0;n<e.length;n++){r[n]=[];for(var 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G=z[V],Z=G.indexOf(\"y\"),j=G.slice(0,Z),N=G.slice(Z),H=s&&l?r:void 0;if(H=Jht(f,j,N,H),H){var re=n;if(!a){var ae=k0(e,j,\"x\"),_e=k0(e,N,\"y\");re=LP(e,[ae],[_e],G);for(var Ee=0;Ee<re.length;Ee++){var Ce=re[Ee],ge=Ce.cd[0],ie=ge.trace;if(Ce._module.name===\"scattergl\"&&!ge.t.xpx){var Se=ie.x,ke=ie.y,be=ie._length;ge.t.xpx=[],ge.t.ypx=[];for(var Be=0;Be<be;Be++)ge.t.xpx[Be]=ae.c2p(Se[Be]),ge.t.ypx[Be]=_e.c2p(ke[Be])}Ce._module.name===\"splom\"&&(q[ie.uid]||(q[ie.uid]=!0))}}var Le=Dfe(H,re);u=u.concat(Le),c=c.concat(re)}}var me={points:u};jM(e,c,me);var Pe=h.clickmode,ce=Pe.indexOf(\"event\")>-1&&t;if(!o&&t){var He=Afe(e,!0);if(He.length){var lt=He[0].xref,mt=He[0].yref;if(lt&&mt){var Ht=zfe(He),at=qfe([k0(e,lt,\"x\"),k0(e,mt,\"y\")]);at(me,Ht)}}e._fullLayout._noEmitSelectedAtStart?e._fullLayout._noEmitSelectedAtStart=!1:ce&&WM(e,me),h._reselect=!1}if(!o&&h._deselect){var ct=h._deselect;s=ct.xref,l=ct.yref,Kht(s,l,c)||Ffe(e,s,l,n),ce&&(me.points.length?WM(e,me):NN(e)),h._deselect=!1}return{eventData:me,selectionTesters:r}}function Yht(e){var t=e.calcdata;if(t)for(var r=0;r<t.length;r++){var n=t[r][0],i=n.trace,a=e._fullLayout._splomScenes;if(a){var o=a[i.uid];o&&(o.selectBatch=[])}}}function Kht(e,t,r){for(var n=0;n<r.length;n++){var i=r[n];if(i.xaxis&&i.xaxis._id===e&&i.yaxis&&i.yaxis._id===t)return!0}return!1}function Ffe(e,t,r,n){n=LP(e,[k0(e,t,\"x\")],[k0(e,r,\"y\")],t+r);for(var i=0;i<n.length;i++){var a=n[i];a._module.selectPoints(a,!1)}jM(e,n)}function Jht(e,t,r,n){for(var i,a=0;a<e.length;a++){var o=e[a];if(!(t!==o.xref||r!==o.yref))if(i){var s=!!o.subtract;i=Pfe(i,o,s),n=ON(i)}else i=[o],n=FN(o)}return n}function Afe(e,t){for(var r=[],n=e._fullLayout,i=n.selections,a=i.length,o=0;o<a;o++)if(!(t&&o!==n._activeSelectionIndex)){var s=i[o];if(s){var l=s.xref,u=s.yref,c=k0(e,l,\"x\"),f=k0(e,u,\"y\"),h,d,v,m,b;if(s.type===\"rect\"){b=[];var p=w_(c,s.x0),k=w_(c,s.x1),M=w_(f,s.y0),T=w_(f,s.y1);b=[[p,M],[p,T],[k,T],[k,M]],h=Math.min(p,k),d=Math.max(p,k),v=Math.min(M,T),m=Math.max(M,T),b.xmin=h,b.xmax=d,b.ymin=v,b.ymax=m,b.xref=l,b.yref=u,b.subtract=!1,b.isRect=!0,r.push(b)}else if(s.type===\"path\")for(var L=s.path.split(\"Z\"),x=[],C=0;C<L.length;C++){var S=L[C];if(S){S+=\"Z\";var g=_fe.extractPathCoords(S,xfe.paramIsX,\"raw\"),P=_fe.extractPathCoords(S,xfe.paramIsY,\"raw\");h=1/0,d=-1/0,v=1/0,m=-1/0,b=[];for(var E=0;E<g.length;E++){var z=w_(c,g[E]),q=w_(f,P[E]);b.push([z,q]),h=Math.min(z,h),d=Math.max(z,d),v=Math.min(q,v),m=Math.max(q,m)}b.xmin=h,b.xmax=d,b.ymin=v,b.ymax=m,b.xref=l,b.yref=u,b.subtract=BN(b,x),x.push(b),r.push(b)}}}}return r}function BN(e,t){for(var r=!1,n=0;n<t.length;n++)for(var i=t[n],a=0;a<e.length;a++)if(Tht(e[a],i)){r=!r;break}return r}function w_(e,t){return e.type===\"date\"&&(t=t.replace(\"_\",\" \")),e.type===\"log\"?e.c2p(t):e.r2p(t,null,e.calendar)}function zfe(e){for(var 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i=0,a=n._length,o=n.p2l(i+t)-n.p2l(i),s=n.p2l(a+t)-n.p2l(a),l=(o+s)/2;n.range=[n.l2r(n._rl[0]-l),n.l2r(n._rl[1]-l)]}else n.range=[n.l2r(n._rl[0]-t/n._m),n.l2r(n._rl[1]-t/n._m)];n.limitRange&&n.limitRange()}}}function Idt(e){return 1-(e>=0?Math.min(e,.9):1/(1/Math.max(e,-.3)+3.222))}function Rdt(e,t,r){return e?e===\"nsew\"?r?\"\":t===\"pan\"?\"move\":\"crosshair\":e.toLowerCase()+\"-resize\":\"pointer\"}function khe(e,t,r,n,i){return e.append(\"path\").attr(\"class\",\"zoombox\").style({fill:t>.2?\"rgba(0,0,0,0)\":\"rgba(255,255,255,0)\",\"stroke-width\":0}).attr(\"transform\",Ahe(r,n)).attr(\"d\",i+\"Z\")}function Che(e,t,r){return e.append(\"path\").attr(\"class\",\"zoombox-corners\").style({fill:ghe.background,stroke:ghe.defaultLine,\"stroke-width\":1,opacity:0}).attr(\"transform\",Ahe(t,r)).attr(\"d\",\"M0,0Z\")}function Lhe(e,t,r,n,i,a){e.attr(\"d\",n+\"M\"+r.l+\",\"+r.t+\"v\"+r.h+\"h\"+r.w+\"v-\"+r.h+\"h-\"+r.w+\"Z\"),Phe(e,t,i,a)}function 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0?e.onmousewheel=t:e.isAddedWheelEvent||(e.isAddedWheelEvent=!0,e.addEventListener(\"wheel\",t,{passive:!1}));else{var r=e.onwheel!==void 0?\"wheel\":\"mousewheel\";e._onwheel&&e.removeEventListener(r,e._onwheel),e._onwheel=t,e.addEventListener(r,t,{passive:!1})}}function The(e){var t=[];for(var r in e)t.push(e[r]);return t}Dhe.exports={makeDragBox:Ldt,makeDragger:Mhe,makeRectDragger:Ehe,makeZoombox:khe,makeCorners:Che,updateZoombox:Lhe,xyCorners:zP,transitionZoombox:Phe,removeZoombox:WN,showDoubleClickNotifier:Ihe,attachWheelEventHandler:Rhe}});var KN=ye(OP=>{\"use strict\";var zdt=Fa(),qP=ef(),qdt=yv(),Odt=Eg(),Ig=YN().makeDragBox,gd=Rh().DRAGGERSIZE;OP.initInteractions=function(t){var r=t._fullLayout;if(t._context.staticPlot){zdt.select(t).selectAll(\".drag\").remove();return}if(!(!r._has(\"cartesian\")&&!r._has(\"splom\"))){var n=Object.keys(r._plots||{}).sort(function(a,o){if((r._plots[a].mainplot&&!0)===(r._plots[o].mainplot&&!0)){var s=a.split(\"y\"),l=o.split(\"y\");return s[0]===l[0]?Number(s[1]||1)-Number(l[1]||1):Number(s[0]||1)-Number(l[0]||1)}return r._plots[a].mainplot?1:-1});n.forEach(function(a){var o=r._plots[a],s=o.xaxis,l=o.yaxis;if(!o.mainplot){var u=Ig(t,o,s._offset,l._offset,s._length,l._length,\"ns\",\"ew\");u.onmousemove=function(h){t._fullLayout._rehover=function(){t._fullLayout._hoversubplot===a&&t._fullLayout._plots[a]&&qP.hover(t,h,a)},qP.hover(t,h,a),t._fullLayout._lasthover=u,t._fullLayout._hoversubplot=a},u.onmouseout=function(h){t._dragging||(t._fullLayout._hoversubplot=null,qdt.unhover(t,h))},t._context.showAxisDragHandles&&(Ig(t,o,s._offset-gd,l._offset-gd,gd,gd,\"n\",\"w\"),Ig(t,o,s._offset+s._length,l._offset-gd,gd,gd,\"n\",\"e\"),Ig(t,o,s._offset-gd,l._offset+l._length,gd,gd,\"s\",\"w\"),Ig(t,o,s._offset+s._length,l._offset+l._length,gd,gd,\"s\",\"e\"))}if(t._context.showAxisDragHandles){if(a===s._mainSubplot){var 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s=t._length,l=e._fullLayout,u=r._id,c=n._id,f=l._firstScatter[gve(t)]===t.uid,h=(xU(t,l,r,n)||{}).orientation,d=t.fill;r._minDtick=0,n._minDtick=0;var v={padded:!0},m={padded:!0};o&&(v.ppad=m.ppad=o);var b=s<2||i[0]!==i[s-1]||a[0]!==a[s-1];b&&(d===\"tozerox\"||d===\"tonextx\"&&(f||h===\"h\"))?v.tozero=!0:!(t.error_y||{}).visible&&(d===\"tonexty\"||d===\"tozeroy\"||!_U.hasMarkers(t)&&!_U.hasText(t))&&(v.padded=!1,v.ppad=0),b&&(d===\"tozeroy\"||d===\"tonexty\"&&(f||h===\"v\"))?m.tozero=!0:(d===\"tonextx\"||d===\"tozerox\")&&(m.padded=!1),u&&(t._extremes[u]=s4.findExtremes(r,i,v)),c&&(t._extremes[c]=s4.findExtremes(n,a,m))}function vve(e,t){if(_U.hasMarkers(e)){var r=e.marker,n=1.6*(e.marker.sizeref||1),i;if(e.marker.sizemode===\"area\"?i=function(u){return Math.max(Math.sqrt((u||0)/n),3)}:i=function(u){return Math.max((u||0)/n,3)},yU.isArrayOrTypedArray(r.size)){var a={type:\"linear\"};s4.setConvert(a);for(var o=a.makeCalcdata(e.marker,\"size\"),s=new 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r=t.xaxis,n=t.yaxis,i=e._fullLayout,a=e._fullData,o=e.calcdata,s=[],l=[],u=0;u<a.length;u++){var c=a[u];if(c.visible===!0&&jpt.traceIs(c,\"bar\")&&c.xaxis===r._id&&c.yaxis===n._id&&(c.orientation===\"h\"?s.push(o[u]):l.push(o[u]),c._computePh))for(var f=e.calcdata[u],h=0;h<f.length;h++)typeof f[h].ph0==\"function\"&&(f[h].ph0=f[h].ph0()),typeof f[h].ph1==\"function\"&&(f[h].ph1=f[h].ph1())}var d={xCat:r.type===\"category\"||r.type===\"multicategory\",yCat:n.type===\"category\"||n.type===\"multicategory\",mode:i.barmode,norm:i.barnorm,gap:i.bargap,groupgap:i.bargroupgap};wU(e,r,n,l,d),wU(e,n,r,s,d)}function wU(e,t,r,n,i){if(n.length){var a,o,s,l,u;switch(Qpt(r,n),i.mode){case\"overlay\":bU(e,t,r,n,i);break;case\"group\":for(a=[],o=[],s=0;s<n.length;s++)l=n[s],u=l[0].trace,u.offset===void 0?o.push(l):a.push(l);o.length&&e0t(e,t,r,o,i),a.length&&bU(e,t,r,a,i);break;case\"stack\":case\"relative\":for(a=[],o=[],s=0;s<n.length;s++)l=n[s],u=l[0].trace,u.base===void 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i=e._fullLayout,a=r.positions,o=r.distinctPositions,s=r.minDiff,l=r.traces,u=l.length,c=a.length!==o.length,f=s*(1-n.gap),h,d,v,m;if(t._id===\"angularaxis\")h=f,d=h*(1-(n.groupgap||0)),v=-d/2;else{var b=Wpt(i,t._id)+l[0][0].trace.orientation;m=i._alignmentOpts[b]||{}}for(var p=0;p<u;p++){var k=l[p],M=k[0].trace;if(t._id!==\"angularaxis\"){var T=m[M.alignmentgroup]||{},L=Object.keys(T.offsetGroups||{}).length;L?h=f/L:h=c?f/u:f,d=h*(1-(n.groupgap||0)),L?v=((2*M._offsetIndex+1-L)*h-d)/2:v=c?((2*p+1-u)*h-d)/2:-d/2}var x=k[0].t;x.barwidth=d,x.offsetindex=M._offsetIndex||0,x.poffset=v,x.bargroupwidth=f,x.bardelta=s}r.binWidth=l[0][0].t.barwidth/100,r0t(r),i0t(t,r),t._id===\"angularaxis\"?bve(t,r):bve(t,r,c)}function r0t(e){var t=e.traces,r,n;for(r=0;r<t.length;r++){var i=t[r],a=i[0],o=a.trace,s=a.t,l=o._offset||o.offset,u=s.poffset,c;if(By(l)){for(c=Array.prototype.slice.call(l,0,i.length),n=0;n<c.length;n++)B0(c[n])||(c[n]=u);for(n=c.length;n<i.length;n++)c.push(u);s.poffset=c}else l!==void 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T=k?b[v]:b,L=M?p[v]:p;h=f.p+T,d=h+L,c.push(h,d)}}u._extremes[e._id]=l4.findExtremes(e,c,{padded:!1})}}function wve(e,t){for(var r=t.traces,n=vT(e),i=0;i<r.length;i++){for(var a=r[i],o=a[0].trace,s=o.type===\"scatter\",l=o.orientation===\"v\",u=[],c=!1,f=0;f<a.length;f++){var h=a[f],d=s?0:h.b,v=s?l?h.y:h.x:d+h.s;h[n]=v,u.push(v),h.hasB&&u.push(d),(!h.hasB||!h.b)&&(c=!0)}let{ppadminus:m,ppadplus:b}=SU(o,a);o._extremes[e._id]=l4.findExtremes(e,u,{tozero:c,padded:!0,ppadplus:b,ppadminus:m})}}function n0t(e,t,r){var n=vT(e),i=t.traces,a,o,s,l,u,c,f;for(l=0;l<i.length;l++)if(a=i[l],o=a[0].trace,o.type===\"funnel\")for(f=a[0].t.offsetindex,u=0;u<a.length;u++)c=a[u],c.s!==dT&&t.put(c.p,f,-.5*c.s);for(l=0;l<i.length;l++){a=i[l],o=a[0].trace,s=o.type===\"funnel\",f=o.type===\"barpolar\"?0:a[0].t.offsetindex;var h=[];for(u=0;u<a.length;u++)if(c=a[u],c.s!==dT){var d;s?d=c.s:d=c.s+c.b;var 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wT=ye((Qsr,Xve)=>{\"use strict\";var Wve=Ru();Xve.exports=function(t,r){var n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].trace,l,u,c,f,h=!Wve.hasMarkers(s)&&!Wve.hasText(s);if(h)return[];if(r===!1)for(l=0;l<n.length;l++)n[l].selected=0;else for(l=0;l<n.length;l++)u=n[l],c=i.c2p(u.x),f=a.c2p(u.y),u.i!==null&&r.contains([c,f],!1,l,t)?(o.push({pointNumber:u.i,x:i.c2d(u.x),y:a.c2d(u.y)}),u.selected=1):u.selected=0;return o}});var Yve=ye((elr,Zve)=>{\"use strict\";Zve.exports={xaxis:{valType:\"subplotid\",dflt:\"x\",editType:\"calc+clearAxisTypes\"},yaxis:{valType:\"subplotid\",dflt:\"y\",editType:\"calc+clearAxisTypes\"}}});var FU=ye((tlr,$ve)=>{\"use strict\";var f4=Oa().traceIs,DU=W3();$ve.exports=function(t,r,n,i){n(\"autotypenumbers\",i.autotypenumbersDflt);var a=n(\"type\",(i.splomStash||{}).type);a===\"-\"&&(A0t(r,i.data),r.type===\"-\"?r.type=\"linear\":t.type=r.type)};function A0t(e,t){if(e.type===\"-\"){var r=e._id,n=r.charAt(0),i;r.indexOf(\"scene\")!==-1&&(r=n);var 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rr={letter:G,font:r.font,outerTicks:d[q],showGrid:!v[q],data:[],bgColor:z,calendar:r.calendar,automargin:!0,visibleDflt:!1,reverseDflt:!1,autotypenumbersDflt:i,splomStash:((r._splomAxes||{})[G]||{})[V]};H(\"uirevision\",r.uirevision),j.type=ge[V]||\"linear\",hpe(Z,j,H,rr,r),dpe(Z,j,H,{letter:G,counterAxes:Ee[G],overlayableAxes:_e(G,q),grid:r.grid}),H(\"fixedrange\"),H(\"modebardisable\"),Se(),j._input=Z}var tt=qU(\"rangeslider\",\"handleDefaults\"),je=qU(\"rangeselector\",\"handleDefaults\");for(m=0;m<S.length;m++)q=S[m],Z=t[q],j=r[q],tt(t,r,q),j.type===\"date\"&&je(Z,j,r,g,j.calendar),H(\"fixedrange\"),H(\"modebardisable\");for(m=0;m<g.length;m++){q=g[m],Z=t[q],j=r[q];var Ue=r[Fm(j.anchor)],Ae=qU(\"rangeslider\",\"isVisible\")(Ue);H(\"fixedrange\",Ae),H(\"modebardisable\")}W0t.handleDefaults(t,r,{axIds:Ce.concat(ie).sort(OU.idSort),axHasImage:h})}});var xpe=ye((slr,_pe)=>{\"use strict\";var Z0t=Fa(),ype=Oa(),_I=Pr(),Qp=So(),xI=ho();_pe.exports=function(t,r,n,i){var 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a=Og.traceLayerClasses,o=e._fullLayout,s=o._zindices,l=o._modules,u,c,f,h=[],d=[],v=0;v<s.length;v++)for(var m=s[v],b=0;b<l.length;b++){u=l[b];var p=u.name,k=bpe.modules[p].categories;if(k.svg){var M=u.layerName||p+\"layer\",T=M+(v?Number(v)+1:\"\"),L=u.plot;c=wpe(r,L,m),f=c[0],r=c[1],f.length&&h.push({i:a.indexOf(M),zindex:v,className:T,plotMethod:L,cdModule:f}),k.zoomScale&&d.push(\".\"+T)}}h.sort(function(S,g){return(S.zindex||0)-(g.zindex||0)||S.i-g.i});var x=t.plot.selectAll(\"g.mlayer\").data(h,function(S){return S.className});if(x.enter().append(\"g\").attr(\"class\",function(S){return S.className}).classed(\"mlayer\",!0).classed(\"rangeplot\",t.isRangePlot),x.exit().remove(),x.order(),x.each(function(S){var g=wI.select(this),P=S.className;S.plotMethod(e,t,S.cdModule,g,n,i),Og.clipOnAxisFalseQuery.indexOf(\".\"+P)===-1&&K0t.setClipUrl(g,t.layerClipId,e)}),o._has(\"scattergl\")&&(u=bpe.getModule(\"scattergl\"),f=wpe(r,u)[0],u.plot(e,t,f)),!e._context.staticPlot&&(t._hasClipOnAxisFalse&&(t.clipOnAxisFalseTraces=t.plot.selectAll(Og.clipOnAxisFalseQuery.join(\",\")).selectAll(\".trace\")),d.length)){var C=t.plot.selectAll(d.join(\",\")).selectAll(\".trace\");t.zoomScalePts=C.selectAll(\"path.point\"),t.zoomScaleTxt=C.selectAll(\".textpoint\")}}xv.clean=function(e,t,r,n){var i=n._plots||{},a=t._plots||{},o=n._subplots||{},s,l,u;if(n._hasOnlyLargeSploms&&!t._hasOnlyLargeSploms)for(u in i)s=i[u],s.plotgroup&&s.plotgroup.remove();var c=n._has&&n._has(\"gl\"),f=t._has&&t._has(\"gl\");if(c&&!f)for(u in i)s=i[u],s._scene&&s._scene.destroy();if(o.xaxis&&o.yaxis){var h=M_.listIds({_fullLayout:n});for(l=0;l<h.length;l++){var 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v=t._cartesianlayer.selectAll(\".subplot\").data(f,String);v.enter().append(\"g\").attr(\"class\",function(m){return\"subplot \"+m[0]}),v.order(),v.exit().call(Spe,t),v.each(function(m){var b=m[0],p=b.indexOf(s2),k=p!==-1,M=k?b.slice(0,p):b,T=t._plots[b];T||(T=o2.extendFlat({},t._plots[M]),T&&(T.id=b,t._plots[b]=T,t._subplots.cartesian.push(b))),T&&(T.plotgroup=wI.select(this),Ape(e,T),k||(T.draglayer=iu(t._draggers,\"g\",b)))})};xv.rangePlot=function(e,t,r){Ape(e,t),Tpe(e,t,r),Y0t.style(e)};function $0t(e){var t=e._fullLayout,r=t._zindices.length,n=t._subplots.cartesian,i=n.length,a,o,s,l,u,c,f=[],h=[];for(a=0;a<i;a++){s=n[a],l=t._plots[s],u=l.xaxis,c=l.yaxis;var d=u._mainAxis,v=c._mainAxis,m=d._id+v._id,b=t._plots[m];l.overlays=[],m!==s&&b?(l.mainplot=m,l.mainplotinfo=b,h.push(s)):(l.mainplot=void 0,l.mainplotinfo=void 0,f.push(s))}for(a=0;a<h.length;a++)s=h[a],l=t._plots[s],l.mainplotinfo.overlays.push(l);var p=f.concat(h),k=[];for(a=0;a<i;a++){s=p[a],l=t._plots[s],u=l.xaxis,c=l.yaxis;for(var M=[],T=1;T<=r;T++){var L=\"\";for(T>1&&(L+=s2+T),M.push(s+L),o=0;o<l.overlays.length;o++)M.push(l.overlays[o].id+L)}M=M.concat([u.layer,c.layer,u.overlaying||\"\",c.overlaying||\"\"]),k.push(M)}return k}function Ape(e,t){var r=e._fullLayout,n=t.plotgroup,i=t.id,a=i.indexOf(s2),o=a!==-1,s=Og.layerValue2layerClass[t.xaxis.layer],l=Og.layerValue2layerClass[t.yaxis.layer],u=r._hasOnlyLargeSploms,c=r._zindices.length>1,f=t.mainplotinfo;if(!t.mainplot||c)if(u)t.xlines=iu(n,\"path\",\"xlines-above\"),t.ylines=iu(n,\"path\",\"ylines-above\"),t.xaxislayer=iu(n,\"g\",\"xaxislayer-above\"),t.yaxislayer=iu(n,\"g\",\"yaxislayer-above\");else{if(!o){var 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d=iu(n,\"g\",\"layer-between\");t.shapelayerBetween=iu(d,\"g\",\"shapelayer\"),t.imagelayerBetween=iu(d,\"g\",\"imagelayer\"),iu(n,\"path\",\"xlines-below\"),iu(n,\"path\",\"ylines-below\"),t.overlinesBelow=iu(n,\"g\",\"overlines-below\"),iu(n,\"g\",\"xaxislayer-below\"),iu(n,\"g\",\"yaxislayer-below\"),t.overaxesBelow=iu(n,\"g\",\"overaxes-below\")}t.overplot=iu(n,\"g\",\"overplot\"),t.plot=iu(t.overplot,\"g\",i),f&&c?t.zerolinelayerAbove=f.zerolinelayerAbove:t.zerolinelayerAbove=iu(n,\"g\",\"zerolinelayer-above\"),o||(t.xlines=iu(n,\"path\",\"xlines-above\"),t.ylines=iu(n,\"path\",\"ylines-above\"),t.overlinesAbove=iu(n,\"g\",\"overlines-above\"),iu(n,\"g\",\"xaxislayer-above\"),iu(n,\"g\",\"yaxislayer-above\"),t.overaxesAbove=iu(n,\"g\",\"overaxes-above\"),t.xlines=n.select(\".xlines-\"+s),t.ylines=n.select(\".ylines-\"+l),t.xaxislayer=n.select(\".xaxislayer-\"+s),t.yaxislayer=n.select(\".yaxislayer-\"+l))}else{var v=f.plotgroup,m=i+\"-x\",b=i+\"-y\";t.minorGridlayer=f.minorGridlayer,t.gridlayer=f.gridlayer,t.zerolinelayer=f.zerolinelayer,t.zerolinelayerAbove=f.zerolinelayerAbove,iu(f.overlinesBelow,\"path\",m),iu(f.overlinesBelow,\"path\",b),iu(f.overaxesBelow,\"g\",m),iu(f.overaxesBelow,\"g\",b),t.plot=iu(f.overplot,\"g\",i),iu(f.overlinesAbove,\"path\",m),iu(f.overlinesAbove,\"path\",b),iu(f.overaxesAbove,\"g\",m),iu(f.overaxesAbove,\"g\",b),t.xlines=v.select(\".overlines-\"+s).select(\".\"+m),t.ylines=v.select(\".overlines-\"+l).select(\".\"+b),t.xaxislayer=v.select(\".overaxes-\"+s).select(\".\"+m),t.yaxislayer=v.select(\".overaxes-\"+l).select(\".\"+b)}o||(u||(bI(t.minorGridlayer,\"g\",t.xaxis._id),bI(t.minorGridlayer,\"g\",t.yaxis._id),t.minorGridlayer.selectAll(\"g\").map(function(p){return p[0]}).sort(M_.idSort),bI(t.gridlayer,\"g\",t.xaxis._id),bI(t.gridlayer,\"g\",t.yaxis._id),t.gridlayer.selectAll(\"g\").map(function(p){return p[0]}).sort(M_.idSort)),t.xlines.style(\"fill\",\"none\").classed(\"crisp\",!0),t.ylines.style(\"fill\",\"none\").classed(\"crisp\",!0))}function Spe(e,t){if(e){var r={};e.each(function(l){var u=l[0],c=wI.select(this);c.remove(),Mpe(u,t),r[u]=!0});for(var n in t._plots)for(var i=t._plots[n],a=i.overlays||[],o=0;o<a.length;o++){var s=a[o];r[s.id]&&s.plot.selectAll(\".trace\").remove()}}}function Mpe(e,t){t._draggers.selectAll(\"g.\"+e).remove(),t._defs.select(\"#clip\"+t._uid+e+\"plot\").remove()}xv.toSVG=function(e){var t=e._fullLayout._glimages,r=wI.select(e).selectAll(\".svg-container\"),n=r.filter(function(a,o){return o===r.size()-1}).selectAll(\".gl-canvas-context, .gl-canvas-focus\");function i(){var a=this,o=a.toDataURL(\"image/png\"),s=t.append(\"svg:image\");s.attr({xmlns:J0t.svg,\"xlink:href\":o,preserveAspectRatio:\"none\",x:0,y:0,width:a.style.width,height:a.style.height})}n.each(i)};xv.updateFx=KN().updateFx});var kpe=ye((ulr,Epe)=>{\"use strict\";var TI=Ru();Epe.exports={hasLines:TI.hasLines,hasMarkers:TI.hasMarkers,hasText:TI.hasText,isBubble:TI.isBubble,attributes:pf(),layoutAttributes:iL(),supplyDefaults:Qde(),crossTraceDefaults:vU(),supplyLayoutDefaults:ive(),calc:O0().calc,crossTraceCalc:kve(),arraysToCalcdata:Dm(),plot:mT(),colorbar:Qd(),formatLabels:fI(),style:sp().style,styleOnSelect:sp().styleOnSelect,hoverPoints:bT(),selectPoints:wT(),animatable:!0,moduleType:\"trace\",name:\"scatter\",basePlotModule:mh(),categories:[\"cartesian\",\"svg\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\",\"zoomScale\"],meta:{}}});var Ppe=ye((clr,Lpe)=>{\"use strict\";var Q0t=Fa(),egt=ka(),Cpe=UN(),BU=Pr(),tgt=BU.strScale,rgt=BU.strRotate,igt=BU.strTranslate;Lpe.exports=function(t,r,n){var i=t.node(),a=Cpe[n.arrowhead||0],o=Cpe[n.startarrowhead||0],s=(n.arrowwidth||1)*(n.arrowsize||1),l=(n.arrowwidth||1)*(n.startarrowsize||1),u=r.indexOf(\"start\")>=0,c=r.indexOf(\"end\")>=0,f=a.backoff*s+n.standoff,h=o.backoff*l+n.startstandoff,d,v,m,b;if(i.nodeName===\"line\"){d={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},v={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var p=d.x-v.x,k=d.y-v.y;if(m=Math.atan2(k,p),b=m+Math.PI,f&&h&&f+h>Math.sqrt(p*p+k*k)){V();return}if(f){if(f*f>p*p+k*k){V();return}var M=f*Math.cos(m),T=f*Math.sin(m);v.x+=M,v.y+=T,t.attr({x2:v.x,y2:v.y})}if(h){if(h*h>p*p+k*k){V();return}var L=h*Math.cos(m),x=h*Math.sin(m);d.x-=L,d.y-=x,t.attr({x1:d.x,y1:d.y})}}else if(i.nodeName===\"path\"){var C=i.getTotalLength(),S=\"\";if(C<f+h){V();return}var g=i.getPointAtLength(0),P=i.getPointAtLength(.1);m=Math.atan2(g.y-P.y,g.x-P.x),d=i.getPointAtLength(Math.min(h,C)),S=\"0px,\"+h+\"px,\";var 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r=e._fullLayout,n=r.annotations[t]||{},i=g4.getFromId(e,n.xref),a=g4.getFromId(e,n.yref);i&&i.setScale(),a&&a.setScale(),Fpe(e,n,t,!1,i,a)}function E_(e,t,r,n,i){var a=i[r],o=i[r+\"ref\"],s=r.indexOf(\"y\")!==-1,l=g4.getRefType(o)===\"domain\",u=s?n.h:n.w;return e?l?a+(s?-t:t)/e._length:e.p2r(e.r2p(a)+t):a+(s?-t:t)/u}function Fpe(e,t,r,n,i,a){var o=e._fullLayout,s=e._fullLayout._size,l=e._context.edits,u,c;n?(u=\"annotation-\"+n,c=n+\".annotations\"):(u=\"annotation\",c=\"annotations\");var f=agt(e.layout,c,t),h=f.modifyBase,d=f.modifyItem,v=f.getUpdateObj;o._infolayer.selectAll(\".\"+u+'[data-index=\"'+r+'\"]').remove();var m=\"clip\"+o._uid+\"_ann\"+r;if(!t._input||t.visible===!1){Ipe.selectAll(\"#\"+m).remove();return}var b={x:{},y:{}},p=+t.textangle||0,k=o._infolayer.append(\"g\").classed(u,!0).attr(\"data-index\",String(r)).style(\"opacity\",t.opacity),M=k.append(\"g\").classed(\"annotation-text-g\",!0),T=l[t.showarrow?\"annotationTail\":\"annotationPosition\"],L=t.captureevents||l.annotationText||T;function x(H){var re={index:r,annotation:t._input,fullAnnotation:t,event:H};return n&&(re.subplotId=n),re}var C=M.append(\"g\").style(\"pointer-events\",L?\"all\":null).call(GU,\"pointer\").on(\"click\",function(){e._dragging=!1,e.emit(\"plotly_clickannotation\",x(Ipe.event))});t.hovertext&&C.on(\"mouseover\",function(){var H=t.hoverlabel,re=H.font,ae=this.getBoundingClientRect(),_e=e.getBoundingClientRect();Rpe.loneHover({x0:ae.left-_e.left,x1:ae.right-_e.left,y:(ae.top+ae.bottom)/2-_e.top,text:t.hovertext,color:H.bgcolor,borderColor:H.bordercolor,fontFamily:re.family,fontSize:re.size,fontColor:re.color,fontWeight:re.weight,fontStyle:re.style,fontVariant:re.variant,fontShadow:re.fontShadow,fontLineposition:re.fontLineposition,fontTextcase:re.fontTextcase},{container:o._hoverlayer.node(),outerContainer:o._paper.node(),gd:e})}).on(\"mouseout\",function(){Rpe.loneUnhover(o._hoverlayer.node())});var S=t.borderwidth,g=t.borderpad,P=S+g,E=C.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",S+\"px\").call(l2.stroke,t.bordercolor).call(l2.fill,t.bgcolor),z=t.width||t.height,q=o._topclips.selectAll(\"#\"+m).data(z?[0]:[]);q.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",m).append(\"rect\"),q.exit().remove();var 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jr=P+qr-Ee.top,gt=P+ei-Ee.left;Z.call(VU.positionText,gt,jr).call(Ny.setClipUrl,z?m:null,e)}q.select(\"rect\").call(Ny.setRect,P,P,ie,Se),E.call(Ny.setRect,S/2,S/2,ke-S,be-S),C.call(Ny.setTranslate,Math.round(b.x.text-ke/2),Math.round(b.y.text-be/2)),M.attr({transform:\"rotate(\"+p+\",\"+b.x.text+\",\"+b.y.text+\")\"});var Ge=function(We,et){k.selectAll(\".annotation-arrow-g\").remove();var xt=b.x.head,At=b.y.head,Kt=b.x.tail+We,Qt=b.y.tail+et,Mr=b.x.text+We,Gr=b.y.text+et,Ir=k_.rotationXYMatrix(p,Mr,Gr),Yr=k_.apply2DTransform(Ir),_i=k_.apply2DTransform2(Ir),Pi=+E.attr(\"width\"),ai=+E.attr(\"height\"),mi=Mr-.5*Pi,un=mi+Pi,Fn=Gr-.5*ai,An=Fn+ai,Hn=[[mi,Fn,mi,An],[mi,An,un,An],[un,An,un,Fn],[un,Fn,mi,Fn]].map(_i);if(!Hn.reduce(function($r,Ri){return $r^!!k_.segmentsIntersect(xt,At,xt+1e6,At+1e6,Ri[0],Ri[1],Ri[2],Ri[3])},!1)){Hn.forEach(function($r){var Ri=k_.segmentsIntersect(Kt,Qt,xt,At,$r[0],$r[1],$r[2],$r[3]);Ri&&(Kt=Ri.x,Qt=Ri.y)});var 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Mf(e,t,r){this.gd=e,this.container=t,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll(\"rect.scrollbox-bg\").data([0]),this.bg.exit().on(\".drag\",null).on(\"wheel\",null).remove(),this.bg.enter().append(\"rect\").classed(\"scrollbox-bg\",!0).style(\"pointer-events\",\"all\").attr({opacity:0,x:0,y:0,width:0,height:0})}Mf.barWidth=2;Mf.barLength=20;Mf.barRadius=2;Mf.barPad=1;Mf.barColor=\"#808BA4\";Mf.prototype.enable=function(t,r,n){var i=this.gd._fullLayout,a=i.width,o=i.height;this.position=t;var s=this.position.l,l=this.position.w,u=this.position.t,c=this.position.h,f=this.position.direction,h=f===\"down\",d=f===\"left\",v=f===\"right\",m=f===\"up\",b=l,p=c,k,M,T,L;!h&&!d&&!v&&!m&&(this.position.direction=\"down\",h=!0);var x=h||m;x?(k=s,M=k+b,h?(T=u,L=Math.min(T+p,o),p=L-T):(L=u+p,T=Math.max(L-p,0),p=L-T)):(T=u,L=T+p,d?(M=s+b,k=Math.max(M-b,0),b=M-k):(k=s,M=Math.min(k+b,a),b=M-k)),this._box={l:k,t:T,w:b,h:p};var C=l>b,S=Mf.barLength+2*Mf.barPad,g=Mf.barWidth+2*Mf.barPad,P=s,E=u+c;E+g>o&&(E=o-g);var z=this.container.selectAll(\"rect.scrollbar-horizontal\").data(C?[0]:[]);z.exit().on(\".drag\",null).remove(),z.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call($0e.fill,Mf.barColor),C?(this.hbar=z.attr({rx:Mf.barRadius,ry:Mf.barRadius,x:P,y:E,width:S,height:g}),this._hbarXMin=P+S/2,this._hbarTranslateMax=b-S):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var q=c>p,V=Mf.barWidth+2*Mf.barPad,G=Mf.barLength+2*Mf.barPad,Z=s+l,j=u;Z+V>a&&(Z=a-V);var N=this.container.selectAll(\"rect.scrollbar-vertical\").data(q?[0]:[]);N.exit().on(\".drag\",null).remove(),N.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call($0e.fill,Mf.barColor),q?(this.vbar=N.attr({rx:Mf.barRadius,ry:Mf.barRadius,x:Z,y:j,width:V,height:G}),this._vbarYMin=j+G/2,this._vbarTranslateMax=p-G):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var H=this.id,re=k-.5,ae=q?M+V+.5:M+.5,_e=T-.5,Ee=C?L+g+.5:L+.5,Ce=i._topdefs.selectAll(\"#\"+H).data(C||q?[0]:[]);if(Ce.exit().remove(),Ce.enter().append(\"clipPath\").attr(\"id\",H).append(\"rect\"),C||q?(this._clipRect=Ce.select(\"rect\").attr({x:Math.floor(re),y:Math.floor(_e),width:Math.ceil(ae)-Math.floor(re),height:Math.ceil(Ee)-Math.floor(_e)}),this.container.call(ET.setClipUrl,H,this.gd),this.bg.attr({x:s,y:u,width:l,height:c})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(ET.setClipUrl,null),delete this._clipRect),C||q){var ge=Ng.behavior.drag().on(\"dragstart\",function(){Ng.event.sourceEvent.preventDefault()}).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(ge);var ie=Ng.behavior.drag().on(\"dragstart\",function(){Ng.event.sourceEvent.preventDefault(),Ng.event.sourceEvent.stopPropagation()}).on(\"drag\",this._onBarDrag.bind(this));C&&this.hbar.on(\".drag\",null).call(ie),q&&this.vbar.on(\".drag\",null).call(ie)}this.setTranslate(r,n)};Mf.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(ET.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)};Mf.prototype._onBoxDrag=function(){var t=this.translateX,r=this.translateY;this.hbar&&(t-=Ng.event.dx),this.vbar&&(r-=Ng.event.dy),this.setTranslate(t,r)};Mf.prototype._onBoxWheel=function(){var t=this.translateX,r=this.translateY;this.hbar&&(t+=Ng.event.deltaY),this.vbar&&(r+=Ng.event.deltaY),this.setTranslate(t,r)};Mf.prototype._onBarDrag=function(){var t=this.translateX,r=this.translateY;if(this.hbar){var n=t+this._hbarXMin,i=n+this._hbarTranslateMax,a=kI.constrain(Ng.event.x,n,i),o=(a-n)/(i-n),s=this.position.w-this._box.w;t=o*s}if(this.vbar){var l=r+this._vbarYMin,u=l+this._vbarTranslateMax,c=kI.constrain(Ng.event.y,l,u),f=(c-l)/(u-l),h=this.position.h-this._box.h;r=f*h}this.setTranslate(t,r)};Mf.prototype.setTranslate=function(t,r){var n=this.position.w-this._box.w,i=this.position.h-this._box.h;if(t=kI.constrain(t||0,0,n),r=kI.constrain(r||0,0,i),this.translateX=t,this.translateY=r,this.container.call(ET.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-r),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+r-.5)}),this.hbar){var a=t/n;this.hbar.call(ET.setTranslate,t+a*this._hbarTranslateMax,r)}if(this.vbar){var o=r/i;this.vbar.call(ET.setTranslate,t,r+o*this._vbarTranslateMax)}}});var cge=ye((Ulr,uge)=>{\"use strict\";var kT=Fa(),y4=Mc(),_4=ka(),CT=So(),t0=Pr(),CI=Zl(),cmt=vl().arrayEditor,rge=Dh().LINE_SPACING,es=MI(),fmt=ege();uge.exports=function(t){var r=t._fullLayout,n=t0.filterVisible(r[es.name]);function i(h){y4.autoMargin(t,sge(h))}var a=r._menulayer.selectAll(\"g.\"+es.containerClassName).data(n.length>0?[0]:[]);if(a.enter().append(\"g\").classed(es.containerClassName,!0).style(\"cursor\",\"pointer\"),a.exit().each(function(){kT.select(this).selectAll(\"g.\"+es.headerGroupClassName).each(i)}).remove(),n.length!==0){var o=a.selectAll(\"g.\"+es.headerGroupClassName).data(n,hmt);o.enter().append(\"g\").classed(es.headerGroupClassName,!0);for(var s=t0.ensureSingle(a,\"g\",es.dropdownButtonGroupClassName,function(h){h.style(\"pointer-events\",\"all\")}),l=0;l<n.length;l++){var u=n[l];ymt(t,u)}var c=\"updatemenus\"+r._uid,f=new fmt(t,s,c);o.enter().size()&&(s.node().parentNode.appendChild(s.node()),s.call(aV)),o.exit().each(function(h){s.call(aV),i(h)}).remove(),o.each(function(h){var d=kT.select(this),v=h.type===\"dropdown\"?s:null;y4.manageCommandObserver(t,h,h.buttons,function(m){nV(t,h,h.buttons[m.index],d,v,f,m.index,!0)}),h.type===\"dropdown\"?(nge(t,d,s,f,h),ige(s,h)&&x4(t,d,s,f,h)):x4(t,d,null,null,h)})}};function hmt(e){return e._index}function dmt(e){return+e.attr(es.menuIndexAttrName)==-1}function ige(e,t){return+e.attr(es.menuIndexAttrName)===t._index}function nV(e,t,r,n,i,a,o,s){t.active=o,cmt(e.layout,es.name,t).applyUpdate(\"active\",o),t.type===\"buttons\"?x4(e,n,null,null,t):t.type===\"dropdown\"&&(i.attr(es.menuIndexAttrName,\"-1\"),nge(e,n,i,a,t),s||x4(e,n,i,a,t))}function nge(e,t,r,n,i){var a=t0.ensureSingle(t,\"g\",es.headerClassName,function(h){h.style(\"pointer-events\",\"all\")}),o=i._dims,s=i.active,l=i.buttons[s]||es.blankHeaderOpts,u={y:i.pad.t,yPad:0,x:i.pad.l,xPad:0,index:0},c={width:o.headerWidth,height:o.headerHeight};a.call(oV,i,l,e).call(lge,i,u,c);var f=t0.ensureSingle(t,\"text\",es.headerArrowClassName,function(h){h.attr(\"text-anchor\",\"end\").call(CT.font,i.font).text(es.arrowSymbol[i.direction])});f.attr({x:o.headerWidth-es.arrowOffsetX+i.pad.l,y:o.headerHeight/2+es.textOffsetY+i.pad.t}),a.on(\"click\",function(){r.call(aV,String(ige(r,i)?-1:i._index)),x4(e,t,r,n,i)}),a.on(\"mouseover\",function(){a.call(age)}),a.on(\"mouseout\",function(){a.call(oge,i)}),CT.setTranslate(t,o.lx,o.ly)}function x4(e,t,r,n,i){r||(r=t,r.attr(\"pointer-events\",\"all\"));var a=!dmt(r)||i.type===\"buttons\"?i.buttons:[],o=i.type===\"dropdown\"?es.dropdownButtonClassName:es.buttonClassName,s=r.selectAll(\"g.\"+o).data(t0.filterVisible(a)),l=s.enter().append(\"g\").classed(o,!0),u=s.exit();i.type===\"dropdown\"?(l.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),u.transition().attr(\"opacity\",\"0\").remove()):u.remove();var c=0,f=0,h=i._dims,d=[\"up\",\"down\"].indexOf(i.direction)!==-1;i.type===\"dropdown\"&&(d?f=h.headerHeight+es.gapButtonHeader:c=h.headerWidth+es.gapButtonHeader),i.type===\"dropdown\"&&i.direction===\"up\"&&(f=-es.gapButtonHeader+es.gapButton-h.openHeight),i.type===\"dropdown\"&&i.direction===\"left\"&&(c=-es.gapButtonHeader+es.gapButton-h.openWidth);var v={x:h.lx+c+i.pad.l,y:h.ly+f+i.pad.t,yPad:es.gapButton,xPad:es.gapButton,index:0},m={l:v.x+i.borderwidth,t:v.y+i.borderwidth};s.each(function(b,p){var k=kT.select(this);k.call(oV,i,b,e).call(lge,i,v),k.on(\"click\",function(){kT.event.defaultPrevented||(b.execute&&(b.args2&&i.active===p?(nV(e,i,b,t,r,n,-1),y4.executeAPICommand(e,b.method,b.args2)):(nV(e,i,b,t,r,n,p),y4.executeAPICommand(e,b.method,b.args))),e.emit(\"plotly_buttonclicked\",{menu:i,button:b,active:i.active}))}),k.on(\"mouseover\",function(){k.call(age)}),k.on(\"mouseout\",function(){k.call(oge,i),s.call(tge,i)})}),s.call(tge,i),d?(m.w=Math.max(h.openWidth,h.headerWidth),m.h=v.y-m.t):(m.w=v.x-m.l,m.h=Math.max(h.openHeight,h.headerHeight)),m.direction=i.direction,n&&(s.size()?vmt(e,t,r,n,i,m):pmt(n))}function vmt(e,t,r,n,i,a){var o=i.direction,s=o===\"up\"||o===\"down\",l=i._dims,u=i.active,c,f,h;if(s)for(f=0,h=0;h<u;h++)f+=l.heights[h]+es.gapButton;else for(c=0,h=0;h<u;h++)c+=l.widths[h]+es.gapButton;n.enable(a,c,f),n.hbar&&n.hbar.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),n.vbar&&n.vbar.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\")}function pmt(e){var t=!!e.hbar,r=!!e.vbar;t&&e.hbar.transition().attr(\"opacity\",\"0\").each(\"end\",function(){t=!1,r||e.disable()}),r&&e.vbar.transition().attr(\"opacity\",\"0\").each(\"end\",function(){r=!1,t||e.disable()})}function oV(e,t,r,n){e.call(gmt,t).call(mmt,t,r,n)}function gmt(e,t){var r=t0.ensureSingle(e,\"rect\",es.itemRectClassName,function(n){n.attr({rx:es.rx,ry:es.ry,\"shape-rendering\":\"crispEdges\"})});r.call(_4.stroke,t.bordercolor).call(_4.fill,t.bgcolor).style(\"stroke-width\",t.borderwidth+\"px\")}function mmt(e,t,r,n){var i=t0.ensureSingle(e,\"text\",es.itemTextClassName,function(s){s.attr({\"text-anchor\":\"start\",\"data-notex\":1})}),a=r.label,o=n._fullLayout._meta;o&&(a=t0.templateString(a,o)),i.call(CT.font,t.font).text(a).call(CI.convertToTspans,n)}function tge(e,t){var r=t.active;e.each(function(n,i){var a=kT.select(this);i===r&&t.showactive&&a.select(\"rect.\"+es.itemRectClassName).call(_4.fill,es.activeColor)})}function age(e){e.select(\"rect.\"+es.itemRectClassName).call(_4.fill,es.hoverColor)}function oge(e,t){e.select(\"rect.\"+es.itemRectClassName).call(_4.fill,t.bgcolor)}function ymt(e,t){var r=t._dims={width1:0,height1:0,heights:[],widths:[],totalWidth:0,totalHeight:0,openWidth:0,openHeight:0,lx:0,ly:0},n=CT.tester.selectAll(\"g.\"+es.dropdownButtonClassName).data(t0.filterVisible(t.buttons));n.enter().append(\"g\").classed(es.dropdownButtonClassName,!0);var i=[\"up\",\"down\"].indexOf(t.direction)!==-1;n.each(function(c,f){var h=kT.select(this);h.call(oV,t,c,e);var d=h.select(\".\"+es.itemTextClassName),v=d.node()&&CT.bBox(d.node()).width,m=Math.max(v+es.textPadX,es.minWidth),b=t.font.size*rge,p=CI.lineCount(d),k=Math.max(b*p,es.minHeight)+es.textOffsetY;k=Math.ceil(k),m=Math.ceil(m),r.widths[f]=m,r.heights[f]=k,r.height1=Math.max(r.height1,k),r.width1=Math.max(r.width1,m),i?(r.totalWidth=Math.max(r.totalWidth,m),r.openWidth=r.totalWidth,r.totalHeight+=k+es.gapButton,r.openHeight+=k+es.gapButton):(r.totalWidth+=m+es.gapButton,r.openWidth+=m+es.gapButton,r.totalHeight=Math.max(r.totalHeight,k),r.openHeight=r.totalHeight)}),i?r.totalHeight-=es.gapButton:r.totalWidth-=es.gapButton,r.headerWidth=r.width1+es.arrowPadX,r.headerHeight=r.height1,t.type===\"dropdown\"&&(i?(r.width1+=es.arrowPadX,r.totalHeight=r.height1):r.totalWidth=r.width1,r.totalWidth+=es.arrowPadX),n.remove();var a=r.totalWidth+t.pad.l+t.pad.r,o=r.totalHeight+t.pad.t+t.pad.b,s=e._fullLayout._size;r.lx=s.l+s.w*t.x,r.ly=s.t+s.h*(1-t.y);var l=\"left\";t0.isRightAnchor(t)&&(r.lx-=a,l=\"right\"),t0.isCenterAnchor(t)&&(r.lx-=a/2,l=\"center\");var u=\"top\";t0.isBottomAnchor(t)&&(r.ly-=o,u=\"bottom\"),t0.isMiddleAnchor(t)&&(r.ly-=o/2,u=\"middle\"),r.totalWidth=Math.ceil(r.totalWidth),r.totalHeight=Math.ceil(r.totalHeight),r.lx=Math.round(r.lx),r.ly=Math.round(r.ly),y4.autoMargin(e,sge(t),{x:t.x,y:t.y,l:a*({right:1,center:.5}[l]||0),r:a*({left:1,center:.5}[l]||0),b:o*({top:1,middle:.5}[u]||0),t:o*({bottom:1,middle:.5}[u]||0)})}function sge(e){return es.autoMarginIdRoot+e._index}function lge(e,t,r,n){n=n||{};var i=e.select(\".\"+es.itemRectClassName),a=e.select(\".\"+es.itemTextClassName),o=t.borderwidth,s=r.index,l=t._dims;CT.setTranslate(e,o+r.x,o+r.y);var u=[\"up\",\"down\"].indexOf(t.direction)!==-1,c=n.height||(u?l.heights[s]:l.height1);i.attr({x:0,y:0,width:n.width||(u?l.width1:l.widths[s]),height:c});var f=t.font.size*rge,h=CI.lineCount(a),d=(h-1)*f/2;CI.positionText(a,es.textOffsetX,c/2-d+es.textOffsetY),u?r.y+=l.heights[s]+r.yPad:r.x+=l.widths[s]+r.xPad,r.index++}function aV(e,t){e.attr(es.menuIndexAttrName,t||\"-1\").selectAll(\"g.\"+es.dropdownButtonClassName).remove()}});var hge=ye((Vlr,fge)=>{\"use strict\";var _mt=MI();fge.exports={moduleType:\"component\",name:_mt.name,layoutAttributes:iV(),supplyLayoutDefaults:J0e(),draw:cge()}});var b4=ye((Glr,dge)=>{\"use strict\";dge.exports={name:\"sliders\",containerClassName:\"slider-container\",groupClassName:\"slider-group\",inputAreaClass:\"slider-input-area\",railRectClass:\"slider-rail-rect\",railTouchRectClass:\"slider-rail-touch-rect\",gripRectClass:\"slider-grip-rect\",tickRectClass:\"slider-tick-rect\",inputProxyClass:\"slider-input-proxy\",labelsClass:\"slider-labels\",labelGroupClass:\"slider-label-group\",labelClass:\"slider-label\",currentValueClass:\"slider-current-value\",railHeight:5,menuIndexAttrName:\"slider-active-index\",autoMarginIdRoot:\"slider-\",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:\"#bec8d9\",railBgColor:\"#f8fafc\",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:\"#bec8d9\",gripBgColor:\"#f6f8fa\",gripBgActiveColor:\"#dbdde0\",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:\"#333\",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:\"#333\",minorTickLength:4,currentValuePadding:8,currentValueInset:0}});var sV=ye((Hlr,gge)=>{\"use strict\";var vge=ec(),xmt=q6(),bmt=Ao().extendDeepAll,wmt=mc().overrideAll,Tmt=YS(),pge=vl().templatedArray,f2=b4(),Amt=pge(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});gge.exports=wmt(pge(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:Amt,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:bmt(xmt({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:Tmt.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:vge({})},font:vge({}),activebgcolor:{valType:\"color\",dflt:f2.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:f2.railBgColor},bordercolor:{valType:\"color\",dflt:f2.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:f2.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:f2.tickLength},tickcolor:{valType:\"color\",dflt:f2.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:f2.minorTickLength}}),\"arraydraw\",\"from-root\")});var xge=ye((jlr,_ge)=>{\"use strict\";var LT=Pr(),mge=Kd(),yge=sV(),Smt=b4(),Mmt=Smt.name,Emt=yge.steps;_ge.exports=function(t,r){mge(t,r,{name:Mmt,handleItemDefaults:kmt})};function kmt(e,t,r){function n(f,h){return LT.coerce(e,t,yge,f,h)}for(var i=mge(e,t,{name:\"steps\",handleItemDefaults:Cmt}),a=0,o=0;o<i.length;o++)i[o].visible&&a++;var s;if(a<2?s=t.visible=!1:s=n(\"visible\"),!!s){t._stepCount=a;var l=t._visibleSteps=LT.filterVisible(i),u=n(\"active\");(i[u]||{}).visible||(t.active=l[0]._index),n(\"x\"),n(\"y\"),LT.noneOrAll(e,t,[\"x\",\"y\"]),n(\"xanchor\"),n(\"yanchor\"),n(\"len\"),n(\"lenmode\"),n(\"pad.t\"),n(\"pad.r\"),n(\"pad.b\"),n(\"pad.l\"),LT.coerceFont(n,\"font\",r.font);var c=n(\"currentvalue.visible\");c&&(n(\"currentvalue.xanchor\"),n(\"currentvalue.prefix\"),n(\"currentvalue.suffix\"),n(\"currentvalue.offset\"),LT.coerceFont(n,\"currentvalue.font\",t.font)),n(\"transition.duration\"),n(\"transition.easing\"),n(\"bgcolor\"),n(\"activebgcolor\"),n(\"bordercolor\"),n(\"borderwidth\"),n(\"ticklen\"),n(\"tickwidth\"),n(\"tickcolor\"),n(\"minorticklen\")}}function Cmt(e,t){function r(a,o){return LT.coerce(e,t,Emt,a,o)}var n;if(e.method!==\"skip\"&&!Array.isArray(e.args)?n=t.visible=!1:n=r(\"visible\"),n){r(\"method\"),r(\"args\");var i=r(\"label\",\"step-\"+t._index);r(\"value\",i),r(\"execute\")}}});var Lge=ye((Wlr,Cge)=>{\"use strict\";var Ug=Fa(),LI=Mc(),C_=ka(),Vg=So(),r0=Pr(),Lmt=r0.strTranslate,w4=Zl(),Pmt=vl().arrayEditor,Rs=b4(),cV=Dh(),Tge=cV.LINE_SPACING,lV=cV.FROM_TL,uV=cV.FROM_BR;Cge.exports=function(t){var r=t._context.staticPlot,n=t._fullLayout,i=Imt(n,t),a=n._infolayer.selectAll(\"g.\"+Rs.containerClassName).data(i.length>0?[0]:[]);a.enter().append(\"g\").classed(Rs.containerClassName,!0).style(\"cursor\",r?null:\"ew-resize\");function o(c){c._commandObserver&&(c._commandObserver.remove(),delete c._commandObserver),LI.autoMargin(t,Age(c))}if(a.exit().each(function(){Ug.select(this).selectAll(\"g.\"+Rs.groupClassName).each(o)}).remove(),i.length!==0){var s=a.selectAll(\"g.\"+Rs.groupClassName).data(i,Rmt);s.enter().append(\"g\").classed(Rs.groupClassName,!0),s.exit().each(o).remove();for(var l=0;l<i.length;l++){var u=i[l];Dmt(t,u)}s.each(function(c){var f=Ug.select(this);Bmt(c),LI.manageCommandObserver(t,c,c._visibleSteps,function(h){var d=f.data()[0];d.active!==h.index&&(d._dragging||Mge(t,f,d,h.index,!1,!0))}),Fmt(t,Ug.select(this),c)})}};function Age(e){return Rs.autoMarginIdRoot+e._index}function Imt(e,t){for(var r=e[Rs.name],n=[],i=0;i<r.length;i++){var a=r[i];a.visible&&(a._gd=t,n.push(a))}return n}function Rmt(e){return e._index}function Dmt(e,t){var r=Vg.tester.selectAll(\"g.\"+Rs.labelGroupClass).data(t._visibleSteps);r.enter().append(\"g\").classed(Rs.labelGroupClass,!0);var n=0,i=0;r.each(function(v){var m=Ug.select(this),b=Sge(m,{step:v},t),p=b.node();if(p){var k=Vg.bBox(p);i=Math.max(i,k.height),n=Math.max(n,k.width)}}),r.remove();var a=t._dims={};a.inputAreaWidth=Math.max(Rs.railWidth,Rs.gripHeight);var o=e._fullLayout._size;a.lx=o.l+o.w*t.x,a.ly=o.t+o.h*(1-t.y),t.lenmode===\"fraction\"?a.outerLength=Math.round(o.w*t.len):a.outerLength=t.len,a.inputAreaStart=0,a.inputAreaLength=Math.round(a.outerLength-t.pad.l-t.pad.r);var s=a.inputAreaLength-2*Rs.stepInset,l=s/(t._stepCount-1),u=n+Rs.labelPadding;if(a.labelStride=Math.max(1,Math.ceil(u/l)),a.labelHeight=i,a.currentValueMaxWidth=0,a.currentValueHeight=0,a.currentValueTotalHeight=0,a.currentValueMaxLines=1,t.currentvalue.visible){var c=Vg.tester.append(\"g\");r.each(function(v){var m=PI(c,t,v.label),b=m.node()&&Vg.bBox(m.node())||{width:0,height:0},p=w4.lineCount(m);a.currentValueMaxWidth=Math.max(a.currentValueMaxWidth,Math.ceil(b.width)),a.currentValueHeight=Math.max(a.currentValueHeight,Math.ceil(b.height)),a.currentValueMaxLines=Math.max(a.currentValueMaxLines,p)}),a.currentValueTotalHeight=a.currentValueHeight+t.currentvalue.offset,c.remove()}a.height=a.currentValueTotalHeight+Rs.tickOffset+t.ticklen+Rs.labelOffset+a.labelHeight+t.pad.t+t.pad.b;var f=\"left\";r0.isRightAnchor(t)&&(a.lx-=a.outerLength,f=\"right\"),r0.isCenterAnchor(t)&&(a.lx-=a.outerLength/2,f=\"center\");var h=\"top\";r0.isBottomAnchor(t)&&(a.ly-=a.height,h=\"bottom\"),r0.isMiddleAnchor(t)&&(a.ly-=a.height/2,h=\"middle\"),a.outerLength=Math.ceil(a.outerLength),a.height=Math.ceil(a.height),a.lx=Math.round(a.lx),a.ly=Math.round(a.ly);var d={y:t.y,b:a.height*uV[h],t:a.height*lV[h]};t.lenmode===\"fraction\"?(d.l=0,d.xl=t.x-t.len*lV[f],d.r=0,d.xr=t.x+t.len*uV[f]):(d.x=t.x,d.l=a.outerLength*lV[f],d.r=a.outerLength*uV[f]),LI.autoMargin(e,Age(t),d)}function Fmt(e,t,r){(r.steps[r.active]||{}).visible||(r.active=r._visibleSteps[0]._index),t.call(PI,r).call(Umt,r).call(qmt,r).call(Omt,r).call(Nmt,e,r).call(zmt,e,r);var n=r._dims;Vg.setTranslate(t,n.lx+r.pad.l,n.ly+r.pad.t),t.call(kge,r,!1),t.call(PI,r)}function PI(e,t,r){if(t.currentvalue.visible){var n=t._dims,i,a;switch(t.currentvalue.xanchor){case\"right\":i=n.inputAreaLength-Rs.currentValueInset-n.currentValueMaxWidth,a=\"left\";break;case\"center\":i=n.inputAreaLength*.5,a=\"middle\";break;default:i=Rs.currentValueInset,a=\"left\"}var o=r0.ensureSingle(e,\"text\",Rs.labelClass,function(h){h.attr({\"text-anchor\":a,\"data-notex\":1})}),s=t.currentvalue.prefix?t.currentvalue.prefix:\"\";if(typeof r==\"string\")s+=r;else{var l=t.steps[t.active].label,u=t._gd._fullLayout._meta;u&&(l=r0.templateString(l,u)),s+=l}t.currentvalue.suffix&&(s+=t.currentvalue.suffix),o.call(Vg.font,t.currentvalue.font).text(s).call(w4.convertToTspans,t._gd);var c=w4.lineCount(o),f=(n.currentValueMaxLines+1-c)*t.currentvalue.font.size*Tge;return w4.positionText(o,i,f),o}}function zmt(e,t,r){var n=r0.ensureSingle(e,\"rect\",Rs.gripRectClass,function(i){i.call(Ege,t,e,r).style(\"pointer-events\",\"all\")});n.attr({width:Rs.gripWidth,height:Rs.gripHeight,rx:Rs.gripRadius,ry:Rs.gripRadius}).call(C_.stroke,r.bordercolor).call(C_.fill,r.bgcolor).style(\"stroke-width\",r.borderwidth+\"px\")}function Sge(e,t,r){var n=r0.ensureSingle(e,\"text\",Rs.labelClass,function(o){o.attr({\"text-anchor\":\"middle\",\"data-notex\":1})}),i=t.step.label,a=r._gd._fullLayout._meta;return a&&(i=r0.templateString(i,a)),n.call(Vg.font,r.font).text(i).call(w4.convertToTspans,r._gd),n}function qmt(e,t){var r=r0.ensureSingle(e,\"g\",Rs.labelsClass),n=t._dims,i=r.selectAll(\"g.\"+Rs.labelGroupClass).data(n.labelSteps);i.enter().append(\"g\").classed(Rs.labelGroupClass,!0),i.exit().remove(),i.each(function(a){var o=Ug.select(this);o.call(Sge,a,t),Vg.setTranslate(o,fV(t,a.fraction),Rs.tickOffset+t.ticklen+t.font.size*Tge+Rs.labelOffset+n.currentValueTotalHeight)})}function bge(e,t,r,n,i){var a=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[a]._index;o!==r.active&&Mge(e,t,r,o,!0,i)}function Mge(e,t,r,n,i,a){var o=r.active;r.active=n,Pmt(e.layout,Rs.name,r).applyUpdate(\"active\",n);var s=r.steps[r.active];t.call(kge,r,a),t.call(PI,r),e.emit(\"plotly_sliderchange\",{slider:r,step:r.steps[r.active],interaction:i,previousActive:o}),s&&s.method&&i&&(t._nextMethod?(t._nextMethod.step=s,t._nextMethod.doCallback=i,t._nextMethod.doTransition=a):(t._nextMethod={step:s,doCallback:i,doTransition:a},t._nextMethodRaf=window.requestAnimationFrame(function(){var l=t._nextMethod.step;l.method&&(l.execute&&LI.executeAPICommand(e,l.method,l.args),t._nextMethod=null,t._nextMethodRaf=null)})))}function Ege(e,t,r){if(t._context.staticPlot)return;var n=r.node(),i=Ug.select(t);function a(){return r.data()[0]}function o(){var s=a();t.emit(\"plotly_sliderstart\",{slider:s});var l=r.select(\".\"+Rs.gripRectClass);Ug.event.stopPropagation(),Ug.event.preventDefault(),l.call(C_.fill,s.activebgcolor);var u=wge(s,Ug.mouse(n)[0]);bge(t,r,s,u,!0),s._dragging=!0;function c(){var h=a(),d=wge(h,Ug.mouse(n)[0]);bge(t,r,h,d,!1)}i.on(\"mousemove\",c),i.on(\"touchmove\",c);function f(){var h=a();h._dragging=!1,l.call(C_.fill,h.bgcolor),i.on(\"mouseup\",null),i.on(\"mousemove\",null),i.on(\"touchend\",null),i.on(\"touchmove\",null),t.emit(\"plotly_sliderend\",{slider:h,step:h.steps[h.active]})}i.on(\"mouseup\",f),i.on(\"touchend\",f)}e.on(\"mousedown\",o),e.on(\"touchstart\",o)}function Omt(e,t){var r=e.selectAll(\"rect.\"+Rs.tickRectClass).data(t._visibleSteps),n=t._dims;r.enter().append(\"rect\").classed(Rs.tickRectClass,!0),r.exit().remove(),r.attr({width:t.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each(function(i,a){var o=a%n.labelStride===0,s=Ug.select(this);s.attr({height:o?t.ticklen:t.minorticklen}).call(C_.fill,t.tickcolor),Vg.setTranslate(s,fV(t,a/(t._stepCount-1))-.5*t.tickwidth,(o?Rs.tickOffset:Rs.minorTickOffset)+n.currentValueTotalHeight)})}function Bmt(e){var t=e._dims;t.labelSteps=[];for(var r=e._stepCount,n=0;n<r;n+=t.labelStride)t.labelSteps.push({fraction:n/(r-1),step:e._visibleSteps[n]})}function kge(e,t,r){for(var n=e.select(\"rect.\"+Rs.gripRectClass),i=0,a=0;a<t._stepCount;a++)if(t._visibleSteps[a]._index===t.active){i=a;break}var o=fV(t,i/(t._stepCount-1));if(!t._invokingCommand){var s=n;r&&t.transition.duration>0&&(s=s.transition().duration(t.transition.duration).ease(t.transition.easing)),s.attr(\"transform\",Lmt(o-Rs.gripWidth*.5,t._dims.currentValueTotalHeight))}}function fV(e,t){var r=e._dims;return r.inputAreaStart+Rs.stepInset+(r.inputAreaLength-2*Rs.stepInset)*Math.min(1,Math.max(0,t))}function wge(e,t){var r=e._dims;return Math.min(1,Math.max(0,(t-Rs.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*Rs.stepInset-2*r.inputAreaStart)))}function Nmt(e,t,r){var n=r._dims,i=r0.ensureSingle(e,\"rect\",Rs.railTouchRectClass,function(a){a.call(Ege,t,e,r).style(\"pointer-events\",\"all\")});i.attr({width:n.inputAreaLength,height:Math.max(n.inputAreaWidth,Rs.tickOffset+r.ticklen+n.labelHeight)}).call(C_.fill,r.bgcolor).attr(\"opacity\",0),Vg.setTranslate(i,0,n.currentValueTotalHeight)}function Umt(e,t){var r=t._dims,n=r.inputAreaLength-Rs.railInset*2,i=r0.ensureSingle(e,\"rect\",Rs.railRectClass);i.attr({width:n,height:Rs.railWidth,rx:Rs.railRadius,ry:Rs.railRadius,\"shape-rendering\":\"crispEdges\"}).call(C_.stroke,t.bordercolor).call(C_.fill,t.bgcolor).style(\"stroke-width\",t.borderwidth+\"px\"),Vg.setTranslate(i,Rs.railInset,(r.inputAreaWidth-Rs.railWidth)*.5+r.currentValueTotalHeight)}});var Ige=ye((Xlr,Pge)=>{\"use strict\";var Vmt=b4();Pge.exports={moduleType:\"component\",name:Vmt.name,layoutAttributes:sV(),supplyLayoutDefaults:xge(),draw:Lge()}});var II=ye((Zlr,Dge)=>{\"use strict\";var Rge=Ih();Dge.exports={bgcolor:{valType:\"color\",dflt:Rge.background,editType:\"plot\"},bordercolor:{valType:\"color\",dflt:Rge.defaultLine,editType:\"plot\"},borderwidth:{valType:\"integer\",dflt:0,min:0,editType:\"plot\"},autorange:{valType:\"boolean\",dflt:!0,editType:\"calc\",impliedEdits:{\"range[0]\":void 0,\"range[1]\":void 0}},range:{valType:\"info_array\",items:[{valType:\"any\",editType:\"calc\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"calc\",impliedEdits:{\"^autorange\":!1}}],editType:\"calc\",impliedEdits:{autorange:!1}},thickness:{valType:\"number\",dflt:.15,min:0,max:1,editType:\"plot\"},visible:{valType:\"boolean\",dflt:!0,editType:\"calc\"},editType:\"calc\"}});var hV=ye((Ylr,Fge)=>{\"use strict\";Fge.exports={_isSubplotObj:!0,rangemode:{valType:\"enumerated\",values:[\"auto\",\"fixed\",\"match\"],dflt:\"match\",editType:\"calc\"},range:{valType:\"info_array\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},editType:\"calc\"}});var RI=ye((Klr,zge)=>{\"use strict\";zge.exports={name:\"rangeslider\",containerClassName:\"rangeslider-container\",bgClassName:\"rangeslider-bg\",rangePlotClassName:\"rangeslider-rangeplot\",maskMinClassName:\"rangeslider-mask-min\",maskMaxClassName:\"rangeslider-mask-max\",slideBoxClassName:\"rangeslider-slidebox\",grabberMinClassName:\"rangeslider-grabber-min\",grabAreaMinClassName:\"rangeslider-grabarea-min\",handleMinClassName:\"rangeslider-handle-min\",grabberMaxClassName:\"rangeslider-grabber-max\",grabAreaMaxClassName:\"rangeslider-grabarea-max\",handleMaxClassName:\"rangeslider-handle-max\",maskMinOppAxisClassName:\"rangeslider-mask-min-opp-axis\",maskMaxOppAxisClassName:\"rangeslider-mask-max-opp-axis\",maskColor:\"rgba(0,0,0,0.4)\",maskOppAxisColor:\"rgba(0,0,0,0.2)\",slideBoxFill:\"transparent\",slideBoxCursor:\"ew-resize\",grabAreaFill:\"transparent\",grabAreaCursor:\"col-resize\",grabAreaWidth:10,handleWidth:4,handleRadius:1,handleStrokeWidth:1,extraPad:15}});var Bge=ye(FI=>{\"use strict\";var Gmt=df(),Hmt=Zl(),qge=RI(),jmt=Dh().LINE_SPACING,DI=qge.name;function Oge(e){var t=e&&e[DI];return t&&t.visible}FI.isVisible=Oge;FI.makeData=function(e){for(var t=Gmt.list({_fullLayout:e},\"x\",!0),r=e.margin,n=[],i=0;i<t.length;i++){var a=t[i];if(Oge(a)){n.push(a);var o=a[DI];o._id=DI+a._id,o._height=(e.height-r.b-r.t)*o.thickness,o._offsetShift=Math.floor(o.borderwidth/2)}}e._rangeSliderData=n};FI.autoMarginOpts=function(e,t){var r=e._fullLayout,n=t[DI],i=t._id.charAt(0),a=0,o=0;if(t.side===\"bottom\"&&(a=t._depth,t.title.text!==r._dfltTitle[i])){o=1.5*t.title.font.size+10+n._offsetShift;var s=(t.title.text.match(Hmt.BR_TAG_ALL)||[]).length;o+=s*t.title.font.size*jmt}return{x:0,y:t._counterDomainMin,l:0,r:0,t:0,b:n._height+a+Math.max(r.margin.b,o),pad:qge.extraPad+n._offsetShift*2}}});var Gge=ye(($lr,Vge)=>{\"use strict\";var zI=Pr(),Nge=vl(),Uge=df(),Wmt=II(),Xmt=hV();Vge.exports=function(t,r,n){var i=t[n],a=r[n];if(!(i.rangeslider||r._requestRangeslider[a._id]))return;zI.isPlainObject(i.rangeslider)||(i.rangeslider={});var o=i.rangeslider,s=Nge.newContainer(a,\"rangeslider\");function l(L,x){return zI.coerce(o,s,Wmt,L,x)}var u,c;function f(L,x){return zI.coerce(u,c,Xmt,L,x)}var h=l(\"visible\");if(h){l(\"bgcolor\",r.plot_bgcolor),l(\"bordercolor\"),l(\"borderwidth\"),l(\"thickness\"),l(\"autorange\",!a.isValidRange(o.range)),l(\"range\");var d=r._subplots;if(d)for(var v=d.cartesian.filter(function(L){return L.slice(0,Math.max(0,L.indexOf(\"y\")))===Uge.name2id(n)}).map(function(L){return L.slice(L.indexOf(\"y\"),L.length)}),m=zI.simpleMap(v,Uge.id2name),b=0;b<m.length;b++){var p=m[b];u=o[p]||{},c=Nge.newContainer(s,p,\"yaxis\");var k=r[p],M;u.range&&k.isValidRange(u.range)&&(M=\"fixed\");var T=f(\"rangemode\",M);T!==\"match\"&&f(\"range\",k.range.slice())}s._input=o}}});var jge=ye((Qlr,Hge)=>{\"use strict\";var Zmt=df().list,Ymt=Mg().getAutoRange,Kmt=RI();Hge.exports=function(t){for(var r=Zmt(t,\"x\",!0),n=0;n<r.length;n++){var i=r[n],a=i[Kmt.name];a&&a.visible&&a.autorange&&(a._input.autorange=!0,a._input.range=a.range=Ymt(t,i))}}});var Yge=ye((eur,Zge)=>{\"use strict\";var qI=Fa(),Jmt=Oa(),$mt=Mc(),Jf=Pr(),OI=Jf.strTranslate,Xge=So(),L_=ka(),Qmt=qb(),eyt=mh(),dV=df(),tyt=yv(),ryt=Eg(),tl=RI();Zge.exports=function(e){for(var t=e._fullLayout,r=t._rangeSliderData,n=0;n<r.length;n++){var i=r[n][tl.name];i._clipId=i._id+\"-\"+t._uid}function a(s){return s._name}var o=t._infolayer.selectAll(\"g.\"+tl.containerClassName).data(r,a);o.exit().each(function(s){var l=s[tl.name];t._topdefs.select(\"#\"+l._clipId).remove()}).remove(),r.length!==0&&(o.enter().append(\"g\").classed(tl.containerClassName,!0).attr(\"pointer-events\",\"all\"),o.each(function(s){var l=qI.select(this),u=s[tl.name],c=t[dV.id2name(s.anchor)],f=u[dV.id2name(s.anchor)];if(u.range){var 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Ue=e.select(\".\"+gf.cbtitle),Ae=Ue.select(\"text\"),rt=[-l/2,l/2],St=Ue.select(\".h\"+ke._id+\"title-math-group\").node(),Tt=15.6;Ae.node()&&(Tt=parseInt(Ae.node().style.fontSize,10)*Vme);var dt;if(St?(dt=Gg.bBox(St),ct=dt.width,at=dt.height,at>Tt&&(rt[1]-=(at-Tt)/2)):Ae.node()&&!Ae.classed(gf.jsPlaceholder)&&(dt=Gg.bBox(Ae.node()),ct=dt.width,at=dt.height),n){if(at){if(at+=5,S===\"top\")ke.domain[1]-=at/T.h,rt[1]*=-1;else{ke.domain[0]+=at/T.h;var Et=i1t.lineCount(Ae);rt[1]+=(1-Et)*Tt}Ue.attr(\"transform\",Hg(rt[0],rt[1])),ke.setScale()}}else ct&&(S===\"right\"&&(ke.domain[0]+=(ct+lt/2)/T.w),Ue.attr(\"transform\",Hg(rt[0],rt[1])),ke.setScale())}e.selectAll(\".\"+gf.cbfills+\",.\"+gf.cblines).attr(\"transform\",n?Hg(0,Math.round(T.h*(1-ke.domain[1]))):Hg(Math.round(T.w*ke.domain[0]),0)),mt.attr(\"transform\",n?Hg(0,Math.round(-T.t)):Hg(Math.round(-T.l),0));var pt=e.select(\".\"+gf.cbfills).selectAll(\"rect.\"+gf.cbfill).attr(\"style\",\"\").data(V);pt.enter().append(\"rect\").classed(gf.cbfill,!0).attr(\"style\",\"\"),pt.exit().remove();var jt=g.map(ke.c2p).map(Math.round).sort(function(qr,jr){return qr-jr});pt.each(function(qr,jr){var gt=[jr===0?g[0]:(V[jr]+V[jr-1])/2,jr===V.length-1?g[1]:(V[jr]+V[jr+1])/2].map(ke.c2p).map(Math.round);n&&(gt[1]=U0.constrain(gt[1]+(gt[1]>gt[0])?1:-1,jt[0],jt[1]));var Ge=I_.select(this).attr(n?\"x\":\"y\",_e).attr(n?\"y\":\"x\",I_.min(gt)).attr(n?\"width\":\"height\",Math.max(Z,2)).attr(n?\"height\":\"width\",Math.max(I_.max(gt)-I_.min(gt),2));if(t._fillgradient)Gg.gradient(Ge,r,t._id,n?\"vertical\":\"horizontalreversed\",t._fillgradient,\"fill\");else{var Je=E(qr).replace(\"e-\",\"\");Ge.attr(\"fill\",EV(Je).toHexString())}});var or=e.select(\".\"+gf.cblines).selectAll(\"path.\"+gf.cbline).data(x.color&&x.width?G:[]);or.enter().append(\"path\").classed(gf.cbline,!0),or.exit().remove(),or.each(function(qr){var jr=_e,gt=Math.round(ke.c2p(qr))+x.width/2%1;I_.select(this).attr(\"d\",\"M\"+(n?jr+\",\"+gt:gt+\",\"+jr)+(n?\"h\":\"v\")+Z).call(Gg.lineGroupStyle,x.width,P(qr),x.dash)}),mt.selectAll(\"g.\"+ke._id+\"tick,path\").remove();var mr=_e+Z+(l||0)/2-(t.ticks===\"outside\"?1:0),Ar=Vy.calcTicks(ke),ei=Vy.getTickSigns(ke)[2];return Vy.drawTicks(r,ke,{vals:ke.ticks===\"inside\"?Vy.clipEnds(ke,Ar):Ar,layer:mt,path:Vy.makeTickPath(ke,mr,ei),transFn:Vy.makeTransTickFn(ke)}),Vy.drawLabels(r,ke,{vals:Ar,layer:mt,transFn:Vy.makeTransTickLabelFn(ke),labelFns:Vy.makeLabelFns(ke,mr)})}function je(){var Ue,Ae=Z+l/2;He.indexOf(\"inside\")===-1&&(Ue=Gg.bBox(mt.node()),Ae+=n?Ue.width:Ue.height),Ht=ce.select(\"text\");var rt=0,St=n&&S===\"top\",Tt=!n&&S===\"right\",dt=0;if(Ht.node()&&!Ht.classed(gf.jsPlaceholder)){var Et,pt=ce.select(\".h\"+ke._id+\"title-math-group\").node();pt&&(n&&be||!n&&!be)?(Ue=Gg.bBox(pt),rt=Ue.width,Et=Ue.height):(Ue=Gg.bBox(ce.node()),rt=Ue.right-T.l-(n?_e:Se),Et=Ue.bottom-T.t-(n?Se:_e),!n&&S===\"top\"&&(Ae+=Ue.height,dt=Ue.height)),Tt&&(Ht.attr(\"transform\",Hg(rt/2+lt/2,0)),rt*=2),Ae=Math.max(Ae,n?rt:Et)}var jt=(n?d:v)*2+Ae+u+l/2,or=0;!n&&C.text&&h===\"bottom\"&&b<=0&&(or=jt/2,jt+=or,dt+=or),M._hColorbarMoveTitle=or,M._hColorbarMoveCBTitle=dt;var mr=u+l,Ar=(n?_e:Se)-mr/2-(n?d:0),ei=(n?Se:_e)-(n?N:v+dt-or);e.select(\".\"+gf.cbbg).attr(\"x\",Ar).attr(\"y\",ei).attr(n?\"width\":\"height\",Math.max(jt-or,2)).attr(n?\"height\":\"width\",Math.max(N+mr,2)).call(CV.fill,c).call(CV.stroke,t.bordercolor).style(\"stroke-width\",u);var qr=Tt?Math.max(rt-10,0):0;e.selectAll(\".\"+gf.cboutline).attr(\"x\",(n?_e:Se+d)+qr).attr(\"y\",(n?Se+v-N:_e)+(St?at:0)).attr(n?\"width\":\"height\",Math.max(Z,2)).attr(n?\"height\":\"width\",Math.max(N-(n?2*v+at:2*d+qr),2)).call(CV.stroke,t.outlinecolor).style({fill:\"none\",\"stroke-width\":l});var jr=n?Ee*jt:0,gt=n?0:(1-Ce)*jt-dt;if(jr=k?T.l-jr:-jr,gt=p?T.t-gt:-gt,e.attr(\"transform\",Hg(jr,gt)),!n&&(u||EV(c).getAlpha()&&!EV.equals(M.paper_bgcolor,c))){var Ge=mt.selectAll(\"text\"),Je=Ge[0].length,We=e.select(\".\"+gf.cbbg).node(),et=Gg.bBox(We),xt=Gg.getTranslate(e),At=2;Ge.each(function(mi,un){var Fn=0,An=Je-1;if(un===Fn||un===An){var Hn=Gg.bBox(this),Qn=Gg.getTranslate(this),Vi;if(un===An){var Jn=Hn.right+Qn.x,Yn=et.right+xt.x+Se-u-At+m;Vi=Yn-Jn,Vi>0&&(Vi=0)}else if(un===Fn){var Gt=Hn.left+Qn.x,wt=et.left+xt.x+Se+u+At;Vi=wt-Gt,Vi<0&&(Vi=0)}Vi&&(Je<3?this.setAttribute(\"transform\",\"translate(\"+Vi+\",0) 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n=t.orientation===\"v\",i=r._fullLayout,a=i._size,o,s,l;HI.init({element:e.node(),gd:r,prepFn:function(){o=e.attr(\"transform\"),kV(e)},moveFn:function(u,c){e.attr(\"transform\",o+Hg(u,c)),s=HI.align((n?t._uFrac:t._vFrac)+u/a.w,n?t._thickFrac:t._lenFrac,0,1,t.xanchor),l=HI.align((n?t._vFrac:1-t._uFrac)-c/a.h,n?t._lenFrac:t._thickFrac,0,1,t.yanchor);var f=HI.getCursor(s,l,t.xanchor,t.yanchor);kV(e,f)},doneFn:function(){if(kV(e),s!==void 0&&l!==void 0){var u={};u[t._propPrefix+\"x\"]=s,u[t._propPrefix+\"y\"]=l,t._traceIndex!==void 0?Ume.call(\"_guiRestyle\",r,u,t._traceIndex):Ume.call(\"_guiRelayout\",r,u)}}})}function h1t(e,t,r){var n=t._levels,i=[],a=[],o,s,l=n.end+n.size/100,u=n.size,c=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(s=0;s<1e5&&(o=n.start+s*u,!(u>0?o>=l:o<=l));s++)o>c&&o<f&&i.push(o);if(t._fillgradient)a=[0];else if(typeof t._fillcolor==\"function\"){var h=t._filllevels;if(h)for(l=h.end+h.size/100,u=h.size,s=0;s<1e5&&(o=h.start+s*u,!(u>0?o>=l:o<=l));s++)o>r[0]&&o<r[1]&&a.push(o);else a=i.map(function(d){return d-n.size/2}),a.push(a[a.length-1]+n.size)}else t._fillcolor&&typeof t._fillcolor==\"string\"&&(a=[0]);return n.size<0&&(i.reverse(),a.reverse()),{line:i,fill:a}}function d1t(e,t,r){var n=e._fullLayout,i=t.orientation===\"v\",a={type:\"linear\",range:r,tickmode:t.tickmode,nticks:t.nticks,tick0:t.tick0,dtick:t.dtick,tickvals:t.tickvals,ticktext:t.ticktext,ticks:t.ticks,ticklen:t.ticklen,tickwidth:t.tickwidth,tickcolor:t.tickcolor,showticklabels:t.showticklabels,labelalias:t.labelalias,ticklabelposition:t.ticklabelposition,ticklabeloverflow:t.ticklabeloverflow,ticklabelstep:t.ticklabelstep,tickfont:t.tickfont,tickangle:t.tickangle,tickformat:t.tickformat,exponentformat:t.exponentformat,minexponent:t.minexponent,separatethousands:t.separatethousands,showexponent:t.showexponent,showtickprefix:t.showtickprefix,tickprefix:t.tickprefix,showticksuffix:t.showticksuffix,ticksuffix:t.ticksuffix,title:t.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+t._id},l={letter:o,font:n.font,noAutotickangles:o===\"y\",noHover:!0,noTickson:!0,noTicklabelmode:!0,noInsideRange:!0,calendar:n.calendar};function u(c,f){return 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ZI=ye((Cur,bye)=>{\"use strict\";bye.exports={barmode:{valType:\"enumerated\",values:[\"stack\",\"group\",\"overlay\",\"relative\"],dflt:\"group\",editType:\"calc\"},barnorm:{valType:\"enumerated\",values:[\"\",\"fraction\",\"percent\"],dflt:\"\",editType:\"calc\"},bargap:{valType:\"number\",min:0,max:1,editType:\"calc\"},bargroupgap:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},barcornerradius:{valType:\"any\",editType:\"calc\"}}});var YI=ye((Lur,Aye)=>{\"use strict\";var D1t=ka(),wye=pv().hasColorscale,Tye=td(),F1t=Pr().coercePattern;Aye.exports=function(t,r,n,i,a){var o=n(\"marker.color\",i),s=wye(t,\"marker\");s&&Tye(t,r,a,n,{prefix:\"marker.\",cLetter:\"c\"}),n(\"marker.line.color\",D1t.defaultLine),wye(t,\"marker.line\")&&Tye(t,r,a,n,{prefix:\"marker.line.\",cLetter:\"c\"}),n(\"marker.line.width\"),n(\"marker.opacity\"),F1t(n,\"marker.pattern\",o,s),n(\"selected.marker.color\"),n(\"unselected.marker.color\")}});var i0=ye((Pur,Lye)=>{\"use strict\";var 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M_t(e,t,r,n,i,a){var o=!!a.isHorizontal,s=!!a.constrained,l=a.angle||0,u=i.width,c=i.height,f=Math.abs(t-e),h=Math.abs(n-r),d;o?d=h>2*Gv?Gv:0:d=f>2*Gv?Gv:0;var v=1;s&&(v=o?Math.min(1,h/c):Math.min(1,f/u));var m=i1e(l),b=n1e(i,m),p=(o?b.x:b.y)/2,k=(i.left+i.right)/2,M=(i.top+i.bottom)/2,T=(e+t)/2,L=(r+n)/2,x=0,C=0,S=o?Om(t,e):Om(r,n);return o?(T=t-S*d,x=S*p):(L=n+S*d,C=-S*p),{textX:k,textY:M,targetX:T,targetY:L,anchorX:x,anchorY:C,scale:v,rotate:m}}function E_t(e,t,r,n,i){var a=t[0].trace,o=a.texttemplate,s;return o?s=C_t(e,t,r,n,i):a.textinfo?s=L_t(t,r,n,i):s=DT.getValue(a.text,r),DT.coerceString(m_t,s)}function k_t(e,t){var r=DT.getValue(e.textposition,t);return DT.coerceEnumerated(y_t,r)}function C_t(e,t,r,n,i){var a=t[0].trace,o=Fd.castOption(a,r,\"texttemplate\");if(!o)return\"\";var s=a.type===\"histogram\",l=a.type===\"waterfall\",u=a.type===\"funnel\",c=a.orientation===\"h\",f,h,d,v;c?(f=\"y\",h=i,d=\"x\",v=n):(f=\"x\",h=n,d=\"y\",v=i);function m(x){return 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d=rxt.distinctVals(f);(n.type===\"category\"||n.type===\"multicategory\")&&(d.minDiff=1);var v=d.minDiff/2;O1e.minDtick(n,d.minDiff,d.vals[0],!0);var m=e===\"violin\"?\"_numViolins\":\"_numBoxes\",b=a[m],p=a[e+\"mode\"]===\"group\"&&b>1,k=1-a[e+\"gap\"],M=1-a[e+\"groupgap\"];for(l=0;l<r.length;l++){c=i[r[l]];var T=c[0].trace,L=c[0].t,x=T.width,C=T.side,S,g,P,E;if(x)S=g=E=x/2,P=0;else if(S=v,p){var z=ixt(a,n._id)+T.orientation,q=a._alignmentOpts[z]||{},V=q[T.alignmentgroup]||{},G=Object.keys(V.offsetGroups||{}).length,Z=G||b,j=G?T._offsetIndex:L.num;g=S*k*M/Z,P=2*S*(-.5+(j+.5)/Z)*k,E=S*k/Z}else g=S*k*M,P=0,E=S;L.dPos=S,L.bPos=P,L.bdPos=g,L.wHover=E;var N,H,re=P+g,ae,_e,Ee,Ce,ge,ie,Se=!!x,ke=(T.boxpoints||T.points)&&h>0;if(C===\"positive\"?(N=S*(x?1:.5),ae=re,H=ae=P):C===\"negative\"?(N=ae=P,H=S*(x?1:.5),_e=re):(N=H=S,ae=_e=re),ke){var be=T.pointpos,Be=T.jitter,Le=T.marker.size/2,me=0;be+Be>=0&&(me=re*(be+Be),me>N?(Se=!0,ge=Le,Ee=me):me>ae&&(ge=Le,Ee=N)),me<=N&&(Ee=N);var Pe=0;be-Be<=0&&(Pe=-re*(be-Be),Pe>H?(Se=!0,ie=Le,Ce=Pe):Pe>_e&&(ie=Le,Ce=H)),Pe<=H&&(Ce=H)}else Ee=N,Ce=H;var ce=new Array(c.length);for(u=0;u<c.length;u++)ce[u]=c[u].pos;T._extremes[o]=O1e.findExtremes(n,ce,{padded:Se,vpadminus:Ce,vpadplus:Ee,vpadLinearized:!0,ppadminus:{x:ie,y:ge}[s],ppadplus:{x:ge,y:ie}[s]})}}}U1e.exports={crossTraceCalc:nxt,setPositionOffset:N1e}});var n8=ye((Kur,W1e)=>{\"use strict\";var OT=Fa(),m2=Pr(),axt=So(),V1e=5,oxt=.01;function sxt(e,t,r,n){var i=e._context.staticPlot,a=t.xaxis,o=t.yaxis;m2.makeTraceGroups(n,r,\"trace boxes\").each(function(s){var l=OT.select(this),u=s[0],c=u.t,f=u.trace;if(c.wdPos=c.bdPos*f.whiskerwidth,f.visible!==!0||c.empty){l.remove();return}var h,d;f.orientation===\"h\"?(h=o,d=a):(h=a,d=o),G1e(l,{pos:h,val:d},f,c,i),H1e(l,{x:a,y:o},f,c),j1e(l,{pos:h,val:d},f,c)})}function G1e(e,t,r,n,i){var a=r.orientation===\"h\",o=t.val,s=t.pos,l=!!s.rangebreaks,u=n.bPos,c=n.wdPos||0,f=n.bPosPxOffset||0,h=r.whiskerwidth||0,d=r.showwhiskers!==!1,v=r.notched||!1,m=v?1-2*r.notchwidth:1,b,p;Array.isArray(n.bdPos)?(b=n.bdPos[0],p=n.bdPos[1]):(b=n.bdPos,p=n.bdPos);var k=e.selectAll(\"path.box\").data(r.type!==\"violin\"||r.box.visible?m2.identity:[]);k.enter().append(\"path\").style(\"vector-effect\",i?\"none\":\"non-scaling-stroke\").attr(\"class\",\"box\"),k.exit().remove(),k.each(function(M){if(M.empty)return OT.select(this).attr(\"d\",\"M0,0Z\");var T=s.c2l(M.pos+u,!0),L=s.l2p(T-b)+f,x=s.l2p(T+p)+f,C=l?(L+x)/2:s.l2p(T)+f,S=r.whiskerwidth,g=l?L*S+(1-S)*C:s.l2p(T-c)+f,P=l?x*S+(1-S)*C:s.l2p(T+c)+f,E=s.l2p(T-b*m)+f,z=s.l2p(T+p*m)+f,q=r.sizemode===\"sd\",V=o.c2p(q?M.mean-M.sd:M.q1,!0),G=q?o.c2p(M.mean+M.sd,!0):o.c2p(M.q3,!0),Z=m2.constrain(q?o.c2p(M.mean,!0):o.c2p(M.med,!0),Math.min(V,G)+1,Math.max(V,G)-1),j=M.lf===void 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h.forEach(function(d){d.t=n,d.trace=r}),h},c=e.selectAll(\"g.points\").data(l?u:[]);c.enter().append(\"g\").attr(\"class\",\"points\"),c.exit().remove();var f=c.selectAll(\"path\").data(function(h){var d,v=h.pts2,m=Math.max((h.max-h.min)/10,h.q3-h.q1),b=m*1e-9,p=m*oxt,k=[],M=0,T;if(r.jitter){if(m===0)for(M=1,k=new Array(v.length),d=0;d<v.length;d++)k[d]=1;else for(d=0;d<v.length;d++){var L=Math.max(0,d-V1e),x=v[L].v,C=Math.min(v.length-1,d+V1e),S=v[C].v;l!==\"all\"&&(v[d].v<h.lf?S=Math.min(S,h.lf):x=Math.max(x,h.uf));var g=Math.sqrt(p*(C-L)/(S-x+b))||0;g=m2.constrain(Math.abs(g),0,1),k.push(g),M=Math.max(g,M)}T=r.jitter*2/(M||1)}for(d=0;d<v.length;d++){var P=v[d],E=P.v,z=r.jitter?T*k[d]*(m2.pseudoRandom()-.5):0,q=h.pos+s+o*(r.pointpos+z);r.orientation===\"h\"?(P.y=q,P.x=E):(P.x=q,P.y=E),l===\"suspectedoutliers\"&&E<h.uo&&E>h.lo&&(P.so=!0)}return v});f.enter().append(\"path\").classed(\"point\",!0),f.exit().remove(),f.call(axt.translatePoints,i,a)}function j1e(e,t,r,n){var i=t.val,a=t.pos,o=!!a.rangebreaks,s=n.bPos,l=n.bPosPxOffset||0,u=r.boxmean||(r.meanline||{}).visible,c,f;Array.isArray(n.bdPos)?(c=n.bdPos[0],f=n.bdPos[1]):(c=n.bdPos,f=n.bdPos);var h=e.selectAll(\"path.mean\").data(r.type===\"box\"&&r.boxmean||r.type===\"violin\"&&r.box.visible&&r.meanline.visible?m2.identity:[]);h.enter().append(\"path\").attr(\"class\",\"mean\").style({fill:\"none\",\"vector-effect\":\"non-scaling-stroke\"}),h.exit().remove(),h.each(function(d){var v=a.c2l(d.pos+s,!0),m=a.l2p(v-c)+l,b=a.l2p(v+f)+l,p=o?(m+b)/2:a.l2p(v)+l,k=i.c2p(d.mean,!0),M=i.c2p(d.mean-d.sd,!0),T=i.c2p(d.mean+d.sd,!0);r.orientation===\"h\"?OT.select(this).attr(\"d\",\"M\"+k+\",\"+m+\"V\"+b+(u===\"sd\"?\"m0,0L\"+M+\",\"+p+\"L\"+k+\",\"+m+\"L\"+T+\",\"+p+\"Z\":\"\")):OT.select(this).attr(\"d\",\"M\"+m+\",\"+k+\"H\"+b+(u===\"sd\"?\"m0,0L\"+p+\",\"+M+\"L\"+m+\",\"+k+\"L\"+p+\",\"+T+\"Z\":\"\"))})}W1e.exports={plot:sxt,plotBoxAndWhiskers:G1e,plotPoints:H1e,plotBoxMean:j1e}});var a8=ye((Jur,X1e)=>{\"use 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cxt=ho(),ZV=Pr(),D_=ef(),Z1e=ka(),fxt=ZV.fillText;function hxt(e,t,r,n){var i=e.cd,a=i[0].trace,o=a.hoveron,s=[],l;return o.indexOf(\"boxes\")!==-1&&(s=s.concat(Y1e(e,t,r,n))),o.indexOf(\"points\")!==-1&&(l=K1e(e,t,r)),n===\"closest\"?l?[l]:s:(l&&s.push(l),s)}function Y1e(e,t,r,n){var i=e.cd,a=e.xa,o=e.ya,s=i[0].trace,l=i[0].t,u=s.type===\"violin\",c,f,h,d,v,m,b,p,k,M,T,L=l.bdPos,x,C,S=l.wHover,g=function(Le){return h.c2l(Le.pos)+l.bPos-h.c2l(m)};u&&s.side!==\"both\"?(s.side===\"positive\"&&(k=function(Le){var me=g(Le);return D_.inbox(me,me+S,M)},x=L,C=0),s.side===\"negative\"&&(k=function(Le){var me=g(Le);return D_.inbox(me-S,me,M)},x=0,C=L)):(k=function(Le){var me=g(Le);return D_.inbox(me-S,me+S,M)},x=C=L);var P;u?P=function(Le){return D_.inbox(Le.span[0]-v,Le.span[1]-v,M)}:P=function(Le){return D_.inbox(Le.min-v,Le.max-v,M)},s.orientation===\"h\"?(v=t,m=r,b=P,p=k,c=\"y\",h=o,f=\"x\",d=a):(v=r,m=t,b=k,p=P,c=\"x\",h=a,f=\"y\",d=o);var E=Math.min(1,L/Math.abs(h.r2c(h.range[1])-h.r2c(h.range[0])));M=e.maxHoverDistance-E,T=e.maxSpikeDistance-E;function z(Le){return(b(Le)+p(Le))/2}var q=D_.getDistanceFunction(n,b,p,z);if(D_.getClosest(i,q,e),e.index===!1)return[];var V=i[e.index],G=s.line.color,Z=(s.marker||{}).color;Z1e.opacity(G)&&s.line.width?e.color=G:Z1e.opacity(Z)&&s.boxpoints?e.color=Z:e.color=s.fillcolor,e[c+\"0\"]=h.c2p(V.pos+l.bPos-C,!0),e[c+\"1\"]=h.c2p(V.pos+l.bPos+x,!0),e[c+\"LabelVal\"]=V.orig_p!==void 0?V.orig_p:V.pos;var j=c+\"Spike\";e.spikeDistance=z(V)*T/M,e[j]=h.c2p(V.pos,!0);var N=s.boxmean||s.sizemode===\"sd\"||(s.meanline||{}).visible,H=s.boxpoints||s.points,re=H&&N?[\"max\",\"uf\",\"q3\",\"med\",\"mean\",\"q1\",\"lf\",\"min\"]:H&&!N?[\"max\",\"uf\",\"q3\",\"med\",\"q1\",\"lf\",\"min\"]:!H&&N?[\"max\",\"q3\",\"med\",\"mean\",\"q1\",\"min\"]:[\"max\",\"q3\",\"med\",\"q1\",\"min\"],ae=d.range[1]<d.range[0];s.orientation===(ae?\"v\":\"h\")&&re.reverse();for(var _e=e.spikeDistance,Ee=e[j],Ce=[],ge=0;ge<re.length;ge++){var ie=re[ge];if(ie in V){var Se=V[ie],ke=d.c2p(Se,!0),be=ZV.extendFlat({},e);be.attr=ie,be[f+\"0\"]=be[f+\"1\"]=ke,be[f+\"LabelVal\"]=Se,be[f+\"Label\"]=(l.labels?l.labels[ie]+\" \":\"\")+cxt.hoverLabelText(d,Se,s[f+\"hoverformat\"]),be.hoverOnBox=!0,ie===\"mean\"&&\"sd\"in V&&(s.boxmean===\"sd\"||s.sizemode===\"sd\")&&(be[f+\"err\"]=V.sd),be.hovertemplate=!1,Ce.push(be)}}e.name=\"\",e.spikeDistance=void 0,e[j]=void 0;for(var Be=0;Be<Ce.length;Be++)Ce[Be].attr!==\"med\"?(Ce[Be].name=\"\",Ce[Be].spikeDistance=void 0,Ce[Be][j]=void 0):(Ce[Be].spikeDistance=_e,Ce[Be][j]=Ee);return Ce}function K1e(e,t,r){for(var n=e.cd,i=e.xa,a=e.ya,o=n[0].trace,s=i.c2p(t),l=a.c2p(r),u,c=function(P){var E=Math.max(3,P.mrc||0);return Math.max(Math.abs(i.c2p(P.x)-s)-E,1-3/E)},f=function(P){var E=Math.max(3,P.mrc||0);return Math.max(Math.abs(a.c2p(P.y)-l)-E,1-3/E)},h=D_.quadrature(c,f),d=!1,v,m,b=0;b<n.length;b++){v=n[b];for(var p=0;p<(v.pts||[]).length;p++){m=v.pts[p];var k=h(m);k<=e.distance&&(e.distance=k,d=[b,p])}}if(!d)return!1;v=n[d[0]],m=v.pts[d[1]];var M=i.c2p(m.x,!0),T=a.c2p(m.y,!0),L=m.mrc||1;u=ZV.extendFlat({},e,{index:m.i,color:(o.marker||{}).color,name:o.name,x0:M-L,x1:M+L,y0:T-L,y1:T+L,spikeDistance:e.distance,hovertemplate:o.hovertemplate});var x=v.orig_p,C=x!==void 0?x:v.pos,S;o.orientation===\"h\"?(S=a,u.xLabelVal=m.x,u.yLabelVal=C):(S=i,u.xLabelVal=C,u.yLabelVal=m.y);var g=S._id.charAt(0);return u[g+\"Spike\"]=S.c2p(v.pos,!0),fxt(m,o,u),u}J1e.exports={hoverPoints:hxt,hoverOnBoxes:Y1e,hoverOnPoints:K1e}});var Q1e=ye((Qur,$1e)=>{\"use strict\";$1e.exports=function(t,r){return r.hoverOnBox&&(t.hoverOnBox=r.hoverOnBox),\"xVal\"in r&&(t.x=r.xVal),\"yVal\"in r&&(t.y=r.yVal),r.xa&&(t.xaxis=r.xa),r.ya&&(t.yaxis=r.ya),t}});var KV=ye((ecr,e_e)=>{\"use strict\";e_e.exports=function(t,r){var 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BT=ye((icr,o_e)=>{\"use strict\";var 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Cxe=ye((Pcr,kxe)=>{\"use strict\";var Mxe=Oa(),q4=Pr(),Exe=ka(),ybt=i0().handleText,_bt=YI(),xbt=A8();kxe.exports=function(t,r,n,i){function a(k,M){return q4.coerce(t,r,xbt,k,M)}var o=a(\"x\"),s=a(\"y\"),l=a(\"cumulative.enabled\");l&&(a(\"cumulative.direction\"),a(\"cumulative.currentbin\")),a(\"text\");var u=a(\"textposition\");ybt(t,r,i,a,u,{moduleHasSelected:!0,moduleHasUnselected:!0,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),a(\"xhoverformat\"),a(\"yhoverformat\");var c=a(\"orientation\",s&&!o?\"h\":\"v\"),f=c===\"v\"?\"x\":\"y\",h=c===\"v\"?\"y\":\"x\",d=o&&s?Math.min(q4.minRowLength(o)&&q4.minRowLength(s)):q4.minRowLength(r[f]||[]);if(!d){r.visible=!1;return}r._length=d;var v=Mxe.getComponentMethod(\"calendars\",\"handleTraceDefaults\");v(t,r,[\"x\",\"y\"],i);var 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j.type}function p(G,Z,j){var N=G.uid+\"__\"+j;Z||(Z=N);var H=b(G,j),re=G[j+\"calendar\"]||\"\",ae=n[Z],_e=!0;ae&&(H===ae.axType&&re===ae.calendar?(_e=!1,ae.traces.push(G),ae.dirs.push(j)):(Z=N,H!==ae.axType&&O4.warn([\"Attempted to group the bins of trace\",G.index,\"set on a\",\"type:\"+H,\"axis\",\"with bins on\",\"type:\"+ae.axType,\"axis.\"].join(\" \")),re!==ae.calendar&&O4.warn([\"Attempted to group the bins of trace\",G.index,\"set with a\",re,\"calendar\",\"with bins\",ae.calendar?\"on a \"+ae.calendar+\" calendar\":\"w/o a set calendar\"].join(\" \")))),_e&&(n[Z]={traces:[G],dirs:[j],axType:H,calendar:G[j+\"calendar\"]||\"\"}),G[\"_\"+j+\"bingroup\"]=Z}for(f=0;f<t.length;f++)if(s=t[f],S8(s,\"histogram\")){if(i.push(s),delete s._xautoBinFinished,delete s._yautoBinFinished,s.type===\"histogram\"){var k=v(\"marker.cornerradius\",r.barcornerradius);s.marker&&(s.marker.cornerradius=Tbt(k))}S8(s,\"2dMap\")||wbt(s._input,s,r,v,r.barmode)}var M=r._alignmentOpts||{};for(f=0;f<i.length;f++){if(s=i[f],u=\"\",!S8(s,\"2dMap\")){if(c=m(s),r.barmode===\"group\"&&s.alignmentgroup){var T=s[c+\"axis\"],L=pG(r,T)+s.orientation;(M[L]||{})[s.alignmentgroup]&&(u=L)}!u&&r.barmode!==\"overlay\"&&(u=pG(r,s.xaxis)+pG(r,s.yaxis)+m(s))}u?(a[u]||(a[u]=[]),a[u].push(s)):o.push(s)}for(u in a){if(l=a[u],l.length===1){o.push(l[0]);continue}var x=!1;for(l.length&&(s=l[0],x=v(\"bingroup\")),u=x||u,f=0;f<l.length;f++){s=l[f];var C=s._input.bingroup;C&&C!==u&&O4.warn([\"Trace\",s.index,\"must match\",\"within bingroup\",u+\".\",\"Ignoring its bingroup:\",C,\"setting.\"].join(\" \")),s.bingroup=u,p(s,u,m(s))}}for(f=0;f<o.length;f++){s=o[f];var S=v(\"bingroup\");if(S8(s,\"2dMap\"))for(d=0;d<2;d++){c=Abt[d];var g=v(c+\"bingroup\",S?S+\"__\"+c:null);p(s,g,c)}else p(s,S,m(s))}for(u in n){var P=n[u];for(l=P.traces,h=0;h<Pxe.length;h++){var 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a};jv.createLineClip=function(e,t,r,n){var i=r._fullLayout._clips,a=t?\"clipline\"+n:null,o=i.selectAll(\"#\"+a).data(t?[0]:[]);return o.exit().remove(),o.enter().append(\"clipPath\").classed(\"contourlineclip\",!0).attr(\"id\",a),Wy.setClipUrl(e,a,r),o};jv.labelFormatter=function(e,t){var r=e._fullLayout,n=t.trace,i=n.contours,a={type:\"linear\",_id:\"ycontour\",showexponent:\"all\",exponentformat:\"B\"};if(i.labelformat)a.tickformat=i.labelformat,Dbe(a,r);else{var o=l2t.extractOpts(n);if(o&&o.colorbar&&o.colorbar._axis)a=o.colorbar._axis;else{if(i.type===\"constraint\"){var s=i.value;zd.isArrayOrTypedArray(s)?a.range=[s[0],s[s.length-1]]:a.range=[s,s]}else a.range=[i.start,i.end],a.nticks=(i.end-i.start)/i.size;a.range[0]===a.range[1]&&(a.range[1]+=a.range[0]||1),a.nticks||(a.nticks=1e3),Dbe(a,r),Rbe.prepTicks(a),a._tmin=null,a._tmax=null}}return function(l){return Rbe.tickText(a,l).text}};jv.calcTextOpts=function(e,t,r,n){var i=t(e);r.text(i).call(Fbe.convertToTspans,n);var 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Xbe=ye((ffr,Wbe)=>{\"use strict\";Wbe.exports={attributes:bG(),supplyDefaults:dbe(),crossTraceDefaults:M8(),calc:MG(),plot:F8().plot,layerName:\"contourlayer\",style:q8(),colorbar:O8(),hoverPoints:zG(),moduleType:\"trace\",name:\"histogram2dcontour\",basePlotModule:mh(),categories:[\"cartesian\",\"svg\",\"2dMap\",\"contour\",\"histogram\",\"showLegend\"],meta:{}}});var Ybe=ye((hfr,Zbe)=>{\"use strict\";Zbe.exports=Xbe()});var qG=ye((dfr,t2e)=>{\"use strict\";var Kbe=Eo(),M2t=wG(),Qbe=ka(),Jbe=Qbe.addOpacity,E2t=Qbe.opacity,e2e=C8(),$be=Pr().isArrayOrTypedArray,k2t=e2e.CONSTRAINT_REDUCTION,C2t=e2e.COMPARISON_OPS2;t2e.exports=function(t,r,n,i,a,o){var s=r.contours,l,u,c,f=n(\"contours.operation\");if(s._operation=k2t[f],L2t(n,s),f===\"=\"?l=s.showlines=!0:(l=n(\"contours.showlines\"),c=n(\"fillcolor\",Jbe((t.line||{}).color||a,.5))),l){var h=c&&E2t(c)?Jbe(r.fillcolor,1):a;u=n(\"line.color\",h),n(\"line.width\",2),n(\"line.dash\")}n(\"line.smoothing\"),M2t(n,i,u,o)};function L2t(e,t){var 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g2e=ye((yfr,p2e)=>{\"use strict\";var d2e=Pr(),V2t=Pm(),XT=Ru(),G2t=$p(),H2t=D0(),v2e=fT(),j2t=F0(),W2t=Fg(),X2t=BG();p2e.exports=function(t,r,n,i){function a(h,d){return d2e.coerce(t,r,X2t,h,d)}var o=a(\"a\"),s=a(\"b\"),l=a(\"c\"),u;if(o?(u=o.length,s?(u=Math.min(u,s.length),l&&(u=Math.min(u,l.length))):l?u=Math.min(u,l.length):u=0):s&&l&&(u=Math.min(s.length,l.length)),!u){r.visible=!1;return}r._length=u,a(\"sum\"),a(\"text\"),a(\"hovertext\"),r.hoveron!==\"fills\"&&(a(\"hovertemplate\"),a(\"hovertemplatefallback\"));var c=u<V2t.PTS_LINESONLY?\"lines+markers\":\"lines\";a(\"mode\",c),XT.hasMarkers(r)&&G2t(t,r,n,i,a,{gradient:!0}),XT.hasLines(r)&&(H2t(t,r,n,i,a,{backoff:!0}),v2e(t,r,a),a(\"connectgaps\")),XT.hasText(r)&&(a(\"texttemplate\"),a(\"texttemplatefallback\"),j2t(t,r,i,a));var f=[];(XT.hasMarkers(r)||XT.hasText(r))&&(a(\"cliponaxis\"),a(\"marker.maxdisplayed\"),f.push(\"points\")),a(\"fill\"),r.fill!==\"none\"&&(W2t(t,r,n,a),XT.hasLines(r)||v2e(t,r,a)),(r.fill===\"tonext\"||r.fill===\"toself\")&&f.push(\"fills\"),a(\"hoveron\",f.join(\"+\")||\"points\"),d2e.coerceSelectionMarkerOpacity(r,a)}});var y2e=ye((_fr,m2e)=>{\"use strict\";var NG=ho();m2e.exports=function(t,r,n){var i={},a=n[r.subplot]._subplot;return i.aLabel=NG.tickText(a.aaxis,t.a,!0).text,i.bLabel=NG.tickText(a.baxis,t.b,!0).text,i.cLabel=NG.tickText(a.caxis,t.c,!0).text,i}});var w2e=ye((xfr,b2e)=>{\"use strict\";var UG=Eo(),Z2t=z0(),Y2t=Dm(),K2t=q0(),J2t=O0().calcMarkerSize,_2e=[\"a\",\"b\",\"c\"],x2e={a:[\"b\",\"c\"],b:[\"a\",\"c\"],c:[\"a\",\"b\"]};b2e.exports=function(t,r){var n=t._fullLayout[r.subplot],i=n.sum,a=r.sum||i,o={a:r.a,b:r.b,c:r.c},s=r.ids,l,u,c,f,h,d;for(l=0;l<_2e.length;l++)if(c=_2e[l],!o[c]){for(h=o[x2e[c][0]],d=o[x2e[c][1]],f=new 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o.x0=Math.max(Math.min(o.x0,c),u),o.x1=Math.max(Math.min(o.x1,c),u),a}var f=o.cd[o.index],h=o.trace,d=o.subplot;o.a=f.a,o.b=f.b,o.c=f.c,o.xLabelVal=void 0,o.yLabelVal=void 0;var v={};v[h.subplot]={_subplot:d};var m=h._module.formatLabels(f,h,v);o.aLabel=m.aLabel,o.bLabel=m.bLabel,o.cLabel=m.cLabel;var b=f.hi||h.hoverinfo,p=[];function k(T,L){p.push(T._hovertitle+\": \"+L)}if(!h.hovertemplate){var M=b.split(\"+\");M.indexOf(\"all\")!==-1&&(M=[\"a\",\"b\",\"c\"]),M.indexOf(\"a\")!==-1&&k(d.aaxis,o.aLabel),M.indexOf(\"b\")!==-1&&k(d.baxis,o.bLabel),M.indexOf(\"c\")!==-1&&k(d.caxis,o.cLabel)}return o.extraText=p.join(\"<br>\"),o.hovertemplate=h.hovertemplate,a}});var k2e=ye((Tfr,E2e)=>{\"use strict\";E2e.exports=function(t,r,n,i,a){if(r.xa&&(t.xaxis=r.xa),r.ya&&(t.yaxis=r.ya),i[a]){var o=i[a];t.a=o.a,t.b=o.b,t.c=o.c}else t.a=r.a,t.b=r.b,t.c=r.c;return t}});var B2e=ye((Afr,O2e)=>{\"use strict\";var D2e=Fa(),ewt=fd(),VG=Oa(),Xy=Pr(),Vm=Xy.strTranslate,N8=Xy._,YT=ka(),U8=So(),X4=xm(),GG=Ao().extendFlat,twt=Mc(),q_=ho(),C2e=yv(),L2e=ef(),F2e=Cg(),P2e=F2e.freeMode,rwt=F2e.rectMode,HG=qb(),iwt=qf().prepSelect,nwt=qf().selectOnClick,awt=qf().clearOutline,owt=qf().clearSelectionsCache,z2e=Rh();function q2e(e,t){this.id=e.id,this.graphDiv=e.graphDiv,this.init(t),this.makeFramework(t),this.updateFx(t),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}O2e.exports=q2e;var Gm=q2e.prototype;Gm.init=function(e){this.container=e._ternarylayer,this.defs=e._defs,this.layoutId=e._uid,this.traceHash={},this.layers={}};Gm.plot=function(e,t){var r=this,n=t[r.id],i=t._size;r._hasClipOnAxisFalse=!1;for(var a=0;a<e.length;a++){var o=e[a][0].trace;if(o.cliponaxis===!1){r._hasClipOnAxisFalse=!0;break}}r.updateLayers(n),r.adjustLayout(n,i),twt.generalUpdatePerTraceModule(r.graphDiv,r,e,n),r.layers.plotbg.select(\"path\").call(YT.fill,n.bgcolor)};Gm.makeFramework=function(e){var t=this,r=t.graphDiv,n=e[t.id],i=t.clipId=\"clip\"+t.layoutId+t.id,a=t.clipIdRelative=\"clip-relative\"+t.layoutId+t.id;t.clipDef=Xy.ensureSingleById(e._clips,\"clipPath\",i,function(o){o.append(\"path\").attr(\"d\",\"M0,0Z\")}),t.clipDefRelative=Xy.ensureSingleById(e._clips,\"clipPath\",a,function(o){o.append(\"path\").attr(\"d\",\"M0,0Z\")}),t.plotContainer=Xy.ensureSingle(t.container,\"g\",t.id),t.updateLayers(n),U8.setClipUrl(t.layers.backplot,i,r),U8.setClipUrl(t.layers.grids,i,r)};Gm.updateFx=function(e){e._ternarylayer.selectAll(\"g.toplevel\").style(\"cursor\",e.dragmode===\"pan\"?\"move\":\"crosshair\")};Gm.updateLayers=function(e){var t=this,r=t.layers,n=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];e.aaxis.layer===\"below traces\"&&n.push(\"aaxis\",\"aline\"),e.baxis.layer===\"below traces\"&&n.push(\"baxis\",\"bline\"),e.caxis.layer===\"below traces\"&&n.push(\"caxis\",\"cline\"),n.push(\"frontplot\"),e.aaxis.layer===\"above 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r=this,n=e.domain,i=(n.x[0]+n.x[1])/2,a=(n.y[0]+n.y[1])/2,o=n.x[1]-n.x[0],s=n.y[1]-n.y[0],l=o*t.w,u=s*t.h,c=e.sum,f=e.aaxis.min,h=e.baxis.min,d=e.caxis.min,v,m,b,p,k,M;l>ZT*u?(p=u,b=p*ZT):(b=l,p=b/ZT),k=o*b/l,M=s*p/u,v=t.l+t.w*i-b/2,m=t.t+t.h*(1-a)-p/2,r.x0=v,r.y0=m,r.w=b,r.h=p,r.sum=c,r.xaxis={type:\"linear\",range:[f+2*d-c,c-f-2*h],domain:[i-k/2,i+k/2],_id:\"x\"},X4(r.xaxis,r.graphDiv._fullLayout),r.xaxis.setScale(),r.xaxis.isPtWithinRange=function(V){return V.a>=r.aaxis.range[0]&&V.a<=r.aaxis.range[1]&&V.b>=r.baxis.range[1]&&V.b<=r.baxis.range[0]&&V.c>=r.caxis.range[1]&&V.c<=r.caxis.range[0]},r.yaxis={type:\"linear\",range:[f,c-h-d],domain:[a-M/2,a+M/2],_id:\"y\"},X4(r.yaxis,r.graphDiv._fullLayout),r.yaxis.setScale(),r.yaxis.isPtWithinRange=function(){return!0};var T=r.yaxis.domain[0],L=r.aaxis=GG({},e.aaxis,{range:[f,c-h-d],side:\"left\",tickangle:(+e.aaxis.tickangle||0)-30,domain:[T,T+M*ZT],anchor:\"free\",position:0,_id:\"y\",_length:b});X4(L,r.graphDiv._fullLayout),L.setScale();var x=r.baxis=GG({},e.baxis,{range:[c-f-d,h],side:\"bottom\",domain:r.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:b});X4(x,r.graphDiv._fullLayout),x.setScale();var C=r.caxis=GG({},e.caxis,{range:[c-f-h,d],side:\"right\",tickangle:(+e.caxis.tickangle||0)+30,domain:[T,T+M*ZT],anchor:\"free\",position:0,_id:\"y\",_length:b});X4(C,r.graphDiv._fullLayout),C.setScale();var S=\"M\"+v+\",\"+(m+p)+\"h\"+b+\"l-\"+b/2+\",-\"+p+\"Z\";r.clipDef.select(\"path\").attr(\"d\",S),r.layers.plotbg.select(\"path\").attr(\"d\",S);var g=\"M0,\"+p+\"h\"+b+\"l-\"+b/2+\",-\"+p+\"Z\";r.clipDefRelative.select(\"path\").attr(\"d\",g);var 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s=W8.select(this),l=o[0].trace,u=N_.ensureSingle(s,\"g\",\"lines\");if(!l.connector||!l.connector.visible||!l.connector.line.width){u.remove();return}var c=l.orientation===\"h\",f=u.selectAll(\"g.line\").data(N_.identity);f.enter().append(\"g\").classed(\"line\",!0),f.exit().remove();var h=f.size();f.each(function(d,v){if(!(v!==h-1&&!d.cNext)){var m=Xwe(d,i,a,c),b=m[0],p=m[1],k=\"\";b[3]!==void 0&&p[3]!==void 0&&(c?(k+=\"M\"+b[0]+\",\"+p[1]+\"L\"+b[2]+\",\"+p[2],k+=\"M\"+b[1]+\",\"+p[1]+\"L\"+b[3]+\",\"+p[2]):(k+=\"M\"+b[1]+\",\"+p[1]+\"L\"+b[2]+\",\"+p[3],k+=\"M\"+b[1]+\",\"+p[0]+\"L\"+b[2]+\",\"+p[2])),k===\"\"&&(k=\"M0,0Z\"),N_.ensureSingle(W8.select(this),\"path\").attr(\"d\",k).call(Wwe.setClipUrl,t.layerClipId,e)}})})}function Xwe(e,t,r,n){var i=[],a=[],o=n?t:r,s=n?r:t;return i[0]=o.c2p(e.s0,!0),a[0]=s.c2p(e.p0,!0),i[1]=o.c2p(e.s1,!0),a[1]=s.c2p(e.p1,!0),i[2]=o.c2p(e.nextS0,!0),a[2]=s.c2p(e.nextP0,!0),i[3]=o.c2p(e.nextS1,!0),a[3]=s.c2p(e.nextP1,!0),n?[i,a]:[a,i]}});var $we=ye(($fr,Jwe)=>{\"use strict\";var Y4=Fa(),Kwe=So(),cH=ka(),m3t=Z1().DESELECTDIM,y3t=V0(),_3t=bv().resizeText,x3t=y3t.styleTextPoints;function b3t(e,t,r){var n=r||Y4.select(e).selectAll('g[class^=\"funnellayer\"]').selectAll(\"g.trace\");_3t(e,n,\"funnel\"),n.style(\"opacity\",function(i){return i[0].trace.opacity}),n.each(function(i){var a=Y4.select(this),o=i[0].trace;a.selectAll(\".point > path\").each(function(s){if(!s.isBlank){var l=o.marker;Y4.select(this).call(cH.fill,s.mc||l.color).call(cH.stroke,s.mlc||l.line.color).call(Kwe.dashLine,l.line.dash,s.mlw||l.line.width).style(\"opacity\",o.selectedpoints&&!s.selected?m3t:1)}}),x3t(a,o,e),a.selectAll(\".regions\").each(function(){Y4.select(this).selectAll(\"path\").style(\"stroke-width\",0).call(cH.fill,o.connector.fillcolor)}),a.selectAll(\".lines\").each(function(){var s=o.connector.line;Kwe.lineGroupStyle(Y4.select(this).selectAll(\"path\"),s.width,s.color,s.dash)})})}Jwe.exports={style:b3t}});var t3e=ye((Qfr,e3e)=>{\"use 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_c=qm(),hH=pf().line,A3t=Vl(),c3e=vf().axisHoverFormat,{hovertemplateAttrs:S3t,texttemplateAttrs:M3t,templatefallbackAttrs:f3e}=Pl(),h3e=u3e(),eA=Ao().extendFlat,E3t=ka();function dH(e){return{marker:{color:eA({},_c.marker.color,{arrayOk:!1,editType:\"style\"}),line:{color:eA({},_c.marker.line.color,{arrayOk:!1,editType:\"style\"}),width:eA({},_c.marker.line.width,{arrayOk:!1,editType:\"style\"}),editType:\"style\"},editType:\"style\"},editType:\"style\"}}d3e.exports={measure:{valType:\"data_array\",dflt:[],editType:\"calc\"},base:{valType:\"number\",dflt:null,arrayOk:!1,editType:\"calc\"},x:_c.x,x0:_c.x0,dx:_c.dx,y:_c.y,y0:_c.y0,dy:_c.dy,xperiod:_c.xperiod,yperiod:_c.yperiod,xperiod0:_c.xperiod0,yperiod0:_c.yperiod0,xperiodalignment:_c.xperiodalignment,yperiodalignment:_c.yperiodalignment,xhoverformat:c3e(\"x\"),yhoverformat:c3e(\"y\"),hovertext:_c.hovertext,hovertemplate:S3t({},{keys:h3e.eventDataKeys}),hovertemplatefallback:f3e(),hoverinfo:eA({},A3t.hoverinfo,{flags:[\"name\",\"x\",\"y\",\"text\",\"initial\",\"delta\",\"final\"]}),textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"initial\",\"delta\",\"final\"],extras:[\"none\"],editType:\"plot\",arrayOk:!1},texttemplate:M3t({editType:\"plot\"},{keys:h3e.eventDataKeys.concat([\"label\"])}),texttemplatefallback:f3e({editType:\"plot\"}),text:_c.text,textposition:_c.textposition,insidetextanchor:_c.insidetextanchor,textangle:_c.textangle,textfont:_c.textfont,insidetextfont:_c.insidetextfont,outsidetextfont:_c.outsidetextfont,constraintext:_c.constraintext,cliponaxis:_c.cliponaxis,orientation:_c.orientation,offset:_c.offset,width:_c.width,increasing:dH(\"increasing\"),decreasing:dH(\"decreasing\"),totals:dH(\"intermediate sums and total\"),connector:{line:{color:eA({},hH.color,{dflt:E3t.defaultLine}),width:eA({},hH.width,{editType:\"plot\"}),dash:hH.dash,editType:\"plot\"},mode:{valType:\"enumerated\",values:[\"spanning\",\"between\"],dflt:\"between\",editType:\"plot\"},visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},editType:\"plot\"},offsetgroup:_c.offsetgroup,alignmentgroup:_c.alignmentgroup,zorder:_c.zorder}});var pH=ye((ahr,v3e)=>{\"use strict\";v3e.exports={waterfallmode:{valType:\"enumerated\",values:[\"group\",\"overlay\"],dflt:\"group\",editType:\"calc\"},waterfallgap:{valType:\"number\",min:0,max:1,editType:\"calc\"},waterfallgroupgap:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"}}});var tA=ye((ohr,p3e)=>{\"use strict\";p3e.exports={INCREASING:{COLOR:\"#3D9970\",SYMBOL:\"\\u25B2\"},DECREASING:{COLOR:\"#FF4136\",SYMBOL:\"\\u25BC\"}}});var mH=ye((shr,_3e)=>{\"use strict\";var g3e=Pr(),k3t=i2(),C3t=i0().handleText,L3t=cT(),P3t=Dg(),m3e=vH(),I3t=ka(),y3e=tA(),R3t=y3e.INCREASING.COLOR,D3t=y3e.DECREASING.COLOR,F3t=\"#4499FF\";function gH(e,t,r){e(t+\".marker.color\",r),e(t+\".marker.line.color\",I3t.defaultLine),e(t+\".marker.line.width\")}function z3t(e,t,r,n){function i(u,c){return g3e.coerce(e,t,m3e,u,c)}var a=L3t(e,t,n,i);if(!a){t.visible=!1;return}P3t(e,t,n,i),i(\"xhoverformat\"),i(\"yhoverformat\"),i(\"measure\"),i(\"orientation\",t.x&&!t.y?\"h\":\"v\"),i(\"base\"),i(\"offset\"),i(\"width\"),i(\"text\"),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\");var o=i(\"textposition\");C3t(e,t,n,i,o,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),t.textposition!==\"none\"&&(i(\"texttemplate\"),i(\"texttemplatefallback\"),t.texttemplate||i(\"textinfo\")),gH(i,\"increasing\",R3t),gH(i,\"decreasing\",D3t),gH(i,\"totals\",F3t);var s=i(\"connector.visible\");if(s){i(\"connector.mode\");var l=i(\"connector.line.width\");l&&(i(\"connector.line.color\"),i(\"connector.line.dash\"))}i(\"zorder\")}function q3t(e,t){var r,n;function i(o){return g3e.coerce(n._input,n,m3e,o)}if(t.waterfallmode===\"group\")for(var a=0;a<e.length;a++)n=e[a],r=n._input,k3t(r,n,t,i,t.waterfallmode)}_3e.exports={supplyDefaults:z3t,crossTraceDefaults:q3t}});var b3e=ye((lhr,x3e)=>{\"use strict\";var O3t=Pr(),B3t=pH();x3e.exports=function(e,t,r){var n=!1;function i(s,l){return O3t.coerce(e,t,B3t,s,l)}for(var a=0;a<r.length;a++){var o=r[a];if(o.visible&&o.type===\"waterfall\"){n=!0;break}}n&&(i(\"waterfallmode\"),i(\"waterfallgap\",.2),i(\"waterfallgroupgap\"))}});var E3e=ye((uhr,M3e)=>{\"use strict\";var w3e=ho(),T3e=zg(),A3e=Pr().mergeArray,N3t=q0(),S3e=fs().BADNUM;function yH(e){return e===\"a\"||e===\"absolute\"}function _H(e){return e===\"t\"||e===\"total\"}M3e.exports=function(t,r){var n=w3e.getFromId(t,r.xaxis||\"x\"),i=w3e.getFromId(t,r.yaxis||\"y\"),a,o,s,l,u,c;r.orientation===\"h\"?(a=n.makeCalcdata(r,\"x\"),s=i.makeCalcdata(r,\"y\"),l=T3e(r,i,\"y\",s),u=!!r.yperiodalignment,c=\"y\"):(a=i.makeCalcdata(r,\"y\"),s=n.makeCalcdata(r,\"x\"),l=T3e(r,n,\"x\",s),u=!!r.xperiodalignment,c=\"x\"),o=l.vals;for(var f=Math.min(o.length,a.length),h=new Array(f),d=0,v,m=!1,b=0;b<f;b++){var p=a[b]||0,k=!1;(a[b]!==S3e||_H(r.measure[b])||yH(r.measure[b]))&&b+1<f&&(a[b+1]!==S3e||_H(r.measure[b+1])||yH(r.measure[b+1]))&&(k=!0);var M=h[b]={i:b,p:o[b],s:p,rawS:p,cNext:k};yH(r.measure[b])?(d=M.s,M.isSum=!0,M.dir=\"totals\",M.s=d):_H(r.measure[b])?(M.isSum=!0,M.dir=\"totals\",M.s=d):(M.isSum=!1,M.dir=M.rawS<0?\"decreasing\":\"increasing\",v=M.s,M.s=d+v,d+=v),M.dir===\"totals\"&&(m=!0),u&&(h[b].orig_p=s[b],h[b][c+\"End\"]=l.ends[b],h[b][c+\"Start\"]=l.starts[b]),r.ids&&(M.id=String(r.ids[b])),M.v=(r.base||0)+d}return h.length&&(h[0].hasTotals=m),A3e(r.text,h,\"tx\"),A3e(r.hovertext,h,\"htx\"),N3t(h,r),h}});var L3e=ye((chr,C3e)=>{\"use strict\";var k3e=n2().setGroupPositions;C3e.exports=function(t,r){var n=t._fullLayout,i=t._fullData,a=t.calcdata,o=r.xaxis,s=r.yaxis,l=[],u=[],c=[],f,h;for(h=0;h<i.length;h++){var d=i[h];d.visible===!0&&d.xaxis===o._id&&d.yaxis===s._id&&d.type===\"waterfall\"&&(f=a[h],d.orientation===\"h\"?c.push(f):u.push(f),l.push(f))}var v={mode:n.waterfallmode,norm:n.waterfallnorm,gap:n.waterfallgap,groupgap:n.waterfallgroupgap};for(k3e(t,o,s,u,v),k3e(t,s,o,c,v),h=0;h<l.length;h++){f=l[h];for(var m=0;m<f.length;m++){var b=f[m];b.isSum===!1&&(b.s0+=m===0?0:f[m-1].s),m+1<f.length&&(f[m].nextP0=f[m+1].p0,f[m].nextS0=f[m+1].s0)}}}});var R3e=ye((fhr,I3e)=>{\"use strict\";var P3e=Fa(),X8=Pr(),U3t=So(),rA=fs().BADNUM,V3t=g2(),G3t=bv().clearMinTextSize;I3e.exports=function(t,r,n,i){var a=t._fullLayout;G3t(\"waterfall\",a),V3t.plot(t,r,n,i,{mode:a.waterfallmode,norm:a.waterfallmode,gap:a.waterfallgap,groupgap:a.waterfallgroupgap}),H3t(t,r,n,i)};function H3t(e,t,r,n){var i=t.xaxis,a=t.yaxis;X8.makeTraceGroups(n,r,\"trace bars\").each(function(o){var s=P3e.select(this),l=o[0].trace,u=X8.ensureSingle(s,\"g\",\"lines\");if(!l.connector||!l.connector.visible){u.remove();return}var c=l.orientation===\"h\",f=l.connector.mode,h=u.selectAll(\"g.line\").data(X8.identity);h.enter().append(\"g\").classed(\"line\",!0),h.exit().remove();var d=h.size();h.each(function(v,m){if(!(m!==d-1&&!v.cNext)){var 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t=e._readableState;nu(\"emitReadable_\",t.destroyed,t.length,t.ended),!t.destroyed&&(t.length||t.ended)&&(e.emit(\"readable\"),t.emittedReadable=!1),t.needReadable=!t.flowing&&!t.ended&&t.length<=t.highWaterMark,Nj(e)}function Oj(e,t){t.readingMore||(t.readingMore=!0,process.nextTick(WMt,e,t))}function WMt(e,t){for(;!t.reading&&!t.ended&&(t.length<t.highWaterMark||t.flowing&&t.length===0);){var r=t.length;if(nu(\"maybeReadMore read 0\"),e.read(0),r===t.length)break}t.readingMore=!1}Bu.prototype._read=function(e){dE(this,new NMt(\"_read()\"))};Bu.prototype.pipe=function(e,t){var r=this,n=this._readableState;switch(n.pipesCount){case 0:n.pipes=e;break;case 1:n.pipes=[n.pipes,e];break;default:n.pipes.push(e);break}n.pipesCount+=1,nu(\"pipe count=%d opts=%j\",n.pipesCount,t);var i=(!t||t.end!==!1)&&e!==process.stdout&&e!==process.stderr,a=i?s:m;n.endEmitted?process.nextTick(a):r.once(\"end\",a),e.on(\"unpipe\",o);function 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r=vE.prototype.on.call(this,e,t),n=this._readableState;return e===\"data\"?(n.readableListening=this.listenerCount(\"readable\")>0,n.flowing!==!1&&this.resume()):e===\"readable\"&&!n.endEmitted&&!n.readableListening&&(n.readableListening=n.needReadable=!0,n.flowing=!1,n.emittedReadable=!1,nu(\"on readable\",n.length,n.reading),n.length?OR(this):n.reading||process.nextTick(ZMt,this)),r};Bu.prototype.addListener=Bu.prototype.on;Bu.prototype.removeListener=function(e,t){var r=vE.prototype.removeListener.call(this,e,t);return e===\"readable\"&&process.nextTick(PSe,this),r};Bu.prototype.removeAllListeners=function(e){var t=vE.prototype.removeAllListeners.apply(this,arguments);return(e===\"readable\"||e===void 0)&&process.nextTick(PSe,this),t};function PSe(e){var t=e._readableState;t.readableListening=e.listenerCount(\"readable\")>0,t.resumeScheduled&&!t.paused?t.flowing=!0:e.listenerCount(\"data\")>0&&e.resume()}function ZMt(e){nu(\"readable nexttick read 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this._readableState.flowing},set:function(t){this._readableState&&(this._readableState.flowing=t)}});Bu._fromList=ISe;Object.defineProperty(Bu.prototype,\"readableLength\",{enumerable:!1,get:function(){return this._readableState.length}});function ISe(e,t){if(t.length===0)return null;var r;return t.objectMode?r=t.buffer.shift():!e||e>=t.length?(t.decoder?r=t.buffer.join(\"\"):t.buffer.length===1?r=t.buffer.first():r=t.buffer.concat(t.length),t.buffer.clear()):r=t.buffer.consume(e,t.decoder),r}function qj(e){var t=e._readableState;nu(\"endReadable\",t.endEmitted),t.endEmitted||(t.ended=!0,process.nextTick(JMt,t,e))}function JMt(e,t){if(nu(\"endReadableNT\",e.endEmitted,e.length),!e.endEmitted&&e.length===0&&(e.endEmitted=!0,t.readable=!1,t.emit(\"end\"),e.autoDestroy)){var r=t._writableState;(!r||r.autoDestroy&&r.finished)&&t.destroy()}}typeof Symbol==\"function\"&&(Bu.from=function(e,t){return Dj===void 0&&(Dj=ASe()),Dj(Bu,e,t)});function RSe(e,t){for(var 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+       "`)+'Actual message: \"'.concat(t&&t.message,'\"'),stackStartFn:e})}throw t}}Ef.throws=function e(t){for(var r=arguments.length,n=new Array(r>1?r-1:0),i=1;i<r;i++)n[i-1]=arguments[i];D4e.apply(void 0,[e,I4e(t)].concat(n))};Ef.rejects=function e(t){for(var r=arguments.length,n=new Array(r>1?r-1:0),i=1;i<r;i++)n[i-1]=arguments[i];return R4e(t).then(function(a){return D4e.apply(void 0,[e,a].concat(n))})};Ef.doesNotThrow=function e(t){for(var r=arguments.length,n=new Array(r>1?r-1:0),i=1;i<r;i++)n[i-1]=arguments[i];F4e.apply(void 0,[e,I4e(t)].concat(n))};Ef.doesNotReject=function e(t){for(var r=arguments.length,n=new Array(r>1?r-1:0),i=1;i<r;i++)n[i-1]=arguments[i];return R4e(t).then(function(a){return F4e.apply(void 0,[e,a].concat(n))})};Ef.ifError=function e(t){if(t!=null){var r=\"ifError got unwanted exception: \";Zg(t)===\"object\"&&typeof t.message==\"string\"?t.message.length===0&&t.constructor?r+=t.constructor.name:r+=t.message:r+=oD(t);var n=new J_({actual:t,expected:null,operator:\"ifError\",message:r,stackStartFn:e}),i=t.stack;if(typeof i==\"string\"){var a=i.split(`\n",
+       "`);a.shift();for(var o=n.stack.split(`\n",
+       "`),s=0;s<a.length;s++){var l=o.indexOf(a[s]);if(l!==-1){o=o.slice(0,l);break}}n.stack=\"\".concat(o.join(`\n",
+       "`),`\n",
+       "`).concat(a.join(`\n",
+       "`))}throw n}};function z4e(e,t,r,n,i){if(!sD(t))throw new EA(\"regexp\",\"RegExp\",t);var a=i===\"match\";if(typeof e!=\"string\"||lD(t,e)!==a){if(r instanceof Error)throw r;var o=!r;r=r||(typeof e!=\"string\"?'The \"string\" argument must be of type string. Received type '+\"\".concat(Zg(e),\" (\").concat(oD(e),\")\"):(a?\"The input did not match the regular expression \":\"The input was expected to not match the regular expression \")+\"\".concat(oD(t),`. Input:\n",
+       "\n",
+       "`).concat(oD(e),`\n",
+       "`));var s=new J_({actual:e,expected:t,message:r,operator:i,stackStartFn:n});throw s.generatedMessage=o,s}}Ef.match=function e(t,r,n){z4e(t,r,n,e,\"match\")};Ef.doesNotMatch=function e(t,r,n){z4e(t,r,n,e,\"doesNotMatch\")};function q4e(){for(var e=arguments.length,t=new Array(e),r=0;r<e;r++)t[r]=arguments[r];C4e.apply(void 0,[q4e,t.length].concat(t))}Ef.strict=ukt(q4e,Ef,{equal:Ef.strictEqual,deepEqual:Ef.deepStrictEqual,notEqual:Ef.notStrictEqual,notDeepEqual:Ef.notDeepStrictEqual});Ef.strict.strict=Ef.strict});var N4e=ye((nvr,B4e)=>{var IE=1e3,RE=IE*60,DE=RE*60,FE=DE*24,fkt=FE*365.25;B4e.exports=function(e,t){t=t||{};var r=typeof e;if(r===\"string\"&&e.length>0)return hkt(e);if(r===\"number\"&&isNaN(e)===!1)return t.long?vkt(e):dkt(e);throw new Error(\"val is not a non-empty string or a valid number. val=\"+JSON.stringify(e))};function hkt(e){if(e=String(e),!(e.length>100)){var t=/^((?:\\d+)?\\.?\\d+) *(milliseconds?|msecs?|ms|seconds?|secs?|s|minutes?|mins?|m|hours?|hrs?|h|days?|d|years?|yrs?|y)?$/i.exec(e);if(t){var r=parseFloat(t[1]),n=(t[2]||\"ms\").toLowerCase();switch(n){case\"years\":case\"year\":case\"yrs\":case\"yr\":case\"y\":return r*fkt;case\"days\":case\"day\":case\"d\":return r*FE;case\"hours\":case\"hour\":case\"hrs\":case\"hr\":case\"h\":return r*DE;case\"minutes\":case\"minute\":case\"mins\":case\"min\":case\"m\":return r*RE;case\"seconds\":case\"second\":case\"secs\":case\"sec\":case\"s\":return r*IE;case\"milliseconds\":case\"millisecond\":case\"msecs\":case\"msec\":case\"ms\":return r;default:return}}}}function dkt(e){return e>=FE?Math.round(e/FE)+\"d\":e>=DE?Math.round(e/DE)+\"h\":e>=RE?Math.round(e/RE)+\"m\":e>=IE?Math.round(e/IE)+\"s\":e+\"ms\"}function vkt(e){return fD(e,FE,\"day\")||fD(e,DE,\"hour\")||fD(e,RE,\"minute\")||fD(e,IE,\"second\")||e+\" ms\"}function fD(e,t,r){if(!(e<t))return e<t*1.5?Math.floor(e/t)+\" \"+r:Math.ceil(e/t)+\" \"+r+\"s\"}});var V4e=ye((Lc,U4e)=>{Lc=U4e.exports=pW.debug=pW.default=pW;Lc.coerce=_kt;Lc.disable=mkt;Lc.enable=gkt;Lc.enabled=ykt;Lc.humanize=N4e();Lc.names=[];Lc.skips=[];Lc.formatters={};var vW;function pkt(e){var t=0,r;for(r in e)t=(t<<5)-t+e.charCodeAt(r),t|=0;return Lc.colors[Math.abs(t)%Lc.colors.length]}function pW(e){function t(){if(t.enabled){var r=t,n=+new Date,i=n-(vW||n);r.diff=i,r.prev=vW,r.curr=n,vW=n;for(var a=new Array(arguments.length),o=0;o<a.length;o++)a[o]=arguments[o];a[0]=Lc.coerce(a[0]),typeof a[0]!=\"string\"&&a.unshift(\"%O\");var s=0;a[0]=a[0].replace(/%([a-zA-Z%])/g,function(u,c){if(u===\"%%\")return u;s++;var f=Lc.formatters[c];if(typeof f==\"function\"){var h=a[s];u=f.call(r,h),a.splice(s,1),s--}return u}),Lc.formatArgs.call(r,a);var l=t.log||Lc.log||console.log.bind(console);l.apply(r,a)}}return t.namespace=e,t.enabled=Lc.enabled(e),t.useColors=Lc.useColors(),t.color=pkt(e),typeof Lc.init==\"function\"&&Lc.init(t),t}function gkt(e){Lc.save(e),Lc.names=[],Lc.skips=[];for(var t=(typeof e==\"string\"?e:\"\").split(/[\\s,]+/),r=t.length,n=0;n<r;n++)t[n]&&(e=t[n].replace(/\\*/g,\".*?\"),e[0]===\"-\"?Lc.skips.push(new RegExp(\"^\"+e.substr(1)+\"$\")):Lc.names.push(new RegExp(\"^\"+e+\"$\")))}function mkt(){Lc.enable(\"\")}function ykt(e){var t,r;for(t=0,r=Lc.skips.length;t<r;t++)if(Lc.skips[t].test(e))return!1;for(t=0,r=Lc.names.length;t<r;t++)if(Lc.names[t].test(e))return!0;return!1}function _kt(e){return e instanceof Error?e.stack||e.message:e}});var j4e=ye((up,H4e)=>{up=H4e.exports=V4e();up.log=wkt;up.formatArgs=bkt;up.save=Tkt;up.load=G4e;up.useColors=xkt;up.storage=typeof chrome!=\"undefined\"&&typeof chrome.storage!=\"undefined\"?chrome.storage.local:Akt();up.colors=[\"lightseagreen\",\"forestgreen\",\"goldenrod\",\"dodgerblue\",\"darkorchid\",\"crimson\"];function xkt(){return typeof window!=\"undefined\"&&window.process&&window.process.type===\"renderer\"?!0:typeof document!=\"undefined\"&&document.documentElement&&document.documentElement.style&&document.documentElement.style.WebkitAppearance||typeof window!=\"undefined\"&&window.console&&(window.console.firebug||window.console.exception&&window.console.table)||typeof navigator!=\"undefined\"&&navigator.userAgent&&navigator.userAgent.toLowerCase().match(/firefox\\/(\\d+)/)&&parseInt(RegExp.$1,10)>=31||typeof navigator!=\"undefined\"&&navigator.userAgent&&navigator.userAgent.toLowerCase().match(/applewebkit\\/(\\d+)/)}up.formatters.j=function(e){try{return JSON.stringify(e)}catch(t){return\"[UnexpectedJSONParseError]: \"+t.message}};function bkt(e){var t=this.useColors;if(e[0]=(t?\"%c\":\"\")+this.namespace+(t?\" %c\":\" \")+e[0]+(t?\"%c \":\" \")+\"+\"+up.humanize(this.diff),!!t){var r=\"color: \"+this.color;e.splice(1,0,r,\"color: inherit\");var n=0,i=0;e[0].replace(/%[a-zA-Z%]/g,function(a){a!==\"%%\"&&(n++,a===\"%c\"&&(i=n))}),e.splice(i,0,r)}}function wkt(){return typeof console==\"object\"&&console.log&&Function.prototype.apply.call(console.log,console,arguments)}function Tkt(e){try{e==null?up.storage.removeItem(\"debug\"):up.storage.debug=e}catch(t){}}function G4e(){var e;try{e=up.storage.debug}catch(t){}return!e&&typeof process!=\"undefined\"&&\"env\"in process&&(e=process.env.DEBUG),e}up.enable(G4e());function Akt(){try{return window.localStorage}catch(e){}}});var Q4e=ye((avr,$4e)=>{var kA=_E(),$_=j4e()(\"stream-parser\");$4e.exports=Mkt;var X4e=-1,hD=0,Skt=1,Z4e=2;function Mkt(e){var t=e&&typeof e._transform==\"function\",r=e&&typeof e._write==\"function\";if(!t&&!r)throw new Error(\"must pass a Writable or Transform stream in\");$_(\"extending Parser into stream\"),e._bytes=Ekt,e._skipBytes=kkt,t&&(e._passthrough=Ckt),t?e._transform=Pkt:e._write=Lkt}function zE(e){$_(\"initializing parser stream\"),e._parserBytesLeft=0,e._parserBuffers=[],e._parserBuffered=0,e._parserState=X4e,e._parserCallback=null,typeof e.push==\"function\"&&(e._parserOutput=e.push.bind(e)),e._parserInit=!0}function Ekt(e,t){kA(!this._parserCallback,'there is already a \"callback\" set!'),kA(isFinite(e)&&e>0,'can only buffer a finite number of bytes > 0, got \"'+e+'\"'),this._parserInit||zE(this),$_(\"buffering %o bytes\",e),this._parserBytesLeft=e,this._parserCallback=t,this._parserState=hD}function kkt(e,t){kA(!this._parserCallback,'there is already a \"callback\" set!'),kA(e>0,'can only skip > 0 bytes, got \"'+e+'\"'),this._parserInit||zE(this),$_(\"skipping %o bytes\",e),this._parserBytesLeft=e,this._parserCallback=t,this._parserState=Skt}function Ckt(e,t){kA(!this._parserCallback,'There is already a \"callback\" set!'),kA(e>0,'can only pass through > 0 bytes, got \"'+e+'\"'),this._parserInit||zE(this),$_(\"passing through %o bytes\",e),this._parserBytesLeft=e,this._parserCallback=t,this._parserState=Z4e}function Lkt(e,t,r){this._parserInit||zE(this),$_(\"write(%o bytes)\",e.length),typeof t==\"function\"&&(r=t),K4e(this,e,null,r)}function Pkt(e,t,r){this._parserInit||zE(this),$_(\"transform(%o bytes)\",e.length),typeof t!=\"function\"&&(t=this._parserOutput),K4e(this,e,t,r)}function Y4e(e,t,r,n){return e._parserBytesLeft<=0?n(new Error(\"got data but not currently parsing anything\")):t.length<=e._parserBytesLeft?function(){return W4e(e,t,r,n)}:function(){var i=t.slice(0,e._parserBytesLeft);return W4e(e,i,r,function(a){if(a)return n(a);if(t.length>i.length)return function(){return Y4e(e,t.slice(i.length),r,n)}})}}function W4e(e,t,r,n){if(e._parserBytesLeft-=t.length,$_(\"%o bytes left for stream piece\",e._parserBytesLeft),e._parserState===hD?(e._parserBuffers.push(t),e._parserBuffered+=t.length):e._parserState===Z4e&&r(t),e._parserBytesLeft===0){var i=e._parserCallback;if(i&&e._parserState===hD&&e._parserBuffers.length>1&&(t=Buffer.concat(e._parserBuffers,e._parserBuffered)),e._parserState!==hD&&(t=null),e._parserCallback=null,e._parserBuffered=0,e._parserState=X4e,e._parserBuffers.splice(0),i){var a=[];t&&a.push(t),r&&a.push(r);var o=i.length>a.length;o&&a.push(J4e(n));var s=i.apply(e,a);if(!o||n===s)return n}}else return n}var K4e=J4e(Y4e);function J4e(e){return function(){for(var t=e.apply(this,arguments);typeof t==\"function\";)t=t();return t}}});var rc=ye($y=>{\"use strict\";var eEe=WSe().Transform,Ikt=Q4e();function qE(){eEe.call(this,{readableObjectMode:!0})}qE.prototype=Object.create(eEe.prototype);qE.prototype.constructor=qE;Ikt(qE.prototype);$y.ParserStream=qE;$y.sliceEq=function(e,t,r){for(var n=t,i=0;i<r.length;)if(e[n++]!==r[i++])return!1;return!0};$y.str2arr=function(e,t){var r=[],n=0;if(t&&t===\"hex\")for(;n<e.length;)r.push(parseInt(e.slice(n,n+2),16)),n+=2;else for(;n<e.length;n++)r.push(e.charCodeAt(n)&255);return r};$y.readUInt16LE=function(e,t){return e[t]|e[t+1]<<8};$y.readUInt16BE=function(e,t){return e[t+1]|e[t]<<8};$y.readUInt32LE=function(e,t){return e[t]|e[t+1]<<8|e[t+2]<<16|e[t+3]*16777216};$y.readUInt32BE=function(e,t){return e[t+3]|e[t+2]<<8|e[t+1]<<16|e[t]*16777216};function dD(e,t,r){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=new Error().stack||\"\",this.name=this.constructor.name,this.message=e,t&&(this.code=t),r&&(this.statusCode=r)}dD.prototype=Object.create(Error.prototype);dD.prototype.constructor=dD;$y.ProbeError=dD});var tEe=ye((svr,vD)=>{\"use strict\";var CA=rc().readUInt16BE,mW=rc().readUInt32BE;function OE(e,t){if(e.length<4+t)return null;var r=mW(e,t);return e.length<r+t||r<8?null:{boxtype:String.fromCharCode.apply(null,e.slice(t+4,t+8)),data:e.slice(t+8,t+r),end:t+r}}vD.exports.unbox=OE;function Rkt(e,t){for(var r=0;;){var n=OE(e,r);if(!n)break;switch(n.boxtype){case\"ispe\":t.sizes.push({width:mW(n.data,4),height:mW(n.data,8)});break;case\"irot\":t.transforms.push({type:\"irot\",value:n.data[0]&3});break;case\"imir\":t.transforms.push({type:\"imir\",value:n.data[0]&1});break}r=n.end}}function gW(e,t,r){for(var n=0,i=0;i<r;i++)n=n*256+(e[t+i]||0);return n}function Dkt(e,t){for(var r=e[4]>>4&15,n=e[4]&15,i=e[5]>>4&15,a=CA(e,6),o=8,s=0;s<a;s++){var l=CA(e,o);o+=2;var u=CA(e,o);o+=2;var c=gW(e,o,i);o+=i;var f=CA(e,o);if(o+=2,u===0&&f===1){var h=gW(e,o,r),d=gW(e,o+r,n);t.item_loc[l]={length:d,offset:h+c}}o+=f*(r+n)}}function Fkt(e,t){for(var r=CA(e,4),n=6,i=0;i<r;i++){var a=OE(e,n);if(!a)break;if(a.boxtype===\"infe\"){for(var o=CA(a.data,4),s=\"\",l=8;l<a.data.length&&a.data[l];l++)s+=String.fromCharCode(a.data[l]);t.item_inf[s]=o}n=a.end}}function zkt(e,t){for(var r=0;;){var n=OE(e,r);if(!n)break;n.boxtype===\"ipco\"&&Rkt(n.data,t),r=n.end}}function qkt(e,t){for(var r=4;;){var n=OE(e,r);if(!n)break;n.boxtype===\"iprp\"&&zkt(n.data,t),n.boxtype===\"iloc\"&&Dkt(n.data,t),n.boxtype===\"iinf\"&&Fkt(n.data,t),r=n.end}}function Okt(e){var t=e.reduce(function(i,a){return i.width>a.width||i.width===a.width&&i.height>a.height?i:a}),r=e.reduce(function(i,a){return i.height>a.height||i.height===a.height&&i.width>a.width?i:a}),n;return t.width>r.height||t.width===r.height&&t.height>r.width?n=t:n=r,n}vD.exports.readSizeFromMeta=function(e){var t={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(qkt(e,t),!!t.sizes.length){var r=Okt(t.sizes),n=1;t.transforms.forEach(function(a){var o={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},s={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(a.type===\"imir\"&&(a.value===0?n=s[n]:(n=s[n],n=o[n],n=o[n])),a.type===\"irot\")for(var l=0;l<a.value;l++)n=o[n]});var i=null;return t.item_inf.Exif&&(i=t.item_loc[t.item_inf.Exif]),{width:r.width,height:r.height,orientation:t.transforms.length?n:null,variants:t.sizes,exif_location:i}}};vD.exports.getMimeType=function(e){var t=String.fromCharCode.apply(null,e.slice(0,4)),r={};r[t]=!0;for(var n=8;n<e.length;n+=4)r[String.fromCharCode.apply(null,e.slice(n,n+4))]=!0;if(!(!r.mif1&&!r.msf1&&!r.miaf))return t===\"avif\"||t===\"avis\"||t===\"avio\"?{type:\"avif\",mime:\"image/avif\"}:t===\"heic\"||t===\"heix\"?{type:\"heic\",mime:\"image/heic\"}:t===\"hevc\"||t===\"hevx\"?{type:\"heic\",mime:\"image/heic-sequence\"}:r.avif||r.avis?{type:\"avif\",mime:\"image/avif\"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:\"heif\",mime:\"image/heif-sequence\"}:{type:\"heif\",mime:\"image/heif\"}:{type:\"avif\",mime:\"image/avif\"}}});var gD=ye((lvr,yW)=>{\"use strict\";function pD(e,t){var r=new Error(e);return r.code=t,r}function Bkt(e){try{return decodeURIComponent(escape(e))}catch(t){return e}}function Qy(e,t,r){this.input=e.subarray(t,r),this.start=t;var n=String.fromCharCode.apply(null,this.input.subarray(0,4));if(n!==\"II*\\0\"&&n!==\"MM\\0*\")throw pD(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=n[0]===\"M\"}Qy.prototype.each=function(e){this.aborted=!1;var t=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:t}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,e)}};Qy.prototype.read_uint16=function(e){var t=this.input;if(e+2>t.length)throw pD(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?t[e]*256+t[e+1]:t[e]+t[e+1]*256};Qy.prototype.read_uint32=function(e){var t=this.input;if(e+4>t.length)throw pD(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?t[e]*16777216+t[e+1]*65536+t[e+2]*256+t[e+3]:t[e]+t[e+1]*256+t[e+2]*65536+t[e+3]*16777216};Qy.prototype.is_subifd_link=function(e,t){return e===0&&t===34665||e===0&&t===34853||e===34665&&t===40965};Qy.prototype.exif_format_length=function(e){switch(e){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}};Qy.prototype.exif_format_read=function(e,t){var r;switch(e){case 1:case 2:return r=this.input[t],r;case 6:return r=this.input[t],r|(r&128)*33554430;case 3:return r=this.read_uint16(t),r;case 8:return r=this.read_uint16(t),r|(r&32768)*131070;case 4:return r=this.read_uint32(t),r;case 9:return r=this.read_uint32(t),r|0;case 5:case 10:case 11:case 12:return null;case 7:return null;default:return null}};Qy.prototype.scan_ifd=function(e,t,r){var n=this.read_uint16(t);t+=2;for(var i=0;i<n;i++){var a=this.read_uint16(t),o=this.read_uint16(t+2),s=this.read_uint32(t+4),l=this.exif_format_length(o),u=s*l,c=u<=4?t+8:this.read_uint32(t+8),f=!1;if(c+u>this.input.length)throw pD(\"unexpected EOF\",\"EBADDATA\");for(var h=[],d=c,v=0;v<s;v++,d+=l){var m=this.exif_format_read(o,d);if(m===null){h=null;break}h.push(m)}Array.isArray(h)&&o===2&&(h=Bkt(String.fromCharCode.apply(null,h)),h&&h[h.length-1]===\"\\0\"&&(h=h.slice(0,-1))),this.is_subifd_link(e,a)&&Array.isArray(h)&&Number.isInteger(h[0])&&h[0]>0&&(this.ifds_to_read.push({id:a,offset:h[0]}),f=!0);var b={is_big_endian:this.big_endian,ifd:e,tag:a,format:o,count:s,entry_offset:t+this.start,data_length:u,data_offset:c+this.start,value:h,is_subifd_link:f};if(r(b)===!1){this.aborted=!0;return}t+=12}e===0&&this.ifds_to_read.push({id:1,offset:this.read_uint32(t)})};yW.exports.ExifParser=Qy;yW.exports.get_orientation=function(e){var t=0;try{return new Qy(e,0,e.length).each(function(r){if(r.ifd===0&&r.tag===274&&Array.isArray(r.value))return t=r.value[0],!1}),t}catch(r){return-1}}});var iEe=ye((uvr,rEe)=>{\"use strict\";var Nkt=rc().str2arr,Ukt=rc().sliceEq,Vkt=rc().readUInt32BE,mD=tEe(),Gkt=gD(),Hkt=Nkt(\"ftyp\");rEe.exports=function(e){if(Ukt(e,4,Hkt)){var t=mD.unbox(e,0);if(t){var r=mD.getMimeType(t.data);if(r){for(var n,i=t.end;;){var a=mD.unbox(e,i);if(!a)break;if(i=a.end,a.boxtype===\"mdat\")return;if(a.boxtype===\"meta\"){n=a.data;break}}if(n){var o=mD.readSizeFromMeta(n);if(o){var s={width:o.width,height:o.height,type:r.type,mime:r.mime,wUnits:\"px\",hUnits:\"px\"};if(o.variants.length>1&&(s.variants=o.variants),o.orientation&&(s.orientation=o.orientation),o.exif_location&&o.exif_location.offset+o.exif_location.length<=e.length){var l=Vkt(e,o.exif_location.offset),u=e.slice(o.exif_location.offset+l+4,o.exif_location.offset+o.exif_location.length),c=Gkt.get_orientation(u);c>0&&(s.orientation=c)}return s}}}}}}});var oEe=ye((cvr,aEe)=>{\"use strict\";var jkt=rc().str2arr,Wkt=rc().sliceEq,nEe=rc().readUInt16LE,Xkt=jkt(\"BM\");aEe.exports=function(e){if(!(e.length<26)&&Wkt(e,0,Xkt))return{width:nEe(e,18),height:nEe(e,22),type:\"bmp\",mime:\"image/bmp\",wUnits:\"px\",hUnits:\"px\"}}});var fEe=ye((fvr,cEe)=>{\"use strict\";var uEe=rc().str2arr,sEe=rc().sliceEq,lEe=rc().readUInt16LE,Zkt=uEe(\"GIF87a\"),Ykt=uEe(\"GIF89a\");cEe.exports=function(e){if(!(e.length<10)&&!(!sEe(e,0,Zkt)&&!sEe(e,0,Ykt)))return{width:lEe(e,6),height:lEe(e,8),type:\"gif\",mime:\"image/gif\",wUnits:\"px\",hUnits:\"px\"}}});var vEe=ye((hvr,dEe)=>{\"use strict\";var _W=rc().readUInt16LE,Kkt=0,Jkt=1,hEe=16;dEe.exports=function(e){var t=_W(e,0),r=_W(e,2),n=_W(e,4);if(!(t!==Kkt||r!==Jkt||!n)){for(var i=[],a={width:0,height:0},o=0;o<n;o++){var s=e[6+hEe*o]||256,l=e[6+hEe*o+1]||256,u={width:s,height:l};i.push(u),(s>a.width||l>a.height)&&(a=u)}return{width:a.width,height:a.height,variants:i,type:\"ico\",mime:\"image/x-icon\",wUnits:\"px\",hUnits:\"px\"}}}});var gEe=ye((dvr,pEe)=>{\"use strict\";var xW=rc().readUInt16BE,$kt=rc().str2arr,Qkt=rc().sliceEq,eCt=gD(),tCt=$kt(\"Exif\\0\\0\");pEe.exports=function(e){if(!(e.length<2)&&!(e[0]!==255||e[1]!==216||e[2]!==255))for(var t=2;;){for(;;){if(e.length-t<2)return;if(e[t++]===255)break}for(var 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i.width?LA(a)?{width:a,height:a/f,type:\"svg\",mime:\"image/svg+xml\",wUnits:Zm(i.width),hUnits:Zm(i.width)}:void 0:i.height?LA(o)?{width:o*f,height:o,type:\"svg\",mime:\"image/svg+xml\",wUnits:Zm(i.height),hUnits:Zm(i.height)}:void 0:{width:u,height:c,type:\"svg\",mime:\"image/svg+xml\",wUnits:Zm(l.width),hUnits:Zm(l.height)}}}}}});var IEe=ye((mvr,PEe)=>{\"use strict\";var LEe=rc().str2arr,kEe=rc().sliceEq,pCt=rc().readUInt16LE,gCt=rc().readUInt16BE,mCt=rc().readUInt32LE,yCt=rc().readUInt32BE,_Ct=LEe(\"II*\\0\"),xCt=LEe(\"MM\\0*\");function yD(e,t,r){return r?gCt(e,t):pCt(e,t)}function bW(e,t,r){return r?yCt(e,t):mCt(e,t)}function CEe(e,t,r){var n=yD(e,t+2,r),i=bW(e,t+4,r);return i!==1||n!==3&&n!==4?null:n===3?yD(e,t+8,r):bW(e,t+8,r)}PEe.exports=function(e){if(!(e.length<8)&&!(!kEe(e,0,_Ct)&&!kEe(e,0,xCt))){var t=e[0]===77,r=bW(e,4,t)-8;if(!(r<0)){var n=r+8;if(!(e.length-n<2)){var i=yD(e,n+0,t)*12;if(!(i<=0)&&(n+=2,!(e.length-n<i))){var 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t=12,r=null,n=0,i=wW(e,4)+8;if(!(i>e.length)){for(;t+8<i;){if(e[t]===0){t++;continue}var a=String.fromCharCode.apply(null,e.slice(t,t+4)),o=wW(e,t+4);a===\"VP8 \"&&o>=10?r=r||ACt(e,t+8):a===\"VP8L\"&&o>=9?r=r||SCt(e,t+8):a===\"VP8X\"&&o>=10?r=r||MCt(e,t+8):a===\"EXIF\"&&(n=bCt.get_orientation(e.slice(t+8,t+8+o)),t=1/0),t+=8+o}if(r)return n>0&&(r.orientation=n),r}}}});var BEe=ye((_vr,OEe)=>{\"use strict\";OEe.exports={avif:iEe(),bmp:oEe(),gif:fEe(),ico:vEe(),jpeg:gEe(),png:bEe(),psd:AEe(),svg:EEe(),tiff:IEe(),webp:qEe()}});var NEe=ye((xvr,AW)=>{\"use strict\";var TW=BEe();function ECt(e){for(var t=Object.keys(TW),r=0;r<t.length;r++){var n=TW[t[r]](e);if(n)return n}return null}AW.exports=function(t){return ECt(t)};AW.exports.parsers=TW});var VEe=ye(UEe=>{\"use strict\";var kCt=NEe(),CCt=Oy().IMAGE_URL_PREFIX,LCt=w2().Buffer;UEe.getImageSize=function(e){var t=e.replace(CCt,\"\"),r=new LCt(t,\"base64\");return kCt(r)}});var jEe=ye((wvr,HEe)=>{\"use strict\";var 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bD=Vl(),jCt=Cc().attributes,WCt=ec(),XCt=Ih(),{hovertemplateAttrs:ZCt,texttemplateAttrs:YCt,templatefallbackAttrs:ske}=Pl(),Q_=Ao().extendFlat,KCt=Pd().pattern,wD=WCt({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});lke.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:XCt.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},pattern:KCt,editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:Q_({},bD.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:ZCt({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),hovertemplatefallback:ske(),texttemplate:YCt({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplatefallback:ske({editType:\"plot\"}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:Q_({},wD,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:Q_({},wD,{}),outsidetextfont:Q_({},wD,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},showlegend:Q_({},bD.showlegend,{arrayOk:!0}),legend:Q_({},bD.legend,{arrayOk:!0}),legendrank:Q_({},bD.legendrank,{arrayOk:!0}),title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:Q_({},wD,{}),position:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"plot\"},editType:\"plot\"},domain:jCt({name:\"pie\",trace:!0,editType:\"calc\"}),hole:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},sort:{valType:\"boolean\",dflt:!0,editType:\"calc\"},direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"],dflt:\"counterclockwise\",editType:\"calc\"},rotation:{valType:\"angle\",dflt:0,editType:\"calc\"},pull:{valType:\"number\",min:0,max:1,dflt:0,arrayOk:!0,editType:\"calc\"}}});var O2=ye((Lvr,fke)=>{\"use strict\";var JCt=Eo(),BE=Pr(),$Ct=q2(),QCt=Cc().defaults,e6t=i0().handleText,t6t=Pr().coercePattern;function uke(e,t){var r=BE.isArrayOrTypedArray(e),n=BE.isArrayOrTypedArray(t),i=Math.min(r?e.length:1/0,n?t.length:1/0);if(isFinite(i)||(i=0),i&&n){for(var a,o=0;o<i;o++){var s=t[o];if(JCt(s)&&s>0){a=!0;break}}a||(i=0)}return{hasLabels:r,hasValues:n,len:i}}function cke(e,t,r,n,i){var a=n(\"marker.line.width\");a&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var o=n(\"marker.colors\");t6t(n,\"marker.pattern\",o),e.marker&&!t.marker.pattern.fgcolor&&(t.marker.pattern.fgcolor=e.marker.colors),t.marker.pattern.bgcolor||(t.marker.pattern.bgcolor=r.paper_bgcolor)}function r6t(e,t,r,n){function i(k,M){return BE.coerce(e,t,$Ct,k,M)}var a=i(\"labels\"),o=i(\"values\"),s=uke(a,o),l=s.len;if(t._hasLabels=s.hasLabels,t._hasValues=s.hasValues,!t._hasLabels&&t._hasValues&&(i(\"label0\"),i(\"dlabel\")),!l){t.visible=!1;return}t._length=l,cke(e,t,n,i,!0),i(\"scalegroup\");var u=i(\"text\"),c=i(\"texttemplate\");i(\"texttemplatefallback\");var f;if(c||(f=i(\"textinfo\",BE.isArrayOrTypedArray(u)?\"text+percent\":\"percent\")),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\"),c||f&&f!==\"none\"){var h=i(\"textposition\");e6t(e,t,n,i,h,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1});var d=Array.isArray(h)||h===\"auto\",v=d||h===\"outside\";v&&i(\"automargin\"),(h===\"inside\"||h===\"auto\"||Array.isArray(h))&&i(\"insidetextorientation\")}else f===\"none\"&&i(\"textposition\",\"none\");QCt(t,n,i);var m=i(\"hole\"),b=i(\"title.text\");if(b){var p=i(\"title.position\",m?\"middle center\":\"top center\");!m&&p===\"middle center\"&&(t.title.position=\"top center\"),BE.coerceFont(i,\"title.font\",n.font)}i(\"sort\"),i(\"direction\"),i(\"rotation\"),i(\"pull\")}fke.exports={handleLabelsAndValues:uke,handleMarkerDefaults:cke,supplyDefaults:r6t}});var TD=ye((Pvr,hke)=>{\"use strict\";hke.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}});var vke=ye((Ivr,dke)=>{\"use 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x.v-L.v}),r[0]&&(r[0].vTotal=v),r}function pke(e){return function(r,n){return!r||(r=SW(r),!r.isValid())?!1:(r=o6t.addOpacity(r,r.getAlpha()),e[n]||(e[n]=r),r)}}function u6t(e,t){var r=(t||{}).type;r||(r=\"pie\");var n=e._fullLayout,i=e.calcdata,a=n[r+\"colorway\"],o=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=gke(a,s6t));for(var s=0,l=0;l<i.length;l++){var u=i[l],c=u[0].trace.type;if(c===r)for(var f=0;f<u.length;f++){var h=u[f];h.color===!1&&(o[h.label]?h.color=o[h.label]:(o[h.label]=h.color=a[s%a.length],s++))}}}function gke(e,t){var r,n=JSON.stringify(e),i=t[n];if(!i){for(i=e.slice(),r=0;r<e.length;r++)i.push(SW(e[r]).lighten(20).toHexString());for(r=0;r<e.length;r++)i.push(SW(e[r]).darken(20).toHexString());t[n]=i}return i}mke.exports={calc:l6t,crossTraceCalc:u6t,makePullColorFn:pke,generateExtendedColors:gke}});var _ke=ye((Dvr,yke)=>{\"use strict\";var c6t=ip().appendArrayMultiPointValues;yke.exports=function(t,r){var n={curveNumber:r.index,pointNumbers:t.pts,data:r._input,fullData:r,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return t.pts.length===1&&(n.pointNumber=n.i=t.pts[0]),c6t(n,r,t.pts),r.type===\"funnelarea\"&&(delete n.v,delete n.i),n}});var ED=ye((Fvr,Nke)=>{\"use strict\";var Fp=Fa(),f6t=Mc(),AD=ef(),Ske=ka(),e1=So(),iv=Pr(),h6t=iv.strScale,xke=iv.strTranslate,MW=Zl(),Mke=bv(),d6t=Mke.recordMinTextSize,v6t=Mke.clearMinTextSize,Eke=S_().TEXTPAD,ns=m_(),SD=_ke(),bke=Pr().isValidTextValue;function p6t(e,t){var r=e._context.staticPlot,n=e._fullLayout,i=n._size;v6t(\"pie\",n),Lke(t,e),qke(t,i);var a=iv.makeTraceGroups(n._pielayer,t,\"trace\").each(function(o){var s=Fp.select(this),l=o[0],u=l.trace;A6t(o),s.attr(\"stroke-linejoin\",\"round\"),s.each(function(){var c=Fp.select(this).selectAll(\"g.slice\").data(o);c.enter().append(\"g\").classed(\"slice\",!0),c.exit().remove();var f=[[[],[]],[[],[]]],h=!1;c.each(function(T,L){if(T.hidden){Fp.select(this).selectAll(\"path,g\").remove();return}T.pointNumber=T.i,T.curveNumber=u.index,f[T.pxmid[1]<0?0:1][T.pxmid[0]<0?0:1].push(T);var x=l.cx,C=l.cy,S=Fp.select(this),g=S.selectAll(\"path.surface\").data([T]);if(g.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),S.call(kke,e,o),u.pull){var P=+ns.castOption(u.pull,T.pts)||0;P>0&&(x+=P*T.pxmid[0],C+=P*T.pxmid[1])}T.cxFinal=x,T.cyFinal=C;function E(N,H,re,ae){var _e=ae*(H[0]-N[0]),Ee=ae*(H[1]-N[1]);return\"a\"+ae*l.r+\",\"+ae*l.r+\" 0 \"+T.largeArc+(re?\" 1 \":\" 0 \")+_e+\",\"+Ee}var z=u.hole;if(T.v===l.vTotal){var q=\"M\"+(x+T.px0[0])+\",\"+(C+T.px0[1])+E(T.px0,T.pxmid,!0,1)+E(T.pxmid,T.px0,!0,1)+\"Z\";z?g.attr(\"d\",\"M\"+(x+z*T.px0[0])+\",\"+(C+z*T.px0[1])+E(T.px0,T.pxmid,!1,z)+E(T.pxmid,T.px0,!1,z)+\"Z\"+q):g.attr(\"d\",q)}else{var V=E(T.px0,T.px1,!0,1);if(z){var G=1-z;g.attr(\"d\",\"M\"+(x+z*T.px1[0])+\",\"+(C+z*T.px1[1])+E(T.px1,T.px0,!1,z)+\"l\"+G*T.px0[0]+\",\"+G*T.px0[1]+V+\"Z\")}else g.attr(\"d\",\"M\"+x+\",\"+C+\"l\"+T.px0[0]+\",\"+T.px0[1]+V+\"Z\")}Oke(e,T,l);var Z=ns.castOption(u.textposition,T.pts),j=S.selectAll(\"g.slicetext\").data(T.text&&Z!==\"none\"?[0]:[]);j.enter().append(\"g\").classed(\"slicetext\",!0),j.exit().remove(),j.each(function(){var N=iv.ensureSingle(Fp.select(this),\"text\",\"\",function(ie){ie.attr(\"data-notex\",1)}),H=iv.ensureUniformFontSize(e,Z===\"outside\"?m6t(u,T,n.font):Cke(u,T,n.font));N.text(T.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(e1.font,H).call(MW.convertToTspans,e);var re=e1.bBox(N.node()),ae;if(Z===\"outside\")ae=Ake(re,T);else if(ae=Pke(re,T,l),Z===\"auto\"&&ae.scale<1){var _e=iv.ensureUniformFontSize(e,u.outsidetextfont);N.call(e1.font,_e),re=e1.bBox(N.node()),ae=Ake(re,T)}var Ee=ae.textPosAngle,Ce=Ee===void 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v=e1.bBox(s.node()),m=u.domain,b=i.w*(m.x[1]-m.x[0]),p=i.h*(m.y[1]-m.y[0]),k=(.5*b-l.r)/i.w,M=(.5*p-l.r)/i.h;f6t.autoMargin(e,\"pie.\"+u.uid+\".automargin\",{xl:m.x[0]-k,xr:m.x[1]+k,yb:m.y[0]-M,yt:m.y[1]+M,l:Math.max(l.cx-l.r-v.left,0),r:Math.max(v.right-(l.cx+l.r),0),b:Math.max(v.bottom-(l.cy+l.r),0),t:Math.max(l.cy-l.r-v.top,0),pad:5})}})});setTimeout(function(){a.selectAll(\"tspan\").each(function(){var o=Fp.select(this);o.attr(\"dy\")&&o.attr(\"dy\",o.attr(\"dy\"))})},0)}function g6t(e,t){e.each(function(r){var n=Fp.select(this);if(!r.labelExtraX&&!r.labelExtraY){n.select(\"path.textline\").remove();return}var i=n.select(\"g.slicetext text\");r.transform.targetX+=r.labelExtraX,r.transform.targetY+=r.labelExtraY,iv.setTransormAndDisplay(i,r.transform);var a=r.cxFinal+r.pxmid[0],o=r.cyFinal+r.pxmid[1],s=\"M\"+a+\",\"+o,l=(r.yLabelMax-r.yLabelMin)*(r.pxmid[0]<0?-1:1)/4;if(r.labelExtraX){var u=r.labelExtraX*r.pxmid[1]/r.pxmid[0],c=r.yLabelMid+r.labelExtraY-(r.cyFinal+r.pxmid[1]);Math.abs(u)>Math.abs(c)?s+=\"l\"+c*r.pxmid[0]/r.pxmid[1]+\",\"+c+\"H\"+(a+r.labelExtraX+l):s+=\"l\"+r.labelExtraX+\",\"+u+\"v\"+(c-u)+\"h\"+l}else s+=\"V\"+(r.yLabelMid+r.labelExtraY)+\"h\"+l;iv.ensureSingle(n,\"path\",\"textline\").call(Ske.stroke,t.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,t.outsidetextfont.size/8),d:s,fill:\"none\"})})}function kke(e,t,r){var n=r[0],i=n.cx,a=n.cy,o=n.trace,s=o.type===\"funnelarea\";\"_hasHoverLabel\"in o||(o._hasHoverLabel=!1),\"_hasHoverEvent\"in o||(o._hasHoverEvent=!1),e.on(\"mouseover\",function(l){var u=t._fullLayout,c=t._fullData[o.index];if(!(t._dragging||u.hovermode===!1)){var f=c.hoverinfo;if(Array.isArray(f)&&(f=AD.castHoverinfo({hoverinfo:[ns.castOption(f,l.pts)],_module:o._module},u,0)),f===\"all\"&&(f=\"label+text+value+percent+name\"),c.hovertemplate||f!==\"none\"&&f!==\"skip\"&&f){var h=l.rInscribed||0,d=i+l.pxmid[0]*(1-h),v=a+l.pxmid[1]*(1-h),m=u.separators,b=[];if(f&&f.indexOf(\"label\")!==-1&&b.push(l.label),l.text=ns.castOption(c.hovertext||c.text,l.pts),f&&f.indexOf(\"text\")!==-1){var p=l.text;iv.isValidTextValue(p)&&b.push(p)}l.value=l.v,l.valueLabel=ns.formatPieValue(l.v,m),f&&f.indexOf(\"value\")!==-1&&b.push(l.valueLabel),l.percent=l.v/n.vTotal,l.percentLabel=ns.formatPiePercent(l.percent,m),f&&f.indexOf(\"percent\")!==-1&&b.push(l.percentLabel);var k=c.hoverlabel,M=k.font,T=[];AD.loneHover({trace:o,x0:d-h*n.r,x1:d+h*n.r,y:v,_x0:s?i+l.TL[0]:d-h*n.r,_x1:s?i+l.TR[0]:d+h*n.r,_y0:s?a+l.TL[1]:v-h*n.r,_y1:s?a+l.BL[1]:v+h*n.r,text:b.join(\"<br>\"),name:c.hovertemplate||f.indexOf(\"name\")!==-1?c.name:void 0,idealAlign:l.pxmid[0]<0?\"left\":\"right\",color:ns.castOption(k.bgcolor,l.pts)||l.color,borderColor:ns.castOption(k.bordercolor,l.pts),fontFamily:ns.castOption(M.family,l.pts),fontSize:ns.castOption(M.size,l.pts),fontColor:ns.castOption(M.color,l.pts),nameLength:ns.castOption(k.namelength,l.pts),textAlign:ns.castOption(k.align,l.pts),hovertemplate:ns.castOption(c.hovertemplate,l.pts),hovertemplateLabels:l,eventData:[SD(l,c)]},{container:u._hoverlayer.node(),outerContainer:u._paper.node(),gd:t,inOut_bbox:T}),l.bbox=T[0],o._hasHoverLabel=!0}o._hasHoverEvent=!0,t.emit(\"plotly_hover\",{points:[SD(l,c)],event:Fp.event})}}),e.on(\"mouseout\",function(l){var u=t._fullLayout,c=t._fullData[o.index],f=Fp.select(this).datum();o._hasHoverEvent&&(l.originalEvent=Fp.event,t.emit(\"plotly_unhover\",{points:[SD(f,c)],event:Fp.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(AD.loneUnhover(u._hoverlayer.node()),o._hasHoverLabel=!1)}),e.on(\"click\",function(l){var u=t._fullLayout,c=t._fullData[o.index];t._dragging||u.hovermode===!1||(t._hoverdata=[SD(l,c)],AD.click(t,Fp.event))})}function m6t(e,t,r){var n=ns.castOption(e.outsidetextfont.color,t.pts)||ns.castOption(e.textfont.color,t.pts)||r.color,i=ns.castOption(e.outsidetextfont.family,t.pts)||ns.castOption(e.textfont.family,t.pts)||r.family,a=ns.castOption(e.outsidetextfont.size,t.pts)||ns.castOption(e.textfont.size,t.pts)||r.size,o=ns.castOption(e.outsidetextfont.weight,t.pts)||ns.castOption(e.textfont.weight,t.pts)||r.weight,s=ns.castOption(e.outsidetextfont.style,t.pts)||ns.castOption(e.textfont.style,t.pts)||r.style,l=ns.castOption(e.outsidetextfont.variant,t.pts)||ns.castOption(e.textfont.variant,t.pts)||r.variant,u=ns.castOption(e.outsidetextfont.textcase,t.pts)||ns.castOption(e.textfont.textcase,t.pts)||r.textcase,c=ns.castOption(e.outsidetextfont.lineposition,t.pts)||ns.castOption(e.textfont.lineposition,t.pts)||r.lineposition,f=ns.castOption(e.outsidetextfont.shadow,t.pts)||ns.castOption(e.textfont.shadow,t.pts)||r.shadow;return{color:n,family:i,size:a,weight:o,style:s,variant:l,textcase:u,lineposition:c,shadow:f}}function Cke(e,t,r){var n=ns.castOption(e.insidetextfont.color,t.pts);!n&&e._input.textfont&&(n=ns.castOption(e._input.textfont.color,t.pts));var i=ns.castOption(e.insidetextfont.family,t.pts)||ns.castOption(e.textfont.family,t.pts)||r.family,a=ns.castOption(e.insidetextfont.size,t.pts)||ns.castOption(e.textfont.size,t.pts)||r.size,o=ns.castOption(e.insidetextfont.weight,t.pts)||ns.castOption(e.textfont.weight,t.pts)||r.weight,s=ns.castOption(e.insidetextfont.style,t.pts)||ns.castOption(e.textfont.style,t.pts)||r.style,l=ns.castOption(e.insidetextfont.variant,t.pts)||ns.castOption(e.textfont.variant,t.pts)||r.variant,u=ns.castOption(e.insidetextfont.textcase,t.pts)||ns.castOption(e.textfont.textcase,t.pts)||r.textcase,c=ns.castOption(e.insidetextfont.lineposition,t.pts)||ns.castOption(e.textfont.lineposition,t.pts)||r.lineposition,f=ns.castOption(e.insidetextfont.shadow,t.pts)||ns.castOption(e.textfont.shadow,t.pts)||r.shadow;return{color:n||Ske.contrast(t.color),family:i,size:a,weight:o,style:s,variant:l,textcase:u,lineposition:c,shadow:f}}function Lke(e,t){for(var r,n,i=0;i<e.length;i++)if(r=e[i][0],n=r.trace,n.title.text){var a=n.title.text;n._meta&&(a=iv.templateString(a,n._meta));var o=e1.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(e1.font,n.title.font).call(MW.convertToTspans,t),s=e1.bBox(o.node(),!0);r.titleBox={width:s.width,height:s.height},o.remove()}}function Pke(e,t,r){var n=r.r||t.rpx1,i=t.rInscribed,a=t.startangle===t.stopangle;if(a)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var o=t.ring,s=o===1&&Math.abs(t.startangle-t.stopangle)===Math.PI*2,l=t.halfangle,u=t.midangle,c=r.trace.insidetextorientation,f=c===\"horizontal\",h=c===\"tangential\",d=c===\"radial\",v=c===\"auto\",m=[],b;if(!v){var p=function(S,g){if(y6t(t,S)){var 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s=o.split(\"+\"),l=function(T){return s.indexOf(T)!==-1},u=l(\"label\"),c=l(\"text\"),f=l(\"value\"),h=l(\"percent\"),d=n.separators,v;if(v=u?[t.label]:[],c){var m=ns.getFirstFilled(i.text,t.pts);bke(m)&&v.push(m)}f&&v.push(ns.formatPieValue(t.v,d)),h&&v.push(ns.formatPiePercent(t.v/r.vTotal,d)),t.text=v.join(\"<br>\")}function b(T){return{label:T.label,value:T.v,valueLabel:ns.formatPieValue(T.v,n.separators),percent:T.v/r.vTotal,percentLabel:ns.formatPiePercent(T.v/r.vTotal,n.separators),color:T.color,text:T.text,customdata:iv.castOption(i,T.i,\"customdata\")}}if(a){var p=iv.castOption(i,t.i,\"texttemplate\");if(!p)t.text=\"\";else{var k=b(t),M=ns.getFirstFilled(i.text,t.pts);(bke(M)||M===\"\")&&(k.text=M),t.text=iv.texttemplateString({data:[k,i._meta],fallback:i.texttemplatefallback,labels:k,locale:e._fullLayout._d3locale,template:p})}}}function Bke(e,t){var r=e.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(t.left+t.right)/2,o=(t.top+t.bottom)/2;e.textX=a*n-o*i,e.textY=a*i+o*n,e.noCenter=!0}Nke.exports={plot:p6t,formatSliceLabel:Oke,transformInsideText:Pke,determineInsideTextFont:Cke,positionTitleOutside:Fke,prerenderTitles:Lke,layoutAreas:qke,attachFxHandlers:kke,computeTransform:Bke}});var Gke=ye((zvr,Vke)=>{\"use strict\";var Uke=Fa(),S6t=J3(),M6t=bv().resizeText;Vke.exports=function(t){var r=t._fullLayout._pielayer.selectAll(\".trace\");M6t(t,r,\"pie\"),r.each(function(n){var i=n[0],a=i.trace,o=Uke.select(this);o.style({opacity:a.opacity}),o.selectAll(\"path.surface\").each(function(s){Uke.select(this).call(S6t,s,a,t)})})}});var jke=ye(IA=>{\"use strict\";var Hke=Mc();IA.name=\"pie\";IA.plot=function(e,t,r,n){Hke.plotBasePlot(IA.name,e,t,r,n)};IA.clean=function(e,t,r,n){Hke.cleanBasePlot(IA.name,e,t,r,n)}});var Xke=ye((Ovr,Wke)=>{\"use 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E6t=Vl(),{hovertemplateAttrs:k6t,texttemplateAttrs:C6t,templatefallbackAttrs:Qke}=Pl(),L6t=Tu(),P6t=Cc().attributes,t1=q2(),eCe=kW(),NE=Ao().extendFlat,I6t=Pd().pattern;tCe.exports={labels:{valType:\"data_array\",editType:\"calc\"},parents:{valType:\"data_array\",editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},branchvalues:{valType:\"enumerated\",values:[\"remainder\",\"total\"],dflt:\"remainder\",editType:\"calc\"},count:{valType:\"flaglist\",flags:[\"branches\",\"leaves\"],dflt:\"leaves\",editType:\"calc\"},level:{valType:\"any\",editType:\"plot\",anim:!0},maxdepth:{valType:\"integer\",editType:\"plot\",dflt:-1},marker:NE({colors:{valType:\"data_array\",editType:\"calc\"},line:{color:NE({},t1.marker.line.color,{dflt:null}),width:NE({},t1.marker.line.width,{dflt:1}),editType:\"calc\"},pattern:I6t,editType:\"calc\"},L6t(\"marker\",{colorAttr:\"colors\",anim:!1})),leaf:{opacity:{valType:\"number\",editType:\"style\",min:0,max:1},editType:\"plot\"},text:t1.text,textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"current path\",\"percent root\",\"percent entry\",\"percent parent\"],extras:[\"none\"],editType:\"plot\"},texttemplate:C6t({editType:\"plot\"},{keys:eCe.eventDataKeys.concat([\"label\",\"value\"])}),texttemplatefallback:Qke({editType:\"plot\"}),hovertext:t1.hovertext,hoverinfo:NE({},E6t.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"name\",\"current path\",\"percent root\",\"percent entry\",\"percent parent\"],dflt:\"label+text+value+name\"}),hovertemplate:k6t({},{keys:eCe.eventDataKeys}),hovertemplatefallback:Qke(),textfont:t1.textfont,insidetextorientation:t1.insidetextorientation,insidetextfont:t1.insidetextfont,outsidetextfont:NE({},t1.outsidetextfont,{}),rotation:{valType:\"angle\",dflt:0,editType:\"plot\"},sort:t1.sort,root:{color:{valType:\"color\",editType:\"calc\",dflt:\"rgba(0,0,0,0)\"},editType:\"calc\"},domain:P6t({name:\"sunburst\",trace:!0,editType:\"calc\"})}});var CW=ye((Gvr,rCe)=>{\"use 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ie=rk.select(this),Se=Av.ensureSingle(ie,\"path\",\"surface\",function(Pe){Pe.style(\"pointer-events\",i?\"none\":\"all\")});ge.rpx0=q(ge.y0),ge.rpx1=q(ge.y1),ge.xmid=(ge.x0+ge.x1)/2,ge.pxmid=V(ge.rpx1,ge.xmid),ge.midangle=-(ge.xmid-Math.PI/2),ge.startangle=-(ge.x0-Math.PI/2),ge.stopangle=-(ge.x1-Math.PI/2),ge.halfangle=.5*Math.min(Av.angleDelta(ge.x0,ge.x1)||Math.PI,Math.PI),ge.ring=1-ge.rpx0/ge.rpx1,ge.rInscribed=OLt(ge,c),o?Se.transition().attrTween(\"d\",function(Pe){var ce=_e(Pe);return function(He){return G(ce(He))}}):Se.attr(\"d\",G),ie.call(zLt,h,e,t,{eventDataKeys:QW.eventDataKeys,transitionTime:QW.CLICK_TRANSITION_TIME,transitionEasing:QW.CLICK_TRANSITION_EASING}).call(Il.setSliceCursor,e,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:e._transitioning}),Se.call(DLt,ge,c,e);var ke=Av.ensureSingle(ie,\"g\",\"slicetext\"),be=Av.ensureSingle(ke,\"text\",\"\",function(Pe){Pe.attr(\"data-notex\",1)}),Be=Av.ensureUniformFontSize(e,Il.determineTextFont(c,ge,a.font));be.text(YD.formatSliceLabel(ge,h,c,t,a)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(P6e.font,Be).call(CLt.convertToTspans,e);var Le=P6e.bBox(be.node());ge.transform=RLt(Le,ge,u),ge.transform.targetX=Z(ge),ge.transform.targetY=j(ge);var me=function(Pe,ce){var He=Pe.transform;return ILt(He,ce),He.fontSize=Be.size,I6e(c.type,He,a),Av.getTextTransform(He)};o?be.transition().attrTween(\"transform\",function(Pe){var ce=Ee(Pe);return function(He){return me(ce(He),Le)}}):be.attr(\"transform\",me(ge,Le))});function ae(ge){var ie=Il.getPtId(ge),Se=x[ie],ke=x[Il.getPtId(h)],be;if(ke){var Be=(ge.x1>ke.x1?2*Math.PI:0)+E;be=ge.rpx1<ke.rpx1?{x0:ge.x0,x1:ge.x1,rpx0:0,rpx1:0}:{x0:Be,x1:Be,rpx0:ge.rpx0,rpx1:ge.rpx1}}else{var Le,me=Il.getPtId(ge.parent);l.each(function(mt){if(Il.getPtId(mt)===me)return Le=mt});var Pe=Le.children,ce;Pe.forEach(function(mt,Ht){if(Il.getPtId(mt)===ie)return ce=Ht});var He=Pe.length,lt=Jg(Le.x0,Le.x1);be={rpx0:k,rpx1:k,x0:lt(ce/He),x1:lt((ce+1)/He)}}return Jg(Se,be)}function _e(ge){var ie=x[Il.getPtId(ge)],Se,ke={x0:ge.x0,x1:ge.x1,rpx0:ge.rpx0,rpx1:ge.rpx1};if(ie)Se=ie;else if(L)if(ge.parent)if(N){var be=(ge.x1>N?2*Math.PI:0)+E;Se={x0:be,x1:be}}else Se={rpx0:k,rpx1:k},Av.extendFlat(Se,Ce(ge));else Se={rpx0:0,rpx1:0};else Se={x0:E,x1:E};return Jg(Se,ke)}function Ee(ge){var ie=x[Il.getPtId(ge)],Se,ke=ge.transform;if(ie)Se=ie;else if(Se={rpx1:ge.rpx1,transform:{textPosAngle:ke.textPosAngle,scale:0,rotate:ke.rotate,rCenter:ke.rCenter,x:ke.x,y:ke.y}},L)if(ge.parent)if(N){var be=ge.x1>N?2*Math.PI:0;Se.x0=Se.x1=be}else Av.extendFlat(Se,Ce(ge));else Se.x0=Se.x1=E;else Se.x0=Se.x1=E;var 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C={};u.label&&(C.label=u.label),u.hasOwnProperty(\"v\")&&(C.value=u.v,C.valueLabel=Il.formatValue(u.v,s)),C.currentPath=Il.getPath(e.data),f||(C.percentParent=d/Il.getValue(h),C.percentParentLabel=Il.formatPercent(C.percentParent,s),C.parent=Il.getPtLabel(h)),C.percentEntry=d/Il.getValue(t),C.percentEntryLabel=Il.formatPercent(C.percentEntry,s),C.entry=Il.getPtLabel(t),C.percentRoot=d/Il.getValue(c),C.percentRootLabel=Il.formatPercent(C.percentRoot,s),C.root=Il.getPtLabel(c),u.hasOwnProperty(\"color\")&&(C.color=u.color);var S=Av.castOption(r,u.i,\"text\");return(Av.isValidTextValue(S)||S===\"\")&&(C.text=S),C.customdata=Av.castOption(r,u.i,\"customdata\"),Av.texttemplateString({data:[C,r._meta],fallback:r.texttemplatefallback,labels:C,locale:i._d3locale,template:x})};function OLt(e){return e.rpx0===0&&Av.isFullCircle([e.x0,e.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(e.halfangle)),e.ring/2))}function D6e(e){return BLt(e.rpx1,e.transform.textPosAngle)}function 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u=a(\"tiling.packing\");u===\"squarify\"&&a(\"tiling.squarifyratio\"),a(\"tiling.flip\"),a(\"tiling.pad\");var c=a(\"text\");a(\"texttemplate\"),a(\"texttemplatefallback\"),r.texttemplate||a(\"textinfo\",Z6e.isArrayOrTypedArray(c)?\"text+label\":\"label\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var f=a(\"pathbar.visible\"),h=\"auto\";ZLt(t,r,i,a,h,{hasPathbar:f,moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),a(\"textposition\");var d=r.textposition.indexOf(\"bottom\")!==-1;KLt(t,r,i,a);var v=r._hasColorscale=JLt(t,\"marker\",\"colors\")||(t.marker||{}).coloraxis;v?$Lt(t,r,i,a,{prefix:\"marker.\",cLetter:\"c\"}):a(\"marker.depthfade\",!(r.marker.colors||[]).length);var m=r.textfont.size*2;a(\"marker.pad.t\",d?m/4:m),a(\"marker.pad.l\",m/4),a(\"marker.pad.r\",m/4),a(\"marker.pad.b\",d?m:m/4),a(\"marker.cornerradius\"),r._hovered={marker:{line:{width:2,color:WLt.contrast(i.paper_bgcolor)}}},f&&(a(\"pathbar.thickness\",r.pathbar.textfont.size+2*YLt),a(\"pathbar.side\"),a(\"pathbar.edgeshape\")),a(\"sort\"),a(\"root.color\"),XLt(r,i,a),r._length=null}});var Q6e=ye((X0r,$6e)=>{\"use strict\";var QLt=Pr(),ePt=tX();$6e.exports=function(t,r){function n(i,a){return QLt.coerce(t,r,ePt,i,a)}n(\"treemapcolorway\",r.colorway),n(\"extendtreemapcolors\")}});var iX=ye(rX=>{\"use strict\";var eLe=HE();rX.calc=function(e,t){return eLe.calc(e,t)};rX.crossTraceCalc=function(e){return eLe._runCrossTraceCalc(\"treemap\",e)}});var nX=ye((Y0r,tLe)=>{\"use strict\";tLe.exports=function e(t,r,n){var i;n.swapXY&&(i=t.x0,t.x0=t.y0,t.y0=i,i=t.x1,t.x1=t.y1,t.y1=i),n.flipX&&(i=t.x0,t.x0=r[0]-t.x1,t.x1=r[0]-i),n.flipY&&(i=t.y0,t.y0=r[1]-t.y1,t.y1=r[1]-i);var a=t.children;if(a)for(var o=0;o<a.length;o++)e(a[o],r,n)}});var aX=ye((K0r,rLe)=>{\"use strict\";var HA=VE(),tPt=nX();rLe.exports=function(t,r,n){var i=n.flipX,a=n.flipY,o=n.packing===\"dice-slice\",s=n.pad[a?\"bottom\":\"top\"],l=n.pad[i?\"right\":\"left\"],u=n.pad[i?\"left\":\"right\"],c=n.pad[a?\"top\":\"bottom\"],f;o&&(f=l,l=s,s=f,f=u,u=c,c=f);var h=HA.treemap().tile(rPt(n.packing,n.squarifyratio)).paddingInner(n.pad.inner).paddingLeft(l).paddingRight(u).paddingTop(s).paddingBottom(c).size(o?[r[1],r[0]]:r)(t);return(o||i||a)&&tPt(h,r,{swapXY:o,flipX:i,flipY:a}),h};function rPt(e,t){switch(e){case\"squarify\":return HA.treemapSquarify.ratio(t);case\"binary\":return HA.treemapBinary;case\"dice\":return HA.treemapDice;case\"slice\":return HA.treemapSlice;default:return HA.treemapSliceDice}}});var $D=ye((J0r,oLe)=>{\"use strict\";var iLe=Fa(),jA=ka(),nLe=Pr(),oX=n1(),iPt=bv().resizeText,nPt=ZD();function aPt(e){var 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pX=ye((sgr,DLe)=>{\"use strict\";DLe.exports={iciclecolorway:{valType:\"colorlist\",editType:\"calc\"},extendiciclecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}});var OLe=ye((lgr,qLe)=>{\"use strict\";var FLe=Pr(),PPt=vX(),IPt=ka(),RPt=Cc().defaults,DPt=i0().handleText,FPt=S_().TEXTPAD,zPt=O2().handleMarkerDefaults,zLe=tc(),qPt=zLe.hasColorscale,OPt=zLe.handleDefaults;qLe.exports=function(t,r,n,i){function a(d,v){return FLe.coerce(t,r,PPt,d,v)}var o=a(\"labels\"),s=a(\"parents\");if(!o||!o.length||!s||!s.length){r.visible=!1;return}var l=a(\"values\");l&&l.length?a(\"branchvalues\"):a(\"count\"),a(\"level\"),a(\"maxdepth\"),a(\"tiling.orientation\"),a(\"tiling.flip\"),a(\"tiling.pad\");var u=a(\"text\");a(\"texttemplate\"),a(\"texttemplatefallback\"),r.texttemplate||a(\"textinfo\",FLe.isArrayOrTypedArray(u)?\"text+label\":\"label\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var 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GLe=ye((fgr,VLe)=>{\"use strict\";var UPt=VE(),VPt=nX();VLe.exports=function(t,r,n){var i=n.flipX,a=n.flipY,o=n.orientation===\"h\",s=n.maxDepth,l=r[0],u=r[1];s&&(l=(t.height+1)*r[0]/Math.min(t.height+1,s),u=(t.height+1)*r[1]/Math.min(t.height+1,s));var c=UPt.partition().padding(n.pad.inner).size(o?[r[1],l]:[r[0],u])(t);return(o||i||a)&&VPt(c,r,{swapXY:o,flipX:i,flipY:a}),c}});var yX=ye((hgr,ZLe)=>{\"use strict\";var HLe=Fa(),jLe=ka(),WLe=Pr(),GPt=bv().resizeText,HPt=ZD();function jPt(e){var t=e._fullLayout._iciclelayer.selectAll(\".trace\");GPt(e,t,\"icicle\"),t.each(function(r){var n=HLe.select(this),i=r[0],a=i.trace;n.style(\"opacity\",a.opacity),n.selectAll(\"path.surface\").each(function(o){HLe.select(this).call(XLe,o,a,e)})})}function XLe(e,t,r,n){var 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je=+je,Ue=Ue>>>0,Ae||Be(this,je,Ue,4,4294967295,0),this[Ue]=je>>>24,this[Ue+1]=je>>>16,this[Ue+2]=je>>>8,this[Ue+3]=je&255,Ue+4},m.prototype.writeIntLE=function(je,Ue,Ae,rt){if(je=+je,Ue=Ue>>>0,!rt){var St=Math.pow(2,8*Ae-1);Be(this,je,Ue,Ae,St-1,-St)}var Tt=0,dt=1,Et=0;for(this[Ue]=je&255;++Tt<Ae&&(dt*=256);)je<0&&Et===0&&this[Ue+Tt-1]!==0&&(Et=1),this[Ue+Tt]=(je/dt>>0)-Et&255;return Ue+Ae},m.prototype.writeIntBE=function(je,Ue,Ae,rt){if(je=+je,Ue=Ue>>>0,!rt){var St=Math.pow(2,8*Ae-1);Be(this,je,Ue,Ae,St-1,-St)}var Tt=Ae-1,dt=1,Et=0;for(this[Ue+Tt]=je&255;--Tt>=0&&(dt*=256);)je<0&&Et===0&&this[Ue+Tt+1]!==0&&(Et=1),this[Ue+Tt]=(je/dt>>0)-Et&255;return Ue+Ae},m.prototype.writeInt8=function(je,Ue,Ae){return je=+je,Ue=Ue>>>0,Ae||Be(this,je,Ue,1,127,-128),je<0&&(je=255+je+1),this[Ue]=je&255,Ue+1},m.prototype.writeInt16LE=function(je,Ue,Ae){return je=+je,Ue=Ue>>>0,Ae||Be(this,je,Ue,2,32767,-32768),this[Ue]=je&255,this[Ue+1]=je>>>8,Ue+2},m.prototype.writeInt16BE=function(je,Ue,Ae){return 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H,re;this.length>N.length?(H=this,re=N):(H=N,re=this);for(var ae=0;ae<re.length;ae++)this.words[ae]=H.words[ae]^re.words[ae];if(this!==H)for(;ae<H.length;ae++)this.words[ae]=H.words[ae];return this.length=H.length,this.strip()},f.prototype.ixor=function(N){return u((this.negative|N.negative)===0),this.iuxor(N)},f.prototype.xor=function(N){return this.length>N.length?this.clone().ixor(N):N.clone().ixor(this)},f.prototype.uxor=function(N){return this.length>N.length?this.clone().iuxor(N):N.clone().iuxor(this)},f.prototype.inotn=function(N){u(typeof N==\"number\"&&N>=0);var H=Math.ceil(N/26)|0,re=N%26;this._expand(H),re>0&&H--;for(var ae=0;ae<H;ae++)this.words[ae]=~this.words[ae]&67108863;return re>0&&(this.words[ae]=~this.words[ae]&67108863>>26-re),this.strip()},f.prototype.notn=function(N){return this.clone().inotn(N)},f.prototype.setn=function(N,H){u(typeof N==\"number\"&&N>=0);var re=N/26|0,ae=N%26;return 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N.negative!==0&&this.negative===0?(N.negative=0,H=this.sub(N),N.negative^=1,H):N.negative===0&&this.negative!==0?(this.negative=0,H=N.sub(this),this.negative=1,H):this.length>N.length?this.clone().iadd(N):N.clone().iadd(this)},f.prototype.isub=function(N){if(N.negative!==0){N.negative=0;var H=this.iadd(N);return N.negative=1,H._normSign()}else if(this.negative!==0)return this.negative=0,this.iadd(N),this.negative=1,this._normSign();var re=this.cmp(N);if(re===0)return this.negative=0,this.length=1,this.words[0]=0,this;var ae,_e;re>0?(ae=this,_e=N):(ae=N,_e=this);for(var Ee=0,Ce=0;Ce<_e.length;Ce++)H=(ae.words[Ce]|0)-(_e.words[Ce]|0)+Ee,Ee=H>>26,this.words[Ce]=H&67108863;for(;Ee!==0&&Ce<ae.length;Ce++)H=(ae.words[Ce]|0)+Ee,Ee=H>>26,this.words[Ce]=H&67108863;if(Ee===0&&Ce<ae.length&&ae!==this)for(;Ce<ae.length;Ce++)this.words[Ce]=ae.words[Ce];return this.length=Math.max(this.length,Ce),ae!==this&&(this.negative=1),this.strip()},f.prototype.sub=function(N){return this.clone().isub(N)};function T(j,N,H){H.negative=N.negative^j.negative;var re=j.length+N.length|0;H.length=re,re=re-1|0;var ae=j.words[0]|0,_e=N.words[0]|0,Ee=ae*_e,Ce=Ee&67108863,ge=Ee/67108864|0;H.words[0]=Ce;for(var ie=1;ie<re;ie++){for(var Se=ge>>>26,ke=ge&67108863,be=Math.min(ie,N.length-1),Be=Math.max(0,ie-j.length+1);Be<=be;Be++){var Le=ie-Be|0;ae=j.words[Le]|0,_e=N.words[Be]|0,Ee=ae*_e+ke,Se+=Ee/67108864|0,ke=Ee&67108863}H.words[ie]=ke|0,ge=Se|0}return ge!==0?H.words[ie]=ge|0:H.length--,H.strip()}var L=function(N,H,re){var ae=N.words,_e=H.words,Ee=re.words,Ce=0,ge,ie,Se,ke=ae[0]|0,be=ke&8191,Be=ke>>>13,Le=ae[1]|0,me=Le&8191,Pe=Le>>>13,ce=ae[2]|0,He=ce&8191,lt=ce>>>13,mt=ae[3]|0,Ht=mt&8191,at=mt>>>13,ct=ae[4]|0,ar=ct&8191,Vt=ct>>>13,rr=ae[5]|0,tt=rr&8191,je=rr>>>13,Ue=ae[6]|0,Ae=Ue&8191,rt=Ue>>>13,St=ae[7]|0,Tt=St&8191,dt=St>>>13,Et=ae[8]|0,pt=Et&8191,jt=Et>>>13,or=ae[9]|0,mr=or&8191,Ar=or>>>13,ei=_e[0]|0,qr=ei&8191,jr=ei>>>13,gt=_e[1]|0,Ge=gt&8191,Je=gt>>>13,We=_e[2]|0,et=We&8191,xt=We>>>13,At=_e[3]|0,Kt=At&8191,Qt=At>>>13,Mr=_e[4]|0,Gr=Mr&8191,Ir=Mr>>>13,Yr=_e[5]|0,_i=Yr&8191,Pi=Yr>>>13,ai=_e[6]|0,mi=ai&8191,un=ai>>>13,Fn=_e[7]|0,An=Fn&8191,Hn=Fn>>>13,Qn=_e[8]|0,Vi=Qn&8191,Jn=Qn>>>13,Yn=_e[9]|0,Gt=Yn&8191,wt=Yn>>>13;re.negative=N.negative^H.negative,re.length=19,ge=Math.imul(be,qr),ie=Math.imul(be,jr),ie=ie+Math.imul(Be,qr)|0,Se=Math.imul(Be,jr);var tr=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(tr>>>26)|0,tr&=67108863,ge=Math.imul(me,qr),ie=Math.imul(me,jr),ie=ie+Math.imul(Pe,qr)|0,Se=Math.imul(Pe,jr),ge=ge+Math.imul(be,Ge)|0,ie=ie+Math.imul(be,Je)|0,ie=ie+Math.imul(Be,Ge)|0,Se=Se+Math.imul(Be,Je)|0;var ir=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ir>>>26)|0,ir&=67108863,ge=Math.imul(He,qr),ie=Math.imul(He,jr),ie=ie+Math.imul(lt,qr)|0,Se=Math.imul(lt,jr),ge=ge+Math.imul(me,Ge)|0,ie=ie+Math.imul(me,Je)|0,ie=ie+Math.imul(Pe,Ge)|0,Se=Se+Math.imul(Pe,Je)|0,ge=ge+Math.imul(be,et)|0,ie=ie+Math.imul(be,xt)|0,ie=ie+Math.imul(Be,et)|0,Se=Se+Math.imul(Be,xt)|0;var wr=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(wr>>>26)|0,wr&=67108863,ge=Math.imul(Ht,qr),ie=Math.imul(Ht,jr),ie=ie+Math.imul(at,qr)|0,Se=Math.imul(at,jr),ge=ge+Math.imul(He,Ge)|0,ie=ie+Math.imul(He,Je)|0,ie=ie+Math.imul(lt,Ge)|0,Se=Se+Math.imul(lt,Je)|0,ge=ge+Math.imul(me,et)|0,ie=ie+Math.imul(me,xt)|0,ie=ie+Math.imul(Pe,et)|0,Se=Se+Math.imul(Pe,xt)|0,ge=ge+Math.imul(be,Kt)|0,ie=ie+Math.imul(be,Qt)|0,ie=ie+Math.imul(Be,Kt)|0,Se=Se+Math.imul(Be,Qt)|0;var Xr=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Xr>>>26)|0,Xr&=67108863,ge=Math.imul(ar,qr),ie=Math.imul(ar,jr),ie=ie+Math.imul(Vt,qr)|0,Se=Math.imul(Vt,jr),ge=ge+Math.imul(Ht,Ge)|0,ie=ie+Math.imul(Ht,Je)|0,ie=ie+Math.imul(at,Ge)|0,Se=Se+Math.imul(at,Je)|0,ge=ge+Math.imul(He,et)|0,ie=ie+Math.imul(He,xt)|0,ie=ie+Math.imul(lt,et)|0,Se=Se+Math.imul(lt,xt)|0,ge=ge+Math.imul(me,Kt)|0,ie=ie+Math.imul(me,Qt)|0,ie=ie+Math.imul(Pe,Kt)|0,Se=Se+Math.imul(Pe,Qt)|0,ge=ge+Math.imul(be,Gr)|0,ie=ie+Math.imul(be,Ir)|0,ie=ie+Math.imul(Be,Gr)|0,Se=Se+Math.imul(Be,Ir)|0;var ti=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ti>>>26)|0,ti&=67108863,ge=Math.imul(tt,qr),ie=Math.imul(tt,jr),ie=ie+Math.imul(je,qr)|0,Se=Math.imul(je,jr),ge=ge+Math.imul(ar,Ge)|0,ie=ie+Math.imul(ar,Je)|0,ie=ie+Math.imul(Vt,Ge)|0,Se=Se+Math.imul(Vt,Je)|0,ge=ge+Math.imul(Ht,et)|0,ie=ie+Math.imul(Ht,xt)|0,ie=ie+Math.imul(at,et)|0,Se=Se+Math.imul(at,xt)|0,ge=ge+Math.imul(He,Kt)|0,ie=ie+Math.imul(He,Qt)|0,ie=ie+Math.imul(lt,Kt)|0,Se=Se+Math.imul(lt,Qt)|0,ge=ge+Math.imul(me,Gr)|0,ie=ie+Math.imul(me,Ir)|0,ie=ie+Math.imul(Pe,Gr)|0,Se=Se+Math.imul(Pe,Ir)|0,ge=ge+Math.imul(be,_i)|0,ie=ie+Math.imul(be,Pi)|0,ie=ie+Math.imul(Be,_i)|0,Se=Se+Math.imul(Be,Pi)|0;var $r=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+($r>>>26)|0,$r&=67108863,ge=Math.imul(Ae,qr),ie=Math.imul(Ae,jr),ie=ie+Math.imul(rt,qr)|0,Se=Math.imul(rt,jr),ge=ge+Math.imul(tt,Ge)|0,ie=ie+Math.imul(tt,Je)|0,ie=ie+Math.imul(je,Ge)|0,Se=Se+Math.imul(je,Je)|0,ge=ge+Math.imul(ar,et)|0,ie=ie+Math.imul(ar,xt)|0,ie=ie+Math.imul(Vt,et)|0,Se=Se+Math.imul(Vt,xt)|0,ge=ge+Math.imul(Ht,Kt)|0,ie=ie+Math.imul(Ht,Qt)|0,ie=ie+Math.imul(at,Kt)|0,Se=Se+Math.imul(at,Qt)|0,ge=ge+Math.imul(He,Gr)|0,ie=ie+Math.imul(He,Ir)|0,ie=ie+Math.imul(lt,Gr)|0,Se=Se+Math.imul(lt,Ir)|0,ge=ge+Math.imul(me,_i)|0,ie=ie+Math.imul(me,Pi)|0,ie=ie+Math.imul(Pe,_i)|0,Se=Se+Math.imul(Pe,Pi)|0,ge=ge+Math.imul(be,mi)|0,ie=ie+Math.imul(be,un)|0,ie=ie+Math.imul(Be,mi)|0,Se=Se+Math.imul(Be,un)|0;var Ri=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Ri>>>26)|0,Ri&=67108863,ge=Math.imul(Tt,qr),ie=Math.imul(Tt,jr),ie=ie+Math.imul(dt,qr)|0,Se=Math.imul(dt,jr),ge=ge+Math.imul(Ae,Ge)|0,ie=ie+Math.imul(Ae,Je)|0,ie=ie+Math.imul(rt,Ge)|0,Se=Se+Math.imul(rt,Je)|0,ge=ge+Math.imul(tt,et)|0,ie=ie+Math.imul(tt,xt)|0,ie=ie+Math.imul(je,et)|0,Se=Se+Math.imul(je,xt)|0,ge=ge+Math.imul(ar,Kt)|0,ie=ie+Math.imul(ar,Qt)|0,ie=ie+Math.imul(Vt,Kt)|0,Se=Se+Math.imul(Vt,Qt)|0,ge=ge+Math.imul(Ht,Gr)|0,ie=ie+Math.imul(Ht,Ir)|0,ie=ie+Math.imul(at,Gr)|0,Se=Se+Math.imul(at,Ir)|0,ge=ge+Math.imul(He,_i)|0,ie=ie+Math.imul(He,Pi)|0,ie=ie+Math.imul(lt,_i)|0,Se=Se+Math.imul(lt,Pi)|0,ge=ge+Math.imul(me,mi)|0,ie=ie+Math.imul(me,un)|0,ie=ie+Math.imul(Pe,mi)|0,Se=Se+Math.imul(Pe,un)|0,ge=ge+Math.imul(be,An)|0,ie=ie+Math.imul(be,Hn)|0,ie=ie+Math.imul(Be,An)|0,Se=Se+Math.imul(Be,Hn)|0;var Hi=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Hi>>>26)|0,Hi&=67108863,ge=Math.imul(pt,qr),ie=Math.imul(pt,jr),ie=ie+Math.imul(jt,qr)|0,Se=Math.imul(jt,jr),ge=ge+Math.imul(Tt,Ge)|0,ie=ie+Math.imul(Tt,Je)|0,ie=ie+Math.imul(dt,Ge)|0,Se=Se+Math.imul(dt,Je)|0,ge=ge+Math.imul(Ae,et)|0,ie=ie+Math.imul(Ae,xt)|0,ie=ie+Math.imul(rt,et)|0,Se=Se+Math.imul(rt,xt)|0,ge=ge+Math.imul(tt,Kt)|0,ie=ie+Math.imul(tt,Qt)|0,ie=ie+Math.imul(je,Kt)|0,Se=Se+Math.imul(je,Qt)|0,ge=ge+Math.imul(ar,Gr)|0,ie=ie+Math.imul(ar,Ir)|0,ie=ie+Math.imul(Vt,Gr)|0,Se=Se+Math.imul(Vt,Ir)|0,ge=ge+Math.imul(Ht,_i)|0,ie=ie+Math.imul(Ht,Pi)|0,ie=ie+Math.imul(at,_i)|0,Se=Se+Math.imul(at,Pi)|0,ge=ge+Math.imul(He,mi)|0,ie=ie+Math.imul(He,un)|0,ie=ie+Math.imul(lt,mi)|0,Se=Se+Math.imul(lt,un)|0,ge=ge+Math.imul(me,An)|0,ie=ie+Math.imul(me,Hn)|0,ie=ie+Math.imul(Pe,An)|0,Se=Se+Math.imul(Pe,Hn)|0,ge=ge+Math.imul(be,Vi)|0,ie=ie+Math.imul(be,Jn)|0,ie=ie+Math.imul(Be,Vi)|0,Se=Se+Math.imul(Be,Jn)|0;var en=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(en>>>26)|0,en&=67108863,ge=Math.imul(mr,qr),ie=Math.imul(mr,jr),ie=ie+Math.imul(Ar,qr)|0,Se=Math.imul(Ar,jr),ge=ge+Math.imul(pt,Ge)|0,ie=ie+Math.imul(pt,Je)|0,ie=ie+Math.imul(jt,Ge)|0,Se=Se+Math.imul(jt,Je)|0,ge=ge+Math.imul(Tt,et)|0,ie=ie+Math.imul(Tt,xt)|0,ie=ie+Math.imul(dt,et)|0,Se=Se+Math.imul(dt,xt)|0,ge=ge+Math.imul(Ae,Kt)|0,ie=ie+Math.imul(Ae,Qt)|0,ie=ie+Math.imul(rt,Kt)|0,Se=Se+Math.imul(rt,Qt)|0,ge=ge+Math.imul(tt,Gr)|0,ie=ie+Math.imul(tt,Ir)|0,ie=ie+Math.imul(je,Gr)|0,Se=Se+Math.imul(je,Ir)|0,ge=ge+Math.imul(ar,_i)|0,ie=ie+Math.imul(ar,Pi)|0,ie=ie+Math.imul(Vt,_i)|0,Se=Se+Math.imul(Vt,Pi)|0,ge=ge+Math.imul(Ht,mi)|0,ie=ie+Math.imul(Ht,un)|0,ie=ie+Math.imul(at,mi)|0,Se=Se+Math.imul(at,un)|0,ge=ge+Math.imul(He,An)|0,ie=ie+Math.imul(He,Hn)|0,ie=ie+Math.imul(lt,An)|0,Se=Se+Math.imul(lt,Hn)|0,ge=ge+Math.imul(me,Vi)|0,ie=ie+Math.imul(me,Jn)|0,ie=ie+Math.imul(Pe,Vi)|0,Se=Se+Math.imul(Pe,Jn)|0,ge=ge+Math.imul(be,Gt)|0,ie=ie+Math.imul(be,wt)|0,ie=ie+Math.imul(Be,Gt)|0,Se=Se+Math.imul(Be,wt)|0;var fn=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(fn>>>26)|0,fn&=67108863,ge=Math.imul(mr,Ge),ie=Math.imul(mr,Je),ie=ie+Math.imul(Ar,Ge)|0,Se=Math.imul(Ar,Je),ge=ge+Math.imul(pt,et)|0,ie=ie+Math.imul(pt,xt)|0,ie=ie+Math.imul(jt,et)|0,Se=Se+Math.imul(jt,xt)|0,ge=ge+Math.imul(Tt,Kt)|0,ie=ie+Math.imul(Tt,Qt)|0,ie=ie+Math.imul(dt,Kt)|0,Se=Se+Math.imul(dt,Qt)|0,ge=ge+Math.imul(Ae,Gr)|0,ie=ie+Math.imul(Ae,Ir)|0,ie=ie+Math.imul(rt,Gr)|0,Se=Se+Math.imul(rt,Ir)|0,ge=ge+Math.imul(tt,_i)|0,ie=ie+Math.imul(tt,Pi)|0,ie=ie+Math.imul(je,_i)|0,Se=Se+Math.imul(je,Pi)|0,ge=ge+Math.imul(ar,mi)|0,ie=ie+Math.imul(ar,un)|0,ie=ie+Math.imul(Vt,mi)|0,Se=Se+Math.imul(Vt,un)|0,ge=ge+Math.imul(Ht,An)|0,ie=ie+Math.imul(Ht,Hn)|0,ie=ie+Math.imul(at,An)|0,Se=Se+Math.imul(at,Hn)|0,ge=ge+Math.imul(He,Vi)|0,ie=ie+Math.imul(He,Jn)|0,ie=ie+Math.imul(lt,Vi)|0,Se=Se+Math.imul(lt,Jn)|0,ge=ge+Math.imul(me,Gt)|0,ie=ie+Math.imul(me,wt)|0,ie=ie+Math.imul(Pe,Gt)|0,Se=Se+Math.imul(Pe,wt)|0;var yn=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(yn>>>26)|0,yn&=67108863,ge=Math.imul(mr,et),ie=Math.imul(mr,xt),ie=ie+Math.imul(Ar,et)|0,Se=Math.imul(Ar,xt),ge=ge+Math.imul(pt,Kt)|0,ie=ie+Math.imul(pt,Qt)|0,ie=ie+Math.imul(jt,Kt)|0,Se=Se+Math.imul(jt,Qt)|0,ge=ge+Math.imul(Tt,Gr)|0,ie=ie+Math.imul(Tt,Ir)|0,ie=ie+Math.imul(dt,Gr)|0,Se=Se+Math.imul(dt,Ir)|0,ge=ge+Math.imul(Ae,_i)|0,ie=ie+Math.imul(Ae,Pi)|0,ie=ie+Math.imul(rt,_i)|0,Se=Se+Math.imul(rt,Pi)|0,ge=ge+Math.imul(tt,mi)|0,ie=ie+Math.imul(tt,un)|0,ie=ie+Math.imul(je,mi)|0,Se=Se+Math.imul(je,un)|0,ge=ge+Math.imul(ar,An)|0,ie=ie+Math.imul(ar,Hn)|0,ie=ie+Math.imul(Vt,An)|0,Se=Se+Math.imul(Vt,Hn)|0,ge=ge+Math.imul(Ht,Vi)|0,ie=ie+Math.imul(Ht,Jn)|0,ie=ie+Math.imul(at,Vi)|0,Se=Se+Math.imul(at,Jn)|0,ge=ge+Math.imul(He,Gt)|0,ie=ie+Math.imul(He,wt)|0,ie=ie+Math.imul(lt,Gt)|0,Se=Se+Math.imul(lt,wt)|0;var Mn=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Mn>>>26)|0,Mn&=67108863,ge=Math.imul(mr,Kt),ie=Math.imul(mr,Qt),ie=ie+Math.imul(Ar,Kt)|0,Se=Math.imul(Ar,Qt),ge=ge+Math.imul(pt,Gr)|0,ie=ie+Math.imul(pt,Ir)|0,ie=ie+Math.imul(jt,Gr)|0,Se=Se+Math.imul(jt,Ir)|0,ge=ge+Math.imul(Tt,_i)|0,ie=ie+Math.imul(Tt,Pi)|0,ie=ie+Math.imul(dt,_i)|0,Se=Se+Math.imul(dt,Pi)|0,ge=ge+Math.imul(Ae,mi)|0,ie=ie+Math.imul(Ae,un)|0,ie=ie+Math.imul(rt,mi)|0,Se=Se+Math.imul(rt,un)|0,ge=ge+Math.imul(tt,An)|0,ie=ie+Math.imul(tt,Hn)|0,ie=ie+Math.imul(je,An)|0,Se=Se+Math.imul(je,Hn)|0,ge=ge+Math.imul(ar,Vi)|0,ie=ie+Math.imul(ar,Jn)|0,ie=ie+Math.imul(Vt,Vi)|0,Se=Se+Math.imul(Vt,Jn)|0,ge=ge+Math.imul(Ht,Gt)|0,ie=ie+Math.imul(Ht,wt)|0,ie=ie+Math.imul(at,Gt)|0,Se=Se+Math.imul(at,wt)|0;var Ba=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Ba>>>26)|0,Ba&=67108863,ge=Math.imul(mr,Gr),ie=Math.imul(mr,Ir),ie=ie+Math.imul(Ar,Gr)|0,Se=Math.imul(Ar,Ir),ge=ge+Math.imul(pt,_i)|0,ie=ie+Math.imul(pt,Pi)|0,ie=ie+Math.imul(jt,_i)|0,Se=Se+Math.imul(jt,Pi)|0,ge=ge+Math.imul(Tt,mi)|0,ie=ie+Math.imul(Tt,un)|0,ie=ie+Math.imul(dt,mi)|0,Se=Se+Math.imul(dt,un)|0,ge=ge+Math.imul(Ae,An)|0,ie=ie+Math.imul(Ae,Hn)|0,ie=ie+Math.imul(rt,An)|0,Se=Se+Math.imul(rt,Hn)|0,ge=ge+Math.imul(tt,Vi)|0,ie=ie+Math.imul(tt,Jn)|0,ie=ie+Math.imul(je,Vi)|0,Se=Se+Math.imul(je,Jn)|0,ge=ge+Math.imul(ar,Gt)|0,ie=ie+Math.imul(ar,wt)|0,ie=ie+Math.imul(Vt,Gt)|0,Se=Se+Math.imul(Vt,wt)|0;var ua=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ua>>>26)|0,ua&=67108863,ge=Math.imul(mr,_i),ie=Math.imul(mr,Pi),ie=ie+Math.imul(Ar,_i)|0,Se=Math.imul(Ar,Pi),ge=ge+Math.imul(pt,mi)|0,ie=ie+Math.imul(pt,un)|0,ie=ie+Math.imul(jt,mi)|0,Se=Se+Math.imul(jt,un)|0,ge=ge+Math.imul(Tt,An)|0,ie=ie+Math.imul(Tt,Hn)|0,ie=ie+Math.imul(dt,An)|0,Se=Se+Math.imul(dt,Hn)|0,ge=ge+Math.imul(Ae,Vi)|0,ie=ie+Math.imul(Ae,Jn)|0,ie=ie+Math.imul(rt,Vi)|0,Se=Se+Math.imul(rt,Jn)|0,ge=ge+Math.imul(tt,Gt)|0,ie=ie+Math.imul(tt,wt)|0,ie=ie+Math.imul(je,Gt)|0,Se=Se+Math.imul(je,wt)|0;var ma=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ma>>>26)|0,ma&=67108863,ge=Math.imul(mr,mi),ie=Math.imul(mr,un),ie=ie+Math.imul(Ar,mi)|0,Se=Math.imul(Ar,un),ge=ge+Math.imul(pt,An)|0,ie=ie+Math.imul(pt,Hn)|0,ie=ie+Math.imul(jt,An)|0,Se=Se+Math.imul(jt,Hn)|0,ge=ge+Math.imul(Tt,Vi)|0,ie=ie+Math.imul(Tt,Jn)|0,ie=ie+Math.imul(dt,Vi)|0,Se=Se+Math.imul(dt,Jn)|0,ge=ge+Math.imul(Ae,Gt)|0,ie=ie+Math.imul(Ae,wt)|0,ie=ie+Math.imul(rt,Gt)|0,Se=Se+Math.imul(rt,wt)|0;var Wa=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Wa>>>26)|0,Wa&=67108863,ge=Math.imul(mr,An),ie=Math.imul(mr,Hn),ie=ie+Math.imul(Ar,An)|0,Se=Math.imul(Ar,Hn),ge=ge+Math.imul(pt,Vi)|0,ie=ie+Math.imul(pt,Jn)|0,ie=ie+Math.imul(jt,Vi)|0,Se=Se+Math.imul(jt,Jn)|0,ge=ge+Math.imul(Tt,Gt)|0,ie=ie+Math.imul(Tt,wt)|0,ie=ie+Math.imul(dt,Gt)|0,Se=Se+Math.imul(dt,wt)|0;var za=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(za>>>26)|0,za&=67108863,ge=Math.imul(mr,Vi),ie=Math.imul(mr,Jn),ie=ie+Math.imul(Ar,Vi)|0,Se=Math.imul(Ar,Jn),ge=ge+Math.imul(pt,Gt)|0,ie=ie+Math.imul(pt,wt)|0,ie=ie+Math.imul(jt,Gt)|0,Se=Se+Math.imul(jt,wt)|0;var Xo=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Xo>>>26)|0,Xo&=67108863,ge=Math.imul(mr,Gt),ie=Math.imul(mr,wt),ie=ie+Math.imul(Ar,Gt)|0,Se=Math.imul(Ar,wt);var da=(Ce+ge|0)+((ie&8191)<<13)|0;return Ce=(Se+(ie>>>13)|0)+(da>>>26)|0,da&=67108863,Ee[0]=tr,Ee[1]=ir,Ee[2]=wr,Ee[3]=Xr,Ee[4]=ti,Ee[5]=$r,Ee[6]=Ri,Ee[7]=Hi,Ee[8]=en,Ee[9]=fn,Ee[10]=yn,Ee[11]=Mn,Ee[12]=Ba,Ee[13]=ua,Ee[14]=ma,Ee[15]=Wa,Ee[16]=za,Ee[17]=Xo,Ee[18]=da,Ce!==0&&(Ee[19]=Ce,re.length++),re};Math.imul||(L=T);function x(j,N,H){H.negative=N.negative^j.negative,H.length=j.length+N.length;for(var re=0,ae=0,_e=0;_e<H.length-1;_e++){var Ee=ae;ae=0;for(var Ce=re&67108863,ge=Math.min(_e,N.length-1),ie=Math.max(0,_e-j.length+1);ie<=ge;ie++){var Se=_e-ie,ke=j.words[Se]|0,be=N.words[ie]|0,Be=ke*be,Le=Be&67108863;Ee=Ee+(Be/67108864|0)|0,Le=Le+Ce|0,Ce=Le&67108863,Ee=Ee+(Le>>>26)|0,ae+=Ee>>>26,Ee&=67108863}H.words[_e]=Ce,re=Ee,Ee=ae}return re!==0?H.words[_e]=re:H.length--,H.strip()}function C(j,N,H){var re=new S;return re.mulp(j,N,H)}f.prototype.mulTo=function(N,H){var re,ae=this.length+N.length;return this.length===10&&N.length===10?re=L(this,N,H):ae<63?re=T(this,N,H):ae<1024?re=x(this,N,H):re=C(this,N,H),re};function S(j,N){this.x=j,this.y=N}S.prototype.makeRBT=function(N){for(var H=new Array(N),re=f.prototype._countBits(N)-1,ae=0;ae<N;ae++)H[ae]=this.revBin(ae,re,N);return H},S.prototype.revBin=function(N,H,re){if(N===0||N===re-1)return N;for(var ae=0,_e=0;_e<H;_e++)ae|=(N&1)<<H-_e-1,N>>=1;return ae},S.prototype.permute=function(N,H,re,ae,_e,Ee){for(var Ce=0;Ce<Ee;Ce++)ae[Ce]=H[N[Ce]],_e[Ce]=re[N[Ce]]},S.prototype.transform=function(N,H,re,ae,_e,Ee){this.permute(Ee,N,H,re,ae,_e);for(var Ce=1;Ce<_e;Ce<<=1)for(var ge=Ce<<1,ie=Math.cos(2*Math.PI/ge),Se=Math.sin(2*Math.PI/ge),ke=0;ke<_e;ke+=ge)for(var be=ie,Be=Se,Le=0;Le<Ce;Le++){var me=re[ke+Le],Pe=ae[ke+Le],ce=re[ke+Le+Ce],He=ae[ke+Le+Ce],lt=be*ce-Be*He;He=be*He+Be*ce,ce=lt,re[ke+Le]=me+ce,ae[ke+Le]=Pe+He,re[ke+Le+Ce]=me-ce,ae[ke+Le+Ce]=Pe-He,Le!==ge&&(lt=ie*be-Se*Be,Be=ie*Be+Se*be,be=lt)}},S.prototype.guessLen13b=function(N,H){var re=Math.max(H,N)|1,ae=re&1,_e=0;for(re=re/2|0;re;re=re>>>1)_e++;return 1<<_e+1+ae},S.prototype.conjugate=function(N,H,re){if(!(re<=1))for(var ae=0;ae<re/2;ae++){var _e=N[ae];N[ae]=N[re-ae-1],N[re-ae-1]=_e,_e=H[ae],H[ae]=-H[re-ae-1],H[re-ae-1]=-_e}},S.prototype.normalize13b=function(N,H){for(var re=0,ae=0;ae<H/2;ae++){var _e=Math.round(N[2*ae+1]/H)*8192+Math.round(N[2*ae]/H)+re;N[ae]=_e&67108863,_e<67108864?re=0:re=_e/67108864|0}return N},S.prototype.convert13b=function(N,H,re,ae){for(var _e=0,Ee=0;Ee<H;Ee++)_e=_e+(N[Ee]|0),re[2*Ee]=_e&8191,_e=_e>>>13,re[2*Ee+1]=_e&8191,_e=_e>>>13;for(Ee=2*H;Ee<ae;++Ee)re[Ee]=0;u(_e===0),u((_e&-8192)===0)},S.prototype.stub=function(N){for(var H=new Array(N),re=0;re<N;re++)H[re]=0;return H},S.prototype.mulp=function(N,H,re){var ae=2*this.guessLen13b(N.length,H.length),_e=this.makeRBT(ae),Ee=this.stub(ae),Ce=new Array(ae),ge=new Array(ae),ie=new Array(ae),Se=new Array(ae),ke=new Array(ae),be=new Array(ae),Be=re.words;Be.length=ae,this.convert13b(N.words,N.length,Ce,ae),this.convert13b(H.words,H.length,Se,ae),this.transform(Ce,Ee,ge,ie,ae,_e),this.transform(Se,Ee,ke,be,ae,_e);for(var Le=0;Le<ae;Le++){var me=ge[Le]*ke[Le]-ie[Le]*be[Le];ie[Le]=ge[Le]*be[Le]+ie[Le]*ke[Le],ge[Le]=me}return this.conjugate(ge,ie,ae),this.transform(ge,ie,Be,Ee,ae,_e),this.conjugate(Be,Ee,ae),this.normalize13b(Be,ae),re.negative=N.negative^H.negative,re.length=N.length+H.length,re.strip()},f.prototype.mul=function(N){var H=new f(null);return H.words=new Array(this.length+N.length),this.mulTo(N,H)},f.prototype.mulf=function(N){var H=new f(null);return H.words=new Array(this.length+N.length),C(this,N,H)},f.prototype.imul=function(N){return this.clone().mulTo(N,this)},f.prototype.imuln=function(N){u(typeof N==\"number\"),u(N<67108864);for(var H=0,re=0;re<this.length;re++){var ae=(this.words[re]|0)*N,_e=(ae&67108863)+(H&67108863);H>>=26,H+=ae/67108864|0,H+=_e>>>26,this.words[re]=_e&67108863}return H!==0&&(this.words[re]=H,this.length++),this.length=N===0?1:this.length,this},f.prototype.muln=function(N){return this.clone().imuln(N)},f.prototype.sqr=function(){return this.mul(this)},f.prototype.isqr=function(){return this.imul(this.clone())},f.prototype.pow=function(N){var H=M(N);if(H.length===0)return new f(1);for(var re=this,ae=0;ae<H.length&&H[ae]===0;ae++,re=re.sqr());if(++ae<H.length)for(var _e=re.sqr();ae<H.length;ae++,_e=_e.sqr())H[ae]!==0&&(re=re.mul(_e));return re},f.prototype.iushln=function(N){u(typeof N==\"number\"&&N>=0);var H=N%26,re=(N-H)/26,ae=67108863>>>26-H<<26-H,_e;if(H!==0){var Ee=0;for(_e=0;_e<this.length;_e++){var Ce=this.words[_e]&ae,ge=(this.words[_e]|0)-Ce<<H;this.words[_e]=ge|Ee,Ee=Ce>>>26-H}Ee&&(this.words[_e]=Ee,this.length++)}if(re!==0){for(_e=this.length-1;_e>=0;_e--)this.words[_e+re]=this.words[_e];for(_e=0;_e<re;_e++)this.words[_e]=0;this.length+=re}return this.strip()},f.prototype.ishln=function(N){return u(this.negative===0),this.iushln(N)},f.prototype.iushrn=function(N,H,re){u(typeof N==\"number\"&&N>=0);var ae;H?ae=(H-H%26)/26:ae=0;var _e=N%26,Ee=Math.min((N-_e)/26,this.length),Ce=67108863^67108863>>>_e<<_e,ge=re;if(ae-=Ee,ae=Math.max(0,ae),ge){for(var ie=0;ie<Ee;ie++)ge.words[ie]=this.words[ie];ge.length=Ee}if(Ee!==0)if(this.length>Ee)for(this.length-=Ee,ie=0;ie<this.length;ie++)this.words[ie]=this.words[ie+Ee];else this.words[0]=0,this.length=1;var Se=0;for(ie=this.length-1;ie>=0&&(Se!==0||ie>=ae);ie--){var ke=this.words[ie]|0;this.words[ie]=Se<<26-_e|ke>>>_e,Se=ke&Ce}return ge&&Se!==0&&(ge.words[ge.length++]=Se),this.length===0&&(this.words[0]=0,this.length=1),this.strip()},f.prototype.ishrn=function(N,H,re){return u(this.negative===0),this.iushrn(N,H,re)},f.prototype.shln=function(N){return this.clone().ishln(N)},f.prototype.ushln=function(N){return this.clone().iushln(N)},f.prototype.shrn=function(N){return this.clone().ishrn(N)},f.prototype.ushrn=function(N){return this.clone().iushrn(N)},f.prototype.testn=function(N){u(typeof N==\"number\"&&N>=0);var H=N%26,re=(N-H)/26,ae=1<<H;if(this.length<=re)return!1;var _e=this.words[re];return!!(_e&ae)},f.prototype.imaskn=function(N){u(typeof N==\"number\"&&N>=0);var H=N%26,re=(N-H)/26;if(u(this.negative===0,\"imaskn works only with positive numbers\"),this.length<=re)return this;if(H!==0&&re++,this.length=Math.min(re,this.length),H!==0){var ae=67108863^67108863>>>H<<H;this.words[this.length-1]&=ae}return this.length===0&&(this.words[0]=0,this.length=1),this.strip()},f.prototype.maskn=function(N){return this.clone().imaskn(N)},f.prototype.iaddn=function(N){return u(typeof N==\"number\"),u(N<67108864),N<0?this.isubn(-N):this.negative!==0?this.length===1&&(this.words[0]|0)<N?(this.words[0]=N-(this.words[0]|0),this.negative=0,this):(this.negative=0,this.isubn(N),this.negative=1,this):this._iaddn(N)},f.prototype._iaddn=function(N){this.words[0]+=N;for(var H=0;H<this.length&&this.words[H]>=67108864;H++)this.words[H]-=67108864,H===this.length-1?this.words[H+1]=1:this.words[H+1]++;return this.length=Math.max(this.length,H+1),this},f.prototype.isubn=function(N){if(u(typeof N==\"number\"),u(N<67108864),N<0)return this.iaddn(-N);if(this.negative!==0)return this.negative=0,this.iaddn(N),this.negative=1,this;if(this.words[0]-=N,this.length===1&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var H=0;H<this.length&&this.words[H]<0;H++)this.words[H]+=67108864,this.words[H+1]-=1;return this.strip()},f.prototype.addn=function(N){return this.clone().iaddn(N)},f.prototype.subn=function(N){return this.clone().isubn(N)},f.prototype.iabs=function(){return this.negative=0,this},f.prototype.abs=function(){return this.clone().iabs()},f.prototype._ishlnsubmul=function(N,H,re){var ae=N.length+re,_e;this._expand(ae);var Ee,Ce=0;for(_e=0;_e<N.length;_e++){Ee=(this.words[_e+re]|0)+Ce;var ge=(N.words[_e]|0)*H;Ee-=ge&67108863,Ce=(Ee>>26)-(ge/67108864|0),this.words[_e+re]=Ee&67108863}for(;_e<this.length-re;_e++)Ee=(this.words[_e+re]|0)+Ce,Ce=Ee>>26,this.words[_e+re]=Ee&67108863;if(Ce===0)return this.strip();for(u(Ce===-1),Ce=0,_e=0;_e<this.length;_e++)Ee=-(this.words[_e]|0)+Ce,Ce=Ee>>26,this.words[_e]=Ee&67108863;return this.negative=1,this.strip()},f.prototype._wordDiv=function(N,H){var re=this.length-N.length,ae=this.clone(),_e=N,Ee=_e.words[_e.length-1]|0,Ce=this._countBits(Ee);re=26-Ce,re!==0&&(_e=_e.ushln(re),ae.iushln(re),Ee=_e.words[_e.length-1]|0);var ge=ae.length-_e.length,ie;if(H!==\"mod\"){ie=new f(null),ie.length=ge+1,ie.words=new Array(ie.length);for(var Se=0;Se<ie.length;Se++)ie.words[Se]=0}var ke=ae.clone()._ishlnsubmul(_e,1,ge);ke.negative===0&&(ae=ke,ie&&(ie.words[ge]=1));for(var be=ge-1;be>=0;be--){var Be=(ae.words[_e.length+be]|0)*67108864+(ae.words[_e.length+be-1]|0);for(Be=Math.min(Be/Ee|0,67108863),ae._ishlnsubmul(_e,Be,be);ae.negative!==0;)Be--,ae.negative=0,ae._ishlnsubmul(_e,1,be),ae.isZero()||(ae.negative^=1);ie&&(ie.words[be]=Be)}return ie&&ie.strip(),ae.strip(),H!==\"div\"&&re!==0&&ae.iushrn(re),{div:ie||null,mod:ae}},f.prototype.divmod=function(N,H,re){if(u(!N.isZero()),this.isZero())return{div:new f(0),mod:new f(0)};var ae,_e,Ee;return this.negative!==0&&N.negative===0?(Ee=this.neg().divmod(N,H),H!==\"mod\"&&(ae=Ee.div.neg()),H!==\"div\"&&(_e=Ee.mod.neg(),re&&_e.negative!==0&&_e.iadd(N)),{div:ae,mod:_e}):this.negative===0&&N.negative!==0?(Ee=this.divmod(N.neg(),H),H!==\"mod\"&&(ae=Ee.div.neg()),{div:ae,mod:Ee.mod}):(this.negative&N.negative)!==0?(Ee=this.neg().divmod(N.neg(),H),H!==\"div\"&&(_e=Ee.mod.neg(),re&&_e.negative!==0&&_e.isub(N)),{div:Ee.div,mod:_e}):N.length>this.length||this.cmp(N)<0?{div:new f(0),mod:this}:N.length===1?H===\"div\"?{div:this.divn(N.words[0]),mod:null}:H===\"mod\"?{div:null,mod:new f(this.modn(N.words[0]))}:{div:this.divn(N.words[0]),mod:new f(this.modn(N.words[0]))}:this._wordDiv(N,H)},f.prototype.div=function(N){return this.divmod(N,\"div\",!1).div},f.prototype.mod=function(N){return this.divmod(N,\"mod\",!1).mod},f.prototype.umod=function(N){return this.divmod(N,\"mod\",!0).mod},f.prototype.divRound=function(N){var H=this.divmod(N);if(H.mod.isZero())return H.div;var re=H.div.negative!==0?H.mod.isub(N):H.mod,ae=N.ushrn(1),_e=N.andln(1),Ee=re.cmp(ae);return Ee<0||_e===1&&Ee===0?H.div:H.div.negative!==0?H.div.isubn(1):H.div.iaddn(1)},f.prototype.modn=function(N){u(N<=67108863);for(var H=(1<<26)%N,re=0,ae=this.length-1;ae>=0;ae--)re=(H*re+(this.words[ae]|0))%N;return re},f.prototype.idivn=function(N){u(N<=67108863);for(var H=0,re=this.length-1;re>=0;re--){var ae=(this.words[re]|0)+H*67108864;this.words[re]=ae/N|0,H=ae%N}return this.strip()},f.prototype.divn=function(N){return this.clone().idivn(N)},f.prototype.egcd=function(N){u(N.negative===0),u(!N.isZero());var H=this,re=N.clone();H.negative!==0?H=H.umod(N):H=H.clone();for(var ae=new f(1),_e=new f(0),Ee=new f(0),Ce=new f(1),ge=0;H.isEven()&&re.isEven();)H.iushrn(1),re.iushrn(1),++ge;for(var ie=re.clone(),Se=H.clone();!H.isZero();){for(var ke=0,be=1;(H.words[0]&be)===0&&ke<26;++ke,be<<=1);if(ke>0)for(H.iushrn(ke);ke-- >0;)(ae.isOdd()||_e.isOdd())&&(ae.iadd(ie),_e.isub(Se)),ae.iushrn(1),_e.iushrn(1);for(var Be=0,Le=1;(re.words[0]&Le)===0&&Be<26;++Be,Le<<=1);if(Be>0)for(re.iushrn(Be);Be-- >0;)(Ee.isOdd()||Ce.isOdd())&&(Ee.iadd(ie),Ce.isub(Se)),Ee.iushrn(1),Ce.iushrn(1);H.cmp(re)>=0?(H.isub(re),ae.isub(Ee),_e.isub(Ce)):(re.isub(H),Ee.isub(ae),Ce.isub(_e))}return{a:Ee,b:Ce,gcd:re.iushln(ge)}},f.prototype._invmp=function(N){u(N.negative===0),u(!N.isZero());var H=this,re=N.clone();H.negative!==0?H=H.umod(N):H=H.clone();for(var ae=new f(1),_e=new f(0),Ee=re.clone();H.cmpn(1)>0&&re.cmpn(1)>0;){for(var Ce=0,ge=1;(H.words[0]&ge)===0&&Ce<26;++Ce,ge<<=1);if(Ce>0)for(H.iushrn(Ce);Ce-- >0;)ae.isOdd()&&ae.iadd(Ee),ae.iushrn(1);for(var ie=0,Se=1;(re.words[0]&Se)===0&&ie<26;++ie,Se<<=1);if(ie>0)for(re.iushrn(ie);ie-- >0;)_e.isOdd()&&_e.iadd(Ee),_e.iushrn(1);H.cmp(re)>=0?(H.isub(re),ae.isub(_e)):(re.isub(H),_e.isub(ae))}var ke;return H.cmpn(1)===0?ke=ae:ke=_e,ke.cmpn(0)<0&&ke.iadd(N),ke},f.prototype.gcd=function(N){if(this.isZero())return N.abs();if(N.isZero())return this.abs();var H=this.clone(),re=N.clone();H.negative=0,re.negative=0;for(var ae=0;H.isEven()&&re.isEven();ae++)H.iushrn(1),re.iushrn(1);do{for(;H.isEven();)H.iushrn(1);for(;re.isEven();)re.iushrn(1);var _e=H.cmp(re);if(_e<0){var Ee=H;H=re,re=Ee}else if(_e===0||re.cmpn(1)===0)break;H.isub(re)}while(!0);return re.iushln(ae)},f.prototype.invm=function(N){return this.egcd(N).a.umod(N)},f.prototype.isEven=function(){return(this.words[0]&1)===0},f.prototype.isOdd=function(){return(this.words[0]&1)===1},f.prototype.andln=function(N){return this.words[0]&N},f.prototype.bincn=function(N){u(typeof N==\"number\");var H=N%26,re=(N-H)/26,ae=1<<H;if(this.length<=re)return this._expand(re+1),this.words[re]|=ae,this;for(var _e=ae,Ee=re;_e!==0&&Ee<this.length;Ee++){var Ce=this.words[Ee]|0;Ce+=_e,_e=Ce>>>26,Ce&=67108863,this.words[Ee]=Ce}return _e!==0&&(this.words[Ee]=_e,this.length++),this},f.prototype.isZero=function(){return this.length===1&&this.words[0]===0},f.prototype.cmpn=function(N){var H=N<0;if(this.negative!==0&&!H)return-1;if(this.negative===0&&H)return 1;this.strip();var re;if(this.length>1)re=1;else{H&&(N=-N),u(N<=67108863,\"Number is too big\");var ae=this.words[0]|0;re=ae===N?0:ae<N?-1:1}return this.negative!==0?-re|0:re},f.prototype.cmp=function(N){if(this.negative!==0&&N.negative===0)return-1;if(this.negative===0&&N.negative!==0)return 1;var H=this.ucmp(N);return this.negative!==0?-H|0:H},f.prototype.ucmp=function(N){if(this.length>N.length)return 1;if(this.length<N.length)return-1;for(var H=0,re=this.length-1;re>=0;re--){var ae=this.words[re]|0,_e=N.words[re]|0;if(ae!==_e){ae<_e?H=-1:ae>_e&&(H=1);break}}return H},f.prototype.gtn=function(N){return this.cmpn(N)===1},f.prototype.gt=function(N){return this.cmp(N)===1},f.prototype.gten=function(N){return this.cmpn(N)>=0},f.prototype.gte=function(N){return this.cmp(N)>=0},f.prototype.ltn=function(N){return this.cmpn(N)===-1},f.prototype.lt=function(N){return this.cmp(N)===-1},f.prototype.lten=function(N){return this.cmpn(N)<=0},f.prototype.lte=function(N){return this.cmp(N)<=0},f.prototype.eqn=function(N){return this.cmpn(N)===0},f.prototype.eq=function(N){return this.cmp(N)===0},f.red=function(N){return new G(N)},f.prototype.toRed=function(N){return u(!this.red,\"Already a number in reduction context\"),u(this.negative===0,\"red works only with positives\"),N.convertTo(this)._forceRed(N)},f.prototype.fromRed=function(){return u(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},f.prototype._forceRed=function(N){return this.red=N,this},f.prototype.forceRed=function(N){return u(!this.red,\"Already a number in reduction context\"),this._forceRed(N)},f.prototype.redAdd=function(N){return u(this.red,\"redAdd works only with red numbers\"),this.red.add(this,N)},f.prototype.redIAdd=function(N){return u(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,N)},f.prototype.redSub=function(N){return u(this.red,\"redSub works only with red numbers\"),this.red.sub(this,N)},f.prototype.redISub=function(N){return u(this.red,\"redISub works only with red numbers\"),this.red.isub(this,N)},f.prototype.redShl=function(N){return u(this.red,\"redShl works only with red numbers\"),this.red.shl(this,N)},f.prototype.redMul=function(N){return u(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,N),this.red.mul(this,N)},f.prototype.redIMul=function(N){return u(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,N),this.red.imul(this,N)},f.prototype.redSqr=function(){return u(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},f.prototype.redISqr=function(){return u(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},f.prototype.redSqrt=function(){return u(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},f.prototype.redInvm=function(){return u(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},f.prototype.redNeg=function(){return u(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},f.prototype.redPow=function(N){return u(this.red&&!N.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,N)};var g={k256:null,p224:null,p192:null,p25519:null};function P(j,N){this.name=j,this.p=new f(N,16),this.n=this.p.bitLength(),this.k=new f(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}P.prototype._tmp=function(){var N=new f(null);return N.words=new Array(Math.ceil(this.n/13)),N},P.prototype.ireduce=function(N){var H=N,re;do this.split(H,this.tmp),H=this.imulK(H),H=H.iadd(this.tmp),re=H.bitLength();while(re>this.n);var ae=re<this.n?-1:H.ucmp(this.p);return ae===0?(H.words[0]=0,H.length=1):ae>0?H.isub(this.p):H.strip!==void 0?H.strip():H._strip(),H},P.prototype.split=function(N,H){N.iushrn(this.n,0,H)},P.prototype.imulK=function(N){return N.imul(this.k)};function E(){P.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}c(E,P),E.prototype.split=function(N,H){for(var re=4194303,ae=Math.min(N.length,9),_e=0;_e<ae;_e++)H.words[_e]=N.words[_e];if(H.length=ae,N.length<=9){N.words[0]=0,N.length=1;return}var Ee=N.words[9];for(H.words[H.length++]=Ee&re,_e=10;_e<N.length;_e++){var Ce=N.words[_e]|0;N.words[_e-10]=(Ce&re)<<4|Ee>>>22,Ee=Ce}Ee>>>=22,N.words[_e-10]=Ee,Ee===0&&N.length>10?N.length-=10:N.length-=9},E.prototype.imulK=function(N){N.words[N.length]=0,N.words[N.length+1]=0,N.length+=2;for(var H=0,re=0;re<N.length;re++){var ae=N.words[re]|0;H+=ae*977,N.words[re]=H&67108863,H=ae*64+(H/67108864|0)}return N.words[N.length-1]===0&&(N.length--,N.words[N.length-1]===0&&N.length--),N};function z(){P.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}c(z,P);function q(){P.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}c(q,P);function V(){P.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}c(V,P),V.prototype.imulK=function(N){for(var H=0,re=0;re<N.length;re++){var ae=(N.words[re]|0)*19+H,_e=ae&67108863;ae>>>=26,N.words[re]=_e,H=ae}return H!==0&&(N.words[N.length++]=H),N},f._prime=function(N){if(g[N])return g[N];var H;if(N===\"k256\")H=new E;else if(N===\"p224\")H=new z;else if(N===\"p192\")H=new q;else if(N===\"p25519\")H=new V;else throw new Error(\"Unknown prime \"+N);return g[N]=H,H};function G(j){if(typeof j==\"string\"){var N=f._prime(j);this.m=N.p,this.prime=N}else u(j.gtn(1),\"modulus must be greater than 1\"),this.m=j,this.prime=null}G.prototype._verify1=function(N){u(N.negative===0,\"red works only with positives\"),u(N.red,\"red works only with red 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H.negative!==0?(H.negative=0,this.imod(H).redNeg()):this.imod(H)},G.prototype.pow=function(N,H){if(H.isZero())return new f(1).toRed(this);if(H.cmpn(1)===0)return N.clone();var re=4,ae=new Array(1<<re);ae[0]=new f(1).toRed(this),ae[1]=N;for(var _e=2;_e<ae.length;_e++)ae[_e]=this.mul(ae[_e-1],N);var Ee=ae[0],Ce=0,ge=0,ie=H.bitLength()%26;for(ie===0&&(ie=26),_e=H.length-1;_e>=0;_e--){for(var Se=H.words[_e],ke=ie-1;ke>=0;ke--){var be=Se>>ke&1;if(Ee!==ae[0]&&(Ee=this.sqr(Ee)),be===0&&Ce===0){ge=0;continue}Ce<<=1,Ce|=be,ge++,!(ge!==re&&(_e!==0||ke!==0))&&(Ee=this.mul(Ee,ae[Ce]),ge=0,Ce=0)}ie=26}return Ee},G.prototype.convertTo=function(N){var H=N.umod(this.m);return H===N?H.clone():H},G.prototype.convertFrom=function(N){var H=N.clone();return H.red=null,H},f.mont=function(N){return new Z(N)};function Z(j){G.call(this,j),this.shift=this.m.bitLength(),this.shift%26!==0&&(this.shift+=26-this.shift%26),this.r=new f(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}c(Z,G),Z.prototype.convertTo=function(N){return this.imod(N.ushln(this.shift))},Z.prototype.convertFrom=function(N){var H=this.imod(N.mul(this.rinv));return H.red=null,H},Z.prototype.imul=function(N,H){if(N.isZero()||H.isZero())return N.words[0]=0,N.length=1,N;var re=N.imul(H),ae=re.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),_e=re.isub(ae).iushrn(this.shift),Ee=_e;return _e.cmp(this.m)>=0?Ee=_e.isub(this.m):_e.cmpn(0)<0&&(Ee=_e.iadd(this.m)),Ee._forceRed(this)},Z.prototype.mul=function(N,H){if(N.isZero()||H.isZero())return new f(0)._forceRed(this);var re=N.mul(H),ae=re.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),_e=re.isub(ae).iushrn(this.shift),Ee=_e;return 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ct}}}function Z(N,H,re,ae,_e,Ee,Ce,ge,ie,Se){var ke=2*N,be=ge*ke,Be=ie[be+H];e:for(var Le=ae,me=ae*ke;Le<_e;++Le,me+=ke){var Pe=Ce[Le];if(Pe!==Se){var ce=Ee[me+H],He=Ee[me+H+N];if(!(Be<ce||He<Be)){for(var lt=H+1;lt<N;++lt){var ce=Ee[me+lt],He=Ee[me+lt+N],mt=ie[be+lt],Ht=ie[be+lt+N];if(He<mt||Ht<ce)continue e}var at=re(Pe,Se);if(at!==void 0)return at}}}}function j(N,H,re,ae,_e,Ee,Ce,ge,ie){q(N,ae+Ce);var Se=0,ke=2*N,be;for(V(Se++,0,0,ae,0,Ce,re?16:0,-1/0,1/0),re||V(Se++,0,0,Ce,0,ae,1,-1/0,1/0);Se>0;){Se-=1;var Be=Se*S,Le=E[Be],me=E[Be+1],Pe=E[Be+2],ce=E[Be+3],He=E[Be+4],lt=E[Be+5],mt=Se*g,Ht=z[mt],at=z[mt+1],ct=lt&1,ar=!!(lt&16),Vt=_e,rr=Ee,tt=ge,je=ie;if(ct&&(Vt=ge,rr=ie,tt=_e,je=Ee),!(lt&2&&(Pe=T(N,Le,me,Pe,Vt,rr,at),me>=Pe))&&!(lt&4&&(me=L(N,Le,me,Pe,Vt,rr,Ht),me>=Pe))){var Ue=Pe-me,Ae=He-ce;if(ar){if(N*Ue*(Ue+Ae)<p){if(be=h.scanComplete(N,Le,H,me,Pe,Vt,rr,ce,He,tt,je),be!==void 0)return be;continue}}else if(N*Math.min(Ue,Ae)<m){if(be=c(N,Le,H,ct,me,Pe,Vt,rr,ce,He,tt,je),be!==void 0)return be;continue}else if(N*Ue*Ae<b){if(be=h.scanBipartite(N,Le,H,ct,me,Pe,Vt,rr,ce,He,tt,je),be!==void 0)return be;continue}var rt=k(N,Le,me,Pe,Vt,rr,Ht,at);if(me<rt)if(N*(rt-me)<m){if(be=f(N,Le+1,H,me,rt,Vt,rr,ce,He,tt,je),be!==void 0)return be}else if(Le===N-2){if(ct?be=h.sweepBipartite(N,H,ce,He,tt,je,me,rt,Vt,rr):be=h.sweepBipartite(N,H,me,rt,Vt,rr,ce,He,tt,je),be!==void 0)return be}else V(Se++,Le+1,me,rt,ce,He,ct,-1/0,1/0),V(Se++,Le+1,ce,He,me,rt,ct^1,-1/0,1/0);if(rt<Pe){var St=d(N,Le,ce,He,tt,je),Tt=tt[ke*St+Le],dt=M(N,Le,St,He,tt,je,Tt);if(dt<He&&V(Se++,Le,rt,Pe,dt,He,(ct|4)+(ar?16:0),Tt,at),ce<St&&V(Se++,Le,rt,Pe,ce,St,(ct|2)+(ar?16:0),Ht,Tt),St+1===dt){if(ar?be=Z(N,Le,H,rt,Pe,Vt,rr,St,tt,je[St]):be=G(N,Le,H,ct,rt,Pe,Vt,rr,St,tt,je[St]),be!==void 0)return be}else if(St<dt){var Et;if(ar){if(Et=x(N,Le,rt,Pe,Vt,rr,Tt),rt<Et){var pt=M(N,Le,rt,Et,Vt,rr,Tt);if(Le===N-2){if(rt<pt&&(be=h.sweepComplete(N,H,rt,pt,Vt,rr,St,dt,tt,je),be!==void 0)||pt<Et&&(be=h.sweepBipartite(N,H,pt,Et,Vt,rr,St,dt,tt,je),be!==void 0))return be}else rt<pt&&V(Se++,Le+1,rt,pt,St,dt,16,-1/0,1/0),pt<Et&&(V(Se++,Le+1,pt,Et,St,dt,0,-1/0,1/0),V(Se++,Le+1,St,dt,pt,Et,1,-1/0,1/0))}}else ct?Et=C(N,Le,rt,Pe,Vt,rr,Tt):Et=x(N,Le,rt,Pe,Vt,rr,Tt),rt<Et&&(Le===N-2?ct?be=h.sweepBipartite(N,H,St,dt,tt,je,rt,Et,Vt,rr):be=h.sweepBipartite(N,H,rt,Et,Vt,rr,St,dt,tt,je):(V(Se++,Le+1,rt,Et,St,dt,ct,-1/0,1/0),V(Se++,Le+1,St,dt,rt,Et,ct^1,-1/0,1/0)))}}}}}}),3545:(function(i,a,o){\"use strict\";i.exports=f;var s=o(8105),l=s(\"lo<p0\"),u=8;function c(h,d,v,m,b,p){for(var k=2*h,M=k*(v+1)+d,T=v+1;T<m;++T,M+=k)for(var L=b[M],x=T,C=k*(T-1);x>v&&b[C+d]>L;--x,C-=k){for(var S=C,g=C+k,P=0;P<k;++P,++S,++g){var E=b[S];b[S]=b[g],b[g]=E}var z=p[x];p[x]=p[x-1],p[x-1]=z}}function f(h,d,v,m,b,p){if(m<=v+1)return v;for(var k=v,M=m,T=m+v>>>1,L=2*h,x=T,C=b[L*T+d];k<M;){if(M-k<u){c(h,d,k,M,b,p),C=b[L*T+d];break}var S=M-k,g=Math.random()*S+k|0,P=b[L*g+d],E=Math.random()*S+k|0,z=b[L*E+d],q=Math.random()*S+k|0,V=b[L*q+d];P<=z?V>=z?(x=E,C=z):P>=V?(x=g,C=P):(x=q,C=V):z>=V?(x=E,C=z):V>=P?(x=g,C=P):(x=q,C=V);for(var j=L*(M-1),N=L*x,G=0;G<L;++G,++j,++N){var Z=b[j];b[j]=b[N],b[N]=Z}var H=p[M-1];p[M-1]=p[x],p[x]=H,x=l(h,d,k,M-1,b,p,C);for(var j=L*(M-1),N=L*x,G=0;G<L;++G,++j,++N){var Z=b[j];b[j]=b[N],b[N]=Z}var H=p[M-1];if(p[M-1]=p[x],p[x]=H,T<x){for(M=x-1;k<M&&b[L*(M-1)+d]===C;)M-=1;M+=1}else if(x<T)for(k=x+1;k<M&&b[L*k+d]===C;)k+=1;else break}return l(h,d,v,T,b,p,b[L*T+d])}}),8105:(function(i){\"use strict\";i.exports=o;var a={\"lo===p0\":s,\"lo<p0\":l,\"lo<=p0\":u,\"hi<=p0\":c,\"lo<p0&&p0<=hi\":h,\"lo<=p0&&p0<=hi\":f,\"!(lo>=p0)&&!(p1>=hi)\":d};function o(v){return a[v]}function s(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+g];if(z===T)if(S===E)S+=1,C+=L;else{for(var q=0;L>q;++q){var V=k[x+q];k[x+q]=k[C],k[C++]=V}var G=M[E];M[E]=M[S],M[S++]=G}}return S}function l(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+g];if(z<T)if(S===E)S+=1,C+=L;else{for(var q=0;L>q;++q){var V=k[x+q];k[x+q]=k[C],k[C++]=V}var G=M[E];M[E]=M[S],M[S++]=G}}return S}function u(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+P];if(z<=T)if(S===E)S+=1,C+=L;else{for(var q=0;L>q;++q){var V=k[x+q];k[x+q]=k[C],k[C++]=V}var G=M[E];M[E]=M[S],M[S++]=G}}return S}function c(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+P];if(z<=T)if(S===E)S+=1,C+=L;else{for(var q=0;L>q;++q){var V=k[x+q];k[x+q]=k[C],k[C++]=V}var G=M[E];M[E]=M[S],M[S++]=G}}return S}function f(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+g],q=k[x+P];if(z<=T&&T<=q)if(S===E)S+=1,C+=L;else{for(var V=0;L>V;++V){var G=k[x+V];k[x+V]=k[C],k[C++]=G}var Z=M[E];M[E]=M[S],M[S++]=Z}}return S}function h(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+g],q=k[x+P];if(z<T&&T<=q)if(S===E)S+=1,C+=L;else{for(var V=0;L>V;++V){var G=k[x+V];k[x+V]=k[C],k[C++]=G}var Z=M[E];M[E]=M[S],M[S++]=Z}}return S}function d(v,m,b,p,k,M,T,L){for(var x=2*v,C=x*b,S=C,g=b,P=m,E=v+m,z=b;p>z;++z,C+=x){var q=k[C+P],V=k[C+E];if(!(q>=T)&&!(L>=V))if(g===z)g+=1,S+=x;else{for(var G=0;x>G;++G){var Z=k[C+G];k[C+G]=k[S],k[S++]=Z}var j=M[z];M[z]=M[g],M[g++]=j}}return g}}),4192:(function(i){\"use strict\";i.exports=o;var a=32;function o(m,b){b<=4*a?s(0,b-1,m):v(0,b-1,m)}function s(m,b,p){for(var k=2*(m+1),M=m+1;M<=b;++M){for(var T=p[k++],L=p[k++],x=M,C=k-2;x-- >m;){var S=p[C-2],g=p[C-1];if(S<T)break;if(S===T&&g<L)break;p[C]=S,p[C+1]=g,C-=2}p[C]=T,p[C+1]=L}}function l(m,b,p){m*=2,b*=2;var k=p[m],M=p[m+1];p[m]=p[b],p[m+1]=p[b+1],p[b]=k,p[b+1]=M}function u(m,b,p){m*=2,b*=2,p[m]=p[b],p[m+1]=p[b+1]}function c(m,b,p,k){m*=2,b*=2,p*=2;var M=k[m],T=k[m+1];k[m]=k[b],k[m+1]=k[b+1],k[b]=k[p],k[b+1]=k[p+1],k[p]=M,k[p+1]=T}function f(m,b,p,k,M){m*=2,b*=2,M[m]=M[b],M[b]=p,M[m+1]=M[b+1],M[b+1]=k}function h(m,b,p){m*=2,b*=2;var k=p[m],M=p[b];return k<M?!1:k===M?p[m+1]>p[b+1]:!0}function d(m,b,p,k){m*=2;var M=k[m];return M<b?!0:M===b?k[m+1]<p:!1}function v(m,b,p){var k=(b-m+1)/6|0,M=m+k,T=b-k,L=m+b>>1,x=L-k,C=L+k,S=M,g=x,P=L,E=C,z=T,q=m+1,V=b-1,G=0;h(S,g,p)&&(G=S,S=g,g=G),h(E,z,p)&&(G=E,E=z,z=G),h(S,P,p)&&(G=S,S=P,P=G),h(g,P,p)&&(G=g,g=P,P=G),h(S,E,p)&&(G=S,S=E,E=G),h(P,E,p)&&(G=P,P=E,E=G),h(g,z,p)&&(G=g,g=z,z=G),h(g,P,p)&&(G=g,g=P,P=G),h(E,z,p)&&(G=E,E=z,z=G);for(var Z=p[2*g],j=p[2*g+1],N=p[2*E],H=p[2*E+1],re=2*S,ae=2*P,_e=2*z,Ee=2*M,Ce=2*L,ge=2*T,ie=0;ie<2;++ie){var Se=p[re+ie],ke=p[ae+ie],be=p[_e+ie];p[Ee+ie]=Se,p[Ce+ie]=ke,p[ge+ie]=be}u(x,m,p),u(C,b,p);for(var Be=q;Be<=V;++Be)if(d(Be,Z,j,p))Be!==q&&l(Be,q,p),++q;else 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T(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}var L=[new T,new T,new T];function x(V,G,Z,j,N){for(var H=V.primalOffset,re=V.primalMinor,ae=V.mirrorOffset,_e=V.mirrorMinor,Ee=j[G],Ce=0;Ce<3;++Ce)if(G!==Ce){var ge=H,ie=ae,Se=re,ke=_e;Ee&1<<Ce&&(ge=ae,ie=H,Se=_e,ke=re),ge[Ce]=Z[0][Ce],ie[Ce]=Z[1][Ce],N[Ce]>0?(Se[Ce]=-1,ke[Ce]=0):(Se[Ce]=0,ke[Ce]=1)}}var C=[0,0,0],S={model:h,view:h,projection:h,_ortho:!1};M.isOpaque=function(){return!0},M.isTransparent=function(){return!1},M.drawTransparent=function(V){};var g=0,P=[0,0,0],E=[0,0,0],z=[0,0,0];M.draw=function(V){V=V||S;for(var Le=this.gl,G=V.model||h,Z=V.view||h,j=V.projection||h,N=this.bounds,H=V._ortho||!1,re=c(G,Z,j,N,H),ae=re.cubeEdges,_e=re.axis,Ee=Z[12],Ce=Z[13],ge=Z[14],ie=Z[15],Se=H?2:1,ke=Se*this.pixelRatio*(j[3]*Ee+j[7]*Ce+j[11]*ge+j[15]*ie)/Le.drawingBufferHeight,be=0;be<3;++be)this.lastCubeProps.cubeEdges[be]=ae[be],this.lastCubeProps.axis[be]=_e[be];for(var Be=L,be=0;be<3;++be)x(L[be],be,this.bounds,ae,_e);for(var Le=this.gl,me=C,be=0;be<3;++be)this.backgroundEnable[be]?me[be]=_e[be]:me[be]=0;this._background.draw(G,Z,j,N,me,this.backgroundColor),this._lines.bind(G,Z,j,this);for(var be=0;be<3;++be){var Pe=[0,0,0];_e[be]>0?Pe[be]=N[1][be]:Pe[be]=N[0][be];for(var ce=0;ce<2;++ce){var He=(be+1+ce)%3,lt=(be+1+(ce^1))%3;this.gridEnable[He]&&this._lines.drawGrid(He,lt,this.bounds,Pe,this.gridColor[He],this.gridWidth[He]*this.pixelRatio)}for(var ce=0;ce<2;++ce){var He=(be+1+ce)%3,lt=(be+1+(ce^1))%3;this.zeroEnable[lt]&&Math.min(N[0][lt],N[1][lt])<=0&&Math.max(N[0][lt],N[1][lt])>=0&&this._lines.drawZero(He,lt,this.bounds,Pe,this.zeroLineColor[lt],this.zeroLineWidth[lt]*this.pixelRatio)}}for(var be=0;be<3;++be){this.lineEnable[be]&&this._lines.drawAxisLine(be,this.bounds,Be[be].primalOffset,this.lineColor[be],this.lineWidth[be]*this.pixelRatio),this.lineMirror[be]&&this._lines.drawAxisLine(be,this.bounds,Be[be].mirrorOffset,this.lineColor[be],this.lineWidth[be]*this.pixelRatio);for(var mt=p(P,Be[be].primalMinor),Ht=p(E,Be[be].mirrorMinor),at=this.lineTickLength,ce=0;ce<3;++ce){var ct=ke/G[5*ce];mt[ce]*=at[ce]*ct,Ht[ce]*=at[ce]*ct}this.lineTickEnable[be]&&this._lines.drawAxisTicks(be,Be[be].primalOffset,mt,this.lineTickColor[be],this.lineTickWidth[be]*this.pixelRatio),this.lineTickMirror[be]&&this._lines.drawAxisTicks(be,Be[be].mirrorOffset,Ht,this.lineTickColor[be],this.lineTickWidth[be]*this.pixelRatio)}this._lines.unbind(),this._text.bind(G,Z,j,this.pixelRatio);var ar,Vt=.5,rr,tt;function je(dt){tt=[0,0,0],tt[dt]=1}function Ue(dt,Et,pt){var jt=(dt+1)%3,or=(dt+2)%3,mr=Et[jt],Ar=Et[or],ei=pt[jt],qr=pt[or];if(mr>0&&qr>0){je(jt);return}else if(mr>0&&qr<0){je(jt);return}else if(mr<0&&qr>0){je(jt);return}else if(mr<0&&qr<0){je(jt);return}else if(Ar>0&&ei>0){je(or);return}else if(Ar>0&&ei<0){je(or);return}else if(Ar<0&&ei>0){je(or);return}else if(Ar<0&&ei<0){je(or);return}}for(var be=0;be<3;++be){for(var Ae=Be[be].primalMinor,rt=Be[be].mirrorMinor,St=p(z,Be[be].primalOffset),ce=0;ce<3;++ce)this.lineTickEnable[be]&&(St[ce]+=ke*Ae[ce]*Math.max(this.lineTickLength[ce],0)/G[5*ce]);var Tt=[0,0,0];if(Tt[be]=1,this.tickEnable[be]){this.tickAngle[be]===-3600?(this.tickAngle[be]=0,this.tickAlign[be]=\"auto\"):this.tickAlign[be]=-1,rr=1,ar=[this.tickAlign[be],Vt,rr],ar[0]===\"auto\"?ar[0]=g:ar[0]=parseInt(\"\"+ar[0]),tt=[0,0,0],Ue(be,Ae,rt);for(var ce=0;ce<3;++ce)St[ce]+=ke*Ae[ce]*this.tickPad[ce]/G[5*ce];this._text.drawTicks(be,this.tickSize[be],this.tickAngle[be],St,this.tickColor[be],Tt,tt,ar)}if(this.labelEnable[be]){rr=0,tt=[0,0,0],this.labels[be].length>4&&(je(be),rr=1),ar=[this.labelAlign[be],Vt,rr],ar[0]===\"auto\"?ar[0]=g:ar[0]=parseInt(\"\"+ar[0]);for(var ce=0;ce<3;++ce)St[ce]+=ke*Ae[ce]*this.labelPad[ce]/G[5*ce];St[be]+=.5*(N[0][be]+N[1][be]),this._text.drawLabel(be,this.labelSize[be],this.labelAngle[be],St,this.labelColor[be],[0,0,0],tt,ar)}}this._text.unbind()},M.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null};function q(V,G){var Z=new k(V);return Z.update(G),Z}}),5304:(function(i,a,o){\"use strict\";i.exports=h;var s=o(2762),l=o(8116),u=o(1879).bg;function c(d,v,m,b){this.gl=d,this.buffer=v,this.vao=m,this.shader=b}var f=c.prototype;f.draw=function(d,v,m,b,p,k){for(var M=!1,T=0;T<3;++T)M=M||p[T];if(M){var L=this.gl;L.enable(L.POLYGON_OFFSET_FILL),L.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:d,view:v,projection:m,bounds:b,enable:p,colors:k},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),L.disable(L.POLYGON_OFFSET_FILL)}},f.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()};function h(d){for(var v=[],m=[],b=0,p=0;p<3;++p)for(var k=(p+1)%3,M=(p+2)%3,T=[0,0,0],L=[0,0,0],x=-1;x<=1;x+=2){m.push(b,b+2,b+1,b+1,b+2,b+3),T[p]=x,L[p]=x;for(var C=-1;C<=1;C+=2){T[k]=C;for(var S=-1;S<=1;S+=2)T[M]=S,v.push(T[0],T[1],T[2],L[0],L[1],L[2]),b+=1}var g=k;k=M,M=g}var P=s(d,new Float32Array(v)),E=s(d,new Uint16Array(m),d.ELEMENT_ARRAY_BUFFER),z=l(d,[{buffer:P,type:d.FLOAT,size:3,offset:0,stride:24},{buffer:P,type:d.FLOAT,size:3,offset:12,stride:24}],E),q=u(d);return q.attributes.position.location=0,q.attributes.normal.location=1,new c(d,P,z,q)}}),6429:(function(i,a,o){\"use strict\";i.exports=x;var s=o(8828),l=o(6760),u=o(5202),c=o(3250),f=new Array(16),h=new Array(8),d=new Array(8),v=new Array(3),m=[0,0,0];(function(){for(var C=0;C<8;++C)h[C]=[1,1,1,1],d[C]=[1,1,1]})();function b(C,S,g){for(var P=0;P<4;++P){C[P]=g[12+P];for(var E=0;E<3;++E)C[P]+=S[E]*g[4*E+P]}}var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function k(C){for(var S=0;S<p.length;++S)if(C=u.positive(C,p[S]),C.length<3)return 0;for(var g=C[0],P=g[0]/g[3],E=g[1]/g[3],z=0,S=1;S+1<C.length;++S){var q=C[S],V=C[S+1],G=q[0]/q[3],Z=q[1]/q[3],j=V[0]/V[3],N=V[1]/V[3],H=G-P,re=Z-E,ae=j-P,_e=N-E;z+=Math.abs(H*_e-re*ae)}return z}var M=[1,1,1],T=[0,0,0],L={cubeEdges:M,axis:T};function x(C,S,g,P,E){l(f,S,C),l(f,g,f);for(var z=0,q=0;q<2;++q){v[2]=P[q][2];for(var V=0;V<2;++V){v[1]=P[V][1];for(var G=0;G<2;++G)v[0]=P[G][0],b(h[z],v,f),z+=1}}for(var Z=-1,q=0;q<8;++q){for(var j=h[q][3],N=0;N<3;++N)d[q][N]=h[q][N]/j;E&&(d[q][2]*=-1),j<0&&(Z<0||d[q][2]<d[Z][2])&&(Z=q)}if(Z<0){Z=0;for(var H=0;H<3;++H){for(var re=(H+2)%3,ae=(H+1)%3,_e=-1,Ee=-1,Ce=0;Ce<2;++Ce){var ge=Ce<<H,ie=ge+(Ce<<re)+(1-Ce<<ae),Se=ge+(1-Ce<<re)+(Ce<<ae);c(d[ge],d[ie],d[Se],m)<0||(Ce?_e=1:Ee=1)}if(_e<0||Ee<0){Ee>_e&&(Z|=1<<H);continue}for(var Ce=0;Ce<2;++Ce){var ge=Ce<<H,ie=ge+(Ce<<re)+(1-Ce<<ae),Se=ge+(1-Ce<<re)+(Ce<<ae),ke=k([h[ge],h[ie],h[Se],h[ge+(1<<re)+(1<<ae)]]);Ce?_e=ke:Ee=ke}if(Ee>_e){Z|=1<<H;continue}}}for(var be=7^Z,Be=-1,q=0;q<8;++q)q===Z||q===be||(Be<0||d[Be][1]>d[q][1])&&(Be=q);for(var Le=-1,q=0;q<3;++q){var me=Be^1<<q;if(!(me===Z||me===be)){Le<0&&(Le=me);var ae=d[me];ae[0]<d[Le][0]&&(Le=me)}}for(var Pe=-1,q=0;q<3;++q){var me=Be^1<<q;if(!(me===Z||me===be||me===Le)){Pe<0&&(Pe=me);var ae=d[me];ae[0]>d[Pe][0]&&(Pe=me)}}var ce=M;ce[0]=ce[1]=ce[2]=0,ce[s.log2(Le^Be)]=Be&Le,ce[s.log2(Be^Pe)]=Be&Pe;var He=Pe^7;He===Z||He===be?(He=Le^7,ce[s.log2(Pe^He)]=He&Pe):ce[s.log2(Le^He)]=He&Le;for(var lt=T,mt=Z,H=0;H<3;++H)mt&1<<H?lt[H]=-1:lt[H]=1;return L}}),501:(function(i,a,o){\"use strict\";i.exports=M;var s=o(2762),l=o(8116),u=o(1879).n,c=[0,0,0],f=[0,0,0],h=[0,0,0],d=[0,0,0],v=[1,1];function m(T){return T[0]=T[1]=T[2]=0,T}function b(T,L){return T[0]=L[0],T[1]=L[1],T[2]=L[2],T}function p(T,L,x,C,S,g,P,E){this.gl=T,this.vertBuffer=L,this.vao=x,this.shader=C,this.tickCount=S,this.tickOffset=g,this.gridCount=P,this.gridOffset=E}var k=p.prototype;k.bind=function(T,L,x){this.shader.bind(),this.shader.uniforms.model=T,this.shader.uniforms.view=L,this.shader.uniforms.projection=x,v[0]=this.gl.drawingBufferWidth,v[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=v,this.vao.bind()},k.unbind=function(){this.vao.unbind()},k.drawAxisLine=function(T,L,x,C,S){var g=m(f);this.shader.uniforms.majorAxis=f,g[T]=L[1][T]-L[0][T],this.shader.uniforms.minorAxis=g;var P=b(d,x);P[T]+=L[0][T],this.shader.uniforms.offset=P,this.shader.uniforms.lineWidth=S,this.shader.uniforms.color=C;var E=m(h);E[(T+2)%3]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,6);var E=m(h);E[(T+1)%3]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,6)},k.drawAxisTicks=function(T,L,x,C,S){if(this.tickCount[T]){var g=m(c);g[T]=1,this.shader.uniforms.majorAxis=g,this.shader.uniforms.offset=L,this.shader.uniforms.minorAxis=x,this.shader.uniforms.color=C,this.shader.uniforms.lineWidth=S;var P=m(h);P[T]=1,this.shader.uniforms.screenAxis=P,this.vao.draw(this.gl.TRIANGLES,this.tickCount[T],this.tickOffset[T])}},k.drawGrid=function(T,L,x,C,S,g){if(this.gridCount[T]){var P=m(f);P[L]=x[1][L]-x[0][L],this.shader.uniforms.minorAxis=P;var E=b(d,C);E[L]+=x[0][L],this.shader.uniforms.offset=E;var z=m(c);z[T]=1,this.shader.uniforms.majorAxis=z;var q=m(h);q[T]=1,this.shader.uniforms.screenAxis=q,this.shader.uniforms.lineWidth=g,this.shader.uniforms.color=S,this.vao.draw(this.gl.TRIANGLES,this.gridCount[T],this.gridOffset[T])}},k.drawZero=function(T,L,x,C,S,g){var P=m(f);this.shader.uniforms.majorAxis=P,P[T]=x[1][T]-x[0][T],this.shader.uniforms.minorAxis=P;var E=b(d,C);E[T]+=x[0][T],this.shader.uniforms.offset=E;var z=m(h);z[L]=1,this.shader.uniforms.screenAxis=z,this.shader.uniforms.lineWidth=g,this.shader.uniforms.color=S,this.vao.draw(this.gl.TRIANGLES,6)},k.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()};function M(T,L,x){var C=[],S=[0,0,0],g=[0,0,0],P=[0,0,0],E=[0,0,0];C.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var z=0;z<3;++z){for(var G=C.length/3|0,q=0;q<x[z].length;++q){var V=+x[z][q].x;C.push(V,0,1,V,1,1,V,0,-1,V,0,-1,V,1,1,V,1,-1)}var j=C.length/3|0;S[z]=G,g[z]=j-G;for(var G=C.length/3|0,Z=0;Z<x[z].length;++Z){var V=+x[z][Z].x;C.push(V,0,1,V,1,1,V,0,-1,V,0,-1,V,1,1,V,1,-1)}var j=C.length/3|0;P[z]=G,E[z]=j-G}var N=s(T,new Float32Array(C)),H=l(T,[{buffer:N,type:T.FLOAT,size:3,stride:0,offset:0}]),re=u(T);return re.attributes.position.location=0,new p(T,N,H,re,g,S,E,P)}}),1879:(function(i,a,o){\"use strict\";var s=o(3236),l=o(9405),u=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec3 offset, majorAxis, minorAxis, screenAxis;\n",
+       "uniform float lineWidth;\n",
+       "uniform vec2 screenShape;\n",
+       "\n",
+       "vec3 project(vec3 p) {\n",
+       "  vec4 pp = projection * (view * (model * vec4(p, 1.0)));\n",
+       "  return pp.xyz / max(pp.w, 0.0001);\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "  vec3 major = position.x * majorAxis;\n",
+       "  vec3 minor = position.y * minorAxis;\n",
+       "\n",
+       "  vec3 vPosition = major + minor + offset;\n",
+       "  vec3 pPosition = project(vPosition);\n",
+       "  vec3 offset = project(vPosition + screenAxis * position.z);\n",
+       "\n",
+       "  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n",
+       "\n",
+       "  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n",
+       "}\n",
+       "`]),c=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "uniform vec4 color;\n",
+       "void main() {\n",
+       "  gl_FragColor = color;\n",
+       "}`]);a.n=function(m){return l(m,u,c,null,[{name:\"position\",type:\"vec3\"}])};var f=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec3 offset, axis, alignDir, alignOpt;\n",
+       "uniform float scale, angle, pixelScale;\n",
+       "uniform vec2 resolution;\n",
+       "\n",
+       "vec3 project(vec3 p) {\n",
+       "  vec4 pp = projection * (view * (model * vec4(p, 1.0)));\n",
+       "  return pp.xyz / max(pp.w, 0.0001);\n",
+       "}\n",
+       "\n",
+       "float computeViewAngle(vec3 a, vec3 b) {\n",
+       "  vec3 A = project(a);\n",
+       "  vec3 B = project(b);\n",
+       "\n",
+       "  return atan(\n",
+       "    (B.y - A.y) * resolution.y,\n",
+       "    (B.x - A.x) * resolution.x\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "const float PI = 3.141592;\n",
+       "const float TWO_PI = 2.0 * PI;\n",
+       "const float HALF_PI = 0.5 * PI;\n",
+       "const float ONE_AND_HALF_PI = 1.5 * PI;\n",
+       "\n",
+       "int option = int(floor(alignOpt.x + 0.001));\n",
+       "float hv_ratio =       alignOpt.y;\n",
+       "bool enableAlign =    (alignOpt.z != 0.0);\n",
+       "\n",
+       "float mod_angle(float a) {\n",
+       "  return mod(a, PI);\n",
+       "}\n",
+       "\n",
+       "float positive_angle(float a) {\n",
+       "  return mod_angle((a < 0.0) ?\n",
+       "    a + TWO_PI :\n",
+       "    a\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "float look_upwards(float a) {\n",
+       "  float b = positive_angle(a);\n",
+       "  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n",
+       "    b - PI :\n",
+       "    b;\n",
+       "}\n",
+       "\n",
+       "float look_horizontal_or_vertical(float a, float ratio) {\n",
+       "  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n",
+       "  // if ratio is set to 0.5 then it is 50%, 50%.\n",
+       "  // when using a higher ratio e.g. 0.75 the result would\n",
+       "  // likely be more horizontal than vertical.\n",
+       "\n",
+       "  float b = positive_angle(a);\n",
+       "\n",
+       "  return\n",
+       "    (b < (      ratio) * HALF_PI) ? 0.0 :\n",
+       "    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n",
+       "    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n",
+       "    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\n",
+       "                                    0.0;\n",
+       "}\n",
+       "\n",
+       "float roundTo(float a, float b) {\n",
+       "  return float(b * floor((a + 0.5 * b) / b));\n",
+       "}\n",
+       "\n",
+       "float look_round_n_directions(float a, int n) {\n",
+       "  float b = positive_angle(a);\n",
+       "  float div = TWO_PI / float(n);\n",
+       "  float c = roundTo(b, div);\n",
+       "  return look_upwards(c);\n",
+       "}\n",
+       "\n",
+       "float applyAlignOption(float rawAngle, float delta) {\n",
+       "  return\n",
+       "    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\n",
+       "    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\n",
+       "    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\n",
+       "    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\n",
+       "    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\n",
+       "                    rawAngle;                // otherwise return back raw input angle\n",
+       "}\n",
+       "\n",
+       "bool isAxisTitle = (axis.x == 0.0) &&\n",
+       "                   (axis.y == 0.0) &&\n",
+       "                   (axis.z == 0.0);\n",
+       "\n",
+       "void main() {\n",
+       "  //Compute world offset\n",
+       "  float axisDistance = position.z;\n",
+       "  vec3 dataPosition = axisDistance * axis + offset;\n",
+       "\n",
+       "  float beta = angle; // i.e. user defined attributes for each tick\n",
+       "\n",
+       "  float axisAngle;\n",
+       "  float clipAngle;\n",
+       "  float flip;\n",
+       "\n",
+       "  if (enableAlign) {\n",
+       "    axisAngle = (isAxisTitle) ? HALF_PI :\n",
+       "                      computeViewAngle(dataPosition, dataPosition + axis);\n",
+       "    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n",
+       "\n",
+       "    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n",
+       "    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n",
+       "\n",
+       "    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n",
+       "                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n",
+       "\n",
+       "    beta += applyAlignOption(clipAngle, flip * PI);\n",
+       "  }\n",
+       "\n",
+       "  //Compute plane offset\n",
+       "  vec2 planeCoord = position.xy * pixelScale;\n",
+       "\n",
+       "  mat2 planeXform = scale * mat2(\n",
+       "     cos(beta), sin(beta),\n",
+       "    -sin(beta), cos(beta)\n",
+       "  );\n",
+       "\n",
+       "  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n",
+       "\n",
+       "  //Compute clip position\n",
+       "  vec3 clipPosition = project(dataPosition);\n",
+       "\n",
+       "  //Apply text offset in clip coordinates\n",
+       "  clipPosition += vec3(viewOffset, 0.0);\n",
+       "\n",
+       "  //Done\n",
+       "  gl_Position = vec4(clipPosition, 1.0);\n",
+       "}\n",
+       "`]),h=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "uniform vec4 color;\n",
+       "void main() {\n",
+       "  gl_FragColor = color;\n",
+       "}`]);a.Q=function(m){return l(m,f,h,null,[{name:\"position\",type:\"vec3\"}])};var d=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "attribute vec3 normal;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec3 enable;\n",
+       "uniform vec3 bounds[2];\n",
+       "\n",
+       "varying vec3 colorChannel;\n",
+       "\n",
+       "void main() {\n",
+       "\n",
+       "  vec3 signAxis = sign(bounds[1] - bounds[0]);\n",
+       "\n",
+       "  vec3 realNormal = signAxis * normal;\n",
+       "\n",
+       "  if(dot(realNormal, enable) > 0.0) {\n",
+       "    vec3 minRange = min(bounds[0], bounds[1]);\n",
+       "    vec3 maxRange = max(bounds[0], bounds[1]);\n",
+       "    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n",
+       "    gl_Position = projection * (view * (model * vec4(nPosition, 1.0)));\n",
+       "  } else {\n",
+       "    gl_Position = vec4(0,0,0,0);\n",
+       "  }\n",
+       "\n",
+       "  colorChannel = abs(realNormal);\n",
+       "}\n",
+       "`]),v=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "uniform vec4 colors[3];\n",
+       "\n",
+       "varying vec3 colorChannel;\n",
+       "\n",
+       "void main() {\n",
+       "  gl_FragColor = colorChannel.x * colors[0] +\n",
+       "                 colorChannel.y * colors[1] +\n",
+       "                 colorChannel.z * colors[2];\n",
+       "}`]);a.bg=function(m){return l(m,d,v,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}}),4935:(function(i,a,o){\"use strict\";i.exports=k;var s=o(2762),l=o(8116),u=o(4359),c=o(1879).Q,f=window||process.global||{},h=f.__TEXT_CACHE||{};f.__TEXT_CACHE={};var d=3;function v(M,T,L,x){this.gl=M,this.shader=T,this.buffer=L,this.vao=x,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var m=v.prototype,b=[0,0];m.bind=function(M,T,L,x){this.vao.bind(),this.shader.bind();var C=this.shader.uniforms;C.model=M,C.view=T,C.projection=L,C.pixelScale=x,b[0]=this.gl.drawingBufferWidth,b[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=b},m.unbind=function(){this.vao.unbind()},m.update=function(M,T,L,x,C){var S=[];function g(H,re,ae,_e,Ee,Ce){var ge=[ae.style,ae.weight,ae.variant,ae.family].join(\"_\"),ie=h[ge];ie||(ie=h[ge]={});var Se=ie[re];Se||(Se=ie[re]=p(re,{triangles:!0,font:ae.family,fontStyle:ae.style,fontWeight:ae.weight,fontVariant:ae.variant,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:Ee,styletags:Ce}));for(var ke=(_e||12)/12,be=Se.positions,Be=Se.cells,Le=0,me=Be.length;Le<me;++Le)for(var Pe=Be[Le],ce=2;ce>=0;--ce){var He=be[Pe[ce]];S.push(ke*He[0],-ke*He[1],H)}}for(var P=[0,0,0],E=[0,0,0],z=[0,0,0],q=[0,0,0],V=1.25,G={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},Z=0;Z<3;++Z){z[Z]=S.length/d|0,g(.5*(M[0][Z]+M[1][Z]),T[Z],L[Z],12,V,G),q[Z]=(S.length/d|0)-z[Z],P[Z]=S.length/d|0;for(var j=0;j<x[Z].length;++j)if(x[Z][j].text){var N={family:x[Z][j].font||C[Z].family,style:C[Z].fontStyle||C[Z].style,weight:C[Z].fontWeight||C[Z].weight,variant:C[Z].fontVariant||C[Z].variant};g(x[Z][j].x,x[Z][j].text,N,x[Z][j].fontSize||12,V,G)}E[Z]=(S.length/d|0)-P[Z]}this.buffer.update(S),this.tickOffset=P,this.tickCount=E,this.labelOffset=z,this.labelCount=q},m.drawTicks=function(M,T,L,x,C,S,g,P){this.tickCount[M]&&(this.shader.uniforms.axis=S,this.shader.uniforms.color=C,this.shader.uniforms.angle=L,this.shader.uniforms.scale=T,this.shader.uniforms.offset=x,this.shader.uniforms.alignDir=g,this.shader.uniforms.alignOpt=P,this.vao.draw(this.gl.TRIANGLES,this.tickCount[M],this.tickOffset[M]))},m.drawLabel=function(M,T,L,x,C,S,g,P){this.labelCount[M]&&(this.shader.uniforms.axis=S,this.shader.uniforms.color=C,this.shader.uniforms.angle=L,this.shader.uniforms.scale=T,this.shader.uniforms.offset=x,this.shader.uniforms.alignDir=g,this.shader.uniforms.alignOpt=P,this.vao.draw(this.gl.TRIANGLES,this.labelCount[M],this.labelOffset[M]))},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()};function p(M,T){try{return u(M,T)}catch(L){return console.warn('error vectorizing text:\"'+M+'\" error:',L),{cells:[],positions:[]}}}function k(M,T,L,x,C,S){var g=s(M),P=l(M,[{buffer:g,size:3}]),E=c(M);E.attributes.position.location=0;var z=new v(M,E,g,P);return z.update(T,L,x,C,S),z}}),6444:(function(i,a){\"use strict\";a.create=s,a.equal=l;function o(u,c){var f=u+\"\",h=f.indexOf(\".\"),d=0;h>=0&&(d=f.length-h-1);var v=Math.pow(10,d),m=Math.round(u*c*v),b=m+\"\";if(b.indexOf(\"e\")>=0)return b;var p=m/v,k=m%v;m<0?(p=-Math.ceil(p)|0,k=-k|0):(p=Math.floor(p)|0,k=k|0);var M=\"\"+p;if(m<0&&(M=\"-\"+M),d){for(var T=\"\"+k;T.length<d;)T=\"0\"+T;return M+\".\"+T}else return M}function s(u,c){for(var f=[],h=0;h<3;++h){for(var d=[],v=.5*(u[0][h]+u[1][h]),m=0;m*c[h]<=u[1][h];++m)d.push({x:m*c[h],text:o(c[h],m)});for(var m=-1;m*c[h]>=u[0][h];--m)d.push({x:m*c[h],text:o(c[h],m)});f.push(d)}return f}function l(u,c){for(var f=0;f<3;++f){if(u[f].length!==c[f].length)return!1;for(var h=0;h<u[f].length;++h){var d=u[f][h],v=c[f][h];if(d.x!==v.x||d.text!==v.text||d.font!==v.font||d.fontColor!==v.fontColor||d.fontSize!==v.fontSize||d.dx!==v.dx||d.dy!==v.dy)return!1}}return!0}}),5445:(function(i,a,o){\"use strict\";i.exports=L;var s=o(5033),l=o(5202),u=o(6429),c=o(6760),f=o(5665),h=o(5352),d=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),v=new Float32Array(16);function m(x,C,S){this.lo=x,this.hi=C,this.pixelsPerDataUnit=S}var b=[0,0,0,1],p=[0,0,0,1];function k(x,C,S,g,P){for(var E=0;E<3;++E){for(var z=b,q=p,V=0;V<3;++V)q[V]=z[V]=S[V];q[3]=z[3]=1,q[E]+=1,h(q,q,C),q[3]<0&&(x[E]=1/0),z[E]-=1,h(z,z,C),z[3]<0&&(x[E]=1/0);var G=(z[0]/z[3]-q[0]/q[3])*g,Z=(z[1]/z[3]-q[1]/q[3])*P;x[E]=.25*Math.sqrt(G*G+Z*Z)}return x}var M=[new m(1/0,-1/0,1/0),new m(1/0,-1/0,1/0),new m(1/0,-1/0,1/0)],T=[0,0,0];function L(x,C,S,g,Z){var E=C.model||d,z=C.view||d,q=C.projection||d,V=C._ortho||!1,G=x.bounds,Z=Z||u(E,z,q,G,V),j=Z.axis;c(v,z,E),c(v,q,v);for(var N=M,H=0;H<3;++H)N[H].lo=1/0,N[H].hi=-1/0,N[H].pixelsPerDataUnit=1/0;var re=s(f(v,v));f(v,v);for(var ae=0;ae<3;++ae){var _e=(ae+1)%3,Ee=(ae+2)%3,Ce=T;e:for(var H=0;H<2;++H){var ge=[];if(j[ae]<0!=!!H){Ce[ae]=G[H][ae];for(var ie=0;ie<2;++ie){Ce[_e]=G[ie^H][_e];for(var Se=0;Se<2;++Se)Ce[Ee]=G[Se^ie^H][Ee],ge.push(Ce.slice())}for(var ke=V?5:4,ie=ke;ie===ke;++ie){if(ge.length===0)continue e;ge=l.positive(ge,re[ie])}for(var ie=0;ie<ge.length;++ie)for(var Ee=ge[ie],be=k(T,v,Ee,S,g),Se=0;Se<3;++Se)N[Se].lo=Math.min(N[Se].lo,Ee[Se]),N[Se].hi=Math.max(N[Se].hi,Ee[Se]),Se!==ae&&(N[Se].pixelsPerDataUnit=Math.min(N[Se].pixelsPerDataUnit,Math.abs(be[Se])))}}}return N}}),2762:(function(i,a,o){\"use strict\";var s=o(1888),l=o(5298),u=o(9618),c=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function f(p,k,M,T,L){this.gl=p,this.type=k,this.handle=M,this.length=T,this.usage=L}var h=f.prototype;h.bind=function(){this.gl.bindBuffer(this.type,this.handle)},h.unbind=function(){this.gl.bindBuffer(this.type,null)},h.dispose=function(){this.gl.deleteBuffer(this.handle)};function d(p,k,M,T,L,x){var C=L.length*L.BYTES_PER_ELEMENT;if(x<0)return p.bufferData(k,L,T),C;if(C+x>M)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return p.bufferSubData(k,x,L),M}function v(p,k){for(var M=s.malloc(p.length,k),T=p.length,L=0;L<T;++L)M[L]=p[L];return M}function m(p,k){for(var M=1,T=k.length-1;T>=0;--T){if(k[T]!==M)return!1;M*=p[T]}return!0}h.update=function(p,k){if(typeof k!=\"number\"&&(k=-1),this.bind(),typeof p==\"object\"&&typeof p.shape!=\"undefined\"){var M=p.dtype;if(c.indexOf(M)<0&&(M=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER){var T=gl.getExtension(\"OES_element_index_uint\");T&&M!==\"uint16\"?M=\"uint32\":M=\"uint16\"}if(M===p.dtype&&m(p.shape,p.stride))p.offset===0&&p.data.length===p.shape[0]?this.length=d(this.gl,this.type,this.length,this.usage,p.data,k):this.length=d(this.gl,this.type,this.length,this.usage,p.data.subarray(p.offset,p.shape[0]),k);else{var L=s.malloc(p.size,M),x=u(L,p.shape);l.assign(x,p),k<0?this.length=d(this.gl,this.type,this.length,this.usage,L,k):this.length=d(this.gl,this.type,this.length,this.usage,L.subarray(0,p.size),k),s.free(L)}}else if(Array.isArray(p)){var C;this.type===this.gl.ELEMENT_ARRAY_BUFFER?C=v(p,\"uint16\"):C=v(p,\"float32\"),k<0?this.length=d(this.gl,this.type,this.length,this.usage,C,k):this.length=d(this.gl,this.type,this.length,this.usage,C.subarray(0,p.length),k),s.free(C)}else if(typeof p==\"object\"&&typeof p.length==\"number\")this.length=d(this.gl,this.type,this.length,this.usage,p,k);else if(typeof p==\"number\"||p===void 0){if(k>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");p=p|0,p<=0&&(p=1),this.gl.bufferData(this.type,p|0,this.usage),this.length=p}else throw new Error(\"gl-buffer: Invalid data type\")};function b(p,k,M,T){if(M=M||p.ARRAY_BUFFER,T=T||p.DYNAMIC_DRAW,M!==p.ARRAY_BUFFER&&M!==p.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(T!==p.DYNAMIC_DRAW&&T!==p.STATIC_DRAW&&T!==p.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var L=p.createBuffer(),x=new f(p,M,L,0,T);return x.update(k),x}i.exports=b}),6405:(function(i,a,o){\"use strict\";var s=o(2931);i.exports=function(u,c){var f=u.positions,h=u.vectors,d={positions:[],vertexIntensity:[],vertexIntensityBounds:u.vertexIntensityBounds,vectors:[],cells:[],coneOffset:u.coneOffset,colormap:u.colormap};if(u.positions.length===0)return c&&(c[0]=[0,0,0],c[1]=[0,0,0]),d;for(var v=0,m=1/0,b=-1/0,p=1/0,k=-1/0,M=1/0,T=-1/0,L=null,x=null,C=[],S=1/0,g=!1,P=u.coneSizemode===\"raw\",E=0;E<f.length;E++){var z=f[E];m=Math.min(z[0],m),b=Math.max(z[0],b),p=Math.min(z[1],p),k=Math.max(z[1],k),M=Math.min(z[2],M),T=Math.max(z[2],T);var q=h[E];if(s.length(q)>v&&(v=s.length(q)),E&&!P){var V=2*s.distance(L,z)/(s.length(x)+s.length(q));V?(S=Math.min(S,V),g=!1):g=!0}g||(L=z,x=q),C.push(q)}var G=[m,p,M],Z=[b,k,T];c&&(c[0]=G,c[1]=Z),v===0&&(v=1);var j=1/v;isFinite(S)||(S=1),d.vectorScale=S;var N=u.coneSize||(P?1:.5);u.absoluteConeSize&&(N=u.absoluteConeSize*j),d.coneScale=N;for(var E=0,H=0;E<f.length;E++)for(var z=f[E],re=z[0],ae=z[1],_e=z[2],Ee=C[E],Ce=s.length(Ee)*j,ge=0,ie=8;ge<ie;ge++){d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vertexIntensity.push(Ce,Ce,Ce),d.vertexIntensity.push(Ce,Ce,Ce);var Se=d.positions.length;d.cells.push([Se-6,Se-5,Se-4],[Se-3,Se-2,Se-1])}return d};var l=o(614);i.exports.createMesh=o(9060),i.exports.createConeMesh=function(u,c){return i.exports.createMesh(u,c,{shaders:l,traceType:\"cone\"})}}),9060:(function(i,a,o){\"use strict\";var s=o(9405),l=o(2762),u=o(8116),c=o(7766),f=o(6760),h=o(7608),d=o(9618),v=o(6729),m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function b(C,S,g,P,E,z,q,V,G,Z,j){this.gl=C,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=S,this.dirty=!0,this.triShader=g,this.pickShader=P,this.trianglePositions=E,this.triangleVectors=z,this.triangleColors=V,this.triangleUVs=G,this.triangleIds=q,this.triangleVAO=Z,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=j,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1]}var p=b.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(C){this.pickId=C};function k(C){for(var S=v({colormap:C,nshades:256,format:\"rgba\"}),g=new Uint8Array(256*4),P=0;P<256;++P){for(var E=S[P],z=0;z<3;++z)g[4*P+z]=E[z];g[4*P+3]=E[3]*255}return d(g,[256,256,4],[4,0,1])}function M(C){for(var S=C.length,g=new Array(S),P=0;P<S;++P)g[P]=C[P][2];return g}p.update=function(C){C=C||{};var S=this.gl;this.dirty=!0,\"lightPosition\"in C&&(this.lightPosition=C.lightPosition),\"opacity\"in C&&(this.opacity=C.opacity),\"ambient\"in C&&(this.ambientLight=C.ambient),\"diffuse\"in C&&(this.diffuseLight=C.diffuse),\"specular\"in C&&(this.specularLight=C.specular),\"roughness\"in C&&(this.roughness=C.roughness),\"fresnel\"in C&&(this.fresnel=C.fresnel),C.tubeScale!==void 0&&(this.tubeScale=C.tubeScale),C.vectorScale!==void 0&&(this.vectorScale=C.vectorScale),C.coneScale!==void 0&&(this.coneScale=C.coneScale),C.coneOffset!==void 0&&(this.coneOffset=C.coneOffset),C.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=S.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=S.LINEAR,this.texture.setPixels(k(C.colormap)),this.texture.generateMipmap());var g=C.cells,P=C.positions,E=C.vectors;if(!(!P||!g||!E)){var z=[],q=[],V=[],G=[],Z=[];this.cells=g,this.positions=P,this.vectors=E;var j=C.meshColor||[1,1,1,1],N=C.vertexIntensity,H=1/0,re=-1/0;if(N)if(C.vertexIntensityBounds)H=+C.vertexIntensityBounds[0],re=+C.vertexIntensityBounds[1];else for(var ae=0;ae<N.length;++ae){var _e=N[ae];H=Math.min(H,_e),re=Math.max(re,_e)}else for(var ae=0;ae<P.length;++ae){var _e=P[ae][2];H=Math.min(H,_e),re=Math.max(re,_e)}N?this.intensity=N:this.intensity=M(P),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var ae=0;ae<P.length;++ae)for(var Ee=P[ae],Ce=0;Ce<3;++Ce)isNaN(Ee[Ce])||!isFinite(Ee[Ce])||(this.bounds[0][Ce]=Math.min(this.bounds[0][Ce],Ee[Ce]),this.bounds[1][Ce]=Math.max(this.bounds[1][Ce],Ee[Ce]));var ge=0;e:for(var ae=0;ae<g.length;++ae){var ie=g[ae];switch(ie.length){case 3:for(var Ce=0;Ce<3;++Ce)for(var Se=ie[Ce],Ee=P[Se],ke=0;ke<3;++ke)if(isNaN(Ee[ke])||!isFinite(Ee[ke]))continue e;for(var Ce=0;Ce<3;++Ce){var Se=ie[2-Ce],Ee=P[Se];z.push(Ee[0],Ee[1],Ee[2],Ee[3]);var be=E[Se];q.push(be[0],be[1],be[2],be[3]||0);var Be=j;Be.length===3?V.push(Be[0],Be[1],Be[2],1):V.push(Be[0],Be[1],Be[2],Be[3]);var Le;N?Le=[(N[Se]-H)/(re-H),0]:Le=[(Ee[2]-H)/(re-H),0],G.push(Le[0],Le[1]),Z.push(ae)}ge+=1;break;default:break}}this.triangleCount=ge,this.trianglePositions.update(z),this.triangleVectors.update(q),this.triangleColors.update(V),this.triangleUVs.update(G),this.triangleIds.update(new Uint32Array(Z))}},p.drawTransparent=p.draw=function(C){C=C||{};for(var S=this.gl,g=C.model||m,P=C.view||m,E=C.projection||m,z=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],q=0;q<3;++q)z[0][q]=Math.max(z[0][q],this.clipBounds[0][q]),z[1][q]=Math.min(z[1][q],this.clipBounds[1][q]);var V={model:g,view:P,projection:E,inverseModel:m.slice(),clipBounds:z,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};V.inverseModel=h(V.inverseModel,V.model),S.disable(S.CULL_FACE),this.texture.bind(0);var G=new Array(16);f(G,V.view,V.model),f(G,V.projection,G),h(G,G);for(var q=0;q<3;++q)V.eyePosition[q]=G[12+q]/G[15];for(var Z=G[15],q=0;q<3;++q)Z+=this.lightPosition[q]*G[4*q+3];for(var q=0;q<3;++q){for(var j=G[12+q],N=0;N<3;++N)j+=G[4*N+q]*this.lightPosition[N];V.lightPosition[q]=j/Z}if(this.triangleCount>0){var H=this.triShader;H.bind(),H.uniforms=V,this.triangleVAO.bind(),S.drawArrays(S.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}},p.drawPick=function(C){C=C||{};for(var S=this.gl,g=C.model||m,P=C.view||m,E=C.projection||m,z=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],q=0;q<3;++q)z[0][q]=Math.max(z[0][q],this.clipBounds[0][q]),z[1][q]=Math.min(z[1][q],this.clipBounds[1][q]);this._model=[].slice.call(g),this._view=[].slice.call(P),this._projection=[].slice.call(E),this._resolution=[S.drawingBufferWidth,S.drawingBufferHeight];var V={model:g,view:P,projection:E,clipBounds:z,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},G=this.pickShader;G.bind(),G.uniforms=V,this.triangleCount>0&&(this.triangleVAO.bind(),S.drawArrays(S.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind())},p.pick=function(C){if(!C||C.id!==this.pickId)return null;var S=C.value[0]+256*C.value[1]+65536*C.value[2],g=this.cells[S],P=this.positions[g[1]].slice(0,3),E={position:P,dataCoordinate:P,index:Math.floor(g[1]/48)};return this.traceType===\"cone\"?E.index=Math.floor(g[1]/48):this.traceType===\"streamtube\"&&(E.intensity=this.intensity[g[1]],E.velocity=this.vectors[g[1]].slice(0,3),E.divergence=this.vectors[g[1]][3],E.index=S),E},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()};function T(C,S){var g=s(C,S.meshShader.vertex,S.meshShader.fragment,null,S.meshShader.attributes);return g.attributes.position.location=0,g.attributes.color.location=2,g.attributes.uv.location=3,g.attributes.vector.location=4,g}function L(C,S){var g=s(C,S.pickShader.vertex,S.pickShader.fragment,null,S.pickShader.attributes);return g.attributes.position.location=0,g.attributes.id.location=1,g.attributes.vector.location=4,g}function x(C,S,g){var P=g.shaders;arguments.length===1&&(S=C,C=S.gl);var E=T(C,P),z=L(C,P),q=c(C,d(new Uint8Array([255,255,255,255]),[1,1,4]));q.generateMipmap(),q.minFilter=C.LINEAR_MIPMAP_LINEAR,q.magFilter=C.LINEAR;var V=l(C),G=l(C),Z=l(C),j=l(C),N=l(C),H=u(C,[{buffer:V,type:C.FLOAT,size:4},{buffer:N,type:C.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Z,type:C.FLOAT,size:4},{buffer:j,type:C.FLOAT,size:2},{buffer:G,type:C.FLOAT,size:4}]),re=new b(C,q,E,z,V,G,N,Z,j,H,g.traceType||\"cone\");return re.update(S),re}i.exports=x}),614:(function(i,a,o){var s=o(3236),l=s([`precision highp float;\n",
+       "\n",
+       "precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "vec3 getOrthogonalVector(vec3 v) {\n",
+       "  // Return up-vector for only-z vector.\n",
+       "  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n",
+       "  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n",
+       "  // Assign z = 0, x = -b, y = a:\n",
+       "  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n",
+       "  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n",
+       "    return normalize(vec3(-v.y, v.x, 0.0));\n",
+       "  } else {\n",
+       "    return normalize(vec3(0.0, v.z, -v.y));\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "// Calculate the cone vertex and normal at the given index.\n",
+       "//\n",
+       "// The returned vertex is for a cone with its top at origin and height of 1.0,\n",
+       "// pointing in the direction of the vector attribute.\n",
+       "//\n",
+       "// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n",
+       "// These vertices are used to make up the triangles of the cone by the following:\n",
+       "//   segment + 0 top vertex\n",
+       "//   segment + 1 perimeter vertex a+1\n",
+       "//   segment + 2 perimeter vertex a\n",
+       "//   segment + 3 center base vertex\n",
+       "//   segment + 4 perimeter vertex a\n",
+       "//   segment + 5 perimeter vertex a+1\n",
+       "// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n",
+       "// To go from index to segment, floor(index / 6)\n",
+       "// To go from segment to angle, 2*pi * (segment/segmentCount)\n",
+       "// To go from index to segment index, index - (segment*6)\n",
+       "//\n",
+       "vec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n",
+       "\n",
+       "  const float segmentCount = 8.0;\n",
+       "\n",
+       "  float index = rawIndex - floor(rawIndex /\n",
+       "    (segmentCount * 6.0)) *\n",
+       "    (segmentCount * 6.0);\n",
+       "\n",
+       "  float segment = floor(0.001 + index/6.0);\n",
+       "  float segmentIndex = index - (segment*6.0);\n",
+       "\n",
+       "  normal = -normalize(d);\n",
+       "\n",
+       "  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n",
+       "    return mix(vec3(0.0), -d, coneOffset);\n",
+       "  }\n",
+       "\n",
+       "  float nextAngle = (\n",
+       "    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n",
+       "    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n",
+       "  ) ? 1.0 : 0.0;\n",
+       "  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n",
+       "\n",
+       "  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n",
+       "  vec3 v2 = v1 - d;\n",
+       "\n",
+       "  vec3 u = getOrthogonalVector(d);\n",
+       "  vec3 v = normalize(cross(u, d));\n",
+       "\n",
+       "  vec3 x = u * cos(angle) * length(d)*0.25;\n",
+       "  vec3 y = v * sin(angle) * length(d)*0.25;\n",
+       "  vec3 v3 = v2 + x + y;\n",
+       "  if (segmentIndex < 3.0) {\n",
+       "    vec3 tx = u * sin(angle);\n",
+       "    vec3 ty = v * -cos(angle);\n",
+       "    vec3 tangent = tx + ty;\n",
+       "    normal = normalize(cross(v3 - v1, tangent));\n",
+       "  }\n",
+       "\n",
+       "  if (segmentIndex == 0.0) {\n",
+       "    return mix(d, vec3(0.0), coneOffset);\n",
+       "  }\n",
+       "  return v3;\n",
+       "}\n",
+       "\n",
+       "attribute vec3 vector;\n",
+       "attribute vec4 color, position;\n",
+       "attribute vec2 uv;\n",
+       "\n",
+       "uniform float vectorScale, coneScale, coneOffset;\n",
+       "uniform mat4 model, view, projection, inverseModel;\n",
+       "uniform vec3 eyePosition, lightPosition;\n",
+       "\n",
+       "varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\n",
+       "varying vec4 f_color;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  // Scale the vector magnitude to stay constant with\n",
+       "  // model & view changes.\n",
+       "  vec3 normal;\n",
+       "  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n",
+       "  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n",
+       "\n",
+       "  //Lighting geometry parameters\n",
+       "  vec4 cameraCoordinate = view * conePosition;\n",
+       "  cameraCoordinate.xyz /= cameraCoordinate.w;\n",
+       "  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n",
+       "  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n",
+       "  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n",
+       "\n",
+       "  // vec4 m_position  = model * vec4(conePosition, 1.0);\n",
+       "  vec4 t_position  = view * conePosition;\n",
+       "  gl_Position      = projection * t_position;\n",
+       "\n",
+       "  f_color          = color;\n",
+       "  f_data           = conePosition.xyz;\n",
+       "  f_position       = position.xyz;\n",
+       "  f_uv             = uv;\n",
+       "}\n",
+       "`]),u=s([`#extension GL_OES_standard_derivatives : enable\n",
+       "\n",
+       "precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "float beckmannDistribution(float x, float roughness) {\n",
+       "  float NdotH = max(x, 0.0001);\n",
+       "  float cos2Alpha = NdotH * NdotH;\n",
+       "  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n",
+       "  float roughness2 = roughness * roughness;\n",
+       "  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n",
+       "  return exp(tan2Alpha / roughness2) / denom;\n",
+       "}\n",
+       "\n",
+       "float cookTorranceSpecular(\n",
+       "  vec3 lightDirection,\n",
+       "  vec3 viewDirection,\n",
+       "  vec3 surfaceNormal,\n",
+       "  float roughness,\n",
+       "  float fresnel) {\n",
+       "\n",
+       "  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n",
+       "  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n",
+       "\n",
+       "  //Half angle vector\n",
+       "  vec3 H = normalize(lightDirection + viewDirection);\n",
+       "\n",
+       "  //Geometric term\n",
+       "  float NdotH = max(dot(surfaceNormal, H), 0.0);\n",
+       "  float VdotH = max(dot(viewDirection, H), 0.000001);\n",
+       "  float LdotH = max(dot(lightDirection, H), 0.000001);\n",
+       "  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n",
+       "  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n",
+       "  float G = min(1.0, min(G1, G2));\n",
+       "  \n",
+       "  //Distribution term\n",
+       "  float D = beckmannDistribution(NdotH, roughness);\n",
+       "\n",
+       "  //Fresnel term\n",
+       "  float F = pow(1.0 - VdotN, fresnel);\n",
+       "\n",
+       "  //Multiply terms and done\n",
+       "  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\n",
+       "uniform sampler2D texture;\n",
+       "\n",
+       "varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\n",
+       "varying vec4 f_color;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
+       "  vec3 N = normalize(f_normal);\n",
+       "  vec3 L = normalize(f_lightDirection);\n",
+       "  vec3 V = normalize(f_eyeDirection);\n",
+       "\n",
+       "  if(gl_FrontFacing) {\n",
+       "    N = -N;\n",
+       "  }\n",
+       "\n",
+       "  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n",
+       "  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n",
+       "\n",
+       "  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n",
+       "  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n",
+       "\n",
+       "  gl_FragColor = litColor * opacity;\n",
+       "}\n",
+       "`]),c=s([`precision highp float;\n",
+       "\n",
+       "precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "vec3 getOrthogonalVector(vec3 v) {\n",
+       "  // Return up-vector for only-z vector.\n",
+       "  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n",
+       "  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n",
+       "  // Assign z = 0, x = -b, y = a:\n",
+       "  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n",
+       "  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n",
+       "    return normalize(vec3(-v.y, v.x, 0.0));\n",
+       "  } else {\n",
+       "    return normalize(vec3(0.0, v.z, -v.y));\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "// Calculate the cone vertex and normal at the given index.\n",
+       "//\n",
+       "// The returned vertex is for a cone with its top at origin and height of 1.0,\n",
+       "// pointing in the direction of the vector attribute.\n",
+       "//\n",
+       "// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n",
+       "// These vertices are used to make up the triangles of the cone by the following:\n",
+       "//   segment + 0 top vertex\n",
+       "//   segment + 1 perimeter vertex a+1\n",
+       "//   segment + 2 perimeter vertex a\n",
+       "//   segment + 3 center base vertex\n",
+       "//   segment + 4 perimeter vertex a\n",
+       "//   segment + 5 perimeter vertex a+1\n",
+       "// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n",
+       "// To go from index to segment, floor(index / 6)\n",
+       "// To go from segment to angle, 2*pi * (segment/segmentCount)\n",
+       "// To go from index to segment index, index - (segment*6)\n",
+       "//\n",
+       "vec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n",
+       "\n",
+       "  const float segmentCount = 8.0;\n",
+       "\n",
+       "  float index = rawIndex - floor(rawIndex /\n",
+       "    (segmentCount * 6.0)) *\n",
+       "    (segmentCount * 6.0);\n",
+       "\n",
+       "  float segment = floor(0.001 + index/6.0);\n",
+       "  float segmentIndex = index - (segment*6.0);\n",
+       "\n",
+       "  normal = -normalize(d);\n",
+       "\n",
+       "  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n",
+       "    return mix(vec3(0.0), -d, coneOffset);\n",
+       "  }\n",
+       "\n",
+       "  float nextAngle = (\n",
+       "    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n",
+       "    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n",
+       "  ) ? 1.0 : 0.0;\n",
+       "  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n",
+       "\n",
+       "  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n",
+       "  vec3 v2 = v1 - d;\n",
+       "\n",
+       "  vec3 u = getOrthogonalVector(d);\n",
+       "  vec3 v = normalize(cross(u, d));\n",
+       "\n",
+       "  vec3 x = u * cos(angle) * length(d)*0.25;\n",
+       "  vec3 y = v * sin(angle) * length(d)*0.25;\n",
+       "  vec3 v3 = v2 + x + y;\n",
+       "  if (segmentIndex < 3.0) {\n",
+       "    vec3 tx = u * sin(angle);\n",
+       "    vec3 ty = v * -cos(angle);\n",
+       "    vec3 tangent = tx + ty;\n",
+       "    normal = normalize(cross(v3 - v1, tangent));\n",
+       "  }\n",
+       "\n",
+       "  if (segmentIndex == 0.0) {\n",
+       "    return mix(d, vec3(0.0), coneOffset);\n",
+       "  }\n",
+       "  return v3;\n",
+       "}\n",
+       "\n",
+       "attribute vec4 vector;\n",
+       "attribute vec4 position;\n",
+       "attribute vec4 id;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform float vectorScale, coneScale, coneOffset;\n",
+       "\n",
+       "varying vec3 f_position;\n",
+       "varying vec4 f_id;\n",
+       "\n",
+       "void main() {\n",
+       "  vec3 normal;\n",
+       "  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n",
+       "  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n",
+       "  gl_Position = projection * (view * conePosition);\n",
+       "  f_id        = id;\n",
+       "  f_position  = position.xyz;\n",
+       "}\n",
+       "`]),f=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3  clipBounds[2];\n",
+       "uniform float pickId;\n",
+       "\n",
+       "varying vec3 f_position;\n",
+       "varying vec4 f_id;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
+       "\n",
+       "  gl_FragColor = vec4(pickId, f_id.xyz);\n",
+       "}`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},a.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}}),737:(function(i){i.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}}),5171:(function(i,a,o){var s=o(737);i.exports=function(u){return s[u]}}),9165:(function(i,a,o){\"use strict\";i.exports=b;var s=o(2762),l=o(8116),u=o(3436),c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(p,k,M,T){this.gl=p,this.shader=T,this.buffer=k,this.vao=M,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var h=f.prototype;h.isOpaque=function(){return!this.hasAlpha},h.isTransparent=function(){return this.hasAlpha},h.drawTransparent=h.draw=function(p){var k=this.gl,M=this.shader.uniforms;this.shader.bind();var T=M.view=p.view||c,L=M.projection=p.projection||c;M.model=p.model||c,M.clipBounds=this.clipBounds,M.opacity=this.opacity;var x=T[12],C=T[13],S=T[14],g=T[15],P=p._ortho||!1,E=P?2:1,z=E*this.pixelRatio*(L[3]*x+L[7]*C+L[11]*S+L[15]*g)/k.drawingBufferHeight;this.vao.bind();for(var q=0;q<3;++q)k.lineWidth(this.lineWidth[q]*this.pixelRatio),M.capSize=this.capSize[q]*z,this.lineCount[q]&&k.drawArrays(k.LINES,this.lineOffset[q],this.lineCount[q]);this.vao.unbind()};function d(p,k){for(var M=0;M<3;++M)p[0][M]=Math.min(p[0][M],k[M]),p[1][M]=Math.max(p[1][M],k[M])}var v=(function(){for(var p=new Array(3),k=0;k<3;++k){for(var M=[],T=1;T<=2;++T)for(var L=-1;L<=1;L+=2){var x=(T+k)%3,C=[0,0,0];C[x]=L,M.push(C)}p[k]=M}return p})();function m(p,k,M,T){for(var L=v[T],x=0;x<L.length;++x){var C=L[x];p.push(k[0],k[1],k[2],M[0],M[1],M[2],M[3],C[0],C[1],C[2])}return L.length}h.update=function(p){p=p||{},\"lineWidth\"in p&&(this.lineWidth=p.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in p&&(this.capSize=p.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,\"opacity\"in p&&(this.opacity=+p.opacity,this.opacity<1&&(this.hasAlpha=!0));var k=p.color||[[0,0,0],[0,0,0],[0,0,0]],M=p.position,T=p.error;if(Array.isArray(k[0])||(k=[k,k,k]),M&&T){var L=[],x=M.length,C=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var S=0;S<3;++S){this.lineOffset[S]=C;e:for(var g=0;g<x;++g){for(var P=M[g],E=0;E<3;++E)if(isNaN(P[E])||!isFinite(P[E]))continue e;var z=T[g],q=k[S];if(Array.isArray(q[0])&&(q=k[g]),q.length===3?q=[q[0],q[1],q[2],1]:q.length===4&&(q=[q[0],q[1],q[2],q[3]],!this.hasAlpha&&q[3]<1&&(this.hasAlpha=!0)),!(isNaN(z[0][S])||isNaN(z[1][S]))){if(z[0][S]<0){var V=P.slice();V[S]+=z[0][S],L.push(P[0],P[1],P[2],q[0],q[1],q[2],q[3],0,0,0,V[0],V[1],V[2],q[0],q[1],q[2],q[3],0,0,0),d(this.bounds,V),C+=2+m(L,V,q,S)}if(z[1][S]>0){var V=P.slice();V[S]+=z[1][S],L.push(P[0],P[1],P[2],q[0],q[1],q[2],q[3],0,0,0,V[0],V[1],V[2],q[0],q[1],q[2],q[3],0,0,0),d(this.bounds,V),C+=2+m(L,V,q,S)}}}this.lineCount[S]=C-this.lineOffset[S]}this.buffer.update(L)}},h.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()};function b(p){var k=p.gl,M=s(k),T=l(k,[{buffer:M,type:k.FLOAT,size:3,offset:0,stride:40},{buffer:M,type:k.FLOAT,size:4,offset:12,stride:40},{buffer:M,type:k.FLOAT,size:3,offset:28,stride:40}]),L=u(k);L.attributes.position.location=0,L.attributes.color.location=1,L.attributes.offset.location=2;var x=new f(k,M,T,L);return x.update(p),x}}),3436:(function(i,a,o){\"use strict\";var s=o(3236),l=o(9405),u=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position, offset;\n",
+       "attribute vec4 color;\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform float capSize;\n",
+       "varying vec4 fragColor;\n",
+       "varying vec3 fragPosition;\n",
+       "\n",
+       "void main() {\n",
+       "  vec4 worldPosition  = model * vec4(position, 1.0);\n",
+       "  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n",
+       "  gl_Position         = projection * (view * worldPosition);\n",
+       "  fragColor           = color;\n",
+       "  fragPosition        = position;\n",
+       "}`]),c=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform float opacity;\n",
+       "varying vec3 fragPosition;\n",
+       "varying vec4 fragColor;\n",
+       "\n",
+       "void main() {\n",
+       "  if (\n",
+       "    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n",
+       "    fragColor.a * opacity == 0.\n",
+       "  ) discard;\n",
+       "\n",
+       "  gl_FragColor = opacity * fragColor;\n",
+       "}`]);i.exports=function(f){return l(f,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}}),2260:(function(i,a,o){\"use strict\";var s=o(7766);i.exports=C;var l=null,u,c,f,h;function d(S){var g=S.getParameter(S.FRAMEBUFFER_BINDING),P=S.getParameter(S.RENDERBUFFER_BINDING),E=S.getParameter(S.TEXTURE_BINDING_2D);return[g,P,E]}function v(S,g){S.bindFramebuffer(S.FRAMEBUFFER,g[0]),S.bindRenderbuffer(S.RENDERBUFFER,g[1]),S.bindTexture(S.TEXTURE_2D,g[2])}function m(S,g){var P=S.getParameter(g.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(P+1);for(var E=0;E<=P;++E){for(var z=new Array(P),q=0;q<E;++q)z[q]=S.COLOR_ATTACHMENT0+q;for(var q=E;q<P;++q)z[q]=S.NONE;l[E]=z}}function b(S){switch(S){case u:throw new Error(\"gl-fbo: Framebuffer unsupported\");case c:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case f:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case h:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function p(S,g,P,E,z,q){if(!E)return null;var V=s(S,g,P,z,E);return V.magFilter=S.NEAREST,V.minFilter=S.NEAREST,V.mipSamples=1,V.bind(),S.framebufferTexture2D(S.FRAMEBUFFER,q,S.TEXTURE_2D,V.handle,0),V}function k(S,g,P,E,z){var q=S.createRenderbuffer();return S.bindRenderbuffer(S.RENDERBUFFER,q),S.renderbufferStorage(S.RENDERBUFFER,E,g,P),S.framebufferRenderbuffer(S.FRAMEBUFFER,z,S.RENDERBUFFER,q),q}function M(S){var g=d(S.gl),P=S.gl,E=S.handle=P.createFramebuffer(),z=S._shape[0],q=S._shape[1],V=S.color.length,G=S._ext,Z=S._useStencil,j=S._useDepth,N=S._colorType;P.bindFramebuffer(P.FRAMEBUFFER,E);for(var H=0;H<V;++H)S.color[H]=p(P,z,q,N,P.RGBA,P.COLOR_ATTACHMENT0+H);V===0?(S._color_rb=k(P,z,q,P.RGBA4,P.COLOR_ATTACHMENT0),G&&G.drawBuffersWEBGL(l[0])):V>1&&G.drawBuffersWEBGL(l[V]);var re=P.getExtension(\"WEBGL_depth_texture\");re?Z?S.depth=p(P,z,q,re.UNSIGNED_INT_24_8_WEBGL,P.DEPTH_STENCIL,P.DEPTH_STENCIL_ATTACHMENT):j&&(S.depth=p(P,z,q,P.UNSIGNED_SHORT,P.DEPTH_COMPONENT,P.DEPTH_ATTACHMENT)):j&&Z?S._depth_rb=k(P,z,q,P.DEPTH_STENCIL,P.DEPTH_STENCIL_ATTACHMENT):j?S._depth_rb=k(P,z,q,P.DEPTH_COMPONENT16,P.DEPTH_ATTACHMENT):Z&&(S._depth_rb=k(P,z,q,P.STENCIL_INDEX,P.STENCIL_ATTACHMENT));var ae=P.checkFramebufferStatus(P.FRAMEBUFFER);if(ae!==P.FRAMEBUFFER_COMPLETE){S._destroyed=!0,P.bindFramebuffer(P.FRAMEBUFFER,null),P.deleteFramebuffer(S.handle),S.handle=null,S.depth&&(S.depth.dispose(),S.depth=null),S._depth_rb&&(P.deleteRenderbuffer(S._depth_rb),S._depth_rb=null);for(var H=0;H<S.color.length;++H)S.color[H].dispose(),S.color[H]=null;S._color_rb&&(P.deleteRenderbuffer(S._color_rb),S._color_rb=null),v(P,g),b(ae)}v(P,g)}function T(S,g,P,E,z,q,V,G){this.gl=S,this._shape=[g|0,P|0],this._destroyed=!1,this._ext=G,this.color=new Array(z);for(var Z=0;Z<z;++Z)this.color[Z]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=E,this._useDepth=q,this._useStencil=V;var j=this,N=[g|0,P|0];Object.defineProperties(N,{0:{get:function(){return j._shape[0]},set:function(H){return j.width=H}},1:{get:function(){return j._shape[1]},set:function(H){return j.height=H}}}),this._shapeVector=N,M(this)}var L=T.prototype;function x(S,g,P){if(S._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(!(S._shape[0]===g&&S._shape[1]===P)){var E=S.gl,z=E.getParameter(E.MAX_RENDERBUFFER_SIZE);if(g<0||g>z||P<0||P>z)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");S._shape[0]=g,S._shape[1]=P;for(var q=d(E),V=0;V<S.color.length;++V)S.color[V].shape=S._shape;S._color_rb&&(E.bindRenderbuffer(E.RENDERBUFFER,S._color_rb),E.renderbufferStorage(E.RENDERBUFFER,E.RGBA4,S._shape[0],S._shape[1])),S.depth&&(S.depth.shape=S._shape),S._depth_rb&&(E.bindRenderbuffer(E.RENDERBUFFER,S._depth_rb),S._useDepth&&S._useStencil?E.renderbufferStorage(E.RENDERBUFFER,E.DEPTH_STENCIL,S._shape[0],S._shape[1]):S._useDepth?E.renderbufferStorage(E.RENDERBUFFER,E.DEPTH_COMPONENT16,S._shape[0],S._shape[1]):S._useStencil&&E.renderbufferStorage(E.RENDERBUFFER,E.STENCIL_INDEX,S._shape[0],S._shape[1])),E.bindFramebuffer(E.FRAMEBUFFER,S.handle);var G=E.checkFramebufferStatus(E.FRAMEBUFFER);G!==E.FRAMEBUFFER_COMPLETE&&(S.dispose(),v(E,q),b(G)),v(E,q)}}Object.defineProperties(L,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(S){if(Array.isArray(S)||(S=[S|0,S|0]),S.length!==2)throw new Error(\"gl-fbo: Shape vector must be length 2\");var g=S[0]|0,P=S[1]|0;return x(this,g,P),[g,P]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(S){return S=S|0,x(this,S,this._shape[1]),S},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(S){return S=S|0,x(this,this._shape[0],S),S},enumerable:!1}}),L.bind=function(){if(!this._destroyed){var S=this.gl;S.bindFramebuffer(S.FRAMEBUFFER,this.handle),S.viewport(0,0,this._shape[0],this._shape[1])}},L.dispose=function(){if(!this._destroyed){this._destroyed=!0;var S=this.gl;S.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(S.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var g=0;g<this.color.length;++g)this.color[g].dispose(),this.color[g]=null;this._color_rb&&(S.deleteRenderbuffer(this._color_rb),this._color_rb=null)}};function C(S,g,P,E){u||(u=S.FRAMEBUFFER_UNSUPPORTED,c=S.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,f=S.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,h=S.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var z=S.getExtension(\"WEBGL_draw_buffers\");if(!l&&z&&m(S,z),Array.isArray(g)&&(E=P,P=g[1]|0,g=g[0]|0),typeof g!=\"number\")throw new Error(\"gl-fbo: Missing shape parameter\");var q=S.getParameter(S.MAX_RENDERBUFFER_SIZE);if(g<0||g>q||P<0||P>q)throw new Error(\"gl-fbo: Parameters are too large for FBO\");E=E||{};var V=1;if(\"color\"in E){if(V=Math.max(E.color|0,0),V<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(V>1)if(z){if(V>S.getParameter(z.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+V+\" draw buffers\")}else throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\")}var G=S.UNSIGNED_BYTE,Z=S.getExtension(\"OES_texture_float\");if(E.float&&V>0){if(!Z)throw new Error(\"gl-fbo: Context does not support floating point textures\");G=S.FLOAT}else E.preferFloat&&V>0&&Z&&(G=S.FLOAT);var j=!0;\"depth\"in E&&(j=!!E.depth);var N=!1;return\"stencil\"in E&&(N=!!E.stencil),new T(S,g,P,G,V,j,N,z)}}),2992:(function(i,a,o){var s=o(3387).sprintf,l=o(5171),u=o(1848),c=o(1085);i.exports=f;function f(h,d,v){\"use strict\";var m=u(d)||\"of unknown name (see npm glsl-shader-name)\",b=\"unknown type\";v!==void 0&&(b=v===l.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var p=s(`Error compiling %s shader %s:\n",
+       "`,b,m),k=s(\"%s%s\",p,h),M=h.split(`\n",
+       "`),T={},L=0;L<M.length;L++){var x=M[L];if(!(x===\"\"||x===\"\\0\")){var C=parseInt(x.split(\":\")[2]);if(isNaN(C))throw new Error(s(\"Could not parse error: %s\",x));T[C]=x}}for(var S=c(d).split(`\n",
+       "`),L=0;L<S.length;L++)if(!(!T[L+3]&&!T[L+2]&&!T[L+1])){var g=S[L];if(p+=g+`\n",
+       "`,T[L+1]){var P=T[L+1];P=P.substr(P.split(\":\",3).join(\":\").length+1).trim(),p+=s(`^^^ %s\n",
+       "\n",
+       "`,P)}}return{long:p.trim(),short:k.trim()}}}),7319:(function(i,a,o){var s=o(3236),l=o(9405),u=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position, nextPosition;\n",
+       "attribute float arcLength, lineWidth;\n",
+       "attribute vec4 color;\n",
+       "\n",
+       "uniform vec2 screenShape;\n",
+       "uniform float pixelRatio;\n",
+       "uniform mat4 model, view, projection;\n",
+       "\n",
+       "varying vec4 fragColor;\n",
+       "varying vec3 worldPosition;\n",
+       "varying float pixelArcLength;\n",
+       "\n",
+       "vec4 project(vec3 p) {\n",
+       "  return projection * (view * (model * vec4(p, 1.0)));\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "  vec4 startPoint = project(position);\n",
+       "  vec4 endPoint   = project(nextPosition);\n",
+       "\n",
+       "  vec2 A = startPoint.xy / startPoint.w;\n",
+       "  vec2 B =   endPoint.xy /   endPoint.w;\n",
+       "\n",
+       "  float clipAngle = atan(\n",
+       "    (B.y - A.y) * screenShape.y,\n",
+       "    (B.x - A.x) * screenShape.x\n",
+       "  );\n",
+       "\n",
+       "  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n",
+       "    sin(clipAngle),\n",
+       "    -cos(clipAngle)\n",
+       "  ) / screenShape;\n",
+       "\n",
+       "  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n",
+       "\n",
+       "  worldPosition = position;\n",
+       "  pixelArcLength = arcLength;\n",
+       "  fragColor = color;\n",
+       "}\n",
+       "`]),c=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3      clipBounds[2];\n",
+       "uniform sampler2D dashTexture;\n",
+       "uniform float     dashScale;\n",
+       "uniform float     opacity;\n",
+       "\n",
+       "varying vec3    worldPosition;\n",
+       "varying float   pixelArcLength;\n",
+       "varying vec4    fragColor;\n",
+       "\n",
+       "void main() {\n",
+       "  if (\n",
+       "    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n",
+       "    fragColor.a * opacity == 0.\n",
+       "  ) discard;\n",
+       "\n",
+       "  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n",
+       "  if(dashWeight < 0.5) {\n",
+       "    discard;\n",
+       "  }\n",
+       "  gl_FragColor = fragColor * opacity;\n",
+       "}\n",
+       "`]),f=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "#define FLOAT_MAX  1.70141184e38\n",
+       "#define FLOAT_MIN  1.17549435e-38\n",
+       "\n",
+       "// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\n",
+       "vec4 packFloat(float v) {\n",
+       "  float av = abs(v);\n",
+       "\n",
+       "  //Handle special cases\n",
+       "  if(av < FLOAT_MIN) {\n",
+       "    return vec4(0.0, 0.0, 0.0, 0.0);\n",
+       "  } else if(v > FLOAT_MAX) {\n",
+       "    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n",
+       "  } else if(v < -FLOAT_MAX) {\n",
+       "    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n",
+       "  }\n",
+       "\n",
+       "  vec4 c = vec4(0,0,0,0);\n",
+       "\n",
+       "  //Compute exponent and mantissa\n",
+       "  float e = floor(log2(av));\n",
+       "  float m = av * pow(2.0, -e) - 1.0;\n",
+       "\n",
+       "  //Unpack mantissa\n",
+       "  c[1] = floor(128.0 * m);\n",
+       "  m -= c[1] / 128.0;\n",
+       "  c[2] = floor(32768.0 * m);\n",
+       "  m -= c[2] / 32768.0;\n",
+       "  c[3] = floor(8388608.0 * m);\n",
+       "\n",
+       "  //Unpack exponent\n",
+       "  float ebias = e + 127.0;\n",
+       "  c[0] = floor(ebias / 2.0);\n",
+       "  ebias -= c[0] * 2.0;\n",
+       "  c[1] += floor(ebias) * 128.0;\n",
+       "\n",
+       "  //Unpack sign bit\n",
+       "  c[0] += 128.0 * step(0.0, -v);\n",
+       "\n",
+       "  //Scale back to range\n",
+       "  return c / 255.0;\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform float pickId;\n",
+       "uniform vec3 clipBounds[2];\n",
+       "\n",
+       "varying vec3 worldPosition;\n",
+       "varying float pixelArcLength;\n",
+       "varying vec4 fragColor;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n",
+       "\n",
+       "  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n",
+       "}`]),h=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];a.createShader=function(d){return l(d,u,c,null,h)},a.createPickShader=function(d){return l(d,u,f,null,h)}}),5714:(function(i,a,o){\"use strict\";i.exports=S;var s=o(2762),l=o(8116),u=o(7766),c=new Uint8Array(4),f=new Float32Array(c.buffer);function h(g,P,E,z){return c[0]=z,c[1]=E,c[2]=P,c[3]=g,f[0]}var d=o(2478),v=o(9618),m=o(7319),b=m.createShader,p=m.createPickShader,k=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function M(g,P){for(var E=0,z=0;z<3;++z){var q=g[z]-P[z];E+=q*q}return Math.sqrt(E)}function T(g){for(var P=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],E=0;E<3;++E)P[0][E]=Math.max(g[0][E],P[0][E]),P[1][E]=Math.min(g[1][E],P[1][E]);return P}function L(g,P,E,z){this.arcLength=g,this.position=P,this.index=E,this.dataCoordinate=z}function x(g,P,E,z,q,V){this.gl=g,this.shader=P,this.pickShader=E,this.buffer=z,this.vao=q,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=V,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var C=x.prototype;C.isTransparent=function(){return this.hasAlpha},C.isOpaque=function(){return!this.hasAlpha},C.pickSlots=1,C.setPickBase=function(g){this.pickId=g},C.drawTransparent=C.draw=function(g){if(this.vertexCount){var P=this.gl,E=this.shader,z=this.vao;E.bind(),E.uniforms={model:g.model||k,view:g.view||k,projection:g.projection||k,clipBounds:T(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[P.drawingBufferWidth,P.drawingBufferHeight],pixelRatio:this.pixelRatio},z.bind(),z.draw(P.TRIANGLE_STRIP,this.vertexCount),z.unbind()}},C.drawPick=function(g){if(this.vertexCount){var P=this.gl,E=this.pickShader,z=this.vao;E.bind(),E.uniforms={model:g.model||k,view:g.view||k,projection:g.projection||k,pickId:this.pickId,clipBounds:T(this.clipBounds),screenShape:[P.drawingBufferWidth,P.drawingBufferHeight],pixelRatio:this.pixelRatio},z.bind(),z.draw(P.TRIANGLE_STRIP,this.vertexCount),z.unbind()}},C.update=function(g){var P,E;this.dirty=!0;var z=!!g.connectGaps;\"dashScale\"in g&&(this.dashScale=g.dashScale),this.hasAlpha=!1,\"opacity\"in g&&(this.opacity=+g.opacity,this.opacity<1&&(this.hasAlpha=!0));var q=[],V=[],G=[],Z=0,j=0,N=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],H=g.position||g.positions;if(H){var re=g.color||g.colors||[0,0,0,1],ae=g.lineWidth||1,_e=!1;e:for(P=1;P<H.length;++P){var Ee=H[P-1],Ce=H[P];for(V.push(Z),G.push(Ee.slice()),E=0;E<3;++E){if(isNaN(Ee[E])||isNaN(Ce[E])||!isFinite(Ee[E])||!isFinite(Ce[E])){if(!z&&q.length>0){for(var ge=0;ge<24;++ge)q.push(q[q.length-12]);j+=2,_e=!0}continue e}N[0][E]=Math.min(N[0][E],Ee[E],Ce[E]),N[1][E]=Math.max(N[1][E],Ee[E],Ce[E])}var ie,Se;Array.isArray(re[0])?(ie=re.length>P-1?re[P-1]:re.length>0?re[re.length-1]:[0,0,0,1],Se=re.length>P?re[P]:re.length>0?re[re.length-1]:[0,0,0,1]):ie=Se=re,ie.length===3&&(ie=[ie[0],ie[1],ie[2],1]),Se.length===3&&(Se=[Se[0],Se[1],Se[2],1]),!this.hasAlpha&&ie[3]<1&&(this.hasAlpha=!0);var ke;Array.isArray(ae)?ke=ae.length>P-1?ae[P-1]:ae.length>0?ae[ae.length-1]:[0,0,0,1]:ke=ae;var be=Z;if(Z+=M(Ee,Ce),_e){for(E=0;E<2;++E)q.push(Ee[0],Ee[1],Ee[2],Ce[0],Ce[1],Ce[2],be,ke,ie[0],ie[1],ie[2],ie[3]);j+=2,_e=!1}q.push(Ee[0],Ee[1],Ee[2],Ce[0],Ce[1],Ce[2],be,ke,ie[0],ie[1],ie[2],ie[3],Ee[0],Ee[1],Ee[2],Ce[0],Ce[1],Ce[2],be,-ke,ie[0],ie[1],ie[2],ie[3],Ce[0],Ce[1],Ce[2],Ee[0],Ee[1],Ee[2],Z,-ke,Se[0],Se[1],Se[2],Se[3],Ce[0],Ce[1],Ce[2],Ee[0],Ee[1],Ee[2],Z,ke,Se[0],Se[1],Se[2],Se[3]),j+=4}}if(this.buffer.update(q),V.push(Z),G.push(H[H.length-1].slice()),this.bounds=N,this.vertexCount=j,this.points=G,this.arcLength=V,\"dashes\"in g){var Be=g.dashes,Le=Be.slice();for(Le.unshift(0),P=1;P<Le.length;++P)Le[P]=Le[P-1]+Le[P];var me=v(new Array(256*4),[256,1,4]);for(P=0;P<256;++P){for(E=0;E<4;++E)me.set(P,0,E,0);d.le(Le,Le[Le.length-1]*P/255)&1?me.set(P,0,0,0):me.set(P,0,0,255)}this.texture.setPixels(me)}},C.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},C.pick=function(g){if(!g||g.id!==this.pickId)return null;var P=h(g.value[0],g.value[1],g.value[2],0),E=d.le(this.arcLength,P);if(E<0)return null;if(E===this.arcLength.length-1)return new L(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),E);for(var z=this.points[E],q=this.points[Math.min(E+1,this.points.length-1)],V=(P-this.arcLength[E])/(this.arcLength[E+1]-this.arcLength[E]),G=1-V,Z=[0,0,0],j=0;j<3;++j)Z[j]=G*z[j]+V*q[j];var N=Math.min(V<.5?E:E+1,this.points.length-1);return new L(P,Z,N,this.points[N])};function S(g){var P=g.gl||g.scene&&g.scene.gl,E=b(P);E.attributes.position.location=0,E.attributes.nextPosition.location=1,E.attributes.arcLength.location=2,E.attributes.lineWidth.location=3,E.attributes.color.location=4;var z=p(P);z.attributes.position.location=0,z.attributes.nextPosition.location=1,z.attributes.arcLength.location=2,z.attributes.lineWidth.location=3,z.attributes.color.location=4;for(var q=s(P),V=l(P,[{buffer:q,size:3,offset:0,stride:48},{buffer:q,size:3,offset:12,stride:48},{buffer:q,size:1,offset:24,stride:48},{buffer:q,size:1,offset:28,stride:48},{buffer:q,size:4,offset:32,stride:48}]),G=v(new Array(256*4),[256,1,4]),Z=0;Z<1024;++Z)G.data[Z]=255;var j=u(P,G);j.wrap=P.REPEAT;var N=new x(P,E,z,q,V,j);return N.update(g),N}}),1903:(function(i){i.exports=a;function a(o){var s=new Float32Array(16);return s[0]=o[0],s[1]=o[1],s[2]=o[2],s[3]=o[3],s[4]=o[4],s[5]=o[5],s[6]=o[6],s[7]=o[7],s[8]=o[8],s[9]=o[9],s[10]=o[10],s[11]=o[11],s[12]=o[12],s[13]=o[13],s[14]=o[14],s[15]=o[15],s}}),6864:(function(i){i.exports=a;function a(){var o=new Float32Array(16);return o[0]=1,o[1]=0,o[2]=0,o[3]=0,o[4]=0,o[5]=1,o[6]=0,o[7]=0,o[8]=0,o[9]=0,o[10]=1,o[11]=0,o[12]=0,o[13]=0,o[14]=0,o[15]=1,o}}),9921:(function(i){i.exports=a;function a(o){var s=o[0],l=o[1],u=o[2],c=o[3],f=o[4],h=o[5],d=o[6],v=o[7],m=o[8],b=o[9],p=o[10],k=o[11],M=o[12],T=o[13],L=o[14],x=o[15],C=s*h-l*f,S=s*d-u*f,g=s*v-c*f,P=l*d-u*h,E=l*v-c*h,z=u*v-c*d,q=m*T-b*M,V=m*L-p*M,G=m*x-k*M,Z=b*L-p*T,j=b*x-k*T,N=p*x-k*L;return C*N-S*j+g*Z+P*G-E*V+z*q}}),7399:(function(i){i.exports=a;function a(o,s){var l=s[0],u=s[1],c=s[2],f=s[3],h=l+l,d=u+u,v=c+c,m=l*h,b=u*h,p=u*d,k=c*h,M=c*d,T=c*v,L=f*h,x=f*d,C=f*v;return o[0]=1-p-T,o[1]=b+C,o[2]=k-x,o[3]=0,o[4]=b-C,o[5]=1-m-T,o[6]=M+L,o[7]=0,o[8]=k+x,o[9]=M-L,o[10]=1-m-p,o[11]=0,o[12]=0,o[13]=0,o[14]=0,o[15]=1,o}}),6743:(function(i){i.exports=a;function a(o,s,l){var u=s[0],c=s[1],f=s[2],h=s[3],d=u+u,v=c+c,m=f+f,b=u*d,p=u*v,k=u*m,M=c*v,T=c*m,L=f*m,x=h*d,C=h*v,S=h*m;return o[0]=1-(M+L),o[1]=p+S,o[2]=k-C,o[3]=0,o[4]=p-S,o[5]=1-(b+L),o[6]=T+x,o[7]=0,o[8]=k+C,o[9]=T-x,o[10]=1-(b+M),o[11]=0,o[12]=l[0],o[13]=l[1],o[14]=l[2],o[15]=1,o}}),7894:(function(i){i.exports=a;function a(o){return o[0]=1,o[1]=0,o[2]=0,o[3]=0,o[4]=0,o[5]=1,o[6]=0,o[7]=0,o[8]=0,o[9]=0,o[10]=1,o[11]=0,o[12]=0,o[13]=0,o[14]=0,o[15]=1,o}}),7608:(function(i){i.exports=a;function a(o,s){var l=s[0],u=s[1],c=s[2],f=s[3],h=s[4],d=s[5],v=s[6],m=s[7],b=s[8],p=s[9],k=s[10],M=s[11],T=s[12],L=s[13],x=s[14],C=s[15],S=l*d-u*h,g=l*v-c*h,P=l*m-f*h,E=u*v-c*d,z=u*m-f*d,q=c*m-f*v,V=b*L-p*T,G=b*x-k*T,Z=b*C-M*T,j=p*x-k*L,N=p*C-M*L,H=k*C-M*x,re=S*H-g*N+P*j+E*Z-z*G+q*V;return re?(re=1/re,o[0]=(d*H-v*N+m*j)*re,o[1]=(c*N-u*H-f*j)*re,o[2]=(L*q-x*z+C*E)*re,o[3]=(k*z-p*q-M*E)*re,o[4]=(v*Z-h*H-m*G)*re,o[5]=(l*H-c*Z+f*G)*re,o[6]=(x*P-T*q-C*g)*re,o[7]=(b*q-k*P+M*g)*re,o[8]=(h*N-d*Z+m*V)*re,o[9]=(u*Z-l*N-f*V)*re,o[10]=(T*z-L*P+C*S)*re,o[11]=(p*P-b*z-M*S)*re,o[12]=(d*G-h*j-v*V)*re,o[13]=(l*j-u*G+c*V)*re,o[14]=(L*g-T*E-x*S)*re,o[15]=(b*E-p*g+k*S)*re,o):null}}),6582:(function(i,a,o){var s=o(7894);i.exports=l;function l(u,c,f,h){var d,v,m,b,p,k,M,T,L,x,C=c[0],S=c[1],g=c[2],P=h[0],E=h[1],z=h[2],q=f[0],V=f[1],G=f[2];return Math.abs(C-q)<1e-6&&Math.abs(S-V)<1e-6&&Math.abs(g-G)<1e-6?s(u):(M=C-q,T=S-V,L=g-G,x=1/Math.sqrt(M*M+T*T+L*L),M*=x,T*=x,L*=x,d=E*L-z*T,v=z*M-P*L,m=P*T-E*M,x=Math.sqrt(d*d+v*v+m*m),x?(x=1/x,d*=x,v*=x,m*=x):(d=0,v=0,m=0),b=T*m-L*v,p=L*d-M*m,k=M*v-T*d,x=Math.sqrt(b*b+p*p+k*k),x?(x=1/x,b*=x,p*=x,k*=x):(b=0,p=0,k=0),u[0]=d,u[1]=b,u[2]=M,u[3]=0,u[4]=v,u[5]=p,u[6]=T,u[7]=0,u[8]=m,u[9]=k,u[10]=L,u[11]=0,u[12]=-(d*C+v*S+m*g),u[13]=-(b*C+p*S+k*g),u[14]=-(M*C+T*S+L*g),u[15]=1,u)}}),6760:(function(i){i.exports=a;function a(o,s,l){var u=s[0],c=s[1],f=s[2],h=s[3],d=s[4],v=s[5],m=s[6],b=s[7],p=s[8],k=s[9],M=s[10],T=s[11],L=s[12],x=s[13],C=s[14],S=s[15],g=l[0],P=l[1],E=l[2],z=l[3];return o[0]=g*u+P*d+E*p+z*L,o[1]=g*c+P*v+E*k+z*x,o[2]=g*f+P*m+E*M+z*C,o[3]=g*h+P*b+E*T+z*S,g=l[4],P=l[5],E=l[6],z=l[7],o[4]=g*u+P*d+E*p+z*L,o[5]=g*c+P*v+E*k+z*x,o[6]=g*f+P*m+E*M+z*C,o[7]=g*h+P*b+E*T+z*S,g=l[8],P=l[9],E=l[10],z=l[11],o[8]=g*u+P*d+E*p+z*L,o[9]=g*c+P*v+E*k+z*x,o[10]=g*f+P*m+E*M+z*C,o[11]=g*h+P*b+E*T+z*S,g=l[12],P=l[13],E=l[14],z=l[15],o[12]=g*u+P*d+E*p+z*L,o[13]=g*c+P*v+E*k+z*x,o[14]=g*f+P*m+E*M+z*C,o[15]=g*h+P*b+E*T+z*S,o}}),4040:(function(i){i.exports=a;function a(o,s,l,u,c,f,h){var d=1/(s-l),v=1/(u-c),m=1/(f-h);return o[0]=-2*d,o[1]=0,o[2]=0,o[3]=0,o[4]=0,o[5]=-2*v,o[6]=0,o[7]=0,o[8]=0,o[9]=0,o[10]=2*m,o[11]=0,o[12]=(s+l)*d,o[13]=(c+u)*v,o[14]=(h+f)*m,o[15]=1,o}}),4772:(function(i){i.exports=a;function a(o,s,l,u,c){var f=1/Math.tan(s/2),h=1/(u-c);return o[0]=f/l,o[1]=0,o[2]=0,o[3]=0,o[4]=0,o[5]=f,o[6]=0,o[7]=0,o[8]=0,o[9]=0,o[10]=(c+u)*h,o[11]=-1,o[12]=0,o[13]=0,o[14]=2*c*u*h,o[15]=0,o}}),6079:(function(i){i.exports=a;function a(o,s,l,u){var c=u[0],f=u[1],h=u[2],d=Math.sqrt(c*c+f*f+h*h),v,m,b,p,k,M,T,L,x,C,S,g,P,E,z,q,V,G,Z,j,N,H,re,ae;return Math.abs(d)<1e-6?null:(d=1/d,c*=d,f*=d,h*=d,v=Math.sin(l),m=Math.cos(l),b=1-m,p=s[0],k=s[1],M=s[2],T=s[3],L=s[4],x=s[5],C=s[6],S=s[7],g=s[8],P=s[9],E=s[10],z=s[11],q=c*c*b+m,V=f*c*b+h*v,G=h*c*b-f*v,Z=c*f*b-h*v,j=f*f*b+m,N=h*f*b+c*v,H=c*h*b+f*v,re=f*h*b-c*v,ae=h*h*b+m,o[0]=p*q+L*V+g*G,o[1]=k*q+x*V+P*G,o[2]=M*q+C*V+E*G,o[3]=T*q+S*V+z*G,o[4]=p*Z+L*j+g*N,o[5]=k*Z+x*j+P*N,o[6]=M*Z+C*j+E*N,o[7]=T*Z+S*j+z*N,o[8]=p*H+L*re+g*ae,o[9]=k*H+x*re+P*ae,o[10]=M*H+C*re+E*ae,o[11]=T*H+S*re+z*ae,s!==o&&(o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15]),o)}}),5567:(function(i){i.exports=a;function a(o,s,l){var u=Math.sin(l),c=Math.cos(l),f=s[4],h=s[5],d=s[6],v=s[7],m=s[8],b=s[9],p=s[10],k=s[11];return s!==o&&(o[0]=s[0],o[1]=s[1],o[2]=s[2],o[3]=s[3],o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15]),o[4]=f*c+m*u,o[5]=h*c+b*u,o[6]=d*c+p*u,o[7]=v*c+k*u,o[8]=m*c-f*u,o[9]=b*c-h*u,o[10]=p*c-d*u,o[11]=k*c-v*u,o}}),2408:(function(i){i.exports=a;function a(o,s,l){var u=Math.sin(l),c=Math.cos(l),f=s[0],h=s[1],d=s[2],v=s[3],m=s[8],b=s[9],p=s[10],k=s[11];return s!==o&&(o[4]=s[4],o[5]=s[5],o[6]=s[6],o[7]=s[7],o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15]),o[0]=f*c-m*u,o[1]=h*c-b*u,o[2]=d*c-p*u,o[3]=v*c-k*u,o[8]=f*u+m*c,o[9]=h*u+b*c,o[10]=d*u+p*c,o[11]=v*u+k*c,o}}),7089:(function(i){i.exports=a;function a(o,s,l){var u=Math.sin(l),c=Math.cos(l),f=s[0],h=s[1],d=s[2],v=s[3],m=s[4],b=s[5],p=s[6],k=s[7];return s!==o&&(o[8]=s[8],o[9]=s[9],o[10]=s[10],o[11]=s[11],o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15]),o[0]=f*c+m*u,o[1]=h*c+b*u,o[2]=d*c+p*u,o[3]=v*c+k*u,o[4]=m*c-f*u,o[5]=b*c-h*u,o[6]=p*c-d*u,o[7]=k*c-v*u,o}}),2504:(function(i){i.exports=a;function a(o,s,l){var u=l[0],c=l[1],f=l[2];return o[0]=s[0]*u,o[1]=s[1]*u,o[2]=s[2]*u,o[3]=s[3]*u,o[4]=s[4]*c,o[5]=s[5]*c,o[6]=s[6]*c,o[7]=s[7]*c,o[8]=s[8]*f,o[9]=s[9]*f,o[10]=s[10]*f,o[11]=s[11]*f,o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15],o}}),7656:(function(i){i.exports=a;function a(o,s,l){var u=l[0],c=l[1],f=l[2],h,d,v,m,b,p,k,M,T,L,x,C;return s===o?(o[12]=s[0]*u+s[4]*c+s[8]*f+s[12],o[13]=s[1]*u+s[5]*c+s[9]*f+s[13],o[14]=s[2]*u+s[6]*c+s[10]*f+s[14],o[15]=s[3]*u+s[7]*c+s[11]*f+s[15]):(h=s[0],d=s[1],v=s[2],m=s[3],b=s[4],p=s[5],k=s[6],M=s[7],T=s[8],L=s[9],x=s[10],C=s[11],o[0]=h,o[1]=d,o[2]=v,o[3]=m,o[4]=b,o[5]=p,o[6]=k,o[7]=M,o[8]=T,o[9]=L,o[10]=x,o[11]=C,o[12]=h*u+b*c+T*f+s[12],o[13]=d*u+p*c+L*f+s[13],o[14]=v*u+k*c+x*f+s[14],o[15]=m*u+M*c+C*f+s[15]),o}}),5665:(function(i){i.exports=a;function a(o,s){if(o===s){var l=s[1],u=s[2],c=s[3],f=s[6],h=s[7],d=s[11];o[1]=s[4],o[2]=s[8],o[3]=s[12],o[4]=l,o[6]=s[9],o[7]=s[13],o[8]=u,o[9]=f,o[11]=s[14],o[12]=c,o[13]=h,o[14]=d}else o[0]=s[0],o[1]=s[4],o[2]=s[8],o[3]=s[12],o[4]=s[1],o[5]=s[5],o[6]=s[9],o[7]=s[13],o[8]=s[2],o[9]=s[6],o[10]=s[10],o[11]=s[14],o[12]=s[3],o[13]=s[7],o[14]=s[11],o[15]=s[15];return o}}),7626:(function(i,a,o){\"use strict\";var s=o(2642),l=o(9346);i.exports=d;function u(v,m){for(var b=[0,0,0,0],p=0;p<4;++p)for(var k=0;k<4;++k)b[k]+=v[4*p+k]*m[p];return b}function c(v,m,b,p,k){for(var M=u(p,u(b,u(m,[v[0],v[1],v[2],1]))),T=0;T<3;++T)M[T]/=M[3];return[.5*k[0]*(1+M[0]),.5*k[1]*(1-M[1])]}function f(v,m){if(v.length===2){for(var b=0,p=0,k=0;k<2;++k)b+=Math.pow(m[k]-v[0][k],2),p+=Math.pow(m[k]-v[1][k],2);return b=Math.sqrt(b),p=Math.sqrt(p),b+p<1e-6?[1,0]:[p/(b+p),b/(p+b)]}else if(v.length===3){var M=[0,0];return l(v[0],v[1],v[2],m,M),s(v,M)}return[]}function h(v,m){for(var b=[0,0,0],p=0;p<v.length;++p)for(var k=v[p],M=m[p],T=0;T<3;++T)b[T]+=M*k[T];return b}function d(v,m,b,p,k,M){if(v.length===1)return[0,v[0].slice()];for(var T=new Array(v.length),L=0;L<v.length;++L)T[L]=c(v[L],b,p,k,M);for(var x=0,C=1/0,L=0;L<T.length;++L){for(var S=0,g=0;g<2;++g)S+=Math.pow(T[L][g]-m[g],2);S<C&&(C=S,x=L)}for(var P=f(T,m),E=0,L=0;L<3;++L){if(P[L]<-.001||P[L]>1.0001)return null;E+=P[L]}return Math.abs(E-1)>.001?null:[x,h(v,P),P]}}),840:(function(i,a,o){var s=o(3236),l=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position, normal;\n",
+       "attribute vec4 color;\n",
+       "attribute vec2 uv;\n",
+       "\n",
+       "uniform mat4 model\n",
+       "           , view\n",
+       "           , projection\n",
+       "           , inverseModel;\n",
+       "uniform vec3 eyePosition\n",
+       "           , lightPosition;\n",
+       "\n",
+       "varying vec3 f_normal\n",
+       "           , f_lightDirection\n",
+       "           , f_eyeDirection\n",
+       "           , f_data;\n",
+       "varying vec4 f_color;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "vec4 project(vec3 p) {\n",
+       "  return projection * (view * (model * vec4(p, 1.0)));\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "  gl_Position      = project(position);\n",
+       "\n",
+       "  //Lighting geometry parameters\n",
+       "  vec4 cameraCoordinate = view * vec4(position , 1.0);\n",
+       "  cameraCoordinate.xyz /= cameraCoordinate.w;\n",
+       "  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n",
+       "  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n",
+       "  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n",
+       "\n",
+       "  f_color          = color;\n",
+       "  f_data           = position;\n",
+       "  f_uv             = uv;\n",
+       "}\n",
+       "`]),u=s([`#extension GL_OES_standard_derivatives : enable\n",
+       "\n",
+       "precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "float beckmannDistribution(float x, float roughness) {\n",
+       "  float NdotH = max(x, 0.0001);\n",
+       "  float cos2Alpha = NdotH * NdotH;\n",
+       "  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n",
+       "  float roughness2 = roughness * roughness;\n",
+       "  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n",
+       "  return exp(tan2Alpha / roughness2) / denom;\n",
+       "}\n",
+       "\n",
+       "float cookTorranceSpecular(\n",
+       "  vec3 lightDirection,\n",
+       "  vec3 viewDirection,\n",
+       "  vec3 surfaceNormal,\n",
+       "  float roughness,\n",
+       "  float fresnel) {\n",
+       "\n",
+       "  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n",
+       "  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n",
+       "\n",
+       "  //Half angle vector\n",
+       "  vec3 H = normalize(lightDirection + viewDirection);\n",
+       "\n",
+       "  //Geometric term\n",
+       "  float NdotH = max(dot(surfaceNormal, H), 0.0);\n",
+       "  float VdotH = max(dot(viewDirection, H), 0.000001);\n",
+       "  float LdotH = max(dot(lightDirection, H), 0.000001);\n",
+       "  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n",
+       "  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n",
+       "  float G = min(1.0, min(G1, G2));\n",
+       "  \n",
+       "  //Distribution term\n",
+       "  float D = beckmannDistribution(NdotH, roughness);\n",
+       "\n",
+       "  //Fresnel term\n",
+       "  float F = pow(1.0 - VdotN, fresnel);\n",
+       "\n",
+       "  //Multiply terms and done\n",
+       "  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n",
+       "}\n",
+       "\n",
+       "//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform float roughness\n",
+       "            , fresnel\n",
+       "            , kambient\n",
+       "            , kdiffuse\n",
+       "            , kspecular;\n",
+       "uniform sampler2D texture;\n",
+       "\n",
+       "varying vec3 f_normal\n",
+       "           , f_lightDirection\n",
+       "           , f_eyeDirection\n",
+       "           , f_data;\n",
+       "varying vec4 f_color;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  if (f_color.a == 0.0 ||\n",
+       "    outOfRange(clipBounds[0], clipBounds[1], f_data)\n",
+       "  ) discard;\n",
+       "\n",
+       "  vec3 N = normalize(f_normal);\n",
+       "  vec3 L = normalize(f_lightDirection);\n",
+       "  vec3 V = normalize(f_eyeDirection);\n",
+       "\n",
+       "  if(gl_FrontFacing) {\n",
+       "    N = -N;\n",
+       "  }\n",
+       "\n",
+       "  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n",
+       "  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n",
+       "\n",
+       "  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n",
+       "\n",
+       "  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n",
+       "  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n",
+       "\n",
+       "  gl_FragColor = litColor * f_color.a;\n",
+       "}\n",
+       "`]),c=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "attribute vec4 color;\n",
+       "attribute vec2 uv;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "\n",
+       "varying vec4 f_color;\n",
+       "varying vec3 f_data;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  gl_Position = projection * (view * (model * vec4(position, 1.0)));\n",
+       "  f_color = color;\n",
+       "  f_data  = position;\n",
+       "  f_uv    = uv;\n",
+       "}`]),f=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform sampler2D texture;\n",
+       "uniform float opacity;\n",
+       "\n",
+       "varying vec4 f_color;\n",
+       "varying vec3 f_data;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n",
+       "\n",
+       "  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n",
+       "}`]),h=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "attribute vec4 color;\n",
+       "attribute vec2 uv;\n",
+       "attribute float pointSize;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec3 clipBounds[2];\n",
+       "\n",
+       "varying vec4 f_color;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n",
+       "\n",
+       "    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n",
+       "  } else {\n",
+       "    gl_Position = projection * (view * (model * vec4(position, 1.0)));\n",
+       "  }\n",
+       "  gl_PointSize = pointSize;\n",
+       "  f_color = color;\n",
+       "  f_uv = uv;\n",
+       "}`]),d=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "uniform sampler2D texture;\n",
+       "uniform float opacity;\n",
+       "\n",
+       "varying vec4 f_color;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n",
+       "  if(dot(pointR, pointR) > 0.25) {\n",
+       "    discard;\n",
+       "  }\n",
+       "  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n",
+       "}`]),v=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "attribute vec4 id;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "\n",
+       "varying vec3 f_position;\n",
+       "varying vec4 f_id;\n",
+       "\n",
+       "void main() {\n",
+       "  gl_Position = projection * (view * (model * vec4(position, 1.0)));\n",
+       "  f_id        = id;\n",
+       "  f_position  = position;\n",
+       "}`]),m=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3  clipBounds[2];\n",
+       "uniform float pickId;\n",
+       "\n",
+       "varying vec3 f_position;\n",
+       "varying vec4 f_id;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
+       "\n",
+       "  gl_FragColor = vec4(pickId, f_id.xyz);\n",
+       "}`]),b=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "attribute vec3  position;\n",
+       "attribute float pointSize;\n",
+       "attribute vec4  id;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec3 clipBounds[2];\n",
+       "\n",
+       "varying vec3 f_position;\n",
+       "varying vec4 f_id;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n",
+       "\n",
+       "    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n",
+       "  } else {\n",
+       "    gl_Position  = projection * (view * (model * vec4(position, 1.0)));\n",
+       "    gl_PointSize = pointSize;\n",
+       "  }\n",
+       "  f_id         = id;\n",
+       "  f_position   = position;\n",
+       "}`]),p=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "\n",
+       "void main() {\n",
+       "  gl_Position = projection * (view * (model * vec4(position, 1.0)));\n",
+       "}`]),k=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "uniform vec3 contourColor;\n",
+       "\n",
+       "void main() {\n",
+       "  gl_FragColor = vec4(contourColor, 1.0);\n",
+       "}\n",
+       "`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},a.wireShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},a.pointShader={vertex:h,fragment:d,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},a.pickShader={vertex:v,fragment:m,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},a.pointPickShader={vertex:b,fragment:m,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},a.contourShader={vertex:p,fragment:k,attributes:[{name:\"position\",type:\"vec3\"}]}}),7201:(function(i,a,o){\"use strict\";var s=1e-6,l=1e-6,u=o(9405),c=o(2762),f=o(8116),h=o(7766),d=o(8406),v=o(6760),m=o(7608),b=o(9618),p=o(6729),k=o(7765),M=o(1888),T=o(840),L=o(7626),x=T.meshShader,C=T.wireShader,S=T.pointShader,g=T.pickShader,P=T.pointPickShader,E=T.contourShader,z=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function q(ge,ie,Se,ke,be,Be,Le,me,Pe,ce,He,lt,mt,Ht,at,ct,ar,Vt,rr,tt,je,Ue,Ae,rt,St,Tt,dt){this.gl=ge,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=ie,this.dirty=!0,this.triShader=Se,this.lineShader=ke,this.pointShader=be,this.pickShader=Be,this.pointPickShader=Le,this.contourShader=me,this.trianglePositions=Pe,this.triangleColors=He,this.triangleNormals=mt,this.triangleUVs=lt,this.triangleIds=ce,this.triangleVAO=Ht,this.triangleCount=0,this.lineWidth=1,this.edgePositions=at,this.edgeColors=ar,this.edgeUVs=Vt,this.edgeIds=ct,this.edgeVAO=rr,this.edgeCount=0,this.pointPositions=tt,this.pointColors=Ue,this.pointUVs=Ae,this.pointSizes=rt,this.pointIds=je,this.pointVAO=St,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=Tt,this.contourVAO=dt,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=z,this._view=z,this._projection=z,this._resolution=[1,1]}var V=q.prototype;V.isOpaque=function(){return!this.hasAlpha},V.isTransparent=function(){return this.hasAlpha},V.pickSlots=1,V.setPickBase=function(ge){this.pickId=ge};function G(ge,ie){if(!ie||!ie.length)return 1;for(var Se=0;Se<ie.length;++Se){if(ie.length<2)return 1;if(ie[Se][0]===ge)return ie[Se][1];if(ie[Se][0]>ge&&Se>0){var ke=(ie[Se][0]-ge)/(ie[Se][0]-ie[Se-1][0]);return ie[Se][1]*(1-ke)+ke*ie[Se-1][1]}}return 1}function Z(ge,ie){for(var Se=p({colormap:ge,nshades:256,format:\"rgba\"}),ke=new Uint8Array(256*4),be=0;be<256;++be){for(var Be=Se[be],Le=0;Le<3;++Le)ke[4*be+Le]=Be[Le];ie?ke[4*be+3]=255*G(be/255,ie):ke[4*be+3]=255*Be[3]}return b(ke,[256,256,4],[4,0,1])}function j(ge){for(var ie=ge.length,Se=new Array(ie),ke=0;ke<ie;++ke)Se[ke]=ge[ke][2];return Se}V.highlight=function(ge){if(!ge||!this.contourEnable){this.contourCount=0;return}for(var ie=k(this.cells,this.intensity,ge.intensity),Se=ie.cells,ke=ie.vertexIds,be=ie.vertexWeights,Be=Se.length,Le=M.mallocFloat32(6*Be),me=0,Pe=0;Pe<Be;++Pe)for(var ce=Se[Pe],He=0;He<2;++He){var lt=ce[0];ce.length===2&&(lt=ce[He]);for(var mt=ke[lt][0],Ht=ke[lt][1],at=be[lt],ct=1-at,ar=this.positions[mt],Vt=this.positions[Ht],rr=0;rr<3;++rr)Le[me++]=at*ar[rr]+ct*Vt[rr]}this.contourCount=me/3|0,this.contourPositions.update(Le.subarray(0,me)),M.free(Le)},V.update=function(ge){ge=ge||{};var ie=this.gl;this.dirty=!0,\"contourEnable\"in ge&&(this.contourEnable=ge.contourEnable),\"contourColor\"in ge&&(this.contourColor=ge.contourColor),\"lineWidth\"in ge&&(this.lineWidth=ge.lineWidth),\"lightPosition\"in ge&&(this.lightPosition=ge.lightPosition),this.hasAlpha=!1,\"opacity\"in ge&&(this.opacity=ge.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in ge&&(this.opacityscale=ge.opacityscale,this.hasAlpha=!0),\"ambient\"in ge&&(this.ambientLight=ge.ambient),\"diffuse\"in ge&&(this.diffuseLight=ge.diffuse),\"specular\"in ge&&(this.specularLight=ge.specular),\"roughness\"in ge&&(this.roughness=ge.roughness),\"fresnel\"in ge&&(this.fresnel=ge.fresnel),ge.texture?(this.texture.dispose(),this.texture=h(ie,ge.texture)):ge.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=ie.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=ie.LINEAR,this.texture.setPixels(Z(ge.colormap,this.opacityscale)),this.texture.generateMipmap());var Se=ge.cells,ke=ge.positions;if(!(!ke||!Se)){var be=[],Be=[],Le=[],me=[],Pe=[],ce=[],He=[],lt=[],mt=[],Ht=[],at=[],ct=[],ar=[],Vt=[];this.cells=Se,this.positions=ke;var rr=ge.vertexNormals,tt=ge.cellNormals,je=ge.vertexNormalsEpsilon===void 0?s:ge.vertexNormalsEpsilon,Ue=ge.faceNormalsEpsilon===void 0?l:ge.faceNormalsEpsilon;ge.useFacetNormals&&!tt&&(tt=d.faceNormals(Se,ke,Ue)),!tt&&!rr&&(rr=d.vertexNormals(Se,ke,je));var Ae=ge.vertexColors,rt=ge.cellColors,St=ge.meshColor||[1,1,1,1],Tt=ge.vertexUVs,dt=ge.vertexIntensity,Et=ge.cellUVs,pt=ge.cellIntensity,jt=1/0,or=-1/0;if(!Tt&&!Et)if(dt)if(ge.vertexIntensityBounds)jt=+ge.vertexIntensityBounds[0],or=+ge.vertexIntensityBounds[1];else for(var mr=0;mr<dt.length;++mr){var Ar=dt[mr];jt=Math.min(jt,Ar),or=Math.max(or,Ar)}else if(pt)if(ge.cellIntensityBounds)jt=+ge.cellIntensityBounds[0],or=+ge.cellIntensityBounds[1];else for(var mr=0;mr<pt.length;++mr){var Ar=pt[mr];jt=Math.min(jt,Ar),or=Math.max(or,Ar)}else for(var mr=0;mr<ke.length;++mr){var Ar=ke[mr][2];jt=Math.min(jt,Ar),or=Math.max(or,Ar)}dt?this.intensity=dt:pt?this.intensity=pt:this.intensity=j(ke),this.pickVertex=!(pt||rt);var ei=ge.pointSizes,qr=ge.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var mr=0;mr<ke.length;++mr)for(var jr=ke[mr],gt=0;gt<3;++gt)isNaN(jr[gt])||!isFinite(jr[gt])||(this.bounds[0][gt]=Math.min(this.bounds[0][gt],jr[gt]),this.bounds[1][gt]=Math.max(this.bounds[1][gt],jr[gt]));var Ge=0,Je=0,We=0;e:for(var mr=0;mr<Se.length;++mr){var et=Se[mr];switch(et.length){case 1:for(var xt=et[0],jr=ke[xt],gt=0;gt<3;++gt)if(isNaN(jr[gt])||!isFinite(jr[gt]))continue e;Ht.push(jr[0],jr[1],jr[2]);var At;Ae?At=Ae[xt]:rt?At=rt[mr]:At=St,this.opacityscale&&dt?Be.push(At[0],At[1],At[2],this.opacity*G((dt[xt]-jt)/(or-jt),this.opacityscale)):At.length===3?at.push(At[0],At[1],At[2],this.opacity):(at.push(At[0],At[1],At[2],At[3]*this.opacity),At[3]<1&&(this.hasAlpha=!0));var Kt;Tt?Kt=Tt[xt]:dt?Kt=[(dt[xt]-jt)/(or-jt),0]:Et?Kt=Et[mr]:pt?Kt=[(pt[mr]-jt)/(or-jt),0]:Kt=[(jr[2]-jt)/(or-jt),0],ct.push(Kt[0],Kt[1]),ei?ar.push(ei[xt]):ar.push(qr),Vt.push(mr),We+=1;break;case 2:for(var gt=0;gt<2;++gt)for(var xt=et[gt],jr=ke[xt],Qt=0;Qt<3;++Qt)if(isNaN(jr[Qt])||!isFinite(jr[Qt]))continue e;for(var gt=0;gt<2;++gt){var xt=et[gt],jr=ke[xt];ce.push(jr[0],jr[1],jr[2]);var At;Ae?At=Ae[xt]:rt?At=rt[mr]:At=St,this.opacityscale&&dt?Be.push(At[0],At[1],At[2],this.opacity*G((dt[xt]-jt)/(or-jt),this.opacityscale)):At.length===3?He.push(At[0],At[1],At[2],this.opacity):(He.push(At[0],At[1],At[2],At[3]*this.opacity),At[3]<1&&(this.hasAlpha=!0));var Kt;Tt?Kt=Tt[xt]:dt?Kt=[(dt[xt]-jt)/(or-jt),0]:Et?Kt=Et[mr]:pt?Kt=[(pt[mr]-jt)/(or-jt),0]:Kt=[(jr[2]-jt)/(or-jt),0],lt.push(Kt[0],Kt[1]),mt.push(mr)}Je+=1;break;case 3:for(var gt=0;gt<3;++gt)for(var xt=et[gt],jr=ke[xt],Qt=0;Qt<3;++Qt)if(isNaN(jr[Qt])||!isFinite(jr[Qt]))continue e;for(var gt=0;gt<3;++gt){var xt=et[2-gt],jr=ke[xt];be.push(jr[0],jr[1],jr[2]);var At;Ae?At=Ae[xt]:rt?At=rt[mr]:At=St,At?this.opacityscale&&dt?Be.push(At[0],At[1],At[2],this.opacity*G((dt[xt]-jt)/(or-jt),this.opacityscale)):At.length===3?Be.push(At[0],At[1],At[2],this.opacity):(Be.push(At[0],At[1],At[2],At[3]*this.opacity),At[3]<1&&(this.hasAlpha=!0)):Be.push(.5,.5,.5,1);var Kt;Tt?Kt=Tt[xt]:dt?Kt=[(dt[xt]-jt)/(or-jt),0]:Et?Kt=Et[mr]:pt?Kt=[(pt[mr]-jt)/(or-jt),0]:Kt=[(jr[2]-jt)/(or-jt),0],me.push(Kt[0],Kt[1]);var Mr;rr?Mr=rr[xt]:Mr=tt[mr],Le.push(Mr[0],Mr[1],Mr[2]),Pe.push(mr)}Ge+=1;break;default:break}}this.pointCount=We,this.edgeCount=Je,this.triangleCount=Ge,this.pointPositions.update(Ht),this.pointColors.update(at),this.pointUVs.update(ct),this.pointSizes.update(ar),this.pointIds.update(new Uint32Array(Vt)),this.edgePositions.update(ce),this.edgeColors.update(He),this.edgeUVs.update(lt),this.edgeIds.update(new Uint32Array(mt)),this.trianglePositions.update(be),this.triangleColors.update(Be),this.triangleUVs.update(me),this.triangleNormals.update(Le),this.triangleIds.update(new Uint32Array(Pe))}},V.drawTransparent=V.draw=function(ge){ge=ge||{};for(var ie=this.gl,Se=ge.model||z,ke=ge.view||z,be=ge.projection||z,Be=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],Le=0;Le<3;++Le)Be[0][Le]=Math.max(Be[0][Le],this.clipBounds[0][Le]),Be[1][Le]=Math.min(Be[1][Le],this.clipBounds[1][Le]);var me={model:Se,view:ke,projection:be,inverseModel:z.slice(),clipBounds:Be,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};me.inverseModel=m(me.inverseModel,me.model),ie.disable(ie.CULL_FACE),this.texture.bind(0);var Pe=new Array(16);v(Pe,me.view,me.model),v(Pe,me.projection,Pe),m(Pe,Pe);for(var Le=0;Le<3;++Le)me.eyePosition[Le]=Pe[12+Le]/Pe[15];for(var ce=Pe[15],Le=0;Le<3;++Le)ce+=this.lightPosition[Le]*Pe[4*Le+3];for(var Le=0;Le<3;++Le){for(var He=Pe[12+Le],lt=0;lt<3;++lt)He+=Pe[4*lt+Le]*this.lightPosition[lt];me.lightPosition[Le]=He/ce}if(this.triangleCount>0){var mt=this.triShader;mt.bind(),mt.uniforms=me,this.triangleVAO.bind(),ie.drawArrays(ie.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var mt=this.lineShader;mt.bind(),mt.uniforms=me,this.edgeVAO.bind(),ie.lineWidth(this.lineWidth*this.pixelRatio),ie.drawArrays(ie.LINES,0,this.edgeCount*2),this.edgeVAO.unbind()}if(this.pointCount>0){var mt=this.pointShader;mt.bind(),mt.uniforms=me,this.pointVAO.bind(),ie.drawArrays(ie.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var mt=this.contourShader;mt.bind(),mt.uniforms=me,this.contourVAO.bind(),ie.drawArrays(ie.LINES,0,this.contourCount),this.contourVAO.unbind()}},V.drawPick=function(ge){ge=ge||{};for(var ie=this.gl,Se=ge.model||z,ke=ge.view||z,be=ge.projection||z,Be=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],Le=0;Le<3;++Le)Be[0][Le]=Math.max(Be[0][Le],this.clipBounds[0][Le]),Be[1][Le]=Math.min(Be[1][Le],this.clipBounds[1][Le]);this._model=[].slice.call(Se),this._view=[].slice.call(ke),this._projection=[].slice.call(be),this._resolution=[ie.drawingBufferWidth,ie.drawingBufferHeight];var me={model:Se,view:ke,projection:be,clipBounds:Be,pickId:this.pickId/255},Pe=this.pickShader;if(Pe.bind(),Pe.uniforms=me,this.triangleCount>0&&(this.triangleVAO.bind(),ie.drawArrays(ie.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),ie.lineWidth(this.lineWidth*this.pixelRatio),ie.drawArrays(ie.LINES,0,this.edgeCount*2),this.edgeVAO.unbind()),this.pointCount>0){var Pe=this.pointPickShader;Pe.bind(),Pe.uniforms=me,this.pointVAO.bind(),ie.drawArrays(ie.POINTS,0,this.pointCount),this.pointVAO.unbind()}},V.pick=function(ge){if(!ge||ge.id!==this.pickId)return null;for(var ie=ge.value[0]+256*ge.value[1]+65536*ge.value[2],Se=this.cells[ie],ke=this.positions,be=new Array(Se.length),Be=0;Be<Se.length;++Be)be[Be]=ke[Se[Be]];var Le=ge.coord[0],me=ge.coord[1];if(!this.pickVertex){var Pe=this.positions[Se[0]],ce=this.positions[Se[1]],He=this.positions[Se[2]],lt=[(Pe[0]+ce[0]+He[0])/3,(Pe[1]+ce[1]+He[1])/3,(Pe[2]+ce[2]+He[2])/3];return{_cellCenter:!0,position:[Le,me],index:ie,cell:Se,cellId:ie,intensity:this.intensity[ie],dataCoordinate:lt}}var mt=L(be,[Le*this.pixelRatio,this._resolution[1]-me*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!mt)return null;for(var Ht=mt[2],at=0,Be=0;Be<Se.length;++Be)at+=Ht[Be]*this.intensity[Se[Be]];return{position:mt[1],index:Se[mt[0]],cell:Se,cellId:ie,intensity:at,dataCoordinate:this.positions[Se[mt[0]]]}},V.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()};function N(ge){var ie=u(ge,x.vertex,x.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie.attributes.normal.location=4,ie}function H(ge){var ie=u(ge,C.vertex,C.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie}function re(ge){var ie=u(ge,S.vertex,S.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie.attributes.pointSize.location=4,ie}function ae(ge){var ie=u(ge,g.vertex,g.fragment);return ie.attributes.position.location=0,ie.attributes.id.location=1,ie}function _e(ge){var ie=u(ge,P.vertex,P.fragment);return ie.attributes.position.location=0,ie.attributes.id.location=1,ie.attributes.pointSize.location=4,ie}function Ee(ge){var ie=u(ge,E.vertex,E.fragment);return ie.attributes.position.location=0,ie}function Ce(ge,ie){arguments.length===1&&(ie=ge,ge=ie.gl);var Se=ge.getExtension(\"OES_standard_derivatives\")||ge.getExtension(\"MOZ_OES_standard_derivatives\")||ge.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!Se)throw new Error(\"derivatives not supported\");var ke=N(ge),be=H(ge),Be=re(ge),Le=ae(ge),me=_e(ge),Pe=Ee(ge),ce=h(ge,b(new Uint8Array([255,255,255,255]),[1,1,4]));ce.generateMipmap(),ce.minFilter=ge.LINEAR_MIPMAP_LINEAR,ce.magFilter=ge.LINEAR;var He=c(ge),lt=c(ge),mt=c(ge),Ht=c(ge),at=c(ge),ct=f(ge,[{buffer:He,type:ge.FLOAT,size:3},{buffer:at,type:ge.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:lt,type:ge.FLOAT,size:4},{buffer:mt,type:ge.FLOAT,size:2},{buffer:Ht,type:ge.FLOAT,size:3}]),ar=c(ge),Vt=c(ge),rr=c(ge),tt=c(ge),je=f(ge,[{buffer:ar,type:ge.FLOAT,size:3},{buffer:tt,type:ge.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Vt,type:ge.FLOAT,size:4},{buffer:rr,type:ge.FLOAT,size:2}]),Ue=c(ge),Ae=c(ge),rt=c(ge),St=c(ge),Tt=c(ge),dt=f(ge,[{buffer:Ue,type:ge.FLOAT,size:3},{buffer:Tt,type:ge.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Ae,type:ge.FLOAT,size:4},{buffer:rt,type:ge.FLOAT,size:2},{buffer:St,type:ge.FLOAT,size:1}]),Et=c(ge),pt=f(ge,[{buffer:Et,type:ge.FLOAT,size:3}]),jt=new q(ge,ce,ke,be,Be,Le,me,Pe,He,at,lt,mt,Ht,ct,ar,tt,Vt,rr,je,Ue,Tt,Ae,rt,St,dt,Et,pt);return jt.update(ie),jt}i.exports=Ce}),4437:(function(i,a,o){\"use strict\";i.exports=d;var s=o(3025),l=o(6296),u=o(351),c=o(8512),f=o(24),h=o(7520);function d(v,m){v=v||document.body,m=m||{};var b=[.01,1/0];\"distanceLimits\"in m&&(b[0]=m.distanceLimits[0],b[1]=m.distanceLimits[1]),\"zoomMin\"in m&&(b[0]=m.zoomMin),\"zoomMax\"in m&&(b[1]=m.zoomMax);var p=l({center:m.center||[0,0,0],up:m.up||[0,1,0],eye:m.eye||[0,0,10],mode:m.mode||\"orbit\",distanceLimits:b}),k=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],M=0,T=v.clientWidth,L=v.clientHeight,x={keyBindingMode:\"rotate\",enableWheel:!0,view:p,element:v,delay:m.delay||16,rotateSpeed:m.rotateSpeed||1,zoomSpeed:m.zoomSpeed||1,translateSpeed:m.translateSpeed||1,flipX:!!m.flipX,flipY:!!m.flipY,modes:p.modes,_ortho:m._ortho||m.projection&&m.projection.type===\"orthographic\"||!1,tick:function(){var C=s(),S=this.delay,g=C-2*S;p.idle(C-S),p.recalcMatrix(g),p.flush(C-(100+S*2));for(var P=!0,E=p.computedMatrix,z=0;z<16;++z)P=P&&k[z]===E[z],k[z]=E[z];var q=v.clientWidth===T&&v.clientHeight===L;return T=v.clientWidth,L=v.clientHeight,P?!q:(M=Math.exp(p.computedRadius[0]),!0)},lookAt:function(C,S,g){p.lookAt(p.lastT(),C,S,g)},rotate:function(C,S,g){p.rotate(p.lastT(),C,S,g)},pan:function(C,S,g){p.pan(p.lastT(),C,S,g)},translate:function(C,S,g){p.translate(p.lastT(),C,S,g)}};return Object.defineProperties(x,{matrix:{get:function(){return p.computedMatrix},set:function(C){return p.setMatrix(p.lastT(),C),p.computedMatrix},enumerable:!0},mode:{get:function(){return p.getMode()},set:function(C){var S=p.computedUp.slice(),g=p.computedEye.slice(),P=p.computedCenter.slice();if(p.setMode(C),C===\"turntable\"){var E=s();p._active.lookAt(E,g,P,S),p._active.lookAt(E+500,g,P,[0,0,1]),p._active.flush(E)}return p.getMode()},enumerable:!0},center:{get:function(){return p.computedCenter},set:function(C){return p.lookAt(p.lastT(),null,C),p.computedCenter},enumerable:!0},eye:{get:function(){return p.computedEye},set:function(C){return p.lookAt(p.lastT(),C),p.computedEye},enumerable:!0},up:{get:function(){return p.computedUp},set:function(C){return p.lookAt(p.lastT(),null,null,C),p.computedUp},enumerable:!0},distance:{get:function(){return M},set:function(C){return p.setDistance(p.lastT(),C),C},enumerable:!0},distanceLimits:{get:function(){return p.getDistanceLimits(b)},set:function(C){return p.setDistanceLimits(C),C},enumerable:!0}}),v.addEventListener(\"contextmenu\",function(C){return C.preventDefault(),!1}),x._lastX=-1,x._lastY=-1,x._lastMods={shift:!1,control:!1,alt:!1,meta:!1},x.enableMouseListeners=function(){x.mouseListener=u(v,C),v.addEventListener(\"touchstart\",function(S){var g=f(S.changedTouches[0],v);C(0,g[0],g[1],x._lastMods),C(1,g[0],g[1],x._lastMods)},h?{passive:!0}:!1),v.addEventListener(\"touchmove\",function(S){var g=f(S.changedTouches[0],v);C(1,g[0],g[1],x._lastMods),S.preventDefault()},h?{passive:!1}:!1),v.addEventListener(\"touchend\",function(S){C(0,x._lastX,x._lastY,x._lastMods)},h?{passive:!0}:!1);function C(S,g,P,E){var z=x.keyBindingMode;if(z!==!1){var q=z===\"rotate\",V=z===\"pan\",G=z===\"zoom\",Z=!!E.control,j=!!E.alt,N=!!E.shift,H=!!(S&1),re=!!(S&2),ae=!!(S&4),_e=1/v.clientHeight,Ee=_e*(g-x._lastX),Ce=_e*(P-x._lastY),ge=x.flipX?1:-1,ie=x.flipY?1:-1,Se=Math.PI*x.rotateSpeed,ke=s();if(x._lastX!==-1&&x._lastY!==-1&&((q&&H&&!Z&&!j&&!N||H&&!Z&&!j&&N)&&p.rotate(ke,ge*Se*Ee,-ie*Se*Ce,0),(V&&H&&!Z&&!j&&!N||re||H&&Z&&!j&&!N)&&p.pan(ke,-x.translateSpeed*Ee*M,x.translateSpeed*Ce*M,0),G&&H&&!Z&&!j&&!N||ae||H&&!Z&&j&&!N)){var be=-x.zoomSpeed*Ce/window.innerHeight*(ke-p.lastT())*100;p.pan(ke,0,0,M*(Math.exp(be)-1))}return x._lastX=g,x._lastY=P,x._lastMods=E,!0}}x.wheelListener=c(v,function(S,g){if(x.keyBindingMode!==!1&&x.enableWheel){var P=x.flipX?1:-1,E=x.flipY?1:-1,z=s();if(Math.abs(S)>Math.abs(g))p.rotate(z,0,0,-S*P*Math.PI*x.rotateSpeed/window.innerWidth);else if(!x._ortho){var q=-x.zoomSpeed*E*g/window.innerHeight*(z-p.lastT())/20;p.pan(z,0,0,M*(Math.exp(q)-1))}}},!0)},x.enableMouseListeners(),x}}),799:(function(i,a,o){var s=o(3236),l=o(9405),u=s([`precision mediump float;\n",
+       "#define GLSLIFY 1\n",
+       "attribute vec2 position;\n",
+       "varying vec2 uv;\n",
+       "void main() {\n",
+       "  uv = position;\n",
+       "  gl_Position = vec4(position, 0, 1);\n",
+       "}`]),c=s([`precision mediump float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "uniform sampler2D accumBuffer;\n",
+       "varying vec2 uv;\n",
+       "\n",
+       "void main() {\n",
+       "  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n",
+       "  gl_FragColor = min(vec4(1,1,1,1), accum);\n",
+       "}`]);i.exports=function(f){return l(f,u,c,null,[{name:\"position\",type:\"vec2\"}])}}),4100:(function(i,a,o){\"use strict\";var s=o(4437),l=o(3837),u=o(5445),c=o(4449),f=o(3589),h=o(2260),d=o(7169),v=o(351),m=o(4772),b=o(4040),p=o(799),k=o(9216)({tablet:!0,featureDetect:!0});i.exports={createScene:C,createCamera:s};function M(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function T(g,P){var E=null;try{E=g.getContext(\"webgl\",P),E||(E=g.getContext(\"experimental-webgl\",P))}catch(z){return null}return E}function L(g){var P=Math.round(Math.log(Math.abs(g))/Math.log(10));if(P<0){var E=Math.round(Math.pow(10,-P));return Math.ceil(g*E)/E}else if(P>0){var E=Math.round(Math.pow(10,P));return Math.ceil(g/E)*E}return Math.ceil(g)}function x(g){return typeof g==\"boolean\"?g:!0}function C(g){g=g||{},g.camera=g.camera||{};var P=g.canvas;if(!P)if(P=document.createElement(\"canvas\"),g.container){var E=g.container;E.appendChild(P)}else document.body.appendChild(P);var z=g.gl;if(z||(g.glOptions&&(k=!!g.glOptions.preserveDrawingBuffer),z=T(P,g.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:k})),!z)throw new Error(\"webgl not supported\");var q=g.bounds||[[-10,-10,-10],[10,10,10]],V=new M,G=h(z,z.drawingBufferWidth,z.drawingBufferHeight,{preferFloat:!k}),Z=p(z),j=g.cameraObject&&g.cameraObject._ortho===!0||g.camera.projection&&g.camera.projection.type===\"orthographic\"||!1,N={eye:g.camera.eye||[2,0,0],center:g.camera.center||[0,0,0],up:g.camera.up||[0,1,0],zoomMin:g.camera.zoomMax||.1,zoomMax:g.camera.zoomMin||100,mode:g.camera.mode||\"turntable\",_ortho:j},H=g.axes||{},re=l(z,H);re.enable=!H.disable;var ae=g.spikes||{},_e=c(z,ae),Ee=[],Ce=[],ge=[],ie=[],Se=!0,Le=!0,ke=new Array(16),be=new Array(16),Be={view:null,projection:ke,model:be,_ortho:!1},Le=!0,me=[z.drawingBufferWidth,z.drawingBufferHeight],Pe=g.cameraObject||s(P,N),ce={gl:z,contextLost:!1,pixelRatio:g.pixelRatio||1,canvas:P,selection:V,camera:Pe,axes:re,axesPixels:null,spikes:_e,bounds:q,objects:Ee,shape:me,aspect:g.aspectRatio||[1,1,1],pickRadius:g.pickRadius||10,zNear:g.zNear||.01,zFar:g.zFar||1e3,fovy:g.fovy||Math.PI/4,clearColor:g.clearColor||[0,0,0,0],autoResize:x(g.autoResize),autoBounds:x(g.autoBounds),autoScale:!!g.autoScale,autoCenter:x(g.autoCenter),clipToBounds:x(g.clipToBounds),snapToData:!!g.snapToData,onselect:g.onselect||null,onrender:g.onrender||null,onclick:g.onclick||null,cameraParams:Be,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(tt){this.aspect[0]=tt.x,this.aspect[1]=tt.y,this.aspect[2]=tt.z,Le=!0},setBounds:function(tt,je){this.bounds[0][tt]=je.min,this.bounds[1][tt]=je.max},setClearColor:function(tt){this.clearColor=tt},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},He=[z.drawingBufferWidth/ce.pixelRatio|0,z.drawingBufferHeight/ce.pixelRatio|0];function lt(){if(!ce._stopped&&ce.autoResize){var tt=P.parentNode,je=1,Ue=1;tt&&tt!==document.body?(je=tt.clientWidth,Ue=tt.clientHeight):(je=window.innerWidth,Ue=window.innerHeight);var Ae=Math.ceil(je*ce.pixelRatio)|0,rt=Math.ceil(Ue*ce.pixelRatio)|0;if(Ae!==P.width||rt!==P.height){P.width=Ae,P.height=rt;var St=P.style;St.position=St.position||\"absolute\",St.left=\"0px\",St.top=\"0px\",St.width=je+\"px\",St.height=Ue+\"px\",Se=!0}}}ce.autoResize&&lt(),window.addEventListener(\"resize\",lt);function mt(){for(var tt=Ee.length,je=ie.length,Ue=0;Ue<je;++Ue)ge[Ue]=0;e:for(var Ue=0;Ue<tt;++Ue){var Ae=Ee[Ue],rt=Ae.pickSlots;if(!rt){Ce[Ue]=-1;continue}for(var St=0;St<je;++St)if(ge[St]+rt<255){Ce[Ue]=St,Ae.setPickBase(ge[St]+1),ge[St]+=rt;continue e}var Tt=f(z,me);Ce[Ue]=je,ie.push(Tt),ge.push(rt),Ae.setPickBase(1),je+=1}for(;je>0&&ge[je-1]===0;)ge.pop(),ie.pop().dispose()}ce.update=function(tt){ce._stopped||(tt=tt||{},Se=!0,Le=!0)},ce.add=function(tt){ce._stopped||(tt.axes=re,Ee.push(tt),Ce.push(-1),Se=!0,Le=!0,mt())},ce.remove=function(tt){if(!ce._stopped){var je=Ee.indexOf(tt);je<0||(Ee.splice(je,1),Ce.pop(),Se=!0,Le=!0,mt())}},ce.dispose=function(){if(!ce._stopped&&(ce._stopped=!0,window.removeEventListener(\"resize\",lt),P.removeEventListener(\"webglcontextlost\",Ht),ce.mouseListener.enabled=!1,!ce.contextLost)){re.dispose(),_e.dispose();for(var tt=0;tt<Ee.length;++tt)Ee[tt].dispose();G.dispose();for(var tt=0;tt<ie.length;++tt)ie[tt].dispose();Z.dispose(),z=null,re=null,_e=null,Ee=[]}},ce._mouseRotating=!1,ce._prevButtons=0,ce.enableMouseListeners=function(){ce.mouseListener=v(P,function(tt,je,Ue){if(!ce._stopped){var Ae=ie.length,rt=Ee.length,St=V.object;V.distance=1/0,V.mouse[0]=je,V.mouse[1]=Ue,V.object=null,V.screen=null,V.dataCoordinate=V.dataPosition=null;var Tt=!1;if(tt&&ce._prevButtons)ce._mouseRotating=!0;else{ce._mouseRotating&&(Le=!0),ce._mouseRotating=!1;for(var dt=0;dt<Ae;++dt){var Et=ie[dt].query(je,He[1]-Ue-1,ce.pickRadius);if(Et){if(Et.distance>V.distance)continue;for(var pt=0;pt<rt;++pt){var jt=Ee[pt];if(Ce[pt]===dt){var or=jt.pick(Et);or&&(V.buttons=tt,V.screen=Et.coord,V.distance=Et.distance,V.object=jt,V.index=or.distance,V.dataPosition=or.position,V.dataCoordinate=or.dataCoordinate,V.data=or,Tt=!0)}}}}}St&&St!==V.object&&(St.highlight&&St.highlight(null),Se=!0),V.object&&(V.object.highlight&&V.object.highlight(V.data),Se=!0),Tt=Tt||V.object!==St,Tt&&ce.onselect&&ce.onselect(V),tt&1&&!(ce._prevButtons&1)&&ce.onclick&&ce.onclick(V),ce._prevButtons=tt}})};function Ht(){if(ce.contextLost)return!0;z.isContextLost()&&(ce.contextLost=!0,ce.mouseListener.enabled=!1,ce.selection.object=null,ce.oncontextloss&&ce.oncontextloss())}P.addEventListener(\"webglcontextlost\",Ht);function at(){if(!Ht()){z.colorMask(!0,!0,!0,!0),z.depthMask(!0),z.disable(z.BLEND),z.enable(z.DEPTH_TEST),z.depthFunc(z.LEQUAL);for(var tt=Ee.length,je=ie.length,Ue=0;Ue<je;++Ue){var Ae=ie[Ue];Ae.shape=He,Ae.begin();for(var rt=0;rt<tt;++rt)if(Ce[rt]===Ue){var St=Ee[rt];St.drawPick&&(St.pixelRatio=1,St.drawPick(Be))}Ae.end()}}}var ct=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],ar=[ct[0].slice(),ct[1].slice()];function Vt(){if(!Ht()){lt();var tt=ce.camera.tick();Be.view=ce.camera.matrix,Se=Se||tt,Le=Le||tt,re.pixelRatio=ce.pixelRatio,_e.pixelRatio=ce.pixelRatio;var je=Ee.length,Ue=ct[0],Ae=ct[1];Ue[0]=Ue[1]=Ue[2]=1/0,Ae[0]=Ae[1]=Ae[2]=-1/0;for(var rt=0;rt<je;++rt){var St=Ee[rt];St.pixelRatio=ce.pixelRatio,St.axes=ce.axes,Se=Se||!!St.dirty,Le=Le||!!St.dirty;var Tt=St.bounds;if(Tt)for(var dt=Tt[0],Et=Tt[1],pt=0;pt<3;++pt)Ue[pt]=Math.min(Ue[pt],dt[pt]),Ae[pt]=Math.max(Ae[pt],Et[pt])}var jt=ce.bounds;if(ce.autoBounds)for(var pt=0;pt<3;++pt){if(Ae[pt]<Ue[pt])Ue[pt]=-1,Ae[pt]=1;else{Ue[pt]===Ae[pt]&&(Ue[pt]-=1,Ae[pt]+=1);var or=.05*(Ae[pt]-Ue[pt]);Ue[pt]=Ue[pt]-or,Ae[pt]=Ae[pt]+or}jt[0][pt]=Ue[pt],jt[1][pt]=Ae[pt]}for(var mr=!1,pt=0;pt<3;++pt)mr=mr||ar[0][pt]!==jt[0][pt]||ar[1][pt]!==jt[1][pt],ar[0][pt]=jt[0][pt],ar[1][pt]=jt[1][pt];if(Le=Le||mr,Se=Se||mr,!!Se){if(mr){for(var Ar=[0,0,0],rt=0;rt<3;++rt)Ar[rt]=L((jt[1][rt]-jt[0][rt])/10);re.autoTicks?re.update({bounds:jt,tickSpacing:Ar}):re.update({bounds:jt})}var ei=z.drawingBufferWidth,qr=z.drawingBufferHeight;me[0]=ei,me[1]=qr,He[0]=Math.max(ei/ce.pixelRatio,1)|0,He[1]=Math.max(qr/ce.pixelRatio,1)|0,S(ce,j);for(var rt=0;rt<je;++rt){var St=Ee[rt];St.axesBounds=jt,ce.clipToBounds&&(St.clipBounds=jt)}V.object&&(ce.snapToData?_e.position=V.dataCoordinate:_e.position=V.dataPosition,_e.bounds=jt),Le&&(Le=!1,at()),ce.axesPixels=u(ce.axes,Be,ei,qr),ce.onrender&&ce.onrender(),z.bindFramebuffer(z.FRAMEBUFFER,null),z.viewport(0,0,ei,qr),ce.clearRGBA(),z.depthMask(!0),z.colorMask(!0,!0,!0,!0),z.enable(z.DEPTH_TEST),z.depthFunc(z.LEQUAL),z.disable(z.BLEND),z.disable(z.CULL_FACE);var jr=!1;re.enable&&(jr=jr||re.isTransparent(),re.draw(Be)),_e.axes=re,V.object&&_e.draw(Be),z.disable(z.CULL_FACE);for(var rt=0;rt<je;++rt){var St=Ee[rt];St.axes=re,St.pixelRatio=ce.pixelRatio,St.isOpaque&&St.isOpaque()&&St.draw(Be),St.isTransparent&&St.isTransparent()&&(jr=!0)}if(jr){G.shape=me,G.bind(),z.clear(z.DEPTH_BUFFER_BIT),z.colorMask(!1,!1,!1,!1),z.depthMask(!0),z.depthFunc(z.LESS),re.enable&&re.isTransparent()&&re.drawTransparent(Be);for(var rt=0;rt<je;++rt){var St=Ee[rt];St.isOpaque&&St.isOpaque()&&St.draw(Be)}z.enable(z.BLEND),z.blendEquation(z.FUNC_ADD),z.blendFunc(z.ONE,z.ONE_MINUS_SRC_ALPHA),z.colorMask(!0,!0,!0,!0),z.depthMask(!1),z.clearColor(0,0,0,0),z.clear(z.COLOR_BUFFER_BIT),re.isTransparent()&&re.drawTransparent(Be);for(var rt=0;rt<je;++rt){var St=Ee[rt];St.isTransparent&&St.isTransparent()&&St.drawTransparent(Be)}z.bindFramebuffer(z.FRAMEBUFFER,null),z.blendFunc(z.ONE,z.ONE_MINUS_SRC_ALPHA),z.disable(z.DEPTH_TEST),Z.bind(),G.color[0].bind(0),Z.uniforms.accumBuffer=0,d(z),z.disable(z.BLEND)}Se=!1;for(var rt=0;rt<je;++rt)Ee[rt].dirty=!1}}}function rr(){ce._stopped||ce.contextLost||(Vt(),requestAnimationFrame(rr))}return ce.enableMouseListeners(),rr(),ce.redraw=function(){ce._stopped||(Se=!0,Vt())},ce}function S(g,P){var E=g.bounds,z=g.cameraParams,q=z.projection,V=z.model,G=g.gl.drawingBufferWidth,Z=g.gl.drawingBufferHeight,j=g.zNear,N=g.zFar,H=g.fovy,re=G/Z;P?(b(q,-re,re,-1,1,j,N),z._ortho=!0):(m(q,H,re,j,N),z._ortho=!1);for(var ae=0;ae<16;++ae)V[ae]=0;V[15]=1;for(var _e=0,ae=0;ae<3;++ae)_e=Math.max(_e,E[1][ae]-E[0][ae]);for(var ae=0;ae<3;++ae)g.autoScale?V[5*ae]=g.aspect[ae]/(E[1][ae]-E[0][ae]):V[5*ae]=1/_e,g.autoCenter&&(V[12+ae]=-V[5*ae]*.5*(E[0][ae]+E[1][ae]))}}),783:(function(i){i.exports=a;function a(o,s,l,u){var c=s[0],f=s[1],h=s[2],d=s[3],v=l[0],m=l[1],b=l[2],p=l[3],k,M,T,L,x;return M=c*v+f*m+h*b+d*p,M<0&&(M=-M,v=-v,m=-m,b=-b,p=-p),1-M>1e-6?(k=Math.acos(M),T=Math.sin(k),L=Math.sin((1-u)*k)/T,x=Math.sin(u*k)/T):(L=1-u,x=u),o[0]=L*c+x*v,o[1]=L*f+x*m,o[2]=L*h+x*b,o[3]=L*d+x*p,o}}),5964:(function(i){\"use strict\";i.exports=function(a){return!a&&a!==0?\"\":a.toString()}}),9366:(function(i,a,o){\"use strict\";var s=o(4359);i.exports=u;var l={};function u(c,f,h){var d=[f.style,f.weight,f.variant,f.family].join(\"_\"),v=l[d];if(v||(v=l[d]={}),c in v)return v[c];var m={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:f.family,fontStyle:f.style,fontWeight:f.weight,fontVariant:f.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0}};m.triangles=!0;var b=s(c,m);m.triangles=!1;var p=s(c,m),k,M;if(h&&h!==1){for(k=0;k<b.positions.length;++k)for(M=0;M<b.positions[k].length;++M)b.positions[k][M]/=h;for(k=0;k<p.positions.length;++k)for(M=0;M<p.positions[k].length;++M)p.positions[k][M]/=h}var T=[[1/0,1/0],[-1/0,-1/0]],L=p.positions.length;for(k=0;k<L;++k){var x=p.positions[k];for(M=0;M<2;++M)T[0][M]=Math.min(T[0][M],x[M]),T[1][M]=Math.max(T[1][M],x[M])}return v[c]=[b,p,T]}}),1283:(function(i,a,o){var s=o(9405),l=o(3236),u=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "attribute vec4 color;\n",
+       "attribute vec2 glyph;\n",
+       "attribute vec4 id;\n",
+       "\n",
+       "uniform vec4 highlightId;\n",
+       "uniform float highlightScale;\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec3 clipBounds[2];\n",
+       "\n",
+       "varying vec4 interpColor;\n",
+       "varying vec4 pickId;\n",
+       "varying vec3 dataCoordinate;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n",
+       "\n",
+       "    gl_Position = vec4(0,0,0,0);\n",
+       "  } else {\n",
+       "    float scale = 1.0;\n",
+       "    if(distance(highlightId, id) < 0.0001) {\n",
+       "      scale = highlightScale;\n",
+       "    }\n",
+       "\n",
+       "    vec4 worldPosition = model * vec4(position, 1);\n",
+       "    vec4 viewPosition = view * worldPosition;\n",
+       "    viewPosition = viewPosition / viewPosition.w;\n",
+       "    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n",
+       "\n",
+       "    gl_Position = clipPosition;\n",
+       "    interpColor = color;\n",
+       "    pickId = id;\n",
+       "    dataCoordinate = position;\n",
+       "  }\n",
+       "}`]),c=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "attribute vec4 color;\n",
+       "attribute vec2 glyph;\n",
+       "attribute vec4 id;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec2 screenSize;\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform float highlightScale, pixelRatio;\n",
+       "uniform vec4 highlightId;\n",
+       "\n",
+       "varying vec4 interpColor;\n",
+       "varying vec4 pickId;\n",
+       "varying vec3 dataCoordinate;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n",
+       "\n",
+       "    gl_Position = vec4(0,0,0,0);\n",
+       "  } else {\n",
+       "    float scale = pixelRatio;\n",
+       "    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n",
+       "      scale *= highlightScale;\n",
+       "    }\n",
+       "\n",
+       "    vec4 worldPosition = model * vec4(position, 1.0);\n",
+       "    vec4 viewPosition = view * worldPosition;\n",
+       "    vec4 clipPosition = projection * viewPosition;\n",
+       "    clipPosition /= clipPosition.w;\n",
+       "\n",
+       "    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n",
+       "    interpColor = color;\n",
+       "    pickId = id;\n",
+       "    dataCoordinate = position;\n",
+       "  }\n",
+       "}`]),f=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "attribute vec3 position;\n",
+       "attribute vec4 color;\n",
+       "attribute vec2 glyph;\n",
+       "attribute vec4 id;\n",
+       "\n",
+       "uniform float highlightScale;\n",
+       "uniform vec4 highlightId;\n",
+       "uniform vec3 axes[2];\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec2 screenSize;\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform float scale, pixelRatio;\n",
+       "\n",
+       "varying vec4 interpColor;\n",
+       "varying vec4 pickId;\n",
+       "varying vec3 dataCoordinate;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n",
+       "\n",
+       "    gl_Position = vec4(0,0,0,0);\n",
+       "  } else {\n",
+       "    float lscale = pixelRatio * scale;\n",
+       "    if(distance(highlightId, id) < 0.0001) {\n",
+       "      lscale *= highlightScale;\n",
+       "    }\n",
+       "\n",
+       "    vec4 clipCenter   = projection * (view * (model * vec4(position, 1)));\n",
+       "    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n",
+       "    vec4 clipPosition = projection * (view * (model * vec4(dataPosition, 1)));\n",
+       "\n",
+       "    gl_Position = clipPosition;\n",
+       "    interpColor = color;\n",
+       "    pickId = id;\n",
+       "    dataCoordinate = dataPosition;\n",
+       "  }\n",
+       "}\n",
+       "`]),h=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3 fragClipBounds[2];\n",
+       "uniform float opacity;\n",
+       "\n",
+       "varying vec4 interpColor;\n",
+       "varying vec3 dataCoordinate;\n",
+       "\n",
+       "void main() {\n",
+       "  if (\n",
+       "    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n",
+       "    interpColor.a * opacity == 0.\n",
+       "  ) discard;\n",
+       "  gl_FragColor = interpColor * opacity;\n",
+       "}\n",
+       "`]),d=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3 fragClipBounds[2];\n",
+       "uniform float pickGroup;\n",
+       "\n",
+       "varying vec4 pickId;\n",
+       "varying vec3 dataCoordinate;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n",
+       "\n",
+       "  gl_FragColor = vec4(pickGroup, pickId.bgr);\n",
+       "}`]),v=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],m={vertex:u,fragment:h,attributes:v},b={vertex:c,fragment:h,attributes:v},p={vertex:f,fragment:h,attributes:v},k={vertex:u,fragment:d,attributes:v},M={vertex:c,fragment:d,attributes:v},T={vertex:f,fragment:d,attributes:v};function L(x,C){var S=s(x,C),g=S.attributes;return g.position.location=0,g.color.location=1,g.glyph.location=2,g.id.location=3,S}a.createPerspective=function(x){return L(x,m)},a.createOrtho=function(x){return L(x,b)},a.createProject=function(x){return L(x,p)},a.createPickPerspective=function(x){return L(x,k)},a.createPickOrtho=function(x){return L(x,M)},a.createPickProject=function(x){return L(x,T)}}),8418:(function(i,a,o){\"use strict\";var s=o(5219),l=o(2762),u=o(8116),c=o(1888),f=o(6760),h=o(1283),d=o(9366),v=o(5964),m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],b=ArrayBuffer,p=DataView;function k(be){return b.isView(be)&&!(be instanceof p)}function M(be){return Array.isArray(be)||k(be)}i.exports=ke;function T(be,Be){var Le=be[0],me=be[1],Pe=be[2],ce=be[3];return be[0]=Be[0]*Le+Be[4]*me+Be[8]*Pe+Be[12]*ce,be[1]=Be[1]*Le+Be[5]*me+Be[9]*Pe+Be[13]*ce,be[2]=Be[2]*Le+Be[6]*me+Be[10]*Pe+Be[14]*ce,be[3]=Be[3]*Le+Be[7]*me+Be[11]*Pe+Be[15]*ce,be}function L(be,Be,Le,me){return T(me,me,Le),T(me,me,Be),T(me,me,be)}function x(be,Be){this.index=be,this.dataCoordinate=this.position=Be}function C(be){return be===!0||be>1?1:be}function S(be,Be,Le,me,Pe,ce,He,lt,mt,Ht,at,ct){this.gl=be,this.pixelRatio=1,this.shader=Be,this.orthoShader=Le,this.projectShader=me,this.pointBuffer=Pe,this.colorBuffer=ce,this.glyphBuffer=He,this.idBuffer=lt,this.vao=mt,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[.6666666666666666,.6666666666666666,.6666666666666666],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=Ht,this.pickOrthoShader=at,this.pickProjectShader=ct,this.points=[],this._selectResult=new x(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}var g=S.prototype;g.pickSlots=1,g.setPickBase=function(be){this.pickId=be},g.isTransparent=function(){if(this.hasAlpha)return!0;for(var be=0;be<3;++be)if(this.axesProject[be]&&this.projectHasAlpha)return!0;return!1},g.isOpaque=function(){if(!this.hasAlpha)return!0;for(var be=0;be<3;++be)if(this.axesProject[be]&&!this.projectHasAlpha)return!0;return!1};var P=[0,0],E=[0,0,0],z=[0,0,0],q=[0,0,0,1],V=[0,0,0,1],G=m.slice(),Z=[0,0,0],j=[[0,0,0],[0,0,0]];function N(be){return be[0]=be[1]=be[2]=0,be}function H(be,Be){return be[0]=Be[0],be[1]=Be[1],be[2]=Be[2],be[3]=1,be}function re(be,Be,Le,me){return be[0]=Be[0],be[1]=Be[1],be[2]=Be[2],be[Le]=me,be}function ae(be){for(var Be=j,Le=0;Le<2;++Le)for(var me=0;me<3;++me)Be[Le][me]=Math.max(Math.min(be[Le][me],1e8),-1e8);return Be}function _e(be,Be,Le,me){var Pe=Be.axesProject,ce=Be.gl,He=be.uniforms,lt=Le.model||m,mt=Le.view||m,Ht=Le.projection||m,at=Be.axesBounds,ct=ae(Be.clipBounds),ar;Be.axes&&Be.axes.lastCubeProps?ar=Be.axes.lastCubeProps.axis:ar=[1,1,1],P[0]=2/ce.drawingBufferWidth,P[1]=2/ce.drawingBufferHeight,be.bind(),He.view=mt,He.projection=Ht,He.screenSize=P,He.highlightId=Be.highlightId,He.highlightScale=Be.highlightScale,He.clipBounds=ct,He.pickGroup=Be.pickId/255,He.pixelRatio=me;for(var Vt=0;Vt<3;++Vt)if(Pe[Vt]){He.scale=Be.projectScale[Vt],He.opacity=Be.projectOpacity[Vt];for(var rr=G,tt=0;tt<16;++tt)rr[tt]=0;for(var tt=0;tt<4;++tt)rr[5*tt]=1;rr[5*Vt]=0,ar[Vt]<0?rr[12+Vt]=at[0][Vt]:rr[12+Vt]=at[1][Vt],f(rr,lt,rr),He.model=rr;var je=(Vt+1)%3,Ue=(Vt+2)%3,Ae=N(E),rt=N(z);Ae[je]=1,rt[Ue]=1;var St=L(Ht,mt,lt,H(q,Ae)),Tt=L(Ht,mt,lt,H(V,rt));if(Math.abs(St[1])>Math.abs(Tt[1])){var dt=St;St=Tt,Tt=dt,dt=Ae,Ae=rt,rt=dt;var Et=je;je=Ue,Ue=Et}St[0]<0&&(Ae[je]=-1),Tt[1]>0&&(rt[Ue]=-1);for(var pt=0,jt=0,tt=0;tt<4;++tt)pt+=Math.pow(lt[4*je+tt],2),jt+=Math.pow(lt[4*Ue+tt],2);Ae[je]/=Math.sqrt(pt),rt[Ue]/=Math.sqrt(jt),He.axes[0]=Ae,He.axes[1]=rt,He.fragClipBounds[0]=re(Z,ct[0],Vt,-1e8),He.fragClipBounds[1]=re(Z,ct[1],Vt,1e8),Be.vao.bind(),Be.vao.draw(ce.TRIANGLES,Be.vertexCount),Be.lineWidth>0&&(ce.lineWidth(Be.lineWidth*me),Be.vao.draw(ce.LINES,Be.lineVertexCount,Be.vertexCount)),Be.vao.unbind()}}var Ee=[-1e8,-1e8,-1e8],Ce=[1e8,1e8,1e8],ge=[Ee,Ce];function ie(be,Be,Le,me,Pe,ce,He){var lt=Le.gl;if((ce===Le.projectHasAlpha||He)&&_e(Be,Le,me,Pe),ce===Le.hasAlpha||He){be.bind();var mt=be.uniforms;mt.model=me.model||m,mt.view=me.view||m,mt.projection=me.projection||m,P[0]=2/lt.drawingBufferWidth,P[1]=2/lt.drawingBufferHeight,mt.screenSize=P,mt.highlightId=Le.highlightId,mt.highlightScale=Le.highlightScale,mt.fragClipBounds=ge,mt.clipBounds=Le.axes.bounds,mt.opacity=Le.opacity,mt.pickGroup=Le.pickId/255,mt.pixelRatio=Pe,Le.vao.bind(),Le.vao.draw(lt.TRIANGLES,Le.vertexCount),Le.lineWidth>0&&(lt.lineWidth(Le.lineWidth*Pe),Le.vao.draw(lt.LINES,Le.lineVertexCount,Le.vertexCount)),Le.vao.unbind()}}g.draw=function(be){var Be=this.useOrtho?this.orthoShader:this.shader;ie(Be,this.projectShader,this,be,this.pixelRatio,!1,!1)},g.drawTransparent=function(be){var Be=this.useOrtho?this.orthoShader:this.shader;ie(Be,this.projectShader,this,be,this.pixelRatio,!0,!1)},g.drawPick=function(be){var Be=this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader;ie(Be,this.pickProjectShader,this,be,1,!0,!0)},g.pick=function(be){if(!be||be.id!==this.pickId)return null;var Be=be.value[2]+(be.value[1]<<8)+(be.value[0]<<16);if(Be>=this.pointCount||Be<0)return null;var Le=this.points[Be],me=this._selectResult;me.index=Be;for(var Pe=0;Pe<3;++Pe)me.position[Pe]=me.dataCoordinate[Pe]=Le[Pe];return me},g.highlight=function(be){if(!be)this.highlightId=[1,1,1,1];else{var Be=be.index,Le=Be&255,me=Be>>8&255,Pe=Be>>16&255;this.highlightId=[Le/255,me/255,Pe/255,0]}};function Se(be,Be,Le,me){var Pe;M(be)?Be<be.length?Pe=be[Be]:Pe=void 0:Pe=be,Pe=v(Pe);var ce=!0;s(Pe)&&(Pe=\"\\u25BC\",ce=!1),Le||(Le={});var He=Le.family;M(He)&&(He=He[Be]),He||(He=\"normal\");var lt=Le.weight;M(lt)&&(lt=lt[Be]),lt||(lt=\"normal\");var mt=Le.style;M(mt)&&(mt=mt[Be]),mt||(mt=\"normal\");var Ht=Le.variant;M(Ht)&&(Ht=Ht[Be]),Ht||(Ht=\"normal\");var at=d(Pe,{family:He,weight:lt,style:mt,variant:Ht},me),at=d(Pe,Le,me);return{mesh:at[0],lines:at[1],bounds:at[2],visible:ce}}g.update=function(be){if(be=be||{},\"perspective\"in be&&(this.useOrtho=!be.perspective),\"orthographic\"in be&&(this.useOrtho=!!be.orthographic),\"lineWidth\"in be&&(this.lineWidth=be.lineWidth),\"project\"in be)if(M(be.project))this.axesProject=be.project;else{var Be=!!be.project;this.axesProject=[Be,Be,Be]}if(\"projectScale\"in be)if(M(be.projectScale))this.projectScale=be.projectScale.slice();else{var Le=+be.projectScale;this.projectScale=[Le,Le,Le]}if(this.projectHasAlpha=!1,\"projectOpacity\"in be){if(M(be.projectOpacity))this.projectOpacity=be.projectOpacity.slice();else{var Le=+be.projectOpacity;this.projectOpacity=[Le,Le,Le]}for(var me=0;me<3;++me)this.projectOpacity[me]=C(this.projectOpacity[me]),this.projectOpacity[me]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in be&&(this.opacity=C(be.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var Pe=be.position,ce={family:be.font||\"normal\",style:be.fontStyle||\"normal\",weight:be.fontWeight||\"normal\",variant:be.fontVariant||\"normal\"},He=be.alignment||[0,0],lt,mt;if(He.length===2)lt=He[0],mt=He[1];else{lt=[],mt=[];for(var me=0;me<He.length;++me)lt[me]=He[me][0],mt[me]=He[me][1]}var Ht=[1/0,1/0,1/0],at=[-1/0,-1/0,-1/0],ct=be.glyph,ar=be.color,Vt=be.size,rr=be.angle,tt=be.lineColor,je=-1,Ue=0,Ae=0,rt=0;if(Pe.length){rt=Pe.length;e:for(var me=0;me<rt;++me){for(var St=Pe[me],Tt=0;Tt<3;++Tt)if(isNaN(St[Tt])||!isFinite(St[Tt]))continue e;var dt=Se(ct,me,ce,this.pixelRatio),Et=dt.mesh,pt=dt.lines,jt=dt.bounds;Ue+=Et.cells.length*3,Ae+=pt.edges.length*2}}var or=Ue+Ae,mr=c.mallocFloat(3*or),Ar=c.mallocFloat(4*or),ei=c.mallocFloat(2*or),qr=c.mallocUint32(or);if(or>0){var jr=0,gt=Ue,Ge=[0,0,0,1],Je=[0,0,0,1],We=M(ar)&&M(ar[0]),et=M(tt)&&M(tt[0]);e:for(var me=0;me<rt;++me){je+=1;for(var St=Pe[me],Tt=0;Tt<3;++Tt){if(isNaN(St[Tt])||!isFinite(St[Tt]))continue e;at[Tt]=Math.max(at[Tt],St[Tt]),Ht[Tt]=Math.min(Ht[Tt],St[Tt])}var dt=Se(ct,me,ce,this.pixelRatio),Et=dt.mesh,pt=dt.lines,jt=dt.bounds,xt=dt.visible;if(!xt)Ge=[1,1,1,0];else if(M(ar)){var At;if(We?me<ar.length?At=ar[me]:At=[0,0,0,0]:At=ar,At.length===3){for(var Tt=0;Tt<3;++Tt)Ge[Tt]=At[Tt];Ge[3]=1}else if(At.length===4){for(var Tt=0;Tt<4;++Tt)Ge[Tt]=At[Tt];!this.hasAlpha&&At[3]<1&&(this.hasAlpha=!0)}}else Ge[0]=Ge[1]=Ge[2]=0,Ge[3]=1;if(!xt)Je=[1,1,1,0];else if(M(tt)){var At;if(et?me<tt.length?At=tt[me]:At=[0,0,0,0]:At=tt,At.length===3){for(var Tt=0;Tt<3;++Tt)Je[Tt]=At[Tt];Je[Tt]=1}else if(At.length===4){for(var Tt=0;Tt<4;++Tt)Je[Tt]=At[Tt];!this.hasAlpha&&At[3]<1&&(this.hasAlpha=!0)}}else Je[0]=Je[1]=Je[2]=0,Je[3]=1;var Kt=.5;xt?M(Vt)?me<Vt.length?Kt=+Vt[me]:Kt=12:Vt?Kt=+Vt:this.useOrtho&&(Kt=12):Kt=0;var Qt=0;M(rr)?me<rr.length?Qt=+rr[me]:Qt=0:rr&&(Qt=+rr);for(var Mr=Math.cos(Qt),Gr=Math.sin(Qt),St=Pe[me],Tt=0;Tt<3;++Tt)at[Tt]=Math.max(at[Tt],St[Tt]),Ht[Tt]=Math.min(Ht[Tt],St[Tt]);var Ir=lt,Yr=mt,Ir=0;M(lt)?me<lt.length?Ir=lt[me]:Ir=0:lt&&(Ir=lt);var Yr=0;M(mt)?me<mt.length?Yr=mt[me]:Yr=0:mt&&(Yr=mt),Ir*=Ir>0?1-jt[0][0]:Ir<0?1+jt[1][0]:1,Yr*=Yr>0?1-jt[0][1]:Yr<0?1+jt[1][1]:1;for(var _i=[Ir,Yr],Fn=Et.cells||[],An=Et.positions||[],Tt=0;Tt<Fn.length;++Tt)for(var Pi=Fn[Tt],ai=0;ai<3;++ai){for(var mi=0;mi<3;++mi)mr[3*jr+mi]=St[mi];for(var mi=0;mi<4;++mi)Ar[4*jr+mi]=Ge[mi];qr[jr]=je;var un=An[Pi[ai]];ei[2*jr]=Kt*(Mr*un[0]-Gr*un[1]+_i[0]),ei[2*jr+1]=Kt*(Gr*un[0]+Mr*un[1]+_i[1]),jr+=1}for(var Fn=pt.edges,An=pt.positions,Tt=0;Tt<Fn.length;++Tt)for(var Pi=Fn[Tt],ai=0;ai<2;++ai){for(var mi=0;mi<3;++mi)mr[3*gt+mi]=St[mi];for(var mi=0;mi<4;++mi)Ar[4*gt+mi]=Je[mi];qr[gt]=je;var un=An[Pi[ai]];ei[2*gt]=Kt*(Mr*un[0]-Gr*un[1]+_i[0]),ei[2*gt+1]=Kt*(Gr*un[0]+Mr*un[1]+_i[1]),gt+=1}}}this.bounds=[Ht,at],this.points=Pe,this.pointCount=Pe.length,this.vertexCount=Ue,this.lineVertexCount=Ae,this.pointBuffer.update(mr),this.colorBuffer.update(Ar),this.glyphBuffer.update(ei),this.idBuffer.update(qr),c.free(mr),c.free(Ar),c.free(ei),c.free(qr)},g.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()};function ke(be){var Be=be.gl,Le=h.createPerspective(Be),me=h.createOrtho(Be),Pe=h.createProject(Be),ce=h.createPickPerspective(Be),He=h.createPickOrtho(Be),lt=h.createPickProject(Be),mt=l(Be),Ht=l(Be),at=l(Be),ct=l(Be),ar=u(Be,[{buffer:mt,size:3,type:Be.FLOAT},{buffer:Ht,size:4,type:Be.FLOAT},{buffer:at,size:2,type:Be.FLOAT},{buffer:ct,size:4,type:Be.UNSIGNED_BYTE,normalized:!0}]),Vt=new S(Be,Le,me,Pe,mt,Ht,at,ct,ar,ce,He,lt);return Vt.update(be),Vt}}),3589:(function(i,a,o){\"use strict\";i.exports=m;var s=o(2260),l=o(1888),u=o(9618),c=o(8828).nextPow2,f=function(b,p,k){for(var M=1e8,T=-1,L=-1,x=b.shape[0],C=b.shape[1],S=0;S<x;S++)for(var g=0;g<C;g++){var P=b.get(S,g,0),E=b.get(S,g,1),z=b.get(S,g,2),q=b.get(S,g,3);if(P<255||E<255||z<255||q<255){var V=p-S,G=k-g,Z=V*V+G*G;Z<M&&(M=Z,T=S,L=g)}}return[T,L,M]};function h(b,p,k,M,T){this.coord=[b,p],this.id=k,this.value=M,this.distance=T}function d(b,p,k){this.gl=b,this.fbo=p,this.buffer=k,this._readTimeout=null;var M=this;this._readCallback=function(){M.gl&&(p.bind(),b.readPixels(0,0,p.shape[0],p.shape[1],b.RGBA,b.UNSIGNED_BYTE,M.buffer),M._readTimeout=null)}}var v=d.prototype;Object.defineProperty(v,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(b){if(this.gl){this.fbo.shape=b;var p=this.fbo.shape[0],k=this.fbo.shape[1];if(k*p*4>this.buffer.length){l.free(this.buffer);for(var M=this.buffer=l.mallocUint8(c(k*p*4)),T=0;T<k*p*4;++T)M[T]=255}return b}}}),v.begin=function(){var b=this.gl,p=this.shape;b&&(this.fbo.bind(),b.clearColor(1,1,1,1),b.clear(b.COLOR_BUFFER_BIT|b.DEPTH_BUFFER_BIT))},v.end=function(){var b=this.gl;b&&(b.bindFramebuffer(b.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},v.query=function(b,p,k){if(!this.gl)return null;var M=this.fbo.shape.slice();b=b|0,p=p|0,typeof k!=\"number\"&&(k=1);var T=Math.min(Math.max(b-k,0),M[0])|0,L=Math.min(Math.max(b+k,0),M[0])|0,x=Math.min(Math.max(p-k,0),M[1])|0,C=Math.min(Math.max(p+k,0),M[1])|0;if(L<=T||C<=x)return null;var S=[L-T,C-x],g=u(this.buffer,[S[0],S[1],4],[4,M[0]*4,1],4*(T+M[0]*x)),P=f(g.hi(S[0],S[1],1),k,k),E=P[0],z=P[1];if(E<0||Math.pow(this.radius,2)<P[2])return null;var q=g.get(E,z,0),V=g.get(E,z,1),G=g.get(E,z,2),Z=g.get(E,z,3);return new h(E+T|0,z+x|0,q,[V,G,Z],Math.sqrt(P[2]))},v.dispose=function(){this.gl&&(this.fbo.dispose(),l.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))};function m(b,p){var k=p[0],M=p[1],T={},L=s(b,k,M,T),x=l.mallocUint8(k*M*4);return new d(b,L,x)}}),9405:(function(i,a,o){\"use strict\";var s=o(3327),l=o(8731),u=o(216),c=o(5091),f=o(2145),h=o(8866);function d(p){this.gl=p,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var v=d.prototype;v.bind=function(){this.program||this._relink();var p,k=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),M=this.gl.lastAttribCount;if(k>M)for(p=M;p<k;p++)this.gl.enableVertexAttribArray(p);else if(M>k)for(p=k;p<M;p++)this.gl.disableVertexAttribArray(p);this.gl.lastAttribCount=k,this.gl.useProgram(this.program)},v.dispose=function(){for(var p=this.gl.lastAttribCount,k=0;k<p;k++)this.gl.disableVertexAttribArray(k);this.gl.lastAttribCount=0,this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null};function m(p,k){return p.name<k.name?-1:1}v.update=function(p,k,M,T){if(!k||arguments.length===1){var L=p;p=L.vertex,k=L.fragment,M=L.uniforms,T=L.attributes}var x=this,C=x.gl,S=x._vref;x._vref=c.shader(C,C.VERTEX_SHADER,p),S&&S.dispose(),x.vertShader=x._vref.shader;var g=this._fref;if(x._fref=c.shader(C,C.FRAGMENT_SHADER,k),g&&g.dispose(),x.fragShader=x._fref.shader,!M||!T){var P=C.createProgram();if(C.attachShader(P,x.fragShader),C.attachShader(P,x.vertShader),C.linkProgram(P),!C.getProgramParameter(P,C.LINK_STATUS)){var E=C.getProgramInfoLog(P);throw new h(E,\"Error linking program:\"+E)}M=M||f.uniforms(C,P),T=T||f.attributes(C,P),C.deleteProgram(P)}T=T.slice(),T.sort(m);var z=[],q=[],V=[],G;for(G=0;G<T.length;++G){var Z=T[G];if(Z.type.indexOf(\"mat\")>=0){for(var j=Z.type.charAt(Z.type.length-1)|0,N=new Array(j),H=0;H<j;++H)N[H]=V.length,q.push(Z.name+\"[\"+H+\"]\"),typeof Z.location==\"number\"?V.push(Z.location+H):Array.isArray(Z.location)&&Z.location.length===j&&typeof Z.location[H]==\"number\"?V.push(Z.location[H]|0):V.push(-1);z.push({name:Z.name,type:Z.type,locations:N})}else z.push({name:Z.name,type:Z.type,locations:[V.length]}),q.push(Z.name),typeof Z.location==\"number\"?V.push(Z.location|0):V.push(-1)}var re=0;for(G=0;G<V.length;++G)if(V[G]<0){for(;V.indexOf(re)>=0;)re+=1;V[G]=re}var ae=new Array(M.length);function _e(){x.program=c.program(C,x._vref,x._fref,q,V);for(var Ee=0;Ee<M.length;++Ee)ae[Ee]=C.getUniformLocation(x.program,M[Ee].name)}_e(),x._relink=_e,x.types={uniforms:u(M),attributes:u(T)},x.attributes=l(C,x,z,V),Object.defineProperty(x,\"uniforms\",s(C,x,M,ae))};function b(p,k,M,T,L){var x=new d(p);return x.update(k,M,T,L),x}i.exports=b}),8866:(function(i){function a(o,s,l){this.shortMessage=s||\"\",this.longMessage=l||\"\",this.rawError=o||\"\",this.message=\"gl-shader: \"+(s||o||\"\")+(l?`\n",
+       "`+l:\"\"),this.stack=new Error().stack}a.prototype=new Error,a.prototype.name=\"GLError\",a.prototype.constructor=a,i.exports=a}),8731:(function(i,a,o){\"use strict\";i.exports=d;var s=o(8866);function l(v,m,b,p,k,M){this._gl=v,this._wrapper=m,this._index=b,this._locations=p,this._dimension=k,this._constFunc=M}var u=l.prototype;u.pointer=function(m,b,p,k){var M=this,T=M._gl,L=M._locations[M._index];T.vertexAttribPointer(L,M._dimension,m||T.FLOAT,!!b,p||0,k||0),T.enableVertexAttribArray(L)},u.set=function(v,m,b,p){return this._constFunc(this._locations[this._index],v,m,b,p)},Object.defineProperty(u,\"location\",{get:function(){return this._locations[this._index]},set:function(v){return v!==this._locations[this._index]&&(this._locations[this._index]=v|0,this._wrapper.program=null),v|0}});var c=[function(v,m,b){return b.length===void 0?v.vertexAttrib1f(m,b):v.vertexAttrib1fv(m,b)},function(v,m,b,p){return b.length===void 0?v.vertexAttrib2f(m,b,p):v.vertexAttrib2fv(m,b)},function(v,m,b,p,k){return b.length===void 0?v.vertexAttrib3f(m,b,p,k):v.vertexAttrib3fv(m,b)},function(v,m,b,p,k,M){return b.length===void 0?v.vertexAttrib4f(m,b,p,k,M):v.vertexAttrib4fv(m,b)}];function f(v,m,b,p,k,M,T){var L=c[k],x=new l(v,m,b,p,k,L);Object.defineProperty(M,T,{set:function(C){return v.disableVertexAttribArray(p[b]),L(v,p[b],C),C},get:function(){return x},enumerable:!0})}function h(v,m,b,p,k,M,T){for(var L=new Array(k),x=new Array(k),C=0;C<k;++C)f(v,m,b[C],p,k,L,C),x[C]=L[C];Object.defineProperty(L,\"location\",{set:function(P){if(Array.isArray(P))for(var E=0;E<k;++E)x[E].location=P[E];else for(var E=0;E<k;++E)x[E].location=P+E;return P},get:function(){for(var P=new Array(k),E=0;E<k;++E)P[E]=p[b[E]];return P},enumerable:!0}),L.pointer=function(P,E,z,q){P=P||v.FLOAT,E=!!E,z=z||k*k,q=q||0;for(var V=0;V<k;++V){var G=p[b[V]];v.vertexAttribPointer(G,k,P,E,z,q+V*k),v.enableVertexAttribArray(G)}};var S=new Array(k),g=v[\"vertexAttrib\"+k+\"fv\"];Object.defineProperty(M,T,{set:function(P){for(var E=0;E<k;++E){var z=p[b[E]];if(v.disableVertexAttribArray(z),Array.isArray(P[0]))g.call(v,z,P[E]);else{for(var q=0;q<k;++q)S[q]=P[k*E+q];g.call(v,z,S)}}return P},get:function(){return L},enumerable:!0})}function d(v,m,b,p){for(var k={},M=0,T=b.length;M<T;++M){var L=b[M],x=L.name,C=L.type,S=L.locations;switch(C){case\"bool\":case\"int\":case\"float\":f(v,m,S[0],p,1,k,x);break;default:if(C.indexOf(\"vec\")>=0){var g=C.charCodeAt(C.length-1)-48;if(g<2||g>4)throw new s(\"\",\"Invalid data type for attribute \"+x+\": \"+C);f(v,m,S[0],p,g,k,x)}else if(C.indexOf(\"mat\")>=0){var g=C.charCodeAt(C.length-1)-48;if(g<2||g>4)throw new s(\"\",\"Invalid data type for attribute \"+x+\": \"+C);h(v,m,S,p,g,k,x)}else throw new s(\"\",\"Unknown data type for attribute \"+x+\": \"+C);break}}return k}}),3327:(function(i,a,o){\"use strict\";var s=o(216),l=o(8866);i.exports=f;function u(h){return function(){return h}}function c(h,d){for(var v=new Array(h),m=0;m<h;++m)v[m]=d;return v}function f(h,d,v,m){function b(C){return function(S,g,P){return S.getUniform(g.program,P[C])}}function p(C){return function(g){for(var P=k(\"\",C),E=0;E<P.length;++E){var z=P[E],q=z[0],V=z[1];if(m[V]){var G=g;if(typeof q==\"string\"&&(q.indexOf(\".\")===0||q.indexOf(\"[\")===0)){var Z=q;if(q.indexOf(\".\")===0&&(Z=q.slice(1)),Z.indexOf(\"]\")===Z.length-1){var j=Z.indexOf(\"[\"),N=Z.slice(0,j),H=Z.slice(j+1,Z.length-1);G=N?g[N][H]:g[H]}else G=g[Z]}var re=v[V].type,ae;switch(re){case\"bool\":case\"int\":case\"sampler2D\":case\"samplerCube\":h.uniform1i(m[V],G);break;case\"float\":h.uniform1f(m[V],G);break;default:var _e=re.indexOf(\"vec\");if(0<=_e&&_e<=1&&re.length===4+_e){if(ae=re.charCodeAt(re.length-1)-48,ae<2||ae>4)throw new l(\"\",\"Invalid data type\");switch(re.charAt(0)){case\"b\":case\"i\":h[\"uniform\"+ae+\"iv\"](m[V],G);break;case\"v\":h[\"uniform\"+ae+\"fv\"](m[V],G);break;default:throw new l(\"\",\"Unrecognized data type for vector \"+name+\": \"+re)}}else if(re.indexOf(\"mat\")===0&&re.length===4){if(ae=re.charCodeAt(re.length-1)-48,ae<2||ae>4)throw new l(\"\",\"Invalid uniform dimension type for matrix \"+name+\": \"+re);h[\"uniformMatrix\"+ae+\"fv\"](m[V],!1,G);break}else throw new l(\"\",\"Unknown uniform data type for \"+name+\": \"+re)}}}}}function k(C,S){if(typeof S!=\"object\")return[[C,S]];var g=[];for(var P in S){var E=S[P],z=C;parseInt(P)+\"\"===P?z+=\"[\"+P+\"]\":z+=\".\"+P,typeof E==\"object\"?g.push.apply(g,k(z,E)):g.push([z,E])}return g}function M(C){switch(C){case\"bool\":return!1;case\"int\":case\"sampler2D\":case\"samplerCube\":return 0;case\"float\":return 0;default:var S=C.indexOf(\"vec\");if(0<=S&&S<=1&&C.length===4+S){var g=C.charCodeAt(C.length-1)-48;if(g<2||g>4)throw new l(\"\",\"Invalid data type\");return C.charAt(0)===\"b\"?c(g,!1):c(g,0)}else if(C.indexOf(\"mat\")===0&&C.length===4){var g=C.charCodeAt(C.length-1)-48;if(g<2||g>4)throw new l(\"\",\"Invalid uniform dimension type for matrix \"+name+\": \"+C);return c(g*g,0)}else throw new l(\"\",\"Unknown uniform data type for \"+name+\": \"+C)}}function T(C,S,g){if(typeof g==\"object\"){var P=L(g);Object.defineProperty(C,S,{get:u(P),set:p(g),enumerable:!0,configurable:!1})}else m[g]?Object.defineProperty(C,S,{get:b(g),set:p(g),enumerable:!0,configurable:!1}):C[S]=M(v[g].type)}function L(C){var S;if(Array.isArray(C)){S=new Array(C.length);for(var g=0;g<C.length;++g)T(S,g,C[g])}else{S={};for(var P in C)T(S,P,C[P])}return S}var x=s(v,!0);return{get:u(L(x)),set:p(x),enumerable:!0,configurable:!0}}}),216:(function(i){\"use strict\";i.exports=a;function a(o,s){for(var l={},u=0;u<o.length;++u)for(var c=o[u].name,f=c.split(\".\"),h=l,d=0;d<f.length;++d){var v=f[d].split(\"[\");if(v.length>1){v[0]in h||(h[v[0]]=[]),h=h[v[0]];for(var m=1;m<v.length;++m){var b=parseInt(v[m]);m<v.length-1||d<f.length-1?(b in h||(m<v.length-1?h[b]=[]:h[b]={}),h=h[b]):s?h[b]=u:h[b]=o[u].type}}else d<f.length-1?(v[0]in h||(h[v[0]]={}),h=h[v[0]]):s?h[v[0]]=u:h[v[0]]=o[u].type}return l}}),2145:(function(i,a){\"use strict\";a.uniforms=u,a.attributes=c;var o={FLOAT:\"float\",FLOAT_VEC2:\"vec2\",FLOAT_VEC3:\"vec3\",FLOAT_VEC4:\"vec4\",INT:\"int\",INT_VEC2:\"ivec2\",INT_VEC3:\"ivec3\",INT_VEC4:\"ivec4\",BOOL:\"bool\",BOOL_VEC2:\"bvec2\",BOOL_VEC3:\"bvec3\",BOOL_VEC4:\"bvec4\",FLOAT_MAT2:\"mat2\",FLOAT_MAT3:\"mat3\",FLOAT_MAT4:\"mat4\",SAMPLER_2D:\"sampler2D\",SAMPLER_CUBE:\"samplerCube\"},s=null;function l(f,h){if(!s){var d=Object.keys(o);s={};for(var v=0;v<d.length;++v){var m=d[v];s[f[m]]=o[m]}}return s[h]}function u(f,h){for(var d=f.getProgramParameter(h,f.ACTIVE_UNIFORMS),v=[],m=0;m<d;++m){var b=f.getActiveUniform(h,m);if(b){var p=l(f,b.type);if(b.size>1)for(var k=0;k<b.size;++k)v.push({name:b.name.replace(\"[0]\",\"[\"+k+\"]\"),type:p});else v.push({name:b.name,type:p})}}return v}function c(f,h){for(var d=f.getProgramParameter(h,f.ACTIVE_ATTRIBUTES),v=[],m=0;m<d;++m){var b=f.getActiveAttrib(h,m);b&&v.push({name:b.name,type:l(f,b.type)})}return v}}),5091:(function(i,a,o){\"use strict\";a.shader=k,a.program=M;var s=o(8866),l=o(2992),u=typeof WeakMap==\"undefined\"?o(606):WeakMap,c=new u,f=0;function h(T,L,x,C,S,g,P){this.id=T,this.src=L,this.type=x,this.shader=C,this.count=g,this.programs=[],this.cache=P}h.prototype.dispose=function(){if(--this.count===0){for(var T=this.cache,L=T.gl,x=this.programs,C=0,S=x.length;C<S;++C){var g=T.programs[x[C]];g&&(delete T.programs[C],L.deleteProgram(g))}L.deleteShader(this.shader),delete T.shaders[this.type===L.FRAGMENT_SHADER|0][this.src]}};function d(T){this.gl=T,this.shaders=[{},{}],this.programs={}}var v=d.prototype;function m(T,L,x){var C=T.createShader(L);if(T.shaderSource(C,x),T.compileShader(C),!T.getShaderParameter(C,T.COMPILE_STATUS)){var S=T.getShaderInfoLog(C);try{var g=l(S,x,L)}catch(P){throw console.warn(\"Failed to format compiler error: \"+P),new s(S,`Error compiling shader:\n",
+       "`+S)}throw new s(S,g.short,g.long)}return C}v.getShaderReference=function(T,L){var x=this.gl,C=this.shaders[T===x.FRAGMENT_SHADER|0],S=C[L];if(!S||!x.isShader(S.shader)){var g=m(x,T,L);S=C[L]=new h(f++,L,T,g,[],1,this)}else S.count+=1;return S};function b(T,L,x,C,S){var g=T.createProgram();T.attachShader(g,L),T.attachShader(g,x);for(var P=0;P<C.length;++P)T.bindAttribLocation(g,S[P],C[P]);if(T.linkProgram(g),!T.getProgramParameter(g,T.LINK_STATUS)){var E=T.getProgramInfoLog(g);throw new s(E,\"Error linking program: \"+E)}return g}v.getProgram=function(T,L,x,C){var S=[T.id,L.id,x.join(\":\"),C.join(\":\")].join(\"@\"),g=this.programs[S];return(!g||!this.gl.isProgram(g))&&(this.programs[S]=g=b(this.gl,T.shader,L.shader,x,C),T.programs.push(S),L.programs.push(S)),g};function p(T){var L=c.get(T);return L||(L=new d(T),c.set(T,L)),L}function k(T,L,x){return p(T).getShaderReference(L,x)}function M(T,L,x,C,S){return p(T).getProgram(L,x,C,S)}}),1493:(function(i,a,o){\"use strict\";var s=o(3236),l=o(9405),u=s([`precision mediump float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec3 position, color;\n",
+       "attribute float weight;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform vec3 coordinates[3];\n",
+       "uniform vec4 colors[3];\n",
+       "uniform vec2 screenShape;\n",
+       "uniform float lineWidth;\n",
+       "\n",
+       "varying vec4 fragColor;\n",
+       "\n",
+       "void main() {\n",
+       "  vec3 vertexPosition = mix(coordinates[0],\n",
+       "    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\n",
+       "\n",
+       "  vec4 clipPos = projection * (view * (model * vec4(vertexPosition, 1.0)));\n",
+       "  vec2 clipOffset = (projection * (view * (model * vec4(color, 0.0)))).xy;\n",
+       "  vec2 delta = weight * clipOffset * screenShape;\n",
+       "  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n",
+       "\n",
+       "  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n",
+       "  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n",
+       "}\n",
+       "`]),c=s([`precision mediump float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "varying vec4 fragColor;\n",
+       "\n",
+       "void main() {\n",
+       "  gl_FragColor = fragColor;\n",
+       "}`]);i.exports=function(f){return l(f,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}}),4449:(function(i,a,o){\"use strict\";var s=o(2762),l=o(8116),u=o(1493);i.exports=b;var c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(p,k,M,T){this.gl=p,this.buffer=k,this.vao=M,this.shader=T,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var h=f.prototype,d=[0,0,0],v=[0,0,0],m=[0,0];h.isTransparent=function(){return!1},h.drawTransparent=function(p){},h.draw=function(p){var k=this.gl,M=this.vao,T=this.shader;M.bind(),T.bind();var L=p.model||c,x=p.view||c,C=p.projection||c,S;this.axes&&(S=this.axes.lastCubeProps.axis);for(var g=d,P=v,E=0;E<3;++E)S&&S[E]<0?(g[E]=this.bounds[0][E],P[E]=this.bounds[1][E]):(g[E]=this.bounds[1][E],P[E]=this.bounds[0][E]);m[0]=k.drawingBufferWidth,m[1]=k.drawingBufferHeight,T.uniforms.model=L,T.uniforms.view=x,T.uniforms.projection=C,T.uniforms.coordinates=[this.position,g,P],T.uniforms.colors=this.colors,T.uniforms.screenShape=m;for(var E=0;E<3;++E)T.uniforms.lineWidth=this.lineWidth[E]*this.pixelRatio,this.enabled[E]&&(M.draw(k.TRIANGLES,6,6*E),this.drawSides[E]&&M.draw(k.TRIANGLES,12,18+12*E));M.unbind()},h.update=function(p){p&&(\"bounds\"in p&&(this.bounds=p.bounds),\"position\"in p&&(this.position=p.position),\"lineWidth\"in p&&(this.lineWidth=p.lineWidth),\"colors\"in p&&(this.colors=p.colors),\"enabled\"in p&&(this.enabled=p.enabled),\"drawSides\"in p&&(this.drawSides=p.drawSides))},h.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()};function b(p,k){var M=[];function T(g,P,E,z,q,V){var G=[g,P,E,0,0,0,1];G[z+3]=1,G[z]=q,M.push.apply(M,G),G[6]=-1,M.push.apply(M,G),G[z]=V,M.push.apply(M,G),M.push.apply(M,G),G[6]=1,M.push.apply(M,G),G[z]=q,M.push.apply(M,G)}T(0,0,0,0,0,1),T(0,0,0,1,0,1),T(0,0,0,2,0,1),T(1,0,0,1,-1,1),T(1,0,0,2,-1,1),T(0,1,0,0,-1,1),T(0,1,0,2,-1,1),T(0,0,1,0,-1,1),T(0,0,1,1,-1,1);var L=s(p,M),x=l(p,[{type:p.FLOAT,buffer:L,size:3,offset:0,stride:28},{type:p.FLOAT,buffer:L,size:3,offset:12,stride:28},{type:p.FLOAT,buffer:L,size:1,offset:24,stride:28}]),C=u(p);C.attributes.position.location=0,C.attributes.color.location=1,C.attributes.weight.location=2;var S=new f(p,L,x,C);return S.update(k),S}}),6740:(function(i,a,o){var s=o(3236),l=s([`precision highp float;\n",
+       "\n",
+       "precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "vec3 getOrthogonalVector(vec3 v) {\n",
+       "  // Return up-vector for only-z vector.\n",
+       "  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n",
+       "  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n",
+       "  // Assign z = 0, x = -b, y = a:\n",
+       "  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n",
+       "  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n",
+       "    return normalize(vec3(-v.y, v.x, 0.0));\n",
+       "  } else {\n",
+       "    return normalize(vec3(0.0, v.z, -v.y));\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "// Calculate the tube vertex and normal at the given index.\n",
+       "//\n",
+       "// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n",
+       "//\n",
+       "// Each tube segment is made up of a ring of vertices.\n",
+       "// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n",
+       "// The indexes of tube segments run from 0 to 8.\n",
+       "//\n",
+       "vec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n",
+       "  float segmentCount = 8.0;\n",
+       "\n",
+       "  float angle = 2.0 * 3.14159 * (index / segmentCount);\n",
+       "\n",
+       "  vec3 u = getOrthogonalVector(d);\n",
+       "  vec3 v = normalize(cross(u, d));\n",
+       "\n",
+       "  vec3 x = u * cos(angle) * length(d);\n",
+       "  vec3 y = v * sin(angle) * length(d);\n",
+       "  vec3 v3 = x + y;\n",
+       "\n",
+       "  normal = normalize(v3);\n",
+       "\n",
+       "  return v3;\n",
+       "}\n",
+       "\n",
+       "attribute vec4 vector;\n",
+       "attribute vec4 color, position;\n",
+       "attribute vec2 uv;\n",
+       "\n",
+       "uniform float vectorScale, tubeScale;\n",
+       "uniform mat4 model, view, projection, inverseModel;\n",
+       "uniform vec3 eyePosition, lightPosition;\n",
+       "\n",
+       "varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\n",
+       "varying vec4 f_color;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  // Scale the vector magnitude to stay constant with\n",
+       "  // model & view changes.\n",
+       "  vec3 normal;\n",
+       "  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n",
+       "  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n",
+       "\n",
+       "  //Lighting geometry parameters\n",
+       "  vec4 cameraCoordinate = view * tubePosition;\n",
+       "  cameraCoordinate.xyz /= cameraCoordinate.w;\n",
+       "  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n",
+       "  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n",
+       "  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n",
+       "\n",
+       "  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n",
+       "  vec4 t_position  = view * tubePosition;\n",
+       "  gl_Position      = projection * t_position;\n",
+       "\n",
+       "  f_color          = color;\n",
+       "  f_data           = tubePosition.xyz;\n",
+       "  f_position       = position.xyz;\n",
+       "  f_uv             = uv;\n",
+       "}\n",
+       "`]),u=s([`#extension GL_OES_standard_derivatives : enable\n",
+       "\n",
+       "precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "float beckmannDistribution(float x, float roughness) {\n",
+       "  float NdotH = max(x, 0.0001);\n",
+       "  float cos2Alpha = NdotH * NdotH;\n",
+       "  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n",
+       "  float roughness2 = roughness * roughness;\n",
+       "  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n",
+       "  return exp(tan2Alpha / roughness2) / denom;\n",
+       "}\n",
+       "\n",
+       "float cookTorranceSpecular(\n",
+       "  vec3 lightDirection,\n",
+       "  vec3 viewDirection,\n",
+       "  vec3 surfaceNormal,\n",
+       "  float roughness,\n",
+       "  float fresnel) {\n",
+       "\n",
+       "  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n",
+       "  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n",
+       "\n",
+       "  //Half angle vector\n",
+       "  vec3 H = normalize(lightDirection + viewDirection);\n",
+       "\n",
+       "  //Geometric term\n",
+       "  float NdotH = max(dot(surfaceNormal, H), 0.0);\n",
+       "  float VdotH = max(dot(viewDirection, H), 0.000001);\n",
+       "  float LdotH = max(dot(lightDirection, H), 0.000001);\n",
+       "  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n",
+       "  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n",
+       "  float G = min(1.0, min(G1, G2));\n",
+       "  \n",
+       "  //Distribution term\n",
+       "  float D = beckmannDistribution(NdotH, roughness);\n",
+       "\n",
+       "  //Fresnel term\n",
+       "  float F = pow(1.0 - VdotN, fresnel);\n",
+       "\n",
+       "  //Multiply terms and done\n",
+       "  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\n",
+       "uniform sampler2D texture;\n",
+       "\n",
+       "varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\n",
+       "varying vec4 f_color;\n",
+       "varying vec2 f_uv;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
+       "  vec3 N = normalize(f_normal);\n",
+       "  vec3 L = normalize(f_lightDirection);\n",
+       "  vec3 V = normalize(f_eyeDirection);\n",
+       "\n",
+       "  if(gl_FrontFacing) {\n",
+       "    N = -N;\n",
+       "  }\n",
+       "\n",
+       "  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n",
+       "  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n",
+       "\n",
+       "  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n",
+       "  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n",
+       "\n",
+       "  gl_FragColor = litColor * opacity;\n",
+       "}\n",
+       "`]),c=s([`precision highp float;\n",
+       "\n",
+       "precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "vec3 getOrthogonalVector(vec3 v) {\n",
+       "  // Return up-vector for only-z vector.\n",
+       "  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n",
+       "  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n",
+       "  // Assign z = 0, x = -b, y = a:\n",
+       "  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n",
+       "  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n",
+       "    return normalize(vec3(-v.y, v.x, 0.0));\n",
+       "  } else {\n",
+       "    return normalize(vec3(0.0, v.z, -v.y));\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "// Calculate the tube vertex and normal at the given index.\n",
+       "//\n",
+       "// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n",
+       "//\n",
+       "// Each tube segment is made up of a ring of vertices.\n",
+       "// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n",
+       "// The indexes of tube segments run from 0 to 8.\n",
+       "//\n",
+       "vec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n",
+       "  float segmentCount = 8.0;\n",
+       "\n",
+       "  float angle = 2.0 * 3.14159 * (index / segmentCount);\n",
+       "\n",
+       "  vec3 u = getOrthogonalVector(d);\n",
+       "  vec3 v = normalize(cross(u, d));\n",
+       "\n",
+       "  vec3 x = u * cos(angle) * length(d);\n",
+       "  vec3 y = v * sin(angle) * length(d);\n",
+       "  vec3 v3 = x + y;\n",
+       "\n",
+       "  normal = normalize(v3);\n",
+       "\n",
+       "  return v3;\n",
+       "}\n",
+       "\n",
+       "attribute vec4 vector;\n",
+       "attribute vec4 position;\n",
+       "attribute vec4 id;\n",
+       "\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform float tubeScale;\n",
+       "\n",
+       "varying vec3 f_position;\n",
+       "varying vec4 f_id;\n",
+       "\n",
+       "void main() {\n",
+       "  vec3 normal;\n",
+       "  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n",
+       "  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n",
+       "\n",
+       "  gl_Position = projection * (view * tubePosition);\n",
+       "  f_id        = id;\n",
+       "  f_position  = position.xyz;\n",
+       "}\n",
+       "`]),f=s([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3  clipBounds[2];\n",
+       "uniform float pickId;\n",
+       "\n",
+       "varying vec3 f_position;\n",
+       "varying vec4 f_id;\n",
+       "\n",
+       "void main() {\n",
+       "  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
+       "\n",
+       "  gl_FragColor = vec4(pickId, f_id.xyz);\n",
+       "}`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},a.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}}),7815:(function(i,a,o){\"use strict\";var s=o(2931),l=o(9970),u=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],c=function(T,L,x,C){for(var S=T.points,g=T.velocities,P=T.divergences,E=[],z=[],q=[],V=[],G=[],Z=[],j=0,N=0,H=l.create(),re=l.create(),ae=8,_e=0;_e<S.length;_e++){var Ee=S[_e],Ce=g[_e],ge=P[_e];L===0&&(ge=x*.05),N=s.length(Ce)/C,H=l.create(),s.copy(H,Ce),H[3]=ge;for(var ie=0;ie<ae;ie++)G[ie]=[Ee[0],Ee[1],Ee[2],ie];if(V.length>0)for(var ie=0;ie<ae;ie++){var Se=(ie+1)%ae;E.push(V[ie],G[ie],G[Se],G[Se],V[Se],V[ie]),q.push(re,H,H,H,re,re),Z.push(j,N,N,N,j,j);var ke=E.length;z.push([ke-6,ke-5,ke-4],[ke-3,ke-2,ke-1])}var be=V;V=G,G=be;var Be=re;re=H,H=Be;var Le=j;j=N,N=Le}return{positions:E,cells:z,vectors:q,vertexIntensity:Z}},f=function(T,L,x,C){for(var S=0,g=0;g<T.length;g++)for(var P=T[g].velocities,E=0;E<P.length;E++)S=Math.max(S,s.length(P[E]));for(var z=T.map(function(_e){return c(_e,x,C,S)}),q=[],V=[],G=[],Z=[],g=0;g<z.length;g++){var j=z[g],N=q.length;q=q.concat(j.positions),G=G.concat(j.vectors),Z=Z.concat(j.vertexIntensity);for(var E=0;E<j.cells.length;E++){var H=j.cells[E],re=[];V.push(re);for(var ae=0;ae<H.length;ae++)re.push(H[ae]+N)}}return{positions:q,cells:V,vectors:G,vertexIntensity:Z,colormap:L}},h=function(T,L){var x=T.length,C;for(C=0;C<x;C++){var S=T[C];if(S===L)return C;if(S>L)return C-1}return C},d=function(T,L,x){return T<L?L:T>x?x:T},v=function(T,L,x){var C=L.vectors,S=L.meshgrid,g=T[0],P=T[1],E=T[2],z=S[0].length,q=S[1].length,V=S[2].length,G=h(S[0],g),Z=h(S[1],P),j=h(S[2],E),N=G+1,H=Z+1,re=j+1;if(G=d(G,0,z-1),N=d(N,0,z-1),Z=d(Z,0,q-1),H=d(H,0,q-1),j=d(j,0,V-1),re=d(re,0,V-1),G<0||Z<0||j<0||N>z-1||H>q-1||re>V-1)return s.create();var ae=S[0][G],_e=S[0][N],Ee=S[1][Z],Ce=S[1][H],ge=S[2][j],ie=S[2][re],Se=(g-ae)/(_e-ae),ke=(P-Ee)/(Ce-Ee),be=(E-ge)/(ie-ge);isFinite(Se)||(Se=.5),isFinite(ke)||(ke=.5),isFinite(be)||(be=.5);var Be,Le,me,Pe,ce,He;switch(x.reversedX&&(G=z-1-G,N=z-1-N),x.reversedY&&(Z=q-1-Z,H=q-1-H),x.reversedZ&&(j=V-1-j,re=V-1-re),x.filled){case 5:ce=j,He=re,me=Z*V,Pe=H*V,Be=G*V*q,Le=N*V*q;break;case 4:ce=j,He=re,Be=G*V,Le=N*V,me=Z*V*z,Pe=H*V*z;break;case 3:me=Z,Pe=H,ce=j*q,He=re*q,Be=G*q*V,Le=N*q*V;break;case 2:me=Z,Pe=H,Be=G*q,Le=N*q,ce=j*q*z,He=re*q*z;break;case 1:Be=G,Le=N,ce=j*z,He=re*z,me=Z*z*V,Pe=H*z*V;break;default:Be=G,Le=N,me=Z*z,Pe=H*z,ce=j*z*q,He=re*z*q;break}var lt=C[Be+me+ce],mt=C[Be+me+He],Ht=C[Be+Pe+ce],at=C[Be+Pe+He],ct=C[Le+me+ce],ar=C[Le+me+He],Vt=C[Le+Pe+ce],rr=C[Le+Pe+He],tt=s.create(),je=s.create(),Ue=s.create(),Ae=s.create();s.lerp(tt,lt,ct,Se),s.lerp(je,mt,ar,Se),s.lerp(Ue,Ht,Vt,Se),s.lerp(Ae,at,rr,Se);var rt=s.create(),St=s.create();s.lerp(rt,tt,Ue,ke),s.lerp(St,je,Ae,ke);var Tt=s.create();return s.lerp(Tt,rt,St,be),Tt},m=function(T,L){var x=L[0],C=L[1],S=L[2];return T[0]=x<0?-x:x,T[1]=C<0?-C:C,T[2]=S<0?-S:S,T},b=function(T){var L=1/0;T.sort(function(g,P){return g-P});for(var x=T.length,C=1;C<x;C++){var S=Math.abs(T[C]-T[C-1]);S<L&&(L=S)}return L},p=function(T){for(var L=[],x=[],C=[],S={},g={},P={},E=T.length,z=0;z<E;z++){var q=T[z],V=q[0],G=q[1],Z=q[2];S[V]||(L.push(V),S[V]=!0),g[G]||(x.push(G),g[G]=!0),P[Z]||(C.push(Z),P[Z]=!0)}var j=b(L),N=b(x),H=b(C),re=Math.min(j,N,H);return isFinite(re)?re:1};i.exports=function(T,L){var x=T.startingPositions,C=T.maxLength||1e3,S=T.tubeSize||1,g=T.absoluteTubeSize,P=T.gridFill||\"+x+y+z\",E={};P.indexOf(\"-x\")!==-1&&(E.reversedX=!0),P.indexOf(\"-y\")!==-1&&(E.reversedY=!0),P.indexOf(\"-z\")!==-1&&(E.reversedZ=!0),E.filled=u.indexOf(P.replace(/-/g,\"\").replace(/\\+/g,\"\"));var z=T.getVelocity||function(ar){return v(ar,T,E)},q=T.getDivergence||function(ar,Vt){var rr=s.create(),tt=1e-4;s.add(rr,ar,[tt,0,0]);var je=z(rr);s.subtract(je,je,Vt),s.scale(je,je,1/tt),s.add(rr,ar,[0,tt,0]);var Ue=z(rr);s.subtract(Ue,Ue,Vt),s.scale(Ue,Ue,1/tt),s.add(rr,ar,[0,0,tt]);var Ae=z(rr);return s.subtract(Ae,Ae,Vt),s.scale(Ae,Ae,1/tt),s.add(rr,je,Ue),s.add(rr,rr,Ae),rr},V=[],G=L[0][0],Z=L[0][1],j=L[0][2],N=L[1][0],H=L[1][1],re=L[1][2],ae=function(ar){var Vt=ar[0],rr=ar[1],tt=ar[2];return!(Vt<G||Vt>N||rr<Z||rr>H||tt<j||tt>re)},_e=s.distance(L[0],L[1]),Ee=10*_e/C,Ce=Ee*Ee,ge=1,ie=0,Se=x.length;Se>1&&(ge=p(x));for(var ke=0;ke<Se;ke++){var be=s.create();s.copy(be,x[ke]);var Be=[be],Le=[],me=z(be),Pe=be;Le.push(me);var ce=[],He=q(be,me),lt=s.length(He);isFinite(lt)&&lt>ie&&(ie=lt),ce.push(lt),V.push({points:Be,velocities:Le,divergences:ce});for(var mt=0;mt<C*100&&Be.length<C&&ae(be);){mt++;var Ht=s.clone(me),at=s.squaredLength(Ht);if(at===0)break;if(at>Ce&&s.scale(Ht,Ht,Ee/Math.sqrt(at)),s.add(Ht,Ht,be),me=z(Ht),s.squaredDistance(Pe,Ht)-Ce>-1e-4*Ce){Be.push(Ht),Pe=Ht,Le.push(me);var He=q(Ht,me),lt=s.length(He);isFinite(lt)&&lt>ie&&(ie=lt),ce.push(lt)}be=Ht}}var ct=f(V,T.colormap,ie,ge);return g?ct.tubeScale=g:(ie===0&&(ie=1),ct.tubeScale=S*.5*ge/ie),ct};var k=o(6740),M=o(6405).createMesh;i.exports.createTubeMesh=function(T,L){return M(T,L,{shaders:k,traceType:\"streamtube\"})}}),990:(function(i,a,o){var s=o(9405),l=o(3236),u=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec4 uv;\n",
+       "attribute vec3 f;\n",
+       "attribute vec3 normal;\n",
+       "\n",
+       "uniform vec3 objectOffset;\n",
+       "uniform mat4 model, view, projection, inverseModel;\n",
+       "uniform vec3 lightPosition, eyePosition;\n",
+       "uniform sampler2D colormap;\n",
+       "\n",
+       "varying float value, kill;\n",
+       "varying vec3 worldCoordinate;\n",
+       "varying vec2 planeCoordinate;\n",
+       "varying vec3 lightDirection, eyeDirection, surfaceNormal;\n",
+       "varying vec4 vColor;\n",
+       "\n",
+       "void main() {\n",
+       "  vec3 localCoordinate = vec3(uv.zw, f.x);\n",
+       "  worldCoordinate = objectOffset + localCoordinate;\n",
+       "  mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));\n",
+       "  vec4 worldPosition = (model * objectOffsetTranslation) * vec4(localCoordinate, 1.0);\n",
+       "  vec4 clipPosition = projection * (view * worldPosition);\n",
+       "  gl_Position = clipPosition;\n",
+       "  kill = f.y;\n",
+       "  value = f.z;\n",
+       "  planeCoordinate = uv.xy;\n",
+       "\n",
+       "  vColor = texture2D(colormap, vec2(value, value));\n",
+       "\n",
+       "  //Lighting geometry parameters\n",
+       "  vec4 cameraCoordinate = view * worldPosition;\n",
+       "  cameraCoordinate.xyz /= cameraCoordinate.w;\n",
+       "  lightDirection = lightPosition - cameraCoordinate.xyz;\n",
+       "  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n",
+       "  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n",
+       "}\n",
+       "`]),c=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "float beckmannDistribution(float x, float roughness) {\n",
+       "  float NdotH = max(x, 0.0001);\n",
+       "  float cos2Alpha = NdotH * NdotH;\n",
+       "  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n",
+       "  float roughness2 = roughness * roughness;\n",
+       "  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n",
+       "  return exp(tan2Alpha / roughness2) / denom;\n",
+       "}\n",
+       "\n",
+       "float beckmannSpecular(\n",
+       "  vec3 lightDirection,\n",
+       "  vec3 viewDirection,\n",
+       "  vec3 surfaceNormal,\n",
+       "  float roughness) {\n",
+       "  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec3 lowerBound, upperBound;\n",
+       "uniform float contourTint;\n",
+       "uniform vec4 contourColor;\n",
+       "uniform sampler2D colormap;\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\n",
+       "uniform float vertexColor;\n",
+       "\n",
+       "varying float value, kill;\n",
+       "varying vec3 worldCoordinate;\n",
+       "varying vec3 lightDirection, eyeDirection, surfaceNormal;\n",
+       "varying vec4 vColor;\n",
+       "\n",
+       "void main() {\n",
+       "  if (\n",
+       "    kill > 0.0 ||\n",
+       "    vColor.a == 0.0 ||\n",
+       "    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n",
+       "  ) discard;\n",
+       "\n",
+       "  vec3 N = normalize(surfaceNormal);\n",
+       "  vec3 V = normalize(eyeDirection);\n",
+       "  vec3 L = normalize(lightDirection);\n",
+       "\n",
+       "  if(gl_FrontFacing) {\n",
+       "    N = -N;\n",
+       "  }\n",
+       "\n",
+       "  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n",
+       "  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n",
+       "\n",
+       "  //decide how to interpolate color \\u2014 in vertex or in fragment\n",
+       "  vec4 surfaceColor =\n",
+       "    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n",
+       "    step(.5, vertexColor) * vColor;\n",
+       "\n",
+       "  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n",
+       "\n",
+       "  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n",
+       "}\n",
+       "`]),f=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute vec4 uv;\n",
+       "attribute float f;\n",
+       "\n",
+       "uniform vec3 objectOffset;\n",
+       "uniform mat3 permutation;\n",
+       "uniform mat4 model, view, projection;\n",
+       "uniform float height, zOffset;\n",
+       "uniform sampler2D colormap;\n",
+       "\n",
+       "varying float value, kill;\n",
+       "varying vec3 worldCoordinate;\n",
+       "varying vec2 planeCoordinate;\n",
+       "varying vec3 lightDirection, eyeDirection, surfaceNormal;\n",
+       "varying vec4 vColor;\n",
+       "\n",
+       "void main() {\n",
+       "  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n",
+       "  worldCoordinate = objectOffset + dataCoordinate;\n",
+       "  mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));\n",
+       "  vec4 worldPosition = (model * objectOffsetTranslation) * vec4(dataCoordinate, 1.0);\n",
+       "\n",
+       "  vec4 clipPosition = projection * (view * worldPosition);\n",
+       "  clipPosition.z += zOffset;\n",
+       "\n",
+       "  gl_Position = clipPosition;\n",
+       "  value = f + objectOffset.z;\n",
+       "  kill = -1.0;\n",
+       "  planeCoordinate = uv.zw;\n",
+       "\n",
+       "  vColor = texture2D(colormap, vec2(value, value));\n",
+       "\n",
+       "  //Don't do lighting for contours\n",
+       "  surfaceNormal   = vec3(1,0,0);\n",
+       "  eyeDirection    = vec3(0,1,0);\n",
+       "  lightDirection  = vec3(0,0,1);\n",
+       "}\n",
+       "`]),h=l([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "bool outOfRange(float a, float b, float p) {\n",
+       "  return ((p > max(a, b)) || \n",
+       "          (p < min(a, b)));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+       "  return (outOfRange(a.x, b.x, p.x) ||\n",
+       "          outOfRange(a.y, b.y, p.y) ||\n",
+       "          outOfRange(a.z, b.z, p.z));\n",
+       "}\n",
+       "\n",
+       "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+       "  return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+       "}\n",
+       "\n",
+       "uniform vec2 shape;\n",
+       "uniform vec3 clipBounds[2];\n",
+       "uniform float pickId;\n",
+       "\n",
+       "varying float value, kill;\n",
+       "varying vec3 worldCoordinate;\n",
+       "varying vec2 planeCoordinate;\n",
+       "varying vec3 surfaceNormal;\n",
+       "\n",
+       "vec2 splitFloat(float v) {\n",
+       "  float vh = 255.0 * v;\n",
+       "  float upper = floor(vh);\n",
+       "  float lower = fract(vh);\n",
+       "  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "  if ((kill > 0.0) ||\n",
+       "      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n",
+       "\n",
+       "  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n",
+       "  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n",
+       "  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n",
+       "}\n",
+       "`]);a.createShader=function(d){var v=s(d,u,c,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v.attributes.normal.location=2,v},a.createPickShader=function(d){var v=s(d,u,h,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v.attributes.normal.location=2,v},a.createContourShader=function(d){var v=s(d,f,c,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v},a.createPickContourShader=function(d){var v=s(d,f,h,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v}}),9499:(function(i,a,o){\"use strict\";i.exports=Be;var s=o(8828),l=o(2762),u=o(8116),c=o(7766),f=o(1888),h=o(6729),d=o(5298),v=o(9994),m=o(9618),b=o(3711),p=o(6760),k=o(7608),M=o(2478),T=o(6199),L=o(990),x=L.createShader,C=L.createContourShader,S=L.createPickShader,g=L.createPickContourShader,P=40,E=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],z=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],q=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];(function(){for(var Le=0;Le<3;++Le){var me=q[Le],Pe=(Le+1)%3,ce=(Le+2)%3;me[Pe+0]=1,me[ce+3]=1,me[Le+6]=1}})();function V(Le,me,Pe,ce,He){this.position=Le,this.index=me,this.uv=Pe,this.level=ce,this.dataCoordinate=He}var G=256;function Z(Le,me,Pe,ce,He,lt,mt,Ht,at,ct,ar,Vt,rr,tt,je){this.gl=Le,this.shape=me,this.bounds=Pe,this.objectOffset=je,this.intensityBounds=[],this._shader=ce,this._pickShader=He,this._coordinateBuffer=lt,this._vao=mt,this._colorMap=Ht,this._contourShader=at,this._contourPickShader=ct,this._contourBuffer=ar,this._contourVAO=Vt,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new V([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=rr,this._dynamicVAO=tt,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[m(f.mallocFloat(1024),[0,0]),m(f.mallocFloat(1024),[0,0]),m(f.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var j=Z.prototype;j.genColormap=function(Le,me){var Pe=!1,ce=v([h({colormap:Le,nshades:G,format:\"rgba\"}).map(function(He,lt){var mt=me?N(lt/255,me):He[3];return mt<1&&(Pe=!0),[He[0],He[1],He[2],255*mt]})]);return d.divseq(ce,255),this.hasAlphaScale=Pe,ce},j.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},j.isOpaque=function(){return!this.isTransparent()},j.pickSlots=1,j.setPickBase=function(Le){this.pickId=Le};function N(Le,me){if(!me||!me.length)return 1;for(var Pe=0;Pe<me.length;++Pe){if(me.length<2)return 1;if(me[Pe][0]===Le)return me[Pe][1];if(me[Pe][0]>Le&&Pe>0){var ce=(me[Pe][0]-Le)/(me[Pe][0]-me[Pe-1][0]);return me[Pe][1]*(1-ce)+ce*me[Pe-1][1]}}return 1}var H=[0,0,0],re={showSurface:!1,showContour:!1,projections:[E.slice(),E.slice(),E.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function ae(Le,me){var Pe,ce,He,lt=me.axes&&me.axes.lastCubeProps.axis||H,mt=me.showSurface,Ht=me.showContour;for(Pe=0;Pe<3;++Pe)for(mt=mt||me.surfaceProject[Pe],ce=0;ce<3;++ce)Ht=Ht||me.contourProject[Pe][ce];for(Pe=0;Pe<3;++Pe){var at=re.projections[Pe];for(ce=0;ce<16;++ce)at[ce]=0;for(ce=0;ce<4;++ce)at[5*ce]=1;at[5*Pe]=0,at[12+Pe]=me.axesBounds[+(lt[Pe]>0)][Pe],p(at,Le.model,at);var ct=re.clipBounds[Pe];for(He=0;He<2;++He)for(ce=0;ce<3;++ce)ct[He][ce]=Le.clipBounds[He][ce];ct[0][Pe]=-1e8,ct[1][Pe]=1e8}return re.showSurface=mt,re.showContour=Ht,re}var _e={model:E,view:E,projection:E,inverseModel:E.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},Ee=E.slice(),Ce=[1,0,0,0,1,0,0,0,1];function ge(Le,me){Le=Le||{};var Pe=this.gl;Pe.disable(Pe.CULL_FACE),this._colorMap.bind(0);var ce=_e;ce.model=Le.model||E,ce.view=Le.view||E,ce.projection=Le.projection||E,ce.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],ce.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],ce.objectOffset=this.objectOffset,ce.contourColor=this.contourColor[0],ce.inverseModel=k(ce.inverseModel,ce.model);for(var He=0;He<2;++He)for(var lt=ce.clipBounds[He],mt=0;mt<3;++mt)lt[mt]=Math.min(Math.max(this.clipBounds[He][mt],-1e8),1e8);ce.kambient=this.ambientLight,ce.kdiffuse=this.diffuseLight,ce.kspecular=this.specularLight,ce.roughness=this.roughness,ce.fresnel=this.fresnel,ce.opacity=this.opacity,ce.height=0,ce.permutation=Ce,ce.vertexColor=this.vertexColor;var Ht=Ee;for(p(Ht,ce.view,ce.model),p(Ht,ce.projection,Ht),k(Ht,Ht),He=0;He<3;++He)ce.eyePosition[He]=Ht[12+He]/Ht[15];var at=Ht[15];for(He=0;He<3;++He)at+=this.lightPosition[He]*Ht[4*He+3];for(He=0;He<3;++He){var ct=Ht[12+He];for(mt=0;mt<3;++mt)ct+=Ht[4*mt+He]*this.lightPosition[mt];ce.lightPosition[He]=ct/at}var ar=ae(ce,this);if(ar.showSurface){for(this._shader.bind(),this._shader.uniforms=ce,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(Pe.TRIANGLES,this._vertexCount),He=0;He<3;++He)!this.surfaceProject[He]||!this.vertexCount||(this._shader.uniforms.model=ar.projections[He],this._shader.uniforms.clipBounds=ar.clipBounds[He],this._vao.draw(Pe.TRIANGLES,this._vertexCount));this._vao.unbind()}if(ar.showContour){var Vt=this._contourShader;ce.kambient=1,ce.kdiffuse=0,ce.kspecular=0,ce.opacity=1,Vt.bind(),Vt.uniforms=ce;var rr=this._contourVAO;for(rr.bind(),He=0;He<3;++He)for(Vt.uniforms.permutation=q[He],Pe.lineWidth(this.contourWidth[He]*this.pixelRatio),mt=0;mt<this.contourLevels[He].length;++mt)mt===this.highlightLevel[He]?(Vt.uniforms.contourColor=this.highlightColor[He],Vt.uniforms.contourTint=this.highlightTint[He]):(mt===0||mt-1===this.highlightLevel[He])&&(Vt.uniforms.contourColor=this.contourColor[He],Vt.uniforms.contourTint=this.contourTint[He]),this._contourCounts[He][mt]&&(Vt.uniforms.height=this.contourLevels[He][mt],rr.draw(Pe.LINES,this._contourCounts[He][mt],this._contourOffsets[He][mt]));for(He=0;He<3;++He)for(Vt.uniforms.model=ar.projections[He],Vt.uniforms.clipBounds=ar.clipBounds[He],mt=0;mt<3;++mt)if(this.contourProject[He][mt]){Vt.uniforms.permutation=q[mt],Pe.lineWidth(this.contourWidth[mt]*this.pixelRatio);for(var tt=0;tt<this.contourLevels[mt].length;++tt)tt===this.highlightLevel[mt]?(Vt.uniforms.contourColor=this.highlightColor[mt],Vt.uniforms.contourTint=this.highlightTint[mt]):(tt===0||tt-1===this.highlightLevel[mt])&&(Vt.uniforms.contourColor=this.contourColor[mt],Vt.uniforms.contourTint=this.contourTint[mt]),this._contourCounts[mt][tt]&&(Vt.uniforms.height=this.contourLevels[mt][tt],rr.draw(Pe.LINES,this._contourCounts[mt][tt],this._contourOffsets[mt][tt]))}for(rr.unbind(),rr=this._dynamicVAO,rr.bind(),He=0;He<3;++He)if(this._dynamicCounts[He]!==0)for(Vt.uniforms.model=ce.model,Vt.uniforms.clipBounds=ce.clipBounds,Vt.uniforms.permutation=q[He],Pe.lineWidth(this.dynamicWidth[He]*this.pixelRatio),Vt.uniforms.contourColor=this.dynamicColor[He],Vt.uniforms.contourTint=this.dynamicTint[He],Vt.uniforms.height=this.dynamicLevel[He],rr.draw(Pe.LINES,this._dynamicCounts[He],this._dynamicOffsets[He]),mt=0;mt<3;++mt)this.contourProject[mt][He]&&(Vt.uniforms.model=ar.projections[mt],Vt.uniforms.clipBounds=ar.clipBounds[mt],rr.draw(Pe.LINES,this._dynamicCounts[He],this._dynamicOffsets[He]));rr.unbind()}}j.draw=function(Le){return ge.call(this,Le,!1)},j.drawTransparent=function(Le){return ge.call(this,Le,!0)};var ie={model:E,view:E,projection:E,inverseModel:E,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};j.drawPick=function(Le){Le=Le||{};var me=this.gl;me.disable(me.CULL_FACE);var Pe=ie;Pe.model=Le.model||E,Pe.view=Le.view||E,Pe.projection=Le.projection||E,Pe.shape=this._field[2].shape,Pe.pickId=this.pickId/255,Pe.lowerBound=this.bounds[0],Pe.upperBound=this.bounds[1],Pe.objectOffset=this.objectOffset,Pe.permutation=Ce;for(var ce=0;ce<2;++ce)for(var He=Pe.clipBounds[ce],lt=0;lt<3;++lt)He[lt]=Math.min(Math.max(this.clipBounds[ce][lt],-1e8),1e8);var mt=ae(Pe,this);if(mt.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=Pe,this._vao.bind(),this._vao.draw(me.TRIANGLES,this._vertexCount),ce=0;ce<3;++ce)this.surfaceProject[ce]&&(this._pickShader.uniforms.model=mt.projections[ce],this._pickShader.uniforms.clipBounds=mt.clipBounds[ce],this._vao.draw(me.TRIANGLES,this._vertexCount));this._vao.unbind()}if(mt.showContour){var Ht=this._contourPickShader;Ht.bind(),Ht.uniforms=Pe;var at=this._contourVAO;for(at.bind(),lt=0;lt<3;++lt)for(me.lineWidth(this.contourWidth[lt]*this.pixelRatio),Ht.uniforms.permutation=q[lt],ce=0;ce<this.contourLevels[lt].length;++ce)this._contourCounts[lt][ce]&&(Ht.uniforms.height=this.contourLevels[lt][ce],at.draw(me.LINES,this._contourCounts[lt][ce],this._contourOffsets[lt][ce]));for(ce=0;ce<3;++ce)for(Ht.uniforms.model=mt.projections[ce],Ht.uniforms.clipBounds=mt.clipBounds[ce],lt=0;lt<3;++lt)if(this.contourProject[ce][lt]){Ht.uniforms.permutation=q[lt],me.lineWidth(this.contourWidth[lt]*this.pixelRatio);for(var ct=0;ct<this.contourLevels[lt].length;++ct)this._contourCounts[lt][ct]&&(Ht.uniforms.height=this.contourLevels[lt][ct],at.draw(me.LINES,this._contourCounts[lt][ct],this._contourOffsets[lt][ct]))}at.unbind()}},j.pick=function(Le){if(!Le||Le.id!==this.pickId)return null;var me=this._field[2].shape,Pe=this._pickResult,ce=me[0]*(Le.value[0]+(Le.value[2]>>4)/16)/255,He=Math.floor(ce),lt=ce-He,mt=me[1]*(Le.value[1]+(Le.value[2]&15)/16)/255,Ht=Math.floor(mt),at=mt-Ht;He+=1,Ht+=1;var ct=Pe.position;ct[0]=ct[1]=ct[2]=0;for(var ar=0;ar<2;++ar)for(var Vt=ar?lt:1-lt,rr=0;rr<2;++rr)for(var tt=rr?at:1-at,je=He+ar,Ue=Ht+rr,Ae=Vt*tt,rt=0;rt<3;++rt)ct[rt]+=this._field[rt].get(je,Ue)*Ae;for(var St=this._pickResult.level,Tt=0;Tt<3;++Tt)if(St[Tt]=M.le(this.contourLevels[Tt],ct[Tt]),St[Tt]<0)this.contourLevels[Tt].length>0&&(St[Tt]=0);else if(St[Tt]<this.contourLevels[Tt].length-1){var dt=this.contourLevels[Tt][St[Tt]],Et=this.contourLevels[Tt][St[Tt]+1];Math.abs(dt-ct[Tt])>Math.abs(Et-ct[Tt])&&(St[Tt]+=1)}for(Pe.index[0]=lt<.5?He:He+1,Pe.index[1]=at<.5?Ht:Ht+1,Pe.uv[0]=ce/me[0],Pe.uv[1]=mt/me[1],rt=0;rt<3;++rt)Pe.dataCoordinate[rt]=this._field[rt].get(Pe.index[0],Pe.index[1]);return Pe},j.padField=function(Le,me){var Pe=me.shape.slice(),ce=Le.shape.slice();d.assign(Le.lo(1,1).hi(Pe[0],Pe[1]),me),d.assign(Le.lo(1).hi(Pe[0],1),me.hi(Pe[0],1)),d.assign(Le.lo(1,ce[1]-1).hi(Pe[0],1),me.lo(0,Pe[1]-1).hi(Pe[0],1)),d.assign(Le.lo(0,1).hi(1,Pe[1]),me.hi(1)),d.assign(Le.lo(ce[0]-1,1).hi(1,Pe[1]),me.lo(Pe[0]-1)),Le.set(0,0,me.get(0,0)),Le.set(0,ce[1]-1,me.get(0,Pe[1]-1)),Le.set(ce[0]-1,0,me.get(Pe[0]-1,0)),Le.set(ce[0]-1,ce[1]-1,me.get(Pe[0]-1,Pe[1]-1))};function Se(Le,me){return Array.isArray(Le)?[me(Le[0]),me(Le[1]),me(Le[2])]:[me(Le),me(Le),me(Le)]}function ke(Le){return Array.isArray(Le)?Le.length===3?[Le[0],Le[1],Le[2],1]:[Le[0],Le[1],Le[2],Le[3]]:[0,0,0,1]}function be(Le){if(Array.isArray(Le)){if(Array.isArray(Le))return[ke(Le[0]),ke(Le[1]),ke(Le[2])];var me=ke(Le);return[me.slice(),me.slice(),me.slice()]}}j.update=function(Le){Le=Le||{},this.objectOffset=Le.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in Le&&(this.contourWidth=Se(Le.contourWidth,Number)),\"showContour\"in Le&&(this.showContour=Se(Le.showContour,Boolean)),\"showSurface\"in Le&&(this.showSurface=!!Le.showSurface),\"contourTint\"in Le&&(this.contourTint=Se(Le.contourTint,Boolean)),\"contourColor\"in Le&&(this.contourColor=be(Le.contourColor)),\"contourProject\"in Le&&(this.contourProject=Se(Le.contourProject,function(yn){return Se(yn,Boolean)})),\"surfaceProject\"in Le&&(this.surfaceProject=Le.surfaceProject),\"dynamicColor\"in Le&&(this.dynamicColor=be(Le.dynamicColor)),\"dynamicTint\"in Le&&(this.dynamicTint=Se(Le.dynamicTint,Number)),\"dynamicWidth\"in Le&&(this.dynamicWidth=Se(Le.dynamicWidth,Number)),\"opacity\"in Le&&(this.opacity=Le.opacity),\"opacityscale\"in Le&&(this.opacityscale=Le.opacityscale),\"colorBounds\"in Le&&(this.colorBounds=Le.colorBounds),\"vertexColor\"in Le&&(this.vertexColor=Le.vertexColor?1:0),\"colormap\"in Le&&this._colorMap.setPixels(this.genColormap(Le.colormap,this.opacityscale));var me=Le.field||Le.coords&&Le.coords[2]||null,Pe=!1;if(me||(this._field[2].shape[0]||this._field[2].shape[2]?me=this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):me=this._field[2].hi(0,0)),\"field\"in Le||\"coords\"in Le){var ce=(me.shape[0]+2)*(me.shape[1]+2);ce>this._field[2].data.length&&(f.freeFloat(this._field[2].data),this._field[2].data=f.mallocFloat(s.nextPow2(ce))),this._field[2]=m(this._field[2].data,[me.shape[0]+2,me.shape[1]+2]),this.padField(this._field[2],me),this.shape=me.shape.slice();for(var He=this.shape,lt=0;lt<2;++lt)this._field[2].size>this._field[lt].data.length&&(f.freeFloat(this._field[lt].data),this._field[lt].data=f.mallocFloat(this._field[2].size)),this._field[lt]=m(this._field[lt].data,[He[0]+2,He[1]+2]);if(Le.coords){var mt=Le.coords;if(!Array.isArray(mt)||mt.length!==3)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(lt=0;lt<2;++lt){var Ht=mt[lt];for(rr=0;rr<2;++rr)if(Ht.shape[rr]!==He[rr])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[lt],Ht)}}else if(Le.ticks){var at=Le.ticks;if(!Array.isArray(at)||at.length!==2)throw new Error(\"gl-surface: invalid ticks\");for(lt=0;lt<2;++lt){var ct=at[lt];if((Array.isArray(ct)||ct.length)&&(ct=m(ct)),ct.shape[0]!==He[lt])throw new Error(\"gl-surface: invalid tick length\");var ar=m(ct.data,He);ar.stride[lt]=ct.stride[0],ar.stride[lt^1]=0,this.padField(this._field[lt],ar)}}else{for(lt=0;lt<2;++lt){var Vt=[0,0];Vt[lt]=1,this._field[lt]=m(this._field[lt].data,[He[0]+2,He[1]+2],Vt,0)}this._field[0].set(0,0,0);for(var rr=0;rr<He[0];++rr)this._field[0].set(rr+1,0,rr);for(this._field[0].set(He[0]+1,0,He[0]-1),this._field[1].set(0,0,0),rr=0;rr<He[1];++rr)this._field[1].set(0,rr+1,rr);this._field[1].set(0,He[1]+1,He[1]-1)}var tt=this._field,je=m(f.mallocFloat(tt[2].size*3*2),[3,He[0]+2,He[1]+2,2]);for(lt=0;lt<3;++lt)T(je.pick(lt),tt[lt],\"mirror\");var Ue=m(f.mallocFloat(tt[2].size*3),[He[0]+2,He[1]+2,3]);for(lt=0;lt<He[0]+2;++lt)for(rr=0;rr<He[1]+2;++rr){var Ae=je.get(0,lt,rr,0),rt=je.get(0,lt,rr,1),St=je.get(1,lt,rr,0),Tt=je.get(1,lt,rr,1),dt=je.get(2,lt,rr,0),Et=je.get(2,lt,rr,1),pt=St*Et-Tt*dt,jt=dt*rt-Et*Ae,or=Ae*Tt-rt*St,mr=Math.sqrt(pt*pt+jt*jt+or*or);mr<1e-8?(mr=Math.max(Math.abs(pt),Math.abs(jt),Math.abs(or)),mr<1e-8?(or=1,jt=pt=0,mr=1):mr=1/mr):mr=1/Math.sqrt(mr),Ue.set(lt,rr,0,pt*mr),Ue.set(lt,rr,1,jt*mr),Ue.set(lt,rr,2,or*mr)}f.free(je.data);var Ar=[1/0,1/0,1/0],ei=[-1/0,-1/0,-1/0],qr=1/0,jr=-1/0,gt=(He[0]-1)*(He[1]-1)*6,Ge=f.mallocFloat(s.nextPow2(10*gt)),Je=0,We=0;for(lt=0;lt<He[0]-1;++lt)e:for(rr=0;rr<He[1]-1;++rr){for(var et=0;et<2;++et)for(var xt=0;xt<2;++xt)for(var At=0;At<3;++At){var Kt=this._field[At].get(1+lt+et,1+rr+xt);if(isNaN(Kt)||!isFinite(Kt))continue e}for(At=0;At<6;++At){var Qt=lt+z[At][0],Mr=rr+z[At][1],Gr=this._field[0].get(Qt+1,Mr+1),Ir=this._field[1].get(Qt+1,Mr+1);Kt=this._field[2].get(Qt+1,Mr+1),pt=Ue.get(Qt+1,Mr+1,0),jt=Ue.get(Qt+1,Mr+1,1),or=Ue.get(Qt+1,Mr+1,2),Le.intensity&&(Yr=Le.intensity.get(Qt,Mr));var Yr=Le.intensity?Le.intensity.get(Qt,Mr):Kt+this.objectOffset[2];Ge[Je++]=Qt,Ge[Je++]=Mr,Ge[Je++]=Gr,Ge[Je++]=Ir,Ge[Je++]=Kt,Ge[Je++]=0,Ge[Je++]=Yr,Ge[Je++]=pt,Ge[Je++]=jt,Ge[Je++]=or,Ar[0]=Math.min(Ar[0],Gr+this.objectOffset[0]),Ar[1]=Math.min(Ar[1],Ir+this.objectOffset[1]),Ar[2]=Math.min(Ar[2],Kt+this.objectOffset[2]),qr=Math.min(qr,Yr),ei[0]=Math.max(ei[0],Gr+this.objectOffset[0]),ei[1]=Math.max(ei[1],Ir+this.objectOffset[1]),ei[2]=Math.max(ei[2],Kt+this.objectOffset[2]),jr=Math.max(jr,Yr),We+=1}}for(Le.intensityBounds&&(qr=+Le.intensityBounds[0],jr=+Le.intensityBounds[1]),lt=6;lt<Je;lt+=10)Ge[lt]=(Ge[lt]-qr)/(jr-qr);this._vertexCount=We,this._coordinateBuffer.update(Ge.subarray(0,Je)),f.freeFloat(Ge),f.free(Ue.data),this.bounds=[Ar,ei],this.intensity=Le.intensity||this._field[2],(this.intensityBounds[0]!==qr||this.intensityBounds[1]!==jr)&&(Pe=!0),this.intensityBounds=[qr,jr]}if(\"levels\"in Le){var _i=Le.levels;for(Array.isArray(_i[0])?_i=_i.slice():_i=[[],[],_i],lt=0;lt<3;++lt)_i[lt]=_i[lt].slice(),_i[lt].sort(function(yn,Mn){return yn-Mn});for(lt=0;lt<3;++lt)for(rr=0;rr<_i[lt].length;++rr)_i[lt][rr]-=this.objectOffset[lt];e:for(lt=0;lt<3;++lt){if(_i[lt].length!==this.contourLevels[lt].length){Pe=!0;break}for(rr=0;rr<_i[lt].length;++rr)if(_i[lt][rr]!==this.contourLevels[lt][rr]){Pe=!0;break e}}this.contourLevels=_i}if(Pe){tt=this._field,He=this.shape;for(var Pi=[],ai=0;ai<3;++ai){var mi=this.contourLevels[ai],un=[],Fn=[],An=[0,0,0];for(lt=0;lt<mi.length;++lt){var Hn=b(this._field[ai],mi[lt]);un.push(Pi.length/5|0),We=0;e:for(rr=0;rr<Hn.cells.length;++rr){var Qn=Hn.cells[rr];for(At=0;At<2;++At){var Vi=Hn.positions[Qn[At]],Jn=Vi[0],Yn=Math.floor(Jn)|0,Gt=Jn-Yn,wt=Vi[1],tr=Math.floor(wt)|0,ir=wt-tr,wr=!1;t:for(var Xr=0;Xr<3;++Xr){An[Xr]=0;var ti=(ai+Xr+1)%3;for(et=0;et<2;++et){var $r=et?Gt:1-Gt;for(Qt=Math.min(Math.max(Yn+et,0),He[0])|0,xt=0;xt<2;++xt){var Ri=xt?ir:1-ir;if(Mr=Math.min(Math.max(tr+xt,0),He[1])|0,Xr<2?Kt=this._field[ti].get(Qt,Mr):Kt=(this.intensity.get(Qt,Mr)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(Kt)||isNaN(Kt)){wr=!0;break t}var Hi=$r*Ri;An[Xr]+=Hi*Kt}}}if(!wr)Pi.push(An[0],An[1],Vi[0],Vi[1],An[2]),We+=1;else{if(At>0){for(var en=0;en<5;++en)Pi.pop();We-=1}continue e}}}Fn.push(We)}this._contourOffsets[ai]=un,this._contourCounts[ai]=Fn}var fn=f.mallocFloat(Pi.length);for(lt=0;lt<Pi.length;++lt)fn[lt]=Pi[lt];this._contourBuffer.update(fn),f.freeFloat(fn)}},j.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var Le=0;Le<3;++Le)f.freeFloat(this._field[Le].data)},j.highlight=function(Le){var me;if(!Le){this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],this.highlightLevel=[-1,-1,-1];return}for(me=0;me<3;++me)this.enableHighlight[me]?this.highlightLevel[me]=Le.level[me]:this.highlightLevel[me]=-1;var Pe;for(this.snapToData?Pe=Le.dataCoordinate:Pe=Le.position,me=0;me<3;++me)Pe[me]-=this.objectOffset[me];if(!((!this.enableDynamic[0]||Pe[0]===this.dynamicLevel[0])&&(!this.enableDynamic[1]||Pe[1]===this.dynamicLevel[1])&&(!this.enableDynamic[2]||Pe[2]===this.dynamicLevel[2]))){for(var ce=0,He=this.shape,lt=f.mallocFloat(12*He[0]*He[1]),mt=0;mt<3;++mt){if(!this.enableDynamic[mt]){this.dynamicLevel[mt]=NaN,this._dynamicCounts[mt]=0;continue}this.dynamicLevel[mt]=Pe[mt];var Ht=(mt+1)%3,at=(mt+2)%3,ct=this._field[mt],ar=this._field[Ht],Vt=this._field[at],rr=b(ct,Pe[mt]),tt=rr.cells,je=rr.positions;for(this._dynamicOffsets[mt]=ce,me=0;me<tt.length;++me)for(var Ue=tt[me],Ae=0;Ae<2;++Ae){var rt=je[Ue[Ae]],St=+rt[0],Tt=St|0,dt=Math.min(Tt+1,He[0])|0,Et=St-Tt,pt=1-Et,jt=+rt[1],or=jt|0,mr=Math.min(or+1,He[1])|0,Ar=jt-or,ei=1-Ar,qr=pt*ei,jr=pt*Ar,gt=Et*ei,Ge=Et*Ar,Je=qr*ar.get(Tt,or)+jr*ar.get(Tt,mr)+gt*ar.get(dt,or)+Ge*ar.get(dt,mr),We=qr*Vt.get(Tt,or)+jr*Vt.get(Tt,mr)+gt*Vt.get(dt,or)+Ge*Vt.get(dt,mr);if(isNaN(Je)||isNaN(We)){Ae&&(ce-=1);break}lt[2*ce+0]=Je,lt[2*ce+1]=We,ce+=1}this._dynamicCounts[mt]=ce-this._dynamicOffsets[mt]}this._dynamicBuffer.update(lt.subarray(0,2*ce)),f.freeFloat(lt)}};function Be(Le){var me=Le.gl,Pe=x(me),ce=S(me),He=C(me),lt=g(me),mt=l(me),Ht=u(me,[{buffer:mt,size:4,stride:P,offset:0},{buffer:mt,size:3,stride:P,offset:16},{buffer:mt,size:3,stride:P,offset:28}]),at=l(me),ct=u(me,[{buffer:at,size:4,stride:20,offset:0},{buffer:at,size:1,stride:20,offset:16}]),ar=l(me),Vt=u(me,[{buffer:ar,size:2,type:me.FLOAT}]),rr=c(me,1,G,me.RGBA,me.UNSIGNED_BYTE);rr.minFilter=me.LINEAR,rr.magFilter=me.LINEAR;var tt=new Z(me,[0,0],[[0,0,0],[0,0,0]],Pe,ce,mt,Ht,rr,He,lt,at,ct,ar,Vt,[0,0,0]),je={levels:[[],[],[]]};for(var Ue in Le)je[Ue]=Le[Ue];return je.colormap=je.colormap||\"jet\",tt.update(je),tt}}),7766:(function(i,a,o){\"use strict\";var s=o(9618),l=o(5298),u=o(1888);i.exports=g;var c=null,f=null,h=null;function d(P){c=[P.LINEAR,P.NEAREST_MIPMAP_LINEAR,P.LINEAR_MIPMAP_NEAREST,P.LINEAR_MIPMAP_NEAREST],f=[P.NEAREST,P.LINEAR,P.NEAREST_MIPMAP_NEAREST,P.NEAREST_MIPMAP_LINEAR,P.LINEAR_MIPMAP_NEAREST,P.LINEAR_MIPMAP_LINEAR],h=[P.REPEAT,P.CLAMP_TO_EDGE,P.MIRRORED_REPEAT]}function v(P){return typeof HTMLCanvasElement!=\"undefined\"&&P instanceof HTMLCanvasElement||typeof HTMLImageElement!=\"undefined\"&&P instanceof HTMLImageElement||typeof HTMLVideoElement!=\"undefined\"&&P instanceof HTMLVideoElement||typeof ImageData!=\"undefined\"&&P instanceof ImageData}var m=function(P,E){l.muls(P,E,255)};function b(P,E,z){var q=P.gl,V=q.getParameter(q.MAX_TEXTURE_SIZE);if(E<0||E>V||z<0||z>V)throw new Error(\"gl-texture2d: Invalid texture size\");return P._shape=[E,z],P.bind(),q.texImage2D(q.TEXTURE_2D,0,P.format,E,z,0,P.format,P.type,null),P._mipLevels=[0],P}function p(P,E,z,q,V,G){this.gl=P,this.handle=E,this.format=V,this.type=G,this._shape=[z,q],this._mipLevels=[0],this._magFilter=P.NEAREST,this._minFilter=P.NEAREST,this._wrapS=P.CLAMP_TO_EDGE,this._wrapT=P.CLAMP_TO_EDGE,this._anisoSamples=1;var Z=this,j=[this._wrapS,this._wrapT];Object.defineProperties(j,[{get:function(){return Z._wrapS},set:function(H){return Z.wrapS=H}},{get:function(){return Z._wrapT},set:function(H){return Z.wrapT=H}}]),this._wrapVector=j;var N=[this._shape[0],this._shape[1]];Object.defineProperties(N,[{get:function(){return Z._shape[0]},set:function(H){return Z.width=H}},{get:function(){return Z._shape[1]},set:function(H){return Z.height=H}}]),this._shapeVector=N}var k=p.prototype;Object.defineProperties(k,{minFilter:{get:function(){return this._minFilter},set:function(P){this.bind();var E=this.gl;if(this.type===E.FLOAT&&c.indexOf(P)>=0&&(E.getExtension(\"OES_texture_float_linear\")||(P=E.NEAREST)),f.indexOf(P)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+P);return E.texParameteri(E.TEXTURE_2D,E.TEXTURE_MIN_FILTER,P),this._minFilter=P}},magFilter:{get:function(){return this._magFilter},set:function(P){this.bind();var E=this.gl;if(this.type===E.FLOAT&&c.indexOf(P)>=0&&(E.getExtension(\"OES_texture_float_linear\")||(P=E.NEAREST)),f.indexOf(P)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+P);return E.texParameteri(E.TEXTURE_2D,E.TEXTURE_MAG_FILTER,P),this._magFilter=P}},mipSamples:{get:function(){return this._anisoSamples},set:function(P){var E=this._anisoSamples;if(this._anisoSamples=Math.max(P,1)|0,E!==this._anisoSamples){var z=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");z&&this.gl.texParameterf(this.gl.TEXTURE_2D,z.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(P){if(this.bind(),h.indexOf(P)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+P);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,P),this._wrapS=P}},wrapT:{get:function(){return this._wrapT},set:function(P){if(this.bind(),h.indexOf(P)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+P);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,P),this._wrapT=P}},wrap:{get:function(){return this._wrapVector},set:function(P){if(Array.isArray(P)||(P=[P,P]),P.length!==2)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var E=0;E<2;++E)if(h.indexOf(P[E])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+P);this._wrapS=P[0],this._wrapT=P[1];var z=this.gl;return this.bind(),z.texParameteri(z.TEXTURE_2D,z.TEXTURE_WRAP_S,this._wrapS),z.texParameteri(z.TEXTURE_2D,z.TEXTURE_WRAP_T,this._wrapT),P}},shape:{get:function(){return this._shapeVector},set:function(P){if(!Array.isArray(P))P=[P|0,P|0];else if(P.length!==2)throw new Error(\"gl-texture2d: Invalid texture shape\");return b(this,P[0]|0,P[1]|0),[P[0]|0,P[1]|0]}},width:{get:function(){return this._shape[0]},set:function(P){return P=P|0,b(this,P,this._shape[1]),P}},height:{get:function(){return this._shape[1]},set:function(P){return P=P|0,b(this,this._shape[0],P),P}}}),k.bind=function(P){var E=this.gl;return P!==void 0&&E.activeTexture(E.TEXTURE0+(P|0)),E.bindTexture(E.TEXTURE_2D,this.handle),P!==void 0?P|0:E.getParameter(E.ACTIVE_TEXTURE)-E.TEXTURE0},k.dispose=function(){this.gl.deleteTexture(this.handle)},k.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var P=Math.min(this._shape[0],this._shape[1]),E=0;P>0;++E,P>>>=1)this._mipLevels.indexOf(E)<0&&this._mipLevels.push(E)},k.setPixels=function(P,E,z,q){var V=this.gl;this.bind(),Array.isArray(E)?(q=z,z=E[1]|0,E=E[0]|0):(E=E||0,z=z||0),q=q||0;var G=v(P)?P:P.raw;if(G){var Z=this._mipLevels.indexOf(q)<0;Z?(V.texImage2D(V.TEXTURE_2D,0,this.format,this.format,this.type,G),this._mipLevels.push(q)):V.texSubImage2D(V.TEXTURE_2D,q,E,z,this.format,this.type,G)}else if(P.shape&&P.stride&&P.data){if(P.shape.length<2||E+P.shape[1]>this._shape[1]>>>q||z+P.shape[0]>this._shape[0]>>>q||E<0||z<0)throw new Error(\"gl-texture2d: Texture dimensions are out of bounds\");T(V,E,z,q,this.format,this.type,this._mipLevels,P)}else throw new Error(\"gl-texture2d: Unsupported data type\")};function M(P,E){return P.length===3?E[2]===1&&E[1]===P[0]*P[2]&&E[0]===P[2]:E[0]===1&&E[1]===P[0]}function T(P,E,z,q,V,G,Z,j){var N=j.dtype,H=j.shape.slice();if(H.length<2||H.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var re=0,ae=0,_e=M(H,j.stride.slice());N===\"float32\"?re=P.FLOAT:N===\"float64\"?(re=P.FLOAT,_e=!1,N=\"float32\"):N===\"uint8\"?re=P.UNSIGNED_BYTE:(re=P.UNSIGNED_BYTE,_e=!1,N=\"uint8\");var Ee=1;if(H.length===2)ae=P.LUMINANCE,H=[H[0],H[1],1],j=s(j.data,H,[j.stride[0],j.stride[1],1],j.offset);else if(H.length===3){if(H[2]===1)ae=P.ALPHA;else if(H[2]===2)ae=P.LUMINANCE_ALPHA;else if(H[2]===3)ae=P.RGB;else if(H[2]===4)ae=P.RGBA;else throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");Ee=H[2]}else throw new Error(\"gl-texture2d: Invalid shape for texture\");if((ae===P.LUMINANCE||ae===P.ALPHA)&&(V===P.LUMINANCE||V===P.ALPHA)&&(ae=V),ae!==V)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var Ce=j.size,ge=Z.indexOf(q)<0;if(ge&&Z.push(q),re===G&&_e)j.offset===0&&j.data.length===Ce?ge?P.texImage2D(P.TEXTURE_2D,q,V,H[0],H[1],0,V,G,j.data):P.texSubImage2D(P.TEXTURE_2D,q,E,z,H[0],H[1],V,G,j.data):ge?P.texImage2D(P.TEXTURE_2D,q,V,H[0],H[1],0,V,G,j.data.subarray(j.offset,j.offset+Ce)):P.texSubImage2D(P.TEXTURE_2D,q,E,z,H[0],H[1],V,G,j.data.subarray(j.offset,j.offset+Ce));else{var ie;G===P.FLOAT?ie=u.mallocFloat32(Ce):ie=u.mallocUint8(Ce);var 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z=this.shape[0],q=this.shape[1],V=this.shape[2],G=this.shape[3],Z=this.shape[4],j=this.stride[0],N=this.stride[1],H=this.stride[2],re=this.stride[3],ae=this.stride[4],_e=this.offset,Ee=0,Ce=Math.ceil;return typeof C==\"number\"&&(Ee=C|0,Ee<0?(_e+=j*(z-1),z=Ce(-z/Ee)):z=Ce(z/Ee),j*=Ee),typeof S==\"number\"&&(Ee=S|0,Ee<0?(_e+=N*(q-1),q=Ce(-q/Ee)):q=Ce(q/Ee),N*=Ee),typeof g==\"number\"&&(Ee=g|0,Ee<0?(_e+=H*(V-1),V=Ce(-V/Ee)):V=Ce(V/Ee),H*=Ee),typeof P==\"number\"&&(Ee=P|0,Ee<0?(_e+=re*(G-1),G=Ce(-G/Ee)):G=Ce(G/Ee),re*=Ee),typeof E==\"number\"&&(Ee=E|0,Ee<0?(_e+=ae*(Z-1),Z=Ce(-Z/Ee)):Z=Ce(Z/Ee),ae*=Ee),new T(this.data,z,q,V,G,Z,j,N,H,re,ae,_e)},L.transpose=function(C,S,g,P,E){C=C===void 0?0:C|0,S=S===void 0?1:S|0,g=g===void 0?2:g|0,P=P===void 0?3:P|0,E=E===void 0?4:E|0;var z=this.shape,q=this.stride;return new T(this.data,z[C],z[S],z[g],z[P],z[E],q[C],q[S],q[g],q[P],q[E],this.offset)},L.pick=function(C,S,g,P,E){var z=[],q=[],V=this.offset;typeof 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Int32Array]\":return\"int32\";case\"[object Uint8ClampedArray]\":return\"uint8_clamped\";case\"[object Uint8Array]\":return\"uint8\";case\"[object Uint16Array]\":return\"uint16\";case\"[object Uint32Array]\":return\"uint32\";case\"[object BigInt64Array]\":return\"bigint64\";case\"[object BigUint64Array]\":return\"biguint64\"}return Array.isArray(b)?\"array\":\"generic\"}var v={generic:[],buffer:[],array:[],float32:[],float64:[],int8:[],int16:[],int32:[],uint8_clamped:[],uint8:[],uint16:[],uint32:[],bigint64:[],biguint64:[]};function m(b,p,k,M){if(b===void 0){var g=v.array[0];return g([])}else typeof b==\"number\"&&(b=[b]);p===void 0&&(p=[b.length]);var T=p.length;if(k===void 0){k=new Array(T);for(var L=T-1,x=1;L>=0;--L)k[L]=x,x*=p[L]}if(M===void 0){M=0;for(var L=0;L<T;++L)k[L]<0&&(M-=(p[L]-1)*k[L])}for(var C=d(b),S=v[C];S.length<=T+1;)S.push(h(C,S.length-1));var g=S[T+1];return g(b,p,k,M)}i.exports=m}),1278:(function(i,a,o){\"use strict\";var 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g=0;g<3;++g){for(var P=0,E=0;E<3;++E)P+=T[g+4*E]*x[E];T[12+g]=-P}},b.getMatrix=function(k,M){this.recalcMatrix(k);var T=this.computedMatrix;if(M){for(var L=0;L<16;++L)M[L]=T[L];return M}return T},b.idle=function(k){this.center.idle(k),this.radius.idle(k),this.rotation.idle(k)},b.flush=function(k){this.center.flush(k),this.radius.flush(k),this.rotation.flush(k)},b.pan=function(k,M,T,L){M=M||0,T=T||0,L=L||0,this.recalcMatrix(k);var x=this.computedMatrix,C=x[1],S=x[5],g=x[9],P=h(C,S,g);C/=P,S/=P,g/=P;var E=x[0],z=x[4],q=x[8],V=E*C+z*S+q*g;E-=C*V,z-=S*V,q-=g*V;var G=h(E,z,q);E/=G,z/=G,q/=G;var Z=x[2],j=x[6],N=x[10],H=Z*C+j*S+N*g,re=Z*E+j*z+N*q;Z-=H*C+re*E,j-=H*S+re*z,N-=H*g+re*q;var ae=h(Z,j,N);Z/=ae,j/=ae,N/=ae;var _e=E*M+C*T,Ee=z*M+S*T,Ce=q*M+g*T;this.center.move(k,_e,Ee,Ce);var ge=Math.exp(this.computedRadius[0]);ge=Math.max(1e-4,ge+L),this.radius.set(k,Math.log(ge))},b.rotate=function(k,M,T,L){this.recalcMatrix(k),M=M||0,T=T||0;var x=this.computedMatrix,C=x[0],S=x[4],g=x[8],P=x[1],E=x[5],z=x[9],q=x[2],V=x[6],G=x[10],Z=M*C+T*P,j=M*S+T*E,N=M*g+T*z,H=-(V*N-G*j),re=-(G*Z-q*N),ae=-(q*j-V*Z),_e=Math.sqrt(Math.max(0,1-Math.pow(H,2)-Math.pow(re,2)-Math.pow(ae,2))),Ee=d(H,re,ae,_e);Ee>1e-6?(H/=Ee,re/=Ee,ae/=Ee,_e/=Ee):(H=re=ae=0,_e=1);var Ce=this.computedRotation,ge=Ce[0],ie=Ce[1],Se=Ce[2],ke=Ce[3],be=ge*_e+ke*H+ie*ae-Se*re,Be=ie*_e+ke*re+Se*H-ge*ae,Le=Se*_e+ke*ae+ge*re-ie*H,me=ke*_e-ge*H-ie*re-Se*ae;if(L){H=q,re=V,ae=G;var Pe=Math.sin(L)/h(H,re,ae);H*=Pe,re*=Pe,ae*=Pe,_e=Math.cos(M),be=be*_e+me*H+Be*ae-Le*re,Be=Be*_e+me*re+Le*H-be*ae,Le=Le*_e+me*ae+be*re-Be*H,me=me*_e-be*H-Be*re-Le*ae}var ce=d(be,Be,Le,me);ce>1e-6?(be/=ce,Be/=ce,Le/=ce,me/=ce):(be=Be=Le=0,me=1),this.rotation.set(k,be,Be,Le,me)},b.lookAt=function(k,M,T,L){this.recalcMatrix(k),T=T||this.computedCenter,M=M||this.computedEye,L=L||this.computedUp;var x=this.computedMatrix;l(x,M,T,L);var C=this.computedRotation;f(C,x[0],x[1],x[2],x[4],x[5],x[6],x[8],x[9],x[10]),v(C,C),this.rotation.set(k,C[0],C[1],C[2],C[3]);for(var S=0,g=0;g<3;++g)S+=Math.pow(T[g]-M[g],2);this.radius.set(k,.5*Math.log(Math.max(S,1e-6))),this.center.set(k,T[0],T[1],T[2])},b.translate=function(k,M,T,L){this.center.move(k,M||0,T||0,L||0)},b.setMatrix=function(k,M){var T=this.computedRotation;f(T,M[0],M[1],M[2],M[4],M[5],M[6],M[8],M[9],M[10]),v(T,T),this.rotation.set(k,T[0],T[1],T[2],T[3]);var L=this.computedMatrix;c(L,M);var x=L[15];if(Math.abs(x)>1e-6){var C=L[12]/x,S=L[13]/x,g=L[14]/x;this.recalcMatrix(k);var P=Math.exp(this.computedRadius[0]);this.center.set(k,C-L[2]*P,S-L[6]*P,g-L[10]*P),this.radius.idle(k)}else 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9:m.push([l(h,f,p[3],p[1]),l(h,f,p[0],p[1]),l(h,f,p[0],p[2])],[l(h,f,p[3],p[2]),l(h,f,p[3],p[1]),l(h,f,p[0],p[2])]);break;case 10:m.push([l(h,f,p[1],p[0]),l(h,f,p[3],p[0]),l(h,f,p[1],p[2])],[l(h,f,p[3],p[0]),l(h,f,p[3],p[2]),l(h,f,p[1],p[2])]);break;case 11:m.push([l(h,f,p[1],p[2]),l(h,f,p[0],p[2]),l(h,f,p[3],p[2])]);break;case 12:m.push([l(h,f,p[3],p[0]),l(h,f,p[2],p[0]),l(h,f,p[2],p[1])],[l(h,f,p[3],p[1]),l(h,f,p[3],p[0]),l(h,f,p[2],p[1])]);break;case 13:m.push([l(h,f,p[0],p[1]),l(h,f,p[2],p[1]),l(h,f,p[3],p[1])]);break;case 14:m.push([l(h,f,p[2],p[0]),l(h,f,p[1],p[0]),l(h,f,p[3],p[0])]);break;case 15:break}}else if(k===3){var M=(d[p[0]]<<0)+(d[p[1]]<<1)+(d[p[2]]<<2);if(M===0||M===7)continue;switch(M){case 0:break;case 1:m.push([l(h,f,p[0],p[2]),l(h,f,p[0],p[1])]);break;case 2:m.push([l(h,f,p[1],p[0]),l(h,f,p[1],p[2])]);break;case 3:m.push([l(h,f,p[0],p[2]),l(h,f,p[1],p[2])]);break;case 4:m.push([l(h,f,p[2],p[1]),l(h,f,p[2],p[0])]);break;case 5:m.push([l(h,f,p[2],p[1]),l(h,f,p[0],p[1])]);break;case 6:m.push([l(h,f,p[1],p[0]),l(h,f,p[2],p[0])]);break;case 7:break}}else if(k===2){var M=(d[p[0]]<<0)+(d[p[1]]<<1);if(M===0||M===3)continue;switch(M){case 0:break;case 1:m.push([l(h,f,p[0],p[1])]);break;case 2:m.push([l(h,f,p[1],p[0])]);break;case 3:break}}}return m}return u}];function o(s){return a[s]()}}),6803:(function(i,a,o){\"use strict\";var s,l=o(8828),u=o(1755);function c(P){for(var E=0,z=Math.max,q=0,V=P.length;q<V;++q)E=z(E,P[q].length);return E-1}s=c;function f(P){for(var E=-1,z=Math.max,q=0,V=P.length;q<V;++q)for(var G=P[q],Z=0,j=G.length;Z<j;++Z)E=z(E,G[Z]);return E+1}s=f;function h(P){for(var E=new Array(P.length),z=0,q=P.length;z<q;++z)E[z]=P[z].slice(0);return E}s=h;function d(P,E){var z=P.length,q=P.length-E.length,V=Math.min;if(q)return q;switch(z){case 0:return 0;case 1:return P[0]-E[0];case 2:var H=P[0]+P[1]-E[0]-E[1];return H||V(P[0],P[1])-V(E[0],E[1]);case 3:var 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V=P[q],G=0;G<V.length;++G)for(var Z=G+1;Z<V.length;++Z)z.link(V[G],V[Z]);for(var j=[],N=z.ranks,q=0;q<N.length;++q)N[q]=-1;for(var q=0;q<P.length;++q){var H=z.find(P[q][0]);N[H]<0?(N[H]=j.length,j.push([P[q].slice(0)])):j[N[H]].push(P[q].slice(0))}return j}function S(P){for(var E=b(m(L(P,0))),z=new u(E.length),q=0;q<P.length;++q)for(var V=P[q],G=0;G<V.length;++G)for(var Z=p(E,[V[G]]),j=G+1;j<V.length;++j)z.link(Z,p(E,[V[j]]));for(var N=[],H=z.ranks,q=0;q<H.length;++q)H[q]=-1;for(var q=0;q<P.length;++q){var re=z.find(p(E,[P[q][0]]));H[re]<0?(H[re]=N.length,N.push([P[q].slice(0)])):N[H[re]].push(P[q].slice(0))}return N}function g(P,E){return E?C(P,E):S(P)}s=g}),3105:(function(i,a){\"use strict\";\"use restrict\";var o=32;a.INT_BITS=o,a.INT_MAX=2147483647,a.INT_MIN=-1<<o-1,a.sign=function(u){return(u>0)-(u<0)},a.abs=function(u){var c=u>>o-1;return(u^c)-c},a.min=function(u,c){return c^(u^c)&-(u<c)},a.max=function(u,c){return u^(u^c)&-(u<c)},a.isPow2=function(u){return!(u&u-1)&&!!u},a.log2=function(u){var c,f;return c=(u>65535)<<4,u>>>=c,f=(u>255)<<3,u>>>=f,c|=f,f=(u>15)<<2,u>>>=f,c|=f,f=(u>3)<<1,u>>>=f,c|=f,c|u>>1},a.log10=function(u){return u>=1e9?9:u>=1e8?8:u>=1e7?7:u>=1e6?6:u>=1e5?5:u>=1e4?4:u>=1e3?3:u>=100?2:u>=10?1:0},a.popCount=function(u){return u=u-(u>>>1&1431655765),u=(u&858993459)+(u>>>2&858993459),(u+(u>>>4)&252645135)*16843009>>>24};function s(u){var c=32;return u&=-u,u&&c--,u&65535&&(c-=16),u&16711935&&(c-=8),u&252645135&&(c-=4),u&858993459&&(c-=2),u&1431655765&&(c-=1),c}a.countTrailingZeros=s,a.nextPow2=function(u){return u+=u===0,--u,u|=u>>>1,u|=u>>>2,u|=u>>>4,u|=u>>>8,u|=u>>>16,u+1},a.prevPow2=function(u){return u|=u>>>1,u|=u>>>2,u|=u>>>4,u|=u>>>8,u|=u>>>16,u-(u>>>1)},a.parity=function(u){return u^=u>>>16,u^=u>>>8,u^=u>>>4,u&=15,27030>>>u&1};var l=new Array(256);(function(u){for(var c=0;c<256;++c){var 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u=u>>>c&1227133513,u=(u|u>>>2)&3272356035,u=(u|u>>>4)&251719695,u=(u|u>>>8)&4278190335,u=(u|u>>>16)&1023,u<<22>>22},a.nextCombination=function(u){var c=u|u-1;return c+1|(~c&-~c)-1>>>s(u)+1}}),2014:(function(i,a,o){\"use strict\";\"use restrict\";var s=o(3105),l=o(4623);function u(g){for(var P=0,E=Math.max,z=0,q=g.length;z<q;++z)P=E(P,g[z].length);return P-1}a.dimension=u;function c(g){for(var P=-1,E=Math.max,z=0,q=g.length;z<q;++z)for(var V=g[z],G=0,Z=V.length;G<Z;++G)P=E(P,V[G]);return P+1}a.countVertices=c;function f(g){for(var P=new Array(g.length),E=0,z=g.length;E<z;++E)P[E]=g[E].slice(0);return P}a.cloneCells=f;function h(g,P){var E=g.length,z=g.length-P.length,q=Math.min;if(z)return z;switch(E){case 0:return 0;case 1:return g[0]-P[0];case 2:var N=g[0]+g[1]-P[0]-P[1];return N||q(g[0],g[1])-q(P[0],P[1]);case 3:var V=g[0]+g[1],G=P[0]+P[1];if(N=V+g[2]-(G+P[2]),N)return N;var Z=q(g[0],g[1]),j=q(P[0],P[1]),N=q(Z,g[2])-q(j,P[2]);return N||q(Z+g[2],V)-q(j+P[2],G);default:var 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b(T,L,x,C,S,g,P,E){this.center=s(x),this.up=s(C),this.right=s(S),this.radius=s([g]),this.angle=s([P,E]),this.angle.bounds=[[-1/0,-Math.PI/2],[1/0,Math.PI/2]],this.setDistanceLimits(T,L),this.computedCenter=this.center.curve(0),this.computedUp=this.up.curve(0),this.computedRight=this.right.curve(0),this.computedRadius=this.radius.curve(0),this.computedAngle=this.angle.curve(0),this.computedToward=[0,0,0],this.computedEye=[0,0,0],this.computedMatrix=new Array(16);for(var z=0;z<16;++z)this.computedMatrix[z]=.5;this.recalcMatrix(0)}var p=b.prototype;p.setDistanceLimits=function(T,L){T>0?T=Math.log(T):T=-1/0,L>0?L=Math.log(L):L=1/0,L=Math.max(L,T),this.radius.bounds[0][0]=T,this.radius.bounds[1][0]=L},p.getDistanceLimits=function(T){var L=this.radius.bounds[0];return T?(T[0]=Math.exp(L[0][0]),T[1]=Math.exp(L[1][0]),T):[Math.exp(L[0][0]),Math.exp(L[1][0])]},p.recalcMatrix=function(T){this.center.curve(T),this.up.curve(T),this.right.curve(T),this.radius.curve(T),this.angle.curve(T);for(var L=this.computedUp,x=this.computedRight,C=0,S=0,g=0;g<3;++g)S+=L[g]*x[g],C+=L[g]*L[g];for(var P=Math.sqrt(C),E=0,g=0;g<3;++g)x[g]-=L[g]*S/C,E+=x[g]*x[g],L[g]/=P;for(var z=Math.sqrt(E),g=0;g<3;++g)x[g]/=z;var q=this.computedToward;c(q,L,x),f(q,q);for(var V=Math.exp(this.computedRadius[0]),G=this.computedAngle[0],Z=this.computedAngle[1],j=Math.cos(G),N=Math.sin(G),H=Math.cos(Z),re=Math.sin(Z),ae=this.computedCenter,_e=j*H,Ee=N*H,Ce=re,ge=-j*re,ie=-N*re,Se=H,ke=this.computedEye,be=this.computedMatrix,g=0;g<3;++g){var Be=_e*x[g]+Ee*q[g]+Ce*L[g];be[4*g+1]=ge*x[g]+ie*q[g]+Se*L[g],be[4*g+2]=Be,be[4*g+3]=0}var Le=be[1],me=be[5],Pe=be[9],ce=be[2],He=be[6],lt=be[10],mt=me*lt-Pe*He,Ht=Pe*ce-Le*lt,at=Le*He-me*ce,ct=d(mt,Ht,at);mt/=ct,Ht/=ct,at/=ct,be[0]=mt,be[4]=Ht,be[8]=at;for(var g=0;g<3;++g)ke[g]=ae[g]+be[2+4*g]*V;for(var g=0;g<3;++g){for(var E=0,ar=0;ar<3;++ar)E+=be[g+4*ar]*ke[ar];be[12+g]=-E}be[15]=1},p.getMatrix=function(T,L){this.recalcMatrix(T);var x=this.computedMatrix;if(L){for(var C=0;C<16;++C)L[C]=x[C];return L}return x};var k=[0,0,0];p.rotate=function(T,L,x,C){if(this.angle.move(T,L,x),C){this.recalcMatrix(T);var S=this.computedMatrix;k[0]=S[2],k[1]=S[6],k[2]=S[10];for(var g=this.computedUp,P=this.computedRight,E=this.computedToward,z=0;z<3;++z)S[4*z]=g[z],S[4*z+1]=P[z],S[4*z+2]=E[z];u(S,S,C,k);for(var z=0;z<3;++z)g[z]=S[4*z],P[z]=S[4*z+1];this.up.set(T,g[0],g[1],g[2]),this.right.set(T,P[0],P[1],P[2])}},p.pan=function(T,L,x,C){L=L||0,x=x||0,C=C||0,this.recalcMatrix(T);var S=this.computedMatrix,g=Math.exp(this.computedRadius[0]),P=S[1],E=S[5],z=S[9],q=d(P,E,z);P/=q,E/=q,z/=q;var V=S[0],G=S[4],Z=S[8],j=V*P+G*E+Z*z;V-=P*j,G-=E*j,Z-=z*j;var N=d(V,G,Z);V/=N,G/=N,Z/=N;var H=V*L+P*x,re=G*L+E*x,ae=Z*L+z*x;this.center.move(T,H,re,ae);var _e=Math.exp(this.computedRadius[0]);_e=Math.max(1e-4,_e+C),this.radius.set(T,Math.log(_e))},p.translate=function(T,L,x,C){this.center.move(T,L||0,x||0,C||0)},p.setMatrix=function(T,L,x,C){var S=1;typeof x==\"number\"&&(S=x|0),(S<0||S>3)&&(S=1);var g=(S+2)%3,P=(S+1)%3;L||(this.recalcMatrix(T),L=this.computedMatrix);var E=L[S],z=L[S+4],q=L[S+8];if(C){var G=Math.abs(E),Z=Math.abs(z),j=Math.abs(q),N=Math.max(G,Z,j);G===N?(E=E<0?-1:1,z=q=0):j===N?(q=q<0?-1:1,E=z=0):(z=z<0?-1:1,E=q=0)}else{var V=d(E,z,q);E/=V,z/=V,q/=V}var H=L[g],re=L[g+4],ae=L[g+8],_e=H*E+re*z+ae*q;H-=E*_e,re-=z*_e,ae-=q*_e;var Ee=d(H,re,ae);H/=Ee,re/=Ee,ae/=Ee;var Ce=z*ae-q*re,ge=q*H-E*ae,ie=E*re-z*H,Se=d(Ce,ge,ie);Ce/=Se,ge/=Se,ie/=Se,this.center.jump(T,Ue,Ae,rt),this.radius.idle(T),this.up.jump(T,E,z,q),this.right.jump(T,H,re,ae);var ke,be;if(S===2){var Be=L[1],Le=L[5],me=L[9],Pe=Be*H+Le*re+me*ae,ce=Be*Ce+Le*ge+me*ie;Ht<0?ke=-Math.PI/2:ke=Math.PI/2,be=Math.atan2(ce,Pe)}else{var He=L[2],lt=L[6],mt=L[10],Ht=He*E+lt*z+mt*q,at=He*H+lt*re+mt*ae,ct=He*Ce+lt*ge+mt*ie;ke=Math.asin(v(Ht)),be=Math.atan2(ct,at)}this.angle.jump(T,be,ke),this.recalcMatrix(T);var ar=L[2],Vt=L[6],rr=L[10],tt=this.computedMatrix;l(tt,L);var je=tt[15],Ue=tt[12]/je,Ae=tt[13]/je,rt=tt[14]/je,St=Math.exp(this.computedRadius[0]);this.center.jump(T,Ue-ar*St,Ae-Vt*St,rt-rr*St)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(T){this.center.idle(T),this.up.idle(T),this.right.idle(T),this.radius.idle(T),this.angle.idle(T)},p.flush=function(T){this.center.flush(T),this.up.flush(T),this.right.flush(T),this.radius.flush(T),this.angle.flush(T)},p.setDistance=function(T,L){L>0&&this.radius.set(T,Math.log(L))},p.lookAt=function(T,L,x,C){this.recalcMatrix(T),L=L||this.computedEye,x=x||this.computedCenter,C=C||this.computedUp;var S=C[0],g=C[1],P=C[2],E=d(S,g,P);if(!(E<1e-6)){S/=E,g/=E,P/=E;var z=L[0]-x[0],q=L[1]-x[1],V=L[2]-x[2],G=d(z,q,V);if(!(G<1e-6)){z/=G,q/=G,V/=G;var Z=this.computedRight,j=Z[0],N=Z[1],H=Z[2],re=S*j+g*N+P*H;j-=re*S,N-=re*g,H-=re*P;var 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Uint16Array;case\"uint32\":return Uint32Array;case\"float32\":return Float32Array;case\"float64\":return Float64Array;case\"array\":return Array;case\"uint8_clamped\":return Uint8ClampedArray}}});var lx=ye((Rgr,NPe)=>{\"use strict\";var IIt=qPe(),nF=ak(),RIt=iF();NPe.exports=function(t,r){(r===\"float\"||!r)&&(r=\"array\"),r===\"uint\"&&(r=\"uint8\"),r===\"uint_clamped\"&&(r=\"uint8_clamped\");var n=RIt(r),i=new n(4),a=r!==\"uint8\"&&r!==\"uint8_clamped\";return(!t.length||typeof t==\"string\")&&(t=IIt(t),t[0]/=255,t[1]/=255,t[2]/=255),DIt(t)?(i[0]=t[0],i[1]=t[1],i[2]=t[2],i[3]=t[3]!=null?t[3]:255,a&&(i[0]/=255,i[1]/=255,i[2]/=255,i[3]/=255),i):(a?(i[0]=t[0],i[1]=t[1],i[2]=t[2],i[3]=t[3]!=null?t[3]:1):(i[0]=nF(Math.floor(t[0]*255),0,255),i[1]=nF(Math.floor(t[1]*255),0,255),i[2]=nF(Math.floor(t[2]*255),0,255),i[3]=t[3]==null?255:nF(Math.floor(t[3]*255),0,255)),i)};function DIt(e){return!!(e instanceof Uint8Array||e instanceof Uint8ClampedArray||Array.isArray(e)&&(e[0]>1||e[0]===0)&&(e[1]>1||e[1]===0)&&(e[2]>1||e[2]===0)&&(!e[3]||e[3]>1))}});var a1=ye((Dgr,UPe)=>{\"use strict\";var FIt=lx();function zIt(e){return e?FIt(e):[0,0,0,1]}UPe.exports=zIt});var o1=ye((Fgr,ZPe)=>{\"use strict\";var WPe=Eo(),qIt=fd(),aF=lx(),oF=tc(),OIt=Ih().defaultLine,VPe=Yd().isArrayOrTypedArray,EX=aF(OIt),XPe=1;function GPe(e,t){var r=e;return r[3]*=t,r}function HPe(e){if(WPe(e))return EX;var t=aF(e);return t.length?t:EX}function jPe(e){return WPe(e)?e:XPe}function BIt(e,t,r){var n=e.color;n&&n._inputArray&&(n=n._inputArray);var i=VPe(n),a=VPe(t),o=oF.extractOpts(e),s=[],l,u,c,f,h;if(o.colorscale!==void 0?l=oF.makeColorScaleFuncFromTrace(e):l=HPe,i?u=function(v,m){return v[m]===void 0?EX:aF(l(v[m]))}:u=HPe,a?c=function(v,m){return v[m]===void 0?XPe:jPe(v[m])}:c=jPe,i||a)for(var d=0;d<r;d++)f=u(n,d),h=c(t,d),s[d]=GPe(f,h);else s=GPe(aF(n),t);return s}function NIt(e){var t=oF.extractOpts(e),r=t.colorscale;return t.reversescale&&(r=oF.flipScale(t.colorscale)),r.map(function(n){var i=n[0],a=qIt(n[1]),o=a.toRgb();return{index:i,rgb:[o.r,o.g,o.b,o.a]}})}ZPe.exports={formatColor:BIt,parseColorScale:NIt}});var kX=ye((zgr,YPe)=>{\"use strict\";YPe.exports={solid:[[],0],dot:[[.5,1],200],dash:[[.5,1],50],longdash:[[.5,1],10],dashdot:[[.5,.625,.875,1],50],longdashdot:[[.5,.7,.8,1],10]}});var sF=ye((qgr,KPe)=>{\"use strict\";KPe.exports={circle:\"\\u25CF\",\"circle-open\":\"\\u25CB\",square:\"\\u25A0\",\"square-open\":\"\\u25A1\",diamond:\"\\u25C6\",\"diamond-open\":\"\\u25C7\",cross:\"+\",x:\"\\u274C\"}});var $Pe=ye((Ogr,JPe)=>{\"use strict\";var UIt=Oa();function CX(e,t,r,n){if(!t||!t.visible)return null;for(var i=UIt.getComponentMethod(\"errorbars\",\"makeComputeError\")(t),a=new Array(e.length),o=0;o<e.length;o++){var s=i(+e[o],o);if(n.type===\"log\"){var l=n.c2l(e[o]),u=e[o]-s[0],c=e[o]+s[1];if(a[o]=[(n.c2l(u,!0)-l)*r,(n.c2l(c,!0)-l)*r],u>0){var f=n.c2l(u);n._lowerLogErrorBound||(n._lowerLogErrorBound=f),n._lowerErrorBound=Math.min(n._lowerLogErrorBound,f)}}else a[o]=[-s[0]*r,s[1]*r]}return a}function VIt(e){for(var t=0;t<e.length;t++)if(e[t])return e[t].length;return 0}function GIt(e,t,r){var n=[CX(e.x,e.error_x,t[0],r.xaxis),CX(e.y,e.error_y,t[1],r.yaxis),CX(e.z,e.error_z,t[2],r.zaxis)],i=VIt(n);if(i===0)return null;for(var a=new Array(i),o=0;o<i;o++){for(var s=[[0,0,0],[0,0,0]],l=0;l<3;l++)if(n[l])for(var u=0;u<2;u++)s[u][l]=n[l][o][u];a[o]=s}return a}JPe.exports=GIt});var sIe=ye((Bgr,oIe)=>{\"use strict\";var HIt=qd().gl_line3d,QPe=qd().gl_scatter3d,jIt=qd().gl_error3d,WIt=qd().gl_mesh3d,XIt=qd().delaunay_triangulate,s1=Pr(),nIe=a1(),lF=o1().formatColor,ZIt=U3(),LX=kX(),YIt=sF(),KIt=ho(),JIt=ip().appendArrayPointValue,$It=$Pe();function aIe(e,t){this.scene=e,this.uid=t,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode=\"\",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var IX=aIe.prototype;IX.handlePick=function(e){if(e.object&&(e.object===this.linePlot||e.object===this.delaunayMesh||e.object===this.textMarkers||e.object===this.scatterPlot)){var t=e.index=e.data.index;return e.object.highlight&&e.object.highlight(null),this.scatterPlot&&(e.object=this.scatterPlot,this.scatterPlot.highlight(e.data)),e.textLabel=\"\",this.textLabels&&(s1.isArrayOrTypedArray(this.textLabels)?(this.textLabels[t]||this.textLabels[t]===0)&&(e.textLabel=this.textLabels[t]):e.textLabel=this.textLabels),e.traceCoordinate=[this.data.x[t],this.data.y[t],this.data.z[t]],!0}};function QIt(e,t,r){var n=(r+1)%3,i=(r+2)%3,a=[],o=[],s;for(s=0;s<e.length;++s){var 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0&&(l*=e.marker.opacity),n={gl:this.scene.glplot.gl,position:s.position,color:s.scatterColor,size:s.scatterSize,glyph:s.scatterMarker,opacity:l,orthographic:!0,lineWidth:s.scatterLineWidth,lineColor:s.scatterLineColor,project:s.project,projectScale:s.projectScale,projectOpacity:s.projectOpacity},this.mode.indexOf(\"markers\")!==-1?this.scatterPlot?this.scatterPlot.update(n):(this.scatterPlot=QPe(n),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),a={gl:this.scene.glplot.gl,position:s.position,glyph:s.text,color:s.textColor,size:s.textSize,angle:s.textAngle,alignment:s.textOffset,font:s.textFontFamily,fontWeight:s.textFontWeight,fontStyle:s.textFontStyle,fontVariant:s.textFontVariant,orthographic:!0,lineWidth:0,project:!1,opacity:e.opacity},this.textLabels=e.hovertext||e.text,this.mode.indexOf(\"text\")!==-1?this.textMarkers?this.textMarkers.update(a):(this.textMarkers=QPe(a),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),i={gl:this.scene.glplot.gl,position:s.position,color:s.errorColor,error:s.errorBounds,lineWidth:s.errorLineWidth,capSize:s.errorCapSize,opacity:e.opacity},this.errorBars?s.errorBounds?this.errorBars.update(i):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):s.errorBounds&&(this.errorBars=jIt(i),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),s.delaunayAxis>=0){var u=QIt(s.position,s.delaunayColor,s.delaunayAxis);u.opacity=e.opacity,this.delaunayMesh?this.delaunayMesh.update(u):(u.gl=t,this.delaunayMesh=WIt(u),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)};IX.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())};function a8t(e,t){var r=new aIe(e,t.uid);return r.update(t),r}oIe.exports=a8t});var qX=ye((Ngr,fIe)=>{\"use strict\";var l1=pf(),o8t=ec(),zX=Tu(),RX=vf().axisHoverFormat,{hovertemplateAttrs:s8t,texttemplateAttrs:l8t,templatefallbackAttrs:lIe}=Pl(),uIe=Vl(),u8t=kX(),c8t=sF(),$g=Ao().extendFlat,f8t=mc().overrideAll,cIe=r_(),h8t=l1.line,Q2=l1.marker,d8t=Q2.line,v8t=$g({width:h8t.width,dash:{valType:\"enumerated\",values:cIe(u8t),dflt:\"solid\"}},zX(\"line\"));function DX(e){return{show:{valType:\"boolean\",dflt:!1},opacity:{valType:\"number\",min:0,max:1,dflt:1},scale:{valType:\"number\",min:0,max:10,dflt:2/3}}}var FX=fIe.exports=f8t({x:l1.x,y:l1.y,z:{valType:\"data_array\"},text:$g({},l1.text,{}),texttemplate:l8t(),texttemplatefallback:lIe({editType:\"calc\"}),hovertext:$g({},l1.hovertext,{}),hovertemplate:s8t(),hovertemplatefallback:lIe(),xhoverformat:RX(\"x\"),yhoverformat:RX(\"y\"),zhoverformat:RX(\"z\"),mode:$g({},l1.mode,{dflt:\"lines+markers\"}),surfaceaxis:{valType:\"enumerated\",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:\"color\"},projection:{x:DX(\"x\"),y:DX(\"y\"),z:DX(\"z\")},connectgaps:l1.connectgaps,line:v8t,marker:$g({symbol:{valType:\"enumerated\",values:cIe(c8t),dflt:\"circle\",arrayOk:!0},size:$g({},Q2.size,{dflt:8}),sizeref:Q2.sizeref,sizemin:Q2.sizemin,sizemode:Q2.sizemode,opacity:$g({},Q2.opacity,{arrayOk:!1}),colorbar:Q2.colorbar,line:$g({width:$g({},d8t.width,{arrayOk:!1})},zX(\"marker.line\"))},zX(\"marker\")),textposition:$g({},l1.textposition,{dflt:\"top center\"}),textfont:o8t({noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0,editType:\"calc\",colorEditType:\"style\",arrayOk:!0,variantValues:[\"normal\",\"small-caps\"]}),opacity:uIe.opacity,hoverinfo:$g({},uIe.hoverinfo)},\"calc\",\"nested\");FX.x.editType=FX.y.editType=FX.z.editType=\"calc+clearAxisTypes\"});var vIe=ye((Ugr,dIe)=>{\"use strict\";var hIe=Oa(),p8t=Pr(),OX=Ru(),g8t=$p(),m8t=D0(),y8t=F0(),_8t=qX();dIe.exports=function(t,r,n,i){function a(d,v){return p8t.coerce(t,r,_8t,d,v)}var o=x8t(t,r,a,i);if(!o){r.visible=!1;return}a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),a(\"xhoverformat\"),a(\"yhoverformat\"),a(\"zhoverformat\"),a(\"mode\"),OX.hasMarkers(r)&&g8t(t,r,n,i,a,{noAngle:!0,noLineDash:!0,noSelect:!0}),OX.hasLines(r)&&(a(\"connectgaps\"),m8t(t,r,n,i,a)),OX.hasText(r)&&(a(\"texttemplate\"),a(\"texttemplatefallback\"),y8t(t,r,i,a,{noSelect:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}));var s=(r.line||{}).color,l=(r.marker||{}).color;a(\"surfaceaxis\")>=0&&a(\"surfacecolor\",s||l);for(var u=[\"x\",\"y\",\"z\"],c=0;c<3;++c){var f=\"projection.\"+u[c];a(f+\".show\")&&(a(f+\".opacity\"),a(f+\".scale\"))}var h=hIe.getComponentMethod(\"errorbars\",\"supplyDefaults\");h(t,r,s||l||n,{axis:\"z\"}),h(t,r,s||l||n,{axis:\"y\",inherit:\"z\"}),h(t,r,s||l||n,{axis:\"x\",inherit:\"z\"})};function x8t(e,t,r,n){var i=0,a=r(\"x\"),o=r(\"y\"),s=r(\"z\"),l=hIe.getComponentMethod(\"calendars\",\"handleTraceDefaults\");return l(e,t,[\"x\",\"y\",\"z\"],n),a&&o&&s&&(i=Math.min(a.length,o.length,s.length),t._length=t._xlength=t._ylength=t._zlength=i),i}});var gIe=ye((Vgr,pIe)=>{\"use strict\";var b8t=Dm(),w8t=z0();pIe.exports=function(t,r){var n=[{x:!1,y:!1,trace:r,t:{}}];return b8t(n,r),w8t(t,r),n}});var yIe=ye((Ggr,mIe)=>{mIe.exports=T8t;function T8t(e,t){if(typeof e!=\"string\")throw new TypeError(\"must specify type string\");if(t=t||{},typeof document==\"undefined\"&&!t.canvas)return null;var r=t.canvas||document.createElement(\"canvas\");typeof t.width==\"number\"&&(r.width=t.width),typeof t.height==\"number\"&&(r.height=t.height);var n=t,i;try{var a=[e];e.indexOf(\"webgl\")===0&&a.push(\"experimental-\"+e);for(var o=0;o<a.length;o++)if(i=r.getContext(a[o],n),i)return i}catch(s){i=null}return i||null}});var xIe=ye((Hgr,_Ie)=>{var A8t=yIe();_Ie.exports=function(t){return A8t(\"webgl\",t)}});var BX=ye((jgr,wIe)=>{\"use strict\";var bIe=ka(),S8t=function(){};wIe.exports=function(t){for(var r in t)typeof t[r]==\"function\"&&(t[r]=S8t);t.destroy=function(){t.container.parentNode.removeChild(t.container)};var n=document.createElement(\"div\");n.className=\"no-webgl\",n.style.cursor=\"pointer\",n.style.fontSize=\"24px\",n.style.color=bIe.defaults[0],n.style.position=\"absolute\",n.style.left=n.style.top=\"0px\",n.style.width=n.style.height=\"100%\",n.style[\"background-color\"]=bIe.lightLine,n.style[\"z-index\"]=30;var i=document.createElement(\"p\");return i.textContent=\"WebGL is not supported by your browser - visit https://get.webgl.org for more info\",i.style.position=\"relative\",i.style.top=\"50%\",i.style.left=\"50%\",i.style.height=\"30%\",i.style.width=\"50%\",i.style.margin=\"-15% 0 0 -25%\",n.appendChild(i),t.container.appendChild(n),t.container.style.background=\"#FFFFFF\",t.container.onclick=function(){window.open(\"https://get.webgl.org\")},!1}});var SIe=ye((Wgr,AIe)=>{\"use strict\";var ew=a1(),M8t=Pr(),E8t=[\"xaxis\",\"yaxis\",\"zaxis\"];function TIe(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickFontWeight=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open 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k8t=TIe.prototype;k8t.merge=function(e,t){for(var r=this,n=0;n<3;++n){var i=t[E8t[n]];if(!i.visible){r.tickEnable[n]=!1,r.labelEnable[n]=!1,r.lineEnable[n]=!1,r.lineTickEnable[n]=!1,r.gridEnable[n]=!1,r.zeroEnable[n]=!1,r.backgroundEnable[n]=!1;continue}r.labels[n]=e._meta?M8t.templateString(i.title.text,e._meta):i.title.text,\"font\"in i.title&&(i.title.font.color&&(r.labelColor[n]=ew(i.title.font.color)),i.title.font.family&&(r.labelFont[n]=i.title.font.family),i.title.font.size&&(r.labelSize[n]=i.title.font.size),i.title.font.weight&&(r.labelFontWeight[n]=i.title.font.weight),i.title.font.style&&(r.labelFontStyle[n]=i.title.font.style),i.title.font.variant&&(r.labelFontVariant[n]=i.title.font.variant)),\"showline\"in i&&(r.lineEnable[n]=i.showline),\"linecolor\"in i&&(r.lineColor[n]=ew(i.linecolor)),\"linewidth\"in i&&(r.lineWidth[n]=i.linewidth),\"showgrid\"in i&&(r.gridEnable[n]=i.showgrid),\"gridcolor\"in i&&(r.gridColor[n]=ew(i.gridcolor)),\"gridwidth\"in i&&(r.gridWidth[n]=i.gridwidth),i.type===\"log\"?r.zeroEnable[n]=!1:\"zeroline\"in i&&(r.zeroEnable[n]=i.zeroline),\"zerolinecolor\"in i&&(r.zeroLineColor[n]=ew(i.zerolinecolor)),\"zerolinewidth\"in i&&(r.zeroLineWidth[n]=i.zerolinewidth),\"ticks\"in i&&i.ticks?r.lineTickEnable[n]=!0:r.lineTickEnable[n]=!1,\"ticklen\"in i&&(r.lineTickLength[n]=r._defaultLineTickLength[n]=i.ticklen),\"tickcolor\"in i&&(r.lineTickColor[n]=ew(i.tickcolor)),\"tickwidth\"in i&&(r.lineTickWidth[n]=i.tickwidth),\"tickangle\"in i&&(r.tickAngle[n]=i.tickangle===\"auto\"?-3600:Math.PI*-i.tickangle/180),\"showticklabels\"in i&&(r.tickEnable[n]=i.showticklabels),\"tickfont\"in i&&(i.tickfont.color&&(r.tickColor[n]=ew(i.tickfont.color)),i.tickfont.family&&(r.tickFont[n]=i.tickfont.family),i.tickfont.size&&(r.tickSize[n]=i.tickfont.size),i.tickfont.weight&&(r.tickFontWeight[n]=i.tickfont.weight),i.tickfont.style&&(r.tickFontStyle[n]=i.tickfont.style),i.tickfont.variant&&(r.tickFontVariant[n]=i.tickfont.variant)),\"mirror\"in i?[\"ticks\",\"all\",\"allticks\"].indexOf(i.mirror)!==-1?(r.lineTickMirror[n]=!0,r.lineMirror[n]=!0):i.mirror===!0?(r.lineTickMirror[n]=!1,r.lineMirror[n]=!0):(r.lineTickMirror[n]=!1,r.lineMirror[n]=!1):r.lineMirror[n]=!1,\"showbackground\"in i&&i.showbackground!==!1?(r.backgroundEnable[n]=!0,r.backgroundColor[n]=ew(i.backgroundcolor)):r.backgroundEnable[n]=!1}};function C8t(e,t){var r=new TIe;return r.merge(e,t),r}AIe.exports=C8t});var kIe=ye((Xgr,EIe)=>{\"use strict\";var L8t=a1(),P8t=[\"xaxis\",\"yaxis\",\"zaxis\"];function MIe(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}var I8t=MIe.prototype;I8t.merge=function(e){for(var t=0;t<3;++t){var r=e[P8t[t]];if(!r.visible){this.enabled[t]=!1,this.drawSides[t]=!1;continue}this.enabled[t]=r.showspikes,this.colors[t]=L8t(r.spikecolor),this.drawSides[t]=r.spikesides,this.lineWidth[t]=r.spikethickness}};function R8t(e){var t=new MIe;return t.merge(e),t}EIe.exports=R8t});var PIe=ye((Zgr,LIe)=>{\"use strict\";LIe.exports=O8t;var CIe=ho(),D8t=Pr(),F8t=[\"xaxis\",\"yaxis\",\"zaxis\"],z8t=[0,0,0];function q8t(e){for(var t=new Array(3),r=0;r<3;++r){for(var n=e[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;t[r]=i}return t}function O8t(e){for(var t=e.axesOptions,r=e.glplot.axesPixels,n=e.fullSceneLayout,i=[[],[],[]],a=0;a<3;++a){var o=n[F8t[a]];if(o._length=(r[a].hi-r[a].lo)*r[a].pixelsPerDataUnit/e.dataScale[a],Math.abs(o._length)===1/0||isNaN(o._length))i[a]=[];else{o._input_range=o.range.slice(),o.range[0]=r[a].lo/e.dataScale[a],o.range[1]=r[a].hi/e.dataScale[a],o._m=1/(e.dataScale[a]*r[a].pixelsPerDataUnit),o.range[0]===o.range[1]&&(o.range[0]-=1,o.range[1]+=1);var s=o.tickmode;if(o.tickmode===\"auto\"){o.tickmode=\"linear\";var l=o.nticks||D8t.constrain(o._length/40,4,9);CIe.autoTicks(o,Math.abs(o.range[1]-o.range[0])/l)}for(var u=CIe.calcTicks(o,{msUTC:!0}),c=0;c<u.length;++c)u[c].x=u[c].x*e.dataScale[a],o.type===\"date\"&&(u[c].text=u[c].text.replace(/\\<br\\>/g,\" \"));i[a]=u,o.tickmode=s}}t.ticks=i;for(var a=0;a<3;++a){z8t[a]=.5*(e.glplot.bounds[0][a]+e.glplot.bounds[1][a]);for(var c=0;c<2;++c)t.bounds[c][a]=e.glplot.bounds[c][a]}e.contourLevels=q8t(i)}});var OIe=ye((Ygr,qIe)=>{\"use strict\";var DIe=qd().gl_plot3d,B8t=DIe.createCamera,IIe=DIe.createScene,N8t=xIe(),U8t=GL(),fF=Oa(),cp=Pr(),cF=cp.preserveDrawingBuffer(),hF=ho(),Qg=ef(),V8t=a1(),G8t=BX(),H8t=JU(),j8t=SIe(),W8t=kIe(),X8t=PIe(),Z8t=Mg().applyAutorangeOptions,ok,uF,FIe=!1;function zIe(e,t){var r=document.createElement(\"div\"),n=e.container;this.graphDiv=e.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=e.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=t,this.id=e.id||\"scene\",this.fullSceneLayout=t[this.id],this.plotArgs=[[],{},{}],this.axesOptions=j8t(t,t[this.id]),this.spikeOptions=W8t(t[this.id]),this.container=r,this.staticMode=!!e.staticPlot,this.pixelRatio=this.pixelRatio||e.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=fF.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=fF.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var Sv=zIe.prototype;Sv.prepareOptions=function(){var e=this,t={canvas:e.canvas,gl:e.gl,glOptions:{preserveDrawingBuffer:cF,premultipliedAlpha:!0,antialias:!0},container:e.container,axes:e.axesOptions,spikes:e.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:e.camera,pixelRatio:e.pixelRatio};if(e.staticMode){if(!uF&&(ok=document.createElement(\"canvas\"),uF=N8t({canvas:ok,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}),!uF))throw new Error(\"error creating static canvas/context for image server\");t.gl=uF,t.canvas=ok}return t};var RIe=!0;Sv.tryCreatePlot=function(){var e=this,t=e.prepareOptions(),r=!0;try{e.glplot=IIe(t)}catch(n){if(e.staticMode||!RIe||cF)r=!1;else{cp.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{cF=t.glOptions.preserveDrawingBuffer=!0,e.glplot=IIe(t)}catch(i){cF=t.glOptions.preserveDrawingBuffer=!1,r=!1}}}return RIe=!1,r};Sv.initializeGLCamera=function(){var e=this,t=e.fullSceneLayout.camera,r=t.projection.type===\"orthographic\";e.camera=B8t(e.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:\"orbit\"})};Sv.initializeGLPlot=function(){var e=this;e.initializeGLCamera();var t=e.tryCreatePlot();if(!t)return G8t(e);e.traces={},e.make4thDimension();var r=e.graphDiv,n=r.layout,i=function(){var o={};return e.isCameraChanged(n)&&(o[e.id+\".camera\"]=e.getCamera()),e.isAspectChanged(n)&&(o[e.id+\".aspectratio\"]=e.glplot.getAspectratio(),n[e.id].aspectmode!==\"manual\"&&(e.fullSceneLayout.aspectmode=n[e.id].aspectmode=o[e.id+\".aspectmode\"]=\"manual\")),o},a=function(o){if(o.fullSceneLayout.dragmode!==!1){var s=i();o.saveLayout(n),o.graphDiv.emit(\"plotly_relayout\",s)}};return e.glplot.canvas&&(e.glplot.canvas.addEventListener(\"mouseup\",function(){a(e)}),e.glplot.canvas.addEventListener(\"touchstart\",function(){FIe=!0}),e.glplot.canvas.addEventListener(\"wheel\",function(o){if(r._context._scrollZoom.gl3d){if(e.camera._ortho){var s=o.deltaX>o.deltaY?1.1:.9090909090909091,l=e.glplot.getAspectratio();e.glplot.setAspectratio({x:s*l.x,y:s*l.y,z:s*l.z})}a(e)}},U8t?{passive:!1}:!1),e.glplot.canvas.addEventListener(\"mousemove\",function(){if(e.fullSceneLayout.dragmode!==!1&&e.camera.mouseListener.buttons!==0){var o=i();e.graphDiv.emit(\"plotly_relayouting\",o)}}),e.staticMode||e.glplot.canvas.addEventListener(\"webglcontextlost\",function(o){r&&r.emit&&r.emit(\"plotly_webglcontextlost\",{event:o,layer:e.id})},!1)),e.glplot.oncontextloss=function(){e.recoverContext()},e.glplot.onrender=function(){e.render()},!0};Sv.render=function(){var e=this,t=e.graphDiv,r,n=e.svgContainer,i=e.container.getBoundingClientRect();t._fullLayout._calcInverseTransform(t);var a=t._fullLayout._invScaleX,o=t._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),X8t(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,f=e.glplot.selection,h=0;h<u.length;++h)r=e.traces[u[h]],r.data.hoverinfo!==\"skip\"&&r.handlePick(f)&&(c=r),r.setContourLevels&&r.setContourLevels();function d(P,E,z){var q=e.fullSceneLayout[P+\"axis\"];return q.type!==\"log\"&&(E=q.d2l(E)),hF.hoverLabelText(q,E,z)}if(c!==null){var v=H8t(e.glplot.cameraParams,f.dataCoordinate);r=c.data;var m=t._fullData[r.index],b=f.index,p={xLabel:d(\"x\",f.traceCoordinate[0],r.xhoverformat),yLabel:d(\"y\",f.traceCoordinate[1],r.yhoverformat),zLabel:d(\"z\",f.traceCoordinate[2],r.zhoverformat)},k=Qg.castHoverinfo(m,e.fullLayout,b),M=(k||\"\").split(\"+\"),T=k&&k===\"all\";!m.hovertemplate&&!T&&(M.indexOf(\"x\")===-1&&(p.xLabel=void 0),M.indexOf(\"y\")===-1&&(p.yLabel=void 0),M.indexOf(\"z\")===-1&&(p.zLabel=void 0),M.indexOf(\"text\")===-1&&(f.textLabel=void 0),M.indexOf(\"name\")===-1&&(c.name=void 0));var L,x=[];r.type===\"cone\"||r.type===\"streamtube\"?(p.uLabel=d(\"x\",f.traceCoordinate[3],r.uhoverformat),(T||M.indexOf(\"u\")!==-1)&&x.push(\"u: \"+p.uLabel),p.vLabel=d(\"y\",f.traceCoordinate[4],r.vhoverformat),(T||M.indexOf(\"v\")!==-1)&&x.push(\"v: \"+p.vLabel),p.wLabel=d(\"z\",f.traceCoordinate[5],r.whoverformat),(T||M.indexOf(\"w\")!==-1)&&x.push(\"w: \"+p.wLabel),p.normLabel=f.traceCoordinate[6].toPrecision(3),(T||M.indexOf(\"norm\")!==-1)&&x.push(\"norm: \"+p.normLabel),r.type===\"streamtube\"&&(p.divergenceLabel=f.traceCoordinate[7].toPrecision(3),(T||M.indexOf(\"divergence\")!==-1)&&x.push(\"divergence: \"+p.divergenceLabel)),f.textLabel&&x.push(f.textLabel),L=x.join(\"<br>\")):r.type===\"isosurface\"||r.type===\"volume\"?(p.valueLabel=hF.hoverLabelText(e._mockAxis,e._mockAxis.d2l(f.traceCoordinate[3]),r.valuehoverformat),x.push(\"value: \"+p.valueLabel),f.textLabel&&x.push(f.textLabel),L=x.join(\"<br>\")):L=f.textLabel;var C={x:f.traceCoordinate[0],y:f.traceCoordinate[1],z:f.traceCoordinate[2],data:m._input,fullData:m,curveNumber:m.index,pointNumber:b};Qg.appendArrayPointValue(C,m,b),r._module.eventData&&(C=m._module.eventData(C,f,m,{},b));var S={points:[C]};if(e.fullSceneLayout.hovermode){var g=[];Qg.loneHover({trace:m,x:(.5+.5*v[0]/v[3])*s,y:(.5-.5*v[1]/v[3])*l,xLabel:p.xLabel,yLabel:p.yLabel,zLabel:p.zLabel,text:L,name:c.name,color:Qg.castHoverOption(m,b,\"bgcolor\")||c.color,borderColor:Qg.castHoverOption(m,b,\"bordercolor\"),fontFamily:Qg.castHoverOption(m,b,\"font.family\"),fontSize:Qg.castHoverOption(m,b,\"font.size\"),fontColor:Qg.castHoverOption(m,b,\"font.color\"),nameLength:Qg.castHoverOption(m,b,\"namelength\"),textAlign:Qg.castHoverOption(m,b,\"align\"),hovertemplate:cp.castOption(m,b,\"hovertemplate\"),hovertemplateLabels:cp.extendFlat({},C,p),eventData:[C]},{container:n,gd:t,inOut_bbox:g}),C.bbox=g[0]}f.distance<5&&(f.buttons||FIe)?t.emit(\"plotly_click\",S):t.emit(\"plotly_hover\",S),this.oldEventData=S}else Qg.loneUnhover(n),this.oldEventData&&t.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)};Sv.recoverContext=function(){var e=this;e.glplot.dispose();var t=function(){if(e.glplot.gl.isContextLost()){requestAnimationFrame(t);return}if(!e.initializeGLPlot()){cp.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\");return}e.plot.apply(e,e.plotArgs)};requestAnimationFrame(t)};var sk=[\"xaxis\",\"yaxis\",\"zaxis\"];function Y8t(e,t,r){for(var n=e.fullSceneLayout,i=0;i<3;i++){var a=sk[i],o=a.charAt(0),s=n[a],l=t[o],u=t[o+\"calendar\"],c=t[\"_\"+o+\"length\"];if(!cp.isArrayOrTypedArray(l))r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1);else for(var f,h=0;h<(c||l.length);h++)if(cp.isArrayOrTypedArray(l[h]))for(var d=0;d<l[h].length;++d)f=s.d2l(l[h][d],0,u),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f));else f=s.d2l(l[h],0,u),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f))}}function K8t(e,t){for(var r=e.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=sk[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(t[0][i]=Math.min(t[0][i],c),t[1][i]=Math.max(t[1][i],c))}}}Sv.plot=function(e,t,r){var n=this;if(n.plotArgs=[e,t,r],!n.glplot.contextLost){var i,a,o,s,l,u,c=t[n.id],f=r[n.id];n.fullLayout=t,n.fullSceneLayout=c,n.axesOptions.merge(t,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(V8t(c.bgcolor)),n.setConvert(l),e?Array.isArray(e)||(e=[e]):e=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<e.length;++o)i=e[o],!(i.visible!==!0||i._length===0)&&Y8t(this,i,h);K8t(this,h);var d=[1,1,1];for(s=0;s<3;++s)h[1][s]===h[0][s]?d[s]=1:d[s]=1/(h[1][s]-h[0][s]);for(n.dataScale=d,n.convertAnnotations(this),o=0;o<e.length;++o)i=e[o],!(i.visible!==!0||i._length===0)&&(a=n.traces[i.uid],a?a.data.type===i.type?a.update(i):(a.dispose(),a=i._module.plot(this,i),n.traces[i.uid]=a):(a=i._module.plot(this,i),n.traces[i.uid]=a),a.name=i.name);var v=Object.keys(n.traces);e:for(o=0;o<v.length;++o){for(s=0;s<e.length;++s)if(e[s].uid===v[o]&&e[s].visible===!0&&e[s]._length!==0)continue e;a=n.traces[v[o]],a.dispose(),delete n.traces[v[o]]}n.glplot.objects.sort(function(ae,_e){return ae._trace.data.index-_e._trace.data.index});var m=[[0,0,0],[0,0,0]],b=[],p={};for(o=0;o<3;++o){l=c[sk[o]],u=l.type,u in p?(p[u].acc*=d[o],p[u].count+=1):p[u]={acc:d[o],count:1};var k;if(l.autorange){m[0][o]=1/0,m[1][o]=-1/0;var M=n.glplot.objects,T=n.fullSceneLayout.annotations||[],L=l._name.charAt(0);for(s=0;s<M.length;s++){var x=M[s],C=x.bounds,S=x._trace.data._pad||0;x.constructor.name===\"ErrorBars\"&&l._lowerLogErrorBound?m[0][o]=Math.min(m[0][o],l._lowerLogErrorBound):m[0][o]=Math.min(m[0][o],C[0][o]/d[o]-S),m[1][o]=Math.max(m[1][o],C[1][o]/d[o]+S)}for(s=0;s<T.length;s++){var g=T[s];if(g.visible){var P=l.r2l(g[L]);m[0][o]=Math.min(m[0][o],P),m[1][o]=Math.max(m[1][o],P)}}if(\"rangemode\"in l&&l.rangemode===\"tozero\"&&(m[0][o]=Math.min(m[0][o],0),m[1][o]=Math.max(m[1][o],0)),m[0][o]>m[1][o])m[0][o]=-1,m[1][o]=1;else{var E=m[1][o]-m[0][o];m[0][o]-=E/32,m[1][o]+=E/32}if(k=[m[0][o],m[1][o]],k=Z8t(k,l),m[0][o]=k[0],m[1][o]=k[1],l.isReversed()){var z=m[0][o];m[0][o]=m[1][o],m[1][o]=z}}else k=l.range,m[0][o]=l.r2l(k[0]),m[1][o]=l.r2l(k[1]);m[0][o]===m[1][o]&&(m[0][o]-=1,m[1][o]+=1),b[o]=m[1][o]-m[0][o],l.range=[m[0][o],m[1][o]],l.limitRange(),n.glplot.setBounds(o,{min:l.range[0]*d[o],max:l.range[1]*d[o]})}var q,V=c.aspectmode;if(V===\"cube\")q=[1,1,1];else if(V===\"manual\"){var G=c.aspectratio;q=[G.x,G.y,G.z]}else if(V===\"auto\"||V===\"data\"){var Z=[1,1,1];for(o=0;o<3;++o){l=c[sk[o]],u=l.type;var j=p[u];Z[o]=Math.pow(j.acc,1/j.count)/d[o]}V===\"data\"||Math.max.apply(null,Z)/Math.min.apply(null,Z)<=4?q=Z:q=[1,1,1]}else throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");c.aspectratio.x=f.aspectratio.x=q[0],c.aspectratio.y=f.aspectratio.y=q[1],c.aspectratio.z=f.aspectratio.z=q[2],n.glplot.setAspectratio(c.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=c.aspectmode);var N=c.domain||null,H=t._size||null;if(N&&H){var re=n.container.style;re.position=\"absolute\",re.left=H.l+N.x[0]*H.w+\"px\",re.top=H.t+(1-N.y[1])*H.h+\"px\",re.width=H.w*(N.x[1]-N.x[0])+\"px\",re.height=H.h*(N.y[1]-N.y[0])+\"px\"}n.glplot.redraw()}};Sv.destroy=function(){var e=this;e.glplot&&(e.camera.mouseListener.enabled=!1,e.container.removeEventListener(\"wheel\",e.camera.wheelListener),e.camera=null,e.glplot.dispose(),e.container.parentNode.removeChild(e.container),e.glplot=null)};function J8t(e){return[[e.eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]}function $8t(e){return{up:{x:e.up[0],y:e.up[1],z:e.up[2]},center:{x:e.center[0],y:e.center[1],z:e.center[2]},eye:{x:e.eye[0],y:e.eye[1],z:e.eye[2]},projection:{type:e._ortho===!0?\"orthographic\":\"perspective\"}}}Sv.getCamera=function(){var e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),$8t(e.camera)};Sv.setViewport=function(e){var t=this,r=e.camera;t.camera.lookAt.apply(this,J8t(r)),t.glplot.setAspectratio(e.aspectratio);var n=r.projection.type===\"orthographic\",i=t.camera._ortho;n!==i&&(t.glplot.redraw(),t.glplot.clearRGBA(),t.glplot.dispose(),t.initializeGLPlot())};Sv.isCameraChanged=function(e){var t=this,r=t.getCamera(),n=cp.nestedProperty(e,t.id+\".camera\"),i=n.get();function a(u,c,f,h){var d=[\"up\",\"center\",\"eye\"],v=[\"x\",\"y\",\"z\"];return c[d[f]]&&u[d[f]][v[h]]===c[d[f]][v[h]]}var o=!1;if(i===void 0)o=!0;else{for(var s=0;s<3;s++)for(var l=0;l<3;l++)if(!a(r,i,s,l)){o=!0;break}(!i.projection||r.projection&&r.projection.type!==i.projection.type)&&(o=!0)}return o};Sv.isAspectChanged=function(e){var t=this,r=t.glplot.getAspectratio(),n=cp.nestedProperty(e,t.id+\".aspectratio\"),i=n.get();return i===void 0||i.x!==r.x||i.y!==r.y||i.z!==r.z};Sv.saveLayout=function(e){var t=this,r=t.fullLayout,n,i,a,o,s,l,u=t.isCameraChanged(e),c=t.isAspectChanged(e),f=u||c;if(f){var h={};if(u&&(n=t.getCamera(),i=cp.nestedProperty(e,t.id+\".camera\"),a=i.get(),h[t.id+\".camera\"]=a),c&&(o=t.glplot.getAspectratio(),s=cp.nestedProperty(e,t.id+\".aspectratio\"),l=s.get(),h[t.id+\".aspectratio\"]=l),fF.call(\"_storeDirectGUIEdit\",e,r._preGUI,h),u){i.set(n);var d=cp.nestedProperty(r,t.id+\".camera\");d.set(n)}if(c){s.set(o);var v=cp.nestedProperty(r,t.id+\".aspectratio\");v.set(o),t.glplot.redraw()}}return f};Sv.updateFx=function(e,t){var r=this,n=r.camera;if(n)if(e===\"orbit\")n.mode=\"orbit\",n.keyBindingMode=\"rotate\";else if(e===\"turntable\"){n.up=[0,0,1],n.mode=\"turntable\",n.keyBindingMode=\"rotate\";var i=r.graphDiv,a=i._fullLayout,o=r.fullSceneLayout.camera,s=o.up.x,l=o.up.y,u=o.up.z;if(u/Math.sqrt(s*s+l*l+u*u)<.999){var c=r.id+\".camera.up\",f={x:0,y:0,z:1},h={};h[c]=f;var d=i.layout;fF.call(\"_storeDirectGUIEdit\",d,a._preGUI,h),o.up=f,cp.nestedProperty(d,c).set(f)}}else n.keyBindingMode=e;r.fullSceneLayout.hovermode=t};function Q8t(e,t,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<t;++a)for(var o=0;o<4;++o){var s=4*(t*n+a)+o,l=4*(t*i+a)+o,u=e[s];e[s]=e[l],e[l]=u}}function eRt(e,t,r){for(var n=0;n<r;++n)for(var i=0;i<t;++i){var a=4*(t*n+i),o=e[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)e[a+l]=Math.min(s*e[a+l],255)}}Sv.toImage=function(e){var t=this;e||(e=\"png\"),t.staticMode&&t.container.appendChild(ok),t.glplot.redraw();var r=t.glplot.gl,n=r.drawingBufferWidth,i=r.drawingBufferHeight;r.bindFramebuffer(r.FRAMEBUFFER,null);var a=new Uint8Array(n*i*4);r.readPixels(0,0,n,i,r.RGBA,r.UNSIGNED_BYTE,a),Q8t(a,n,i),eRt(a,n,i);var o=document.createElement(\"canvas\");o.width=n,o.height=i;var s=o.getContext(\"2d\",{willReadFrequently:!0}),l=s.createImageData(n,i);l.data.set(a),s.putImageData(l,0,0);var u;switch(e){case\"jpeg\":u=o.toDataURL(\"image/jpeg\");break;case\"webp\":u=o.toDataURL(\"image/webp\");break;default:u=o.toDataURL(\"image/png\")}return t.staticMode&&t.container.removeChild(ok),u};Sv.setConvert=function(){for(var e=this,t=0;t<3;t++){var r=e.fullSceneLayout[sk[t]];hF.setConvert(r,e.fullLayout),r.setScale=cp.noop}};Sv.make4thDimension=function(){var e=this,t=e.graphDiv,r=t._fullLayout;e._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},hF.setConvert(e._mockAxis,r)};qIe.exports=zIe});var NIe=ye((Kgr,BIe)=>{\"use strict\";BIe.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}});var UX=ye((Jgr,UIe)=>{\"use strict\";var tRt=ka(),xs=Rd(),NX=Ao().extendFlat,rRt=mc().overrideAll;UIe.exports=rRt({visible:xs.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:tRt.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:xs.color,categoryorder:xs.categoryorder,categoryarray:xs.categoryarray,title:{text:xs.title.text,font:xs.title.font},type:NX({},xs.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:xs.autotypenumbers,autorange:xs.autorange,autorangeoptions:{minallowed:xs.autorangeoptions.minallowed,maxallowed:xs.autorangeoptions.maxallowed,clipmin:xs.autorangeoptions.clipmin,clipmax:xs.autorangeoptions.clipmax,include:xs.autorangeoptions.include,editType:\"plot\"},rangemode:xs.rangemode,minallowed:xs.minallowed,maxallowed:xs.maxallowed,range:NX({},xs.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:xs.minor.tickmode,nticks:xs.nticks,tick0:xs.tick0,dtick:xs.dtick,tickvals:xs.tickvals,ticktext:xs.ticktext,ticks:xs.ticks,mirror:xs.mirror,ticklen:xs.ticklen,tickwidth:xs.tickwidth,tickcolor:xs.tickcolor,showticklabels:xs.showticklabels,labelalias:xs.labelalias,tickfont:xs.tickfont,tickangle:xs.tickangle,tickprefix:xs.tickprefix,showtickprefix:xs.showtickprefix,ticksuffix:xs.ticksuffix,showticksuffix:xs.showticksuffix,showexponent:xs.showexponent,exponentformat:xs.exponentformat,minexponent:xs.minexponent,separatethousands:xs.separatethousands,tickformat:xs.tickformat,tickformatstops:xs.tickformatstops,hoverformat:xs.hoverformat,showline:xs.showline,linecolor:xs.linecolor,linewidth:xs.linewidth,showgrid:xs.showgrid,gridcolor:NX({},xs.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:xs.gridwidth,zeroline:xs.zeroline,zerolinecolor:xs.zerolinecolor,zerolinewidth:xs.zerolinewidth},\"plot\",\"from-root\")});var jX=ye(($gr,VIe)=>{\"use strict\";var VX=UX(),iRt=Cc().attributes,GX=Ao().extendFlat,nRt=Pr().counterRegex;function HX(e,t,r){return{x:{valType:\"number\",dflt:e,editType:\"camera\"},y:{valType:\"number\",dflt:t,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}VIe.exports={_arrayAttrRegexps:[nRt(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:GX(HX(0,0,1),{}),center:GX(HX(0,0,0),{}),eye:GX(HX(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:iRt({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:VX,yaxis:VX,zaxis:VX,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\"}});var WIe=ye((Qgr,jIe)=>{\"use strict\";var aRt=fd().mix,GIe=Pr(),oRt=vl(),sRt=UX(),lRt=FU(),uRt=v4(),HIe=[\"xaxis\",\"yaxis\",\"zaxis\"],cRt=13600/187;jIe.exports=function(t,r,n){var i,a;function o(u,c){return GIe.coerce(i,a,sRt,u,c)}for(var s=0;s<HIe.length;s++){var l=HIe[s];i=t[l]||{},a=oRt.newContainer(r,l),a._id=l[0]+n.scene,a._name=l,lRt(i,a,o,n),uRt(i,a,o,{font:n.font,letter:l[0],data:n.data,showGrid:!0,noAutotickangles:!0,noMinorloglabels:!0,noTicklabelindex:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelshift:!0,noTicklabelstandoff:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,noUnifiedhovertitle:!0,bgColor:n.bgColor,calendar:n.calendar},n.fullLayout),o(\"gridcolor\",aRt(a.color,n.bgColor,cRt).toRgbString()),o(\"title.text\",l[0]),a.setScale=GIe.noop,o(\"showspikes\")&&(o(\"spikesides\"),o(\"spikethickness\"),o(\"spikecolor\",a.color)),o(\"showaxeslabels\"),o(\"showbackground\")&&o(\"backgroundcolor\")}}});var KIe=ye((emr,YIe)=>{\"use strict\";var fRt=Pr(),hRt=ka(),dRt=Oa(),vRt=O_(),pRt=WIe(),XIe=jX(),gRt=Id().getSubplotData,ZIe=\"gl3d\";YIe.exports=function(t,r,n){var i=r._basePlotModules.length>1;function a(o){if(!i){var s=fRt.validate(t[o],XIe[o]);if(s)return t[o]}}vRt(t,r,n,{type:ZIe,attributes:XIe,handleDefaults:mRt,fullLayout:r,font:r.font,fullData:n,getDfltFromLayout:a,autotypenumbersDflt:r.autotypenumbers,paper_bgcolor:r.paper_bgcolor,calendar:r.calendar})};function mRt(e,t,r,n){for(var i=r(\"bgcolor\"),a=hRt.combine(i,n.paper_bgcolor),o=[\"up\",\"center\",\"eye\"],s=0;s<o.length;s++)r(\"camera.\"+o[s]+\".x\"),r(\"camera.\"+o[s]+\".y\"),r(\"camera.\"+o[s]+\".z\");r(\"camera.projection.type\");var l=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),u=l?\"manual\":\"auto\",c=r(\"aspectmode\",u);l||(e.aspectratio=t.aspectratio={x:1,y:1,z:1},c===\"manual\"&&(t.aspectmode=\"auto\"),e.aspectmode=t.aspectmode);var f=gRt(n.fullData,ZIe,n.id);pRt(e,t,{font:n.font,scene:n.id,data:f,bgColor:a,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),dRt.getComponentMethod(\"annotations3d\",\"handleDefaults\")(e,t,n);var h=n.getDfltFromLayout(\"dragmode\");if(h!==!1&&!h)if(h=\"orbit\",e.camera&&e.camera.up){var d=e.camera.up.x,v=e.camera.up.y,m=e.camera.up.z;m!==0&&(!d||!v||!m||m/Math.sqrt(d*d+v*v+m*m)>.999)&&(h=\"turntable\")}else h=\"turntable\";r(\"dragmode\",h),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}});var ux=ye(fp=>{\"use strict\";var yRt=mc().overrideAll,_Rt=X1(),xRt=OIe(),bRt=Id().getSubplotData,wRt=Pr(),TRt=Wp(),JA=\"gl3d\",WX=\"scene\";fp.name=JA;fp.attr=WX;fp.idRoot=WX;fp.idRegex=fp.attrRegex=wRt.counterRegex(\"scene\");fp.attributes=NIe();fp.layoutAttributes=jX();fp.baseLayoutAttrOverrides=yRt({hoverlabel:_Rt.hoverlabel},\"plot\",\"nested\");fp.supplyLayoutDefaults=KIe();fp.plot=function(t){for(var r=t._fullLayout,n=t._fullData,i=r._subplots[JA],a=0;a<i.length;a++){var o=i[a],s=bRt(n,JA,o),l=r[o],u=l.camera,c=l._scene;c||(c=new xRt({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},r),l._scene=c),c.viewInitial||(c.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),c.plot(s,r,t.layout)}};fp.clean=function(e,t,r,n){for(var i=n._subplots[JA]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}};fp.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[JA],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._scene,l=s.toImage(\"png\"),u=t._glimages.append(\"svg:image\");u.attr({xmlns:TRt.svg,\"xlink:href\":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}};fp.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var r=t.slice(5);return r===\"1\"&&(r=\"\"),WX+r}};fp.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[JA],n=0;n<r.length;n++){var i=t[r[n]]._scene;i.updateFx(t.dragmode,t.hovermode)}}});var $Ie=ye((rmr,JIe)=>{\"use strict\";JIe.exports={plot:sIe(),attributes:qX(),markerSymbols:sF(),supplyDefaults:vIe(),colorbar:[{container:\"marker\",min:\"cmin\",max:\"cmax\"},{container:\"line\",min:\"cmin\",max:\"cmax\"}],calc:gIe(),moduleType:\"trace\",name:\"scatter3d\",basePlotModule:ux(),categories:[\"gl3d\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}});var e8e=ye((imr,QIe)=>{\"use strict\";QIe.exports=$Ie()});var lk=ye((nmr,i8e)=>{\"use strict\";var t8e=ka(),ARt=Tu(),XX=vf().axisHoverFormat,{hovertemplateAttrs:SRt,templatefallbackAttrs:MRt}=Pl(),r8e=Vl(),ZX=Ao().extendFlat,ERt=mc().overrideAll;function YX(e){return{valType:\"boolean\",dflt:!1}}function KX(e){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:YX(\"x\"),y:YX(\"y\"),z:YX(\"z\")},color:{valType:\"color\",dflt:t8e.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:t8e.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var JX=i8e.exports=ERt(ZX({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:SRt(),hovertemplatefallback:MRt(),xhoverformat:XX(\"x\"),yhoverformat:XX(\"y\"),zhoverformat:XX(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},ARt(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:KX(\"x\"),y:KX(\"y\"),z:KX(\"z\")},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05,description:\"Represents the level that incident rays are reflected in a single direction, causing shine.\"},roughness:{valType:\"number\",min:0,max:1,dflt:.5,description:\"Alters specular reflection; the rougher the surface, the wider and less contrasty the shine.\"},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},hoverinfo:ZX({},r8e.hoverinfo),showlegend:ZX({},r8e.showlegend,{dflt:!1})}),\"calc\",\"nested\");JX.x.editType=JX.y.editType=JX.z.editType=\"calc+clearAxisTypes\"});var QX=ye((amr,o8e)=>{\"use strict\";var kRt=Oa(),n8e=Pr(),CRt=td(),LRt=lk(),$X=.1;function PRt(e,t){for(var r=[],n=32,i=0;i<n;i++){var a=i/(n-1),o=t+(1-t)*(1-Math.pow(Math.sin(e*a*Math.PI),2));r.push([a,Math.max(0,Math.min(1,o))])}return r}function IRt(e){var t=0;if(!Array.isArray(e)||e.length<2||!e[0]||!e[e.length-1]||+e[0][0]!=0||+e[e.length-1][0]!=1)return!1;for(var r=0;r<e.length;r++){var n=e[r];if(n.length!==2||+n[0]<t)return!1;t=+n[0]}return!0}function RRt(e,t,r,n){var i,a;function o(b,p){return n8e.coerce(e,t,LRt,b,p)}var s=o(\"x\"),l=o(\"y\"),u=o(\"z\");if(!u||!u.length||s&&s.length<1||l&&l.length<1){t.visible=!1;return}t._xlength=Array.isArray(s)&&n8e.isArrayOrTypedArray(s[0])?u.length:u[0].length,t._ylength=u.length;var c=kRt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");c(e,t,[\"x\",\"y\",\"z\"],n),o(\"text\"),o(\"hovertext\"),o(\"hovertemplate\"),o(\"hovertemplatefallback\"),o(\"xhoverformat\"),o(\"yhoverformat\"),o(\"zhoverformat\"),[\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"hidesurface\",\"connectgaps\",\"opacity\"].forEach(function(b){o(b)});var f=o(\"surfacecolor\"),h=[\"x\",\"y\",\"z\"];for(i=0;i<3;++i){var d=\"contours.\"+h[i],v=o(d+\".show\"),m=o(d+\".highlight\");if(v||m)for(a=0;a<3;++a)o(d+\".project.\"+h[a]);v&&(o(d+\".color\"),o(d+\".width\"),o(d+\".usecolormap\")),m&&(o(d+\".highlightcolor\"),o(d+\".highlightwidth\")),o(d+\".start\"),o(d+\".end\"),o(d+\".size\")}CRt(e,t,n,o,{prefix:\"\",cLetter:\"c\"}),a8e(e,t,n,o),t._length=null}function a8e(e,t,r,n){var i=n(\"opacityscale\");i===\"max\"?t.opacityscale=[[0,$X],[1,1]]:i===\"min\"?t.opacityscale=[[0,1],[1,$X]]:i===\"extremes\"?t.opacityscale=PRt(1,$X):IRt(i)||(t.opacityscale=void 0)}o8e.exports={supplyDefaults:RRt,opacityscaleDefaults:a8e}});var u8e=ye((omr,l8e)=>{\"use strict\";var s8e=gv();l8e.exports=function(t,r){r.surfacecolor?s8e(t,r,{vals:r.surfacecolor,containerStr:\"\",cLetter:\"c\"}):s8e(t,r,{vals:r.z,containerStr:\"\",cLetter:\"c\"})}});var p8e=ye((smr,v8e)=>{\"use strict\";var DRt=qd().gl_surface3d,$A=qd().ndarray,FRt=qd().ndarray_linear_interpolate.d2,zRt=d8(),qRt=v8(),uk=Pr().isArrayOrTypedArray,ORt=o1().parseColorScale,c8e=a1(),BRt=tc().extractOpts;function h8e(e,t,r){this.scene=e,this.uid=r,this.surface=t,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var em=h8e.prototype;em.getXat=function(e,t,r,n){var i=uk(this.data.x)?uk(this.data.x[0])?this.data.x[t][e]:this.data.x[e]:e;return r===void 0?i:n.d2l(i,0,r)};em.getYat=function(e,t,r,n){var i=uk(this.data.y)?uk(this.data.y[0])?this.data.y[t][e]:this.data.y[t]:t;return r===void 0?i:n.d2l(i,0,r)};em.getZat=function(e,t,r,n){var i=this.data.z[t][e];return i===null&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[t][e]),r===void 0?i:n.d2l(i,0,r)};em.handlePick=function(e){if(e.object===this.surface){var t=(e.data.index[0]-1)/this.dataScaleX-1,r=(e.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(t),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);e.index=[n,i],e.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],e.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=e.dataCoordinate[a];o!=null&&(e.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return uk(s)&&s[i]&&s[i][n]!==void 0?e.textLabel=s[i][n]:s?e.textLabel=s:e.textLabel=\"\",e.data.dataCoordinate=e.dataCoordinate.slice(),this.surface.highlight(e.data),this.scene.glplot.spikes.position=e.dataCoordinate,!0}};function NRt(e){var t=e[0].rgb,r=e[e.length-1].rgb;return t[0]===r[0]&&t[1]===r[1]&&t[2]===r[2]&&t[3]===r[3]}var QA=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function URt(e,t){if(e<t)return 0;for(var r=0;Math.floor(e%t)===0;)e/=t,r++;return r}function eZ(e){for(var t=[],r=0;r<QA.length;r++){var n=QA[r];t.push(URt(e,n))}return t}function VRt(e){for(var t=eZ(e),r=e,n=0;n<QA.length;n++)if(t[n]>0){r=QA[n];break}return r}function GRt(e,t){if(!(e<1||t<1)){for(var r=eZ(e),n=eZ(t),i=1,a=0;a<QA.length;a++)i*=Math.pow(QA[a],Math.max(r[a],n[a]));return i}}function HRt(e){if(e.length!==0){for(var t=1,r=0;r<e.length;r++)t=GRt(t,e[r]);return t}}em.calcXnums=function(e){var t,r=[];for(t=1;t<e;t++){var n=this.getXat(t-1,0),i=this.getXat(t,0);i!==n&&n!==void 0&&n!==null&&i!==void 0&&i!==null?r[t-1]=Math.abs(i-n):r[t-1]=0}var a=0;for(t=1;t<e;t++)a+=r[t-1];for(t=1;t<e;t++)r[t-1]===0?r[t-1]=1:r[t-1]=Math.round(a/r[t-1]);return r};em.calcYnums=function(e){var t,r=[];for(t=1;t<e;t++){var n=this.getYat(0,t-1),i=this.getYat(0,t);i!==n&&n!==void 0&&n!==null&&i!==void 0&&i!==null?r[t-1]=Math.abs(i-n):r[t-1]=0}var a=0;for(t=1;t<e;t++)a+=r[t-1];for(t=1;t<e;t++)r[t-1]===0?r[t-1]=1:r[t-1]=Math.round(a/r[t-1]);return r};var d8e=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],f8e=d8e[9],dF=d8e[13];em.estimateScale=function(e,t){for(var r=t===0?this.calcXnums(e):this.calcYnums(e),n=1+HRt(r);n<f8e;)n*=2;for(;n>dF;)n--,n/=VRt(n),n++,n<f8e&&(n=dF);var i=Math.round(n/e);return i>1?i:1};function jRt(e,t,r){var n=r[8]+r[2]*t[0]+r[5]*t[1];return e[0]=(r[6]+r[0]*t[0]+r[3]*t[1])/n,e[1]=(r[7]+r[1]*t[0]+r[4]*t[1])/n,e}function WRt(e,t,r){return XRt(e,t,jRt,r),e}function XRt(e,t,r,n){for(var i=[0,0],a=e.shape[0],o=e.shape[1],s=0;s<a;s++)for(var l=0;l<o;l++)r(i,[s,l],n),e.set(s,l,FRt(t,i[0],i[1]));return e}em.refineCoords=function(e){for(var t=this.dataScaleX,r=this.dataScaleY,n=e[0].shape[0],i=e[0].shape[1],a=Math.floor(e[0].shape[0]*t+1)|0,o=Math.floor(e[0].shape[1]*r+1)|0,s=1+n+1,l=1+i+1,u=$A(new Float32Array(s*l),[s,l]),c=[1/t,0,0,0,1/r,0,0,0,1],f=0;f<e.length;++f){this.surface.padField(u,e[f]);var h=$A(new Float32Array(a*o),[a,o]);WRt(h,u,c),e[f]=h}};function ZRt(e,t){for(var r=!1,n=0;n<e.length;n++)if(t===e[n]){r=!0;break}r===!1&&e.push(t)}em.setContourLevels=function(){var e=[[],[],[]],t=[!1,!1,!1],r=!1,n,i,a;for(n=0;n<3;++n)if(this.showContour[n]&&(r=!0,this.contourSize[n]>0&&this.contourStart[n]!==null&&this.contourEnd[n]!==null&&this.contourEnd[n]>this.contourStart[n]))for(t[n]=!0,i=this.contourStart[n];i<this.contourEnd[n];i+=this.contourSize[n])a=i*this.scene.dataScale[n],ZRt(e[n],a);if(r){var o=[[],[],[]];for(n=0;n<3;++n)this.showContour[n]&&(o[n]=t[n]?e[n]:this.scene.contourLevels[n]);this.surface.update({levels:o})}};em.update=function(e){var t=this.scene,r=t.fullSceneLayout,n=this.surface,i=ORt(e),a=t.dataScale,o=e.z[0].length,s=e._ylength,l=t.contourLevels;this.data=e;var u,c,f,h,d=[];for(u=0;u<3;u++)for(d[u]=[],c=0;c<o;c++)d[u][c]=[];for(c=0;c<o;c++)for(f=0;f<s;f++)d[0][c][f]=this.getXat(c,f,e.xcalendar,r.xaxis),d[1][c][f]=this.getYat(c,f,e.ycalendar,r.yaxis),d[2][c][f]=this.getZat(c,f,e.zcalendar,r.zaxis);if(e.connectgaps)for(e._emptypoints=qRt(d[2]),zRt(d[2],e._emptypoints),e._interpolatedZ=[],c=0;c<o;c++)for(e._interpolatedZ[c]=[],f=0;f<s;f++)e._interpolatedZ[c][f]=d[2][c][f];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h==null?d[u][c][f]=NaN:h=d[u][c][f]*=a[u];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h!=null&&(this.minValues[u]>h&&(this.minValues[u]=h),this.maxValues[u]<h&&(this.maxValues[u]=h));for(u=0;u<3;u++)this.objectOffset[u]=.5*(this.minValues[u]+this.maxValues[u]);for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h!=null&&(d[u][c][f]-=this.objectOffset[u]);var v=[$A(new Float32Array(o*s),[o,s]),$A(new Float32Array(o*s),[o,s]),$A(new Float32Array(o*s),[o,s])];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)v[u].set(c,f,d[u][c][f]);d=[];var m={colormap:i,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!e.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:e.opacityscale,opacity:e.opacity},b=BRt(e);if(m.intensityBounds=[b.min,b.max],e.surfacecolor){var p=$A(new Float32Array(o*s),[o,s]);for(c=0;c<o;c++)for(f=0;f<s;f++)p.set(c,f,e.surfacecolor[f][c]);v.push(p)}else m.intensityBounds[0]*=a[2],m.intensityBounds[1]*=a[2];(dF<v[0].shape[0]||dF<v[0].shape[1])&&(this.refineData=!1),this.refineData===!0&&(this.dataScaleX=this.estimateScale(v[0].shape[0],0),this.dataScaleY=this.estimateScale(v[0].shape[1],1),(this.dataScaleX!==1||this.dataScaleY!==1)&&this.refineCoords(v)),e.surfacecolor&&(m.intensity=v.pop());var k=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(u=0;u<3;++u){var T=e.contours[M[u]];k[u]=T.highlight,m.showContour[u]=T.show||T.highlight,m.showContour[u]&&(m.contourProject[u]=[T.project.x,T.project.y,T.project.z],T.show?(this.showContour[u]=!0,m.levels[u]=l[u],n.highlightColor[u]=m.contourColor[u]=c8e(T.color),T.usecolormap?n.highlightTint[u]=m.contourTint[u]=0:n.highlightTint[u]=m.contourTint[u]=1,m.contourWidth[u]=T.width,this.contourStart[u]=T.start,this.contourEnd[u]=T.end,this.contourSize[u]=T.size):(this.showContour[u]=!1,this.contourStart[u]=null,this.contourEnd[u]=null,this.contourSize[u]=0),T.highlight&&(m.dynamicColor[u]=c8e(T.highlightcolor),m.dynamicWidth[u]=T.highlightwidth))}NRt(i)&&(m.vertexColor=!0),m.objectOffset=this.objectOffset,m.coords=v,n.update(m),n.visible=e.visible,n.enableDynamic=k,n.enableHighlight=k,n.snapToData=!0,\"lighting\"in e&&(n.ambientLight=e.lighting.ambient,n.diffuseLight=e.lighting.diffuse,n.specularLight=e.lighting.specular,n.roughness=e.lighting.roughness,n.fresnel=e.lighting.fresnel),\"lightposition\"in e&&(n.lightPosition=[e.lightposition.x,e.lightposition.y,e.lightposition.z])};em.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()};function YRt(e,t){var r=e.glplot.gl,n=DRt({gl:r}),i=new h8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}v8e.exports=YRt});var m8e=ye((lmr,g8e)=>{\"use strict\";g8e.exports={attributes:lk(),supplyDefaults:QX().supplyDefaults,colorbar:{min:\"cmin\",max:\"cmax\"},calc:u8e(),plot:p8e(),moduleType:\"trace\",name:\"surface\",basePlotModule:ux(),categories:[\"gl3d\",\"2dMap\",\"showLegend\"],meta:{}}});var _8e=ye((umr,y8e)=>{\"use strict\";y8e.exports=m8e()});var e5=ye((cmr,b8e)=>{\"use strict\";var KRt=Tu(),tZ=vf().axisHoverFormat,{hovertemplateAttrs:JRt,templatefallbackAttrs:$Rt}=Pl(),cx=lk(),x8e=Vl(),fx=Ao().extendFlat;b8e.exports=fx({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:JRt({editType:\"calc\"}),hovertemplatefallback:$Rt({editType:\"calc\"}),xhoverformat:tZ(\"x\"),yhoverformat:tZ(\"y\"),zhoverformat:tZ(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"}},KRt(\"\",{colorAttr:\"`intensity`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:cx.opacity,flatshading:{valType:\"boolean\",dflt:!1,editType:\"calc\"},contour:{show:fx({},cx.contours.x.show,{}),color:cx.contours.x.color,width:cx.contours.x.width,editType:\"calc\"},lightposition:{x:fx({},cx.lightposition.x,{dflt:1e5}),y:fx({},cx.lightposition.y,{dflt:1e5}),z:fx({},cx.lightposition.z,{dflt:0}),editType:\"calc\"},lighting:fx({vertexnormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-12,editType:\"calc\",description:\"Epsilon for vertex normals calculation avoids math issues arising from degenerate geometry.\"},facenormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-6,editType:\"calc\",description:\"Epsilon for face normals calculation avoids math issues arising from degenerate geometry.\"},editType:\"calc\"},cx.lighting),hoverinfo:fx({},x8e.hoverinfo,{editType:\"calc\"}),showlegend:fx({},x8e.showlegend,{dflt:!1})})});var pF=ye((fmr,T8e)=>{\"use strict\";var QRt=Tu(),vF=vf().axisHoverFormat,{hovertemplateAttrs:eDt,templatefallbackAttrs:tDt}=Pl(),ck=e5(),w8e=Vl(),rZ=Ao().extendFlat,rDt=mc().overrideAll;function iZ(e){return{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}}function nZ(e){return{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}}var t5=T8e.exports=rDt(rZ({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:iZ(\"x\"),y:iZ(\"y\"),z:iZ(\"z\")},caps:{x:nZ(\"x\"),y:nZ(\"y\"),z:nZ(\"z\")},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:eDt(),hovertemplatefallback:tDt(),xhoverformat:vF(\"x\"),yhoverformat:vF(\"y\"),zhoverformat:vF(\"z\"),valuehoverformat:vF(\"value\",1),showlegend:rZ({},w8e.showlegend,{dflt:!1})},QRt(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:ck.opacity,lightposition:ck.lightposition,lighting:ck.lighting,flatshading:ck.flatshading,contour:ck.contour,hoverinfo:rZ({},w8e.hoverinfo)}),\"calc\",\"nested\");t5.flatshading.dflt=!0;t5.lighting.facenormalsepsilon.dflt=0;t5.x.editType=t5.y.editType=t5.z.editType=t5.value.editType=\"calc+clearAxisTypes\"});var aZ=ye((hmr,S8e)=>{\"use strict\";var iDt=Pr(),nDt=Oa(),aDt=pF(),oDt=td();function sDt(e,t,r,n){function i(a,o){return iDt.coerce(e,t,aDt,a,o)}A8e(e,t,r,n,i)}function A8e(e,t,r,n,i){var a=i(\"isomin\"),o=i(\"isomax\");o!=null&&a!==void 0&&a!==null&&a>o&&(t.isomin=null,t.isomax=null);var s=i(\"x\"),l=i(\"y\"),u=i(\"z\"),c=i(\"value\");if(!s||!s.length||!l||!l.length||!u||!u.length||!c||!c.length){t.visible=!1;return}var f=nDt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");f(e,t,[\"x\",\"y\",\"z\"],n),i(\"valuehoverformat\"),[\"x\",\"y\",\"z\"].forEach(function(m){i(m+\"hoverformat\");var b=\"caps.\"+m,p=i(b+\".show\");p&&i(b+\".fill\");var k=\"slices.\"+m,M=i(k+\".show\");M&&(i(k+\".fill\"),i(k+\".locations\"))});var h=i(\"spaceframe.show\");h&&i(\"spaceframe.fill\");var d=i(\"surface.show\");d&&(i(\"surface.count\"),i(\"surface.fill\"),i(\"surface.pattern\"));var v=i(\"contour.show\");v&&(i(\"contour.color\"),i(\"contour.width\")),[\"text\",\"hovertext\",\"hovertemplate\",\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lighting.vertexnormalsepsilon\",\"lighting.facenormalsepsilon\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"flatshading\",\"opacity\"].forEach(function(m){i(m)}),oDt(e,t,n,i,{prefix:\"\",cLetter:\"c\"}),t._length=null}S8e.exports={supplyDefaults:sDt,supplyIsoDefaults:A8e}});var gF=ye((dmr,E8e)=>{\"use strict\";var sZ=Pr(),lDt=gv();function uDt(e,t){t._len=Math.min(t.u.length,t.v.length,t.w.length,t.x.length,t.y.length,t.z.length),t._u=$m(t.u,t._len),t._v=$m(t.v,t._len),t._w=$m(t.w,t._len),t._x=$m(t.x,t._len),t._y=$m(t.y,t._len),t._z=$m(t.z,t._len);var r=M8e(t);t._gridFill=r.fill,t._Xs=r.Xs,t._Ys=r.Ys,t._Zs=r.Zs,t._len=r.len;var n=0,i,a,o;t.starts&&(i=$m(t.starts.x||[]),a=$m(t.starts.y||[]),o=$m(t.starts.z||[]),n=Math.min(i.length,a.length,o.length)),t._startsX=i||[],t._startsY=a||[],t._startsZ=o||[];var s=0,l=1/0,u;for(u=0;u<t._len;u++){var c=t._u[u],f=t._v[u],h=t._w[u],d=Math.sqrt(c*c+f*f+h*h);s=Math.max(s,d),l=Math.min(l,d)}for(lDt(e,t,{vals:[l,s],containerStr:\"\",cLetter:\"c\"}),u=0;u<n;u++){var v=i[u];r.xMax=Math.max(r.xMax,v),r.xMin=Math.min(r.xMin,v);var m=a[u];r.yMax=Math.max(r.yMax,m),r.yMin=Math.min(r.yMin,m);var b=o[u];r.zMax=Math.max(r.zMax,b),r.zMin=Math.min(r.zMin,b)}t._slen=n,t._normMax=s,t._xbnds=[r.xMin,r.xMax],t._ybnds=[r.yMin,r.yMax],t._zbnds=[r.zMin,r.zMax]}function M8e(e){var t=e._x,r=e._y,n=e._z,i=e._len,a,o,s,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,d=1/0,v=\"\",m,b,p,k,M,T,L,x,C;for(i&&(k=t[0],T=r[0],x=n[0]),i>1&&(M=t[i-1],L=r[i-1],C=n[i-1]),a=0;a<i;a++)l=Math.max(l,t[a]),u=Math.min(u,t[a]),c=Math.max(c,r[a]),f=Math.min(f,r[a]),h=Math.max(h,n[a]),d=Math.min(d,n[a]),!m&&t[a]!==k&&(m=!0,v+=\"x\"),!b&&r[a]!==T&&(b=!0,v+=\"y\"),!p&&n[a]!==x&&(p=!0,v+=\"z\");m||(v+=\"x\"),b||(v+=\"y\"),p||(v+=\"z\");var S=oZ(e._x),g=oZ(e._y),P=oZ(e._z);v=v.replace(\"x\",(k>M?\"-\":\"+\")+\"x\"),v=v.replace(\"y\",(T>L?\"-\":\"+\")+\"y\"),v=v.replace(\"z\",(x>C?\"-\":\"+\")+\"z\");var E=function(){i=0,S=[],g=[],P=[]};(!i||i<S.length*g.length*P.length)&&E();var z=function(Be){return Be===\"x\"?t:Be===\"y\"?r:n},q=function(Be){return Be===\"x\"?S:Be===\"y\"?g:P},V=function(Be){return Be[i-1]<Be[0]?-1:1},G=z(v[1]),Z=z(v[3]),j=z(v[5]),N=q(v[1]).length,H=q(v[3]).length,re=q(v[5]).length,ae=!1,_e=function(Be,Le,me){return N*(H*Be+Le)+me},Ee=V(z(v[1])),Ce=V(z(v[3])),ge=V(z(v[5]));for(a=0;a<re-1;a++){for(o=0;o<H-1;o++){for(s=0;s<N-1;s++){var ie=_e(a,o,s),Se=_e(a,o,s+1),ke=_e(a,o+1,s),be=_e(a+1,o,s);if((!(G[ie]*Ee<G[Se]*Ee)||!(Z[ie]*Ce<Z[ke]*Ce)||!(j[ie]*ge<j[be]*ge))&&(ae=!0),ae)break}if(ae)break}if(ae)break}return ae&&(sZ.warn(\"Encountered arbitrary coordinates! Unable to input data grid.\"),E()),{xMin:u,yMin:f,zMin:d,xMax:l,yMax:c,zMax:h,Xs:S,Ys:g,Zs:P,len:i,fill:v}}function oZ(e){return sZ.distinctVals(e).vals}function $m(e,t){if(t===void 0&&(t=e.length),sZ.isTypedArray(e))return e.subarray(0,t);for(var r=[],n=0;n<t;n++)r[n]=+e[n];return r}E8e.exports={calc:uDt,filter:$m,processGrid:M8e}});var lZ=ye((vmr,k8e)=>{\"use strict\";var cDt=gv(),fDt=gF().processGrid,mF=gF().filter;k8e.exports=function(t,r){r._len=Math.min(r.x.length,r.y.length,r.z.length,r.value.length),r._x=mF(r.x,r._len),r._y=mF(r.y,r._len),r._z=mF(r.z,r._len),r._value=mF(r.value,r._len);var n=fDt(r);r._gridFill=n.fill,r._Xs=n.Xs,r._Ys=n.Ys,r._Zs=n.Zs,r._len=n.len;for(var i=1/0,a=-1/0,o=0;o<r._len;o++){var s=r._value[o];i=Math.min(i,s),a=Math.max(a,s)}r._minValues=i,r._maxValues=a,r._vMin=r.isomin===void 0||r.isomin===null?i:r.isomin,r._vMax=r.isomax===void 0||r.isomax===null?a:r.isomax,cDt(t,r,{vals:[r._vMin,r._vMax],containerStr:\"\",cLetter:\"c\"})}});var r5=ye((pmr,C8e)=>{\"use strict\";C8e.exports=function(t,r,n,i){i=i||t.length;for(var a=new Array(i),o=0;o<i;o++)a[o]=[t[o],r[o],n[o]];return a}});var yF=ye((gmr,R8e)=>{\"use strict\";var hDt=qd().gl_mesh3d,dDt=o1().parseColorScale,vDt=Pr().isArrayOrTypedArray,pDt=a1(),gDt=tc().extractOpts,L8e=r5(),fk=function(e,t){for(var r=t.length-1;r>0;r--){var n=Math.min(t[r],t[r-1]),i=Math.max(t[r],t[r-1]);if(i>n&&n<e&&e<=i)return{id:r,distRatio:(i-e)/(i-n)}}return{id:0,distRatio:0}};function P8e(e,t,r){this.scene=e,this.uid=r,this.mesh=t,this.name=\"\",this.data=null,this.showContour=!1}var uZ=P8e.prototype;uZ.handlePick=function(e){if(e.object===this.mesh){var t=e.data.index,r=this.data._meshX[t],n=this.data._meshY[t],i=this.data._meshZ[t],a=this.data._Ys.length,o=this.data._Zs.length,s=fk(r,this.data._Xs).id,l=fk(n,this.data._Ys).id,u=fk(i,this.data._Zs).id,c=e.index=u+o*l+o*a*s;e.traceCoordinate=[this.data._meshX[c],this.data._meshY[c],this.data._meshZ[c],this.data._value[c]];var f=this.data.hovertext||this.data.text;return vDt(f)&&f[c]!==void 0?e.textLabel=f[c]:f&&(e.textLabel=f),!0}};uZ.update=function(e){var t=this.scene,r=t.fullSceneLayout;this.data=I8e(e);function n(l,u,c,f){return u.map(function(h){return l.d2l(h,0,f)*c})}var i=L8e(n(r.xaxis,e._meshX,t.dataScale[0],e.xcalendar),n(r.yaxis,e._meshY,t.dataScale[1],e.ycalendar),n(r.zaxis,e._meshZ,t.dataScale[2],e.zcalendar)),a=L8e(e._meshI,e._meshJ,e._meshK),o={positions:i,cells:a,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,contourEnable:e.contour.show,contourColor:pDt(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading},s=gDt(e);o.vertexIntensity=e._meshIntensity,o.vertexIntensityBounds=[s.min,s.max],o.colormap=dDt(e),this.mesh.update(o)};uZ.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var mDt=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"];function I8e(e){e._meshI=[],e._meshJ=[],e._meshK=[];var t=e.surface.show,r=e.spaceframe.show,n=e.surface.fill,i=e.spaceframe.fill,a=!1,o=!1,s=0,l,u,c=e._Xs,f=e._Ys,h=e._Zs,d=c.length,v=f.length,m=h.length,b=mDt.indexOf(e._gridFill.replace(/-/g,\"\").replace(/\\+/g,\"\")),p=function(St,Tt,dt){switch(b){case 5:return dt+m*Tt+m*v*St;case 4:return dt+m*St+m*d*Tt;case 3:return Tt+v*dt+v*m*St;case 2:return Tt+v*St+v*d*dt;case 1:return St+d*dt+d*m*Tt;default:return St+d*Tt+d*v*dt}},k=e._minValues,M=e._maxValues,T=e._vMin,L=e._vMax,x,C,S,g;function P(St,Tt,dt){for(var Et=g.length,pt=u;pt<Et;pt++)if(St===x[pt]&&Tt===C[pt]&&dt===S[pt])return pt;return-1}function E(){u=l}function z(){x=[],C=[],S=[],g=[],l=0,E()}function q(St,Tt,dt,Et){return x.push(St),C.push(Tt),S.push(dt),g.push(Et),l++,l-1}function V(St,Tt,dt){return e._meshI.push(St),e._meshJ.push(Tt),e._meshK.push(dt),s++,s-1}function G(St,Tt,dt){for(var Et=[],pt=0;pt<St.length;pt++)Et[pt]=(St[pt]+Tt[pt]+dt[pt])/3;return Et}function Z(St,Tt,dt){for(var Et=[],pt=0;pt<St.length;pt++)Et[pt]=St[pt]*(1-dt)+dt*Tt[pt];return Et}var j;function N(St){j=St}function H(St,Tt){var dt=St[0],Et=St[1],pt=St[2],jt=G(dt,Et,pt),or=Math.sqrt(1-j),mr=Z(jt,dt,or),Ar=Z(jt,Et,or),ei=Z(jt,pt,or),qr=Tt[0],jr=Tt[1],gt=Tt[2];return{xyzv:[[dt,Et,Ar],[Ar,mr,dt],[Et,pt,ei],[ei,Ar,Et],[pt,dt,mr],[mr,ei,pt]],abc:[[qr,jr,-1],[-1,-1,qr],[jr,gt,-1],[-1,-1,jr],[gt,qr,-1],[-1,-1,gt]]}}function re(St,Tt){return St===\"all\"||St===null?!0:St.indexOf(Tt)>-1}function ae(St,Tt){return St===null?Tt:St}function _e(St,Tt,dt){E();var Et=[Tt],pt=[dt];if(j>=1)Et=[Tt],pt=[dt];else if(j>0){var jt=H(Tt,dt);Et=jt.xyzv,pt=jt.abc}for(var or=0;or<Et.length;or++){Tt=Et[or],dt=pt[or];for(var mr=[],Ar=0;Ar<3;Ar++){var ei=Tt[Ar][0],qr=Tt[Ar][1],jr=Tt[Ar][2],gt=Tt[Ar][3],Ge=dt[Ar]>-1?dt[Ar]:P(ei,qr,jr);Ge>-1?mr[Ar]=Ge:mr[Ar]=q(ei,qr,jr,ae(St,gt))}V(mr[0],mr[1],mr[2])}}function Ee(St,Tt,dt){var Et=function(pt,jt,or){_e(St,[Tt[pt],Tt[jt],Tt[or]],[dt[pt],dt[jt],dt[or]])};Et(0,1,2),Et(2,3,0)}function Ce(St,Tt,dt){var Et=function(pt,jt,or){_e(St,[Tt[pt],Tt[jt],Tt[or]],[dt[pt],dt[jt],dt[or]])};Et(0,1,2),Et(3,0,1),Et(2,3,0),Et(1,2,3)}function ge(St,Tt,dt,Et){var pt=St[3];pt<dt&&(pt=dt),pt>Et&&(pt=Et);for(var jt=(St[3]-pt)/(St[3]-Tt[3]+1e-9),or=[],mr=0;mr<4;mr++)or[mr]=(1-jt)*St[mr]+jt*Tt[mr];return or}function ie(St,Tt,dt){return St>=Tt&&St<=dt}function Se(St){var Tt=.001*(L-T);return St>=T-Tt&&St<=L+Tt}function ke(St){for(var Tt=[],dt=0;dt<4;dt++){var Et=St[dt];Tt.push([e._x[Et],e._y[Et],e._z[Et],e._value[Et]])}return Tt}var be=3;function Be(St,Tt,dt,Et,pt,jt){jt||(jt=1),dt=[-1,-1,-1];var or=!1,mr=[ie(Tt[0][3],Et,pt),ie(Tt[1][3],Et,pt),ie(Tt[2][3],Et,pt)];if(!mr[0]&&!mr[1]&&!mr[2])return!1;var Ar=function(qr,jr,gt){return Se(jr[0][3])&&Se(jr[1][3])&&Se(jr[2][3])?(_e(qr,jr,gt),!0):jt<be?Be(qr,jr,gt,T,L,++jt):!1};if(mr[0]&&mr[1]&&mr[2])return Ar(St,Tt,dt)||or;var ei=!1;return[[0,1,2],[2,0,1],[1,2,0]].forEach(function(qr){if(mr[qr[0]]&&mr[qr[1]]&&!mr[qr[2]]){var jr=Tt[qr[0]],gt=Tt[qr[1]],Ge=Tt[qr[2]],Je=ge(Ge,jr,Et,pt),We=ge(Ge,gt,Et,pt);or=Ar(St,[We,Je,jr],[-1,-1,dt[qr[0]]])||or,or=Ar(St,[jr,gt,We],[dt[qr[0]],dt[qr[1]],-1])||or,ei=!0}}),ei||[[0,1,2],[1,2,0],[2,0,1]].forEach(function(qr){if(mr[qr[0]]&&!mr[qr[1]]&&!mr[qr[2]]){var jr=Tt[qr[0]],gt=Tt[qr[1]],Ge=Tt[qr[2]],Je=ge(gt,jr,Et,pt),We=ge(Ge,jr,Et,pt);or=Ar(St,[We,Je,jr],[-1,-1,dt[qr[0]]])||or,ei=!0}}),or}function Le(St,Tt,dt,Et){var pt=!1,jt=ke(Tt),or=[ie(jt[0][3],dt,Et),ie(jt[1][3],dt,Et),ie(jt[2][3],dt,Et),ie(jt[3][3],dt,Et)];if(!or[0]&&!or[1]&&!or[2]&&!or[3])return pt;if(or[0]&&or[1]&&or[2]&&or[3])return o&&(pt=Ce(St,jt,Tt)||pt),pt;var mr=!1;return[[0,1,2,3],[3,0,1,2],[2,3,0,1],[1,2,3,0]].forEach(function(Ar){if(or[Ar[0]]&&or[Ar[1]]&&or[Ar[2]]&&!or[Ar[3]]){var ei=jt[Ar[0]],qr=jt[Ar[1]],jr=jt[Ar[2]],gt=jt[Ar[3]];if(o)pt=_e(St,[ei,qr,jr],[Tt[Ar[0]],Tt[Ar[1]],Tt[Ar[2]]])||pt;else{var Ge=ge(gt,ei,dt,Et),Je=ge(gt,qr,dt,Et),We=ge(gt,jr,dt,Et);pt=_e(null,[Ge,Je,We],[-1,-1,-1])||pt}mr=!0}}),mr||([[0,1,2,3],[1,2,3,0],[2,3,0,1],[3,0,1,2],[0,2,3,1],[1,3,2,0]].forEach(function(Ar){if(or[Ar[0]]&&or[Ar[1]]&&!or[Ar[2]]&&!or[Ar[3]]){var ei=jt[Ar[0]],qr=jt[Ar[1]],jr=jt[Ar[2]],gt=jt[Ar[3]],Ge=ge(jr,ei,dt,Et),Je=ge(jr,qr,dt,Et),We=ge(gt,qr,dt,Et),et=ge(gt,ei,dt,Et);o?(pt=_e(St,[ei,et,Ge],[Tt[Ar[0]],-1,-1])||pt,pt=_e(St,[qr,Je,We],[Tt[Ar[1]],-1,-1])||pt):pt=Ee(null,[Ge,Je,We,et],[-1,-1,-1,-1])||pt,mr=!0}}),mr)||[[0,1,2,3],[1,2,3,0],[2,3,0,1],[3,0,1,2]].forEach(function(Ar){if(or[Ar[0]]&&!or[Ar[1]]&&!or[Ar[2]]&&!or[Ar[3]]){var ei=jt[Ar[0]],qr=jt[Ar[1]],jr=jt[Ar[2]],gt=jt[Ar[3]],Ge=ge(qr,ei,dt,Et),Je=ge(jr,ei,dt,Et),We=ge(gt,ei,dt,Et);o?(pt=_e(St,[ei,Ge,Je],[Tt[Ar[0]],-1,-1])||pt,pt=_e(St,[ei,Je,We],[Tt[Ar[0]],-1,-1])||pt,pt=_e(St,[ei,We,Ge],[Tt[Ar[0]],-1,-1])||pt):pt=_e(null,[Ge,Je,We],[-1,-1,-1])||pt,mr=!0}}),pt}function me(St,Tt,dt,Et,pt,jt,or,mr,Ar,ei,qr){var jr=!1;return a&&(re(St,\"A\")&&(jr=Le(null,[Tt,dt,Et,jt],ei,qr)||jr),re(St,\"B\")&&(jr=Le(null,[dt,Et,pt,Ar],ei,qr)||jr),re(St,\"C\")&&(jr=Le(null,[dt,jt,or,Ar],ei,qr)||jr),re(St,\"D\")&&(jr=Le(null,[Et,jt,mr,Ar],ei,qr)||jr),re(St,\"E\")&&(jr=Le(null,[dt,Et,jt,Ar],ei,qr)||jr)),o&&(jr=Le(St,[dt,Et,jt,Ar],ei,qr)||jr),jr}function Pe(St,Tt,dt,Et,pt,jt,or,mr){return[mr[0]===!0?!0:Be(St,ke([Tt,dt,Et]),[Tt,dt,Et],jt,or),mr[1]===!0?!0:Be(St,ke([Et,pt,Tt]),[Et,pt,Tt],jt,or)]}function ce(St,Tt,dt,Et,pt,jt,or,mr,Ar){return mr?Pe(St,Tt,dt,pt,Et,jt,or,Ar):Pe(St,dt,pt,Et,Tt,jt,or,Ar)}function He(St,Tt,dt,Et,pt,jt,or){var mr=!1,Ar,ei,qr,jr,gt=function(){mr=Be(St,[Ar,ei,qr],[-1,-1,-1],pt,jt)||mr,mr=Be(St,[qr,jr,Ar],[-1,-1,-1],pt,jt)||mr},Ge=or[0],Je=or[1],We=or[2];return Ge&&(Ar=Z(ke([p(Tt,dt-0,Et-0)])[0],ke([p(Tt-1,dt-0,Et-0)])[0],Ge),ei=Z(ke([p(Tt,dt-0,Et-1)])[0],ke([p(Tt-1,dt-0,Et-1)])[0],Ge),qr=Z(ke([p(Tt,dt-1,Et-1)])[0],ke([p(Tt-1,dt-1,Et-1)])[0],Ge),jr=Z(ke([p(Tt,dt-1,Et-0)])[0],ke([p(Tt-1,dt-1,Et-0)])[0],Ge),gt()),Je&&(Ar=Z(ke([p(Tt-0,dt,Et-0)])[0],ke([p(Tt-0,dt-1,Et-0)])[0],Je),ei=Z(ke([p(Tt-0,dt,Et-1)])[0],ke([p(Tt-0,dt-1,Et-1)])[0],Je),qr=Z(ke([p(Tt-1,dt,Et-1)])[0],ke([p(Tt-1,dt-1,Et-1)])[0],Je),jr=Z(ke([p(Tt-1,dt,Et-0)])[0],ke([p(Tt-1,dt-1,Et-0)])[0],Je),gt()),We&&(Ar=Z(ke([p(Tt-0,dt-0,Et)])[0],ke([p(Tt-0,dt-0,Et-1)])[0],We),ei=Z(ke([p(Tt-0,dt-1,Et)])[0],ke([p(Tt-0,dt-1,Et-1)])[0],We),qr=Z(ke([p(Tt-1,dt-1,Et)])[0],ke([p(Tt-1,dt-1,Et-1)])[0],We),jr=Z(ke([p(Tt-1,dt-0,Et)])[0],ke([p(Tt-1,dt-0,Et-1)])[0],We),gt()),mr}function lt(St,Tt,dt,Et,pt,jt,or,mr,Ar,ei,qr,jr){var gt=St;return jr?(a&&St===\"even\"&&(gt=null),me(gt,Tt,dt,Et,pt,jt,or,mr,Ar,ei,qr)):(a&&St===\"odd\"&&(gt=null),me(gt,Ar,mr,or,jt,pt,Et,dt,Tt,ei,qr))}function mt(St,Tt,dt,Et,pt){for(var jt=[],or=0,mr=0;mr<Tt.length;mr++)for(var Ar=Tt[mr],ei=1;ei<m;ei++)for(var qr=1;qr<v;qr++)jt.push(ce(St,p(Ar,qr-1,ei-1),p(Ar,qr-1,ei),p(Ar,qr,ei-1),p(Ar,qr,ei),dt,Et,(Ar+qr+ei)%2,pt&&pt[or]?pt[or]:[])),or++;return jt}function Ht(St,Tt,dt,Et,pt){for(var jt=[],or=0,mr=0;mr<Tt.length;mr++)for(var Ar=Tt[mr],ei=1;ei<d;ei++)for(var qr=1;qr<m;qr++)jt.push(ce(St,p(ei-1,Ar,qr-1),p(ei,Ar,qr-1),p(ei-1,Ar,qr),p(ei,Ar,qr),dt,Et,(ei+Ar+qr)%2,pt&&pt[or]?pt[or]:[])),or++;return jt}function at(St,Tt,dt,Et,pt){for(var jt=[],or=0,mr=0;mr<Tt.length;mr++)for(var Ar=Tt[mr],ei=1;ei<v;ei++)for(var qr=1;qr<d;qr++)jt.push(ce(St,p(qr-1,ei-1,Ar),p(qr-1,ei,Ar),p(qr,ei-1,Ar),p(qr,ei,Ar),dt,Et,(qr+ei+Ar)%2,pt&&pt[or]?pt[or]:[])),or++;return jt}function ct(St,Tt,dt){for(var Et=1;Et<m;Et++)for(var pt=1;pt<v;pt++)for(var jt=1;jt<d;jt++)lt(St,p(jt-1,pt-1,Et-1),p(jt-1,pt-1,Et),p(jt-1,pt,Et-1),p(jt-1,pt,Et),p(jt,pt-1,Et-1),p(jt,pt-1,Et),p(jt,pt,Et-1),p(jt,pt,Et),Tt,dt,(jt+pt+Et)%2)}function ar(St,Tt,dt){o=!0,ct(St,Tt,dt),o=!1}function Vt(St,Tt,dt){a=!0,ct(St,Tt,dt),a=!1}function rr(St,Tt,dt,Et,pt,jt){for(var or=[],mr=0,Ar=0;Ar<Tt.length;Ar++)for(var ei=Tt[Ar],qr=1;qr<m;qr++)for(var jr=1;jr<v;jr++)or.push(He(St,ei,jr,qr,dt,Et,pt[Ar],jt&&jt[mr]?jt[mr]:[])),mr++;return or}function tt(St,Tt,dt,Et,pt,jt){for(var or=[],mr=0,Ar=0;Ar<Tt.length;Ar++)for(var ei=Tt[Ar],qr=1;qr<d;qr++)for(var jr=1;jr<m;jr++)or.push(He(St,qr,ei,jr,dt,Et,pt[Ar],jt&&jt[mr]?jt[mr]:[])),mr++;return or}function je(St,Tt,dt,Et,pt,jt){for(var or=[],mr=0,Ar=0;Ar<Tt.length;Ar++)for(var ei=Tt[Ar],qr=1;qr<v;qr++)for(var jr=1;jr<d;jr++)or.push(He(St,jr,qr,ei,dt,Et,pt[Ar],jt&&jt[mr]?jt[mr]:[])),mr++;return or}function Ue(St,Tt){for(var dt=[],Et=St;Et<Tt;Et++)dt.push(Et);return dt}function Ae(){for(var St=0;St<d;St++)for(var Tt=0;Tt<v;Tt++)for(var dt=0;dt<m;dt++){var Et=p(St,Tt,dt);q(e._x[Et],e._y[Et],e._z[Et],e._value[Et])}}function rt(){z(),Ae();var St=null;if(r&&i&&(N(i),ar(St,T,L)),t&&n){N(n);for(var Tt=e.surface.pattern,dt=e.surface.count,Et=0;Et<dt;Et++){var pt=dt===1?.5:Et/(dt-1),jt=(1-pt)*T+pt*L,or=Math.abs(jt-k),mr=Math.abs(jt-M),Ar=or>mr?[k,jt]:[jt,M];Vt(Tt,Ar[0],Ar[1])}}var ei=[[Math.min(T,M),Math.max(T,M)],[Math.min(k,L),Math.max(k,L)]];[\"x\",\"y\",\"z\"].forEach(function(qr){for(var jr=[],gt=0;gt<ei.length;gt++){var Ge=0,Je=ei[gt][0],We=ei[gt][1],et=e.slices[qr];if(et.show&&et.fill){N(et.fill);var xt=[],At=[],Kt=[];if(et.locations.length)for(var Qt=0;Qt<et.locations.length;Qt++){var Mr=fk(et.locations[Qt],qr===\"x\"?c:qr===\"y\"?f:h);Mr.distRatio===0?xt.push(Mr.id):Mr.id>0&&(At.push(Mr.id),qr===\"x\"?Kt.push([Mr.distRatio,0,0]):qr===\"y\"?Kt.push([0,Mr.distRatio,0]):Kt.push([0,0,Mr.distRatio]))}else qr===\"x\"?xt=Ue(1,d-1):qr===\"y\"?xt=Ue(1,v-1):xt=Ue(1,m-1);At.length>0&&(qr===\"x\"?jr[Ge]=rr(St,At,Je,We,Kt,jr[Ge]):qr===\"y\"?jr[Ge]=tt(St,At,Je,We,Kt,jr[Ge]):jr[Ge]=je(St,At,Je,We,Kt,jr[Ge]),Ge++),xt.length>0&&(qr===\"x\"?jr[Ge]=mt(St,xt,Je,We,jr[Ge]):qr===\"y\"?jr[Ge]=Ht(St,xt,Je,We,jr[Ge]):jr[Ge]=at(St,xt,Je,We,jr[Ge]),Ge++)}var Gr=e.caps[qr];Gr.show&&Gr.fill&&(N(Gr.fill),qr===\"x\"?jr[Ge]=mt(St,[0,d-1],Je,We,jr[Ge]):qr===\"y\"?jr[Ge]=Ht(St,[0,v-1],Je,We,jr[Ge]):jr[Ge]=at(St,[0,m-1],Je,We,jr[Ge]),Ge++)}}),s===0&&z(),e._meshX=x,e._meshY=C,e._meshZ=S,e._meshIntensity=g,e._Xs=c,e._Ys=f,e._Zs=h}return rt(),e}function yDt(e,t){var r=e.glplot.gl,n=hDt({gl:r}),i=new P8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}R8e.exports={findNearestOnAxis:fk,generateIsoMeshes:I8e,createIsosurfaceTrace:yDt}});var F8e=ye((mmr,D8e)=>{\"use strict\";D8e.exports={attributes:pF(),supplyDefaults:aZ().supplyDefaults,calc:lZ(),colorbar:{min:\"cmin\",max:\"cmax\"},plot:yF().createIsosurfaceTrace,moduleType:\"trace\",name:\"isosurface\",basePlotModule:ux(),categories:[\"gl3d\",\"showLegend\"],meta:{}}});var q8e=ye((ymr,z8e)=>{\"use strict\";z8e.exports=F8e()});var fZ=ye((_mr,B8e)=>{\"use strict\";var _Dt=Tu(),xh=pF(),xDt=lk(),O8e=Vl(),cZ=Ao().extendFlat,bDt=mc().overrideAll,_F=B8e.exports=bDt(cZ({x:xh.x,y:xh.y,z:xh.z,value:xh.value,isomin:xh.isomin,isomax:xh.isomax,surface:xh.surface,spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:1}},slices:xh.slices,caps:xh.caps,text:xh.text,hovertext:xh.hovertext,xhoverformat:xh.xhoverformat,yhoverformat:xh.yhoverformat,zhoverformat:xh.zhoverformat,valuehoverformat:xh.valuehoverformat,hovertemplate:xh.hovertemplate,hovertemplatefallback:xh.hovertemplatefallback},_Dt(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{colorbar:xh.colorbar,opacity:xh.opacity,opacityscale:xDt.opacityscale,lightposition:xh.lightposition,lighting:xh.lighting,flatshading:xh.flatshading,contour:xh.contour,hoverinfo:cZ({},O8e.hoverinfo),showlegend:cZ({},O8e.showlegend,{dflt:!1})}),\"calc\",\"nested\");_F.x.editType=_F.y.editType=_F.z.editType=_F.value.editType=\"calc+clearAxisTypes\"});var U8e=ye((xmr,N8e)=>{\"use strict\";var wDt=Pr(),TDt=fZ(),ADt=aZ().supplyIsoDefaults,SDt=QX().opacityscaleDefaults;N8e.exports=function(t,r,n,i){function a(o,s){return wDt.coerce(t,r,TDt,o,s)}ADt(t,r,n,i,a),SDt(t,r,i,a)}});var j8e=ye((bmr,H8e)=>{\"use strict\";var MDt=qd().gl_mesh3d,EDt=o1().parseColorScale,kDt=Pr().isArrayOrTypedArray,CDt=a1(),LDt=tc().extractOpts,V8e=r5(),hZ=yF().findNearestOnAxis,PDt=yF().generateIsoMeshes;function G8e(e,t,r){this.scene=e,this.uid=r,this.mesh=t,this.name=\"\",this.data=null,this.showContour=!1}var dZ=G8e.prototype;dZ.handlePick=function(e){if(e.object===this.mesh){var t=e.data.index,r=this.data._meshX[t],n=this.data._meshY[t],i=this.data._meshZ[t],a=this.data._Ys.length,o=this.data._Zs.length,s=hZ(r,this.data._Xs).id,l=hZ(n,this.data._Ys).id,u=hZ(i,this.data._Zs).id,c=e.index=u+o*l+o*a*s;e.traceCoordinate=[this.data._meshX[c],this.data._meshY[c],this.data._meshZ[c],this.data._value[c]];var f=this.data.hovertext||this.data.text;return kDt(f)&&f[c]!==void 0?e.textLabel=f[c]:f&&(e.textLabel=f),!0}};dZ.update=function(e){var t=this.scene,r=t.fullSceneLayout;this.data=PDt(e);function n(l,u,c,f){return u.map(function(h){return l.d2l(h,0,f)*c})}var i=V8e(n(r.xaxis,e._meshX,t.dataScale[0],e.xcalendar),n(r.yaxis,e._meshY,t.dataScale[1],e.ycalendar),n(r.zaxis,e._meshZ,t.dataScale[2],e.zcalendar)),a=V8e(e._meshI,e._meshJ,e._meshK),o={positions:i,cells:a,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,opacityscale:e.opacityscale,contourEnable:e.contour.show,contourColor:CDt(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading},s=LDt(e);o.vertexIntensity=e._meshIntensity,o.vertexIntensityBounds=[s.min,s.max],o.colormap=EDt(e),this.mesh.update(o)};dZ.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};function IDt(e,t){var r=e.glplot.gl,n=MDt({gl:r}),i=new G8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}H8e.exports=IDt});var X8e=ye((wmr,W8e)=>{\"use strict\";W8e.exports={attributes:fZ(),supplyDefaults:U8e(),calc:lZ(),colorbar:{min:\"cmin\",max:\"cmax\"},plot:j8e(),moduleType:\"trace\",name:\"volume\",basePlotModule:ux(),categories:[\"gl3d\",\"showLegend\"],meta:{}}});var Y8e=ye((Tmr,Z8e)=>{\"use strict\";Z8e.exports=X8e()});var $8e=ye((Amr,J8e)=>{\"use strict\";var RDt=Oa(),K8e=Pr(),DDt=td(),FDt=e5();J8e.exports=function(t,r,n,i){function a(c,f){return K8e.coerce(t,r,FDt,c,f)}function o(c){var f=c.map(function(h){var d=a(h);return d&&K8e.isArrayOrTypedArray(d)?d:null});return f.every(function(h){return h&&h.length===f[0].length})&&f}var s=o([\"x\",\"y\",\"z\"]);if(!s){r.visible=!1;return}if(o([\"i\",\"j\",\"k\"]),r.i&&(!r.j||!r.k)||r.j&&(!r.k||!r.i)||r.k&&(!r.i||!r.j)){r.visible=!1;return}var 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Ie=2-n(xe)/G(2*M/3);return[ve*G(6*M)/(2*Ie),v(xe)*q((4-Ie*Ie)/3)]};function An(){return t.geoProjection(Fn).scale(165.664)}function Hn(ve,xe){var Ie=G(M*(4+M));return[2/Ie*ve*(1+G(1-4*xe*xe/(M*M))),4/Ie*xe]}Hn.invert=function(ve,xe){var Ie=G(M*(4+M))/2;return[ve*Ie/(1+G(1-xe*xe*(4+M)/(4*M))),xe*Ie/2]};function Qn(){return t.geoProjection(Hn).scale(180.739)}function Vi(ve,xe){var Ie=(2+T)*m(xe);xe/=2;for(var ze=0,Qe=1/0;ze<10&&n(Qe)>p;ze++){var Xe=o(xe);xe-=Qe=(xe+m(xe)*(Xe+2)-Ie)/(2*Xe*(1+Xe))}return[2/G(M*(4+M))*ve*(1+o(xe)),2*G(M/(4+M))*m(xe)]}Vi.invert=function(ve,xe){var Ie=xe*G((4+M)/M)/2,ze=q(Ie),Qe=o(ze);return[ve/(2/G(M*(4+M))*(1+Qe)),q((ze+Ie*(Qe+2))/(2+T))]};function Jn(){return t.geoProjection(Vi).scale(180.739)}function Yn(ve,xe){return[ve*(1+o(xe))/G(2+M),2*xe/G(2+M)]}Yn.invert=function(ve,xe){var Ie=G(2+M),ze=xe*Ie/2;return[Ie*ve/(1+o(ze)),ze]};function Gt(){return t.geoProjection(Yn).scale(173.044)}function wt(ve,xe){for(var 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t.geoProjection(vo).scale(153.93)}var Ct=da(5/6*M,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Sr(){return t.geoProjection(Ct).scale(130.945)}function Jr(ve,xe){var Ie=ve*ve,ze=xe*xe;return[ve*(1-.162388*ze)*(.87-952426e-9*Ie*Ie),xe*(1+ze/12)]}Jr.invert=function(ve,xe){var Ie=ve,ze=xe,Qe=50,Xe;do{var nt=ze*ze;ze-=Xe=(ze*(1+nt/12)-xe)/(1+nt/4)}while(n(Xe)>p&&--Qe>0);Qe=50,ve/=1-.162388*nt;do{var qt=(qt=Ie*Ie)*qt;Ie-=Xe=(Ie*(.87-952426e-9*qt)-ve)/(.87-.00476213*qt)}while(n(Xe)>p&&--Qe>0);return[Ie,ze]};function hi(){return t.geoProjection(Jr).scale(131.747)}var hn=da(2.6516,-.76534,.19123,-.047094,1.36289,-.13965,.031762);function Sn(){return t.geoProjection(hn).scale(131.087)}function En(ve){var xe=ve(T,0)[0]-ve(-T,0)[0];function Ie(ze,Qe){var Xe=ze>0?-.5:.5,nt=ve(ze+Xe*M,Qe);return nt[0]-=Xe*xe,nt}return ve.invert&&(Ie.invert=function(ze,Qe){var Xe=ze>0?-.5:.5,nt=ve.invert(ze+Xe*xe,Qe),qt=nt[0]-Xe*M;return qt<-M?qt+=2*M:qt>M&&(qt-=2*M),nt[0]=qt,nt}),Ie}function ki(ve,xe){var Ie=v(ve),ze=v(xe),Qe=o(xe),Xe=o(ve)*Qe,nt=m(ve)*Qe,qt=m(ze*xe);ve=n(a(nt,qt)),xe=q(Xe),n(ve-T)>p&&(ve%=T);var Wt=_n(ve>M/4?T-ve:ve,xe);return ve>M/4&&(qt=Wt[0],Wt[0]=-Wt[1],Wt[1]=-qt),Wt[0]*=Ie,Wt[1]*=-ze,Wt}ki.invert=function(ve,xe){n(ve)>1&&(ve=v(ve)*2-ve),n(xe)>1&&(xe=v(xe)*2-xe);var Ie=v(ve),ze=v(xe),Qe=-Ie*ve,Xe=-ze*xe,nt=Xe/Qe<1,qt=ya(nt?Xe:Qe,nt?Qe:Xe),Wt=qt[0],nr=qt[1],fr=o(nr);return nt&&(Wt=-T-Wt),[Ie*(a(m(Wt)*fr,-m(nr))+M),ze*q(o(Wt)*fr)]};function _n(ve,xe){if(xe===T)return[0,0];var Ie=m(xe),ze=Ie*Ie,Qe=ze*ze,Xe=1+Qe,nt=1+3*Qe,qt=1-Qe,Wt=q(1/G(Xe)),nr=qt+ze*Xe*Wt,fr=(1-Ie)/nr,br=G(fr),Fr=fr*Xe,Hr=G(Fr),ri=br*qt,Ci,cn;if(ve===0)return[0,-(ri+ze*Hr)];var sn=o(xe),Ln=1/sn,Kn=2*Ie*sn,Aa=(-3*ze+Wt*nt)*Kn,fa=(-nr*sn-(1-Ie)*Aa)/(nr*nr),$a=.5*fa/br,Co=qt*$a-2*ze*br*Kn,Qa=ze*Xe*fa+fr*nt*Kn,mo=-Ln*Kn,Bo=-Ln*Qa,Ps=-2*Ln*Co,Ts=4*ve/M,wo;if(ve>.222*M||xe<M/4&&ve>.175*M){if(Ci=(ri+ze*G(Fr*(1+Qe)-ri*ri))/(1+Qe),ve>M/4)return[Ci,Ci];var To=Ci,hl=.5*Ci;Ci=.5*(hl+To),cn=50;do{var 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ve?(ze=_o(ve,Ie),xe?(Qe=_o(xe,1-Ie),Xe=Qe[1]*Qe[1]+Ie*ze[0]*ze[0]*Qe[0]*Qe[0],[[ze[0]*Qe[2]/Xe,ze[1]*ze[2]*Qe[0]*Qe[1]/Xe],[ze[1]*Qe[1]/Xe,-ze[0]*ze[2]*Qe[0]*Qe[2]/Xe],[ze[2]*Qe[1]*Qe[2]/Xe,-Ie*ze[0]*ze[1]*Qe[0]/Xe]]):[[ze[0],0],[ze[1],0],[ze[2],0]]):(Qe=_o(xe,1-Ie),[[0,Qe[0]/Qe[1]],[1/Qe[1],0],[Qe[2]/Qe[1],0]])}function _o(ve,xe){var Ie,ze,Qe,Xe,nt;if(xe<p)return Xe=m(ve),ze=o(ve),Ie=xe*(ve-Xe*ze)/4,[Xe-Ie*ze,ze+Ie*Xe,1-xe*Xe*Xe/2,ve-Ie];if(xe>=1-p)return Ie=(1-xe)/4,ze=N(ve),Xe=Z(ve),Qe=1/ze,nt=ze*j(ve),[Xe+Ie*(nt-ve)/(ze*ze),Qe-Ie*Xe*Qe*(nt-ve),Qe+Ie*Xe*Qe*(nt+ve),2*i(s(ve))-T+Ie*(nt-ve)/ze];var qt=[1,0,0,0,0,0,0,0,0],Wt=[G(xe),0,0,0,0,0,0,0,0],nr=0;for(ze=G(1-xe),nt=1;n(Wt[nr]/qt[nr])>p&&nr<8;)Ie=qt[nr++],Wt[nr]=(Ie-ze)/2,qt[nr]=(Ie+ze)/2,ze=G(Ie*ze),nt*=2;Qe=nt*qt[nr]*ve;do Xe=Wt[nr]*m(ze=Qe)/qt[nr],Qe=(q(Xe)+Qe)/2;while(--nr);return[m(Qe),Xe=o(Qe),Xe/o(Qe-ze),Qe]}function No(ve,xe,Ie){var ze=n(ve),Qe=n(xe),Xe=j(Qe);if(ze){var nt=1/m(ze),qt=1/(b(ze)*b(ze)),Wt=-(qt+Ie*(Xe*Xe*nt*nt)-1+Ie),nr=(Ie-1)*qt,fr=(-Wt+G(Wt*Wt-4*nr))/2;return[po(i(1/G(fr)),Ie)*v(ve),po(i(G((fr/qt-1)/Ie)),1-Ie)*v(xe)]}return[0,po(i(Xe),1-Ie)*v(xe)]}function po(ve,xe){if(!xe)return ve;if(xe===1)return u(b(ve/2+L));for(var Ie=1,ze=G(1-xe),Qe=G(xe),Xe=0;n(Qe)>p;Xe++){if(ve%M){var nt=i(ze*b(ve)/Ie);nt<0&&(nt+=M),ve+=nt+~~(ve/M)*M}else ve+=ve;Qe=(Ie+ze)/2,ze=G(Ie*ze),Qe=((Ie=Qe)-ze)/2}return ve/(h(2,Xe)*Ie)}function Lo(ve,xe){var Ie=(C-1)/(C+1),ze=G(1-Ie*Ie),Qe=po(T,ze*ze),Xe=-1,nt=u(b(M/4+n(xe)/2)),qt=s(Xe*nt)/G(Ie),Wt=ko(qt*o(Xe*ve),qt*m(Xe*ve)),nr=No(Wt[0],Wt[1],ze*ze);return[-nr[1],(xe>=0?1:-1)*(.5*Qe-nr[0])]}function ko(ve,xe){var Ie=ve*ve,ze=xe+1,Qe=1-Ie-xe*xe;return[.5*((ve>=0?T:-T)-a(Qe,2*ve)),-.25*u(Qe*Qe+4*Ie)+.5*u(ze*ze+Ie)]}function Ds(ve,xe){var Ie=xe[0]*xe[0]+xe[1]*xe[1];return[(ve[0]*xe[0]+ve[1]*xe[1])/Ie,(ve[1]*xe[0]-ve[0]*xe[1])/Ie]}Lo.invert=function(ve,xe){var Ie=(C-1)/(C+1),ze=G(1-Ie*Ie),Qe=po(T,ze*ze),Xe=-1,nt=Ma(.5*Qe-xe,-ve,ze*ze),qt=Ds(nt[0],nt[1]),Wt=a(qt[1],qt[0])/Xe;return[Wt,2*i(s(.5/Xe*u(Ie*qt[0]*qt[0]+Ie*qt[1]*qt[1])))-T]};function Fs(){return t.geoProjection(En(Lo)).scale(151.496)}function ll(ve){var xe=m(ve),Ie=o(ve),ze=ul(ve);ze.invert=ul(-ve);function Qe(Xe,nt){var qt=ze(Xe,nt);Xe=qt[0],nt=qt[1];var Wt=m(nt),nr=o(nt),fr=o(Xe),br=V(xe*Wt+Ie*nr*fr),Fr=m(br),Hr=n(Fr)>p?br/Fr:1;return[Hr*Ie*m(Xe),(n(Xe)>T?Hr:-Hr)*(xe*nr-Ie*Wt*fr)]}return Qe.invert=function(Xe,nt){var qt=G(Xe*Xe+nt*nt),Wt=-m(qt),nr=o(qt),fr=qt*nr,br=-nt*Wt,Fr=qt*xe,Hr=G(fr*fr+br*br-Fr*Fr),ri=a(fr*Fr+br*Hr,br*Fr-fr*Hr),Ci=(qt>T?-1:1)*a(Xe*Wt,qt*o(ri)*nr+nt*m(ri)*Wt);return ze.invert(Ci,ri)},Qe}function ul(ve){var xe=m(ve),Ie=o(ve);return function(ze,Qe){var Xe=o(Qe),nt=o(ze)*Xe,qt=m(ze)*Xe,Wt=m(Qe);return[a(qt,nt*Ie-Wt*xe),q(Wt*Ie+nt*xe)]}}function Fl(){var ve=0,xe=t.geoProjectionMutator(ll),Ie=xe(ve),ze=Ie.rotate,Qe=Ie.stream,Xe=t.geoCircle();return Ie.parallel=function(nt){if(!arguments.length)return ve*P;var qt=Ie.rotate();return xe(ve=nt*E).rotate(qt)},Ie.rotate=function(nt){return arguments.length?(ze.call(Ie,[nt[0],nt[1]-ve*P]),Xe.center([-nt[0],-nt[1]]),Ie):(nt=ze.call(Ie),nt[1]+=ve*P,nt)},Ie.stream=function(nt){return nt=Qe(nt),nt.sphere=function(){nt.polygonStart();var qt=.01,Wt=Xe.radius(90-qt)().coordinates[0],nr=Wt.length-1,fr=-1,br;for(nt.lineStart();++fr<nr;)nt.point((br=Wt[fr])[0],br[1]);for(nt.lineEnd(),Wt=Xe.radius(90+qt)().coordinates[0],nr=Wt.length-1,nt.lineStart();--fr>=0;)nt.point((br=Wt[fr])[0],br[1]);nt.lineEnd(),nt.polygonEnd()},nt},Ie.scale(79.4187).parallel(45).clipAngle(180-.001)}var us=3,rl=q(1-1/us)*P,As=Yr(0);function cl(ve){var xe=rl*E,Ie=xt(M,xe)[0]-xt(-M,xe)[0],ze=As(0,xe)[1],Qe=xt(0,xe)[1],Xe=S-Qe,nt=g/ve,qt=4/g,Wt=ze+Xe*Xe*4/g;function nr(fr,br){var Fr,Hr=n(br);if(Hr>xe){var 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JDe=ye((syr,KDe)=>{\"use strict\";var nd=Fa(),jZ=Pr(),s7t=Oa(),a5=Math.PI/180,nw=180/Math.PI,XZ={cursor:\"pointer\"},ZZ={cursor:\"auto\"};function l7t(e,t){var r=e.projection,n;return t._isScoped?n=u7t:t._isClipped?n=f7t:n=c7t,n(e,r)}KDe.exports=l7t;function YZ(e,t){return nd.behavior.zoom().translate(t.translate()).scale(t.scale())}function KZ(e,t,r){var n=e.id,i=e.graphDiv,a=i.layout,o=a[n],s=i._fullLayout,l=s[n],u={},c={};function f(h,d){u[n+\".\"+h]=jZ.nestedProperty(o,h).get(),s7t.call(\"_storeDirectGUIEdit\",a,s._preGUI,u);var v=jZ.nestedProperty(l,h);v.get()!==d&&(v.set(d),jZ.nestedProperty(o,h).set(d),c[n+\".\"+h]=d)}r(f),f(\"projection.scale\",t.scale()/e.fitScale),f(\"fitbounds\",!1),i.emit(\"plotly_relayout\",c)}function u7t(e,t){var r=YZ(e,t);function n(){nd.select(this).style(XZ)}function i(){t.scale(nd.event.scale).translate(nd.event.translate),e.render(!0);var 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a=t[this.id],o=[],s=!1;for(var l in hp.layerNameToAdjective)if(l!==\"frame\"&&a[\"show\"+l]){s=!0;break}for(var u=!1,c=0;c<e.length;c++){var f=e[0][0].trace;f._geo=i,f.locationmode&&(s=!0);var h=f.marker;if(h){var d=h.angle,v=h.angleref;(d||v===\"north\"||v===\"previous\")&&(u=!0)}}if(this._hasMarkerAngles=u,s){var m=rFe.getTopojsonName(a);(i.topojson===null||m!==i.topojsonName)&&(i.topojsonName=m,PlotlyGeoAssets.topojson[i.topojsonName]===void 0&&o.push(i.fetchTopojson()))}o=o.concat(tFe.fetchTraceGeoData(e)),r.push(new Promise(function(b,p){Promise.all(o).then(function(){i.topojson=PlotlyGeoAssets.topojson[i.topojsonName],i.update(e,t),b()}).catch(p)}))};tm.fetchTopojson=function(){var e=this,t=rFe.getTopojsonPath(e.topojsonURL,e.topojsonName);return new Promise(function(r,n){u1.json(t,function(i,a){if(i)return i.status===404?n(new Error([\"plotly.js could not find topojson file at\",t+\".\",\"Make sure the *topojsonURL* plot config option\",\"is set properly.\"].join(\" \"))):n(new 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fFe=ye((cyr,cFe)=>{\"use strict\";var VF=Pr(),B7t=O_(),N7t=Id().getSubplotData,{unwrapLonRange:U7t}=ey(),GF=hk(),V7t=eY(),uFe=GF.axesNames;cFe.exports=function(t,r,n){B7t(t,r,n,{type:\"geo\",attributes:V7t,handleDefaults:G7t,fullData:n,partition:\"y\"})};function G7t(e,t,r,n){var i=N7t(n.fullData,\"geo\",n.id),a=i.map(function(ae){return ae.index}),o=r(\"resolution\"),s=r(\"scope\"),l=GF.scopeDefaults[s],u=r(\"projection.type\",l.projType),c=t._isAlbersUsa=u===\"albers usa\";c&&(s=t.scope=\"usa\");var f=t._isScoped=s!==\"world\",h=t._isSatellite=u===\"satellite\",d=t._isConic=u.indexOf(\"conic\")!==-1||u===\"albers\",v=t._isClipped=!!GF.lonaxisSpan[u];if(e.visible===!1){var m=VF.extendDeep({},t._template);m.showcoastlines=!1,m.showcountries=!1,m.showframe=!1,m.showlakes=!1,m.showland=!1,m.showocean=!1,m.showrivers=!1,m.showsubunits=!1,m.lonaxis&&(m.lonaxis.showgrid=!1),m.lataxis&&(m.lataxis.showgrid=!1),t._template=m}for(var b=r(\"visible\"),p,k=0;k<uFe.length;k++){var M=uFe[k],T=[30,10][k],L;if(f)L=l[M+\"Range\"];else{var x=GF[M+\"Span\"],C=(x[u]||x[\"*\"])/2,S=r(\"projection.rotation.\"+M.slice(0,3),l.projRotate[k]);L=[S-C,S+C]}var g=r(M+\".range\",L);r(M+\".tick0\"),r(M+\".dtick\",T),p=r(M+\".showgrid\",b?void 0:!1),p&&(r(M+\".gridcolor\"),r(M+\".gridwidth\"),r(M+\".griddash\")),t[M]._ax={type:\"linear\",_id:M.slice(0,3),_traceIndices:a,setScale:VF.identity,c2l:VF.identity,r2l:VF.identity,autorange:!0,range:g.slice(),_m:1,_input:{}}}var P=t.lonaxis.range,E=t.lataxis.range;let[z,q]=U7t(P);var V=(z+q)/2,G;if(!c){var Z=f?l.projRotate:[V,0,0];G=r(\"projection.rotation.lon\",Z[0]),r(\"projection.rotation.lat\",Z[1]),r(\"projection.rotation.roll\",Z[2]),p=r(\"showcoastlines\",!f&&b),p&&(r(\"coastlinecolor\"),r(\"coastlinewidth\")),p=r(\"showocean\",b?void 0:!1),p&&r(\"oceancolor\")}var j,N;if(c?(j=-96.6,N=38.7):(j=f?V:G,N=(E[0]+E[1])/2),r(\"center.lon\",j),r(\"center.lat\",N),h&&(r(\"projection.tilt\"),r(\"projection.distance\")),d){var 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wqt=Pze(),Tqt=lM(),Aqt=Bze(),Sqt=cY(),Mqt=Gze(),hY=document.createElement(\"canvas\"),dp=hY.getContext(\"2d\");Hze.exports=Eqt;function Eqt(e,t){if(!Sqt(e))throw Error(\"Argument should be valid svg path string\");t||(t={});var r,n;t.shape?(r=t.shape[0],n=t.shape[1]):(r=hY.width=t.w||t.width||200,n=hY.height=t.h||t.height||200);var i=Math.min(r,n),a=t.stroke||0,o=t.viewbox||t.viewBox||wqt(e),s=[r/(o[2]-o[0]),n/(o[3]-o[1])],l=Math.min(s[0]||0,s[1]||0)/2;if(dp.fillStyle=\"black\",dp.fillRect(0,0,r,n),dp.fillStyle=\"white\",a&&(typeof a!=\"number\"&&(a=1),a>0?dp.strokeStyle=\"white\":dp.strokeStyle=\"black\",dp.lineWidth=Math.abs(a)),dp.translate(r*.5,n*.5),dp.scale(l,l),kqt()){var u=new Path2D(e);dp.fill(u),a&&dp.stroke(u)}else{var c=Tqt(e);Aqt(dp,c),dp.fill(),a&&dp.stroke()}dp.setTransform(1,0,0,1,0,0);var f=Mqt(dp,{cutoff:t.cutoff!=null?t.cutoff:.5,radius:t.radius!=null?t.radius:i*.5});return f}var rz;function kqt(){if(rz!=null)return rz;var e=document.createElement(\"canvas\").getContext(\"2d\");if(e.canvas.width=e.canvas.height=1,!window.Path2D)return rz=!1;var t=new Path2D(\"M0,0h1v1h-1v-1Z\");e.fillStyle=\"black\",e.fill(t);var r=e.getImageData(0,0,1,1);return rz=r&&r.data&&r.data[3]===255}});var cw=ye((Zyr,r7e)=>{\"use strict\";var nz=Eo(),Cqt=jze(),iz=lx(),Lqt=Oa(),d5=Pr(),bh=d5.isArrayOrTypedArray,f5=So(),Wze=df(),Xze=o1().formatColor,h5=Ru(),Pqt=U3(),vY=KF(),Tk=gx(),Iqt=Z1().DESELECTDIM,Zze={start:1,left:1,end:-1,right:-1,middle:0,center:0,bottom:1,top:-1},Rqt=ip().appendArrayPointValue;function Dqt(e,t){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=e._context.plotGlPixelRatio;if(t.visible!==!0)return n;if(h5.hasText(t)&&(n.text=t7e(e,t),n.textSel=Kze(e,t,t.selected),n.textUnsel=Kze(e,t,t.unselected)),h5.hasMarkers(t)&&(n.marker=gY(e,t),n.markerSel=pY(e,t,t.selected),n.markerUnsel=pY(e,t,t.unselected),!t.unselected&&bh(t.marker.opacity))){var a=t.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=Iqt*a[r]}if(h5.hasLines(t)){n.line={overlay:!0,thickness:t.line.width*i,color:t.line.color,opacity:t.opacity};var o=(Tk.DASHES[t.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=t.line.width*i;n.line.dashes=o}return t.error_x&&t.error_x.visible&&(n.errorX=Jze(t,t.error_x,i)),t.error_y&&t.error_y.visible&&(n.errorY=Jze(t,t.error_y,i)),t.fill&&t.fill!==\"none\"&&(n.fill={closed:!0,fill:t.fillcolor,thickness:0}),n}function t7e(e,t){var r=e._fullLayout,n=t._length,i=t.textfont,a=t.textposition,o=bh(a)?a:[a],s=i.color,l=i.size,u=i.family,c=i.weight,f=i.style,h=i.variant,d={},v,m=e._context.plotGlPixelRatio,b=t.texttemplate;if(b){d.text=[];var p=r._d3locale,k=Array.isArray(b),M=k?Math.min(b.length,n):n,T=k?function(P){return b[P]}:function(){return b};for(v=0;v<M;v++){var L={i:v},x=t._module.formatLabels(L,t,r),C={};Rqt(C,t,v),d.text.push(d5.texttemplateString({data:[C,L,t._meta],fallback:t.texttemplatefallback,labels:x,locale:p,template:T(v)}))}}else bh(t.text)&&t.text.length<n?d.text=t.text.slice():d.text=t.text;if(bh(d.text))for(v=d.text.length;v<n;v++)d.text[v]=\"\";for(d.opacity=t.opacity,d.font={},d.align=[],d.baseline=[],v=0;v<o.length;v++){var S=o[v].split(/\\s+/);switch(S[1]){case\"left\":d.align.push(\"right\");break;case\"right\":d.align.push(\"left\");break;default:d.align.push(S[1])}switch(S[0]){case\"top\":d.baseline.push(\"bottom\");break;case\"bottom\":d.baseline.push(\"top\");break;default:d.baseline.push(S[0])}}if(bh(s))for(d.color=new Array(n),v=0;v<n;v++)d.color[v]=s[v];else d.color=s;if(bh(l)||Array.isArray(u)||bh(c)||Array.isArray(f)||Array.isArray(h))for(d.font=new Array(n),v=0;v<n;v++){var g=d.font[v]={};g.size=(d5.isTypedArray(l)?l[v]:bh(l)?nz(l[v])?l[v]:0:l)*m,g.family=Array.isArray(u)?u[v]:u,g.weight=Yze(bh(c)?c[v]:c),g.style=Array.isArray(f)?f[v]:f,g.variant=Array.isArray(h)?h[v]:h}else d.font={size:l*m,family:u,weight:Yze(c),style:f,variant:h};return d}function Yze(e){return e<=1e3?e>500?\"bold\":\"normal\":e}function gY(e,t){var r=t._length,n=t.marker,i={},a,o=bh(n.symbol),s=bh(n.angle),l=bh(n.color),u=bh(n.line.color),c=bh(n.opacity),f=bh(n.size),h=bh(n.line.width),d;if(o||(d=vY.isOpenSymbol(n.symbol)),o||l||u||c||s){i.symbols=new Array(r),i.angles=new Array(r),i.colors=new Array(r),i.borderColors=new Array(r);var v=n.symbol,m=n.angle,b=Xze(n,n.opacity,r),p=Xze(n.line,n.opacity,r);if(!bh(p[0])){var k=p;for(p=Array(r),a=0;a<r;a++)p[a]=k}if(!bh(b[0])){var M=b;for(b=Array(r),a=0;a<r;a++)b[a]=M}if(!bh(v)){var T=v;for(v=Array(r),a=0;a<r;a++)v[a]=T}if(!bh(m)){var L=m;for(m=Array(r),a=0;a<r;a++)m[a]=L}for(i.symbols=v,i.angles=m,i.colors=b,i.borderColors=p,a=0;a<r;a++)o&&(d=vY.isOpenSymbol(n.symbol[a])),d&&(p[a]=b[a].slice(),b[a]=b[a].slice(),b[a][3]=0);for(i.opacity=t.opacity,i.markers=new Array(r),a=0;a<r;a++)i.markers[a]=e7e({mx:i.symbols[a],ma:i.angles[a]},t)}else d?(i.color=iz(n.color,\"uint8\"),i.color[3]=0,i.borderColor=iz(n.color,\"uint8\")):(i.color=iz(n.color,\"uint8\"),i.borderColor=iz(n.line.color,\"uint8\")),i.opacity=t.opacity*n.opacity,i.marker=e7e({mx:n.symbol,ma:n.angle},t);var x=1,C=Pqt(t,x),S;if(f||h){var g=i.sizes=new Array(r),P=i.borderSizes=new Array(r),E=0,z;if(f){for(a=0;a<r;a++)g[a]=C(n.size[a]),E+=g[a];z=E/r}else for(S=C(n.size),a=0;a<r;a++)g[a]=S;if(h)for(a=0;a<r;a++)P[a]=n.line.width[a];else for(S=n.line.width,a=0;a<r;a++)P[a]=S;i.sizeAvg=z}else i.size=C(n&&n.size||10),i.borderSizes=C(n.line.width);return i}function pY(e,t,r){var n=t.marker,i={};return r&&(r.marker&&r.marker.symbol?i=gY(e,d5.extendFlat({},n,r.marker)):r.marker&&(r.marker.size&&(i.size=r.marker.size),r.marker.color&&(i.colors=r.marker.color),r.marker.opacity!==void 0&&(i.opacity=r.marker.opacity))),i}function Kze(e,t,r){var n={};if(!r)return n;if(r.textfont){var i={opacity:1,text:t.text,texttemplate:t.texttemplate,textposition:t.textposition,textfont:d5.extendFlat({},t.textfont)};r.textfont&&d5.extendFlat(i.textfont,r.textfont),n=t7e(e,i)}return n}function Jze(e,t,r){var n={capSize:t.width*2*r,lineWidth:t.thickness*r,color:t.color};return t.copy_ystyle&&(n=e.error_y),n}var $ze=Tk.SYMBOL_SDF_SIZE,uw=Tk.SYMBOL_SIZE,Qze=Tk.SYMBOL_STROKE,dY={},Fqt=f5.symbolFuncs[0](uw*.05);function e7e(e,t){var r=e.mx;if(r===\"circle\")return null;var n,i,a=f5.symbolNumber(r),o=f5.symbolFuncs[a%100],s=!!f5.symbolNoDot[a%100],l=!!f5.symbolNoFill[a%100],u=vY.isDotSymbol(r);if(e.ma&&(r+=\"_\"+e.ma),dY[r])return dY[r];var c=f5.getMarkerAngle(e,t);return u&&!s?n=o(uw*1.1,c)+Fqt:n=o(uw,c),i=Cqt(n,{w:$ze,h:$ze,viewBox:[-uw,-uw,uw,uw],stroke:l?Qze:-Qze}),dY[r]=i,i||null}function zqt(e,t,r){var n=r.length,i=n/2,a,o;if(h5.hasLines(t)&&i)if(t.line.shape===\"hv\"){for(a=[],o=0;o<i-1;o++)isNaN(r[o*2])||isNaN(r[o*2+1])?a.push(NaN,NaN,NaN,NaN):(a.push(r[o*2],r[o*2+1]),!isNaN(r[o*2+2])&&!isNaN(r[o*2+3])?a.push(r[o*2+2],r[o*2+1]):a.push(NaN,NaN));a.push(r[n-2],r[n-1])}else if(t.line.shape===\"hvh\"){for(a=[],o=0;o<i-1;o++)if(isNaN(r[o*2])||isNaN(r[o*2+1])||isNaN(r[o*2+2])||isNaN(r[o*2+3]))!isNaN(r[o*2])&&!isNaN(r[o*2+1])?a.push(r[o*2],r[o*2+1]):a.push(NaN,NaN),a.push(NaN,NaN);else{var s=(r[o*2]+r[o*2+2])/2;a.push(r[o*2],r[o*2+1],s,r[o*2+1],s,r[o*2+3])}a.push(r[n-2],r[n-1])}else if(t.line.shape===\"vhv\"){for(a=[],o=0;o<i-1;o++)if(isNaN(r[o*2])||isNaN(r[o*2+1])||isNaN(r[o*2+2])||isNaN(r[o*2+3]))!isNaN(r[o*2])&&!isNaN(r[o*2+1])?a.push(r[o*2],r[o*2+1]):a.push(NaN,NaN),a.push(NaN,NaN);else{var l=(r[o*2+1]+r[o*2+3])/2;a.push(r[o*2],r[o*2+1],r[o*2],l,r[o*2+2],l)}a.push(r[n-2],r[n-1])}else if(t.line.shape===\"vh\"){for(a=[],o=0;o<i-1;o++)isNaN(r[o*2])||isNaN(r[o*2+1])?a.push(NaN,NaN,NaN,NaN):(a.push(r[o*2],r[o*2+1]),!isNaN(r[o*2+2])&&!isNaN(r[o*2+3])?a.push(r[o*2],r[o*2+3]):a.push(NaN,NaN));a.push(r[n-2],r[n-1])}else a=r;var u=!1;for(o=0;o<a.length;o++)if(isNaN(a[o])){u=!0;break}var c=u||a.length>Tk.TOO_MANY_POINTS||h5.hasMarkers(t)?\"rect\":\"round\";if(u&&t.connectgaps){var f=a[0],h=a[1];for(o=0;o<a.length;o+=2)isNaN(a[o])||isNaN(a[o+1])?(a[o]=f,a[o+1]=h):(f=a[o],h=a[o+1])}return{join:c,positions:a}}function qqt(e,t,r,n,i){var a=Lqt.getComponentMethod(\"errorbars\",\"makeComputeError\"),o=Wze.getFromId(e,t.xaxis,\"x\"),s=Wze.getFromId(e,t.yaxis,\"y\"),l=r.length/2,u={};function c(f,h){var d=h._id.charAt(0),v=t[\"error_\"+d];if(v&&v.visible&&(h.type===\"linear\"||h.type===\"log\")){for(var m=a(v),b={x:0,y:1}[d],p={x:[0,1,2,3],y:[2,3,0,1]}[d],k=new Float64Array(4*l),M=1/0,T=-1/0,L=0,x=0;L<l;L++,x+=4){var C=f[L];if(nz(C)){var S=r[L*2+b],g=m(C,L),P=g[0],E=g[1];if(nz(P)&&nz(E)){var z=C-P,q=C+E;k[x+p[0]]=S-h.c2l(z),k[x+p[1]]=h.c2l(q)-S,k[x+p[2]]=0,k[x+p[3]]=0,M=Math.min(M,C-P),T=Math.max(T,C+E)}}}u[d]={positions:r,errors:k,_bnds:[M,T]}}}return c(n,o),c(i,s),u}function Oqt(e,t,r,n){var i=t._length,a={},o;if(h5.hasMarkers(t)){var s=r.font,l=r.align,u=r.baseline;for(a.offset=new Array(i),o=0;o<i;o++){var c=n.sizes?n.sizes[o]:n.size,f=bh(s)?s[o].size:s.size,h=bh(l)?l.length>1?l[o]:l[0]:l,d=bh(u)?u.length>1?u[o]:u[0]:u,v=Zze[h],m=Zze[d],b=c?c/.8+1:0,p=-m*b-m*.5;a.offset[o]=[v*b/f,p/f]}}return a}r7e.exports={style:Dqt,markerStyle:gY,markerSelection:pY,linePositions:zqt,errorBarPositions:qqt,textPosition:Oqt}});var mY=ye((Yyr,i7e)=>{\"use strict\";var az=Pr();i7e.exports=function(t,r){var n=r._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return r._scene||(n=r._scene={},n.init=function(){az.extendFlat(n,a,i)},n.init(),n.update=function(s){var l=az.repeat(s,n.count);if(n.fill2d&&n.fill2d.update(l),n.scatter2d&&n.scatter2d.update(l),n.line2d&&n.line2d.update(l),n.error2d&&n.error2d.update(l.concat(l)),n.select2d&&n.select2d.update(l),n.glText)for(var u=0;u<n.count;u++)n.glText[u].update(s)},n.draw=function(){for(var s=n.count,l=n.fill2d,u=n.error2d,c=n.line2d,f=n.scatter2d,h=n.glText,d=n.select2d,v=n.selectBatch,m=n.unselectBatch,b=0;b<s;b++){if(l&&n.fillOrder[b]&&l.draw(n.fillOrder[b]),c&&n.lineOptions[b]&&c.draw(b),u&&(n.errorXOptions[b]&&u.draw(b),n.errorYOptions[b]&&u.draw(b+s)),f&&n.markerOptions[b])if(m[b].length){var p=az.repeat([],n.count);p[b]=m[b],f.draw(p)}else v[b].length||f.draw(b);h[b]&&n.textOptions[b]&&h[b].render()}d&&d.draw(v),n.dirty=!1},n.destroy=function(){n.fill2d&&n.fill2d.destroy&&n.fill2d.destroy(),n.scatter2d&&n.scatter2d.destroy&&n.scatter2d.destroy(),n.error2d&&n.error2d.destroy&&n.error2d.destroy(),n.line2d&&n.line2d.destroy&&n.line2d.destroy(),n.select2d&&n.select2d.destroy&&n.select2d.destroy(),n.glText&&n.glText.forEach(function(s){s.destroy&&s.destroy()}),n.lineOptions=null,n.fillOptions=null,n.markerOptions=null,n.markerSelectedOptions=null,n.markerUnselectedOptions=null,n.errorXOptions=null,n.errorYOptions=null,n.textOptions=null,n.textSelectedOptions=null,n.textUnselectedOptions=null,n.selectBatch=null,n.unselectBatch=null,r._scene=null}),n.dirty||az.extendFlat(n,i),n}});var u7e=ye((Kyr,l7e)=>{\"use strict\";var Bqt=$F(),v5=Pr(),n7e=df(),Nqt=Mg().findExtremes,a7e=zg(),yY=O0(),Uqt=yY.calcMarkerSize,Vqt=yY.calcAxisExpansion,Gqt=yY.setFirstScatter,Hqt=z0(),p5=cw(),jqt=mY(),o7e=fs().BADNUM,Wqt=gx().TOO_MANY_POINTS;l7e.exports=function(t,r){var n=t._fullLayout,i=r._xA=n7e.getFromId(t,r.xaxis,\"x\"),a=r._yA=n7e.getFromId(t,r.yaxis,\"y\"),o=n._plots[r.xaxis+r.yaxis],s=r._length,l=s>=Wqt,u=s*2,c={},f,h=i.makeCalcdata(r,\"x\"),d=a.makeCalcdata(r,\"y\"),v=a7e(r,i,\"x\",h),m=a7e(r,a,\"y\",d),b=v.vals,p=m.vals;r._x=b,r._y=p,r.xperiodalignment&&(r._origX=h,r._xStarts=v.starts,r._xEnds=v.ends),r.yperiodalignment&&(r._origY=d,r._yStarts=m.starts,r._yEnds=m.ends);var k=new Array(u),M=new Array(s);for(f=0;f<s;f++)k[f*2]=b[f]===o7e?NaN:b[f],k[f*2+1]=p[f]===o7e?NaN:p[f],M[f]=f;if(i.type===\"log\")for(f=0;f<u;f+=2)k[f]=i.c2l(k[f]);if(a.type===\"log\")for(f=1;f<u;f+=2)k[f]=a.c2l(k[f]);l&&i.type!==\"log\"&&a.type!==\"log\"?c.tree=Bqt(k):c.ids=M,Hqt(t,r);var T=Xqt(t,o,r,k,b,p),L=jqt(t,o);Gqt(n,r);var x;return l?T.marker&&(x=T.marker.sizeAvg||Math.max(T.marker.size,3)):x=Uqt(r,s),Vqt(t,r,i,a,b,p,x),T.errorX&&s7e(r,i,T.errorX),T.errorY&&s7e(r,a,T.errorY),T.fill&&!L.fill2d&&(L.fill2d=!0),T.marker&&!L.scatter2d&&(L.scatter2d=!0),T.line&&!L.line2d&&(L.line2d=!0),(T.errorX||T.errorY)&&!L.error2d&&(L.error2d=!0),T.text&&!L.glText&&(L.glText=!0),T.marker&&(T.marker.snap=s),L.lineOptions.push(T.line),L.errorXOptions.push(T.errorX),L.errorYOptions.push(T.errorY),L.fillOptions.push(T.fill),L.markerOptions.push(T.marker),L.markerSelectedOptions.push(T.markerSel),L.markerUnselectedOptions.push(T.markerUnsel),L.textOptions.push(T.text),L.textSelectedOptions.push(T.textSel),L.textUnselectedOptions.push(T.textUnsel),L.selectBatch.push([]),L.unselectBatch.push([]),c._scene=L,c.index=L.count,c.x=b,c.y=p,c.positions=k,L.count++,[{x:!1,y:!1,t:c,trace:r}]};function s7e(e,t,r){var n=e._extremes[t._id],i=Nqt(t,r._bnds,{padded:!0});n.min=n.min.concat(i.min),n.max=n.max.concat(i.max)}function Xqt(e,t,r,n,i,a){var o=p5.style(e,r);if(o.marker&&(o.marker.positions=n),o.line&&n.length>1&&v5.extendFlat(o.line,p5.linePositions(e,r,n)),o.errorX||o.errorY){var s=p5.errorBarPositions(e,r,n,i,a);o.errorX&&v5.extendFlat(o.errorX,s.x),o.errorY&&v5.extendFlat(o.errorY,s.y)}return o.text&&(v5.extendFlat(o.text,{positions:n},p5.textPosition(e,r,o.text,o.marker)),v5.extendFlat(o.textSel,{positions:n},p5.textPosition(e,r,o.text,o.markerSel)),v5.extendFlat(o.textUnsel,{positions:n},p5.textPosition(e,r,o.text,o.markerUnsel))),o}});var _Y=ye((Jyr,f7e)=>{\"use strict\";var c7e=Pr(),Zqt=ka(),Yqt=Z1().DESELECTDIM;function Kqt(e){var t=e[0],r=t.trace,n=t.t,i=n._scene,a=n.index,o=i.selectBatch[a],s=i.unselectBatch[a],l=i.textOptions[a],u=i.textSelectedOptions[a]||{},c=i.textUnselectedOptions[a]||{},f=c7e.extendFlat({},l),h,d;if(o.length||s.length){var v=u.color,m=c.color,b=l.color,p=c7e.isArrayOrTypedArray(b);for(f.color=new Array(r._length),h=0;h<o.length;h++)d=o[h],f.color[d]=v||(p?b[d]:b);for(h=0;h<s.length;h++){d=s[h];var k=p?b[d]:b;f.color[d]=m||(v?k:Zqt.addOpacity(k,Yqt))}}i.glText[a].update(f)}f7e.exports={styleTextSelection:Kqt}});var xY=ye(($yr,d7e)=>{\"use strict\";var h7e=Ru(),Jqt=_Y().styleTextSelection;d7e.exports=function(t,r){var n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].trace,l=n[0].t,u=s._length,c=l.x,f=l.y,h=l._scene,d=l.index;if(!h)return o;var v=h7e.hasText(s),m=h7e.hasMarkers(s),b=!m&&!v;if(s.visible!==!0||b)return o;var p=[],k=[];if(r!==!1&&!r.degenerate)for(var M=0;M<u;M++)r.contains([l.xpx[M],l.ypx[M]],!1,M,t)?(p.push(M),o.push({pointNumber:M,x:i.c2d(c[M]),y:a.c2d(f[M])})):k.push(M);if(m){var T=h.scatter2d;if(!p.length&&!k.length){var L=new Array(h.count);L[d]=h.markerOptions[d],T.update.apply(T,L)}else if(!h.selectBatch[d].length&&!h.unselectBatch[d].length){var x=new Array(h.count);x[d]=h.markerUnselectedOptions[d],T.update.apply(T,x)}}return h.selectBatch[d]=p,h.unselectBatch[d]=k,v&&Jqt(n),o}});var p7e=ye((Qyr,v7e)=>{\"use strict\";var $qt=YF();v7e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:mh(),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:_k(),supplyDefaults:KFe(),crossTraceDefaults:vU(),colorbar:Qd(),formatLabels:$Fe(),calc:u7e(),hoverPoints:$qt.hoverPoints,selectPoints:xY(),meta:{}}});var m7e=ye((e1r,sz)=>{\"use strict\";var oz=ak();sz.exports=g7e;sz.exports.to=g7e;sz.exports.from=Qqt;function g7e(e,t){t==null&&(t=!0);var r=e[0],n=e[1],i=e[2],a=e[3];a==null&&(a=t?1:255),t&&(r*=255,n*=255,i*=255,a*=255),r=oz(r,0,255)&255,n=oz(n,0,255)&255,i=oz(i,0,255)&255,a=oz(a,0,255)&255;var o=r*16777216+(n<<16)+(i<<8)+a;return o}function Qqt(e,t){e=+e;var r=e>>>24,n=(e&16711680)>>>16,i=(e&65280)>>>8,a=e&255;return t===!1?[r,n,i,a]:[r/255,n/255,i/255,a/255]}});var Nh=ye((t1r,_7e)=>{\"use strict\";var y7e=Object.getOwnPropertySymbols,eOt=Object.prototype.hasOwnProperty,tOt=Object.prototype.propertyIsEnumerable;function rOt(e){if(e==null)throw new TypeError(\"Object.assign cannot be called with null or undefined\");return Object(e)}function iOt(){try{if(!Object.assign)return!1;var e=new String(\"abc\");if(e[5]=\"de\",Object.getOwnPropertyNames(e)[0]===\"5\")return!1;for(var t={},r=0;r<10;r++)t[\"_\"+String.fromCharCode(r)]=r;var n=Object.getOwnPropertyNames(t).map(function(a){return t[a]});if(n.join(\"\")!==\"0123456789\")return!1;var i={};return\"abcdefghijklmnopqrst\".split(\"\").forEach(function(a){i[a]=a}),Object.keys(Object.assign({},i)).join(\"\")===\"abcdefghijklmnopqrst\"}catch(a){return!1}}_7e.exports=iOt()?Object.assign:function(e,t){for(var r,n=rOt(e),i,a=1;a<arguments.length;a++){r=Object(arguments[a]);for(var o in r)eOt.call(r,o)&&(n[o]=r[o]);if(y7e){i=y7e(r);for(var s=0;s<i.length;s++)tOt.call(r,i[s])&&(n[i[s]]=r[i[s]])}}return n}});var b7e=ye((r1r,x7e)=>{x7e.exports=function(e){typeof e==\"string\"&&(e=[e]);for(var t=[].slice.call(arguments,1),r=[],n=0;n<e.length-1;n++)r.push(e[n],t[n]||\"\");return r.push(e[n]),r.join(\"\")}});var bY=ye((i1r,w7e)=>{\"use strict\";w7e.exports=function(t,r,n){Array.isArray(n)||(n=[].slice.call(arguments,2));for(var i=0,a=n.length;i<a;i++){var o=n[i];for(var s in o)if(!(r[s]!==void 0&&!Array.isArray(r[s])&&t[s]===r[s])&&s in r){var l;if(o[s]===!0)l=r[s];else{if(o[s]===!1)continue;if(typeof o[s]==\"function\"&&(l=o[s](r[s],t,r),l===void 0))continue}t[s]=l}}return t}});var A7e=ye((n1r,T7e)=>{\"use strict\";T7e.exports=typeof navigator!=\"undefined\"&&(/MSIE/.test(navigator.userAgent)||/Trident\\//.test(navigator.appVersion))});var lz=ye((a1r,g5)=>{\"use strict\";g5.exports=Ak;g5.exports.float32=g5.exports.float=Ak;g5.exports.fract32=g5.exports.fract=nOt;var S7e=new Float32Array(1);function nOt(e,t){if(e.length){if(e instanceof Float32Array)return new Float32Array(e.length);t instanceof Float32Array||(t=Ak(e));for(var r=0,n=t.length;r<n;r++)t[r]=e[r]-t[r];return t}return Ak(e-Ak(e))}function Ak(e){return e.length?e instanceof Float32Array?e:new Float32Array(e):(S7e[0]=e,S7e[0])}});var TY=ye((o1r,k7e)=>{\"use strict\";function aOt(e,t){var r=e==null?null:typeof Symbol!=\"undefined\"&&e[Symbol.iterator]||e[\"@@iterator\"];if(r!=null){var n,i,a,o,s=[],l=!0,u=!1;try{if(a=(r=r.call(e)).next,t===0){if(Object(r)!==r)return;l=!1}else for(;!(l=(n=a.call(r)).done)&&(s.push(n.value),s.length!==t);l=!0);}catch(c){u=!0,i=c}finally{try{if(!l&&r.return!=null&&(o=r.return(),Object(o)!==o))return}finally{if(u)throw i}}return s}}function oOt(e,t){return uOt(e)||aOt(e,t)||E7e(e,t)||hOt()}function sOt(e){return lOt(e)||cOt(e)||E7e(e)||fOt()}function lOt(e){if(Array.isArray(e))return wY(e)}function uOt(e){if(Array.isArray(e))return e}function cOt(e){if(typeof Symbol!=\"undefined\"&&e[Symbol.iterator]!=null||e[\"@@iterator\"]!=null)return Array.from(e)}function E7e(e,t){if(e){if(typeof e==\"string\")return wY(e,t);var r=Object.prototype.toString.call(e).slice(8,-1);if(r===\"Object\"&&e.constructor&&(r=e.constructor.name),r===\"Map\"||r===\"Set\")return Array.from(e);if(r===\"Arguments\"||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r))return wY(e,t)}}function wY(e,t){(t==null||t>e.length)&&(t=e.length);for(var r=0,n=new Array(t);r<t;r++)n[r]=e[r];return n}function fOt(){throw new TypeError(`Invalid attempt to spread non-iterable instance.\n",
+       "In order to be iterable, non-array objects must have a [Symbol.iterator]() method.`)}function hOt(){throw new TypeError(`Invalid attempt to destructure non-iterable instance.\n",
+       "In order to be iterable, non-array objects must have a [Symbol.iterator]() method.`)}var dOt=lx(),vOt=ow(),pOt=m7e(),gOt=$F(),fw=Nh(),uz=b7e(),mOt=ry(),yOt=bY(),_Ot=sw(),M7e=A7e(),cz=lz(),xOt=c5(),bOt=av;function av(e,t){var r=this;if(!(this instanceof av))return new av(e,t);typeof e==\"function\"?(t||(t={}),t.regl=e):(t=e,e=null),t&&t.length&&(t.positions=t),e=t.regl;var n=e._gl,i,a=[],o={},s=[],l=[null],u=[null],c=255,f=100;this.tooManyColors=M7e,i=e.texture({data:new Uint8Array(c*4),width:c,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),fw(this,{regl:e,gl:n,groups:s,markerCache:u,markerTextures:l,palette:a,paletteIds:o,paletteTexture:i,maxColors:c,maxSize:f,canvas:n.canvas}),this.update(t);var h={uniforms:{constPointSize:!!t.constPointSize,opacity:e.prop(\"opacity\"),paletteSize:function(b,p){return[r.tooManyColors?0:c,i.height]},pixelRatio:e.context(\"pixelRatio\"),scale:e.prop(\"scale\"),scaleFract:e.prop(\"scaleFract\"),translate:e.prop(\"translate\"),translateFract:e.prop(\"translateFract\"),markerTexture:e.prop(\"markerTexture\"),paletteTexture:i},attributes:{x:function(b,p){return p.xAttr||{buffer:p.positionBuffer,stride:8,offset:0}},y:function(b,p){return p.yAttr||{buffer:p.positionBuffer,stride:8,offset:4}},xFract:function(b,p){return p.xAttr?{constant:[0,0]}:{buffer:p.positionFractBuffer,stride:8,offset:0}},yFract:function(b,p){return p.yAttr?{constant:[0,0]}:{buffer:p.positionFractBuffer,stride:8,offset:4}},size:function(b,p){return p.size.length?{buffer:p.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(p.size*255/r.maxSize)]}},borderSize:function(b,p){return p.borderSize.length?{buffer:p.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(p.borderSize*255/r.maxSize)]}},colorId:function(b,p){return p.color.length?{buffer:p.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(p.color*4,p.color*4+4):[p.color]}},borderColorId:function(b,p){return p.borderColor.length?{buffer:p.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(p.borderColor*4,p.borderColor*4+4):[p.borderColor]}},isActive:function(b,p){return p.activation===!0?{constant:[1]}:p.activation?p.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:e.prop(\"viewport\")},viewport:e.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:e.prop(\"elements\"),count:e.prop(\"count\"),offset:e.prop(\"offset\"),primitive:\"points\"},d=fw({},h);d.frag=uz([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "uniform float opacity;\n",
+       "uniform sampler2D markerTexture;\n",
+       "\n",
+       "varying vec4 fragColor, fragBorderColor;\n",
+       "varying float fragWidth, fragBorderColorLevel, fragColorLevel;\n",
+       "\n",
+       "float smoothStep(float x, float y) {\n",
+       "  return 1.0 / (1.0 + exp(50.0*(x - y)));\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n",
+       "\n",
+       "  // max-distance alpha\n",
+       "  if (dist < 0.003) discard;\n",
+       "\n",
+       "  // null-border case\n",
+       "  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n",
+       "    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n",
+       "    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n",
+       "  }\n",
+       "  else {\n",
+       "    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n",
+       "    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n",
+       "\n",
+       "    vec4 color = fragBorderColor;\n",
+       "    color.a *= borderColorAmt;\n",
+       "    color = mix(color, fragColor, colorAmt);\n",
+       "    color.a *= opacity;\n",
+       "\n",
+       "    gl_FragColor = color;\n",
+       "  }\n",
+       "\n",
+       "}\n",
+       "`]),d.vert=uz([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute float x, y, xFract, yFract;\n",
+       "attribute float size, borderSize;\n",
+       "attribute vec4 colorId, borderColorId;\n",
+       "attribute float isActive;\n",
+       "\n",
+       "// \\`invariant\\` effectively turns off optimizations for the position.\n",
+       "// We need this because -fast-math on M1 Macs is re-ordering\n",
+       "// floating point operations in a way that causes floating point\n",
+       "// precision limits to put points in the wrong locations.\n",
+       "invariant gl_Position;\n",
+       "\n",
+       "uniform bool constPointSize;\n",
+       "uniform float pixelRatio;\n",
+       "uniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\n",
+       "uniform sampler2D paletteTexture;\n",
+       "\n",
+       "const float maxSize = 100.;\n",
+       "const float borderLevel = .5;\n",
+       "\n",
+       "varying vec4 fragColor, fragBorderColor;\n",
+       "varying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n",
+       "\n",
+       "float pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n",
+       "\n",
+       "bool isDirect = (paletteSize.x < 1.);\n",
+       "\n",
+       "vec4 getColor(vec4 id) {\n",
+       "  return isDirect ? id / 255. : texture2D(paletteTexture,\n",
+       "    vec2(\n",
+       "      (id.x + .5) / paletteSize.x,\n",
+       "      (id.y + .5) / paletteSize.y\n",
+       "    )\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "  // ignore inactive points\n",
+       "  if (isActive == 0.) return;\n",
+       "\n",
+       "  vec2 position = vec2(x, y);\n",
+       "  vec2 positionFract = vec2(xFract, yFract);\n",
+       "\n",
+       "  vec4 color = getColor(colorId);\n",
+       "  vec4 borderColor = getColor(borderColorId);\n",
+       "\n",
+       "  float size = size * maxSize / 255.;\n",
+       "  float borderSize = borderSize * maxSize / 255.;\n",
+       "\n",
+       "  gl_PointSize = 2. * size * pointSizeScale;\n",
+       "  fragPointSize = size * pixelRatio;\n",
+       "\n",
+       "  vec2 pos = (position + translate) * scale\n",
+       "      + (positionFract + translateFract) * scale\n",
+       "      + (position + translate) * scaleFract\n",
+       "      + (positionFract + translateFract) * scaleFract;\n",
+       "\n",
+       "  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n",
+       "\n",
+       "  fragColor = color;\n",
+       "  fragBorderColor = borderColor;\n",
+       "  fragWidth = 1. / gl_PointSize;\n",
+       "\n",
+       "  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n",
+       "  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n",
+       "}\n",
+       "`]),this.drawMarker=e(d);var v=fw({},h);v.frag=uz([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "varying vec4 fragColor, fragBorderColor;\n",
+       "varying float fragBorderRadius, fragWidth;\n",
+       "\n",
+       "uniform float opacity;\n",
+       "\n",
+       "float smoothStep(float edge0, float edge1, float x) {\n",
+       "\tfloat t;\n",
+       "\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n",
+       "\treturn t * t * (3.0 - 2.0 * t);\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "\tfloat radius, alpha = 1.0, delta = fragWidth;\n",
+       "\n",
+       "\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\n",
+       "\n",
+       "\tif (radius > 1.0 + delta) {\n",
+       "\t\tdiscard;\n",
+       "\t}\n",
+       "\n",
+       "\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\n",
+       "\n",
+       "\tfloat borderRadius = fragBorderRadius;\n",
+       "\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\n",
+       "\tvec4 color = mix(fragColor, fragBorderColor, ratio);\n",
+       "\tcolor.a *= alpha * opacity;\n",
+       "\tgl_FragColor = color;\n",
+       "}\n",
+       "`]),v.vert=uz([`precision highp float;\n",
+       "#define GLSLIFY 1\n",
+       "\n",
+       "attribute float x, y, xFract, yFract;\n",
+       "attribute float size, borderSize;\n",
+       "attribute vec4 colorId, borderColorId;\n",
+       "attribute float isActive;\n",
+       "\n",
+       "// \\`invariant\\` effectively turns off optimizations for the position.\n",
+       "// We need this because -fast-math on M1 Macs is re-ordering\n",
+       "// floating point operations in a way that causes floating point\n",
+       "// precision limits to put points in the wrong locations.\n",
+       "invariant gl_Position;\n",
+       "\n",
+       "uniform bool constPointSize;\n",
+       "uniform float pixelRatio;\n",
+       "uniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\n",
+       "uniform sampler2D paletteTexture;\n",
+       "\n",
+       "const float maxSize = 100.;\n",
+       "\n",
+       "varying vec4 fragColor, fragBorderColor;\n",
+       "varying float fragBorderRadius, fragWidth;\n",
+       "\n",
+       "float pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n",
+       "\n",
+       "bool isDirect = (paletteSize.x < 1.);\n",
+       "\n",
+       "vec4 getColor(vec4 id) {\n",
+       "  return isDirect ? id / 255. : texture2D(paletteTexture,\n",
+       "    vec2(\n",
+       "      (id.x + .5) / paletteSize.x,\n",
+       "      (id.y + .5) / paletteSize.y\n",
+       "    )\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "  // ignore inactive points\n",
+       "  if (isActive == 0.) return;\n",
+       "\n",
+       "  vec2 position = vec2(x, y);\n",
+       "  vec2 positionFract = vec2(xFract, yFract);\n",
+       "\n",
+       "  vec4 color = getColor(colorId);\n",
+       "  vec4 borderColor = getColor(borderColorId);\n",
+       "\n",
+       "  float size = size * maxSize / 255.;\n",
+       "  float borderSize = borderSize * maxSize / 255.;\n",
+       "\n",
+       "  gl_PointSize = (size + borderSize) * pointSizeScale;\n",
+       "\n",
+       "  vec2 pos = (position + translate) * scale\n",
+       "      + (positionFract + translateFract) * scale\n",
+       "      + (position + translate) * scaleFract\n",
+       "      + (positionFract + translateFract) * scaleFract;\n",
+       "\n",
+       "  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n",
+       "\n",
+       "  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n",
+       "  fragColor = color;\n",
+       "  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n",
+       "  fragWidth = 1. / gl_PointSize;\n",
+       "}\n",
+       "`]),M7e&&(v.frag=v.frag.replace(\"smoothstep\",\"smoothStep\"),d.frag=d.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=e(v)}av.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4};av.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this};av.prototype.draw=function(){for(var e=this,t=arguments.length,r=new Array(t),n=0;n<t;n++)r[n]=arguments[n];var i=this.groups;if(r.length===1&&Array.isArray(r[0])&&(r[0][0]===null||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach(function(o,s){e.drawItem(s)});return this};av.prototype.drawItem=function(e,t){var r=this.groups,n=r[e];if(typeof t==\"number\"&&(e=t,n=r[t],t=null),!!(n&&n.count&&n.opacity)){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,t));for(var i=[],a=1;a<n.activation.length;a++)!n.activation[a]||n.activation[a]!==!0&&!n.activation[a].data.length||i.push.apply(i,sOt(this.getMarkerDrawOptions(a,n,t)));i.length&&this.drawMarker(i)}};av.prototype.getMarkerDrawOptions=function(e,t,r){var n=t.range,i=t.tree,a=t.viewport,o=t.activation,s=t.selectionBuffer,l=t.count,u=this.regl;if(!i)return r?[fw({},t,{markerTexture:this.markerTextures[e],activation:o[e],count:r.length,elements:r,offset:0})]:[fw({},t,{markerTexture:this.markerTextures[e],activation:o[e],offset:0})];var c=[],f=i.range(n,{lod:!0,px:[(n[2]-n[0])/a.width,(n[3]-n[1])/a.height]});if(r){for(var h=o[e],d=h.data,v=new Uint8Array(l),m=0;m<r.length;m++){var b=r[m];v[b]=d?d[b]:1}s.subdata(v)}for(var p=f.length;p--;){var k=oOt(f[p],2),M=k[0],T=k[1];c.push(fw({},t,{markerTexture:this.markerTextures[e],activation:r?s:o[e],offset:M,count:T-M}))}return c};av.prototype.update=function(){for(var e=this,t=arguments.length,r=new Array(t),n=0;n<t;n++)r[n]=arguments[n];if(r.length){r.length===1&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,l=this.maxColors,u=this.palette;this.groups=i=r.map(function(c,f){var h=i[f];if(c===void 0)return h;c===null?c={positions:null}:typeof c==\"function\"?c={ondraw:c}:typeof c[0]==\"number\"&&(c={positions:c}),c=mOt(c,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"}),c.positions===null&&(c.positions=[]),c.tooManyColors!=null&&(e.tooManyColors=c.tooManyColors),h||(i[f]=h={id:f,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},c=fw({},av.defaults,c)),c.positions&&!(\"marker\"in c)&&(c.marker=h.marker,delete h.marker),c.marker&&!(\"positions\"in c)&&(c.positions=h.positions,delete h.positions);var d=0,v=0;if(yOt(h,c,[{snap:!0,size:function(j,N){return j==null&&(j=av.defaults.size),d+=j&&j.length?1:0,j},borderSize:function(j,N){return j==null&&(j=av.defaults.borderSize),d+=j&&j.length?1:0,j},opacity:parseFloat,color:function(j,N){return j==null&&(j=av.defaults.color),j=e.updateColor(j),v++,j},borderColor:function(j,N){return j==null&&(j=av.defaults.borderColor),j=e.updateColor(j),v++,j},bounds:function(j,N,H){return\"range\"in H||(H.range=null),j},positions:function(j,N,H){var re=N.snap,ae=N.positionBuffer,_e=N.positionFractBuffer,Ee=N.selectionBuffer;if(j.x||j.y)return j.x.length?N.xAttr={buffer:o.buffer(j.x),offset:0,stride:4,count:j.x.length}:N.xAttr={buffer:j.x.buffer,offset:j.x.offset*4||0,stride:(j.x.stride||1)*4,count:j.x.count},j.y.length?N.yAttr={buffer:o.buffer(j.y),offset:0,stride:4,count:j.y.length}:N.yAttr={buffer:j.y.buffer,offset:j.y.offset*4||0,stride:(j.y.stride||1)*4,count:j.y.count},N.count=Math.max(N.xAttr.count,N.yAttr.count),j;j=_Ot(j,\"float64\");var Ce=N.count=Math.floor(j.length/2),ge=N.bounds=Ce?vOt(j,2):null;if(!H.range&&!N.range&&(delete N.range,H.range=ge),!H.marker&&!N.marker&&(delete N.marker,H.marker=null),re&&(re===!0||Ce>re)?N.tree=gOt(j,{bounds:ge}):re&&re.length&&(N.tree=re),N.tree){var ie={primitive:\"points\",usage:\"static\",data:N.tree,type:\"uint32\"};N.elements?N.elements(ie):N.elements=o.elements(ie)}var Se=cz.float32(j);ae({data:Se,usage:\"dynamic\"});var ke=cz.fract32(j,Se);return _e({data:ke,usage:\"dynamic\"}),Ee({data:new Uint8Array(Ce),type:\"uint8\",usage:\"stream\"}),j}},{marker:function(j,N,H){var re=N.activation;if(re.forEach(function(ke){return ke&&ke.destroy&&ke.destroy()}),re.length=0,!j||typeof j[0]==\"number\"){var ae=e.addMarker(j);re[ae]=!0}else{for(var _e=[],Ee=0,Ce=Math.min(j.length,N.count);Ee<Ce;Ee++){var ge=e.addMarker(j[Ee]);_e[ge]||(_e[ge]=new Uint8Array(N.count)),_e[ge][Ee]=1}for(var ie=0;ie<_e.length;ie++)if(_e[ie]){var Se={data:_e[ie],type:\"uint8\",usage:\"static\"};re[ie]?re[ie](Se):re[ie]=o.buffer(Se),re[ie].data=_e[ie]}}return j},range:function(j,N,H){var re=N.bounds;if(re)return j||(j=re),N.scale=[1/(j[2]-j[0]),1/(j[3]-j[1])],N.translate=[-j[0],-j[1]],N.scaleFract=cz.fract(N.scale),N.translateFract=cz.fract(N.translate),j},viewport:function(j){var N=xOt(j||[a.drawingBufferWidth,a.drawingBufferHeight]);return N}}]),d){var m=h,b=m.count,p=m.size,k=m.borderSize,M=m.sizeBuffer,T=new Uint8Array(b*2);if(p.length||k.length)for(var L=0;L<b;L++)T[L*2]=Math.round((p[L]==null?p:p[L])*255/s),T[L*2+1]=Math.round((k[L]==null?k:k[L])*255/s);M({data:T,usage:\"dynamic\"})}if(v){var x=h,C=x.count,S=x.color,g=x.borderColor,P=x.colorBuffer,E;if(e.tooManyColors){if(S.length||g.length){E=new Uint8Array(C*8);for(var z=0;z<C;z++){var q=S[z];E[z*8]=u[q*4],E[z*8+1]=u[q*4+1],E[z*8+2]=u[q*4+2],E[z*8+3]=u[q*4+3];var V=g[z];E[z*8+4]=u[V*4],E[z*8+5]=u[V*4+1],E[z*8+6]=u[V*4+2],E[z*8+7]=u[V*4+3]}}}else if(S.length||g.length){E=new Uint8Array(C*4+2);for(var G=0;G<C;G++)S[G]!=null&&(E[G*4]=S[G]%l,E[G*4+1]=Math.floor(S[G]/l)),g[G]!=null&&(E[G*4+2]=g[G]%l,E[G*4+3]=Math.floor(g[G]/l))}P({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return h})}};av.prototype.addMarker=function(e){var t=this.markerTextures,r=this.regl,n=this.markerCache,i=e==null?0:n.indexOf(e);if(i>=0)return i;var a;if(e instanceof Uint8Array||e instanceof Uint8ClampedArray)a=e;else{a=new Uint8Array(e.length);for(var o=0,s=e.length;o<s;o++)a[o]=e[o]*255}var l=Math.floor(Math.sqrt(a.length));return i=t.length,n.push(e),t.push(r.texture({channels:1,data:a,radius:l,mag:\"linear\",min:\"linear\"})),i};av.prototype.updateColor=function(e){var t=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(e)||(e=[e]);var i=[];if(typeof e[0]==\"number\"){var a=[];if(Array.isArray(e))for(var o=0;o<e.length;o+=4)a.push(e.slice(o,o+4));else for(var s=0;s<e.length;s+=4)a.push(e.subarray(s,s+4));e=a}for(var l=0;l<e.length;l++){var u=e[l];u=dOt(u,\"uint8\");var c=pOt(u,!1);if(t[c]==null){var f=r.length;t[c]=Math.floor(f/4),r[f]=u[0],r[f+1]=u[1],r[f+2]=u[2],r[f+3]=u[3]}i[l]=t[c]}return!this.tooManyColors&&r.length>n*4&&(this.tooManyColors=!0),this.updatePalette(r),i.length===1?i[0]:i};av.prototype.updatePalette=function(e){if(!this.tooManyColors){var t=this.maxColors,r=this.paletteTexture,n=Math.ceil(e.length*.25/t);if(n>1){e=e.slice();for(var i=e.length*.25%t;i<n*t;i++)e.push(0,0,0,0)}r.height<n&&r.resize(t,n),r.subimage({width:Math.min(e.length*.25,t),height:n,data:e},0,0)}};av.prototype.destroy=function(){return this.groups.forEach(function(e){e.sizeBuffer.destroy(),e.positionBuffer.destroy(),e.positionFractBuffer.destroy(),e.colorBuffer.destroy(),e.activation.forEach(function(t){return t&&t.destroy&&t.destroy()}),e.selectionBuffer.destroy(),e.elements&&e.elements.destroy()}),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach(function(e){return 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+       "precision highp float;\n",
+       "\n",
+       "attribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\n",
+       "attribute vec4 color;\n",
+       "attribute float lineEnd, lineTop;\n",
+       "\n",
+       "uniform vec2 scale, scaleFract, translate, translateFract;\n",
+       "uniform float thickness, pixelRatio, id, depth;\n",
+       "uniform vec4 viewport;\n",
+       "\n",
+       "varying vec4 fragColor;\n",
+       "varying vec2 tangent;\n",
+       "\n",
+       "vec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\n",
+       "\t// the order is important\n",
+       "\treturn position * scale + translate\n",
+       "       + positionFract * scale + translateFract\n",
+       "       + position * scaleFract\n",
+       "       + positionFract * scaleFract;\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "\tfloat lineStart = 1. - lineEnd;\n",
+       "\tfloat lineOffset = lineTop * 2. - 1.;\n",
+       "\n",
+       "\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\n",
+       "\ttangent = normalize(diff * scale * viewport.zw);\n",
+       "\tvec2 normal = vec2(-tangent.y, tangent.x);\n",
+       "\n",
+       "\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\n",
+       "\t\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\n",
+       "\n",
+       "\t\t+ thickness * normal * .5 * lineOffset / viewport.zw;\n",
+       "\n",
+       "\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n",
+       "\n",
+       "\tfragColor = color / 255.;\n",
+       "}\n",
+       "`,lUt=`\n",
+       "precision highp float;\n",
+       "\n",
+       "uniform float dashLength, pixelRatio, thickness, opacity, id;\n",
+       "uniform sampler2D dashTexture;\n",
+       "\n",
+       "varying vec4 fragColor;\n",
+       "varying vec2 tangent;\n",
+       "\n",
+       "void main() {\n",
+       "\tfloat alpha = 1.;\n",
+       "\n",
+       "\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n",
+       "\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n",
+       "\n",
+       "\tgl_FragColor = fragColor;\n",
+       "\tgl_FragColor.a *= alpha * opacity * dash;\n",
+       "}\n",
+       "`,uUt=`\n",
+       "precision highp float;\n",
+       "\n",
+       "attribute vec2 position, positionFract;\n",
+       "\n",
+       "uniform vec4 color;\n",
+       "uniform vec2 scale, scaleFract, translate, translateFract;\n",
+       "uniform float pixelRatio, id;\n",
+       "uniform vec4 viewport;\n",
+       "uniform float opacity;\n",
+       "\n",
+       "varying vec4 fragColor;\n",
+       "\n",
+       "const float MAX_LINES = 256.;\n",
+       "\n",
+       "void main() {\n",
+       "\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n",
+       "\n",
+       "\tvec2 position = position * scale + translate\n",
+       "       + positionFract * scale + translateFract\n",
+       "       + position * scaleFract\n",
+       "       + positionFract * scaleFract;\n",
+       "\n",
+       "\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n",
+       "\n",
+       "\tfragColor = color / 255.;\n",
+       "\tfragColor.a *= opacity;\n",
+       "}\n",
+       "`,cUt=`\n",
+       "precision highp float;\n",
+       "varying vec4 fragColor;\n",
+       "\n",
+       "void main() {\n",
+       "\tgl_FragColor = fragColor;\n",
+       "}\n",
+       "`,fUt=`\n",
+       "precision highp float;\n",
+       "\n",
+       "attribute vec2 aCoord, bCoord, nextCoord, prevCoord;\n",
+       "attribute vec4 aColor, bColor;\n",
+       "attribute float lineEnd, lineTop;\n",
+       "\n",
+       "uniform vec2 scale, translate;\n",
+       "uniform float thickness, pixelRatio, id, depth;\n",
+       "uniform vec4 viewport;\n",
+       "uniform float miterLimit, miterMode;\n",
+       "\n",
+       "varying vec4 fragColor;\n",
+       "varying vec4 startCutoff, endCutoff;\n",
+       "varying vec2 tangent;\n",
+       "varying vec2 startCoord, endCoord;\n",
+       "varying float enableStartMiter, enableEndMiter;\n",
+       "\n",
+       "const float REVERSE_THRESHOLD = -.875;\n",
+       "const float MIN_DIFF = 1e-6;\n",
+       "\n",
+       "// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n",
+       "// TODO: precalculate dot products, normalize things beforehead etc.\n",
+       "// TODO: refactor to rectangular algorithm\n",
+       "\n",
+       "float distToLine(vec2 p, vec2 a, vec2 b) {\n",
+       "\tvec2 diff = b - a;\n",
+       "\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n",
+       "\treturn dot(p - a, perp);\n",
+       "}\n",
+       "\n",
+       "bool isNaN( float val ){\n",
+       "  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n",
+       "\n",
+       "  vec2 adjustedScale;\n",
+       "  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n",
+       "  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n",
+       "\n",
+       "  vec2 scaleRatio = adjustedScale * viewport.zw;\n",
+       "\tvec2 normalWidth = thickness / scaleRatio;\n",
+       "\n",
+       "\tfloat lineStart = 1. - lineEnd;\n",
+       "\tfloat lineBot = 1. - lineTop;\n",
+       "\n",
+       "\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n",
+       "\n",
+       "\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n",
+       "\n",
+       "\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n",
+       "\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n",
+       "\n",
+       "\n",
+       "\tvec2 prevDiff = aCoord - prevCoord;\n",
+       "\tvec2 currDiff = bCoord - aCoord;\n",
+       "\tvec2 nextDiff = nextCoord - bCoord;\n",
+       "\n",
+       "\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n",
+       "\tvec2 currTangent = normalize(currDiff * scaleRatio);\n",
+       "\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n",
+       "\n",
+       "\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n",
+       "\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n",
+       "\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n",
+       "\n",
+       "\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n",
+       "\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n",
+       "\n",
+       "\t// collapsed/unidirectional segment cases\n",
+       "\t// FIXME: there should be more elegant solution\n",
+       "\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n",
+       "\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n",
+       "\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n",
+       "\t\tstartJoinDirection = currNormal;\n",
+       "\t}\n",
+       "\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n",
+       "\t\tendJoinDirection = currNormal;\n",
+       "\t}\n",
+       "\tif (aCoord == bCoord) {\n",
+       "\t\tendJoinDirection = startJoinDirection;\n",
+       "\t\tcurrNormal = prevNormal;\n",
+       "\t\tcurrTangent = prevTangent;\n",
+       "\t}\n",
+       "\n",
+       "\ttangent = currTangent;\n",
+       "\n",
+       "\t//calculate join shifts relative to normals\n",
+       "\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n",
+       "\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n",
+       "\n",
+       "\tfloat startMiterRatio = abs(1. / startJoinShift);\n",
+       "\tfloat endMiterRatio = abs(1. / endJoinShift);\n",
+       "\n",
+       "\tvec2 startJoin = startJoinDirection * startMiterRatio;\n",
+       "\tvec2 endJoin = endJoinDirection * endMiterRatio;\n",
+       "\n",
+       "\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n",
+       "\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n",
+       "\tstartBotJoin = -startTopJoin;\n",
+       "\n",
+       "\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n",
+       "\tendBotJoin = -endTopJoin;\n",
+       "\n",
+       "\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n",
+       "\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n",
+       "\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n",
+       "\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n",
+       "\n",
+       "\t//miter anti-clipping\n",
+       "\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n",
+       "\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n",
+       "\n",
+       "\t//prevent close to reverse direction switch\n",
+       "\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n",
+       "\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n",
+       "\n",
+       "\tif (prevReverse) {\n",
+       "\t\t//make join rectangular\n",
+       "\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n",
+       "\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n",
+       "\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n",
+       "\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n",
+       "\t}\n",
+       "\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n",
+       "\t\t//handle miter clipping\n",
+       "\t\tbTopCoord -= normalWidth * endTopJoin;\n",
+       "\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n",
+       "\t}\n",
+       "\n",
+       "\tif (nextReverse) {\n",
+       "\t\t//make join rectangular\n",
+       "\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n",
+       "\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n",
+       "\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n",
+       "\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n",
+       "\t}\n",
+       "\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n",
+       "\t\t//handle miter clipping\n",
+       "\t\taBotCoord -= normalWidth * startBotJoin;\n",
+       "\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n",
+       "\t}\n",
+       "\n",
+       "\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n",
+       "\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n",
+       "\n",
+       "\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n",
+       "\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n",
+       "\n",
+       "\t//position is normalized 0..1 coord on the screen\n",
+       "\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n",
+       "\n",
+       "\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n",
+       "\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n",
+       "\n",
+       "\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n",
+       "\n",
+       "\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n",
+       "\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n",
+       "\n",
+       "\t//bevel miter cutoffs\n",
+       "\tif (miterMode == 1.) {\n",
+       "\t\tif (enableStartMiter == 1.) {\n",
+       "\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n",
+       "\t\t\tstartCutoff = vec4(aCoord, aCoord);\n",
+       "\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n",
+       "\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n",
+       "\t\t\tstartCutoff += viewport.xyxy;\n",
+       "\t\t\tstartCutoff += startMiterWidth.xyxy;\n",
+       "\t\t}\n",
+       "\n",
+       "\t\tif (enableEndMiter == 1.) {\n",
+       "\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n",
+       "\t\t\tendCutoff = vec4(bCoord, bCoord);\n",
+       "\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n",
+       "\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n",
+       "\t\t\tendCutoff += viewport.xyxy;\n",
+       "\t\t\tendCutoff += endMiterWidth.xyxy;\n",
+       "\t\t}\n",
+       "\t}\n",
+       "\n",
+       "\t//round miter cutoffs\n",
+       "\telse if (miterMode == 2.) {\n",
+       "\t\tif (enableStartMiter == 1.) {\n",
+       "\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n",
+       "\t\t\tstartCutoff = vec4(aCoord, aCoord);\n",
+       "\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n",
+       "\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n",
+       "\t\t\tstartCutoff += viewport.xyxy;\n",
+       "\t\t\tstartCutoff += startMiterWidth.xyxy;\n",
+       "\t\t}\n",
+       "\n",
+       "\t\tif (enableEndMiter == 1.) {\n",
+       "\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n",
+       "\t\t\tendCutoff = vec4(bCoord, bCoord);\n",
+       "\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n",
+       "\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n",
+       "\t\t\tendCutoff += viewport.xyxy;\n",
+       "\t\t\tendCutoff += endMiterWidth.xyxy;\n",
+       "\t\t}\n",
+       "\t}\n",
+       "}\n",
+       "`,hUt=`\n",
+       "precision highp float;\n",
+       "\n",
+       "uniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\n",
+       "uniform sampler2D dashTexture;\n",
+       "\n",
+       "varying vec4 fragColor;\n",
+       "varying vec2 tangent;\n",
+       "varying vec4 startCutoff, endCutoff;\n",
+       "varying vec2 startCoord, endCoord;\n",
+       "varying float enableStartMiter, enableEndMiter;\n",
+       "\n",
+       "float distToLine(vec2 p, vec2 a, vec2 b) {\n",
+       "\tvec2 diff = b - a;\n",
+       "\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n",
+       "\treturn dot(p - a, perp);\n",
+       "}\n",
+       "\n",
+       "void main() {\n",
+       "\tfloat alpha = 1., distToStart, distToEnd;\n",
+       "\tfloat cutoff = thickness * .5;\n",
+       "\n",
+       "\t//bevel miter\n",
+       "\tif (miterMode == 1.) {\n",
+       "\t\tif (enableStartMiter == 1.) {\n",
+       "\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n",
+       "\t\t\tif (distToStart < -1.) {\n",
+       "\t\t\t\tdiscard;\n",
+       "\t\t\t\treturn;\n",
+       "\t\t\t}\n",
+       "\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n",
+       "\t\t}\n",
+       "\n",
+       "\t\tif (enableEndMiter == 1.) {\n",
+       "\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n",
+       "\t\t\tif (distToEnd < -1.) {\n",
+       "\t\t\t\tdiscard;\n",
+       "\t\t\t\treturn;\n",
+       "\t\t\t}\n",
+       "\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n",
+       "\t\t}\n",
+       "\t}\n",
+       "\n",
+       "\t// round miter\n",
+       "\telse if (miterMode == 2.) {\n",
+       "\t\tif (enableStartMiter == 1.) {\n",
+       "\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n",
+       "\t\t\tif (distToStart < 0.) {\n",
+       "\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n",
+       "\n",
+       "\t\t\t\tif(radius > cutoff + .5) {\n",
+       "\t\t\t\t\tdiscard;\n",
+       "\t\t\t\t\treturn;\n",
+       "\t\t\t\t}\n",
+       "\n",
+       "\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n",
+       "\t\t\t}\n",
+       "\t\t}\n",
+       "\n",
+       "\t\tif (enableEndMiter == 1.) {\n",
+       "\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n",
+       "\t\t\tif (distToEnd < 0.) {\n",
+       "\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n",
+       "\n",
+       "\t\t\t\tif(radius > cutoff + .5) {\n",
+       "\t\t\t\t\tdiscard;\n",
+       "\t\t\t\t\treturn;\n",
+       "\t\t\t\t}\n",
+       "\n",
+       "\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n",
+       "\t\t\t}\n",
+       "\t\t}\n",
+       "\t}\n",
+       "\n",
+       "\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n",
+       "\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n",
+       "\n",
+       "\tgl_FragColor = fragColor;\n",
+       "\tgl_FragColor.a *= alpha * opacity * dash;\n",
+       "}\n",
+       "`;COe.exports=Wc;function Wc(e,t){if(!(this instanceof Wc))return new Wc(e,t);if(typeof e==\"function\"?(t||(t={}),t.regl=e):t=e,t.length&&(t.positions=t),e=t.regl,!e.hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=e._gl,this.regl=e,this.passes=[],this.shaders=Wc.shaders.has(e)?Wc.shaders.get(e):Wc.shaders.set(e,Wc.createShaders(e)).get(e),this.update(t)}Wc.dashMult=2;Wc.maxPatternLength=256;Wc.precisionThreshold=3e6;Wc.maxPoints=1e4;Wc.maxLines=2048;Wc.shaders=new aUt;Wc.createShaders=function(e){let t=e.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),r={primitive:\"triangle strip\",instances:e.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:(o,s)=>s.join===\"round\"?2:1,miterLimit:e.prop(\"miterLimit\"),scale:e.prop(\"scale\"),scaleFract:e.prop(\"scaleFract\"),translateFract:e.prop(\"translateFract\"),translate:e.prop(\"translate\"),thickness:e.prop(\"thickness\"),dashTexture:e.prop(\"dashTexture\"),opacity:e.prop(\"opacity\"),pixelRatio:e.context(\"pixelRatio\"),id:e.prop(\"id\"),dashLength:e.prop(\"dashLength\"),viewport:(o,s)=>[s.viewport.x,s.viewport.y,o.viewportWidth,o.viewportHeight],depth:e.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:(o,s)=>!s.overlay},stencil:{enable:!1},scissor:{enable:!0,box:e.prop(\"viewport\")},viewport:e.prop(\"viewport\")},n=e(QY({vert:sUt,frag:lUt,attributes:{lineEnd:{buffer:t,divisor:0,stride:8,offset:0},lineTop:{buffer:t,divisor:0,stride:8,offset:4},aCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},r)),i;try{i=e(QY({cull:{enable:!0,face:\"back\"},vert:fUt,frag:hUt,attributes:{lineEnd:{buffer:t,divisor:0,stride:8,offset:0},lineTop:{buffer:t,divisor:0,stride:8,offset:4},aColor:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},r))}catch(o){i=n}return{fill:e({primitive:\"triangle\",elements:(o,s)=>s.triangles,offset:0,vert:uUt,frag:cUt,uniforms:{scale:e.prop(\"scale\"),color:e.prop(\"fill\"),scaleFract:e.prop(\"scaleFract\"),translateFract:e.prop(\"translateFract\"),translate:e.prop(\"translate\"),opacity:e.prop(\"opacity\"),pixelRatio:e.context(\"pixelRatio\"),id:e.prop(\"id\"),viewport:(o,s)=>[s.viewport.x,s.viewport.y,o.viewportWidth,o.viewportHeight]},attributes:{position:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:r.blend,depth:{enable:!1},scissor:r.scissor,stencil:r.stencil,viewport:r.viewport}),rect:n,miter:i}};Wc.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null};Wc.prototype.render=function(...e){e.length&&this.update(...e),this.draw()};Wc.prototype.draw=function(...e){return(e.length?e:this.passes).forEach((t,r)=>{if(t&&Array.isArray(t))return this.draw(...t);typeof t==\"number\"&&(t=this.passes[t]),t&&t.count>1&&t.opacity&&(this.regl._refresh(),t.fill&&t.triangles&&t.triangles.length>2&&this.shaders.fill(t),t.thickness&&(t.scale[0]*t.viewport.width>Wc.precisionThreshold||t.scale[1]*t.viewport.height>Wc.precisionThreshold?this.shaders.rect(t):t.join===\"rect\"||!t.join&&(t.thickness<=2||t.count>=Wc.maxPoints)?this.shaders.rect(t):this.shaders.miter(t)))}),this};Wc.prototype.update=function(e){if(!e)return;e.length!=null?typeof e[0]==\"number\"&&(e=[{positions:e}]):Array.isArray(e)||(e=[e]);let{regl:t,gl:r}=this;if(e.forEach((i,a)=>{let o=this.passes[a];if(i!==void 0){if(i===null){this.passes[a]=null;return}if(typeof i[0]==\"number\"&&(i={positions:i}),i=tUt(i,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin join type mode\",miterLimit:\"miterlimit miterLimit\",dashes:\"dash dashes dasharray dash-array dashArray\",color:\"color colour stroke colors colours stroke-color strokeColor\",fill:\"fill fill-color fillColor\",opacity:\"alpha opacity\",overlay:\"overlay crease overlap intersect\",close:\"closed close closed-path closePath\",range:\"range dataBox\",viewport:\"viewport viewBox\",hole:\"holes hole hollow\",splitNull:\"splitNull\"}),o||(this.passes[a]=o={id:a,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:t.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:\"linear\",min:\"linear\"}),colorBuffer:t.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array}),positionBuffer:t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),positionFractBuffer:t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array})},i=QY({},Wc.defaults,i)),i.thickness!=null&&(o.thickness=parseFloat(i.thickness)),i.opacity!=null&&(o.opacity=parseFloat(i.opacity)),i.miterLimit!=null&&(o.miterLimit=parseFloat(i.miterLimit)),i.overlay!=null&&(o.overlay=!!i.overlay,a<Wc.maxLines&&(o.depth=2*(Wc.maxLines-1-a%Wc.maxLines)/Wc.maxLines-1)),i.join!=null&&(o.join=i.join),i.hole!=null&&(o.hole=i.hole),i.fill!=null&&(o.fill=i.fill?Mz(i.fill,\"uint8\"):null),i.viewport!=null&&(o.viewport=kOe(i.viewport)),o.viewport||(o.viewport=kOe([r.drawingBufferWidth,r.drawingBufferHeight])),i.close!=null&&(o.close=i.close),i.positions===null&&(i.positions=[]),i.positions){let u,c;if(i.positions.x&&i.positions.y){let v=i.positions.x,m=i.positions.y;c=o.count=Math.max(v.length,m.length),u=new Float64Array(c*2);for(let b=0;b<c;b++)u[b*2]=v[b],u[b*2+1]=m[b]}else u=rUt(i.positions,\"float64\"),c=o.count=Math.floor(u.length/2);let f=o.bounds=eUt(u,2);if(o.fill){let v=[],m={},b=0;for(let p=0,k=0,M=o.count;p<M;p++){let T=u[p*2],L=u[p*2+1];isNaN(T)||isNaN(L)||T==null||L==null?(T=u[b*2],L=u[b*2+1],m[p]=b):b=p,v[k++]=T,v[k++]=L}if(i.splitNull){o.count-1 in m||(m[o.count]=o.count-1);let p=Object.keys(m).map(Number).sort((L,x)=>L-x),k=[],M=0,T=o.hole!=null?o.hole[0]:null;if(T!=null){let L=oUt(p,x=>x>=T);p=p.slice(0,L),p.push(T)}for(let L=0;L<p.length;L++){let x=v.slice(M*2,p[L]*2).concat(T?v.slice(T*2):[]),C=(o.hole||[]).map(g=>g-T+(p[L]-M)),S=EOe(x,C);S=S.map(g=>g+M+(g+M<p[L]?0:T-p[L])),k.push(...S),M=p[L]+1}for(let L=0,x=k.length;L<x;L++)m[k[L]]!=null&&(k[L]=m[k[L]]);o.triangles=k}else{let p=EOe(v,o.hole||[]);for(let k=0,M=p.length;k<M;k++)m[p[k]]!=null&&(p[k]=m[p[k]]);o.triangles=p}}let h=new Float64Array(u);iUt(h,2,f);let d=new Float64Array(c*2+6);o.close?u[0]===u[c*2-2]&&u[1]===u[c*2-1]?(d[0]=h[c*2-4],d[1]=h[c*2-3]):(d[0]=h[c*2-2],d[1]=h[c*2-1]):(d[0]=h[0],d[1]=h[1]),d.set(h,2),o.close?u[0]===u[c*2-2]&&u[1]===u[c*2-1]?(d[c*2+2]=h[2],d[c*2+3]=h[3],o.count-=1):(d[c*2+2]=h[0],d[c*2+3]=h[1],d[c*2+4]=h[2],d[c*2+5]=h[3]):(d[c*2+2]=h[c*2-2],d[c*2+3]=h[c*2-1],d[c*2+4]=h[c*2-2],d[c*2+5]=h[c*2-1]);var s=nUt(d);o.positionBuffer(s);var l=$Y(d,s);o.positionFractBuffer(l)}if(i.range?o.range=i.range:o.range||(o.range=o.bounds),(i.range||i.positions)&&o.count){let u=o.bounds,c=u[2]-u[0],f=u[3]-u[1],h=o.range[2]-o.range[0],d=o.range[3]-o.range[1];o.scale=[c/h,f/d],o.translate=[-o.range[0]/h+u[0]/h||0,-o.range[1]/d+u[1]/d||0],o.scaleFract=$Y(o.scale),o.translateFract=$Y(o.translate)}if(i.dashes){let u=0,c;if(!i.dashes||i.dashes.length<2)u=1,c=new Uint8Array([255,255,255,255,255,255,255,255]);else{u=0;for(let d=0;d<i.dashes.length;++d)u+=i.dashes[d];c=new Uint8Array(u*Wc.dashMult);let f=0,h=255;for(let d=0;d<2;d++)for(let v=0;v<i.dashes.length;++v){for(let m=0,b=i.dashes[v]*Wc.dashMult*.5;m<b;++m)c[f++]=h;h^=255}}o.dashLength=u,o.dashTexture({channels:1,data:c,width:c.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(i.color){let u=o.count,c=i.color;c||(c=\"transparent\");let f=new Uint8Array(u*4+4);if(!Array.isArray(c)||typeof c[0]==\"number\"){let h=Mz(c,\"uint8\");for(let d=0;d<u+1;d++)f.set(h,d*4)}else{for(let h=0;h<u;h++){let d=Mz(c[h],\"uint8\");f.set(d,h*4)}f.set(Mz(c[0],\"uint8\"),u*4)}o.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:f})}}}),e.length<this.passes.length){for(let i=e.length;i<this.passes.length;i++){let a=this.passes[i];a&&(a.colorBuffer.destroy(),a.positionBuffer.destroy(),a.dashTexture.destroy())}this.passes.length=e.length}let n=[];for(let i=0;i<this.passes.length;i++)this.passes[i]!==null&&n.push(this.passes[i]);return this.passes=n,this};Wc.prototype.destroy=function(){return this.passes.forEach(e=>{e.colorBuffer.destroy(),e.positionBuffer.destroy(),e.dashTexture.destroy()}),this.passes.length=0,this}});var DOe=ye((C_r,ROe)=>{\"use strict\";var dUt=ow(),vUt=lx(),pUt=bY(),gUt=ry(),LOe=Nh(),POe=sw(),{float32:mUt,fract32:tK}=lz();ROe.exports=yUt;var IOe=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]];function yUt(e,t){if(typeof e==\"function\"?(t||(t={}),t.regl=e):t=e,t.length&&(t.positions=t),e=t.regl,!e.hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");let r=e._gl,n,i,a,o,s,l,u={color:\"black\",capSize:5,lineWidth:1,opacity:1,viewport:null,range:null,offset:0,count:0,bounds:null,positions:[],errors:[]},c=[];return o=e.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array(0)}),i=e.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),a=e.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),s=e.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),l=e.buffer({usage:\"static\",type:\"float\",data:IOe}),v(t),n=e({vert:`\n",
+       "\t\tprecision highp float;\n",
+       "\n",
+       "\t\tattribute vec2 position, positionFract;\n",
+       "\t\tattribute vec4 error;\n",
+       "\t\tattribute vec4 color;\n",
+       "\n",
+       "\t\tattribute vec2 direction, lineOffset, capOffset;\n",
+       "\n",
+       "\t\tuniform vec4 viewport;\n",
+       "\t\tuniform float lineWidth, capSize;\n",
+       "\t\tuniform vec2 scale, scaleFract, translate, translateFract;\n",
+       "\n",
+       "\t\tvarying vec4 fragColor;\n",
+       "\n",
+       "\t\tvoid main() {\n",
+       "\t\t\tfragColor = color / 255.;\n",
+       "\n",
+       "\t\t\tvec2 pixelOffset = lineWidth * lineOffset + (capSize + lineWidth) * capOffset;\n",
+       "\n",
+       "\t\t\tvec2 dxy = -step(.5, direction.xy) * error.xz + step(direction.xy, vec2(-.5)) * error.yw;\n",
+       "\n",
+       "\t\t\tvec2 position = position + dxy;\n",
+       "\n",
+       "\t\t\tvec2 pos = (position + translate) * scale\n",
+       "\t\t\t\t+ (positionFract + translateFract) * scale\n",
+       "\t\t\t\t+ (position + translate) * scaleFract\n",
+       "\t\t\t\t+ (positionFract + translateFract) * scaleFract;\n",
+       "\n",
+       "\t\t\tpos += pixelOffset / viewport.zw;\n",
+       "\n",
+       "\t\t\tgl_Position = vec4(pos * 2. - 1., 0, 1);\n",
+       "\t\t}\n",
+       "\t\t`,frag:`\n",
+       "\t\tprecision highp float;\n",
+       "\n",
+       "\t\tvarying vec4 fragColor;\n",
+       "\n",
+       "\t\tuniform float opacity;\n",
+       "\n",
+       "\t\tvoid main() {\n",
+       "\t\t\tgl_FragColor = fragColor;\n",
+       "\t\t\tgl_FragColor.a *= opacity;\n",
+       "\t\t}\n",
+       "\t\t`,uniforms:{range:e.prop(\"range\"),lineWidth:e.prop(\"lineWidth\"),capSize:e.prop(\"capSize\"),opacity:e.prop(\"opacity\"),scale:e.prop(\"scale\"),translate:e.prop(\"translate\"),scaleFract:e.prop(\"scaleFract\"),translateFract:e.prop(\"translateFract\"),viewport:(b,p)=>[p.viewport.x,p.viewport.y,b.viewportWidth,b.viewportHeight]},attributes:{color:{buffer:o,offset:(b,p)=>p.offset*4,divisor:1},position:{buffer:i,offset:(b,p)=>p.offset*8,divisor:1},positionFract:{buffer:a,offset:(b,p)=>p.offset*8,divisor:1},error:{buffer:s,offset:(b,p)=>p.offset*16,divisor:1},direction:{buffer:l,stride:24,offset:0},lineOffset:{buffer:l,stride:24,offset:8},capOffset:{buffer:l,stride:24,offset:16}},primitive:\"triangles\",blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst 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S||(S=E),g.scale=[1/(S[2]-S[0]),1/(S[3]-S[1])],g.translate=[-S[0],-S[1]],g.scaleFract=tK(g.scale),g.translateFract=tK(g.translate),S},viewport:S=>{let g;return Array.isArray(S)?g={x:S[0],y:S[1],width:S[2]-S[0],height:S[3]-S[1]}:S?(g={x:S.x||S.left||0,y:S.y||S.top||0},S.right?g.width=S.right-g.x:g.width=S.w||S.width||0,S.bottom?g.height=S.bottom-g.y:g.height=S.h||S.height||0):g={x:0,y:0,width:r.drawingBufferWidth,height:r.drawingBufferHeight},g}}]),C}),p||k){let L=c.reduce((g,P,E)=>g+(P?P.count:0),0),x=new Float64Array(L*2),C=new Uint8Array(L*4),S=new Float32Array(L*4);c.forEach((g,P)=>{if(!g)return;let{positions:E,count:z,offset:q,color:V,errors:G}=g;z&&(C.set(V,q*4),S.set(G,q*4),x.set(E,q*2))});var M=mUt(x);i(M);var T=tK(x,M);a(T),o(C),s(S)}}function m(){i.destroy(),a.destroy(),o.destroy(),s.destroy(),l.destroy()}}});var qOe=ye((L_r,zOe)=>{var FOe=/[\\'\\\"]/;zOe.exports=function(t){return 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HOe=ye((q_r,GOe)=>{\"use strict\";var VOe=UOe();GOe.exports=function(t,r,n){if(t==null)throw Error(\"First argument should be a string\");if(r==null)throw Error(\"Separator should be a string or a RegExp\");n?(typeof n==\"string\"||Array.isArray(n))&&(n={ignore:n}):n={},n.escape==null&&(n.escape=!0),n.ignore==null?n.ignore=[\"[]\",\"()\",\"{}\",\"<>\",'\"\"',\"''\",\"``\",\"\\u201C\\u201D\",\"\\xAB\\xBB\"]:(typeof n.ignore==\"string\"&&(n.ignore=[n.ignore]),n.ignore=n.ignore.map(function(f){return f.length===1&&(f=f+f),f}));var i=VOe.parse(t,{flat:!0,brackets:n.ignore}),a=i[0],o=a.split(r);if(n.escape){for(var s=[],l=0;l<o.length;l++){var u=o[l],c=o[l+1];u[u.length-1]===\"\\\\\"&&u[u.length-2]!==\"\\\\\"?(s.push(u+r+c),l++):s.push(u)}o=s}for(var l=0;l<o.length;l++)i[0]=o[l],o[l]=VOe.stringify(i,{flat:!0});return o}});var jOe=ye((O_r,AUt)=>{AUt.exports=[\"xx-small\",\"x-small\",\"small\",\"medium\",\"large\",\"x-large\",\"xx-large\",\"larger\",\"smaller\"]});var lK=ye((B_r,WOe)=>{\"use 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si(Ut,Oe),Ut.width=0,Ut.height=0,Bi(Ut,Nt),Ut.width=Ut.width||(Oe.width>>Ft)-It,Ut.height=Ut.height||(Oe.height>>Ft)-Bt,al(Oe),Nn(Ut,_n,It,Bt,Ft),io(),Cn(Ut),ht}function cr(Nt,Ne){var $e=Nt|0,st=Ne|0||$e;if($e===Oe.width&&st===Oe.height)return ht;ht.width=Oe.width=$e,ht.height=Oe.height=st,al(Oe);for(var It=0;Oe.mipmask>>It;++It){var Bt=$e>>It,Ft=st>>It;if(!Bt||!Ft)break;Me.texImage2D(_n,It,Oe.format,Bt,Ft,0,Oe.format,Oe.type,null)}return io(),Nr.profile&&(Oe.stats.size=cc(Oe.internalformat,Oe.type,$e,st,!1,!1)),ht}return ht(ne,we),ht.subimage=Yt,ht.resize=cr,ht._reglType=\"texture2d\",ht._texture=Oe,Nr.profile&&(ht.stats=Oe.stats),ht.destroy=function(){Oe.decRef()},ht}function J(ne,we,Oe,ht,Yt,cr){var Nt=new wa(ya);ss[Nt.id]=Nt,Or.cubeCount++;var Ne=new Array(6);function $e(Bt,Ft,Ut,zt,_t,Dt){var vt,lr=Nt.texInfo;for(zo.call(lr),vt=0;vt<6;++vt)Ne[vt]=Ga();if(typeof Bt==\"number\"||!Bt){var _r=Bt|0||1;for(vt=0;vt<6;++vt)sa(Ne[vt],_r,_r)}else if(typeof 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It(Bt){var Ft=Bt|0;if(Ft!==Nt.width){$e.width=Nt.width=Ft,$e.height=Nt.height=Ft,al(Nt);for(var Ut=0;Ut<6;++Ut)for(var zt=0;Nt.mipmask>>zt;++zt)Me.texImage2D(ea+Ut,zt,Nt.format,Ft>>zt,Ft>>zt,0,Nt.format,Nt.type,null);return io(),Nr.profile&&(Nt.stats.size=cc(Nt.internalformat,Nt.type,$e.width,$e.height,!1,!0)),$e}}return $e(ne,we,Oe,ht,Yt,cr),$e.subimage=st,$e.resize=It,$e._reglType=\"textureCube\",$e._texture=Nt,Nr.profile&&($e.stats=Nt.stats),$e.destroy=function(){Nt.decRef()},$e}function O(){for(var ne=0;ne<Vs;++ne)Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Ys[ne]=null;gt(ss).forEach(Y),Or.cubeCount=0,Or.textureCount=0}Nr.profile&&(Or.getTotalTextureSize=function(){var ne=0;return Object.keys(ss).forEach(function(we){ne+=ss[we].stats.size}),ne});function K(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null)}gt(ss).forEach(function(Oe){Oe.texture=Me.createTexture(),Me.bindTexture(Oe.target,Oe.texture);for(var ht=0;ht<32;++ht)if((Oe.mipmask&1<<ht)!==0)if(Oe.target===_n)Me.texImage2D(_n,ht,Oe.internalformat,Oe.width>>ht,Oe.height>>ht,0,Oe.internalformat,Oe.type,null);else for(var Yt=0;Yt<6;++Yt)Me.texImage2D(ea+Yt,ht,Oe.internalformat,Oe.width>>ht,Oe.height>>ht,0,Oe.internalformat,Oe.type,null);nl(Oe.texInfo,Oe.target)})}function de(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null),Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Me.bindTexture(ya,null)}}return{create2D:D,createCube:J,clear:O,getTexture:function(ne){return null},restore:K,refresh:de}}var Bl=36161,Kc=32854,Rc=32855,gs=36194,Wf=33189,Wh=36168,rh=34041,lf=35907,Sh=34836,Mu=34842,ih=34843,js=[];js[Kc]=2,js[Rc]=2,js[gs]=2,js[Wf]=2,js[Wh]=1,js[rh]=4,js[lf]=4,js[Sh]=16,js[Mu]=8,js[ih]=6;function Eu(Me,bt,Ot){return js[Me]*bt*Ot}var Dc=function(Me,bt,Ot,Lr,Vr){var Or={rgba4:Kc,rgb565:gs,\"rgb5 a1\":Rc,depth:Wf,stencil:Wh,\"depth 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Qi(){gt(Ni).forEach(function(Wi){Wi.renderbuffer=Me.createRenderbuffer(),Me.bindRenderbuffer(Bl,Wi.renderbuffer),Me.renderbufferStorage(Bl,Wi.format,Wi.width,Wi.height)}),Me.bindRenderbuffer(Bl,null)}return{create:Vn,clear:function(){gt(Ni).forEach(Si)},restore:Qi}},ks=36160,bc=36161,hu=3553,_u=34069,il=36064,nh=36096,Mh=36128,zu=33306,Fc=36053,wc=36193,bd=5121,xf=5126,Pf=6407,qu=6408,bf=[];bf[qu]=4,bf[Pf]=3;var jl=[];jl[bd]=1,jl[xf]=4,jl[wc]=2;function uf(Me,bt,Ot,Lr,Vr,Or){var Nr={cur:null,next:null,dirty:!1,setFBO:null},vi=[\"rgba\"],Ni=[\"rgba4\",\"rgb565\",\"rgb5 a1\"];bt.ext_srgb&&Ni.push(\"srgba\"),bt.ext_color_buffer_half_float&&Ni.push(\"rgba16f\",\"rgb16f\"),bt.webgl_color_buffer_float&&Ni.push(\"rgba32f\");var qi=[\"uint8\"];bt.oes_texture_half_float&&qi.push(\"half float\",\"float16\"),bt.oes_texture_float&&qi.push(\"float\",\"float32\");function Si(Ui,Bi,pn){this.target=Ui,this.texture=Bi,this.renderbuffer=pn;var Nn=0,aa=0;Bi?(Nn=Bi.width,aa=Bi.height):pn&&(Nn=pn.width,aa=pn.height),this.width=Nn,this.height=aa}function Vn(Ui){Ui&&(Ui.texture&&Ui.texture._texture.decRef(),Ui.renderbuffer&&Ui.renderbuffer._renderbuffer.decRef())}function Qi(Ui,Bi,pn){if(Ui)if(Ui.texture){var Nn=Ui.texture._texture,aa=Math.max(1,Nn.width),Ki=Math.max(1,Nn.height);Nn.refCount+=1}else{var Cn=Ui.renderbuffer._renderbuffer;Cn.refCount+=1}}function Wi(Ui,Bi){Bi&&(Bi.texture?Me.framebufferTexture2D(ks,Ui,Bi.target,Bi.texture._texture.texture,0):Me.framebufferRenderbuffer(ks,Ui,bc,Bi.renderbuffer._renderbuffer.renderbuffer))}function oi(Ui){var Bi=hu,pn=null,Nn=null,aa=Ui;typeof Ui==\"object\"&&(aa=Ui.data,\"target\"in Ui&&(Bi=Ui.target|0));var Ki=aa._reglType;return Ki===\"texture2d\"||Ki===\"textureCube\"?pn=aa:Ki===\"renderbuffer\"&&(Nn=aa,Bi=bc),new Si(Bi,pn,Nn)}function Tr(Ui,Bi,pn,Nn,aa){if(pn){var Ki=Lr.create2D({width:Ui,height:Bi,format:Nn,type:aa});return Ki._texture.refCount=0,new Si(hu,Ki,null)}else{var Cn=Vr.create({width:Ui,height:Bi,format:Nn});return Cn._renderbuffer.refCount=0,new Si(bc,null,Cn)}}function Zr(Ui){return Ui&&(Ui.texture||Ui.renderbuffer)}function yi(Ui,Bi,pn){Ui&&(Ui.texture?Ui.texture.resize(Bi,pn):Ui.renderbuffer&&Ui.renderbuffer.resize(Bi,pn),Ui.width=Bi,Ui.height=pn)}var Ii=0,ui={};function nn(){this.id=Ii++,ui[this.id]=this,this.framebuffer=Me.createFramebuffer(),this.width=0,this.height=0,this.colorAttachments=[],this.depthAttachment=null,this.stencilAttachment=null,this.depthStencilAttachment=null}function Zi(Ui){Ui.colorAttachments.forEach(Vn),Vn(Ui.depthAttachment),Vn(Ui.stencilAttachment),Vn(Ui.depthStencilAttachment)}function qn(Ui){var Bi=Ui.framebuffer;Me.deleteFramebuffer(Bi),Ui.framebuffer=null,Or.framebufferCount--,delete ui[Ui.id]}function fi(Ui){var Bi;Me.bindFramebuffer(ks,Ui.framebuffer);var pn=Ui.colorAttachments;for(Bi=0;Bi<pn.length;++Bi)Wi(il+Bi,pn[Bi]);for(Bi=pn.length;Bi<Ot.maxColorAttachments;++Bi)Me.framebufferTexture2D(ks,il+Bi,hu,null,0);Me.framebufferTexture2D(ks,zu,hu,null,0),Me.framebufferTexture2D(ks,nh,hu,null,0),Me.framebufferTexture2D(ks,Mh,hu,null,0),Wi(nh,Ui.depthAttachment),Wi(Mh,Ui.stencilAttachment),Wi(zu,Ui.depthStencilAttachment);var Nn=Me.checkFramebufferStatus(ks);Me.isContextLost(),Me.bindFramebuffer(ks,Nr.next?Nr.next.framebuffer:null),Nr.cur=Nr.next,Me.getError()}function si(Ui,Bi){var pn=new nn;Or.framebufferCount++;function Nn(Ki,Cn){var ia,sa=0,ba=0,is=!0,Zs=!0,Ga=null,El=!0,zo=\"rgba\",Qs=\"uint8\",nl=1,Ul=null,ss=null,Vs=null,Ys=!1;if(typeof Ki==\"number\")sa=Ki|0,ba=Cn|0||sa;else if(!Ki)sa=ba=1;else{var wa=Ki;if(\"shape\"in wa){var al=wa.shape;sa=al[0],ba=al[1]}else\"radius\"in wa&&(sa=ba=wa.radius),\"width\"in wa&&(sa=wa.width),\"height\"in wa&&(ba=wa.height);(\"color\"in wa||\"colors\"in wa)&&(Ga=wa.color||wa.colors,Array.isArray(Ga)),Ga||(\"colorCount\"in wa&&(nl=wa.colorCount|0),\"colorTexture\"in wa&&(El=!!wa.colorTexture,zo=\"rgba4\"),\"colorType\"in wa&&(Qs=wa.colorType,El||(Qs===\"half float\"||Qs===\"float16\"?zo=\"rgba16f\":(Qs===\"float\"||Qs===\"float32\")&&(zo=\"rgba32f\"))),\"colorFormat\"in wa&&(zo=wa.colorFormat,vi.indexOf(zo)>=0?El=!0:Ni.indexOf(zo)>=0&&(El=!1))),(\"depthTexture\"in wa||\"depthStencilTexture\"in wa)&&(Ys=!!(wa.depthTexture||wa.depthStencilTexture)),\"depth\"in wa&&(typeof wa.depth==\"boolean\"?is=wa.depth:(Ul=wa.depth,Zs=!1)),\"stencil\"in wa&&(typeof wa.stencil==\"boolean\"?Zs=wa.stencil:(ss=wa.stencil,is=!1)),\"depthStencil\"in wa&&(typeof wa.depthStencil==\"boolean\"?is=Zs=wa.depthStencil:(Vs=wa.depthStencil,is=!1,Zs=!1))}var io=null,Y=null,D=null,J=null;if(Array.isArray(Ga))io=Ga.map(oi);else if(Ga)io=[oi(Ga)];else for(io=new Array(nl),ia=0;ia<nl;++ia)io[ia]=Tr(sa,ba,El,zo,Qs);sa=sa||io[0].width,ba=ba||io[0].height,Ul?Y=oi(Ul):is&&!Zs&&(Y=Tr(sa,ba,Ys,\"depth\",\"uint32\")),ss?D=oi(ss):Zs&&!is&&(D=Tr(sa,ba,!1,\"stencil\",\"uint8\")),Vs?J=oi(Vs):!Ul&&!ss&&Zs&&is&&(J=Tr(sa,ba,Ys,\"depth stencil\",\"depth stencil\"));var O=null;for(ia=0;ia<io.length;++ia)if(Qi(io[ia],sa,ba),io[ia]&&io[ia].texture){var K=bf[io[ia].texture._texture.format]*jl[io[ia].texture._texture.type];O===null&&(O=K)}return Qi(Y,sa,ba),Qi(D,sa,ba),Qi(J,sa,ba),Zi(pn),pn.width=sa,pn.height=ba,pn.colorAttachments=io,pn.depthAttachment=Y,pn.stencilAttachment=D,pn.depthStencilAttachment=J,Nn.color=io.map(Zr),Nn.depth=Zr(Y),Nn.stencil=Zr(D),Nn.depthStencil=Zr(J),Nn.width=pn.width,Nn.height=pn.height,fi(pn),Nn}function aa(Ki,Cn){var ia=Math.max(Ki|0,1),sa=Math.max(Cn|0||ia,1);if(ia===pn.width&&sa===pn.height)return Nn;for(var ba=pn.colorAttachments,is=0;is<ba.length;++is)yi(ba[is],ia,sa);return yi(pn.depthAttachment,ia,sa),yi(pn.stencilAttachment,ia,sa),yi(pn.depthStencilAttachment,ia,sa),pn.width=Nn.width=ia,pn.height=Nn.height=sa,fi(pn),Nn}return Nn(Ui,Bi),e(Nn,{resize:aa,_reglType:\"framebuffer\",_framebuffer:pn,destroy:function(){qn(pn),Zi(pn)},use:function(Ki){Nr.setFBO({framebuffer:Nn},Ki)}})}function mn(Ui){var Bi=Array(6);function pn(aa){var Ki,Cn={color:null},ia=0,sa=null,ba=\"rgba\",is=\"uint8\",Zs=1;if(typeof aa==\"number\")ia=aa|0;else if(!aa)ia=1;else{var Ga=aa;if(\"shape\"in Ga){var El=Ga.shape;ia=El[0]}else\"radius\"in Ga&&(ia=Ga.radius|0),\"width\"in Ga?(ia=Ga.width|0,\"height\"in Ga):\"height\"in Ga&&(ia=Ga.height|0);(\"color\"in Ga||\"colors\"in Ga)&&(sa=Ga.color||Ga.colors,Array.isArray(sa)),sa||(\"colorCount\"in Ga&&(Zs=Ga.colorCount|0),\"colorType\"in Ga&&(is=Ga.colorType),\"colorFormat\"in Ga&&(ba=Ga.colorFormat)),\"depth\"in Ga&&(Cn.depth=Ga.depth),\"stencil\"in Ga&&(Cn.stencil=Ga.stencil),\"depthStencil\"in Ga&&(Cn.depthStencil=Ga.depthStencil)}var zo;if(sa)if(Array.isArray(sa))for(zo=[],Ki=0;Ki<sa.length;++Ki)zo[Ki]=sa[Ki];else zo=[sa];else{zo=Array(Zs);var Qs={radius:ia,format:ba,type:is};for(Ki=0;Ki<Zs;++Ki)zo[Ki]=Lr.createCube(Qs)}for(Cn.color=Array(zo.length),Ki=0;Ki<zo.length;++Ki){var nl=zo[Ki];ia=ia||nl.width,Cn.color[Ki]={target:_u,data:zo[Ki]}}for(Ki=0;Ki<6;++Ki){for(var Ul=0;Ul<zo.length;++Ul)Cn.color[Ul].target=_u+Ki;Ki>0&&(Cn.depth=Bi[0].depth,Cn.stencil=Bi[0].stencil,Cn.depthStencil=Bi[0].depthStencil),Bi[Ki]?Bi[Ki](Cn):Bi[Ki]=si(Cn)}return e(pn,{width:ia,height:ia,color:zo})}function Nn(aa){var Ki,Cn=aa|0;if(Cn===pn.width)return pn;var ia=pn.color;for(Ki=0;Ki<ia.length;++Ki)ia[Ki].resize(Cn);for(Ki=0;Ki<6;++Ki)Bi[Ki].resize(Cn);return pn.width=pn.height=Cn,pn}return pn(Ui),e(pn,{faces:Bi,resize:Nn,_reglType:\"framebufferCube\",destroy:function(){Bi.forEach(function(aa){aa.destroy()})}})}function Ji(){Nr.cur=null,Nr.next=null,Nr.dirty=!0,gt(ui).forEach(function(Ui){Ui.framebuffer=Me.createFramebuffer(),fi(Ui)})}return e(Nr,{getFramebuffer:function(Ui){if(typeof Ui==\"function\"&&Ui._reglType===\"framebuffer\"){var Bi=Ui._framebuffer;if(Bi instanceof nn)return Bi}return null},create:si,createCube:mn,clear:function(){gt(ui).forEach(qn)},restore:Ji})}var Xh=5126,If=34962,Cs=34963;function du(){this.state=0,this.x=0,this.y=0,this.z=0,this.w=0,this.buffer=null,this.size=0,this.normalized=!1,this.type=Xh,this.offset=0,this.stride=0,this.divisor=0}function ku(Me,bt,Ot,Lr,Vr,Or,Nr){for(var vi=Ot.maxAttributes,Ni=new Array(vi),qi=0;qi<vi;++qi)Ni[qi]=new du;var Si=0,Vn={},Qi={Record:du,scope:{},state:Ni,currentVAO:null,targetVAO:null,restore:oi()?Zi:function(){},createVAO:qn,getVAO:Zr,destroyBuffer:Wi,setVAO:oi()?yi:Ii,clear:oi()?ui:function(){}};function Wi(fi){for(var si=0;si<Ni.length;++si){var mn=Ni[si];mn.buffer===fi&&(Me.disableVertexAttribArray(si),mn.buffer=null)}}function oi(){return bt.oes_vertex_array_object}function Tr(){return bt.angle_instanced_arrays}function Zr(fi){return typeof fi==\"function\"&&fi._vao?fi._vao:null}function yi(fi){if(fi!==Qi.currentVAO){var si=oi();fi?si.bindVertexArrayOES(fi.vao):si.bindVertexArrayOES(null),Qi.currentVAO=fi}}function Ii(fi){if(fi!==Qi.currentVAO){if(fi)fi.bindAttrs();else{for(var si=Tr(),mn=0;mn<Ni.length;++mn){var Ji=Ni[mn];Ji.buffer?(Me.enableVertexAttribArray(mn),Ji.buffer.bind(),Me.vertexAttribPointer(mn,Ji.size,Ji.type,Ji.normalized,Ji.stride,Ji.offfset),si&&Ji.divisor&&si.vertexAttribDivisorANGLE(mn,Ji.divisor)):(Me.disableVertexAttribArray(mn),Me.vertexAttrib4f(mn,Ji.x,Ji.y,Ji.z,Ji.w))}Nr.elements?Me.bindBuffer(Cs,Nr.elements.buffer.buffer):Me.bindBuffer(Cs,null)}Qi.currentVAO=fi}}function ui(){gt(Vn).forEach(function(fi){fi.destroy()})}function nn(){this.id=++Si,this.attributes=[],this.elements=null,this.ownsElements=!1,this.count=0,this.offset=0,this.instances=-1,this.primitive=4;var fi=oi();fi?this.vao=fi.createVertexArrayOES():this.vao=null,Vn[this.id]=this,this.buffers=[]}nn.prototype.bindAttrs=function(){for(var fi=Tr(),si=this.attributes,mn=0;mn<si.length;++mn){var Ji=si[mn];Ji.buffer?(Me.enableVertexAttribArray(mn),Me.bindBuffer(If,Ji.buffer.buffer),Me.vertexAttribPointer(mn,Ji.size,Ji.type,Ji.normalized,Ji.stride,Ji.offset),fi&&Ji.divisor&&fi.vertexAttribDivisorANGLE(mn,Ji.divisor)):(Me.disableVertexAttribArray(mn),Me.vertexAttrib4f(mn,Ji.x,Ji.y,Ji.z,Ji.w))}for(var Ui=si.length;Ui<vi;++Ui)Me.disableVertexAttribArray(Ui);var Bi=Or.getElements(this.elements);Bi?Me.bindBuffer(Cs,Bi.buffer.buffer):Me.bindBuffer(Cs,null)},nn.prototype.refresh=function(){var fi=oi();fi&&(fi.bindVertexArrayOES(this.vao),this.bindAttrs(),Qi.currentVAO=null,fi.bindVertexArrayOES(null))},nn.prototype.destroy=function(){if(this.vao){var fi=oi();this===Qi.currentVAO&&(Qi.currentVAO=null,fi.bindVertexArrayOES(null)),fi.deleteVertexArrayOES(this.vao),this.vao=null}this.ownsElements&&(this.elements.destroy(),this.elements=null,this.ownsElements=!1),Vn[this.id]&&(delete Vn[this.id],Lr.vaoCount-=1)};function Zi(){var fi=oi();fi&&gt(Vn).forEach(function(si){si.refresh()})}function qn(fi){var si=new nn;Lr.vaoCount+=1;function mn(Ji){var Ui;if(Array.isArray(Ji))Ui=Ji,si.elements&&si.ownsElements&&si.elements.destroy(),si.elements=null,si.ownsElements=!1,si.offset=0,si.count=0,si.instances=-1,si.primitive=4;else{if(Ji.elements){var Bi=Ji.elements;si.ownsElements?typeof Bi==\"function\"&&Bi._reglType===\"elements\"?(si.elements.destroy(),si.ownsElements=!1):(si.elements(Bi),si.ownsElements=!1):Or.getElements(Ji.elements)?(si.elements=Ji.elements,si.ownsElements=!1):(si.elements=Or.create(Ji.elements),si.ownsElements=!0)}else 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Wu(Me,bt,Ot,Lr){var Vr={},Or={};function Nr(Tr,Zr,yi,Ii){this.name=Tr,this.id=Zr,this.location=yi,this.info=Ii}function vi(Tr,Zr){for(var yi=0;yi<Tr.length;++yi)if(Tr[yi].id===Zr.id){Tr[yi].location=Zr.location;return}Tr.push(Zr)}function Ni(Tr,Zr,yi){var Ii=Tr===Xf?Vr:Or,ui=Ii[Zr];if(!ui){var nn=bt.str(Zr);ui=Me.createShader(Tr),Me.shaderSource(ui,nn),Me.compileShader(ui),Ii[Zr]=ui}return ui}var qi={},Si=[],Vn=0;function Qi(Tr,Zr){this.id=Vn++,this.fragId=Tr,this.vertId=Zr,this.program=null,this.uniforms=[],this.attributes=[],this.refCount=1,Lr.profile&&(this.stats={uniformsCount:0,attributesCount:0})}function Wi(Tr,Zr,yi){var Ii,ui,nn=Ni(Xf,Tr.fragId),Zi=Ni(Us,Tr.vertId),qn=Tr.program=Me.createProgram();if(Me.attachShader(qn,nn),Me.attachShader(qn,Zi),yi)for(Ii=0;Ii<yi.length;++Ii){var fi=yi[Ii];Me.bindAttribLocation(qn,fi[0],fi[1])}Me.linkProgram(qn);var si=Me.getProgramParameter(qn,wf);Lr.profile&&(Tr.stats.uniformsCount=si);var mn=Tr.uniforms;for(Ii=0;Ii<si;++Ii)if(ui=Me.getActiveUniform(qn,Ii),ui)if(ui.size>1)for(var Ji=0;Ji<ui.size;++Ji){var Ui=ui.name.replace(\"[0]\",\"[\"+Ji+\"]\");vi(mn,new Nr(Ui,bt.id(Ui),Me.getUniformLocation(qn,Ui),ui))}else vi(mn,new Nr(ui.name,bt.id(ui.name),Me.getUniformLocation(qn,ui.name),ui));var Bi=Me.getProgramParameter(qn,zc);Lr.profile&&(Tr.stats.attributesCount=Bi);var pn=Tr.attributes;for(Ii=0;Ii<Bi;++Ii)ui=Me.getActiveAttrib(qn,Ii),ui&&vi(pn,new Nr(ui.name,bt.id(ui.name),Me.getAttribLocation(qn,ui.name),ui))}Lr.profile&&(Ot.getMaxUniformsCount=function(){var Tr=0;return Si.forEach(function(Zr){Zr.stats.uniformsCount>Tr&&(Tr=Zr.stats.uniformsCount)}),Tr},Ot.getMaxAttributesCount=function(){var Tr=0;return Si.forEach(function(Zr){Zr.stats.attributesCount>Tr&&(Tr=Zr.stats.attributesCount)}),Tr});function oi(){Vr={},Or={};for(var Tr=0;Tr<Si.length;++Tr)Wi(Si[Tr],null,Si[Tr].attributes.map(function(Zr){return[Zr.location,Zr.name]}))}return{clear:function(){var Tr=Me.deleteShader.bind(Me);gt(Vr).forEach(Tr),Vr={},gt(Or).forEach(Tr),Or={},Si.forEach(function(Zr){Me.deleteProgram(Zr.program)}),Si.length=0,qi={},Ot.shaderCount=0},program:function(Tr,Zr,yi,Ii){var ui=qi[Zr];ui||(ui=qi[Zr]={});var nn=ui[Tr];if(nn&&(nn.refCount++,!Ii))return nn;var Zi=new Qi(Zr,Tr);return Ot.shaderCount++,Wi(Zi,yi,Ii),nn||(ui[Tr]=Zi),Si.push(Zi),e(Zi,{destroy:function(){if(Zi.refCount--,Zi.refCount<=0){Me.deleteProgram(Zi.program);var qn=Si.indexOf(Zi);Si.splice(qn,1),Ot.shaderCount--}ui[Zi.vertId].refCount<=0&&(Me.deleteShader(Or[Zi.vertId]),delete Or[Zi.vertId],delete qi[Zi.fragId][Zi.vertId]),Object.keys(qi[Zi.fragId]).length||(Me.deleteShader(Vr[Zi.fragId]),delete Vr[Zi.fragId],delete qi[Zi.fragId])}})},restore:oi,shader:Ni,frag:-1,vert:-1}}var Rf=6408,Xu=5121,cf=3333,Zf=5126;function Wl(Me,bt,Ot,Lr,Vr,Or,Nr){function vi(Si){var Vn;bt.next===null?Vn=Xu:Vn=bt.next.colorAttachments[0].texture._texture.type;var 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bt=ah?\"0123456789ABCDEF\":\"0123456789abcdef\",Ot=\"\",Lr,Vr=0;Vr<Me.length;Vr++)Lr=Me.charCodeAt(Vr),Ot+=bt.charAt(Lr>>>4&15)+bt.charAt(Lr&15);return Ot}function Oc(Me){for(var bt=\"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/\",Ot=\"\",Lr=Me.length,Vr=0;Vr<Lr;Vr+=3)for(var Or=Me.charCodeAt(Vr)<<16|(Vr+1<Lr?Me.charCodeAt(Vr+1)<<8:0)|(Vr+2<Lr?Me.charCodeAt(Vr+2):0),Nr=0;Nr<4;Nr++)Vr*8+Nr*6>Me.length*8?Ot+=Zu:Ot+=bt.charAt(Or>>>6*(3-Nr)&63);return Ot}function ff(Me,bt){var Ot=bt.length,Lr=Array(),Vr,Or,Nr,vi,Ni=Array(Math.ceil(Me.length/2));for(Vr=0;Vr<Ni.length;Vr++)Ni[Vr]=Me.charCodeAt(Vr*2)<<8|Me.charCodeAt(Vr*2+1);for(;Ni.length>0;){for(vi=Array(),Nr=0,Vr=0;Vr<Ni.length;Vr++)Nr=(Nr<<16)+Ni[Vr],Or=Math.floor(Nr/Ot),Nr-=Or*Ot,(vi.length>0||Or>0)&&(vi[vi.length]=Or);Lr[Lr.length]=Nr,Ni=vi}var qi=\"\";for(Vr=Lr.length-1;Vr>=0;Vr--)qi+=bt.charAt(Lr[Vr]);var Si=Math.ceil(Me.length*8/(Math.log(bt.length)/Math.log(2)));for(Vr=qi.length;Vr<Si;Vr++)qi=bt[0]+qi;return qi}function fc(Me){for(var bt=\"\",Ot=-1,Lr,Vr;++Ot<Me.length;)Lr=Me.charCodeAt(Ot),Vr=Ot+1<Me.length?Me.charCodeAt(Ot+1):0,55296<=Lr&&Lr<=56319&&56320<=Vr&&Vr<=57343&&(Lr=65536+((Lr&1023)<<10)+(Vr&1023),Ot++),Lr<=127?bt+=String.fromCharCode(Lr):Lr<=2047?bt+=String.fromCharCode(192|Lr>>>6&31,128|Lr&63):Lr<=65535?bt+=String.fromCharCode(224|Lr>>>12&15,128|Lr>>>6&63,128|Lr&63):Lr<=2097151&&(bt+=String.fromCharCode(240|Lr>>>18&7,128|Lr>>>12&63,128|Lr>>>6&63,128|Lr&63));return bt}function Bc(Me){for(var bt=Array(Me.length>>2),Ot=0;Ot<bt.length;Ot++)bt[Ot]=0;for(var Ot=0;Ot<Me.length*8;Ot+=8)bt[Ot>>5]|=(Me.charCodeAt(Ot/8)&255)<<24-Ot%32;return bt}function kt(Me){for(var bt=\"\",Ot=0;Ot<Me.length*32;Ot+=8)bt+=String.fromCharCode(Me[Ot>>5]>>>24-Ot%32&255);return bt}function Zt(Me,bt){return Me>>>bt|Me<<32-bt}function Er(Me,bt){return Me>>>bt}function xr(Me,bt,Ot){return Me&bt^~Me&Ot}function Kr(Me,bt,Ot){return Me&bt^Me&Ot^bt&Ot}function Mi(Me){return Zt(Me,2)^Zt(Me,13)^Zt(Me,22)}function 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lr.def(ci+\")\")}),{frag:Ut,vert:zt,progVar:Dt,program:_t}}function Ga(Ne,$e){var st=Ne.static,It=Ne.dynamic,Bt={},Ft=!1;function Ut(){if(ve in st){var tn=st[ve];return tn!==null&&qi.getVAO(tn)===null&&(tn=qi.createVAO(tn)),Ft=!0,Bt.vao=tn,kn(function(Oi){var Gi=qi.getVAO(tn);return Gi?Oi.link(Gi):\"null\"})}else if(ve in It){Ft=!0;var Ei=It[ve];return Bn(Ei,function(Oi,Gi){var on=Oi.invoke(Gi,Ei);return Gi.def(Oi.shared.vao+\".getVAO(\"+on+\")\")})}return null}var zt=Ut(),_t=!1;function Dt(){if(Mt in st){var tn=st[Mt];if(Bt.elements=tn,Li(tn)){var Ei=Bt.elements=Or.create(tn,!0);tn=Or.getElements(Ei),_t=!0}else tn&&(tn=Or.getElements(tn),_t=!0);var Oi=kn(function(on,zn){if(tn){var Ja=on.link(tn);return on.ELEMENTS=Ja,Ja}return on.ELEMENTS=null,null});return Oi.value=tn,Oi}else if(Mt in It){_t=!0;var Gi=It[Mt];return Bn(Gi,function(on,zn){var Ja=on.shared,co=Ja.isBufferArgs,ts=Ja.elements,so=on.invoke(zn,Gi),Yo=zn.def(\"null\"),ms=zn.def(co,\"(\",so,\")\"),ou=on.cond(ms).then(Yo,\"=\",ts,\".createStream(\",so,\");\").else(Yo,\"=\",ts,\".getElements(\",so,\");\");return zn.entry(ou),zn.exit(on.cond(ms).then(ts,\".destroyStream(\",Yo,\");\")),on.ELEMENTS=Yo,Yo})}else if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(on,zn){return zn.def(on.shared.vao+\".currentVAO?\"+on.shared.elements+\".getElements(\"+on.shared.vao+\".currentVAO.elements):null\")});return null}var vt=Dt();function lr(){if(Jt in st){var tn=st[Jt];return Bt.primitive=tn,kn(function(Oi,Gi){return Mn[tn]})}else if(Jt in It){var Ei=It[Jt];return Bn(Ei,function(Oi,Gi){var on=Oi.constants.primTypes,zn=Oi.invoke(Gi,Ei);return Gi.def(on,\"[\",zn,\"]\")})}else{if(_t)return Pn(vt)?vt.value?kn(function(Oi,Gi){return Gi.def(Oi.ELEMENTS,\".primType\")}):kn(function(){return Sl}):new Qr(vt.thisDep,vt.contextDep,vt.propDep,function(Oi,Gi){var on=Oi.ELEMENTS;return Gi.def(on,\"?\",on,\".primType:\",Sl)});if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(Oi,Gi){return Gi.def(Oi.shared.vao+\".currentVAO?\"+Oi.shared.vao+\".currentVAO.primitive:\"+Sl)})}return null}function _r(tn,Ei){if(tn in st){var Oi=st[tn]|0;return Ei?Bt.offset=Oi:Bt.instances=Oi,kn(function(on,zn){return Ei&&(on.OFFSET=Oi),Oi})}else if(tn in It){var Gi=It[tn];return Bn(Gi,function(on,zn){var Ja=on.invoke(zn,Gi);return Ei&&(on.OFFSET=Ja),Ja})}else if(Ei){if(_t)return kn(function(on,zn){return on.OFFSET=0,0});if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(on,zn){return zn.def(on.shared.vao+\".currentVAO?\"+on.shared.vao+\".currentVAO.offset:0\")})}else if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(on,zn){return zn.def(on.shared.vao+\".currentVAO?\"+on.shared.vao+\".currentVAO.instances:-1\")});return null}var Br=_r(yr,!0);function Di(){if($t in st){var tn=st[$t]|0;return Bt.count=tn,kn(function(){return tn})}else if($t in It){var Ei=It[$t];return Bn(Ei,function(zn,Ja){var co=zn.invoke(Ja,Ei);return co})}else if(_t)if(Pn(vt)){if(vt)return Br?new Qr(Br.thisDep,Br.contextDep,Br.propDep,function(zn,Ja){var co=Ja.def(zn.ELEMENTS,\".vertCount-\",zn.OFFSET);return co}):kn(function(zn,Ja){return Ja.def(zn.ELEMENTS,\".vertCount\")});var Oi=kn(function(){return-1});return Oi}else{var Gi=new Qr(vt.thisDep||Br.thisDep,vt.contextDep||Br.contextDep,vt.propDep||Br.propDep,function(zn,Ja){var co=zn.ELEMENTS;return zn.OFFSET?Ja.def(co,\"?\",co,\".vertCount-\",zn.OFFSET,\":-1\"):Ja.def(co,\"?\",co,\".vertCount:-1\")});return Gi}else if(Ft){var on=new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(zn,Ja){return Ja.def(zn.shared.vao,\".currentVAO?\",zn.shared.vao,\".currentVAO.count:-1\")});return on}return null}var ci=lr(),bi=Di(),gn=_r(Cr,!1);return{elements:vt,primitive:ci,count:bi,instances:gn,offset:Br,vao:zt,vaoActive:Ft,elementsActive:_t,static:Bt}}function El(Ne,$e){var st=Ne.static,It=Ne.dynamic,Bt={};return fi.forEach(function(Ft){var Ut=Ji(Ft);function zt(_t,Dt){if(Ft in st){var vt=_t(st[Ft]);Bt[Ut]=kn(function(){return vt})}else if(Ft in It){var lr=It[Ft];Bt[Ut]=Bn(lr,function(_r,Br){return Dt(_r,Br,_r.invoke(Br,lr))})}}switch(Ft){case Cu:case Ho:case ws:case Vc:case va:case B:case Uc:case Ac:case Va:case rs:return zt(function(_t){return _t},function(_t,Dt,vt){return vt});case no:return zt(function(_t){return Wr[_t]},function(_t,Dt,vt){var lr=_t.constants.compareFuncs;return Dt.def(lr,\"[\",vt,\"]\")});case ys:return zt(function(_t){return _t},function(_t,Dt,vt){var lr=Dt.def(\"+\",vt,\"[0]\"),_r=Dt.def(\"+\",vt,\"[1]\");return[lr,_r]});case Ls:return zt(function(_t){var Dt=\"srcRGB\"in _t?_t.srcRGB:_t.src,vt=\"srcAlpha\"in _t?_t.srcAlpha:_t.src,lr=\"dstRGB\"in _t?_t.dstRGB:_t.dst,_r=\"dstAlpha\"in _t?_t.dstAlpha:_t.dst;return[xi[Dt],xi[lr],xi[vt],xi[_r]]},function(_t,Dt,vt){var lr=_t.constants.blendFuncs;function _r(Ei,Oi){var 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Dt=_t.cmp||\"keep\",vt=_t.ref||0,lr=\"mask\"in _t?_t.mask:-1;return[Wr[Dt],vt,lr]},function(_t,Dt,vt){var lr=_t.constants.compareFuncs,_r=Dt.def('\"cmp\" in ',vt,\"?\",lr,\"[\",vt,\".cmp]\",\":\",ot),Br=Dt.def(vt,\".ref|0\"),Di=Dt.def('\"mask\" in ',vt,\"?\",vt,\".mask|0:-1\");return[_r,Br,Di]});case ue:case w:return zt(function(_t){var Dt=_t.fail||\"keep\",vt=_t.zfail||\"keep\",lr=_t.zpass||\"keep\";return[Ft===w?se:X,ii[Dt],ii[vt],ii[lr]]},function(_t,Dt,vt){var lr=_t.constants.stencilOps;function _r(Br){return Dt.def('\"',Br,'\" in ',vt,\"?\",lr,\"[\",vt,\".\",Br,\"]:\",ot)}return[Ft===w?se:X,_r(\"fail\"),_r(\"zfail\"),_r(\"zpass\")]});case xu:return zt(function(_t){var Dt=_t.factor|0,vt=_t.units|0;return[Dt,vt]},function(_t,Dt,vt){var lr=Dt.def(vt,\".factor|0\"),_r=Dt.def(vt,\".units|0\");return[lr,_r]});case Yu:return zt(function(_t){var Dt=0;return _t===\"front\"?Dt=X:_t===\"back\"&&(Dt=se),Dt},function(_t,Dt,vt){return Dt.def(vt,'===\"front\"?',X,\":\",se)});case pu:return 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Iz,xc=function(t){yVt(t)?(t={regl:t},this.gl=t.regl._gl):this.gl=oVt(t),this.shader=UBe.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||aVt({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),UBe.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(cVt(t)?t:{})};xc.prototype.createShader=function(){var t=this.regl,r=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(i,a){return[a.atlas.width,a.atlas.height]},atlasDim:function(i,a){return[a.atlas.cols,a.atlas.rows]},atlas:function(i,a){return a.atlas.texture},charStep:function(i,a){return a.atlas.step},em:function(i,a){return a.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:`\n",
+       "\t\t\tprecision highp float;\n",
+       "\t\t\tattribute float width, charOffset, char;\n",
+       "\t\t\tattribute vec2 position;\n",
+       "\t\t\tuniform float fontSize, charStep, em, align, baseline;\n",
+       "\t\t\tuniform vec4 viewport;\n",
+       "\t\t\tuniform vec4 color;\n",
+       "\t\t\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\n",
+       "\t\t\tvarying vec2 charCoord, charId;\n",
+       "\t\t\tvarying float charWidth;\n",
+       "\t\t\tvarying vec4 fontColor;\n",
+       "\t\t\tvoid main () {\n",
+       "\t\t\t\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\n",
+       "\t\t\t\t\t+ vec2(positionOffset.x, -positionOffset.y)))\n",
+       "\t\t\t\t\t/ (viewport.zw * scale.xy);\n",
+       "\n",
+       "\t\t\t\tvec2 position = (position + translate) * scale;\n",
+       "\t\t\t\tposition += offset * scale;\n",
+       "\n",
+       "\t\t\t\tcharCoord = position * viewport.zw + viewport.xy;\n",
+       "\n",
+       "\t\t\t\tgl_Position = vec4(position * 2. - 1., 0, 1);\n",
+       "\n",
+       "\t\t\t\tgl_PointSize = charStep;\n",
+       "\n",
+       "\t\t\t\tcharId.x = mod(char, atlasDim.x);\n",
+       "\t\t\t\tcharId.y = floor(char / atlasDim.x);\n",
+       "\n",
+       "\t\t\t\tcharWidth = width * em;\n",
+       "\n",
+       "\t\t\t\tfontColor = color / 255.;\n",
+       "\t\t\t}`,frag:`\n",
+       "\t\t\tprecision highp float;\n",
+       "\t\t\tuniform float fontSize, charStep, opacity;\n",
+       "\t\t\tuniform vec2 atlasSize;\n",
+       "\t\t\tuniform vec4 viewport;\n",
+       "\t\t\tuniform sampler2D atlas;\n",
+       "\t\t\tvarying vec4 fontColor;\n",
+       "\t\t\tvarying vec2 charCoord, charId;\n",
+       "\t\t\tvarying float charWidth;\n",
+       "\n",
+       "\t\t\tfloat lightness(vec4 color) {\n",
+       "\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n",
+       "\t\t\t}\n",
+       "\n",
+       "\t\t\tvoid main () {\n",
+       "\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n",
+       "\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n",
+       "\n",
+       "\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n",
+       "\t\t\t\tuv.y = charStep - uv.y;\n",
+       "\n",
+       "\t\t\t\t// ignore points outside of character bounding box\n",
+       "\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n",
+       "\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n",
+       "\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n",
+       "\n",
+       "\t\t\t\tuv += charId * charStep;\n",
+       "\t\t\t\tuv = uv / atlasSize;\n",
+       "\n",
+       "\t\t\t\tvec4 color = fontColor;\n",
+       "\t\t\t\tvec4 mask = texture2D(atlas, uv);\n",
+       "\n",
+       "\t\t\t\tfloat maskY = lightness(mask);\n",
+       "\t\t\t\t// float colorY = lightness(color);\n",
+       "\t\t\t\tcolor.a *= maskY;\n",
+       "\t\t\t\tcolor.a *= opacity;\n",
+       "\n",
+       "\t\t\t\t// color.a += .1;\n",
+       "\n",
+       "\t\t\t\t// antialiasing, see yiq color space y-channel formula\n",
+       "\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n",
+       "\n",
+       "\t\t\t\tgl_FragColor = color;\n",
+       "\t\t\t}`}),n={};return{regl:t,draw:r,atlas:n}};xc.prototype.update=function(t){var r=this;if(typeof t==\"string\")t={text:t};else if(!t)return;t=nVt(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0),t.opacity!=null&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map(function(Ce){return parseFloat(Ce)}):this.opacity=parseFloat(t.opacity)),t.viewport!=null&&(this.viewport=uVt(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),this.viewport==null&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),t.kerning!=null&&(this.kerning=t.kerning),t.offset!=null&&(typeof t.offset==\"number\"&&(t.offset=[t.offset,0]),this.positionOffset=gVt(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),!this.font.length&&!t.font&&(t.font=xc.baseFontSize+\"px sans-serif\");var n=!1,i=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach(function(Ce,ge){if(typeof Ce==\"string\")try{Ce=w5.parse(Ce)}catch(He){Ce=w5.parse(xc.baseFontSize+\"px \"+Ce)}else{var ie=Ce.style,Se=Ce.weight,ke=Ce.stretch,be=Ce.variant;Ce=w5.parse(w5.stringify(Ce)),ie&&(Ce.style=ie),Se&&(Ce.weight=Se),ke&&(Ce.stretch=ke),be&&(Ce.variant=be)}var Be=w5.stringify({size:xc.baseFontSize,family:Ce.family,stretch:Rz?Ce.stretch:void 0,variant:Ce.variant,weight:Ce.weight,style:Ce.style}),Le=fVt(Ce.size),me=Math.round(Le[0]*hVt(Le[1]));if(me!==r.fontSize[ge]&&(i=!0,r.fontSize[ge]=me),(!r.font[ge]||Be!=r.font[ge].baseString)&&(n=!0,r.font[ge]=xc.fonts[Be],!r.font[ge])){var Pe=Ce.family.join(\", \"),ce=[Ce.style];Ce.style!=Ce.variant&&ce.push(Ce.variant),Ce.variant!=Ce.weight&&ce.push(Ce.weight),Rz&&Ce.weight!=Ce.stretch&&ce.push(Ce.stretch),r.font[ge]={baseString:Be,family:Pe,weight:Ce.weight,stretch:Ce.stretch,style:Ce.style,variant:Ce.variant,width:{},kerning:{},metrics:pVt(Pe,{origin:\"top\",fontSize:xc.baseFontSize,fontStyle:ce.join(\" \")})},xc.fonts[Be]=r.font[ge]}}),(n||i)&&this.font.forEach(function(Ce,ge){var ie=w5.stringify({size:r.fontSize[ge],family:Ce.family,stretch:Rz?Ce.stretch:void 0,variant:Ce.variant,weight:Ce.weight,style:Ce.style});if(r.fontAtlas[ge]=r.shader.atlas[ie],!r.fontAtlas[ge]){var Se=Ce.metrics;r.shader.atlas[ie]=r.fontAtlas[ge]={fontString:ie,step:Math.ceil(r.fontSize[ge]*Se.bottom*.5)*2,em:r.fontSize[ge],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:r.regl.texture()}}t.text==null&&(t.text=r.text)}),typeof t.text==\"string\"&&t.position&&t.position.length>2){for(var a=Array(t.position.length*.5),o=0;o<a.length;o++)a[o]=t.text;t.text=a}var s;if(t.text!=null||n){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var l=1;l<t.text.length;l++)this.textOffsets[l]=this.textOffsets[l-1]+t.text[l-1].length,this.count+=t.text[l].length,this.counts.push(t.text[l].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];s=[],this.font.forEach(function(Ce,ge){xc.atlasContext.font=Ce.baseString;for(var ie=r.fontAtlas[ge],Se=0;Se<r.text.length;Se++){var ke=r.text.charAt(Se);if(ie.ids[ke]==null&&(ie.ids[ke]=ie.chars.length,ie.chars.push(ke),s.push(ke)),Ce.width[ke]==null&&(Ce.width[ke]=xc.atlasContext.measureText(ke).width/xc.baseFontSize,r.kerning)){var be=[];for(var Be in Ce.width)be.push(Be+ke,ke+Be);vVt(Ce.kerning,dVt(Ce.family,{pairs:be}))}}})}if(t.position)if(t.position.length>2){for(var u=!t.position[0].length,c=Tx.mallocFloat(this.count*2),f=0,h=0;f<this.counts.length;f++){var d=this.counts[f];if(u)for(var v=0;v<d;v++)c[h++]=t.position[f*2],c[h++]=t.position[f*2+1];else for(var m=0;m<d;m++)c[h++]=t.position[f][0],c[h++]=t.position[f][1]}this.position.call?this.position({type:\"float\",data:c}):this.position=this.regl.buffer({type:\"float\",data:c}),Tx.freeFloat(c)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||n){var b=Tx.mallocUint8(this.count),p=Tx.mallocFloat(this.count*2);this.textWidth=[];for(var k=0,M=0;k<this.counts.length;k++){for(var T=this.counts[k],L=this.font[k]||this.font[0],x=this.fontAtlas[k]||this.fontAtlas[0],C=0;C<T;C++){var S=this.text.charAt(M),g=this.text.charAt(M-1);if(b[M]=x.ids[S],p[M*2]=L.width[S],C){var P=p[M*2-2],E=p[M*2],z=p[M*2-1],q=z+P*.5+E*.5;if(this.kerning){var V=L.kerning[g+S];V&&(q+=V*.001)}p[M*2+1]=q}else p[M*2+1]=p[M*2]*.5;M++}this.textWidth.push(p.length?p[M*2-2]*.5+p[M*2-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:b,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:p,type:\"float\",usage:\"stream\"}),Tx.freeUint8(b),Tx.freeFloat(p),s.length&&this.font.forEach(function(Ce,ge){var ie=r.fontAtlas[ge],Se=ie.step,ke=Math.floor(xc.maxAtlasSize/Se),be=Math.min(ke,ie.chars.length),Be=Math.ceil(ie.chars.length/be),Le=NBe(be*Se),me=NBe(Be*Se);ie.width=Le,ie.height=me,ie.rows=Be,ie.cols=be,ie.em&&ie.texture({data:lVt({canvas:xc.atlasCanvas,font:ie.fontString,chars:ie.chars,shape:[Le,me],step:[Se,Se]})})})}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map(function(Ce,ge){var ie=Array.isArray(r.align)?r.align.length>1?r.align[ge]:r.align[0]:r.align;if(typeof ie==\"number\")return ie;switch(ie){case\"right\":case\"end\":return-Ce;case\"center\":case\"centre\":case\"middle\":return-Ce*.5}return 0})),this.baseline==null&&t.baseline==null&&(t.baseline=0),t.baseline!=null&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map(function(Ce,ge){var ie=(r.font[ge]||r.font[0]).metrics,Se=0;return Se+=ie.bottom*.5,typeof Ce==\"number\"?Se+=Ce-ie.baseline:Se+=-ie[Ce],Se*=-1,Se})),t.color!=null)if(t.color||(t.color=\"transparent\"),typeof t.color==\"string\"||!isNaN(t.color))this.color=_K(t.color,\"uint8\");else{var G;if(typeof t.color[0]==\"number\"&&t.color.length>this.counts.length){var Z=t.color.length;G=Tx.mallocUint8(Z);for(var j=(t.color.subarray||t.color.slice).bind(t.color),N=0;N<Z;N+=4)G.set(_K(j(N,N+4),\"uint8\"),N)}else{var H=t.color.length;G=Tx.mallocUint8(H*4);for(var re=0;re<H;re++)G.set(_K(t.color[re]||0,\"uint8\"),re*4)}this.color=G}if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity){var ae=this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2;if(ae){var _e=Math.max(this.position.length*.5||0,this.color.length*.25||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,this.positionOffset.length*.5||0);this.batch=Array(_e);for(var Ee=0;Ee<this.batch.length;Ee++)this.batch[Ee]={count:this.counts.length>1?this.counts[Ee]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[Ee]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(Ee*4,Ee*4+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[Ee]:this.opacity,baseline:this.baselineOffset[Ee]!=null?this.baselineOffset[Ee]:this.baselineOffset[0],align:this.align?this.alignOffset[Ee]!=null?this.alignOffset[Ee]:this.alignOffset[0]:0,atlas:this.fontAtlas[Ee]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(Ee*2,Ee*2+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]}};xc.prototype.destroy=function(){};xc.prototype.kerning=!0;xc.prototype.position={constant:new Float32Array(2)};xc.prototype.translate=null;xc.prototype.scale=null;xc.prototype.font=null;xc.prototype.text=\"\";xc.prototype.positionOffset=[0,0];xc.prototype.opacity=1;xc.prototype.color=new Uint8Array([0,0,0,255]);xc.prototype.alignOffset=[0,0];xc.maxAtlasSize=1024;xc.atlasCanvas=document.createElement(\"canvas\");xc.atlasContext=xc.atlasCanvas.getContext(\"2d\",{alpha:!1});xc.baseFontSize=64;xc.fonts={};function yVt(e){return typeof e==\"function\"&&e._gl&&e.prop&&e.texture&&e.buffer}VBe.exports=xc});var HBe=ye((xK,bK)=>{(function(e,t){typeof xK==\"object\"&&typeof 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si(ne,we){ne.internalformat=we.internalformat,ne.format=we.format,ne.type=we.type,ne.compressed=we.compressed,ne.premultiplyAlpha=we.premultiplyAlpha,ne.flipY=we.flipY,ne.unpackAlignment=we.unpackAlignment,ne.colorSpace=we.colorSpace,ne.width=we.width,ne.height=we.height,ne.channels=we.channels}function mn(ne,we){if(!(typeof we!=\"object\"||!we)){if(\"premultiplyAlpha\"in we&&(ne.premultiplyAlpha=we.premultiplyAlpha),\"flipY\"in we&&(ne.flipY=we.flipY),\"alignment\"in we&&(ne.unpackAlignment=we.alignment),\"colorSpace\"in we&&(ne.colorSpace=Vn[we.colorSpace]),\"type\"in we){var Oe=we.type;ne.type=Qi[Oe]}var ht=ne.width,Yt=ne.height,cr=ne.channels,Nt=!1;\"shape\"in we?(ht=we.shape[0],Yt=we.shape[1],we.shape.length===3&&(cr=we.shape[2],Nt=!0)):(\"radius\"in we&&(ht=Yt=we.radius),\"width\"in we&&(ht=we.width),\"height\"in we&&(Yt=we.height),\"channels\"in we&&(cr=we.channels,Nt=!0)),ne.width=ht|0,ne.height=Yt|0,ne.channels=cr|0;var Ne=!1;if(\"format\"in we){var 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we)&&ne.type===Ns&&(ne.type=Yc(Oe));else if(of(Oe))ne.channels=ne.channels||4,eh(ne,Oe),ne.alignment=1,ne.needsFree=!0;else if(jr(Oe)){var cr=Oe.data;!Array.isArray(cr)&&ne.type===Ns&&(ne.type=Yc(cr));var Nt=Oe.shape,Ne=Oe.stride,$e,st,It,Bt,Ft,Ut;Nt.length===3?(It=Nt[2],Ut=Ne[2]):(It=1,Ut=1),$e=Nt[0],st=Nt[1],Bt=Ne[0],Ft=Ne[1],ne.alignment=1,ne.width=$e,ne.height=st,ne.channels=It,ne.format=ne.internalformat=Hs[It],ne.needsFree=!0,jf(ne,cr,Bt,Ft,Ut,Oe.offset)}else if(Hf(Oe)||Ic(Oe)||yf(Oe))Hf(Oe)||Ic(Oe)?ne.element=Oe:ne.element=Oe.canvas,ne.width=ne.element.width,ne.height=ne.element.height,ne.channels=4;else if(Ol(Oe))ne.element=Oe,ne.width=Oe.width,ne.height=Oe.height,ne.channels=4;else if(Ah(Oe))ne.element=Oe,ne.width=Oe.naturalWidth,ne.height=Oe.naturalHeight,ne.channels=4;else if(Qf(Oe))ne.element=Oe,ne.width=Oe.videoWidth,ne.height=Oe.videoHeight,ne.channels=4;else if(Hu(Oe)){var zt=ne.width||Oe[0].length,_t=ne.height||Oe.length,Dt=ne.channels;En(Oe[0][0])?Dt=Dt||Oe[0][0].length:Dt=Dt||1;for(var vt=Ge.shape(Oe),lr=1,_r=0;_r<vt.length;++_r)lr*=vt[_r];var Br=th(ne,lr);Ge.flatten(Oe,vt,\"\",Br),ju(ne,Br),ne.alignment=1,ne.width=zt,ne.height=_t,ne.channels=Dt,ne.format=ne.internalformat=Hs[Dt],ne.needsFree=!0}ne.type===oa||ne.type}function pn(ne,we,Oe){var ht=ne.element,Yt=ne.data,cr=ne.internalformat,Nt=ne.format,Ne=ne.type,$e=ne.width,st=ne.height;Ji(ne),ht?Me.texImage2D(we,Oe,Nt,Nt,Ne,ht):ne.compressed?Me.compressedTexImage2D(we,Oe,cr,$e,st,0,Yt):ne.needsCopy?(Lr(),Me.copyTexImage2D(we,Oe,Nt,ne.xOffset,ne.yOffset,$e,st,0)):Me.texImage2D(we,Oe,Nt,$e,st,0,Nt,Ne,Yt||null)}function Nn(ne,we,Oe,ht,Yt){var cr=ne.element,Nt=ne.data,Ne=ne.internalformat,$e=ne.format,st=ne.type,It=ne.width,Bt=ne.height;Ji(ne),cr?Me.texSubImage2D(we,Yt,Oe,ht,$e,st,cr):ne.compressed?Me.compressedTexSubImage2D(we,Yt,Oe,ht,Ne,It,Bt,Nt):ne.needsCopy?(Lr(),Me.copyTexSubImage2D(we,Yt,Oe,ht,ne.xOffset,ne.yOffset,It,Bt)):Me.texSubImage2D(we,Yt,Oe,ht,It,Bt,$e,st,Nt)}var aa=[];function Ki(){return aa.pop()||new Ui}function Cn(ne){ne.needsFree&&H.freeType(ne.data),Ui.call(ne),aa.push(ne)}function ia(){fi.call(this),this.genMipmaps=!1,this.mipmapHint=sc,this.mipmask=0,this.images=Array(16)}function sa(ne,we,Oe){var ht=ne.images[0]=Ki();ne.mipmask=1,ht.width=ne.width=we,ht.height=ne.height=Oe,ht.channels=ne.channels=4}function ba(ne,we){var Oe=null;if(_f(we))Oe=ne.images[0]=Ki(),si(Oe,ne),Bi(Oe,we),ne.mipmask=1;else if(mn(ne,we),Array.isArray(we.mipmap))for(var ht=we.mipmap,Yt=0;Yt<ht.length;++Yt)Oe=ne.images[Yt]=Ki(),si(Oe,ne),Oe.width>>=Yt,Oe.height>>=Yt,Bi(Oe,ht[Yt]),ne.mipmask|=1<<Yt;else Oe=ne.images[0]=Ki(),si(Oe,ne),Bi(Oe,we),ne.mipmask=1;si(ne,ne.images[0]),ne.compressed&&(ne.internalformat===Io||ne.internalformat===ls||ne.internalformat===Yl||ne.internalformat)}function is(ne,we){for(var Oe=ne.images,ht=0;ht<Oe.length;++ht){if(!Oe[ht])return;pn(Oe[ht],we,ht)}}var Zs=[];function Ga(){var ne=Zs.pop()||new ia;fi.call(ne),ne.mipmask=0;for(var we=0;we<16;++we)ne.images[we]=null;return ne}function El(ne){for(var we=ne.images,Oe=0;Oe<we.length;++Oe)we[Oe]&&Cn(we[Oe]),we[Oe]=null;Zs.push(ne)}function zo(){this.minFilter=pl,this.magFilter=pl,this.wrapS=Pc,this.wrapT=Pc,this.anisotropic=1,this.genMipmaps=!1,this.mipmapHint=sc}function Qs(ne,we){if(\"min\"in we){var Oe=we.min;ne.minFilter=Si[Oe],Es.indexOf(ne.minFilter)>=0&&!(\"faces\"in we)&&(ne.genMipmaps=!0)}if(\"mag\"in we){var ht=we.mag;ne.magFilter=qi[ht]}var Yt=ne.wrapS,cr=ne.wrapT;if(\"wrap\"in we){var Nt=we.wrap;typeof Nt==\"string\"?Yt=cr=Ni[Nt]:Array.isArray(Nt)&&(Yt=Ni[Nt[0]],cr=Ni[Nt[1]])}else{if(\"wrapS\"in we){var Ne=we.wrapS;Yt=Ni[Ne]}if(\"wrapT\"in we){var $e=we.wrapT;cr=Ni[$e]}}if(ne.wrapS=Yt,ne.wrapT=cr,\"anisotropic\"in we){var st=we.anisotropic;ne.anisotropic=we.anisotropic}if(\"mipmap\"in we){var It=!1;switch(typeof we.mipmap){case\"string\":ne.mipmapHint=vi[we.mipmap],ne.genMipmaps=!0,It=!0;break;case\"boolean\":It=ne.genMipmaps=we.mipmap;break;case\"object\":ne.genMipmaps=!1,It=!0;break;default:}It&&!(\"min\"in we)&&(ne.minFilter=Jl)}}function nl(ne,we){Me.texParameteri(we,Vf,ne.minFilter),Me.texParameteri(we,nf,ne.magFilter),Me.texParameteri(we,qo,ne.wrapS),Me.texParameteri(we,Oo,ne.wrapT),bt.ext_texture_filter_anisotropic&&Me.texParameteri(we,cs,ne.anisotropic),ne.genMipmaps&&(Me.hint(Cf,ne.mipmapHint),Me.generateMipmap(we))}var Ul=0,ss={},Vs=Ot.maxTextureUnits,Ys=Array(Vs).map(function(){return null});function wa(ne){fi.call(this),this.mipmask=0,this.internalformat=Ma,this.id=Ul++,this.refCount=1,this.target=ne,this.texture=Me.createTexture(),this.unit=-1,this.bindCount=0,this.texInfo=new zo,Nr.profile&&(this.stats={size:0})}function al(ne){Me.activeTexture(Fu),Me.bindTexture(ne.target,ne.texture)}function io(){var ne=Ys[0];ne?Me.bindTexture(ne.target,ne.texture):Me.bindTexture(_n,null)}function Y(ne){var we=ne.texture,Oe=ne.unit,ht=ne.target;Oe>=0&&(Me.activeTexture(Fu+Oe),Me.bindTexture(ht,null),Ys[Oe]=null),Me.deleteTexture(we),ne.texture=null,ne.params=null,ne.pixels=null,ne.refCount=0,delete ss[ne.id],Or.textureCount--}e(wa.prototype,{bind:function(){var ne=this;ne.bindCount+=1;var we=ne.unit;if(we<0){for(var Oe=0;Oe<Vs;++Oe){var ht=Ys[Oe];if(ht){if(ht.bindCount>0)continue;ht.unit=-1}Ys[Oe]=ne,we=Oe;break}we>=Vs,Nr.profile&&Or.maxTextureUnits<we+1&&(Or.maxTextureUnits=we+1),ne.unit=we,Me.activeTexture(Fu+we),Me.bindTexture(ne.target,ne.texture)}return we},unbind:function(){this.bindCount-=1},decRef:function(){--this.refCount<=0&&Y(this)}});function D(ne,we){var Oe=new wa(_n);ss[Oe.id]=Oe,Or.textureCount++;function ht(Nt,Ne){var $e=Oe.texInfo;zo.call($e);var st=Ga();return typeof Nt==\"number\"?typeof Ne==\"number\"?sa(st,Nt|0,Ne|0):sa(st,Nt|0,Nt|0):Nt?(Qs($e,Nt),ba(st,Nt)):sa(st,1,1),$e.genMipmaps&&(st.mipmask=(st.width<<1)-1),Oe.mipmask=st.mipmask,si(Oe,st),Oe.internalformat=st.internalformat,ht.width=st.width,ht.height=st.height,al(Oe),is(st,_n),nl($e,_n),io(),El(st),Nr.profile&&(Oe.stats.size=cc(Oe.internalformat,Oe.type,st.width,st.height,$e.genMipmaps,!1)),ht.format=yi[Oe.internalformat],ht.type=Ii[Oe.type],ht.mag=ui[$e.magFilter],ht.min=nn[$e.minFilter],ht.wrapS=Zi[$e.wrapS],ht.wrapT=Zi[$e.wrapT],ht}function Yt(Nt,Ne,$e,st){var It=Ne|0,Bt=$e|0,Ft=st|0,Ut=Ki();return si(Ut,Oe),Ut.width=0,Ut.height=0,Bi(Ut,Nt),Ut.width=Ut.width||(Oe.width>>Ft)-It,Ut.height=Ut.height||(Oe.height>>Ft)-Bt,al(Oe),Nn(Ut,_n,It,Bt,Ft),io(),Cn(Ut),ht}function cr(Nt,Ne){var $e=Nt|0,st=Ne|0||$e;if($e===Oe.width&&st===Oe.height)return ht;ht.width=Oe.width=$e,ht.height=Oe.height=st,al(Oe);for(var It=0;Oe.mipmask>>It;++It){var Bt=$e>>It,Ft=st>>It;if(!Bt||!Ft)break;Me.texImage2D(_n,It,Oe.format,Bt,Ft,0,Oe.format,Oe.type,null)}return io(),Nr.profile&&(Oe.stats.size=cc(Oe.internalformat,Oe.type,$e,st,!1,!1)),ht}return ht(ne,we),ht.subimage=Yt,ht.resize=cr,ht._reglType=\"texture2d\",ht._texture=Oe,Nr.profile&&(ht.stats=Oe.stats),ht.destroy=function(){Oe.decRef()},ht}function J(ne,we,Oe,ht,Yt,cr){var Nt=new wa(ya);ss[Nt.id]=Nt,Or.cubeCount++;var Ne=new Array(6);function $e(Bt,Ft,Ut,zt,_t,Dt){var vt,lr=Nt.texInfo;for(zo.call(lr),vt=0;vt<6;++vt)Ne[vt]=Ga();if(typeof Bt==\"number\"||!Bt){var _r=Bt|0||1;for(vt=0;vt<6;++vt)sa(Ne[vt],_r,_r)}else if(typeof Bt==\"object\")if(Ft)ba(Ne[0],Bt),ba(Ne[1],Ft),ba(Ne[2],Ut),ba(Ne[3],zt),ba(Ne[4],_t),ba(Ne[5],Dt);else if(Qs(lr,Bt),mn(Nt,Bt),\"faces\"in Bt){var Br=Bt.faces;for(vt=0;vt<6;++vt)si(Ne[vt],Nt),ba(Ne[vt],Br[vt])}else for(vt=0;vt<6;++vt)ba(Ne[vt],Bt);for(si(Nt,Ne[0]),lr.genMipmaps?Nt.mipmask=(Ne[0].width<<1)-1:Nt.mipmask=Ne[0].mipmask,Nt.internalformat=Ne[0].internalformat,$e.width=Ne[0].width,$e.height=Ne[0].height,al(Nt),vt=0;vt<6;++vt)is(Ne[vt],ea+vt);for(nl(lr,ya),io(),Nr.profile&&(Nt.stats.size=cc(Nt.internalformat,Nt.type,$e.width,$e.height,lr.genMipmaps,!0)),$e.format=yi[Nt.internalformat],$e.type=Ii[Nt.type],$e.mag=ui[lr.magFilter],$e.min=nn[lr.minFilter],$e.wrapS=Zi[lr.wrapS],$e.wrapT=Zi[lr.wrapT],vt=0;vt<6;++vt)El(Ne[vt]);return $e}function st(Bt,Ft,Ut,zt,_t){var Dt=Ut|0,vt=zt|0,lr=_t|0,_r=Ki();return si(_r,Nt),_r.width=0,_r.height=0,Bi(_r,Ft),_r.width=_r.width||(Nt.width>>lr)-Dt,_r.height=_r.height||(Nt.height>>lr)-vt,al(Nt),Nn(_r,ea+Bt,Dt,vt,lr),io(),Cn(_r),$e}function It(Bt){var Ft=Bt|0;if(Ft!==Nt.width){$e.width=Nt.width=Ft,$e.height=Nt.height=Ft,al(Nt);for(var Ut=0;Ut<6;++Ut)for(var zt=0;Nt.mipmask>>zt;++zt)Me.texImage2D(ea+Ut,zt,Nt.format,Ft>>zt,Ft>>zt,0,Nt.format,Nt.type,null);return io(),Nr.profile&&(Nt.stats.size=cc(Nt.internalformat,Nt.type,$e.width,$e.height,!1,!0)),$e}}return $e(ne,we,Oe,ht,Yt,cr),$e.subimage=st,$e.resize=It,$e._reglType=\"textureCube\",$e._texture=Nt,Nr.profile&&($e.stats=Nt.stats),$e.destroy=function(){Nt.decRef()},$e}function O(){for(var ne=0;ne<Vs;++ne)Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Ys[ne]=null;gt(ss).forEach(Y),Or.cubeCount=0,Or.textureCount=0}Nr.profile&&(Or.getTotalTextureSize=function(){var ne=0;return Object.keys(ss).forEach(function(we){ne+=ss[we].stats.size}),ne});function K(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null)}gt(ss).forEach(function(Oe){Oe.texture=Me.createTexture(),Me.bindTexture(Oe.target,Oe.texture);for(var ht=0;ht<32;++ht)if((Oe.mipmask&1<<ht)!==0)if(Oe.target===_n)Me.texImage2D(_n,ht,Oe.internalformat,Oe.width>>ht,Oe.height>>ht,0,Oe.internalformat,Oe.type,null);else for(var Yt=0;Yt<6;++Yt)Me.texImage2D(ea+Yt,ht,Oe.internalformat,Oe.width>>ht,Oe.height>>ht,0,Oe.internalformat,Oe.type,null);nl(Oe.texInfo,Oe.target)})}function de(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null),Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Me.bindTexture(ya,null)}}return{create2D:D,createCube:J,clear:O,getTexture:function(ne){return null},restore:K,refresh:de}}var Bl=36161,Kc=32854,Rc=32855,gs=36194,Wf=33189,Wh=36168,rh=34041,lf=35907,Sh=34836,Mu=34842,ih=34843,js=[];js[Kc]=2,js[Rc]=2,js[gs]=2,js[Wf]=2,js[Wh]=1,js[rh]=4,js[lf]=4,js[Sh]=16,js[Mu]=8,js[ih]=6;function Eu(Me,bt,Ot){return js[Me]*bt*Ot}var Dc=function(Me,bt,Ot,Lr,Vr){var Or={rgba4:Kc,rgb565:gs,\"rgb5 a1\":Rc,depth:Wf,stencil:Wh,\"depth stencil\":rh};bt.ext_srgb&&(Or.srgba=lf),bt.ext_color_buffer_half_float&&(Or.rgba16f=Mu,Or.rgb16f=ih),bt.webgl_color_buffer_float&&(Or.rgba32f=Sh);var Nr=[];Object.keys(Or).forEach(function(Wi){var oi=Or[Wi];Nr[oi]=Wi});var vi=0,Ni={};function qi(Wi){this.id=vi++,this.refCount=1,this.renderbuffer=Wi,this.format=Kc,this.width=0,this.height=0,Vr.profile&&(this.stats={size:0})}qi.prototype.decRef=function(){--this.refCount<=0&&Si(this)};function Si(Wi){var oi=Wi.renderbuffer;Me.bindRenderbuffer(Bl,null),Me.deleteRenderbuffer(oi),Wi.renderbuffer=null,Wi.refCount=0,delete Ni[Wi.id],Lr.renderbufferCount--}function Vn(Wi,oi){var Tr=new qi(Me.createRenderbuffer());Ni[Tr.id]=Tr,Lr.renderbufferCount++;function Zr(Ii,ui){var nn=0,Zi=0,qn=Kc;if(typeof Ii==\"object\"&&Ii){var fi=Ii;if(\"shape\"in fi){var si=fi.shape;nn=si[0]|0,Zi=si[1]|0}else\"radius\"in fi&&(nn=Zi=fi.radius|0),\"width\"in fi&&(nn=fi.width|0),\"height\"in fi&&(Zi=fi.height|0);\"format\"in fi&&(qn=Or[fi.format])}else typeof Ii==\"number\"?(nn=Ii|0,typeof ui==\"number\"?Zi=ui|0:Zi=nn):Ii||(nn=Zi=1);if(!(nn===Tr.width&&Zi===Tr.height&&qn===Tr.format))return Zr.width=Tr.width=nn,Zr.height=Tr.height=Zi,Tr.format=qn,Me.bindRenderbuffer(Bl,Tr.renderbuffer),Me.renderbufferStorage(Bl,qn,nn,Zi),Vr.profile&&(Tr.stats.size=Eu(Tr.format,Tr.width,Tr.height)),Zr.format=Nr[Tr.format],Zr}function yi(Ii,ui){var nn=Ii|0,Zi=ui|0||nn;return nn===Tr.width&&Zi===Tr.height||(Zr.width=Tr.width=nn,Zr.height=Tr.height=Zi,Me.bindRenderbuffer(Bl,Tr.renderbuffer),Me.renderbufferStorage(Bl,Tr.format,nn,Zi),Vr.profile&&(Tr.stats.size=Eu(Tr.format,Tr.width,Tr.height))),Zr}return Zr(Wi,oi),Zr.resize=yi,Zr._reglType=\"renderbuffer\",Zr._renderbuffer=Tr,Vr.profile&&(Zr.stats=Tr.stats),Zr.destroy=function(){Tr.decRef()},Zr}Vr.profile&&(Lr.getTotalRenderbufferSize=function(){var Wi=0;return Object.keys(Ni).forEach(function(oi){Wi+=Ni[oi].stats.size}),Wi});function Qi(){gt(Ni).forEach(function(Wi){Wi.renderbuffer=Me.createRenderbuffer(),Me.bindRenderbuffer(Bl,Wi.renderbuffer),Me.renderbufferStorage(Bl,Wi.format,Wi.width,Wi.height)}),Me.bindRenderbuffer(Bl,null)}return{create:Vn,clear:function(){gt(Ni).forEach(Si)},restore:Qi}},ks=36160,bc=36161,hu=3553,_u=34069,il=36064,nh=36096,Mh=36128,zu=33306,Fc=36053,wc=36193,bd=5121,xf=5126,Pf=6407,qu=6408,bf=[];bf[qu]=4,bf[Pf]=3;var jl=[];jl[bd]=1,jl[xf]=4,jl[wc]=2;function uf(Me,bt,Ot,Lr,Vr,Or){var Nr={cur:null,next:null,dirty:!1,setFBO:null},vi=[\"rgba\"],Ni=[\"rgba4\",\"rgb565\",\"rgb5 a1\"];bt.ext_srgb&&Ni.push(\"srgba\"),bt.ext_color_buffer_half_float&&Ni.push(\"rgba16f\",\"rgb16f\"),bt.webgl_color_buffer_float&&Ni.push(\"rgba32f\");var qi=[\"uint8\"];bt.oes_texture_half_float&&qi.push(\"half float\",\"float16\"),bt.oes_texture_float&&qi.push(\"float\",\"float32\");function Si(Ui,Bi,pn){this.target=Ui,this.texture=Bi,this.renderbuffer=pn;var Nn=0,aa=0;Bi?(Nn=Bi.width,aa=Bi.height):pn&&(Nn=pn.width,aa=pn.height),this.width=Nn,this.height=aa}function Vn(Ui){Ui&&(Ui.texture&&Ui.texture._texture.decRef(),Ui.renderbuffer&&Ui.renderbuffer._renderbuffer.decRef())}function Qi(Ui,Bi,pn){if(Ui)if(Ui.texture){var Nn=Ui.texture._texture,aa=Math.max(1,Nn.width),Ki=Math.max(1,Nn.height);Nn.refCount+=1}else{var Cn=Ui.renderbuffer._renderbuffer;Cn.refCount+=1}}function Wi(Ui,Bi){Bi&&(Bi.texture?Me.framebufferTexture2D(ks,Ui,Bi.target,Bi.texture._texture.texture,0):Me.framebufferRenderbuffer(ks,Ui,bc,Bi.renderbuffer._renderbuffer.renderbuffer))}function oi(Ui){var Bi=hu,pn=null,Nn=null,aa=Ui;typeof Ui==\"object\"&&(aa=Ui.data,\"target\"in Ui&&(Bi=Ui.target|0));var Ki=aa._reglType;return Ki===\"texture2d\"||Ki===\"textureCube\"?pn=aa:Ki===\"renderbuffer\"&&(Nn=aa,Bi=bc),new Si(Bi,pn,Nn)}function Tr(Ui,Bi,pn,Nn,aa){if(pn){var Ki=Lr.create2D({width:Ui,height:Bi,format:Nn,type:aa});return Ki._texture.refCount=0,new Si(hu,Ki,null)}else{var Cn=Vr.create({width:Ui,height:Bi,format:Nn});return Cn._renderbuffer.refCount=0,new Si(bc,null,Cn)}}function Zr(Ui){return Ui&&(Ui.texture||Ui.renderbuffer)}function yi(Ui,Bi,pn){Ui&&(Ui.texture?Ui.texture.resize(Bi,pn):Ui.renderbuffer&&Ui.renderbuffer.resize(Bi,pn),Ui.width=Bi,Ui.height=pn)}var Ii=0,ui={};function nn(){this.id=Ii++,ui[this.id]=this,this.framebuffer=Me.createFramebuffer(),this.width=0,this.height=0,this.colorAttachments=[],this.depthAttachment=null,this.stencilAttachment=null,this.depthStencilAttachment=null}function Zi(Ui){Ui.colorAttachments.forEach(Vn),Vn(Ui.depthAttachment),Vn(Ui.stencilAttachment),Vn(Ui.depthStencilAttachment)}function qn(Ui){var Bi=Ui.framebuffer;Me.deleteFramebuffer(Bi),Ui.framebuffer=null,Or.framebufferCount--,delete ui[Ui.id]}function fi(Ui){var Bi;Me.bindFramebuffer(ks,Ui.framebuffer);var pn=Ui.colorAttachments;for(Bi=0;Bi<pn.length;++Bi)Wi(il+Bi,pn[Bi]);for(Bi=pn.length;Bi<Ot.maxColorAttachments;++Bi)Me.framebufferTexture2D(ks,il+Bi,hu,null,0);Me.framebufferTexture2D(ks,zu,hu,null,0),Me.framebufferTexture2D(ks,nh,hu,null,0),Me.framebufferTexture2D(ks,Mh,hu,null,0),Wi(nh,Ui.depthAttachment),Wi(Mh,Ui.stencilAttachment),Wi(zu,Ui.depthStencilAttachment);var Nn=Me.checkFramebufferStatus(ks);Me.isContextLost(),Me.bindFramebuffer(ks,Nr.next?Nr.next.framebuffer:null),Nr.cur=Nr.next,Me.getError()}function si(Ui,Bi){var pn=new nn;Or.framebufferCount++;function Nn(Ki,Cn){var ia,sa=0,ba=0,is=!0,Zs=!0,Ga=null,El=!0,zo=\"rgba\",Qs=\"uint8\",nl=1,Ul=null,ss=null,Vs=null,Ys=!1;if(typeof Ki==\"number\")sa=Ki|0,ba=Cn|0||sa;else if(!Ki)sa=ba=1;else{var wa=Ki;if(\"shape\"in wa){var al=wa.shape;sa=al[0],ba=al[1]}else\"radius\"in wa&&(sa=ba=wa.radius),\"width\"in wa&&(sa=wa.width),\"height\"in wa&&(ba=wa.height);(\"color\"in wa||\"colors\"in wa)&&(Ga=wa.color||wa.colors,Array.isArray(Ga)),Ga||(\"colorCount\"in wa&&(nl=wa.colorCount|0),\"colorTexture\"in wa&&(El=!!wa.colorTexture,zo=\"rgba4\"),\"colorType\"in wa&&(Qs=wa.colorType,El||(Qs===\"half float\"||Qs===\"float16\"?zo=\"rgba16f\":(Qs===\"float\"||Qs===\"float32\")&&(zo=\"rgba32f\"))),\"colorFormat\"in wa&&(zo=wa.colorFormat,vi.indexOf(zo)>=0?El=!0:Ni.indexOf(zo)>=0&&(El=!1))),(\"depthTexture\"in wa||\"depthStencilTexture\"in wa)&&(Ys=!!(wa.depthTexture||wa.depthStencilTexture)),\"depth\"in wa&&(typeof wa.depth==\"boolean\"?is=wa.depth:(Ul=wa.depth,Zs=!1)),\"stencil\"in wa&&(typeof wa.stencil==\"boolean\"?Zs=wa.stencil:(ss=wa.stencil,is=!1)),\"depthStencil\"in wa&&(typeof wa.depthStencil==\"boolean\"?is=Zs=wa.depthStencil:(Vs=wa.depthStencil,is=!1,Zs=!1))}var io=null,Y=null,D=null,J=null;if(Array.isArray(Ga))io=Ga.map(oi);else if(Ga)io=[oi(Ga)];else for(io=new Array(nl),ia=0;ia<nl;++ia)io[ia]=Tr(sa,ba,El,zo,Qs);sa=sa||io[0].width,ba=ba||io[0].height,Ul?Y=oi(Ul):is&&!Zs&&(Y=Tr(sa,ba,Ys,\"depth\",\"uint32\")),ss?D=oi(ss):Zs&&!is&&(D=Tr(sa,ba,!1,\"stencil\",\"uint8\")),Vs?J=oi(Vs):!Ul&&!ss&&Zs&&is&&(J=Tr(sa,ba,Ys,\"depth stencil\",\"depth stencil\"));var O=null;for(ia=0;ia<io.length;++ia)if(Qi(io[ia],sa,ba),io[ia]&&io[ia].texture){var K=bf[io[ia].texture._texture.format]*jl[io[ia].texture._texture.type];O===null&&(O=K)}return Qi(Y,sa,ba),Qi(D,sa,ba),Qi(J,sa,ba),Zi(pn),pn.width=sa,pn.height=ba,pn.colorAttachments=io,pn.depthAttachment=Y,pn.stencilAttachment=D,pn.depthStencilAttachment=J,Nn.color=io.map(Zr),Nn.depth=Zr(Y),Nn.stencil=Zr(D),Nn.depthStencil=Zr(J),Nn.width=pn.width,Nn.height=pn.height,fi(pn),Nn}function aa(Ki,Cn){var ia=Math.max(Ki|0,1),sa=Math.max(Cn|0||ia,1);if(ia===pn.width&&sa===pn.height)return Nn;for(var ba=pn.colorAttachments,is=0;is<ba.length;++is)yi(ba[is],ia,sa);return yi(pn.depthAttachment,ia,sa),yi(pn.stencilAttachment,ia,sa),yi(pn.depthStencilAttachment,ia,sa),pn.width=Nn.width=ia,pn.height=Nn.height=sa,fi(pn),Nn}return Nn(Ui,Bi),e(Nn,{resize:aa,_reglType:\"framebuffer\",_framebuffer:pn,destroy:function(){qn(pn),Zi(pn)},use:function(Ki){Nr.setFBO({framebuffer:Nn},Ki)}})}function mn(Ui){var Bi=Array(6);function pn(aa){var Ki,Cn={color:null},ia=0,sa=null,ba=\"rgba\",is=\"uint8\",Zs=1;if(typeof aa==\"number\")ia=aa|0;else if(!aa)ia=1;else{var Ga=aa;if(\"shape\"in Ga){var El=Ga.shape;ia=El[0]}else\"radius\"in Ga&&(ia=Ga.radius|0),\"width\"in Ga?(ia=Ga.width|0,\"height\"in Ga):\"height\"in Ga&&(ia=Ga.height|0);(\"color\"in Ga||\"colors\"in Ga)&&(sa=Ga.color||Ga.colors,Array.isArray(sa)),sa||(\"colorCount\"in Ga&&(Zs=Ga.colorCount|0),\"colorType\"in Ga&&(is=Ga.colorType),\"colorFormat\"in Ga&&(ba=Ga.colorFormat)),\"depth\"in Ga&&(Cn.depth=Ga.depth),\"stencil\"in Ga&&(Cn.stencil=Ga.stencil),\"depthStencil\"in Ga&&(Cn.depthStencil=Ga.depthStencil)}var zo;if(sa)if(Array.isArray(sa))for(zo=[],Ki=0;Ki<sa.length;++Ki)zo[Ki]=sa[Ki];else zo=[sa];else{zo=Array(Zs);var Qs={radius:ia,format:ba,type:is};for(Ki=0;Ki<Zs;++Ki)zo[Ki]=Lr.createCube(Qs)}for(Cn.color=Array(zo.length),Ki=0;Ki<zo.length;++Ki){var nl=zo[Ki];ia=ia||nl.width,Cn.color[Ki]={target:_u,data:zo[Ki]}}for(Ki=0;Ki<6;++Ki){for(var Ul=0;Ul<zo.length;++Ul)Cn.color[Ul].target=_u+Ki;Ki>0&&(Cn.depth=Bi[0].depth,Cn.stencil=Bi[0].stencil,Cn.depthStencil=Bi[0].depthStencil),Bi[Ki]?Bi[Ki](Cn):Bi[Ki]=si(Cn)}return e(pn,{width:ia,height:ia,color:zo})}function Nn(aa){var Ki,Cn=aa|0;if(Cn===pn.width)return pn;var ia=pn.color;for(Ki=0;Ki<ia.length;++Ki)ia[Ki].resize(Cn);for(Ki=0;Ki<6;++Ki)Bi[Ki].resize(Cn);return pn.width=pn.height=Cn,pn}return pn(Ui),e(pn,{faces:Bi,resize:Nn,_reglType:\"framebufferCube\",destroy:function(){Bi.forEach(function(aa){aa.destroy()})}})}function Ji(){Nr.cur=null,Nr.next=null,Nr.dirty=!0,gt(ui).forEach(function(Ui){Ui.framebuffer=Me.createFramebuffer(),fi(Ui)})}return e(Nr,{getFramebuffer:function(Ui){if(typeof Ui==\"function\"&&Ui._reglType===\"framebuffer\"){var Bi=Ui._framebuffer;if(Bi instanceof nn)return Bi}return null},create:si,createCube:mn,clear:function(){gt(ui).forEach(qn)},restore:Ji})}var Xh=5126,If=34962,Cs=34963;function du(){this.state=0,this.x=0,this.y=0,this.z=0,this.w=0,this.buffer=null,this.size=0,this.normalized=!1,this.type=Xh,this.offset=0,this.stride=0,this.divisor=0}function ku(Me,bt,Ot,Lr,Vr,Or,Nr){for(var vi=Ot.maxAttributes,Ni=new Array(vi),qi=0;qi<vi;++qi)Ni[qi]=new du;var Si=0,Vn={},Qi={Record:du,scope:{},state:Ni,currentVAO:null,targetVAO:null,restore:oi()?Zi:function(){},createVAO:qn,getVAO:Zr,destroyBuffer:Wi,setVAO:oi()?yi:Ii,clear:oi()?ui:function(){}};function Wi(fi){for(var si=0;si<Ni.length;++si){var mn=Ni[si];mn.buffer===fi&&(Me.disableVertexAttribArray(si),mn.buffer=null)}}function oi(){return bt.oes_vertex_array_object}function Tr(){return bt.angle_instanced_arrays}function Zr(fi){return typeof fi==\"function\"&&fi._vao?fi._vao:null}function yi(fi){if(fi!==Qi.currentVAO){var si=oi();fi?si.bindVertexArrayOES(fi.vao):si.bindVertexArrayOES(null),Qi.currentVAO=fi}}function Ii(fi){if(fi!==Qi.currentVAO){if(fi)fi.bindAttrs();else{for(var si=Tr(),mn=0;mn<Ni.length;++mn){var Ji=Ni[mn];Ji.buffer?(Me.enableVertexAttribArray(mn),Ji.buffer.bind(),Me.vertexAttribPointer(mn,Ji.size,Ji.type,Ji.normalized,Ji.stride,Ji.offfset),si&&Ji.divisor&&si.vertexAttribDivisorANGLE(mn,Ji.divisor)):(Me.disableVertexAttribArray(mn),Me.vertexAttrib4f(mn,Ji.x,Ji.y,Ji.z,Ji.w))}Nr.elements?Me.bindBuffer(Cs,Nr.elements.buffer.buffer):Me.bindBuffer(Cs,null)}Qi.currentVAO=fi}}function ui(){gt(Vn).forEach(function(fi){fi.destroy()})}function nn(){this.id=++Si,this.attributes=[],this.elements=null,this.ownsElements=!1,this.count=0,this.offset=0,this.instances=-1,this.primitive=4;var fi=oi();fi?this.vao=fi.createVertexArrayOES():this.vao=null,Vn[this.id]=this,this.buffers=[]}nn.prototype.bindAttrs=function(){for(var fi=Tr(),si=this.attributes,mn=0;mn<si.length;++mn){var Ji=si[mn];Ji.buffer?(Me.enableVertexAttribArray(mn),Me.bindBuffer(If,Ji.buffer.buffer),Me.vertexAttribPointer(mn,Ji.size,Ji.type,Ji.normalized,Ji.stride,Ji.offset),fi&&Ji.divisor&&fi.vertexAttribDivisorANGLE(mn,Ji.divisor)):(Me.disableVertexAttribArray(mn),Me.vertexAttrib4f(mn,Ji.x,Ji.y,Ji.z,Ji.w))}for(var Ui=si.length;Ui<vi;++Ui)Me.disableVertexAttribArray(Ui);var Bi=Or.getElements(this.elements);Bi?Me.bindBuffer(Cs,Bi.buffer.buffer):Me.bindBuffer(Cs,null)},nn.prototype.refresh=function(){var fi=oi();fi&&(fi.bindVertexArrayOES(this.vao),this.bindAttrs(),Qi.currentVAO=null,fi.bindVertexArrayOES(null))},nn.prototype.destroy=function(){if(this.vao){var fi=oi();this===Qi.currentVAO&&(Qi.currentVAO=null,fi.bindVertexArrayOES(null)),fi.deleteVertexArrayOES(this.vao),this.vao=null}this.ownsElements&&(this.elements.destroy(),this.elements=null,this.ownsElements=!1),Vn[this.id]&&(delete Vn[this.id],Lr.vaoCount-=1)};function Zi(){var fi=oi();fi&&gt(Vn).forEach(function(si){si.refresh()})}function qn(fi){var si=new nn;Lr.vaoCount+=1;function mn(Ji){var Ui;if(Array.isArray(Ji))Ui=Ji,si.elements&&si.ownsElements&&si.elements.destroy(),si.elements=null,si.ownsElements=!1,si.offset=0,si.count=0,si.instances=-1,si.primitive=4;else{if(Ji.elements){var Bi=Ji.elements;si.ownsElements?typeof Bi==\"function\"&&Bi._reglType===\"elements\"?(si.elements.destroy(),si.ownsElements=!1):(si.elements(Bi),si.ownsElements=!1):Or.getElements(Ji.elements)?(si.elements=Ji.elements,si.ownsElements=!1):(si.elements=Or.create(Ji.elements),si.ownsElements=!0)}else si.elements=null,si.ownsElements=!1;Ui=Ji.attributes,si.offset=0,si.count=-1,si.instances=-1,si.primitive=4,si.elements&&(si.count=si.elements._elements.vertCount,si.primitive=si.elements._elements.primType),\"offset\"in Ji&&(si.offset=Ji.offset|0),\"count\"in Ji&&(si.count=Ji.count|0),\"instances\"in Ji&&(si.instances=Ji.instances|0),\"primitive\"in Ji&&(si.primitive=Mn[Ji.primitive])}var pn={},Nn=si.attributes;Nn.length=Ui.length;for(var aa=0;aa<Ui.length;++aa){var Ki=Ui[aa],Cn=Nn[aa]=new du,ia=Ki.data||Ki;if(Array.isArray(ia)||qr(ia)||jr(ia)){var sa;si.buffers[aa]&&(sa=si.buffers[aa],qr(ia)&&sa._buffer.byteLength>=ia.byteLength?sa.subdata(ia):(sa.destroy(),si.buffers[aa]=null)),si.buffers[aa]||(sa=si.buffers[aa]=Vr.create(Ki,If,!1,!0)),Cn.buffer=Vr.getBuffer(sa),Cn.size=Cn.buffer.dimension|0,Cn.normalized=!1,Cn.type=Cn.buffer.dtype,Cn.offset=0,Cn.stride=0,Cn.divisor=0,Cn.state=1,pn[aa]=1}else Vr.getBuffer(Ki)?(Cn.buffer=Vr.getBuffer(Ki),Cn.size=Cn.buffer.dimension|0,Cn.normalized=!1,Cn.type=Cn.buffer.dtype,Cn.offset=0,Cn.stride=0,Cn.divisor=0,Cn.state=1):Vr.getBuffer(Ki.buffer)?(Cn.buffer=Vr.getBuffer(Ki.buffer),Cn.size=(+Ki.size||Cn.buffer.dimension)|0,Cn.normalized=!!Ki.normalized||!1,\"type\"in Ki?Cn.type=un[Ki.type]:Cn.type=Cn.buffer.dtype,Cn.offset=(Ki.offset||0)|0,Cn.stride=(Ki.stride||0)|0,Cn.divisor=(Ki.divisor||0)|0,Cn.state=1):\"x\"in Ki&&(Cn.x=+Ki.x||0,Cn.y=+Ki.y||0,Cn.z=+Ki.z||0,Cn.w=+Ki.w||0,Cn.state=2)}for(var ba=0;ba<si.buffers.length;++ba)!pn[ba]&&si.buffers[ba]&&(si.buffers[ba].destroy(),si.buffers[ba]=null);return si.refresh(),mn}return mn.destroy=function(){for(var Ji=0;Ji<si.buffers.length;++Ji)si.buffers[Ji]&&si.buffers[Ji].destroy();si.buffers.length=0,si.ownsElements&&(si.elements.destroy(),si.elements=null,si.ownsElements=!1),si.destroy()},mn._vao=si,mn._reglType=\"vao\",mn(fi)}return Qi}var Xf=35632,Us=35633,wf=35718,zc=35721;function Wu(Me,bt,Ot,Lr){var Vr={},Or={};function Nr(Tr,Zr,yi,Ii){this.name=Tr,this.id=Zr,this.location=yi,this.info=Ii}function vi(Tr,Zr){for(var yi=0;yi<Tr.length;++yi)if(Tr[yi].id===Zr.id){Tr[yi].location=Zr.location;return}Tr.push(Zr)}function Ni(Tr,Zr,yi){var Ii=Tr===Xf?Vr:Or,ui=Ii[Zr];if(!ui){var nn=bt.str(Zr);ui=Me.createShader(Tr),Me.shaderSource(ui,nn),Me.compileShader(ui),Ii[Zr]=ui}return ui}var qi={},Si=[],Vn=0;function Qi(Tr,Zr){this.id=Vn++,this.fragId=Tr,this.vertId=Zr,this.program=null,this.uniforms=[],this.attributes=[],this.refCount=1,Lr.profile&&(this.stats={uniformsCount:0,attributesCount:0})}function Wi(Tr,Zr,yi){var Ii,ui,nn=Ni(Xf,Tr.fragId),Zi=Ni(Us,Tr.vertId),qn=Tr.program=Me.createProgram();if(Me.attachShader(qn,nn),Me.attachShader(qn,Zi),yi)for(Ii=0;Ii<yi.length;++Ii){var fi=yi[Ii];Me.bindAttribLocation(qn,fi[0],fi[1])}Me.linkProgram(qn);var si=Me.getProgramParameter(qn,wf);Lr.profile&&(Tr.stats.uniformsCount=si);var mn=Tr.uniforms;for(Ii=0;Ii<si;++Ii)if(ui=Me.getActiveUniform(qn,Ii),ui)if(ui.size>1)for(var Ji=0;Ji<ui.size;++Ji){var Ui=ui.name.replace(\"[0]\",\"[\"+Ji+\"]\");vi(mn,new Nr(Ui,bt.id(Ui),Me.getUniformLocation(qn,Ui),ui))}else vi(mn,new Nr(ui.name,bt.id(ui.name),Me.getUniformLocation(qn,ui.name),ui));var Bi=Me.getProgramParameter(qn,zc);Lr.profile&&(Tr.stats.attributesCount=Bi);var pn=Tr.attributes;for(Ii=0;Ii<Bi;++Ii)ui=Me.getActiveAttrib(qn,Ii),ui&&vi(pn,new Nr(ui.name,bt.id(ui.name),Me.getAttribLocation(qn,ui.name),ui))}Lr.profile&&(Ot.getMaxUniformsCount=function(){var Tr=0;return Si.forEach(function(Zr){Zr.stats.uniformsCount>Tr&&(Tr=Zr.stats.uniformsCount)}),Tr},Ot.getMaxAttributesCount=function(){var Tr=0;return Si.forEach(function(Zr){Zr.stats.attributesCount>Tr&&(Tr=Zr.stats.attributesCount)}),Tr});function oi(){Vr={},Or={};for(var Tr=0;Tr<Si.length;++Tr)Wi(Si[Tr],null,Si[Tr].attributes.map(function(Zr){return[Zr.location,Zr.name]}))}return{clear:function(){var Tr=Me.deleteShader.bind(Me);gt(Vr).forEach(Tr),Vr={},gt(Or).forEach(Tr),Or={},Si.forEach(function(Zr){Me.deleteProgram(Zr.program)}),Si.length=0,qi={},Ot.shaderCount=0},program:function(Tr,Zr,yi,Ii){var ui=qi[Zr];ui||(ui=qi[Zr]={});var nn=ui[Tr];if(nn&&(nn.refCount++,!Ii))return nn;var Zi=new Qi(Zr,Tr);return Ot.shaderCount++,Wi(Zi,yi,Ii),nn||(ui[Tr]=Zi),Si.push(Zi),e(Zi,{destroy:function(){if(Zi.refCount--,Zi.refCount<=0){Me.deleteProgram(Zi.program);var qn=Si.indexOf(Zi);Si.splice(qn,1),Ot.shaderCount--}ui[Zi.vertId].refCount<=0&&(Me.deleteShader(Or[Zi.vertId]),delete Or[Zi.vertId],delete qi[Zi.fragId][Zi.vertId]),Object.keys(qi[Zi.fragId]).length||(Me.deleteShader(Vr[Zi.fragId]),delete Vr[Zi.fragId],delete qi[Zi.fragId])}})},restore:oi,shader:Ni,frag:-1,vert:-1}}var Rf=6408,Xu=5121,cf=3333,Zf=5126;function Wl(Me,bt,Ot,Lr,Vr,Or,Nr){function vi(Si){var Vn;bt.next===null?Vn=Xu:Vn=bt.next.colorAttachments[0].texture._texture.type;var 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bt=ah?\"0123456789ABCDEF\":\"0123456789abcdef\",Ot=\"\",Lr,Vr=0;Vr<Me.length;Vr++)Lr=Me.charCodeAt(Vr),Ot+=bt.charAt(Lr>>>4&15)+bt.charAt(Lr&15);return Ot}function Oc(Me){for(var bt=\"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/\",Ot=\"\",Lr=Me.length,Vr=0;Vr<Lr;Vr+=3)for(var Or=Me.charCodeAt(Vr)<<16|(Vr+1<Lr?Me.charCodeAt(Vr+1)<<8:0)|(Vr+2<Lr?Me.charCodeAt(Vr+2):0),Nr=0;Nr<4;Nr++)Vr*8+Nr*6>Me.length*8?Ot+=Zu:Ot+=bt.charAt(Or>>>6*(3-Nr)&63);return Ot}function ff(Me,bt){var Ot=bt.length,Lr=Array(),Vr,Or,Nr,vi,Ni=Array(Math.ceil(Me.length/2));for(Vr=0;Vr<Ni.length;Vr++)Ni[Vr]=Me.charCodeAt(Vr*2)<<8|Me.charCodeAt(Vr*2+1);for(;Ni.length>0;){for(vi=Array(),Nr=0,Vr=0;Vr<Ni.length;Vr++)Nr=(Nr<<16)+Ni[Vr],Or=Math.floor(Nr/Ot),Nr-=Or*Ot,(vi.length>0||Or>0)&&(vi[vi.length]=Or);Lr[Lr.length]=Nr,Ni=vi}var qi=\"\";for(Vr=Lr.length-1;Vr>=0;Vr--)qi+=bt.charAt(Lr[Vr]);var Si=Math.ceil(Me.length*8/(Math.log(bt.length)/Math.log(2)));for(Vr=qi.length;Vr<Si;Vr++)qi=bt[0]+qi;return 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st[Ze]==\"string\";if(It){if(Object.keys($e.dynamic).length>0)return null;var Bt=$e.static,Ft=Object.keys(Bt);if(Ft.length>0&&typeof Bt[Ft[0]]==\"number\"){for(var Ut=[],zt=0;zt<Ft.length;++zt)Ut.push([Bt[Ft[zt]]|0,Ft[zt]]);return Ut}}return null}function Zs(Ne,$e,st){var It=Ne.static,Bt=Ne.dynamic;function Ft(vt){if(vt in It){var lr=bt.id(It[vt]),_r=kn(function(){return lr});return _r.id=lr,_r}else if(vt in Bt){var Br=Bt[vt];return Bn(Br,function(Di,ci){var bi=Di.invoke(ci,Br),gn=ci.def(Di.shared.strings,\".id(\",bi,\")\");return gn})}return null}var Ut=Ft(ut),zt=Ft(Ze),_t=null,Dt;return Pn(Ut)&&Pn(zt)?(_t=Si.program(zt.id,Ut.id,null,st),Dt=kn(function(vt,lr){return vt.link(_t)})):Dt=new Qr(Ut&&Ut.thisDep||zt&&zt.thisDep,Ut&&Ut.contextDep||zt&&zt.contextDep,Ut&&Ut.propDep||zt&&zt.propDep,function(vt,lr){var _r=vt.shared.shader,Br;Ut?Br=Ut.append(vt,lr):Br=lr.def(_r,\".\",ut);var Di;zt?Di=zt.append(vt,lr):Di=lr.def(_r,\".\",Ze);var ci=_r+\".program(\"+Di+\",\"+Br;return 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Ja=on.shared,co=Ja.isBufferArgs,ts=Ja.elements,so=on.invoke(zn,Gi),Yo=zn.def(\"null\"),ms=zn.def(co,\"(\",so,\")\"),ou=on.cond(ms).then(Yo,\"=\",ts,\".createStream(\",so,\");\").else(Yo,\"=\",ts,\".getElements(\",so,\");\");return zn.entry(ou),zn.exit(on.cond(ms).then(ts,\".destroyStream(\",Yo,\");\")),on.ELEMENTS=Yo,Yo})}else if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(on,zn){return zn.def(on.shared.vao+\".currentVAO?\"+on.shared.elements+\".getElements(\"+on.shared.vao+\".currentVAO.elements):null\")});return null}var vt=Dt();function lr(){if(Jt in st){var tn=st[Jt];return Bt.primitive=tn,kn(function(Oi,Gi){return Mn[tn]})}else if(Jt in It){var Ei=It[Jt];return Bn(Ei,function(Oi,Gi){var on=Oi.constants.primTypes,zn=Oi.invoke(Gi,Ei);return Gi.def(on,\"[\",zn,\"]\")})}else{if(_t)return Pn(vt)?vt.value?kn(function(Oi,Gi){return Gi.def(Oi.ELEMENTS,\".primType\")}):kn(function(){return Sl}):new Qr(vt.thisDep,vt.contextDep,vt.propDep,function(Oi,Gi){var on=Oi.ELEMENTS;return Gi.def(on,\"?\",on,\".primType:\",Sl)});if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(Oi,Gi){return Gi.def(Oi.shared.vao+\".currentVAO?\"+Oi.shared.vao+\".currentVAO.primitive:\"+Sl)})}return null}function _r(tn,Ei){if(tn in st){var Oi=st[tn]|0;return Ei?Bt.offset=Oi:Bt.instances=Oi,kn(function(on,zn){return Ei&&(on.OFFSET=Oi),Oi})}else if(tn in It){var Gi=It[tn];return Bn(Gi,function(on,zn){var Ja=on.invoke(zn,Gi);return Ei&&(on.OFFSET=Ja),Ja})}else if(Ei){if(_t)return kn(function(on,zn){return on.OFFSET=0,0});if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(on,zn){return zn.def(on.shared.vao+\".currentVAO?\"+on.shared.vao+\".currentVAO.offset:0\")})}else if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(on,zn){return zn.def(on.shared.vao+\".currentVAO?\"+on.shared.vao+\".currentVAO.instances:-1\")});return null}var Br=_r(yr,!0);function Di(){if($t in st){var tn=st[$t]|0;return Bt.count=tn,kn(function(){return tn})}else if($t in It){var Ei=It[$t];return Bn(Ei,function(zn,Ja){var co=zn.invoke(Ja,Ei);return co})}else if(_t)if(Pn(vt)){if(vt)return Br?new Qr(Br.thisDep,Br.contextDep,Br.propDep,function(zn,Ja){var co=Ja.def(zn.ELEMENTS,\".vertCount-\",zn.OFFSET);return co}):kn(function(zn,Ja){return Ja.def(zn.ELEMENTS,\".vertCount\")});var Oi=kn(function(){return-1});return Oi}else{var Gi=new Qr(vt.thisDep||Br.thisDep,vt.contextDep||Br.contextDep,vt.propDep||Br.propDep,function(zn,Ja){var co=zn.ELEMENTS;return zn.OFFSET?Ja.def(co,\"?\",co,\".vertCount-\",zn.OFFSET,\":-1\"):Ja.def(co,\"?\",co,\".vertCount:-1\")});return Gi}else if(Ft){var on=new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(zn,Ja){return Ja.def(zn.shared.vao,\".currentVAO?\",zn.shared.vao,\".currentVAO.count:-1\")});return on}return null}var ci=lr(),bi=Di(),gn=_r(Cr,!1);return{elements:vt,primitive:ci,count:bi,instances:gn,offset:Br,vao:zt,vaoActive:Ft,elementsActive:_t,static:Bt}}function El(Ne,$e){var st=Ne.static,It=Ne.dynamic,Bt={};return fi.forEach(function(Ft){var Ut=Ji(Ft);function zt(_t,Dt){if(Ft in st){var vt=_t(st[Ft]);Bt[Ut]=kn(function(){return vt})}else if(Ft in It){var lr=It[Ft];Bt[Ut]=Bn(lr,function(_r,Br){return Dt(_r,Br,_r.invoke(Br,lr))})}}switch(Ft){case Cu:case Ho:case ws:case Vc:case va:case B:case Uc:case Ac:case Va:case rs:return zt(function(_t){return _t},function(_t,Dt,vt){return vt});case no:return zt(function(_t){return Wr[_t]},function(_t,Dt,vt){var lr=_t.constants.compareFuncs;return Dt.def(lr,\"[\",vt,\"]\")});case ys:return zt(function(_t){return _t},function(_t,Dt,vt){var lr=Dt.def(\"+\",vt,\"[0]\"),_r=Dt.def(\"+\",vt,\"[1]\");return[lr,_r]});case Ls:return zt(function(_t){var Dt=\"srcRGB\"in _t?_t.srcRGB:_t.src,vt=\"srcAlpha\"in _t?_t.srcAlpha:_t.src,lr=\"dstRGB\"in _t?_t.dstRGB:_t.dst,_r=\"dstAlpha\"in _t?_t.dstAlpha:_t.dst;return[xi[Dt],xi[lr],xi[vt],xi[_r]]},function(_t,Dt,vt){var lr=_t.constants.blendFuncs;function _r(Ei,Oi){var Gi=Dt.def('\"',Ei,Oi,'\" in ',vt,\"?\",vt,\".\",Ei,Oi,\":\",vt,\".\",Ei);return Gi}var Br=_r(\"src\",\"RGB\"),Di=_r(\"dst\",\"RGB\"),ci=Dt.def(lr,\"[\",Br,\"]\"),bi=Dt.def(lr,\"[\",_r(\"src\",\"Alpha\"),\"]\"),gn=Dt.def(lr,\"[\",Di,\"]\"),tn=Dt.def(lr,\"[\",_r(\"dst\",\"Alpha\"),\"]\");return[ci,gn,bi,tn]});case Ws:return zt(function(_t){if(typeof _t==\"string\")return[yi[_t],yi[_t]];if(typeof _t==\"object\")return[yi[_t.rgb],yi[_t.alpha]]},function(_t,Dt,vt){var lr=_t.constants.blendEquations,_r=Dt.def(),Br=Dt.def(),Di=_t.cond(\"typeof \",vt,'===\"string\"');return Di.then(_r,\"=\",Br,\"=\",lr,\"[\",vt,\"];\"),Di.else(_r,\"=\",lr,\"[\",vt,\".rgb];\",Br,\"=\",lr,\"[\",vt,\".alpha];\"),Dt(Di),[_r,Br]});case ml:return zt(function(_t){return S(4,function(Dt){return+_t[Dt]})},function(_t,Dt,vt){return S(4,function(lr){return Dt.def(\"+\",vt,\"[\",lr,\"]\")})});case hc:return zt(function(_t){return _t|0},function(_t,Dt,vt){return Dt.def(vt,\"|0\")});case Ku:return zt(function(_t){var Dt=_t.cmp||\"keep\",vt=_t.ref||0,lr=\"mask\"in _t?_t.mask:-1;return[Wr[Dt],vt,lr]},function(_t,Dt,vt){var lr=_t.constants.compareFuncs,_r=Dt.def('\"cmp\" in ',vt,\"?\",lr,\"[\",vt,\".cmp]\",\":\",ot),Br=Dt.def(vt,\".ref|0\"),Di=Dt.def('\"mask\" in ',vt,\"?\",vt,\".mask|0:-1\");return[_r,Br,Di]});case ue:case w:return zt(function(_t){var Dt=_t.fail||\"keep\",vt=_t.zfail||\"keep\",lr=_t.zpass||\"keep\";return[Ft===w?se:X,ii[Dt],ii[vt],ii[lr]]},function(_t,Dt,vt){var lr=_t.constants.stencilOps;function _r(Br){return Dt.def('\"',Br,'\" in ',vt,\"?\",lr,\"[\",vt,\".\",Br,\"]:\",ot)}return[Ft===w?se:X,_r(\"fail\"),_r(\"zfail\"),_r(\"zpass\")]});case xu:return zt(function(_t){var Dt=_t.factor|0,vt=_t.units|0;return[Dt,vt]},function(_t,Dt,vt){var lr=Dt.def(vt,\".factor|0\"),_r=Dt.def(vt,\".units|0\");return[lr,_r]});case Yu:return zt(function(_t){var Dt=0;return _t===\"front\"?Dt=X:_t===\"back\"&&(Dt=se),Dt},function(_t,Dt,vt){return Dt.def(vt,'===\"front\"?',X,\":\",se)});case pu:return zt(function(_t){return _t},function(_t,Dt,vt){return vt});case Nc:return zt(function(_t){return di[_t]},function(_t,Dt,vt){return Dt.def(vt+'===\"cw\"?'+Te+\":\"+qe)});case Ql:return zt(function(_t){return _t.map(function(Dt){return!!Dt})},function(_t,Dt,vt){return S(4,function(lr){return\"!!\"+vt+\"[\"+lr+\"]\"})});case oo:return zt(function(_t){var Dt=\"value\"in _t?_t.value:1,vt=!!_t.invert;return[Dt,vt]},function(_t,Dt,vt){var lr=Dt.def('\"value\" in ',vt,\"?+\",vt,\".value:1\"),_r=Dt.def(\"!!\",vt,\".invert\");return[lr,_r]})}}),Bt}function zo(Ne,$e){var st=Ne.static,It=Ne.dynamic,Bt={};return Object.keys(st).forEach(function(Ft){var Ut=st[Ft],zt;if(typeof Ut==\"number\"||typeof Ut==\"boolean\")zt=kn(function(){return Ut});else if(typeof Ut==\"function\"){var _t=Ut._reglType;_t===\"texture2d\"||_t===\"textureCube\"?zt=kn(function(Dt){return Dt.link(Ut)}):(_t===\"framebuffer\"||_t===\"framebufferCube\")&&(zt=kn(function(Dt){return Dt.link(Ut.color[0])}))}else 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bi=Ut.divisor|0;_t.buffer=Dt,_t.state=La,_t.size=Br,_t.normalized=Di,_t.type=ci||Dt.dtype,_t.offset=lr,_t.stride=_r,_t.divisor=bi}}Bt[Ft]=kn(function(gn,tn){var Ei=gn.attribCache;if(zt in Ei)return Ei[zt];var Oi={isStream:!1};return Object.keys(_t).forEach(function(Gi){Oi[Gi]=_t[Gi]}),_t.buffer&&(Oi.buffer=gn.link(_t.buffer),Oi.type=Oi.type||Oi.buffer+\".dtype\"),Ei[zt]=Oi,Oi})}),Object.keys(It).forEach(function(Ft){var Ut=It[Ft];function zt(_t,Dt){var vt=_t.invoke(Dt,Ut),lr=_t.shared,_r=_t.constants,Br=lr.isBufferArgs,Di=lr.buffer,ci={isStream:Dt.def(!1)},bi=new Zr;bi.state=La,Object.keys(bi).forEach(function(Oi){ci[Oi]=Dt.def(\"\"+bi[Oi])});var gn=ci.buffer,tn=ci.type;Dt(\"if(\",Br,\"(\",vt,\")){\",ci.isStream,\"=true;\",gn,\"=\",Di,\".createStream(\",br,\",\",vt,\");\",tn,\"=\",gn,\".dtype;\",\"}else{\",gn,\"=\",Di,\".getBuffer(\",vt,\");\",\"if(\",gn,\"){\",tn,\"=\",gn,\".dtype;\",'}else if(\"constant\" in ',vt,\"){\",ci.state,\"=\",Ua,\";\",\"if(typeof \"+vt+'.constant === 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Ft==\"boolean\"?\"\"+Ft:Ut.link(Ft)})}),Object.keys(st).forEach(function(Bt){var Ft=st[Bt];It[Bt]=Bn(Ft,function(Ut,zt){return Ut.invoke(zt,Ft)})}),It}function Ul(Ne,$e,st,It,Bt){var Ft=Ne.static,Ut=Ne.dynamic,zt=is(Ne,$e),_t=sa(Ne,Bt),Dt=ba(Ne,_t,Bt),vt=Ga(Ne,Bt),lr=El(Ne,Bt),_r=Zs(Ne,Bt,zt);function Br(Ei){var Oi=Dt[Ei];Oi&&(lr[Ei]=Oi)}Br(ee),Br(Ji(Q));var Di=Object.keys(lr).length>0,ci={framebuffer:_t,draw:vt,shader:_r,state:lr,dirty:Di,scopeVAO:null,drawVAO:null,useVAO:!1,attributes:{}};if(ci.profile=ia(Ne,Bt),ci.uniforms=zo(st,Bt),ci.drawVAO=ci.scopeVAO=vt.vao,!ci.drawVAO&&_r.program&&!zt&&Ot.angle_instanced_arrays&&vt.static.elements){var bi=!0,gn=_r.program.attributes.map(function(Ei){var Oi=$e.static[Ei];return bi=bi&&!!Oi,Oi});if(bi&&gn.length>0){var tn=qi.getVAO(qi.createVAO({attributes:gn,elements:vt.static.elements}));ci.drawVAO=new Qr(null,null,null,function(Ei,Oi){return Ei.link(tn)}),ci.useVAO=!0}}return zt?ci.useVAO=!0:ci.attributes=Qs($e,Bt),ci.context=nl(It,Bt),ci}function ss(Ne,$e,st){var It=Ne.shared,Bt=It.context,Ft=Ne.scope();Object.keys(st).forEach(function(Ut){$e.save(Bt,\".\"+Ut);var zt=st[Ut],_t=zt.append(Ne,$e);Array.isArray(_t)?Ft(Bt,\".\",Ut,\"=[\",_t.join(),\"];\"):Ft(Bt,\".\",Ut,\"=\",_t,\";\")}),$e(Ft)}function Vs(Ne,$e,st,It){var Bt=Ne.shared,Ft=Bt.gl,Ut=Bt.framebuffer,zt;ui&&(zt=$e.def(Bt.extensions,\".webgl_draw_buffers\"));var _t=Ne.constants,Dt=_t.drawBuffer,vt=_t.backBuffer,lr;st?lr=st.append(Ne,$e):lr=$e.def(Ut,\".next\"),It||$e(\"if(\",lr,\"!==\",Ut,\".cur){\"),$e(\"if(\",lr,\"){\",Ft,\".bindFramebuffer(\",zr,\",\",lr,\".framebuffer);\"),ui&&$e(zt,\".drawBuffersWEBGL(\",Dt,\"[\",lr,\".colorAttachments.length]);\"),$e(\"}else{\",Ft,\".bindFramebuffer(\",zr,\",null);\"),ui&&$e(zt,\".drawBuffersWEBGL(\",vt,\");\"),$e(\"}\",Ut,\".cur=\",lr,\";\"),It||$e(\"}\")}function Ys(Ne,$e,st){var It=Ne.shared,Bt=It.gl,Ft=Ne.current,Ut=Ne.next,zt=It.current,_t=It.next,Dt=Ne.cond(zt,\".dirty\");fi.forEach(function(vt){var lr=Ji(vt);if(!(lr in st.state)){var _r,Br;if(lr in Ut){_r=Ut[lr],Br=Ft[lr];var Di=S(Zi[lr].length,function(bi){return Dt.def(_r,\"[\",bi,\"]\")});Dt(Ne.cond(Di.map(function(bi,gn){return bi+\"!==\"+Br+\"[\"+gn+\"]\"}).join(\"||\")).then(Bt,\".\",mn[lr],\"(\",Di,\");\",Di.map(function(bi,gn){return Br+\"[\"+gn+\"]=\"+bi}).join(\";\"),\";\"))}else{_r=Dt.def(_t,\".\",lr);var ci=Ne.cond(_r,\"!==\",zt,\".\",lr);Dt(ci),lr in si?ci(Ne.cond(_r).then(Bt,\".enable(\",si[lr],\");\").else(Bt,\".disable(\",si[lr],\");\"),zt,\".\",lr,\"=\",_r,\";\"):ci(Bt,\".\",mn[lr],\"(\",_r,\");\",zt,\".\",lr,\"=\",_r,\";\")}}}),Object.keys(st.state).length===0&&Dt(zt,\".dirty=false;\"),$e(Dt)}function wa(Ne,$e,st,It){var Bt=Ne.shared,Ft=Ne.current,Ut=Bt.current,zt=Bt.gl,_t;Ti(Object.keys(st)).forEach(function(Dt){var vt=st[Dt];if(!(It&&!It(vt))){var lr=vt.append(Ne,$e);if(si[Dt]){var 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i=t[n][0],a=i.trace,o=r._splomScenes[a.uid];a.type===\"splom\"&&o&&o.matrix&&yGt(e,a,o)}}function yGt(e,t,r){for(var n=r.matrixOptions.data.length,i=t._visibleDims,a=r.viewOpts.ranges=new Array(n),o=0;o<i.length;o++){var s=i[o],l=a[o]=new Array(4),u=jNe(e,t._diag[s][0]);u&&(l[0]=u.r2l(u.range[0]),l[2]=u.r2l(u.range[1]));var c=jNe(e,t._diag[s][1]);c&&(l[1]=c.r2l(c.range[0]),l[3]=c.r2l(c.range[1]))}r.selectBatch.length||r.unselectBatch.length?r.matrix.update({ranges:a},{ranges:a}):r.matrix.update({ranges:a})}function IK(e){var t=e._fullLayout,r=t._glcanvas.data()[0].regl,n=t._splomGrid;n||(n=t._splomGrid=hGt(r)),n.update(_Gt(e))}function _Gt(e){var t=e._context.plotGlPixelRatio,r=e._fullLayout,n=r._size,i=[0,0,r.width*t,r.height*t],a={},o;function s(x,C,S,g,P,E){S*=t,g*=t,P*=t,E*=t;var z=C[x+\"color\"],q=C[x+\"width\"],V=String(z+q);V in a?a[V].data.push(NaN,NaN,S,g,P,E):a[V]={data:[S,g,P,E],join:\"rect\",thickness:q*t,color:z,viewport:i,range:i,overlay:!1}}for(o in r._splomSubplots){var l=r._plots[o],u=l.xaxis,c=l.yaxis,f=u._gridVals,h=c._gridVals,d=u._offset,v=u._length,m=c._length,b=n.b+c.domain[0]*n.h,p=-c._m,k=-p*c.r2l(c.range[0],c.calendar),M,T;if(u.showgrid)for(o=0;o<f.length;o++)M=d+u.l2p(f[o].x),s(\"grid\",u,M,b,M,b+m);if(c.showgrid)for(o=0;o<h.length;o++)T=b+k+p*h[o].x,s(\"grid\",c,d,T,d+v,T);WNe(e,u,c)&&(M=d+u.l2p(0),s(\"zeroline\",u,M,b,M,b+m)),WNe(e,c,u)&&(T=b+k+0,s(\"zeroline\",c,d,T,d+v,T))}var L=[];for(o in a)L.push(a[o]);return L}function xGt(e,t,r,n){var i={},a;if(n._splomScenes){for(a=0;a<e.length;a++){var o=e[a];o.type===\"splom\"&&(i[o.uid]=1)}for(a=0;a<r.length;a++){var s=r[a];if(!i[s.uid]){var l=n._splomScenes[s.uid];l&&l.destroy&&l.destroy(),n._splomScenes[s.uid]=null,delete n._splomScenes[s.uid]}}}Object.keys(n._splomScenes||{}).length===0&&delete n._splomScenes,n._splomGrid&&!t._hasOnlyLargeSploms&&n._hasOnlyLargeSploms&&(n._splomGrid.destroy(),n._splomGrid=null,delete n._splomGrid),Mx.clean(e,t,r,n)}YNe.exports={name:XNe,attr:Mx.attr,attrRegex:Mx.attrRegex,layoutAttributes:Mx.layoutAttributes,supplyLayoutDefaults:Mx.supplyLayoutDefaults,drawFramework:Mx.drawFramework,plot:gGt,drag:mGt,updateGrid:IK,clean:xGt,updateFx:Mx.updateFx,toSVG:Mx.toSVG,reglPrecompiled:ZNe}});var QNe=ye((yxr,$Ne)=>{\"use strict\";var JNe=HNe();JNe.basePlotModule=KNe(),$Ne.exports=JNe});var tUe=ye((_xr,eUe)=>{\"use strict\";eUe.exports=QNe()});var FK=ye((xxr,rUe)=>{\"use strict\";var bGt=Tu(),RK=Rd(),DK=ec(),wGt=Cc().attributes,Gz=Ao().extendFlat,TGt=vl().templatedArray;rUe.exports={domain:wGt({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:DK({editType:\"plot\"}),tickfont:DK({autoShadowDflt:!0,editType:\"plot\"}),rangefont:DK({editType:\"plot\"}),dimensions:TGt(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:Gz({},RK.tickvals,{editType:\"plot\"}),ticktext:Gz({},RK.ticktext,{editType:\"plot\"}),tickformat:Gz({},RK.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:Gz({editType:\"calc\"},bGt(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"})),unselected:{line:{color:{valType:\"color\",dflt:\"#7f7f7f\",editType:\"plot\"},opacity:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"plot\"},editType:\"plot\"},editType:\"plot\"}}});var Vk=ye((bxr,iUe)=>{\"use strict\";iUe.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:[\"contextLineLayer\",\"focusLineLayer\",\"pickLineLayer\"],axisTitleOffset:28,axisExtentOffset:10,bar:{width:4,captureWidth:10,fillColor:\"magenta\",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:\"axis-extent-text\",parcoordsLineLayers:\"parcoords-line-layers\",parcoordsLineLayer:\"parcoords-lines\",parcoords:\"parcoords\",parcoordsControlView:\"parcoords-control-view\",yAxis:\"y-axis\",axisOverlays:\"axis-overlays\",axis:\"axis\",axisHeading:\"axis-heading\",axisTitle:\"axis-title\",axisExtent:\"axis-extent\",axisExtentTop:\"axis-extent-top\",axisExtentTopText:\"axis-extent-top-text\",axisExtentBottom:\"axis-extent-bottom\",axisExtentBottomText:\"axis-extent-bottom-text\",axisBrush:\"axis-brush\"},id:{filterBarPattern:\"filter-bar-pattern\"}}});var ay=ye((wxr,aUe)=>{\"use strict\";var AGt=$S();function nUe(e){return[e]}aUe.exports={keyFun:function(e){return e.key},repeat:nUe,descend:AGt,wrap:nUe,unwrap:function(e){return e[0]}}});var OK=ye((Txr,gUe)=>{\"use strict\";var Th=Vk(),im=Fa(),SGt=ay().keyFun,Hz=ay().repeat,E5=Pr().sorterAsc,MGt=Pr().strTranslate,oUe=Th.bar.snapRatio;function sUe(e,t){return e*(1-oUe)+t*oUe}var lUe=Th.bar.snapClose;function EGt(e,t){return e*(1-lUe)+t*lUe}function Wz(e,t,r,n){if(kGt(r,n))return r;var i=e?-1:1,a=0,o=t.length-1;if(i<0){var s=a;a=o,o=s}for(var l=t[a],u=l,c=a;i*c<i*o;c+=i){var f=c+i,h=t[f];if(i*r<i*EGt(l,h))return sUe(l,u);if(i*r<i*h||f===o)return sUe(h,l);u=l,l=h}}function kGt(e,t){for(var r=0;r<t.length;r++)if(e>=t[r][0]&&e<=t[r][1])return!0;return!1}function CGt(e){e.attr(\"x\",-Th.bar.captureWidth/2).attr(\"width\",Th.bar.captureWidth)}function LGt(e){e.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function PGt(e){if(!e.brush.filterSpecified)return\"0,\"+e.height;for(var t=uUe(e.brush.filter.getConsolidated(),e.height),r=[0],n,i,a,o=t.length?t[0][0]:null,s=0;s<t.length;s++)n=t[s],i=n[1]-n[0],r.push(o),r.push(i),a=s+1,a<t.length&&(o=t[a][0]-n[1]);return r.push(e.height),r}function uUe(e,t){return e.map(function(r){return r.map(function(n){return Math.max(0,n*t)}).sort(E5)})}function IGt(e,t){var r=Th.bar.handleHeight;if(!(t>e[1]+r||t<e[0]-r))return t>=.9*e[1]+.1*e[0]?\"n\":t<=.9*e[0]+.1*e[1]?\"s\":\"ns\"}function cUe(){im.select(document.body).style(\"cursor\",null)}function qK(e){e.attr(\"stroke-dasharray\",PGt)}function jz(e,t){var r=im.select(e).selectAll(\".highlight, .highlight-shadow\"),n=t?r.transition().duration(Th.bar.snapDuration).each(\"end\",t):r;qK(n)}function fUe(e,t){var r=e.brush,n=r.filterSpecified,i=NaN,a={},o;if(n){var s=e.height,l=r.filter.getConsolidated(),u=uUe(l,s),c=NaN,f=NaN,h=NaN;for(o=0;o<=u.length;o++){var d=u[o];if(d&&d[0]<=t&&t<=d[1]){c=o;break}else if(f=o?o-1:NaN,d&&d[0]>t){h=o;break}}if(i=c,isNaN(i)&&(isNaN(f)||isNaN(h)?i=isNaN(f)?h:f:i=t-u[f][1]<u[h][0]-t?f:h),!isNaN(i)){var v=u[i],m=IGt(v,t);m&&(a.interval=l[i],a.intervalPix=v,a.region=m)}}if(e.ordinal&&!a.region){var b=e.unitTickvals,p=e.unitToPaddedPx.invert(t);for(o=0;o<b.length;o++){var k=[b[Math.max(o-1,0)]*.25+b[o]*.75,b[Math.min(o+1,b.length-1)]*.25+b[o]*.75];if(p>=k[0]&&p<=k[1]){a.clickableOrdinalRange=k;break}}}return a}function RGt(e,t){im.event.sourceEvent.stopPropagation();var r=t.height-im.mouse(e)[1]-2*Th.verticalPadding,n=t.unitToPaddedPx.invert(r),i=t.brush,a=fUe(t,r),o=a.interval,s=i.svgBrush;if(s.wasDragged=!1,s.grabbingBar=a.region===\"ns\",s.grabbingBar){var l=o.map(t.unitToPaddedPx);s.grabPoint=r-l[0]-Th.verticalPadding,s.barLength=l[1]-l[0]}s.clickableOrdinalRange=a.clickableOrdinalRange,s.stayingIntervals=t.multiselect&&i.filterSpecified?i.filter.getConsolidated():[],o&&(s.stayingIntervals=s.stayingIntervals.filter(function(u){return u[0]!==o[0]&&u[1]!==o[1]})),s.startExtent=a.region?o[a.region===\"s\"?1:0]:n,t.parent.inBrushDrag=!0,s.brushStartCallback()}function hUe(e,t){im.event.sourceEvent.stopPropagation();var r=t.height-im.mouse(e)[1]-2*Th.verticalPadding,n=t.brush.svgBrush;n.wasDragged=!0,n._dragging=!0,n.grabbingBar?n.newExtent=[r-n.grabPoint,r+n.barLength-n.grabPoint].map(t.unitToPaddedPx.invert):n.newExtent=[n.startExtent,t.unitToPaddedPx.invert(r)].sort(E5),t.brush.filterSpecified=!0,n.extent=n.stayingIntervals.concat([n.newExtent]),n.brushCallback(t),jz(e.parentNode)}function DGt(e,t){var r=t.brush,n=r.filter,i=r.svgBrush;i._dragging||(dUe(e,t),hUe(e,t),t.brush.svgBrush.wasDragged=!1),i._dragging=!1;var a=im.event;a.sourceEvent.stopPropagation();var o=i.grabbingBar;if(i.grabbingBar=!1,i.grabLocation=void 0,t.parent.inBrushDrag=!1,cUe(),!i.wasDragged){i.wasDragged=void 0,i.clickableOrdinalRange?r.filterSpecified&&t.multiselect?i.extent.push(i.clickableOrdinalRange):(i.extent=[i.clickableOrdinalRange],r.filterSpecified=!0):o?(i.extent=i.stayingIntervals,i.extent.length===0&&zK(r)):zK(r),i.brushCallback(t),jz(e.parentNode),i.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);return}var s=function(){n.set(n.getConsolidated())};if(t.ordinal){var l=t.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),i.newExtent=[Wz(0,l,i.newExtent[0],i.stayingIntervals),Wz(1,l,i.newExtent[1],i.stayingIntervals)];var u=i.newExtent[1]>i.newExtent[0];i.extent=i.stayingIntervals.concat(u?[i.newExtent]:[]),i.extent.length||zK(r),i.brushCallback(t),u?jz(e.parentNode,s):(s(),jz(e.parentNode))}else s();i.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}function dUe(e,t){var r=t.height-im.mouse(e)[1]-2*Th.verticalPadding,n=fUe(t,r),i=\"crosshair\";n.clickableOrdinalRange?i=\"pointer\":n.region&&(i=n.region+\"-resize\"),im.select(document.body).style(\"cursor\",i)}function FGt(e){e.on(\"mousemove\",function(t){im.event.preventDefault(),t.parent.inBrushDrag||dUe(this,t)}).on(\"mouseleave\",function(t){t.parent.inBrushDrag||cUe()}).call(im.behavior.drag().on(\"dragstart\",function(t){RGt(this,t)}).on(\"drag\",function(t){hUe(this,t)}).on(\"dragend\",function(t){DGt(this,t)}))}function vUe(e,t){return e[0]-t[0]}function zGt(e,t,r){var n=r._context.staticPlot,i=e.selectAll(\".background\").data(Hz);i.enter().append(\"rect\").classed(\"background\",!0).call(CGt).call(LGt).style(\"pointer-events\",n?\"none\":\"auto\").attr(\"transform\",MGt(0,Th.verticalPadding)),i.call(FGt).attr(\"height\",function(s){return s.height-Th.verticalPadding});var a=e.selectAll(\".highlight-shadow\").data(Hz);a.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-Th.bar.width/2).attr(\"stroke-width\",Th.bar.width+Th.bar.strokeWidth).attr(\"stroke\",t).attr(\"opacity\",Th.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),a.attr(\"y1\",function(s){return s.height}).call(qK);var o=e.selectAll(\".highlight\").data(Hz);o.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-Th.bar.width/2).attr(\"stroke-width\",Th.bar.width-Th.bar.strokeWidth).attr(\"stroke\",Th.bar.fillColor).attr(\"opacity\",Th.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),o.attr(\"y1\",function(s){return s.height}).call(qK)}function qGt(e,t,r){var n=e.selectAll(\".\"+Th.cn.axisBrush).data(Hz,SGt);n.enter().append(\"g\").classed(Th.cn.axisBrush,!0),zGt(n,t,r)}function OGt(e){return e.svgBrush.extent.map(function(t){return t.slice()})}function zK(e){e.filterSpecified=!1,e.svgBrush.extent=[[-1/0,1/0]]}function BGt(e){return function(r){var n=r.brush,i=OGt(n),a=i.slice();n.filter.set(a),e()}}function pUe(e){for(var t=e.slice(),r=[],n,i=t.shift();i;){for(n=i.slice();(i=t.shift())&&i[0]<=n[1];)n[1]=Math.max(n[1],i[1]);r.push(n)}return r.length===1&&r[0][0]>r[0][1]&&(r=[]),r}function NGt(){var e=[],t,r;return{set:function(n){e=n.map(function(i){return i.slice().sort(E5)}).sort(vUe),e.length===1&&e[0][0]===-1/0&&e[0][1]===1/0&&(e=[[0,-1]]),t=pUe(e),r=e.reduce(function(i,a){return[Math.min(i[0],a[0]),Math.max(i[1],a[1])]},[1/0,-1/0])},get:function(){return e.slice()},getConsolidated:function(){return t},getBounds:function(){return r}}}function UGt(e,t,r,n,i,a){var o=NGt();return o.set(r),{filter:o,filterSpecified:t,svgBrush:{extent:[],brushStartCallback:n,brushCallback:BGt(i),brushEndCallback:a}}}function VGt(e,t){if(Array.isArray(e[0])?(e=e.map(function(n){return n.sort(E5)}),t.multiselect?e=pUe(e.sort(vUe)):e=[e[0]]):e=[e.sort(E5)],t.tickvals){var r=t.tickvals.slice().sort(E5);if(e=e.map(function(n){var i=[Wz(0,r,n[0],[]),Wz(1,r,n[1],[])];if(i[1]>i[0])return i}).filter(function(n){return n}),!e.length)return}return e.length>1?e:e[0]}gUe.exports={makeBrush:UGt,ensureAxisBrush:qGt,cleanRanges:VGt}});var _Ue=ye((Axr,yUe)=>{\"use strict\";var Ex=Pr(),GGt=pv().hasColorscale,HGt=td(),jGt=Cc().defaults,WGt=Kd(),XGt=ho(),mUe=FK(),ZGt=OK(),BK=Vk().maxDimensionCount,YGt=zz();function KGt(e,t,r,n,i){var a=i(\"line.color\",r);if(GGt(e,\"line\")&&Ex.isArrayOrTypedArray(a)){if(a.length)return i(\"line.colorscale\"),HGt(e,t,n,i,{prefix:\"line.\",cLetter:\"c\"}),a.length;t.line.color=r}return 1/0}function JGt(e,t,r,n){function i(u,c){return Ex.coerce(e,t,mUe.dimensions,u,c)}var a=i(\"values\"),o=i(\"visible\");if(a&&a.length||(o=t.visible=!1),o){i(\"label\"),i(\"tickvals\"),i(\"ticktext\"),i(\"tickformat\");var s=i(\"range\");t._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:s},XGt.setConvert(t._ax,n.layout),i(\"multiselect\");var l=i(\"constraintrange\");l&&(t.constraintrange=ZGt.cleanRanges(l,t))}}yUe.exports=function(t,r,n,i){function a(c,f){return Ex.coerce(t,r,mUe,c,f)}var o=t.dimensions;Array.isArray(o)&&o.length>BK&&(Ex.log(\"parcoords traces support up to \"+BK+\" dimensions at the moment\"),o.splice(BK));var s=WGt(t,r,{name:\"dimensions\",layout:i,handleItemDefaults:JGt}),l=KGt(t,r,n,i,a);jGt(r,i,a),(!Array.isArray(s)||!s.length)&&(r.visible=!1),YGt(r,s,\"values\",l);var u=Ex.extendFlat({},i.font,{size:Math.round(i.font.size/1.2)});Ex.coerceFont(a,\"labelfont\",u),Ex.coerceFont(a,\"tickfont\",u,{autoShadowDflt:!0}),Ex.coerceFont(a,\"rangefont\",u),a(\"labelangle\"),a(\"labelside\"),a(\"unselected.line.color\"),a(\"unselected.line.opacity\")}});var bUe=ye((Sxr,xUe)=>{\"use strict\";var $Gt=Pr().isArrayOrTypedArray,NK=tc(),QGt=ay().wrap;xUe.exports=function(t,r){var n,i;return NK.hasColorscale(r,\"line\")&&$Gt(r.line.color)?(n=r.line.color,i=NK.extractOpts(r.line).colorscale,NK.calc(t,r,{vals:n,containerStr:\"line\",cLetter:\"c\"})):(n=eHt(r._length),i=[[0,r.line.color],[1,r.line.color]]),QGt({lineColor:n,cscale:i})};function eHt(e){for(var t=new Array(e),r=0;r<e;r++)t[r]=.5;return t}});function tHt(e){var c,f;var t,r=[],n=1,i;if(typeof e==\"number\")return{space:\"rgb\",values:[e>>>16,(e&65280)>>>8,e&255],alpha:1};if(typeof e==\"number\")return{space:\"rgb\",values:[e>>>16,(e&65280)>>>8,e&255],alpha:1};if(e=String(e).toLowerCase(),UK.default[e])r=UK.default[e].slice(),i=\"rgb\";else if(e===\"transparent\")n=0,i=\"rgb\",r=[0,0,0];else if(e[0]===\"#\"){var a=e.slice(1),o=a.length,s=o<=4;n=1,s?(r=[parseInt(a[0]+a[0],16),parseInt(a[1]+a[1],16),parseInt(a[2]+a[2],16)],o===4&&(n=parseInt(a[3]+a[3],16)/255)):(r=[parseInt(a[0]+a[1],16),parseInt(a[2]+a[3],16),parseInt(a[4]+a[5],16)],o===8&&(n=parseInt(a[6]+a[7],16)/255)),r[0]||(r[0]=0),r[1]||(r[1]=0),r[2]||(r[2]=0),i=\"rgb\"}else if(t=/^((?:rgba?|hs[lvb]a?|hwba?|cmyk?|xy[zy]|gray|lab|lchu?v?|[ly]uv|lms|oklch|oklab|color))\\s*\\(([^\\)]*)\\)/.exec(e)){var l=t[1];i=l.replace(/a$/,\"\");var u=i===\"cmyk\"?4:i===\"gray\"?1:3;r=t[2].trim().split(/\\s*[,\\/]\\s*|\\s+/),i===\"color\"&&(i=r.shift()),r=r.map(function(h,d){if(h[h.length-1]===\"%\")return h=parseFloat(h)/100,d===3?h:i===\"rgb\"?h*255:i[0]===\"h\"||i[0]===\"l\"&&!d?h*100:i===\"lab\"?h*125:i===\"lch\"?d<2?h*150:h*360:i[0]===\"o\"&&!d?h:i===\"oklab\"?h*.4:i===\"oklch\"?d<2?h*.4:h*360:h;if(i[d]===\"h\"||d===2&&i[i.length-1]===\"h\"){if(wUe[h]!==void 0)return wUe[h];if(h.endsWith(\"deg\"))return parseFloat(h);if(h.endsWith(\"turn\"))return parseFloat(h)*360;if(h.endsWith(\"grad\"))return parseFloat(h)*360/400;if(h.endsWith(\"rad\"))return parseFloat(h)*180/Math.PI}return h===\"none\"?0:parseFloat(h)}),n=r.length>u?r.pop():1}else/[0-9](?:\\s|\\/|,)/.test(e)&&(r=e.match(/([0-9]+)/g).map(function(h){return parseFloat(h)}),i=((f=(c=e.match(/([a-z])/ig))==null?void 0:c.join(\"\"))==null?void 0:f.toLowerCase())||\"rgb\");return{space:i,values:r,alpha:n}}var UK,TUe,wUe,AUe=gu(()=>{UK=Rtt(MX(),1),TUe=tHt,wUe={red:0,orange:60,yellow:120,green:180,blue:240,purple:300}});var Gk,VK=gu(()=>{Gk={name:\"rgb\",min:[0,0,0],max:[255,255,255],channel:[\"red\",\"green\",\"blue\"],alias:[\"RGB\"]}});var Xz,SUe=gu(()=>{VK();Xz={name:\"hsl\",min:[0,0,0],max:[360,100,100],channel:[\"hue\",\"saturation\",\"lightness\"],alias:[\"HSL\"],rgb:function(e){var t=e[0]/360,r=e[1]/100,n=e[2]/100,i,a,o,s,l,u=0;if(r===0)return l=n*255,[l,l,l];for(a=n<.5?n*(1+r):n+r-n*r,i=2*n-a,s=[0,0,0];u<3;)o=t+1/3*-(u-1),o<0?o++:o>1&&o--,l=6*o<1?i+(a-i)*6*o:2*o<1?a:3*o<2?i+(a-i)*(2/3-o)*6:i,s[u++]=l*255;return s}};Gk.hsl=function(e){var t=e[0]/255,r=e[1]/255,n=e[2]/255,i=Math.min(t,r,n),a=Math.max(t,r,n),o=a-i,s,l,u;return a===i?s=0:t===a?s=(r-n)/o:r===a?s=2+(n-t)/o:n===a&&(s=4+(t-r)/o),s=Math.min(s*60,360),s<0&&(s+=360),u=(i+a)/2,a===i?l=0:u<=.5?l=o/(a+i):l=o/(2-a-i),[s,l*100,u*100]}});var EUe={};bee(EUe,{default:()=>MUe});function MUe(e){Array.isArray(e)&&e.raw&&(e=String.raw(...arguments)),e instanceof Number&&(e=+e);var t,r,n,i=TUe(e);if(!i.space)return[];let a=i.space[0]===\"h\"?Xz.min:Gk.min,o=i.space[0]===\"h\"?Xz.max:Gk.max;return t=Array(3),t[0]=Math.min(Math.max(i.values[0],a[0]),o[0]),t[1]=Math.min(Math.max(i.values[1],a[1]),o[1]),t[2]=Math.min(Math.max(i.values[2],a[2]),o[2]),i.space[0]===\"h\"&&(t=Xz.rgb(t)),t.push(Math.min(Math.max(i.alpha,0),1)),t}var kUe=gu(()=>{AUe();VK();SUe()});var GK=ye(Zz=>{\"use strict\";var rHt=Pr().isTypedArray;Zz.convertTypedArray=function(e){return rHt(e)?Array.prototype.slice.call(e):e};Zz.isOrdinal=function(e){return!!e.tickvals};Zz.isVisible=function(e){return e.visible||!(\"visible\"in e)}});var OUe=ye((Dxr,qUe)=>{\"use strict\";var iHt=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"attribute vec4 p01_04, p05_08, p09_12, p13_16,\",\"               p17_20, p21_24, p25_28, p29_32,\",\"               p33_36, p37_40, p41_44, p45_48,\",\"               p49_52, p53_56, p57_60, colors;\",\"\",\"uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\",\"             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\",\"\",\"uniform vec2 resolution, viewBoxPos, viewBoxSize;\",\"uniform float maskHeight;\",\"uniform float drwLayer; // 0: context, 1: focus, 2: pick\",\"uniform vec4 contextColor;\",\"uniform sampler2D maskTexture, palette;\",\"\",\"bool isPick    = (drwLayer > 1.5);\",\"bool isContext = (drwLayer < 0.5);\",\"\",\"const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\",\"const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\",\"\",\"float val(mat4 p, mat4 v) {\",\"    return dot(matrixCompMult(p, v) * UNITS, UNITS);\",\"}\",\"\",\"float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\",\"    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\",\"    return y1 * (1.0 - ratio) + y2 * ratio;\",\"}\",\"\",\"int iMod(int a, int b) {\",\"    return a - b * (a / b);\",\"}\",\"\",\"bool fOutside(float p, float lo, float hi) {\",\"    return (lo < hi) && (lo > p || p > hi);\",\"}\",\"\",\"bool vOutside(vec4 p, vec4 lo, vec4 hi) {\",\"    return (\",\"        fOutside(p[0], lo[0], hi[0]) ||\",\"        fOutside(p[1], lo[1], hi[1]) ||\",\"        fOutside(p[2], lo[2], hi[2]) ||\",\"        fOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool mOutside(mat4 p, mat4 lo, mat4 hi) {\",\"    return (\",\"        vOutside(p[0], lo[0], hi[0]) ||\",\"        vOutside(p[1], lo[1], hi[1]) ||\",\"        vOutside(p[2], lo[2], hi[2]) ||\",\"        vOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    return mOutside(A, loA, hiA) ||\",\"           mOutside(B, loB, hiB) ||\",\"           mOutside(C, loC, hiC) ||\",\"           mOutside(D, loD, hiD);\",\"}\",\"\",\"bool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    mat4 pnts[4];\",\"    pnts[0] = A;\",\"    pnts[1] = B;\",\"    pnts[2] = C;\",\"    pnts[3] = D;\",\"\",\"    for(int i = 0; i < 4; ++i) {\",\"        for(int j = 0; j < 4; ++j) {\",\"            for(int k = 0; k < 4; ++k) {\",\"                if(0 == iMod(\",\"                    int(255.0 * texture2D(maskTexture,\",\"                        vec2(\",\"                            (float(i * 2 + j / 2) + 0.5) / 8.0,\",\"                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\",\"                        ))[3]\",\"                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\",\"                    2\",\"                )) return true;\",\"            }\",\"        }\",\"    }\",\"    return false;\",\"}\",\"\",\"vec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float x = 0.5 * sign(v) + 0.5;\",\"    float y = axisY(x, A, B, C, D);\",\"    float z = 1.0 - abs(v);\",\"\",\"    z += isContext ? 0.0 : 2.0 * float(\",\"        outsideBoundingBox(A, B, C, D) ||\",\"        outsideRasterMask(A, B, C, D)\",\"    );\",\"\",\"    return vec4(\",\"        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\",\"        z,\",\"        1.0\",\"    );\",\"}\",\"\",\"void main() {\",\"    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\",\"    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\",\"    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\",\"    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\",\"\",\"    float v = colors[3];\",\"\",\"    gl_Position = position(isContext, v, A, B, C, D);\",\"\",\"    fragColor =\",\"        isContext ? vec4(contextColor) :\",\"        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\",\"}\"].join(`\n",
+       "`),nHt=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"void main() {\",\"    gl_FragColor = fragColor;\",\"}\"].join(`\n",
+       "`),Hk=Vk().maxDimensionCount,DUe=Pr(),CUe=1e-6,Yz=2048,aHt=new Uint8Array(4),LUe=new Uint8Array(4),PUe={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function oHt(e){e.read({x:0,y:0,width:1,height:1,data:aHt})}function FUe(e,t,r,n,i){var a=e._gl;a.enable(a.SCISSOR_TEST),a.scissor(t,r,n,i),e.clear({color:[0,0,0,0],depth:1})}function sHt(e,t,r,n,i,a){var o=a.key;function s(l){var u=Math.min(n,i-l*n);l===0&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],FUe(e,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),!r.clearOnly&&(a.count=2*u,a.offset=2*l*n,t(a),l*n+u<i&&(r.currentRafs[o]=window.requestAnimationFrame(function(){s(l+1)})),r.drawCompleted=!1)}r.drawCompleted||(oHt(e),r.drawCompleted=!0),s(0)}function lHt(e){return Math.max(CUe,Math.min(1-CUe,e))}function uHt(e,t){for(var r=new Array(256),n=0;n<256;n++)r[n]=e(n/255).concat(t);return r}function HK(e,t){return(e>>>8*t)%256/255}function cHt(e,t,r){for(var n=new Array(e*(Hk+4)),i=0,a=0;a<e;a++){for(var o=0;o<Hk;o++)n[i++]=o<t.length?t[o].paddedUnitValues[a]:.5;n[i++]=HK(a,2),n[i++]=HK(a,1),n[i++]=HK(a,0),n[i++]=lHt(r[a])}return n}function fHt(e,t,r){for(var n=new Array(t*8),i=0,a=0;a<t;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=e*4+s,u=r[a*64+l];l===63&&o===0&&(u*=-1),n[i++]=u}return n}function IUe(e){var t=\"0\"+e;return t.slice(-2)}function zUe(e){return e<Hk?\"p\"+IUe(e+1)+\"_\"+IUe(e+4):\"colors\"}function hHt(e,t,r){for(var n=0;n<=Hk;n+=4)e[zUe(n)](fHt(n/4,t,r))}function dHt(e){for(var t={},r=0;r<=Hk;r+=4)t[zUe(r)]=e.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return t}function vHt(e,t,r,n,i,a,o,s,l,u,c,f,h,d){for(var v=[[],[]],m=0;m<64;m++)v[0][m]=m===i?1:0,v[1][m]=m===a?1:0;o*=d,s*=d,l*=d,u*=d;var b=e.lines.canvasOverdrag*d,p=e.domain,k=e.canvasWidth*d,M=e.canvasHeight*d,T=e.pad.l*d,L=e.pad.b*d,x=e.layoutHeight*d,C=e.layoutWidth*d,S=e.deselectedLines.color,g=e.deselectedLines.opacity,P=DUe.extendFlat({key:c,resolution:[k,M],viewBoxPos:[o+b,s],viewBoxSize:[l,u],i0:i,i1:a,dim0A:v[0].slice(0,16),dim0B:v[0].slice(16,32),dim0C:v[0].slice(32,48),dim0D:v[0].slice(48,64),dim1A:v[1].slice(0,16),dim1B:v[1].slice(16,32),dim1C:v[1].slice(32,48),dim1D:v[1].slice(48,64),drwLayer:f,contextColor:[S[0]/255,S[1]/255,S[2]/255,g!==\"auto\"?S[3]*g:Math.max(1/255,Math.pow(1/e.lines.color.length,1/3))],scissorX:(n===t?0:o+b)+(T-b)+C*p.x[0],scissorWidth:(n===r?k-o+b:l+.5)+(n===t?o+b:0),scissorY:s+L+x*p.y[0],scissorHeight:u,viewportX:T-b+C*p.x[0],viewportY:L+x*p.y[0],viewportWidth:k,viewportHeight:M},h);return P}function RUe(e){var t=Yz-1,r=Math.max(0,Math.floor(e[0]*t),0),n=Math.min(t,Math.ceil(e[1]*t),t);return[Math.min(r,n),Math.max(r,n)]}qUe.exports=function(e,t){var r=t.context,n=t.pick,i=t.regl,a=i._gl,o=a.getParameter(a.ALIASED_LINE_WIDTH_RANGE),s=Math.max(o[0],Math.min(o[1],t.viewModel.plotGlPixelRatio)),l={currentRafs:{},drawCompleted:!0,clearOnly:!1},u,c,f,h,d=dHt(i),v,m=i.texture(PUe),b=[];k(t);var p=i({profile:!1,blend:{enable:r,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:1,dstAlpha:1},equation:{rgb:\"add\",alpha:\"add\"},color:[0,0,0,0]},depth:{enable:!r,mask:!0,func:\"less\",range:[0,1]},cull:{enable:!0,face:\"back\"},scissor:{enable:!0,box:{x:i.prop(\"scissorX\"),y:i.prop(\"scissorY\"),width:i.prop(\"scissorWidth\"),height:i.prop(\"scissorHeight\")}},viewport:{x:i.prop(\"viewportX\"),y:i.prop(\"viewportY\"),width:i.prop(\"viewportWidth\"),height:i.prop(\"viewportHeight\")},dither:!1,vert:iHt,frag:nHt,primitive:\"lines\",lineWidth:s,attributes:d,uniforms:{resolution:i.prop(\"resolution\"),viewBoxPos:i.prop(\"viewBoxPos\"),viewBoxSize:i.prop(\"viewBoxSize\"),dim0A:i.prop(\"dim0A\"),dim1A:i.prop(\"dim1A\"),dim0B:i.prop(\"dim0B\"),dim1B:i.prop(\"dim1B\"),dim0C:i.prop(\"dim0C\"),dim1C:i.prop(\"dim1C\"),dim0D:i.prop(\"dim0D\"),dim1D:i.prop(\"dim1D\"),loA:i.prop(\"loA\"),hiA:i.prop(\"hiA\"),loB:i.prop(\"loB\"),hiB:i.prop(\"hiB\"),loC:i.prop(\"loC\"),hiC:i.prop(\"hiC\"),loD:i.prop(\"loD\"),hiD:i.prop(\"hiD\"),palette:m,contextColor:i.prop(\"contextColor\"),maskTexture:i.prop(\"maskTexture\"),drwLayer:i.prop(\"drwLayer\"),maskHeight:i.prop(\"maskHeight\")},offset:i.prop(\"offset\"),count:i.prop(\"count\")});function k(S){u=S.model,c=S.viewModel,f=c.dimensions.slice(),h=f[0]?f[0].values.length:0;var g=u.lines,P=n?g.color.map(function(z,q){return q/g.color.length}):g.color,E=cHt(h,f,P);hHt(d,h,E),!r&&!n&&(m=i.texture(DUe.extendFlat({data:uHt(u.unitToColor,255)},PUe)))}function M(S){var g,P,E,z=[[],[]];for(E=0;E<64;E++){var q=!S&&E<f.length?f[E].brush.filter.getBounds():[-1/0,1/0];z[0][E]=q[0],z[1][E]=q[1]}var V=Yz*8,G=new Array(V);for(g=0;g<V;g++)G[g]=255;if(!S)for(g=0;g<f.length;g++){var Z=g%8,j=(g-Z)/8,N=Math.pow(2,Z),H=f[g],re=H.brush.filter.get();if(!(re.length<2)){var ae=RUe(re[0])[1];for(P=1;P<re.length;P++){var _e=RUe(re[P]);for(E=ae+1;E<_e[0];E++)G[E*8+j]&=~N;ae=Math.max(ae,_e[1])}}}var Ee={shape:[8,Yz],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:G};return 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i(\"line.colorscale\"),BHt(e,t,n,i,{prefix:\"line.\",cLetter:\"c\"}),a.length;t.line.color=r}return 1/0}function jHt(e,t){function r(u,c){return C5.coerce(e,t,mVe.dimensions,u,c)}var n=r(\"values\"),i=r(\"visible\");if(n&&n.length||(i=t.visible=!1),i){r(\"label\"),r(\"displayindex\",t._index);var a=e.categoryarray,o=C5.isArrayOrTypedArray(a)&&a.length>0||GHt(a),s;o&&(s=\"array\");var l=r(\"categoryorder\",s);l===\"array\"?(r(\"categoryarray\"),r(\"ticktext\")):(delete e.categoryarray,delete e.ticktext),!o&&l===\"array\"&&(t.categoryorder=\"trace\")}}yVe.exports=function(t,r,n,i){function a(u,c){return C5.coerce(t,r,mVe,u,c)}var o=UHt(t,r,{name:\"dimensions\",handleItemDefaults:jHt}),s=HHt(t,r,n,i,a);NHt(r,i,a),(!Array.isArray(o)||!o.length)&&(r.visible=!1),VHt(r,o,\"values\",s),a(\"hoveron\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),a(\"arrangement\"),a(\"bundlecolors\"),a(\"sortpaths\"),a(\"counts\");var l=i.font;C5.coerceFont(a,\"labelfont\",l,{overrideDflt:{size:Math.round(l.size)}}),C5.coerceFont(a,\"tickfont\",l,{autoShadowDflt:!0,overrideDflt:{size:Math.round(l.size/1.2)}})}});var bVe=ye((Vxr,xVe)=>{\"use strict\";var WHt=ay().wrap,XHt=pv().hasColorscale,ZHt=gv(),YHt=aO(),KHt=So(),Wk=Pr(),JHt=Eo();xVe.exports=function(t,r){var n=Wk.filterVisible(r.dimensions);if(n.length===0)return[];var i=n.map(function(g){var P;if(g.categoryorder===\"trace\")P=null;else if(g.categoryorder===\"array\")P=g.categoryarray;else{P=YHt(g.values);for(var E=!0,z=0;z<P.length;z++)if(!JHt(P[z])){E=!1;break}P.sort(E?Wk.sorterAsc:void 0),g.categoryorder===\"category descending\"&&(P=P.reverse())}return njt(g.values,P)}),a,o,s;Wk.isArrayOrTypedArray(r.counts)?a=r.counts:a=[r.counts],ajt(n),n.forEach(function(g,P){ojt(g,i[P])});var l=r.line,u;l?(XHt(r,\"line\")&&ZHt(t,r,{vals:r.line.color,containerStr:\"line\",cLetter:\"c\"}),u=KHt.tryColorscale(l)):u=Wk.identity;function c(g){var P,E;return Wk.isArrayOrTypedArray(l.color)?(P=l.color[g%l.color.length],E=P):P=l.color,{color:u(P),rawColor:E}}var f=n[0].values.length,h={},d=i.map(function(g){return g.inds});s=0;var v,m;for(v=0;v<f;v++){var b=[];for(m=0;m<d.length;m++)b.push(d[m][v]);o=a[v%a.length],s+=o;var p=c(v),k=b+\"-\"+p.rawColor;h[k]===void 0&&(h[k]=rjt(b,p.color,p.rawColor)),ijt(h[k],v,o)}var M=n.map(function(g,P){return QHt(P,g._index,g._displayindex,g.label,s)});for(v=0;v<f;v++)for(o=a[v%a.length],m=0;m<M.length;m++){var T=M[m].containerInd,L=i[m].inds[v],x=M[m].categories;if(x[L]===void 0){var C=r.dimensions[T]._categoryarray[L],S=r.dimensions[T]._ticktext[L];x[L]=ejt(m,L,C,S)}tjt(x[L],v,o)}return WHt($Ht(M,h,s))};function $Ht(e,t,r){var n=e.map(function(i){return i.categories.length}).reduce(function(i,a){return Math.max(i,a)});return{dimensions:e,paths:t,trace:void 0,maxCats:n,count:r}}function QHt(e,t,r,n,i){return{dimensionInd:e,containerInd:t,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}function ejt(e,t,r,n){return{dimensionInd:e,categoryInd:t,categoryValue:r,displayInd:t,categoryLabel:n,valueInds:[],count:0,dragY:null}}function tjt(e,t,r){e.valueInds.push(t),e.count+=r}function rjt(e,t,r){return{categoryInds:e,color:t,rawColor:r,valueInds:[],count:0}}function ijt(e,t,r){e.valueInds.push(t),e.count+=r}function njt(e,t){t==null?t=[]:t=t.map(function(u){return u});var r={},n={},i=[];t.forEach(function(u,c){r[u]=0,n[u]=c});for(var a=0;a<e.length;a++){var o=e[a],s;r[o]===void 0?(r[o]=1,s=t.push(o)-1,n[o]=s):(r[o]++,s=n[o]),i.push(s)}var l=t.map(function(u){return r[u]});return{uniqueValues:t,uniqueCounts:l,inds:i}}function ajt(e){var t=e.map(function(n){return n.displayindex}),r;if(sjt(t))for(r=0;r<e.length;r++)e[r]._displayindex=e[r].displayindex;else for(r=0;r<e.length;r++)e[r]._displayindex=r}function ojt(e,t){e._categoryarray=t.uniqueValues,e.ticktext===null||e.ticktext===void 0?e._ticktext=[]:e._ticktext=e.ticktext.slice();for(var r=e._ticktext.length;r<t.uniqueValues.length;r++)e._ticktext.push(t.uniqueValues[r])}function sjt(e){for(var t=new Array(e.length),r=0;r<e.length;r++){if(e[r]<0||e[r]>=e.length||t[e[r]]!==void 0)return!1;t[e[r]]=!0}return!0}});var LVe=ye((Gxr,CVe)=>{\"use strict\";var Dl=Fa(),ljt=(W2(),gb(j2)).interpolateNumber,ujt=JP(),Yk=ef(),kx=Pr(),Xk=kx.strTranslate,wVe=So(),QK=fd(),cjt=Zl();function fjt(e,t,r,n){var i=t._context.staticPlot,a=e.map(Mjt.bind(0,t,r)),o=n.selectAll(\"g.parcatslayer\").data([null]);o.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",i?\"none\":\"all\");var s=o.selectAll(\"g.trace.parcats\").data(a,m1),l=s.enter().append(\"g\").attr(\"class\",\"trace parcats\");s.attr(\"transform\",function(k){return Xk(k.x,k.y)}),l.append(\"g\").attr(\"class\",\"paths\");var u=s.select(\"g.paths\"),c=u.selectAll(\"path.path\").data(function(k){return k.paths},m1);c.attr(\"fill\",function(k){return k.model.color});var f=c.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",function(k){return k.model.color}).attr(\"fill-opacity\",0);rJ(f),c.attr(\"d\",function(k){return k.svgD}),f.empty()||c.sort(eJ),c.exit().remove(),c.on(\"mouseover\",hjt).on(\"mouseout\",djt).on(\"click\",vjt),l.append(\"g\").attr(\"class\",\"dimensions\");var h=s.select(\"g.dimensions\"),d=h.selectAll(\"g.dimension\").data(function(k){return k.dimensions},m1);d.enter().append(\"g\").attr(\"class\",\"dimension\"),d.attr(\"transform\",function(k){return Xk(k.x,0)}),d.exit().remove();var v=d.selectAll(\"g.category\").data(function(k){return k.categories},m1),m=v.enter().append(\"g\").attr(\"class\",\"category\");v.attr(\"transform\",function(k){return Xk(0,k.y)}),m.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),v.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",function(k){return k.width}).attr(\"height\",function(k){return k.height}),AVe(m);var b=v.selectAll(\"rect.bandrect\").data(function(k){return k.bands},m1);b.each(function(){kx.raiseToTop(this)}),b.attr(\"fill\",function(k){return k.color});var p=b.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",function(k){return k.color}).attr(\"fill-opacity\",0);b.attr(\"fill\",function(k){return k.color}).attr(\"width\",function(k){return k.width}).attr(\"height\",function(k){return k.height}).attr(\"y\",function(k){return k.y}).attr(\"cursor\",function(k){return 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k.width/2}).attr(\"y\",-5).text(function(k,M){return M===0?k.parcatsViewModel.model.dimensions[k.model.dimensionInd].dimensionLabel:null}).each(function(k){wVe.font(Dl.select(this),k.parcatsViewModel.labelfont)}),v.selectAll(\"rect.bandrect\").on(\"mouseover\",bjt).on(\"mouseout\",wjt),v.exit().remove(),d.call(Dl.behavior.drag().origin(function(k){return{x:k.x,y:0}}).on(\"dragstart\",Tjt).on(\"drag\",Ajt).on(\"dragend\",Sjt)),s.each(function(k){k.traceSelection=Dl.select(this),k.pathSelection=Dl.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),k.dimensionSelection=Dl.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")}),s.exit().remove()}CVe.exports=function(e,t,r,n){fjt(r,e,n,t)};function m1(e){return e.key}function Zk(e){var t=e.parcatsViewModel.dimensions.length,r=e.parcatsViewModel.dimensions[t-1].model.dimensionInd;return e.model.dimensionInd===r}function eJ(e,t){return e.model.rawColor>t.model.rawColor?1:e.model.rawColor<t.model.rawColor?-1:0}function hjt(e){if(!e.parcatsViewModel.dragDimension&&e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1){kx.raiseToTop(this),iJ(Dl.select(this));var t=Kk(e),r=tJ(e);if(e.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:t,event:Dl.event,constraints:r}),e.parcatsViewModel.hoverinfoItems.indexOf(\"none\")===-1){var n=Dl.mouse(this)[0],i=e.parcatsViewModel.graphDiv,a=e.parcatsViewModel.trace,o=i._fullLayout,s=o._paperdiv.node().getBoundingClientRect(),l=e.parcatsViewModel.graphDiv.getBoundingClientRect(),u,c,f;for(f=0;f<e.leftXs.length-1;f++)if(e.leftXs[f]+e.dimWidths[f]-2<=n&&n<=e.leftXs[f+1]+2){var h=e.parcatsViewModel.dimensions[f],d=e.parcatsViewModel.dimensions[f+1];u=(h.x+h.width+d.x)/2,c=(e.topYs[f]+e.topYs[f+1]+e.height)/2;break}var v=e.parcatsViewModel.x+u,m=e.parcatsViewModel.y+c,b=QK.mostReadable(e.model.color,[\"black\",\"white\"]),p=e.model.count,k=p/e.parcatsViewModel.model.count,M={countLabel:p,probabilityLabel:k.toFixed(3)},T=[];e.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&T.push([\"Count:\",M.countLabel].join(\" \")),e.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&T.push([\"P:\",M.probabilityLabel].join(\" \"));var L=T.join(\"<br>\"),x=Dl.mouse(i)[0];Yk.loneHover({trace:a,x:v-s.left+l.left,y:m-s.top+l.top,text:L,color:e.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:b,idealAlign:x<v?\"right\":\"left\",hovertemplate:(a.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:a._input,fullData:a,count:p,probability:k}]},{container:o._hoverlayer.node(),outerContainer:o._paper.node(),gd:i})}}}function djt(e){if(!e.parcatsViewModel.dragDimension&&(rJ(Dl.select(this)),Yk.loneUnhover(e.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),e.parcatsViewModel.pathSelection.sort(eJ),e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1)){var t=Kk(e),r=tJ(e);e.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:t,event:Dl.event,constraints:r})}}function Kk(e){for(var t=[],r=MVe(e.parcatsViewModel),n=0;n<e.model.valueInds.length;n++){var i=e.model.valueInds[n];t.push({curveNumber:r,pointNumber:i})}return t}function tJ(e){for(var t={},r=e.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[e.model.categoryInds[n]];t[i.containerInd]=a.categoryValue}return e.model.rawColor!==void 0&&(t.color=e.model.rawColor),t}function vjt(e){if(e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1){var t=Kk(e),r=tJ(e);e.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:t,event:Dl.event,constraints:r})}}function rJ(e){e.attr(\"fill\",function(t){return t.model.color}).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function iJ(e){e.attr(\"fill-opacity\",.8).attr(\"stroke\",function(t){return QK.mostReadable(t.model.color,[\"black\",\"white\"])}).attr(\"stroke-width\",.3)}function pjt(e){e.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}function AVe(e){e.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function gjt(e){e.attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}function nJ(e){e.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function $z(e){var t=e.parcatsViewModel.pathSelection,r=e.categoryViewModel.model.dimensionInd,n=e.categoryViewModel.model.categoryInd;return t.filter(function(i){return i.model.categoryInds[r]===n&&i.model.color===e.color})}function mjt(e){var t=Dl.select(e.parentNode).selectAll(\"rect.bandrect\");t.each(function(r){var n=$z(r);iJ(n),n.each(function(){kx.raiseToTop(this)})}),pjt(Dl.select(e.parentNode))}function yjt(e){var t=Dl.select(e).datum(),r=$z(t);iJ(r),r.each(function(){kx.raiseToTop(this)}),Dl.select(e.parentNode).selectAll(\"rect.bandrect\").filter(function(n){return n.color===t.color}).each(function(){kx.raiseToTop(this),gjt(Dl.select(this))})}function aJ(e,t,r){var n=Dl.select(e).datum(),i=n.categoryViewModel.model,a=n.parcatsViewModel.graphDiv,o=Dl.select(e.parentNode).selectAll(\"rect.bandrect\"),s=[];o.each(function(u){var c=$z(u);c.each(function(f){Array.prototype.push.apply(s,Kk(f))})});var l={};l[i.dimensionInd]=i.categoryValue,a.emit(t,{points:s,event:r,constraints:l})}function oJ(e,t,r){var n=Dl.select(e).datum(),i=n.categoryViewModel.model,a=n.parcatsViewModel.graphDiv,o=$z(n),s=[];o.each(function(u){Array.prototype.push.apply(s,Kk(u))});var l={};l[i.dimensionInd]=i.categoryValue,n.rawColor!==void 0&&(l.color=n.rawColor),a.emit(t,{points:s,event:r,constraints:l})}function SVe(e,t,r){e._fullLayout._calcInverseTransform(e);var n=e._fullLayout._invScaleX,i=e._fullLayout._invScaleY,a=Dl.select(r.parentNode).select(\"rect.catrect\"),o=a.node().getBoundingClientRect(),s=a.datum(),l=s.parcatsViewModel,u=l.model.dimensions[s.model.dimensionInd],c=l.trace,f=o.top+o.height/2,h,d;l.dimensions.length>1&&u.displayInd===l.dimensions.length-1?(h=o.left,d=\"left\"):(h=o.left+o.width,d=\"right\");var v=s.model.count,m=s.model.categoryLabel,b=v/s.parcatsViewModel.model.count,p={countLabel:v,categoryLabel:m,probabilityLabel:b.toFixed(3)},k=[];s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&k.push([\"Count:\",p.countLabel].join(\" \")),s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&k.push([\"P(\"+p.categoryLabel+\"):\",p.probabilityLabel].join(\" \"));var M=k.join(\"<br>\");return{trace:c,x:n*(h-t.left),y:i*(f-t.top),text:M,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:d,hovertemplate:c.hovertemplate,hovertemplateLabels:p,eventData:[{data:c._input,fullData:c,count:v,category:m,probability:b}]}}function _jt(e,t,r){var n=[];return Dl.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each(function(){var i=this;n.push(SVe(e,t,i))}),n}function xjt(e,t,r){e._fullLayout._calcInverseTransform(e);var n=e._fullLayout._invScaleX,i=e._fullLayout._invScaleY,a=r.getBoundingClientRect(),o=Dl.select(r).datum(),s=o.categoryViewModel,l=s.parcatsViewModel,u=l.model.dimensions[s.model.dimensionInd],c=l.trace,f=a.y+a.height/2,h,d;l.dimensions.length>1&&u.displayInd===l.dimensions.length-1?(h=a.left,d=\"left\"):(h=a.left+a.width,d=\"right\");var v=s.model.categoryLabel,m=o.parcatsViewModel.model.count,b=0;o.categoryViewModel.bands.forEach(function(P){P.color===o.color&&(b+=P.count)});var p=s.model.count,k=0;l.pathSelection.each(function(P){P.model.color===o.color&&(k+=P.model.count)});var M=b/m,T=b/k,L=b/p,x={countLabel:b,categoryLabel:v,probabilityLabel:M.toFixed(3)},C=[];s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&C.push([\"Count:\",x.countLabel].join(\" \")),s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&(C.push(\"P(color \\u2229 \"+v+\"): \"+x.probabilityLabel),C.push(\"P(\"+v+\" | color): \"+T.toFixed(3)),C.push(\"P(color | \"+v+\"): \"+L.toFixed(3)));var S=C.join(\"<br>\"),g=QK.mostReadable(o.color,[\"black\",\"white\"]);return{trace:c,x:n*(h-t.left),y:i*(f-t.top),text:S,color:o.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:g,fontSize:10,idealAlign:d,hovertemplate:c.hovertemplate,hovertemplateLabels:x,eventData:[{data:c._input,fullData:c,category:v,count:m,probability:M,categorycount:p,colorcount:k,bandcolorcount:b}]}}function bjt(e){if(!e.parcatsViewModel.dragDimension&&e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1){var t=Dl.mouse(this)[1];if(t<-1)return;var r=e.parcatsViewModel.graphDiv,n=r._fullLayout,i=n._paperdiv.node().getBoundingClientRect(),a=e.parcatsViewModel.hoveron,o=this;if(a===\"color\"?(yjt(o),oJ(o,\"plotly_hover\",Dl.event)):(mjt(o),aJ(o,\"plotly_hover\",Dl.event)),e.parcatsViewModel.hoverinfoItems.indexOf(\"none\")===-1){var s;a===\"category\"?s=SVe(r,i,o):a===\"color\"?s=xjt(r,i,o):a===\"dimension\"&&(s=_jt(r,i,o)),s&&Yk.loneHover(s,{container:n._hoverlayer.node(),outerContainer:n._paper.node(),gd:r})}}}function wjt(e){var t=e.parcatsViewModel;if(!t.dragDimension&&(rJ(t.pathSelection),AVe(t.dimensionSelection.selectAll(\"g.category\")),nJ(t.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),Yk.loneUnhover(t.graphDiv._fullLayout._hoverlayer.node()),t.pathSelection.sort(eJ),t.hoverinfoItems.indexOf(\"skip\")===-1)){var r=e.parcatsViewModel.hoveron,n=this;r===\"color\"?oJ(n,\"plotly_unhover\",Dl.event):aJ(n,\"plotly_unhover\",Dl.event)}}function Tjt(e){e.parcatsViewModel.arrangement!==\"fixed\"&&(e.dragDimensionDisplayInd=e.model.displayInd,e.initialDragDimensionDisplayInds=e.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),e.dragHasMoved=!1,e.dragCategoryDisplayInd=null,Dl.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each(function(t){var r=Dl.mouse(this)[0],n=Dl.mouse(this)[1];-2<=r&&r<=t.width+2&&-2<=n&&n<=t.height+2&&(e.dragCategoryDisplayInd=t.model.displayInd,e.initialDragCategoryDisplayInds=e.model.categories.map(function(i){return i.displayInd}),t.model.dragY=t.y,kx.raiseToTop(this.parentNode),Dl.select(this.parentNode).selectAll(\"rect.bandrect\").each(function(i){i.y<n&&n<=i.y+i.height&&(e.potentialClickBand=this)}))}),e.parcatsViewModel.dragDimension=e,Yk.loneUnhover(e.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function Ajt(e){if(e.parcatsViewModel.arrangement!==\"fixed\"&&(e.dragHasMoved=!0,e.dragDimensionDisplayInd!==null)){var t=e.dragDimensionDisplayInd,r=t-1,n=t+1,i=e.parcatsViewModel.dimensions[t];if(e.dragCategoryDisplayInd!==null){var a=i.categories[e.dragCategoryDisplayInd];a.model.dragY+=Dl.event.dy;var o=a.model.dragY,s=a.model.displayInd,l=i.categories,u=l[s-1],c=l[s+1];u!==void 0&&o<u.y+u.height/2&&(a.model.displayInd=u.model.displayInd,u.model.displayInd=s),c!==void 0&&o+a.height>c.y+c.height/2&&(a.model.displayInd=c.model.displayInd,c.model.displayInd=s),e.dragCategoryDisplayInd=a.model.displayInd}if(e.dragCategoryDisplayInd===null||e.parcatsViewModel.arrangement===\"freeform\"){i.model.dragX=Dl.event.x;var f=e.parcatsViewModel.dimensions[r],h=e.parcatsViewModel.dimensions[n];f!==void 0&&i.model.dragX<f.x+f.width&&(i.model.displayInd=f.model.displayInd,f.model.displayInd=t),h!==void 0&&i.model.dragX+i.width>h.x&&(i.model.displayInd=h.model.displayInd,h.model.displayInd=e.dragDimensionDisplayInd),e.dragDimensionDisplayInd=i.model.displayInd}lJ(e.parcatsViewModel),sJ(e.parcatsViewModel),kVe(e.parcatsViewModel),EVe(e.parcatsViewModel)}}function Sjt(e){if(e.parcatsViewModel.arrangement!==\"fixed\"&&e.dragDimensionDisplayInd!==null){Dl.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var t={},r=MVe(e.parcatsViewModel),n=e.parcatsViewModel.model.dimensions.map(function(h){return h.displayInd}),i=e.initialDragDimensionDisplayInds.some(function(h,d){return h!==n[d]});i&&n.forEach(function(h,d){var v=e.parcatsViewModel.model.dimensions[d].containerInd;t[\"dimensions[\"+v+\"].displayindex\"]=h});var a=!1;if(e.dragCategoryDisplayInd!==null){var o=e.model.categories.map(function(h){return h.displayInd});if(a=e.initialDragCategoryDisplayInds.some(function(h,d){return h!==o[d]}),a){var s=e.model.categories.slice().sort(function(h,d){return h.displayInd-d.displayInd}),l=s.map(function(h){return h.categoryValue}),u=s.map(function(h){return h.categoryLabel});t[\"dimensions[\"+e.model.containerInd+\"].categoryarray\"]=[l],t[\"dimensions[\"+e.model.containerInd+\"].ticktext\"]=[u],t[\"dimensions[\"+e.model.containerInd+\"].categoryorder\"]=\"array\"}}if(e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1&&!e.dragHasMoved&&e.potentialClickBand&&(e.parcatsViewModel.hoveron===\"color\"?oJ(e.potentialClickBand,\"plotly_click\",Dl.event.sourceEvent):aJ(e.potentialClickBand,\"plotly_click\",Dl.event.sourceEvent)),e.model.dragX=null,e.dragCategoryDisplayInd!==null){var c=e.parcatsViewModel.dimensions[e.dragDimensionDisplayInd].categories[e.dragCategoryDisplayInd];c.model.dragY=null,e.dragCategoryDisplayInd=null}e.dragDimensionDisplayInd=null,e.parcatsViewModel.dragDimension=null,e.dragHasMoved=null,e.potentialClickBand=null,lJ(e.parcatsViewModel),sJ(e.parcatsViewModel);var f=Dl.transition().duration(300).ease(\"cubic-in-out\");f.each(function(){kVe(e.parcatsViewModel,!0),EVe(e.parcatsViewModel,!0)}).each(\"end\",function(){(i||a)&&ujt.restyle(e.parcatsViewModel.graphDiv,t,[r])})}}function MVe(e){for(var t,r=e.graphDiv._fullData,n=0;n<r.length;n++)if(e.key===r[n].uid){t=n;break}return t}function EVe(e,t){t===void 0&&(t=!1);function r(n){return t?n.transition():n}e.pathSelection.data(function(n){return n.paths},m1),r(e.pathSelection).attr(\"d\",function(n){return n.svgD})}function kVe(e,t){t===void 0&&(t=!1);function r(l){return t?l.transition():l}e.dimensionSelection.data(function(l){return l.dimensions},m1);var n=e.dimensionSelection.selectAll(\"g.category\").data(function(l){return l.categories},m1);r(e.dimensionSelection).attr(\"transform\",function(l){return Xk(l.x,0)}),r(n).attr(\"transform\",function(l){return Xk(0,l.y)});var i=n.select(\".dimlabel\");i.text(function(l,u){return u===0?l.parcatsViewModel.model.dimensions[l.model.dimensionInd].dimensionLabel:null});var a=n.select(\".catlabel\");a.attr(\"text-anchor\",function(l){return Zk(l)?\"start\":\"end\"}).attr(\"x\",function(l){return Zk(l)?l.width+5:-5}).each(function(l){var u,c;Zk(l)?(u=l.width+5,c=\"start\"):(u=-5,c=\"end\"),Dl.select(this).selectAll(\"tspan\").attr(\"x\",u).attr(\"text-anchor\",c)});var o=n.selectAll(\"rect.bandrect\").data(function(l){return l.bands},m1),s=o.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",function(l){return l.color}).attr(\"fill-opacity\",0);o.attr(\"fill\",function(l){return l.color}).attr(\"width\",function(l){return l.width}).attr(\"height\",function(l){return l.height}).attr(\"y\",function(l){return l.y}),nJ(s),o.each(function(){kx.raiseToTop(this)}),o.exit().remove()}function Mjt(e,t,r){var n=r[0],i=t.margin||{l:80,r:80,t:100,b:80},a=n.trace,o=a.domain,s=t.width,l=t.height,u=Math.floor(s*(o.x[1]-o.x[0])),c=Math.floor(l*(o.y[1]-o.y[0])),f=o.x[0]*s+i.l,h=t.height-o.y[1]*t.height+i.t,d=a.line.shape,v;a.hoverinfo===\"all\"?v=[\"count\",\"probability\"]:v=(a.hoverinfo||\"\").split(\"+\");var m={trace:a,key:a.uid,model:n,x:f,y:h,width:u,height:c,hoveron:a.hoveron,hoverinfoItems:v,arrangement:a.arrangement,bundlecolors:a.bundlecolors,sortpaths:a.sortpaths,labelfont:a.labelfont,categorylabelfont:a.tickfont,pathShape:d,dragDimension:null,margin:i,paths:[],dimensions:[],graphDiv:e,traceSelection:null,pathSelection:null,dimensionSelection:null};return n.dimensions&&(lJ(m),sJ(m)),m}function TVe(e,t,r,n,i){var a=[],o=[],s,l;for(l=0;l<r.length-1;l++)s=ljt(r[l]+e[l],e[l+1]),a.push(s(i)),o.push(s(1-i));var u=\"M \"+e[0]+\",\"+t[0];for(u+=\"l\"+r[0]+\",0 \",l=1;l<r.length;l++)u+=\"C\"+a[l-1]+\",\"+t[l-1]+\" \"+o[l-1]+\",\"+t[l]+\" \"+e[l]+\",\"+t[l],u+=\"l\"+r[l]+\",0 \";for(u+=\"l0,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",l=r.length-2;l>=0;l--)u+=\"C\"+o[l]+\",\"+(t[l+1]+n)+\" \"+a[l]+\",\"+(t[l]+n)+\" \"+(e[l]+r[l])+\",\"+(t[l]+n),u+=\"l-\"+r[l]+\",0 \";return u+=\"Z\",u}function sJ(e){var t=e.dimensions,r=e.model,n=t.map(function(q){return q.categories.map(function(V){return V.y})}),i=e.model.dimensions.map(function(q){return q.categories.map(function(V){return V.displayInd})}),a=e.model.dimensions.map(function(q){return q.displayInd}),o=e.dimensions.map(function(q){return q.model.dimensionInd}),s=t.map(function(q){return q.x}),l=t.map(function(q){return q.width}),u=[];for(var c in r.paths)r.paths.hasOwnProperty(c)&&u.push(r.paths[c]);function f(q){var V=q.categoryInds.map(function(Z,j){return i[j][Z]}),G=o.map(function(Z){return V[Z]});return G}u.sort(function(q,V){var G=f(q),Z=f(V);return e.sortpaths===\"backward\"&&(G.reverse(),Z.reverse()),G.push(q.valueInds[0]),Z.push(V.valueInds[0]),e.bundlecolors&&(G.unshift(q.rawColor),Z.unshift(V.rawColor)),G<Z?-1:G>Z?1:0});for(var h=new Array(u.length),d=t[0].model.count,v=t[0].categories.map(function(q){return q.height}).reduce(function(q,V){return q+V}),m=0;m<u.length;m++){var b=u[m],p;d>0?p=v*(b.count/d):p=0;for(var k=new Array(n.length),M=0;M<b.categoryInds.length;M++){var T=b.categoryInds[M],L=i[M][T],x=a[M];k[x]=n[x][L],n[x][L]+=p;var C=e.dimensions[x].categories[L],S=C.bands.length,g=C.bands[S-1];if(g===void 0||b.rawColor!==g.rawColor){var P=g===void 0?0:g.y+g.height;C.bands.push({key:P,color:b.color,rawColor:b.rawColor,height:p,width:C.width,count:b.count,y:P,categoryViewModel:C,parcatsViewModel:e})}else{var E=C.bands[S-1];E.height+=p,E.count+=b.count}}var z;e.pathShape===\"hspline\"?z=TVe(s,k,l,p,.5):z=TVe(s,k,l,p,0),h[m]={key:b.valueInds[0],model:b,height:p,leftXs:s,topYs:k,dimWidths:l,svgD:z,parcatsViewModel:e}}e.paths=h}function lJ(e){var t=e.model.dimensions.map(function(o){return{displayInd:o.displayInd,dimensionInd:o.dimensionInd}});t.sort(function(o,s){return o.displayInd-s.displayInd});var r=[];for(var n in t){var i=t[n].dimensionInd,a=e.model.dimensions[i];r.push(Ejt(e,a))}e.dimensions=r}function Ejt(e,t){var r=40,n=16,i=e.model.dimensions.length,a=t.displayInd,o,s,l;i>1?o=(e.width-2*r-n)/(i-1):o=0,s=r,l=s+o*a;var u=[],c=e.model.maxCats,f=t.categories.length,h=8,d=t.count,v=e.height-h*(c-1),m,b,p,k,M,T=(c-f)*h/2,L=t.categories.map(function(x){return{displayInd:x.displayInd,categoryInd:x.categoryInd}});for(L.sort(function(x,C){return x.displayInd-C.displayInd}),M=0;M<f;M++)k=L[M].categoryInd,b=t.categories[k],d>0?m=b.count/d*v:m=0,p={key:b.valueInds[0],model:b,width:n,height:m,y:b.dragY!==null?b.dragY:T,bands:[],parcatsViewModel:e},T=T+m+h,u.push(p);return{key:t.dimensionInd,x:t.dragX!==null?t.dragX:l,y:0,width:n,model:t,categories:u,parcatsViewModel:e,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}});var uJ=ye((Hxr,PVe)=>{\"use strict\";var kjt=LVe();PVe.exports=function(t,r,n,i){var a=t._fullLayout,o=a._paper,s=a._size;kjt(t,o,r,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},n,i)}});var RVe=ye(Qz=>{\"use strict\";var Cjt=Id().getModuleCalcData,Ljt=uJ(),IVe=\"parcats\";Qz.name=IVe;Qz.plot=function(e,t,r,n){var i=Cjt(e.calcdata,IVe);if(i.length){var a=i[0];Ljt(e,a,r,n)}};Qz.clean=function(e,t,r,n){var i=n._has&&n._has(\"parcats\"),a=t._has&&t._has(\"parcats\");i&&!a&&n._paperdiv.selectAll(\".parcats\").remove()}});var FVe=ye((Wxr,DVe)=>{\"use strict\";DVe.exports={attributes:$K(),supplyDefaults:_Ve(),calc:bVe(),plot:uJ(),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:RVe(),categories:[\"noOpacity\"],meta:{}}});var qVe=ye((Xxr,zVe)=>{\"use strict\";zVe.exports=FVe()});var y1=ye((Zxr,HVe)=>{\"use strict\";var Pjt=r_(),OVe=\"1.13.4\",VVe='\\xA9 <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">OpenStreetMap</a> contributors',BVe=['\\xA9 <a target=\"_blank\" href=\"https://carto.com/\">Carto</a>',VVe].join(\" \"),NVe=['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a 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custom plotly.js bundle is not using the correct mapbox-gl version\",\"Please install @plotly/mapbox-gl@\"+OVe+\".\"].join(`\n",
+       "`),noAccessTokenErrorMsg:[\"Missing Mapbox access token.\",\"Mapbox trace type require a Mapbox access token to be registered.\",\"For example:\",\"  Plotly.newPlot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });\",\"More info here: https://www.mapbox.com/help/define-access-token/\"].join(`\n",
+       "`),missingStyleErrorMsg:[\"No valid mapbox style found, please set `mapbox.style` to one of:\",UVe.join(\", \"),\"or register a Mapbox access token to use a Mapbox-served style.\"].join(`\n",
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fJ=ye((Jxr,QVe)=>{\"use strict\";var Wjt=[\"Metropolis Black Italic\",\"Metropolis Black\",\"Metropolis Bold Italic\",\"Metropolis Bold\",\"Metropolis Extra Bold Italic\",\"Metropolis Extra Bold\",\"Metropolis Extra Light Italic\",\"Metropolis Extra Light\",\"Metropolis Light Italic\",\"Metropolis Light\",\"Metropolis Medium Italic\",\"Metropolis Medium\",\"Metropolis Regular Italic\",\"Metropolis Regular\",\"Metropolis Semi Bold Italic\",\"Metropolis Semi Bold\",\"Metropolis Thin Italic\",\"Metropolis Thin\",\"Open Sans Bold Italic\",\"Open Sans Bold\",\"Open Sans Extrabold Italic\",\"Open Sans Extrabold\",\"Open Sans Italic\",\"Open Sans Light Italic\",\"Open Sans Light\",\"Open Sans Regular\",\"Open Sans Semibold Italic\",\"Open Sans Semibold\",\"Klokantech Noto Sans Bold\",\"Klokantech Noto Sans CJK Bold\",\"Klokantech Noto Sans CJK Regular\",\"Klokantech Noto Sans Italic\",\"Klokantech Noto Sans Regular\"];QVe.exports={isSupportedFont:function(e){return Wjt.indexOf(e)!==-1}}});var rGe=ye(($xr,tGe)=>{\"use strict\";var Qk=Pr(),hJ=Ru(),Xjt=$p(),Zjt=D0(),Yjt=F0(),Kjt=Fg(),eGe=e7(),Jjt=fJ().isSupportedFont;tGe.exports=function(t,r,n,i){function a(p,k){return Qk.coerce(t,r,eGe,p,k)}function o(p,k){return Qk.coerce2(t,r,eGe,p,k)}var s=$jt(t,r,a);if(!s){r.visible=!1;return}if(a(\"text\"),a(\"texttemplate\"),a(\"texttemplatefallback\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),a(\"mode\"),a(\"below\"),hJ.hasMarkers(r)){Xjt(t,r,n,i,a,{noLine:!0,noAngle:!0}),a(\"marker.allowoverlap\"),a(\"marker.angle\");var l=r.marker;l.symbol!==\"circle\"&&(Qk.isArrayOrTypedArray(l.size)&&(l.size=l.size[0]),Qk.isArrayOrTypedArray(l.color)&&(l.color=l.color[0]))}hJ.hasLines(r)&&(Zjt(t,r,n,i,a,{noDash:!0}),a(\"connectgaps\"));var u=o(\"cluster.maxzoom\"),c=o(\"cluster.step\"),f=o(\"cluster.color\",r.marker&&r.marker.color||n),h=o(\"cluster.size\"),d=o(\"cluster.opacity\"),v=u!==!1||c!==!1||f!==!1||h!==!1||d!==!1,m=a(\"cluster.enabled\",v);if(m||hJ.hasText(r)){var 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vGe(e,t,r,n){this.type=\"scattermapbox\",this.subplot=e,this.uid=t,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+t+\"-fill\",line:\"source-\"+t+\"-line\",circle:\"source-\"+t+\"-circle\",symbol:\"source-\"+t+\"-symbol\",cluster:\"source-\"+t+\"-circle\",clusterCount:\"source-\"+t+\"-circle\"},this.layerIds={fill:I5+t+\"-fill\",line:I5+t+\"-line\",circle:I5+t+\"-circle\",symbol:I5+t+\"-symbol\",cluster:I5+t+\"-cluster\",clusterCount:I5+t+\"-cluster-count\"},this.below=null}var eC=vGe.prototype;eC.addSource=function(e,t,r){var n={type:\"geojson\",data:t.geojson};r&&r.enabled&&uWt.extendFlat(n,{cluster:!0,clusterMaxZoom:r.maxzoom});var i=this.subplot.map.getSource(this.sourceIds[e]);i?i.setData(t.geojson):this.subplot.map.addSource(this.sourceIds[e],n)};eC.setSourceData=function(e,t){this.subplot.map.getSource(this.sourceIds[e]).setData(t.geojson)};eC.addLayer=function(e,t,r){var n={type:t.type,id:this.layerIds[e],source:this.sourceIds[e],layout:t.layout,paint:t.paint};t.filter&&(n.filter=t.filter);for(var i=this.layerIds[e],a,o=this.subplot.getMapLayers(),s=0;s<o.length;s++)if(o[s].id===i){a=!0;break}a?(this.subplot.setOptions(i,\"setLayoutProperty\",n.layout),n.layout.visibility===\"visible\"&&this.subplot.setOptions(i,\"setPaintProperty\",n.paint)):this.subplot.addLayer(n,r)};eC.update=function(t){var r=t[0].trace,n=this.subplot,i=n.map,a=dGe(n.gd,t),o=n.belowLookup[\"trace-\"+this.uid],s=!!(r.cluster&&r.cluster.enabled),l=!!this.clusterEnabled,u=this;function c(M){M||u.addSource(\"circle\",a.circle,r.cluster);for(var T=ng.cluster,L=0;L<T.length;L++){var x=T[L],C=a[x];u.addLayer(x,C,o)}}function f(M){for(var T=ng.cluster,L=T.length-1;L>=0;L--){var x=T[L];i.removeLayer(u.layerIds[x])}M||i.removeSource(u.sourceIds.circle)}function h(M){for(var T=ng.nonCluster,L=0;L<T.length;L++){var x=T[L],C=a[x];M||u.addSource(x,C),u.addLayer(x,C,o)}}function d(M){for(var T=ng.nonCluster,L=T.length-1;L>=0;L--){var x=T[L];i.removeLayer(u.layerIds[x]),M||i.removeSource(u.sourceIds[x])}}function v(M){l?f(M):d(M)}function m(M){s?c(M):h(M)}function b(){for(var M=s?ng.cluster:ng.nonCluster,T=0;T<M.length;T++){var L=M[T],x=a[L];x&&(n.setOptions(u.layerIds[L],\"setLayoutProperty\",x.layout),x.layout.visibility===\"visible\"&&(L!==\"cluster\"&&u.setSourceData(L,x),n.setOptions(u.layerIds[L],\"setPaintProperty\",x.paint)))}}var p=this.isHidden,k=r.visible!==!0;k?p||v():p?k||m():l!==s?(v(),m()):(this.below!==o&&(v(!0),m(!0)),b()),this.clusterEnabled=s,this.isHidden=k,this.below=o,t[0].trace._glTrace=this};eC.dispose=function(){for(var t=this.subplot.map,r=this.clusterEnabled?ng.cluster:ng.nonCluster,n=r.length-1;n>=0;n--){var i=r[n];t.removeLayer(this.layerIds[i]),t.removeSource(this.sourceIds[i])}};pGe.exports=function(t,r){var n=r[0].trace,i=n.cluster&&n.cluster.enabled,a=n.visible!==!0,o=new vGe(t,n.uid,i,a),s=dGe(t.gd,r),l=o.below=t.belowLookup[\"trace-\"+n.uid],u,c,f;if(i)for(o.addSource(\"circle\",s.circle,n.cluster),u=0;u<ng.cluster.length;u++)c=ng.cluster[u],f=s[c],o.addLayer(c,f,l);else for(u=0;u<ng.nonCluster.length;u++)c=ng.nonCluster[u],f=s[c],o.addSource(c,f,n.cluster),o.addLayer(c,f,l);return r[0].trace._glTrace=o,o}});var a7=ye((ibr,yGe)=>{\"use strict\";var cWt=ef(),mJ=Pr(),fWt=xT(),hWt=mJ.fillText,dWt=fs().BADNUM,vWt=y1().traceLayerPrefix;function pWt(e,t,r){var n=e.cd,i=n[0].trace,a=e.xa,o=e.ya,s=e.subplot,l=[],u=vWt+i.uid+\"-circle\",c=i.cluster&&i.cluster.enabled;if(c){var f=s.map.queryRenderedFeatures(null,{layers:[u]});l=f.map(function(S){return S.id})}var h=t>=0?Math.floor((t+180)/360):Math.ceil((t-180)/360),d=h*360,v=t-d;function m(S){var g=S.lonlat;if(g[0]===dWt||c&&l.indexOf(S.i+1)===-1)return 1/0;var P=mJ.modHalf(g[0],360),E=g[1],z=s.project([P,E]),q=z.x-a.c2p([v,E]),V=z.y-o.c2p([P,r]),G=Math.max(3,S.mrc||0);return Math.max(Math.sqrt(q*q+V*V)-G,1-3/G)}if(cWt.getClosest(n,m,e),e.index!==!1){var b=n[e.index],p=b.lonlat,k=[mJ.modHalf(p[0],360)+d,p[1]],M=a.c2p(k),T=o.c2p(k),L=b.mrc||1;e.x0=M-L,e.x1=M+L,e.y0=T-L,e.y1=T+L;var x={};x[i.subplot]={_subplot:s};var C=i._module.formatLabels(b,i,x);return e.lonLabel=C.lonLabel,e.latLabel=C.latLabel,e.color=fWt(i,b),e.extraText=mGe(i,b,n[0].t.labels),e.hovertemplate=i.hovertemplate,[e]}}function mGe(e,t,r){if(e.hovertemplate)return;var n=t.hi||e.hoverinfo,i=n.split(\"+\"),a=i.indexOf(\"all\")!==-1,o=i.indexOf(\"lon\")!==-1,s=i.indexOf(\"lat\")!==-1,l=t.lonlat,u=[];function c(f){return f+\"\\xB0\"}return a||o&&s?u.push(\"(\"+c(l[1])+\", \"+c(l[0])+\")\"):o?u.push(r.lon+c(l[0])):s&&u.push(r.lat+c(l[1])),(a||i.indexOf(\"text\")!==-1)&&hWt(t,e,u),u.join(\"<br>\")}yGe.exports={hoverPoints:pWt,getExtraText:mGe}});var xGe=ye((nbr,_Ge)=>{\"use strict\";_Ge.exports=function(t,r){return t.lon=r.lon,t.lat=r.lat,t}});var wGe=ye((abr,bGe)=>{\"use strict\";var gWt=Pr(),mWt=Ru(),yWt=fs().BADNUM;bGe.exports=function(t,r){var n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].trace,l;if(!mWt.hasMarkers(s))return[];if(r===!1)for(l=0;l<n.length;l++)n[l].selected=0;else for(l=0;l<n.length;l++){var u=n[l],c=u.lonlat;if(c[0]!==yWt){var f=[gWt.modHalf(c[0],360),c[1]],h=[i.c2p(f),a.c2p(f)];r.contains(h,null,l,t)?(o.push({pointNumber:l,lon:c[0],lat:c[1]}),u.selected=1):u.selected=0}}return o}});var xJ=ye((yJ,_J)=>{(function(e,t){typeof yJ==\"object\"&&typeof _J!=\"undefined\"?_J.exports=t():(e=e||self,e.mapboxgl=t())})(yJ,(function(){\"use strict\";var e,t,r;function n(i,a){if(!e)e=a;else if(!t)t=a;else{var o=\"var sharedChunk = {}; (\"+e+\")(sharedChunk); (\"+t+\")(sharedChunk);\",s={};e(s),r=a(s),typeof window!=\"undefined\"&&(r.workerUrl=window.URL.createObjectURL(new Blob([o],{type:\"text/javascript\"})))}}return n([\"exports\"],function(i){\"use strict\";function a(y,_){return _={exports:{}},y(_,_.exports),_.exports}var o=\"1.13.4\",s=l;function 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it=_;it<I;it++)this.paintVertexArray.emplace(it,U,$);this.maxValue=Math.max(this.maxValue,Math.abs(U),Math.abs($))}},_r.prototype.upload=function(_){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=_.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},_r.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},_r.prototype.setUniform=function(_,I){var U=this.useIntegerZoom?Math.floor(I.zoom):I.zoom,$=p(this.expression.interpolationFactor(U,this.zoom,this.zoom+1),0,1);_.set($)},_r.prototype.getBinding=function(_,I,U){return new $e(_,I)};var Br=function(_,I,U,$,oe,he){this.expression=_,this.type=I,this.useIntegerZoom=U,this.zoom=$,this.layerId=he,this.zoomInPaintVertexArray=new oe,this.zoomOutPaintVertexArray=new 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sr=_;sr<I;sr++)this.zoomInPaintVertexArray.emplace(sr,yt.tl[0],yt.tl[1],yt.br[0],yt.br[1],it.tl[0],it.tl[1],it.br[0],it.br[1],yt.pixelRatio,it.pixelRatio),this.zoomOutPaintVertexArray.emplace(sr,yt.tl[0],yt.tl[1],yt.br[0],yt.br[1],Lt.tl[0],Lt.tl[1],Lt.br[0],Lt.br[1],yt.pixelRatio,Lt.pixelRatio)}},Br.prototype.upload=function(_){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=_.createVertexBuffer(this.zoomInPaintVertexArray,D.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=_.createVertexBuffer(this.zoomOutPaintVertexArray,D.members,this.expression.isStateDependent))},Br.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var Di=function(_,I,U){this.binders={},this._buffers=[];var $=[];for(var oe in 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it=oe[De],yt=oe[De+1],Lt=he[De],sr=he[De+1],gr=[it,yt,sr,Lt,it];so(I,gr)&&(U=Math.min(U,SS(I,gr)))}return U===1/0?!1:U}function MS(y,_,I,U){for(var $=[],oe=[],he=U[8]*_,De=U[9]*_,it=U[10]*_,yt=U[11]*_,Lt=U[8]*I,sr=U[9]*I,gr=U[10]*I,hr=U[11]*I,Rr=0,ni=y;Rr<ni.length;Rr+=1){for(var ji=ni[Rr],wi=[],bn=[],rn=0,xn=ji;rn<xn.length;rn+=1){var In=xn[rn],Xn=In.x,ga=In.y,ha=U[0]*Xn+U[4]*ga+U[12],eo=U[1]*Xn+U[5]*ga+U[13],qa=U[2]*Xn+U[6]*ga+U[14],Za=U[3]*Xn+U[7]*ga+U[15],Jo=ha+he,to=eo+De,ao=qa+it,_s=Za+yt,jo=ha+Lt,kl=eo+sr,Iu=qa+gr,Cl=Za+hr,Ll=new u(Jo/_s,to/_s);Ll.z=ao/_s,wi.push(Ll);var yl=new u(jo/Cl,kl/Cl);yl.z=Iu/Cl,bn.push(yl)}$.push(wi),oe.push(bn)}return[$,oe]}function fq(y,_,I,U){for(var $=[],oe=0,he=y;oe<he.length;oe+=1){var De=he[oe],it=[De.x,De.y,U,1];my(it,it,_),$.push(new u(it[0]/it[3],it[1]/it[3]))}return $}var 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U.id})};sh.prototype.populate=function(_,I,U){this.hasPattern=Jw(\"line\",this.layers,I);for(var $=this.layers[0].layout.get(\"line-sort-key\"),oe=[],he=0,De=_;he<De.length;he+=1){var it=De[he],yt=it.feature,Lt=it.id,sr=it.index,gr=it.sourceLayerIndex,hr=this.layers[0]._featureFilter.needGeometry,Rr=Ja(yt,hr);if(this.layers[0]._featureFilter.filter(new Un(this.zoom),Rr,U)){var ni=$?$.evaluate(Rr,{},U):void 0,ji={id:Lt,properties:yt.properties,type:yt.type,sourceLayerIndex:gr,index:sr,geometry:hr?Rr.geometry:zn(yt),patterns:{},sortKey:ni};oe.push(ji)}}$&&oe.sort(function(qa,Za){return qa.sortKey-Za.sortKey});for(var wi=0,bn=oe;wi<bn.length;wi+=1){var rn=bn[wi],xn=rn,In=xn.geometry,Xn=xn.index,ga=xn.sourceLayerIndex;if(this.hasPattern){var ha=sb(\"line\",this.layers,rn,this.zoom,I);this.patternFeatures.push(ha)}else this.addFeature(rn,In,Xn,U,{});var eo=_[Xn].feature;I.featureIndex.insert(eo,In,Xn,ga,this.index)}},sh.prototype.update=function(_,I,U){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(_,I,this.stateDependentLayers,U)},sh.prototype.addFeatures=function(_,I,U){for(var $=0,oe=this.patternFeatures;$<oe.length;$+=1){var he=oe[$];this.addFeature(he,he.geometry,he.index,I,U)}},sh.prototype.isEmpty=function(){return this.layoutVertexArray.length===0},sh.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},sh.prototype.upload=function(_){this.uploaded||(this.layoutVertexArray2.length!==0&&(this.layoutVertexBuffer2=_.createVertexBuffer(this.layoutVertexArray2,r6)),this.layoutVertexBuffer=_.createVertexBuffer(this.layoutVertexArray,t6),this.indexBuffer=_.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(_),this.uploaded=!0},sh.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},sh.prototype.lineFeatureClips=function(_){if(_.properties&&_.properties.hasOwnProperty(\"mapbox_clip_start\")&&_.properties.hasOwnProperty(\"mapbox_clip_end\")){var I=+_.properties.mapbox_clip_start,U=+_.properties.mapbox_clip_end;return{start:I,end:U}}},sh.prototype.addFeature=function(_,I,U,$,oe){var he=this.layers[0].layout,De=he.get(\"line-join\").evaluate(_,{}),it=he.get(\"line-cap\"),yt=he.get(\"line-miter-limit\"),Lt=he.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(_);for(var sr=0,gr=I;sr<gr.length;sr+=1){var hr=gr[sr];this.addLine(hr,_,De,it,yt,Lt)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,_,U,oe,$)},sh.prototype.addLine=function(_,I,U,$,oe,he){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(var De=0;De<_.length-1;De++)this.totalDistance+=_[De].dist(_[De+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}for(var it=ES[I.type]===\"Polygon\",yt=_.length;yt>=2&&_[yt-1].equals(_[yt-2]);)yt--;for(var Lt=0;Lt<yt-1&&_[Lt].equals(_[Lt+1]);)Lt++;if(!(yt<(it?3:2))){U===\"bevel\"&&(oe=1.05);var sr=this.overscaling<=16?cb*Ei/(512*this.overscaling):0,gr=this.segments.prepareSegment(yt*10,this.layoutVertexArray,this.indexArray),hr,Rr=void 0,ni=void 0,ji=void 0,wi=void 0;this.e1=this.e2=-1,it&&(hr=_[yt-2],wi=_[Lt].sub(hr)._unit()._perp());for(var bn=Lt;bn<yt;bn++)if(ni=bn===yt-1?it?_[Lt+1]:void 0:_[bn+1],!(ni&&_[bn].equals(ni))){wi&&(ji=wi),hr&&(Rr=hr),hr=_[bn],wi=ni?ni.sub(hr)._unit()._perp():ji,ji=ji||wi;var rn=ji.add(wi);(rn.x!==0||rn.y!==0)&&rn._unit();var xn=ji.x*wi.x+ji.y*wi.y,In=rn.x*wi.x+rn.y*wi.y,Xn=In!==0?1/In:1/0,ga=2*Math.sqrt(2-2*In),ha=In<i6&&Rr&&ni,eo=ji.x*wi.y-ji.y*wi.x>0;if(ha&&bn>Lt){var qa=hr.dist(Rr);if(qa>2*sr){var Za=hr.sub(hr.sub(Rr)._mult(sr/qa)._round());this.updateDistance(Rr,Za),this.addCurrentVertex(Za,ji,0,0,gr),Rr=Za}}var 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ni=De.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,ni),De.primitiveLength++),oe?this.e2=ni:this.e1=ni},sh.prototype.updateScaledDistance=function(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance},sh.prototype.updateDistance=function(_,I){this.distance+=_.dist(I),this.updateScaledDistance()},X(\"LineBucket\",sh,{omit:[\"layers\",\"patternFeatures\"]});var CS=new Or({\"line-cap\":new Me(Rn.layout_line[\"line-cap\"]),\"line-join\":new bt(Rn.layout_line[\"line-join\"]),\"line-miter-limit\":new Me(Rn.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Me(Rn.layout_line[\"line-round-limit\"]),\"line-sort-key\":new bt(Rn.layout_line[\"line-sort-key\"])}),LS=new Or({\"line-opacity\":new bt(Rn.paint_line[\"line-opacity\"]),\"line-color\":new bt(Rn.paint_line[\"line-color\"]),\"line-translate\":new 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y.push(this.readBoolean());var _=uo(this);for(y=y||[];this.pos<_;)y.push(this.readBoolean());return y},readPackedFloat:function(y){if(this.type!==la.Bytes)return y.push(this.readFloat());var _=uo(this);for(y=y||[];this.pos<_;)y.push(this.readFloat());return y},readPackedDouble:function(y){if(this.type!==la.Bytes)return y.push(this.readDouble());var _=uo(this);for(y=y||[];this.pos<_;)y.push(this.readDouble());return y},readPackedFixed32:function(y){if(this.type!==la.Bytes)return y.push(this.readFixed32());var _=uo(this);for(y=y||[];this.pos<_;)y.push(this.readFixed32());return y},readPackedSFixed32:function(y){if(this.type!==la.Bytes)return y.push(this.readSFixed32());var _=uo(this);for(y=y||[];this.pos<_;)y.push(this.readSFixed32());return y},readPackedFixed64:function(y){if(this.type!==la.Bytes)return y.push(this.readFixed64());var _=uo(this);for(y=y||[];this.pos<_;)y.push(this.readFixed64());return y},readPackedSFixed64:function(y){if(this.type!==la.Bytes)return y.push(this.readSFixed64());var _=uo(this);for(y=y||[];this.pos<_;)y.push(this.readSFixed64());return y},skip:function(y){var _=y&7;if(_===la.Varint)for(;this.buf[this.pos++]>127;);else if(_===la.Bytes)this.pos=this.readVarint()+this.pos;else if(_===la.Fixed32)this.pos+=4;else if(_===la.Fixed64)this.pos+=8;else throw new Error(\"Unimplemented type: \"+_)},writeTag:function(y,_){this.writeVarint(y<<3|_)},realloc:function(y){for(var _=this.length||16;_<this.pos+y;)_*=2;if(_!==this.length){var I=new Uint8Array(_);I.set(this.buf),this.buf=I,this.length=_}},finish:function(){return this.length=this.pos,this.pos=0,this.buf.subarray(0,this.length)},writeFixed32:function(y){this.realloc(4),Df(this.buf,y,this.pos),this.pos+=4},writeSFixed32:function(y){this.realloc(4),Df(this.buf,y,this.pos),this.pos+=4},writeFixed64:function(y){this.realloc(8),Df(this.buf,y&-1,this.pos),Df(this.buf,Math.floor(y*fo),this.pos+4),this.pos+=8},writeSFixed64:function(y){this.realloc(8),Df(this.buf,y&-1,this.pos),Df(this.buf,Math.floor(y*fo),this.pos+4),this.pos+=8},writeVarint:function(y){if(y=+y||0,y>268435455||y<0){Ju(y,this);return}this.realloc(4),this.buf[this.pos++]=y&127|(y>127?128:0),!(y<=127)&&(this.buf[this.pos++]=(y>>>=7)&127|(y>127?128:0),!(y<=127)&&(this.buf[this.pos++]=(y>>>=7)&127|(y>127?128:0),!(y<=127)&&(this.buf[this.pos++]=y>>>7&127)))},writeSVarint:function(y){this.writeVarint(y<0?-y*2-1:y*2)},writeBoolean:function(y){this.writeVarint(!!y)},writeString:function(y){y=String(y),this.realloc(y.length*4),this.pos++;var _=this.pos;this.pos=pc(this.buf,y,this.pos);var I=this.pos-_;I>=128&&Qv(_,I,this),this.pos=_-1,this.writeVarint(I),this.pos+=I},writeFloat:function(y){this.realloc(4),Vo.write(this.buf,y,this.pos,!0,23,4),this.pos+=4},writeDouble:function(y){this.realloc(8),Vo.write(this.buf,y,this.pos,!0,52,8),this.pos+=8},writeBytes:function(y){var _=y.length;this.writeVarint(_),this.realloc(_);for(var I=0;I<_;I++)this.buf[this.pos++]=y[I]},writeRawMessage:function(y,_){this.pos++;var I=this.pos;y(_,this);var U=this.pos-I;U>=128&&Qv(I,U,this),this.pos=I-1,this.writeVarint(U),this.pos+=U},writeMessage:function(y,_,I){this.writeTag(y,la.Bytes),this.writeRawMessage(_,I)},writePackedVarint:function(y,_){_.length&&this.writeMessage(y,ud,_)},writePackedSVarint:function(y,_){_.length&&this.writeMessage(y,Ch,_)},writePackedBoolean:function(y,_){_.length&&this.writeMessage(y,jd,_)},writePackedFloat:function(y,_){_.length&&this.writeMessage(y,Gd,_)},writePackedDouble:function(y,_){_.length&&this.writeMessage(y,Hd,_)},writePackedFixed32:function(y,_){_.length&&this.writeMessage(y,Af,_)},writePackedSFixed32:function(y,_){_.length&&this.writeMessage(y,Lh,_)},writePackedFixed64:function(y,_){_.length&&this.writeMessage(y,Ed,_)},writePackedSFixed64:function(y,_){_.length&&this.writeMessage(y,cd,_)},writeBytesField:function(y,_){this.writeTag(y,la.Bytes),this.writeBytes(_)},writeFixed32Field:function(y,_){this.writeTag(y,la.Fixed32),this.writeFixed32(_)},writeSFixed32Field:function(y,_){this.writeTag(y,la.Fixed32),this.writeSFixed32(_)},writeFixed64Field:function(y,_){this.writeTag(y,la.Fixed64),this.writeFixed64(_)},writeSFixed64Field:function(y,_){this.writeTag(y,la.Fixed64),this.writeSFixed64(_)},writeVarintField:function(y,_){this.writeTag(y,la.Varint),this.writeVarint(_)},writeSVarintField:function(y,_){this.writeTag(y,la.Varint),this.writeSVarint(_)},writeStringField:function(y,_){this.writeTag(y,la.Bytes),this.writeString(_)},writeFloatField:function(y,_){this.writeTag(y,la.Fixed32),this.writeFloat(_)},writeDoubleField:function(y,_){this.writeTag(y,la.Fixed64),this.writeDouble(_)},writeBooleanField:function(y,_){this.writeVarintField(y,!!_)}};function Ro(y,_,I){var U=I.buf,$,oe;if(oe=U[I.pos++],$=(oe&112)>>4,oe<128||(oe=U[I.pos++],$|=(oe&127)<<3,oe<128)||(oe=U[I.pos++],$|=(oe&127)<<10,oe<128)||(oe=U[I.pos++],$|=(oe&127)<<17,oe<128)||(oe=U[I.pos++],$|=(oe&127)<<24,oe<128)||(oe=U[I.pos++],$|=(oe&1)<<31,oe<128))return $o(y,$,_);throw new Error(\"Expected varint not more than 10 bytes\")}function uo(y){return y.type===la.Bytes?y.readVarint()+y.pos:y.pos+1}function $o(y,_,I){return I?_*4294967296+(y>>>0):(_>>>0)*4294967296+(y>>>0)}function Ju(y,_){var I,U;if(y>=0?(I=y%4294967296|0,U=y/4294967296|0):(I=~(-y%4294967296),U=~(-y/4294967296),I^4294967295?I=I+1|0:(I=0,U=U+1|0)),y>=18446744073709552e3||y<-18446744073709552e3)throw new Error(\"Given varint doesn't fit into 10 bytes\");_.realloc(10),Ou(I,U,_),kh(U,_)}function Ou(y,_,I){I.buf[I.pos++]=y&127|128,y>>>=7,I.buf[I.pos++]=y&127|128,y>>>=7,I.buf[I.pos++]=y&127|128,y>>>=7,I.buf[I.pos++]=y&127|128,y>>>=7,I.buf[I.pos]=y&127}function kh(y,_){var I=(y&7)<<4;_.buf[_.pos++]|=I|((y>>>=3)?128:0),y&&(_.buf[_.pos++]=y&127|((y>>>=7)?128:0),y&&(_.buf[_.pos++]=y&127|((y>>>=7)?128:0),y&&(_.buf[_.pos++]=y&127|((y>>>=7)?128:0),y&&(_.buf[_.pos++]=y&127|((y>>>=7)?128:0),y&&(_.buf[_.pos++]=y&127)))))}function Qv(y,_,I){var U=_<=16383?1:_<=2097151?2:_<=268435455?3:Math.floor(Math.log(_)/(Math.LN2*7));I.realloc(U);for(var $=I.pos-1;$>=y;$--)I.buf[$+U]=I.buf[$]}function ud(y,_){for(var I=0;I<y.length;I++)_.writeVarint(y[I])}function Ch(y,_){for(var I=0;I<y.length;I++)_.writeSVarint(y[I])}function Gd(y,_){for(var I=0;I<y.length;I++)_.writeFloat(y[I])}function Hd(y,_){for(var I=0;I<y.length;I++)_.writeDouble(y[I])}function jd(y,_){for(var I=0;I<y.length;I++)_.writeBoolean(y[I])}function Af(y,_){for(var I=0;I<y.length;I++)_.writeFixed32(y[I])}function Lh(y,_){for(var I=0;I<y.length;I++)_.writeSFixed32(y[I])}function Ed(y,_){for(var I=0;I<y.length;I++)_.writeFixed64(y[I])}function cd(y,_){for(var I=0;I<y.length;I++)_.writeSFixed64(y[I])}function Yh(y,_){return(y[_]|y[_+1]<<8|y[_+2]<<16)+y[_+3]*16777216}function Df(y,_,I){y[I]=_,y[I+1]=_>>>8,y[I+2]=_>>>16,y[I+3]=_>>>24}function Fv(y,_){return(y[_]|y[_+1]<<8|y[_+2]<<16)+(y[_+3]<<24)}function uv(y,_,I){for(var U=\"\",$=_;$<I;){var oe=y[$],he=null,De=oe>239?4:oe>223?3:oe>191?2:1;if($+De>I)break;var it,yt,Lt;De===1?oe<128&&(he=oe):De===2?(it=y[$+1],(it&192)===128&&(he=(oe&31)<<6|it&63,he<=127&&(he=null))):De===3?(it=y[$+1],yt=y[$+2],(it&192)===128&&(yt&192)===128&&(he=(oe&15)<<12|(it&63)<<6|yt&63,(he<=2047||he>=55296&&he<=57343)&&(he=null))):De===4&&(it=y[$+1],yt=y[$+2],Lt=y[$+3],(it&192)===128&&(yt&192)===128&&(Lt&192)===128&&(he=(oe&15)<<18|(it&63)<<12|(yt&63)<<6|Lt&63,(he<=65535||he>=1114112)&&(he=null))),he===null?(he=65533,De=1):he>65535&&(he-=65536,U+=String.fromCharCode(he>>>10&1023|55296),he=56320|he&1023),U+=String.fromCharCode(he),$+=De}return U}function ru(y,_,I){return Wn.decode(y.subarray(_,I))}function pc(y,_,I){for(var U=0,$,oe;U<_.length;U++){if($=_.charCodeAt(U),$>55295&&$<57344)if(oe)if($<56320){y[I++]=239,y[I++]=191,y[I++]=189,oe=$;continue}else $=oe-55296<<10|$-56320|65536,oe=null;else{$>56319||U+1===_.length?(y[I++]=239,y[I++]=191,y[I++]=189):oe=$;continue}else oe&&(y[I++]=239,y[I++]=191,y[I++]=189,oe=null);$<128?y[I++]=$:($<2048?y[I++]=$>>6|192:($<65536?y[I++]=$>>12|224:(y[I++]=$>>18|240,y[I++]=$>>12&63|128),y[I++]=$>>6&63|128),y[I++]=$&63|128)}return I}var $u=3;function zv(y,_,I){y===1&&I.readMessage(hf,_)}function hf(y,_,I){if(y===3){var U=I.readMessage(B1,{}),$=U.id,oe=U.bitmap,he=U.width,De=U.height,it=U.left,yt=U.top,Lt=U.advance;_.push({id:$,bitmap:new Dv({width:he+2*$u,height:De+2*$u},oe),metrics:{width:he,height:De,left:it,top:yt,advance:Lt}})}}function B1(y,_,I){y===1?_.id=I.readVarint():y===2?_.bitmap=I.readBytes():y===3?_.width=I.readVarint():y===4?_.height=I.readVarint():y===5?_.left=I.readSVarint():y===6?_.top=I.readSVarint():y===7&&(_.advance=I.readVarint())}function p0(y){return new Xa(y).readFields(zv,[])}var Gp=$u;function ep(y){for(var _=0,I=0,U=0,$=y;U<$.length;U+=1){var oe=$[U];_+=oe.w*oe.h,I=Math.max(I,oe.w)}y.sort(function(ji,wi){return wi.h-ji.h});for(var he=Math.max(Math.ceil(Math.sqrt(_/.95)),I),De=[{x:0,y:0,w:he,h:1/0}],it=0,yt=0,Lt=0,sr=y;Lt<sr.length;Lt+=1)for(var gr=sr[Lt],hr=De.length-1;hr>=0;hr--){var Rr=De[hr];if(!(gr.w>Rr.w||gr.h>Rr.h)){if(gr.x=Rr.x,gr.y=Rr.y,yt=Math.max(yt,gr.y+gr.h),it=Math.max(it,gr.x+gr.w),gr.w===Rr.w&&gr.h===Rr.h){var ni=De.pop();hr<De.length&&(De[hr]=ni)}else gr.h===Rr.h?(Rr.x+=gr.w,Rr.w-=gr.w):gr.w===Rr.w?(Rr.y+=gr.h,Rr.h-=gr.h):(De.push({x:Rr.x+gr.w,y:Rr.y,w:Rr.w-gr.w,h:gr.h}),Rr.y+=gr.h,Rr.h-=gr.h);break}}return{w:it,h:yt,fill:_/(it*yt)||0}}var Gc=1,Yf=function(_,I){var U=I.pixelRatio,$=I.version,oe=I.stretchX,he=I.stretchY,De=I.content;this.paddedRect=_,this.pixelRatio=U,this.stretchX=oe,this.stretchY=he,this.content=De,this.version=$},tp={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};tp.tl.get=function(){return[this.paddedRect.x+Gc,this.paddedRect.y+Gc]},tp.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-Gc,this.paddedRect.y+this.paddedRect.h-Gc]},tp.tlbr.get=function(){return this.tl.concat(this.br)},tp.displaySize.get=function(){return[(this.paddedRect.w-Gc*2)/this.pixelRatio,(this.paddedRect.h-Gc*2)/this.pixelRatio]},Object.defineProperties(Yf.prototype,tp);var yg=function(_,I){var U={},$={};this.haveRenderCallbacks=[];var oe=[];this.addImages(_,U,oe),this.addImages(I,$,oe);var he=ep(oe),De=he.w,it=he.h,yt=new Eh({width:De||1,height:it||1});for(var Lt in _){var sr=_[Lt],gr=U[Lt].paddedRect;Eh.copy(sr.data,yt,{x:0,y:0},{x:gr.x+Gc,y:gr.y+Gc},sr.data)}for(var hr in I){var Rr=I[hr],ni=$[hr].paddedRect,ji=ni.x+Gc,wi=ni.y+Gc,bn=Rr.data.width,rn=Rr.data.height;Eh.copy(Rr.data,yt,{x:0,y:0},{x:ji,y:wi},Rr.data),Eh.copy(Rr.data,yt,{x:0,y:rn-1},{x:ji,y:wi-1},{width:bn,height:1}),Eh.copy(Rr.data,yt,{x:0,y:0},{x:ji,y:wi+rn},{width:bn,height:1}),Eh.copy(Rr.data,yt,{x:bn-1,y:0},{x:ji-1,y:wi},{width:1,height:rn}),Eh.copy(Rr.data,yt,{x:0,y:0},{x:ji+bn,y:wi},{width:1,height:rn})}this.image=yt,this.iconPositions=U,this.patternPositions=$};yg.prototype.addImages=function(_,I,U){for(var $ in _){var oe=_[$],he={x:0,y:0,w:oe.data.width+2*Gc,h:oe.data.height+2*Gc};U.push(he),I[$]=new Yf(he,oe),oe.hasRenderCallback&&this.haveRenderCallbacks.push($)}},yg.prototype.patchUpdatedImages=function(_,I){_.dispatchRenderCallbacks(this.haveRenderCallbacks);for(var U in 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oe=_.sections[$];oe.image?U.addImageSection(oe):U.addTextSection(oe,I)}return U},Kh.prototype.length=function(){return this.text.length},Kh.prototype.getSection=function(_){return this.sections[this.sectionIndex[_]]},Kh.prototype.getSectionIndex=function(_){return this.sectionIndex[_]},Kh.prototype.getCharCode=function(_){return this.text.charCodeAt(_)},Kh.prototype.verticalizePunctuation=function(){this.text=ln(this.text)},Kh.prototype.trim=function(){for(var _=0,I=0;I<this.text.length&&m0[this.text.charCodeAt(I)];I++)_++;for(var U=this.text.length,$=this.text.length-1;$>=0&&$>=_&&m0[this.text.charCodeAt($)];$--)U--;this.text=this.text.substring(_,U),this.sectionIndex=this.sectionIndex.slice(_,U)},Kh.prototype.substring=function(_,I){var U=new Kh;return U.text=this.text.substring(_,I),U.sectionIndex=this.sectionIndex.slice(_,I),U.sections=this.sections,U},Kh.prototype.toString=function(){return this.text},Kh.prototype.getMaxScale=function(){var _=this;return this.sectionIndex.reduce(function(I,U){return Math.max(I,_.sections[U].scale)},0)},Kh.prototype.addTextSection=function(_,I){this.text+=_.text,this.sections.push(by.forText(_.scale,_.fontStack||I));for(var U=this.sections.length-1,$=0;$<_.text.length;++$)this.sectionIndex.push(U)},Kh.prototype.addImageSection=function(_){var I=_.image?_.image.name:\"\";if(I.length===0){re(\"Can't add FormattedSection with an empty image.\");return}var U=this.getNextImageSectionCharCode();if(!U){re(\"Reached maximum number of images \"+(g0-r3+2));return}this.text+=String.fromCharCode(U),this.sections.push(by.forImage(I)),this.sectionIndex.push(this.sections.length-1)},Kh.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=g0?null:++this.imageSectionID:(this.imageSectionID=r3,this.imageSectionID)};function vq(y,_){for(var I=[],U=y.text,$=0,oe=0,he=_;oe<he.length;oe+=1){var De=he[oe];I.push(y.substring($,De)),$=De}return 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I=this.layers[0],U=I._unevaluatedLayout._values;this.textSizeData=xp(this.zoom,U[\"text-size\"]),this.iconSizeData=xp(this.zoom,U[\"icon-size\"]);var $=this.layers[0].layout,oe=$.get(\"symbol-sort-key\"),he=$.get(\"symbol-z-order\");this.canOverlap=$.get(\"text-allow-overlap\")||$.get(\"icon-allow-overlap\")||$.get(\"text-ignore-placement\")||$.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=he!==\"viewport-y\"&&oe.constantOr(1)!==void 0;var De=he===\"viewport-y\"||he===\"auto\"&&!this.sortFeaturesByKey;this.sortFeaturesByY=De&&this.canOverlap,$.get(\"symbol-placement\")===\"point\"&&(this.writingModes=$.get(\"text-writing-mode\").map(function(it){return cv[it]})),this.stateDependentLayerIds=this.layers.filter(function(it){return it.isStateDependent()}).map(function(it){return it.id}),this.sourceID=_.sourceID};Pu.prototype.createArrays=function(){this.text=new u3(new ci(this.layers,this.zoom,function(_){return/^text/.test(_)})),this.icon=new u3(new ci(this.layers,this.zoom,function(_){return/^icon/.test(_)})),this.glyphOffsetArray=new Ul,this.lineVertexArray=new ss,this.symbolInstances=new nl},Pu.prototype.calculateGlyphDependencies=function(_,I,U,$,oe){for(var he=0;he<_.length;he++)if(I[_.charCodeAt(he)]=!0,(U||$)&&oe){var De=zi[_.charAt(he)];De&&(I[De.charCodeAt(0)]=!0)}},Pu.prototype.populate=function(_,I,U){var $=this.layers[0],oe=$.layout,he=oe.get(\"text-font\"),De=oe.get(\"text-field\"),it=oe.get(\"icon-image\"),yt=(De.value.kind!==\"constant\"||De.value.value instanceof $l&&!De.value.value.isEmpty()||De.value.value.toString().length>0)&&(he.value.kind!==\"constant\"||he.value.value.length>0),Lt=it.value.kind!==\"constant\"||!!it.value.value||Object.keys(it.parameters).length>0,sr=oe.get(\"symbol-sort-key\");if(this.features=[],!(!yt&&!Lt)){for(var gr=I.iconDependencies,hr=I.glyphDependencies,Rr=I.availableImages,ni=new Un(this.zoom),ji=0,wi=_;ji<wi.length;ji+=1){var bn=wi[ji],rn=bn.feature,xn=bn.id,In=bn.index,Xn=bn.sourceLayerIndex,ga=$._featureFilter.needGeometry,ha=Ja(rn,ga);if($._featureFilter.filter(ni,ha,U)){ga||(ha.geometry=zn(rn));var eo=void 0;if(yt){var qa=$.getValueAndResolveTokens(\"text-field\",ha,U,Rr),Za=$l.factory(qa);itt(Za)&&(this.hasRTLText=!0),(!this.hasRTLText||yo()===\"unavailable\"||this.hasRTLText&&Ms.isParsed())&&(eo=pi(Za,$,ha))}var Jo=void 0;if(Lt){var to=$.getValueAndResolveTokens(\"icon-image\",ha,U,Rr);to instanceof fl?Jo=to:Jo=fl.fromString(to)}if(!(!eo&&!Jo)){var ao=this.sortFeaturesByKey?sr.evaluate(ha,{},U):void 0,_s={id:xn,text:eo,icon:Jo,index:In,sourceLayerIndex:Xn,geometry:ha.geometry,properties:rn.properties,type:ttt[rn.type],sortKey:ao};if(this.features.push(_s),Jo&&(gr[Jo.name]=!0),eo){var jo=he.evaluate(ha,{},U).join(\",\"),kl=oe.get(\"text-rotation-alignment\")===\"map\"&&oe.get(\"symbol-placement\")!==\"point\";this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(cv.vertical)>=0;for(var Iu=0,Cl=eo.sections;Iu<Cl.length;Iu+=1){var Ll=Cl[Iu];if(Ll.image)gr[Ll.image.name]=!0;else{var yl=Xt(eo.toString()),Qu=Ll.fontStack||jo,gc=hr[Qu]=hr[Qu]||{};this.calculateGlyphDependencies(Ll.text,gc,kl,this.allowVerticalPlacement,yl)}}}}}}oe.get(\"symbol-placement\")===\"line\"&&(this.features=vn(this.features)),this.sortFeaturesByKey&&this.features.sort(function(Sf,Ff){return Sf.sortKey-Ff.sortKey})}},Pu.prototype.update=function(_,I,U){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(_,I,this.layers,U),this.icon.programConfigurations.updatePaintArrays(_,I,this.layers,U))},Pu.prototype.isEmpty=function(){return this.symbolInstances.length===0&&!this.hasRTLText},Pu.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Pu.prototype.upload=function(_){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(_),this.iconCollisionBox.upload(_)),this.text.upload(_,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(_,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Pu.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Pu.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Pu.prototype.addToLineVertexArray=function(_,I){var U=this.lineVertexArray.length;if(_.segment!==void 0){for(var $=_.dist(I[_.segment+1]),oe=_.dist(I[_.segment]),he={},De=_.segment+1;De<I.length;De++)he[De]={x:I[De].x,y:I[De].y,tileUnitDistanceFromAnchor:$},De<I.length-1&&($+=I[De+1].dist(I[De]));for(var it=_.segment||0;it>=0;it--)he[it]={x:I[it].x,y:I[it].y,tileUnitDistanceFromAnchor:oe},it>0&&(oe+=I[it-1].dist(I[it]));for(var yt=0;yt<I.length;yt++){var Lt=he[yt];this.lineVertexArray.emplaceBack(Lt.x,Lt.y,Lt.tileUnitDistanceFromAnchor)}}return{lineStartIndex:U,lineLength:this.lineVertexArray.length-U}},Pu.prototype.addSymbols=function(_,I,U,$,oe,he,De,it,yt,Lt,sr,gr){for(var hr=_.indexArray,Rr=_.layoutVertexArray,ni=_.segments.prepareSegment(4*I.length,Rr,hr,this.canOverlap?he.sortKey:void 0),ji=this.glyphOffsetArray.length,wi=ni.vertexLength,bn=this.allowVerticalPlacement&&De===cv.vertical?Math.PI/2:0,rn=he.text&&he.text.sections,xn=0;xn<I.length;xn++){var In=I[xn],Xn=In.tl,ga=In.tr,ha=In.bl,eo=In.br,qa=In.tex,Za=In.pixelOffsetTL,Jo=In.pixelOffsetBR,to=In.minFontScaleX,ao=In.minFontScaleY,_s=In.glyphOffset,jo=In.isSDF,kl=In.sectionIndex,Iu=ni.vertexLength,Cl=_s[1];f6(Rr,it.x,it.y,Xn.x,Cl+Xn.y,qa.x,qa.y,U,jo,Za.x,Za.y,to,ao),f6(Rr,it.x,it.y,ga.x,Cl+ga.y,qa.x+qa.w,qa.y,U,jo,Jo.x,Za.y,to,ao),f6(Rr,it.x,it.y,ha.x,Cl+ha.y,qa.x,qa.y+qa.h,U,jo,Za.x,Jo.y,to,ao),f6(Rr,it.x,it.y,eo.x,Cl+eo.y,qa.x+qa.w,qa.y+qa.h,U,jo,Jo.x,Jo.y,to,ao),bq(_.dynamicLayoutVertexArray,it,bn),hr.emplaceBack(Iu,Iu+1,Iu+2),hr.emplaceBack(Iu+1,Iu+2,Iu+3),ni.vertexLength+=4,ni.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(_s[0]),(xn===I.length-1||kl!==I[xn+1].sectionIndex)&&_.programConfigurations.populatePaintArrays(Rr.length,he,he.index,{},gr,rn&&rn[kl])}_.placedSymbolArray.emplaceBack(it.x,it.y,ji,this.glyphOffsetArray.length-ji,wi,yt,Lt,it.segment,U?U[0]:0,U?U[1]:0,$[0],$[1],De,0,!1,0,sr)},Pu.prototype._addCollisionDebugVertex=function(_,I,U,$,oe,he){return I.emplaceBack(0,0),_.emplaceBack(U.x,U.y,$,oe,Math.round(he.x),Math.round(he.y))},Pu.prototype.addCollisionDebugVertices=function(_,I,U,$,oe,he,De){var it=oe.segments.prepareSegment(4,oe.layoutVertexArray,oe.indexArray),yt=it.vertexLength,Lt=oe.layoutVertexArray,sr=oe.collisionVertexArray,gr=De.anchorX,hr=De.anchorY;this._addCollisionDebugVertex(Lt,sr,he,gr,hr,new u(_,I)),this._addCollisionDebugVertex(Lt,sr,he,gr,hr,new u(U,I)),this._addCollisionDebugVertex(Lt,sr,he,gr,hr,new u(U,$)),this._addCollisionDebugVertex(Lt,sr,he,gr,hr,new u(_,$)),it.vertexLength+=4;var Rr=oe.indexArray;Rr.emplaceBack(yt,yt+1),Rr.emplaceBack(yt+1,yt+2),Rr.emplaceBack(yt+2,yt+3),Rr.emplaceBack(yt+3,yt),it.primitiveLength+=4},Pu.prototype.addDebugCollisionBoxes=function(_,I,U,$){for(var oe=_;oe<I;oe++){var he=this.collisionBoxArray.get(oe),De=he.x1,it=he.y1,yt=he.x2,Lt=he.y2;this.addCollisionDebugVertices(De,it,yt,Lt,$?this.textCollisionBox:this.iconCollisionBox,he.anchorPoint,U)}},Pu.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new DS(Ji,ft.members,sa),this.iconCollisionBox=new DS(Ji,ft.members,sa);for(var _=0;_<this.symbolInstances.length;_++){var I=this.symbolInstances.get(_);this.addDebugCollisionBoxes(I.textBoxStartIndex,I.textBoxEndIndex,I,!0),this.addDebugCollisionBoxes(I.verticalTextBoxStartIndex,I.verticalTextBoxEndIndex,I,!0),this.addDebugCollisionBoxes(I.iconBoxStartIndex,I.iconBoxEndIndex,I,!1),this.addDebugCollisionBoxes(I.verticalIconBoxStartIndex,I.verticalIconBoxEndIndex,I,!1)}},Pu.prototype._deserializeCollisionBoxesForSymbol=function(_,I,U,$,oe,he,De,it,yt){for(var Lt={},sr=I;sr<U;sr++){var gr=_.get(sr);Lt.textBox={x1:gr.x1,y1:gr.y1,x2:gr.x2,y2:gr.y2,anchorPointX:gr.anchorPointX,anchorPointY:gr.anchorPointY},Lt.textFeatureIndex=gr.featureIndex;break}for(var hr=$;hr<oe;hr++){var Rr=_.get(hr);Lt.verticalTextBox={x1:Rr.x1,y1:Rr.y1,x2:Rr.x2,y2:Rr.y2,anchorPointX:Rr.anchorPointX,anchorPointY:Rr.anchorPointY},Lt.verticalTextFeatureIndex=Rr.featureIndex;break}for(var ni=he;ni<De;ni++){var ji=_.get(ni);Lt.iconBox={x1:ji.x1,y1:ji.y1,x2:ji.x2,y2:ji.y2,anchorPointX:ji.anchorPointX,anchorPointY:ji.anchorPointY},Lt.iconFeatureIndex=ji.featureIndex;break}for(var wi=it;wi<yt;wi++){var bn=_.get(wi);Lt.verticalIconBox={x1:bn.x1,y1:bn.y1,x2:bn.x2,y2:bn.y2,anchorPointX:bn.anchorPointX,anchorPointY:bn.anchorPointY},Lt.verticalIconFeatureIndex=bn.featureIndex;break}return Lt},Pu.prototype.deserializeCollisionBoxes=function(_){this.collisionArrays=[];for(var I=0;I<this.symbolInstances.length;I++){var U=this.symbolInstances.get(I);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(_,U.textBoxStartIndex,U.textBoxEndIndex,U.verticalTextBoxStartIndex,U.verticalTextBoxEndIndex,U.iconBoxStartIndex,U.iconBoxEndIndex,U.verticalIconBoxStartIndex,U.verticalIconBoxEndIndex))}},Pu.prototype.hasTextData=function(){return this.text.segments.get().length>0},Pu.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Pu.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Pu.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Pu.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Pu.prototype.addIndicesForPlacedSymbol=function(_,I){for(var U=_.placedSymbolArray.get(I),$=U.vertexStartIndex+U.numGlyphs*4,oe=U.vertexStartIndex;oe<$;oe+=4)_.indexArray.emplaceBack(oe,oe+1,oe+2),_.indexArray.emplaceBack(oe+1,oe+2,oe+3)},Pu.prototype.getSortedSymbolIndexes=function(_){if(this.sortedAngle===_&&this.symbolInstanceIndexes!==void 0)return this.symbolInstanceIndexes;for(var I=Math.sin(_),U=Math.cos(_),$=[],oe=[],he=[],De=0;De<this.symbolInstances.length;++De){he.push(De);var it=this.symbolInstances.get(De);$.push(Math.round(I*it.anchorX+U*it.anchorY)|0),oe.push(it.featureIndex)}return he.sort(function(yt,Lt){return $[yt]-$[Lt]||oe[Lt]-oe[yt]}),he},Pu.prototype.addToSortKeyRanges=function(_,I){var U=this.sortKeyRanges[this.sortKeyRanges.length-1];U&&U.sortKey===I?U.symbolInstanceEnd=_+1:this.sortKeyRanges.push({sortKey:I,symbolInstanceStart:_,symbolInstanceEnd:_+1})},Pu.prototype.sortFeatures=function(_){var I=this;if(this.sortFeaturesByY&&this.sortedAngle!==_&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(_),this.sortedAngle=_,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var U=0,$=this.symbolInstanceIndexes;U<$.length;U+=1){var oe=$[U],he=this.symbolInstances.get(oe);this.featureSortOrder.push(he.featureIndex),[he.rightJustifiedTextSymbolIndex,he.centerJustifiedTextSymbolIndex,he.leftJustifiedTextSymbolIndex].forEach(function(De,it,yt){De>=0&&yt.indexOf(De)===it&&I.addIndicesForPlacedSymbol(I.text,De)}),he.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,he.verticalPlacedTextSymbolIndex),he.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,he.placedIconSymbolIndex),he.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,he.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},X(\"SymbolBucket\",Pu,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Pu.MAX_GLYPHS=65535,Pu.addDynamicAttributes=bq;function ntt(y,_){return _.replace(/{([^{}]+)}/g,function(I,U){return U in y?String(y[U]):\"\"})}var att=new Or({\"symbol-placement\":new Me(Rn.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Me(Rn.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Me(Rn.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new bt(Rn.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new Me(Rn.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new Me(Rn.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new Me(Rn.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Me(Rn.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Me(Rn.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new bt(Rn.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Me(Rn.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Me(Rn.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new bt(Rn.layout_symbol[\"icon-image\"]),\"icon-rotate\":new bt(Rn.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new Me(Rn.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Me(Rn.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new bt(Rn.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new bt(Rn.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Me(Rn.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Me(Rn.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Me(Rn.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new bt(Rn.layout_symbol[\"text-field\"]),\"text-font\":new bt(Rn.layout_symbol[\"text-font\"]),\"text-size\":new bt(Rn.layout_symbol[\"text-size\"]),\"text-max-width\":new bt(Rn.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Me(Rn.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new bt(Rn.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new bt(Rn.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new bt(Rn.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new Me(Rn.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new bt(Rn.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Me(Rn.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new Me(Rn.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new bt(Rn.layout_symbol[\"text-rotate\"]),\"text-padding\":new Me(Rn.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Me(Rn.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new bt(Rn.layout_symbol[\"text-transform\"]),\"text-offset\":new bt(Rn.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Me(Rn.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new Me(Rn.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Me(Rn.layout_symbol[\"text-optional\"])}),ott=new Or({\"icon-opacity\":new bt(Rn.paint_symbol[\"icon-opacity\"]),\"icon-color\":new bt(Rn.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new bt(Rn.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new bt(Rn.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new bt(Rn.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Me(Rn.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Me(Rn.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new bt(Rn.paint_symbol[\"text-opacity\"]),\"text-color\":new bt(Rn.paint_symbol[\"text-color\"],{runtimeType:ql,getOverride:function(y){return y.textColor},hasOverride:function(y){return!!y.textColor}}),\"text-halo-color\":new bt(Rn.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new bt(Rn.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new bt(Rn.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Me(Rn.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Me(Rn.paint_symbol[\"text-translate-anchor\"])}),wq={paint:ott,layout:att},c3=function(_){this.type=_.property.overrides?_.property.overrides.runtimeType:ac,this.defaultValue=_};c3.prototype.evaluate=function(_){if(_.formattedSection){var I=this.defaultValue.property.overrides;if(I&&I.hasOverride(_.formattedSection))return I.getOverride(_.formattedSection)}return _.feature&&_.featureState?this.defaultValue.evaluate(_.feature,_.featureState):this.defaultValue.property.specification.default},c3.prototype.eachChild=function(_){if(!this.defaultValue.isConstant()){var I=this.defaultValue.value;_(I._styleExpression.expression)}},c3.prototype.outputDefined=function(){return!1},c3.prototype.serialize=function(){return null},X(\"FormatSectionOverride\",c3,{omit:[\"defaultValue\"]});var stt=(function(y){function _(I){y.call(this,I,wq)}return y&&(_.__proto__=y),_.prototype=Object.create(y&&y.prototype),_.prototype.constructor=_,_.prototype.recalculate=function(U,$){if(y.prototype.recalculate.call(this,U,$),this.layout.get(\"icon-rotation-alignment\")===\"auto\"&&(this.layout.get(\"symbol-placement\")!==\"point\"?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),this.layout.get(\"text-rotation-alignment\")===\"auto\"&&(this.layout.get(\"symbol-placement\")!==\"point\"?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),this.layout.get(\"text-pitch-alignment\")===\"auto\"&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),this.layout.get(\"icon-pitch-alignment\")===\"auto\"&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),this.layout.get(\"symbol-placement\")===\"point\"){var oe=this.layout.get(\"text-writing-mode\");if(oe){for(var he=[],De=0,it=oe;De<it.length;De+=1){var yt=it[De];he.indexOf(yt)<0&&he.push(yt)}this.layout._values[\"text-writing-mode\"]=he}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},_.prototype.getValueAndResolveTokens=function(U,$,oe,he){var De=this.layout.get(U).evaluate($,{},oe,he),it=this._unevaluatedLayout._values[U];return!it.isDataDriven()&&!Va(it.value)&&De?ntt($.properties,De):De},_.prototype.createBucket=function(U){return new Pu(U)},_.prototype.queryRadius=function(){return 0},_.prototype.queryIntersectsFeature=function(){return!1},_.prototype._setPaintOverrides=function(){for(var U=0,$=wq.paint.overridableProperties;U<$.length;U+=1){var oe=$[U];if(_.hasPaintOverride(this.layout,oe)){var he=this.paint.get(oe),De=new c3(he),it=new Ac(De,he.property.specification),yt=null;he.value.kind===\"constant\"||he.value.kind===\"source\"?yt=new Vc(\"source\",it):yt=new hc(\"composite\",it,he.value.zoomStops,he.value._interpolationType),this.paint._values[oe]=new dl(he.property,yt,he.parameters)}}},_.prototype._handleOverridablePaintPropertyUpdate=function(U,$,oe){return!this.layout||$.isDataDriven()||oe.isDataDriven()?!1:_.hasPaintOverride(this.layout,U)},_.hasPaintOverride=function(U,$){var oe=U.get(\"text-field\"),he=wq.paint.properties[$],De=!1,it=function(sr){for(var gr=0,hr=sr;gr<hr.length;gr+=1){var Rr=hr[gr];if(he.overrides&&he.overrides.hasOverride(Rr)){De=!0;return}}};if(oe.value.kind===\"constant\"&&oe.value.value instanceof $l)it(oe.value.value.sections);else if(oe.value.kind===\"source\"){var yt=function(sr){if(!De)if(sr instanceof Go&&Es(sr.value)===pl){var gr=sr.value;it(gr.sections)}else sr instanceof Gu?it(sr.sections):sr.eachChild(yt)},Lt=oe.value;Lt._styleExpression&&yt(Lt._styleExpression.expression)}return De},_})(vi),ltt=new Or({\"background-color\":new Me(Rn.paint_background[\"background-color\"]),\"background-pattern\":new Lr(Rn.paint_background[\"background-pattern\"]),\"background-opacity\":new Me(Rn.paint_background[\"background-opacity\"])}),utt={paint:ltt},ctt=(function(y){function _(I){y.call(this,I,utt)}return y&&(_.__proto__=y),_.prototype=Object.create(y&&y.prototype),_.prototype.constructor=_,_})(vi),ftt=new Or({\"raster-opacity\":new Me(Rn.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new Me(Rn.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new Me(Rn.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new Me(Rn.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new Me(Rn.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new Me(Rn.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new Me(Rn.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new Me(Rn.paint_raster[\"raster-fade-duration\"])}),htt={paint:ftt},dtt=(function(y){function _(I){y.call(this,I,htt)}return y&&(_.__proto__=y),_.prototype=Object.create(y&&y.prototype),_.prototype.constructor=_,_})(vi);function vtt(y){var _=[],I=y.id;return I===void 0&&_.push({message:\"layers.\"+I+': missing required property \"id\"'}),y.render===void 0&&_.push({message:\"layers.\"+I+': missing required method \"render\"'}),y.renderingMode&&y.renderingMode!==\"2d\"&&y.renderingMode!==\"3d\"&&_.push({message:\"layers.\"+I+': property \"renderingMode\" must be either \"2d\" or \"3d\"'}),_}var ptt=(function(y){function _(I){y.call(this,I,{}),this.implementation=I}return y&&(_.__proto__=y),_.prototype=Object.create(y&&y.prototype),_.prototype.constructor=_,_.prototype.is3D=function(){return this.implementation.renderingMode===\"3d\"},_.prototype.hasOffscreenPass=function(){return this.implementation.prerender!==void 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I={};if(!_)return I;for(var U=function(){var he=oe[$],De=he.layerIds.map(function(sr){return _.getLayer(sr)}).filter(Boolean);if(De.length!==0){he.layers=De,he.stateDependentLayerIds&&(he.stateDependentLayers=he.stateDependentLayerIds.map(function(sr){return De.filter(function(gr){return gr.id===sr})[0]}));for(var it=0,yt=De;it<yt.length;it+=1){var Lt=yt[it];I[Lt.id]=he}}},$=0,oe=y;$<oe.length;$+=1)U();return I}var d6=function(_){this._stringToNumber={},this._numberToString=[];for(var I=0;I<_.length;I++){var U=_[I];this._stringToNumber[U]=I,this._numberToString[I]=U}};d6.prototype.encode=function(_){return this._stringToNumber[_]},d6.prototype.decode=function(_){return this._numberToString[_]};var v6=function(_,I,U,$,oe){this.type=\"Feature\",this._vectorTileFeature=_,_._z=I,_._x=U,_._y=$,this.properties=_.properties,this.id=oe},Sq={geometry:{configurable:!0}};Sq.geometry.get=function(){return this._geometry===void 0&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Sq.geometry.set=function(y){this._geometry=y},v6.prototype.toJSON=function(){var _={geometry:this.geometry};for(var I in this)I===\"_geometry\"||I===\"_vectorTileFeature\"||(_[I]=this[I]);return _},Object.defineProperties(v6.prototype,Sq);var h3=function(){this.state={},this.stateChanges={},this.deletedStates={}};h3.prototype.updateState=function(_,I,U){var $=String(I);if(this.stateChanges[_]=this.stateChanges[_]||{},this.stateChanges[_][$]=this.stateChanges[_][$]||{},x(this.stateChanges[_][$],U),this.deletedStates[_]===null){this.deletedStates[_]={};for(var oe in this.state[_])oe!==$&&(this.deletedStates[_][oe]=null)}else{var he=this.deletedStates[_]&&this.deletedStates[_][$]===null;if(he){this.deletedStates[_][$]={};for(var De in this.state[_][$])U[De]||(this.deletedStates[_][$][De]=null)}else for(var it in U){var yt=this.deletedStates[_]&&this.deletedStates[_][$]&&this.deletedStates[_][$][it]===null;yt&&delete this.deletedStates[_][$][it]}}},h3.prototype.removeFeatureState=function(_,I,U){var $=this.deletedStates[_]===null;if(!$){var oe=String(I);if(this.deletedStates[_]=this.deletedStates[_]||{},U&&I!==void 0)this.deletedStates[_][oe]!==null&&(this.deletedStates[_][oe]=this.deletedStates[_][oe]||{},this.deletedStates[_][oe][U]=null);else if(I!==void 0){var he=this.stateChanges[_]&&this.stateChanges[_][oe];if(he){this.deletedStates[_][oe]={};for(U in this.stateChanges[_][oe])this.deletedStates[_][oe][U]=null}else this.deletedStates[_][oe]=null}else this.deletedStates[_]=null}},h3.prototype.getState=function(_,I){var U=String(I),$=this.state[_]||{},oe=this.stateChanges[_]||{},he=x({},$[U],oe[U]);if(this.deletedStates[_]===null)return{};if(this.deletedStates[_]){var De=this.deletedStates[_][I];if(De===null)return{};for(var it in De)delete he[it]}return he},h3.prototype.initializeTileState=function(_,I){_.setFeatureState(this.state,I)},h3.prototype.coalesceChanges=function(_,I){var U={};for(var $ in this.stateChanges){this.state[$]=this.state[$]||{};var oe={};for(var he in this.stateChanges[$])this.state[$][he]||(this.state[$][he]={}),x(this.state[$][he],this.stateChanges[$][he]),oe[he]=this.state[$][he];U[$]=oe}for(var De in this.deletedStates){this.state[De]=this.state[De]||{};var it={};if(this.deletedStates[De]===null)for(var yt in this.state[De])it[yt]={},this.state[De][yt]={};else for(var Lt in this.deletedStates[De]){var sr=this.deletedStates[De][Lt]===null;if(sr)this.state[De][Lt]={};else for(var gr=0,hr=Object.keys(this.deletedStates[De][Lt]);gr<hr.length;gr+=1){var Rr=hr[gr];delete this.state[De][Lt][Rr]}it[Lt]=this.state[De][Lt]}U[De]=U[De]||{},x(U[De],it)}if(this.stateChanges={},this.deletedStates={},Object.keys(U).length!==0)for(var ni in _){var ji=_[ni];ji.setFeatureState(U,I)}};var Ty=function(_,I){this.tileID=_,this.x=_.canonical.x,this.y=_.canonical.y,this.z=_.canonical.z,this.grid=new au(Ei,16,0),this.grid3D=new au(Ei,16,0),this.featureIndexArray=new Ys,this.promoteId=I};Ty.prototype.insert=function(_,I,U,$,oe,he){var De=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(U,$,oe);for(var it=he?this.grid3D:this.grid,yt=0;yt<I.length;yt++){for(var Lt=I[yt],sr=[1/0,1/0,-1/0,-1/0],gr=0;gr<Lt.length;gr++){var hr=Lt[gr];sr[0]=Math.min(sr[0],hr.x),sr[1]=Math.min(sr[1],hr.y),sr[2]=Math.max(sr[2],hr.x),sr[3]=Math.max(sr[3],hr.y)}sr[0]<Ei&&sr[1]<Ei&&sr[2]>=0&&sr[3]>=0&&it.insert(De,sr[0],sr[1],sr[2],sr[3])}},Ty.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new mg.VectorTile(new Xa(this.rawTileData)).layers,this.sourceLayerCoder=new d6(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Ty.prototype.query=function(_,I,U,$){var oe=this;this.loadVTLayers();for(var he=_.params||{},De=Ei/_.tileSize/_.scale,it=xe(he.filter),yt=_.queryGeometry,Lt=_.queryPadding*De,sr=pee(yt),gr=this.grid.query(sr.minX-Lt,sr.minY-Lt,sr.maxX+Lt,sr.maxY+Lt),hr=pee(_.cameraQueryGeometry),Rr=this.grid3D.query(hr.minX-Lt,hr.minY-Lt,hr.maxX+Lt,hr.maxY+Lt,function(Xn,ga,ha,eo){return mp(_.cameraQueryGeometry,Xn-Lt,ga-Lt,ha+Lt,eo+Lt)}),ni=0,ji=Rr;ni<ji.length;ni+=1){var wi=ji[ni];gr.push(wi)}gr.sort(Att);for(var bn={},rn,xn=function(Xn){var ga=gr[Xn];if(ga!==rn){rn=ga;var ha=oe.featureIndexArray.get(ga),eo=null;oe.loadMatchingFeature(bn,ha.bucketIndex,ha.sourceLayerIndex,ha.featureIndex,it,he.layers,he.availableImages,I,U,$,function(qa,Za,Jo){return eo||(eo=zn(qa)),Za.queryIntersectsFeature(yt,qa,Jo,eo,oe.z,_.transform,De,_.pixelPosMatrix)})}},In=0;In<gr.length;In++)xn(In);return bn},Ty.prototype.loadMatchingFeature=function(_,I,U,$,oe,he,De,it,yt,Lt,sr){var gr=this.bucketLayerIDs[I];if(!(he&&!N(he,gr))){var hr=this.sourceLayerCoder.decode(U),Rr=this.vtLayers[hr],ni=Rr.feature($);if(oe.needGeometry){var ji=Ja(ni,!0);if(!oe.filter(new Un(this.tileID.overscaledZ),ji,this.tileID.canonical))return}else if(!oe.filter(new Un(this.tileID.overscaledZ),ni))return;for(var wi=this.getId(ni,hr),bn=0;bn<gr.length;bn++){var rn=gr[bn];if(!(he&&he.indexOf(rn)<0)){var xn=it[rn];if(xn){var In={};wi!==void 0&&Lt&&(In=Lt.getState(xn.sourceLayer||\"_geojsonTileLayer\",wi));var Xn=x({},yt[rn]);Xn.paint=vee(Xn.paint,xn.paint,ni,In,De),Xn.layout=vee(Xn.layout,xn.layout,ni,In,De);var ga=!sr||sr(ni,xn,In);if(ga){var ha=new v6(ni,this.z,this.x,this.y,wi);ha.layer=Xn;var eo=_[rn];eo===void 0&&(eo=_[rn]=[]),eo.push({featureIndex:$,feature:ha,intersectionZ:ga})}}}}}},Ty.prototype.lookupSymbolFeatures=function(_,I,U,$,oe,he,De,it){var yt={};this.loadVTLayers();for(var Lt=xe(oe),sr=0,gr=_;sr<gr.length;sr+=1){var hr=gr[sr];this.loadMatchingFeature(yt,U,$,hr,Lt,he,De,it,I)}return yt},Ty.prototype.hasLayer=function(_){for(var I=0,U=this.bucketLayerIDs;I<U.length;I+=1)for(var $=U[I],oe=0,he=$;oe<he.length;oe+=1){var De=he[oe];if(_===De)return!0}return!1},Ty.prototype.getId=function(_,I){var U=_.id;if(this.promoteId){var $=typeof this.promoteId==\"string\"?this.promoteId:this.promoteId[I];U=_.properties[$],typeof U==\"boolean\"&&(U=Number(U))}return U},X(\"FeatureIndex\",Ty,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});function vee(y,_,I,U,$){return G(y,function(oe,he){var De=_ instanceof Sc?_.get(he):null;return De&&De.evaluate?De.evaluate(I,U,$):De})}function pee(y){for(var _=1/0,I=1/0,U=-1/0,$=-1/0,oe=0,he=y;oe<he.length;oe+=1){var De=he[oe];_=Math.min(_,De.x),I=Math.min(I,De.y),U=Math.max(U,De.x),$=Math.max($,De.y)}return{minX:_,minY:I,maxX:U,maxY:$}}function Att(y,_){return _-y}var Stt=3e4,Jh=function(_,I){this.tileID=_,this.uid=g(),this.uses=0,this.tileSize=I,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};Jh.prototype.registerFadeDuration=function(_){var I=_+this.timeAdded;I<lt.now()||this.fadeEndTime&&I<this.fadeEndTime||(this.fadeEndTime=I)},Jh.prototype.wasRequested=function(){return this.state===\"errored\"||this.state===\"loaded\"||this.state===\"reloading\"},Jh.prototype.loadVectorData=function(_,I,U){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",!_){this.collisionBoxArray=new Ga;return}_.featureIndex&&(this.latestFeatureIndex=_.featureIndex,_.rawTileData?(this.latestRawTileData=_.rawTileData,this.latestFeatureIndex.rawTileData=_.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=_.collisionBoxArray,this.buckets=Ttt(_.buckets,I.style),this.hasSymbolBuckets=!1;for(var $ in this.buckets){var oe=this.buckets[$];if(oe instanceof Pu)if(this.hasSymbolBuckets=!0,U)oe.justReloaded=!0;else break}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var he in this.buckets){var De=this.buckets[he];if(De instanceof Pu&&De.hasRTLText){this.hasRTLText=!0,Xs();break}}this.queryPadding=0;for(var it in this.buckets){var yt=this.buckets[it];this.queryPadding=Math.max(this.queryPadding,I.style.getLayer(it).queryRadius(yt))}_.imageAtlas&&(this.imageAtlas=_.imageAtlas),_.glyphAtlasImage&&(this.glyphAtlasImage=_.glyphAtlasImage)},Jh.prototype.unloadVectorData=function(){for(var _ in this.buckets)this.buckets[_].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},Jh.prototype.getBucket=function(_){return this.buckets[_.id]},Jh.prototype.upload=function(_){for(var I in this.buckets){var U=this.buckets[I];U.uploadPending()&&U.upload(_)}var $=_.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new db(_,this.imageAtlas.image,$.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new db(_,this.glyphAtlasImage,$.ALPHA),this.glyphAtlasImage=null)},Jh.prototype.prepare=function(_){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(_,this.imageAtlasTexture)},Jh.prototype.queryRenderedFeatures=function(_,I,U,$,oe,he,De,it,yt,Lt){return!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData?{}:this.latestFeatureIndex.query({queryGeometry:$,cameraQueryGeometry:oe,scale:he,tileSize:this.tileSize,pixelPosMatrix:Lt,transform:it,params:De,queryPadding:this.queryPadding*yt},_,I,U)},Jh.prototype.querySourceFeatures=function(_,I){var U=this.latestFeatureIndex;if(!(!U||!U.rawTileData)){var $=U.loadVTLayers(),oe=I?I.sourceLayer:\"\",he=$._geojsonTileLayer||$[oe];if(he)for(var De=xe(I&&I.filter),it=this.tileID.canonical,yt=it.z,Lt=it.x,sr=it.y,gr={z:yt,x:Lt,y:sr},hr=0;hr<he.length;hr++){var Rr=he.feature(hr);if(De.needGeometry){var ni=Ja(Rr,!0);if(!De.filter(new Un(this.tileID.overscaledZ),ni,this.tileID.canonical))continue}else if(!De.filter(new Un(this.tileID.overscaledZ),Rr))continue;var ji=U.getId(Rr,oe),wi=new v6(Rr,yt,Lt,sr,ji);wi.tile=gr,_.push(wi)}}},Jh.prototype.hasData=function(){return this.state===\"loaded\"||this.state===\"reloading\"||this.state===\"expired\"},Jh.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},Jh.prototype.setExpiryData=function(_){var I=this.expirationTime;if(_.cacheControl){var U=ge(_.cacheControl);U[\"max-age\"]&&(this.expirationTime=Date.now()+U[\"max-age\"]*1e3)}else _.expires&&(this.expirationTime=new Date(_.expires).getTime());if(this.expirationTime){var $=Date.now(),oe=!1;if(this.expirationTime>$)oe=!1;else if(!I)oe=!0;else if(this.expirationTime<I)oe=!0;else{var he=this.expirationTime-I;he?this.expirationTime=$+Math.max(he,Stt):oe=!0}oe?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},Jh.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-new Date().getTime(),Math.pow(2,31)-1)},Jh.prototype.setFeatureState=function(_,I){if(!(!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData||Object.keys(_).length===0)){var U=this.latestFeatureIndex.loadVTLayers();for(var $ in this.buckets)if(I.style.hasLayer($)){var oe=this.buckets[$],he=oe.layers[0].sourceLayer||\"_geojsonTileLayer\",De=U[he],it=_[he];if(!(!De||!it||Object.keys(it).length===0)){oe.update(it,De,this.imageAtlas&&this.imageAtlas.patternPositions||{});var yt=I&&I.style&&I.style.getLayer($);yt&&(this.queryPadding=Math.max(this.queryPadding,yt.queryRadius(oe)))}}}},Jh.prototype.holdingForFade=function(){return this.symbolFadeHoldUntil!==void 0},Jh.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<lt.now()},Jh.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},Jh.prototype.setHoldDuration=function(_){this.symbolFadeHoldUntil=lt.now()+_},Jh.prototype.setDependencies=function(_,I){for(var U={},$=0,oe=I;$<oe.length;$+=1){var he=oe[$];U[he]=!0}this.dependencies[_]=U},Jh.prototype.hasDependency=function(_,I){for(var U=0,$=_;U<$.length;U+=1){var oe=$[U],he=this.dependencies[oe];if(he)for(var De=0,it=I;De<it.length;De+=1){var yt=it[De];if(he[yt])return!0}}return!1};var Mtt=[\"type\",\"source\",\"source-layer\",\"minzoom\",\"maxzoom\",\"filter\",\"layout\"],V1=f.performance,gee=function(_){this._marks={start:[_.url,\"start\"].join(\"#\"),end:[_.url,\"end\"].join(\"#\"),measure:_.url.toString()},V1.mark(this._marks.start)};gee.prototype.finish=function(){V1.mark(this._marks.end);var _=V1.getEntriesByName(this._marks.measure);return 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strict\";function a(Gt){var wt=typeof Gt;if(wt===\"number\"||wt===\"boolean\"||wt===\"string\"||Gt===void 0||Gt===null)return JSON.stringify(Gt);if(Array.isArray(Gt)){for(var tr=\"[\",ir=0,wr=Gt;ir<wr.length;ir+=1){var Xr=wr[ir];tr+=a(Xr)+\",\"}return tr+\"]\"}for(var ti=Object.keys(Gt).sort(),$r=\"{\",Ri=0;Ri<ti.length;Ri++)$r+=JSON.stringify(ti[Ri])+\":\"+a(Gt[ti[Ri]])+\",\";return $r+\"}\"}function o(Gt){for(var wt=\"\",tr=0,ir=i.refProperties;tr<ir.length;tr+=1){var wr=ir[tr];wt+=\"/\"+a(Gt[wr])}return wt}function s(Gt,wt){for(var tr={},ir=0;ir<Gt.length;ir++){var wr=wt&&wt[Gt[ir].id]||o(Gt[ir]);wt&&(wt[Gt[ir].id]=wr);var Xr=tr[wr];Xr||(Xr=tr[wr]=[]),Xr.push(Gt[ir])}var ti=[];for(var $r in tr)ti.push(tr[$r]);return ti}var l=function(wt){this.keyCache={},wt&&this.replace(wt)};l.prototype.replace=function(wt){this._layerConfigs={},this._layers={},this.update(wt,[])},l.prototype.update=function(wt,tr){for(var ir=this,wr=0,Xr=wt;wr<Xr.length;wr+=1){var ti=Xr[wr];this._layerConfigs[ti.id]=ti;var $r=this._layers[ti.id]=i.createStyleLayer(ti);$r._featureFilter=i.featureFilter($r.filter),this.keyCache[ti.id]&&delete this.keyCache[ti.id]}for(var Ri=0,Hi=tr;Ri<Hi.length;Ri+=1){var en=Hi[Ri];delete this.keyCache[en],delete this._layerConfigs[en],delete this._layers[en]}this.familiesBySource={};for(var fn=s(i.values(this._layerConfigs),this.keyCache),yn=0,Mn=fn;yn<Mn.length;yn+=1){var Ba=Mn[yn],ua=Ba.map(function(jn){return ir._layers[jn.id]}),ma=ua[0];if(ma.visibility!==\"none\"){var Wa=ma.source||\"\",za=this.familiesBySource[Wa];za||(za=this.familiesBySource[Wa]={});var Xo=ma.sourceLayer||\"_geojsonTileLayer\",da=za[Xo];da||(da=za[Xo]=[]),da.push(ua)}}};var u=1,c=function(wt){var tr={},ir=[];for(var wr in wt){var Xr=wt[wr],ti=tr[wr]={};for(var $r in Xr){var Ri=Xr[+$r];if(!(!Ri||Ri.bitmap.width===0||Ri.bitmap.height===0)){var Hi={x:0,y:0,w:Ri.bitmap.width+2*u,h:Ri.bitmap.height+2*u};ir.push(Hi),ti[$r]={rect:Hi,metrics:Ri.metrics}}}}var en=i.potpack(ir),fn=en.w,yn=en.h,Mn=new i.AlphaImage({width:fn||1,height:yn||1});for(var Ba in wt){var ua=wt[Ba];for(var ma in ua){var Wa=ua[+ma];if(!(!Wa||Wa.bitmap.width===0||Wa.bitmap.height===0)){var za=tr[Ba][ma].rect;i.AlphaImage.copy(Wa.bitmap,Mn,{x:0,y:0},{x:za.x+u,y:za.y+u},Wa.bitmap)}}}this.image=Mn,this.positions=tr};i.register(\"GlyphAtlas\",c);var f=function(wt){this.tileID=new i.OverscaledTileID(wt.tileID.overscaledZ,wt.tileID.wrap,wt.tileID.canonical.z,wt.tileID.canonical.x,wt.tileID.canonical.y),this.uid=wt.uid,this.zoom=wt.zoom,this.pixelRatio=wt.pixelRatio,this.tileSize=wt.tileSize,this.source=wt.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=wt.showCollisionBoxes,this.collectResourceTiming=!!wt.collectResourceTiming,this.returnDependencies=!!wt.returnDependencies,this.promoteId=wt.promoteId};f.prototype.parse=function(wt,tr,ir,wr,Xr){var ti=this;this.status=\"parsing\",this.data=wt,this.collisionBoxArray=new i.CollisionBoxArray;var $r=new i.DictionaryCoder(Object.keys(wt.layers).sort()),Ri=new i.FeatureIndex(this.tileID,this.promoteId);Ri.bucketLayerIDs=[];var Hi={},en={featureIndex:Ri,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:ir},fn=tr.familiesBySource[this.source];for(var yn in fn){var Mn=wt.layers[yn];if(Mn){Mn.version===1&&i.warnOnce('Vector tile source \"'+this.source+'\" layer \"'+yn+'\" does not use vector tile spec v2 and therefore may have some rendering errors.');for(var Ba=$r.encode(yn),ua=[],ma=0;ma<Mn.length;ma++){var Wa=Mn.feature(ma),za=Ri.getId(Wa,yn);ua.push({feature:Wa,id:za,index:ma,sourceLayerIndex:Ba})}for(var Xo=0,da=fn[yn];Xo<da.length;Xo+=1){var jn=da[Xo],Ha=jn[0];if(!(Ha.minzoom&&this.zoom<Math.floor(Ha.minzoom))&&!(Ha.maxzoom&&this.zoom>=Ha.maxzoom)&&Ha.visibility!==\"none\"){h(jn,this.zoom,ir);var vo=Hi[Ha.id]=Ha.createBucket({index:Ri.bucketLayerIDs.length,layers:jn,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:Ba,sourceID:this.source});vo.populate(ua,en,this.tileID.canonical),Ri.bucketLayerIDs.push(jn.map(function(ki){return ki.id}))}}}}var Gn,Ct,Sr,Jr,hi=i.mapObject(en.glyphDependencies,function(ki){return Object.keys(ki).map(Number)});Object.keys(hi).length?wr.send(\"getGlyphs\",{uid:this.uid,stacks:hi},function(ki,_n){Gn||(Gn=ki,Ct=_n,En.call(ti))}):Ct={};var hn=Object.keys(en.iconDependencies);hn.length?wr.send(\"getImages\",{icons:hn,source:this.source,tileID:this.tileID,type:\"icons\"},function(ki,_n){Gn||(Gn=ki,Sr=_n,En.call(ti))}):Sr={};var Sn=Object.keys(en.patternDependencies);Sn.length?wr.send(\"getImages\",{icons:Sn,source:this.source,tileID:this.tileID,type:\"patterns\"},function(ki,_n){Gn||(Gn=ki,Jr=_n,En.call(ti))}):Jr={},En.call(this);function En(){if(Gn)return Xr(Gn);if(Ct&&Sr&&Jr){var ki=new c(Ct),_n=new i.ImageAtlas(Sr,Jr);for(var ya in Hi){var ea=Hi[ya];ea instanceof i.SymbolBucket?(h(ea.layers,this.zoom,ir),i.performSymbolLayout(ea,Ct,ki.positions,Sr,_n.iconPositions,this.showCollisionBoxes,this.tileID.canonical)):ea.hasPattern&&(ea instanceof i.LineBucket||ea instanceof i.FillBucket||ea instanceof i.FillExtrusionBucket)&&(h(ea.layers,this.zoom,ir),ea.addFeatures(en,this.tileID.canonical,_n.patternPositions))}this.status=\"done\",Xr(null,{buckets:i.values(Hi).filter(function(Ma){return!Ma.isEmpty()}),featureIndex:Ri,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:ki.image,imageAtlas:_n,glyphMap:this.returnDependencies?Ct:null,iconMap:this.returnDependencies?Sr:null,glyphPositions:this.returnDependencies?ki.positions:null})}}};function h(Gt,wt,tr){for(var ir=new i.EvaluationParameters(wt),wr=0,Xr=Gt;wr<Xr.length;wr+=1){var ti=Xr[wr];ti.recalculate(ir,tr)}}function d(Gt,wt){var tr=i.getArrayBuffer(Gt.request,function(ir,wr,Xr,ti){ir?wt(ir):wr&&wt(null,{vectorTile:new i.vectorTile.VectorTile(new i.pbf(wr)),rawData:wr,cacheControl:Xr,expires:ti})});return function(){tr.cancel(),wt()}}var v=function(wt,tr,ir,wr){this.actor=wt,this.layerIndex=tr,this.availableImages=ir,this.loadVectorData=wr||d,this.loading={},this.loaded={}};v.prototype.loadTile=function(wt,tr){var ir=this,wr=wt.uid;this.loading||(this.loading={});var Xr=wt&&wt.request&&wt.request.collectResourceTiming?new i.RequestPerformance(wt.request):!1,ti=this.loading[wr]=new f(wt);ti.abort=this.loadVectorData(wt,function($r,Ri){if(delete ir.loading[wr],$r||!Ri)return ti.status=\"done\",ir.loaded[wr]=ti,tr($r);var Hi=Ri.rawData,en={};Ri.expires&&(en.expires=Ri.expires),Ri.cacheControl&&(en.cacheControl=Ri.cacheControl);var fn={};if(Xr){var yn=Xr.finish();yn&&(fn.resourceTiming=JSON.parse(JSON.stringify(yn)))}ti.vectorTile=Ri.vectorTile,ti.parse(Ri.vectorTile,ir.layerIndex,ir.availableImages,ir.actor,function(Mn,Ba){if(Mn||!Ba)return tr(Mn);tr(null,i.extend({rawTileData:Hi.slice(0)},Ba,en,fn))}),ir.loaded=ir.loaded||{},ir.loaded[wr]=ti})},v.prototype.reloadTile=function(wt,tr){var ir=this,wr=this.loaded,Xr=wt.uid,ti=this;if(wr&&wr[Xr]){var $r=wr[Xr];$r.showCollisionBoxes=wt.showCollisionBoxes;var Ri=function(Hi,en){var fn=$r.reloadCallback;fn&&(delete $r.reloadCallback,$r.parse($r.vectorTile,ti.layerIndex,ir.availableImages,ti.actor,fn)),tr(Hi,en)};$r.status===\"parsing\"?$r.reloadCallback=Ri:$r.status===\"done\"&&($r.vectorTile?$r.parse($r.vectorTile,this.layerIndex,this.availableImages,this.actor,Ri):Ri())}},v.prototype.abortTile=function(wt,tr){var ir=this.loading,wr=wt.uid;ir&&ir[wr]&&ir[wr].abort&&(ir[wr].abort(),delete ir[wr]),tr()},v.prototype.removeTile=function(wt,tr){var ir=this.loaded,wr=wt.uid;ir&&ir[wr]&&delete ir[wr],tr()};var m=i.window.ImageBitmap,b=function(){this.loaded={}};b.prototype.loadTile=function(wt,tr){var ir=wt.uid,wr=wt.encoding,Xr=wt.rawImageData,ti=m&&Xr instanceof m?this.getImageData(Xr):Xr,$r=new i.DEMData(ir,ti,wr);this.loaded=this.loaded||{},this.loaded[ir]=$r,tr(null,$r)},b.prototype.getImageData=function(wt){(!this.offscreenCanvas||!this.offscreenCanvasContext)&&(this.offscreenCanvas=new OffscreenCanvas(wt.width,wt.height),this.offscreenCanvasContext=this.offscreenCanvas.getContext(\"2d\")),this.offscreenCanvas.width=wt.width,this.offscreenCanvas.height=wt.height,this.offscreenCanvasContext.drawImage(wt,0,0,wt.width,wt.height);var tr=this.offscreenCanvasContext.getImageData(-1,-1,wt.width+2,wt.height+2);return this.offscreenCanvasContext.clearRect(0,0,this.offscreenCanvas.width,this.offscreenCanvas.height),new i.RGBAImage({width:tr.width,height:tr.height},tr.data)},b.prototype.removeTile=function(wt){var tr=this.loaded,ir=wt.uid;tr&&tr[ir]&&delete tr[ir]};var p=k;function k(Gt,wt){var tr=Gt&&Gt.type,ir;if(tr===\"FeatureCollection\")for(ir=0;ir<Gt.features.length;ir++)k(Gt.features[ir],wt);else if(tr===\"GeometryCollection\")for(ir=0;ir<Gt.geometries.length;ir++)k(Gt.geometries[ir],wt);else if(tr===\"Feature\")k(Gt.geometry,wt);else if(tr===\"Polygon\")M(Gt.coordinates,wt);else if(tr===\"MultiPolygon\")for(ir=0;ir<Gt.coordinates.length;ir++)M(Gt.coordinates[ir],wt);return Gt}function M(Gt,wt){if(Gt.length!==0){T(Gt[0],wt);for(var tr=1;tr<Gt.length;tr++)T(Gt[tr],!wt)}}function T(Gt,wt){for(var tr=0,ir=0,wr=Gt.length,Xr=wr-1;ir<wr;Xr=ir++)tr+=(Gt[ir][0]-Gt[Xr][0])*(Gt[Xr][1]+Gt[ir][1]);tr>=0!=!!wt&&Gt.reverse()}var L=i.vectorTile.VectorTileFeature.prototype.toGeoJSON,x=function(wt){this._feature=wt,this.extent=i.EXTENT,this.type=wt.type,this.properties=wt.tags,\"id\"in wt&&!isNaN(wt.id)&&(this.id=parseInt(wt.id,10))};x.prototype.loadGeometry=function(){if(this._feature.type===1){for(var wt=[],tr=0,ir=this._feature.geometry;tr<ir.length;tr+=1){var wr=ir[tr];wt.push([new i.Point$1(wr[0],wr[1])])}return wt}else{for(var Xr=[],ti=0,$r=this._feature.geometry;ti<$r.length;ti+=1){for(var Ri=$r[ti],Hi=[],en=0,fn=Ri;en<fn.length;en+=1){var yn=fn[en];Hi.push(new i.Point$1(yn[0],yn[1]))}Xr.push(Hi)}return Xr}},x.prototype.toGeoJSON=function(wt,tr,ir){return L.call(this,wt,tr,ir)};var C=function(wt){this.layers={_geojsonTileLayer:this},this.name=\"_geojsonTileLayer\",this.extent=i.EXTENT,this.length=wt.length,this._features=wt};C.prototype.feature=function(wt){return new x(this._features[wt])};var S=i.vectorTile.VectorTileFeature,g=P;function P(Gt,wt){this.options=wt||{},this.features=Gt,this.length=Gt.length}P.prototype.feature=function(Gt){return new E(this.features[Gt],this.options.extent)};function E(Gt,wt){this.id=typeof Gt.id==\"number\"?Gt.id:void 0,this.type=Gt.type,this.rawGeometry=Gt.type===1?[Gt.geometry]:Gt.geometry,this.properties=Gt.tags,this.extent=wt||4096}E.prototype.loadGeometry=function(){var Gt=this.rawGeometry;this.geometry=[];for(var wt=0;wt<Gt.length;wt++){for(var tr=Gt[wt],ir=[],wr=0;wr<tr.length;wr++)ir.push(new i.Point$1(tr[wr][0],tr[wr][1]));this.geometry.push(ir)}return this.geometry},E.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var Gt=this.geometry,wt=1/0,tr=-1/0,ir=1/0,wr=-1/0,Xr=0;Xr<Gt.length;Xr++)for(var ti=Gt[Xr],$r=0;$r<ti.length;$r++){var Ri=ti[$r];wt=Math.min(wt,Ri.x),tr=Math.max(tr,Ri.x),ir=Math.min(ir,Ri.y),wr=Math.max(wr,Ri.y)}return[wt,ir,tr,wr]},E.prototype.toGeoJSON=S.prototype.toGeoJSON;var z=Z,q=Z,V=j,G=g;function Z(Gt){var wt=new i.pbf;return N(Gt,wt),wt.finish()}function j(Gt,wt){wt=wt||{};var tr={};for(var ir in Gt)tr[ir]=new g(Gt[ir].features,wt),tr[ir].name=ir,tr[ir].version=wt.version,tr[ir].extent=wt.extent;return Z({layers:tr})}function N(Gt,wt){for(var tr in Gt.layers)wt.writeMessage(3,H,Gt.layers[tr])}function H(Gt,wt){wt.writeVarintField(15,Gt.version||1),wt.writeStringField(1,Gt.name||\"\"),wt.writeVarintField(5,Gt.extent||4096);var tr,ir={keys:[],values:[],keycache:{},valuecache:{}};for(tr=0;tr<Gt.length;tr++)ir.feature=Gt.feature(tr),wt.writeMessage(2,re,ir);var wr=ir.keys;for(tr=0;tr<wr.length;tr++)wt.writeStringField(3,wr[tr]);var Xr=ir.values;for(tr=0;tr<Xr.length;tr++)wt.writeMessage(4,ge,Xr[tr])}function re(Gt,wt){var tr=Gt.feature;tr.id!==void 0&&wt.writeVarintField(1,tr.id),wt.writeMessage(2,ae,Gt),wt.writeVarintField(3,tr.type),wt.writeMessage(4,Ce,tr)}function ae(Gt,wt){var tr=Gt.feature,ir=Gt.keys,wr=Gt.values,Xr=Gt.keycache,ti=Gt.valuecache;for(var $r in tr.properties){var Ri=Xr[$r];typeof Ri==\"undefined\"&&(ir.push($r),Ri=ir.length-1,Xr[$r]=Ri),wt.writeVarint(Ri);var Hi=tr.properties[$r],en=typeof Hi;en!==\"string\"&&en!==\"boolean\"&&en!==\"number\"&&(Hi=JSON.stringify(Hi));var fn=en+\":\"+Hi,yn=ti[fn];typeof yn==\"undefined\"&&(wr.push(Hi),yn=wr.length-1,ti[fn]=yn),wt.writeVarint(yn)}}function _e(Gt,wt){return(wt<<3)+(Gt&7)}function Ee(Gt){return Gt<<1^Gt>>31}function Ce(Gt,wt){for(var tr=Gt.loadGeometry(),ir=Gt.type,wr=0,Xr=0,ti=tr.length,$r=0;$r<ti;$r++){var Ri=tr[$r],Hi=1;ir===1&&(Hi=Ri.length),wt.writeVarint(_e(1,Hi));for(var en=ir===3?Ri.length-1:Ri.length,fn=0;fn<en;fn++){fn===1&&ir!==1&&wt.writeVarint(_e(2,en-1));var yn=Ri[fn].x-wr,Mn=Ri[fn].y-Xr;wt.writeVarint(Ee(yn)),wt.writeVarint(Ee(Mn)),wr+=yn,Xr+=Mn}ir===3&&wt.writeVarint(_e(7,1))}}function ge(Gt,wt){var tr=typeof Gt;tr===\"string\"?wt.writeStringField(1,Gt):tr===\"boolean\"?wt.writeBooleanField(7,Gt):tr===\"number\"&&(Gt%1!==0?wt.writeDoubleField(3,Gt):Gt<0?wt.writeSVarintField(6,Gt):wt.writeVarintField(5,Gt))}z.fromVectorTileJs=q,z.fromGeojsonVt=V,z.GeoJSONWrapper=G;function ie(Gt,wt,tr,ir,wr,Xr){if(!(wr-ir<=tr)){var ti=ir+wr>>1;Se(Gt,wt,ti,ir,wr,Xr%2),ie(Gt,wt,tr,ir,ti-1,Xr+1),ie(Gt,wt,tr,ti+1,wr,Xr+1)}}function Se(Gt,wt,tr,ir,wr,Xr){for(;wr>ir;){if(wr-ir>600){var ti=wr-ir+1,$r=tr-ir+1,Ri=Math.log(ti),Hi=.5*Math.exp(2*Ri/3),en=.5*Math.sqrt(Ri*Hi*(ti-Hi)/ti)*($r-ti/2<0?-1:1),fn=Math.max(ir,Math.floor(tr-$r*Hi/ti+en)),yn=Math.min(wr,Math.floor(tr+(ti-$r)*Hi/ti+en));Se(Gt,wt,tr,fn,yn,Xr)}var Mn=wt[2*tr+Xr],Ba=ir,ua=wr;for(ke(Gt,wt,ir,tr),wt[2*wr+Xr]>Mn&&ke(Gt,wt,ir,wr);Ba<ua;){for(ke(Gt,wt,Ba,ua),Ba++,ua--;wt[2*Ba+Xr]<Mn;)Ba++;for(;wt[2*ua+Xr]>Mn;)ua--}wt[2*ir+Xr]===Mn?ke(Gt,wt,ir,ua):(ua++,ke(Gt,wt,ua,wr)),ua<=tr&&(ir=ua+1),tr<=ua&&(wr=ua-1)}}function ke(Gt,wt,tr,ir){be(Gt,tr,ir),be(wt,2*tr,2*ir),be(wt,2*tr+1,2*ir+1)}function be(Gt,wt,tr){var ir=Gt[wt];Gt[wt]=Gt[tr],Gt[tr]=ir}function Be(Gt,wt,tr,ir,wr,Xr,ti){for(var $r=[0,Gt.length-1,0],Ri=[],Hi,en;$r.length;){var fn=$r.pop(),yn=$r.pop(),Mn=$r.pop();if(yn-Mn<=ti){for(var Ba=Mn;Ba<=yn;Ba++)Hi=wt[2*Ba],en=wt[2*Ba+1],Hi>=tr&&Hi<=wr&&en>=ir&&en<=Xr&&Ri.push(Gt[Ba]);continue}var ua=Math.floor((Mn+yn)/2);Hi=wt[2*ua],en=wt[2*ua+1],Hi>=tr&&Hi<=wr&&en>=ir&&en<=Xr&&Ri.push(Gt[ua]);var ma=(fn+1)%2;(fn===0?tr<=Hi:ir<=en)&&($r.push(Mn),$r.push(ua-1),$r.push(ma)),(fn===0?wr>=Hi:Xr>=en)&&($r.push(ua+1),$r.push(yn),$r.push(ma))}return Ri}function Le(Gt,wt,tr,ir,wr,Xr){for(var ti=[0,Gt.length-1,0],$r=[],Ri=wr*wr;ti.length;){var Hi=ti.pop(),en=ti.pop(),fn=ti.pop();if(en-fn<=Xr){for(var yn=fn;yn<=en;yn++)me(wt[2*yn],wt[2*yn+1],tr,ir)<=Ri&&$r.push(Gt[yn]);continue}var Mn=Math.floor((fn+en)/2),Ba=wt[2*Mn],ua=wt[2*Mn+1];me(Ba,ua,tr,ir)<=Ri&&$r.push(Gt[Mn]);var ma=(Hi+1)%2;(Hi===0?tr-wr<=Ba:ir-wr<=ua)&&(ti.push(fn),ti.push(Mn-1),ti.push(ma)),(Hi===0?tr+wr>=Ba:ir+wr>=ua)&&(ti.push(Mn+1),ti.push(en),ti.push(ma))}return $r}function me(Gt,wt,tr,ir){var wr=Gt-tr,Xr=wt-ir;return wr*wr+Xr*Xr}var Pe=function(Gt){return Gt[0]},ce=function(Gt){return Gt[1]},He=function(wt,tr,ir,wr,Xr){tr===void 0&&(tr=Pe),ir===void 0&&(ir=ce),wr===void 0&&(wr=64),Xr===void 0&&(Xr=Float64Array),this.nodeSize=wr,this.points=wt;for(var ti=wt.length<65536?Uint16Array:Uint32Array,$r=this.ids=new ti(wt.length),Ri=this.coords=new Xr(wt.length*2),Hi=0;Hi<wt.length;Hi++)$r[Hi]=Hi,Ri[2*Hi]=tr(wt[Hi]),Ri[2*Hi+1]=ir(wt[Hi]);ie($r,Ri,wr,0,$r.length-1,0)};He.prototype.range=function(wt,tr,ir,wr){return Be(this.ids,this.coords,wt,tr,ir,wr,this.nodeSize)},He.prototype.within=function(wt,tr,ir){return Le(this.ids,this.coords,wt,tr,ir,this.nodeSize)};var lt={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(Gt){return Gt}},mt=function(wt){this.options=Ue(Object.create(lt),wt),this.trees=new Array(this.options.maxZoom+1)};mt.prototype.load=function(wt){var tr=this.options,ir=tr.log,wr=tr.minZoom,Xr=tr.maxZoom,ti=tr.nodeSize;ir&&console.time(\"total time\");var $r=\"prepare \"+wt.length+\" points\";ir&&console.time($r),this.points=wt;for(var Ri=[],Hi=0;Hi<wt.length;Hi++)wt[Hi].geometry&&Ri.push(at(wt[Hi],Hi));this.trees[Xr+1]=new He(Ri,Ae,rt,ti,Float32Array),ir&&console.timeEnd($r);for(var en=Xr;en>=wr;en--){var fn=+Date.now();Ri=this._cluster(Ri,en),this.trees[en]=new He(Ri,Ae,rt,ti,Float32Array),ir&&console.log(\"z%d: %d clusters in %dms\",en,Ri.length,+Date.now()-fn)}return ir&&console.timeEnd(\"total time\"),this},mt.prototype.getClusters=function(wt,tr){var ir=((wt[0]+180)%360+360)%360-180,wr=Math.max(-90,Math.min(90,wt[1])),Xr=wt[2]===180?180:((wt[2]+180)%360+360)%360-180,ti=Math.max(-90,Math.min(90,wt[3]));if(wt[2]-wt[0]>=360)ir=-180,Xr=180;else if(ir>Xr){var $r=this.getClusters([ir,wr,180,ti],tr),Ri=this.getClusters([-180,wr,Xr,ti],tr);return $r.concat(Ri)}for(var Hi=this.trees[this._limitZoom(tr)],en=Hi.range(Vt(ir),rr(ti),Vt(Xr),rr(wr)),fn=[],yn=0,Mn=en;yn<Mn.length;yn+=1){var Ba=Mn[yn],ua=Hi.points[Ba];fn.push(ua.numPoints?ct(ua):this.points[ua.index])}return fn},mt.prototype.getChildren=function(wt){var tr=this._getOriginId(wt),ir=this._getOriginZoom(wt),wr=\"No cluster with the specified id.\",Xr=this.trees[ir];if(!Xr)throw new Error(wr);var ti=Xr.points[tr];if(!ti)throw new Error(wr);for(var $r=this.options.radius/(this.options.extent*Math.pow(2,ir-1)),Ri=Xr.within(ti.x,ti.y,$r),Hi=[],en=0,fn=Ri;en<fn.length;en+=1){var yn=fn[en],Mn=Xr.points[yn];Mn.parentId===wt&&Hi.push(Mn.numPoints?ct(Mn):this.points[Mn.index])}if(Hi.length===0)throw new Error(wr);return Hi},mt.prototype.getLeaves=function(wt,tr,ir){tr=tr||10,ir=ir||0;var wr=[];return this._appendLeaves(wr,wt,tr,ir,0),wr},mt.prototype.getTile=function(wt,tr,ir){var wr=this.trees[this._limitZoom(wt)],Xr=Math.pow(2,wt),ti=this.options,$r=ti.extent,Ri=ti.radius,Hi=Ri/$r,en=(ir-Hi)/Xr,fn=(ir+1+Hi)/Xr,yn={features:[]};return this._addTileFeatures(wr.range((tr-Hi)/Xr,en,(tr+1+Hi)/Xr,fn),wr.points,tr,ir,Xr,yn),tr===0&&this._addTileFeatures(wr.range(1-Hi/Xr,en,1,fn),wr.points,Xr,ir,Xr,yn),tr===Xr-1&&this._addTileFeatures(wr.range(0,en,Hi/Xr,fn),wr.points,-1,ir,Xr,yn),yn.features.length?yn:null},mt.prototype.getClusterExpansionZoom=function(wt){for(var tr=this._getOriginZoom(wt)-1;tr<=this.options.maxZoom;){var ir=this.getChildren(wt);if(tr++,ir.length!==1)break;wt=ir[0].properties.cluster_id}return tr},mt.prototype._appendLeaves=function(wt,tr,ir,wr,Xr){for(var ti=this.getChildren(tr),$r=0,Ri=ti;$r<Ri.length;$r+=1){var Hi=Ri[$r],en=Hi.properties;if(en&&en.cluster?Xr+en.point_count<=wr?Xr+=en.point_count:Xr=this._appendLeaves(wt,en.cluster_id,ir,wr,Xr):Xr<wr?Xr++:wt.push(Hi),wt.length===ir)break}return Xr},mt.prototype._addTileFeatures=function(wt,tr,ir,wr,Xr,ti){for(var $r=0,Ri=wt;$r<Ri.length;$r+=1){var Hi=Ri[$r],en=tr[Hi],fn=en.numPoints,yn={type:1,geometry:[[Math.round(this.options.extent*(en.x*Xr-ir)),Math.round(this.options.extent*(en.y*Xr-wr))]],tags:fn?ar(en):this.points[en.index].properties},Mn=void 0;fn?Mn=en.id:this.options.generateId?Mn=en.index:this.points[en.index].id&&(Mn=this.points[en.index].id),Mn!==void 0&&(yn.id=Mn),ti.features.push(yn)}},mt.prototype._limitZoom=function(wt){return Math.max(this.options.minZoom,Math.min(+wt,this.options.maxZoom+1))},mt.prototype._cluster=function(wt,tr){for(var ir=[],wr=this.options,Xr=wr.radius,ti=wr.extent,$r=wr.reduce,Ri=wr.minPoints,Hi=Xr/(ti*Math.pow(2,tr)),en=0;en<wt.length;en++){var fn=wt[en];if(!(fn.zoom<=tr)){fn.zoom=tr;for(var yn=this.trees[tr+1],Mn=yn.within(fn.x,fn.y,Hi),Ba=fn.numPoints||1,ua=Ba,ma=0,Wa=Mn;ma<Wa.length;ma+=1){var za=Wa[ma],Xo=yn.points[za];Xo.zoom>tr&&(ua+=Xo.numPoints||1)}if(ua>=Ri){for(var da=fn.x*Ba,jn=fn.y*Ba,Ha=$r&&Ba>1?this._map(fn,!0):null,vo=(en<<5)+(tr+1)+this.points.length,Gn=0,Ct=Mn;Gn<Ct.length;Gn+=1){var Sr=Ct[Gn],Jr=yn.points[Sr];if(!(Jr.zoom<=tr)){Jr.zoom=tr;var hi=Jr.numPoints||1;da+=Jr.x*hi,jn+=Jr.y*hi,Jr.parentId=vo,$r&&(Ha||(Ha=this._map(fn,!0)),$r(Ha,this._map(Jr)))}}fn.parentId=vo,ir.push(Ht(da/ua,jn/ua,vo,ua,Ha))}else if(ir.push(fn),ua>1)for(var hn=0,Sn=Mn;hn<Sn.length;hn+=1){var En=Sn[hn],ki=yn.points[En];ki.zoom<=tr||(ki.zoom=tr,ir.push(ki))}}}return ir},mt.prototype._getOriginId=function(wt){return wt-this.points.length>>5},mt.prototype._getOriginZoom=function(wt){return(wt-this.points.length)%32},mt.prototype._map=function(wt,tr){if(wt.numPoints)return tr?Ue({},wt.properties):wt.properties;var ir=this.points[wt.index].properties,wr=this.options.map(ir);return tr&&wr===ir?Ue({},wr):wr};function Ht(Gt,wt,tr,ir,wr){return{x:Gt,y:wt,zoom:1/0,id:tr,parentId:-1,numPoints:ir,properties:wr}}function at(Gt,wt){var tr=Gt.geometry.coordinates,ir=tr[0],wr=tr[1];return{x:Vt(ir),y:rr(wr),zoom:1/0,index:wt,parentId:-1}}function ct(Gt){return{type:\"Feature\",id:Gt.id,properties:ar(Gt),geometry:{type:\"Point\",coordinates:[tt(Gt.x),je(Gt.y)]}}}function ar(Gt){var wt=Gt.numPoints,tr=wt>=1e4?Math.round(wt/1e3)+\"k\":wt>=1e3?Math.round(wt/100)/10+\"k\":wt;return Ue(Ue({},Gt.properties),{cluster:!0,cluster_id:Gt.id,point_count:wt,point_count_abbreviated:tr})}function Vt(Gt){return Gt/360+.5}function rr(Gt){var wt=Math.sin(Gt*Math.PI/180),tr=.5-.25*Math.log((1+wt)/(1-wt))/Math.PI;return tr<0?0:tr>1?1:tr}function tt(Gt){return(Gt-.5)*360}function je(Gt){var wt=(180-Gt*360)*Math.PI/180;return 360*Math.atan(Math.exp(wt))/Math.PI-90}function Ue(Gt,wt){for(var tr in wt)Gt[tr]=wt[tr];return Gt}function Ae(Gt){return Gt.x}function rt(Gt){return Gt.y}function St(Gt,wt,tr,ir){for(var wr=ir,Xr=tr-wt>>1,ti=tr-wt,$r,Ri=Gt[wt],Hi=Gt[wt+1],en=Gt[tr],fn=Gt[tr+1],yn=wt+3;yn<tr;yn+=3){var Mn=Tt(Gt[yn],Gt[yn+1],Ri,Hi,en,fn);if(Mn>wr)$r=yn,wr=Mn;else if(Mn===wr){var Ba=Math.abs(yn-Xr);Ba<ti&&($r=yn,ti=Ba)}}wr>ir&&($r-wt>3&&St(Gt,wt,$r,ir),Gt[$r+2]=wr,tr-$r>3&&St(Gt,$r,tr,ir))}function Tt(Gt,wt,tr,ir,wr,Xr){var ti=wr-tr,$r=Xr-ir;if(ti!==0||$r!==0){var Ri=((Gt-tr)*ti+(wt-ir)*$r)/(ti*ti+$r*$r);Ri>1?(tr=wr,ir=Xr):Ri>0&&(tr+=ti*Ri,ir+=$r*Ri)}return ti=Gt-tr,$r=wt-ir,ti*ti+$r*$r}function dt(Gt,wt,tr,ir){var wr={id:typeof Gt==\"undefined\"?null:Gt,type:wt,geometry:tr,tags:ir,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return Et(wr),wr}function Et(Gt){var wt=Gt.geometry,tr=Gt.type;if(tr===\"Point\"||tr===\"MultiPoint\"||tr===\"LineString\")pt(Gt,wt);else if(tr===\"Polygon\"||tr===\"MultiLineString\")for(var ir=0;ir<wt.length;ir++)pt(Gt,wt[ir]);else if(tr===\"MultiPolygon\")for(ir=0;ir<wt.length;ir++)for(var wr=0;wr<wt[ir].length;wr++)pt(Gt,wt[ir][wr])}function pt(Gt,wt){for(var tr=0;tr<wt.length;tr+=3)Gt.minX=Math.min(Gt.minX,wt[tr]),Gt.minY=Math.min(Gt.minY,wt[tr+1]),Gt.maxX=Math.max(Gt.maxX,wt[tr]),Gt.maxY=Math.max(Gt.maxY,wt[tr+1])}function jt(Gt,wt){var tr=[];if(Gt.type===\"FeatureCollection\")for(var ir=0;ir<Gt.features.length;ir++)or(tr,Gt.features[ir],wt,ir);else Gt.type===\"Feature\"?or(tr,Gt,wt):or(tr,{geometry:Gt},wt);return tr}function or(Gt,wt,tr,ir){if(wt.geometry){var wr=wt.geometry.coordinates,Xr=wt.geometry.type,ti=Math.pow(tr.tolerance/((1<<tr.maxZoom)*tr.extent),2),$r=[],Ri=wt.id;if(tr.promoteId?Ri=wt.properties[tr.promoteId]:tr.generateId&&(Ri=ir||0),Xr===\"Point\")mr(wr,$r);else if(Xr===\"MultiPoint\")for(var Hi=0;Hi<wr.length;Hi++)mr(wr[Hi],$r);else if(Xr===\"LineString\")Ar(wr,$r,ti,!1);else if(Xr===\"MultiLineString\")if(tr.lineMetrics){for(Hi=0;Hi<wr.length;Hi++)$r=[],Ar(wr[Hi],$r,ti,!1),Gt.push(dt(Ri,\"LineString\",$r,wt.properties));return}else ei(wr,$r,ti,!1);else if(Xr===\"Polygon\")ei(wr,$r,ti,!0);else if(Xr===\"MultiPolygon\")for(Hi=0;Hi<wr.length;Hi++){var en=[];ei(wr[Hi],en,ti,!0),$r.push(en)}else if(Xr===\"GeometryCollection\"){for(Hi=0;Hi<wt.geometry.geometries.length;Hi++)or(Gt,{id:Ri,geometry:wt.geometry.geometries[Hi],properties:wt.properties},tr,ir);return}else throw new Error(\"Input data is not a valid GeoJSON object.\");Gt.push(dt(Ri,Xr,$r,wt.properties))}}function mr(Gt,wt){wt.push(qr(Gt[0])),wt.push(jr(Gt[1])),wt.push(0)}function Ar(Gt,wt,tr,ir){for(var wr,Xr,ti=0,$r=0;$r<Gt.length;$r++){var Ri=qr(Gt[$r][0]),Hi=jr(Gt[$r][1]);wt.push(Ri),wt.push(Hi),wt.push(0),$r>0&&(ir?ti+=(wr*Hi-Ri*Xr)/2:ti+=Math.sqrt(Math.pow(Ri-wr,2)+Math.pow(Hi-Xr,2))),wr=Ri,Xr=Hi}var en=wt.length-3;wt[2]=1,St(wt,0,en,tr),wt[en+2]=1,wt.size=Math.abs(ti),wt.start=0,wt.end=wt.size}function ei(Gt,wt,tr,ir){for(var wr=0;wr<Gt.length;wr++){var Xr=[];Ar(Gt[wr],Xr,tr,ir),wt.push(Xr)}}function qr(Gt){return Gt/360+.5}function jr(Gt){var wt=Math.sin(Gt*Math.PI/180),tr=.5-.25*Math.log((1+wt)/(1-wt))/Math.PI;return tr<0?0:tr>1?1:tr}function gt(Gt,wt,tr,ir,wr,Xr,ti,$r){if(tr/=wt,ir/=wt,Xr>=tr&&ti<ir)return Gt;if(ti<tr||Xr>=ir)return null;for(var Ri=[],Hi=0;Hi<Gt.length;Hi++){var en=Gt[Hi],fn=en.geometry,yn=en.type,Mn=wr===0?en.minX:en.minY,Ba=wr===0?en.maxX:en.maxY;if(Mn>=tr&&Ba<ir){Ri.push(en);continue}else if(Ba<tr||Mn>=ir)continue;var ua=[];if(yn===\"Point\"||yn===\"MultiPoint\")Ge(fn,ua,tr,ir,wr);else if(yn===\"LineString\")Je(fn,ua,tr,ir,wr,!1,$r.lineMetrics);else if(yn===\"MultiLineString\")et(fn,ua,tr,ir,wr,!1);else if(yn===\"Polygon\")et(fn,ua,tr,ir,wr,!0);else if(yn===\"MultiPolygon\")for(var ma=0;ma<fn.length;ma++){var Wa=[];et(fn[ma],Wa,tr,ir,wr,!0),Wa.length&&ua.push(Wa)}if(ua.length){if($r.lineMetrics&&yn===\"LineString\"){for(ma=0;ma<ua.length;ma++)Ri.push(dt(en.id,yn,ua[ma],en.tags));continue}(yn===\"LineString\"||yn===\"MultiLineString\")&&(ua.length===1?(yn=\"LineString\",ua=ua[0]):yn=\"MultiLineString\"),(yn===\"Point\"||yn===\"MultiPoint\")&&(yn=ua.length===3?\"Point\":\"MultiPoint\"),Ri.push(dt(en.id,yn,ua,en.tags))}}return Ri.length?Ri:null}function Ge(Gt,wt,tr,ir,wr){for(var Xr=0;Xr<Gt.length;Xr+=3){var ti=Gt[Xr+wr];ti>=tr&&ti<=ir&&(wt.push(Gt[Xr]),wt.push(Gt[Xr+1]),wt.push(Gt[Xr+2]))}}function Je(Gt,wt,tr,ir,wr,Xr,ti){for(var $r=We(Gt),Ri=wr===0?At:Kt,Hi=Gt.start,en,fn,yn=0;yn<Gt.length-3;yn+=3){var Mn=Gt[yn],Ba=Gt[yn+1],ua=Gt[yn+2],ma=Gt[yn+3],Wa=Gt[yn+4],za=wr===0?Mn:Ba,Xo=wr===0?ma:Wa,da=!1;ti&&(en=Math.sqrt(Math.pow(Mn-ma,2)+Math.pow(Ba-Wa,2))),za<tr?Xo>tr&&(fn=Ri($r,Mn,Ba,ma,Wa,tr),ti&&($r.start=Hi+en*fn)):za>ir?Xo<ir&&(fn=Ri($r,Mn,Ba,ma,Wa,ir),ti&&($r.start=Hi+en*fn)):xt($r,Mn,Ba,ua),Xo<tr&&za>=tr&&(fn=Ri($r,Mn,Ba,ma,Wa,tr),da=!0),Xo>ir&&za<=ir&&(fn=Ri($r,Mn,Ba,ma,Wa,ir),da=!0),!Xr&&da&&(ti&&($r.end=Hi+en*fn),wt.push($r),$r=We(Gt)),ti&&(Hi+=en)}var jn=Gt.length-3;Mn=Gt[jn],Ba=Gt[jn+1],ua=Gt[jn+2],za=wr===0?Mn:Ba,za>=tr&&za<=ir&&xt($r,Mn,Ba,ua),jn=$r.length-3,Xr&&jn>=3&&($r[jn]!==$r[0]||$r[jn+1]!==$r[1])&&xt($r,$r[0],$r[1],$r[2]),$r.length&&wt.push($r)}function We(Gt){var wt=[];return wt.size=Gt.size,wt.start=Gt.start,wt.end=Gt.end,wt}function et(Gt,wt,tr,ir,wr,Xr){for(var ti=0;ti<Gt.length;ti++)Je(Gt[ti],wt,tr,ir,wr,Xr,!1)}function xt(Gt,wt,tr,ir){Gt.push(wt),Gt.push(tr),Gt.push(ir)}function At(Gt,wt,tr,ir,wr,Xr){var ti=(Xr-wt)/(ir-wt);return Gt.push(Xr),Gt.push(tr+(wr-tr)*ti),Gt.push(1),ti}function Kt(Gt,wt,tr,ir,wr,Xr){var ti=(Xr-tr)/(wr-tr);return Gt.push(wt+(ir-wt)*ti),Gt.push(Xr),Gt.push(1),ti}function Qt(Gt,wt){var tr=wt.buffer/wt.extent,ir=Gt,wr=gt(Gt,1,-1-tr,tr,0,-1,2,wt),Xr=gt(Gt,1,1-tr,2+tr,0,-1,2,wt);return(wr||Xr)&&(ir=gt(Gt,1,-tr,1+tr,0,-1,2,wt)||[],wr&&(ir=Mr(wr,1).concat(ir)),Xr&&(ir=ir.concat(Mr(Xr,-1)))),ir}function Mr(Gt,wt){for(var tr=[],ir=0;ir<Gt.length;ir++){var wr=Gt[ir],Xr=wr.type,ti;if(Xr===\"Point\"||Xr===\"MultiPoint\"||Xr===\"LineString\")ti=Gr(wr.geometry,wt);else if(Xr===\"MultiLineString\"||Xr===\"Polygon\"){ti=[];for(var $r=0;$r<wr.geometry.length;$r++)ti.push(Gr(wr.geometry[$r],wt))}else if(Xr===\"MultiPolygon\")for(ti=[],$r=0;$r<wr.geometry.length;$r++){for(var Ri=[],Hi=0;Hi<wr.geometry[$r].length;Hi++)Ri.push(Gr(wr.geometry[$r][Hi],wt));ti.push(Ri)}tr.push(dt(wr.id,Xr,ti,wr.tags))}return tr}function Gr(Gt,wt){var tr=[];tr.size=Gt.size,Gt.start!==void 0&&(tr.start=Gt.start,tr.end=Gt.end);for(var ir=0;ir<Gt.length;ir+=3)tr.push(Gt[ir]+wt,Gt[ir+1],Gt[ir+2]);return tr}function Ir(Gt,wt){if(Gt.transformed)return Gt;var tr=1<<Gt.z,ir=Gt.x,wr=Gt.y,Xr,ti,$r;for(Xr=0;Xr<Gt.features.length;Xr++){var Ri=Gt.features[Xr],Hi=Ri.geometry,en=Ri.type;if(Ri.geometry=[],en===1)for(ti=0;ti<Hi.length;ti+=2)Ri.geometry.push(Yr(Hi[ti],Hi[ti+1],wt,tr,ir,wr));else for(ti=0;ti<Hi.length;ti++){var fn=[];for($r=0;$r<Hi[ti].length;$r+=2)fn.push(Yr(Hi[ti][$r],Hi[ti][$r+1],wt,tr,ir,wr));Ri.geometry.push(fn)}}return Gt.transformed=!0,Gt}function Yr(Gt,wt,tr,ir,wr,Xr){return[Math.round(tr*(Gt*ir-wr)),Math.round(tr*(wt*ir-Xr))]}function _i(Gt,wt,tr,ir,wr){for(var Xr=wt===wr.maxZoom?0:wr.tolerance/((1<<wt)*wr.extent),ti={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:tr,y:ir,z:wt,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},$r=0;$r<Gt.length;$r++){ti.numFeatures++,Pi(ti,Gt[$r],Xr,wr);var Ri=Gt[$r].minX,Hi=Gt[$r].minY,en=Gt[$r].maxX,fn=Gt[$r].maxY;Ri<ti.minX&&(ti.minX=Ri),Hi<ti.minY&&(ti.minY=Hi),en>ti.maxX&&(ti.maxX=en),fn>ti.maxY&&(ti.maxY=fn)}return ti}function Pi(Gt,wt,tr,ir){var wr=wt.geometry,Xr=wt.type,ti=[];if(Xr===\"Point\"||Xr===\"MultiPoint\")for(var $r=0;$r<wr.length;$r+=3)ti.push(wr[$r]),ti.push(wr[$r+1]),Gt.numPoints++,Gt.numSimplified++;else if(Xr===\"LineString\")ai(ti,wr,Gt,tr,!1,!1);else if(Xr===\"MultiLineString\"||Xr===\"Polygon\")for($r=0;$r<wr.length;$r++)ai(ti,wr[$r],Gt,tr,Xr===\"Polygon\",$r===0);else if(Xr===\"MultiPolygon\")for(var Ri=0;Ri<wr.length;Ri++){var Hi=wr[Ri];for($r=0;$r<Hi.length;$r++)ai(ti,Hi[$r],Gt,tr,!0,$r===0)}if(ti.length){var en=wt.tags||null;if(Xr===\"LineString\"&&ir.lineMetrics){en={};for(var fn in wt.tags)en[fn]=wt.tags[fn];en.mapbox_clip_start=wr.start/wr.size,en.mapbox_clip_end=wr.end/wr.size}var yn={geometry:ti,type:Xr===\"Polygon\"||Xr===\"MultiPolygon\"?3:Xr===\"LineString\"||Xr===\"MultiLineString\"?2:1,tags:en};wt.id!==null&&(yn.id=wt.id),Gt.features.push(yn)}}function ai(Gt,wt,tr,ir,wr,Xr){var ti=ir*ir;if(ir>0&&wt.size<(wr?ti:ir)){tr.numPoints+=wt.length/3;return}for(var $r=[],Ri=0;Ri<wt.length;Ri+=3)(ir===0||wt[Ri+2]>ti)&&(tr.numSimplified++,$r.push(wt[Ri]),$r.push(wt[Ri+1])),tr.numPoints++;wr&&mi($r,Xr),Gt.push($r)}function mi(Gt,wt){for(var tr=0,ir=0,wr=Gt.length,Xr=wr-2;ir<wr;Xr=ir,ir+=2)tr+=(Gt[ir]-Gt[Xr])*(Gt[ir+1]+Gt[Xr+1]);if(tr>0===wt)for(ir=0,wr=Gt.length;ir<wr/2;ir+=2){var ti=Gt[ir],$r=Gt[ir+1];Gt[ir]=Gt[wr-2-ir],Gt[ir+1]=Gt[wr-1-ir],Gt[wr-2-ir]=ti,Gt[wr-1-ir]=$r}}function un(Gt,wt){return new Fn(Gt,wt)}function Fn(Gt,wt){wt=this.options=Hn(Object.create(this.options),wt);var tr=wt.debug;if(tr&&console.time(\"preprocess data\"),wt.maxZoom<0||wt.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(wt.promoteId&&wt.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var ir=jt(Gt,wt);this.tiles={},this.tileCoords=[],tr&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",wt.indexMaxZoom,wt.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),ir=Qt(ir,wt),ir.length&&this.splitTile(ir,0,0,0),tr&&(ir.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}Fn.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Fn.prototype.splitTile=function(Gt,wt,tr,ir,wr,Xr,ti){for(var $r=[Gt,wt,tr,ir],Ri=this.options,Hi=Ri.debug;$r.length;){ir=$r.pop(),tr=$r.pop(),wt=$r.pop(),Gt=$r.pop();var en=1<<wt,fn=An(wt,tr,ir),yn=this.tiles[fn];if(!yn&&(Hi>1&&console.time(\"creation\"),yn=this.tiles[fn]=_i(Gt,wt,tr,ir,Ri),this.tileCoords.push({z:wt,x:tr,y:ir}),Hi)){Hi>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",wt,tr,ir,yn.numFeatures,yn.numPoints,yn.numSimplified),console.timeEnd(\"creation\"));var Mn=\"z\"+wt;this.stats[Mn]=(this.stats[Mn]||0)+1,this.total++}if(yn.source=Gt,wr){if(wt===Ri.maxZoom||wt===wr)continue;var Ba=1<<wr-wt;if(tr!==Math.floor(Xr/Ba)||ir!==Math.floor(ti/Ba))continue}else if(wt===Ri.indexMaxZoom||yn.numPoints<=Ri.indexMaxPoints)continue;if(yn.source=null,Gt.length!==0){Hi>1&&console.time(\"clipping\");var ua=.5*Ri.buffer/Ri.extent,ma=.5-ua,Wa=.5+ua,za=1+ua,Xo,da,jn,Ha,vo,Gn;Xo=da=jn=Ha=null,vo=gt(Gt,en,tr-ua,tr+Wa,0,yn.minX,yn.maxX,Ri),Gn=gt(Gt,en,tr+ma,tr+za,0,yn.minX,yn.maxX,Ri),Gt=null,vo&&(Xo=gt(vo,en,ir-ua,ir+Wa,1,yn.minY,yn.maxY,Ri),da=gt(vo,en,ir+ma,ir+za,1,yn.minY,yn.maxY,Ri),vo=null),Gn&&(jn=gt(Gn,en,ir-ua,ir+Wa,1,yn.minY,yn.maxY,Ri),Ha=gt(Gn,en,ir+ma,ir+za,1,yn.minY,yn.maxY,Ri),Gn=null),Hi>1&&console.timeEnd(\"clipping\"),$r.push(Xo||[],wt+1,tr*2,ir*2),$r.push(da||[],wt+1,tr*2,ir*2+1),$r.push(jn||[],wt+1,tr*2+1,ir*2),$r.push(Ha||[],wt+1,tr*2+1,ir*2+1)}}},Fn.prototype.getTile=function(Gt,wt,tr){var ir=this.options,wr=ir.extent,Xr=ir.debug;if(Gt<0||Gt>24)return null;var ti=1<<Gt;wt=(wt%ti+ti)%ti;var $r=An(Gt,wt,tr);if(this.tiles[$r])return Ir(this.tiles[$r],wr);Xr>1&&console.log(\"drilling down to z%d-%d-%d\",Gt,wt,tr);for(var Ri=Gt,Hi=wt,en=tr,fn;!fn&&Ri>0;)Ri--,Hi=Math.floor(Hi/2),en=Math.floor(en/2),fn=this.tiles[An(Ri,Hi,en)];return!fn||!fn.source?null:(Xr>1&&console.log(\"found parent tile z%d-%d-%d\",Ri,Hi,en),Xr>1&&console.time(\"drilling down\"),this.splitTile(fn.source,Ri,Hi,en,Gt,wt,tr),Xr>1&&console.timeEnd(\"drilling down\"),this.tiles[$r]?Ir(this.tiles[$r],wr):null)};function An(Gt,wt,tr){return((1<<Gt)*tr+wt)*32+Gt}function Hn(Gt,wt){for(var tr in wt)Gt[tr]=wt[tr];return Gt}function Qn(Gt,wt){var tr=Gt.tileID.canonical;if(!this._geoJSONIndex)return wt(null,null);var ir=this._geoJSONIndex.getTile(tr.z,tr.x,tr.y);if(!ir)return wt(null,null);var wr=new C(ir.features),Xr=z(wr);(Xr.byteOffset!==0||Xr.byteLength!==Xr.buffer.byteLength)&&(Xr=new Uint8Array(Xr)),wt(null,{vectorTile:wr,rawData:Xr.buffer})}var Vi=(function(Gt){function wt(tr,ir,wr,Xr){Gt.call(this,tr,ir,wr,Qn),Xr&&(this.loadGeoJSON=Xr)}return Gt&&(wt.__proto__=Gt),wt.prototype=Object.create(Gt&&Gt.prototype),wt.prototype.constructor=wt,wt.prototype.loadData=function(ir,wr){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=wr,this._pendingLoadDataParams=ir,this._state&&this._state!==\"Idle\"?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},wt.prototype._loadData=function(){var ir=this;if(!(!this._pendingCallback||!this._pendingLoadDataParams)){var wr=this._pendingCallback,Xr=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var ti=Xr&&Xr.request&&Xr.request.collectResourceTiming?new i.RequestPerformance(Xr.request):!1;this.loadGeoJSON(Xr,function($r,Ri){if($r||!Ri)return wr($r);if(typeof Ri!=\"object\")return wr(new Error(\"Input data given to '\"+Xr.source+\"' is not a valid GeoJSON object.\"));p(Ri,!0);try{if(Xr.filter){var Hi=i.createExpression(Xr.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(Hi.result===\"error\")throw new Error(Hi.value.map(function(Mn){return Mn.key+\": \"+Mn.message}).join(\", \"));var en=Ri.features.filter(function(Mn){return Hi.value.evaluate({zoom:0},Mn)});Ri={type:\"FeatureCollection\",features:en}}ir._geoJSONIndex=Xr.cluster?new mt(Jn(Xr)).load(Ri.features):un(Ri,Xr.geojsonVtOptions)}catch(Mn){return wr(Mn)}ir.loaded={};var fn={};if(ti){var yn=ti.finish();yn&&(fn.resourceTiming={},fn.resourceTiming[Xr.source]=JSON.parse(JSON.stringify(yn)))}wr(null,fn)})}},wt.prototype.coalesce=function(){this._state===\"Coalescing\"?this._state=\"Idle\":this._state===\"NeedsLoadData\"&&(this._state=\"Coalescing\",this._loadData())},wt.prototype.reloadTile=function(ir,wr){var Xr=this.loaded,ti=ir.uid;return Xr&&Xr[ti]?Gt.prototype.reloadTile.call(this,ir,wr):this.loadTile(ir,wr)},wt.prototype.loadGeoJSON=function(ir,wr){if(ir.request)i.getJSON(ir.request,wr);else if(typeof ir.data==\"string\")try{return wr(null,JSON.parse(ir.data))}catch(Xr){return wr(new Error(\"Input data given to '\"+ir.source+\"' is not a valid GeoJSON object.\"))}else return wr(new Error(\"Input data given to '\"+ir.source+\"' is not a valid GeoJSON object.\"))},wt.prototype.removeSource=function(ir,wr){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),wr()},wt.prototype.getClusterExpansionZoom=function(ir,wr){try{wr(null,this._geoJSONIndex.getClusterExpansionZoom(ir.clusterId))}catch(Xr){wr(Xr)}},wt.prototype.getClusterChildren=function(ir,wr){try{wr(null,this._geoJSONIndex.getChildren(ir.clusterId))}catch(Xr){wr(Xr)}},wt.prototype.getClusterLeaves=function(ir,wr){try{wr(null,this._geoJSONIndex.getLeaves(ir.clusterId,ir.limit,ir.offset))}catch(Xr){wr(Xr)}},wt})(v);function Jn(Gt){var wt=Gt.superclusterOptions,tr=Gt.clusterProperties;if(!tr||!wt)return wt;for(var ir={},wr={},Xr={accumulated:null,zoom:0},ti={properties:null},$r=Object.keys(tr),Ri=0,Hi=$r;Ri<Hi.length;Ri+=1){var en=Hi[Ri],fn=tr[en],yn=fn[0],Mn=fn[1],Ba=i.createExpression(Mn),ua=i.createExpression(typeof yn==\"string\"?[yn,[\"accumulated\"],[\"get\",en]]:yn);ir[en]=Ba.value,wr[en]=ua.value}return wt.map=function(ma){ti.properties=ma;for(var Wa={},za=0,Xo=$r;za<Xo.length;za+=1){var da=Xo[za];Wa[da]=ir[da].evaluate(Xr,ti)}return Wa},wt.reduce=function(ma,Wa){ti.properties=Wa;for(var za=0,Xo=$r;za<Xo.length;za+=1){var da=Xo[za];Xr.accumulated=ma[da],ma[da]=wr[da].evaluate(Xr,ti)}},wt}var Yn=function(wt){var tr=this;this.self=wt,this.actor=new i.Actor(wt,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:v,geojson:Vi},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(ir,wr){if(tr.workerSourceTypes[ir])throw new Error('Worker source with name \"'+ir+'\" already registered.');tr.workerSourceTypes[ir]=wr},this.self.registerRTLTextPlugin=function(ir){if(i.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");i.plugin.applyArabicShaping=ir.applyArabicShaping,i.plugin.processBidirectionalText=ir.processBidirectionalText,i.plugin.processStyledBidirectionalText=ir.processStyledBidirectionalText}};return Yn.prototype.setReferrer=function(wt,tr){this.referrer=tr},Yn.prototype.setImages=function(wt,tr,ir){this.availableImages[wt]=tr;for(var wr in this.workerSources[wt]){var Xr=this.workerSources[wt][wr];for(var ti in Xr)Xr[ti].availableImages=tr}ir()},Yn.prototype.setLayers=function(wt,tr,ir){this.getLayerIndex(wt).replace(tr),ir()},Yn.prototype.updateLayers=function(wt,tr,ir){this.getLayerIndex(wt).update(tr.layers,tr.removedIds),ir()},Yn.prototype.loadTile=function(wt,tr,ir){this.getWorkerSource(wt,tr.type,tr.source).loadTile(tr,ir)},Yn.prototype.loadDEMTile=function(wt,tr,ir){this.getDEMWorkerSource(wt,tr.source).loadTile(tr,ir)},Yn.prototype.reloadTile=function(wt,tr,ir){this.getWorkerSource(wt,tr.type,tr.source).reloadTile(tr,ir)},Yn.prototype.abortTile=function(wt,tr,ir){this.getWorkerSource(wt,tr.type,tr.source).abortTile(tr,ir)},Yn.prototype.removeTile=function(wt,tr,ir){this.getWorkerSource(wt,tr.type,tr.source).removeTile(tr,ir)},Yn.prototype.removeDEMTile=function(wt,tr){this.getDEMWorkerSource(wt,tr.source).removeTile(tr)},Yn.prototype.removeSource=function(wt,tr,ir){if(!(!this.workerSources[wt]||!this.workerSources[wt][tr.type]||!this.workerSources[wt][tr.type][tr.source])){var wr=this.workerSources[wt][tr.type][tr.source];delete this.workerSources[wt][tr.type][tr.source],wr.removeSource!==void 0?wr.removeSource(tr,ir):ir()}},Yn.prototype.loadWorkerSource=function(wt,tr,ir){try{this.self.importScripts(tr.url),ir()}catch(wr){ir(wr.toString())}},Yn.prototype.syncRTLPluginState=function(wt,tr,ir){try{i.plugin.setState(tr);var wr=i.plugin.getPluginURL();if(i.plugin.isLoaded()&&!i.plugin.isParsed()&&wr!=null){this.self.importScripts(wr);var Xr=i.plugin.isParsed(),ti=Xr?void 0:new Error(\"RTL Text Plugin failed to import scripts from \"+wr);ir(ti,Xr)}}catch($r){ir($r.toString())}},Yn.prototype.getAvailableImages=function(wt){var tr=this.availableImages[wt];return tr||(tr=[]),tr},Yn.prototype.getLayerIndex=function(wt){var tr=this.layerIndexes[wt];return tr||(tr=this.layerIndexes[wt]=new l),tr},Yn.prototype.getWorkerSource=function(wt,tr,ir){var wr=this;if(this.workerSources[wt]||(this.workerSources[wt]={}),this.workerSources[wt][tr]||(this.workerSources[wt][tr]={}),!this.workerSources[wt][tr][ir]){var Xr={send:function(ti,$r,Ri){wr.actor.send(ti,$r,Ri,wt)}};this.workerSources[wt][tr][ir]=new this.workerSourceTypes[tr](Xr,this.getLayerIndex(wt),this.getAvailableImages(wt))}return this.workerSources[wt][tr][ir]},Yn.prototype.getDEMWorkerSource=function(wt,tr){return this.demWorkerSources[wt]||(this.demWorkerSources[wt]={}),this.demWorkerSources[wt][tr]||(this.demWorkerSources[wt][tr]=new b),this.demWorkerSources[wt][tr]},Yn.prototype.enforceCacheSizeLimit=function(wt,tr){i.enforceCacheSizeLimit(tr)},typeof WorkerGlobalScope!=\"undefined\"&&typeof self!=\"undefined\"&&self instanceof WorkerGlobalScope&&(self.worker=new Yn(self)),Yn}),n([\"./shared\"],function(i){\"use strict\";var 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Array.prototype&&Array.prototype.every&&Array.prototype.filter&&Array.prototype.forEach&&Array.prototype.indexOf&&Array.prototype.lastIndexOf&&Array.prototype.map&&Array.prototype.some&&Array.prototype.reduce&&Array.prototype.reduceRight&&Array.isArray}function de(){return Function.prototype&&Function.prototype.bind}function ne(){return Object.keys&&Object.create&&Object.getPrototypeOf&&Object.getOwnPropertyNames&&Object.isSealed&&Object.isFrozen&&Object.isExtensible&&Object.getOwnPropertyDescriptor&&Object.defineProperty&&Object.defineProperties&&Object.seal&&Object.freeze&&Object.preventExtensions}function we(){return\"JSON\"in window&&\"parse\"in JSON&&\"stringify\"in JSON}function Oe(){if(!(\"Worker\"in window&&\"Blob\"in window&&\"URL\"in window))return!1;var It=new Blob([\"\"],{type:\"text/javascript\"}),Bt=URL.createObjectURL(It),Ft,Ut;try{Ut=new Worker(Bt),Ft=!0}catch(zt){Ft=!1}return Ut&&Ut.terminate(),URL.revokeObjectURL(Bt),Ft}function ht(){return\"Uint8ClampedArray\"in 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O&&h?Y.addEventListener(D,J,O):Y.addEventListener(D,J,O.capture)},o.removeEventListener=function(Y,D,J,O){O===void 0&&(O={}),\"passive\"in O&&h?Y.removeEventListener(D,J,O):Y.removeEventListener(D,J,O.capture)};var v=function(Y){Y.preventDefault(),Y.stopPropagation(),i.window.removeEventListener(\"click\",v,!0)};o.suppressClick=function(){i.window.addEventListener(\"click\",v,!0),i.window.setTimeout(function(){i.window.removeEventListener(\"click\",v,!0)},0)},o.mousePos=function(Y,D){var J=Y.getBoundingClientRect();return new i.Point(D.clientX-J.left-Y.clientLeft,D.clientY-J.top-Y.clientTop)},o.touchPos=function(Y,D){for(var J=Y.getBoundingClientRect(),O=[],K=0;K<D.length;K++)O.push(new i.Point(D[K].clientX-J.left-Y.clientLeft,D[K].clientY-J.top-Y.clientTop));return O},o.mouseButton=function(Y){return typeof i.window.InstallTrigger!=\"undefined\"&&Y.button===2&&Y.ctrlKey&&i.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:Y.button},o.remove=function(Y){Y.parentNode&&Y.parentNode.removeChild(Y)};function m(Y,D,J){var O,K,de,ne=i.browser.devicePixelRatio>1?\"@2x\":\"\",we=i.getJSON(D.transformRequest(D.normalizeSpriteURL(Y,ne,\".json\"),i.ResourceType.SpriteJSON),function(Yt,cr){we=null,de||(de=Yt,O=cr,ht())}),Oe=i.getImage(D.transformRequest(D.normalizeSpriteURL(Y,ne,\".png\"),i.ResourceType.SpriteImage),function(Yt,cr){Oe=null,de||(de=Yt,K=cr,ht())});function ht(){if(de)J(de);else if(O&&K){var Yt=i.browser.getImageData(K),cr={};for(var Nt in O){var Ne=O[Nt],$e=Ne.width,st=Ne.height,It=Ne.x,Bt=Ne.y,Ft=Ne.sdf,Ut=Ne.pixelRatio,zt=Ne.stretchX,_t=Ne.stretchY,Dt=Ne.content,vt=new i.RGBAImage({width:$e,height:st});i.RGBAImage.copy(Yt,vt,{x:It,y:Bt},{x:0,y:0},{width:$e,height:st}),cr[Nt]={data:vt,pixelRatio:Ut,sdf:Ft,stretchX:zt,stretchY:_t,content:Dt}}J(null,cr)}}return{cancel:function(){we&&(we.cancel(),we=null),Oe&&(Oe.cancel(),Oe=null)}}}function b(Y){var D=Y.userImage;if(D&&D.render){var J=D.render();if(J)return Y.data.replace(new Uint8Array(D.data.buffer)),!0}return!1}var p=1,k=(function(Y){function D(){Y.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new i.RGBAImage({width:1,height:1}),this.dirty=!0}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.isLoaded=function(){return this.loaded},D.prototype.setLoaded=function(O){if(this.loaded!==O&&(this.loaded=O,O)){for(var K=0,de=this.requestors;K<de.length;K+=1){var ne=de[K],we=ne.ids,Oe=ne.callback;this._notify(we,Oe)}this.requestors=[]}},D.prototype.getImage=function(O){return this.images[O]},D.prototype.addImage=function(O,K){this._validate(O,K)&&(this.images[O]=K)},D.prototype._validate=function(O,K){var de=!0;return this._validateStretch(K.stretchX,K.data&&K.data.width)||(this.fire(new i.ErrorEvent(new Error('Image \"'+O+'\" has invalid \"stretchX\" value'))),de=!1),this._validateStretch(K.stretchY,K.data&&K.data.height)||(this.fire(new i.ErrorEvent(new Error('Image \"'+O+'\" has invalid \"stretchY\" value'))),de=!1),this._validateContent(K.content,K)||(this.fire(new i.ErrorEvent(new Error('Image \"'+O+'\" has invalid \"content\" value'))),de=!1),de},D.prototype._validateStretch=function(O,K){if(!O)return!0;for(var de=0,ne=0,we=O;ne<we.length;ne+=1){var Oe=we[ne];if(Oe[0]<de||Oe[1]<Oe[0]||K<Oe[1])return!1;de=Oe[1]}return!0},D.prototype._validateContent=function(O,K){return O?!(O.length!==4||O[0]<0||K.data.width<O[0]||O[1]<0||K.data.height<O[1]||O[2]<0||K.data.width<O[2]||O[3]<0||K.data.height<O[3]||O[2]<O[0]||O[3]<O[1]):!0},D.prototype.updateImage=function(O,K){var de=this.images[O];K.version=de.version+1,this.images[O]=K,this.updatedImages[O]=!0},D.prototype.removeImage=function(O){var K=this.images[O];delete this.images[O],delete this.patterns[O],K.userImage&&K.userImage.onRemove&&K.userImage.onRemove()},D.prototype.listImages=function(){return Object.keys(this.images)},D.prototype.getImages=function(O,K){var de=!0;if(!this.isLoaded())for(var ne=0,we=O;ne<we.length;ne+=1){var Oe=we[ne];this.images[Oe]||(de=!1)}this.isLoaded()||de?this._notify(O,K):this.requestors.push({ids:O,callback:K})},D.prototype._notify=function(O,K){for(var de={},ne=0,we=O;ne<we.length;ne+=1){var Oe=we[ne];this.images[Oe]||this.fire(new i.Event(\"styleimagemissing\",{id:Oe}));var ht=this.images[Oe];ht?de[Oe]={data:ht.data.clone(),pixelRatio:ht.pixelRatio,sdf:ht.sdf,version:ht.version,stretchX:ht.stretchX,stretchY:ht.stretchY,content:ht.content,hasRenderCallback:!!(ht.userImage&&ht.userImage.render)}:i.warnOnce('Image \"'+Oe+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}K(null,de)},D.prototype.getPixelSize=function(){var O=this.atlasImage,K=O.width,de=O.height;return{width:K,height:de}},D.prototype.getPattern=function(O){var K=this.patterns[O],de=this.getImage(O);if(!de)return null;if(K&&K.position.version===de.version)return K.position;if(K)K.position.version=de.version;else{var ne=de.data.width+p*2,we=de.data.height+p*2,Oe={w:ne,h:we,x:0,y:0},ht=new i.ImagePosition(Oe,de);this.patterns[O]={bin:Oe,position:ht}}return this._updatePatternAtlas(),this.patterns[O].position},D.prototype.bind=function(O){var K=O.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new i.Texture(O,this.atlasImage,K.RGBA),this.atlasTexture.bind(K.LINEAR,K.CLAMP_TO_EDGE)},D.prototype._updatePatternAtlas=function(){var O=[];for(var K in this.patterns)O.push(this.patterns[K].bin);var de=i.potpack(O),ne=de.w,we=de.h,Oe=this.atlasImage;Oe.resize({width:ne||1,height:we||1});for(var ht in this.patterns){var Yt=this.patterns[ht],cr=Yt.bin,Nt=cr.x+p,Ne=cr.y+p,$e=this.images[ht].data,st=$e.width,It=$e.height;i.RGBAImage.copy($e,Oe,{x:0,y:0},{x:Nt,y:Ne},{width:st,height:It}),i.RGBAImage.copy($e,Oe,{x:0,y:It-1},{x:Nt,y:Ne-1},{width:st,height:1}),i.RGBAImage.copy($e,Oe,{x:0,y:0},{x:Nt,y:Ne+It},{width:st,height:1}),i.RGBAImage.copy($e,Oe,{x:st-1,y:0},{x:Nt-1,y:Ne},{width:1,height:It}),i.RGBAImage.copy($e,Oe,{x:0,y:0},{x:Nt+st,y:Ne},{width:1,height:It})}this.dirty=!0},D.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},D.prototype.dispatchRenderCallbacks=function(O){for(var K=0,de=O;K<de.length;K+=1){var ne=de[K];if(!this.callbackDispatchedThisFrame[ne]){this.callbackDispatchedThisFrame[ne]=!0;var we=this.images[ne],Oe=b(we);Oe&&this.updateImage(ne,we)}}},D})(i.Evented);function M(Y,D,J,O,K){var de=D*256,ne=de+255,we=O.transformRequest(O.normalizeGlyphsURL(J).replace(\"{fontstack}\",Y).replace(\"{range}\",de+\"-\"+ne),i.ResourceType.Glyphs);i.getArrayBuffer(we,function(Oe,ht){if(Oe)K(Oe);else if(ht){for(var Yt={},cr=0,Nt=i.parseGlyphPBF(ht);cr<Nt.length;cr+=1){var Ne=Nt[cr];Yt[Ne.id]=Ne}K(null,Yt)}})}var T=C,L=C,x=1e20;function C(Y,D,J,O,K,de){this.fontSize=Y||24,this.buffer=D===void 0?3:D,this.cutoff=O||.25,this.fontFamily=K||\"sans-serif\",this.fontWeight=de||\"normal\",this.radius=J||8;var ne=this.size=this.fontSize+this.buffer*2;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=ne,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(ne*ne),this.gridInner=new Float64Array(ne*ne),this.f=new Float64Array(ne),this.d=new Float64Array(ne),this.z=new Float64Array(ne+1),this.v=new Int16Array(ne),this.middle=Math.round(ne/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}C.prototype.draw=function(Y){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(Y,this.buffer,this.middle);for(var D=this.ctx.getImageData(0,0,this.size,this.size),J=new Uint8ClampedArray(this.size*this.size),O=0;O<this.size*this.size;O++){var K=D.data[O*4+3]/255;this.gridOuter[O]=K===1?0:K===0?x:Math.pow(Math.max(0,.5-K),2),this.gridInner[O]=K===1?x:K===0?0:Math.pow(Math.max(0,K-.5),2)}for(S(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),S(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),O=0;O<this.size*this.size;O++){var de=this.gridOuter[O]-this.gridInner[O];J[O]=Math.max(0,Math.min(255,Math.round(255-255*(de/this.radius+this.cutoff))))}return J};function S(Y,D,J,O,K,de,ne){for(var we=0;we<D;we++){for(var Oe=0;Oe<J;Oe++)O[Oe]=Y[Oe*D+we];for(g(O,K,de,ne,J),Oe=0;Oe<J;Oe++)Y[Oe*D+we]=K[Oe]}for(Oe=0;Oe<J;Oe++){for(we=0;we<D;we++)O[we]=Y[Oe*D+we];for(g(O,K,de,ne,D),we=0;we<D;we++)Y[Oe*D+we]=Math.sqrt(K[we])}}function g(Y,D,J,O,K){J[0]=0,O[0]=-x,O[1]=+x;for(var de=1,ne=0;de<K;de++){for(var we=(Y[de]+de*de-(Y[J[ne]]+J[ne]*J[ne]))/(2*de-2*J[ne]);we<=O[ne];)ne--,we=(Y[de]+de*de-(Y[J[ne]]+J[ne]*J[ne]))/(2*de-2*J[ne]);ne++,J[ne]=de,O[ne]=we,O[ne+1]=+x}for(de=0,ne=0;de<K;de++){for(;O[ne+1]<de;)ne++;D[de]=(de-J[ne])*(de-J[ne])+Y[J[ne]]}}T.default=L;var P=function(D,J){this.requestManager=D,this.localIdeographFontFamily=J,this.entries={}};P.prototype.setURL=function(D){this.url=D},P.prototype.getGlyphs=function(D,J){var O=this,K=[];for(var de in D)for(var ne=0,we=D[de];ne<we.length;ne+=1){var Oe=we[ne];K.push({stack:de,id:Oe})}i.asyncAll(K,function(ht,Yt){var cr=ht.stack,Nt=ht.id,Ne=O.entries[cr];Ne||(Ne=O.entries[cr]={glyphs:{},requests:{},ranges:{}});var $e=Ne.glyphs[Nt];if($e!==void 0){Yt(null,{stack:cr,id:Nt,glyph:$e});return}if($e=O._tinySDF(Ne,cr,Nt),$e){Ne.glyphs[Nt]=$e,Yt(null,{stack:cr,id:Nt,glyph:$e});return}var st=Math.floor(Nt/256);if(st*256>65535){Yt(new Error(\"glyphs > 65535 not supported\"));return}if(Ne.ranges[st]){Yt(null,{stack:cr,id:Nt,glyph:$e});return}var It=Ne.requests[st];It||(It=Ne.requests[st]=[],P.loadGlyphRange(cr,st,O.url,O.requestManager,function(Bt,Ft){if(Ft){for(var Ut in Ft)O._doesCharSupportLocalGlyph(+Ut)||(Ne.glyphs[+Ut]=Ft[+Ut]);Ne.ranges[st]=!0}for(var zt=0,_t=It;zt<_t.length;zt+=1){var Dt=_t[zt];Dt(Bt,Ft)}delete Ne.requests[st]})),It.push(function(Bt,Ft){Bt?Yt(Bt):Ft&&Yt(null,{stack:cr,id:Nt,glyph:Ft[Nt]||null})})},function(ht,Yt){if(ht)J(ht);else if(Yt){for(var cr={},Nt=0,Ne=Yt;Nt<Ne.length;Nt+=1){var $e=Ne[Nt],st=$e.stack,It=$e.id,Bt=$e.glyph;(cr[st]||(cr[st]={}))[It]=Bt&&{id:Bt.id,bitmap:Bt.bitmap.clone(),metrics:Bt.metrics}}J(null,cr)}})},P.prototype._doesCharSupportLocalGlyph=function(D){return!!this.localIdeographFontFamily&&(i.isChar[\"CJK Unified Ideographs\"](D)||i.isChar[\"Hangul Syllables\"](D)||i.isChar.Hiragana(D)||i.isChar.Katakana(D))},P.prototype._tinySDF=function(D,J,O){var K=this.localIdeographFontFamily;if(K&&this._doesCharSupportLocalGlyph(O)){var de=D.tinySDF;if(!de){var ne=\"400\";/bold/i.test(J)?ne=\"900\":/medium/i.test(J)?ne=\"500\":/light/i.test(J)&&(ne=\"200\"),de=D.tinySDF=new P.TinySDF(24,3,8,.25,K,ne)}return{id:O,bitmap:new i.AlphaImage({width:30,height:30},de.draw(String.fromCharCode(O))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},P.loadGlyphRange=M,P.TinySDF=T;var E=function(){this.specification=i.styleSpec.light.position};E.prototype.possiblyEvaluate=function(D,J){return i.sphericalToCartesian(D.expression.evaluate(J))},E.prototype.interpolate=function(D,J,O){return{x:i.number(D.x,J.x,O),y:i.number(D.y,J.y,O),z:i.number(D.z,J.z,O)}};var z=new i.Properties({anchor:new i.DataConstantProperty(i.styleSpec.light.anchor),position:new E,color:new i.DataConstantProperty(i.styleSpec.light.color),intensity:new i.DataConstantProperty(i.styleSpec.light.intensity)}),q=\"-transition\",V=(function(Y){function D(J){Y.call(this),this._transitionable=new i.Transitionable(z),this.setLight(J),this._transitioning=this._transitionable.untransitioned()}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getLight=function(){return this._transitionable.serialize()},D.prototype.setLight=function(O,K){if(K===void 0&&(K={}),!this._validate(i.validateLight,O,K))for(var de in O){var ne=O[de];i.endsWith(de,q)?this._transitionable.setTransition(de.slice(0,-q.length),ne):this._transitionable.setValue(de,ne)}},D.prototype.updateTransitions=function(O){this._transitioning=this._transitionable.transitioned(O,this._transitioning)},D.prototype.hasTransition=function(){return this._transitioning.hasTransition()},D.prototype.recalculate=function(O){this.properties=this._transitioning.possiblyEvaluate(O)},D.prototype._validate=function(O,K,de){return de&&de.validate===!1?!1:i.emitValidationErrors(this,O.call(i.validateStyle,i.extend({value:K,style:{glyphs:!0,sprite:!0},styleSpec:i.styleSpec})))},D})(i.Evented),G=function(D,J){this.width=D,this.height=J,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};G.prototype.getDash=function(D,J){var O=D.join(\",\")+String(J);return this.dashEntry[O]||(this.dashEntry[O]=this.addDash(D,J)),this.dashEntry[O]},G.prototype.getDashRanges=function(D,J,O){var K=D.length%2===1,de=[],ne=K?-D[D.length-1]*O:0,we=D[0]*O,Oe=!0;de.push({left:ne,right:we,isDash:Oe,zeroLength:D[0]===0});for(var ht=D[0],Yt=1;Yt<D.length;Yt++){Oe=!Oe;var cr=D[Yt];ne=ht*O,ht+=cr,we=ht*O,de.push({left:ne,right:we,isDash:Oe,zeroLength:cr===0})}return de},G.prototype.addRoundDash=function(D,J,O){for(var K=J/2,de=-O;de<=O;de++)for(var ne=this.nextRow+O+de,we=this.width*ne,Oe=0,ht=D[Oe],Yt=0;Yt<this.width;Yt++){Yt/ht.right>1&&(ht=D[++Oe]);var cr=Math.abs(Yt-ht.left),Nt=Math.abs(Yt-ht.right),Ne=Math.min(cr,Nt),$e=void 0,st=de/O*(K+1);if(ht.isDash){var It=K-Math.abs(st);$e=Math.sqrt(Ne*Ne+It*It)}else $e=K-Math.sqrt(Ne*Ne+st*st);this.data[we+Yt]=Math.max(0,Math.min(255,$e+128))}},G.prototype.addRegularDash=function(D){for(var J=D.length-1;J>=0;--J){var O=D[J],K=D[J+1];O.zeroLength?D.splice(J,1):K&&K.isDash===O.isDash&&(K.left=O.left,D.splice(J,1))}var de=D[0],ne=D[D.length-1];de.isDash===ne.isDash&&(de.left=ne.left-this.width,ne.right=de.right+this.width);for(var we=this.width*this.nextRow,Oe=0,ht=D[Oe],Yt=0;Yt<this.width;Yt++){Yt/ht.right>1&&(ht=D[++Oe]);var cr=Math.abs(Yt-ht.left),Nt=Math.abs(Yt-ht.right),Ne=Math.min(cr,Nt),$e=ht.isDash?Ne:-Ne;this.data[we+Yt]=Math.max(0,Math.min(255,$e+128))}},G.prototype.addDash=function(D,J){var O=J?7:0,K=2*O+1;if(this.nextRow+K>this.height)return i.warnOnce(\"LineAtlas out of space\"),null;for(var de=0,ne=0;ne<D.length;ne++)de+=D[ne];if(de!==0){var we=this.width/de,Oe=this.getDashRanges(D,this.width,we);J?this.addRoundDash(Oe,we,O):this.addRegularDash(Oe)}var ht={y:(this.nextRow+O+.5)/this.height,height:2*O/this.height,width:de};return this.nextRow+=K,this.dirty=!0,ht},G.prototype.bind=function(D){var J=D.gl;this.texture?(J.bindTexture(J.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,J.texSubImage2D(J.TEXTURE_2D,0,0,0,this.width,this.height,J.ALPHA,J.UNSIGNED_BYTE,this.data))):(this.texture=J.createTexture(),J.bindTexture(J.TEXTURE_2D,this.texture),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_WRAP_S,J.REPEAT),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_WRAP_T,J.REPEAT),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_MIN_FILTER,J.LINEAR),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_MAG_FILTER,J.LINEAR),J.texImage2D(J.TEXTURE_2D,0,J.ALPHA,this.width,this.height,0,J.ALPHA,J.UNSIGNED_BYTE,this.data))};var Z=function Y(D,J){this.workerPool=D,this.actors=[],this.currentActor=0,this.id=i.uniqueId();for(var O=this.workerPool.acquire(this.id),K=0;K<O.length;K++){var de=O[K],ne=new Y.Actor(de,J,this.id);ne.name=\"Worker \"+K,this.actors.push(ne)}};Z.prototype.broadcast=function(D,J,O){O=O||function(){},i.asyncAll(this.actors,function(K,de){K.send(D,J,de)},O)},Z.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},Z.prototype.remove=function(){this.actors.forEach(function(D){D.remove()}),this.actors=[],this.workerPool.release(this.id)},Z.Actor=i.Actor;function j(Y,D,J){var O=function(K,de){if(K)return J(K);if(de){var ne=i.pick(i.extend(de,Y),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);de.vector_layers&&(ne.vectorLayers=de.vector_layers,ne.vectorLayerIds=ne.vectorLayers.map(function(we){return we.id})),ne.tiles=D.canonicalizeTileset(ne,Y.url),J(null,ne)}};return Y.url?i.getJSON(D.transformRequest(D.normalizeSourceURL(Y.url),i.ResourceType.Source),O):i.browser.frame(function(){return O(null,Y)})}var N=function(D,J,O){this.bounds=i.LngLatBounds.convert(this.validateBounds(D)),this.minzoom=J||0,this.maxzoom=O||24};N.prototype.validateBounds=function(D){return!Array.isArray(D)||D.length!==4?[-180,-90,180,90]:[Math.max(-180,D[0]),Math.max(-90,D[1]),Math.min(180,D[2]),Math.min(90,D[3])]},N.prototype.contains=function(D){var J=Math.pow(2,D.z),O={minX:Math.floor(i.mercatorXfromLng(this.bounds.getWest())*J),minY:Math.floor(i.mercatorYfromLat(this.bounds.getNorth())*J),maxX:Math.ceil(i.mercatorXfromLng(this.bounds.getEast())*J),maxY:Math.ceil(i.mercatorYfromLat(this.bounds.getSouth())*J)},K=D.x>=O.minX&&D.x<O.maxX&&D.y>=O.minY&&D.y<O.maxY;return K};var H=(function(Y){function D(J,O,K,de){if(Y.call(this),this.id=J,this.dispatcher=K,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,i.extend(this,i.pick(O,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=i.extend({type:\"vector\"},O),this._collectResourceTiming=O.collectResourceTiming,this.tileSize!==512)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(de)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var O=this;this._loaded=!1,this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=j(this._options,this.map._requestManager,function(K,de){O._tileJSONRequest=null,O._loaded=!0,K?O.fire(new i.ErrorEvent(K)):de&&(i.extend(O,de),de.bounds&&(O.tileBounds=new N(de.bounds,O.minzoom,O.maxzoom)),i.postTurnstileEvent(de.tiles,O.map._requestManager._customAccessToken),i.postMapLoadEvent(de.tiles,O.map._getMapId(),O.map._requestManager._skuToken,O.map._requestManager._customAccessToken),O.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),O.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},D.prototype.loaded=function(){return this._loaded},D.prototype.hasTile=function(O){return!this.tileBounds||this.tileBounds.contains(O.canonical)},D.prototype.onAdd=function(O){this.map=O,this.load()},D.prototype.setSourceProperty=function(O){this._tileJSONRequest&&this._tileJSONRequest.cancel(),O();var K=this.map.style.sourceCaches[this.id];K.clearTiles(),this.load()},D.prototype.setTiles=function(O){var K=this;return this.setSourceProperty(function(){K._options.tiles=O}),this},D.prototype.setUrl=function(O){var K=this;return this.setSourceProperty(function(){K.url=O,K._options.url=O}),this},D.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},D.prototype.serialize=function(){return i.extend({},this._options)},D.prototype.loadTile=function(O,K){var de=this.map._requestManager.normalizeTileURL(O.tileID.canonical.url(this.tiles,this.scheme)),ne={request:this.map._requestManager.transformRequest(de,i.ResourceType.Tile),uid:O.uid,tileID:O.tileID,zoom:O.tileID.overscaledZ,tileSize:this.tileSize*O.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:i.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};ne.request.collectResourceTiming=this._collectResourceTiming,!O.actor||O.state===\"expired\"?(O.actor=this.dispatcher.getActor(),O.request=O.actor.send(\"loadTile\",ne,we.bind(this))):O.state===\"loading\"?O.reloadCallback=K:O.request=O.actor.send(\"reloadTile\",ne,we.bind(this));function we(Oe,ht){if(delete O.request,O.aborted)return K(null);if(Oe&&Oe.status!==404)return K(Oe);ht&&ht.resourceTiming&&(O.resourceTiming=ht.resourceTiming),this.map._refreshExpiredTiles&&ht&&O.setExpiryData(ht),O.loadVectorData(ht,this.map.painter),i.cacheEntryPossiblyAdded(this.dispatcher),K(null),O.reloadCallback&&(this.loadTile(O,O.reloadCallback),O.reloadCallback=null)}},D.prototype.abortTile=function(O){O.request&&(O.request.cancel(),delete O.request),O.actor&&O.actor.send(\"abortTile\",{uid:O.uid,type:this.type,source:this.id},void 0)},D.prototype.unloadTile=function(O){O.unloadVectorData(),O.actor&&O.actor.send(\"removeTile\",{uid:O.uid,type:this.type,source:this.id},void 0)},D.prototype.hasTransition=function(){return!1},D})(i.Evented),re=(function(Y){function D(J,O,K,de){Y.call(this),this.id=J,this.dispatcher=K,this.setEventedParent(de),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=i.extend({type:\"raster\"},O),i.extend(this,i.pick(O,[\"url\",\"scheme\",\"tileSize\"]))}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var O=this;this._loaded=!1,this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=j(this._options,this.map._requestManager,function(K,de){O._tileJSONRequest=null,O._loaded=!0,K?O.fire(new i.ErrorEvent(K)):de&&(i.extend(O,de),de.bounds&&(O.tileBounds=new N(de.bounds,O.minzoom,O.maxzoom)),i.postTurnstileEvent(de.tiles),i.postMapLoadEvent(de.tiles,O.map._getMapId(),O.map._requestManager._skuToken),O.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),O.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},D.prototype.loaded=function(){return this._loaded},D.prototype.onAdd=function(O){this.map=O,this.load()},D.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},D.prototype.serialize=function(){return i.extend({},this._options)},D.prototype.hasTile=function(O){return!this.tileBounds||this.tileBounds.contains(O.canonical)},D.prototype.loadTile=function(O,K){var de=this,ne=this.map._requestManager.normalizeTileURL(O.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);O.request=i.getImage(this.map._requestManager.transformRequest(ne,i.ResourceType.Tile),function(we,Oe){if(delete O.request,O.aborted)O.state=\"unloaded\",K(null);else if(we)O.state=\"errored\",K(we);else if(Oe){de.map._refreshExpiredTiles&&O.setExpiryData(Oe),delete Oe.cacheControl,delete Oe.expires;var ht=de.map.painter.context,Yt=ht.gl;O.texture=de.map.painter.getTileTexture(Oe.width),O.texture?O.texture.update(Oe,{useMipmap:!0}):(O.texture=new i.Texture(ht,Oe,Yt.RGBA,{useMipmap:!0}),O.texture.bind(Yt.LINEAR,Yt.CLAMP_TO_EDGE,Yt.LINEAR_MIPMAP_NEAREST),ht.extTextureFilterAnisotropic&&Yt.texParameterf(Yt.TEXTURE_2D,ht.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,ht.extTextureFilterAnisotropicMax)),O.state=\"loaded\",i.cacheEntryPossiblyAdded(de.dispatcher),K(null)}})},D.prototype.abortTile=function(O,K){O.request&&(O.request.cancel(),delete O.request),K()},D.prototype.unloadTile=function(O,K){O.texture&&this.map.painter.saveTileTexture(O.texture),K()},D.prototype.hasTransition=function(){return!1},D})(i.Evented),ae=(function(Y){function D(J,O,K,de){Y.call(this,J,O,K,de),this.type=\"raster-dem\",this.maxzoom=22,this._options=i.extend({type:\"raster-dem\"},O),this.encoding=O.encoding||\"mapbox\"}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},D.prototype.loadTile=function(O,K){var de=this.map._requestManager.normalizeTileURL(O.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);O.request=i.getImage(this.map._requestManager.transformRequest(de,i.ResourceType.Tile),ne.bind(this)),O.neighboringTiles=this._getNeighboringTiles(O.tileID);function ne(Oe,ht){if(delete O.request,O.aborted)O.state=\"unloaded\",K(null);else if(Oe)O.state=\"errored\",K(Oe);else if(ht){this.map._refreshExpiredTiles&&O.setExpiryData(ht),delete ht.cacheControl,delete ht.expires;var Yt=i.window.ImageBitmap&&ht instanceof i.window.ImageBitmap&&i.offscreenCanvasSupported(),cr=Yt?ht:i.browser.getImageData(ht,1),Nt={uid:O.uid,coord:O.tileID,source:this.id,rawImageData:cr,encoding:this.encoding};(!O.actor||O.state===\"expired\")&&(O.actor=this.dispatcher.getActor(),O.actor.send(\"loadDEMTile\",Nt,we.bind(this)))}}function we(Oe,ht){Oe&&(O.state=\"errored\",K(Oe)),ht&&(O.dem=ht,O.needsHillshadePrepare=!0,O.state=\"loaded\",K(null))}},D.prototype._getNeighboringTiles=function(O){var K=O.canonical,de=Math.pow(2,K.z),ne=(K.x-1+de)%de,we=K.x===0?O.wrap-1:O.wrap,Oe=(K.x+1+de)%de,ht=K.x+1===de?O.wrap+1:O.wrap,Yt={};return Yt[new i.OverscaledTileID(O.overscaledZ,we,K.z,ne,K.y).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,ht,K.z,Oe,K.y).key]={backfilled:!1},K.y>0&&(Yt[new i.OverscaledTileID(O.overscaledZ,we,K.z,ne,K.y-1).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,O.wrap,K.z,K.x,K.y-1).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,ht,K.z,Oe,K.y-1).key]={backfilled:!1}),K.y+1<de&&(Yt[new i.OverscaledTileID(O.overscaledZ,we,K.z,ne,K.y+1).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,O.wrap,K.z,K.x,K.y+1).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,ht,K.z,Oe,K.y+1).key]={backfilled:!1}),Yt},D.prototype.unloadTile=function(O){O.demTexture&&this.map.painter.saveTileTexture(O.demTexture),O.fbo&&(O.fbo.destroy(),delete O.fbo),O.dem&&delete O.dem,delete O.neighboringTiles,O.state=\"unloaded\",O.actor&&O.actor.send(\"removeDEMTile\",{uid:O.uid,source:this.id})},D})(re),_e=(function(Y){function D(J,O,K,de){Y.call(this),this.id=J,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=K.getActor(),this.setEventedParent(de),this._data=O.data,this._options=i.extend({},O),this._collectResourceTiming=O.collectResourceTiming,this._resourceTiming=[],O.maxzoom!==void 0&&(this.maxzoom=O.maxzoom),O.type&&(this.type=O.type),O.attribution&&(this.attribution=O.attribution),this.promoteId=O.promoteId;var ne=i.EXTENT/this.tileSize;this.workerOptions=i.extend({source:this.id,cluster:O.cluster||!1,geojsonVtOptions:{buffer:(O.buffer!==void 0?O.buffer:128)*ne,tolerance:(O.tolerance!==void 0?O.tolerance:.375)*ne,extent:i.EXTENT,maxZoom:this.maxzoom,lineMetrics:O.lineMetrics||!1,generateId:O.generateId||!1},superclusterOptions:{maxZoom:O.clusterMaxZoom!==void 0?Math.min(O.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,minPoints:Math.max(2,O.clusterMinPoints||2),extent:i.EXTENT,radius:(O.clusterRadius||50)*ne,log:!1,generateId:O.generateId||!1},clusterProperties:O.clusterProperties,filter:O.filter},O.workerOptions)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var O=this;this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData(function(K){if(K){O.fire(new i.ErrorEvent(K));return}var de={dataType:\"source\",sourceDataType:\"metadata\"};O._collectResourceTiming&&O._resourceTiming&&O._resourceTiming.length>0&&(de.resourceTiming=O._resourceTiming,O._resourceTiming=[]),O.fire(new i.Event(\"data\",de))})},D.prototype.onAdd=function(O){this.map=O,this.load()},D.prototype.setData=function(O){var K=this;return this._data=O,this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData(function(de){if(de){K.fire(new i.ErrorEvent(de));return}var ne={dataType:\"source\",sourceDataType:\"content\"};K._collectResourceTiming&&K._resourceTiming&&K._resourceTiming.length>0&&(ne.resourceTiming=K._resourceTiming,K._resourceTiming=[]),K.fire(new i.Event(\"data\",ne))}),this},D.prototype.getClusterExpansionZoom=function(O,K){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:O,source:this.id},K),this},D.prototype.getClusterChildren=function(O,K){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:O,source:this.id},K),this},D.prototype.getClusterLeaves=function(O,K,de,ne){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:O,limit:K,offset:de},ne),this},D.prototype._updateWorkerData=function(O){var K=this;this._loaded=!1;var de=i.extend({},this.workerOptions),ne=this._data;typeof ne==\"string\"?(de.request=this.map._requestManager.transformRequest(i.browser.resolveURL(ne),i.ResourceType.Source),de.request.collectResourceTiming=this._collectResourceTiming):de.data=JSON.stringify(ne),this.actor.send(this.type+\".loadData\",de,function(we,Oe){K._removed||Oe&&Oe.abandoned||(K._loaded=!0,Oe&&Oe.resourceTiming&&Oe.resourceTiming[K.id]&&(K._resourceTiming=Oe.resourceTiming[K.id].slice(0)),K.actor.send(K.type+\".coalesce\",{source:de.source},null),O(we))})},D.prototype.loaded=function(){return this._loaded},D.prototype.loadTile=function(O,K){var de=this,ne=O.actor?\"reloadTile\":\"loadTile\";O.actor=this.actor;var we={type:this.type,uid:O.uid,tileID:O.tileID,zoom:O.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:i.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};O.request=this.actor.send(ne,we,function(Oe,ht){return delete O.request,O.unloadVectorData(),O.aborted?K(null):Oe?K(Oe):(O.loadVectorData(ht,de.map.painter,ne===\"reloadTile\"),K(null))})},D.prototype.abortTile=function(O){O.request&&(O.request.cancel(),delete O.request),O.aborted=!0},D.prototype.unloadTile=function(O){O.unloadVectorData(),this.actor.send(\"removeTile\",{uid:O.uid,type:this.type,source:this.id})},D.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},D.prototype.serialize=function(){return i.extend({},this._options,{type:this.type,data:this._data})},D.prototype.hasTransition=function(){return!1},D})(i.Evented),Ee=i.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),Ce=(function(Y){function D(J,O,K,de){Y.call(this),this.id=J,this.dispatcher=K,this.coordinates=O.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(de),this.options=O}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(O,K){var de=this;this._loaded=!1,this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,i.getImage(this.map._requestManager.transformRequest(this.url,i.ResourceType.Image),function(ne,we){de._loaded=!0,ne?de.fire(new i.ErrorEvent(ne)):we&&(de.image=we,O&&(de.coordinates=O),K&&K(),de._finishLoading())})},D.prototype.loaded=function(){return this._loaded},D.prototype.updateImage=function(O){var K=this;return!this.image||!O.url?this:(this.options.url=O.url,this.load(O.coordinates,function(){K.texture=null}),this)},D.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},D.prototype.onAdd=function(O){this.map=O,this.load()},D.prototype.setCoordinates=function(O){var K=this;this.coordinates=O;var de=O.map(i.MercatorCoordinate.fromLngLat);this.tileID=ge(de),this.minzoom=this.maxzoom=this.tileID.z;var ne=de.map(function(we){return K.tileID.getTilePoint(we)._round()});return this._boundsArray=new i.StructArrayLayout4i8,this._boundsArray.emplaceBack(ne[0].x,ne[0].y,0,0),this._boundsArray.emplaceBack(ne[1].x,ne[1].y,i.EXTENT,0),this._boundsArray.emplaceBack(ne[3].x,ne[3].y,0,i.EXTENT),this._boundsArray.emplaceBack(ne[2].x,ne[2].y,i.EXTENT,i.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},D.prototype.prepare=function(){if(!(Object.keys(this.tiles).length===0||!this.image)){var O=this.map.painter.context,K=O.gl;this.boundsBuffer||(this.boundsBuffer=O.createVertexBuffer(this._boundsArray,Ee.members)),this.boundsSegments||(this.boundsSegments=i.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new i.Texture(O,this.image,K.RGBA),this.texture.bind(K.LINEAR,K.CLAMP_TO_EDGE));for(var de in this.tiles){var ne=this.tiles[de];ne.state!==\"loaded\"&&(ne.state=\"loaded\",ne.texture=this.texture)}}},D.prototype.loadTile=function(O,K){this.tileID&&this.tileID.equals(O.tileID.canonical)?(this.tiles[String(O.tileID.wrap)]=O,O.buckets={},K(null)):(O.state=\"errored\",K(null))},D.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},D.prototype.hasTransition=function(){return!1},D})(i.Evented);function ge(Y){for(var D=1/0,J=1/0,O=-1/0,K=-1/0,de=0,ne=Y;de<ne.length;de+=1){var we=ne[de];D=Math.min(D,we.x),J=Math.min(J,we.y),O=Math.max(O,we.x),K=Math.max(K,we.y)}var Oe=O-D,ht=K-J,Yt=Math.max(Oe,ht),cr=Math.max(0,Math.floor(-Math.log(Yt)/Math.LN2)),Nt=Math.pow(2,cr);return new i.CanonicalTileID(cr,Math.floor((D+O)/2*Nt),Math.floor((J+K)/2*Nt))}var ie=(function(Y){function D(J,O,K,de){Y.call(this,J,O,K,de),this.roundZoom=!0,this.type=\"video\",this.options=O}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var O=this;this._loaded=!1;var K=this.options;this.urls=[];for(var de=0,ne=K.urls;de<ne.length;de+=1){var we=ne[de];this.urls.push(this.map._requestManager.transformRequest(we,i.ResourceType.Source).url)}i.getVideo(this.urls,function(Oe,ht){O._loaded=!0,Oe?O.fire(new i.ErrorEvent(Oe)):ht&&(O.video=ht,O.video.loop=!0,O.video.addEventListener(\"playing\",function(){O.map.triggerRepaint()}),O.map&&O.video.play(),O._finishLoading())})},D.prototype.pause=function(){this.video&&this.video.pause()},D.prototype.play=function(){this.video&&this.video.play()},D.prototype.seek=function(O){if(this.video){var K=this.video.seekable;O<K.start(0)||O>K.end(0)?this.fire(new i.ErrorEvent(new i.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+K.start(0)+\" and \"+K.end(0)+\"-second mark.\"))):this.video.currentTime=O}},D.prototype.getVideo=function(){return this.video},D.prototype.onAdd=function(O){this.map||(this.map=O,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},D.prototype.prepare=function(){if(!(Object.keys(this.tiles).length===0||this.video.readyState<2)){var O=this.map.painter.context,K=O.gl;this.boundsBuffer||(this.boundsBuffer=O.createVertexBuffer(this._boundsArray,Ee.members)),this.boundsSegments||(this.boundsSegments=i.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(K.LINEAR,K.CLAMP_TO_EDGE),K.texSubImage2D(K.TEXTURE_2D,0,0,0,K.RGBA,K.UNSIGNED_BYTE,this.video)):(this.texture=new i.Texture(O,this.video,K.RGBA),this.texture.bind(K.LINEAR,K.CLAMP_TO_EDGE));for(var de in this.tiles){var ne=this.tiles[de];ne.state!==\"loaded\"&&(ne.state=\"loaded\",ne.texture=this.texture)}}},D.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},D.prototype.hasTransition=function(){return this.video&&!this.video.paused},D})(Ce),Se=(function(Y){function D(J,O,K,de){Y.call(this,J,O,K,de),O.coordinates?(!Array.isArray(O.coordinates)||O.coordinates.length!==4||O.coordinates.some(function(ne){return!Array.isArray(ne)||ne.length!==2||ne.some(function(we){return typeof we!=\"number\"})}))&&this.fire(new i.ErrorEvent(new i.ValidationError(\"sources.\"+J,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new i.ErrorEvent(new i.ValidationError(\"sources.\"+J,null,'missing required property \"coordinates\"'))),O.animate&&typeof O.animate!=\"boolean\"&&this.fire(new i.ErrorEvent(new i.ValidationError(\"sources.\"+J,null,'optional \"animate\" property must be a boolean value'))),O.canvas?typeof O.canvas!=\"string\"&&!(O.canvas instanceof i.window.HTMLCanvasElement)&&this.fire(new i.ErrorEvent(new i.ValidationError(\"sources.\"+J,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new i.ErrorEvent(new i.ValidationError(\"sources.\"+J,null,'missing required property \"canvas\"'))),this.options=O,this.animate=O.animate!==void 0?O.animate:!0}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){if(this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof i.window.HTMLCanvasElement?this.options.canvas:i.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()){this.fire(new i.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\")));return}this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading()},D.prototype.getCanvas=function(){return this.canvas},D.prototype.onAdd=function(O){this.map=O,this.load(),this.canvas&&this.animate&&this.play()},D.prototype.onRemove=function(){this.pause()},D.prototype.prepare=function(){var O=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,O=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,O=!0),!this._hasInvalidDimensions()&&Object.keys(this.tiles).length!==0){var K=this.map.painter.context,de=K.gl;this.boundsBuffer||(this.boundsBuffer=K.createVertexBuffer(this._boundsArray,Ee.members)),this.boundsSegments||(this.boundsSegments=i.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(O||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new i.Texture(K,this.canvas,de.RGBA,{premultiply:!0});for(var ne in this.tiles){var we=this.tiles[ne];we.state!==\"loaded\"&&(we.state=\"loaded\",we.texture=this.texture)}}},D.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},D.prototype.hasTransition=function(){return this._playing},D.prototype._hasInvalidDimensions=function(){for(var O=0,K=[this.canvas.width,this.canvas.height];O<K.length;O+=1){var de=K[O];if(isNaN(de)||de<=0)return!0}return!1},D})(Ce),ke={vector:H,raster:re,\"raster-dem\":ae,geojson:_e,video:ie,image:Ce,canvas:Se},be=function(Y,D,J,O){var K=new ke[D.type](Y,D,J,O);if(K.id!==Y)throw new Error(\"Expected Source id to be \"+Y+\" instead of \"+K.id);return i.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],K),K},Be=function(Y){return ke[Y]},Le=function(Y,D){ke[Y]=D};function me(Y,D){var J=i.identity([]);return i.translate(J,J,[1,1,0]),i.scale(J,J,[Y.width*.5,Y.height*.5,1]),i.multiply(J,J,Y.calculatePosMatrix(D.toUnwrapped()))}function Pe(Y,D,J){if(Y)for(var O=0,K=Y;O<K.length;O+=1){var de=K[O],ne=D[de];if(ne&&ne.source===J&&ne.type===\"fill-extrusion\")return!0}else for(var we in D){var Oe=D[we];if(Oe.source===J&&Oe.type===\"fill-extrusion\")return!0}return!1}function ce(Y,D,J,O,K,de){var ne=Pe(K&&K.layers,D,Y.id),we=de.maxPitchScaleFactor(),Oe=Y.tilesIn(O,we,ne);Oe.sort(mt);for(var ht=[],Yt=0,cr=Oe;Yt<cr.length;Yt+=1){var Nt=cr[Yt];ht.push({wrappedTileID:Nt.tileID.wrapped().key,queryResults:Nt.tile.queryRenderedFeatures(D,J,Y._state,Nt.queryGeometry,Nt.cameraQueryGeometry,Nt.scale,K,de,we,me(Y.transform,Nt.tileID))})}var Ne=Ht(ht);for(var $e in Ne)Ne[$e].forEach(function(st){var It=st.feature,Bt=Y.getFeatureState(It.layer[\"source-layer\"],It.id);It.source=It.layer.source,It.layer[\"source-layer\"]&&(It.sourceLayer=It.layer[\"source-layer\"]),It.state=Bt});return Ne}function He(Y,D,J,O,K,de,ne){for(var we={},Oe=de.queryRenderedSymbols(O),ht=[],Yt=0,cr=Object.keys(Oe).map(Number);Yt<cr.length;Yt+=1){var Nt=cr[Yt];ht.push(ne[Nt])}ht.sort(mt);for(var Ne=function(){var Ft=st[$e],Ut=Ft.featureIndex.lookupSymbolFeatures(Oe[Ft.bucketInstanceId],D,Ft.bucketIndex,Ft.sourceLayerIndex,K.filter,K.layers,K.availableImages,Y);for(var zt in Ut){var _t=we[zt]=we[zt]||[],Dt=Ut[zt];Dt.sort(function(Br,Di){var ci=Ft.featureSortOrder;if(ci){var bi=ci.indexOf(Br.featureIndex),gn=ci.indexOf(Di.featureIndex);return gn-bi}else 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Fn=519,An=function(D,J,O){this.func=D,this.mask=J,this.range=O};An.ReadOnly=!1,An.ReadWrite=!0,An.disabled=new An(Fn,An.ReadOnly,[0,1]);var Hn=519,Qn=7680,Vi=function(D,J,O,K,de,ne){this.test=D,this.ref=J,this.mask=O,this.fail=K,this.depthFail=de,this.pass=ne};Vi.disabled=new Vi({func:Hn,mask:0},0,0,Qn,Qn,Qn);var Jn=0,Yn=1,Gt=771,wt=function(D,J,O){this.blendFunction=D,this.blendColor=J,this.mask=O};wt.Replace=[Yn,Jn],wt.disabled=new wt(wt.Replace,i.Color.transparent,[!1,!1,!1,!1]),wt.unblended=new wt(wt.Replace,i.Color.transparent,[!0,!0,!0,!0]),wt.alphaBlended=new wt([Yn,Gt],i.Color.transparent,[!0,!0,!0,!0]);var tr=1029,ir=2305,wr=function(D,J,O){this.enable=D,this.mode=J,this.frontFace=O};wr.disabled=new wr(!1,tr,ir),wr.backCCW=new wr(!0,tr,ir);var Xr=function(D){this.gl=D,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new tt(this),this.clearDepth=new je(this),this.clearStencil=new Ue(this),this.colorMask=new Ae(this),this.depthMask=new rt(this),this.stencilMask=new St(this),this.stencilFunc=new Tt(this),this.stencilOp=new dt(this),this.stencilTest=new Et(this),this.depthRange=new pt(this),this.depthTest=new jt(this),this.depthFunc=new or(this),this.blend=new mr(this),this.blendFunc=new Ar(this),this.blendColor=new ei(this),this.blendEquation=new qr(this),this.cullFace=new jr(this),this.cullFaceSide=new gt(this),this.frontFace=new Ge(this),this.program=new Je(this),this.activeTexture=new We(this),this.viewport=new et(this),this.bindFramebuffer=new xt(this),this.bindRenderbuffer=new At(this),this.bindTexture=new Kt(this),this.bindVertexBuffer=new Qt(this),this.bindElementBuffer=new Mr(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new Gr(this),this.pixelStoreUnpack=new Ir(this),this.pixelStoreUnpackPremultiplyAlpha=new Yr(this),this.pixelStoreUnpackFlipY=new _i(this),this.extTextureFilterAnisotropic=D.getExtension(\"EXT_texture_filter_anisotropic\")||D.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||D.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=D.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=D.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(D.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=D.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=D.getExtension(\"EXT_disjoint_timer_query\"),this.maxTextureSize=D.getParameter(D.MAX_TEXTURE_SIZE)};Xr.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Xr.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Xr.prototype.createIndexBuffer=function(D,J){return new ct(this,D,J)},Xr.prototype.createVertexBuffer=function(D,J,O){return new Vt(this,D,J,O)},Xr.prototype.createRenderbuffer=function(D,J,O){var K=this.gl,de=K.createRenderbuffer();return this.bindRenderbuffer.set(de),K.renderbufferStorage(K.RENDERBUFFER,D,J,O),this.bindRenderbuffer.set(null),de},Xr.prototype.createFramebuffer=function(D,J,O){return new un(this,D,J,O)},Xr.prototype.clear=function(D){var J=D.color,O=D.depth,K=this.gl,de=0;J&&(de|=K.COLOR_BUFFER_BIT,this.clearColor.set(J),this.colorMask.set([!0,!0,!0,!0])),typeof O!=\"undefined\"&&(de|=K.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(O),this.depthMask.set(!0)),K.clear(de)},Xr.prototype.setCullFace=function(D){D.enable===!1?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(D.mode),this.frontFace.set(D.frontFace))},Xr.prototype.setDepthMode=function(D){D.func===this.gl.ALWAYS&&!D.mask?this.depthTest.set(!1):(this.depthTest.set(!0),this.depthFunc.set(D.func),this.depthMask.set(D.mask),this.depthRange.set(D.range))},Xr.prototype.setStencilMode=function(D){D.test.func===this.gl.ALWAYS&&!D.mask?this.stencilTest.set(!1):(this.stencilTest.set(!0),this.stencilMask.set(D.mask),this.stencilOp.set([D.fail,D.depthFail,D.pass]),this.stencilFunc.set({func:D.test.func,ref:D.ref,mask:D.test.mask}))},Xr.prototype.setColorMode=function(D){i.deepEqual(D.blendFunction,wt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(D.blendFunction),this.blendColor.set(D.blendColor)),this.colorMask.set(D.mask)},Xr.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var ti=(function(Y){function D(J,O,K){var de=this;Y.call(this),this.id=J,this.dispatcher=K,this.on(\"data\",function(ne){ne.dataType===\"source\"&&ne.sourceDataType===\"metadata\"&&(de._sourceLoaded=!0),de._sourceLoaded&&!de._paused&&ne.dataType===\"source\"&&ne.sourceDataType===\"content\"&&(de.reload(),de.transform&&de.update(de.transform))}),this.on(\"error\",function(){de._sourceErrored=!0}),this._source=be(J,O,K,this),this._tiles={},this._cache=new at(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new i.SourceFeatureState}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.onAdd=function(O){this.map=O,this._maxTileCacheSize=O?O._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(O)},D.prototype.onRemove=function(O){this._source&&this._source.onRemove&&this._source.onRemove(O)},D.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;for(var O in this._tiles){var K=this._tiles[O];if(K.state!==\"loaded\"&&K.state!==\"errored\")return!1}return!0},D.prototype.getSource=function(){return this._source},D.prototype.pause=function(){this._paused=!0},D.prototype.resume=function(){if(this._paused){var O=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,O&&this.reload(),this.transform&&this.update(this.transform)}},D.prototype._loadTile=function(O,K){return this._source.loadTile(O,K)},D.prototype._unloadTile=function(O){if(this._source.unloadTile)return this._source.unloadTile(O,function(){})},D.prototype._abortTile=function(O){if(this._source.abortTile)return this._source.abortTile(O,function(){})},D.prototype.serialize=function(){return this._source.serialize()},D.prototype.prepare=function(O){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(var K in this._tiles){var de=this._tiles[K];de.upload(O),de.prepare(this.map.style.imageManager)}},D.prototype.getIds=function(){return i.values(this._tiles).map(function(O){return O.tileID}).sort($r).map(function(O){return O.key})},D.prototype.getRenderableIds=function(O){var K=this,de=[];for(var ne in this._tiles)this._isIdRenderable(ne,O)&&de.push(this._tiles[ne]);return O?de.sort(function(we,Oe){var ht=we.tileID,Yt=Oe.tileID,cr=new i.Point(ht.canonical.x,ht.canonical.y)._rotate(K.transform.angle),Nt=new i.Point(Yt.canonical.x,Yt.canonical.y)._rotate(K.transform.angle);return ht.overscaledZ-Yt.overscaledZ||Nt.y-cr.y||Nt.x-cr.x}).map(function(we){return we.tileID.key}):de.map(function(we){return we.tileID}).sort($r).map(function(we){return we.key})},D.prototype.hasRenderableParent=function(O){var K=this.findLoadedParent(O,0);return K?this._isIdRenderable(K.tileID.key):!1},D.prototype._isIdRenderable=function(O,K){return this._tiles[O]&&this._tiles[O].hasData()&&!this._coveredTiles[O]&&(K||!this._tiles[O].holdingForFade())},D.prototype.reload=function(){if(this._paused){this._shouldReloadOnResume=!0;return}this._cache.reset();for(var O in this._tiles)this._tiles[O].state!==\"errored\"&&this._reloadTile(O,\"reloading\")},D.prototype._reloadTile=function(O,K){var de=this._tiles[O];de&&(de.state!==\"loading\"&&(de.state=K),this._loadTile(de,this._tileLoaded.bind(this,de,O,K)))},D.prototype._tileLoaded=function(O,K,de,ne){if(ne){O.state=\"errored\",ne.status!==404?this._source.fire(new i.ErrorEvent(ne,{tile:O})):this.update(this.transform);return}O.timeAdded=i.browser.now(),de===\"expired\"&&(O.refreshedUponExpiration=!0),this._setTileReloadTimer(K,O),this.getSource().type===\"raster-dem\"&&O.dem&&this._backfillDEM(O),this._state.initializeTileState(O,this.map?this.map.painter:null),this._source.fire(new i.Event(\"data\",{dataType:\"source\",tile:O,coord:O.tileID}))},D.prototype._backfillDEM=function(O){for(var K=this.getRenderableIds(),de=0;de<K.length;de++){var ne=K[de];if(O.neighboringTiles&&O.neighboringTiles[ne]){var we=this.getTileByID(ne);Oe(O,we),Oe(we,O)}}function Oe(ht,Yt){ht.needsHillshadePrepare=!0;var cr=Yt.tileID.canonical.x-ht.tileID.canonical.x,Nt=Yt.tileID.canonical.y-ht.tileID.canonical.y,Ne=Math.pow(2,ht.tileID.canonical.z),$e=Yt.tileID.key;cr===0&&Nt===0||Math.abs(Nt)>1||(Math.abs(cr)>1&&(Math.abs(cr+Ne)===1?cr+=Ne:Math.abs(cr-Ne)===1&&(cr-=Ne)),!(!Yt.dem||!ht.dem)&&(ht.dem.backfillBorder(Yt.dem,cr,Nt),ht.neighboringTiles&&ht.neighboringTiles[$e]&&(ht.neighboringTiles[$e].backfilled=!0)))}},D.prototype.getTile=function(O){return this.getTileByID(O.key)},D.prototype.getTileByID=function(O){return this._tiles[O]},D.prototype._retainLoadedChildren=function(O,K,de,ne){for(var we in this._tiles){var Oe=this._tiles[we];if(!(ne[we]||!Oe.hasData()||Oe.tileID.overscaledZ<=K||Oe.tileID.overscaledZ>de)){for(var ht=Oe.tileID;Oe&&Oe.tileID.overscaledZ>K+1;){var Yt=Oe.tileID.scaledTo(Oe.tileID.overscaledZ-1);Oe=this._tiles[Yt.key],Oe&&Oe.hasData()&&(ht=Yt)}for(var cr=ht;cr.overscaledZ>K;)if(cr=cr.scaledTo(cr.overscaledZ-1),O[cr.key]){ne[ht.key]=ht;break}}}},D.prototype.findLoadedParent=function(O,K){if(O.key in this._loadedParentTiles){var de=this._loadedParentTiles[O.key];return de&&de.tileID.overscaledZ>=K?de:null}for(var ne=O.overscaledZ-1;ne>=K;ne--){var we=O.scaledTo(ne),Oe=this._getLoadedTile(we);if(Oe)return Oe}},D.prototype._getLoadedTile=function(O){var K=this._tiles[O.key];if(K&&K.hasData())return K;var de=this._cache.getByKey(O.wrapped().key);return de},D.prototype.updateCacheSize=function(O){var K=Math.ceil(O.width/this._source.tileSize)+1,de=Math.ceil(O.height/this._source.tileSize)+1,ne=K*de,we=5,Oe=Math.floor(ne*we),ht=typeof this._maxTileCacheSize==\"number\"?Math.min(this._maxTileCacheSize,Oe):Oe;this._cache.setMaxSize(ht)},D.prototype.handleWrapJump=function(O){var K=this._prevLng===void 0?O:this._prevLng,de=O-K,ne=de/360,we=Math.round(ne);if(this._prevLng=O,we){var Oe={};for(var ht in this._tiles){var Yt=this._tiles[ht];Yt.tileID=Yt.tileID.unwrapTo(Yt.tileID.wrap+we),Oe[Yt.tileID.key]=Yt}this._tiles=Oe;for(var cr in this._timers)clearTimeout(this._timers[cr]),delete this._timers[cr];for(var Nt in this._tiles){var Ne=this._tiles[Nt];this._setTileReloadTimer(Nt,Ne)}}},D.prototype.update=function(O){var K=this;if(this.transform=O,!(!this._sourceLoaded||this._paused)){this.updateCacheSize(O),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={};var de;this.used?this._source.tileID?de=O.getVisibleUnwrappedCoordinates(this._source.tileID).map(function(Br){return new i.OverscaledTileID(Br.canonical.z,Br.wrap,Br.canonical.z,Br.canonical.x,Br.canonical.y)}):(de=O.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(de=de.filter(function(Br){return K._source.hasTile(Br)}))):de=[];var ne=O.coveringZoomLevel(this._source),we=Math.max(ne-D.maxOverzooming,this._source.minzoom),Oe=Math.max(ne+D.maxUnderzooming,this._source.minzoom),ht=this._updateRetainedTiles(de,ne);if(Ri(this._source.type)){for(var Yt={},cr={},Nt=Object.keys(ht),Ne=0,$e=Nt;Ne<$e.length;Ne+=1){var st=$e[Ne],It=ht[st],Bt=this._tiles[st];if(!(!Bt||Bt.fadeEndTime&&Bt.fadeEndTime<=i.browser.now())){var Ft=this.findLoadedParent(It,we);Ft&&(this._addTile(Ft.tileID),Yt[Ft.tileID.key]=Ft.tileID),cr[st]=It}}this._retainLoadedChildren(cr,ne,Oe,ht);for(var Ut in Yt)ht[Ut]||(this._coveredTiles[Ut]=!0,ht[Ut]=Yt[Ut])}for(var zt in ht)this._tiles[zt].clearFadeHold();for(var _t=i.keysDifference(this._tiles,ht),Dt=0,vt=_t;Dt<vt.length;Dt+=1){var lr=vt[Dt],_r=this._tiles[lr];_r.hasSymbolBuckets&&!_r.holdingForFade()?_r.setHoldDuration(this.map._fadeDuration):(!_r.hasSymbolBuckets||_r.symbolFadeFinished())&&this._removeTile(lr)}this._updateLoadedParentTileCache()}},D.prototype.releaseSymbolFadeTiles=function(){for(var O in this._tiles)this._tiles[O].holdingForFade()&&this._removeTile(O)},D.prototype._updateRetainedTiles=function(O,K){for(var de={},ne={},we=Math.max(K-D.maxOverzooming,this._source.minzoom),Oe=Math.max(K+D.maxUnderzooming,this._source.minzoom),ht={},Yt=0,cr=O;Yt<cr.length;Yt+=1){var Nt=cr[Yt],Ne=this._addTile(Nt);de[Nt.key]=Nt,!Ne.hasData()&&K<this._source.maxzoom&&(ht[Nt.key]=Nt)}this._retainLoadedChildren(ht,K,Oe,de);for(var $e=0,st=O;$e<st.length;$e+=1){var It=st[$e],Bt=this._tiles[It.key];if(!Bt.hasData()){if(K+1>this._source.maxzoom){var Ft=It.children(this._source.maxzoom)[0],Ut=this.getTile(Ft);if(Ut&&Ut.hasData()){de[Ft.key]=Ft;continue}}else{var zt=It.children(this._source.maxzoom);if(de[zt[0].key]&&de[zt[1].key]&&de[zt[2].key]&&de[zt[3].key])continue}for(var _t=Bt.wasRequested(),Dt=It.overscaledZ-1;Dt>=we;--Dt){var vt=It.scaledTo(Dt);if(ne[vt.key]||(ne[vt.key]=!0,Bt=this.getTile(vt),!Bt&&_t&&(Bt=this._addTile(vt)),Bt&&(de[vt.key]=vt,_t=Bt.wasRequested(),Bt.hasData())))break}}}return de},D.prototype._updateLoadedParentTileCache=function(){this._loadedParentTiles={};for(var O in this._tiles){for(var K=[],de=void 0,ne=this._tiles[O].tileID;ne.overscaledZ>0;){if(ne.key in this._loadedParentTiles){de=this._loadedParentTiles[ne.key];break}K.push(ne.key);var we=ne.scaledTo(ne.overscaledZ-1);if(de=this._getLoadedTile(we),de)break;ne=we}for(var Oe=0,ht=K;Oe<ht.length;Oe+=1){var Yt=ht[Oe];this._loadedParentTiles[Yt]=de}}},D.prototype._addTile=function(O){var K=this._tiles[O.key];if(K)return K;K=this._cache.getAndRemove(O),K&&(this._setTileReloadTimer(O.key,K),K.tileID=O,this._state.initializeTileState(K,this.map?this.map.painter:null),this._cacheTimers[O.key]&&(clearTimeout(this._cacheTimers[O.key]),delete this._cacheTimers[O.key],this._setTileReloadTimer(O.key,K)));var de=!!K;return de||(K=new i.Tile(O,this._source.tileSize*O.overscaleFactor()),this._loadTile(K,this._tileLoaded.bind(this,K,O.key,K.state))),K?(K.uses++,this._tiles[O.key]=K,de||this._source.fire(new i.Event(\"dataloading\",{tile:K,coord:K.tileID,dataType:\"source\"})),K):null},D.prototype._setTileReloadTimer=function(O,K){var de=this;O in this._timers&&(clearTimeout(this._timers[O]),delete this._timers[O]);var ne=K.getExpiryTimeout();ne&&(this._timers[O]=setTimeout(function(){de._reloadTile(O,\"expired\"),delete de._timers[O]},ne))},D.prototype._removeTile=function(O){var K=this._tiles[O];K&&(K.uses--,delete this._tiles[O],this._timers[O]&&(clearTimeout(this._timers[O]),delete this._timers[O]),!(K.uses>0)&&(K.hasData()&&K.state!==\"reloading\"?this._cache.add(K.tileID,K,K.getExpiryTimeout()):(K.aborted=!0,this._abortTile(K),this._unloadTile(K))))},D.prototype.clearTiles=function(){this._shouldReloadOnResume=!1,this._paused=!1;for(var O in this._tiles)this._removeTile(O);this._cache.reset()},D.prototype.tilesIn=function(O,K,de){var ne=this,we=[],Oe=this.transform;if(!Oe)return we;for(var ht=de?Oe.getCameraQueryGeometry(O):O,Yt=O.map(function(Dt){return Oe.pointCoordinate(Dt)}),cr=ht.map(function(Dt){return Oe.pointCoordinate(Dt)}),Nt=this.getIds(),Ne=1/0,$e=1/0,st=-1/0,It=-1/0,Bt=0,Ft=cr;Bt<Ft.length;Bt+=1){var Ut=Ft[Bt];Ne=Math.min(Ne,Ut.x),$e=Math.min($e,Ut.y),st=Math.max(st,Ut.x),It=Math.max(It,Ut.y)}for(var zt=function(Dt){var vt=ne._tiles[Nt[Dt]];if(!vt.holdingForFade()){var lr=vt.tileID,_r=Math.pow(2,Oe.zoom-vt.tileID.overscaledZ),Br=K*vt.queryPadding*i.EXTENT/vt.tileSize/_r,Di=[lr.getTilePoint(new i.MercatorCoordinate(Ne,$e)),lr.getTilePoint(new i.MercatorCoordinate(st,It))];if(Di[0].x-Br<i.EXTENT&&Di[0].y-Br<i.EXTENT&&Di[1].x+Br>=0&&Di[1].y+Br>=0){var ci=Yt.map(function(gn){return lr.getTilePoint(gn)}),bi=cr.map(function(gn){return lr.getTilePoint(gn)});we.push({tile:vt,tileID:lr,queryGeometry:ci,cameraQueryGeometry:bi,scale:_r})}}},_t=0;_t<Nt.length;_t++)zt(_t);return we},D.prototype.getVisibleCoordinates=function(O){for(var K=this,de=this.getRenderableIds(O).map(function(ht){return K._tiles[ht].tileID}),ne=0,we=de;ne<we.length;ne+=1){var Oe=we[ne];Oe.posMatrix=this.transform.calculatePosMatrix(Oe.toUnwrapped())}return de},D.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Ri(this._source.type))for(var O in this._tiles){var K=this._tiles[O];if(K.fadeEndTime!==void 0&&K.fadeEndTime>=i.browser.now())return!0}return!1},D.prototype.setFeatureState=function(O,K,de){O=O||\"_geojsonTileLayer\",this._state.updateState(O,K,de)},D.prototype.removeFeatureState=function(O,K,de){O=O||\"_geojsonTileLayer\",this._state.removeFeatureState(O,K,de)},D.prototype.getFeatureState=function(O,K){return O=O||\"_geojsonTileLayer\",this._state.getState(O,K)},D.prototype.setDependencies=function(O,K,de){var ne=this._tiles[O];ne&&ne.setDependencies(K,de)},D.prototype.reloadTilesForDependencies=function(O,K){for(var de in this._tiles){var ne=this._tiles[de];ne.hasDependency(O,K)&&this._reloadTile(de,\"reloading\")}this._cache.filter(function(we){return!we.hasDependency(O,K)})},D})(i.Evented);ti.maxOverzooming=10,ti.maxUnderzooming=3;function $r(Y,D){var J=Math.abs(Y.wrap*2)-+(Y.wrap<0),O=Math.abs(D.wrap*2)-+(D.wrap<0);return Y.overscaledZ-D.overscaledZ||O-J||D.canonical.y-Y.canonical.y||D.canonical.x-Y.canonical.x}function Ri(Y){return Y===\"raster\"||Y===\"image\"||Y===\"video\"}function Hi(){return new i.window.Worker(io.workerUrl)}var en=\"mapboxgl_preloaded_worker_pool\",fn=function(){this.active={}};fn.prototype.acquire=function(D){if(!this.workers)for(this.workers=[];this.workers.length<fn.workerCount;)this.workers.push(new Hi);return this.active[D]=!0,this.workers.slice()},fn.prototype.release=function(D){delete this.active[D],this.numActive()===0&&(this.workers.forEach(function(J){J.terminate()}),this.workers=null)},fn.prototype.isPreloaded=function(){return!!this.active[en]},fn.prototype.numActive=function(){return Object.keys(this.active).length};var yn=Math.floor(i.browser.hardwareConcurrency/2);fn.workerCount=Math.max(Math.min(yn,6),1);var Mn;function Ba(){return Mn||(Mn=new fn),Mn}function ua(){var Y=Ba();Y.acquire(en)}function ma(){var Y=Mn;Y&&(Y.isPreloaded()&&Y.numActive()===1?(Y.release(en),Mn=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. 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da={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function jn(Y,D,J){J.push({command:da.addSource,args:[Y,D[Y]]})}function Ha(Y,D,J){D.push({command:da.removeSource,args:[Y]}),J[Y]=!0}function vo(Y,D,J,O){Ha(Y,J,O),jn(Y,D,J)}function Gn(Y,D,J){var O;for(O in Y[J])if(Y[J].hasOwnProperty(O)&&O!==\"data\"&&!i.deepEqual(Y[J][O],D[J][O]))return!1;for(O in D[J])if(D[J].hasOwnProperty(O)&&O!==\"data\"&&!i.deepEqual(Y[J][O],D[J][O]))return!1;return!0}function Ct(Y,D,J,O){Y=Y||{},D=D||{};var K;for(K in Y)Y.hasOwnProperty(K)&&(D.hasOwnProperty(K)||Ha(K,J,O));for(K in D)D.hasOwnProperty(K)&&(Y.hasOwnProperty(K)?i.deepEqual(Y[K],D[K])||(Y[K].type===\"geojson\"&&D[K].type===\"geojson\"&&Gn(Y,D,K)?J.push({command:da.setGeoJSONSourceData,args:[K,D[K].data]}):vo(K,D,J,O)):jn(K,D,J))}function Sr(Y,D,J,O,K,de){Y=Y||{},D=D||{};var ne;for(ne in Y)Y.hasOwnProperty(ne)&&(i.deepEqual(Y[ne],D[ne])||J.push({command:de,args:[O,ne,D[ne],K]}));for(ne in D)!D.hasOwnProperty(ne)||Y.hasOwnProperty(ne)||i.deepEqual(Y[ne],D[ne])||J.push({command:de,args:[O,ne,D[ne],K]})}function Jr(Y){return Y.id}function hi(Y,D){return Y[D.id]=D,Y}function hn(Y,D,J){Y=Y||[],D=D||[];var O=Y.map(Jr),K=D.map(Jr),de=Y.reduce(hi,{}),ne=D.reduce(hi,{}),we=O.slice(),Oe=Object.create(null),ht,Yt,cr,Nt,Ne,$e,st;for(ht=0,Yt=0;ht<O.length;ht++)cr=O[ht],ne.hasOwnProperty(cr)?Yt++:(J.push({command:da.removeLayer,args:[cr]}),we.splice(we.indexOf(cr,Yt),1));for(ht=0,Yt=0;ht<K.length;ht++)cr=K[K.length-1-ht],we[we.length-1-ht]!==cr&&(de.hasOwnProperty(cr)?(J.push({command:da.removeLayer,args:[cr]}),we.splice(we.lastIndexOf(cr,we.length-Yt),1)):Yt++,$e=we[we.length-ht],J.push({command:da.addLayer,args:[ne[cr],$e]}),we.splice(we.length-ht,0,cr),Oe[cr]=!0);for(ht=0;ht<K.length;ht++)if(cr=K[ht],Nt=de[cr],Ne=ne[cr],!(Oe[cr]||i.deepEqual(Nt,Ne))){if(!i.deepEqual(Nt.source,Ne.source)||!i.deepEqual(Nt[\"source-layer\"],Ne[\"source-layer\"])||!i.deepEqual(Nt.type,Ne.type)){J.push({command:da.removeLayer,args:[cr]}),$e=we[we.lastIndexOf(cr)+1],J.push({command:da.addLayer,args:[Ne,$e]});continue}Sr(Nt.layout,Ne.layout,J,cr,null,da.setLayoutProperty),Sr(Nt.paint,Ne.paint,J,cr,null,da.setPaintProperty),i.deepEqual(Nt.filter,Ne.filter)||J.push({command:da.setFilter,args:[cr,Ne.filter]}),(!i.deepEqual(Nt.minzoom,Ne.minzoom)||!i.deepEqual(Nt.maxzoom,Ne.maxzoom))&&J.push({command:da.setLayerZoomRange,args:[cr,Ne.minzoom,Ne.maxzoom]});for(st in Nt)Nt.hasOwnProperty(st)&&(st===\"layout\"||st===\"paint\"||st===\"filter\"||st===\"metadata\"||st===\"minzoom\"||st===\"maxzoom\"||(st.indexOf(\"paint.\")===0?Sr(Nt[st],Ne[st],J,cr,st.slice(6),da.setPaintProperty):i.deepEqual(Nt[st],Ne[st])||J.push({command:da.setLayerProperty,args:[cr,st,Ne[st]]})));for(st in Ne)!Ne.hasOwnProperty(st)||Nt.hasOwnProperty(st)||st===\"layout\"||st===\"paint\"||st===\"filter\"||st===\"metadata\"||st===\"minzoom\"||st===\"maxzoom\"||(st.indexOf(\"paint.\")===0?Sr(Nt[st],Ne[st],J,cr,st.slice(6),da.setPaintProperty):i.deepEqual(Nt[st],Ne[st])||J.push({command:da.setLayerProperty,args:[cr,st,Ne[st]]}))}}function Sn(Y,D){if(!Y)return[{command:da.setStyle,args:[D]}];var J=[];try{if(!i.deepEqual(Y.version,D.version))return[{command:da.setStyle,args:[D]}];i.deepEqual(Y.center,D.center)||J.push({command:da.setCenter,args:[D.center]}),i.deepEqual(Y.zoom,D.zoom)||J.push({command:da.setZoom,args:[D.zoom]}),i.deepEqual(Y.bearing,D.bearing)||J.push({command:da.setBearing,args:[D.bearing]}),i.deepEqual(Y.pitch,D.pitch)||J.push({command:da.setPitch,args:[D.pitch]}),i.deepEqual(Y.sprite,D.sprite)||J.push({command:da.setSprite,args:[D.sprite]}),i.deepEqual(Y.glyphs,D.glyphs)||J.push({command:da.setGlyphs,args:[D.glyphs]}),i.deepEqual(Y.transition,D.transition)||J.push({command:da.setTransition,args:[D.transition]}),i.deepEqual(Y.light,D.light)||J.push({command:da.setLight,args:[D.light]});var O={},K=[];Ct(Y.sources,D.sources,K,O);var de=[];Y.layers&&Y.layers.forEach(function(ne){O[ne.source]?J.push({command:da.removeLayer,args:[ne.id]}):de.push(ne)}),J=J.concat(K),hn(de,D.layers,J)}catch(ne){console.warn(\"Unable to compute style diff:\",ne),J=[{command:da.setStyle,args:[D]}]}return J}var En=function(D,J){this.reset(D,J)};En.prototype.reset=function(D,J){this.points=D||[],this._distances=[0];for(var O=1;O<this.points.length;O++)this._distances[O]=this._distances[O-1]+this.points[O].dist(this.points[O-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(J||0,this.length*.5),this.paddedLength=this.length-this.padding*2},En.prototype.lerp=function(D){if(this.points.length===1)return this.points[0];D=i.clamp(D,0,1);for(var J=1,O=this._distances[J],K=D*this.paddedLength+this.padding;O<K&&J<this._distances.length;)O=this._distances[++J];var de=J-1,ne=this._distances[de],we=O-ne,Oe=we>0?(K-ne)/we:0;return this.points[de].mult(1-Oe).add(this.points[J].mult(Oe))};var ki=function(D,J,O){var K=this.boxCells=[],de=this.circleCells=[];this.xCellCount=Math.ceil(D/O),this.yCellCount=Math.ceil(J/O);for(var ne=0;ne<this.xCellCount*this.yCellCount;ne++)K.push([]),de.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=D,this.height=J,this.xScale=this.xCellCount/D,this.yScale=this.yCellCount/J,this.boxUid=0,this.circleUid=0};ki.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},ki.prototype.insert=function(D,J,O,K,de){this._forEachCell(J,O,K,de,this._insertBoxCell,this.boxUid++),this.boxKeys.push(D),this.bboxes.push(J),this.bboxes.push(O),this.bboxes.push(K),this.bboxes.push(de)},ki.prototype.insertCircle=function(D,J,O,K){this._forEachCell(J-K,O-K,J+K,O+K,this._insertCircleCell,this.circleUid++),this.circleKeys.push(D),this.circles.push(J),this.circles.push(O),this.circles.push(K)},ki.prototype._insertBoxCell=function(D,J,O,K,de,ne){this.boxCells[de].push(ne)},ki.prototype._insertCircleCell=function(D,J,O,K,de,ne){this.circleCells[de].push(ne)},ki.prototype._query=function(D,J,O,K,de,ne){if(O<0||D>this.width||K<0||J>this.height)return de?!1:[];var we=[];if(D<=0&&J<=0&&this.width<=O&&this.height<=K){if(de)return!0;for(var Oe=0;Oe<this.boxKeys.length;Oe++)we.push({key:this.boxKeys[Oe],x1:this.bboxes[Oe*4],y1:this.bboxes[Oe*4+1],x2:this.bboxes[Oe*4+2],y2:this.bboxes[Oe*4+3]});for(var ht=0;ht<this.circleKeys.length;ht++){var Yt=this.circles[ht*3],cr=this.circles[ht*3+1],Nt=this.circles[ht*3+2];we.push({key:this.circleKeys[ht],x1:Yt-Nt,y1:cr-Nt,x2:Yt+Nt,y2:cr+Nt})}return ne?we.filter(ne):we}else{var Ne={hitTest:de,seenUids:{box:{},circle:{}}};return this._forEachCell(D,J,O,K,this._queryCell,we,Ne,ne),de?we.length>0:we}},ki.prototype._queryCircle=function(D,J,O,K,de){var ne=D-O,we=D+O,Oe=J-O,ht=J+O;if(we<0||ne>this.width||ht<0||Oe>this.height)return K?!1:[];var Yt=[],cr={hitTest:K,circle:{x:D,y:J,radius:O},seenUids:{box:{},circle:{}}};return this._forEachCell(ne,Oe,we,ht,this._queryCellCircle,Yt,cr,de),K?Yt.length>0:Yt},ki.prototype.query=function(D,J,O,K,de){return this._query(D,J,O,K,!1,de)},ki.prototype.hitTest=function(D,J,O,K,de){return this._query(D,J,O,K,!0,de)},ki.prototype.hitTestCircle=function(D,J,O,K){return this._queryCircle(D,J,O,!0,K)},ki.prototype._queryCell=function(D,J,O,K,de,ne,we,Oe){var ht=we.seenUids,Yt=this.boxCells[de];if(Yt!==null)for(var cr=this.bboxes,Nt=0,Ne=Yt;Nt<Ne.length;Nt+=1){var $e=Ne[Nt];if(!ht.box[$e]){ht.box[$e]=!0;var st=$e*4;if(D<=cr[st+2]&&J<=cr[st+3]&&O>=cr[st+0]&&K>=cr[st+1]&&(!Oe||Oe(this.boxKeys[$e]))){if(we.hitTest)return ne.push(!0),!0;ne.push({key:this.boxKeys[$e],x1:cr[st],y1:cr[st+1],x2:cr[st+2],y2:cr[st+3]})}}}var It=this.circleCells[de];if(It!==null)for(var Bt=this.circles,Ft=0,Ut=It;Ft<Ut.length;Ft+=1){var zt=Ut[Ft];if(!ht.circle[zt]){ht.circle[zt]=!0;var _t=zt*3;if(this._circleAndRectCollide(Bt[_t],Bt[_t+1],Bt[_t+2],D,J,O,K)&&(!Oe||Oe(this.circleKeys[zt]))){if(we.hitTest)return ne.push(!0),!0;var Dt=Bt[_t],vt=Bt[_t+1],lr=Bt[_t+2];ne.push({key:this.circleKeys[zt],x1:Dt-lr,y1:vt-lr,x2:Dt+lr,y2:vt+lr})}}}},ki.prototype._queryCellCircle=function(D,J,O,K,de,ne,we,Oe){var ht=we.circle,Yt=we.seenUids,cr=this.boxCells[de];if(cr!==null)for(var Nt=this.bboxes,Ne=0,$e=cr;Ne<$e.length;Ne+=1){var st=$e[Ne];if(!Yt.box[st]){Yt.box[st]=!0;var It=st*4;if(this._circleAndRectCollide(ht.x,ht.y,ht.radius,Nt[It+0],Nt[It+1],Nt[It+2],Nt[It+3])&&(!Oe||Oe(this.boxKeys[st])))return ne.push(!0),!0}}var Bt=this.circleCells[de];if(Bt!==null)for(var Ft=this.circles,Ut=0,zt=Bt;Ut<zt.length;Ut+=1){var _t=zt[Ut];if(!Yt.circle[_t]){Yt.circle[_t]=!0;var Dt=_t*3;if(this._circlesCollide(Ft[Dt],Ft[Dt+1],Ft[Dt+2],ht.x,ht.y,ht.radius)&&(!Oe||Oe(this.circleKeys[_t])))return ne.push(!0),!0}}},ki.prototype._forEachCell=function(D,J,O,K,de,ne,we,Oe){for(var ht=this._convertToXCellCoord(D),Yt=this._convertToYCellCoord(J),cr=this._convertToXCellCoord(O),Nt=this._convertToYCellCoord(K),Ne=ht;Ne<=cr;Ne++)for(var $e=Yt;$e<=Nt;$e++){var st=this.xCellCount*$e+Ne;if(de.call(this,D,J,O,K,st,ne,we,Oe))return}},ki.prototype._convertToXCellCoord=function(D){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(D*this.xScale)))},ki.prototype._convertToYCellCoord=function(D){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(D*this.yScale)))},ki.prototype._circlesCollide=function(D,J,O,K,de,ne){var we=K-D,Oe=de-J,ht=O+ne;return ht*ht>we*we+Oe*Oe},ki.prototype._circleAndRectCollide=function(D,J,O,K,de,ne,we){var Oe=(ne-K)/2,ht=Math.abs(D-(K+Oe));if(ht>Oe+O)return!1;var Yt=(we-de)/2,cr=Math.abs(J-(de+Yt));if(cr>Yt+O)return!1;if(ht<=Oe||cr<=Yt)return!0;var Nt=ht-Oe,Ne=cr-Yt;return Nt*Nt+Ne*Ne<=O*O};function _n(Y,D,J,O,K){var de=i.create();return D?(i.scale(de,de,[1/K,1/K,1]),J||i.rotateZ(de,de,O.angle)):i.multiply(de,O.labelPlaneMatrix,Y),de}function ya(Y,D,J,O,K){if(D){var de=i.clone(Y);return i.scale(de,de,[K,K,1]),J||i.rotateZ(de,de,-O.angle),de}else return O.glCoordMatrix}function ea(Y,D){var J=[Y.x,Y.y,0,1];Fl(J,J,D);var O=J[3];return{point:new i.Point(J[0]/O,J[1]/O),signedDistanceFromCamera:O}}function Ma(Y,D){return .5+.5*(Y/D)}function _o(Y,D){var J=Y[0]/Y[3],O=Y[1]/Y[3],K=J>=-D[0]&&J<=D[0]&&O>=-D[1]&&O<=D[1];return K}function No(Y,D,J,O,K,de,ne,we){var Oe=O?Y.textSizeData:Y.iconSizeData,ht=i.evaluateSizeForZoom(Oe,J.transform.zoom),Yt=[256/J.width*2+1,256/J.height*2+1],cr=O?Y.text.dynamicLayoutVertexArray:Y.icon.dynamicLayoutVertexArray;cr.clear();for(var Nt=Y.lineVertexArray,Ne=O?Y.text.placedSymbolArray:Y.icon.placedSymbolArray,$e=J.transform.width/J.transform.height,st=!1,It=0;It<Ne.length;It++){var Bt=Ne.get(It);if(Bt.hidden||Bt.writingMode===i.WritingMode.vertical&&!st){ul(Bt.numGlyphs,cr);continue}st=!1;var Ft=[Bt.anchorX,Bt.anchorY,0,1];if(i.transformMat4(Ft,Ft,D),!_o(Ft,Yt)){ul(Bt.numGlyphs,cr);continue}var Ut=Ft[3],zt=Ma(J.transform.cameraToCenterDistance,Ut),_t=i.evaluateSizeForFeature(Oe,ht,Bt),Dt=ne?_t/zt:_t*zt,vt=new i.Point(Bt.anchorX,Bt.anchorY),lr=ea(vt,K).point,_r={},Br=ko(Bt,Dt,!1,we,D,K,de,Y.glyphOffsetArray,Nt,cr,lr,vt,_r,$e);st=Br.useVertical,(Br.notEnoughRoom||st||Br.needsFlipping&&ko(Bt,Dt,!0,we,D,K,de,Y.glyphOffsetArray,Nt,cr,lr,vt,_r,$e).notEnoughRoom)&&ul(Bt.numGlyphs,cr)}O?Y.text.dynamicLayoutVertexBuffer.updateData(cr):Y.icon.dynamicLayoutVertexBuffer.updateData(cr)}function po(Y,D,J,O,K,de,ne,we,Oe,ht,Yt){var cr=we.glyphStartIndex+we.numGlyphs,Nt=we.lineStartIndex,Ne=we.lineStartIndex+we.lineLength,$e=D.getoffsetX(we.glyphStartIndex),st=D.getoffsetX(cr-1),It=Fs(Y*$e,J,O,K,de,ne,we.segment,Nt,Ne,Oe,ht,Yt);if(!It)return null;var Bt=Fs(Y*st,J,O,K,de,ne,we.segment,Nt,Ne,Oe,ht,Yt);return Bt?{first:It,last:Bt}:null}function Lo(Y,D,J,O){if(Y===i.WritingMode.horizontal){var K=Math.abs(J.y-D.y),de=Math.abs(J.x-D.x)*O;if(K>de)return{useVertical:!0}}return(Y===i.WritingMode.vertical?D.y<J.y:D.x>J.x)?{needsFlipping:!0}:null}function ko(Y,D,J,O,K,de,ne,we,Oe,ht,Yt,cr,Nt,Ne){var $e=D/24,st=Y.lineOffsetX*$e,It=Y.lineOffsetY*$e,Bt;if(Y.numGlyphs>1){var Ft=Y.glyphStartIndex+Y.numGlyphs,Ut=Y.lineStartIndex,zt=Y.lineStartIndex+Y.lineLength,_t=po($e,we,st,It,J,Yt,cr,Y,Oe,de,Nt);if(!_t)return{notEnoughRoom:!0};var Dt=ea(_t.first.point,ne).point,vt=ea(_t.last.point,ne).point;if(O&&!J){var lr=Lo(Y.writingMode,Dt,vt,Ne);if(lr)return lr}Bt=[_t.first];for(var _r=Y.glyphStartIndex+1;_r<Ft-1;_r++)Bt.push(Fs($e*we.getoffsetX(_r),st,It,J,Yt,cr,Y.segment,Ut,zt,Oe,de,Nt));Bt.push(_t.last)}else{if(O&&!J){var Br=ea(cr,K).point,Di=Y.lineStartIndex+Y.segment+1,ci=new i.Point(Oe.getx(Di),Oe.gety(Di)),bi=ea(ci,K),gn=bi.signedDistanceFromCamera>0?bi.point:Ds(cr,ci,Br,1,K),tn=Lo(Y.writingMode,Br,gn,Ne);if(tn)return tn}var Ei=Fs($e*we.getoffsetX(Y.glyphStartIndex),st,It,J,Yt,cr,Y.segment,Y.lineStartIndex,Y.lineStartIndex+Y.lineLength,Oe,de,Nt);if(!Ei)return{notEnoughRoom:!0};Bt=[Ei]}for(var Oi=0,Gi=Bt;Oi<Gi.length;Oi+=1){var on=Gi[Oi];i.addDynamicAttributes(ht,on.point,on.angle)}return{}}function Ds(Y,D,J,O,K){var de=ea(Y.add(Y.sub(D)._unit()),K).point,ne=J.sub(de);return J.add(ne._mult(O/ne.mag()))}function Fs(Y,D,J,O,K,de,ne,we,Oe,ht,Yt,cr){var Nt=O?Y-D:Y+D,Ne=Nt>0?1:-1,$e=0;O&&(Ne*=-1,$e=Math.PI),Ne<0&&($e+=Math.PI);for(var st=Ne>0?we+ne:we+ne+1,It=K,Bt=K,Ft=0,Ut=0,zt=Math.abs(Nt),_t=[];Ft+Ut<=zt;){if(st+=Ne,st<we||st>=Oe)return null;if(Bt=It,_t.push(It),It=cr[st],It===void 0){var Dt=new i.Point(ht.getx(st),ht.gety(st)),vt=ea(Dt,Yt);if(vt.signedDistanceFromCamera>0)It=cr[st]=vt.point;else{var lr=st-Ne,_r=Ft===0?de:new i.Point(ht.getx(lr),ht.gety(lr));It=Ds(_r,Dt,Bt,zt-Ft+1,Yt)}}Ft+=Ut,Ut=Bt.dist(It)}var Br=(zt-Ft)/Ut,Di=It.sub(Bt),ci=Di.mult(Br)._add(Bt);ci._add(Di._unit()._perp()._mult(J*Ne));var bi=$e+Math.atan2(It.y-Bt.y,It.x-Bt.x);return _t.push(ci),{point:ci,angle:bi,path:_t}}var ll=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ul(Y,D){for(var J=0;J<Y;J++){var O=D.length;D.resize(O+4),D.float32.set(ll,O*3)}}function Fl(Y,D,J){var O=D[0],K=D[1];return Y[0]=J[0]*O+J[4]*K+J[12],Y[1]=J[1]*O+J[5]*K+J[13],Y[3]=J[3]*O+J[7]*K+J[15],Y}var us=100,rl=function(D,J,O){J===void 0&&(J=new ki(D.width+2*us,D.height+2*us,25)),O===void 0&&(O=new ki(D.width+2*us,D.height+2*us,25)),this.transform=D,this.grid=J,this.ignoredGrid=O,this.pitchfactor=Math.cos(D._pitch)*D.cameraToCenterDistance,this.screenRightBoundary=D.width+us,this.screenBottomBoundary=D.height+us,this.gridRightBoundary=D.width+2*us,this.gridBottomBoundary=D.height+2*us};rl.prototype.placeCollisionBox=function(D,J,O,K,de){var ne=this.projectAndGetPerspectiveRatio(K,D.anchorPointX,D.anchorPointY),we=O*ne.perspectiveRatio,Oe=D.x1*we+ne.point.x,ht=D.y1*we+ne.point.y,Yt=D.x2*we+ne.point.x,cr=D.y2*we+ne.point.y;return!this.isInsideGrid(Oe,ht,Yt,cr)||!J&&this.grid.hitTest(Oe,ht,Yt,cr,de)?{box:[],offscreen:!1}:{box:[Oe,ht,Yt,cr],offscreen:this.isOffscreen(Oe,ht,Yt,cr)}},rl.prototype.placeCollisionCircles=function(D,J,O,K,de,ne,we,Oe,ht,Yt,cr,Nt,Ne){var $e=[],st=new i.Point(J.anchorX,J.anchorY),It=ea(st,ne),Bt=Ma(this.transform.cameraToCenterDistance,It.signedDistanceFromCamera),Ft=Yt?de/Bt:de*Bt,Ut=Ft/i.ONE_EM,zt=ea(st,we).point,_t={},Dt=J.lineOffsetX*Ut,vt=J.lineOffsetY*Ut,lr=po(Ut,K,Dt,vt,!1,zt,st,J,O,we,_t),_r=!1,Br=!1,Di=!0;if(lr){for(var ci=Nt*.5*Bt+Ne,bi=new i.Point(-us,-us),gn=new i.Point(this.screenRightBoundary,this.screenBottomBoundary),tn=new En,Ei=lr.first,Oi=lr.last,Gi=[],on=Ei.path.length-1;on>=1;on--)Gi.push(Ei.path[on]);for(var zn=1;zn<Oi.path.length;zn++)Gi.push(Oi.path[zn]);var Ja=ci*2.5;if(Oe){var co=Gi.map(function(Iv){return ea(Iv,Oe)});co.some(function(Iv){return Iv.signedDistanceFromCamera<=0})?Gi=[]:Gi=co.map(function(Iv){return Iv.point})}var ts=[];if(Gi.length>0){for(var so=Gi[0].clone(),Yo=Gi[0].clone(),ms=1;ms<Gi.length;ms++)so.x=Math.min(so.x,Gi[ms].x),so.y=Math.min(so.y,Gi[ms].y),Yo.x=Math.max(Yo.x,Gi[ms].x),Yo.y=Math.max(Yo.y,Gi[ms].y);so.x>=bi.x&&Yo.x<=gn.x&&so.y>=bi.y&&Yo.y<=gn.y?ts=[Gi]:Yo.x<bi.x||so.x>gn.x||Yo.y<bi.y||so.y>gn.y?ts=[]:ts=i.clipLine([Gi],bi.x,bi.y,gn.x,gn.y)}for(var ou=0,Cv=ts;ou<Cv.length;ou+=1){var Lv=Cv[ou];tn.reset(Lv,ci*.25);var wd=0;tn.length<=.5*ci?wd=1:wd=Math.ceil(tn.paddedLength/Ja)+1;for(var Kv=0;Kv<wd;Kv++){var hg=Kv/Math.max(wd-1,1),gp=tn.lerp(hg),Td=gp.x+us,mp=gp.y+us;$e.push(Td,mp,ci,0);var Vd=Td-ci,Ad=mp-ci,Pv=Td+ci,Jv=mp+ci;if(Di=Di&&this.isOffscreen(Vd,Ad,Pv,Jv),Br=Br||this.isInsideGrid(Vd,Ad,Pv,Jv),!D&&this.grid.hitTestCircle(Td,mp,ci,cr)&&(_r=!0,!ht))return{circles:[],offscreen:!1,collisionDetected:_r}}}}return{circles:!ht&&_r||!Br?[]:$e,offscreen:Di,collisionDetected:_r}},rl.prototype.queryRenderedSymbols=function(D){if(D.length===0||this.grid.keysLength()===0&&this.ignoredGrid.keysLength()===0)return{};for(var J=[],O=1/0,K=1/0,de=-1/0,ne=-1/0,we=0,Oe=D;we<Oe.length;we+=1){var ht=Oe[we],Yt=new i.Point(ht.x+us,ht.y+us);O=Math.min(O,Yt.x),K=Math.min(K,Yt.y),de=Math.max(de,Yt.x),ne=Math.max(ne,Yt.y),J.push(Yt)}for(var cr=this.grid.query(O,K,de,ne).concat(this.ignoredGrid.query(O,K,de,ne)),Nt={},Ne={},$e=0,st=cr;$e<st.length;$e+=1){var It=st[$e],Bt=It.key;if(Nt[Bt.bucketInstanceId]===void 0&&(Nt[Bt.bucketInstanceId]={}),!Nt[Bt.bucketInstanceId][Bt.featureIndex]){var Ft=[new i.Point(It.x1,It.y1),new i.Point(It.x2,It.y1),new i.Point(It.x2,It.y2),new i.Point(It.x1,It.y2)];i.polygonIntersectsPolygon(J,Ft)&&(Nt[Bt.bucketInstanceId][Bt.featureIndex]=!0,Ne[Bt.bucketInstanceId]===void 0&&(Ne[Bt.bucketInstanceId]=[]),Ne[Bt.bucketInstanceId].push(Bt.featureIndex))}}return Ne},rl.prototype.insertCollisionBox=function(D,J,O,K,de){var ne=J?this.ignoredGrid:this.grid,we={bucketInstanceId:O,featureIndex:K,collisionGroupID:de};ne.insert(we,D[0],D[1],D[2],D[3])},rl.prototype.insertCollisionCircles=function(D,J,O,K,de){for(var ne=J?this.ignoredGrid:this.grid,we={bucketInstanceId:O,featureIndex:K,collisionGroupID:de},Oe=0;Oe<D.length;Oe+=4)ne.insertCircle(we,D[Oe],D[Oe+1],D[Oe+2])},rl.prototype.projectAndGetPerspectiveRatio=function(D,J,O){var K=[J,O,0,1];Fl(K,K,D);var de=new i.Point((K[0]/K[3]+1)/2*this.transform.width+us,(-K[1]/K[3]+1)/2*this.transform.height+us);return{point:de,perspectiveRatio:.5+.5*(this.transform.cameraToCenterDistance/K[3])}},rl.prototype.isOffscreen=function(D,J,O,K){return O<us||D>=this.screenRightBoundary||K<us||J>this.screenBottomBoundary},rl.prototype.isInsideGrid=function(D,J,O,K){return O>=0&&D<this.gridRightBoundary&&K>=0&&J<this.gridBottomBoundary},rl.prototype.getViewportMatrix=function(){var D=i.identity([]);return i.translate(D,D,[-us,-us,0]),D};function As(Y,D,J){return D*(i.EXTENT/(Y.tileSize*Math.pow(2,J-Y.tileID.overscaledZ)))}var cl=function(D,J,O,K){D?this.opacity=Math.max(0,Math.min(1,D.opacity+(D.placed?J:-J))):this.opacity=K&&O?1:0,this.placed=O};cl.prototype.isHidden=function(){return this.opacity===0&&!this.placed};var Ks=function(D,J,O,K,de){this.text=new cl(D?D.text:null,J,O,de),this.icon=new cl(D?D.icon:null,J,K,de)};Ks.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var zs=function(D,J,O){this.text=D,this.icon=J,this.skipFade=O},Io=function(){this.invProjMatrix=i.create(),this.viewportMatrix=i.create(),this.circles=[]},ls=function(D,J,O,K,de){this.bucketInstanceId=D,this.featureIndex=J,this.sourceLayerIndex=O,this.bucketIndex=K,this.tileID=de},Yl=function(D){this.crossSourceCollisions=D,this.maxGroupID=0,this.collisionGroups={}};Yl.prototype.get=function(D){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[D]){var J=++this.maxGroupID;this.collisionGroups[D]={ID:J,predicate:function(O){return O.collisionGroupID===J}}}return this.collisionGroups[D]};function Su(Y,D,J,O,K){var de=i.getAnchorAlignment(Y),ne=de.horizontalAlign,we=de.verticalAlign,Oe=-(ne-.5)*D,ht=-(we-.5)*J,Yt=i.evaluateVariableOffset(Y,O);return new i.Point(Oe+Yt[0]*K,ht+Yt[1]*K)}function nc(Y,D,J,O,K,de){var ne=Y.x1,we=Y.x2,Oe=Y.y1,ht=Y.y2,Yt=Y.anchorPointX,cr=Y.anchorPointY,Nt=new i.Point(D,J);return O&&Nt._rotate(K?de:-de),{x1:ne+Nt.x,y1:Oe+Nt.y,x2:we+Nt.x,y2:ht+Nt.y,anchorPointX:Yt,anchorPointY:cr}}var bs=function(D,J,O,K){this.transform=D.clone(),this.collisionIndex=new rl(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=J,this.retainedQueryData={},this.collisionGroups=new Yl(O),this.collisionCircleArrays={},this.prevPlacement=K,K&&(K.prevPlacement=void 0),this.placedOrientations={}};bs.prototype.getBucketParts=function(D,J,O,K){var de=O.getBucket(J),ne=O.latestFeatureIndex;if(!(!de||!ne||J.id!==de.layerIds[0])){var we=O.collisionBoxArray,Oe=de.layers[0].layout,ht=Math.pow(2,this.transform.zoom-O.tileID.overscaledZ),Yt=O.tileSize/i.EXTENT,cr=this.transform.calculatePosMatrix(O.tileID.toUnwrapped()),Nt=Oe.get(\"text-pitch-alignment\")===\"map\",Ne=Oe.get(\"text-rotation-alignment\")===\"map\",$e=As(O,1,this.transform.zoom),st=_n(cr,Nt,Ne,this.transform,$e),It=null;if(Nt){var Bt=ya(cr,Nt,Ne,this.transform,$e);It=i.multiply([],this.transform.labelPlaneMatrix,Bt)}this.retainedQueryData[de.bucketInstanceId]=new ls(de.bucketInstanceId,ne,de.sourceLayerIndex,de.index,O.tileID);var Ft={bucket:de,layout:Oe,posMatrix:cr,textLabelPlaneMatrix:st,labelToScreenMatrix:It,scale:ht,textPixelRatio:Yt,holdingForFade:O.holdingForFade(),collisionBoxArray:we,partiallyEvaluatedTextSize:i.evaluateSizeForZoom(de.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(de.sourceID)};if(K)for(var Ut=0,zt=de.sortKeyRanges;Ut<zt.length;Ut+=1){var _t=zt[Ut],Dt=_t.sortKey,vt=_t.symbolInstanceStart,lr=_t.symbolInstanceEnd;D.push({sortKey:Dt,symbolInstanceStart:vt,symbolInstanceEnd:lr,parameters:Ft})}else D.push({symbolInstanceStart:0,symbolInstanceEnd:de.symbolInstances.length,parameters:Ft})}},bs.prototype.attemptAnchorPlacement=function(D,J,O,K,de,ne,we,Oe,ht,Yt,cr,Nt,Ne,$e,st){var It=[Nt.textOffset0,Nt.textOffset1],Bt=Su(D,O,K,It,de),Ft=this.collisionIndex.placeCollisionBox(nc(J,Bt.x,Bt.y,ne,we,this.transform.angle),cr,Oe,ht,Yt.predicate);if(st){var Ut=this.collisionIndex.placeCollisionBox(nc(st,Bt.x,Bt.y,ne,we,this.transform.angle),cr,Oe,ht,Yt.predicate);if(Ut.box.length===0)return}if(Ft.box.length>0){var zt;return this.prevPlacement&&this.prevPlacement.variableOffsets[Nt.crossTileID]&&this.prevPlacement.placements[Nt.crossTileID]&&this.prevPlacement.placements[Nt.crossTileID].text&&(zt=this.prevPlacement.variableOffsets[Nt.crossTileID].anchor),this.variableOffsets[Nt.crossTileID]={textOffset:It,width:O,height:K,anchor:D,textBoxScale:de,prevAnchor:zt},this.markUsedJustification(Ne,D,Nt,$e),Ne.allowVerticalPlacement&&(this.markUsedOrientation(Ne,$e,Nt),this.placedOrientations[Nt.crossTileID]=$e),{shift:Bt,placedGlyphBoxes:Ft}}},bs.prototype.placeLayerBucketPart=function(D,J,O){var K=this,de=D.parameters,ne=de.bucket,we=de.layout,Oe=de.posMatrix,ht=de.textLabelPlaneMatrix,Yt=de.labelToScreenMatrix,cr=de.textPixelRatio,Nt=de.holdingForFade,Ne=de.collisionBoxArray,$e=de.partiallyEvaluatedTextSize,st=de.collisionGroup,It=we.get(\"text-optional\"),Bt=we.get(\"icon-optional\"),Ft=we.get(\"text-allow-overlap\"),Ut=we.get(\"icon-allow-overlap\"),zt=we.get(\"text-rotation-alignment\")===\"map\",_t=we.get(\"text-pitch-alignment\")===\"map\",Dt=we.get(\"icon-text-fit\")!==\"none\",vt=we.get(\"symbol-z-order\")===\"viewport-y\",lr=Ft&&(Ut||!ne.hasIconData()||Bt),_r=Ut&&(Ft||!ne.hasTextData()||It);!ne.collisionArrays&&Ne&&ne.deserializeCollisionBoxes(Ne);var Br=function(Ei,Oi){if(!J[Ei.crossTileID]){if(Nt){K.placements[Ei.crossTileID]=new zs(!1,!1,!1);return}var Gi=!1,on=!1,zn=!0,Ja=null,co={box:null,offscreen:null},ts={box:null,offscreen:null},so=null,Yo=null,ms=null,ou=0,Cv=0,Lv=0;Oi.textFeatureIndex?ou=Oi.textFeatureIndex:Ei.useRuntimeCollisionCircles&&(ou=Ei.featureIndex),Oi.verticalTextFeatureIndex&&(Cv=Oi.verticalTextFeatureIndex);var wd=Oi.textBox;if(wd){var Kv=function(vc){var tu=i.WritingMode.horizontal;if(ne.allowVerticalPlacement&&!vc&&K.prevPlacement){var Sd=K.prevPlacement.placedOrientations[Ei.crossTileID];Sd&&(K.placedOrientations[Ei.crossTileID]=Sd,tu=Sd,K.markUsedOrientation(ne,tu,Ei))}return tu},hg=function(vc,tu){if(ne.allowVerticalPlacement&&Ei.numVerticalGlyphVertices>0&&Oi.verticalTextBox)for(var Sd=0,gy=ne.writingModes;Sd<gy.length;Sd+=1){var P1=gy[Sd];if(P1===i.WritingMode.vertical?(co=tu(),ts=co):co=vc(),co&&co.box&&co.box.length)break}else co=vc()};if(we.get(\"text-variable-anchor\")){var Vd=we.get(\"text-variable-anchor\");if(K.prevPlacement&&K.prevPlacement.variableOffsets[Ei.crossTileID]){var Ad=K.prevPlacement.variableOffsets[Ei.crossTileID];Vd.indexOf(Ad.anchor)>0&&(Vd=Vd.filter(function(vc){return vc!==Ad.anchor}),Vd.unshift(Ad.anchor))}var Pv=function(vc,tu,Sd){for(var gy=vc.x2-vc.x1,P1=vc.y2-vc.y1,wu=Ei.textBoxScale,Kx=Dt&&!Ut?tu:null,lm={box:[],offscreen:!1},Uw=Ft?Vd.length*2:Vd.length,Rv=0;Rv<Uw;++Rv){var um=Vd[Rv%Vd.length],Vw=Rv>=Vd.length,Jx=K.attemptAnchorPlacement(um,vc,gy,P1,wu,zt,_t,cr,Oe,st,Vw,Ei,ne,Sd,Kx);if(Jx&&(lm=Jx.placedGlyphBoxes,lm&&lm.box&&lm.box.length)){Gi=!0,Ja=Jx.shift;break}}return lm},Jv=function(){return Pv(wd,Oi.iconBox,i.WritingMode.horizontal)},Iv=function(){var vc=Oi.verticalTextBox,tu=co&&co.box&&co.box.length;return ne.allowVerticalPlacement&&!tu&&Ei.numVerticalGlyphVertices>0&&vc?Pv(vc,Oi.verticalIconBox,i.WritingMode.vertical):{box:null,offscreen:null}};hg(Jv,Iv),co&&(Gi=co.box,zn=co.offscreen);var dy=Kv(co&&co.box);if(!Gi&&K.prevPlacement){var dg=K.prevPlacement.variableOffsets[Ei.crossTileID];dg&&(K.variableOffsets[Ei.crossTileID]=dg,K.markUsedJustification(ne,dg.anchor,Ei,dy))}}else{var gp=function(vc,tu){var Sd=K.collisionIndex.placeCollisionBox(vc,Ft,cr,Oe,st.predicate);return Sd&&Sd.box&&Sd.box.length&&(K.markUsedOrientation(ne,tu,Ei),K.placedOrientations[Ei.crossTileID]=tu),Sd},Td=function(){return gp(wd,i.WritingMode.horizontal)},mp=function(){var vc=Oi.verticalTextBox;return ne.allowVerticalPlacement&&Ei.numVerticalGlyphVertices>0&&vc?gp(vc,i.WritingMode.vertical):{box:null,offscreen:null}};hg(Td,mp),Kv(co&&co.box&&co.box.length)}}if(so=co,Gi=so&&so.box&&so.box.length>0,zn=so&&so.offscreen,Ei.useRuntimeCollisionCircles){var oh=ne.text.placedSymbolArray.get(Ei.centerJustifiedTextSymbolIndex),vg=i.evaluateSizeForFeature(ne.textSizeData,$e,oh),vy=we.get(\"text-padding\"),Zh=Ei.collisionCircleDiameter;Yo=K.collisionIndex.placeCollisionCircles(Ft,oh,ne.lineVertexArray,ne.glyphOffsetArray,vg,Oe,ht,Yt,O,_t,st.predicate,Zh,vy),Gi=Ft||Yo.circles.length>0&&!Yo.collisionDetected,zn=zn&&Yo.offscreen}if(Oi.iconFeatureIndex&&(Lv=Oi.iconFeatureIndex),Oi.iconBox){var am=function(vc){var tu=Dt&&Ja?nc(vc,Ja.x,Ja.y,zt,_t,K.transform.angle):vc;return K.collisionIndex.placeCollisionBox(tu,Ut,cr,Oe,st.predicate)};ts&&ts.box&&ts.box.length&&Oi.verticalIconBox?(ms=am(Oi.verticalIconBox),on=ms.box.length>0):(ms=am(Oi.iconBox),on=ms.box.length>0),zn=zn&&ms.offscreen}var C1=It||Ei.numHorizontalGlyphVertices===0&&Ei.numVerticalGlyphVertices===0,L1=Bt||Ei.numIconVertices===0;if(!C1&&!L1?on=Gi=on&&Gi:L1?C1||(on=on&&Gi):Gi=on&&Gi,Gi&&so&&so.box&&(ts&&ts.box&&Cv?K.collisionIndex.insertCollisionBox(so.box,we.get(\"text-ignore-placement\"),ne.bucketInstanceId,Cv,st.ID):K.collisionIndex.insertCollisionBox(so.box,we.get(\"text-ignore-placement\"),ne.bucketInstanceId,ou,st.ID)),on&&ms&&K.collisionIndex.insertCollisionBox(ms.box,we.get(\"icon-ignore-placement\"),ne.bucketInstanceId,Lv,st.ID),Yo&&(Gi&&K.collisionIndex.insertCollisionCircles(Yo.circles,we.get(\"text-ignore-placement\"),ne.bucketInstanceId,ou,st.ID),O)){var py=ne.bucketInstanceId,om=K.collisionCircleArrays[py];om===void 0&&(om=K.collisionCircleArrays[py]=new Io);for(var sm=0;sm<Yo.circles.length;sm+=4)om.circles.push(Yo.circles[sm+0]),om.circles.push(Yo.circles[sm+1]),om.circles.push(Yo.circles[sm+2]),om.circles.push(Yo.collisionDetected?1:0)}K.placements[Ei.crossTileID]=new zs(Gi||lr,on||_r,zn||ne.justReloaded),J[Ei.crossTileID]=!0}};if(vt)for(var Di=ne.getSortedSymbolIndexes(this.transform.angle),ci=Di.length-1;ci>=0;--ci){var bi=Di[ci];Br(ne.symbolInstances.get(bi),ne.collisionArrays[bi])}else for(var gn=D.symbolInstanceStart;gn<D.symbolInstanceEnd;gn++)Br(ne.symbolInstances.get(gn),ne.collisionArrays[gn]);if(O&&ne.bucketInstanceId in this.collisionCircleArrays){var tn=this.collisionCircleArrays[ne.bucketInstanceId];i.invert(tn.invProjMatrix,Oe),tn.viewportMatrix=this.collisionIndex.getViewportMatrix()}ne.justReloaded=!1},bs.prototype.markUsedJustification=function(D,J,O,K){var de={left:O.leftJustifiedTextSymbolIndex,center:O.centerJustifiedTextSymbolIndex,right:O.rightJustifiedTextSymbolIndex},ne;K===i.WritingMode.vertical?ne=O.verticalPlacedTextSymbolIndex:ne=de[i.getAnchorJustification(J)];for(var we=[O.leftJustifiedTextSymbolIndex,O.centerJustifiedTextSymbolIndex,O.rightJustifiedTextSymbolIndex,O.verticalPlacedTextSymbolIndex],Oe=0,ht=we;Oe<ht.length;Oe+=1){var Yt=ht[Oe];Yt>=0&&(ne>=0&&Yt!==ne?D.text.placedSymbolArray.get(Yt).crossTileID=0:D.text.placedSymbolArray.get(Yt).crossTileID=O.crossTileID)}},bs.prototype.markUsedOrientation=function(D,J,O){for(var K=J===i.WritingMode.horizontal||J===i.WritingMode.horizontalOnly?J:0,de=J===i.WritingMode.vertical?J:0,ne=[O.leftJustifiedTextSymbolIndex,O.centerJustifiedTextSymbolIndex,O.rightJustifiedTextSymbolIndex],we=0,Oe=ne;we<Oe.length;we+=1){var ht=Oe[we];D.text.placedSymbolArray.get(ht).placedOrientation=K}O.verticalPlacedTextSymbolIndex&&(D.text.placedSymbolArray.get(O.verticalPlacedTextSymbolIndex).placedOrientation=de)},bs.prototype.commit=function(D){this.commitTime=D,this.zoomAtLastRecencyCheck=this.transform.zoom;var J=this.prevPlacement,O=!1;this.prevZoomAdjustment=J?J.zoomAdjustment(this.transform.zoom):0;var K=J?J.symbolFadeChange(D):1,de=J?J.opacities:{},ne=J?J.variableOffsets:{},we=J?J.placedOrientations:{};for(var Oe in this.placements){var ht=this.placements[Oe],Yt=de[Oe];Yt?(this.opacities[Oe]=new Ks(Yt,K,ht.text,ht.icon),O=O||ht.text!==Yt.text.placed||ht.icon!==Yt.icon.placed):(this.opacities[Oe]=new Ks(null,K,ht.text,ht.icon,ht.skipFade),O=O||ht.text||ht.icon)}for(var cr in de){var Nt=de[cr];if(!this.opacities[cr]){var Ne=new Ks(Nt,K,!1,!1);Ne.isHidden()||(this.opacities[cr]=Ne,O=O||Nt.text.placed||Nt.icon.placed)}}for(var $e in ne)!this.variableOffsets[$e]&&this.opacities[$e]&&!this.opacities[$e].isHidden()&&(this.variableOffsets[$e]=ne[$e]);for(var st in we)!this.placedOrientations[st]&&this.opacities[st]&&!this.opacities[st].isHidden()&&(this.placedOrientations[st]=we[st]);O?this.lastPlacementChangeTime=D:typeof this.lastPlacementChangeTime!=\"number\"&&(this.lastPlacementChangeTime=J?J.lastPlacementChangeTime:D)},bs.prototype.updateLayerOpacities=function(D,J){for(var O={},K=0,de=J;K<de.length;K+=1){var ne=de[K],we=ne.getBucket(D);we&&ne.latestFeatureIndex&&D.id===we.layerIds[0]&&this.updateBucketOpacities(we,O,ne.collisionBoxArray)}},bs.prototype.updateBucketOpacities=function(D,J,O){var K=this;D.hasTextData()&&D.text.opacityVertexArray.clear(),D.hasIconData()&&D.icon.opacityVertexArray.clear(),D.hasIconCollisionBoxData()&&D.iconCollisionBox.collisionVertexArray.clear(),D.hasTextCollisionBoxData()&&D.textCollisionBox.collisionVertexArray.clear();var de=D.layers[0].layout,ne=new Ks(null,0,!1,!1,!0),we=de.get(\"text-allow-overlap\"),Oe=de.get(\"icon-allow-overlap\"),ht=de.get(\"text-variable-anchor\"),Yt=de.get(\"text-rotation-alignment\")===\"map\",cr=de.get(\"text-pitch-alignment\")===\"map\",Nt=de.get(\"icon-text-fit\")!==\"none\",Ne=new Ks(null,0,we&&(Oe||!D.hasIconData()||de.get(\"icon-optional\")),Oe&&(we||!D.hasTextData()||de.get(\"text-optional\")),!0);!D.collisionArrays&&O&&(D.hasIconCollisionBoxData()||D.hasTextCollisionBoxData())&&D.deserializeCollisionBoxes(O);for(var $e=function(Ft,Ut,zt){for(var _t=0;_t<Ut/4;_t++)Ft.opacityVertexArray.emplaceBack(zt)},st=function(Ft){var Ut=D.symbolInstances.get(Ft),zt=Ut.numHorizontalGlyphVertices,_t=Ut.numVerticalGlyphVertices,Dt=Ut.crossTileID,vt=J[Dt],lr=K.opacities[Dt];vt?lr=ne:lr||(lr=Ne,K.opacities[Dt]=lr),J[Dt]=!0;var _r=zt>0||_t>0,Br=Ut.numIconVertices>0,Di=K.placedOrientations[Ut.crossTileID],ci=Di===i.WritingMode.vertical,bi=Di===i.WritingMode.horizontal||Di===i.WritingMode.horizontalOnly;if(_r){var gn=ac(lr.text),tn=ci?oa:gn;$e(D.text,zt,tn);var Ei=bi?oa:gn;$e(D.text,_t,Ei);var Oi=lr.text.isHidden();[Ut.rightJustifiedTextSymbolIndex,Ut.centerJustifiedTextSymbolIndex,Ut.leftJustifiedTextSymbolIndex].forEach(function(Lv){Lv>=0&&(D.text.placedSymbolArray.get(Lv).hidden=Oi||ci?1:0)}),Ut.verticalPlacedTextSymbolIndex>=0&&(D.text.placedSymbolArray.get(Ut.verticalPlacedTextSymbolIndex).hidden=Oi||bi?1:0);var Gi=K.variableOffsets[Ut.crossTileID];Gi&&K.markUsedJustification(D,Gi.anchor,Ut,Di);var on=K.placedOrientations[Ut.crossTileID];on&&(K.markUsedJustification(D,\"left\",Ut,on),K.markUsedOrientation(D,on,Ut))}if(Br){var zn=ac(lr.icon),Ja=!(Nt&&Ut.verticalPlacedIconSymbolIndex&&ci);if(Ut.placedIconSymbolIndex>=0){var co=Ja?zn:oa;$e(D.icon,Ut.numIconVertices,co),D.icon.placedSymbolArray.get(Ut.placedIconSymbolIndex).hidden=lr.icon.isHidden()}if(Ut.verticalPlacedIconSymbolIndex>=0){var ts=Ja?oa:zn;$e(D.icon,Ut.numVerticalIconVertices,ts),D.icon.placedSymbolArray.get(Ut.verticalPlacedIconSymbolIndex).hidden=lr.icon.isHidden()}}if(D.hasIconCollisionBoxData()||D.hasTextCollisionBoxData()){var so=D.collisionArrays[Ft];if(so){var Yo=new i.Point(0,0);if(so.textBox||so.verticalTextBox){var ms=!0;if(ht){var ou=K.variableOffsets[Dt];ou?(Yo=Su(ou.anchor,ou.width,ou.height,ou.textOffset,ou.textBoxScale),Yt&&Yo._rotate(cr?K.transform.angle:-K.transform.angle)):ms=!1}so.textBox&&Rn(D.textCollisionBox.collisionVertexArray,lr.text.placed,!ms||ci,Yo.x,Yo.y),so.verticalTextBox&&Rn(D.textCollisionBox.collisionVertexArray,lr.text.placed,!ms||bi,Yo.x,Yo.y)}var Cv=!!(!bi&&so.verticalIconBox);so.iconBox&&Rn(D.iconCollisionBox.collisionVertexArray,lr.icon.placed,Cv,Nt?Yo.x:0,Nt?Yo.y:0),so.verticalIconBox&&Rn(D.iconCollisionBox.collisionVertexArray,lr.icon.placed,!Cv,Nt?Yo.x:0,Nt?Yo.y:0)}}},It=0;It<D.symbolInstances.length;It++)st(It);if(D.sortFeatures(this.transform.angle),this.retainedQueryData[D.bucketInstanceId]&&(this.retainedQueryData[D.bucketInstanceId].featureSortOrder=D.featureSortOrder),D.hasTextData()&&D.text.opacityVertexBuffer&&D.text.opacityVertexBuffer.updateData(D.text.opacityVertexArray),D.hasIconData()&&D.icon.opacityVertexBuffer&&D.icon.opacityVertexBuffer.updateData(D.icon.opacityVertexArray),D.hasIconCollisionBoxData()&&D.iconCollisionBox.collisionVertexBuffer&&D.iconCollisionBox.collisionVertexBuffer.updateData(D.iconCollisionBox.collisionVertexArray),D.hasTextCollisionBoxData()&&D.textCollisionBox.collisionVertexBuffer&&D.textCollisionBox.collisionVertexBuffer.updateData(D.textCollisionBox.collisionVertexArray),D.bucketInstanceId in this.collisionCircleArrays){var Bt=this.collisionCircleArrays[D.bucketInstanceId];D.placementInvProjMatrix=Bt.invProjMatrix,D.placementViewportMatrix=Bt.viewportMatrix,D.collisionCircleArray=Bt.circles,delete this.collisionCircleArrays[D.bucketInstanceId]}},bs.prototype.symbolFadeChange=function(D){return this.fadeDuration===0?1:(D-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},bs.prototype.zoomAdjustment=function(D){return Math.max(0,(this.transform.zoom-D)/1.5)},bs.prototype.hasTransitions=function(D){return this.stale||D-this.lastPlacementChangeTime<this.fadeDuration},bs.prototype.stillRecent=function(D,J){var O=this.zoomAtLastRecencyCheck===J?1-this.zoomAdjustment(J):1;return this.zoomAtLastRecencyCheck=J,this.commitTime+this.fadeDuration*O>D},bs.prototype.setStale=function(){this.stale=!0};function Rn(Y,D,J,O,K){Y.emplaceBack(D?1:0,J?1:0,O||0,K||0),Y.emplaceBack(D?1:0,J?1:0,O||0,K||0),Y.emplaceBack(D?1:0,J?1:0,O||0,K||0),Y.emplaceBack(D?1:0,J?1:0,O||0,K||0)}var _a=Math.pow(2,25),Vu=Math.pow(2,24),zl=Math.pow(2,17),xo=Math.pow(2,16),Kl=Math.pow(2,9),Ns=Math.pow(2,8),Hl=Math.pow(2,1);function ac(Y){if(Y.opacity===0&&!Y.placed)return 0;if(Y.opacity===1&&Y.placed)return 4294967295;var D=Y.placed?1:0,J=Math.floor(Y.opacity*127);return J*_a+D*Vu+J*zl+D*xo+J*Kl+D*Ns+J*Hl+D}var oa=0,qo=function(D){this._sortAcrossTiles=D.layout.get(\"symbol-z-order\")!==\"viewport-y\"&&D.layout.get(\"symbol-sort-key\").constantOr(1)!==void 0,this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};qo.prototype.continuePlacement=function(D,J,O,K,de){for(var ne=this._bucketParts;this._currentTileIndex<D.length;){var we=D[this._currentTileIndex];if(J.getBucketParts(ne,K,we,this._sortAcrossTiles),this._currentTileIndex++,de())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,ne.sort(function(ht,Yt){return ht.sortKey-Yt.sortKey}));this._currentPartIndex<ne.length;){var Oe=ne[this._currentPartIndex];if(J.placeLayerBucketPart(Oe,this._seenCrossTileIDs,O),this._currentPartIndex++,de())return!0}return!1};var Oo=function(D,J,O,K,de,ne,we){this.placement=new bs(D,de,ne,we),this._currentPlacementIndex=J.length-1,this._forceFullPlacement=O,this._showCollisionBoxes=K,this._done=!1};Oo.prototype.isDone=function(){return this._done},Oo.prototype.continuePlacement=function(D,J,O){for(var K=this,de=i.browser.now(),ne=function(){var cr=i.browser.now()-de;return K._forceFullPlacement?!1:cr>2};this._currentPlacementIndex>=0;){var we=D[this._currentPlacementIndex],Oe=J[we],ht=this.placement.collisionIndex.transform.zoom;if(Oe.type===\"symbol\"&&(!Oe.minzoom||Oe.minzoom<=ht)&&(!Oe.maxzoom||Oe.maxzoom>ht)){this._inProgressLayer||(this._inProgressLayer=new qo(Oe));var Yt=this._inProgressLayer.continuePlacement(O[Oe.source],this.placement,this._showCollisionBoxes,Oe,ne);if(Yt)return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Oo.prototype.commit=function(D){return this.placement.commit(D),this.placement};var ql=512/i.EXTENT/2,Pc=function(D,J,O){this.tileID=D,this.indexedSymbolInstances={},this.bucketInstanceId=O;for(var K=0;K<J.length;K++){var de=J.get(K),ne=de.key;this.indexedSymbolInstances[ne]||(this.indexedSymbolInstances[ne]=[]),this.indexedSymbolInstances[ne].push({crossTileID:de.crossTileID,coord:this.getScaledCoordinates(de,D)})}};Pc.prototype.getScaledCoordinates=function(D,J){var O=J.canonical.z-this.tileID.canonical.z,K=ql/Math.pow(2,O);return{x:Math.floor((J.canonical.x*i.EXTENT+D.anchorX)*K),y:Math.floor((J.canonical.y*i.EXTENT+D.anchorY)*K)}},Pc.prototype.findMatches=function(D,J,O){for(var K=this.tileID.canonical.z<J.canonical.z?1:Math.pow(2,this.tileID.canonical.z-J.canonical.z),de=0;de<D.length;de++){var ne=D.get(de);if(!ne.crossTileID){var we=this.indexedSymbolInstances[ne.key];if(we)for(var Oe=this.getScaledCoordinates(ne,J),ht=0,Yt=we;ht<Yt.length;ht+=1){var cr=Yt[ht];if(Math.abs(cr.coord.x-Oe.x)<=K&&Math.abs(cr.coord.y-Oe.y)<=K&&!O[cr.crossTileID]){O[cr.crossTileID]=!0,ne.crossTileID=cr.crossTileID;break}}}}};var Do=function(){this.maxCrossTileID=0};Do.prototype.generate=function(){return++this.maxCrossTileID};var nf=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};nf.prototype.handleWrapJump=function(D){var J=Math.round((D-this.lng)/360);if(J!==0)for(var O in this.indexes){var K=this.indexes[O],de={};for(var ne in K){var we=K[ne];we.tileID=we.tileID.unwrapTo(we.tileID.wrap+J),de[we.tileID.key]=we}this.indexes[O]=de}this.lng=D},nf.prototype.addBucket=function(D,J,O){if(this.indexes[D.overscaledZ]&&this.indexes[D.overscaledZ][D.key]){if(this.indexes[D.overscaledZ][D.key].bucketInstanceId===J.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(D.overscaledZ,this.indexes[D.overscaledZ][D.key])}for(var K=0;K<J.symbolInstances.length;K++){var de=J.symbolInstances.get(K);de.crossTileID=0}this.usedCrossTileIDs[D.overscaledZ]||(this.usedCrossTileIDs[D.overscaledZ]={});var ne=this.usedCrossTileIDs[D.overscaledZ];for(var we in this.indexes){var Oe=this.indexes[we];if(Number(we)>D.overscaledZ)for(var ht in Oe){var Yt=Oe[ht];Yt.tileID.isChildOf(D)&&Yt.findMatches(J.symbolInstances,D,ne)}else{var cr=D.scaledTo(Number(we)),Nt=Oe[cr.key];Nt&&Nt.findMatches(J.symbolInstances,D,ne)}}for(var Ne=0;Ne<J.symbolInstances.length;Ne++){var $e=J.symbolInstances.get(Ne);$e.crossTileID||($e.crossTileID=O.generate(),ne[$e.crossTileID]=!0)}return this.indexes[D.overscaledZ]===void 0&&(this.indexes[D.overscaledZ]={}),this.indexes[D.overscaledZ][D.key]=new Pc(D,J.symbolInstances,J.bucketInstanceId),!0},nf.prototype.removeBucketCrossTileIDs=function(D,J){for(var O in J.indexedSymbolInstances)for(var K=0,de=J.indexedSymbolInstances[O];K<de.length;K+=1){var ne=de[K];delete this.usedCrossTileIDs[D][ne.crossTileID]}},nf.prototype.removeStaleBuckets=function(D){var J=!1;for(var O in this.indexes){var K=this.indexes[O];for(var de in K)D[K[de].bucketInstanceId]||(this.removeBucketCrossTileIDs(O,K[de]),delete K[de],J=!0)}return J};var Vf=function(){this.layerIndexes={},this.crossTileIDs=new Do,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Vf.prototype.addLayer=function(D,J,O){var K=this.layerIndexes[D.id];K===void 0&&(K=this.layerIndexes[D.id]=new nf);var de=!1,ne={};K.handleWrapJump(O);for(var we=0,Oe=J;we<Oe.length;we+=1){var ht=Oe[we],Yt=ht.getBucket(D);!Yt||D.id!==Yt.layerIds[0]||(Yt.bucketInstanceId||(Yt.bucketInstanceId=++this.maxBucketInstanceId),K.addBucket(ht.tileID,Yt,this.crossTileIDs)&&(de=!0),ne[Yt.bucketInstanceId]=!0)}return K.removeStaleBuckets(ne)&&(de=!0),de},Vf.prototype.pruneUnusedLayers=function(D){var J={};D.forEach(function(K){J[K]=!0});for(var O in this.layerIndexes)J[O]||delete this.layerIndexes[O]};var pl=function(Y,D){return i.emitValidationErrors(Y,D&&D.filter(function(J){return J.identifier!==\"source.canvas\"}))},Zc=i.pick(da,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Jl=i.pick(da,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),qs=Xo(),yu=(function(Y){function D(J,O){var K=this;O===void 0&&(O={}),Y.call(this),this.map=J,this.dispatcher=new Z(Ba(),this),this.imageManager=new k,this.imageManager.setEventedParent(this),this.glyphManager=new P(J._requestManager,O.localIdeographFontFamily),this.lineAtlas=new G(256,512),this.crossTileSymbolIndex=new Vf,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new i.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",i.getReferrer());var de=this;this._rtlTextPluginCallback=D.registerForPluginStateChange(function(ne){var we={pluginStatus:ne.pluginStatus,pluginURL:ne.pluginURL};de.dispatcher.broadcast(\"syncRTLPluginState\",we,function(Oe,ht){if(i.triggerPluginCompletionEvent(Oe),ht){var Yt=ht.every(function(Nt){return Nt});if(Yt)for(var cr in de.sourceCaches)de.sourceCaches[cr].reload()}})}),this.on(\"data\",function(ne){if(!(ne.dataType!==\"source\"||ne.sourceDataType!==\"metadata\")){var we=K.sourceCaches[ne.sourceId];if(we){var Oe=we.getSource();if(!(!Oe||!Oe.vectorLayerIds))for(var ht in K._layers){var Yt=K._layers[ht];Yt.source===Oe.id&&K._validateLayer(Yt)}}}})}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.loadURL=function(O,K){var de=this;K===void 0&&(K={}),this.fire(new i.Event(\"dataloading\",{dataType:\"style\"}));var ne=typeof K.validate==\"boolean\"?K.validate:!i.isMapboxURL(O);O=this.map._requestManager.normalizeStyleURL(O,K.accessToken);var we=this.map._requestManager.transformRequest(O,i.ResourceType.Style);this._request=i.getJSON(we,function(Oe,ht){de._request=null,Oe?de.fire(new i.ErrorEvent(Oe)):ht&&de._load(ht,ne)})},D.prototype.loadJSON=function(O,K){var de=this;K===void 0&&(K={}),this.fire(new i.Event(\"dataloading\",{dataType:\"style\"})),this._request=i.browser.frame(function(){de._request=null,de._load(O,K.validate!==!1)})},D.prototype.loadEmpty=function(){this.fire(new i.Event(\"dataloading\",{dataType:\"style\"})),this._load(qs,!1)},D.prototype._load=function(O,K){if(!(K&&pl(this,i.validateStyle(O)))){this._loaded=!0,this.stylesheet=O;for(var de in O.sources)this.addSource(de,O.sources[de],{validate:!1});O.sprite?this._loadSprite(O.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(O.glyphs);var ne=za(this.stylesheet.layers);this._order=ne.map(function(Yt){return Yt.id}),this._layers={},this._serializedLayers={};for(var we=0,Oe=ne;we<Oe.length;we+=1){var ht=Oe[we];ht=i.createStyleLayer(ht),ht.setEventedParent(this,{layer:{id:ht.id}}),this._layers[ht.id]=ht,this._serializedLayers[ht.id]=ht.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new V(this.stylesheet.light),this.fire(new i.Event(\"data\",{dataType:\"style\"})),this.fire(new i.Event(\"style.load\"))}},D.prototype._loadSprite=function(O){var K=this;this._spriteRequest=m(O,this.map._requestManager,function(de,ne){if(K._spriteRequest=null,de)K.fire(new i.ErrorEvent(de));else if(ne)for(var we in ne)K.imageManager.addImage(we,ne[we]);K.imageManager.setLoaded(!0),K._availableImages=K.imageManager.listImages(),K.dispatcher.broadcast(\"setImages\",K._availableImages),K.fire(new i.Event(\"data\",{dataType:\"style\"}))})},D.prototype._validateLayer=function(O){var K=this.sourceCaches[O.source];if(K){var de=O.sourceLayer;if(de){var ne=K.getSource();(ne.type===\"geojson\"||ne.vectorLayerIds&&ne.vectorLayerIds.indexOf(de)===-1)&&this.fire(new i.ErrorEvent(new Error('Source layer \"'+de+'\" does not exist on source \"'+ne.id+'\" as specified by style layer \"'+O.id+'\"')))}}},D.prototype.loaded=function(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(var O in this.sourceCaches)if(!this.sourceCaches[O].loaded())return!1;return!!this.imageManager.isLoaded()},D.prototype._serializeLayers=function(O){for(var K=[],de=0,ne=O;de<ne.length;de+=1){var we=ne[de],Oe=this._layers[we];Oe.type!==\"custom\"&&K.push(Oe.serialize())}return K},D.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var O in this.sourceCaches)if(this.sourceCaches[O].hasTransition())return!0;for(var K in this._layers)if(this._layers[K].hasTransition())return!0;return!1},D.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},D.prototype.update=function(O){if(this._loaded){var K=this._changed;if(this._changed){var de=Object.keys(this._updatedLayers),ne=Object.keys(this._removedLayers);(de.length||ne.length)&&this._updateWorkerLayers(de,ne);for(var we in this._updatedSources){var Oe=this._updatedSources[we];Oe===\"reload\"?this._reloadSource(we):Oe===\"clear\"&&this._clearSource(we)}this._updateTilesForChangedImages();for(var ht in this._updatedPaintProps)this._layers[ht].updateTransitions(O);this.light.updateTransitions(O),this._resetUpdates()}var Yt={};for(var cr in this.sourceCaches){var Nt=this.sourceCaches[cr];Yt[cr]=Nt.used,Nt.used=!1}for(var Ne=0,$e=this._order;Ne<$e.length;Ne+=1){var st=$e[Ne],It=this._layers[st];It.recalculate(O,this._availableImages),!It.isHidden(O.zoom)&&It.source&&(this.sourceCaches[It.source].used=!0)}for(var Bt in Yt){var Ft=this.sourceCaches[Bt];Yt[Bt]!==Ft.used&&Ft.fire(new i.Event(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:Bt}))}this.light.recalculate(O),this.z=O.zoom,K&&this.fire(new i.Event(\"data\",{dataType:\"style\"}))}},D.prototype._updateTilesForChangedImages=function(){var O=Object.keys(this._changedImages);if(O.length){for(var K in this.sourceCaches)this.sourceCaches[K].reloadTilesForDependencies([\"icons\",\"patterns\"],O);this._changedImages={}}},D.prototype._updateWorkerLayers=function(O,K){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(O),removedIds:K})},D.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},D.prototype.setState=function(O){var K=this;if(this._checkLoaded(),pl(this,i.validateStyle(O)))return!1;O=i.clone$1(O),O.layers=za(O.layers);var de=Sn(this.serialize(),O).filter(function(we){return!(we.command in Jl)});if(de.length===0)return!1;var ne=de.filter(function(we){return!(we.command in Zc)});if(ne.length>0)throw new Error(\"Unimplemented: \"+ne.map(function(we){return we.command}).join(\", \")+\".\");return de.forEach(function(we){we.command!==\"setTransition\"&&K[we.command].apply(K,we.args)}),this.stylesheet=O,!0},D.prototype.addImage=function(O,K){if(this.getImage(O))return this.fire(new i.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(O,K),this._afterImageUpdated(O)},D.prototype.updateImage=function(O,K){this.imageManager.updateImage(O,K)},D.prototype.getImage=function(O){return this.imageManager.getImage(O)},D.prototype.removeImage=function(O){if(!this.getImage(O))return this.fire(new i.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(O),this._afterImageUpdated(O)},D.prototype._afterImageUpdated=function(O){this._availableImages=this.imageManager.listImages(),this._changedImages[O]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new i.Event(\"data\",{dataType:\"style\"}))},D.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},D.prototype.addSource=function(O,K,de){var ne=this;if(de===void 0&&(de={}),this._checkLoaded(),this.sourceCaches[O]!==void 0)throw new Error(\"There is already a source with this ID\");if(!K.type)throw new Error(\"The type property must be defined, but only the following properties were given: \"+Object.keys(K).join(\", \")+\".\");var we=[\"vector\",\"raster\",\"geojson\",\"video\",\"image\"],Oe=we.indexOf(K.type)>=0;if(!(Oe&&this._validate(i.validateStyle.source,\"sources.\"+O,K,null,de))){this.map&&this.map._collectResourceTiming&&(K.collectResourceTiming=!0);var ht=this.sourceCaches[O]=new ti(O,K,this.dispatcher);ht.style=this,ht.setEventedParent(this,function(){return{isSourceLoaded:ne.loaded(),source:ht.serialize(),sourceId:O}}),ht.onAdd(this.map),this._changed=!0}},D.prototype.removeSource=function(O){if(this._checkLoaded(),this.sourceCaches[O]===void 0)throw new Error(\"There is no source with this ID\");for(var K in this._layers)if(this._layers[K].source===O)return this.fire(new i.ErrorEvent(new Error('Source \"'+O+'\" cannot be removed while layer \"'+K+'\" is using it.')));var de=this.sourceCaches[O];delete this.sourceCaches[O],delete this._updatedSources[O],de.fire(new i.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:O})),de.setEventedParent(null),de.clearTiles(),de.onRemove&&de.onRemove(this.map),this._changed=!0},D.prototype.setGeoJSONSourceData=function(O,K){this._checkLoaded();var de=this.sourceCaches[O].getSource();de.setData(K),this._changed=!0},D.prototype.getSource=function(O){return this.sourceCaches[O]&&this.sourceCaches[O].getSource()},D.prototype.addLayer=function(O,K,de){de===void 0&&(de={}),this._checkLoaded();var ne=O.id;if(this.getLayer(ne)){this.fire(new i.ErrorEvent(new Error('Layer with id \"'+ne+'\" already exists on this map')));return}var we;if(O.type===\"custom\"){if(pl(this,i.validateCustomStyleLayer(O)))return;we=i.createStyleLayer(O)}else{if(typeof O.source==\"object\"&&(this.addSource(ne,O.source),O=i.clone$1(O),O=i.extend(O,{source:ne})),this._validate(i.validateStyle.layer,\"layers.\"+ne,O,{arrayIndex:-1},de))return;we=i.createStyleLayer(O),this._validateLayer(we),we.setEventedParent(this,{layer:{id:ne}}),this._serializedLayers[we.id]=we.serialize()}var Oe=K?this._order.indexOf(K):this._order.length;if(K&&Oe===-1){this.fire(new i.ErrorEvent(new Error('Layer with id \"'+K+'\" does not exist on this map.')));return}if(this._order.splice(Oe,0,ne),this._layerOrderChanged=!0,this._layers[ne]=we,this._removedLayers[ne]&&we.source&&we.type!==\"custom\"){var ht=this._removedLayers[ne];delete this._removedLayers[ne],ht.type!==we.type?this._updatedSources[we.source]=\"clear\":(this._updatedSources[we.source]=\"reload\",this.sourceCaches[we.source].pause())}this._updateLayer(we),we.onAdd&&we.onAdd(this.map)},D.prototype.moveLayer=function(O,K){this._checkLoaded(),this._changed=!0;var de=this._layers[O];if(!de){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be moved.\")));return}if(O!==K){var ne=this._order.indexOf(O);this._order.splice(ne,1);var we=K?this._order.indexOf(K):this._order.length;if(K&&we===-1){this.fire(new i.ErrorEvent(new Error('Layer with id \"'+K+'\" does not exist on this map.')));return}this._order.splice(we,0,O),this._layerOrderChanged=!0}},D.prototype.removeLayer=function(O){this._checkLoaded();var K=this._layers[O];if(!K){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be removed.\")));return}K.setEventedParent(null);var de=this._order.indexOf(O);this._order.splice(de,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[O]=K,delete this._layers[O],delete this._serializedLayers[O],delete this._updatedLayers[O],delete this._updatedPaintProps[O],K.onRemove&&K.onRemove(this.map)},D.prototype.getLayer=function(O){return this._layers[O]},D.prototype.hasLayer=function(O){return O in this._layers},D.prototype.setLayerZoomRange=function(O,K,de){this._checkLoaded();var ne=this.getLayer(O);if(!ne){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot have zoom extent.\")));return}ne.minzoom===K&&ne.maxzoom===de||(K!=null&&(ne.minzoom=K),de!=null&&(ne.maxzoom=de),this._updateLayer(ne))},D.prototype.setFilter=function(O,K,de){de===void 0&&(de={}),this._checkLoaded();var ne=this.getLayer(O);if(!ne){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be filtered.\")));return}if(!i.deepEqual(ne.filter,K)){if(K==null){ne.filter=void 0,this._updateLayer(ne);return}this._validate(i.validateStyle.filter,\"layers.\"+ne.id+\".filter\",K,null,de)||(ne.filter=i.clone$1(K),this._updateLayer(ne))}},D.prototype.getFilter=function(O){return i.clone$1(this.getLayer(O).filter)},D.prototype.setLayoutProperty=function(O,K,de,ne){ne===void 0&&(ne={}),this._checkLoaded();var we=this.getLayer(O);if(!we){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be styled.\")));return}i.deepEqual(we.getLayoutProperty(K),de)||(we.setLayoutProperty(K,de,ne),this._updateLayer(we))},D.prototype.getLayoutProperty=function(O,K){var de=this.getLayer(O);if(!de){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style.\")));return}return de.getLayoutProperty(K)},D.prototype.setPaintProperty=function(O,K,de,ne){ne===void 0&&(ne={}),this._checkLoaded();var we=this.getLayer(O);if(!we){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be styled.\")));return}if(!i.deepEqual(we.getPaintProperty(K),de)){var Oe=we.setPaintProperty(K,de,ne);Oe&&this._updateLayer(we),this._changed=!0,this._updatedPaintProps[O]=!0}},D.prototype.getPaintProperty=function(O,K){return this.getLayer(O).getPaintProperty(K)},D.prototype.setFeatureState=function(O,K){this._checkLoaded();var de=O.source,ne=O.sourceLayer,we=this.sourceCaches[de];if(we===void 0){this.fire(new i.ErrorEvent(new Error(\"The source '\"+de+\"' does not exist in the map's style.\")));return}var Oe=we.getSource().type;if(Oe===\"geojson\"&&ne){this.fire(new i.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\")));return}if(Oe===\"vector\"&&!ne){this.fire(new i.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));return}O.id===void 0&&this.fire(new i.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),we.setFeatureState(ne,O.id,K)},D.prototype.removeFeatureState=function(O,K){this._checkLoaded();var de=O.source,ne=this.sourceCaches[de];if(ne===void 0){this.fire(new i.ErrorEvent(new Error(\"The source '\"+de+\"' does not exist in the map's style.\")));return}var we=ne.getSource().type,Oe=we===\"vector\"?O.sourceLayer:void 0;if(we===\"vector\"&&!Oe){this.fire(new i.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));return}if(K&&typeof O.id!=\"string\"&&typeof O.id!=\"number\"){this.fire(new i.ErrorEvent(new Error(\"A feature id is required to remove its specific state property.\")));return}ne.removeFeatureState(Oe,O.id,K)},D.prototype.getFeatureState=function(O){this._checkLoaded();var K=O.source,de=O.sourceLayer,ne=this.sourceCaches[K];if(ne===void 0){this.fire(new i.ErrorEvent(new Error(\"The source '\"+K+\"' does not exist in the map's style.\")));return}var we=ne.getSource().type;if(we===\"vector\"&&!de){this.fire(new i.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));return}return O.id===void 0&&this.fire(new i.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),ne.getFeatureState(de,O.id)},D.prototype.getTransition=function(){return i.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},D.prototype.serialize=function(){return i.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:i.mapObject(this.sourceCaches,function(O){return O.serialize()}),layers:this._serializeLayers(this._order)},function(O){return O!==void 0})},D.prototype._updateLayer=function(O){this._updatedLayers[O.id]=!0,O.source&&!this._updatedSources[O.source]&&this.sourceCaches[O.source].getSource().type!==\"raster\"&&(this._updatedSources[O.source]=\"reload\",this.sourceCaches[O.source].pause()),this._changed=!0},D.prototype._flattenAndSortRenderedFeatures=function(O){for(var K=this,de=function(bi){return K._layers[bi].type===\"fill-extrusion\"},ne={},we=[],Oe=this._order.length-1;Oe>=0;Oe--){var ht=this._order[Oe];if(de(ht)){ne[ht]=Oe;for(var Yt=0,cr=O;Yt<cr.length;Yt+=1){var Nt=cr[Yt],Ne=Nt[ht];if(Ne)for(var $e=0,st=Ne;$e<st.length;$e+=1){var It=st[$e];we.push(It)}}}}we.sort(function(bi,gn){return gn.intersectionZ-bi.intersectionZ});for(var Bt=[],Ft=this._order.length-1;Ft>=0;Ft--){var Ut=this._order[Ft];if(de(Ut))for(var zt=we.length-1;zt>=0;zt--){var _t=we[zt].feature;if(ne[_t.layer.id]<Ft)break;Bt.push(_t),we.pop()}else for(var Dt=0,vt=O;Dt<vt.length;Dt+=1){var lr=vt[Dt],_r=lr[Ut];if(_r)for(var Br=0,Di=_r;Br<Di.length;Br+=1){var ci=Di[Br];Bt.push(ci.feature)}}}return Bt},D.prototype.queryRenderedFeatures=function(O,K,de){K&&K.filter&&this._validate(i.validateStyle.filter,\"queryRenderedFeatures.filter\",K.filter,null,K);var ne={};if(K&&K.layers){if(!Array.isArray(K.layers))return this.fire(new i.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var we=0,Oe=K.layers;we<Oe.length;we+=1){var ht=Oe[we],Yt=this._layers[ht];if(!Yt)return this.fire(new i.ErrorEvent(new Error(\"The layer '\"+ht+\"' does not exist in the map's style and cannot be queried for features.\"))),[];ne[Yt.source]=!0}}var cr=[];K.availableImages=this._availableImages;for(var Nt in this.sourceCaches)K.layers&&!ne[Nt]||cr.push(ce(this.sourceCaches[Nt],this._layers,this._serializedLayers,O,K,de));return this.placement&&cr.push(He(this._layers,this._serializedLayers,this.sourceCaches,O,K,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(cr)},D.prototype.querySourceFeatures=function(O,K){K&&K.filter&&this._validate(i.validateStyle.filter,\"querySourceFeatures.filter\",K.filter,null,K);var de=this.sourceCaches[O];return de?lt(de,K):[]},D.prototype.addSourceType=function(O,K,de){if(D.getSourceType(O))return de(new Error('A source type called \"'+O+'\" already exists.'));if(D.setSourceType(O,K),!K.workerSourceURL)return de(null,null);this.dispatcher.broadcast(\"loadWorkerSource\",{name:O,url:K.workerSourceURL},de)},D.prototype.getLight=function(){return this.light.getLight()},D.prototype.setLight=function(O,K){K===void 0&&(K={}),this._checkLoaded();var de=this.light.getLight(),ne=!1;for(var we in O)if(!i.deepEqual(O[we],de[we])){ne=!0;break}if(ne){var Oe={now:i.browser.now(),transition:i.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(O,K),this.light.updateTransitions(Oe)}},D.prototype._validate=function(O,K,de,ne,we){return we===void 0&&(we={}),we&&we.validate===!1?!1:pl(this,O.call(i.validateStyle,i.extend({key:K,style:this.serialize(),value:de,styleSpec:i.styleSpec},ne)))},D.prototype._remove=function(){this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),i.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback);for(var O in this._layers){var K=this._layers[O];K.setEventedParent(null)}for(var de in this.sourceCaches)this.sourceCaches[de].clearTiles(),this.sourceCaches[de].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},D.prototype._clearSource=function(O){this.sourceCaches[O].clearTiles()},D.prototype._reloadSource=function(O){this.sourceCaches[O].resume(),this.sourceCaches[O].reload()},D.prototype._updateSources=function(O){for(var K in this.sourceCaches)this.sourceCaches[K].update(O)},D.prototype._generateCollisionBoxes=function(){for(var O in this.sourceCaches)this._reloadSource(O)},D.prototype._updatePlacement=function(O,K,de,ne,we){we===void 0&&(we=!1);for(var Oe=!1,ht=!1,Yt={},cr=0,Nt=this._order;cr<Nt.length;cr+=1){var Ne=Nt[cr],$e=this._layers[Ne];if($e.type===\"symbol\"){if(!Yt[$e.source]){var st=this.sourceCaches[$e.source];Yt[$e.source]=st.getRenderableIds(!0).map(function(Dt){return st.getTileByID(Dt)}).sort(function(Dt,vt){return vt.tileID.overscaledZ-Dt.tileID.overscaledZ||(Dt.tileID.isLessThan(vt.tileID)?-1:1)})}var It=this.crossTileSymbolIndex.addLayer($e,Yt[$e.source],O.center.lng);Oe=Oe||It}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),we=we||this._layerOrderChanged||de===0,(we||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(i.browser.now(),O.zoom))&&(this.pauseablePlacement=new Oo(O,this._order,we,K,de,ne,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,Yt),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(i.browser.now()),ht=!0),Oe&&this.pauseablePlacement.placement.setStale()),ht||Oe)for(var Bt=0,Ft=this._order;Bt<Ft.length;Bt+=1){var Ut=Ft[Bt],zt=this._layers[Ut];zt.type===\"symbol\"&&this.placement.updateLayerOpacities(zt,Yt[zt.source])}var _t=!this.pauseablePlacement.isDone()||this.placement.hasTransitions(i.browser.now());return _t},D.prototype._releaseSymbolFadeTiles=function(){for(var O in this.sourceCaches)this.sourceCaches[O].releaseSymbolFadeTiles()},D.prototype.getImages=function(O,K,de){this.imageManager.getImages(K.icons,de),this._updateTilesForChangedImages();var ne=this.sourceCaches[K.source];ne&&ne.setDependencies(K.tileID.key,K.type,K.icons)},D.prototype.getGlyphs=function(O,K,de){this.glyphManager.getGlyphs(K.stacks,de)},D.prototype.getResource=function(O,K,de){return i.makeRequest(K,de)},D})(i.Evented);yu.getSourceType=Be,yu.setSourceType=Le,yu.registerForPluginStateChange=i.registerForPluginStateChange;var oc=i.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Cf=`#ifdef GL_ES\n",
+       "precision mediump float;\n",
+       "#else\n",
+       "#if !defined(lowp)\n",
+       "#define lowp\n",
+       "#endif\n",
+       "#if !defined(mediump)\n",
+       "#define mediump\n",
+       "#endif\n",
+       "#if !defined(highp)\n",
+       "#define highp\n",
+       "#endif\n",
+       "#endif`,sc=`#ifdef GL_ES\n",
+       "precision highp float;\n",
+       "#else\n",
+       "#if !defined(lowp)\n",
+       "#define lowp\n",
+       "#endif\n",
+       "#if !defined(mediump)\n",
+       "#define mediump\n",
+       "#endif\n",
+       "#if !defined(highp)\n",
+       "#define highp\n",
+       "#endif\n",
+       "#endif\n",
+       "vec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n",
+       ");}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}`,jh=`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Lf=\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\",cs=`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,af=\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\",Gf=`varying vec3 v_data;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define mediump float radius\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define highp vec4 stroke_color\n",
+       "#pragma mapbox: define mediump float stroke_width\n",
+       "#pragma mapbox: define lowp float stroke_opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize mediump float radius\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize highp vec4 stroke_color\n",
+       "#pragma mapbox: initialize mediump float stroke_width\n",
+       "#pragma mapbox: initialize lowp float stroke_opacity\n",
+       "vec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,$l=`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define mediump float radius\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define highp vec4 stroke_color\n",
+       "#pragma mapbox: define mediump float stroke_width\n",
+       "#pragma mapbox: define lowp float stroke_opacity\n",
+       "void main(void) {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize mediump float radius\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize highp vec4 stroke_color\n",
+       "#pragma mapbox: initialize mediump float stroke_width\n",
+       "#pragma mapbox: initialize lowp float stroke_opacity\n",
+       "vec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`,fl=\"void main() {gl_FragColor=vec4(1.0);}\",lc=\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\",Fu=`uniform highp float u_intensity;varying vec2 v_extrude;\n",
+       "#pragma mapbox: define highp float weight\n",
+       "#define GAUSS_COEF 0.3989422804014327\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp float weight\n",
+       "float d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Es=`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n",
+       "#pragma mapbox: define highp float weight\n",
+       "#pragma mapbox: define mediump float radius\n",
+       "const highp float ZERO=1.0/255.0/16.0;\n",
+       "#define GAUSS_COEF 0.3989422804014327\n",
+       "void main(void) {\n",
+       "#pragma mapbox: initialize highp float weight\n",
+       "#pragma mapbox: initialize mediump float radius\n",
+       "vec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}`,Hs=`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(0.0);\n",
+       "#endif\n",
+       "}`,Go=\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\",ps=\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",uc=\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\",bl=\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",Gu=\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\",Os=\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",od=\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\",Po=`#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "gl_FragColor=color*opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,sd=`attribute vec2 a_pos;uniform mat4 u_matrix;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "gl_Position=u_matrix*vec4(a_pos,0,1);}`,Ko=`varying vec2 v_pos;\n",
+       "#pragma mapbox: define highp vec4 outline_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 outline_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Pa=`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n",
+       "#pragma mapbox: define highp vec4 outline_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 outline_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`,of=`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Hu=`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`,wl=`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Hf=`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`,Ic=`varying vec4 v_color;void main() {gl_FragColor=v_color;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,yf=`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n",
+       "#pragma mapbox: define highp float base\n",
+       "#pragma mapbox: define highp float height\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp float base\n",
+       "#pragma mapbox: initialize highp float height\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "vec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`,Ol=`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n",
+       "#pragma mapbox: define lowp float base\n",
+       "#pragma mapbox: define lowp float height\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float base\n",
+       "#pragma mapbox: initialize lowp float height\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Ah=`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n",
+       "#pragma mapbox: define lowp float base\n",
+       "#pragma mapbox: define lowp float height\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float base\n",
+       "#pragma mapbox: initialize lowp float height\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n",
+       "? a_pos\n",
+       ": vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`,Qf=`#ifdef GL_ES\n",
+       "precision highp float;\n",
+       "#endif\n",
+       "uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,_f=\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\",Yc=`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n",
+       "#define PI 3.141592653589793\n",
+       "void main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,eh=\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\",th=`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,ju=`\n",
+       "#define scale 0.015873016\n",
+       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float width\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}`,jf=`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,cc=`\n",
+       "#define scale 0.015873016\n",
+       "attribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float width\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}`,sf=`uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Bl=`\n",
+       "#define scale 0.015873016\n",
+       "#define LINE_DISTANCE_SCALE 2.0\n",
+       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define mediump float width\n",
+       "#pragma mapbox: define lowp float floorwidth\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "#pragma mapbox: initialize lowp float floorwidth\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`,Kc=`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float width\n",
+       "#pragma mapbox: define lowp float floorwidth\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "#pragma mapbox: initialize lowp float floorwidth\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Rc=`\n",
+       "#define scale 0.015873016\n",
+       "#define LINE_DISTANCE_SCALE 2.0\n",
+       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float width\n",
+       "#pragma mapbox: define lowp float floorwidth\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "#pragma mapbox: initialize lowp float floorwidth\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`,gs=`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Wf=\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\",Wh=`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "lowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,rh=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}`,lf=`#define SDF_PX 8.0\n",
+       "uniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n",
+       "#pragma mapbox: define highp vec4 fill_color\n",
+       "#pragma mapbox: define highp vec4 halo_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float halo_width\n",
+       "#pragma mapbox: define lowp float halo_blur\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 fill_color\n",
+       "#pragma mapbox: initialize highp vec4 halo_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float halo_width\n",
+       "#pragma mapbox: initialize lowp float halo_blur\n",
+       "float EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,Sh=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n",
+       "#pragma mapbox: define highp vec4 fill_color\n",
+       "#pragma mapbox: define highp vec4 halo_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float halo_width\n",
+       "#pragma mapbox: define lowp float halo_blur\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 fill_color\n",
+       "#pragma mapbox: initialize highp vec4 halo_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float halo_width\n",
+       "#pragma mapbox: initialize lowp float halo_blur\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`,Mu=`#define SDF_PX 8.0\n",
+       "#define SDF 1.0\n",
+       "#define ICON 0.0\n",
+       "uniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n",
+       "#pragma mapbox: define highp vec4 fill_color\n",
+       "#pragma mapbox: define highp vec4 halo_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float halo_width\n",
+       "#pragma mapbox: define lowp float halo_blur\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 fill_color\n",
+       "#pragma mapbox: initialize highp vec4 halo_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float halo_width\n",
+       "#pragma mapbox: initialize lowp float halo_blur\n",
+       "float fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "return;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,ih=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n",
+       "#pragma mapbox: define highp vec4 fill_color\n",
+       "#pragma mapbox: define highp vec4 halo_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float halo_width\n",
+       "#pragma mapbox: define lowp float halo_blur\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 fill_color\n",
+       "#pragma mapbox: initialize highp vec4 halo_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float halo_width\n",
+       "#pragma mapbox: initialize lowp float halo_blur\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`,js=Us(Cf,sc),Eu=Us(jh,Lf),Dc=Us(cs,af),ks=Us(Gf,$l),bc=Us(fl,lc),hu=Us(Fu,Es),_u=Us(Hs,Go),il=Us(ps,uc),nh=Us(bl,Gu),Mh=Us(Os,od),zu=Us(Po,sd),Fc=Us(Ko,Pa),wc=Us(of,Hu),bd=Us(wl,Hf),xf=Us(Ic,yf),Pf=Us(Ol,Ah),qu=Us(Qf,_f),bf=Us(Yc,eh),jl=Us(th,ju),uf=Us(jf,cc),Xh=Us(sf,Bl),If=Us(Kc,Rc),Cs=Us(gs,Wf),du=Us(Wh,rh),ku=Us(lf,Sh),Xf=Us(Mu,ih);function Us(Y,D){var J=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,O=D.match(/attribute ([\\w]+) ([\\w]+)/g),K=Y.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),de=D.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),ne=de?de.concat(K):K,we={};return Y=Y.replace(J,function(Oe,ht,Yt,cr,Nt){return we[Nt]=!0,ht===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
+       "varying `+Yt+\" \"+cr+\" \"+Nt+`;\n",
+       "#else\n",
+       "uniform `+Yt+\" \"+cr+\" u_\"+Nt+`;\n",
+       "#endif\n",
+       "`:`\n",
+       "#ifdef HAS_UNIFORM_u_`+Nt+`\n",
+       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
+       "#endif\n",
+       "`}),D=D.replace(J,function(Oe,ht,Yt,cr,Nt){var Ne=cr===\"float\"?\"vec2\":\"vec4\",$e=Nt.match(/color/)?\"color\":Ne;return we[Nt]?ht===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
+       "uniform lowp float u_`+Nt+`_t;\n",
+       "attribute `+Yt+\" \"+Ne+\" a_\"+Nt+`;\n",
+       "varying `+Yt+\" \"+cr+\" \"+Nt+`;\n",
+       "#else\n",
+       "uniform `+Yt+\" \"+cr+\" u_\"+Nt+`;\n",
+       "#endif\n",
+       "`:$e===\"vec4\"?`\n",
+       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
+       "    `+Nt+\" = a_\"+Nt+`;\n",
+       "#else\n",
+       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
+       "#endif\n",
+       "`:`\n",
+       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
+       "    `+Nt+\" = unpack_mix_\"+$e+\"(a_\"+Nt+\", u_\"+Nt+`_t);\n",
+       "#else\n",
+       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
+       "#endif\n",
+       "`:ht===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
+       "uniform lowp float u_`+Nt+`_t;\n",
+       "attribute `+Yt+\" \"+Ne+\" a_\"+Nt+`;\n",
+       "#else\n",
+       "uniform `+Yt+\" \"+cr+\" u_\"+Nt+`;\n",
+       "#endif\n",
+       "`:$e===\"vec4\"?`\n",
+       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
+       "    `+Yt+\" \"+cr+\" \"+Nt+\" = a_\"+Nt+`;\n",
+       "#else\n",
+       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
+       "#endif\n",
+       "`:`\n",
+       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
+       "    `+Yt+\" \"+cr+\" \"+Nt+\" = unpack_mix_\"+$e+\"(a_\"+Nt+\", u_\"+Nt+`_t);\n",
+       "#else\n",
+       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
+       "#endif\n",
+       "`}),{fragmentSource:Y,vertexSource:D,staticAttributes:O,staticUniforms:ne}}var wf=Object.freeze({__proto__:null,prelude:js,background:Eu,backgroundPattern:Dc,circle:ks,clippingMask:bc,heatmap:hu,heatmapTexture:_u,collisionBox:il,collisionCircle:nh,debug:Mh,fill:zu,fillOutline:Fc,fillOutlinePattern:wc,fillPattern:bd,fillExtrusion:xf,fillExtrusionPattern:Pf,hillshadePrepare:qu,hillshade:bf,line:jl,lineGradient:uf,linePattern:Xh,lineSDF:If,raster:Cs,symbolIcon:du,symbolSDF:ku,symbolTextAndIcon:Xf}),zc=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};zc.prototype.bind=function(D,J,O,K,de,ne,we,Oe){this.context=D;for(var ht=this.boundPaintVertexBuffers.length!==K.length,Yt=0;!ht&&Yt<K.length;Yt++)this.boundPaintVertexBuffers[Yt]!==K[Yt]&&(ht=!0);var cr=!this.vao||this.boundProgram!==J||this.boundLayoutVertexBuffer!==O||ht||this.boundIndexBuffer!==de||this.boundVertexOffset!==ne||this.boundDynamicVertexBuffer!==we||this.boundDynamicVertexBuffer2!==Oe;!D.extVertexArrayObject||cr?this.freshBind(J,O,K,de,ne,we,Oe):(D.bindVertexArrayOES.set(this.vao),we&&we.bind(),de&&de.dynamicDraw&&de.bind(),Oe&&Oe.bind())},zc.prototype.freshBind=function(D,J,O,K,de,ne,we){var Oe,ht=D.numAttributes,Yt=this.context,cr=Yt.gl;if(Yt.extVertexArrayObject)this.vao&&this.destroy(),this.vao=Yt.extVertexArrayObject.createVertexArrayOES(),Yt.bindVertexArrayOES.set(this.vao),Oe=0,this.boundProgram=D,this.boundLayoutVertexBuffer=J,this.boundPaintVertexBuffers=O,this.boundIndexBuffer=K,this.boundVertexOffset=de,this.boundDynamicVertexBuffer=ne,this.boundDynamicVertexBuffer2=we;else{Oe=Yt.currentNumAttributes||0;for(var Nt=ht;Nt<Oe;Nt++)cr.disableVertexAttribArray(Nt)}J.enableAttributes(cr,D);for(var Ne=0,$e=O;Ne<$e.length;Ne+=1){var st=$e[Ne];st.enableAttributes(cr,D)}ne&&ne.enableAttributes(cr,D),we&&we.enableAttributes(cr,D),J.bind(),J.setVertexAttribPointers(cr,D,de);for(var It=0,Bt=O;It<Bt.length;It+=1){var Ft=Bt[It];Ft.bind(),Ft.setVertexAttribPointers(cr,D,de)}ne&&(ne.bind(),ne.setVertexAttribPointers(cr,D,de)),K&&K.bind(),we&&(we.bind(),we.setVertexAttribPointers(cr,D,de)),Yt.currentNumAttributes=ht},zc.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};function Wu(Y){for(var D=[],J=0;J<Y.length;J++)if(Y[J]!==null){var O=Y[J].split(\" \");D.push(O.pop())}return D}var Rf=function(D,J,O,K,de,ne){var we=D.gl;this.program=we.createProgram();for(var Oe=Wu(O.staticAttributes),ht=K?K.getBinderAttributes():[],Yt=Oe.concat(ht),cr=O.staticUniforms?Wu(O.staticUniforms):[],Nt=K?K.getBinderUniforms():[],Ne=cr.concat(Nt),$e=[],st=0,It=Ne;st<It.length;st+=1){var Bt=It[st];$e.indexOf(Bt)<0&&$e.push(Bt)}var Ft=K?K.defines():[];ne&&Ft.push(\"#define OVERDRAW_INSPECTOR;\");var Ut=Ft.concat(js.fragmentSource,O.fragmentSource).join(`\n",
+       "`),zt=Ft.concat(js.vertexSource,O.vertexSource).join(`\n",
+       "`),_t=we.createShader(we.FRAGMENT_SHADER);if(we.isContextLost()){this.failedToCreate=!0;return}we.shaderSource(_t,Ut),we.compileShader(_t),we.attachShader(this.program,_t);var Dt=we.createShader(we.VERTEX_SHADER);if(we.isContextLost()){this.failedToCreate=!0;return}we.shaderSource(Dt,zt),we.compileShader(Dt),we.attachShader(this.program,Dt),this.attributes={};var vt={};this.numAttributes=Yt.length;for(var lr=0;lr<this.numAttributes;lr++)Yt[lr]&&(we.bindAttribLocation(this.program,lr,Yt[lr]),this.attributes[Yt[lr]]=lr);we.linkProgram(this.program),we.deleteShader(Dt),we.deleteShader(_t);for(var _r=0;_r<$e.length;_r++){var Br=$e[_r];if(Br&&!vt[Br]){var Di=we.getUniformLocation(this.program,Br);Di&&(vt[Br]=Di)}}this.fixedUniforms=de(D,vt),this.binderUniforms=K?K.getUniforms(D,vt):[]};Rf.prototype.draw=function(D,J,O,K,de,ne,we,Oe,ht,Yt,cr,Nt,Ne,$e,st,It){var Bt,Ft=D.gl;if(!this.failedToCreate){D.program.set(this.program),D.setDepthMode(O),D.setStencilMode(K),D.setColorMode(de),D.setCullFace(ne);for(var Ut in this.fixedUniforms)this.fixedUniforms[Ut].set(we[Ut]);$e&&$e.setUniforms(D,this.binderUniforms,Nt,{zoom:Ne});for(var zt=(Bt={},Bt[Ft.LINES]=2,Bt[Ft.TRIANGLES]=3,Bt[Ft.LINE_STRIP]=1,Bt)[J],_t=0,Dt=cr.get();_t<Dt.length;_t+=1){var vt=Dt[_t],lr=vt.vaos||(vt.vaos={}),_r=lr[Oe]||(lr[Oe]=new zc);_r.bind(D,this,ht,$e?$e.getPaintVertexBuffers():[],Yt,vt.vertexOffset,st,It),Ft.drawElements(J,vt.primitiveLength*zt,Ft.UNSIGNED_SHORT,vt.primitiveOffset*zt*2)}}};function Xu(Y,D,J){var O=1/As(J,1,D.transform.tileZoom),K=Math.pow(2,J.tileID.overscaledZ),de=J.tileSize*Math.pow(2,D.transform.tileZoom)/K,ne=de*(J.tileID.canonical.x+J.tileID.wrap*K),we=de*J.tileID.canonical.y;return{u_image:0,u_texsize:J.imageAtlasTexture.size,u_scale:[O,Y.fromScale,Y.toScale],u_fade:Y.t,u_pixel_coord_upper:[ne>>16,we>>16],u_pixel_coord_lower:[ne&65535,we&65535]}}function cf(Y,D,J,O){var K=J.imageManager.getPattern(Y.from.toString()),de=J.imageManager.getPattern(Y.to.toString()),ne=J.imageManager.getPixelSize(),we=ne.width,Oe=ne.height,ht=Math.pow(2,O.tileID.overscaledZ),Yt=O.tileSize*Math.pow(2,J.transform.tileZoom)/ht,cr=Yt*(O.tileID.canonical.x+O.tileID.wrap*ht),Nt=Yt*O.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:K.tl,u_pattern_br_a:K.br,u_pattern_tl_b:de.tl,u_pattern_br_b:de.br,u_texsize:[we,Oe],u_mix:D.t,u_pattern_size_a:K.displaySize,u_pattern_size_b:de.displaySize,u_scale_a:D.fromScale,u_scale_b:D.toScale,u_tile_units_to_pixels:1/As(O,1,J.transform.tileZoom),u_pixel_coord_upper:[cr>>16,Nt>>16],u_pixel_coord_lower:[cr&65535,Nt&65535]}}var Zf=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_lightpos:new i.Uniform3f(Y,D.u_lightpos),u_lightintensity:new i.Uniform1f(Y,D.u_lightintensity),u_lightcolor:new i.Uniform3f(Y,D.u_lightcolor),u_vertical_gradient:new i.Uniform1f(Y,D.u_vertical_gradient),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},Wl=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_lightpos:new i.Uniform3f(Y,D.u_lightpos),u_lightintensity:new i.Uniform1f(Y,D.u_lightintensity),u_lightcolor:new i.Uniform3f(Y,D.u_lightcolor),u_vertical_gradient:new i.Uniform1f(Y,D.u_vertical_gradient),u_height_factor:new i.Uniform1f(Y,D.u_height_factor),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},ah=function(Y,D,J,O){var K=D.style.light,de=K.properties.get(\"position\"),ne=[de.x,de.y,de.z],we=i.create$1();K.properties.get(\"anchor\")===\"viewport\"&&i.fromRotation(we,-D.transform.angle),i.transformMat3(ne,ne,we);var Oe=K.properties.get(\"color\");return{u_matrix:Y,u_lightpos:ne,u_lightintensity:K.properties.get(\"intensity\"),u_lightcolor:[Oe.r,Oe.g,Oe.b],u_vertical_gradient:+J,u_opacity:O}},Zu=function(Y,D,J,O,K,de,ne){return i.extend(ah(Y,D,J,O),Xu(de,D,ne),{u_height_factor:-Math.pow(2,K.overscaledZ)/ne.tileSize/8})},qc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},Tc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},Tl=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world)}},vu=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},Oc=function(Y){return{u_matrix:Y}},ff=function(Y,D,J,O){return i.extend(Oc(Y),Xu(J,D,O))},fc=function(Y,D){return{u_matrix:Y,u_world:D}},Bc=function(Y,D,J,O,K){return i.extend(ff(Y,D,J,O),{u_world:K})},kt=function(Y,D){return{u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_scale_with_map:new i.Uniform1i(Y,D.u_scale_with_map),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_extrude_scale:new i.Uniform2f(Y,D.u_extrude_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},Zt=function(Y,D,J,O){var K=Y.transform,de,ne;if(O.paint.get(\"circle-pitch-alignment\")===\"map\"){var we=As(J,1,K.zoom);de=!0,ne=[we,we]}else de=!1,ne=K.pixelsToGLUnits;return{u_camera_to_center_distance:K.cameraToCenterDistance,u_scale_with_map:+(O.paint.get(\"circle-pitch-scale\")===\"map\"),u_matrix:Y.translatePosMatrix(D.posMatrix,J,O.paint.get(\"circle-translate\"),O.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+de,u_device_pixel_ratio:i.browser.devicePixelRatio,u_extrude_scale:ne}},Er=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pixels_to_tile_units:new i.Uniform1f(Y,D.u_pixels_to_tile_units),u_extrude_scale:new i.Uniform2f(Y,D.u_extrude_scale),u_overscale_factor:new i.Uniform1f(Y,D.u_overscale_factor)}},xr=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_inv_matrix:new i.UniformMatrix4f(Y,D.u_inv_matrix),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_viewport_size:new i.Uniform2f(Y,D.u_viewport_size)}},Kr=function(Y,D,J){var O=As(J,1,D.zoom),K=Math.pow(2,D.zoom-J.tileID.overscaledZ),de=J.tileID.overscaleFactor();return{u_matrix:Y,u_camera_to_center_distance:D.cameraToCenterDistance,u_pixels_to_tile_units:O,u_extrude_scale:[D.pixelsToGLUnits[0]/(O*K),D.pixelsToGLUnits[1]/(O*K)],u_overscale_factor:de}},Mi=function(Y,D,J){return{u_matrix:Y,u_inv_matrix:D,u_camera_to_center_distance:J.cameraToCenterDistance,u_viewport_size:[J.width,J.height]}},Xi=function(Y,D){return{u_color:new i.UniformColor(Y,D.u_color),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_overlay:new i.Uniform1i(Y,D.u_overlay),u_overlay_scale:new i.Uniform1f(Y,D.u_overlay_scale)}},dn=function(Y,D,J){return J===void 0&&(J=1),{u_matrix:Y,u_color:D,u_overlay:0,u_overlay_scale:J}},wn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},On=function(Y){return{u_matrix:Y}},Yi=function(Y,D){return{u_extrude_scale:new i.Uniform1f(Y,D.u_extrude_scale),u_intensity:new i.Uniform1f(Y,D.u_intensity),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},$i=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world),u_image:new i.Uniform1i(Y,D.u_image),u_color_ramp:new i.Uniform1i(Y,D.u_color_ramp),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},an=function(Y,D,J,O){return{u_matrix:Y,u_extrude_scale:As(D,1,J),u_intensity:O}},Fi=function(Y,D,J,O){var K=i.create();i.ortho(K,0,Y.width,Y.height,0,0,1);var de=Y.context.gl;return{u_matrix:K,u_world:[de.drawingBufferWidth,de.drawingBufferHeight],u_image:J,u_color_ramp:O,u_opacity:D.paint.get(\"heatmap-opacity\")}},ta=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_latrange:new i.Uniform2f(Y,D.u_latrange),u_light:new i.Uniform2f(Y,D.u_light),u_shadow:new i.UniformColor(Y,D.u_shadow),u_highlight:new i.UniformColor(Y,D.u_highlight),u_accent:new i.UniformColor(Y,D.u_accent)}},Ca=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_dimension:new i.Uniform2f(Y,D.u_dimension),u_zoom:new i.Uniform1f(Y,D.u_zoom),u_unpack:new i.Uniform4f(Y,D.u_unpack)}},Ra=function(Y,D,J){var O=J.paint.get(\"hillshade-shadow-color\"),K=J.paint.get(\"hillshade-highlight-color\"),de=J.paint.get(\"hillshade-accent-color\"),ne=J.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);J.paint.get(\"hillshade-illumination-anchor\")===\"viewport\"&&(ne-=Y.transform.angle);var we=!Y.options.moving;return{u_matrix:Y.transform.calculatePosMatrix(D.tileID.toUnwrapped(),we),u_image:0,u_latrange:Ua(Y,D.tileID),u_light:[J.paint.get(\"hillshade-exaggeration\"),ne],u_shadow:O,u_highlight:K,u_accent:de}},La=function(Y,D){var J=D.stride,O=i.create();return i.ortho(O,0,i.EXTENT,-i.EXTENT,0,0,1),i.translate(O,O,[0,-i.EXTENT,0]),{u_matrix:O,u_image:1,u_dimension:[J,J],u_zoom:Y.overscaledZ,u_unpack:D.getUnpackVector()}};function Ua(Y,D){var J=Math.pow(2,D.canonical.z),O=D.canonical.y;return[new i.MercatorCoordinate(0,O/J).toLngLat().lat,new i.MercatorCoordinate(0,(O+1)/J).toLngLat().lat]}var Zn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels)}},Dn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_image:new i.Uniform1i(Y,D.u_image),u_image_height:new i.Uniform1f(Y,D.u_image_height)}},Ka=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_image:new i.Uniform1i(Y,D.u_image),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},bo=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_patternscale_a:new i.Uniform2f(Y,D.u_patternscale_a),u_patternscale_b:new i.Uniform2f(Y,D.u_patternscale_b),u_sdfgamma:new i.Uniform1f(Y,D.u_sdfgamma),u_image:new i.Uniform1i(Y,D.u_image),u_tex_y_a:new i.Uniform1f(Y,D.u_tex_y_a),u_tex_y_b:new i.Uniform1f(Y,D.u_tex_y_b),u_mix:new i.Uniform1f(Y,D.u_mix)}},Zo=function(Y,D,J){var O=Y.transform;return{u_matrix:ml(Y,D,J),u_ratio:1/As(D,1,O.zoom),u_device_pixel_ratio:i.browser.devicePixelRatio,u_units_to_pixels:[1/O.pixelsToGLUnits[0],1/O.pixelsToGLUnits[1]]}},Ss=function(Y,D,J,O){return i.extend(Zo(Y,D,J),{u_image:0,u_image_height:O})},as=function(Y,D,J,O){var K=Y.transform,de=Ho(D,K);return{u_matrix:ml(Y,D,J),u_texsize:D.imageAtlasTexture.size,u_ratio:1/As(D,1,K.zoom),u_device_pixel_ratio:i.browser.devicePixelRatio,u_image:0,u_scale:[de,O.fromScale,O.toScale],u_fade:O.t,u_units_to_pixels:[1/K.pixelsToGLUnits[0],1/K.pixelsToGLUnits[1]]}},ws=function(Y,D,J,O,K){var de=Y.transform,ne=Y.lineAtlas,we=Ho(D,de),Oe=J.layout.get(\"line-cap\")===\"round\",ht=ne.getDash(O.from,Oe),Yt=ne.getDash(O.to,Oe),cr=ht.width*K.fromScale,Nt=Yt.width*K.toScale;return i.extend(Zo(Y,D,J),{u_patternscale_a:[we/cr,-ht.height/2],u_patternscale_b:[we/Nt,-Yt.height/2],u_sdfgamma:ne.width/(Math.min(cr,Nt)*256*i.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:ht.y,u_tex_y_b:Yt.y,u_mix:K.t})};function Ho(Y,D){return 1/As(Y,1,D.tileZoom)}function ml(Y,D,J){return Y.translatePosMatrix(D.tileID.posMatrix,D,J.paint.get(\"line-translate\"),J.paint.get(\"line-translate-anchor\"))}var Ws=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_tl_parent:new i.Uniform2f(Y,D.u_tl_parent),u_scale_parent:new i.Uniform1f(Y,D.u_scale_parent),u_buffer_scale:new i.Uniform1f(Y,D.u_buffer_scale),u_fade_t:new i.Uniform1f(Y,D.u_fade_t),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_image0:new i.Uniform1i(Y,D.u_image0),u_image1:new i.Uniform1i(Y,D.u_image1),u_brightness_low:new i.Uniform1f(Y,D.u_brightness_low),u_brightness_high:new i.Uniform1f(Y,D.u_brightness_high),u_saturation_factor:new i.Uniform1f(Y,D.u_saturation_factor),u_contrast_factor:new i.Uniform1f(Y,D.u_contrast_factor),u_spin_weights:new i.Uniform3f(Y,D.u_spin_weights)}},Ls=function(Y,D,J,O,K){return{u_matrix:Y,u_tl_parent:D,u_scale_parent:J,u_buffer_scale:1,u_fade_t:O.mix,u_opacity:O.opacity*K.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:K.paint.get(\"raster-brightness-min\"),u_brightness_high:K.paint.get(\"raster-brightness-max\"),u_saturation_factor:ys(K.paint.get(\"raster-saturation\")),u_contrast_factor:no(K.paint.get(\"raster-contrast\")),u_spin_weights:va(K.paint.get(\"raster-hue-rotate\"))}};function va(Y){Y*=Math.PI/180;var D=Math.sin(Y),J=Math.cos(Y);return[(2*J+1)/3,(-Math.sqrt(3)*D-J+1)/3,(Math.sqrt(3)*D-J+1)/3]}function no(Y){return Y>0?1/(1-Y):1+Y}function ys(Y){return Y>0?1-1/(1.001-Y):-Y}var rs=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texture:new i.Uniform1i(Y,D.u_texture)}},Ql=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texture:new i.Uniform1i(Y,D.u_texture),u_gamma_scale:new i.Uniform1f(Y,D.u_gamma_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_is_halo:new i.Uniform1i(Y,D.u_is_halo)}},Cu=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texsize_icon:new i.Uniform2f(Y,D.u_texsize_icon),u_texture:new i.Uniform1i(Y,D.u_texture),u_texture_icon:new i.Uniform1i(Y,D.u_texture_icon),u_gamma_scale:new i.Uniform1f(Y,D.u_gamma_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_is_halo:new i.Uniform1i(Y,D.u_is_halo)}},Yu=function(Y,D,J,O,K,de,ne,we,Oe,ht){var Yt=K.transform;return{u_is_size_zoom_constant:+(Y===\"constant\"||Y===\"source\"),u_is_size_feature_constant:+(Y===\"constant\"||Y===\"camera\"),u_size_t:D?D.uSizeT:0,u_size:D?D.uSize:0,u_camera_to_center_distance:Yt.cameraToCenterDistance,u_pitch:Yt.pitch/360*2*Math.PI,u_rotate_symbol:+J,u_aspect_ratio:Yt.width/Yt.height,u_fade_change:K.options.fadeDuration?K.symbolFadeChange:1,u_matrix:de,u_label_plane_matrix:ne,u_coord_matrix:we,u_is_text:+Oe,u_pitch_with_map:+O,u_texsize:ht,u_texture:0}},Nc=function(Y,D,J,O,K,de,ne,we,Oe,ht,Yt){var cr=K.transform;return i.extend(Yu(Y,D,J,O,K,de,ne,we,Oe,ht),{u_gamma_scale:O?Math.cos(cr._pitch)*cr.cameraToCenterDistance:1,u_device_pixel_ratio:i.browser.devicePixelRatio,u_is_halo:+Yt})},pu=function(Y,D,J,O,K,de,ne,we,Oe,ht){return i.extend(Nc(Y,D,J,O,K,de,ne,we,!0,Oe,!0),{u_texsize_icon:ht,u_texture_icon:1})},Uc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_color:new i.UniformColor(Y,D.u_color)}},xu=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_image:new i.Uniform1i(Y,D.u_image),u_pattern_tl_a:new i.Uniform2f(Y,D.u_pattern_tl_a),u_pattern_br_a:new i.Uniform2f(Y,D.u_pattern_br_a),u_pattern_tl_b:new i.Uniform2f(Y,D.u_pattern_tl_b),u_pattern_br_b:new i.Uniform2f(Y,D.u_pattern_br_b),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_mix:new i.Uniform1f(Y,D.u_mix),u_pattern_size_a:new i.Uniform2f(Y,D.u_pattern_size_a),u_pattern_size_b:new i.Uniform2f(Y,D.u_pattern_size_b),u_scale_a:new i.Uniform1f(Y,D.u_scale_a),u_scale_b:new i.Uniform1f(Y,D.u_scale_b),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_tile_units_to_pixels:new i.Uniform1f(Y,D.u_tile_units_to_pixels)}},Ac=function(Y,D,J){return{u_matrix:Y,u_opacity:D,u_color:J}},Va=function(Y,D,J,O,K,de){return i.extend(cf(O,de,J,K),{u_matrix:Y,u_opacity:D})},oo={fillExtrusion:Zf,fillExtrusionPattern:Wl,fill:qc,fillPattern:Tc,fillOutline:Tl,fillOutlinePattern:vu,circle:kt,collisionBox:Er,collisionCircle:xr,debug:Xi,clippingMask:wn,heatmap:Yi,heatmapTexture:$i,hillshade:ta,hillshadePrepare:Ca,line:Zn,lineGradient:Dn,linePattern:Ka,lineSDF:bo,raster:Ws,symbolIcon:rs,symbolSDF:Ql,symbolTextAndIcon:Cu,background:Uc,backgroundPattern:xu},Vc;function hc(Y,D,J,O,K,de,ne){for(var we=Y.context,Oe=we.gl,ht=Y.useProgram(\"collisionBox\"),Yt=[],cr=0,Nt=0,Ne=0;Ne<O.length;Ne++){var $e=O[Ne],st=D.getTile($e),It=st.getBucket(J);if(It){var Bt=$e.posMatrix;(K[0]!==0||K[1]!==0)&&(Bt=Y.translatePosMatrix($e.posMatrix,st,K,de));var Ft=ne?It.textCollisionBox:It.iconCollisionBox,Ut=It.collisionCircleArray;if(Ut.length>0){var zt=i.create(),_t=Bt;i.mul(zt,It.placementInvProjMatrix,Y.transform.glCoordMatrix),i.mul(zt,zt,It.placementViewportMatrix),Yt.push({circleArray:Ut,circleOffset:Nt,transform:_t,invTransform:zt}),cr+=Ut.length/4,Nt=cr}Ft&&ht.draw(we,Oe.LINES,An.disabled,Vi.disabled,Y.colorModeForRenderPass(),wr.disabled,Kr(Bt,Y.transform,st),J.id,Ft.layoutVertexBuffer,Ft.indexBuffer,Ft.segments,null,Y.transform.zoom,null,null,Ft.collisionVertexBuffer)}}if(!(!ne||!Yt.length)){var Dt=Y.useProgram(\"collisionCircle\"),vt=new i.StructArrayLayout2f1f2i16;vt.resize(cr*4),vt._trim();for(var lr=0,_r=0,Br=Yt;_r<Br.length;_r+=1)for(var Di=Br[_r],ci=0;ci<Di.circleArray.length/4;ci++){var bi=ci*4,gn=Di.circleArray[bi+0],tn=Di.circleArray[bi+1],Ei=Di.circleArray[bi+2],Oi=Di.circleArray[bi+3];vt.emplace(lr++,gn,tn,Ei,Oi,0),vt.emplace(lr++,gn,tn,Ei,Oi,1),vt.emplace(lr++,gn,tn,Ei,Oi,2),vt.emplace(lr++,gn,tn,Ei,Oi,3)}(!Vc||Vc.length<cr*2)&&(Vc=Ku(cr));for(var Gi=we.createIndexBuffer(Vc,!0),on=we.createVertexBuffer(vt,i.collisionCircleLayout.members,!0),zn=0,Ja=Yt;zn<Ja.length;zn+=1){var co=Ja[zn],ts=Mi(co.transform,co.invTransform,Y.transform);Dt.draw(we,Oe.TRIANGLES,An.disabled,Vi.disabled,Y.colorModeForRenderPass(),wr.disabled,ts,J.id,on,Gi,i.SegmentVector.simpleSegment(0,co.circleOffset*2,co.circleArray.length,co.circleArray.length/2),null,Y.transform.zoom,null,null,null)}on.destroy(),Gi.destroy()}}function Ku(Y){var D=Y*2,J=new i.StructArrayLayout3ui6;J.resize(D),J._trim();for(var O=0;O<D;O++){var K=O*6;J.uint16[K+0]=O*4+0,J.uint16[K+1]=O*4+1,J.uint16[K+2]=O*4+2,J.uint16[K+3]=O*4+2,J.uint16[K+4]=O*4+3,J.uint16[K+5]=O*4+0}return J}var ue=i.identity(new Float32Array(16));function w(Y,D,J,O,K){if(Y.renderPass===\"translucent\"){var de=Vi.disabled,ne=Y.colorModeForRenderPass(),we=J.layout.get(\"text-variable-anchor\");we&&Q(O,Y,J,D,J.layout.get(\"text-rotation-alignment\"),J.layout.get(\"text-pitch-alignment\"),K),J.paint.get(\"icon-opacity\").constantOr(1)!==0&&Fe(Y,D,J,O,!1,J.paint.get(\"icon-translate\"),J.paint.get(\"icon-translate-anchor\"),J.layout.get(\"icon-rotation-alignment\"),J.layout.get(\"icon-pitch-alignment\"),J.layout.get(\"icon-keep-upright\"),de,ne),J.paint.get(\"text-opacity\").constantOr(1)!==0&&Fe(Y,D,J,O,!0,J.paint.get(\"text-translate\"),J.paint.get(\"text-translate-anchor\"),J.layout.get(\"text-rotation-alignment\"),J.layout.get(\"text-pitch-alignment\"),J.layout.get(\"text-keep-upright\"),de,ne),D.map.showCollisionBoxes&&(hc(Y,D,J,O,J.paint.get(\"text-translate\"),J.paint.get(\"text-translate-anchor\"),!0),hc(Y,D,J,O,J.paint.get(\"icon-translate\"),J.paint.get(\"icon-translate-anchor\"),!1))}}function B(Y,D,J,O,K,de){var ne=i.getAnchorAlignment(Y),we=ne.horizontalAlign,Oe=ne.verticalAlign,ht=-(we-.5)*D,Yt=-(Oe-.5)*J,cr=i.evaluateVariableOffset(Y,O);return new i.Point((ht/K+cr[0])*de,(Yt/K+cr[1])*de)}function Q(Y,D,J,O,K,de,ne){for(var we=D.transform,Oe=K===\"map\",ht=de===\"map\",Yt=0,cr=Y;Yt<cr.length;Yt+=1){var Nt=cr[Yt],Ne=O.getTile(Nt),$e=Ne.getBucket(J);if(!(!$e||!$e.text||!$e.text.segments.get().length)){var st=$e.textSizeData,It=i.evaluateSizeForZoom(st,we.zoom),Bt=As(Ne,1,D.transform.zoom),Ft=_n(Nt.posMatrix,ht,Oe,D.transform,Bt),Ut=J.layout.get(\"icon-text-fit\")!==\"none\"&&$e.hasIconData();if(It){var zt=Math.pow(2,we.zoom-Ne.tileID.overscaledZ);ee($e,Oe,ht,ne,i.symbolSize,we,Ft,Nt.posMatrix,zt,It,Ut)}}}}function ee(Y,D,J,O,K,de,ne,we,Oe,ht,Yt){var cr=Y.text.placedSymbolArray,Nt=Y.text.dynamicLayoutVertexArray,Ne=Y.icon.dynamicLayoutVertexArray,$e={};Nt.clear();for(var st=0;st<cr.length;st++){var It=cr.get(st),Bt=Y.allowVerticalPlacement&&!It.placedOrientation,Ft=!It.hidden&&It.crossTileID&&!Bt?O[It.crossTileID]:null;if(!Ft)ul(It.numGlyphs,Nt);else{var Ut=new i.Point(It.anchorX,It.anchorY),zt=ea(Ut,J?we:ne),_t=Ma(de.cameraToCenterDistance,zt.signedDistanceFromCamera),Dt=K.evaluateSizeForFeature(Y.textSizeData,ht,It)*_t/i.ONE_EM;J&&(Dt*=Y.tilePixelRatio/Oe);for(var vt=Ft.width,lr=Ft.height,_r=Ft.anchor,Br=Ft.textOffset,Di=Ft.textBoxScale,ci=B(_r,vt,lr,Br,Di,Dt),bi=J?ea(Ut.add(ci),ne).point:zt.point.add(D?ci.rotate(-de.angle):ci),gn=Y.allowVerticalPlacement&&It.placedOrientation===i.WritingMode.vertical?Math.PI/2:0,tn=0;tn<It.numGlyphs;tn++)i.addDynamicAttributes(Nt,bi,gn);Yt&&It.associatedIconIndex>=0&&($e[It.associatedIconIndex]={shiftedAnchor:bi,angle:gn})}}if(Yt){Ne.clear();for(var Ei=Y.icon.placedSymbolArray,Oi=0;Oi<Ei.length;Oi++){var Gi=Ei.get(Oi);if(Gi.hidden)ul(Gi.numGlyphs,Ne);else{var on=$e[Oi];if(!on)ul(Gi.numGlyphs,Ne);else for(var zn=0;zn<Gi.numGlyphs;zn++)i.addDynamicAttributes(Ne,on.shiftedAnchor,on.angle)}}Y.icon.dynamicLayoutVertexBuffer.updateData(Ne)}Y.text.dynamicLayoutVertexBuffer.updateData(Nt)}function le(Y,D,J){return J.iconsInText&&D?\"symbolTextAndIcon\":Y?\"symbolSDF\":\"symbolIcon\"}function Fe(Y,D,J,O,K,de,ne,we,Oe,ht,Yt,cr){for(var Nt=Y.context,Ne=Nt.gl,$e=Y.transform,st=we===\"map\",It=Oe===\"map\",Bt=st&&J.layout.get(\"symbol-placement\")!==\"point\",Ft=st&&!It&&!Bt,Ut=J.layout.get(\"symbol-sort-key\").constantOr(1)!==void 0,zt=!1,_t=Y.depthModeForSublayer(0,An.ReadOnly),Dt=J.layout.get(\"text-variable-anchor\"),vt=[],lr=0,_r=O;lr<_r.length;lr+=1){var Br=_r[lr],Di=D.getTile(Br),ci=Di.getBucket(J);if(ci){var bi=K?ci.text:ci.icon;if(!(!bi||!bi.segments.get().length)){var gn=bi.programConfigurations.get(J.id),tn=K||ci.sdfIcons,Ei=K?ci.textSizeData:ci.iconSizeData,Oi=It||$e.pitch!==0,Gi=Y.useProgram(le(tn,K,ci),gn),on=i.evaluateSizeForZoom(Ei,$e.zoom),zn=void 0,Ja=[0,0],co=void 0,ts=void 0,so=null,Yo=void 0;if(K){if(co=Di.glyphAtlasTexture,ts=Ne.LINEAR,zn=Di.glyphAtlasTexture.size,ci.iconsInText){Ja=Di.imageAtlasTexture.size,so=Di.imageAtlasTexture;var ms=Ei.kind===\"composite\"||Ei.kind===\"camera\";Yo=Oi||Y.options.rotating||Y.options.zooming||ms?Ne.LINEAR:Ne.NEAREST}}else{var ou=J.layout.get(\"icon-size\").constantOr(0)!==1||ci.iconsNeedLinear;co=Di.imageAtlasTexture,ts=tn||Y.options.rotating||Y.options.zooming||ou||Oi?Ne.LINEAR:Ne.NEAREST,zn=Di.imageAtlasTexture.size}var Cv=As(Di,1,Y.transform.zoom),Lv=_n(Br.posMatrix,It,st,Y.transform,Cv),wd=ya(Br.posMatrix,It,st,Y.transform,Cv),Kv=Dt&&ci.hasTextData(),hg=J.layout.get(\"icon-text-fit\")!==\"none\"&&Kv&&ci.hasIconData();Bt&&No(ci,Br.posMatrix,Y,K,Lv,wd,It,ht);var gp=Y.translatePosMatrix(Br.posMatrix,Di,de,ne),Td=Bt||K&&Dt||hg?ue:Lv,mp=Y.translatePosMatrix(wd,Di,de,ne,!0),Vd=tn&&J.paint.get(K?\"text-halo-width\":\"icon-halo-width\").constantOr(1)!==0,Ad=void 0;tn?ci.iconsInText?Ad=pu(Ei.kind,on,Ft,It,Y,gp,Td,mp,zn,Ja):Ad=Nc(Ei.kind,on,Ft,It,Y,gp,Td,mp,K,zn,!0):Ad=Yu(Ei.kind,on,Ft,It,Y,gp,Td,mp,K,zn);var Pv={program:Gi,buffers:bi,uniformValues:Ad,atlasTexture:co,atlasTextureIcon:so,atlasInterpolation:ts,atlasInterpolationIcon:Yo,isSDF:tn,hasHalo:Vd};if(Ut&&ci.canOverlap){zt=!0;for(var Jv=bi.segments.get(),Iv=0,dy=Jv;Iv<dy.length;Iv+=1){var dg=dy[Iv];vt.push({segments:new i.SegmentVector([dg]),sortKey:dg.sortKey,state:Pv})}}else vt.push({segments:bi.segments,sortKey:0,state:Pv})}}}zt&&vt.sort(function(C1,L1){return C1.sortKey-L1.sortKey});for(var oh=0,vg=vt;oh<vg.length;oh+=1){var vy=vg[oh],Zh=vy.state;if(Nt.activeTexture.set(Ne.TEXTURE0),Zh.atlasTexture.bind(Zh.atlasInterpolation,Ne.CLAMP_TO_EDGE),Zh.atlasTextureIcon&&(Nt.activeTexture.set(Ne.TEXTURE1),Zh.atlasTextureIcon&&Zh.atlasTextureIcon.bind(Zh.atlasInterpolationIcon,Ne.CLAMP_TO_EDGE)),Zh.isSDF){var am=Zh.uniformValues;Zh.hasHalo&&(am.u_is_halo=1,Ze(Zh.buffers,vy.segments,J,Y,Zh.program,_t,Yt,cr,am)),am.u_is_halo=0}Ze(Zh.buffers,vy.segments,J,Y,Zh.program,_t,Yt,cr,Zh.uniformValues)}}function Ze(Y,D,J,O,K,de,ne,we,Oe){var ht=O.context,Yt=ht.gl;K.draw(ht,Yt.TRIANGLES,de,ne,we,wr.disabled,Oe,J.id,Y.layoutVertexBuffer,Y.indexBuffer,D,J.paint,O.transform.zoom,Y.programConfigurations.get(J.id),Y.dynamicLayoutVertexBuffer,Y.opacityVertexBuffer)}function ut(Y,D,J,O){if(Y.renderPass===\"translucent\"){var K=J.paint.get(\"circle-opacity\"),de=J.paint.get(\"circle-stroke-width\"),ne=J.paint.get(\"circle-stroke-opacity\"),we=J.layout.get(\"circle-sort-key\").constantOr(1)!==void 0;if(!(K.constantOr(1)===0&&(de.constantOr(1)===0||ne.constantOr(1)===0))){for(var Oe=Y.context,ht=Oe.gl,Yt=Y.depthModeForSublayer(0,An.ReadOnly),cr=Vi.disabled,Nt=Y.colorModeForRenderPass(),Ne=[],$e=0;$e<O.length;$e++){var st=O[$e],It=D.getTile(st),Bt=It.getBucket(J);if(Bt){var Ft=Bt.programConfigurations.get(J.id),Ut=Y.useProgram(\"circle\",Ft),zt=Bt.layoutVertexBuffer,_t=Bt.indexBuffer,Dt=Zt(Y,st,It,J),vt={programConfiguration:Ft,program:Ut,layoutVertexBuffer:zt,indexBuffer:_t,uniformValues:Dt};if(we)for(var lr=Bt.segments.get(),_r=0,Br=lr;_r<Br.length;_r+=1){var Di=Br[_r];Ne.push({segments:new i.SegmentVector([Di]),sortKey:Di.sortKey,state:vt})}else Ne.push({segments:Bt.segments,sortKey:0,state:vt})}}we&&Ne.sort(function(co,ts){return co.sortKey-ts.sortKey});for(var ci=0,bi=Ne;ci<bi.length;ci+=1){var gn=bi[ci],tn=gn.state,Ei=tn.programConfiguration,Oi=tn.program,Gi=tn.layoutVertexBuffer,on=tn.indexBuffer,zn=tn.uniformValues,Ja=gn.segments;Oi.draw(Oe,ht.TRIANGLES,Yt,cr,Nt,wr.disabled,zn,J.id,Gi,on,Ja,J.paint,Y.transform.zoom,Ei)}}}}function Mt(Y,D,J,O){if(J.paint.get(\"heatmap-opacity\")!==0)if(Y.renderPass===\"offscreen\"){var K=Y.context,de=K.gl,ne=Vi.disabled,we=new wt([de.ONE,de.ONE],i.Color.transparent,[!0,!0,!0,!0]);Jt(K,Y,J),K.clear({color:i.Color.transparent});for(var Oe=0;Oe<O.length;Oe++){var ht=O[Oe];if(!D.hasRenderableParent(ht)){var Yt=D.getTile(ht),cr=Yt.getBucket(J);if(cr){var Nt=cr.programConfigurations.get(J.id),Ne=Y.useProgram(\"heatmap\",Nt),$e=Y.transform,st=$e.zoom;Ne.draw(K,de.TRIANGLES,An.disabled,ne,we,wr.disabled,an(ht.posMatrix,Yt,st,J.paint.get(\"heatmap-intensity\")),J.id,cr.layoutVertexBuffer,cr.indexBuffer,cr.segments,J.paint,Y.transform.zoom,Nt)}}}K.viewport.set([0,0,Y.width,Y.height])}else Y.renderPass===\"translucent\"&&(Y.context.setColorMode(Y.colorModeForRenderPass()),yr(Y,J))}function Jt(Y,D,J){var O=Y.gl;Y.activeTexture.set(O.TEXTURE1),Y.viewport.set([0,0,D.width/4,D.height/4]);var K=J.heatmapFbo;if(K)O.bindTexture(O.TEXTURE_2D,K.colorAttachment.get()),Y.bindFramebuffer.set(K.framebuffer);else{var de=O.createTexture();O.bindTexture(O.TEXTURE_2D,de),O.texParameteri(O.TEXTURE_2D,O.TEXTURE_WRAP_S,O.CLAMP_TO_EDGE),O.texParameteri(O.TEXTURE_2D,O.TEXTURE_WRAP_T,O.CLAMP_TO_EDGE),O.texParameteri(O.TEXTURE_2D,O.TEXTURE_MIN_FILTER,O.LINEAR),O.texParameteri(O.TEXTURE_2D,O.TEXTURE_MAG_FILTER,O.LINEAR),K=J.heatmapFbo=Y.createFramebuffer(D.width/4,D.height/4,!1),$t(Y,D,de,K)}}function $t(Y,D,J,O){var K=Y.gl,de=Y.extRenderToTextureHalfFloat?Y.extTextureHalfFloat.HALF_FLOAT_OES:K.UNSIGNED_BYTE;K.texImage2D(K.TEXTURE_2D,0,K.RGBA,D.width/4,D.height/4,0,K.RGBA,de,null),O.colorAttachment.set(J)}function yr(Y,D){var J=Y.context,O=J.gl,K=D.heatmapFbo;if(K){J.activeTexture.set(O.TEXTURE0),O.bindTexture(O.TEXTURE_2D,K.colorAttachment.get()),J.activeTexture.set(O.TEXTURE1);var de=D.colorRampTexture;de||(de=D.colorRampTexture=new i.Texture(J,D.colorRamp,O.RGBA)),de.bind(O.LINEAR,O.CLAMP_TO_EDGE),Y.useProgram(\"heatmapTexture\").draw(J,O.TRIANGLES,An.disabled,Vi.disabled,Y.colorModeForRenderPass(),wr.disabled,Fi(Y,D,0,1),D.id,Y.viewportBuffer,Y.quadTriangleIndexBuffer,Y.viewportSegments,D.paint,Y.transform.zoom)}}function Cr(Y,D,J,O){if(Y.renderPass===\"translucent\"){var K=J.paint.get(\"line-opacity\"),de=J.paint.get(\"line-width\");if(!(K.constantOr(1)===0||de.constantOr(1)===0))for(var ne=Y.depthModeForSublayer(0,An.ReadOnly),we=Y.colorModeForRenderPass(),Oe=J.paint.get(\"line-dasharray\"),ht=J.paint.get(\"line-pattern\"),Yt=ht.constantOr(1),cr=J.paint.get(\"line-gradient\"),Nt=J.getCrossfadeParameters(),Ne=Yt?\"linePattern\":Oe?\"lineSDF\":cr?\"lineGradient\":\"line\",$e=Y.context,st=$e.gl,It=!0,Bt=0,Ft=O;Bt<Ft.length;Bt+=1){var Ut=Ft[Bt],zt=D.getTile(Ut);if(!(Yt&&!zt.patternsLoaded())){var _t=zt.getBucket(J);if(_t){var Dt=_t.programConfigurations.get(J.id),vt=Y.context.program.get(),lr=Y.useProgram(Ne,Dt),_r=It||lr.program!==vt,Br=ht.constantOr(null);if(Br&&zt.imageAtlas){var Di=zt.imageAtlas,ci=Di.patternPositions[Br.to.toString()],bi=Di.patternPositions[Br.from.toString()];ci&&bi&&Dt.setConstantPatternPositions(ci,bi)}var gn=Yt?as(Y,zt,J,Nt):Oe?ws(Y,zt,J,Oe,Nt):cr?Ss(Y,zt,J,_t.lineClipsArray.length):Zo(Y,zt,J);if(Yt)$e.activeTexture.set(st.TEXTURE0),zt.imageAtlasTexture.bind(st.LINEAR,st.CLAMP_TO_EDGE),Dt.updatePaintBuffers(Nt);else if(Oe&&(_r||Y.lineAtlas.dirty))$e.activeTexture.set(st.TEXTURE0),Y.lineAtlas.bind($e);else if(cr){var tn=_t.gradients[J.id],Ei=tn.texture;if(J.gradientVersion!==tn.version){var Oi=256;if(J.stepInterpolant){var Gi=D.getSource().maxzoom,on=Ut.canonical.z===Gi?Math.ceil(1<<Y.transform.maxZoom-Ut.canonical.z):1,zn=_t.maxLineLength/i.EXTENT,Ja=1024,co=zn*Ja*on;Oi=i.clamp(i.nextPowerOfTwo(co),256,$e.maxTextureSize)}tn.gradient=i.renderColorRamp({expression:J.gradientExpression(),evaluationKey:\"lineProgress\",resolution:Oi,image:tn.gradient||void 0,clips:_t.lineClipsArray}),tn.texture?tn.texture.update(tn.gradient):tn.texture=new i.Texture($e,tn.gradient,st.RGBA),tn.version=J.gradientVersion,Ei=tn.texture}$e.activeTexture.set(st.TEXTURE0),Ei.bind(J.stepInterpolant?st.NEAREST:st.LINEAR,st.CLAMP_TO_EDGE)}lr.draw($e,st.TRIANGLES,ne,Y.stencilModeForClipping(Ut),we,wr.disabled,gn,J.id,_t.layoutVertexBuffer,_t.indexBuffer,_t.segments,J.paint,Y.transform.zoom,Dt,_t.layoutVertexBuffer2),It=!1}}}}}function ve(Y,D,J,O){var K=J.paint.get(\"fill-color\"),de=J.paint.get(\"fill-opacity\");if(de.constantOr(1)!==0){var ne=Y.colorModeForRenderPass(),we=J.paint.get(\"fill-pattern\"),Oe=Y.opaquePassEnabledForLayer()&&!we.constantOr(1)&&K.constantOr(i.Color.transparent).a===1&&de.constantOr(0)===1?\"opaque\":\"translucent\";if(Y.renderPass===Oe){var ht=Y.depthModeForSublayer(1,Y.renderPass===\"opaque\"?An.ReadWrite:An.ReadOnly);xe(Y,D,J,O,ht,ne,!1)}if(Y.renderPass===\"translucent\"&&J.paint.get(\"fill-antialias\")){var Yt=Y.depthModeForSublayer(J.getPaintProperty(\"fill-outline-color\")?2:0,An.ReadOnly);xe(Y,D,J,O,Yt,ne,!0)}}}function xe(Y,D,J,O,K,de,ne){var we=Y.context.gl,Oe=J.paint.get(\"fill-pattern\"),ht=Oe&&Oe.constantOr(1),Yt=J.getCrossfadeParameters(),cr,Nt,Ne,$e,st;ne?(Nt=ht&&!J.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",cr=we.LINES):(Nt=ht?\"fillPattern\":\"fill\",cr=we.TRIANGLES);for(var It=0,Bt=O;It<Bt.length;It+=1){var Ft=Bt[It],Ut=D.getTile(Ft);if(!(ht&&!Ut.patternsLoaded())){var zt=Ut.getBucket(J);if(zt){var _t=zt.programConfigurations.get(J.id),Dt=Y.useProgram(Nt,_t);ht&&(Y.context.activeTexture.set(we.TEXTURE0),Ut.imageAtlasTexture.bind(we.LINEAR,we.CLAMP_TO_EDGE),_t.updatePaintBuffers(Yt));var vt=Oe.constantOr(null);if(vt&&Ut.imageAtlas){var lr=Ut.imageAtlas,_r=lr.patternPositions[vt.to.toString()],Br=lr.patternPositions[vt.from.toString()];_r&&Br&&_t.setConstantPatternPositions(_r,Br)}var Di=Y.translatePosMatrix(Ft.posMatrix,Ut,J.paint.get(\"fill-translate\"),J.paint.get(\"fill-translate-anchor\"));if(!ne)$e=zt.indexBuffer,st=zt.segments,Ne=ht?ff(Di,Y,Yt,Ut):Oc(Di);else{$e=zt.indexBuffer2,st=zt.segments2;var ci=[we.drawingBufferWidth,we.drawingBufferHeight];Ne=Nt===\"fillOutlinePattern\"&&ht?Bc(Di,Y,Yt,Ut,ci):fc(Di,ci)}Dt.draw(Y.context,cr,K,Y.stencilModeForClipping(Ft),de,wr.disabled,Ne,J.id,zt.layoutVertexBuffer,$e,st,J.paint,Y.transform.zoom,_t)}}}}function Ie(Y,D,J,O){var K=J.paint.get(\"fill-extrusion-opacity\");if(K!==0&&Y.renderPass===\"translucent\"){var de=new An(Y.context.gl.LEQUAL,An.ReadWrite,Y.depthRangeFor3D);if(K===1&&!J.paint.get(\"fill-extrusion-pattern\").constantOr(1)){var ne=Y.colorModeForRenderPass();ze(Y,D,J,O,de,Vi.disabled,ne)}else ze(Y,D,J,O,de,Vi.disabled,wt.disabled),ze(Y,D,J,O,de,Y.stencilModeFor3D(),Y.colorModeForRenderPass())}}function ze(Y,D,J,O,K,de,ne){for(var we=Y.context,Oe=we.gl,ht=J.paint.get(\"fill-extrusion-pattern\"),Yt=ht.constantOr(1),cr=J.getCrossfadeParameters(),Nt=J.paint.get(\"fill-extrusion-opacity\"),Ne=0,$e=O;Ne<$e.length;Ne+=1){var st=$e[Ne],It=D.getTile(st),Bt=It.getBucket(J);if(Bt){var Ft=Bt.programConfigurations.get(J.id),Ut=Y.useProgram(Yt?\"fillExtrusionPattern\":\"fillExtrusion\",Ft);Yt&&(Y.context.activeTexture.set(Oe.TEXTURE0),It.imageAtlasTexture.bind(Oe.LINEAR,Oe.CLAMP_TO_EDGE),Ft.updatePaintBuffers(cr));var zt=ht.constantOr(null);if(zt&&It.imageAtlas){var _t=It.imageAtlas,Dt=_t.patternPositions[zt.to.toString()],vt=_t.patternPositions[zt.from.toString()];Dt&&vt&&Ft.setConstantPatternPositions(Dt,vt)}var lr=Y.translatePosMatrix(st.posMatrix,It,J.paint.get(\"fill-extrusion-translate\"),J.paint.get(\"fill-extrusion-translate-anchor\")),_r=J.paint.get(\"fill-extrusion-vertical-gradient\"),Br=Yt?Zu(lr,Y,_r,Nt,st,cr,It):ah(lr,Y,_r,Nt);Ut.draw(we,we.gl.TRIANGLES,K,de,ne,wr.backCCW,Br,J.id,Bt.layoutVertexBuffer,Bt.indexBuffer,Bt.segments,J.paint,Y.transform.zoom,Ft)}}}function Qe(Y,D,J,O){if(!(Y.renderPass!==\"offscreen\"&&Y.renderPass!==\"translucent\")){for(var K=Y.context,de=Y.depthModeForSublayer(0,An.ReadOnly),ne=Y.colorModeForRenderPass(),we=Y.renderPass===\"translucent\"?Y.stencilConfigForOverlap(O):[{},O],Oe=we[0],ht=we[1],Yt=0,cr=ht;Yt<cr.length;Yt+=1){var Nt=cr[Yt],Ne=D.getTile(Nt);Ne.needsHillshadePrepare&&Y.renderPass===\"offscreen\"?nt(Y,Ne,J,de,Vi.disabled,ne):Y.renderPass===\"translucent\"&&Xe(Y,Ne,J,de,Oe[Nt.overscaledZ],ne)}K.viewport.set([0,0,Y.width,Y.height])}}function Xe(Y,D,J,O,K,de){var ne=Y.context,we=ne.gl,Oe=D.fbo;if(Oe){var ht=Y.useProgram(\"hillshade\");ne.activeTexture.set(we.TEXTURE0),we.bindTexture(we.TEXTURE_2D,Oe.colorAttachment.get());var Yt=Ra(Y,D,J);ht.draw(ne,we.TRIANGLES,O,K,de,wr.disabled,Yt,J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments)}}function nt(Y,D,J,O,K,de){var ne=Y.context,we=ne.gl,Oe=D.dem;if(Oe&&Oe.data){var ht=Oe.dim,Yt=Oe.stride,cr=Oe.getPixels();if(ne.activeTexture.set(we.TEXTURE1),ne.pixelStoreUnpackPremultiplyAlpha.set(!1),D.demTexture=D.demTexture||Y.getTileTexture(Yt),D.demTexture){var Nt=D.demTexture;Nt.update(cr,{premultiply:!1}),Nt.bind(we.NEAREST,we.CLAMP_TO_EDGE)}else D.demTexture=new i.Texture(ne,cr,we.RGBA,{premultiply:!1}),D.demTexture.bind(we.NEAREST,we.CLAMP_TO_EDGE);ne.activeTexture.set(we.TEXTURE0);var Ne=D.fbo;if(!Ne){var $e=new i.Texture(ne,{width:ht,height:ht,data:null},we.RGBA);$e.bind(we.LINEAR,we.CLAMP_TO_EDGE),Ne=D.fbo=ne.createFramebuffer(ht,ht,!0),Ne.colorAttachment.set($e.texture)}ne.bindFramebuffer.set(Ne.framebuffer),ne.viewport.set([0,0,ht,ht]),Y.useProgram(\"hillshadePrepare\").draw(ne,we.TRIANGLES,O,K,de,wr.disabled,La(D.tileID,Oe),J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments),D.needsHillshadePrepare=!1}}function qt(Y,D,J,O){if(Y.renderPass===\"translucent\"&&J.paint.get(\"raster-opacity\")!==0&&O.length)for(var K=Y.context,de=K.gl,ne=D.getSource(),we=Y.useProgram(\"raster\"),Oe=Y.colorModeForRenderPass(),ht=ne instanceof Ce?[{},O]:Y.stencilConfigForOverlap(O),Yt=ht[0],cr=ht[1],Nt=cr[cr.length-1].overscaledZ,Ne=!Y.options.moving,$e=0,st=cr;$e<st.length;$e+=1){var It=st[$e],Bt=Y.depthModeForSublayer(It.overscaledZ-Nt,J.paint.get(\"raster-opacity\")===1?An.ReadWrite:An.ReadOnly,de.LESS),Ft=D.getTile(It),Ut=Y.transform.calculatePosMatrix(It.toUnwrapped(),Ne);Ft.registerFadeDuration(J.paint.get(\"raster-fade-duration\"));var zt=D.findLoadedParent(It,0),_t=Wt(Ft,zt,D,J,Y.transform),Dt=void 0,vt=void 0,lr=J.paint.get(\"raster-resampling\")===\"nearest\"?de.NEAREST:de.LINEAR;K.activeTexture.set(de.TEXTURE0),Ft.texture.bind(lr,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST),K.activeTexture.set(de.TEXTURE1),zt?(zt.texture.bind(lr,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST),Dt=Math.pow(2,zt.tileID.overscaledZ-Ft.tileID.overscaledZ),vt=[Ft.tileID.canonical.x*Dt%1,Ft.tileID.canonical.y*Dt%1]):Ft.texture.bind(lr,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST);var _r=Ls(Ut,vt||[0,0],Dt||1,_t,J);ne instanceof Ce?we.draw(K,de.TRIANGLES,Bt,Vi.disabled,Oe,wr.disabled,_r,J.id,ne.boundsBuffer,Y.quadTriangleIndexBuffer,ne.boundsSegments):we.draw(K,de.TRIANGLES,Bt,Yt[It.overscaledZ],Oe,wr.disabled,_r,J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments)}}function Wt(Y,D,J,O,K){var de=O.paint.get(\"raster-fade-duration\");if(de>0){var ne=i.browser.now(),we=(ne-Y.timeAdded)/de,Oe=D?(ne-D.timeAdded)/de:-1,ht=J.getSource(),Yt=K.coveringZoomLevel({tileSize:ht.tileSize,roundZoom:ht.roundZoom}),cr=!D||Math.abs(D.tileID.overscaledZ-Yt)>Math.abs(Y.tileID.overscaledZ-Yt),Nt=cr&&Y.refreshedUponExpiration?1:i.clamp(cr?we:1-Oe,0,1);return Y.refreshedUponExpiration&&we>=1&&(Y.refreshedUponExpiration=!1),D?{opacity:1,mix:1-Nt}:{opacity:Nt,mix:0}}else return{opacity:1,mix:0}}function nr(Y,D,J){var O=J.paint.get(\"background-color\"),K=J.paint.get(\"background-opacity\");if(K!==0){var de=Y.context,ne=de.gl,we=Y.transform,Oe=we.tileSize,ht=J.paint.get(\"background-pattern\");if(!Y.isPatternMissing(ht)){var Yt=!ht&&O.a===1&&K===1&&Y.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(Y.renderPass===Yt){var cr=Vi.disabled,Nt=Y.depthModeForSublayer(0,Yt===\"opaque\"?An.ReadWrite:An.ReadOnly),Ne=Y.colorModeForRenderPass(),$e=Y.useProgram(ht?\"backgroundPattern\":\"background\"),st=we.coveringTiles({tileSize:Oe});ht&&(de.activeTexture.set(ne.TEXTURE0),Y.imageManager.bind(Y.context));for(var It=J.getCrossfadeParameters(),Bt=0,Ft=st;Bt<Ft.length;Bt+=1){var Ut=Ft[Bt],zt=Y.transform.calculatePosMatrix(Ut.toUnwrapped()),_t=ht?Va(zt,K,Y,ht,{tileID:Ut,tileSize:Oe},It):Ac(zt,K,O);$e.draw(de,ne.TRIANGLES,Nt,cr,Ne,wr.disabled,_t,J.id,Y.tileExtentBuffer,Y.quadTriangleIndexBuffer,Y.tileExtentSegments)}}}}}var fr=new i.Color(1,0,0,1),br=new i.Color(0,1,0,1),Fr=new i.Color(0,0,1,1),Hr=new i.Color(1,0,1,1),ri=new i.Color(0,1,1,1);function Ci(Y){var D=Y.transform.padding,J=3;sn(Y,Y.transform.height-(D.top||0),J,fr),sn(Y,D.bottom||0,J,br),Ln(Y,D.left||0,J,Fr),Ln(Y,Y.transform.width-(D.right||0),J,Hr);var O=Y.transform.centerPoint;cn(Y,O.x,Y.transform.height-O.y,ri)}function cn(Y,D,J,O){var K=20,de=2;Kn(Y,D-de/2,J-K/2,de,K,O),Kn(Y,D-K/2,J-de/2,K,de,O)}function sn(Y,D,J,O){Kn(Y,0,D+J/2,Y.transform.width,J,O)}function Ln(Y,D,J,O){Kn(Y,D-J/2,0,J,Y.transform.height,O)}function Kn(Y,D,J,O,K,de){var ne=Y.context,we=ne.gl;we.enable(we.SCISSOR_TEST),we.scissor(D*i.browser.devicePixelRatio,J*i.browser.devicePixelRatio,O*i.browser.devicePixelRatio,K*i.browser.devicePixelRatio),ne.clear({color:de}),we.disable(we.SCISSOR_TEST)}function Aa(Y,D,J){for(var O=0;O<J.length;O++)fa(Y,D,J[O])}function fa(Y,D,J){var O=Y.context,K=O.gl,de=J.posMatrix,ne=Y.useProgram(\"debug\"),we=An.disabled,Oe=Vi.disabled,ht=Y.colorModeForRenderPass(),Yt=\"$debug\";O.activeTexture.set(K.TEXTURE0),Y.emptyTexture.bind(K.LINEAR,K.CLAMP_TO_EDGE),ne.draw(O,K.LINE_STRIP,we,Oe,ht,wr.disabled,dn(de,i.Color.red),Yt,Y.debugBuffer,Y.tileBorderIndexBuffer,Y.debugSegments);var cr=D.getTileByID(J.key).latestRawTileData,Nt=cr&&cr.byteLength||0,Ne=Math.floor(Nt/1024),$e=D.getTile(J).tileSize,st=512/Math.min($e,512)*(J.overscaledZ/Y.transform.zoom)*.5,It=J.canonical.toString();J.overscaledZ!==J.canonical.z&&(It+=\" => \"+J.overscaledZ);var Bt=It+\" \"+Ne+\"kb\";$a(Y,Bt),ne.draw(O,K.TRIANGLES,we,Oe,wt.alphaBlended,wr.disabled,dn(de,i.Color.transparent,st),Yt,Y.debugBuffer,Y.quadTriangleIndexBuffer,Y.debugSegments)}function $a(Y,D){Y.initDebugOverlayCanvas();var J=Y.debugOverlayCanvas,O=Y.context.gl,K=Y.debugOverlayCanvas.getContext(\"2d\");K.clearRect(0,0,J.width,J.height),K.shadowColor=\"white\",K.shadowBlur=2,K.lineWidth=1.5,K.strokeStyle=\"white\",K.textBaseline=\"top\",K.font=\"bold 36px Open Sans, sans-serif\",K.fillText(D,5,5),K.strokeText(D,5,5),Y.debugOverlayTexture.update(J),Y.debugOverlayTexture.bind(O.LINEAR,O.CLAMP_TO_EDGE)}function Co(Y,D,J){var O=Y.context,K=J.implementation;if(Y.renderPass===\"offscreen\"){var de=K.prerender;de&&(Y.setCustomLayerDefaults(),O.setColorMode(Y.colorModeForRenderPass()),de.call(K,O.gl,Y.transform.customLayerMatrix()),O.setDirty(),Y.setBaseState())}else if(Y.renderPass===\"translucent\"){Y.setCustomLayerDefaults(),O.setColorMode(Y.colorModeForRenderPass()),O.setStencilMode(Vi.disabled);var ne=K.renderingMode===\"3d\"?new An(Y.context.gl.LEQUAL,An.ReadWrite,Y.depthRangeFor3D):Y.depthModeForSublayer(0,An.ReadOnly);O.setDepthMode(ne),K.render(O.gl,Y.transform.customLayerMatrix()),O.setDirty(),Y.setBaseState(),O.bindFramebuffer.set(null)}}var Qa={symbol:w,circle:ut,heatmap:Mt,line:Cr,fill:ve,\"fill-extrusion\":Ie,hillshade:Qe,raster:qt,background:nr,debug:Aa,custom:Co},mo=function(D,J){this.context=new Xr(D),this.transform=J,this._tileTextures={},this.setup(),this.numSublayers=ti.maxUnderzooming+ti.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Vf,this.gpuTimers={}};mo.prototype.resize=function(D,J){if(this.width=D*i.browser.devicePixelRatio,this.height=J*i.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var O=0,K=this.style._order;O<K.length;O+=1){var de=K[O];this.style._layers[de].resize()}},mo.prototype.setup=function(){var D=this.context,J=new i.StructArrayLayout2i4;J.emplaceBack(0,0),J.emplaceBack(i.EXTENT,0),J.emplaceBack(0,i.EXTENT),J.emplaceBack(i.EXTENT,i.EXTENT),this.tileExtentBuffer=D.createVertexBuffer(J,oc.members),this.tileExtentSegments=i.SegmentVector.simpleSegment(0,0,4,2);var O=new i.StructArrayLayout2i4;O.emplaceBack(0,0),O.emplaceBack(i.EXTENT,0),O.emplaceBack(0,i.EXTENT),O.emplaceBack(i.EXTENT,i.EXTENT),this.debugBuffer=D.createVertexBuffer(O,oc.members),this.debugSegments=i.SegmentVector.simpleSegment(0,0,4,5);var K=new i.StructArrayLayout4i8;K.emplaceBack(0,0,0,0),K.emplaceBack(i.EXTENT,0,i.EXTENT,0),K.emplaceBack(0,i.EXTENT,0,i.EXTENT),K.emplaceBack(i.EXTENT,i.EXTENT,i.EXTENT,i.EXTENT),this.rasterBoundsBuffer=D.createVertexBuffer(K,Ee.members),this.rasterBoundsSegments=i.SegmentVector.simpleSegment(0,0,4,2);var de=new i.StructArrayLayout2i4;de.emplaceBack(0,0),de.emplaceBack(1,0),de.emplaceBack(0,1),de.emplaceBack(1,1),this.viewportBuffer=D.createVertexBuffer(de,oc.members),this.viewportSegments=i.SegmentVector.simpleSegment(0,0,4,2);var ne=new i.StructArrayLayout1ui2;ne.emplaceBack(0),ne.emplaceBack(1),ne.emplaceBack(3),ne.emplaceBack(2),ne.emplaceBack(0),this.tileBorderIndexBuffer=D.createIndexBuffer(ne);var we=new i.StructArrayLayout3ui6;we.emplaceBack(0,1,2),we.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=D.createIndexBuffer(we),this.emptyTexture=new i.Texture(D,{width:1,height:1,data:new Uint8Array([0,0,0,0])},D.gl.RGBA);var Oe=this.context.gl;this.stencilClearMode=new Vi({func:Oe.ALWAYS,mask:0},0,255,Oe.ZERO,Oe.ZERO,Oe.ZERO)},mo.prototype.clearStencil=function(){var D=this.context,J=D.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var O=i.create();i.ortho(O,0,this.width,this.height,0,0,1),i.scale(O,O,[J.drawingBufferWidth,J.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(D,J.TRIANGLES,An.disabled,this.stencilClearMode,wt.disabled,wr.disabled,On(O),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},mo.prototype._renderTileClippingMasks=function(D,J){if(!(this.currentStencilSource===D.source||!D.isTileClipped()||!J||!J.length)){this.currentStencilSource=D.source;var O=this.context,K=O.gl;this.nextStencilID+J.length>256&&this.clearStencil(),O.setColorMode(wt.disabled),O.setDepthMode(An.disabled);var de=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var ne=0,we=J;ne<we.length;ne+=1){var Oe=we[ne],ht=this._tileClippingMaskIDs[Oe.key]=this.nextStencilID++;de.draw(O,K.TRIANGLES,An.disabled,new Vi({func:K.ALWAYS,mask:0},ht,255,K.KEEP,K.KEEP,K.REPLACE),wt.disabled,wr.disabled,On(Oe.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},mo.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var D=this.nextStencilID++,J=this.context.gl;return new Vi({func:J.NOTEQUAL,mask:255},D,255,J.KEEP,J.KEEP,J.REPLACE)},mo.prototype.stencilModeForClipping=function(D){var J=this.context.gl;return new Vi({func:J.EQUAL,mask:255},this._tileClippingMaskIDs[D.key],0,J.KEEP,J.KEEP,J.REPLACE)},mo.prototype.stencilConfigForOverlap=function(D){var J,O=this.context.gl,K=D.sort(function(ht,Yt){return Yt.overscaledZ-ht.overscaledZ}),de=K[K.length-1].overscaledZ,ne=K[0].overscaledZ-de+1;if(ne>1){this.currentStencilSource=void 0,this.nextStencilID+ne>256&&this.clearStencil();for(var we={},Oe=0;Oe<ne;Oe++)we[Oe+de]=new Vi({func:O.GEQUAL,mask:255},Oe+this.nextStencilID,255,O.KEEP,O.KEEP,O.REPLACE);return this.nextStencilID+=ne,[we,K]}return[(J={},J[de]=Vi.disabled,J),K]},mo.prototype.colorModeForRenderPass=function(){var D=this.context.gl;if(this._showOverdrawInspector){var J=8,O=1/J;return new wt([D.CONSTANT_COLOR,D.ONE],new i.Color(O,O,O,0),[!0,!0,!0,!0])}else return this.renderPass===\"opaque\"?wt.unblended:wt.alphaBlended},mo.prototype.depthModeForSublayer=function(D,J,O){if(!this.opaquePassEnabledForLayer())return An.disabled;var K=1-((1+this.currentLayer)*this.numSublayers+D)*this.depthEpsilon;return new An(O||this.context.gl.LEQUAL,J,[K,K])},mo.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},mo.prototype.render=function(D,J){var O=this;this.style=D,this.options=J,this.lineAtlas=D.lineAtlas,this.imageManager=D.imageManager,this.glyphManager=D.glyphManager,this.symbolFadeChange=D.placement.symbolFadeChange(i.browser.now()),this.imageManager.beginFrame();var K=this.style._order,de=this.style.sourceCaches;for(var ne in de){var we=de[ne];we.used&&we.prepare(this.context)}var Oe={},ht={},Yt={};for(var cr in de){var Nt=de[cr];Oe[cr]=Nt.getVisibleCoordinates(),ht[cr]=Oe[cr].slice().reverse(),Yt[cr]=Nt.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var Ne=0;Ne<K.length;Ne++){var $e=K[Ne];if(this.style._layers[$e].is3D()){this.opaquePassCutoff=Ne;break}}this.renderPass=\"offscreen\";for(var st=0,It=K;st<It.length;st+=1){var Bt=It[st],Ft=this.style._layers[Bt];if(!(!Ft.hasOffscreenPass()||Ft.isHidden(this.transform.zoom))){var Ut=ht[Ft.source];Ft.type!==\"custom\"&&!Ut.length||this.renderLayer(this,de[Ft.source],Ft,Ut)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:J.showOverdrawInspector?i.Color.black:i.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=J.showOverdrawInspector,this.depthRangeFor3D=[0,1-(D._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=K.length-1;this.currentLayer>=0;this.currentLayer--){var zt=this.style._layers[K[this.currentLayer]],_t=de[zt.source],Dt=Oe[zt.source];this._renderTileClippingMasks(zt,Dt),this.renderLayer(this,_t,zt,Dt)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<K.length;this.currentLayer++){var vt=this.style._layers[K[this.currentLayer]],lr=de[vt.source],_r=(vt.type===\"symbol\"?Yt:ht)[vt.source];this._renderTileClippingMasks(vt,Oe[vt.source]),this.renderLayer(this,lr,vt,_r)}if(this.options.showTileBoundaries){var Br,Di,ci=i.values(this.style._layers);ci.forEach(function(bi){bi.source&&!bi.isHidden(O.transform.zoom)&&(bi.source!==(Di&&Di.id)&&(Di=O.style.sourceCaches[bi.source]),(!Br||Br.getSource().maxzoom<Di.getSource().maxzoom)&&(Br=Di))}),Br&&Qa.debug(this,Br,Br.getVisibleCoordinates())}this.options.showPadding&&Ci(this),this.context.setDefault()},mo.prototype.renderLayer=function(D,J,O,K){O.isHidden(this.transform.zoom)||O.type!==\"background\"&&O.type!==\"custom\"&&!K.length||(this.id=O.id,this.gpuTimingStart(O),Qa[O.type](D,J,O,K,this.style.placement.variableOffsets),this.gpuTimingEnd())},mo.prototype.gpuTimingStart=function(D){if(this.options.gpuTiming){var J=this.context.extTimerQuery,O=this.gpuTimers[D.id];O||(O=this.gpuTimers[D.id]={calls:0,cpuTime:0,query:J.createQueryEXT()}),O.calls++,J.beginQueryEXT(J.TIME_ELAPSED_EXT,O.query)}},mo.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var D=this.context.extTimerQuery;D.endQueryEXT(D.TIME_ELAPSED_EXT)}},mo.prototype.collectGpuTimers=function(){var D=this.gpuTimers;return this.gpuTimers={},D},mo.prototype.queryGpuTimers=function(D){var J={};for(var O in D){var K=D[O],de=this.context.extTimerQuery,ne=de.getQueryObjectEXT(K.query,de.QUERY_RESULT_EXT)/(1e3*1e3);de.deleteQueryEXT(K.query),J[O]=ne}return J},mo.prototype.translatePosMatrix=function(D,J,O,K,de){if(!O[0]&&!O[1])return D;var ne=de?K===\"map\"?this.transform.angle:0:K===\"viewport\"?-this.transform.angle:0;if(ne){var we=Math.sin(ne),Oe=Math.cos(ne);O=[O[0]*Oe-O[1]*we,O[0]*we+O[1]*Oe]}var ht=[de?O[0]:As(J,O[0],this.transform.zoom),de?O[1]:As(J,O[1],this.transform.zoom),0],Yt=new Float32Array(16);return i.translate(Yt,D,ht),Yt},mo.prototype.saveTileTexture=function(D){var J=this._tileTextures[D.size[0]];J?J.push(D):this._tileTextures[D.size[0]]=[D]},mo.prototype.getTileTexture=function(D){var J=this._tileTextures[D];return J&&J.length>0?J.pop():null},mo.prototype.isPatternMissing=function(D){if(!D)return!1;if(!D.from||!D.to)return!0;var J=this.imageManager.getPattern(D.from.toString()),O=this.imageManager.getPattern(D.to.toString());return!J||!O},mo.prototype.useProgram=function(D,J){this.cache=this.cache||{};var O=\"\"+D+(J?J.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[O]||(this.cache[O]=new Rf(this.context,D,wf[D],J,oo[D],this._showOverdrawInspector)),this.cache[O]},mo.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},mo.prototype.setBaseState=function(){var D=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(D.FUNC_ADD)},mo.prototype.initDebugOverlayCanvas=function(){if(this.debugOverlayCanvas==null){this.debugOverlayCanvas=i.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var D=this.context.gl;this.debugOverlayTexture=new i.Texture(this.context,this.debugOverlayCanvas,D.RGBA)}},mo.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var Bo=function(D,J){this.points=D,this.planes=J};Bo.fromInvProjectionMatrix=function(D,J,O){var K=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]],de=Math.pow(2,O),ne=K.map(function(ht){return i.transformMat4([],ht,D)}).map(function(ht){return i.scale$1([],ht,1/ht[3]/J*de)}),we=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]],Oe=we.map(function(ht){var Yt=i.sub([],ne[ht[0]],ne[ht[1]]),cr=i.sub([],ne[ht[2]],ne[ht[1]]),Nt=i.normalize([],i.cross([],Yt,cr)),Ne=-i.dot(Nt,ne[ht[1]]);return Nt.concat(Ne)});return new Bo(ne,Oe)};var Ps=function(D,J){this.min=D,this.max=J,this.center=i.scale$2([],i.add([],this.min,this.max),.5)};Ps.prototype.quadrant=function(D){for(var J=[D%2===0,D<2],O=i.clone$2(this.min),K=i.clone$2(this.max),de=0;de<J.length;de++)O[de]=J[de]?this.min[de]:this.center[de],K[de]=J[de]?this.center[de]:this.max[de];return K[2]=this.max[2],new Ps(O,K)},Ps.prototype.distanceX=function(D){var J=Math.max(Math.min(this.max[0],D[0]),this.min[0]);return J-D[0]},Ps.prototype.distanceY=function(D){var J=Math.max(Math.min(this.max[1],D[1]),this.min[1]);return J-D[1]},Ps.prototype.intersects=function(D){for(var J=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],O=!0,K=0;K<D.planes.length;K++){for(var de=D.planes[K],ne=0,we=0;we<J.length;we++)ne+=i.dot$1(de,J[we])>=0;if(ne===0)return 0;ne!==J.length&&(O=!1)}if(O)return 2;for(var Oe=0;Oe<3;Oe++){for(var ht=Number.MAX_VALUE,Yt=-Number.MAX_VALUE,cr=0;cr<D.points.length;cr++){var Nt=D.points[cr][Oe]-this.min[Oe];ht=Math.min(ht,Nt),Yt=Math.max(Yt,Nt)}if(Yt<0||ht>this.max[Oe]-this.min[Oe])return 0}return 1};var Ts=function(D,J,O,K){if(D===void 0&&(D=0),J===void 0&&(J=0),O===void 0&&(O=0),K===void 0&&(K=0),isNaN(D)||D<0||isNaN(J)||J<0||isNaN(O)||O<0||isNaN(K)||K<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=D,this.bottom=J,this.left=O,this.right=K};Ts.prototype.interpolate=function(D,J,O){return J.top!=null&&D.top!=null&&(this.top=i.number(D.top,J.top,O)),J.bottom!=null&&D.bottom!=null&&(this.bottom=i.number(D.bottom,J.bottom,O)),J.left!=null&&D.left!=null&&(this.left=i.number(D.left,J.left,O)),J.right!=null&&D.right!=null&&(this.right=i.number(D.right,J.right,O)),this},Ts.prototype.getCenter=function(D,J){var O=i.clamp((this.left+D-this.right)/2,0,D),K=i.clamp((this.top+J-this.bottom)/2,0,J);return new i.Point(O,K)},Ts.prototype.equals=function(D){return this.top===D.top&&this.bottom===D.bottom&&this.left===D.left&&this.right===D.right},Ts.prototype.clone=function(){return new Ts(this.top,this.bottom,this.left,this.right)},Ts.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var wo=function(D,J,O,K,de){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=de===void 0?!0:de,this._minZoom=D||0,this._maxZoom=J||22,this._minPitch=O==null?0:O,this._maxPitch=K==null?60:K,this.setMaxBounds(),this.width=0,this.height=0,this._center=new i.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Ts,this._posMatrixCache={},this._alignedPosMatrixCache={}},To={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};wo.prototype.clone=function(){var D=new wo(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return D.tileSize=this.tileSize,D.latRange=this.latRange,D.width=this.width,D.height=this.height,D._center=this._center,D.zoom=this.zoom,D.angle=this.angle,D._fov=this._fov,D._pitch=this._pitch,D._unmodified=this._unmodified,D._edgeInsets=this._edgeInsets.clone(),D._calcMatrices(),D},To.minZoom.get=function(){return this._minZoom},To.minZoom.set=function(Y){this._minZoom!==Y&&(this._minZoom=Y,this.zoom=Math.max(this.zoom,Y))},To.maxZoom.get=function(){return this._maxZoom},To.maxZoom.set=function(Y){this._maxZoom!==Y&&(this._maxZoom=Y,this.zoom=Math.min(this.zoom,Y))},To.minPitch.get=function(){return this._minPitch},To.minPitch.set=function(Y){this._minPitch!==Y&&(this._minPitch=Y,this.pitch=Math.max(this.pitch,Y))},To.maxPitch.get=function(){return this._maxPitch},To.maxPitch.set=function(Y){this._maxPitch!==Y&&(this._maxPitch=Y,this.pitch=Math.min(this.pitch,Y))},To.renderWorldCopies.get=function(){return this._renderWorldCopies},To.renderWorldCopies.set=function(Y){Y===void 0?Y=!0:Y===null&&(Y=!1),this._renderWorldCopies=Y},To.worldSize.get=function(){return this.tileSize*this.scale},To.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},To.size.get=function(){return new i.Point(this.width,this.height)},To.bearing.get=function(){return-this.angle/Math.PI*180},To.bearing.set=function(Y){var D=-i.wrap(Y,-180,180)*Math.PI/180;this.angle!==D&&(this._unmodified=!1,this.angle=D,this._calcMatrices(),this.rotationMatrix=i.create$2(),i.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},To.pitch.get=function(){return this._pitch/Math.PI*180},To.pitch.set=function(Y){var D=i.clamp(Y,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==D&&(this._unmodified=!1,this._pitch=D,this._calcMatrices())},To.fov.get=function(){return this._fov/Math.PI*180},To.fov.set=function(Y){Y=Math.max(.01,Math.min(60,Y)),this._fov!==Y&&(this._unmodified=!1,this._fov=Y/180*Math.PI,this._calcMatrices())},To.zoom.get=function(){return this._zoom},To.zoom.set=function(Y){var D=Math.min(Math.max(Y,this.minZoom),this.maxZoom);this._zoom!==D&&(this._unmodified=!1,this._zoom=D,this.scale=this.zoomScale(D),this.tileZoom=Math.floor(D),this.zoomFraction=D-this.tileZoom,this._constrain(),this._calcMatrices())},To.center.get=function(){return this._center},To.center.set=function(Y){Y.lat===this._center.lat&&Y.lng===this._center.lng||(this._unmodified=!1,this._center=Y,this._constrain(),this._calcMatrices())},To.padding.get=function(){return this._edgeInsets.toJSON()},To.padding.set=function(Y){this._edgeInsets.equals(Y)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,Y,1),this._calcMatrices())},To.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},wo.prototype.isPaddingEqual=function(D){return this._edgeInsets.equals(D)},wo.prototype.interpolatePadding=function(D,J,O){this._unmodified=!1,this._edgeInsets.interpolate(D,J,O),this._constrain(),this._calcMatrices()},wo.prototype.coveringZoomLevel=function(D){var J=(D.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/D.tileSize));return Math.max(0,J)},wo.prototype.getVisibleUnwrappedCoordinates=function(D){var J=[new i.UnwrappedTileID(0,D)];if(this._renderWorldCopies)for(var O=this.pointCoordinate(new i.Point(0,0)),K=this.pointCoordinate(new i.Point(this.width,0)),de=this.pointCoordinate(new i.Point(this.width,this.height)),ne=this.pointCoordinate(new i.Point(0,this.height)),we=Math.floor(Math.min(O.x,K.x,de.x,ne.x)),Oe=Math.floor(Math.max(O.x,K.x,de.x,ne.x)),ht=1,Yt=we-ht;Yt<=Oe+ht;Yt++)Yt!==0&&J.push(new i.UnwrappedTileID(Yt,D));return J},wo.prototype.coveringTiles=function(D){var J=this.coveringZoomLevel(D),O=J;if(D.minzoom!==void 0&&J<D.minzoom)return[];D.maxzoom!==void 0&&J>D.maxzoom&&(J=D.maxzoom);var K=i.MercatorCoordinate.fromLngLat(this.center),de=Math.pow(2,J),ne=[de*K.x,de*K.y,0],we=Bo.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,J),Oe=D.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(Oe=J);var ht=3,Yt=function(ci){return{aabb:new Ps([ci*de,0,0],[(ci+1)*de,de,0]),zoom:0,x:0,y:0,wrap:ci,fullyVisible:!1}},cr=[],Nt=[],Ne=J,$e=D.reparseOverscaled?O:J;if(this._renderWorldCopies)for(var st=1;st<=3;st++)cr.push(Yt(-st)),cr.push(Yt(st));for(cr.push(Yt(0));cr.length>0;){var It=cr.pop(),Bt=It.x,Ft=It.y,Ut=It.fullyVisible;if(!Ut){var zt=It.aabb.intersects(we);if(zt===0)continue;Ut=zt===2}var _t=It.aabb.distanceX(ne),Dt=It.aabb.distanceY(ne),vt=Math.max(Math.abs(_t),Math.abs(Dt)),lr=ht+(1<<Ne-It.zoom)-2;if(It.zoom===Ne||vt>lr&&It.zoom>=Oe){Nt.push({tileID:new i.OverscaledTileID(It.zoom===Ne?$e:It.zoom,It.wrap,It.zoom,Bt,Ft),distanceSq:i.sqrLen([ne[0]-.5-Bt,ne[1]-.5-Ft])});continue}for(var _r=0;_r<4;_r++){var Br=(Bt<<1)+_r%2,Di=(Ft<<1)+(_r>>1);cr.push({aabb:It.aabb.quadrant(_r),zoom:It.zoom+1,x:Br,y:Di,wrap:It.wrap,fullyVisible:Ut})}}return Nt.sort(function(ci,bi){return ci.distanceSq-bi.distanceSq}).map(function(ci){return ci.tileID})},wo.prototype.resize=function(D,J){this.width=D,this.height=J,this.pixelsToGLUnits=[2/D,-2/J],this._constrain(),this._calcMatrices()},To.unmodified.get=function(){return this._unmodified},wo.prototype.zoomScale=function(D){return Math.pow(2,D)},wo.prototype.scaleZoom=function(D){return Math.log(D)/Math.LN2},wo.prototype.project=function(D){var J=i.clamp(D.lat,-this.maxValidLatitude,this.maxValidLatitude);return new i.Point(i.mercatorXfromLng(D.lng)*this.worldSize,i.mercatorYfromLat(J)*this.worldSize)},wo.prototype.unproject=function(D){return new i.MercatorCoordinate(D.x/this.worldSize,D.y/this.worldSize).toLngLat()},To.point.get=function(){return this.project(this.center)},wo.prototype.setLocationAtPoint=function(D,J){var O=this.pointCoordinate(J),K=this.pointCoordinate(this.centerPoint),de=this.locationCoordinate(D),ne=new i.MercatorCoordinate(de.x-(O.x-K.x),de.y-(O.y-K.y));this.center=this.coordinateLocation(ne),this._renderWorldCopies&&(this.center=this.center.wrap())},wo.prototype.locationPoint=function(D){return this.coordinatePoint(this.locationCoordinate(D))},wo.prototype.pointLocation=function(D){return this.coordinateLocation(this.pointCoordinate(D))},wo.prototype.locationCoordinate=function(D){return i.MercatorCoordinate.fromLngLat(D)},wo.prototype.coordinateLocation=function(D){return D.toLngLat()},wo.prototype.pointCoordinate=function(D){var J=0,O=[D.x,D.y,0,1],K=[D.x,D.y,1,1];i.transformMat4(O,O,this.pixelMatrixInverse),i.transformMat4(K,K,this.pixelMatrixInverse);var de=O[3],ne=K[3],we=O[0]/de,Oe=K[0]/ne,ht=O[1]/de,Yt=K[1]/ne,cr=O[2]/de,Nt=K[2]/ne,Ne=cr===Nt?0:(J-cr)/(Nt-cr);return new i.MercatorCoordinate(i.number(we,Oe,Ne)/this.worldSize,i.number(ht,Yt,Ne)/this.worldSize)},wo.prototype.coordinatePoint=function(D){var J=[D.x*this.worldSize,D.y*this.worldSize,0,1];return i.transformMat4(J,J,this.pixelMatrix),new i.Point(J[0]/J[3],J[1]/J[3])},wo.prototype.getBounds=function(){return new i.LngLatBounds().extend(this.pointLocation(new i.Point(0,0))).extend(this.pointLocation(new i.Point(this.width,0))).extend(this.pointLocation(new i.Point(this.width,this.height))).extend(this.pointLocation(new i.Point(0,this.height)))},wo.prototype.getMaxBounds=function(){return!this.latRange||this.latRange.length!==2||!this.lngRange||this.lngRange.length!==2?null:new i.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]])},wo.prototype.setMaxBounds=function(D){D?(this.lngRange=[D.getWest(),D.getEast()],this.latRange=[D.getSouth(),D.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},wo.prototype.calculatePosMatrix=function(D,J){J===void 0&&(J=!1);var O=D.key,K=J?this._alignedPosMatrixCache:this._posMatrixCache;if(K[O])return K[O];var de=D.canonical,ne=this.worldSize/this.zoomScale(de.z),we=de.x+Math.pow(2,de.z)*D.wrap,Oe=i.identity(new Float64Array(16));return i.translate(Oe,Oe,[we*ne,de.y*ne,0]),i.scale(Oe,Oe,[ne/i.EXTENT,ne/i.EXTENT,1]),i.multiply(Oe,J?this.alignedProjMatrix:this.projMatrix,Oe),K[O]=new Float32Array(Oe),K[O]},wo.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},wo.prototype._constrain=function(){if(!(!this.center||!this.width||!this.height||this._constraining)){this._constraining=!0;var D=-90,J=90,O=-180,K=180,de,ne,we,Oe,ht=this.size,Yt=this._unmodified;if(this.latRange){var cr=this.latRange;D=i.mercatorYfromLat(cr[1])*this.worldSize,J=i.mercatorYfromLat(cr[0])*this.worldSize,de=J-D<ht.y?ht.y/(J-D):0}if(this.lngRange){var Nt=this.lngRange;O=i.mercatorXfromLng(Nt[0])*this.worldSize,K=i.mercatorXfromLng(Nt[1])*this.worldSize,ne=K-O<ht.x?ht.x/(K-O):0}var Ne=this.point,$e=Math.max(ne||0,de||0);if($e){this.center=this.unproject(new i.Point(ne?(K+O)/2:Ne.x,de?(J+D)/2:Ne.y)),this.zoom+=this.scaleZoom($e),this._unmodified=Yt,this._constraining=!1;return}if(this.latRange){var st=Ne.y,It=ht.y/2;st-It<D&&(Oe=D+It),st+It>J&&(Oe=J-It)}if(this.lngRange){var Bt=Ne.x,Ft=ht.x/2;Bt-Ft<O&&(we=O+Ft),Bt+Ft>K&&(we=K-Ft)}(we!==void 0||Oe!==void 0)&&(this.center=this.unproject(new i.Point(we!==void 0?we:Ne.x,Oe!==void 0?Oe:Ne.y))),this._unmodified=Yt,this._constraining=!1}},wo.prototype._calcMatrices=function(){if(this.height){var D=this._fov/2,J=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(D)*this.height;var O=Math.PI/2+this._pitch,K=this._fov*(.5+J.y/this.height),de=Math.sin(K)*this.cameraToCenterDistance/Math.sin(i.clamp(Math.PI-O-K,.01,Math.PI-.01)),ne=this.point,we=ne.x,Oe=ne.y,ht=Math.cos(Math.PI/2-this._pitch)*de+this.cameraToCenterDistance,Yt=ht*1.01,cr=this.height/50,Nt=new Float64Array(16);i.perspective(Nt,this._fov,this.width/this.height,cr,Yt),Nt[8]=-J.x*2/this.width,Nt[9]=J.y*2/this.height,i.scale(Nt,Nt,[1,-1,1]),i.translate(Nt,Nt,[0,0,-this.cameraToCenterDistance]),i.rotateX(Nt,Nt,this._pitch),i.rotateZ(Nt,Nt,this.angle),i.translate(Nt,Nt,[-we,-Oe,0]),this.mercatorMatrix=i.scale([],Nt,[this.worldSize,this.worldSize,this.worldSize]),i.scale(Nt,Nt,[1,1,i.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=Nt,this.invProjMatrix=i.invert([],this.projMatrix);var Ne=this.width%2/2,$e=this.height%2/2,st=Math.cos(this.angle),It=Math.sin(this.angle),Bt=we-Math.round(we)+st*Ne+It*$e,Ft=Oe-Math.round(Oe)+st*$e+It*Ne,Ut=new Float64Array(Nt);if(i.translate(Ut,Ut,[Bt>.5?Bt-1:Bt,Ft>.5?Ft-1:Ft,0]),this.alignedProjMatrix=Ut,Nt=i.create(),i.scale(Nt,Nt,[this.width/2,-this.height/2,1]),i.translate(Nt,Nt,[1,-1,0]),this.labelPlaneMatrix=Nt,Nt=i.create(),i.scale(Nt,Nt,[1,-1,1]),i.translate(Nt,Nt,[-1,-1,0]),i.scale(Nt,Nt,[2/this.width,2/this.height,1]),this.glCoordMatrix=Nt,this.pixelMatrix=i.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),Nt=i.invert(new Float64Array(16),this.pixelMatrix),!Nt)throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=Nt,this._posMatrixCache={},this._alignedPosMatrixCache={}}},wo.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var D=this.pointCoordinate(new i.Point(0,0)),J=[D.x*this.worldSize,D.y*this.worldSize,0,1],O=i.transformMat4(J,J,this.pixelMatrix);return O[3]/this.cameraToCenterDistance},wo.prototype.getCameraPoint=function(){var D=this._pitch,J=Math.tan(D)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new i.Point(0,J))},wo.prototype.getCameraQueryGeometry=function(D){var J=this.getCameraPoint();if(D.length===1)return[D[0],J];for(var O=J.x,K=J.y,de=J.x,ne=J.y,we=0,Oe=D;we<Oe.length;we+=1){var ht=Oe[we];O=Math.min(O,ht.x),K=Math.min(K,ht.y),de=Math.max(de,ht.x),ne=Math.max(ne,ht.y)}return[new i.Point(O,K),new i.Point(de,K),new i.Point(de,ne),new i.Point(O,ne),new i.Point(O,K)]},Object.defineProperties(wo.prototype,To);function hl(Y,D){var J=!1,O=null,K=function(){O=null,J&&(Y(),O=setTimeout(K,D),J=!1)};return function(){return J=!0,O||K(),O}}var Nl=function(D){this._hashName=D&&encodeURIComponent(D),i.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=hl(this._updateHashUnthrottled.bind(this),30*1e3/100)};Nl.prototype.addTo=function(D){return this._map=D,i.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Nl.prototype.remove=function(){return i.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Nl.prototype.getHashString=function(D){var J=this._map.getCenter(),O=Math.round(this._map.getZoom()*100)/100,K=Math.ceil((O*Math.LN2+Math.log(512/360/.5))/Math.LN10),de=Math.pow(10,K),ne=Math.round(J.lng*de)/de,we=Math.round(J.lat*de)/de,Oe=this._map.getBearing(),ht=this._map.getPitch(),Yt=\"\";if(D?Yt+=\"/\"+ne+\"/\"+we+\"/\"+O:Yt+=O+\"/\"+we+\"/\"+ne,(Oe||ht)&&(Yt+=\"/\"+Math.round(Oe*10)/10),ht&&(Yt+=\"/\"+Math.round(ht)),this._hashName){var cr=this._hashName,Nt=!1,Ne=i.window.location.hash.slice(1).split(\"&\").map(function($e){var st=$e.split(\"=\")[0];return st===cr?(Nt=!0,st+\"=\"+Yt):$e}).filter(function($e){return $e});return Nt||Ne.push(cr+\"=\"+Yt),\"#\"+Ne.join(\"&\")}return\"#\"+Yt},Nl.prototype._getCurrentHash=function(){var D=this,J=i.window.location.hash.replace(\"#\",\"\");if(this._hashName){var O;return J.split(\"&\").map(function(K){return K.split(\"=\")}).forEach(function(K){K[0]===D._hashName&&(O=K)}),(O&&O[1]||\"\").split(\"/\")}return J.split(\"/\")},Nl.prototype._onHashChange=function(){var D=this._getCurrentHash();if(D.length>=3&&!D.some(function(O){return isNaN(O)})){var J=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(D[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+D[2],+D[1]],zoom:+D[0],bearing:J,pitch:+(D[4]||0)}),!0}return!1},Nl.prototype._updateHashUnthrottled=function(){var D=i.window.location.href.replace(/(#.+)?$/,this.getHashString());try{i.window.history.replaceState(i.window.history.state,null,D)}catch(J){}};var Lu={linearity:.3,easing:i.bezier(0,0,.3,1)},au=i.extend({deceleration:2500,maxSpeed:1400},Lu),Js=i.extend({deceleration:20,maxSpeed:1400},Lu),eu=i.extend({deceleration:1e3,maxSpeed:360},Lu),dc=i.extend({deceleration:1e3,maxSpeed:90},Lu),Al=function(D){this._map=D,this.clear()};Al.prototype.clear=function(){this._inertiaBuffer=[]},Al.prototype.record=function(D){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:i.browser.now(),settings:D})},Al.prototype._drainInertiaBuffer=function(){for(var D=this._inertiaBuffer,J=i.browser.now(),O=160;D.length>0&&J-D[0].time>O;)D.shift()},Al.prototype._onMoveEnd=function(D){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var J={zoom:0,bearing:0,pitch:0,pan:new i.Point(0,0),pinchAround:void 0,around:void 0},O=0,K=this._inertiaBuffer;O<K.length;O+=1){var de=K[O],ne=de.settings;J.zoom+=ne.zoomDelta||0,J.bearing+=ne.bearingDelta||0,J.pitch+=ne.pitchDelta||0,ne.panDelta&&J.pan._add(ne.panDelta),ne.around&&(J.around=ne.around),ne.pinchAround&&(J.pinchAround=ne.pinchAround)}var we=this._inertiaBuffer[this._inertiaBuffer.length-1],Oe=we.time-this._inertiaBuffer[0].time,ht={};if(J.pan.mag()){var Yt=X(J.pan.mag(),Oe,i.extend({},au,D||{}));ht.offset=J.pan.mult(Yt.amount/J.pan.mag()),ht.center=this._map.transform.center,Sl(ht,Yt)}if(J.zoom){var cr=X(J.zoom,Oe,Js);ht.zoom=this._map.transform.zoom+cr.amount,Sl(ht,cr)}if(J.bearing){var Nt=X(J.bearing,Oe,eu);ht.bearing=this._map.transform.bearing+i.clamp(Nt.amount,-179,179),Sl(ht,Nt)}if(J.pitch){var Ne=X(J.pitch,Oe,dc);ht.pitch=this._map.transform.pitch+Ne.amount,Sl(ht,Ne)}if(ht.zoom||ht.bearing){var $e=J.pinchAround===void 0?J.around:J.pinchAround;ht.around=$e?this._map.unproject($e):this._map.getCenter()}return this.clear(),i.extend(ht,{noMoveStart:!0})}};function Sl(Y,D){(!Y.duration||Y.duration<D.duration)&&(Y.duration=D.duration,Y.easing=D.easing)}function X(Y,D,J){var O=J.maxSpeed,K=J.linearity,de=J.deceleration,ne=i.clamp(Y*K/(D/1e3),-O,O),we=Math.abs(ne)/(de*K);return{easing:J.easing,duration:we*1e3,amount:ne*(we/2)}}var se=(function(Y){function D(O,K,de,ne){ne===void 0&&(ne={});var we=o.mousePos(K.getCanvasContainer(),de),Oe=K.unproject(we);Y.call(this,O,i.extend({point:we,lngLat:Oe,originalEvent:de},ne)),this._defaultPrevented=!1,this.target=K}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D})(i.Event),Te=(function(Y){function D(O,K,de){var ne=O===\"touchend\"?de.changedTouches:de.touches,we=o.touchPos(K.getCanvasContainer(),ne),Oe=we.map(function(cr){return K.unproject(cr)}),ht=we.reduce(function(cr,Nt,Ne,$e){return cr.add(Nt.div($e.length))},new i.Point(0,0)),Yt=K.unproject(ht);Y.call(this,O,{points:we,point:ht,lngLats:Oe,lngLat:Yt,originalEvent:de}),this._defaultPrevented=!1}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D})(i.Event),qe=(function(Y){function D(O,K,de){Y.call(this,O,{originalEvent:de}),this._defaultPrevented=!1}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D})(i.Event),Ve=function(D,J){this._map=D,this._clickTolerance=J.clickTolerance};Ve.prototype.reset=function(){delete this._mousedownPos},Ve.prototype.wheel=function(D){return this._firePreventable(new qe(D.type,this._map,D))},Ve.prototype.mousedown=function(D,J){return this._mousedownPos=J,this._firePreventable(new se(D.type,this._map,D))},Ve.prototype.mouseup=function(D){this._map.fire(new se(D.type,this._map,D))},Ve.prototype.click=function(D,J){this._mousedownPos&&this._mousedownPos.dist(J)>=this._clickTolerance||this._map.fire(new se(D.type,this._map,D))},Ve.prototype.dblclick=function(D){return this._firePreventable(new se(D.type,this._map,D))},Ve.prototype.mouseover=function(D){this._map.fire(new se(D.type,this._map,D))},Ve.prototype.mouseout=function(D){this._map.fire(new se(D.type,this._map,D))},Ve.prototype.touchstart=function(D){return this._firePreventable(new Te(D.type,this._map,D))},Ve.prototype.touchmove=function(D){this._map.fire(new Te(D.type,this._map,D))},Ve.prototype.touchend=function(D){this._map.fire(new Te(D.type,this._map,D))},Ve.prototype.touchcancel=function(D){this._map.fire(new Te(D.type,this._map,D))},Ve.prototype._firePreventable=function(D){if(this._map.fire(D),D.defaultPrevented)return{}},Ve.prototype.isEnabled=function(){return!0},Ve.prototype.isActive=function(){return!1},Ve.prototype.enable=function(){},Ve.prototype.disable=function(){};var Ye=function(D){this._map=D};Ye.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Ye.prototype.mousemove=function(D){this._map.fire(new se(D.type,this._map,D))},Ye.prototype.mousedown=function(){this._delayContextMenu=!0},Ye.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new se(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Ye.prototype.contextmenu=function(D){this._delayContextMenu?this._contextMenuEvent=D:this._map.fire(new se(D.type,this._map,D)),this._map.listens(\"contextmenu\")&&D.preventDefault()},Ye.prototype.isEnabled=function(){return!0},Ye.prototype.isActive=function(){return!1},Ye.prototype.enable=function(){},Ye.prototype.disable=function(){};var Pt=function(D,J){this._map=D,this._el=D.getCanvasContainer(),this._container=D.getContainer(),this._clickTolerance=J.clickTolerance||1};Pt.prototype.isEnabled=function(){return!!this._enabled},Pt.prototype.isActive=function(){return!!this._active},Pt.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Pt.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Pt.prototype.mousedown=function(D,J){this.isEnabled()&&D.shiftKey&&D.button===0&&(o.disableDrag(),this._startPos=this._lastPos=J,this._active=!0)},Pt.prototype.mousemoveWindow=function(D,J){if(this._active){var O=J;if(!(this._lastPos.equals(O)||!this._box&&O.dist(this._startPos)<this._clickTolerance)){var K=this._startPos;this._lastPos=O,this._box||(this._box=o.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",D));var de=Math.min(K.x,O.x),ne=Math.max(K.x,O.x),we=Math.min(K.y,O.y),Oe=Math.max(K.y,O.y);o.setTransform(this._box,\"translate(\"+de+\"px,\"+we+\"px)\"),this._box.style.width=ne-de+\"px\",this._box.style.height=Oe-we+\"px\"}}},Pt.prototype.mouseupWindow=function(D,J){var O=this;if(this._active&&D.button===0){var K=this._startPos,de=J;if(this.reset(),o.suppressClick(),K.x===de.x&&K.y===de.y)this._fireEvent(\"boxzoomcancel\",D);else return this._map.fire(new i.Event(\"boxzoomend\",{originalEvent:D})),{cameraAnimation:function(ne){return ne.fitScreenCoordinates(K,de,O._map.getBearing(),{linear:!0})}}}},Pt.prototype.keydown=function(D){this._active&&D.keyCode===27&&(this.reset(),this._fireEvent(\"boxzoomcancel\",D))},Pt.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(o.remove(this._box),this._box=null),o.enableDrag(),delete this._startPos,delete this._lastPos},Pt.prototype._fireEvent=function(D,J){return this._map.fire(new i.Event(D,{originalEvent:J}))};function ot(Y,D){for(var J={},O=0;O<Y.length;O++)J[Y[O].identifier]=D[O];return J}function Xt(Y){for(var D=new i.Point(0,0),J=0,O=Y;J<O.length;J+=1){var K=O[J];D._add(K)}return D.div(Y.length)}var dr=500,pr=500,Ur=30,zr=function(D){this.reset(),this.numTouches=D.numTouches};zr.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},zr.prototype.touchstart=function(D,J,O){(this.centroid||O.length>this.numTouches)&&(this.aborted=!0),!this.aborted&&(this.startTime===void 0&&(this.startTime=D.timeStamp),O.length===this.numTouches&&(this.centroid=Xt(J),this.touches=ot(O,J)))},zr.prototype.touchmove=function(D,J,O){if(!(this.aborted||!this.centroid)){var K=ot(O,J);for(var de in this.touches){var ne=this.touches[de],we=K[de];(!we||we.dist(ne)>Ur)&&(this.aborted=!0)}}},zr.prototype.touchend=function(D,J,O){if((!this.centroid||D.timeStamp-this.startTime>pr)&&(this.aborted=!0),O.length===0){var K=!this.aborted&&this.centroid;if(this.reset(),K)return K}};var gi=function(D){this.singleTap=new zr(D),this.numTaps=D.numTaps,this.reset()};gi.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},gi.prototype.touchstart=function(D,J,O){this.singleTap.touchstart(D,J,O)},gi.prototype.touchmove=function(D,J,O){this.singleTap.touchmove(D,J,O)},gi.prototype.touchend=function(D,J,O){var K=this.singleTap.touchend(D,J,O);if(K){var de=D.timeStamp-this.lastTime<dr,ne=!this.lastTap||this.lastTap.dist(K)<Ur;if((!de||!ne)&&this.reset(),this.count++,this.lastTime=D.timeStamp,this.lastTap=K,this.count===this.numTaps)return this.reset(),K}};var xi=function(){this._zoomIn=new gi({numTouches:1,numTaps:2}),this._zoomOut=new gi({numTouches:2,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},xi.prototype.touchstart=function(D,J,O){this._zoomIn.touchstart(D,J,O),this._zoomOut.touchstart(D,J,O)},xi.prototype.touchmove=function(D,J,O){this._zoomIn.touchmove(D,J,O),this._zoomOut.touchmove(D,J,O)},xi.prototype.touchend=function(D,J,O){var K=this,de=this._zoomIn.touchend(D,J,O),ne=this._zoomOut.touchend(D,J,O);if(de)return this._active=!0,D.preventDefault(),setTimeout(function(){return K.reset()},0),{cameraAnimation:function(we){return we.easeTo({duration:300,zoom:we.getZoom()+1,around:we.unproject(de)},{originalEvent:D})}};if(ne)return this._active=!0,D.preventDefault(),setTimeout(function(){return K.reset()},0),{cameraAnimation:function(we){return we.easeTo({duration:300,zoom:we.getZoom()-1,around:we.unproject(ne)},{originalEvent:D})}}},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var Wr=0,ii=2,di={};di[Wr]=1,di[ii]=2;function Li(Y,D){var J=di[D];return Y.buttons===void 0||(Y.buttons&J)!==J}var Ti=function(D){this.reset(),this._clickTolerance=D.clickTolerance||1};Ti.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Ti.prototype._correctButton=function(D,J){return!1},Ti.prototype._move=function(D,J){return{}},Ti.prototype.mousedown=function(D,J){if(!this._lastPoint){var O=o.mouseButton(D);this._correctButton(D,O)&&(this._lastPoint=J,this._eventButton=O)}},Ti.prototype.mousemoveWindow=function(D,J){var O=this._lastPoint;if(O){if(D.preventDefault(),Li(D,this._eventButton)){this.reset();return}if(!(!this._moved&&J.dist(O)<this._clickTolerance))return this._moved=!0,this._lastPoint=J,this._move(O,J)}},Ti.prototype.mouseupWindow=function(D){if(this._lastPoint){var J=o.mouseButton(D);J===this._eventButton&&(this._moved&&o.suppressClick(),this.reset())}},Ti.prototype.enable=function(){this._enabled=!0},Ti.prototype.disable=function(){this._enabled=!1,this.reset()},Ti.prototype.isEnabled=function(){return this._enabled},Ti.prototype.isActive=function(){return this._active};var Qr=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.mousedown=function(O,K){Y.prototype.mousedown.call(this,O,K),this._lastPoint&&(this._active=!0)},D.prototype._correctButton=function(O,K){return K===Wr&&!O.ctrlKey},D.prototype._move=function(O,K){return{around:K,panDelta:K.sub(O)}},D})(Ti),Pn=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype._correctButton=function(O,K){return K===Wr&&O.ctrlKey||K===ii},D.prototype._move=function(O,K){var de=.8,ne=(K.x-O.x)*de;if(ne)return this._active=!0,{bearingDelta:ne}},D.prototype.contextmenu=function(O){O.preventDefault()},D})(Ti),kn=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype._correctButton=function(O,K){return K===Wr&&O.ctrlKey||K===ii},D.prototype._move=function(O,K){var de=-.5,ne=(K.y-O.y)*de;if(ne)return this._active=!0,{pitchDelta:ne}},D.prototype.contextmenu=function(O){O.preventDefault()},D})(Ti),Bn=function(D){this._minTouches=1,this._clickTolerance=D.clickTolerance||1,this.reset()};Bn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new i.Point(0,0)},Bn.prototype.touchstart=function(D,J,O){return this._calculateTransform(D,J,O)},Bn.prototype.touchmove=function(D,J,O){if(!(!this._active||O.length<this._minTouches))return D.preventDefault(),this._calculateTransform(D,J,O)},Bn.prototype.touchend=function(D,J,O){this._calculateTransform(D,J,O),this._active&&O.length<this._minTouches&&this.reset()},Bn.prototype.touchcancel=function(){this.reset()},Bn.prototype._calculateTransform=function(D,J,O){O.length>0&&(this._active=!0);var K=ot(O,J),de=new i.Point(0,0),ne=new i.Point(0,0),we=0;for(var Oe in K){var ht=K[Oe],Yt=this._touches[Oe];Yt&&(de._add(ht),ne._add(ht.sub(Yt)),we++,K[Oe]=ht)}if(this._touches=K,!(we<this._minTouches||!ne.mag())){var cr=ne.div(we);if(this._sum._add(cr),!(this._sum.mag()<this._clickTolerance)){var Nt=de.div(we);return{around:Nt,panDelta:cr}}}},Bn.prototype.enable=function(){this._enabled=!0},Bn.prototype.disable=function(){this._enabled=!1,this.reset()},Bn.prototype.isEnabled=function(){return this._enabled},Bn.prototype.isActive=function(){return this._active};var na=function(){this.reset()};na.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},na.prototype._start=function(D){},na.prototype._move=function(D,J,O){return{}},na.prototype.touchstart=function(D,J,O){this._firstTwoTouches||O.length<2||(this._firstTwoTouches=[O[0].identifier,O[1].identifier],this._start([J[0],J[1]]))},na.prototype.touchmove=function(D,J,O){if(this._firstTwoTouches){D.preventDefault();var K=this._firstTwoTouches,de=K[0],ne=K[1],we=Ea(O,J,de),Oe=Ea(O,J,ne);if(!(!we||!Oe)){var ht=this._aroundCenter?null:we.add(Oe).div(2);return this._move([we,Oe],ht,D)}}},na.prototype.touchend=function(D,J,O){if(this._firstTwoTouches){var K=this._firstTwoTouches,de=K[0],ne=K[1],we=Ea(O,J,de),Oe=Ea(O,J,ne);we&&Oe||(this._active&&o.suppressClick(),this.reset())}},na.prototype.touchcancel=function(){this.reset()},na.prototype.enable=function(D){this._enabled=!0,this._aroundCenter=!!D&&D.around===\"center\"},na.prototype.disable=function(){this._enabled=!1,this.reset()},na.prototype.isEnabled=function(){return this._enabled},na.prototype.isActive=function(){return this._active};function Ea(Y,D,J){for(var O=0;O<Y.length;O++)if(Y[O].identifier===J)return D[O]}var Ia=.1;function yo(Y,D){return Math.log(Y/D)/Math.LN2}var Da=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),delete this._distance,delete this._startDistance},D.prototype._start=function(O){this._startDistance=this._distance=O[0].dist(O[1])},D.prototype._move=function(O,K){var de=this._distance;if(this._distance=O[0].dist(O[1]),!(!this._active&&Math.abs(yo(this._distance,this._startDistance))<Ia))return this._active=!0,{zoomDelta:yo(this._distance,de),pinchAround:K}},D})(na),go=25;function Is(Y,D){return Y.angleWith(D)*180/Math.PI}var Ms=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},D.prototype._start=function(O){this._startVector=this._vector=O[0].sub(O[1]),this._minDiameter=O[0].dist(O[1])},D.prototype._move=function(O,K){var de=this._vector;if(this._vector=O[0].sub(O[1]),!(!this._active&&this._isBelowThreshold(this._vector)))return this._active=!0,{bearingDelta:Is(this._vector,de),pinchAround:K}},D.prototype._isBelowThreshold=function(O){this._minDiameter=Math.min(this._minDiameter,O.mag());var K=Math.PI*this._minDiameter,de=go/K*360,ne=Is(O,this._startVector);return Math.abs(ne)<de},D})(na);function Xs(Y){return Math.abs(Y.y)>Math.abs(Y.x)}var Un=100,ja=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},D.prototype._start=function(O){this._lastPoints=O,Xs(O[0].sub(O[1]))&&(this._valid=!1)},D.prototype._move=function(O,K,de){var ne=O[0].sub(this._lastPoints[0]),we=O[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(ne,we,de.timeStamp),!!this._valid){this._lastPoints=O,this._active=!0;var Oe=(ne.y+we.y)/2,ht=-.5;return{pitchDelta:Oe*ht}}},D.prototype.gestureBeginsVertically=function(O,K,de){if(this._valid!==void 0)return this._valid;var ne=2,we=O.mag()>=ne,Oe=K.mag()>=ne;if(!(!we&&!Oe)){if(!we||!Oe)return this._firstMove===void 0&&(this._firstMove=de),de-this._firstMove<Un?void 0:!1;var ht=O.y>0==K.y>0;return Xs(O)&&Xs(K)&&ht}},D})(na),Fo={panStep:100,bearingStep:15,pitchStep:10},Uo=function(){var D=Fo;this._panStep=D.panStep,this._bearingStep=D.bearingStep,this._pitchStep=D.pitchStep,this._rotationDisabled=!1};Uo.prototype.reset=function(){this._active=!1},Uo.prototype.keydown=function(D){var J=this;if(!(D.altKey||D.ctrlKey||D.metaKey)){var O=0,K=0,de=0,ne=0,we=0;switch(D.keyCode){case 61:case 107:case 171:case 187:O=1;break;case 189:case 109:case 173:O=-1;break;case 37:D.shiftKey?K=-1:(D.preventDefault(),ne=-1);break;case 39:D.shiftKey?K=1:(D.preventDefault(),ne=1);break;case 38:D.shiftKey?de=1:(D.preventDefault(),we=-1);break;case 40:D.shiftKey?de=-1:(D.preventDefault(),we=1);break;default:return}return this._rotationDisabled&&(K=0,de=0),{cameraAnimation:function(Oe){var ht=Oe.getZoom();Oe.easeTo({duration:300,easeId:\"keyboardHandler\",easing:$s,zoom:O?Math.round(ht)+O*(D.shiftKey?2:1):ht,bearing:Oe.getBearing()+K*J._bearingStep,pitch:Oe.getPitch()+de*J._pitchStep,offset:[-ne*J._panStep,-we*J._panStep],center:Oe.getCenter()},{originalEvent:D})}}}},Uo.prototype.enable=function(){this._enabled=!0},Uo.prototype.disable=function(){this._enabled=!1,this.reset()},Uo.prototype.isEnabled=function(){return this._enabled},Uo.prototype.isActive=function(){return this._active},Uo.prototype.disableRotation=function(){this._rotationDisabled=!0},Uo.prototype.enableRotation=function(){this._rotationDisabled=!1};function $s(Y){return Y*(2-Y)}var Ml=4.000244140625,bu=1/100,dl=1/450,Sc=2,Me=function(D,J){this._map=D,this._el=D.getCanvasContainer(),this._handler=J,this._delta=0,this._defaultZoomRate=bu,this._wheelZoomRate=dl,i.bindAll([\"_onTimeout\"],this)};Me.prototype.setZoomRate=function(D){this._defaultZoomRate=D},Me.prototype.setWheelZoomRate=function(D){this._wheelZoomRate=D},Me.prototype.isEnabled=function(){return!!this._enabled},Me.prototype.isActive=function(){return!!this._active||this._finishTimeout!==void 0},Me.prototype.isZooming=function(){return!!this._zooming},Me.prototype.enable=function(D){this.isEnabled()||(this._enabled=!0,this._aroundCenter=D&&D.around===\"center\")},Me.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Me.prototype.wheel=function(D){if(this.isEnabled()){var J=D.deltaMode===i.window.WheelEvent.DOM_DELTA_LINE?D.deltaY*40:D.deltaY,O=i.browser.now(),K=O-(this._lastWheelEventTime||0);this._lastWheelEventTime=O,J!==0&&J%Ml===0?this._type=\"wheel\":J!==0&&Math.abs(J)<4?this._type=\"trackpad\":K>400?(this._type=null,this._lastValue=J,this._timeout=setTimeout(this._onTimeout,40,D)):this._type||(this._type=Math.abs(K*J)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,J+=this._lastValue)),D.shiftKey&&J&&(J=J/4),this._type&&(this._lastWheelEvent=D,this._delta-=J,this._active||this._start(D)),D.preventDefault()}},Me.prototype._onTimeout=function(D){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(D)},Me.prototype._start=function(D){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var J=o.mousePos(this._el,D);this._around=i.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(J)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},Me.prototype.renderFrame=function(){var D=this;if(this._frameId&&(this._frameId=null,!!this.isActive())){var J=this._map.transform;if(this._delta!==0){var O=this._type===\"wheel\"&&Math.abs(this._delta)>Ml?this._wheelZoomRate:this._defaultZoomRate,K=Sc/(1+Math.exp(-Math.abs(this._delta*O)));this._delta<0&&K!==0&&(K=1/K);var de=typeof this._targetZoom==\"number\"?J.zoomScale(this._targetZoom):J.scale;this._targetZoom=Math.min(J.maxZoom,Math.max(J.minZoom,J.scaleZoom(de*K))),this._type===\"wheel\"&&(this._startZoom=J.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var ne=typeof this._targetZoom==\"number\"?this._targetZoom:J.zoom,we=this._startZoom,Oe=this._easing,ht=!1,Yt;if(this._type===\"wheel\"&&we&&Oe){var cr=Math.min((i.browser.now()-this._lastWheelEventTime)/200,1),Nt=Oe(cr);Yt=i.number(we,ne,Nt),cr<1?this._frameId||(this._frameId=!0):ht=!0}else Yt=ne,ht=!0;return this._active=!0,ht&&(this._active=!1,this._finishTimeout=setTimeout(function(){D._zooming=!1,D._handler._triggerRenderFrame(),delete D._targetZoom,delete D._finishTimeout},200)),{noInertia:!0,needsRenderFrame:!ht,zoomDelta:Yt-J.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},Me.prototype._smoothOutEasing=function(D){var J=i.ease;if(this._prevEase){var O=this._prevEase,K=(i.browser.now()-O.start)/O.duration,de=O.easing(K+.01)-O.easing(K),ne=.27/Math.sqrt(de*de+1e-4)*.01,we=Math.sqrt(.27*.27-ne*ne);J=i.bezier(ne,we,.25,1)}return this._prevEase={start:i.browser.now(),duration:D,easing:J},J},Me.prototype.reset=function(){this._active=!1};var bt=function(D,J){this._clickZoom=D,this._tapZoom=J};bt.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},bt.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},bt.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},bt.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var Ot=function(){this.reset()};Ot.prototype.reset=function(){this._active=!1},Ot.prototype.dblclick=function(D,J){return D.preventDefault(),{cameraAnimation:function(O){O.easeTo({duration:300,zoom:O.getZoom()+(D.shiftKey?-1:1),around:O.unproject(J)},{originalEvent:D})}}},Ot.prototype.enable=function(){this._enabled=!0},Ot.prototype.disable=function(){this._enabled=!1,this.reset()},Ot.prototype.isEnabled=function(){return this._enabled},Ot.prototype.isActive=function(){return this._active};var Lr=function(){this._tap=new gi({numTouches:1,numTaps:1}),this.reset()};Lr.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},Lr.prototype.touchstart=function(D,J,O){this._swipePoint||(this._tapTime&&D.timeStamp-this._tapTime>dr&&this.reset(),this._tapTime?O.length>0&&(this._swipePoint=J[0],this._swipeTouch=O[0].identifier):this._tap.touchstart(D,J,O))},Lr.prototype.touchmove=function(D,J,O){if(!this._tapTime)this._tap.touchmove(D,J,O);else if(this._swipePoint){if(O[0].identifier!==this._swipeTouch)return;var K=J[0],de=K.y-this._swipePoint.y;return this._swipePoint=K,D.preventDefault(),this._active=!0,{zoomDelta:de/128}}},Lr.prototype.touchend=function(D,J,O){if(this._tapTime)this._swipePoint&&O.length===0&&this.reset();else{var K=this._tap.touchend(D,J,O);K&&(this._tapTime=D.timeStamp)}},Lr.prototype.touchcancel=function(){this.reset()},Lr.prototype.enable=function(){this._enabled=!0},Lr.prototype.disable=function(){this._enabled=!1,this.reset()},Lr.prototype.isEnabled=function(){return this._enabled},Lr.prototype.isActive=function(){return this._active};var Vr=function(D,J,O){this._el=D,this._mousePan=J,this._touchPan=O};Vr.prototype.enable=function(D){this._inertiaOptions=D||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},Vr.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},Vr.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},Vr.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var Or=function(D,J,O){this._pitchWithRotate=D.pitchWithRotate,this._mouseRotate=J,this._mousePitch=O};Or.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},Or.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},Or.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},Or.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var Nr=function(D,J,O,K){this._el=D,this._touchZoom=J,this._touchRotate=O,this._tapDragZoom=K,this._rotationDisabled=!1,this._enabled=!0};Nr.prototype.enable=function(D){this._touchZoom.enable(D),this._rotationDisabled||this._touchRotate.enable(D),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},Nr.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},Nr.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},Nr.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},Nr.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},Nr.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var vi=function(Y){return Y.zoom||Y.drag||Y.pitch||Y.rotate},Ni=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D})(i.Event);function qi(Y){return Y.panDelta&&Y.panDelta.mag()||Y.zoomDelta||Y.bearingDelta||Y.pitchDelta}var Si=function(D,J){this._map=D,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Al(D),this._bearingSnap=J.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(J),i.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var O=this._el;this._listeners=[[O,\"touchstart\",{passive:!0}],[O,\"touchmove\",{passive:!1}],[O,\"touchend\",void 0],[O,\"touchcancel\",void 0],[O,\"mousedown\",void 0],[O,\"mousemove\",void 0],[O,\"mouseup\",void 0],[i.window.document,\"mousemove\",{capture:!0}],[i.window.document,\"mouseup\",void 0],[O,\"mouseover\",void 0],[O,\"mouseout\",void 0],[O,\"dblclick\",void 0],[O,\"click\",void 0],[O,\"keydown\",{capture:!1}],[O,\"keyup\",void 0],[O,\"wheel\",{passive:!1}],[O,\"contextmenu\",void 0],[i.window,\"blur\",void 0]];for(var K=0,de=this._listeners;K<de.length;K+=1){var ne=de[K],we=ne[0],Oe=ne[1],ht=ne[2];o.addEventListener(we,Oe,we===i.window.document?this.handleWindowEvent:this.handleEvent,ht)}};Si.prototype.destroy=function(){for(var D=0,J=this._listeners;D<J.length;D+=1){var O=J[D],K=O[0],de=O[1],ne=O[2];o.removeEventListener(K,de,K===i.window.document?this.handleWindowEvent:this.handleEvent,ne)}},Si.prototype._addDefaultHandlers=function(D){var J=this._map,O=J.getCanvasContainer();this._add(\"mapEvent\",new Ve(J,D));var K=J.boxZoom=new Pt(J,D);this._add(\"boxZoom\",K);var de=new xi,ne=new Ot;J.doubleClickZoom=new bt(ne,de),this._add(\"tapZoom\",de),this._add(\"clickZoom\",ne);var we=new Lr;this._add(\"tapDragZoom\",we);var Oe=J.touchPitch=new ja;this._add(\"touchPitch\",Oe);var ht=new Pn(D),Yt=new kn(D);J.dragRotate=new Or(D,ht,Yt),this._add(\"mouseRotate\",ht,[\"mousePitch\"]),this._add(\"mousePitch\",Yt,[\"mouseRotate\"]);var cr=new Qr(D),Nt=new Bn(D);J.dragPan=new Vr(O,cr,Nt),this._add(\"mousePan\",cr),this._add(\"touchPan\",Nt,[\"touchZoom\",\"touchRotate\"]);var Ne=new Ms,$e=new Da;J.touchZoomRotate=new Nr(O,$e,Ne,we),this._add(\"touchRotate\",Ne,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",$e,[\"touchPan\",\"touchRotate\"]);var st=J.scrollZoom=new Me(J,this);this._add(\"scrollZoom\",st,[\"mousePan\"]);var It=J.keyboard=new Uo;this._add(\"keyboard\",It),this._add(\"blockableMapEvent\",new Ye(J));for(var Bt=0,Ft=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];Bt<Ft.length;Bt+=1){var Ut=Ft[Bt];D.interactive&&D[Ut]&&J[Ut].enable(D[Ut])}},Si.prototype._add=function(D,J,O){this._handlers.push({handlerName:D,handler:J,allowed:O}),this._handlersById[D]=J},Si.prototype.stop=function(D){if(!this._updatingCamera){for(var J=0,O=this._handlers;J<O.length;J+=1){var K=O[J],de=K.handler;de.reset()}this._inertia.clear(),this._fireEvents({},{},D),this._changes=[]}},Si.prototype.isActive=function(){for(var D=0,J=this._handlers;D<J.length;D+=1){var O=J[D],K=O.handler;if(K.isActive())return!0}return!1},Si.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Si.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Si.prototype.isMoving=function(){return!!vi(this._eventsInProgress)||this.isZooming()},Si.prototype._blockedByActive=function(D,J,O){for(var K in D)if(K!==O&&(!J||J.indexOf(K)<0))return!0;return!1},Si.prototype.handleWindowEvent=function(D){this.handleEvent(D,D.type+\"Window\")},Si.prototype._getMapTouches=function(D){for(var J=[],O=0,K=D;O<K.length;O+=1){var de=K[O],ne=de.target;this._el.contains(ne)&&J.push(de)}return J},Si.prototype.handleEvent=function(D,J){if(D.type===\"blur\"){this.stop(!0);return}this._updatingCamera=!0;for(var O=D.type===\"renderFrame\"?void 0:D,K={needsRenderFrame:!1},de={},ne={},we=D.touches?this._getMapTouches(D.touches):void 0,Oe=we?o.touchPos(this._el,we):o.mousePos(this._el,D),ht=0,Yt=this._handlers;ht<Yt.length;ht+=1){var cr=Yt[ht],Nt=cr.handlerName,Ne=cr.handler,$e=cr.allowed;if(Ne.isEnabled()){var st=void 0;this._blockedByActive(ne,$e,Nt)?Ne.reset():Ne[J||D.type]&&(st=Ne[J||D.type](D,Oe,we),this.mergeHandlerResult(K,de,st,Nt,O),st&&st.needsRenderFrame&&this._triggerRenderFrame()),(st||Ne.isActive())&&(ne[Nt]=Ne)}}var It={};for(var Bt in this._previousActiveHandlers)ne[Bt]||(It[Bt]=O);this._previousActiveHandlers=ne,(Object.keys(It).length||qi(K))&&(this._changes.push([K,de,It]),this._triggerRenderFrame()),(Object.keys(ne).length||qi(K))&&this._map._stop(!0),this._updatingCamera=!1;var Ft=K.cameraAnimation;Ft&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],Ft(this._map))},Si.prototype.mergeHandlerResult=function(D,J,O,K,de){if(O){i.extend(D,O);var ne={handlerName:K,originalEvent:O.originalEvent||de};O.zoomDelta!==void 0&&(J.zoom=ne),O.panDelta!==void 0&&(J.drag=ne),O.pitchDelta!==void 0&&(J.pitch=ne),O.bearingDelta!==void 0&&(J.rotate=ne)}},Si.prototype._applyChanges=function(){for(var D={},J={},O={},K=0,de=this._changes;K<de.length;K+=1){var ne=de[K],we=ne[0],Oe=ne[1],ht=ne[2];we.panDelta&&(D.panDelta=(D.panDelta||new i.Point(0,0))._add(we.panDelta)),we.zoomDelta&&(D.zoomDelta=(D.zoomDelta||0)+we.zoomDelta),we.bearingDelta&&(D.bearingDelta=(D.bearingDelta||0)+we.bearingDelta),we.pitchDelta&&(D.pitchDelta=(D.pitchDelta||0)+we.pitchDelta),we.around!==void 0&&(D.around=we.around),we.pinchAround!==void 0&&(D.pinchAround=we.pinchAround),we.noInertia&&(D.noInertia=we.noInertia),i.extend(J,Oe),i.extend(O,ht)}this._updateMapTransform(D,J,O),this._changes=[]},Si.prototype._updateMapTransform=function(D,J,O){var K=this._map,de=K.transform;if(!qi(D))return this._fireEvents(J,O,!0);var ne=D.panDelta,we=D.zoomDelta,Oe=D.bearingDelta,ht=D.pitchDelta,Yt=D.around,cr=D.pinchAround;cr!==void 0&&(Yt=cr),K._stop(!0),Yt=Yt||K.transform.centerPoint;var Nt=de.pointLocation(ne?Yt.sub(ne):Yt);Oe&&(de.bearing+=Oe),ht&&(de.pitch+=ht),we&&(de.zoom+=we),de.setLocationAtPoint(Nt,Yt),this._map._update(),D.noInertia||this._inertia.record(D),this._fireEvents(J,O,!0)},Si.prototype._fireEvents=function(D,J,O){var K=this,de=vi(this._eventsInProgress),ne=vi(D),we={};for(var Oe in D){var ht=D[Oe],Yt=ht.originalEvent;this._eventsInProgress[Oe]||(we[Oe+\"start\"]=Yt),this._eventsInProgress[Oe]=D[Oe]}!de&&ne&&this._fireEvent(\"movestart\",ne.originalEvent);for(var cr in we)this._fireEvent(cr,we[cr]);ne&&this._fireEvent(\"move\",ne.originalEvent);for(var Nt in D){var Ne=D[Nt],$e=Ne.originalEvent;this._fireEvent(Nt,$e)}var st={},It;for(var Bt in this._eventsInProgress){var Ft=this._eventsInProgress[Bt],Ut=Ft.handlerName,zt=Ft.originalEvent;this._handlersById[Ut].isActive()||(delete this._eventsInProgress[Bt],It=J[Ut]||zt,st[Bt+\"end\"]=It)}for(var _t in st)this._fireEvent(_t,st[_t]);var Dt=vi(this._eventsInProgress);if(O&&(de||ne)&&!Dt){this._updatingCamera=!0;var vt=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),lr=function(_r){return _r!==0&&-K._bearingSnap<_r&&_r<K._bearingSnap};vt?(lr(vt.bearing||this._map.getBearing())&&(vt.bearing=0),this._map.easeTo(vt,{originalEvent:It})):(this._map.fire(new i.Event(\"moveend\",{originalEvent:It})),lr(this._map.getBearing())&&this._map.resetNorth()),this._updatingCamera=!1}},Si.prototype._fireEvent=function(D,J){this._map.fire(new i.Event(D,J?{originalEvent:J}:{}))},Si.prototype._requestFrame=function(){var D=this;return this._map.triggerRepaint(),this._map._renderTaskQueue.add(function(J){delete D._frameId,D.handleEvent(new Ni(\"renderFrame\",{timeStamp:J})),D._applyChanges()})},Si.prototype._triggerRenderFrame=function(){this._frameId===void 0&&(this._frameId=this._requestFrame())};var Vn=(function(Y){function D(J,O){Y.call(this),this._moving=!1,this._zooming=!1,this.transform=J,this._bearingSnap=O.bearingSnap,i.bindAll([\"_renderFrameCallback\"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getCenter=function(){return new i.LngLat(this.transform.center.lng,this.transform.center.lat)},D.prototype.setCenter=function(O,K){return this.jumpTo({center:O},K)},D.prototype.panBy=function(O,K,de){return O=i.Point.convert(O).mult(-1),this.panTo(this.transform.center,i.extend({offset:O},K),de)},D.prototype.panTo=function(O,K,de){return this.easeTo(i.extend({center:O},K),de)},D.prototype.getZoom=function(){return this.transform.zoom},D.prototype.setZoom=function(O,K){return this.jumpTo({zoom:O},K),this},D.prototype.zoomTo=function(O,K,de){return this.easeTo(i.extend({zoom:O},K),de)},D.prototype.zoomIn=function(O,K){return this.zoomTo(this.getZoom()+1,O,K),this},D.prototype.zoomOut=function(O,K){return this.zoomTo(this.getZoom()-1,O,K),this},D.prototype.getBearing=function(){return this.transform.bearing},D.prototype.setBearing=function(O,K){return this.jumpTo({bearing:O},K),this},D.prototype.getPadding=function(){return this.transform.padding},D.prototype.setPadding=function(O,K){return this.jumpTo({padding:O},K),this},D.prototype.rotateTo=function(O,K,de){return this.easeTo(i.extend({bearing:O},K),de)},D.prototype.resetNorth=function(O,K){return this.rotateTo(0,i.extend({duration:1e3},O),K),this},D.prototype.resetNorthPitch=function(O,K){return this.easeTo(i.extend({bearing:0,pitch:0,duration:1e3},O),K),this},D.prototype.snapToNorth=function(O,K){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(O,K):this},D.prototype.getPitch=function(){return this.transform.pitch},D.prototype.setPitch=function(O,K){return this.jumpTo({pitch:O},K),this},D.prototype.cameraForBounds=function(O,K){O=i.LngLatBounds.convert(O);var de=K&&K.bearing||0;return this._cameraForBoxAndBearing(O.getNorthWest(),O.getSouthEast(),de,K)},D.prototype._cameraForBoxAndBearing=function(O,K,de,ne){var we={top:0,bottom:0,right:0,left:0};if(ne=i.extend({padding:we,offset:[0,0],maxZoom:this.transform.maxZoom},ne),typeof ne.padding==\"number\"){var Oe=ne.padding;ne.padding={top:Oe,bottom:Oe,right:Oe,left:Oe}}ne.padding=i.extend(we,ne.padding);var ht=this.transform,Yt=ht.padding,cr=ht.project(i.LngLat.convert(O)),Nt=ht.project(i.LngLat.convert(K)),Ne=cr.rotate(-de*Math.PI/180),$e=Nt.rotate(-de*Math.PI/180),st=new i.Point(Math.max(Ne.x,$e.x),Math.max(Ne.y,$e.y)),It=new i.Point(Math.min(Ne.x,$e.x),Math.min(Ne.y,$e.y)),Bt=st.sub(It),Ft=(ht.width-(Yt.left+Yt.right+ne.padding.left+ne.padding.right))/Bt.x,Ut=(ht.height-(Yt.top+Yt.bottom+ne.padding.top+ne.padding.bottom))/Bt.y;if(Ut<0||Ft<0){i.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\");return}var zt=Math.min(ht.scaleZoom(ht.scale*Math.min(Ft,Ut)),ne.maxZoom),_t=typeof ne.offset.x==\"number\"?new i.Point(ne.offset.x,ne.offset.y):i.Point.convert(ne.offset),Dt=(ne.padding.left-ne.padding.right)/2,vt=(ne.padding.top-ne.padding.bottom)/2,lr=new i.Point(Dt,vt),_r=lr.rotate(de*Math.PI/180),Br=_t.add(_r),Di=Br.mult(ht.scale/ht.zoomScale(zt)),ci=ht.unproject(cr.add(Nt).div(2).sub(Di));return{center:ci,zoom:zt,bearing:de}},D.prototype.fitBounds=function(O,K,de){return this._fitInternal(this.cameraForBounds(O,K),K,de)},D.prototype.fitScreenCoordinates=function(O,K,de,ne,we){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(i.Point.convert(O)),this.transform.pointLocation(i.Point.convert(K)),de,ne),ne,we)},D.prototype._fitInternal=function(O,K,de){return O?(K=i.extend(O,K),delete K.padding,K.linear?this.easeTo(K,de):this.flyTo(K,de)):this},D.prototype.jumpTo=function(O,K){this.stop();var de=this.transform,ne=!1,we=!1,Oe=!1;return\"zoom\"in O&&de.zoom!==+O.zoom&&(ne=!0,de.zoom=+O.zoom),O.center!==void 0&&(de.center=i.LngLat.convert(O.center)),\"bearing\"in O&&de.bearing!==+O.bearing&&(we=!0,de.bearing=+O.bearing),\"pitch\"in O&&de.pitch!==+O.pitch&&(Oe=!0,de.pitch=+O.pitch),O.padding!=null&&!de.isPaddingEqual(O.padding)&&(de.padding=O.padding),this.fire(new i.Event(\"movestart\",K)).fire(new i.Event(\"move\",K)),ne&&this.fire(new i.Event(\"zoomstart\",K)).fire(new i.Event(\"zoom\",K)).fire(new i.Event(\"zoomend\",K)),we&&this.fire(new i.Event(\"rotatestart\",K)).fire(new i.Event(\"rotate\",K)).fire(new i.Event(\"rotateend\",K)),Oe&&this.fire(new i.Event(\"pitchstart\",K)).fire(new i.Event(\"pitch\",K)).fire(new i.Event(\"pitchend\",K)),this.fire(new i.Event(\"moveend\",K))},D.prototype.easeTo=function(O,K){var de=this;this._stop(!1,O.easeId),O=i.extend({offset:[0,0],duration:500,easing:i.ease},O),(O.animate===!1||!O.essential&&i.browser.prefersReducedMotion)&&(O.duration=0);var ne=this.transform,we=this.getZoom(),Oe=this.getBearing(),ht=this.getPitch(),Yt=this.getPadding(),cr=\"zoom\"in O?+O.zoom:we,Nt=\"bearing\"in O?this._normalizeBearing(O.bearing,Oe):Oe,Ne=\"pitch\"in O?+O.pitch:ht,$e=\"padding\"in O?O.padding:ne.padding,st=i.Point.convert(O.offset),It=ne.centerPoint.add(st),Bt=ne.pointLocation(It),Ft=i.LngLat.convert(O.center||Bt);this._normalizeCenter(Ft);var Ut=ne.project(Bt),zt=ne.project(Ft).sub(Ut),_t=ne.zoomScale(cr-we),Dt,vt;O.around&&(Dt=i.LngLat.convert(O.around),vt=ne.locationPoint(Dt));var lr={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||cr!==we,this._rotating=this._rotating||Oe!==Nt,this._pitching=this._pitching||Ne!==ht,this._padding=!ne.isPaddingEqual($e),this._easeId=O.easeId,this._prepareEase(K,O.noMoveStart,lr),this._ease(function(_r){if(de._zooming&&(ne.zoom=i.number(we,cr,_r)),de._rotating&&(ne.bearing=i.number(Oe,Nt,_r)),de._pitching&&(ne.pitch=i.number(ht,Ne,_r)),de._padding&&(ne.interpolatePadding(Yt,$e,_r),It=ne.centerPoint.add(st)),Dt)ne.setLocationAtPoint(Dt,vt);else{var Br=ne.zoomScale(ne.zoom-we),Di=cr>we?Math.min(2,_t):Math.max(.5,_t),ci=Math.pow(Di,1-_r),bi=ne.unproject(Ut.add(zt.mult(_r*ci)).mult(Br));ne.setLocationAtPoint(ne.renderWorldCopies?bi.wrap():bi,It)}de._fireMoveEvents(K)},function(_r){de._afterEase(K,_r)},O),this},D.prototype._prepareEase=function(O,K,de){de===void 0&&(de={}),this._moving=!0,!K&&!de.moving&&this.fire(new i.Event(\"movestart\",O)),this._zooming&&!de.zooming&&this.fire(new i.Event(\"zoomstart\",O)),this._rotating&&!de.rotating&&this.fire(new i.Event(\"rotatestart\",O)),this._pitching&&!de.pitching&&this.fire(new i.Event(\"pitchstart\",O))},D.prototype._fireMoveEvents=function(O){this.fire(new i.Event(\"move\",O)),this._zooming&&this.fire(new i.Event(\"zoom\",O)),this._rotating&&this.fire(new i.Event(\"rotate\",O)),this._pitching&&this.fire(new i.Event(\"pitch\",O))},D.prototype._afterEase=function(O,K){if(!(this._easeId&&K&&this._easeId===K)){delete this._easeId;var de=this._zooming,ne=this._rotating,we=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,de&&this.fire(new i.Event(\"zoomend\",O)),ne&&this.fire(new i.Event(\"rotateend\",O)),we&&this.fire(new i.Event(\"pitchend\",O)),this.fire(new i.Event(\"moveend\",O))}},D.prototype.flyTo=function(O,K){var de=this;if(!O.essential&&i.browser.prefersReducedMotion){var ne=i.pick(O,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(ne,K)}this.stop(),O=i.extend({offset:[0,0],speed:1.2,curve:1.42,easing:i.ease},O);var we=this.transform,Oe=this.getZoom(),ht=this.getBearing(),Yt=this.getPitch(),cr=this.getPadding(),Nt=\"zoom\"in O?i.clamp(+O.zoom,we.minZoom,we.maxZoom):Oe,Ne=\"bearing\"in O?this._normalizeBearing(O.bearing,ht):ht,$e=\"pitch\"in O?+O.pitch:Yt,st=\"padding\"in O?O.padding:we.padding,It=we.zoomScale(Nt-Oe),Bt=i.Point.convert(O.offset),Ft=we.centerPoint.add(Bt),Ut=we.pointLocation(Ft),zt=i.LngLat.convert(O.center||Ut);this._normalizeCenter(zt);var _t=we.project(Ut),Dt=we.project(zt).sub(_t),vt=O.curve,lr=Math.max(we.width,we.height),_r=lr/It,Br=Dt.mag();if(\"minZoom\"in O){var Di=i.clamp(Math.min(O.minZoom,Oe,Nt),we.minZoom,we.maxZoom),ci=lr/we.zoomScale(Di-Oe);vt=Math.sqrt(ci/Br*2)}var bi=vt*vt;function gn(so){var Yo=(_r*_r-lr*lr+(so?-1:1)*bi*bi*Br*Br)/(2*(so?_r:lr)*bi*Br);return Math.log(Math.sqrt(Yo*Yo+1)-Yo)}function tn(so){return(Math.exp(so)-Math.exp(-so))/2}function Ei(so){return(Math.exp(so)+Math.exp(-so))/2}function Oi(so){return tn(so)/Ei(so)}var Gi=gn(0),on=function(so){return Ei(Gi)/Ei(Gi+vt*so)},zn=function(so){return lr*((Ei(Gi)*Oi(Gi+vt*so)-tn(Gi))/bi)/Br},Ja=(gn(1)-Gi)/vt;if(Math.abs(Br)<1e-6||!isFinite(Ja)){if(Math.abs(lr-_r)<1e-6)return this.easeTo(O,K);var co=_r<lr?-1:1;Ja=Math.abs(Math.log(_r/lr))/vt,zn=function(){return 0},on=function(so){return Math.exp(co*vt*so)}}if(\"duration\"in O)O.duration=+O.duration;else{var ts=\"screenSpeed\"in O?+O.screenSpeed/vt:+O.speed;O.duration=1e3*Ja/ts}return O.maxDuration&&O.duration>O.maxDuration&&(O.duration=0),this._zooming=!0,this._rotating=ht!==Ne,this._pitching=$e!==Yt,this._padding=!we.isPaddingEqual(st),this._prepareEase(K,!1),this._ease(function(so){var Yo=so*Ja,ms=1/on(Yo);we.zoom=so===1?Nt:Oe+we.scaleZoom(ms),de._rotating&&(we.bearing=i.number(ht,Ne,so)),de._pitching&&(we.pitch=i.number(Yt,$e,so)),de._padding&&(we.interpolatePadding(cr,st,so),Ft=we.centerPoint.add(Bt));var ou=so===1?zt:we.unproject(_t.add(Dt.mult(zn(Yo))).mult(ms));we.setLocationAtPoint(we.renderWorldCopies?ou.wrap():ou,Ft),de._fireMoveEvents(K)},function(){return de._afterEase(K)},O),this},D.prototype.isEasing=function(){return!!this._easeFrameId},D.prototype.stop=function(){return this._stop()},D.prototype._stop=function(O,K){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var de=this._onEaseEnd;delete this._onEaseEnd,de.call(this,K)}if(!O){var ne=this.handlers;ne&&ne.stop(!1)}return this},D.prototype._ease=function(O,K,de){de.animate===!1||de.duration===0?(O(1),K()):(this._easeStart=i.browser.now(),this._easeOptions=de,this._onEaseFrame=O,this._onEaseEnd=K,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},D.prototype._renderFrameCallback=function(){var O=Math.min((i.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(O)),O<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},D.prototype._normalizeBearing=function(O,K){O=i.wrap(O,-180,180);var de=Math.abs(O-K);return Math.abs(O-360-K)<de&&(O-=360),Math.abs(O+360-K)<de&&(O+=360),O},D.prototype._normalizeCenter=function(O){var K=this.transform;if(!(!K.renderWorldCopies||K.lngRange)){var de=O.lng-K.center.lng;O.lng+=de>180?-360:de<-180?360:0}},D})(i.Evented),Qi=function(D){D===void 0&&(D={}),this.options=D,i.bindAll([\"_toggleAttribution\",\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Qi.prototype.getDefaultPosition=function(){return\"bottom-right\"},Qi.prototype.onAdd=function(D){var J=this.options&&this.options.compact;return this._map=D,this._container=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._compactButton=o.create(\"button\",\"mapboxgl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=o.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),this._innerContainer.setAttribute(\"role\",\"list\"),J&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),J===void 0&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Qi.prototype.onRemove=function(){o.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Qi.prototype._setElementTitle=function(D,J){var O=this._map._getUIString(\"AttributionControl.\"+J);D.title=O,D.setAttribute(\"aria-label\",O)},Qi.prototype._toggleAttribution=function(){this._container.classList.contains(\"mapboxgl-compact-show\")?(this._container.classList.remove(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"false\")):(this._container.classList.add(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"true\"))},Qi.prototype._updateEditLink=function(){var D=this._editLink;D||(D=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var J=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||i.config.ACCESS_TOKEN}];if(D){var O=J.reduce(function(K,de,ne){return de.value&&(K+=de.key+\"=\"+de.value+(ne<J.length-1?\"&\":\"\")),K},\"?\");D.href=i.config.FEEDBACK_URL+\"/\"+O+(this._map._hash?this._map._hash.getHashString(!0):\"\"),D.rel=\"noopener nofollow\",this._setElementTitle(D,\"MapFeedback\")}},Qi.prototype._updateData=function(D){D&&(D.sourceDataType===\"metadata\"||D.sourceDataType===\"visibility\"||D.dataType===\"style\")&&(this._updateAttributions(),this._updateEditLink())},Qi.prototype._updateAttributions=function(){if(this._map.style){var D=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?D=D.concat(this.options.customAttribution.map(function(Oe){return typeof Oe!=\"string\"?\"\":Oe})):typeof this.options.customAttribution==\"string\"&&D.push(this.options.customAttribution)),this._map.style.stylesheet){var J=this._map.style.stylesheet;this.styleOwner=J.owner,this.styleId=J.id}var O=this._map.style.sourceCaches;for(var K in O){var de=O[K];if(de.used){var ne=de.getSource();ne.attribution&&D.indexOf(ne.attribution)<0&&D.push(ne.attribution)}}D.sort(function(Oe,ht){return Oe.length-ht.length}),D=D.filter(function(Oe,ht){for(var Yt=ht+1;Yt<D.length;Yt++)if(D[Yt].indexOf(Oe)>=0)return!1;return!0});var we=D.join(\" | \");we!==this._attribHTML&&(this._attribHTML=we,D.length?(this._innerContainer.innerHTML=we,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Qi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\",\"mapboxgl-compact-show\")};var Wi=function(){i.bindAll([\"_updateLogo\"],this),i.bindAll([\"_updateCompact\"],this)};Wi.prototype.onAdd=function(D){this._map=D,this._container=o.create(\"div\",\"mapboxgl-ctrl\");var J=o.create(\"a\",\"mapboxgl-ctrl-logo\");return J.target=\"_blank\",J.rel=\"noopener nofollow\",J.href=\"https://www.mapbox.com/\",J.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),J.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(J),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Wi.prototype.onRemove=function(){o.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Wi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Wi.prototype._updateLogo=function(D){(!D||D.sourceDataType===\"metadata\")&&(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Wi.prototype._logoRequired=function(){if(this._map.style){var D=this._map.style.sourceCaches;for(var J in D){var O=D[J].getSource();if(O.mapbox_logo)return!0}return!1}},Wi.prototype._updateCompact=function(){var D=this._container.children;if(D.length){var J=D[0];this._map.getCanvasContainer().offsetWidth<250?J.classList.add(\"mapboxgl-compact\"):J.classList.remove(\"mapboxgl-compact\")}};var oi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};oi.prototype.add=function(D){var J=++this._id,O=this._queue;return O.push({callback:D,id:J,cancelled:!1}),J},oi.prototype.remove=function(D){for(var J=this._currentlyRunning,O=J?this._queue.concat(J):this._queue,K=0,de=O;K<de.length;K+=1){var ne=de[K];if(ne.id===D){ne.cancelled=!0;return}}},oi.prototype.run=function(D){D===void 0&&(D=0);var J=this._currentlyRunning=this._queue;this._queue=[];for(var O=0,K=J;O<K.length;O+=1){var de=K[O];if(!de.cancelled&&(de.callback(D),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},oi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Tr={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},Zr=i.window.HTMLImageElement,yi=i.window.HTMLElement,Ii=i.window.ImageBitmap,ui=-2,nn=22,Zi=0,qn=60,fi={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:ui,maxZoom:nn,minPitch:Zi,maxPitch:qn,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},si=(function(Y){function D(O){var K=this;if(O=i.extend({},fi,O),O.minZoom!=null&&O.maxZoom!=null&&O.minZoom>O.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(O.minPitch!=null&&O.maxPitch!=null&&O.minPitch>O.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(O.minPitch!=null&&O.minPitch<Zi)throw new Error(\"minPitch must be greater than or equal to \"+Zi);if(O.maxPitch!=null&&O.maxPitch>qn)throw new Error(\"maxPitch must be less than or equal to \"+qn);var de=new wo(O.minZoom,O.maxZoom,O.minPitch,O.maxPitch,O.renderWorldCopies);if(Y.call(this,de,O),this._interactive=O.interactive,this._maxTileCacheSize=O.maxTileCacheSize,this._failIfMajorPerformanceCaveat=O.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=O.preserveDrawingBuffer,this._antialias=O.antialias,this._trackResize=O.trackResize,this._bearingSnap=O.bearingSnap,this._refreshExpiredTiles=O.refreshExpiredTiles,this._fadeDuration=O.fadeDuration,this._crossSourceCollisions=O.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=O.collectResourceTiming,this._renderTaskQueue=new oi,this._controls=[],this._mapId=i.uniqueId(),this._locale=i.extend({},Tr,O.locale),this._clickTolerance=O.clickTolerance,this._requestManager=new i.RequestManager(O.transformRequest,O.accessToken),typeof O.container==\"string\"){if(this._container=i.window.document.getElementById(O.container),!this._container)throw new Error(\"Container '\"+O.container+\"' not found.\")}else if(O.container instanceof yi)this._container=O.container;else throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");if(O.maxBounds&&this.setMaxBounds(O.maxBounds),i.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_onMapScroll\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),this.painter===void 0)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",function(){return K._update(!1)}),this.on(\"moveend\",function(){return K._update(!1)}),this.on(\"zoom\",function(){return K._update(!0)}),typeof i.window!=\"undefined\"&&(i.window.addEventListener(\"online\",this._onWindowOnline,!1),i.window.addEventListener(\"resize\",this._onWindowResize,!1),i.window.addEventListener(\"orientationchange\",this._onWindowResize,!1)),this.handlers=new Si(this,O);var ne=typeof O.hash==\"string\"&&O.hash||void 0;this._hash=O.hash&&new Nl(ne).addTo(this),(!this._hash||!this._hash._onHashChange())&&(this.jumpTo({center:O.center,zoom:O.zoom,bearing:O.bearing,pitch:O.pitch}),O.bounds&&(this.resize(),this.fitBounds(O.bounds,i.extend({},O.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=O.localIdeographFontFamily,O.style&&this.setStyle(O.style,{localIdeographFontFamily:O.localIdeographFontFamily}),O.attributionControl&&this.addControl(new Qi({customAttribution:O.customAttribution})),this.addControl(new Wi,O.logoPosition),this.on(\"style.load\",function(){K.transform.unmodified&&K.jumpTo(K.style.stylesheet)}),this.on(\"data\",function(we){K._update(we.dataType===\"style\"),K.fire(new i.Event(we.dataType+\"data\",we))}),this.on(\"dataloading\",function(we){K.fire(new i.Event(we.dataType+\"dataloading\",we))})}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return D.prototype._getMapId=function(){return this._mapId},D.prototype.addControl=function(K,de){if(de===void 0&&(K.getDefaultPosition?de=K.getDefaultPosition():de=\"top-right\"),!K||!K.onAdd)return this.fire(new i.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var ne=K.onAdd(this);this._controls.push(K);var we=this._controlPositions[de];return de.indexOf(\"bottom\")!==-1?we.insertBefore(ne,we.firstChild):we.appendChild(ne),this},D.prototype.removeControl=function(K){if(!K||!K.onRemove)return this.fire(new i.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var de=this._controls.indexOf(K);return de>-1&&this._controls.splice(de,1),K.onRemove(this),this},D.prototype.hasControl=function(K){return this._controls.indexOf(K)>-1},D.prototype.resize=function(K){var de=this._containerDimensions(),ne=de[0],we=de[1];this._resizeCanvas(ne,we),this.transform.resize(ne,we),this.painter.resize(ne,we);var Oe=!this._moving;return Oe&&(this.stop(),this.fire(new i.Event(\"movestart\",K)).fire(new i.Event(\"move\",K))),this.fire(new i.Event(\"resize\",K)),Oe&&this.fire(new i.Event(\"moveend\",K)),this},D.prototype.getBounds=function(){return this.transform.getBounds()},D.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},D.prototype.setMaxBounds=function(K){return this.transform.setMaxBounds(i.LngLatBounds.convert(K)),this._update()},D.prototype.setMinZoom=function(K){if(K=K==null?ui:K,K>=ui&&K<=this.transform.maxZoom)return this.transform.minZoom=K,this._update(),this.getZoom()<K&&this.setZoom(K),this;throw new Error(\"minZoom must be between \"+ui+\" and the current maxZoom, inclusive\")},D.prototype.getMinZoom=function(){return this.transform.minZoom},D.prototype.setMaxZoom=function(K){if(K=K==null?nn:K,K>=this.transform.minZoom)return this.transform.maxZoom=K,this._update(),this.getZoom()>K&&this.setZoom(K),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},D.prototype.getMaxZoom=function(){return this.transform.maxZoom},D.prototype.setMinPitch=function(K){if(K=K==null?Zi:K,K<Zi)throw new Error(\"minPitch must be greater than or equal to \"+Zi);if(K>=Zi&&K<=this.transform.maxPitch)return this.transform.minPitch=K,this._update(),this.getPitch()<K&&this.setPitch(K),this;throw new Error(\"minPitch must be between \"+Zi+\" and the current maxPitch, inclusive\")},D.prototype.getMinPitch=function(){return this.transform.minPitch},D.prototype.setMaxPitch=function(K){if(K=K==null?qn:K,K>qn)throw new Error(\"maxPitch must be less than or equal to \"+qn);if(K>=this.transform.minPitch)return this.transform.maxPitch=K,this._update(),this.getPitch()>K&&this.setPitch(K),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},D.prototype.getMaxPitch=function(){return this.transform.maxPitch},D.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},D.prototype.setRenderWorldCopies=function(K){return this.transform.renderWorldCopies=K,this._update()},D.prototype.project=function(K){return this.transform.locationPoint(i.LngLat.convert(K))},D.prototype.unproject=function(K){return this.transform.pointLocation(i.Point.convert(K))},D.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},D.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},D.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},D.prototype._createDelegatedListener=function(K,de,ne){var we=this,Oe;if(K===\"mouseenter\"||K===\"mouseover\"){var ht=!1,Yt=function(It){var Bt=we.getLayer(de)?we.queryRenderedFeatures(It.point,{layers:[de]}):[];Bt.length?ht||(ht=!0,ne.call(we,new se(K,we,It.originalEvent,{features:Bt}))):ht=!1},cr=function(){ht=!1};return{layer:de,listener:ne,delegates:{mousemove:Yt,mouseout:cr}}}else if(K===\"mouseleave\"||K===\"mouseout\"){var Nt=!1,Ne=function(It){var Bt=we.getLayer(de)?we.queryRenderedFeatures(It.point,{layers:[de]}):[];Bt.length?Nt=!0:Nt&&(Nt=!1,ne.call(we,new se(K,we,It.originalEvent)))},$e=function(It){Nt&&(Nt=!1,ne.call(we,new se(K,we,It.originalEvent)))};return{layer:de,listener:ne,delegates:{mousemove:Ne,mouseout:$e}}}else{var st=function(It){var Bt=we.getLayer(de)?we.queryRenderedFeatures(It.point,{layers:[de]}):[];Bt.length&&(It.features=Bt,ne.call(we,It),delete It.features)};return{layer:de,listener:ne,delegates:(Oe={},Oe[K]=st,Oe)}}},D.prototype.on=function(K,de,ne){if(ne===void 0)return Y.prototype.on.call(this,K,de);var we=this._createDelegatedListener(K,de,ne);this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[K]=this._delegatedListeners[K]||[],this._delegatedListeners[K].push(we);for(var Oe in we.delegates)this.on(Oe,we.delegates[Oe]);return this},D.prototype.once=function(K,de,ne){if(ne===void 0)return Y.prototype.once.call(this,K,de);var we=this._createDelegatedListener(K,de,ne);for(var Oe in we.delegates)this.once(Oe,we.delegates[Oe]);return this},D.prototype.off=function(K,de,ne){var we=this;if(ne===void 0)return Y.prototype.off.call(this,K,de);var Oe=function(ht){for(var Yt=ht[K],cr=0;cr<Yt.length;cr++){var Nt=Yt[cr];if(Nt.layer===de&&Nt.listener===ne){for(var Ne in Nt.delegates)we.off(Ne,Nt.delegates[Ne]);return Yt.splice(cr,1),we}}};return this._delegatedListeners&&this._delegatedListeners[K]&&Oe(this._delegatedListeners),this},D.prototype.queryRenderedFeatures=function(K,de){if(!this.style)return[];de===void 0&&K!==void 0&&!(K instanceof i.Point)&&!Array.isArray(K)&&(de=K,K=void 0),de=de||{},K=K||[[0,0],[this.transform.width,this.transform.height]];var ne;if(K instanceof i.Point||typeof K[0]==\"number\")ne=[i.Point.convert(K)];else{var we=i.Point.convert(K[0]),Oe=i.Point.convert(K[1]);ne=[we,new i.Point(Oe.x,we.y),Oe,new i.Point(we.x,Oe.y),we]}return this.style.queryRenderedFeatures(ne,de,this.transform)},D.prototype.querySourceFeatures=function(K,de){return this.style.querySourceFeatures(K,de)},D.prototype.setStyle=function(K,de){return de=i.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},de),de.diff!==!1&&de.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&K?(this._diffStyle(K,de),this):(this._localIdeographFontFamily=de.localIdeographFontFamily,this._updateStyle(K,de))},D.prototype._getUIString=function(K){var de=this._locale[K];if(de==null)throw new Error(\"Missing UI string '\"+K+\"'\");return de},D.prototype._updateStyle=function(K,de){if(this.style&&(this.style.setEventedParent(null),this.style._remove()),K)this.style=new yu(this,de||{});else return delete this.style,this;return this.style.setEventedParent(this,{style:this.style}),typeof K==\"string\"?this.style.loadURL(K):this.style.loadJSON(K),this},D.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new yu(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},D.prototype._diffStyle=function(K,de){var ne=this;if(typeof K==\"string\"){var we=this._requestManager.normalizeStyleURL(K),Oe=this._requestManager.transformRequest(we,i.ResourceType.Style);i.getJSON(Oe,function(ht,Yt){ht?ne.fire(new i.ErrorEvent(ht)):Yt&&ne._updateDiff(Yt,de)})}else typeof K==\"object\"&&this._updateDiff(K,de)},D.prototype._updateDiff=function(K,de){try{this.style.setState(K)&&this._update(!0)}catch(ne){i.warnOnce(\"Unable to perform style diff: \"+(ne.message||ne.error||ne)+\".  Rebuilding the style from scratch.\"),this._updateStyle(K,de)}},D.prototype.getStyle=function(){if(this.style)return this.style.serialize()},D.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():i.warnOnce(\"There is no style added to the map.\")},D.prototype.addSource=function(K,de){return this._lazyInitEmptyStyle(),this.style.addSource(K,de),this._update(!0)},D.prototype.isSourceLoaded=function(K){var de=this.style&&this.style.sourceCaches[K];if(de===void 0){this.fire(new i.ErrorEvent(new Error(\"There is no source with ID '\"+K+\"'\")));return}return de.loaded()},D.prototype.areTilesLoaded=function(){var K=this.style&&this.style.sourceCaches;for(var de in K){var ne=K[de],we=ne._tiles;for(var Oe in we){var ht=we[Oe];if(!(ht.state===\"loaded\"||ht.state===\"errored\"))return!1}}return!0},D.prototype.addSourceType=function(K,de,ne){return this._lazyInitEmptyStyle(),this.style.addSourceType(K,de,ne)},D.prototype.removeSource=function(K){return this.style.removeSource(K),this._update(!0)},D.prototype.getSource=function(K){return this.style.getSource(K)},D.prototype.addImage=function(K,de,ne){ne===void 0&&(ne={});var we=ne.pixelRatio;we===void 0&&(we=1);var Oe=ne.sdf;Oe===void 0&&(Oe=!1);var ht=ne.stretchX,Yt=ne.stretchY,cr=ne.content;this._lazyInitEmptyStyle();var Nt=0;if(de instanceof Zr||Ii&&de instanceof Ii){var Ne=i.browser.getImageData(de),$e=Ne.width,st=Ne.height,It=Ne.data;this.style.addImage(K,{data:new i.RGBAImage({width:$e,height:st},It),pixelRatio:we,stretchX:ht,stretchY:Yt,content:cr,sdf:Oe,version:Nt})}else{if(de.width===void 0||de.height===void 0)return this.fire(new i.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var Bt=de.width,Ft=de.height,Ut=de.data,zt=de;this.style.addImage(K,{data:new i.RGBAImage({width:Bt,height:Ft},new Uint8Array(Ut)),pixelRatio:we,stretchX:ht,stretchY:Yt,content:cr,sdf:Oe,version:Nt,userImage:zt}),zt.onAdd&&zt.onAdd(this,K)}},D.prototype.updateImage=function(K,de){var ne=this.style.getImage(K);if(!ne)return this.fire(new i.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var we=de instanceof Zr||Ii&&de instanceof Ii?i.browser.getImageData(de):de,Oe=we.width,ht=we.height,Yt=we.data;if(Oe===void 0||ht===void 0)return this.fire(new i.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(Oe!==ne.data.width||ht!==ne.data.height)return this.fire(new i.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var cr=!(de instanceof Zr||Ii&&de instanceof Ii);ne.data.replace(Yt,cr),this.style.updateImage(K,ne)},D.prototype.hasImage=function(K){return K?!!this.style.getImage(K):(this.fire(new i.ErrorEvent(new Error(\"Missing required image id\"))),!1)},D.prototype.removeImage=function(K){this.style.removeImage(K)},D.prototype.loadImage=function(K,de){i.getImage(this._requestManager.transformRequest(K,i.ResourceType.Image),de)},D.prototype.listImages=function(){return this.style.listImages()},D.prototype.addLayer=function(K,de){return this._lazyInitEmptyStyle(),this.style.addLayer(K,de),this._update(!0)},D.prototype.moveLayer=function(K,de){return this.style.moveLayer(K,de),this._update(!0)},D.prototype.removeLayer=function(K){return this.style.removeLayer(K),this._update(!0)},D.prototype.getLayer=function(K){return this.style.getLayer(K)},D.prototype.setLayerZoomRange=function(K,de,ne){return this.style.setLayerZoomRange(K,de,ne),this._update(!0)},D.prototype.setFilter=function(K,de,ne){return ne===void 0&&(ne={}),this.style.setFilter(K,de,ne),this._update(!0)},D.prototype.getFilter=function(K){return this.style.getFilter(K)},D.prototype.setPaintProperty=function(K,de,ne,we){return we===void 0&&(we={}),this.style.setPaintProperty(K,de,ne,we),this._update(!0)},D.prototype.getPaintProperty=function(K,de){return this.style.getPaintProperty(K,de)},D.prototype.setLayoutProperty=function(K,de,ne,we){return we===void 0&&(we={}),this.style.setLayoutProperty(K,de,ne,we),this._update(!0)},D.prototype.getLayoutProperty=function(K,de){return this.style.getLayoutProperty(K,de)},D.prototype.setLight=function(K,de){return de===void 0&&(de={}),this._lazyInitEmptyStyle(),this.style.setLight(K,de),this._update(!0)},D.prototype.getLight=function(){return this.style.getLight()},D.prototype.setFeatureState=function(K,de){return this.style.setFeatureState(K,de),this._update()},D.prototype.removeFeatureState=function(K,de){return this.style.removeFeatureState(K,de),this._update()},D.prototype.getFeatureState=function(K){return this.style.getFeatureState(K)},D.prototype.getContainer=function(){return this._container},D.prototype.getCanvasContainer=function(){return this._canvasContainer},D.prototype.getCanvas=function(){return this._canvas},D.prototype._containerDimensions=function(){var K=0,de=0;return this._container&&(K=this._container.clientWidth||400,de=this._container.clientHeight||300),[K,de]},D.prototype._detectMissingCSS=function(){var K=i.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\");K!==\"rgb(250, 128, 114)\"&&i.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},D.prototype._setupContainer=function(){var K=this._container;K.classList.add(\"mapboxgl-map\");var de=this._missingCSSCanary=o.create(\"div\",\"mapboxgl-canary\",K);de.style.visibility=\"hidden\",this._detectMissingCSS();var ne=this._canvasContainer=o.create(\"div\",\"mapboxgl-canvas-container\",K);this._interactive&&ne.classList.add(\"mapboxgl-interactive\"),this._canvas=o.create(\"canvas\",\"mapboxgl-canvas\",ne),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\"),this._canvas.setAttribute(\"role\",\"region\");var we=this._containerDimensions();this._resizeCanvas(we[0],we[1]);var Oe=this._controlContainer=o.create(\"div\",\"mapboxgl-control-container\",K),ht=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(Yt){ht[Yt]=o.create(\"div\",\"mapboxgl-ctrl-\"+Yt,Oe)}),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)},D.prototype._resizeCanvas=function(K,de){var ne=i.browser.devicePixelRatio||1;this._canvas.width=ne*K,this._canvas.height=ne*de,this._canvas.style.width=K+\"px\",this._canvas.style.height=de+\"px\"},D.prototype._setupPainter=function(){var K=i.extend({},a.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),de=this._canvas.getContext(\"webgl\",K)||this._canvas.getContext(\"experimental-webgl\",K);if(!de){this.fire(new i.ErrorEvent(new Error(\"Failed to initialize WebGL\")));return}this.painter=new mo(de,this.transform),i.webpSupported.testSupport(de)},D.prototype._contextLost=function(K){K.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new i.Event(\"webglcontextlost\",{originalEvent:K}))},D.prototype._contextRestored=function(K){this._setupPainter(),this.resize(),this._update(),this.fire(new i.Event(\"webglcontextrestored\",{originalEvent:K}))},D.prototype._onMapScroll=function(K){if(K.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},D.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},D.prototype._update=function(K){return this.style?(this._styleDirty=this._styleDirty||K,this._sourcesDirty=!0,this.triggerRepaint(),this):this},D.prototype._requestRenderFrame=function(K){return this._update(),this._renderTaskQueue.add(K)},D.prototype._cancelRenderFrame=function(K){this._renderTaskQueue.remove(K)},D.prototype._render=function(K){var de=this,ne,we=0,Oe=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(ne=Oe.createQueryEXT(),Oe.beginQueryEXT(Oe.TIME_ELAPSED_EXT,ne),we=i.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(K),!this._removed){var ht=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var Yt=this.transform.zoom,cr=i.browser.now();this.style.zoomHistory.update(Yt,cr);var Nt=new i.EvaluationParameters(Yt,{now:cr,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),Ne=Nt.crossFadingFactor();(Ne!==1||Ne!==this._crossFadingFactor)&&(ht=!0,this._crossFadingFactor=Ne),this.style.update(Nt)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new i.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new i.Event(\"load\"))),this.style&&(this.style.hasTransitions()||ht)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var $e=i.browser.now()-we;Oe.endQueryEXT(Oe.TIME_ELAPSED_EXT,ne),setTimeout(function(){var Bt=Oe.getQueryObjectEXT(ne,Oe.QUERY_RESULT_EXT)/1e6;Oe.deleteQueryEXT(ne),de.fire(new i.Event(\"gpu-timing-frame\",{cpuTime:$e,gpuTime:Bt}))},50)}if(this.listens(\"gpu-timing-layer\")){var st=this.painter.collectGpuTimers();setTimeout(function(){var Bt=de.painter.queryGpuTimers(st);de.fire(new i.Event(\"gpu-timing-layer\",{layerTimes:Bt}))},50)}var It=this._sourcesDirty||this._styleDirty||this._placementDirty;return It||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new i.Event(\"idle\")),this._loaded&&!this._fullyLoaded&&!It&&(this._fullyLoaded=!0),this}},D.prototype.remove=function(){this._hash&&this._hash.remove();for(var K=0,de=this._controls;K<de.length;K+=1){var ne=de[K];ne.onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof i.window!=\"undefined\"&&(i.window.removeEventListener(\"resize\",this._onWindowResize,!1),i.window.removeEventListener(\"orientationchange\",this._onWindowResize,!1),i.window.removeEventListener(\"online\",this._onWindowOnline,!1));var we=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");we&&we.loseContext&&we.loseContext(),mn(this._canvasContainer),mn(this._controlContainer),mn(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new i.Event(\"remove\"))},D.prototype.triggerRepaint=function(){var K=this;this.style&&!this._frame&&(this._frame=i.browser.frame(function(de){K._frame=null,K._render(de)}))},D.prototype._onWindowOnline=function(){this._update()},D.prototype._onWindowResize=function(K){this._trackResize&&this.resize({originalEvent:K})._update()},J.showTileBoundaries.get=function(){return!!this._showTileBoundaries},J.showTileBoundaries.set=function(O){this._showTileBoundaries!==O&&(this._showTileBoundaries=O,this._update())},J.showPadding.get=function(){return!!this._showPadding},J.showPadding.set=function(O){this._showPadding!==O&&(this._showPadding=O,this._update())},J.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},J.showCollisionBoxes.set=function(O){this._showCollisionBoxes!==O&&(this._showCollisionBoxes=O,O?this.style._generateCollisionBoxes():this._update())},J.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},J.showOverdrawInspector.set=function(O){this._showOverdrawInspector!==O&&(this._showOverdrawInspector=O,this._update())},J.repaint.get=function(){return!!this._repaint},J.repaint.set=function(O){this._repaint!==O&&(this._repaint=O,this.triggerRepaint())},J.vertices.get=function(){return!!this._vertices},J.vertices.set=function(O){this._vertices=O,this._update()},D.prototype._setCacheLimits=function(K,de){i.setCacheLimits(K,de)},J.version.get=function(){return i.version},Object.defineProperties(D.prototype,J),D})(Vn);function mn(Y){Y.parentNode&&Y.parentNode.removeChild(Y)}var Ji={showCompass:!0,showZoom:!0,visualizePitch:!1},Ui=function(D){var J=this;this.options=i.extend({},Ji,D),this._container=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",function(O){return O.preventDefault()}),this.options.showZoom&&(i.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",function(O){return J._map.zoomIn({},{originalEvent:O})}),o.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",function(O){return J._map.zoomOut({},{originalEvent:O})}),o.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(i.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",function(O){J.options.visualizePitch?J._map.resetNorthPitch({},{originalEvent:O}):J._map.resetNorth({},{originalEvent:O})}),this._compassIcon=o.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Ui.prototype._updateZoomButtons=function(){var D=this._map.getZoom(),J=D===this._map.getMaxZoom(),O=D===this._map.getMinZoom();this._zoomInButton.disabled=J,this._zoomOutButton.disabled=O,this._zoomInButton.setAttribute(\"aria-disabled\",J.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",O.toString())},Ui.prototype._rotateCompassArrow=function(){var D=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=D},Ui.prototype.onAdd=function(D){return this._map=D,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Bi(this._map,this._compass,this.options.visualizePitch)),this._container},Ui.prototype.onRemove=function(){o.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Ui.prototype._createButton=function(D,J){var O=o.create(\"button\",D,this._container);return O.type=\"button\",O.addEventListener(\"click\",J),O},Ui.prototype._setButtonTitle=function(D,J){var O=this._map._getUIString(\"NavigationControl.\"+J);D.title=O,D.setAttribute(\"aria-label\",O)};var Bi=function(D,J,O){O===void 0&&(O=!1),this._clickTolerance=10,this.element=J,this.mouseRotate=new Pn({clickTolerance:D.dragRotate._mouseRotate._clickTolerance}),this.map=D,O&&(this.mousePitch=new kn({clickTolerance:D.dragRotate._mousePitch._clickTolerance})),i.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),o.addEventListener(J,\"mousedown\",this.mousedown),o.addEventListener(J,\"touchstart\",this.touchstart,{passive:!1}),o.addEventListener(J,\"touchmove\",this.touchmove),o.addEventListener(J,\"touchend\",this.touchend),o.addEventListener(J,\"touchcancel\",this.reset)};Bi.prototype.down=function(D,J){this.mouseRotate.mousedown(D,J),this.mousePitch&&this.mousePitch.mousedown(D,J),o.disableDrag()},Bi.prototype.move=function(D,J){var O=this.map,K=this.mouseRotate.mousemoveWindow(D,J);if(K&&K.bearingDelta&&O.setBearing(O.getBearing()+K.bearingDelta),this.mousePitch){var de=this.mousePitch.mousemoveWindow(D,J);de&&de.pitchDelta&&O.setPitch(O.getPitch()+de.pitchDelta)}},Bi.prototype.off=function(){var D=this.element;o.removeEventListener(D,\"mousedown\",this.mousedown),o.removeEventListener(D,\"touchstart\",this.touchstart,{passive:!1}),o.removeEventListener(D,\"touchmove\",this.touchmove),o.removeEventListener(D,\"touchend\",this.touchend),o.removeEventListener(D,\"touchcancel\",this.reset),this.offTemp()},Bi.prototype.offTemp=function(){o.enableDrag(),o.removeEventListener(i.window,\"mousemove\",this.mousemove),o.removeEventListener(i.window,\"mouseup\",this.mouseup)},Bi.prototype.mousedown=function(D){this.down(i.extend({},D,{ctrlKey:!0,preventDefault:function(){return D.preventDefault()}}),o.mousePos(this.element,D)),o.addEventListener(i.window,\"mousemove\",this.mousemove),o.addEventListener(i.window,\"mouseup\",this.mouseup)},Bi.prototype.mousemove=function(D){this.move(D,o.mousePos(this.element,D))},Bi.prototype.mouseup=function(D){this.mouseRotate.mouseupWindow(D),this.mousePitch&&this.mousePitch.mouseupWindow(D),this.offTemp()},Bi.prototype.touchstart=function(D){D.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=o.touchPos(this.element,D.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return D.preventDefault()}},this._startPos))},Bi.prototype.touchmove=function(D){D.targetTouches.length!==1?this.reset():(this._lastPos=o.touchPos(this.element,D.targetTouches)[0],this.move({preventDefault:function(){return D.preventDefault()}},this._lastPos))},Bi.prototype.touchend=function(D){D.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Bi.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};function pn(Y,D,J){if(Y=new i.LngLat(Y.lng,Y.lat),D){var O=new i.LngLat(Y.lng-360,Y.lat),K=new i.LngLat(Y.lng+360,Y.lat),de=J.locationPoint(Y).distSqr(D);J.locationPoint(O).distSqr(D)<de?Y=O:J.locationPoint(K).distSqr(D)<de&&(Y=K)}for(;Math.abs(Y.lng-J.center.lng)>180;){var ne=J.locationPoint(Y);if(ne.x>=0&&ne.y>=0&&ne.x<=J.width&&ne.y<=J.height)break;Y.lng>J.center.lng?Y.lng-=360:Y.lng+=360}return Y}var Nn={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function aa(Y,D,J){var O=Y.classList;for(var K in Nn)O.remove(\"mapboxgl-\"+J+\"-anchor-\"+K);O.add(\"mapboxgl-\"+J+\"-anchor-\"+D)}var Ki=(function(Y){function D(J,O){if(Y.call(this),(J instanceof i.window.HTMLElement||O)&&(J=i.extend({element:J},O)),i.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=J&&J.anchor||\"center\",this._color=J&&J.color||\"#3FB1CE\",this._scale=J&&J.scale||1,this._draggable=J&&J.draggable||!1,this._clickTolerance=J&&J.clickTolerance||0,this._isDragging=!1,this._state=\"inactive\",this._rotation=J&&J.rotation||0,this._rotationAlignment=J&&J.rotationAlignment||\"auto\",this._pitchAlignment=J&&J.pitchAlignment&&J.pitchAlignment!==\"auto\"?J.pitchAlignment:this._rotationAlignment,!J||!J.element){this._defaultMarker=!0,this._element=o.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var K=o.createNS(\"http://www.w3.org/2000/svg\",\"svg\"),de=41,ne=27;K.setAttributeNS(null,\"display\",\"block\"),K.setAttributeNS(null,\"height\",de+\"px\"),K.setAttributeNS(null,\"width\",ne+\"px\"),K.setAttributeNS(null,\"viewBox\",\"0 0 \"+ne+\" \"+de);var we=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");we.setAttributeNS(null,\"stroke\",\"none\"),we.setAttributeNS(null,\"stroke-width\",\"1\"),we.setAttributeNS(null,\"fill\",\"none\"),we.setAttributeNS(null,\"fill-rule\",\"evenodd\");var Oe=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");Oe.setAttributeNS(null,\"fill-rule\",\"nonzero\");var ht=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");ht.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),ht.setAttributeNS(null,\"fill\",\"#000000\");for(var Yt=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}],cr=0,Nt=Yt;cr<Nt.length;cr+=1){var Ne=Nt[cr],$e=o.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");$e.setAttributeNS(null,\"opacity\",\"0.04\"),$e.setAttributeNS(null,\"cx\",\"10.5\"),$e.setAttributeNS(null,\"cy\",\"5.80029008\"),$e.setAttributeNS(null,\"rx\",Ne.rx),$e.setAttributeNS(null,\"ry\",Ne.ry),ht.appendChild($e)}var st=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");st.setAttributeNS(null,\"fill\",this._color);var It=o.createNS(\"http://www.w3.org/2000/svg\",\"path\");It.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),st.appendChild(It);var Bt=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");Bt.setAttributeNS(null,\"opacity\",\"0.25\"),Bt.setAttributeNS(null,\"fill\",\"#000000\");var Ft=o.createNS(\"http://www.w3.org/2000/svg\",\"path\");Ft.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),Bt.appendChild(Ft);var Ut=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");Ut.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),Ut.setAttributeNS(null,\"fill\",\"#FFFFFF\");var zt=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");zt.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var _t=o.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_t.setAttributeNS(null,\"fill\",\"#000000\"),_t.setAttributeNS(null,\"opacity\",\"0.25\"),_t.setAttributeNS(null,\"cx\",\"5.5\"),_t.setAttributeNS(null,\"cy\",\"5.5\"),_t.setAttributeNS(null,\"r\",\"5.4999962\");var Dt=o.createNS(\"http://www.w3.org/2000/svg\",\"circle\");Dt.setAttributeNS(null,\"fill\",\"#FFFFFF\"),Dt.setAttributeNS(null,\"cx\",\"5.5\"),Dt.setAttributeNS(null,\"cy\",\"5.5\"),Dt.setAttributeNS(null,\"r\",\"5.4999962\"),zt.appendChild(_t),zt.appendChild(Dt),Oe.appendChild(ht),Oe.appendChild(st),Oe.appendChild(Bt),Oe.appendChild(Ut),Oe.appendChild(zt),K.appendChild(Oe),K.setAttributeNS(null,\"height\",de*this._scale+\"px\"),K.setAttributeNS(null,\"width\",ne*this._scale+\"px\"),this._element.appendChild(K),this._offset=i.Point.convert(J&&J.offset||[0,-14])}else this._element=J.element,this._offset=i.Point.convert(J&&J.offset||[0,0]);this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",function(vt){vt.preventDefault()}),this._element.addEventListener(\"mousedown\",function(vt){vt.preventDefault()}),aa(this._element,this._anchor,\"marker\"),this._popup=null}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.addTo=function(O){return this.remove(),this._map=O,O.getCanvasContainer().appendChild(this._element),O.on(\"move\",this._update),O.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},D.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),o.remove(this._element),this._popup&&this._popup.remove(),this},D.prototype.getLngLat=function(){return this._lngLat},D.prototype.setLngLat=function(O){return this._lngLat=i.LngLat.convert(O),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},D.prototype.getElement=function(){return this._element},D.prototype.setPopup=function(O){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),O){if(!(\"offset\"in O.options)){var K=38.1,de=13.5,ne=Math.sqrt(Math.pow(de,2)/2);O.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-K],\"bottom-left\":[ne,(K-de+ne)*-1],\"bottom-right\":[-ne,(K-de+ne)*-1],left:[de,(K-de)*-1],right:[-de,(K-de)*-1]}:this._offset}this._popup=O,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},D.prototype._onKeyPress=function(O){var K=O.code,de=O.charCode||O.keyCode;(K===\"Space\"||K===\"Enter\"||de===32||de===13)&&this.togglePopup()},D.prototype._onMapClick=function(O){var K=O.originalEvent.target,de=this._element;this._popup&&(K===de||de.contains(K))&&this.togglePopup()},D.prototype.getPopup=function(){return this._popup},D.prototype.togglePopup=function(){var O=this._popup;if(O)O.isOpen()?O.remove():O.addTo(this._map);else return this;return this},D.prototype._update=function(O){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=pn(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var K=\"\";this._rotationAlignment===\"viewport\"||this._rotationAlignment===\"auto\"?K=\"rotateZ(\"+this._rotation+\"deg)\":this._rotationAlignment===\"map\"&&(K=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var de=\"\";this._pitchAlignment===\"viewport\"||this._pitchAlignment===\"auto\"?de=\"rotateX(0deg)\":this._pitchAlignment===\"map\"&&(de=\"rotateX(\"+this._map.getPitch()+\"deg)\"),(!O||O.type===\"moveend\")&&(this._pos=this._pos.round()),o.setTransform(this._element,Nn[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+de+\" \"+K)}},D.prototype.getOffset=function(){return this._offset},D.prototype.setOffset=function(O){return this._offset=i.Point.convert(O),this._update(),this},D.prototype._onMove=function(O){if(!this._isDragging){var K=this._clickTolerance||this._map._clickTolerance;this._isDragging=O.point.dist(this._pointerdownPos)>=K}this._isDragging&&(this._pos=O.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",this._state===\"pending\"&&(this._state=\"active\",this.fire(new i.Event(\"dragstart\"))),this.fire(new i.Event(\"drag\")))},D.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),this._state===\"active\"&&this.fire(new i.Event(\"dragend\")),this._state=\"inactive\"},D.prototype._addDragHandler=function(O){this._element.contains(O.originalEvent.target)&&(O.preventDefault(),this._positionDelta=O.point.sub(this._pos).add(this._offset),this._pointerdownPos=O.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},D.prototype.setDraggable=function(O){return this._draggable=!!O,this._map&&(O?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},D.prototype.isDraggable=function(){return this._draggable},D.prototype.setRotation=function(O){return this._rotation=O||0,this._update(),this},D.prototype.getRotation=function(){return this._rotation},D.prototype.setRotationAlignment=function(O){return this._rotationAlignment=O||\"auto\",this._update(),this},D.prototype.getRotationAlignment=function(){return this._rotationAlignment},D.prototype.setPitchAlignment=function(O){return this._pitchAlignment=O&&O!==\"auto\"?O:this._rotationAlignment,this._update(),this},D.prototype.getPitchAlignment=function(){return this._pitchAlignment},D})(i.Evented),Cn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},ia;function sa(Y){ia!==void 0?Y(ia):i.window.navigator.permissions!==void 0?i.window.navigator.permissions.query({name:\"geolocation\"}).then(function(D){ia=D.state!==\"denied\",Y(ia)}):(ia=!!i.window.navigator.geolocation,Y(ia))}var ba=0,is=!1,Zs=(function(Y){function D(J){Y.call(this),this.options=i.extend({},Cn,J),i.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.onAdd=function(O){return this._map=O,this._container=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),sa(this._setupUI),this._container},D.prototype.onRemove=function(){this._geolocationWatchID!==void 0&&(i.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),o.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,ba=0,is=!1},D.prototype._isOutOfMapMaxBounds=function(O){var K=this._map.getMaxBounds(),de=O.coords;return K&&(de.longitude<K.getWest()||de.longitude>K.getEast()||de.latitude<K.getSouth()||de.latitude>K.getNorth())},D.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break}},D.prototype._onSuccess=function(O){if(this._map){if(this._isOutOfMapMaxBounds(O)){this._setErrorState(),this.fire(new i.Event(\"outofmaxbounds\",O)),this._updateMarker(),this._finish();return}if(this.options.trackUserLocation)switch(this._lastKnownPosition=O,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break}this.options.showUserLocation&&this._watchState!==\"OFF\"&&this._updateMarker(O),(!this.options.trackUserLocation||this._watchState===\"ACTIVE_LOCK\")&&this._updateCamera(O),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new i.Event(\"geolocate\",O)),this._finish()}},D.prototype._updateCamera=function(O){var K=new i.LngLat(O.coords.longitude,O.coords.latitude),de=O.coords.accuracy,ne=this._map.getBearing(),we=i.extend({bearing:ne},this.options.fitBoundsOptions);this._map.fitBounds(K.toBounds(de),we,{geolocateSource:!0})},D.prototype._updateMarker=function(O){if(O){var K=new i.LngLat(O.coords.longitude,O.coords.latitude);this._accuracyCircleMarker.setLngLat(K).addTo(this._map),this._userLocationDotMarker.setLngLat(K).addTo(this._map),this._accuracy=O.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},D.prototype._updateCircleRadius=function(){var O=this._map._container.clientHeight/2,K=this._map.unproject([0,O]),de=this._map.unproject([1,O]),ne=K.distanceTo(de),we=Math.ceil(2*this._accuracy/ne);this._circleElement.style.width=we+\"px\",this._circleElement.style.height=we+\"px\"},D.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},D.prototype._onError=function(O){if(this._map){if(this.options.trackUserLocation)if(O.code===1){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var K=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=K,this._geolocateButton.setAttribute(\"aria-label\",K),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(O.code===3&&is)return;this._setErrorState()}this._watchState!==\"OFF\"&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new i.Event(\"error\",O)),this._finish()}},D.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},D.prototype._setupUI=function(O){var K=this;if(this._container.addEventListener(\"contextmenu\",function(we){return we.preventDefault()}),this._geolocateButton=o.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),o.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",O===!1){i.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var de=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=de,this._geolocateButton.setAttribute(\"aria-label\",de)}else{var ne=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=ne,this._geolocateButton.setAttribute(\"aria-label\",ne)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=o.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Ki(this._dotElement),this._circleElement=o.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Ki({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",function(we){var Oe=we.originalEvent&&we.originalEvent.type===\"resize\";!we.geolocateSource&&K._watchState===\"ACTIVE_LOCK\"&&!Oe&&(K._watchState=\"BACKGROUND\",K._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),K._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),K.fire(new i.Event(\"trackuserlocationend\")))})},D.prototype.trigger=function(){if(!this._setup)return i.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new i.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":ba--,is=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new i.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new i.Event(\"trackuserlocationstart\"));break}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\");break}if(this._watchState===\"OFF\"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),ba++;var O;ba>1?(O={maximumAge:6e5,timeout:0},is=!0):(O=this.options.positionOptions,is=!1),this._geolocationWatchID=i.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,O)}}else i.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},D.prototype._clearWatch=function(){i.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},D})(i.Evented),Ga={maxWidth:100,unit:\"metric\"},El=function(D){this.options=i.extend({},Ga,D),i.bindAll([\"_onMove\",\"setUnit\"],this)};El.prototype.getDefaultPosition=function(){return\"bottom-left\"},El.prototype._onMove=function(){zo(this._map,this._container,this.options)},El.prototype.onAdd=function(D){return this._map=D,this._container=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",D.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},El.prototype.onRemove=function(){o.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},El.prototype.setUnit=function(D){this.options.unit=D,zo(this._map,this._container,this.options)};function zo(Y,D,J){var O=J&&J.maxWidth||100,K=Y._container.clientHeight/2,de=Y.unproject([0,K]),ne=Y.unproject([O,K]),we=de.distanceTo(ne);if(J&&J.unit===\"imperial\"){var Oe=3.2808*we;if(Oe>5280){var ht=Oe/5280;Qs(D,O,ht,Y._getUIString(\"ScaleControl.Miles\"))}else Qs(D,O,Oe,Y._getUIString(\"ScaleControl.Feet\"))}else if(J&&J.unit===\"nautical\"){var Yt=we/1852;Qs(D,O,Yt,Y._getUIString(\"ScaleControl.NauticalMiles\"))}else we>=1e3?Qs(D,O,we/1e3,Y._getUIString(\"ScaleControl.Kilometers\")):Qs(D,O,we,Y._getUIString(\"ScaleControl.Meters\"))}function Qs(Y,D,J,O){var K=Ul(J),de=K/J;Y.style.width=D*de+\"px\",Y.innerHTML=K+\"&nbsp;\"+O}function nl(Y){var D=Math.pow(10,Math.ceil(-Math.log(Y)/Math.LN10));return Math.round(Y*D)/D}function Ul(Y){var D=Math.pow(10,(\"\"+Math.floor(Y)).length-1),J=Y/D;return J=J>=10?10:J>=5?5:J>=3?3:J>=2?2:J>=1?1:nl(J),D*J}var ss=function(D){this._fullscreen=!1,D&&D.container&&(D.container instanceof i.window.HTMLElement?this._container=D.container:i.warnOnce(\"Full screen control 'container' must be a DOM element.\")),i.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in i.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in i.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in i.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in i.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};ss.prototype.onAdd=function(D){return this._map=D,this._container||(this._container=this._map.getContainer()),this._controlContainer=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",i.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},ss.prototype.onRemove=function(){o.remove(this._controlContainer),this._map=null,i.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ss.prototype._checkFullscreenSupport=function(){return!!(i.window.document.fullscreenEnabled||i.window.document.mozFullScreenEnabled||i.window.document.msFullscreenEnabled||i.window.document.webkitFullscreenEnabled)},ss.prototype._setupUI=function(){var D=this._fullscreenButton=o.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);o.create(\"span\",\"mapboxgl-ctrl-icon\",D).setAttribute(\"aria-hidden\",!0),D.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),i.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ss.prototype._updateTitle=function(){var D=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",D),this._fullscreenButton.title=D},ss.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},ss.prototype._isFullscreen=function(){return this._fullscreen},ss.prototype._changeIcon=function(){var D=i.window.document.fullscreenElement||i.window.document.mozFullScreenElement||i.window.document.webkitFullscreenElement||i.window.document.msFullscreenElement;D===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},ss.prototype._onClickFullscreen=function(){this._isFullscreen()?i.window.document.exitFullscreen?i.window.document.exitFullscreen():i.window.document.mozCancelFullScreen?i.window.document.mozCancelFullScreen():i.window.document.msExitFullscreen?i.window.document.msExitFullscreen():i.window.document.webkitCancelFullScreen&&i.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Vs={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},Ys=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \"),wa=(function(Y){function D(J){Y.call(this),this.options=i.extend(Object.create(Vs),J),i.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.addTo=function(O){return this._map&&this.remove(),this._map=O,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new i.Event(\"open\")),this},D.prototype.isOpen=function(){return!!this._map},D.prototype.remove=function(){return this._content&&o.remove(this._content),this._container&&(o.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new i.Event(\"close\")),this},D.prototype.getLngLat=function(){return this._lngLat},D.prototype.setLngLat=function(O){return this._lngLat=i.LngLat.convert(O),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},D.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},D.prototype.getElement=function(){return this._container},D.prototype.setText=function(O){return this.setDOMContent(i.window.document.createTextNode(O))},D.prototype.setHTML=function(O){var K=i.window.document.createDocumentFragment(),de=i.window.document.createElement(\"body\"),ne;for(de.innerHTML=O;ne=de.firstChild,!!ne;)K.appendChild(ne);return this.setDOMContent(K)},D.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},D.prototype.setMaxWidth=function(O){return this.options.maxWidth=O,this._update(),this},D.prototype.setDOMContent=function(O){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=o.create(\"div\",\"mapboxgl-popup-content\",this._container);return this._content.appendChild(O),this._createCloseButton(),this._update(),this._focusFirstElement(),this},D.prototype.addClassName=function(O){this._container&&this._container.classList.add(O)},D.prototype.removeClassName=function(O){this._container&&this._container.classList.remove(O)},D.prototype.setOffset=function(O){return this.options.offset=O,this._update(),this},D.prototype.toggleClassName=function(O){if(this._container)return this._container.classList.toggle(O)},D.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=o.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},D.prototype._onMouseUp=function(O){this._update(O.point)},D.prototype._onMouseMove=function(O){this._update(O.point)},D.prototype._onDrag=function(O){this._update(O.point)},D.prototype._update=function(O){var K=this,de=this._lngLat||this._trackPointer;if(!(!this._map||!de||!this._content)&&(this._container||(this._container=o.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=o.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach(function(Ne){return K._container.classList.add(Ne)}),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=pn(this._lngLat,this._pos,this._map.transform)),!(this._trackPointer&&!O))){var ne=this._pos=this._trackPointer&&O?O:this._map.project(this._lngLat),we=this.options.anchor,Oe=al(this.options.offset);if(!we){var ht=this._container.offsetWidth,Yt=this._container.offsetHeight,cr;ne.y+Oe.bottom.y<Yt?cr=[\"top\"]:ne.y>this._map.transform.height-Yt?cr=[\"bottom\"]:cr=[],ne.x<ht/2?cr.push(\"left\"):ne.x>this._map.transform.width-ht/2&&cr.push(\"right\"),cr.length===0?we=\"bottom\":we=cr.join(\"-\")}var Nt=ne.add(Oe[we]).round();o.setTransform(this._container,Nn[we]+\" translate(\"+Nt.x+\"px,\"+Nt.y+\"px)\"),aa(this._container,we,\"popup\")}},D.prototype._focusFirstElement=function(){if(!(!this.options.focusAfterOpen||!this._container)){var O=this._container.querySelector(Ys);O&&O.focus()}},D.prototype._onClose=function(){this.remove()},D})(i.Evented);function al(Y){if(Y)if(typeof Y==\"number\"){var D=Math.round(Math.sqrt(.5*Math.pow(Y,2)));return{center:new i.Point(0,0),top:new i.Point(0,Y),\"top-left\":new i.Point(D,D),\"top-right\":new i.Point(-D,D),bottom:new i.Point(0,-Y),\"bottom-left\":new i.Point(D,-D),\"bottom-right\":new i.Point(-D,-D),left:new i.Point(Y,0),right:new i.Point(-Y,0)}}else if(Y instanceof i.Point||Array.isArray(Y)){var J=i.Point.convert(Y);return{center:J,top:J,\"top-left\":J,\"top-right\":J,bottom:J,\"bottom-left\":J,\"bottom-right\":J,left:J,right:J}}else return{center:i.Point.convert(Y.center||[0,0]),top:i.Point.convert(Y.top||[0,0]),\"top-left\":i.Point.convert(Y[\"top-left\"]||[0,0]),\"top-right\":i.Point.convert(Y[\"top-right\"]||[0,0]),bottom:i.Point.convert(Y.bottom||[0,0]),\"bottom-left\":i.Point.convert(Y[\"bottom-left\"]||[0,0]),\"bottom-right\":i.Point.convert(Y[\"bottom-right\"]||[0,0]),left:i.Point.convert(Y.left||[0,0]),right:i.Point.convert(Y.right||[0,0])};else return al(new i.Point(0,0))}var io={version:i.version,supported:a,setRTLTextPlugin:i.setRTLTextPlugin,getRTLTextPluginStatus:i.getRTLTextPluginStatus,Map:si,NavigationControl:Ui,GeolocateControl:Zs,AttributionControl:Qi,ScaleControl:El,FullscreenControl:ss,Popup:wa,Marker:Ki,Style:yu,LngLat:i.LngLat,LngLatBounds:i.LngLatBounds,Point:i.Point,MercatorCoordinate:i.MercatorCoordinate,Evented:i.Evented,config:i.config,prewarm:ua,clearPrewarmedResources:ma,get accessToken(){return i.config.ACCESS_TOKEN},set accessToken(Y){i.config.ACCESS_TOKEN=Y},get baseApiUrl(){return i.config.API_URL},set baseApiUrl(Y){i.config.API_URL=Y},get workerCount(){return fn.workerCount},set workerCount(Y){fn.workerCount=Y},get maxParallelImageRequests(){return i.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(Y){i.config.MAX_PARALLEL_IMAGE_REQUESTS=Y},clearStorage:function(D){i.clearTileCache(D)},workerUrl:\"\"};return io}),r}))});var EGe=ye((obr,MGe)=>{\"use strict\";var gw=Pr(),_Wt=Zl().sanitizeHTML,xWt=vJ(),TGe=y1();function AGe(e,t){this.subplot=e,this.uid=e.uid+\"-\"+t,this.index=t,this.idSource=\"source-\"+this.uid,this.idLayer=TGe.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var ag=AGe.prototype;ag.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=o7(t)};ag.needsNewImage=function(e){var t=this.subplot.map;return t.getSource(this.idSource)&&this.sourceType===\"image\"&&e.sourcetype===\"image\"&&(this.source!==e.source||JSON.stringify(this.coordinates)!==JSON.stringify(e.coordinates))};ag.needsNewSource=function(e){return this.sourceType!==e.sourcetype||JSON.stringify(this.source)!==JSON.stringify(e.source)||this.layerType!==e.type};ag.needsNewLayer=function(e){return this.layerType!==e.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]};ag.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]};ag.updateImage=function(e){var t=this.subplot.map;t.getSource(this.idSource).updateImage({url:e.source,coordinates:e.coordinates});var r=this.findFollowingMapboxLayerId(this.lookupBelow());r!==null&&this.subplot.map.moveLayer(this.idLayer,r)};ag.updateSource=function(e){var t=this.subplot.map;if(t.getSource(this.idSource)&&t.removeSource(this.idSource),this.sourceType=e.sourcetype,this.source=e.source,!!o7(e)){var r=bWt(e);t.addSource(this.idSource,r)}};ag.findFollowingMapboxLayerId=function(e){if(e===\"traces\")for(var t=this.subplot.getMapLayers(),r=0;r<t.length;r++){var n=t[r].id;if(typeof n==\"string\"&&n.indexOf(TGe.traceLayerPrefix)===0){e=n;break}}return e};ag.updateLayer=function(e){var t=this.subplot,r=SGe(e),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),o7(e)&&t.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":e.sourcelayer||\"\",type:e.type,minzoom:e.minzoom,maxzoom:e.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=e.type,this.below=n};ag.updateStyle=function(e){if(o7(e)){var t=SGe(e);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",t.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",t.paint)}};ag.removeLayer=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer)};ag.dispose=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),e.getSource(this.idSource)&&e.removeSource(this.idSource)};function o7(e){if(!e.visible)return!1;var t=e.source;if(Array.isArray(t)&&t.length>0){for(var r=0;r<t.length;r++)if(typeof t[r]!=\"string\"||t[r].length===0)return!1;return!0}return gw.isPlainObject(t)||typeof t==\"string\"&&t.length>0}function SGe(e){var t={},r={};switch(e.type){case\"circle\":gw.extendFlat(r,{\"circle-radius\":e.circle.radius,\"circle-color\":e.color,\"circle-opacity\":e.opacity});break;case\"line\":gw.extendFlat(r,{\"line-width\":e.line.width,\"line-color\":e.color,\"line-opacity\":e.opacity,\"line-dasharray\":e.line.dash});break;case\"fill\":gw.extendFlat(r,{\"fill-color\":e.color,\"fill-outline-color\":e.fill.outlinecolor,\"fill-opacity\":e.opacity});break;case\"symbol\":var n=e.symbol,i=xWt(n.textposition,n.iconsize);gw.extendFlat(t,{\"icon-image\":n.icon+\"-15\",\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":i.anchor,\"text-offset\":i.offset,\"symbol-placement\":n.placement}),gw.extendFlat(r,{\"icon-color\":e.color,\"text-color\":n.textfont.color,\"text-opacity\":e.opacity});break;case\"raster\":gw.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":e.opacity});break}return{layout:t,paint:r}}function bWt(e){var t=e.sourcetype,r=e.source,n={type:t},i;return t===\"geojson\"?i=\"data\":t===\"vector\"?i=typeof r==\"string\"?\"url\":\"tiles\":t===\"raster\"?(i=\"tiles\",n.tileSize=256):t===\"image\"&&(i=\"url\",n.coordinates=e.coordinates),n[i]=r,e.sourceattribution&&(n.attribution=_Wt(e.sourceattribution)),n}MGe.exports=function(t,r,n){var i=new AGe(t,r);return i.update(n),i}});var zGe=ye((sbr,FGe)=>{\"use strict\";var bJ=xJ(),wJ=Pr(),PGe=ey(),kGe=Oa(),wWt=ho(),TWt=yv(),s7=ef(),IGe=Cg(),AWt=IGe.drawMode,SWt=IGe.selectMode,MWt=qf().prepSelect,EWt=qf().clearOutline,kWt=qf().clearSelectionsCache,CWt=qf().selectOnClick,Cx=y1(),LWt=EGe();function RGe(e,t){this.id=t,this.gd=e;var r=e._fullLayout,n=e._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+\"-\"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var Gh=RGe.prototype;Gh.plot=function(e,t,r){var n=this,i=t[n.id];n.map&&i.accesstoken!==n.accessToken&&(n.map.remove(),n.map=null,n.styleObj=null,n.traceHash={},n.layerList=[]);var a;n.map?a=new Promise(function(o,s){n.updateMap(e,t,o,s)}):a=new Promise(function(o,s){n.createMap(e,t,o,s)}),r.push(a)};Gh.createMap=function(e,t,r,n){var i=this,a=t[i.id],o=i.styleObj=DGe(a.style,t);i.accessToken=a.accesstoken;var s=a.bounds,l=s?[[s.west,s.south],[s.east,s.north]]:null,u=i.map=new bJ.Map({container:i.div,style:o.style,center:TJ(a.center),zoom:a.zoom,bearing:a.bearing,pitch:a.pitch,maxBounds:l,interactive:!i.isStatic,preserveDrawingBuffer:i.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new bJ.AttributionControl({compact:!0}));u._canvas.style.left=\"0px\",u._canvas.style.top=\"0px\",i.rejectOnError(n),i.isStatic||i.initFx(e,t);var c=[];c.push(new Promise(function(f){u.once(\"load\",f)})),c=c.concat(PGe.fetchTraceGeoData(e)),Promise.all(c).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Gh.updateMap=function(e,t,r,n){var i=this,a=i.map,o=t[this.id];i.rejectOnError(n);var s=[],l=DGe(o.style,t);JSON.stringify(i.styleObj)!==JSON.stringify(l)&&(i.styleObj=l,a.setStyle(l.style),i.traceHash={},s.push(new Promise(function(u){a.once(\"styledata\",u)}))),s=s.concat(PGe.fetchTraceGeoData(e)),Promise.all(s).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Gh.fillBelowLookup=function(e,t){var r=t[this.id],n=r.layers,i,a,o=this.belowLookup={},s=!1;for(i=0;i<e.length;i++){var l=e[i][0].trace,u=l._module;typeof l.below==\"string\"?a=l.below:u.getBelow&&(a=u.getBelow(l,this)),a===\"\"&&(s=!0),o[\"trace-\"+l.uid]=a||\"\"}for(i=0;i<n.length;i++){var c=n[i];typeof c.below==\"string\"?a=c.below:s?a=\"traces\":a=\"\",o[\"layout-\"+i]=a}var f={},h,d;for(h in o)a=o[h],f[a]?f[a].push(h):f[a]=[h];for(a in f){var v=f[a];if(v.length>1)for(i=0;i<v.length;i++)h=v[i],h.indexOf(\"trace-\")===0?(d=h.split(\"trace-\")[1],this.traceHash[d]&&(this.traceHash[d].below=null)):h.indexOf(\"layout-\")===0&&(d=h.split(\"layout-\")[1],this.layerList[d]&&(this.layerList[d].below=null))}};var CGe={choroplethmapbox:0,densitymapbox:1,scattermapbox:2};Gh.updateData=function(e){var t=this.traceHash,r,n,i,a,o=e.slice().sort(function(f,h){return CGe[f[0].trace.type]-CGe[h[0].trace.type]});for(i=0;i<o.length;i++){var s=o[i];n=s[0].trace,r=t[n.uid];var l=!1;r&&(r.type===n.type?(r.update(s),l=!0):r.dispose()),!l&&n._module&&(t[n.uid]=n._module.plot(this,s))}var u=Object.keys(t);e:for(i=0;i<u.length;i++){var c=u[i];for(a=0;a<e.length;a++)if(n=e[a][0].trace,c===n.uid)continue e;r=t[c],r.dispose(),delete t[c]}};Gh.updateLayout=function(e){var t=this.map,r=e[this.id];!this.dragging&&!this.wheeling&&(t.setCenter(TJ(r.center)),t.setZoom(r.zoom),t.setBearing(r.bearing),t.setPitch(r.pitch)),this.updateLayers(e),this.updateFramework(e),this.updateFx(e),this.map.resize(),this.gd._context._scrollZoom.mapbox?t.scrollZoom.enable():t.scrollZoom.disable()};Gh.resolveOnRender=function(e){var t=this.map;t.on(\"render\",function r(){t.loaded()&&(t.off(\"render\",r),setTimeout(e,10))})};Gh.rejectOnError=function(e){var t=this.map;function r(){e(new Error(Cx.mapOnErrorMsg))}t.once(\"error\",r),t.once(\"style.error\",r),t.once(\"source.error\",r),t.once(\"tile.error\",r),t.once(\"layer.error\",r)};Gh.createFramework=function(e){var t=this,r=t.div=document.createElement(\"div\");r.id=t.uid,r.style.position=\"absolute\",t.container.appendChild(r),t.xaxis={_id:\"x\",c2p:function(n){return t.project(n).x}},t.yaxis={_id:\"y\",c2p:function(n){return t.project(n).y}},t.updateFramework(e),t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},wWt.setConvert(t.mockAxis,e)};Gh.initFx=function(e,t){var r=this,n=r.gd,i=r.map;i.on(\"moveend\",function(s){if(r.map){var l=n._fullLayout;if(s.originalEvent||r.wheeling){var u=l[r.id];kGe.call(\"_storeDirectGUIEdit\",n.layout,l._preGUI,r.getViewEdits(u));var c=r.getView();u._input.center=u.center=c.center,u._input.zoom=u.zoom=c.zoom,u._input.bearing=u.bearing=c.bearing,u._input.pitch=u.pitch=c.pitch,n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(c))}s.originalEvent&&s.originalEvent.type===\"mouseup\"?r.dragging=!1:r.wheeling&&(r.wheeling=!1),l._rehover&&l._rehover()}}),i.on(\"wheel\",function(){r.wheeling=!0}),i.on(\"mousemove\",function(s){var l=r.div.getBoundingClientRect(),u=[s.originalEvent.offsetX,s.originalEvent.offsetY];s.target.getBoundingClientRect=function(){return l},r.xaxis.p2c=function(){return i.unproject(u).lng},r.yaxis.p2c=function(){return i.unproject(u).lat},n._fullLayout._rehover=function(){n._fullLayout._hoversubplot===r.id&&n._fullLayout[r.id]&&s7.hover(n,s,r.id)},s7.hover(n,s,r.id),n._fullLayout._hoversubplot=r.id});function a(){s7.loneUnhover(t._hoverlayer)}i.on(\"dragstart\",function(){r.dragging=!0,a()}),i.on(\"zoomstart\",a),i.on(\"mouseout\",function(){n._fullLayout._hoversubplot=null});function o(){var s=r.getView();n.emit(\"plotly_relayouting\",r.getViewEditsWithDerived(s))}i.on(\"drag\",o),i.on(\"zoom\",o),i.on(\"dblclick\",function(){var s=n._fullLayout[r.id];kGe.call(\"_storeDirectGUIEdit\",n.layout,n._fullLayout._preGUI,r.getViewEdits(s));var l=r.viewInitial;i.setCenter(TJ(l.center)),i.setZoom(l.zoom),i.setBearing(l.bearing),i.setPitch(l.pitch);var u=r.getView();s._input.center=s.center=u.center,s._input.zoom=s.zoom=u.zoom,s._input.bearing=s.bearing=u.bearing,s._input.pitch=s.pitch=u.pitch,n.emit(\"plotly_doubleclick\",null),n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(u))}),r.clearOutline=function(){kWt(r.dragOptions),EWt(r.dragOptions.gd)},r.onClickInPanFn=function(s){return function(l){var u=n._fullLayout.clickmode;u.indexOf(\"select\")>-1&&CWt(l.originalEvent,n,[r.xaxis],[r.yaxis],r.id,s),u.indexOf(\"event\")>-1&&s7.click(n,l.originalEvent)}}};Gh.updateFx=function(e){var t=this,r=t.map,n=t.gd;if(t.isStatic)return;function i(l){var u=t.map.unproject(l);return[u.lng,u.lat]}var a=e.dragmode,o;o=function(l,u){if(u.isRect){var c=l.range={};c[t.id]=[i([u.xmin,u.ymin]),i([u.xmax,u.ymax])]}else{var f=l.lassoPoints={};f[t.id]=u.map(i)}};var s=t.dragOptions;t.dragOptions=wJ.extendDeep(s||{},{dragmode:e.dragmode,element:t.div,gd:n,plotinfo:{id:t.id,domain:e[t.id].domain,xaxis:t.xaxis,yaxis:t.yaxis,fillRangeItems:o},xaxes:[t.xaxis],yaxes:[t.yaxis],subplot:t.id}),r.off(\"click\",t.onClickInPanHandler),SWt(a)||AWt(a)?(r.dragPan.disable(),r.on(\"zoomstart\",t.clearOutline),t.dragOptions.prepFn=function(l,u,c){MWt(l,u,c,t.dragOptions,a)},TWt.init(t.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",t.clearOutline),t.div.onmousedown=null,t.div.ontouchstart=null,t.div.removeEventListener(\"touchstart\",t.div._ontouchstart),t.onClickInPanHandler=t.onClickInPanFn(t.dragOptions),r.on(\"click\",t.onClickInPanHandler))};Gh.updateFramework=function(e){var t=e[this.id].domain,r=e._size,n=this.div.style;n.width=r.w*(t.x[1]-t.x[0])+\"px\",n.height=r.h*(t.y[1]-t.y[0])+\"px\",n.left=r.l+t.x[0]*r.w+\"px\",n.top=r.t+(1-t.y[1])*r.h+\"px\",this.xaxis._offset=r.l+t.x[0]*r.w,this.xaxis._length=r.w*(t.x[1]-t.x[0]),this.yaxis._offset=r.t+(1-t.y[1])*r.h,this.yaxis._length=r.h*(t.y[1]-t.y[0])};Gh.updateLayers=function(e){var t=e[this.id],r=t.layers,n=this.layerList,i;if(r.length!==n.length){for(i=0;i<n.length;i++)n[i].dispose();for(n=this.layerList=[],i=0;i<r.length;i++)n.push(LWt(this,i,r[i]))}else for(i=0;i<r.length;i++)n[i].update(r[i])};Gh.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))};Gh.toImage=function(){return this.map.stop(),this.map.getCanvas().toDataURL()};Gh.setOptions=function(e,t,r){for(var n in r)this.map[t](e,n,r[n])};Gh.getMapLayers=function(){return this.map.getStyle().layers};Gh.addLayer=function(e,t){var r=this.map;if(typeof t==\"string\"){if(t===\"\"){r.addLayer(e,t);return}for(var n=this.getMapLayers(),i=0;i<n.length;i++)if(t===n[i].id){r.addLayer(e,t);return}wJ.warn([\"Trying to add layer with *below* value\",t,\"referencing a layer that does not exist\",\"or that does not yet exist.\"].join(\" \"))}r.addLayer(e)};Gh.project=function(e){return this.map.project(new bJ.LngLat(e[0],e[1]))};Gh.getView=function(){var e=this.map,t=e.getCenter(),r=t.lng,n=t.lat,i={lon:r,lat:n},a=e.getCanvas(),o=parseInt(a.style.width),s=parseInt(a.style.height);return{center:i,zoom:e.getZoom(),bearing:e.getBearing(),pitch:e.getPitch(),_derived:{coordinates:[e.unproject([0,0]).toArray(),e.unproject([o,0]).toArray(),e.unproject([o,s]).toArray(),e.unproject([0,s]).toArray()]}}};Gh.getViewEdits=function(e){for(var t=this.id,r=[\"center\",\"zoom\",\"bearing\",\"pitch\"],n={},i=0;i<r.length;i++){var a=r[i];n[t+\".\"+a]=e[a]}return n};Gh.getViewEditsWithDerived=function(e){var t=this.id,r=this.getViewEdits(e);return r[t+\"._derived\"]=e._derived,r};function DGe(e,t){var r={};if(wJ.isPlainObject(e))r.id=e.id,r.style=e;else if(typeof e==\"string\")if(r.id=e,Cx.styleValuesMapbox.indexOf(e)!==-1)r.style=LGe(e);else if(Cx.stylesNonMapbox[e]){r.style=Cx.stylesNonMapbox[e];var n=r.style.sources[\"plotly-\"+e],i=n?n.tiles:void 0;i&&i[0]&&i[0].slice(-9)===\"?api_key=\"&&(i[0]+=t._mapboxAccessToken)}else r.style=e;else r.id=Cx.styleValueDflt,r.style=LGe(Cx.styleValueDflt);return r.transition={duration:0,delay:0},r}function LGe(e){return Cx.styleUrlPrefix+e+\"-\"+Cx.styleUrlSuffix}function TJ(e){return[e.lon,e.lat]}FGe.exports=RGe});var BGe=ye((lbr,OGe)=>{\"use strict\";var AJ=Pr(),PWt=O_(),IWt=Kd(),qGe=Jk();OGe.exports=function(t,r,n){PWt(t,r,n,{type:\"mapbox\",attributes:qGe,handleDefaults:RWt,partition:\"y\",accessToken:r._mapboxAccessToken})};function RWt(e,t,r,n){r(\"accesstoken\",n.accessToken),r(\"style\"),r(\"center.lon\"),r(\"center.lat\"),r(\"zoom\"),r(\"bearing\"),r(\"pitch\");var i=r(\"bounds.west\"),a=r(\"bounds.east\"),o=r(\"bounds.south\"),s=r(\"bounds.north\");(i===void 0||a===void 0||o===void 0||s===void 0)&&delete t.bounds,IWt(e,t,{name:\"layers\",handleItemDefaults:DWt}),t._input=e}function DWt(e,t){function r(l,u){return AJ.coerce(e,t,qGe.layers,l,u)}var n=r(\"visible\");if(n){var i=r(\"sourcetype\"),a=i===\"raster\"||i===\"image\";r(\"source\"),r(\"sourceattribution\"),i===\"vector\"&&r(\"sourcelayer\"),i===\"image\"&&r(\"coordinates\");var o;a&&(o=\"raster\");var s=r(\"type\",o);a&&s!==\"raster\"&&(s=t.type=\"raster\",AJ.log(\"Source types *raster* and *image* must drawn *raster* layer type.\")),r(\"below\"),r(\"color\"),r(\"opacity\"),r(\"minzoom\"),r(\"maxzoom\"),s===\"circle\"&&r(\"circle.radius\"),s===\"line\"&&(r(\"line.width\"),r(\"line.dash\")),s===\"fill\"&&r(\"fill.outlinecolor\"),s===\"symbol\"&&(r(\"symbol.icon\"),r(\"symbol.iconsize\"),r(\"symbol.text\"),AJ.coerceFont(r,\"symbol.textfont\",void 0,{noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}),r(\"symbol.textposition\"),r(\"symbol.placement\"))}}});var l7=ye(Np=>{\"use strict\";var NGe=xJ(),nm=Pr(),SJ=nm.strTranslate,FWt=nm.strScale,zWt=Id().getSubplotCalcData,qWt=Wp(),OWt=Fa(),UGe=So(),BWt=Zl(),NWt=zGe(),Lx=\"mapbox\",ly=Np.constants=y1();Np.name=Lx;Np.attr=\"subplot\";Np.idRoot=Lx;Np.idRegex=Np.attrRegex=nm.counterRegex(Lx);var UWt=[\"mapbox subplots and traces are deprecated!\",\"Please consider switching to `map` subplots and traces.\",\"Learn more at: https://plotly.com/python/maplibre-migration/\",\"as well as https://plotly.com/javascript/maplibre-migration/\"].join(\" \");Np.attributes={subplot:{valType:\"subplotid\",dflt:\"mapbox\",editType:\"calc\"}};Np.layoutAttributes=Jk();Np.supplyLayoutDefaults=BGe();var VGe=!0;Np.plot=function(t){VGe&&(VGe=!1,nm.warn(UWt));var r=t._fullLayout,n=t.calcdata,i=r._subplots[Lx];if(NGe.version!==ly.requiredVersion)throw new Error(ly.wrongVersionErrorMsg);var a=VWt(t,i);NGe.accessToken=a;for(var o=0;o<i.length;o++){var s=i[o],l=zWt(n,Lx,s),u=r[s],c=u._subplot;c||(c=new NWt(t,s),r[s]._subplot=c),c.viewInitial||(c.viewInitial={center:nm.extendFlat({},u.center),zoom:u.zoom,bearing:u.bearing,pitch:u.pitch}),c.plot(l,r,t._promises)}};Np.clean=function(e,t,r,n){for(var i=n._subplots[Lx]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._subplot&&n[o]._subplot.destroy()}};Np.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[Lx],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._subplot,l=s.toImage(\"png\"),u=t._glimages.append(\"svg:image\");u.attr({xmlns:qWt.svg,\"xlink:href\":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"});var c=OWt.select(a._subplot.div),f=c.select(\".mapboxgl-ctrl-logo\").node().offsetParent===null;if(!f){var h=t._glimages.append(\"g\");h.attr(\"transform\",SJ(n.l+n.w*o.x[0]+10,n.t+n.h*(1-o.y[0])-31)),h.append(\"path\").attr(\"d\",ly.mapboxLogo.path0).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"}),h.append(\"path\").attr(\"d\",ly.mapboxLogo.path1).style(\"opacity\",.35).style(\"enable-background\",\"new\"),h.append(\"path\").attr(\"d\",ly.mapboxLogo.path2).style(\"opacity\",.35).style(\"enable-background\",\"new\"),h.append(\"polygon\").attr(\"points\",ly.mapboxLogo.polygon).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"})}var d=c.select(\".mapboxgl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),v=t._glimages.append(\"g\"),m=v.append(\"text\");m.text(d).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":d});var b=UGe.bBox(m.node()),p=n.w*(o.x[1]-o.x[0]);if(b.width>p/2){var k=d.split(\"|\").join(\"<br>\");m.text(k).attr(\"data-unformatted\",k).call(BWt.convertToTspans,e),b=UGe.bBox(m.node())}m.attr(\"transform\",SJ(-3,-b.height+8)),v.insert(\"rect\",\".static-attribution\").attr({x:-b.width-6,y:-b.height-3,width:b.width+6,height:b.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var M=1;b.width+6>p&&(M=p/(b.width+6));var T=[n.l+n.w*o.x[1],n.t+n.h*(1-o.y[0])];v.attr(\"transform\",SJ(T[0],T[1])+FWt(M))}};function VWt(e,t){var r=e._fullLayout,n=e._context;if(n.mapboxAccessToken===\"\")return\"\";for(var i=[],a=[],o=!1,s=!1,l=0;l<t.length;l++){var u=r[t[l]],c=u.accesstoken;GGe(u.style)&&(c?nm.pushUnique(i,c):(GGe(u._input.style)&&(nm.error(\"Uses Mapbox map style, but did not set an access token.\"),o=!0),s=!0)),c&&nm.pushUnique(a,c)}if(s){var f=o?ly.noAccessTokenErrorMsg:ly.missingStyleErrorMsg;throw nm.error(f),new Error(f)}return i.length?(i.length>1&&nm.warn(ly.multipleTokensErrorMsg),i[0]):(a.length&&nm.log([\"Listed mapbox access token(s)\",a.join(\",\"),\"but did not use a Mapbox map style, ignoring token(s).\"].join(\" \")),\"\")}function GGe(e){return typeof e==\"string\"&&(ly.styleValuesMapbox.indexOf(e)!==-1||e.indexOf(\"mapbox://\")===0||e.indexOf(\"stamen\")===0)}Np.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[Lx],n=0;n<r.length;n++){var i=t[r[n]]._subplot;i.updateFx(t)}}});var jGe=ye((fbr,HGe)=>{\"use strict\";var cbr=[\"*scattermapbox* trace is deprecated!\",\"Please consider switching to the *scattermap* trace type and `map` subplots.\",\"Learn more at: https://plotly.com/python/maplibre-migration/\",\"as well as https://plotly.com/javascript/maplibre-migration/\"].join(\" \");HGe.exports={attributes:e7(),supplyDefaults:rGe(),colorbar:Qd(),formatLabels:dJ(),calc:TF(),plot:gGe(),hoverPoints:a7().hoverPoints,eventData:xGe(),selectPoints:wGe(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.update(t)}},moduleType:\"trace\",name:\"scattermapbox\",basePlotModule:l7(),categories:[\"mapbox\",\"gl\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}});var XGe=ye((hbr,WGe)=>{\"use strict\";WGe.exports=jGe()});var MJ=ye((dbr,ZGe)=>{\"use strict\";var _1=l5(),GWt=Tu(),{hovertemplateAttrs:HWt,templatefallbackAttrs:jWt}=Pl(),WWt=Vl(),Px=Ao().extendFlat;ZGe.exports=Px({locations:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},geojson:{valType:\"any\",editType:\"calc\"},featureidkey:Px({},_1.featureidkey,{}),below:{valType:\"string\",editType:\"plot\"},text:_1.text,hovertext:_1.hovertext,marker:{line:{color:Px({},_1.marker.line.color,{editType:\"plot\"}),width:Px({},_1.marker.line.width,{editType:\"plot\"}),editType:\"calc\"},opacity:Px({},_1.marker.opacity,{editType:\"plot\"}),editType:\"calc\"},selected:{marker:{opacity:Px({},_1.selected.marker.opacity,{editType:\"plot\"}),editType:\"plot\"},editType:\"plot\"},unselected:{marker:{opacity:Px({},_1.unselected.marker.opacity,{editType:\"plot\"}),editType:\"plot\"},editType:\"plot\"},hoverinfo:_1.hoverinfo,hovertemplate:HWt({},{keys:[\"properties\"]}),hovertemplatefallback:jWt(),showlegend:Px({},WWt.showlegend,{dflt:!1})},GWt(\"\",{cLetter:\"z\",editTypeOverride:\"calc\"}))});var KGe=ye((vbr,YGe)=>{\"use strict\";var tC=Pr(),XWt=td(),ZWt=MJ();YGe.exports=function(t,r,n,i){function a(c,f){return tC.coerce(t,r,ZWt,c,f)}var o=a(\"locations\"),s=a(\"z\"),l=a(\"geojson\");if(!tC.isArrayOrTypedArray(o)||!o.length||!tC.isArrayOrTypedArray(s)||!s.length||!(typeof l==\"string\"&&l!==\"\"||tC.isPlainObject(l))){r.visible=!1;return}a(\"featureidkey\"),r._length=Math.min(o.length,s.length),a(\"below\"),a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var u=a(\"marker.line.width\");u&&a(\"marker.line.color\"),a(\"marker.opacity\"),XWt(t,r,i,a,{prefix:\"\",cLetter:\"z\"}),tC.coerceSelectionMarkerOpacity(r,a)}});var EJ=ye((pbr,QGe)=>{\"use strict\";var YWt=Eo(),x1=Pr(),KWt=tc(),JWt=So(),$Wt=hx().makeBlank,JGe=ey();function QWt(e){var t=e[0].trace,r=t.visible===!0&&t._length!==0,n={layout:{visibility:\"none\"},paint:{}},i={layout:{visibility:\"none\"},paint:{}},a=t._opts={fill:n,line:i,geojson:$Wt()};if(!r)return a;var o=JGe.extractTraceFeature(e);if(!o)return a;var s=KWt.makeColorScaleFuncFromTrace(t),l=t.marker,u=l.line||{},c;x1.isArrayOrTypedArray(l.opacity)&&(c=function(k){var M=k.mo;return YWt(M)?+x1.constrain(M,0,1):0});var f;x1.isArrayOrTypedArray(u.color)&&(f=function(k){return k.mlc});var h;x1.isArrayOrTypedArray(u.width)&&(h=function(k){return k.mlw});for(var d=0;d<e.length;d++){var v=e[d],m=v.fOut;if(m){var b=m.properties;b.fc=s(v.z),c&&(b.mo=c(v)),f&&(b.mlc=f(v)),h&&(b.mlw=h(v)),v.ct=b.ct,v._polygons=JGe.feature2polygons(m)}}var p=c?{type:\"identity\",property:\"mo\"}:l.opacity;return x1.extendFlat(n.paint,{\"fill-color\":{type:\"identity\",property:\"fc\"},\"fill-opacity\":p}),x1.extendFlat(i.paint,{\"line-color\":f?{type:\"identity\",property:\"mlc\"}:u.color,\"line-width\":h?{type:\"identity\",property:\"mlw\"}:u.width,\"line-opacity\":p}),n.layout.visibility=\"visible\",i.layout.visibility=\"visible\",a.geojson={type:\"FeatureCollection\",features:o},$Ge(e),a}function $Ge(e){var t=e[0].trace,r=t._opts,n;if(t.selectedpoints){for(var i=JWt.makeSelectedPointStyleFns(t),a=0;a<e.length;a++){var o=e[a];o.fOut&&(o.fOut.properties.mo2=i.selectedOpacityFn(o))}n={type:\"identity\",property:\"mo2\"}}else n=x1.isArrayOrTypedArray(t.marker.opacity)?{type:\"identity\",property:\"mo\"}:t.marker.opacity;return x1.extendFlat(r.fill.paint,{\"fill-opacity\":n}),x1.extendFlat(r.line.paint,{\"line-opacity\":n}),r}QGe.exports={convert:QWt,convertOnSelect:$Ge}});var nHe=ye((gbr,iHe)=>{\"use strict\";var tHe=EJ().convert,eXt=EJ().convertOnSelect,eHe=y1().traceLayerPrefix;function rHe(e,t){this.type=\"choroplethmapbox\",this.subplot=e,this.uid=t,this.sourceId=\"source-\"+t,this.layerList=[[\"fill\",eHe+t+\"-fill\"],[\"line\",eHe+t+\"-line\"]],this.below=null}var R5=rHe.prototype;R5.update=function(e){this._update(tHe(e)),e[0].trace._glTrace=this};R5.updateOnSelect=function(e){this._update(eXt(e))};R5._update=function(e){var t=this.subplot,r=this.layerList,n=t.belowLookup[\"trace-\"+this.uid];t.map.getSource(this.sourceId).setData(e.geojson),n!==this.below&&(this._removeLayers(),this._addLayers(e,n),this.below=n);for(var i=0;i<r.length;i++){var a=r[i],o=a[0],s=a[1],l=e[o];t.setOptions(s,\"setLayoutProperty\",l.layout),l.layout.visibility===\"visible\"&&t.setOptions(s,\"setPaintProperty\",l.paint)}};R5._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},t)}};R5._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};R5.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};iHe.exports=function(t,r){var n=r[0].trace,i=new rHe(t,n.uid),a=i.sourceId,o=tHe(r),s=i.below=t.belowLookup[\"trace-\"+n.uid];return t.map.addSource(a,{type:\"geojson\",data:o.geojson}),i._addLayers(o,s),r[0].trace._glTrace=i,i}});var oHe=ye((ybr,aHe)=>{\"use strict\";var mbr=[\"*choroplethmapbox* trace is deprecated!\",\"Please consider switching to the *choroplethmap* trace type and `map` subplots.\",\"Learn more at: https://plotly.com/python/maplibre-migration/\",\"as well as https://plotly.com/javascript/maplibre-migration/\"].join(\" \");aHe.exports={attributes:MJ(),supplyDefaults:KGe(),colorbar:F_(),calc:HF(),plot:nHe(),hoverPoints:WF(),eventData:XF(),selectPoints:ZF(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.updateOnSelect(t)}},getBelow:function(e,t){for(var r=t.getMapLayers(),n=r.length-2;n>=0;n--){var i=r[n].id;if(typeof i==\"string\"&&i.indexOf(\"water\")===0){for(var a=n+1;a<r.length;a++)if(i=r[a].id,typeof i==\"string\"&&i.indexOf(\"plotly-\")===-1)return i}}},moduleType:\"trace\",name:\"choroplethmapbox\",basePlotModule:l7(),categories:[\"mapbox\",\"gl\",\"noOpacity\",\"showLegend\"],meta:{hr_name:\"choropleth_mapbox\"}}});var lHe=ye((_br,sHe)=>{\"use strict\";sHe.exports=oHe()});var CJ=ye((xbr,cHe)=>{\"use strict\";var tXt=Tu(),{hovertemplateAttrs:rXt,templatefallbackAttrs:iXt}=Pl(),uHe=Vl(),u7=e7(),kJ=Ao().extendFlat;cHe.exports=kJ({lon:u7.lon,lat:u7.lat,z:{valType:\"data_array\",editType:\"calc\"},radius:{valType:\"number\",editType:\"plot\",arrayOk:!0,min:1,dflt:30},below:{valType:\"string\",editType:\"plot\"},text:u7.text,hovertext:u7.hovertext,hoverinfo:kJ({},uHe.hoverinfo,{flags:[\"lon\",\"lat\",\"z\",\"text\",\"name\"]}),hovertemplate:rXt(),hovertemplatefallback:iXt(),showlegend:kJ({},uHe.showlegend,{dflt:!1})},tXt(\"\",{cLetter:\"z\",editTypeOverride:\"calc\"}))});var hHe=ye((bbr,fHe)=>{\"use strict\";var nXt=Pr(),aXt=td(),oXt=CJ();fHe.exports=function(t,r,n,i){function a(u,c){return nXt.coerce(t,r,oXt,u,c)}var o=a(\"lon\")||[],s=a(\"lat\")||[],l=Math.min(o.length,s.length);if(!l){r.visible=!1;return}r._length=l,a(\"z\"),a(\"radius\"),a(\"below\"),a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),aXt(t,r,i,a,{prefix:\"\",cLetter:\"z\"})}});var pHe=ye((wbr,vHe)=>{\"use strict\";var LJ=Eo(),sXt=Pr().isArrayOrTypedArray,PJ=fs().BADNUM,lXt=gv(),dHe=Pr()._;vHe.exports=function(t,r){for(var n=r._length,i=new Array(n),a=r.z,o=sXt(a)&&a.length,s=0;s<n;s++){var l=i[s]={},u=r.lon[s],c=r.lat[s];if(l.lonlat=LJ(u)&&LJ(c)?[+u,+c]:[PJ,PJ],o){var f=a[s];l.z=LJ(f)?f:PJ}}return lXt(t,r,{vals:o?a:[0,1],containerStr:\"\",cLetter:\"z\"}),n&&(i[0].t={labels:{lat:dHe(t,\"lat:\")+\" \",lon:dHe(t,\"lon:\")+\" \"}}),i}});var xHe=ye((Tbr,_He)=>{\"use strict\";var uXt=Eo(),IJ=Pr(),gHe=ka(),mHe=tc(),yHe=fs().BADNUM,cXt=hx().makeBlank;_He.exports=function(t){var r=t[0].trace,n=r.visible===!0&&r._length!==0,i={layout:{visibility:\"none\"},paint:{}},a=r._opts={heatmap:i,geojson:cXt()};if(!n)return a;var o=[],s,l=r.z,u=r.radius,c=IJ.isArrayOrTypedArray(l)&&l.length,f=IJ.isArrayOrTypedArray(u);for(s=0;s<t.length;s++){var h=t[s],d=h.lonlat;if(d[0]!==yHe){var v={};if(c){var m=h.z;v.z=m!==yHe?m:0}f&&(v.r=uXt(u[s])&&u[s]>0?+u[s]:0),o.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:d},properties:v})}}var b=mHe.extractOpts(r),p=b.reversescale?mHe.flipScale(b.colorscale):b.colorscale,k=p[0][1],M=gHe.opacity(k)<1?k:gHe.addOpacity(k,0),T=[\"interpolate\",[\"linear\"],[\"heatmap-density\"],0,M];for(s=1;s<p.length;s++)T.push(p[s][0],p[s][1]);var L=[\"interpolate\",[\"linear\"],[\"get\",\"z\"],b.min,0,b.max,1];return IJ.extendFlat(a.heatmap.paint,{\"heatmap-weight\":c?L:1/(b.max-b.min),\"heatmap-color\":T,\"heatmap-radius\":f?{type:\"identity\",property:\"r\"}:r.radius,\"heatmap-opacity\":r.opacity}),a.geojson={type:\"FeatureCollection\",features:o},a.heatmap.layout.visibility=\"visible\",a}});var AHe=ye((Abr,THe)=>{\"use strict\";var bHe=xHe(),fXt=y1().traceLayerPrefix;function wHe(e,t){this.type=\"densitymapbox\",this.subplot=e,this.uid=t,this.sourceId=\"source-\"+t,this.layerList=[[\"heatmap\",fXt+t+\"-heatmap\"]],this.below=null}var c7=wHe.prototype;c7.update=function(e){var t=this.subplot,r=this.layerList,n=bHe(e),i=t.belowLookup[\"trace-\"+this.uid];t.map.getSource(this.sourceId).setData(n.geojson),i!==this.below&&(this._removeLayers(),this._addLayers(n,i),this.below=i);for(var a=0;a<r.length;a++){var o=r[a],s=o[0],l=o[1],u=n[s];t.setOptions(l,\"setLayoutProperty\",u.layout),u.layout.visibility===\"visible\"&&t.setOptions(l,\"setPaintProperty\",u.paint)}};c7._addLayers=function(e,t){for(var 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RJ=ye((Fbr,JHe)=>{\"use strict\";var FXt=[\"Metropolis Black Italic\",\"Metropolis Black\",\"Metropolis Bold Italic\",\"Metropolis Bold\",\"Metropolis Extra Bold Italic\",\"Metropolis Extra Bold\",\"Metropolis Extra Light Italic\",\"Metropolis Extra Light\",\"Metropolis Light Italic\",\"Metropolis Light\",\"Metropolis Medium Italic\",\"Metropolis Medium\",\"Metropolis Regular Italic\",\"Metropolis Regular\",\"Metropolis Semi Bold Italic\",\"Metropolis Semi Bold\",\"Metropolis Thin Italic\",\"Metropolis Thin\",\"Open Sans Bold Italic\",\"Open Sans Bold\",\"Open Sans Extrabold Italic\",\"Open Sans Extrabold\",\"Open Sans Italic\",\"Open Sans Light Italic\",\"Open Sans Light\",\"Open Sans Regular\",\"Open Sans Semibold Italic\",\"Open Sans Semibold\",\"Klokantech Noto Sans Bold\",\"Klokantech Noto Sans CJK Bold\",\"Klokantech Noto Sans CJK Regular\",\"Klokantech Noto Sans Italic\",\"Klokantech Noto Sans Regular\"];JHe.exports={isSupportedFont:function(e){return FXt.indexOf(e)!==-1}}});var eje=ye((zbr,QHe)=>{\"use strict\";var nC=Pr(),DJ=Ru(),zXt=$p(),qXt=D0(),OXt=F0(),BXt=Fg(),$He=h7(),NXt=RJ().isSupportedFont;QHe.exports=function(t,r,n,i){function a(p,k){return nC.coerce(t,r,$He,p,k)}function o(p,k){return nC.coerce2(t,r,$He,p,k)}var s=UXt(t,r,a);if(!s){r.visible=!1;return}if(a(\"text\"),a(\"texttemplate\"),a(\"texttemplatefallback\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),a(\"mode\"),a(\"below\"),DJ.hasMarkers(r)){zXt(t,r,n,i,a,{noLine:!0,noAngle:!0}),a(\"marker.allowoverlap\"),a(\"marker.angle\");var l=r.marker;l.symbol!==\"circle\"&&(nC.isArrayOrTypedArray(l.size)&&(l.size=l.size[0]),nC.isArrayOrTypedArray(l.color)&&(l.color=l.color[0]))}DJ.hasLines(r)&&(qXt(t,r,n,i,a,{noDash:!0}),a(\"connectgaps\"));var u=o(\"cluster.maxzoom\"),c=o(\"cluster.step\"),f=o(\"cluster.color\",r.marker&&r.marker.color||n),h=o(\"cluster.size\"),d=o(\"cluster.opacity\"),v=u!==!1||c!==!1||f!==!1||h!==!1||d!==!1,m=a(\"cluster.enabled\",v);if(m||DJ.hasText(r)){var b=i.font.family;OXt(t,r,i,a,{noSelect:!0,noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0,font:{family:NXt(b)?b:\"Open Sans Regular\",weight:i.font.weight,style:i.font.style,size:i.font.size,color:i.font.color}})}a(\"fill\"),r.fill!==\"none\"&&BXt(t,r,n,a),nC.coerceSelectionMarkerOpacity(r,a)};function UXt(e,t,r){var n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length);return t._length=a,a}});var FJ=ye((qbr,rje)=>{\"use strict\";var tje=ho();rje.exports=function(t,r,n){var i={},a=n[r.subplot]._subplot,o=a.mockAxis,s=t.lonlat;return i.lonLabel=tje.tickText(o,o.c2l(s[0]),!0).text,i.latLabel=tje.tickText(o,o.c2l(s[1]),!0).text,i}});var zJ=ye((Obr,nje)=>{\"use strict\";var ije=Pr();nje.exports=function(t,r){var n=t.split(\" 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p;if((a||o)&&(p=v7.calcTraceToLineCoords(r)),a&&(h.geojson=v7.makePolygon(p),h.layout.visibility=\"visible\",sv.extendFlat(h.paint,{\"fill-color\":n.fillcolor})),o&&(d.geojson=v7.makeLine(p),d.layout.visibility=\"visible\",sv.extendFlat(d.paint,{\"line-width\":n.line.width,\"line-color\":n.line.color,\"line-opacity\":n.opacity})),u){var k=KXt(r);v.geojson=k.geojson,v.layout.visibility=\"visible\",f&&(v.filter=[\"!\",[\"has\",\"point_count\"]],b.cluster={type:\"circle\",filter:[\"has\",\"point_count\"],layout:{visibility:\"visible\"},paint:{\"circle-color\":OJ(n.cluster.color,n.cluster.step),\"circle-radius\":OJ(n.cluster.size,n.cluster.step),\"circle-opacity\":OJ(n.cluster.opacity,n.cluster.step)}},b.clusterCount={type:\"symbol\",filter:[\"has\",\"point_count\"],paint:{},layout:{\"text-field\":\"{point_count_abbreviated}\",\"text-font\":oje(n),\"text-size\":12}}),sv.extendFlat(v.paint,{\"circle-color\":k.mcc,\"circle-radius\":k.mrc,\"circle-opacity\":k.mo})}if(u&&f&&(v.filter=[\"!\",[\"has\",\"point_count\"]]),(c||l)&&(m.geojson=JXt(r,t),sv.extendFlat(m.layout,{visibility:\"visible\",\"icon-image\":\"{symbol}-15\",\"text-field\":\"{text}\"}),c&&(sv.extendFlat(m.layout,{\"icon-size\":n.marker.size/10}),\"angle\"in n.marker&&n.marker.angle!==\"auto\"&&sv.extendFlat(m.layout,{\"icon-rotate\":{type:\"identity\",property:\"angle\"},\"icon-rotation-alignment\":\"map\"}),m.layout[\"icon-allow-overlap\"]=n.marker.allowoverlap,sv.extendFlat(m.paint,{\"icon-opacity\":n.opacity*n.marker.opacity,\"icon-color\":n.marker.color})),l)){var M=(n.marker||{}).size,T=WXt(n.textposition,M);sv.extendFlat(m.layout,{\"text-size\":n.textfont.size,\"text-anchor\":T.anchor,\"text-offset\":T.offset,\"text-font\":oje(n)}),sv.extendFlat(m.paint,{\"text-color\":n.textfont.color,\"text-opacity\":n.opacity})}return b};function d7(e){return{type:e,geojson:v7.makeBlank(),layout:{visibility:\"none\"},filter:null,paint:{}}}function KXt(e){var t=e[0].trace,r=t.marker,n=t.selectedpoints,i=sv.isArrayOrTypedArray(r.color),a=sv.isArrayOrTypedArray(r.size),o=sv.isArrayOrTypedArray(r.opacity),s;function l(M){return t.opacity*M}function u(M){return M/2}var c;i&&(aje.hasColorscale(t,\"marker\")?c=aje.makeColorScaleFuncFromTrace(r):c=sv.identity);var f;a&&(f=HXt(t));var h;o&&(h=function(M){var T=sje(M)?+sv.constrain(M,0,1):0;return l(T)});var d=[];for(s=0;s<e.length;s++){var v=e[s],m=v.lonlat;if(!lje(m)){var b={};c&&(b.mcc=v.mcc=c(v.mc)),f&&(b.mrc=v.mrc=f(v.ms)),h&&(b.mo=h(v.mo)),n&&(b.selected=v.selected||0),d.push({type:\"Feature\",id:s+1,geometry:{type:\"Point\",coordinates:m},properties:b})}}var p;if(n)for(p=GXt.makeSelectedPointStyleFns(t),s=0;s<d.length;s++){var k=d[s].properties;p.selectedOpacityFn&&(k.mo=l(p.selectedOpacityFn(k))),p.selectedColorFn&&(k.mcc=p.selectedColorFn(k)),p.selectedSizeFn&&(k.mrc=p.selectedSizeFn(k))}return{geojson:{type:\"FeatureCollection\",features:d},mcc:i||p&&p.selectedColorFn?{type:\"identity\",property:\"mcc\"}:r.color,mrc:a||p&&p.selectedSizeFn?{type:\"identity\",property:\"mrc\"}:u(r.size),mo:o||p&&p.selectedOpacityFn?{type:\"identity\",property:\"mo\"}:l(r.opacity)}}function JXt(e,t){for(var r=t._fullLayout,n=e[0].trace,i=n.marker||{},a=i.symbol,o=i.angle,s=a!==\"circle\"?qJ(a):g7,l=o!==\"auto\"?qJ(o,!0):g7,u=p7.hasText(n)?qJ(n.text):g7,c=[],f=0;f<e.length;f++){var h=e[f];if(!lje(h.lonlat)){var d=n.texttemplate,v;if(d){var m=Array.isArray(d)?d[f]||\"\":d,b=n._module.formatLabels(h,n,r),p={};XXt(p,n,h.i),v=sv.texttemplateString({data:[p,h,n._meta],fallback:n.texttemplatefallback,labels:b,locale:r._d3locale,template:m})}else v=u(f);v&&(v=v.replace(ZXt,\"\").replace(YXt,`\n",
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hje(e,t,r,n){this.type=\"scattermap\",this.subplot=e,this.uid=t,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+t+\"-fill\",line:\"source-\"+t+\"-line\",circle:\"source-\"+t+\"-circle\",symbol:\"source-\"+t+\"-symbol\",cluster:\"source-\"+t+\"-circle\",clusterCount:\"source-\"+t+\"-circle\"},this.layerIds={fill:z5+t+\"-fill\",line:z5+t+\"-line\",circle:z5+t+\"-circle\",symbol:z5+t+\"-symbol\",cluster:z5+t+\"-cluster\",clusterCount:z5+t+\"-cluster-count\"},this.below=null}var aC=hje.prototype;aC.addSource=function(e,t,r){var n={type:\"geojson\",data:t.geojson};r&&r.enabled&&$Xt.extendFlat(n,{cluster:!0,clusterMaxZoom:r.maxzoom});var i=this.subplot.map.getSource(this.sourceIds[e]);i?i.setData(t.geojson):this.subplot.map.addSource(this.sourceIds[e],n)};aC.setSourceData=function(e,t){this.subplot.map.getSource(this.sourceIds[e]).setData(t.geojson)};aC.addLayer=function(e,t,r){var n={type:t.type,id:this.layerIds[e],source:this.sourceIds[e],layout:t.layout,paint:t.paint};t.filter&&(n.filter=t.filter);for(var i=this.layerIds[e],a,o=this.subplot.getMapLayers(),s=0;s<o.length;s++)if(o[s].id===i){a=!0;break}a?(this.subplot.setOptions(i,\"setLayoutProperty\",n.layout),n.layout.visibility===\"visible\"&&this.subplot.setOptions(i,\"setPaintProperty\",n.paint)):this.subplot.addLayer(n,r)};aC.update=function(t){var r=t[0].trace,n=this.subplot,i=n.map,a=fje(n.gd,t),o=n.belowLookup[\"trace-\"+this.uid],s=!!(r.cluster&&r.cluster.enabled),l=!!this.clusterEnabled,u=this;function c(M){M||u.addSource(\"circle\",a.circle,r.cluster);for(var T=og.cluster,L=0;L<T.length;L++){var x=T[L],C=a[x];u.addLayer(x,C,o)}}function f(M){for(var T=og.cluster,L=T.length-1;L>=0;L--){var x=T[L];i.removeLayer(u.layerIds[x])}M||i.removeSource(u.sourceIds.circle)}function h(M){for(var T=og.nonCluster,L=0;L<T.length;L++){var x=T[L],C=a[x];M||u.addSource(x,C),u.addLayer(x,C,o)}}function d(M){for(var T=og.nonCluster,L=T.length-1;L>=0;L--){var x=T[L];i.removeLayer(u.layerIds[x]),M||i.removeSource(u.sourceIds[x])}}function v(M){l?f(M):d(M)}function m(M){s?c(M):h(M)}function b(){for(var M=s?og.cluster:og.nonCluster,T=0;T<M.length;T++){var L=M[T],x=a[L];x&&(n.setOptions(u.layerIds[L],\"setLayoutProperty\",x.layout),x.layout.visibility===\"visible\"&&(L!==\"cluster\"&&u.setSourceData(L,x),n.setOptions(u.layerIds[L],\"setPaintProperty\",x.paint)))}}var p=this.isHidden,k=r.visible!==!0;k?p||v():p?k||m():l!==s?(v(),m()):(this.below!==o&&(v(!0),m(!0)),b()),this.clusterEnabled=s,this.isHidden=k,this.below=o,t[0].trace._glTrace=this};aC.dispose=function(){for(var t=this.subplot.map,r=this.clusterEnabled?og.cluster:og.nonCluster,n=r.length-1;n>=0;n--){var i=r[n];t.removeLayer(this.layerIds[i]),t.removeSource(this.sourceIds[i])}};dje.exports=function(t,r){var n=r[0].trace,i=n.cluster&&n.cluster.enabled,a=n.visible!==!0,o=new hje(t,n.uid,i,a),s=fje(t.gd,r),l=o.below=t.belowLookup[\"trace-\"+n.uid],u,c,f;if(i)for(o.addSource(\"circle\",s.circle,n.cluster),u=0;u<og.cluster.length;u++)c=og.cluster[u],f=s[c],o.addLayer(c,f,l);else for(u=0;u<og.nonCluster.length;u++)c=og.nonCluster[u],f=s[c],o.addSource(c,f,n.cluster),o.addLayer(c,f,l);return r[0].trace._glTrace=o,o}});var m7=ye((Ubr,gje)=>{\"use strict\";var QXt=ef(),BJ=Pr(),eZt=xT(),tZt=BJ.fillText,rZt=fs().BADNUM,iZt=Ix().traceLayerPrefix;function nZt(e,t,r){var n=e.cd,i=n[0].trace,a=e.xa,o=e.ya,s=e.subplot,l=[],u=iZt+i.uid+\"-circle\",c=i.cluster&&i.cluster.enabled;if(c){var f=s.map.queryRenderedFeatures(null,{layers:[u]});l=f.map(function(S){return S.id})}var h=t>=0?Math.floor((t+180)/360):Math.ceil((t-180)/360),d=h*360,v=t-d;function m(S){var g=S.lonlat;if(g[0]===rZt||c&&l.indexOf(S.i+1)===-1)return 1/0;var P=BJ.modHalf(g[0],360),E=g[1],z=s.project([P,E]),q=z.x-a.c2p([v,E]),V=z.y-o.c2p([P,r]),G=Math.max(3,S.mrc||0);return Math.max(Math.sqrt(q*q+V*V)-G,1-3/G)}if(QXt.getClosest(n,m,e),e.index!==!1){var b=n[e.index],p=b.lonlat,k=[BJ.modHalf(p[0],360)+d,p[1]],M=a.c2p(k),T=o.c2p(k),L=b.mrc||1;e.x0=M-L,e.x1=M+L,e.y0=T-L,e.y1=T+L;var x={};x[i.subplot]={_subplot:s};var C=i._module.formatLabels(b,i,x);return e.lonLabel=C.lonLabel,e.latLabel=C.latLabel,e.color=eZt(i,b),e.extraText=pje(i,b,n[0].t.labels),e.hovertemplate=i.hovertemplate,[e]}}function pje(e,t,r){if(e.hovertemplate)return;var n=t.hi||e.hoverinfo,i=n.split(\"+\"),a=i.indexOf(\"all\")!==-1,o=i.indexOf(\"lon\")!==-1,s=i.indexOf(\"lat\")!==-1,l=t.lonlat,u=[];function c(f){return f+\"\\xB0\"}return a||o&&s?u.push(\"(\"+c(l[1])+\", \"+c(l[0])+\")\"):o?u.push(r.lon+c(l[0])):s&&u.push(r.lat+c(l[1])),(a||i.indexOf(\"text\")!==-1)&&tZt(t,e,u),u.join(\"<br>\")}gje.exports={hoverPoints:nZt,getExtraText:pje}});var yje=ye((Vbr,mje)=>{\"use strict\";mje.exports=function(t,r){return t.lon=r.lon,t.lat=r.lat,t}});var xje=ye((Gbr,_je)=>{\"use strict\";var 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0,minimumFractionDigits:this.minFractionDigits?this.minFractionDigits.evaluate(A):void 0,maximumFractionDigits:this.maxFractionDigits?this.maxFractionDigits.evaluate(A):void 0}).format(this.number.evaluate(A))}eachChild(A){A(this.number),this.locale&&A(this.locale),this.currency&&A(this.currency),this.minFractionDigits&&A(this.minFractionDigits),this.maxFractionDigits&&A(this.maxFractionDigits)}outputDefined(){return!1}}class Yl{constructor(A){this.type=ei,this.sections=A}static parse(A,F){if(A.length<2)return F.error(\"Expected at least one argument.\");let W=A[1];if(!Array.isArray(W)&&typeof W==\"object\")return F.error(\"First argument must be an image or text section.\");let te=[],fe=!1;for(let pe=1;pe<=A.length-1;++pe){let Re=A[pe];if(fe&&typeof Re==\"object\"&&!Array.isArray(Re)){fe=!1;let Ke=null;if(Re[\"font-scale\"]&&(Ke=F.parse(Re[\"font-scale\"],1,dt),!Ke))return null;let ft=null;if(Re[\"text-font\"]&&(ft=F.parse(Re[\"text-font\"],1,Ge(Et)),!ft))return null;let 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F=(180+R[0])/360,W=(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+R[1]*Math.PI/360)))/360,te=Math.pow(2,A.z);return[Math.round(F*te*bs),Math.round(W*te*bs)]}function _a(R,A){let F=Math.pow(2,A.z);return[(te=(R[0]/bs+A.x)/F,360*te-180),(W=(R[1]/bs+A.y)/F,360/Math.PI*Math.atan(Math.exp((180-360*W)*Math.PI/180))-90)];var W,te}function Vu(R,A){R[0]=Math.min(R[0],A[0]),R[1]=Math.min(R[1],A[1]),R[2]=Math.max(R[2],A[0]),R[3]=Math.max(R[3],A[1])}function zl(R,A){return!(R[0]<=A[0]||R[2]>=A[2]||R[1]<=A[1]||R[3]>=A[3])}function xo(R,A,F){let W=R[0]-A[0],te=R[1]-A[1],fe=R[0]-F[0],pe=R[1]-F[1];return W*pe-fe*te==0&&W*fe<=0&&te*pe<=0}function Kl(R,A,F,W){return(te=[W[0]-F[0],W[1]-F[1]])[0]*(fe=[A[0]-R[0],A[1]-R[1]])[1]-te[1]*fe[0]!=0&&!(!Oo(R,A,F,W)||!Oo(F,W,R,A));var te,fe}function Ns(R,A,F){for(let W of F)for(let te=0;te<W.length-1;++te)if(Kl(R,A,W[te],W[te+1]))return!0;return!1}function Hl(R,A,F=!1){let W=!1;for(let Re of A)for(let Ke=0;Ke<Re.length-1;Ke++){if(xo(R,Re[Ke],Re[Ke+1]))return 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this.first?(this.first=!1,this.lastIntegerZoom=W,this.lastIntegerZoomTime=0,this.lastZoom=A,this.lastFloorZoom=W,!0):(this.lastFloorZoom>W?(this.lastIntegerZoom=W+1,this.lastIntegerZoomTime=F):this.lastFloorZoom<W&&(this.lastIntegerZoom=W,this.lastIntegerZoomTime=F),A!==this.lastZoom&&(this.lastZoom=A,this.lastFloorZoom=W,!0))}}let Dn={\"Latin-1 Supplement\":R=>R>=128&&R<=255,\"Hangul Jamo\":R=>R>=4352&&R<=4607,Khmer:R=>R>=6016&&R<=6143,\"General Punctuation\":R=>R>=8192&&R<=8303,\"Letterlike Symbols\":R=>R>=8448&&R<=8527,\"Number Forms\":R=>R>=8528&&R<=8591,\"Miscellaneous Technical\":R=>R>=8960&&R<=9215,\"Control Pictures\":R=>R>=9216&&R<=9279,\"Optical Character Recognition\":R=>R>=9280&&R<=9311,\"Enclosed Alphanumerics\":R=>R>=9312&&R<=9471,\"Geometric Shapes\":R=>R>=9632&&R<=9727,\"Miscellaneous Symbols\":R=>R>=9728&&R<=9983,\"Miscellaneous Symbols and Arrows\":R=>R>=11008&&R<=11263,\"Ideographic Description Characters\":R=>R>=12272&&R<=12287,\"CJK Symbols and 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ws=Zo([\"Bopo\",\"Hani\",\"Hira\",\"Kana\",\"Kits\",\"Nshu\",\"Tang\",\"Yiii\"]);function Ho(R){return!(R!==746&&R!==747&&(R<4352||!(Dn[\"CJK Compatibility Forms\"](R)&&!(R>=65097&&R<=65103)||Dn[\"CJK Compatibility\"](R)||Dn[\"CJK Strokes\"](R)||!(!Dn[\"CJK Symbols and Punctuation\"](R)||R>=12296&&R<=12305||R>=12308&&R<=12319||R===12336)||Dn[\"Enclosed CJK Letters and Months\"](R)||Dn[\"Ideographic Description Characters\"](R)||Dn.Kanbun(R)||Dn.Katakana(R)&&R!==12540||!(!Dn[\"Halfwidth and Fullwidth Forms\"](R)||R===65288||R===65289||R===65293||R>=65306&&R<=65310||R===65339||R===65341||R===65343||R>=65371&&R<=65503||R===65507||R>=65512&&R<=65519)||!(!Dn[\"Small Form Variants\"](R)||R>=65112&&R<=65118||R>=65123&&R<=65126)||Dn[\"Vertical Forms\"](R)||Dn[\"Yijing Hexagram Symbols\"](R)||new RegExp(\"\\\\p{sc=Cans}\",\"u\").test(String.fromCodePoint(R))||new RegExp(\"\\\\p{sc=Hang}\",\"u\").test(String.fromCodePoint(R))||ws.test(String.fromCodePoint(R)))))}function ml(R){return!(Ho(R)||(function(A){return!!(Dn[\"Latin-1 Supplement\"](A)&&(A===167||A===169||A===174||A===177||A===188||A===189||A===190||A===215||A===247)||Dn[\"General Punctuation\"](A)&&(A===8214||A===8224||A===8225||A===8240||A===8241||A===8251||A===8252||A===8258||A===8263||A===8264||A===8265||A===8273)||Dn[\"Letterlike Symbols\"](A)||Dn[\"Number Forms\"](A)||Dn[\"Miscellaneous Technical\"](A)&&(A>=8960&&A<=8967||A>=8972&&A<=8991||A>=8996&&A<=9e3||A===9003||A>=9085&&A<=9114||A>=9150&&A<=9165||A===9167||A>=9169&&A<=9179||A>=9186&&A<=9215)||Dn[\"Control Pictures\"](A)&&A!==9251||Dn[\"Optical Character Recognition\"](A)||Dn[\"Enclosed Alphanumerics\"](A)||Dn[\"Geometric Shapes\"](A)||Dn[\"Miscellaneous Symbols\"](A)&&!(A>=9754&&A<=9759)||Dn[\"Miscellaneous Symbols and Arrows\"](A)&&(A>=11026&&A<=11055||A>=11088&&A<=11097||A>=11192&&A<=11243)||Dn[\"CJK Symbols and Punctuation\"](A)||Dn.Katakana(A)||Dn[\"Private Use Area\"](A)||Dn[\"CJK Compatibility Forms\"](A)||Dn[\"Small Form 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class{constructor(){this.applyArabicShaping=null,this.processBidirectionalText=null,this.processStyledBidirectionalText=null,this.pluginStatus=\"unavailable\",this.pluginURL=null}setState(R){this.pluginStatus=R.pluginStatus,this.pluginURL=R.pluginURL}getState(){return{pluginStatus:this.pluginStatus,pluginURL:this.pluginURL}}setMethods(R){this.applyArabicShaping=R.applyArabicShaping,this.processBidirectionalText=R.processBidirectionalText,this.processStyledBidirectionalText=R.processStyledBidirectionalText}isParsed(){return this.applyArabicShaping!=null&&this.processBidirectionalText!=null&&this.processStyledBidirectionalText!=null}getPluginURL(){return this.pluginURL}getRTLTextPluginStatus(){return this.pluginStatus}};class rs{constructor(A,F){this.zoom=A,F?(this.now=F.now,this.fadeDuration=F.fadeDuration,this.zoomHistory=F.zoomHistory,this.transition=F.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Zn,this.transition={})}isSupportedScript(A){return(function(F,W){for(let te of F)if(!va(te.charCodeAt(0),W))return!1;return!0})(A,ys.getRTLTextPluginStatus()===\"loaded\")}crossFadingFactor(){return this.fadeDuration===0?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)}getCrossfadeParameters(){let A=this.zoom,F=A-Math.floor(A),W=this.crossFadingFactor();return A>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:F+(1-F)*W}:{fromScale:.5,toScale:1,t:1-(1-W)*F}}}class Ql{constructor(A,F){this.property=A,this.value=F,this.expression=(function(W,te){if(Wf(W))return new il(W,te);if(Dc(W)){let fe=_u(W,te);if(fe.result===\"error\")throw new Error(fe.value.map((pe=>`${pe.key}: ${pe.message}`)).join(\", \"));return fe.value}{let fe=W;return te.type===\"color\"&&typeof W==\"string\"?fe=ir.parse(W):te.type!==\"padding\"||typeof W!=\"number\"&&!Array.isArray(W)?te.type===\"variableAnchorOffsetCollection\"&&Array.isArray(W)&&(fe=Hi.parse(W)):fe=$r.parse(W),{kind:\"constant\",evaluate:()=>fe}}})(F===void 0?A.specification.default:F,A.specification)}isDataDriven(){return this.expression.kind===\"source\"||this.expression.kind===\"composite\"}possiblyEvaluate(A,F,W){return this.property.possiblyEvaluate(this,A,F,W)}}class Cu{constructor(A){this.property=A,this.value=new Ql(A,void 0)}transitioned(A,F){return new Nc(this.property,this.value,F,L({},A.transition,this.transition),A.now)}untransitioned(){return new Nc(this.property,this.value,null,{},0)}}class Yu{constructor(A){this._properties=A,this._values=Object.create(A.defaultTransitionablePropertyValues)}getValue(A){return g(this._values[A].value.value)}setValue(A,F){Object.prototype.hasOwnProperty.call(this._values,A)||(this._values[A]=new Cu(this._values[A].property)),this._values[A].value=new Ql(this._values[A].property,F===null?void 0:g(F))}getTransition(A){return g(this._values[A].transition)}setTransition(A,F){Object.prototype.hasOwnProperty.call(this._values,A)||(this._values[A]=new Cu(this._values[A].property)),this._values[A].transition=g(F)||void 0}serialize(){let A={};for(let F of Object.keys(this._values)){let W=this.getValue(F);W!==void 0&&(A[F]=W);let te=this.getTransition(F);te!==void 0&&(A[`${F}-transition`]=te)}return A}transitioned(A,F){let W=new pu(this._properties);for(let te of Object.keys(this._values))W._values[te]=this._values[te].transitioned(A,F._values[te]);return W}untransitioned(){let A=new pu(this._properties);for(let F of Object.keys(this._values))A._values[F]=this._values[F].untransitioned();return A}}class Nc{constructor(A,F,W,te,fe){this.property=A,this.value=F,this.begin=fe+te.delay||0,this.end=this.begin+te.duration||0,A.specification.transition&&(te.delay||te.duration)&&(this.prior=W)}possiblyEvaluate(A,F,W){let te=A.now||0,fe=this.value.possiblyEvaluate(A,F,W),pe=this.prior;if(pe){if(te>this.end)return this.prior=null,fe;if(this.value.isDataDriven())return this.prior=null,fe;if(te<this.begin)return pe.possiblyEvaluate(A,F,W);{let Re=(te-this.begin)/(this.end-this.begin);return this.property.interpolate(pe.possiblyEvaluate(A,F,W),fe,(function(Ke){if(Ke<=0)return 0;if(Ke>=1)return 1;let ft=Ke*Ke,Rt=ft*Ke;return 4*(Ke<.5?Rt:3*(Ke-ft)+Rt-.75)})(Re))}}return fe}}class pu{constructor(A){this._properties=A,this._values=Object.create(A.defaultTransitioningPropertyValues)}possiblyEvaluate(A,F,W){let te=new Ac(this._properties);for(let fe of Object.keys(this._values))te._values[fe]=this._values[fe].possiblyEvaluate(A,F,W);return te}hasTransition(){for(let A of Object.keys(this._values))if(this._values[A].prior)return!0;return!1}}class Uc{constructor(A){this._properties=A,this._values=Object.create(A.defaultPropertyValues)}hasValue(A){return this._values[A].value!==void 0}getValue(A){return g(this._values[A].value)}setValue(A,F){this._values[A]=new Ql(this._values[A].property,F===null?void 0:g(F))}serialize(){let A={};for(let F of Object.keys(this._values)){let W=this.getValue(F);W!==void 0&&(A[F]=W)}return A}possiblyEvaluate(A,F,W){let te=new Ac(this._properties);for(let fe of Object.keys(this._values))te._values[fe]=this._values[fe].possiblyEvaluate(A,F,W);return te}}class xu{constructor(A,F,W){this.property=A,this.value=F,this.parameters=W}isConstant(){return this.value.kind===\"constant\"}constantOr(A){return this.value.kind===\"constant\"?this.value.value:A}evaluate(A,F,W,te){return this.property.evaluate(this.value,this.parameters,A,F,W,te)}}class Ac{constructor(A){this._properties=A,this._values=Object.create(A.defaultPossiblyEvaluatedValues)}get(A){return this._values[A]}}class Va{constructor(A){this.specification=A}possiblyEvaluate(A,F){if(A.isDataDriven())throw new Error(\"Value should not be data driven\");return A.expression.evaluate(F)}interpolate(A,F,W){let te=Lo[this.specification.type];return te?te(A,F,W):A}}class oo{constructor(A,F){this.specification=A,this.overrides=F}possiblyEvaluate(A,F,W,te){return new xu(this,A.expression.kind===\"constant\"||A.expression.kind===\"camera\"?{kind:\"constant\",value:A.expression.evaluate(F,null,{},W,te)}:A.expression,F)}interpolate(A,F,W){if(A.value.kind!==\"constant\"||F.value.kind!==\"constant\")return A;if(A.value.value===void 0||F.value.value===void 0)return new xu(this,{kind:\"constant\",value:void 0},A.parameters);let te=Lo[this.specification.type];if(te){let fe=te(A.value.value,F.value.value,W);return new xu(this,{kind:\"constant\",value:fe},A.parameters)}return A}evaluate(A,F,W,te,fe,pe){return A.kind===\"constant\"?A.value:A.evaluate(F,W,te,fe,pe)}}class Vc extends oo{possiblyEvaluate(A,F,W,te){if(A.value===void 0)return new xu(this,{kind:\"constant\",value:void 0},F);if(A.expression.kind===\"constant\"){let fe=A.expression.evaluate(F,null,{},W,te),pe=A.property.specification.type===\"resolvedImage\"&&typeof fe!=\"string\"?fe.name:fe,Re=this._calculate(pe,pe,pe,F);return new xu(this,{kind:\"constant\",value:Re},F)}if(A.expression.kind===\"camera\"){let fe=this._calculate(A.expression.evaluate({zoom:F.zoom-1}),A.expression.evaluate({zoom:F.zoom}),A.expression.evaluate({zoom:F.zoom+1}),F);return new xu(this,{kind:\"constant\",value:fe},F)}return new xu(this,A.expression,F)}evaluate(A,F,W,te,fe,pe){if(A.kind===\"source\"){let Re=A.evaluate(F,W,te,fe,pe);return this._calculate(Re,Re,Re,F)}return A.kind===\"composite\"?this._calculate(A.evaluate({zoom:Math.floor(F.zoom)-1},W,te),A.evaluate({zoom:Math.floor(F.zoom)},W,te),A.evaluate({zoom:Math.floor(F.zoom)+1},W,te),F):A.value}_calculate(A,F,W,te){return te.zoom>te.zoomHistory.lastIntegerZoom?{from:A,to:F}:{from:W,to:F}}interpolate(A){return A}}class hc{constructor(A){this.specification=A}possiblyEvaluate(A,F,W,te){if(A.value!==void 0){if(A.expression.kind===\"constant\"){let fe=A.expression.evaluate(F,null,{},W,te);return this._calculate(fe,fe,fe,F)}return this._calculate(A.expression.evaluate(new rs(Math.floor(F.zoom-1),F)),A.expression.evaluate(new rs(Math.floor(F.zoom),F)),A.expression.evaluate(new rs(Math.floor(F.zoom+1),F)),F)}}_calculate(A,F,W,te){return te.zoom>te.zoomHistory.lastIntegerZoom?{from:A,to:F}:{from:W,to:F}}interpolate(A){return A}}class Ku{constructor(A){this.specification=A}possiblyEvaluate(A,F,W,te){return!!A.expression.evaluate(F,null,{},W,te)}interpolate(){return!1}}class ue{constructor(A){this.properties=A,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[];for(let F in A){let W=A[F];W.specification.overridable&&this.overridableProperties.push(F);let te=this.defaultPropertyValues[F]=new Ql(W,void 0),fe=this.defaultTransitionablePropertyValues[F]=new Cu(W);this.defaultTransitioningPropertyValues[F]=fe.untransitioned(),this.defaultPossiblyEvaluatedValues[F]=te.possiblyEvaluate({})}}}Fi(\"DataDrivenProperty\",oo),Fi(\"DataConstantProperty\",Va),Fi(\"CrossFadedDataDrivenProperty\",Vc),Fi(\"CrossFadedProperty\",hc),Fi(\"ColorRampProperty\",Ku);let w=\"-transition\";class B extends Pe{constructor(A,F){if(super(),this.id=A.id,this.type=A.type,this._featureFilter={filter:()=>!0,needGeometry:!1},A.type!==\"custom\"&&(this.metadata=A.metadata,this.minzoom=A.minzoom,this.maxzoom=A.maxzoom,A.type!==\"background\"&&(this.source=A.source,this.sourceLayer=A[\"source-layer\"],this.filter=A.filter),F.layout&&(this._unevaluatedLayout=new Uc(F.layout)),F.paint)){this._transitionablePaint=new Yu(F.paint);for(let W in A.paint)this.setPaintProperty(W,A.paint[W],{validate:!1});for(let W in A.layout)this.setLayoutProperty(W,A.layout[W],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Ac(F.paint)}}getCrossfadeParameters(){return this._crossfadeParameters}getLayoutProperty(A){return A===\"visibility\"?this.visibility:this._unevaluatedLayout.getValue(A)}setLayoutProperty(A,F,W={}){F!=null&&this._validate(On,`layers.${this.id}.layout.${A}`,A,F,W)||(A!==\"visibility\"?this._unevaluatedLayout.setValue(A,F):this.visibility=F)}getPaintProperty(A){return A.endsWith(w)?this._transitionablePaint.getTransition(A.slice(0,-11)):this._transitionablePaint.getValue(A)}setPaintProperty(A,F,W={}){if(F!=null&&this._validate(wn,`layers.${this.id}.paint.${A}`,A,F,W))return!1;if(A.endsWith(w))return this._transitionablePaint.setTransition(A.slice(0,-11),F||void 0),!1;{let te=this._transitionablePaint._values[A],fe=te.property.specification[\"property-type\"]===\"cross-faded-data-driven\",pe=te.value.isDataDriven(),Re=te.value;this._transitionablePaint.setValue(A,F),this._handleSpecialPaintPropertyUpdate(A);let Ke=this._transitionablePaint._values[A].value;return Ke.isDataDriven()||pe||fe||this._handleOverridablePaintPropertyUpdate(A,Re,Ke)}}_handleSpecialPaintPropertyUpdate(A){}_handleOverridablePaintPropertyUpdate(A,F,W){return!1}isHidden(A){return!!(this.minzoom&&A<this.minzoom)||!!(this.maxzoom&&A>=this.maxzoom)||this.visibility===\"none\"}updateTransitions(A){this._transitioningPaint=this._transitionablePaint.transitioned(A,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculate(A,F){A.getCrossfadeParameters&&(this._crossfadeParameters=A.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(A,void 0,F)),this.paint=this._transitioningPaint.possiblyEvaluate(A,void 0,F)}serialize(){let A={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(A.layout=A.layout||{},A.layout.visibility=this.visibility),S(A,((F,W)=>!(F===void 0||W===\"layout\"&&!Object.keys(F).length||W===\"paint\"&&!Object.keys(F).length)))}_validate(A,F,W,te,fe={}){return(!fe||fe.validate!==!1)&&Yi(this,A.call(Mi,{key:F,layerType:this.type,objectKey:W,value:te,styleSpec:ce,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(let A in this.paint._values){let F=this.paint.get(A);if(F instanceof xu&&Bl(F.property.specification)&&(F.value.kind===\"source\"||F.value.kind===\"composite\")&&F.value.isStateDependent)return!0}return!1}}let Q={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class ee{constructor(A,F){this._structArray=A,this._pos1=F*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class le{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(A,F){return A._trim(),F&&(A.isTransferred=!0,F.push(A.arrayBuffer)),{length:A.length,arrayBuffer:A.arrayBuffer}}static deserialize(A){let F=Object.create(this.prototype);return F.arrayBuffer=A.arrayBuffer,F.length=A.length,F.capacity=A.arrayBuffer.byteLength/F.bytesPerElement,F._refreshViews(),F}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(A){this.reserve(A),this.length=A}reserve(A){if(A>this.capacity){this.capacity=Math.max(A,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);let F=this.uint8;this._refreshViews(),F&&this.uint8.set(F)}}_refreshViews(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")}}function Fe(R,A=1){let F=0,W=0;return{members:R.map((te=>{let fe=Q[te.type].BYTES_PER_ELEMENT,pe=F=Ze(F,Math.max(A,fe)),Re=te.components||1;return W=Math.max(W,fe),F+=fe*Re,{name:te.name,type:te.type,components:Re,offset:pe}})),size:Ze(F,Math.max(W,A)),alignment:A}}function Ze(R,A){return Math.ceil(R/A)*A}class ut extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let te=2*A;return this.int16[te+0]=F,this.int16[te+1]=W,A}}ut.prototype.bytesPerElement=4,Fi(\"StructArrayLayout2i4\",ut);class Mt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=3*A;return this.int16[fe+0]=F,this.int16[fe+1]=W,this.int16[fe+2]=te,A}}Mt.prototype.bytesPerElement=6,Fi(\"StructArrayLayout3i6\",Mt);class Jt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te){let fe=this.length;return this.resize(fe+1),this.emplace(fe,A,F,W,te)}emplace(A,F,W,te,fe){let pe=4*A;return this.int16[pe+0]=F,this.int16[pe+1]=W,this.int16[pe+2]=te,this.int16[pe+3]=fe,A}}Jt.prototype.bytesPerElement=8,Fi(\"StructArrayLayout4i8\",Jt);class $t extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe){let Re=this.length;return this.resize(Re+1),this.emplace(Re,A,F,W,te,fe,pe)}emplace(A,F,W,te,fe,pe,Re){let Ke=6*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.int16[Ke+2]=te,this.int16[Ke+3]=fe,this.int16[Ke+4]=pe,this.int16[Ke+5]=Re,A}}$t.prototype.bytesPerElement=12,Fi(\"StructArrayLayout2i4i12\",$t);class yr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe){let Re=this.length;return this.resize(Re+1),this.emplace(Re,A,F,W,te,fe,pe)}emplace(A,F,W,te,fe,pe,Re){let Ke=4*A,ft=8*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.uint8[ft+4]=te,this.uint8[ft+5]=fe,this.uint8[ft+6]=pe,this.uint8[ft+7]=Re,A}}yr.prototype.bytesPerElement=8,Fi(\"StructArrayLayout2i4ub8\",yr);class Cr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let te=2*A;return this.float32[te+0]=F,this.float32[te+1]=W,A}}Cr.prototype.bytesPerElement=8,Fi(\"StructArrayLayout2f8\",Cr);class ve extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft,Rt){let er=this.length;return this.resize(er+1),this.emplace(er,A,F,W,te,fe,pe,Re,Ke,ft,Rt)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er){let ur=10*A;return this.uint16[ur+0]=F,this.uint16[ur+1]=W,this.uint16[ur+2]=te,this.uint16[ur+3]=fe,this.uint16[ur+4]=pe,this.uint16[ur+5]=Re,this.uint16[ur+6]=Ke,this.uint16[ur+7]=ft,this.uint16[ur+8]=Rt,this.uint16[ur+9]=er,A}}ve.prototype.bytesPerElement=20,Fi(\"StructArrayLayout10ui20\",ve);class xe extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur){let vr=this.length;return this.resize(vr+1),this.emplace(vr,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr){let kr=12*A;return this.int16[kr+0]=F,this.int16[kr+1]=W,this.int16[kr+2]=te,this.int16[kr+3]=fe,this.uint16[kr+4]=pe,this.uint16[kr+5]=Re,this.uint16[kr+6]=Ke,this.uint16[kr+7]=ft,this.int16[kr+8]=Rt,this.int16[kr+9]=er,this.int16[kr+10]=ur,this.int16[kr+11]=vr,A}}xe.prototype.bytesPerElement=24,Fi(\"StructArrayLayout4i4ui4i24\",xe);class Ie extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=3*A;return this.float32[fe+0]=F,this.float32[fe+1]=W,this.float32[fe+2]=te,A}}Ie.prototype.bytesPerElement=12,Fi(\"StructArrayLayout3f12\",Ie);class ze extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.uint32[1*A+0]=F,A}}ze.prototype.bytesPerElement=4,Fi(\"StructArrayLayout1ul4\",ze);class Qe extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft){let Rt=this.length;return this.resize(Rt+1),this.emplace(Rt,A,F,W,te,fe,pe,Re,Ke,ft)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt){let er=10*A,ur=5*A;return this.int16[er+0]=F,this.int16[er+1]=W,this.int16[er+2]=te,this.int16[er+3]=fe,this.int16[er+4]=pe,this.int16[er+5]=Re,this.uint32[ur+3]=Ke,this.uint16[er+8]=ft,this.uint16[er+9]=Rt,A}}Qe.prototype.bytesPerElement=20,Fi(\"StructArrayLayout6i1ul2ui20\",Qe);class Xe extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe){let Re=this.length;return this.resize(Re+1),this.emplace(Re,A,F,W,te,fe,pe)}emplace(A,F,W,te,fe,pe,Re){let Ke=6*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.int16[Ke+2]=te,this.int16[Ke+3]=fe,this.int16[Ke+4]=pe,this.int16[Ke+5]=Re,A}}Xe.prototype.bytesPerElement=12,Fi(\"StructArrayLayout2i2i2i12\",Xe);class nt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe){let pe=this.length;return this.resize(pe+1),this.emplace(pe,A,F,W,te,fe)}emplace(A,F,W,te,fe,pe){let Re=4*A,Ke=8*A;return this.float32[Re+0]=F,this.float32[Re+1]=W,this.float32[Re+2]=te,this.int16[Ke+6]=fe,this.int16[Ke+7]=pe,A}}nt.prototype.bytesPerElement=16,Fi(\"StructArrayLayout2f1f2i16\",nt);class qt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe){let Re=this.length;return this.resize(Re+1),this.emplace(Re,A,F,W,te,fe,pe)}emplace(A,F,W,te,fe,pe,Re){let Ke=16*A,ft=4*A,Rt=8*A;return this.uint8[Ke+0]=F,this.uint8[Ke+1]=W,this.float32[ft+1]=te,this.float32[ft+2]=fe,this.int16[Rt+6]=pe,this.int16[Rt+7]=Re,A}}qt.prototype.bytesPerElement=16,Fi(\"StructArrayLayout2ub2f2i16\",qt);class Wt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=3*A;return this.uint16[fe+0]=F,this.uint16[fe+1]=W,this.uint16[fe+2]=te,A}}Wt.prototype.bytesPerElement=6,Fi(\"StructArrayLayout3ui6\",Wt);class nr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi){let vn=this.length;return this.resize(vn+1),this.emplace(vn,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi,vn){let zi=24*A,ln=12*A,Tn=48*A;return this.int16[zi+0]=F,this.int16[zi+1]=W,this.uint16[zi+2]=te,this.uint16[zi+3]=fe,this.uint32[ln+2]=pe,this.uint32[ln+3]=Re,this.uint32[ln+4]=Ke,this.uint16[zi+10]=ft,this.uint16[zi+11]=Rt,this.uint16[zi+12]=er,this.float32[ln+7]=ur,this.float32[ln+8]=vr,this.uint8[Tn+36]=kr,this.uint8[Tn+37]=Dr,this.uint8[Tn+38]=li,this.uint32[ln+10]=pi,this.int16[zi+22]=vn,A}}nr.prototype.bytesPerElement=48,Fi(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",nr);class fr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi,vn,zi,ln,Tn,pa,ro,Vo,Xa,la,Mo,fo){let lo=this.length;return this.resize(lo+1),this.emplace(lo,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi,vn,zi,ln,Tn,pa,ro,Vo,Xa,la,Mo,fo)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi,vn,zi,ln,Tn,pa,ro,Vo,Xa,la,Mo,fo,lo){let Wn=32*A,Ro=16*A;return this.int16[Wn+0]=F,this.int16[Wn+1]=W,this.int16[Wn+2]=te,this.int16[Wn+3]=fe,this.int16[Wn+4]=pe,this.int16[Wn+5]=Re,this.int16[Wn+6]=Ke,this.int16[Wn+7]=ft,this.uint16[Wn+8]=Rt,this.uint16[Wn+9]=er,this.uint16[Wn+10]=ur,this.uint16[Wn+11]=vr,this.uint16[Wn+12]=kr,this.uint16[Wn+13]=Dr,this.uint16[Wn+14]=li,this.uint16[Wn+15]=pi,this.uint16[Wn+16]=vn,this.uint16[Wn+17]=zi,this.uint16[Wn+18]=ln,this.uint16[Wn+19]=Tn,this.uint16[Wn+20]=pa,this.uint16[Wn+21]=ro,this.uint16[Wn+22]=Vo,this.uint32[Ro+12]=Xa,this.float32[Ro+13]=la,this.float32[Ro+14]=Mo,this.uint16[Wn+30]=fo,this.uint16[Wn+31]=lo,A}}fr.prototype.bytesPerElement=64,Fi(\"StructArrayLayout8i15ui1ul2f2ui64\",fr);class br extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.float32[1*A+0]=F,A}}br.prototype.bytesPerElement=4,Fi(\"StructArrayLayout1f4\",br);class Fr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=3*A;return this.uint16[6*A+0]=F,this.float32[fe+1]=W,this.float32[fe+2]=te,A}}Fr.prototype.bytesPerElement=12,Fi(\"StructArrayLayout1ui2f12\",Fr);class Hr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=4*A;return this.uint32[2*A+0]=F,this.uint16[fe+2]=W,this.uint16[fe+3]=te,A}}Hr.prototype.bytesPerElement=8,Fi(\"StructArrayLayout1ul2ui8\",Hr);class ri extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let te=2*A;return this.uint16[te+0]=F,this.uint16[te+1]=W,A}}ri.prototype.bytesPerElement=4,Fi(\"StructArrayLayout2ui4\",ri);class Ci extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.uint16[1*A+0]=F,A}}Ci.prototype.bytesPerElement=2,Fi(\"StructArrayLayout1ui2\",Ci);class cn extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,te){let fe=this.length;return this.resize(fe+1),this.emplace(fe,A,F,W,te)}emplace(A,F,W,te,fe){let pe=4*A;return this.float32[pe+0]=F,this.float32[pe+1]=W,this.float32[pe+2]=te,this.float32[pe+3]=fe,A}}cn.prototype.bytesPerElement=16,Fi(\"StructArrayLayout4f16\",cn);class sn extends ee{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new u(this.anchorPointX,this.anchorPointY)}}sn.prototype.size=20;class Ln extends Qe{get(A){return new sn(this,A)}}Fi(\"CollisionBoxArray\",Ln);class Kn extends ee{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(A){this._structArray.uint8[this._pos1+37]=A}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(A){this._structArray.uint8[this._pos1+38]=A}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(A){this._structArray.uint32[this._pos4+10]=A}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}Kn.prototype.size=48;class Aa extends nr{get(A){return new Kn(this,A)}}Fi(\"PlacedSymbolArray\",Aa);class fa extends ee{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(A){this._structArray.uint32[this._pos4+12]=A}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}fa.prototype.size=64;class $a extends fr{get(A){return new fa(this,A)}}Fi(\"SymbolInstanceArray\",$a);class Co extends br{getoffsetX(A){return this.float32[1*A+0]}}Fi(\"GlyphOffsetArray\",Co);class Qa extends Mt{getx(A){return this.int16[3*A+0]}gety(A){return this.int16[3*A+1]}gettileUnitDistanceFromAnchor(A){return this.int16[3*A+2]}}Fi(\"SymbolLineVertexArray\",Qa);class mo extends ee{get textAnchor(){return this._structArray.uint16[this._pos2+0]}get textOffset0(){return this._structArray.float32[this._pos4+1]}get textOffset1(){return this._structArray.float32[this._pos4+2]}}mo.prototype.size=12;class Bo extends Fr{get(A){return new mo(this,A)}}Fi(\"TextAnchorOffsetArray\",Bo);class Ps extends ee{get featureIndex(){return this._structArray.uint32[this._pos4+0]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+2]}get bucketIndex(){return this._structArray.uint16[this._pos2+3]}}Ps.prototype.size=8;class Ts extends Hr{get(A){return new Ps(this,A)}}Fi(\"FeatureIndexArray\",Ts);class wo extends ut{}class To extends ut{}class hl extends ut{}class Nl extends $t{}class Lu extends yr{}class au extends Cr{}class Js extends ve{}class eu extends xe{}class dc extends Ie{}class Al extends ze{}class Sl extends Xe{}class X extends qt{}class se extends Wt{}class Te extends ri{}let qe=Fe([{name:\"a_pos\",components:2,type:\"Int16\"}],4),{members:Ve}=qe;class Ye{constructor(A=[]){this.segments=A}prepareSegment(A,F,W,te){let fe=this.segments[this.segments.length-1];return A>Ye.MAX_VERTEX_ARRAY_LENGTH&&E(`Max vertices per segment is ${Ye.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${A}`),(!fe||fe.vertexLength+A>Ye.MAX_VERTEX_ARRAY_LENGTH||fe.sortKey!==te)&&(fe={vertexOffset:F.length,primitiveOffset:W.length,vertexLength:0,primitiveLength:0},te!==void 0&&(fe.sortKey=te),this.segments.push(fe)),fe}get(){return this.segments}destroy(){for(let A of this.segments)for(let F in A.vaos)A.vaos[F].destroy()}static simpleSegment(A,F,W,te){return new Ye([{vertexOffset:A,primitiveOffset:F,vertexLength:W,primitiveLength:te,vaos:{},sortKey:0}])}}function Pt(R,A){return 256*(R=M(Math.floor(R),0,255))+M(Math.floor(A),0,255)}Ye.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Fi(\"SegmentVector\",Ye);let ot=Fe([{name:\"a_pattern_from\",components:4,type:\"Uint16\"},{name:\"a_pattern_to\",components:4,type:\"Uint16\"},{name:\"a_pixel_ratio_from\",components:1,type:\"Uint16\"},{name:\"a_pixel_ratio_to\",components:1,type:\"Uint16\"}]);var Xt={exports:{}},dr={exports:{}};dr.exports=function(R,A){var F,W,te,fe,pe,Re,Ke,ft;for(W=R.length-(F=3&R.length),te=A,pe=3432918353,Re=461845907,ft=0;ft<W;)Ke=255&R.charCodeAt(ft)|(255&R.charCodeAt(++ft))<<8|(255&R.charCodeAt(++ft))<<16|(255&R.charCodeAt(++ft))<<24,++ft,te=27492+(65535&(fe=5*(65535&(te=(te^=Ke=(65535&(Ke=(Ke=(65535&Ke)*pe+(((Ke>>>16)*pe&65535)<<16)&4294967295)<<15|Ke>>>17))*Re+(((Ke>>>16)*Re&65535)<<16)&4294967295)<<13|te>>>19))+((5*(te>>>16)&65535)<<16)&4294967295))+((58964+(fe>>>16)&65535)<<16);switch(Ke=0,F){case 3:Ke^=(255&R.charCodeAt(ft+2))<<16;case 2:Ke^=(255&R.charCodeAt(ft+1))<<8;case 1:te^=Ke=(65535&(Ke=(Ke=(65535&(Ke^=255&R.charCodeAt(ft)))*pe+(((Ke>>>16)*pe&65535)<<16)&4294967295)<<15|Ke>>>17))*Re+(((Ke>>>16)*Re&65535)<<16)&4294967295}return te^=R.length,te=2246822507*(65535&(te^=te>>>16))+((2246822507*(te>>>16)&65535)<<16)&4294967295,te=3266489909*(65535&(te^=te>>>13))+((3266489909*(te>>>16)&65535)<<16)&4294967295,(te^=te>>>16)>>>0};var pr=dr.exports,Ur={exports:{}};Ur.exports=function(R,A){for(var F,W=R.length,te=A^W,fe=0;W>=4;)F=1540483477*(65535&(F=255&R.charCodeAt(fe)|(255&R.charCodeAt(++fe))<<8|(255&R.charCodeAt(++fe))<<16|(255&R.charCodeAt(++fe))<<24))+((1540483477*(F>>>16)&65535)<<16),te=1540483477*(65535&te)+((1540483477*(te>>>16)&65535)<<16)^(F=1540483477*(65535&(F^=F>>>24))+((1540483477*(F>>>16)&65535)<<16)),W-=4,++fe;switch(W){case 3:te^=(255&R.charCodeAt(fe+2))<<16;case 2:te^=(255&R.charCodeAt(fe+1))<<8;case 1:te=1540483477*(65535&(te^=255&R.charCodeAt(fe)))+((1540483477*(te>>>16)&65535)<<16)}return te=1540483477*(65535&(te^=te>>>13))+((1540483477*(te>>>16)&65535)<<16),(te^=te>>>15)>>>0};var zr=pr,gi=Ur.exports;Xt.exports=zr,Xt.exports.murmur3=zr,Xt.exports.murmur2=gi;var xi=o(Xt.exports);class Wr{constructor(){this.ids=[],this.positions=[],this.indexed=!1}add(A,F,W,te){this.ids.push(ii(A)),this.positions.push(F,W,te)}getPositions(A){if(!this.indexed)throw new Error(\"Trying to get index, but feature positions are not indexed\");let F=ii(A),W=0,te=this.ids.length-1;for(;W<te;){let pe=W+te>>1;this.ids[pe]>=F?te=pe:W=pe+1}let fe=[];for(;this.ids[W]===F;)fe.push({index:this.positions[3*W],start:this.positions[3*W+1],end:this.positions[3*W+2]}),W++;return fe}static serialize(A,F){let W=new Float64Array(A.ids),te=new Uint32Array(A.positions);return di(W,te,0,W.length-1),F&&F.push(W.buffer,te.buffer),{ids:W,positions:te}}static deserialize(A){let F=new Wr;return F.ids=A.ids,F.positions=A.positions,F.indexed=!0,F}}function ii(R){let A=+R;return!isNaN(A)&&A<=Number.MAX_SAFE_INTEGER?A:xi(String(R))}function di(R,A,F,W){for(;F<W;){let te=R[F+W>>1],fe=F-1,pe=W+1;for(;;){do fe++;while(R[fe]<te);do pe--;while(R[pe]>te);if(fe>=pe)break;Li(R,fe,pe),Li(A,3*fe,3*pe),Li(A,3*fe+1,3*pe+1),Li(A,3*fe+2,3*pe+2)}pe-F<W-pe?(di(R,A,F,pe),F=pe+1):(di(R,A,pe+1,W),W=pe)}}function Li(R,A,F){let W=R[A];R[A]=R[F],R[F]=W}Fi(\"FeaturePositionMap\",Wr);class Ti{constructor(A,F){this.gl=A.gl,this.location=F}}class Qr extends Ti{constructor(A,F){super(A,F),this.current=0}set(A){this.current!==A&&(this.current=A,this.gl.uniform1f(this.location,A))}}class Pn extends Ti{constructor(A,F){super(A,F),this.current=[0,0,0,0]}set(A){A[0]===this.current[0]&&A[1]===this.current[1]&&A[2]===this.current[2]&&A[3]===this.current[3]||(this.current=A,this.gl.uniform4f(this.location,A[0],A[1],A[2],A[3]))}}class kn extends Ti{constructor(A,F){super(A,F),this.current=ir.transparent}set(A){A.r===this.current.r&&A.g===this.current.g&&A.b===this.current.b&&A.a===this.current.a||(this.current=A,this.gl.uniform4f(this.location,A.r,A.g,A.b,A.a))}}let Bn=new Float32Array(16);function na(R){return[Pt(255*R.r,255*R.g),Pt(255*R.b,255*R.a)]}class Ea{constructor(A,F,W){this.value=A,this.uniformNames=F.map((te=>`u_${te}`)),this.type=W}setUniform(A,F,W){A.set(W.constantOr(this.value))}getBinding(A,F,W){return this.type===\"color\"?new kn(A,F):new Qr(A,F)}}class Ia{constructor(A,F){this.uniformNames=F.map((W=>`u_${W}`)),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1}setConstantPatternPositions(A,F){this.pixelRatioFrom=F.pixelRatio,this.pixelRatioTo=A.pixelRatio,this.patternFrom=F.tlbr,this.patternTo=A.tlbr}setUniform(A,F,W,te){let fe=te===\"u_pattern_to\"?this.patternTo:te===\"u_pattern_from\"?this.patternFrom:te===\"u_pixel_ratio_to\"?this.pixelRatioTo:te===\"u_pixel_ratio_from\"?this.pixelRatioFrom:null;fe&&A.set(fe)}getBinding(A,F,W){return W.substr(0,9)===\"u_pattern\"?new Pn(A,F):new Qr(A,F)}}class yo{constructor(A,F,W,te){this.expression=A,this.type=W,this.maxValue=0,this.paintVertexAttributes=F.map((fe=>({name:`a_${fe}`,type:\"Float32\",components:W===\"color\"?2:1,offset:0}))),this.paintVertexArray=new te}populatePaintArray(A,F,W,te,fe){let pe=this.paintVertexArray.length,Re=this.expression.evaluate(new rs(0),F,{},te,[],fe);this.paintVertexArray.resize(A),this._setPaintValue(pe,A,Re)}updatePaintArray(A,F,W,te){let fe=this.expression.evaluate({zoom:0},W,te);this._setPaintValue(A,F,fe)}_setPaintValue(A,F,W){if(this.type===\"color\"){let te=na(W);for(let fe=A;fe<F;fe++)this.paintVertexArray.emplace(fe,te[0],te[1])}else{for(let te=A;te<F;te++)this.paintVertexArray.emplace(te,W);this.maxValue=Math.max(this.maxValue,Math.abs(W))}}upload(A){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=A.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}}class Da{constructor(A,F,W,te,fe,pe){this.expression=A,this.uniformNames=F.map((Re=>`u_${Re}_t`)),this.type=W,this.useIntegerZoom=te,this.zoom=fe,this.maxValue=0,this.paintVertexAttributes=F.map((Re=>({name:`a_${Re}`,type:\"Float32\",components:W===\"color\"?4:2,offset:0}))),this.paintVertexArray=new pe}populatePaintArray(A,F,W,te,fe){let pe=this.expression.evaluate(new rs(this.zoom),F,{},te,[],fe),Re=this.expression.evaluate(new rs(this.zoom+1),F,{},te,[],fe),Ke=this.paintVertexArray.length;this.paintVertexArray.resize(A),this._setPaintValue(Ke,A,pe,Re)}updatePaintArray(A,F,W,te){let fe=this.expression.evaluate({zoom:this.zoom},W,te),pe=this.expression.evaluate({zoom:this.zoom+1},W,te);this._setPaintValue(A,F,fe,pe)}_setPaintValue(A,F,W,te){if(this.type===\"color\"){let fe=na(W),pe=na(te);for(let Re=A;Re<F;Re++)this.paintVertexArray.emplace(Re,fe[0],fe[1],pe[0],pe[1])}else{for(let fe=A;fe<F;fe++)this.paintVertexArray.emplace(fe,W,te);this.maxValue=Math.max(this.maxValue,Math.abs(W),Math.abs(te))}}upload(A){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=A.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}setUniform(A,F){let W=this.useIntegerZoom?Math.floor(F.zoom):F.zoom,te=M(this.expression.interpolationFactor(W,this.zoom,this.zoom+1),0,1);A.set(te)}getBinding(A,F,W){return new Qr(A,F)}}class go{constructor(A,F,W,te,fe,pe){this.expression=A,this.type=F,this.useIntegerZoom=W,this.zoom=te,this.layerId=pe,this.zoomInPaintVertexArray=new fe,this.zoomOutPaintVertexArray=new fe}populatePaintArray(A,F,W){let te=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(A),this.zoomOutPaintVertexArray.resize(A),this._setPaintValues(te,A,F.patterns&&F.patterns[this.layerId],W)}updatePaintArray(A,F,W,te,fe){this._setPaintValues(A,F,W.patterns&&W.patterns[this.layerId],fe)}_setPaintValues(A,F,W,te){if(!te||!W)return;let{min:fe,mid:pe,max:Re}=W,Ke=te[fe],ft=te[pe],Rt=te[Re];if(Ke&&ft&&Rt)for(let er=A;er<F;er++)this.zoomInPaintVertexArray.emplace(er,ft.tl[0],ft.tl[1],ft.br[0],ft.br[1],Ke.tl[0],Ke.tl[1],Ke.br[0],Ke.br[1],ft.pixelRatio,Ke.pixelRatio),this.zoomOutPaintVertexArray.emplace(er,ft.tl[0],ft.tl[1],ft.br[0],ft.br[1],Rt.tl[0],Rt.tl[1],Rt.br[0],Rt.br[1],ft.pixelRatio,Rt.pixelRatio)}upload(A){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=A.createVertexBuffer(this.zoomInPaintVertexArray,ot.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=A.createVertexBuffer(this.zoomOutPaintVertexArray,ot.members,this.expression.isStateDependent))}destroy(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()}}class Is{constructor(A,F,W){this.binders={},this._buffers=[];let te=[];for(let fe in A.paint._values){if(!W(fe))continue;let pe=A.paint.get(fe);if(!(pe instanceof xu&&Bl(pe.property.specification)))continue;let Re=Xs(fe,A.type),Ke=pe.value,ft=pe.property.specification.type,Rt=pe.property.useIntegerZoom,er=pe.property.specification[\"property-type\"],ur=er===\"cross-faded\"||er===\"cross-faded-data-driven\";if(Ke.kind===\"constant\")this.binders[fe]=ur?new Ia(Ke.value,Re):new Ea(Ke.value,Re,ft),te.push(`/u_${fe}`);else if(Ke.kind===\"source\"||ur){let vr=Un(fe,ft,\"source\");this.binders[fe]=ur?new go(Ke,ft,Rt,F,vr,A.id):new yo(Ke,Re,ft,vr),te.push(`/a_${fe}`)}else{let vr=Un(fe,ft,\"composite\");this.binders[fe]=new Da(Ke,Re,ft,Rt,F,vr),te.push(`/z_${fe}`)}}this.cacheKey=te.sort().join(\"\")}getMaxValue(A){let F=this.binders[A];return F instanceof yo||F instanceof Da?F.maxValue:0}populatePaintArrays(A,F,W,te,fe){for(let pe in this.binders){let Re=this.binders[pe];(Re instanceof yo||Re instanceof Da||Re instanceof go)&&Re.populatePaintArray(A,F,W,te,fe)}}setConstantPatternPositions(A,F){for(let W in this.binders){let te=this.binders[W];te instanceof Ia&&te.setConstantPatternPositions(A,F)}}updatePaintArrays(A,F,W,te,fe){let pe=!1;for(let Re in A){let Ke=F.getPositions(Re);for(let ft of Ke){let Rt=W.feature(ft.index);for(let er in this.binders){let ur=this.binders[er];if((ur instanceof yo||ur instanceof Da||ur instanceof go)&&ur.expression.isStateDependent===!0){let vr=te.paint.get(er);ur.expression=vr.value,ur.updatePaintArray(ft.start,ft.end,Rt,A[Re],fe),pe=!0}}}}return pe}defines(){let A=[];for(let F in this.binders){let W=this.binders[F];(W instanceof Ea||W instanceof Ia)&&A.push(...W.uniformNames.map((te=>`#define HAS_UNIFORM_${te}`)))}return A}getBinderAttributes(){let A=[];for(let F in this.binders){let W=this.binders[F];if(W instanceof yo||W instanceof Da)for(let te=0;te<W.paintVertexAttributes.length;te++)A.push(W.paintVertexAttributes[te].name);else if(W instanceof go)for(let te=0;te<ot.members.length;te++)A.push(ot.members[te].name)}return A}getBinderUniforms(){let A=[];for(let F in this.binders){let W=this.binders[F];if(W instanceof Ea||W instanceof Ia||W instanceof Da)for(let te of W.uniformNames)A.push(te)}return A}getPaintVertexBuffers(){return this._buffers}getUniforms(A,F){let W=[];for(let te in this.binders){let fe=this.binders[te];if(fe instanceof Ea||fe instanceof Ia||fe instanceof Da){for(let pe of fe.uniformNames)if(F[pe]){let Re=fe.getBinding(A,F[pe],pe);W.push({name:pe,property:te,binding:Re})}}}return W}setUniforms(A,F,W,te){for(let{name:fe,property:pe,binding:Re}of F)this.binders[pe].setUniform(Re,te,W.get(pe),fe)}updatePaintBuffers(A){this._buffers=[];for(let F in this.binders){let W=this.binders[F];if(A&&W instanceof go){let te=A.fromScale===2?W.zoomInPaintVertexBuffer:W.zoomOutPaintVertexBuffer;te&&this._buffers.push(te)}else(W instanceof yo||W instanceof Da)&&W.paintVertexBuffer&&this._buffers.push(W.paintVertexBuffer)}}upload(A){for(let F in this.binders){let W=this.binders[F];(W instanceof yo||W instanceof Da||W instanceof go)&&W.upload(A)}this.updatePaintBuffers()}destroy(){for(let A in this.binders){let F=this.binders[A];(F instanceof yo||F instanceof Da||F instanceof go)&&F.destroy()}}}class Ms{constructor(A,F,W=(()=>!0)){this.programConfigurations={};for(let te of A)this.programConfigurations[te.id]=new Is(te,F,W);this.needsUpload=!1,this._featureMap=new Wr,this._bufferOffset=0}populatePaintArrays(A,F,W,te,fe,pe){for(let Re in this.programConfigurations)this.programConfigurations[Re].populatePaintArrays(A,F,te,fe,pe);F.id!==void 0&&this._featureMap.add(F.id,W,this._bufferOffset,A),this._bufferOffset=A,this.needsUpload=!0}updatePaintArrays(A,F,W,te){for(let fe of W)this.needsUpload=this.programConfigurations[fe.id].updatePaintArrays(A,this._featureMap,F,fe,te)||this.needsUpload}get(A){return this.programConfigurations[A]}upload(A){if(this.needsUpload){for(let F in this.programConfigurations)this.programConfigurations[F].upload(A);this.needsUpload=!1}}destroy(){for(let A in 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er=[];for(W=0;W<Ke.length;W++)er[W]=Ke[W][0];Rt(Ke=er);break;case 2:for(W=0;W<Ke.length;W++)Rt(Ke[W]);break;case 3:for(Ke=(function(vr){var kr=vr.length;if(kr<=1)return[vr];for(var Dr,li,pi=[],vn=0;vn<kr;vn++){var zi=gn(vr[vn]);zi!==0&&(li===void 0&&(li=zi<0),li===zi<0?(Dr&&pi.push(Dr),Dr=[vr[vn]]):Dr.push(vr[vn]))}return Dr&&pi.push(Dr),pi})(Ke),W=0;W<Ke.length;W++)for(te=0;te<Ke[W].length;te++)Rt(Ke[W][te])}Ke.length===1?Ke=Ke[0]:ft=\"Multi\"+ft;var ur={type:\"Feature\",geometry:{type:ft,coordinates:Ke},properties:this.properties};return\"id\"in this&&(ur.id=this.id),ur};var tn=Di,Ei=Oi;function Oi(R,A){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=R,this._keys=[],this._values=[],this._features=[],R.readFields(Gi,this,A),this.length=this._features.length}function Gi(R,A,F){R===15?A.version=F.readVarint():R===1?A.name=F.readString():R===5?A.extent=F.readVarint():R===2?A._features.push(F.pos):R===3?A._keys.push(F.readString()):R===4&&A._values.push((function(W){for(var te=null,fe=W.readVarint()+W.pos;W.pos<fe;){var pe=W.readVarint()>>3;te=pe===1?W.readString():pe===2?W.readFloat():pe===3?W.readDouble():pe===4?W.readVarint64():pe===5?W.readVarint():pe===6?W.readSVarint():pe===7?W.readBoolean():null}return te})(F))}Oi.prototype.feature=function(R){if(R<0||R>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[R];var A=this._pbf.readVarint()+this._pbf.pos;return new tn(this._pbf,A,this.extent,this._keys,this._values)};var on=Ei;function zn(R,A,F){if(R===3){var W=new on(F,F.readVarint()+F.pos);W.length&&(A[W.name]=W)}}_r.VectorTile=function(R,A){this.layers=R.readFields(zn,{},A)},_r.VectorTileFeature=Di,_r.VectorTileLayer=Ei;let Ja=_r.VectorTileFeature.types,co=Math.pow(2,13);function ts(R,A,F,W,te,fe,pe,Re){R.emplaceBack(A,F,2*Math.floor(W*co)+pe,te*co*2,fe*co*2,Math.round(Re))}class so{constructor(A){this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map((F=>F.id)),this.index=A.index,this.hasPattern=!1,this.layoutVertexArray=new Nl,this.centroidVertexArray=new wo,this.indexArray=new se,this.programConfigurations=new Ms(A.layers,A.zoom),this.segments=new Ye,this.stateDependentLayerIds=this.layers.filter((F=>F.isStateDependent())).map((F=>F.id))}populate(A,F,W){this.features=[],this.hasPattern=st(\"fill-extrusion\",this.layers,F);for(let{feature:te,id:fe,index:pe,sourceLayerIndex:Re}of A){let Ke=this.layers[0]._featureFilter.needGeometry,ft=Ml(te,Ke);if(!this.layers[0]._featureFilter.filter(new rs(this.zoom),ft,W))continue;let Rt={id:fe,sourceLayerIndex:Re,index:pe,geometry:Ke?ft.geometry:$s(te),properties:te.properties,type:te.type,patterns:{}};this.hasPattern?this.features.push(It(\"fill-extrusion\",this.layers,Rt,this.zoom,F)):this.addFeature(Rt,Rt.geometry,pe,W,{}),F.featureIndex.insert(te,Rt.geometry,pe,Re,this.index,!0)}}addFeatures(A,F,W){for(let te of this.features){let{geometry:fe}=te;this.addFeature(te,fe,te.index,F,W)}}update(A,F,W){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(A,F,this.stateDependentLayers,W)}isEmpty(){return this.layoutVertexArray.length===0&&this.centroidVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(A){this.uploaded||(this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,lr),this.centroidVertexBuffer=A.createVertexBuffer(this.centroidVertexArray,vt.members,!0),this.indexBuffer=A.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(A),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.centroidVertexBuffer.destroy())}addFeature(A,F,W,te,fe){for(let pe of Cf(F,500)){let Re={x:0,y:0,vertexCount:0},Ke=0;for(let kr of pe)Ke+=kr.length;let ft=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray);for(let kr of pe){if(kr.length===0||ms(kr))continue;let Dr=0;for(let li=0;li<kr.length;li++){let pi=kr[li];if(li>=1){let vn=kr[li-1];if(!Yo(pi,vn)){ft.vertexLength+4>Ye.MAX_VERTEX_ARRAY_LENGTH&&(ft=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));let zi=pi.sub(vn)._perp()._unit(),ln=vn.dist(pi);Dr+ln>32768&&(Dr=0),ts(this.layoutVertexArray,pi.x,pi.y,zi.x,zi.y,0,0,Dr),ts(this.layoutVertexArray,pi.x,pi.y,zi.x,zi.y,0,1,Dr),Re.x+=2*pi.x,Re.y+=2*pi.y,Re.vertexCount+=2,Dr+=ln,ts(this.layoutVertexArray,vn.x,vn.y,zi.x,zi.y,0,0,Dr),ts(this.layoutVertexArray,vn.x,vn.y,zi.x,zi.y,0,1,Dr),Re.x+=2*vn.x,Re.y+=2*vn.y,Re.vertexCount+=2;let Tn=ft.vertexLength;this.indexArray.emplaceBack(Tn,Tn+2,Tn+1),this.indexArray.emplaceBack(Tn+1,Tn+2,Tn+3),ft.vertexLength+=4,ft.primitiveLength+=2}}}}if(ft.vertexLength+Ke>Ye.MAX_VERTEX_ARRAY_LENGTH&&(ft=this.segments.prepareSegment(Ke,this.layoutVertexArray,this.indexArray)),Ja[A.type]!==\"Polygon\")continue;let Rt=[],er=[],ur=ft.vertexLength;for(let kr of pe)if(kr.length!==0){kr!==pe[0]&&er.push(Rt.length/2);for(let Dr=0;Dr<kr.length;Dr++){let li=kr[Dr];ts(this.layoutVertexArray,li.x,li.y,0,0,1,1,0),Re.x+=li.x,Re.y+=li.y,Re.vertexCount+=1,Rt.push(li.x),Rt.push(li.y)}}let vr=zo(Rt,er);for(let kr=0;kr<vr.length;kr+=3)this.indexArray.emplaceBack(ur+vr[kr],ur+vr[kr+2],ur+vr[kr+1]);ft.primitiveLength+=vr.length/3,ft.vertexLength+=Ke;for(let kr=0;kr<Re.vertexCount;kr++){let Dr=Math.floor(Re.x/Re.vertexCount),li=Math.floor(Re.y/Re.vertexCount);this.centroidVertexArray.emplaceBack(Dr,li)}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,A,W,fe,te)}}function Yo(R,A){return R.x===A.x&&(R.x<0||R.x>ja)||R.y===A.y&&(R.y<0||R.y>ja)}function ms(R){return R.every((A=>A.x<0))||R.every((A=>A.x>ja))||R.every((A=>A.y<0))||R.every((A=>A.y>ja))}let ou;Fi(\"FillExtrusionBucket\",so,{omit:[\"layers\",\"features\"]});var Cv={get paint(){return ou=ou||new ue({\"fill-extrusion-opacity\":new Va(ce[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new oo(ce[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new Va(ce[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new Va(ce[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new Vc(ce[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new oo(ce[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new oo(ce[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new Va(ce[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})}};class Lv extends B{constructor(A){super(A,Cv)}createBucket(A){return new so(A)}queryRadius(){return Vn(this.paint.get(\"fill-extrusion-translate\"))}is3D(){return!0}queryIntersectsFeature(A,F,W,te,fe,pe,Re,Ke){let ft=Qi(A,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),pe.angle,Re),Rt=this.paint.get(\"fill-extrusion-height\").evaluate(F,W),er=this.paint.get(\"fill-extrusion-base\").evaluate(F,W),ur=(function(kr,Dr,li,pi){let vn=[];for(let zi of kr){let ln=[zi.x,zi.y,0,1];qn(ln,ln,Dr),vn.push(new u(ln[0]/ln[3],ln[1]/ln[3]))}return vn})(ft,Ke),vr=(function(kr,Dr,li,pi){let vn=[],zi=[],ln=pi[8]*Dr,Tn=pi[9]*Dr,pa=pi[10]*Dr,ro=pi[11]*Dr,Vo=pi[8]*li,Xa=pi[9]*li,la=pi[10]*li,Mo=pi[11]*li;for(let fo of kr){let lo=[],Wn=[];for(let Ro of fo){let uo=Ro.x,$o=Ro.y,Ju=pi[0]*uo+pi[4]*$o+pi[12],Ou=pi[1]*uo+pi[5]*$o+pi[13],kh=pi[2]*uo+pi[6]*$o+pi[14],Qv=pi[3]*uo+pi[7]*$o+pi[15],ud=kh+pa,Ch=Qv+ro,Gd=Ju+Vo,Hd=Ou+Xa,jd=kh+la,Af=Qv+Mo,Lh=new u((Ju+ln)/Ch,(Ou+Tn)/Ch);Lh.z=ud/Ch,lo.push(Lh);let Ed=new u(Gd/Af,Hd/Af);Ed.z=jd/Af,Wn.push(Ed)}vn.push(lo),zi.push(Wn)}return[vn,zi]})(te,er,Rt,Ke);return(function(kr,Dr,li){let pi=1/0;bt(li,Dr)&&(pi=Kv(li,Dr[0]));for(let vn=0;vn<Dr.length;vn++){let zi=Dr[vn],ln=kr[vn];for(let Tn=0;Tn<zi.length-1;Tn++){let pa=zi[Tn],ro=[pa,zi[Tn+1],ln[Tn+1],ln[Tn],pa];Sc(li,ro)&&(pi=Math.min(pi,Kv(li,ro)))}}return pi!==1/0&&pi})(vr[0],vr[1],ur)}}function wd(R,A){return R.x*A.x+R.y*A.y}function Kv(R,A){if(R.length===1){let F=0,W=A[F++],te;for(;!te||W.equals(te);)if(te=A[F++],!te)return 1/0;for(;F<A.length;F++){let fe=A[F],pe=R[0],Re=te.sub(W),Ke=fe.sub(W),ft=pe.sub(W),Rt=wd(Re,Re),er=wd(Re,Ke),ur=wd(Ke,Ke),vr=wd(ft,Re),kr=wd(ft,Ke),Dr=Rt*ur-er*er,li=(ur*vr-er*kr)/Dr,pi=(Rt*kr-er*vr)/Dr,vn=W.z*(1-li-pi)+te.z*li+fe.z*pi;if(isFinite(vn))return vn}return 1/0}{let F=1/0;for(let W of A)F=Math.min(F,W.z);return F}}let hg=Fe([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4),{members:gp}=hg,Td=Fe([{name:\"a_uv_x\",components:1,type:\"Float32\"},{name:\"a_split_index\",components:1,type:\"Float32\"}]),{members:mp}=Td,Vd=_r.VectorTileFeature.types,Ad=Math.cos(Math.PI/180*37.5),Pv=Math.pow(2,14)/.5;class Jv{constructor(A){this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map((F=>F.id)),this.index=A.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((F=>{this.gradients[F.id]={}})),this.layoutVertexArray=new Lu,this.layoutVertexArray2=new au,this.indexArray=new se,this.programConfigurations=new Ms(A.layers,A.zoom),this.segments=new Ye,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((F=>F.isStateDependent())).map((F=>F.id))}populate(A,F,W){this.hasPattern=st(\"line\",this.layers,F);let te=this.layers[0].layout.get(\"line-sort-key\"),fe=!te.isConstant(),pe=[];for(let{feature:Re,id:Ke,index:ft,sourceLayerIndex:Rt}of A){let er=this.layers[0]._featureFilter.needGeometry,ur=Ml(Re,er);if(!this.layers[0]._featureFilter.filter(new rs(this.zoom),ur,W))continue;let vr=fe?te.evaluate(ur,{},W):void 0,kr={id:Ke,properties:Re.properties,type:Re.type,sourceLayerIndex:Rt,index:ft,geometry:er?ur.geometry:$s(Re),patterns:{},sortKey:vr};pe.push(kr)}fe&&pe.sort(((Re,Ke)=>Re.sortKey-Ke.sortKey));for(let Re of pe){let{geometry:Ke,index:ft,sourceLayerIndex:Rt}=Re;if(this.hasPattern){let er=It(\"line\",this.layers,Re,this.zoom,F);this.patternFeatures.push(er)}else this.addFeature(Re,Ke,ft,W,{});F.featureIndex.insert(A[ft].feature,Ke,ft,Rt,this.index)}}update(A,F,W){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(A,F,this.stateDependentLayers,W)}addFeatures(A,F,W){for(let te of this.patternFeatures)this.addFeature(te,te.geometry,te.index,F,W)}isEmpty(){return this.layoutVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(A){this.uploaded||(this.layoutVertexArray2.length!==0&&(this.layoutVertexBuffer2=A.createVertexBuffer(this.layoutVertexArray2,mp)),this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,gp),this.indexBuffer=A.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(A),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}lineFeatureClips(A){if(A.properties&&Object.prototype.hasOwnProperty.call(A.properties,\"mapbox_clip_start\")&&Object.prototype.hasOwnProperty.call(A.properties,\"mapbox_clip_end\"))return{start:+A.properties.mapbox_clip_start,end:+A.properties.mapbox_clip_end}}addFeature(A,F,W,te,fe){let pe=this.layers[0].layout,Re=pe.get(\"line-join\").evaluate(A,{}),Ke=pe.get(\"line-cap\"),ft=pe.get(\"line-miter-limit\"),Rt=pe.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(A);for(let er of F)this.addLine(er,A,Re,Ke,ft,Rt);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,A,W,fe,te)}addLine(A,F,W,te,fe,pe){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(let pi=0;pi<A.length-1;pi++)this.totalDistance+=A[pi].dist(A[pi+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}let Re=Vd[F.type]===\"Polygon\",Ke=A.length;for(;Ke>=2&&A[Ke-1].equals(A[Ke-2]);)Ke--;let ft=0;for(;ft<Ke-1&&A[ft].equals(A[ft+1]);)ft++;if(Ke<(Re?3:2))return;W===\"bevel\"&&(fe=1.05);let Rt=this.overscaling<=16?15*ja/(512*this.overscaling):0,er=this.segments.prepareSegment(10*Ke,this.layoutVertexArray,this.indexArray),ur,vr,kr,Dr,li;this.e1=this.e2=-1,Re&&(ur=A[Ke-2],li=A[ft].sub(ur)._unit()._perp());for(let pi=ft;pi<Ke;pi++){if(kr=pi===Ke-1?Re?A[ft+1]:void 0:A[pi+1],kr&&A[pi].equals(kr))continue;li&&(Dr=li),ur&&(vr=ur),ur=A[pi],li=kr?kr.sub(ur)._unit()._perp():Dr,Dr=Dr||li;let vn=Dr.add(li);vn.x===0&&vn.y===0||vn._unit();let zi=Dr.x*li.x+Dr.y*li.y,ln=vn.x*li.x+vn.y*li.y,Tn=ln!==0?1/ln:1/0,pa=2*Math.sqrt(2-2*ln),ro=ln<Ad&&vr&&kr,Vo=Dr.x*li.y-Dr.y*li.x>0;if(ro&&pi>ft){let Mo=ur.dist(vr);if(Mo>2*Rt){let fo=ur.sub(ur.sub(vr)._mult(Rt/Mo)._round());this.updateDistance(vr,fo),this.addCurrentVertex(fo,Dr,0,0,er),vr=fo}}let Xa=vr&&kr,la=Xa?W:Re?\"butt\":te;if(Xa&&la===\"round\"&&(Tn<pe?la=\"miter\":Tn<=2&&(la=\"fakeround\")),la===\"miter\"&&Tn>fe&&(la=\"bevel\"),la===\"bevel\"&&(Tn>2&&(la=\"flipbevel\"),Tn<fe&&(la=\"miter\")),vr&&this.updateDistance(vr,ur),la===\"miter\")vn._mult(Tn),this.addCurrentVertex(ur,vn,0,0,er);else if(la===\"flipbevel\"){if(Tn>100)vn=li.mult(-1);else{let Mo=Tn*Dr.add(li).mag()/Dr.sub(li).mag();vn._perp()._mult(Mo*(Vo?-1:1))}this.addCurrentVertex(ur,vn,0,0,er),this.addCurrentVertex(ur,vn.mult(-1),0,0,er)}else if(la===\"bevel\"||la===\"fakeround\"){let Mo=-Math.sqrt(Tn*Tn-1),fo=Vo?Mo:0,lo=Vo?0:Mo;if(vr&&this.addCurrentVertex(ur,Dr,fo,lo,er),la===\"fakeround\"){let Wn=Math.round(180*pa/Math.PI/20);for(let Ro=1;Ro<Wn;Ro++){let uo=Ro/Wn;if(uo!==.5){let Ju=uo-.5;uo+=uo*Ju*(uo-1)*((1.0904+zi*(zi*(3.55645-1.43519*zi)-3.2452))*Ju*Ju+(.848013+zi*(.215638*zi-1.06021)))}let $o=li.sub(Dr)._mult(uo)._add(Dr)._unit()._mult(Vo?-1:1);this.addHalfVertex(ur,$o.x,$o.y,!1,Vo,0,er)}}kr&&this.addCurrentVertex(ur,li,-fo,-lo,er)}else if(la===\"butt\")this.addCurrentVertex(ur,vn,0,0,er);else if(la===\"square\"){let Mo=vr?1:-1;this.addCurrentVertex(ur,vn,Mo,Mo,er)}else la===\"round\"&&(vr&&(this.addCurrentVertex(ur,Dr,0,0,er),this.addCurrentVertex(ur,Dr,1,1,er,!0)),kr&&(this.addCurrentVertex(ur,li,-1,-1,er,!0),this.addCurrentVertex(ur,li,0,0,er)));if(ro&&pi<Ke-1){let Mo=ur.dist(kr);if(Mo>2*Rt){let fo=ur.add(kr.sub(ur)._mult(Rt/Mo)._round());this.updateDistance(ur,fo),this.addCurrentVertex(fo,li,0,0,er),ur=fo}}}}addCurrentVertex(A,F,W,te,fe,pe=!1){let Re=F.y*te-F.x,Ke=-F.y-F.x*te;this.addHalfVertex(A,F.x+F.y*W,F.y-F.x*W,pe,!1,W,fe),this.addHalfVertex(A,Re,Ke,pe,!0,-te,fe),this.distance>Pv/2&&this.totalDistance===0&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(A,F,W,te,fe,pe))}addHalfVertex({x:A,y:F},W,te,fe,pe,Re,Ke){let ft=.5*(this.lineClips?this.scaledDistance*(Pv-1):this.scaledDistance);this.layoutVertexArray.emplaceBack((A<<1)+(fe?1:0),(F<<1)+(pe?1:0),Math.round(63*W)+128,Math.round(63*te)+128,1+(Re===0?0:Re<0?-1:1)|(63&ft)<<2,ft>>6),this.lineClips&&this.layoutVertexArray2.emplaceBack((this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start),this.lineClipsArray.length);let Rt=Ke.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,Rt),Ke.primitiveLength++),pe?this.e2=Rt:this.e1=Rt}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(A,F){this.distance+=A.dist(F),this.updateScaledDistance()}}let Iv,dy;Fi(\"LineBucket\",Jv,{omit:[\"layers\",\"patternFeatures\"]});var dg={get paint(){return dy=dy||new ue({\"line-opacity\":new oo(ce.paint_line[\"line-opacity\"]),\"line-color\":new oo(ce.paint_line[\"line-color\"]),\"line-translate\":new Va(ce.paint_line[\"line-translate\"]),\"line-translate-anchor\":new Va(ce.paint_line[\"line-translate-anchor\"]),\"line-width\":new oo(ce.paint_line[\"line-width\"]),\"line-gap-width\":new oo(ce.paint_line[\"line-gap-width\"]),\"line-offset\":new oo(ce.paint_line[\"line-offset\"]),\"line-blur\":new oo(ce.paint_line[\"line-blur\"]),\"line-dasharray\":new hc(ce.paint_line[\"line-dasharray\"]),\"line-pattern\":new Vc(ce.paint_line[\"line-pattern\"]),\"line-gradient\":new Ku(ce.paint_line[\"line-gradient\"])})},get layout(){return Iv=Iv||new ue({\"line-cap\":new Va(ce.layout_line[\"line-cap\"]),\"line-join\":new oo(ce.layout_line[\"line-join\"]),\"line-miter-limit\":new Va(ce.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Va(ce.layout_line[\"line-round-limit\"]),\"line-sort-key\":new oo(ce.layout_line[\"line-sort-key\"])})}};class oh extends oo{possiblyEvaluate(A,F){return F=new rs(Math.floor(F.zoom),{now:F.now,fadeDuration:F.fadeDuration,zoomHistory:F.zoomHistory,transition:F.transition}),super.possiblyEvaluate(A,F)}evaluate(A,F,W,te){return F=L({},F,{zoom:Math.floor(F.zoom)}),super.evaluate(A,F,W,te)}}let vg;class vy extends B{constructor(A){super(A,dg),this.gradientVersion=0,vg||(vg=new oh(dg.paint.properties[\"line-width\"].specification),vg.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(A){if(A===\"line-gradient\"){let F=this.gradientExpression();this.stepInterpolant=!!(function(W){return W._styleExpression!==void 0})(F)&&F._styleExpression.expression instanceof _n,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values[\"line-gradient\"].value.expression}recalculate(A,F){super.recalculate(A,F),this.paint._values[\"line-floorwidth\"]=vg.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,A)}createBucket(A){return new Jv(A)}queryRadius(A){let F=A,W=Zh(Si(\"line-width\",this,F),Si(\"line-gap-width\",this,F)),te=Si(\"line-offset\",this,F);return W/2+Math.abs(te)+Vn(this.paint.get(\"line-translate\"))}queryIntersectsFeature(A,F,W,te,fe,pe,Re){let Ke=Qi(A,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),pe.angle,Re),ft=Re/2*Zh(this.paint.get(\"line-width\").evaluate(F,W),this.paint.get(\"line-gap-width\").evaluate(F,W)),Rt=this.paint.get(\"line-offset\").evaluate(F,W);return Rt&&(te=(function(er,ur){let vr=[];for(let kr=0;kr<er.length;kr++){let Dr=er[kr],li=[];for(let pi=0;pi<Dr.length;pi++){let vn=Dr[pi-1],zi=Dr[pi],ln=Dr[pi+1],Tn=pi===0?new u(0,0):zi.sub(vn)._unit()._perp(),pa=pi===Dr.length-1?new u(0,0):ln.sub(zi)._unit()._perp(),ro=Tn._add(pa)._unit(),Vo=ro.x*pa.x+ro.y*pa.y;Vo!==0&&ro._mult(1/Vo),li.push(ro._mult(ur)._add(zi))}vr.push(li)}return vr})(te,Rt*Re)),(function(er,ur,vr){for(let kr=0;kr<ur.length;kr++){let Dr=ur[kr];if(er.length>=3){for(let 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zC(R,A){let{expression:F}=A;if(F.kind===\"constant\")return{kind:\"constant\",layoutSize:F.evaluate(new rs(R+1))};if(F.kind===\"source\")return{kind:\"source\"};{let{zoomStops:W,interpolationType:te}=F,fe=0;for(;fe<W.length&&W[fe]<=R;)fe++;fe=Math.max(0,fe-1);let pe=fe;for(;pe<W.length&&W[pe]<R+1;)pe++;pe=Math.min(W.length-1,pe);let Re=W[fe],Ke=W[pe];return F.kind===\"composite\"?{kind:\"composite\",minZoom:Re,maxZoom:Ke,interpolationType:te}:{kind:\"camera\",minZoom:Re,maxZoom:Ke,minSize:F.evaluate(new rs(Re)),maxSize:F.evaluate(new rs(Ke)),interpolationType:te}}}function vS(R,A,F){let W=\"never\",te=R.get(A);return te?W=te:R.get(F)&&(W=\"always\"),W}let iq=_r.VectorTileFeature.types,nq=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function Zw(R,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur){let vr=Re?Math.min(fm,Math.round(Re[0])):0,kr=Re?Math.min(fm,Math.round(Re[1])):0;R.emplaceBack(A,F,Math.round(32*W),Math.round(32*te),fe,pe,(vr<<1)+(Ke?1:0),kr,16*ft,16*Rt,256*er,256*ur)}function pS(R,A,F){R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F)}function gS(R){for(let A of R.sections)if(no(A.text))return!0;return!1}class mS{constructor(A){this.layoutVertexArray=new eu,this.indexArray=new se,this.programConfigurations=A,this.segments=new Ye,this.dynamicLayoutVertexArray=new dc,this.opacityVertexArray=new Al,this.hasVisibleVertices=!1,this.placedSymbolArray=new Aa}isEmpty(){return this.layoutVertexArray.length===0&&this.indexArray.length===0&&this.dynamicLayoutVertexArray.length===0&&this.opacityVertexArray.length===0}upload(A,F,W,te){this.isEmpty()||(W&&(this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,am.members),this.indexBuffer=A.createIndexBuffer(this.indexArray,F),this.dynamicLayoutVertexBuffer=A.createVertexBuffer(this.dynamicLayoutVertexArray,C1.members,!0),this.opacityVertexBuffer=A.createVertexBuffer(this.opacityVertexArray,nq,!0),this.opacityVertexBuffer.itemSize=1),(W||te)&&this.programConfigurations.upload(A))}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())}}Fi(\"SymbolBuffers\",mS);class hm{constructor(A,F,W){this.layoutVertexArray=new A,this.layoutAttributes=F,this.indexArray=new W,this.segments=new Ye,this.collisionVertexArray=new X}upload(A){this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=A.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=A.createVertexBuffer(this.collisionVertexArray,L1.members,!0)}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())}}Fi(\"CollisionBuffers\",hm);class D1{constructor(A){this.collisionBoxArray=A.collisionBoxArray,this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map((pe=>pe.id)),this.index=A.index,this.pixelRatio=A.pixelRatio,this.sourceLayerIndex=A.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Ii([]),this.placementViewportMatrix=Ii([]);let F=this.layers[0]._unevaluatedLayout._values;this.textSizeData=zC(this.zoom,F[\"text-size\"]),this.iconSizeData=zC(this.zoom,F[\"icon-size\"]);let W=this.layers[0].layout,te=W.get(\"symbol-sort-key\"),fe=W.get(\"symbol-z-order\");this.canOverlap=vS(W,\"text-overlap\",\"text-allow-overlap\")!==\"never\"||vS(W,\"icon-overlap\",\"icon-allow-overlap\")!==\"never\"||W.get(\"text-ignore-placement\")||W.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=fe!==\"viewport-y\"&&!te.isConstant(),this.sortFeaturesByY=(fe===\"viewport-y\"||fe===\"auto\"&&!this.sortFeaturesByKey)&&this.canOverlap,W.get(\"symbol-placement\")===\"point\"&&(this.writingModes=W.get(\"text-writing-mode\").map((pe=>i.ah[pe]))),this.stateDependentLayerIds=this.layers.filter((pe=>pe.isStateDependent())).map((pe=>pe.id)),this.sourceID=A.sourceID}createArrays(){this.text=new mS(new Ms(this.layers,this.zoom,(A=>/^text/.test(A)))),this.icon=new mS(new Ms(this.layers,this.zoom,(A=>/^icon/.test(A)))),this.glyphOffsetArray=new Co,this.lineVertexArray=new Qa,this.symbolInstances=new $a,this.textAnchorOffsets=new Bo}calculateGlyphDependencies(A,F,W,te,fe){for(let pe=0;pe<A.length;pe++)if(F[A.charCodeAt(pe)]=!0,(W||te)&&fe){let Re=vc[A.charAt(pe)];Re&&(F[Re.charCodeAt(0)]=!0)}}populate(A,F,W){let te=this.layers[0],fe=te.layout,pe=fe.get(\"text-font\"),Re=fe.get(\"text-field\"),Ke=fe.get(\"icon-image\"),ft=(Re.value.kind!==\"constant\"||Re.value.value instanceof ti&&!Re.value.value.isEmpty()||Re.value.value.toString().length>0)&&(pe.value.kind!==\"constant\"||pe.value.value.length>0),Rt=Ke.value.kind!==\"constant\"||!!Ke.value.value||Object.keys(Ke.parameters).length>0,er=fe.get(\"symbol-sort-key\");if(this.features=[],!ft&&!Rt)return;let ur=F.iconDependencies,vr=F.glyphDependencies,kr=F.availableImages,Dr=new rs(this.zoom);for(let{feature:li,id:pi,index:vn,sourceLayerIndex:zi}of A){let ln=te._featureFilter.needGeometry,Tn=Ml(li,ln);if(!te._featureFilter.filter(Dr,Tn,W))continue;let pa,ro;if(ln||(Tn.geometry=$s(li)),ft){let Xa=te.getValueAndResolveTokens(\"text-field\",Tn,W,kr),la=ti.factory(Xa),Mo=this.hasRTLText=this.hasRTLText||gS(la);(!Mo||ys.getRTLTextPluginStatus()===\"unavailable\"||Mo&&ys.isParsed())&&(pa=sm(la,te,Tn))}if(Rt){let Xa=te.getValueAndResolveTokens(\"icon-image\",Tn,W,kr);ro=Xa instanceof en?Xa:en.fromString(Xa)}if(!pa&&!ro)continue;let Vo=this.sortFeaturesByKey?er.evaluate(Tn,{},W):void 0;if(this.features.push({id:pi,text:pa,icon:ro,index:vn,sourceLayerIndex:zi,geometry:Tn.geometry,properties:li.properties,type:iq[li.type],sortKey:Vo}),ro&&(ur[ro.name]=!0),pa){let Xa=pe.evaluate(Tn,{},W).join(\",\"),la=fe.get(\"text-rotation-alignment\")!==\"viewport\"&&fe.get(\"symbol-placement\")!==\"point\";this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(i.ah.vertical)>=0;for(let Mo of pa.sections)if(Mo.image)ur[Mo.image.name]=!0;else{let fo=Ka(pa.toString()),lo=Mo.fontStack||Xa,Wn=vr[lo]=vr[lo]||{};this.calculateGlyphDependencies(Mo.text,Wn,la,this.allowVerticalPlacement,fo)}}}fe.get(\"symbol-placement\")===\"line\"&&(this.features=(function(li){let pi={},vn={},zi=[],ln=0;function Tn(Xa){zi.push(li[Xa]),ln++}function pa(Xa,la,Mo){let fo=vn[Xa];return delete vn[Xa],vn[la]=fo,zi[fo].geometry[0].pop(),zi[fo].geometry[0]=zi[fo].geometry[0].concat(Mo[0]),fo}function ro(Xa,la,Mo){let fo=pi[la];return delete pi[la],pi[Xa]=fo,zi[fo].geometry[0].shift(),zi[fo].geometry[0]=Mo[0].concat(zi[fo].geometry[0]),fo}function Vo(Xa,la,Mo){let fo=Mo?la[0][la[0].length-1]:la[0][0];return`${Xa}:${fo.x}:${fo.y}`}for(let Xa=0;Xa<li.length;Xa++){let la=li[Xa],Mo=la.geometry,fo=la.text?la.text.toString():null;if(!fo){Tn(Xa);continue}let lo=Vo(fo,Mo),Wn=Vo(fo,Mo,!0);if(lo in vn&&Wn in pi&&vn[lo]!==pi[Wn]){let Ro=ro(lo,Wn,Mo),uo=pa(lo,Wn,zi[Ro].geometry);delete pi[lo],delete vn[Wn],vn[Vo(fo,zi[uo].geometry,!0)]=uo,zi[Ro].geometry=null}else lo in vn?pa(lo,Wn,Mo):Wn in pi?ro(lo,Wn,Mo):(Tn(Xa),pi[lo]=ln-1,vn[Wn]=ln-1)}return zi.filter((Xa=>Xa.geometry))})(this.features)),this.sortFeaturesByKey&&this.features.sort(((li,pi)=>li.sortKey-pi.sortKey))}update(A,F,W){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(A,F,this.layers,W),this.icon.programConfigurations.updatePaintArrays(A,F,this.layers,W))}isEmpty(){return this.symbolInstances.length===0&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(A){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(A),this.iconCollisionBox.upload(A)),this.text.upload(A,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(A,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(A,F){let W=this.lineVertexArray.length;if(A.segment!==void 0){let te=A.dist(F[A.segment+1]),fe=A.dist(F[A.segment]),pe={};for(let Re=A.segment+1;Re<F.length;Re++)pe[Re]={x:F[Re].x,y:F[Re].y,tileUnitDistanceFromAnchor:te},Re<F.length-1&&(te+=F[Re+1].dist(F[Re]));for(let Re=A.segment||0;Re>=0;Re--)pe[Re]={x:F[Re].x,y:F[Re].y,tileUnitDistanceFromAnchor:fe},Re>0&&(fe+=F[Re-1].dist(F[Re]));for(let Re=0;Re<F.length;Re++){let Ke=pe[Re];this.lineVertexArray.emplaceBack(Ke.x,Ke.y,Ke.tileUnitDistanceFromAnchor)}}return{lineStartIndex:W,lineLength:this.lineVertexArray.length-W}}addSymbols(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur){let vr=A.indexArray,kr=A.layoutVertexArray,Dr=A.segments.prepareSegment(4*F.length,kr,vr,this.canOverlap?pe.sortKey:void 0),li=this.glyphOffsetArray.length,pi=Dr.vertexLength,vn=this.allowVerticalPlacement&&Re===i.ah.vertical?Math.PI/2:0,zi=pe.text&&pe.text.sections;for(let ln=0;ln<F.length;ln++){let{tl:Tn,tr:pa,bl:ro,br:Vo,tex:Xa,pixelOffsetTL:la,pixelOffsetBR:Mo,minFontScaleX:fo,minFontScaleY:lo,glyphOffset:Wn,isSDF:Ro,sectionIndex:uo}=F[ln],$o=Dr.vertexLength,Ju=Wn[1];Zw(kr,Ke.x,Ke.y,Tn.x,Ju+Tn.y,Xa.x,Xa.y,W,Ro,la.x,la.y,fo,lo),Zw(kr,Ke.x,Ke.y,pa.x,Ju+pa.y,Xa.x+Xa.w,Xa.y,W,Ro,Mo.x,la.y,fo,lo),Zw(kr,Ke.x,Ke.y,ro.x,Ju+ro.y,Xa.x,Xa.y+Xa.h,W,Ro,la.x,Mo.y,fo,lo),Zw(kr,Ke.x,Ke.y,Vo.x,Ju+Vo.y,Xa.x+Xa.w,Xa.y+Xa.h,W,Ro,Mo.x,Mo.y,fo,lo),pS(A.dynamicLayoutVertexArray,Ke,vn),vr.emplaceBack($o,$o+1,$o+2),vr.emplaceBack($o+1,$o+2,$o+3),Dr.vertexLength+=4,Dr.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(Wn[0]),ln!==F.length-1&&uo===F[ln+1].sectionIndex||A.programConfigurations.populatePaintArrays(kr.length,pe,pe.index,{},ur,zi&&zi[uo])}A.placedSymbolArray.emplaceBack(Ke.x,Ke.y,li,this.glyphOffsetArray.length-li,pi,ft,Rt,Ke.segment,W?W[0]:0,W?W[1]:0,te[0],te[1],Re,0,!1,0,er)}_addCollisionDebugVertex(A,F,W,te,fe,pe){return F.emplaceBack(0,0),A.emplaceBack(W.x,W.y,te,fe,Math.round(pe.x),Math.round(pe.y))}addCollisionDebugVertices(A,F,W,te,fe,pe,Re){let Ke=fe.segments.prepareSegment(4,fe.layoutVertexArray,fe.indexArray),ft=Ke.vertexLength,Rt=fe.layoutVertexArray,er=fe.collisionVertexArray,ur=Re.anchorX,vr=Re.anchorY;this._addCollisionDebugVertex(Rt,er,pe,ur,vr,new u(A,F)),this._addCollisionDebugVertex(Rt,er,pe,ur,vr,new u(W,F)),this._addCollisionDebugVertex(Rt,er,pe,ur,vr,new u(W,te)),this._addCollisionDebugVertex(Rt,er,pe,ur,vr,new u(A,te)),Ke.vertexLength+=4;let kr=fe.indexArray;kr.emplaceBack(ft,ft+1),kr.emplaceBack(ft+1,ft+2),kr.emplaceBack(ft+2,ft+3),kr.emplaceBack(ft+3,ft),Ke.primitiveLength+=4}addDebugCollisionBoxes(A,F,W,te){for(let fe=A;fe<F;fe++){let pe=this.collisionBoxArray.get(fe);this.addCollisionDebugVertices(pe.x1,pe.y1,pe.x2,pe.y2,te?this.textCollisionBox:this.iconCollisionBox,pe.anchorPoint,W)}}generateCollisionDebugBuffers(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new hm(Sl,py.members,Te),this.iconCollisionBox=new hm(Sl,py.members,Te);for(let A=0;A<this.symbolInstances.length;A++){let F=this.symbolInstances.get(A);this.addDebugCollisionBoxes(F.textBoxStartIndex,F.textBoxEndIndex,F,!0),this.addDebugCollisionBoxes(F.verticalTextBoxStartIndex,F.verticalTextBoxEndIndex,F,!0),this.addDebugCollisionBoxes(F.iconBoxStartIndex,F.iconBoxEndIndex,F,!1),this.addDebugCollisionBoxes(F.verticalIconBoxStartIndex,F.verticalIconBoxEndIndex,F,!1)}}_deserializeCollisionBoxesForSymbol(A,F,W,te,fe,pe,Re,Ke,ft){let Rt={};for(let er=F;er<W;er++){let ur=A.get(er);Rt.textBox={x1:ur.x1,y1:ur.y1,x2:ur.x2,y2:ur.y2,anchorPointX:ur.anchorPointX,anchorPointY:ur.anchorPointY},Rt.textFeatureIndex=ur.featureIndex;break}for(let er=te;er<fe;er++){let ur=A.get(er);Rt.verticalTextBox={x1:ur.x1,y1:ur.y1,x2:ur.x2,y2:ur.y2,anchorPointX:ur.anchorPointX,anchorPointY:ur.anchorPointY},Rt.verticalTextFeatureIndex=ur.featureIndex;break}for(let er=pe;er<Re;er++){let ur=A.get(er);Rt.iconBox={x1:ur.x1,y1:ur.y1,x2:ur.x2,y2:ur.y2,anchorPointX:ur.anchorPointX,anchorPointY:ur.anchorPointY},Rt.iconFeatureIndex=ur.featureIndex;break}for(let er=Ke;er<ft;er++){let ur=A.get(er);Rt.verticalIconBox={x1:ur.x1,y1:ur.y1,x2:ur.x2,y2:ur.y2,anchorPointX:ur.anchorPointX,anchorPointY:ur.anchorPointY},Rt.verticalIconFeatureIndex=ur.featureIndex;break}return Rt}deserializeCollisionBoxes(A){this.collisionArrays=[];for(let F=0;F<this.symbolInstances.length;F++){let W=this.symbolInstances.get(F);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(A,W.textBoxStartIndex,W.textBoxEndIndex,W.verticalTextBoxStartIndex,W.verticalTextBoxEndIndex,W.iconBoxStartIndex,W.iconBoxEndIndex,W.verticalIconBoxStartIndex,W.verticalIconBoxEndIndex))}}hasTextData(){return this.text.segments.get().length>0}hasIconData(){return this.icon.segments.get().length>0}hasDebugData(){return this.textCollisionBox&&this.iconCollisionBox}hasTextCollisionBoxData(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0}hasIconCollisionBoxData(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0}addIndicesForPlacedSymbol(A,F){let W=A.placedSymbolArray.get(F),te=W.vertexStartIndex+4*W.numGlyphs;for(let fe=W.vertexStartIndex;fe<te;fe+=4)A.indexArray.emplaceBack(fe,fe+1,fe+2),A.indexArray.emplaceBack(fe+1,fe+2,fe+3)}getSortedSymbolIndexes(A){if(this.sortedAngle===A&&this.symbolInstanceIndexes!==void 0)return this.symbolInstanceIndexes;let F=Math.sin(A),W=Math.cos(A),te=[],fe=[],pe=[];for(let Re=0;Re<this.symbolInstances.length;++Re){pe.push(Re);let Ke=this.symbolInstances.get(Re);te.push(0|Math.round(F*Ke.anchorX+W*Ke.anchorY)),fe.push(Ke.featureIndex)}return pe.sort(((Re,Ke)=>te[Re]-te[Ke]||fe[Ke]-fe[Re])),pe}addToSortKeyRanges(A,F){let W=this.sortKeyRanges[this.sortKeyRanges.length-1];W&&W.sortKey===F?W.symbolInstanceEnd=A+1:this.sortKeyRanges.push({sortKey:F,symbolInstanceStart:A,symbolInstanceEnd:A+1})}sortFeatures(A){if(this.sortFeaturesByY&&this.sortedAngle!==A&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(A),this.sortedAngle=A,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(let F of this.symbolInstanceIndexes){let W=this.symbolInstances.get(F);this.featureSortOrder.push(W.featureIndex),[W.rightJustifiedTextSymbolIndex,W.centerJustifiedTextSymbolIndex,W.leftJustifiedTextSymbolIndex].forEach(((te,fe,pe)=>{te>=0&&pe.indexOf(te)===fe&&this.addIndicesForPlacedSymbol(this.text,te)})),W.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,W.verticalPlacedTextSymbolIndex),W.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,W.placedIconSymbolIndex),W.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,W.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let Tf,ib;Fi(\"SymbolBucket\",D1,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),D1.MAX_GLYPHS=65535,D1.addDynamicAttributes=pS;var Yw={get paint(){return ib=ib||new ue({\"icon-opacity\":new oo(ce.paint_symbol[\"icon-opacity\"]),\"icon-color\":new oo(ce.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new oo(ce.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new oo(ce.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new oo(ce.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Va(ce.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Va(ce.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new oo(ce.paint_symbol[\"text-opacity\"]),\"text-color\":new oo(ce.paint_symbol[\"text-color\"],{runtimeType:jt,getOverride:R=>R.textColor,hasOverride:R=>!!R.textColor}),\"text-halo-color\":new oo(ce.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new oo(ce.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new oo(ce.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Va(ce.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Va(ce.paint_symbol[\"text-translate-anchor\"])})},get layout(){return Tf=Tf||new ue({\"symbol-placement\":new Va(ce.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Va(ce.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Va(ce.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new oo(ce.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new Va(ce.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new Va(ce.layout_symbol[\"icon-allow-overlap\"]),\"icon-overlap\":new Va(ce.layout_symbol[\"icon-overlap\"]),\"icon-ignore-placement\":new Va(ce.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Va(ce.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Va(ce.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new oo(ce.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Va(ce.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Va(ce.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new oo(ce.layout_symbol[\"icon-image\"]),\"icon-rotate\":new oo(ce.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new oo(ce.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Va(ce.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new oo(ce.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new oo(ce.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Va(ce.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Va(ce.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Va(ce.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new oo(ce.layout_symbol[\"text-field\"]),\"text-font\":new oo(ce.layout_symbol[\"text-font\"]),\"text-size\":new oo(ce.layout_symbol[\"text-size\"]),\"text-max-width\":new oo(ce.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Va(ce.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new oo(ce.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new oo(ce.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new oo(ce.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new Va(ce.layout_symbol[\"text-variable-anchor\"]),\"text-variable-anchor-offset\":new oo(ce.layout_symbol[\"text-variable-anchor-offset\"]),\"text-anchor\":new oo(ce.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Va(ce.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new Va(ce.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new oo(ce.layout_symbol[\"text-rotate\"]),\"text-padding\":new Va(ce.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Va(ce.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new oo(ce.layout_symbol[\"text-transform\"]),\"text-offset\":new oo(ce.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Va(ce.layout_symbol[\"text-allow-overlap\"]),\"text-overlap\":new Va(ce.layout_symbol[\"text-overlap\"]),\"text-ignore-placement\":new Va(ce.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Va(ce.layout_symbol[\"text-optional\"])})}};class nb{constructor(A){if(A.property.overrides===void 0)throw new Error(\"overrides must be provided to instantiate FormatSectionOverride class\");this.type=A.property.overrides?A.property.overrides.runtimeType:Tt,this.defaultValue=A}evaluate(A){if(A.formattedSection){let F=this.defaultValue.property.overrides;if(F&&F.hasOverride(A.formattedSection))return F.getOverride(A.formattedSection)}return A.feature&&A.featureState?this.defaultValue.evaluate(A.feature,A.featureState):this.defaultValue.property.specification.default}eachChild(A){this.defaultValue.isConstant()||A(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}Fi(\"FormatSectionOverride\",nb,{omit:[\"defaultValue\"]});class yy extends B{constructor(A){super(A,Yw)}recalculate(A,F){if(super.recalculate(A,F),this.layout.get(\"icon-rotation-alignment\")===\"auto\"&&(this.layout._values[\"icon-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-rotation-alignment\")===\"auto\"&&(this.layout._values[\"text-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-pitch-alignment\")===\"auto\"&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")===\"map\"?\"map\":\"viewport\"),this.layout.get(\"icon-pitch-alignment\")===\"auto\"&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),this.layout.get(\"symbol-placement\")===\"point\"){let W=this.layout.get(\"text-writing-mode\");if(W){let te=[];for(let fe of W)te.indexOf(fe)<0&&te.push(fe);this.layout._values[\"text-writing-mode\"]=te}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()}getValueAndResolveTokens(A,F,W,te){let fe=this.layout.get(A).evaluate(F,{},W,te),pe=this._unevaluatedLayout._values[A];return pe.isDataDriven()||Dc(pe.value)||!fe?fe:(function(Re,Ke){return Ke.replace(/{([^{}]+)}/g,((ft,Rt)=>Re&&Rt in Re?String(Re[Rt]):\"\"))})(F.properties,fe)}createBucket(A){return new D1(A)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error(\"Should take a different path in FeatureIndex\")}_setPaintOverrides(){for(let A of Yw.paint.overridableProperties){if(!yy.hasPaintOverride(this.layout,A))continue;let F=this.paint.get(A),W=new nb(F),te=new Eu(W,F.property.specification),fe=null;fe=F.value.kind===\"constant\"||F.value.kind===\"source\"?new bc(\"source\",te):new hu(\"composite\",te,F.value.zoomStops),this.paint._values[A]=new xu(F.property,fe,F.parameters)}}_handleOverridablePaintPropertyUpdate(A,F,W){return!(!this.layout||F.isDataDriven()||W.isDataDriven())&&yy.hasPaintOverride(this.layout,A)}static hasPaintOverride(A,F){let W=A.get(\"text-field\"),te=Yw.paint.properties[F],fe=!1,pe=Re=>{for(let Ke of Re)if(te.overrides&&te.overrides.hasOverride(Ke))return void(fe=!0)};if(W.value.kind===\"constant\"&&W.value.value instanceof ti)pe(W.value.value.sections);else if(W.value.kind===\"source\"){let Re=ft=>{fe||(ft instanceof ua&&Mn(ft.value)===ei?pe(ft.value.sections):ft instanceof Yl?pe(ft.sections):ft.eachChild(Re))},Ke=W.value;Ke._styleExpression&&Re(Ke._styleExpression.expression)}return fe}}let qC;var ab={get paint(){return qC=qC||new ue({\"background-color\":new Va(ce.paint_background[\"background-color\"]),\"background-pattern\":new hc(ce.paint_background[\"background-pattern\"]),\"background-opacity\":new Va(ce.paint_background[\"background-opacity\"])})}};class aq extends B{constructor(A){super(A,ab)}}let yS;var OC={get paint(){return yS=yS||new ue({\"raster-opacity\":new Va(ce.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new Va(ce.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new Va(ce.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new Va(ce.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new Va(ce.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new Va(ce.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new Va(ce.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new Va(ce.paint_raster[\"raster-fade-duration\"])})}};class ob extends B{constructor(A){super(A,OC)}}class _S extends B{constructor(A){super(A,{}),this.onAdd=F=>{this.implementation.onAdd&&this.implementation.onAdd(F,F.painter.context.gl)},this.onRemove=F=>{this.implementation.onRemove&&this.implementation.onRemove(F,F.painter.context.gl)},this.implementation=A}is3D(){return this.implementation.renderingMode===\"3d\"}hasOffscreenPass(){return this.implementation.prerender!==void 0}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error(\"Custom layers cannot be serialized\")}}class xS{constructor(A){this._methodToThrottle=A,this._triggered=!1,typeof MessageChannel!=\"undefined\"&&(this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._methodToThrottle()})}trigger(){this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((()=>{this._triggered=!1,this._methodToThrottle()}),0))}remove(){delete this._channel,this._methodToThrottle=()=>{}}}let bS=63710088e-1;class pg{constructor(A,F){if(isNaN(A)||isNaN(F))throw new Error(`Invalid LngLat object: (${A}, ${F})`);if(this.lng=+A,this.lat=+F,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}wrap(){return new pg(T(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(A){let F=Math.PI/180,W=this.lat*F,te=A.lat*F,fe=Math.sin(W)*Math.sin(te)+Math.cos(W)*Math.cos(te)*Math.cos((A.lng-this.lng)*F);return bS*Math.acos(Math.min(fe,1))}static convert(A){if(A instanceof pg)return A;if(Array.isArray(A)&&(A.length===2||A.length===3))return new pg(Number(A[0]),Number(A[1]));if(!Array.isArray(A)&&typeof A==\"object\"&&A!==null)return new pg(Number(\"lng\"in A?A.lng:A.lon),Number(A.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")}}let F1=2*Math.PI*bS;function BC(R){return F1*Math.cos(R*Math.PI/180)}function Kw(R){return(180+R)/360}function NC(R){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+R*Math.PI/360)))/360}function Jw(R,A){return R/BC(A)}function sb(R){return 360/Math.PI*Math.atan(Math.exp((180-360*R)*Math.PI/180))-90}class lb{constructor(A,F,W=0){this.x=+A,this.y=+F,this.z=+W}static fromLngLat(A,F=0){let W=pg.convert(A);return new lb(Kw(W.lng),NC(W.lat),Jw(F,W.lat))}toLngLat(){return new pg(360*this.x-180,sb(this.y))}toAltitude(){return this.z*BC(sb(this.y))}meterInMercatorCoordinateUnits(){return 1/F1*(A=sb(this.y),1/Math.cos(A*Math.PI/180));var A}}function yp(R,A,F){var W=2*Math.PI*6378137/256/Math.pow(2,F);return[R*W-2*Math.PI*6378137/2,A*W-2*Math.PI*6378137/2]}class wS{constructor(A,F,W){if(!(function(te,fe,pe){return!(te<0||te>25||pe<0||pe>=Math.pow(2,te)||fe<0||fe>=Math.pow(2,te))})(A,F,W))throw new Error(`x=${F}, y=${W}, z=${A} outside of bounds. 0<=x<${Math.pow(2,A)}, 0<=y<${Math.pow(2,A)} 0<=z<=25 `);this.z=A,this.x=F,this.y=W,this.key=ub(0,A,A,F,W)}equals(A){return this.z===A.z&&this.x===A.x&&this.y===A.y}url(A,F,W){let te=(pe=this.y,Re=this.z,Ke=yp(256*(fe=this.x),256*(pe=Math.pow(2,Re)-pe-1),Re),ft=yp(256*(fe+1),256*(pe+1),Re),Ke[0]+\",\"+Ke[1]+\",\"+ft[0]+\",\"+ft[1]);var fe,pe,Re,Ke,ft;let Rt=(function(er,ur,vr){let kr,Dr=\"\";for(let li=er;li>0;li--)kr=1<<li-1,Dr+=(ur&kr?1:0)+(vr&kr?2:0);return Dr})(this.z,this.x,this.y);return A[(this.x+this.y)%A.length].replace(/{prefix}/g,(this.x%16).toString(16)+(this.y%16).toString(16)).replace(/{z}/g,String(this.z)).replace(/{x}/g,String(this.x)).replace(/{y}/g,String(W===\"tms\"?Math.pow(2,this.z)-this.y-1:this.y)).replace(/{ratio}/g,F>1?\"@2x\":\"\").replace(/{quadkey}/g,Rt).replace(/{bbox-epsg-3857}/g,te)}isChildOf(A){let F=this.z-A.z;return F>0&&A.x===this.x>>F&&A.y===this.y>>F}getTilePoint(A){let F=Math.pow(2,this.z);return new u((A.x*F-this.x)*ja,(A.y*F-this.y)*ja)}toString(){return`${this.z}/${this.x}/${this.y}`}}class UC{constructor(A,F){this.wrap=A,this.canonical=F,this.key=ub(A,F.z,F.z,F.x,F.y)}}class $v{constructor(A,F,W,te,fe){if(A<W)throw new Error(`overscaledZ should be >= z; overscaledZ = ${A}; z = ${W}`);this.overscaledZ=A,this.wrap=F,this.canonical=new wS(W,+te,+fe),this.key=ub(F,A,W,te,fe)}clone(){return new $v(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(A){return this.overscaledZ===A.overscaledZ&&this.wrap===A.wrap&&this.canonical.equals(A.canonical)}scaledTo(A){if(A>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${A}; overscaledZ = ${this.overscaledZ}`);let F=this.canonical.z-A;return A>this.canonical.z?new $v(A,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new $v(A,this.wrap,A,this.canonical.x>>F,this.canonical.y>>F)}calculateScaledKey(A,F){if(A>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${A}; overscaledZ = ${this.overscaledZ}`);let W=this.canonical.z-A;return A>this.canonical.z?ub(this.wrap*+F,A,this.canonical.z,this.canonical.x,this.canonical.y):ub(this.wrap*+F,A,A,this.canonical.x>>W,this.canonical.y>>W)}isChildOf(A){if(A.wrap!==this.wrap)return!1;let F=this.canonical.z-A.canonical.z;return A.overscaledZ===0||A.overscaledZ<this.overscaledZ&&A.canonical.x===this.canonical.x>>F&&A.canonical.y===this.canonical.y>>F}children(A){if(this.overscaledZ>=A)return[new $v(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];let F=this.canonical.z+1,W=2*this.canonical.x,te=2*this.canonical.y;return[new $v(F,this.wrap,F,W,te),new $v(F,this.wrap,F,W+1,te),new $v(F,this.wrap,F,W,te+1),new $v(F,this.wrap,F,W+1,te+1)]}isLessThan(A){return this.wrap<A.wrap||!(this.wrap>A.wrap)&&(this.overscaledZ<A.overscaledZ||!(this.overscaledZ>A.overscaledZ)&&(this.canonical.x<A.canonical.x||!(this.canonical.x>A.canonical.x)&&this.canonical.y<A.canonical.y))}wrapped(){return new $v(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)}unwrapTo(A){return new $v(this.overscaledZ,A,this.canonical.z,this.canonical.x,this.canonical.y)}overscaleFactor(){return Math.pow(2,this.overscaledZ-this.canonical.z)}toUnwrapped(){return new UC(this.wrap,this.canonical)}toString(){return`${this.overscaledZ}/${this.canonical.x}/${this.canonical.y}`}getTilePoint(A){return this.canonical.getTilePoint(new lb(A.x-this.wrap,A.y))}}function ub(R,A,F,W,te){(R*=2)<0&&(R=-1*R-1);let fe=1<<F;return(fe*fe*R+fe*te+W).toString(36)+F.toString(36)+A.toString(36)}Fi(\"CanonicalTileID\",wS),Fi(\"OverscaledTileID\",$v,{omit:[\"posMatrix\"]});class VC{constructor(A,F,W,te=1,fe=1,pe=1,Re=0){if(this.uid=A,F.height!==F.width)throw new RangeError(\"DEM tiles must be square\");if(W&&![\"mapbox\",\"terrarium\",\"custom\"].includes(W))return void E(`\"${W}\" is not a valid encoding type. Valid types include \"mapbox\", \"terrarium\" and \"custom\".`);this.stride=F.height;let Ke=this.dim=F.height-2;switch(this.data=new Uint32Array(F.data.buffer),W){case\"terrarium\":this.redFactor=256,this.greenFactor=1,this.blueFactor=1/256,this.baseShift=32768;break;case\"custom\":this.redFactor=te,this.greenFactor=fe,this.blueFactor=pe,this.baseShift=Re;break;default:this.redFactor=6553.6,this.greenFactor=25.6,this.blueFactor=.1,this.baseShift=1e4}for(let ft=0;ft<Ke;ft++)this.data[this._idx(-1,ft)]=this.data[this._idx(0,ft)],this.data[this._idx(Ke,ft)]=this.data[this._idx(Ke-1,ft)],this.data[this._idx(ft,-1)]=this.data[this._idx(ft,0)],this.data[this._idx(ft,Ke)]=this.data[this._idx(ft,Ke-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(Ke,-1)]=this.data[this._idx(Ke-1,0)],this.data[this._idx(-1,Ke)]=this.data[this._idx(0,Ke-1)],this.data[this._idx(Ke,Ke)]=this.data[this._idx(Ke-1,Ke-1)],this.min=Number.MAX_SAFE_INTEGER,this.max=Number.MIN_SAFE_INTEGER;for(let ft=0;ft<Ke;ft++)for(let Rt=0;Rt<Ke;Rt++){let er=this.get(ft,Rt);er>this.max&&(this.max=er),er<this.min&&(this.min=er)}}get(A,F){let W=new Uint8Array(this.data.buffer),te=4*this._idx(A,F);return this.unpack(W[te],W[te+1],W[te+2])}getUnpackVector(){return[this.redFactor,this.greenFactor,this.blueFactor,this.baseShift]}_idx(A,F){if(A<-1||A>=this.dim+1||F<-1||F>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(F+1)*this.stride+(A+1)}unpack(A,F,W){return A*this.redFactor+F*this.greenFactor+W*this.blueFactor-this.baseShift}getPixels(){return new Ki({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(A,F,W){if(this.dim!==A.dim)throw new Error(\"dem dimension mismatch\");let te=F*this.dim,fe=F*this.dim+this.dim,pe=W*this.dim,Re=W*this.dim+this.dim;switch(F){case-1:te=fe-1;break;case 1:fe=te+1}switch(W){case-1:pe=Re-1;break;case 1:Re=pe+1}let Ke=-F*this.dim,ft=-W*this.dim;for(let Rt=pe;Rt<Re;Rt++)for(let er=te;er<fe;er++)this.data[this._idx(er,Rt)]=A.data[this._idx(er+Ke,Rt+ft)]}}Fi(\"DEMData\",VC);class GC{constructor(A){this._stringToNumber={},this._numberToString=[];for(let F=0;F<A.length;F++){let W=A[F];this._stringToNumber[W]=F,this._numberToString[F]=W}}encode(A){return this._stringToNumber[A]}decode(A){if(A>=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${A} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[A]}}class TS{constructor(A,F,W,te,fe){this.type=\"Feature\",this._vectorTileFeature=A,A._z=F,A._x=W,A._y=te,this.properties=A.properties,this.id=fe}get geometry(){return this._geometry===void 0&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry}set geometry(A){this._geometry=A}toJSON(){let A={geometry:this.geometry};for(let F in this)F!==\"_geometry\"&&F!==\"_vectorTileFeature\"&&(A[F]=this[F]);return A}}class _y{constructor(A,F){this.tileID=A,this.x=A.canonical.x,this.y=A.canonical.y,this.z=A.canonical.z,this.grid=new $i(ja,16,0),this.grid3D=new $i(ja,16,0),this.featureIndexArray=new Ts,this.promoteId=F}insert(A,F,W,te,fe,pe){let Re=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(W,te,fe);let Ke=pe?this.grid3D:this.grid;for(let ft=0;ft<F.length;ft++){let Rt=F[ft],er=[1/0,1/0,-1/0,-1/0];for(let ur=0;ur<Rt.length;ur++){let vr=Rt[ur];er[0]=Math.min(er[0],vr.x),er[1]=Math.min(er[1],vr.y),er[2]=Math.max(er[2],vr.x),er[3]=Math.max(er[3],vr.y)}er[0]<ja&&er[1]<ja&&er[2]>=0&&er[3]>=0&&Ke.insert(Re,er[0],er[1],er[2],er[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=new _r.VectorTile(new fS(this.rawTileData)).layers,this.sourceLayerCoder=new GC(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers}query(A,F,W,te){this.loadVTLayers();let fe=A.params||{},pe=ja/A.tileSize/A.scale,Re=Fc(fe.filter),Ke=A.queryGeometry,ft=A.queryPadding*pe,Rt=jC(Ke),er=this.grid.query(Rt.minX-ft,Rt.minY-ft,Rt.maxX+ft,Rt.maxY+ft),ur=jC(A.cameraQueryGeometry),vr=this.grid3D.query(ur.minX-ft,ur.minY-ft,ur.maxX+ft,ur.maxY+ft,((li,pi,vn,zi)=>(function(ln,Tn,pa,ro,Vo){for(let la of ln)if(Tn<=la.x&&pa<=la.y&&ro>=la.x&&Vo>=la.y)return!0;let Xa=[new u(Tn,pa),new u(Tn,Vo),new u(ro,Vo),new u(ro,pa)];if(ln.length>2){for(let la of Xa)if(Ni(ln,la))return!0}for(let la=0;la<ln.length-1;la++)if(qi(ln[la],ln[la+1],Xa))return!0;return!1})(A.cameraQueryGeometry,li-ft,pi-ft,vn+ft,zi+ft)));for(let li of vr)er.push(li);er.sort(oq);let kr={},Dr;for(let li=0;li<er.length;li++){let pi=er[li];if(pi===Dr)continue;Dr=pi;let vn=this.featureIndexArray.get(pi),zi=null;this.loadMatchingFeature(kr,vn.bucketIndex,vn.sourceLayerIndex,vn.featureIndex,Re,fe.layers,fe.availableImages,F,W,te,((ln,Tn,pa)=>(zi||(zi=$s(ln)),Tn.queryIntersectsFeature(Ke,ln,pa,zi,this.z,A.transform,pe,A.pixelPosMatrix))))}return kr}loadMatchingFeature(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er){let ur=this.bucketLayerIDs[F];if(pe&&!(function(li,pi){for(let vn=0;vn<li.length;vn++)if(pi.indexOf(li[vn])>=0)return!0;return!1})(pe,ur))return;let vr=this.sourceLayerCoder.decode(W),kr=this.vtLayers[vr].feature(te);if(fe.needGeometry){let li=Ml(kr,!0);if(!fe.filter(new rs(this.tileID.overscaledZ),li,this.tileID.canonical))return}else if(!fe.filter(new rs(this.tileID.overscaledZ),kr))return;let Dr=this.getId(kr,vr);for(let li=0;li<ur.length;li++){let pi=ur[li];if(pe&&pe.indexOf(pi)<0)continue;let vn=Ke[pi];if(!vn)continue;let zi={};Dr&&Rt&&(zi=Rt.getState(vn.sourceLayer||\"_geojsonTileLayer\",Dr));let ln=L({},ft[pi]);ln.paint=HC(ln.paint,vn.paint,kr,zi,Re),ln.layout=HC(ln.layout,vn.layout,kr,zi,Re);let Tn=!er||er(kr,vn,zi);if(!Tn)continue;let pa=new TS(kr,this.z,this.x,this.y,Dr);pa.layer=ln;let ro=A[pi];ro===void 0&&(ro=A[pi]=[]),ro.push({featureIndex:te,feature:pa,intersectionZ:Tn})}}lookupSymbolFeatures(A,F,W,te,fe,pe,Re,Ke){let ft={};this.loadVTLayers();let Rt=Fc(fe);for(let er of A)this.loadMatchingFeature(ft,W,te,er,Rt,pe,Re,Ke,F);return ft}hasLayer(A){for(let F of this.bucketLayerIDs)for(let W of F)if(A===W)return!0;return!1}getId(A,F){let W=A.id;return this.promoteId&&(W=A.properties[typeof this.promoteId==\"string\"?this.promoteId:this.promoteId[F]],typeof W==\"boolean\"&&(W=Number(W))),W}}function HC(R,A,F,W,te){return C(R,((fe,pe)=>{let Re=A instanceof Ac?A.get(pe):null;return Re&&Re.evaluate?Re.evaluate(F,W,te):Re}))}function jC(R){let A=1/0,F=1/0,W=-1/0,te=-1/0;for(let fe of R)A=Math.min(A,fe.x),F=Math.min(F,fe.y),W=Math.max(W,fe.x),te=Math.max(te,fe.y);return{minX:A,minY:F,maxX:W,maxY:te}}function oq(R,A){return A-R}function WC(R,A,F,W,te){let fe=[];for(let pe=0;pe<R.length;pe++){let Re=R[pe],Ke;for(let ft=0;ft<Re.length-1;ft++){let Rt=Re[ft],er=Re[ft+1];Rt.x<A&&er.x<A||(Rt.x<A?Rt=new u(A,Rt.y+(A-Rt.x)/(er.x-Rt.x)*(er.y-Rt.y))._round():er.x<A&&(er=new u(A,Rt.y+(A-Rt.x)/(er.x-Rt.x)*(er.y-Rt.y))._round()),Rt.y<F&&er.y<F||(Rt.y<F?Rt=new u(Rt.x+(F-Rt.y)/(er.y-Rt.y)*(er.x-Rt.x),F)._round():er.y<F&&(er=new u(Rt.x+(F-Rt.y)/(er.y-Rt.y)*(er.x-Rt.x),F)._round()),Rt.x>=W&&er.x>=W||(Rt.x>=W?Rt=new u(W,Rt.y+(W-Rt.x)/(er.x-Rt.x)*(er.y-Rt.y))._round():er.x>=W&&(er=new u(W,Rt.y+(W-Rt.x)/(er.x-Rt.x)*(er.y-Rt.y))._round()),Rt.y>=te&&er.y>=te||(Rt.y>=te?Rt=new u(Rt.x+(te-Rt.y)/(er.y-Rt.y)*(er.x-Rt.x),te)._round():er.y>=te&&(er=new u(Rt.x+(te-Rt.y)/(er.y-Rt.y)*(er.x-Rt.x),te)._round()),Ke&&Rt.equals(Ke[Ke.length-1])||(Ke=[Rt],fe.push(Ke)),Ke.push(er)))))}}return fe}Fi(\"FeatureIndex\",_y,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});class gg extends u{constructor(A,F,W,te){super(A,F),this.angle=W,te!==void 0&&(this.segment=te)}clone(){return new gg(this.x,this.y,this.angle,this.segment)}}function AS(R,A,F,W,te){if(A.segment===void 0||F===0)return!0;let fe=A,pe=A.segment+1,Re=0;for(;Re>-F/2;){if(pe--,pe<0)return!1;Re-=R[pe].dist(fe),fe=R[pe]}Re+=R[pe].dist(R[pe+1]),pe++;let Ke=[],ft=0;for(;Re<F/2;){let Rt=R[pe],er=R[pe+1];if(!er)return!1;let ur=R[pe-1].angleTo(Rt)-Rt.angleTo(er);for(ur=Math.abs((ur+3*Math.PI)%(2*Math.PI)-Math.PI),Ke.push({distance:Re,angleDelta:ur}),ft+=ur;Re-Ke[0].distance>W;)ft-=Ke.shift().angleDelta;if(ft>te)return!1;pe++,Re+=Rt.dist(er)}return!0}function XC(R){let A=0;for(let F=0;F<R.length-1;F++)A+=R[F].dist(R[F+1]);return A}function ZC(R,A,F){return R?.6*A*F:0}function YC(R,A){return Math.max(R?R.right-R.left:0,A?A.right-A.left:0)}function sq(R,A,F,W,te,fe){let pe=ZC(F,te,fe),Re=YC(F,W)*fe,Ke=0,ft=XC(R)/2;for(let Rt=0;Rt<R.length-1;Rt++){let er=R[Rt],ur=R[Rt+1],vr=er.dist(ur);if(Ke+vr>ft){let kr=(ft-Ke)/vr,Dr=Lo.number(er.x,ur.x,kr),li=Lo.number(er.y,ur.y,kr),pi=new gg(Dr,li,ur.angleTo(er),Rt);return pi._round(),!pe||AS(R,pi,Re,pe,A)?pi:void 0}Ke+=vr}}function lq(R,A,F,W,te,fe,pe,Re,Ke){let ft=ZC(W,fe,pe),Rt=YC(W,te),er=Rt*pe,ur=R[0].x===0||R[0].x===Ke||R[0].y===0||R[0].y===Ke;return A-er<A/4&&(A=er+A/4),KC(R,ur?A/2*Re%A:(Rt/2+2*fe)*pe*Re%A,A,ft,F,er,ur,!1,Ke)}function KC(R,A,F,W,te,fe,pe,Re,Ke){let ft=fe/2,Rt=XC(R),er=0,ur=A-F,vr=[];for(let kr=0;kr<R.length-1;kr++){let Dr=R[kr],li=R[kr+1],pi=Dr.dist(li),vn=li.angleTo(Dr);for(;ur+F<er+pi;){ur+=F;let zi=(ur-er)/pi,ln=Lo.number(Dr.x,li.x,zi),Tn=Lo.number(Dr.y,li.y,zi);if(ln>=0&&ln<Ke&&Tn>=0&&Tn<Ke&&ur-ft>=0&&ur+ft<=Rt){let pa=new gg(ln,Tn,vn,kr);pa._round(),W&&!AS(R,pa,fe,W,te)||vr.push(pa)}}er+=pi}return Re||vr.length||pe||(vr=KC(R,er/2,F,W,te,fe,pe,!0,Ke)),vr}Fi(\"Anchor\",gg);let z1=Md;function JC(R,A,F,W){let te=[],fe=R.image,pe=fe.pixelRatio,Re=fe.paddedRect.w-2*z1,Ke=fe.paddedRect.h-2*z1,ft={x1:R.left,y1:R.top,x2:R.right,y2:R.bottom},Rt=fe.stretchX||[[0,Re]],er=fe.stretchY||[[0,Ke]],ur=(Wn,Ro)=>Wn+Ro[1]-Ro[0],vr=Rt.reduce(ur,0),kr=er.reduce(ur,0),Dr=Re-vr,li=Ke-kr,pi=0,vn=vr,zi=0,ln=kr,Tn=0,pa=Dr,ro=0,Vo=li;if(fe.content&&W){let Wn=fe.content,Ro=Wn[2]-Wn[0],uo=Wn[3]-Wn[1];(fe.textFitWidth||fe.textFitHeight)&&(ft=DC(R)),pi=mg(Rt,0,Wn[0]),zi=mg(er,0,Wn[1]),vn=mg(Rt,Wn[0],Wn[2]),ln=mg(er,Wn[1],Wn[3]),Tn=Wn[0]-pi,ro=Wn[1]-zi,pa=Ro-vn,Vo=uo-ln}let Xa=ft.x1,la=ft.y1,Mo=ft.x2-Xa,fo=ft.y2-la,lo=(Wn,Ro,uo,$o)=>{let Ju=$w(Wn.stretch-pi,vn,Mo,Xa),Ou=q1(Wn.fixed-Tn,pa,Wn.stretch,vr),kh=$w(Ro.stretch-zi,ln,fo,la),Qv=q1(Ro.fixed-ro,Vo,Ro.stretch,kr),ud=$w(uo.stretch-pi,vn,Mo,Xa),Ch=q1(uo.fixed-Tn,pa,uo.stretch,vr),Gd=$w($o.stretch-zi,ln,fo,la),Hd=q1($o.fixed-ro,Vo,$o.stretch,kr),jd=new u(Ju,kh),Af=new u(ud,kh),Lh=new u(ud,Gd),Ed=new u(Ju,Gd),cd=new u(Ou/pe,Qv/pe),Yh=new u(Ch/pe,Hd/pe),Df=A*Math.PI/180;if(Df){let ru=Math.sin(Df),pc=Math.cos(Df),$u=[pc,-ru,ru,pc];jd._matMult($u),Af._matMult($u),Ed._matMult($u),Lh._matMult($u)}let Fv=Wn.stretch+Wn.fixed,uv=Ro.stretch+Ro.fixed;return{tl:jd,tr:Af,bl:Ed,br:Lh,tex:{x:fe.paddedRect.x+z1+Fv,y:fe.paddedRect.y+z1+uv,w:uo.stretch+uo.fixed-Fv,h:$o.stretch+$o.fixed-uv},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:cd,pixelOffsetBR:Yh,minFontScaleX:pa/pe/Mo,minFontScaleY:Vo/pe/fo,isSDF:F}};if(W&&(fe.stretchX||fe.stretchY)){let Wn=$C(Rt,Dr,vr),Ro=$C(er,li,kr);for(let uo=0;uo<Wn.length-1;uo++){let $o=Wn[uo],Ju=Wn[uo+1];for(let 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A=this.data[0],F=this.data.pop();return--this.length>0&&(this.data[0]=F,this._down(0)),A}peek(){return this.data[0]}_up(A){let{data:F,compare:W}=this,te=F[A];for(;A>0;){let fe=A-1>>1,pe=F[fe];if(W(te,pe)>=0)break;F[A]=pe,A=fe}F[A]=te}_down(A){let{data:F,compare:W}=this,te=this.length>>1,fe=F[A];for(;A<te;){let pe=1+(A<<1),Re=pe+1;if(Re<this.length&&W(F[Re],F[pe])<0&&(pe=Re),W(F[pe],fe)>=0)break;F[A]=F[pe],A=pe}F[A]=fe}}function uq(R,A=1,F=!1){let W=1/0,te=1/0,fe=-1/0,pe=-1/0,Re=R[0];for(let vr=0;vr<Re.length;vr++){let kr=Re[vr];(!vr||kr.x<W)&&(W=kr.x),(!vr||kr.y<te)&&(te=kr.y),(!vr||kr.x>fe)&&(fe=kr.x),(!vr||kr.y>pe)&&(pe=kr.y)}let Ke=Math.min(fe-W,pe-te),ft=Ke/2,Rt=new Vp([],cq);if(Ke===0)return new u(W,te);for(let vr=W;vr<fe;vr+=Ke)for(let kr=te;kr<pe;kr+=Ke)Rt.push(new O1(vr+ft,kr+ft,ft,R));let er=(function(vr){let kr=0,Dr=0,li=0,pi=vr[0];for(let vn=0,zi=pi.length,ln=zi-1;vn<zi;ln=vn++){let Tn=pi[vn],pa=pi[ln],ro=Tn.x*pa.y-pa.x*Tn.y;Dr+=(Tn.x+pa.x)*ro,li+=(Tn.y+pa.y)*ro,kr+=3*ro}return new O1(Dr/kr,li/kr,0,vr)})(R),ur=Rt.length;for(;Rt.length;){let vr=Rt.pop();(vr.d>er.d||!er.d)&&(er=vr,F&&console.log(\"found best %d after %d probes\",Math.round(1e4*vr.d)/1e4,ur)),vr.max-er.d<=A||(ft=vr.h/2,Rt.push(new O1(vr.p.x-ft,vr.p.y-ft,ft,R)),Rt.push(new O1(vr.p.x+ft,vr.p.y-ft,ft,R)),Rt.push(new O1(vr.p.x-ft,vr.p.y+ft,ft,R)),Rt.push(new O1(vr.p.x+ft,vr.p.y+ft,ft,R)),ur+=4)}return F&&(console.log(`num probes: ${ur}`),console.log(`best distance: ${er.d}`)),er.p}function cq(R,A){return A.max-R.max}function O1(R,A,F,W){this.p=new u(R,A),this.h=F,this.d=(function(te,fe){let pe=!1,Re=1/0;for(let Ke=0;Ke<fe.length;Ke++){let ft=fe[Ke];for(let Rt=0,er=ft.length,ur=er-1;Rt<er;ur=Rt++){let vr=ft[Rt],kr=ft[ur];vr.y>te.y!=kr.y>te.y&&te.x<(kr.x-vr.x)*(te.y-vr.y)/(kr.y-vr.y)+vr.x&&(pe=!pe),Re=Math.min(Re,Nr(te,vr,kr))}}return(pe?1:-1)*Math.sqrt(Re)})(this.p,W),this.max=this.d+this.h*Math.SQRT2}var ld;i.aq=void 0,(ld=i.aq||(i.aq={}))[ld.center=1]=\"center\",ld[ld.left=2]=\"left\",ld[ld.right=3]=\"right\",ld[ld.top=4]=\"top\",ld[ld.bottom=5]=\"bottom\",ld[ld[\"top-left\"]=6]=\"top-left\",ld[ld[\"top-right\"]=7]=\"top-right\",ld[ld[\"bottom-left\"]=8]=\"bottom-left\",ld[ld[\"bottom-right\"]=9]=\"bottom-right\";let vm=7,xy=Number.POSITIVE_INFINITY;function SS(R,A){return A[1]!==xy?(function(F,W,te){let fe=0,pe=0;switch(W=Math.abs(W),te=Math.abs(te),F){case\"top-right\":case\"top-left\":case\"top\":pe=te-vm;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":pe=-te+vm}switch(F){case\"top-right\":case\"bottom-right\":case\"right\":fe=-W;break;case\"top-left\":case\"bottom-left\":case\"left\":fe=W}return[fe,pe]})(R,A[0],A[1]):(function(F,W){let te=0,fe=0;W<0&&(W=0);let pe=W/Math.SQRT2;switch(F){case\"top-right\":case\"top-left\":fe=pe-vm;break;case\"bottom-right\":case\"bottom-left\":fe=-pe+vm;break;case\"bottom\":fe=-W+vm;break;case\"top\":fe=W-vm}switch(F){case\"top-right\":case\"bottom-right\":te=-pe;break;case\"top-left\":case\"bottom-left\":te=pe;break;case\"left\":te=W;break;case\"right\":te=-W}return[te,fe]})(R,A[0])}function QC(R,A,F){var W;let te=R.layout,fe=(W=te.get(\"text-variable-anchor-offset\"))===null||W===void 0?void 0:W.evaluate(A,{},F);if(fe){let Re=fe.values,Ke=[];for(let ft=0;ft<Re.length;ft+=2){let Rt=Ke[ft]=Re[ft],er=Re[ft+1].map((ur=>ur*tu));Rt.startsWith(\"top\")?er[1]-=vm:Rt.startsWith(\"bottom\")&&(er[1]+=vm),Ke[ft+1]=er}return new Hi(Ke)}let pe=te.get(\"text-variable-anchor\");if(pe){let Re;Re=R._unevaluatedLayout.getValue(\"text-radial-offset\")!==void 0?[te.get(\"text-radial-offset\").evaluate(A,{},F)*tu,xy]:te.get(\"text-offset\").evaluate(A,{},F).map((ft=>ft*tu));let Ke=[];for(let ft of pe)Ke.push(ft,SS(ft,Re));return new Hi(Ke)}return null}function MS(R){switch(R){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function fq(R,A,F,W,te,fe,pe,Re,Ke,ft,Rt){let er=fe.textMaxSize.evaluate(A,{});er===void 0&&(er=pe);let ur=R.layers[0].layout,vr=ur.get(\"icon-offset\").evaluate(A,{},Rt),kr=t6(F.horizontal),Dr=pe/24,li=R.tilePixelRatio*Dr,pi=R.tilePixelRatio*er/24,vn=R.tilePixelRatio*Re,zi=R.tilePixelRatio*ur.get(\"symbol-spacing\"),ln=ur.get(\"text-padding\")*R.tilePixelRatio,Tn=(function(Wn,Ro,uo,$o=1){let Ju=Wn.get(\"icon-padding\").evaluate(Ro,{},uo),Ou=Ju&&Ju.values;return[Ou[0]*$o,Ou[1]*$o,Ou[2]*$o,Ou[3]*$o]})(ur,A,Rt,R.tilePixelRatio),pa=ur.get(\"text-max-angle\")/180*Math.PI,ro=ur.get(\"text-rotation-alignment\")!==\"viewport\"&&ur.get(\"symbol-placement\")!==\"point\",Vo=ur.get(\"icon-rotation-alignment\")===\"map\"&&ur.get(\"symbol-placement\")!==\"point\",Xa=ur.get(\"symbol-placement\"),la=zi/2,Mo=ur.get(\"icon-text-fit\"),fo;W&&Mo!==\"none\"&&(R.allowVerticalPlacement&&F.vertical&&(fo=FC(W,F.vertical,Mo,ur.get(\"icon-text-fit-padding\"),vr,Dr)),kr&&(W=FC(W,kr,Mo,ur.get(\"icon-text-fit-padding\"),vr,Dr)));let lo=(Wn,Ro)=>{Ro.x<0||Ro.x>=ja||Ro.y<0||Ro.y>=ja||(function(uo,$o,Ju,Ou,kh,Qv,ud,Ch,Gd,Hd,jd,Af,Lh,Ed,cd,Yh,Df,Fv,uv,ru,pc,$u,zv,hf,B1){let p0=uo.addToLineVertexArray($o,Ju),Gp,ep,Gc,Yf,tp=0,yg=0,cv=0,N1=0,PS=-1,r3=-1,g0={},by=xi(\"\");if(uo.allowVerticalPlacement&&Ou.vertical){let kd=Ch.layout.get(\"text-rotate\").evaluate(pc,{},hf)+90;Gc=new 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this._layers[et];this.familiesBySource={};let We=i.bk(Object.values(this._layerConfigs),this.keyCache);for(let et of We){let xt=et.map((Ir=>this._layers[Ir.id])),At=xt[0];if(At.visibility===\"none\")continue;let Kt=At.source||\"\",Qt=this.familiesBySource[Kt];Qt||(Qt=this.familiesBySource[Kt]={});let Mr=At.sourceLayer||\"_geojsonTileLayer\",Gr=Qt[Mr];Gr||(Gr=Qt[Mr]=[]),Gr.push(xt)}}}class o{constructor(Ge){let Je={},We=[];for(let Kt in Ge){let Qt=Ge[Kt],Mr=Je[Kt]={};for(let Gr in Qt){let Ir=Qt[+Gr];if(!Ir||Ir.bitmap.width===0||Ir.bitmap.height===0)continue;let Yr={x:0,y:0,w:Ir.bitmap.width+2,h:Ir.bitmap.height+2};We.push(Yr),Mr[Gr]={rect:Yr,metrics:Ir.metrics}}}let{w:et,h:xt}=i.p(We),At=new i.o({width:et||1,height:xt||1});for(let Kt in Ge){let Qt=Ge[Kt];for(let Mr in Qt){let Gr=Qt[+Mr];if(!Gr||Gr.bitmap.width===0||Gr.bitmap.height===0)continue;let Ir=Je[Kt][Mr].rect;i.o.copy(Gr.bitmap,At,{x:0,y:0},{x:Ir.x+1,y:Ir.y+1},Gr.bitmap)}}this.image=At,this.positions=Je}}i.bl(\"GlyphAtlas\",o);class s{constructor(Ge){this.tileID=new i.S(Ge.tileID.overscaledZ,Ge.tileID.wrap,Ge.tileID.canonical.z,Ge.tileID.canonical.x,Ge.tileID.canonical.y),this.uid=Ge.uid,this.zoom=Ge.zoom,this.pixelRatio=Ge.pixelRatio,this.tileSize=Ge.tileSize,this.source=Ge.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=Ge.showCollisionBoxes,this.collectResourceTiming=!!Ge.collectResourceTiming,this.returnDependencies=!!Ge.returnDependencies,this.promoteId=Ge.promoteId,this.inFlightDependencies=[]}parse(Ge,Je,We,et){return i._(this,void 0,void 0,(function*(){this.status=\"parsing\",this.data=Ge,this.collisionBoxArray=new i.a5;let xt=new i.bm(Object.keys(Ge.layers).sort()),At=new i.bn(this.tileID,this.promoteId);At.bucketLayerIDs=[];let Kt={},Qt={featureIndex:At,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:We},Mr=Je.familiesBySource[this.source];for(let Qn in Mr){let Vi=Ge.layers[Qn];if(!Vi)continue;Vi.version===1&&i.w(`Vector tile source \"${this.source}\" layer \"${Qn}\" does not use vector tile spec v2 and therefore may have some rendering errors.`);let Jn=xt.encode(Qn),Yn=[];for(let Gt=0;Gt<Vi.length;Gt++){let wt=Vi.feature(Gt),tr=At.getId(wt,Qn);Yn.push({feature:wt,id:tr,index:Gt,sourceLayerIndex:Jn})}for(let Gt of Mr[Qn]){let wt=Gt[0];wt.source!==this.source&&i.w(`layer.source = ${wt.source} does not equal this.source = ${this.source}`),wt.minzoom&&this.zoom<Math.floor(wt.minzoom)||wt.maxzoom&&this.zoom>=wt.maxzoom||wt.visibility!==\"none\"&&(l(Gt,this.zoom,We),(Kt[wt.id]=wt.createBucket({index:At.bucketLayerIDs.length,layers:Gt,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:Jn,sourceID:this.source})).populate(Yn,Qt,this.tileID.canonical),At.bucketLayerIDs.push(Gt.map((tr=>tr.id))))}}let Gr=i.aF(Qt.glyphDependencies,(Qn=>Object.keys(Qn).map(Number)));this.inFlightDependencies.forEach((Qn=>Qn==null?void 0:Qn.abort())),this.inFlightDependencies=[];let Ir=Promise.resolve({});if(Object.keys(Gr).length){let Qn=new AbortController;this.inFlightDependencies.push(Qn),Ir=et.sendAsync({type:\"GG\",data:{stacks:Gr,source:this.source,tileID:this.tileID,type:\"glyphs\"}},Qn)}let Yr=Object.keys(Qt.iconDependencies),_i=Promise.resolve({});if(Yr.length){let Qn=new AbortController;this.inFlightDependencies.push(Qn),_i=et.sendAsync({type:\"GI\",data:{icons:Yr,source:this.source,tileID:this.tileID,type:\"icons\"}},Qn)}let Pi=Object.keys(Qt.patternDependencies),ai=Promise.resolve({});if(Pi.length){let Qn=new AbortController;this.inFlightDependencies.push(Qn),ai=et.sendAsync({type:\"GI\",data:{icons:Pi,source:this.source,tileID:this.tileID,type:\"patterns\"}},Qn)}let[mi,un,Fn]=yield Promise.all([Ir,_i,ai]),An=new o(mi),Hn=new i.bo(un,Fn);for(let Qn in Kt){let Vi=Kt[Qn];Vi instanceof i.a6?(l(Vi.layers,this.zoom,We),i.bp({bucket:Vi,glyphMap:mi,glyphPositions:An.positions,imageMap:un,imagePositions:Hn.iconPositions,showCollisionBoxes:this.showCollisionBoxes,canonical:this.tileID.canonical})):Vi.hasPattern&&(Vi instanceof i.bq||Vi instanceof i.br||Vi instanceof i.bs)&&(l(Vi.layers,this.zoom,We),Vi.addFeatures(Qt,this.tileID.canonical,Hn.patternPositions))}return this.status=\"done\",{buckets:Object.values(Kt).filter((Qn=>!Qn.isEmpty())),featureIndex:At,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:An.image,imageAtlas:Hn,glyphMap:this.returnDependencies?mi:null,iconMap:this.returnDependencies?un:null,glyphPositions:this.returnDependencies?An.positions:null}}))}}function l(gt,Ge,Je){let We=new i.z(Ge);for(let et of gt)et.recalculate(We,Je)}class u{constructor(Ge,Je,We){this.actor=Ge,this.layerIndex=Je,this.availableImages=We,this.fetching={},this.loading={},this.loaded={}}loadVectorTile(Ge,Je){return i._(this,void 0,void 0,(function*(){let We=yield i.l(Ge.request,Je);try{return{vectorTile:new i.bt.VectorTile(new i.bu(We.data)),rawData:We.data,cacheControl:We.cacheControl,expires:We.expires}}catch(et){let xt=new Uint8Array(We.data),At=`Unable to parse the tile at ${Ge.request.url}, `;throw At+=xt[0]===31&&xt[1]===139?\"please make sure the data is not gzipped and that you have configured the relevant header in the server\":`got error: ${et.message}`,new Error(At)}}))}loadTile(Ge){return i._(this,void 0,void 0,(function*(){let Je=Ge.uid,We=!!(Ge&&Ge.request&&Ge.request.collectResourceTiming)&&new i.bv(Ge.request),et=new s(Ge);this.loading[Je]=et;let xt=new AbortController;et.abort=xt;try{let At=yield this.loadVectorTile(Ge,xt);if(delete this.loading[Je],!At)return null;let Kt=At.rawData,Qt={};At.expires&&(Qt.expires=At.expires),At.cacheControl&&(Qt.cacheControl=At.cacheControl);let Mr={};if(We){let Ir=We.finish();Ir&&(Mr.resourceTiming=JSON.parse(JSON.stringify(Ir)))}et.vectorTile=At.vectorTile;let Gr=et.parse(At.vectorTile,this.layerIndex,this.availableImages,this.actor);this.loaded[Je]=et,this.fetching[Je]={rawTileData:Kt,cacheControl:Qt,resourceTiming:Mr};try{let Ir=yield Gr;return i.e({rawTileData:Kt.slice(0)},Ir,Qt,Mr)}finally{delete this.fetching[Je]}}catch(At){throw delete this.loading[Je],et.status=\"done\",this.loaded[Je]=et,At}}))}reloadTile(Ge){return i._(this,void 0,void 0,(function*(){let Je=Ge.uid;if(!this.loaded||!this.loaded[Je])throw new Error(\"Should not be trying to reload a tile that was never loaded or has been removed\");let We=this.loaded[Je];if(We.showCollisionBoxes=Ge.showCollisionBoxes,We.status===\"parsing\"){let et=yield We.parse(We.vectorTile,this.layerIndex,this.availableImages,this.actor),xt;if(this.fetching[Je]){let{rawTileData:At,cacheControl:Kt,resourceTiming:Qt}=this.fetching[Je];delete this.fetching[Je],xt=i.e({rawTileData:At.slice(0)},et,Kt,Qt)}else xt=et;return xt}if(We.status===\"done\"&&We.vectorTile)return We.parse(We.vectorTile,this.layerIndex,this.availableImages,this.actor)}))}abortTile(Ge){return i._(this,void 0,void 0,(function*(){let Je=this.loading,We=Ge.uid;Je&&Je[We]&&Je[We].abort&&(Je[We].abort.abort(),delete Je[We])}))}removeTile(Ge){return i._(this,void 0,void 0,(function*(){this.loaded&&this.loaded[Ge.uid]&&delete this.loaded[Ge.uid]}))}}class c{constructor(){this.loaded={}}loadTile(Ge){return i._(this,void 0,void 0,(function*(){let{uid:Je,encoding:We,rawImageData:et,redFactor:xt,greenFactor:At,blueFactor:Kt,baseShift:Qt}=Ge,Mr=et.width+2,Gr=et.height+2,Ir=i.b(et)?new i.R({width:Mr,height:Gr},yield i.bw(et,-1,-1,Mr,Gr)):et,Yr=new i.bx(Je,Ir,We,xt,At,Kt,Qt);return this.loaded=this.loaded||{},this.loaded[Je]=Yr,Yr}))}removeTile(Ge){let Je=this.loaded,We=Ge.uid;Je&&Je[We]&&delete Je[We]}}function f(gt,Ge){if(gt.length!==0){h(gt[0],Ge);for(var Je=1;Je<gt.length;Je++)h(gt[Je],!Ge)}}function h(gt,Ge){for(var Je=0,We=0,et=0,xt=gt.length,At=xt-1;et<xt;At=et++){var Kt=(gt[et][0]-gt[At][0])*(gt[At][1]+gt[et][1]),Qt=Je+Kt;We+=Math.abs(Je)>=Math.abs(Kt)?Je-Qt+Kt:Kt-Qt+Je,Je=Qt}Je+We>=0!=!!Ge&&gt.reverse()}var d=i.by((function gt(Ge,Je){var We,et=Ge&&Ge.type;if(et===\"FeatureCollection\")for(We=0;We<Ge.features.length;We++)gt(Ge.features[We],Je);else if(et===\"GeometryCollection\")for(We=0;We<Ge.geometries.length;We++)gt(Ge.geometries[We],Je);else if(et===\"Feature\")gt(Ge.geometry,Je);else if(et===\"Polygon\")f(Ge.coordinates,Je);else if(et===\"MultiPolygon\")for(We=0;We<Ge.coordinates.length;We++)f(Ge.coordinates[We],Je);return Ge}));let v=i.bt.VectorTileFeature.prototype.toGeoJSON;var m={exports:{}},b=i.bz,p=i.bt.VectorTileFeature,k=M;function M(gt,Ge){this.options=Ge||{},this.features=gt,this.length=gt.length}function T(gt,Ge){this.id=typeof gt.id==\"number\"?gt.id:void 0,this.type=gt.type,this.rawGeometry=gt.type===1?[gt.geometry]:gt.geometry,this.properties=gt.tags,this.extent=Ge||4096}M.prototype.feature=function(gt){return new T(this.features[gt],this.options.extent)},T.prototype.loadGeometry=function(){var gt=this.rawGeometry;this.geometry=[];for(var Ge=0;Ge<gt.length;Ge++){for(var Je=gt[Ge],We=[],et=0;et<Je.length;et++)We.push(new b(Je[et][0],Je[et][1]));this.geometry.push(We)}return this.geometry},T.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var gt=this.geometry,Ge=1/0,Je=-1/0,We=1/0,et=-1/0,xt=0;xt<gt.length;xt++)for(var At=gt[xt],Kt=0;Kt<At.length;Kt++){var Qt=At[Kt];Ge=Math.min(Ge,Qt.x),Je=Math.max(Je,Qt.x),We=Math.min(We,Qt.y),et=Math.max(et,Qt.y)}return[Ge,We,Je,et]},T.prototype.toGeoJSON=p.prototype.toGeoJSON;var L=i.bA,x=k;function C(gt){var Ge=new L;return(function(Je,We){for(var et in Je.layers)We.writeMessage(3,S,Je.layers[et])})(gt,Ge),Ge.finish()}function S(gt,Ge){var Je;Ge.writeVarintField(15,gt.version||1),Ge.writeStringField(1,gt.name||\"\"),Ge.writeVarintField(5,gt.extent||4096);var We={keys:[],values:[],keycache:{},valuecache:{}};for(Je=0;Je<gt.length;Je++)We.feature=gt.feature(Je),Ge.writeMessage(2,g,We);var et=We.keys;for(Je=0;Je<et.length;Je++)Ge.writeStringField(3,et[Je]);var xt=We.values;for(Je=0;Je<xt.length;Je++)Ge.writeMessage(4,V,xt[Je])}function g(gt,Ge){var Je=gt.feature;Je.id!==void 0&&Ge.writeVarintField(1,Je.id),Ge.writeMessage(2,P,gt),Ge.writeVarintField(3,Je.type),Ge.writeMessage(4,q,Je)}function P(gt,Ge){var Je=gt.feature,We=gt.keys,et=gt.values,xt=gt.keycache,At=gt.valuecache;for(var Kt in Je.properties){var Qt=Je.properties[Kt],Mr=xt[Kt];if(Qt!==null){Mr===void 0&&(We.push(Kt),xt[Kt]=Mr=We.length-1),Ge.writeVarint(Mr);var Gr=typeof Qt;Gr!==\"string\"&&Gr!==\"boolean\"&&Gr!==\"number\"&&(Qt=JSON.stringify(Qt));var Ir=Gr+\":\"+Qt,Yr=At[Ir];Yr===void 0&&(et.push(Qt),At[Ir]=Yr=et.length-1),Ge.writeVarint(Yr)}}}function E(gt,Ge){return(Ge<<3)+(7&gt)}function z(gt){return gt<<1^gt>>31}function q(gt,Ge){for(var Je=gt.loadGeometry(),We=gt.type,et=0,xt=0,At=Je.length,Kt=0;Kt<At;Kt++){var Qt=Je[Kt],Mr=1;We===1&&(Mr=Qt.length),Ge.writeVarint(E(1,Mr));for(var Gr=We===3?Qt.length-1:Qt.length,Ir=0;Ir<Gr;Ir++){Ir===1&&We!==1&&Ge.writeVarint(E(2,Gr-1));var Yr=Qt[Ir].x-et,_i=Qt[Ir].y-xt;Ge.writeVarint(z(Yr)),Ge.writeVarint(z(_i)),et+=Yr,xt+=_i}We===3&&Ge.writeVarint(E(7,1))}}function V(gt,Ge){var Je=typeof gt;Je===\"string\"?Ge.writeStringField(1,gt):Je===\"boolean\"?Ge.writeBooleanField(7,gt):Je===\"number\"&&(gt%1!=0?Ge.writeDoubleField(3,gt):gt<0?Ge.writeSVarintField(6,gt):Ge.writeVarintField(5,gt))}m.exports=C,m.exports.fromVectorTileJs=C,m.exports.fromGeojsonVt=function(gt,Ge){Ge=Ge||{};var Je={};for(var We in gt)Je[We]=new x(gt[We].features,Ge),Je[We].name=We,Je[We].version=Ge.version,Je[We].extent=Ge.extent;return C({layers:Je})},m.exports.GeoJSONWrapper=x;var G=i.by(m.exports);let Z={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:gt=>gt},j=Math.fround||(N=new Float32Array(1),gt=>(N[0]=+gt,N[0]));var N;let H=3,re=5,ae=6;class _e{constructor(Ge){this.options=Object.assign(Object.create(Z),Ge),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[]}load(Ge){let{log:Je,minZoom:We,maxZoom:et}=this.options;Je&&console.time(\"total time\");let xt=`prepare ${Ge.length} points`;Je&&console.time(xt),this.points=Ge;let At=[];for(let Qt=0;Qt<Ge.length;Qt++){let Mr=Ge[Qt];if(!Mr.geometry)continue;let[Gr,Ir]=Mr.geometry.coordinates,Yr=j(ge(Gr)),_i=j(ie(Ir));At.push(Yr,_i,1/0,Qt,-1,1),this.options.reduce&&At.push(0)}let Kt=this.trees[et+1]=this._createTree(At);Je&&console.timeEnd(xt);for(let Qt=et;Qt>=We;Qt--){let Mr=+Date.now();Kt=this.trees[Qt]=this._createTree(this._cluster(Kt,Qt)),Je&&console.log(\"z%d: %d clusters in %dms\",Qt,Kt.numItems,+Date.now()-Mr)}return Je&&console.timeEnd(\"total time\"),this}getClusters(Ge,Je){let We=((Ge[0]+180)%360+360)%360-180,et=Math.max(-90,Math.min(90,Ge[1])),xt=Ge[2]===180?180:((Ge[2]+180)%360+360)%360-180,At=Math.max(-90,Math.min(90,Ge[3]));if(Ge[2]-Ge[0]>=360)We=-180,xt=180;else if(We>xt){let Ir=this.getClusters([We,et,180,At],Je),Yr=this.getClusters([-180,et,xt,At],Je);return Ir.concat(Yr)}let Kt=this.trees[this._limitZoom(Je)],Qt=Kt.range(ge(We),ie(At),ge(xt),ie(et)),Mr=Kt.data,Gr=[];for(let Ir of Qt){let Yr=this.stride*Ir;Gr.push(Mr[Yr+re]>1?Ee(Mr,Yr,this.clusterProps):this.points[Mr[Yr+H]])}return Gr}getChildren(Ge){let Je=this._getOriginId(Ge),We=this._getOriginZoom(Ge),et=\"No cluster with the specified id.\",xt=this.trees[We];if(!xt)throw new Error(et);let At=xt.data;if(Je*this.stride>=At.length)throw new Error(et);let Kt=this.options.radius/(this.options.extent*Math.pow(2,We-1)),Qt=xt.within(At[Je*this.stride],At[Je*this.stride+1],Kt),Mr=[];for(let Gr of Qt){let Ir=Gr*this.stride;At[Ir+4]===Ge&&Mr.push(At[Ir+re]>1?Ee(At,Ir,this.clusterProps):this.points[At[Ir+H]])}if(Mr.length===0)throw new Error(et);return Mr}getLeaves(Ge,Je,We){let et=[];return this._appendLeaves(et,Ge,Je=Je||10,We=We||0,0),et}getTile(Ge,Je,We){let et=this.trees[this._limitZoom(Ge)],xt=Math.pow(2,Ge),{extent:At,radius:Kt}=this.options,Qt=Kt/At,Mr=(We-Qt)/xt,Gr=(We+1+Qt)/xt,Ir={features:[]};return this._addTileFeatures(et.range((Je-Qt)/xt,Mr,(Je+1+Qt)/xt,Gr),et.data,Je,We,xt,Ir),Je===0&&this._addTileFeatures(et.range(1-Qt/xt,Mr,1,Gr),et.data,xt,We,xt,Ir),Je===xt-1&&this._addTileFeatures(et.range(0,Mr,Qt/xt,Gr),et.data,-1,We,xt,Ir),Ir.features.length?Ir:null}getClusterExpansionZoom(Ge){let Je=this._getOriginZoom(Ge)-1;for(;Je<=this.options.maxZoom;){let We=this.getChildren(Ge);if(Je++,We.length!==1)break;Ge=We[0].properties.cluster_id}return Je}_appendLeaves(Ge,Je,We,et,xt){let At=this.getChildren(Je);for(let Kt of At){let Qt=Kt.properties;if(Qt&&Qt.cluster?xt+Qt.point_count<=et?xt+=Qt.point_count:xt=this._appendLeaves(Ge,Qt.cluster_id,We,et,xt):xt<et?xt++:Ge.push(Kt),Ge.length===We)break}return xt}_createTree(Ge){let Je=new i.av(Ge.length/this.stride|0,this.options.nodeSize,Float32Array);for(let We=0;We<Ge.length;We+=this.stride)Je.add(Ge[We],Ge[We+1]);return Je.finish(),Je.data=Ge,Je}_addTileFeatures(Ge,Je,We,et,xt,At){for(let Kt of Ge){let Qt=Kt*this.stride,Mr=Je[Qt+re]>1,Gr,Ir,Yr;if(Mr)Gr=Ce(Je,Qt,this.clusterProps),Ir=Je[Qt],Yr=Je[Qt+1];else{let ai=this.points[Je[Qt+H]];Gr=ai.properties;let[mi,un]=ai.geometry.coordinates;Ir=ge(mi),Yr=ie(un)}let _i={type:1,geometry:[[Math.round(this.options.extent*(Ir*xt-We)),Math.round(this.options.extent*(Yr*xt-et))]],tags:Gr},Pi;Pi=Mr||this.options.generateId?Je[Qt+H]:this.points[Je[Qt+H]].id,Pi!==void 0&&(_i.id=Pi),At.features.push(_i)}}_limitZoom(Ge){return Math.max(this.options.minZoom,Math.min(Math.floor(+Ge),this.options.maxZoom+1))}_cluster(Ge,Je){let{radius:We,extent:et,reduce:xt,minPoints:At}=this.options,Kt=We/(et*Math.pow(2,Je)),Qt=Ge.data,Mr=[],Gr=this.stride;for(let Ir=0;Ir<Qt.length;Ir+=Gr){if(Qt[Ir+2]<=Je)continue;Qt[Ir+2]=Je;let Yr=Qt[Ir],_i=Qt[Ir+1],Pi=Ge.within(Qt[Ir],Qt[Ir+1],Kt),ai=Qt[Ir+re],mi=ai;for(let un of Pi){let Fn=un*Gr;Qt[Fn+2]>Je&&(mi+=Qt[Fn+re])}if(mi>ai&&mi>=At){let un,Fn=Yr*ai,An=_i*ai,Hn=-1,Qn=((Ir/Gr|0)<<5)+(Je+1)+this.points.length;for(let Vi of Pi){let Jn=Vi*Gr;if(Qt[Jn+2]<=Je)continue;Qt[Jn+2]=Je;let Yn=Qt[Jn+re];Fn+=Qt[Jn]*Yn,An+=Qt[Jn+1]*Yn,Qt[Jn+4]=Qn,xt&&(un||(un=this._map(Qt,Ir,!0),Hn=this.clusterProps.length,this.clusterProps.push(un)),xt(un,this._map(Qt,Jn)))}Qt[Ir+4]=Qn,Mr.push(Fn/mi,An/mi,1/0,Qn,-1,mi),xt&&Mr.push(Hn)}else{for(let un=0;un<Gr;un++)Mr.push(Qt[Ir+un]);if(mi>1)for(let un of Pi){let Fn=un*Gr;if(!(Qt[Fn+2]<=Je)){Qt[Fn+2]=Je;for(let An=0;An<Gr;An++)Mr.push(Qt[Fn+An])}}}}return Mr}_getOriginId(Ge){return Ge-this.points.length>>5}_getOriginZoom(Ge){return(Ge-this.points.length)%32}_map(Ge,Je,We){if(Ge[Je+re]>1){let At=this.clusterProps[Ge[Je+ae]];return We?Object.assign({},At):At}let et=this.points[Ge[Je+H]].properties,xt=this.options.map(et);return We&&xt===et?Object.assign({},xt):xt}}function Ee(gt,Ge,Je){return{type:\"Feature\",id:gt[Ge+H],properties:Ce(gt,Ge,Je),geometry:{type:\"Point\",coordinates:[(We=gt[Ge],360*(We-.5)),Se(gt[Ge+1])]}};var We}function Ce(gt,Ge,Je){let We=gt[Ge+re],et=We>=1e4?`${Math.round(We/1e3)}k`:We>=1e3?Math.round(We/100)/10+\"k\":We,xt=gt[Ge+ae],At=xt===-1?{}:Object.assign({},Je[xt]);return Object.assign(At,{cluster:!0,cluster_id:gt[Ge+H],point_count:We,point_count_abbreviated:et})}function ge(gt){return gt/360+.5}function ie(gt){let Ge=Math.sin(gt*Math.PI/180),Je=.5-.25*Math.log((1+Ge)/(1-Ge))/Math.PI;return Je<0?0:Je>1?1:Je}function Se(gt){let Ge=(180-360*gt)*Math.PI/180;return 360*Math.atan(Math.exp(Ge))/Math.PI-90}function ke(gt,Ge,Je,We){let et=We,xt=Ge+(Je-Ge>>1),At,Kt=Je-Ge,Qt=gt[Ge],Mr=gt[Ge+1],Gr=gt[Je],Ir=gt[Je+1];for(let Yr=Ge+3;Yr<Je;Yr+=3){let _i=be(gt[Yr],gt[Yr+1],Qt,Mr,Gr,Ir);if(_i>et)At=Yr,et=_i;else if(_i===et){let Pi=Math.abs(Yr-xt);Pi<Kt&&(At=Yr,Kt=Pi)}}et>We&&(At-Ge>3&&ke(gt,Ge,At,We),gt[At+2]=et,Je-At>3&&ke(gt,At,Je,We))}function be(gt,Ge,Je,We,et,xt){let At=et-Je,Kt=xt-We;if(At!==0||Kt!==0){let Qt=((gt-Je)*At+(Ge-We)*Kt)/(At*At+Kt*Kt);Qt>1?(Je=et,We=xt):Qt>0&&(Je+=At*Qt,We+=Kt*Qt)}return At=gt-Je,Kt=Ge-We,At*At+Kt*Kt}function Be(gt,Ge,Je,We){let et={id:gt==null?null:gt,type:Ge,geometry:Je,tags:We,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};if(Ge===\"Point\"||Ge===\"MultiPoint\"||Ge===\"LineString\")Le(et,Je);else if(Ge===\"Polygon\")Le(et,Je[0]);else if(Ge===\"MultiLineString\")for(let xt of Je)Le(et,xt);else if(Ge===\"MultiPolygon\")for(let xt of Je)Le(et,xt[0]);return et}function Le(gt,Ge){for(let Je=0;Je<Ge.length;Je+=3)gt.minX=Math.min(gt.minX,Ge[Je]),gt.minY=Math.min(gt.minY,Ge[Je+1]),gt.maxX=Math.max(gt.maxX,Ge[Je]),gt.maxY=Math.max(gt.maxY,Ge[Je+1])}function me(gt,Ge,Je,We){if(!Ge.geometry)return;let et=Ge.geometry.coordinates;if(et&&et.length===0)return;let xt=Ge.geometry.type,At=Math.pow(Je.tolerance/((1<<Je.maxZoom)*Je.extent),2),Kt=[],Qt=Ge.id;if(Je.promoteId?Qt=Ge.properties[Je.promoteId]:Je.generateId&&(Qt=We||0),xt===\"Point\")Pe(et,Kt);else if(xt===\"MultiPoint\")for(let Mr of et)Pe(Mr,Kt);else if(xt===\"LineString\")ce(et,Kt,At,!1);else if(xt===\"MultiLineString\"){if(Je.lineMetrics){for(let Mr of et)Kt=[],ce(Mr,Kt,At,!1),gt.push(Be(Qt,\"LineString\",Kt,Ge.properties));return}He(et,Kt,At,!1)}else if(xt===\"Polygon\")He(et,Kt,At,!0);else{if(xt!==\"MultiPolygon\"){if(xt===\"GeometryCollection\"){for(let Mr of Ge.geometry.geometries)me(gt,{id:Qt,geometry:Mr,properties:Ge.properties},Je,We);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(let Mr of et){let Gr=[];He(Mr,Gr,At,!0),Kt.push(Gr)}}gt.push(Be(Qt,xt,Kt,Ge.properties))}function Pe(gt,Ge){Ge.push(lt(gt[0]),mt(gt[1]),0)}function ce(gt,Ge,Je,We){let et,xt,At=0;for(let Qt=0;Qt<gt.length;Qt++){let Mr=lt(gt[Qt][0]),Gr=mt(gt[Qt][1]);Ge.push(Mr,Gr,0),Qt>0&&(At+=We?(et*Gr-Mr*xt)/2:Math.sqrt(Math.pow(Mr-et,2)+Math.pow(Gr-xt,2))),et=Mr,xt=Gr}let Kt=Ge.length-3;Ge[2]=1,ke(Ge,0,Kt,Je),Ge[Kt+2]=1,Ge.size=Math.abs(At),Ge.start=0,Ge.end=Ge.size}function He(gt,Ge,Je,We){for(let et=0;et<gt.length;et++){let xt=[];ce(gt[et],xt,Je,We),Ge.push(xt)}}function lt(gt){return gt/360+.5}function mt(gt){let Ge=Math.sin(gt*Math.PI/180),Je=.5-.25*Math.log((1+Ge)/(1-Ge))/Math.PI;return Je<0?0:Je>1?1:Je}function Ht(gt,Ge,Je,We,et,xt,At,Kt){if(We/=Ge,xt>=(Je/=Ge)&&At<We)return gt;if(At<Je||xt>=We)return null;let Qt=[];for(let Mr of gt){let Gr=Mr.geometry,Ir=Mr.type,Yr=et===0?Mr.minX:Mr.minY,_i=et===0?Mr.maxX:Mr.maxY;if(Yr>=Je&&_i<We){Qt.push(Mr);continue}if(_i<Je||Yr>=We)continue;let Pi=[];if(Ir===\"Point\"||Ir===\"MultiPoint\")at(Gr,Pi,Je,We,et);else if(Ir===\"LineString\")ct(Gr,Pi,Je,We,et,!1,Kt.lineMetrics);else if(Ir===\"MultiLineString\")Vt(Gr,Pi,Je,We,et,!1);else if(Ir===\"Polygon\")Vt(Gr,Pi,Je,We,et,!0);else if(Ir===\"MultiPolygon\")for(let ai of Gr){let mi=[];Vt(ai,mi,Je,We,et,!0),mi.length&&Pi.push(mi)}if(Pi.length){if(Kt.lineMetrics&&Ir===\"LineString\"){for(let ai of Pi)Qt.push(Be(Mr.id,Ir,ai,Mr.tags));continue}Ir!==\"LineString\"&&Ir!==\"MultiLineString\"||(Pi.length===1?(Ir=\"LineString\",Pi=Pi[0]):Ir=\"MultiLineString\"),Ir!==\"Point\"&&Ir!==\"MultiPoint\"||(Ir=Pi.length===3?\"Point\":\"MultiPoint\"),Qt.push(Be(Mr.id,Ir,Pi,Mr.tags))}}return Qt.length?Qt:null}function at(gt,Ge,Je,We,et){for(let xt=0;xt<gt.length;xt+=3){let At=gt[xt+et];At>=Je&&At<=We&&rr(Ge,gt[xt],gt[xt+1],gt[xt+2])}}function ct(gt,Ge,Je,We,et,xt,At){let Kt=ar(gt),Qt=et===0?tt:je,Mr,Gr,Ir=gt.start;for(let mi=0;mi<gt.length-3;mi+=3){let un=gt[mi],Fn=gt[mi+1],An=gt[mi+2],Hn=gt[mi+3],Qn=gt[mi+4],Vi=et===0?un:Fn,Jn=et===0?Hn:Qn,Yn=!1;At&&(Mr=Math.sqrt(Math.pow(un-Hn,2)+Math.pow(Fn-Qn,2))),Vi<Je?Jn>Je&&(Gr=Qt(Kt,un,Fn,Hn,Qn,Je),At&&(Kt.start=Ir+Mr*Gr)):Vi>We?Jn<We&&(Gr=Qt(Kt,un,Fn,Hn,Qn,We),At&&(Kt.start=Ir+Mr*Gr)):rr(Kt,un,Fn,An),Jn<Je&&Vi>=Je&&(Gr=Qt(Kt,un,Fn,Hn,Qn,Je),Yn=!0),Jn>We&&Vi<=We&&(Gr=Qt(Kt,un,Fn,Hn,Qn,We),Yn=!0),!xt&&Yn&&(At&&(Kt.end=Ir+Mr*Gr),Ge.push(Kt),Kt=ar(gt)),At&&(Ir+=Mr)}let Yr=gt.length-3,_i=gt[Yr],Pi=gt[Yr+1],ai=et===0?_i:Pi;ai>=Je&&ai<=We&&rr(Kt,_i,Pi,gt[Yr+2]),Yr=Kt.length-3,xt&&Yr>=3&&(Kt[Yr]!==Kt[0]||Kt[Yr+1]!==Kt[1])&&rr(Kt,Kt[0],Kt[1],Kt[2]),Kt.length&&Ge.push(Kt)}function ar(gt){let Ge=[];return Ge.size=gt.size,Ge.start=gt.start,Ge.end=gt.end,Ge}function Vt(gt,Ge,Je,We,et,xt){for(let At of gt)ct(At,Ge,Je,We,et,xt,!1)}function rr(gt,Ge,Je,We){gt.push(Ge,Je,We)}function tt(gt,Ge,Je,We,et,xt){let At=(xt-Ge)/(We-Ge);return rr(gt,xt,Je+(et-Je)*At,1),At}function je(gt,Ge,Je,We,et,xt){let At=(xt-Je)/(et-Je);return rr(gt,Ge+(We-Ge)*At,xt,1),At}function Ue(gt,Ge){let Je=[];for(let We=0;We<gt.length;We++){let et=gt[We],xt=et.type,At;if(xt===\"Point\"||xt===\"MultiPoint\"||xt===\"LineString\")At=Ae(et.geometry,Ge);else if(xt===\"MultiLineString\"||xt===\"Polygon\"){At=[];for(let Kt of et.geometry)At.push(Ae(Kt,Ge))}else if(xt===\"MultiPolygon\"){At=[];for(let Kt of et.geometry){let Qt=[];for(let Mr of Kt)Qt.push(Ae(Mr,Ge));At.push(Qt)}}Je.push(Be(et.id,xt,At,et.tags))}return Je}function Ae(gt,Ge){let Je=[];Je.size=gt.size,gt.start!==void 0&&(Je.start=gt.start,Je.end=gt.end);for(let We=0;We<gt.length;We+=3)Je.push(gt[We]+Ge,gt[We+1],gt[We+2]);return Je}function rt(gt,Ge){if(gt.transformed)return gt;let Je=1<<gt.z,We=gt.x,et=gt.y;for(let xt of gt.features){let At=xt.geometry,Kt=xt.type;if(xt.geometry=[],Kt===1)for(let Qt=0;Qt<At.length;Qt+=2)xt.geometry.push(St(At[Qt],At[Qt+1],Ge,Je,We,et));else for(let Qt=0;Qt<At.length;Qt++){let Mr=[];for(let Gr=0;Gr<At[Qt].length;Gr+=2)Mr.push(St(At[Qt][Gr],At[Qt][Gr+1],Ge,Je,We,et));xt.geometry.push(Mr)}}return gt.transformed=!0,gt}function St(gt,Ge,Je,We,et,xt){return[Math.round(Je*(gt*We-et)),Math.round(Je*(Ge*We-xt))]}function Tt(gt,Ge,Je,We,et){let xt=Ge===et.maxZoom?0:et.tolerance/((1<<Ge)*et.extent),At={features:[],numPoints:0,numSimplified:0,numFeatures:gt.length,source:null,x:Je,y:We,z:Ge,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0};for(let Kt of gt)dt(At,Kt,xt,et);return At}function dt(gt,Ge,Je,We){let et=Ge.geometry,xt=Ge.type,At=[];if(gt.minX=Math.min(gt.minX,Ge.minX),gt.minY=Math.min(gt.minY,Ge.minY),gt.maxX=Math.max(gt.maxX,Ge.maxX),gt.maxY=Math.max(gt.maxY,Ge.maxY),xt===\"Point\"||xt===\"MultiPoint\")for(let Kt=0;Kt<et.length;Kt+=3)At.push(et[Kt],et[Kt+1]),gt.numPoints++,gt.numSimplified++;else if(xt===\"LineString\")Et(At,et,gt,Je,!1,!1);else if(xt===\"MultiLineString\"||xt===\"Polygon\")for(let Kt=0;Kt<et.length;Kt++)Et(At,et[Kt],gt,Je,xt===\"Polygon\",Kt===0);else if(xt===\"MultiPolygon\")for(let Kt=0;Kt<et.length;Kt++){let Qt=et[Kt];for(let Mr=0;Mr<Qt.length;Mr++)Et(At,Qt[Mr],gt,Je,!0,Mr===0)}if(At.length){let Kt=Ge.tags||null;if(xt===\"LineString\"&&We.lineMetrics){Kt={};for(let Mr in Ge.tags)Kt[Mr]=Ge.tags[Mr];Kt.mapbox_clip_start=et.start/et.size,Kt.mapbox_clip_end=et.end/et.size}let Qt={geometry:At,type:xt===\"Polygon\"||xt===\"MultiPolygon\"?3:xt===\"LineString\"||xt===\"MultiLineString\"?2:1,tags:Kt};Ge.id!==null&&(Qt.id=Ge.id),gt.features.push(Qt)}}function Et(gt,Ge,Je,We,et,xt){let At=We*We;if(We>0&&Ge.size<(et?At:We))return void(Je.numPoints+=Ge.length/3);let Kt=[];for(let Qt=0;Qt<Ge.length;Qt+=3)(We===0||Ge[Qt+2]>At)&&(Je.numSimplified++,Kt.push(Ge[Qt],Ge[Qt+1])),Je.numPoints++;et&&(function(Qt,Mr){let Gr=0;for(let Ir=0,Yr=Qt.length,_i=Yr-2;Ir<Yr;_i=Ir,Ir+=2)Gr+=(Qt[Ir]-Qt[_i])*(Qt[Ir+1]+Qt[_i+1]);if(Gr>0===Mr)for(let Ir=0,Yr=Qt.length;Ir<Yr/2;Ir+=2){let _i=Qt[Ir],Pi=Qt[Ir+1];Qt[Ir]=Qt[Yr-2-Ir],Qt[Ir+1]=Qt[Yr-1-Ir],Qt[Yr-2-Ir]=_i,Qt[Yr-1-Ir]=Pi}})(Kt,xt),gt.push(Kt)}let pt={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0};class jt{constructor(Ge,Je){let We=(Je=this.options=(function(xt,At){for(let Kt in At)xt[Kt]=At[Kt];return xt})(Object.create(pt),Je)).debug;if(We&&console.time(\"preprocess data\"),Je.maxZoom<0||Je.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(Je.promoteId&&Je.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");let et=(function(xt,At){let Kt=[];if(xt.type===\"FeatureCollection\")for(let Qt=0;Qt<xt.features.length;Qt++)me(Kt,xt.features[Qt],At,Qt);else me(Kt,xt.type===\"Feature\"?xt:{geometry:xt},At);return Kt})(Ge,Je);this.tiles={},this.tileCoords=[],We&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",Je.indexMaxZoom,Je.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),et=(function(xt,At){let Kt=At.buffer/At.extent,Qt=xt,Mr=Ht(xt,1,-1-Kt,Kt,0,-1,2,At),Gr=Ht(xt,1,1-Kt,2+Kt,0,-1,2,At);return(Mr||Gr)&&(Qt=Ht(xt,1,-Kt,1+Kt,0,-1,2,At)||[],Mr&&(Qt=Ue(Mr,1).concat(Qt)),Gr&&(Qt=Qt.concat(Ue(Gr,-1)))),Qt})(et,Je),et.length&&this.splitTile(et,0,0,0),We&&(et.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}splitTile(Ge,Je,We,et,xt,At,Kt){let Qt=[Ge,Je,We,et],Mr=this.options,Gr=Mr.debug;for(;Qt.length;){et=Qt.pop(),We=Qt.pop(),Je=Qt.pop(),Ge=Qt.pop();let Ir=1<<Je,Yr=or(Je,We,et),_i=this.tiles[Yr];if(!_i&&(Gr>1&&console.time(\"creation\"),_i=this.tiles[Yr]=Tt(Ge,Je,We,et,Mr),this.tileCoords.push({z:Je,x:We,y:et}),Gr)){Gr>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",Je,We,et,_i.numFeatures,_i.numPoints,_i.numSimplified),console.timeEnd(\"creation\"));let Yn=`z${Je}`;this.stats[Yn]=(this.stats[Yn]||0)+1,this.total++}if(_i.source=Ge,xt==null){if(Je===Mr.indexMaxZoom||_i.numPoints<=Mr.indexMaxPoints)continue}else{if(Je===Mr.maxZoom||Je===xt)continue;if(xt!=null){let Yn=xt-Je;if(We!==At>>Yn||et!==Kt>>Yn)continue}}if(_i.source=null,Ge.length===0)continue;Gr>1&&console.time(\"clipping\");let Pi=.5*Mr.buffer/Mr.extent,ai=.5-Pi,mi=.5+Pi,un=1+Pi,Fn=null,An=null,Hn=null,Qn=null,Vi=Ht(Ge,Ir,We-Pi,We+mi,0,_i.minX,_i.maxX,Mr),Jn=Ht(Ge,Ir,We+ai,We+un,0,_i.minX,_i.maxX,Mr);Ge=null,Vi&&(Fn=Ht(Vi,Ir,et-Pi,et+mi,1,_i.minY,_i.maxY,Mr),An=Ht(Vi,Ir,et+ai,et+un,1,_i.minY,_i.maxY,Mr),Vi=null),Jn&&(Hn=Ht(Jn,Ir,et-Pi,et+mi,1,_i.minY,_i.maxY,Mr),Qn=Ht(Jn,Ir,et+ai,et+un,1,_i.minY,_i.maxY,Mr),Jn=null),Gr>1&&console.timeEnd(\"clipping\"),Qt.push(Fn||[],Je+1,2*We,2*et),Qt.push(An||[],Je+1,2*We,2*et+1),Qt.push(Hn||[],Je+1,2*We+1,2*et),Qt.push(Qn||[],Je+1,2*We+1,2*et+1)}}getTile(Ge,Je,We){Ge=+Ge,Je=+Je,We=+We;let et=this.options,{extent:xt,debug:At}=et;if(Ge<0||Ge>24)return null;let Kt=1<<Ge,Qt=or(Ge,Je=Je+Kt&Kt-1,We);if(this.tiles[Qt])return rt(this.tiles[Qt],xt);At>1&&console.log(\"drilling down to z%d-%d-%d\",Ge,Je,We);let Mr,Gr=Ge,Ir=Je,Yr=We;for(;!Mr&&Gr>0;)Gr--,Ir>>=1,Yr>>=1,Mr=this.tiles[or(Gr,Ir,Yr)];return Mr&&Mr.source?(At>1&&(console.log(\"found parent tile z%d-%d-%d\",Gr,Ir,Yr),console.time(\"drilling down\")),this.splitTile(Mr.source,Gr,Ir,Yr,Ge,Je,We),At>1&&console.timeEnd(\"drilling down\"),this.tiles[Qt]?rt(this.tiles[Qt],xt):null):null}}function or(gt,Ge,Je){return 32*((1<<gt)*Je+Ge)+gt}function mr(gt,Ge){return Ge?gt.properties[Ge]:gt.id}function Ar(gt,Ge){if(gt==null)return!0;if(gt.type===\"Feature\")return mr(gt,Ge)!=null;if(gt.type===\"FeatureCollection\"){let Je=new Set;for(let We of gt.features){let et=mr(We,Ge);if(et==null||Je.has(et))return!1;Je.add(et)}return!0}return!1}function ei(gt,Ge){let Je=new Map;if(gt!=null)if(gt.type===\"Feature\")Je.set(mr(gt,Ge),gt);else for(let We of gt.features)Je.set(mr(We,Ge),We);return Je}class qr extends u{constructor(){super(...arguments),this._dataUpdateable=new Map}loadVectorTile(Ge,Je){return i._(this,void 0,void 0,(function*(){let We=Ge.tileID.canonical;if(!this._geoJSONIndex)throw new Error(\"Unable to parse the data into a cluster or geojson\");let et=this._geoJSONIndex.getTile(We.z,We.x,We.y);if(!et)return null;let xt=new class{constructor(Kt){this.layers={_geojsonTileLayer:this},this.name=\"_geojsonTileLayer\",this.extent=i.X,this.length=Kt.length,this._features=Kt}feature(Kt){return new class{constructor(Qt){this._feature=Qt,this.extent=i.X,this.type=Qt.type,this.properties=Qt.tags,\"id\"in Qt&&!isNaN(Qt.id)&&(this.id=parseInt(Qt.id,10))}loadGeometry(){if(this._feature.type===1){let Qt=[];for(let Mr of this._feature.geometry)Qt.push([new i.P(Mr[0],Mr[1])]);return Qt}{let Qt=[];for(let Mr of this._feature.geometry){let Gr=[];for(let Ir of Mr)Gr.push(new i.P(Ir[0],Ir[1]));Qt.push(Gr)}return Qt}}toGeoJSON(Qt,Mr,Gr){return v.call(this,Qt,Mr,Gr)}}(this._features[Kt])}}(et.features),At=G(xt);return At.byteOffset===0&&At.byteLength===At.buffer.byteLength||(At=new Uint8Array(At)),{vectorTile:xt,rawData:At.buffer}}))}loadData(Ge){return i._(this,void 0,void 0,(function*(){var Je;(Je=this._pendingRequest)===null||Je===void 0||Je.abort();let We=!!(Ge&&Ge.request&&Ge.request.collectResourceTiming)&&new i.bv(Ge.request);this._pendingRequest=new AbortController;try{this._pendingData=this.loadAndProcessGeoJSON(Ge,this._pendingRequest),this._geoJSONIndex=Ge.cluster?new _e((function({superclusterOptions:At,clusterProperties:Kt}){if(!Kt||!At)return At;let Qt={},Mr={},Gr={accumulated:null,zoom:0},Ir={properties:null},Yr=Object.keys(Kt);for(let _i of Yr){let[Pi,ai]=Kt[_i],mi=i.bC(ai),un=i.bC(typeof Pi==\"string\"?[Pi,[\"accumulated\"],[\"get\",_i]]:Pi);Qt[_i]=mi.value,Mr[_i]=un.value}return At.map=_i=>{Ir.properties=_i;let Pi={};for(let ai of Yr)Pi[ai]=Qt[ai].evaluate(Gr,Ir);return Pi},At.reduce=(_i,Pi)=>{Ir.properties=Pi;for(let ai of Yr)Gr.accumulated=_i[ai],_i[ai]=Mr[ai].evaluate(Gr,Ir)},At})(Ge)).load((yield this._pendingData).features):(et=yield this._pendingData,new jt(et,Ge.geojsonVtOptions)),this.loaded={};let xt={};if(We){let At=We.finish();At&&(xt.resourceTiming={},xt.resourceTiming[Ge.source]=JSON.parse(JSON.stringify(At)))}return xt}catch(xt){if(delete this._pendingRequest,i.bB(xt))return{abandoned:!0};throw xt}var et}))}getData(){return i._(this,void 0,void 0,(function*(){return this._pendingData}))}reloadTile(Ge){let Je=this.loaded;return Je&&Je[Ge.uid]?super.reloadTile(Ge):this.loadTile(Ge)}loadAndProcessGeoJSON(Ge,Je){return i._(this,void 0,void 0,(function*(){let We=yield this.loadGeoJSON(Ge,Je);if(delete this._pendingRequest,typeof We!=\"object\")throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`);if(d(We,!0),Ge.filter){let et=i.bC(Ge.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(et.result===\"error\")throw new Error(et.value.map((At=>`${At.key}: ${At.message}`)).join(\", \"));We={type:\"FeatureCollection\",features:We.features.filter((At=>et.value.evaluate({zoom:0},At)))}}return We}))}loadGeoJSON(Ge,Je){return i._(this,void 0,void 0,(function*(){let{promoteId:We}=Ge;if(Ge.request){let et=yield i.h(Ge.request,Je);return this._dataUpdateable=Ar(et.data,We)?ei(et.data,We):void 0,et.data}if(typeof Ge.data==\"string\")try{let et=JSON.parse(Ge.data);return this._dataUpdateable=Ar(et,We)?ei(et,We):void 0,et}catch(et){throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`)}if(!Ge.dataDiff)throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`);if(!this._dataUpdateable)throw new Error(`Cannot update existing geojson data in ${Ge.source}`);return(function(et,xt,At){var Kt,Qt,Mr,Gr;if(xt.removeAll&&et.clear(),xt.remove)for(let Ir of xt.remove)et.delete(Ir);if(xt.add)for(let Ir of xt.add){let Yr=mr(Ir,At);Yr!=null&&et.set(Yr,Ir)}if(xt.update)for(let Ir of xt.update){let Yr=et.get(Ir.id);if(Yr==null)continue;let _i=!Ir.removeAllProperties&&(((Kt=Ir.removeProperties)===null||Kt===void 0?void 0:Kt.length)>0||((Qt=Ir.addOrUpdateProperties)===null||Qt===void 0?void 0:Qt.length)>0);if((Ir.newGeometry||Ir.removeAllProperties||_i)&&(Yr=Object.assign({},Yr),et.set(Ir.id,Yr),_i&&(Yr.properties=Object.assign({},Yr.properties))),Ir.newGeometry&&(Yr.geometry=Ir.newGeometry),Ir.removeAllProperties)Yr.properties={};else if(((Mr=Ir.removeProperties)===null||Mr===void 0?void 0:Mr.length)>0)for(let Pi of Ir.removeProperties)Object.prototype.hasOwnProperty.call(Yr.properties,Pi)&&delete Yr.properties[Pi];if(((Gr=Ir.addOrUpdateProperties)===null||Gr===void 0?void 0:Gr.length)>0)for(let{key:Pi,value:ai}of Ir.addOrUpdateProperties)Yr.properties[Pi]=ai}})(this._dataUpdateable,Ge.dataDiff,We),{type:\"FeatureCollection\",features:Array.from(this._dataUpdateable.values())}}))}removeSource(Ge){return i._(this,void 0,void 0,(function*(){this._pendingRequest&&this._pendingRequest.abort()}))}getClusterExpansionZoom(Ge){return this._geoJSONIndex.getClusterExpansionZoom(Ge.clusterId)}getClusterChildren(Ge){return this._geoJSONIndex.getChildren(Ge.clusterId)}getClusterLeaves(Ge){return this._geoJSONIndex.getLeaves(Ge.clusterId,Ge.limit,Ge.offset)}}class jr{constructor(Ge){this.self=Ge,this.actor=new i.F(Ge),this.layerIndexes={},this.availableImages={},this.workerSources={},this.demWorkerSources={},this.externalWorkerSourceTypes={},this.self.registerWorkerSource=(Je,We)=>{if(this.externalWorkerSourceTypes[Je])throw new Error(`Worker source with name \"${Je}\" already registered.`);this.externalWorkerSourceTypes[Je]=We},this.self.addProtocol=i.bi,this.self.removeProtocol=i.bj,this.self.registerRTLTextPlugin=Je=>{if(i.bD.isParsed())throw new Error(\"RTL text plugin already registered.\");i.bD.setMethods(Je)},this.actor.registerMessageHandler(\"LDT\",((Je,We)=>this._getDEMWorkerSource(Je,We.source).loadTile(We))),this.actor.registerMessageHandler(\"RDT\",((Je,We)=>i._(this,void 0,void 0,(function*(){this._getDEMWorkerSource(Je,We.source).removeTile(We)})))),this.actor.registerMessageHandler(\"GCEZ\",((Je,We)=>i._(this,void 0,void 0,(function*(){return this._getWorkerSource(Je,We.type,We.source).getClusterExpansionZoom(We)})))),this.actor.registerMessageHandler(\"GCC\",((Je,We)=>i._(this,void 0,void 0,(function*(){return this._getWorkerSource(Je,We.type,We.source).getClusterChildren(We)})))),this.actor.registerMessageHandler(\"GCL\",((Je,We)=>i._(this,void 0,void 0,(function*(){return this._getWorkerSource(Je,We.type,We.source).getClusterLeaves(We)})))),this.actor.registerMessageHandler(\"LD\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).loadData(We))),this.actor.registerMessageHandler(\"GD\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).getData())),this.actor.registerMessageHandler(\"LT\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).loadTile(We))),this.actor.registerMessageHandler(\"RT\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).reloadTile(We))),this.actor.registerMessageHandler(\"AT\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).abortTile(We))),this.actor.registerMessageHandler(\"RMT\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).removeTile(We))),this.actor.registerMessageHandler(\"RS\",((Je,We)=>i._(this,void 0,void 0,(function*(){if(!this.workerSources[Je]||!this.workerSources[Je][We.type]||!this.workerSources[Je][We.type][We.source])return;let et=this.workerSources[Je][We.type][We.source];delete this.workerSources[Je][We.type][We.source],et.removeSource!==void 0&&et.removeSource(We)})))),this.actor.registerMessageHandler(\"RM\",(Je=>i._(this,void 0,void 0,(function*(){delete this.layerIndexes[Je],delete this.availableImages[Je],delete this.workerSources[Je],delete this.demWorkerSources[Je]})))),this.actor.registerMessageHandler(\"SR\",((Je,We)=>i._(this,void 0,void 0,(function*(){this.referrer=We})))),this.actor.registerMessageHandler(\"SRPS\",((Je,We)=>this._syncRTLPluginState(Je,We))),this.actor.registerMessageHandler(\"IS\",((Je,We)=>i._(this,void 0,void 0,(function*(){this.self.importScripts(We)})))),this.actor.registerMessageHandler(\"SI\",((Je,We)=>this._setImages(Je,We))),this.actor.registerMessageHandler(\"UL\",((Je,We)=>i._(this,void 0,void 0,(function*(){this._getLayerIndex(Je).update(We.layers,We.removedIds)})))),this.actor.registerMessageHandler(\"SL\",((Je,We)=>i._(this,void 0,void 0,(function*(){this._getLayerIndex(Je).replace(We)}))))}_setImages(Ge,Je){return i._(this,void 0,void 0,(function*(){this.availableImages[Ge]=Je;for(let We in this.workerSources[Ge]){let et=this.workerSources[Ge][We];for(let xt in et)et[xt].availableImages=Je}}))}_syncRTLPluginState(Ge,Je){return i._(this,void 0,void 0,(function*(){if(i.bD.isParsed())return i.bD.getState();if(Je.pluginStatus!==\"loading\")return i.bD.setState(Je),Je;let We=Je.pluginURL;if(this.self.importScripts(We),i.bD.isParsed()){let et={pluginStatus:\"loaded\",pluginURL:We};return i.bD.setState(et),et}throw i.bD.setState({pluginStatus:\"error\",pluginURL:\"\"}),new Error(`RTL Text Plugin failed to import scripts from ${We}`)}))}_getAvailableImages(Ge){let Je=this.availableImages[Ge];return Je||(Je=[]),Je}_getLayerIndex(Ge){let Je=this.layerIndexes[Ge];return Je||(Je=this.layerIndexes[Ge]=new a),Je}_getWorkerSource(Ge,Je,We){if(this.workerSources[Ge]||(this.workerSources[Ge]={}),this.workerSources[Ge][Je]||(this.workerSources[Ge][Je]={}),!this.workerSources[Ge][Je][We]){let et={sendAsync:(xt,At)=>(xt.targetMapId=Ge,this.actor.sendAsync(xt,At))};switch(Je){case\"vector\":this.workerSources[Ge][Je][We]=new u(et,this._getLayerIndex(Ge),this._getAvailableImages(Ge));break;case\"geojson\":this.workerSources[Ge][Je][We]=new qr(et,this._getLayerIndex(Ge),this._getAvailableImages(Ge));break;default:this.workerSources[Ge][Je][We]=new this.externalWorkerSourceTypes[Je](et,this._getLayerIndex(Ge),this._getAvailableImages(Ge))}}return this.workerSources[Ge][Je][We]}_getDEMWorkerSource(Ge,Je){return this.demWorkerSources[Ge]||(this.demWorkerSources[Ge]={}),this.demWorkerSources[Ge][Je]||(this.demWorkerSources[Ge][Je]=new c),this.demWorkerSources[Ge][Je]}}return i.i(self)&&(self.worker=new jr(self)),jr})),r(\"index\",[\"exports\",\"./shared\"],(function(i,a){\"use strict\";var o=\"4.7.1\";let s,l,u={now:typeof performance!=\"undefined\"&&performance&&performance.now?performance.now.bind(performance):Date.now.bind(Date),frameAsync:ue=>new Promise(((w,B)=>{let 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0&&(ee.className=B),Q&&Q.appendChild(ee),ee}static createNS(w,B){return window.document.createElementNS(w,B)}static disableDrag(){c.docStyle&&c.selectProp&&(c.userSelect=c.docStyle[c.selectProp],c.docStyle[c.selectProp]=\"none\")}static enableDrag(){c.docStyle&&c.selectProp&&(c.docStyle[c.selectProp]=c.userSelect)}static setTransform(w,B){w.style[c.transformProp]=B}static addEventListener(w,B,Q,ee={}){w.addEventListener(B,Q,\"passive\"in ee?ee:ee.capture)}static removeEventListener(w,B,Q,ee={}){w.removeEventListener(B,Q,\"passive\"in ee?ee:ee.capture)}static suppressClickInternal(w){w.preventDefault(),w.stopPropagation(),window.removeEventListener(\"click\",c.suppressClickInternal,!0)}static suppressClick(){window.addEventListener(\"click\",c.suppressClickInternal,!0),window.setTimeout((()=>{window.removeEventListener(\"click\",c.suppressClickInternal,!0)}),0)}static getScale(w){let B=w.getBoundingClientRect();return{x:B.width/w.offsetWidth||1,y:B.height/w.offsetHeight||1,boundingClientRect:B}}static getPoint(w,B,Q){let ee=B.boundingClientRect;return new a.P((Q.clientX-ee.left)/B.x-w.clientLeft,(Q.clientY-ee.top)/B.y-w.clientTop)}static mousePos(w,B){let Q=c.getScale(w);return c.getPoint(w,Q,B)}static touchPos(w,B){let Q=[],ee=c.getScale(w);for(let le=0;le<B.length;le++)Q.push(c.getPoint(w,ee,B[le]));return Q}static mouseButton(w){return w.button}static remove(w){w.parentNode&&w.parentNode.removeChild(w)}}c.docStyle=typeof window!=\"undefined\"&&window.document&&window.document.documentElement.style,c.selectProp=c.testProp([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]),c.transformProp=c.testProp([\"transform\",\"WebkitTransform\"]);let f={supported:!1,testSupport:function(ue){!v&&d&&(m?b(ue):h=ue)}},h,d,v=!1,m=!1;function b(ue){let w=ue.createTexture();ue.bindTexture(ue.TEXTURE_2D,w);try{if(ue.texImage2D(ue.TEXTURE_2D,0,ue.RGBA,ue.RGBA,ue.UNSIGNED_BYTE,d),ue.isContextLost())return;f.supported=!0}catch(B){}ue.deleteTexture(w),v=!0}var p;typeof document!=\"undefined\"&&(d=document.createElement(\"img\"),d.onload=()=>{h&&b(h),h=null,m=!0},d.onerror=()=>{v=!0,h=null},d.src=\"data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=\"),(function(ue){let w,B,Q,ee;ue.resetRequestQueue=()=>{w=[],B=0,Q=0,ee={}},ue.addThrottleControl=ut=>{let Mt=Q++;return ee[Mt]=ut,Mt},ue.removeThrottleControl=ut=>{delete ee[ut],Fe()},ue.getImage=(ut,Mt,Jt=!0)=>new Promise((($t,yr)=>{f.supported&&(ut.headers||(ut.headers={}),ut.headers.accept=\"image/webp,*/*\"),a.e(ut,{type:\"image\"}),w.push({abortController:Mt,requestParameters:ut,supportImageRefresh:Jt,state:\"queued\",onError:Cr=>{yr(Cr)},onSuccess:Cr=>{$t(Cr)}}),Fe()}));let le=ut=>a._(this,void 0,void 0,(function*(){ut.state=\"running\";let{requestParameters:Mt,supportImageRefresh:Jt,onError:$t,onSuccess:yr,abortController:Cr}=ut,ve=Jt===!1&&!a.i(self)&&!a.g(Mt.url)&&(!Mt.headers||Object.keys(Mt.headers).reduce(((ze,Qe)=>ze&&Qe===\"accept\"),!0));B++;let xe=ve?Ze(Mt,Cr):a.m(Mt,Cr);try{let ze=yield xe;delete ut.abortController,ut.state=\"completed\",ze.data instanceof HTMLImageElement||a.b(ze.data)?yr(ze):ze.data&&yr({data:yield(Ie=ze.data,typeof createImageBitmap==\"function\"?a.d(Ie):a.f(Ie)),cacheControl:ze.cacheControl,expires:ze.expires})}catch(ze){delete ut.abortController,$t(ze)}finally{B--,Fe()}var Ie})),Fe=()=>{let ut=(()=>{for(let Mt of Object.keys(ee))if(ee[Mt]())return!0;return!1})()?a.a.MAX_PARALLEL_IMAGE_REQUESTS_PER_FRAME:a.a.MAX_PARALLEL_IMAGE_REQUESTS;for(let Mt=B;Mt<ut&&w.length>0;Mt++){let Jt=w.shift();Jt.abortController.signal.aborted?Mt--:le(Jt)}},Ze=(ut,Mt)=>new Promise(((Jt,$t)=>{let yr=new Image,Cr=ut.url,ve=ut.credentials;ve&&ve===\"include\"?yr.crossOrigin=\"use-credentials\":(ve&&ve===\"same-origin\"||!a.s(Cr))&&(yr.crossOrigin=\"anonymous\"),Mt.signal.addEventListener(\"abort\",(()=>{yr.src=\"\",$t(a.c())})),yr.fetchPriority=\"high\",yr.onload=()=>{yr.onerror=yr.onload=null,Jt({data:yr})},yr.onerror=()=>{yr.onerror=yr.onload=null,Mt.signal.aborted||$t(new Error(\"Could not load image. Please make sure to use a supported image type such as PNG or JPEG. Note that SVGs are not supported.\"))},yr.src=Cr}))})(p||(p={})),p.resetRequestQueue();class k{constructor(w){this._transformRequestFn=w}transformRequest(w,B){return this._transformRequestFn&&this._transformRequestFn(w,B)||{url:w}}setTransformRequest(w){this._transformRequestFn=w}}function M(ue){var w=new a.A(3);return w[0]=ue[0],w[1]=ue[1],w[2]=ue[2],w}var T,L=function(ue,w,B){return ue[0]=w[0]-B[0],ue[1]=w[1]-B[1],ue[2]=w[2]-B[2],ue};T=new a.A(3),a.A!=Float32Array&&(T[0]=0,T[1]=0,T[2]=0);var x=function(ue){var w=ue[0],B=ue[1];return w*w+B*B};function C(ue){let w=[];if(typeof ue==\"string\")w.push({id:\"default\",url:ue});else if(ue&&ue.length>0){let B=[];for(let{id:Q,url:ee}of ue){let le=`${Q}${ee}`;B.indexOf(le)===-1&&(B.push(le),w.push({id:Q,url:ee}))}}return w}function S(ue,w,B){let Q=ue.split(\"?\");return Q[0]+=`${w}${B}`,Q.join(\"?\")}(function(){var ue=new a.A(2);a.A!=Float32Array&&(ue[0]=0,ue[1]=0)})();class g{constructor(w,B,Q,ee){this.context=w,this.format=Q,this.texture=w.gl.createTexture(),this.update(B,ee)}update(w,B,Q){let{width:ee,height:le}=w,Fe=!(this.size&&this.size[0]===ee&&this.size[1]===le||Q),{context:Ze}=this,{gl:ut}=Ze;if(this.useMipmap=!!(B&&B.useMipmap),ut.bindTexture(ut.TEXTURE_2D,this.texture),Ze.pixelStoreUnpackFlipY.set(!1),Ze.pixelStoreUnpack.set(1),Ze.pixelStoreUnpackPremultiplyAlpha.set(this.format===ut.RGBA&&(!B||B.premultiply!==!1)),Fe)this.size=[ee,le],w instanceof HTMLImageElement||w instanceof HTMLCanvasElement||w instanceof HTMLVideoElement||w instanceof ImageData||a.b(w)?ut.texImage2D(ut.TEXTURE_2D,0,this.format,this.format,ut.UNSIGNED_BYTE,w):ut.texImage2D(ut.TEXTURE_2D,0,this.format,ee,le,0,this.format,ut.UNSIGNED_BYTE,w.data);else{let{x:Mt,y:Jt}=Q||{x:0,y:0};w instanceof HTMLImageElement||w instanceof HTMLCanvasElement||w instanceof HTMLVideoElement||w instanceof ImageData||a.b(w)?ut.texSubImage2D(ut.TEXTURE_2D,0,Mt,Jt,ut.RGBA,ut.UNSIGNED_BYTE,w):ut.texSubImage2D(ut.TEXTURE_2D,0,Mt,Jt,ee,le,ut.RGBA,ut.UNSIGNED_BYTE,w.data)}this.useMipmap&&this.isSizePowerOfTwo()&&ut.generateMipmap(ut.TEXTURE_2D)}bind(w,B,Q){let{context:ee}=this,{gl:le}=ee;le.bindTexture(le.TEXTURE_2D,this.texture),Q!==le.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(Q=le.LINEAR),w!==this.filter&&(le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MAG_FILTER,w),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MIN_FILTER,Q||w),this.filter=w),B!==this.wrap&&(le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_S,B),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_T,B),this.wrap=B)}isSizePowerOfTwo(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0}destroy(){let{gl:w}=this.context;w.deleteTexture(this.texture),this.texture=null}}function P(ue){let{userImage:w}=ue;return!!(w&&w.render&&w.render())&&(ue.data.replace(new Uint8Array(w.data.buffer)),!0)}class E extends a.E{constructor(){super(),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new a.R({width:1,height:1}),this.dirty=!0}isLoaded(){return this.loaded}setLoaded(w){if(this.loaded!==w&&(this.loaded=w,w)){for(let{ids:B,promiseResolve:Q}of this.requestors)Q(this._getImagesForIds(B));this.requestors=[]}}getImage(w){let B=this.images[w];if(B&&!B.data&&B.spriteData){let Q=B.spriteData;B.data=new a.R({width:Q.width,height:Q.height},Q.context.getImageData(Q.x,Q.y,Q.width,Q.height).data),B.spriteData=null}return B}addImage(w,B){if(this.images[w])throw new Error(`Image id ${w} already exist, use updateImage instead`);this._validate(w,B)&&(this.images[w]=B)}_validate(w,B){let Q=!0,ee=B.data||B.spriteData;return this._validateStretch(B.stretchX,ee&&ee.width)||(this.fire(new a.j(new Error(`Image \"${w}\" has invalid \"stretchX\" value`))),Q=!1),this._validateStretch(B.stretchY,ee&&ee.height)||(this.fire(new a.j(new Error(`Image \"${w}\" has invalid \"stretchY\" value`))),Q=!1),this._validateContent(B.content,B)||(this.fire(new a.j(new Error(`Image \"${w}\" has invalid \"content\" value`))),Q=!1),Q}_validateStretch(w,B){if(!w)return!0;let Q=0;for(let ee of w){if(ee[0]<Q||ee[1]<ee[0]||B<ee[1])return!1;Q=ee[1]}return!0}_validateContent(w,B){if(!w)return!0;if(w.length!==4)return!1;let Q=B.spriteData,ee=Q&&Q.width||B.data.width,le=Q&&Q.height||B.data.height;return!(w[0]<0||ee<w[0]||w[1]<0||le<w[1]||w[2]<0||ee<w[2]||w[3]<0||le<w[3]||w[2]<w[0]||w[3]<w[1])}updateImage(w,B,Q=!0){let ee=this.getImage(w);if(Q&&(ee.data.width!==B.data.width||ee.data.height!==B.data.height))throw new Error(`size mismatch between old image (${ee.data.width}x${ee.data.height}) and new image (${B.data.width}x${B.data.height}).`);B.version=ee.version+1,this.images[w]=B,this.updatedImages[w]=!0}removeImage(w){let B=this.images[w];delete this.images[w],delete this.patterns[w],B.userImage&&B.userImage.onRemove&&B.userImage.onRemove()}listImages(){return Object.keys(this.images)}getImages(w){return new Promise(((B,Q)=>{let ee=!0;if(!this.isLoaded())for(let le of w)this.images[le]||(ee=!1);this.isLoaded()||ee?B(this._getImagesForIds(w)):this.requestors.push({ids:w,promiseResolve:B})}))}_getImagesForIds(w){let B={};for(let Q of w){let ee=this.getImage(Q);ee||(this.fire(new a.k(\"styleimagemissing\",{id:Q})),ee=this.getImage(Q)),ee?B[Q]={data:ee.data.clone(),pixelRatio:ee.pixelRatio,sdf:ee.sdf,version:ee.version,stretchX:ee.stretchX,stretchY:ee.stretchY,content:ee.content,textFitWidth:ee.textFitWidth,textFitHeight:ee.textFitHeight,hasRenderCallback:!!(ee.userImage&&ee.userImage.render)}:a.w(`Image \"${Q}\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.`)}return B}getPixelSize(){let{width:w,height:B}=this.atlasImage;return{width:w,height:B}}getPattern(w){let B=this.patterns[w],Q=this.getImage(w);if(!Q)return null;if(B&&B.position.version===Q.version)return B.position;if(B)B.position.version=Q.version;else{let ee={w:Q.data.width+2,h:Q.data.height+2,x:0,y:0},le=new a.I(ee,Q);this.patterns[w]={bin:ee,position:le}}return this._updatePatternAtlas(),this.patterns[w].position}bind(w){let B=w.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new g(w,this.atlasImage,B.RGBA),this.atlasTexture.bind(B.LINEAR,B.CLAMP_TO_EDGE)}_updatePatternAtlas(){let w=[];for(let le in this.patterns)w.push(this.patterns[le].bin);let{w:B,h:Q}=a.p(w),ee=this.atlasImage;ee.resize({width:B||1,height:Q||1});for(let le in this.patterns){let{bin:Fe}=this.patterns[le],Ze=Fe.x+1,ut=Fe.y+1,Mt=this.getImage(le).data,Jt=Mt.width,$t=Mt.height;a.R.copy(Mt,ee,{x:0,y:0},{x:Ze,y:ut},{width:Jt,height:$t}),a.R.copy(Mt,ee,{x:0,y:$t-1},{x:Ze,y:ut-1},{width:Jt,height:1}),a.R.copy(Mt,ee,{x:0,y:0},{x:Ze,y:ut+$t},{width:Jt,height:1}),a.R.copy(Mt,ee,{x:Jt-1,y:0},{x:Ze-1,y:ut},{width:1,height:$t}),a.R.copy(Mt,ee,{x:0,y:0},{x:Ze+Jt,y:ut},{width:1,height:$t})}this.dirty=!0}beginFrame(){this.callbackDispatchedThisFrame={}}dispatchRenderCallbacks(w){for(let B of w){if(this.callbackDispatchedThisFrame[B])continue;this.callbackDispatchedThisFrame[B]=!0;let Q=this.getImage(B);Q||a.w(`Image with ID: \"${B}\" was not found`),P(Q)&&this.updateImage(B,Q)}}}let z=1e20;function q(ue,w,B,Q,ee,le,Fe,Ze,ut){for(let Mt=w;Mt<w+Q;Mt++)V(ue,B*le+Mt,le,ee,Fe,Ze,ut);for(let Mt=B;Mt<B+ee;Mt++)V(ue,Mt*le+w,1,Q,Fe,Ze,ut)}function V(ue,w,B,Q,ee,le,Fe){le[0]=0,Fe[0]=-z,Fe[1]=z,ee[0]=ue[w];for(let Ze=1,ut=0,Mt=0;Ze<Q;Ze++){ee[Ze]=ue[w+Ze*B];let Jt=Ze*Ze;do{let $t=le[ut];Mt=(ee[Ze]-ee[$t]+Jt-$t*$t)/(Ze-$t)/2}while(Mt<=Fe[ut]&&--ut>-1);ut++,le[ut]=Ze,Fe[ut]=Mt,Fe[ut+1]=z}for(let Ze=0,ut=0;Ze<Q;Ze++){for(;Fe[ut+1]<Ze;)ut++;let Mt=le[ut],Jt=Ze-Mt;ue[w+Ze*B]=ee[Mt]+Jt*Jt}}class G{constructor(w,B){this.requestManager=w,this.localIdeographFontFamily=B,this.entries={}}setURL(w){this.url=w}getGlyphs(w){return a._(this,void 0,void 0,(function*(){let B=[];for(let le in w)for(let Fe of w[le])B.push(this._getAndCacheGlyphsPromise(le,Fe));let Q=yield Promise.all(B),ee={};for(let{stack:le,id:Fe,glyph:Ze}of Q)ee[le]||(ee[le]={}),ee[le][Fe]=Ze&&{id:Ze.id,bitmap:Ze.bitmap.clone(),metrics:Ze.metrics};return ee}))}_getAndCacheGlyphsPromise(w,B){return a._(this,void 0,void 0,(function*(){let Q=this.entries[w];Q||(Q=this.entries[w]={glyphs:{},requests:{},ranges:{}});let ee=Q.glyphs[B];if(ee!==void 0)return{stack:w,id:B,glyph:ee};if(ee=this._tinySDF(Q,w,B),ee)return Q.glyphs[B]=ee,{stack:w,id:B,glyph:ee};let le=Math.floor(B/256);if(256*le>65535)throw new Error(\"glyphs > 65535 not supported\");if(Q.ranges[le])return{stack:w,id:B,glyph:ee};if(!this.url)throw new Error(\"glyphsUrl is not set\");if(!Q.requests[le]){let Ze=G.loadGlyphRange(w,le,this.url,this.requestManager);Q.requests[le]=Ze}let Fe=yield Q.requests[le];for(let Ze in Fe)this._doesCharSupportLocalGlyph(+Ze)||(Q.glyphs[+Ze]=Fe[+Ze]);return Q.ranges[le]=!0,{stack:w,id:B,glyph:Fe[B]||null}}))}_doesCharSupportLocalGlyph(w){return!!this.localIdeographFontFamily&&new RegExp(\"\\\\p{Ideo}|\\\\p{sc=Hang}|\\\\p{sc=Hira}|\\\\p{sc=Kana}\",\"u\").test(String.fromCodePoint(w))}_tinySDF(w,B,Q){let ee=this.localIdeographFontFamily;if(!ee||!this._doesCharSupportLocalGlyph(Q))return;let le=w.tinySDF;if(!le){let Ze=\"400\";/bold/i.test(B)?Ze=\"900\":/medium/i.test(B)?Ze=\"500\":/light/i.test(B)&&(Ze=\"200\"),le=w.tinySDF=new G.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:ee,fontWeight:Ze})}let Fe=le.draw(String.fromCharCode(Q));return{id:Q,bitmap:new a.o({width:Fe.width||60,height:Fe.height||60},Fe.data),metrics:{width:Fe.glyphWidth/2||24,height:Fe.glyphHeight/2||24,left:Fe.glyphLeft/2+.5||0,top:Fe.glyphTop/2-27.5||-8,advance:Fe.glyphAdvance/2||24,isDoubleResolution:!0}}}}G.loadGlyphRange=function(ue,w,B,Q){return a._(this,void 0,void 0,(function*(){let ee=256*w,le=ee+255,Fe=Q.transformRequest(B.replace(\"{fontstack}\",ue).replace(\"{range}\",`${ee}-${le}`),\"Glyphs\"),Ze=yield a.l(Fe,new AbortController);if(!Ze||!Ze.data)throw new Error(`Could not load glyph range. range: ${w}, ${ee}-${le}`);let ut={};for(let Mt of a.n(Ze.data))ut[Mt.id]=Mt;return ut}))},G.TinySDF=class{constructor({fontSize:ue=24,buffer:w=3,radius:B=8,cutoff:Q=.25,fontFamily:ee=\"sans-serif\",fontWeight:le=\"normal\",fontStyle:Fe=\"normal\"}={}){this.buffer=w,this.cutoff=Q,this.radius=B;let Ze=this.size=ue+4*w,ut=this._createCanvas(Ze),Mt=this.ctx=ut.getContext(\"2d\",{willReadFrequently:!0});Mt.font=`${Fe} ${le} ${ue}px ${ee}`,Mt.textBaseline=\"alphabetic\",Mt.textAlign=\"left\",Mt.fillStyle=\"black\",this.gridOuter=new Float64Array(Ze*Ze),this.gridInner=new Float64Array(Ze*Ze),this.f=new Float64Array(Ze),this.z=new Float64Array(Ze+1),this.v=new Uint16Array(Ze)}_createCanvas(ue){let w=document.createElement(\"canvas\");return w.width=w.height=ue,w}draw(ue){let{width:w,actualBoundingBoxAscent:B,actualBoundingBoxDescent:Q,actualBoundingBoxLeft:ee,actualBoundingBoxRight:le}=this.ctx.measureText(ue),Fe=Math.ceil(B),Ze=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(le-ee))),ut=Math.min(this.size-this.buffer,Fe+Math.ceil(Q)),Mt=Ze+2*this.buffer,Jt=ut+2*this.buffer,$t=Math.max(Mt*Jt,0),yr=new Uint8ClampedArray($t),Cr={data:yr,width:Mt,height:Jt,glyphWidth:Ze,glyphHeight:ut,glyphTop:Fe,glyphLeft:0,glyphAdvance:w};if(Ze===0||ut===0)return Cr;let{ctx:ve,buffer:xe,gridInner:Ie,gridOuter:ze}=this;ve.clearRect(xe,xe,Ze,ut),ve.fillText(ue,xe,xe+Fe);let Qe=ve.getImageData(xe,xe,Ze,ut);ze.fill(z,0,$t),Ie.fill(0,0,$t);for(let Xe=0;Xe<ut;Xe++)for(let nt=0;nt<Ze;nt++){let qt=Qe.data[4*(Xe*Ze+nt)+3]/255;if(qt===0)continue;let Wt=(Xe+xe)*Mt+nt+xe;if(qt===1)ze[Wt]=0,Ie[Wt]=z;else{let nr=.5-qt;ze[Wt]=nr>0?nr*nr:0,Ie[Wt]=nr<0?nr*nr:0}}q(ze,0,0,Mt,Jt,Mt,this.f,this.v,this.z),q(Ie,xe,xe,Ze,ut,Mt,this.f,this.v,this.z);for(let Xe=0;Xe<$t;Xe++){let nt=Math.sqrt(ze[Xe])-Math.sqrt(Ie[Xe]);yr[Xe]=Math.round(255-255*(nt/this.radius+this.cutoff))}return Cr}};class Z{constructor(){this.specification=a.v.light.position}possiblyEvaluate(w,B){return a.x(w.expression.evaluate(B))}interpolate(w,B,Q){return{x:a.y.number(w.x,B.x,Q),y:a.y.number(w.y,B.y,Q),z:a.y.number(w.z,B.z,Q)}}}let j;class N extends a.E{constructor(w){super(),j=j||new a.q({anchor:new a.D(a.v.light.anchor),position:new Z,color:new a.D(a.v.light.color),intensity:new a.D(a.v.light.intensity)}),this._transitionable=new a.T(j),this.setLight(w),this._transitioning=this._transitionable.untransitioned()}getLight(){return this._transitionable.serialize()}setLight(w,B={}){if(!this._validate(a.r,w,B))for(let Q in w){let ee=w[Q];Q.endsWith(\"-transition\")?this._transitionable.setTransition(Q.slice(0,-11),ee):this._transitionable.setValue(Q,ee)}}updateTransitions(w){this._transitioning=this._transitionable.transitioned(w,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(w){this.properties=this._transitioning.possiblyEvaluate(w)}_validate(w,B,Q){return(!Q||Q.validate!==!1)&&a.t(this,w.call(a.u,{value:B,style:{glyphs:!0,sprite:!0},styleSpec:a.v}))}}let H=new a.q({\"sky-color\":new a.D(a.v.sky[\"sky-color\"]),\"horizon-color\":new a.D(a.v.sky[\"horizon-color\"]),\"fog-color\":new a.D(a.v.sky[\"fog-color\"]),\"fog-ground-blend\":new a.D(a.v.sky[\"fog-ground-blend\"]),\"horizon-fog-blend\":new a.D(a.v.sky[\"horizon-fog-blend\"]),\"sky-horizon-blend\":new a.D(a.v.sky[\"sky-horizon-blend\"]),\"atmosphere-blend\":new a.D(a.v.sky[\"atmosphere-blend\"])});class re extends a.E{constructor(w){super(),this._transitionable=new a.T(H),this.setSky(w),this._transitioning=this._transitionable.untransitioned(),this.recalculate(new a.z(0))}setSky(w,B={}){if(!this._validate(a.B,w,B)){w||(w={\"sky-color\":\"transparent\",\"horizon-color\":\"transparent\",\"fog-color\":\"transparent\",\"fog-ground-blend\":1,\"atmosphere-blend\":0});for(let Q in w){let ee=w[Q];Q.endsWith(\"-transition\")?this._transitionable.setTransition(Q.slice(0,-11),ee):this._transitionable.setValue(Q,ee)}}}getSky(){return this._transitionable.serialize()}updateTransitions(w){this._transitioning=this._transitionable.transitioned(w,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(w){this.properties=this._transitioning.possiblyEvaluate(w)}_validate(w,B,Q={}){return(Q==null?void 0:Q.validate)!==!1&&a.t(this,w.call(a.u,a.e({value:B,style:{glyphs:!0,sprite:!0},styleSpec:a.v})))}calculateFogBlendOpacity(w){return w<60?0:w<70?(w-60)/10:1}}class ae{constructor(w,B){this.width=w,this.height=B,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}}getDash(w,B){let Q=w.join(\",\")+String(B);return this.dashEntry[Q]||(this.dashEntry[Q]=this.addDash(w,B)),this.dashEntry[Q]}getDashRanges(w,B,Q){let ee=[],le=w.length%2==1?-w[w.length-1]*Q:0,Fe=w[0]*Q,Ze=!0;ee.push({left:le,right:Fe,isDash:Ze,zeroLength:w[0]===0});let ut=w[0];for(let Mt=1;Mt<w.length;Mt++){Ze=!Ze;let Jt=w[Mt];le=ut*Q,ut+=Jt,Fe=ut*Q,ee.push({left:le,right:Fe,isDash:Ze,zeroLength:Jt===0})}return ee}addRoundDash(w,B,Q){let ee=B/2;for(let le=-Q;le<=Q;le++){let Fe=this.width*(this.nextRow+Q+le),Ze=0,ut=w[Ze];for(let Mt=0;Mt<this.width;Mt++){Mt/ut.right>1&&(ut=w[++Ze]);let Jt=Math.abs(Mt-ut.left),$t=Math.abs(Mt-ut.right),yr=Math.min(Jt,$t),Cr,ve=le/Q*(ee+1);if(ut.isDash){let xe=ee-Math.abs(ve);Cr=Math.sqrt(yr*yr+xe*xe)}else Cr=ee-Math.sqrt(yr*yr+ve*ve);this.data[Fe+Mt]=Math.max(0,Math.min(255,Cr+128))}}}addRegularDash(w){for(let Ze=w.length-1;Ze>=0;--Ze){let ut=w[Ze],Mt=w[Ze+1];ut.zeroLength?w.splice(Ze,1):Mt&&Mt.isDash===ut.isDash&&(Mt.left=ut.left,w.splice(Ze,1))}let B=w[0],Q=w[w.length-1];B.isDash===Q.isDash&&(B.left=Q.left-this.width,Q.right=B.right+this.width);let ee=this.width*this.nextRow,le=0,Fe=w[le];for(let Ze=0;Ze<this.width;Ze++){Ze/Fe.right>1&&(Fe=w[++le]);let ut=Math.abs(Ze-Fe.left),Mt=Math.abs(Ze-Fe.right),Jt=Math.min(ut,Mt);this.data[ee+Ze]=Math.max(0,Math.min(255,(Fe.isDash?Jt:-Jt)+128))}}addDash(w,B){let Q=B?7:0,ee=2*Q+1;if(this.nextRow+ee>this.height)return a.w(\"LineAtlas out of space\"),null;let le=0;for(let Ze=0;Ze<w.length;Ze++)le+=w[Ze];if(le!==0){let Ze=this.width/le,ut=this.getDashRanges(w,this.width,Ze);B?this.addRoundDash(ut,Ze,Q):this.addRegularDash(ut)}let Fe={y:(this.nextRow+Q+.5)/this.height,height:2*Q/this.height,width:le};return this.nextRow+=ee,this.dirty=!0,Fe}bind(w){let B=w.gl;this.texture?(B.bindTexture(B.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,B.texSubImage2D(B.TEXTURE_2D,0,0,0,this.width,this.height,B.ALPHA,B.UNSIGNED_BYTE,this.data))):(this.texture=B.createTexture(),B.bindTexture(B.TEXTURE_2D,this.texture),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_WRAP_S,B.REPEAT),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_WRAP_T,B.REPEAT),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_MIN_FILTER,B.LINEAR),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_MAG_FILTER,B.LINEAR),B.texImage2D(B.TEXTURE_2D,0,B.ALPHA,this.width,this.height,0,B.ALPHA,B.UNSIGNED_BYTE,this.data))}}let _e=\"maplibre_preloaded_worker_pool\";class Ee{constructor(){this.active={}}acquire(w){if(!this.workers)for(this.workers=[];this.workers.length<Ee.workerCount;)this.workers.push(new Worker(a.a.WORKER_URL));return this.active[w]=!0,this.workers.slice()}release(w){delete this.active[w],this.numActive()===0&&(this.workers.forEach((B=>{B.terminate()})),this.workers=null)}isPreloaded(){return!!this.active[_e]}numActive(){return Object.keys(this.active).length}}let Ce=Math.floor(u.hardwareConcurrency/2),ge,ie;function Se(){return ge||(ge=new Ee),ge}Ee.workerCount=a.C(globalThis)?Math.max(Math.min(Ce,3),1):1;class ke{constructor(w,B){this.workerPool=w,this.actors=[],this.currentActor=0,this.id=B;let Q=this.workerPool.acquire(B);for(let ee=0;ee<Q.length;ee++){let le=new a.F(Q[ee],B);le.name=`Worker ${ee}`,this.actors.push(le)}if(!this.actors.length)throw new Error(\"No actors found\")}broadcast(w,B){let Q=[];for(let ee of this.actors)Q.push(ee.sendAsync({type:w,data:B}));return Promise.all(Q)}getActor(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]}remove(w=!0){this.actors.forEach((B=>{B.remove()})),this.actors=[],w&&this.workerPool.release(this.id)}registerMessageHandler(w,B){for(let Q of this.actors)Q.registerMessageHandler(w,B)}}function be(){return ie||(ie=new ke(Se(),a.G),ie.registerMessageHandler(\"GR\",((ue,w,B)=>a.m(w,B)))),ie}function Be(ue,w){let B=a.H();return a.J(B,B,[1,1,0]),a.K(B,B,[.5*ue.width,.5*ue.height,1]),a.L(B,B,ue.calculatePosMatrix(w.toUnwrapped()))}function Le(ue,w,B,Q,ee,le){let Fe=(function($t,yr,Cr){if($t)for(let ve of $t){let xe=yr[ve];if(xe&&xe.source===Cr&&xe.type===\"fill-extrusion\")return!0}else for(let ve in yr){let xe=yr[ve];if(xe.source===Cr&&xe.type===\"fill-extrusion\")return!0}return!1})(ee&&ee.layers,w,ue.id),Ze=le.maxPitchScaleFactor(),ut=ue.tilesIn(Q,Ze,Fe);ut.sort(me);let Mt=[];for(let $t of ut)Mt.push({wrappedTileID:$t.tileID.wrapped().key,queryResults:$t.tile.queryRenderedFeatures(w,B,ue._state,$t.queryGeometry,$t.cameraQueryGeometry,$t.scale,ee,le,Ze,Be(ue.transform,$t.tileID))});let Jt=(function($t){let yr={},Cr={};for(let ve of $t){let xe=ve.queryResults,Ie=ve.wrappedTileID,ze=Cr[Ie]=Cr[Ie]||{};for(let Qe in xe){let Xe=xe[Qe],nt=ze[Qe]=ze[Qe]||{},qt=yr[Qe]=yr[Qe]||[];for(let Wt of Xe)nt[Wt.featureIndex]||(nt[Wt.featureIndex]=!0,qt.push(Wt))}}return yr})(Mt);for(let $t in Jt)Jt[$t].forEach((yr=>{let Cr=yr.feature,ve=ue.getFeatureState(Cr.layer[\"source-layer\"],Cr.id);Cr.source=Cr.layer.source,Cr.layer[\"source-layer\"]&&(Cr.sourceLayer=Cr.layer[\"source-layer\"]),Cr.state=ve}));return Jt}function me(ue,w){let B=ue.tileID,Q=w.tileID;return B.overscaledZ-Q.overscaledZ||B.canonical.y-Q.canonical.y||B.wrap-Q.wrap||B.canonical.x-Q.canonical.x}function Pe(ue,w,B){return a._(this,void 0,void 0,(function*(){let Q=ue;if(ue.url?Q=(yield a.h(w.transformRequest(ue.url,\"Source\"),B)).data:yield u.frameAsync(B),!Q)return null;let ee=a.M(a.e(Q,ue),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);return\"vector_layers\"in Q&&Q.vector_layers&&(ee.vectorLayerIds=Q.vector_layers.map((le=>le.id))),ee}))}class ce{constructor(w,B){w&&(B?this.setSouthWest(w).setNorthEast(B):Array.isArray(w)&&(w.length===4?this.setSouthWest([w[0],w[1]]).setNorthEast([w[2],w[3]]):this.setSouthWest(w[0]).setNorthEast(w[1])))}setNorthEast(w){return this._ne=w instanceof a.N?new a.N(w.lng,w.lat):a.N.convert(w),this}setSouthWest(w){return this._sw=w instanceof a.N?new a.N(w.lng,w.lat):a.N.convert(w),this}extend(w){let B=this._sw,Q=this._ne,ee,le;if(w instanceof a.N)ee=w,le=w;else{if(!(w instanceof ce))return Array.isArray(w)?w.length===4||w.every(Array.isArray)?this.extend(ce.convert(w)):this.extend(a.N.convert(w)):w&&(\"lng\"in w||\"lon\"in w)&&\"lat\"in w?this.extend(a.N.convert(w)):this;if(ee=w._sw,le=w._ne,!ee||!le)return this}return B||Q?(B.lng=Math.min(ee.lng,B.lng),B.lat=Math.min(ee.lat,B.lat),Q.lng=Math.max(le.lng,Q.lng),Q.lat=Math.max(le.lat,Q.lat)):(this._sw=new a.N(ee.lng,ee.lat),this._ne=new a.N(le.lng,le.lat)),this}getCenter(){return new a.N((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new a.N(this.getWest(),this.getNorth())}getSouthEast(){return new a.N(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(w){let{lng:B,lat:Q}=a.N.convert(w),ee=this._sw.lng<=B&&B<=this._ne.lng;return this._sw.lng>this._ne.lng&&(ee=this._sw.lng>=B&&B>=this._ne.lng),this._sw.lat<=Q&&Q<=this._ne.lat&&ee}static convert(w){return w instanceof ce?w:w&&new ce(w)}static fromLngLat(w,B=0){let Q=360*B/40075017,ee=Q/Math.cos(Math.PI/180*w.lat);return new ce(new a.N(w.lng-ee,w.lat-Q),new a.N(w.lng+ee,w.lat+Q))}adjustAntiMeridian(){let w=new a.N(this._sw.lng,this._sw.lat),B=new a.N(this._ne.lng,this._ne.lat);return new ce(w,w.lng>B.lng?new a.N(B.lng+360,B.lat):B)}}class He{constructor(w,B,Q){this.bounds=ce.convert(this.validateBounds(w)),this.minzoom=B||0,this.maxzoom=Q||24}validateBounds(w){return Array.isArray(w)&&w.length===4?[Math.max(-180,w[0]),Math.max(-90,w[1]),Math.min(180,w[2]),Math.min(90,w[3])]:[-180,-90,180,90]}contains(w){let B=Math.pow(2,w.z),Q=Math.floor(a.O(this.bounds.getWest())*B),ee=Math.floor(a.Q(this.bounds.getNorth())*B),le=Math.ceil(a.O(this.bounds.getEast())*B),Fe=Math.ceil(a.Q(this.bounds.getSouth())*B);return w.x>=Q&&w.x<le&&w.y>=ee&&w.y<Fe}}class lt extends a.E{constructor(w,B,Q,ee){if(super(),this.id=w,this.dispatcher=Q,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,a.e(this,a.M(B,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=a.e({type:\"vector\"},B),this._collectResourceTiming=B.collectResourceTiming,this.tileSize!==512)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(ee)}load(){return a._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new a.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=new AbortController;try{let w=yield Pe(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,this.map.style.sourceCaches[this.id].clearTiles(),w&&(a.e(this,w),w.bounds&&(this.tileBounds=new He(w.bounds,this.minzoom,this.maxzoom)),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}catch(w){this._tileJSONRequest=null,this.fire(new a.j(w))}}))}loaded(){return this._loaded}hasTile(w){return!this.tileBounds||this.tileBounds.contains(w.canonical)}onAdd(w){this.map=w,this.load()}setSourceProperty(w){this._tileJSONRequest&&this._tileJSONRequest.abort(),w(),this.load()}setTiles(w){return this.setSourceProperty((()=>{this._options.tiles=w})),this}setUrl(w){return this.setSourceProperty((()=>{this.url=w,this._options.url=w})),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}serialize(){return a.e({},this._options)}loadTile(w){return a._(this,void 0,void 0,(function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),Q={request:this.map._requestManager.transformRequest(B,\"Tile\"),uid:w.uid,tileID:w.tileID,zoom:w.tileID.overscaledZ,tileSize:this.tileSize*w.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};Q.request.collectResourceTiming=this._collectResourceTiming;let ee=\"RT\";if(w.actor&&w.state!==\"expired\"){if(w.state===\"loading\")return new Promise(((le,Fe)=>{w.reloadPromise={resolve:le,reject:Fe}}))}else w.actor=this.dispatcher.getActor(),ee=\"LT\";w.abortController=new AbortController;try{let le=yield w.actor.sendAsync({type:ee,data:Q},w.abortController);if(delete w.abortController,w.aborted)return;this._afterTileLoadWorkerResponse(w,le)}catch(le){if(delete w.abortController,w.aborted)return;if(le&&le.status!==404)throw le;this._afterTileLoadWorkerResponse(w,null)}}))}_afterTileLoadWorkerResponse(w,B){if(B&&B.resourceTiming&&(w.resourceTiming=B.resourceTiming),B&&this.map._refreshExpiredTiles&&w.setExpiryData(B),w.loadVectorData(B,this.map.painter),w.reloadPromise){let Q=w.reloadPromise;w.reloadPromise=null,this.loadTile(w).then(Q.resolve).catch(Q.reject)}}abortTile(w){return a._(this,void 0,void 0,(function*(){w.abortController&&(w.abortController.abort(),delete w.abortController),w.actor&&(yield w.actor.sendAsync({type:\"AT\",data:{uid:w.uid,type:this.type,source:this.id}}))}))}unloadTile(w){return a._(this,void 0,void 0,(function*(){w.unloadVectorData(),w.actor&&(yield w.actor.sendAsync({type:\"RMT\",data:{uid:w.uid,type:this.type,source:this.id}}))}))}hasTransition(){return!1}}class mt extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.dispatcher=Q,this.setEventedParent(ee),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=a.e({type:\"raster\"},B),a.e(this,a.M(B,[\"url\",\"scheme\",\"tileSize\"]))}load(){return a._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new a.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=new AbortController;try{let w=yield Pe(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,w&&(a.e(this,w),w.bounds&&(this.tileBounds=new He(w.bounds,this.minzoom,this.maxzoom)),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}catch(w){this._tileJSONRequest=null,this.fire(new a.j(w))}}))}loaded(){return this._loaded}onAdd(w){this.map=w,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}setSourceProperty(w){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null),w(),this.load()}setTiles(w){return this.setSourceProperty((()=>{this._options.tiles=w})),this}setUrl(w){return this.setSourceProperty((()=>{this.url=w,this._options.url=w})),this}serialize(){return a.e({},this._options)}hasTile(w){return!this.tileBounds||this.tileBounds.contains(w.canonical)}loadTile(w){return a._(this,void 0,void 0,(function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);w.abortController=new AbortController;try{let Q=yield p.getImage(this.map._requestManager.transformRequest(B,\"Tile\"),w.abortController,this.map._refreshExpiredTiles);if(delete w.abortController,w.aborted)return void(w.state=\"unloaded\");if(Q&&Q.data){this.map._refreshExpiredTiles&&Q.cacheControl&&Q.expires&&w.setExpiryData({cacheControl:Q.cacheControl,expires:Q.expires});let ee=this.map.painter.context,le=ee.gl,Fe=Q.data;w.texture=this.map.painter.getTileTexture(Fe.width),w.texture?w.texture.update(Fe,{useMipmap:!0}):(w.texture=new g(ee,Fe,le.RGBA,{useMipmap:!0}),w.texture.bind(le.LINEAR,le.CLAMP_TO_EDGE,le.LINEAR_MIPMAP_NEAREST)),w.state=\"loaded\"}}catch(Q){if(delete w.abortController,w.aborted)w.state=\"unloaded\";else if(Q)throw w.state=\"errored\",Q}}))}abortTile(w){return a._(this,void 0,void 0,(function*(){w.abortController&&(w.abortController.abort(),delete w.abortController)}))}unloadTile(w){return a._(this,void 0,void 0,(function*(){w.texture&&this.map.painter.saveTileTexture(w.texture)}))}hasTransition(){return!1}}class Ht extends mt{constructor(w,B,Q,ee){super(w,B,Q,ee),this.type=\"raster-dem\",this.maxzoom=22,this._options=a.e({type:\"raster-dem\"},B),this.encoding=B.encoding||\"mapbox\",this.redFactor=B.redFactor,this.greenFactor=B.greenFactor,this.blueFactor=B.blueFactor,this.baseShift=B.baseShift}loadTile(w){return a._(this,void 0,void 0,(function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),Q=this.map._requestManager.transformRequest(B,\"Tile\");w.neighboringTiles=this._getNeighboringTiles(w.tileID),w.abortController=new AbortController;try{let ee=yield p.getImage(Q,w.abortController,this.map._refreshExpiredTiles);if(delete w.abortController,w.aborted)return void(w.state=\"unloaded\");if(ee&&ee.data){let le=ee.data;this.map._refreshExpiredTiles&&ee.cacheControl&&ee.expires&&w.setExpiryData({cacheControl:ee.cacheControl,expires:ee.expires});let Fe=a.b(le)&&a.U()?le:yield this.readImageNow(le),Ze={type:this.type,uid:w.uid,source:this.id,rawImageData:Fe,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};if(!w.actor||w.state===\"expired\"){w.actor=this.dispatcher.getActor();let ut=yield w.actor.sendAsync({type:\"LDT\",data:Ze});w.dem=ut,w.needsHillshadePrepare=!0,w.needsTerrainPrepare=!0,w.state=\"loaded\"}}}catch(ee){if(delete w.abortController,w.aborted)w.state=\"unloaded\";else if(ee)throw w.state=\"errored\",ee}}))}readImageNow(w){return a._(this,void 0,void 0,(function*(){if(typeof VideoFrame!=\"undefined\"&&a.V()){let B=w.width+2,Q=w.height+2;try{return new a.R({width:B,height:Q},yield a.W(w,-1,-1,B,Q))}catch(ee){}}return u.getImageData(w,1)}))}_getNeighboringTiles(w){let B=w.canonical,Q=Math.pow(2,B.z),ee=(B.x-1+Q)%Q,le=B.x===0?w.wrap-1:w.wrap,Fe=(B.x+1+Q)%Q,Ze=B.x+1===Q?w.wrap+1:w.wrap,ut={};return ut[new a.S(w.overscaledZ,le,B.z,ee,B.y).key]={backfilled:!1},ut[new a.S(w.overscaledZ,Ze,B.z,Fe,B.y).key]={backfilled:!1},B.y>0&&(ut[new a.S(w.overscaledZ,le,B.z,ee,B.y-1).key]={backfilled:!1},ut[new a.S(w.overscaledZ,w.wrap,B.z,B.x,B.y-1).key]={backfilled:!1},ut[new a.S(w.overscaledZ,Ze,B.z,Fe,B.y-1).key]={backfilled:!1}),B.y+1<Q&&(ut[new a.S(w.overscaledZ,le,B.z,ee,B.y+1).key]={backfilled:!1},ut[new a.S(w.overscaledZ,w.wrap,B.z,B.x,B.y+1).key]={backfilled:!1},ut[new a.S(w.overscaledZ,Ze,B.z,Fe,B.y+1).key]={backfilled:!1}),ut}unloadTile(w){return a._(this,void 0,void 0,(function*(){w.demTexture&&this.map.painter.saveTileTexture(w.demTexture),w.fbo&&(w.fbo.destroy(),delete w.fbo),w.dem&&delete w.dem,delete w.neighboringTiles,w.state=\"unloaded\",w.actor&&(yield w.actor.sendAsync({type:\"RDT\",data:{type:this.type,uid:w.uid,source:this.id}}))}))}}class at extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._pendingLoads=0,this.actor=Q.getActor(),this.setEventedParent(ee),this._data=B.data,this._options=a.e({},B),this._collectResourceTiming=B.collectResourceTiming,B.maxzoom!==void 0&&(this.maxzoom=B.maxzoom),B.type&&(this.type=B.type),B.attribution&&(this.attribution=B.attribution),this.promoteId=B.promoteId;let le=a.X/this.tileSize;B.clusterMaxZoom!==void 0&&this.maxzoom<=B.clusterMaxZoom&&a.w(`The maxzoom value \"${this.maxzoom}\" is expected to be greater than the clusterMaxZoom value \"${B.clusterMaxZoom}\".`),this.workerOptions=a.e({source:this.id,cluster:B.cluster||!1,geojsonVtOptions:{buffer:(B.buffer!==void 0?B.buffer:128)*le,tolerance:(B.tolerance!==void 0?B.tolerance:.375)*le,extent:a.X,maxZoom:this.maxzoom,lineMetrics:B.lineMetrics||!1,generateId:B.generateId||!1},superclusterOptions:{maxZoom:B.clusterMaxZoom!==void 0?B.clusterMaxZoom:this.maxzoom-1,minPoints:Math.max(2,B.clusterMinPoints||2),extent:a.X,radius:(B.clusterRadius||50)*le,log:!1,generateId:B.generateId||!1},clusterProperties:B.clusterProperties,filter:B.filter},B.workerOptions),typeof this.promoteId==\"string\"&&(this.workerOptions.promoteId=this.promoteId)}load(){return a._(this,void 0,void 0,(function*(){yield this._updateWorkerData()}))}onAdd(w){this.map=w,this.load()}setData(w){return this._data=w,this._updateWorkerData(),this}updateData(w){return this._updateWorkerData(w),this}getData(){return a._(this,void 0,void 0,(function*(){let w=a.e({type:this.type},this.workerOptions);return this.actor.sendAsync({type:\"GD\",data:w})}))}setClusterOptions(w){return this.workerOptions.cluster=w.cluster,w&&(w.clusterRadius!==void 0&&(this.workerOptions.superclusterOptions.radius=w.clusterRadius),w.clusterMaxZoom!==void 0&&(this.workerOptions.superclusterOptions.maxZoom=w.clusterMaxZoom)),this._updateWorkerData(),this}getClusterExpansionZoom(w){return this.actor.sendAsync({type:\"GCEZ\",data:{type:this.type,clusterId:w,source:this.id}})}getClusterChildren(w){return this.actor.sendAsync({type:\"GCC\",data:{type:this.type,clusterId:w,source:this.id}})}getClusterLeaves(w,B,Q){return this.actor.sendAsync({type:\"GCL\",data:{type:this.type,source:this.id,clusterId:w,limit:B,offset:Q}})}_updateWorkerData(w){return a._(this,void 0,void 0,(function*(){let B=a.e({type:this.type},this.workerOptions);w?B.dataDiff=w:typeof this._data==\"string\"?(B.request=this.map._requestManager.transformRequest(u.resolveURL(this._data),\"Source\"),B.request.collectResourceTiming=this._collectResourceTiming):B.data=JSON.stringify(this._data),this._pendingLoads++,this.fire(new a.k(\"dataloading\",{dataType:\"source\"}));try{let Q=yield this.actor.sendAsync({type:\"LD\",data:B});if(this._pendingLoads--,this._removed||Q.abandoned)return void this.fire(new a.k(\"dataabort\",{dataType:\"source\"}));let ee=null;Q.resourceTiming&&Q.resourceTiming[this.id]&&(ee=Q.resourceTiming[this.id].slice(0));let le={dataType:\"source\"};this._collectResourceTiming&&ee&&ee.length>0&&a.e(le,{resourceTiming:ee}),this.fire(new a.k(\"data\",Object.assign(Object.assign({},le),{sourceDataType:\"metadata\"}))),this.fire(new a.k(\"data\",Object.assign(Object.assign({},le),{sourceDataType:\"content\"})))}catch(Q){if(this._pendingLoads--,this._removed)return void this.fire(new a.k(\"dataabort\",{dataType:\"source\"}));this.fire(new a.j(Q))}}))}loaded(){return this._pendingLoads===0}loadTile(w){return a._(this,void 0,void 0,(function*(){let B=w.actor?\"RT\":\"LT\";w.actor=this.actor;let Q={type:this.type,uid:w.uid,tileID:w.tileID,zoom:w.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};w.abortController=new AbortController;let ee=yield this.actor.sendAsync({type:B,data:Q},w.abortController);delete w.abortController,w.unloadVectorData(),w.aborted||w.loadVectorData(ee,this.map.painter,B===\"RT\")}))}abortTile(w){return a._(this,void 0,void 0,(function*(){w.abortController&&(w.abortController.abort(),delete w.abortController),w.aborted=!0}))}unloadTile(w){return a._(this,void 0,void 0,(function*(){w.unloadVectorData(),yield this.actor.sendAsync({type:\"RMT\",data:{uid:w.uid,type:this.type,source:this.id}})}))}onRemove(){this._removed=!0,this.actor.sendAsync({type:\"RS\",data:{type:this.type,source:this.id}})}serialize(){return a.e({},this._options,{type:this.type,data:this._data})}hasTransition(){return!1}}var ct=a.Y([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]);class ar extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.dispatcher=Q,this.coordinates=B.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(ee),this.options=B}load(w){return a._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new a.k(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,this._request=new AbortController;try{let B=yield p.getImage(this.map._requestManager.transformRequest(this.url,\"Image\"),this._request);this._request=null,this._loaded=!0,B&&B.data&&(this.image=B.data,w&&(this.coordinates=w),this._finishLoading())}catch(B){this._request=null,this._loaded=!0,this.fire(new a.j(B))}}))}loaded(){return this._loaded}updateImage(w){return w.url?(this._request&&(this._request.abort(),this._request=null),this.options.url=w.url,this.load(w.coordinates).finally((()=>{this.texture=null})),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))}onAdd(w){this.map=w,this.load()}onRemove(){this._request&&(this._request.abort(),this._request=null)}setCoordinates(w){this.coordinates=w;let B=w.map(a.Z.fromLngLat);this.tileID=(function(ee){let le=1/0,Fe=1/0,Ze=-1/0,ut=-1/0;for(let yr of ee)le=Math.min(le,yr.x),Fe=Math.min(Fe,yr.y),Ze=Math.max(Ze,yr.x),ut=Math.max(ut,yr.y);let Mt=Math.max(Ze-le,ut-Fe),Jt=Math.max(0,Math.floor(-Math.log(Mt)/Math.LN2)),$t=Math.pow(2,Jt);return new a.a1(Jt,Math.floor((le+Ze)/2*$t),Math.floor((Fe+ut)/2*$t))})(B),this.minzoom=this.maxzoom=this.tileID.z;let Q=B.map((ee=>this.tileID.getTilePoint(ee)._round()));return this._boundsArray=new a.$,this._boundsArray.emplaceBack(Q[0].x,Q[0].y,0,0),this._boundsArray.emplaceBack(Q[1].x,Q[1].y,a.X,0),this._boundsArray.emplaceBack(Q[3].x,Q[3].y,0,a.X),this._boundsArray.emplaceBack(Q[2].x,Q[2].y,a.X,a.X),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this}prepare(){if(Object.keys(this.tiles).length===0||!this.image)return;let w=this.map.painter.context,B=w.gl;this.boundsBuffer||(this.boundsBuffer=w.createVertexBuffer(this._boundsArray,ct.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture||(this.texture=new g(w,this.image,B.RGBA),this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE));let Q=!1;for(let ee in this.tiles){let le=this.tiles[ee];le.state!==\"loaded\"&&(le.state=\"loaded\",le.texture=this.texture,Q=!0)}Q&&this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}loadTile(w){return a._(this,void 0,void 0,(function*(){this.tileID&&this.tileID.equals(w.tileID.canonical)?(this.tiles[String(w.tileID.wrap)]=w,w.buckets={}):w.state=\"errored\"}))}serialize(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}}hasTransition(){return!1}}class Vt extends ar{constructor(w,B,Q,ee){super(w,B,Q,ee),this.roundZoom=!0,this.type=\"video\",this.options=B}load(){return a._(this,void 0,void 0,(function*(){this._loaded=!1;let w=this.options;this.urls=[];for(let B of w.urls)this.urls.push(this.map._requestManager.transformRequest(B,\"Source\").url);try{let B=yield a.a3(this.urls);if(this._loaded=!0,!B)return;this.video=B,this.video.loop=!0,this.video.addEventListener(\"playing\",(()=>{this.map.triggerRepaint()})),this.map&&this.video.play(),this._finishLoading()}catch(B){this.fire(new a.j(B))}}))}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(w){if(this.video){let B=this.video.seekable;w<B.start(0)||w>B.end(0)?this.fire(new a.j(new a.a2(`sources.${this.id}`,null,`Playback for this video can be set only between the ${B.start(0)} and ${B.end(0)}-second mark.`))):this.video.currentTime=w}}getVideo(){return this.video}onAdd(w){this.map||(this.map=w,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}prepare(){if(Object.keys(this.tiles).length===0||this.video.readyState<2)return;let w=this.map.painter.context,B=w.gl;this.boundsBuffer||(this.boundsBuffer=w.createVertexBuffer(this._boundsArray,ct.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE),B.texSubImage2D(B.TEXTURE_2D,0,0,0,B.RGBA,B.UNSIGNED_BYTE,this.video)):(this.texture=new g(w,this.video,B.RGBA),this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE));let Q=!1;for(let ee in this.tiles){let le=this.tiles[ee];le.state!==\"loaded\"&&(le.state=\"loaded\",le.texture=this.texture,Q=!0)}Q&&this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}hasTransition(){return this.video&&!this.video.paused}}class rr extends ar{constructor(w,B,Q,ee){super(w,B,Q,ee),B.coordinates?Array.isArray(B.coordinates)&&B.coordinates.length===4&&!B.coordinates.some((le=>!Array.isArray(le)||le.length!==2||le.some((Fe=>typeof Fe!=\"number\"))))||this.fire(new a.j(new a.a2(`sources.${w}`,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new a.j(new a.a2(`sources.${w}`,null,'missing required property \"coordinates\"'))),B.animate&&typeof B.animate!=\"boolean\"&&this.fire(new a.j(new a.a2(`sources.${w}`,null,'optional \"animate\" property must be a boolean value'))),B.canvas?typeof B.canvas==\"string\"||B.canvas instanceof HTMLCanvasElement||this.fire(new a.j(new a.a2(`sources.${w}`,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new a.j(new a.a2(`sources.${w}`,null,'missing required property \"canvas\"'))),this.options=B,this.animate=B.animate===void 0||B.animate}load(){return a._(this,void 0,void 0,(function*(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new a.j(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())}))}getCanvas(){return this.canvas}onAdd(w){this.map=w,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}prepare(){let w=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,w=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,w=!0),this._hasInvalidDimensions()||Object.keys(this.tiles).length===0)return;let B=this.map.painter.context,Q=B.gl;this.boundsBuffer||(this.boundsBuffer=B.createVertexBuffer(this._boundsArray,ct.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture?(w||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new g(B,this.canvas,Q.RGBA,{premultiply:!0});let ee=!1;for(let le in this.tiles){let Fe=this.tiles[le];Fe.state!==\"loaded\"&&(Fe.state=\"loaded\",Fe.texture=this.texture,ee=!0)}ee&&this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"canvas\",coordinates:this.coordinates}}hasTransition(){return this._playing}_hasInvalidDimensions(){for(let w of[this.canvas.width,this.canvas.height])if(isNaN(w)||w<=0)return!0;return!1}}let tt={},je=ue=>{switch(ue){case\"geojson\":return at;case\"image\":return ar;case\"raster\":return mt;case\"raster-dem\":return Ht;case\"vector\":return lt;case\"video\":return Vt;case\"canvas\":return rr}return tt[ue]},Ue=\"RTLPluginLoaded\";class Ae extends a.E{constructor(){super(...arguments),this.status=\"unavailable\",this.url=null,this.dispatcher=be()}_syncState(w){return this.status=w,this.dispatcher.broadcast(\"SRPS\",{pluginStatus:w,pluginURL:this.url}).catch((B=>{throw this.status=\"error\",B}))}getRTLTextPluginStatus(){return this.status}clearRTLTextPlugin(){this.status=\"unavailable\",this.url=null}setRTLTextPlugin(w){return a._(this,arguments,void 0,(function*(B,Q=!1){if(this.url)throw new Error(\"setRTLTextPlugin cannot be called multiple times.\");if(this.url=u.resolveURL(B),!this.url)throw new Error(`requested url ${B} is invalid`);if(this.status===\"unavailable\"){if(!Q)return this._requestImport();this.status=\"deferred\",this._syncState(this.status)}else if(this.status===\"requested\")return this._requestImport()}))}_requestImport(){return a._(this,void 0,void 0,(function*(){yield this._syncState(\"loading\"),this.status=\"loaded\",this.fire(new a.k(Ue))}))}lazyLoad(){this.status===\"unavailable\"?this.status=\"requested\":this.status===\"deferred\"&&this._requestImport()}}let rt=null;function St(){return rt||(rt=new Ae),rt}class Tt{constructor(w,B){this.timeAdded=0,this.fadeEndTime=0,this.tileID=w,this.uid=a.a4(),this.uses=0,this.tileSize=B,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttCoords={},this.expiredRequestCount=0,this.state=\"loading\"}registerFadeDuration(w){let B=w+this.timeAdded;B<this.fadeEndTime||(this.fadeEndTime=B)}wasRequested(){return this.state===\"errored\"||this.state===\"loaded\"||this.state===\"reloading\"}clearTextures(w){this.demTexture&&w.saveTileTexture(this.demTexture),this.demTexture=null}loadVectorData(w,B,Q){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",w){w.featureIndex&&(this.latestFeatureIndex=w.featureIndex,w.rawTileData?(this.latestRawTileData=w.rawTileData,this.latestFeatureIndex.rawTileData=w.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=w.collisionBoxArray,this.buckets=(function(ee,le){let Fe={};if(!le)return Fe;for(let Ze of ee){let ut=Ze.layerIds.map((Mt=>le.getLayer(Mt))).filter(Boolean);if(ut.length!==0){Ze.layers=ut,Ze.stateDependentLayerIds&&(Ze.stateDependentLayers=Ze.stateDependentLayerIds.map((Mt=>ut.filter((Jt=>Jt.id===Mt))[0])));for(let Mt of ut)Fe[Mt.id]=Ze}}return Fe})(w.buckets,B.style),this.hasSymbolBuckets=!1;for(let ee in this.buckets){let le=this.buckets[ee];if(le instanceof a.a6){if(this.hasSymbolBuckets=!0,!Q)break;le.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(let ee in this.buckets){let le=this.buckets[ee];if(le instanceof a.a6&&le.hasRTLText){this.hasRTLText=!0,St().lazyLoad();break}}this.queryPadding=0;for(let ee in this.buckets){let le=this.buckets[ee];this.queryPadding=Math.max(this.queryPadding,B.style.getLayer(ee).queryRadius(le))}w.imageAtlas&&(this.imageAtlas=w.imageAtlas),w.glyphAtlasImage&&(this.glyphAtlasImage=w.glyphAtlasImage)}else this.collisionBoxArray=new a.a5}unloadVectorData(){for(let w in this.buckets)this.buckets[w].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"}getBucket(w){return this.buckets[w.id]}upload(w){for(let Q in this.buckets){let ee=this.buckets[Q];ee.uploadPending()&&ee.upload(w)}let B=w.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new g(w,this.imageAtlas.image,B.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new g(w,this.glyphAtlasImage,B.ALPHA),this.glyphAtlasImage=null)}prepare(w){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(w,this.imageAtlasTexture)}queryRenderedFeatures(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:ee,cameraQueryGeometry:le,scale:Fe,tileSize:this.tileSize,pixelPosMatrix:Jt,transform:ut,params:Ze,queryPadding:this.queryPadding*Mt},w,B,Q):{}}querySourceFeatures(w,B){let Q=this.latestFeatureIndex;if(!Q||!Q.rawTileData)return;let ee=Q.loadVTLayers(),le=B&&B.sourceLayer?B.sourceLayer:\"\",Fe=ee._geojsonTileLayer||ee[le];if(!Fe)return;let Ze=a.a7(B&&B.filter),{z:ut,x:Mt,y:Jt}=this.tileID.canonical,$t={z:ut,x:Mt,y:Jt};for(let yr=0;yr<Fe.length;yr++){let Cr=Fe.feature(yr);if(Ze.needGeometry){let Ie=a.a8(Cr,!0);if(!Ze.filter(new a.z(this.tileID.overscaledZ),Ie,this.tileID.canonical))continue}else if(!Ze.filter(new a.z(this.tileID.overscaledZ),Cr))continue;let ve=Q.getId(Cr,le),xe=new a.a9(Cr,ut,Mt,Jt,ve);xe.tile=$t,w.push(xe)}}hasData(){return this.state===\"loaded\"||this.state===\"reloading\"||this.state===\"expired\"}patternsLoaded(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length}setExpiryData(w){let B=this.expirationTime;if(w.cacheControl){let Q=a.aa(w.cacheControl);Q[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*Q[\"max-age\"])}else w.expires&&(this.expirationTime=new Date(w.expires).getTime());if(this.expirationTime){let Q=Date.now(),ee=!1;if(this.expirationTime>Q)ee=!1;else if(B)if(this.expirationTime<B)ee=!0;else{let le=this.expirationTime-B;le?this.expirationTime=Q+Math.max(le,3e4):ee=!0}else ee=!0;ee?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}}getExpiryTimeout(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-new Date().getTime(),Math.pow(2,31)-1)}setFeatureState(w,B){if(!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData||Object.keys(w).length===0)return;let Q=this.latestFeatureIndex.loadVTLayers();for(let ee in this.buckets){if(!B.style.hasLayer(ee))continue;let le=this.buckets[ee],Fe=le.layers[0].sourceLayer||\"_geojsonTileLayer\",Ze=Q[Fe],ut=w[Fe];if(!Ze||!ut||Object.keys(ut).length===0)continue;le.update(ut,Ze,this.imageAtlas&&this.imageAtlas.patternPositions||{});let Mt=B&&B.style&&B.style.getLayer(ee);Mt&&(this.queryPadding=Math.max(this.queryPadding,Mt.queryRadius(le)))}}holdingForFade(){return this.symbolFadeHoldUntil!==void 0}symbolFadeFinished(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<u.now()}clearFadeHold(){this.symbolFadeHoldUntil=void 0}setHoldDuration(w){this.symbolFadeHoldUntil=u.now()+w}setDependencies(w,B){let Q={};for(let ee of B)Q[ee]=!0;this.dependencies[w]=Q}hasDependency(w,B){for(let Q of w){let ee=this.dependencies[Q];if(ee){for(let le of B)if(ee[le])return!0}}return!1}}class dt{constructor(w,B){this.max=w,this.onRemove=B,this.reset()}reset(){for(let w in this.data)for(let B of this.data[w])B.timeout&&clearTimeout(B.timeout),this.onRemove(B.value);return this.data={},this.order=[],this}add(w,B,Q){let ee=w.wrapped().key;this.data[ee]===void 0&&(this.data[ee]=[]);let le={value:B,timeout:void 0};if(Q!==void 0&&(le.timeout=setTimeout((()=>{this.remove(w,le)}),Q)),this.data[ee].push(le),this.order.push(ee),this.order.length>this.max){let Fe=this._getAndRemoveByKey(this.order[0]);Fe&&this.onRemove(Fe)}return this}has(w){return w.wrapped().key in this.data}getAndRemove(w){return this.has(w)?this._getAndRemoveByKey(w.wrapped().key):null}_getAndRemoveByKey(w){let B=this.data[w].shift();return B.timeout&&clearTimeout(B.timeout),this.data[w].length===0&&delete this.data[w],this.order.splice(this.order.indexOf(w),1),B.value}getByKey(w){let B=this.data[w];return B?B[0].value:null}get(w){return this.has(w)?this.data[w.wrapped().key][0].value:null}remove(w,B){if(!this.has(w))return this;let Q=w.wrapped().key,ee=B===void 0?0:this.data[Q].indexOf(B),le=this.data[Q][ee];return this.data[Q].splice(ee,1),le.timeout&&clearTimeout(le.timeout),this.data[Q].length===0&&delete this.data[Q],this.onRemove(le.value),this.order.splice(this.order.indexOf(Q),1),this}setMaxSize(w){for(this.max=w;this.order.length>this.max;){let B=this._getAndRemoveByKey(this.order[0]);B&&this.onRemove(B)}return this}filter(w){let B=[];for(let Q in this.data)for(let ee of this.data[Q])w(ee.value)||B.push(ee);for(let Q of B)this.remove(Q.value.tileID,Q)}}class Et{constructor(){this.state={},this.stateChanges={},this.deletedStates={}}updateState(w,B,Q){let ee=String(B);if(this.stateChanges[w]=this.stateChanges[w]||{},this.stateChanges[w][ee]=this.stateChanges[w][ee]||{},a.e(this.stateChanges[w][ee],Q),this.deletedStates[w]===null){this.deletedStates[w]={};for(let le in this.state[w])le!==ee&&(this.deletedStates[w][le]=null)}else if(this.deletedStates[w]&&this.deletedStates[w][ee]===null){this.deletedStates[w][ee]={};for(let le in this.state[w][ee])Q[le]||(this.deletedStates[w][ee][le]=null)}else for(let le in Q)this.deletedStates[w]&&this.deletedStates[w][ee]&&this.deletedStates[w][ee][le]===null&&delete this.deletedStates[w][ee][le]}removeFeatureState(w,B,Q){if(this.deletedStates[w]===null)return;let ee=String(B);if(this.deletedStates[w]=this.deletedStates[w]||{},Q&&B!==void 0)this.deletedStates[w][ee]!==null&&(this.deletedStates[w][ee]=this.deletedStates[w][ee]||{},this.deletedStates[w][ee][Q]=null);else if(B!==void 0)if(this.stateChanges[w]&&this.stateChanges[w][ee])for(Q in this.deletedStates[w][ee]={},this.stateChanges[w][ee])this.deletedStates[w][ee][Q]=null;else this.deletedStates[w][ee]=null;else this.deletedStates[w]=null}getState(w,B){let Q=String(B),ee=a.e({},(this.state[w]||{})[Q],(this.stateChanges[w]||{})[Q]);if(this.deletedStates[w]===null)return{};if(this.deletedStates[w]){let le=this.deletedStates[w][B];if(le===null)return{};for(let Fe in le)delete ee[Fe]}return ee}initializeTileState(w,B){w.setFeatureState(this.state,B)}coalesceChanges(w,B){let Q={};for(let ee in this.stateChanges){this.state[ee]=this.state[ee]||{};let le={};for(let Fe in this.stateChanges[ee])this.state[ee][Fe]||(this.state[ee][Fe]={}),a.e(this.state[ee][Fe],this.stateChanges[ee][Fe]),le[Fe]=this.state[ee][Fe];Q[ee]=le}for(let ee in this.deletedStates){this.state[ee]=this.state[ee]||{};let le={};if(this.deletedStates[ee]===null)for(let Fe in this.state[ee])le[Fe]={},this.state[ee][Fe]={};else for(let Fe in this.deletedStates[ee]){if(this.deletedStates[ee][Fe]===null)this.state[ee][Fe]={};else for(let Ze of Object.keys(this.deletedStates[ee][Fe]))delete this.state[ee][Fe][Ze];le[Fe]=this.state[ee][Fe]}Q[ee]=Q[ee]||{},a.e(Q[ee],le)}if(this.stateChanges={},this.deletedStates={},Object.keys(Q).length!==0)for(let ee in w)w[ee].setFeatureState(Q,B)}}class pt extends a.E{constructor(w,B,Q){super(),this.id=w,this.dispatcher=Q,this.on(\"data\",(ee=>this._dataHandler(ee))),this.on(\"dataloading\",(()=>{this._sourceErrored=!1})),this.on(\"error\",(()=>{this._sourceErrored=this._source.loaded()})),this._source=((ee,le,Fe,Ze)=>{let ut=new(je(le.type))(ee,le,Fe,Ze);if(ut.id!==ee)throw new Error(`Expected Source id to be ${ee} instead of ${ut.id}`);return ut})(w,B,Q,this),this._tiles={},this._cache=new dt(0,(ee=>this._unloadTile(ee))),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new Et,this._didEmitContent=!1,this._updated=!1}onAdd(w){this.map=w,this._maxTileCacheSize=w?w._maxTileCacheSize:null,this._maxTileCacheZoomLevels=w?w._maxTileCacheZoomLevels:null,this._source&&this._source.onAdd&&this._source.onAdd(w)}onRemove(w){this.clearTiles(),this._source&&this._source.onRemove&&this._source.onRemove(w)}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;if(!(this.used===void 0&&this.usedForTerrain===void 0||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(let w in this._tiles){let B=this._tiles[w];if(B.state!==\"loaded\"&&B.state!==\"errored\")return!1}return!0}getSource(){return this._source}pause(){this._paused=!0}resume(){if(!this._paused)return;let w=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,w&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(w,B,Q){return a._(this,void 0,void 0,(function*(){try{yield this._source.loadTile(w),this._tileLoaded(w,B,Q)}catch(ee){w.state=\"errored\",ee.status!==404?this._source.fire(new a.j(ee,{tile:w})):this.update(this.transform,this.terrain)}}))}_unloadTile(w){this._source.unloadTile&&this._source.unloadTile(w)}_abortTile(w){this._source.abortTile&&this._source.abortTile(w),this._source.fire(new a.k(\"dataabort\",{tile:w,coord:w.tileID,dataType:\"source\"}))}serialize(){return this._source.serialize()}prepare(w){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(let B in this._tiles){let Q=this._tiles[B];Q.upload(w),Q.prepare(this.map.style.imageManager)}}getIds(){return Object.values(this._tiles).map((w=>w.tileID)).sort(jt).map((w=>w.key))}getRenderableIds(w){let B=[];for(let Q in this._tiles)this._isIdRenderable(Q,w)&&B.push(this._tiles[Q]);return w?B.sort(((Q,ee)=>{let le=Q.tileID,Fe=ee.tileID,Ze=new a.P(le.canonical.x,le.canonical.y)._rotate(this.transform.angle),ut=new a.P(Fe.canonical.x,Fe.canonical.y)._rotate(this.transform.angle);return le.overscaledZ-Fe.overscaledZ||ut.y-Ze.y||ut.x-Ze.x})).map((Q=>Q.tileID.key)):B.map((Q=>Q.tileID)).sort(jt).map((Q=>Q.key))}hasRenderableParent(w){let B=this.findLoadedParent(w,0);return!!B&&this._isIdRenderable(B.tileID.key)}_isIdRenderable(w,B){return this._tiles[w]&&this._tiles[w].hasData()&&!this._coveredTiles[w]&&(B||!this._tiles[w].holdingForFade())}reload(){if(this._paused)this._shouldReloadOnResume=!0;else{this._cache.reset();for(let w in this._tiles)this._tiles[w].state!==\"errored\"&&this._reloadTile(w,\"reloading\")}}_reloadTile(w,B){return a._(this,void 0,void 0,(function*(){let Q=this._tiles[w];Q&&(Q.state!==\"loading\"&&(Q.state=B),yield this._loadTile(Q,w,B))}))}_tileLoaded(w,B,Q){w.timeAdded=u.now(),Q===\"expired\"&&(w.refreshedUponExpiration=!0),this._setTileReloadTimer(B,w),this.getSource().type===\"raster-dem\"&&w.dem&&this._backfillDEM(w),this._state.initializeTileState(w,this.map?this.map.painter:null),w.aborted||this._source.fire(new a.k(\"data\",{dataType:\"source\",tile:w,coord:w.tileID}))}_backfillDEM(w){let B=this.getRenderableIds();for(let ee=0;ee<B.length;ee++){let le=B[ee];if(w.neighboringTiles&&w.neighboringTiles[le]){let Fe=this.getTileByID(le);Q(w,Fe),Q(Fe,w)}}function Q(ee,le){ee.needsHillshadePrepare=!0,ee.needsTerrainPrepare=!0;let Fe=le.tileID.canonical.x-ee.tileID.canonical.x,Ze=le.tileID.canonical.y-ee.tileID.canonical.y,ut=Math.pow(2,ee.tileID.canonical.z),Mt=le.tileID.key;Fe===0&&Ze===0||Math.abs(Ze)>1||(Math.abs(Fe)>1&&(Math.abs(Fe+ut)===1?Fe+=ut:Math.abs(Fe-ut)===1&&(Fe-=ut)),le.dem&&ee.dem&&(ee.dem.backfillBorder(le.dem,Fe,Ze),ee.neighboringTiles&&ee.neighboringTiles[Mt]&&(ee.neighboringTiles[Mt].backfilled=!0)))}}getTile(w){return this.getTileByID(w.key)}getTileByID(w){return this._tiles[w]}_retainLoadedChildren(w,B,Q,ee){for(let le in this._tiles){let Fe=this._tiles[le];if(ee[le]||!Fe.hasData()||Fe.tileID.overscaledZ<=B||Fe.tileID.overscaledZ>Q)continue;let Ze=Fe.tileID;for(;Fe&&Fe.tileID.overscaledZ>B+1;){let Mt=Fe.tileID.scaledTo(Fe.tileID.overscaledZ-1);Fe=this._tiles[Mt.key],Fe&&Fe.hasData()&&(Ze=Mt)}let ut=Ze;for(;ut.overscaledZ>B;)if(ut=ut.scaledTo(ut.overscaledZ-1),w[ut.key]){ee[Ze.key]=Ze;break}}}findLoadedParent(w,B){if(w.key in this._loadedParentTiles){let Q=this._loadedParentTiles[w.key];return Q&&Q.tileID.overscaledZ>=B?Q:null}for(let Q=w.overscaledZ-1;Q>=B;Q--){let ee=w.scaledTo(Q),le=this._getLoadedTile(ee);if(le)return le}}findLoadedSibling(w){return this._getLoadedTile(w)}_getLoadedTile(w){let B=this._tiles[w.key];return B&&B.hasData()?B:this._cache.getByKey(w.wrapped().key)}updateCacheSize(w){let B=Math.ceil(w.width/this._source.tileSize)+1,Q=Math.ceil(w.height/this._source.tileSize)+1,ee=Math.floor(B*Q*(this._maxTileCacheZoomLevels===null?a.a.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels)),le=typeof this._maxTileCacheSize==\"number\"?Math.min(this._maxTileCacheSize,ee):ee;this._cache.setMaxSize(le)}handleWrapJump(w){let B=Math.round((w-(this._prevLng===void 0?w:this._prevLng))/360);if(this._prevLng=w,B){let Q={};for(let ee in this._tiles){let le=this._tiles[ee];le.tileID=le.tileID.unwrapTo(le.tileID.wrap+B),Q[le.tileID.key]=le}this._tiles=Q;for(let ee in this._timers)clearTimeout(this._timers[ee]),delete this._timers[ee];for(let ee in this._tiles)this._setTileReloadTimer(ee,this._tiles[ee])}}_updateCoveredAndRetainedTiles(w,B,Q,ee,le,Fe){let Ze={},ut={},Mt=Object.keys(w),Jt=u.now();for(let $t of Mt){let yr=w[$t],Cr=this._tiles[$t];if(!Cr||Cr.fadeEndTime!==0&&Cr.fadeEndTime<=Jt)continue;let ve=this.findLoadedParent(yr,B),xe=this.findLoadedSibling(yr),Ie=ve||xe||null;Ie&&(this._addTile(Ie.tileID),Ze[Ie.tileID.key]=Ie.tileID),ut[$t]=yr}this._retainLoadedChildren(ut,ee,Q,w);for(let $t in Ze)w[$t]||(this._coveredTiles[$t]=!0,w[$t]=Ze[$t]);if(Fe){let $t={},yr={};for(let Cr of le)this._tiles[Cr.key].hasData()?$t[Cr.key]=Cr:yr[Cr.key]=Cr;for(let Cr in yr){let ve=yr[Cr].children(this._source.maxzoom);this._tiles[ve[0].key]&&this._tiles[ve[1].key]&&this._tiles[ve[2].key]&&this._tiles[ve[3].key]&&($t[ve[0].key]=w[ve[0].key]=ve[0],$t[ve[1].key]=w[ve[1].key]=ve[1],$t[ve[2].key]=w[ve[2].key]=ve[2],$t[ve[3].key]=w[ve[3].key]=ve[3],delete yr[Cr])}for(let Cr in yr){let ve=yr[Cr],xe=this.findLoadedParent(ve,this._source.minzoom),Ie=this.findLoadedSibling(ve),ze=xe||Ie||null;if(ze){$t[ze.tileID.key]=w[ze.tileID.key]=ze.tileID;for(let Qe in $t)$t[Qe].isChildOf(ze.tileID)&&delete $t[Qe]}}for(let Cr in this._tiles)$t[Cr]||(this._coveredTiles[Cr]=!0)}}update(w,B){if(!this._sourceLoaded||this._paused)return;let Q;this.transform=w,this.terrain=B,this.updateCacheSize(w),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used||this.usedForTerrain?this._source.tileID?Q=w.getVisibleUnwrappedCoordinates(this._source.tileID).map((Jt=>new a.S(Jt.canonical.z,Jt.wrap,Jt.canonical.z,Jt.canonical.x,Jt.canonical.y))):(Q=w.coveringTiles({tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:B}),this._source.hasTile&&(Q=Q.filter((Jt=>this._source.hasTile(Jt))))):Q=[];let ee=w.coveringZoomLevel(this._source),le=Math.max(ee-pt.maxOverzooming,this._source.minzoom),Fe=Math.max(ee+pt.maxUnderzooming,this._source.minzoom);if(this.usedForTerrain){let Jt={};for(let $t of Q)if($t.canonical.z>this._source.minzoom){let yr=$t.scaledTo($t.canonical.z-1);Jt[yr.key]=yr;let Cr=$t.scaledTo(Math.max(this._source.minzoom,Math.min($t.canonical.z,5)));Jt[Cr.key]=Cr}Q=Q.concat(Object.values(Jt))}let Ze=Q.length===0&&!this._updated&&this._didEmitContent;this._updated=!0,Ze&&this.fire(new a.k(\"data\",{sourceDataType:\"idle\",dataType:\"source\",sourceId:this.id}));let ut=this._updateRetainedTiles(Q,ee);or(this._source.type)&&this._updateCoveredAndRetainedTiles(ut,le,Fe,ee,Q,B);for(let Jt in ut)this._tiles[Jt].clearFadeHold();let Mt=a.ab(this._tiles,ut);for(let Jt of Mt){let $t=this._tiles[Jt];$t.hasSymbolBuckets&&!$t.holdingForFade()?$t.setHoldDuration(this.map._fadeDuration):$t.hasSymbolBuckets&&!$t.symbolFadeFinished()||this._removeTile(Jt)}this._updateLoadedParentTileCache(),this._updateLoadedSiblingTileCache()}releaseSymbolFadeTiles(){for(let w in this._tiles)this._tiles[w].holdingForFade()&&this._removeTile(w)}_updateRetainedTiles(w,B){var Q;let ee={},le={},Fe=Math.max(B-pt.maxOverzooming,this._source.minzoom),Ze=Math.max(B+pt.maxUnderzooming,this._source.minzoom),ut={};for(let Mt of w){let Jt=this._addTile(Mt);ee[Mt.key]=Mt,Jt.hasData()||B<this._source.maxzoom&&(ut[Mt.key]=Mt)}this._retainLoadedChildren(ut,B,Ze,ee);for(let Mt of w){let Jt=this._tiles[Mt.key];if(Jt.hasData())continue;if(B+1>this._source.maxzoom){let yr=Mt.children(this._source.maxzoom)[0],Cr=this.getTile(yr);if(Cr&&Cr.hasData()){ee[yr.key]=yr;continue}}else{let yr=Mt.children(this._source.maxzoom);if(ee[yr[0].key]&&ee[yr[1].key]&&ee[yr[2].key]&&ee[yr[3].key])continue}let $t=Jt.wasRequested();for(let yr=Mt.overscaledZ-1;yr>=Fe;--yr){let Cr=Mt.scaledTo(yr);if(le[Cr.key])break;if(le[Cr.key]=!0,Jt=this.getTile(Cr),!Jt&&$t&&(Jt=this._addTile(Cr)),Jt){let ve=Jt.hasData();if((ve||!(!((Q=this.map)===null||Q===void 0)&&Q.cancelPendingTileRequestsWhileZooming)||$t)&&(ee[Cr.key]=Cr),$t=Jt.wasRequested(),ve)break}}}return ee}_updateLoadedParentTileCache(){this._loadedParentTiles={};for(let w in this._tiles){let B=[],Q,ee=this._tiles[w].tileID;for(;ee.overscaledZ>0;){if(ee.key in this._loadedParentTiles){Q=this._loadedParentTiles[ee.key];break}B.push(ee.key);let le=ee.scaledTo(ee.overscaledZ-1);if(Q=this._getLoadedTile(le),Q)break;ee=le}for(let le of B)this._loadedParentTiles[le]=Q}}_updateLoadedSiblingTileCache(){this._loadedSiblingTiles={};for(let w in this._tiles){let B=this._tiles[w].tileID,Q=this._getLoadedTile(B);this._loadedSiblingTiles[B.key]=Q}}_addTile(w){let B=this._tiles[w.key];if(B)return B;B=this._cache.getAndRemove(w),B&&(this._setTileReloadTimer(w.key,B),B.tileID=w,this._state.initializeTileState(B,this.map?this.map.painter:null),this._cacheTimers[w.key]&&(clearTimeout(this._cacheTimers[w.key]),delete this._cacheTimers[w.key],this._setTileReloadTimer(w.key,B)));let Q=B;return B||(B=new Tt(w,this._source.tileSize*w.overscaleFactor()),this._loadTile(B,w.key,B.state)),B.uses++,this._tiles[w.key]=B,Q||this._source.fire(new a.k(\"dataloading\",{tile:B,coord:B.tileID,dataType:\"source\"})),B}_setTileReloadTimer(w,B){w in this._timers&&(clearTimeout(this._timers[w]),delete this._timers[w]);let Q=B.getExpiryTimeout();Q&&(this._timers[w]=setTimeout((()=>{this._reloadTile(w,\"expired\"),delete this._timers[w]}),Q))}_removeTile(w){let B=this._tiles[w];B&&(B.uses--,delete this._tiles[w],this._timers[w]&&(clearTimeout(this._timers[w]),delete this._timers[w]),B.uses>0||(B.hasData()&&B.state!==\"reloading\"?this._cache.add(B.tileID,B,B.getExpiryTimeout()):(B.aborted=!0,this._abortTile(B),this._unloadTile(B))))}_dataHandler(w){let B=w.sourceDataType;w.dataType===\"source\"&&B===\"metadata\"&&(this._sourceLoaded=!0),this._sourceLoaded&&!this._paused&&w.dataType===\"source\"&&B===\"content\"&&(this.reload(),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0)}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(let w in this._tiles)this._removeTile(w);this._cache.reset()}tilesIn(w,B,Q){let ee=[],le=this.transform;if(!le)return ee;let Fe=Q?le.getCameraQueryGeometry(w):w,Ze=w.map((ve=>le.pointCoordinate(ve,this.terrain))),ut=Fe.map((ve=>le.pointCoordinate(ve,this.terrain))),Mt=this.getIds(),Jt=1/0,$t=1/0,yr=-1/0,Cr=-1/0;for(let ve of ut)Jt=Math.min(Jt,ve.x),$t=Math.min($t,ve.y),yr=Math.max(yr,ve.x),Cr=Math.max(Cr,ve.y);for(let ve=0;ve<Mt.length;ve++){let xe=this._tiles[Mt[ve]];if(xe.holdingForFade())continue;let Ie=xe.tileID,ze=Math.pow(2,le.zoom-xe.tileID.overscaledZ),Qe=B*xe.queryPadding*a.X/xe.tileSize/ze,Xe=[Ie.getTilePoint(new a.Z(Jt,$t)),Ie.getTilePoint(new a.Z(yr,Cr))];if(Xe[0].x-Qe<a.X&&Xe[0].y-Qe<a.X&&Xe[1].x+Qe>=0&&Xe[1].y+Qe>=0){let nt=Ze.map((Wt=>Ie.getTilePoint(Wt))),qt=ut.map((Wt=>Ie.getTilePoint(Wt)));ee.push({tile:xe,tileID:Ie,queryGeometry:nt,cameraQueryGeometry:qt,scale:ze})}}return ee}getVisibleCoordinates(w){let B=this.getRenderableIds(w).map((Q=>this._tiles[Q].tileID));for(let Q of B)Q.posMatrix=this.transform.calculatePosMatrix(Q.toUnwrapped());return B}hasTransition(){if(this._source.hasTransition())return!0;if(or(this._source.type)){let w=u.now();for(let B in this._tiles)if(this._tiles[B].fadeEndTime>=w)return!0}return!1}setFeatureState(w,B,Q){this._state.updateState(w=w||\"_geojsonTileLayer\",B,Q)}removeFeatureState(w,B,Q){this._state.removeFeatureState(w=w||\"_geojsonTileLayer\",B,Q)}getFeatureState(w,B){return this._state.getState(w=w||\"_geojsonTileLayer\",B)}setDependencies(w,B,Q){let ee=this._tiles[w];ee&&ee.setDependencies(B,Q)}reloadTilesForDependencies(w,B){for(let Q in this._tiles)this._tiles[Q].hasDependency(w,B)&&this._reloadTile(Q,\"reloading\");this._cache.filter((Q=>!Q.hasDependency(w,B)))}}function jt(ue,w){let B=Math.abs(2*ue.wrap)-+(ue.wrap<0),Q=Math.abs(2*w.wrap)-+(w.wrap<0);return ue.overscaledZ-w.overscaledZ||Q-B||w.canonical.y-ue.canonical.y||w.canonical.x-ue.canonical.x}function or(ue){return ue===\"raster\"||ue===\"image\"||ue===\"video\"}pt.maxOverzooming=10,pt.maxUnderzooming=3;class mr{constructor(w,B){this.reset(w,B)}reset(w,B){this.points=w||[],this._distances=[0];for(let Q=1;Q<this.points.length;Q++)this._distances[Q]=this._distances[Q-1]+this.points[Q].dist(this.points[Q-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(B||0,.5*this.length),this.paddedLength=this.length-2*this.padding}lerp(w){if(this.points.length===1)return this.points[0];w=a.ac(w,0,1);let B=1,Q=this._distances[B],ee=w*this.paddedLength+this.padding;for(;Q<ee&&B<this._distances.length;)Q=this._distances[++B];let le=B-1,Fe=this._distances[le],Ze=Q-Fe,ut=Ze>0?(ee-Fe)/Ze:0;return this.points[le].mult(1-ut).add(this.points[B].mult(ut))}}function Ar(ue,w){let B=!0;return ue===\"always\"||ue!==\"never\"&&w!==\"never\"||(B=!1),B}class ei{constructor(w,B,Q){let ee=this.boxCells=[],le=this.circleCells=[];this.xCellCount=Math.ceil(w/Q),this.yCellCount=Math.ceil(B/Q);for(let Fe=0;Fe<this.xCellCount*this.yCellCount;Fe++)ee.push([]),le.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=w,this.height=B,this.xScale=this.xCellCount/w,this.yScale=this.yCellCount/B,this.boxUid=0,this.circleUid=0}keysLength(){return this.boxKeys.length+this.circleKeys.length}insert(w,B,Q,ee,le){this._forEachCell(B,Q,ee,le,this._insertBoxCell,this.boxUid++),this.boxKeys.push(w),this.bboxes.push(B),this.bboxes.push(Q),this.bboxes.push(ee),this.bboxes.push(le)}insertCircle(w,B,Q,ee){this._forEachCell(B-ee,Q-ee,B+ee,Q+ee,this._insertCircleCell,this.circleUid++),this.circleKeys.push(w),this.circles.push(B),this.circles.push(Q),this.circles.push(ee)}_insertBoxCell(w,B,Q,ee,le,Fe){this.boxCells[le].push(Fe)}_insertCircleCell(w,B,Q,ee,le,Fe){this.circleCells[le].push(Fe)}_query(w,B,Q,ee,le,Fe,Ze){if(Q<0||w>this.width||ee<0||B>this.height)return[];let ut=[];if(w<=0&&B<=0&&this.width<=Q&&this.height<=ee){if(le)return[{key:null,x1:w,y1:B,x2:Q,y2:ee}];for(let Mt=0;Mt<this.boxKeys.length;Mt++)ut.push({key:this.boxKeys[Mt],x1:this.bboxes[4*Mt],y1:this.bboxes[4*Mt+1],x2:this.bboxes[4*Mt+2],y2:this.bboxes[4*Mt+3]});for(let Mt=0;Mt<this.circleKeys.length;Mt++){let Jt=this.circles[3*Mt],$t=this.circles[3*Mt+1],yr=this.circles[3*Mt+2];ut.push({key:this.circleKeys[Mt],x1:Jt-yr,y1:$t-yr,x2:Jt+yr,y2:$t+yr})}}else this._forEachCell(w,B,Q,ee,this._queryCell,ut,{hitTest:le,overlapMode:Fe,seenUids:{box:{},circle:{}}},Ze);return ut}query(w,B,Q,ee){return this._query(w,B,Q,ee,!1,null)}hitTest(w,B,Q,ee,le,Fe){return this._query(w,B,Q,ee,!0,le,Fe).length>0}hitTestCircle(w,B,Q,ee,le){let Fe=w-Q,Ze=w+Q,ut=B-Q,Mt=B+Q;if(Ze<0||Fe>this.width||Mt<0||ut>this.height)return!1;let Jt=[];return this._forEachCell(Fe,ut,Ze,Mt,this._queryCellCircle,Jt,{hitTest:!0,overlapMode:ee,circle:{x:w,y:B,radius:Q},seenUids:{box:{},circle:{}}},le),Jt.length>0}_queryCell(w,B,Q,ee,le,Fe,Ze,ut){let{seenUids:Mt,hitTest:Jt,overlapMode:$t}=Ze,yr=this.boxCells[le];if(yr!==null){let ve=this.bboxes;for(let xe of yr)if(!Mt.box[xe]){Mt.box[xe]=!0;let Ie=4*xe,ze=this.boxKeys[xe];if(w<=ve[Ie+2]&&B<=ve[Ie+3]&&Q>=ve[Ie+0]&&ee>=ve[Ie+1]&&(!ut||ut(ze))&&(!Jt||!Ar($t,ze.overlapMode))&&(Fe.push({key:ze,x1:ve[Ie],y1:ve[Ie+1],x2:ve[Ie+2],y2:ve[Ie+3]}),Jt))return!0}}let Cr=this.circleCells[le];if(Cr!==null){let ve=this.circles;for(let xe of Cr)if(!Mt.circle[xe]){Mt.circle[xe]=!0;let Ie=3*xe,ze=this.circleKeys[xe];if(this._circleAndRectCollide(ve[Ie],ve[Ie+1],ve[Ie+2],w,B,Q,ee)&&(!ut||ut(ze))&&(!Jt||!Ar($t,ze.overlapMode))){let Qe=ve[Ie],Xe=ve[Ie+1],nt=ve[Ie+2];if(Fe.push({key:ze,x1:Qe-nt,y1:Xe-nt,x2:Qe+nt,y2:Xe+nt}),Jt)return!0}}}return!1}_queryCellCircle(w,B,Q,ee,le,Fe,Ze,ut){let{circle:Mt,seenUids:Jt,overlapMode:$t}=Ze,yr=this.boxCells[le];if(yr!==null){let ve=this.bboxes;for(let xe of yr)if(!Jt.box[xe]){Jt.box[xe]=!0;let Ie=4*xe,ze=this.boxKeys[xe];if(this._circleAndRectCollide(Mt.x,Mt.y,Mt.radius,ve[Ie+0],ve[Ie+1],ve[Ie+2],ve[Ie+3])&&(!ut||ut(ze))&&!Ar($t,ze.overlapMode))return Fe.push(!0),!0}}let Cr=this.circleCells[le];if(Cr!==null){let ve=this.circles;for(let xe of Cr)if(!Jt.circle[xe]){Jt.circle[xe]=!0;let Ie=3*xe,ze=this.circleKeys[xe];if(this._circlesCollide(ve[Ie],ve[Ie+1],ve[Ie+2],Mt.x,Mt.y,Mt.radius)&&(!ut||ut(ze))&&!Ar($t,ze.overlapMode))return Fe.push(!0),!0}}}_forEachCell(w,B,Q,ee,le,Fe,Ze,ut){let Mt=this._convertToXCellCoord(w),Jt=this._convertToYCellCoord(B),$t=this._convertToXCellCoord(Q),yr=this._convertToYCellCoord(ee);for(let Cr=Mt;Cr<=$t;Cr++)for(let ve=Jt;ve<=yr;ve++)if(le.call(this,w,B,Q,ee,this.xCellCount*ve+Cr,Fe,Ze,ut))return}_convertToXCellCoord(w){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(w*this.xScale)))}_convertToYCellCoord(w){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(w*this.yScale)))}_circlesCollide(w,B,Q,ee,le,Fe){let Ze=ee-w,ut=le-B,Mt=Q+Fe;return Mt*Mt>Ze*Ze+ut*ut}_circleAndRectCollide(w,B,Q,ee,le,Fe,Ze){let ut=(Fe-ee)/2,Mt=Math.abs(w-(ee+ut));if(Mt>ut+Q)return!1;let Jt=(Ze-le)/2,$t=Math.abs(B-(le+Jt));if($t>Jt+Q)return!1;if(Mt<=ut||$t<=Jt)return!0;let yr=Mt-ut,Cr=$t-Jt;return yr*yr+Cr*Cr<=Q*Q}}function qr(ue,w,B,Q,ee){let le=a.H();return w?(a.K(le,le,[1/ee,1/ee,1]),B||a.ad(le,le,Q.angle)):a.L(le,Q.labelPlaneMatrix,ue),le}function jr(ue,w,B,Q,ee){if(w){let le=a.ae(ue);return a.K(le,le,[ee,ee,1]),B||a.ad(le,le,-Q.angle),le}return Q.glCoordMatrix}function gt(ue,w,B,Q){let ee;Q?(ee=[ue,w,Q(ue,w),1],a.af(ee,ee,B)):(ee=[ue,w,0,1],ai(ee,ee,B));let le=ee[3];return{point:new a.P(ee[0]/le,ee[1]/le),signedDistanceFromCamera:le,isOccluded:!1}}function Ge(ue,w){return .5+ue/w*.5}function Je(ue,w){return ue.x>=-w[0]&&ue.x<=w[0]&&ue.y>=-w[1]&&ue.y<=w[1]}function We(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve){let xe=Q?ue.textSizeData:ue.iconSizeData,Ie=a.ag(xe,B.transform.zoom),ze=[256/B.width*2+1,256/B.height*2+1],Qe=Q?ue.text.dynamicLayoutVertexArray:ue.icon.dynamicLayoutVertexArray;Qe.clear();let Xe=ue.lineVertexArray,nt=Q?ue.text.placedSymbolArray:ue.icon.placedSymbolArray,qt=B.transform.width/B.transform.height,Wt=!1;for(let nr=0;nr<nt.length;nr++){let fr=nt.get(nr);if(fr.hidden||fr.writingMode===a.ah.vertical&&!Wt){Pi(fr.numGlyphs,Qe);continue}Wt=!1;let br=gt(fr.anchorX,fr.anchorY,w,ve);if(!Je(br.point,ze)){Pi(fr.numGlyphs,Qe);continue}let Fr=Ge(B.transform.cameraToCenterDistance,br.signedDistanceFromCamera),Hr=a.ai(xe,Ie,fr),ri=Fe?Hr/Fr:Hr*Fr,Ci={getElevation:ve,labelPlaneMatrix:ee,lineVertexArray:Xe,pitchWithMap:Fe,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},projection:Mt,tileAnchorPoint:new a.P(fr.anchorX,fr.anchorY),unwrappedTileID:Jt,width:$t,height:yr,translation:Cr},cn=At(Ci,fr,ri,!1,Ze,w,le,ue.glyphOffsetArray,Qe,qt,ut);Wt=cn.useVertical,(cn.notEnoughRoom||Wt||cn.needsFlipping&&At(Ci,fr,ri,!0,Ze,w,le,ue.glyphOffsetArray,Qe,qt,ut).notEnoughRoom)&&Pi(fr.numGlyphs,Qe)}Q?ue.text.dynamicLayoutVertexBuffer.updateData(Qe):ue.icon.dynamicLayoutVertexBuffer.updateData(Qe)}function et(ue,w,B,Q,ee,le,Fe,Ze){let ut=le.glyphStartIndex+le.numGlyphs,Mt=le.lineStartIndex,Jt=le.lineStartIndex+le.lineLength,$t=w.getoffsetX(le.glyphStartIndex),yr=w.getoffsetX(ut-1),Cr=Yr(ue*$t,B,Q,ee,le.segment,Mt,Jt,Ze,Fe);if(!Cr)return null;let ve=Yr(ue*yr,B,Q,ee,le.segment,Mt,Jt,Ze,Fe);return ve?Ze.projectionCache.anyProjectionOccluded?null:{first:Cr,last:ve}:null}function xt(ue,w,B,Q){return ue===a.ah.horizontal&&Math.abs(B.y-w.y)>Math.abs(B.x-w.x)*Q?{useVertical:!0}:(ue===a.ah.vertical?w.y<B.y:w.x>B.x)?{needsFlipping:!0}:null}function At(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt){let $t=B/24,yr=w.lineOffsetX*$t,Cr=w.lineOffsetY*$t,ve;if(w.numGlyphs>1){let xe=w.glyphStartIndex+w.numGlyphs,Ie=w.lineStartIndex,ze=w.lineStartIndex+w.lineLength,Qe=et($t,Ze,yr,Cr,Q,w,Jt,ue);if(!Qe)return{notEnoughRoom:!0};let Xe=gt(Qe.first.point.x,Qe.first.point.y,Fe,ue.getElevation).point,nt=gt(Qe.last.point.x,Qe.last.point.y,Fe,ue.getElevation).point;if(ee&&!Q){let qt=xt(w.writingMode,Xe,nt,Mt);if(qt)return qt}ve=[Qe.first];for(let qt=w.glyphStartIndex+1;qt<xe-1;qt++)ve.push(Yr($t*Ze.getoffsetX(qt),yr,Cr,Q,w.segment,Ie,ze,ue,Jt));ve.push(Qe.last)}else{if(ee&&!Q){let Ie=gt(ue.tileAnchorPoint.x,ue.tileAnchorPoint.y,le,ue.getElevation).point,ze=w.lineStartIndex+w.segment+1,Qe=new a.P(ue.lineVertexArray.getx(ze),ue.lineVertexArray.gety(ze)),Xe=gt(Qe.x,Qe.y,le,ue.getElevation),nt=Xe.signedDistanceFromCamera>0?Xe.point:(function(Wt,nr,fr,br,Fr,Hr){return Kt(Wt,nr,fr,1,Fr,Hr)})(ue.tileAnchorPoint,Qe,Ie,0,le,ue),qt=xt(w.writingMode,Ie,nt,Mt);if(qt)return qt}let xe=Yr($t*Ze.getoffsetX(w.glyphStartIndex),yr,Cr,Q,w.segment,w.lineStartIndex,w.lineStartIndex+w.lineLength,ue,Jt);if(!xe||ue.projectionCache.anyProjectionOccluded)return{notEnoughRoom:!0};ve=[xe]}for(let xe of ve)a.aj(ut,xe.point,xe.angle);return{}}function Kt(ue,w,B,Q,ee,le){let Fe=ue.add(ue.sub(w)._unit()),Ze=ee!==void 0?gt(Fe.x,Fe.y,ee,le.getElevation).point:Mr(Fe.x,Fe.y,le).point,ut=B.sub(Ze);return B.add(ut._mult(Q/ut.mag()))}function Qt(ue,w,B){let Q=w.projectionCache;if(Q.projections[ue])return Q.projections[ue];let ee=new a.P(w.lineVertexArray.getx(ue),w.lineVertexArray.gety(ue)),le=Mr(ee.x,ee.y,w);if(le.signedDistanceFromCamera>0)return Q.projections[ue]=le.point,Q.anyProjectionOccluded=Q.anyProjectionOccluded||le.isOccluded,le.point;let Fe=ue-B.direction;return(function(Ze,ut,Mt,Jt,$t){return Kt(Ze,ut,Mt,Jt,void 0,$t)})(B.distanceFromAnchor===0?w.tileAnchorPoint:new a.P(w.lineVertexArray.getx(Fe),w.lineVertexArray.gety(Fe)),ee,B.previousVertex,B.absOffsetX-B.distanceFromAnchor+1,w)}function Mr(ue,w,B){let Q=ue+B.translation[0],ee=w+B.translation[1],le;return!B.pitchWithMap&&B.projection.useSpecialProjectionForSymbols?(le=B.projection.projectTileCoordinates(Q,ee,B.unwrappedTileID,B.getElevation),le.point.x=(.5*le.point.x+.5)*B.width,le.point.y=(.5*-le.point.y+.5)*B.height):(le=gt(Q,ee,B.labelPlaneMatrix,B.getElevation),le.isOccluded=!1),le}function Gr(ue,w,B){return ue._unit()._perp()._mult(w*B)}function Ir(ue,w,B,Q,ee,le,Fe,Ze,ut){if(Ze.projectionCache.offsets[ue])return Ze.projectionCache.offsets[ue];let Mt=B.add(w);if(ue+ut.direction<Q||ue+ut.direction>=ee)return Ze.projectionCache.offsets[ue]=Mt,Mt;let Jt=Qt(ue+ut.direction,Ze,ut),$t=Gr(Jt.sub(B),Fe,ut.direction),yr=B.add($t),Cr=Jt.add($t);return Ze.projectionCache.offsets[ue]=a.ak(le,Mt,yr,Cr)||Mt,Ze.projectionCache.offsets[ue]}function Yr(ue,w,B,Q,ee,le,Fe,Ze,ut){let Mt=Q?ue-w:ue+w,Jt=Mt>0?1:-1,$t=0;Q&&(Jt*=-1,$t=Math.PI),Jt<0&&($t+=Math.PI);let yr,Cr=Jt>0?le+ee:le+ee+1;Ze.projectionCache.cachedAnchorPoint?yr=Ze.projectionCache.cachedAnchorPoint:(yr=Mr(Ze.tileAnchorPoint.x,Ze.tileAnchorPoint.y,Ze).point,Ze.projectionCache.cachedAnchorPoint=yr);let ve,xe,Ie=yr,ze=yr,Qe=0,Xe=0,nt=Math.abs(Mt),qt=[],Wt;for(;Qe+Xe<=nt;){if(Cr+=Jt,Cr<le||Cr>=Fe)return null;Qe+=Xe,ze=Ie,xe=ve;let br={absOffsetX:nt,direction:Jt,distanceFromAnchor:Qe,previousVertex:ze};if(Ie=Qt(Cr,Ze,br),B===0)qt.push(ze),Wt=Ie.sub(ze);else{let Fr,Hr=Ie.sub(ze);Fr=Hr.mag()===0?Gr(Qt(Cr+Jt,Ze,br).sub(Ie),B,Jt):Gr(Hr,B,Jt),xe||(xe=ze.add(Fr)),ve=Ir(Cr,Fr,Ie,le,Fe,xe,B,Ze,br),qt.push(xe),Wt=ve.sub(xe)}Xe=Wt.mag()}let nr=Wt._mult((nt-Qe)/Xe)._add(xe||ze),fr=$t+Math.atan2(Ie.y-ze.y,Ie.x-ze.x);return qt.push(nr),{point:nr,angle:ut?fr:0,path:qt}}let _i=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function Pi(ue,w){for(let B=0;B<ue;B++){let Q=w.length;w.resize(Q+4),w.float32.set(_i,3*Q)}}function ai(ue,w,B){let Q=w[0],ee=w[1];return ue[0]=B[0]*Q+B[4]*ee+B[12],ue[1]=B[1]*Q+B[5]*ee+B[13],ue[3]=B[3]*Q+B[7]*ee+B[15],ue}let mi=100;class un{constructor(w,B,Q=new ei(w.width+200,w.height+200,25),ee=new ei(w.width+200,w.height+200,25)){this.transform=w,this.mapProjection=B,this.grid=Q,this.ignoredGrid=ee,this.pitchFactor=Math.cos(w._pitch)*w.cameraToCenterDistance,this.screenRightBoundary=w.width+mi,this.screenBottomBoundary=w.height+mi,this.gridRightBoundary=w.width+200,this.gridBottomBoundary=w.height+200,this.perspectiveRatioCutoff=.6}placeCollisionBox(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t){let yr=w.anchorPointX+ut[0],Cr=w.anchorPointY+ut[1],ve=this.projectAndGetPerspectiveRatio(ee,yr,Cr,le,Jt),xe=Q*ve.perspectiveRatio,Ie;if(Fe||Ze)Ie=this._projectCollisionBox(w,xe,ee,le,Fe,Ze,ut,ve,Jt,$t);else{let qt=ve.point.x+($t?$t.x*xe:0),Wt=ve.point.y+($t?$t.y*xe:0);Ie={allPointsOccluded:!1,box:[qt+w.x1*xe,Wt+w.y1*xe,qt+w.x2*xe,Wt+w.y2*xe]}}let[ze,Qe,Xe,nt]=Ie.box;return this.mapProjection.useSpecialProjectionForSymbols&&(Fe?Ie.allPointsOccluded:this.mapProjection.isOccluded(yr,Cr,le))||ve.perspectiveRatio<this.perspectiveRatioCutoff||!this.isInsideGrid(ze,Qe,Xe,nt)||B!==\"always\"&&this.grid.hitTest(ze,Qe,Xe,nt,B,Mt)?{box:[ze,Qe,Xe,nt],placeable:!1,offscreen:!1}:{box:[ze,Qe,Xe,nt],placeable:!0,offscreen:this.isOffscreen(ze,Qe,Xe,nt)}}placeCollisionCircles(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve,xe,Ie){let ze=[],Qe=new a.P(B.anchorX,B.anchorY),Xe=this.getPerspectiveRatio(Fe,Qe.x,Qe.y,Ze,Ie),nt=($t?le/Xe:le*Xe)/a.ap,qt={getElevation:Ie,labelPlaneMatrix:ut,lineVertexArray:Q,pitchWithMap:$t,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},projection:this.mapProjection,tileAnchorPoint:Qe,unwrappedTileID:Ze,width:this.transform.width,height:this.transform.height,translation:xe},Wt=et(nt,ee,B.lineOffsetX*nt,B.lineOffsetY*nt,!1,B,!1,qt),nr=!1,fr=!1,br=!0;if(Wt){let Fr=.5*Cr*Xe+ve,Hr=new a.P(-100,-100),ri=new a.P(this.screenRightBoundary,this.screenBottomBoundary),Ci=new mr,cn=Wt.first,sn=Wt.last,Ln=[];for(let fa=cn.path.length-1;fa>=1;fa--)Ln.push(cn.path[fa]);for(let fa=1;fa<sn.path.length;fa++)Ln.push(sn.path[fa]);let Kn=2.5*Fr;if(Mt){let fa=this.projectPathToScreenSpace(Ln,qt,Mt);Ln=fa.some(($a=>$a.signedDistanceFromCamera<=0))?[]:fa.map(($a=>$a.point))}let Aa=[];if(Ln.length>0){let fa=Ln[0].clone(),$a=Ln[0].clone();for(let Co=1;Co<Ln.length;Co++)fa.x=Math.min(fa.x,Ln[Co].x),fa.y=Math.min(fa.y,Ln[Co].y),$a.x=Math.max($a.x,Ln[Co].x),$a.y=Math.max($a.y,Ln[Co].y);Aa=fa.x>=Hr.x&&$a.x<=ri.x&&fa.y>=Hr.y&&$a.y<=ri.y?[Ln]:$a.x<Hr.x||fa.x>ri.x||$a.y<Hr.y||fa.y>ri.y?[]:a.al([Ln],Hr.x,Hr.y,ri.x,ri.y)}for(let fa of Aa){Ci.reset(fa,.25*Fr);let $a=0;$a=Ci.length<=.5*Fr?1:Math.ceil(Ci.paddedLength/Kn)+1;for(let Co=0;Co<$a;Co++){let Qa=Co/Math.max($a-1,1),mo=Ci.lerp(Qa),Bo=mo.x+mi,Ps=mo.y+mi;ze.push(Bo,Ps,Fr,0);let Ts=Bo-Fr,wo=Ps-Fr,To=Bo+Fr,hl=Ps+Fr;if(br=br&&this.isOffscreen(Ts,wo,To,hl),fr=fr||this.isInsideGrid(Ts,wo,To,hl),w!==\"always\"&&this.grid.hitTestCircle(Bo,Ps,Fr,w,yr)&&(nr=!0,!Jt))return{circles:[],offscreen:!1,collisionDetected:nr}}}}return{circles:!Jt&&nr||!fr||Xe<this.perspectiveRatioCutoff?[]:ze,offscreen:br,collisionDetected:nr}}projectPathToScreenSpace(w,B,Q){return w.map((ee=>gt(ee.x,ee.y,Q,B.getElevation)))}queryRenderedSymbols(w){if(w.length===0||this.grid.keysLength()===0&&this.ignoredGrid.keysLength()===0)return{};let B=[],Q=1/0,ee=1/0,le=-1/0,Fe=-1/0;for(let Jt of w){let $t=new a.P(Jt.x+mi,Jt.y+mi);Q=Math.min(Q,$t.x),ee=Math.min(ee,$t.y),le=Math.max(le,$t.x),Fe=Math.max(Fe,$t.y),B.push($t)}let Ze=this.grid.query(Q,ee,le,Fe).concat(this.ignoredGrid.query(Q,ee,le,Fe)),ut={},Mt={};for(let Jt of Ze){let $t=Jt.key;if(ut[$t.bucketInstanceId]===void 0&&(ut[$t.bucketInstanceId]={}),ut[$t.bucketInstanceId][$t.featureIndex])continue;let yr=[new a.P(Jt.x1,Jt.y1),new a.P(Jt.x2,Jt.y1),new a.P(Jt.x2,Jt.y2),new a.P(Jt.x1,Jt.y2)];a.am(B,yr)&&(ut[$t.bucketInstanceId][$t.featureIndex]=!0,Mt[$t.bucketInstanceId]===void 0&&(Mt[$t.bucketInstanceId]=[]),Mt[$t.bucketInstanceId].push($t.featureIndex))}return Mt}insertCollisionBox(w,B,Q,ee,le,Fe){(Q?this.ignoredGrid:this.grid).insert({bucketInstanceId:ee,featureIndex:le,collisionGroupID:Fe,overlapMode:B},w[0],w[1],w[2],w[3])}insertCollisionCircles(w,B,Q,ee,le,Fe){let Ze=Q?this.ignoredGrid:this.grid,ut={bucketInstanceId:ee,featureIndex:le,collisionGroupID:Fe,overlapMode:B};for(let Mt=0;Mt<w.length;Mt+=4)Ze.insertCircle(ut,w[Mt],w[Mt+1],w[Mt+2])}projectAndGetPerspectiveRatio(w,B,Q,ee,le){let Fe;le?(Fe=[B,Q,le(B,Q),1],a.af(Fe,Fe,w)):(Fe=[B,Q,0,1],ai(Fe,Fe,w));let Ze=Fe[3];return{point:new a.P((Fe[0]/Ze+1)/2*this.transform.width+mi,(-Fe[1]/Ze+1)/2*this.transform.height+mi),perspectiveRatio:.5+this.transform.cameraToCenterDistance/Ze*.5,isOccluded:!1,signedDistanceFromCamera:Ze}}getPerspectiveRatio(w,B,Q,ee,le){let Fe=this.mapProjection.useSpecialProjectionForSymbols?this.mapProjection.projectTileCoordinates(B,Q,ee,le):gt(B,Q,w,le);return .5+this.transform.cameraToCenterDistance/Fe.signedDistanceFromCamera*.5}isOffscreen(w,B,Q,ee){return Q<mi||w>=this.screenRightBoundary||ee<mi||B>this.screenBottomBoundary}isInsideGrid(w,B,Q,ee){return Q>=0&&w<this.gridRightBoundary&&ee>=0&&B<this.gridBottomBoundary}getViewportMatrix(){let w=a.an([]);return a.J(w,w,[-100,-100,0]),w}_projectCollisionBox(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt){let $t=new a.P(1,0),yr=new a.P(0,1),Cr=new a.P(w.anchorPointX+Ze[0],w.anchorPointY+Ze[1]);if(Fe&&!le){let br=this.projectAndGetPerspectiveRatio(Q,Cr.x+1,Cr.y,ee,Mt).point.sub(ut.point).unit(),Fr=Math.atan(br.y/br.x)+(br.x<0?Math.PI:0),Hr=Math.sin(Fr),ri=Math.cos(Fr);$t=new a.P(ri,Hr),yr=new a.P(-Hr,ri)}else if(!Fe&&le){let br=-this.transform.angle,Fr=Math.sin(br),Hr=Math.cos(br);$t=new a.P(Hr,Fr),yr=new a.P(-Fr,Hr)}let ve=ut.point,xe=B;if(le){ve=Cr;let br=this.transform.zoom-Math.floor(this.transform.zoom);xe=Math.pow(2,-br),xe*=this.mapProjection.getPitchedTextCorrection(this.transform,Cr,ee),Jt||(xe*=a.ac(.5+ut.signedDistanceFromCamera/this.transform.cameraToCenterDistance*.5,0,4))}Jt&&(ve=ve.add($t.mult(Jt.x*xe)).add(yr.mult(Jt.y*xe)));let Ie=w.x1*xe,ze=w.x2*xe,Qe=(Ie+ze)/2,Xe=w.y1*xe,nt=w.y2*xe,qt=(Xe+nt)/2,Wt=[{offsetX:Ie,offsetY:Xe},{offsetX:Qe,offsetY:Xe},{offsetX:ze,offsetY:Xe},{offsetX:ze,offsetY:qt},{offsetX:ze,offsetY:nt},{offsetX:Qe,offsetY:nt},{offsetX:Ie,offsetY:nt},{offsetX:Ie,offsetY:qt}],nr=[];for(let{offsetX:br,offsetY:Fr}of Wt)nr.push(new a.P(ve.x+$t.x*br+yr.x*Fr,ve.y+$t.y*br+yr.y*Fr));let fr=!1;if(le){let br=nr.map((Fr=>this.projectAndGetPerspectiveRatio(Q,Fr.x,Fr.y,ee,Mt)));fr=br.some((Fr=>!Fr.isOccluded)),nr=br.map((Fr=>Fr.point))}else fr=!0;return{box:a.ao(nr),allPointsOccluded:!fr}}}function Fn(ue,w,B){return w*(a.X/(ue.tileSize*Math.pow(2,B-ue.tileID.overscaledZ)))}class An{constructor(w,B,Q,ee){this.opacity=w?Math.max(0,Math.min(1,w.opacity+(w.placed?B:-B))):ee&&Q?1:0,this.placed=Q}isHidden(){return this.opacity===0&&!this.placed}}class Hn{constructor(w,B,Q,ee,le){this.text=new An(w?w.text:null,B,Q,le),this.icon=new An(w?w.icon:null,B,ee,le)}isHidden(){return this.text.isHidden()&&this.icon.isHidden()}}class Qn{constructor(w,B,Q){this.text=w,this.icon=B,this.skipFade=Q}}class Vi{constructor(){this.invProjMatrix=a.H(),this.viewportMatrix=a.H(),this.circles=[]}}class Jn{constructor(w,B,Q,ee,le){this.bucketInstanceId=w,this.featureIndex=B,this.sourceLayerIndex=Q,this.bucketIndex=ee,this.tileID=le}}class Yn{constructor(w){this.crossSourceCollisions=w,this.maxGroupID=0,this.collisionGroups={}}get(w){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[w]){let B=++this.maxGroupID;this.collisionGroups[w]={ID:B,predicate:Q=>Q.collisionGroupID===B}}return this.collisionGroups[w]}}function Gt(ue,w,B,Q,ee){let{horizontalAlign:le,verticalAlign:Fe}=a.au(ue);return new a.P(-(le-.5)*w+Q[0]*ee,-(Fe-.5)*B+Q[1]*ee)}class wt{constructor(w,B,Q,ee,le,Fe){this.transform=w.clone(),this.terrain=Q,this.collisionIndex=new un(this.transform,B),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=ee,this.retainedQueryData={},this.collisionGroups=new Yn(le),this.collisionCircleArrays={},this.collisionBoxArrays=new Map,this.prevPlacement=Fe,Fe&&(Fe.prevPlacement=void 0),this.placedOrientations={}}_getTerrainElevationFunc(w){let B=this.terrain;return B?(Q,ee)=>B.getElevation(w,Q,ee):null}getBucketParts(w,B,Q,ee){let le=Q.getBucket(B),Fe=Q.latestFeatureIndex;if(!le||!Fe||B.id!==le.layerIds[0])return;let Ze=Q.collisionBoxArray,ut=le.layers[0].layout,Mt=le.layers[0].paint,Jt=Math.pow(2,this.transform.zoom-Q.tileID.overscaledZ),$t=Q.tileSize/a.X,yr=Q.tileID.toUnwrapped(),Cr=this.transform.calculatePosMatrix(yr),ve=ut.get(\"text-pitch-alignment\")===\"map\",xe=ut.get(\"text-rotation-alignment\")===\"map\",Ie=Fn(Q,1,this.transform.zoom),ze=this.collisionIndex.mapProjection.translatePosition(this.transform,Q,Mt.get(\"text-translate\"),Mt.get(\"text-translate-anchor\")),Qe=this.collisionIndex.mapProjection.translatePosition(this.transform,Q,Mt.get(\"icon-translate\"),Mt.get(\"icon-translate-anchor\")),Xe=qr(Cr,ve,xe,this.transform,Ie),nt=null;if(ve){let Wt=jr(Cr,ve,xe,this.transform,Ie);nt=a.L([],this.transform.labelPlaneMatrix,Wt)}this.retainedQueryData[le.bucketInstanceId]=new Jn(le.bucketInstanceId,Fe,le.sourceLayerIndex,le.index,Q.tileID);let qt={bucket:le,layout:ut,translationText:ze,translationIcon:Qe,posMatrix:Cr,unwrappedTileID:yr,textLabelPlaneMatrix:Xe,labelToScreenMatrix:nt,scale:Jt,textPixelRatio:$t,holdingForFade:Q.holdingForFade(),collisionBoxArray:Ze,partiallyEvaluatedTextSize:a.ag(le.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(le.sourceID)};if(ee)for(let Wt of le.sortKeyRanges){let{sortKey:nr,symbolInstanceStart:fr,symbolInstanceEnd:br}=Wt;w.push({sortKey:nr,symbolInstanceStart:fr,symbolInstanceEnd:br,parameters:qt})}else w.push({symbolInstanceStart:0,symbolInstanceEnd:le.symbolInstances.length,parameters:qt})}attemptAnchorPlacement(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve,xe,Ie,ze,Qe,Xe){let nt=a.aq[w.textAnchor],qt=[w.textOffset0,w.textOffset1],Wt=Gt(nt,Q,ee,qt,le),nr=this.collisionIndex.placeCollisionBox(B,yr,ut,Mt,Jt,Ze,Fe,Ie,$t.predicate,Xe,Wt);if((!Qe||this.collisionIndex.placeCollisionBox(Qe,yr,ut,Mt,Jt,Ze,Fe,ze,$t.predicate,Xe,Wt).placeable)&&nr.placeable){let fr;if(this.prevPlacement&&this.prevPlacement.variableOffsets[Cr.crossTileID]&&this.prevPlacement.placements[Cr.crossTileID]&&this.prevPlacement.placements[Cr.crossTileID].text&&(fr=this.prevPlacement.variableOffsets[Cr.crossTileID].anchor),Cr.crossTileID===0)throw new Error(\"symbolInstance.crossTileID can't be 0\");return this.variableOffsets[Cr.crossTileID]={textOffset:qt,width:Q,height:ee,anchor:nt,textBoxScale:le,prevAnchor:fr},this.markUsedJustification(ve,nt,Cr,xe),ve.allowVerticalPlacement&&(this.markUsedOrientation(ve,xe,Cr),this.placedOrientations[Cr.crossTileID]=xe),{shift:Wt,placedGlyphBoxes:nr}}}placeLayerBucketPart(w,B,Q){let{bucket:ee,layout:le,translationText:Fe,translationIcon:Ze,posMatrix:ut,unwrappedTileID:Mt,textLabelPlaneMatrix:Jt,labelToScreenMatrix:$t,textPixelRatio:yr,holdingForFade:Cr,collisionBoxArray:ve,partiallyEvaluatedTextSize:xe,collisionGroup:Ie}=w.parameters,ze=le.get(\"text-optional\"),Qe=le.get(\"icon-optional\"),Xe=a.ar(le,\"text-overlap\",\"text-allow-overlap\"),nt=Xe===\"always\",qt=a.ar(le,\"icon-overlap\",\"icon-allow-overlap\"),Wt=qt===\"always\",nr=le.get(\"text-rotation-alignment\")===\"map\",fr=le.get(\"text-pitch-alignment\")===\"map\",br=le.get(\"icon-text-fit\")!==\"none\",Fr=le.get(\"symbol-z-order\")===\"viewport-y\",Hr=nt&&(Wt||!ee.hasIconData()||Qe),ri=Wt&&(nt||!ee.hasTextData()||ze);!ee.collisionArrays&&ve&&ee.deserializeCollisionBoxes(ve);let Ci=this._getTerrainElevationFunc(this.retainedQueryData[ee.bucketInstanceId].tileID),cn=(sn,Ln,Kn)=>{var Aa,fa;if(B[sn.crossTileID])return;if(Cr)return void(this.placements[sn.crossTileID]=new Qn(!1,!1,!1));let $a=!1,Co=!1,Qa=!0,mo=null,Bo={box:null,placeable:!1,offscreen:null},Ps={box:null,placeable:!1,offscreen:null},Ts=null,wo=null,To=null,hl=0,Nl=0,Lu=0;Ln.textFeatureIndex?hl=Ln.textFeatureIndex:sn.useRuntimeCollisionCircles&&(hl=sn.featureIndex),Ln.verticalTextFeatureIndex&&(Nl=Ln.verticalTextFeatureIndex);let au=Ln.textBox;if(au){let Al=Te=>{let qe=a.ah.horizontal;if(ee.allowVerticalPlacement&&!Te&&this.prevPlacement){let Ve=this.prevPlacement.placedOrientations[sn.crossTileID];Ve&&(this.placedOrientations[sn.crossTileID]=Ve,qe=Ve,this.markUsedOrientation(ee,qe,sn))}return qe},Sl=(Te,qe)=>{if(ee.allowVerticalPlacement&&sn.numVerticalGlyphVertices>0&&Ln.verticalTextBox){for(let Ve of ee.writingModes)if(Ve===a.ah.vertical?(Bo=qe(),Ps=Bo):Bo=Te(),Bo&&Bo.placeable)break}else Bo=Te()},X=sn.textAnchorOffsetStartIndex,se=sn.textAnchorOffsetEndIndex;if(se===X){let Te=(qe,Ve)=>{let Ye=this.collisionIndex.placeCollisionBox(qe,Xe,yr,ut,Mt,fr,nr,Fe,Ie.predicate,Ci);return Ye&&Ye.placeable&&(this.markUsedOrientation(ee,Ve,sn),this.placedOrientations[sn.crossTileID]=Ve),Ye};Sl((()=>Te(au,a.ah.horizontal)),(()=>{let qe=Ln.verticalTextBox;return ee.allowVerticalPlacement&&sn.numVerticalGlyphVertices>0&&qe?Te(qe,a.ah.vertical):{box:null,offscreen:null}})),Al(Bo&&Bo.placeable)}else{let Te=a.aq[(fa=(Aa=this.prevPlacement)===null||Aa===void 0?void 0:Aa.variableOffsets[sn.crossTileID])===null||fa===void 0?void 0:fa.anchor],qe=(Ye,Pt,ot)=>{let Xt=Ye.x2-Ye.x1,dr=Ye.y2-Ye.y1,pr=sn.textBoxScale,Ur=br&&qt===\"never\"?Pt:null,zr=null,gi=Xe===\"never\"?1:2,xi=\"never\";Te&&gi++;for(let Wr=0;Wr<gi;Wr++){for(let ii=X;ii<se;ii++){let di=ee.textAnchorOffsets.get(ii);if(Te&&di.textAnchor!==Te)continue;let Li=this.attemptAnchorPlacement(di,Ye,Xt,dr,pr,nr,fr,yr,ut,Mt,Ie,xi,sn,ee,ot,Fe,Ze,Ur,Ci);if(Li&&(zr=Li.placedGlyphBoxes,zr&&zr.placeable))return $a=!0,mo=Li.shift,zr}Te?Te=null:xi=Xe}return Q&&!zr&&(zr={box:this.collisionIndex.placeCollisionBox(au,\"always\",yr,ut,Mt,fr,nr,Fe,Ie.predicate,Ci,new a.P(0,0)).box,offscreen:!1,placeable:!1}),zr};Sl((()=>qe(au,Ln.iconBox,a.ah.horizontal)),(()=>{let Ye=Ln.verticalTextBox;return ee.allowVerticalPlacement&&(!Bo||!Bo.placeable)&&sn.numVerticalGlyphVertices>0&&Ye?qe(Ye,Ln.verticalIconBox,a.ah.vertical):{box:null,occluded:!0,offscreen:null}})),Bo&&($a=Bo.placeable,Qa=Bo.offscreen);let Ve=Al(Bo&&Bo.placeable);if(!$a&&this.prevPlacement){let Ye=this.prevPlacement.variableOffsets[sn.crossTileID];Ye&&(this.variableOffsets[sn.crossTileID]=Ye,this.markUsedJustification(ee,Ye.anchor,sn,Ve))}}}if(Ts=Bo,$a=Ts&&Ts.placeable,Qa=Ts&&Ts.offscreen,sn.useRuntimeCollisionCircles){let Al=ee.text.placedSymbolArray.get(sn.centerJustifiedTextSymbolIndex),Sl=a.ai(ee.textSizeData,xe,Al),X=le.get(\"text-padding\");wo=this.collisionIndex.placeCollisionCircles(Xe,Al,ee.lineVertexArray,ee.glyphOffsetArray,Sl,ut,Mt,Jt,$t,Q,fr,Ie.predicate,sn.collisionCircleDiameter,X,Fe,Ci),wo.circles.length&&wo.collisionDetected&&!Q&&a.w(\"Collisions detected, but collision boxes are not shown\"),$a=nt||wo.circles.length>0&&!wo.collisionDetected,Qa=Qa&&wo.offscreen}if(Ln.iconFeatureIndex&&(Lu=Ln.iconFeatureIndex),Ln.iconBox){let Al=Sl=>this.collisionIndex.placeCollisionBox(Sl,qt,yr,ut,Mt,fr,nr,Ze,Ie.predicate,Ci,br&&mo?mo:void 0);Ps&&Ps.placeable&&Ln.verticalIconBox?(To=Al(Ln.verticalIconBox),Co=To.placeable):(To=Al(Ln.iconBox),Co=To.placeable),Qa=Qa&&To.offscreen}let Js=ze||sn.numHorizontalGlyphVertices===0&&sn.numVerticalGlyphVertices===0,eu=Qe||sn.numIconVertices===0;Js||eu?eu?Js||(Co=Co&&$a):$a=Co&&$a:Co=$a=Co&&$a;let dc=Co&&To.placeable;if($a&&Ts.placeable&&this.collisionIndex.insertCollisionBox(Ts.box,Xe,le.get(\"text-ignore-placement\"),ee.bucketInstanceId,Ps&&Ps.placeable&&Nl?Nl:hl,Ie.ID),dc&&this.collisionIndex.insertCollisionBox(To.box,qt,le.get(\"icon-ignore-placement\"),ee.bucketInstanceId,Lu,Ie.ID),wo&&$a&&this.collisionIndex.insertCollisionCircles(wo.circles,Xe,le.get(\"text-ignore-placement\"),ee.bucketInstanceId,hl,Ie.ID),Q&&this.storeCollisionData(ee.bucketInstanceId,Kn,Ln,Ts,To,wo),sn.crossTileID===0)throw new Error(\"symbolInstance.crossTileID can't be 0\");if(ee.bucketInstanceId===0)throw new Error(\"bucket.bucketInstanceId can't be 0\");this.placements[sn.crossTileID]=new Qn($a||Hr,Co||ri,Qa||ee.justReloaded),B[sn.crossTileID]=!0};if(Fr){if(w.symbolInstanceStart!==0)throw new Error(\"bucket.bucketInstanceId should be 0\");let sn=ee.getSortedSymbolIndexes(this.transform.angle);for(let Ln=sn.length-1;Ln>=0;--Ln){let Kn=sn[Ln];cn(ee.symbolInstances.get(Kn),ee.collisionArrays[Kn],Kn)}}else for(let sn=w.symbolInstanceStart;sn<w.symbolInstanceEnd;sn++)cn(ee.symbolInstances.get(sn),ee.collisionArrays[sn],sn);if(Q&&ee.bucketInstanceId in this.collisionCircleArrays){let sn=this.collisionCircleArrays[ee.bucketInstanceId];a.as(sn.invProjMatrix,ut),sn.viewportMatrix=this.collisionIndex.getViewportMatrix()}ee.justReloaded=!1}storeCollisionData(w,B,Q,ee,le,Fe){if(Q.textBox||Q.iconBox){let Ze,ut;this.collisionBoxArrays.has(w)?Ze=this.collisionBoxArrays.get(w):(Ze=new Map,this.collisionBoxArrays.set(w,Ze)),Ze.has(B)?ut=Ze.get(B):(ut={text:null,icon:null},Ze.set(B,ut)),Q.textBox&&(ut.text=ee.box),Q.iconBox&&(ut.icon=le.box)}if(Fe){let Ze=this.collisionCircleArrays[w];Ze===void 0&&(Ze=this.collisionCircleArrays[w]=new Vi);for(let ut=0;ut<Fe.circles.length;ut+=4)Ze.circles.push(Fe.circles[ut+0]),Ze.circles.push(Fe.circles[ut+1]),Ze.circles.push(Fe.circles[ut+2]),Ze.circles.push(Fe.collisionDetected?1:0)}}markUsedJustification(w,B,Q,ee){let le;le=ee===a.ah.vertical?Q.verticalPlacedTextSymbolIndex:{left:Q.leftJustifiedTextSymbolIndex,center:Q.centerJustifiedTextSymbolIndex,right:Q.rightJustifiedTextSymbolIndex}[a.at(B)];let Fe=[Q.leftJustifiedTextSymbolIndex,Q.centerJustifiedTextSymbolIndex,Q.rightJustifiedTextSymbolIndex,Q.verticalPlacedTextSymbolIndex];for(let Ze of Fe)Ze>=0&&(w.text.placedSymbolArray.get(Ze).crossTileID=le>=0&&Ze!==le?0:Q.crossTileID)}markUsedOrientation(w,B,Q){let ee=B===a.ah.horizontal||B===a.ah.horizontalOnly?B:0,le=B===a.ah.vertical?B:0,Fe=[Q.leftJustifiedTextSymbolIndex,Q.centerJustifiedTextSymbolIndex,Q.rightJustifiedTextSymbolIndex];for(let Ze of Fe)w.text.placedSymbolArray.get(Ze).placedOrientation=ee;Q.verticalPlacedTextSymbolIndex&&(w.text.placedSymbolArray.get(Q.verticalPlacedTextSymbolIndex).placedOrientation=le)}commit(w){this.commitTime=w,this.zoomAtLastRecencyCheck=this.transform.zoom;let B=this.prevPlacement,Q=!1;this.prevZoomAdjustment=B?B.zoomAdjustment(this.transform.zoom):0;let ee=B?B.symbolFadeChange(w):1,le=B?B.opacities:{},Fe=B?B.variableOffsets:{},Ze=B?B.placedOrientations:{};for(let ut in this.placements){let Mt=this.placements[ut],Jt=le[ut];Jt?(this.opacities[ut]=new Hn(Jt,ee,Mt.text,Mt.icon),Q=Q||Mt.text!==Jt.text.placed||Mt.icon!==Jt.icon.placed):(this.opacities[ut]=new Hn(null,ee,Mt.text,Mt.icon,Mt.skipFade),Q=Q||Mt.text||Mt.icon)}for(let ut in le){let Mt=le[ut];if(!this.opacities[ut]){let Jt=new Hn(Mt,ee,!1,!1);Jt.isHidden()||(this.opacities[ut]=Jt,Q=Q||Mt.text.placed||Mt.icon.placed)}}for(let ut in Fe)this.variableOffsets[ut]||!this.opacities[ut]||this.opacities[ut].isHidden()||(this.variableOffsets[ut]=Fe[ut]);for(let ut in Ze)this.placedOrientations[ut]||!this.opacities[ut]||this.opacities[ut].isHidden()||(this.placedOrientations[ut]=Ze[ut]);if(B&&B.lastPlacementChangeTime===void 0)throw new Error(\"Last placement time for previous placement is not defined\");Q?this.lastPlacementChangeTime=w:typeof this.lastPlacementChangeTime!=\"number\"&&(this.lastPlacementChangeTime=B?B.lastPlacementChangeTime:w)}updateLayerOpacities(w,B){let Q={};for(let ee of B){let le=ee.getBucket(w);le&&ee.latestFeatureIndex&&w.id===le.layerIds[0]&&this.updateBucketOpacities(le,ee.tileID,Q,ee.collisionBoxArray)}}updateBucketOpacities(w,B,Q,ee){w.hasTextData()&&(w.text.opacityVertexArray.clear(),w.text.hasVisibleVertices=!1),w.hasIconData()&&(w.icon.opacityVertexArray.clear(),w.icon.hasVisibleVertices=!1),w.hasIconCollisionBoxData()&&w.iconCollisionBox.collisionVertexArray.clear(),w.hasTextCollisionBoxData()&&w.textCollisionBox.collisionVertexArray.clear();let le=w.layers[0],Fe=le.layout,Ze=new Hn(null,0,!1,!1,!0),ut=Fe.get(\"text-allow-overlap\"),Mt=Fe.get(\"icon-allow-overlap\"),Jt=le._unevaluatedLayout.hasValue(\"text-variable-anchor\")||le._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),$t=Fe.get(\"text-rotation-alignment\")===\"map\",yr=Fe.get(\"text-pitch-alignment\")===\"map\",Cr=Fe.get(\"icon-text-fit\")!==\"none\",ve=new Hn(null,0,ut&&(Mt||!w.hasIconData()||Fe.get(\"icon-optional\")),Mt&&(ut||!w.hasTextData()||Fe.get(\"text-optional\")),!0);!w.collisionArrays&&ee&&(w.hasIconCollisionBoxData()||w.hasTextCollisionBoxData())&&w.deserializeCollisionBoxes(ee);let xe=(ze,Qe,Xe)=>{for(let nt=0;nt<Qe/4;nt++)ze.opacityVertexArray.emplaceBack(Xe);ze.hasVisibleVertices=ze.hasVisibleVertices||Xe!==fn},Ie=this.collisionBoxArrays.get(w.bucketInstanceId);for(let ze=0;ze<w.symbolInstances.length;ze++){let Qe=w.symbolInstances.get(ze),{numHorizontalGlyphVertices:Xe,numVerticalGlyphVertices:nt,crossTileID:qt}=Qe,Wt=this.opacities[qt];Q[qt]?Wt=Ze:Wt||(Wt=ve,this.opacities[qt]=Wt),Q[qt]=!0;let nr=Qe.numIconVertices>0,fr=this.placedOrientations[Qe.crossTileID],br=fr===a.ah.vertical,Fr=fr===a.ah.horizontal||fr===a.ah.horizontalOnly;if(Xe>0||nt>0){let ri=en(Wt.text);xe(w.text,Xe,br?fn:ri),xe(w.text,nt,Fr?fn:ri);let Ci=Wt.text.isHidden();[Qe.rightJustifiedTextSymbolIndex,Qe.centerJustifiedTextSymbolIndex,Qe.leftJustifiedTextSymbolIndex].forEach((Ln=>{Ln>=0&&(w.text.placedSymbolArray.get(Ln).hidden=Ci||br?1:0)})),Qe.verticalPlacedTextSymbolIndex>=0&&(w.text.placedSymbolArray.get(Qe.verticalPlacedTextSymbolIndex).hidden=Ci||Fr?1:0);let cn=this.variableOffsets[Qe.crossTileID];cn&&this.markUsedJustification(w,cn.anchor,Qe,fr);let sn=this.placedOrientations[Qe.crossTileID];sn&&(this.markUsedJustification(w,\"left\",Qe,sn),this.markUsedOrientation(w,sn,Qe))}if(nr){let ri=en(Wt.icon),Ci=!(Cr&&Qe.verticalPlacedIconSymbolIndex&&br);Qe.placedIconSymbolIndex>=0&&(xe(w.icon,Qe.numIconVertices,Ci?ri:fn),w.icon.placedSymbolArray.get(Qe.placedIconSymbolIndex).hidden=Wt.icon.isHidden()),Qe.verticalPlacedIconSymbolIndex>=0&&(xe(w.icon,Qe.numVerticalIconVertices,Ci?fn:ri),w.icon.placedSymbolArray.get(Qe.verticalPlacedIconSymbolIndex).hidden=Wt.icon.isHidden())}let Hr=Ie&&Ie.has(ze)?Ie.get(ze):{text:null,icon:null};if(w.hasIconCollisionBoxData()||w.hasTextCollisionBoxData()){let ri=w.collisionArrays[ze];if(ri){let Ci=new a.P(0,0);if(ri.textBox||ri.verticalTextBox){let cn=!0;if(Jt){let sn=this.variableOffsets[qt];sn?(Ci=Gt(sn.anchor,sn.width,sn.height,sn.textOffset,sn.textBoxScale),$t&&Ci._rotate(yr?this.transform.angle:-this.transform.angle)):cn=!1}if(ri.textBox||ri.verticalTextBox){let sn;ri.textBox&&(sn=br),ri.verticalTextBox&&(sn=Fr),tr(w.textCollisionBox.collisionVertexArray,Wt.text.placed,!cn||sn,Hr.text,Ci.x,Ci.y)}}if(ri.iconBox||ri.verticalIconBox){let cn=!!(!Fr&&ri.verticalIconBox),sn;ri.iconBox&&(sn=cn),ri.verticalIconBox&&(sn=!cn),tr(w.iconCollisionBox.collisionVertexArray,Wt.icon.placed,sn,Hr.icon,Cr?Ci.x:0,Cr?Ci.y:0)}}}}if(w.sortFeatures(this.transform.angle),this.retainedQueryData[w.bucketInstanceId]&&(this.retainedQueryData[w.bucketInstanceId].featureSortOrder=w.featureSortOrder),w.hasTextData()&&w.text.opacityVertexBuffer&&w.text.opacityVertexBuffer.updateData(w.text.opacityVertexArray),w.hasIconData()&&w.icon.opacityVertexBuffer&&w.icon.opacityVertexBuffer.updateData(w.icon.opacityVertexArray),w.hasIconCollisionBoxData()&&w.iconCollisionBox.collisionVertexBuffer&&w.iconCollisionBox.collisionVertexBuffer.updateData(w.iconCollisionBox.collisionVertexArray),w.hasTextCollisionBoxData()&&w.textCollisionBox.collisionVertexBuffer&&w.textCollisionBox.collisionVertexBuffer.updateData(w.textCollisionBox.collisionVertexArray),w.text.opacityVertexArray.length!==w.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${w.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${w.text.layoutVertexArray.length}) / 4`);if(w.icon.opacityVertexArray.length!==w.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${w.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${w.icon.layoutVertexArray.length}) / 4`);if(w.bucketInstanceId in this.collisionCircleArrays){let ze=this.collisionCircleArrays[w.bucketInstanceId];w.placementInvProjMatrix=ze.invProjMatrix,w.placementViewportMatrix=ze.viewportMatrix,w.collisionCircleArray=ze.circles,delete this.collisionCircleArrays[w.bucketInstanceId]}}symbolFadeChange(w){return this.fadeDuration===0?1:(w-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(w){return Math.max(0,(this.transform.zoom-w)/1.5)}hasTransitions(w){return this.stale||w-this.lastPlacementChangeTime<this.fadeDuration}stillRecent(w,B){let Q=this.zoomAtLastRecencyCheck===B?1-this.zoomAdjustment(B):1;return this.zoomAtLastRecencyCheck=B,this.commitTime+this.fadeDuration*Q>w}setStale(){this.stale=!0}}function tr(ue,w,B,Q,ee,le){Q&&Q.length!==0||(Q=[0,0,0,0]);let Fe=Q[0]-mi,Ze=Q[1]-mi,ut=Q[2]-mi,Mt=Q[3]-mi;ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,Fe,Ze),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,ut,Ze),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,ut,Mt),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,Fe,Mt)}let ir=Math.pow(2,25),wr=Math.pow(2,24),Xr=Math.pow(2,17),ti=Math.pow(2,16),$r=Math.pow(2,9),Ri=Math.pow(2,8),Hi=Math.pow(2,1);function en(ue){if(ue.opacity===0&&!ue.placed)return 0;if(ue.opacity===1&&ue.placed)return 4294967295;let w=ue.placed?1:0,B=Math.floor(127*ue.opacity);return B*ir+w*wr+B*Xr+w*ti+B*$r+w*Ri+B*Hi+w}let fn=0;function yn(){return{isOccluded:(ue,w,B)=>!1,getPitchedTextCorrection:(ue,w,B)=>1,get useSpecialProjectionForSymbols(){return!1},projectTileCoordinates(ue,w,B,Q){throw new Error(\"Not implemented.\")},translatePosition:(ue,w,B,Q)=>(function(ee,le,Fe,Ze,ut=!1){if(!Fe[0]&&!Fe[1])return[0,0];let Mt=ut?Ze===\"map\"?ee.angle:0:Ze===\"viewport\"?-ee.angle:0;if(Mt){let Jt=Math.sin(Mt),$t=Math.cos(Mt);Fe=[Fe[0]*$t-Fe[1]*Jt,Fe[0]*Jt+Fe[1]*$t]}return[ut?Fe[0]:Fn(le,Fe[0],ee.zoom),ut?Fe[1]:Fn(le,Fe[1],ee.zoom)]})(ue,w,B,Q),getCircleRadiusCorrection:ue=>1}}class Mn{constructor(w){this._sortAcrossTiles=w.layout.get(\"symbol-z-order\")!==\"viewport-y\"&&!w.layout.get(\"symbol-sort-key\").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(w,B,Q,ee,le){let Fe=this._bucketParts;for(;this._currentTileIndex<w.length;)if(B.getBucketParts(Fe,ee,w[this._currentTileIndex],this._sortAcrossTiles),this._currentTileIndex++,le())return!0;for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,Fe.sort(((Ze,ut)=>Ze.sortKey-ut.sortKey)));this._currentPartIndex<Fe.length;)if(B.placeLayerBucketPart(Fe[this._currentPartIndex],this._seenCrossTileIDs,Q),this._currentPartIndex++,le())return!0;return!1}}class Ba{constructor(w,B,Q,ee,le,Fe,Ze,ut){this.placement=new wt(w,yn(),B,Fe,Ze,ut),this._currentPlacementIndex=Q.length-1,this._forceFullPlacement=ee,this._showCollisionBoxes=le,this._done=!1}isDone(){return this._done}continuePlacement(w,B,Q){let ee=u.now(),le=()=>!this._forceFullPlacement&&u.now()-ee>2;for(;this._currentPlacementIndex>=0;){let Fe=B[w[this._currentPlacementIndex]],Ze=this.placement.collisionIndex.transform.zoom;if(Fe.type===\"symbol\"&&(!Fe.minzoom||Fe.minzoom<=Ze)&&(!Fe.maxzoom||Fe.maxzoom>Ze)){if(this._inProgressLayer||(this._inProgressLayer=new Mn(Fe)),this._inProgressLayer.continuePlacement(Q[Fe.source],this.placement,this._showCollisionBoxes,Fe,le))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(w){return this.placement.commit(w),this.placement}}let ua=512/a.X/2;class ma{constructor(w,B,Q){this.tileID=w,this.bucketInstanceId=Q,this._symbolsByKey={};let ee=new Map;for(let le=0;le<B.length;le++){let Fe=B.get(le),Ze=Fe.key,ut=ee.get(Ze);ut?ut.push(Fe):ee.set(Ze,[Fe])}for(let[le,Fe]of ee){let Ze={positions:Fe.map((ut=>({x:Math.floor(ut.anchorX*ua),y:Math.floor(ut.anchorY*ua)}))),crossTileIDs:Fe.map((ut=>ut.crossTileID))};if(Ze.positions.length>128){let ut=new a.av(Ze.positions.length,16,Uint16Array);for(let{x:Mt,y:Jt}of Ze.positions)ut.add(Mt,Jt);ut.finish(),delete Ze.positions,Ze.index=ut}this._symbolsByKey[le]=Ze}}getScaledCoordinates(w,B){let{x:Q,y:ee,z:le}=this.tileID.canonical,{x:Fe,y:Ze,z:ut}=B.canonical,Mt=ua/Math.pow(2,ut-le),Jt=(Ze*a.X+w.anchorY)*Mt,$t=ee*a.X*ua;return{x:Math.floor((Fe*a.X+w.anchorX)*Mt-Q*a.X*ua),y:Math.floor(Jt-$t)}}findMatches(w,B,Q){let ee=this.tileID.canonical.z<B.canonical.z?1:Math.pow(2,this.tileID.canonical.z-B.canonical.z);for(let le=0;le<w.length;le++){let Fe=w.get(le);if(Fe.crossTileID)continue;let Ze=this._symbolsByKey[Fe.key];if(!Ze)continue;let ut=this.getScaledCoordinates(Fe,B);if(Ze.index){let Mt=Ze.index.range(ut.x-ee,ut.y-ee,ut.x+ee,ut.y+ee).sort();for(let Jt of Mt){let $t=Ze.crossTileIDs[Jt];if(!Q[$t]){Q[$t]=!0,Fe.crossTileID=$t;break}}}else if(Ze.positions)for(let Mt=0;Mt<Ze.positions.length;Mt++){let Jt=Ze.positions[Mt],$t=Ze.crossTileIDs[Mt];if(Math.abs(Jt.x-ut.x)<=ee&&Math.abs(Jt.y-ut.y)<=ee&&!Q[$t]){Q[$t]=!0,Fe.crossTileID=$t;break}}}}getCrossTileIDsLists(){return Object.values(this._symbolsByKey).map((({crossTileIDs:w})=>w))}}class Wa{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class za{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(w){let B=Math.round((w-this.lng)/360);if(B!==0)for(let Q in this.indexes){let ee=this.indexes[Q],le={};for(let Fe in ee){let Ze=ee[Fe];Ze.tileID=Ze.tileID.unwrapTo(Ze.tileID.wrap+B),le[Ze.tileID.key]=Ze}this.indexes[Q]=le}this.lng=w}addBucket(w,B,Q){if(this.indexes[w.overscaledZ]&&this.indexes[w.overscaledZ][w.key]){if(this.indexes[w.overscaledZ][w.key].bucketInstanceId===B.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(w.overscaledZ,this.indexes[w.overscaledZ][w.key])}for(let le=0;le<B.symbolInstances.length;le++)B.symbolInstances.get(le).crossTileID=0;this.usedCrossTileIDs[w.overscaledZ]||(this.usedCrossTileIDs[w.overscaledZ]={});let ee=this.usedCrossTileIDs[w.overscaledZ];for(let le in this.indexes){let Fe=this.indexes[le];if(Number(le)>w.overscaledZ)for(let Ze in Fe){let ut=Fe[Ze];ut.tileID.isChildOf(w)&&ut.findMatches(B.symbolInstances,w,ee)}else{let Ze=Fe[w.scaledTo(Number(le)).key];Ze&&Ze.findMatches(B.symbolInstances,w,ee)}}for(let le=0;le<B.symbolInstances.length;le++){let Fe=B.symbolInstances.get(le);Fe.crossTileID||(Fe.crossTileID=Q.generate(),ee[Fe.crossTileID]=!0)}return this.indexes[w.overscaledZ]===void 0&&(this.indexes[w.overscaledZ]={}),this.indexes[w.overscaledZ][w.key]=new ma(w,B.symbolInstances,B.bucketInstanceId),!0}removeBucketCrossTileIDs(w,B){for(let Q of B.getCrossTileIDsLists())for(let ee of Q)delete this.usedCrossTileIDs[w][ee]}removeStaleBuckets(w){let B=!1;for(let Q in this.indexes){let ee=this.indexes[Q];for(let le in ee)w[ee[le].bucketInstanceId]||(this.removeBucketCrossTileIDs(Q,ee[le]),delete ee[le],B=!0)}return B}}class Xo{constructor(){this.layerIndexes={},this.crossTileIDs=new Wa,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}}addLayer(w,B,Q){let ee=this.layerIndexes[w.id];ee===void 0&&(ee=this.layerIndexes[w.id]=new za);let le=!1,Fe={};ee.handleWrapJump(Q);for(let Ze of B){let ut=Ze.getBucket(w);ut&&w.id===ut.layerIds[0]&&(ut.bucketInstanceId||(ut.bucketInstanceId=++this.maxBucketInstanceId),ee.addBucket(Ze.tileID,ut,this.crossTileIDs)&&(le=!0),Fe[ut.bucketInstanceId]=!0)}return ee.removeStaleBuckets(Fe)&&(le=!0),le}pruneUnusedLayers(w){let B={};w.forEach((Q=>{B[Q]=!0}));for(let Q in this.layerIndexes)B[Q]||delete this.layerIndexes[Q]}}let da=(ue,w)=>a.t(ue,w&&w.filter((B=>B.identifier!==\"source.canvas\"))),jn=a.aw();class Ha extends a.E{constructor(w,B={}){super(),this._rtlPluginLoaded=()=>{for(let Q in this.sourceCaches){let ee=this.sourceCaches[Q].getSource().type;ee!==\"vector\"&&ee!==\"geojson\"||this.sourceCaches[Q].reload()}},this.map=w,this.dispatcher=new ke(Se(),w._getMapId()),this.dispatcher.registerMessageHandler(\"GG\",((Q,ee)=>this.getGlyphs(Q,ee))),this.dispatcher.registerMessageHandler(\"GI\",((Q,ee)=>this.getImages(Q,ee))),this.imageManager=new E,this.imageManager.setEventedParent(this),this.glyphManager=new G(w._requestManager,B.localIdeographFontFamily),this.lineAtlas=new ae(256,512),this.crossTileSymbolIndex=new Xo,this._spritesImagesIds={},this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new a.ax,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"SR\",a.ay()),St().on(Ue,this._rtlPluginLoaded),this.on(\"data\",(Q=>{if(Q.dataType!==\"source\"||Q.sourceDataType!==\"metadata\")return;let ee=this.sourceCaches[Q.sourceId];if(!ee)return;let le=ee.getSource();if(le&&le.vectorLayerIds)for(let Fe in this._layers){let Ze=this._layers[Fe];Ze.source===le.id&&this._validateLayer(Ze)}}))}loadURL(w,B={},Q){this.fire(new a.k(\"dataloading\",{dataType:\"style\"})),B.validate=typeof B.validate!=\"boolean\"||B.validate;let ee=this.map._requestManager.transformRequest(w,\"Style\");this._loadStyleRequest=new AbortController;let le=this._loadStyleRequest;a.h(ee,this._loadStyleRequest).then((Fe=>{this._loadStyleRequest=null,this._load(Fe.data,B,Q)})).catch((Fe=>{this._loadStyleRequest=null,Fe&&!le.signal.aborted&&this.fire(new a.j(Fe))}))}loadJSON(w,B={},Q){this.fire(new a.k(\"dataloading\",{dataType:\"style\"})),this._frameRequest=new AbortController,u.frameAsync(this._frameRequest).then((()=>{this._frameRequest=null,B.validate=B.validate!==!1,this._load(w,B,Q)})).catch((()=>{}))}loadEmpty(){this.fire(new a.k(\"dataloading\",{dataType:\"style\"})),this._load(jn,{validate:!1})}_load(w,B,Q){var ee;let le=B.transformStyle?B.transformStyle(Q,w):w;if(!B.validate||!da(this,a.u(le))){this._loaded=!0,this.stylesheet=le;for(let Fe in le.sources)this.addSource(Fe,le.sources[Fe],{validate:!1});le.sprite?this._loadSprite(le.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(le.glyphs),this._createLayers(),this.light=new N(this.stylesheet.light),this.sky=new re(this.stylesheet.sky),this.map.setTerrain((ee=this.stylesheet.terrain)!==null&&ee!==void 0?ee:null),this.fire(new a.k(\"data\",{dataType:\"style\"})),this.fire(new a.k(\"style.load\"))}}_createLayers(){let w=a.az(this.stylesheet.layers);this.dispatcher.broadcast(\"SL\",w),this._order=w.map((B=>B.id)),this._layers={},this._serializedLayers=null;for(let B of w){let Q=a.aA(B);Q.setEventedParent(this,{layer:{id:B.id}}),this._layers[B.id]=Q}}_loadSprite(w,B=!1,Q=void 0){let ee;this.imageManager.setLoaded(!1),this._spriteRequest=new AbortController,(function(le,Fe,Ze,ut){return a._(this,void 0,void 0,(function*(){let Mt=C(le),Jt=Ze>1?\"@2x\":\"\",$t={},yr={};for(let{id:Cr,url:ve}of Mt){let xe=Fe.transformRequest(S(ve,Jt,\".json\"),\"SpriteJSON\");$t[Cr]=a.h(xe,ut);let Ie=Fe.transformRequest(S(ve,Jt,\".png\"),\"SpriteImage\");yr[Cr]=p.getImage(Ie,ut)}return yield Promise.all([...Object.values($t),...Object.values(yr)]),(function(Cr,ve){return a._(this,void 0,void 0,(function*(){let xe={};for(let Ie in Cr){xe[Ie]={};let ze=u.getImageCanvasContext((yield ve[Ie]).data),Qe=(yield Cr[Ie]).data;for(let Xe in Qe){let{width:nt,height:qt,x:Wt,y:nr,sdf:fr,pixelRatio:br,stretchX:Fr,stretchY:Hr,content:ri,textFitWidth:Ci,textFitHeight:cn}=Qe[Xe];xe[Ie][Xe]={data:null,pixelRatio:br,sdf:fr,stretchX:Fr,stretchY:Hr,content:ri,textFitWidth:Ci,textFitHeight:cn,spriteData:{width:nt,height:qt,x:Wt,y:nr,context:ze}}}}return xe}))})($t,yr)}))})(w,this.map._requestManager,this.map.getPixelRatio(),this._spriteRequest).then((le=>{if(this._spriteRequest=null,le)for(let Fe in le){this._spritesImagesIds[Fe]=[];let Ze=this._spritesImagesIds[Fe]?this._spritesImagesIds[Fe].filter((ut=>!(ut in le))):[];for(let ut of Ze)this.imageManager.removeImage(ut),this._changedImages[ut]=!0;for(let ut in le[Fe]){let Mt=Fe===\"default\"?ut:`${Fe}:${ut}`;this._spritesImagesIds[Fe].push(Mt),Mt in this.imageManager.images?this.imageManager.updateImage(Mt,le[Fe][ut],!1):this.imageManager.addImage(Mt,le[Fe][ut]),B&&(this._changedImages[Mt]=!0)}}})).catch((le=>{this._spriteRequest=null,ee=le,this.fire(new a.j(ee))})).finally((()=>{this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),B&&(this._changed=!0),this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new a.k(\"data\",{dataType:\"style\"})),Q&&Q(ee)}))}_unloadSprite(){for(let w of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(w),this._changedImages[w]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new a.k(\"data\",{dataType:\"style\"}))}_validateLayer(w){let B=this.sourceCaches[w.source];if(!B)return;let Q=w.sourceLayer;if(!Q)return;let ee=B.getSource();(ee.type===\"geojson\"||ee.vectorLayerIds&&ee.vectorLayerIds.indexOf(Q)===-1)&&this.fire(new a.j(new Error(`Source layer \"${Q}\" does not exist on source \"${ee.id}\" as specified by style layer \"${w.id}\".`)))}loaded(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(let w in this.sourceCaches)if(!this.sourceCaches[w].loaded())return!1;return!!this.imageManager.isLoaded()}_serializeByIds(w,B=!1){let Q=this._serializedAllLayers();if(!w||w.length===0)return Object.values(B?a.aB(Q):Q);let ee=[];for(let le of w)if(Q[le]){let Fe=B?a.aB(Q[le]):Q[le];ee.push(Fe)}return ee}_serializedAllLayers(){let w=this._serializedLayers;if(w)return w;w=this._serializedLayers={};let B=Object.keys(this._layers);for(let Q of B){let ee=this._layers[Q];ee.type!==\"custom\"&&(w[Q]=ee.serialize())}return w}hasTransitions(){if(this.light&&this.light.hasTransition()||this.sky&&this.sky.hasTransition())return!0;for(let w in this.sourceCaches)if(this.sourceCaches[w].hasTransition())return!0;for(let w in this._layers)if(this._layers[w].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error(\"Style is not done loading.\")}update(w){if(!this._loaded)return;let B=this._changed;if(B){let ee=Object.keys(this._updatedLayers),le=Object.keys(this._removedLayers);(ee.length||le.length)&&this._updateWorkerLayers(ee,le);for(let Fe in this._updatedSources){let Ze=this._updatedSources[Fe];if(Ze===\"reload\")this._reloadSource(Fe);else{if(Ze!==\"clear\")throw new Error(`Invalid action ${Ze}`);this._clearSource(Fe)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(let Fe in this._updatedPaintProps)this._layers[Fe].updateTransitions(w);this.light.updateTransitions(w),this.sky.updateTransitions(w),this._resetUpdates()}let Q={};for(let ee in this.sourceCaches){let le=this.sourceCaches[ee];Q[ee]=le.used,le.used=!1}for(let ee of this._order){let le=this._layers[ee];le.recalculate(w,this._availableImages),!le.isHidden(w.zoom)&&le.source&&(this.sourceCaches[le.source].used=!0)}for(let ee in Q){let le=this.sourceCaches[ee];!!Q[ee]!=!!le.used&&le.fire(new a.k(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:ee}))}this.light.recalculate(w),this.sky.recalculate(w),this.z=w.zoom,B&&this.fire(new a.k(\"data\",{dataType:\"style\"}))}_updateTilesForChangedImages(){let w=Object.keys(this._changedImages);if(w.length){for(let B in this.sourceCaches)this.sourceCaches[B].reloadTilesForDependencies([\"icons\",\"patterns\"],w);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(let w in this.sourceCaches)this.sourceCaches[w].reloadTilesForDependencies([\"glyphs\"],[\"\"]);this._glyphsDidChange=!1}}_updateWorkerLayers(w,B){this.dispatcher.broadcast(\"UL\",{layers:this._serializeByIds(w,!1),removedIds:B})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(w,B={}){var Q;this._checkLoaded();let ee=this.serialize();if(w=B.transformStyle?B.transformStyle(ee,w):w,((Q=B.validate)===null||Q===void 0||Q)&&da(this,a.u(w)))return!1;(w=a.aB(w)).layers=a.az(w.layers);let le=a.aC(ee,w),Fe=this._getOperationsToPerform(le);if(Fe.unimplemented.length>0)throw new Error(`Unimplemented: ${Fe.unimplemented.join(\", \")}.`);if(Fe.operations.length===0)return!1;for(let Ze of Fe.operations)Ze();return this.stylesheet=w,this._serializedLayers=null,!0}_getOperationsToPerform(w){let B=[],Q=[];for(let ee of w)switch(ee.command){case\"setCenter\":case\"setZoom\":case\"setBearing\":case\"setPitch\":continue;case\"addLayer\":B.push((()=>this.addLayer.apply(this,ee.args)));break;case\"removeLayer\":B.push((()=>this.removeLayer.apply(this,ee.args)));break;case\"setPaintProperty\":B.push((()=>this.setPaintProperty.apply(this,ee.args)));break;case\"setLayoutProperty\":B.push((()=>this.setLayoutProperty.apply(this,ee.args)));break;case\"setFilter\":B.push((()=>this.setFilter.apply(this,ee.args)));break;case\"addSource\":B.push((()=>this.addSource.apply(this,ee.args)));break;case\"removeSource\":B.push((()=>this.removeSource.apply(this,ee.args)));break;case\"setLayerZoomRange\":B.push((()=>this.setLayerZoomRange.apply(this,ee.args)));break;case\"setLight\":B.push((()=>this.setLight.apply(this,ee.args)));break;case\"setGeoJSONSourceData\":B.push((()=>this.setGeoJSONSourceData.apply(this,ee.args)));break;case\"setGlyphs\":B.push((()=>this.setGlyphs.apply(this,ee.args)));break;case\"setSprite\":B.push((()=>this.setSprite.apply(this,ee.args)));break;case\"setSky\":B.push((()=>this.setSky.apply(this,ee.args)));break;case\"setTerrain\":B.push((()=>this.map.setTerrain.apply(this,ee.args)));break;case\"setTransition\":B.push((()=>{}));break;default:Q.push(ee.command)}return{operations:B,unimplemented:Q}}addImage(w,B){if(this.getImage(w))return this.fire(new a.j(new Error(`An image named \"${w}\" already exists.`)));this.imageManager.addImage(w,B),this._afterImageUpdated(w)}updateImage(w,B){this.imageManager.updateImage(w,B)}getImage(w){return this.imageManager.getImage(w)}removeImage(w){if(!this.getImage(w))return this.fire(new a.j(new Error(`An image named \"${w}\" does not exist.`)));this.imageManager.removeImage(w),this._afterImageUpdated(w)}_afterImageUpdated(w){this._availableImages=this.imageManager.listImages(),this._changedImages[w]=!0,this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new a.k(\"data\",{dataType:\"style\"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(w,B,Q={}){if(this._checkLoaded(),this.sourceCaches[w]!==void 0)throw new Error(`Source \"${w}\" already exists.`);if(!B.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(B).join(\", \")}.`);if([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(B.type)>=0&&this._validate(a.u.source,`sources.${w}`,B,null,Q))return;this.map&&this.map._collectResourceTiming&&(B.collectResourceTiming=!0);let ee=this.sourceCaches[w]=new pt(w,B,this.dispatcher);ee.style=this,ee.setEventedParent(this,(()=>({isSourceLoaded:ee.loaded(),source:ee.serialize(),sourceId:w}))),ee.onAdd(this.map),this._changed=!0}removeSource(w){if(this._checkLoaded(),this.sourceCaches[w]===void 0)throw new Error(\"There is no source with this ID\");for(let Q in this._layers)if(this._layers[Q].source===w)return this.fire(new a.j(new Error(`Source \"${w}\" cannot be removed while layer \"${Q}\" is using it.`)));let B=this.sourceCaches[w];delete this.sourceCaches[w],delete this._updatedSources[w],B.fire(new a.k(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:w})),B.setEventedParent(null),B.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(w,B){if(this._checkLoaded(),this.sourceCaches[w]===void 0)throw new Error(`There is no source with this ID=${w}`);let Q=this.sourceCaches[w].getSource();if(Q.type!==\"geojson\")throw new Error(`geojsonSource.type is ${Q.type}, which is !== 'geojson`);Q.setData(B),this._changed=!0}getSource(w){return this.sourceCaches[w]&&this.sourceCaches[w].getSource()}addLayer(w,B,Q={}){this._checkLoaded();let ee=w.id;if(this.getLayer(ee))return void this.fire(new a.j(new Error(`Layer \"${ee}\" already exists on this map.`)));let le;if(w.type===\"custom\"){if(da(this,a.aD(w)))return;le=a.aA(w)}else{if(\"source\"in w&&typeof w.source==\"object\"&&(this.addSource(ee,w.source),w=a.aB(w),w=a.e(w,{source:ee})),this._validate(a.u.layer,`layers.${ee}`,w,{arrayIndex:-1},Q))return;le=a.aA(w),this._validateLayer(le),le.setEventedParent(this,{layer:{id:ee}})}let Fe=B?this._order.indexOf(B):this._order.length;if(B&&Fe===-1)this.fire(new a.j(new Error(`Cannot add layer \"${ee}\" before non-existing layer \"${B}\".`)));else{if(this._order.splice(Fe,0,ee),this._layerOrderChanged=!0,this._layers[ee]=le,this._removedLayers[ee]&&le.source&&le.type!==\"custom\"){let Ze=this._removedLayers[ee];delete this._removedLayers[ee],Ze.type!==le.type?this._updatedSources[le.source]=\"clear\":(this._updatedSources[le.source]=\"reload\",this.sourceCaches[le.source].pause())}this._updateLayer(le),le.onAdd&&le.onAdd(this.map)}}moveLayer(w,B){if(this._checkLoaded(),this._changed=!0,!this._layers[w])return void this.fire(new a.j(new Error(`The layer '${w}' does not exist in the map's style and cannot be moved.`)));if(w===B)return;let Q=this._order.indexOf(w);this._order.splice(Q,1);let ee=B?this._order.indexOf(B):this._order.length;B&&ee===-1?this.fire(new a.j(new Error(`Cannot move layer \"${w}\" before non-existing layer \"${B}\".`))):(this._order.splice(ee,0,w),this._layerOrderChanged=!0)}removeLayer(w){this._checkLoaded();let B=this._layers[w];if(!B)return void this.fire(new a.j(new Error(`Cannot remove non-existing layer \"${w}\".`)));B.setEventedParent(null);let Q=this._order.indexOf(w);this._order.splice(Q,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[w]=B,delete this._layers[w],this._serializedLayers&&delete this._serializedLayers[w],delete this._updatedLayers[w],delete this._updatedPaintProps[w],B.onRemove&&B.onRemove(this.map)}getLayer(w){return this._layers[w]}getLayersOrder(){return[...this._order]}hasLayer(w){return w in this._layers}setLayerZoomRange(w,B,Q){this._checkLoaded();let ee=this.getLayer(w);ee?ee.minzoom===B&&ee.maxzoom===Q||(B!=null&&(ee.minzoom=B),Q!=null&&(ee.maxzoom=Q),this._updateLayer(ee)):this.fire(new a.j(new Error(`Cannot set the zoom range of non-existing layer \"${w}\".`)))}setFilter(w,B,Q={}){this._checkLoaded();let ee=this.getLayer(w);if(ee){if(!a.aE(ee.filter,B))return B==null?(ee.filter=void 0,void this._updateLayer(ee)):void(this._validate(a.u.filter,`layers.${ee.id}.filter`,B,null,Q)||(ee.filter=a.aB(B),this._updateLayer(ee)))}else this.fire(new a.j(new Error(`Cannot filter non-existing layer \"${w}\".`)))}getFilter(w){return a.aB(this.getLayer(w).filter)}setLayoutProperty(w,B,Q,ee={}){this._checkLoaded();let le=this.getLayer(w);le?a.aE(le.getLayoutProperty(B),Q)||(le.setLayoutProperty(B,Q,ee),this._updateLayer(le)):this.fire(new a.j(new Error(`Cannot style non-existing layer \"${w}\".`)))}getLayoutProperty(w,B){let Q=this.getLayer(w);if(Q)return Q.getLayoutProperty(B);this.fire(new a.j(new Error(`Cannot get style of non-existing layer \"${w}\".`)))}setPaintProperty(w,B,Q,ee={}){this._checkLoaded();let le=this.getLayer(w);le?a.aE(le.getPaintProperty(B),Q)||(le.setPaintProperty(B,Q,ee)&&this._updateLayer(le),this._changed=!0,this._updatedPaintProps[w]=!0,this._serializedLayers=null):this.fire(new a.j(new Error(`Cannot style non-existing layer \"${w}\".`)))}getPaintProperty(w,B){return this.getLayer(w).getPaintProperty(B)}setFeatureState(w,B){this._checkLoaded();let Q=w.source,ee=w.sourceLayer,le=this.sourceCaches[Q];if(le===void 0)return void this.fire(new a.j(new Error(`The source '${Q}' does not exist in the map's style.`)));let Fe=le.getSource().type;Fe===\"geojson\"&&ee?this.fire(new a.j(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):Fe!==\"vector\"||ee?(w.id===void 0&&this.fire(new a.j(new Error(\"The feature id parameter must be provided.\"))),le.setFeatureState(ee,w.id,B)):this.fire(new a.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}removeFeatureState(w,B){this._checkLoaded();let Q=w.source,ee=this.sourceCaches[Q];if(ee===void 0)return void this.fire(new a.j(new Error(`The source '${Q}' does not exist in the map's style.`)));let le=ee.getSource().type,Fe=le===\"vector\"?w.sourceLayer:void 0;le!==\"vector\"||Fe?B&&typeof w.id!=\"string\"&&typeof w.id!=\"number\"?this.fire(new a.j(new Error(\"A feature id is required to remove its specific state property.\"))):ee.removeFeatureState(Fe,w.id,B):this.fire(new a.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}getFeatureState(w){this._checkLoaded();let B=w.source,Q=w.sourceLayer,ee=this.sourceCaches[B];if(ee!==void 0)return ee.getSource().type!==\"vector\"||Q?(w.id===void 0&&this.fire(new a.j(new Error(\"The feature id parameter must be provided.\"))),ee.getFeatureState(Q,w.id)):void this.fire(new a.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));this.fire(new a.j(new Error(`The source '${B}' does not exist in the map's style.`)))}getTransition(){return a.e({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)}serialize(){if(!this._loaded)return;let w=a.aF(this.sourceCaches,(le=>le.serialize())),B=this._serializeByIds(this._order,!0),Q=this.map.getTerrain()||void 0,ee=this.stylesheet;return a.aG({version:ee.version,name:ee.name,metadata:ee.metadata,light:ee.light,sky:ee.sky,center:ee.center,zoom:ee.zoom,bearing:ee.bearing,pitch:ee.pitch,sprite:ee.sprite,glyphs:ee.glyphs,transition:ee.transition,sources:w,layers:B,terrain:Q},(le=>le!==void 0))}_updateLayer(w){this._updatedLayers[w.id]=!0,w.source&&!this._updatedSources[w.source]&&this.sourceCaches[w.source].getSource().type!==\"raster\"&&(this._updatedSources[w.source]=\"reload\",this.sourceCaches[w.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(w){let B=Fe=>this._layers[Fe].type===\"fill-extrusion\",Q={},ee=[];for(let Fe=this._order.length-1;Fe>=0;Fe--){let Ze=this._order[Fe];if(B(Ze)){Q[Ze]=Fe;for(let ut of w){let Mt=ut[Ze];if(Mt)for(let Jt of Mt)ee.push(Jt)}}}ee.sort(((Fe,Ze)=>Ze.intersectionZ-Fe.intersectionZ));let le=[];for(let Fe=this._order.length-1;Fe>=0;Fe--){let Ze=this._order[Fe];if(B(Ze))for(let ut=ee.length-1;ut>=0;ut--){let Mt=ee[ut].feature;if(Q[Mt.layer.id]<Fe)break;le.push(Mt),ee.pop()}else for(let ut of w){let Mt=ut[Ze];if(Mt)for(let Jt of Mt)le.push(Jt.feature)}}return le}queryRenderedFeatures(w,B,Q){B&&B.filter&&this._validate(a.u.filter,\"queryRenderedFeatures.filter\",B.filter,null,B);let ee={};if(B&&B.layers){if(!Array.isArray(B.layers))return this.fire(new a.j(new Error(\"parameters.layers must be an Array.\"))),[];for(let Ze of B.layers){let ut=this._layers[Ze];if(!ut)return this.fire(new a.j(new Error(`The layer '${Ze}' does not exist in the map's style and cannot be queried for features.`))),[];ee[ut.source]=!0}}let le=[];B.availableImages=this._availableImages;let Fe=this._serializedAllLayers();for(let Ze in this.sourceCaches)B.layers&&!ee[Ze]||le.push(Le(this.sourceCaches[Ze],this._layers,Fe,w,B,Q));return this.placement&&le.push((function(Ze,ut,Mt,Jt,$t,yr,Cr){let ve={},xe=yr.queryRenderedSymbols(Jt),Ie=[];for(let ze of Object.keys(xe).map(Number))Ie.push(Cr[ze]);Ie.sort(me);for(let ze of Ie){let Qe=ze.featureIndex.lookupSymbolFeatures(xe[ze.bucketInstanceId],ut,ze.bucketIndex,ze.sourceLayerIndex,$t.filter,$t.layers,$t.availableImages,Ze);for(let Xe in Qe){let nt=ve[Xe]=ve[Xe]||[],qt=Qe[Xe];qt.sort(((Wt,nr)=>{let fr=ze.featureSortOrder;if(fr){let br=fr.indexOf(Wt.featureIndex);return fr.indexOf(nr.featureIndex)-br}return nr.featureIndex-Wt.featureIndex}));for(let Wt of qt)nt.push(Wt)}}for(let ze in ve)ve[ze].forEach((Qe=>{let Xe=Qe.feature,nt=Mt[Ze[ze].source].getFeatureState(Xe.layer[\"source-layer\"],Xe.id);Xe.source=Xe.layer.source,Xe.layer[\"source-layer\"]&&(Xe.sourceLayer=Xe.layer[\"source-layer\"]),Xe.state=nt}));return ve})(this._layers,Fe,this.sourceCaches,w,B,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(le)}querySourceFeatures(w,B){B&&B.filter&&this._validate(a.u.filter,\"querySourceFeatures.filter\",B.filter,null,B);let Q=this.sourceCaches[w];return Q?(function(ee,le){let Fe=ee.getRenderableIds().map((Mt=>ee.getTileByID(Mt))),Ze=[],ut={};for(let Mt=0;Mt<Fe.length;Mt++){let Jt=Fe[Mt],$t=Jt.tileID.canonical.key;ut[$t]||(ut[$t]=!0,Jt.querySourceFeatures(Ze,le))}return Ze})(Q,B):[]}getLight(){return this.light.getLight()}setLight(w,B={}){this._checkLoaded();let Q=this.light.getLight(),ee=!1;for(let Fe in w)if(!a.aE(w[Fe],Q[Fe])){ee=!0;break}if(!ee)return;let le={now:u.now(),transition:a.e({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(w,B),this.light.updateTransitions(le)}getSky(){var w;return(w=this.stylesheet)===null||w===void 0?void 0:w.sky}setSky(w,B={}){let Q=this.getSky(),ee=!1;if(!w&&!Q)return;if(w&&!Q)ee=!0;else if(!w&&Q)ee=!0;else for(let Fe in w)if(!a.aE(w[Fe],Q[Fe])){ee=!0;break}if(!ee)return;let le={now:u.now(),transition:a.e({duration:300,delay:0},this.stylesheet.transition)};this.stylesheet.sky=w,this.sky.setSky(w,B),this.sky.updateTransitions(le)}_validate(w,B,Q,ee,le={}){return(!le||le.validate!==!1)&&da(this,w.call(a.u,a.e({key:B,style:this.serialize(),value:Q,styleSpec:a.v},ee)))}_remove(w=!0){this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._loadStyleRequest&&(this._loadStyleRequest.abort(),this._loadStyleRequest=null),this._spriteRequest&&(this._spriteRequest.abort(),this._spriteRequest=null),St().off(Ue,this._rtlPluginLoaded);for(let B in this._layers)this._layers[B].setEventedParent(null);for(let B in this.sourceCaches){let Q=this.sourceCaches[B];Q.setEventedParent(null),Q.onRemove(this.map)}this.imageManager.setEventedParent(null),this.setEventedParent(null),w&&this.dispatcher.broadcast(\"RM\",void 0),this.dispatcher.remove(w)}_clearSource(w){this.sourceCaches[w].clearTiles()}_reloadSource(w){this.sourceCaches[w].resume(),this.sourceCaches[w].reload()}_updateSources(w){for(let B in this.sourceCaches)this.sourceCaches[B].update(w,this.map.terrain)}_generateCollisionBoxes(){for(let w in this.sourceCaches)this._reloadSource(w)}_updatePlacement(w,B,Q,ee,le=!1){let Fe=!1,Ze=!1,ut={};for(let Mt of this._order){let Jt=this._layers[Mt];if(Jt.type!==\"symbol\")continue;if(!ut[Jt.source]){let yr=this.sourceCaches[Jt.source];ut[Jt.source]=yr.getRenderableIds(!0).map((Cr=>yr.getTileByID(Cr))).sort(((Cr,ve)=>ve.tileID.overscaledZ-Cr.tileID.overscaledZ||(Cr.tileID.isLessThan(ve.tileID)?-1:1)))}let $t=this.crossTileSymbolIndex.addLayer(Jt,ut[Jt.source],w.center.lng);Fe=Fe||$t}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((le=le||this._layerOrderChanged||Q===0)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(u.now(),w.zoom))&&(this.pauseablePlacement=new Ba(w,this.map.terrain,this._order,le,B,Q,ee,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,ut),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(u.now()),Ze=!0),Fe&&this.pauseablePlacement.placement.setStale()),Ze||Fe)for(let Mt of this._order){let Jt=this._layers[Mt];Jt.type===\"symbol\"&&this.placement.updateLayerOpacities(Jt,ut[Jt.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(u.now())}_releaseSymbolFadeTiles(){for(let w in this.sourceCaches)this.sourceCaches[w].releaseSymbolFadeTiles()}getImages(w,B){return a._(this,void 0,void 0,(function*(){let Q=yield this.imageManager.getImages(B.icons);this._updateTilesForChangedImages();let ee=this.sourceCaches[B.source];return ee&&ee.setDependencies(B.tileID.key,B.type,B.icons),Q}))}getGlyphs(w,B){return a._(this,void 0,void 0,(function*(){let Q=yield this.glyphManager.getGlyphs(B.stacks),ee=this.sourceCaches[B.source];return ee&&ee.setDependencies(B.tileID.key,B.type,[\"\"]),Q}))}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(w,B={}){this._checkLoaded(),w&&this._validate(a.u.glyphs,\"glyphs\",w,null,B)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=w,this.glyphManager.entries={},this.glyphManager.setURL(w))}addSprite(w,B,Q={},ee){this._checkLoaded();let le=[{id:w,url:B}],Fe=[...C(this.stylesheet.sprite),...le];this._validate(a.u.sprite,\"sprite\",Fe,null,Q)||(this.stylesheet.sprite=Fe,this._loadSprite(le,!0,ee))}removeSprite(w){this._checkLoaded();let B=C(this.stylesheet.sprite);if(B.find((Q=>Q.id===w))){if(this._spritesImagesIds[w])for(let Q of this._spritesImagesIds[w])this.imageManager.removeImage(Q),this._changedImages[Q]=!0;B.splice(B.findIndex((Q=>Q.id===w)),1),this.stylesheet.sprite=B.length>0?B:void 0,delete this._spritesImagesIds[w],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new a.k(\"data\",{dataType:\"style\"}))}else this.fire(new a.j(new Error(`Sprite \"${w}\" doesn't exists on this map.`)))}getSprite(){return C(this.stylesheet.sprite)}setSprite(w,B={},Q){this._checkLoaded(),w&&this._validate(a.u.sprite,\"sprite\",w,null,B)||(this.stylesheet.sprite=w,w?this._loadSprite(w,!0,Q):(this._unloadSprite(),Q&&Q(null)))}}var vo=a.Y([{name:\"a_pos\",type:\"Int16\",components:2}]);let Gn={prelude:Ct(`#ifdef GL_ES\n",
+       "precision mediump float;\n",
+       "#else\n",
+       "#if !defined(lowp)\n",
+       "#define lowp\n",
+       "#endif\n",
+       "#if !defined(mediump)\n",
+       "#define mediump\n",
+       "#endif\n",
+       "#if !defined(highp)\n",
+       "#define highp\n",
+       "#endif\n",
+       "#endif\n",
+       "`,`#ifdef GL_ES\n",
+       "precision highp float;\n",
+       "#else\n",
+       "#if !defined(lowp)\n",
+       "#define lowp\n",
+       "#endif\n",
+       "#if !defined(mediump)\n",
+       "#define mediump\n",
+       "#endif\n",
+       "#if !defined(highp)\n",
+       "#define highp\n",
+       "#endif\n",
+       "#endif\n",
+       "vec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n",
+       ");}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\n",
+       "#ifdef TERRAIN3D\n",
+       "uniform sampler2D u_terrain;uniform float u_terrain_dim;uniform mat4 u_terrain_matrix;uniform vec4 u_terrain_unpack;uniform float u_terrain_exaggeration;uniform highp sampler2D u_depth;\n",
+       "#endif\n",
+       "const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitShifts=vec4(1.)/bitSh;highp float unpack(highp vec4 color) {return dot(color,bitShifts);}highp float depthOpacity(vec3 frag) {\n",
+       "#ifdef TERRAIN3D\n",
+       "highp float d=unpack(texture2D(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));\n",
+       "#else\n",
+       "return 1.0;\n",
+       "#endif\n",
+       "}float calculate_visibility(vec4 pos) {\n",
+       "#ifdef TERRAIN3D\n",
+       "vec3 frag=pos.xyz/pos.w;highp float d=depthOpacity(frag);if (d > 0.95) return 1.0;return (d+depthOpacity(frag+vec3(0.0,0.01,0.0)))/2.0;\n",
+       "#else\n",
+       "return 1.0;\n",
+       "#endif\n",
+       "}float ele(vec2 pos) {\n",
+       "#ifdef TERRAIN3D\n",
+       "vec4 rgb=(texture2D(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;\n",
+       "#else\n",
+       "return 0.0;\n",
+       "#endif\n",
+       "}float get_elevation(vec2 pos) {\n",
+       "#ifdef TERRAIN3D\n",
+       "vec2 coord=(u_terrain_matrix*vec4(pos,0.0,1.0)).xy*u_terrain_dim+1.0;vec2 f=fract(coord);vec2 c=(floor(coord)+0.5)/(u_terrain_dim+2.0);float d=1.0/(u_terrain_dim+2.0);float tl=ele(c);float tr=ele(c+vec2(d,0.0));float bl=ele(c+vec2(0.0,d));float br=ele(c+vec2(d,d));float elevation=mix(mix(tl,tr,f.x),mix(bl,br,f.x),f.y);return elevation*u_terrain_exaggeration;\n",
+       "#else\n",
+       "return 0.0;\n",
+       "#endif\n",
+       "}`),background:Ct(`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),backgroundPattern:Ct(`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),circle:Ct(`varying vec3 v_data;varying float v_visibility;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define mediump float radius\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define highp vec4 stroke_color\n",
+       "#pragma mapbox: define mediump float stroke_width\n",
+       "#pragma mapbox: define lowp float stroke_opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize mediump float radius\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize highp vec4 stroke_color\n",
+       "#pragma mapbox: initialize mediump float stroke_width\n",
+       "#pragma mapbox: initialize lowp float stroke_opacity\n",
+       "vec2 extrude=v_data.xy;float extrude_length=length(extrude);float antialiased_blur=v_data.z;float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;varying float v_visibility;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define mediump float radius\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define highp vec4 stroke_color\n",
+       "#pragma mapbox: define mediump float stroke_width\n",
+       "#pragma mapbox: define lowp float stroke_opacity\n",
+       "void main(void) {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize mediump float radius\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize highp vec4 stroke_color\n",
+       "#pragma mapbox: initialize mediump float stroke_width\n",
+       "#pragma mapbox: initialize lowp float stroke_opacity\n",
+       "vec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);float ele=get_elevation(circle_center);v_visibility=calculate_visibility(u_matrix*vec4(circle_center,ele,1.0));if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,ele,1);} else {gl_Position=u_matrix*vec4(circle_center,ele,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}float antialiasblur=-max(1.0/u_device_pixel_ratio/(radius+stroke_width),blur);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`),clippingMask:Ct(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),heatmap:Ct(`uniform highp float u_intensity;varying vec2 v_extrude;\n",
+       "#pragma mapbox: define highp float weight\n",
+       "#define GAUSS_COEF 0.3989422804014327\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp float weight\n",
+       "float d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n",
+       "#pragma mapbox: define highp float weight\n",
+       "#pragma mapbox: define mediump float radius\n",
+       "const highp float ZERO=1.0/255.0/16.0;\n",
+       "#define GAUSS_COEF 0.3989422804014327\n",
+       "void main(void) {\n",
+       "#pragma mapbox: initialize highp float weight\n",
+       "#pragma mapbox: initialize mediump float radius\n",
+       "vec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,get_elevation(floor(a_pos*0.5)),1);gl_Position=u_matrix*pos;}`),heatmapTexture:Ct(`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(0.0);\n",
+       "#endif\n",
+       "}`,\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),collisionBox:Ct(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_anchor_pos;attribute vec2 a_placed;attribute vec2 a_box_real;uniform mat4 u_matrix;uniform vec2 u_pixel_extrude_scale;varying float v_placed;varying float v_notUsed;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}void main() {gl_Position=projectTileWithElevation(a_anchor_pos,get_elevation(a_anchor_pos));gl_Position.xy=((a_box_real+0.5)*u_pixel_extrude_scale*2.0-1.0)*vec2(1.0,-1.0)*gl_Position.w;if (gl_Position.z/gl_Position.w < 1.1) {gl_Position.z=0.5;}v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),collisionCircle:Ct(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),debug:Ct(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,get_elevation(a_pos),1);}\"),fill:Ct(`#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "gl_FragColor=color*opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`attribute vec2 a_pos;uniform mat4 u_matrix;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "gl_Position=u_matrix*vec4(a_pos,0,1);}`),fillOutline:Ct(`varying vec2 v_pos;\n",
+       "#pragma mapbox: define highp vec4 outline_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 outline_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n",
+       "#pragma mapbox: define highp vec4 outline_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 outline_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillOutlinePattern:Ct(`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillPattern:Ct(`#ifdef GL_ES\n",
+       "precision highp float;\n",
+       "#endif\n",
+       "uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`),fillExtrusion:Ct(`varying vec4 v_color;void main() {gl_FragColor=v_color;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n",
+       "#ifdef TERRAIN3D\n",
+       "attribute vec2 a_centroid;\n",
+       "#endif\n",
+       "varying vec4 v_color;\n",
+       "#pragma mapbox: define highp float base\n",
+       "#pragma mapbox: define highp float height\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp float base\n",
+       "#pragma mapbox: initialize highp float height\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "vec3 normal=a_normal_ed.xyz;\n",
+       "#ifdef TERRAIN3D\n",
+       "float height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\n",
+       "#else\n",
+       "float height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n",
+       "#endif\n",
+       "base=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`),fillExtrusionPattern:Ct(`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n",
+       "#pragma mapbox: define lowp float base\n",
+       "#pragma mapbox: define lowp float height\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float base\n",
+       "#pragma mapbox: initialize lowp float height\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n",
+       "#ifdef TERRAIN3D\n",
+       "attribute vec2 a_centroid;\n",
+       "#endif\n",
+       "varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n",
+       "#pragma mapbox: define lowp float base\n",
+       "#pragma mapbox: define lowp float height\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float base\n",
+       "#pragma mapbox: initialize lowp float height\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;\n",
+       "#ifdef TERRAIN3D\n",
+       "float height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\n",
+       "#else\n",
+       "float height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n",
+       "#endif\n",
+       "base=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n",
+       "? a_pos\n",
+       ": vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`),hillshadePrepare:Ct(`#ifdef GL_ES\n",
+       "precision highp float;\n",
+       "#endif\n",
+       "uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hillshade:Ct(`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n",
+       "#define PI 3.141592653589793\n",
+       "void main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),line:Ct(`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`\n",
+       "#define scale 0.015873016\n",
+       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float width\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n",
+       "#ifdef TERRAIN3D\n",
+       "v_gamma_scale=1.0;\n",
+       "#else\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "#endif\n",
+       "v_width2=vec2(outset,inset);}`),lineGradient:Ct(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`\n",
+       "#define scale 0.015873016\n",
+       "attribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float width\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n",
+       "#ifdef TERRAIN3D\n",
+       "v_gamma_scale=1.0;\n",
+       "#else\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "#endif\n",
+       "v_width2=vec2(outset,inset);}`),linePattern:Ct(`#ifdef GL_ES\n",
+       "precision highp float;\n",
+       "#endif\n",
+       "uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`\n",
+       "#define scale 0.015873016\n",
+       "#define LINE_DISTANCE_SCALE 2.0\n",
+       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define mediump float width\n",
+       "#pragma mapbox: define lowp float floorwidth\n",
+       "#pragma mapbox: define lowp vec4 pattern_from\n",
+       "#pragma mapbox: define lowp vec4 pattern_to\n",
+       "#pragma mapbox: define lowp float pixel_ratio_from\n",
+       "#pragma mapbox: define lowp float pixel_ratio_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "#pragma mapbox: initialize lowp float floorwidth\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
+       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
+       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n",
+       "#ifdef TERRAIN3D\n",
+       "v_gamma_scale=1.0;\n",
+       "#else\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "#endif\n",
+       "v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`),lineSDF:Ct(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float width\n",
+       "#pragma mapbox: define lowp float floorwidth\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "#pragma mapbox: initialize lowp float floorwidth\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`\n",
+       "#define scale 0.015873016\n",
+       "#define LINE_DISTANCE_SCALE 2.0\n",
+       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n",
+       "#pragma mapbox: define highp vec4 color\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float width\n",
+       "#pragma mapbox: define lowp float floorwidth\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 color\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "#pragma mapbox: initialize lowp float floorwidth\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n",
+       "#ifdef TERRAIN3D\n",
+       "v_gamma_scale=1.0;\n",
+       "#else\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "#endif\n",
+       "v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`),raster:Ct(`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),symbolIcon:Ct(`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "lowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_tex;varying float v_fade_opacity;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}gl_Position=finalPos;v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));}`),symbolSDF:Ct(`#define SDF_PX 8.0\n",
+       "uniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n",
+       "#pragma mapbox: define highp vec4 fill_color\n",
+       "#pragma mapbox: define highp vec4 halo_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float halo_width\n",
+       "#pragma mapbox: define lowp float halo_blur\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 fill_color\n",
+       "#pragma mapbox: initialize highp vec4 halo_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float halo_width\n",
+       "#pragma mapbox: initialize lowp float halo_blur\n",
+       "float EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);inner_edge=inner_edge+gamma*gamma_scale;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);if (u_is_halo) {lowp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha=min(smoothstep(halo_edge-gamma_scaled,halo_edge+gamma_scaled,dist),1.0-alpha);}gl_FragColor=color*(alpha*opacity*fade_opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_data0;varying vec3 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n",
+       "#pragma mapbox: define highp vec4 fill_color\n",
+       "#pragma mapbox: define highp vec4 halo_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float halo_width\n",
+       "#pragma mapbox: define lowp float halo_blur\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 fill_color\n",
+       "#pragma mapbox: initialize highp vec4 halo_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float halo_width\n",
+       "#pragma mapbox: initialize lowp float halo_blur\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`),symbolTextAndIcon:Ct(`#define SDF_PX 8.0\n",
+       "#define SDF 1.0\n",
+       "#define ICON 0.0\n",
+       "uniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n",
+       "#pragma mapbox: define highp vec4 fill_color\n",
+       "#pragma mapbox: define highp vec4 halo_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float halo_width\n",
+       "#pragma mapbox: define lowp float halo_blur\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 fill_color\n",
+       "#pragma mapbox: initialize highp vec4 halo_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float halo_width\n",
+       "#pragma mapbox: initialize lowp float halo_blur\n",
+       "float fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "return;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "gl_FragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec4 v_data0;varying vec4 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n",
+       "#pragma mapbox: define highp vec4 fill_color\n",
+       "#pragma mapbox: define highp vec4 halo_color\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define lowp float halo_width\n",
+       "#pragma mapbox: define lowp float halo_blur\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize highp vec4 fill_color\n",
+       "#pragma mapbox: initialize highp vec4 halo_color\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize lowp float halo_width\n",
+       "#pragma mapbox: initialize lowp float halo_blur\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`),terrain:Ct(\"uniform sampler2D u_texture;uniform vec4 u_fog_color;uniform vec4 u_horizon_color;uniform float u_fog_ground_blend;uniform float u_fog_ground_blend_opacity;uniform float u_horizon_fog_blend;varying vec2 v_texture_pos;varying float v_fog_depth;const float gamma=2.2;vec4 gammaToLinear(vec4 color) {return pow(color,vec4(gamma));}vec4 linearToGamma(vec4 color) {return pow(color,vec4(1.0/gamma));}void main() {vec4 surface_color=texture2D(u_texture,v_texture_pos);if (v_fog_depth > u_fog_ground_blend) {vec4 surface_color_linear=gammaToLinear(surface_color);float blend_color=smoothstep(0.0,1.0,max((v_fog_depth-u_horizon_fog_blend)/(1.0-u_horizon_fog_blend),0.0));vec4 fog_horizon_color_linear=mix(gammaToLinear(u_fog_color),gammaToLinear(u_horizon_color),blend_color);float factor_fog=max(v_fog_depth-u_fog_ground_blend,0.0)/(1.0-u_fog_ground_blend);gl_FragColor=linearToGamma(mix(surface_color_linear,fog_horizon_color_linear,pow(factor_fog,2.0)*u_fog_ground_blend_opacity));} else {gl_FragColor=surface_color;}}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform mat4 u_fog_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;varying float v_fog_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);vec4 pos=u_fog_matrix*vec4(a_pos3d.xy,ele,1.0);v_fog_depth=pos.z/pos.w*0.5+0.5;}\"),terrainDepth:Ct(\"varying float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {gl_FragColor=pack(v_depth);}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying float v_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);v_depth=gl_Position.z/gl_Position.w;}\"),terrainCoords:Ct(\"precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;varying vec2 v_texture_pos;void main() {vec4 rgba=texture2D(u_texture,v_texture_pos);gl_FragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);}\"),sky:Ct(\"uniform vec4 u_sky_color;uniform vec4 u_horizon_color;uniform float u_horizon;uniform float u_sky_horizon_blend;void main() {float y=gl_FragCoord.y;if (y > u_horizon) {float blend=y-u_horizon;if (blend < u_sky_horizon_blend) {gl_FragColor=mix(u_sky_color,u_horizon_color,pow(1.0-blend/u_sky_horizon_blend,2.0));} else {gl_FragColor=u_sky_color;}}}\",\"attribute vec2 a_pos;void main() {gl_Position=vec4(a_pos,1.0,1.0);}\")};function Ct(ue,w){let B=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,Q=w.match(/attribute ([\\w]+) ([\\w]+)/g),ee=ue.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),le=w.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),Fe=le?le.concat(ee):ee,Ze={};return{fragmentSource:ue=ue.replace(B,((ut,Mt,Jt,$t,yr)=>(Ze[yr]=!0,Mt===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${yr}\n",
+       "varying ${Jt} ${$t} ${yr};\n",
+       "#else\n",
+       "uniform ${Jt} ${$t} u_${yr};\n",
+       "#endif\n",
+       "`:`\n",
+       "#ifdef HAS_UNIFORM_u_${yr}\n",
+       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "#endif\n",
+       "`))),vertexSource:w=w.replace(B,((ut,Mt,Jt,$t,yr)=>{let Cr=$t===\"float\"?\"vec2\":\"vec4\",ve=yr.match(/color/)?\"color\":Cr;return Ze[yr]?Mt===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${yr}\n",
+       "uniform lowp float u_${yr}_t;\n",
+       "attribute ${Jt} ${Cr} a_${yr};\n",
+       "varying ${Jt} ${$t} ${yr};\n",
+       "#else\n",
+       "uniform ${Jt} ${$t} u_${yr};\n",
+       "#endif\n",
+       "`:ve===\"vec4\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${yr}\n",
+       "    ${yr} = a_${yr};\n",
+       "#else\n",
+       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "#endif\n",
+       "`:`\n",
+       "#ifndef HAS_UNIFORM_u_${yr}\n",
+       "    ${yr} = unpack_mix_${ve}(a_${yr}, u_${yr}_t);\n",
+       "#else\n",
+       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "#endif\n",
+       "`:Mt===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${yr}\n",
+       "uniform lowp float u_${yr}_t;\n",
+       "attribute ${Jt} ${Cr} a_${yr};\n",
+       "#else\n",
+       "uniform ${Jt} ${$t} u_${yr};\n",
+       "#endif\n",
+       "`:ve===\"vec4\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${yr}\n",
+       "    ${Jt} ${$t} ${yr} = a_${yr};\n",
+       "#else\n",
+       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "#endif\n",
+       "`:`\n",
+       "#ifndef HAS_UNIFORM_u_${yr}\n",
+       "    ${Jt} ${$t} ${yr} = unpack_mix_${ve}(a_${yr}, u_${yr}_t);\n",
+       "#else\n",
+       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "#endif\n",
+       "`})),staticAttributes:Q,staticUniforms:Fe}}class Sr{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(w,B,Q,ee,le,Fe,Ze,ut,Mt){this.context=w;let Jt=this.boundPaintVertexBuffers.length!==ee.length;for(let $t=0;!Jt&&$t<ee.length;$t++)this.boundPaintVertexBuffers[$t]!==ee[$t]&&(Jt=!0);!this.vao||this.boundProgram!==B||this.boundLayoutVertexBuffer!==Q||Jt||this.boundIndexBuffer!==le||this.boundVertexOffset!==Fe||this.boundDynamicVertexBuffer!==Ze||this.boundDynamicVertexBuffer2!==ut||this.boundDynamicVertexBuffer3!==Mt?this.freshBind(B,Q,ee,le,Fe,Ze,ut,Mt):(w.bindVertexArray.set(this.vao),Ze&&Ze.bind(),le&&le.dynamicDraw&&le.bind(),ut&&ut.bind(),Mt&&Mt.bind())}freshBind(w,B,Q,ee,le,Fe,Ze,ut){let Mt=w.numAttributes,Jt=this.context,$t=Jt.gl;this.vao&&this.destroy(),this.vao=Jt.createVertexArray(),Jt.bindVertexArray.set(this.vao),this.boundProgram=w,this.boundLayoutVertexBuffer=B,this.boundPaintVertexBuffers=Q,this.boundIndexBuffer=ee,this.boundVertexOffset=le,this.boundDynamicVertexBuffer=Fe,this.boundDynamicVertexBuffer2=Ze,this.boundDynamicVertexBuffer3=ut,B.enableAttributes($t,w);for(let yr of Q)yr.enableAttributes($t,w);Fe&&Fe.enableAttributes($t,w),Ze&&Ze.enableAttributes($t,w),ut&&ut.enableAttributes($t,w),B.bind(),B.setVertexAttribPointers($t,w,le);for(let yr of Q)yr.bind(),yr.setVertexAttribPointers($t,w,le);Fe&&(Fe.bind(),Fe.setVertexAttribPointers($t,w,le)),ee&&ee.bind(),Ze&&(Ze.bind(),Ze.setVertexAttribPointers($t,w,le)),ut&&(ut.bind(),ut.setVertexAttribPointers($t,w,le)),Jt.currentNumAttributes=Mt}destroy(){this.vao&&(this.context.deleteVertexArray(this.vao),this.vao=null)}}let Jr=(ue,w,B,Q,ee)=>({u_matrix:ue,u_texture:0,u_ele_delta:w,u_fog_matrix:B,u_fog_color:Q?Q.properties.get(\"fog-color\"):a.aM.white,u_fog_ground_blend:Q?Q.properties.get(\"fog-ground-blend\"):1,u_fog_ground_blend_opacity:Q?Q.calculateFogBlendOpacity(ee):0,u_horizon_color:Q?Q.properties.get(\"horizon-color\"):a.aM.white,u_horizon_fog_blend:Q?Q.properties.get(\"horizon-fog-blend\"):1});function hi(ue){let w=[];for(let B=0;B<ue.length;B++){if(ue[B]===null)continue;let Q=ue[B].split(\" \");w.push(Q.pop())}return w}class hn{constructor(w,B,Q,ee,le,Fe){let Ze=w.gl;this.program=Ze.createProgram();let ut=hi(B.staticAttributes),Mt=Q?Q.getBinderAttributes():[],Jt=ut.concat(Mt),$t=Gn.prelude.staticUniforms?hi(Gn.prelude.staticUniforms):[],yr=B.staticUniforms?hi(B.staticUniforms):[],Cr=Q?Q.getBinderUniforms():[],ve=$t.concat(yr).concat(Cr),xe=[];for(let Wt of ve)xe.indexOf(Wt)<0&&xe.push(Wt);let Ie=Q?Q.defines():[];le&&Ie.push(\"#define OVERDRAW_INSPECTOR;\"),Fe&&Ie.push(\"#define TERRAIN3D;\");let ze=Ie.concat(Gn.prelude.fragmentSource,B.fragmentSource).join(`\n",
+       "`),Qe=Ie.concat(Gn.prelude.vertexSource,B.vertexSource).join(`\n",
+       "`),Xe=Ze.createShader(Ze.FRAGMENT_SHADER);if(Ze.isContextLost())return void(this.failedToCreate=!0);if(Ze.shaderSource(Xe,ze),Ze.compileShader(Xe),!Ze.getShaderParameter(Xe,Ze.COMPILE_STATUS))throw new Error(`Could not compile fragment shader: ${Ze.getShaderInfoLog(Xe)}`);Ze.attachShader(this.program,Xe);let nt=Ze.createShader(Ze.VERTEX_SHADER);if(Ze.isContextLost())return void(this.failedToCreate=!0);if(Ze.shaderSource(nt,Qe),Ze.compileShader(nt),!Ze.getShaderParameter(nt,Ze.COMPILE_STATUS))throw new Error(`Could not compile vertex shader: ${Ze.getShaderInfoLog(nt)}`);Ze.attachShader(this.program,nt),this.attributes={};let qt={};this.numAttributes=Jt.length;for(let Wt=0;Wt<this.numAttributes;Wt++)Jt[Wt]&&(Ze.bindAttribLocation(this.program,Wt,Jt[Wt]),this.attributes[Jt[Wt]]=Wt);if(Ze.linkProgram(this.program),!Ze.getProgramParameter(this.program,Ze.LINK_STATUS))throw new Error(`Program failed to link: ${Ze.getProgramInfoLog(this.program)}`);Ze.deleteShader(nt),Ze.deleteShader(Xe);for(let Wt=0;Wt<xe.length;Wt++){let nr=xe[Wt];if(nr&&!qt[nr]){let fr=Ze.getUniformLocation(this.program,nr);fr&&(qt[nr]=fr)}}this.fixedUniforms=ee(w,qt),this.terrainUniforms=((Wt,nr)=>({u_depth:new a.aH(Wt,nr.u_depth),u_terrain:new a.aH(Wt,nr.u_terrain),u_terrain_dim:new a.aI(Wt,nr.u_terrain_dim),u_terrain_matrix:new a.aJ(Wt,nr.u_terrain_matrix),u_terrain_unpack:new a.aK(Wt,nr.u_terrain_unpack),u_terrain_exaggeration:new a.aI(Wt,nr.u_terrain_exaggeration)}))(w,qt),this.binderUniforms=Q?Q.getUniforms(w,qt):[]}draw(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve,xe,Ie,ze,Qe){let Xe=w.gl;if(this.failedToCreate)return;if(w.program.set(this.program),w.setDepthMode(Q),w.setStencilMode(ee),w.setColorMode(le),w.setCullFace(Fe),ut){w.activeTexture.set(Xe.TEXTURE2),Xe.bindTexture(Xe.TEXTURE_2D,ut.depthTexture),w.activeTexture.set(Xe.TEXTURE3),Xe.bindTexture(Xe.TEXTURE_2D,ut.texture);for(let qt in this.terrainUniforms)this.terrainUniforms[qt].set(ut[qt])}for(let qt in this.fixedUniforms)this.fixedUniforms[qt].set(Ze[qt]);xe&&xe.setUniforms(w,this.binderUniforms,Cr,{zoom:ve});let nt=0;switch(B){case Xe.LINES:nt=2;break;case Xe.TRIANGLES:nt=3;break;case Xe.LINE_STRIP:nt=1}for(let qt of yr.get()){let Wt=qt.vaos||(qt.vaos={});(Wt[Mt]||(Wt[Mt]=new Sr)).bind(w,this,Jt,xe?xe.getPaintVertexBuffers():[],$t,qt.vertexOffset,Ie,ze,Qe),Xe.drawElements(B,qt.primitiveLength*nt,Xe.UNSIGNED_SHORT,qt.primitiveOffset*nt*2)}}}function Sn(ue,w,B){let Q=1/Fn(B,1,w.transform.tileZoom),ee=Math.pow(2,B.tileID.overscaledZ),le=B.tileSize*Math.pow(2,w.transform.tileZoom)/ee,Fe=le*(B.tileID.canonical.x+B.tileID.wrap*ee),Ze=le*B.tileID.canonical.y;return{u_image:0,u_texsize:B.imageAtlasTexture.size,u_scale:[Q,ue.fromScale,ue.toScale],u_fade:ue.t,u_pixel_coord_upper:[Fe>>16,Ze>>16],u_pixel_coord_lower:[65535&Fe,65535&Ze]}}let En=(ue,w,B,Q)=>{let ee=w.style.light,le=ee.properties.get(\"position\"),Fe=[le.x,le.y,le.z],Ze=(function(){var Mt=new a.A(9);return a.A!=Float32Array&&(Mt[1]=0,Mt[2]=0,Mt[3]=0,Mt[5]=0,Mt[6]=0,Mt[7]=0),Mt[0]=1,Mt[4]=1,Mt[8]=1,Mt})();ee.properties.get(\"anchor\")===\"viewport\"&&(function(Mt,Jt){var $t=Math.sin(Jt),yr=Math.cos(Jt);Mt[0]=yr,Mt[1]=$t,Mt[2]=0,Mt[3]=-$t,Mt[4]=yr,Mt[5]=0,Mt[6]=0,Mt[7]=0,Mt[8]=1})(Ze,-w.transform.angle),(function(Mt,Jt,$t){var yr=Jt[0],Cr=Jt[1],ve=Jt[2];Mt[0]=yr*$t[0]+Cr*$t[3]+ve*$t[6],Mt[1]=yr*$t[1]+Cr*$t[4]+ve*$t[7],Mt[2]=yr*$t[2]+Cr*$t[5]+ve*$t[8]})(Fe,Fe,Ze);let ut=ee.properties.get(\"color\");return{u_matrix:ue,u_lightpos:Fe,u_lightintensity:ee.properties.get(\"intensity\"),u_lightcolor:[ut.r,ut.g,ut.b],u_vertical_gradient:+B,u_opacity:Q}},ki=(ue,w,B,Q,ee,le,Fe)=>a.e(En(ue,w,B,Q),Sn(le,w,Fe),{u_height_factor:-Math.pow(2,ee.overscaledZ)/Fe.tileSize/8}),_n=ue=>({u_matrix:ue}),ya=(ue,w,B,Q)=>a.e(_n(ue),Sn(B,w,Q)),ea=(ue,w)=>({u_matrix:ue,u_world:w}),Ma=(ue,w,B,Q,ee)=>a.e(ya(ue,w,B,Q),{u_world:ee}),_o=(ue,w,B,Q)=>{let ee=ue.transform,le,Fe;if(Q.paint.get(\"circle-pitch-alignment\")===\"map\"){let Ze=Fn(B,1,ee.zoom);le=!0,Fe=[Ze,Ze]}else le=!1,Fe=ee.pixelsToGLUnits;return{u_camera_to_center_distance:ee.cameraToCenterDistance,u_scale_with_map:+(Q.paint.get(\"circle-pitch-scale\")===\"map\"),u_matrix:ue.translatePosMatrix(w.posMatrix,B,Q.paint.get(\"circle-translate\"),Q.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+le,u_device_pixel_ratio:ue.pixelRatio,u_extrude_scale:Fe}},No=(ue,w,B)=>({u_matrix:ue,u_inv_matrix:w,u_camera_to_center_distance:B.cameraToCenterDistance,u_viewport_size:[B.width,B.height]}),po=(ue,w,B=1)=>({u_matrix:ue,u_color:w,u_overlay:0,u_overlay_scale:B}),Lo=ue=>({u_matrix:ue}),ko=(ue,w,B,Q)=>({u_matrix:ue,u_extrude_scale:Fn(w,1,B),u_intensity:Q}),Ds=(ue,w,B,Q)=>{let ee=a.H();a.aP(ee,0,ue.width,ue.height,0,0,1);let le=ue.context.gl;return{u_matrix:ee,u_world:[le.drawingBufferWidth,le.drawingBufferHeight],u_image:B,u_color_ramp:Q,u_opacity:w.paint.get(\"heatmap-opacity\")}};function Fs(ue,w){let B=Math.pow(2,w.canonical.z),Q=w.canonical.y;return[new a.Z(0,Q/B).toLngLat().lat,new a.Z(0,(Q+1)/B).toLngLat().lat]}let ll=(ue,w,B,Q)=>{let ee=ue.transform;return{u_matrix:As(ue,w,B,Q),u_ratio:1/Fn(w,1,ee.zoom),u_device_pixel_ratio:ue.pixelRatio,u_units_to_pixels:[1/ee.pixelsToGLUnits[0],1/ee.pixelsToGLUnits[1]]}},ul=(ue,w,B,Q,ee)=>a.e(ll(ue,w,B,ee),{u_image:0,u_image_height:Q}),Fl=(ue,w,B,Q,ee)=>{let le=ue.transform,Fe=rl(w,le);return{u_matrix:As(ue,w,B,ee),u_texsize:w.imageAtlasTexture.size,u_ratio:1/Fn(w,1,le.zoom),u_device_pixel_ratio:ue.pixelRatio,u_image:0,u_scale:[Fe,Q.fromScale,Q.toScale],u_fade:Q.t,u_units_to_pixels:[1/le.pixelsToGLUnits[0],1/le.pixelsToGLUnits[1]]}},us=(ue,w,B,Q,ee,le)=>{let Fe=ue.lineAtlas,Ze=rl(w,ue.transform),ut=B.layout.get(\"line-cap\")===\"round\",Mt=Fe.getDash(Q.from,ut),Jt=Fe.getDash(Q.to,ut),$t=Mt.width*ee.fromScale,yr=Jt.width*ee.toScale;return a.e(ll(ue,w,B,le),{u_patternscale_a:[Ze/$t,-Mt.height/2],u_patternscale_b:[Ze/yr,-Jt.height/2],u_sdfgamma:Fe.width/(256*Math.min($t,yr)*ue.pixelRatio)/2,u_image:0,u_tex_y_a:Mt.y,u_tex_y_b:Jt.y,u_mix:ee.t})};function rl(ue,w){return 1/Fn(ue,1,w.tileZoom)}function As(ue,w,B,Q){return ue.translatePosMatrix(Q?Q.posMatrix:w.tileID.posMatrix,w,B.paint.get(\"line-translate\"),B.paint.get(\"line-translate-anchor\"))}let cl=(ue,w,B,Q,ee)=>{return{u_matrix:ue,u_tl_parent:w,u_scale_parent:B,u_buffer_scale:1,u_fade_t:Q.mix,u_opacity:Q.opacity*ee.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:ee.paint.get(\"raster-brightness-min\"),u_brightness_high:ee.paint.get(\"raster-brightness-max\"),u_saturation_factor:(Fe=ee.paint.get(\"raster-saturation\"),Fe>0?1-1/(1.001-Fe):-Fe),u_contrast_factor:(le=ee.paint.get(\"raster-contrast\"),le>0?1/(1-le):1+le),u_spin_weights:Ks(ee.paint.get(\"raster-hue-rotate\"))};var le,Fe};function Ks(ue){ue*=Math.PI/180;let w=Math.sin(ue),B=Math.cos(ue);return[(2*B+1)/3,(-Math.sqrt(3)*w-B+1)/3,(Math.sqrt(3)*w-B+1)/3]}let zs=(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr)=>{let ve=Fe.transform;return{u_is_size_zoom_constant:+(ue===\"constant\"||ue===\"source\"),u_is_size_feature_constant:+(ue===\"constant\"||ue===\"camera\"),u_size_t:w?w.uSizeT:0,u_size:w?w.uSize:0,u_camera_to_center_distance:ve.cameraToCenterDistance,u_pitch:ve.pitch/360*2*Math.PI,u_rotate_symbol:+B,u_aspect_ratio:ve.width/ve.height,u_fade_change:Fe.options.fadeDuration?Fe.symbolFadeChange:1,u_matrix:Ze,u_label_plane_matrix:ut,u_coord_matrix:Mt,u_is_text:+$t,u_pitch_with_map:+Q,u_is_along_line:ee,u_is_variable_anchor:le,u_texsize:yr,u_texture:0,u_translation:Jt,u_pitched_scale:Cr}},Io=(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve)=>{let xe=Fe.transform;return a.e(zs(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,ve),{u_gamma_scale:Q?Math.cos(xe._pitch)*xe.cameraToCenterDistance:1,u_device_pixel_ratio:Fe.pixelRatio,u_is_halo:+Cr})},ls=(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr)=>a.e(Io(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,!0,$t,!0,Cr),{u_texsize_icon:yr,u_texture_icon:1}),Yl=(ue,w,B)=>({u_matrix:ue,u_opacity:w,u_color:B}),Su=(ue,w,B,Q,ee,le)=>a.e((function(Fe,Ze,ut,Mt){let 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a.aO(ue,w.u_units_to_pixels),u_patternscale_a:new a.aO(ue,w.u_patternscale_a),u_patternscale_b:new a.aO(ue,w.u_patternscale_b),u_sdfgamma:new a.aI(ue,w.u_sdfgamma),u_image:new a.aH(ue,w.u_image),u_tex_y_a:new a.aI(ue,w.u_tex_y_a),u_tex_y_b:new a.aI(ue,w.u_tex_y_b),u_mix:new a.aI(ue,w.u_mix)}),raster:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_tl_parent:new a.aO(ue,w.u_tl_parent),u_scale_parent:new a.aI(ue,w.u_scale_parent),u_buffer_scale:new a.aI(ue,w.u_buffer_scale),u_fade_t:new a.aI(ue,w.u_fade_t),u_opacity:new a.aI(ue,w.u_opacity),u_image0:new a.aH(ue,w.u_image0),u_image1:new a.aH(ue,w.u_image1),u_brightness_low:new a.aI(ue,w.u_brightness_low),u_brightness_high:new a.aI(ue,w.u_brightness_high),u_saturation_factor:new a.aI(ue,w.u_saturation_factor),u_contrast_factor:new a.aI(ue,w.u_contrast_factor),u_spin_weights:new a.aN(ue,w.u_spin_weights)}),symbolIcon:(ue,w)=>({u_is_size_zoom_constant:new a.aH(ue,w.u_is_size_zoom_constant),u_is_size_feature_constant:new 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a.aI(ue,w.u_size_t),u_size:new a.aI(ue,w.u_size),u_camera_to_center_distance:new a.aI(ue,w.u_camera_to_center_distance),u_pitch:new a.aI(ue,w.u_pitch),u_rotate_symbol:new a.aH(ue,w.u_rotate_symbol),u_aspect_ratio:new a.aI(ue,w.u_aspect_ratio),u_fade_change:new a.aI(ue,w.u_fade_change),u_matrix:new a.aJ(ue,w.u_matrix),u_label_plane_matrix:new a.aJ(ue,w.u_label_plane_matrix),u_coord_matrix:new a.aJ(ue,w.u_coord_matrix),u_is_text:new a.aH(ue,w.u_is_text),u_pitch_with_map:new a.aH(ue,w.u_pitch_with_map),u_is_along_line:new a.aH(ue,w.u_is_along_line),u_is_variable_anchor:new a.aH(ue,w.u_is_variable_anchor),u_texsize:new a.aO(ue,w.u_texsize),u_texture:new a.aH(ue,w.u_texture),u_gamma_scale:new a.aI(ue,w.u_gamma_scale),u_device_pixel_ratio:new a.aI(ue,w.u_device_pixel_ratio),u_is_halo:new a.aH(ue,w.u_is_halo),u_translation:new a.aO(ue,w.u_translation),u_pitched_scale:new a.aI(ue,w.u_pitched_scale)}),symbolTextAndIcon:(ue,w)=>({u_is_size_zoom_constant:new 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a.aH(ue,w.u_is_halo),u_translation:new a.aO(ue,w.u_translation),u_pitched_scale:new a.aI(ue,w.u_pitched_scale)}),background:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_opacity:new a.aI(ue,w.u_opacity),u_color:new a.aL(ue,w.u_color)}),backgroundPattern:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_opacity:new a.aI(ue,w.u_opacity),u_image:new a.aH(ue,w.u_image),u_pattern_tl_a:new a.aO(ue,w.u_pattern_tl_a),u_pattern_br_a:new a.aO(ue,w.u_pattern_br_a),u_pattern_tl_b:new a.aO(ue,w.u_pattern_tl_b),u_pattern_br_b:new a.aO(ue,w.u_pattern_br_b),u_texsize:new a.aO(ue,w.u_texsize),u_mix:new a.aI(ue,w.u_mix),u_pattern_size_a:new a.aO(ue,w.u_pattern_size_a),u_pattern_size_b:new a.aO(ue,w.u_pattern_size_b),u_scale_a:new a.aI(ue,w.u_scale_a),u_scale_b:new a.aI(ue,w.u_scale_b),u_pixel_coord_upper:new a.aO(ue,w.u_pixel_coord_upper),u_pixel_coord_lower:new a.aO(ue,w.u_pixel_coord_lower),u_tile_units_to_pixels:new a.aI(ue,w.u_tile_units_to_pixels)}),terrain:(ue,w)=>({u_matrix:new 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ee=w.gl;this.buffer=ee.createBuffer(),this.dynamicDraw=!!Q,this.context.unbindVAO(),w.bindElementBuffer.set(this.buffer),ee.bufferData(ee.ELEMENT_ARRAY_BUFFER,B.arrayBuffer,this.dynamicDraw?ee.DYNAMIC_DRAW:ee.STATIC_DRAW),this.dynamicDraw||delete B.arrayBuffer}bind(){this.context.bindElementBuffer.set(this.buffer)}updateData(w){let B=this.context.gl;if(!this.dynamicDraw)throw new Error(\"Attempted to update data while not in dynamic mode.\");this.context.unbindVAO(),this.bind(),B.bufferSubData(B.ELEMENT_ARRAY_BUFFER,0,w.arrayBuffer)}destroy(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)}}let Rn={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"};class _a{constructor(w,B,Q,ee){this.length=B.length,this.attributes=Q,this.itemSize=B.bytesPerElement,this.dynamicDraw=ee,this.context=w;let le=w.gl;this.buffer=le.createBuffer(),w.bindVertexBuffer.set(this.buffer),le.bufferData(le.ARRAY_BUFFER,B.arrayBuffer,this.dynamicDraw?le.DYNAMIC_DRAW:le.STATIC_DRAW),this.dynamicDraw||delete B.arrayBuffer}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(w){if(w.length!==this.length)throw new Error(`Length of new data is ${w.length}, which doesn't match current length of ${this.length}`);let B=this.context.gl;this.bind(),B.bufferSubData(B.ARRAY_BUFFER,0,w.arrayBuffer)}enableAttributes(w,B){for(let Q=0;Q<this.attributes.length;Q++){let ee=B.attributes[this.attributes[Q].name];ee!==void 0&&w.enableVertexAttribArray(ee)}}setVertexAttribPointers(w,B,Q){for(let ee=0;ee<this.attributes.length;ee++){let le=this.attributes[ee],Fe=B.attributes[le.name];Fe!==void 0&&w.vertexAttribPointer(Fe,le.components,w[Rn[le.type]],!1,this.itemSize,le.offset+this.itemSize*(Q||0))}}destroy(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)}}let Vu=new WeakMap;function zl(ue){var w;if(Vu.has(ue))return Vu.get(ue);{let B=(w=ue.getParameter(ue.VERSION))===null||w===void 0?void 0:w.startsWith(\"WebGL 2.0\");return Vu.set(ue,B),B}}class xo{constructor(w){this.gl=w.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1}get(){return this.current}set(w){}getDefault(){return this.default}setDefault(){this.set(this.default)}}class Kl extends xo{getDefault(){return a.aM.transparent}set(w){let B=this.current;(w.r!==B.r||w.g!==B.g||w.b!==B.b||w.a!==B.a||this.dirty)&&(this.gl.clearColor(w.r,w.g,w.b,w.a),this.current=w,this.dirty=!1)}}class Ns extends xo{getDefault(){return 1}set(w){(w!==this.current||this.dirty)&&(this.gl.clearDepth(w),this.current=w,this.dirty=!1)}}class Hl extends xo{getDefault(){return 0}set(w){(w!==this.current||this.dirty)&&(this.gl.clearStencil(w),this.current=w,this.dirty=!1)}}class ac extends xo{getDefault(){return[!0,!0,!0,!0]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||w[3]!==B[3]||this.dirty)&&(this.gl.colorMask(w[0],w[1],w[2],w[3]),this.current=w,this.dirty=!1)}}class oa extends xo{getDefault(){return!0}set(w){(w!==this.current||this.dirty)&&(this.gl.depthMask(w),this.current=w,this.dirty=!1)}}class qo extends xo{getDefault(){return 255}set(w){(w!==this.current||this.dirty)&&(this.gl.stencilMask(w),this.current=w,this.dirty=!1)}}class Oo extends xo{getDefault(){return{func:this.gl.ALWAYS,ref:0,mask:255}}set(w){let B=this.current;(w.func!==B.func||w.ref!==B.ref||w.mask!==B.mask||this.dirty)&&(this.gl.stencilFunc(w.func,w.ref,w.mask),this.current=w,this.dirty=!1)}}class ql extends xo{getDefault(){let w=this.gl;return[w.KEEP,w.KEEP,w.KEEP]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||this.dirty)&&(this.gl.stencilOp(w[0],w[1],w[2]),this.current=w,this.dirty=!1)}}class Pc extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.STENCIL_TEST):B.disable(B.STENCIL_TEST),this.current=w,this.dirty=!1}}class Do extends xo{getDefault(){return[0,1]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||this.dirty)&&(this.gl.depthRange(w[0],w[1]),this.current=w,this.dirty=!1)}}class nf extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.DEPTH_TEST):B.disable(B.DEPTH_TEST),this.current=w,this.dirty=!1}}class Vf extends xo{getDefault(){return this.gl.LESS}set(w){(w!==this.current||this.dirty)&&(this.gl.depthFunc(w),this.current=w,this.dirty=!1)}}class pl extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.BLEND):B.disable(B.BLEND),this.current=w,this.dirty=!1}}class Zc extends xo{getDefault(){let w=this.gl;return[w.ONE,w.ZERO]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||this.dirty)&&(this.gl.blendFunc(w[0],w[1]),this.current=w,this.dirty=!1)}}class Jl extends xo{getDefault(){return a.aM.transparent}set(w){let B=this.current;(w.r!==B.r||w.g!==B.g||w.b!==B.b||w.a!==B.a||this.dirty)&&(this.gl.blendColor(w.r,w.g,w.b,w.a),this.current=w,this.dirty=!1)}}class qs extends xo{getDefault(){return this.gl.FUNC_ADD}set(w){(w!==this.current||this.dirty)&&(this.gl.blendEquation(w),this.current=w,this.dirty=!1)}}class yu extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.CULL_FACE):B.disable(B.CULL_FACE),this.current=w,this.dirty=!1}}class oc extends xo{getDefault(){return this.gl.BACK}set(w){(w!==this.current||this.dirty)&&(this.gl.cullFace(w),this.current=w,this.dirty=!1)}}class Cf extends xo{getDefault(){return this.gl.CCW}set(w){(w!==this.current||this.dirty)&&(this.gl.frontFace(w),this.current=w,this.dirty=!1)}}class sc extends xo{getDefault(){return null}set(w){(w!==this.current||this.dirty)&&(this.gl.useProgram(w),this.current=w,this.dirty=!1)}}class jh extends xo{getDefault(){return this.gl.TEXTURE0}set(w){(w!==this.current||this.dirty)&&(this.gl.activeTexture(w),this.current=w,this.dirty=!1)}}class Lf extends xo{getDefault(){let w=this.gl;return[0,0,w.drawingBufferWidth,w.drawingBufferHeight]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||w[3]!==B[3]||this.dirty)&&(this.gl.viewport(w[0],w[1],w[2],w[3]),this.current=w,this.dirty=!1)}}class cs extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindFramebuffer(B.FRAMEBUFFER,w),this.current=w,this.dirty=!1}}class af extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindRenderbuffer(B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class Gf extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindTexture(B.TEXTURE_2D,w),this.current=w,this.dirty=!1}}class $l extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindBuffer(B.ARRAY_BUFFER,w),this.current=w,this.dirty=!1}}class fl extends xo{getDefault(){return null}set(w){let B=this.gl;B.bindBuffer(B.ELEMENT_ARRAY_BUFFER,w),this.current=w,this.dirty=!1}}class lc extends xo{getDefault(){return null}set(w){var B;if(w===this.current&&!this.dirty)return;let Q=this.gl;zl(Q)?Q.bindVertexArray(w):(B=Q.getExtension(\"OES_vertex_array_object\"))===null||B===void 0||B.bindVertexArrayOES(w),this.current=w,this.dirty=!1}}class Fu extends xo{getDefault(){return 4}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_ALIGNMENT,w),this.current=w,this.dirty=!1}}class Es extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_PREMULTIPLY_ALPHA_WEBGL,w),this.current=w,this.dirty=!1}}class Hs extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_FLIP_Y_WEBGL,w),this.current=w,this.dirty=!1}}class Go extends xo{constructor(w,B){super(w),this.context=w,this.parent=B}getDefault(){return null}}class ps extends Go{setDirty(){this.dirty=!0}set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferTexture2D(B.FRAMEBUFFER,B.COLOR_ATTACHMENT0,B.TEXTURE_2D,w,0),this.current=w,this.dirty=!1}}class uc extends Go{set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferRenderbuffer(B.FRAMEBUFFER,B.DEPTH_ATTACHMENT,B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class bl extends Go{set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferRenderbuffer(B.FRAMEBUFFER,B.DEPTH_STENCIL_ATTACHMENT,B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class Gu{constructor(w,B,Q,ee,le){this.context=w,this.width=B,this.height=Q;let Fe=w.gl,Ze=this.framebuffer=Fe.createFramebuffer();if(this.colorAttachment=new ps(w,Ze),ee)this.depthAttachment=le?new bl(w,Ze):new uc(w,Ze);else if(le)throw new Error(\"Stencil cannot be set without depth\");if(Fe.checkFramebufferStatus(Fe.FRAMEBUFFER)!==Fe.FRAMEBUFFER_COMPLETE)throw new Error(\"Framebuffer is not complete\")}destroy(){let w=this.context.gl,B=this.colorAttachment.get();if(B&&w.deleteTexture(B),this.depthAttachment){let Q=this.depthAttachment.get();Q&&w.deleteRenderbuffer(Q)}w.deleteFramebuffer(this.framebuffer)}}class Os{constructor(w,B,Q){this.blendFunction=w,this.blendColor=B,this.mask=Q}}Os.Replace=[1,0],Os.disabled=new Os(Os.Replace,a.aM.transparent,[!1,!1,!1,!1]),Os.unblended=new Os(Os.Replace,a.aM.transparent,[!0,!0,!0,!0]),Os.alphaBlended=new Os([1,771],a.aM.transparent,[!0,!0,!0,!0]);class od{constructor(w){var B,Q;if(this.gl=w,this.clearColor=new Kl(this),this.clearDepth=new Ns(this),this.clearStencil=new Hl(this),this.colorMask=new ac(this),this.depthMask=new oa(this),this.stencilMask=new qo(this),this.stencilFunc=new Oo(this),this.stencilOp=new ql(this),this.stencilTest=new Pc(this),this.depthRange=new Do(this),this.depthTest=new nf(this),this.depthFunc=new Vf(this),this.blend=new pl(this),this.blendFunc=new Zc(this),this.blendColor=new Jl(this),this.blendEquation=new qs(this),this.cullFace=new yu(this),this.cullFaceSide=new oc(this),this.frontFace=new Cf(this),this.program=new sc(this),this.activeTexture=new jh(this),this.viewport=new Lf(this),this.bindFramebuffer=new cs(this),this.bindRenderbuffer=new af(this),this.bindTexture=new Gf(this),this.bindVertexBuffer=new $l(this),this.bindElementBuffer=new fl(this),this.bindVertexArray=new lc(this),this.pixelStoreUnpack=new Fu(this),this.pixelStoreUnpackPremultiplyAlpha=new Es(this),this.pixelStoreUnpackFlipY=new Hs(this),this.extTextureFilterAnisotropic=w.getExtension(\"EXT_texture_filter_anisotropic\")||w.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||w.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=w.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.maxTextureSize=w.getParameter(w.MAX_TEXTURE_SIZE),zl(w)){this.HALF_FLOAT=w.HALF_FLOAT;let ee=w.getExtension(\"EXT_color_buffer_half_float\");this.RGBA16F=(B=w.RGBA16F)!==null&&B!==void 0?B:ee==null?void 0:ee.RGBA16F_EXT,this.RGB16F=(Q=w.RGB16F)!==null&&Q!==void 0?Q:ee==null?void 0:ee.RGB16F_EXT,w.getExtension(\"EXT_color_buffer_float\")}else{w.getExtension(\"EXT_color_buffer_half_float\"),w.getExtension(\"OES_texture_half_float_linear\");let ee=w.getExtension(\"OES_texture_half_float\");this.HALF_FLOAT=ee==null?void 0:ee.HALF_FLOAT_OES}}setDefault(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()}setDirty(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.bindVertexArray.dirty=!0,this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0}createIndexBuffer(w,B){return new bs(this,w,B)}createVertexBuffer(w,B,Q){return new _a(this,w,B,Q)}createRenderbuffer(w,B,Q){let ee=this.gl,le=ee.createRenderbuffer();return this.bindRenderbuffer.set(le),ee.renderbufferStorage(ee.RENDERBUFFER,w,B,Q),this.bindRenderbuffer.set(null),le}createFramebuffer(w,B,Q,ee){return new Gu(this,w,B,Q,ee)}clear({color:w,depth:B,stencil:Q}){let ee=this.gl,le=0;w&&(le|=ee.COLOR_BUFFER_BIT,this.clearColor.set(w),this.colorMask.set([!0,!0,!0,!0])),B!==void 0&&(le|=ee.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(B),this.depthMask.set(!0)),Q!==void 0&&(le|=ee.STENCIL_BUFFER_BIT,this.clearStencil.set(Q),this.stencilMask.set(255)),ee.clear(le)}setCullFace(w){w.enable===!1?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(w.mode),this.frontFace.set(w.frontFace))}setDepthMode(w){w.func!==this.gl.ALWAYS||w.mask?(this.depthTest.set(!0),this.depthFunc.set(w.func),this.depthMask.set(w.mask),this.depthRange.set(w.range)):this.depthTest.set(!1)}setStencilMode(w){w.test.func!==this.gl.ALWAYS||w.mask?(this.stencilTest.set(!0),this.stencilMask.set(w.mask),this.stencilOp.set([w.fail,w.depthFail,w.pass]),this.stencilFunc.set({func:w.test.func,ref:w.ref,mask:w.test.mask})):this.stencilTest.set(!1)}setColorMode(w){a.aE(w.blendFunction,Os.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(w.blendFunction),this.blendColor.set(w.blendColor)),this.colorMask.set(w.mask)}createVertexArray(){var w;return zl(this.gl)?this.gl.createVertexArray():(w=this.gl.getExtension(\"OES_vertex_array_object\"))===null||w===void 0?void 0:w.createVertexArrayOES()}deleteVertexArray(w){var B;return zl(this.gl)?this.gl.deleteVertexArray(w):(B=this.gl.getExtension(\"OES_vertex_array_object\"))===null||B===void 0?void 0:B.deleteVertexArrayOES(w)}unbindVAO(){this.bindVertexArray.set(null)}}class Po{constructor(w,B,Q){this.func=w,this.mask=B,this.range=Q}}Po.ReadOnly=!1,Po.ReadWrite=!0,Po.disabled=new Po(519,Po.ReadOnly,[0,1]);let sd=7680;class Ko{constructor(w,B,Q,ee,le,Fe){this.test=w,this.ref=B,this.mask=Q,this.fail=ee,this.depthFail=le,this.pass=Fe}}Ko.disabled=new Ko({func:519,mask:0},0,0,sd,sd,sd);class Pa{constructor(w,B,Q){this.enable=w,this.mode=B,this.frontFace=Q}}let of;function Hu(ue,w,B,Q,ee){let le=ue.context,Fe=le.gl,Ze=ue.useProgram(\"collisionBox\"),ut=[],Mt=0,Jt=0;for(let ze=0;ze<Q.length;ze++){let Qe=Q[ze],Xe=w.getTile(Qe).getBucket(B);if(!Xe)continue;let nt=ee?Xe.textCollisionBox:Xe.iconCollisionBox,qt=Xe.collisionCircleArray;if(qt.length>0){let Wt=a.H();a.aQ(Wt,Xe.placementInvProjMatrix,ue.transform.glCoordMatrix),a.aQ(Wt,Wt,Xe.placementViewportMatrix),ut.push({circleArray:qt,circleOffset:Jt,transform:Qe.posMatrix,invTransform:Wt,coord:Qe}),Mt+=qt.length/4,Jt=Mt}nt&&Ze.draw(le,Fe.LINES,Po.disabled,Ko.disabled,ue.colorModeForRenderPass(),Pa.disabled,{u_matrix:Qe.posMatrix,u_pixel_extrude_scale:[1/($t=ue.transform).width,1/$t.height]},ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(Qe),B.id,nt.layoutVertexBuffer,nt.indexBuffer,nt.segments,null,ue.transform.zoom,null,null,nt.collisionVertexBuffer)}var $t;if(!ee||!ut.length)return;let yr=ue.useProgram(\"collisionCircle\"),Cr=new a.aR;Cr.resize(4*Mt),Cr._trim();let ve=0;for(let ze of ut)for(let Qe=0;Qe<ze.circleArray.length/4;Qe++){let Xe=4*Qe,nt=ze.circleArray[Xe+0],qt=ze.circleArray[Xe+1],Wt=ze.circleArray[Xe+2],nr=ze.circleArray[Xe+3];Cr.emplace(ve++,nt,qt,Wt,nr,0),Cr.emplace(ve++,nt,qt,Wt,nr,1),Cr.emplace(ve++,nt,qt,Wt,nr,2),Cr.emplace(ve++,nt,qt,Wt,nr,3)}(!of||of.length<2*Mt)&&(of=(function(ze){let Qe=2*ze,Xe=new a.aT;Xe.resize(Qe),Xe._trim();for(let nt=0;nt<Qe;nt++){let qt=6*nt;Xe.uint16[qt+0]=4*nt+0,Xe.uint16[qt+1]=4*nt+1,Xe.uint16[qt+2]=4*nt+2,Xe.uint16[qt+3]=4*nt+2,Xe.uint16[qt+4]=4*nt+3,Xe.uint16[qt+5]=4*nt+0}return Xe})(Mt));let xe=le.createIndexBuffer(of,!0),Ie=le.createVertexBuffer(Cr,a.aS.members,!0);for(let ze of ut){let Qe=No(ze.transform,ze.invTransform,ue.transform);yr.draw(le,Fe.TRIANGLES,Po.disabled,Ko.disabled,ue.colorModeForRenderPass(),Pa.disabled,Qe,ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(ze.coord),B.id,Ie,xe,a.a0.simpleSegment(0,2*ze.circleOffset,ze.circleArray.length,ze.circleArray.length/2),null,ue.transform.zoom,null,null,null)}Ie.destroy(),xe.destroy()}Pa.disabled=new Pa(!1,1029,2305),Pa.backCCW=new Pa(!0,1029,2305);let wl=a.an(new Float32Array(16));function Hf(ue,w,B,Q,ee,le){let{horizontalAlign:Fe,verticalAlign:Ze}=a.au(ue);return new a.P((-(Fe-.5)*w/ee+Q[0])*le,(-(Ze-.5)*B/ee+Q[1])*le)}function Ic(ue,w,B,Q,ee,le){let Fe=w.tileAnchorPoint.add(new a.P(w.translation[0],w.translation[1]));if(w.pitchWithMap){let Ze=Q.mult(le);B||(Ze=Ze.rotate(-ee));let ut=Fe.add(Ze);return gt(ut.x,ut.y,w.labelPlaneMatrix,w.getElevation).point}if(B){let Ze=Mr(w.tileAnchorPoint.x+1,w.tileAnchorPoint.y,w).point.sub(ue),ut=Math.atan(Ze.y/Ze.x)+(Ze.x<0?Math.PI:0);return ue.add(Q.rotate(ut))}return ue.add(Q)}function yf(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr){let ve=ue.text.placedSymbolArray,xe=ue.text.dynamicLayoutVertexArray,Ie=ue.icon.dynamicLayoutVertexArray,ze={};xe.clear();for(let Qe=0;Qe<ve.length;Qe++){let Xe=ve.get(Qe),nt=Xe.hidden||!Xe.crossTileID||ue.allowVerticalPlacement&&!Xe.placedOrientation?null:Q[Xe.crossTileID];if(nt){let qt=new a.P(Xe.anchorX,Xe.anchorY),Wt={getElevation:Cr,width:ee.width,height:ee.height,labelPlaneMatrix:le,lineVertexArray:null,pitchWithMap:B,projection:Jt,projectionCache:null,tileAnchorPoint:qt,translation:$t,unwrappedTileID:yr},nr=B?gt(qt.x,qt.y,Fe,Cr):Mr(qt.x,qt.y,Wt),fr=Ge(ee.cameraToCenterDistance,nr.signedDistanceFromCamera),br=a.ai(ue.textSizeData,ut,Xe)*fr/a.ap;B&&(br*=ue.tilePixelRatio/Ze);let{width:Fr,height:Hr,anchor:ri,textOffset:Ci,textBoxScale:cn}=nt,sn=Hf(ri,Fr,Hr,Ci,cn,br),Ln=Jt.getPitchedTextCorrection(ee,qt.add(new a.P($t[0],$t[1])),yr),Kn=Ic(nr.point,Wt,w,sn,ee.angle,Ln),Aa=ue.allowVerticalPlacement&&Xe.placedOrientation===a.ah.vertical?Math.PI/2:0;for(let fa=0;fa<Xe.numGlyphs;fa++)a.aj(xe,Kn,Aa);Mt&&Xe.associatedIconIndex>=0&&(ze[Xe.associatedIconIndex]={shiftedAnchor:Kn,angle:Aa})}else Pi(Xe.numGlyphs,xe)}if(Mt){Ie.clear();let Qe=ue.icon.placedSymbolArray;for(let Xe=0;Xe<Qe.length;Xe++){let nt=Qe.get(Xe);if(nt.hidden)Pi(nt.numGlyphs,Ie);else{let qt=ze[Xe];if(qt)for(let Wt=0;Wt<nt.numGlyphs;Wt++)a.aj(Ie,qt.shiftedAnchor,qt.angle);else Pi(nt.numGlyphs,Ie)}}ue.icon.dynamicLayoutVertexBuffer.updateData(Ie)}ue.text.dynamicLayoutVertexBuffer.updateData(xe)}function Ol(ue,w,B){return B.iconsInText&&w?\"symbolTextAndIcon\":ue?\"symbolSDF\":\"symbolIcon\"}function Ah(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t){let yr=ue.context,Cr=yr.gl,ve=ue.transform,xe=yn(),Ie=Ze===\"map\",ze=ut===\"map\",Qe=Ze!==\"viewport\"&&B.layout.get(\"symbol-placement\")!==\"point\",Xe=Ie&&!ze&&!Qe,nt=!ze&&Qe,qt=!B.layout.get(\"symbol-sort-key\").isConstant(),Wt=!1,nr=ue.depthModeForSublayer(0,Po.ReadOnly),fr=B._unevaluatedLayout.hasValue(\"text-variable-anchor\")||B._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),br=[],Fr=xe.getCircleRadiusCorrection(ve);for(let Hr of Q){let ri=w.getTile(Hr),Ci=ri.getBucket(B);if(!Ci)continue;let cn=ee?Ci.text:Ci.icon;if(!cn||!cn.segments.get().length||!cn.hasVisibleVertices)continue;let sn=cn.programConfigurations.get(B.id),Ln=ee||Ci.sdfIcons,Kn=ee?Ci.textSizeData:Ci.iconSizeData,Aa=ze||ve.pitch!==0,fa=ue.useProgram(Ol(Ln,ee,Ci),sn),$a=a.ag(Kn,ve.zoom),Co=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(Hr),Qa,mo,Bo,Ps,Ts=[0,0],wo=null;if(ee)mo=ri.glyphAtlasTexture,Bo=Cr.LINEAR,Qa=ri.glyphAtlasTexture.size,Ci.iconsInText&&(Ts=ri.imageAtlasTexture.size,wo=ri.imageAtlasTexture,Ps=Aa||ue.options.rotating||ue.options.zooming||Kn.kind===\"composite\"||Kn.kind===\"camera\"?Cr.LINEAR:Cr.NEAREST);else{let Ye=B.layout.get(\"icon-size\").constantOr(0)!==1||Ci.iconsNeedLinear;mo=ri.imageAtlasTexture,Bo=Ln||ue.options.rotating||ue.options.zooming||Ye||Aa?Cr.LINEAR:Cr.NEAREST,Qa=ri.imageAtlasTexture.size}let To=Fn(ri,1,ue.transform.zoom),hl=nt?Hr.posMatrix:wl,Nl=qr(hl,ze,Ie,ue.transform,To),Lu=jr(hl,ze,Ie,ue.transform,To),au=jr(Hr.posMatrix,ze,Ie,ue.transform,To),Js=xe.translatePosition(ue.transform,ri,le,Fe),eu=fr&&Ci.hasTextData(),dc=B.layout.get(\"icon-text-fit\")!==\"none\"&&eu&&Ci.hasIconData();if(Qe){let Ye=ue.style.map.terrain?(ot,Xt)=>ue.style.map.terrain.getElevation(Hr,ot,Xt):null,Pt=B.layout.get(\"text-rotation-alignment\")===\"map\";We(Ci,Hr.posMatrix,ue,ee,Nl,au,ze,Mt,Pt,xe,Hr.toUnwrapped(),ve.width,ve.height,Js,Ye)}let Al=Hr.posMatrix,Sl=ee&&fr||dc,X=Qe||Sl?wl:Nl,se=Lu,Te=Ln&&B.paint.get(ee?\"text-halo-width\":\"icon-halo-width\").constantOr(1)!==0,qe;qe=Ln?Ci.iconsInText?ls(Kn.kind,$a,Xe,ze,Qe,Sl,ue,Al,X,se,Js,Qa,Ts,Fr):Io(Kn.kind,$a,Xe,ze,Qe,Sl,ue,Al,X,se,Js,ee,Qa,!0,Fr):zs(Kn.kind,$a,Xe,ze,Qe,Sl,ue,Al,X,se,Js,ee,Qa,Fr);let Ve={program:fa,buffers:cn,uniformValues:qe,atlasTexture:mo,atlasTextureIcon:wo,atlasInterpolation:Bo,atlasInterpolationIcon:Ps,isSDF:Ln,hasHalo:Te};if(qt&&Ci.canOverlap){Wt=!0;let Ye=cn.segments.get();for(let Pt of Ye)br.push({segments:new a.a0([Pt]),sortKey:Pt.sortKey,state:Ve,terrainData:Co})}else br.push({segments:cn.segments,sortKey:0,state:Ve,terrainData:Co})}Wt&&br.sort(((Hr,ri)=>Hr.sortKey-ri.sortKey));for(let Hr of br){let ri=Hr.state;if(yr.activeTexture.set(Cr.TEXTURE0),ri.atlasTexture.bind(ri.atlasInterpolation,Cr.CLAMP_TO_EDGE),ri.atlasTextureIcon&&(yr.activeTexture.set(Cr.TEXTURE1),ri.atlasTextureIcon&&ri.atlasTextureIcon.bind(ri.atlasInterpolationIcon,Cr.CLAMP_TO_EDGE)),ri.isSDF){let Ci=ri.uniformValues;ri.hasHalo&&(Ci.u_is_halo=1,Qf(ri.buffers,Hr.segments,B,ue,ri.program,nr,Jt,$t,Ci,Hr.terrainData)),Ci.u_is_halo=0}Qf(ri.buffers,Hr.segments,B,ue,ri.program,nr,Jt,$t,ri.uniformValues,Hr.terrainData)}}function Qf(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt){let Jt=Q.context;ee.draw(Jt,Jt.gl.TRIANGLES,le,Fe,Ze,Pa.disabled,ut,Mt,B.id,ue.layoutVertexBuffer,ue.indexBuffer,w,B.paint,Q.transform.zoom,ue.programConfigurations.get(B.id),ue.dynamicLayoutVertexBuffer,ue.opacityVertexBuffer)}function _f(ue,w,B,Q){let ee=ue.context,le=ee.gl,Fe=Ko.disabled,Ze=new Os([le.ONE,le.ONE],a.aM.transparent,[!0,!0,!0,!0]),ut=w.getBucket(B);if(!ut)return;let Mt=Q.key,Jt=B.heatmapFbos.get(Mt);Jt||(Jt=eh(ee,w.tileSize,w.tileSize),B.heatmapFbos.set(Mt,Jt)),ee.bindFramebuffer.set(Jt.framebuffer),ee.viewport.set([0,0,w.tileSize,w.tileSize]),ee.clear({color:a.aM.transparent});let $t=ut.programConfigurations.get(B.id),yr=ue.useProgram(\"heatmap\",$t),Cr=ue.style.map.terrain.getTerrainData(Q);yr.draw(ee,le.TRIANGLES,Po.disabled,Fe,Ze,Pa.disabled,ko(Q.posMatrix,w,ue.transform.zoom,B.paint.get(\"heatmap-intensity\")),Cr,B.id,ut.layoutVertexBuffer,ut.indexBuffer,ut.segments,B.paint,ue.transform.zoom,$t)}function Yc(ue,w,B){let Q=ue.context,ee=Q.gl;Q.setColorMode(ue.colorModeForRenderPass());let le=th(Q,w),Fe=B.key,Ze=w.heatmapFbos.get(Fe);Ze&&(Q.activeTexture.set(ee.TEXTURE0),ee.bindTexture(ee.TEXTURE_2D,Ze.colorAttachment.get()),Q.activeTexture.set(ee.TEXTURE1),le.bind(ee.LINEAR,ee.CLAMP_TO_EDGE),ue.useProgram(\"heatmapTexture\").draw(Q,ee.TRIANGLES,Po.disabled,Ko.disabled,ue.colorModeForRenderPass(),Pa.disabled,Ds(ue,w,0,1),null,w.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments,w.paint,ue.transform.zoom),Ze.destroy(),w.heatmapFbos.delete(Fe))}function eh(ue,w,B){var Q,ee;let le=ue.gl,Fe=le.createTexture();le.bindTexture(le.TEXTURE_2D,Fe),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_S,le.CLAMP_TO_EDGE),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_T,le.CLAMP_TO_EDGE),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MIN_FILTER,le.LINEAR),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MAG_FILTER,le.LINEAR);let Ze=(Q=ue.HALF_FLOAT)!==null&&Q!==void 0?Q:le.UNSIGNED_BYTE,ut=(ee=ue.RGBA16F)!==null&&ee!==void 0?ee:le.RGBA;le.texImage2D(le.TEXTURE_2D,0,ut,w,B,0,le.RGBA,Ze,null);let Mt=ue.createFramebuffer(w,B,!1,!1);return Mt.colorAttachment.set(Fe),Mt}function th(ue,w){return w.colorRampTexture||(w.colorRampTexture=new g(ue,w.colorRamp,ue.gl.RGBA)),w.colorRampTexture}function ju(ue,w,B,Q,ee){if(!B||!Q||!Q.imageAtlas)return;let le=Q.imageAtlas.patternPositions,Fe=le[B.to.toString()],Ze=le[B.from.toString()];if(!Fe&&Ze&&(Fe=Ze),!Ze&&Fe&&(Ze=Fe),!Fe||!Ze){let ut=ee.getPaintProperty(w);Fe=le[ut],Ze=le[ut]}Fe&&Ze&&ue.setConstantPatternPositions(Fe,Ze)}function jf(ue,w,B,Q,ee,le,Fe){let Ze=ue.context.gl,ut=\"fill-pattern\",Mt=B.paint.get(ut),Jt=Mt&&Mt.constantOr(1),$t=B.getCrossfadeParameters(),yr,Cr,ve,xe,Ie;Fe?(Cr=Jt&&!B.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",yr=Ze.LINES):(Cr=Jt?\"fillPattern\":\"fill\",yr=Ze.TRIANGLES);let ze=Mt.constantOr(null);for(let Qe of Q){let Xe=w.getTile(Qe);if(Jt&&!Xe.patternsLoaded())continue;let nt=Xe.getBucket(B);if(!nt)continue;let qt=nt.programConfigurations.get(B.id),Wt=ue.useProgram(Cr,qt),nr=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(Qe);Jt&&(ue.context.activeTexture.set(Ze.TEXTURE0),Xe.imageAtlasTexture.bind(Ze.LINEAR,Ze.CLAMP_TO_EDGE),qt.updatePaintBuffers($t)),ju(qt,ut,ze,Xe,B);let fr=nr?Qe:null,br=ue.translatePosMatrix(fr?fr.posMatrix:Qe.posMatrix,Xe,B.paint.get(\"fill-translate\"),B.paint.get(\"fill-translate-anchor\"));if(Fe){xe=nt.indexBuffer2,Ie=nt.segments2;let Fr=[Ze.drawingBufferWidth,Ze.drawingBufferHeight];ve=Cr===\"fillOutlinePattern\"&&Jt?Ma(br,ue,$t,Xe,Fr):ea(br,Fr)}else xe=nt.indexBuffer,Ie=nt.segments,ve=Jt?ya(br,ue,$t,Xe):_n(br);Wt.draw(ue.context,yr,ee,ue.stencilModeForClipping(Qe),le,Pa.disabled,ve,nr,B.id,nt.layoutVertexBuffer,xe,Ie,B.paint,ue.transform.zoom,qt)}}function cc(ue,w,B,Q,ee,le,Fe){let Ze=ue.context,ut=Ze.gl,Mt=\"fill-extrusion-pattern\",Jt=B.paint.get(Mt),$t=Jt.constantOr(1),yr=B.getCrossfadeParameters(),Cr=B.paint.get(\"fill-extrusion-opacity\"),ve=Jt.constantOr(null);for(let xe of Q){let Ie=w.getTile(xe),ze=Ie.getBucket(B);if(!ze)continue;let Qe=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(xe),Xe=ze.programConfigurations.get(B.id),nt=ue.useProgram($t?\"fillExtrusionPattern\":\"fillExtrusion\",Xe);$t&&(ue.context.activeTexture.set(ut.TEXTURE0),Ie.imageAtlasTexture.bind(ut.LINEAR,ut.CLAMP_TO_EDGE),Xe.updatePaintBuffers(yr)),ju(Xe,Mt,ve,Ie,B);let qt=ue.translatePosMatrix(xe.posMatrix,Ie,B.paint.get(\"fill-extrusion-translate\"),B.paint.get(\"fill-extrusion-translate-anchor\")),Wt=B.paint.get(\"fill-extrusion-vertical-gradient\"),nr=$t?ki(qt,ue,Wt,Cr,xe,yr,Ie):En(qt,ue,Wt,Cr);nt.draw(Ze,Ze.gl.TRIANGLES,ee,le,Fe,Pa.backCCW,nr,Qe,B.id,ze.layoutVertexBuffer,ze.indexBuffer,ze.segments,B.paint,ue.transform.zoom,Xe,ue.style.map.terrain&&ze.centroidVertexBuffer)}}function sf(ue,w,B,Q,ee,le,Fe){let Ze=ue.context,ut=Ze.gl,Mt=B.fbo;if(!Mt)return;let Jt=ue.useProgram(\"hillshade\"),$t=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(w);Ze.activeTexture.set(ut.TEXTURE0),ut.bindTexture(ut.TEXTURE_2D,Mt.colorAttachment.get()),Jt.draw(Ze,ut.TRIANGLES,ee,le,Fe,Pa.disabled,((yr,Cr,ve,xe)=>{let Ie=ve.paint.get(\"hillshade-shadow-color\"),ze=ve.paint.get(\"hillshade-highlight-color\"),Qe=ve.paint.get(\"hillshade-accent-color\"),Xe=ve.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);ve.paint.get(\"hillshade-illumination-anchor\")===\"viewport\"&&(Xe-=yr.transform.angle);let nt=!yr.options.moving;return{u_matrix:xe?xe.posMatrix:yr.transform.calculatePosMatrix(Cr.tileID.toUnwrapped(),nt),u_image:0,u_latrange:Fs(0,Cr.tileID),u_light:[ve.paint.get(\"hillshade-exaggeration\"),Xe],u_shadow:Ie,u_highlight:ze,u_accent:Qe}})(ue,B,Q,$t?w:null),$t,Q.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments)}function Bl(ue,w,B,Q,ee,le){let Fe=ue.context,Ze=Fe.gl,ut=w.dem;if(ut&&ut.data){let Mt=ut.dim,Jt=ut.stride,$t=ut.getPixels();if(Fe.activeTexture.set(Ze.TEXTURE1),Fe.pixelStoreUnpackPremultiplyAlpha.set(!1),w.demTexture=w.demTexture||ue.getTileTexture(Jt),w.demTexture){let Cr=w.demTexture;Cr.update($t,{premultiply:!1}),Cr.bind(Ze.NEAREST,Ze.CLAMP_TO_EDGE)}else w.demTexture=new g(Fe,$t,Ze.RGBA,{premultiply:!1}),w.demTexture.bind(Ze.NEAREST,Ze.CLAMP_TO_EDGE);Fe.activeTexture.set(Ze.TEXTURE0);let yr=w.fbo;if(!yr){let Cr=new g(Fe,{width:Mt,height:Mt,data:null},Ze.RGBA);Cr.bind(Ze.LINEAR,Ze.CLAMP_TO_EDGE),yr=w.fbo=Fe.createFramebuffer(Mt,Mt,!0,!1),yr.colorAttachment.set(Cr.texture)}Fe.bindFramebuffer.set(yr.framebuffer),Fe.viewport.set([0,0,Mt,Mt]),ue.useProgram(\"hillshadePrepare\").draw(Fe,Ze.TRIANGLES,Q,ee,le,Pa.disabled,((Cr,ve)=>{let xe=ve.stride,Ie=a.H();return a.aP(Ie,0,a.X,-a.X,0,0,1),a.J(Ie,Ie,[0,-a.X,0]),{u_matrix:Ie,u_image:1,u_dimension:[xe,xe],u_zoom:Cr.overscaledZ,u_unpack:ve.getUnpackVector()}})(w.tileID,ut),null,B.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments),w.needsHillshadePrepare=!1}}function Kc(ue,w,B,Q,ee,le){let Fe=Q.paint.get(\"raster-fade-duration\");if(!le&&Fe>0){let Ze=u.now(),ut=(Ze-ue.timeAdded)/Fe,Mt=w?(Ze-w.timeAdded)/Fe:-1,Jt=B.getSource(),$t=ee.coveringZoomLevel({tileSize:Jt.tileSize,roundZoom:Jt.roundZoom}),yr=!w||Math.abs(w.tileID.overscaledZ-$t)>Math.abs(ue.tileID.overscaledZ-$t),Cr=yr&&ue.refreshedUponExpiration?1:a.ac(yr?ut:1-Mt,0,1);return ue.refreshedUponExpiration&&ut>=1&&(ue.refreshedUponExpiration=!1),w?{opacity:1,mix:1-Cr}:{opacity:Cr,mix:0}}return{opacity:1,mix:0}}let Rc=new a.aM(1,0,0,1),gs=new a.aM(0,1,0,1),Wf=new a.aM(0,0,1,1),Wh=new a.aM(1,0,1,1),rh=new a.aM(0,1,1,1);function lf(ue,w,B,Q){Mu(ue,0,w+B/2,ue.transform.width,B,Q)}function Sh(ue,w,B,Q){Mu(ue,w-B/2,0,B,ue.transform.height,Q)}function Mu(ue,w,B,Q,ee,le){let Fe=ue.context,Ze=Fe.gl;Ze.enable(Ze.SCISSOR_TEST),Ze.scissor(w*ue.pixelRatio,B*ue.pixelRatio,Q*ue.pixelRatio,ee*ue.pixelRatio),Fe.clear({color:le}),Ze.disable(Ze.SCISSOR_TEST)}function ih(ue,w,B){let Q=ue.context,ee=Q.gl,le=B.posMatrix,Fe=ue.useProgram(\"debug\"),Ze=Po.disabled,ut=Ko.disabled,Mt=ue.colorModeForRenderPass(),Jt=\"$debug\",$t=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(B);Q.activeTexture.set(ee.TEXTURE0);let yr=w.getTileByID(B.key).latestRawTileData,Cr=Math.floor((yr&&yr.byteLength||0)/1024),ve=w.getTile(B).tileSize,xe=512/Math.min(ve,512)*(B.overscaledZ/ue.transform.zoom)*.5,Ie=B.canonical.toString();B.overscaledZ!==B.canonical.z&&(Ie+=` => ${B.overscaledZ}`),(function(ze,Qe){ze.initDebugOverlayCanvas();let Xe=ze.debugOverlayCanvas,nt=ze.context.gl,qt=ze.debugOverlayCanvas.getContext(\"2d\");qt.clearRect(0,0,Xe.width,Xe.height),qt.shadowColor=\"white\",qt.shadowBlur=2,qt.lineWidth=1.5,qt.strokeStyle=\"white\",qt.textBaseline=\"top\",qt.font=\"bold 36px Open Sans, sans-serif\",qt.fillText(Qe,5,5),qt.strokeText(Qe,5,5),ze.debugOverlayTexture.update(Xe),ze.debugOverlayTexture.bind(nt.LINEAR,nt.CLAMP_TO_EDGE)})(ue,`${Ie} ${Cr}kB`),Fe.draw(Q,ee.TRIANGLES,Ze,ut,Os.alphaBlended,Pa.disabled,po(le,a.aM.transparent,xe),null,Jt,ue.debugBuffer,ue.quadTriangleIndexBuffer,ue.debugSegments),Fe.draw(Q,ee.LINE_STRIP,Ze,ut,Mt,Pa.disabled,po(le,a.aM.red),$t,Jt,ue.debugBuffer,ue.tileBorderIndexBuffer,ue.debugSegments)}function js(ue,w,B){let Q=ue.context,ee=Q.gl,le=ue.colorModeForRenderPass(),Fe=new Po(ee.LEQUAL,Po.ReadWrite,ue.depthRangeFor3D),Ze=ue.useProgram(\"terrain\"),ut=w.getTerrainMesh();Q.bindFramebuffer.set(null),Q.viewport.set([0,0,ue.width,ue.height]);for(let Mt of B){let Jt=ue.renderToTexture.getTexture(Mt),$t=w.getTerrainData(Mt.tileID);Q.activeTexture.set(ee.TEXTURE0),ee.bindTexture(ee.TEXTURE_2D,Jt.texture);let yr=ue.transform.calculatePosMatrix(Mt.tileID.toUnwrapped()),Cr=w.getMeshFrameDelta(ue.transform.zoom),ve=ue.transform.calculateFogMatrix(Mt.tileID.toUnwrapped()),xe=Jr(yr,Cr,ve,ue.style.sky,ue.transform.pitch);Ze.draw(Q,ee.TRIANGLES,Fe,Ko.disabled,le,Pa.backCCW,xe,$t,\"terrain\",ut.vertexBuffer,ut.indexBuffer,ut.segments)}}class Eu{constructor(w,B,Q){this.vertexBuffer=w,this.indexBuffer=B,this.segments=Q}destroy(){this.vertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.vertexBuffer=null,this.indexBuffer=null,this.segments=null}}class Dc{constructor(w,B){this.context=new od(w),this.transform=B,this._tileTextures={},this.terrainFacilitator={dirty:!0,matrix:a.an(new Float64Array(16)),renderTime:0},this.setup(),this.numSublayers=pt.maxUnderzooming+pt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Xo}resize(w,B,Q){if(this.width=Math.floor(w*Q),this.height=Math.floor(B*Q),this.pixelRatio=Q,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(let ee of this.style._order)this.style._layers[ee].resize()}setup(){let w=this.context,B=new a.aX;B.emplaceBack(0,0),B.emplaceBack(a.X,0),B.emplaceBack(0,a.X),B.emplaceBack(a.X,a.X),this.tileExtentBuffer=w.createVertexBuffer(B,vo.members),this.tileExtentSegments=a.a0.simpleSegment(0,0,4,2);let Q=new a.aX;Q.emplaceBack(0,0),Q.emplaceBack(a.X,0),Q.emplaceBack(0,a.X),Q.emplaceBack(a.X,a.X),this.debugBuffer=w.createVertexBuffer(Q,vo.members),this.debugSegments=a.a0.simpleSegment(0,0,4,5);let ee=new a.$;ee.emplaceBack(0,0,0,0),ee.emplaceBack(a.X,0,a.X,0),ee.emplaceBack(0,a.X,0,a.X),ee.emplaceBack(a.X,a.X,a.X,a.X),this.rasterBoundsBuffer=w.createVertexBuffer(ee,ct.members),this.rasterBoundsSegments=a.a0.simpleSegment(0,0,4,2);let le=new a.aX;le.emplaceBack(0,0),le.emplaceBack(1,0),le.emplaceBack(0,1),le.emplaceBack(1,1),this.viewportBuffer=w.createVertexBuffer(le,vo.members),this.viewportSegments=a.a0.simpleSegment(0,0,4,2);let Fe=new a.aZ;Fe.emplaceBack(0),Fe.emplaceBack(1),Fe.emplaceBack(3),Fe.emplaceBack(2),Fe.emplaceBack(0),this.tileBorderIndexBuffer=w.createIndexBuffer(Fe);let Ze=new a.aY;Ze.emplaceBack(0,1,2),Ze.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=w.createIndexBuffer(Ze);let ut=this.context.gl;this.stencilClearMode=new Ko({func:ut.ALWAYS,mask:0},0,255,ut.ZERO,ut.ZERO,ut.ZERO)}clearStencil(){let w=this.context,B=w.gl;this.nextStencilID=1,this.currentStencilSource=void 0;let Q=a.H();a.aP(Q,0,this.width,this.height,0,0,1),a.K(Q,Q,[B.drawingBufferWidth,B.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(w,B.TRIANGLES,Po.disabled,this.stencilClearMode,Os.disabled,Pa.disabled,Lo(Q),null,\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(w,B){if(this.currentStencilSource===w.source||!w.isTileClipped()||!B||!B.length)return;this.currentStencilSource=w.source;let Q=this.context,ee=Q.gl;this.nextStencilID+B.length>256&&this.clearStencil(),Q.setColorMode(Os.disabled),Q.setDepthMode(Po.disabled);let le=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(let Fe of B){let Ze=this._tileClippingMaskIDs[Fe.key]=this.nextStencilID++,ut=this.style.map.terrain&&this.style.map.terrain.getTerrainData(Fe);le.draw(Q,ee.TRIANGLES,Po.disabled,new Ko({func:ee.ALWAYS,mask:0},Ze,255,ee.KEEP,ee.KEEP,ee.REPLACE),Os.disabled,Pa.disabled,Lo(Fe.posMatrix),ut,\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();let w=this.nextStencilID++,B=this.context.gl;return new Ko({func:B.NOTEQUAL,mask:255},w,255,B.KEEP,B.KEEP,B.REPLACE)}stencilModeForClipping(w){let B=this.context.gl;return new Ko({func:B.EQUAL,mask:255},this._tileClippingMaskIDs[w.key],0,B.KEEP,B.KEEP,B.REPLACE)}stencilConfigForOverlap(w){let B=this.context.gl,Q=w.sort(((Fe,Ze)=>Ze.overscaledZ-Fe.overscaledZ)),ee=Q[Q.length-1].overscaledZ,le=Q[0].overscaledZ-ee+1;if(le>1){this.currentStencilSource=void 0,this.nextStencilID+le>256&&this.clearStencil();let Fe={};for(let Ze=0;Ze<le;Ze++)Fe[Ze+ee]=new Ko({func:B.GEQUAL,mask:255},Ze+this.nextStencilID,255,B.KEEP,B.KEEP,B.REPLACE);return this.nextStencilID+=le,[Fe,Q]}return[{[ee]:Ko.disabled},Q]}colorModeForRenderPass(){let w=this.context.gl;return this._showOverdrawInspector?new Os([w.CONSTANT_COLOR,w.ONE],new a.aM(.125,.125,.125,0),[!0,!0,!0,!0]):this.renderPass===\"opaque\"?Os.unblended:Os.alphaBlended}depthModeForSublayer(w,B,Q){if(!this.opaquePassEnabledForLayer())return Po.disabled;let ee=1-((1+this.currentLayer)*this.numSublayers+w)*this.depthEpsilon;return new Po(Q||this.context.gl.LEQUAL,B,[ee,ee])}opaquePassEnabledForLayer(){return this.currentLayer<this.opaquePassCutoff}render(w,B){this.style=w,this.options=B,this.lineAtlas=w.lineAtlas,this.imageManager=w.imageManager,this.glyphManager=w.glyphManager,this.symbolFadeChange=w.placement.symbolFadeChange(u.now()),this.imageManager.beginFrame();let Q=this.style._order,ee=this.style.sourceCaches,le={},Fe={},Ze={};for(let ut in ee){let Mt=ee[ut];Mt.used&&Mt.prepare(this.context),le[ut]=Mt.getVisibleCoordinates(),Fe[ut]=le[ut].slice().reverse(),Ze[ut]=Mt.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(let ut=0;ut<Q.length;ut++)if(this.style._layers[Q[ut]].is3D()){this.opaquePassCutoff=ut;break}this.maybeDrawDepthAndCoords(!1),this.renderToTexture&&(this.renderToTexture.prepareForRender(this.style,this.transform.zoom),this.opaquePassCutoff=0),this.renderPass=\"offscreen\";for(let ut of Q){let Mt=this.style._layers[ut];if(!Mt.hasOffscreenPass()||Mt.isHidden(this.transform.zoom))continue;let Jt=Fe[Mt.source];(Mt.type===\"custom\"||Jt.length)&&this.renderLayer(this,ee[Mt.source],Mt,Jt)}if(this.context.bindFramebuffer.set(null),this.context.clear({color:B.showOverdrawInspector?a.aM.black:a.aM.transparent,depth:1}),this.clearStencil(),this.style.sky&&(function(ut,Mt){let Jt=ut.context,$t=Jt.gl,yr=((ze,Qe,Xe)=>({u_sky_color:ze.properties.get(\"sky-color\"),u_horizon_color:ze.properties.get(\"horizon-color\"),u_horizon:(Qe.height/2+Qe.getHorizon())*Xe,u_sky_horizon_blend:ze.properties.get(\"sky-horizon-blend\")*Qe.height/2*Xe}))(Mt,ut.style.map.transform,ut.pixelRatio),Cr=new Po($t.LEQUAL,Po.ReadWrite,[0,1]),ve=Ko.disabled,xe=ut.colorModeForRenderPass(),Ie=ut.useProgram(\"sky\");if(!Mt.mesh){let ze=new a.aX;ze.emplaceBack(-1,-1),ze.emplaceBack(1,-1),ze.emplaceBack(1,1),ze.emplaceBack(-1,1);let Qe=new a.aY;Qe.emplaceBack(0,1,2),Qe.emplaceBack(0,2,3),Mt.mesh=new Eu(Jt.createVertexBuffer(ze,vo.members),Jt.createIndexBuffer(Qe),a.a0.simpleSegment(0,0,ze.length,Qe.length))}Ie.draw(Jt,$t.TRIANGLES,Cr,ve,xe,Pa.disabled,yr,void 0,\"sky\",Mt.mesh.vertexBuffer,Mt.mesh.indexBuffer,Mt.mesh.segments)})(this,this.style.sky),this._showOverdrawInspector=B.showOverdrawInspector,this.depthRangeFor3D=[0,1-(w._order.length+2)*this.numSublayers*this.depthEpsilon],!this.renderToTexture)for(this.renderPass=\"opaque\",this.currentLayer=Q.length-1;this.currentLayer>=0;this.currentLayer--){let ut=this.style._layers[Q[this.currentLayer]],Mt=ee[ut.source],Jt=le[ut.source];this._renderTileClippingMasks(ut,Jt),this.renderLayer(this,Mt,ut,Jt)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<Q.length;this.currentLayer++){let ut=this.style._layers[Q[this.currentLayer]],Mt=ee[ut.source];if(this.renderToTexture&&this.renderToTexture.renderLayer(ut))continue;let Jt=(ut.type===\"symbol\"?Ze:Fe)[ut.source];this._renderTileClippingMasks(ut,le[ut.source]),this.renderLayer(this,Mt,ut,Jt)}if(this.options.showTileBoundaries){let ut=(function(Mt,Jt){let $t=null,yr=Object.values(Mt._layers).flatMap((Ie=>Ie.source&&!Ie.isHidden(Jt)?[Mt.sourceCaches[Ie.source]]:[])),Cr=yr.filter((Ie=>Ie.getSource().type===\"vector\")),ve=yr.filter((Ie=>Ie.getSource().type!==\"vector\")),xe=Ie=>{(!$t||$t.getSource().maxzoom<Ie.getSource().maxzoom)&&($t=Ie)};return Cr.forEach((Ie=>xe(Ie))),$t||ve.forEach((Ie=>xe(Ie))),$t})(this.style,this.transform.zoom);ut&&(function(Mt,Jt,$t){for(let yr=0;yr<$t.length;yr++)ih(Mt,Jt,$t[yr])})(this,ut,ut.getVisibleCoordinates())}this.options.showPadding&&(function(ut){let Mt=ut.transform.padding;lf(ut,ut.transform.height-(Mt.top||0),3,Rc),lf(ut,Mt.bottom||0,3,gs),Sh(ut,Mt.left||0,3,Wf),Sh(ut,ut.transform.width-(Mt.right||0),3,Wh);let Jt=ut.transform.centerPoint;(function($t,yr,Cr,ve){Mu($t,yr-1,Cr-10,2,20,ve),Mu($t,yr-10,Cr-1,20,2,ve)})(ut,Jt.x,ut.transform.height-Jt.y,rh)})(this),this.context.setDefault()}maybeDrawDepthAndCoords(w){if(!this.style||!this.style.map||!this.style.map.terrain)return;let B=this.terrainFacilitator.matrix,Q=this.transform.modelViewProjectionMatrix,ee=this.terrainFacilitator.dirty;ee||(ee=w?!a.a_(B,Q):!a.a$(B,Q)),ee||(ee=this.style.map.terrain.sourceCache.tilesAfterTime(this.terrainFacilitator.renderTime).length>0),ee&&(a.b0(B,Q),this.terrainFacilitator.renderTime=Date.now(),this.terrainFacilitator.dirty=!1,(function(le,Fe){let Ze=le.context,ut=Ze.gl,Mt=Os.unblended,Jt=new Po(ut.LEQUAL,Po.ReadWrite,[0,1]),$t=Fe.getTerrainMesh(),yr=Fe.sourceCache.getRenderableTiles(),Cr=le.useProgram(\"terrainDepth\");Ze.bindFramebuffer.set(Fe.getFramebuffer(\"depth\").framebuffer),Ze.viewport.set([0,0,le.width/devicePixelRatio,le.height/devicePixelRatio]),Ze.clear({color:a.aM.transparent,depth:1});for(let ve of yr){let xe=Fe.getTerrainData(ve.tileID),Ie={u_matrix:le.transform.calculatePosMatrix(ve.tileID.toUnwrapped()),u_ele_delta:Fe.getMeshFrameDelta(le.transform.zoom)};Cr.draw(Ze,ut.TRIANGLES,Jt,Ko.disabled,Mt,Pa.backCCW,Ie,xe,\"terrain\",$t.vertexBuffer,$t.indexBuffer,$t.segments)}Ze.bindFramebuffer.set(null),Ze.viewport.set([0,0,le.width,le.height])})(this,this.style.map.terrain),(function(le,Fe){let Ze=le.context,ut=Ze.gl,Mt=Os.unblended,Jt=new Po(ut.LEQUAL,Po.ReadWrite,[0,1]),$t=Fe.getTerrainMesh(),yr=Fe.getCoordsTexture(),Cr=Fe.sourceCache.getRenderableTiles(),ve=le.useProgram(\"terrainCoords\");Ze.bindFramebuffer.set(Fe.getFramebuffer(\"coords\").framebuffer),Ze.viewport.set([0,0,le.width/devicePixelRatio,le.height/devicePixelRatio]),Ze.clear({color:a.aM.transparent,depth:1}),Fe.coordsIndex=[];for(let xe of Cr){let Ie=Fe.getTerrainData(xe.tileID);Ze.activeTexture.set(ut.TEXTURE0),ut.bindTexture(ut.TEXTURE_2D,yr.texture);let ze={u_matrix:le.transform.calculatePosMatrix(xe.tileID.toUnwrapped()),u_terrain_coords_id:(255-Fe.coordsIndex.length)/255,u_texture:0,u_ele_delta:Fe.getMeshFrameDelta(le.transform.zoom)};ve.draw(Ze,ut.TRIANGLES,Jt,Ko.disabled,Mt,Pa.backCCW,ze,Ie,\"terrain\",$t.vertexBuffer,$t.indexBuffer,$t.segments),Fe.coordsIndex.push(xe.tileID.key)}Ze.bindFramebuffer.set(null),Ze.viewport.set([0,0,le.width,le.height])})(this,this.style.map.terrain))}renderLayer(w,B,Q,ee){if(!Q.isHidden(this.transform.zoom)&&(Q.type===\"background\"||Q.type===\"custom\"||(ee||[]).length))switch(this.id=Q.id,Q.type){case\"symbol\":(function(le,Fe,Ze,ut,Mt){if(le.renderPass!==\"translucent\")return;let Jt=Ko.disabled,$t=le.colorModeForRenderPass();(Ze._unevaluatedLayout.hasValue(\"text-variable-anchor\")||Ze._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"))&&(function(yr,Cr,ve,xe,Ie,ze,Qe,Xe,nt){let qt=Cr.transform,Wt=yn(),nr=Ie===\"map\",fr=ze===\"map\";for(let br of yr){let Fr=xe.getTile(br),Hr=Fr.getBucket(ve);if(!Hr||!Hr.text||!Hr.text.segments.get().length)continue;let ri=a.ag(Hr.textSizeData,qt.zoom),Ci=Fn(Fr,1,Cr.transform.zoom),cn=qr(br.posMatrix,fr,nr,Cr.transform,Ci),sn=ve.layout.get(\"icon-text-fit\")!==\"none\"&&Hr.hasIconData();if(ri){let Ln=Math.pow(2,qt.zoom-Fr.tileID.overscaledZ),Kn=Cr.style.map.terrain?(fa,$a)=>Cr.style.map.terrain.getElevation(br,fa,$a):null,Aa=Wt.translatePosition(qt,Fr,Qe,Xe);yf(Hr,nr,fr,nt,qt,cn,br.posMatrix,Ln,ri,sn,Wt,Aa,br.toUnwrapped(),Kn)}}})(ut,le,Ze,Fe,Ze.layout.get(\"text-rotation-alignment\"),Ze.layout.get(\"text-pitch-alignment\"),Ze.paint.get(\"text-translate\"),Ze.paint.get(\"text-translate-anchor\"),Mt),Ze.paint.get(\"icon-opacity\").constantOr(1)!==0&&Ah(le,Fe,Ze,ut,!1,Ze.paint.get(\"icon-translate\"),Ze.paint.get(\"icon-translate-anchor\"),Ze.layout.get(\"icon-rotation-alignment\"),Ze.layout.get(\"icon-pitch-alignment\"),Ze.layout.get(\"icon-keep-upright\"),Jt,$t),Ze.paint.get(\"text-opacity\").constantOr(1)!==0&&Ah(le,Fe,Ze,ut,!0,Ze.paint.get(\"text-translate\"),Ze.paint.get(\"text-translate-anchor\"),Ze.layout.get(\"text-rotation-alignment\"),Ze.layout.get(\"text-pitch-alignment\"),Ze.layout.get(\"text-keep-upright\"),Jt,$t),Fe.map.showCollisionBoxes&&(Hu(le,Fe,Ze,ut,!0),Hu(le,Fe,Ze,ut,!1))})(w,B,Q,ee,this.style.placement.variableOffsets);break;case\"circle\":(function(le,Fe,Ze,ut){if(le.renderPass!==\"translucent\")return;let Mt=Ze.paint.get(\"circle-opacity\"),Jt=Ze.paint.get(\"circle-stroke-width\"),$t=Ze.paint.get(\"circle-stroke-opacity\"),yr=!Ze.layout.get(\"circle-sort-key\").isConstant();if(Mt.constantOr(1)===0&&(Jt.constantOr(1)===0||$t.constantOr(1)===0))return;let Cr=le.context,ve=Cr.gl,xe=le.depthModeForSublayer(0,Po.ReadOnly),Ie=Ko.disabled,ze=le.colorModeForRenderPass(),Qe=[];for(let Xe=0;Xe<ut.length;Xe++){let nt=ut[Xe],qt=Fe.getTile(nt),Wt=qt.getBucket(Ze);if(!Wt)continue;let nr=Wt.programConfigurations.get(Ze.id),fr=le.useProgram(\"circle\",nr),br=Wt.layoutVertexBuffer,Fr=Wt.indexBuffer,Hr=le.style.map.terrain&&le.style.map.terrain.getTerrainData(nt),ri={programConfiguration:nr,program:fr,layoutVertexBuffer:br,indexBuffer:Fr,uniformValues:_o(le,nt,qt,Ze),terrainData:Hr};if(yr){let Ci=Wt.segments.get();for(let cn of Ci)Qe.push({segments:new a.a0([cn]),sortKey:cn.sortKey,state:ri})}else Qe.push({segments:Wt.segments,sortKey:0,state:ri})}yr&&Qe.sort(((Xe,nt)=>Xe.sortKey-nt.sortKey));for(let Xe of Qe){let{programConfiguration:nt,program:qt,layoutVertexBuffer:Wt,indexBuffer:nr,uniformValues:fr,terrainData:br}=Xe.state;qt.draw(Cr,ve.TRIANGLES,xe,Ie,ze,Pa.disabled,fr,br,Ze.id,Wt,nr,Xe.segments,Ze.paint,le.transform.zoom,nt)}})(w,B,Q,ee);break;case\"heatmap\":(function(le,Fe,Ze,ut){if(Ze.paint.get(\"heatmap-opacity\")===0)return;let Mt=le.context;if(le.style.map.terrain){for(let Jt of ut){let $t=Fe.getTile(Jt);Fe.hasRenderableParent(Jt)||(le.renderPass===\"offscreen\"?_f(le,$t,Ze,Jt):le.renderPass===\"translucent\"&&Yc(le,Ze,Jt))}Mt.viewport.set([0,0,le.width,le.height])}else le.renderPass===\"offscreen\"?(function(Jt,$t,yr,Cr){let ve=Jt.context,xe=ve.gl,Ie=Ko.disabled,ze=new Os([xe.ONE,xe.ONE],a.aM.transparent,[!0,!0,!0,!0]);(function(Qe,Xe,nt){let qt=Qe.gl;Qe.activeTexture.set(qt.TEXTURE1),Qe.viewport.set([0,0,Xe.width/4,Xe.height/4]);let Wt=nt.heatmapFbos.get(a.aU);Wt?(qt.bindTexture(qt.TEXTURE_2D,Wt.colorAttachment.get()),Qe.bindFramebuffer.set(Wt.framebuffer)):(Wt=eh(Qe,Xe.width/4,Xe.height/4),nt.heatmapFbos.set(a.aU,Wt))})(ve,Jt,yr),ve.clear({color:a.aM.transparent});for(let Qe=0;Qe<Cr.length;Qe++){let Xe=Cr[Qe];if($t.hasRenderableParent(Xe))continue;let nt=$t.getTile(Xe),qt=nt.getBucket(yr);if(!qt)continue;let Wt=qt.programConfigurations.get(yr.id),nr=Jt.useProgram(\"heatmap\",Wt),{zoom:fr}=Jt.transform;nr.draw(ve,xe.TRIANGLES,Po.disabled,Ie,ze,Pa.disabled,ko(Xe.posMatrix,nt,fr,yr.paint.get(\"heatmap-intensity\")),null,yr.id,qt.layoutVertexBuffer,qt.indexBuffer,qt.segments,yr.paint,Jt.transform.zoom,Wt)}ve.viewport.set([0,0,Jt.width,Jt.height])})(le,Fe,Ze,ut):le.renderPass===\"translucent\"&&(function(Jt,$t){let yr=Jt.context,Cr=yr.gl;yr.setColorMode(Jt.colorModeForRenderPass());let ve=$t.heatmapFbos.get(a.aU);ve&&(yr.activeTexture.set(Cr.TEXTURE0),Cr.bindTexture(Cr.TEXTURE_2D,ve.colorAttachment.get()),yr.activeTexture.set(Cr.TEXTURE1),th(yr,$t).bind(Cr.LINEAR,Cr.CLAMP_TO_EDGE),Jt.useProgram(\"heatmapTexture\").draw(yr,Cr.TRIANGLES,Po.disabled,Ko.disabled,Jt.colorModeForRenderPass(),Pa.disabled,Ds(Jt,$t,0,1),null,$t.id,Jt.viewportBuffer,Jt.quadTriangleIndexBuffer,Jt.viewportSegments,$t.paint,Jt.transform.zoom))})(le,Ze)})(w,B,Q,ee);break;case\"line\":(function(le,Fe,Ze,ut){if(le.renderPass!==\"translucent\")return;let Mt=Ze.paint.get(\"line-opacity\"),Jt=Ze.paint.get(\"line-width\");if(Mt.constantOr(1)===0||Jt.constantOr(1)===0)return;let $t=le.depthModeForSublayer(0,Po.ReadOnly),yr=le.colorModeForRenderPass(),Cr=Ze.paint.get(\"line-dasharray\"),ve=Ze.paint.get(\"line-pattern\"),xe=ve.constantOr(1),Ie=Ze.paint.get(\"line-gradient\"),ze=Ze.getCrossfadeParameters(),Qe=xe?\"linePattern\":Cr?\"lineSDF\":Ie?\"lineGradient\":\"line\",Xe=le.context,nt=Xe.gl,qt=!0;for(let Wt of ut){let nr=Fe.getTile(Wt);if(xe&&!nr.patternsLoaded())continue;let fr=nr.getBucket(Ze);if(!fr)continue;let br=fr.programConfigurations.get(Ze.id),Fr=le.context.program.get(),Hr=le.useProgram(Qe,br),ri=qt||Hr.program!==Fr,Ci=le.style.map.terrain&&le.style.map.terrain.getTerrainData(Wt),cn=ve.constantOr(null);if(cn&&nr.imageAtlas){let Kn=nr.imageAtlas,Aa=Kn.patternPositions[cn.to.toString()],fa=Kn.patternPositions[cn.from.toString()];Aa&&fa&&br.setConstantPatternPositions(Aa,fa)}let sn=Ci?Wt:null,Ln=xe?Fl(le,nr,Ze,ze,sn):Cr?us(le,nr,Ze,Cr,ze,sn):Ie?ul(le,nr,Ze,fr.lineClipsArray.length,sn):ll(le,nr,Ze,sn);if(xe)Xe.activeTexture.set(nt.TEXTURE0),nr.imageAtlasTexture.bind(nt.LINEAR,nt.CLAMP_TO_EDGE),br.updatePaintBuffers(ze);else if(Cr&&(ri||le.lineAtlas.dirty))Xe.activeTexture.set(nt.TEXTURE0),le.lineAtlas.bind(Xe);else if(Ie){let Kn=fr.gradients[Ze.id],Aa=Kn.texture;if(Ze.gradientVersion!==Kn.version){let fa=256;if(Ze.stepInterpolant){let $a=Fe.getSource().maxzoom,Co=Wt.canonical.z===$a?Math.ceil(1<<le.transform.maxZoom-Wt.canonical.z):1;fa=a.ac(a.aV(fr.maxLineLength/a.X*1024*Co),256,Xe.maxTextureSize)}Kn.gradient=a.aW({expression:Ze.gradientExpression(),evaluationKey:\"lineProgress\",resolution:fa,image:Kn.gradient||void 0,clips:fr.lineClipsArray}),Kn.texture?Kn.texture.update(Kn.gradient):Kn.texture=new g(Xe,Kn.gradient,nt.RGBA),Kn.version=Ze.gradientVersion,Aa=Kn.texture}Xe.activeTexture.set(nt.TEXTURE0),Aa.bind(Ze.stepInterpolant?nt.NEAREST:nt.LINEAR,nt.CLAMP_TO_EDGE)}Hr.draw(Xe,nt.TRIANGLES,$t,le.stencilModeForClipping(Wt),yr,Pa.disabled,Ln,Ci,Ze.id,fr.layoutVertexBuffer,fr.indexBuffer,fr.segments,Ze.paint,le.transform.zoom,br,fr.layoutVertexBuffer2),qt=!1}})(w,B,Q,ee);break;case\"fill\":(function(le,Fe,Ze,ut){let Mt=Ze.paint.get(\"fill-color\"),Jt=Ze.paint.get(\"fill-opacity\");if(Jt.constantOr(1)===0)return;let $t=le.colorModeForRenderPass(),yr=Ze.paint.get(\"fill-pattern\"),Cr=le.opaquePassEnabledForLayer()&&!yr.constantOr(1)&&Mt.constantOr(a.aM.transparent).a===1&&Jt.constantOr(0)===1?\"opaque\":\"translucent\";if(le.renderPass===Cr){let ve=le.depthModeForSublayer(1,le.renderPass===\"opaque\"?Po.ReadWrite:Po.ReadOnly);jf(le,Fe,Ze,ut,ve,$t,!1)}if(le.renderPass===\"translucent\"&&Ze.paint.get(\"fill-antialias\")){let ve=le.depthModeForSublayer(Ze.getPaintProperty(\"fill-outline-color\")?2:0,Po.ReadOnly);jf(le,Fe,Ze,ut,ve,$t,!0)}})(w,B,Q,ee);break;case\"fill-extrusion\":(function(le,Fe,Ze,ut){let Mt=Ze.paint.get(\"fill-extrusion-opacity\");if(Mt!==0&&le.renderPass===\"translucent\"){let Jt=new Po(le.context.gl.LEQUAL,Po.ReadWrite,le.depthRangeFor3D);if(Mt!==1||Ze.paint.get(\"fill-extrusion-pattern\").constantOr(1))cc(le,Fe,Ze,ut,Jt,Ko.disabled,Os.disabled),cc(le,Fe,Ze,ut,Jt,le.stencilModeFor3D(),le.colorModeForRenderPass());else{let $t=le.colorModeForRenderPass();cc(le,Fe,Ze,ut,Jt,Ko.disabled,$t)}}})(w,B,Q,ee);break;case\"hillshade\":(function(le,Fe,Ze,ut){if(le.renderPass!==\"offscreen\"&&le.renderPass!==\"translucent\")return;let Mt=le.context,Jt=le.depthModeForSublayer(0,Po.ReadOnly),$t=le.colorModeForRenderPass(),[yr,Cr]=le.renderPass===\"translucent\"?le.stencilConfigForOverlap(ut):[{},ut];for(let ve of Cr){let xe=Fe.getTile(ve);xe.needsHillshadePrepare!==void 0&&xe.needsHillshadePrepare&&le.renderPass===\"offscreen\"?Bl(le,xe,Ze,Jt,Ko.disabled,$t):le.renderPass===\"translucent\"&&sf(le,ve,xe,Ze,Jt,yr[ve.overscaledZ],$t)}Mt.viewport.set([0,0,le.width,le.height])})(w,B,Q,ee);break;case\"raster\":(function(le,Fe,Ze,ut){if(le.renderPass!==\"translucent\"||Ze.paint.get(\"raster-opacity\")===0||!ut.length)return;let Mt=le.context,Jt=Mt.gl,$t=Fe.getSource(),yr=le.useProgram(\"raster\"),Cr=le.colorModeForRenderPass(),[ve,xe]=$t instanceof ar?[{},ut]:le.stencilConfigForOverlap(ut),Ie=xe[xe.length-1].overscaledZ,ze=!le.options.moving;for(let Qe of xe){let Xe=le.depthModeForSublayer(Qe.overscaledZ-Ie,Ze.paint.get(\"raster-opacity\")===1?Po.ReadWrite:Po.ReadOnly,Jt.LESS),nt=Fe.getTile(Qe);nt.registerFadeDuration(Ze.paint.get(\"raster-fade-duration\"));let qt=Fe.findLoadedParent(Qe,0),Wt=Fe.findLoadedSibling(Qe),nr=Kc(nt,qt||Wt||null,Fe,Ze,le.transform,le.style.map.terrain),fr,br,Fr=Ze.paint.get(\"raster-resampling\")===\"nearest\"?Jt.NEAREST:Jt.LINEAR;Mt.activeTexture.set(Jt.TEXTURE0),nt.texture.bind(Fr,Jt.CLAMP_TO_EDGE,Jt.LINEAR_MIPMAP_NEAREST),Mt.activeTexture.set(Jt.TEXTURE1),qt?(qt.texture.bind(Fr,Jt.CLAMP_TO_EDGE,Jt.LINEAR_MIPMAP_NEAREST),fr=Math.pow(2,qt.tileID.overscaledZ-nt.tileID.overscaledZ),br=[nt.tileID.canonical.x*fr%1,nt.tileID.canonical.y*fr%1]):nt.texture.bind(Fr,Jt.CLAMP_TO_EDGE,Jt.LINEAR_MIPMAP_NEAREST),nt.texture.useMipmap&&Mt.extTextureFilterAnisotropic&&le.transform.pitch>20&&Jt.texParameterf(Jt.TEXTURE_2D,Mt.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,Mt.extTextureFilterAnisotropicMax);let Hr=le.style.map.terrain&&le.style.map.terrain.getTerrainData(Qe),ri=Hr?Qe:null,Ci=ri?ri.posMatrix:le.transform.calculatePosMatrix(Qe.toUnwrapped(),ze),cn=cl(Ci,br||[0,0],fr||1,nr,Ze);$t instanceof ar?yr.draw(Mt,Jt.TRIANGLES,Xe,Ko.disabled,Cr,Pa.disabled,cn,Hr,Ze.id,$t.boundsBuffer,le.quadTriangleIndexBuffer,$t.boundsSegments):yr.draw(Mt,Jt.TRIANGLES,Xe,ve[Qe.overscaledZ],Cr,Pa.disabled,cn,Hr,Ze.id,le.rasterBoundsBuffer,le.quadTriangleIndexBuffer,le.rasterBoundsSegments)}})(w,B,Q,ee);break;case\"background\":(function(le,Fe,Ze,ut){let Mt=Ze.paint.get(\"background-color\"),Jt=Ze.paint.get(\"background-opacity\");if(Jt===0)return;let $t=le.context,yr=$t.gl,Cr=le.transform,ve=Cr.tileSize,xe=Ze.paint.get(\"background-pattern\");if(le.isPatternMissing(xe))return;let Ie=!xe&&Mt.a===1&&Jt===1&&le.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(le.renderPass!==Ie)return;let ze=Ko.disabled,Qe=le.depthModeForSublayer(0,Ie===\"opaque\"?Po.ReadWrite:Po.ReadOnly),Xe=le.colorModeForRenderPass(),nt=le.useProgram(xe?\"backgroundPattern\":\"background\"),qt=ut||Cr.coveringTiles({tileSize:ve,terrain:le.style.map.terrain});xe&&($t.activeTexture.set(yr.TEXTURE0),le.imageManager.bind(le.context));let Wt=Ze.getCrossfadeParameters();for(let nr of qt){let fr=ut?nr.posMatrix:le.transform.calculatePosMatrix(nr.toUnwrapped()),br=xe?Su(fr,Jt,le,xe,{tileID:nr,tileSize:ve},Wt):Yl(fr,Jt,Mt),Fr=le.style.map.terrain&&le.style.map.terrain.getTerrainData(nr);nt.draw($t,yr.TRIANGLES,Qe,ze,Xe,Pa.disabled,br,Fr,Ze.id,le.tileExtentBuffer,le.quadTriangleIndexBuffer,le.tileExtentSegments)}})(w,0,Q,ee);break;case\"custom\":(function(le,Fe,Ze){let ut=le.context,Mt=Ze.implementation;if(le.renderPass===\"offscreen\"){let Jt=Mt.prerender;Jt&&(le.setCustomLayerDefaults(),ut.setColorMode(le.colorModeForRenderPass()),Jt.call(Mt,ut.gl,le.transform.customLayerMatrix()),ut.setDirty(),le.setBaseState())}else if(le.renderPass===\"translucent\"){le.setCustomLayerDefaults(),ut.setColorMode(le.colorModeForRenderPass()),ut.setStencilMode(Ko.disabled);let Jt=Mt.renderingMode===\"3d\"?new Po(le.context.gl.LEQUAL,Po.ReadWrite,le.depthRangeFor3D):le.depthModeForSublayer(0,Po.ReadOnly);ut.setDepthMode(Jt),Mt.render(ut.gl,le.transform.customLayerMatrix(),{farZ:le.transform.farZ,nearZ:le.transform.nearZ,fov:le.transform._fov,modelViewProjectionMatrix:le.transform.modelViewProjectionMatrix,projectionMatrix:le.transform.projectionMatrix}),ut.setDirty(),le.setBaseState(),ut.bindFramebuffer.set(null)}})(w,0,Q)}}translatePosMatrix(w,B,Q,ee,le){if(!Q[0]&&!Q[1])return w;let Fe=le?ee===\"map\"?this.transform.angle:0:ee===\"viewport\"?-this.transform.angle:0;if(Fe){let Mt=Math.sin(Fe),Jt=Math.cos(Fe);Q=[Q[0]*Jt-Q[1]*Mt,Q[0]*Mt+Q[1]*Jt]}let Ze=[le?Q[0]:Fn(B,Q[0],this.transform.zoom),le?Q[1]:Fn(B,Q[1],this.transform.zoom),0],ut=new Float32Array(16);return a.J(ut,w,Ze),ut}saveTileTexture(w){let B=this._tileTextures[w.size[0]];B?B.push(w):this._tileTextures[w.size[0]]=[w]}getTileTexture(w){let B=this._tileTextures[w];return B&&B.length>0?B.pop():null}isPatternMissing(w){if(!w)return!1;if(!w.from||!w.to)return!0;let B=this.imageManager.getPattern(w.from.toString()),Q=this.imageManager.getPattern(w.to.toString());return!B||!Q}useProgram(w,B){this.cache=this.cache||{};let Q=w+(B?B.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\")+(this.style.map.terrain?\"/terrain\":\"\");return this.cache[Q]||(this.cache[Q]=new hn(this.context,Gn[w],B,nc[w],this._showOverdrawInspector,this.style.map.terrain)),this.cache[Q]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){let w=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(w.FUNC_ADD)}initDebugOverlayCanvas(){this.debugOverlayCanvas==null&&(this.debugOverlayCanvas=document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new g(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))}destroy(){this.debugOverlayTexture&&this.debugOverlayTexture.destroy()}overLimit(){let{drawingBufferWidth:w,drawingBufferHeight:B}=this.context.gl;return this.width!==w||this.height!==B}}class ks{constructor(w,B){this.points=w,this.planes=B}static fromInvProjectionMatrix(w,B,Q){let ee=Math.pow(2,Q),le=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((Ze=>{let ut=1/(Ze=a.af([],Ze,w))[3]/B*ee;return a.b1(Ze,Ze,[ut,ut,1/Ze[3],ut])})),Fe=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((Ze=>{let ut=(function(yr,Cr){var ve=Cr[0],xe=Cr[1],Ie=Cr[2],ze=ve*ve+xe*xe+Ie*Ie;return ze>0&&(ze=1/Math.sqrt(ze)),yr[0]=Cr[0]*ze,yr[1]=Cr[1]*ze,yr[2]=Cr[2]*ze,yr})([],(function(yr,Cr,ve){var xe=Cr[0],Ie=Cr[1],ze=Cr[2],Qe=ve[0],Xe=ve[1],nt=ve[2];return yr[0]=Ie*nt-ze*Xe,yr[1]=ze*Qe-xe*nt,yr[2]=xe*Xe-Ie*Qe,yr})([],L([],le[Ze[0]],le[Ze[1]]),L([],le[Ze[2]],le[Ze[1]]))),Mt=-((Jt=ut)[0]*($t=le[Ze[1]])[0]+Jt[1]*$t[1]+Jt[2]*$t[2]);var Jt,$t;return ut.concat(Mt)}));return new ks(le,Fe)}}class bc{constructor(w,B){this.min=w,this.max=B,this.center=(function(Q,ee,le){return Q[0]=.5*ee[0],Q[1]=.5*ee[1],Q[2]=.5*ee[2],Q})([],(function(Q,ee,le){return Q[0]=ee[0]+le[0],Q[1]=ee[1]+le[1],Q[2]=ee[2]+le[2],Q})([],this.min,this.max))}quadrant(w){let B=[w%2==0,w<2],Q=M(this.min),ee=M(this.max);for(let le=0;le<B.length;le++)Q[le]=B[le]?this.min[le]:this.center[le],ee[le]=B[le]?this.center[le]:this.max[le];return ee[2]=this.max[2],new bc(Q,ee)}distanceX(w){return Math.max(Math.min(this.max[0],w[0]),this.min[0])-w[0]}distanceY(w){return Math.max(Math.min(this.max[1],w[1]),this.min[1])-w[1]}intersects(w){let B=[[this.min[0],this.min[1],this.min[2],1],[this.max[0],this.min[1],this.min[2],1],[this.max[0],this.max[1],this.min[2],1],[this.min[0],this.max[1],this.min[2],1],[this.min[0],this.min[1],this.max[2],1],[this.max[0],this.min[1],this.max[2],1],[this.max[0],this.max[1],this.max[2],1],[this.min[0],this.max[1],this.max[2],1]],Q=!0;for(let ee=0;ee<w.planes.length;ee++){let le=w.planes[ee],Fe=0;for(let Ze=0;Ze<B.length;Ze++)a.b2(le,B[Ze])>=0&&Fe++;if(Fe===0)return 0;Fe!==B.length&&(Q=!1)}if(Q)return 2;for(let ee=0;ee<3;ee++){let le=Number.MAX_VALUE,Fe=-Number.MAX_VALUE;for(let Ze=0;Ze<w.points.length;Ze++){let ut=w.points[Ze][ee]-this.min[ee];le=Math.min(le,ut),Fe=Math.max(Fe,ut)}if(Fe<0||le>this.max[ee]-this.min[ee])return 0}return 1}}class hu{constructor(w=0,B=0,Q=0,ee=0){if(isNaN(w)||w<0||isNaN(B)||B<0||isNaN(Q)||Q<0||isNaN(ee)||ee<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=w,this.bottom=B,this.left=Q,this.right=ee}interpolate(w,B,Q){return B.top!=null&&w.top!=null&&(this.top=a.y.number(w.top,B.top,Q)),B.bottom!=null&&w.bottom!=null&&(this.bottom=a.y.number(w.bottom,B.bottom,Q)),B.left!=null&&w.left!=null&&(this.left=a.y.number(w.left,B.left,Q)),B.right!=null&&w.right!=null&&(this.right=a.y.number(w.right,B.right,Q)),this}getCenter(w,B){let Q=a.ac((this.left+w-this.right)/2,0,w),ee=a.ac((this.top+B-this.bottom)/2,0,B);return new a.P(Q,ee)}equals(w){return this.top===w.top&&this.bottom===w.bottom&&this.left===w.left&&this.right===w.right}clone(){return new hu(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}let _u=85.051129;class il{constructor(w,B,Q,ee,le){this.tileSize=512,this._renderWorldCopies=le===void 0||!!le,this._minZoom=w||0,this._maxZoom=B||22,this._minPitch=Q==null?0:Q,this._maxPitch=ee==null?60:ee,this.setMaxBounds(),this.width=0,this.height=0,this._center=new a.N(0,0),this._elevation=0,this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new hu,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={},this.minElevationForCurrentTile=0}clone(){let w=new il(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return w.apply(this),w}apply(w){this.tileSize=w.tileSize,this.latRange=w.latRange,this.lngRange=w.lngRange,this.width=w.width,this.height=w.height,this._center=w._center,this._elevation=w._elevation,this.minElevationForCurrentTile=w.minElevationForCurrentTile,this.zoom=w.zoom,this.angle=w.angle,this._fov=w._fov,this._pitch=w._pitch,this._unmodified=w._unmodified,this._edgeInsets=w._edgeInsets.clone(),this._calcMatrices()}get minZoom(){return this._minZoom}set minZoom(w){this._minZoom!==w&&(this._minZoom=w,this.zoom=Math.max(this.zoom,w))}get maxZoom(){return this._maxZoom}set maxZoom(w){this._maxZoom!==w&&(this._maxZoom=w,this.zoom=Math.min(this.zoom,w))}get minPitch(){return this._minPitch}set minPitch(w){this._minPitch!==w&&(this._minPitch=w,this.pitch=Math.max(this.pitch,w))}get maxPitch(){return this._maxPitch}set maxPitch(w){this._maxPitch!==w&&(this._maxPitch=w,this.pitch=Math.min(this.pitch,w))}get renderWorldCopies(){return this._renderWorldCopies}set renderWorldCopies(w){w===void 0?w=!0:w===null&&(w=!1),this._renderWorldCopies=w}get worldSize(){return this.tileSize*this.scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new a.P(this.width,this.height)}get bearing(){return-this.angle/Math.PI*180}set bearing(w){let B=-a.b3(w,-180,180)*Math.PI/180;this.angle!==B&&(this._unmodified=!1,this.angle=B,this._calcMatrices(),this.rotationMatrix=(function(){var Q=new a.A(4);return a.A!=Float32Array&&(Q[1]=0,Q[2]=0),Q[0]=1,Q[3]=1,Q})(),(function(Q,ee,le){var Fe=ee[0],Ze=ee[1],ut=ee[2],Mt=ee[3],Jt=Math.sin(le),$t=Math.cos(le);Q[0]=Fe*$t+ut*Jt,Q[1]=Ze*$t+Mt*Jt,Q[2]=Fe*-Jt+ut*$t,Q[3]=Ze*-Jt+Mt*$t})(this.rotationMatrix,this.rotationMatrix,this.angle))}get pitch(){return this._pitch/Math.PI*180}set pitch(w){let B=a.ac(w,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==B&&(this._unmodified=!1,this._pitch=B,this._calcMatrices())}get fov(){return this._fov/Math.PI*180}set fov(w){w=Math.max(.01,Math.min(60,w)),this._fov!==w&&(this._unmodified=!1,this._fov=w/180*Math.PI,this._calcMatrices())}get zoom(){return this._zoom}set zoom(w){let B=Math.min(Math.max(w,this.minZoom),this.maxZoom);this._zoom!==B&&(this._unmodified=!1,this._zoom=B,this.tileZoom=Math.max(0,Math.floor(B)),this.scale=this.zoomScale(B),this._constrain(),this._calcMatrices())}get center(){return this._center}set center(w){w.lat===this._center.lat&&w.lng===this._center.lng||(this._unmodified=!1,this._center=w,this._constrain(),this._calcMatrices())}get elevation(){return this._elevation}set elevation(w){w!==this._elevation&&(this._elevation=w,this._constrain(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}set padding(w){this._edgeInsets.equals(w)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,w,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this.width,this.height)}isPaddingEqual(w){return this._edgeInsets.equals(w)}interpolatePadding(w,B,Q){this._unmodified=!1,this._edgeInsets.interpolate(w,B,Q),this._constrain(),this._calcMatrices()}coveringZoomLevel(w){let B=(w.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/w.tileSize));return Math.max(0,B)}getVisibleUnwrappedCoordinates(w){let B=[new a.b4(0,w)];if(this._renderWorldCopies){let Q=this.pointCoordinate(new a.P(0,0)),ee=this.pointCoordinate(new a.P(this.width,0)),le=this.pointCoordinate(new a.P(this.width,this.height)),Fe=this.pointCoordinate(new a.P(0,this.height)),Ze=Math.floor(Math.min(Q.x,ee.x,le.x,Fe.x)),ut=Math.floor(Math.max(Q.x,ee.x,le.x,Fe.x)),Mt=1;for(let Jt=Ze-Mt;Jt<=ut+Mt;Jt++)Jt!==0&&B.push(new a.b4(Jt,w))}return B}coveringTiles(w){var B,Q;let ee=this.coveringZoomLevel(w),le=ee;if(w.minzoom!==void 0&&ee<w.minzoom)return[];w.maxzoom!==void 0&&ee>w.maxzoom&&(ee=w.maxzoom);let Fe=this.pointCoordinate(this.getCameraPoint()),Ze=a.Z.fromLngLat(this.center),ut=Math.pow(2,ee),Mt=[ut*Fe.x,ut*Fe.y,0],Jt=[ut*Ze.x,ut*Ze.y,0],$t=ks.fromInvProjectionMatrix(this.invModelViewProjectionMatrix,this.worldSize,ee),yr=w.minzoom||0;!w.terrain&&this.pitch<=60&&this._edgeInsets.top<.1&&(yr=ee);let Cr=w.terrain?2/Math.min(this.tileSize,w.tileSize)*this.tileSize:3,ve=Xe=>({aabb:new bc([Xe*ut,0,0],[(Xe+1)*ut,ut,0]),zoom:0,x:0,y:0,wrap:Xe,fullyVisible:!1}),xe=[],Ie=[],ze=ee,Qe=w.reparseOverscaled?le:ee;if(this._renderWorldCopies)for(let Xe=1;Xe<=3;Xe++)xe.push(ve(-Xe)),xe.push(ve(Xe));for(xe.push(ve(0));xe.length>0;){let Xe=xe.pop(),nt=Xe.x,qt=Xe.y,Wt=Xe.fullyVisible;if(!Wt){let Hr=Xe.aabb.intersects($t);if(Hr===0)continue;Wt=Hr===2}let nr=w.terrain?Mt:Jt,fr=Xe.aabb.distanceX(nr),br=Xe.aabb.distanceY(nr),Fr=Math.max(Math.abs(fr),Math.abs(br));if(Xe.zoom===ze||Fr>Cr+(1<<ze-Xe.zoom)-2&&Xe.zoom>=yr){let Hr=ze-Xe.zoom,ri=Mt[0]-.5-(nt<<Hr),Ci=Mt[1]-.5-(qt<<Hr);Ie.push({tileID:new a.S(Xe.zoom===ze?Qe:Xe.zoom,Xe.wrap,Xe.zoom,nt,qt),distanceSq:x([Jt[0]-.5-nt,Jt[1]-.5-qt]),tileDistanceToCamera:Math.sqrt(ri*ri+Ci*Ci)})}else for(let Hr=0;Hr<4;Hr++){let ri=(nt<<1)+Hr%2,Ci=(qt<<1)+(Hr>>1),cn=Xe.zoom+1,sn=Xe.aabb.quadrant(Hr);if(w.terrain){let Ln=new a.S(cn,Xe.wrap,cn,ri,Ci),Kn=w.terrain.getMinMaxElevation(Ln),Aa=(B=Kn.minElevation)!==null&&B!==void 0?B:this.elevation,fa=(Q=Kn.maxElevation)!==null&&Q!==void 0?Q:this.elevation;sn=new bc([sn.min[0],sn.min[1],Aa],[sn.max[0],sn.max[1],fa])}xe.push({aabb:sn,zoom:cn,x:ri,y:Ci,wrap:Xe.wrap,fullyVisible:Wt})}}return Ie.sort(((Xe,nt)=>Xe.distanceSq-nt.distanceSq)).map((Xe=>Xe.tileID))}resize(w,B){this.width=w,this.height=B,this.pixelsToGLUnits=[2/w,-2/B],this._constrain(),this._calcMatrices()}get unmodified(){return this._unmodified}zoomScale(w){return Math.pow(2,w)}scaleZoom(w){return Math.log(w)/Math.LN2}project(w){let B=a.ac(w.lat,-85.051129,_u);return new a.P(a.O(w.lng)*this.worldSize,a.Q(B)*this.worldSize)}unproject(w){return new a.Z(w.x/this.worldSize,w.y/this.worldSize).toLngLat()}get point(){return this.project(this.center)}getCameraPosition(){return{lngLat:this.pointLocation(this.getCameraPoint()),altitude:Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter+this.elevation}}recalculateZoom(w){let B=this.elevation,Q=Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter,ee=this.pointLocation(this.centerPoint,w),le=w.getElevationForLngLatZoom(ee,this.tileZoom);if(!(this.elevation-le))return;let Fe=Q+B-le,Ze=Math.cos(this._pitch)*this.cameraToCenterDistance/Fe/a.b5(1,ee.lat),ut=this.scaleZoom(Ze/this.tileSize);this._elevation=le,this._center=ee,this.zoom=ut}setLocationAtPoint(w,B){let Q=this.pointCoordinate(B),ee=this.pointCoordinate(this.centerPoint),le=this.locationCoordinate(w),Fe=new a.Z(le.x-(Q.x-ee.x),le.y-(Q.y-ee.y));this.center=this.coordinateLocation(Fe),this._renderWorldCopies&&(this.center=this.center.wrap())}locationPoint(w,B){return B?this.coordinatePoint(this.locationCoordinate(w),B.getElevationForLngLatZoom(w,this.tileZoom),this.pixelMatrix3D):this.coordinatePoint(this.locationCoordinate(w))}pointLocation(w,B){return this.coordinateLocation(this.pointCoordinate(w,B))}locationCoordinate(w){return a.Z.fromLngLat(w)}coordinateLocation(w){return w&&w.toLngLat()}pointCoordinate(w,B){if(B){let yr=B.pointCoordinate(w);if(yr!=null)return yr}let Q=[w.x,w.y,0,1],ee=[w.x,w.y,1,1];a.af(Q,Q,this.pixelMatrixInverse),a.af(ee,ee,this.pixelMatrixInverse);let le=Q[3],Fe=ee[3],Ze=Q[1]/le,ut=ee[1]/Fe,Mt=Q[2]/le,Jt=ee[2]/Fe,$t=Mt===Jt?0:(0-Mt)/(Jt-Mt);return new a.Z(a.y.number(Q[0]/le,ee[0]/Fe,$t)/this.worldSize,a.y.number(Ze,ut,$t)/this.worldSize)}coordinatePoint(w,B=0,Q=this.pixelMatrix){let ee=[w.x*this.worldSize,w.y*this.worldSize,B,1];return a.af(ee,ee,Q),new a.P(ee[0]/ee[3],ee[1]/ee[3])}getBounds(){let w=Math.max(0,this.height/2-this.getHorizon());return new ce().extend(this.pointLocation(new a.P(0,w))).extend(this.pointLocation(new a.P(this.width,w))).extend(this.pointLocation(new a.P(this.width,this.height))).extend(this.pointLocation(new a.P(0,this.height)))}getMaxBounds(){return this.latRange&&this.latRange.length===2&&this.lngRange&&this.lngRange.length===2?new ce([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null}getHorizon(){return Math.tan(Math.PI/2-this._pitch)*this.cameraToCenterDistance*.85}setMaxBounds(w){w?(this.lngRange=[w.getWest(),w.getEast()],this.latRange=[w.getSouth(),w.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-85.051129,_u])}calculateTileMatrix(w){let B=w.canonical,Q=this.worldSize/this.zoomScale(B.z),ee=B.x+Math.pow(2,B.z)*w.wrap,le=a.an(new Float64Array(16));return a.J(le,le,[ee*Q,B.y*Q,0]),a.K(le,le,[Q/a.X,Q/a.X,1]),le}calculatePosMatrix(w,B=!1){let Q=w.key,ee=B?this._alignedPosMatrixCache:this._posMatrixCache;if(ee[Q])return ee[Q];let le=this.calculateTileMatrix(w);return a.L(le,B?this.alignedModelViewProjectionMatrix:this.modelViewProjectionMatrix,le),ee[Q]=new Float32Array(le),ee[Q]}calculateFogMatrix(w){let B=w.key,Q=this._fogMatrixCache;if(Q[B])return Q[B];let ee=this.calculateTileMatrix(w);return a.L(ee,this.fogMatrix,ee),Q[B]=new Float32Array(ee),Q[B]}customLayerMatrix(){return this.mercatorMatrix.slice()}getConstrained(w,B){B=a.ac(+B,this.minZoom,this.maxZoom);let Q={center:new a.N(w.lng,w.lat),zoom:B},ee=this.lngRange;if(!this._renderWorldCopies&&ee===null){let Xe=179.9999999999;ee=[-Xe,Xe]}let le=this.tileSize*this.zoomScale(Q.zoom),Fe=0,Ze=le,ut=0,Mt=le,Jt=0,$t=0,{x:yr,y:Cr}=this.size;if(this.latRange){let Xe=this.latRange;Fe=a.Q(Xe[1])*le,Ze=a.Q(Xe[0])*le,Ze-Fe<Cr&&(Jt=Cr/(Ze-Fe))}ee&&(ut=a.b3(a.O(ee[0])*le,0,le),Mt=a.b3(a.O(ee[1])*le,0,le),Mt<ut&&(Mt+=le),Mt-ut<yr&&($t=yr/(Mt-ut)));let{x:ve,y:xe}=this.project.call({worldSize:le},w),Ie,ze,Qe=Math.max($t||0,Jt||0);if(Qe){let Xe=new a.P($t?(Mt+ut)/2:ve,Jt?(Ze+Fe)/2:xe);return Q.center=this.unproject.call({worldSize:le},Xe).wrap(),Q.zoom+=this.scaleZoom(Qe),Q}if(this.latRange){let Xe=Cr/2;xe-Xe<Fe&&(ze=Fe+Xe),xe+Xe>Ze&&(ze=Ze-Xe)}if(ee){let Xe=(ut+Mt)/2,nt=ve;this._renderWorldCopies&&(nt=a.b3(ve,Xe-le/2,Xe+le/2));let qt=yr/2;nt-qt<ut&&(Ie=ut+qt),nt+qt>Mt&&(Ie=Mt-qt)}if(Ie!==void 0||ze!==void 0){let Xe=new a.P(Ie!=null?Ie:ve,ze!=null?ze:xe);Q.center=this.unproject.call({worldSize:le},Xe).wrap()}return Q}_constrain(){if(!this.center||!this.width||!this.height||this._constraining)return;this._constraining=!0;let w=this._unmodified,{center:B,zoom:Q}=this.getConstrained(this.center,this.zoom);this.center=B,this.zoom=Q,this._unmodified=w,this._constraining=!1}_calcMatrices(){if(!this.height)return;let w=this.centerOffset,B=this.point.x,Q=this.point.y;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height,this._pixelPerMeter=a.b5(1,this.center.lat)*this.worldSize;let ee=a.an(new Float64Array(16));a.K(ee,ee,[this.width/2,-this.height/2,1]),a.J(ee,ee,[1,-1,0]),this.labelPlaneMatrix=ee,ee=a.an(new Float64Array(16)),a.K(ee,ee,[1,-1,1]),a.J(ee,ee,[-1,-1,0]),a.K(ee,ee,[2/this.width,2/this.height,1]),this.glCoordMatrix=ee;let le=this.cameraToCenterDistance+this._elevation*this._pixelPerMeter/Math.cos(this._pitch),Fe=Math.min(this.elevation,this.minElevationForCurrentTile),Ze=le-Fe*this._pixelPerMeter/Math.cos(this._pitch),ut=Fe<0?Ze:le,Mt=Math.PI/2+this._pitch,Jt=this._fov*(.5+w.y/this.height),$t=Math.sin(Jt)*ut/Math.sin(a.ac(Math.PI-Mt-Jt,.01,Math.PI-.01)),yr=this.getHorizon(),Cr=2*Math.atan(yr/this.cameraToCenterDistance)*(.5+w.y/(2*yr)),ve=Math.sin(Cr)*ut/Math.sin(a.ac(Math.PI-Mt-Cr,.01,Math.PI-.01)),xe=Math.min($t,ve);this.farZ=1.01*(Math.cos(Math.PI/2-this._pitch)*xe+ut),this.nearZ=this.height/50,ee=new Float64Array(16),a.b6(ee,this._fov,this.width/this.height,this.nearZ,this.farZ),ee[8]=2*-w.x/this.width,ee[9]=2*w.y/this.height,this.projectionMatrix=a.ae(ee),a.K(ee,ee,[1,-1,1]),a.J(ee,ee,[0,0,-this.cameraToCenterDistance]),a.b7(ee,ee,this._pitch),a.ad(ee,ee,this.angle),a.J(ee,ee,[-B,-Q,0]),this.mercatorMatrix=a.K([],ee,[this.worldSize,this.worldSize,this.worldSize]),a.K(ee,ee,[1,1,this._pixelPerMeter]),this.pixelMatrix=a.L(new Float64Array(16),this.labelPlaneMatrix,ee),a.J(ee,ee,[0,0,-this.elevation]),this.modelViewProjectionMatrix=ee,this.invModelViewProjectionMatrix=a.as([],ee),this.fogMatrix=new Float64Array(16),a.b6(this.fogMatrix,this._fov,this.width/this.height,le,this.farZ),this.fogMatrix[8]=2*-w.x/this.width,this.fogMatrix[9]=2*w.y/this.height,a.K(this.fogMatrix,this.fogMatrix,[1,-1,1]),a.J(this.fogMatrix,this.fogMatrix,[0,0,-this.cameraToCenterDistance]),a.b7(this.fogMatrix,this.fogMatrix,this._pitch),a.ad(this.fogMatrix,this.fogMatrix,this.angle),a.J(this.fogMatrix,this.fogMatrix,[-B,-Q,0]),a.K(this.fogMatrix,this.fogMatrix,[1,1,this._pixelPerMeter]),a.J(this.fogMatrix,this.fogMatrix,[0,0,-this.elevation]),this.pixelMatrix3D=a.L(new Float64Array(16),this.labelPlaneMatrix,ee);let Ie=this.width%2/2,ze=this.height%2/2,Qe=Math.cos(this.angle),Xe=Math.sin(this.angle),nt=B-Math.round(B)+Qe*Ie+Xe*ze,qt=Q-Math.round(Q)+Qe*ze+Xe*Ie,Wt=new Float64Array(ee);if(a.J(Wt,Wt,[nt>.5?nt-1:nt,qt>.5?qt-1:qt,0]),this.alignedModelViewProjectionMatrix=Wt,ee=a.as(new Float64Array(16),this.pixelMatrix),!ee)throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=ee,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={}}maxPitchScaleFactor(){if(!this.pixelMatrixInverse)return 1;let w=this.pointCoordinate(new a.P(0,0)),B=[w.x*this.worldSize,w.y*this.worldSize,0,1];return a.af(B,B,this.pixelMatrix)[3]/this.cameraToCenterDistance}getCameraPoint(){let w=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new a.P(0,w))}getCameraQueryGeometry(w){let B=this.getCameraPoint();if(w.length===1)return[w[0],B];{let Q=B.x,ee=B.y,le=B.x,Fe=B.y;for(let Ze of w)Q=Math.min(Q,Ze.x),ee=Math.min(ee,Ze.y),le=Math.max(le,Ze.x),Fe=Math.max(Fe,Ze.y);return[new a.P(Q,ee),new a.P(le,ee),new a.P(le,Fe),new a.P(Q,Fe),new a.P(Q,ee)]}}lngLatToCameraDepth(w,B){let Q=this.locationCoordinate(w),ee=[Q.x*this.worldSize,Q.y*this.worldSize,B,1];return a.af(ee,ee,this.modelViewProjectionMatrix),ee[2]/ee[3]}}function nh(ue,w){let B,Q=!1,ee=null,le=null,Fe=()=>{ee=null,Q&&(ue.apply(le,B),ee=setTimeout(Fe,w),Q=!1)};return(...Ze)=>(Q=!0,le=this,B=Ze,ee||Fe(),ee)}class Mh{constructor(w){this._getCurrentHash=()=>{let B=window.location.hash.replace(\"#\",\"\");if(this._hashName){let Q;return B.split(\"&\").map((ee=>ee.split(\"=\"))).forEach((ee=>{ee[0]===this._hashName&&(Q=ee)})),(Q&&Q[1]||\"\").split(\"/\")}return B.split(\"/\")},this._onHashChange=()=>{let B=this._getCurrentHash();if(B.length>=3&&!B.some((Q=>isNaN(Q)))){let Q=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(B[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+B[2],+B[1]],zoom:+B[0],bearing:Q,pitch:+(B[4]||0)}),!0}return!1},this._updateHashUnthrottled=()=>{let B=window.location.href.replace(/(#.*)?$/,this.getHashString());window.history.replaceState(window.history.state,null,B)},this._removeHash=()=>{let B=this._getCurrentHash();if(B.length===0)return;let Q=B.join(\"/\"),ee=Q;ee.split(\"&\").length>0&&(ee=ee.split(\"&\")[0]),this._hashName&&(ee=`${this._hashName}=${Q}`);let le=window.location.hash.replace(ee,\"\");le.startsWith(\"#&\")?le=le.slice(0,1)+le.slice(2):le===\"#\"&&(le=\"\");let Fe=window.location.href.replace(/(#.+)?$/,le);Fe=Fe.replace(\"&&\",\"&\"),window.history.replaceState(window.history.state,null,Fe)},this._updateHash=nh(this._updateHashUnthrottled,300),this._hashName=w&&encodeURIComponent(w)}addTo(w){return this._map=w,addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this}remove(){return removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),this._removeHash(),delete this._map,this}getHashString(w){let B=this._map.getCenter(),Q=Math.round(100*this._map.getZoom())/100,ee=Math.ceil((Q*Math.LN2+Math.log(512/360/.5))/Math.LN10),le=Math.pow(10,ee),Fe=Math.round(B.lng*le)/le,Ze=Math.round(B.lat*le)/le,ut=this._map.getBearing(),Mt=this._map.getPitch(),Jt=\"\";if(Jt+=w?`/${Fe}/${Ze}/${Q}`:`${Q}/${Ze}/${Fe}`,(ut||Mt)&&(Jt+=\"/\"+Math.round(10*ut)/10),Mt&&(Jt+=`/${Math.round(Mt)}`),this._hashName){let $t=this._hashName,yr=!1,Cr=window.location.hash.slice(1).split(\"&\").map((ve=>{let xe=ve.split(\"=\")[0];return xe===$t?(yr=!0,`${xe}=${Jt}`):ve})).filter((ve=>ve));return yr||Cr.push(`${$t}=${Jt}`),`#${Cr.join(\"&\")}`}return`#${Jt}`}}let zu={linearity:.3,easing:a.b8(0,0,.3,1)},Fc=a.e({deceleration:2500,maxSpeed:1400},zu),wc=a.e({deceleration:20,maxSpeed:1400},zu),bd=a.e({deceleration:1e3,maxSpeed:360},zu),xf=a.e({deceleration:1e3,maxSpeed:90},zu);class Pf{constructor(w){this._map=w,this.clear()}clear(){this._inertiaBuffer=[]}record(w){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:u.now(),settings:w})}_drainInertiaBuffer(){let w=this._inertiaBuffer,B=u.now();for(;w.length>0&&B-w[0].time>160;)w.shift()}_onMoveEnd(w){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;let B={zoom:0,bearing:0,pitch:0,pan:new a.P(0,0),pinchAround:void 0,around:void 0};for(let{settings:le}of this._inertiaBuffer)B.zoom+=le.zoomDelta||0,B.bearing+=le.bearingDelta||0,B.pitch+=le.pitchDelta||0,le.panDelta&&B.pan._add(le.panDelta),le.around&&(B.around=le.around),le.pinchAround&&(B.pinchAround=le.pinchAround);let Q=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,ee={};if(B.pan.mag()){let le=bf(B.pan.mag(),Q,a.e({},Fc,w||{}));ee.offset=B.pan.mult(le.amount/B.pan.mag()),ee.center=this._map.transform.center,qu(ee,le)}if(B.zoom){let le=bf(B.zoom,Q,wc);ee.zoom=this._map.transform.zoom+le.amount,qu(ee,le)}if(B.bearing){let le=bf(B.bearing,Q,bd);ee.bearing=this._map.transform.bearing+a.ac(le.amount,-179,179),qu(ee,le)}if(B.pitch){let le=bf(B.pitch,Q,xf);ee.pitch=this._map.transform.pitch+le.amount,qu(ee,le)}if(ee.zoom||ee.bearing){let le=B.pinchAround===void 0?B.around:B.pinchAround;ee.around=le?this._map.unproject(le):this._map.getCenter()}return this.clear(),a.e(ee,{noMoveStart:!0})}}function qu(ue,w){(!ue.duration||ue.duration<w.duration)&&(ue.duration=w.duration,ue.easing=w.easing)}function bf(ue,w,B){let{maxSpeed:Q,linearity:ee,deceleration:le}=B,Fe=a.ac(ue*ee/(w/1e3),-Q,Q),Ze=Math.abs(Fe)/(le*ee);return{easing:B.easing,duration:1e3*Ze,amount:Fe*(Ze/2)}}class jl extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q,ee={}){let le=c.mousePos(B.getCanvas(),Q),Fe=B.unproject(le);super(w,a.e({point:le,lngLat:Fe,originalEvent:Q},ee)),this._defaultPrevented=!1,this.target=B}}class uf extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q){let ee=w===\"touchend\"?Q.changedTouches:Q.touches,le=c.touchPos(B.getCanvasContainer(),ee),Fe=le.map((ut=>B.unproject(ut))),Ze=le.reduce(((ut,Mt,Jt,$t)=>ut.add(Mt.div($t.length))),new a.P(0,0));super(w,{points:le,point:Ze,lngLats:Fe,lngLat:B.unproject(Ze),originalEvent:Q}),this._defaultPrevented=!1}}class Xh extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q){super(w,{originalEvent:Q}),this._defaultPrevented=!1}}class If{constructor(w,B){this._map=w,this._clickTolerance=B.clickTolerance}reset(){delete this._mousedownPos}wheel(w){return this._firePreventable(new Xh(w.type,this._map,w))}mousedown(w,B){return this._mousedownPos=B,this._firePreventable(new jl(w.type,this._map,w))}mouseup(w){this._map.fire(new jl(w.type,this._map,w))}click(w,B){this._mousedownPos&&this._mousedownPos.dist(B)>=this._clickTolerance||this._map.fire(new jl(w.type,this._map,w))}dblclick(w){return this._firePreventable(new jl(w.type,this._map,w))}mouseover(w){this._map.fire(new jl(w.type,this._map,w))}mouseout(w){this._map.fire(new jl(w.type,this._map,w))}touchstart(w){return this._firePreventable(new uf(w.type,this._map,w))}touchmove(w){this._map.fire(new uf(w.type,this._map,w))}touchend(w){this._map.fire(new uf(w.type,this._map,w))}touchcancel(w){this._map.fire(new uf(w.type,this._map,w))}_firePreventable(w){if(this._map.fire(w),w.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Cs{constructor(w){this._map=w}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(w){this._map.fire(new jl(w.type,this._map,w))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new jl(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(w){this._delayContextMenu?this._contextMenuEvent=w:this._ignoreContextMenu||this._map.fire(new jl(w.type,this._map,w)),this._map.listens(\"contextmenu\")&&w.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class du{constructor(w){this._map=w}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(w){return this.transform.pointLocation(a.P.convert(w),this._map.terrain)}}class ku{constructor(w,B){this._map=w,this._tr=new du(w),this._el=w.getCanvasContainer(),this._container=w.getContainer(),this._clickTolerance=B.clickTolerance||1}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(w,B){this.isEnabled()&&w.shiftKey&&w.button===0&&(c.disableDrag(),this._startPos=this._lastPos=B,this._active=!0)}mousemoveWindow(w,B){if(!this._active)return;let Q=B;if(this._lastPos.equals(Q)||!this._box&&Q.dist(this._startPos)<this._clickTolerance)return;let ee=this._startPos;this._lastPos=Q,this._box||(this._box=c.create(\"div\",\"maplibregl-boxzoom\",this._container),this._container.classList.add(\"maplibregl-crosshair\"),this._fireEvent(\"boxzoomstart\",w));let le=Math.min(ee.x,Q.x),Fe=Math.max(ee.x,Q.x),Ze=Math.min(ee.y,Q.y),ut=Math.max(ee.y,Q.y);c.setTransform(this._box,`translate(${le}px,${Ze}px)`),this._box.style.width=Fe-le+\"px\",this._box.style.height=ut-Ze+\"px\"}mouseupWindow(w,B){if(!this._active||w.button!==0)return;let Q=this._startPos,ee=B;if(this.reset(),c.suppressClick(),Q.x!==ee.x||Q.y!==ee.y)return this._map.fire(new a.k(\"boxzoomend\",{originalEvent:w})),{cameraAnimation:le=>le.fitScreenCoordinates(Q,ee,this._tr.bearing,{linear:!0})};this._fireEvent(\"boxzoomcancel\",w)}keydown(w){this._active&&w.keyCode===27&&(this.reset(),this._fireEvent(\"boxzoomcancel\",w))}reset(){this._active=!1,this._container.classList.remove(\"maplibregl-crosshair\"),this._box&&(c.remove(this._box),this._box=null),c.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(w,B){return this._map.fire(new a.k(w,{originalEvent:B}))}}function Xf(ue,w){if(ue.length!==w.length)throw new Error(`The number of touches and points are not equal - touches ${ue.length}, points ${w.length}`);let B={};for(let Q=0;Q<ue.length;Q++)B[ue[Q].identifier]=w[Q];return B}class Us{constructor(w){this.reset(),this.numTouches=w.numTouches}reset(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1}touchstart(w,B,Q){(this.centroid||Q.length>this.numTouches)&&(this.aborted=!0),this.aborted||(this.startTime===void 0&&(this.startTime=w.timeStamp),Q.length===this.numTouches&&(this.centroid=(function(ee){let le=new a.P(0,0);for(let Fe of ee)le._add(Fe);return le.div(ee.length)})(B),this.touches=Xf(Q,B)))}touchmove(w,B,Q){if(this.aborted||!this.centroid)return;let ee=Xf(Q,B);for(let le in this.touches){let Fe=ee[le];(!Fe||Fe.dist(this.touches[le])>30)&&(this.aborted=!0)}}touchend(w,B,Q){if((!this.centroid||w.timeStamp-this.startTime>500)&&(this.aborted=!0),Q.length===0){let ee=!this.aborted&&this.centroid;if(this.reset(),ee)return ee}}}class wf{constructor(w){this.singleTap=new Us(w),this.numTaps=w.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(w,B,Q){this.singleTap.touchstart(w,B,Q)}touchmove(w,B,Q){this.singleTap.touchmove(w,B,Q)}touchend(w,B,Q){let ee=this.singleTap.touchend(w,B,Q);if(ee){let le=w.timeStamp-this.lastTime<500,Fe=!this.lastTap||this.lastTap.dist(ee)<30;if(le&&Fe||this.reset(),this.count++,this.lastTime=w.timeStamp,this.lastTap=ee,this.count===this.numTaps)return this.reset(),ee}}}class zc{constructor(w){this._tr=new du(w),this._zoomIn=new wf({numTouches:1,numTaps:2}),this._zoomOut=new wf({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(w,B,Q){this._zoomIn.touchstart(w,B,Q),this._zoomOut.touchstart(w,B,Q)}touchmove(w,B,Q){this._zoomIn.touchmove(w,B,Q),this._zoomOut.touchmove(w,B,Q)}touchend(w,B,Q){let ee=this._zoomIn.touchend(w,B,Q),le=this._zoomOut.touchend(w,B,Q),Fe=this._tr;return ee?(this._active=!0,w.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:Ze=>Ze.easeTo({duration:300,zoom:Fe.zoom+1,around:Fe.unproject(ee)},{originalEvent:w})}):le?(this._active=!0,w.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:Ze=>Ze.easeTo({duration:300,zoom:Fe.zoom-1,around:Fe.unproject(le)},{originalEvent:w})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class Wu{constructor(w){this._enabled=!!w.enable,this._moveStateManager=w.moveStateManager,this._clickTolerance=w.clickTolerance||1,this._moveFunction=w.move,this._activateOnStart=!!w.activateOnStart,w.assignEvents(this),this.reset()}reset(w){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(w)}_move(...w){let B=this._moveFunction(...w);if(B.bearingDelta||B.pitchDelta||B.around||B.panDelta)return this._active=!0,B}dragStart(w,B){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(w)&&(this._moveStateManager.startMove(w),this._lastPoint=B.length?B[0]:B,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(w,B){if(!this.isEnabled())return;let Q=this._lastPoint;if(!Q)return;if(w.preventDefault(),!this._moveStateManager.isValidMoveEvent(w))return void this.reset(w);let ee=B.length?B[0]:B;return!this._moved&&ee.dist(Q)<this._clickTolerance?void 0:(this._moved=!0,this._lastPoint=ee,this._move(Q,ee))}dragEnd(w){this.isEnabled()&&this._lastPoint&&this._moveStateManager.isValidEndEvent(w)&&(this._moved&&c.suppressClick(),this.reset(w))}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}getClickTolerance(){return this._clickTolerance}}let Rf={0:1,2:2};class Xu{constructor(w){this._correctEvent=w.checkCorrectEvent}startMove(w){let B=c.mouseButton(w);this._eventButton=B}endMove(w){delete this._eventButton}isValidStartEvent(w){return this._correctEvent(w)}isValidMoveEvent(w){return!(function(B,Q){let ee=Rf[Q];return B.buttons===void 0||(B.buttons&ee)!==ee})(w,this._eventButton)}isValidEndEvent(w){return c.mouseButton(w)===this._eventButton}}class cf{constructor(){this._firstTouch=void 0}_isOneFingerTouch(w){return w.targetTouches.length===1}_isSameTouchEvent(w){return w.targetTouches[0].identifier===this._firstTouch}startMove(w){this._firstTouch=w.targetTouches[0].identifier}endMove(w){delete this._firstTouch}isValidStartEvent(w){return this._isOneFingerTouch(w)}isValidMoveEvent(w){return this._isOneFingerTouch(w)&&this._isSameTouchEvent(w)}isValidEndEvent(w){return this._isOneFingerTouch(w)&&this._isSameTouchEvent(w)}}let Zf=ue=>{ue.mousedown=ue.dragStart,ue.mousemoveWindow=ue.dragMove,ue.mouseup=ue.dragEnd,ue.contextmenu=w=>{w.preventDefault()}},Wl=({enable:ue,clickTolerance:w,bearingDegreesPerPixelMoved:B=.8})=>{let Q=new Xu({checkCorrectEvent:ee=>c.mouseButton(ee)===0&&ee.ctrlKey||c.mouseButton(ee)===2});return new Wu({clickTolerance:w,move:(ee,le)=>({bearingDelta:(le.x-ee.x)*B}),moveStateManager:Q,enable:ue,assignEvents:Zf})},ah=({enable:ue,clickTolerance:w,pitchDegreesPerPixelMoved:B=-.5})=>{let Q=new Xu({checkCorrectEvent:ee=>c.mouseButton(ee)===0&&ee.ctrlKey||c.mouseButton(ee)===2});return new Wu({clickTolerance:w,move:(ee,le)=>({pitchDelta:(le.y-ee.y)*B}),moveStateManager:Q,enable:ue,assignEvents:Zf})};class Zu{constructor(w,B){this._clickTolerance=w.clickTolerance||1,this._map=B,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new a.P(0,0)}_shouldBePrevented(w){return w<(this._map.cooperativeGestures.isEnabled()?2:1)}touchstart(w,B,Q){return this._calculateTransform(w,B,Q)}touchmove(w,B,Q){if(this._active){if(!this._shouldBePrevented(Q.length))return w.preventDefault(),this._calculateTransform(w,B,Q);this._map.cooperativeGestures.notifyGestureBlocked(\"touch_pan\",w)}}touchend(w,B,Q){this._calculateTransform(w,B,Q),this._active&&this._shouldBePrevented(Q.length)&&this.reset()}touchcancel(){this.reset()}_calculateTransform(w,B,Q){Q.length>0&&(this._active=!0);let ee=Xf(Q,B),le=new a.P(0,0),Fe=new a.P(0,0),Ze=0;for(let Mt in ee){let Jt=ee[Mt],$t=this._touches[Mt];$t&&(le._add(Jt),Fe._add(Jt.sub($t)),Ze++,ee[Mt]=Jt)}if(this._touches=ee,this._shouldBePrevented(Ze)||!Fe.mag())return;let ut=Fe.div(Ze);return this._sum._add(ut),this._sum.mag()<this._clickTolerance?void 0:{around:le.div(Ze),panDelta:ut}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class qc{constructor(){this.reset()}reset(){this._active=!1,delete this._firstTwoTouches}touchstart(w,B,Q){this._firstTwoTouches||Q.length<2||(this._firstTwoTouches=[Q[0].identifier,Q[1].identifier],this._start([B[0],B[1]]))}touchmove(w,B,Q){if(!this._firstTwoTouches)return;w.preventDefault();let[ee,le]=this._firstTwoTouches,Fe=Tc(Q,B,ee),Ze=Tc(Q,B,le);if(!Fe||!Ze)return;let ut=this._aroundCenter?null:Fe.add(Ze).div(2);return this._move([Fe,Ze],ut,w)}touchend(w,B,Q){if(!this._firstTwoTouches)return;let[ee,le]=this._firstTwoTouches,Fe=Tc(Q,B,ee),Ze=Tc(Q,B,le);Fe&&Ze||(this._active&&c.suppressClick(),this.reset())}touchcancel(){this.reset()}enable(w){this._enabled=!0,this._aroundCenter=!!w&&w.around===\"center\"}disable(){this._enabled=!1,this.reset()}isEnabled(){return!!this._enabled}isActive(){return!!this._active}}function Tc(ue,w,B){for(let Q=0;Q<ue.length;Q++)if(ue[Q].identifier===B)return w[Q]}function Tl(ue,w){return Math.log(ue/w)/Math.LN2}class vu extends qc{reset(){super.reset(),delete this._distance,delete this._startDistance}_start(w){this._startDistance=this._distance=w[0].dist(w[1])}_move(w,B){let Q=this._distance;if(this._distance=w[0].dist(w[1]),this._active||!(Math.abs(Tl(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:Tl(this._distance,Q),pinchAround:B}}}function Oc(ue,w){return 180*ue.angleWith(w)/Math.PI}class ff extends qc{reset(){super.reset(),delete this._minDiameter,delete this._startVector,delete this._vector}_start(w){this._startVector=this._vector=w[0].sub(w[1]),this._minDiameter=w[0].dist(w[1])}_move(w,B,Q){let ee=this._vector;if(this._vector=w[0].sub(w[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:Oc(this._vector,ee),pinchAround:B}}_isBelowThreshold(w){this._minDiameter=Math.min(this._minDiameter,w.mag());let B=25/(Math.PI*this._minDiameter)*360,Q=Oc(w,this._startVector);return Math.abs(Q)<B}}function fc(ue){return Math.abs(ue.y)>Math.abs(ue.x)}class Bc extends qc{constructor(w){super(),this._currentTouchCount=0,this._map=w}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(w,B,Q){super.touchstart(w,B,Q),this._currentTouchCount=Q.length}_start(w){this._lastPoints=w,fc(w[0].sub(w[1]))&&(this._valid=!1)}_move(w,B,Q){if(this._map.cooperativeGestures.isEnabled()&&this._currentTouchCount<3)return;let ee=w[0].sub(this._lastPoints[0]),le=w[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(ee,le,Q.timeStamp),this._valid?(this._lastPoints=w,this._active=!0,{pitchDelta:(ee.y+le.y)/2*-.5}):void 0}gestureBeginsVertically(w,B,Q){if(this._valid!==void 0)return this._valid;let ee=w.mag()>=2,le=B.mag()>=2;if(!ee&&!le)return;if(!ee||!le)return this._firstMove===void 0&&(this._firstMove=Q),Q-this._firstMove<100&&void 0;let Fe=w.y>0==B.y>0;return fc(w)&&fc(B)&&Fe}}let kt={panStep:100,bearingStep:15,pitchStep:10};class Zt{constructor(w){this._tr=new du(w);let B=kt;this._panStep=B.panStep,this._bearingStep=B.bearingStep,this._pitchStep=B.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(w){if(w.altKey||w.ctrlKey||w.metaKey)return;let B=0,Q=0,ee=0,le=0,Fe=0;switch(w.keyCode){case 61:case 107:case 171:case 187:B=1;break;case 189:case 109:case 173:B=-1;break;case 37:w.shiftKey?Q=-1:(w.preventDefault(),le=-1);break;case 39:w.shiftKey?Q=1:(w.preventDefault(),le=1);break;case 38:w.shiftKey?ee=1:(w.preventDefault(),Fe=-1);break;case 40:w.shiftKey?ee=-1:(w.preventDefault(),Fe=1);break;default:return}return this._rotationDisabled&&(Q=0,ee=0),{cameraAnimation:Ze=>{let ut=this._tr;Ze.easeTo({duration:300,easeId:\"keyboardHandler\",easing:Er,zoom:B?Math.round(ut.zoom)+B*(w.shiftKey?2:1):ut.zoom,bearing:ut.bearing+Q*this._bearingStep,pitch:ut.pitch+ee*this._pitchStep,offset:[-le*this._panStep,-Fe*this._panStep],center:ut.center},{originalEvent:w})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function Er(ue){return ue*(2-ue)}let xr=4.000244140625;class Kr{constructor(w,B){this._onTimeout=Q=>{this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(Q)},this._map=w,this._tr=new du(w),this._triggerRenderFrame=B,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222}setZoomRate(w){this._defaultZoomRate=w}setWheelZoomRate(w){this._wheelZoomRate=w}isEnabled(){return!!this._enabled}isActive(){return!!this._active||this._finishTimeout!==void 0}isZooming(){return!!this._zooming}enable(w){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!w&&w.around===\"center\")}disable(){this.isEnabled()&&(this._enabled=!1)}_shouldBePrevented(w){return!!this._map.cooperativeGestures.isEnabled()&&!(w.ctrlKey||this._map.cooperativeGestures.isBypassed(w))}wheel(w){if(!this.isEnabled())return;if(this._shouldBePrevented(w))return void this._map.cooperativeGestures.notifyGestureBlocked(\"wheel_zoom\",w);let B=w.deltaMode===WheelEvent.DOM_DELTA_LINE?40*w.deltaY:w.deltaY,Q=u.now(),ee=Q-(this._lastWheelEventTime||0);this._lastWheelEventTime=Q,B!==0&&B%xr==0?this._type=\"wheel\":B!==0&&Math.abs(B)<4?this._type=\"trackpad\":ee>400?(this._type=null,this._lastValue=B,this._timeout=setTimeout(this._onTimeout,40,w)):this._type||(this._type=Math.abs(ee*B)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,B+=this._lastValue)),w.shiftKey&&B&&(B/=4),this._type&&(this._lastWheelEvent=w,this._delta-=B,this._active||this._start(w)),w.preventDefault()}_start(w){if(!this._delta)return;this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);let B=c.mousePos(this._map.getCanvas(),w),Q=this._tr;this._around=B.y>Q.transform.height/2-Q.transform.getHorizon()?a.N.convert(this._aroundCenter?Q.center:Q.unproject(B)):a.N.convert(Q.center),this._aroundPoint=Q.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._triggerRenderFrame())}renderFrame(){if(!this._frameId||(this._frameId=null,!this.isActive()))return;let w=this._tr.transform;if(this._delta!==0){let ut=this._type===\"wheel\"&&Math.abs(this._delta)>xr?this._wheelZoomRate:this._defaultZoomRate,Mt=2/(1+Math.exp(-Math.abs(this._delta*ut)));this._delta<0&&Mt!==0&&(Mt=1/Mt);let Jt=typeof this._targetZoom==\"number\"?w.zoomScale(this._targetZoom):w.scale;this._targetZoom=Math.min(w.maxZoom,Math.max(w.minZoom,w.scaleZoom(Jt*Mt))),this._type===\"wheel\"&&(this._startZoom=w.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}let B=typeof this._targetZoom==\"number\"?this._targetZoom:w.zoom,Q=this._startZoom,ee=this._easing,le,Fe=!1,Ze=u.now()-this._lastWheelEventTime;if(this._type===\"wheel\"&&Q&&ee&&Ze){let ut=Math.min(Ze/200,1),Mt=ee(ut);le=a.y.number(Q,B,Mt),ut<1?this._frameId||(this._frameId=!0):Fe=!0}else le=B,Fe=!0;return this._active=!0,Fe&&(this._active=!1,this._finishTimeout=setTimeout((()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!Fe,zoomDelta:le-w.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(w){let B=a.b9;if(this._prevEase){let Q=this._prevEase,ee=(u.now()-Q.start)/Q.duration,le=Q.easing(ee+.01)-Q.easing(ee),Fe=.27/Math.sqrt(le*le+1e-4)*.01,Ze=Math.sqrt(.0729-Fe*Fe);B=a.b8(Fe,Ze,.25,1)}return this._prevEase={start:u.now(),duration:w,easing:B},B}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class Mi{constructor(w,B){this._clickZoom=w,this._tapZoom=B}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class Xi{constructor(w){this._tr=new du(w),this.reset()}reset(){this._active=!1}dblclick(w,B){return w.preventDefault(),{cameraAnimation:Q=>{Q.easeTo({duration:300,zoom:this._tr.zoom+(w.shiftKey?-1:1),around:this._tr.unproject(B)},{originalEvent:w})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class dn{constructor(){this._tap=new wf({numTouches:1,numTaps:1}),this.reset()}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(w,B,Q){if(!this._swipePoint)if(this._tapTime){let ee=B[0],le=w.timeStamp-this._tapTime<500,Fe=this._tapPoint.dist(ee)<30;le&&Fe?Q.length>0&&(this._swipePoint=ee,this._swipeTouch=Q[0].identifier):this.reset()}else this._tap.touchstart(w,B,Q)}touchmove(w,B,Q){if(this._tapTime){if(this._swipePoint){if(Q[0].identifier!==this._swipeTouch)return;let ee=B[0],le=ee.y-this._swipePoint.y;return this._swipePoint=ee,w.preventDefault(),this._active=!0,{zoomDelta:le/128}}}else this._tap.touchmove(w,B,Q)}touchend(w,B,Q){if(this._tapTime)this._swipePoint&&Q.length===0&&this.reset();else{let ee=this._tap.touchend(w,B,Q);ee&&(this._tapTime=w.timeStamp,this._tapPoint=ee)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class wn{constructor(w,B,Q){this._el=w,this._mousePan=B,this._touchPan=Q}enable(w){this._inertiaOptions=w||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"maplibregl-touch-drag-pan\")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"maplibregl-touch-drag-pan\")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class On{constructor(w,B,Q){this._pitchWithRotate=w.pitchWithRotate,this._mouseRotate=B,this._mousePitch=Q}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()}}class Yi{constructor(w,B,Q,ee){this._el=w,this._touchZoom=B,this._touchRotate=Q,this._tapDragZoom=ee,this._rotationDisabled=!1,this._enabled=!0}enable(w){this._touchZoom.enable(w),this._rotationDisabled||this._touchRotate.enable(w),this._tapDragZoom.enable(),this._el.classList.add(\"maplibregl-touch-zoom-rotate\")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"maplibregl-touch-zoom-rotate\")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}class $i{constructor(w,B){this._bypassKey=navigator.userAgent.indexOf(\"Mac\")!==-1?\"metaKey\":\"ctrlKey\",this._map=w,this._options=B,this._enabled=!1}isActive(){return!1}reset(){}_setupUI(){if(this._container)return;let w=this._map.getCanvasContainer();w.classList.add(\"maplibregl-cooperative-gestures\"),this._container=c.create(\"div\",\"maplibregl-cooperative-gesture-screen\",w);let B=this._map._getUIString(\"CooperativeGesturesHandler.WindowsHelpText\");this._bypassKey===\"metaKey\"&&(B=this._map._getUIString(\"CooperativeGesturesHandler.MacHelpText\"));let Q=this._map._getUIString(\"CooperativeGesturesHandler.MobileHelpText\"),ee=document.createElement(\"div\");ee.className=\"maplibregl-desktop-message\",ee.textContent=B,this._container.appendChild(ee);let le=document.createElement(\"div\");le.className=\"maplibregl-mobile-message\",le.textContent=Q,this._container.appendChild(le),this._container.setAttribute(\"aria-hidden\",\"true\")}_destroyUI(){this._container&&(c.remove(this._container),this._map.getCanvasContainer().classList.remove(\"maplibregl-cooperative-gestures\")),delete this._container}enable(){this._setupUI(),this._enabled=!0}disable(){this._enabled=!1,this._destroyUI()}isEnabled(){return this._enabled}isBypassed(w){return w[this._bypassKey]}notifyGestureBlocked(w,B){this._enabled&&(this._map.fire(new a.k(\"cooperativegestureprevented\",{gestureType:w,originalEvent:B})),this._container.classList.add(\"maplibregl-show\"),setTimeout((()=>{this._container.classList.remove(\"maplibregl-show\")}),100))}}let an=ue=>ue.zoom||ue.drag||ue.pitch||ue.rotate;class Fi extends a.k{}function ta(ue){return ue.panDelta&&ue.panDelta.mag()||ue.zoomDelta||ue.bearingDelta||ue.pitchDelta}class Ca{constructor(w,B){this.handleWindowEvent=ee=>{this.handleEvent(ee,`${ee.type}Window`)},this.handleEvent=(ee,le)=>{if(ee.type===\"blur\")return void this.stop(!0);this._updatingCamera=!0;let Fe=ee.type===\"renderFrame\"?void 0:ee,Ze={needsRenderFrame:!1},ut={},Mt={},Jt=ee.touches,$t=Jt?this._getMapTouches(Jt):void 0,yr=$t?c.touchPos(this._map.getCanvas(),$t):c.mousePos(this._map.getCanvas(),ee);for(let{handlerName:xe,handler:Ie,allowed:ze}of this._handlers){if(!Ie.isEnabled())continue;let Qe;this._blockedByActive(Mt,ze,xe)?Ie.reset():Ie[le||ee.type]&&(Qe=Ie[le||ee.type](ee,yr,$t),this.mergeHandlerResult(Ze,ut,Qe,xe,Fe),Qe&&Qe.needsRenderFrame&&this._triggerRenderFrame()),(Qe||Ie.isActive())&&(Mt[xe]=Ie)}let Cr={};for(let xe in this._previousActiveHandlers)Mt[xe]||(Cr[xe]=Fe);this._previousActiveHandlers=Mt,(Object.keys(Cr).length||ta(Ze))&&(this._changes.push([Ze,ut,Cr]),this._triggerRenderFrame()),(Object.keys(Mt).length||ta(Ze))&&this._map._stop(!0),this._updatingCamera=!1;let{cameraAnimation:ve}=Ze;ve&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],ve(this._map))},this._map=w,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Pf(w),this._bearingSnap=B.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(B);let Q=this._el;this._listeners=[[Q,\"touchstart\",{passive:!0}],[Q,\"touchmove\",{passive:!1}],[Q,\"touchend\",void 0],[Q,\"touchcancel\",void 0],[Q,\"mousedown\",void 0],[Q,\"mousemove\",void 0],[Q,\"mouseup\",void 0],[document,\"mousemove\",{capture:!0}],[document,\"mouseup\",void 0],[Q,\"mouseover\",void 0],[Q,\"mouseout\",void 0],[Q,\"dblclick\",void 0],[Q,\"click\",void 0],[Q,\"keydown\",{capture:!1}],[Q,\"keyup\",void 0],[Q,\"wheel\",{passive:!1}],[Q,\"contextmenu\",void 0],[window,\"blur\",void 0]];for(let[ee,le,Fe]of this._listeners)c.addEventListener(ee,le,ee===document?this.handleWindowEvent:this.handleEvent,Fe)}destroy(){for(let[w,B,Q]of this._listeners)c.removeEventListener(w,B,w===document?this.handleWindowEvent:this.handleEvent,Q)}_addDefaultHandlers(w){let B=this._map,Q=B.getCanvasContainer();this._add(\"mapEvent\",new If(B,w));let ee=B.boxZoom=new ku(B,w);this._add(\"boxZoom\",ee),w.interactive&&w.boxZoom&&ee.enable();let le=B.cooperativeGestures=new $i(B,w.cooperativeGestures);this._add(\"cooperativeGestures\",le),w.cooperativeGestures&&le.enable();let Fe=new zc(B),Ze=new Xi(B);B.doubleClickZoom=new Mi(Ze,Fe),this._add(\"tapZoom\",Fe),this._add(\"clickZoom\",Ze),w.interactive&&w.doubleClickZoom&&B.doubleClickZoom.enable();let ut=new dn;this._add(\"tapDragZoom\",ut);let Mt=B.touchPitch=new Bc(B);this._add(\"touchPitch\",Mt),w.interactive&&w.touchPitch&&B.touchPitch.enable(w.touchPitch);let Jt=Wl(w),$t=ah(w);B.dragRotate=new On(w,Jt,$t),this._add(\"mouseRotate\",Jt,[\"mousePitch\"]),this._add(\"mousePitch\",$t,[\"mouseRotate\"]),w.interactive&&w.dragRotate&&B.dragRotate.enable();let yr=(({enable:Qe,clickTolerance:Xe})=>{let nt=new Xu({checkCorrectEvent:qt=>c.mouseButton(qt)===0&&!qt.ctrlKey});return new Wu({clickTolerance:Xe,move:(qt,Wt)=>({around:Wt,panDelta:Wt.sub(qt)}),activateOnStart:!0,moveStateManager:nt,enable:Qe,assignEvents:Zf})})(w),Cr=new Zu(w,B);B.dragPan=new wn(Q,yr,Cr),this._add(\"mousePan\",yr),this._add(\"touchPan\",Cr,[\"touchZoom\",\"touchRotate\"]),w.interactive&&w.dragPan&&B.dragPan.enable(w.dragPan);let ve=new ff,xe=new vu;B.touchZoomRotate=new Yi(Q,xe,ve,ut),this._add(\"touchRotate\",ve,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",xe,[\"touchPan\",\"touchRotate\"]),w.interactive&&w.touchZoomRotate&&B.touchZoomRotate.enable(w.touchZoomRotate);let Ie=B.scrollZoom=new Kr(B,(()=>this._triggerRenderFrame()));this._add(\"scrollZoom\",Ie,[\"mousePan\"]),w.interactive&&w.scrollZoom&&B.scrollZoom.enable(w.scrollZoom);let ze=B.keyboard=new Zt(B);this._add(\"keyboard\",ze),w.interactive&&w.keyboard&&B.keyboard.enable(),this._add(\"blockableMapEvent\",new Cs(B))}_add(w,B,Q){this._handlers.push({handlerName:w,handler:B,allowed:Q}),this._handlersById[w]=B}stop(w){if(!this._updatingCamera){for(let{handler:B}of this._handlers)B.reset();this._inertia.clear(),this._fireEvents({},{},w),this._changes=[]}}isActive(){for(let{handler:w}of this._handlers)if(w.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return!!an(this._eventsInProgress)||this.isZooming()}_blockedByActive(w,B,Q){for(let ee in w)if(ee!==Q&&(!B||B.indexOf(ee)<0))return!0;return!1}_getMapTouches(w){let B=[];for(let Q of w)this._el.contains(Q.target)&&B.push(Q);return B}mergeHandlerResult(w,B,Q,ee,le){if(!Q)return;a.e(w,Q);let Fe={handlerName:ee,originalEvent:Q.originalEvent||le};Q.zoomDelta!==void 0&&(B.zoom=Fe),Q.panDelta!==void 0&&(B.drag=Fe),Q.pitchDelta!==void 0&&(B.pitch=Fe),Q.bearingDelta!==void 0&&(B.rotate=Fe)}_applyChanges(){let w={},B={},Q={};for(let[ee,le,Fe]of this._changes)ee.panDelta&&(w.panDelta=(w.panDelta||new a.P(0,0))._add(ee.panDelta)),ee.zoomDelta&&(w.zoomDelta=(w.zoomDelta||0)+ee.zoomDelta),ee.bearingDelta&&(w.bearingDelta=(w.bearingDelta||0)+ee.bearingDelta),ee.pitchDelta&&(w.pitchDelta=(w.pitchDelta||0)+ee.pitchDelta),ee.around!==void 0&&(w.around=ee.around),ee.pinchAround!==void 0&&(w.pinchAround=ee.pinchAround),ee.noInertia&&(w.noInertia=ee.noInertia),a.e(B,le),a.e(Q,Fe);this._updateMapTransform(w,B,Q),this._changes=[]}_updateMapTransform(w,B,Q){let ee=this._map,le=ee._getTransformForUpdate(),Fe=ee.terrain;if(!(ta(w)||Fe&&this._terrainMovement))return this._fireEvents(B,Q,!0);let{panDelta:Ze,zoomDelta:ut,bearingDelta:Mt,pitchDelta:Jt,around:$t,pinchAround:yr}=w;yr!==void 0&&($t=yr),ee._stop(!0),$t=$t||ee.transform.centerPoint;let Cr=le.pointLocation(Ze?$t.sub(Ze):$t);Mt&&(le.bearing+=Mt),Jt&&(le.pitch+=Jt),ut&&(le.zoom+=ut),Fe?this._terrainMovement||!B.drag&&!B.zoom?B.drag&&this._terrainMovement?le.center=le.pointLocation(le.centerPoint.sub(Ze)):le.setLocationAtPoint(Cr,$t):(this._terrainMovement=!0,this._map._elevationFreeze=!0,le.setLocationAtPoint(Cr,$t)):le.setLocationAtPoint(Cr,$t),ee._applyUpdatedTransform(le),this._map._update(),w.noInertia||this._inertia.record(w),this._fireEvents(B,Q,!0)}_fireEvents(w,B,Q){let ee=an(this._eventsInProgress),le=an(w),Fe={};for(let $t in w){let{originalEvent:yr}=w[$t];this._eventsInProgress[$t]||(Fe[`${$t}start`]=yr),this._eventsInProgress[$t]=w[$t]}!ee&&le&&this._fireEvent(\"movestart\",le.originalEvent);for(let $t in Fe)this._fireEvent($t,Fe[$t]);le&&this._fireEvent(\"move\",le.originalEvent);for(let $t in w){let{originalEvent:yr}=w[$t];this._fireEvent($t,yr)}let Ze={},ut;for(let $t in this._eventsInProgress){let{handlerName:yr,originalEvent:Cr}=this._eventsInProgress[$t];this._handlersById[yr].isActive()||(delete this._eventsInProgress[$t],ut=B[yr]||Cr,Ze[`${$t}end`]=ut)}for(let $t in Ze)this._fireEvent($t,Ze[$t]);let Mt=an(this._eventsInProgress),Jt=(ee||le)&&!Mt;if(Jt&&this._terrainMovement){this._map._elevationFreeze=!1,this._terrainMovement=!1;let $t=this._map._getTransformForUpdate();$t.recalculateZoom(this._map.terrain),this._map._applyUpdatedTransform($t)}if(Q&&Jt){this._updatingCamera=!0;let $t=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),yr=Cr=>Cr!==0&&-this._bearingSnap<Cr&&Cr<this._bearingSnap;!$t||!$t.essential&&u.prefersReducedMotion?(this._map.fire(new a.k(\"moveend\",{originalEvent:ut})),yr(this._map.getBearing())&&this._map.resetNorth()):(yr($t.bearing||this._map.getBearing())&&($t.bearing=0),$t.freezeElevation=!0,this._map.easeTo($t,{originalEvent:ut})),this._updatingCamera=!1}}_fireEvent(w,B){this._map.fire(new a.k(w,B?{originalEvent:B}:{}))}_requestFrame(){return this._map.triggerRepaint(),this._map._renderTaskQueue.add((w=>{delete this._frameId,this.handleEvent(new Fi(\"renderFrame\",{timeStamp:w})),this._applyChanges()}))}_triggerRenderFrame(){this._frameId===void 0&&(this._frameId=this._requestFrame())}}class Ra extends a.E{constructor(w,B){super(),this._renderFrameCallback=()=>{let Q=Math.min((u.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(Q)),Q<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=w,this._bearingSnap=B.bearingSnap,this.on(\"moveend\",(()=>{delete this._requestedCameraState}))}getCenter(){return new a.N(this.transform.center.lng,this.transform.center.lat)}setCenter(w,B){return this.jumpTo({center:w},B)}panBy(w,B,Q){return w=a.P.convert(w).mult(-1),this.panTo(this.transform.center,a.e({offset:w},B),Q)}panTo(w,B,Q){return this.easeTo(a.e({center:w},B),Q)}getZoom(){return this.transform.zoom}setZoom(w,B){return this.jumpTo({zoom:w},B),this}zoomTo(w,B,Q){return this.easeTo(a.e({zoom:w},B),Q)}zoomIn(w,B){return this.zoomTo(this.getZoom()+1,w,B),this}zoomOut(w,B){return this.zoomTo(this.getZoom()-1,w,B),this}getBearing(){return this.transform.bearing}setBearing(w,B){return this.jumpTo({bearing:w},B),this}getPadding(){return this.transform.padding}setPadding(w,B){return this.jumpTo({padding:w},B),this}rotateTo(w,B,Q){return this.easeTo(a.e({bearing:w},B),Q)}resetNorth(w,B){return this.rotateTo(0,a.e({duration:1e3},w),B),this}resetNorthPitch(w,B){return this.easeTo(a.e({bearing:0,pitch:0,duration:1e3},w),B),this}snapToNorth(w,B){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(w,B):this}getPitch(){return this.transform.pitch}setPitch(w,B){return this.jumpTo({pitch:w},B),this}cameraForBounds(w,B){w=ce.convert(w).adjustAntiMeridian();let Q=B&&B.bearing||0;return this._cameraForBoxAndBearing(w.getNorthWest(),w.getSouthEast(),Q,B)}_cameraForBoxAndBearing(w,B,Q,ee){let le={top:0,bottom:0,right:0,left:0};if(typeof(ee=a.e({padding:le,offset:[0,0],maxZoom:this.transform.maxZoom},ee)).padding==\"number\"){let Hr=ee.padding;ee.padding={top:Hr,bottom:Hr,right:Hr,left:Hr}}ee.padding=a.e(le,ee.padding);let Fe=this.transform,Ze=Fe.padding,ut=new ce(w,B),Mt=Fe.project(ut.getNorthWest()),Jt=Fe.project(ut.getNorthEast()),$t=Fe.project(ut.getSouthEast()),yr=Fe.project(ut.getSouthWest()),Cr=a.ba(-Q),ve=Mt.rotate(Cr),xe=Jt.rotate(Cr),Ie=$t.rotate(Cr),ze=yr.rotate(Cr),Qe=new a.P(Math.max(ve.x,xe.x,ze.x,Ie.x),Math.max(ve.y,xe.y,ze.y,Ie.y)),Xe=new a.P(Math.min(ve.x,xe.x,ze.x,Ie.x),Math.min(ve.y,xe.y,ze.y,Ie.y)),nt=Qe.sub(Xe),qt=(Fe.width-(Ze.left+Ze.right+ee.padding.left+ee.padding.right))/nt.x,Wt=(Fe.height-(Ze.top+Ze.bottom+ee.padding.top+ee.padding.bottom))/nt.y;if(Wt<0||qt<0)return void a.w(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\");let nr=Math.min(Fe.scaleZoom(Fe.scale*Math.min(qt,Wt)),ee.maxZoom),fr=a.P.convert(ee.offset),br=new a.P((ee.padding.left-ee.padding.right)/2,(ee.padding.top-ee.padding.bottom)/2).rotate(a.ba(Q)),Fr=fr.add(br).mult(Fe.scale/Fe.zoomScale(nr));return{center:Fe.unproject(Mt.add($t).div(2).sub(Fr)),zoom:nr,bearing:Q}}fitBounds(w,B,Q){return this._fitInternal(this.cameraForBounds(w,B),B,Q)}fitScreenCoordinates(w,B,Q,ee,le){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(a.P.convert(w)),this.transform.pointLocation(a.P.convert(B)),Q,ee),ee,le)}_fitInternal(w,B,Q){return w?(delete(B=a.e(w,B)).padding,B.linear?this.easeTo(B,Q):this.flyTo(B,Q)):this}jumpTo(w,B){this.stop();let Q=this._getTransformForUpdate(),ee=!1,le=!1,Fe=!1;return\"zoom\"in w&&Q.zoom!==+w.zoom&&(ee=!0,Q.zoom=+w.zoom),w.center!==void 0&&(Q.center=a.N.convert(w.center)),\"bearing\"in w&&Q.bearing!==+w.bearing&&(le=!0,Q.bearing=+w.bearing),\"pitch\"in w&&Q.pitch!==+w.pitch&&(Fe=!0,Q.pitch=+w.pitch),w.padding==null||Q.isPaddingEqual(w.padding)||(Q.padding=w.padding),this._applyUpdatedTransform(Q),this.fire(new a.k(\"movestart\",B)).fire(new a.k(\"move\",B)),ee&&this.fire(new a.k(\"zoomstart\",B)).fire(new a.k(\"zoom\",B)).fire(new a.k(\"zoomend\",B)),le&&this.fire(new a.k(\"rotatestart\",B)).fire(new a.k(\"rotate\",B)).fire(new a.k(\"rotateend\",B)),Fe&&this.fire(new a.k(\"pitchstart\",B)).fire(new a.k(\"pitch\",B)).fire(new a.k(\"pitchend\",B)),this.fire(new a.k(\"moveend\",B))}calculateCameraOptionsFromTo(w,B,Q,ee=0){let le=a.Z.fromLngLat(w,B),Fe=a.Z.fromLngLat(Q,ee),Ze=Fe.x-le.x,ut=Fe.y-le.y,Mt=Fe.z-le.z,Jt=Math.hypot(Ze,ut,Mt);if(Jt===0)throw new Error(\"Can't calculate camera options with same From and To\");let $t=Math.hypot(Ze,ut),yr=this.transform.scaleZoom(this.transform.cameraToCenterDistance/Jt/this.transform.tileSize),Cr=180*Math.atan2(Ze,-ut)/Math.PI,ve=180*Math.acos($t/Jt)/Math.PI;return ve=Mt<0?90-ve:90+ve,{center:Fe.toLngLat(),zoom:yr,pitch:ve,bearing:Cr}}easeTo(w,B){var Q;this._stop(!1,w.easeId),((w=a.e({offset:[0,0],duration:500,easing:a.b9},w)).animate===!1||!w.essential&&u.prefersReducedMotion)&&(w.duration=0);let ee=this._getTransformForUpdate(),le=ee.zoom,Fe=ee.bearing,Ze=ee.pitch,ut=ee.padding,Mt=\"bearing\"in w?this._normalizeBearing(w.bearing,Fe):Fe,Jt=\"pitch\"in w?+w.pitch:Ze,$t=\"padding\"in w?w.padding:ee.padding,yr=a.P.convert(w.offset),Cr=ee.centerPoint.add(yr),ve=ee.pointLocation(Cr),{center:xe,zoom:Ie}=ee.getConstrained(a.N.convert(w.center||ve),(Q=w.zoom)!==null&&Q!==void 0?Q:le);this._normalizeCenter(xe,ee);let ze=ee.project(ve),Qe=ee.project(xe).sub(ze),Xe=ee.zoomScale(Ie-le),nt,qt;w.around&&(nt=a.N.convert(w.around),qt=ee.locationPoint(nt));let Wt={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||Ie!==le,this._rotating=this._rotating||Fe!==Mt,this._pitching=this._pitching||Jt!==Ze,this._padding=!ee.isPaddingEqual($t),this._easeId=w.easeId,this._prepareEase(B,w.noMoveStart,Wt),this.terrain&&this._prepareElevation(xe),this._ease((nr=>{if(this._zooming&&(ee.zoom=a.y.number(le,Ie,nr)),this._rotating&&(ee.bearing=a.y.number(Fe,Mt,nr)),this._pitching&&(ee.pitch=a.y.number(Ze,Jt,nr)),this._padding&&(ee.interpolatePadding(ut,$t,nr),Cr=ee.centerPoint.add(yr)),this.terrain&&!w.freezeElevation&&this._updateElevation(nr),nt)ee.setLocationAtPoint(nt,qt);else{let fr=ee.zoomScale(ee.zoom-le),br=Ie>le?Math.min(2,Xe):Math.max(.5,Xe),Fr=Math.pow(br,1-nr),Hr=ee.unproject(ze.add(Qe.mult(nr*Fr)).mult(fr));ee.setLocationAtPoint(ee.renderWorldCopies?Hr.wrap():Hr,Cr)}this._applyUpdatedTransform(ee),this._fireMoveEvents(B)}),(nr=>{this.terrain&&w.freezeElevation&&this._finalizeElevation(),this._afterEase(B,nr)}),w),this}_prepareEase(w,B,Q={}){this._moving=!0,B||Q.moving||this.fire(new a.k(\"movestart\",w)),this._zooming&&!Q.zooming&&this.fire(new a.k(\"zoomstart\",w)),this._rotating&&!Q.rotating&&this.fire(new a.k(\"rotatestart\",w)),this._pitching&&!Q.pitching&&this.fire(new a.k(\"pitchstart\",w))}_prepareElevation(w){this._elevationCenter=w,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(w,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(w){this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);let B=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(w<1&&B!==this._elevationTarget){let Q=this._elevationTarget-this._elevationStart;this._elevationStart+=w*(Q-(B-(Q*w+this._elevationStart))/(1-w)),this._elevationTarget=B}this.transform.elevation=a.y.number(this._elevationStart,this._elevationTarget,w)}_finalizeElevation(){this._elevationFreeze=!1,this.transform.recalculateZoom(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate||this.terrain?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_elevateCameraIfInsideTerrain(w){let B=w.getCameraPosition(),Q=this.terrain.getElevationForLngLatZoom(B.lngLat,w.zoom);if(B.altitude<Q){let ee=this.calculateCameraOptionsFromTo(B.lngLat,Q,w.center,w.elevation);return{pitch:ee.pitch,zoom:ee.zoom}}return{}}_applyUpdatedTransform(w){let B=[];if(this.terrain&&B.push((ee=>this._elevateCameraIfInsideTerrain(ee))),this.transformCameraUpdate&&B.push((ee=>this.transformCameraUpdate(ee))),!B.length)return;let Q=w.clone();for(let ee of B){let le=Q.clone(),{center:Fe,zoom:Ze,pitch:ut,bearing:Mt,elevation:Jt}=ee(le);Fe&&(le.center=Fe),Ze!==void 0&&(le.zoom=Ze),ut!==void 0&&(le.pitch=ut),Mt!==void 0&&(le.bearing=Mt),Jt!==void 0&&(le.elevation=Jt),Q.apply(le)}this.transform.apply(Q)}_fireMoveEvents(w){this.fire(new a.k(\"move\",w)),this._zooming&&this.fire(new a.k(\"zoom\",w)),this._rotating&&this.fire(new a.k(\"rotate\",w)),this._pitching&&this.fire(new a.k(\"pitch\",w))}_afterEase(w,B){if(this._easeId&&B&&this._easeId===B)return;delete this._easeId;let Q=this._zooming,ee=this._rotating,le=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,Q&&this.fire(new a.k(\"zoomend\",w)),ee&&this.fire(new a.k(\"rotateend\",w)),le&&this.fire(new a.k(\"pitchend\",w)),this.fire(new a.k(\"moveend\",w))}flyTo(w,B){var Q;if(!w.essential&&u.prefersReducedMotion){let Ln=a.M(w,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(Ln,B)}this.stop(),w=a.e({offset:[0,0],speed:1.2,curve:1.42,easing:a.b9},w);let ee=this._getTransformForUpdate(),le=ee.zoom,Fe=ee.bearing,Ze=ee.pitch,ut=ee.padding,Mt=\"bearing\"in w?this._normalizeBearing(w.bearing,Fe):Fe,Jt=\"pitch\"in w?+w.pitch:Ze,$t=\"padding\"in w?w.padding:ee.padding,yr=a.P.convert(w.offset),Cr=ee.centerPoint.add(yr),ve=ee.pointLocation(Cr),{center:xe,zoom:Ie}=ee.getConstrained(a.N.convert(w.center||ve),(Q=w.zoom)!==null&&Q!==void 0?Q:le);this._normalizeCenter(xe,ee);let ze=ee.zoomScale(Ie-le),Qe=ee.project(ve),Xe=ee.project(xe).sub(Qe),nt=w.curve,qt=Math.max(ee.width,ee.height),Wt=qt/ze,nr=Xe.mag();if(\"minZoom\"in w){let Ln=a.ac(Math.min(w.minZoom,le,Ie),ee.minZoom,ee.maxZoom),Kn=qt/ee.zoomScale(Ln-le);nt=Math.sqrt(Kn/nr*2)}let fr=nt*nt;function br(Ln){let Kn=(Wt*Wt-qt*qt+(Ln?-1:1)*fr*fr*nr*nr)/(2*(Ln?Wt:qt)*fr*nr);return Math.log(Math.sqrt(Kn*Kn+1)-Kn)}function Fr(Ln){return(Math.exp(Ln)-Math.exp(-Ln))/2}function Hr(Ln){return(Math.exp(Ln)+Math.exp(-Ln))/2}let ri=br(!1),Ci=function(Ln){return Hr(ri)/Hr(ri+nt*Ln)},cn=function(Ln){return qt*((Hr(ri)*(Fr(Kn=ri+nt*Ln)/Hr(Kn))-Fr(ri))/fr)/nr;var Kn},sn=(br(!0)-ri)/nt;if(Math.abs(nr)<1e-6||!isFinite(sn)){if(Math.abs(qt-Wt)<1e-6)return this.easeTo(w,B);let Ln=Wt<qt?-1:1;sn=Math.abs(Math.log(Wt/qt))/nt,cn=()=>0,Ci=Kn=>Math.exp(Ln*nt*Kn)}return w.duration=\"duration\"in w?+w.duration:1e3*sn/(\"screenSpeed\"in w?+w.screenSpeed/nt:+w.speed),w.maxDuration&&w.duration>w.maxDuration&&(w.duration=0),this._zooming=!0,this._rotating=Fe!==Mt,this._pitching=Jt!==Ze,this._padding=!ee.isPaddingEqual($t),this._prepareEase(B,!1),this.terrain&&this._prepareElevation(xe),this._ease((Ln=>{let Kn=Ln*sn,Aa=1/Ci(Kn);ee.zoom=Ln===1?Ie:le+ee.scaleZoom(Aa),this._rotating&&(ee.bearing=a.y.number(Fe,Mt,Ln)),this._pitching&&(ee.pitch=a.y.number(Ze,Jt,Ln)),this._padding&&(ee.interpolatePadding(ut,$t,Ln),Cr=ee.centerPoint.add(yr)),this.terrain&&!w.freezeElevation&&this._updateElevation(Ln);let fa=Ln===1?xe:ee.unproject(Qe.add(Xe.mult(cn(Kn))).mult(Aa));ee.setLocationAtPoint(ee.renderWorldCopies?fa.wrap():fa,Cr),this._applyUpdatedTransform(ee),this._fireMoveEvents(B)}),(()=>{this.terrain&&w.freezeElevation&&this._finalizeElevation(),this._afterEase(B)}),w),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(w,B){var Q;if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){let ee=this._onEaseEnd;delete this._onEaseEnd,ee.call(this,B)}return w||(Q=this.handlers)===null||Q===void 0||Q.stop(!1),this}_ease(w,B,Q){Q.animate===!1||Q.duration===0?(w(1),B()):(this._easeStart=u.now(),this._easeOptions=Q,this._onEaseFrame=w,this._onEaseEnd=B,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(w,B){w=a.b3(w,-180,180);let Q=Math.abs(w-B);return Math.abs(w-360-B)<Q&&(w-=360),Math.abs(w+360-B)<Q&&(w+=360),w}_normalizeCenter(w,B){if(!B.renderWorldCopies||B.lngRange)return;let Q=w.lng-B.center.lng;w.lng+=Q>180?-360:Q<-180?360:0}queryTerrainElevation(w){return this.terrain?this.terrain.getElevationForLngLatZoom(a.N.convert(w),this.transform.tileZoom)-this.transform.elevation:null}}let La={compact:!0,customAttribution:'<a href=\"https://maplibre.org/\" target=\"_blank\">MapLibre</a>'};class Ua{constructor(w=La){this._toggleAttribution=()=>{this._container.classList.contains(\"maplibregl-compact\")&&(this._container.classList.contains(\"maplibregl-compact-show\")?(this._container.setAttribute(\"open\",\"\"),this._container.classList.remove(\"maplibregl-compact-show\")):(this._container.classList.add(\"maplibregl-compact-show\"),this._container.removeAttribute(\"open\")))},this._updateData=B=>{!B||B.sourceDataType!==\"metadata\"&&B.sourceDataType!==\"visibility\"&&B.dataType!==\"style\"&&B.type!==\"terrain\"||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact===!1?this._container.setAttribute(\"open\",\"\"):this._container.classList.contains(\"maplibregl-compact\")||this._container.classList.contains(\"maplibregl-attrib-empty\")||(this._container.setAttribute(\"open\",\"\"),this._container.classList.add(\"maplibregl-compact\",\"maplibregl-compact-show\")):(this._container.setAttribute(\"open\",\"\"),this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.remove(\"maplibregl-compact\",\"maplibregl-compact-show\"))},this._updateCompactMinimize=()=>{this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.contains(\"maplibregl-compact-show\")&&this._container.classList.remove(\"maplibregl-compact-show\")},this.options=w}getDefaultPosition(){return\"bottom-right\"}onAdd(w){return this._map=w,this._compact=this.options.compact,this._container=c.create(\"details\",\"maplibregl-ctrl maplibregl-ctrl-attrib\"),this._compactButton=c.create(\"summary\",\"maplibregl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=c.create(\"div\",\"maplibregl-ctrl-attrib-inner\",this._container),this._updateAttributions(),this._updateCompact(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"terrain\",this._updateData),this._map.on(\"resize\",this._updateCompact),this._map.on(\"drag\",this._updateCompactMinimize),this._container}onRemove(){c.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"terrain\",this._updateData),this._map.off(\"resize\",this._updateCompact),this._map.off(\"drag\",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(w,B){let Q=this._map._getUIString(`AttributionControl.${B}`);w.title=Q,w.setAttribute(\"aria-label\",Q)}_updateAttributions(){if(!this._map.style)return;let w=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?w=w.concat(this.options.customAttribution.map((ee=>typeof ee!=\"string\"?\"\":ee))):typeof this.options.customAttribution==\"string\"&&w.push(this.options.customAttribution)),this._map.style.stylesheet){let ee=this._map.style.stylesheet;this.styleOwner=ee.owner,this.styleId=ee.id}let B=this._map.style.sourceCaches;for(let ee in B){let le=B[ee];if(le.used||le.usedForTerrain){let Fe=le.getSource();Fe.attribution&&w.indexOf(Fe.attribution)<0&&w.push(Fe.attribution)}}w=w.filter((ee=>String(ee).trim())),w.sort(((ee,le)=>ee.length-le.length)),w=w.filter(((ee,le)=>{for(let Fe=le+1;Fe<w.length;Fe++)if(w[Fe].indexOf(ee)>=0)return!1;return!0}));let Q=w.join(\" | \");Q!==this._attribHTML&&(this._attribHTML=Q,w.length?(this._innerContainer.innerHTML=Q,this._container.classList.remove(\"maplibregl-attrib-empty\")):this._container.classList.add(\"maplibregl-attrib-empty\"),this._updateCompact(),this._editLink=null)}}class Zn{constructor(w={}){this._updateCompact=()=>{let B=this._container.children;if(B.length){let Q=B[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact!==!1&&Q.classList.add(\"maplibregl-compact\"):Q.classList.remove(\"maplibregl-compact\")}},this.options=w}getDefaultPosition(){return\"bottom-left\"}onAdd(w){this._map=w,this._compact=this.options&&this.options.compact,this._container=c.create(\"div\",\"maplibregl-ctrl\");let B=c.create(\"a\",\"maplibregl-ctrl-logo\");return B.target=\"_blank\",B.rel=\"noopener nofollow\",B.href=\"https://maplibre.org/\",B.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),B.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(B),this._container.style.display=\"block\",this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container}onRemove(){c.remove(this._container),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._compact=void 0}}class Dn{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(w){let B=++this._id;return this._queue.push({callback:w,id:B,cancelled:!1}),B}remove(w){let B=this._currentlyRunning,Q=B?this._queue.concat(B):this._queue;for(let ee of Q)if(ee.id===w)return void(ee.cancelled=!0)}run(w=0){if(this._currentlyRunning)throw new Error(\"Attempting to run(), but is already running.\");let B=this._currentlyRunning=this._queue;this._queue=[];for(let Q of B)if(!Q.cancelled&&(Q.callback(w),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}var Ka=a.Y([{name:\"a_pos3d\",type:\"Int16\",components:3}]);class bo extends a.E{constructor(w){super(),this.sourceCache=w,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.deltaZoom=1,w.usedForTerrain=!0,w.tileSize=this.tileSize*2**this.deltaZoom}destruct(){this.sourceCache.usedForTerrain=!1,this.sourceCache.tileSize=null}update(w,B){this.sourceCache.update(w,B),this._renderableTilesKeys=[];let Q={};for(let ee of w.coveringTiles({tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:B}))Q[ee.key]=!0,this._renderableTilesKeys.push(ee.key),this._tiles[ee.key]||(ee.posMatrix=new Float64Array(16),a.aP(ee.posMatrix,0,a.X,0,a.X,0,1),this._tiles[ee.key]=new Tt(ee,this.tileSize));for(let ee in this._tiles)Q[ee]||delete this._tiles[ee]}freeRtt(w){for(let B in this._tiles){let Q=this._tiles[B];(!w||Q.tileID.equals(w)||Q.tileID.isChildOf(w)||w.isChildOf(Q.tileID))&&(Q.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map((w=>this.getTileByID(w)))}getTileByID(w){return this._tiles[w]}getTerrainCoords(w){let B={};for(let Q of this._renderableTilesKeys){let ee=this._tiles[Q].tileID;if(ee.canonical.equals(w.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16),a.aP(le.posMatrix,0,a.X,0,a.X,0,1),B[Q]=le}else if(ee.canonical.isChildOf(w.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16);let Fe=ee.canonical.z-w.canonical.z,Ze=ee.canonical.x-(ee.canonical.x>>Fe<<Fe),ut=ee.canonical.y-(ee.canonical.y>>Fe<<Fe),Mt=a.X>>Fe;a.aP(le.posMatrix,0,Mt,0,Mt,0,1),a.J(le.posMatrix,le.posMatrix,[-Ze*Mt,-ut*Mt,0]),B[Q]=le}else if(w.canonical.isChildOf(ee.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16);let Fe=w.canonical.z-ee.canonical.z,Ze=w.canonical.x-(w.canonical.x>>Fe<<Fe),ut=w.canonical.y-(w.canonical.y>>Fe<<Fe),Mt=a.X>>Fe;a.aP(le.posMatrix,0,a.X,0,a.X,0,1),a.J(le.posMatrix,le.posMatrix,[Ze*Mt,ut*Mt,0]),a.K(le.posMatrix,le.posMatrix,[1/2**Fe,1/2**Fe,0]),B[Q]=le}}return B}getSourceTile(w,B){let Q=this.sourceCache._source,ee=w.overscaledZ-this.deltaZoom;if(ee>Q.maxzoom&&(ee=Q.maxzoom),ee<Q.minzoom)return null;this._sourceTileCache[w.key]||(this._sourceTileCache[w.key]=w.scaledTo(ee).key);let le=this.sourceCache.getTileByID(this._sourceTileCache[w.key]);if((!le||!le.dem)&&B)for(;ee>=Q.minzoom&&(!le||!le.dem);)le=this.sourceCache.getTileByID(w.scaledTo(ee--).key);return le}tilesAfterTime(w=Date.now()){return Object.values(this._tiles).filter((B=>B.timeAdded>=w))}}class Zo{constructor(w,B,Q){this.painter=w,this.sourceCache=new bo(B),this.options=Q,this.exaggeration=typeof Q.exaggeration==\"number\"?Q.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}getDEMElevation(w,B,Q,ee=a.X){var le;if(!(B>=0&&B<ee&&Q>=0&&Q<ee))return 0;let Fe=this.getTerrainData(w),Ze=(le=Fe.tile)===null||le===void 0?void 0:le.dem;if(!Ze)return 0;let ut=(function(ve,xe,Ie){var ze=xe[0],Qe=xe[1];return ve[0]=Ie[0]*ze+Ie[4]*Qe+Ie[12],ve[1]=Ie[1]*ze+Ie[5]*Qe+Ie[13],ve})([],[B/ee*a.X,Q/ee*a.X],Fe.u_terrain_matrix),Mt=[ut[0]*Ze.dim,ut[1]*Ze.dim],Jt=Math.floor(Mt[0]),$t=Math.floor(Mt[1]),yr=Mt[0]-Jt,Cr=Mt[1]-$t;return Ze.get(Jt,$t)*(1-yr)*(1-Cr)+Ze.get(Jt+1,$t)*yr*(1-Cr)+Ze.get(Jt,$t+1)*(1-yr)*Cr+Ze.get(Jt+1,$t+1)*yr*Cr}getElevationForLngLatZoom(w,B){if(!a.bb(B,w.wrap()))return 0;let{tileID:Q,mercatorX:ee,mercatorY:le}=this._getOverscaledTileIDFromLngLatZoom(w,B);return this.getElevation(Q,ee%a.X,le%a.X,a.X)}getElevation(w,B,Q,ee=a.X){return this.getDEMElevation(w,B,Q,ee)*this.exaggeration}getTerrainData(w){if(!this._emptyDemTexture){let ee=this.painter.context,le=new a.R({width:1,height:1},new Uint8Array(4));this._emptyDepthTexture=new g(ee,le,ee.gl.RGBA,{premultiply:!1}),this._emptyDemUnpack=[0,0,0,0],this._emptyDemTexture=new g(ee,new a.R({width:1,height:1}),ee.gl.RGBA,{premultiply:!1}),this._emptyDemTexture.bind(ee.gl.NEAREST,ee.gl.CLAMP_TO_EDGE),this._emptyDemMatrix=a.an([])}let B=this.sourceCache.getSourceTile(w,!0);if(B&&B.dem&&(!B.demTexture||B.needsTerrainPrepare)){let ee=this.painter.context;B.demTexture=this.painter.getTileTexture(B.dem.stride),B.demTexture?B.demTexture.update(B.dem.getPixels(),{premultiply:!1}):B.demTexture=new g(ee,B.dem.getPixels(),ee.gl.RGBA,{premultiply:!1}),B.demTexture.bind(ee.gl.NEAREST,ee.gl.CLAMP_TO_EDGE),B.needsTerrainPrepare=!1}let Q=B&&B+B.tileID.key+w.key;if(Q&&!this._demMatrixCache[Q]){let ee=this.sourceCache.sourceCache._source.maxzoom,le=w.canonical.z-B.tileID.canonical.z;w.overscaledZ>w.canonical.z&&(w.canonical.z>=ee?le=w.canonical.z-ee:a.w(\"cannot calculate elevation if elevation maxzoom > source.maxzoom\"));let Fe=w.canonical.x-(w.canonical.x>>le<<le),Ze=w.canonical.y-(w.canonical.y>>le<<le),ut=a.bc(new Float64Array(16),[1/(a.X<<le),1/(a.X<<le),0]);a.J(ut,ut,[Fe*a.X,Ze*a.X,0]),this._demMatrixCache[w.key]={matrix:ut,coord:w}}return{u_depth:2,u_terrain:3,u_terrain_dim:B&&B.dem&&B.dem.dim||1,u_terrain_matrix:Q?this._demMatrixCache[w.key].matrix:this._emptyDemMatrix,u_terrain_unpack:B&&B.dem&&B.dem.getUnpackVector()||this._emptyDemUnpack,u_terrain_exaggeration:this.exaggeration,texture:(B&&B.demTexture||this._emptyDemTexture).texture,depthTexture:(this._fboDepthTexture||this._emptyDepthTexture).texture,tile:B}}getFramebuffer(w){let B=this.painter,Q=B.width/devicePixelRatio,ee=B.height/devicePixelRatio;return!this._fbo||this._fbo.width===Q&&this._fbo.height===ee||(this._fbo.destroy(),this._fboCoordsTexture.destroy(),this._fboDepthTexture.destroy(),delete this._fbo,delete this._fboDepthTexture,delete this._fboCoordsTexture),this._fboCoordsTexture||(this._fboCoordsTexture=new g(B.context,{width:Q,height:ee,data:null},B.context.gl.RGBA,{premultiply:!1}),this._fboCoordsTexture.bind(B.context.gl.NEAREST,B.context.gl.CLAMP_TO_EDGE)),this._fboDepthTexture||(this._fboDepthTexture=new g(B.context,{width:Q,height:ee,data:null},B.context.gl.RGBA,{premultiply:!1}),this._fboDepthTexture.bind(B.context.gl.NEAREST,B.context.gl.CLAMP_TO_EDGE)),this._fbo||(this._fbo=B.context.createFramebuffer(Q,ee,!0,!1),this._fbo.depthAttachment.set(B.context.createRenderbuffer(B.context.gl.DEPTH_COMPONENT16,Q,ee))),this._fbo.colorAttachment.set(w===\"coords\"?this._fboCoordsTexture.texture:this._fboDepthTexture.texture),this._fbo}getCoordsTexture(){let w=this.painter.context;if(this._coordsTexture)return this._coordsTexture;let B=new Uint8Array(this._coordsTextureSize*this._coordsTextureSize*4);for(let le=0,Fe=0;le<this._coordsTextureSize;le++)for(let Ze=0;Ze<this._coordsTextureSize;Ze++,Fe+=4)B[Fe+0]=255&Ze,B[Fe+1]=255&le,B[Fe+2]=Ze>>8<<4|le>>8,B[Fe+3]=0;let Q=new a.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(B.buffer)),ee=new g(w,Q,w.gl.RGBA,{premultiply:!1});return ee.bind(w.gl.NEAREST,w.gl.CLAMP_TO_EDGE),this._coordsTexture=ee,ee}pointCoordinate(w){this.painter.maybeDrawDepthAndCoords(!0);let B=new Uint8Array(4),Q=this.painter.context,ee=Q.gl,le=Math.round(w.x*this.painter.pixelRatio/devicePixelRatio),Fe=Math.round(w.y*this.painter.pixelRatio/devicePixelRatio),Ze=Math.round(this.painter.height/devicePixelRatio);Q.bindFramebuffer.set(this.getFramebuffer(\"coords\").framebuffer),ee.readPixels(le,Ze-Fe-1,1,1,ee.RGBA,ee.UNSIGNED_BYTE,B),Q.bindFramebuffer.set(null);let ut=B[0]+(B[2]>>4<<8),Mt=B[1]+((15&B[2])<<8),Jt=this.coordsIndex[255-B[3]],$t=Jt&&this.sourceCache.getTileByID(Jt);if(!$t)return null;let yr=this._coordsTextureSize,Cr=(1<<$t.tileID.canonical.z)*yr;return new a.Z(($t.tileID.canonical.x*yr+ut)/Cr+$t.tileID.wrap,($t.tileID.canonical.y*yr+Mt)/Cr,this.getElevation($t.tileID,ut,Mt,yr))}depthAtPoint(w){let B=new Uint8Array(4),Q=this.painter.context,ee=Q.gl;return Q.bindFramebuffer.set(this.getFramebuffer(\"depth\").framebuffer),ee.readPixels(w.x,this.painter.height/devicePixelRatio-w.y-1,1,1,ee.RGBA,ee.UNSIGNED_BYTE,B),Q.bindFramebuffer.set(null),(B[0]/16777216+B[1]/65536+B[2]/256+B[3])/256}getTerrainMesh(){if(this._mesh)return this._mesh;let w=this.painter.context,B=new a.bd,Q=new a.aY,ee=this.meshSize,le=a.X/ee,Fe=ee*ee;for(let $t=0;$t<=ee;$t++)for(let yr=0;yr<=ee;yr++)B.emplaceBack(yr*le,$t*le,0);for(let $t=0;$t<Fe;$t+=ee+1)for(let yr=0;yr<ee;yr++)Q.emplaceBack(yr+$t,ee+yr+$t+1,ee+yr+$t+2),Q.emplaceBack(yr+$t,ee+yr+$t+2,yr+$t+1);let Ze=B.length,ut=Ze+2*(ee+1);for(let $t of[0,1])for(let yr=0;yr<=ee;yr++)for(let Cr of[0,1])B.emplaceBack(yr*le,$t*a.X,Cr);for(let $t=0;$t<2*ee;$t+=2)Q.emplaceBack(ut+$t,ut+$t+1,ut+$t+3),Q.emplaceBack(ut+$t,ut+$t+3,ut+$t+2),Q.emplaceBack(Ze+$t,Ze+$t+3,Ze+$t+1),Q.emplaceBack(Ze+$t,Ze+$t+2,Ze+$t+3);let Mt=B.length,Jt=Mt+2*(ee+1);for(let $t of[0,1])for(let yr=0;yr<=ee;yr++)for(let Cr of[0,1])B.emplaceBack($t*a.X,yr*le,Cr);for(let $t=0;$t<2*ee;$t+=2)Q.emplaceBack(Mt+$t,Mt+$t+1,Mt+$t+3),Q.emplaceBack(Mt+$t,Mt+$t+3,Mt+$t+2),Q.emplaceBack(Jt+$t,Jt+$t+3,Jt+$t+1),Q.emplaceBack(Jt+$t,Jt+$t+2,Jt+$t+3);return this._mesh=new Eu(w.createVertexBuffer(B,Ka.members),w.createIndexBuffer(Q),a.a0.simpleSegment(0,0,B.length,Q.length)),this._mesh}getMeshFrameDelta(w){return 2*Math.PI*a.be/Math.pow(2,w)/5}getMinTileElevationForLngLatZoom(w,B){var Q;let{tileID:ee}=this._getOverscaledTileIDFromLngLatZoom(w,B);return(Q=this.getMinMaxElevation(ee).minElevation)!==null&&Q!==void 0?Q:0}getMinMaxElevation(w){let B=this.getTerrainData(w).tile,Q={minElevation:null,maxElevation:null};return B&&B.dem&&(Q.minElevation=B.dem.min*this.exaggeration,Q.maxElevation=B.dem.max*this.exaggeration),Q}_getOverscaledTileIDFromLngLatZoom(w,B){let Q=a.Z.fromLngLat(w.wrap()),ee=(1<<B)*a.X,le=Q.x*ee,Fe=Q.y*ee,Ze=Math.floor(le/a.X),ut=Math.floor(Fe/a.X);return{tileID:new a.S(B,0,B,Ze,ut),mercatorX:le,mercatorY:Fe}}}class Ss{constructor(w,B,Q){this._context=w,this._size=B,this._tileSize=Q,this._objects=[],this._recentlyUsed=[],this._stamp=0}destruct(){for(let w of this._objects)w.texture.destroy(),w.fbo.destroy()}_createObject(w){let B=this._context.createFramebuffer(this._tileSize,this._tileSize,!0,!0),Q=new g(this._context,{width:this._tileSize,height:this._tileSize,data:null},this._context.gl.RGBA);return Q.bind(this._context.gl.LINEAR,this._context.gl.CLAMP_TO_EDGE),B.depthAttachment.set(this._context.createRenderbuffer(this._context.gl.DEPTH_STENCIL,this._tileSize,this._tileSize)),B.colorAttachment.set(Q.texture),{id:w,fbo:B,texture:Q,stamp:-1,inUse:!1}}getObjectForId(w){return this._objects[w]}useObject(w){w.inUse=!0,this._recentlyUsed=this._recentlyUsed.filter((B=>w.id!==B)),this._recentlyUsed.push(w.id)}stampObject(w){w.stamp=++this._stamp}getOrCreateFreeObject(){for(let B of this._recentlyUsed)if(!this._objects[B].inUse)return this._objects[B];if(this._objects.length>=this._size)throw new Error(\"No free RenderPool available, call freeAllObjects() required!\");let w=this._createObject(this._objects.length);return this._objects.push(w),w}freeObject(w){w.inUse=!1}freeAllObjects(){for(let w of this._objects)this.freeObject(w)}isFull(){return!(this._objects.length<this._size)&&this._objects.some((w=>!w.inUse))===!1}}let as={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0};class ws{constructor(w,B){this.painter=w,this.terrain=B,this.pool=new Ss(w.context,30,B.sourceCache.tileSize*B.qualityFactor)}destruct(){this.pool.destruct()}getTexture(w){return this.pool.getObjectForId(w.rtt[this._stacks.length-1].id).texture}prepareForRender(w,B){this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.sourceCache.getRenderableTiles(),this._renderableLayerIds=w._order.filter((Q=>!w._layers[Q].isHidden(B))),this._coordsDescendingInv={};for(let Q in w.sourceCaches){this._coordsDescendingInv[Q]={};let ee=w.sourceCaches[Q].getVisibleCoordinates();for(let le of ee){let Fe=this.terrain.sourceCache.getTerrainCoords(le);for(let Ze in Fe)this._coordsDescendingInv[Q][Ze]||(this._coordsDescendingInv[Q][Ze]=[]),this._coordsDescendingInv[Q][Ze].push(Fe[Ze])}}this._coordsDescendingInvStr={};for(let Q of w._order){let ee=w._layers[Q],le=ee.source;if(as[ee.type]&&!this._coordsDescendingInvStr[le]){this._coordsDescendingInvStr[le]={};for(let Fe in this._coordsDescendingInv[le])this._coordsDescendingInvStr[le][Fe]=this._coordsDescendingInv[le][Fe].map((Ze=>Ze.key)).sort().join()}}for(let Q of this._renderableTiles)for(let ee in this._coordsDescendingInvStr){let le=this._coordsDescendingInvStr[ee][Q.tileID.key];le&&le!==Q.rttCoords[ee]&&(Q.rtt=[])}}renderLayer(w){if(w.isHidden(this.painter.transform.zoom))return!1;let B=w.type,Q=this.painter,ee=this._renderableLayerIds[this._renderableLayerIds.length-1]===w.id;if(as[B]&&(this._prevType&&as[this._prevType]||this._stacks.push([]),this._prevType=B,this._stacks[this._stacks.length-1].push(w.id),!ee))return!0;if(as[this._prevType]||as[B]&&ee){this._prevType=B;let le=this._stacks.length-1,Fe=this._stacks[le]||[];for(let Ze of this._renderableTiles){if(this.pool.isFull()&&(js(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(Ze),Ze.rtt[le]){let Mt=this.pool.getObjectForId(Ze.rtt[le].id);if(Mt.stamp===Ze.rtt[le].stamp){this.pool.useObject(Mt);continue}}let ut=this.pool.getOrCreateFreeObject();this.pool.useObject(ut),this.pool.stampObject(ut),Ze.rtt[le]={id:ut.id,stamp:ut.stamp},Q.context.bindFramebuffer.set(ut.fbo.framebuffer),Q.context.clear({color:a.aM.transparent,stencil:0}),Q.currentStencilSource=void 0;for(let Mt=0;Mt<Fe.length;Mt++){let Jt=Q.style._layers[Fe[Mt]],$t=Jt.source?this._coordsDescendingInv[Jt.source][Ze.tileID.key]:[Ze.tileID];Q.context.viewport.set([0,0,ut.fbo.width,ut.fbo.height]),Q._renderTileClippingMasks(Jt,$t),Q.renderLayer(Q,Q.style.sourceCaches[Jt.source],Jt,$t),Jt.source&&(Ze.rttCoords[Jt.source]=this._coordsDescendingInvStr[Jt.source][Ze.tileID.key])}}return js(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects(),as[B]}return!1}}let Ho={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"MapLibre logo\",\"Map.Title\":\"Map\",\"Marker.Title\":\"Map marker\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"Popup.Close\":\"Close popup\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\",\"TerrainControl.Enable\":\"Enable terrain\",\"TerrainControl.Disable\":\"Disable terrain\",\"CooperativeGesturesHandler.WindowsHelpText\":\"Use Ctrl + scroll to zoom the map\",\"CooperativeGesturesHandler.MacHelpText\":\"Use \\u2318 + scroll to zoom the map\",\"CooperativeGesturesHandler.MobileHelpText\":\"Use two fingers to move the map\"},ml=o,Ws={hash:!1,interactive:!0,bearingSnap:7,attributionControl:La,maplibreLogo:!1,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,refreshExpiredTiles:!0,scrollZoom:!0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,cooperativeGestures:!1,trackResize:!0,center:[0,0],zoom:0,bearing:0,pitch:0,renderWorldCopies:!0,maxTileCacheSize:null,maxTileCacheZoomLevels:a.a.MAX_TILE_CACHE_ZOOM_LEVELS,transformRequest:null,transformCameraUpdate:null,fadeDuration:300,crossSourceCollisions:!0,clickTolerance:3,localIdeographFontFamily:\"sans-serif\",pitchWithRotate:!0,validateStyle:!0,maxCanvasSize:[4096,4096],cancelPendingTileRequestsWhileZooming:!0},Ls=ue=>{ue.touchstart=ue.dragStart,ue.touchmoveWindow=ue.dragMove,ue.touchend=ue.dragEnd},va={showCompass:!0,showZoom:!0,visualizePitch:!1};class no{constructor(w,B,Q=!1){this.mousedown=Fe=>{this.startMouse(a.e({},Fe,{ctrlKey:!0,preventDefault:()=>Fe.preventDefault()}),c.mousePos(this.element,Fe)),c.addEventListener(window,\"mousemove\",this.mousemove),c.addEventListener(window,\"mouseup\",this.mouseup)},this.mousemove=Fe=>{this.moveMouse(Fe,c.mousePos(this.element,Fe))},this.mouseup=Fe=>{this.mouseRotate.dragEnd(Fe),this.mousePitch&&this.mousePitch.dragEnd(Fe),this.offTemp()},this.touchstart=Fe=>{Fe.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=c.touchPos(this.element,Fe.targetTouches)[0],this.startTouch(Fe,this._startPos),c.addEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.addEventListener(window,\"touchend\",this.touchend))},this.touchmove=Fe=>{Fe.targetTouches.length!==1?this.reset():(this._lastPos=c.touchPos(this.element,Fe.targetTouches)[0],this.moveTouch(Fe,this._lastPos))},this.touchend=Fe=>{Fe.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),delete this._startPos,delete this._lastPos,this.offTemp()},this.reset=()=>{this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),this.touchRotate.reset(),this.touchPitch&&this.touchPitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10;let ee=w.dragRotate._mouseRotate.getClickTolerance(),le=w.dragRotate._mousePitch.getClickTolerance();this.element=B,this.mouseRotate=Wl({clickTolerance:ee,enable:!0}),this.touchRotate=(({enable:Fe,clickTolerance:Ze,bearingDegreesPerPixelMoved:ut=.8})=>{let Mt=new cf;return new Wu({clickTolerance:Ze,move:(Jt,$t)=>({bearingDelta:($t.x-Jt.x)*ut}),moveStateManager:Mt,enable:Fe,assignEvents:Ls})})({clickTolerance:ee,enable:!0}),this.map=w,Q&&(this.mousePitch=ah({clickTolerance:le,enable:!0}),this.touchPitch=(({enable:Fe,clickTolerance:Ze,pitchDegreesPerPixelMoved:ut=-.5})=>{let Mt=new cf;return new Wu({clickTolerance:Ze,move:(Jt,$t)=>({pitchDelta:($t.y-Jt.y)*ut}),moveStateManager:Mt,enable:Fe,assignEvents:Ls})})({clickTolerance:le,enable:!0})),c.addEventListener(B,\"mousedown\",this.mousedown),c.addEventListener(B,\"touchstart\",this.touchstart,{passive:!1}),c.addEventListener(B,\"touchcancel\",this.reset)}startMouse(w,B){this.mouseRotate.dragStart(w,B),this.mousePitch&&this.mousePitch.dragStart(w,B),c.disableDrag()}startTouch(w,B){this.touchRotate.dragStart(w,B),this.touchPitch&&this.touchPitch.dragStart(w,B),c.disableDrag()}moveMouse(w,B){let Q=this.map,{bearingDelta:ee}=this.mouseRotate.dragMove(w,B)||{};if(ee&&Q.setBearing(Q.getBearing()+ee),this.mousePitch){let{pitchDelta:le}=this.mousePitch.dragMove(w,B)||{};le&&Q.setPitch(Q.getPitch()+le)}}moveTouch(w,B){let Q=this.map,{bearingDelta:ee}=this.touchRotate.dragMove(w,B)||{};if(ee&&Q.setBearing(Q.getBearing()+ee),this.touchPitch){let{pitchDelta:le}=this.touchPitch.dragMove(w,B)||{};le&&Q.setPitch(Q.getPitch()+le)}}off(){let w=this.element;c.removeEventListener(w,\"mousedown\",this.mousedown),c.removeEventListener(w,\"touchstart\",this.touchstart,{passive:!1}),c.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.removeEventListener(window,\"touchend\",this.touchend),c.removeEventListener(w,\"touchcancel\",this.reset),this.offTemp()}offTemp(){c.enableDrag(),c.removeEventListener(window,\"mousemove\",this.mousemove),c.removeEventListener(window,\"mouseup\",this.mouseup),c.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.removeEventListener(window,\"touchend\",this.touchend)}}let ys;function rs(ue,w,B){let Q=new a.N(ue.lng,ue.lat);if(ue=new a.N(ue.lng,ue.lat),w){let ee=new a.N(ue.lng-360,ue.lat),le=new a.N(ue.lng+360,ue.lat),Fe=B.locationPoint(ue).distSqr(w);B.locationPoint(ee).distSqr(w)<Fe?ue=ee:B.locationPoint(le).distSqr(w)<Fe&&(ue=le)}for(;Math.abs(ue.lng-B.center.lng)>180;){let ee=B.locationPoint(ue);if(ee.x>=0&&ee.y>=0&&ee.x<=B.width&&ee.y<=B.height)break;ue.lng>B.center.lng?ue.lng-=360:ue.lng+=360}return ue.lng!==Q.lng&&B.locationPoint(ue).y>B.height/2-B.getHorizon()?ue:Q}let Ql={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Cu(ue,w,B){let Q=ue.classList;for(let ee in Ql)Q.remove(`maplibregl-${B}-anchor-${ee}`);Q.add(`maplibregl-${B}-anchor-${w}`)}class Yu extends a.E{constructor(w){if(super(),this._onKeyPress=B=>{let Q=B.code,ee=B.charCode||B.keyCode;Q!==\"Space\"&&Q!==\"Enter\"&&ee!==32&&ee!==13||this.togglePopup()},this._onMapClick=B=>{let Q=B.originalEvent.target,ee=this._element;this._popup&&(Q===ee||ee.contains(Q))&&this.togglePopup()},this._update=B=>{var Q;if(!this._map)return;let ee=this._map.loaded()&&!this._map.isMoving();((B==null?void 0:B.type)===\"terrain\"||(B==null?void 0:B.type)===\"render\"&&!ee)&&this._map.once(\"render\",this._update),this._lngLat=this._map.transform.renderWorldCopies?rs(this._lngLat,this._flatPos,this._map.transform):(Q=this._lngLat)===null||Q===void 0?void 0:Q.wrap(),this._flatPos=this._pos=this._map.project(this._lngLat)._add(this._offset),this._map.terrain&&(this._flatPos=this._map.transform.locationPoint(this._lngLat)._add(this._offset));let le=\"\";this._rotationAlignment===\"viewport\"||this._rotationAlignment===\"auto\"?le=`rotateZ(${this._rotation}deg)`:this._rotationAlignment===\"map\"&&(le=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let Fe=\"\";this._pitchAlignment===\"viewport\"||this._pitchAlignment===\"auto\"?Fe=\"rotateX(0deg)\":this._pitchAlignment===\"map\"&&(Fe=`rotateX(${this._map.getPitch()}deg)`),this._subpixelPositioning||B&&B.type!==\"moveend\"||(this._pos=this._pos.round()),c.setTransform(this._element,`${Ql[this._anchor]} translate(${this._pos.x}px, ${this._pos.y}px) ${Fe} ${le}`),u.frameAsync(new AbortController).then((()=>{this._updateOpacity(B&&B.type===\"moveend\")})).catch((()=>{}))},this._onMove=B=>{if(!this._isDragging){let Q=this._clickTolerance||this._map._clickTolerance;this._isDragging=B.point.dist(this._pointerdownPos)>=Q}this._isDragging&&(this._pos=B.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",this._state===\"pending\"&&(this._state=\"active\",this.fire(new a.k(\"dragstart\"))),this.fire(new a.k(\"drag\")))},this._onUp=()=>{this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),this._state===\"active\"&&this.fire(new a.k(\"dragend\")),this._state=\"inactive\"},this._addDragHandler=B=>{this._element.contains(B.originalEvent.target)&&(B.preventDefault(),this._positionDelta=B.point.sub(this._pos).add(this._offset),this._pointerdownPos=B.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},this._anchor=w&&w.anchor||\"center\",this._color=w&&w.color||\"#3FB1CE\",this._scale=w&&w.scale||1,this._draggable=w&&w.draggable||!1,this._clickTolerance=w&&w.clickTolerance||0,this._subpixelPositioning=w&&w.subpixelPositioning||!1,this._isDragging=!1,this._state=\"inactive\",this._rotation=w&&w.rotation||0,this._rotationAlignment=w&&w.rotationAlignment||\"auto\",this._pitchAlignment=w&&w.pitchAlignment&&w.pitchAlignment!==\"auto\"?w.pitchAlignment:this._rotationAlignment,this.setOpacity(),this.setOpacity(w==null?void 0:w.opacity,w==null?void 0:w.opacityWhenCovered),w&&w.element)this._element=w.element,this._offset=a.P.convert(w&&w.offset||[0,0]);else{this._defaultMarker=!0,this._element=c.create(\"div\");let B=c.createNS(\"http://www.w3.org/2000/svg\",\"svg\"),Q=41,ee=27;B.setAttributeNS(null,\"display\",\"block\"),B.setAttributeNS(null,\"height\",`${Q}px`),B.setAttributeNS(null,\"width\",`${ee}px`),B.setAttributeNS(null,\"viewBox\",`0 0 ${ee} ${Q}`);let le=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");le.setAttributeNS(null,\"stroke\",\"none\"),le.setAttributeNS(null,\"stroke-width\",\"1\"),le.setAttributeNS(null,\"fill\",\"none\"),le.setAttributeNS(null,\"fill-rule\",\"evenodd\");let Fe=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");Fe.setAttributeNS(null,\"fill-rule\",\"nonzero\");let Ze=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");Ze.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),Ze.setAttributeNS(null,\"fill\",\"#000000\");let ut=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];for(let ze of ut){let Qe=c.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");Qe.setAttributeNS(null,\"opacity\",\"0.04\"),Qe.setAttributeNS(null,\"cx\",\"10.5\"),Qe.setAttributeNS(null,\"cy\",\"5.80029008\"),Qe.setAttributeNS(null,\"rx\",ze.rx),Qe.setAttributeNS(null,\"ry\",ze.ry),Ze.appendChild(Qe)}let Mt=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");Mt.setAttributeNS(null,\"fill\",this._color);let Jt=c.createNS(\"http://www.w3.org/2000/svg\",\"path\");Jt.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),Mt.appendChild(Jt);let $t=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");$t.setAttributeNS(null,\"opacity\",\"0.25\"),$t.setAttributeNS(null,\"fill\",\"#000000\");let yr=c.createNS(\"http://www.w3.org/2000/svg\",\"path\");yr.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),$t.appendChild(yr);let Cr=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");Cr.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),Cr.setAttributeNS(null,\"fill\",\"#FFFFFF\");let ve=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");ve.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");let xe=c.createNS(\"http://www.w3.org/2000/svg\",\"circle\");xe.setAttributeNS(null,\"fill\",\"#000000\"),xe.setAttributeNS(null,\"opacity\",\"0.25\"),xe.setAttributeNS(null,\"cx\",\"5.5\"),xe.setAttributeNS(null,\"cy\",\"5.5\"),xe.setAttributeNS(null,\"r\",\"5.4999962\");let Ie=c.createNS(\"http://www.w3.org/2000/svg\",\"circle\");Ie.setAttributeNS(null,\"fill\",\"#FFFFFF\"),Ie.setAttributeNS(null,\"cx\",\"5.5\"),Ie.setAttributeNS(null,\"cy\",\"5.5\"),Ie.setAttributeNS(null,\"r\",\"5.4999962\"),ve.appendChild(xe),ve.appendChild(Ie),Fe.appendChild(Ze),Fe.appendChild(Mt),Fe.appendChild($t),Fe.appendChild(Cr),Fe.appendChild(ve),B.appendChild(Fe),B.setAttributeNS(null,\"height\",Q*this._scale+\"px\"),B.setAttributeNS(null,\"width\",ee*this._scale+\"px\"),this._element.appendChild(B),this._offset=a.P.convert(w&&w.offset||[0,-14])}if(this._element.classList.add(\"maplibregl-marker\"),this._element.addEventListener(\"dragstart\",(B=>{B.preventDefault()})),this._element.addEventListener(\"mousedown\",(B=>{B.preventDefault()})),Cu(this._element,this._anchor,\"marker\"),w&&w.className)for(let B of w.className.split(\" \"))this._element.classList.add(B);this._popup=null}addTo(w){return this.remove(),this._map=w,this._element.setAttribute(\"aria-label\",w._getUIString(\"Marker.Title\")),w.getCanvasContainer().appendChild(this._element),w.on(\"move\",this._update),w.on(\"moveend\",this._update),w.on(\"terrain\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this}remove(){return this._opacityTimeout&&(clearTimeout(this._opacityTimeout),delete this._opacityTimeout),this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"terrain\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),c.remove(this._element),this._popup&&this._popup.remove(),this}getLngLat(){return this._lngLat}setLngLat(w){return this._lngLat=a.N.convert(w),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(w){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),w){if(!(\"offset\"in w.options)){let ee=Math.abs(13.5)/Math.SQRT2;w.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[ee,-1*(38.1-13.5+ee)],\"bottom-right\":[-ee,-1*(38.1-13.5+ee)],left:[13.5,-1*(38.1-13.5)],right:[-13.5,-1*(38.1-13.5)]}:this._offset}this._popup=w,this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this}setSubpixelPositioning(w){return this._subpixelPositioning=w,this}getPopup(){return this._popup}togglePopup(){let w=this._popup;return this._element.style.opacity===this._opacityWhenCovered?this:w?(w.isOpen()?w.remove():(w.setLngLat(this._lngLat),w.addTo(this._map)),this):this}_updateOpacity(w=!1){var B,Q;if(!(!((B=this._map)===null||B===void 0)&&B.terrain))return void(this._element.style.opacity!==this._opacity&&(this._element.style.opacity=this._opacity));if(w)this._opacityTimeout=null;else{if(this._opacityTimeout)return;this._opacityTimeout=setTimeout((()=>{this._opacityTimeout=null}),100)}let ee=this._map,le=ee.terrain.depthAtPoint(this._pos),Fe=ee.terrain.getElevationForLngLatZoom(this._lngLat,ee.transform.tileZoom);if(ee.transform.lngLatToCameraDepth(this._lngLat,Fe)-le<.006)return void(this._element.style.opacity=this._opacity);let Ze=-this._offset.y/ee.transform._pixelPerMeter,ut=Math.sin(ee.getPitch()*Math.PI/180)*Ze,Mt=ee.terrain.depthAtPoint(new a.P(this._pos.x,this._pos.y-this._offset.y)),Jt=ee.transform.lngLatToCameraDepth(this._lngLat,Fe+ut)-Mt>.006;!((Q=this._popup)===null||Q===void 0)&&Q.isOpen()&&Jt&&this._popup.remove(),this._element.style.opacity=Jt?this._opacityWhenCovered:this._opacity}getOffset(){return this._offset}setOffset(w){return this._offset=a.P.convert(w),this._update(),this}addClassName(w){this._element.classList.add(w)}removeClassName(w){this._element.classList.remove(w)}toggleClassName(w){return this._element.classList.toggle(w)}setDraggable(w){return this._draggable=!!w,this._map&&(w?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(w){return this._rotation=w||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(w){return this._rotationAlignment=w||\"auto\",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(w){return this._pitchAlignment=w&&w!==\"auto\"?w:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}setOpacity(w,B){return w===void 0&&B===void 0&&(this._opacity=\"1\",this._opacityWhenCovered=\"0.2\"),w!==void 0&&(this._opacity=w),B!==void 0&&(this._opacityWhenCovered=B),this._map&&this._updateOpacity(!0),this}}let Nc={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},pu=0,Uc=!1,xu={maxWidth:100,unit:\"metric\"};function Ac(ue,w,B){let Q=B&&B.maxWidth||100,ee=ue._container.clientHeight/2,le=ue.unproject([0,ee]),Fe=ue.unproject([Q,ee]),Ze=le.distanceTo(Fe);if(B&&B.unit===\"imperial\"){let ut=3.2808*Ze;ut>5280?Va(w,Q,ut/5280,ue._getUIString(\"ScaleControl.Miles\")):Va(w,Q,ut,ue._getUIString(\"ScaleControl.Feet\"))}else B&&B.unit===\"nautical\"?Va(w,Q,Ze/1852,ue._getUIString(\"ScaleControl.NauticalMiles\")):Ze>=1e3?Va(w,Q,Ze/1e3,ue._getUIString(\"ScaleControl.Kilometers\")):Va(w,Q,Ze,ue._getUIString(\"ScaleControl.Meters\"))}function Va(ue,w,B,Q){let ee=(function(le){let Fe=Math.pow(10,`${Math.floor(le)}`.length-1),Ze=le/Fe;return Ze=Ze>=10?10:Ze>=5?5:Ze>=3?3:Ze>=2?2:Ze>=1?1:(function(ut){let Mt=Math.pow(10,Math.ceil(-Math.log(ut)/Math.LN10));return Math.round(ut*Mt)/Mt})(Ze),Fe*Ze})(B);ue.style.width=w*(ee/B)+\"px\",ue.innerHTML=`${ee}&nbsp;${Q}`}let oo={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\",subpixelPositioning:!1},Vc=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \");function hc(ue){if(ue){if(typeof ue==\"number\"){let w=Math.round(Math.abs(ue)/Math.SQRT2);return{center:new a.P(0,0),top:new a.P(0,ue),\"top-left\":new a.P(w,w),\"top-right\":new a.P(-w,w),bottom:new a.P(0,-ue),\"bottom-left\":new a.P(w,-w),\"bottom-right\":new a.P(-w,-w),left:new a.P(ue,0),right:new a.P(-ue,0)}}if(ue instanceof a.P||Array.isArray(ue)){let w=a.P.convert(ue);return{center:w,top:w,\"top-left\":w,\"top-right\":w,bottom:w,\"bottom-left\":w,\"bottom-right\":w,left:w,right:w}}return{center:a.P.convert(ue.center||[0,0]),top:a.P.convert(ue.top||[0,0]),\"top-left\":a.P.convert(ue[\"top-left\"]||[0,0]),\"top-right\":a.P.convert(ue[\"top-right\"]||[0,0]),bottom:a.P.convert(ue.bottom||[0,0]),\"bottom-left\":a.P.convert(ue[\"bottom-left\"]||[0,0]),\"bottom-right\":a.P.convert(ue[\"bottom-right\"]||[0,0]),left:a.P.convert(ue.left||[0,0]),right:a.P.convert(ue.right||[0,0])}}return hc(new a.P(0,0))}let Ku=o;i.AJAXError=a.bh,i.Evented=a.E,i.LngLat=a.N,i.MercatorCoordinate=a.Z,i.Point=a.P,i.addProtocol=a.bi,i.config=a.a,i.removeProtocol=a.bj,i.AttributionControl=Ua,i.BoxZoomHandler=ku,i.CanvasSource=rr,i.CooperativeGesturesHandler=$i,i.DoubleClickZoomHandler=Mi,i.DragPanHandler=wn,i.DragRotateHandler=On,i.EdgeInsets=hu,i.FullscreenControl=class extends a.E{constructor(ue={}){super(),this._onFullscreenChange=()=>{var w;let B=window.document.fullscreenElement||window.document.mozFullScreenElement||window.document.webkitFullscreenElement||window.document.msFullscreenElement;for(;!((w=B==null?void 0:B.shadowRoot)===null||w===void 0)&&w.fullscreenElement;)B=B.shadowRoot.fullscreenElement;B===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,ue&&ue.container&&(ue.container instanceof HTMLElement?this._container=ue.container:a.w(\"Full screen control 'container' must be a DOM element.\")),\"onfullscreenchange\"in document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in document&&(this._fullscreenchange=\"MSFullscreenChange\")}onAdd(ue){return this._map=ue,this._container||(this._container=this._map.getContainer()),this._controlContainer=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),this._controlContainer}onRemove(){c.remove(this._controlContainer),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){let ue=this._fullscreenButton=c.create(\"button\",\"maplibregl-ctrl-fullscreen\",this._controlContainer);c.create(\"span\",\"maplibregl-ctrl-icon\",ue).setAttribute(\"aria-hidden\",\"true\"),ue.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){let ue=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",ue),this._fullscreenButton.title=ue}_getTitle(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")}_isFullscreen(){return this._fullscreen}_handleFullscreenChange(){this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-fullscreen\"),this._updateTitle(),this._fullscreen?(this.fire(new a.k(\"fullscreenstart\")),this._prevCooperativeGesturesEnabled=this._map.cooperativeGestures.isEnabled(),this._map.cooperativeGestures.disable()):(this.fire(new a.k(\"fullscreenend\")),this._prevCooperativeGesturesEnabled&&this._map.cooperativeGestures.enable())}_exitFullscreen(){window.document.exitFullscreen?window.document.exitFullscreen():window.document.mozCancelFullScreen?window.document.mozCancelFullScreen():window.document.msExitFullscreen?window.document.msExitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle(\"maplibregl-pseudo-fullscreen\"),this._handleFullscreenChange(),this._map.resize()}},i.GeoJSONSource=at,i.GeolocateControl=class extends a.E{constructor(ue){super(),this._onSuccess=w=>{if(this._map){if(this._isOutOfMapMaxBounds(w))return this._setErrorState(),this.fire(new a.k(\"outofmaxbounds\",w)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=w,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\");break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}this.options.showUserLocation&&this._watchState!==\"OFF\"&&this._updateMarker(w),this.options.trackUserLocation&&this._watchState!==\"ACTIVE_LOCK\"||this._updateCamera(w),this.options.showUserLocation&&this._dotElement.classList.remove(\"maplibregl-user-location-dot-stale\"),this.fire(new a.k(\"geolocate\",w)),this._finish()}},this._updateCamera=w=>{let B=new a.N(w.coords.longitude,w.coords.latitude),Q=w.coords.accuracy,ee=this._map.getBearing(),le=a.e({bearing:ee},this.options.fitBoundsOptions),Fe=ce.fromLngLat(B,Q);this._map.fitBounds(Fe,le,{geolocateSource:!0})},this._updateMarker=w=>{if(w){let B=new a.N(w.coords.longitude,w.coords.latitude);this._accuracyCircleMarker.setLngLat(B).addTo(this._map),this._userLocationDotMarker.setLngLat(B).addTo(this._map),this._accuracy=w.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onZoom=()=>{this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},this._onError=w=>{if(this._map){if(this.options.trackUserLocation)if(w.code===1){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;let B=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=B,this._geolocateButton.setAttribute(\"aria-label\",B),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(w.code===3&&Uc)return;this._setErrorState()}this._watchState!==\"OFF\"&&this.options.showUserLocation&&this._dotElement.classList.add(\"maplibregl-user-location-dot-stale\"),this.fire(new a.k(\"error\",w)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._setupUI=()=>{this._map&&(this._container.addEventListener(\"contextmenu\",(w=>w.preventDefault())),this._geolocateButton=c.create(\"button\",\"maplibregl-ctrl-geolocate\",this._container),c.create(\"span\",\"maplibregl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",\"true\"),this._geolocateButton.type=\"button\",this._geolocateButton.disabled=!0)},this._finishSetupUI=w=>{if(this._map){if(w===!1){a.w(\"Geolocation support is not available so the GeolocateControl will be disabled.\");let B=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=B,this._geolocateButton.setAttribute(\"aria-label\",B)}else{let B=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.disabled=!1,this._geolocateButton.title=B,this._geolocateButton.setAttribute(\"aria-label\",B)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=c.create(\"div\",\"maplibregl-user-location-dot\"),this._userLocationDotMarker=new Yu({element:this._dotElement}),this._circleElement=c.create(\"div\",\"maplibregl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Yu({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",(()=>this.trigger())),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(B=>{B.geolocateSource||this._watchState!==\"ACTIVE_LOCK\"||B.originalEvent&&B.originalEvent.type===\"resize\"||(this._watchState=\"BACKGROUND\",this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this.fire(new a.k(\"trackuserlocationend\")),this.fire(new a.k(\"userlocationlostfocus\")))}))}},this.options=a.e({},Nc,ue)}onAdd(ue){return this._map=ue,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),(function(){return a._(this,arguments,void 0,(function*(w=!1){if(ys!==void 0&&!w)return ys;if(window.navigator.permissions===void 0)return ys=!!window.navigator.geolocation,ys;try{ys=(yield window.navigator.permissions.query({name:\"geolocation\"})).state!==\"denied\"}catch(B){ys=!!window.navigator.geolocation}return ys}))})().then((w=>this._finishSetupUI(w))),this._container}onRemove(){this._geolocationWatchID!==void 0&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),c.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,pu=0,Uc=!1}_isOutOfMapMaxBounds(ue){let w=this._map.getMaxBounds(),B=ue.coords;return w&&(B.longitude<w.getWest()||B.longitude>w.getEast()||B.latitude<w.getSouth()||B.latitude>w.getNorth())}_setErrorState(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"ACTIVE_ERROR\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadius(){let ue=this._map.getBounds(),w=ue.getSouthEast(),B=ue.getNorthEast(),Q=w.distanceTo(B),ee=Math.ceil(this._accuracy/(Q/this._map._container.clientHeight)*2);this._circleElement.style.width=`${ee}px`,this._circleElement.style.height=`${ee}px`}trigger(){if(!this._setup)return a.w(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new a.k(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":pu--,Uc=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this.fire(new a.k(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new a.k(\"trackuserlocationstart\")),this.fire(new a.k(\"userlocationfocus\"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"OFF\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if(this._watchState===\"OFF\"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){let ue;this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),pu++,pu>1?(ue={maximumAge:6e5,timeout:0},Uc=!0):(ue=this.options.positionOptions,Uc=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,ue)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)}},i.Hash=Mh,i.ImageSource=ar,i.KeyboardHandler=Zt,i.LngLatBounds=ce,i.LogoControl=Zn,i.Map=class extends Ra{constructor(ue){a.bf.mark(a.bg.create);let w=Object.assign(Object.assign({},Ws),ue);if(w.minZoom!=null&&w.maxZoom!=null&&w.minZoom>w.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(w.minPitch!=null&&w.maxPitch!=null&&w.minPitch>w.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(w.minPitch!=null&&w.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(w.maxPitch!=null&&w.maxPitch>85)throw new Error(\"maxPitch must be less than or equal to 85\");if(super(new il(w.minZoom,w.maxZoom,w.minPitch,w.maxPitch,w.renderWorldCopies),{bearingSnap:w.bearingSnap}),this._idleTriggered=!1,this._crossFadingFactor=1,this._renderTaskQueue=new Dn,this._controls=[],this._mapId=a.a4(),this._contextLost=B=>{B.preventDefault(),this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this.fire(new a.k(\"webglcontextlost\",{originalEvent:B}))},this._contextRestored=B=>{this._setupPainter(),this.resize(),this._update(),this.fire(new a.k(\"webglcontextrestored\",{originalEvent:B}))},this._onMapScroll=B=>{if(B.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=w.interactive,this._maxTileCacheSize=w.maxTileCacheSize,this._maxTileCacheZoomLevels=w.maxTileCacheZoomLevels,this._failIfMajorPerformanceCaveat=w.failIfMajorPerformanceCaveat===!0,this._preserveDrawingBuffer=w.preserveDrawingBuffer===!0,this._antialias=w.antialias===!0,this._trackResize=w.trackResize===!0,this._bearingSnap=w.bearingSnap,this._refreshExpiredTiles=w.refreshExpiredTiles===!0,this._fadeDuration=w.fadeDuration,this._crossSourceCollisions=w.crossSourceCollisions===!0,this._collectResourceTiming=w.collectResourceTiming===!0,this._locale=Object.assign(Object.assign({},Ho),w.locale),this._clickTolerance=w.clickTolerance,this._overridePixelRatio=w.pixelRatio,this._maxCanvasSize=w.maxCanvasSize,this.transformCameraUpdate=w.transformCameraUpdate,this.cancelPendingTileRequestsWhileZooming=w.cancelPendingTileRequestsWhileZooming===!0,this._imageQueueHandle=p.addThrottleControl((()=>this.isMoving())),this._requestManager=new k(w.transformRequest),typeof w.container==\"string\"){if(this._container=document.getElementById(w.container),!this._container)throw new Error(`Container '${w.container}' not found.`)}else{if(!(w.container instanceof HTMLElement))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=w.container}if(w.maxBounds&&this.setMaxBounds(w.maxBounds),this._setupContainer(),this._setupPainter(),this.on(\"move\",(()=>this._update(!1))).on(\"moveend\",(()=>this._update(!1))).on(\"zoom\",(()=>this._update(!0))).on(\"terrain\",(()=>{this.painter.terrainFacilitator.dirty=!0,this._update(!0)})).once(\"idle\",(()=>{this._idleTriggered=!0})),typeof window!=\"undefined\"){addEventListener(\"online\",this._onWindowOnline,!1);let B=!1,Q=nh((ee=>{this._trackResize&&!this._removed&&(this.resize(ee),this.redraw())}),50);this._resizeObserver=new ResizeObserver((ee=>{B?Q(ee):B=!0})),this._resizeObserver.observe(this._container)}this.handlers=new Ca(this,w),this._hash=w.hash&&new Mh(typeof w.hash==\"string\"&&w.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:w.center,zoom:w.zoom,bearing:w.bearing,pitch:w.pitch}),w.bounds&&(this.resize(),this.fitBounds(w.bounds,a.e({},w.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=w.localIdeographFontFamily,this._validateStyle=w.validateStyle,w.style&&this.setStyle(w.style,{localIdeographFontFamily:w.localIdeographFontFamily}),w.attributionControl&&this.addControl(new Ua(typeof w.attributionControl==\"boolean\"?void 0:w.attributionControl)),w.maplibreLogo&&this.addControl(new Zn,w.logoPosition),this.on(\"style.load\",(()=>{this.transform.unmodified&&this.jumpTo(this.style.stylesheet)})),this.on(\"data\",(B=>{this._update(B.dataType===\"style\"),this.fire(new a.k(`${B.dataType}data`,B))})),this.on(\"dataloading\",(B=>{this.fire(new a.k(`${B.dataType}dataloading`,B))})),this.on(\"dataabort\",(B=>{this.fire(new a.k(\"sourcedataabort\",B))}))}_getMapId(){return this._mapId}addControl(ue,w){if(w===void 0&&(w=ue.getDefaultPosition?ue.getDefaultPosition():\"top-right\"),!ue||!ue.onAdd)return this.fire(new a.j(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));let B=ue.onAdd(this);this._controls.push(ue);let Q=this._controlPositions[w];return w.indexOf(\"bottom\")!==-1?Q.insertBefore(B,Q.firstChild):Q.appendChild(B),this}removeControl(ue){if(!ue||!ue.onRemove)return this.fire(new a.j(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));let w=this._controls.indexOf(ue);return w>-1&&this._controls.splice(w,1),ue.onRemove(this),this}hasControl(ue){return this._controls.indexOf(ue)>-1}calculateCameraOptionsFromTo(ue,w,B,Q){return Q==null&&this.terrain&&(Q=this.terrain.getElevationForLngLatZoom(B,this.transform.tileZoom)),super.calculateCameraOptionsFromTo(ue,w,B,Q)}resize(ue){var w;let B=this._containerDimensions(),Q=B[0],ee=B[1],le=this._getClampedPixelRatio(Q,ee);if(this._resizeCanvas(Q,ee,le),this.painter.resize(Q,ee,le),this.painter.overLimit()){let Ze=this.painter.context.gl;this._maxCanvasSize=[Ze.drawingBufferWidth,Ze.drawingBufferHeight];let ut=this._getClampedPixelRatio(Q,ee);this._resizeCanvas(Q,ee,ut),this.painter.resize(Q,ee,ut)}this.transform.resize(Q,ee),(w=this._requestedCameraState)===null||w===void 0||w.resize(Q,ee);let Fe=!this._moving;return Fe&&(this.stop(),this.fire(new a.k(\"movestart\",ue)).fire(new a.k(\"move\",ue))),this.fire(new a.k(\"resize\",ue)),Fe&&this.fire(new a.k(\"moveend\",ue)),this}_getClampedPixelRatio(ue,w){let{0:B,1:Q}=this._maxCanvasSize,ee=this.getPixelRatio(),le=ue*ee,Fe=w*ee;return Math.min(le>B?B/le:1,Fe>Q?Q/Fe:1)*ee}getPixelRatio(){var ue;return(ue=this._overridePixelRatio)!==null&&ue!==void 0?ue:devicePixelRatio}setPixelRatio(ue){this._overridePixelRatio=ue,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(ue){return this.transform.setMaxBounds(ce.convert(ue)),this._update()}setMinZoom(ue){if((ue=ue==null?-2:ue)>=-2&&ue<=this.transform.maxZoom)return this.transform.minZoom=ue,this._update(),this.getZoom()<ue&&this.setZoom(ue),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")}getMinZoom(){return this.transform.minZoom}setMaxZoom(ue){if((ue=ue==null?22:ue)>=this.transform.minZoom)return this.transform.maxZoom=ue,this._update(),this.getZoom()>ue&&this.setZoom(ue),this;throw new Error(\"maxZoom must be greater than the current minZoom\")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(ue){if((ue=ue==null?0:ue)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(ue>=0&&ue<=this.transform.maxPitch)return this.transform.minPitch=ue,this._update(),this.getPitch()<ue&&this.setPitch(ue),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")}getMinPitch(){return this.transform.minPitch}setMaxPitch(ue){if((ue=ue==null?60:ue)>85)throw new Error(\"maxPitch must be less than or equal to 85\");if(ue>=this.transform.minPitch)return this.transform.maxPitch=ue,this._update(),this.getPitch()>ue&&this.setPitch(ue),this;throw new Error(\"maxPitch must be greater than the current minPitch\")}getMaxPitch(){return this.transform.maxPitch}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(ue){return this.transform.renderWorldCopies=ue,this._update()}project(ue){return this.transform.locationPoint(a.N.convert(ue),this.style&&this.terrain)}unproject(ue){return this.transform.pointLocation(a.P.convert(ue),this.terrain)}isMoving(){var ue;return this._moving||((ue=this.handlers)===null||ue===void 0?void 0:ue.isMoving())}isZooming(){var ue;return this._zooming||((ue=this.handlers)===null||ue===void 0?void 0:ue.isZooming())}isRotating(){var ue;return this._rotating||((ue=this.handlers)===null||ue===void 0?void 0:ue.isRotating())}_createDelegatedListener(ue,w,B){if(ue===\"mouseenter\"||ue===\"mouseover\"){let Q=!1;return{layers:w,listener:B,delegates:{mousemove:le=>{let Fe=w.filter((ut=>this.getLayer(ut))),Ze=Fe.length!==0?this.queryRenderedFeatures(le.point,{layers:Fe}):[];Ze.length?Q||(Q=!0,B.call(this,new jl(ue,this,le.originalEvent,{features:Ze}))):Q=!1},mouseout:()=>{Q=!1}}}}if(ue===\"mouseleave\"||ue===\"mouseout\"){let Q=!1;return{layers:w,listener:B,delegates:{mousemove:Fe=>{let Ze=w.filter((ut=>this.getLayer(ut)));(Ze.length!==0?this.queryRenderedFeatures(Fe.point,{layers:Ze}):[]).length?Q=!0:Q&&(Q=!1,B.call(this,new jl(ue,this,Fe.originalEvent)))},mouseout:Fe=>{Q&&(Q=!1,B.call(this,new jl(ue,this,Fe.originalEvent)))}}}}{let Q=ee=>{let le=w.filter((Ze=>this.getLayer(Ze))),Fe=le.length!==0?this.queryRenderedFeatures(ee.point,{layers:le}):[];Fe.length&&(ee.features=Fe,B.call(this,ee),delete ee.features)};return{layers:w,listener:B,delegates:{[ue]:Q}}}}_saveDelegatedListener(ue,w){this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[ue]=this._delegatedListeners[ue]||[],this._delegatedListeners[ue].push(w)}_removeDelegatedListener(ue,w,B){if(!this._delegatedListeners||!this._delegatedListeners[ue])return;let Q=this._delegatedListeners[ue];for(let ee=0;ee<Q.length;ee++){let le=Q[ee];if(le.listener===B&&le.layers.length===w.length&&le.layers.every((Fe=>w.includes(Fe)))){for(let Fe in le.delegates)this.off(Fe,le.delegates[Fe]);return void Q.splice(ee,1)}}}on(ue,w,B){if(B===void 0)return super.on(ue,w);let Q=this._createDelegatedListener(ue,typeof w==\"string\"?[w]:w,B);this._saveDelegatedListener(ue,Q);for(let ee in Q.delegates)this.on(ee,Q.delegates[ee]);return this}once(ue,w,B){if(B===void 0)return super.once(ue,w);let Q=typeof w==\"string\"?[w]:w,ee=this._createDelegatedListener(ue,Q,B);for(let le in ee.delegates){let Fe=ee.delegates[le];ee.delegates[le]=(...Ze)=>{this._removeDelegatedListener(ue,Q,B),Fe(...Ze)}}this._saveDelegatedListener(ue,ee);for(let le in ee.delegates)this.once(le,ee.delegates[le]);return this}off(ue,w,B){return B===void 0?super.off(ue,w):(this._removeDelegatedListener(ue,typeof w==\"string\"?[w]:w,B),this)}queryRenderedFeatures(ue,w){if(!this.style)return[];let B,Q=ue instanceof a.P||Array.isArray(ue),ee=Q?ue:[[0,0],[this.transform.width,this.transform.height]];if(w=w||(Q?{}:ue)||{},ee instanceof a.P||typeof ee[0]==\"number\")B=[a.P.convert(ee)];else{let le=a.P.convert(ee[0]),Fe=a.P.convert(ee[1]);B=[le,new a.P(Fe.x,le.y),Fe,new a.P(le.x,Fe.y),le]}return this.style.queryRenderedFeatures(B,w,this.transform)}querySourceFeatures(ue,w){return this.style.querySourceFeatures(ue,w)}setStyle(ue,w){return(w=a.e({},{localIdeographFontFamily:this._localIdeographFontFamily,validate:this._validateStyle},w)).diff!==!1&&w.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&ue?(this._diffStyle(ue,w),this):(this._localIdeographFontFamily=w.localIdeographFontFamily,this._updateStyle(ue,w))}setTransformRequest(ue){return this._requestManager.setTransformRequest(ue),this}_getUIString(ue){let w=this._locale[ue];if(w==null)throw new Error(`Missing UI string '${ue}'`);return w}_updateStyle(ue,w){if(w.transformStyle&&this.style&&!this.style._loaded)return void this.style.once(\"style.load\",(()=>this._updateStyle(ue,w)));let B=this.style&&w.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!ue)),ue?(this.style=new Ha(this,w||{}),this.style.setEventedParent(this,{style:this.style}),typeof ue==\"string\"?this.style.loadURL(ue,w,B):this.style.loadJSON(ue,w,B),this):(delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new Ha(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(ue,w){if(typeof ue==\"string\"){let B=this._requestManager.transformRequest(ue,\"Style\");a.h(B,new AbortController).then((Q=>{this._updateDiff(Q.data,w)})).catch((Q=>{Q&&this.fire(new a.j(Q))}))}else typeof ue==\"object\"&&this._updateDiff(ue,w)}_updateDiff(ue,w){try{this.style.setState(ue,w)&&this._update(!0)}catch(B){a.w(`Unable to perform style diff: ${B.message||B.error||B}.  Rebuilding the style from scratch.`),this._updateStyle(ue,w)}}getStyle(){if(this.style)return this.style.serialize()}isStyleLoaded(){return this.style?this.style.loaded():a.w(\"There is no style added to the map.\")}addSource(ue,w){return this._lazyInitEmptyStyle(),this.style.addSource(ue,w),this._update(!0)}isSourceLoaded(ue){let w=this.style&&this.style.sourceCaches[ue];if(w!==void 0)return w.loaded();this.fire(new a.j(new Error(`There is no source with ID '${ue}'`)))}setTerrain(ue){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off(\"data\",this._terrainDataCallback),ue){let w=this.style.sourceCaches[ue.source];if(!w)throw new Error(`cannot load terrain, because there exists no source with ID: ${ue.source}`);this.terrain===null&&w.reload();for(let B in this.style._layers){let Q=this.style._layers[B];Q.type===\"hillshade\"&&Q.source===ue.source&&a.w(\"You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.\")}this.terrain=new Zo(this.painter,w,ue),this.painter.renderToTexture=new ws(this.painter,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._terrainDataCallback=B=>{B.dataType===\"style\"?this.terrain.sourceCache.freeRtt():B.dataType===\"source\"&&B.tile&&(B.sourceId!==ue.source||this._elevationFreeze||(this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.terrain.sourceCache.freeRtt(B.tile.tileID))},this.style.on(\"data\",this._terrainDataCallback)}else this.terrain&&this.terrain.sourceCache.destruct(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform.minElevationForCurrentTile=0,this.transform.elevation=0;return this.fire(new a.k(\"terrain\",{terrain:ue})),this}getTerrain(){var ue,w;return(w=(ue=this.terrain)===null||ue===void 0?void 0:ue.options)!==null&&w!==void 0?w:null}areTilesLoaded(){let ue=this.style&&this.style.sourceCaches;for(let w in ue){let B=ue[w]._tiles;for(let Q in B){let ee=B[Q];if(ee.state!==\"loaded\"&&ee.state!==\"errored\")return!1}}return!0}removeSource(ue){return this.style.removeSource(ue),this._update(!0)}getSource(ue){return this.style.getSource(ue)}addImage(ue,w,B={}){let{pixelRatio:Q=1,sdf:ee=!1,stretchX:le,stretchY:Fe,content:Ze,textFitWidth:ut,textFitHeight:Mt}=B;if(this._lazyInitEmptyStyle(),!(w instanceof HTMLImageElement||a.b(w))){if(w.width===void 0||w.height===void 0)return this.fire(new a.j(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));{let{width:Jt,height:$t,data:yr}=w,Cr=w;return this.style.addImage(ue,{data:new a.R({width:Jt,height:$t},new Uint8Array(yr)),pixelRatio:Q,stretchX:le,stretchY:Fe,content:Ze,textFitWidth:ut,textFitHeight:Mt,sdf:ee,version:0,userImage:Cr}),Cr.onAdd&&Cr.onAdd(this,ue),this}}{let{width:Jt,height:$t,data:yr}=u.getImageData(w);this.style.addImage(ue,{data:new a.R({width:Jt,height:$t},yr),pixelRatio:Q,stretchX:le,stretchY:Fe,content:Ze,textFitWidth:ut,textFitHeight:Mt,sdf:ee,version:0})}}updateImage(ue,w){let B=this.style.getImage(ue);if(!B)return this.fire(new a.j(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));let Q=w instanceof HTMLImageElement||a.b(w)?u.getImageData(w):w,{width:ee,height:le,data:Fe}=Q;if(ee===void 0||le===void 0)return this.fire(new a.j(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(ee!==B.data.width||le!==B.data.height)return this.fire(new a.j(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));let Ze=!(w instanceof HTMLImageElement||a.b(w));return B.data.replace(Fe,Ze),this.style.updateImage(ue,B),this}getImage(ue){return this.style.getImage(ue)}hasImage(ue){return ue?!!this.style.getImage(ue):(this.fire(new a.j(new Error(\"Missing required image id\"))),!1)}removeImage(ue){this.style.removeImage(ue)}loadImage(ue){return p.getImage(this._requestManager.transformRequest(ue,\"Image\"),new AbortController)}listImages(){return this.style.listImages()}addLayer(ue,w){return this._lazyInitEmptyStyle(),this.style.addLayer(ue,w),this._update(!0)}moveLayer(ue,w){return this.style.moveLayer(ue,w),this._update(!0)}removeLayer(ue){return this.style.removeLayer(ue),this._update(!0)}getLayer(ue){return this.style.getLayer(ue)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(ue,w,B){return this.style.setLayerZoomRange(ue,w,B),this._update(!0)}setFilter(ue,w,B={}){return this.style.setFilter(ue,w,B),this._update(!0)}getFilter(ue){return this.style.getFilter(ue)}setPaintProperty(ue,w,B,Q={}){return this.style.setPaintProperty(ue,w,B,Q),this._update(!0)}getPaintProperty(ue,w){return this.style.getPaintProperty(ue,w)}setLayoutProperty(ue,w,B,Q={}){return this.style.setLayoutProperty(ue,w,B,Q),this._update(!0)}getLayoutProperty(ue,w){return this.style.getLayoutProperty(ue,w)}setGlyphs(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(ue,w),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(ue,w,B={}){return this._lazyInitEmptyStyle(),this.style.addSprite(ue,w,B,(Q=>{Q||this._update(!0)})),this}removeSprite(ue){return this._lazyInitEmptyStyle(),this.style.removeSprite(ue),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setSprite(ue,w,(B=>{B||this._update(!0)})),this}setLight(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setLight(ue,w),this._update(!0)}getLight(){return this.style.getLight()}setSky(ue){return this._lazyInitEmptyStyle(),this.style.setSky(ue),this._update(!0)}getSky(){return this.style.getSky()}setFeatureState(ue,w){return this.style.setFeatureState(ue,w),this._update()}removeFeatureState(ue,w){return this.style.removeFeatureState(ue,w),this._update()}getFeatureState(ue){return this.style.getFeatureState(ue)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let ue=0,w=0;return this._container&&(ue=this._container.clientWidth||400,w=this._container.clientHeight||300),[ue,w]}_setupContainer(){let ue=this._container;ue.classList.add(\"maplibregl-map\");let w=this._canvasContainer=c.create(\"div\",\"maplibregl-canvas-container\",ue);this._interactive&&w.classList.add(\"maplibregl-interactive\"),this._canvas=c.create(\"canvas\",\"maplibregl-canvas\",w),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",this._interactive?\"0\":\"-1\"),this._canvas.setAttribute(\"aria-label\",this._getUIString(\"Map.Title\")),this._canvas.setAttribute(\"role\",\"region\");let B=this._containerDimensions(),Q=this._getClampedPixelRatio(B[0],B[1]);this._resizeCanvas(B[0],B[1],Q);let ee=this._controlContainer=c.create(\"div\",\"maplibregl-control-container\",ue),le=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((Fe=>{le[Fe]=c.create(\"div\",`maplibregl-ctrl-${Fe} `,ee)})),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)}_resizeCanvas(ue,w,B){this._canvas.width=Math.floor(B*ue),this._canvas.height=Math.floor(B*w),this._canvas.style.width=`${ue}px`,this._canvas.style.height=`${w}px`}_setupPainter(){let ue={alpha:!0,stencil:!0,depth:!0,failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1},w=null;this._canvas.addEventListener(\"webglcontextcreationerror\",(Q=>{w={requestedAttributes:ue},Q&&(w.statusMessage=Q.statusMessage,w.type=Q.type)}),{once:!0});let B=this._canvas.getContext(\"webgl2\",ue)||this._canvas.getContext(\"webgl\",ue);if(!B){let Q=\"Failed to initialize WebGL\";throw w?(w.message=Q,new Error(JSON.stringify(w))):new Error(Q)}this.painter=new Dc(B,this.transform),f.testSupport(B)}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(ue){return this.style&&this.style._loaded?(this._styleDirty=this._styleDirty||ue,this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(ue){return this._update(),this._renderTaskQueue.add(ue)}_cancelRenderFrame(ue){this._renderTaskQueue.remove(ue)}_render(ue){let w=this._idleTriggered?this._fadeDuration:0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(ue),this._removed)return;let B=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;let ee=this.transform.zoom,le=u.now();this.style.zoomHistory.update(ee,le);let Fe=new a.z(ee,{now:le,fadeDuration:w,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),Ze=Fe.crossFadingFactor();Ze===1&&Ze===this._crossFadingFactor||(B=!0,this._crossFadingFactor=Ze),this.style.update(Fe)}this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.sourceCache.update(this.transform,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._elevationFreeze||(this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform.minElevationForCurrentTile=0,this.transform.elevation=0),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,w,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:w,showPadding:this.showPadding}),this.fire(new a.k(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,a.bf.mark(a.bg.load),this.fire(new a.k(\"load\"))),this.style&&(this.style.hasTransitions()||B)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();let Q=this._sourcesDirty||this._styleDirty||this._placementDirty;return Q||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new a.k(\"idle\")),!this._loaded||this._fullyLoaded||Q||(this._fullyLoaded=!0,a.bf.mark(a.bg.fullLoad)),this}redraw(){return this.style&&(this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._render(0)),this}remove(){var ue;this._hash&&this._hash.remove();for(let B of this._controls)B.onRemove(this);this._controls=[],this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof window!=\"undefined\"&&removeEventListener(\"online\",this._onWindowOnline,!1),p.removeThrottleControl(this._imageQueueHandle),(ue=this._resizeObserver)===null||ue===void 0||ue.disconnect();let w=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");w!=null&&w.loseContext&&w.loseContext(),this._canvas.removeEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.removeEventListener(\"webglcontextlost\",this._contextLost,!1),c.remove(this._canvasContainer),c.remove(this._controlContainer),this._container.classList.remove(\"maplibregl-map\"),a.bf.clearMetrics(),this._removed=!0,this.fire(new a.k(\"remove\"))}triggerRepaint(){this.style&&!this._frameRequest&&(this._frameRequest=new AbortController,u.frameAsync(this._frameRequest).then((ue=>{a.bf.frame(ue),this._frameRequest=null,this._render(ue)})).catch((()=>{})))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(ue){this._showTileBoundaries!==ue&&(this._showTileBoundaries=ue,this._update())}get showPadding(){return!!this._showPadding}set showPadding(ue){this._showPadding!==ue&&(this._showPadding=ue,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(ue){this._showCollisionBoxes!==ue&&(this._showCollisionBoxes=ue,ue?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(ue){this._showOverdrawInspector!==ue&&(this._showOverdrawInspector=ue,this._update())}get repaint(){return!!this._repaint}set repaint(ue){this._repaint!==ue&&(this._repaint=ue,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(ue){this._vertices=ue,this._update()}get version(){return ml}getCameraTargetElevation(){return this.transform.elevation}},i.MapMouseEvent=jl,i.MapTouchEvent=uf,i.MapWheelEvent=Xh,i.Marker=Yu,i.NavigationControl=class{constructor(ue){this._updateZoomButtons=()=>{let w=this._map.getZoom(),B=w===this._map.getMaxZoom(),Q=w===this._map.getMinZoom();this._zoomInButton.disabled=B,this._zoomOutButton.disabled=Q,this._zoomInButton.setAttribute(\"aria-disabled\",B.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",Q.toString())},this._rotateCompassArrow=()=>{let w=this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${this._map.transform.angle*(180/Math.PI)}deg)`:`rotate(${this._map.transform.angle*(180/Math.PI)}deg)`;this._compassIcon.style.transform=w},this._setButtonTitle=(w,B)=>{let Q=this._map._getUIString(`NavigationControl.${B}`);w.title=Q,w.setAttribute(\"aria-label\",Q)},this.options=a.e({},va,ue),this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(w=>w.preventDefault())),this.options.showZoom&&(this._zoomInButton=this._createButton(\"maplibregl-ctrl-zoom-in\",(w=>this._map.zoomIn({},{originalEvent:w}))),c.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",\"true\"),this._zoomOutButton=this._createButton(\"maplibregl-ctrl-zoom-out\",(w=>this._map.zoomOut({},{originalEvent:w}))),c.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",\"true\")),this.options.showCompass&&(this._compass=this._createButton(\"maplibregl-ctrl-compass\",(w=>{this.options.visualizePitch?this._map.resetNorthPitch({},{originalEvent:w}):this._map.resetNorth({},{originalEvent:w})})),this._compassIcon=c.create(\"span\",\"maplibregl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",\"true\"))}onAdd(ue){return this._map=ue,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new no(this._map,this._compass,this.options.visualizePitch)),this._container}onRemove(){c.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map}_createButton(ue,w){let B=c.create(\"button\",ue,this._container);return B.type=\"button\",B.addEventListener(\"click\",w),B}},i.Popup=class extends a.E{constructor(ue){super(),this.remove=()=>(this._content&&c.remove(this._content),this._container&&(c.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\"),delete this._map,this.fire(new a.k(\"close\"))),this),this._onMouseUp=w=>{this._update(w.point)},this._onMouseMove=w=>{this._update(w.point)},this._onDrag=w=>{this._update(w.point)},this._update=w=>{var B;if(!this._map||!this._lngLat&&!this._trackPointer||!this._content)return;if(!this._container){if(this._container=c.create(\"div\",\"maplibregl-popup\",this._map.getContainer()),this._tip=c.create(\"div\",\"maplibregl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className)for(let Ze of this.options.className.split(\" \"))this._container.classList.add(Ze);this._closeButton&&this._closeButton.setAttribute(\"aria-label\",this._map._getUIString(\"Popup.Close\")),this._trackPointer&&this._container.classList.add(\"maplibregl-popup-track-pointer\")}if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._lngLat=this._map.transform.renderWorldCopies&&!this._trackPointer?rs(this._lngLat,this._flatPos,this._map.transform):(B=this._lngLat)===null||B===void 0?void 0:B.wrap(),this._trackPointer&&!w)return;let Q=this._flatPos=this._pos=this._trackPointer&&w?w:this._map.project(this._lngLat);this._map.terrain&&(this._flatPos=this._trackPointer&&w?w:this._map.transform.locationPoint(this._lngLat));let ee=this.options.anchor,le=hc(this.options.offset);if(!ee){let Ze=this._container.offsetWidth,ut=this._container.offsetHeight,Mt;Mt=Q.y+le.bottom.y<ut?[\"top\"]:Q.y>this._map.transform.height-ut?[\"bottom\"]:[],Q.x<Ze/2?Mt.push(\"left\"):Q.x>this._map.transform.width-Ze/2&&Mt.push(\"right\"),ee=Mt.length===0?\"bottom\":Mt.join(\"-\")}let Fe=Q.add(le[ee]);this.options.subpixelPositioning||(Fe=Fe.round()),c.setTransform(this._container,`${Ql[ee]} translate(${Fe.x}px,${Fe.y}px)`),Cu(this._container,ee,\"popup\")},this._onClose=()=>{this.remove()},this.options=a.e(Object.create(oo),ue)}addTo(ue){return this._map&&this.remove(),this._map=ue,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new a.k(\"open\")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(ue){return this._lngLat=a.N.convert(ue),this._pos=null,this._flatPos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._flatPos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")),this}getElement(){return this._container}setText(ue){return this.setDOMContent(document.createTextNode(ue))}setHTML(ue){let w=document.createDocumentFragment(),B=document.createElement(\"body\"),Q;for(B.innerHTML=ue;Q=B.firstChild,Q;)w.appendChild(Q);return this.setDOMContent(w)}getMaxWidth(){var ue;return(ue=this._container)===null||ue===void 0?void 0:ue.style.maxWidth}setMaxWidth(ue){return this.options.maxWidth=ue,this._update(),this}setDOMContent(ue){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=c.create(\"div\",\"maplibregl-popup-content\",this._container);return this._content.appendChild(ue),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(ue){return this._container&&this._container.classList.add(ue),this}removeClassName(ue){return this._container&&this._container.classList.remove(ue),this}setOffset(ue){return this.options.offset=ue,this._update(),this}toggleClassName(ue){if(this._container)return this._container.classList.toggle(ue)}setSubpixelPositioning(ue){this.options.subpixelPositioning=ue}_createCloseButton(){this.options.closeButton&&(this._closeButton=c.create(\"button\",\"maplibregl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;let ue=this._container.querySelector(Vc);ue&&ue.focus()}},i.RasterDEMTileSource=Ht,i.RasterTileSource=mt,i.ScaleControl=class{constructor(ue){this._onMove=()=>{Ac(this._map,this._container,this.options)},this.setUnit=w=>{this.options.unit=w,Ac(this._map,this._container,this.options)},this.options=Object.assign(Object.assign({},xu),ue)}getDefaultPosition(){return\"bottom-left\"}onAdd(ue){return this._map=ue,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-scale\",ue.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container}onRemove(){c.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0}},i.ScrollZoomHandler=Kr,i.Style=Ha,i.TerrainControl=class{constructor(ue){this._toggleTerrain=()=>{this._map.getTerrain()?this._map.setTerrain(null):this._map.setTerrain(this.options),this._updateTerrainIcon()},this._updateTerrainIcon=()=>{this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain\"),this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain-enabled\"),this._map.terrain?(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain-enabled\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.Disable\")):(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.Enable\"))},this.options=ue}onAdd(ue){return this._map=ue,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._terrainButton=c.create(\"button\",\"maplibregl-ctrl-terrain\",this._container),c.create(\"span\",\"maplibregl-ctrl-icon\",this._terrainButton).setAttribute(\"aria-hidden\",\"true\"),this._terrainButton.type=\"button\",this._terrainButton.addEventListener(\"click\",this._toggleTerrain),this._updateTerrainIcon(),this._map.on(\"terrain\",this._updateTerrainIcon),this._container}onRemove(){c.remove(this._container),this._map.off(\"terrain\",this._updateTerrainIcon),this._map=void 0}},i.TwoFingersTouchPitchHandler=Bc,i.TwoFingersTouchRotateHandler=ff,i.TwoFingersTouchZoomHandler=vu,i.TwoFingersTouchZoomRotateHandler=Yi,i.VectorTileSource=lt,i.VideoSource=Vt,i.addSourceType=(ue,w)=>a._(void 0,void 0,void 0,(function*(){if(je(ue))throw new Error(`A source type called \"${ue}\" already exists.`);((B,Q)=>{tt[B]=Q})(ue,w)})),i.clearPrewarmedResources=function(){let ue=ge;ue&&(ue.isPreloaded()&&ue.numActive()===1?(ue.release(_e),ge=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},i.getMaxParallelImageRequests=function(){return a.a.MAX_PARALLEL_IMAGE_REQUESTS},i.getRTLTextPluginStatus=function(){return St().getRTLTextPluginStatus()},i.getVersion=function(){return Ku},i.getWorkerCount=function(){return Ee.workerCount},i.getWorkerUrl=function(){return a.a.WORKER_URL},i.importScriptInWorkers=function(ue){return be().broadcast(\"IS\",ue)},i.prewarm=function(){Se().acquire(_e)},i.setMaxParallelImageRequests=function(ue){a.a.MAX_PARALLEL_IMAGE_REQUESTS=ue},i.setRTLTextPlugin=function(ue,w){return St().setRTLTextPlugin(ue,w)},i.setWorkerCount=function(ue){Ee.workerCount=ue},i.setWorkerUrl=function(ue){a.a.WORKER_URL=ue}}));var n=e;return n}))});var Eje=ye((v2r,Mje)=>{\"use strict\";var yw=Pr(),lZt=Zl().sanitizeHTML,uZt=zJ(),Tje=Ix();function Aje(e,t){this.subplot=e,this.uid=e.uid+\"-\"+t,this.index=t,this.idSource=\"source-\"+this.uid,this.idLayer=Tje.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var sg=Aje.prototype;sg.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=y7(t)};sg.needsNewImage=function(e){var t=this.subplot.map;return t.getSource(this.idSource)&&this.sourceType===\"image\"&&e.sourcetype===\"image\"&&(this.source!==e.source||JSON.stringify(this.coordinates)!==JSON.stringify(e.coordinates))};sg.needsNewSource=function(e){return this.sourceType!==e.sourcetype||JSON.stringify(this.source)!==JSON.stringify(e.source)||this.layerType!==e.type};sg.needsNewLayer=function(e){return this.layerType!==e.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]};sg.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]};sg.updateImage=function(e){var t=this.subplot.map;t.getSource(this.idSource).updateImage({url:e.source,coordinates:e.coordinates});var r=this.findFollowingMapLayerId(this.lookupBelow());r!==null&&this.subplot.map.moveLayer(this.idLayer,r)};sg.updateSource=function(e){var t=this.subplot.map;if(t.getSource(this.idSource)&&t.removeSource(this.idSource),this.sourceType=e.sourcetype,this.source=e.source,!!y7(e)){var r=cZt(e);t.addSource(this.idSource,r)}};sg.findFollowingMapLayerId=function(e){if(e===\"traces\")for(var t=this.subplot.getMapLayers(),r=0;r<t.length;r++){var n=t[r].id;if(typeof n==\"string\"&&n.indexOf(Tje.traceLayerPrefix)===0){e=n;break}}return e};sg.updateLayer=function(e){var t=this.subplot,r=Sje(e),n=this.lookupBelow(),i=this.findFollowingMapLayerId(n);this.removeLayer(),y7(e)&&t.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":e.sourcelayer||\"\",type:e.type,minzoom:e.minzoom,maxzoom:e.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=e.type,this.below=n};sg.updateStyle=function(e){if(y7(e)){var t=Sje(e);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",t.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",t.paint)}};sg.removeLayer=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer)};sg.dispose=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),e.getSource(this.idSource)&&e.removeSource(this.idSource)};function y7(e){if(!e.visible)return!1;var t=e.source;if(Array.isArray(t)&&t.length>0){for(var r=0;r<t.length;r++)if(typeof t[r]!=\"string\"||t[r].length===0)return!1;return!0}return yw.isPlainObject(t)||typeof t==\"string\"&&t.length>0}function Sje(e){var t={},r={};switch(e.type){case\"circle\":yw.extendFlat(r,{\"circle-radius\":e.circle.radius,\"circle-color\":e.color,\"circle-opacity\":e.opacity});break;case\"line\":yw.extendFlat(r,{\"line-width\":e.line.width,\"line-color\":e.color,\"line-opacity\":e.opacity,\"line-dasharray\":e.line.dash});break;case\"fill\":yw.extendFlat(r,{\"fill-color\":e.color,\"fill-outline-color\":e.fill.outlinecolor,\"fill-opacity\":e.opacity});break;case\"symbol\":var n=e.symbol,i=uZt(n.textposition,n.iconsize);yw.extendFlat(t,{\"icon-image\":n.icon+\"-15\",\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":i.anchor,\"text-offset\":i.offset,\"symbol-placement\":n.placement}),yw.extendFlat(r,{\"icon-color\":e.color,\"text-color\":n.textfont.color,\"text-opacity\":e.opacity});break;case\"raster\":yw.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":e.opacity});break}return{layout:t,paint:r}}function cZt(e){var t=e.sourcetype,r=e.source,n={type:t},i;return t===\"geojson\"?i=\"data\":t===\"vector\"?i=typeof r==\"string\"?\"url\":\"tiles\":t===\"raster\"?(i=\"tiles\",n.tileSize=256):t===\"image\"&&(i=\"url\",n.coordinates=e.coordinates),n[i]=r,e.sourceattribution&&(n.attribution=lZt(e.sourceattribution)),n}Mje.exports=function(t,r,n){var i=new Aje(t,r);return i.update(n),i}});var Fje=ye((p2r,Dje)=>{\"use strict\";var VJ=wje(),GJ=Pr(),Lje=ey(),kje=Oa(),fZt=ho(),hZt=yv(),_7=ef(),Pje=Cg(),dZt=Pje.drawMode,vZt=Pje.selectMode,pZt=qf().prepSelect,gZt=qf().clearOutline,mZt=qf().clearSelectionsCache,yZt=qf().selectOnClick,_w=Ix(),_Zt=Eje();function Ije(e,t){this.id=t,this.gd=e;var r=e._fullLayout,n=e._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+\"-\"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var Hh=Ije.prototype;Hh.plot=function(e,t,r){var n=this,i;n.map?i=new Promise(function(a,o){n.updateMap(e,t,a,o)}):i=new Promise(function(a,o){n.createMap(e,t,a,o)}),r.push(i)};Hh.createMap=function(e,t,r,n){var i=this,a=t[i.id],o=i.styleObj=Rje(a.style),s=a.bounds,l=s?[[s.west,s.south],[s.east,s.north]]:null,u=i.map=new VJ.Map({container:i.div,style:o.style,center:HJ(a.center),zoom:a.zoom,bearing:a.bearing,pitch:a.pitch,maxBounds:l,interactive:!i.isStatic,preserveDrawingBuffer:i.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new VJ.AttributionControl({compact:!0})),c={};u.on(\"styleimagemissing\",function(h){var d=h.id;if(!c[d]&&d.includes(\"-15\")){c[d]=!0;var v=new Image(15,15);v.onload=function(){u.addImage(d,v)},v.crossOrigin=\"Anonymous\",v.src=\"https://unpkg.com/maki@2.1.0/icons/\"+d+\".svg\"}}),u.setTransformRequest(function(h){return h=h.replace(\"https://fonts.openmaptiles.org/Open Sans Extrabold\",\"https://fonts.openmaptiles.org/Open Sans Extra Bold\"),h=h.replace(\"https://tiles.basemaps.cartocdn.com/fonts/Open Sans Extrabold\",\"https://fonts.openmaptiles.org/Open Sans Extra Bold\"),h=h.replace(\"https://fonts.openmaptiles.org/Open Sans Regular,Arial Unicode MS Regular\",\"https://fonts.openmaptiles.org/Klokantech Noto Sans Regular\"),{url:h}}),u._canvas.style.left=\"0px\",u._canvas.style.top=\"0px\",i.rejectOnError(n),i.isStatic||i.initFx(e,t);var f=[];f.push(new Promise(function(h){u.once(\"load\",h)})),f=f.concat(Lje.fetchTraceGeoData(e)),Promise.all(f).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Hh.updateMap=function(e,t,r,n){var i=this,a=i.map,o=t[this.id];i.rejectOnError(n);var s=[],l=Rje(o.style);JSON.stringify(i.styleObj)!==JSON.stringify(l)&&(i.styleObj=l,a.setStyle(l.style),i.traceHash={},s.push(new Promise(function(u){a.once(\"styledata\",u)}))),s=s.concat(Lje.fetchTraceGeoData(e)),Promise.all(s).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Hh.fillBelowLookup=function(e,t){var r=t[this.id],n=r.layers,i,a,o=this.belowLookup={},s=!1;for(i=0;i<e.length;i++){var l=e[i][0].trace,u=l._module;typeof l.below==\"string\"?a=l.below:u.getBelow&&(a=u.getBelow(l,this)),a===\"\"&&(s=!0),o[\"trace-\"+l.uid]=a||\"\"}for(i=0;i<n.length;i++){var c=n[i];typeof c.below==\"string\"?a=c.below:s?a=\"traces\":a=\"\",o[\"layout-\"+i]=a}var f={},h,d;for(h in o)a=o[h],f[a]?f[a].push(h):f[a]=[h];for(a in f){var v=f[a];if(v.length>1)for(i=0;i<v.length;i++)h=v[i],h.indexOf(\"trace-\")===0?(d=h.split(\"trace-\")[1],this.traceHash[d]&&(this.traceHash[d].below=null)):h.indexOf(\"layout-\")===0&&(d=h.split(\"layout-\")[1],this.layerList[d]&&(this.layerList[d].below=null))}};var Cje={choroplethmap:0,densitymap:1,scattermap:2};Hh.updateData=function(e){var t=this.traceHash,r,n,i,a,o=e.slice().sort(function(f,h){return Cje[f[0].trace.type]-Cje[h[0].trace.type]});for(i=0;i<o.length;i++){var s=o[i];n=s[0].trace,r=t[n.uid];var l=!1;r&&(r.type===n.type?(r.update(s),l=!0):r.dispose()),!l&&n._module&&(t[n.uid]=n._module.plot(this,s))}var u=Object.keys(t);e:for(i=0;i<u.length;i++){var c=u[i];for(a=0;a<e.length;a++)if(n=e[a][0].trace,c===n.uid)continue e;r=t[c],r.dispose(),delete t[c]}};Hh.updateLayout=function(e){var t=this.map,r=e[this.id];!this.dragging&&!this.wheeling&&(t.setCenter(HJ(r.center)),t.setZoom(r.zoom),t.setBearing(r.bearing),t.setPitch(r.pitch)),this.updateLayers(e),this.updateFramework(e),this.updateFx(e),this.map.resize(),this.gd._context._scrollZoom.map?t.scrollZoom.enable():t.scrollZoom.disable()};Hh.resolveOnRender=function(e){var t=this.map;t.on(\"render\",function r(){t.loaded()&&(t.off(\"render\",r),setTimeout(e,10))})};Hh.rejectOnError=function(e){var t=this.map;function r(){e(new Error(_w.mapOnErrorMsg))}t.once(\"error\",r),t.once(\"style.error\",r),t.once(\"source.error\",r),t.once(\"tile.error\",r),t.once(\"layer.error\",r)};Hh.createFramework=function(e){var t=this,r=t.div=document.createElement(\"div\");r.id=t.uid,r.style.position=\"absolute\",t.container.appendChild(r),t.xaxis={_id:\"x\",c2p:function(n){return t.project(n).x}},t.yaxis={_id:\"y\",c2p:function(n){return t.project(n).y}},t.updateFramework(e),t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},fZt.setConvert(t.mockAxis,e)};Hh.initFx=function(e,t){var r=this,n=r.gd,i=r.map;i.on(\"moveend\",function(s){if(r.map){var l=n._fullLayout;if(s.originalEvent||r.wheeling){var u=l[r.id];kje.call(\"_storeDirectGUIEdit\",n.layout,l._preGUI,r.getViewEdits(u));var c=r.getView();u._input.center=u.center=c.center,u._input.zoom=u.zoom=c.zoom,u._input.bearing=u.bearing=c.bearing,u._input.pitch=u.pitch=c.pitch,n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(c))}s.originalEvent&&s.originalEvent.type===\"mouseup\"?r.dragging=!1:r.wheeling&&(r.wheeling=!1),l&&l._rehover&&l._rehover()}}),i.on(\"wheel\",function(){r.wheeling=!0}),i.on(\"mousemove\",function(s){var l=r.div.getBoundingClientRect(),u=[s.originalEvent.offsetX,s.originalEvent.offsetY];s.target.getBoundingClientRect=function(){return l},r.xaxis.p2c=function(){return i.unproject(u).lng},r.yaxis.p2c=function(){return i.unproject(u).lat},n._fullLayout._rehover=function(){n._fullLayout._hoversubplot===r.id&&n._fullLayout[r.id]&&_7.hover(n,s,r.id)},_7.hover(n,s,r.id),n._fullLayout._hoversubplot=r.id});function a(){_7.loneUnhover(t._hoverlayer)}i.on(\"dragstart\",function(){r.dragging=!0,a()}),i.on(\"zoomstart\",a),i.on(\"mouseout\",function(){n._fullLayout._hoversubplot=null});function o(){var s=r.getView();n.emit(\"plotly_relayouting\",r.getViewEditsWithDerived(s))}i.on(\"drag\",o),i.on(\"zoom\",o),i.on(\"dblclick\",function(){var s=n._fullLayout[r.id];kje.call(\"_storeDirectGUIEdit\",n.layout,n._fullLayout._preGUI,r.getViewEdits(s));var l=r.viewInitial;i.setCenter(HJ(l.center)),i.setZoom(l.zoom),i.setBearing(l.bearing),i.setPitch(l.pitch);var u=r.getView();s._input.center=s.center=u.center,s._input.zoom=s.zoom=u.zoom,s._input.bearing=s.bearing=u.bearing,s._input.pitch=s.pitch=u.pitch,n.emit(\"plotly_doubleclick\",null),n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(u))}),r.clearOutline=function(){mZt(r.dragOptions),gZt(r.dragOptions.gd)},r.onClickInPanFn=function(s){return function(l){var u=n._fullLayout.clickmode;u.indexOf(\"select\")>-1&&yZt(l.originalEvent,n,[r.xaxis],[r.yaxis],r.id,s),u.indexOf(\"event\")>-1&&_7.click(n,l.originalEvent)}}};Hh.updateFx=function(e){var t=this,r=t.map,n=t.gd;if(t.isStatic)return;function i(l){var u=t.map.unproject(l);return[u.lng,u.lat]}var a=e.dragmode,o;o=function(l,u){if(u.isRect){var c=l.range={};c[t.id]=[i([u.xmin,u.ymin]),i([u.xmax,u.ymax])]}else{var f=l.lassoPoints={};f[t.id]=u.map(i)}};var s=t.dragOptions;t.dragOptions=GJ.extendDeep(s||{},{dragmode:e.dragmode,element:t.div,gd:n,plotinfo:{id:t.id,domain:e[t.id].domain,xaxis:t.xaxis,yaxis:t.yaxis,fillRangeItems:o},xaxes:[t.xaxis],yaxes:[t.yaxis],subplot:t.id}),r.off(\"click\",t.onClickInPanHandler),vZt(a)||dZt(a)?(r.dragPan.disable(),r.on(\"zoomstart\",t.clearOutline),t.dragOptions.prepFn=function(l,u,c){pZt(l,u,c,t.dragOptions,a)},hZt.init(t.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",t.clearOutline),t.div.onmousedown=null,t.div.ontouchstart=null,t.div.removeEventListener(\"touchstart\",t.div._ontouchstart),t.onClickInPanHandler=t.onClickInPanFn(t.dragOptions),r.on(\"click\",t.onClickInPanHandler))};Hh.updateFramework=function(e){var t=e[this.id].domain,r=e._size,n=this.div.style;n.width=r.w*(t.x[1]-t.x[0])+\"px\",n.height=r.h*(t.y[1]-t.y[0])+\"px\",n.left=r.l+t.x[0]*r.w+\"px\",n.top=r.t+(1-t.y[1])*r.h+\"px\",this.xaxis._offset=r.l+t.x[0]*r.w,this.xaxis._length=r.w*(t.x[1]-t.x[0]),this.yaxis._offset=r.t+(1-t.y[1])*r.h,this.yaxis._length=r.h*(t.y[1]-t.y[0])};Hh.updateLayers=function(e){var t=e[this.id],r=t.layers,n=this.layerList,i;if(r.length!==n.length){for(i=0;i<n.length;i++)n[i].dispose();for(n=this.layerList=[],i=0;i<r.length;i++)n.push(_Zt(this,i,r[i]))}else for(i=0;i<r.length;i++)n[i].update(r[i])};Hh.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))};Hh.toImage=function(){return this.map.stop(),this.map.getCanvas().toDataURL()};Hh.setOptions=function(e,t,r){for(var n in r)this.map[t](e,n,r[n])};Hh.getMapLayers=function(){return this.map.getStyle().layers};Hh.addLayer=function(e,t){var r=this.map;if(typeof t==\"string\"){if(t===\"\"){r.addLayer(e,t);return}for(var n=this.getMapLayers(),i=0;i<n.length;i++)if(t===n[i].id){r.addLayer(e,t);return}GJ.warn([\"Trying to add layer with *below* value\",t,\"referencing a layer that does not exist\",\"or that does not yet exist.\"].join(\" \"))}r.addLayer(e)};Hh.project=function(e){return this.map.project(new VJ.LngLat(e[0],e[1]))};Hh.getView=function(){var e=this.map,t=e.getCenter(),r=t.lng,n=t.lat,i={lon:r,lat:n},a=e.getCanvas(),o=parseInt(a.style.width),s=parseInt(a.style.height);return{center:i,zoom:e.getZoom(),bearing:e.getBearing(),pitch:e.getPitch(),_derived:{coordinates:[e.unproject([0,0]).toArray(),e.unproject([o,0]).toArray(),e.unproject([o,s]).toArray(),e.unproject([0,s]).toArray()]}}};Hh.getViewEdits=function(e){for(var t=this.id,r=[\"center\",\"zoom\",\"bearing\",\"pitch\"],n={},i=0;i<r.length;i++){var a=r[i];n[t+\".\"+a]=e[a]}return n};Hh.getViewEditsWithDerived=function(e){var t=this.id,r=this.getViewEdits(e);return r[t+\"._derived\"]=e._derived,r};function Rje(e){var t={};return GJ.isPlainObject(e)?(t.id=e.id,t.style=e):typeof e==\"string\"?(t.id=e,_w.stylesMap[e]?t.style=_w.stylesMap[e]:t.style=e):(t.id=_w.styleValueDflt,t.style=xZt(_w.styleValueDflt)),t.transition={duration:0,delay:0},t}function xZt(e){return _w.styleUrlPrefix+e+\"-\"+_w.styleUrlSuffix}function HJ(e){return[e.lon,e.lat]}Dje.exports=Ije});var Oje=ye((g2r,qje)=>{\"use strict\";var jJ=Pr(),bZt=O_(),wZt=Kd(),zje=rC();qje.exports=function(t,r,n){bZt(t,r,n,{type:\"map\",attributes:zje,handleDefaults:TZt,partition:\"y\"})};function TZt(e,t,r){r(\"style\"),r(\"center.lon\"),r(\"center.lat\"),r(\"zoom\"),r(\"bearing\"),r(\"pitch\");var n=r(\"bounds.west\"),i=r(\"bounds.east\"),a=r(\"bounds.south\"),o=r(\"bounds.north\");(n===void 0||i===void 0||a===void 0||o===void 0)&&delete t.bounds,wZt(e,t,{name:\"layers\",handleItemDefaults:AZt}),t._input=e}function AZt(e,t){function r(l,u){return jJ.coerce(e,t,zje.layers,l,u)}var n=r(\"visible\");if(n){var i=r(\"sourcetype\"),a=i===\"raster\"||i===\"image\";r(\"source\"),r(\"sourceattribution\"),i===\"vector\"&&r(\"sourcelayer\"),i===\"image\"&&r(\"coordinates\");var o;a&&(o=\"raster\");var s=r(\"type\",o);a&&s!==\"raster\"&&(s=t.type=\"raster\",jJ.log(\"Source types *raster* and *image* must drawn *raster* layer type.\")),r(\"below\"),r(\"color\"),r(\"opacity\"),r(\"minzoom\"),r(\"maxzoom\"),s===\"circle\"&&r(\"circle.radius\"),s===\"line\"&&(r(\"line.width\"),r(\"line.dash\")),s===\"fill\"&&r(\"fill.outlinecolor\"),s===\"symbol\"&&(r(\"symbol.icon\"),r(\"symbol.iconsize\"),r(\"symbol.text\"),jJ.coerceFont(r,\"symbol.textfont\",void 0,{noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}),r(\"symbol.textposition\"),r(\"symbol.placement\"))}}});var b7=ye(u0=>{\"use strict\";var x7=Pr(),Bje=x7.strTranslate,SZt=x7.strScale,MZt=Id().getSubplotCalcData,EZt=Wp(),kZt=Fa(),Nje=So(),CZt=Zl(),LZt=Fje(),Rx=\"map\";u0.name=Rx;u0.attr=\"subplot\";u0.idRoot=Rx;u0.idRegex=u0.attrRegex=x7.counterRegex(Rx);u0.attributes={subplot:{valType:\"subplotid\",dflt:\"map\",editType:\"calc\"}};u0.layoutAttributes=rC();u0.supplyLayoutDefaults=Oje();u0.plot=function(t){for(var r=t._fullLayout,n=t.calcdata,i=r._subplots[Rx],a=0;a<i.length;a++){var o=i[a],s=MZt(n,Rx,o),l=r[o],u=l._subplot;u||(u=new LZt(t,o),r[o]._subplot=u),u.viewInitial||(u.viewInitial={center:x7.extendFlat({},l.center),zoom:l.zoom,bearing:l.bearing,pitch:l.pitch}),u.plot(s,r,t._promises)}};u0.clean=function(e,t,r,n){for(var i=n._subplots[Rx]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._subplot&&n[o]._subplot.destroy()}};u0.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[Rx],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._subplot,l=s.toImage(\"png\"),u=t._glimages.append(\"svg:image\");u.attr({xmlns:EZt.svg,\"xlink:href\":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"});var c=kZt.select(a._subplot.div),f=c.select(\".maplibregl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),h=t._glimages.append(\"g\"),d=h.append(\"text\");d.text(f).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":f});var v=Nje.bBox(d.node()),m=n.w*(o.x[1]-o.x[0]);if(v.width>m/2){var b=f.split(\"|\").join(\"<br>\");d.text(b).attr(\"data-unformatted\",b).call(CZt.convertToTspans,e),v=Nje.bBox(d.node())}d.attr(\"transform\",Bje(-3,-v.height+8)),h.insert(\"rect\",\".static-attribution\").attr({x:-v.width-6,y:-v.height-3,width:v.width+6,height:v.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var p=1;v.width+6>m&&(p=m/(v.width+6));var k=[n.l+n.w*o.x[1],n.t+n.h*(1-o.y[0])];h.attr(\"transform\",Bje(k[0],k[1])+SZt(p))}};u0.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[Rx],n=0;n<r.length;n++){var i=t[r[n]]._subplot;i.updateFx(t)}}});var Vje=ye((y2r,Uje)=>{\"use strict\";Uje.exports={attributes:h7(),supplyDefaults:eje(),colorbar:Qd(),formatLabels:FJ(),calc:TF(),plot:vje(),hoverPoints:m7().hoverPoints,eventData:yje(),selectPoints:xje(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.update(t)}},moduleType:\"trace\",name:\"scattermap\",basePlotModule:b7(),categories:[\"map\",\"gl\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}});var Hje=ye((_2r,Gje)=>{\"use strict\";Gje.exports=Vje()});var WJ=ye((x2r,jje)=>{\"use strict\";var b1=l5(),PZt=Tu(),{hovertemplateAttrs:IZt,templatefallbackAttrs:RZt}=Pl(),DZt=Vl(),Dx=Ao().extendFlat;jje.exports=Dx({locations:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},geojson:{valType:\"any\",editType:\"calc\"},featureidkey:Dx({},b1.featureidkey,{}),below:{valType:\"string\",editType:\"plot\"},text:b1.text,hovertext:b1.hovertext,marker:{line:{color:Dx({},b1.marker.line.color,{editType:\"plot\"}),width:Dx({},b1.marker.line.width,{editType:\"plot\"}),editType:\"calc\"},opacity:Dx({},b1.marker.opacity,{editType:\"plot\"}),editType:\"calc\"},selected:{marker:{opacity:Dx({},b1.selected.marker.opacity,{editType:\"plot\"}),editType:\"plot\"},editType:\"plot\"},unselected:{marker:{opacity:Dx({},b1.unselected.marker.opacity,{editType:\"plot\"}),editType:\"plot\"},editType:\"plot\"},hoverinfo:b1.hoverinfo,hovertemplate:IZt({},{keys:[\"properties\"]}),hovertemplatefallback:RZt(),showlegend:Dx({},DZt.showlegend,{dflt:!1})},PZt(\"\",{cLetter:\"z\",editTypeOverride:\"calc\"}))});var Xje=ye((b2r,Wje)=>{\"use strict\";var oC=Pr(),FZt=td(),zZt=WJ();Wje.exports=function(t,r,n,i){function a(c,f){return oC.coerce(t,r,zZt,c,f)}var o=a(\"locations\"),s=a(\"z\"),l=a(\"geojson\");if(!oC.isArrayOrTypedArray(o)||!o.length||!oC.isArrayOrTypedArray(s)||!s.length||!(typeof l==\"string\"&&l!==\"\"||oC.isPlainObject(l))){r.visible=!1;return}a(\"featureidkey\"),r._length=Math.min(o.length,s.length),a(\"below\"),a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var u=a(\"marker.line.width\");u&&a(\"marker.line.color\"),a(\"marker.opacity\"),FZt(t,r,i,a,{prefix:\"\",cLetter:\"z\"}),oC.coerceSelectionMarkerOpacity(r,a)}});var XJ=ye((w2r,Kje)=>{\"use strict\";var qZt=Eo(),w1=Pr(),OZt=tc(),BZt=So(),NZt=hx().makeBlank,Zje=ey();function UZt(e){var t=e[0].trace,r=t.visible===!0&&t._length!==0,n={layout:{visibility:\"none\"},paint:{}},i={layout:{visibility:\"none\"},paint:{}},a=t._opts={fill:n,line:i,geojson:NZt()};if(!r)return a;var o=Zje.extractTraceFeature(e);if(!o)return a;var s=OZt.makeColorScaleFuncFromTrace(t),l=t.marker,u=l.line||{},c;w1.isArrayOrTypedArray(l.opacity)&&(c=function(k){var M=k.mo;return qZt(M)?+w1.constrain(M,0,1):0});var f;w1.isArrayOrTypedArray(u.color)&&(f=function(k){return k.mlc});var h;w1.isArrayOrTypedArray(u.width)&&(h=function(k){return k.mlw});for(var d=0;d<e.length;d++){var v=e[d],m=v.fOut;if(m){var b=m.properties;b.fc=s(v.z),c&&(b.mo=c(v)),f&&(b.mlc=f(v)),h&&(b.mlw=h(v)),v.ct=b.ct,v._polygons=Zje.feature2polygons(m)}}var p=c?{type:\"identity\",property:\"mo\"}:l.opacity;return w1.extendFlat(n.paint,{\"fill-color\":{type:\"identity\",property:\"fc\"},\"fill-opacity\":p}),w1.extendFlat(i.paint,{\"line-color\":f?{type:\"identity\",property:\"mlc\"}:u.color,\"line-width\":h?{type:\"identity\",property:\"mlw\"}:u.width,\"line-opacity\":p}),n.layout.visibility=\"visible\",i.layout.visibility=\"visible\",a.geojson={type:\"FeatureCollection\",features:o},Yje(e),a}function Yje(e){var t=e[0].trace,r=t._opts,n;if(t.selectedpoints){for(var i=BZt.makeSelectedPointStyleFns(t),a=0;a<e.length;a++){var o=e[a];o.fOut&&(o.fOut.properties.mo2=i.selectedOpacityFn(o))}n={type:\"identity\",property:\"mo2\"}}else n=w1.isArrayOrTypedArray(t.marker.opacity)?{type:\"identity\",property:\"mo\"}:t.marker.opacity;return w1.extendFlat(r.fill.paint,{\"fill-opacity\":n}),w1.extendFlat(r.line.paint,{\"line-opacity\":n}),r}Kje.exports={convert:UZt,convertOnSelect:Yje}});var tWe=ye((T2r,eWe)=>{\"use strict\";var $je=XJ().convert,VZt=XJ().convertOnSelect,Jje=Ix().traceLayerPrefix;function Qje(e,t){this.type=\"choroplethmap\",this.subplot=e,this.uid=t,this.sourceId=\"source-\"+t,this.layerList=[[\"fill\",Jje+t+\"-fill\"],[\"line\",Jje+t+\"-line\"]],this.below=null}var q5=Qje.prototype;q5.update=function(e){this._update($je(e)),e[0].trace._glTrace=this};q5.updateOnSelect=function(e){this._update(VZt(e))};q5._update=function(e){var 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A\"+at.circularPathData.rightLargeArcRadius+\" \"+at.circularPathData.rightLargeArcRadius+\" 0 0 0 \"+at.circularPathData.rightFullExtent+\" \"+at.circularPathData.verticalRightInnerExtent+\" L\"+at.circularPathData.rightFullExtent+\" \"+(at.circularPathData.targetY-at.circularPathData.rightSmallArcRadius)+\" A\"+at.circularPathData.rightLargeArcRadius+\" \"+at.circularPathData.rightSmallArcRadius+\" 0 0 0 \"+at.circularPathData.rightInnerExtent+\" \"+at.circularPathData.targetY+\" L\"+at.circularPathData.targetX+\" \"+at.circularPathData.targetY:ct=\"M\"+at.circularPathData.sourceX+\" \"+at.circularPathData.sourceY+\" L\"+at.circularPathData.leftInnerExtent+\" \"+at.circularPathData.sourceY+\" A\"+at.circularPathData.leftLargeArcRadius+\" \"+at.circularPathData.leftSmallArcRadius+\" 0 0 1 \"+at.circularPathData.leftFullExtent+\" \"+(at.circularPathData.sourceY+at.circularPathData.leftSmallArcRadius)+\" L\"+at.circularPathData.leftFullExtent+\" \"+at.circularPathData.verticalLeftInnerExtent+\" A\"+at.circularPathData.leftLargeArcRadius+\" \"+at.circularPathData.leftLargeArcRadius+\" 0 0 1 \"+at.circularPathData.leftInnerExtent+\" \"+at.circularPathData.verticalFullExtent+\" L\"+at.circularPathData.rightInnerExtent+\" \"+at.circularPathData.verticalFullExtent+\" A\"+at.circularPathData.rightLargeArcRadius+\" \"+at.circularPathData.rightLargeArcRadius+\" 0 0 1 \"+at.circularPathData.rightFullExtent+\" \"+at.circularPathData.verticalRightInnerExtent+\" L\"+at.circularPathData.rightFullExtent+\" \"+(at.circularPathData.targetY+at.circularPathData.rightSmallArcRadius)+\" A\"+at.circularPathData.rightLargeArcRadius+\" \"+at.circularPathData.rightSmallArcRadius+\" 0 0 1 \"+at.circularPathData.rightInnerExtent+\" \"+at.circularPathData.targetY+\" L\"+at.circularPathData.targetX+\" \"+at.circularPathData.targetY,ct}function ae(at,ct){return ie(at)==ie(ct)?at.circularLinkType==\"bottom\"?Ee(at,ct):_e(at,ct):ie(ct)-ie(at)}function _e(at,ct){return at.y0-ct.y0}function Ee(at,ct){return ct.y0-at.y0}function Ce(at,ct){return at.y1-ct.y1}function ge(at,ct){return ct.y1-at.y1}function ie(at){return at.target.column-at.source.column}function Se(at){return at.target.x0-at.source.x1}function ke(at,ct){var ar=V(at),Vt=Se(ct)/Math.tan(ar),rr=lt(at)==\"up\"?at.y1+Vt:at.y1-Vt;return rr}function be(at,ct){var ar=V(at),Vt=Se(ct)/Math.tan(ar),rr=lt(at)==\"up\"?at.y1-Vt:at.y1+Vt;return rr}function Be(at,ct,ar,Vt){at.links.forEach(function(rr){if(!rr.circular&&rr.target.column-rr.source.column>1){var tt=rr.source.column+1,je=rr.target.column-1,Ue=1,Ae=je-tt+1;for(Ue=1;tt<=je;tt++,Ue++)at.nodes.forEach(function(rt){if(rt.column==tt){var St=Ue/(Ae+1),Tt=Math.pow(1-St,3),dt=3*St*Math.pow(1-St,2),Et=3*Math.pow(St,2)*(1-St),pt=Math.pow(St,3),jt=Tt*rr.y0+dt*rr.y0+Et*rr.y1+pt*rr.y1,or=jt-rr.width/2,mr=jt+rr.width/2,Ar;or>rt.y0&&or<rt.y1?(Ar=rt.y1-or+10,Ar=rt.circularLinkType==\"bottom\"?Ar:-Ar,rt=me(rt,Ar,ct,ar),at.nodes.forEach(function(ei){C(ei,Vt)==C(rt,Vt)||ei.column!=rt.column||Le(rt,ei)&&me(ei,Ar,ct,ar)})):mr>rt.y0&&mr<rt.y1?(Ar=mr-rt.y0+10,rt=me(rt,Ar,ct,ar),at.nodes.forEach(function(ei){C(ei,Vt)==C(rt,Vt)||ei.column!=rt.column||ei.y0<rt.y1&&ei.y1>rt.y1&&me(ei,Ar,ct,ar)})):or<rt.y0&&mr>rt.y1&&(Ar=mr-rt.y0+10,rt=me(rt,Ar,ct,ar),at.nodes.forEach(function(ei){C(ei,Vt)==C(rt,Vt)||ei.column!=rt.column||ei.y0<rt.y1&&ei.y1>rt.y1&&me(ei,Ar,ct,ar)}))}})}})}function Le(at,ct){return at.y0>ct.y0&&at.y0<ct.y1||at.y1>ct.y0&&at.y1<ct.y1?!0:at.y0<ct.y0&&at.y1>ct.y1}function me(at,ct,ar,Vt){return at.y0+ct>=ar&&at.y1+ct<=Vt&&(at.y0=at.y0+ct,at.y1=at.y1+ct,at.targetLinks.forEach(function(rr){rr.y1=rr.y1+ct}),at.sourceLinks.forEach(function(rr){rr.y0=rr.y0+ct})),at}function Pe(at,ct,ar,Vt){at.nodes.forEach(function(rr){Vt&&rr.y+(rr.y1-rr.y0)>ct&&(rr.y=rr.y-(rr.y+(rr.y1-rr.y0)-ct));var tt=at.links.filter(function(Ae){return C(Ae.source,ar)==C(rr,ar)}),je=tt.length;je>1&&tt.sort(function(Ae,rt){if(!Ae.circular&&!rt.circular){if(Ae.target.column==rt.target.column)return Ae.y1-rt.y1;if(He(Ae,rt)){if(Ae.target.column>rt.target.column){var St=be(rt,Ae);return Ae.y1-St}if(rt.target.column>Ae.target.column){var Tt=be(Ae,rt);return Tt-rt.y1}}else return Ae.y1-rt.y1}if(Ae.circular&&!rt.circular)return Ae.circularLinkType==\"top\"?-1:1;if(rt.circular&&!Ae.circular)return rt.circularLinkType==\"top\"?1:-1;if(Ae.circular&&rt.circular)return Ae.circularLinkType===rt.circularLinkType&&Ae.circularLinkType==\"top\"?Ae.target.column===rt.target.column?Ae.target.y1-rt.target.y1:rt.target.column-Ae.target.column:Ae.circularLinkType===rt.circularLinkType&&Ae.circularLinkType==\"bottom\"?Ae.target.column===rt.target.column?rt.target.y1-Ae.target.y1:Ae.target.column-rt.target.column:Ae.circularLinkType==\"top\"?-1:1});var Ue=rr.y0;tt.forEach(function(Ae){Ae.y0=Ue+Ae.width/2,Ue=Ue+Ae.width}),tt.forEach(function(Ae,rt){if(Ae.circularLinkType==\"bottom\"){var St=rt+1,Tt=0;for(St;St<je;St++)Tt=Tt+tt[St].width;Ae.y0=rr.y1-Tt-Ae.width/2}})})}function ce(at,ct,ar){at.nodes.forEach(function(Vt){var rr=at.links.filter(function(Ue){return C(Ue.target,ar)==C(Vt,ar)}),tt=rr.length;tt>1&&rr.sort(function(Ue,Ae){if(!Ue.circular&&!Ae.circular){if(Ue.source.column==Ae.source.column)return Ue.y0-Ae.y0;if(He(Ue,Ae)){if(Ae.source.column<Ue.source.column){var rt=ke(Ae,Ue);return Ue.y0-rt}if(Ue.source.column<Ae.source.column){var St=ke(Ue,Ae);return St-Ae.y0}}else return Ue.y0-Ae.y0}if(Ue.circular&&!Ae.circular)return Ue.circularLinkType==\"top\"?-1:1;if(Ae.circular&&!Ue.circular)return Ae.circularLinkType==\"top\"?1:-1;if(Ue.circular&&Ae.circular)return Ue.circularLinkType===Ae.circularLinkType&&Ue.circularLinkType==\"top\"?Ue.source.column===Ae.source.column?Ue.source.y1-Ae.source.y1:Ue.source.column-Ae.source.column:Ue.circularLinkType===Ae.circularLinkType&&Ue.circularLinkType==\"bottom\"?Ue.source.column===Ae.source.column?Ue.source.y1-Ae.source.y1:Ae.source.column-Ue.source.column:Ue.circularLinkType==\"top\"?-1:1});var je=Vt.y0;rr.forEach(function(Ue){Ue.y1=je+Ue.width/2,je=je+Ue.width}),rr.forEach(function(Ue,Ae){if(Ue.circularLinkType==\"bottom\"){var rt=Ae+1,St=0;for(rt;rt<tt;rt++)St=St+rr[rt].width;Ue.y1=Vt.y1-St-Ue.width/2}})})}function He(at,ct){return lt(at)==lt(ct)}function lt(at){return at.y0-at.y1>0?\"up\":\"down\"}function mt(at,ct){return C(at.source,ct)==C(at.target,ct)}function Ht(at,ct,ar){var Vt=at.nodes,rr=at.links,tt=!1,je=!1;if(rr.forEach(function(dt){dt.circularLinkType==\"top\"?tt=!0:dt.circularLinkType==\"bottom\"&&(je=!0)}),tt==!1||je==!1){var Ue=t.min(Vt,function(dt){return dt.y0}),Ae=t.max(Vt,function(dt){return dt.y1}),rt=Ae-Ue,St=ar-ct,Tt=St/rt;Vt.forEach(function(dt){var Et=(dt.y1-dt.y0)*Tt;dt.y0=(dt.y0-Ue)*Tt,dt.y1=dt.y0+Et}),rr.forEach(function(dt){dt.y0=(dt.y0-Ue)*Tt,dt.y1=(dt.y1-Ue)*Tt,dt.width=dt.width*Tt})}}e.sankeyCircular=E,e.sankeyCenter=u,e.sankeyLeft=o,e.sankeyRight=s,e.sankeyJustify=l,Object.defineProperty(e,\"__esModule\",{value:!0})}))});var i$=ye((U2r,uXe)=>{\"use strict\";uXe.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:\"linear\",cn:{sankey:\"sankey\",sankeyLinks:\"sankey-links\",sankeyLink:\"sankey-link\",sankeyNodeSet:\"sankey-node-set\",sankeyNode:\"sankey-node\",nodeRect:\"node-rect\",nodeLabel:\"node-label\"}}});var wXe=ye((V2r,bXe)=>{\"use strict\";var cXe=QWe(),xYt=(W2(),gb(j2)).interpolateNumber,N5=Fa(),lC=nXe(),bYt=lXe(),Nu=i$(),U5=fd(),xw=ka(),wYt=So(),T1=Pr(),o$=T1.strTranslate,TYt=T1.strRotate,s$=ay(),uC=s$.keyFun,F7=s$.repeat,gXe=s$.unwrap,fXe=Zl(),AYt=Oa(),mXe=Dh(),SYt=mXe.CAP_SHIFT,MYt=mXe.LINE_SPACING,EYt=3;function kYt(e,t,r){var n=gXe(t),i=n.trace,a=i.domain,o=i.orientation===\"h\",s=i.node.pad,l=i.node.thickness,u={justify:lC.sankeyJustify,left:lC.sankeyLeft,right:lC.sankeyRight,center:lC.sankeyCenter}[i.node.align],c=e.width*(a.x[1]-a.x[0]),f=e.height*(a.y[1]-a.y[0]),h=n._nodes,d=n._links,v=n.circular,m;v?m=bYt.sankeyCircular().circularLinkGap(0):m=lC.sankey(),m.iterations(Nu.sankeyIterations).size(o?[c,f]:[f,c]).nodeWidth(l).nodePadding(s).nodeId(function(V){return V.pointNumber}).nodeAlign(u).nodes(h).links(d);var b=m();m.nodePadding()<s&&T1.warn(\"node.pad was reduced to \",m.nodePadding(),\" to fit within the figure.\");var p,k,M;for(var T in n._groupLookup){var L=parseInt(n._groupLookup[T]),x;for(p=0;p<b.nodes.length;p++)if(b.nodes[p].pointNumber===L){x=b.nodes[p];break}if(x){var C={pointNumber:parseInt(T),x0:x.x0,x1:x.x1,y0:x.y0,y1:x.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};b.nodes.unshift(C),x.childrenNodes.unshift(C)}}function S(){for(p=0;p<b.nodes.length;p++){var V=b.nodes[p],G={},Z,j;for(k=0;k<V.targetLinks.length;k++)j=V.targetLinks[k],Z=j.source.pointNumber+\":\"+j.target.pointNumber,G.hasOwnProperty(Z)||(G[Z]=[]),G[Z].push(j);var N=Object.keys(G);for(k=0;k<N.length;k++){Z=N[k];var H=G[Z],re=0,ae={};for(M=0;M<H.length;M++)j=H[M],ae[j.label]||(ae[j.label]=0),ae[j.label]+=j.value,re+=j.value;for(M=0;M<H.length;M++)j=H[M],j.flow={value:re,labelConcentration:ae[j.label]/re,concentration:j.value/re,links:H},j.concentrationscale&&(j.color=U5(j.concentrationscale(j.flow.labelConcentration)))}var _e=0;for(k=0;k<V.sourceLinks.length;k++)_e+=V.sourceLinks[k].value;for(k=0;k<V.sourceLinks.length;k++)j=V.sourceLinks[k],j.concentrationOut=j.value/_e;var Ee=0;for(k=0;k<V.targetLinks.length;k++)Ee+=V.targetLinks[k].value;for(k=0;k<V.targetLinks.length;k++)j=V.targetLinks[k],j.concenrationIn=j.value/Ee}}S();function g(V){V.forEach(function(G){var Z,j,N=0,H=G.length,re;for(G.sort(function(ae,_e){return ae.y0-_e.y0}),re=0;re<H;++re)Z=G[re],Z.y0>=N||(j=N-Z.y0,j>1e-6&&(Z.y0+=j,Z.y1+=j)),N=Z.y1+s})}function P(V){var G=V.map(function(_e,Ee){return{x0:_e.x0,index:Ee}}).sort(function(_e,Ee){return _e.x0-Ee.x0}),Z=[],j=-1,N,H=-1/0,re;for(p=0;p<G.length;p++){var ae=V[G[p].index];ae.x0>H+l&&(j+=1,N=ae.x0),H=ae.x0,Z[j]||(Z[j]=[]),Z[j].push(ae),re=N-ae.x0,ae.x0+=re,ae.x1+=re}return Z}if(i.node.x.length&&i.node.y.length){for(p=0;p<Math.min(i.node.x.length,i.node.y.length,b.nodes.length);p++)if(i.node.x[p]&&i.node.y[p]){var E=[i.node.x[p]*c,i.node.y[p]*f];b.nodes[p].x0=E[0]-l/2,b.nodes[p].x1=E[0]+l/2;var z=b.nodes[p].y1-b.nodes[p].y0;b.nodes[p].y0=E[1]-z/2,b.nodes[p].y1=E[1]+z/2}if(i.arrangement===\"snap\"){h=b.nodes;var q=P(h);g(q)}m.update(b)}return{circular:v,key:r,trace:i,guid:T1.randstr(),horizontal:o,width:c,height:f,nodePad:i.node.pad,nodeLineColor:i.node.line.color,nodeLineWidth:i.node.line.width,linkLineColor:i.link.line.color,linkLineWidth:i.link.line.width,linkArrowLength:i.link.arrowlen,valueFormat:i.valueformat,valueSuffix:i.valuesuffix,textFont:i.textfont,translateX:a.x[0]*e.width+e.margin.l,translateY:e.height-a.y[1]*e.height+e.margin.t,dragParallel:o?f:c,dragPerpendicular:o?c:f,arrangement:i.arrangement,sankey:m,graph:b,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function CYt(e,t,r){var n=U5(t.color),i=U5(t.hovercolor),a=t.source.label+\"|\"+t.target.label,o=a+\"__\"+r;return t.trace=e.trace,t.curveNumber=e.trace.index,{circular:e.circular,key:o,traceId:e.key,pointNumber:t.pointNumber,link:t,tinyColorHue:xw.tinyRGB(n),tinyColorAlpha:n.getAlpha(),tinyColorHoverHue:xw.tinyRGB(i),tinyColorHoverAlpha:i.getAlpha(),linkPath:l$,linkLineColor:e.linkLineColor,linkLineWidth:e.linkLineWidth,linkArrowLength:e.linkArrowLength,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,parent:e,interactionState:e.interactionState,flow:t.flow}}function LYt(e,t){var r=\"\",n=e.width/2,i=e.circularPathData,a=i.sourceX+i.verticalBuffer<i.targetX,o=i.rightFullExtent-i.rightLargeArcRadius-t<=i.leftFullExtent-n,s=Math.abs(i.rightFullExtent-i.leftFullExtent-n)<n;return e.circularLinkType===\"top\"?(r=\"M \"+(i.targetX-t)+\" \"+(i.targetY+n)+\" L \"+(i.rightInnerExtent-t)+\" \"+(i.targetY+n)+\"A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightSmallArcRadius+n)+\" 0 0 1 \"+(i.rightFullExtent-n-t)+\" \"+(i.targetY-i.rightSmallArcRadius)+\"L \"+(i.rightFullExtent-n-t)+\" \"+i.verticalRightInnerExtent,a&&o?r+=\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 1 \"+(i.rightFullExtent+n-t-(i.rightLargeArcRadius-n))+\" \"+(i.verticalRightInnerExtent-(i.rightLargeArcRadius+n))+\" L \"+(i.rightFullExtent+n-(i.rightLargeArcRadius-n)-t)+\" \"+(i.verticalRightInnerExtent-(i.rightLargeArcRadius+n))+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftLargeArcRadius+n)+\" 0 0 1 \"+(i.leftFullExtent+n)+\" \"+i.verticalRightInnerExtent:a?r+=\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightLargeArcRadius-n)+\" 0 0 0 \"+(i.rightFullExtent-n-t-(i.rightLargeArcRadius-n))+\" \"+(i.verticalRightInnerExtent-(i.rightLargeArcRadius-n))+\" L \"+(i.leftFullExtent+n+(i.rightLargeArcRadius-n))+\" \"+(i.verticalRightInnerExtent-(i.rightLargeArcRadius-n))+\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftLargeArcRadius-n)+\" 0 0 0 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent:r+=\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 1 \"+(i.rightInnerExtent-t)+\" \"+(i.verticalFullExtent-n)+\" L \"+i.leftInnerExtent+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftLargeArcRadius+n)+\" 0 0 1 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent,r+=\" L \"+(i.leftFullExtent+n)+\" \"+(i.sourceY-i.leftSmallArcRadius)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 1 \"+i.leftInnerExtent+\" \"+(i.sourceY+n)+\" L \"+i.sourceX+\" \"+(i.sourceY+n)+\" L \"+i.sourceX+\" \"+(i.sourceY-n)+\" L \"+i.leftInnerExtent+\" \"+(i.sourceY-n)+\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftSmallArcRadius-n)+\" 0 0 0 \"+(i.leftFullExtent-n)+\" \"+(i.sourceY-i.leftSmallArcRadius)+\" L \"+(i.leftFullExtent-n)+\" \"+i.verticalLeftInnerExtent,a&&o?r+=\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 0 \"+(i.leftFullExtent-n)+\" \"+(i.verticalFullExtent+n)+\"L\"+(i.rightFullExtent+n-t)+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 0 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent:a?r+=\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 1 \"+(i.leftFullExtent+n)+\" \"+(i.verticalFullExtent-n)+\" L \"+(i.rightFullExtent-n-t)+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 1 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent:r+=\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftLargeArcRadius-n)+\" 0 0 0 \"+i.leftInnerExtent+\" \"+(i.verticalFullExtent+n)+\" L \"+(i.rightInnerExtent-t)+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightLargeArcRadius-n)+\" 0 0 0 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent,r+=\" L \"+(i.rightFullExtent+n-t)+\" \"+(i.targetY-i.rightSmallArcRadius)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 0 \"+(i.rightInnerExtent-t)+\" \"+(i.targetY-n)+\" L \"+(i.targetX-t)+\" \"+(i.targetY-n)+(t>0?\" L \"+i.targetX+\" \"+i.targetY:\"\")+\"Z\"):(r=\"M \"+(i.targetX-t)+\" \"+(i.targetY-n)+\"  L \"+(i.rightInnerExtent-t)+\" \"+(i.targetY-n)+\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightSmallArcRadius+n)+\" 0 0 0 \"+(i.rightFullExtent-n-t)+\" \"+(i.targetY+i.rightSmallArcRadius)+\" L \"+(i.rightFullExtent-n-t)+\" \"+i.verticalRightInnerExtent,a&&o?r+=\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.rightInnerExtent-n-t)+\" \"+(i.verticalFullExtent+n)+\" L \"+(i.rightFullExtent+n-t-(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent:a?r+=\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.rightFullExtent-t-n-(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent-n)+\" L \"+(i.leftFullExtent+n+(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent:r+=\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.rightInnerExtent-t)+\" \"+(i.verticalFullExtent+n)+\" L \"+i.leftInnerExtent+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftLargeArcRadius+n)+\" 0 0 0 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent,r+=\" L \"+(i.leftFullExtent+n)+\" \"+(i.sourceY+i.leftSmallArcRadius)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 0 \"+i.leftInnerExtent+\" \"+(i.sourceY-n)+\" L \"+i.sourceX+\" \"+(i.sourceY-n)+\" L \"+i.sourceX+\" \"+(i.sourceY+n)+\" L \"+i.leftInnerExtent+\" \"+(i.sourceY+n)+\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftSmallArcRadius-n)+\" 0 0 1 \"+(i.leftFullExtent-n)+\" \"+(i.sourceY+i.leftSmallArcRadius)+\" L \"+(i.leftFullExtent-n)+\" \"+i.verticalLeftInnerExtent,a&&o?r+=\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.leftFullExtent-n-(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent-n)+\" L \"+(i.rightFullExtent+n-t+(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent:a?r+=\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.leftFullExtent+n)+\" \"+(i.verticalFullExtent+n)+\" L \"+(i.rightFullExtent-t-n)+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent:r+=\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftLargeArcRadius-n)+\" 0 0 1 \"+i.leftInnerExtent+\" \"+(i.verticalFullExtent-n)+\" L \"+(i.rightInnerExtent-t)+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightLargeArcRadius-n)+\" 0 0 1 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent,r+=\" L 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IXe=ye(bw=>{\"use strict\";var VYt=mc().overrideAll,GYt=Id().getModuleCalcData,HYt=f$(),jYt=X1(),WYt=Eg(),XYt=yv(),ZYt=qf().prepSelect,h$=Pr(),YYt=Oa(),q7=\"sankey\";bw.name=q7;bw.baseLayoutAttrOverrides=VYt({hoverlabel:jYt.hoverlabel},\"plot\",\"nested\");bw.plot=function(e){var t=GYt(e.calcdata,q7)[0];HYt(e,t),bw.updateFx(e)};bw.clean=function(e,t,r,n){var i=n._has&&n._has(q7),a=t._has&&t._has(q7);i&&!a&&(n._paperdiv.selectAll(\".sankey\").remove(),n._paperdiv.selectAll(\".bgsankey\").remove())};bw.updateFx=function(e){for(var t=0;t<e._fullData.length;t++)KYt(e,t)};function KYt(e,t){var r=e._fullData[t],n=e._fullLayout,i=n.dragmode,a=n.dragmode===\"pan\"?\"move\":\"crosshair\",o=r._bgRect;if(o&&!(i===\"pan\"||i===\"zoom\")){WYt(o,a);var s={_id:\"x\",c2p:h$.identity,_offset:r._sankey.translateX,_length:r._sankey.width},l={_id:\"y\",c2p:h$.identity,_offset:r._sankey.translateY,_length:r._sankey.height},u={gd:e,element:o.node(),plotinfo:{id:t,xaxis:s,yaxis:l,fillRangeItems:h$.noop},subplot:t,xaxes:[s],yaxes:[l],doneFnCompleted:function(c){var f=e._fullData[t],h,d=f.node.groups.slice(),v=[];function m(M){for(var T=f._sankey.graph.nodes,L=0;L<T.length;L++)if(T[L].pointNumber===M)return T[L]}for(var b=0;b<c.length;b++){var p=m(c[b].pointNumber);if(p)if(p.group){for(var k=0;k<p.childrenNodes.length;k++)v.push(p.childrenNodes[k].pointNumber);d[p.pointNumber-f.node._count]=!1}else v.push(p.pointNumber)}h=d.filter(Boolean).concat([v]),YYt.call(\"_guiRestyle\",e,{\"node.groups\":[h]},t)}};u.prepFn=function(c,f,h){ZYt(c,f,h,u,i)},XYt.init(u)}}});var DXe=ye((j2r,RXe)=>{\"use strict\";RXe.exports=function(t,r){for(var n=t.cd,i=[],a=n[0].trace,o=a._sankey.graph.nodes,s=0;s<o.length;s++){var l=o[s];if(!l.partOfGroup){var 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p$=ye((K2r,XXe)=>{\"use strict\";XXe.exports={defaultNumberFontSize:80,bulletNumberDomainSize:.25,bulletPadding:.025,innerRadius:.75,valueThickness:.5,titlePadding:5,horizontalPadding:10}});var KXe=ye((J2r,YXe)=>{\"use strict\";var uy=Pr(),N7=v$(),tKt=Cc().defaults,ZXe=vl(),rKt=Kd(),B7=p$(),iKt=Pb(),nKt=B3(),aKt=l_(),oKt=u_();function sKt(e,t,r,n){function i(x,C){return uy.coerce(e,t,N7,x,C)}tKt(t,n,i),i(\"mode\"),t._hasNumber=t.mode.indexOf(\"number\")!==-1,t._hasDelta=t.mode.indexOf(\"delta\")!==-1,t._hasGauge=t.mode.indexOf(\"gauge\")!==-1;var a=i(\"value\");t._range=[0,typeof a==\"number\"?1.5*a:1];var o=new Array(2),s;if(t._hasNumber){i(\"number.valueformat\");var l=uy.extendFlat({},n.font);l.size=void 0,uy.coerceFont(i,\"number.font\",l),t.number.font.size===void 0&&(t.number.font.size=B7.defaultNumberFontSize,o[0]=!0),i(\"number.prefix\"),i(\"number.suffix\"),s=t.number.font.size}var u;if(t._hasDelta){var c=uy.extendFlat({},n.font);c.size=void 0,uy.coerceFont(i,\"delta.font\",c),t.delta.font.size===void 0&&(t.delta.font.size=(t._hasNumber?.5:1)*(s||B7.defaultNumberFontSize),o[1]=!0),i(\"delta.reference\",t.value),i(\"delta.relative\"),i(\"delta.valueformat\",t.delta.relative?\"2%\":\"\"),i(\"delta.increasing.symbol\"),i(\"delta.increasing.color\"),i(\"delta.decreasing.symbol\"),i(\"delta.decreasing.color\"),i(\"delta.position\"),i(\"delta.prefix\"),i(\"delta.suffix\"),u=t.delta.font.size}t._scaleNumbers=(!t._hasNumber||o[0])&&(!t._hasDelta||o[1])||!1;var f=uy.extendFlat({},n.font);f.size=.25*(s||u||B7.defaultNumberFontSize),uy.coerceFont(i,\"title.font\",f),i(\"title.text\");var h,d,v,m;function b(x,C){return uy.coerce(h,d,N7.gauge,x,C)}function p(x,C){return uy.coerce(v,m,N7.gauge.axis,x,C)}if(t._hasGauge){h=e.gauge,h||(h={}),d=ZXe.newContainer(t,\"gauge\"),b(\"shape\");var k=t._isBullet=t.gauge.shape===\"bullet\";k||i(\"title.align\",\"center\");var M=t._isAngular=t.gauge.shape===\"angular\";M||i(\"align\",\"center\"),b(\"bgcolor\",n.paper_bgcolor),b(\"borderwidth\"),b(\"bordercolor\"),b(\"bar.color\"),b(\"bar.line.color\"),b(\"bar.line.width\");var T=B7.valueThickness*(t.gauge.shape===\"bullet\"?.5:1);b(\"bar.thickness\",T),rKt(h,d,{name:\"steps\",handleItemDefaults:lKt}),b(\"threshold.value\"),b(\"threshold.thickness\"),b(\"threshold.line.width\"),b(\"threshold.line.color\"),v={},h&&(v=h.axis||{}),m=ZXe.newContainer(d,\"axis\"),p(\"visible\"),t._range=p(\"range\",t._range);var L={font:n.font,noAutotickangles:!0,outerTicks:!0,noTicklabelshift:!0,noTicklabelstandoff:!0};iKt(v,m,p,\"linear\"),oKt(v,m,p,\"linear\",L),aKt(v,m,p,\"linear\",L),nKt(v,m,p,L)}else i(\"title.align\",\"center\"),i(\"align\",\"center\"),t._isAngular=t._isBullet=!1;t._length=null}function lKt(e,t){function r(n,i){return 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a=t._fullLayout,o;vC(n)&&i&&(o=i()),qx.makeTraceGroups(a._indicatorlayer,r,\"trace\").each(function(s){var l=s[0],u=l.trace,c=Mw.select(this),f=u._hasGauge,h=u._isAngular,d=u._isBullet,v=u.domain,m={w:a._size.w*(v.x[1]-v.x[0]),h:a._size.h*(v.y[1]-v.y[0]),l:a._size.l+a._size.w*v.x[0],r:a._size.r+a._size.w*(1-v.x[1]),t:a._size.t+a._size.h*(1-v.y[1]),b:a._size.b+a._size.h*v.y[0]},b=m.l+m.w/2,p=m.t+m.h/2,k=Math.min(m.w/2,m.h),M=ww.innerRadius*k,T,L,x,C=u.align||\"center\";if(L=p,!f)T=m.l+Tw[C]*m.w,x=function(j){return tZe(j,m.w,m.h)};else if(h&&(T=b,L=p+k/2,x=function(j){return bKt(j,.9*M)}),d){var S=ww.bulletPadding,g=1-ww.bulletNumberDomainSize+S;T=m.l+(g+(1-g)*Tw[C])*m.w,x=function(j){return tZe(j,(ww.bulletNumberDomainSize-S)*m.w,m.h)}}_Kt(t,c,s,{numbersX:T,numbersY:L,numbersScaler:x,transitionOpts:n,onComplete:o});var P,E;f&&(P={range:u.gauge.axis.range,color:u.gauge.bgcolor,line:{color:u.gauge.bordercolor,width:0},thickness:1},E={range:u.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:u.gauge.bordercolor,width:u.gauge.borderwidth},thickness:1});var z=c.selectAll(\"g.angular\").data(h?s:[]);z.exit().remove();var q=c.selectAll(\"g.angularaxis\").data(h?s:[]);q.exit().remove(),h&&yKt(t,c,s,{radius:k,innerRadius:M,gauge:z,layer:q,size:m,gaugeBg:P,gaugeOutline:E,transitionOpts:n,onComplete:o});var V=c.selectAll(\"g.bullet\").data(d?s:[]);V.exit().remove();var G=c.selectAll(\"g.bulletaxis\").data(d?s:[]);G.exit().remove(),d&&mKt(t,c,s,{gauge:V,layer:G,size:m,gaugeBg:P,gaugeOutline:E,transitionOpts:n,onComplete:o});var Z=c.selectAll(\"text.title\").data(s);Z.exit().remove(),Z.enter().append(\"text\").classed(\"title\",!0),Z.attr(\"text-anchor\",function(){return d?g$.right:g$[u.title.align]}).text(u.title.text).call(Sw.font,u.title.font).call(V7.convertToTspans,t),Z.attr(\"transform\",function(){var j=m.l+m.w*Tw[u.title.align],N,H=ww.titlePadding,re=Sw.bBox(Z.node());if(f){if(h)if(u.gauge.axis.visible){var ae=Sw.bBox(q.node());N=ae.top-H-re.bottom}else N=m.t+m.h/2-k/2-re.bottom-H;d&&(N=L-(re.top+re.bottom)/2,j=m.l-ww.bulletPadding*m.w)}else N=u._numbersTop-H-re.bottom;return dC(j,N)})})};function mKt(e,t,r,n){var i=r[0].trace,a=n.gauge,o=n.layer,s=n.gaugeBg,l=n.gaugeOutline,u=n.size,c=i.domain,f=n.transitionOpts,h=n.onComplete,d,v,m,b,p;a.enter().append(\"g\").classed(\"bullet\",!0),a.attr(\"transform\",dC(u.l,u.t)),o.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),o.selectAll(\"g.xbulletaxistick,path,text\").remove();var k=u.h,M=i.gauge.bar.thickness*k,T=c.x[0],L=c.x[0]+(c.x[1]-c.x[0])*(i._hasNumber||i._hasDelta?1-ww.bulletNumberDomainSize:1);d=hC(e,i.gauge.axis),d._id=\"xbulletaxis\",d.domain=[T,L],d.setScale(),v=lv.calcTicks(d),m=lv.makeTransTickFn(d),b=lv.getTickSigns(d)[2],p=u.t+u.h,d.visible&&(lv.drawTicks(e,d,{vals:d.ticks===\"inside\"?lv.clipEnds(d,v):v,layer:o,path:lv.makeTickPath(d,p,b),transFn:m}),lv.drawLabels(e,d,{vals:v,layer:o,transFn:m,labelFns:lv.makeLabelFns(d,p)}));function x(q){q.attr(\"width\",function(V){return Math.max(0,d.c2p(V.range[1])-d.c2p(V.range[0]))}).attr(\"x\",function(V){return d.c2p(V.range[0])}).attr(\"y\",function(V){return .5*(1-V.thickness)*k}).attr(\"height\",function(V){return V.thickness*k})}var C=[s].concat(i.gauge.steps),S=a.selectAll(\"g.bg-bullet\").data(C);S.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),S.select(\"rect\").call(x).call(Aw),S.exit().remove();var g=a.selectAll(\"g.value-bullet\").data([i.gauge.bar]);g.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),g.select(\"rect\").attr(\"height\",M).attr(\"y\",(k-M)/2).call(Aw),vC(f)?g.select(\"rect\").transition().duration(f.duration).ease(f.easing).each(\"end\",function(){h&&h()}).each(\"interrupt\",function(){h&&h()}).attr(\"width\",Math.max(0,d.c2p(Math.min(i.gauge.axis.range[1],r[0].y)))):g.select(\"rect\").attr(\"width\",typeof r[0].y==\"number\"?Math.max(0,d.c2p(Math.min(i.gauge.axis.range[1],r[0].y))):0),g.exit().remove();var 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f=eS(e,Q5([t,0])),h=f[0],d=f[1];return[\"M\"+o+\",\"+s,\"A\"+i+\",\"+i+\" 0 0,\"+(0<r?0:1)+\" \"+h+\",\"+d,\"A\"+i+\",\"+i+\" 0 0,\"+(n<0?0:1)+u+\",\"+c].join(\" \")}return[\"M\"+o+\",\"+s,\"A\"+i+\",\"+i+\" 0 0,\"+(n<r?0:1)+\" \"+u+\",\"+c].join(\" \")}QJe.exports={smith:Q5,reactanceArc:yQt,resistanceArc:_Qt,smithTransform:eS}});var aQ=ye((b3r,l$e)=>{\"use strict\";var Cw=Fa(),xQt=fd(),Pw=Oa(),Xc=Pr(),fy=Xc.strRotate,xd=Xc.strTranslate,iQ=ka(),bC=So(),bQt=Mc(),pp=ho(),wQt=xm(),TQt=Q$(),AQt=Mg().doAutoRange,M1=YN(),w9=yv(),e$e=ef(),SQt=qb(),MQt=qf().prepSelect,EQt=qf().selectOnClick,nQ=qf().clearOutline,t$e=Eg(),r$e=SM(),i$e=RM().redrawReglTraces,kQt=Dh().MID_SHIFT,Nx=m9(),E1=_9(),T9=rQ(),x9=T9.smith,CQt=T9.reactanceArc,LQt=T9.resistanceArc,b9=T9.smithTransform,PQt=Xc._,n$e=Xc.mod,Ux=Xc.deg2rad,Lw=Xc.rad2deg;function a$e(e,t,r){this.isSmith=r||!1,this.id=t,this.gd=e,this._hasClipOnAxisFalse=null,this.vangles=null,this.radialAxisAngle=null,this.traceHash={},this.layers={},this.clipPaths={},this.clipIds={},this.viewInitial={};var n=e._fullLayout,i=\"clip\"+n._uid+t;this.clipIds.forTraces=i+\"-for-traces\",this.clipPaths.forTraces=n._clips.append(\"clipPath\").attr(\"id\",this.clipIds.forTraces),this.clipPaths.forTraces.append(\"path\"),this.framework=n[\"_\"+(r?\"smith\":\"polar\")+\"layer\"].append(\"g\").attr(\"class\",t),this.getHole=function(a){return this.isSmith?0:a.hole},this.getSector=function(a){return this.isSmith?[0,360]:a.sector},this.getRadial=function(a){return this.isSmith?a.realaxis:a.radialaxis},this.getAngular=function(a){return this.isSmith?a.imaginaryaxis:a.angularaxis},r||(this.radialTickLayout=null,this.angularTickLayout=null)}var Nd=a$e.prototype;l$e.exports=function(t,r,n){return new a$e(t,r,n)};Nd.plot=function(e,t){for(var r=this,n=t[r.id],i=!1,a=0;a<e.length;a++){var o=e[a][0].trace;if(o.cliponaxis===!1){i=!0;break}}r._hasClipOnAxisFalse=i,r.updateLayers(t,n),r.updateLayout(t,n),bQt.generalUpdatePerTraceModule(r.gd,r,e,n),r.updateFx(t,n),r.isSmith&&(delete n.realaxis.range,delete n.imaginaryaxis.range)};Nd.updateLayers=function(e,t){var r=this,n=r.isSmith,i=r.layers,a=r.getRadial(t),o=r.getAngular(t),s=Nx.layerNames,l=s.indexOf(\"frontplot\"),u=s.slice(0,l),c=o.layer===\"below traces\",f=a.layer===\"below traces\";c&&u.push(\"angular-line\"),f&&u.push(\"radial-line\"),c&&u.push(\"angular-axis\"),f&&u.push(\"radial-axis\"),u.push(\"frontplot\"),c||u.push(\"angular-line\"),f||u.push(\"radial-line\"),c||u.push(\"angular-axis\"),f||u.push(\"radial-axis\");var h=(n?\"smith\":\"polar\")+\"sublayer\",d=r.framework.selectAll(\".\"+h).data(u,String);d.enter().append(\"g\").attr(\"class\",function(v){return h+\" \"+v}).each(function(v){var m=i[v]=Cw.select(this);switch(v){case\"frontplot\":n||m.append(\"g\").classed(\"barlayer\",!0),m.append(\"g\").classed(\"scatterlayer\",!0);break;case\"backplot\":m.append(\"g\").classed(\"maplayer\",!0);break;case\"plotbg\":i.bg=m.append(\"path\");break;case\"radial-grid\":m.style(\"fill\",\"none\");break;case\"angular-grid\":m.style(\"fill\",\"none\");break;case\"radial-line\":m.append(\"line\").style(\"fill\",\"none\");break;case\"angular-line\":m.append(\"path\").style(\"fill\",\"none\");break}}),d.order()};Nd.updateLayout=function(e,t){var r=this,n=r.layers,i=e._size,a=r.getRadial(t),o=r.getAngular(t),s=t.domain.x,l=t.domain.y;r.xOffset=i.l+i.w*s[0],r.yOffset=i.t+i.h*(1-l[1]);var u=r.xLength=i.w*(s[1]-s[0]),c=r.yLength=i.h*(l[1]-l[0]),f=r.getSector(t);r.sectorInRad=f.map(Ux);var h=r.sectorBBox=IQt(f),d=h[2]-h[0],v=h[3]-h[1],m=c/u,b=Math.abs(v/d),p,k,M,T,L;m>b?(p=u,k=u*b,L=(c-k)/i.h/2,M=[s[0],s[1]],T=[l[0]+L,l[1]-L]):(p=c/b,k=c,L=(u-p)/i.w/2,M=[s[0]+L,s[1]-L],T=[l[0],l[1]]),r.xLength2=p,r.yLength2=k,r.xDomain2=M,r.yDomain2=T;var x=r.xOffset2=i.l+i.w*M[0],C=r.yOffset2=i.t+i.h*(1-T[1]),S=r.radius=p/d,g=r.innerRadius=r.getHole(t)*S,P=r.cx=x-S*h[0],E=r.cy=C+S*h[3],z=r.cxx=P-x,q=r.cyy=E-C,V=a.side,G;V===\"counterclockwise\"?(G=V,V=\"top\"):V===\"clockwise\"&&(G=V,V=\"bottom\"),r.radialAxis=r.mockAxis(e,t,a,{_id:\"x\",side:V,_trueSide:G,domain:[g/i.w,S/i.w]}),r.angularAxis=r.mockAxis(e,t,o,{side:\"right\",domain:[0,Math.PI],autorange:!1}),r.doAutoRange(e,t),r.updateAngularAxis(e,t),r.updateRadialAxis(e,t),r.updateRadialAxisTitle(e,t),r.xaxis=r.mockCartesianAxis(e,t,{_id:\"x\",domain:M}),r.yaxis=r.mockCartesianAxis(e,t,{_id:\"y\",domain:T});var Z=r.pathSubplot();r.clipPaths.forTraces.select(\"path\").attr(\"d\",Z).attr(\"transform\",xd(z,q)),n.frontplot.attr(\"transform\",xd(x,C)).call(bC.setClipUrl,r._hasClipOnAxisFalse?null:r.clipIds.forTraces,r.gd),n.bg.attr(\"d\",Z).attr(\"transform\",xd(P,E)).call(iQ.fill,t.bgcolor)};Nd.mockAxis=function(e,t,r,n){var i=Xc.extendFlat({},r,n);return TQt(i,t,e),i};Nd.mockCartesianAxis=function(e,t,r){var n=this,i=n.isSmith,a=r._id,o=Xc.extendFlat({type:\"linear\"},r);wQt(o,e);var s={x:[0,2],y:[1,3]};return o.setRange=function(){var l=n.sectorBBox,u=s[a],c=n.radialAxis._rl,f=(c[1]-c[0])/(1-n.getHole(t));o.range=[l[u[0]]*f,l[u[1]]*f]},o.isPtWithinRange=a===\"x\"&&!i?function(l){return n.isPtInside(l)}:function(){return!0},o.setRange(),o.setScale(),o};Nd.doAutoRange=function(e,t){var r=this,n=r.gd,i=r.radialAxis,a=r.getRadial(t);AQt(n,i);var o=i.range;if(a.range=o.slice(),a._input.range=o.slice(),i._rl=[i.r2l(o[0],null,\"gregorian\"),i.r2l(o[1],null,\"gregorian\")],i.minallowed!==void 0){var s=i.r2l(i.minallowed);i._rl[0]>i._rl[1]?i._rl[1]=Math.max(i._rl[1],s):i._rl[0]=Math.max(i._rl[0],s)}if(i.maxallowed!==void 0){var l=i.r2l(i.maxallowed);i._rl[0]<i._rl[1]?i._rl[1]=Math.min(i._rl[1],l):i._rl[0]=Math.min(i._rl[0],l)}};Nd.updateRadialAxis=function(e,t){var r=this,n=r.gd,i=r.layers,a=r.radius,o=r.innerRadius,s=r.cx,l=r.cy,u=r.getRadial(t),c=n$e(r.getSector(t)[0],360),f=r.radialAxis,h=o<a,d=r.isSmith;d||(r.fillViewInitialKey(\"radialaxis.angle\",u.angle),r.fillViewInitialKey(\"radialaxis.range\",f.range.slice()),f.setGeometry()),f.tickangle===\"auto\"&&c>90&&c<=270&&(f.tickangle=180);var v=d?function(S){var g=b9(r,x9([S.x,0]));return xd(g[0]-s,g[1]-l)}:function(S){return xd(f.l2p(S.x)+o,0)},m=d?function(S){return LQt(r,S.x,-1/0,1/0)}:function(S){return r.pathArc(f.r2p(S.x)+o)},b=o$e(u);if(r.radialTickLayout!==b&&(i[\"radial-axis\"].selectAll(\".xtick\").remove(),r.radialTickLayout=b),h){f.setScale();var p=0,k=d?(f.tickvals||[]).filter(function(S){return S>=0}).map(function(S){return pp.tickText(f,S,!0,!1)}):pp.calcTicks(f),M=d?k:pp.clipEnds(f,k),T=pp.getTickSigns(f)[2];d&&((f.ticks===\"top\"&&f.side===\"bottom\"||f.ticks===\"bottom\"&&f.side===\"top\")&&(T=-T),f.ticks===\"top\"&&f.side===\"top\"&&(p=-f.ticklen),f.ticks===\"bottom\"&&f.side===\"bottom\"&&(p=f.ticklen)),pp.drawTicks(n,f,{vals:k,layer:i[\"radial-axis\"],path:pp.makeTickPath(f,0,T),transFn:v,crisp:!1}),pp.drawGrid(n,f,{vals:M,layer:i[\"radial-grid\"],path:m,transFn:Xc.noop,crisp:!1}),pp.drawLabels(n,f,{vals:k,layer:i[\"radial-axis\"],transFn:v,labelFns:pp.makeLabelFns(f,p)})}var L=r.radialAxisAngle=r.vangles?Lw(s$e(Ux(u.angle),r.vangles)):u.angle,x=xd(s,l),C=x+fy(-L);wC(i[\"radial-axis\"],h&&(u.showticklabels||u.ticks),{transform:C}),wC(i[\"radial-grid\"],h&&u.showgrid,{transform:d?\"\":x}),wC(i[\"radial-line\"].select(\"line\"),h&&u.showline,{x1:d?-a:o,y1:0,x2:a,y2:0,transform:C}).attr(\"stroke-width\",u.linewidth).call(iQ.stroke,u.linecolor)};Nd.updateRadialAxisTitle=function(e,t,r){if(!this.isSmith){var n=this,i=n.gd,a=n.radius,o=n.cx,s=n.cy,l=n.getRadial(t),u=n.id+\"title\",c=0;if(l.title){var f=bC.bBox(n.layers[\"radial-axis\"].node()).height,h=l.title.font.size,d=l.side;c=d===\"top\"?h:d===\"counterclockwise\"?-(f+h*.4):f+h*.8}var v=r!==void 0?r:n.radialAxisAngle,m=Ux(v),b=Math.cos(m),p=Math.sin(m),k=o+a/2*b+c*p,M=s-a/2*p+c*b;n.layers[\"radial-axis-title\"]=SQt.draw(i,u,{propContainer:l,propName:n.id+\".radialaxis.title.text\",placeholder:PQt(i,\"Click to enter radial axis title\"),attributes:{x:k,y:M,\"text-anchor\":\"middle\"},transform:{rotate:-v}})}};Nd.updateAngularAxis=function(e,t){var r=this,n=r.gd,i=r.layers,a=r.radius,o=r.innerRadius,s=r.cx,l=r.cy,u=r.getAngular(t),c=r.angularAxis,f=r.isSmith;f||(r.fillViewInitialKey(\"angularaxis.rotation\",u.rotation),c.setGeometry(),c.setScale());var h=f?function(g){var P=b9(r,x9([0,g.x]));return Math.atan2(P[0]-s,P[1]-l)-Math.PI/2}:function(g){return c.t2g(g.x)};c.type===\"linear\"&&c.thetaunit===\"radians\"&&(c.tick0=Lw(c.tick0),c.dtick=Lw(c.dtick));var d=function(g){return xd(s+a*Math.cos(g),l-a*Math.sin(g))},v=f?function(g){var P=b9(r,x9([0,g.x]));return xd(P[0],P[1])}:function(g){return d(h(g))},m=f?function(g){var P=b9(r,x9([0,g.x])),E=Math.atan2(P[0]-s,P[1]-l)-Math.PI/2;return xd(P[0],P[1])+fy(-Lw(E))}:function(g){var P=h(g);return d(P)+fy(-Lw(P))},b=f?function(g){return CQt(r,g.x,0,1/0)}:function(g){var P=h(g),E=Math.cos(P),z=Math.sin(P);return\"M\"+[s+o*E,l-o*z]+\"L\"+[s+a*E,l-a*z]},p=pp.makeLabelFns(c,0),k=p.labelStandoff,M={};M.xFn=function(g){var P=h(g);return Math.cos(P)*k},M.yFn=function(g){var P=h(g),E=Math.sin(P)>0?.2:1;return-Math.sin(P)*(k+g.fontSize*E)+Math.abs(Math.cos(P))*(g.fontSize*kQt)},M.anchorFn=function(g){var P=h(g),E=Math.cos(P);return Math.abs(E)<.1?\"middle\":E>0?\"start\":\"end\"},M.heightFn=function(g,P,E){var z=h(g);return-.5*(1+Math.sin(z))*E};var T=o$e(u);r.angularTickLayout!==T&&(i[\"angular-axis\"].selectAll(\".\"+c._id+\"tick\").remove(),r.angularTickLayout=T);var L=f?[1/0].concat(c.tickvals||[]).map(function(g){return pp.tickText(c,g,!0,!1)}):pp.calcTicks(c);f&&(L[0].text=\"\\u221E\",L[0].fontSize*=1.75);var x;if(t.gridshape===\"linear\"?(x=L.map(h),Xc.angleDelta(x[0],x[1])<0&&(x=x.slice().reverse())):x=null,r.vangles=x,c.type===\"category\"&&(L=L.filter(function(g){return Xc.isAngleInsideSector(h(g),r.sectorInRad)})),c.visible){var C=c.ticks===\"inside\"?-1:1,S=(c.linewidth||1)/2;pp.drawTicks(n,c,{vals:L,layer:i[\"angular-axis\"],path:\"M\"+C*S+\",0h\"+C*c.ticklen,transFn:m,crisp:!1}),pp.drawGrid(n,c,{vals:L,layer:i[\"angular-grid\"],path:b,transFn:Xc.noop,crisp:!1}),pp.drawLabels(n,c,{vals:L,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:v,labelFns:M})}wC(i[\"angular-line\"].select(\"path\"),u.showline,{d:r.pathSubplot(),transform:xd(s,l)}).attr(\"stroke-width\",u.linewidth).call(iQ.stroke,u.linecolor)};Nd.updateFx=function(e,t){if(!this.gd._context.staticPlot){var r=!this.isSmith;r&&(this.updateAngularDrag(e),this.updateRadialDrag(e,t,0),this.updateRadialDrag(e,t,1)),this.updateHoverAndMainDrag(e)}};Nd.updateHoverAndMainDrag=function(e){var t=this,r=t.isSmith,n=t.gd,i=t.layers,a=e._zoomlayer,o=Nx.MINZOOM,s=Nx.OFFEDGE,l=t.radius,u=t.innerRadius,c=t.cx,f=t.cy,h=t.cxx,d=t.cyy,v=t.sectorInRad,m=t.vangles,b=t.radialAxis,p=E1.clampTiny,k=E1.findXYatLength,M=E1.findEnclosingVertexAngles,T=Nx.cornerHalfWidth,L=Nx.cornerLen/2,x,C,S=M1.makeDragger(i,\"path\",\"maindrag\",e.dragmode===!1?\"none\":\"crosshair\");Cw.select(S).attr(\"d\",t.pathSubplot()).attr(\"transform\",xd(c,f)),S.onmousemove=function(ce){e$e.hover(n,ce,t.id),n._fullLayout._lasthover=S,n._fullLayout._hoversubplot=t.id},S.onmouseout=function(ce){n._dragging||w9.unhover(n,ce)};var g={element:S,gd:n,subplot:t.id,plotinfo:{id:t.id,xaxis:t.xaxis,yaxis:t.yaxis},xaxes:[t.xaxis],yaxes:[t.yaxis]},P,E,z,q,V,G,Z,j,N;function H(ce,He){return Math.sqrt(ce*ce+He*He)}function re(ce,He){return H(ce-h,He-d)}function ae(ce,He){return Math.atan2(d-He,ce-h)}function _e(ce,He){return[ce*Math.cos(He),ce*Math.sin(-He)]}function Ee(ce,He){if(ce===0)return t.pathSector(2*T);var lt=L/ce,mt=He-lt,Ht=He+lt,at=Math.max(0,Math.min(ce,l)),ct=at-T,ar=at+T;return\"M\"+_e(ct,mt)+\"A\"+[ct,ct]+\" 0,0,0 \"+_e(ct,Ht)+\"L\"+_e(ar,Ht)+\"A\"+[ar,ar]+\" 0,0,1 \"+_e(ar,mt)+\"Z\"}function Ce(ce,He,lt){if(ce===0)return t.pathSector(2*T);var mt=_e(ce,He),Ht=_e(ce,lt),at=p((mt[0]+Ht[0])/2),ct=p((mt[1]+Ht[1])/2),ar,Vt;if(at&&ct){var rr=ct/at,tt=-1/rr,je=k(T,rr,at,ct);ar=k(L,tt,je[0][0],je[0][1]),Vt=k(L,tt,je[1][0],je[1][1])}else{var Ue,Ae;ct?(Ue=L,Ae=T):(Ue=T,Ae=L),ar=[[at-Ue,ct-Ae],[at+Ue,ct-Ae]],Vt=[[at-Ue,ct+Ae],[at+Ue,ct+Ae]]}return\"M\"+ar.join(\"L\")+\"L\"+Vt.reverse().join(\"L\")+\"Z\"}function ge(){z=null,q=null,V=t.pathSubplot(),G=!1;var ce=n._fullLayout[t.id];Z=xQt(ce.bgcolor).getLuminance(),j=M1.makeZoombox(a,Z,c,f,V),j.attr(\"fill-rule\",\"evenodd\"),N=M1.makeCorners(a,c,f),nQ(n)}function ie(ce,He){return He=Math.max(Math.min(He,l),u),ce<s?ce=0:l-ce<s?ce=l:He<s?He=0:l-He<s&&(He=l),Math.abs(He-ce)>o?(ce<He?(z=ce,q=He):(z=He,q=ce),!0):(z=null,q=null,!1)}function Se(ce,He){ce=ce||V,He=He||\"M0,0Z\",j.attr(\"d\",ce),N.attr(\"d\",He),M1.transitionZoombox(j,N,G,Z),G=!0;var lt={};me(lt),n.emit(\"plotly_relayouting\",lt)}function ke(ce,He){ce=ce*x,He=He*C;var lt=P+ce,mt=E+He,Ht=re(P,E),at=Math.min(re(lt,mt),l),ct=ae(P,E),ar,Vt;ie(Ht,at)&&(ar=V+t.pathSector(q),z&&(ar+=t.pathSector(z)),Vt=Ee(z,ct)+Ee(q,ct)),Se(ar,Vt)}function be(ce,He,lt,mt){var Ht=E1.findIntersectionXY(lt,mt,lt,[ce-h,d-He]);return H(Ht[0],Ht[1])}function Be(ce,He){var lt=P+ce,mt=E+He,Ht=ae(P,E),at=ae(lt,mt),ct=M(Ht,m),ar=M(at,m),Vt=be(P,E,ct[0],ct[1]),rr=Math.min(be(lt,mt,ar[0],ar[1]),l),tt,je;ie(Vt,rr)&&(tt=V+t.pathSector(q),z&&(tt+=t.pathSector(z)),je=[Ce(z,ct[0],ct[1]),Ce(q,ct[0],ct[1])].join(\" \")),Se(tt,je)}function Le(){if(M1.removeZoombox(n),!(z===null||q===null)){var ce={};me(ce),M1.showDoubleClickNotifier(n),Pw.call(\"_guiRelayout\",n,ce)}}function me(ce){var He=b._rl,lt=(He[1]-He[0])/(1-u/l)/l,mt=[He[0]+(z-u)*lt,He[0]+(q-u)*lt];ce[t.id+\".radialaxis.range\"]=mt}function Pe(ce,He){var lt=n._fullLayout.clickmode;if(M1.removeZoombox(n),ce===2){var mt={};for(var Ht in t.viewInitial)mt[t.id+\".\"+Ht]=t.viewInitial[Ht];n.emit(\"plotly_doubleclick\",null),Pw.call(\"_guiRelayout\",n,mt)}lt.indexOf(\"select\")>-1&&ce===1&&EQt(He,n,[t.xaxis],[t.yaxis],t.id,g),lt.indexOf(\"event\")>-1&&e$e.click(n,He,t.id)}g.prepFn=function(ce,He,lt){var mt=n._fullLayout.dragmode,Ht=S.getBoundingClientRect();n._fullLayout._calcInverseTransform(n);var at=n._fullLayout._invTransform;x=n._fullLayout._invScaleX,C=n._fullLayout._invScaleY;var ct=Xc.apply3DTransform(at)(He-Ht.left,lt-Ht.top);if(P=ct[0],E=ct[1],m){var ar=E1.findPolygonOffset(l,v[0],v[1],m);P+=h+ar[0],E+=d+ar[1]}switch(mt){case\"zoom\":g.clickFn=Pe,r||(m?g.moveFn=Be:g.moveFn=ke,g.doneFn=Le,ge(ce,He,lt));break;case\"select\":case\"lasso\":MQt(ce,He,lt,g,mt);break}},w9.init(g)};Nd.updateRadialDrag=function(e,t,r){var n=this,i=n.gd,a=n.layers,o=n.radius,s=n.innerRadius,l=n.cx,u=n.cy,c=n.radialAxis,f=Nx.radialDragBoxSize,h=f/2;if(!c.visible)return;var d=Ux(n.radialAxisAngle),v=c._rl,m=v[0],b=v[1],p=v[r],k=.75*(v[1]-v[0])/(1-n.getHole(t))/o,M,T,L;r?(M=l+(o+h)*Math.cos(d),T=u-(o+h)*Math.sin(d),L=\"radialdrag\"):(M=l+(s-h)*Math.cos(d),T=u-(s-h)*Math.sin(d),L=\"radialdrag-inner\");var x=M1.makeRectDragger(a,L,\"crosshair\",-h,-h,f,f),C={element:x,gd:i};e.dragmode===!1&&(C.dragmode=!1),wC(Cw.select(x),c.visible&&s<o,{transform:xd(M,T)});var S,g,P;function E(Z,j){if(S)S(Z,j);else{var N=[Z,-j],H=[Math.cos(d),Math.sin(d)],re=Math.abs(Xc.dot(N,H)/Math.sqrt(Xc.dot(N,N)));isNaN(re)||(S=re<.5?V:G)}var ae={};z(ae),i.emit(\"plotly_relayouting\",ae)}function z(Z){g!==null?Z[n.id+\".radialaxis.angle\"]=g:P!==null&&(Z[n.id+\".radialaxis.range[\"+r+\"]\"]=P)}function 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Otr=Id().getSubplotCalcData,Btr=Pr().counterRegex,Ntr=aQ(),zQe=pQ(),qQe=zQe.attr,Rw=zQe.name,FQe=Btr(Rw),OQe={};OQe[qQe]={valType:\"subplotid\",dflt:Rw,editType:\"calc\"};function Utr(e){for(var t=e._fullLayout,r=e.calcdata,n=t._subplots[Rw],i=0;i<n.length;i++){var a=n[i],o=Otr(r,Rw,a),s=t[a]._subplot;s||(s=Ntr(e,a,!0),t[a]._subplot=s),s.plot(o,t,e._promises)}}function Vtr(e,t,r,n){for(var i=n._subplots[Rw]||[],a=0;a<i.length;a++){var o=i[a],s=n[o]._subplot;if(!t[o]&&s){s.framework.remove();for(var l in s.clipPaths)s.clipPaths[l].remove()}}}BQe.exports={attr:qQe,name:Rw,idRoot:Rw,idRegex:FQe,attrRegex:FQe,attributes:OQe,layoutAttributes:gQ(),supplyLayoutDefaults:DQe(),plot:Utr,clean:Vtr,toSVG:mh().toSVG}});var yQ=ye((nTr,VQe)=>{\"use 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jQe=ye((aTr,HQe)=>{\"use strict\";var z9=Pr(),lS=Ru(),Xtr=$p(),Ztr=D0(),GQe=fT(),Ytr=F0(),Ktr=Fg(),Jtr=Pm().PTS_LINESONLY,$tr=yQ();HQe.exports=function(t,r,n,i){function a(l,u){return z9.coerce(t,r,$tr,l,u)}var o=Qtr(t,r,i,a);if(!o){r.visible=!1;return}a(\"mode\",o<Jtr?\"lines+markers\":\"lines\"),a(\"text\"),a(\"hovertext\"),r.hoveron!==\"fills\"&&(a(\"hovertemplate\"),a(\"hovertemplatefallback\")),lS.hasMarkers(r)&&Xtr(t,r,n,i,a,{gradient:!0}),lS.hasLines(r)&&(Ztr(t,r,n,i,a,{backoff:!0}),GQe(t,r,a),a(\"connectgaps\")),lS.hasText(r)&&(a(\"texttemplate\"),a(\"texttemplatefallback\"),Ytr(t,r,i,a));var s=[];(lS.hasMarkers(r)||lS.hasText(r))&&(a(\"cliponaxis\"),a(\"marker.maxdisplayed\"),s.push(\"points\")),a(\"fill\"),r.fill!==\"none\"&&(Ktr(t,r,n,a),lS.hasLines(r)||GQe(t,r,a)),(r.fill===\"tonext\"||r.fill===\"toself\")&&s.push(\"fills\"),a(\"hoveron\",s.join(\"+\")||\"points\"),z9.coerceSelectionMarkerOpacity(r,a)};function Qtr(e,t,r,n){var i=n(\"real\"),a=n(\"imag\"),o;return 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i=r.layers.frontplot.select(\"g.scatterlayer\"),a=r.xaxis,o=r.yaxis,s={xaxis:a,yaxis:o,plot:r.framework,layerClipId:r._hasClipOnAxisFalse?r.clipIds.forTraces:null},l=0;l<n.length;l++)for(var u=n[l],c=0;c<u.length;c++){c===0&&(u[0].trace._xA=a,u[0].trace._yA=o);var f=u[c],h=f.real;if(h===$Qe)f.x=f.y=$Qe;else{var d=srr([h,f.imag]);f.x=d[0],f.y=d[1]}}arr(t,s,n,i)}});var iet=ye((uTr,ret)=>{\"use strict\";var lrr=bT();function urr(e,t,r,n){var i=lrr(e,t,r,n);if(!(!i||i[0].index===!1)){var a=i[0];if(a.index===void 0)return i;var o=e.subplot,s=a.cd[a.index],l=a.trace;if(o.isPtInside(s))return a.xLabelVal=void 0,a.yLabelVal=void 0,tet(s,l,o,a),a.hovertemplate=l.hovertemplate,i}}function tet(e,t,r,n){var i=r.radialAxis,a=r.angularAxis;i._hovertitle=\"real\",a._hovertitle=\"imag\";var o={};o[t.subplot]={_subplot:r};var s=t._module.formatLabels(e,t,o);n.realLabel=s.realLabel,n.imagLabel=s.imagLabel;var l=e.hi||t.hoverinfo,u=[];function c(h,d){u.push(h._hovertitle+\": 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t=this._year!==e._year?this._year-e._year:this._month!==e._month?this.monthOfYear()-e.monthOfYear():this._day-e._day;return t===0?0:t<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(e){return this._calendar.fromJD(e)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(e){return this._calendar.fromJSDate(e)},toString:function(){return(this.year()<0?\"-\":\"\")+q9(Math.abs(this.year()),4)+\"-\"+q9(this.month(),2)+\"-\"+q9(this.day(),2)}});function xQ(){this.shortYearCutoff=\"+10\"}O9(xQ.prototype,{_validateLevel:0,newDate:function(e,t,r){return e==null?this.today():(e.year&&(this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate),r=e.day(),t=e.month(),e=e.year()),new _Q(this,e,t,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(e){var t=this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[\"\"].invalidYear);return 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a=e.year()+(r===\"y\"?t:0),o=e.monthOfYear()+(r===\"m\"?t:0),i=e.day(),s=function(c){for(;o<c.minMonth;)a--,o+=c.monthsInYear(a);for(var f=c.monthsInYear(a);o>f-1+c.minMonth;)a++,o-=f,f=c.monthsInYear(a)};r===\"y\"?(e.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,e.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):r===\"m\"&&(s(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var l=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,l}catch(u){throw this._validateLevel--,u}},_correctAdd:function(e,t,r,n){if(!this.hasYearZero&&(n===\"y\"||n===\"m\")&&(t[0]===0||e.year()>0!=t[0]>0)){var i={y:[1,1,\"y\"],m:[1,this.monthsInYear(-1),\"m\"],w:[this.daysInWeek(),this.daysInYear(-1),\"d\"],d:[1,this.daysInYear(-1),\"d\"]}[n],a=r<0?-1:1;t=this._add(e,r*i[0]+a*i[1],i[2])}return e.date(t[0],t[1],t[2])},set:function(e,t,r){this._validate(e,this.minMonth,this.minDay,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate);var n=r===\"y\"?t:e.year(),i=r===\"m\"?t:e.month(),a=r===\"d\"?t:e.day();return(r===\"y\"||r===\"m\")&&(a=Math.min(a,this.daysInMonth(n,i))),e.date(n,i,a)},isValid:function(e,t,r){this._validateLevel++;var n=this.hasYearZero||e!==0;if(n){var i=this.newDate(e,t,this.minDay);n=t>=this.minMonth&&t-this.minMonth<this.monthsInYear(i)&&r>=this.minDay&&r-this.minDay<this.daysInMonth(i)}return this._validateLevel--,n},toJSDate:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate);return Gs.instance().fromJD(this.toJD(n)).toJSDate()},fromJSDate:function(e){return this.fromJD(Gs.instance().fromJSDate(e).toJD())},_validate:function(e,t,r,n){if(e.year){if(this._validateLevel===0&&this.name!==e.calendar().name)throw(Gs.local.differentCalendars||Gs.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this.local.name).replace(/\\{1\\}/,e.calendar().local.name);return e}try{if(this._validateLevel++,this._validateLevel===1&&!this.isValid(e,t,r))throw n.replace(/\\{0\\}/,this.local.name);var i=this.newDate(e,t,r);return this._validateLevel--,i}catch(a){throw this._validateLevel--,a}}});function bQ(e){this.local=this.regionalOptions[e]||this.regionalOptions[\"\"]}bQ.prototype=new xQ;O9(bQ.prototype,{name:\"Gregorian\",jdEpoch:17214255e-1,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Gregorian\",epochs:[\"BCE\",\"CE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[\"\"].invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===0&&(r%100!==0||r%400===0)},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Gs.local.invalidMonth||Gs.regionalOptions[\"\"].invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate);e=n.year(),t=n.month(),r=n.day(),e<0&&e++,t<3&&(t+=12,e--);var i=Math.floor(e/100),a=2-i+Math.floor(i/4);return Math.floor(365.25*(e+4716))+Math.floor(30.6001*(t+1))+r+a-1524.5},fromJD:function(e){var t=Math.floor(e+.5),r=Math.floor((t-186721625e-2)/36524.25);r=t+1+r-Math.floor(r/4);var n=r+1524,i=Math.floor((n-122.1)/365.25),a=Math.floor(365.25*i),o=Math.floor((n-a)/30.6001),s=n-a-Math.floor(o*30.6001),l=o-(o>13.5?13:1),u=i-(l>2.5?4716:4715);return u<=0&&u--,this.newDate(u,l,s)},toJSDate:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate),i=new Date(n.year(),n.month()-1,n.day());return i.setHours(0),i.setMinutes(0),i.setSeconds(0),i.setMilliseconds(0),i.setHours(i.getHours()>12?i.getHours()+2:0),i},fromJSDate:function(e){return this.newDate(e.getFullYear(),e.getMonth()+1,e.getDate())}});var Gs=cet.exports=new uet;Gs.cdate=_Q;Gs.baseCalendar=xQ;Gs.calendars.gregorian=bQ});var fet=ye(()=>{var wQ=Nh(),Ud=kv();wQ(Ud.regionalOptions[\"\"],{invalidArguments:\"Invalid arguments\",invalidFormat:\"Cannot format a date from another calendar\",missingNumberAt:\"Missing number at position {0}\",unknownNameAt:\"Unknown name at position {0}\",unexpectedLiteralAt:\"Unexpected literal at position {0}\",unexpectedText:\"Additional text found at end\"});Ud.local=Ud.regionalOptions[\"\"];wQ(Ud.cdate.prototype,{formatDate:function(e,t){return typeof e!=\"string\"&&(t=e,e=\"\"),this._calendar.formatDate(e||\"\",this,t)}});wQ(Ud.baseCalendar.prototype,{UNIX_EPOCH:Ud.instance().newDate(1970,1,1).toJD(),SECS_PER_DAY:1440*60,TICKS_EPOCH:Ud.instance().jdEpoch,TICKS_PER_DAY:1440*60*1e7,ATOM:\"yyyy-mm-dd\",COOKIE:\"D, dd M yyyy\",FULL:\"DD, MM d, yyyy\",ISO_8601:\"yyyy-mm-dd\",JULIAN:\"J\",RFC_822:\"D, d M yy\",RFC_850:\"DD, dd-M-yy\",RFC_1036:\"D, d M yy\",RFC_1123:\"D, d M yyyy\",RFC_2822:\"D, d M yyyy\",RSS:\"D, d M yy\",TICKS:\"!\",TIMESTAMP:\"@\",W3C:\"yyyy-mm-dd\",formatDate:function(e,t,r){if(typeof e!=\"string\"&&(r=t,t=e,e=\"\"),!t)return\"\";if(t.calendar()!==this)throw Ud.local.invalidFormat||Ud.regionalOptions[\"\"].invalidFormat;e=e||this.local.dateFormat,r=r||{};for(var n=r.dayNamesShort||this.local.dayNamesShort,i=r.dayNames||this.local.dayNames,a=r.monthNumbers||this.local.monthNumbers,o=r.monthNamesShort||this.local.monthNamesShort,s=r.monthNames||this.local.monthNames,l=r.calculateWeek||this.local.calculateWeek,u=function(T,L){for(var x=1;M+x<e.length&&e.charAt(M+x)===T;)x++;return M+=x-1,Math.floor(x/(L||1))>1},c=function(T,L,x,C){var S=\"\"+L;if(u(T,C))for(;S.length<x;)S=\"0\"+S;return S},f=function(T,L,x,C){return u(T)?C[L]:x[L]},h=this,d=function(T){return typeof a==\"function\"?a.call(h,T,u(\"m\")):b(c(\"m\",T.month(),2))},v=function(T,L){return L?typeof s==\"function\"?s.call(h,T):s[T.month()-h.minMonth]:typeof o==\"function\"?o.call(h,T):o[T.month()-h.minMonth]},m=this.local.digits,b=function(T){return r.localNumbers&&m?m(T):T},p=\"\",k=!1,M=0;M<e.length;M++)if(k)e.charAt(M)===\"'\"&&!u(\"'\")?k=!1:p+=e.charAt(M);else switch(e.charAt(M)){case\"d\":p+=b(c(\"d\",t.day(),2));break;case\"D\":p+=f(\"D\",t.dayOfWeek(),n,i);break;case\"o\":p+=c(\"o\",t.dayOfYear(),3);break;case\"w\":p+=c(\"w\",t.weekOfYear(),2);break;case\"m\":p+=d(t);break;case\"M\":p+=v(t,u(\"M\"));break;case\"y\":p+=u(\"y\",2)?t.year():(t.year()%100<10?\"0\":\"\")+t.year()%100;break;case\"Y\":u(\"Y\",2),p+=t.formatYear();break;case\"J\":p+=t.toJD();break;case\"@\":p+=(t.toJD()-this.UNIX_EPOCH)*this.SECS_PER_DAY;break;case\"!\":p+=(t.toJD()-this.TICKS_EPOCH)*this.TICKS_PER_DAY;break;case\"'\":u(\"'\")?p+=\"'\":k=!0;break;default:p+=e.charAt(M)}return p},parseDate:function(e,t,r){if(t==null)throw Ud.local.invalidArguments||Ud.regionalOptions[\"\"].invalidArguments;if(t=typeof t==\"object\"?t.toString():t+\"\",t===\"\")return null;e=e||this.local.dateFormat,r=r||{};var n=r.shortYearCutoff||this.shortYearCutoff;n=typeof n!=\"string\"?n:this.today().year()%100+parseInt(n,10);for(var i=r.dayNamesShort||this.local.dayNamesShort,a=r.dayNames||this.local.dayNames,o=r.parseMonth||this.local.parseMonth,s=r.monthNumbers||this.local.monthNumbers,l=r.monthNamesShort||this.local.monthNamesShort,u=r.monthNames||this.local.monthNames,c=-1,f=-1,h=-1,d=-1,v=-1,m=!1,b=!1,p=function(z,q){for(var V=1;g+V<e.length&&e.charAt(g+V)===z;)V++;return g+=V-1,Math.floor(V/(q||1))>1},k=function(z,q){var V=p(z,q),G=[2,3,V?4:2,V?4:2,10,11,20][\"oyYJ@!\".indexOf(z)+1],Z=new RegExp(\"^-?\\\\d{1,\"+G+\"}\"),j=t.substring(S).match(Z);if(!j)throw(Ud.local.missingNumberAt||Ud.regionalOptions[\"\"].missingNumberAt).replace(/\\{0\\}/,S);return S+=j[0].length,parseInt(j[0],10)},M=this,T=function(){if(typeof s==\"function\"){p(\"m\");var z=s.call(M,t.substring(S));return S+=z.length,z}return k(\"m\")},L=function(z,q,V,G){for(var Z=p(z,G)?V:q,j=0;j<Z.length;j++)if(t.substr(S,Z[j].length).toLowerCase()===Z[j].toLowerCase())return S+=Z[j].length,j+M.minMonth;throw(Ud.local.unknownNameAt||Ud.regionalOptions[\"\"].unknownNameAt).replace(/\\{0\\}/,S)},x=function(){if(typeof u==\"function\"){var z=p(\"M\")?u.call(M,t.substring(S)):l.call(M,t.substring(S));return S+=z.length,z}return L(\"M\",l,u)},C=function(){if(t.charAt(S)!==e.charAt(g))throw(Ud.local.unexpectedLiteralAt||Ud.regionalOptions[\"\"].unexpectedLiteralAt).replace(/\\{0\\}/,S);S++},S=0,g=0;g<e.length;g++)if(b)e.charAt(g)===\"'\"&&!p(\"'\")?b=!1:C();else switch(e.charAt(g)){case\"d\":d=k(\"d\");break;case\"D\":L(\"D\",i,a);break;case\"o\":v=k(\"o\");break;case\"w\":k(\"w\");break;case\"m\":h=T();break;case\"M\":h=x();break;case\"y\":var P=g;m=!p(\"y\",2),g=P,f=k(\"y\",2);break;case\"Y\":f=k(\"Y\",2);break;case\"J\":c=k(\"J\")+.5,t.charAt(S)===\".\"&&(S++,k(\"J\"));break;case\"@\":c=k(\"@\")/this.SECS_PER_DAY+this.UNIX_EPOCH;break;case\"!\":c=k(\"!\")/this.TICKS_PER_DAY+this.TICKS_EPOCH;break;case\"*\":S=t.length;break;case\"'\":p(\"'\")?C():b=!0;break;default:C()}if(S<t.length)throw Ud.local.unexpectedText||Ud.regionalOptions[\"\"].unexpectedText;if(f===-1?f=this.today().year():f<100&&m&&(f+=n===-1?1900:this.today().year()-this.today().year()%100-(f<=n?0:100)),typeof h==\"string\"&&(h=o.call(this,f,h)),v>-1){h=1,d=v;for(var E=this.daysInMonth(f,h);d>E;E=this.daysInMonth(f,h))h++,d-=E}return c>-1?this.fromJD(c):this.newDate(f,h,d)},determineDate:function(e,t,r,n,i){r&&typeof r!=\"object\"&&(i=n,n=r,r=null),typeof n!=\"string\"&&(i=n,n=\"\");var a=this,o=function(s){try{return a.parseDate(n,s,i)}catch(f){}s=s.toLowerCase();for(var l=(s.match(/^c/)&&r?r.newDate():null)||a.today(),u=/([+-]?[0-9]+)\\s*(d|w|m|y)?/g,c=u.exec(s);c;)l.add(parseInt(c[1],10),c[2]||\"d\"),c=u.exec(s);return l};return t=t?t.newDate():null,e=e==null?t:typeof e==\"string\"?o(e):typeof e==\"number\"?isNaN(e)||e===1/0||e===-1/0?t:a.today().add(e,\"d\"):a.newDate(e),e}})});var het=ye(()=>{var jx=kv(),crr=Nh(),TQ=jx.instance();function B9(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}B9.prototype=new jx.baseCalendar;crr(B9.prototype,{name:\"Chinese\",jdEpoch:17214255e-1,hasYearZero:!1,minMonth:0,firstMonth:0,minDay:1,regionalOptions:{\"\":{name:\"Chinese\",epochs:[\"BEC\",\"EC\"],monthNumbers:function(e,t){if(typeof e==\"string\"){var r=e.match(hrr);return r?r[0]:\"\"}var n=this._validateYear(e),i=e.month(),a=\"\"+this.toChineseMonth(n,i);return t&&a.length<2&&(a=\"0\"+a),this.isIntercalaryMonth(n,i)&&(a+=\"i\"),a},monthNames:function(e){if(typeof e==\"string\"){var t=e.match(drr);return t?t[0]:\"\"}var r=this._validateYear(e),n=e.month(),i=this.toChineseMonth(r,n),a=[\"\\u4E00\\u6708\",\"\\u4E8C\\u6708\",\"\\u4E09\\u6708\",\"\\u56DB\\u6708\",\"\\u4E94\\u6708\",\"\\u516D\\u6708\",\"\\u4E03\\u6708\",\"\\u516B\\u6708\",\"\\u4E5D\\u6708\",\"\\u5341\\u6708\",\"\\u5341\\u4E00\\u6708\",\"\\u5341\\u4E8C\\u6708\"][i-1];return this.isIntercalaryMonth(r,n)&&(a=\"\\u95F0\"+a),a},monthNamesShort:function(e){if(typeof e==\"string\"){var t=e.match(vrr);return t?t[0]:\"\"}var r=this._validateYear(e),n=e.month(),i=this.toChineseMonth(r,n),a=[\"\\u4E00\",\"\\u4E8C\",\"\\u4E09\",\"\\u56DB\",\"\\u4E94\",\"\\u516D\",\"\\u4E03\",\"\\u516B\",\"\\u4E5D\",\"\\u5341\",\"\\u5341\\u4E00\",\"\\u5341\\u4E8C\"][i-1];return this.isIntercalaryMonth(r,n)&&(a=\"\\u95F0\"+a),a},parseMonth:function(e,t){e=this._validateYear(e);var r=parseInt(t),n;if(isNaN(r))t[0]===\"\\u95F0\"&&(n=!0,t=t.substring(1)),t[t.length-1]===\"\\u6708\"&&(t=t.substring(0,t.length-1)),r=1+[\"\\u4E00\",\"\\u4E8C\",\"\\u4E09\",\"\\u56DB\",\"\\u4E94\",\"\\u516D\",\"\\u4E03\",\"\\u516B\",\"\\u4E5D\",\"\\u5341\",\"\\u5341\\u4E00\",\"\\u5341\\u4E8C\"].indexOf(t);else{var i=t[t.length-1];n=i===\"i\"||i===\"I\"}var a=this.toMonthIndex(e,r,n);return a},dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},_validateYear:function(e,t){if(e.year&&(e=e.year()),typeof e!=\"number\"||e<1888||e>2111)throw t.replace(/\\{0\\}/,this.local.name);return e},toMonthIndex:function(e,t,r){var n=this.intercalaryMonth(e),i=r&&t!==n;if(i||t<1||t>12)throw jx.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var a;return n?!r&&t<=n?a=t-1:a=t:a=t-1,a},toChineseMonth:function(e,t){e.year&&(e=e.year(),t=e.month());var r=this.intercalaryMonth(e),n=r?12:11;if(t<0||t>n)throw jx.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var i;return r?t<r?i=t+1:i=t:i=t+1,i},intercalaryMonth:function(e){e=this._validateYear(e);var t=Wx[e-Wx[0]],r=t>>13;return r},isIntercalaryMonth:function(e,t){e.year&&(e=e.year(),t=e.month());var r=this.intercalaryMonth(e);return!!r&&r===t},leapYear:function(e){return this.intercalaryMonth(e)!==0},weekOfYear:function(e,t,r){var n=this._validateYear(e,jx.local.invalidyear),i=Xx[n-Xx[0]],a=i>>9&4095,o=i>>5&15,s=i&31,l;l=TQ.newDate(a,o,s),l.add(4-(l.dayOfWeek()||7),\"d\");var u=this.toJD(e,t,r)-l.toJD();return 1+Math.floor(u/7)},monthsInYear:function(e){return this.leapYear(e)?13:12},daysInMonth:function(e,t){e.year&&(t=e.month(),e=e.year()),e=this._validateYear(e);var r=Wx[e-Wx[0]],n=r>>13,i=n?12:11;if(t>i)throw jx.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var a=r&1<<12-t?30:29;return a},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,a,r,jx.local.invalidDate);e=this._validateYear(n.year()),t=n.month(),r=n.day();var i=this.isIntercalaryMonth(e,t),a=this.toChineseMonth(e,t),o=grr(e,a,r,i);return TQ.toJD(o.year,o.month,o.day)},fromJD:function(e){var t=TQ.fromJD(e),r=prr(t.year(),t.month(),t.day()),n=this.toMonthIndex(r.year,r.month,r.isIntercalary);return this.newDate(r.year,n,r.day)},fromString:function(e){var t=e.match(frr),r=this._validateYear(+t[1]),n=+t[2],i=!!t[3],a=this.toMonthIndex(r,n,i),o=+t[4];return this.newDate(r,a,o)},add:function(e,t,r){var n=e.year(),i=e.month(),a=this.isIntercalaryMonth(n,i),o=this.toChineseMonth(n,i),s=Object.getPrototypeOf(B9.prototype).add.call(this,e,t,r);if(r===\"y\"){var l=s.year(),u=s.month(),c=this.isIntercalaryMonth(l,o),f=a&&c?this.toMonthIndex(l,o,!0):this.toMonthIndex(l,o,!1);f!==u&&s.month(f)}return s}});var frr=/^\\s*(-?\\d\\d\\d\\d|\\d\\d)[-/](\\d?\\d)([iI]?)[-/](\\d?\\d)/m,hrr=/^\\d?\\d[iI]?/m,drr=/^闰?十?[一二三四五六七八九]?月/m,vrr=/^闰?十?[一二三四五六七八九]?/m;jx.calendars.chinese=B9;var 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this._validate(e,this.minMonth,this.minDay,Dw.local.invalidYear||Dw.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Dw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===13&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,Dw.local.invalidDate);return e=n.year(),e<0&&e++,n.day()+(n.month()-1)*30+(e-1)*365+Math.floor(e/4)+this.jdEpoch-1},fromJD:function(e){var t=Math.floor(e)+.5-this.jdEpoch,r=Math.floor((t-Math.floor((t+366)/1461))/365)+1;r<=0&&r--,t=Math.floor(e)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(t/30)+1,i=t-(n-1)*30+1;return this.newDate(r,n,i)}});Dw.calendars.coptic=AQ});var vet=ye(()=>{var k1=kv(),yrr=Nh();function SQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}SQ.prototype=new k1.baseCalendar;yrr(SQ.prototype,{name:\"Discworld\",jdEpoch:17214255e-1,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(e){return this._validate(e,this.minMonth,this.minDay,k1.local.invalidYear),!1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,k1.local.invalidYear),13},daysInYear:function(e){return this._validate(e,this.minMonth,this.minDay,k1.local.invalidYear),400},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,k1.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,k1.local.invalidDate);return(n.day()+1)%8},weekDay:function(e,t,r){var n=this.dayOfWeek(e,t,r);return n>=2&&n<=6},extraInfo:function(e,t,r){var n=this._validate(e,t,r,k1.local.invalidDate);return{century:_rr[Math.floor((n.year()-1)/100)+1]||\"\"}},toJD:function(e,t,r){var n=this._validate(e,t,r,k1.local.invalidDate);return e=n.year()+(n.year()<0?1:0),t=n.month(),r=n.day(),r+(t>1?16:0)+(t>2?(t-2)*32:0)+(e-1)*400+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e+.5)-Math.floor(this.jdEpoch)-1;var t=Math.floor(e/400)+1;e-=(t-1)*400,e+=e>15?16:0;var r=Math.floor(e/32)+1,n=e-(r-1)*32+1;return this.newDate(t<=0?t-1:t,r,n)}});var _rr={20:\"Fruitbat\",21:\"Anchovy\"};k1.calendars.discworld=SQ});var pet=ye(()=>{var Fw=kv(),xrr=Nh();function MQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}MQ.prototype=new Fw.baseCalendar;xrr(MQ.prototype,{name:\"Ethiopian\",jdEpoch:17242205e-1,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Ethiopian\",epochs:[\"BEE\",\"EE\"],monthNames:[\"Meskerem\",\"Tikemet\",\"Hidar\",\"Tahesas\",\"Tir\",\"Yekatit\",\"Megabit\",\"Miazia\",\"Genbot\",\"Sene\",\"Hamle\",\"Nehase\",\"Pagume\"],monthNamesShort:[\"Mes\",\"Tik\",\"Hid\",\"Tah\",\"Tir\",\"Yek\",\"Meg\",\"Mia\",\"Gen\",\"Sen\",\"Ham\",\"Neh\",\"Pag\"],dayNames:[\"Ehud\",\"Segno\",\"Maksegno\",\"Irob\",\"Hamus\",\"Arb\",\"Kidame\"],dayNamesShort:[\"Ehu\",\"Seg\",\"Mak\",\"Iro\",\"Ham\",\"Arb\",\"Kid\"],dayNamesMin:[\"Eh\",\"Se\",\"Ma\",\"Ir\",\"Ha\",\"Ar\",\"Ki\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Fw.local.invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===3||r%4===-1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Fw.local.invalidYear||Fw.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Fw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===13&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,Fw.local.invalidDate);return e=n.year(),e<0&&e++,n.day()+(n.month()-1)*30+(e-1)*365+Math.floor(e/4)+this.jdEpoch-1},fromJD:function(e){var t=Math.floor(e)+.5-this.jdEpoch,r=Math.floor((t-Math.floor((t+366)/1461))/365)+1;r<=0&&r--,t=Math.floor(e)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(t/30)+1,i=t-(n-1)*30+1;return this.newDate(r,n,i)}});Fw.calendars.ethiopian=MQ});var get=ye(()=>{var Zx=kv(),brr=Nh();function EQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}EQ.prototype=new Zx.baseCalendar;brr(EQ.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Zx.local.invalidYear);return this._leapYear(t.year())},_leapYear:function(e){return e=e<0?e+1:e,N9(e*7+1,19)<7},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Zx.local.invalidYear),this._leapYear(e.year?e.year():e)?13:12},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Zx.local.invalidYear);return e=t.year(),this.toJD(e===-1?1:e+1,7,1)-this.toJD(e,7,1)},daysInMonth:function(e,t){return e.year&&(t=e.month(),e=e.year()),this._validate(e,t,this.minDay,Zx.local.invalidMonth),t===12&&this.leapYear(e)||t===8&&N9(this.daysInYear(e),10)===5?30:t===9&&N9(this.daysInYear(e),10)===3?29:this.daysPerMonth[t-1]},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==6},extraInfo:function(e,t,r){var n=this._validate(e,t,r,Zx.local.invalidDate);return{yearType:(this.leapYear(n)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(n)%10-3]}},toJD:function(e,t,r){var n=this._validate(e,t,r,Zx.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=e<=0?e+1:e,a=this.jdEpoch+this._delay1(i)+this._delay2(i)+r+1;if(t<7){for(var o=7;o<=this.monthsInYear(e);o++)a+=this.daysInMonth(e,o);for(var o=1;o<t;o++)a+=this.daysInMonth(e,o)}else for(var o=7;o<t;o++)a+=this.daysInMonth(e,o);return a},_delay1:function(e){var t=Math.floor((235*e-234)/19),r=12084+13753*t,n=t*29+Math.floor(r/25920);return N9(3*(n+1),7)<3&&n++,n},_delay2:function(e){var t=this._delay1(e-1),r=this._delay1(e),n=this._delay1(e+1);return n-r===356?2:r-t===382?1:0},fromJD:function(e){e=Math.floor(e)+.5;for(var t=Math.floor((e-this.jdEpoch)*98496/35975351)-1;e>=this.toJD(t===-1?1:t+1,7,1);)t++;for(var r=e<this.toJD(t,1,1)?7:1;e>this.toJD(t,r,this.daysInMonth(t,r));)r++;var n=e-this.toJD(t,r,1)+1;return this.newDate(t,r,n)}});function N9(e,t){return e-t*Math.floor(e/t)}Zx.calendars.hebrew=EQ});var met=ye(()=>{var AC=kv(),wrr=Nh();function kQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}kQ.prototype=new AC.baseCalendar;wrr(kQ.prototype,{name:\"Islamic\",jdEpoch:19484395e-1,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012Bs\",\"Yawm al-jum'a\",\"Yawm 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t=Math.floor((30*(e-this.jdEpoch)+10646)/10631);t=t<=0?t-1:t;var r=Math.min(12,Math.ceil((e-29-this.toJD(t,1,1))/29.5)+1),n=e-this.toJD(t,r,1)+1;return this.newDate(t,r,n)}});AC.calendars.islamic=kQ});var yet=ye(()=>{var SC=kv(),Trr=Nh();function CQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}CQ.prototype=new SC.baseCalendar;Trr(CQ.prototype,{name:\"Julian\",jdEpoch:17214235e-1,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,SC.local.invalidYear),r=t.year()<0?t.year()+1:t.year();return r%4===0},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,SC.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,SC.local.invalidDate);return e=n.year(),t=n.month(),r=n.day(),e<0&&e++,t<=2&&(e--,t+=12),Math.floor(365.25*(e+4716))+Math.floor(30.6001*(t+1))+r-1524.5},fromJD:function(e){var t=Math.floor(e+.5),r=t+1524,n=Math.floor((r-122.1)/365.25),i=Math.floor(365.25*n),a=Math.floor((r-i)/30.6001),o=a-Math.floor(a<14?1:13),s=n-Math.floor(o>2?4716:4715),l=r-i-Math.floor(30.6001*a);return s<=0&&s--,this.newDate(s,o,l)}});SC.calendars.julian=CQ});var xet=ye(()=>{var fg=kv(),Arr=Nh();function PQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}PQ.prototype=new fg.baseCalendar;Arr(PQ.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(e){return this._validate(e,this.minMonth,this.minDay,fg.local.invalidYear),!1},formatYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,fg.local.invalidYear);e=t.year();var r=Math.floor(e/400);e=e%400,e+=e<0?400:0;var n=Math.floor(e/20);return r+\".\"+n+\".\"+e%20},forYear:function(e){if(e=e.split(\".\"),e.length<3)throw\"Invalid Mayan year\";for(var t=0,r=0;r<e.length;r++){var n=parseInt(e[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";t=t*20+n}return t},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,fg.local.invalidYear),18},weekOfYear:function(e,t,r){return this._validate(e,t,r,fg.local.invalidDate),0},daysInYear:function(e){return this._validate(e,this.minMonth,this.minDay,fg.local.invalidYear),360},daysInMonth:function(e,t){return this._validate(e,t,this.minDay,fg.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,fg.local.invalidDate);return n.day()},weekDay:function(e,t,r){return this._validate(e,t,r,fg.local.invalidDate),!0},extraInfo:function(e,t,r){var n=this._validate(e,t,r,fg.local.invalidDate),i=n.toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(e){e-=this.jdEpoch;var t=LQ(e+8+340,365);return[Math.floor(t/20)+1,LQ(t,20)]},_toTzolkin:function(e){return e-=this.jdEpoch,[_et(e+20,20),_et(e+4,13)]},toJD:function(e,t,r){var n=this._validate(e,t,r,fg.local.invalidDate);return n.day()+n.month()*20+n.year()*360+this.jdEpoch},fromJD:function(e){e=Math.floor(e)+.5-this.jdEpoch;var t=Math.floor(e/360);e=e%360,e+=e<0?360:0;var r=Math.floor(e/20),n=e%20;return this.newDate(t,r,n)}});function LQ(e,t){return e-t*Math.floor(e/t)}function _et(e,t){return LQ(e-1,t)+1}fg.calendars.mayan=PQ});var wet=ye(()=>{var zw=kv(),Srr=Nh();function IQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}IQ.prototype=new zw.baseCalendar;var bet=zw.instance(\"gregorian\");Srr(IQ.prototype,{name:\"Nanakshahi\",jdEpoch:22576735e-1,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,zw.local.invalidYear||zw.regionalOptions[\"\"].invalidYear);return bet.leapYear(t.year()+(t.year()<1?1:0)+1469)},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,zw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,zw.local.invalidMonth),i=n.year();i<0&&i++;for(var a=n.day(),o=1;o<n.month();o++)a+=this.daysPerMonth[o-1];return a+bet.toJD(i+1468,3,13)},fromJD:function(e){e=Math.floor(e+.5);for(var t=Math.floor((e-(this.jdEpoch-1))/366);e>=this.toJD(t+1,1,1);)t++;for(var r=e-Math.floor(this.toJD(t,1,1)+.5)+1,n=1;r>this.daysInMonth(t,n);)r-=this.daysInMonth(t,n),n++;return this.newDate(t,n,r)}});zw.calendars.nanakshahi=IQ});var Tet=ye(()=>{var qw=kv(),Mrr=Nh();function RQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}RQ.prototype=new qw.baseCalendar;Mrr(RQ.prototype,{name:\"Nepali\",jdEpoch:17007095e-1,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(e){return this.daysInYear(e)!==this.daysPerYear},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,qw.local.invalidYear);if(e=t.year(),typeof this.NEPALI_CALENDAR_DATA[e]==\"undefined\")return this.daysPerYear;for(var r=0,n=this.minMonth;n<=12;n++)r+=this.NEPALI_CALENDAR_DATA[e][n];return r},daysInMonth:function(e,t){return e.year&&(t=e.month(),e=e.year()),this._validate(e,t,this.minDay,qw.local.invalidMonth),typeof this.NEPALI_CALENDAR_DATA[e]==\"undefined\"?this.daysPerMonth[t-1]:this.NEPALI_CALENDAR_DATA[e][t]},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==6},toJD:function(e,t,r){var n=this._validate(e,t,r,qw.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=qw.instance(),a=0,o=t,s=e;this._createMissingCalendarData(e);var l=e-(o>9||o===9&&r>=this.NEPALI_CALENDAR_DATA[s][0]?56:57);for(t!==9&&(a=r,o--);o!==9;)o<=0&&(o=12,s--),a+=this.NEPALI_CALENDAR_DATA[s][o],o--;return t===9?(a+=r-this.NEPALI_CALENDAR_DATA[s][0],a<0&&(a+=i.daysInYear(l))):a+=this.NEPALI_CALENDAR_DATA[s][9]-this.NEPALI_CALENDAR_DATA[s][0],i.newDate(l,1,1).add(a,\"d\").toJD()},fromJD:function(e){var t=qw.instance(),r=t.fromJD(e),n=r.year(),i=r.dayOfYear(),a=n+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)o++,o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var u=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,u)},_createMissingCalendarData:function(e){var t=this.daysPerMonth.slice(0);t.unshift(17);for(var r=e-1;r<e+2;r++)typeof this.NEPALI_CALENDAR_DATA[r]==\"undefined\"&&(this.NEPALI_CALENDAR_DATA[r]=t)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,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Aet=ye(()=>{var uS=kv(),Err=Nh();function V9(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}function U9(e){var t=e-475;e<0&&t++;var r=.242197,n=r*t,i=r*(t+1),a=n-Math.floor(n),o=i-Math.floor(i);return a>o}V9.prototype=new uS.baseCalendar;Err(V9.prototype,{name:\"Persian\",jdEpoch:19483205e-1,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Persian\",epochs:[\"BP\",\"AP\"],monthNames:[\"Farvardin\",\"Ordibehesht\",\"Khordad\",\"Tir\",\"Mordad\",\"Shahrivar\",\"Mehr\",\"Aban\",\"Azar\",\"Dey\",\"Bahman\",\"Esfand\"],monthNamesShort:[\"Far\",\"Ord\",\"Kho\",\"Tir\",\"Mor\",\"Sha\",\"Meh\",\"Aba\",\"Aza\",\"Dey\",\"Bah\",\"Esf\"],dayNames:[\"Yekshanbeh\",\"Doshanbeh\",\"Seshanbeh\",\"Chah\\u0101rshanbeh\",\"Panjshanbeh\",\"Jom'eh\",\"Shanbeh\"],dayNamesShort:[\"Yek\",\"Do\",\"Se\",\"Cha\",\"Panj\",\"Jom\",\"Sha\"],dayNamesMin:[\"Ye\",\"Do\",\"Se\",\"Ch\",\"Pa\",\"Jo\",\"Sh\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,uS.local.invalidYear);return U9(t.year())},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-((n.dayOfWeek()+1)%7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,uS.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,uS.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=0;if(e>0)for(var a=1;a<e;a++)U9(a)&&i++;else if(e<0)for(var a=e;a<0;a++)U9(a)&&i--;return r+(t<=7?(t-1)*31:(t-1)*30+6)+(e>0?e-1:e)*365+i+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e)+.5;var t=475+(e-this.toJD(475,1,1))/365.242197,r=Math.floor(t);r<=0&&r--,e>this.toJD(r,12,U9(r)?30:29)&&(r++,r===0&&r++);var n=e-this.toJD(r,1,1)+1,i=n<=186?Math.ceil(n/31):Math.ceil((n-6)/30),a=e-this.toJD(r,i,1)+1;return this.newDate(r,i,a)}});uS.calendars.persian=V9;uS.calendars.jalali=V9});var Met=ye(()=>{var Ow=kv(),krr=Nh(),G9=Ow.instance();function DQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}DQ.prototype=new Ow.baseCalendar;krr(DQ.prototype,{name:\"Taiwan\",jdEpoch:24194025e-1,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Ow.local.invalidYear),r=this._t2gYear(t.year());return G9.leapYear(r)},weekOfYear:function(i,t,r){var n=this._validate(i,this.minMonth,this.minDay,Ow.local.invalidYear),i=this._t2gYear(n.year());return G9.weekOfYear(i,n.month(),n.day())},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Ow.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,Ow.local.invalidDate),i=this._t2gYear(n.year());return G9.toJD(i,n.month(),n.day())},fromJD:function(e){var t=G9.fromJD(e),r=this._g2tYear(t.year());return this.newDate(r,t.month(),t.day())},_t2gYear:function(e){return e+this.yearsOffset+(e>=-this.yearsOffset&&e<=-1?1:0)},_g2tYear:function(e){return e-this.yearsOffset-(e>=1&&e<=this.yearsOffset?1:0)}});Ow.calendars.taiwan=DQ});var Eet=ye(()=>{var Bw=kv(),Crr=Nh(),H9=Bw.instance();function FQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}FQ.prototype=new Bw.baseCalendar;Crr(FQ.prototype,{name:\"Thai\",jdEpoch:15230985e-1,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Bw.local.invalidYear),r=this._t2gYear(t.year());return H9.leapYear(r)},weekOfYear:function(i,t,r){var n=this._validate(i,this.minMonth,this.minDay,Bw.local.invalidYear),i=this._t2gYear(n.year());return H9.weekOfYear(i,n.month(),n.day())},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Bw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,Bw.local.invalidDate),i=this._t2gYear(n.year());return H9.toJD(i,n.month(),n.day())},fromJD:function(e){var t=H9.fromJD(e),r=this._g2tYear(t.year());return this.newDate(r,t.month(),t.day())},_t2gYear:function(e){return e-this.yearsOffset-(e>=1&&e<=this.yearsOffset?1:0)},_g2tYear:function(e){return e+this.yearsOffset+(e>=-this.yearsOffset&&e<=-1?1:0)}});Bw.calendars.thai=FQ});var ket=ye(()=>{var Nw=kv(),Lrr=Nh();function zQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}zQ.prototype=new Nw.baseCalendar;Lrr(zQ.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012Bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Nw.local.invalidYear);return this.daysInYear(t.year())===355},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){for(var t=0,r=1;r<=12;r++)t+=this.daysInMonth(e,r);return t},daysInMonth:function(e,t){for(var r=this._validate(e,t,this.minDay,Nw.local.invalidMonth),n=r.toJD()-24e5+.5,i=0,a=0;a<Yx.length;a++){if(Yx[a]>n)return Yx[i]-Yx[i-1];i++}return 30},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,Nw.local.invalidDate),i=12*(n.year()-1)+n.month()-15292,a=n.day()+Yx[i-1]-1;return a+24e5-.5},fromJD:function(e){for(var t=e-24e5+.5,r=0,n=0;n<Yx.length&&!(Yx[n]>t);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),o=a+1,s=i-12*a,l=t-Yx[r-1]+1;return this.newDate(o,s,l)},isValid:function(e,t,r){var n=Nw.baseCalendar.prototype.isValid.apply(this,arguments);return n&&(e=e.year!=null?e.year:e,n=e>=1276&&e<=1500),n},_validate:function(e,t,r,n){var i=Nw.baseCalendar.prototype._validate.apply(this,arguments);if(i.year<1276||i.year>1500)throw n.replace(/\\{0\\}/,this.local.name);return i}});Nw.calendars.ummalqura=zQ;var 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Let=ye((GTr,Cet)=>{\"use strict\";Cet.exports=kv();fet();het();det();vet();pet();get();met();yet();xet();wet();Tet();Aet();Met();Eet();ket()});var qet=ye((HTr,zet)=>{\"use strict\";var Iet=Let(),MC=Pr(),Ret=fs(),Prr=Ret.EPOCHJD,Irr=Ret.ONEDAY,BQ={valType:\"enumerated\",values:MC.sortObjectKeys(Iet.calendars),editType:\"calc\",dflt:\"gregorian\"},Det=function(e,t,r,n){var i={};return i[r]=BQ,MC.coerce(e,t,i,r,n)},Rrr=function(e,t,r,n){for(var i=0;i<r.length;i++)Det(e,t,r[i]+\"calendar\",n.calendar)},Drr={chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},Frr={chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},zrr={chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},j9=\"##\",qrr={d:{0:\"dd\",\"-\":\"d\"},e:{0:\"d\",\"-\":\"d\"},a:{0:\"D\",\"-\":\"D\"},A:{0:\"DD\",\"-\":\"DD\"},j:{0:\"oo\",\"-\":\"o\"},W:{0:\"ww\",\"-\":\"w\"},m:{0:\"mm\",\"-\":\"m\"},b:{0:\"M\",\"-\":\"M\"},B:{0:\"MM\",\"-\":\"MM\"},y:{0:\"yy\",\"-\":\"yy\"},Y:{0:\"yyyy\",\"-\":\"yyyy\"},U:j9,w:j9,c:{0:\"D M d %X yyyy\",\"-\":\"D M d %X yyyy\"},x:{0:\"mm/dd/yyyy\",\"-\":\"mm/dd/yyyy\"}};function Orr(e,t,r){for(var n=Math.floor((t+.05)/Irr)+Prr,i=Fet(r).fromJD(n),a=0,o,s,l,u,c;(a=e.indexOf(\"%\",a))!==-1;)o=e.charAt(a+1),o===\"0\"||o===\"-\"||o===\"_\"?(l=3,s=e.charAt(a+2),o===\"_\"&&(o=\"-\")):(s=o,o=\"0\",l=2),u=qrr[s],u?(u===j9?c=j9:c=i.formatDate(u[o]),e=e.slice(0,a)+c+e.slice(a+l),a+=c.length):a+=l;return e}var Pet={};function Fet(e){var t=Pet[e];return t||(t=Pet[e]=Iet.instance(e),t)}function EC(e){return MC.extendFlat({},BQ,{description:e})}function NQ(e){return\"Sets the calendar system to use with `\"+e+\"` date data.\"}var OQ={xcalendar:EC(NQ(\"x\"))},hy=MC.extendFlat({},OQ,{ycalendar:EC(NQ(\"y\"))}),qQ=MC.extendFlat({},hy,{zcalendar:EC(NQ(\"z\"))}),cS=EC([\"Sets the calendar system to use for `range` and `tick0`\",\"if this is a date axis. This does not set the calendar for\",\"interpreting data on this axis, that's specified in the trace\",\"or via the global `layout.calendar`\"].join(\" \"));zet.exports={moduleType:\"component\",name:\"calendars\",schema:{traces:{scatter:hy,bar:hy,box:hy,heatmap:hy,contour:hy,histogram:hy,histogram2d:hy,histogram2dcontour:hy,scatter3d:qQ,surface:qQ,mesh3d:qQ,scattergl:hy,ohlc:OQ,candlestick:OQ},layout:{calendar:EC([\"Sets the default calendar system to use for interpreting and\",\"displaying dates throughout the plot.\"].join(\" \"))},subplots:{xaxis:{calendar:cS},yaxis:{calendar:cS},scene:{xaxis:{calendar:cS},yaxis:{calendar:cS},zaxis:{calendar:cS}},polar:{radialaxis:{calendar:cS}}}},layoutAttributes:BQ,handleDefaults:Det,handleTraceDefaults:Rrr,CANONICAL_SUNDAY:Frr,CANONICAL_TICK:Drr,DFLTRANGE:zrr,getCal:Fet,worldCalFmt:Orr}});var Bet=ye((jTr,Oet)=>{\"use strict\";Oet.exports=qet()});var Brr=ye((WTr,Uet)=>{var Net=pye();Net.register([m1e(),n_e(),yxe(),Bxe(),Qxe(),Ybe(),l2e(),K2e(),Swe(),s3e(),Z3e(),oke(),Yke(),N6e(),kLe(),aPe(),CPe(),e8e(),_8e(),q8e(),Y8e(),uRe(),ARe(),NRe(),yFe(),OFe(),tNe(),tUe(),fVe(),qVe(),XGe(),lHe(),IHe(),Hje(),aWe(),CWe(),OXe(),lZe(),GZe(),fKe(),zKe(),iJe(),CJe(),GJe(),V$e(),sQe(),MQe(),set(),Bet()]);Uet.exports=Net});return Brr();})();\n",
+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
+       "/*! ieee754. BSD-3-Clause License. Feross Aboukhadijeh <https://feross.org/opensource> */\n",
+       "/*!\n",
+       " * Determine if an object is a Buffer\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
+       "/*!\n",
+       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT license.\n",
+       " */\n",
+       "/*!\n",
+       " * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT License.\n",
+       " */\n",
+       "/*! Bundled license information:\n",
+       "\n",
+       "native-promise-only/lib/npo.src.js:\n",
+       "  (*! Native Promise Only\n",
+       "      v0.8.1 (c) Kyle Simpson\n",
+       "      MIT License: http://getify.mit-license.org\n",
+       "  *)\n",
+       "\n",
+       "polybooljs/index.js:\n",
+       "  (*\n",
+       "   * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
+       "   * @license MIT\n",
+       "   * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
+       "   *)\n",
+       "\n",
+       "ieee754/index.js:\n",
+       "  (*! ieee754. BSD-3-Clause License. Feross Aboukhadijeh <https://feross.org/opensource> *)\n",
+       "\n",
+       "buffer/index.js:\n",
+       "  (*!\n",
+       "   * The buffer module from node.js, for the browser.\n",
+       "   *\n",
+       "   * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       "   * @license  MIT\n",
+       "   *)\n",
+       "\n",
+       "safe-buffer/index.js:\n",
+       "  (*! safe-buffer. MIT License. Feross Aboukhadijeh <https://feross.org/opensource> *)\n",
+       "\n",
+       "assert/build/internal/util/comparisons.js:\n",
+       "  (*!\n",
+       "   * The buffer module from node.js, for the browser.\n",
+       "   *\n",
+       "   * @author   Feross Aboukhadijeh <feross@feross.org> <http://feross.org>\n",
+       "   * @license  MIT\n",
+       "   *)\n",
+       "\n",
+       "object-assign/index.js:\n",
+       "  (*\n",
+       "  object-assign\n",
+       "  (c) Sindre Sorhus\n",
+       "  @license MIT\n",
+       "  *)\n",
+       "\n",
+       "maplibre-gl/dist/maplibre-gl.js:\n",
+       "  (**\n",
+       "   * MapLibre GL JS\n",
+       "   * @license 3-Clause BSD. Full text of license: https://github.com/maplibre/maplibre-gl-js/blob/v4.7.1/LICENSE.txt\n",
+       "   *)\n",
+       "*/\n",
+       "\n",
+       "window.Plotly = Plotly;\n",
+       "return Plotly;\n",
+       "}));</script>\n",
+       "        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
     "import warnings\n",
     "\n",
     "import mlflow\n",
+    "import plotly\n",
     "from mlflow import MlflowClient\n",
     "\n",
     "import darts.metrics as metrics\n",
@@ -54,10 +3951,9 @@
     "\n",
     "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
     "set_option(\"plotting.use_darts_style\", True)\n",
+    "plotly.offline.init_notebook_mode()\n",
     "\n",
     "PLOTLY_KWARGS = dict(\n",
-    "    width=800,\n",
-    "    height=400,\n",
     "    legend=dict(yanchor=\"top\", y=0.99, xanchor=\"left\", x=0.01),\n",
     ")"
    ]
@@ -69,7 +3965,7 @@
    "source": [
     "## 1. MLflow setup\n",
     "\n",
-    "Point MLflow at a tracking backend and create an experiment. We use a temporary SQLite database so this notebook runs self-contained; in production, set `tracking_uri` to your team's MLflow server or local database."
+    "Point MLflow at a tracking backend and create an experiment. We use a temporary directory so this notebook runs self-contained: run metadata goes to a SQLite database, and artifacts (models, JSON files) are stored alongside it. In production, set `tracking_uri` to your team's MLflow server or local database."
    ]
   },
   {
@@ -82,33 +3978,38 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2026/09/03 16:10:25 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
-      "2026/09/03 16:10:25 INFO mlflow.store.db.utils: Updating database tables\n",
-      "2026/09/03 16:10:26 INFO mlflow.tracking.fluent: Experiment with name 'darts-mlflow-examples' does not exist. Creating a new experiment.\n"
+      "2026/09/03 17:38:23 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+      "2026/09/03 17:38:23 INFO mlflow.store.db.utils: Updating database tables\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tracking URI: sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmps5wn0e4a/mlflow.db\n",
+      "Tracking URI: sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp35vve02p/mlflow.db\n",
       "Experiment: darts-mlflow-examples\n",
       "\n",
       "To explore runs in the UI:\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmps5wn0e4a/mlflow.db\n"
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp35vve02p/mlflow.db\n"
      ]
     }
    ],
    "source": [
     "tmpdir = tempfile.mkdtemp()\n",
     "mlflow_db = os.path.join(tmpdir, \"mlflow.db\")\n",
+    "artifact_root = os.path.join(tmpdir, \"mlruns\")\n",
     "\n",
     "EXPERIMENT_NAME = \"darts-mlflow-examples\"\n",
     "mlflow.set_tracking_uri(f\"sqlite:///{mlflow_db}\")\n",
-    "mlflow.set_experiment(EXPERIMENT_NAME)\n",
+    "mlflow.set_experiment(\n",
+    "    experiment_id=mlflow.create_experiment(\n",
+    "        EXPERIMENT_NAME,\n",
+    "        artifact_location=artifact_root,\n",
+    "    )\n",
+    ")\n",
     "\n",
     "print(f\"Tracking URI: {mlflow.get_tracking_uri()}\")\n",
-    "print(f\"Experiment: {mlflow.get_experiment_by_name('darts-mlflow-examples').name}\")\n",
+    "print(f\"Experiment: {mlflow.get_experiment_by_name(EXPERIMENT_NAME).name}\")\n",
     "print(\n",
     "    f\"\\nTo explore runs in the UI:\\n  mlflow ui --backend-store-uri sqlite:///{mlflow_db}\"\n",
     ")"
@@ -318,7 +4219,6 @@
           "yref": "y domain"
          }
         ],
-        "height": 400,
         "hovermode": "x unified",
         "legend": {
          "x": 0.01,
@@ -411,14 +4311,40 @@
         "title": {
          "text": "Air Passengers"
         },
-        "width": 800,
         "xaxis": {
          "title": {
           "text": "Month"
          }
         }
        }
-      }
+      },
+      "text/html": [
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-      "2026/09/03 16:10:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
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+       "\n",
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@@ -904,7 +4855,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2026/09/03 16:10:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+      "2026/09/03 17:38:25 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
+      "2026/09/03 17:38:25 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
      ]
     },
     {
@@ -912,20 +4864,7 @@
      "output_type": "stream",
      "text": [
       "Finished run: linear_regression\n",
-      "Finished run: random_forest\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/09/03 16:10:29 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Finished run: random_forest\n",
       "Finished run: linear_regression_lags24\n"
      ]
     }
@@ -984,7 +4923,7 @@
      "text": [
       "\n",
       "To explore runs in the UI:\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmps5wn0e4a/mlflow.db\n"
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp35vve02p/mlflow.db\n"
      ]
     }
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@@ -1087,19 +5026,19 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>fb3ad151ba8c4192bb82857a5e255994</td>\n",
+       "      <td>b887a1aef1904bc69e4c39e9049c1420</td>\n",
        "      <td>linear_regression</td>\n",
        "      <td>18.870433</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>41484b2e9f794a3a96ee6f89f4d789e4</td>\n",
+       "      <td>40f92ba313d44527ad0b252ca68217ef</td>\n",
        "      <td>linear_regression_lags24</td>\n",
        "      <td>24.509846</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2302431c94a348d09dcd46bbc10dcdab</td>\n",
+       "      <td>ba40cdb96e5b40289919ca4c1b205dce</td>\n",
        "      <td>random_forest</td>\n",
        "      <td>77.316667</td>\n",
        "    </tr>\n",
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-       "1  41484b2e9f794a3a96ee6f89f4d789e4  linear_regression_lags24   \n",
-       "2  2302431c94a348d09dcd46bbc10dcdab             random_forest   \n",
+       "0  b887a1aef1904bc69e4c39e9049c1420         linear_regression   \n",
+       "1  40f92ba313d44527ad0b252ca68217ef  linear_regression_lags24   \n",
+       "2  ba40cdb96e5b40289919ca4c1b205dce             random_forest   \n",
        "\n",
        "   metrics.backtest_agg_mae  \n",
        "0                 18.870433  \n",
@@ -1127,7 +5066,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Best run by backtest_agg_mae: linear_regression (fb3ad151ba8c4192bb82857a5e255994)\n"
+      "Best run by backtest_agg_mae: linear_regression (b887a1aef1904bc69e4c39e9049c1420)\n"
      ]
     }
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@@ -1166,7 +5105,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Best model URI: models:/m-d142c7979f29495d90aa74b3e91638e0\n",
+      "Best model URI: models:/m-4f23e6ce02f144cdb164b78ab5dc2dc6\n",
       "Aliases set: @champion\n"
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@@ -1510,14 +5448,40 @@
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          "text": "Production inference — darts-air-passengers-forecaster@champion"
         },
-        "width": 800,
         "xaxis": {
          "title": {
           "text": "Month"
          }
         }
        }
-      }
+      },
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From b8ba85f037e7cf6892f1869e62e48ce7dd5e59dc Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 4 Sep 2026 11:19:58 +0200
Subject: [PATCH 151/154] udpate notebook

---
 examples/29-MLflow-examples.ipynb | 1432 ++++++++++++-----------------
 1 file changed, 578 insertions(+), 854 deletions(-)

diff --git a/examples/29-MLflow-examples.ipynb b/examples/29-MLflow-examples.ipynb
index 30a177f907..f8ba551244 100644
--- a/examples/29-MLflow-examples.ipynb
+++ b/examples/29-MLflow-examples.ipynb
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "11bca7fd",
    "metadata": {},
    "outputs": [
@@ -45,7 +45,7 @@
        "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
        "        </script>\n",
        "        <script>/**\n",
-       "* plotly.js v3.7.0\n",
+       "* plotly.js v4.0.0\n",
        "* Copyright 2012-2026, Plotly, Inc.\n",
        "* All rights reserved.\n",
        "* Licensed under the MIT license\n",
@@ -58,93 +58,96 @@
        "   root.moduleName = factory();\n",
        "  }\n",
        "} (typeof self !== \"undefined\" ? self : this, () => {\n",
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t=[\"ERROR:\"];for(e=0;e<arguments.length;e++)t.push(arguments[e]);console.error.apply(console,t)}if(b3.notifyOnLogging>0){var r=[];for(e=0;e<arguments.length;e++)r.push(arguments[e]);Vq(r.join(\"<br>\"),\"stick\")}}});var I6=ye((gir,Tte)=>{\"use strict\";Tte.exports=function(){}});var Hq=ye((mir,Ate)=>{\"use strict\";Ate.exports=function(t,r){if(r instanceof RegExp){for(var n=r.toString(),i=0;i<t.length;i++)if(t[i]instanceof RegExp&&t[i].toString()===n)return t;t.push(r)}else(r||r===0)&&t.indexOf(r)===-1&&t.push(r);return t}});var Mte=ye((yir,Ste)=>{Ste.exports=vrt;function vrt(){var e=new Float32Array(16);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var kte=ye((_ir,Ete)=>{Ete.exports=prt;function prt(e){var t=new Float32Array(16);return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}});var Lte=ye((xir,Cte)=>{Cte.exports=grt;function grt(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}});var jq=ye((bir,Pte)=>{Pte.exports=mrt;function mrt(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var Rte=ye((wir,Ite)=>{Ite.exports=yrt;function yrt(e,t){if(e===t){var r=t[1],n=t[2],i=t[3],a=t[6],o=t[7],s=t[11];e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=r,e[6]=t[9],e[7]=t[13],e[8]=n,e[9]=a,e[11]=t[14],e[12]=i,e[13]=o,e[14]=s}else 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r=t[0],n=t[1],i=t[2],a=t[3],o=t[4],s=t[5],l=t[6],u=t[7],c=t[8],f=t[9],h=t[10],d=t[11],v=t[12],m=t[13],b=t[14],p=t[15];return e[0]=s*(h*p-d*b)-f*(l*p-u*b)+m*(l*d-u*h),e[1]=-(n*(h*p-d*b)-f*(i*p-a*b)+m*(i*d-a*h)),e[2]=n*(l*p-u*b)-s*(i*p-a*b)+m*(i*u-a*l),e[3]=-(n*(l*d-u*h)-s*(i*d-a*h)+f*(i*u-a*l)),e[4]=-(o*(h*p-d*b)-c*(l*p-u*b)+v*(l*d-u*h)),e[5]=r*(h*p-d*b)-c*(i*p-a*b)+v*(i*d-a*h),e[6]=-(r*(l*p-u*b)-o*(i*p-a*b)+v*(i*u-a*l)),e[7]=r*(l*d-u*h)-o*(i*d-a*h)+c*(i*u-a*l),e[8]=o*(f*p-d*m)-c*(s*p-u*m)+v*(s*d-u*f),e[9]=-(r*(f*p-d*m)-c*(n*p-a*m)+v*(n*d-a*f)),e[10]=r*(s*p-u*m)-o*(n*p-a*m)+v*(n*u-a*s),e[11]=-(r*(s*d-u*f)-o*(n*d-a*f)+c*(n*u-a*s)),e[12]=-(o*(f*b-h*m)-c*(s*b-l*m)+v*(s*h-l*f)),e[13]=r*(f*b-h*m)-c*(n*b-i*m)+v*(n*h-i*f),e[14]=-(r*(s*b-l*m)-o*(n*b-i*m)+v*(n*l-i*s)),e[15]=r*(s*h-l*f)-o*(n*h-i*f)+c*(n*l-i*s),e}});var Bte=ye((Sir,Ote)=>{Ote.exports=brt;function brt(e){var t=e[0],r=e[1],n=e[2],i=e[3],a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],f=e[10],h=e[11],d=e[12],v=e[13],m=e[14],b=e[15],p=t*o-r*a,k=t*s-n*a,M=t*l-i*a,T=r*s-n*o,L=r*l-i*o,x=n*l-i*s,C=u*v-c*d,S=u*m-f*d,g=u*b-h*d,P=c*m-f*v,E=c*b-h*v,z=f*b-h*m;return p*z-k*E+M*P+T*g-L*S+x*C}});var Ute=ye((Mir,Nte)=>{Nte.exports=wrt;function wrt(e,t,r){var n=t[0],i=t[1],a=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],f=t[8],h=t[9],d=t[10],v=t[11],m=t[12],b=t[13],p=t[14],k=t[15],M=r[0],T=r[1],L=r[2],x=r[3];return e[0]=M*n+T*s+L*f+x*m,e[1]=M*i+T*l+L*h+x*b,e[2]=M*a+T*u+L*d+x*p,e[3]=M*o+T*c+L*v+x*k,M=r[4],T=r[5],L=r[6],x=r[7],e[4]=M*n+T*s+L*f+x*m,e[5]=M*i+T*l+L*h+x*b,e[6]=M*a+T*u+L*d+x*p,e[7]=M*o+T*c+L*v+x*k,M=r[8],T=r[9],L=r[10],x=r[11],e[8]=M*n+T*s+L*f+x*m,e[9]=M*i+T*l+L*h+x*b,e[10]=M*a+T*u+L*d+x*p,e[11]=M*o+T*c+L*v+x*k,M=r[12],T=r[13],L=r[14],x=r[15],e[12]=M*n+T*s+L*f+x*m,e[13]=M*i+T*l+L*h+x*b,e[14]=M*a+T*u+L*d+x*p,e[15]=M*o+T*c+L*v+x*k,e}});var Gte=ye((Eir,Vte)=>{Vte.exports=Trt;function Trt(e,t,r){var n=r[0],i=r[1],a=r[2],o,s,l,u,c,f,h,d,v,m,b,p;return t===e?(e[12]=t[0]*n+t[4]*i+t[8]*a+t[12],e[13]=t[1]*n+t[5]*i+t[9]*a+t[13],e[14]=t[2]*n+t[6]*i+t[10]*a+t[14],e[15]=t[3]*n+t[7]*i+t[11]*a+t[15]):(o=t[0],s=t[1],l=t[2],u=t[3],c=t[4],f=t[5],h=t[6],d=t[7],v=t[8],m=t[9],b=t[10],p=t[11],e[0]=o,e[1]=s,e[2]=l,e[3]=u,e[4]=c,e[5]=f,e[6]=h,e[7]=d,e[8]=v,e[9]=m,e[10]=b,e[11]=p,e[12]=o*n+c*i+v*a+t[12],e[13]=s*n+f*i+m*a+t[13],e[14]=l*n+h*i+b*a+t[14],e[15]=u*n+d*i+p*a+t[15]),e}});var jte=ye((kir,Hte)=>{Hte.exports=Art;function Art(e,t,r){var n=r[0],i=r[1],a=r[2];return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*i,e[5]=t[5]*i,e[6]=t[6]*i,e[7]=t[7]*i,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}});var Xte=ye((Cir,Wte)=>{Wte.exports=Srt;function Srt(e,t,r,n){var i=n[0],a=n[1],o=n[2],s=Math.sqrt(i*i+a*a+o*o),l,u,c,f,h,d,v,m,b,p,k,M,T,L,x,C,S,g,P,E,z,q,V,G;return Math.abs(s)<1e-6?null:(s=1/s,i*=s,a*=s,o*=s,l=Math.sin(r),u=Math.cos(r),c=1-u,f=t[0],h=t[1],d=t[2],v=t[3],m=t[4],b=t[5],p=t[6],k=t[7],M=t[8],T=t[9],L=t[10],x=t[11],C=i*i*c+u,S=a*i*c+o*l,g=o*i*c-a*l,P=i*a*c-o*l,E=a*a*c+u,z=o*a*c+i*l,q=i*o*c+a*l,V=a*o*c-i*l,G=o*o*c+u,e[0]=f*C+m*S+M*g,e[1]=h*C+b*S+T*g,e[2]=d*C+p*S+L*g,e[3]=v*C+k*S+x*g,e[4]=f*P+m*E+M*z,e[5]=h*P+b*E+T*z,e[6]=d*P+p*E+L*z,e[7]=v*P+k*E+x*z,e[8]=f*q+m*V+M*G,e[9]=h*q+b*V+T*G,e[10]=d*q+p*V+L*G,e[11]=v*q+k*V+x*G,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}});var Yte=ye((Lir,Zte)=>{Zte.exports=Mrt;function Mrt(e,t,r){var n=Math.sin(r),i=Math.cos(r),a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],h=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*i+u*n,e[5]=o*i+c*n,e[6]=s*i+f*n,e[7]=l*i+h*n,e[8]=u*i-a*n,e[9]=c*i-o*n,e[10]=f*i-s*n,e[11]=h*i-l*n,e}});var Jte=ye((Pir,Kte)=>{Kte.exports=Ert;function Ert(e,t,r){var n=Math.sin(r),i=Math.cos(r),a=t[0],o=t[1],s=t[2],l=t[3],u=t[8],c=t[9],f=t[10],h=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*i-u*n,e[1]=o*i-c*n,e[2]=s*i-f*n,e[3]=l*i-h*n,e[8]=a*n+u*i,e[9]=o*n+c*i,e[10]=s*n+f*i,e[11]=l*n+h*i,e}});var Qte=ye((Iir,$te)=>{$te.exports=krt;function krt(e,t,r){var n=Math.sin(r),i=Math.cos(r),a=t[0],o=t[1],s=t[2],l=t[3],u=t[4],c=t[5],f=t[6],h=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*i+u*n,e[1]=o*i+c*n,e[2]=s*i+f*n,e[3]=l*i+h*n,e[4]=u*i-a*n,e[5]=c*i-o*n,e[6]=f*i-s*n,e[7]=h*i-l*n,e}});var tre=ye((Rir,ere)=>{ere.exports=Crt;function Crt(e,t,r){var n,i,a,o=r[0],s=r[1],l=r[2],u=Math.sqrt(o*o+s*s+l*l);return Math.abs(u)<1e-6?null:(u=1/u,o*=u,s*=u,l*=u,n=Math.sin(t),i=Math.cos(t),a=1-i,e[0]=o*o*a+i,e[1]=s*o*a+l*n,e[2]=l*o*a-s*n,e[3]=0,e[4]=o*s*a-l*n,e[5]=s*s*a+i,e[6]=l*s*a+o*n,e[7]=0,e[8]=o*l*a+s*n,e[9]=s*l*a-o*n,e[10]=l*l*a+i,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}});var ire=ye((Dir,rre)=>{rre.exports=Lrt;function Lrt(e,t,r){var n=t[0],i=t[1],a=t[2],o=t[3],s=n+n,l=i+i,u=a+a,c=n*s,f=n*l,h=n*u,d=i*l,v=i*u,m=a*u,b=o*s,p=o*l,k=o*u;return e[0]=1-(d+m),e[1]=f+k,e[2]=h-p,e[3]=0,e[4]=f-k,e[5]=1-(c+m),e[6]=v+b,e[7]=0,e[8]=h+p,e[9]=v-b,e[10]=1-(c+d),e[11]=0,e[12]=r[0],e[13]=r[1],e[14]=r[2],e[15]=1,e}});var are=ye((Fir,nre)=>{nre.exports=Prt;function Prt(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var sre=ye((zir,ore)=>{ore.exports=Irt;function Irt(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}});var ure=ye((qir,lre)=>{lre.exports=Rrt;function Rrt(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=n,e[6]=r,e[7]=0,e[8]=0,e[9]=-r,e[10]=n,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var fre=ye((Oir,cre)=>{cre.exports=Drt;function Drt(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=n,e[1]=0,e[2]=-r,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=r,e[9]=0,e[10]=n,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var dre=ye((Bir,hre)=>{hre.exports=Frt;function Frt(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=n,e[1]=r,e[2]=0,e[3]=0,e[4]=-r,e[5]=n,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var pre=ye((Nir,vre)=>{vre.exports=zrt;function zrt(e,t){var r=t[0],n=t[1],i=t[2],a=t[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,f=n*s,h=i*o,d=i*s,v=i*l,m=a*o,b=a*s,p=a*l;return e[0]=1-f-v,e[1]=c+p,e[2]=h-b,e[3]=0,e[4]=c-p,e[5]=1-u-v,e[6]=d+m,e[7]=0,e[8]=h+b,e[9]=d-m,e[10]=1-u-f,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var mre=ye((Uir,gre)=>{gre.exports=qrt;function qrt(e,t,r,n,i,a,o){var s=1/(r-t),l=1/(i-n),u=1/(a-o);return e[0]=a*2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=a*2*l,e[6]=0,e[7]=0,e[8]=(r+t)*s,e[9]=(i+n)*l,e[10]=(o+a)*u,e[11]=-1,e[12]=0,e[13]=0,e[14]=o*a*2*u,e[15]=0,e}});var _re=ye((Vir,yre)=>{yre.exports=Ort;function Ort(e,t,r,n,i){var a=1/Math.tan(t/2),o=1/(n-i);return e[0]=a/r,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=a,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=(i+n)*o,e[11]=-1,e[12]=0,e[13]=0,e[14]=2*i*n*o,e[15]=0,e}});var bre=ye((Gir,xre)=>{xre.exports=Brt;function Brt(e,t,r,n){var i=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),o=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(o+s),u=2/(i+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=u,e[6]=0,e[7]=0,e[8]=-((o-s)*l*.5),e[9]=(i-a)*u*.5,e[10]=n/(r-n),e[11]=-1,e[12]=0,e[13]=0,e[14]=n*r/(r-n),e[15]=0,e}});var Tre=ye((Hir,wre)=>{wre.exports=Nrt;function Nrt(e,t,r,n,i,a,o){var s=1/(t-r),l=1/(n-i),u=1/(a-o);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*u,e[11]=0,e[12]=(t+r)*s,e[13]=(i+n)*l,e[14]=(o+a)*u,e[15]=1,e}});var Sre=ye((jir,Are)=>{var Urt=jq();Are.exports=Vrt;function Vrt(e,t,r,n){var i,a,o,s,l,u,c,f,h,d,v=t[0],m=t[1],b=t[2],p=n[0],k=n[1],M=n[2],T=r[0],L=r[1],x=r[2];return Math.abs(v-T)<1e-6&&Math.abs(m-L)<1e-6&&Math.abs(b-x)<1e-6?Urt(e):(c=v-T,f=m-L,h=b-x,d=1/Math.sqrt(c*c+f*f+h*h),c*=d,f*=d,h*=d,i=k*h-M*f,a=M*c-p*h,o=p*f-k*c,d=Math.sqrt(i*i+a*a+o*o),d?(d=1/d,i*=d,a*=d,o*=d):(i=0,a=0,o=0),s=f*o-h*a,l=h*i-c*o,u=c*a-f*i,d=Math.sqrt(s*s+l*l+u*u),d?(d=1/d,s*=d,l*=d,u*=d):(s=0,l=0,u=0),e[0]=i,e[1]=s,e[2]=c,e[3]=0,e[4]=a,e[5]=l,e[6]=f,e[7]=0,e[8]=o,e[9]=u,e[10]=h,e[11]=0,e[12]=-(i*v+a*m+o*b),e[13]=-(s*v+l*m+u*b),e[14]=-(c*v+f*m+h*b),e[15]=1,e)}});var Ere=ye((Wir,Mre)=>{Mre.exports=Grt;function Grt(e){return\"mat4(\"+e[0]+\", \"+e[1]+\", \"+e[2]+\", \"+e[3]+\", \"+e[4]+\", \"+e[5]+\", \"+e[6]+\", \"+e[7]+\", \"+e[8]+\", \"+e[9]+\", \"+e[10]+\", \"+e[11]+\", \"+e[12]+\", \"+e[13]+\", \"+e[14]+\", \"+e[15]+\")\"}});var Wq=ye((Xir,kre)=>{kre.exports={create:Mte(),clone:kte(),copy:Lte(),identity:jq(),transpose:Rte(),invert:Fte(),adjoint:qte(),determinant:Bte(),multiply:Ute(),translate:Gte(),scale:jte(),rotate:Xte(),rotateX:Yte(),rotateY:Jte(),rotateZ:Qte(),fromRotation:tre(),fromRotationTranslation:ire(),fromScaling:are(),fromTranslation:sre(),fromXRotation:ure(),fromYRotation:fre(),fromZRotation:dre(),fromQuat:pre(),frustum:mre(),perspective:_re(),perspectiveFromFieldOfView:bre(),ortho:Tre(),lookAt:Sre(),str:Ere()}});var R6=ye(fh=>{\"use strict\";var Hrt=Wq();fh.init2dArray=function(e,t){for(var r=new Array(e),n=0;n<e;n++)r[n]=new Array(t);return r};fh.transposeRagged=function(e){var t=0,r=e.length,n,i;for(n=0;n<r;n++)t=Math.max(t,e[n].length);var a=new Array(t);for(n=0;n<t;n++)for(a[n]=new Array(r),i=0;i<r;i++)a[n][i]=e[i][n];return a};fh.dot=function(e,t){if(!(e.length&&t.length)||e.length!==t.length)return null;var r=e.length,n,i;if(e[0].length)for(n=new Array(r),i=0;i<r;i++)n[i]=fh.dot(e[i],t);else if(t[0].length){var a=fh.transposeRagged(t);for(n=new Array(a.length),i=0;i<a.length;i++)n[i]=fh.dot(e,a[i])}else for(n=0,i=0;i<r;i++)n+=e[i]*t[i];return n};fh.translationMatrix=function(e,t){return[[1,0,e],[0,1,t],[0,0,1]]};fh.rotationMatrix=function(e){var t=e*Math.PI/180;return[[Math.cos(t),-Math.sin(t),0],[Math.sin(t),Math.cos(t),0],[0,0,1]]};fh.rotationXYMatrix=function(e,t,r){return fh.dot(fh.dot(fh.translationMatrix(t,r),fh.rotationMatrix(e)),fh.translationMatrix(-t,-r))};fh.apply3DTransform=function(e){return function(){var t=arguments,r=arguments.length===1?t[0]:[t[0],t[1],t[2]||0];return fh.dot(e,[r[0],r[1],r[2],1]).slice(0,3)}};fh.apply2DTransform=function(e){return function(){var t=arguments;t.length===3&&(t=t[0]);var r=arguments.length===1?t[0]:[t[0],t[1]];return 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.maplibregl-ctrl-icon{background-image:url(\"data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' width='29' height='29' viewBox='0 0 29 29'%3E%3Cpath d='M10 13c-.75 0-1.5.75-1.5 1.5S9.25 16 10 16h9c.75 0 1.5-.75 1.5-1.5S19.75 13 19 13z'/%3E%3C/svg%3E\")}.maplibregl-ctrl button.maplibregl-ctrl-zoom-in .maplibregl-ctrl-icon{background-image:url(\"data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' width='29' height='29' viewBox='0 0 29 29'%3E%3Cpath d='M14.5 8.5c-.75 0-1.5.75-1.5 1.5v3h-3c-.75 0-1.5.75-1.5 1.5S9.25 16 10 16h3v3c0 .75.75 1.5 1.5 1.5S16 19.75 16 19v-3h3c.75 0 1.5-.75 1.5-1.5S19.75 13 19 13h-3v-3c0-.75-.75-1.5-1.5-1.5'/%3E%3C/svg%3E\")}}.maplibregl-ctrl button.maplibregl-ctrl-fullscreen .maplibregl-ctrl-icon{background-image:url(\"data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' width='29' height='29' fill='%23333' viewBox='0 0 29 29'%3E%3Cpath d='M24 16v5.5c0 1.75-.75 2.5-2.5 2.5H16v-1l3-1.5-4-5.5 1-1 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d='M24 16v5.5c0 1.75-.75 2.5-2.5 2.5H16v-1l3-1.5-4-5.5 1-1 5.5 4 1.5-3zM6 16l1.5 3 5.5-4 1 1-4 5.5 3 1.5v1H7.5C5.75 24 5 23.25 5 21.5V16zm7-11v1l-3 1.5 4 5.5-1 1-5.5-4L6 13H5V7.5C5 5.75 5.75 5 7.5 5zm11 2.5c0-1.75-.75-2.5-2.5-2.5H16v1l3 1.5-4 5.5 1 1 5.5-4 1.5 3h1z'/%3E%3C/svg%3E\")}.maplibregl-ctrl button.maplibregl-ctrl-shrink .maplibregl-ctrl-icon{background-image:url(\"data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' width='29' height='29' fill='%23fff' viewBox='0 0 29 29'%3E%3Cpath d='M18.5 16c-1.75 0-2.5.75-2.5 2.5V24h1l1.5-3 5.5 4 1-1-4-5.5 3-1.5v-1zM13 18.5c0-1.75-.75-2.5-2.5-2.5H5v1l3 1.5L4 24l1 1 5.5-4 1.5 3h1zm3-8c0 1.75.75 2.5 2.5 2.5H24v-1l-3-1.5L25 5l-1-1-5.5 4L17 5h-1zM10.5 13c1.75 0 2.5-.75 2.5-2.5V5h-1l-1.5 3L5 4 4 5l4 5.5L5 12v1z'/%3E%3C/svg%3E\")}}@media (forced-colors:active) and (prefers-color-scheme:light){.maplibregl-ctrl button.maplibregl-ctrl-fullscreen 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vs(t,r,\"M\"+n+\",0L0,\"+n+\"L-\"+n+\",0L0,-\"+n+\"Z\")}},cross:{n:3,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.4,2),i=ca(e*1.2,2);return vs(t,r,\"M\"+i+\",\"+n+\"H\"+n+\"V\"+i+\"H-\"+n+\"V\"+n+\"H-\"+i+\"V-\"+n+\"H-\"+n+\"V-\"+i+\"H\"+n+\"V-\"+n+\"H\"+i+\"Z\")}},x:{n:4,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.8/Moe,2),i=\"l\"+n+\",\"+n,a=\"l\"+n+\",-\"+n,o=\"l-\"+n+\",-\"+n,s=\"l-\"+n+\",\"+n;return vs(t,r,\"M0,\"+n+i+a+o+a+o+s+o+s+i+s+i+\"Z\")}},\"triangle-up\":{n:5,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*2/f_,2),i=ca(e/2,2),a=ca(e,2);return vs(t,r,\"M-\"+n+\",\"+i+\"H\"+n+\"L0,-\"+a+\"Z\")}},\"triangle-down\":{n:6,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*2/f_,2),i=ca(e/2,2),a=ca(e,2);return vs(t,r,\"M-\"+n+\",-\"+i+\"H\"+n+\"L0,\"+a+\"Z\")}},\"triangle-left\":{n:7,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*2/f_,2),i=ca(e/2,2),a=ca(e,2);return vs(t,r,\"M\"+i+\",-\"+n+\"V\"+n+\"L-\"+a+\",0Z\")}},\"triangle-right\":{n:8,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*2/f_,2),i=ca(e/2,2),a=ca(e,2);return vs(t,r,\"M-\"+i+\",-\"+n+\"V\"+n+\"L\"+a+\",0Z\")}},\"triangle-ne\":{n:9,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.6,2),i=ca(e*1.2,2);return vs(t,r,\"M-\"+i+\",-\"+n+\"H\"+n+\"V\"+i+\"Z\")}},\"triangle-se\":{n:10,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.6,2),i=ca(e*1.2,2);return vs(t,r,\"M\"+n+\",-\"+i+\"V\"+n+\"H-\"+i+\"Z\")}},\"triangle-sw\":{n:11,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.6,2),i=ca(e*1.2,2);return vs(t,r,\"M\"+i+\",\"+n+\"H-\"+n+\"V-\"+i+\"Z\")}},\"triangle-nw\":{n:12,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.6,2),i=ca(e*1.2,2);return vs(t,r,\"M-\"+n+\",\"+i+\"V-\"+n+\"H\"+i+\"Z\")}},pentagon:{n:13,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.951,2),i=ca(e*.588,2),a=ca(-e,2),o=ca(e*-.309,2),s=ca(e*.809,2);return vs(t,r,\"M\"+n+\",\"+o+\"L\"+i+\",\"+s+\"H-\"+i+\"L-\"+n+\",\"+o+\"L0,\"+a+\"Z\")}},hexagon:{n:14,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e/2,2),a=ca(e*f_/2,2);return vs(t,r,\"M\"+a+\",-\"+i+\"V\"+i+\"L0,\"+n+\"L-\"+a+\",\"+i+\"V-\"+i+\"L0,-\"+n+\"Z\")}},hexagon2:{n:15,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e/2,2),a=ca(e*f_/2,2);return vs(t,r,\"M-\"+i+\",\"+a+\"H\"+i+\"L\"+n+\",0L\"+i+\",-\"+a+\"H-\"+i+\"L-\"+n+\",0Z\")}},octagon:{n:16,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.924,2),i=ca(e*.383,2);return vs(t,r,\"M-\"+i+\",-\"+n+\"H\"+i+\"L\"+n+\",-\"+i+\"V\"+i+\"L\"+i+\",\"+n+\"H-\"+i+\"L-\"+n+\",\"+i+\"V-\"+i+\"Z\")}},star:{n:17,f:function(e,t,r){if(ds(t))return hs;var n=e*1.4,i=ca(n*.225,2),a=ca(n*.951,2),o=ca(n*.363,2),s=ca(n*.588,2),l=ca(-n,2),u=ca(n*-.309,2),c=ca(n*.118,2),f=ca(n*.809,2),h=ca(n*.382,2);return vs(t,r,\"M\"+i+\",\"+u+\"H\"+a+\"L\"+o+\",\"+c+\"L\"+s+\",\"+f+\"L0,\"+h+\"L-\"+s+\",\"+f+\"L-\"+o+\",\"+c+\"L-\"+a+\",\"+u+\"H-\"+i+\"L0,\"+l+\"Z\")}},hexagram:{n:18,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.66,2),i=ca(e*.38,2),a=ca(e*.76,2);return vs(t,r,\"M-\"+a+\",0l-\"+i+\",-\"+n+\"h\"+a+\"l\"+i+\",-\"+n+\"l\"+i+\",\"+n+\"h\"+a+\"l-\"+i+\",\"+n+\"l\"+i+\",\"+n+\"h-\"+a+\"l-\"+i+\",\"+n+\"l-\"+i+\",-\"+n+\"h-\"+a+\"Z\")}},\"star-triangle-up\":{n:19,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*f_*.8,2),i=ca(e*.8,2),a=ca(e*1.6,2),o=ca(e*4,2),s=\"A \"+o+\",\"+o+\" 0 0 1 \";return vs(t,r,\"M-\"+n+\",\"+i+s+n+\",\"+i+s+\"0,-\"+a+s+\"-\"+n+\",\"+i+\"Z\")}},\"star-triangle-down\":{n:20,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*f_*.8,2),i=ca(e*.8,2),a=ca(e*1.6,2),o=ca(e*4,2),s=\"A \"+o+\",\"+o+\" 0 0 1 \";return vs(t,r,\"M\"+n+\",-\"+i+s+\"-\"+n+\",-\"+i+s+\"0,\"+a+s+n+\",-\"+i+\"Z\")}},\"star-square\":{n:21,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.1,2),i=ca(e*2,2),a=\"A \"+i+\",\"+i+\" 0 0 1 \";return vs(t,r,\"M-\"+n+\",-\"+n+a+\"-\"+n+\",\"+n+a+n+\",\"+n+a+n+\",-\"+n+a+\"-\"+n+\",-\"+n+\"Z\")}},\"star-diamond\":{n:22,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.4,2),i=ca(e*1.9,2),a=\"A \"+i+\",\"+i+\" 0 0 1 \";return vs(t,r,\"M-\"+n+\",0\"+a+\"0,\"+n+a+n+\",0\"+a+\"0,-\"+n+a+\"-\"+n+\",0Z\")}},\"diamond-tall\":{n:23,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*.7,2),i=ca(e*1.4,2);return vs(t,r,\"M0,\"+i+\"L\"+n+\",0L0,-\"+i+\"L-\"+n+\",0Z\")}},\"diamond-wide\":{n:24,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.4,2),i=ca(e*.7,2);return vs(t,r,\"M0,\"+i+\"L\"+n+\",0L0,-\"+i+\"L-\"+n+\",0Z\")}},hourglass:{n:25,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",\"+n+\"H-\"+n+\"L\"+n+\",-\"+n+\"H-\"+n+\"Z\")},noDot:!0},bowtie:{n:26,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",\"+n+\"V-\"+n+\"L-\"+n+\",\"+n+\"V-\"+n+\"Z\")},noDot:!0},\"circle-cross\":{n:27,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M0,\"+n+\"V-\"+n+\"M\"+n+\",0H-\"+n+\"M\"+n+\",0A\"+n+\",\"+n+\" 0 1,1 0,-\"+n+\"A\"+n+\",\"+n+\" 0 0,1 \"+n+\",0Z\")},needLine:!0,noDot:!0},\"circle-x\":{n:28,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e/Moe,2);return vs(t,r,\"M\"+i+\",\"+i+\"L-\"+i+\",-\"+i+\"M\"+i+\",-\"+i+\"L-\"+i+\",\"+i+\"M\"+n+\",0A\"+n+\",\"+n+\" 0 1,1 0,-\"+n+\"A\"+n+\",\"+n+\" 0 0,1 \"+n+\",0Z\")},needLine:!0,noDot:!0},\"square-cross\":{n:29,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M0,\"+n+\"V-\"+n+\"M\"+n+\",0H-\"+n+\"M\"+n+\",\"+n+\"H-\"+n+\"V-\"+n+\"H\"+n+\"Z\")},needLine:!0,noDot:!0},\"square-x\":{n:30,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",\"+n+\"L-\"+n+\",-\"+n+\"M\"+n+\",-\"+n+\"L-\"+n+\",\"+n+\"M\"+n+\",\"+n+\"H-\"+n+\"V-\"+n+\"H\"+n+\"Z\")},needLine:!0,noDot:!0},\"diamond-cross\":{n:31,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.3,2);return vs(t,r,\"M\"+n+\",0L0,\"+n+\"L-\"+n+\",0L0,-\"+n+\"ZM0,-\"+n+\"V\"+n+\"M-\"+n+\",0H\"+n)},needLine:!0,noDot:!0},\"diamond-x\":{n:32,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.3,2),i=ca(e*.65,2);return vs(t,r,\"M\"+n+\",0L0,\"+n+\"L-\"+n+\",0L0,-\"+n+\"ZM-\"+i+\",-\"+i+\"L\"+i+\",\"+i+\"M-\"+i+\",\"+i+\"L\"+i+\",-\"+i)},needLine:!0,noDot:!0},\"cross-thin\":{n:33,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.4,2);return vs(t,r,\"M0,\"+n+\"V-\"+n+\"M\"+n+\",0H-\"+n)},needLine:!0,noDot:!0,noFill:!0},\"x-thin\":{n:34,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",\"+n+\"L-\"+n+\",-\"+n+\"M\"+n+\",-\"+n+\"L-\"+n+\",\"+n)},needLine:!0,noDot:!0,noFill:!0},asterisk:{n:35,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*.85,2);return vs(t,r,\"M0,\"+n+\"V-\"+n+\"M\"+n+\",0H-\"+n+\"M\"+i+\",\"+i+\"L-\"+i+\",-\"+i+\"M\"+i+\",-\"+i+\"L-\"+i+\",\"+i)},needLine:!0,noDot:!0,noFill:!0},hash:{n:36,f:function(e,t,r){if(ds(t))return hs;var n=ca(e/2,2),i=ca(e,2);return vs(t,r,\"M\"+n+\",\"+i+\"V-\"+i+\"M\"+(n-i)+\",-\"+i+\"V\"+i+\"M\"+i+\",\"+n+\"H-\"+i+\"M-\"+i+\",\"+(n-i)+\"H\"+i)},needLine:!0,noFill:!0},\"y-up\":{n:37,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*1.6,2),a=ca(e*.8,2);return vs(t,r,\"M-\"+n+\",\"+a+\"L0,0M\"+n+\",\"+a+\"L0,0M0,-\"+i+\"L0,0\")},needLine:!0,noDot:!0,noFill:!0},\"y-down\":{n:38,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*1.6,2),a=ca(e*.8,2);return vs(t,r,\"M-\"+n+\",-\"+a+\"L0,0M\"+n+\",-\"+a+\"L0,0M0,\"+i+\"L0,0\")},needLine:!0,noDot:!0,noFill:!0},\"y-left\":{n:39,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*1.6,2),a=ca(e*.8,2);return vs(t,r,\"M\"+a+\",\"+n+\"L0,0M\"+a+\",-\"+n+\"L0,0M-\"+i+\",0L0,0\")},needLine:!0,noDot:!0,noFill:!0},\"y-right\":{n:40,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.2,2),i=ca(e*1.6,2),a=ca(e*.8,2);return vs(t,r,\"M-\"+a+\",\"+n+\"L0,0M-\"+a+\",-\"+n+\"L0,0M\"+i+\",0L0,0\")},needLine:!0,noDot:!0,noFill:!0},\"line-ew\":{n:41,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.4,2);return vs(t,r,\"M\"+n+\",0H-\"+n)},needLine:!0,noDot:!0,noFill:!0},\"line-ns\":{n:42,f:function(e,t,r){if(ds(t))return hs;var n=ca(e*1.4,2);return vs(t,r,\"M0,\"+n+\"V-\"+n)},needLine:!0,noDot:!0,noFill:!0},\"line-ne\":{n:43,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",-\"+n+\"L-\"+n+\",\"+n)},needLine:!0,noDot:!0,noFill:!0},\"line-nw\":{n:44,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2);return vs(t,r,\"M\"+n+\",\"+n+\"L-\"+n+\",-\"+n)},needLine:!0,noDot:!0,noFill:!0},\"arrow-up\":{n:45,f:function(e,t,r){if(ds(t))return hs;var n=ca(e,2),i=ca(e*2,2);return 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c=s[u],f=c[0],h=a,d=o;if(f===\"M\"||f===\"L\")a=+c[1],o=+c[2];else if(f===\"m\"||f===\"l\")a+=+c[1],o+=+c[2];else if(f===\"H\")a=+c[1];else if(f===\"h\")a+=+c[1];else if(f===\"V\")o=+c[1];else if(f===\"v\")o+=+c[1];else if(f===\"A\"){a=+c[1],o=+c[2];var v=n(i,[+c[6],+c[7]]);c[6]=v[0],c[7]=v[1],c[3]=+c[3]+e}(f===\"H\"||f===\"V\")&&(f=\"L\"),(f===\"h\"||f===\"v\")&&(f=\"l\"),(f===\"m\"||f===\"l\")&&(a-=h,o-=d);var m=n(i,[a,o]);(f===\"H\"||f===\"V\")&&(f=\"L\"),(f===\"M\"||f===\"L\"||f===\"m\"||f===\"l\")&&(c[1]=m[0],c[2]=m[1]),c[0]=f,l+=c[0]+c.slice(1).join(\",\")}return koe=l,l}});var So=ye((Sar,$oe)=>{\"use strict\";var dd=Fa(),Du=Pr(),ist=Du.numberFormat,Fb=Eo(),$O=fd(),mL=Oa(),Jd=ka(),nst=tc(),cM=Du.strTranslate,yL=Zl(),ast=Wp(),ost=Dh(),sst=ost.LINE_SPACING,Voe=Z1().DESELECTDIM,lst=Ru(),ust=U3(),cst=ip().appendArrayPointValue,Ta=$oe.exports={};Ta.font=function(e,t){var r=t.variant,n=t.style,i=t.weight,a=t.color,o=t.size,s=t.family,l=t.shadow,u=t.lineposition,c=t.textcase;s&&e.style(\"font-family\",s),o+1&&e.style(\"font-size\",o+\"px\"),a&&e.call(Jd.fill,a),i&&e.style(\"font-weight\",i),n&&e.style(\"font-style\",n),r&&e.style(\"font-variant\",r),c&&e.style(\"text-transform\",XO(hst(c))),l&&e.style(\"text-shadow\",l===\"auto\"?yL.makeTextShadow(Jd.contrast(a)):XO(l)),u&&e.style(\"text-decoration-line\",XO(dst(u)))};function XO(e){return e===\"none\"?void 0:e}var fst={normal:\"none\",lower:\"lowercase\",upper:\"uppercase\",\"word caps\":\"capitalize\"};function hst(e){return fst[e]}function dst(e){return e.replace(\"under\",\"underline\").replace(\"over\",\"overline\").replace(\"through\",\"line-through\").split(\"+\").join(\" 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a=i[0].trace,o=a.xcalendar,s=a.ycalendar,l=mL.traceIs(a,\"bar-like\")?\".bartext\":\".point,.textpoint\";e.selectAll(l).each(function(u){Ta.hideOutsideRangePoint(u,dd.select(this),r,n,o,s)})})}};Ta.crispRound=function(e,t,r){return!t||!Fb(t)?r||0:e._context.staticPlot?t:t<1?1:Math.round(t)};Ta.singleLineStyle=function(e,t,r,n,i){t.style(\"fill\",\"none\");var a=(((e||[])[0]||{}).trace||{}).line||{},o=r||a.width||0,s=i||a.dash||\"\";Jd.stroke(t,n||a.color),Ta.dashLine(t,s,o)};Ta.lineGroupStyle=function(e,t,r,n){e.style(\"fill\",\"none\").each(function(i){var a=(((i||[])[0]||{}).trace||{}).line||{},o=t||a.width||0,s=n||a.dash||\"\";dd.select(this).call(Jd.stroke,r||a.color).call(Ta.dashLine,s,o)})};Ta.dashLine=function(e,t,r){r=+r||0,t=Ta.dashStyle(t,r),e.style({\"stroke-dasharray\":t,\"stroke-width\":r+\"px\"})};Ta.dashStyle=function(e,t){t=+t||1;var r=Math.max(t,3);return e===\"solid\"?e=\"\":e===\"dot\"?e=r+\"px,\"+r+\"px\":e===\"dash\"?e=3*r+\"px,\"+3*r+\"px\":e===\"longdash\"?e=5*r+\"px,\"+5*r+\"px\":e===\"dashdot\"?e=3*r+\"px,\"+r+\"px,\"+r+\"px,\"+r+\"px\":e===\"longdashdot\"&&(e=5*r+\"px,\"+2*r+\"px,\"+r+\"px,\"+2*r+\"px\"),e};function Goe(e,t,r,n){var i=t.fillpattern,a=t.fillgradient,o=Ta.getPatternAttr,s=i&&(o(i.shape,0,\"\")||o(i.path,0,\"\"));if(s){var l=o(i.bgcolor,0,null),u=o(i.fgcolor,0,null),c=i.fgopacity,f=o(i.size,0,8),h=o(i.solidity,0,.3),d=t.uid;Ta.pattern(e,\"point\",r,d,s,f,h,void 0,i.fillmode,l,u,c)}else if(a&&a.type!==\"none\"){var v=a.type,m=\"scatterfill-\"+t.uid;if(n&&(m=\"legendfill-\"+t.uid),!n&&(a.start!==void 0||a.stop!==void 0)){var b,p;v===\"horizontal\"?(b={x:a.start,y:0},p={x:a.stop,y:0}):v===\"vertical\"&&(b={x:0,y:a.start},p={x:0,y:a.stop}),b.x=t._xA.c2p(b.x===void 0?t._extremes.x.min[0].val:b.x,!0),b.y=t._yA.c2p(b.y===void 0?t._extremes.y.min[0].val:b.y,!0),p.x=t._xA.c2p(p.x===void 0?t._extremes.x.max[0].val:p.x,!0),p.y=t._yA.c2p(p.y===void 0?t._extremes.y.max[0].val:p.y,!0),e.call(Woe,r,m,\"linear\",a.colorscale,\"fill\",b,p,!0,!1)}else v===\"horizontal\"&&(v=v+\"reversed\"),e.call(Ta.gradient,r,m,v,a.colorscale,\"fill\")}else t.fillcolor&&e.call(Jd.fill,t.fillcolor)}Ta.singleFillStyle=function(e,t){var r=dd.select(e.node()),n=r.data(),i=((n[0]||[])[0]||{}).trace||{};Goe(e,i,t,!1)};Ta.fillGroupStyle=function(e,t,r){e.style(\"stroke-width\",0).each(function(n){var i=dd.select(this);n[0].trace&&Goe(i,n[0].trace,t,r)})};var Roe=Ioe();Ta.symbolNames=[];Ta.symbolFuncs=[];Ta.symbolBackOffs=[];Ta.symbolNeedLines={};Ta.symbolNoDot={};Ta.symbolNoFill={};Ta.symbolList=[];Object.keys(Roe).forEach(function(e){var t=Roe[e],r=t.n;Ta.symbolList.push(r,String(r),e,r+100,String(r+100),e+\"-open\"),Ta.symbolNames[r]=e,Ta.symbolFuncs[r]=t.f,Ta.symbolBackOffs[r]=t.backoff||0,t.needLine&&(Ta.symbolNeedLines[r]=!0),t.noDot?Ta.symbolNoDot[r]=!0:Ta.symbolList.push(r+200,String(r+200),e+\"-dot\",r+300,String(r+300),e+\"-open-dot\"),t.noFill&&(Ta.symbolNoFill[r]=!0)});var vst=Ta.symbolNames.length,pst=\"M0,0.5L0.5,0L0,-0.5L-0.5,0Z\";Ta.symbolNumber=function(e){if(Fb(e))e=+e;else if(typeof e==\"string\"){var t=0;e.indexOf(\"-open\")>0&&(t=100,e=e.replace(\"-open\",\"\")),e.indexOf(\"-dot\")>0&&(t+=200,e=e.replace(\"-dot\",\"\")),e=Ta.symbolNames.indexOf(e),e>=0&&(e+=t)}return e%100>=vst||e>=400?0:Math.floor(Math.max(e,0))};function Hoe(e,t,r,n){var i=e%100;return Ta.symbolFuncs[i](t,r,n)+(e>=200?pst:\"\")}var Doe=ist(\"~f\"),joe={radial:{type:\"radial\"},radialreversed:{type:\"radial\",reversed:!0},horizontal:{type:\"linear\",start:{x:1,y:0},stop:{x:0,y:0}},horizontalreversed:{type:\"linear\",start:{x:1,y:0},stop:{x:0,y:0},reversed:!0},vertical:{type:\"linear\",start:{x:0,y:1},stop:{x:0,y:0}},verticalreversed:{type:\"linear\",start:{x:0,y:1},stop:{x:0,y:0},reversed:!0}};Ta.gradient=function(e,t,r,n,i,a){var o=joe[n];return Woe(e,t,r,o.type,i,a,o.start,o.stop,!1,o.reversed)};function Woe(e,t,r,n,i,a,o,s,l,u){var c=i.length,f;n===\"linear\"?f={node:\"linearGradient\",attrs:{x1:o.x,y1:o.y,x2:s.x,y2:s.y,gradientUnits:l?\"userSpaceOnUse\":\"objectBoundingBox\"},reversed:u}:n===\"radial\"&&(f={node:\"radialGradient\",reversed:u});for(var h=new Array(c),d=0;d<c;d++)f.reversed?h[c-1-d]=[Doe((1-i[d][0])*100),i[d][1]]:h[d]=[Doe(i[d][0]*100),i[d][1]];var v=t._fullLayout,m=\"g\"+v._uid+\"-\"+r,b=v._defs.select(\".gradients\").selectAll(\"#\"+m).data([n+h.join(\";\")],Du.identity);b.exit().remove(),b.enter().append(f.node).each(function(){var p=dd.select(this);f.attrs&&p.attr(f.attrs),p.attr(\"id\",m);var k=p.selectAll(\"stop\").data(h);k.exit().remove(),k.enter().append(\"stop\"),k.each(function(M){var T=$O(M[1]);dd.select(this).attr({offset:M[0]+\"%\",\"stop-color\":Jd.tinyRGB(T),\"stop-opacity\":T.getAlpha()})})}),e.style(a,QO(m,t)).style(a+\"-opacity\",null),e.classed(\"gradient_filled\",!0)}Ta.pattern=function(e,t,r,n,i,a,o,s,l,u,c,f){var h=t===\"legend\";s&&(l===\"overlay\"?(u=s,c=Jd.contrast(u)):(u=void 0,c=s));var d=r._fullLayout,v=\"p\"+d._uid+\"-\"+n,m,b,p=function(q,V,G,Z,j){return Z+(j-Z)*(q-V)/(G-V)},k,M,T,L,x={},C=$O(c),S=Jd.tinyRGB(C),g=C.getAlpha(),P=f*g;switch(i){case\"/\":m=a*Math.sqrt(2),b=a*Math.sqrt(2),k=\"M-\"+m/4+\",\"+b/4+\"l\"+m/2+\",-\"+b/2+\"M0,\"+b+\"L\"+m+\",0M\"+m/4*3+\",\"+b/4*5+\"l\"+m/2+\",-\"+b/2,M=o*a,L=\"path\",x={d:k,opacity:P,stroke:S,\"stroke-width\":M+\"px\"};break;case\"\\\\\":m=a*Math.sqrt(2),b=a*Math.sqrt(2),k=\"M\"+m/4*3+\",-\"+b/4+\"l\"+m/2+\",\"+b/2+\"M0,0L\"+m+\",\"+b+\"M-\"+m/4+\",\"+b/4*3+\"l\"+m/2+\",\"+b/2,M=o*a,L=\"path\",x={d:k,opacity:P,stroke:S,\"stroke-width\":M+\"px\"};break;case\"x\":m=a*Math.sqrt(2),b=a*Math.sqrt(2),k=\"M-\"+m/4+\",\"+b/4+\"l\"+m/2+\",-\"+b/2+\"M0,\"+b+\"L\"+m+\",0M\"+m/4*3+\",\"+b/4*5+\"l\"+m/2+\",-\"+b/2+\"M\"+m/4*3+\",-\"+b/4+\"l\"+m/2+\",\"+b/2+\"M0,0L\"+m+\",\"+b+\"M-\"+m/4+\",\"+b/4*3+\"l\"+m/2+\",\"+b/2,M=a-a*Math.sqrt(1-o),L=\"path\",x={d:k,opacity:P,stroke:S,\"stroke-width\":M+\"px\"};break;case\"|\":m=a,b=a,L=\"path\",k=\"M\"+m/2+\",0L\"+m/2+\",\"+b,M=o*a,L=\"path\",x={d:k,opacity:P,stroke:S,\"stroke-width\":M+\"px\"};break;case\"-\":m=a,b=a,L=\"path\",k=\"M0,\"+b/2+\"L\"+m+\",\"+b/2,M=o*a,L=\"path\",x={d:k,opacity:P,stroke:S,\"stroke-width\":M+\"px\"};break;case\"+\":m=a,b=a,L=\"path\",k=\"M\"+m/2+\",0L\"+m/2+\",\"+b+\"M0,\"+b/2+\"L\"+m+\",\"+b/2,M=a-a*Math.sqrt(1-o),L=\"path\",x={d:k,opacity:P,stroke:S,\"stroke-width\":M+\"px\"};break;case\".\":m=a,b=a,o<Math.PI/4?T=Math.sqrt(o*a*a/Math.PI):T=p(o,Math.PI/4,1,a/2,a/Math.sqrt(2)),L=\"circle\",x={cx:m/2,cy:b/2,r:T,opacity:P,fill:S};break;default:m=a,b=a,L=\"path\",x={d:i,opacity:P,fill:S};break}var E=[i||\"noSh\",u||\"noBg\",c||\"noFg\",a,o].join(\";\"),z=d._defs.select(\".patterns\").selectAll(\"#\"+v).data([E],Du.identity);z.exit().remove(),z.enter().append(\"pattern\").each(function(){var q=dd.select(this);if(q.attr({id:v,width:m+\"px\",height:b+\"px\",patternUnits:\"userSpaceOnUse\",patternTransform:h?\"scale(0.8)\":\"\"}),u){var V=$O(u),G=Jd.tinyRGB(V),Z=V.getAlpha(),j=q.selectAll(\"rect\").data([0]);j.exit().remove(),j.enter().append(\"rect\").attr({width:m+\"px\",height:b+\"px\",fill:G,\"fill-opacity\":Z})}var N=q.selectAll(L).data([0]);N.exit().remove(),N.enter().append(L).attr(x)}),e.style(\"fill\",QO(v,r)).style(\"fill-opacity\",null),e.classed(\"pattern_filled\",!0)};Ta.initGradients=function(e){var t=e._fullLayout,r=Du.ensureSingle(t._defs,\"g\",\"gradients\");r.selectAll(\"linearGradient,radialGradient\").remove(),dd.select(e).selectAll(\".gradient_filled\").classed(\"gradient_filled\",!1)};Ta.initPatterns=function(e){var t=e._fullLayout,r=Du.ensureSingle(t._defs,\"g\",\"patterns\");r.selectAll(\"pattern\").remove(),dd.select(e).selectAll(\".pattern_filled\").classed(\"pattern_filled\",!1)};Ta.getPatternAttr=function(e,t,r){return e&&Du.isArrayOrTypedArray(e)?t<e.length?e[t]:r:e};Ta.pointStyle=function(e,t,r,n){if(e.size()){var i=Ta.makePointStyleFns(t);e.each(function(a){Ta.singlePointStyle(a,dd.select(this),t,i,r,n)})}};Ta.singlePointStyle=function(e,t,r,n,i,a){var o=r.marker,s=o.line;if(a&&a.i>=0&&e.i===void 0&&(e.i=a.i),t.style(\"opacity\",n.selectedOpacityFn?n.selectedOpacityFn(e):e.mo===void 0?o.opacity:e.mo),n.ms2mrc){var l;e.ms===\"various\"||o.size===\"various\"?l=3:l=n.ms2mrc(e.ms),e.mrc=l,n.selectedSizeFn&&(l=e.mrc=n.selectedSizeFn(e));var u=Ta.symbolNumber(e.mx||o.symbol)||0;e.om=u%200>=100;var c=tB(e,r),f=eB(e,r);t.attr(\"d\",Hoe(u,l,c,f))}var h=!1,d,v,m;if(e.so)m=s.outlierwidth,v=s.outliercolor,d=o.outliercolor;else{var b=(s||{}).width;m=(e.mlw+1||b+1||(e.trace?(e.trace.marker.line||{}).width:0)+1)-1||0,\"mlc\"in e?v=e.mlcc=n.lineScale(e.mlc):Du.isArrayOrTypedArray(s.color)?v=Jd.defaultLine:v=s.color,Du.isArrayOrTypedArray(o.color)&&(d=Jd.defaultLine,h=!0),\"mc\"in e?d=e.mcc=n.markerScale(e.mc):d=o.color||o.colors||\"rgba(0,0,0,0)\",n.selectedColorFn&&(d=n.selectedColorFn(e))}let p=e.mld||(s||{}).dash;if(p&&Ta.dashLine(t,p,m),e.om)t.call(Jd.stroke,d).style({\"stroke-width\":(m||1)+\"px\",fill:\"none\"});else{t.style(\"stroke-width\",(e.isBlank?0:m)+\"px\");var k=o.gradient,M=e.mgt;M?h=!0:M=k&&k.type,Du.isArrayOrTypedArray(M)&&(M=M[0],joe[M]||(M=0));var T=o.pattern,L=Ta.getPatternAttr,x=T&&(L(T.shape,e.i,\"\")||L(T.path,e.i,\"\"));if(M&&M!==\"none\"){var C=e.mgc;C?h=!0:C=k.color;var S=r.uid;h&&(S+=\"-\"+e.i),Ta.gradient(t,i,S,M,[[0,C],[1,d]],\"fill\")}else if(x){var g=!1,P=T.fgcolor;!P&&a&&a.color&&(P=a.color,g=!0);var E=L(P,e.i,a&&a.color||null),z=L(T.bgcolor,e.i,null),q=T.fgopacity,V=L(T.size,e.i,8),G=L(T.solidity,e.i,.3);g=g||e.mcc||Du.isArrayOrTypedArray(T.shape)||Du.isArrayOrTypedArray(T.path)||Du.isArrayOrTypedArray(T.bgcolor)||Du.isArrayOrTypedArray(T.fgcolor)||Du.isArrayOrTypedArray(T.size)||Du.isArrayOrTypedArray(T.solidity);var Z=r.uid;g&&(Z+=\"-\"+e.i),Ta.pattern(t,\"point\",i,Z,x,V,G,e.mcc,T.fillmode,z,E,q)}else Du.isArrayOrTypedArray(d)?Jd.fill(t,d[e.i]):Jd.fill(t,d);m&&Jd.stroke(t,v)}};Ta.makePointStyleFns=function(e){var t={},r=e.marker;return t.markerScale=Ta.tryColorscale(r,\"\"),t.lineScale=Ta.tryColorscale(r,\"line\"),mL.traceIs(e,\"symbols\")&&(t.ms2mrc=lst.isBubble(e)?ust(e):function(){return(r.size||6)/2}),e.selectedpoints&&Du.extendFlat(t,Ta.makeSelectedPointStyleFns(e)),t};Ta.makeSelectedPointStyleFns=function(e){var t={},r=e.selected||{},n=e.unselected||{},i=e.marker||{},a=r.marker||{},o=n.marker||{},s=i.opacity,l=a.opacity,u=o.opacity,c=l!==void 0,f=u!==void 0;(Du.isArrayOrTypedArray(s)||c||f)&&(t.selectedOpacityFn=function(T){var L=T.mo===void 0?i.opacity:T.mo;return T.selected?c?l:L:f?u:Voe*L});var h=i.color,d=a.color,v=o.color;(d||v)&&(t.selectedColorFn=function(T){var L=T.mcc||h;return T.selected?d||L:v||L});var m=i.size,b=a.size,p=o.size,k=b!==void 0,M=p!==void 0;return mL.traceIs(e,\"symbols\")&&(k||M)&&(t.selectedSizeFn=function(T){var L=T.mrc||m/2;return T.selected?k?b/2:L:M?p/2:L}),t};Ta.makeSelectedTextStyleFns=function(e){var t={},r=e.selected||{},n=e.unselected||{},i=e.textfont||{},a=r.textfont||{},o=n.textfont||{},s=i.color,l=a.color,u=o.color;return t.selectedTextColorFn=function(c){var f=c.tc||s;return c.selected?l||f:u||(l?f:Jd.addOpacity(f,Voe))},t};Ta.selectedPointStyle=function(e,t){if(!(!e.size()||!t.selectedpoints)){var r=Ta.makeSelectedPointStyleFns(t),n=t.marker||{},i=[];r.selectedOpacityFn&&i.push(function(a,o){a.style(\"opacity\",r.selectedOpacityFn(o))}),r.selectedColorFn&&i.push(function(a,o){Jd.fill(a,r.selectedColorFn(o))}),r.selectedSizeFn&&i.push(function(a,o){var s=o.mx||n.symbol||0,l=r.selectedSizeFn(o);a.attr(\"d\",Hoe(Ta.symbolNumber(s),l,tB(o,t),eB(o,t))),o.mrc2=l}),i.length&&e.each(function(a){for(var o=dd.select(this),s=0;s<i.length;s++)i[s](o,a)})}};Ta.tryColorscale=function(e,t){var r=t?Du.nestedProperty(e,t).get():e;if(r){var n=r.color;if((r.colorscale||r._colorAx)&&Du.isArrayOrTypedArray(n))return nst.makeColorScaleFuncFromTrace(r)}return Du.identity};var ZO={start:1,end:-1,middle:0,bottom:1,top:-1};function Xoe(e,t,r,n,i){var a=dd.select(e.node().parentNode),o=t.indexOf(\"top\")!==-1?\"top\":t.indexOf(\"bottom\")!==-1?\"bottom\":\"middle\",s=t.indexOf(\"left\")!==-1?\"end\":t.indexOf(\"right\")!==-1?\"start\":\"middle\",l=n?n/.8+1:0,u=(yL.lineCount(e)-1)*sst+1,c=ZO[s]*l,f=r*.75+ZO[o]*l+(ZO[o]-1)*u*r/2;e.attr(\"text-anchor\",s),i||a.attr(\"transform\",cM(c,f))}function Zoe(e,t){var r=e.ts||t.textfont.size;return Fb(r)&&r>0?r:0}Ta.textPointStyle=function(e,t,r){if(e.size()){var n;if(t.selectedpoints){var i=Ta.makeSelectedTextStyleFns(t);n=i.selectedTextColorFn}var a=t.texttemplate,o=r._fullLayout;e.each(function(s){var l=dd.select(this),u=a?Du.extractOption(s,t,\"txt\",\"texttemplate\"):Du.extractOption(s,t,\"tx\",\"text\");if(!u&&u!==0){l.remove();return}if(a){var c=t._module.formatLabels,f=c?c(s,t,o):{},h={};cst(h,t,s.i),u=Du.texttemplateString({data:[h,s,t._meta],fallback:t.texttemplatefallback,labels:f,locale:o._d3locale,template:u})}var d=s.tp||t.textposition,v=Zoe(s,t),m=n?n(s):s.tc||t.textfont.color;l.call(Ta.font,{family:s.tf||t.textfont.family,weight:s.tw||t.textfont.weight,style:s.ty||t.textfont.style,variant:s.tv||t.textfont.variant,textcase:s.tC||t.textfont.textcase,lineposition:s.tE||t.textfont.lineposition,shadow:s.tS||t.textfont.shadow,size:v,color:m}).text(u).call(yL.convertToTspans,r).call(Xoe,d,v,s.mrc)})}};Ta.selectedTextStyle=function(e,t){if(!(!e.size()||!t.selectedpoints)){var r=Ta.makeSelectedTextStyleFns(t);e.each(function(n){var i=dd.select(this),a=r.selectedTextColorFn(n),o=n.tp||t.textposition,s=Zoe(n,t);Jd.fill(i,a);var l=mL.traceIs(t,\"bar-like\");Xoe(i,o,s,n.mrc2||n.mrc,l)})}};var Foe=.5;Ta.smoothopen=function(e,t){if(e.length<3)return\"M\"+e.join(\"L\");var r=\"M\"+e[0],n=[],i;for(i=1;i<e.length-1;i++)n.push(gL(e[i-1],e[i],e[i+1],t));for(r+=\"Q\"+n[0][0]+\" \"+e[1],i=2;i<e.length-1;i++)r+=\"C\"+n[i-2][1]+\" \"+n[i-1][0]+\" \"+e[i];return r+=\"Q\"+n[e.length-3][1]+\" 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L=t.minallowed,x=t.maxallowed;if(!(L===void 0&&x===void 0)){T||(T=\"range\");var C=yc.nestedProperty(t,T).get(),S=yc.simpleMap(C,t.r2l),g=S[1]<S[0];g&&S.reverse();var P=yc.simpleMap([L,x],t.r2l);if(L!==void 0&&S[0]<P[0]&&(C[g?1:0]=L,S[0]=P[0]),x!==void 0&&S[1]>P[1]&&(C[g?0:1]=x,S[1]=P[1]),S[0]>=S[1])if(L!==void 0){var E=P[0]+1;x!==void 0&&(E=Math.min(E,P[1])),C[g?0:1]=t.l2r(E)}else x!==void 0&&(C[g?1:0]=t.l2r(P[1]-1))}},t.cleanRange=function(T,L){t._cleanRange(T,L),t.limitRange(T)},t._cleanRange=function(T,L){L||(L={}),T||(T=\"range\");var x=yc.nestedProperty(t,T).get(),C,S;if(t.type===\"date\"?S=yc.dfltRange(t.calendar):i===\"y\"?S=EL.DFLTRANGEY:t._name===\"realaxis\"?S=[0,1]:S=L.dfltRange||EL.DFLTRANGEX,S=S.slice(),(t.rangemode===\"tozero\"||t.rangemode===\"nonnegative\")&&(S[0]=0),!x||x.length!==2){yc.nestedProperty(t,T).set(S);return}var g=x[0]===null,P=x[1]===null;for(t.type===\"date\"&&!t.autorange&&(x[0]=yc.cleanDate(x[0],Sg,t.calendar),x[1]=yc.cleanDate(x[1],Sg,t.calendar)),C=0;C<2;C++)if(t.type===\"date\"){if(!yc.isDateTime(x[C],t.calendar)){t[T]=S;break}if(t.r2l(x[0])===t.r2l(x[1])){var E=yc.constrain(t.r2l(x[0]),yc.MIN_MS+1e3,yc.MAX_MS-1e3);x[0]=t.l2r(E-1e3),x[1]=t.l2r(E+1e3);break}}else{if(!ym(x[C]))if(!(g||P)&&ym(x[1-C]))x[C]=x[1-C]*(C?10:.1);else{t[T]=S;break}if(x[C]<-bL?x[C]=-bL:x[C]>bL&&(x[C]=bL),x[0]===x[1]){var z=Math.max(1,Math.abs(x[0]*1e-6));x[0]-=z,x[1]+=z}}},t.setScale=function(T){var L=r._size;if(t.overlaying){var x=AL.getFromId({_fullLayout:r},t.overlaying);t.domain=x.domain}var C=T&&t._r?\"_r\":\"range\",S=t.calendar;t.cleanRange(C);var 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x&&L&&L.reverseDflt?x=\"reversed\":T&&(T[0]===null&&T[1]===null?x=!0:T[0]===null&&T[1]!==null?x=\"min\":T[0]!==null&&T[1]===null&&(x=\"max\")),x},t.isReversed=function(){var T=t.autorange;return T===\"reversed\"||T===\"min reversed\"||T===\"max reversed\"},t.isPtWithinRange=function(T,L){var x=t.c2l(T[i],null,L),C=t.r2l(t.range[0]),S=t.r2l(t.range[1]);return C<S?C<=x&&x<=S:S<=x&&x<=C},t._emptyCategories=function(){t._categories=[],t._categoriesMap={}},t.clearCalc=function(){var T=t._matchGroup;if(T){var L=null,x=null;for(var C in T){var S=r[AL.id2name(C)];if(S._categories){L=S._categories,x=S._categoriesMap;break}}L&&x?(t._categories=L,t._categoriesMap=x):t._emptyCategories()}else t._emptyCategories();if(t._initialCategories)for(var g=0;g<t._initialCategories.length;g++)u(t._initialCategories[g])},t.sortByInitialCategories=function(){var T=[];if(t._emptyCategories(),t._initialCategories)for(var L=0;L<t._initialCategories.length;L++)u(t._initialCategories[L]);T=T.concat(t._traceIndices);var x=t._matchGroup;for(var C in x)if(n!==C){var S=r[AL.id2name(C)];S._categories=t._categories,S._categoriesMap=t._categoriesMap,T=T.concat(S._traceIndices)}return T};var M=r._d3locale;t.type===\"date\"&&(t._dateFormat=M?M.timeFormat:Mst,t._extraFormat=r._extraFormat),t._separators=r.separators,t._numFormat=M?M.numberFormat:Est,delete t._minDtick,delete t._forceTick0}});var W3=ye((kar,fse)=>{\"use strict\";var lse=Eo(),oB=Pr(),Pst=fs().BADNUM,kL=oB.isArrayOrTypedArray,Ist=oB.isDateTime,Rst=oB.cleanNumber,use=Math.round;fse.exports=function(t,r,n){var i=t,a=n.noMultiCategory;if(kL(i)&&!i.length)return\"-\";if(!a&&Ost(i))return\"multicategory\";if(a&&Array.isArray(i[0])){for(var o=[],s=0;s<i.length;s++)if(kL(i[s]))for(var l=0;l<i[s].length;l++)o.push(i[s][l]);i=o}if(zst(i,r))return\"date\";var u=n.autotypenumbers!==\"strict\";return qst(i,u)?\"category\":Fst(i,u)?\"linear\":\"-\"};function 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a=e._dtickInit!==e.dtick;/%[fLQsSMX]/.test(i)||(/%[HI]/.test(i)?(t=wm,a&&!n&&e.dtick<wm&&(e.dtick=wm)):/%p/.test(i)?(t=p_,a&&!n&&e.dtick<p_&&(e.dtick=p_)):/%[Aadejuwx]/.test(i)?(t=qv,a&&!n&&e.dtick<qv&&(e.dtick=qv)):/%[UVW]/.test(i)?(t=Zp,a&&!n&&e.dtick<Zp&&(e.dtick=Zp)):/%[Bbm]/.test(i)?(t=Y3,a&&(n?dB(e.dtick)<1:e.dtick<zL)&&(e.dtick=\"M1\")):/%[q]/.test(i)?(t=pB,a&&(n?dB(e.dtick)<3:e.dtick<FL)&&(e.dtick=\"M3\")):/%[Yy]/.test(i)&&(t=RL,a&&(n?dB(e.dtick)<12:e.dtick<DL)&&(e.dtick=\"M12\")))}n=r(),n&&e.tick0===e._dowTick0&&(e.tick0=e._rawTick0),e._definedDelta=t}function dlt(e,t,r){for(var n=0;n<e.length;n++){var i=e[n].value,a=n,o=n+1;n<e.length-1?(a=n,o=n+1):n>0?(a=n-1,o=n):(a=n,o=n);var s=e[a].value,l=e[o].value,u=Math.abs(l-s),c=r||u,f=0;c>=DL?u>=DL&&u<=Qst?f=u:f=RL:r===pB&&c>=FL?u>=FL&&u<=elt?f=u:f=pB:c>=zL?u>=zL&&u<=tlt?f=u:f=Y3:r===Zp&&c>=Zp?f=Zp:c>=qv?f=qv:r===p_&&c>=p_?f=p_:r===wm&&c>=wm&&(f=wm);var h;f>=u&&(f=u,h=!0);var d=i+f;if(t.rangebreaks&&f>0){for(var v=84,m=0,b=0;b<v;b++){var p=(b+.5)/v;t.maskBreaks(i*(1-p)+p*d)!==BL&&m++}f*=m/v,f||(e[n].drop=!0),h&&u>Zp&&(f=u)}(f>0||n===0)&&(e[n].periodX=i+f/2)}}$n.calcTicks=function(t,r){for(var n=t.type,i=t.calendar,a=t.ticklabelstep,o=t.ticklabelmode===\"period\",s=t.range[0]>t.range[1],l=!t.ticklabelindex||Wo.isArrayOrTypedArray(t.ticklabelindex)?t.ticklabelindex:[t.ticklabelindex],u=Wo.simpleMap(t.range,t.r2l,void 0,void 0,r),c=u[1]<u[0],f=Math.min(u[0],u[1]),h=Math.max(u[0],u[1]),d=Math.max(1e3,t._length||0),v=[],m=[],b=[],p=[],k=[],M=t.minor&&(t.minor.ticks||t.minor.showgrid),T=M||l,L=1;L>=(T?0:1);L--){var x=!L;L?(t._dtickInit=t.dtick,t._tick0Init=t.tick0):T&&(t.minor._dtickInit=t.minor.dtick,t.minor._tick0Init=t.minor.tick0);var C=L?t:Wo.extendFlat({},t,T?t.minor:{minor:{}});if(x?$n.prepMinorTicks(C,t,r):$n.prepTicks(C,r),C.tickmode===\"array\"){L?(b=[],v=Mse(t,!x)):(p=[],m=Mse(t,!x));continue}if(C.tickmode===\"sync\"){b=[],v=vlt(t);continue}var S=wB(u),g=S[0],P=S[1],E=zh(C.dtick),z=n===\"log\"&&!(E||C.dtick.charAt(0)===\"L\"),q=$n.tickFirst(C,r);if(L){if(t._tmin=q,q<g!==c)break;(n===\"category\"||n===\"multicategory\")&&(P=c?Math.max(-.5,P):Math.min(t._categories.length-.5,P))}var V=null,G=q,Z;if(L){var j;E?j=t.dtick:n===\"date\"?typeof t.dtick==\"string\"&&t.dtick.charAt(0)===\"M\"&&(j=Y3*t.dtick.substring(1)):j=t._roughDTick,Z=Math.round((t.r2l(G)-t.r2l(t.tick0))/j)-1}var N=C.dtick;for(C.rangebreaks&&C._tick0Init!==C.tick0&&(G=vB(G,t),c||(G=$n.tickIncrement(G,N,!c,i))),(L||l)&&o&&(G=$n.tickIncrement(G,N,!c,i),L&&Z--);c?G>=P:G<=P;G=$n.tickIncrement(G,N,c,i)){if(L&&Z++,C.rangebreaks&&!c){if(G<g)continue;if(C.maskBreaks(G)===BL&&vB(G,C)>=h)break}if(b.length>d||G===V)break;V=G;var H={value:G};L?(z&&G!==(G|0)&&(H.simpleLabel=!0),a>1&&Z%a&&(H.skipLabel=!0),b.push(H)):(H.minor=!0,p.push(H))}}if(!p||p.length<3)l=!1;else{var re=(p[2].value-p[1].value)*(s?-1:1);Blt(re,t.tickformat)||(l=!1,p=p.slice(1))}if(!l)k=b;else{var ae=b.concat(p);o&&b.length&&(ae=ae.slice(1)),ae=ae.sort(function(Vt,rr){return Vt.value-rr.value}).filter(function(Vt,rr,tt){return rr===0||Vt.value!==tt[rr-1].value});var _e=ae.map(function(Vt,rr){return Vt.minor===void 0&&!Vt.skipLabel?rr:null}).filter(function(Vt){return Vt!==null});_e.forEach(function(Vt){l.map(function(rr){var tt=Vt+rr;tt>=0&&tt<ae.length&&Wo.pushUnique(k,ae[tt])})}),b.forEach(function(Vt){Vt.skipLabel=k.indexOf(Vt)===-1})}if(M){var Ee=t.minor.ticks===\"inside\"&&t.ticks===\"outside\"||t.minor.ticks===\"outside\"&&t.ticks===\"inside\";if(!Ee){for(var Ce=b.map(function(Vt){return Vt.value}),ge=[],ie=0;ie<p.length;ie++){var Se=p[ie],ke=Se.value;if(Ce.indexOf(ke)===-1){for(var be=!1,Be=0;!be&&Be<b.length;Be++)1e7+b[Be].value===1e7+ke&&(be=!0);be||ge.push(Se)}}p=ge}}o&&dlt(k,t,t._definedDelta);var Le;if(t.rangebreaks){var me=t._id.charAt(0)===\"y\",Pe=1;t.tickmode===\"auto\"&&(Pe=t.tickfont?t.tickfont.size:12);var ce=NaN;for(Le=b.length-1;Le>-1;Le--){if(b[Le].drop){b.splice(Le,1);continue}b[Le].value=vB(b[Le].value,t);var He=t.c2p(b[Le].value);(me?ce>He-Pe:ce<He+Pe)?b.splice(c?Le+1:Le,1):ce=He}}MB(t)&&Math.abs(u[1]-u[0])===360&&b.pop(),t._tmax=(b[b.length-1]||{}).value,t._prevDateHead=\"\",t._inCalcTicks=!0;var lt,mt=function(Vt){Vt.text=\"\",t._prevDateHead=lt};b=b.concat(p);function Ht(Vt,rr){var tt=$n.tickText(Vt,rr.value,!1,rr.simpleLabel),je=rr.periodX;return je!==void 0&&(tt.periodX=je,(je>h||je<f)&&(je>h&&(tt.periodX=h),je<f&&(tt.periodX=f),mt(tt))),tt}var at;for(Le=0;Le<b.length;Le++){var ct=b[Le].minor,ar=b[Le].value;ct?(l&&k.indexOf(b[Le])!==-1?at=Ht(t,b[Le]):at={x:ar},at.minor=!0,m.push(at)):(lt=t._prevDateHead,at=Ht(t,b[Le]),b[Le].skipLabel&&mt(at),v.push(at))}return o&&l&&m.length&&(m[0].noTick=!0),v=v.concat(m),t._inCalcTicks=!1,o&&v.length&&(v[0].noTick=!0),v};function Rse(e,t){return e.rangebreaks&&(t=t.filter(function(r){return e.maskBreaks(r.x)!==BL})),t}function vlt(e){var t=e._mainAxis,r=[];if(t._vals){for(var n=0;n<t._vals.length;n++)if(!t._vals[n].noTick){var i=t.l2p(t._vals[n].x),a=e.p2l(i),o=$n.tickText(e,a);t._vals[n].minor&&(o.minor=!0,o.text=\"\"),r.push(o)}}return r=Rse(e,r),r}function Mse(e,t){var r=Wo.simpleMap(e.range,e.r2l),n=wB(r),i=Math.min(n[0],n[1]),a=Math.max(n[0],n[1]),o=e.type===\"category\"?e.d2l_noadd:e.d2l;e.type===\"log\"&&String(e.dtick).charAt(0)!==\"L\"&&(e.dtick=\"L\"+Math.pow(10,Math.floor(Math.min(e.range[0],e.range[1]))-1));for(var s=[],l=0;l<=1;l++)if(!(t!==void 0&&(t&&l||t===!1&&!l))&&!(l&&!e.minor)){var u=l?e.minor.tickvals:e.tickvals,c=l?[]:e.ticktext;if(u){Wo.isArrayOrTypedArray(c)||(c=[]);for(var f=0;f<u.length;f++){var h=o(u[f]);if(h>i&&h<a){var d=$n.tickText(e,h,!1,String(c[f]));l&&(d.minor=!0,d.text=\"\"),s.push(d)}}}}return s=Rse(e,s),s}var PL=[2,5,10],Ese=[1,2,3,6,12],kse=[1,2,5,10,15,30],plt=[1,2,3,7,14],Dse=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],Fse=[-.301,0,.301,.699,1],glt=[15,30,45,90,180];function bm(e,t,r){return t*Wo.roundUp(e/t,r)}$n.autoTicks=function(e,t,r){var n;function i(f){return Math.pow(f,Math.floor(Math.log(t)/Math.LN10))}if(e.type===\"date\"){e.tick0=Wo.dateTick0(e.calendar,0);var a=2*t;if(a>RL)t/=RL,n=i(10),e.dtick=\"M\"+12*bm(t,n,PL);else if(a>Y3)t/=Y3,e.dtick=\"M\"+bm(t,1,Ese);else if(a>qv){if(e.dtick=bm(t,qv,e._hasDayOfWeekBreaks?[1,2,7,14]:plt),!r){var o=$n.getTickFormat(e),s=e.ticklabelmode===\"period\";s&&(e._rawTick0=e.tick0),/%[uVW]/.test(o)?e.tick0=Wo.dateTick0(e.calendar,2):e.tick0=Wo.dateTick0(e.calendar,1),s&&(e._dowTick0=e.tick0)}}else a>wm?e.dtick=bm(t,wm,Ese):a>pM?e.dtick=bm(t,pM,kse):a>qL?e.dtick=bm(t,qL,kse):(n=i(10),e.dtick=bm(t,n,PL))}else if(e.type===\"log\"){e.tick0=0;var l=Wo.simpleMap(e.range,e.r2l);if(e._isMinor&&(t*=1.5),t>.7)e.dtick=Math.ceil(t);else if(Math.abs(l[1]-l[0])<1){var u=1.5*Math.abs((l[1]-l[0])/t);t=Math.abs(Math.pow(10,l[1])-Math.pow(10,l[0]))/u,n=i(10),e.dtick=\"L\"+bm(t,n,PL)}else e.dtick=t>.3?\"D2\":\"D1\"}else e.type===\"category\"||e.type===\"multicategory\"?(e.tick0=0,e.dtick=Math.ceil(Math.max(t,1))):MB(e)?(e.tick0=0,n=1,e.dtick=bm(t,n,glt)):(e.tick0=0,n=i(10),e.dtick=bm(t,n,PL));if(e.dtick===0&&(e.dtick=1),!zh(e.dtick)&&typeof e.dtick!=\"string\"){var c=e.dtick;throw e.dtick=1,\"ax.dtick error: \"+String(c)}};function zse(e){var t=e.dtick;if(e._tickexponent=0,!zh(t)&&typeof t!=\"string\"&&(t=1),(e.type===\"category\"||e.type===\"multicategory\")&&(e._tickround=null),e.type===\"date\"){var r=e.r2l(e.tick0),n=e.l2r(r).replace(/(^-|i)/g,\"\"),i=n.length;if(String(t).charAt(0)===\"M\")i>10||n.slice(5)!==\"01-01\"?e._tickround=\"d\":e._tickround=+t.slice(1)%12===0?\"y\":\"m\";else if(t>=qv&&i<=10||t>=qv*15)e._tickround=\"d\";else if(t>=pM&&i<=16||t>=wm)e._tickround=\"M\";else if(t>=qL&&i<=19||t>=pM)e._tickround=\"S\";else{var a=e.l2r(r+t).replace(/^-/,\"\").length;e._tickround=Math.max(i,a)-20,e._tickround<0&&(e._tickround=4)}}else if(zh(t)||t.charAt(0)===\"L\"){var o=e.range.map(e.r2d||Number);zh(t)||(t=Number(t.slice(1))),e._tickround=2-Math.floor(Math.log(t)/Math.LN10+.01);var s=Math.max(Math.abs(o[0]),Math.abs(o[1])),l=Math.floor(Math.log(s)/Math.LN10+.01),u=e.minexponent===void 0?3:e.minexponent;Math.abs(l)>u&&(K3(e.exponentformat)&&e.exponentformat!==\"SI extended\"&&!TB(l)||K3(e.exponentformat)&&e.exponentformat===\"SI extended\"&&!AB(l)?e._tickexponent=3*Math.round((l-1)/3):e._tickexponent=l)}else e._tickround=null}$n.tickIncrement=function(e,t,r,n){var i=r?-1:1;if(zh(t))return Wo.increment(e,i*t);var a=t.charAt(0),o=i*Number(t.slice(1));if(a===\"M\")return Wo.incrementMonth(e,o,n);if(a===\"L\")return Math.log(Math.pow(10,e)+o)/Math.LN10;if(a===\"D\"){var s=t===\"D2\"?Fse:Dse,l=e+i*.01,u=Wo.roundUp(Wo.mod(l,1),s,r);return Math.floor(l)+Math.log(w0.round(Math.pow(10,u),1))/Math.LN10}throw\"unrecognized dtick \"+String(t)};$n.tickFirst=function(e,t){var r=e.r2l||Number,n=Wo.simpleMap(e.range,r,void 0,void 0,t),i=n[1]<n[0],a=i?Math.floor:Math.ceil,o=wB(n)[0],s=e.dtick,l=r(e.tick0);if(zh(s)){var u=a((o-l)/s)*s+l;return(e.type===\"category\"||e.type===\"multicategory\")&&(u=Wo.constrain(u,0,e._categories.length-1)),u}var c=s.charAt(0),f=Number(s.slice(1));if(c===\"M\"){for(var h=0,d=l,v,m,b;h<10;){if(v=$n.tickIncrement(d,s,i,e.calendar),(v-o)*(d-o)<=0)return i?Math.min(d,v):Math.max(d,v);m=(o-(d+v)/2)/(v-d),b=c+(Math.abs(Math.round(m))||1)*f,d=$n.tickIncrement(d,b,m<0?!i:i,e.calendar),h++}return Wo.error(\"tickFirst did not converge\",e),d}else{if(c===\"L\")return Math.log(a((Math.pow(10,o)-l)/f)*f+l)/Math.LN10;if(c===\"D\"){var p=s===\"D2\"?Fse:Dse,k=Wo.roundUp(Wo.mod(o,1),p,i);return Math.floor(o)+Math.log(w0.round(Math.pow(10,k),1))/Math.LN10}else throw\"unrecognized dtick \"+String(s)}};$n.tickText=function(e,t,r,n){var i=qse(e,t),a=e.tickmode===\"array\",o=r||a,s=e.type,l=s===\"category\"?e.d2l_noadd:e.d2l,u,c=function(b){var p=e.l2p(b);return p>=0&&p<=e._length?b:null};if(a&&Wo.isArrayOrTypedArray(e.ticktext)){var f=Wo.simpleMap(e.range,e.r2l),h=(Math.abs(f[1]-f[0])-(e._lBreaks||0))/1e4;for(u=0;u<e.ticktext.length&&!(Math.abs(t-l(e.tickvals[u]))<h);u++);if(u<e.ticktext.length)return i.text=String(e.ticktext[u]),i.xbnd=[c(i.x-.5),c(i.x+e.dtick-.5)],i}function d(b){if(b===void 0)return!0;if(r)return b===\"none\";var p={first:e._tmin,last:e._tmax}[b];return b!==\"all\"&&t!==p}var v=r?\"never\":e.exponentformat!==\"none\"&&d(e.showexponent)?\"hide\":\"\";if(s===\"date\"?mlt(e,i,r,o):s===\"log\"?ylt(e,i,r,o,v):s===\"category\"?_lt(e,i):s===\"multicategory\"?xlt(e,i,r):MB(e)?wlt(e,i,r,o,v):blt(e,i,r,o,v),n||(e.tickprefix&&!d(e.showtickprefix)&&(i.text=e.tickprefix+i.text),e.ticksuffix&&!d(e.showticksuffix)&&(i.text+=e.ticksuffix)),e.labelalias&&e.labelalias.hasOwnProperty(i.text)){var m=e.labelalias[i.text];typeof m==\"string\"&&(i.text=m)}return(e.tickson===\"boundaries\"||e.showdividers)&&(i.xbnd=[c(i.x-.5),c(i.x+e.dtick-.5)]),i};$n.hoverLabelText=function(e,t,r){r&&(e=Wo.extendFlat({},e,{hoverformat:r}));var n=Wo.isArrayOrTypedArray(t)?t[0]:t,i=Wo.isArrayOrTypedArray(t)?t[1]:void 0;if(i!==void 0&&i!==n)return $n.hoverLabelText(e,n,r)+\" - \"+$n.hoverLabelText(e,i,r);var a=e.type===\"log\"&&n<=0,o=$n.tickText(e,e.c2l(a?-n:n),\"hover\").text;return a?n===0?\"0\":Bb+o:o};function qse(e,t,r){var n=e.tickfont||{};return{x:t,dx:0,dy:0,text:r||\"\",fontSize:n.size,font:n.family,fontWeight:n.weight,fontStyle:n.style,fontVariant:n.variant,fontTextcase:n.textcase,fontLineposition:n.lineposition,fontShadow:n.shadow,fontColor:n.color}}function mlt(e,t,r,n){var i=e._tickround,a=r&&e.hoverformat||$n.getTickFormat(e);n=!a&&n,n&&(zh(i)?i=4:i={y:\"m\",m:\"d\",d:\"M\",M:\"S\",S:4}[i]);var o=Wo.formatDate(t.x,a,i,e._dateFormat,e.calendar,e._extraFormat),s,l=o.indexOf(`\n",
-       "`);if(l!==-1&&(s=o.slice(l+1),o=o.slice(0,l)),n&&(s!==void 0&&(o===\"00:00:00\"||o===\"00:00\")?(o=s,s=\"\"):o.length===8&&(o=o.replace(/:00$/,\"\"))),s)if(r)i===\"d\"?o+=\", \"+s:o=s+(o?\", \"+o:\"\");else if(!e._inCalcTicks||e._prevDateHead!==s)e._prevDateHead=s,o+=\"<br>\"+s;else{var u=yM(e),c=e._trueSide||e.side;(!u&&c===\"top\"||u&&c===\"bottom\")&&(o+=\"<br> \")}t.text=o}function ylt(e,t,r,n,i){var a=e.dtick,o=t.x,s=e.tickformat,l=typeof a==\"string\"&&a.charAt(0);if(i===\"never\"&&(i=\"\"),n&&l!==\"L\"&&(a=\"L3\",l=\"L\"),s||l===\"L\")t.text=mM(Math.pow(10,o),e,i,n);else if(zh(a)||l===\"D\"&&(e.minorloglabels===\"complete\"||Wo.mod(o+.01,1)<.1)){var u;e.minorloglabels===\"complete\"&&!(Wo.mod(o+.01,1)<.1)&&(u=!0,t.fontSize*=.75);var c=Math.pow(10,o).toExponential(0),f=c.split(\"e\"),h=+f[1],d=Math.abs(h),v=e.exponentformat;v===\"power\"||K3(v)&&v!==\"SI extended\"&&TB(h)||K3(v)&&v===\"SI 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Le=Object.keys(t._linepositions||{});for(a=0;a<Le.length;a++){o=Le[a],s=l._plots[o];var me=t._linepositions[o]||[],Pe=me[0],ce=me[1],He=me[2],lt=$n.makeTickPath(t,Pe,He?_e[0]:Ee[0],{minor:He})+$n.makeTickPath(t,ce,He?_e[1]:Ee[1],{minor:He});$n.drawTicks(e,t,{vals:g,layer:s[c+\"axislayer\"],path:lt,transFn:C})}}var mt=[];if(mt.push(function(){return $n.drawLabels(e,t,{vals:M,layer:m,plotinfo:s,transFn:S,labelFns:$n.makeLabelFns(t,p)})}),t.type===\"multicategory\"){var Ht={x:2,y:10}[c];mt.push(function(){var ct={x:\"height\",y:\"width\"}[c],ar=x()[ct]+Ht+(t._tickAngles[u+\"tick\"]?t.tickfont.size*gM:0);return $n.drawLabels(e,t,{vals:Elt(t,M),layer:m,cls:u+\"tick2\",repositionOnUpdate:!0,secondary:!0,transFn:C,labelFns:$n.makeLabelFns(t,p+ar*_e[4])})}),mt.push(function(){return t._depth=_e[4]*(x(\"tick2\")[t.side]-p),Plt(e,t,{vals:G,layer:m,path:$n.makeTickPath(t,p,_e[4],{len:t._depth}),transFn:C})})}else t.title.hasOwnProperty(\"standoff\")&&mt.push(function(){t._depth=_e[4]*(x()[t.side]-p)});var at=dM.getComponentMethod(\"rangeslider\",\"isVisible\")(t);return!r.skipTitle&&!(at&&t.side===\"bottom\")&&mt.push(function(){return Ilt(e,t)}),mt.push(function(){var ct=t.side.charAt(0),ar=nlt[t.side].charAt(0),Vt=$n.getPxPosition(e,t),rr=z?t.ticklen:0,tt,je,Ue,Ae;(t.automargin||at||t._shiftPusher)&&(t.type===\"multicategory\"?tt=x(\"tick2\"):(tt=x(),c===\"x\"&&ct===\"b\"&&(t._depth=Math.max(tt.width>0?tt.bottom-Vt:0,rr))));var rt=0,St=0;if(t._shiftPusher&&(rt=Math.max(rr,tt.height>0?ct===\"l\"?Vt-tt.left:tt.right-Vt:0),t.title.text!==l._dfltTitle[c]&&(St=(t._titleStandoff||0)+(t._titleScoot||0),ct===\"l\"&&(St+=Lse(t))),t._fullDepth=Math.max(rt,St)),t.automargin){je={x:0,y:0,r:0,l:0,t:0,b:0};var Tt=[0,1],dt=typeof t._shift==\"number\"?t._shift:0;if(c===\"x\"){if(ct===\"b\"?je[ct]=t._depth:(je[ct]=t._depth=Math.max(tt.width>0?Vt-tt.top:0,rr),Tt.reverse()),tt.width>0){var 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t.automargin==\"string\"&&(Cse(je,t.automargin),Cse(Ue,t.automargin)),X3.autoMargin(e,SB(t),je),X3.autoMargin(e,Nse(t),Ue),X3.autoMargin(e,Use(t),Ae)}),Wo.syncOrAsync(mt)}};function Cse(e,t){if(e){var r=Object.keys(wse).reduce(function(n,i){return t.indexOf(i)!==-1&&wse[i].forEach(function(a){n[a]=1}),n},{});Object.keys(e).forEach(function(n){r[n]||(n.length===1?e[n]=0:delete e[n])})}}function Mlt(e,t){var r=[],n,i=function(a,o){var s=a.xbnd[o];s!==null&&r.push(Wo.extendFlat({},a,{x:s}))};if(t.length){for(n=0;n<t.length;n++)i(t[n],0);i(t[n-1],1)}return r}function Elt(e,t){for(var r=[],n={},i=0;i<t.length;i++){var a=t[i];n[a.text2]?n[a.text2].push(a.x):n[a.text2]=[a.x]}for(var o in n)r.push(qse(e,Wo.interp(n[o],.5),o));return r}function klt(e,t){var r=[],n,i,a=t.length&&t[t.length-1].x<t[0].x,o=function(l,u){var c=l.xbnd[u];c!==null&&r.push(Wo.extendFlat({},l,{x:c}))};if(e.showdividers&&t.length){for(n=0;n<t.length;n++){var s=t[n];s.text2!==i&&o(s,a?1:0),i=s.text2}o(t[n-1],a?0:1)}return 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Se=ie.K===\"tick\"&&ie.L===\"text\"&&t.ticklabelmode===\"period\",ke=n._plots[t._mainSubplot],be;if(ie.K===gB.K){var Be=a?ke.zerolinelayerAbove:ke.zerolinelayer;be=Be.selectAll(\".\"+t._id+\"zl\")}else ie.K===yB.K?be=ke.minorGridlayer.selectAll(\".\"+t._id):ie.K===mB.K?be=ke.gridlayer.selectAll(\".\"+t._id):be=ke[t._id.charAt(0)+\"axislayer\"];be.each(function(){var Le=w0.select(this);ie.L&&(Le=Le.selectAll(ie.L)),Le.each(function(me){var Pe=t.l2p(Se?_B(me):me.x)+t._offset,ce=w0.select(this);Pe<t[\"_visibleLabelMax_\"+Ce._id]&&Pe>t[\"_visibleLabelMin_\"+Ce._id]?ce.style(\"display\",\"none\"):ie.K===\"tick\"&&!ge&&ce.node().style.display!==\"none\"&&ce.style(\"display\",null)})})})})},d(f,c+1?c:u);function v(){return h.length&&Promise.all(h)}var m=null;function b(){if(d(f,u),s.length&&t.autotickangles&&(t.type!==\"log\"||String(t.dtick).charAt(0)!==\"D\")){m=t.autotickangles[0];var H=0,re=[],ae,_e=1;f.each(function(je){H=Math.max(H,je.fontSize);var 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a._offset+a._length};function Lse(e){var t=e.title.font.size,r=(e.title.text.match(Ob.BR_TAG_ALL)||[]).length;return e.title.hasOwnProperty(\"standoff\")?t*(Nb+r*gM):r?t*(r+1)*gM:t}function Ilt(e,t){var r=e._fullLayout,n=t._id,i=n.charAt(0),a=t.title.font.size,o,s=(t.title.text.match(Ob.BR_TAG_ALL)||[]).length;if(t.title.hasOwnProperty(\"standoff\"))t.side===\"bottom\"||t.side===\"right\"?o=t._depth+t.title.standoff+a*Nb:(t.side===\"top\"||t.side===\"left\")&&(o=t._depth+t.title.standoff+a*(hM+s*gM));else{var l=yM(t);if(t.type===\"multicategory\")o=t._depth;else{var u=1.5*a;l&&(u=.5*a,t.ticks===\"outside\"&&(u+=t.ticklen)),o=10+u+(t.linewidth?t.linewidth-1:0)}l||(i===\"x\"?o+=t.side===\"top\"?a*(t.showticklabels?1:0):a*(t.showticklabels?1.5:.5):o+=t.side===\"right\"?a*(t.showticklabels?1:.5):a*(t.showticklabels?.5:0))}var 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lt=XL(He,n,v);j=Math.max(j,lt),N+=lt}),P=null;var H=0;if(u){var re=0,ae=0,_e=0;t.each(function(){var He=0,lt=0;np.select(this).selectAll(\"g.traces\").each(function(Ht){var at=XL(Ht,n,v),ct=Ht[0].height;ph.setTranslate(this,E[0],E[1]+f+d+ct/2+lt),lt+=ct,He=Math.max(He,at),T[Ht[0].trace.legendgroup]=He});var mt=He+d;ae>0&&mt+f+ae>n._maxWidth?(H=Math.max(H,ae),ae=0,_e+=re+M,re=lt):re=Math.max(re,lt),ph.setTranslate(this,ae,_e),ae+=mt}),n._width=Math.max(H,ae)+f,n._height=_e+re+m}else{var Ee=r.size(),Ce=N+h+(Ee-1)*d<n._maxWidth,ge=0,ie=0,Se=0,ke=0;r.each(function(He){var lt=He[0].height,mt=XL(He,n,v,u),Ht=Ce?mt:j;c||(Ht+=d),Ht+f+ie-d>=n._maxWidth&&(H=Math.max(H,ke),ie=0,Se+=ge,n._height+=ge,ge=0),ph.setTranslate(this,E[0]+f+ie,E[1]+f+Se+lt/2+d),ke=ie+mt+d,ie+=Ht,ge=Math.max(ge,lt)}),Ce?(n._width=ie+h,n._height=ge+m):(n._width=Math.max(H,ke)+h,n._height+=ge+m)}}n._width=Math.ceil(Math.max(n._width+E[0],n._titleWidth+2*(f+qh.titlePad))),n._height=Math.ceil(Math.max(n._height+E[1],n._titleHeight+2*(f+qh.itemGap))),n._effHeight=Math.min(n._height,n._maxHeight);var be=e._context.edits,Be=be.legendText||be.legendPosition;r.each(function(He){var lt=np.select(this).select(\".\"+o+\"toggle\"),mt=He[0].height,Ht=He[0].trace.legendgroup,at=XL(He,n,v);u&&Ht!==\"\"&&(at=T[Ht]);var ct=Be?v:P||at;!l&&!c&&(ct+=d/2),ph.setRect(lt,0,-mt/2,ct,mt)});var Le=i.select(\".\"+o+\"titletext\");Le.node()&&yut(Le,n,f);var me=i.select(\".\"+o+\"titletoggle\");if(me.size()&&Le.node()){var 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Ht(Vi,Jn){for(H=0;H<G.length;H++)if(re=G[H],!(!re||!re[0]||!re[0].trace)&&(ae=re[0].trace,!(ae.visible!==!0||ae._length===0)&&[\"carpet\",\"contourcarpet\"].indexOf(ae._module.name)===-1)){if(Ce=h,kg.isUnifiedHover(Ce)&&(Ce=Ce.charAt(0)),ae.type===\"splom\"?(Ee=0,_e=a[Ee]):(_e=kg.getSubplot(ae),Ee=a.indexOf(_e)),Se={cd:re,trace:ae,xa:C[Ee],ya:S[Ee],maxHoverDistance:z,maxSpikeDistance:q,index:!1,distance:Math.min(mt,z),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:vd.defaultLine,name:ae.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},s[_e]&&(Se.subplot=s[_e]._subplot),s._splomScenes&&s._splomScenes[ae.uid]&&(Se.scene=s._splomScenes[ae.uid]),Ce===\"array\"){var Yn=t[H];if(\"pointNumber\"in Yn){let Hi=re[Yn.pointNumber];Hi&&(Se.index=Yn.pointNumber,Hi.lonlat?(ge=Hi.lonlat[0],ie=Hi.lonlat[1]):(ge=Hi.x,ie=Hi.y)),Ce=\"closest\"}else Ce=\"\",\"xval\"in Yn&&(ge=Yn.xval,Ce=\"x\"),\"yval\"in Yn&&(ie=Yn.yval,Ce=Ce?\"closest\":\"y\")}else Vi!==void 0&&Jn!==void 0?(ge=Vi,ie=Jn):(ge=Z[Ee],ie=j[Ee]);if(ke=V.length,z!==0)if(ae._module&&ae._module.hoverPoints){var Gt=ae._module.hoverPoints(Se,ge,ie,Ce,{finiteRange:!0,hoverLayer:s._hoverlayer,hoversubplots:l,gd:e});if(Gt)for(var wt,tr=0;tr<Gt.length;tr++)wt=Gt[tr],Ry(wt.x0)&&Ry(wt.y0)&&V.push(Uut(wt,h))}else zf.log(\"Unrecognized trace type in hover:\",ae);if(h===\"closest\"&&V.length>ke&&(V.splice(0,ke),mt=V[0].distance),f&&q!==0&&V.length===0){Se.distance=q,Se.index=!1;var ir=ae._module.hoverPoints(Se,ge,ie,\"closest\",{hoverLayer:s._hoverlayer});if(ir&&(ir=ir.filter(function(Hi){return Hi.spikeDistance<=q})),ir&&ir.length){var wr,Xr=ir.filter(function(Hi){return Hi.xa.showspikes&&Hi.xa.spikesnap!==\"hovered data\"});if(Xr.length){var ti=Xr[0];Ry(ti.x0)&&Ry(ti.y0)&&(wr=ct(ti),(!be.vLinePoint||be.vLinePoint.spikeDistance>wr.spikeDistance)&&(be.vLinePoint=wr))}var $r=ir.filter(function(Hi){return Hi.ya.showspikes&&Hi.ya.spikesnap!==\"hovered data\"});if($r.length){var Ri=$r[0];Ry(Ri.x0)&&Ry(Ri.y0)&&(wr=ct(Ri),(!be.hLinePoint||be.hLinePoint.spikeDistance>wr.spikeDistance)&&(be.hLinePoint=wr))}}}}}Ht();function at(Vi,Jn,Yn){for(var Gt=null,wt=1/0,tr,ir=0;ir<Vi.length;ir++)m&&m._id!==Vi[ir].xa._id||b&&b._id!==Vi[ir].ya._id||(tr=Vi[ir].spikeDistance,Yn&&ir===0&&(tr=-1/0),tr<=wt&&tr<=Jn&&(Gt=Vi[ir],wt=tr));return Gt}function ct(Vi){return Vi?{xa:Vi.xa,ya:Vi.ya,x:Vi.xSpike!==void 0?Vi.xSpike:(Vi.x0+Vi.x1)/2,y:Vi.ySpike!==void 0?Vi.ySpike:(Vi.y0+Vi.y1)/2,distance:Vi.distance,spikeDistance:Vi.spikeDistance,curveNumber:Vi.trace.index,color:Vi.color,pointNumber:Vi.index}:null}var ar={fullLayout:s,container:s._hoverlayer,event:t},Vt=e._spikepoints,rr={vLinePoint:be.vLinePoint,hLinePoint:be.hLinePoint};e._spikepoints=rr;var tt=function(){var Vi=V.filter(function(Yn){return m&&m._id===Yn.xa._id&&b&&b._id===Yn.ya._id}),Jn=V.filter(function(Yn){return!(m&&m._id===Yn.xa._id&&b&&b._id===Yn.ya._id)});Vi.sort(Ble),Jn.sort(Ble),V=Vi.concat(Jn),V=Gut(V,h)};tt();var je=h.charAt(0),Ue=(je===\"x\"||je===\"y\")&&V[0]&&zut[V[0].trace.type];if(f&&q!==0&&V.length!==0){var Ae=V.filter(function(Vi){return Vi.ya.showspikes}),rt=at(Ae,q,Ue);be.hLinePoint=ct(rt);var St=V.filter(function(Vi){return Vi.xa.showspikes}),Tt=at(St,q,Ue);be.vLinePoint=ct(Tt)}if(V.length===0){var dt=$L.unhoverRaw(e,t);if(f&&(be.hLinePoint!==null||be.vLinePoint!==null)&&Vle(Vt)&&Ule(e,be,ar),s.hoveranywhere&&!n&&i){var Et=e._hoverdata;Et&&Et.length&&(e.emit(\"plotly_unhover\",{event:t,points:Et}),e._hoverdata=[]),Qn([])}return dt}if(f&&Vle(Vt)&&Ule(e,be,ar),kg.isXYhover(Ce)&&V[0].length!==0&&V[0].trace.type!==\"splom\"){var pt=V[0];Ole[pt.trace.type]?V=V.filter(Vi=>Vi.trace.index===pt.trace.index):V=[pt];var jt=V.length,or=Hle(\"x\",pt,s),mr=Hle(\"y\",pt,s);Ht(or,mr);var Ar=[],ei={},qr=0,jr=function(Vi){var Jn=Ole[Vi.trace.type]?Wle(Vi):Vi.trace.index;if(!ei[Jn])qr++,ei[Jn]=qr,Ar.push(Vi);else{var Yn=ei[Jn]-1,Gt=Ar[Yn];Yn>0&&Math.abs(Vi.distance)<Math.abs(Gt.distance)&&(Ar[Yn]=Vi)}},gt;for(gt=0;gt<jt;gt++)jr(V[gt]);for(gt=V.length-1;gt>jt-1;gt--)jr(V[gt]);V=Ar,tt()}var Ge=e._hoverdata,Je=[],We=Kle(e),et=Jle(e);for(let Vi of V){var xt=kg.makeEventData(Vi,Vi.trace,Vi.cd);if(Vi.hovertemplate!==!1){var At=!1;Vi.cd[Vi.index]&&Vi.cd[Vi.index].ht&&(At=Vi.cd[Vi.index].ht),Vi.hovertemplate=At||Vi.trace.hovertemplate||!1}if(Vi.xa&&Vi.ya){var Kt=Vi.x0+Vi.xa._offset,Qt=Vi.x1+Vi.xa._offset,Mr=Vi.y0+Vi.ya._offset,Gr=Vi.y1+Vi.ya._offset,Ir=Math.min(Kt,Qt),Yr=Math.max(Kt,Qt),_i=Math.min(Mr,Gr),Pi=Math.max(Mr,Gr);xt.bbox={x0:Ir+et,x1:Yr+et,y0:_i+We,y1:Pi+We},xt.xPixel=(Kt+Qt)/2,xt.yPixel=(Mr+Gr)/2}Vi.eventData=[xt],Je.push(xt)}e._hoverdata=Je;var ai=h===\"y\"&&(G.length>1||V.length>1)||h===\"closest\"&&Be&&V.length>1,mi=vd.combine(s.plot_bgcolor||vd.background,s.paper_bgcolor),un=Xle(V,{gd:e,hovermode:h,rotateLabels:ai,bgColor:mi,container:s._hoverlayer,outerContainer:s._paper.node(),commonLabelOpts:s.hoverlabel,hoverdistance:s.hoverdistance}),Fn=un.hoverLabels;if(kg.isUnifiedHover(h)||(But(Fn,ai,s,un.commonLabelBoundingBox),Yle(Fn,ai,s._invScaleX,s._invScaleY)),i&&i.tagName){var An=iT.getComponentMethod(\"annotations\",\"hasClickToShow\")(e,Je);Cut(Wb.select(i),An?\"pointer\":\"\")}var Hn=Vut(e,t,Ge);if(!i||n||!Hn&&!s.hoveranywhere)return;Ge&&Hn&&e.emit(\"plotly_unhover\",{event:t,points:Ge}),Qn(e._hoverdata);function Qn(Vi){e.emit(\"plotly_hover\",{event:t,points:Vi,xaxes:C,yaxes:S,xvals:Z,yvals:j})}}function Wle(e){return[e.trace.index,e.index,e.x0,e.y0,e.name,e.attr,e.xa?e.xa._id:\"\",e.ya?e.ya._id:\"\"].join(\",\")}var Out=/<extra>([\\s\\S]*)<\\/extra>/;function Xle(e,t){var r=t.gd,n=r._fullLayout,i=t.hovermode,a=t.rotateLabels,o=t.bgColor,s=t.container,l=t.outerContainer,u=t.commonLabelOpts||{};if(e.length===0)return[[]];var c=t.fontFamily||Xb.HOVERFONT,f=t.fontSize||Xb.HOVERFONTSIZE,h=t.fontWeight||n.font.weight,d=t.fontStyle||n.font.style,v=t.fontVariant||n.font.variant,m=t.fontTextcase||n.font.textcase,b=t.fontLineposition||n.font.lineposition,p=t.fontShadow||n.font.shadow,k=e[0],M=k.xa,T=k.ya,L=i.charAt(0),x=L+\"Label\",C=k[x];if(C===void 0&&M.type===\"multicategory\")for(var S=0;S<e.length&&(C=e[S][x],C===void 0);S++);var g=rT(r,l),P=g.top,E=g.width,z=g.height,q=C!==void 0&&k.distance<=t.hoverdistance&&(i===\"x\"||i===\"y\");if(q){var V=!0,G,Z;for(G=0;G<e.length;G++)if(V&&e[G].zLabel===void 0&&(V=!1),Z=e[G].hoverinfo||e[G].trace.hoverinfo,Z){var j=Array.isArray(Z)?Z:Z.split(\"+\");if(j.indexOf(\"all\")===-1&&j.indexOf(i)===-1){q=!1;break}}V&&(q=!1)}var N=s.selectAll(\"g.axistext\").data(q?[0]:[]);N.enter().append(\"g\").classed(\"axistext\",!0),N.exit().remove();var H={minX:0,maxX:0,minY:0,maxY:0};if(N.each(function(){var dt=Wb.select(this),Et=zf.ensureSingle(dt,\"path\",\"\",function(_i){_i.style({\"stroke-width\":\"1px\"})}),pt=zf.ensureSingle(dt,\"text\",\"\",function(_i){_i.attr(\"data-notex\",1)}),jt=u.bgcolor||vd.defaultLine,or=u.bordercolor||vd.contrast(jt),mr=vd.contrast(jt),Ar=u.font,ei={weight:Ar.weight||h,style:Ar.style||d,variant:Ar.variant||v,textcase:Ar.textcase||m,lineposition:Ar.lineposition||b,shadow:Ar.shadow||p,family:Ar.family||c,size:Ar.size||f,color:Ar.color||mr};Et.style({fill:jt,stroke:or}),pt.text(C).call(Am.font,ei).call(A0.positionText,0,0).call(A0.convertToTspans,r),dt.attr(\"transform\",\"\");var qr=rT(r,pt.node()),jr,gt;if(i===\"x\"){var Ge=M.side===\"top\"?\"-\":\"\";pt.attr(\"text-anchor\",\"middle\").call(A0.positionText,0,M.side===\"top\"?P-qr.bottom-Qc-ol:P-qr.top+Qc+ol),jr=M._offset+(k.x0+k.x1)/2,gt=T._offset+(M.side===\"top\"?0:T._length);var Je=qr.width/2+ol,We=jr;jr<Je?We=Je:jr>n.width-Je&&(We=n.width-Je),Et.attr(\"d\",\"M\"+(jr-We)+\",0L\"+(jr-We+Qc)+\",\"+Ge+Qc+\"H\"+Je+\"v\"+Ge+(ol*2+qr.height)+\"H\"+-Je+\"V\"+Ge+Qc+\"H\"+(jr-We-Qc)+\"Z\"),jr=We,H.minX=jr-Je,H.maxX=jr+Je,M.side===\"top\"?(H.minY=gt-(ol*2+qr.height),H.maxY=gt-ol):(H.minY=gt+ol,H.maxY=gt+(ol*2+qr.height))}else{var et,xt,At;T.side===\"right\"?(et=\"start\",xt=1,At=\"\",jr=M._offset+M._length):(et=\"end\",xt=-1,At=\"-\",jr=M._offset),gt=T._offset+(k.y0+k.y1)/2,pt.attr(\"text-anchor\",et),Et.attr(\"d\",\"M0,0L\"+At+Qc+\",\"+Qc+\"V\"+(ol+qr.height/2)+\"h\"+At+(ol*2+qr.width)+\"V-\"+(ol+qr.height/2)+\"H\"+At+Qc+\"V-\"+Qc+\"Z\"),H.minY=gt-(ol+qr.height/2),H.maxY=gt+(ol+qr.height/2),T.side===\"right\"?(H.minX=jr+Qc,H.maxX=jr+Qc+(ol*2+qr.width)):(H.minX=jr-Qc-(ol*2+qr.width),H.maxX=jr-Qc);var Kt=qr.height/2,Qt=P-qr.top-Kt,Mr=\"clip\"+n._uid+\"commonlabel\"+T._id,Gr;if(jr<qr.width+2*ol+Qc){Gr=\"M-\"+(Qc+ol)+\"-\"+Kt+\"h-\"+(qr.width-ol)+\"V\"+Kt+\"h\"+(qr.width-ol)+\"Z\";var Ir=qr.width-jr+ol;A0.positionText(pt,Ir,Qt),et===\"end\"&&pt.selectAll(\"tspan\").each(function(){var _i=Wb.select(this),Pi=Am.tester.append(\"text\").text(_i.text()).call(Am.font,ei),ai=rT(r,Pi.node());Math.round(ai.width)<Math.round(qr.width)&&_i.attr(\"x\",Ir-ai.width),Pi.remove()})}else A0.positionText(pt,xt*(ol+Qc),Qt),Gr=null;var Yr=n._topclips.selectAll(\"#\"+Mr).data(Gr?[0]:[]);Yr.enter().append(\"clipPath\").attr(\"id\",Mr).append(\"path\"),Yr.exit().remove(),Yr.select(\"path\").attr(\"d\",Gr),Am.setClipUrl(pt,Gr?Mr:null,r)}dt.attr(\"transform\",KB(jr,gt))}),kg.isUnifiedHover(i)){s.selectAll(\"g.hovertext\").remove();let dt=e.filter(Et=>Et.hoverinfo!==\"none\");if(dt.length===0)return[];var re=n.hoverlabel,ae=re.font,_e=dt[0],Ee=((i===\"x unified\"?_e.xa:_e.ya).unifiedhovertitle||{}).text,Ce=Ee?zf.hovertemplateString({data:i===\"x unified\"?[{xa:_e.xa,x:_e.xVal}]:[{ya:_e.ya,y:_e.yVal}],fallback:_e.trace.hovertemplatefallback,locale:n._d3locale,template:Ee}):C,ge={showlegend:!0,legend:{title:{text:Ce,font:ae},font:ae,bgcolor:re.bgcolor,bordercolor:re.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:n.legend?n.legend.traceorder:void 0,orientation:\"v\"}},ie={font:ae};Put(ge,ie,r._fullData);var Se=ie.legend;Se.entries=[];for(var ke=0;ke<dt.length;ke++){var be=dt[ke];if(be.hoverinfo!==\"none\"){var Be=Nle(be,!0,i,n,C),Le=Be[0],me=Be[1];be.name=me,me!==\"\"?be.text=me+\" : \"+Le:be.text=Le;var Pe=be.cd[be.index];Pe&&(Pe.mc&&(be.mc=Pe.mc),Pe.mcc&&(be.mc=Pe.mcc),Pe.mlc&&(be.mlc=Pe.mlc),Pe.mlcc&&(be.mlc=Pe.mlcc),Pe.mlw&&(be.mlw=Pe.mlw),Pe.mrc&&(be.mrc=Pe.mrc),Pe.dir&&(be.dir=Pe.dir)),be._distinct=!0,Se.entries.push([be])}}Se.entries.sort(function(Et,pt){var jt=n.hoversort;if(jt===\"value descending\"||jt===\"value ascending\"){var or=i.charAt(0)===\"x\"?\"y\":\"x\",mr=Et[0][or+\"LabelVal\"],Ar=pt[0][or+\"LabelVal\"];if(mr!==Ar)return jt===\"value descending\"?Ar-mr:mr-Ar}return Et[0].trace.index-pt[0].trace.index}),Se.layer=s,Se._inHover=!0,Se._groupTitleFont=re.grouptitlefont,Iut(r,Se);var ce=s.select(\"g.legend\"),He=rT(r,ce.node()),lt=He.width+2*ol,mt=He.height+2*ol,Ht=dt[0],at=(Ht.x0+Ht.x1)/2,ct=(Ht.y0+Ht.y1)/2,ar=!(iT.traceIs(Ht.trace,\"bar-like\")||iT.traceIs(Ht.trace,\"box-violin\")),Vt,rr;L===\"y\"?ar?(rr=ct-ol,Vt=ct+ol):(rr=Math.min.apply(null,dt.map(function(Et){return Math.min(Et.y0,Et.y1)})),Vt=Math.max.apply(null,dt.map(function(Et){return Math.max(Et.y0,Et.y1)}))):rr=Vt=zf.mean(dt.map(function(Et){return(Et.y0+Et.y1)/2}))-mt/2;var tt,je;L===\"x\"?ar?(tt=at+ol,je=at-ol):(tt=Math.max.apply(null,dt.map(function(Et){return Math.max(Et.x0,Et.x1)})),je=Math.min.apply(null,dt.map(function(Et){return Math.min(Et.x0,Et.x1)}))):tt=je=zf.mean(dt.map(function(Et){return(Et.x0+Et.x1)/2}))-lt/2;var Ue=M._offset,Ae=T._offset;Vt+=Ae,tt+=Ue,je+=Ue-lt,rr+=Ae-mt;var rt,St;return tt+lt<E&&tt>=0?rt=tt:je+lt<E&&je>=0?rt=je:Ue+lt<E?rt=Ue:tt-at<at-je+lt?rt=E-lt:rt=0,rt+=ol,Vt+mt<z&&Vt>=0?St=Vt:rr+mt<z&&rr>=0?St=rr:Ae+mt<z?St=Ae:Vt-ct<ct-rr+mt?St=z-mt:St=0,St+=ol,ce.attr(\"transform\",KB(rt-1,St-1)),ce}var Tt=s.selectAll(\"g.hovertext\").data(e,function(dt){return Wle(dt)});return Tt.enter().append(\"g\").classed(\"hovertext\",!0).each(function(){var dt=Wb.select(this);dt.append(\"rect\").call(vd.fill,vd.addOpacity(o,.8)),dt.append(\"text\").classed(\"name\",!0),dt.append(\"path\").style(\"stroke-width\",\"1px\"),dt.append(\"text\").classed(\"nums\",!0).call(Am.font,{weight:h,style:d,variant:v,textcase:m,lineposition:b,shadow:p,family:c,size:f})}),Tt.exit().remove(),Tt.each(function(dt){var Et=Wb.select(this).attr(\"transform\",\"\"),pt=dt.color;Array.isArray(pt)&&(pt=pt[dt.eventData[0].pointNumber]);var jt=dt.bgcolor||pt,or=vd.combine(vd.opacity(jt)?jt:vd.defaultLine,o),mr=vd.combine(vd.opacity(pt)?pt:vd.defaultLine,o),Ar=dt.borderColor||vd.contrast(or),ei=Nle(dt,q,i,n,C,Et),qr=ei[0],jr=ei[1],gt=Et.select(\"text.nums\").call(Am.font,{family:dt.fontFamily||c,size:dt.fontSize||f,color:dt.fontColor||Ar,weight:dt.fontWeight||h,style:dt.fontStyle||d,variant:dt.fontVariant||v,textcase:dt.fontTextcase||m,lineposition:dt.fontLineposition||b,shadow:dt.fontShadow||p}).text(qr).attr(\"data-notex\",1).call(A0.positionText,0,0).call(A0.convertToTspans,r),Ge=Et.select(\"text.name\"),Je=0,We=0;if(jr&&jr!==qr){Ge.call(Am.font,{family:dt.fontFamily||c,size:dt.fontSize||f,color:mr,weight:dt.fontWeight||h,style:dt.fontStyle||d,variant:dt.fontVariant||v,textcase:dt.fontTextcase||m,lineposition:dt.fontLineposition||b,shadow:dt.fontShadow||p}).text(jr).attr(\"data-notex\",1).call(A0.positionText,0,0).call(A0.convertToTspans,r);var et=rT(r,Ge.node());Je=et.width+2*ol,We=et.height+2*ol}else Ge.remove(),Et.select(\"rect\").remove();Et.select(\"path\").style({fill:or,stroke:Ar});var xt=dt.xa._offset+(dt.x0+dt.x1)/2,At=dt.ya._offset+(dt.y0+dt.y1)/2,Kt=Math.abs(dt.x1-dt.x0),Qt=Math.abs(dt.y1-dt.y0),Mr=rT(r,gt.node()),Gr=Mr.width/n._invScaleX,Ir=Mr.height/n._invScaleY;dt.ty0=(P-Mr.top)/n._invScaleY,dt.bx=Gr+2*ol,dt.by=Math.max(Ir+2*ol,We),dt.anchor=\"start\",dt.txwidth=Gr,dt.tx2width=Je,dt.offset=0;var Yr=(Gr+Qc+ol+Je)*n._invScaleX,_i,Pi;if(a)dt.pos=xt,_i=At+Qt/2+Yr<=z,Pi=At-Qt/2-Yr>=0,(dt.idealAlign===\"top\"||!_i)&&Pi?(At-=Qt/2,dt.anchor=\"end\"):_i?(At+=Qt/2,dt.anchor=\"start\"):dt.anchor=\"middle\",dt.crossPos=At;else{if(dt.pos=At,_i=xt+Kt/2+Yr<=E,Pi=xt-Kt/2-Yr>=0,(dt.idealAlign===\"left\"||!_i)&&Pi)xt-=Kt/2,dt.anchor=\"end\";else if(_i)xt+=Kt/2,dt.anchor=\"start\";else{dt.anchor=\"middle\";var ai=Yr/2,mi=xt+ai-E,un=xt-ai;mi>0&&(xt-=mi),un<0&&(xt+=-un)}dt.crossPos=xt}gt.attr(\"text-anchor\",dt.anchor),Je&&Ge.attr(\"text-anchor\",dt.anchor),Et.attr(\"transform\",KB(xt,At)+(a?Eut(jle):\"\"))}),{hoverLabels:Tt,commonLabelBoundingBox:H}}function Nle(e,t,r,n,i,a){var f,h;var o=\"\",s=\"\";e.nameOverride!==void 0&&(e.name=e.nameOverride),e.name&&(e.trace._meta&&(e.name=zf.templateString(e.name,e.trace._meta)),o=Gle(e.name,e.nameLength));var l=r.charAt(0),u=l===\"x\"?\"y\":\"x\";e.zLabel!==void 0?(e.xLabel!==void 0&&(s+=\"x: \"+e.xLabel+\"<br>\"),e.yLabel!==void 0&&(s+=\"y: \"+e.yLabel+\"<br>\"),e.trace.type!==\"choropleth\"&&e.trace.type!==\"choroplethmapbox\"&&e.trace.type!==\"choroplethmap\"&&(s+=(s?\"z: \":\"\")+e.zLabel)):t&&e[l+\"Label\"]===i?s=e[u+\"Label\"]||\"\":e.xLabel===void 0?e.yLabel!==void 0&&e.trace.type!==\"scattercarpet\"&&(s=e.yLabel):e.yLabel===void 0?s=e.xLabel:s=\"(\"+e.xLabel+\", \"+e.yLabel+\")\",(e.text||e.text===0)&&!Array.isArray(e.text)&&(s+=(s?\"<br>\":\"\")+e.text),e.extraText!==void 0&&(s+=(s?\"<br>\":\"\")+e.extraText),a&&s===\"\"&&!e.hovertemplate&&(o===\"\"&&a.remove(),s=o),(h=(f=e.trace)==null?void 0:f.hoverlabel)!=null&&h.split&&(e.hovertemplate=\"\");let{hovertemplate:c=!1}=e;if(c){let d=e.hovertemplateLabels||e;e[l+\"Label\"]!==i&&(d[l+\"other\"]=d[l+\"Val\"],d[l+\"otherLabel\"]=d[l+\"Label\"]),s=zf.hovertemplateString({data:[e.eventData[0]||{},e.trace._meta],fallback:e.trace.hovertemplatefallback,labels:d,locale:n._d3locale,template:c}),s=s.replace(Out,(v,m)=>(o=Gle(m,e.nameLength),\"\"))}return[s,o]}function But(e,t,r,n){var i=t?\"xa\":\"ya\",a=t?\"ya\":\"xa\",o=0,s=1,l=e.size(),u=new Array(l),c=0,f=n.minX,h=n.maxX,d=n.minY,v=n.maxY,m=function(Z){return Z*r._invScaleX},b=function(Z){return Z*r._invScaleY};e.each(function(Z){var j=Z[i],N=Z[a],H=j._id.charAt(0)===\"x\",re=j.range;c===0&&re&&re[0]>re[1]!==H&&(s=-1);var 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s=i.label.textposition,l=i.label.textangle,u=i.label.padding,c=i.type,f=Math.PI/180*a,h=Math.sin(f),d=Math.cos(f),v=i.label.xanchor,m=i.label.yanchor,b,p,k,M;if(c===\"line\"){s===\"start\"?(b=e,p=t):s===\"end\"?(b=r,p=n):(b=(e+r)/2,p=(t+n)/2),v===\"auto\"&&(s===\"start\"?l===\"auto\"?r>e?v=\"left\":r<e?v=\"right\":v=\"center\":r>e?v=\"right\":r<e?v=\"left\":v=\"center\":s===\"end\"?l===\"auto\"?r>e?v=\"right\":r<e?v=\"left\":v=\"center\":r>e?v=\"left\":r<e?v=\"right\":v=\"center\":v=\"center\");var T={left:1,center:0,right:-1},L={bottom:-1,middle:0,top:1};if(l===\"auto\"){var x=L[m];k=-u*h*x,M=u*d*x}else{var C=T[v],S=L[m];k=u*C,M=u*S}b=b+k,p=p+M}else k=u+3,s.indexOf(\"right\")!==-1?(b=Math.max(e,r)-k,v===\"auto\"&&(v=\"right\")):s.indexOf(\"left\")!==-1?(b=Math.min(e,r)+k,v===\"auto\"&&(v=\"left\")):(b=(e+r)/2,v===\"auto\"&&(v=\"center\")),s.indexOf(\"top\")!==-1?p=Math.min(t,n):s.indexOf(\"bottom\")!==-1?p=Math.max(t,n):p=(t+n)/2,M=u,m===\"bottom\"?p=p-M:m===\"top\"&&(p=p+M);var g=Nct[m],P=i.label.font.size,E=o.height,z=(E*g-P)*h,q=-(E*g-P)*d;return{textx:b+z,texty:p+q,xanchor:v}}});var cP=ye((xor,Xue)=>{\"use strict\";var Gct=Pr(),Hct=Gct.strTranslate,Nue=yv(),Gue=Cg(),jct=Gue.drawMode,Hue=Gue.selectMode,jue=Oa(),Uue=ka(),uP=rP(),Wct=uP.i000,Xct=uP.i090,Zct=uP.i180,Yct=uP.i270,Kct=s_(),Wue=Kct.clearOutlineControllers,sN=y_(),lP=sN.pointsOnRectangle,oN=sN.pointsOnEllipse,Jct=sN.writePaths,$ct=sP().newShapes,Qct=sP().createShapeObj,eft=nN(),tft=aN();Xue.exports=function e(t,r,n,i){i||(i=0);var a=n.gd;function o(){e(t,r,n,i++),(oN(t[0])||n.hasText)&&s({redrawing:!0})}function s(j){var N={};n.isActiveShape!==void 0&&(n.isActiveShape=!1,N=$ct(r,n)),n.isActiveSelection!==void 0&&(n.isActiveSelection=!1,N=eft(r,n),a._fullLayout._reselect=!0),Object.keys(N).length&&jue.call((j||{}).redrawing?\"relayout\":\"_guiRelayout\",a,N)}var l=a._fullLayout,u=l._zoomlayer,c=n.dragmode,f=jct(c),h=Hue(c);(f||h)&&(a._fullLayout._outlining=!0),Wue(a),r.attr(\"d\",Jct(t));var 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be=ge[1]-ie,Be=ge[2]-Se,Le=Be?5:Math.max(Math.min(25,Math.abs(be)-5),5),me=be?5:Math.max(Math.min(25,Math.abs(Be)-5),5);ke.classed(Be?\"cursor-ew-resize\":\"cursor-ns-resize\",!0).attr(\"width\",Le).attr(\"height\",me).attr(\"x\",ie-Le/2).attr(\"y\",Se-me/2).attr(\"transform\",Hct(be/2,Be/2))}else ke.classed(\"cursor-grab\",!0).attr(\"r\",5).attr(\"cx\",ie).attr(\"cy\",Se);d[N][Ee]={element:ke.node(),gd:a,prepFn:L,doneFn:C,clickFn:g},Nue.init(d[N][Ee])}}}function E(j,N){if(t.length)for(var H=0;H<t.length;H++)for(var re=0;re<t[H].length;re++)for(var ae=0;ae+2<t[H][re].length;ae+=2)t[H][re][ae+1]=p[H][re][ae+1]+j,t[H][re][ae+2]=p[H][re][ae+2]+N}function z(j,N){E(j,N),o()}function q(j){m=+j.srcElement.getAttribute(\"data-i\"),m||(m=0),v[m].moveFn=z}function V(){s()}function G(j){j===2&&ift(a)}function Z(){if(v=[],!!t.length){var j=0;v[j]={element:r[0][0],gd:a,prepFn:q,doneFn:V,clickFn:G},Nue.init(v[j])}}};function rft(e,t){for(var r=0;r<t.length;r++){var n=t[r];e[r]=[];for(var 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t=e._fullLayout;t._shapeUpperLayer.selectAll(\"path\").remove(),t._shapeLowerLayer.selectAll(\"path\").remove(),t._shapeUpperLayer.selectAll(\"text\").remove(),t._shapeLowerLayer.selectAll(\"text\").remove();for(var r in t._plots){var n=t._plots[r].shapelayer;n&&(n.selectAll(\"path\").remove(),n.selectAll(\"text\").remove())}for(var i=0;i<t.shapes.length;i++)t.shapes[i].visible===!0&&tce(e,i)}function fP(e){return!!e._fullLayout._outlining}function vP(e){return!e._context.edits.shapePosition}function tce(e,t){e._fullLayout._paperdiv.selectAll('.shapelayer [data-index=\"'+t+'\"]').remove();var r=Ep.makeShapesOptionsAndPlotinfo(e,t),n=r.options,i=r.plotinfo;if(!n._input||n.visible!==!0)return;let a=Array.isArray(n.xref)||Array.isArray(n.yref);if(n.layer===\"above\")s(e._fullLayout._shapeUpperLayer);else if(n.xref.includes(\"paper\")||n.yref.includes(\"paper\"))s(e._fullLayout._shapeLowerLayer);else if(n.layer===\"between\"&&!a)s(i.shapelayerBetween);else if(i._hadPlotinfo){var 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a=i.append(\"div\").classed(\"plotly-cloud-dialog\",!0),o=a.append(\"div\").classed(\"plotly-cloud-dialog-box\",!0);o.append(\"div\").classed(\"plotly-cloud-dialog-title\",!0).text(gP(t,\"Share with Plotly Cloud\")),o.append(\"div\").classed(\"plotly-cloud-dialog-message\",!0).text(gP(t,\"This chart and its data will be sent to\")+\" \"+r+\".\");var s=o.append(\"div\").classed(\"plotly-cloud-dialog-buttons\",!0);function l(){a.remove(),document.removeEventListener(\"keydown\",u)}function u(c){(c.key===\"Escape\"||c.keyCode===27)&&l()}document.addEventListener(\"keydown\",u),a.on(\"click\",function(){nce.event.target===a.node()&&l()}),s.append(\"button\").classed(\"plotly-cloud-dialog-btn\",!0).classed(\"plotly-cloud-dialog-btn--cancel\",!0).text(gP(t,\"Cancel\")).on(\"click\",l),s.append(\"button\").classed(\"plotly-cloud-dialog-btn\",!0).classed(\"plotly-cloud-dialog-btn--confirm\",!0).text(gP(t,\"Share\")).on(\"click\",function(){l(),n()})}});var vN=ye((Tor,vce)=>{\"use strict\";var S0=Oa(),dft=Mc(),sce=df(),Gl=eP(),vft=pP().eraseActiveShape,pft=oce(),mP=Pr(),sl=mP._,_l=vce.exports={};_l.toImage={name:\"toImage\",title:function(e){var t=e._context.toImageButtonOptions||{},r=t.format||\"png\";return r===\"png\"?sl(e,\"Download plot as a PNG\"):sl(e,\"Download plot\")},icon:Gl.camera,click:function(e){var t=e._context.toImageButtonOptions,r={format:t.format||\"png\"};mP.notifier(sl(e,\"Taking snapshot - this may take a few seconds\"),\"long\",e),[\"filename\",\"width\",\"height\",\"scale\"].forEach(function(n){n in t&&(r[n]=t[n])}),S0.call(\"downloadImage\",e,r).then(function(n){mP.notifier(sl(e,\"Snapshot succeeded\")+\" - \"+n,\"long\",e)}).catch(function(){mP.notifier(sl(e,\"Sorry, there was a problem downloading your snapshot!\"),\"long\",e)})}};_l.sendChartToCloud={name:\"sendChartToCloud\",title:function(e){return sl(e,\"Share with Plotly Cloud\")},icon:Gl.cloudupload,click:function(e){var t=(window.PLOTLYENV||{}).BASE_URL||e._context.plotlyServerURL;if(!t){console.error(\"No destination URL provided (plotlyServerURL is not set)\");return}try{new URL(t)}catch(r){console.error(\"Invalid plotlyServerURL: \"+t);return}pft(e,t,function(){dft.sendDataToCloud(e,t)})}};_l.sendDataToCloud=_l.sendChartToCloud;_l.zoom2d={name:\"zoom2d\",_cat:\"zoom\",title:function(e){return sl(e,\"Zoom\")},attr:\"dragmode\",val:\"zoom\",icon:Gl.zoombox,click:Bv};_l.pan2d={name:\"pan2d\",_cat:\"pan\",title:function(e){return sl(e,\"Pan\")},attr:\"dragmode\",val:\"pan\",icon:Gl.pan,click:Bv};_l.select2d={name:\"select2d\",_cat:\"select\",title:function(e){return sl(e,\"Box Select\")},attr:\"dragmode\",val:\"select\",icon:Gl.selectbox,click:Bv};_l.lasso2d={name:\"lasso2d\",_cat:\"lasso\",title:function(e){return sl(e,\"Lasso Select\")},attr:\"dragmode\",val:\"lasso\",icon:Gl.lasso,click:Bv};_l.drawclosedpath={name:\"drawclosedpath\",title:function(e){return sl(e,\"Draw closed 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0&&(o[v+\".showspikes\"]=u._showSpikeInitial,l===\"on\"&&!u._showSpikeInitial&&(l=\"off\"));else{var b=[u.r2l(u.range[0]),u.r2l(u.range[1])],p=[h*b[0]+d*b[1],h*b[1]+d*b[0]];o[v+\".range[0]\"]=u.l2r(p[0]),o[v+\".range[1]\"]=u.l2r(p[1])}}else n===\"hovermode\"&&(i===\"x\"||i===\"y\")&&(i=a._isHoriz?\"y\":\"x\",r.setAttribute(\"data-val\",i)),o[n]=i;a._cartesianSpikesEnabled=l,S0.call(\"_guiRelayout\",e,o)}_l.zoom3d={name:\"zoom3d\",_cat:\"zoom\",title:function(e){return sl(e,\"Zoom\")},attr:\"scene.dragmode\",val:\"zoom\",icon:Gl.zoombox,click:yP};_l.pan3d={name:\"pan3d\",_cat:\"pan\",title:function(e){return sl(e,\"Pan\")},attr:\"scene.dragmode\",val:\"pan\",icon:Gl.pan,click:yP};_l.orbitRotation={name:\"orbitRotation\",title:function(e){return sl(e,\"Orbital rotation\")},attr:\"scene.dragmode\",val:\"orbit\",icon:Gl[\"3d_rotate\"],click:yP};_l.tableRotation={name:\"tableRotation\",title:function(e){return sl(e,\"Turntable 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c=s[u],f=c+\".camera\",h=c+\".aspectratio\",d=c+\".aspectmode\",v=o[c]._scene,m;i?(l[f+\".up\"]=v.viewInitial.up,l[f+\".eye\"]=v.viewInitial.eye,l[f+\".center\"]=v.viewInitial.center,m=!0):a&&(l[f+\".up\"]=null,l[f+\".eye\"]=null,l[f+\".center\"]=null,m=!0),m&&(l[h+\".x\"]=v.viewInitial.aspectratio.x,l[h+\".y\"]=v.viewInitial.aspectratio.y,l[h+\".z\"]=v.viewInitial.aspectratio.z,l[d]=v.viewInitial.aspectmode)}S0.call(\"_guiRelayout\",e,l)}_l.hoverClosest3d={name:\"hoverClosest3d\",_cat:\"hoverclosest\",title:function(e){return sl(e,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:Gl.tooltip_basic,gravity:\"ne\",click:gft};function lce(e,t){var r=t.currentTarget,n=r._previousVal,i=e._fullLayout,a=i._subplots.gl3d||[],o=[\"xaxis\",\"yaxis\",\"zaxis\"],s={},l={};if(n)l=n,r._previousVal=null;else{for(var u=0;u<a.length;u++){var c=a[u],f=i[c],h=c+\".hovermode\";s[h]=f.hovermode,l[h]=!1;for(var d=0;d<3;d++){var v=o[d],m=c+\".\"+v+\".showspikes\";l[m]=!1,s[m]=f[v].showspikes}}r._previousVal=s}return l}function gft(e,t){var r=lce(e,t);S0.call(\"_guiRelayout\",e,r)}_l.zoomInGeo={name:\"zoomInGeo\",_cat:\"zoomin\",title:function(e){return sl(e,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:Gl.zoom_plus,click:dN};_l.zoomOutGeo={name:\"zoomOutGeo\",_cat:\"zoomout\",title:function(e){return sl(e,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:Gl.zoom_minus,click:dN};_l.resetGeo={name:\"resetGeo\",_cat:\"reset\",title:function(e){return sl(e,\"Reset\")},attr:\"reset\",val:null,icon:Gl.autoscale,click:dN};_l.hoverClosestGeo={name:\"hoverClosestGeo\",_cat:\"hoverclosest\",title:function(e){return sl(e,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:Gl.tooltip_basic,gravity:\"ne\",click:cce};function dN(e,t){for(var r=t.currentTarget,n=r.getAttribute(\"data-attr\"),i=r.getAttribute(\"data-val\")||!0,a=e._fullLayout,o=a._subplots.geo||[],s=0;s<o.length;s++){var l=o[s],u=a[l];if(n===\"zoom\"){var c=u.projection.scale,f=i===\"in\"?2*c:.5*c;S0.call(\"_guiRelayout\",e,l+\".projection.scale\",f)}}n===\"reset\"&&oT(e,\"geo\")}_l.hoverClosestPie={name:\"hoverClosestPie\",_cat:\"hoverclosest\",title:function(e){return sl(e,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:\"closest\",icon:Gl.tooltip_basic,gravity:\"ne\",click:cce};function uce(e){var t=e._fullLayout;return t.hovermode?!1:t._has(\"cartesian\")?t._isHoriz?\"y\":\"x\":\"closest\"}function cce(e){var t=uce(e);S0.call(\"_guiRelayout\",e,\"hovermode\",t)}_l.resetViewSankey={name:\"resetSankeyGroup\",title:function(e){return sl(e,\"Reset view\")},icon:Gl.home,click:function(e){for(var t={\"node.groups\":[],\"node.x\":[],\"node.y\":[]},r=0;r<e._fullData.length;r++){var n=e._fullData[r]._viewInitial;t[\"node.groups\"].push(n.node.groups.slice()),t[\"node.x\"].push(n.node.x.slice()),t[\"node.y\"].push(n.node.y.slice())}S0.call(\"restyle\",e,t)}};_l.toggleHover={name:\"toggleHover\",title:function(e){return sl(e,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:Gl.tooltip_basic,gravity:\"ne\",click:function(e,t){var r=lce(e,t);r.hovermode=uce(e),S0.call(\"_guiRelayout\",e,r)}};_l.resetViews={name:\"resetViews\",title:function(e){return sl(e,\"Reset views\")},icon:Gl.home,click:function(e,t){var r=t.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),Bv(e,t),r.setAttribute(\"data-attr\",\"resetLastSave\"),hN(e,t),oT(e,\"geo\"),oT(e,\"mapbox\"),oT(e,\"map\")}};_l.toggleSpikelines={name:\"toggleSpikelines\",title:function(e){return sl(e,\"Toggle Spike Lines\")},icon:Gl.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(e){var t=e._fullLayout,r=t._cartesianSpikesEnabled;t._cartesianSpikesEnabled=r===\"on\"?\"off\":\"on\",S0.call(\"_guiRelayout\",e,mft(e))}};function mft(e){for(var t=e._fullLayout,r=t._cartesianSpikesEnabled===\"on\",n=sce.list(e,null,!0),i={},a=0;a<n.length;a++){var o=n[a];i[o._name+\".showspikes\"]=r?!0:o._showSpikeInitial}return i}_l.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(e){return sl(e,\"Reset view\")},attr:\"reset\",icon:Gl.home,click:function(e){oT(e,\"mapbox\")}};_l.resetViewMap={name:\"resetViewMap\",_cat:\"resetView\",title:function(e){return sl(e,\"Reset view\")},attr:\"reset\",icon:Gl.home,click:function(e){oT(e,\"map\")}};_l.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(e){return sl(e,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:Gl.zoom_plus,click:fce};_l.zoomInMap={name:\"zoomInMap\",_cat:\"zoomin\",title:function(e){return sl(e,\"Zoom 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pce=vN(),yft=Object.keys(pce),gce=[\"drawline\",\"drawopenpath\",\"drawclosedpath\",\"drawcircle\",\"drawrect\",\"eraseshape\"],mce=[\"v1hovermode\",\"hoverclosest\",\"hovercompare\",\"togglehover\",\"togglespikelines\"].concat(gce),sT=[],_ft=function(e){if(mce.indexOf(e._cat||e.name)===-1){var t=e.name,r=(e._cat||e.name).toLowerCase();sT.indexOf(t)===-1&&sT.push(t),sT.indexOf(r)===-1&&sT.push(r)}};yft.forEach(function(e){_ft(pce[e])});sT.sort();yce.exports={DRAW_MODES:gce,backButtons:mce,foreButtons:sT}});var gN=ye((Mor,_ce)=>{\"use strict\";var Sor=pN();_ce.exports={editType:\"modebar\",orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\",editType:\"modebar\"},bgcolor:{valType:\"color\",editType:\"modebar\"},color:{valType:\"color\",editType:\"modebar\"},activecolor:{valType:\"color\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},add:{valType:\"string\",arrayOk:!0,dflt:\"\",editType:\"modebar\"},remove:{valType:\"string\",arrayOk:!0,dflt:\"\",editType:\"modebar\"}}});var bce=ye((Eor,xce)=>{\"use strict\";var xft=Pr(),CM=ka(),bft=vl(),wft=gN();xce.exports=function(t,r){var n=t.modebar||{},i=bft.newContainer(r,\"modebar\");function a(s,l){return xft.coerce(n,i,wft,s,l)}a(\"orientation\"),a(\"bgcolor\",CM.addOpacity(r.paper_bgcolor,.5));var o=CM.contrast(CM.rgb(r.modebar.bgcolor));a(\"color\",CM.addOpacity(o,.3)),a(\"activecolor\",CM.addOpacity(o,.7)),a(\"uirevision\",r.uirevision),a(\"add\"),a(\"remove\")}});var Sce=ye((kor,Ace)=>{\"use strict\";var mN=Fa(),Tft=Eo(),_P=Pr(),wce=eP(),Aft=OS().version,Sft=new DOMParser;function Tce(e){this.container=e.container,this.element=document.createElement(\"div\"),this.update(e.graphInfo,e.buttons),this.container.appendChild(this.element)}var Cm=Tce.prototype;Cm.update=function(e,t){this.graphInfo=e;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this.element.setAttribute(\"role\",\"toolbar\"),this._uid=i,this.element.className=\"modebar modebar--custom\",r.displayModeBar===\"hover\"&&(this.element.className+=\" modebar--hover ease-bg\"),n.modebar.orientation===\"v\"&&(this.element.className+=\" vertical\",t=t.reverse());var a=n.modebar,o=\"#\"+i+\" .modebar-group\";document.querySelectorAll(o).forEach(function(f){f.style.backgroundColor=a.bgcolor});var s=!this.hasButtons(t),l=this.hasLogo!==r.displaylogo,u=this.locale!==r.locale;if(this.locale=r.locale,(s||l||u)&&(this.removeAllButtons(),this.updateButtons(t),r.watermark||r.displaylogo)){var c=this.getLogo();r.watermark&&(c.className=c.className+\" watermark\"),n.modebar.orientation===\"v\"?this.element.insertBefore(c,this.element.childNodes[0]):this.element.appendChild(c),this.hasLogo=!0}this.updateActiveButton(),_P.setStyleOnHover(\"#\"+i+\" .modebar-btn\",\".active\",\".icon path\",\"fill: \"+a.activecolor,\"fill: \"+a.color,this.element)};Cm.updateButtons=function(e){var t=this;this.buttons=e,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach(function(r){var n=t.createGroup();r.forEach(function(i){var a=i.name;if(!a)throw new Error(\"must provide button 'name' in button config\");if(t.buttonsNames.indexOf(a)!==-1)throw new Error(\"button name '\"+a+\"' is taken\");t.buttonsNames.push(a);var o=t.createButton(i);t.buttonElements.push(o),n.appendChild(o)}),t.element.appendChild(n)})};Cm.createGroup=function(){var e=document.createElement(\"div\");e.className=\"modebar-group\";var t=this.graphInfo._fullLayout.modebar;return e.style.backgroundColor=t.bgcolor,e};Cm.createButton=function(e){var t=this,r=document.createElement(\"button\");r.setAttribute(\"type\",\"button\"),r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var n=e.title;n===void 0?n=e.name:typeof n==\"function\"&&(n=n(this.graphInfo)),(n||n===0)&&(r.setAttribute(\"data-title\",n),r.setAttribute(\"aria-label\",n)),e.attr!==void 0&&r.setAttribute(\"data-attr\",e.attr);var i=e.val;i!==void 0&&(typeof i==\"function\"&&(i=i(this.graphInfo)),r.setAttribute(\"data-val\",i));var a=e.click;if(typeof a!=\"function\")throw new Error(\"must provide button 'click' function in button config\");r.addEventListener(\"click\",function(s){e.click(t.graphInfo,s),t.updateActiveButton(s.currentTarget)}),r.setAttribute(\"data-toggle\",e.toggle||!1),e.toggle&&mN.select(r).classed(\"active\",!0);var o=e.icon;return typeof o==\"function\"?r.appendChild(o()):r.appendChild(this.createIcon(o||wce.question)),r.setAttribute(\"data-gravity\",e.gravity||\"n\"),r};Cm.createIcon=function(e){var t=Tft(e.height)?Number(e.height):e.ascent-e.descent,r=\"http://www.w3.org/2000/svg\",n;if(e.path){n=document.createElementNS(r,\"svg\"),n.setAttribute(\"viewBox\",[0,0,e.width,t].join(\" \")),n.setAttribute(\"class\",\"icon\");var i=document.createElementNS(r,\"path\");i.setAttribute(\"d\",e.path),e.transform?i.setAttribute(\"transform\",e.transform):e.ascent!==void 0&&i.setAttribute(\"transform\",\"matrix(1 0 0 -1 0 \"+e.ascent+\")\"),n.appendChild(i)}if(e.svg){var a=Sft.parseFromString(e.svg,\"application/xml\");n=a.childNodes[0]}return n.setAttribute(\"height\",\"1em\"),n.setAttribute(\"width\",\"1em\"),n};Cm.updateActiveButton=function(e){var t=this.graphInfo._fullLayout,r=e!==void 0?e.getAttribute(\"data-attr\"):null;this.buttonElements.forEach(function(n){var i=n.getAttribute(\"data-val\")||!0,a=n.getAttribute(\"data-attr\"),o=n.getAttribute(\"data-toggle\")===\"true\",s=mN.select(n),l=function(f,h){var d=t.modebar,v=f.querySelector(\".icon path\");v&&(h||f.matches(\":hover\")?v.style.fill=d.activecolor:v.style.fill=d.color)};if(o){if(a===r){var u=!s.classed(\"active\");s.classed(\"active\",u),l(n,u)}}else{var c=a===null?a:_P.nestedProperty(t,a).get();s.classed(\"active\",c===i),l(n,c===i)}})};Cm.hasButtons=function(e){var t=this.buttons;if(!t||e.length!==t.length)return!1;for(var r=0;r<e.length;++r){if(e[r].length!==t[r].length)return!1;for(var n=0;n<e[r].length;n++)if(e[r][n].name!==t[r][n].name)return!1}return!0};function Mft(e){return e+\" (v\"+Aft+\")\"}Cm.getLogo=function(){var e=this.createGroup(),t=document.createElement(\"a\");return t.href=\"https://plotly.com/\",t.target=\"_blank\",t.setAttribute(\"data-title\",Mft(_P._(this.graphInfo,\"Produced with Plotly.js\"))),t.className=\"modebar-btn plotlyjsicon modebar-btn--logo\",t.appendChild(this.createIcon(wce.newplotlylogo)),e.appendChild(t),e};Cm.removeAllButtons=function(){for(;this.element.firstChild;)this.element.removeChild(this.element.firstChild);this.hasLogo=!1};Cm.destroy=function(){_P.removeElement(this.container.querySelector(\".modebar\"))};function Eft(e,t){var r=e._fullLayout,n=new Tce({graphInfo:e,container:r._modebardiv.node(),buttons:t});return r._privateplot&&mN.select(n.element).append(\"span\").classed(\"badge-private float--left\",!0).text(\"PRIVATE\"),n}Ace.exports=Eft});var kce=ye((Cor,Ece)=>{\"use strict\";var kft=df(),Mce=Ru(),yN=Oa(),Cft=ip().isUnifiedHover,Lft=Sce(),xP=vN(),Pft=pN().DRAW_MODES,Ift=Pr().extendDeep;Ece.exports=function(t){var r=t._fullLayout,n=t._context,i=r._modeBar;if(!n.displayModeBar&&!n.watermark){i&&(i.destroy(),delete r._modeBar);return}if(!Array.isArray(n.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(n.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var a=n.modeBarButtons,o;Array.isArray(a)&&a.length?o=Oft(a):!n.displayModeBar&&n.watermark?o=[]:o=Rft(t),i?i.update(t,o):r._modeBar=Lft(t,o)};function Rft(e){var t=e._fullLayout,r=e._fullData,n=e._context;function i(N,H){if(typeof H==\"string\"){if(H.toLowerCase()===N.toLowerCase())return!0}else{var re=H.name,ae=H._cat||H.name;if(re===N||ae===N.toLowerCase())return!0}return!1}var a=t.modebar.add;typeof a==\"string\"&&(a=[a]);var o=t.modebar.remove;typeof o==\"string\"&&(o=[o]);var s=n.modeBarButtonsToAdd.concat(a.filter(function(N){for(var H=0;H<n.modeBarButtonsToRemove.length;H++)if(i(N,n.modeBarButtonsToRemove[H]))return!1;return!0})),l=n.modeBarButtonsToRemove.concat(o.filter(function(N){for(var H=0;H<n.modeBarButtonsToAdd.length;H++)if(i(N,n.modeBarButtonsToAdd[H]))return!1;return!0})),u=t._has(\"cartesian\"),c=t._has(\"gl3d\"),f=t._has(\"geo\"),h=t._has(\"pie\"),d=t._has(\"funnelarea\"),v=t._has(\"ternary\"),m=t._has(\"mapbox\"),b=t._has(\"map\"),p=t._has(\"polar\"),k=t._has(\"smith\"),M=t._has(\"sankey\"),T=Dft(t),L=Cft(t.hovermode),x=[];function C(N){if(N.length){for(var H=[],re=0;re<N.length;re++){for(var ae=N[re],_e=xP[ae],Ee=_e.name.toLowerCase(),Ce=(_e._cat||_e.name).toLowerCase(),ge=!1,ie=0;ie<l.length;ie++){var Se=l[ie].toLowerCase();if(Se===Ee||Se===Ce){ge=!0;break}}ge||H.push(xP[ae])}x.push(H)}}var S=[\"toImage\"];n.showSendToCloud?S.push(\"sendChartToCloud\"):n.showEditInChartStudio&&(console.warn([\"*showEditInChartStudio* is deprecated.\",\"Use *showSendToCloud* instead.\"].join(\" \")),S.push(\"sendChartToCloud\")),C(S);var g=[],P=[],E=[],z=[];(u||h||d||v)+f+c+m+b+p+k>1?(P=[\"toggleHover\"],E=[\"resetViews\"]):f?(g=[\"zoomInGeo\",\"zoomOutGeo\"],P=[\"hoverClosestGeo\"],E=[\"resetGeo\"]):c?(P=[\"hoverClosest3d\"],E=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):m?(g=[\"zoomInMapbox\",\"zoomOutMapbox\"],P=[\"toggleHover\"],E=[\"resetViewMapbox\"]):b?(g=[\"zoomInMap\",\"zoomOutMap\"],P=[\"toggleHover\"],E=[\"resetViewMap\"]):h?P=[\"hoverClosestPie\"]:M?(P=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],E=[\"resetViewSankey\"]):P=[\"toggleHover\"],u&&P.push(\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"),(zft(r)||L)&&(P=[]),u&&!T&&(g=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],E[0]!==\"resetViews\"&&(E=[\"resetScale2d\"])),c?z=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:u&&!T||v?z=[\"zoom2d\",\"pan2d\"]:m||b||f?z=[\"pan2d\"]:p&&(z=[\"zoom2d\"]),Fft(r)&&z.push(\"select2d\",\"lasso2d\");var q=[],V=function(N){q.indexOf(N)===-1&&P.indexOf(N)!==-1&&q.push(N)};if(Array.isArray(s)){for(var G=[],Z=0;Z<s.length;Z++){var j=s[Z];typeof j==\"string\"?(j=j.toLowerCase(),Pft.indexOf(j)!==-1?(t._has(\"mapbox\")||t._has(\"map\")||t._has(\"cartesian\"))&&z.push(j):j===\"togglespikelines\"?V(\"toggleSpikelines\"):j===\"togglehover\"?V(\"toggleHover\"):j===\"hovercompare\"?V(\"hoverCompareCartesian\"):j===\"hoverclosest\"?(V(\"hoverClosestCartesian\"),V(\"hoverClosestGeo\"),V(\"hoverClosest3d\"),V(\"hoverClosestPie\")):j===\"v1hovermode\"&&(V(\"hoverClosestCartesian\"),V(\"hoverCompareCartesian\"),V(\"hoverClosestGeo\"),V(\"hoverClosest3d\"),V(\"hoverClosestPie\"))):G.push(j)}s=G}return C(z),C(g.concat(E)),C(q),qft(x,s)}function Dft(e){for(var t=kft.list({_fullLayout:e},null,!0),r=0;r<t.length;r++){var n=t[r].modebardisable;if(!t[r].fixedrange&&n!==\"autoscale+zoominout\"&&n!==\"zoominout+autoscale\")return!1}return!0}function Fft(e){for(var t=!1,r=0;r<e.length&&!t;r++){var n=e[r];!n._module||!n._module.selectPoints||(yN.traceIs(n,\"scatter-like\")?(Mce.hasMarkers(n)||Mce.hasText(n))&&(t=!0):yN.traceIs(n,\"box-violin\")?(n.boxpoints===\"all\"||n.points===\"all\")&&(t=!0):t=!0)}return t}function zft(e){for(var t=0;t<e.length;t++)if(!yN.traceIs(e[t],\"noHover\"))return!1;return!0}function qft(e,t){if(t.length)if(Array.isArray(t[0]))for(var r=0;r<t.length;r++)e.push(t[r]);else e.push(t);return e}function Oft(e){for(var t=Ift([],e),r=0;r<t.length;r++)for(var n=t[r],i=0;i<n.length;i++){var a=n[i];if(typeof a==\"string\")if(xP[a]!==void 0)t[r][i]=xP[a];else throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"))}return t}});var _N=ye((Lor,Cce)=>{\"use strict\";Cce.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:gN(),supplyLayoutDefaults:bce(),manage:kce()}});var xN=ye((Por,Lce)=>{\"use strict\";var Bft=Dh().FROM_BL;Lce.exports=function(t,r,n){n===void 0&&(n=Bft[t.constraintoward||\"center\"]);var i=[t.r2l(t.range[0]),t.r2l(t.range[1])],a=i[0]+(i[1]-i[0])*n;t.range=t._input.range=[t.l2r(a+(i[0]-a)*r),t.l2r(a+(i[1]-a)*r)],t.setScale()}});var $b=ye(LM=>{\"use strict\";var Jb=Pr(),bN=Mg(),Lg=df().id2name,Nft=Rd(),Pce=xN(),Uft=xm(),Vft=fs().ALMOST_EQUAL,Gft=Dh().FROM_BL;LM.handleDefaults=function(e,t,r){var n=r.axIds,i=r.axHasImage,a=t._axisConstraintGroups=[],o=t._axisMatchGroups=[],s,l,u,c,f,h,d,v;for(s=0;s<n.length;s++)c=Lg(n[s]),f=e[c],h=t[c],Hft(f,h,{axIds:n,layoutOut:t,hasImage:i[c]});function m(S,g){for(s=0;s<S.length;s++){l=S[s];for(u in l)t[Lg(u)][g]=l}}for(m(o,\"_matchGroup\"),s=0;s<a.length;s++){l=a[s];for(u in l)if(h=t[Lg(u)],h.fixedrange){for(var b in l){var p=Lg(b);(e[p]||{}).fixedrange===!1&&Jb.warn(\"fixedrange was specified as false for axis \"+p+\" but was overridden because another axis in its constraint group has fixedrange true\"),t[p].fixedrange=!0}break}}for(s=0;s<a.length;){l=a[s];for(u in l){h=t[Lg(u)],h._matchGroup&&Object.keys(h._matchGroup).length===Object.keys(l).length&&(a.splice(s,1),s--);break}s++}m(a,\"_constraintGroup\");var k=[\"constrain\",\"range\",\"autorange\",\"rangemode\",\"rangebreaks\",\"categoryorder\",\"categoryarray\"],M=!1,T=!1;function L(){v=h[d],d===\"rangebreaks\"&&(T=h._hasDayOfWeekBreaks)}for(s=0;s<o.length;s++){l=o[s];for(var x=0;x<k.length;x++){d=k[x],v=null;var C;for(u in l)if(c=Lg(u),f=e[c],h=t[c],d in h){if(!h.matches&&(C=h,d in f)){L();break}v===null&&d in f&&L()}if(d===\"range\"&&v&&f.range&&f.range.length===2&&f.range[0]!==null&&f.range[1]!==null&&(M=!0),d===\"autorange\"&&v===null&&M&&(v=!1),v===null&&d in C&&(v=C[d]),v!==null)for(u in l)h=t[Lg(u)],h[d]=d===\"range\"?v.slice():v,d===\"rangebreaks\"&&(h._hasDayOfWeekBreaks=T,Uft(h,t))}}};function Hft(e,t,r){var n=r.axIds,i=r.layoutOut,a=r.hasImage,o=i._axisConstraintGroups,s=i._axisMatchGroups,l=t._id,u=l.charAt(0),c=((i._splomAxes||{})[u]||{})[l]||{},f=t._id,h=f.charAt(0)===\"x\";t._matchGroup=null,t._constraintGroup=null;function d(z,q){return Jb.coerce(e,t,Nft,z,q)}d(\"constrain\",a?\"domain\":\"range\"),Jb.coerce(e,t,{constraintoward:{valType:\"enumerated\",values:h?[\"left\",\"center\",\"right\"]:[\"bottom\",\"middle\",\"top\"],dflt:h?\"center\":\"middle\"}},\"constraintoward\");var v=t.type,m,b,p=[];for(m=0;m<n.length;m++)if(b=n[m],b!==f){var k=i[Lg(b)];k.type===v&&p.push(b)}var M=Dce(o,f);if(M){var T=[];for(m=0;m<p.length;m++)b=p[m],M[b]||T.push(b);p=T}var L=p.length,x,C;L&&(e.matches||c.matches)&&(x=Jb.coerce(e,t,{matches:{valType:\"enumerated\",values:p,dflt:p.indexOf(c.matches)!==-1?c.matches:void 0}},\"matches\"));var S=a&&!h?t.anchor:void 0;if(L&&!x&&(e.scaleanchor||S)&&(C=Jb.coerce(e,t,{scaleanchor:{valType:\"enumerated\",values:p.concat([!1])}},\"scaleanchor\",S)),x){t._matchGroup=wN(s,f,x,1);var g=i[Lg(x)],P=Ice(i,t)/Ice(i,g);h!==(x.charAt(0)===\"x\")&&(P=(h?\"x\":\"y\")+P),wN(o,f,x,P)}else e.matches&&n.indexOf(e.matches)!==-1&&Jb.warn(\"ignored \"+t._name+'.matches: \"'+e.matches+'\" to avoid an infinite loop');if(C){var E=d(\"scaleratio\");E||(E=t.scaleratio=1),wN(o,f,C,E)}else e.scaleanchor&&n.indexOf(e.scaleanchor)!==-1&&Jb.warn(\"ignored \"+t._name+'.scaleanchor: \"'+e.scaleanchor+'\" to avoid either an infinite loop and possibly inconsistent scaleratios, or because this axis declares a *matches* constraint.')}function Ice(e,t){var r=t.domain;return r||(r=e[Lg(t.overlaying)].domain),r[1]-r[0]}function Dce(e,t){for(var r=0;r<e.length;r++)if(e[r][t])return e[r];return null}function wN(e,t,r,n){var i,a,o,s,l,u=Dce(e,t);u===null?(u={},u[t]=1,l=e.length,e.push(u)):l=e.indexOf(u);var c=Object.keys(u);for(i=0;i<e.length;i++)if(o=e[i],i!==l&&o[r]){var f=o[r];for(a=0;a<c.length;a++)s=c[a],o[s]=TN(f,TN(n,u[s]));e.splice(l,1);return}if(n!==1)for(a=0;a<c.length;a++){var h=c[a];u[h]=TN(n,u[h])}u[r]=1}function TN(e,t){var r=\"\",n=\"\",i,a;typeof e==\"string\"&&(r=e.match(/^[xy]*/)[0],i=r.length,e=+e.slice(i)),typeof t==\"string\"&&(n=t.match(/^[xy]*/)[0],a=n.length,t=+t.slice(a));var o=e*t;return!i&&!a?o:!i||!a||r.charAt(0)===n.charAt(0)?r+n+e*t:i===a?o:(i>a?r.slice(a):n.slice(i))+o}function jft(e,t){for(var r=t._size,n=r.h/r.w,i={},a=Object.keys(e),o=0;o<a.length;o++){var s=a[o],l=e[s];if(typeof l==\"string\"){var u=l.match(/^[xy]*/)[0],c=u.length;l=+l.slice(c);for(var f=u.charAt(0)===\"y\"?n:1/n,h=0;h<c;h++)l*=f}i[s]=l}return i}LM.enforce=function(t){var r=t._fullLayout,n=r._axisConstraintGroups||[],i,a,o,s,l,u,c,f;for(i=0;i<n.length;i++){o=jft(n[i],r);var h=Object.keys(o),d=1/0,v=0,m=1/0,b={},p={},k=!1;for(a=0;a<h.length;a++)s=h[a],p[s]=l=r[Lg(s)],l._inputDomain?l.domain=l._inputDomain.slice():l._inputDomain=l.domain.slice(),l._inputRange||(l._inputRange=l.range.slice()),l.setScale(),b[s]=u=Math.abs(l._m)/o[s],d=Math.min(d,u),(l.constrain===\"domain\"||!l._constraintShrinkable)&&(m=Math.min(m,u)),delete l._constraintShrinkable,v=Math.max(v,u),l.constrain===\"domain\"&&(k=!0);if(!(d>Vft*v&&!k)){for(a=0;a<h.length;a++)if(s=h[a],u=b[s],l=p[s],c=l.constrain,u!==m||c===\"domain\")if(f=u/m,c===\"range\")Pce(l,f);else{var M=l._inputDomain,T=(l.domain[1]-l.domain[0])/(M[1]-M[0]),L=(l.r2l(l.range[1])-l.r2l(l.range[0]))/(l.r2l(l._inputRange[1])-l.r2l(l._inputRange[0]));if(f/=T,f*L<1){l.domain=l._input.domain=M.slice(),Pce(l,f);continue}if(L<1&&(l.range=l._input.range=l._inputRange.slice(),f*=L),l.autorange){var x=l.r2l(l.range[0]),C=l.r2l(l.range[1]),S=(x+C)/2,g=S,P=S,E=Math.abs(C-S),z=S-E*f*1.0001,q=S+E*f*1.0001,V=bN.makePadFn(r,l,0),G=bN.makePadFn(r,l,1);Rce(l,f);var Z=Math.abs(l._m),j=bN.concatExtremes(t,l),N=j.min,H=j.max,re,ae;for(ae=0;ae<N.length;ae++)re=N[ae].val-V(N[ae])/Z,re>z&&re<g&&(g=re);for(ae=0;ae<H.length;ae++)re=H[ae].val+G(H[ae])/Z,re<q&&re>P&&(P=re);var _e=(P-g)/(2*E);f/=_e,g=l.l2r(g),P=l.l2r(P),l.range=l._input.range=x<C?[g,P]:[P,g]}Rce(l,f)}}}};LM.getAxisGroup=function(t,r){for(var n=t._axisMatchGroups,i=0;i<n.length;i++){var a=n[i];if(a[r])return\"g\"+i}return r};LM.clean=function(t,r){if(r._inputDomain){for(var n=!1,i=r._id,a=t._fullLayout._axisConstraintGroups,o=0;o<a.length;o++)if(a[o][i]){n=!0;break}(!n||r.constrain!==\"domain\")&&(r._input.domain=r.domain=r._inputDomain,delete r._inputDomain)}};function Rce(e,t){var r=e._inputDomain,n=Gft[e.constraintoward],i=r[0]+(r[1]-r[0])*n;e.domain=e._input.domain=[i+(r[0]-i)/t,i+(r[1]-i)/t],e.setScale()}});var RM=ye(pd=>{\"use 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ce=Le._lw/2;if(Le._id.charAt(0)===\"x\"){if(me){if(Pe===\"top\")return me._offset-n-ce}else return r.t+r.h*(1-(Le.position||0))+ce%1;return me._offset+me._length+n+ce}if(me){if(Pe===\"right\")return me._offset+me._length+n+ce}else return r.l+r.w*(Le.position||0)+ce%1;return me._offset-n-ce}for(a=0;a<i.length;a++){l=i[a];var h=l._anchorAxis;l._linepositions={},l._lw=lT.crispRound(e,l.linewidth,1),l._mainLinePosition=f(l,h,l.side),l._mainMirrorPosition=l.mirror&&h?f(l,h,zy.OPPOSITE_SIDE[l.side]):null}var d=[],v=[],m=[],b=PM.opacity(t.paper_bgcolor)===1&&PM.opacity(t.plot_bgcolor)===1&&t.paper_bgcolor===t.plot_bgcolor;for(o in t._plots)if(s=t._plots[o],s.mainplot)s.bg&&s.bg.remove(),s.bg=void 0;else{var p=s.xaxis.domain,k=s.yaxis.domain,M=s.plotgroup;if(Kft(p,k,m)&&o.indexOf(Yft)===-1){var T=M.node(),L=s.bg=M0.ensureSingle(M,\"rect\",\"bg\");T.insertBefore(L.node(),T.childNodes[0]),v.push(o)}else M.select(\"rect.bg\").remove(),m.push([p,k]),b||(d.push(o),v.push(o))}var x=t._bgLayer.selectAll(\".bg\").data(d);for(x.enter().append(\"rect\").classed(\"bg\",!0),x.exit().remove(),x.each(function(Le){t._plots[Le].bg=wP.select(this)}),a=0;a<v.length;a++)s=t._plots[v[a]],u=s.xaxis,c=s.yaxis,s.bg&&u._offset!==void 0&&c._offset!==void 0&&s.bg.call(lT.setRect,u._offset-n,c._offset-n,u._length+2*n,c._length+2*n).call(PM.fill,t.plot_bgcolor).style(\"stroke-width\",0);if(!t._hasOnlyLargeSploms)for(o in t._plots){s=t._plots[o],u=s.xaxis,c=s.yaxis;var C=s.clipId=\"clip\"+t._uid+o+\"plot\",S=M0.ensureSingleById(t._clips,\"clipPath\",C,function(Le){Le.classed(\"plotclip\",!0).append(\"rect\")});s.clipRect=S.select(\"rect\").attr({width:u._length,height:c._length}),lT.setTranslate(s.plot,u._offset,c._offset);var g,P;s._hasClipOnAxisFalse?(g=null,P=C):(g=C,P=null),lT.setClipUrl(s.plot,g,e),s.layerClipId=P}var E,z,q,V,G,Z,j,N,H,re,ae,_e,Ee;function Ce(Le){return\"M\"+E+\",\"+Le+\"H\"+z}function ge(Le){return\"M\"+u._offset+\",\"+Le+\"h\"+u._length}function ie(Le){return\"M\"+Le+\",\"+N+\"V\"+j}function Se(Le){return c._shift!==void 0&&(Le+=c._shift),\"M\"+Le+\",\"+c._offset+\"v\"+c._length}function ke(Le,me,Pe){if(!Le.showline||o!==Le._mainSubplot)return\"\";if(!Le._anchorAxis)return Pe(Le._mainLinePosition);var ce=me(Le._mainLinePosition);return Le.mirror&&(ce+=me(Le._mainMirrorPosition)),ce}for(o in t._plots){s=t._plots[o],u=s.xaxis,c=s.yaxis;var be=\"M0,0\";qce(u,o)&&(G=bP(u,\"left\",c,i),E=u._offset-(G?n+G:0),Z=bP(u,\"right\",c,i),z=u._offset+u._length+(Z?n+Z:0),q=f(u,c,\"bottom\"),V=f(u,c,\"top\"),Ee=!u._anchorAxis||o!==u._mainSubplot,Ee&&(u.mirror===\"allticks\"||u.mirror===\"all\")&&(u._linepositions[o]=[q,V]),be=ke(u,Ce,ge),Ee&&u.showline&&(u.mirror===\"all\"||u.mirror===\"allticks\")&&(be+=Ce(q)+Ce(V)),s.xlines.style(\"stroke-width\",u._lw+\"px\").call(PM.stroke,u.showline?u.linecolor:\"rgba(0,0,0,0)\")),s.xlines.attr(\"d\",be);var Be=\"M0,0\";qce(c,o)&&(ae=bP(c,\"bottom\",u,i),j=c._offset+c._length+(ae?n:0),_e=bP(c,\"top\",u,i),N=c._offset-(_e?n:0),H=f(c,u,\"left\"),re=f(c,u,\"right\"),Ee=!c._anchorAxis||o!==c._mainSubplot,Ee&&(c.mirror===\"allticks\"||c.mirror===\"all\")&&(c._linepositions[o]=[H,re]),Be=ke(c,ie,Se),Ee&&c.showline&&(c.mirror===\"all\"||c.mirror===\"allticks\")&&(Be+=ie(H)+ie(re)),s.ylines.style(\"stroke-width\",c._lw+\"px\").call(PM.stroke,c.showline?c.linecolor:\"rgba(0,0,0,0)\")),s.ylines.attr(\"d\",Be)}return IM.makeClipPaths(e),Jp.previousPromises(e)}function qce(e,t){return(e.ticks||e.showline)&&(t===e._mainSubplot||e.mirror===\"all\"||e.mirror===\"allticks\")}function Oce(e,t,r){if(!r.showline||!r._lw)return!1;if(r.mirror===\"all\"||r.mirror===\"allticks\")return!0;var n=r._anchorAxis;if(!n)return!1;var i=zy.FROM_BL[t];return r.side===t?n.domain[i]===e.domain[i]:r.mirror&&n.domain[1-i]===e.domain[1-i]}function bP(e,t,r,n){if(Oce(e,t,r))return r._lw;for(var i=0;i<n.length;i++){var a=n[i];if(a._mainAxis===r._mainAxis&&Oce(e,t,a))return a._lw}return 0}pd.drawMainTitle=function(e){var t=e._fullLayout.title,r=e._fullLayout,n=nht(r),i=aht(r),a=iht(r,i),o=rht(r,n);if(Fce.draw(e,\"gtitle\",{propContainer:r,propName:\"title.text\",subtitlePropName:\"title.subtitle.text\",placeholder:r._dfltTitle.plot,subtitlePlaceholder:r._dfltTitle.subtitle,attributes:{x:o,y:a,\"text-anchor\":n,dy:i}}),t.text&&t.automargin){var s=wP.select(e).selectAll(\".gtitle\"),l=lT.bBox(wP.select(e).selectAll(\".g-gtitle\").node()).height,u=eht(e,t,l);if(u>0){tht(e,a,u,l),s.attr({x:o,y:a,\"text-anchor\":n,dy:Bce(t.yanchor)}).call(AN.positionText,o,a);var c=(t.text.match(AN.BR_TAG_ALL)||[]).length;if(c){var f=zy.LINE_SPACING*c+zy.MID_SHIFT;t.y===0&&(f=-f),s.selectAll(\".line\").each(function(){var b=+this.getAttribute(\"dy\").slice(0,-2)-f+\"em\";this.setAttribute(\"dy\",b)})}var h=wP.select(e).selectAll(\".gtitle-subtitle\");if(h.node()){var d=s.node().getBBox(),v=d.y+d.height,m=v+Fce.SUBTITLE_PADDING_EM*t.subtitle.font.size;h.attr({x:o,y:m,\"text-anchor\":n,dy:Bce(t.yanchor)}).call(AN.positionText,o,m)}}}};function $ft(e,t,r,n,i){var a=t.yref===\"paper\"?e._fullLayout._size.h:e._fullLayout.height,o=M0.isTopAnchor(t)?n:n-i,s=r===\"b\"?a-o:o;return M0.isTopAnchor(t)&&r===\"t\"||M0.isBottomAnchor(t)&&r===\"b\"?!1:s<i}function Qft(e,t,r,n,i){var a=0;return r===\"middle\"&&(a+=i/2),e===\"t\"?(r===\"top\"&&(a+=i),a+=n-t*n):(r===\"bottom\"&&(a+=i),a+=t*n),a}function eht(e,t,r){var n=t.y,i=t.yanchor,a=n>.5?\"t\":\"b\",o=e._fullLayout.margin[a],s=0;return t.yref===\"paper\"?s=r+t.pad.t+t.pad.b:t.yref===\"container\"&&(s=Qft(a,n,i,e._fullLayout.height,r)+t.pad.t+t.pad.b),s>o?s:0}function tht(e,t,r,n){var i=\"title.automargin\",a=e._fullLayout.title,o=a.y>.5?\"t\":\"b\",s={x:a.x,y:a.y,t:0,b:0},l={};a.yref===\"paper\"&&$ft(e,a,o,t,n)?s[o]=r:a.yref===\"container\"&&(l[o]=r,e._fullLayout._reservedMargin[i]=l),Jp.allowAutoMargin(e,i),Jp.autoMargin(e,i,s)}function rht(e,t){var r=e.title,n=e._size,i=0;return t===Vce?i=r.pad.l:t===Gce&&(i=-r.pad.r),r.xref===\"paper\"?n.l+n.w*r.x+i:e.width*r.x+i}function iht(e,t){var r=e.title,n=e._size,i=0;return t===\"0em\"||!t?i=-r.pad.b:t===zy.CAP_SHIFT+\"em\"&&(i=r.pad.t),r.y===\"auto\"?n.t/2:r.yref===\"paper\"?n.t+n.h-n.h*r.y+i:e.height-e.height*r.y+i}function Bce(e){return e===\"top\"?zy.CAP_SHIFT+.3+\"em\":e===\"bottom\"?\"-0.3em\":zy.MID_SHIFT+\"em\"}function nht(e){var t=e.title,r=Zft;return M0.isRightAnchor(t)?r=Gce:M0.isLeftAnchor(t)&&(r=Vce),r}function aht(e){var t=e.title,r=\"0em\";return M0.isTopAnchor(t)?r=zy.CAP_SHIFT+\"em\":M0.isMiddleAnchor(t)&&(r=zy.MID_SHIFT+\"em\"),r}pd.doTraceStyle=function(e){var t=e.calcdata,r=[],n;for(n=0;n<t.length;n++){var i=t[n],a=i[0]||{},o=a.trace||{},s=o._module||{},l=s.arraysToCalcdata;l&&l(i,o);var u=s.editStyle;u&&r.push({fn:u,cd0:a})}if(r.length){for(n=0;n<r.length;n++){var c=r[n];c.fn(e,c.cd0)}SN(e),pd.redrawReglTraces(e)}return Jp.style(e),Nv.getComponentMethod(\"legend\",\"draw\")(e),Jp.previousPromises(e)};pd.doColorBars=function(e){return Nv.getComponentMethod(\"colorbar\",\"draw\")(e),Jp.previousPromises(e)};pd.layoutReplot=function(e){var t=e.layout;return e.layout=void 0,Nv.call(\"_doPlot\",e,\"\",t)};pd.doLegend=function(e){return Nv.getComponentMethod(\"legend\",\"draw\")(e),Jp.previousPromises(e)};pd.doTicksRelayout=function(e){return IM.draw(e,\"redraw\"),e._fullLayout._hasOnlyLargeSploms&&(Nv.subplotsRegistry.splom.updateGrid(e),SN(e),pd.redrawReglTraces(e)),pd.drawMainTitle(e),Jp.previousPromises(e)};pd.doModeBar=function(e){var t=e._fullLayout;Nce.manage(e);for(var r=0;r<t._basePlotModules.length;r++){var n=t._basePlotModules[r].updateFx;n&&n(e)}return Jp.previousPromises(e)};pd.doCamera=function(e){for(var t=e._fullLayout,r=t._subplots.gl3d,n=0;n<r.length;n++){var i=t[r[n]],a=i._scene;a.setViewport(i)}};pd.drawData=function(e){var t=e._fullLayout;SN(e);for(var r=t._basePlotModules,n=0;n<r.length;n++)r[n].plot(e);return pd.redrawReglTraces(e),Jp.style(e),Nv.getComponentMethod(\"selections\",\"draw\")(e),Nv.getComponentMethod(\"shapes\",\"draw\")(e),Nv.getComponentMethod(\"annotations\",\"draw\")(e),Nv.getComponentMethod(\"images\",\"draw\")(e),t._replotting=!1,Jp.previousPromises(e)};pd.redrawReglTraces=function(e){var t=e._fullLayout;if(t._has(\"regl\")){var r=e._fullData,n=[],i=[],a,o;for(t._hasOnlyLargeSploms&&t._splomGrid.draw(),a=0;a<r.length;a++){var s=r[a];s.visible===!0&&s._length!==0&&(s.type===\"splom\"?t._splomScenes[s.uid].draw():s.type===\"scattergl\"?M0.pushUnique(n,s.xaxis+s.yaxis):s.type===\"scatterpolargl\"&&M0.pushUnique(i,s.subplot))}for(a=0;a<n.length;a++)o=t._plots[n[a]],o._scene&&o._scene.draw();for(a=0;a<i.length;a++)o=t[i[a]]._subplot,o._scene&&o._scene.draw()}};pd.doAutoRangeAndConstraints=function(e){for(var t=IM.list(e,\"\",!0),r,n={},i=0;i<t.length;i++)if(r=t[i],!n[r._id]){n[r._id]=1,Xft(e,r),zce(e,r);var a=r._matchGroup;if(a)for(var o in a){var s=IM.getFromId(e,o);zce(e,s,r.range),n[o]=1}}Wft(e)};pd.finalDraw=function(e){Nv.getComponentMethod(\"rangeslider\",\"draw\")(e),Nv.getComponentMethod(\"rangeselector\",\"draw\")(e)};pd.drawMarginPushers=function(e){Nv.getComponentMethod(\"legend\",\"draw\")(e),Nv.getComponentMethod(\"rangeselector\",\"draw\")(e),Nv.getComponentMethod(\"sliders\",\"draw\")(e),Nv.getComponentMethod(\"updatemenus\",\"draw\")(e),Nv.getComponentMethod(\"colorbar\",\"draw\")(e)}});var kN=ye((Dor,Zce)=>{\"use strict\";var oht=y_().readPaths,sht=cP(),Hce=s_().clearOutlineControllers,MN=ka(),jce=So(),lht=vl().arrayEditor,Wce=b_(),uht=Wce.getPathString;Zce.exports={draw:TP,drawOne:Xce,activateLastSelection:hht};function TP(e){var t=e._fullLayout;Hce(e),t._selectionLayer.selectAll(\"path\").remove();for(var r in t._plots){var n=t._plots[r].selectionLayer;n&&n.selectAll(\"path\").remove()}for(var i=0;i<t.selections.length;i++)Xce(e,i)}function AP(e){return e._context.editSelection}function Xce(e,t){e._fullLayout._paperdiv.selectAll('.selectionlayer [data-index=\"'+t+'\"]').remove();var r=Wce.makeSelectionsOptionsAndPlotinfo(e,t),n=r.options,i=r.plotinfo;if(!n._input)return;a(e._fullLayout._selectionLayer);function a(o){var s=uht(e,n),l={\"data-index\":t,\"fill-rule\":\"evenodd\",d:s},u=n.opacity,c=\"rgba(0,0,0,0)\",f=n.line.color||MN.contrast(e._fullLayout.plot_bgcolor),h=n.line.width,d=n.line.dash;h||(h=5,d=\"solid\");var v=AP(e)&&e._fullLayout._activeSelectionIndex===t;v&&(c=e._fullLayout.activeselection.fillcolor,u=e._fullLayout.activeselection.opacity);for(var m=[],b=1;b>=0;b--){var p=o.append(\"path\").attr(l).style(\"opacity\",b?.1:u).call(MN.stroke,f).call(MN.fill,c).call(jce.dashLine,b?\"solid\":d,b?4+h:h);if(cht(p,e,n),v){var k=lht(e.layout,\"selections\",n);p.style({cursor:\"move\"});var M={element:p.node(),plotinfo:i,gd:e,editHelpers:k,isActiveSelection:!0},T=oht(s,e);sht(T,p,M)}else p.style(\"pointer-events\",b?\"all\":\"none\");m[b]=p}var L=m[0],x=m[1];x.node().addEventListener(\"click\",function(){return fht(e,L)})}}function cht(e,t,r){var n=r.xref+r.yref;jce.setClipUrl(e,\"clip\"+t._fullLayout._uid+n,t)}function fht(e,t){if(AP(e)){var r=t.node(),n=+r.getAttribute(\"data-index\");if(n>=0){if(n===e._fullLayout._activeSelectionIndex){EN(e);return}e._fullLayout._activeSelectionIndex=n,e._fullLayout._deactivateSelection=EN,TP(e)}}}function hht(e){if(AP(e)){var t=e._fullLayout.selections.length-1;e._fullLayout._activeSelectionIndex=t,e._fullLayout._deactivateSelection=EN,TP(e)}}function EN(e){if(AP(e)){var t=e._fullLayout._activeSelectionIndex;t>=0&&(Hce(e),delete e._fullLayout._activeSelectionIndex,TP(e))}}});var Kce=ye((For,Yce)=>{function dht(){var e,t=0,r=!1;function n(i,a){return e.list.push({type:i,data:a?JSON.parse(JSON.stringify(a)):void 0}),e}return e={list:[],segmentId:function(){return t++},checkIntersection:function(i,a){return n(\"check\",{seg1:i,seg2:a})},segmentChop:function(i,a){return n(\"div_seg\",{seg:i,pt:a}),n(\"chop\",{seg:i,pt:a})},statusRemove:function(i){return n(\"pop_seg\",{seg:i})},segmentUpdate:function(i){return n(\"seg_update\",{seg:i})},segmentNew:function(i,a){return n(\"new_seg\",{seg:i,primary:a})},segmentRemove:function(i){return n(\"rem_seg\",{seg:i})},tempStatus:function(i,a,o){return n(\"temp_status\",{seg:i,above:a,below:o})},rewind:function(i){return n(\"rewind\",{seg:i})},status:function(i,a,o){return n(\"status\",{seg:i,above:a,below:o})},vert:function(i){return i===r?e:(r=i,n(\"vert\",{x:i}))},log:function(i){return typeof i!=\"string\"&&(i=JSON.stringify(i,!1,\"  \")),n(\"log\",{txt:i})},reset:function(){return n(\"reset\")},selected:function(i){return n(\"selected\",{segs:i})},chainStart:function(i){return n(\"chain_start\",{seg:i})},chainRemoveHead:function(i,a){return n(\"chain_rem_head\",{index:i,pt:a})},chainRemoveTail:function(i,a){return n(\"chain_rem_tail\",{index:i,pt:a})},chainNew:function(i,a){return n(\"chain_new\",{pt1:i,pt2:a})},chainMatch:function(i){return n(\"chain_match\",{index:i})},chainClose:function(i){return n(\"chain_close\",{index:i})},chainAddHead:function(i,a){return n(\"chain_add_head\",{index:i,pt:a})},chainAddTail:function(i,a){return n(\"chain_add_tail\",{index:i,pt:a})},chainConnect:function(i,a){return n(\"chain_con\",{index1:i,index2:a})},chainReverse:function(i){return n(\"chain_rev\",{index:i})},chainJoin:function(i,a){return n(\"chain_join\",{index1:i,index2:a})},done:function(){return n(\"done\")}},e}Yce.exports=dht});var $ce=ye((zor,Jce)=>{function vht(e){typeof e!=\"number\"&&(e=1e-10);var t={epsilon:function(r){return typeof r==\"number\"&&(e=r),e},pointAboveOrOnLine:function(r,n,i){var a=n[0],o=n[1],s=i[0],l=i[1],u=r[0],c=r[1];return(s-a)*(c-o)-(l-o)*(u-a)>=-e},pointBetween:function(r,n,i){var a=r[1]-n[1],o=i[0]-n[0],s=r[0]-n[0],l=i[1]-n[1],u=s*o+a*l;if(u<e)return!1;var c=o*o+l*l;return!(u-c>-e)},pointsSameX:function(r,n){return Math.abs(r[0]-n[0])<e},pointsSameY:function(r,n){return Math.abs(r[1]-n[1])<e},pointsSame:function(r,n){return t.pointsSameX(r,n)&&t.pointsSameY(r,n)},pointsCompare:function(r,n){return t.pointsSameX(r,n)?t.pointsSameY(r,n)?0:r[1]<n[1]?-1:1:r[0]<n[0]?-1:1},pointsCollinear:function(r,n,i){var a=r[0]-n[0],o=r[1]-n[1],s=n[0]-i[0],l=n[1]-i[1];return Math.abs(a*l-s*o)<e},linesIntersect:function(r,n,i,a){var o=n[0]-r[0],s=n[1]-r[1],l=a[0]-i[0],u=a[1]-i[1],c=o*u-s*l;if(Math.abs(c)<e)return!1;var f=r[0]-i[0],h=r[1]-i[1],d=(l*h-u*f)/c,v=(o*h-s*f)/c,m={alongA:0,alongB:0,pt:[r[0]+d*o,r[1]+d*s]};return d<=-e?m.alongA=-2:d<e?m.alongA=-1:d-1<=-e?m.alongA=0:d-1<e?m.alongA=1:m.alongA=2,v<=-e?m.alongB=-2:v<e?m.alongB=-1:v-1<=-e?m.alongB=0:v-1<e?m.alongB=1:m.alongB=2,m},pointInsideRegion:function(r,n){for(var i=r[0],a=r[1],o=n[n.length-1][0],s=n[n.length-1][1],l=!1,u=0;u<n.length;u++){var c=n[u][0],f=n[u][1];f-a>e!=s-a>e&&(o-c)*(a-f)/(s-f)+c-i>e&&(l=!l),o=c,s=f}return l}};return t}Jce.exports=vht});var efe=ye((qor,Qce)=>{var pht={create:function(){var e={root:{root:!0,next:null},exists:function(t){return!(t===null||t===e.root)},isEmpty:function(){return e.root.next===null},getHead:function(){return e.root.next},insertBefore:function(t,r){for(var n=e.root,i=e.root.next;i!==null;){if(r(i)){t.prev=i.prev,t.next=i,i.prev.next=t,i.prev=t;return}n=i,i=i.next}n.next=t,t.prev=n,t.next=null},findTransition:function(t){for(var r=e.root,n=e.root.next;n!==null&&!t(n);)r=n,n=n.next;return{before:r===e.root?null:r,after:n,insert:function(i){return i.prev=r,i.next=n,r.next=i,n!==null&&(n.prev=i),i}}}};return e},node:function(e){return e.prev=null,e.next=null,e.remove=function(){e.prev.next=e.next,e.next&&(e.next.prev=e.prev),e.prev=null,e.next=null},e}};Qce.exports=pht});var rfe=ye((Oor,tfe)=>{var DM=efe();function ght(e,t,r){function n(v,m){return{id:r?r.segmentId():-1,start:v,end:m,myFill:{above:null,below:null},otherFill:null}}function i(v,m,b){return{id:r?r.segmentId():-1,start:v,end:m,myFill:{above:b.myFill.above,below:b.myFill.below},otherFill:null}}var a=DM.create();function o(v,m,b,p,k,M){var T=t.pointsCompare(m,k);return T!==0?T:t.pointsSame(b,M)?0:v!==p?v?1:-1:t.pointAboveOrOnLine(b,p?k:M,p?M:k)?1:-1}function s(v,m){a.insertBefore(v,function(b){var p=o(v.isStart,v.pt,m,b.isStart,b.pt,b.other.pt);return p<0})}function l(v,m){var b=DM.node({isStart:!0,pt:v.start,seg:v,primary:m,other:null,status:null});return s(b,v.end),b}function u(v,m,b){var p=DM.node({isStart:!1,pt:m.end,seg:m,primary:b,other:v,status:null});v.other=p,s(p,v.pt)}function c(v,m){var b=l(v,m);return u(b,v,m),b}function f(v,m){r&&r.segmentChop(v.seg,m),v.other.remove(),v.seg.end=m,v.other.pt=m,s(v.other,v.pt)}function h(v,m){var b=i(m,v.seg.end,v.seg);return f(v,m),c(b,v.primary)}function d(v,m){var b=DM.create();function p(G,Z){var j=G.seg.start,N=G.seg.end,H=Z.seg.start,re=Z.seg.end;return t.pointsCollinear(j,H,re)?t.pointsCollinear(N,H,re)||t.pointAboveOrOnLine(N,H,re)?1:-1:t.pointAboveOrOnLine(j,H,re)?1:-1}function k(G){return b.findTransition(function(Z){var j=p(G,Z.ev);return j>0})}function M(G,Z){var j=G.seg,N=Z.seg,H=j.start,re=j.end,ae=N.start,_e=N.end;r&&r.checkIntersection(j,N);var Ee=t.linesIntersect(H,re,ae,_e);if(Ee===!1){if(!t.pointsCollinear(H,re,ae)||t.pointsSame(H,_e)||t.pointsSame(re,ae))return!1;var Ce=t.pointsSame(H,ae),ge=t.pointsSame(re,_e);if(Ce&&ge)return Z;var ie=!Ce&&t.pointBetween(H,ae,_e),Se=!ge&&t.pointBetween(re,ae,_e);if(Ce)return Se?h(Z,re):h(G,_e),Z;ie&&(ge||(Se?h(Z,re):h(G,_e)),h(Z,H))}else Ee.alongA===0&&(Ee.alongB===-1?h(G,ae):Ee.alongB===0?h(G,Ee.pt):Ee.alongB===1&&h(G,_e)),Ee.alongB===0&&(Ee.alongA===-1?h(Z,H):Ee.alongA===0?h(Z,Ee.pt):Ee.alongA===1&&h(Z,re));return!1}for(var T=[];!a.isEmpty();){var L=a.getHead();if(r&&r.vert(L.pt[0]),L.isStart){let G=function(){if(C){var Z=M(L,C);if(Z)return Z}return S?M(L,S):!1};var V=G;r&&r.segmentNew(L.seg,L.primary);var x=k(L),C=x.before?x.before.ev:null,S=x.after?x.after.ev:null;r&&r.tempStatus(L.seg,C?C.seg:!1,S?S.seg:!1);var g=G();if(g){if(e){var P;L.seg.myFill.below===null?P=!0:P=L.seg.myFill.above!==L.seg.myFill.below,P&&(g.seg.myFill.above=!g.seg.myFill.above)}else g.seg.otherFill=L.seg.myFill;r&&r.segmentUpdate(g.seg),L.other.remove(),L.remove()}if(a.getHead()!==L){r&&r.rewind(L.seg);continue}if(e){var P;L.seg.myFill.below===null?P=!0:P=L.seg.myFill.above!==L.seg.myFill.below,S?L.seg.myFill.below=S.seg.myFill.above:L.seg.myFill.below=v,P?L.seg.myFill.above=!L.seg.myFill.below:L.seg.myFill.above=L.seg.myFill.below}else if(L.seg.otherFill===null){var E;S?L.primary===S.primary?E=S.seg.otherFill.above:E=S.seg.myFill.above:E=L.primary?m:v,L.seg.otherFill={above:E,below:E}}r&&r.status(L.seg,C?C.seg:!1,S?S.seg:!1),L.other.status=x.insert(DM.node({ev:L}))}else{var z=L.status;if(z===null)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(b.exists(z.prev)&&b.exists(z.next)&&M(z.prev.ev,z.next.ev),r&&r.statusRemove(z.ev.seg),z.remove(),!L.primary){var q=L.seg.myFill;L.seg.myFill=L.seg.otherFill,L.seg.otherFill=q}T.push(L.seg)}a.getHead().remove()}return r&&r.done(),T}return e?{addRegion:function(v){for(var m,b=v[v.length-1],p=0;p<v.length;p++){m=b,b=v[p];var k=t.pointsCompare(m,b);k!==0&&c(n(k<0?m:b,k<0?b:m),!0)}},calculate:function(v){return d(v,!1)}}:{calculate:function(v,m,b,p){return v.forEach(function(k){c(i(k.start,k.end,k),!0)}),b.forEach(function(k){c(i(k.start,k.end,k),!1)}),d(m,p)}}}tfe.exports=ght});var nfe=ye((Bor,ife)=>{function mht(e,t,r){var n=[],i=[];return e.forEach(function(a){var o=a.start,s=a.end;if(t.pointsSame(o,s)){console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");return}r&&r.chainStart(a);var l={index:0,matches_head:!1,matches_pt1:!1},u={index:0,matches_head:!1,matches_pt1:!1},c=l;function f(V,G,Z){return c.index=V,c.matches_head=G,c.matches_pt1=Z,c===l?(c=u,!1):(c=null,!0)}for(var h=0;h<n.length;h++){var d=n[h],v=d[0],m=d[1],b=d[d.length-1],p=d[d.length-2];if(t.pointsSame(v,o)){if(f(h,!0,!0))break}else if(t.pointsSame(v,s)){if(f(h,!0,!1))break}else if(t.pointsSame(b,o)){if(f(h,!1,!0))break}else if(t.pointsSame(b,s)&&f(h,!1,!1))break}if(c===l){n.push([o,s]),r&&r.chainNew(o,s);return}if(c===u){r&&r.chainMatch(l.index);var k=l.index,M=l.matches_pt1?s:o,T=l.matches_head,d=n[k],L=T?d[0]:d[d.length-1],x=T?d[1]:d[d.length-2],C=T?d[d.length-1]:d[0],S=T?d[d.length-2]:d[1];if(t.pointsCollinear(x,L,M)&&(T?(r&&r.chainRemoveHead(l.index,M),d.shift()):(r&&r.chainRemoveTail(l.index,M),d.pop()),L=x),t.pointsSame(C,M)){n.splice(k,1),t.pointsCollinear(S,C,L)&&(T?(r&&r.chainRemoveTail(l.index,L),d.pop()):(r&&r.chainRemoveHead(l.index,L),d.shift())),r&&r.chainClose(l.index),i.push(d);return}T?(r&&r.chainAddHead(l.index,M),d.unshift(M)):(r&&r.chainAddTail(l.index,M),d.push(M));return}function g(V){r&&r.chainReverse(V),n[V].reverse()}function P(V,G){var Z=n[V],j=n[G],N=Z[Z.length-1],H=Z[Z.length-2],re=j[0],ae=j[1];t.pointsCollinear(H,N,re)&&(r&&r.chainRemoveTail(V,N),Z.pop(),N=H),t.pointsCollinear(N,re,ae)&&(r&&r.chainRemoveHead(G,re),j.shift()),r&&r.chainJoin(V,G),n[V]=Z.concat(j),n.splice(G,1)}var E=l.index,z=u.index;r&&r.chainConnect(E,z);var q=n[E].length<n[z].length;l.matches_head?u.matches_head?q?(g(E),P(E,z)):(g(z),P(z,E)):P(z,E):u.matches_head?P(E,z):q?(g(E),P(z,E)):(g(z),P(E,z))}),i}ife.exports=mht});var ofe=ye((Nor,afe)=>{function FM(e,t,r){var n=[];return e.forEach(function(i){var a=(i.myFill.above?8:0)+(i.myFill.below?4:0)+(i.otherFill&&i.otherFill.above?2:0)+(i.otherFill&&i.otherFill.below?1:0);t[a]!==0&&n.push({id:r?r.segmentId():-1,start:i.start,end:i.end,myFill:{above:t[a]===1,below:t[a]===2},otherFill:null})}),r&&r.selected(n),n}var yht={union:function(e,t){return FM(e,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],t)},intersect:function(e,t){return FM(e,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],t)},difference:function(e,t){return FM(e,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],t)},differenceRev:function(e,t){return FM(e,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],t)},xor:function(e,t){return FM(e,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],t)}};afe.exports=yht});var lfe=ye((Uor,sfe)=>{var _ht={toPolygon:function(e,t){function r(a){if(a.length<=0)return e.segments({inverted:!1,regions:[]});function o(u){var c=u.slice(0,u.length-1);return e.segments({inverted:!1,regions:[c]})}for(var s=o(a[0]),l=1;l<a.length;l++)s=e.selectDifference(e.combine(s,o(a[l])));return s}if(t.type===\"Polygon\")return e.polygon(r(t.coordinates));if(t.type===\"MultiPolygon\"){for(var n=e.segments({inverted:!1,regions:[]}),i=0;i<t.coordinates.length;i++)n=e.selectUnion(e.combine(n,r(t.coordinates[i])));return e.polygon(n)}throw new Error(\"PolyBool: Cannot convert GeoJSON object to PolyBool polygon\")},fromPolygon:function(e,t,r){r=e.polygon(e.segments(r));function n(d,v){return t.pointInsideRegion([(d[0][0]+d[1][0])*.5,(d[0][1]+d[1][1])*.5],v)}function i(d){return{region:d,children:[]}}var a=i(null);function o(d,v){for(var m=0;m<d.children.length;m++){var b=d.children[m];if(n(v,b.region)){o(b,v);return}}for(var p=i(v),m=0;m<d.children.length;m++){var b=d.children[m];n(b.region,v)&&(p.children.push(b),d.children.splice(m,1),m--)}d.children.push(p)}for(var s=0;s<r.regions.length;s++){var l=r.regions[s];l.length<3||o(a,l)}function u(d,v){for(var m=0,b=d[d.length-1][0],p=d[d.length-1][1],k=[],M=0;M<d.length;M++){var T=d[M][0],L=d[M][1];k.push([T,L]),m+=L*b-T*p,b=T,p=L}var x=m<0;return x!==v&&k.reverse(),k.push([k[0][0],k[0][1]]),k}var c=[];function f(d){var v=[u(d.region,!1)];c.push(v);for(var m=0;m<d.children.length;m++)v.push(h(d.children[m]))}function h(d){for(var v=0;v<d.children.length;v++)f(d.children[v]);return u(d.region,!0)}for(var s=0;s<a.children.length;s++)f(a.children[s]);return c.length<=0?{type:\"Polygon\",coordinates:[]}:c.length==1?{type:\"Polygon\",coordinates:c[0]}:{type:\"MultiPolygon\",coordinates:c}}};sfe.exports=_ht});var hfe=ye((Vor,ffe)=>{var xht=Kce(),bht=$ce(),ufe=rfe(),wht=nfe(),zM=ofe(),cfe=lfe(),E0=!1,qM=bht(),kp;kp={buildLog:function(e){return e===!0?E0=xht():e===!1&&(E0=!1),E0===!1?!1:E0.list},epsilon:function(e){return qM.epsilon(e)},segments:function(e){var t=ufe(!0,qM,E0);return e.regions.forEach(t.addRegion),{segments:t.calculate(e.inverted),inverted:e.inverted}},combine:function(e,t){var r=ufe(!1,qM,E0);return{combined:r.calculate(e.segments,e.inverted,t.segments,t.inverted),inverted1:e.inverted,inverted2:t.inverted}},selectUnion:function(e){return{segments:zM.union(e.combined,E0),inverted:e.inverted1||e.inverted2}},selectIntersect:function(e){return{segments:zM.intersect(e.combined,E0),inverted:e.inverted1&&e.inverted2}},selectDifference:function(e){return{segments:zM.difference(e.combined,E0),inverted:e.inverted1&&!e.inverted2}},selectDifferenceRev:function(e){return{segments:zM.differenceRev(e.combined,E0),inverted:!e.inverted1&&e.inverted2}},selectXor:function(e){return{segments:zM.xor(e.combined,E0),inverted:e.inverted1!==e.inverted2}},polygon:function(e){return{regions:wht(e.segments,qM,E0),inverted:e.inverted}},polygonFromGeoJSON:function(e){return cfe.toPolygon(kp,e)},polygonToGeoJSON:function(e){return cfe.fromPolygon(kp,qM,e)},union:function(e,t){return OM(e,t,kp.selectUnion)},intersect:function(e,t){return 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t=[],r=PN(e[0])?0:e[0][0][0],n=r,i=PN(e[0])?0:e[0][0][1],a=i,o=0;o<e.length;o++)if(PN(e[o]))t.push(Uht(e[o]));else{var s=FN(e[o]);s.subtract=!!e[o].subtract,t.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}function l(u,c,f,h){for(var d=!1,v=0;v<t.length;v++)t[v].contains(u,c,f,h)&&(d=!t[v].subtract);return d}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:l,isRect:!1,degenerate:!1}}function Lfe(e,t,r){var n=t._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(e.shiftKey||e.altKey)&&!(GM(a)&&RN(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):(!s||!i.selection)&&HM(r),o||(Mfe(t),n._lastSelectedSubplot=i.id)}function Vht(e){return e._fullLayout._activeShapeIndex>=0}function Ght(e){return e._fullLayout._activeSelectionIndex>=0}function HM(e,t){var r=e.dragmode,n=e.plotinfo,i=e.gd;Vht(i)&&i._fullLayout._deactivateShape(i),Ght(i)&&i._fullLayout._deactivateSelection(i);var a=i._fullLayout,o=a._zoomlayer,s=GM(r),l=DN(r);if(s||l){var u=o.selectAll(\".select-outline-\"+n.id);if(u&&i._fullLayout._outlining){var c;s&&(c=Lht(u,e)),c&&VM.call(\"_guiRelayout\",i,{shapes:c});var f;l&&!qN(e)&&(f=Pht(u,e)),f&&(i._fullLayout._noEmitSelectedAtStart=!0,VM.call(\"_guiRelayout\",i,{selections:f}).then(function(){t&&Iht(i)})),i._fullLayout._outlining=!1}}n.selection={},n.selection.selectionDefs=e.selectionDefs=[],n.selection.mergedPolygons=e.mergedPolygons=[]}function wfe(e){return e._id}function LP(e,t,r,n){if(!e.calcdata)return[];var i=[],a=t.map(wfe),o=r.map(wfe),s,l,u;for(u=0;u<e.calcdata.length;u++)if(s=e.calcdata[u],l=s[0].trace,!(l.visible!==!0||!l._module||!l._module.selectPoints))if(qN({subplot:n})&&(l.subplot===n||l.geo===n))i.push(EP(l._module,s,t[0],r[0]));else if(l.type===\"splom\"){if(l._xaxes[a[0]]&&l._yaxes[o[0]]){var c=EP(l._module,s,t[0],r[0]);c.scene=e._fullLayout._splomScenes[l.uid],i.push(c)}}else if(l.type===\"sankey\"){var f=EP(l._module,s,t[0],r[0]);i.push(f)}else{if(a.indexOf(l.xaxis)===-1&&(!l._xA||!l._xA.overlaying)||o.indexOf(l.yaxis)===-1&&(!l._yA||!l._yA.overlaying))continue;i.push(EP(l._module,s,k0(e,l.xaxis),k0(e,l.yaxis)))}return i}function EP(e,t,r,n){return{_module:e,cd:t,xaxis:r,yaxis:n}}function Hht(e){return e&&Array.isArray(e)&&e[0].hoverOnBox!==!0}function jht(e,t){var r=e[0],n=-1,i=[],a,o;for(o=0;o<t.length;o++)if(a=t[o],r.fullData.index===a.cd[0].trace.index){if(r.hoverOnBox===!0)break;r.pointNumber!==void 0?n=r.pointNumber:r.binNumber!==void 0&&(n=r.binNumber,i=r.pointNumbers);break}return{pointNumber:n,pointNumbers:i,searchInfo:a}}function Tfe(e){var t=e.searchInfo.cd[0].trace,r=e.pointNumber,n=e.pointNumbers,i=n.length>0,a=i?n[0]:r;return t.selectedpoints?t.selectedpoints.indexOf(a)>-1:!1}function Wht(e,t){var 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nhe=Pr(),RP=ho(),hdt=Kd(),ddt=VN(),ahe=b_();ohe.exports=function(t,r){hdt(t,r,{name:\"selections\",handleItemDefaults:vdt});for(var n=r.selections,i=0;i<n.length;i++){var a=n[i];a&&a.path===void 0&&(a.x0===void 0||a.x1===void 0||a.y0===void 0||a.y1===void 0)&&(r.selections[i]=null)}};function vdt(e,t,r){function n(x,C){return nhe.coerce(e,t,ddt,x,C)}var i=n(\"path\"),a=i?\"path\":\"rect\",o=n(\"type\",a),s=o!==\"path\";s&&delete t.path,n(\"opacity\"),n(\"line.color\"),n(\"line.width\"),n(\"line.dash\");for(var l=[\"x\",\"y\"],u=0;u<2;u++){var c=l[u],f={_fullLayout:r},h,d,v,m=RP.coerceRef(e,t,f,c);if(h=RP.getFromId(f,m),h._selectionIndices.push(t._index),v=ahe.rangeToShapePosition(h),d=ahe.shapePositionToRange(h),s){var b=c+\"0\",p=c+\"1\",k=e[b],M=e[p];e[b]=d(e[b],!0),e[p]=d(e[p],!0),RP.coercePosition(t,f,n,m,b),RP.coercePosition(t,f,n,m,p);var T=t[b],L=t[p];T!==void 0&&L!==void 0&&(t[b]=v(T),t[p]=v(L),e[b]=k,e[p]=M)}}s&&nhe.noneOrAll(e,t,[\"x0\",\"x1\",\"y0\",\"y1\"])}});var uhe=ye((isr,lhe)=>{\"use strict\";lhe.exports=function(t,r,n){n(\"newselection.mode\");var i=n(\"newselection.line.width\");i&&(n(\"newselection.line.color\"),n(\"newselection.line.dash\")),n(\"activeselection.fillcolor\"),n(\"activeselection.opacity\")}});var KM=ye((nsr,hhe)=>{\"use strict\";var pdt=Oa(),che=Pr(),fhe=df();hhe.exports=function(t){return function(n,i){var a=n[t];if(Array.isArray(a))for(var o=pdt.subplotsRegistry.cartesian,s=o.idRegex,l=i._subplots,u=l.xaxis,c=l.yaxis,f=l.cartesian,h=i._has(\"cartesian\"),d=0;d<a.length;d++){var v=a[d];if(che.isPlainObject(v)){var m=fhe.cleanId(v.xref,\"x\",!1),b=fhe.cleanId(v.yref,\"y\",!1),p=s.x.test(m),k=s.y.test(b);if(p||k){h||che.pushUnique(i._basePlotModules,o);var M=!1;p&&u.indexOf(m)===-1&&(u.push(m),M=!0),k&&c.indexOf(b)===-1&&(c.push(b),M=!0),M&&p&&k&&f.push(m+b)}}}}}});var qf=ye((asr,vhe)=>{\"use strict\";var 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c=s._mainLinePosition;s.side===\"top\"&&(c-=gd),Ig(t,o,s._offset+s._length*.1,c,s._length*.8,gd,\"\",\"ew\"),Ig(t,o,s._offset,c,s._length*.1,gd,\"\",\"w\"),Ig(t,o,s._offset+s._length*.9,c,s._length*.1,gd,\"\",\"e\")}if(a===l._mainSubplot){var f=l._mainLinePosition;l.side!==\"right\"&&(f-=gd),Ig(t,o,f,l._offset+l._length*.1,gd,l._length*.8,\"ns\",\"\"),Ig(t,o,f,l._offset+l._length*.9,gd,l._length*.1,\"s\",\"\"),Ig(t,o,f,l._offset,gd,l._length*.1,\"n\",\"\")}}});var i=r._hoverlayer.node();i.onmousemove=function(a){a.target=t._fullLayout._lasthover,qP.hover(t,a,r._hoversubplot)},i.onclick=function(a){a.target=t._fullLayout._lasthover,qP.click(t,a)},i.onmousedown=function(a){t._fullLayout._lasthover.onmousedown(a)},OP.updateFx(t)}};OP.updateFx=function(e){var t=e._fullLayout,r=t.dragmode===\"pan\"?\"move\":\"crosshair\";Odt(t._draggers,r)}});var qhe=ye((lsr,zhe)=>{\"use strict\";var Fhe=Oa();zhe.exports=function(t){for(var r=Fhe.layoutArrayContainers,n=Fhe.layoutArrayRegexes,i=t.split(\"[\")[0],a,o,s=0;s<n.length;s++)if(o=t.match(n[s]),o&&o.index===0){a=o[0];break}if(a||(a=r[r.indexOf(i)]),!a)return!1;var l=t.slice(a.length);return l?(o=l.match(/^\\[(0|[1-9][0-9]*)\\](\\.(.+))?$/),o?{array:a,index:Number(o[1]),property:o[3]||\"\"}:!1):{array:a,index:\"\",property:\"\"}}});var Bhe=ye(QM=>{\"use strict\";var Bdt=Sy(),JN=I6(),$M=J1(),Ndt=G6().sorterAsc,$N=Oa();QM.containerArrayMatch=qhe();var Udt=QM.isAddVal=function(t){return t===\"add\"||Bdt(t)},Ohe=QM.isRemoveVal=function(t){return t===null||t===\"remove\"};QM.applyContainerArrayChanges=function(t,r,n,i,a){var o=r.astr,s=$N.getComponentMethod(o,\"supplyLayoutDefaults\"),l=$N.getComponentMethod(o,\"draw\"),u=$N.getComponentMethod(o,\"drawOne\"),c=i.replot||i.recalc||s===JN||l===JN,f=t.layout,h=t._fullLayout;if(n[\"\"]){Object.keys(n).length>1&&$M.warn(\"Full array edits are incompatible with other edits\",o);var d=n[\"\"][\"\"];if(Ohe(d))r.set(null);else 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q;if(M===-1)q=v;else{for(T=Math.max(b.length,T),q=[],L=0;L<v.length&&(C=v[L],!(C>=M));L++)q.push(C);for(L=M;L<T;L++)q.push(L)}for(L=0;L<q.length;L++)u(t,q[L])}else l(t);return!0}});var Xhe=ye(L0=>{\"use strict\";var Ghe=Eo(),Hhe=Oa(),Uv=Pr(),e4=Mc(),jhe=df(),Whe=ka(),t4=jhe.cleanId,Vdt=jhe.getFromTrace,QN=Hhe.traceIs,Gdt=[\"x\",\"y\",\"z\"];L0.clearPromiseQueue=function(e){Array.isArray(e._promises)&&e._promises.length>0&&Uv.log(\"Clearing previous rejected promises from queue.\"),e._promises=[]};L0.cleanLayout=function(e){var t,r;e||(e={}),e.xaxis1&&(e.xaxis||(e.xaxis=e.xaxis1),delete e.xaxis1),e.yaxis1&&(e.yaxis||(e.yaxis=e.yaxis1),delete e.yaxis1),e.scene1&&(e.scene||(e.scene=e.scene1),delete e.scene1);var n=(e4.subplotsRegistry.cartesian||{}).attrRegex,i=(e4.subplotsRegistry.polar||{}).attrRegex,a=(e4.subplotsRegistry.ternary||{}).attrRegex,o=(e4.subplotsRegistry.gl3d||{}).attrRegex,s=Object.keys(e);for(t=0;t<s.length;t++){var l=s[t];if(n&&n.test(l)){var u=e[l];u.anchor&&u.anchor!==\"free\"&&(u.anchor=t4(u.anchor)),u.overlaying&&(u.overlaying=t4(u.overlaying)),u.type||(u.isdate?u.type=\"date\":u.islog?u.type=\"log\":u.isdate===!1&&u.islog===!1&&(u.type=\"linear\")),(u.autorange===\"withzero\"||u.autorange===\"tozero\")&&(u.autorange=!0,u.rangemode=\"tozero\"),u.insiderange&&delete u.range,delete u.islog,delete u.isdate,delete u.categories,BP(u,\"domain\")&&delete u.domain}}var c=Array.isArray(e.annotations)?e.annotations.length:0;for(t=0;t<c;t++){var f=e.annotations[t];Uv.isPlainObject(f)&&(uT(f,\"xref\"),uT(f,\"yref\"))}var h=Array.isArray(e.shapes)?e.shapes.length:0;for(t=0;t<h;t++){var d=e.shapes[t];Uv.isPlainObject(d)&&(uT(d,\"xref\",!0),uT(d,\"yref\",!0))}var v=Array.isArray(e.images)?e.images.length:0;for(t=0;t<v;t++){var m=e.images[t];Uv.isPlainObject(m)&&(uT(m,\"xref\"),uT(m,\"yref\"))}var b=e.legend;return b&&(b.x>3?(b.x=1.02,b.xanchor=\"left\"):b.x<-2&&(b.x=-.02,b.xanchor=\"right\"),b.y>3?(b.y=1.02,b.yanchor=\"bottom\"):b.y<-2&&(b.y=-.02,b.yanchor=\"top\")),e.dragmode===\"rotate\"&&(e.dragmode=\"orbit\"),Whe.clean(e),e.template&&e.template.layout&&L0.cleanLayout(e.template.layout),e};function uT(e,t,r=!1){var n=e[t],i=t.charAt(0);r&&Array.isArray(n)||n&&n!==\"paper\"&&(e[t]=t4(n,i,!0))}L0.cleanData=function(e){for(var t=0;t<e.length;t++){var r=e[t],n;if(r.type===\"histogramy\"&&\"xbins\"in r&&!(\"ybins\"in r)&&(r.ybins=r.xbins,delete r.xbins),r.type===\"histogramy\"&&L0.swapXYData(r),(r.type===\"histogramx\"||r.type===\"histogramy\")&&(r.type=\"histogram\"),\"scl\"in r&&!(\"colorscale\"in r)&&(r.colorscale=r.scl,delete r.scl),\"reversescl\"in r&&!(\"reversescale\"in r)&&(r.reversescale=r.reversescl,delete 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n=[],i=xl.deleteTraces,a=ode,o=[e,n],s=[e,t],l,u;for(ovt(e,t,r),Array.isArray(t)||(t=[t]),t=t.map(function(c){return Sa.extendFlat({},c)}),gh.cleanData(t),l=0;l<t.length;l++)e.data.push(t[l]);for(l=0;l<t.length;l++)n.push(-t.length+l);if(typeof r==\"undefined\")return u=xl.redraw(e),op.add(e,i,o,a,s),u;Array.isArray(r)||(r=[r]);try{tde(e,n,r)}catch(c){throw e.data.splice(e.data.length-t.length,t.length),c}return op.startSequence(e),op.add(e,i,o,a,s),u=xl.moveTraces(e,n,r),op.stopSequence(e),u}function sde(e,t){e=Sa.getGraphDiv(e);var r=[],n=xl.addTraces,i=sde,a=[e,r,t],o=[e,t],s,l;if(typeof t==\"undefined\")throw new Error(\"indices must be an integer or array of integers.\");for(Array.isArray(t)||(t=[t]),HP(e,t,\"indices\"),t=GP(t,e.data.length-1),t.sort(Sa.sorterDes),s=0;s<t.length;s+=1)l=e.data.splice(t[s],1)[0],r.push(l);var u=xl.redraw(e);return op.add(e,n,a,i,o),u}function nU(e,t,r){e=Sa.getGraphDiv(e);var n=[],i=[],a=nU,o=nU,s=[e,r,t],l=[e,t,r],u;if(tde(e,t,r),t=Array.isArray(t)?t:[t],typeof r==\"undefined\")for(r=[],u=0;u<t.length;u++)r.push(-t.length+u);for(r=Array.isArray(r)?r:[r],t=GP(t,e.data.length-1),r=GP(r,e.data.length-1),u=0;u<e.data.length;u++)t.indexOf(u)===-1&&n.push(e.data[u]);for(u=0;u<t.length;u++)i.push({newIndex:r[u],trace:e.data[t[u]]});for(i.sort(function(f,h){return f.newIndex-h.newIndex}),u=0;u<i.length;u+=1)n.splice(i[u].newIndex,0,i[u].trace);e.data=n;var c=xl.redraw(e);return op.add(e,a,s,o,l),c}function jP(e,t,r,n){e=Sa.getGraphDiv(e),gh.clearPromiseQueue(e);var i={};if(typeof t==\"string\")i[t]=r;else if(Sa.isPlainObject(t))i=Sa.extendFlat({},t),n===void 0&&(n=r);else return Sa.warn(\"Restyle fail.\",t,r,n),Promise.reject();Object.keys(i).length&&(e.changed=!0);var a=gh.coerceTraceIndices(e,n),o=lde(e,i,a),s=o.flags;s.calc&&(e.calcdata=void 0),s.clearAxisTypes&&gh.clearAxisTypes(e,a,{});var 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o=[Qo.previousPromises];a.layoutReplot?o.push(Au.layoutReplot):Object.keys(n).length&&(ude(e,a,i)||Qo.supplyDefaults(e),a.legend&&o.push(Au.doLegend),a.layoutstyle&&o.push(Au.layoutStyles),a.axrange&&KP(o,i.rangesAltered),a.ticks&&o.push(Au.doTicksRelayout),a.modebar&&o.push(Au.doModeBar),a.camera&&o.push(Au.doCamera),a.colorbars&&o.push(Au.doColorBars),o.push(i4)),o.push(Qo.rehover,Qo.redrag,Qo.reselect),op.add(e,r4,[e,i.undoit],r4,[e,i.redoit]);var s=Sa.syncOrAsync(o,e);return(!s||!s.then)&&(s=Promise.resolve(e)),s.then(function(){return e.emit(\"plotly_relayout\",i.eventData),e})}function ude(e,t,r){var n=e._fullLayout;if(!t.axrange)return!1;for(var i in t)if(i!==\"axrange\"&&t[i])return!1;var a,o,s=function(d,v){return Sa.coerce(a,o,Ydt,d,v)},l={};for(var u in r.rangesAltered){var c=Vv.id2name(u);if(a=e.layout[c],o=n[c],Zdt(a,o,s,l),o._matchGroup){for(var f in o._matchGroup)if(f!==u){var 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be=ke.property,Be=N||{editType:\"calc\"};f!==\"\"&&be===\"\"&&(NP.isAddVal(g)?k[C]=null:NP.isRemoveVal(g)?k[C]=(Ec(r,c).get()||[])[f]:Sa.warn(\"unrecognized full object value\",t)),A_.update(b,Be),u[c]||(u[c]={});var Le=u[c][f];Le||(Le=u[c][f]={}),Le[be]=g,delete t[C]}else z===\"reverse\"?(G.range?G.range.reverse():(M(V+\".autorange\",!0),G.range=[1,0]),Z.autorange?b.calc=!0:b.plot=!0):(C===\"dragmode\"&&(g===!1&&j!==!1||g!==!1&&j===!1)||n._has(\"scatter-like\")&&n._has(\"regl\")&&C===\"dragmode\"&&(g===\"lasso\"||g===\"select\")&&!(j===\"lasso\"||j===\"select\")?b.plot=!0:N?A_.update(b,N):b.calc=!0,S.set(g))}}for(c in u){var me=NP.applyContainerArrayChanges(e,a(r,c),u[c],b,a);me||(b.plot=!0)}for(var Pe in T){L=Vv.getFromId(e,Pe);var ce=L&&L._constraintGroup;if(ce){b.calc=!0;for(var He in ce)T[He]||(Vv.getFromId(e,He)._constraintShrinkable=!0)}}(dde(e)||t.height||t.width)&&(b.plot=!0);var lt=n.shapes;for(f=0;f<lt.length;f++)if(lt[f].showlegend){b.calc=!0;break}return(b.plot||b.calc)&&(b.layoutReplot=!0),{flags:b,rangesAltered:T,undoit:k,redoit:p,eventData:l}}function dde(e){var t=e._fullLayout,r=t.width,n=t.height;return e.layout.autosize&&Qo.plotAutoSize(e,e.layout,t),t.width!==r||t.height!==n}function XP(e,t,r,n){e=Sa.getGraphDiv(e),gh.clearPromiseQueue(e),Sa.isPlainObject(t)||(t={}),Sa.isPlainObject(r)||(r={}),Object.keys(t).length&&(e.changed=!0),Object.keys(r).length&&(e.changed=!0);var i=gh.coerceTraceIndices(e,n),a=lde(e,Sa.extendFlat({},t),i),o=a.flags,s=hde(e,Sa.extendFlat({},r)),l=s.flags;(o.calc||l.calc)&&(e.calcdata=void 0),o.clearAxisTypes&&gh.clearAxisTypes(e,i,r);var u=[];l.layoutReplot?u.push(Au.layoutReplot):o.fullReplot?u.push(xl._doPlot):(u.push(Qo.previousPromises),ude(e,l,s)||Qo.supplyDefaults(e),o.style&&u.push(Au.doTraceStyle),(o.colorbars||l.colorbars)&&u.push(Au.doColorBars),l.legend&&u.push(Au.doLegend),l.layoutstyle&&u.push(Au.layoutStyles),l.axrange&&KP(u,s.rangesAltered),l.ticks&&u.push(Au.doTicksRelayout),l.modebar&&u.push(Au.doModeBar),l.camera&&u.push(Au.doCamera),u.push(i4)),u.push(Qo.rehover,Qo.redrag,Qo.reselect),op.add(e,XP,[e,a.undoit,s.undoit,a.traces],XP,[e,a.redoit,s.redoit,a.traces]);var c=Sa.syncOrAsync(u,e);return(!c||!c.then)&&(c=Promise.resolve(e)),c.then(function(){return e.emit(\"plotly_update\",{data:a.eventData,layout:s.eventData}),e})}function aU(e){return function(r){r._fullLayout._guiEditing=!0;var n=e.apply(null,arguments);return r._fullLayout._guiEditing=!1,n}}var fvt=[{pattern:/^hiddenlabels/,attr:\"legend.uirevision\"},{pattern:/^((x|y)axis\\d*)\\.((auto)?range|title\\.text)/},{pattern:/axis\\d*\\.showspikes$/,attr:\"modebar.uirevision\"},{pattern:/(hover|drag)mode$/,attr:\"modebar.uirevision\"},{pattern:/^(scene\\d*)\\.camera/},{pattern:/^(geo\\d*)\\.(projection|center|fitbounds)/},{pattern:/^(ternary\\d*\\.[abc]axis)\\.(min|title\\.text)$/},{pattern:/^(polar\\d*\\.radialaxis)\\.((auto)?range|angle|title\\.text)/},{pattern:/^(polar\\d*\\.angularaxis)\\.rotation/},{pattern:/^(mapbox\\d*)\\.(center|zoom|bearing|pitch)/},{pattern:/^(map\\d*)\\.(center|zoom|bearing|pitch)/},{pattern:/^legend\\.(x|y)$/,attr:\"editrevision\"},{pattern:/^(shapes|annotations)/,attr:\"editrevision\"},{pattern:/^selections/,attr:\"selectionrevision\"},{pattern:/^title\\.text$/,attr:\"editrevision\"}],hvt=[{pattern:/^selectedpoints$/,attr:\"selectionrevision\"},{pattern:/(^|value\\.)visible$/,attr:\"legend.uirevision\"},{pattern:/^dimensions\\[\\d+\\]\\.constraintrange/},{pattern:/^node\\.(x|y|groups)/},{pattern:/^level$/},{pattern:/(^|value\\.)name$/},{pattern:/colorbar\\.title\\.text$/},{pattern:/colorbar\\.(x|y)$/,attr:\"editrevision\"}];function 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i2=ye((Rsr,eve)=>{\"use strict\";var Lpt=$b().getAxisGroup;eve.exports=function(t,r,n,i,a){var o=r.orientation,s=r[{v:\"x\",h:\"y\"}[o]+\"axis\"],l=Lpt(n,s)+o,u=n._alignmentOpts||{},c=i(\"alignmentgroup\"),f=u[l];f||(f=u[l]={});var h=f[c];h?h.traces.push(r):h=f[c]={traces:[r],alignmentIndex:Object.keys(f).length,offsetGroups:{}};var d=i(\"offsetgroup\")||\"\",v=h.offsetGroups,m=v[d];r._offsetIndex=0,(a!==\"group\"||d)&&(m||(m=v[d]={offsetIndex:Object.keys(v).length}),r._offsetIndex=m.offsetIndex)}});var vU=ye((Dsr,tve)=>{\"use strict\";var Ppt=Pr(),Ipt=i2(),Rpt=pf();tve.exports=function(t,r){var n,i,a,o=r.scattermode;function s(h){return Ppt.coerce(i._input,i,Rpt,h)}if(r.scattermode===\"group\")for(a=0;a<t.length;a++)i=t[a],i.type===\"scatter\"&&(n=i._input,Ipt(n,i,r,s,o));for(a=0;a<t.length;a++){var l=t[a];if(l.type===\"scatter\"){var u=l.fill;if(!(u===\"none\"||u===\"toself\")&&(l.opacity=void 0,u===\"tonexty\"||u===\"tonextx\"))for(var c=a-1;c>=0;c--){var 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s=t._length,l=e._fullLayout,u=r._id,c=n._id,f=l._firstScatter[gve(t)]===t.uid,h=(xU(t,l,r,n)||{}).orientation,d=t.fill;r._minDtick=0,n._minDtick=0;var v={padded:!0},m={padded:!0};o&&(v.ppad=m.ppad=o);var b=s<2||i[0]!==i[s-1]||a[0]!==a[s-1];b&&(d===\"tozerox\"||d===\"tonextx\"&&(f||h===\"h\"))?v.tozero=!0:!(t.error_y||{}).visible&&(d===\"tonexty\"||d===\"tozeroy\"||!_U.hasMarkers(t)&&!_U.hasText(t))&&(v.padded=!1,v.ppad=0),b&&(d===\"tozeroy\"||d===\"tonexty\"&&(f||h===\"v\"))?m.tozero=!0:(d===\"tonextx\"||d===\"tozerox\")&&(m.padded=!1),u&&(t._extremes[u]=s4.findExtremes(r,i,v)),c&&(t._extremes[c]=s4.findExtremes(n,a,m))}function vve(e,t){if(_U.hasMarkers(e)){var r=e.marker,n=1.6*(e.marker.sizeref||1),i;if(e.marker.sizemode===\"area\"?i=function(u){return Math.max(Math.sqrt((u||0)/n),3)}:i=function(u){return Math.max((u||0)/n,3)},yU.isArrayOrTypedArray(r.size)){var a={type:\"linear\"};s4.setConvert(a);for(var o=a.makeCalcdata(e.marker,\"size\"),s=new 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c=Hpt(i);this.distinctPositions=c.vals,c.vals.length===1&&r!==1/0?this.minDiff=r:this.minDiff=Math.min(c.minDiff,r);var f=(t.posAxis||{}).type;(f===\"category\"||f===\"multicategory\")&&(this.minDiff=1),this.binWidth=this.minDiff,this.bins={}}oI.prototype.put=function(t,r,n){var i=this.getLabel(t,r,n),a=this.bins[i]||0;return this.bins[i]=a+n,a};oI.prototype.get=function(t,r,n){var i=this.getLabel(t,r,n);return this.bins[i]||0};oI.prototype.getLabel=function(t,r,n){var i=n<0&&this.sepNegVal?\"v\":\"^\",a=this.overlapNoMerge?t:Math.round(t/this.binWidth);return i+a+\"g\"+r}});var S_=ye((Vsr,xve)=>{\"use strict\";xve.exports={TEXTPAD:3,eventDataKeys:[\"value\",\"label\"]}});var n2=ye((Gsr,Ave)=>{\"use strict\";var B0=Eo(),{isArrayOrTypedArray:By}=Pr(),{BADNUM:dT}=fs(),jpt=Oa(),l4=ho(),{getAxisGroup:Wpt}=$b(),sI=_ve(),{TEXTPAD:Xpt}=S_(),{LINE_SPACING:Zpt}=Dh(),{BR_TAG_ALL:Ypt}=Zl();function Kpt(e,t){for(var r=t.xaxis,n=t.yaxis,i=e._fullLayout,a=e._fullData,o=e.calcdata,s=[],l=[],u=0;u<a.length;u++){var c=a[u];if(c.visible===!0&&jpt.traceIs(c,\"bar\")&&c.xaxis===r._id&&c.yaxis===n._id&&(c.orientation===\"h\"?s.push(o[u]):l.push(o[u]),c._computePh))for(var f=e.calcdata[u],h=0;h<f.length;h++)typeof f[h].ph0==\"function\"&&(f[h].ph0=f[h].ph0()),typeof f[h].ph1==\"function\"&&(f[h].ph1=f[h].ph1())}var d={xCat:r.type===\"category\"||r.type===\"multicategory\",yCat:n.type===\"category\"||n.type===\"multicategory\",mode:i.barmode,norm:i.barnorm,gap:i.bargap,groupgap:i.bargroupgap};wU(e,r,n,l,d),wU(e,n,r,s,d)}function wU(e,t,r,n,i){if(n.length){var a,o,s,l,u;switch(Qpt(r,n),i.mode){case\"overlay\":bU(e,t,r,n,i);break;case\"group\":for(a=[],o=[],s=0;s<n.length;s++)l=n[s],u=l[0].trace,u.offset===void 0?o.push(l):a.push(l);o.length&&e0t(e,t,r,o,i),a.length&&bU(e,t,r,a,i);break;case\"stack\":case\"relative\":for(a=[],o=[],s=0;s<n.length;s++)l=n[s],u=l[0].trace,u.base===void 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null}:e.d2c;if(By(o)){for(n=0;n<Math.min(o.length,i.length);n++)s=u(o[n],0,l),B0(s)?(i[n].b=+s,i[n].hasB=1):i[n].b=0;for(;n<i.length;n++)i[n].b=0}else{s=u(o,0,l);var c=B0(s);for(s=c?s:0,n=0;n<i.length;n++)i[n].b=s,c&&(i[n].hasB=1)}}}function bU(e,t,r,n,i){for(var a=0;a<n.length;a++){var o=n[a],s=new sI([o],{posAxis:t,sepNegVal:!1,overlapNoMerge:!i.norm});TU(e,t,s,i),i.norm?(Tve(s),AU(r,s,i)):wve(r,s)}}function e0t(e,t,r,n,i){var a=new sI(n,{posAxis:t,sepNegVal:!1,overlapNoMerge:!i.norm});TU(e,t,a,i),a0t(a,t),i.norm?(Tve(a),AU(r,a,i)):wve(r,a)}function t0t(e,t,r,n,i){var a=new sI(n,{posAxis:t,sepNegVal:i.mode===\"relative\",overlapNoMerge:!(i.norm||i.mode===\"stack\"||i.mode===\"relative\")});TU(e,t,a,i),n0t(r,a,i);for(var o=0;o<n.length;o++)for(var s=n[o],l=s[0].t.offsetindex,u=0;u<s.length;u++){var c=s[u];if(c.s!==dT){var f=c.b+c.s===a.get(c.p,l,c.s);f&&(c._outmost=!0)}}i.norm&&AU(r,a,i)}function TU(e,t,r,n){var i=e._fullLayout,a=r.positions,o=r.distinctPositions,s=r.minDiff,l=r.traces,u=l.length,c=a.length!==o.length,f=s*(1-n.gap),h,d,v,m;if(t._id===\"angularaxis\")h=f,d=h*(1-(n.groupgap||0)),v=-d/2;else{var b=Wpt(i,t._id)+l[0][0].trace.orientation;m=i._alignmentOpts[b]||{}}for(var p=0;p<u;p++){var k=l[p],M=k[0].trace;if(t._id!==\"angularaxis\"){var T=m[M.alignmentgroup]||{},L=Object.keys(T.offsetGroups||{}).length;L?h=f/L:h=c?f/u:f,d=h*(1-(n.groupgap||0)),L?v=((2*M._offsetIndex+1-L)*h-d)/2:v=c?((2*p+1-u)*h-d)/2:-d/2}var x=k[0].t;x.barwidth=d,x.offsetindex=M._offsetIndex||0,x.poffset=v,x.bargroupwidth=f,x.bardelta=s}r.binWidth=l[0][0].t.barwidth/100,r0t(r),i0t(t,r),t._id===\"angularaxis\"?bve(t,r):bve(t,r,c)}function r0t(e){var t=e.traces,r,n;for(r=0;r<t.length;r++){var i=t[r],a=i[0],o=a.trace,s=a.t,l=o._offset||o.offset,u=s.poffset,c;if(By(l)){for(c=Array.prototype.slice.call(l,0,i.length),n=0;n<c.length;n++)B0(c[n])||(c[n]=u);for(n=c.length;n<i.length;n++)c.push(u);s.poffset=c}else l!==void 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T=k?b[v]:b,L=M?p[v]:p;h=f.p+T,d=h+L,c.push(h,d)}}u._extremes[e._id]=l4.findExtremes(e,c,{padded:!1})}}function wve(e,t){for(var r=t.traces,n=vT(e),i=0;i<r.length;i++){for(var a=r[i],o=a[0].trace,s=o.type===\"scatter\",l=o.orientation===\"v\",u=[],c=!1,f=0;f<a.length;f++){var h=a[f],d=s?0:h.b,v=s?l?h.y:h.x:d+h.s;h[n]=v,u.push(v),h.hasB&&u.push(d),(!h.hasB||!h.b)&&(c=!0)}let{ppadminus:m,ppadplus:b}=SU(o,a);o._extremes[e._id]=l4.findExtremes(e,u,{tozero:c,padded:!0,ppadplus:b,ppadminus:m})}}function n0t(e,t,r){var n=vT(e),i=t.traces,a,o,s,l,u,c,f;for(l=0;l<i.length;l++)if(a=i[l],o=a[0].trace,o.type===\"funnel\")for(f=a[0].t.offsetindex,u=0;u<a.length;u++)c=a[u],c.s!==dT&&t.put(c.p,f,-.5*c.s);for(l=0;l<i.length;l++){a=i[l],o=a[0].trace,s=o.type===\"funnel\",f=o.type===\"barpolar\"?0:a[0].t.offsetindex;var h=[];for(u=0;u<a.length;u++)if(c=a[u],c.s!==dT){var d;s?d=c.s:d=c.s+c.b;var v=t.put(c.p,f,d),m=v+d;c.b=v,c[n]=m,r.norm||(h.push(m),c.hasB&&h.push(v))}if(!r.norm){let{ppadminus:b,ppadplus:p}=SU(o,a);o._extremes[e._id]=l4.findExtremes(e,h,{tozero:!0,padded:!0,ppadplus:p,ppadminus:b})}}}function Tve(e){for(var t=e.traces,r=0;r<t.length;r++)for(var n=t[r],i=n[0].t.offsetindex,a=0;a<n.length;a++){var o=n[a];o.s!==dT&&e.put(o.p,i,o.b+o.s)}}function a0t(e,t){for(var r=e.traces,n=0;n<r.length;n++){var i=r[n],a=i[0].trace,o=i[0].t.offsetindex;if(a.base===void 0)for(var s=new sI([i],{posAxis:t,sepNegVal:!0,overlapNoMerge:!0}),l=0;l<i.length;l++){var u=i[l];if(u.p!==dT){var c=s.put(u.p,o,u.b+u.s);c&&(u.b=c)}}}}function AU(e,t,r){var n=t.traces,i=vT(e),a=r.norm===\"fraction\"?1:100,o=a/1e9,s=e.l2c(e.c2l(0)),l=r.mode===\"stack\"?a:s;function u(x){return B0(e.c2l(x))&&(x<s-o||x>l+o||!B0(s))}for(var c=0;c<n.length;c++){for(var f=n[c],h=f[0].t.offsetindex,d=f[0].trace,v=[],m=!1,b=!1,p=0;p<f.length;p++){var k=f[p];if(k.s!==dT){var M=Math.abs(a/t.get(k.p,h,k.s));k.b*=M,k.s*=M;var T=k.b,L=T+k.s;k[i]=L,v.push(L),b=b||u(L),k.hasB&&(v.push(T),b=b||u(T)),(!k.hasB||!k.b)&&(m=!0)}}let{ppadminus:x,ppadplus:C}=SU(d,f);d._extremes[e._id]=l4.findExtremes(e,v,{tozero:m,padded:b,ppadplus:C,ppadminus:x})}}function SU(e,t){if(e.orientation===\"v\"&&(e.text||e.texttemplate)&&e.textposition===\"outside\"&&(e.textangle===\"auto\"||e.textangle===0)){let n=function(a){return!a||typeof a!=\"string\"?0:(a.match(Ypt)||[]).length+1};var r;e.texttemplate?r=n(e.texttemplate):r=By(e.text)?Math.max(...e.text.map(a=>n(a))):n(e.text);let i=e.outsidetextfont.size*Zpt*r+Xpt;return{ppadplus:t.some(a=>a.s<0)?i:0,ppadminus:t.some(a=>a.s>=0)?i:0}}return{ppadplus:void 0,ppadminus:void 0}}function o0t(e,t,r,n){for(var i=vT(n),a=0;a<e.length;a++)for(var o=e[a],s=0;s<o.length;s++){var l=o[s],u=l[i];l._sMin=t[u],l._sMax=r[u]}}function s0t(e,t){var r=vT(t),n={},i,a,o,s=1/0,l=-1/0;for(i=0;i<e.length;i++)for(o=e[i],a=0;a<o.length;a++){var u=o[a].p;B0(u)&&(s=Math.min(s,u),l=Math.max(l,u))}var c=1e4/(l-s),f=n.round=function(S){return String(Math.round(c*(S-s)))},h={},d={},v=e.some(function(S){var g=S[0].trace;return\"marker\"in g&&g.marker.cornerradius});for(i=0;i<e.length;i++){o=e[i],o[0].t.extents=n;var m=o[0].t.poffset,b=By(m);for(a=0;a<o.length;a++){var p=o[a],k=p[r]-p.w/2;if(B0(k)){var M=p[r]+p.w/2,T=f(p.p);n[T]?n[T]=[Math.min(k,n[T][0]),Math.max(M,n[T][1])]:n[T]=[k,M]}if(p.p0=p.p+(b?m[a]:m),p.p1=p.p0+p.w,p.s0=p.b,p.s1=p.s0+p.s,v){var L=Math.min(p.s0,p.s1)||0,x=Math.max(p.s0,p.s1)||0,C=p[r];h[C]=C in h?Math.min(h[C],L):L,d[C]=C in d?Math.max(d[C],x):x}}}v&&o0t(e,h,d,t)}function vT(e){return e._id.charAt(0)}Ave.exports={crossTraceCalc:Kpt,setGroupPositions:wU}});var kve=ye((Hsr,Eve)=>{\"use strict\";var Sve=O0(),Mve=n2().setGroupPositions;function l0t(e,t){for(var r=t.xaxis,n=t.yaxis,i=e._fullLayout,a=e._fullData,o=e.calcdata,s=[],l=[],u=0;u<a.length;u++){var 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wT=ye((Qsr,Xve)=>{\"use strict\";var Wve=Ru();Xve.exports=function(t,r){var n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].trace,l,u,c,f,h=!Wve.hasMarkers(s)&&!Wve.hasText(s);if(h)return[];if(r===!1)for(l=0;l<n.length;l++)n[l].selected=0;else for(l=0;l<n.length;l++)u=n[l],c=i.c2p(u.x),f=a.c2p(u.y),u.i!==null&&r.contains([c,f],!1,l,t)?(o.push({pointNumber:u.i,x:i.c2d(u.x),y:a.c2d(u.y)}),u.selected=1):u.selected=0;return o}});var Yve=ye((elr,Zve)=>{\"use strict\";Zve.exports={xaxis:{valType:\"subplotid\",dflt:\"x\",editType:\"calc+clearAxisTypes\"},yaxis:{valType:\"subplotid\",dflt:\"y\",editType:\"calc+clearAxisTypes\"}}});var FU=ye((tlr,$ve)=>{\"use strict\";var f4=Oa().traceIs,DU=W3();$ve.exports=function(t,r,n,i){n(\"autotypenumbers\",i.autotypenumbersDflt);var a=n(\"type\",(i.splomStash||{}).type);a===\"-\"&&(A0t(r,i.data),r.type===\"-\"?r.type=\"linear\":t.type=r.type)};function A0t(e,t){if(e.type===\"-\"){var r=e._id,n=r.charAt(0),i;r.indexOf(\"scene\")!==-1&&(r=n);var a=S0t(t,r,n);if(a){if(a.type===\"histogram\"&&n==={v:\"y\",h:\"x\"}[a.orientation||\"v\"]){e.type=\"linear\";return}var o=n+\"calendar\",s=a[o],l={noMultiCategory:!f4(a,\"cartesian\")||f4(a,\"noMultiCategory\")};if(a.type===\"box\"&&a._hasPreCompStats&&n==={h:\"x\",v:\"y\"}[a.orientation||\"v\"]&&(l.noMultiCategory=!0),l.autotypenumbers=e.autotypenumbers,Jve(a,n)){var u=Kve(a),c=[];for(i=0;i<t.length;i++){var f=t[i];!f4(f,\"box-violin\")||(f[n+\"axis\"]||n)!==r||(f[u]!==void 0?c.push(f[u][0]):f.name!==void 0?c.push(f.name):c.push(\"text\"),f[o]!==s&&(s=void 0))}e.type=DU(c,s,l)}else if(a.type===\"splom\"){var h=a.dimensions,d=h[a._axesDim[r]];d.visible&&(e.type=DU(d.values,s,l))}else e.type=DU(a[n]||[a[n+\"0\"]],s,l)}}}function S0t(e,t,r){for(var n=0;n<e.length;n++){var i=e[n];if(i.type===\"splom\"&&i._length>0&&(i[\"_\"+r+\"axes\"]||{})[t])return i;if((i[r+\"axis\"]||r)===t){if(Jve(i,r))return i;if((i[r]||[]).length||i[r+\"0\"])return i}}}function Kve(e){return{v:\"x\",h:\"y\"}[e.orientation||\"v\"]}function Jve(e,t){var r=Kve(e),n=f4(e,\"box-violin\"),i=f4(e._fullInput||{},\"candlestick\");return n&&!i&&t===r&&e[r]===void 0&&e[r+\"0\"]===void 0}});var dI=ye((rlr,Qve)=>{\"use strict\";var M0t=Yd().isTypedArraySpec;function E0t(e,t){var r=t.dataAttr||e._id.charAt(0),n={},i,a,o;if(t.axData)i=t.axData;else for(i=[],a=0;a<t.data.length;a++){var s=t.data[a];s[r+\"axis\"]===e._id&&i.push(s)}for(a=0;a<i.length;a++){var l=i[a][r];for(o=0;o<l.length;o++){var u=l[o];u!=null&&(n[u]=1)}}return Object.keys(n)}Qve.exports=function(t,r,n,i){if(r.type===\"category\"){var a=t.categoryarray,o=Array.isArray(a)&&a.length>0||M0t(a),s;o&&(s=\"array\");var l=n(\"categoryorder\",s),u;l===\"array\"&&(u=n(\"categoryarray\")),!o&&l===\"array\"&&(l=r.categoryorder=\"trace\"),l===\"trace\"?r._initialCategories=[]:l===\"array\"?r._initialCategories=u.slice():(u=E0t(r,i).sort(),l===\"category ascending\"?r._initialCategories=u:l===\"category 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v=\"\";(!i.noTicklabelposition||f===\"multicategory\")&&(v=d4.coerce(t,r,{ticklabelposition:{valType:\"enumerated\",dflt:\"outside\",values:h===\"period\"?[\"outside\",\"inside\"]:o===\"x\"?[\"outside\",\"inside\",\"outside left\",\"inside left\",\"outside right\",\"inside right\"]:[\"outside\",\"inside\",\"outside top\",\"inside top\",\"outside bottom\",\"inside bottom\"]}},\"ticklabelposition\")),i.noTicklabeloverflow||n(\"ticklabeloverflow\",v.indexOf(\"inside\")!==-1?\"hide past domain\":f===\"category\"||f===\"multicategory\"?\"allow\":\"hide past div\"),ape(r,a),q0t(t,r,n,i),F0t(t,r,n,i),i.noHover||(f!==\"category\"&&n(\"hoverformat\"),i.noUnifiedhovertitle||n(\"unifiedhovertitle.text\"));var m=n(\"color\"),b=m!==zU.color.dflt?m:s.color,p=l.label||a._dfltTitle[o];if(D0t(t,r,n,f,i),!u)return r;n(\"title.text\",p),d4.coerceFont(n,\"title.font\",s,{overrideDflt:{size:d4.bigFont(s.size),color:b}}),ipe(t,r,n,f);var k=i.hasMinor;if(k&&(P0t.newContainer(r,\"minor\"),ipe(t,r,n,f,{isMinor:!0})),R0t(t,r,n,f,i),npe(t,r,n,i),k){var M=i.isMinor;i.isMinor=!0,npe(t,r,n,i),i.isMinor=M}z0t(t,r,n,{dfltColor:m,bgColor:i.bgColor,showGrid:i.showGrid,hasMinor:k,attributes:zU}),k&&r.ticklabelindex==null&&!r.minor.ticks&&!r.minor.showgrid&&delete r.minor,(r.showline||r.ticks)&&n(\"mirror\");var T=f===\"multicategory\";if(!i.noTickson&&(f===\"category\"||T)&&(r.ticks||r.showgrid)&&(T?(n(\"tickson\",\"boundaries\"),delete r.ticklabelposition):n(\"tickson\")),T){var L=n(\"showdividers\");L&&(n(\"dividercolor\"),n(\"dividerwidth\"))}if(f===\"date\")if(I0t(t,r,{name:\"rangebreaks\",inclusionAttr:\"enabled\",handleItemDefaults:B0t}),!r.rangebreaks.length)delete r.rangebreaks;else{for(var x=0;x<r.rangebreaks.length;x++)if(r.rangebreaks[x].pattern===vI){r._hasDayOfWeekBreaks=!0;break}if(ape(r,a),a._has(\"scattergl\")||a._has(\"splom\"))for(var C=0;C<i.data.length;C++){var S=i.data[C];(S.type===\"scattergl\"||S.type===\"splom\")&&(S.visible=!1,d4.warn(S.type+\" traces do not work on axes with rangebreaks. 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a=Og.traceLayerClasses,o=e._fullLayout,s=o._zindices,l=o._modules,u,c,f,h=[],d=[],v=0;v<s.length;v++)for(var m=s[v],b=0;b<l.length;b++){u=l[b];var p=u.name,k=bpe.modules[p].categories;if(k.svg){var M=u.layerName||p+\"layer\",T=M+(v?Number(v)+1:\"\"),L=u.plot;c=wpe(r,L,m),f=c[0],r=c[1],f.length&&h.push({i:a.indexOf(M),zindex:v,className:T,plotMethod:L,cdModule:f}),k.zoomScale&&d.push(\".\"+T)}}h.sort(function(S,g){return(S.zindex||0)-(g.zindex||0)||S.i-g.i});var x=t.plot.selectAll(\"g.mlayer\").data(h,function(S){return S.className});if(x.enter().append(\"g\").attr(\"class\",function(S){return S.className}).classed(\"mlayer\",!0).classed(\"rangeplot\",t.isRangePlot),x.exit().remove(),x.order(),x.each(function(S){var g=wI.select(this),P=S.className;S.plotMethod(e,t,S.cdModule,g,n,i),Og.clipOnAxisFalseQuery.indexOf(\".\"+P)===-1&&K0t.setClipUrl(g,t.layerClipId,e)}),o._has(\"scattergl\")&&(u=bpe.getModule(\"scattergl\"),f=wpe(r,u)[0],u.plot(e,t,f)),!e._context.staticPlot&&(t._hasClipOnAxisFalse&&(t.clipOnAxisFalseTraces=t.plot.selectAll(Og.clipOnAxisFalseQuery.join(\",\")).selectAll(\".trace\")),d.length)){var C=t.plot.selectAll(d.join(\",\")).selectAll(\".trace\");t.zoomScalePts=C.selectAll(\"path.point\"),t.zoomScaleTxt=C.selectAll(\".textpoint\")}}xv.clean=function(e,t,r,n){var i=n._plots||{},a=t._plots||{},o=n._subplots||{},s,l,u;if(n._hasOnlyLargeSploms&&!t._hasOnlyLargeSploms)for(u in i)s=i[u],s.plotgroup&&s.plotgroup.remove();var c=n._has&&n._has(\"gl\"),f=t._has&&t._has(\"gl\");if(c&&!f)for(u in i)s=i[u],s._scene&&s._scene.destroy();if(o.xaxis&&o.yaxis){var h=M_.listIds({_fullLayout:n});for(l=0;l<h.length;l++){var d=h[l];t[M_.id2name(d)]||n._infolayer.selectAll(\".g-\"+d+\"title\").remove()}}var v=n._has&&n._has(\"cartesian\"),m=t._has&&t._has(\"cartesian\");if(v&&!m)Spe(n._cartesianlayer.selectAll(\".subplot\"),n),n._defs.selectAll(\".axesclip\").remove(),delete n._axisConstraintGroups,delete n._axisMatchGroups;else if(o.cartesian)for(l=0;l<o.cartesian.length;l++){var b=o.cartesian[l];if(b.indexOf(s2)===-1&&!a[b]){var p=\".\"+b+\",.\"+b+\"-x,.\"+b+\"-y\";n._cartesianlayer.selectAll(p).remove(),Mpe(b,n)}}};xv.drawFramework=function(e){var t=e._fullLayout,r=e.calcdata,n,i={};for(n=0;n<r.length;n++){var a=r[n][0],o=a.trace,s=o.zorder||0;i[s]||(i[s]=[]),i[s].push(a)}var l=Object.keys(i).map(Number).sort(o2.sorterAsc);l.length||(l=[0]),t._zindices=l;var u=$0t(e),c=u.length,f=[];for(n=0;n<c;n++)f[n]=u[n].slice();for(var h=1;h<l.length;h++){var d=[];for(n=0;n<c;n++)d[n]=u[n].slice(),d[n][0]+=s2+(h+1);f=f.concat(d)}var v=t._cartesianlayer.selectAll(\".subplot\").data(f,String);v.enter().append(\"g\").attr(\"class\",function(m){return\"subplot \"+m[0]}),v.order(),v.exit().call(Spe,t),v.each(function(m){var b=m[0],p=b.indexOf(s2),k=p!==-1,M=k?b.slice(0,p):b,T=t._plots[b];T||(T=o2.extendFlat({},t._plots[M]),T&&(T.id=b,t._plots[b]=T,t._subplots.cartesian.push(b))),T&&(T.plotgroup=wI.select(this),Ape(e,T),k||(T.draglayer=iu(t._draggers,\"g\",b)))})};xv.rangePlot=function(e,t,r){Ape(e,t),Tpe(e,t,r),Y0t.style(e)};function $0t(e){var t=e._fullLayout,r=t._zindices.length,n=t._subplots.cartesian,i=n.length,a,o,s,l,u,c,f=[],h=[];for(a=0;a<i;a++){s=n[a],l=t._plots[s],u=l.xaxis,c=l.yaxis;var d=u._mainAxis,v=c._mainAxis,m=d._id+v._id,b=t._plots[m];l.overlays=[],m!==s&&b?(l.mainplot=m,l.mainplotinfo=b,h.push(s)):(l.mainplot=void 0,l.mainplotinfo=void 0,f.push(s))}for(a=0;a<h.length;a++)s=h[a],l=t._plots[s],l.mainplotinfo.overlays.push(l);var p=f.concat(h),k=[];for(a=0;a<i;a++){s=p[a],l=t._plots[s],u=l.xaxis,c=l.yaxis;for(var M=[],T=1;T<=r;T++){var L=\"\";for(T>1&&(L+=s2+T),M.push(s+L),o=0;o<l.overlays.length;o++)M.push(l.overlays[o].id+L)}M=M.concat([u.layer,c.layer,u.overlaying||\"\",c.overlaying||\"\"]),k.push(M)}return k}function Ape(e,t){var r=e._fullLayout,n=t.plotgroup,i=t.id,a=i.indexOf(s2),o=a!==-1,s=Og.layerValue2layerClass[t.xaxis.layer],l=Og.layerValue2layerClass[t.yaxis.layer],u=r._hasOnlyLargeSploms,c=r._zindices.length>1,f=t.mainplotinfo;if(!t.mainplot||c)if(u)t.xlines=iu(n,\"path\",\"xlines-above\"),t.ylines=iu(n,\"path\",\"ylines-above\"),t.xaxislayer=iu(n,\"g\",\"xaxislayer-above\"),t.yaxislayer=iu(n,\"g\",\"yaxislayer-above\");else{if(!o){var 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d=iu(n,\"g\",\"layer-between\");t.shapelayerBetween=iu(d,\"g\",\"shapelayer\"),t.imagelayerBetween=iu(d,\"g\",\"imagelayer\"),iu(n,\"path\",\"xlines-below\"),iu(n,\"path\",\"ylines-below\"),t.overlinesBelow=iu(n,\"g\",\"overlines-below\"),iu(n,\"g\",\"xaxislayer-below\"),iu(n,\"g\",\"yaxislayer-below\"),t.overaxesBelow=iu(n,\"g\",\"overaxes-below\")}t.overplot=iu(n,\"g\",\"overplot\"),t.plot=iu(t.overplot,\"g\",i),f&&c?t.zerolinelayerAbove=f.zerolinelayerAbove:t.zerolinelayerAbove=iu(n,\"g\",\"zerolinelayer-above\"),o||(t.xlines=iu(n,\"path\",\"xlines-above\"),t.ylines=iu(n,\"path\",\"ylines-above\"),t.overlinesAbove=iu(n,\"g\",\"overlines-above\"),iu(n,\"g\",\"xaxislayer-above\"),iu(n,\"g\",\"yaxislayer-above\"),t.overaxesAbove=iu(n,\"g\",\"overaxes-above\"),t.xlines=n.select(\".xlines-\"+s),t.ylines=n.select(\".ylines-\"+l),t.xaxislayer=n.select(\".xaxislayer-\"+s),t.yaxislayer=n.select(\".yaxislayer-\"+l))}else{var v=f.plotgroup,m=i+\"-x\",b=i+\"-y\";t.minorGridlayer=f.minorGridlayer,t.gridlayer=f.gridlayer,t.zerolinelayer=f.zerolinelayer,t.zerolinelayerAbove=f.zerolinelayerAbove,iu(f.overlinesBelow,\"path\",m),iu(f.overlinesBelow,\"path\",b),iu(f.overaxesBelow,\"g\",m),iu(f.overaxesBelow,\"g\",b),t.plot=iu(f.overplot,\"g\",i),iu(f.overlinesAbove,\"path\",m),iu(f.overlinesAbove,\"path\",b),iu(f.overaxesAbove,\"g\",m),iu(f.overaxesAbove,\"g\",b),t.xlines=v.select(\".overlines-\"+s).select(\".\"+m),t.ylines=v.select(\".overlines-\"+l).select(\".\"+b),t.xaxislayer=v.select(\".overaxes-\"+s).select(\".\"+m),t.yaxislayer=v.select(\".overaxes-\"+l).select(\".\"+b)}o||(u||(bI(t.minorGridlayer,\"g\",t.xaxis._id),bI(t.minorGridlayer,\"g\",t.yaxis._id),t.minorGridlayer.selectAll(\"g\").map(function(p){return p[0]}).sort(M_.idSort),bI(t.gridlayer,\"g\",t.xaxis._id),bI(t.gridlayer,\"g\",t.yaxis._id),t.gridlayer.selectAll(\"g\").map(function(p){return p[0]}).sort(M_.idSort)),t.xlines.style(\"fill\",\"none\").classed(\"crisp\",!0),t.ylines.style(\"fill\",\"none\").classed(\"crisp\",!0))}function Spe(e,t){if(e){var r={};e.each(function(l){var u=l[0],c=wI.select(this);c.remove(),Mpe(u,t),r[u]=!0});for(var n in t._plots)for(var i=t._plots[n],a=i.overlays||[],o=0;o<a.length;o++){var s=a[o];r[s.id]&&s.plot.selectAll(\".trace\").remove()}}}function Mpe(e,t){t._draggers.selectAll(\"g.\"+e).remove(),t._defs.select(\"#clip\"+t._uid+e+\"plot\").remove()}xv.toSVG=function(e){var t=e._fullLayout._glimages,r=wI.select(e).selectAll(\".svg-container\"),n=r.filter(function(a,o){return o===r.size()-1}).selectAll(\".gl-canvas-context, .gl-canvas-focus\");function i(){var a=this,o=a.toDataURL(\"image/png\"),s=t.append(\"svg:image\");s.attr({xmlns:J0t.svg,\"xlink:href\":o,preserveAspectRatio:\"none\",x:0,y:0,width:a.style.width,height:a.style.height})}n.each(i)};xv.updateFx=KN().updateFx});var kpe=ye((ulr,Epe)=>{\"use strict\";var 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jr=P+qr-Ee.top,gt=P+ei-Ee.left;Z.call(VU.positionText,gt,jr).call(Ny.setClipUrl,z?m:null,e)}q.select(\"rect\").call(Ny.setRect,P,P,ie,Se),E.call(Ny.setRect,S/2,S/2,ke-S,be-S),C.call(Ny.setTranslate,Math.round(b.x.text-ke/2),Math.round(b.y.text-be/2)),M.attr({transform:\"rotate(\"+p+\",\"+b.x.text+\",\"+b.y.text+\")\"});var Ge=function(We,et){k.selectAll(\".annotation-arrow-g\").remove();var xt=b.x.head,At=b.y.head,Kt=b.x.tail+We,Qt=b.y.tail+et,Mr=b.x.text+We,Gr=b.y.text+et,Ir=k_.rotationXYMatrix(p,Mr,Gr),Yr=k_.apply2DTransform(Ir),_i=k_.apply2DTransform2(Ir),Pi=+E.attr(\"width\"),ai=+E.attr(\"height\"),mi=Mr-.5*Pi,un=mi+Pi,Fn=Gr-.5*ai,An=Fn+ai,Hn=[[mi,Fn,mi,An],[mi,An,un,An],[un,An,un,Fn],[un,Fn,mi,Fn]].map(_i);if(!Hn.reduce(function($r,Ri){return $r^!!k_.segmentsIntersect(xt,At,xt+1e6,At+1e6,Ri[0],Ri[1],Ri[2],Ri[3])},!1)){Hn.forEach(function($r){var Ri=k_.segmentsIntersect(Kt,Qt,xt,At,$r[0],$r[1],$r[2],$r[3]);Ri&&(Kt=Ri.x,Qt=Ri.y)});var Qn=t.arrowwidth,Vi=t.arrowcolor,Jn=t.arrowside,Yn=k.append(\"g\").style({opacity:l2.opacity(Vi)}).classed(\"annotation-arrow-g\",!0),Gt=Yn.append(\"path\").attr(\"d\",\"M\"+Kt+\",\"+Qt+\"L\"+xt+\",\"+At).style(\"stroke-width\",Qn+\"px\").call(l2.stroke,l2.rgb(Vi));if(ogt(Gt,Jn,t),l.annotationPosition&&Gt.node().parentNode&&!n){var wt=xt,tr=At;if(t.standoff){var ir=Math.sqrt(Math.pow(xt-Kt,2)+Math.pow(At-Qt,2));wt+=t.standoff*(Kt-xt)/ir,tr+=t.standoff*(Qt-At)/ir}var wr=Yn.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(Kt-wt)+\",\"+(Qt-tr),transform:UU(wt,tr)}).style(\"stroke-width\",Qn+6+\"px\").call(l2.stroke,\"rgba(0,0,0,0)\").call(l2.fill,\"rgba(0,0,0,0)\"),Xr,ti;p4.init({element:wr.node(),gd:e,prepFn:function(){var $r=Ny.getTranslate(C);Xr=$r.x,ti=$r.y,i&&i.autorange&&h(i._name+\".autorange\",!0),a&&a.autorange&&h(a._name+\".autorange\",!0)},moveFn:function($r,Ri){var 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r=Npe(e,t),n=r.on,i=r.off.concat(r.explicitOff),a={},o=e._fullLayout.annotations,s,l;if(n.length||i.length){for(s=0;s<n.length;s++)l=Ope(e.layout,\"annotations\",o[n[s]]),l.modifyItem(\"visible\",!0),qpe.extendFlat(a,l.getUpdateObj());for(s=0;s<i.length;s++)l=Ope(e.layout,\"annotations\",o[i[s]]),l.modifyItem(\"visible\",!1),qpe.extendFlat(a,l.getUpdateObj());return lgt.call(\"update\",e,{},a)}}function Npe(e,t){var r=e._fullLayout.annotations,n=[],i=[],a=[],o=(t||[]).length,s,l,u,c,f,h,d,v;for(s=0;s<r.length;s++)if(u=r[s],c=u.clicktoshow,c){for(l=0;l<o;l++)if(f=t[l],h=f.xaxis,d=f.yaxis,h._id===u.xref&&d._id===u.yref&&h.d2r(f.x)===Bpe(u._xclick,h)&&d.d2r(f.y)===Bpe(u._yclick,d)){u.visible?c===\"onout\"?v=i:v=a:v=n,v.push(s);break}l===o&&u.visible&&c===\"onout\"&&i.push(s)}return{on:n,off:i,explicitOff:a}}function Bpe(e,t){return t.type===\"log\"?t.l2r(e):t.d2r(e)}});var jU=ye((dlr,Gpe)=>{\"use strict\";var HU=Pr(),TT=ka();Gpe.exports=function(t,r,n,i){i(\"opacity\");var 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XU=Pr(),c2=ho(),pgt=AI().draw;Xpe.exports=function(t){var r=t._fullLayout,n=XU.filterVisible(r.annotations);if(n.length&&t._fullData.length)return XU.syncOrAsync([pgt,ggt],t)};function ggt(e){var t=e._fullLayout;XU.filterVisible(t.annotations).forEach(function(r){var n=c2.getFromId(e,r.xref),i=c2.getFromId(e,r.yref),a=c2.getRefType(r.xref),o=c2.getRefType(r.yref);r._extremes={},a===\"range\"&&Wpe(r,n),o===\"range\"&&Wpe(r,i)})}function Wpe(e,t){var r=t._id,n=r.charAt(0),i=e[n],a=e[\"a\"+n],o=e[n+\"ref\"],s=e[\"a\"+n+\"ref\"],l=e[\"_\"+n+\"padplus\"],u=e[\"_\"+n+\"padminus\"],c={x:1,y:-1}[n]*e[n+\"shift\"],f=3*e.arrowsize*e.arrowwidth||0,h=f+c,d=f-c,v=3*e.startarrowsize*e.arrowwidth||0,m=v+c,b=v-c,p;if(s===o){var k=c2.findExtremes(t,[t.r2c(i)],{ppadplus:h,ppadminus:d}),M=c2.findExtremes(t,[t.r2c(a)],{ppadplus:Math.max(l,m),ppadminus:Math.max(u,b)});p={min:[k.min[0],M.min[0]],max:[k.max[0],M.max[0]]}}else 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kc=Qb(),_gt=mc().overrideAll,xgt=vl().templatedArray;e0e.exports=_gt(xgt(\"annotation\",{visible:kc.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:kc.xanchor,xshift:kc.xshift,yanchor:kc.yanchor,yshift:kc.yshift,text:kc.text,textangle:kc.textangle,font:kc.font,width:kc.width,height:kc.height,opacity:kc.opacity,align:kc.align,valign:kc.valign,bgcolor:kc.bgcolor,bordercolor:kc.bordercolor,borderpad:kc.borderpad,borderwidth:kc.borderwidth,showarrow:kc.showarrow,arrowcolor:kc.arrowcolor,arrowhead:kc.arrowhead,startarrowhead:kc.startarrowhead,arrowside:kc.arrowside,arrowsize:kc.arrowsize,startarrowsize:kc.startarrowsize,arrowwidth:kc.arrowwidth,standoff:kc.standoff,startstandoff:kc.startstandoff,hovertext:kc.hovertext,hoverlabel:kc.hoverlabel,captureevents:kc.captureevents}),\"calc\",\"from-root\")});var r0e=ye((_lr,t0e)=>{\"use strict\";var YU=Pr(),bgt=ho(),wgt=Kd(),Tgt=jU(),Agt=SI();t0e.exports=function(t,r,n){wgt(t,r,{name:\"annotations\",handleItemDefaults:Sgt,fullLayout:n.fullLayout})};function Sgt(e,t,r,n){function i(s,l){return YU.coerce(e,t,Agt,s,l)}function a(s){var l=s+\"axis\",u={_fullLayout:{}};return u._fullLayout[l]=r[l],bgt.coercePosition(t,u,i,s,s,.5)}var o=i(\"visible\");o&&(Tgt(e,t,n.fullLayout,i),a(\"x\"),a(\"y\"),a(\"z\"),YU.noneOrAll(e,t,[\"x\",\"y\",\"z\"]),t.xref=\"x\",t.yref=\"y\",t.zref=\"z\",i(\"xanchor\"),i(\"yanchor\"),i(\"xshift\"),i(\"yshift\"),t.showarrow&&(t.axref=\"pixel\",t.ayref=\"pixel\",i(\"ax\",-10),i(\"ay\",-30),YU.noneOrAll(e,t,[\"ax\",\"ay\"])))}});var o0e=ye((xlr,a0e)=>{\"use strict\";var i0e=Pr(),n0e=ho();a0e.exports=function(t){for(var r=t.fullSceneLayout,n=r.annotations,i=0;i<n.length;i++)Mgt(n[i],t);t.fullLayout._infolayer.selectAll(\".annotation-\"+t.id).remove()};function Mgt(e,t){var r=t.fullSceneLayout,n=r.domain,i=t.fullLayout._size,a={pdata:null,type:\"linear\",autorange:!1,range:[-1/0,1/0]};e._xa={},i0e.extendFlat(e._xa,a),n0e.setConvert(e._xa),e._xa._offset=i.l+n.x[0]*i.w,e._xa.l2p=function(){return .5*(1+e._pdata[0]/e._pdata[3])*i.w*(n.x[1]-n.x[0])},e._ya={},i0e.extendFlat(e._ya,a),n0e.setConvert(e._ya),e._ya._offset=i.t+(1-n.y[1])*i.h,e._ya.l2p=function(){return .5*(1-e._pdata[1]/e._pdata[3])*i.h*(n.y[1]-n.y[0])}}});var JU=ye((blr,s0e)=>{\"use strict\";function KU(e,t){var r=[0,0,0,0],n,i;for(n=0;n<4;++n)for(i=0;i<4;++i)r[i]+=e[4*n+i]*t[n];return r}function Egt(e,t){var r=KU(e.projection,KU(e.view,KU(e.model,[t[0],t[1],t[2],1])));return r}s0e.exports=Egt});var u0e=ye((wlr,l0e)=>{\"use strict\";var kgt=AI().drawRaw,Cgt=JU(),Lgt=[\"x\",\"y\",\"z\"];l0e.exports=function(t){for(var r=t.fullSceneLayout,n=t.dataScale,i=r.annotations,a=0;a<i.length;a++){for(var o=i[a],s=!1,l=0;l<3;l++){var u=Lgt[l],c=o[u],f=r[u+\"axis\"],h=f.r2fraction(c);if(h<0||h>1){s=!0;break}}s?t.fullLayout._infolayer.select(\".annotation-\"+t.id+'[data-index=\"'+a+'\"]').remove():(o._pdata=Cgt(t.glplot.cameraParams,[r.xaxis.r2l(o.x)*n[0],r.yaxis.r2l(o.y)*n[1],r.zaxis.r2l(o.z)*n[2]]),kgt(t.graphDiv,o,a,t.id,o._xa,o._ya))}}});var h0e=ye((Tlr,f0e)=>{\"use strict\";var Pgt=Oa(),c0e=Pr();f0e.exports={moduleType:\"component\",name:\"annotations3d\",schema:{subplots:{scene:{annotations:SI()}}},layoutAttributes:SI(),handleDefaults:r0e(),includeBasePlot:Igt,convert:o0e(),draw:u0e()};function Igt(e,t){var r=Pgt.subplotsRegistry.gl3d;if(r)for(var n=r.attrRegex,i=Object.keys(e),a=0;a<i.length;a++){var o=i[a];n.test(o)&&(e[o].annotations||[]).length&&(c0e.pushUnique(t._basePlotModules,r),c0e.pushUnique(t._subplots.gl3d,o))}}});var $U=ye((Mlr,g0e)=>{\"use strict\";var 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a.yref!==\"paper\"&&u!==\"domain\"&&(o=N0.getFromId(t,a.yref),s=S0e(o,a,zm.paramIsY),s&&(a._extremes[o._id]=N0.findExtremes(o,s,A0e(a))))}};function w0e(e,t,r){let n=t[r+\"ref\"],i=r===\"x\"?zm.paramIsX:zm.paramIsY;function a(f,h){f===\"paper\"||N0.getRefType(f)===\"domain\"||(o[f]||(o[f]=[]),o[f].push(h))}let o={};if(t.type===\"path\"&&t.path){let f=t.path.match(zm.segmentRE)||[];for(var s=0,l=0;l<f.length;l++){let h=f[l],d=h.charAt(0),v=i[d].drawn;if(v===void 0)continue;let m=h.slice(1).match(zm.paramRE);m&&m.length>v&&(a(n[s],m[v]),s++)}}else a(n[0],t[r+\"0\"]),a(n[1],t[r+\"1\"]);let u={};for(let f in o){let h=N0.getFromId(e,f);if(h){var c=h.type===\"category\"||h.type===\"multicategory\"?h.r2c:h.d2c;h.type===\"date\"&&(c=eV.decodeDate(c)),u[h._id]=o[f].map(c)}}return u}function T0e(e){return M0e(e.line.width,e.xsizemode,e.x0,e.x1,e.path,!1)}function A0e(e){return M0e(e.line.width,e.ysizemode,e.y0,e.y1,e.path,!0)}function M0e(e,t,r,n,i,a){var o=e/2,s=a;if(t===\"pixel\"){var l=i?eV.extractPathCoords(i,a?zm.paramIsY:zm.paramIsX):[r,n],u=QU.aggNums(Math.max,null,l),c=QU.aggNums(Math.min,null,l),f=c<0?Math.abs(c)+o:o,h=u>0?u+o:o;return{ppad:o,ppadplus:s?f:h,ppadminus:s?h:f}}else return{ppad:o}}function S0e(e,t,r){var n=e._id.charAt(0)===\"x\"?\"x\":\"y\",i=e.type===\"category\"||e.type===\"multicategory\",a,o,s=0,l=0,u=i?e.r2c:e.d2c,c=t[n+\"sizemode\"]===\"scaled\";if(c?(a=t[n+\"0\"],o=t[n+\"1\"],i&&(s=t[n+\"0shift\"],l=t[n+\"1shift\"])):(a=t[n+\"anchor\"],o=t[n+\"anchor\"]),a!==void 0)return[u(a)+s,u(o)+l];if(t.path){var f=1/0,h=-1/0,d=t.path.match(zm.segmentRE),v,m,b,p,k;for(e.type===\"date\"&&(u=eV.decodeDate(u)),v=0;v<d.length;v++)m=d[v],b=r[m.charAt(0)].drawn,b!==void 0&&(p=d[v].slice(1).match(zm.paramRE),!(!p||p.length<b)&&(k=u(p[b]),k<f&&(f=k),k>h&&(h=k)));if(h>=f)return[f,h]}}});var P0e=ye((Llr,L0e)=>{\"use strict\";var C0e=pP();L0e.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:$U(),supplyLayoutDefaults:y0e(),supplyDrawNewShapeDefaults:b0e(),includeBasePlot:KM()(\"shapes\"),calcAutorange:k0e(),draw:C0e.draw,drawOne:C0e.drawOne}});var tV=ye((Ilr,R0e)=>{\"use strict\";var I0e=Rh(),Hgt=vl().templatedArray,Plr=ZM();R0e.exports=Hgt(\"image\",{visible:{valType:\"boolean\",dflt:!0,editType:\"arraydraw\"},source:{valType:\"string\",editType:\"arraydraw\"},layer:{valType:\"enumerated\",values:[\"below\",\"above\"],dflt:\"above\",editType:\"arraydraw\"},sizex:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizey:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizing:{valType:\"enumerated\",values:[\"fill\",\"contain\",\"stretch\"],dflt:\"contain\",editType:\"arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,editType:\"arraydraw\"},x:{valType:\"any\",dflt:0,editType:\"arraydraw\"},y:{valType:\"any\",dflt:0,editType:\"arraydraw\"},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"arraydraw\"},yanchor:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"top\",editType:\"arraydraw\"},xref:{valType:\"enumerated\",values:[\"paper\",I0e.idRegex.x.toString()],dflt:\"paper\",editType:\"arraydraw\"},yref:{valType:\"enumerated\",values:[\"paper\",I0e.idRegex.y.toString()],dflt:\"paper\",editType:\"arraydraw\"},editType:\"arraydraw\"})});var F0e=ye((Rlr,D0e)=>{\"use strict\";var jgt=Pr(),rV=ho(),Wgt=Kd(),Xgt=tV(),Zgt=\"images\";D0e.exports=function(t,r){var n={name:Zgt,handleItemDefaults:Ygt};Wgt(t,r,n)};function Ygt(e,t,r){function n(h,d){return jgt.coerce(e,t,Xgt,h,d)}var i=n(\"source\"),a=n(\"visible\",!!i);if(!a)return t;n(\"layer\"),n(\"xanchor\"),n(\"yanchor\"),n(\"sizex\"),n(\"sizey\"),n(\"sizing\"),n(\"opacity\");for(var o={_fullLayout:r},s=[\"x\",\"y\"],l=0;l<2;l++){var u=s[l],c=rV.coerceRef(e,t,o,u,\"paper\",void 0);if(c!==\"paper\"){var f=rV.getFromId(o,c);f._imgIndices.push(t._index)}rV.coercePosition(t,o,n,c,u,0)}return t}});var B0e=ye((Dlr,O0e)=>{\"use strict\";var z0e=Fa(),Kgt=So(),MT=ho(),q0e=df(),Jgt=Wp();O0e.exports=function(t){var r=t._fullLayout,n=[],i={},a=[],o,s;for(s=0;s<r.images.length;s++){var l=r.images[s];if(l.visible)if(l.layer===\"below\"&&l.xref!==\"paper\"&&l.yref!==\"paper\"){o=q0e.ref2id(l.xref)+q0e.ref2id(l.yref);var u=r._plots[o];if(!u){a.push(l);continue}u.mainplot&&(o=u.mainplot.id),i[o]||(i[o]=[]),i[o].push(l)}else l.layer===\"above\"?n.push(l):a.push(l)}var c={x:{left:{sizing:\"xMin\",offset:0},center:{sizing:\"xMid\",offset:-1/2},right:{sizing:\"xMax\",offset:-1}},y:{top:{sizing:\"YMin\",offset:0},middle:{sizing:\"YMid\",offset:-1/2},bottom:{sizing:\"YMax\",offset:-1}}};function f(T){var L=z0e.select(this);if(this._imgSrc!==T.source)if(L.attr(\"xmlns\",Jgt.svg),!t._context.staticPlot||T.source&&T.source.slice(0,5)===\"data:\")L.attr(\"xlink:href\",T.source),this._imgSrc=T.source;else{var x=new Promise(function(C){var S=new Image;this.img=S,S.setAttribute(\"crossOrigin\",\"anonymous\"),S.onerror=g,S.onload=function(){var P=document.createElement(\"canvas\");P.width=this.width,P.height=this.height;var E=P.getContext(\"2d\",{willReadFrequently:!0});E.drawImage(this,0,0);var z=P.toDataURL(\"image/png\");L.attr(\"xlink:href\",z),C()},L.on(\"error\",g),S.src=T.source,this._imgSrc=T.source;function g(){L.remove(),C()}}.bind(this));t._promises.push(x)}}function h(T){var L=z0e.select(this),x=MT.getFromId(t,T.xref),C=MT.getFromId(t,T.yref),S=MT.getRefType(T.xref)===\"domain\",g=MT.getRefType(T.yref)===\"domain\",P=r._size,E,z;x!==void 0?E=typeof T.xref==\"string\"&&S?x._length*T.sizex:Math.abs(x.l2p(T.sizex)-x.l2p(0)):E=T.sizex*P.w,C!==void 0?z=typeof T.yref==\"string\"&&g?C._length*T.sizey:Math.abs(C.l2p(T.sizey)-C.l2p(0)):z=T.sizey*P.h;var q=E*c.x[T.xanchor].offset,V=z*c.y[T.yanchor].offset,G=c.x[T.xanchor].sizing+c.y[T.yanchor].sizing,Z,j;switch(x!==void 0?Z=typeof T.xref==\"string\"&&S?x._length*T.x+x._offset:x.r2p(T.x)+x._offset:Z=T.x*P.w+P.l,Z+=q,C!==void 0?j=typeof T.yref==\"string\"&&g?C._length*(1-T.y)+C._offset:C.r2p(T.y)+C._offset:j=P.h-T.y*P.h+P.t,j+=V,T.sizing){case\"fill\":G+=\" slice\";break;case\"stretch\":G=\"none\";break}L.attr({x:Z,y:j,width:E,height:z,preserveAspectRatio:G,opacity:T.opacity});var N=x&&MT.getRefType(T.xref)!==\"domain\"?x._id:\"\",H=C&&MT.getRefType(T.yref)!==\"domain\"?C._id:\"\",re=N+H;Kgt.setClipUrl(L,re?\"clip\"+r._uid+re:null,t)}function d(T){return[T.xref,T.x,T.sizex,T.yref,T.y,T.sizey].join(\"_\")}function v(T,L){return T._index-L._index}var m=r._imageLowerLayer.selectAll(\"image\").data(a,d),b=r._imageUpperLayer.selectAll(\"image\").data(n,d);m.enter().append(\"image\"),b.enter().append(\"image\"),m.exit().remove(),b.exit().remove(),m.each(function(T){f.bind(this)(T),h.bind(this)(T)}),b.each(function(T){f.bind(this)(T),h.bind(this)(T)}),m.sort(v),b.sort(v);var p=Object.keys(r._plots);for(s=0;s<p.length;s++){o=p[s];var k=r._plots[o];if(k.imagelayer){var M=k.imagelayer.selectAll(\"image\").data(i[o]||[],d);M.enter().append(\"image\"),M.exit().remove(),M.each(function(T){f.bind(this)(T),h.bind(this)(T)}),M.sort(v)}}}});var V0e=ye((Flr,U0e)=>{\"use strict\";var N0e=Eo(),$gt=E6();U0e.exports=function(t,r,n,i){r=r||{};var a=n===\"log\"&&r.type===\"linear\",o=n===\"linear\"&&r.type===\"log\";if(a||o){for(var s=t._fullLayout.images,l=r._id.charAt(0),u,c,f=0;f<s.length;f++)if(u=s[f],c=\"images[\"+f+\"].\",u[l+\"ref\"]===r._id){var h=u[l],d=u[\"size\"+l],v=null,m=null;if(a){v=$gt(h,r.range);var b=d/Math.pow(10,v)/2;m=2*Math.log(b+Math.sqrt(1+b*b))/Math.LN10}else v=Math.pow(10,h),m=v*(Math.pow(10,d/2)-Math.pow(10,-d/2));N0e(v)?N0e(m)||(m=null):(v=null,m=null),i(c+l,v),i(c+\"size\"+l,m)}}}});var H0e=ye((zlr,G0e)=>{\"use strict\";G0e.exports={moduleType:\"component\",name:\"images\",layoutAttributes:tV(),supplyLayoutDefaults:F0e(),includeBasePlot:KM()(\"images\"),draw:B0e(),convertCoords:V0e()}});var MI=ye((qlr,j0e)=>{\"use 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Qgt=ec(),emt=Ih(),tmt=Ao().extendFlat,rmt=mc().overrideAll,imt=q6(),W0e=vl().templatedArray,nmt=W0e(\"button\",{visible:{valType:\"boolean\"},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},args2:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\",dflt:\"\"},execute:{valType:\"boolean\",dflt:!0}});X0e.exports=rmt(W0e(\"updatemenu\",{_arrayAttrRegexps:[/^updatemenus\\[(0|[1-9][0-9]+)\\]\\.buttons/],visible:{valType:\"boolean\"},type:{valType:\"enumerated\",values:[\"dropdown\",\"buttons\"],dflt:\"dropdown\"},direction:{valType:\"enumerated\",values:[\"left\",\"right\",\"up\",\"down\"],dflt:\"down\"},active:{valType:\"integer\",min:-1,dflt:0},showactive:{valType:\"boolean\",dflt:!0},buttons:nmt,x:{valType:\"number\",min:-2,max:3,dflt:-.05},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"right\"},y:{valType:\"number\",min:-2,max:3,dflt:1},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},pad:tmt(imt({editType:\"arraydraw\"}),{}),font:Qgt({}),bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\",dflt:emt.borderLine},borderwidth:{valType:\"number\",min:0,dflt:1,editType:\"arraydraw\"}}),\"arraydraw\",\"from-root\")});var J0e=ye((Blr,K0e)=>{\"use strict\";var EI=Pr(),Z0e=Kd(),Y0e=iV(),amt=MI(),omt=amt.name,smt=Y0e.buttons;K0e.exports=function(t,r){var n={name:omt,handleItemDefaults:lmt};Z0e(t,r,n)};function lmt(e,t,r){function n(o,s){return EI.coerce(e,t,Y0e,o,s)}var i=Z0e(e,t,{name:\"buttons\",handleItemDefaults:umt}),a=n(\"visible\",i.length>0);a&&(n(\"active\"),n(\"direction\"),n(\"type\"),n(\"showactive\"),n(\"x\"),n(\"y\"),EI.noneOrAll(e,t,[\"x\",\"y\"]),n(\"xanchor\"),n(\"yanchor\"),n(\"pad.t\"),n(\"pad.r\"),n(\"pad.b\"),n(\"pad.l\"),EI.coerceFont(n,\"font\",r.font),n(\"bgcolor\",r.paper_bgcolor),n(\"bordercolor\"),n(\"borderwidth\"))}function umt(e,t){function r(i,a){return EI.coerce(e,t,smt,i,a)}var n=r(\"visible\",e.method===\"skip\"||Array.isArray(e.args));n&&(r(\"method\"),r(\"args\"),r(\"args2\"),r(\"label\"),r(\"execute\"))}});var ege=ye((Nlr,Q0e)=>{\"use strict\";Q0e.exports=Mf;var Ng=Fa(),$0e=ka(),ET=So(),kI=Pr();function Mf(e,t,r){this.gd=e,this.container=t,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll(\"rect.scrollbox-bg\").data([0]),this.bg.exit().on(\".drag\",null).on(\"wheel\",null).remove(),this.bg.enter().append(\"rect\").classed(\"scrollbox-bg\",!0).style(\"pointer-events\",\"all\").attr({opacity:0,x:0,y:0,width:0,height:0})}Mf.barWidth=2;Mf.barLength=20;Mf.barRadius=2;Mf.barPad=1;Mf.barColor=\"#808BA4\";Mf.prototype.enable=function(t,r,n){var i=this.gd._fullLayout,a=i.width,o=i.height;this.position=t;var s=this.position.l,l=this.position.w,u=this.position.t,c=this.position.h,f=this.position.direction,h=f===\"down\",d=f===\"left\",v=f===\"right\",m=f===\"up\",b=l,p=c,k,M,T,L;!h&&!d&&!v&&!m&&(this.position.direction=\"down\",h=!0);var x=h||m;x?(k=s,M=k+b,h?(T=u,L=Math.min(T+p,o),p=L-T):(L=u+p,T=Math.max(L-p,0),p=L-T)):(T=u,L=T+p,d?(M=s+b,k=Math.max(M-b,0),b=M-k):(k=s,M=Math.min(k+b,a),b=M-k)),this._box={l:k,t:T,w:b,h:p};var C=l>b,S=Mf.barLength+2*Mf.barPad,g=Mf.barWidth+2*Mf.barPad,P=s,E=u+c;E+g>o&&(E=o-g);var z=this.container.selectAll(\"rect.scrollbar-horizontal\").data(C?[0]:[]);z.exit().on(\".drag\",null).remove(),z.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call($0e.fill,Mf.barColor),C?(this.hbar=z.attr({rx:Mf.barRadius,ry:Mf.barRadius,x:P,y:E,width:S,height:g}),this._hbarXMin=P+S/2,this._hbarTranslateMax=b-S):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var q=c>p,V=Mf.barWidth+2*Mf.barPad,G=Mf.barLength+2*Mf.barPad,Z=s+l,j=u;Z+V>a&&(Z=a-V);var N=this.container.selectAll(\"rect.scrollbar-vertical\").data(q?[0]:[]);N.exit().on(\".drag\",null).remove(),N.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call($0e.fill,Mf.barColor),q?(this.vbar=N.attr({rx:Mf.barRadius,ry:Mf.barRadius,x:Z,y:j,width:V,height:G}),this._vbarYMin=j+G/2,this._vbarTranslateMax=p-G):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var H=this.id,re=k-.5,ae=q?M+V+.5:M+.5,_e=T-.5,Ee=C?L+g+.5:L+.5,Ce=i._topdefs.selectAll(\"#\"+H).data(C||q?[0]:[]);if(Ce.exit().remove(),Ce.enter().append(\"clipPath\").attr(\"id\",H).append(\"rect\"),C||q?(this._clipRect=Ce.select(\"rect\").attr({x:Math.floor(re),y:Math.floor(_e),width:Math.ceil(ae)-Math.floor(re),height:Math.ceil(Ee)-Math.floor(_e)}),this.container.call(ET.setClipUrl,H,this.gd),this.bg.attr({x:s,y:u,width:l,height:c})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(ET.setClipUrl,null),delete this._clipRect),C||q){var ge=Ng.behavior.drag().on(\"dragstart\",function(){Ng.event.sourceEvent.preventDefault()}).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(ge);var ie=Ng.behavior.drag().on(\"dragstart\",function(){Ng.event.sourceEvent.preventDefault(),Ng.event.sourceEvent.stopPropagation()}).on(\"drag\",this._onBarDrag.bind(this));C&&this.hbar.on(\".drag\",null).call(ie),q&&this.vbar.on(\".drag\",null).call(ie)}this.setTranslate(r,n)};Mf.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(ET.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)};Mf.prototype._onBoxDrag=function(){var t=this.translateX,r=this.translateY;this.hbar&&(t-=Ng.event.dx),this.vbar&&(r-=Ng.event.dy),this.setTranslate(t,r)};Mf.prototype._onBoxWheel=function(){var t=this.translateX,r=this.translateY;this.hbar&&(t+=Ng.event.deltaY),this.vbar&&(r+=Ng.event.deltaY),this.setTranslate(t,r)};Mf.prototype._onBarDrag=function(){var t=this.translateX,r=this.translateY;if(this.hbar){var n=t+this._hbarXMin,i=n+this._hbarTranslateMax,a=kI.constrain(Ng.event.x,n,i),o=(a-n)/(i-n),s=this.position.w-this._box.w;t=o*s}if(this.vbar){var l=r+this._vbarYMin,u=l+this._vbarTranslateMax,c=kI.constrain(Ng.event.y,l,u),f=(c-l)/(u-l),h=this.position.h-this._box.h;r=f*h}this.setTranslate(t,r)};Mf.prototype.setTranslate=function(t,r){var n=this.position.w-this._box.w,i=this.position.h-this._box.h;if(t=kI.constrain(t||0,0,n),r=kI.constrain(r||0,0,i),this.translateX=t,this.translateY=r,this.container.call(ET.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-r),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+r-.5)}),this.hbar){var a=t/n;this.hbar.call(ET.setTranslate,t+a*this._hbarTranslateMax,r)}if(this.vbar){var o=r/i;this.vbar.call(ET.setTranslate,t,r+o*this._vbarTranslateMax)}}});var cge=ye((Ulr,uge)=>{\"use strict\";var kT=Fa(),y4=Mc(),_4=ka(),CT=So(),t0=Pr(),CI=Zl(),cmt=vl().arrayEditor,rge=Dh().LINE_SPACING,es=MI(),fmt=ege();uge.exports=function(t){var r=t._fullLayout,n=t0.filterVisible(r[es.name]);function i(h){y4.autoMargin(t,sge(h))}var a=r._menulayer.selectAll(\"g.\"+es.containerClassName).data(n.length>0?[0]:[]);if(a.enter().append(\"g\").classed(es.containerClassName,!0).style(\"cursor\",\"pointer\"),a.exit().each(function(){kT.select(this).selectAll(\"g.\"+es.headerGroupClassName).each(i)}).remove(),n.length!==0){var o=a.selectAll(\"g.\"+es.headerGroupClassName).data(n,hmt);o.enter().append(\"g\").classed(es.headerGroupClassName,!0);for(var s=t0.ensureSingle(a,\"g\",es.dropdownButtonGroupClassName,function(h){h.style(\"pointer-events\",\"all\")}),l=0;l<n.length;l++){var u=n[l];ymt(t,u)}var c=\"updatemenus\"+r._uid,f=new fmt(t,s,c);o.enter().size()&&(s.node().parentNode.appendChild(s.node()),s.call(aV)),o.exit().each(function(h){s.call(aV),i(h)}).remove(),o.each(function(h){var d=kT.select(this),v=h.type===\"dropdown\"?s:null;y4.manageCommandObserver(t,h,h.buttons,function(m){nV(t,h,h.buttons[m.index],d,v,f,m.index,!0)}),h.type===\"dropdown\"?(nge(t,d,s,f,h),ige(s,h)&&x4(t,d,s,f,h)):x4(t,d,null,null,h)})}};function hmt(e){return e._index}function dmt(e){return+e.attr(es.menuIndexAttrName)==-1}function ige(e,t){return+e.attr(es.menuIndexAttrName)===t._index}function nV(e,t,r,n,i,a,o,s){t.active=o,cmt(e.layout,es.name,t).applyUpdate(\"active\",o),t.type===\"buttons\"?x4(e,n,null,null,t):t.type===\"dropdown\"&&(i.attr(es.menuIndexAttrName,\"-1\"),nge(e,n,i,a,t),s||x4(e,n,i,a,t))}function nge(e,t,r,n,i){var a=t0.ensureSingle(t,\"g\",es.headerClassName,function(h){h.style(\"pointer-events\",\"all\")}),o=i._dims,s=i.active,l=i.buttons[s]||es.blankHeaderOpts,u={y:i.pad.t,yPad:0,x:i.pad.l,xPad:0,index:0},c={width:o.headerWidth,height:o.headerHeight};a.call(oV,i,l,e).call(lge,i,u,c);var f=t0.ensureSingle(t,\"text\",es.headerArrowClassName,function(h){h.attr(\"text-anchor\",\"end\").call(CT.font,i.font).text(es.arrowSymbol[i.direction])});f.attr({x:o.headerWidth-es.arrowOffsetX+i.pad.l,y:o.headerHeight/2+es.textOffsetY+i.pad.t}),a.on(\"click\",function(){r.call(aV,String(ige(r,i)?-1:i._index)),x4(e,t,r,n,i)}),a.on(\"mouseover\",function(){a.call(age)}),a.on(\"mouseout\",function(){a.call(oge,i)}),CT.setTranslate(t,o.lx,o.ly)}function x4(e,t,r,n,i){r||(r=t,r.attr(\"pointer-events\",\"all\"));var a=!dmt(r)||i.type===\"buttons\"?i.buttons:[],o=i.type===\"dropdown\"?es.dropdownButtonClassName:es.buttonClassName,s=r.selectAll(\"g.\"+o).data(t0.filterVisible(a)),l=s.enter().append(\"g\").classed(o,!0),u=s.exit();i.type===\"dropdown\"?(l.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),u.transition().attr(\"opacity\",\"0\").remove()):u.remove();var c=0,f=0,h=i._dims,d=[\"up\",\"down\"].indexOf(i.direction)!==-1;i.type===\"dropdown\"&&(d?f=h.headerHeight+es.gapButtonHeader:c=h.headerWidth+es.gapButtonHeader),i.type===\"dropdown\"&&i.direction===\"up\"&&(f=-es.gapButtonHeader+es.gapButton-h.openHeight),i.type===\"dropdown\"&&i.direction===\"left\"&&(c=-es.gapButtonHeader+es.gapButton-h.openWidth);var v={x:h.lx+c+i.pad.l,y:h.ly+f+i.pad.t,yPad:es.gapButton,xPad:es.gapButton,index:0},m={l:v.x+i.borderwidth,t:v.y+i.borderwidth};s.each(function(b,p){var k=kT.select(this);k.call(oV,i,b,e).call(lge,i,v),k.on(\"click\",function(){kT.event.defaultPrevented||(b.execute&&(b.args2&&i.active===p?(nV(e,i,b,t,r,n,-1),y4.executeAPICommand(e,b.method,b.args2)):(nV(e,i,b,t,r,n,p),y4.executeAPICommand(e,b.method,b.args))),e.emit(\"plotly_buttonclicked\",{menu:i,button:b,active:i.active}))}),k.on(\"mouseover\",function(){k.call(age)}),k.on(\"mouseout\",function(){k.call(oge,i),s.call(tge,i)})}),s.call(tge,i),d?(m.w=Math.max(h.openWidth,h.headerWidth),m.h=v.y-m.t):(m.w=v.x-m.l,m.h=Math.max(h.openHeight,h.headerHeight)),m.direction=i.direction,n&&(s.size()?vmt(e,t,r,n,i,m):pmt(n))}function vmt(e,t,r,n,i,a){var o=i.direction,s=o===\"up\"||o===\"down\",l=i._dims,u=i.active,c,f,h;if(s)for(f=0,h=0;h<u;h++)f+=l.heights[h]+es.gapButton;else for(c=0,h=0;h<u;h++)c+=l.widths[h]+es.gapButton;n.enable(a,c,f),n.hbar&&n.hbar.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),n.vbar&&n.vbar.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\")}function pmt(e){var t=!!e.hbar,r=!!e.vbar;t&&e.hbar.transition().attr(\"opacity\",\"0\").each(\"end\",function(){t=!1,r||e.disable()}),r&&e.vbar.transition().attr(\"opacity\",\"0\").each(\"end\",function(){r=!1,t||e.disable()})}function oV(e,t,r,n){e.call(gmt,t).call(mmt,t,r,n)}function gmt(e,t){var r=t0.ensureSingle(e,\"rect\",es.itemRectClassName,function(n){n.attr({rx:es.rx,ry:es.ry,\"shape-rendering\":\"crispEdges\"})});r.call(_4.stroke,t.bordercolor).call(_4.fill,t.bgcolor).style(\"stroke-width\",t.borderwidth+\"px\")}function mmt(e,t,r,n){var i=t0.ensureSingle(e,\"text\",es.itemTextClassName,function(s){s.attr({\"text-anchor\":\"start\",\"data-notex\":1})}),a=r.label,o=n._fullLayout._meta;o&&(a=t0.templateString(a,o)),i.call(CT.font,t.font).text(a).call(CI.convertToTspans,n)}function tge(e,t){var r=t.active;e.each(function(n,i){var a=kT.select(this);i===r&&t.showactive&&a.select(\"rect.\"+es.itemRectClassName).call(_4.fill,es.activeColor)})}function age(e){e.select(\"rect.\"+es.itemRectClassName).call(_4.fill,es.hoverColor)}function oge(e,t){e.select(\"rect.\"+es.itemRectClassName).call(_4.fill,t.bgcolor)}function ymt(e,t){var r=t._dims={width1:0,height1:0,heights:[],widths:[],totalWidth:0,totalHeight:0,openWidth:0,openHeight:0,lx:0,ly:0},n=CT.tester.selectAll(\"g.\"+es.dropdownButtonClassName).data(t0.filterVisible(t.buttons));n.enter().append(\"g\").classed(es.dropdownButtonClassName,!0);var i=[\"up\",\"down\"].indexOf(t.direction)!==-1;n.each(function(c,f){var h=kT.select(this);h.call(oV,t,c,e);var d=h.select(\".\"+es.itemTextClassName),v=d.node()&&CT.bBox(d.node()).width,m=Math.max(v+es.textPadX,es.minWidth),b=t.font.size*rge,p=CI.lineCount(d),k=Math.max(b*p,es.minHeight)+es.textOffsetY;k=Math.ceil(k),m=Math.ceil(m),r.widths[f]=m,r.heights[f]=k,r.height1=Math.max(r.height1,k),r.width1=Math.max(r.width1,m),i?(r.totalWidth=Math.max(r.totalWidth,m),r.openWidth=r.totalWidth,r.totalHeight+=k+es.gapButton,r.openHeight+=k+es.gapButton):(r.totalWidth+=m+es.gapButton,r.openWidth+=m+es.gapButton,r.totalHeight=Math.max(r.totalHeight,k),r.openHeight=r.totalHeight)}),i?r.totalHeight-=es.gapButton:r.totalWidth-=es.gapButton,r.headerWidth=r.width1+es.arrowPadX,r.headerHeight=r.height1,t.type===\"dropdown\"&&(i?(r.width1+=es.arrowPadX,r.totalHeight=r.height1):r.totalWidth=r.width1,r.totalWidth+=es.arrowPadX),n.remove();var a=r.totalWidth+t.pad.l+t.pad.r,o=r.totalHeight+t.pad.t+t.pad.b,s=e._fullLayout._size;r.lx=s.l+s.w*t.x,r.ly=s.t+s.h*(1-t.y);var l=\"left\";t0.isRightAnchor(t)&&(r.lx-=a,l=\"right\"),t0.isCenterAnchor(t)&&(r.lx-=a/2,l=\"center\");var u=\"top\";t0.isBottomAnchor(t)&&(r.ly-=o,u=\"bottom\"),t0.isMiddleAnchor(t)&&(r.ly-=o/2,u=\"middle\"),r.totalWidth=Math.ceil(r.totalWidth),r.totalHeight=Math.ceil(r.totalHeight),r.lx=Math.round(r.lx),r.ly=Math.round(r.ly),y4.autoMargin(e,sge(t),{x:t.x,y:t.y,l:a*({right:1,center:.5}[l]||0),r:a*({left:1,center:.5}[l]||0),b:o*({top:1,middle:.5}[u]||0),t:o*({bottom:1,middle:.5}[u]||0)})}function sge(e){return es.autoMarginIdRoot+e._index}function lge(e,t,r,n){n=n||{};var i=e.select(\".\"+es.itemRectClassName),a=e.select(\".\"+es.itemTextClassName),o=t.borderwidth,s=r.index,l=t._dims;CT.setTranslate(e,o+r.x,o+r.y);var u=[\"up\",\"down\"].indexOf(t.direction)!==-1,c=n.height||(u?l.heights[s]:l.height1);i.attr({x:0,y:0,width:n.width||(u?l.width1:l.widths[s]),height:c});var f=t.font.size*rge,h=CI.lineCount(a),d=(h-1)*f/2;CI.positionText(a,es.textOffsetX,c/2-d+es.textOffsetY),u?r.y+=l.heights[s]+r.yPad:r.x+=l.widths[s]+r.xPad,r.index++}function aV(e,t){e.attr(es.menuIndexAttrName,t||\"-1\").selectAll(\"g.\"+es.dropdownButtonClassName).remove()}});var hge=ye((Vlr,fge)=>{\"use strict\";var _mt=MI();fge.exports={moduleType:\"component\",name:_mt.name,layoutAttributes:iV(),supplyLayoutDefaults:J0e(),draw:cge()}});var b4=ye((Glr,dge)=>{\"use strict\";dge.exports={name:\"sliders\",containerClassName:\"slider-container\",groupClassName:\"slider-group\",inputAreaClass:\"slider-input-area\",railRectClass:\"slider-rail-rect\",railTouchRectClass:\"slider-rail-touch-rect\",gripRectClass:\"slider-grip-rect\",tickRectClass:\"slider-tick-rect\",inputProxyClass:\"slider-input-proxy\",labelsClass:\"slider-labels\",labelGroupClass:\"slider-label-group\",labelClass:\"slider-label\",currentValueClass:\"slider-current-value\",railHeight:5,menuIndexAttrName:\"slider-active-index\",autoMarginIdRoot:\"slider-\",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:\"#bec8d9\",railBgColor:\"#f8fafc\",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:\"#bec8d9\",gripBgColor:\"#f6f8fa\",gripBgActiveColor:\"#dbdde0\",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:\"#333\",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:\"#333\",minorTickLength:4,currentValuePadding:8,currentValueInset:0}});var sV=ye((Hlr,gge)=>{\"use strict\";var vge=ec(),xmt=q6(),bmt=Ao().extendDeepAll,wmt=mc().overrideAll,Tmt=YS(),pge=vl().templatedArray,f2=b4(),Amt=pge(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});gge.exports=wmt(pge(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:Amt,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:bmt(xmt({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:Tmt.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:vge({})},font:vge({}),activebgcolor:{valType:\"color\",dflt:f2.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:f2.railBgColor},bordercolor:{valType:\"color\",dflt:f2.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:f2.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:f2.tickLength},tickcolor:{valType:\"color\",dflt:f2.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:f2.minorTickLength}}),\"arraydraw\",\"from-root\")});var xge=ye((jlr,_ge)=>{\"use strict\";var LT=Pr(),mge=Kd(),yge=sV(),Smt=b4(),Mmt=Smt.name,Emt=yge.steps;_ge.exports=function(t,r){mge(t,r,{name:Mmt,handleItemDefaults:kmt})};function kmt(e,t,r){function n(f,h){return LT.coerce(e,t,yge,f,h)}for(var i=mge(e,t,{name:\"steps\",handleItemDefaults:Cmt}),a=0,o=0;o<i.length;o++)i[o].visible&&a++;var s;if(a<2?s=t.visible=!1:s=n(\"visible\"),!!s){t._stepCount=a;var l=t._visibleSteps=LT.filterVisible(i),u=n(\"active\");(i[u]||{}).visible||(t.active=l[0]._index),n(\"x\"),n(\"y\"),LT.noneOrAll(e,t,[\"x\",\"y\"]),n(\"xanchor\"),n(\"yanchor\"),n(\"len\"),n(\"lenmode\"),n(\"pad.t\"),n(\"pad.r\"),n(\"pad.b\"),n(\"pad.l\"),LT.coerceFont(n,\"font\",r.font);var c=n(\"currentvalue.visible\");c&&(n(\"currentvalue.xanchor\"),n(\"currentvalue.prefix\"),n(\"currentvalue.suffix\"),n(\"currentvalue.offset\"),LT.coerceFont(n,\"currentvalue.font\",t.font)),n(\"transition.duration\"),n(\"transition.easing\"),n(\"bgcolor\"),n(\"activebgcolor\"),n(\"bordercolor\"),n(\"borderwidth\"),n(\"ticklen\"),n(\"tickwidth\"),n(\"tickcolor\"),n(\"minorticklen\")}}function Cmt(e,t){function r(a,o){return LT.coerce(e,t,Emt,a,o)}var n;if(e.method!==\"skip\"&&!Array.isArray(e.args)?n=t.visible=!1:n=r(\"visible\"),n){r(\"method\"),r(\"args\");var i=r(\"label\",\"step-\"+t._index);r(\"value\",i),r(\"execute\")}}});var Lge=ye((Wlr,Cge)=>{\"use strict\";var Ug=Fa(),LI=Mc(),C_=ka(),Vg=So(),r0=Pr(),Lmt=r0.strTranslate,w4=Zl(),Pmt=vl().arrayEditor,Rs=b4(),cV=Dh(),Tge=cV.LINE_SPACING,lV=cV.FROM_TL,uV=cV.FROM_BR;Cge.exports=function(t){var r=t._context.staticPlot,n=t._fullLayout,i=Imt(n,t),a=n._infolayer.selectAll(\"g.\"+Rs.containerClassName).data(i.length>0?[0]:[]);a.enter().append(\"g\").classed(Rs.containerClassName,!0).style(\"cursor\",r?null:\"ew-resize\");function o(c){c._commandObserver&&(c._commandObserver.remove(),delete c._commandObserver),LI.autoMargin(t,Age(c))}if(a.exit().each(function(){Ug.select(this).selectAll(\"g.\"+Rs.groupClassName).each(o)}).remove(),i.length!==0){var s=a.selectAll(\"g.\"+Rs.groupClassName).data(i,Rmt);s.enter().append(\"g\").classed(Rs.groupClassName,!0),s.exit().each(o).remove();for(var l=0;l<i.length;l++){var u=i[l];Dmt(t,u)}s.each(function(c){var f=Ug.select(this);Bmt(c),LI.manageCommandObserver(t,c,c._visibleSteps,function(h){var d=f.data()[0];d.active!==h.index&&(d._dragging||Mge(t,f,d,h.index,!1,!0))}),Fmt(t,Ug.select(this),c)})}};function Age(e){return Rs.autoMarginIdRoot+e._index}function Imt(e,t){for(var r=e[Rs.name],n=[],i=0;i<r.length;i++){var a=r[i];a.visible&&(a._gd=t,n.push(a))}return n}function Rmt(e){return e._index}function Dmt(e,t){var r=Vg.tester.selectAll(\"g.\"+Rs.labelGroupClass).data(t._visibleSteps);r.enter().append(\"g\").classed(Rs.labelGroupClass,!0);var n=0,i=0;r.each(function(v){var m=Ug.select(this),b=Sge(m,{step:v},t),p=b.node();if(p){var k=Vg.bBox(p);i=Math.max(i,k.height),n=Math.max(n,k.width)}}),r.remove();var a=t._dims={};a.inputAreaWidth=Math.max(Rs.railWidth,Rs.gripHeight);var o=e._fullLayout._size;a.lx=o.l+o.w*t.x,a.ly=o.t+o.h*(1-t.y),t.lenmode===\"fraction\"?a.outerLength=Math.round(o.w*t.len):a.outerLength=t.len,a.inputAreaStart=0,a.inputAreaLength=Math.round(a.outerLength-t.pad.l-t.pad.r);var s=a.inputAreaLength-2*Rs.stepInset,l=s/(t._stepCount-1),u=n+Rs.labelPadding;if(a.labelStride=Math.max(1,Math.ceil(u/l)),a.labelHeight=i,a.currentValueMaxWidth=0,a.currentValueHeight=0,a.currentValueTotalHeight=0,a.currentValueMaxLines=1,t.currentvalue.visible){var c=Vg.tester.append(\"g\");r.each(function(v){var m=PI(c,t,v.label),b=m.node()&&Vg.bBox(m.node())||{width:0,height:0},p=w4.lineCount(m);a.currentValueMaxWidth=Math.max(a.currentValueMaxWidth,Math.ceil(b.width)),a.currentValueHeight=Math.max(a.currentValueHeight,Math.ceil(b.height)),a.currentValueMaxLines=Math.max(a.currentValueMaxLines,p)}),a.currentValueTotalHeight=a.currentValueHeight+t.currentvalue.offset,c.remove()}a.height=a.currentValueTotalHeight+Rs.tickOffset+t.ticklen+Rs.labelOffset+a.labelHeight+t.pad.t+t.pad.b;var f=\"left\";r0.isRightAnchor(t)&&(a.lx-=a.outerLength,f=\"right\"),r0.isCenterAnchor(t)&&(a.lx-=a.outerLength/2,f=\"center\");var h=\"top\";r0.isBottomAnchor(t)&&(a.ly-=a.height,h=\"bottom\"),r0.isMiddleAnchor(t)&&(a.ly-=a.height/2,h=\"middle\"),a.outerLength=Math.ceil(a.outerLength),a.height=Math.ceil(a.height),a.lx=Math.round(a.lx),a.ly=Math.round(a.ly);var d={y:t.y,b:a.height*uV[h],t:a.height*lV[h]};t.lenmode===\"fraction\"?(d.l=0,d.xl=t.x-t.len*lV[f],d.r=0,d.xr=t.x+t.len*uV[f]):(d.x=t.x,d.l=a.outerLength*lV[f],d.r=a.outerLength*uV[f]),LI.autoMargin(e,Age(t),d)}function Fmt(e,t,r){(r.steps[r.active]||{}).visible||(r.active=r._visibleSteps[0]._index),t.call(PI,r).call(Umt,r).call(qmt,r).call(Omt,r).call(Nmt,e,r).call(zmt,e,r);var n=r._dims;Vg.setTranslate(t,n.lx+r.pad.l,n.ly+r.pad.t),t.call(kge,r,!1),t.call(PI,r)}function PI(e,t,r){if(t.currentvalue.visible){var n=t._dims,i,a;switch(t.currentvalue.xanchor){case\"right\":i=n.inputAreaLength-Rs.currentValueInset-n.currentValueMaxWidth,a=\"left\";break;case\"center\":i=n.inputAreaLength*.5,a=\"middle\";break;default:i=Rs.currentValueInset,a=\"left\"}var o=r0.ensureSingle(e,\"text\",Rs.labelClass,function(h){h.attr({\"text-anchor\":a,\"data-notex\":1})}),s=t.currentvalue.prefix?t.currentvalue.prefix:\"\";if(typeof r==\"string\")s+=r;else{var l=t.steps[t.active].label,u=t._gd._fullLayout._meta;u&&(l=r0.templateString(l,u)),s+=l}t.currentvalue.suffix&&(s+=t.currentvalue.suffix),o.call(Vg.font,t.currentvalue.font).text(s).call(w4.convertToTspans,t._gd);var c=w4.lineCount(o),f=(n.currentValueMaxLines+1-c)*t.currentvalue.font.size*Tge;return w4.positionText(o,i,f),o}}function zmt(e,t,r){var n=r0.ensureSingle(e,\"rect\",Rs.gripRectClass,function(i){i.call(Ege,t,e,r).style(\"pointer-events\",\"all\")});n.attr({width:Rs.gripWidth,height:Rs.gripHeight,rx:Rs.gripRadius,ry:Rs.gripRadius}).call(C_.stroke,r.bordercolor).call(C_.fill,r.bgcolor).style(\"stroke-width\",r.borderwidth+\"px\")}function Sge(e,t,r){var n=r0.ensureSingle(e,\"text\",Rs.labelClass,function(o){o.attr({\"text-anchor\":\"middle\",\"data-notex\":1})}),i=t.step.label,a=r._gd._fullLayout._meta;return a&&(i=r0.templateString(i,a)),n.call(Vg.font,r.font).text(i).call(w4.convertToTspans,r._gd),n}function qmt(e,t){var r=r0.ensureSingle(e,\"g\",Rs.labelsClass),n=t._dims,i=r.selectAll(\"g.\"+Rs.labelGroupClass).data(n.labelSteps);i.enter().append(\"g\").classed(Rs.labelGroupClass,!0),i.exit().remove(),i.each(function(a){var o=Ug.select(this);o.call(Sge,a,t),Vg.setTranslate(o,fV(t,a.fraction),Rs.tickOffset+t.ticklen+t.font.size*Tge+Rs.labelOffset+n.currentValueTotalHeight)})}function bge(e,t,r,n,i){var a=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[a]._index;o!==r.active&&Mge(e,t,r,o,!0,i)}function Mge(e,t,r,n,i,a){var o=r.active;r.active=n,Pmt(e.layout,Rs.name,r).applyUpdate(\"active\",n);var s=r.steps[r.active];t.call(kge,r,a),t.call(PI,r),e.emit(\"plotly_sliderchange\",{slider:r,step:r.steps[r.active],interaction:i,previousActive:o}),s&&s.method&&i&&(t._nextMethod?(t._nextMethod.step=s,t._nextMethod.doCallback=i,t._nextMethod.doTransition=a):(t._nextMethod={step:s,doCallback:i,doTransition:a},t._nextMethodRaf=window.requestAnimationFrame(function(){var l=t._nextMethod.step;l.method&&(l.execute&&LI.executeAPICommand(e,l.method,l.args),t._nextMethod=null,t._nextMethodRaf=null)})))}function Ege(e,t,r){if(t._context.staticPlot)return;var n=r.node(),i=Ug.select(t);function a(){return r.data()[0]}function o(){var s=a();t.emit(\"plotly_sliderstart\",{slider:s});var l=r.select(\".\"+Rs.gripRectClass);Ug.event.stopPropagation(),Ug.event.preventDefault(),l.call(C_.fill,s.activebgcolor);var u=wge(s,Ug.mouse(n)[0]);bge(t,r,s,u,!0),s._dragging=!0;function c(){var h=a(),d=wge(h,Ug.mouse(n)[0]);bge(t,r,h,d,!1)}i.on(\"mousemove\",c),i.on(\"touchmove\",c);function f(){var h=a();h._dragging=!1,l.call(C_.fill,h.bgcolor),i.on(\"mouseup\",null),i.on(\"mousemove\",null),i.on(\"touchend\",null),i.on(\"touchmove\",null),t.emit(\"plotly_sliderend\",{slider:h,step:h.steps[h.active]})}i.on(\"mouseup\",f),i.on(\"touchend\",f)}e.on(\"mousedown\",o),e.on(\"touchstart\",o)}function Omt(e,t){var r=e.selectAll(\"rect.\"+Rs.tickRectClass).data(t._visibleSteps),n=t._dims;r.enter().append(\"rect\").classed(Rs.tickRectClass,!0),r.exit().remove(),r.attr({width:t.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each(function(i,a){var o=a%n.labelStride===0,s=Ug.select(this);s.attr({height:o?t.ticklen:t.minorticklen}).call(C_.fill,t.tickcolor),Vg.setTranslate(s,fV(t,a/(t._stepCount-1))-.5*t.tickwidth,(o?Rs.tickOffset:Rs.minorTickOffset)+n.currentValueTotalHeight)})}function Bmt(e){var t=e._dims;t.labelSteps=[];for(var r=e._stepCount,n=0;n<r;n+=t.labelStride)t.labelSteps.push({fraction:n/(r-1),step:e._visibleSteps[n]})}function kge(e,t,r){for(var n=e.select(\"rect.\"+Rs.gripRectClass),i=0,a=0;a<t._stepCount;a++)if(t._visibleSteps[a]._index===t.active){i=a;break}var o=fV(t,i/(t._stepCount-1));if(!t._invokingCommand){var s=n;r&&t.transition.duration>0&&(s=s.transition().duration(t.transition.duration).ease(t.transition.easing)),s.attr(\"transform\",Lmt(o-Rs.gripWidth*.5,t._dims.currentValueTotalHeight))}}function fV(e,t){var r=e._dims;return r.inputAreaStart+Rs.stepInset+(r.inputAreaLength-2*Rs.stepInset)*Math.min(1,Math.max(0,t))}function wge(e,t){var r=e._dims;return Math.min(1,Math.max(0,(t-Rs.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*Rs.stepInset-2*r.inputAreaStart)))}function Nmt(e,t,r){var n=r._dims,i=r0.ensureSingle(e,\"rect\",Rs.railTouchRectClass,function(a){a.call(Ege,t,e,r).style(\"pointer-events\",\"all\")});i.attr({width:n.inputAreaLength,height:Math.max(n.inputAreaWidth,Rs.tickOffset+r.ticklen+n.labelHeight)}).call(C_.fill,r.bgcolor).attr(\"opacity\",0),Vg.setTranslate(i,0,n.currentValueTotalHeight)}function Umt(e,t){var r=t._dims,n=r.inputAreaLength-Rs.railInset*2,i=r0.ensureSingle(e,\"rect\",Rs.railRectClass);i.attr({width:n,height:Rs.railWidth,rx:Rs.railRadius,ry:Rs.railRadius,\"shape-rendering\":\"crispEdges\"}).call(C_.stroke,t.bordercolor).call(C_.fill,t.bgcolor).style(\"stroke-width\",t.borderwidth+\"px\"),Vg.setTranslate(i,Rs.railInset,(r.inputAreaWidth-Rs.railWidth)*.5+r.currentValueTotalHeight)}});var Ige=ye((Xlr,Pge)=>{\"use strict\";var Vmt=b4();Pge.exports={moduleType:\"component\",name:Vmt.name,layoutAttributes:sV(),supplyLayoutDefaults:xge(),draw:Lge()}});var II=ye((Zlr,Dge)=>{\"use strict\";var Rge=Ih();Dge.exports={bgcolor:{valType:\"color\",dflt:Rge.background,editType:\"plot\"},bordercolor:{valType:\"color\",dflt:Rge.defaultLine,editType:\"plot\"},borderwidth:{valType:\"integer\",dflt:0,min:0,editType:\"plot\"},autorange:{valType:\"boolean\",dflt:!0,editType:\"calc\",impliedEdits:{\"range[0]\":void 0,\"range[1]\":void 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Gmt=df(),Hmt=Zl(),qge=RI(),jmt=Dh().LINE_SPACING,DI=qge.name;function Oge(e){var t=e&&e[DI];return t&&t.visible}FI.isVisible=Oge;FI.makeData=function(e){for(var t=Gmt.list({_fullLayout:e},\"x\",!0),r=e.margin,n=[],i=0;i<t.length;i++){var a=t[i];if(Oge(a)){n.push(a);var o=a[DI];o._id=DI+a._id,o._height=(e.height-r.b-r.t)*o.thickness,o._offsetShift=Math.floor(o.borderwidth/2)}}e._rangeSliderData=n};FI.autoMarginOpts=function(e,t){var r=e._fullLayout,n=t[DI],i=t._id.charAt(0),a=0,o=0;if(t.side===\"bottom\"&&(a=t._depth,t.title.text!==r._dfltTitle[i])){o=1.5*t.title.font.size+10+n._offsetShift;var s=(t.title.text.match(Hmt.BR_TAG_ALL)||[]).length;o+=s*t.title.font.size*jmt}return{x:0,y:t._counterDomainMin,l:0,r:0,t:0,b:n._height+a+Math.max(r.margin.b,o),pad:qge.extraPad+n._offsetShift*2}}});var Gge=ye(($lr,Vge)=>{\"use strict\";var zI=Pr(),Nge=vl(),Uge=df(),Wmt=II(),Xmt=hV();Vge.exports=function(t,r,n){var i=t[n],a=r[n];if(!(i.rangeslider||r._requestRangeslider[a._id]))return;zI.isPlainObject(i.rangeslider)||(i.rangeslider={});var o=i.rangeslider,s=Nge.newContainer(a,\"rangeslider\");function l(L,x){return zI.coerce(o,s,Wmt,L,x)}var u,c;function f(L,x){return zI.coerce(u,c,Xmt,L,x)}var h=l(\"visible\");if(h){l(\"bgcolor\",r.plot_bgcolor),l(\"bordercolor\"),l(\"borderwidth\"),l(\"thickness\"),l(\"autorange\",!a.isValidRange(o.range)),l(\"range\");var d=r._subplots;if(d)for(var v=d.cartesian.filter(function(L){return L.slice(0,Math.max(0,L.indexOf(\"y\")))===Uge.name2id(n)}).map(function(L){return L.slice(L.indexOf(\"y\"),L.length)}),m=zI.simpleMap(v,Uge.id2name),b=0;b<m.length;b++){var p=m[b];u=o[p]||{},c=Nge.newContainer(s,p,\"yaxis\");var k=r[p],M;u.range&&k.isValidRange(u.range)&&(M=\"fixed\");var T=f(\"rangemode\",M);T!==\"match\"&&f(\"range\",k.range.slice())}s._input=o}}});var jge=ye((Qlr,Hge)=>{\"use strict\";var Zmt=df().list,Ymt=Mg().getAutoRange,Kmt=RI();Hge.exports=function(t){for(var r=Zmt(t,\"x\",!0),n=0;n<r.length;n++){var i=r[n],a=i[Kmt.name];a&&a.visible&&a.autorange&&(a._input.autorange=!0,a._input.range=a.range=Ymt(t,i))}}});var Yge=ye((eur,Zge)=>{\"use strict\";var qI=Fa(),Jmt=Oa(),$mt=Mc(),Jf=Pr(),OI=Jf.strTranslate,Xge=So(),L_=ka(),Qmt=qb(),eyt=mh(),dV=df(),tyt=yv(),ryt=Eg(),tl=RI();Zge.exports=function(e){for(var t=e._fullLayout,r=t._rangeSliderData,n=0;n<r.length;n++){var i=r[n][tl.name];i._clipId=i._id+\"-\"+t._uid}function a(s){return s._name}var o=t._infolayer.selectAll(\"g.\"+tl.containerClassName).data(r,a);o.exit().each(function(s){var l=s[tl.name];t._topdefs.select(\"#\"+l._clipId).remove()}).remove(),r.length!==0&&(o.enter().append(\"g\").classed(tl.containerClassName,!0).attr(\"pointer-events\",\"all\"),o.each(function(s){var l=qI.select(this),u=s[tl.name],c=t[dV.id2name(s.anchor)],f=u[dV.id2name(s.anchor)];if(u.range){var h=Jf.simpleMap(u.range,s.r2l),d=Jf.simpleMap(s.range,s.r2l),v;d[0]<d[1]?v=[Math.min(h[0],d[0]),Math.max(h[1],d[1])]:v=[Math.max(h[0],d[0]),Math.min(h[1],d[1])],u.range=u._input.range=Jf.simpleMap(v,s.l2r)}s.cleanRange(\"rangeslider.range\");var m=t._size,b=s.domain;u._width=m.w*(b[1]-b[0]);var p=Math.round(m.l+m.w*b[0]),k=Math.round(m.t+m.h*(1-s._counterDomainMin)+(s.side===\"bottom\"?s._depth:0)+u._offsetShift+tl.extraPad);l.attr(\"transform\",OI(p,k)),u._rl=Jf.simpleMap(u.range,s.r2l);var M=u._rl[0],T=u._rl[1],L=T-M;if(u.p2d=function(G){return G/u._width*L+M},u.d2p=function(G){return(G-M)/L*u._width},s.rangebreaks){var x=s.locateBreaks(M,T);if(x.length){var C,S,g=0;for(C=0;C<x.length;C++)S=x[C],g+=S.max-S.min;var P=u._width/(T-M-g),E=[-P*M];for(C=0;C<x.length;C++)S=x[C],E.push(E[E.length-1]-P*(S.max-S.min));for(u.d2p=function(G){for(var Z=E[0],j=0;j<x.length;j++){var N=x[j];if(G>=N.max)Z=E[j+1];else if(G<N.min)break}return 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i=e.select(\"rect.\"+tl.slideBoxClassName).node(),a=e.select(\"rect.\"+tl.grabAreaMinClassName).node(),o=e.select(\"rect.\"+tl.grabAreaMaxClassName).node();function s(){var l=qI.event,u=l.target,c=Wge(l),f=c-e.node().getBoundingClientRect().left,h=n.d2p(r._rl[0]),d=n.d2p(r._rl[1]),v=tyt.coverSlip();this.addEventListener(\"touchmove\",m),this.addEventListener(\"touchend\",b),v.addEventListener(\"mousemove\",m),v.addEventListener(\"mouseup\",b);function m(p){var k=Wge(p),M=+k-c,T,L,x;switch(u){case i:if(x=\"ew-resize\",h+M>r._length||d+M<0)return;T=h+M,L=d+M;break;case a:if(x=\"col-resize\",h+M>r._length)return;T=h+M,L=d;break;case o:if(x=\"col-resize\",d+M<0)return;T=h,L=d+M;break;default:x=\"ew-resize\",T=f,L=f+M;break}if(L<T){var C=L;L=T,T=C}n._pixelMin=T,n._pixelMax=L,ryt(qI.select(v),x),nyt(e,t,r,n)}function 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Ome=ye((gur,qme)=>{\"use strict\";var T4=Pr(),zme=mc().overrideAll,A4=TV(),h2={error_x:T4.extendFlat({},A4),error_y:T4.extendFlat({},A4)};delete h2.error_x.copy_zstyle;delete h2.error_y.copy_zstyle;delete h2.error_y.copy_ystyle;var S4={error_x:T4.extendFlat({},A4),error_y:T4.extendFlat({},A4),error_z:T4.extendFlat({},A4)};delete S4.error_x.copy_ystyle;delete S4.error_y.copy_ystyle;delete S4.error_z.copy_ystyle;delete S4.error_z.copy_zstyle;qme.exports={moduleType:\"component\",name:\"errorbars\",schema:{traces:{scatter:h2,bar:h2,histogram:h2,scatter3d:zme(S4,\"calc\",\"nested\"),scattergl:zme(h2,\"calc\",\"nested\")}},supplyDefaults:Ame(),calc:Cme(),makeComputeError:AV(),plot:Ime(),style:Fme(),hoverInfo:t1t};function t1t(e,t,r){(t.error_y||{}).visible&&(r.yerr=e.yh-e.y,t.error_y.symmetric||(r.yerrneg=e.y-e.ys)),(t.error_x||{}).visible&&(r.xerr=e.xh-e.x,t.error_x.symmetric||(r.xerrneg=e.x-e.xs))}});var Nme=ye((mur,Bme)=>{\"use 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pt=e.select(\".\"+gf.cbfills).selectAll(\"rect.\"+gf.cbfill).attr(\"style\",\"\").data(V);pt.enter().append(\"rect\").classed(gf.cbfill,!0).attr(\"style\",\"\"),pt.exit().remove();var jt=g.map(ke.c2p).map(Math.round).sort(function(qr,jr){return qr-jr});pt.each(function(qr,jr){var gt=[jr===0?g[0]:(V[jr]+V[jr-1])/2,jr===V.length-1?g[1]:(V[jr]+V[jr+1])/2].map(ke.c2p).map(Math.round);n&&(gt[1]=U0.constrain(gt[1]+(gt[1]>gt[0])?1:-1,jt[0],jt[1]));var Ge=I_.select(this).attr(n?\"x\":\"y\",_e).attr(n?\"y\":\"x\",I_.min(gt)).attr(n?\"width\":\"height\",Math.max(Z,2)).attr(n?\"height\":\"width\",Math.max(I_.max(gt)-I_.min(gt),2));if(t._fillgradient)Gg.gradient(Ge,r,t._id,n?\"vertical\":\"horizontalreversed\",t._fillgradient,\"fill\");else{var Je=E(qr).replace(\"e-\",\"\");Ge.attr(\"fill\",EV(Je).toHexString())}});var 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ZI=ye((Cur,bye)=>{\"use strict\";bye.exports={barmode:{valType:\"enumerated\",values:[\"stack\",\"group\",\"overlay\",\"relative\"],dflt:\"group\",editType:\"calc\"},barnorm:{valType:\"enumerated\",values:[\"\",\"fraction\",\"percent\"],dflt:\"\",editType:\"calc\"},bargap:{valType:\"number\",min:0,max:1,editType:\"calc\"},bargroupgap:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},barcornerradius:{valType:\"any\",editType:\"calc\"}}});var YI=ye((Lur,Aye)=>{\"use strict\";var D1t=ka(),wye=pv().hasColorscale,Tye=td(),F1t=Pr().coercePattern;Aye.exports=function(t,r,n,i,a){var o=n(\"marker.color\",i),s=wye(t,\"marker\");s&&Tye(t,r,a,n,{prefix:\"marker.\",cLetter:\"c\"}),n(\"marker.line.color\",D1t.defaultLine),wye(t,\"marker.line\")&&Tye(t,r,a,n,{prefix:\"marker.line.\",cLetter:\"c\"}),n(\"marker.line.width\"),n(\"marker.opacity\"),F1t(n,\"marker.pattern\",o,s),n(\"selected.marker.color\"),n(\"unselected.marker.color\")}});var i0=ye((Pur,Lye)=>{\"use strict\";var 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t.barmode;return}l!==\"overlay\"&&n(\"barnorm\"),n(\"bargap\",a&&!o?0:.2),n(\"bargroupgap\");var v=n(\"barcornerradius\");t.barcornerradius=X1t(v)}});var E4=ye((Rur,Iye)=>{\"use strict\";var RT=Pr();Iye.exports=function(t,r){for(var n=0;n<t.length;n++)t[n].i=n;RT.mergeArray(r.text,t,\"tx\"),RT.mergeArray(r.hovertext,t,\"htx\");var i=r.marker;if(i){RT.mergeArray(i.opacity,t,\"mo\",!0),RT.mergeArray(i.color,t,\"mc\");var a=i.line;a&&(RT.mergeArray(a.color,t,\"mlc\"),RT.mergeArrayCastPositive(a.width,t,\"mlw\"))}}});var Oye=ye((Dur,qye)=>{\"use strict\";var Rye=ho(),Dye=zg(),Fye=pv().hasColorscale,zye=gv(),Z1t=E4(),Y1t=q0();qye.exports=function(t,r){var n=Rye.getFromId(t,r.xaxis||\"x\"),i=Rye.getFromId(t,r.yaxis||\"y\"),a,o,s,l,u,c,f={msUTC:!!(r.base||r.base===0)};r.orientation===\"h\"?(a=n.makeCalcdata(r,\"x\",f),s=i.makeCalcdata(r,\"y\"),l=Dye(r,i,\"y\",s),u=!!r.yperiodalignment,c=\"y\"):(a=i.makeCalcdata(r,\"y\",f),s=n.makeCalcdata(r,\"x\"),l=Dye(r,n,\"x\",s),u=!!r.xperiodalignment,c=\"x\"),o=l.vals;for(var h=Math.min(o.length,a.length),d=new Array(h),v=0;v<h;v++)d[v]={p:o[v],s:a[v]},u&&(d[v].orig_p=s[v],d[v][c+\"End\"]=l.ends[v],d[v][c+\"Start\"]=l.starts[v]),r.ids&&(d[v].id=String(r.ids[v]));return Fye(r,\"marker\")&&zye(t,r,{vals:r.marker.color,containerStr:\"marker\",cLetter:\"c\"}),Fye(r,\"marker.line\")&&zye(t,r,{vals:r.marker.line.color,containerStr:\"marker.line\",cLetter:\"c\"}),Z1t(d,r),Y1t(d,r),d}});var bv=ye((Fur,Nye)=>{\"use strict\";var K1t=Fa(),J1t=Pr();function $1t(e,t,r){var n=e._fullLayout,i=n[\"_\"+r+\"Text_minsize\"];if(i){var a=n.uniformtext.mode===\"hide\",o;switch(r){case\"funnelarea\":case\"pie\":case\"sunburst\":o=\"g.slice\";break;case\"treemap\":case\"icicle\":o=\"g.slice, g.pathbar\";break;default:o=\"g.points > g.point\"}t.selectAll(o).each(function(s){var l=s.transform;if(l){l.scale=a&&l.hide?0:i/l.fontSize;var u=K1t.select(this).select(\"text\");J1t.setTransormAndDisplay(u,l)}})}}function Q1t(e,t,r){if(r.uniformtext.mode){var n=Bye(e),i=r.uniformtext.minsize,a=t.scale*t.fontSize;t.hide=a<i,r[n]=r[n]||1/0,t.hide||(r[n]=Math.min(r[n],Math.max(a,i)))}}function e_t(e,t){var r=Bye(e);t[r]=void 0}function Bye(e){return\"_\"+e+\"Text_minsize\"}Nye.exports={recordMinTextSize:Q1t,clearMinTextSize:e_t,resizeText:$1t}});var JI=ye(v2=>{\"use strict\";var t_t=Eo(),r_t=fd(),Uye=Pr().isArrayOrTypedArray;v2.coerceString=function(e,t,r){if(typeof t==\"string\"){if(t||!e.noBlank)return t}else if((typeof t==\"number\"||t===!0)&&!e.strict)return String(t);return r!==void 0?r:e.dflt};v2.coerceNumber=function(e,t,r){if(t_t(t)){t=+t;var n=e.min,i=e.max,a=n!==void 0&&t<n||i!==void 0&&t>i;if(!a)return t}return r!==void 0?r:e.dflt};v2.coerceColor=function(e,t,r){return r_t(t).isValid()?t:r!==void 0?r:e.dflt};v2.coerceEnumerated=function(e,t,r){return e.coerceNumber&&(t=+t),e.values.indexOf(t)!==-1?t:r!==void 0?r:e.dflt};v2.getValue=function(e,t){var r;return Uye(e)?t<e.length&&(r=e[t]):r=e,r};v2.getLineWidth=function(e,t){var r=0<t.mlw?t.mlw:Uye(e.marker.line.width)?0:e.marker.line.width;return r}});var V0=ye((qur,$ye)=>{\"use strict\";var k4=Fa(),i_t=ka(),C4=So(),Vye=Pr(),Gye=Oa(),Hye=bv().resizeText,qV=qm(),n_t=qV.textfont,a_t=qV.insidetextfont,o_t=qV.outsidetextfont,ev=JI();function s_t(e){var t=k4.select(e).selectAll('g[class^=\"barlayer\"]').selectAll(\"g.trace\");Hye(e,t,\"bar\");var r=t.size(),n=e._fullLayout;t.style(\"opacity\",function(i){return i[0].trace.opacity}).each(function(i){(n.barmode===\"stack\"&&r>1||n.bargap===0&&n.bargroupgap===0&&!i[0].trace.marker.line.width)&&k4.select(this).attr(\"shape-rendering\",\"crispEdges\")}),t.selectAll(\"g.points\").each(function(i){var a=k4.select(this),o=i[0].trace;jye(a,o,e)}),Gye.getComponentMethod(\"errorbars\",\"style\")(t)}function jye(e,t,r){C4.pointStyle(e.selectAll(\"path\"),t,r),Wye(e,t,r)}function Wye(e,t,r){e.selectAll(\"text\").each(function(n){var i=k4.select(this),a=Vye.ensureUniformFontSize(r,Xye(i,n,t,r));C4.font(i,a)})}function l_t(e,t,r){var n=t[0].trace;n.selectedpoints?u_t(r,n,e):(jye(r,n,e),Gye.getComponentMethod(\"errorbars\",\"style\")(r))}function u_t(e,t,r){C4.selectedPointStyle(e.selectAll(\"path\"),t),c_t(e.selectAll(\"text\"),t,r)}function c_t(e,t,r){e.each(function(n){var i=k4.select(this),a;if(n.selected){a=Vye.ensureUniformFontSize(r,Xye(i,n,t,r));var o=t.selected.textfont&&t.selected.textfont.color;o&&(a.color=o),C4.font(i,a)}else C4.selectedTextStyle(i,t)})}function Xye(e,t,r,n){var i=n._fullLayout.font,a=r.textfont;if(e.classed(\"bartext-inside\")){var o=Jye(t,r);a=Yye(r,t.i,i,o)}else e.classed(\"bartext-outside\")&&(a=Kye(r,t.i,i));return a}function Zye(e,t,r){return OV(n_t,e.textfont,t,r)}function Yye(e,t,r,n){var i=Zye(e,t,r),a=e._input.textfont===void 0||e._input.textfont.color===void 0||Array.isArray(e.textfont.color)&&e.textfont.color[t]===void 0;return a&&(i={color:i_t.contrast(n),family:i.family,size:i.size,weight:i.weight,style:i.style,variant:i.variant,textcase:i.textcase,lineposition:i.lineposition,shadow:i.shadow}),OV(a_t,e.insidetextfont,t,i)}function Kye(e,t,r){var n=Zye(e,t,r);return OV(o_t,e.outsidetextfont,t,n)}function OV(e,t,r,n){t=t||{};var i=ev.getValue(t.family,r),a=ev.getValue(t.size,r),o=ev.getValue(t.color,r),s=ev.getValue(t.weight,r),l=ev.getValue(t.style,r),u=ev.getValue(t.variant,r),c=ev.getValue(t.textcase,r),f=ev.getValue(t.lineposition,r),h=ev.getValue(t.shadow,r);return{family:ev.coerceString(e.family,i,n.family),size:ev.coerceNumber(e.size,a,n.size),color:ev.coerceColor(e.color,o,n.color),weight:ev.coerceString(e.weight,s,n.weight),style:ev.coerceString(e.style,l,n.style),variant:ev.coerceString(e.variant,u,n.variant),textcase:ev.coerceString(e.variant,c,n.textcase),lineposition:ev.coerceString(e.variant,f,n.lineposition),shadow:ev.coerceString(e.variant,h,n.shadow)}}function Jye(e,t){return t.type===\"waterfall\"?t[e.dir].marker.color:e.mcc||e.mc||t.marker.color}$ye.exports={style:s_t,styleTextPoints:Wye,styleOnSelect:l_t,getInsideTextFont:Yye,getOutsideTextFont:Kye,getBarColor:Jye,resizeText:Hye}});var g2=ye((Our,o1e)=>{\"use strict\";var $I=Fa(),QI=Eo(),Fd=Pr(),f_t=Zl(),h_t=ka(),R_=So(),d_t=Oa(),e8=ho().tickText,Qye=bv(),v_t=Qye.recordMinTextSize,p_t=Qye.clearMinTextSize,BV=V0(),DT=JI(),g_t=S_(),e1e=qm(),m_t=e1e.text,y_t=e1e.textposition,__t=ip().appendArrayPointValue,Gv=g_t.TEXTPAD;function x_t(e){return e.id}function b_t(e){if(e.ids)return x_t}function NV(e){return(e>0)-(e<0)}function Om(e,t){return e<t?1:-1}function w_t(e,t,r,n){var i=[],a=[],o=n?t:r,s=n?r:t;return i[0]=o.c2p(e.s0,!0),a[0]=s.c2p(e.p0,!0),i[1]=o.c2p(e.s1,!0),a[1]=s.c2p(e.p1,!0),n?[i,a]:[a,i]}function t1e(e,t,r,n){if(!t.uniformtext.mode&&r1e(r)){var i;return n&&(i=n()),e.transition().duration(r.duration).ease(r.easing).each(\"end\",function(){i&&i()}).each(\"interrupt\",function(){i&&i()})}else return e}function r1e(e){return e&&e.duration>0}function T_t(e,t,r,n,i,a){var o=t.xaxis,s=t.yaxis,l=e._fullLayout,u=e._context.staticPlot;i||(i={mode:l.barmode,norm:l.barmode,gap:l.bargap,groupgap:l.bargroupgap},p_t(\"bar\",l));var c=Fd.makeTraceGroups(n,r,\"trace bars\").each(function(f){var h=$I.select(this),d=f[0].trace,v=f[0].t,m=d.type===\"waterfall\",b=d.type===\"funnel\",p=d.type===\"histogram\",k=d.type===\"bar\",M=k||b,T=0;m&&d.connector.visible&&d.connector.mode===\"between\"&&(T=d.connector.line.width/2);var L=d.orientation===\"h\",x=r1e(i),C=Fd.ensureSingle(h,\"g\",\"points\"),S=b_t(d),g=C.selectAll(\"g.point\").data(Fd.identity,S);g.enter().append(\"g\").classed(\"point\",!0),g.exit().remove(),g.each(function(E,z){var q=$I.select(this),V=w_t(E,o,s,L),G=V[0][0],Z=V[0][1],j=V[1][0],N=V[1][1],H=(L?Z-G:N-j)===0;H&&M&&DT.getLineWidth(d,E)&&(H=!1),H||(H=!QI(G)||!QI(Z)||!QI(j)||!QI(N)),E.isBlank=H,H&&(L?Z=G:N=j),T&&!H&&(L?(G-=Om(G,Z)*T,Z+=Om(G,Z)*T):(j-=Om(j,N)*T,N+=Om(j,N)*T));var re,ae;if(d.type===\"waterfall\"){if(!H){var _e=d[E.dir].marker;re=_e.line.width,ae=_e.color}}else re=DT.getLineWidth(d,E),ae=E.mc||d.marker.color;function Ee(Ae){var rt=$I.round(re/2%1,2);return i.gap===0&&i.groupgap===0?$I.round(Math.round(Ae)-rt,2):Ae}function Ce(Ae,rt,St){return St&&Ae===rt?Ae:Math.abs(Ae-rt)>=2?Ee(Ae):Ae>rt?Math.ceil(Ae):Math.floor(Ae)}var ge=h_t.opacity(ae),ie=ge<1||re>.01?Ee:Ce;e._context.staticPlot||(G=ie(G,Z,L),Z=ie(Z,G,L),j=ie(j,N,!L),N=ie(N,j,!L));var Se=L?o.c2p:s.c2p,ke;E.s0>0?ke=E._sMax:E.s0<0?ke=E._sMin:ke=E.s1>0?E._sMax:E._sMin;function be(Ae,rt){if(!Ae)return 0;var St=Math.abs(L?N-j:Z-G),Tt=Math.abs(L?Z-G:N-j),dt=ie(Math.abs(Se(ke,!0)-Se(0,!0))),Et=E.hasB?Math.min(St/2,Tt/2):Math.min(St/2,dt),pt;if(rt===\"%\"){var jt=Math.min(50,Ae);pt=St*(jt/100)}else pt=Ae;return ie(Math.max(Math.min(pt,Et),0))}var Be=k||p?be(v.cornerradiusvalue,v.cornerradiusform):0,Le,me,Pe=\"M\"+G+\",\"+j+\"V\"+N+\"H\"+Z+\"V\"+j+\"Z\",ce=0;if(Be&&E.s){var He=NV(E.s0)===0||NV(E.s)===NV(E.s0)?E.s1:E.s0;if(ce=ie(E.hasB?0:Math.abs(Se(ke,!0)-Se(He,!0))),ce<Be){var lt=Om(G,Z),mt=Om(j,N),Ht=lt===-mt?1:0;if(L)if(E.hasB)Le=\"M\"+(G+Be*lt)+\",\"+j+\"A \"+Be+\",\"+Be+\" 0 0 \"+Ht+\" 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Vt=me<Be?Be-Math.sqrt(me*(2*Be-me)):0,rr=ce>0?Math.sqrt(ce*(2*Be-ce)):0,tt=mt>0?Math.max:Math.min;Le=\"M\"+(G+Vt*lt)+\",\"+j+\"V\"+tt(N-(Be-ce)*mt,j)+\"A \"+Be+\",\"+Be+\" 0 0 \"+Ht+\" \"+(G+Be*lt-rr)+\",\"+N+\"H\"+(Z-Be*lt+rr)+\"A \"+Be+\",\"+Be+\" 0 0 \"+Ht+\" \"+(Z-Vt*lt)+\",\"+tt(N-(Be-ce)*mt,j)+\"V\"+j+\"Z\"}}else Le=Pe}else Le=Pe;var je=t1e(Fd.ensureSingle(q,\"path\"),l,i,a);if(je.style(\"vector-effect\",u?\"none\":\"non-scaling-stroke\").attr(\"d\",isNaN((Z-G)*(N-j))||H&&e._context.staticPlot?\"M0,0Z\":Le).call(R_.setClipUrl,t.layerClipId,e),!l.uniformtext.mode&&x){var Ue=R_.makePointStyleFns(d);R_.singlePointStyle(E,je,d,Ue,e)}A_t(e,t,q,f,z,G,Z,j,N,Be,ce,i,a),t.layerClipId&&R_.hideOutsideRangePoint(E,q.select(\"text\"),o,s,d.xcalendar,d.ycalendar)});var P=d.cliponaxis===!1;R_.setClipUrl(h,P?null:t.layerClipId,e)});d_t.getComponentMethod(\"errorbars\",\"plot\")(e,c,t,i)}function A_t(e,t,r,n,i,a,o,s,l,u,c,f,h){var d=t.xaxis,v=t.yaxis,m=e._fullLayout,b;function p(me,Pe,ce){var He=Fd.ensureSingle(me,\"text\").text(Pe).attr({class:\"bartext bartext-\"+b,\"text-anchor\":\"middle\",\"data-notex\":1}).call(R_.font,ce).call(f_t.convertToTspans,e);return He}var k=n[0].trace,M=k.orientation===\"h\",T=E_t(m,n,i,d,v);b=k_t(k,i);var L=f.mode===\"stack\"||f.mode===\"relative\",x=n[i],C=!L||x._outmost,S=x.hasB,g=u&&u-c>Gv;if(!T||b===\"none\"||(x.isBlank||a===o||s===l)&&(b===\"auto\"||b===\"inside\")){r.select(\"text\").remove();return}var P=m.font,E=BV.getBarColor(n[i],k),z=BV.getInsideTextFont(k,i,P,E),q=BV.getOutsideTextFont(k,i,P),V=k.insidetextanchor||\"end\",G=r.datum();M?d.type===\"log\"&&G.s0<=0&&(d.range[0]<d.range[1]?a=0:a=d._length):v.type===\"log\"&&G.s0<=0&&(v.range[0]<v.range[1]?s=v._length:s=0);var Z=Math.abs(o-a),j=Math.abs(l-s),N=Z-2*Gv,H=j-2*Gv,re,ae,_e,Ee,Ce;if(b===\"outside\"&&!C&&!x.hasB&&(b=\"inside\"),b===\"auto\")if(C){b=\"inside\",Ce=Fd.ensureUniformFontSize(e,z),re=p(r,T,Ce),ae=R_.bBox(re.node()),_e=ae.width,Ee=ae.height;var 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i(l){return H0.coerce(n._input,n,R4,l)}for(var a=0;a<e.length;a++){n=e[a];var o=n.type;if(o===\"box\"||o===\"violin\"){r=n._input;var s=t[o+\"mode\"];s===\"group\"&&H_t(r,n,t,i,s)}}}M1e.exports={supplyDefaults:j_t,crossTraceDefaults:W_t,handleSampleDefaults:A1e,handlePointsDefaults:S1e}});var t8=ye((Xur,k1e)=>{\"use strict\";var X_t=Oa(),Z_t=Pr(),Y_t=I4();function E1e(e,t,r,n,i){for(var a=i+\"Layout\",o=!1,s=0;s<r.length;s++){var l=r[s];if(X_t.traceIs(l,a)){o=!0;break}}o&&(n(i+\"mode\"),n(i+\"gap\"),n(i+\"groupgap\"))}function K_t(e,t,r){function n(i,a){return Z_t.coerce(e,t,Y_t,i,a)}E1e(e,t,r,n,\"box\")}k1e.exports={supplyLayoutDefaults:K_t,_supply:E1e}});var HV=ye((Zur,q1e)=>{\"use strict\";var GV=Eo(),r8=ho(),J_t=zg(),yh=Pr(),n0=fs().BADNUM,jy=yh._;q1e.exports=function(t,r){var n=t._fullLayout,i=r8.getFromId(t,r.xaxis||\"x\"),a=r8.getFromId(t,r.yaxis||\"y\"),o=[],s=r.type===\"violin\"?\"_numViolins\":\"_numBoxes\",l,u,c,f,h,d,v;r.orientation===\"h\"?(c=i,f=\"x\",h=a,d=\"y\",v=!!r.yperiodalignment):(c=a,f=\"y\",h=i,d=\"x\",v=!!r.xperiodalignment);var m=$_t(r,d,h,n[s]),b=m[0],p=m[1],k=yh.distinctVals(b,h),M=k.vals,T=k.minDiff/2,L,x,C,S,g,P,E=(r.boxpoints||r.points)===\"all\"?yh.identity:function(Ht){return Ht.v<L.lf||Ht.v>L.uf};if(r._hasPreCompStats){var z=r[f],q=function(Ht){return c.d2c((r[Ht]||[])[l])},V=1/0,G=-1/0;for(l=0;l<r._length;l++){var Z=b[l];if(GV(Z)){if(L={},L.pos=L[d]=Z,v&&p&&(L.orig_p=p[l]),L.q1=q(\"q1\"),L.med=q(\"median\"),L.q3=q(\"q3\"),x=[],z&&yh.isArrayOrTypedArray(z[l]))for(u=0;u<z[l].length;u++)P=c.d2c(z[l][u]),P!==n0&&(g={v:P,i:[l,u]},C1e(g,r,[l,u]),x.push(g));if(L.pts=x.sort(L1e),C=L[f]=x.map(P1e),S=C.length,L.med!==n0&&L.q1!==n0&&L.q3!==n0&&L.med>=L.q1&&L.q3>=L.med){var j=q(\"lowerfence\");L.lf=j!==n0&&j<=L.q1?j:I1e(L,C,S);var N=q(\"upperfence\");L.uf=N!==n0&&N>=L.q3?N:R1e(L,C,S);var H=q(\"mean\");L.mean=H!==n0?H:S?yh.mean(C,S):(L.q1+L.q3)/2;var re=q(\"sd\");L.sd=H!==n0&&re>=0?re:S?yh.stdev(C,S,L.mean):L.q3-L.q1,L.lo=D1e(L),L.uo=F1e(L);var ae=q(\"notchspan\");ae=ae!==n0&&ae>0?ae:z1e(L,S),L.ln=L.med-ae,L.un=L.med+ae;var _e=L.lf,Ee=L.uf;r.boxpoints&&C.length&&(_e=Math.min(_e,C[0]),Ee=Math.max(Ee,C[S-1])),r.notched&&(_e=Math.min(_e,L.ln),Ee=Math.max(Ee,L.un)),L.min=_e,L.max=Ee}else{yh.warn([\"Invalid input - make sure that q1 <= median <= q3\",\"q1 = \"+L.q1,\"median = \"+L.med,\"q3 = \"+L.q3].join(`\n",
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BT=ye((icr,o_e)=>{\"use strict\";var dxt=Tu(),{extendFlat:Pp}=Ao(),vxt=Vl(),{axisHoverFormat:JV}=vf(),pxt=ec(),{hovertemplateAttrs:gxt,templatefallbackAttrs:a_e,texttemplateAttrs:mxt}=Pl(),a0=pf();o_e.exports=Pp({z:{valType:\"data_array\",editType:\"calc\"},x:Pp({},a0.x,{impliedEdits:{xtype:\"array\"}}),x0:Pp({},a0.x0,{impliedEdits:{xtype:\"scaled\"}}),dx:Pp({},a0.dx,{impliedEdits:{xtype:\"scaled\"}}),y:Pp({},a0.y,{impliedEdits:{ytype:\"array\"}}),y0:Pp({},a0.y0,{impliedEdits:{ytype:\"scaled\"}}),dy:Pp({},a0.dy,{impliedEdits:{ytype:\"scaled\"}}),xperiod:Pp({},a0.xperiod,{impliedEdits:{xtype:\"scaled\"}}),yperiod:Pp({},a0.yperiod,{impliedEdits:{ytype:\"scaled\"}}),xperiod0:Pp({},a0.xperiod0,{impliedEdits:{xtype:\"scaled\"}}),yperiod0:Pp({},a0.yperiod0,{impliedEdits:{ytype:\"scaled\"}}),xperiodalignment:Pp({},a0.xperiodalignment,{impliedEdits:{xtype:\"scaled\"}}),yperiodalignment:Pp({},a0.yperiodalignment,{impliedEdits:{ytype:\"scaled\"}}),text:{valType:\"data_array\",editType:\"calc\"},hovertext:{valType:\"data_array\",editType:\"calc\"},transpose:{valType:\"boolean\",dflt:!1,editType:\"calc\"},xtype:{valType:\"enumerated\",values:[\"array\",\"scaled\"],editType:\"calc+clearAxisTypes\"},ytype:{valType:\"enumerated\",values:[\"array\",\"scaled\"],editType:\"calc+clearAxisTypes\"},zsmooth:{valType:\"enumerated\",values:[\"fast\",\"best\",!1],dflt:!1,editType:\"calc\"},hoverongaps:{valType:\"boolean\",dflt:!0,editType:\"none\"},connectgaps:{valType:\"boolean\",editType:\"calc\"},xgap:{valType:\"number\",dflt:0,min:0,editType:\"plot\"},ygap:{valType:\"number\",dflt:0,min:0,editType:\"plot\"},xhoverformat:JV(\"x\"),yhoverformat:JV(\"y\"),zhoverformat:JV(\"z\",1),hovertemplate:gxt(),hovertemplatefallback:a_e(),texttemplate:mxt({arrayOk:!1,editType:\"plot\"},{keys:[\"x\",\"y\",\"z\",\"text\"]}),texttemplatefallback:a_e({editType:\"plot\"}),textfont:pxt({editType:\"plot\",autoSize:!0,autoColor:!0,colorEditType:\"style\"}),showlegend:Pp({},vxt.showlegend,{dflt:!1}),zorder:a0.zorder},dxt(\"\",{cLetter:\"z\",autoColorDflt:!1}))});var s8=ye((ncr,l_e)=>{\"use strict\";var yxt=Eo(),o8=Pr(),_xt=Oa();l_e.exports=function(t,r,n,i,a,o){var s=n(\"z\");a=a||\"x\",o=o||\"y\";var l,u;if(s===void 0||!s.length)return 0;if(o8.isArray1D(s)){l=n(a),u=n(o);var c=o8.minRowLength(l),f=o8.minRowLength(u);if(c===0||f===0)return 0;r._length=Math.min(c,f,s.length)}else{if(l=s_e(a,n),u=s_e(o,n),!xxt(s))return 0;n(\"transpose\"),r._length=null}var h=_xt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");return h(t,r,[a,o],i),!0};function s_e(e,t){var r=t(e),n=r?t(e+\"type\",\"array\"):\"scaled\";return n===\"scaled\"&&(t(e+\"0\"),t(\"d\"+e)),r}function xxt(e){for(var t=!0,r=!1,n=!1,i,a=0;a<e.length;a++){if(i=e[a],!o8.isArrayOrTypedArray(i)){t=!1;break}i.length>0&&(r=!0);for(var o=0;o<i.length;o++)if(yxt(i[o])){n=!0;break}}return t&&r&&n}});var F4=ye((acr,c_e)=>{\"use strict\";var u_e=Pr();c_e.exports=function(t,r){t(\"texttemplate\"),t(\"texttemplatefallback\");var 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z8=Fa(),Ube=So(),x2t=b8(),b2t=FG();Vbe.exports=function(t){var r=z8.select(t).selectAll(\"g.contour\");r.style(\"opacity\",function(n){return n[0].trace.opacity}),r.each(function(n){var i=z8.select(this),a=n[0].trace,o=a.contours,s=a.line,l=o.size||1,u=o.start,c=o.type===\"constraint\",f=!c&&o.coloring===\"lines\",h=!c&&o.coloring===\"fill\",d=f||h?b2t(a):null;i.selectAll(\"g.contourlevel\").each(function(b){z8.select(this).selectAll(\"path\").call(Ube.lineGroupStyle,s.width,f?d(b.level):s.color,s.dash)});var v=o.labelfont;if(i.selectAll(\"g.contourlabels text\").each(function(b){Ube.font(z8.select(this),{weight:v.weight,style:v.style,variant:v.variant,textcase:v.textcase,lineposition:v.lineposition,shadow:v.shadow,family:v.family,size:v.size,color:v.color||(f?d(b.level):s.color)})}),c)i.selectAll(\"g.contourfill path\").style(\"fill\",a.fillcolor);else if(h){var m;i.selectAll(\"g.contourfill path\").style(\"fill\",function(b){return m===void 0&&(m=b.level),d(b.level+.5*l)}),m===void 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Xbe=ye((ffr,Wbe)=>{\"use strict\";Wbe.exports={attributes:bG(),supplyDefaults:dbe(),crossTraceDefaults:M8(),calc:MG(),plot:F8().plot,layerName:\"contourlayer\",style:q8(),colorbar:O8(),hoverPoints:zG(),moduleType:\"trace\",name:\"histogram2dcontour\",basePlotModule:mh(),categories:[\"cartesian\",\"svg\",\"2dMap\",\"contour\",\"histogram\",\"showLegend\"],meta:{}}});var Ybe=ye((hfr,Zbe)=>{\"use strict\";Zbe.exports=Xbe()});var qG=ye((dfr,t2e)=>{\"use strict\";var Kbe=Eo(),M2t=wG(),Qbe=ka(),Jbe=Qbe.addOpacity,E2t=Qbe.opacity,e2e=C8(),$be=Pr().isArrayOrTypedArray,k2t=e2e.CONSTRAINT_REDUCTION,C2t=e2e.COMPARISON_OPS2;t2e.exports=function(t,r,n,i,a,o){var s=r.contours,l,u,c,f=n(\"contours.operation\");if(s._operation=k2t[f],L2t(n,s),f===\"=\"?l=s.showlines=!0:(l=n(\"contours.showlines\"),c=n(\"fillcolor\",Jbe((t.line||{}).color||a,.5))),l){var h=c&&E2t(c)?Jbe(r.fillcolor,1):a;u=n(\"line.color\",h),n(\"line.width\",2),n(\"line.dash\")}n(\"line.smoothing\"),M2t(n,i,u,o)};function L2t(e,t){var 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g2e=ye((yfr,p2e)=>{\"use strict\";var d2e=Pr(),V2t=Pm(),XT=Ru(),G2t=$p(),H2t=D0(),v2e=fT(),j2t=F0(),W2t=Fg(),X2t=BG();p2e.exports=function(t,r,n,i){function a(h,d){return d2e.coerce(t,r,X2t,h,d)}var o=a(\"a\"),s=a(\"b\"),l=a(\"c\"),u;if(o?(u=o.length,s?(u=Math.min(u,s.length),l&&(u=Math.min(u,l.length))):l?u=Math.min(u,l.length):u=0):s&&l&&(u=Math.min(s.length,l.length)),!u){r.visible=!1;return}r._length=u,a(\"sum\"),a(\"text\"),a(\"hovertext\"),r.hoveron!==\"fills\"&&(a(\"hovertemplate\"),a(\"hovertemplatefallback\"));var c=u<V2t.PTS_LINESONLY?\"lines+markers\":\"lines\";a(\"mode\",c),XT.hasMarkers(r)&&G2t(t,r,n,i,a,{gradient:!0}),XT.hasLines(r)&&(H2t(t,r,n,i,a,{backoff:!0}),v2e(t,r,a),a(\"connectgaps\")),XT.hasText(r)&&(a(\"texttemplate\"),a(\"texttemplatefallback\"),j2t(t,r,i,a));var f=[];(XT.hasMarkers(r)||XT.hasText(r))&&(a(\"cliponaxis\"),a(\"marker.maxdisplayed\"),f.push(\"points\")),a(\"fill\"),r.fill!==\"none\"&&(W2t(t,r,n,a),XT.hasLines(r)||v2e(t,r,a)),(r.fill===\"tonext\"||r.fill===\"toself\")&&f.push(\"fills\"),a(\"hoveron\",f.join(\"+\")||\"points\"),d2e.coerceSelectionMarkerOpacity(r,a)}});var y2e=ye((_fr,m2e)=>{\"use strict\";var NG=ho();m2e.exports=function(t,r,n){var i={},a=n[r.subplot]._subplot;return i.aLabel=NG.tickText(a.aaxis,t.a,!0).text,i.bLabel=NG.tickText(a.baxis,t.b,!0).text,i.cLabel=NG.tickText(a.caxis,t.c,!0).text,i}});var w2e=ye((xfr,b2e)=>{\"use strict\";var UG=Eo(),Z2t=z0(),Y2t=Dm(),K2t=q0(),J2t=O0().calcMarkerSize,_2e=[\"a\",\"b\",\"c\"],x2e={a:[\"b\",\"c\"],b:[\"a\",\"c\"],c:[\"a\",\"b\"]};b2e.exports=function(t,r){var n=t._fullLayout[r.subplot],i=n.sum,a=r.sum||i,o={a:r.a,b:r.b,c:r.c},s=r.ids,l,u,c,f,h,d;for(l=0;l<_2e.length;l++)if(c=_2e[l],!o[c]){for(h=o[x2e[c][0]],d=o[x2e[c][1]],f=new 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o.x0=Math.max(Math.min(o.x0,c),u),o.x1=Math.max(Math.min(o.x1,c),u),a}var f=o.cd[o.index],h=o.trace,d=o.subplot;o.a=f.a,o.b=f.b,o.c=f.c,o.xLabelVal=void 0,o.yLabelVal=void 0;var v={};v[h.subplot]={_subplot:d};var m=h._module.formatLabels(f,h,v);o.aLabel=m.aLabel,o.bLabel=m.bLabel,o.cLabel=m.cLabel;var b=f.hi||h.hoverinfo,p=[];function k(T,L){p.push(T._hovertitle+\": \"+L)}if(!h.hovertemplate){var M=b.split(\"+\");M.indexOf(\"all\")!==-1&&(M=[\"a\",\"b\",\"c\"]),M.indexOf(\"a\")!==-1&&k(d.aaxis,o.aLabel),M.indexOf(\"b\")!==-1&&k(d.baxis,o.bLabel),M.indexOf(\"c\")!==-1&&k(d.caxis,o.cLabel)}return o.extraText=p.join(\"<br>\"),o.hovertemplate=h.hovertemplate,a}});var k2e=ye((Tfr,E2e)=>{\"use strict\";E2e.exports=function(t,r,n,i,a){if(r.xa&&(t.xaxis=r.xa),r.ya&&(t.yaxis=r.ya),i[a]){var o=i[a];t.a=o.a,t.b=o.b,t.c=o.c}else t.a=r.a,t.b=r.b,t.c=r.c;return t}});var B2e=ye((Afr,O2e)=>{\"use strict\";var D2e=Fa(),ewt=fd(),VG=Oa(),Xy=Pr(),Vm=Xy.strTranslate,N8=Xy._,YT=ka(),U8=So(),X4=xm(),GG=Ao().extendFlat,twt=Mc(),q_=ho(),C2e=yv(),L2e=ef(),F2e=Cg(),P2e=F2e.freeMode,rwt=F2e.rectMode,HG=qb(),iwt=qf().prepSelect,nwt=qf().selectOnClick,awt=qf().clearOutline,owt=qf().clearSelectionsCache,z2e=Rh();function q2e(e,t){this.id=e.id,this.graphDiv=e.graphDiv,this.init(t),this.makeFramework(t),this.updateFx(t),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}O2e.exports=q2e;var Gm=q2e.prototype;Gm.init=function(e){this.container=e._ternarylayer,this.defs=e._defs,this.layoutId=e._uid,this.traceHash={},this.layers={}};Gm.plot=function(e,t){var r=this,n=t[r.id],i=t._size;r._hasClipOnAxisFalse=!1;for(var a=0;a<e.length;a++){var o=e[a][0].trace;if(o.cliponaxis===!1){r._hasClipOnAxisFalse=!0;break}}r.updateLayers(n),r.adjustLayout(n,i),twt.generalUpdatePerTraceModule(r.graphDiv,r,e,n),r.layers.plotbg.select(\"path\").call(YT.fill,n.bgcolor)};Gm.makeFramework=function(e){var t=this,r=t.graphDiv,n=e[t.id],i=t.clipId=\"clip\"+t.layoutId+t.id,a=t.clipIdRelative=\"clip-relative\"+t.layoutId+t.id;t.clipDef=Xy.ensureSingleById(e._clips,\"clipPath\",i,function(o){o.append(\"path\").attr(\"d\",\"M0,0Z\")}),t.clipDefRelative=Xy.ensureSingleById(e._clips,\"clipPath\",a,function(o){o.append(\"path\").attr(\"d\",\"M0,0Z\")}),t.plotContainer=Xy.ensureSingle(t.container,\"g\",t.id),t.updateLayers(n),U8.setClipUrl(t.layers.backplot,i,r),U8.setClipUrl(t.layers.grids,i,r)};Gm.updateFx=function(e){e._ternarylayer.selectAll(\"g.toplevel\").style(\"cursor\",e.dragmode===\"pan\"?\"move\":\"crosshair\")};Gm.updateLayers=function(e){var t=this,r=t.layers,n=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];e.aaxis.layer===\"below traces\"&&n.push(\"aaxis\",\"aline\"),e.baxis.layer===\"below traces\"&&n.push(\"baxis\",\"bline\"),e.caxis.layer===\"below traces\"&&n.push(\"caxis\",\"cline\"),n.push(\"frontplot\"),e.aaxis.layer===\"above 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r=this,n=e.domain,i=(n.x[0]+n.x[1])/2,a=(n.y[0]+n.y[1])/2,o=n.x[1]-n.x[0],s=n.y[1]-n.y[0],l=o*t.w,u=s*t.h,c=e.sum,f=e.aaxis.min,h=e.baxis.min,d=e.caxis.min,v,m,b,p,k,M;l>ZT*u?(p=u,b=p*ZT):(b=l,p=b/ZT),k=o*b/l,M=s*p/u,v=t.l+t.w*i-b/2,m=t.t+t.h*(1-a)-p/2,r.x0=v,r.y0=m,r.w=b,r.h=p,r.sum=c,r.xaxis={type:\"linear\",range:[f+2*d-c,c-f-2*h],domain:[i-k/2,i+k/2],_id:\"x\"},X4(r.xaxis,r.graphDiv._fullLayout),r.xaxis.setScale(),r.xaxis.isPtWithinRange=function(V){return V.a>=r.aaxis.range[0]&&V.a<=r.aaxis.range[1]&&V.b>=r.baxis.range[1]&&V.b<=r.baxis.range[0]&&V.c>=r.caxis.range[1]&&V.c<=r.caxis.range[0]},r.yaxis={type:\"linear\",range:[f,c-h-d],domain:[a-M/2,a+M/2],_id:\"y\"},X4(r.yaxis,r.graphDiv._fullLayout),r.yaxis.setScale(),r.yaxis.isPtWithinRange=function(){return!0};var T=r.yaxis.domain[0],L=r.aaxis=GG({},e.aaxis,{range:[f,c-h-d],side:\"left\",tickangle:(+e.aaxis.tickangle||0)-30,domain:[T,T+M*ZT],anchor:\"free\",position:0,_id:\"y\",_length:b});X4(L,r.graphDiv._fullLayout),L.setScale();var x=r.baxis=GG({},e.baxis,{range:[c-f-d,h],side:\"bottom\",domain:r.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:b});X4(x,r.graphDiv._fullLayout),x.setScale();var C=r.caxis=GG({},e.caxis,{range:[c-f-h,d],side:\"right\",tickangle:(+e.caxis.tickangle||0)+30,domain:[T,T+M*ZT],anchor:\"free\",position:0,_id:\"y\",_length:b});X4(C,r.graphDiv._fullLayout),C.setScale();var S=\"M\"+v+\",\"+(m+p)+\"h\"+b+\"l-\"+b/2+\",-\"+p+\"Z\";r.clipDef.select(\"path\").attr(\"d\",S),r.layers.plotbg.select(\"path\").attr(\"d\",S);var g=\"M0,\"+p+\"h\"+b+\"l-\"+b/2+\",-\"+p+\"Z\";r.clipDefRelative.select(\"path\").attr(\"d\",g);var P=Vm(v,m);r.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",P),r.clipDefRelative.select(\"path\").attr(\"transform\",null);var E=Vm(v-x._offset,m+p);r.layers.baxis.attr(\"transform\",E),r.layers.bgrid.attr(\"transform\",E);var z=Vm(v+b/2,m)+\"rotate(30)\"+Vm(0,-L._offset);r.layers.aaxis.attr(\"transform\",z),r.layers.agrid.attr(\"transform\",z);var q=Vm(v+b/2,m)+\"rotate(-30)\"+Vm(0,-C._offset);r.layers.caxis.attr(\"transform\",q),r.layers.cgrid.attr(\"transform\",q),r.drawAxes(!0),r.layers.aline.select(\"path\").attr(\"d\",L.showline?\"M\"+v+\",\"+(m+p)+\"l\"+b/2+\",-\"+p:\"M0,0\").call(YT.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),r.layers.bline.select(\"path\").attr(\"d\",x.showline?\"M\"+v+\",\"+(m+p)+\"h\"+b:\"M0,0\").call(YT.stroke,x.linecolor||\"#000\").style(\"stroke-width\",(x.linewidth||0)+\"px\"),r.layers.cline.select(\"path\").attr(\"d\",C.showline?\"M\"+(v+b/2)+\",\"+m+\"l\"+b/2+\",\"+p:\"M0,0\").call(YT.stroke,C.linecolor||\"#000\").style(\"stroke-width\",(C.linewidth||0)+\"px\"),r.graphDiv._context.staticPlot||r.initInteractions(),U8.setClipUrl(r.layers.frontplot,r._hasClipOnAxisFalse?null:r.clipId,r.graphDiv)};Gm.drawAxes=function(e){var t=this,r=t.graphDiv,n=t.id.slice(7)+\"title\",i=t.layers,a=t.aaxis,o=t.baxis,s=t.caxis;if(t.drawAx(a),t.drawAx(o),t.drawAx(s),e){var 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c=t.aaxis,f=t.baxis,h=t.caxis;c.min+f.min+h.min>=a&&(c.min=0,f.min=0,h.min=0,e.aaxis&&delete e.aaxis.min,e.baxis&&delete e.baxis.min,e.caxis&&delete e.caxis.min)}function Ewt(e,t,r,n){var i=G2e[t._name];function a(d,v){return G8.coerce(e,t,i,d,v)}a(\"uirevision\",n.uirevision),t.type=\"linear\";var o=a(\"color\"),s=o!==i.color.dflt?o:r.font.color,l=t._name,u=l.charAt(0).toUpperCase(),c=\"Component \"+u,f=a(\"title.text\",c);t._hovertitle=f===c?f:u,G8.coerceFont(a,\"title.font\",r.font,{overrideDflt:{size:G8.bigFont(r.font.size),color:s}}),a(\"min\"),Awt(e,t,a,\"linear\"),wwt(e,t,a,\"linear\"),bwt(e,t,a,\"linear\",{noAutotickangles:!0,noTicklabelshift:!0,noTicklabelstandoff:!0}),Twt(e,t,a,{outerTicks:!0});var h=a(\"showticklabels\");h&&(G8.coerceFont(a,\"tickfont\",r.font,{overrideDflt:{color:s}}),a(\"tickangle\"),a(\"tickformat\")),Swt(e,t,a,{dfltColor:o,bgColor:r.bgColor,blend:60,showLine:!0,showGrid:!0,noZeroLine:!0,attributes:i}),a(\"hoverformat\"),a(\"layer\")}});var W2e=ye(Z0=>{\"use strict\";var kwt=B2e(),Cwt=Id().getSubplotCalcData,Lwt=Pr().counterRegex,KT=\"ternary\";Z0.name=KT;var Pwt=Z0.attr=\"subplot\";Z0.idRoot=KT;Z0.idRegex=Z0.attrRegex=Lwt(KT);var Iwt=Z0.attributes={};Iwt[Pwt]={valType:\"subplotid\",dflt:\"ternary\",editType:\"calc\"};Z0.layoutAttributes=XG();Z0.supplyLayoutDefaults=j2e();Z0.plot=function(t){for(var r=t._fullLayout,n=t.calcdata,i=r._subplots[KT],a=0;a<i.length;a++){var o=i[a],s=Cwt(n,KT,o),l=r[o]._subplot;l||(l=new kwt({id:o,graphDiv:t,container:r._ternarylayer.node()},r),r[o]._subplot=l),l.plot(s,r,t._promises)}};Z0.clean=function(e,t,r,n){for(var i=n._subplots[KT]||[],a=0;a<i.length;a++){var o=i[a],s=n[o]._subplot;!t[o]&&s&&(s.plotContainer.remove(),s.clipDef.remove(),s.clipDefRelative.remove(),s.layers[\"a-title\"].remove(),s.layers[\"b-title\"].remove(),s.layers[\"c-title\"].remove())}};Z0.updateFx=function(e){var 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Oh=P4(),JT=Ao().extendFlat,J2e=vf().axisHoverFormat;$2e.exports={y:Oh.y,x:Oh.x,x0:Oh.x0,y0:Oh.y0,xhoverformat:J2e(\"x\"),yhoverformat:J2e(\"y\"),name:JT({},Oh.name,{}),orientation:JT({},Oh.orientation,{}),bandwidth:{valType:\"number\",min:0,editType:\"calc\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},scalemode:{valType:\"enumerated\",values:[\"width\",\"count\"],dflt:\"width\",editType:\"calc\"},spanmode:{valType:\"enumerated\",values:[\"soft\",\"hard\",\"manual\"],dflt:\"soft\",editType:\"calc\"},span:{valType:\"info_array\",items:[{valType:\"any\",editType:\"calc\"},{valType:\"any\",editType:\"calc\"}],editType:\"calc\"},line:{color:{valType:\"color\",editType:\"style\"},width:{valType:\"number\",min:0,dflt:2,editType:\"style\"},editType:\"plot\"},fillcolor:Oh.fillcolor,points:JT({},Oh.boxpoints,{}),jitter:JT({},Oh.jitter,{}),pointpos:JT({},Oh.pointpos,{}),width:JT({},Oh.width,{}),marker:Oh.marker,text:Oh.text,hovertext:Oh.hovertext,hovertemplate:Oh.hovertemplate,hovertemplatefallback:Oh.hovertemplatefallback,quartilemethod:Oh.quartilemethod,box:{visible:{valType:\"boolean\",dflt:!1,editType:\"plot\"},width:{valType:\"number\",min:0,max:1,dflt:.25,editType:\"plot\"},fillcolor:{valType:\"color\",editType:\"style\"},line:{color:{valType:\"color\",editType:\"style\"},width:{valType:\"number\",min:0,editType:\"style\"},editType:\"style\"},editType:\"plot\"},meanline:{visible:{valType:\"boolean\",dflt:!1,editType:\"plot\"},color:{valType:\"color\",editType:\"style\"},width:{valType:\"number\",min:0,editType:\"style\"},editType:\"plot\"},side:{valType:\"enumerated\",values:[\"both\",\"positive\",\"negative\"],dflt:\"both\",editType:\"calc\"},offsetgroup:Oh.offsetgroup,alignmentgroup:Oh.alignmentgroup,selected:Oh.selected,unselected:Oh.unselected,hoveron:{valType:\"flaglist\",flags:[\"violins\",\"points\",\"kde\"],dflt:\"violins+points+kde\",extras:[\"all\"],editType:\"style\"},zorder:Oh.zorder}});var JG=ye((Ifr,Q2e)=>{\"use strict\";var YG=I4(),KG=Pr().extendFlat;Q2e.exports={violinmode:KG({},YG.boxmode,{}),violingap:KG({},YG.boxgap,{}),violingroupgap:KG({},YG.boxgroupgap,{})}});var nwe=ye((Rfr,iwe)=>{\"use strict\";var ewe=Pr(),Rwt=ka(),twe=D4(),rwe=ZG();iwe.exports=function(t,r,n,i){function a(L,x){return ewe.coerce(t,r,rwe,L,x)}function o(L,x){return ewe.coerce2(t,r,rwe,L,x)}if(twe.handleSampleDefaults(t,r,a,i),r.visible!==!1){a(\"bandwidth\"),a(\"side\");var s=a(\"width\");s||(a(\"scalegroup\",r.name),a(\"scalemode\"));var l=a(\"span\"),u;Array.isArray(l)&&(u=\"manual\"),a(\"spanmode\",u);var c=a(\"line.color\",(t.marker||{}).color||n),f=a(\"line.width\"),h=a(\"fillcolor\",Rwt.addOpacity(r.line.color,.5));twe.handlePointsDefaults(t,r,a,{prefix:\"\"});var d=o(\"box.width\"),v=o(\"box.fillcolor\",h),m=o(\"box.line.color\",c),b=o(\"box.line.width\",f),p=a(\"box.visible\",!!(d||v||m||b));p||(r.box={visible:!1});var 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uwe=ye((zfr,lwe)=>{\"use strict\";var $G=Pr(),QG=ho(),Bwt=HV(),swe=H8(),Nwt=fs().BADNUM;lwe.exports=function(t,r){var n=Bwt(t,r);if(n[0].t.empty)return n;for(var i=t._fullLayout,a=QG.getFromId(t,r[r.orientation===\"h\"?\"xaxis\":\"yaxis\"]),o=1/0,s=-1/0,l=0,u=0,c=0;c<n.length;c++){var f=n[c],h=f.pts.map(swe.extractVal),d=f.bandwidth=Vwt(r,f,h),v=f.span=Gwt(r,f,a,d);if(f.min===f.max&&d===0)v=f.span=[f.min,f.max],f.density=[{v:1,t:v[0]}],f.bandwidth=d,l=Math.max(l,1);else{var m=v[1]-v[0],b=Math.ceil(m/(d/3)),p=m/b;if(!isFinite(p)||!isFinite(b))return $G.error(\"Something went wrong with computing the violin span\"),n[0].t.empty=!0,n;var k=swe.makeKDE(f,r,h);f.density=new Array(b+1);for(var M=0;M<f.density.length;M++){var T=v[0]+M*p,L=k(T);f.density[M]={v:L,t:T},l=Math.max(l,L)}}u=Math.max(u,h.length),o=Math.min(o,v[0]),s=Math.max(s,v[1])}var x=QG.findExtremes(a,[o,s],{padded:!0});if(r._extremes[a._id]=x,r.width)n[0].t.maxKDE=l;else{var 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bwe=ye((Nfr,xwe)=>{\"use strict\";var Ywt=ka(),iH=Pr(),Kwt=ho(),ywe=YV(),_we=H8();xwe.exports=function(t,r,n,i,a){a||(a={});var o=a.hoverLayer,s=t.cd,l=s[0].trace,u=l.hoveron,c=u.indexOf(\"violins\")!==-1,f=u.indexOf(\"kde\")!==-1,h=[],d,v;if(c||f){var m=ywe.hoverOnBoxes(t,r,n,i);if(f&&m.length>0){var b=t.xa,p=t.ya,k,M,T,L,x;l.orientation===\"h\"?(x=r,k=\"y\",T=p,M=\"x\",L=b):(x=n,k=\"x\",T=b,M=\"y\",L=p);var C=s[t.index];if(x>=C.span[0]&&x<=C.span[1]){var S=iH.extendFlat({},t),g=L.c2p(x,!0),P=_we.getKdeValue(C,l,x),E=_we.getPositionOnKdePath(C,l,g),z=T._offset,q=T._length;S[k+\"0\"]=E[0],S[k+\"1\"]=E[1],S[M+\"0\"]=S[M+\"1\"]=g,S[M+\"Label\"]=M+\": \"+Kwt.hoverLabelText(L,x,l[M+\"hoverformat\"])+\", \"+s[0].t.labels.kde+\" \"+P.toFixed(3);for(var V=0,G=0;G<m.length;G++)if(m[G].attr===\"med\"){V=G;break}S.spikeDistance=m[V].spikeDistance;var Z=k+\"Spike\";S[Z]=m[V][Z],m[V].spikeDistance=void 0,m[V][Z]=void 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s=W8.select(this),l=o[0].trace,u=N_.ensureSingle(s,\"g\",\"lines\");if(!l.connector||!l.connector.visible||!l.connector.line.width){u.remove();return}var c=l.orientation===\"h\",f=u.selectAll(\"g.line\").data(N_.identity);f.enter().append(\"g\").classed(\"line\",!0),f.exit().remove();var h=f.size();f.each(function(d,v){if(!(v!==h-1&&!d.cNext)){var m=Xwe(d,i,a,c),b=m[0],p=m[1],k=\"\";b[3]!==void 0&&p[3]!==void 0&&(c?(k+=\"M\"+b[0]+\",\"+p[1]+\"L\"+b[2]+\",\"+p[2],k+=\"M\"+b[1]+\",\"+p[1]+\"L\"+b[3]+\",\"+p[2]):(k+=\"M\"+b[1]+\",\"+p[1]+\"L\"+b[2]+\",\"+p[3],k+=\"M\"+b[1]+\",\"+p[0]+\"L\"+b[2]+\",\"+p[2])),k===\"\"&&(k=\"M0,0Z\"),N_.ensureSingle(W8.select(this),\"path\").attr(\"d\",k).call(Wwe.setClipUrl,t.layerClipId,e)}})})}function Xwe(e,t,r,n){var i=[],a=[],o=n?t:r,s=n?r:t;return i[0]=o.c2p(e.s0,!0),a[0]=s.c2p(e.p0,!0),i[1]=o.c2p(e.s1,!0),a[1]=s.c2p(e.p1,!0),i[2]=o.c2p(e.nextS0,!0),a[2]=s.c2p(e.nextP0,!0),i[3]=o.c2p(e.nextS1,!0),a[3]=s.c2p(e.nextP1,!0),n?[i,a]:[a,i]}});var $we=ye(($fr,Jwe)=>{\"use strict\";var Y4=Fa(),Kwe=So(),cH=ka(),m3t=Z1().DESELECTDIM,y3t=V0(),_3t=bv().resizeText,x3t=y3t.styleTextPoints;function b3t(e,t,r){var n=r||Y4.select(e).selectAll('g[class^=\"funnellayer\"]').selectAll(\"g.trace\");_3t(e,n,\"funnel\"),n.style(\"opacity\",function(i){return i[0].trace.opacity}),n.each(function(i){var a=Y4.select(this),o=i[0].trace;a.selectAll(\".point > path\").each(function(s){if(!s.isBlank){var l=o.marker;Y4.select(this).call(cH.fill,s.mc||l.color).call(cH.stroke,s.mlc||l.line.color).call(Kwe.dashLine,l.line.dash,s.mlw||l.line.width).style(\"opacity\",o.selectedpoints&&!s.selected?m3t:1)}}),x3t(a,o,e),a.selectAll(\".regions\").each(function(){Y4.select(this).selectAll(\"path\").style(\"stroke-width\",0).call(cH.fill,o.connector.fillcolor)}),a.selectAll(\".lines\").each(function(){var s=o.connector.line;Kwe.lineGroupStyle(Y4.select(this).selectAll(\"path\"),s.width,s.color,s.dash)})})}Jwe.exports={style:b3t}});var t3e=ye((Qfr,e3e)=>{\"use 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_c=qm(),hH=pf().line,A3t=Vl(),c3e=vf().axisHoverFormat,{hovertemplateAttrs:S3t,texttemplateAttrs:M3t,templatefallbackAttrs:f3e}=Pl(),h3e=u3e(),eA=Ao().extendFlat,E3t=ka();function dH(e){return{marker:{color:eA({},_c.marker.color,{arrayOk:!1,editType:\"style\"}),line:{color:eA({},_c.marker.line.color,{arrayOk:!1,editType:\"style\"}),width:eA({},_c.marker.line.width,{arrayOk:!1,editType:\"style\"}),editType:\"style\"},editType:\"style\"},editType:\"style\"}}d3e.exports={measure:{valType:\"data_array\",dflt:[],editType:\"calc\"},base:{valType:\"number\",dflt:null,arrayOk:!1,editType:\"calc\"},x:_c.x,x0:_c.x0,dx:_c.dx,y:_c.y,y0:_c.y0,dy:_c.dy,xperiod:_c.xperiod,yperiod:_c.yperiod,xperiod0:_c.xperiod0,yperiod0:_c.yperiod0,xperiodalignment:_c.xperiodalignment,yperiodalignment:_c.yperiodalignment,xhoverformat:c3e(\"x\"),yhoverformat:c3e(\"y\"),hovertext:_c.hovertext,hovertemplate:S3t({},{keys:h3e.eventDataKeys}),hovertemplatefallback:f3e(),hoverinfo:eA({},A3t.hoverinfo,{flags:[\"name\",\"x\",\"y\",\"text\",\"initial\",\"delta\",\"final\"]}),textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"initial\",\"delta\",\"final\"],extras:[\"none\"],editType:\"plot\",arrayOk:!1},texttemplate:M3t({editType:\"plot\"},{keys:h3e.eventDataKeys.concat([\"label\"])}),texttemplatefallback:f3e({editType:\"plot\"}),text:_c.text,textposition:_c.textposition,insidetextanchor:_c.insidetextanchor,textangle:_c.textangle,textfont:_c.textfont,insidetextfont:_c.insidetextfont,outsidetextfont:_c.outsidetextfont,constraintext:_c.constraintext,cliponaxis:_c.cliponaxis,orientation:_c.orientation,offset:_c.offset,width:_c.width,increasing:dH(\"increasing\"),decreasing:dH(\"decreasing\"),totals:dH(\"intermediate 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g3e=Pr(),k3t=i2(),C3t=i0().handleText,L3t=cT(),P3t=Dg(),m3e=vH(),I3t=ka(),y3e=tA(),R3t=y3e.INCREASING.COLOR,D3t=y3e.DECREASING.COLOR,F3t=\"#4499FF\";function gH(e,t,r){e(t+\".marker.color\",r),e(t+\".marker.line.color\",I3t.defaultLine),e(t+\".marker.line.width\")}function z3t(e,t,r,n){function i(u,c){return g3e.coerce(e,t,m3e,u,c)}var a=L3t(e,t,n,i);if(!a){t.visible=!1;return}P3t(e,t,n,i),i(\"xhoverformat\"),i(\"yhoverformat\"),i(\"measure\"),i(\"orientation\",t.x&&!t.y?\"h\":\"v\"),i(\"base\"),i(\"offset\"),i(\"width\"),i(\"text\"),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\");var o=i(\"textposition\");C3t(e,t,n,i,o,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),t.textposition!==\"none\"&&(i(\"texttemplate\"),i(\"texttemplatefallback\"),t.texttemplate||i(\"textinfo\")),gH(i,\"increasing\",R3t),gH(i,\"decreasing\",D3t),gH(i,\"totals\",F3t);var s=i(\"connector.visible\");if(s){i(\"connector.mode\");var l=i(\"connector.line.width\");l&&(i(\"connector.line.color\"),i(\"connector.line.dash\"))}i(\"zorder\")}function q3t(e,t){var r,n;function i(o){return g3e.coerce(n._input,n,m3e,o)}if(t.waterfallmode===\"group\")for(var a=0;a<e.length;a++)n=e[a],r=n._input,k3t(r,n,t,i,t.waterfallmode)}_3e.exports={supplyDefaults:z3t,crossTraceDefaults:q3t}});var b3e=ye((lhr,x3e)=>{\"use strict\";var O3t=Pr(),B3t=pH();x3e.exports=function(e,t,r){var n=!1;function i(s,l){return O3t.coerce(e,t,B3t,s,l)}for(var a=0;a<r.length;a++){var o=r[a];if(o.visible&&o.type===\"waterfall\"){n=!0;break}}n&&(i(\"waterfallmode\"),i(\"waterfallgap\",.2),i(\"waterfallgroupgap\"))}});var E3e=ye((uhr,M3e)=>{\"use strict\";var w3e=ho(),T3e=zg(),A3e=Pr().mergeArray,N3t=q0(),S3e=fs().BADNUM;function yH(e){return e===\"a\"||e===\"absolute\"}function _H(e){return e===\"t\"||e===\"total\"}M3e.exports=function(t,r){var n=w3e.getFromId(t,r.xaxis||\"x\"),i=w3e.getFromId(t,r.yaxis||\"y\"),a,o,s,l,u,c;r.orientation===\"h\"?(a=n.makeCalcdata(r,\"x\"),s=i.makeCalcdata(r,\"y\"),l=T3e(r,i,\"y\",s),u=!!r.yperiodalignment,c=\"y\"):(a=i.makeCalcdata(r,\"y\"),s=n.makeCalcdata(r,\"x\"),l=T3e(r,n,\"x\",s),u=!!r.xperiodalignment,c=\"x\"),o=l.vals;for(var f=Math.min(o.length,a.length),h=new Array(f),d=0,v,m=!1,b=0;b<f;b++){var p=a[b]||0,k=!1;(a[b]!==S3e||_H(r.measure[b])||yH(r.measure[b]))&&b+1<f&&(a[b+1]!==S3e||_H(r.measure[b+1])||yH(r.measure[b+1]))&&(k=!0);var M=h[b]={i:b,p:o[b],s:p,rawS:p,cNext:k};yH(r.measure[b])?(d=M.s,M.isSum=!0,M.dir=\"totals\",M.s=d):_H(r.measure[b])?(M.isSum=!0,M.dir=\"totals\",M.s=d):(M.isSum=!1,M.dir=M.rawS<0?\"decreasing\":\"increasing\",v=M.s,M.s=d+v,d+=v),M.dir===\"totals\"&&(m=!0),u&&(h[b].orig_p=s[b],h[b][c+\"End\"]=l.ends[b],h[b][c+\"Start\"]=l.starts[b]),r.ids&&(M.id=String(r.ids[b])),M.v=(r.base||0)+d}return h.length&&(h[0].hasTotals=m),A3e(r.text,h,\"tx\"),A3e(r.hovertext,h,\"htx\"),N3t(h,r),h}});var L3e=ye((chr,C3e)=>{\"use strict\";var k3e=n2().setGroupPositions;C3e.exports=function(t,r){var n=t._fullLayout,i=t._fullData,a=t.calcdata,o=r.xaxis,s=r.yaxis,l=[],u=[],c=[],f,h;for(h=0;h<i.length;h++){var d=i[h];d.visible===!0&&d.xaxis===o._id&&d.yaxis===s._id&&d.type===\"waterfall\"&&(f=a[h],d.orientation===\"h\"?c.push(f):u.push(f),l.push(f))}var v={mode:n.waterfallmode,norm:n.waterfallnorm,gap:n.waterfallgap,groupgap:n.waterfallgroupgap};for(k3e(t,o,s,u,v),k3e(t,s,o,c,v),h=0;h<l.length;h++){f=l[h];for(var m=0;m<f.length;m++){var b=f[m];b.isSum===!1&&(b.s0+=m===0?0:f[m-1].s),m+1<f.length&&(f[m].nextP0=f[m+1].p0,f[m].nextS0=f[m+1].s0)}}}});var R3e=ye((fhr,I3e)=>{\"use strict\";var P3e=Fa(),X8=Pr(),U3t=So(),rA=fs().BADNUM,V3t=g2(),G3t=bv().clearMinTextSize;I3e.exports=function(t,r,n,i){var a=t._fullLayout;G3t(\"waterfall\",a),V3t.plot(t,r,n,i,{mode:a.waterfallmode,norm:a.waterfallmode,gap:a.waterfallgap,groupgap:a.waterfallgroupgap}),H3t(t,r,n,i)};function H3t(e,t,r,n){var i=t.xaxis,a=t.yaxis;X8.makeTraceGroups(n,r,\"trace bars\").each(function(o){var s=P3e.select(this),l=o[0].trace,u=X8.ensureSingle(s,\"g\",\"lines\");if(!l.connector||!l.connector.visible){u.remove();return}var c=l.orientation===\"h\",f=l.connector.mode,h=u.selectAll(\"g.line\").data(X8.identity);h.enter().append(\"g\").classed(\"line\",!0),h.exit().remove();var d=h.size();h.each(function(v,m){if(!(m!==d-1&&!v.cNext)){var b=j3t(v,i,a,c),p=b[0],k=b[1],M=\"\";p[0]!==rA&&k[0]!==rA&&p[1]!==rA&&k[1]!==rA&&(f===\"spanning\"&&!v.isSum&&m>0&&(c?M+=\"M\"+p[0]+\",\"+k[1]+\"V\"+k[0]:M+=\"M\"+p[1]+\",\"+k[0]+\"H\"+p[0]),f!==\"between\"&&(v.isSum||m<d-1)&&(c?M+=\"M\"+p[1]+\",\"+k[0]+\"V\"+k[1]:M+=\"M\"+p[0]+\",\"+k[1]+\"H\"+p[1]),p[2]!==rA&&k[2]!==rA&&(c?M+=\"M\"+p[1]+\",\"+k[1]+\"V\"+k[2]:M+=\"M\"+p[1]+\",\"+k[1]+\"H\"+p[2])),M===\"\"&&(M=\"M0,0Z\"),X8.ensureSingle(P3e.select(this),\"path\").attr(\"d\",M).call(U3t.setClipUrl,t.layerClipId,e)}})})}function j3t(e,t,r,n){var i=[],a=[],o=n?t:r,s=n?r:t;return i[0]=o.c2p(e.s0,!0),a[0]=s.c2p(e.p0,!0),i[1]=o.c2p(e.s1,!0),a[1]=s.c2p(e.p1,!0),i[2]=o.c2p(e.nextS0,!0),a[2]=s.c2p(e.nextP0,!0),n?[i,a]:[a,i]}});var q3e=ye((hhr,z3e)=>{\"use strict\";var Z8=Fa(),D3e=So(),F3e=ka(),W3t=Z1().DESELECTDIM,X3t=V0(),Z3t=bv().resizeText,Y3t=X3t.styleTextPoints;function K3t(e,t,r){var 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e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var zie=fe((Cfr,Iie)=>{Iie.exports=dft;function dft(e,t){if(e===t){var r=t[1],n=t[2],a=t[3],i=t[6],o=t[7],s=t[11];e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=r,e[6]=t[9],e[7]=t[13],e[8]=n,e[9]=i,e[11]=t[14],e[12]=a,e[13]=o,e[14]=s}else e[0]=t[0],e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=t[1],e[5]=t[5],e[6]=t[9],e[7]=t[13],e[8]=t[2],e[9]=t[6],e[10]=t[10],e[11]=t[14],e[12]=t[3],e[13]=t[7],e[14]=t[11],e[15]=t[15];return e}});var Bie=fe((Lfr,qie)=>{qie.exports=vft;function vft(e,t){var r=t[0],n=t[1],a=t[2],i=t[3],o=t[4],s=t[5],l=t[6],u=t[7],c=t[8],f=t[9],v=t[10],g=t[11],p=t[12],x=t[13],_=t[14],y=t[15],C=r*s-n*o,k=r*l-a*o,S=r*u-i*o,L=n*l-a*s,w=n*u-i*s,A=a*u-i*l,T=c*x-f*p,m=c*_-v*p,F=c*y-g*p,E=f*_-v*x,I=f*y-g*x,R=v*y-g*_,B=C*R-k*I+S*E+L*F-w*m+A*T;return B?(B=1/B,e[0]=(s*R-l*I+u*E)*B,e[1]=(a*I-n*R-i*E)*B,e[2]=(x*A-_*w+y*L)*B,e[3]=(v*w-f*A-g*L)*B,e[4]=(l*F-o*R-u*m)*B,e[5]=(r*R-a*F+i*m)*B,e[6]=(_*S-p*A-y*k)*B,e[7]=(c*A-v*S+g*k)*B,e[8]=(o*I-s*F+u*T)*B,e[9]=(n*F-r*I-i*T)*B,e[10]=(p*w-x*S+y*C)*B,e[11]=(f*S-c*w-g*C)*B,e[12]=(s*m-o*E-l*T)*B,e[13]=(r*E-n*m+a*T)*B,e[14]=(x*k-p*L-_*C)*B,e[15]=(c*L-f*k+v*C)*B,e):null}});var Nie=fe((Pfr,Oie)=>{Oie.exports=pft;function pft(e,t){var r=t[0],n=t[1],a=t[2],i=t[3],o=t[4],s=t[5],l=t[6],u=t[7],c=t[8],f=t[9],v=t[10],g=t[11],p=t[12],x=t[13],_=t[14],y=t[15];return e[0]=s*(v*y-g*_)-f*(l*y-u*_)+x*(l*g-u*v),e[1]=-(n*(v*y-g*_)-f*(a*y-i*_)+x*(a*g-i*v)),e[2]=n*(l*y-u*_)-s*(a*y-i*_)+x*(a*u-i*l),e[3]=-(n*(l*g-u*v)-s*(a*g-i*v)+f*(a*u-i*l)),e[4]=-(o*(v*y-g*_)-c*(l*y-u*_)+p*(l*g-u*v)),e[5]=r*(v*y-g*_)-c*(a*y-i*_)+p*(a*g-i*v),e[6]=-(r*(l*y-u*_)-o*(a*y-i*_)+p*(a*u-i*l)),e[7]=r*(l*g-u*v)-o*(a*g-i*v)+c*(a*u-i*l),e[8]=o*(f*y-g*x)-c*(s*y-u*x)+p*(s*g-u*f),e[9]=-(r*(f*y-g*x)-c*(n*y-i*x)+p*(n*g-i*f)),e[10]=r*(s*y-u*x)-o*(n*y-i*x)+p*(n*u-i*s),e[11]=-(r*(s*g-u*f)-o*(n*g-i*f)+c*(n*u-i*s)),e[12]=-(o*(f*_-v*x)-c*(s*_-l*x)+p*(s*v-l*f)),e[13]=r*(f*_-v*x)-c*(n*_-a*x)+p*(n*v-a*f),e[14]=-(r*(s*_-l*x)-o*(n*_-a*x)+p*(n*l-a*s)),e[15]=r*(s*v-l*f)-o*(n*v-a*f)+c*(n*l-a*s),e}});var Vie=fe((Ffr,Uie)=>{Uie.exports=gft;function gft(e){var t=e[0],r=e[1],n=e[2],a=e[3],i=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],f=e[10],v=e[11],g=e[12],p=e[13],x=e[14],_=e[15],y=t*o-r*i,C=t*s-n*i,k=t*l-a*i,S=r*s-n*o,L=r*l-a*o,w=n*l-a*s,A=u*p-c*g,T=u*x-f*g,m=u*_-v*g,F=c*x-f*p,E=c*_-v*p,I=f*_-v*x;return y*I-C*E+k*F+S*m-L*T+w*A}});var Hie=fe((Dfr,Gie)=>{Gie.exports=mft;function mft(e,t,r){var n=t[0],a=t[1],i=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],f=t[8],v=t[9],g=t[10],p=t[11],x=t[12],_=t[13],y=t[14],C=t[15],k=r[0],S=r[1],L=r[2],w=r[3];return e[0]=k*n+S*s+L*f+w*x,e[1]=k*a+S*l+L*v+w*_,e[2]=k*i+S*u+L*g+w*y,e[3]=k*o+S*c+L*p+w*C,k=r[4],S=r[5],L=r[6],w=r[7],e[4]=k*n+S*s+L*f+w*x,e[5]=k*a+S*l+L*v+w*_,e[6]=k*i+S*u+L*g+w*y,e[7]=k*o+S*c+L*p+w*C,k=r[8],S=r[9],L=r[10],w=r[11],e[8]=k*n+S*s+L*f+w*x,e[9]=k*a+S*l+L*v+w*_,e[10]=k*i+S*u+L*g+w*y,e[11]=k*o+S*c+L*p+w*C,k=r[12],S=r[13],L=r[14],w=r[15],e[12]=k*n+S*s+L*f+w*x,e[13]=k*a+S*l+L*v+w*_,e[14]=k*i+S*u+L*g+w*y,e[15]=k*o+S*c+L*p+w*C,e}});var Wie=fe((Rfr,jie)=>{jie.exports=yft;function yft(e,t,r){var n=r[0],a=r[1],i=r[2],o,s,l,u,c,f,v,g,p,x,_,y;return t===e?(e[12]=t[0]*n+t[4]*a+t[8]*i+t[12],e[13]=t[1]*n+t[5]*a+t[9]*i+t[13],e[14]=t[2]*n+t[6]*a+t[10]*i+t[14],e[15]=t[3]*n+t[7]*a+t[11]*i+t[15]):(o=t[0],s=t[1],l=t[2],u=t[3],c=t[4],f=t[5],v=t[6],g=t[7],p=t[8],x=t[9],_=t[10],y=t[11],e[0]=o,e[1]=s,e[2]=l,e[3]=u,e[4]=c,e[5]=f,e[6]=v,e[7]=g,e[8]=p,e[9]=x,e[10]=_,e[11]=y,e[12]=o*n+c*a+p*i+t[12],e[13]=s*n+f*a+x*i+t[13],e[14]=l*n+v*a+_*i+t[14],e[15]=u*n+g*a+y*i+t[15]),e}});var Xie=fe((Ifr,Yie)=>{Yie.exports=xft;function xft(e,t,r){var n=r[0],a=r[1],i=r[2];return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*a,e[5]=t[5]*a,e[6]=t[6]*a,e[7]=t[7]*a,e[8]=t[8]*i,e[9]=t[9]*i,e[10]=t[10]*i,e[11]=t[11]*i,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}});var $ie=fe((zfr,Zie)=>{Zie.exports=_ft;function _ft(e,t,r,n){var a=n[0],i=n[1],o=n[2],s=Math.sqrt(a*a+i*i+o*o),l,u,c,f,v,g,p,x,_,y,C,k,S,L,w,A,T,m,F,E,I,R,B,O;return Math.abs(s)<1e-6?null:(s=1/s,a*=s,i*=s,o*=s,l=Math.sin(r),u=Math.cos(r),c=1-u,f=t[0],v=t[1],g=t[2],p=t[3],x=t[4],_=t[5],y=t[6],C=t[7],k=t[8],S=t[9],L=t[10],w=t[11],A=a*a*c+u,T=i*a*c+o*l,m=o*a*c-i*l,F=a*i*c-o*l,E=i*i*c+u,I=o*i*c+a*l,R=a*o*c+i*l,B=i*o*c-a*l,O=o*o*c+u,e[0]=f*A+x*T+k*m,e[1]=v*A+_*T+S*m,e[2]=g*A+y*T+L*m,e[3]=p*A+C*T+w*m,e[4]=f*F+x*E+k*I,e[5]=v*F+_*E+S*I,e[6]=g*F+y*E+L*I,e[7]=p*F+C*E+w*I,e[8]=f*R+x*B+k*O,e[9]=v*R+_*B+S*O,e[10]=g*R+y*B+L*O,e[11]=p*R+C*B+w*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}});var Jie=fe((qfr,Kie)=>{Kie.exports=bft;function bft(e,t,r){var n=Math.sin(r),a=Math.cos(r),i=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],v=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=i*a+u*n,e[5]=o*a+c*n,e[6]=s*a+f*n,e[7]=l*a+v*n,e[8]=u*a-i*n,e[9]=c*a-o*n,e[10]=f*a-s*n,e[11]=v*a-l*n,e}});var eae=fe((Bfr,Qie)=>{Qie.exports=wft;function wft(e,t,r){var n=Math.sin(r),a=Math.cos(r),i=t[0],o=t[1],s=t[2],l=t[3],u=t[8],c=t[9],f=t[10],v=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=i*a-u*n,e[1]=o*a-c*n,e[2]=s*a-f*n,e[3]=l*a-v*n,e[8]=i*n+u*a,e[9]=o*n+c*a,e[10]=s*n+f*a,e[11]=l*n+v*a,e}});var rae=fe((Ofr,tae)=>{tae.exports=Tft;function Tft(e,t,r){var n=Math.sin(r),a=Math.cos(r),i=t[0],o=t[1],s=t[2],l=t[3],u=t[4],c=t[5],f=t[6],v=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=i*a+u*n,e[1]=o*a+c*n,e[2]=s*a+f*n,e[3]=l*a+v*n,e[4]=u*a-i*n,e[5]=c*a-o*n,e[6]=f*a-s*n,e[7]=v*a-l*n,e}});var iae=fe((Nfr,nae)=>{nae.exports=Aft;function Aft(e,t,r){var n,a,i,o=r[0],s=r[1],l=r[2],u=Math.sqrt(o*o+s*s+l*l);return Math.abs(u)<1e-6?null:(u=1/u,o*=u,s*=u,l*=u,n=Math.sin(t),a=Math.cos(t),i=1-a,e[0]=o*o*i+a,e[1]=s*o*i+l*n,e[2]=l*o*i-s*n,e[3]=0,e[4]=o*s*i-l*n,e[5]=s*s*i+a,e[6]=l*s*i+o*n,e[7]=0,e[8]=o*l*i+s*n,e[9]=s*l*i-o*n,e[10]=l*l*i+a,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}});var oae=fe((Ufr,aae)=>{aae.exports=Mft;function Mft(e,t,r){var n=t[0],a=t[1],i=t[2],o=t[3],s=n+n,l=a+a,u=i+i,c=n*s,f=n*l,v=n*u,g=a*l,p=a*u,x=i*u,_=o*s,y=o*l,C=o*u;return e[0]=1-(g+x),e[1]=f+C,e[2]=v-y,e[3]=0,e[4]=f-C,e[5]=1-(c+x),e[6]=p+_,e[7]=0,e[8]=v+y,e[9]=p-_,e[10]=1-(c+g),e[11]=0,e[12]=r[0],e[13]=r[1],e[14]=r[2],e[15]=1,e}});var lae=fe((Vfr,sae)=>{sae.exports=Sft;function Sft(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var cae=fe((Gfr,uae)=>{uae.exports=Eft;function Eft(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}});var hae=fe((Hfr,fae)=>{fae.exports=kft;function kft(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=n,e[6]=r,e[7]=0,e[8]=0,e[9]=-r,e[10]=n,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var vae=fe((jfr,dae)=>{dae.exports=Cft;function Cft(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=n,e[1]=0,e[2]=-r,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=r,e[9]=0,e[10]=n,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var gae=fe((Wfr,pae)=>{pae.exports=Lft;function Lft(e,t){var r=Math.sin(t),n=Math.cos(t);return e[0]=n,e[1]=r,e[2]=0,e[3]=0,e[4]=-r,e[5]=n,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var yae=fe((Yfr,mae)=>{mae.exports=Pft;function Pft(e,t){var r=t[0],n=t[1],a=t[2],i=t[3],o=r+r,s=n+n,l=a+a,u=r*o,c=n*o,f=n*s,v=a*o,g=a*s,p=a*l,x=i*o,_=i*s,y=i*l;return e[0]=1-f-p,e[1]=c+y,e[2]=v-_,e[3]=0,e[4]=c-y,e[5]=1-u-p,e[6]=g+x,e[7]=0,e[8]=v+_,e[9]=g-x,e[10]=1-u-f,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}});var _ae=fe((Xfr,xae)=>{xae.exports=Fft;function Fft(e,t,r,n,a,i,o){var s=1/(r-t),l=1/(a-n),u=1/(i-o);return e[0]=i*2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i*2*l,e[6]=0,e[7]=0,e[8]=(r+t)*s,e[9]=(a+n)*l,e[10]=(o+i)*u,e[11]=-1,e[12]=0,e[13]=0,e[14]=o*i*2*u,e[15]=0,e}});var wae=fe((Zfr,bae)=>{bae.exports=Dft;function Dft(e,t,r,n,a){var i=1/Math.tan(t/2),o=1/(n-a);return e[0]=i/r,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=(a+n)*o,e[11]=-1,e[12]=0,e[13]=0,e[14]=2*a*n*o,e[15]=0,e}});var Aae=fe(($fr,Tae)=>{Tae.exports=Rft;function Rft(e,t,r,n){var a=Math.tan(t.upDegrees*Math.PI/180),i=Math.tan(t.downDegrees*Math.PI/180),o=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(o+s),u=2/(a+i);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=u,e[6]=0,e[7]=0,e[8]=-((o-s)*l*.5),e[9]=(a-i)*u*.5,e[10]=n/(r-n),e[11]=-1,e[12]=0,e[13]=0,e[14]=n*r/(r-n),e[15]=0,e}});var Sae=fe((Kfr,Mae)=>{Mae.exports=Ift;function Ift(e,t,r,n,a,i,o){var s=1/(t-r),l=1/(n-a),u=1/(i-o);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*u,e[11]=0,e[12]=(t+r)*s,e[13]=(a+n)*l,e[14]=(o+i)*u,e[15]=1,e}});var kae=fe((Jfr,Eae)=>{var zft=Pq();Eae.exports=qft;function qft(e,t,r,n){var a,i,o,s,l,u,c,f,v,g,p=t[0],x=t[1],_=t[2],y=n[0],C=n[1],k=n[2],S=r[0],L=r[1],w=r[2];return Math.abs(p-S)<1e-6&&Math.abs(x-L)<1e-6&&Math.abs(_-w)<1e-6?zft(e):(c=p-S,f=x-L,v=_-w,g=1/Math.sqrt(c*c+f*f+v*v),c*=g,f*=g,v*=g,a=C*v-k*f,i=k*c-y*v,o=y*f-C*c,g=Math.sqrt(a*a+i*i+o*o),g?(g=1/g,a*=g,i*=g,o*=g):(a=0,i=0,o=0),s=f*o-v*i,l=v*a-c*o,u=c*i-f*a,g=Math.sqrt(s*s+l*l+u*u),g?(g=1/g,s*=g,l*=g,u*=g):(s=0,l=0,u=0),e[0]=a,e[1]=s,e[2]=c,e[3]=0,e[4]=i,e[5]=l,e[6]=f,e[7]=0,e[8]=o,e[9]=u,e[10]=v,e[11]=0,e[12]=-(a*p+i*x+o*_),e[13]=-(s*p+l*x+u*_),e[14]=-(c*p+f*x+v*_),e[15]=1,e)}});var Lae=fe((Qfr,Cae)=>{Cae.exports=Bft;function Bft(e){return\"mat4(\"+e[0]+\", \"+e[1]+\", \"+e[2]+\", \"+e[3]+\", \"+e[4]+\", \"+e[5]+\", \"+e[6]+\", \"+e[7]+\", \"+e[8]+\", \"+e[9]+\", \"+e[10]+\", \"+e[11]+\", \"+e[12]+\", \"+e[13]+\", \"+e[14]+\", \"+e[15]+\")\"}});var Fq=fe((ehr,Pae)=>{Pae.exports={create:Cie(),clone:Pie(),copy:Die(),identity:Pq(),transpose:zie(),invert:Bie(),adjoint:Nie(),determinant:Vie(),multiply:Hie(),translate:Wie(),scale:Xie(),rotate:$ie(),rotateX:Jie(),rotateY:eae(),rotateZ:rae(),fromRotation:iae(),fromRotationTranslation:oae(),fromScaling:lae(),fromTranslation:cae(),fromXRotation:hae(),fromYRotation:vae(),fromZRotation:gae(),fromQuat:yae(),frustum:_ae(),perspective:wae(),perspectiveFromFieldOfView:Aae(),ortho:Sae(),lookAt:kae(),str:Lae()}});var FE=fe(Lu=>{\"use strict\";var Oft=Fq();Lu.init2dArray=function(e,t){for(var r=new Array(e),n=0;n<e;n++)r[n]=new Array(t);return r};Lu.transposeRagged=function(e){var t=0,r=e.length,n,a;for(n=0;n<r;n++)t=Math.max(t,e[n].length);var i=new Array(t);for(n=0;n<t;n++)for(i[n]=new Array(r),a=0;a<r;a++)i[n][a]=e[a][n];return 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RegExp(rse+\"{3,}\",\"g\"),Sdt=new RegExp(rse+\"$\",\"g\"),Edt=60;function kdt(e){return typeof e.toWellFormed==\"function\"?e.toWellFormed():new TextDecoder().decode(new TextEncoder().encode(e))}nse.exports=function(t,r=Edt){var n=kdt(t!=null?t:\"\").replace(Tdt,\"\").replace(_dt,\" \").replace(bdt,a=>a===YE?a:\"\").toLowerCase().trim().replace(Adt,YE).replace(wdt,\"\").replace(Mdt,YE);return n.length<=r?n:Array.from(n).slice(0,r).join(\"\").replace(Sdt,\"\")}});var Xt=fe((Ihr,gse)=>{\"use strict\";var rT=xi(),Cdt=lb().utcFormat,Ldt=jI().format,cse=aa(),fse=Wa(),hse=fse.FP_SAFE,Pdt=-hse,ase=fse.BADNUM,Ir=gse.exports={};Ir.adjustFormat=function(t){if(!t||/^\\d[.]\\df/.test(t)||/[.]\\d%/.test(t))return t;if(t===\"0.f\")return\"~f\";if(/^\\d%/.test(t))return\"~%\";if(/^\\ds/.test(t))return\"~s\";var r=(t.match(/^[+\\-( ]?/)||[\"\"])[0],n=t.slice(r.length);return!/^[~,.0$#]/.test(n)&&/[&fps]/.test(n)?r+\"~\"+n:t};var ose={};Ir.warnBadFormat=function(e){var t=String(e);ose[t]||(ose[t]=1,Ir.warn('encountered bad format: \"'+t+'\"'))};Ir.noFormat=function(e){return String(e)};Ir.numberFormat=function(e){var t;try{t=Ldt(Ir.adjustFormat(e))}catch(r){return Ir.warnBadFormat(e),Ir.noFormat}return t};Ir.nestedProperty=M4();Ir.keyedContainer=Jte();Ir.relativeAttr=ere();Ir.isPlainObject=em();Ir.toLogRange=rE();Ir.relinkPrivateKeys=ire();var hm=Gf();Ir.isArrayBuffer=hm.isArrayBuffer;Ir.isTypedArray=hm.isTypedArray;Ir.isArrayOrTypedArray=hm.isArrayOrTypedArray;Ir.isArray1D=hm.isArray1D;Ir.ensureArray=hm.ensureArray;Ir.concat=hm.concat;Ir.maxRowLength=hm.maxRowLength;Ir.minRowLength=hm.minRowLength;var dse=(fb(),Gs(cb));Ir.mod=dse.mod;Ir.modHalf=dse.modHalf;var dm=Mie();Ir.valObjectMeta=dm.valObjectMeta;Ir.coerce=dm.coerce;Ir.coerce2=dm.coerce2;Ir.coerceFont=dm.coerceFont;Ir.coercePattern=dm.coercePattern;Ir.coerceHoverinfo=dm.coerceHoverinfo;Ir.coerceSelectionMarkerOpacity=dm.coerceSelectionMarkerOpacity;Ir.validate=dm.validate;var T0=Moe();Ir.dateTime2ms=T0.dateTime2ms;Ir.isDateTime=T0.isDateTime;Ir.ms2DateTime=T0.ms2DateTime;Ir.ms2DateTimeLocal=T0.ms2DateTimeLocal;Ir.cleanDate=T0.cleanDate;Ir.isJSDate=T0.isJSDate;Ir.formatDate=T0.formatDate;Ir.incrementMonth=T0.incrementMonth;Ir.dateTick0=T0.dateTick0;Ir.dfltRange=T0.dfltRange;Ir.findExactDates=T0.findExactDates;Ir.MIN_MS=T0.MIN_MS;Ir.MAX_MS=T0.MAX_MS;var Ax=VE();Ir.findBin=Ax.findBin;Ir.sorterAsc=Ax.sorterAsc;Ir.sorterDes=Ax.sorterDes;Ir.distinctVals=Ax.distinctVals;Ir.roundUp=Ax.roundUp;Ir.sort=Ax.sort;Ir.findIndexOfMin=Ax.findIndexOfMin;Ir.sortObjectKeys=(bx(),Gs(_x)).default;var J1=Eoe();Ir.aggNums=J1.aggNums;Ir.len=J1.len;Ir.mean=J1.mean;Ir.geometricMean=J1.geometricMean;Ir.median=J1.median;Ir.midRange=J1.midRange;Ir.variance=J1.variance;Ir.stdev=J1.stdev;Ir.interp=J1.interp;var Jv=FE();Ir.init2dArray=Jv.init2dArray;Ir.transposeRagged=Jv.transposeRagged;Ir.dot=Jv.dot;Ir.translationMatrix=Jv.translationMatrix;Ir.rotationMatrix=Jv.rotationMatrix;Ir.rotationXYMatrix=Jv.rotationXYMatrix;Ir.apply3DTransform=Jv.apply3DTransform;Ir.apply2DTransform=Jv.apply2DTransform;Ir.apply2DTransform2=Jv.apply2DTransform2;Ir.convertCssMatrix=Jv.convertCssMatrix;Ir.inverseTransformMatrix=Jv.inverseTransformMatrix;var Np=Foe();Ir.deg2rad=Np.deg2rad;Ir.rad2deg=Np.rad2deg;Ir.angleDelta=Np.angleDelta;Ir.angleDist=Np.angleDist;Ir.isFullCircle=Np.isFullCircle;Ir.isAngleInsideSector=Np.isAngleInsideSector;Ir.isPtInsideSector=Np.isPtInsideSector;Ir.pathArc=Np.pathArc;Ir.pathSector=Np.pathSector;Ir.pathAnnulus=Np.pathAnnulus;var Pb=Doe();Ir.isLeftAnchor=Pb.isLeftAnchor;Ir.isCenterAnchor=Pb.isCenterAnchor;Ir.isRightAnchor=Pb.isRightAnchor;Ir.isTopAnchor=Pb.isTopAnchor;Ir.isMiddleAnchor=Pb.isMiddleAnchor;Ir.isBottomAnchor=Pb.isBottomAnchor;var Fb=zoe();Ir.segmentsIntersect=Fb.segmentsIntersect;Ir.segmentDistance=Fb.segmentDistance;Ir.getTextLocation=Fb.getTextLocation;Ir.clearLocationCache=Fb.clearLocationCache;Ir.getVisibleSegment=Fb.getVisibleSegment;Ir.findPointOnPath=Fb.findPointOnPath;var $E=ta();Ir.extendFlat=$E.extendFlat;Ir.extendDeep=$E.extendDeep;Ir.extendDeepAll=$E.extendDeepAll;Ir.extendDeepNoArrays=$E.extendDeepNoArrays;var Yq=W1();Ir.log=Yq.log;Ir.warn=Yq.warn;Ir.error=Yq.error;var Fdt=(Tb(),Gs(wb));Ir.counterRegex=Fdt.counter;var Xq=jE();Ir.throttle=Xq.throttle;Ir.throttleDone=Xq.done;Ir.clearThrottle=Xq.clear;var Qv=X4();Ir.getGraphDiv=Qv.getGraphDiv;Ir.isPlotDiv=Qv.isPlotDiv;Ir.removeElement=Qv.removeElement;Ir.addStyleRule=Qv.addStyleRule;Ir.addRelatedStyleRule=Qv.addRelatedStyleRule;Ir.deleteRelatedStyleRule=Qv.deleteRelatedStyleRule;Ir.setStyleOnHover=Qv.setStyleOnHover;Ir.getFullTransformMatrix=Qv.getFullTransformMatrix;Ir.getElementTransformMatrix=Qv.getElementTransformMatrix;Ir.getElementAndAncestors=Qv.getElementAndAncestors;Ir.equalDomRects=Qv.equalDomRects;Ir.clearResponsive=Ooe();Ir.preserveDrawingBuffer=Voe();Ir.makeTraceGroups=Hoe();Ir._=Woe();Ir.notifier=bq();Ir.filterUnique=Wq();Ir.filterVisible=Zoe();Ir.pushUnique=Lq();Ir.increment=Koe();Ir.cleanNumber=(tse(),Gs(ese)).default;Ir.slugify=ise();Ir.ensureNumber=function(t){return cse(t)?(t=Number(t),t>hse||t<Pdt?ase:t):ase};Ir.isIndex=function(e,t){return t!==void 0&&e>=t?!1:cse(e)&&e>=0&&e%1===0};Ir.noop=PE();Ir.identity=J4();Ir.repeat=function(e,t){for(var r=new Array(t),n=0;n<t;n++)r[n]=e;return r};Ir.swapAttrs=function(e,t,r,n){r||(r=\"x\"),n||(n=\"y\");for(var a=0;a<t.length;a++){var i=t[a],o=Ir.nestedProperty(e,i.replace(\"?\",r)),s=Ir.nestedProperty(e,i.replace(\"?\",n)),l=o.get();o.set(s.get()),s.set(l)}};Ir.raiseToTop=function(t){t.parentNode.appendChild(t)};Ir.cancelTransition=function(e){return e.transition().duration(0)};Ir.constrain=function(e,t,r){return t>r?Math.max(r,Math.min(t,e)):Math.max(t,Math.min(r,e))};Ir.bBoxIntersect=function(e,t,r){return r=r||0,e.left<=t.right+r&&t.left<=e.right+r&&e.top<=t.bottom+r&&t.top<=e.bottom+r};Ir.simpleMap=function(e,t,r,n,a){for(var i=e.length,o=new Array(i),s=0;s<i;s++)o[s]=t(e[s],r,n,a);return o};Ir.randstr=function e(t,r,n,a){if(n||(n=16),r===void 0&&(r=24),r<=0)return\"0\";var i=Math.log(Math.pow(2,r))/Math.log(n),o=\"\",s,l,u;for(s=2;i===1/0;s*=2)i=Math.log(Math.pow(2,r/s))/Math.log(n)*s;var c=i-Math.floor(i);for(s=0;s<Math.floor(i);s++)u=Math.floor(Math.random()*n).toString(n),o=u+o;c&&(l=Math.pow(n,c),u=Math.floor(Math.random()*l).toString(n),o=u+o);var f=parseInt(o,n);return t&&t[o]||f!==1/0&&f>=Math.pow(2,r)?a>10?(Ir.warn(\"randstr failed uniqueness\"),o):e(t,r,n,(a||0)+1):o};Ir.OptionControl=function(e,t){e||(e={}),t||(t=\"opt\");var r={};return r.optionList=[],r._newoption=function(n){n[t]=e,r[n.name]=n,r.optionList.push(n)},r[\"_\"+t]=e,r};Ir.smooth=function(e,t){if(t=Math.round(t)||0,t<2)return e;var r=e.length,n=2*r,a=2*t-1,i=new Array(a),o=new Array(r),s,l,u,c;for(s=0;s<a;s++)i[s]=(1-Math.cos(Math.PI*(s+1)/t))/(2*t);for(s=0;s<r;s++){for(c=0,l=0;l<a;l++)u=s+l+1-t,u<-r?u-=n*Math.round(u/n):u>=n&&(u-=n*Math.floor(u/n)),u<0?u=-1-u:u>=r&&(u=n-1-u),c+=e[u]*i[l];o[s]=c}return o};Ir.syncOrAsync=function(e,t,r){var n,a;function i(){return Ir.syncOrAsync(e,t,r)}for(;e.length;)if(a=e.splice(0,1)[0],n=a(t),n&&n.then)return n.then(i);return r&&r(t)};Ir.stripTrailingSlash=function(e){return e.slice(-1)===\"/\"?e.slice(0,-1):e};Ir.noneOrAll=function(e,t,r){if(e){var n=!1,a=!0,i,o;for(i=0;i<r.length;i++)o=e[r[i]],o!=null?n=!0:a=!1;if(n&&!a)for(i=0;i<r.length;i++)e[r[i]]=t[r[i]]}};Ir.mergeArray=function(e,t,r,n){var a=typeof n==\"function\";if(Ir.isArrayOrTypedArray(e))for(var i=Math.min(e.length,t.length),o=0;o<i;o++){var s=e[o];t[o][r]=a?n(s):s}};Ir.mergeArrayCastPositive=function(e,t,r){return Ir.mergeArray(e,t,r,function(n){var a=+n;return isFinite(a)&&a>0?a:0})};Ir.fillArray=function(e,t,r,n){if(n=n||Ir.identity,Ir.isArrayOrTypedArray(e))for(var a=0;a<t.length;a++)t[a][r]=n(e[a])};Ir.castOption=function(e,t,r,n){n=n||Ir.identity;var a=Ir.nestedProperty(e,r).get();return Ir.isArrayOrTypedArray(a)?Array.isArray(t)&&Ir.isArrayOrTypedArray(a[t[0]])?n(a[t[0]][t[1]]):n(a[t]):a};Ir.extractOption=function(e,t,r,n){if(r in e)return e[r];var a=Ir.nestedProperty(t,n).get();if(!Array.isArray(a))return a};Ir.tagSelected=function(e,t,r){var n=t.selectedpoints;function a(u){return u!==void 0&&u<e.length}for(var i=0;i<n.length;i++){var o=n[i];if(Ir.isIndex(o)||Ir.isArrayOrTypedArray(o)&&Ir.isIndex(o[0])&&Ir.isIndex(o[1])){var s=o,l=r?r[s]:s;a(l)&&(e[l].selected=1)}}};Ir.getTargetArray=function(e,t){var r=t.target;if(typeof r==\"string\"&&r){var n=Ir.nestedProperty(e,r).get();return Ir.isArrayOrTypedArray(n)?n:!1}else if(Ir.isArrayOrTypedArray(r))return r;return!1};function vse(e,t,r){var n={};typeof t!=\"object\"&&(t={});var a=r===\"pieLike\"?-1:3,i=Object.keys(e),o,s,l;for(o=0;o<i.length;o++)s=i[o],l=e[s],!(s.charAt(0)===\"_\"||typeof l==\"function\")&&(s===\"module\"?n[s]=l:Array.isArray(l)?s===\"colorscale\"||a===-1?n[s]=l.slice():n[s]=l.slice(0,a):Ir.isTypedArray(l)?a===-1?n[s]=l.subarray():n[s]=l.subarray(0,a):l&&typeof l==\"object\"?n[s]=vse(e[s],t[s],r):n[s]=l);for(i=Object.keys(t),o=0;o<i.length;o++)s=i[o],l=t[s],(typeof l!=\"object\"||!(s in n)||typeof n[s]!=\"object\")&&(n[s]=l);return n}Ir.minExtend=vse;Ir.titleCase=function(e){return e.charAt(0).toUpperCase()+e.slice(1)};Ir.containsAny=function(e,t){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])!==-1)return!0;return!1};var Ddt=/Version\\/[\\d\\.]+.*Safari/;Ir.isSafari=function(){return Ddt.test(window.navigator.userAgent)};var Rdt=/iPad|iPhone|iPod/;Ir.isIOS=function(){return Rdt.test(window.navigator.userAgent)};var Idt=/Macintosh.+AppleWebKit.+Gecko\\)$/;Ir.isMacWKWebView=()=>Idt.test(window.navigator.userAgent);var zdt=/Firefox\\/(\\d+)\\.\\d+/;Ir.getFirefoxVersion=function(){var e=zdt.exec(window.navigator.userAgent);if(e&&e.length===2){var t=parseInt(e[1]);if(!isNaN(t))return t}return null};Ir.isD3Selection=function(e){return e instanceof rT.selection};Ir.ensureSingle=function(e,t,r,n){var a=e.select(t+(r?\".\"+r:\"\"));if(a.size())return a;var i=e.append(t);return r&&i.classed(r,!0),n&&i.call(n),i};Ir.ensureSingleById=function(e,t,r,n){var a=e.select(t+\"#\"+r);if(a.size())return a;var i=e.append(t).attr(\"id\",r);return n&&i.call(n),i};Ir.objectFromPath=function(e,t){for(var r=e.split(\".\"),n,a=n={},i=0;i<r.length;i++){var o=r[i],s=null,l=r[i].match(/(.*)\\[([0-9]+)\\]/);l?(o=l[1],s=l[2],n=n[o]=[],i===r.length-1?n[s]=t:n[s]={},n=n[s]):(i===r.length-1?n[o]=t:n[o]={},n=n[o])}return a};var qdt=/^([^\\[\\.]+)\\.(.+)?/,Bdt=/^([^\\.]+)\\[([0-9]+)\\](\\.)?(.+)?/;function XE(e){return e.slice(0,2)===\"__\"}Ir.expandObjectPaths=function(e){var t,r,n,a,i,o,s;if(typeof e==\"object\"&&!Array.isArray(e)){for(r in e)if(e.hasOwnProperty(r))if(t=r.match(qdt)){if(a=e[r],n=t[1],XE(n))continue;delete e[r],e[n]=Ir.extendDeepNoArrays(e[n]||{},Ir.objectFromPath(r,Ir.expandObjectPaths(a))[n])}else if(t=r.match(Bdt)){if(a=e[r],n=t[1],XE(n))continue;if(i=parseInt(t[2]),delete e[r],e[n]=e[n]||[],t[3]===\".\")s=t[4],o=e[n][i]=e[n][i]||{},Ir.extendDeepNoArrays(o,Ir.objectFromPath(s,Ir.expandObjectPaths(a)));else{if(XE(n))continue;e[n][i]=Ir.expandObjectPaths(a)}}else{if(XE(r))continue;e[r]=Ir.expandObjectPaths(e[r])}}return e};Ir.numSeparate=function(e,t,r){if(r||(r=!1),typeof t!=\"string\"||t.length===0)throw new Error(\"Separator string required for formatting!\");typeof e==\"number\"&&(e=String(e));var n=/(\\d+)(\\d{3})/,a=t.charAt(0),i=t.charAt(1),o=e.split(\".\"),s=o[0],l=o.length>1?a+o[1]:\"\";if(i&&(o.length>1||s.length>4||r))for(;n.test(s);)s=s.replace(n,\"$1\"+i+\"$2\");return s+l};Ir.TEMPLATE_STRING_REGEX=/%{([^\\s%{}:]*)([:|\\|][^}]*)?}/g;var pse=/^\\w*$/;Ir.templateString=function(e,t){var r={};return e.replace(Ir.TEMPLATE_STRING_REGEX,function(n,a){var i;return pse.test(a)?i=t[a]:(r[a]=r[a]||Ir.nestedProperty(t,a).get,i=r[a](!0)),i!==void 0?i:\"\"})};var Odt={max:10,count:0,name:\"hovertemplate\"};Ir.hovertemplateString=e=>Zq(tl(zo({},e),{opts:Odt}));var Ndt={max:10,count:0,name:\"texttemplate\"};Ir.texttemplateString=e=>Zq(tl(zo({},e),{opts:Ndt}));var Udt=/^(\\S+)([\\*\\/])(-?\\d+(\\.\\d+)?)$/;function Vdt(e){var t=e.match(Udt);return t?{key:t[1],op:t[2],number:Number(t[3])}:{key:e,op:null,number:null}}var Gdt={max:10,count:0,name:\"texttemplate\",parseMultDiv:!0};Ir.texttemplateStringForShapes=e=>Zq(tl(zo({},e),{opts:Gdt}));var sse=/^[:|\\|]/;function Zq({data:e=[],locale:t,fallback:r,labels:n={},opts:a,template:i}){return i.replace(Ir.TEMPLATE_STRING_REGEX,(o,s,l)=>{let u=[\"xother\",\"yother\"].includes(s),c=[\"_xother\",\"_yother\"].includes(s),f=[\"_xother_\",\"_yother_\"].includes(s),v=[\"xother_\",\"yother_\"].includes(s),g=u||c||v||f;(c||f)&&(s=s.substring(1)),(v||f)&&(s=s.substring(0,s.length-1));let p=null,x=null;if(a.parseMultDiv){var _=Vdt(s);s=_.key,p=_.op,x=_.number}let y;if(g){if(n[s]===void 0)return\"\";y=n[s]}else for(let L of e)if(L){if(L.hasOwnProperty(s)){y=L[s];break}if(pse.test(s)||(y=Ir.nestedProperty(L,s).get(!0)),y!==void 0)break}if(y===void 0){let{count:L,max:w,name:A}=a,T=r===!1?o:r;return L<w&&Ir.warn([`Variable '${s}' in ${A} could not be found!`,\"Please verify that the template is correct.\",`Using value: '${T}'.`].join(\" \")),L===w&&Ir.warn(`Too many '${A}' warnings - additional warnings will be suppressed.`),a.count++,T}if(p===\"*\"&&(y*=x),p===\"/\"&&(y/=x),l){var C;if(l[0]===\":\"&&(C=t?t.numberFormat:Ir.numberFormat,y!==\"\"&&(y=C(l.replace(sse,\"\"))(y))),l[0]===\"|\"){C=t?t.timeFormat:Cdt;var k=Ir.dateTime2ms(y);y=Ir.formatDate(k,l.replace(sse,\"\"),!1,C)}}else{var S=s+\"Label\";n.hasOwnProperty(S)&&(y=n[S])}return g&&(y=\"(\"+y+\")\",(c||f)&&(y=\" \"+y),(v||f)&&(y=y+\" \")),y})}var ZE=48,lse=57;Ir.subplotSort=function(e,t){for(var r=Math.min(e.length,t.length)+1,n=0,a=0,i=0;i<r;i++){var o=e.charCodeAt(i)||0,s=t.charCodeAt(i)||0,l=o>=ZE&&o<=lse,u=s>=ZE&&s<=lse;if(l&&(n=10*n+o-ZE),u&&(a=10*a+s-ZE),!l||!u){if(n!==a)return n-a;if(o!==s)return o-s}}return a-n};var Lb=2e9;Ir.seedPseudoRandom=function(){Lb=2e9};Ir.pseudoRandom=function(){var e=Lb;return Lb=(69069*Lb+1)%4294967296,Math.abs(Lb-e)<429496729?Ir.pseudoRandom():Lb/4294967296};Ir.fillText=function(e,t,r){var n=Array.isArray(r)?function(o){r.push(o)}:function(o){r.text=o},a=Ir.extractOption(e,t,\"htx\",\"hovertext\");if(Ir.isValidTextValue(a))return n(a);var i=Ir.extractOption(e,t,\"tx\",\"text\");if(Ir.isValidTextValue(i))return n(i)};Ir.isValidTextValue=function(e){return e||e===0};Ir.formatPercent=function(e,t){t=t||0;for(var r=(Math.round(100*e*Math.pow(10,t))*Math.pow(.1,t)).toFixed(t)+\"%\",n=0;n<t;n++)r.indexOf(\".\")!==-1&&(r=r.replace(\"0%\",\"%\"),r=r.replace(\".%\",\"%\"));return r};Ir.isHidden=function(e){var t=window.getComputedStyle(e).display;return!t||t===\"none\"};Ir.strTranslate=function(e,t){return e||t?\"translate(\"+e+\",\"+t+\")\":\"\"};Ir.strRotate=function(e){return e?\"rotate(\"+e+\")\":\"\"};Ir.strScale=function(e){return e!==1?\"scale(\"+e+\")\":\"\"};Ir.getTextTransform=function(e){var t=e.noCenter,r=e.textX,n=e.textY,a=e.targetX,i=e.targetY,o=e.anchorX||0,s=e.anchorY||0,l=e.rotate,u=e.scale;return u?u>1&&(u=1):u=0,Ir.strTranslate(a-u*(r+o),i-u*(n+s))+Ir.strScale(u)+(l?\"rotate(\"+l+(t?\"\":\" \"+r+\" \"+n)+\")\":\"\")};Ir.setTransormAndDisplay=function(e,t){e.attr(\"transform\",Ir.getTextTransform(t)),e.style(\"display\",t.scale?null:\"none\")};Ir.ensureUniformFontSize=function(e,t){var r=Ir.extendFlat({},t);return r.size=Math.max(t.size,e._fullLayout.uniformtext.minsize||0),r};Ir.join2=function(e,t,r){var n=e.length;return n>1?e.slice(0,-1).join(t)+r+e[n-1]:e.join(t)};Ir.bigFont=function(e){return Math.round(1.2*e)};var use=Ir.getFirefoxVersion(),Hdt=use!==null&&use<86;Ir.getPositionFromD3Event=function(){return Hdt?[rT.event.layerX,rT.event.layerY]:[rT.event.offsetX,rT.event.offsetY]}});var xse=fe(()=>{\"use strict\";var jdt=Xt(),mse={\"X,X div\":'direction:ltr;font-family:\"Open Sans\",verdana,arial,sans-serif;margin:0;padding:0;border:0;',\"X input,X button\":'font-family:\"Open Sans\",verdana,arial,sans-serif;',\"X input:focus,X button:focus\":\"outline:none;\",\"X a\":\"text-decoration:none;\",\"X a:hover\":\"text-decoration:none;\",\"X .crisp\":\"shape-rendering:crispEdges;\",\"X .user-select-none\":\"-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;user-select:none;\",\"X svg a\":\"fill:#447adb;\",\"X svg a:hover\":\"fill:#3c6dc5;\",\"X .main-svg\":\"position:absolute;top:0;left:0;pointer-events:none;\",\"X .main-svg .draglayer\":\"pointer-events:all;\",\"X .cursor-default\":\"cursor:default;\",\"X .cursor-pointer\":\"cursor:pointer;\",\"X 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.modebar--hover>:not(.watermark)\":\"opacity:0;-webkit-transition:opacity .3s ease 0s;-moz-transition:opacity .3s ease 0s;-ms-transition:opacity .3s ease 0s;-o-transition:opacity .3s ease 0s;transition:opacity .3s ease 0s;\",\"X:hover .modebar--hover .modebar-group\":\"opacity:1;\",\"X:focus-within .modebar--hover .modebar-group\":\"opacity:1;\",\"X .modebar-group\":\"float:left;display:inline-block;box-sizing:border-box;padding-left:8px;position:relative;vertical-align:middle;white-space:nowrap;\",\"X .modebar-group a\":\"display:grid;place-content:center;\",\"X .modebar-btn\":\"position:relative;font-size:16px;padding:3px 4px;height:22px;cursor:pointer;line-height:normal;box-sizing:border-box;border:none;background:rgba(0,0,0,0);\",\"X .modebar-btn svg\":\"position:relative;\",\"X .modebar-btn:focus-visible\":\"outline:1px solid #000;outline-offset:1px;border-radius:3px;\",\"X 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0;(hs.isArrayOrTypedArray(s)||c||f)&&(t.selectedOpacityFn=function(S){var L=S.mo===void 0?a.opacity:S.mo;return S.selected?c?l:L:f?u:Wue*L});var v=a.color,g=i.color,p=o.color;(g||p)&&(t.selectedColorFn=function(S){var L=S.mcc||v;return S.selected?g||L:p||L});var x=a.size,_=i.size,y=o.size,C=_!==void 0,k=y!==void 0;return pk.traceIs(e,\"symbols\")&&(C||k)&&(t.selectedSizeFn=function(S){var L=S.mrc||x/2;return S.selected?C?_/2:L:k?y/2:L}),t};hi.makeSelectedTextStyleFns=function(e){var t={},r=e.selected||{},n=e.unselected||{},a=e.textfont||{},i=r.textfont||{},o=n.textfont||{},s=a.color,l=i.color,u=o.color;return t.selectedTextColorFn=function(c){var f=c.tc||s;return c.selected?l||f:u||(l?f:Tc.addOpacity(f,Wue))},t};hi.selectedPointStyle=function(e,t){if(!(!e.size()||!t.selectedpoints)){var r=hi.makeSelectedPointStyleFns(t),n=t.marker||{},a=[];r.selectedOpacityFn&&a.push(function(i,o){i.style(\"opacity\",r.selectedOpacityFn(o))}),r.selectedColorFn&&a.push(function(i,o){Tc.fill(i,r.selectedColorFn(o))}),r.selectedSizeFn&&a.push(function(i,o){var s=o.mx||n.symbol||0,l=r.selectedSizeFn(o);i.attr(\"d\",Xue(hi.symbolNumber(s),l,GB(o,t),VB(o,t))),o.mrc2=l}),a.length&&e.each(function(i){for(var o=Gc.select(this),s=0;s<a.length;s++)a[s](o,i)})}};hi.tryColorscale=function(e,t){var r=t?hs.nestedProperty(e,t).get():e;if(r){var n=r.color;if((r.colorscale||r._colorAx)&&hs.isArrayOrTypedArray(n))return upt.makeColorScaleFuncFromTrace(r)}return hs.identity};var qB={start:1,end:-1,middle:0,bottom:1,top:-1};function Kue(e,t,r,n,a){var i=Gc.select(e.node().parentNode),o=t.indexOf(\"top\")!==-1?\"top\":t.indexOf(\"bottom\")!==-1?\"bottom\":\"middle\",s=t.indexOf(\"left\")!==-1?\"end\":t.indexOf(\"right\")!==-1?\"start\":\"middle\",l=n?n/.8+1:0,u=(gk.lineCount(e)-1)*hpt+1,c=qB[s]*l,f=r*.75+qB[o]*l+(qB[o]-1)*u*r/2;e.attr(\"text-anchor\",s),a||i.attr(\"transform\",fT(c,f))}function Jue(e,t){var r=e.ts||t.textfont.size;return _m(r)&&r>0?r:0}hi.textPointStyle=function(e,t,r){if(e.size()){var n;if(t.selectedpoints){var a=hi.makeSelectedTextStyleFns(t);n=a.selectedTextColorFn}var i=t.texttemplate,o=r._fullLayout;e.each(function(s){var l=Gc.select(this),u=i?hs.extractOption(s,t,\"txt\",\"texttemplate\"):hs.extractOption(s,t,\"tx\",\"text\");if(!u&&u!==0){l.remove();return}if(i){var c=t._module.formatLabels,f=c?c(s,t,o):{},v={};ppt(v,t,s.i),u=hs.texttemplateString({data:[v,s,t._meta],fallback:t.texttemplatefallback,labels:f,locale:o._d3locale,template:u})}var 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L=0;L<t._initialCategories.length;L++)u(t._initialCategories[L]);S=S.concat(t._traceIndices);var w=t._matchGroup;for(var A in w)if(n!==A){var T=r[wk.id2name(A)];T._categories=t._categories,T._categoriesMap=t._categoriesMap,S=S.concat(T._traceIndices)}return S};var k=r._d3locale;t.type===\"date\"&&(t._dateFormat=k?k.timeFormat:Ppt,t._extraFormat=r._extraFormat),t._separators=r.separators,t._numFormat=k?k.numberFormat:Fpt,delete t._minDtick,delete t._forceTick0}});var Yb=fe((Idr,pce)=>{\"use strict\";var hce=aa(),XB=Xt(),zpt=Wa().BADNUM,Sk=XB.isArrayOrTypedArray,qpt=XB.isDateTime,Bpt=XB.cleanNumber,dce=Math.round;pce.exports=function(t,r,n){var a=t,i=n.noMultiCategory;if(Sk(a)&&!a.length)return\"-\";if(!i&&Gpt(a))return\"multicategory\";if(i&&Array.isArray(a[0])){for(var o=[],s=0;s<a.length;s++)if(Sk(a[s]))for(var l=0;l<a[s].length;l++)o.push(a[s][l]);a=o}if(Upt(a,r))return\"date\";var u=n.autotypenumbers!==\"strict\";return Vpt(a,u)?\"category\":Npt(a,u)?\"linear\":\"-\"};function 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n=t._context.doubleClickDelay,a,i=1,o=Ru.ensureSingle(e,\"rect\",r+\"toggle\",function(s){t._context.staticPlot||s.style(\"cursor\",\"pointer\").attr(\"pointer-events\",\"all\"),s.call(TT.fill,\"rgba(0,0,0,0)\")});t._context.staticPlot||(o.on(\"mousedown\",function(){a=new Date().getTime(),a-t._legendMouseDownTime<n?i+=1:(i=1,t._legendMouseDownTime=a)}),o.on(\"mouseup\",function(){if(!(t._dragged||t._editing)){var s=t._fullLayout[r];new Date().getTime()-t._legendMouseDownTime>n&&(i=Math.max(i-1,1)),Ofe(t,s,e,i,Ed.event)}}))}function Egt(e,t,r,n){if(t._fullData.some(function(u){let c=u.legend||\"legend\";return(Array.isArray(c)?c.includes(n):c===n)&&Hx.traceIs(u,\"pie-like\")}))return;let i=t._context.doubleClickDelay;var o,s=1;let l=Ru.ensureSingle(e,\"rect\",n+\"titletoggle\",function(u){t._context.staticPlot||u.style(\"cursor\",\"pointer\").attr(\"pointer-events\",\"all\"),u.call(TT.fill,\"rgba(0,0,0,0)\")});t._context.staticPlot||(l.on(\"mousedown\",function(){o=new 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Ge=jk(De,n,p);j=Math.max(j,Ge),q+=Ge}),F=null;var U=0;if(u){var J=0,ee=0,ce=0;t.each(function(){var De=0,Ge=0;Ed.select(this).selectAll(\"g.traces\").each(function(it){var Oe=jk(it,n,p),ze=it[0].height;Iu.setTranslate(this,E[0],E[1]+f+g+ze/2+Ge),Ge+=ze,De=Math.max(De,Oe),S[it[0].trace.legendgroup]=De});var ht=De+g;ee>0&&ht+f+ee>n._maxWidth?(U=Math.max(U,ee),ee=0,ce+=J+k,J=Ge):J=Math.max(J,Ge),Iu.setTranslate(this,ee,ce),ee+=ht}),n._width=Math.max(U,ee)+f,n._height=ce+J+x}else{var ye=r.size(),xe=q+v+(ye-1)*g<n._maxWidth,ue=0,K=0,_e=0,ne=0;r.each(function(De){var Ge=De[0].height,ht=jk(De,n,p,u),it=xe?ht:j;c||(it+=g),it+f+K-g>=n._maxWidth&&(U=Math.max(U,ne),K=0,_e+=ue,n._height+=ue,ue=0),Iu.setTranslate(this,E[0]+f+K,E[1]+f+_e+Ge/2+g),ne=K+ht+g,K+=it,ue=Math.max(ue,Ge)}),xe?(n._width=K+v,n._height=ue+x):(n._width=Math.max(U,ne)+v,n._height+=ue+x)}}n._width=Math.ceil(Math.max(n._width+E[0],n._titleWidth+2*(f+ic.titlePad))),n._height=Math.ceil(Math.max(n._height+E[1],n._titleHeight+2*(f+ic.itemGap))),n._effHeight=Math.min(n._height,n._maxHeight);var ae=e._context.edits,pe=ae.legendText||ae.legendPosition;r.each(function(De){var Ge=Ed.select(this).select(\".\"+o+\"toggle\"),ht=De[0].height,it=De[0].trace.legendgroup,Oe=jk(De,n,p);u&&it!==\"\"&&(Oe=S[it]);var ze=pe?p:F||Oe;!l&&!c&&(ze+=g/2),Iu.setRect(Ge,0,-ht/2,ze,ht)});var ie=a.select(\".\"+o+\"titletext\");ie.node()&&Agt(ie,n,f);var le=a.select(\".\"+o+\"titletoggle\");if(le.size()&&ie.node()){var me=ie.attr(\"x\")||0,be=ic.titlePad;Iu.setRect(le,me-be,f,n._titleWidth+2*be,n._titleHeight+2*be)}}function Pgt(e,t,r,n){var a=e._fullLayout,i=a[t],o=Xk(i),s=Zk(i),l=i.xref===\"paper\",u=i.yref===\"paper\";e._fullLayout._reservedMargin[t]={};var c=i.y<.5?\"b\":\"t\",f=i.x<.5?\"l\":\"r\",v={r:a.width-r,l:r+i._width,b:a.height-n,t:n+i._effHeight};if(l&&u)return IO.autoMargin(e,t,{x:i.x,y:i.y,l:i._width*t3[o],r:i._width*Rfe[o],b:i._effHeight*Rfe[s],t:i._effHeight*t3[s]});l?e._fullLayout._reservedMargin[t][c]=v[c]:u||i.orientation===\"v\"?e._fullLayout._reservedMargin[t][f]=v[f]:e._fullLayout._reservedMargin[t][c]=v[c]}function Xk(e){return Ru.isRightAnchor(e)?\"right\":Ru.isCenterAnchor(e)?\"center\":\"left\"}function Zk(e){return Ru.isBottomAnchor(e)?\"bottom\":Ru.isMiddleAnchor(e)?\"middle\":\"top\"}});var UO=fe(NO=>{\"use strict\";var Wx=xi(),eg=aa(),tu=Xt(),Fgt=tu.pushUnique,BO=tu.strTranslate,Dgt=tu.strRotate,Rgt=Rb(),nv=as(),Igt=hfe(),Xp=ea(),ac=ni(),$k=ed(),Zp=Yi(),zgt=rc().zindexSeparator,i3=bi(),C0=Sd(),Yx=L4(),qgt=TO(),Bgt=qO(),Xfe=Yx.YANGLE,OO=Math.PI*Xfe/180,Ogt=1/Math.sin(OO),Ngt=Math.cos(OO),Ugt=Math.sin(OO),yl=Yx.HOVERARROWSIZE,go=Yx.HOVERTEXTPAD,Ufe={box:!0,ohlc:!0,violin:!0,candlestick:!0},Vgt={scatter:!0,scattergl:!0,splom:!0};function Vfe(e,t){return e.distance-t.distance}NO.hover=function(t,r,n,a){t=tu.getGraphDiv(t);var i=r.target;tu.throttle(t._fullLayout._uid+Yx.HOVERID,Yx.HOVERMINTIME,function(){Ggt(t,r,n,a,i)})};NO.loneHover=function(t,r){var n=!0;Array.isArray(t)||(n=!1,t=[t]);var a=r.gd,i=Qfe(a),o=ehe(a),s=t.map(function(_){var y=_._x0||_.x0||_.x||0,C=_._x1||_.x1||_.x||0,k=_._y0||_.y0||_.y||0,S=_._y1||_.y1||_.y||0,L=_.eventData;if(L){var w=Math.min(y,C),A=Math.max(y,C),T=Math.min(k,S),m=Math.max(k,S),F=_.trace;if(i3.traceIs(F,\"gl3d\")){var E=a._fullLayout[F.scene]._scene.container,I=E.offsetLeft,R=E.offsetTop;w+=I,A+=I,T+=R,m+=R}L.bbox={x0:w+o,x1:A+o,y0:T+i,y1:m+i},L.xPixel=(y+C)/2,L.yPixel=(k+S)/2,r.inOut_bbox&&r.inOut_bbox.push(L.bbox)}else L=!1;return{color:_.color||ac.defaultLine,x0:_.x0||_.x||0,x1:_.x1||_.x||0,y0:_.y0||_.y||0,y1:_.y1||_.y||0,xLabel:_.xLabel,yLabel:_.yLabel,zLabel:_.zLabel,text:_.text,name:_.name,idealAlign:_.idealAlign,borderColor:_.borderColor,fontFamily:_.fontFamily,fontSize:_.fontSize,fontColor:_.fontColor,fontWeight:_.fontWeight,fontStyle:_.fontStyle,fontVariant:_.fontVariant,nameLength:_.nameLength,textAlign:_.textAlign,trace:_.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:_.hovertemplate||!1,hovertemplateLabels:_.hovertemplateLabels||!1,eventData:L}}),l=!1,u=$fe(s,{gd:a,hovermode:\"closest\",rotateLabels:l,bgColor:r.bgColor||ac.background,container:Wx.select(r.container),outerContainer:r.outerContainer||r.container}),c=u.hoverLabels,f=5,v=0,g=0;c.sort(function(_,y){return _.y0-y.y0}).each(function(_,y){var C=_.y0-_.by/2;C-f<v?_.offset=v-C+f:_.offset=0,v=C+_.by+_.offset,y===r.anchorIndex&&(g=_.offset)}).each(function(_){_.offset-=g});var p=a._fullLayout._invScaleX,x=a._fullLayout._invScaleY;return Jfe(c,l,p,x),n?c:c.node()};function Ggt(e,t,r,n,a){r||(r=\"xy\"),typeof r==\"string\"&&(r=r.split(zgt)[0]);var i=Array.isArray(r)?r:[r],o,s=e._fullLayout,l=s.hoversubplots,u=s._plots||[],c=u[r],f=s._has(\"cartesian\"),v=t.hovermode||s.hovermode,g=(v||\"\").charAt(0)===\"x\",p=(v||\"\").charAt(0)===\"y\",x,_;if(f&&(g||p)&&l===\"axis\"){for(var y=i.length,C=0;C<y;C++)if(o=i[C],u[o]){x=Zp.getFromId(e,o,\"x\"),_=Zp.getFromId(e,o,\"y\");var k=(g?x:_)._subplotsWith;if(k&&k.length)for(var S=0;S<k.length;S++)Fgt(i,k[S])}}if(c&&l!==\"single\"){var L=c.overlays.map(function(Ut){return Ut.id});i=i.concat(L)}for(var w=i.length,A=new Array(w),T=new Array(w),m=!1,F=0;F<w;F++)if(o=i[F],u[o])m=!0,A[F]=u[o].xaxis,T[F]=u[o].yaxis;else if(s[o]&&s[o]._subplot){var E=s[o]._subplot;A[F]=E.xaxis,T[F]=E.yaxis}else{tu.warn(\"Unrecognized subplot: \"+o);return}if(v&&!m&&(v=\"closest\"),[\"x\",\"y\",\"closest\",\"x unified\",\"y unified\"].indexOf(v)===-1||!e.calcdata||e.querySelector(\".zoombox\")||e._dragging)return $k.unhoverRaw(e,t);var I=s.hoverdistance;I===-1&&(I=1/0);var R=s.spikedistance;R===-1&&(R=1/0);var B=[],O=[],V,j,q,U,J,ee,ce,ye,xe,ue,K,_e,ne,ae={hLinePoint:null,vLinePoint:null},pe=!1;if(Array.isArray(t))for(v=\"array\",q=0;q<t.length;q++)J=e.calcdata[t[q].curveNumber||0],J&&(ee=J[0].trace,J[0].trace.hoverinfo!==\"skip\"&&(O.push(J),ee.orientation===\"h\"&&(pe=!0)));else{var ie=e.calcdata.slice();for(ie.sort(function(Ut,_r){var rn=Ut[0].trace.zorder||0,Jr=_r[0].trace.zorder||0;return rn-Jr}),U=0;U<ie.length;U++)J=ie[U],ee=J[0].trace,ee.hoverinfo!==\"skip\"&&C0.isTraceInSubplots(ee,i)&&(O.push(J),ee.orientation===\"h\"&&(pe=!0));var le=!a,me,be;if(le)\"xpx\"in t?me=t.xpx:me=A[0]._length/2,\"ypx\"in t?be=t.ypx:be=T[0]._length/2;else{if(Rgt.triggerHandler(e,\"plotly_beforehover\",t)===!1)return;var De=a.getBoundingClientRect();me=t.clientX-De.left,be=t.clientY-De.top,s._calcInverseTransform(e);var Ge=tu.apply3DTransform(s._invTransform)(me,be);if(me=Ge[0],be=Ge[1],me<0||me>A[0]._length||be<0||be>T[0]._length)return $k.unhoverRaw(e,t)}if(t.pointerX=me+A[0]._offset,t.pointerY=be+T[0]._offset,\"xval\"in t?V=C0.flat(i,t.xval):V=C0.p2c(A,me),\"yval\"in t?j=C0.flat(i,t.yval):j=C0.p2c(T,be),e._hoverPointerX=t.pointerX,e._hoverPointerY=t.pointerY,!eg(V[0])||!eg(j[0]))return tu.warn(\"Fx.hover failed\",t,e),$k.unhoverRaw(e,t)}s.clickanywhere&&(e._hoverXVals=V,e._hoverYVals=j,e._hoverXAxes=A,e._hoverYAxes=T);var ht=1/0;function it(Ut,_r){for(U=0;U<O.length;U++)if(J=O[U],!(!J||!J[0]||!J[0].trace)&&(ee=J[0].trace,!(ee.visible!==!0||ee._length===0)&&[\"carpet\",\"contourcarpet\"].indexOf(ee._module.name)===-1)){if(xe=v,C0.isUnifiedHover(xe)&&(xe=xe.charAt(0)),ee.type===\"splom\"?(ye=0,ce=i[ye]):(ce=C0.getSubplot(ee),ye=i.indexOf(ce)),_e={cd:J,trace:ee,xa:A[ye],ya:T[ye],maxHoverDistance:I,maxSpikeDistance:R,index:!1,distance:Math.min(ht,I),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:ac.defaultLine,name:ee.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},s[ce]&&(_e.subplot=s[ce]._subplot),s._splomScenes&&s._splomScenes[ee.uid]&&(_e.scene=s._splomScenes[ee.uid]),xe===\"array\"){var rn=t[U];if(\"pointNumber\"in rn){let Xi=J[rn.pointNumber];Xi&&(_e.index=rn.pointNumber,Xi.lonlat?(ue=Xi.lonlat[0],K=Xi.lonlat[1]):(ue=Xi.x,K=Xi.y)),xe=\"closest\"}else xe=\"\",\"xval\"in rn&&(ue=rn.xval,xe=\"x\"),\"yval\"in rn&&(K=rn.yval,xe=xe?\"closest\":\"y\")}else Ut!==void 0&&_r!==void 0?(ue=Ut,K=_r):(ue=V[ye],K=j[ye]);if(ne=B.length,I!==0)if(ee._module&&ee._module.hoverPoints){var Jr=ee._module.hoverPoints(_e,ue,K,xe,{finiteRange:!0,hoverLayer:s._hoverlayer,hoversubplots:l,gd:e});if(Jr)for(var qn,ri=0;ri<Jr.length;ri++)qn=Jr[ri],eg(qn.x0)&&eg(qn.y0)&&B.push(Ygt(qn,v))}else tu.log(\"Unrecognized trace type in hover:\",ee);if(v===\"closest\"&&B.length>ne&&(B.splice(0,ne),ht=B[0].distance),f&&R!==0&&B.length===0){_e.distance=R,_e.index=!1;var Jn=ee._module.hoverPoints(_e,ue,K,\"closest\",{hoverLayer:s._hoverlayer});if(Jn&&(Jn=Jn.filter(function(Xi){return Xi.spikeDistance<=R})),Jn&&Jn.length){var Ii,Vi=Jn.filter(function(Xi){return Xi.xa.showspikes&&Xi.xa.spikesnap!==\"hovered data\"});if(Vi.length){var ra=Vi[0];eg(ra.x0)&&eg(ra.y0)&&(Ii=ze(ra),(!ae.vLinePoint||ae.vLinePoint.spikeDistance>Ii.spikeDistance)&&(ae.vLinePoint=Ii))}var ha=Jn.filter(function(Xi){return Xi.ya.showspikes&&Xi.ya.spikesnap!==\"hovered data\"});if(ha.length){var va=ha[0];eg(va.x0)&&eg(va.y0)&&(Ii=ze(va),(!ae.hLinePoint||ae.hLinePoint.spikeDistance>Ii.spikeDistance)&&(ae.hLinePoint=Ii))}}}}}it();function Oe(Ut,_r,rn){for(var Jr=null,qn=1/0,ri,Jn=0;Jn<Ut.length;Jn++)x&&x._id!==Ut[Jn].xa._id||_&&_._id!==Ut[Jn].ya._id||(ri=Ut[Jn].spikeDistance,rn&&Jn===0&&(ri=-1/0),ri<=qn&&ri<=_r&&(Jr=Ut[Jn],qn=ri));return Jr}function ze(Ut){return Ut?{xa:Ut.xa,ya:Ut.ya,x:Ut.xSpike!==void 0?Ut.xSpike:(Ut.x0+Ut.x1)/2,y:Ut.ySpike!==void 0?Ut.ySpike:(Ut.y0+Ut.y1)/2,distance:Ut.distance,spikeDistance:Ut.spikeDistance,curveNumber:Ut.trace.index,color:Ut.color,pointNumber:Ut.index}:null}var ct={fullLayout:s,container:s._hoverlayer,event:t},et=e._spikepoints,qe={vLinePoint:ae.vLinePoint,hLinePoint:ae.hLinePoint};e._spikepoints=qe;var we=function(){var Ut=B.filter(function(rn){return x&&x._id===rn.xa._id&&_&&_._id===rn.ya._id}),_r=B.filter(function(rn){return!(x&&x._id===rn.xa._id&&_&&_._id===rn.ya._id)});Ut.sort(Vfe),_r.sort(Vfe),B=Ut.concat(_r),B=Zgt(B,v)};we();var Le=v.charAt(0),Ke=(Le===\"x\"||Le===\"y\")&&B[0]&&Vgt[B[0].trace.type];if(f&&R!==0&&B.length!==0){var Te=B.filter(function(Ut){return Ut.ya.showspikes}),Ve=Oe(Te,R,Ke);ae.hLinePoint=ze(Ve);var $e=B.filter(function(Ut){return Ut.xa.showspikes}),ot=Oe($e,R,Ke);ae.vLinePoint=ze(ot)}if(B.length===0){var tt=$k.unhoverRaw(e,t);if(f&&(ae.hLinePoint!==null||ae.vLinePoint!==null)&&jfe(et)&&Hfe(e,ae,ct),s.hoveranywhere&&!n&&a){var nt=e._hoverdata;nt&&nt.length&&(e.emit(\"plotly_unhover\",{event:t,points:nt}),e._hoverdata=[]),Qt([]),e._hoverAnywhereActive=!0}return tt}if(f&&jfe(et)&&Hfe(e,ae,ct),C0.isXYhover(xe)&&B[0].length!==0&&B[0].trace.type!==\"splom\"){var We=B[0];Ufe[We.trace.type]?B=B.filter(Ut=>Ut.trace.index===We.trace.index):B=[We];var mt=B.length,bt=Yfe(\"x\",We,s),Yt=Yfe(\"y\",We,s);it(bt,Yt);var jt=[],mr={},or=0,fr=function(Ut){var _r=Ufe[Ut.trace.type]?Zfe(Ut):Ut.trace.index;if(!mr[_r])or++,mr[_r]=or,jt.push(Ut);else{var rn=mr[_r]-1,Jr=jt[rn];rn>0&&Math.abs(Ut.distance)<Math.abs(Jr.distance)&&(jt[rn]=Ut)}},Ot;for(Ot=0;Ot<mt;Ot++)fr(B[Ot]);for(Ot=B.length-1;Ot>mt-1;Ot--)fr(B[Ot]);B=jt,we()}var cr=e._hoverdata,Ct=[],wt=Qfe(e),Tt=ehe(e);for(let Ut of B){var Mt=C0.makeEventData(Ut,Ut.trace,Ut.cd);if(Ut.hovertemplate!==!1){var Lt=!1;Ut.cd[Ut.index]&&Ut.cd[Ut.index].ht&&(Lt=Ut.cd[Ut.index].ht),Ut.hovertemplate=Lt||Ut.trace.hovertemplate||!1}if(Ut.xa&&Ut.ya){var ft=Ut.x0+Ut.xa._offset,kt=Ut.x1+Ut.xa._offset,rr=Ut.y0+Ut.ya._offset,pr=Ut.y1+Ut.ya._offset,Kr=Math.min(ft,kt),Nr=Math.max(ft,kt),dn=Math.min(rr,pr),xr=Math.max(rr,pr);Mt.bbox={x0:Kr+Tt,x1:Nr+Tt,y0:dn+wt,y1:xr+wt},Mt.xPixel=(ft+kt)/2,Mt.yPixel=(rr+pr)/2}Ut.eventData=[Mt],Ct.push(Mt)}e._hoverdata=Ct;var Fr=v===\"y\"&&(O.length>1||B.length>1)||v===\"closest\"&&pe&&B.length>1,Zr=ac.combine(s.plot_bgcolor||ac.background,s.paper_bgcolor),Cn=$fe(B,{gd:e,hovermode:v,rotateLabels:Fr,bgColor:Zr,container:s._hoverlayer,outerContainer:s._paper.node(),commonLabelOpts:s.hoverlabel,hoverdistance:s.hoverdistance}),Dt=Cn.hoverLabels;if(C0.isUnifiedHover(v)||(jgt(Dt,Fr,s,Cn.commonLabelBoundingBox),Jfe(Dt,Fr,s._invScaleX,s._invScaleY)),a&&a.tagName){var At=i3.getComponentMethod(\"annotations\",\"hasClickToShow\")(e,Ct);Igt(Wx.select(a),At?\"pointer\":\"\")}var Vt=Xgt(e,t,cr);if(!a||n||!Vt&&!s.hoveranywhere)return;cr&&Vt&&e.emit(\"plotly_unhover\",{event:t,points:cr}),Qt(e._hoverdata);function Qt(Ut){e.emit(\"plotly_hover\",{event:t,points:Ut,xaxes:A,yaxes:T,xvals:C0.c2dApply(A,V),yvals:C0.c2dApply(T,j),xPixel:t.pointerX,yPixel:t.pointerY})}}function Zfe(e){return[e.trace.index,e.index,e.x0,e.y0,e.name,e.attr,e.xa?e.xa._id:\"\",e.ya?e.ya._id:\"\"].join(\",\")}var Hgt=/<extra>([\\s\\S]*)<\\/extra>/;function $fe(e,t){var r=t.gd,n=r._fullLayout,a=t.hovermode,i=t.rotateLabels,o=t.bgColor,s=t.container,l=t.outerContainer,u=t.commonLabelOpts||{};if(e.length===0)return[[]];var c=t.fontFamily||Yx.HOVERFONT,f=t.fontSize||Yx.HOVERFONTSIZE,v=t.fontWeight||n.font.weight,g=t.fontStyle||n.font.style,p=t.fontVariant||n.font.variant,x=t.fontTextcase||n.font.textcase,_=t.fontLineposition||n.font.lineposition,y=t.fontShadow||n.font.shadow,C=e[0],k=C.xa,S=C.ya,L=a.charAt(0),w=L+\"Label\",A=C[w];if(A===void 0&&k.type===\"multicategory\")for(var T=0;T<e.length&&(A=e[T][w],A===void 0);T++);var m=n3(r,l),F=m.top,E=m.width,I=m.height,R=A!==void 0&&C.distance<=t.hoverdistance&&(a===\"x\"||a===\"y\");if(R){var B=!0,O,V;for(O=0;O<e.length;O++)if(B&&e[O].zLabel===void 0&&(B=!1),V=e[O].hoverinfo||e[O].trace.hoverinfo,V){var j=Array.isArray(V)?V:V.split(\"+\");if(j.indexOf(\"all\")===-1&&j.indexOf(a)===-1){R=!1;break}}B&&(R=!1)}var q=s.selectAll(\"g.axistext\").data(R?[0]:[]);q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove();var U={minX:0,maxX:0,minY:0,maxY:0};if(q.each(function(){var tt=Wx.select(this),nt=tu.ensureSingle(tt,\"path\",\"\",function(dn){dn.style({\"stroke-width\":\"1px\"})}),We=tu.ensureSingle(tt,\"text\",\"\",function(dn){dn.attr(\"data-notex\",1)}),mt=u.bgcolor||ac.defaultLine,bt=u.bordercolor||ac.contrast(mt),Yt=ac.contrast(mt),jt=u.font,mr={weight:jt.weight||v,style:jt.style||g,variant:jt.variant||p,textcase:jt.textcase||x,lineposition:jt.lineposition||_,shadow:jt.shadow||y,family:jt.family||c,size:jt.size||f,color:jt.color||Yt};nt.style({fill:mt,stroke:bt}),We.text(A).call(Xp.font,mr).call(nv.positionText,0,0).call(nv.convertToTspans,r),tt.attr(\"transform\",\"\");var or=n3(r,We.node()),fr,Ot;if(a===\"x\"){var cr=k.side===\"top\"?\"-\":\"\";We.attr(\"text-anchor\",\"middle\").call(nv.positionText,0,k.side===\"top\"?F-or.bottom-yl-go:F-or.top+yl+go),fr=k._offset+(C.x0+C.x1)/2,Ot=S._offset+(k.side===\"top\"?0:S._length);var Ct=or.width/2+go,wt=fr;fr<Ct?wt=Ct:fr>n.width-Ct&&(wt=n.width-Ct),nt.attr(\"d\",\"M\"+(fr-wt)+\",0L\"+(fr-wt+yl)+\",\"+cr+yl+\"H\"+Ct+\"v\"+cr+(go*2+or.height)+\"H\"+-Ct+\"V\"+cr+yl+\"H\"+(fr-wt-yl)+\"Z\"),fr=wt,U.minX=fr-Ct,U.maxX=fr+Ct,k.side===\"top\"?(U.minY=Ot-(go*2+or.height),U.maxY=Ot-go):(U.minY=Ot+go,U.maxY=Ot+(go*2+or.height))}else{var Tt,Mt,Lt;S.side===\"right\"?(Tt=\"start\",Mt=1,Lt=\"\",fr=k._offset+k._length):(Tt=\"end\",Mt=-1,Lt=\"-\",fr=k._offset),Ot=S._offset+(C.y0+C.y1)/2,We.attr(\"text-anchor\",Tt),nt.attr(\"d\",\"M0,0L\"+Lt+yl+\",\"+yl+\"V\"+(go+or.height/2)+\"h\"+Lt+(go*2+or.width)+\"V-\"+(go+or.height/2)+\"H\"+Lt+yl+\"V-\"+yl+\"Z\"),U.minY=Ot-(go+or.height/2),U.maxY=Ot+(go+or.height/2),S.side===\"right\"?(U.minX=fr+yl,U.maxX=fr+yl+(go*2+or.width)):(U.minX=fr-yl-(go*2+or.width),U.maxX=fr-yl);var 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j=V[0],q=V[V.length-1];if(C=j.pmin-j.pos-j.dp+j.size,k=q.pos+q.dp+q.size-j.pmax,C>.01){for(L=V.length-1;L>=0;L--)V[L].dp+=C;y=!1}if(!(k<.01)){if(C<-.01){for(L=V.length-1;L>=0;L--)V[L].dp-=k;y=!1}if(y){var U=0;for(S=0;S<V.length;S++)w=V[S],w.pos+w.dp+w.size>j.pmax&&U++;for(S=V.length-1;S>=0&&!(U<=0);S--)w=V[S],w.pos>j.pmax-1&&(w.del=!0,U--);for(S=0;S<V.length&&!(U<=0);S++)if(w=V[S],w.pos<j.pmin+1)for(w.del=!0,U--,k=w.size*2,L=V.length-1;L>=0;L--)V[L].dp-=k;for(S=V.length-1;S>=0&&!(U<=0);S--)w=V[S],w.pos+w.dp+w.size>j.pmax&&(w.del=!0,U--)}}}for(;!y&&o<=l;){for(o++,y=!0,S=0;S<u.length-1;){var m=u[S],F=u[S+1],E=m[m.length-1],I=F[0];if(C=E.pos+E.dp+E.size-I.pos-I.dp+I.size,C>.01){for(L=F.length-1;L>=0;L--)F[L].dp+=C;for(m.push.apply(m,F),u.splice(S+1,1),A=0,L=m.length-1;L>=0;L--)A+=m[L].dp;for(k=A/m.length,L=m.length-1;L>=0;L--)m[L].dp-=k;y=!1}else S++}u.forEach(T)}for(S=u.length-1;S>=0;S--){var R=u[S];for(L=R.length-1;L>=0;L--){var B=R[L],O=B.datum;O.offset=B.dp,O.del=B.del}}}function Kfe(e,t){var r=0,n=e.offset;return t&&(n*=-Ugt,r=e.offset*Ngt),{x:r,y:n}}function Wgt(e){var t={start:1,end:-1,middle:0}[e.anchor],r=t*(yl+go),n=r+t*(e.txwidth+go),a=e.anchor===\"middle\";return a&&(r-=e.tx2width/2,n+=e.txwidth/2+go),{alignShift:t,textShiftX:r,text2ShiftX:n}}function Jfe(e,t,r,n){var a=function(o){return o*r},i=function(o){return o*n};e.each(function(o){var s=Wx.select(this);if(o.del)return s.remove();var l=s.select(\"text.nums\"),u=o.anchor,c=u===\"end\"?-1:1,f=Wgt(o),v=Kfe(o,t),g=v.x,p=v.y,x=u===\"middle\",_=\"hoverlabel\"in o.trace?o.trace.hoverlabel.showarrow:!0,y;x?y=\"M-\"+a(o.bx/2+o.tx2width/2)+\",\"+i(p-o.by/2)+\"h\"+a(o.bx)+\"v\"+i(o.by)+\"h-\"+a(o.bx)+\"Z\":_?y=\"M0,0L\"+a(c*yl+g)+\",\"+i(yl+p)+\"v\"+i(o.by/2-yl)+\"h\"+a(c*o.bx)+\"v-\"+i(o.by)+\"H\"+a(c*yl+g)+\"V\"+i(p-yl)+\"Z\":y=\"M\"+a(c*yl+g)+\",\"+i(p-o.by/2)+\"h\"+a(c*o.bx)+\"v\"+i(o.by)+\"h\"+a(-c*o.bx)+\"Z\",s.select(\"path\").attr(\"d\",y);var C=g+f.textShiftX,k=p+o.ty0-o.by/2+go,S=o.textAlign||\"auto\";S!==\"auto\"&&(S===\"left\"&&u!==\"start\"?(l.attr(\"text-anchor\",\"start\"),C=x?-o.bx/2-o.tx2width/2+go:-o.bx-go):S===\"right\"&&u!==\"end\"&&(l.attr(\"text-anchor\",\"end\"),C=x?o.bx/2-o.tx2width/2-go:o.bx+go)),l.call(nv.positionText,a(C),i(k)),o.tx2width&&(s.select(\"text.name\").call(nv.positionText,a(f.text2ShiftX+f.alignShift*go+g),i(p+o.ty0-o.by/2+go)),s.select(\"rect\").call(Xp.setRect,a(f.text2ShiftX+(f.alignShift-1)*o.tx2width/2+g),i(p-o.by/2-1),a(o.tx2width),i(o.by+2)))})}function Ygt(e,t){var r=e.index,n=e.trace||{},a=e.cd[0],i=e.cd[r]||{};function o(v){return v||eg(v)&&v===0}var s=Array.isArray(r)?function(v,g){var p=tu.castOption(a,r,v);return o(p)?p:tu.extractOption({},n,\"\",g)}:function(v,g){return tu.extractOption(i,n,v,g)};function l(v,g,p){var 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n=e[0][0],a=t.gd,i=n.getAttribute(\"d\"),o=a._fullLayout.newshape,s=t.plotinfo,l=t.isActiveShape,u=s.xaxis,c=s.yaxis,f=!!s.domain||!s.xaxis,v=!!s.domain||!s.yaxis,g=Mmt(r),p=Lmt(i,a,s,l),x={editable:!0,visible:o.visible,name:o.name,showlegend:o.showlegend,legend:o.legend,legendwidth:o.legendwidth,legendgroup:o.legendgroup,legendgrouptitle:{text:o.legendgrouptitle.text,font:o.legendgrouptitle.font},legendrank:o.legendrank,label:o.label,xref:f?\"paper\":u._id,yref:v?\"paper\":c._id,layer:o.layer,opacity:o.opacity,line:{color:o.line.color,width:o.line.width,dash:o.line.dash}};g||(x.fillcolor=o.fillcolor,x.fillrule=o.fillrule);var _;if(p.length===1&&(_=p[0]),_&&_.length===5&&r===\"drawrect\")x.type=\"rect\",x.x0=_[0][1],x.y0=_[0][2],x.x1=_[2][1],x.y1=_[2][2];else if(_&&r===\"drawline\")x.type=\"line\",x.x0=_[0][1],x.y0=_[0][2],x.x1=_[1][1],x.y1=_[1][2];else if(_&&r===\"drawcircle\"){x.type=\"circle\";var y=_[Lhe][1],C=_[Phe][1],k=_[Fhe][1],S=_[Dhe][1],L=_[Lhe][2],w=_[Phe][2],A=_[Fhe][2],T=_[Dhe][2],m=s.xaxis&&(s.xaxis.type===\"date\"||s.xaxis.type===\"log\"),F=s.yaxis&&(s.yaxis.type===\"date\"||s.yaxis.type===\"log\");m&&(y=km(s.xaxis,y),C=km(s.xaxis,C),k=km(s.xaxis,k),S=km(s.xaxis,S)),F&&(L=km(s.yaxis,L),w=km(s.yaxis,w),A=km(s.yaxis,A),T=km(s.yaxis,T));var E=(C+S)/2,I=(L+A)/2,R=(S-C+k-y)/2,B=(T-w+A-L)/2,O=Fmt({x0:E,y0:I,x1:E+R*Smt,y1:I+B*Emt});m&&(O.x0=n8(s.xaxis,O.x0),O.x1=n8(s.xaxis,O.x1)),F&&(O.y0=n8(s.yaxis,O.y0),O.y1=n8(s.yaxis,O.y1)),x.x0=O.x0,x.y0=O.y0,x.x1=O.x1,x.y1=O.y1}else x.type=\"path\",u&&c&&Dmt(p,u,c),x.path=Pmt(p),_=null;return x}qhe.exports={newShapes:Rmt,createShapeObj:zhe}});var WO=fe((w0r,Bhe)=>{\"use strict\";var Imt=$p(),zmt=Imt.selectMode,qmt=pm(),Bmt=qmt.clearOutline,jO=Em(),Omt=jO.readPaths,Nmt=jO.writePaths,Umt=jO.fixDatesForPaths;Bhe.exports=function(t,r){if(t.length){var n=t[0][0];if(n){var a=n.getAttribute(\"d\"),i=r.gd,o=i._fullLayout.newselection,s=r.plotinfo,l=s.xaxis,u=s.yaxis,c=r.isActiveSelection,f=r.dragmode,v=(i.layout||{}).selections||[];if(!zmt(f)&&c!==void 0){var g=i._fullLayout._activeSelectionIndex;if(g<v.length)switch(i._fullLayout.selections[g].type){case\"rect\":f=\"select\";break;case\"path\":f=\"lasso\";break}}var p=Omt(a,i,s,c),x={xref:l._id,yref:u._id,opacity:o.opacity,line:{color:o.line.color,width:o.line.width,dash:o.line.dash}},_;p.length===1&&(_=p[0]),_&&_.length===5&&f===\"select\"?(x.type=\"rect\",x.x0=_[0][1],x.y0=_[0][2],x.x1=_[2][1],x.y1=_[2][2]):(x.type=\"path\",l&&u&&Umt(p,l,u),x.path=Nmt(p),_=null),Bmt(i);for(var y=r.editHelpers,C=(y||{}).modifyItem,k=[],S=0;S<v.length;S++){var L=i._fullLayout.selections[S];if(!L){k[S]=L;continue}if(k[S]=L._input,c!==void 0&&S===i._fullLayout._activeSelectionIndex){var w=x;switch(L.type){case\"rect\":C(\"x0\",w.x0),C(\"x1\",w.x1),C(\"y0\",w.y0),C(\"y1\",w.y1);break;case\"path\":C(\"path\",w.path);break}}}return c===void 0?(k.push(x),k):y?y.getUpdateObj():{}}}}});var kT=fe((T0r,Ohe)=>{\"use strict\";Ohe.exports={segmentRE:/[MLHVQCTSZ][^MLHVQCTSZ]*/g,paramRE:/[^\\s,]+/g,paramIsX:{M:{0:!0,drawn:0},L:{0:!0,drawn:0},H:{0:!0,drawn:0},V:{},Q:{0:!0,2:!0,drawn:2},C:{0:!0,2:!0,4:!0,drawn:4},T:{0:!0,drawn:0},S:{0:!0,2:!0,drawn:2},Z:{}},paramIsY:{M:{1:!0,drawn:1},L:{1:!0,drawn:1},H:{},V:{0:!0,drawn:0},Q:{1:!0,3:!0,drawn:3},C:{1:!0,3:!0,5:!0,drawn:5},T:{1:!0,drawn:1},S:{1:!0,3:!0,drawn:3},Z:{}},numParams:{M:2,L:2,H:1,V:1,Q:4,C:6,T:2,S:4,Z:0}}});var Lm=fe(gf=>{\"use strict\";var Jp=kT(),Nhe=Xt(),Cm=Yi();gf.rangeToShapePosition=function(e){return e.type===\"log\"?e.r2d:function(t){return t}};gf.shapePositionToRange=function(e){return e.type===\"log\"?e.d2r:function(t){return t}};gf.decodeDate=function(e){return function(t){return t.replace&&(t=t.replace(\"_\",\" \")),e(t)}};gf.encodeDate=function(e){return function(t){return e(t).replace(\" \",\"_\")}};gf.extractPathCoords=function(e,t,r){var n=[],a=e.match(Jp.segmentRE);return a.forEach(function(i){var o=t[i.charAt(0)].drawn;if(o!==void 0){var s=i.slice(1).match(Jp.paramRE);if(!(!s||s.length<o)){var l=s[o],u=r?l:Nhe.cleanNumber(l);n.push(u)}}}),n};gf.countDefiningCoords=function(e,t,r){if(e!==\"path\")return 2;if(!t)return 0;let n=t.match(Jp.segmentRE);if(!n)return 0;let a=r===\"x\"?Jp.paramIsX:Jp.paramIsY;return n.reduce((i,o)=>{let s=o.charAt(0),l=a[s].drawn!==void 0;return i+(l?1:0)},0)};gf.getDataToPixel=function(e,t,r,n,a){var i=e._fullLayout._size,o;if(t)if(a===\"domain\")o=function(l){return t._length*(n?1-l:l)+t._offset};else{var s=gf.shapePositionToRange(t);o=function(l){var u=Kp(t,r);return t._offset+t.r2p(s(l,!0))+u},t.type===\"date\"&&(o=gf.decodeDate(o))}else n?o=function(l){return i.t+i.h*(1-l)}:o=function(l){return i.l+i.w*l};return o};gf.getPixelToData=function(e,t,r,n){var a=e._fullLayout._size,i;if(t)if(n===\"domain\")i=function(s){var l=(s-t._offset)/t._length;return r?1-l:l};else{var o=gf.rangeToShapePosition(t);i=function(s){return o(t.p2r(s-t._offset))}}else r?i=function(s){return 1-(s-a.t)/a.h}:i=function(s){return(s-a.l)/a.w};return i};gf.roundPositionForSharpStrokeRendering=function(e,t){var r=Math.round(t%2)===1,n=Math.round(e);return r?n+.5:n};gf.makeShapesOptionsAndPlotinfo=function(e,t){var r=e._fullLayout.shapes[t]||{},n=e._fullLayout._plots[r.xref+r.yref],a=!!n;return a?n._hadPlotinfo=!0:(n={},r.xref&&r.xref!==\"paper\"&&(n.xaxis=e._fullLayout[r.xref+\"axis\"]),r.yref&&r.yref!==\"paper\"&&(n.yaxis=e._fullLayout[r.yref+\"axis\"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}};gf.makeSelectionsOptionsAndPlotinfo=function(e,t){var r=e._fullLayout.selections[t]||{},n=e._fullLayout._plots[r.xref+r.yref],a=!!n;return a?n._hadPlotinfo=!0:(n={},r.xref&&(n.xaxis=e._fullLayout[r.xref+\"axis\"]),r.yref&&(n.yaxis=e._fullLayout[r.yref+\"axis\"])),{options:r,plotinfo:n}};gf.getPathString=function(e,t){let r=t.type,n=Cm.getRefType(t.xref),a=Cm.getRefType(t.yref),i=e._fullLayout._size;var o,s,l,u,c,f,v,g,p,x,_,y;function C(F,E,I,R){var B;if(F)if(E===\"domain\")R?B=function(O){return F._offset+F._length*(1-O)}:B=function(O){return F._offset+F._length*O};else{let O=gf.shapePositionToRange(F);B=function(V){return F._offset+F.r2p(O(V,!0))},I===\"path\"&&F.type===\"date\"&&(B=gf.decodeDate(B))}else R?B=function(O){return i.t+i.h*(1-O)}:B=function(O){return i.l+i.w*O};return B}if(n===\"array\"?(v=[],o=t.xref.map(function(F){return Cm.getFromId(e,F)}),v=t.xref.map(function(F,E){return C(o[E],Cm.getRefType(F),r,!1)})):(o=Cm.getFromId(e,t.xref),v=C(o,n,r,!1)),a===\"array\"?(g=[],s=t.yref.map(function(F){return Cm.getFromId(e,F)}),g=t.yref.map(function(F,E){return C(s[E],Cm.getRefType(F),r,!0)})):(s=Cm.getFromId(e,t.yref),g=C(s,a,r,!0)),r===\"path\")return Vmt(t,v,g);if(n===\"array\")l=Kp(o[0],t.x0shift),u=Kp(o[1],t.x1shift),p=v[0](t.x0)+l,x=v[1](t.x1)+u;else if(l=Kp(o,t.x0shift),u=Kp(o,t.x1shift),t.xsizemode===\"pixel\"){let F=v(t.xanchor);p=F+t.x0+l,x=F+t.x1+u}else p=v(t.x0)+l,x=v(t.x1)+u;if(a===\"array\")c=Kp(s[0],t.y0shift),f=Kp(s[1],t.y1shift),_=g[0](t.y0)+c,y=g[1](t.y1)+f;else if(c=Kp(s,t.y0shift),f=Kp(s,t.y1shift),t.ysizemode===\"pixel\"){let F=g(t.yanchor);_=F-t.y0+c,y=F-t.y1+f}else _=g(t.y0)+c,y=g(t.y1)+f;if(r===\"line\")return\"M\"+p+\",\"+_+\"L\"+x+\",\"+y;if(r===\"rect\")return\"M\"+p+\",\"+_+\"H\"+x+\"V\"+y+\"H\"+p+\"Z\";var k=(p+x)/2,S=(_+y)/2,L=Math.abs(k-p),w=Math.abs(S-_),A=\"A\"+L+\",\"+w,T=k+L+\",\"+S,m=k+\",\"+(S-w);return\"M\"+T+A+\" 0 1,1 \"+m+A+\" 0 0,1 \"+T+\"Z\"};function Vmt(e,t,r){let n=e.path,a=e.xsizemode,i=e.ysizemode,o=e.xanchor,s=e.yanchor,l=Array.isArray(e.xref),u=Array.isArray(e.yref);var c=0,f=0;return n.replace(Jp.segmentRE,function(v){var g=0,p=v.charAt(0),x=Jp.paramIsX[p],_=Jp.paramIsY[p],y=Jp.numParams[p];let C=x.drawn!==void 0,k=_.drawn!==void 0,S=l?t[c]:t,L=u?r[f]:r;var w=v.slice(1).replace(Jp.paramRE,function(A){return x[g]?a===\"pixel\"?A=S(o)+Number(A):A=S(A):_[g]&&(i===\"pixel\"?A=L(s)-Number(A):A=L(A)),g++,g>y&&(A=\"X\"),A});return g>y&&(w=w.replace(/[\\s,]*X.*/,\"\"),Nhe.log(\"Ignoring extra params in segment \"+v)),C&&c++,k&&f++,p+w})}gf.getPixelShift=Kp;function Kp(e,t){t=t||0;var r=0;return t&&e&&(e.type===\"category\"||e.type===\"multicategory\")&&(r=(e.r2p(1)-e.r2p(0))*t),r}});var YO=fe((M0r,Ghe)=>{\"use strict\";var Gmt=Xt(),Qp=Yi(),Uhe=as(),Vhe=ea(),Hmt=Em().readPaths,Xx=Lm(),jmt=Xx.getPathString,CT=IE(),Wmt=nc().FROM_TL;Ghe.exports=function(t,r,n,a){if(a.selectAll(\".shape-label\").remove(),!!(n.label.text||n.label.texttemplate)){var i;if(n.label.texttemplate){var o={};if(n.type!==\"path\"){var s=Qp.getFromId(t,n.xref),l=Qp.getFromId(t,n.yref);let ee=Array.isArray(n.xref),ce=Array.isArray(n.yref);for(var u in CT){var c=typeof CT[u]==\"function\",f=!ee||CT.simpleXVariables.includes(u),v=!ce||CT.simpleYVariables.includes(u);if(c&&f&&v){var g=CT[u](n,s,l);g!==void 0&&(o[u]=g)}}}i=Gmt.texttemplateStringForShapes({data:[o],fallback:n.label.texttemplatefallback,locale:t._fullLayout._d3locale,template:n.label.texttemplate})}else i=n.label.text;var p={\"data-index\":r},x=n.label.font,_={\"data-notex\":1},y=a.append(\"g\").attr(p).classed(\"shape-label\",!0),C=y.append(\"text\").attr(_).classed(\"shape-label-text\",!0).text(i),k,S,L,w;if(n.path){var A=jmt(t,n),T=Hmt(A,t);k=1/0,L=1/0,S=-1/0,w=-1/0;for(var m=0;m<T.length;m++)for(var F=0;F<T[m].length;F++)for(var E=T[m][F],I=1;I<E.length;I+=2){var R=E[I],B=E[I+1];k=Math.min(k,R),S=Math.max(S,R),L=Math.min(L,B),w=Math.max(w,B)}}else{let ee=Array.isArray(n.xref),ce=Array.isArray(n.yref),ye=Qp.getFromId(t,ee?n.xref[0]:n.xref),xe=Qp.getFromId(t,ee?n.xref[1]:n.xref),ue=Qp.getFromId(t,ce?n.yref[0]:n.yref),K=Qp.getFromId(t,ce?n.yref[1]:n.yref),_e=Qp.getRefType(ee?n.xref[0]:n.xref),ne=Qp.getRefType(ee?n.xref[1]:n.xref),ae=Qp.getRefType(ce?n.yref[0]:n.yref),pe=Qp.getRefType(ce?n.yref[1]:n.yref),ie=(me,be,De,Ge)=>Xx.getDataToPixel(t,De,be,!1,Ge)(me),le=(me,be,De,Ge)=>Xx.getDataToPixel(t,De,be,!0,Ge)(me);if(n.xsizemode===\"pixel\"){let me=ie(n.xanchor,void 0,ye,_e),be=Xx.getPixelShift(ye,n.x0shift),De=Xx.getPixelShift(ye,n.x1shift);k=me+n.x0+be,S=me+n.x1+De}else k=ie(n.x0,n.x0shift,ye,_e),S=ie(n.x1,n.x1shift,xe,ne);if(n.ysizemode===\"pixel\"){let me=le(n.yanchor,void 0,ue,ae),be=Xx.getPixelShift(ue,n.y0shift),De=Xx.getPixelShift(ue,n.y1shift);L=me-n.y0+be,w=me-n.y1+De}else L=le(n.y0,n.y0shift,ue,ae),w=le(n.y1,n.y1shift,K,pe)}var O=n.label.textangle;O===\"auto\"&&(n.type===\"line\"?O=Ymt(k,L,S,w):O=0),C.call(function(ee){return ee.call(Vhe.font,x).attr({}),Uhe.convertToTspans(ee,t),ee});var V=Vhe.bBox(C.node()),j=Xmt(k,L,S,w,n,O,V),q=j.textx,U=j.texty,J=j.xanchor;C.attr({\"text-anchor\":{left:\"start\",center:\"middle\",right:\"end\"}[J],y:U,x:q,transform:\"rotate(\"+O+\",\"+q+\",\"+U+\")\"}).call(Uhe.positionText,q,U)}};function Ymt(e,t,r,n){var a,i;return i=Math.abs(r-e),r>=e?a=t-n:a=n-t,-180/Math.PI*Math.atan2(a,i)}function Xmt(e,t,r,n,a,i,o){var s=a.label.textposition,l=a.label.textangle,u=a.label.padding,c=a.type,f=Math.PI/180*i,v=Math.sin(f),g=Math.cos(f),p=a.label.xanchor,x=a.label.yanchor,_,y,C,k;if(c===\"line\"){s===\"start\"?(_=e,y=t):s===\"end\"?(_=r,y=n):(_=(e+r)/2,y=(t+n)/2),p===\"auto\"&&(s===\"start\"?l===\"auto\"?r>e?p=\"left\":r<e?p=\"right\":p=\"center\":r>e?p=\"right\":r<e?p=\"left\":p=\"center\":s===\"end\"?l===\"auto\"?r>e?p=\"right\":r<e?p=\"left\":p=\"center\":r>e?p=\"left\":r<e?p=\"right\":p=\"center\":p=\"center\");var S={left:1,center:0,right:-1},L={bottom:-1,middle:0,top:1};if(l===\"auto\"){var w=L[x];C=-u*v*w,k=u*g*w}else{var A=S[p],T=L[x];C=u*A,k=u*T}_=_+C,y=y+k}else C=u+3,s.indexOf(\"right\")!==-1?(_=Math.max(e,r)-C,p===\"auto\"&&(p=\"right\")):s.indexOf(\"left\")!==-1?(_=Math.min(e,r)+C,p===\"auto\"&&(p=\"left\")):(_=(e+r)/2,p===\"auto\"&&(p=\"center\")),s.indexOf(\"top\")!==-1?y=Math.min(t,n):s.indexOf(\"bottom\")!==-1?y=Math.max(t,n):y=(t+n)/2,k=u,x===\"bottom\"?y=y-k:x===\"top\"&&(y=y+k);var m=Wmt[x],F=a.label.font.size,E=o.height,I=(E*m-F)*v,R=-(E*m-F)*g;return{textx:_+I,texty:y+R,xanchor:p}}});var l8=fe((S0r,Khe)=>{\"use strict\";var Zmt=Xt(),$mt=Zmt.strTranslate,Hhe=ed(),Yhe=$p(),Kmt=Yhe.drawMode,Xhe=Yhe.selectMode,Zhe=bi(),jhe=ni(),s8=e8(),Jmt=s8.i000,Qmt=s8.i090,eyt=s8.i180,tyt=s8.i270,ryt=pm(),$he=ryt.clearOutlineControllers,ZO=Em(),o8=ZO.pointsOnRectangle,XO=ZO.pointsOnEllipse,nyt=ZO.writePaths,iyt=a8().newShapes,ayt=a8().createShapeObj,oyt=WO(),syt=YO();Khe.exports=function 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n=t.currentTarget,a=n.getAttribute(\"data-val\"),i=e._fullLayout,o=i._subplots[r]||[],s=1.05,l={},u=0;u<o.length;u++){var c=o[u],f=i[c].zoom,v=a===\"in\"?s*f:f/s;l[c+\".zoom\"]=v}iv.call(\"_guiRelayout\",e,l)}function p8(e,t){for(var r=e._fullLayout,n=r._subplots[t]||[],a={},i=0;i<n.length;i++)for(var o=n[i],s=r[o]._subplot,l=s.viewInitial,u=Object.keys(l),c=0;c<u.length;c++){var f=u[c];a[o+\".\"+f]=l[f]}iv.call(\"_guiRelayout\",e,a)}});var nN=fe((P0r,Tde)=>{\"use strict\";var _de=rN(),Lyt=Object.keys(_de),bde=[\"drawline\",\"drawopenpath\",\"drawclosedpath\",\"drawcircle\",\"drawrect\",\"eraseshape\"],wde=[\"v1hovermode\",\"hoverclosest\",\"hovercompare\",\"togglehover\",\"togglespikelines\"].concat(bde),l3=[],Pyt=function(e){if(wde.indexOf(e._cat||e.name)===-1){var t=e.name,r=(e._cat||e.name).toLowerCase();l3.indexOf(t)===-1&&l3.push(t),l3.indexOf(r)===-1&&l3.push(r)}};Lyt.forEach(function(e){Pyt(_de[e])});l3.sort();Tde.exports={DRAW_MODES:bde,backButtons:wde,foreButtons:l3}});var 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Ryt.coerce(n,a,zyt,s,l)}i(\"orientation\"),i(\"bgcolor\",LT.addOpacity(r.paper_bgcolor,.5));var o=LT.contrast(LT.rgb(r.modebar.bgcolor));i(\"color\",LT.addOpacity(o,.3)),i(\"activecolor\",LT.addOpacity(o,.7)),i(\"uirevision\",r.uirevision),i(\"add\"),i(\"remove\")}});var Cde=fe((D0r,kde)=>{\"use strict\";var sN=xi(),qyt=aa(),m8=Xt(),Sde=Jk(),Byt=A4().version,Oyt=new DOMParser;function Ede(e){this.container=e.container,this.element=document.createElement(\"div\"),this.update(e.graphInfo,e.buttons),this.container.appendChild(this.element)}var e1=Ede.prototype;e1.update=function(e,t){this.graphInfo=e;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,a=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",a),this.element.setAttribute(\"role\",\"toolbar\"),this._uid=a,this.element.className=\"modebar modebar--custom\",r.displayModeBar===\"hover\"&&(this.element.className+=\" modebar--hover ease-bg\"),n.modebar.orientation===\"v\"&&(this.element.className+=\" vertical\",t=t.reverse());var i=n.modebar,o=\"#\"+a+\" .modebar-group\";document.querySelectorAll(o).forEach(function(f){f.style.backgroundColor=i.bgcolor});var s=!this.hasButtons(t),l=this.hasLogo!==r.displaylogo,u=this.locale!==r.locale;if(this.locale=r.locale,(s||l||u)&&(this.removeAllButtons(),this.updateButtons(t),r.watermark||r.displaylogo)){var c=this.getLogo();r.watermark&&(c.className=c.className+\" watermark\"),n.modebar.orientation===\"v\"?this.element.insertBefore(c,this.element.childNodes[0]):this.element.appendChild(c),this.hasLogo=!0}this.updateActiveButton(),m8.setStyleOnHover(\"#\"+a+\" .modebar-btn\",\".active\",\".icon path\",\"fill: \"+i.activecolor,\"fill: \"+i.color,this.element)};e1.updateButtons=function(e){var t=this;this.buttons=e,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach(function(r){var n=t.createGroup();r.forEach(function(a){var i=a.name;if(!i)throw new Error(\"must provide button 'name' in button config\");if(t.buttonsNames.indexOf(i)!==-1)throw new Error(\"button name '\"+i+\"' is taken\");t.buttonsNames.push(i);var o=t.createButton(a);t.buttonElements.push(o),n.appendChild(o)}),t.element.appendChild(n)})};e1.createGroup=function(){var e=document.createElement(\"div\");e.className=\"modebar-group\";var t=this.graphInfo._fullLayout.modebar;return e.style.backgroundColor=t.bgcolor,e};e1.createButton=function(e){var t=this,r=document.createElement(\"button\");r.setAttribute(\"type\",\"button\"),r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var n=e.title;n===void 0?n=e.name:typeof n==\"function\"&&(n=n(this.graphInfo)),(n||n===0)&&(r.setAttribute(\"data-title\",n),r.setAttribute(\"aria-label\",n)),e.attr!==void 0&&r.setAttribute(\"data-attr\",e.attr);var a=e.val;a!==void 0&&(typeof a==\"function\"&&(a=a(this.graphInfo)),r.setAttribute(\"data-val\",a));var i=e.click;if(typeof i!=\"function\")throw new Error(\"must provide button 'click' function in button config\");r.addEventListener(\"click\",function(s){e.click(t.graphInfo,s),t.updateActiveButton(s.currentTarget)}),r.setAttribute(\"data-toggle\",e.toggle||!1),e.toggle&&sN.select(r).classed(\"active\",!0);var o=e.icon;return typeof o==\"function\"?r.appendChild(o()):r.appendChild(this.createIcon(o||Sde.question)),r.setAttribute(\"data-gravity\",e.gravity||\"n\"),r};e1.createIcon=function(e){var t=qyt(e.height)?Number(e.height):e.ascent-e.descent,r=\"http://www.w3.org/2000/svg\",n;if(e.path){n=document.createElementNS(r,\"svg\"),n.setAttribute(\"viewBox\",[0,0,e.width,t].join(\" \")),n.setAttribute(\"class\",\"icon\");var a=document.createElementNS(r,\"path\");a.setAttribute(\"d\",e.path),e.transform?a.setAttribute(\"transform\",e.transform):e.ascent!==void 0&&a.setAttribute(\"transform\",\"matrix(1 0 0 -1 0 \"+e.ascent+\")\"),n.appendChild(a)}if(e.svg){var i=Oyt.parseFromString(e.svg,\"application/xml\");n=i.childNodes[0]}return n.setAttribute(\"height\",\"1em\"),n.setAttribute(\"width\",\"1em\"),n};e1.updateActiveButton=function(e){var t=this.graphInfo._fullLayout,r=e!==void 0?e.getAttribute(\"data-attr\"):null;this.buttonElements.forEach(function(n){var a=n.getAttribute(\"data-val\")||!0,i=n.getAttribute(\"data-attr\"),o=n.getAttribute(\"data-toggle\")===\"true\",s=sN.select(n),l=function(f,v){var g=t.modebar,p=f.querySelector(\".icon path\");p&&(v||f.matches(\":hover\")?p.style.fill=g.activecolor:p.style.fill=g.color)};if(o){if(i===r){var u=!s.classed(\"active\");s.classed(\"active\",u),l(n,u)}}else{var c=i===null?i:m8.nestedProperty(t,i).get();s.classed(\"active\",c===a),l(n,c===a)}})};e1.hasButtons=function(e){var t=this.buttons;if(!t||e.length!==t.length)return!1;for(var r=0;r<e.length;++r){if(e[r].length!==t[r].length)return!1;for(var n=0;n<e[r].length;n++)if(e[r][n].name!==t[r][n].name)return!1}return!0};function Nyt(e){return e+\" (v\"+Byt+\")\"}e1.getLogo=function(){var e=this.createGroup(),t=document.createElement(\"a\");return t.href=\"https://plotly.com/\",t.target=\"_blank\",t.setAttribute(\"data-title\",Nyt(m8._(this.graphInfo,\"Produced with Plotly.js\"))),t.className=\"modebar-btn plotlyjsicon modebar-btn--logo\",t.appendChild(this.createIcon(Sde.newplotlylogo)),e.appendChild(t),e};e1.removeAllButtons=function(){for(;this.element.firstChild;)this.element.removeChild(this.element.firstChild);this.hasLogo=!1};e1.destroy=function(){m8.removeElement(this.container.querySelector(\".modebar\"))};function Uyt(e,t){var r=e._fullLayout,n=new Ede({graphInfo:e,container:r._modebardiv.node(),buttons:t});return r._privateplot&&sN.select(n.element).append(\"span\").classed(\"badge-private float--left\",!0).text(\"PRIVATE\"),n}kde.exports=Uyt});var Fde=fe((R0r,Pde)=>{\"use strict\";var Vyt=El(),Lde=Is(),lN=bi(),Gyt=Sd().isUnifiedHover,Hyt=Cde(),y8=rN(),jyt=nN().DRAW_MODES,Wyt=Xt().extendDeep;Pde.exports=function(t){var r=t._fullLayout,n=t._context,a=r._modeBar;if(!n.displayModeBar&&!n.watermark){a&&(a.destroy(),delete r._modeBar);return}if(!Array.isArray(n.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(n.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var i=n.modeBarButtons,o;Array.isArray(i)&&i.length?o=Jyt(i):!n.displayModeBar&&n.watermark?o=[]:o=Yyt(t),a?a.update(t,o):r._modeBar=Hyt(t,o)};function Yyt(e){var t=e._fullLayout,r=e._fullData,n=e._context;function a(j,q){if(typeof q==\"string\"){if(q.toLowerCase()===j.toLowerCase())return!0}else{var U=q.name,J=q._cat||q.name;if(U===j||J===j.toLowerCase())return!0}return!1}var i=t.modebar.add;typeof i==\"string\"&&(i=[i]);var o=t.modebar.remove;typeof o==\"string\"&&(o=[o]);var s=n.modeBarButtonsToAdd.concat(i.filter(function(j){for(var q=0;q<n.modeBarButtonsToRemove.length;q++)if(a(j,n.modeBarButtonsToRemove[q]))return!1;return!0})),l=n.modeBarButtonsToRemove.concat(o.filter(function(j){for(var q=0;q<n.modeBarButtonsToAdd.length;q++)if(a(j,n.modeBarButtonsToAdd[q]))return!1;return!0})),u=t._has(\"cartesian\"),c=t._has(\"gl3d\"),f=t._has(\"geo\"),v=t._has(\"pie\"),g=t._has(\"funnelarea\"),p=t._has(\"ternary\"),x=t._has(\"map\"),_=t._has(\"polar\"),y=t._has(\"smith\"),C=t._has(\"sankey\"),k=Xyt(t),S=Gyt(t.hovermode),L=[];function w(j){if(j.length){for(var q=[],U=0;U<j.length;U++){for(var J=j[U],ee=y8[J],ce=ee.name.toLowerCase(),ye=(ee._cat||ee.name).toLowerCase(),xe=!1,ue=0;ue<l.length;ue++){var K=l[ue].toLowerCase();if(K===ce||K===ye){xe=!0;break}}xe||q.push(y8[J])}L.push(q)}}var A=[\"toImage\"];n.showSendToCloud&&A.push(\"sendChartToCloud\"),w(A);var T=[],m=[],F=[],E=[];(u||v||g||p)+f+c+x+_+y>1?(m=[\"toggleHover\"],F=[\"resetViews\"]):f?(T=[\"zoomInGeo\",\"zoomOutGeo\"],m=[\"hoverClosestGeo\"],F=[\"resetGeo\"]):c?(m=[\"hoverClosest3d\"],F=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):x?(T=[\"zoomInMap\",\"zoomOutMap\"],m=[\"toggleHover\"],F=[\"resetViewMap\"]):v?m=[\"hoverClosestPie\"]:C?(m=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],F=[\"resetViewSankey\"]):m=[\"toggleHover\"],u&&m.push(\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"),($yt(r)||S)&&(m=[]),u&&!k&&(T=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],F[0]!==\"resetViews\"&&(F=[\"resetScale2d\"])),c?E=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:u&&!k||p?E=[\"zoom2d\",\"pan2d\"]:x||f?E=[\"pan2d\"]:_&&(E=[\"zoom2d\"]),Zyt(r)&&E.push(\"select2d\",\"lasso2d\");var I=[],R=function(j){I.indexOf(j)===-1&&m.indexOf(j)!==-1&&I.push(j)};if(Array.isArray(s)){for(var B=[],O=0;O<s.length;O++){var V=s[O];typeof V==\"string\"?(V=V.toLowerCase(),jyt.indexOf(V)!==-1?(t._has(\"map\")||t._has(\"cartesian\"))&&E.push(V):V===\"togglespikelines\"?R(\"toggleSpikelines\"):V===\"togglehover\"?R(\"toggleHover\"):V===\"hovercompare\"?R(\"hoverCompareCartesian\"):V===\"hoverclosest\"?(R(\"hoverClosestCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestPie\")):V===\"v1hovermode\"&&(R(\"hoverClosestCartesian\"),R(\"hoverCompareCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestPie\"))):B.push(V)}s=B}return w(E),w(T.concat(F)),w(I),Kyt(L,s)}function Xyt(e){for(var t=Vyt.list({_fullLayout:e},null,!0),r=0;r<t.length;r++){var n=t[r].modebardisable;if(!t[r].fixedrange&&n!==\"autoscale+zoominout\"&&n!==\"zoominout+autoscale\")return!1}return!0}function Zyt(e){for(var t=!1,r=0;r<e.length&&!t;r++){var n=e[r];!n._module||!n._module.selectPoints||(lN.traceIs(n,\"scatter-like\")&&n.type!==\"quiver\"?(Lde.hasMarkers(n)||Lde.hasText(n))&&(t=!0):lN.traceIs(n,\"box-violin\")?(n.boxpoints===\"all\"||n.points===\"all\")&&(t=!0):t=!0)}return t}function $yt(e){for(var t=0;t<e.length;t++)if(!lN.traceIs(e[t],\"noHover\"))return!1;return!0}function Kyt(e,t){if(t.length)if(Array.isArray(t[0]))for(var r=0;r<t.length;r++)e.push(t[r]);else e.push(t);return e}function Jyt(e){for(var t=Wyt([],e),r=0;r<t.length;r++)for(var n=t[r],a=0;a<n.length;a++){var i=n[a];if(typeof i==\"string\")if(y8[i]!==void 0)t[r][a]=y8[i];else throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"))}return t}});var uN=fe((I0r,Dde)=>{\"use strict\";Dde.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:(oN(),Gs(aN)).default,supplyLayoutDefaults:Mde(),manage:Fde()}});var cN=fe((z0r,Rde)=>{\"use strict\";var Qyt=nc().FROM_BL;Rde.exports=function(t,r,n){n===void 0&&(n=Qyt[t.constraintoward||\"center\"]);var a=[t.r2l(t.range[0]),t.r2l(t.range[1])],i=a[0]+(a[1]-a[0])*n;t.range=t._input.range=[t.l2r(i+(a[0]-i)*r),t.l2r(i+(a[1]-i)*r)],t.setScale()}});var Jx=fe(PT=>{\"use strict\";var Kx=Xt(),fN=rp(),ip=El().id2name,ext=pf(),Ide=cN(),txt=Hp(),rxt=Wa().ALMOST_EQUAL,nxt=nc().FROM_BL;PT.handleDefaults=function(e,t,r){var n=r.axIds,a=r.axHasImage,i=t._axisConstraintGroups=[],o=t._axisMatchGroups=[],s,l,u,c,f,v,g,p;for(s=0;s<n.length;s++)c=ip(n[s]),f=e[c],v=t[c],ixt(f,v,{axIds:n,layoutOut:t,hasImage:a[c]});function x(T,m){for(s=0;s<T.length;s++){l=T[s];for(u in l)t[ip(u)][m]=l}}for(x(o,\"_matchGroup\"),s=0;s<i.length;s++){l=i[s];for(u in l)if(v=t[ip(u)],v.fixedrange){for(var _ in l){var y=ip(_);(e[y]||{}).fixedrange===!1&&Kx.warn(\"fixedrange was specified as false for axis \"+y+\" but was overridden because another axis in its constraint group has fixedrange true\"),t[y].fixedrange=!0}break}}for(s=0;s<i.length;){l=i[s];for(u in l){v=t[ip(u)],v._matchGroup&&Object.keys(v._matchGroup).length===Object.keys(l).length&&(i.splice(s,1),s--);break}s++}x(i,\"_constraintGroup\");var C=[\"constrain\",\"range\",\"autorange\",\"rangemode\",\"rangebreaks\",\"categoryorder\",\"categoryarray\"],k=!1,S=!1;function L(){p=v[g],g===\"rangebreaks\"&&(S=v._hasDayOfWeekBreaks)}for(s=0;s<o.length;s++){l=o[s];for(var w=0;w<C.length;w++){g=C[w],p=null;var A;for(u in l)if(c=ip(u),f=e[c],v=t[c],g in v){if(!v.matches&&(A=v,g in f)){L();break}p===null&&g in f&&L()}if(g===\"range\"&&p&&f.range&&f.range.length===2&&f.range[0]!==null&&f.range[1]!==null&&(k=!0),g===\"autorange\"&&p===null&&k&&(p=!1),p===null&&g in A&&(p=A[g]),p!==null)for(u in l)v=t[ip(u)],v[g]=g===\"range\"?p.slice():p,g===\"rangebreaks\"&&(v._hasDayOfWeekBreaks=S,txt(v,t))}}};function ixt(e,t,r){var n=r.axIds,a=r.layoutOut,i=r.hasImage,o=a._axisConstraintGroups,s=a._axisMatchGroups,l=t._id,u=l.charAt(0),c=((a._splomAxes||{})[u]||{})[l]||{},f=t._id,v=f.charAt(0)===\"x\";t._matchGroup=null,t._constraintGroup=null;function g(I,R){return Kx.coerce(e,t,ext,I,R)}g(\"constrain\",i?\"domain\":\"range\"),Kx.coerce(e,t,{constraintoward:{valType:\"enumerated\",values:v?[\"left\",\"center\",\"right\"]:[\"bottom\",\"middle\",\"top\"],dflt:v?\"center\":\"middle\"}},\"constraintoward\");var p=t.type,x,_,y=[];for(x=0;x<n.length;x++)if(_=n[x],_!==f){var C=a[ip(_)];C.type===p&&y.push(_)}var k=Bde(o,f);if(k){var S=[];for(x=0;x<y.length;x++)_=y[x],k[_]||S.push(_);y=S}var L=y.length,w,A;L&&(e.matches||c.matches)&&(w=Kx.coerce(e,t,{matches:{valType:\"enumerated\",values:y,dflt:y.indexOf(c.matches)!==-1?c.matches:void 0}},\"matches\"));var T=i&&!v?t.anchor:void 0;if(L&&!w&&(e.scaleanchor||T)&&(A=Kx.coerce(e,t,{scaleanchor:{valType:\"enumerated\",values:y.concat([!1])}},\"scaleanchor\",T)),w){t._matchGroup=hN(s,f,w,1);var m=a[ip(w)],F=zde(a,t)/zde(a,m);v!==(w.charAt(0)===\"x\")&&(F=(v?\"x\":\"y\")+F),hN(o,f,w,F)}else e.matches&&n.indexOf(e.matches)!==-1&&Kx.warn(\"ignored \"+t._name+'.matches: \"'+e.matches+'\" to avoid an infinite loop');if(A){var E=g(\"scaleratio\");E||(E=t.scaleratio=1),hN(o,f,A,E)}else e.scaleanchor&&n.indexOf(e.scaleanchor)!==-1&&Kx.warn(\"ignored \"+t._name+'.scaleanchor: \"'+e.scaleanchor+'\" to avoid either an infinite loop and possibly inconsistent scaleratios, or because this axis declares a *matches* constraint.')}function zde(e,t){var r=t.domain;return r||(r=e[ip(t.overlaying)].domain),r[1]-r[0]}function Bde(e,t){for(var r=0;r<e.length;r++)if(e[r][t])return e[r];return null}function hN(e,t,r,n){var a,i,o,s,l,u=Bde(e,t);u===null?(u={},u[t]=1,l=e.length,e.push(u)):l=e.indexOf(u);var c=Object.keys(u);for(a=0;a<e.length;a++)if(o=e[a],a!==l&&o[r]){var f=o[r];for(i=0;i<c.length;i++)s=c[i],o[s]=dN(f,dN(n,u[s]));e.splice(l,1);return}if(n!==1)for(i=0;i<c.length;i++){var v=c[i];u[v]=dN(n,u[v])}u[r]=1}function dN(e,t){var r=\"\",n=\"\",a,i;typeof e==\"string\"&&(r=e.match(/^[xy]*/)[0],a=r.length,e=+e.slice(a)),typeof t==\"string\"&&(n=t.match(/^[xy]*/)[0],i=n.length,t=+t.slice(i));var o=e*t;return!a&&!i?o:!a||!i||r.charAt(0)===n.charAt(0)?r+n+e*t:a===i?o:(a>i?r.slice(i):n.slice(a))+o}function axt(e,t){for(var r=t._size,n=r.h/r.w,a={},i=Object.keys(e),o=0;o<i.length;o++){var s=i[o],l=e[s];if(typeof l==\"string\"){var u=l.match(/^[xy]*/)[0],c=u.length;l=+l.slice(c);for(var f=u.charAt(0)===\"y\"?n:1/n,v=0;v<c;v++)l*=f}a[s]=l}return a}PT.enforce=function(t){var r=t._fullLayout,n=r._axisConstraintGroups||[],a,i,o,s,l,u,c,f;for(a=0;a<n.length;a++){o=axt(n[a],r);var 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w=l.r2l(l.range[0]),A=l.r2l(l.range[1]),T=(w+A)/2,m=T,F=T,E=Math.abs(A-T),I=T-E*f*1.0001,R=T+E*f*1.0001,B=fN.makePadFn(r,l,0),O=fN.makePadFn(r,l,1);qde(l,f);var V=Math.abs(l._m),j=fN.concatExtremes(t,l),q=j.min,U=j.max,J,ee;for(ee=0;ee<q.length;ee++)J=q[ee].val-B(q[ee])/V,J>I&&J<m&&(m=J);for(ee=0;ee<U.length;ee++)J=U[ee].val+O(U[ee])/V,J<R&&J>F&&(F=J);var ce=(F-m)/(2*E);f/=ce,m=l.l2r(m),F=l.l2r(F),l.range=l._input.range=w<A?[m,F]:[F,m]}qde(l,f)}}}};PT.getAxisGroup=function(t,r){for(var n=t._axisMatchGroups,a=0;a<n.length;a++){var i=n[a];if(i[r])return\"g\"+a}return r};PT.clean=function(t,r){if(r._inputDomain){for(var n=!1,a=r._id,i=t._fullLayout._axisConstraintGroups,o=0;o<i.length;o++)if(i[o][a]){n=!0;break}(!n||r.constrain!==\"domain\")&&(r._input.domain=r.domain=r._inputDomain,delete r._inputDomain)}};function qde(e,t){var r=e._inputDomain,n=nxt[e.constraintoward],a=r[0]+(r[1]-r[0])*n;e.domain=e._input.domain=[a+(r[0]-a)/t,a+(r[1]-a)/t],e.setScale()}});var RT=fe(Hc=>{\"use 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w=t._bgLayer.selectAll(\".bg\").data(g);for(w.enter().append(\"rect\").classed(\"bg\",!0),w.exit().remove(),w.each(function(ie){t._plots[ie].bg=_8.select(this)}),i=0;i<p.length;i++)s=t._plots[p[i]],u=s.xaxis,c=s.yaxis,s.bg&&u._offset!==void 0&&c._offset!==void 0&&s.bg.call(u3.setRect,u._offset-n,c._offset-n,u._length+2*n,c._length+2*n).call(FT.fill,t.plot_bgcolor).style(\"stroke-width\",0);if(!t._hasOnlyLargeSploms)for(o in t._plots){s=t._plots[o],u=s.xaxis,c=s.yaxis;var A=s.clipId=\"clip\"+t._uid+o+\"plot\",T=av.ensureSingleById(t._clips,\"clipPath\",A,function(ie){ie.classed(\"plotclip\",!0).append(\"rect\")});s.clipRect=T.select(\"rect\").attr({width:u._length,height:c._length}),u3.setTranslate(s.plot,u._offset,c._offset);var m,F;s._hasClipOnAxisFalse?(m=null,F=A):(m=A,F=null),u3.setClipUrl(s.plot,m,e),s.layerClipId=F}var E,I,R,B,O,V,j,q,U,J,ee,ce,ye;function xe(ie){return\"M\"+E+\",\"+ie+\"H\"+I}function ue(ie){return\"M\"+u._offset+\",\"+ie+\"h\"+u._length}function 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pe=\"M0,0\";Ude(c,o)&&(ee=x8(c,\"bottom\",u,a),j=c._offset+c._length+(ee?n:0),ce=x8(c,\"top\",u,a),q=c._offset-(ce?n:0),U=f(c,u,\"left\"),J=f(c,u,\"right\"),ye=!c._anchorAxis||o!==c._mainSubplot,ye&&(c.mirror===\"allticks\"||c.mirror===\"all\")&&(c._linepositions[o]=[U,J]),pe=ne(c,K,_e),ye&&c.showline&&(c.mirror===\"all\"||c.mirror===\"allticks\")&&(pe+=K(U)+K(J)),s.ylines.style(\"stroke-width\",c._lw+\"px\").call(FT.stroke,c.showline?c.linecolor:\"rgba(0,0,0,0)\")),s.ylines.attr(\"d\",pe)}return DT.makeClipPaths(e),L0.previousPromises(e)}function Ude(e,t){return(e.ticks||e.showline)&&(t===e._mainSubplot||e.mirror===\"all\"||e.mirror===\"allticks\")}function Vde(e,t,r){if(!r.showline||!r._lw)return!1;if(r.mirror===\"all\"||r.mirror===\"allticks\")return!0;var n=r._anchorAxis;if(!n)return!1;var a=ng.FROM_BL[t];return r.side===t?n.domain[a]===e.domain[a]:r.mirror&&n.domain[1-a]===e.domain[1-a]}function x8(e,t,r,n){if(Vde(e,t,r))return r._lw;for(var a=0;a<n.length;a++){var 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g=s.node().getBBox(),p=g.y+g.height,x=p+Ode.SUBTITLE_PADDING_EM*t.subtitle.font.size;v.attr({x:o,y:x,\"text-anchor\":n,dy:Gde(t.yanchor)}).call(vN.positionText,o,x)}}}};function hxt(e,t,r,n,a){var i=t.yref===\"paper\"?e._fullLayout._size.h:e._fullLayout.height,o=av.isTopAnchor(t)?n:n-a,s=r===\"b\"?i-o:o;return av.isTopAnchor(t)&&r===\"t\"||av.isBottomAnchor(t)&&r===\"b\"?!1:s<a}function dxt(e,t,r,n,a){var i=0;return r===\"middle\"&&(i+=a/2),e===\"t\"?(r===\"top\"&&(i+=a),i+=n-t*n):(r===\"bottom\"&&(i+=a),i+=t*n),i}function vxt(e,t,r){var n=t.y,a=t.yanchor,i=n>.5?\"t\":\"b\",o=e._fullLayout.margin[i],s=0;return t.yref===\"paper\"?s=r+t.pad.t+t.pad.b:t.yref===\"container\"&&(s=dxt(i,n,a,e._fullLayout.height,r)+t.pad.t+t.pad.b),s>o?s:0}function pxt(e,t,r,n){var a=\"title.automargin\",i=e._fullLayout.title,o=i.y>.5?\"t\":\"b\",s={x:i.x,y:i.y,t:0,b:0},l={};i.yref===\"paper\"&&hxt(e,i,o,t,n)?s[o]=r:i.yref===\"container\"&&(l[o]=r,e._fullLayout._reservedMargin[a]=l),L0.allowAutoMargin(e,a),L0.autoMargin(e,a,s)}function gxt(e,t){var r=e.title,n=e._size,a=0;return t===Wde?a=r.pad.l:t===Yde&&(a=-r.pad.r),r.xref===\"paper\"?n.l+n.w*r.x+a:e.width*r.x+a}function mxt(e,t){var r=e.title,n=e._size,a=0;return t===\"0em\"||!t?a=-r.pad.b:t===ng.CAP_SHIFT+\"em\"&&(a=r.pad.t),r.y===\"auto\"?n.t/2:r.yref===\"paper\"?n.t+n.h-n.h*r.y+a:e.height-e.height*r.y+a}function Gde(e){return e===\"top\"?ng.CAP_SHIFT+.3+\"em\":e===\"bottom\"?\"-0.3em\":ng.MID_SHIFT+\"em\"}function yxt(e){var t=e.title,r=lxt;return av.isRightAnchor(t)?r=Yde:av.isLeftAnchor(t)&&(r=Wde),r}function xxt(e){var t=e.title,r=\"0em\";return av.isTopAnchor(t)?r=ng.CAP_SHIFT+\"em\":av.isMiddleAnchor(t)&&(r=ng.MID_SHIFT+\"em\"),r}Hc.doTraceStyle=function(e){var t=e.calcdata,r=[],n;for(n=0;n<t.length;n++){var a=t[n],i=a[0]||{},o=i.trace||{},s=o._module||{},l=s.arraysToCalcdata;l&&l(a,o);var u=s.editStyle;u&&r.push({fn:u,cd0:i})}if(r.length){for(n=0;n<r.length;n++){var c=r[n];c.fn(e,c.cd0)}pN(e),Hc.redrawReglTraces(e)}return L0.style(e),nd.getComponentMethod(\"legend\",\"draw\")(e),L0.previousPromises(e)};Hc.doColorBars=function(e){return nd.getComponentMethod(\"colorbar\",\"draw\")(e),L0.previousPromises(e)};Hc.layoutReplot=function(e){var t=e.layout;return e.layout=void 0,nd.call(\"_doPlot\",e,\"\",t)};Hc.doLegend=function(e){return nd.getComponentMethod(\"legend\",\"draw\")(e),L0.previousPromises(e)};Hc.doTicksRelayout=function(e){var t=DT.draw(e,\"redraw\");return e._fullLayout._hasOnlyLargeSploms&&(nd.subplotsRegistry.splom.updateGrid(e),pN(e),Hc.redrawReglTraces(e)),Hc.drawMainTitle(e),Promise.resolve(t).then(function(){return L0.previousPromises(e)})};Hc.doModeBar=function(e){var t=e._fullLayout;Hde.manage(e);for(var 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s=r[i];s.visible===!0&&s._length!==0&&(s.type===\"splom\"?t._splomScenes[s.uid].draw():s.type===\"scattergl\"?av.pushUnique(n,s.xaxis+s.yaxis):s.type===\"scatterpolargl\"&&av.pushUnique(a,s.subplot))}for(i=0;i<n.length;i++)o=t._plots[n[i]],o._scene&&o._scene.draw();for(i=0;i<a.length;i++)o=t[a[i]]._subplot,o._scene&&o._scene.draw()}};Hc.doAutoRangeAndConstraints=function(e){for(var t=DT.list(e,\"\",!0),r,n={},a=0;a<t.length;a++)if(r=t[a],!n[r._id]){n[r._id]=1,sxt(e,r),Nde(e,r);var i=r._matchGroup;if(i)for(var o in i){var s=DT.getFromId(e,o);Nde(e,s,r.range),n[o]=1}}oxt(e)};Hc.finalDraw=function(e){nd.getComponentMethod(\"rangeslider\",\"draw\")(e),nd.getComponentMethod(\"rangeselector\",\"draw\")(e)};Hc.drawMarginPushers=function(e){nd.getComponentMethod(\"legend\",\"draw\")(e),nd.getComponentMethod(\"rangeselector\",\"draw\")(e),nd.getComponentMethod(\"sliders\",\"draw\")(e),nd.getComponentMethod(\"updatemenus\",\"draw\")(e),nd.getComponentMethod(\"colorbar\",\"draw\")(e)}});var yN=fe((O0r,Jde)=>{\"use strict\";var _xt=Em().readPaths,bxt=l8(),Xde=pm().clearOutlineControllers,gN=ni(),Zde=ea(),wxt=Mo().arrayEditor,$de=Lm(),Txt=$de.getPathString;Jde.exports={draw:b8,drawOne:Kde,activateLastSelection:Sxt};function b8(e){var t=e._fullLayout;Xde(e),t._selectionLayer.selectAll(\"path\").remove();for(var r in t._plots){var n=t._plots[r].selectionLayer;n&&n.selectAll(\"path\").remove()}for(var a=0;a<t.selections.length;a++)Kde(e,a)}function w8(e){return e._context.editSelection}function Kde(e,t){e._fullLayout._paperdiv.selectAll('.selectionlayer [data-index=\"'+t+'\"]').remove();var r=$de.makeSelectionsOptionsAndPlotinfo(e,t),n=r.options,a=r.plotinfo;if(!n._input)return;i(e._fullLayout._selectionLayer);function i(o){var s=Txt(e,n),l={\"data-index\":t,\"fill-rule\":\"evenodd\",d:s},u=n.opacity,c=\"rgba(0,0,0,0)\",f=n.line.color||gN.contrast(e._fullLayout.plot_bgcolor),v=n.line.width,g=n.line.dash;v||(v=5,g=\"solid\");var p=w8(e)&&e._fullLayout._activeSelectionIndex===t;p&&(c=e._fullLayout.activeselection.fillcolor,u=e._fullLayout.activeselection.opacity);for(var x=[],_=1;_>=0;_--){var y=o.append(\"path\").attr(l).style(\"opacity\",_?.1:u).call(gN.stroke,f).call(gN.fill,c).call(Zde.dashLine,_?\"solid\":g,_?4+v:v);if(Axt(y,e,n),p){var C=wxt(e.layout,\"selections\",n);y.style({cursor:\"move\"});var k={element:y.node(),plotinfo:a,gd:e,editHelpers:C,isActiveSelection:!0},S=_xt(s,e);bxt(S,y,k)}else y.style(\"pointer-events\",_?\"all\":\"none\");x[_]=y}var L=x[0],w=x[1];w.node().addEventListener(\"click\",function(){return Mxt(e,L)})}}function Axt(e,t,r){var n=r.xref+r.yref;Zde.setClipUrl(e,\"clip\"+t._fullLayout._uid+n,t)}function Mxt(e,t){if(w8(e)){var r=t.node(),n=+r.getAttribute(\"data-index\");if(n>=0){if(n===e._fullLayout._activeSelectionIndex){mN(e);return}e._fullLayout._activeSelectionIndex=n,e._fullLayout._deactivateSelection=mN,b8(e)}}}function Sxt(e){if(w8(e)){var 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n=e.root,a=e.root.next;a!==null;){if(r(a)){t.prev=a.prev,t.next=a,a.prev.next=t,a.prev=t;return}n=a,a=a.next}n.next=t,t.prev=n,t.next=null},findTransition:function(t){for(var r=e.root,n=e.root.next;n!==null&&!t(n);)r=n,n=n.next;return{before:r===e.root?null:r,after:n,insert:function(a){return a.prev=r,a.next=n,r.next=a,n!==null&&(n.prev=a),a}}}};return e},node:function(e){return e.prev=null,e.next=null,e.remove=function(){e.prev.next=e.next,e.next&&(e.next.prev=e.prev),e.prev=null,e.next=null},e}};n0e.exports=Cxt});var o0e=fe((G0r,a0e)=>{var IT=i0e();function Lxt(e,t,r){function n(p,x){return{id:r?r.segmentId():-1,start:p,end:x,myFill:{above:null,below:null},otherFill:null}}function a(p,x,_){return{id:r?r.segmentId():-1,start:p,end:x,myFill:{above:_.myFill.above,below:_.myFill.below},otherFill:null}}var i=IT.create();function o(p,x,_,y,C,k){var S=t.pointsCompare(x,C);return S!==0?S:t.pointsSame(_,k)?0:p!==y?p?1:-1:t.pointAboveOrOnLine(_,y?C:k,y?k:C)?1:-1}function 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ye=t.linesIntersect(U,J,ee,ce);if(ye===!1){if(!t.pointsCollinear(U,J,ee)||t.pointsSame(U,ce)||t.pointsSame(J,ee))return!1;var xe=t.pointsSame(U,ee),ue=t.pointsSame(J,ce);if(xe&&ue)return V;var K=!xe&&t.pointBetween(U,ee,ce),_e=!ue&&t.pointBetween(J,ee,ce);if(xe)return _e?v(V,J):v(O,ce),V;K&&(ue||(_e?v(V,J):v(O,ce)),v(V,U))}else ye.alongA===0&&(ye.alongB===-1?v(O,ee):ye.alongB===0?v(O,ye.pt):ye.alongB===1&&v(O,ce)),ye.alongB===0&&(ye.alongA===-1?v(V,U):ye.alongA===0?v(V,ye.pt):ye.alongA===1&&v(V,J));return!1}for(var S=[];!i.isEmpty();){var L=i.getHead();if(r&&r.vert(L.pt[0]),L.isStart){let O=function(){if(A){var V=k(L,A);if(V)return V}return T?k(L,T):!1};var B=O;r&&r.segmentNew(L.seg,L.primary);var w=C(L),A=w.before?w.before.ev:null,T=w.after?w.after.ev:null;r&&r.tempStatus(L.seg,A?A.seg:!1,T?T.seg:!1);var m=O();if(m){if(e){var F;L.seg.myFill.below===null?F=!0:F=L.seg.myFill.above!==L.seg.myFill.below,F&&(m.seg.myFill.above=!m.seg.myFill.above)}else 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w=a2t(s,v),A=w.pointNumbers.length>0;if(A?o2t(v,w):s2t(v)&&(C=E0e(w))){for(o&&o.remove(),L=0;L<v.length;L++)g=v[L],g._module.selectPoints(g,!1);WT(t,v),jT(i),c&&LN(t)}else{k=e.shiftKey&&(C!==void 0?C:E0e(w)),p=e2t(w.pointNumber,w.searchInfo,k);var T=i.selectionDefs.concat([p]);for(x=kN(T,x),L=0;L<v.length;L++)if(_=v[L]._module.selectPoints(v[L],x),y=z0e(_,v[L]),f.length)for(var m=0;m<y.length;m++)f.push(y[m]);else f=y;if(S={points:f},WT(t,v,S),p&&i&&i.selectionDefs.push(p),o){var F=i.mergedPolygons,E=TN(i.dragmode);C0e(q0e(F,E),o,i)}c&&YT(t,S)}}}function e2t(e,t,r){return{pointNumber:e,searchInfo:t,subtract:!!r}}function bN(e){return\"pointNumber\"in e&&\"searchInfo\"in e}function t2t(e){return{xmin:0,xmax:0,ymin:0,ymax:0,pts:[],contains:function(t,r,n,a){var i=e.searchInfo.cd[0].trace.index,o=a.cd[0].trace.index;return o===i&&n===e.pointNumber},isRect:!1,degenerate:!1,subtract:!!e.subtract}}function kN(e){if(!e.length)return;for(var 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r=e.dragmode,n=e.plotinfo,a=e.gd;r2t(a)&&a._fullLayout._deactivateShape(a),n2t(a)&&a._fullLayout._deactivateSelection(a);var i=a._fullLayout,o=i._zoomlayer,s=HT(r),l=AN(r);if(s||l){var u=o.selectAll(\".select-outline-\"+n.id);if(u&&a._fullLayout._outlining){var c;s&&(c=Hxt(u,e)),c&&GT.call(\"_guiRelayout\",a,{shapes:c});var f;l&&!EN(e)&&(f=jxt(u,e)),f&&(a._fullLayout._noEmitSelectedAtStart=!0,GT.call(\"_guiRelayout\",a,{selections:f}).then(function(){t&&Wxt(a)})),a._fullLayout._outlining=!1}}n.selection={},n.selection.selectionDefs=e.selectionDefs=[],n.selection.mergedPolygons=e.mergedPolygons=[]}function S0e(e){return e._id}function k8(e,t,r,n){if(!e.calcdata)return[];var a=[],i=t.map(S0e),o=r.map(S0e),s,l,u;for(u=0;u<e.calcdata.length;u++)if(s=e.calcdata[u],l=s[0].trace,!(l.visible!==!0||!l._module||!l._module.selectPoints))if(EN({subplot:n})&&(l.subplot===n||l.geo===n))a.push(M8(l._module,s,t[0],r[0]));else if(l.type===\"splom\"){if(l._xaxes[i[0]]&&l._yaxes[o[0]]){var c=M8(l._module,s,t[0],r[0]);c.scene=e._fullLayout._splomScenes[l.uid],a.push(c)}}else if(l.type===\"sankey\"){var f=M8(l._module,s,t[0],r[0]);a.push(f)}else{if(i.indexOf(l.xaxis)===-1&&(!l._xA||!l._xA.overlaying)||o.indexOf(l.yaxis)===-1&&(!l._yA||!l._yA.overlaying))continue;a.push(M8(l._module,s,sv(e,l.xaxis),sv(e,l.yaxis)))}return a}function M8(e,t,r,n){return{_module:e,cd:t,xaxis:r,yaxis:n}}function i2t(e){return e&&Array.isArray(e)&&e[0].hoverOnBox!==!0}function a2t(e,t){var r=e[0],n=-1,a=[],i,o;for(o=0;o<t.length;o++)if(i=t[o],r.fullData.index===i.cd[0].trace.index){if(r.hoverOnBox===!0)break;r.pointNumber!==void 0?n=r.pointNumber:r.binNumber!==void 0&&(n=r.binNumber,a=r.pointNumbers);break}return{pointNumber:n,pointNumbers:a,searchInfo:i}}function E0e(e){var t=e.searchInfo.cd[0].trace,r=e.pointNumber,n=e.pointNumbers,a=n.length>0,i=a?n[0]:r;return t.selectedpoints?t.selectedpoints.indexOf(i)>-1:!1}function o2t(e,t){var r=[],n,a,i,o;for(o=0;o<e.length;o++)n=e[o],n.cd[0].trace.selectedpoints&&n.cd[0].trace.selectedpoints.length>0&&r.push(n);if(r.length===1&&(i=r[0]===t.searchInfo,i&&(a=t.searchInfo.cd[0].trace,a.selectedpoints.length===t.pointNumbers.length))){for(o=0;o<t.pointNumbers.length;o++)if(a.selectedpoints.indexOf(t.pointNumbers[o])<0)return!1;return!0}return!1}function s2t(e){var t=0,r,n,a;for(a=0;a<e.length;a++)if(r=e[a],n=r.cd[0].trace,n.selectedpoints&&(n.selectedpoints.length>1||(t+=n.selectedpoints.length,t>1)))return!1;return t===1}function WT(e,t,r){var n,a;for(n=0;n<t.length;n++){a=t[n].cd[0].trace;var i=e._fullLayout._tracePreGUI[a.uid]||{};i.selectedpoints===void 0&&(i.selectedpoints=a._input.selectedpoints||null)}if(r){var o=r.points||[];for(n=0;n<t.length;n++)a=t[n].cd[0].trace,a._input.selectedpoints=a.selectedpoints=[];for(var s=0;s<o.length;s++){var l=o[s],u=l.data,c=l.pointIndex,f=l.pointIndices;f?[].push.apply(u.selectedpoints,f):u.selectedpoints.push(c)}}else 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mve=yN(),JT=G0e();yve.exports={moduleType:\"component\",name:\"selections\",layoutAttributes:FN(),supplyLayoutDefaults:fve(),supplyDrawNewSelectionDefaults:dve(),includeBasePlot:KT()(\"selections\"),draw:mve.draw,drawOne:mve.drawOne,reselect:JT.reselect,prepSelect:JT.prepSelect,clearOutline:JT.clearOutline,clearSelectionsCache:JT.clearSelectionsCache,selectOnClick:JT.selectOnClick}});var NN=fe((fvr,qve)=>{\"use strict\";var BN=xi(),lv=Xt(),xve=lv.numberFormat,L2t=Uk(),F8=bi(),Eve=lv.strTranslate,P2t=as(),RN=ni(),Dm=ea(),F2t=kl(),_ve=Yi(),D2t=np(),R2t=ed(),kve=$p(),D8=kve.selectingOrDrawing,I2t=kve.freeMode,z2t=nc().FROM_TL,q2t=ST(),B2t=RT().redrawReglTraces,O2t=Hs(),IN=El().getFromId,N2t=Ac().prepSelect,U2t=Ac().clearOutline,V2t=Ac().selectOnClick,DN=cN(),ON=rc(),bve=ON.MINDRAG,kd=ON.MINZOOM,wve=!0;function G2t(e,t,r,n,a,i,o,s){var l=e._fullLayout._zoomlayer,u=o+s===\"nsew\",c=(o+s).length===1,f,v,g,p,x,_,y,C,k,S,L,w,A,T,m,F,E,I,R,B,O,V,j;r+=t.yaxis._shift;function 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wt=(F||w.isSubplotConstrained)&&!mr.fixedrange&&g[mr._id],Tt=(E||w.isSubplotConstrained)&&!or.fixedrange&&p[or._id],Mt,Lt,ft,kt;if(wt?(Mt=cr,ft=s||w.isSubplotConstrained?nt[0]:tt(mr,Mt)):w.xaHash[mr._id]?(Mt=cr,ft=nt[0]*mr._length/f._length):w.yaHash[mr._id]?(Mt=Ct,ft=T===\"ns\"?-nt[1]*mr._length/v._length:tt(mr,Mt,{n:\"top\",s:\"bottom\"}[T])):(Mt=$e(mr,cr,Ct),ft=ot(mr,Mt)),Mt>1&&(mr.maxallowed!==void 0&&F===(mr.range[0]<mr.range[1]?\"e\":\"w\")||mr.minallowed!==void 0&&F===(mr.range[0]<mr.range[1]?\"w\":\"e\"))&&(Mt=1,ft=0),Tt?(Lt=Ct,kt=o||w.isSubplotConstrained?nt[1]:tt(or,Lt)):w.yaHash[or._id]?(Lt=Ct,kt=nt[1]*or._length/v._length):w.xaHash[or._id]?(Lt=cr,kt=A===\"ew\"?-nt[0]*or._length/f._length:tt(or,Lt,{e:\"right\",w:\"left\"}[A])):(Lt=$e(or,cr,Ct),kt=ot(or,Lt)),Lt>1&&(or.maxallowed!==void 0&&E===(or.range[0]<or.range[1]?\"n\":\"s\")||or.minallowed!==void 0&&E===(or.range[0]<or.range[1]?\"s\":\"n\"))&&(Lt=1,kt=0),!(!Mt&&!Lt)){Mt||(Mt=1),Lt||(Lt=1);var rr=mr._offset-ft/Mt,pr=or._offset-kt/Lt;jt.clipRect.call(Dm.setTranslate,ft,kt).call(Dm.setScale,Mt,Lt),jt.plot.call(Dm.setTranslate,rr,pr).call(Dm.setScale,1/Mt,1/Lt),(Mt!==jt.xScaleFactor||Lt!==jt.yScaleFactor)&&(Dm.setPointGroupScale(jt.zoomScalePts,Mt,Lt),Dm.setTextPointsScale(jt.zoomScaleTxt,Mt,Lt)),Dm.hideOutsideRangePoints(jt.clipOnAxisFalseTraces,jt),jt.xScaleFactor=Mt,jt.yScaleFactor=Lt}}}}function $e(nt,We,mt){return nt.fixedrange?0:F&&L.xaHash[nt._id]?We:E&&(L.isSubplotConstrained?L.xaHash:L.yaHash)[nt._id]?mt:0}function ot(nt,We){return We?(nt.range=nt._r.slice(),DN(nt,We),tt(nt,We)):0}function tt(nt,We,mt){return nt._length*(1-We)*z2t[mt||nt.constraintoward||\"middle\"]}return J}function Cve(e,t,r,n){var a=lv.ensureSingle(e.draglayer,t,r,function(i){i.classed(\"drag\",!0).style({fill:\"transparent\",\"stroke-width\":0}).attr(\"data-subplot\",e.id)});return a.call(D2t,n),a.node()}function Lve(e,t,r,n,a,i,o){var s=Cve(e,\"rect\",t,r);return 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t=Math.floor(Math.min(e.b-e.t,e.r-e.l,kd)/2);return\"M\"+(e.l-3.5)+\",\"+(e.t-.5+t)+\"h3v\"+-t+\"h\"+t+\"v-3h-\"+(t+3)+\"ZM\"+(e.r+3.5)+\",\"+(e.t-.5+t)+\"h-3v\"+-t+\"h\"+-t+\"v-3h\"+(t+3)+\"ZM\"+(e.r+3.5)+\",\"+(e.b+.5-t)+\"h-3v\"+t+\"h\"+-t+\"v3h\"+(t+3)+\"ZM\"+(e.l-3.5)+\",\"+(e.b+.5-t)+\"h3v\"+t+\"h\"+t+\"v3h-\"+(t+3)+\"Z\"}function Mve(e,t,r,n,a){for(var i=!1,o={},s={},l,u,c,f,v=(a||{}).xaHash,g=(a||{}).yaHash,p=0;p<t.length;p++){var x=t[p];for(l in r)if(x[l]){for(c in x)!(a&&(v[c]||g[c]))&&!(c.charAt(0)===\"x\"?r:n)[c]&&(o[c]=l);for(u in n)!(a&&(v[u]||g[u]))&&x[u]&&(i=!0)}for(u in n)if(x[u])for(f in x)!(a&&(v[f]||g[f]))&&!(f.charAt(0)===\"x\"?r:n)[f]&&(s[f]=u)}i&&(lv.extendFlat(o,s),s={});var _={},y=[];for(c in o){var C=IN(e,c);y.push(C),_[C._id]=C}var k={},S=[];for(f in s){var L=IN(e,f);S.push(L),k[L._id]=L}return{xaHash:_,yaHash:k,xaxes:y,yaxes:S,xLinks:o,yLinks:s,isSubplotConstrained:i}}function zve(e,t){if(!L2t)e.onwheel!==void 0?e.onwheel=t:e.onmousewheel!==void 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c=s._mainLinePosition;s.side===\"top\"&&(c-=jc),ap(t,o,s._offset+s._length*.1,c,s._length*.8,jc,\"\",\"ew\"),ap(t,o,s._offset,c,s._length*.1,jc,\"\",\"w\"),ap(t,o,s._offset+s._length*.9,c,s._length*.1,jc,\"\",\"e\")}if(i===l._mainSubplot){var f=l._mainLinePosition;l.side!==\"right\"&&(f-=jc),ap(t,o,f,l._offset+l._length*.1,jc,l._length*.8,\"ns\",\"\"),ap(t,o,f,l._offset+l._length*.9,jc,l._length*.1,\"s\",\"\"),ap(t,o,f,l._offset,jc,l._length*.1,\"n\",\"\")}}});var a=r._hoverlayer.node();a.onmousemove=function(i){i.target=t._fullLayout._lasthover,I8.hover(t,i,r._hoversubplot)},a.onclick=function(i){i.target=t._fullLayout._lasthover,I8.click(t,i)},a.onmousedown=function(i){t._fullLayout._lasthover.onmousedown(i)},z8.updateFx(t)}};z8.updateFx=function(e){var t=e._fullLayout,r=t.dragmode===\"pan\"?\"move\":\"crosshair\";K2t(t._draggers,r)}});var Nve=fe((dvr,Ove)=>{\"use strict\";var Bve=bi();Ove.exports=function(t){for(var r=Bve.layoutArrayContainers,n=Bve.layoutArrayRegexes,a=t.split(\"[\")[0],i,o,s=0;s<n.length;s++)if(o=t.match(n[s]),o&&o.index===0){i=o[0];break}if(i||(i=r[r.indexOf(a)]),!i)return!1;var l=t.slice(i.length);return l?(o=l.match(/^\\[(0|[1-9][0-9]*)\\](\\.(.+))?$/),o?{array:i,index:Number(o[1]),property:o[3]||\"\"}:!1):{array:i,index:\"\",property:\"\"}}});var Vve=fe(eA=>{\"use strict\";var J2t=em(),VN=PE(),QT=W1(),Q2t=VE().sorterAsc,GN=bi();eA.containerArrayMatch=Nve();var e_t=eA.isAddVal=function(t){return t===\"add\"||J2t(t)},Uve=eA.isRemoveVal=function(t){return t===null||t===\"remove\"};eA.applyContainerArrayChanges=function(t,r,n,a,i){var o=r.astr,s=GN.getComponentMethod(o,\"supplyLayoutDefaults\"),l=GN.getComponentMethod(o,\"draw\"),u=GN.getComponentMethod(o,\"drawOne\"),c=a.replot||a.recalc||s===VN||l===VN,f=t.layout,v=t._fullLayout;if(n[\"\"]){Object.keys(n).length>1&&QT.warn(\"Full array edits are incompatible with other edits\",o);var g=n[\"\"][\"\"];if(Uve(g))r.set(null);else 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R;if(k===-1)R=p;else{for(S=Math.max(_.length,S),R=[],L=0;L<p.length&&(A=p[L],!(A>=k));L++)R.push(A);for(L=k;L<S;L++)R.push(L)}for(L=0;L<R.length;L++)u(t,R[L])}else l(t);return!0}});var Zve=fe(uv=>{\"use strict\";var Wve=aa(),Yve=bi(),id=Xt(),tA=Hs(),Xve=El(),rA=Xve.cleanId,t_t=Xve.getFromTrace,HN=Yve.traceIs,r_t=[\"x\",\"y\",\"z\"];uv.clearPromiseQueue=function(e){Array.isArray(e._promises)&&e._promises.length>0&&id.log(\"Clearing previous rejected promises from queue.\"),e._promises=[]};uv.cleanLayout=function(e){var t,r;e||(e={}),e.xaxis1&&(e.xaxis||(e.xaxis=e.xaxis1),delete e.xaxis1),e.yaxis1&&(e.yaxis||(e.yaxis=e.yaxis1),delete e.yaxis1),e.scene1&&(e.scene||(e.scene=e.scene1),delete e.scene1);var n=(tA.subplotsRegistry.cartesian||{}).attrRegex,a=(tA.subplotsRegistry.polar||{}).attrRegex,i=(tA.subplotsRegistry.ternary||{}).attrRegex,o=(tA.subplotsRegistry.gl3d||{}).attrRegex,s=Object.keys(e);for(t=0;t<s.length;t++){var l=s[t];if(n&&n.test(l)){var u=e[l];u.anchor&&u.anchor!==\"free\"&&(u.anchor=rA(u.anchor)),u.overlaying&&(u.overlaying=rA(u.overlaying)),u.type||(u.isdate?u.type=\"date\":u.islog?u.type=\"log\":u.isdate===!1&&u.islog===!1&&(u.type=\"linear\")),(u.autorange===\"withzero\"||u.autorange===\"tozero\")&&(u.autorange=!0,u.rangemode=\"tozero\"),u.insiderange&&delete u.range,delete u.islog,delete u.isdate,delete u.categories,q8(u,\"domain\")&&delete u.domain}}var c=Array.isArray(e.annotations)?e.annotations.length:0;for(t=0;t<c;t++){var f=e.annotations[t];id.isPlainObject(f)&&(c3(f,\"xref\"),c3(f,\"yref\"))}var v=Array.isArray(e.shapes)?e.shapes.length:0;for(t=0;t<v;t++){var g=e.shapes[t];id.isPlainObject(g)&&(c3(g,\"xref\",!0),c3(g,\"yref\",!0))}var p=Array.isArray(e.images)?e.images.length:0;for(t=0;t<p;t++){var x=e.images[t];id.isPlainObject(x)&&(c3(x,\"xref\"),c3(x,\"yref\"))}var _=e.legend;return _&&(_.x>3?(_.x=1.02,_.xanchor=\"left\"):_.x<-2&&(_.x=-.02,_.xanchor=\"right\"),_.y>3?(_.y=1.02,_.yanchor=\"bottom\"):_.y<-2&&(_.y=-.02,_.yanchor=\"top\")),e.dragmode===\"rotate\"&&(e.dragmode=\"orbit\"),e.template&&e.template.layout&&uv.cleanLayout(e.template.layout),e};function c3(e,t,r=!1){var n=e[t],a=t.charAt(0);r&&Array.isArray(n)||n&&n!==\"paper\"&&(e[t]=rA(n,a,!0))}uv.cleanData=function(e){for(var t=0;t<e.length;t++){var r=e[t],n;if(r.type===\"histogramy\"&&\"xbins\"in r&&!(\"ybins\"in r)&&(r.ybins=r.xbins,delete r.xbins),r.type===\"histogramy\"&&uv.swapXYData(r),(r.type===\"histogramx\"||r.type===\"histogramy\")&&(r.type=\"histogram\"),\"scl\"in r&&!(\"colorscale\"in r)&&(r.colorscale=r.scl,delete r.scl),\"reversescl\"in r&&!(\"reversescale\"in r)&&(r.reversescale=r.reversescl,delete r.reversescl),r.xaxis&&(r.xaxis=rA(r.xaxis,\"x\")),r.yaxis&&(r.yaxis=rA(r.yaxis,\"y\")),HN(r,\"gl3d\")&&r.scene&&(r.scene=tA.subplotsRegistry.gl3d.cleanId(r.scene)),!HN(r,\"pie-like\")&&!HN(r,\"bar-like\"))if(Array.isArray(r.textposition))for(n=0;n<r.textposition.length;n++)r.textposition[n]=Hve(r.textposition[n]);else r.textposition&&(r.textposition=Hve(r.textposition));var a=Yve.getModule(r);if(a&&a.colorbar){var i=a.colorbar.container,o=i?r[i]:r;o&&o.colorscale&&(o.colorscale===\"YIGnBu\"&&(o.colorscale=\"YlGnBu\"),o.colorscale===\"YIOrRd\"&&(o.colorscale=\"YlOrRd\"))}if(r.type===\"surface\"&&id.isPlainObject(r.contours)){var s=[\"x\",\"y\",\"z\"];for(n=0;n<s.length;n++){var l=r.contours[s[n]];id.isPlainObject(l)&&(l.highlightColor&&(l.highlightcolor=l.highlightColor,delete l.highlightColor),l.highlightWidth&&(l.highlightwidth=l.highlightWidth,delete l.highlightWidth))}}if(r.type===\"candlestick\"||r.type===\"ohlc\"){var u=(r.increasing||{}).showlegend!==!1,c=(r.decreasing||{}).showlegend!==!1,f=Gve(r.increasing),v=Gve(r.decreasing);if(f!==!1&&v!==!1){var g=n_t(f,v,u,c);g&&(r.name=g)}else(f||v)&&!r.name&&(r.name=f||v)}q8(r,\"line\")&&delete r.line,\"marker\"in r&&(q8(r.marker,\"line\")&&delete r.marker.line,q8(r,\"marker\")&&delete r.marker),r.autobinx&&(delete r.autobinx,delete r.xbins),r.autobiny&&(delete r.autobiny,delete r.ybins)}};function Gve(e){if(!id.isPlainObject(e))return!1;var t=e.name;return delete e.name,delete e.showlegend,(typeof t==\"string\"||typeof t==\"number\")&&String(t)}function n_t(e,t,r,n){if(r&&!n)return e;if(n&&!r||!e.trim())return t;if(!t.trim())return e;var a=Math.min(e.length,t.length),i;for(i=0;i<a&&e.charAt(i)===t.charAt(i);i++);var o=e.slice(0,i);return o.trim()}function Hve(e){var t=\"middle\",r=\"center\";return typeof 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nA(e,t,r){e=vi.getGraphDiv(e),zu.clearPromiseQueue(e);var n={};if(typeof t==\"string\")n[t]=r;else if(vi.isPlainObject(t))n=vi.extendFlat({},t);else return vi.warn(\"Relayout fail.\",t,r),Promise.reject();Object.keys(n).length&&(e.changed=!0);var a=vpe(e,n),i=a.flags;i.calc&&(e.calcdata=void 0);var o=[wa.previousPromises];i.layoutReplot?o.push(cs.layoutReplot):Object.keys(n).length&&(fpe(e,i,a)||wa.supplyDefaults(e),i.legend&&o.push(cs.doLegend),i.layoutstyle&&o.push(cs.layoutStyles),i.axrange&&X8(o,a.rangesAltered),i.ticks&&o.push(cs.doTicksRelayout),i.modebar&&o.push(cs.doModeBar),i.camera&&o.push(cs.doCamera),i.colorbars&&o.push(cs.doColorBars),o.push(iA)),o.push(wa.rehover,wa.redrag,wa.reselect),Cd.add(e,nA,[e,a.undoit],nA,[e,a.redoit]);var s=vi.syncOrAsync(o,e);return(!s||!s.then)&&(s=Promise.resolve(e)),s.then(function(){return e.emit(\"plotly_relayout\",a.eventData),e})}function fpe(e,t,r){var n=e._fullLayout;if(!t.axrange)return!1;for(var a in t)if(a!==\"axrange\"&&t[a])return!1;var i,o,s=function(g,p){return vi.coerce(i,o,l_t,g,p)},l={};for(var u in r.rangesAltered){var c=ad.id2name(u);if(i=e.layout[c],o=n[c],s_t(i,o,s,l),o._matchGroup){for(var f in o._matchGroup)if(f!==u){var v=n[ad.id2name(f)];v.autorange=o.autorange,v.range=o.range.slice(),v._input.range=o.range.slice()}}}return!0}function X8(e,t){var r=t?function(n){var a=[],i=!0;for(var o in t){var s=ad.getFromId(n,o);if(a.push(o),(s.ticklabelposition||\"\").indexOf(\"inside\")!==-1&&s._anchorAxis&&a.push(s._anchorAxis._id),s._matchGroup)for(var l in s._matchGroup)t[l]||a.push(l)}return ad.draw(n,a,{skipTitle:i})}:function(n){return ad.draw(n,\"redraw\")};e.push(h_t,cs.doAutoRangeAndConstraints,r,cs.drawData,cs.finalDraw)}var hpe=/^[xyz]axis[0-9]*\\.range(\\[[0|1]\\])?$/,dpe=/^[xyz]axis[0-9]*\\.autorange$/,T_t=/^[xyz]axis[0-9]*\\.domain(\\[[0|1]\\])?$/;function vpe(e,t){var r=e.layout,n=e._fullLayout,a=n._guiEditing,i=O8(n._preGUI,a),o=Object.keys(t),s=ad.list(e),l=vi.extendDeepAll({},t),u={},c,f,v;for(o=Object.keys(t),f=0;f<o.length;f++)if(o[f].indexOf(\"allaxes\")===0){for(v=0;v<s.length;v++){var g=s[v]._id.slice(1),p=g.indexOf(\"scene\")!==-1?g+\".\":\"\",x=o[f].replace(\"allaxes\",p+s[v]._name);t[x]||(t[x]=t[o[f]])}delete t[o[f]]}var _=Rm.layoutFlags(),y={},C={};function k(ht,it){if(Array.isArray(ht)){ht.forEach(function(ze){k(ze,it)});return}if(!(ht in t||zu.hasParent(t,ht))){var Oe=i(r,ht);ht in C||(C[ht]=ig(Oe.get())),it!==void 0&&Oe.set(it)}}var S={},L;function w(ht){var it=ad.name2id(ht.split(\".\")[0]);return S[it]=1,it}for(var A in t){if(zu.hasParent(t,A))throw new Error(\"cannot set \"+A+\" and a parent attribute simultaneously\");for(var T=i(r,A),m=t[A],F=T.parts.length,E=F-1;E>0&&typeof T.parts[E]!=\"string\";)E--;var I=T.parts[E],R=T.parts[E-1]+\".\"+I,B=T.parts.slice(0,E).join(\".\"),O=Ws(e.layout,B).get(),V=Ws(n,B).get(),j=T.get();if(m!==void 0){y[A]=m,C[A]=I===\"reverse\"?m:ig(j);var q=Y8.getLayoutValObject(n,T.parts);if(q&&q.impliedEdits&&m!==null)for(var U in q.impliedEdits)k(vi.relativeAttr(A,U),q.impliedEdits[U]);if([\"width\",\"height\"].indexOf(A)!==-1)if(m){k(\"autosize\",null);var J=A===\"height\"?\"width\":\"height\";k(J,n[J])}else n[A]=e._initialAutoSize[A];else if(A===\"autosize\")k(\"width\",m?null:n.width),k(\"height\",m?null:n.height);else if(R.match(hpe))w(R),Ws(n,B+\"._inputRange\").set(null);else if(R.match(dpe)){w(R),Ws(n,B+\"._inputRange\").set(null);var ee=Ws(n,B).get();ee._inputDomain&&(ee._input.domain=ee._inputDomain.slice())}else R.match(T_t)&&Ws(n,B+\"._inputDomain\").set(null);if(I===\"type\"){L=O;var ce=V.type===\"linear\"&&m===\"log\",ye=V.type===\"log\"&&m===\"linear\";if(ce||ye){if(!L||!L.range)k(B+\".autorange\",!0);else 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ae=ne.property,pe=q||{editType:\"calc\"};f!==\"\"&&ae===\"\"&&(B8.isAddVal(m)?C[A]=null:B8.isRemoveVal(m)?C[A]=(Ws(r,c).get()||[])[f]:vi.warn(\"unrecognized full object value\",t)),Rm.update(_,pe),u[c]||(u[c]={});var ie=u[c][f];ie||(ie=u[c][f]={}),ie[ae]=m,delete t[A]}else I===\"reverse\"?(O.range?O.range.reverse():(k(B+\".autorange\",!0),O.range=[1,0]),V.autorange?_.calc=!0:_.plot=!0):(A===\"dragmode\"&&(m===!1&&j!==!1||m!==!1&&j===!1)||n._has(\"scatter-like\")&&n._has(\"regl\")&&A===\"dragmode\"&&(m===\"lasso\"||m===\"select\")&&!(j===\"lasso\"||j===\"select\")?_.plot=!0:q?Rm.update(_,q):_.calc=!0,T.set(m))}}for(c in u){var le=B8.applyContainerArrayChanges(e,i(r,c),u[c],_,i);le||(_.plot=!0)}for(var me in S){L=ad.getFromId(e,me);var be=L&&L._constraintGroup;if(be){_.calc=!0;for(var De in be)S[De]||(ad.getFromId(e,De)._constraintShrinkable=!0)}}(ppe(e)||t.height||t.width)&&(_.plot=!0);var Ge=n.shapes;for(f=0;f<Ge.length;f++)if(Ge[f].showlegend){_.calc=!0;break}return(_.plot||_.calc)&&(_.layoutReplot=!0),{flags:_,rangesAltered:S,undoit:C,redoit:y,eventData:l}}function ppe(e){var t=e._fullLayout,r=t.width,n=t.height;return e.layout.autosize&&wa.plotAutoSize(e,e.layout,t),t.width!==r||t.height!==n}function j8(e,t,r,n){e=vi.getGraphDiv(e),zu.clearPromiseQueue(e),vi.isPlainObject(t)||(t={}),vi.isPlainObject(r)||(r={}),Object.keys(t).length&&(e.changed=!0),Object.keys(r).length&&(e.changed=!0);var a=zu.coerceTraceIndices(e,n),i=cpe(e,vi.extendFlat({},t),a),o=i.flags,s=vpe(e,vi.extendFlat({},r)),l=s.flags;(o.calc||l.calc)&&(e.calcdata=void 0),o.clearAxisTypes&&zu.clearAxisTypes(e,a,r);var u=[];l.layoutReplot?u.push(cs.layoutReplot):o.fullReplot?u.push(So._doPlot):(u.push(wa.previousPromises),fpe(e,l,s)||wa.supplyDefaults(e),o.style&&u.push(cs.doTraceStyle),(o.colorbars||l.colorbars)&&u.push(cs.doColorBars),l.legend&&u.push(cs.doLegend),l.layoutstyle&&u.push(cs.layoutStyles),l.axrange&&X8(u,s.rangesAltered),l.ticks&&u.push(cs.doTicksRelayout),l.modebar&&u.push(cs.doModeBar),l.camera&&u.push(cs.doCamera),u.push(iA)),u.push(wa.rehover,wa.redrag,wa.reselect),Cd.add(e,j8,[e,i.undoit,s.undoit,i.traces],j8,[e,i.redoit,s.redoit,i.traces]);var c=vi.syncOrAsync(u,e);return(!c||!c.then)&&(c=Promise.resolve(e)),c.then(function(){return e.emit(\"plotly_update\",{data:i.eventData,layout:s.eventData}),e})}function $N(e){return function(r){r._fullLayout._guiEditing=!0;var n=e.apply(null,arguments);return r._fullLayout._guiEditing=!1,n}}var A_t=[{pattern:/^hiddenlabels/,attr:\"legend.uirevision\"},{pattern:/^((x|y)axis\\d*)\\.((auto)?range|title\\.text)/},{pattern:/axis\\d*\\.showspikes$/,attr:\"modebar.uirevision\"},{pattern:/(hover|drag)mode$/,attr:\"modebar.uirevision\"},{pattern:/^(scene\\d*)\\.camera/},{pattern:/^(geo\\d*)\\.(projection|center|fitbounds)/},{pattern:/^(ternary\\d*\\.[abc]axis)\\.(min|title\\.text)$/},{pattern:/^(polar\\d*\\.radialaxis)\\.((auto)?range|angle|title\\.text)/},{pattern:/^(polar\\d*\\.angularaxis)\\.rotation/},{pattern:/^(map\\d*)\\.(center|zoom|bearing|pitch)/},{pattern:/^legend\\.(x|y)$/,attr:\"editrevision\"},{pattern:/^(shapes|annotations)/,attr:\"editrevision\"},{pattern:/^selections/,attr:\"selectionrevision\"},{pattern:/^title\\.text$/,attr:\"editrevision\"}],M_t=[{pattern:/^selectedpoints$/,attr:\"selectionrevision\"},{pattern:/^visible$/,attr:\"legend.uirevision\"},{pattern:/^dimensions\\[\\d+\\]\\.constraintrange/},{pattern:/^node\\.(x|y|groups)/},{pattern:/^level$/},{pattern:/^name$/},{pattern:/colorbar\\.title\\.text$/},{pattern:/colorbar\\.(x|y)$/,attr:\"editrevision\"}];function Kve(e,t){for(var r=0;r<t.length;r++){var n=t[r],a=e.match(n.pattern);if(a){var i=a[1]||\"\";return{head:i,tail:e.slice(i.length+1),attr:n.attr}}}}function Jve(e,t){var r=Ws(t,e).get();if(r!==void 0)return r;var n=e.split(\".\");for(n.pop();n.length>1;)if(n.pop(),r=Ws(t,n.join(\".\")+\".uirevision\").get(),r!==void 0)return r;return t.uirevision}function S_t(e,t){for(var r=0;r<t.length;r++)if(t[r].uid===e)return r;return-1}function E_t(e,t,r){for(var n=0;n<t.length;n++)if(t[n].uid===e)return n;return!t[r]||t[r].uid?-1:r}function Qve(e,t){var r=vi.isPlainObject(e),n=Array.isArray(e);return r||n?(r&&vi.isPlainObject(t)||n&&Array.isArray(t))&&JSON.stringify(e)===JSON.stringify(t):e===t}function k_t(e,t,r,n){var a=n._preGUI,i,o,s,l,u,c,f,v,g,p,x=[],_={},y={};for(i in a){if(u=Kve(i,A_t),u){if(g=u.head,p=u.tail,o=u.attr||g+\".uirevision\",s=Ws(n,o).get(),l=s&&Jve(o,t),l&&l===s){if(c=a[i],c===null&&(c=void 0),f=Ws(t,i),v=f.get(),Qve(v,c)){v===void 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The template has \"+e.templateCount+\" traces, the data has \"+e.dataCount+\" of this type.\";break}return e.msg=t,e}});var qpe=fe(xl=>{\"use strict\";var Mc=Z8();xl._doPlot=Mc._doPlot;xl.newPlot=Mc.newPlot;xl.restyle=Mc.restyle;xl.relayout=Mc.relayout;xl.redraw=Mc.redraw;xl.update=Mc.update;xl._guiRestyle=Mc._guiRestyle;xl._guiRelayout=Mc._guiRelayout;xl._guiUpdate=Mc._guiUpdate;xl._storeDirectGUIEdit=Mc._storeDirectGUIEdit;xl.react=Mc.react;xl.extendTraces=Mc.extendTraces;xl.prependTraces=Mc.prependTraces;xl.addTraces=Mc.addTraces;xl.deleteTraces=Mc.deleteTraces;xl.moveTraces=Mc.moveTraces;xl.purge=Mc.purge;xl.addFrames=Mc.addFrames;xl.deleteFrames=Mc.deleteFrames;xl.animate=Mc.animate;xl.setPlotConfig=Mc.setPlotConfig;var bbt=X4().getGraphDiv,wbt=d8().eraseActiveShape;xl.deleteActiveShape=function(e){return wbt(bbt(e))};xl.toImage=eU();xl.validate=Ape();xl.downloadImage=rU();var zpe=Ipe();xl.makeTemplate=zpe.makeTemplate;xl.validateTemplate=zpe.validateTemplate});var r2=fe((kvr,Bpe)=>{\"use strict\";var iU=Xt(),Tbt=bi();Bpe.exports=function(t,r,n,a){var i=a(\"x\"),o=a(\"y\"),s,l=Tbt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");if(l(t,r,[\"x\",\"y\"],n),i){var u=iU.minRowLength(i);o?s=Math.min(u,iU.minRowLength(o)):(s=u,a(\"y0\"),a(\"dy\"))}else{if(!o)return 0;s=iU.minRowLength(o),a(\"x0\"),a(\"dx\")}return r._length=s,s}});var sp=fe((Cvr,Upe)=>{\"use strict\";var Ope=Xt().dateTick0,Abt=Wa(),Mbt=Abt.ONEWEEK;function Npe(e,t){return e%Mbt===0?Ope(t,1):Ope(t,0)}Upe.exports=function(t,r,n,a,i){if(i||(i={x:!0,y:!0}),i.x){var o=a(\"xperiod\");o&&(a(\"xperiod0\",Npe(o,r.xcalendar)),a(\"xperiodalignment\"))}if(i.y){var s=a(\"yperiod\");s&&(a(\"yperiod0\",Npe(s,r.ycalendar)),a(\"yperiodalignment\"))}}});var Hpe=fe((Lvr,Gpe)=>{\"use strict\";var Vpe=[\"orientation\",\"groupnorm\",\"stackgaps\"];Gpe.exports=function(t,r,n,a){var i=n._scatterStackOpts,o=a(\"stackgroup\");if(o){var s=r.xaxis+r.yaxis,l=i[s];l||(l=i[s]={});var u=l[o],c=!1;u?u.traces.push(r):(u=l[o]={traceIndices:[],traces:[r]},c=!0);for(var f={orientation:r.x&&!r.y?\"h\":\"v\"},v=0;v<Vpe.length;v++){var g=Vpe[v],p=g+\"Found\";if(!u[p]){var x=t[g]!==void 0,_=g===\"orientation\";if((x||c)&&(u[g]=a(g,f[g]),_&&(u.fillDflt=u[g]===\"h\"?\"tonextx\":\"tonexty\"),x&&(u[p]=!0,!c&&(delete u.traces[0][g],_))))for(var y=0;y<u.traces.length-1;y++){var C=u.traces[y];C._input.fill!==C.fill&&(C.fill=u.fillDflt)}}}return u}}});var vv=fe((Pvr,Xpe)=>{\"use strict\";var jpe=ni(),Wpe=Wf().hasColorscale,Ype=Vc(),Sbt=Is();Xpe.exports=function(t,r,n,a,i,o={}){var s=Sbt.isBubble(t),l=(t.line||{}).color,u;if(l&&(n=l),i(\"marker.symbol\"),i(\"marker.opacity\",s?.7:1),i(\"marker.size\"),o.noAngle||(i(\"marker.angle\"),o.noAngleRef||i(\"marker.angleref\"),o.noStandOff||i(\"marker.standoff\")),i(\"marker.color\",n),Wpe(t,\"marker\")&&Ype(t,r,a,i,{prefix:\"marker.\",cLetter:\"c\"}),o.noSelect||(i(\"selected.marker.color\"),i(\"unselected.marker.color\"),i(\"selected.marker.size\"),i(\"unselected.marker.size\")),o.noLine||(l&&!Array.isArray(l)&&r.marker.color!==l?u=l:s?u=jpe.background:u=jpe.defaultLine,i(\"marker.line.color\",u),Wpe(t,\"marker.line\")&&Ype(t,r,a,i,{prefix:\"marker.line.\",cLetter:\"c\"}),i(\"marker.line.width\",s?1:0),o.noLineDash||i(\"marker.line.dash\")),s&&(i(\"marker.sizeref\"),i(\"marker.sizemin\"),i(\"marker.sizemode\")),o.gradient){var c=i(\"marker.gradient.type\");c!==\"none\"&&i(\"marker.gradient.color\")}}});var lp=fe((Fvr,Zpe)=>{\"use strict\";var Ebt=Xt().isArrayOrTypedArray,kbt=Wf().hasColorscale,Cbt=Vc();Zpe.exports=function(t,r,n,a,i,o){o||(o={});var s=(t.marker||{}).color;if(s&&s._inputArray&&(s=s._inputArray),i(\"line.color\",n),kbt(t,\"line\"))Cbt(t,r,a,i,{prefix:\"line.\",cLetter:\"c\"});else{var l=(Ebt(s)?!1:s)||n;i(\"line.color\",l)}i(\"line.width\"),o.noDash||i(\"line.dash\"),o.backoff&&i(\"line.backoff\")}});var f3=fe((Dvr,$pe)=>{\"use strict\";$pe.exports=function(t,r,n){var a=n(\"line.shape\");a===\"spline\"&&n(\"line.smoothing\")}});var up=fe((Rvr,Kpe)=>{\"use strict\";var Lbt=Xt();Kpe.exports=function(e,t,r,n,a){a=a||{},n(\"textposition\"),Lbt.coerceFont(n,\"textfont\",a.font||r.font,a),a.noSelect||(n(\"selected.textfont.color\"),n(\"unselected.textfont.color\"))}});var a1=fe((Ivr,Qpe)=>{\"use strict\";var nC=ni(),Jpe=Xt().isArrayOrTypedArray;function Pbt(e){for(var t=nC.interpolate(e[0][1],e[1][1],.5),r=2;r<e.length;r++){var n=nC.interpolate(e[r-1][1],e[r][1],.5);t=nC.interpolate(t,n,e[r-1][0]/e[r][0])}return t}Qpe.exports=function(t,r,n,a,i){i||(i={});var o=!1;if(r.marker){var s=r.marker.color,l=(r.marker.line||{}).color;s&&!Jpe(s)?o=s:l&&!Jpe(l)&&(o=l)}var u;if(i.moduleHasFillgradient){var c=a(\"fillgradient.type\");if(c!==\"none\"){a(\"fillgradient.start\"),a(\"fillgradient.stop\");var f=a(\"fillgradient.colorscale\");f&&(u=Pbt(f))}}a(\"fillcolor\",nC.addOpacity((r.line||{}).color||o||u||n,.5))}});var n1e=fe((zvr,r1e)=>{\"use strict\";var e1e=Xt(),Fbt=bi(),Dbt=Ol(),Rbt=r1(),h3=Is(),Ibt=r2(),zbt=sp(),qbt=Hpe(),Bbt=vv(),Obt=lp(),t1e=f3(),Nbt=up(),Ubt=a1(),Vbt=Xt().coercePattern;r1e.exports=function(t,r,n,a){function i(g,p){return e1e.coerce(t,r,Dbt,g,p)}var o=Ibt(t,r,a,i);if(o||(r.visible=!1),!!r.visible){zbt(t,r,a,i),i(\"xhoverformat\"),i(\"yhoverformat\"),i(\"zorder\");var s=qbt(t,r,a,i);a.scattermode===\"group\"&&r.orientation===void 0&&i(\"orientation\",\"v\");var l=!s&&o<Rbt.PTS_LINESONLY?\"lines+markers\":\"lines\";i(\"text\"),i(\"hovertext\"),i(\"mode\",l),h3.hasMarkers(r)&&Bbt(t,r,n,a,i,{gradient:!0}),h3.hasLines(r)&&(Obt(t,r,n,a,i,{backoff:!0}),t1e(t,r,i),i(\"connectgaps\"),i(\"line.simplify\")),h3.hasText(r)&&(i(\"texttemplate\"),i(\"texttemplatefallback\"),Nbt(t,r,a,i));var u=[];(h3.hasMarkers(r)||h3.hasText(r))&&(i(\"cliponaxis\"),i(\"marker.maxdisplayed\"),u.push(\"points\")),i(\"fill\",s?s.fillDflt:\"none\"),r.fill!==\"none\"&&(Ubt(t,r,n,i,{moduleHasFillgradient:!0}),h3.hasLines(r)||t1e(t,r,i),Vbt(i,\"fillpattern\",r.fillcolor,!1));var c=(r.line||{}).color,f=(r.marker||{}).color;(r.fill===\"tonext\"||r.fill===\"toself\")&&u.push(\"fills\"),i(\"hoveron\",u.join(\"+\")||\"points\"),r.hoveron!==\"fills\"&&(i(\"hovertemplate\"),i(\"hovertemplatefallback\"));var v=Fbt.getComponentMethod(\"errorbars\",\"supplyDefaults\");v(t,r,c||f||n,{axis:\"y\"}),v(t,r,c||f||n,{axis:\"x\",inherit:\"y\"}),e1e.coerceSelectionMarkerOpacity(r,i)}}});var n2=fe((qvr,i1e)=>{\"use strict\";var Gbt=Jx().getAxisGroup;i1e.exports=function(t,r,n,a,i){var o=r.orientation,s=r[{v:\"x\",h:\"y\"}[o]+\"axis\"],l=Gbt(n,s)+o,u=n._alignmentOpts||{},c=a(\"alignmentgroup\"),f=u[l];f||(f=u[l]={});var v=f[c];v?v.traces.push(r):v=f[c]={traces:[r],alignmentIndex:Object.keys(f).length,offsetGroups:{}};var g=a(\"offsetgroup\")||\"\",p=v.offsetGroups,x=p[g];r._offsetIndex=0,(i!==\"group\"||g)&&(x||(x=p[g]={offsetIndex:Object.keys(p).length}),r._offsetIndex=x.offsetIndex)}});var aU=fe((Bvr,a1e)=>{\"use strict\";var Hbt=Xt(),jbt=n2(),Wbt=Ol();a1e.exports=function(t,r){var n,a,i,o=r.scattermode;function s(v){return Hbt.coerce(a._input,a,Wbt,v)}if(r.scattermode===\"group\")for(i=0;i<t.length;i++)a=t[i],a.type===\"scatter\"&&(n=a._input,jbt(n,a,r,s,o));for(i=0;i<t.length;i++){var l=t[i];if(l.type===\"scatter\"){var u=l.fill;if(!(u===\"none\"||u===\"toself\")&&(l.opacity=void 0,u===\"tonexty\"||u===\"tonextx\"))for(var c=i-1;c>=0;c--){var f=t[c];if(f.type===\"scatter\"&&f.xaxis===l.xaxis&&f.yaxis===l.yaxis){f.opacity=void 0;break}}}}}});var s1e=fe((Ovr,o1e)=>{\"use strict\";var Ybt=Xt(),Xbt=nk();o1e.exports=function(e,t){function r(a,i){return Ybt.coerce(e,t,Xbt,a,i)}var n=t.barmode===\"group\";t.scattermode===\"group\"&&r(\"scattergap\",n?t.bargap:.2)}});var cp=fe((Nvr,u1e)=>{\"use strict\";var Zbt=aa(),l1e=Xt(),$bt=l1e.dateTime2ms,iC=l1e.incrementMonth,Kbt=Wa(),Jbt=Kbt.ONEAVGMONTH;u1e.exports=function(t,r,n,a){if(r.type!==\"date\")return{vals:a};var i=t[n+\"periodalignment\"];if(!i)return{vals:a};var o=t[n+\"period\"],s;if(Zbt(o)){if(o=+o,o<=0)return{vals:a}}else if(typeof o==\"string\"&&o.charAt(0)===\"M\"){var l=+o.substring(1);if(l>0&&Math.round(l)===l)s=l;else return{vals:a}}for(var u=r.calendar,c=i===\"start\",f=i===\"end\",v=t[n+\"period0\"],g=$bt(v,u)||0,p=[],x=[],_=[],y=a.length,C=0;C<y;C++){var k=a[C],S,L,w;if(s){for(S=Math.round((k-g)/(s*Jbt)),w=iC(g,s*S,u);w>k;)w=iC(w,-s,u);for(;w<=k;)w=iC(w,s,u);L=iC(w,-s,u)}else{for(S=Math.round((k-g)/o),w=g+S*o;w>k;)w-=o;for(;w<=k;)w+=o;L=w-o}p[C]=c?L:f?w:(L+w)/2,x[C]=L,_[C]=w}return{vals:p,starts:x,ends:_}}});var pv=fe((Uvr,f1e)=>{\"use strict\";var oU=Wf().hasColorscale,sU=Mh(),c1e=Is();f1e.exports=function(t,r){c1e.hasLines(r)&&oU(r,\"line\")&&sU(t,r,{vals:r.line.color,containerStr:\"line\",cLetter:\"c\"}),c1e.hasMarkers(r)&&(oU(r,\"marker\")&&sU(t,r,{vals:r.marker.color,containerStr:\"marker\",cLetter:\"c\"}),oU(r,\"marker.line\")&&sU(t,r,{vals:r.marker.line.color,containerStr:\"marker.line\",cLetter:\"c\"}))}});var o1=fe((Vvr,h1e)=>{\"use strict\";var ru=Xt();h1e.exports=function(t,r){for(var n=0;n<t.length;n++)t[n].i=n;ru.mergeArray(r.text,t,\"tx\"),ru.mergeArray(r.texttemplate,t,\"txt\"),ru.mergeArray(r.hovertext,t,\"htx\"),ru.mergeArray(r.customdata,t,\"data\"),ru.mergeArray(r.textposition,t,\"tp\"),r.textfont&&(ru.mergeArrayCastPositive(r.textfont.size,t,\"ts\"),ru.mergeArray(r.textfont.color,t,\"tc\"),ru.mergeArray(r.textfont.family,t,\"tf\"),ru.mergeArray(r.textfont.weight,t,\"tw\"),ru.mergeArray(r.textfont.style,t,\"ty\"),ru.mergeArray(r.textfont.variant,t,\"tv\"),ru.mergeArray(r.textfont.textcase,t,\"tC\"),ru.mergeArray(r.textfont.lineposition,t,\"tE\"),ru.mergeArray(r.textfont.shadow,t,\"tS\"));var a=r.marker;if(a){ru.mergeArrayCastPositive(a.size,t,\"ms\"),ru.mergeArrayCastPositive(a.opacity,t,\"mo\"),ru.mergeArray(a.symbol,t,\"mx\"),ru.mergeArray(a.angle,t,\"ma\"),ru.mergeArray(a.standoff,t,\"mf\"),ru.mergeArray(a.color,t,\"mc\");var i=a.line;a.line&&(ru.mergeArray(i.color,t,\"mlc\"),ru.mergeArrayCastPositive(i.width,t,\"mlw\"),ru.mergeArray(i.dash,t,\"mld\"));var o=a.gradient;o&&o.type!==\"none\"&&(ru.mergeArray(o.type,t,\"mgt\"),ru.mergeArray(o.color,t,\"mgc\"))}}});var P0=fe((Gvr,v1e)=>{\"use strict\";var d1e=Xt();v1e.exports=function(t,r){d1e.isArrayOrTypedArray(r.selectedpoints)&&d1e.tagSelected(t,r)}});var gv=fe((Hvr,b1e)=>{\"use strict\";var p1e=aa(),uU=Xt(),lA=Yi(),g1e=cp(),lU=Wa().BADNUM,cU=Is(),Qbt=pv(),e3t=o1(),t3t=P0();function r3t(e,t){var r=e._fullLayout,n=t._xA=lA.getFromId(e,t.xaxis||\"x\",\"x\"),a=t._yA=lA.getFromId(e,t.yaxis||\"y\",\"y\"),i=n.makeCalcdata(t,\"x\"),o=a.makeCalcdata(t,\"y\"),s=g1e(t,n,\"x\",i),l=g1e(t,a,\"y\",o),u=s.vals,c=l.vals,f=t._length,v=new Array(f),g=t.ids,p=fU(t,r,n,a),x=!1,_,y,C,k,S,L;x1e(r,t);var w=\"x\",A=\"y\",T;if(p)uU.pushUnique(p.traceIndices,t.index),_=p.orientation===\"v\",_?(A=\"s\",T=\"x\"):(w=\"s\",T=\"y\"),S=p.stackgaps===\"interpolate\";else{var m=y1e(t,f);m1e(e,t,n,a,u,c,m)}var F=!!t.xperiodalignment,E=!!t.yperiodalignment;for(y=0;y<f;y++){var I=v[y]={},R=p1e(u[y]),B=p1e(c[y]);R&&B?(I[w]=u[y],I[A]=c[y],F&&(I.orig_x=i[y],I.xEnd=s.ends[y],I.xStart=s.starts[y]),E&&(I.orig_y=o[y],I.yEnd=l.ends[y],I.yStart=l.starts[y])):p&&(_?R:B)?(I[T]=_?u[y]:c[y],I.gap=!0,S?(I.s=lU,x=!0):I.s=0):I[w]=I[A]=lU,g&&(I.id=String(g[y]))}if(e3t(v,t),Qbt(e,t),t3t(v,t),p){for(y=0;y<v.length;)v[y][T]===lU?v.splice(y,1):y++;if(uU.sort(v,function(q,U){return q[T]-U[T]||q.i-U.i}),x){for(y=0;y<v.length-1&&v[y].gap;)y++;for(L=v[y].s,L||(L=v[y].s=0),C=0;C<y;C++)v[C].s=L;for(k=v.length-1;k>y&&v[k].gap;)k--;for(L=v[k].s,C=v.length-1;C>k;C--)v[C].s=L;for(;y<k;)if(y++,v[y].gap){for(C=y+1;v[C].gap;)C++;for(var O=v[y-1][T],V=v[y-1].s,j=(v[C].s-V)/(v[C][T]-O);y<C;)v[y].s=V+(v[y][T]-O)*j,y++}}}return v}function m1e(e,t,r,n,a,i,o){var s=t._length,l=e._fullLayout,u=r._id,c=n._id,f=l._firstScatter[_1e(t)]===t.uid,v=(fU(t,l,r,n)||{}).orientation,g=t.fill;r._minDtick=0,n._minDtick=0;var p={padded:!0},x={padded:!0};o&&(p.ppad=x.ppad=o);var _=s<2||a[0]!==a[s-1]||i[0]!==i[s-1];_&&(g===\"tozerox\"||g===\"tonextx\"&&(f||v===\"h\"))?p.tozero=!0:!(t.error_y||{}).visible&&(g===\"tonexty\"||g===\"tozeroy\"||!cU.hasMarkers(t)&&!cU.hasText(t))&&(p.padded=!1,p.ppad=0),_&&(g===\"tozeroy\"||g===\"tonexty\"&&(f||v===\"v\"))?x.tozero=!0:(g===\"tonextx\"||g===\"tozerox\")&&(x.padded=!1),u&&(t._extremes[u]=lA.findExtremes(r,a,p)),c&&(t._extremes[c]=lA.findExtremes(n,i,x))}function y1e(e,t){if(cU.hasMarkers(e)){var r=e.marker,n=1.6*(e.marker.sizeref||1),a;if(e.marker.sizemode===\"area\"?a=function(u){return Math.max(Math.sqrt((u||0)/n),3)}:a=function(u){return Math.max((u||0)/n,3)},uU.isArrayOrTypedArray(r.size)){var i={type:\"linear\"};lA.setConvert(i);for(var o=i.makeCalcdata(e.marker,\"size\"),s=new Array(t),l=0;l<t;l++)s[l]=a(o[l]);return s}else return a(r.size)}}function x1e(e,t){var r=_1e(t),n=e._firstScatter;n[r]||(n[r]=t.uid)}function _1e(e){var t=e.stackgroup;return e.xaxis+e.yaxis+e.type+(t?\"-\"+t:\"\")}function fU(e,t,r,n){var a=e.stackgroup;if(a){var i=t._scatterStackOpts[r._id+n._id][a],o=i.orientation===\"v\"?n:r;if(o.type===\"linear\"||o.type===\"log\")return i}}b1e.exports={calc:r3t,calcMarkerSize:y1e,calcAxisExpansion:m1e,setFirstScatter:x1e,getStackOpts:fU}});var T1e=fe((jvr,w1e)=>{\"use strict\";w1e.exports=aC;var n3t=Xt().distinctVals;function aC(e,t){this.traces=e,this.sepNegVal=t.sepNegVal,this.overlapNoMerge=t.overlapNoMerge;for(var r=1/0,n=t.posAxis._id.charAt(0),a=[],i=0;i<e.length;i++){for(var o=e[i],s=0;s<o.length;s++){var l=o[s],u=l.p;u===void 0&&(u=l[n]),u!==void 0&&a.push(u)}o[0]&&o[0].width1&&(r=Math.min(o[0].width1,r))}this.positions=a;var c=n3t(a);this.distinctPositions=c.vals,c.vals.length===1&&r!==1/0?this.minDiff=r:this.minDiff=Math.min(c.minDiff,r);var f=(t.posAxis||{}).type;(f===\"category\"||f===\"multicategory\")&&(this.minDiff=1),this.binWidth=this.minDiff,this.bins={}}aC.prototype.put=function(t,r,n){var a=this.getLabel(t,r,n),i=this.bins[a]||0;return this.bins[a]=i+n,i};aC.prototype.get=function(t,r,n){var a=this.getLabel(t,r,n);return this.bins[a]||0};aC.prototype.getLabel=function(t,r,n){var a=n<0&&this.sepNegVal?\"v\":\"^\",i=this.overlapNoMerge?t:Math.round(t/this.binWidth);return a+i+\"g\"+r}});var Im=fe((Wvr,A1e)=>{\"use strict\";A1e.exports={TEXTPAD:3,eventDataKeys:[\"value\",\"label\"]}});var i2=fe((Yvr,k1e)=>{\"use strict\";var mv=aa(),{isArrayOrTypedArray:og}=Xt(),{BADNUM:d3}=Wa(),i3t=bi(),uA=Yi(),{getAxisGroup:a3t}=Jx(),oC=T1e(),{TEXTPAD:o3t}=Im(),{LINE_SPACING:s3t}=nc(),{BR_TAG_ALL:l3t}=as();function u3t(e,t){for(var r=t.xaxis,n=t.yaxis,a=e._fullLayout,i=e._fullData,o=e.calcdata,s=[],l=[],u=0;u<i.length;u++){var c=i[u];if(c.visible===!0&&i3t.traceIs(c,\"bar\")&&c.xaxis===r._id&&c.yaxis===n._id&&(c.orientation===\"h\"?s.push(o[u]):l.push(o[u]),c._computePh))for(var f=e.calcdata[u],v=0;v<f.length;v++)typeof f[v].ph0==\"function\"&&(f[v].ph0=f[v].ph0()),typeof f[v].ph1==\"function\"&&(f[v].ph1=f[v].ph1())}var g={xCat:r.type===\"category\"||r.type===\"multicategory\",yCat:n.type===\"category\"||n.type===\"multicategory\",mode:a.barmode,norm:a.barnorm,gap:a.bargap,groupgap:a.bargroupgap};dU(e,r,n,l,g),dU(e,n,r,s,g)}function dU(e,t,r,n,a){if(n.length){var i,o,s,l,u;switch(h3t(r,n),a.mode){case\"overlay\":hU(e,t,r,n,a);break;case\"group\":for(i=[],o=[],s=0;s<n.length;s++)l=n[s],u=l[0].trace,u.offset===void 0?o.push(l):i.push(l);o.length&&d3t(e,t,r,o,a),i.length&&hU(e,t,r,i,a);break;case\"stack\":case\"relative\":for(i=[],o=[],s=0;s<n.length;s++)l=n[s],u=l[0].trace,u.base===void 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null}:e.d2c;if(og(o)){for(n=0;n<Math.min(o.length,a.length);n++)s=u(o[n],0,l),mv(s)?(a[n].b=+s,a[n].hasB=1):a[n].b=0;for(;n<a.length;n++)a[n].b=0}else{s=u(o,0,l);var c=mv(s);for(s=c?s:0,n=0;n<a.length;n++)a[n].b=s,c&&(a[n].hasB=1)}}}function hU(e,t,r,n,a){for(var i=0;i<n.length;i++){var o=n[i],s=new oC([o],{posAxis:t,sepNegVal:!1,overlapNoMerge:!a.norm});vU(e,t,s,a),a.norm?(E1e(s),pU(r,s,a)):S1e(r,s)}}function d3t(e,t,r,n,a){var i=new oC(n,{posAxis:t,sepNegVal:!1,overlapNoMerge:!a.norm});vU(e,t,i,a),y3t(i,t),a.norm?(E1e(i),pU(r,i,a)):S1e(r,i)}function v3t(e,t,r,n,a){var i=new oC(n,{posAxis:t,sepNegVal:a.mode===\"relative\",overlapNoMerge:!(a.norm||a.mode===\"stack\"||a.mode===\"relative\")});vU(e,t,i,a),m3t(r,i,a);for(var o=0;o<n.length;o++)for(var s=n[o],l=s[0].t.offsetindex,u=0;u<s.length;u++){var c=s[u];if(c.s!==d3){var f=c.b+c.s===i.get(c.p,l,c.s);f&&(c._outmost=!0)}}a.norm&&pU(r,i,a)}function vU(e,t,r,n){var a=e._fullLayout,i=r.positions,o=r.distinctPositions,s=r.minDiff,l=r.traces,u=l.length,c=i.length!==o.length,f=s*(1-n.gap),v,g,p,x;if(t._id===\"angularaxis\")v=f,g=v*(1-(n.groupgap||0)),p=-g/2;else{var _=a3t(a,t._id)+l[0][0].trace.orientation;x=a._alignmentOpts[_]||{}}for(var y=0;y<u;y++){var C=l[y],k=C[0].trace;if(t._id!==\"angularaxis\"){var S=x[k.alignmentgroup]||{},L=Object.keys(S.offsetGroups||{}).length;L?v=f/L:v=c?f/u:f,g=v*(1-(n.groupgap||0)),L?p=((2*k._offsetIndex+1-L)*v-g)/2:p=c?((2*y+1-u)*v-g)/2:-g/2}var w=C[0].t;w.barwidth=g,w.offsetindex=k._offsetIndex||0,w.poffset=p,w.bargroupwidth=f,w.bardelta=s}r.binWidth=l[0][0].t.barwidth/100,p3t(r),g3t(t,r),t._id===\"angularaxis\"?M1e(t,r):M1e(t,r,c)}function p3t(e){var t=e.traces,r,n;for(r=0;r<t.length;r++){var a=t[r],i=a[0],o=i.trace,s=i.t,l=o._offset||o.offset,u=s.poffset,c;if(og(l)){for(c=Array.prototype.slice.call(l,0,a.length),n=0;n<c.length;n++)mv(c[n])||(c[n]=u);for(n=c.length;n<a.length;n++)c.push(u);s.poffset=c}else l!==void 0&&(s.poffset=l);var f=o._width||o.width,v=s.barwidth;if(og(f)){var g=Array.prototype.slice.call(f,0,a.length);for(n=0;n<g.length;n++)mv(g[n])||(g[n]=v);for(n=g.length;n<a.length;n++)g.push(v);if(s.barwidth=g,l===void 0){for(c=[],n=0;n<a.length;n++)c.push(u+(v-g[n])/2);s.poffset=c}}else f!==void 0&&(s.barwidth=f,l===void 0&&(s.poffset=u+(v-f)/2))}}function g3t(e,t){for(var r=t.traces,n=v3(e),a=0;a<r.length;a++)for(var i=r[a],o=i[0].t,s=o.poffset,l=og(s),u=o.barwidth,c=og(u),f=0;f<i.length;f++){var v=i[f],g=v.w=c?u[f]:u;v.p===void 0&&(v.p=v[n],v[\"orig_\"+n]=v[n]);var p=(l?s[f]:s)+g/2;v[n]=v.p+p}}function M1e(e,t,r){var n=t.traces,a=t.minDiff,i=a/2;uA.minDtick(e,t.minDiff,t.distinctPositions[0],r);for(var o=0;o<n.length;o++){var s=n[o],l=s[0],u=l.trace,c=[],f,v,g,p;for(p=0;p<s.length;p++)f=s[p],v=f.p-i,g=f.p+i,c.push(v,g);if(u.width||u.offset){var x=l.t,_=x.poffset,y=x.barwidth,C=og(_),k=og(y);for(p=0;p<s.length;p++){f=s[p];var S=C?_[p]:_,L=k?y[p]:y;v=f.p+S,g=v+L,c.push(v,g)}}u._extremes[e._id]=uA.findExtremes(e,c,{padded:!1})}}function S1e(e,t){for(var r=t.traces,n=v3(e),a=0;a<r.length;a++){for(var i=r[a],o=i[0].trace,s=o.type===\"scatter\",l=o.orientation===\"v\",u=[],c=!1,f=0;f<i.length;f++){var v=i[f],g=s?0:v.b,p=s?l?v.y:v.x:g+v.s;v[n]=p,u.push(p),v.hasB&&u.push(g),(!v.hasB||!v.b)&&(c=!0)}let{ppadminus:x,ppadplus:_}=gU(o,i);o._extremes[e._id]=uA.findExtremes(e,u,{tozero:c,padded:!0,ppadplus:_,ppadminus:x})}}function m3t(e,t,r){var n=v3(e),a=t.traces,i,o,s,l,u,c,f;for(l=0;l<a.length;l++)if(i=a[l],o=i[0].trace,o.type===\"funnel\")for(f=i[0].t.offsetindex,u=0;u<i.length;u++)c=i[u],c.s!==d3&&t.put(c.p,f,-.5*c.s);for(l=0;l<a.length;l++){i=a[l],o=i[0].trace,s=o.type===\"funnel\",f=o.type===\"barpolar\"?0:i[0].t.offsetindex;var v=[];for(u=0;u<i.length;u++)if(c=i[u],c.s!==d3){var g;s?g=c.s:g=c.s+c.b;var p=t.put(c.p,f,g),x=p+g;c.b=p,c[n]=x,r.norm||(v.push(x),c.hasB&&v.push(p))}if(!r.norm){let{ppadminus:_,ppadplus:y}=gU(o,i);o._extremes[e._id]=uA.findExtremes(e,v,{tozero:!0,padded:!0,ppadplus:y,ppadminus:_})}}}function E1e(e){for(var t=e.traces,r=0;r<t.length;r++)for(var n=t[r],a=n[0].t.offsetindex,i=0;i<n.length;i++){var o=n[i];o.s!==d3&&e.put(o.p,a,o.b+o.s)}}function y3t(e,t){for(var r=e.traces,n=0;n<r.length;n++){var a=r[n],i=a[0].trace,o=a[0].t.offsetindex;if(i.base===void 0)for(var s=new oC([a],{posAxis:t,sepNegVal:!0,overlapNoMerge:!0}),l=0;l<a.length;l++){var u=a[l];if(u.p!==d3){var c=s.put(u.p,o,u.b+u.s);c&&(u.b=c)}}}}function pU(e,t,r){var n=t.traces,a=v3(e),i=r.norm===\"fraction\"?1:100,o=i/1e9,s=e.l2c(e.c2l(0)),l=r.mode===\"stack\"?i:s;function u(w){return mv(e.c2l(w))&&(w<s-o||w>l+o||!mv(s))}for(var c=0;c<n.length;c++){for(var f=n[c],v=f[0].t.offsetindex,g=f[0].trace,p=[],x=!1,_=!1,y=0;y<f.length;y++){var C=f[y];if(C.s!==d3){var k=Math.abs(i/t.get(C.p,v,C.s));C.b*=k,C.s*=k;var S=C.b,L=S+C.s;C[a]=L,p.push(L),_=_||u(L),C.hasB&&(p.push(S),_=_||u(S)),(!C.hasB||!C.b)&&(x=!0)}}let{ppadminus:w,ppadplus:A}=gU(g,f);g._extremes[e._id]=uA.findExtremes(e,p,{tozero:x,padded:_,ppadplus:A,ppadminus:w})}}function gU(e,t){if(e.orientation===\"v\"&&(e.text||e.texttemplate)&&e.textposition===\"outside\"&&(e.textangle===\"auto\"||e.textangle===0)){let n=function(i){return!i||typeof i!=\"string\"?0:(i.match(l3t)||[]).length+1};var r;e.texttemplate?r=n(e.texttemplate):r=og(e.text)?Math.max(...e.text.map(i=>n(i))):n(e.text);let a=e.outsidetextfont.size*s3t*r+o3t;return{ppadplus:t.some(i=>i.s<0)?a:0,ppadminus:t.some(i=>i.s>=0)?a:0}}return{ppadplus:void 0,ppadminus:void 0}}function x3t(e,t,r,n){for(var a=v3(n),i=0;i<e.length;i++)for(var o=e[i],s=0;s<o.length;s++){var l=o[s],u=l[a];l._sMin=t[u],l._sMax=r[u]}}function _3t(e,t){var r=v3(t),n={},a,i,o,s=1/0,l=-1/0;for(a=0;a<e.length;a++)for(o=e[a],i=0;i<o.length;i++){var u=o[i].p;mv(u)&&(s=Math.min(s,u),l=Math.max(l,u))}var c=1e4/(l-s),f=n.round=function(T){return String(Math.round(c*(T-s)))},v={},g={},p=e.some(function(T){var m=T[0].trace;return\"marker\"in m&&m.marker.cornerradius});for(a=0;a<e.length;a++){o=e[a],o[0].t.extents=n;var x=o[0].t.poffset,_=og(x);for(i=0;i<o.length;i++){var y=o[i],C=y[r]-y.w/2;if(mv(C)){var k=y[r]+y.w/2,S=f(y.p);n[S]?n[S]=[Math.min(C,n[S][0]),Math.max(k,n[S][1])]:n[S]=[C,k]}if(y.p0=y.p+(_?x[i]:x),y.p1=y.p0+y.w,y.s0=y.b,y.s1=y.s0+y.s,p){var L=Math.min(y.s0,y.s1)||0,w=Math.max(y.s0,y.s1)||0,A=y[r];v[A]=A in v?Math.min(v[A],L):L,g[A]=A in g?Math.max(g[A],w):w}}}p&&x3t(e,v,g,t)}function v3(e){return e._id.charAt(0)}k1e.exports={crossTraceCalc:u3t,setGroupPositions:dU}});var F1e=fe((Xvr,P1e)=>{\"use strict\";var C1e=gv(),L1e=i2().setGroupPositions;function b3t(e,t){for(var r=t.xaxis,n=t.yaxis,a=e._fullLayout,i=e._fullData,o=e.calcdata,s=[],l=[],u=0;u<i.length;u++){var c=i[u];c.visible===!0&&c.type===\"scatter\"&&c.xaxis===r._id&&c.yaxis===n._id&&(c.orientation===\"h\"?s.push(o[u]):c.orientation===\"v\"&&l.push(o[u]))}var f={mode:a.scattermode,gap:a.scattergap};L1e(e,r,n,l,f),L1e(e,n,r,s,f)}P1e.exports=function(t,r){t._fullLayout.scattermode===\"group\"&&b3t(t,r);var n=r.xaxis,a=r.yaxis,i=n._id+a._id,o=t._fullLayout._scatterStackOpts[i];if(o){var s=t.calcdata,l,u,c,f,v,g,p,x,_,y,C,k,S,L,w;for(var A in o){y=o[A];var T=y.traceIndices;if(T.length){for(C=y.stackgaps===\"interpolate\",k=y.groupnorm,y.orientation===\"v\"?(S=\"x\",L=\"y\"):(S=\"y\",L=\"x\"),w=new Array(T.length),l=0;l<w.length;l++)w[l]=!1;g=s[T[0]];var m=new Array(g.length);for(l=0;l<g.length;l++)m[l]=g[l][S];for(l=1;l<T.length;l++){for(v=s[T[l]],u=c=0;u<v.length;u++){for(p=v[u][S];p>m[c]&&c<m.length;c++)mU(v,u,m[c],l,w,C,S),u++;if(p!==m[c]){for(f=0;f<l;f++)mU(s[T[f]],c,p,f,w,C,S);m.splice(c,0,p)}c++}for(;c<m.length;c++)mU(v,u,m[c],l,w,C,S),u++}var F=m.length;for(u=0;u<g.length;u++){for(x=g[u][L]=g[u].s,l=1;l<T.length;l++)v=s[T[l]],v[0].trace._rawLength=v[0].trace._length,v[0].trace._length=F,x+=v[u].s,v[u][L]=x;if(k)for(_=(k===\"fraction\"?x:x/100)||1,l=0;l<T.length;l++){var E=s[T[l]][u];E[L]/=_,E.sNorm=E.s/_}}for(l=0;l<T.length;l++){v=s[T[l]];var I=v[0].trace,R=C1e.calcMarkerSize(I,I._rawLength),B=Array.isArray(R);if(R&&w[l]||B){var O=R;for(R=new Array(F),u=0;u<F;u++)R[u]=v[u].gap?0:B?O[v[u].i]:O}var V=new Array(F),j=new Array(F);for(u=0;u<F;u++)V[u]=v[u].x,j[u]=v[u].y;C1e.calcAxisExpansion(t,I,n,a,V,j,R),v[0].t.orientation=y.orientation}}}}};function mU(e,t,r,n,a,i,o){a[n]=!0;var s={i:null,gap:!0,s:0};if(s[o]=r,e.splice(t,0,s),t&&r===e[t-1][o]){var l=e[t-1];s.s=l.s,s.i=l.i,s.gap=l.gap}else i&&(s.s=w3t(e,t,r,o));t||(e[0].t=e[1].t,e[0].trace=e[1].trace,delete e[1].t,delete e[1].trace)}function w3t(e,t,r,n){var a=e[t-1],i=e[t+1];return i?a?a.s+(i.s-a.s)*(r-a[n])/(i[n]-a[n]):i.s:a.s}});var xU=fe((Zvr,B1e)=>{\"use strict\";var 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ue=j[q];ue.contains(f)&&(U.push(ue),ce=Math.min(ce,ue.ymin),ye=Math.max(ye,ue.ymax))}if(U.length===0)return null;ce=Math.max(ce,0),ye=Math.min(ye,l._length),xe=(ce+ye)/2;var K,_e,ne,ae,pe,ie,le;for(q=0;q<U.length;q++)for(_e=U[q].pts,K=1;K<_e.length;K++)ie=_e[K-1][1],le=_e[K][1],ie>xe!=le>=xe&&(ae=_e[K-1][0],pe=_e[K][0],le-ie&&(ne=ae+(pe-ae)*(xe-ie)/(le-ie),J=Math.min(J,ne),ee=Math.max(ee,ne)));return J=Math.max(J,0),ee=Math.min(ee,s._length),{x0:J,x1:ee,y0:xe,y1:xe}}if(v.indexOf(\"fills\")!==-1&&o._fillElement){var B=I(o._fillElement)&&!I(o._fillExclusionElement);if(B){var O=R(o._polygons);O===null&&(O={x0:f[0],x1:f[0],y0:f[1],y1:f[1]});var V=AU.defaultLine;return AU.opacity(o.fillcolor)?V=o.fillcolor:AU.opacity((o.line||{}).color)&&(V=o.line.color),fC.extendFlat(t,{distance:t.maxHoverDistance,x0:O.x0,x1:O.x1,y0:O.y0,y1:O.y1,color:V,hovertemplate:!1}),delete t.index,o.text&&!fC.isArrayOrTypedArray(o.text)?t.text=String(o.text):t.text=o.name,[t]}}}});var a2=fe((npr,K1e)=>{\"use 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i=B3t(t,r,n);if(i){if(i.type===\"histogram\"&&n==={v:\"y\",h:\"x\"}[i.orientation||\"v\"]){e.type=\"linear\";return}var o=n+\"calendar\",s=i[o],l={noMultiCategory:!hC(i,\"cartesian\")||hC(i,\"noMultiCategory\")};if(i.type===\"box\"&&i._hasPreCompStats&&n==={h:\"x\",v:\"y\"}[i.orientation||\"v\"]&&(l.noMultiCategory=!0),l.autotypenumbers=e.autotypenumbers,tge(i,n)){var u=ege(i),c=[];for(a=0;a<t.length;a++){var f=t[a];!hC(f,\"box-violin\")||(f[n+\"axis\"]||n)!==r||(f[u]!==void 0?c.push(f[u][0]):f.name!==void 0?c.push(f.name):c.push(\"text\"),f[o]!==s&&(s=void 0))}e.type=MU(c,s,l)}else if(i.type===\"splom\"){var v=i.dimensions,g=v[i._axesDim[r]];g.visible&&(e.type=MU(g.values,s,l))}else e.type=MU(i[n]||[i[n+\"0\"]],s,l)}}}function B3t(e,t,r){for(var n=0;n<e.length;n++){var a=e[n];if(a.type===\"splom\"&&a._length>0&&(a[\"_\"+r+\"axes\"]||{})[t])return a;if((a[r+\"axis\"]||r)===t){if(tge(a,r))return a;if((a[r]||[]).length||a[r+\"0\"])return a}}}function ege(e){return{v:\"x\",h:\"y\"}[e.orientation||\"v\"]}function tge(e,t){var r=ege(e),n=hC(e,\"box-violin\");return n&&t===r&&e[r]===void 0&&e[r+\"0\"]===void 0}});var dC=fe((opr,nge)=>{\"use strict\";var O3t=Gf().isTypedArraySpec;function N3t(e,t){var r=t.dataAttr||e._id.charAt(0),n={},a,i,o;if(t.axData)a=t.axData;else for(a=[],i=0;i<t.data.length;i++){var s=t.data[i];s[r+\"axis\"]===e._id&&a.push(s)}for(i=0;i<a.length;i++){var l=a[i][r];for(o=0;o<l.length;o++){var u=l[o];u!=null&&(n[u]=1)}}return Object.keys(n)}nge.exports=function(t,r,n,a){if(r.type===\"category\"){var i=t.categoryarray,o=Array.isArray(i)&&i.length>0||O3t(i),s;o&&(s=\"array\");var l=n(\"categoryorder\",s),u;l===\"array\"&&(u=n(\"categoryarray\")),!o&&l===\"array\"&&(l=r.categoryorder=\"trace\"),l===\"trace\"?r._initialCategories=[]:l===\"array\"?r._initialCategories=u.slice():(u=N3t(r,a).sort(),l===\"category ascending\"?r._initialCategories=u:l===\"category descending\"&&(r._initialCategories=u.reverse()))}}});var hA=fe((spr,age)=>{\"use strict\";var ige=ni(),U3t=tc(),V3t=Xt();age.exports=function(t,r,n,a){a=a||{};var i=a.dfltColor;function o(T,m){return V3t.coerce2(t,r,a.attributes,T,m)}var s=o(\"linecolor\",i),l=o(\"linewidth\"),u=n(\"showline\",a.showLine||!!s||!!l);u||(delete r.linecolor,delete r.linewidth);var c=ige.mix(i,a.bgColor,a.blend||U3t.lightFraction),f=o(\"gridcolor\",c),v=o(\"gridwidth\"),g=o(\"griddash\"),p=n(\"showgrid\",a.showGrid||!!f||!!v||!!g);if(p||(delete r.gridcolor,delete r.gridwidth,delete r.griddash),a.hasMinor){var x=ige.mix(r.gridcolor,a.bgColor,67),_=o(\"minor.gridcolor\",x),y=o(\"minor.gridwidth\",r.gridwidth||1),C=o(\"minor.griddash\",r.griddash||\"solid\"),k=n(\"minor.showgrid\",!!_||!!y||!!C);k||(delete r.minor.gridcolor,delete r.minor.gridwidth,delete r.minor.griddash)}if(!a.noZeroLine){var S=o(\"zerolinelayer\"),L=o(\"zerolinecolor\",i),w=o(\"zerolinewidth\"),A=n(\"zeroline\",a.showGrid||!!L||!!w);A||(delete r.zerolinelayer,delete r.zerolinecolor,delete r.zerolinewidth)}}});var vA=fe((lpr,fge)=>{\"use strict\";var oge=aa(),G3t=bi(),dA=Xt(),H3t=Mo(),j3t=Ah(),EU=pf(),sge=Fx(),lge=Ub(),W3t=gm(),Y3t=mm(),X3t=dC(),Z3t=hA(),$3t=gO(),uge=Hp(),vC=rc().WEEKDAY_PATTERN,K3t=rc().HOUR_PATTERN;fge.exports=function(t,r,n,a,i){var o=a.letter,s=a.font||{},l=a.splomStash||{},u=n(\"visible\",!a.visibleDflt),c=r._template||{},f=r.type||c.type||\"-\",v;if(f===\"date\"){var g=G3t.getComponentMethod(\"calendars\",\"handleDefaults\");g(t,r,\"calendar\",a.calendar),a.noTicklabelmode||(v=n(\"ticklabelmode\"))}!a.noTicklabelindex&&(f===\"date\"||f===\"linear\")&&n(\"ticklabelindex\");var p=\"\";(!a.noTicklabelposition||f===\"multicategory\")&&(p=dA.coerce(t,r,{ticklabelposition:{valType:\"enumerated\",dflt:\"outside\",values:v===\"period\"?[\"outside\",\"inside\"]:o===\"x\"?[\"outside\",\"inside\",\"outside left\",\"inside left\",\"outside right\",\"inside right\"]:[\"outside\",\"inside\",\"outside top\",\"inside top\",\"outside bottom\",\"inside 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S=f===\"multicategory\";if(!a.noTickson&&(f===\"category\"||S)&&(r.ticks||r.showgrid)&&(S?(n(\"tickson\",\"boundaries\"),delete r.ticklabelposition):n(\"tickson\")),S){var L=n(\"showdividers\");L&&(n(\"dividercolor\"),n(\"dividerwidth\"))}if(f===\"date\")if(j3t(t,r,{name:\"rangebreaks\",inclusionAttr:\"enabled\",handleItemDefaults:J3t}),!r.rangebreaks.length)delete r.rangebreaks;else{for(var w=0;w<r.rangebreaks.length;w++)if(r.rangebreaks[w].pattern===vC){r._hasDayOfWeekBreaks=!0;break}if(uge(r,i),i._has(\"scattergl\")||i._has(\"splom\"))for(var A=0;A<a.data.length;A++){var T=a.data[A];(T.type===\"scattergl\"||T.type===\"splom\")&&(T.visible=!1,dA.warn(T.type+\" traces do not work on axes with rangebreaks. 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Q3t[e.slice(0,3).toLowerCase()]}});var gC=fe((upr,hge)=>{\"use strict\";var ewt=aa(),pC=Xt();hge.exports=function(t,r,n,a){var i=a.counterAxes||[],o=a.overlayableAxes||[],s=a.letter,l=a.grid,u=a.overlayingDomain,c,f,v,g,p,x;l&&(f=l._domains[s][l._axisMap[r._id]],c=l._anchors[r._id],f&&(v=l[s+\"side\"].split(\" \")[0],g=l.domain[s][v===\"right\"||v===\"top\"?1:0])),f=f||[0,1],c=c||(ewt(t.position)?\"free\":i[0]||\"free\"),v=v||(s===\"x\"?\"bottom\":\"left\"),g=g||0,p=0,x=!1;var _=pC.coerce(t,r,{anchor:{valType:\"enumerated\",values:[\"free\"].concat(i),dflt:c}},\"anchor\"),y=pC.coerce(t,r,{side:{valType:\"enumerated\",values:s===\"x\"?[\"bottom\",\"top\"]:[\"left\",\"right\"],dflt:v}},\"side\");if(_===\"free\"){if(s===\"y\"){var C=n(\"autoshift\");C&&(g=y===\"left\"?u[0]:u[1],x=r.automargin?r.automargin:!0,p=y===\"left\"?-3:3),n(\"shift\",p)}n(\"position\",g)}n(\"automargin\",x);var k=!1;if(o.length&&(k=pC.coerce(t,r,{overlaying:{valType:\"enumerated\",values:[!1].concat(o),dflt:!1}},\"overlaying\")),!k){var S=n(\"domain\",f);S[0]>S[1]-1/4096&&(r.domain=f),pC.noneOrAll(t.domain,r.domain,f),r.tickmode===\"sync\"&&(r.tickmode=\"auto\")}return n(\"layer\"),r}});var bge=fe((cpr,_ge)=>{\"use strict\";var o2=Xt(),dge=ni(),twt=Sd().isUnifiedHover,rwt=VO(),vge=Mo(),nwt=Sb(),pge=pf(),iwt=SU(),gge=vA(),awt=Jx(),mge=gC(),CU=El(),s1=CU.id2name,yge=CU.name2id,owt=rc().AX_ID_PATTERN,xge=bi(),mC=xge.traceIs,kU=xge.getComponentMethod;function yC(e,t,r){Array.isArray(e[t])?e[t].push(r):e[t]=[r]}_ge.exports=function(t,r,n){var a=r.autotypenumbers,i={},o={},s={},l={},u={},c={},f={},v={},g={},p={},x,_;for(x=0;x<n.length;x++){var y=n[x];if(mC(y,\"cartesian\")){var C;if(y.xaxis)C=s1(y.xaxis),yC(i,C,y);else if(y.xaxes)for(_=0;_<y.xaxes.length;_++)yC(i,s1(y.xaxes[_]),y);var k;if(y.yaxis)k=s1(y.yaxis),yC(i,k,y);else if(y.yaxes)for(_=0;_<y.yaxes.length;_++)yC(i,s1(y.yaxes[_]),y);if(y.type===\"funnel\"?y.orientation===\"h\"?(C&&(o[C]=!0),k&&(f[k]=!0)):k&&(s[k]=!0):y.type===\"image\"?(k&&(v[k]=!0),C&&(v[C]=!0)):(k&&(u[k]=!0,c[k]=!0),(!mC(y,\"carpet\")||y.type===\"carpet\"&&!y._cheater)&&C&&(l[C]=!0)),y.type===\"carpet\"&&y._cheater&&C&&(o[C]=!0),mC(y,\"2dMap\")&&(g[C]=!0,g[k]=!0),mC(y,\"oriented\")){var S=y.orientation===\"h\"?k:C;p[S]=!0}}}var L=r._subplots,w=L.xaxis,A=L.yaxis,T=o2.simpleMap(w,s1),m=o2.simpleMap(A,s1),F=T.concat(m),E=dge.background;w.length&&A.length&&(E=o2.coerce(t,r,nwt,\"plot_bgcolor\"));var I=dge.combine(E,r.paper_bgcolor),R,B,O,V,j;function q(){var Ve=i[R]||[];j._traceIndices=Ve.map(function($e){return $e.index}),j._annIndices=[],j._shapeIndices=[],j._selectionIndices=[],j._imgIndices=[],j._subplotsWith=[],j._counterAxes=[],j._name=j._attr=R,j._id=B}function U(Ve,$e){return o2.coerce(V,j,pge,Ve,$e)}function J(Ve,$e){return o2.coerce2(V,j,pge,Ve,$e)}function ee(Ve){return 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i=hp.traceLayerClasses,o=e._fullLayout,s=o._zindices,l=o._modules,u,c,f,v=[],g=[],p=0;p<s.length;p++)for(var x=s[p],_=0;_<l.length;_++){u=l[_];var y=u.name,C=Mge.modules[y].categories;if(C.svg){var k=u.layerName||y+\"layer\",S=k+(p?Number(p)+1:\"\"),L=u.plot;c=Sge(r,L,x),f=c[0],r=c[1],f.length&&v.push({i:i.indexOf(k),zindex:p,className:S,plotMethod:L,cdModule:f}),C.zoomScale&&g.push(\".\"+S)}}v.sort(function(T,m){return(T.zindex||0)-(m.zindex||0)||T.i-m.i});var w=t.plot.selectAll(\"g.mlayer\").data(v,function(T){return T.className});if(w.enter().append(\"g\").attr(\"class\",function(T){return T.className}).classed(\"mlayer\",!0).classed(\"rangeplot\",t.isRangePlot),w.exit().remove(),w.order(),w.each(function(T){var m=wC.select(this),F=T.className;T.plotMethod(e,t,T.cdModule,m,n,a),hp.clipOnAxisFalseQuery.indexOf(\".\"+F)===-1&&uwt.setClipUrl(m,t.layerClipId,e)}),o._has(\"scattergl\")&&(u=Mge.getModule(\"scattergl\"),f=Sge(r,u)[0],u.plot(e,t,f)),!e._context.staticPlot&&(t._hasClipOnAxisFalse&&(t.clipOnAxisFalseTraces=t.plot.selectAll(hp.clipOnAxisFalseQuery.join(\",\")).selectAll(\".trace\")),g.length)){var A=t.plot.selectAll(g.join(\",\")).selectAll(\".trace\");t.zoomScalePts=A.selectAll(\"path.point\"),t.zoomScaleTxt=A.selectAll(\".textpoint\")}}Eh.clean=function(e,t,r,n){var a=n._plots||{},i=t._plots||{},o=n._subplots||{},s,l,u;if(n._hasOnlyLargeSploms&&!t._hasOnlyLargeSploms)for(u in a)s=a[u],s.plotgroup&&s.plotgroup.remove();var c=n._has&&n._has(\"gl\"),f=t._has&&t._has(\"gl\");if(c&&!f)for(u in a)s=a[u],s._scene&&s._scene.destroy();if(o.xaxis&&o.yaxis){var v=zm.listIds({_fullLayout:n});for(l=0;l<v.length;l++){var g=v[l];t[zm.id2name(g)]||n._infolayer.selectAll(\".g-\"+g+\"title\").remove()}}var p=n._has&&n._has(\"cartesian\"),x=t._has&&t._has(\"cartesian\");if(p&&!x)Cge(n._cartesianlayer.selectAll(\".subplot\"),n),n._defs.selectAll(\".axesclip\").remove(),delete n._axisConstraintGroups,delete n._axisMatchGroups;else if(o.cartesian)for(l=0;l<o.cartesian.length;l++){var _=o.cartesian[l];if(_.indexOf(l2)===-1&&!i[_]){var y=\".\"+_+\",.\"+_+\"-x,.\"+_+\"-y\";n._cartesianlayer.selectAll(y).remove(),Lge(_,n)}}};Eh.drawFramework=function(e){var t=e._fullLayout,r=e.calcdata,n,a={};for(n=0;n<r.length;n++){var i=r[n][0],o=i.trace,s=o.zorder||0;a[s]||(a[s]=[]),a[s].push(i)}var l=Object.keys(a).map(Number).sort(s2.sorterAsc);l.length||(l=[0]),t._zindices=l;var u=fwt(e),c=u.length,f=[];for(n=0;n<c;n++)f[n]=u[n].slice();for(var v=1;v<l.length;v++){var g=[];for(n=0;n<c;n++)g[n]=u[n].slice(),g[n][0]+=l2+(v+1);f=f.concat(g)}var p=t._cartesianlayer.selectAll(\".subplot\").data(f,String);p.enter().append(\"g\").attr(\"class\",function(x){return\"subplot \"+x[0]}),p.order(),p.exit().call(Cge,t),p.each(function(x){var _=x[0],y=_.indexOf(l2),C=y!==-1,k=C?_.slice(0,y):_,S=t._plots[_];S||(S=s2.extendFlat({},t._plots[k]),S&&(S.id=_,t._plots[_]=S,t._subplots.cartesian.push(_))),S&&(S.plotgroup=wC.select(this),kge(e,S),C||(S.draglayer=jo(t._draggers,\"g\",_)))})};Eh.rangePlot=function(e,t,r){kge(e,t),Ege(e,t,r),lwt.style(e)};function fwt(e){var t=e._fullLayout,r=t._zindices.length,n=t._subplots.cartesian,a=n.length,i,o,s,l,u,c,f=[],v=[];for(i=0;i<a;i++){s=n[i],l=t._plots[s],u=l.xaxis,c=l.yaxis;var g=u._mainAxis,p=c._mainAxis,x=g._id+p._id,_=t._plots[x];l.overlays=[],x!==s&&_?(l.mainplot=x,l.mainplotinfo=_,v.push(s)):(l.mainplot=void 0,l.mainplotinfo=void 0,f.push(s))}for(i=0;i<v.length;i++)s=v[i],l=t._plots[s],l.mainplotinfo.overlays.push(l);var y=f.concat(v),C=[];for(i=0;i<a;i++){s=y[i],l=t._plots[s],u=l.xaxis,c=l.yaxis;for(var k=[],S=1;S<=r;S++){var L=\"\";for(S>1&&(L+=l2+S),k.push(s+L),o=0;o<l.overlays.length;o++)k.push(l.overlays[o].id+L)}k=k.concat([u.layer,c.layer,u.overlaying||\"\",c.overlaying||\"\"]),C.push(k)}return C}function kge(e,t){var r=e._fullLayout,n=t.plotgroup,a=t.id,i=a.indexOf(l2),o=i!==-1,s=hp.layerValue2layerClass[t.xaxis.layer],l=hp.layerValue2layerClass[t.yaxis.layer],u=r._hasOnlyLargeSploms,c=r._zindices.length>1,f=t.mainplotinfo;if(!t.mainplot||c)if(u)t.xlines=jo(n,\"path\",\"xlines-above\"),t.ylines=jo(n,\"path\",\"ylines-above\"),t.xaxislayer=jo(n,\"g\",\"xaxislayer-above\"),t.yaxislayer=jo(n,\"g\",\"yaxislayer-above\");else{if(!o){var 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p=f.plotgroup,x=a+\"-x\",_=a+\"-y\";t.minorGridlayer=f.minorGridlayer,t.gridlayer=f.gridlayer,t.zerolinelayer=f.zerolinelayer,t.zerolinelayerAbove=f.zerolinelayerAbove,jo(f.overlinesBelow,\"path\",x),jo(f.overlinesBelow,\"path\",_),jo(f.overaxesBelow,\"g\",x),jo(f.overaxesBelow,\"g\",_),t.plot=jo(f.overplot,\"g\",a),jo(f.overlinesAbove,\"path\",x),jo(f.overlinesAbove,\"path\",_),jo(f.overaxesAbove,\"g\",x),jo(f.overaxesAbove,\"g\",_),t.xlines=p.select(\".overlines-\"+s).select(\".\"+x),t.ylines=p.select(\".overlines-\"+l).select(\".\"+_),t.xaxislayer=p.select(\".overaxes-\"+s).select(\".\"+x),t.yaxislayer=p.select(\".overaxes-\"+l).select(\".\"+_)}o||(u||(bC(t.minorGridlayer,\"g\",t.xaxis._id),bC(t.minorGridlayer,\"g\",t.yaxis._id),t.minorGridlayer.selectAll(\"g\").map(function(y){return y[0]}).sort(zm.idSort),bC(t.gridlayer,\"g\",t.xaxis._id),bC(t.gridlayer,\"g\",t.yaxis._id),t.gridlayer.selectAll(\"g\").map(function(y){return 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TC=Is();Pge.exports={hasLines:TC.hasLines,hasMarkers:TC.hasMarkers,hasText:TC.hasText,isBubble:TC.isBubble,attributes:Ol(),layoutAttributes:nk(),supplyDefaults:n1e(),crossTraceDefaults:aU(),supplyLayoutDefaults:s1e(),calc:gv().calc,crossTraceCalc:F1e(),arraysToCalcdata:o1(),plot:m3(),colorbar:Zf(),formatLabels:cC(),style:Ld().style,styleOnSelect:Ld().styleOnSelect,hoverPoints:b3(),selectPoints:a2(),animatable:!0,moduleType:\"trace\",name:\"scatter\",basePlotModule:gu(),categories:[\"cartesian\",\"svg\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\",\"zoomScale\"],meta:{}}});var Ige=fe((vpr,Rge)=>{\"use strict\";var hwt=xi(),dwt=ni(),Dge=PN(),LU=Xt(),vwt=LU.strScale,pwt=LU.strRotate,gwt=LU.strTranslate;Rge.exports=function(t,r,n){var a=t.node(),i=Dge[n.arrowhead||0],o=Dge[n.startarrowhead||0],s=(n.arrowwidth||1)*(n.arrowsize||1),l=(n.arrowwidth||1)*(n.startarrowsize||1),u=r.indexOf(\"start\")>=0,c=r.indexOf(\"end\")>=0,f=i.backoff*s+n.standoff,v=o.backoff*l+n.startstandoff,g,p,x,_;if(a.nodeName===\"line\"){g={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},p={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var y=g.x-p.x,C=g.y-p.y;if(x=Math.atan2(C,y),_=x+Math.PI,f&&v&&f+v>Math.sqrt(y*y+C*C)){B();return}if(f){if(f*f>y*y+C*C){B();return}var k=f*Math.cos(x),S=f*Math.sin(x);p.x+=k,p.y+=S,t.attr({x2:p.x,y2:p.y})}if(v){if(v*v>y*y+C*C){B();return}var L=v*Math.cos(x),w=v*Math.sin(x);g.x-=L,g.y-=w,t.attr({x1:g.x,y1:g.y})}}else if(a.nodeName===\"path\"){var A=a.getTotalLength(),T=\"\";if(A<f+v){B();return}var m=a.getPointAtLength(0),F=a.getPointAtLength(.1);x=Math.atan2(m.y-F.y,m.x-F.x),g=a.getPointAtLength(Math.min(v,A)),T=\"0px,\"+v+\"px,\";var E=a.getPointAtLength(A),I=a.getPointAtLength(A-.1);_=Math.atan2(E.y-I.y,E.x-I.x),p=a.getPointAtLength(Math.max(0,A-f));var R=T?v+f:f;T+=A-R+\"px,\"+A+\"px\",t.style(\"stroke-dasharray\",T)}function B(){t.style(\"stroke-dasharray\",\"0px,100px\")}function O(V,j,q,U){V.path&&(V.noRotate&&(q=0),hwt.select(a.parentNode).append(\"path\").attr({class:t.attr(\"class\"),d:V.path,transform:gwt(j.x,j.y)+pwt(q*180/Math.PI)+vwt(U)}).style({fill:dwt.rgb(n.arrowcolor),\"stroke-width\":0}))}u&&O(o,g,x,l),c&&O(i,p,_,s)}});var AC=fe((ppr,Nge)=>{\"use strict\";var zge=xi(),PU=bi(),mwt=Hs(),Bm=Xt(),FU=Bm.strTranslate,gA=Yi(),u2=ni(),sg=ea(),qge=kl(),DU=as(),RU=np(),pA=ed(),ywt=Mo().arrayEditor,xwt=Ige();Nge.exports={draw:_wt,drawOne:Bge,drawRaw:Oge};function _wt(e){var t=e._fullLayout;t._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<t.annotations.length;r++)t.annotations[r].visible&&Bge(e,r);return mwt.previousPromises(e)}function Bge(e,t){var r=e._fullLayout,n=r.annotations[t]||{},a=gA.getFromId(e,n.xref),i=gA.getFromId(e,n.yref);a&&a.setScale(),i&&i.setScale(),Oge(e,n,t,!1,a,i)}function qm(e,t,r,n,a){var i=a[r],o=a[r+\"ref\"],s=r.indexOf(\"y\")!==-1,l=gA.getRefType(o)===\"domain\",u=s?n.h:n.w;return e?l?i+(s?-t:t)/e._length:e.p2r(e.r2p(i)+t):i+(s?-t:t)/u}function Oge(e,t,r,n,a,i){var o=e._fullLayout,s=e._fullLayout._size,l=e._context.edits,u,c;n?(u=\"annotation-\"+n,c=n+\".annotations\"):(u=\"annotation\",c=\"annotations\");var f=ywt(e.layout,c,t),v=f.modifyBase,g=f.modifyItem,p=f.getUpdateObj;o._infolayer.selectAll(\".\"+u+'[data-index=\"'+r+'\"]').remove();var x=\"clip\"+o._uid+\"_ann\"+r;if(!t._input||t.visible===!1){zge.selectAll(\"#\"+x).remove();return}var _={x:{},y:{}},y=+t.textangle||0,C=o._infolayer.append(\"g\").classed(u,!0).attr(\"data-index\",String(r)).style(\"opacity\",t.opacity),k=C.append(\"g\").classed(\"annotation-text-g\",!0),S=l[t.showarrow?\"annotationTail\":\"annotationPosition\"],L=t.captureevents||l.annotationText||S;function w(U){var J={index:r,annotation:t._input,fullAnnotation:t,event:U};return n&&(J.subplotId=n),J}var A=k.append(\"g\").style(\"pointer-events\",L?\"all\":null).call(RU,\"pointer\").on(\"click\",function(){e._dragging=!1,e.emit(\"plotly_clickannotation\",w(zge.event))});t.hovertext&&A.on(\"mouseover\",function(){var U=t.hoverlabel,J=U.font,ee=this.getBoundingClientRect(),ce=e.getBoundingClientRect();qge.loneHover({x0:ee.left-ce.left,x1:ee.right-ce.left,y:(ee.top+ee.bottom)/2-ce.top,text:t.hovertext,color:U.bgcolor,borderColor:U.bordercolor,fontFamily:J.family,fontSize:J.size,fontColor:J.color,fontWeight:J.weight,fontStyle:J.style,fontVariant:J.variant,fontShadow:J.fontShadow,fontLineposition:J.fontLineposition,fontTextcase:J.fontTextcase},{container:o._hoverlayer.node(),outerContainer:o._paper.node(),gd:e})}).on(\"mouseout\",function(){qge.loneUnhover(o._hoverlayer.node())});var T=t.borderwidth,m=t.borderpad,F=T+m,E=A.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",T+\"px\").call(u2.stroke,t.bordercolor).call(u2.fill,t.bgcolor),I=t.width||t.height,R=o._topclips.selectAll(\"#\"+x).data(I?[0]:[]);R.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",x).append(\"rect\"),R.exit().remove();var B=t.font,O=o._meta?Bm.templateString(t.text,o._meta):t.text,V=A.append(\"text\").classed(\"annotation-text\",!0).text(O);function j(U){return U.call(sg.font,B).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[t.align]||\"middle\"}),DU.convertToTspans(U,e,q),U}function q(){var U=V.selectAll(\"a\");if(U.size()===1&&U.text()===V.text()){var J=A.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":U.attr(\"xlink:href\"),\"xlink:xlink:show\":U.attr(\"xlink:show\")}).style({cursor:\"pointer\"});J.node().appendChild(E.node())}var ee=A.select(\".annotation-text-math-group\"),ce=!ee.empty(),ye=sg.bBox((ce?ee:V).node()),xe=ye.width,ue=ye.height,K=t.width||xe,_e=t.height||ue,ne=Math.round(K+2*F),ae=Math.round(_e+2*F);function pe(wt,Tt){return Tt===\"auto\"&&(wt<1/3?Tt=\"left\":wt>2/3?Tt=\"right\":Tt=\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[Tt]}for(var ie=!1,le=[\"x\",\"y\"],me=0;me<le.length;me++){var be=le[me],De=t[be+\"ref\"]||be,Ge=t[\"a\"+be+\"ref\"],ht={x:a,y:i}[be],it=(y+(be===\"x\"?0:-90))*Math.PI/180,Oe=ne*Math.cos(it),ze=ae*Math.sin(it),ct=Math.abs(Oe)+Math.abs(ze),et=t[be+\"anchor\"],qe=t[be+\"shift\"]*(be===\"x\"?1:-1),we=_[be],Le,Ke,Te,Ve,$e,ot=gA.getRefType(De);if(ht&&ot!==\"domain\"){var tt=ht.r2fraction(t[be]);(tt<0||tt>1)&&(Ge===De?(tt=ht.r2fraction(t[\"a\"+be]),(tt<0||tt>1)&&(ie=!0)):ie=!0),Le=ht._offset+ht.r2p(t[be]),Ve=.5}else{var nt=ot===\"domain\";be===\"x\"?(Te=t[be],Le=nt?ht._offset+ht._length*Te:Le=s.l+s.w*Te):(Te=1-t[be],Le=nt?ht._offset+ht._length*Te:Le=s.t+s.h*Te),Ve=t.showarrow?.5:Te}if(t.showarrow){we.head=Le;var We=t[\"a\"+be];if($e=Oe*pe(.5,t.xanchor)-ze*pe(.5,t.yanchor),Ge===De){var mt=gA.getRefType(Ge);mt===\"domain\"?(be===\"y\"&&(We=1-We),we.tail=ht._offset+ht._length*We):mt===\"paper\"?be===\"y\"?(We=1-We,we.tail=s.t+s.h*We):we.tail=s.l+s.w*We:we.tail=ht._offset+ht.r2p(We),Ke=$e}else we.tail=Le+We,Ke=$e+We;we.text=we.tail+$e;var bt=o[be===\"x\"?\"width\":\"height\"];if(De===\"paper\"&&(we.head=Bm.constrain(we.head,1,bt-1)),Ge===\"pixel\"){var Yt=-Math.max(we.tail-3,we.text),jt=Math.min(we.tail+3,we.text)-bt;Yt>0?(we.tail+=Yt,we.text+=Yt):jt>0&&(we.tail-=jt,we.text-=jt)}we.tail+=qe,we.head+=qe}else $e=ct*pe(Ve,et),Ke=$e,we.text=Le+$e;we.text+=qe,$e+=qe,Ke+=qe,t[\"_\"+be+\"padplus\"]=ct/2+Ke,t[\"_\"+be+\"padminus\"]=ct/2-Ke,t[\"_\"+be+\"size\"]=ct,t[\"_\"+be+\"shift\"]=$e}if(ie){A.remove();return}var mr=0,or=0;if(t.align!==\"left\"&&(mr=(K-xe)*(t.align===\"center\"?.5:1)),t.valign!==\"top\"&&(or=(_e-ue)*(t.valign===\"middle\"?.5:1)),ce)ee.select(\"svg\").attr({x:F+mr-1,y:F+or}).call(sg.setClipUrl,I?x:null,e);else{var fr=F+or-ye.top,Ot=F+mr-ye.left;V.call(DU.positionText,Ot,fr).call(sg.setClipUrl,I?x:null,e)}R.select(\"rect\").call(sg.setRect,F,F,K,_e),E.call(sg.setRect,T/2,T/2,ne-T,ae-T),A.call(sg.setTranslate,Math.round(_.x.text-ne/2),Math.round(_.y.text-ae/2)),k.attr({transform:\"rotate(\"+y+\",\"+_.x.text+\",\"+_.y.text+\")\"});var cr=function(wt,Tt){C.selectAll(\".annotation-arrow-g\").remove();var Mt=_.x.head,Lt=_.y.head,ft=_.x.tail+wt,kt=_.y.tail+Tt,rr=_.x.text+wt,pr=_.y.text+Tt,Kr=Bm.rotationXYMatrix(y,rr,pr),Nr=Bm.apply2DTransform(Kr),dn=Bm.apply2DTransform2(Kr),xr=+E.attr(\"width\"),Fr=+E.attr(\"height\"),Zr=rr-.5*xr,Cn=Zr+xr,Dt=pr-.5*Fr,At=Dt+Fr,Vt=[[Zr,Dt,Zr,At],[Zr,At,Cn,At],[Cn,At,Cn,Dt],[Cn,Dt,Zr,Dt]].map(dn);if(!Vt.reduce(function(ha,va){return ha^!!Bm.segmentsIntersect(Mt,Lt,Mt+1e6,Lt+1e6,va[0],va[1],va[2],va[3])},!1)){Vt.forEach(function(ha){var va=Bm.segmentsIntersect(ft,kt,Mt,Lt,ha[0],ha[1],ha[2],ha[3]);va&&(ft=va.x,kt=va.y)});var Qt=t.arrowwidth,Ut=t.arrowcolor,_r=t.arrowside,rn=C.append(\"g\").style({opacity:u2.opacity(Ut)}).classed(\"annotation-arrow-g\",!0),Jr=rn.append(\"path\").attr(\"d\",\"M\"+ft+\",\"+kt+\"L\"+Mt+\",\"+Lt).style(\"stroke-width\",Qt+\"px\").call(u2.stroke,u2.rgb(Ut));if(xwt(Jr,_r,t),l.annotationPosition&&Jr.node().parentNode&&!n){var qn=Mt,ri=Lt;if(t.standoff){var Jn=Math.sqrt(Math.pow(Mt-ft,2)+Math.pow(Lt-kt,2));qn+=t.standoff*(ft-Mt)/Jn,ri+=t.standoff*(kt-Lt)/Jn}var Ii=rn.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(ft-qn)+\",\"+(kt-ri),transform:FU(qn,ri)}).style(\"stroke-width\",Qt+6+\"px\").call(u2.stroke,\"rgba(0,0,0,0)\").call(u2.fill,\"rgba(0,0,0,0)\"),Vi,ra;pA.init({element:Ii.node(),gd:e,prepFn:function(){var ha=sg.getTranslate(A);Vi=ha.x,ra=ha.y,a&&a.autorange&&v(a._name+\".autorange\",!0),i&&i.autorange&&v(i._name+\".autorange\",!0)},moveFn:function(ha,va){var Xi=Nr(Vi,ra),oa=Xi[0]+ha,Ma=Xi[1]+va;A.call(sg.setTranslate,oa,Ma),g(\"x\",qm(a,ha,\"x\",s,t)),g(\"y\",qm(i,va,\"y\",s,t)),t.axref===t.xref&&g(\"ax\",qm(a,ha,\"ax\",s,t)),t.ayref===t.yref&&g(\"ay\",qm(i,va,\"ay\",s,t)),rn.attr(\"transform\",FU(ha,va)),k.attr({transform:\"rotate(\"+y+\",\"+oa+\",\"+Ma+\")\"})},doneFn:function(){PU.call(\"_guiRelayout\",e,p());var ha=document.querySelector(\".js-notes-box-panel\");ha&&ha.redraw(ha.selectedObj)}})}}};if(t.showarrow&&cr(0,0),S){var Ct;pA.init({element:A.node(),gd:e,prepFn:function(){Ct=k.attr(\"transform\")},moveFn:function(wt,Tt){var Mt=\"pointer\";if(t.showarrow)t.axref===t.xref?g(\"ax\",qm(a,wt,\"ax\",s,t)):g(\"ax\",t.ax+wt),t.ayref===t.yref?g(\"ay\",qm(i,Tt,\"ay\",s.w,t)):g(\"ay\",t.ay+Tt),cr(wt,Tt);else{if(n)return;var Lt,ft;if(a)Lt=qm(a,wt,\"x\",s,t);else{var kt=t._xsize/s.w,rr=t.x+(t._xshift-t.xshift)/s.w-kt/2;Lt=pA.align(rr+wt/s.w,kt,0,1,t.xanchor)}if(i)ft=qm(i,Tt,\"y\",s,t);else{var pr=t._ysize/s.h,Kr=t.y-(t._yshift+t.yshift)/s.h-pr/2;ft=pA.align(Kr-Tt/s.h,pr,0,1,t.yanchor)}g(\"x\",Lt),g(\"y\",ft),(!a||!i)&&(Mt=pA.getCursor(a?.5:Lt,i?.5:ft,t.xanchor,t.yanchor))}k.attr({transform:FU(wt,Tt)+Ct}),RU(A,Mt)},clickFn:function(wt,Tt){t.captureevents&&e.emit(\"plotly_clickannotation\",w(Tt))},doneFn:function(){RU(A),PU.call(\"_guiRelayout\",e,p());var wt=document.querySelector(\".js-notes-box-panel\");wt&&wt.redraw(wt.selectedObj)}})}}l.annotationText?V.call(DU.makeEditable,{delegate:A,gd:e}).call(j).on(\"edit\",function(U){t.text=U,this.call(j),g(\"text\",U),a&&a.autorange&&v(a._name+\".autorange\",!0),i&&i.autorange&&v(i._name+\".autorange\",!0),PU.call(\"_guiRelayout\",e,p())}):V.call(j)}});var Wge=fe((gpr,jge)=>{\"use strict\";var Uge=Xt(),bwt=bi(),Vge=Mo().arrayEditor;jge.exports={hasClickToShow:wwt,onClick:Twt};function wwt(e,t){var r=Hge(e,t);return r.on.length>0||r.explicitOff.length>0}function Twt(e,t){var r=Hge(e,t),n=r.on,a=r.off.concat(r.explicitOff),i={},o=e._fullLayout.annotations,s,l;if(n.length||a.length){for(s=0;s<n.length;s++)l=Vge(e.layout,\"annotations\",o[n[s]]),l.modifyItem(\"visible\",!0),Uge.extendFlat(i,l.getUpdateObj());for(s=0;s<a.length;s++)l=Vge(e.layout,\"annotations\",o[a[s]]),l.modifyItem(\"visible\",!1),Uge.extendFlat(i,l.getUpdateObj());return bwt.call(\"update\",e,{},i)}}function Hge(e,t){var r=e._fullLayout.annotations,n=[],a=[],i=[],o=(t||[]).length,s,l,u,c,f,v,g,p;for(s=0;s<r.length;s++)if(u=r[s],c=u.clicktoshow,c){for(l=0;l<o;l++)if(f=t[l],v=f.xaxis,g=f.yaxis,v._id===u.xref&&g._id===u.yref&&v.d2r(f.x)===Gge(u._xclick,v)&&g.d2r(f.y)===Gge(u._yclick,g)){u.visible?c===\"onout\"?p=a:p=i:p=n,p.push(s);break}l===o&&u.visible&&c===\"onout\"&&a.push(s)}return{on:n,off:a,explicitOff:i}}function Gge(e,t){return t.type===\"log\"?t.l2r(e):t.d2r(e)}});var zU=fe((mpr,Yge)=>{\"use strict\";var IU=Xt(),w3=ni();Yge.exports=function(t,r,n,a){a(\"opacity\");var i=a(\"bgcolor\"),o=a(\"bordercolor\"),s=w3.opacity(o);a(\"borderpad\");var l=a(\"borderwidth\"),u=a(\"showarrow\");a(\"text\",u?\" \":n._dfltTitle.annotation),a(\"textangle\"),IU.coerceFont(a,\"font\",n.font),a(\"width\"),a(\"align\");var c=a(\"height\");if(c&&a(\"valign\"),u){var f=a(\"arrowside\"),v,g;f.indexOf(\"end\")!==-1&&(v=a(\"arrowhead\"),g=a(\"arrowsize\")),f.indexOf(\"start\")!==-1&&(a(\"startarrowhead\",v),a(\"startarrowsize\",g)),a(\"arrowcolor\",s?r.bordercolor:w3.defaultLine),a(\"arrowwidth\",(s&&l||1)*2),a(\"standoff\"),a(\"startstandoff\")}var p=a(\"hovertext\"),x=n.hoverlabel||{};if(p){var _=a(\"hoverlabel.bgcolor\",x.bgcolor||(w3.opacity(i)?w3.rgb(i):w3.defaultLine)),y=a(\"hoverlabel.bordercolor\",x.bordercolor||w3.contrast(_)),C=IU.extendFlat({},x.font);C.color||(C.color=y),IU.coerceFont(a,\"hoverlabel.font\",C)}a(\"captureevents\",!!p)}});var Zge=fe((ypr,Xge)=>{\"use strict\";var qU=Xt(),c2=Yi(),Awt=Ah(),Mwt=zU(),Swt=Fm();Xge.exports=function(t,r){Awt(t,r,{name:\"annotations\",handleItemDefaults:Ewt})};function Ewt(e,t,r){function n(k,S){return qU.coerce(e,t,Swt,k,S)}var a=n(\"visible\"),i=n(\"clicktoshow\");if(a||i){Mwt(e,t,r,n);for(var o=t.showarrow,s=[\"x\",\"y\"],l=[-10,-30],u={_fullLayout:r},c=0;c<2;c++){var f=s[c],v=c2.coerceRef(e,t,u,f,\"\",\"paper\");if(v!==\"paper\"){var g=c2.getFromId(u,v);g._annIndices.push(t._index)}if(c2.coercePosition(t,u,n,v,f,.5),o){var p=\"a\"+f,x=c2.coerceRef(e,t,u,p,\"pixel\",[\"pixel\",\"paper\"]);x!==\"pixel\"&&x!==v&&(x=t[p]=\"pixel\");var _=x===\"pixel\"?l[c]:.4;c2.coercePosition(t,u,n,x,p,_)}n(f+\"anchor\"),n(f+\"shift\")}if(qU.noneOrAll(e,t,[\"x\",\"y\"]),o&&qU.noneOrAll(e,t,[\"ax\",\"ay\"]),i){var y=n(\"xclick\"),C=n(\"yclick\");t._xclick=y===void 0?t.x:c2.cleanPosition(y,u,t.xref),t._yclick=C===void 0?t.y:c2.cleanPosition(C,u,t.yref)}}}});var Jge=fe((xpr,Kge)=>{\"use strict\";var BU=Xt(),f2=Yi(),kwt=AC().draw;Kge.exports=function(t){var r=t._fullLayout,n=BU.filterVisible(r.annotations);if(n.length&&t._fullData.length)return BU.syncOrAsync([kwt,Cwt],t)};function Cwt(e){var t=e._fullLayout;BU.filterVisible(t.annotations).forEach(function(r){var n=f2.getFromId(e,r.xref),a=f2.getFromId(e,r.yref),i=f2.getRefType(r.xref),o=f2.getRefType(r.yref);r._extremes={},i===\"range\"&&$ge(r,n),o===\"range\"&&$ge(r,a)})}function $ge(e,t){var r=t._id,n=r.charAt(0),a=e[n],i=e[\"a\"+n],o=e[n+\"ref\"],s=e[\"a\"+n+\"ref\"],l=e[\"_\"+n+\"padplus\"],u=e[\"_\"+n+\"padminus\"],c={x:1,y:-1}[n]*e[n+\"shift\"],f=3*e.arrowsize*e.arrowwidth||0,v=f+c,g=f-c,p=3*e.startarrowsize*e.arrowwidth||0,x=p+c,_=p-c,y;if(s===o){var C=f2.findExtremes(t,[t.r2c(a)],{ppadplus:v,ppadminus:g}),k=f2.findExtremes(t,[t.r2c(i)],{ppadplus:Math.max(l,x),ppadminus:Math.max(u,_)});y={min:[C.min[0],k.min[0]],max:[C.max[0],k.max[0]]}}else x=i?x+i:x,_=i?_-i:_,y=f2.findExtremes(t,[t.r2c(a)],{ppadplus:Math.max(l,v,x),ppadminus:Math.max(u,g,_)});e._extremes[r]=y}});var eme=fe((_pr,Qge)=>{\"use strict\";var Lwt=aa(),Pwt=rE();Qge.exports=function(t,r,n,a){r=r||{};var i=n===\"log\"&&r.type===\"linear\",o=n===\"linear\"&&r.type===\"log\";if(!(i||o))return;var s=t._fullLayout.annotations,l=r._id.charAt(0),u,c;function f(g){var p=u[g],x=null;i?x=Pwt(p,r.range):x=Math.pow(10,p),Lwt(x)||(x=null),a(c+g,x)}for(var v=0;v<s.length;v++)u=s[v],c=\"annotations[\"+v+\"].\",u[l+\"ref\"]===r._id&&f(l),u[\"a\"+l+\"ref\"]===r._id&&f(\"a\"+l)}});var nme=fe((bpr,rme)=>{\"use strict\";var OU=AC(),tme=Wge();rme.exports={moduleType:\"component\",name:\"annotations\",layoutAttributes:Fm(),supplyLayoutDefaults:Zge(),includeBasePlot:KT()(\"annotations\"),calcAutorange:Jge(),draw:OU.draw,drawOne:OU.drawOne,drawRaw:OU.drawRaw,hasClickToShow:tme.hasClickToShow,onClick:tme.onClick,convertCoords:eme()}});var MC=fe((wpr,ime)=>{\"use strict\";var Ys=Fm(),Fwt=rl().overrideAll,Dwt=Mo().templatedArray;ime.exports=Fwt(Dwt(\"annotation\",{visible:Ys.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:Ys.xanchor,xshift:Ys.xshift,yanchor:Ys.yanchor,yshift:Ys.yshift,text:Ys.text,textangle:Ys.textangle,font:Ys.font,width:Ys.width,height:Ys.height,opacity:Ys.opacity,align:Ys.align,valign:Ys.valign,bgcolor:Ys.bgcolor,bordercolor:Ys.bordercolor,borderpad:Ys.borderpad,borderwidth:Ys.borderwidth,showarrow:Ys.showarrow,arrowcolor:Ys.arrowcolor,arrowhead:Ys.arrowhead,startarrowhead:Ys.startarrowhead,arrowside:Ys.arrowside,arrowsize:Ys.arrowsize,startarrowsize:Ys.startarrowsize,arrowwidth:Ys.arrowwidth,standoff:Ys.standoff,startstandoff:Ys.startstandoff,hovertext:Ys.hovertext,hoverlabel:Ys.hoverlabel,captureevents:Ys.captureevents}),\"calc\",\"from-root\")});var ome=fe((Tpr,ame)=>{\"use strict\";var NU=Xt(),Rwt=Yi(),Iwt=Ah(),zwt=zU(),qwt=MC();ame.exports=function(t,r,n){Iwt(t,r,{name:\"annotations\",handleItemDefaults:Bwt,fullLayout:n.fullLayout})};function Bwt(e,t,r,n){function a(s,l){return NU.coerce(e,t,qwt,s,l)}function i(s){var l=s+\"axis\",u={_fullLayout:{}};return u._fullLayout[l]=r[l],Rwt.coercePosition(t,u,a,s,s,.5)}var o=a(\"visible\");o&&(zwt(e,t,n.fullLayout,a),i(\"x\"),i(\"y\"),i(\"z\"),NU.noneOrAll(e,t,[\"x\",\"y\",\"z\"]),t.xref=\"x\",t.yref=\"y\",t.zref=\"z\",a(\"xanchor\"),a(\"yanchor\"),a(\"xshift\"),a(\"yshift\"),t.showarrow&&(t.axref=\"pixel\",t.ayref=\"pixel\",a(\"ax\",-10),a(\"ay\",-30),NU.noneOrAll(e,t,[\"ax\",\"ay\"])))}});var cme=fe((Apr,ume)=>{\"use strict\";var sme=Xt(),lme=Yi();ume.exports=function(t){for(var r=t.fullSceneLayout,n=r.annotations,a=0;a<n.length;a++)Owt(n[a],t);t.fullLayout._infolayer.selectAll(\".annotation-\"+t.id).remove()};function Owt(e,t){var r=t.fullSceneLayout,n=r.domain,a=t.fullLayout._size,i={pdata:null,type:\"linear\",autorange:!1,range:[-1/0,1/0]};e._xa={},sme.extendFlat(e._xa,i),lme.setConvert(e._xa),e._xa._offset=a.l+n.x[0]*a.w,e._xa.l2p=function(){return .5*(1+e._pdata[0]/e._pdata[3])*a.w*(n.x[1]-n.x[0])},e._ya={},sme.extendFlat(e._ya,i),lme.setConvert(e._ya),e._ya._offset=a.t+(1-n.y[1])*a.h,e._ya.l2p=function(){return .5*(1-e._pdata[1]/e._pdata[3])*a.h*(n.y[1]-n.y[0])}}});var VU=fe((Mpr,fme)=>{\"use strict\";function UU(e,t){var r=[0,0,0,0],n,a;for(n=0;n<4;++n)for(a=0;a<4;++a)r[a]+=e[4*n+a]*t[n];return r}function Nwt(e,t){var r=UU(e.projection,UU(e.view,UU(e.model,[t[0],t[1],t[2],1])));return r}fme.exports=Nwt});var dme=fe((Spr,hme)=>{\"use strict\";var Uwt=AC().drawRaw,Vwt=VU(),Gwt=[\"x\",\"y\",\"z\"];hme.exports=function(t){for(var r=t.fullSceneLayout,n=t.dataScale,a=r.annotations,i=0;i<a.length;i++){for(var o=a[i],s=!1,l=0;l<3;l++){var 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l=a?jU.extractPathCoords(a,i?l1.paramIsY:l1.paramIsX):[r,n],u=HU.aggNums(Math.max,null,l),c=HU.aggNums(Math.min,null,l),f=c<0?Math.abs(c)+o:o,v=u>0?u+o:o;return{ppad:o,ppadplus:s?f:v,ppadminus:s?v:f}}else return{ppad:o}}function Cme(e,t,r){var n=e._id.charAt(0)===\"x\"?\"x\":\"y\",a=e.type===\"category\"||e.type===\"multicategory\",i,o,s=0,l=0,u=a?e.r2c:e.d2c,c=t[n+\"sizemode\"]===\"scaled\";if(c?(i=t[n+\"0\"],o=t[n+\"1\"],a&&(s=t[n+\"0shift\"],l=t[n+\"1shift\"])):(i=t[n+\"anchor\"],o=t[n+\"anchor\"]),i!==void 0)return[u(i)+s,u(o)+l];if(t.path){var f=1/0,v=-1/0,g=t.path.match(l1.segmentRE),p,x,_,y,C;for(e.type===\"date\"&&(u=jU.decodeDate(u)),p=0;p<g.length;p++)x=g[p],_=r[x.charAt(0)].drawn,_!==void 0&&(y=g[p].slice(1).match(l1.paramRE),!(!y||y.length<_)&&(C=u(y[_]),C<f&&(f=C),C>v&&(v=C)));if(v>=f)return[f,v]}}});var Ime=fe((Rpr,Rme)=>{\"use strict\";var Dme=d8();Rme.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:GU(),supplyLayoutDefaults:wme(),supplyDrawNewShapeDefaults:Mme(),includeBasePlot:KT()(\"shapes\"),calcAutorange:Fme(),draw:Dme.draw,drawOne:Dme.drawOne}});var WU=fe((zpr,qme)=>{\"use strict\";var zme=rc(),n5t=Mo().templatedArray,Ipr=ZT();qme.exports=n5t(\"image\",{visible:{valType:\"boolean\",dflt:!0,editType:\"arraydraw\"},source:{valType:\"string\",editType:\"arraydraw\"},layer:{valType:\"enumerated\",values:[\"below\",\"above\"],dflt:\"above\",editType:\"arraydraw\"},sizex:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizey:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizing:{valType:\"enumerated\",values:[\"fill\",\"contain\",\"stretch\"],dflt:\"contain\",editType:\"arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,editType:\"arraydraw\"},x:{valType:\"any\",dflt:0,editType:\"arraydraw\"},y:{valType:\"any\",dflt:0,editType:\"arraydraw\"},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"arraydraw\"},yanchor:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"top\",editType:\"arraydraw\"},xref:{valType:\"enumerated\",values:[\"paper\",zme.idRegex.x.toString()],dflt:\"paper\",editType:\"arraydraw\"},yref:{valType:\"enumerated\",values:[\"paper\",zme.idRegex.y.toString()],dflt:\"paper\",editType:\"arraydraw\"},editType:\"arraydraw\"})});var Ome=fe((qpr,Bme)=>{\"use strict\";var i5t=Xt(),YU=Yi(),a5t=Ah(),o5t=WU(),s5t=\"images\";Bme.exports=function(t,r){var n={name:s5t,handleItemDefaults:l5t};a5t(t,r,n)};function l5t(e,t,r){function n(v,g){return i5t.coerce(e,t,o5t,v,g)}var a=n(\"source\"),i=n(\"visible\",!!a);if(!i)return t;n(\"layer\"),n(\"xanchor\"),n(\"yanchor\"),n(\"sizex\"),n(\"sizey\"),n(\"sizing\"),n(\"opacity\");for(var o={_fullLayout:r},s=[\"x\",\"y\"],l=0;l<2;l++){var u=s[l],c=YU.coerceRef(e,t,o,u,\"paper\",void 0);if(c!==\"paper\"){var f=YU.getFromId(o,c);f._imgIndices.push(t._index)}YU.coercePosition(t,o,n,c,u,0)}return t}});var Gme=fe((Bpr,Vme)=>{\"use strict\";var Nme=xi(),u5t=ea(),M3=Yi(),Ume=El(),c5t=Q0();Vme.exports=function(t){var r=t._fullLayout,n=[],a={},i=[],o,s;for(s=0;s<r.images.length;s++){var l=r.images[s];if(l.visible)if(l.layer===\"below\"&&l.xref!==\"paper\"&&l.yref!==\"paper\"){o=Ume.ref2id(l.xref)+Ume.ref2id(l.yref);var u=r._plots[o];if(!u){i.push(l);continue}u.mainplot&&(o=u.mainplot.id),a[o]||(a[o]=[]),a[o].push(l)}else l.layer===\"above\"?n.push(l):i.push(l)}var c={x:{left:{sizing:\"xMin\",offset:0},center:{sizing:\"xMid\",offset:-1/2},right:{sizing:\"xMax\",offset:-1}},y:{top:{sizing:\"YMin\",offset:0},middle:{sizing:\"YMid\",offset:-1/2},bottom:{sizing:\"YMax\",offset:-1}}};function f(S){var L=Nme.select(this);if(this._imgSrc!==S.source)if(L.attr(\"xmlns\",c5t.svg),!t._context.staticPlot||S.source&&S.source.slice(0,5)===\"data:\")L.attr(\"xlink:href\",S.source),this._imgSrc=S.source;else{var w=new Promise(function(A){var T=new Image;this.img=T,T.setAttribute(\"crossOrigin\",\"anonymous\"),T.onerror=m,T.onload=function(){var F=document.createElement(\"canvas\");F.width=this.width,F.height=this.height;var E=F.getContext(\"2d\",{willReadFrequently:!0});E.drawImage(this,0,0);var I=F.toDataURL(\"image/png\");L.attr(\"xlink:href\",I),A()},L.on(\"error\",m),T.src=S.source,this._imgSrc=S.source;function m(){L.remove(),A()}}.bind(this));t._promises.push(w)}}function v(S){var L=Nme.select(this),w=M3.getFromId(t,S.xref),A=M3.getFromId(t,S.yref),T=M3.getRefType(S.xref)===\"domain\",m=M3.getRefType(S.yref)===\"domain\",F=r._size,E,I;w!==void 0?E=typeof S.xref==\"string\"&&T?w._length*S.sizex:Math.abs(w.l2p(S.sizex)-w.l2p(0)):E=S.sizex*F.w,A!==void 0?I=typeof S.yref==\"string\"&&m?A._length*S.sizey:Math.abs(A.l2p(S.sizey)-A.l2p(0)):I=S.sizey*F.h;var R=E*c.x[S.xanchor].offset,B=I*c.y[S.yanchor].offset,O=c.x[S.xanchor].sizing+c.y[S.yanchor].sizing,V,j;switch(w!==void 0?V=typeof S.xref==\"string\"&&T?w._length*S.x+w._offset:w.r2p(S.x)+w._offset:V=S.x*F.w+F.l,V+=R,A!==void 0?j=typeof S.yref==\"string\"&&m?A._length*(1-S.y)+A._offset:A.r2p(S.y)+A._offset:j=F.h-S.y*F.h+F.t,j+=B,S.sizing){case\"fill\":O+=\" slice\";break;case\"stretch\":O=\"none\";break}L.attr({x:V,y:j,width:E,height:I,preserveAspectRatio:O,opacity:S.opacity});var q=w&&M3.getRefType(S.xref)!==\"domain\"?w._id:\"\",U=A&&M3.getRefType(S.yref)!==\"domain\"?A._id:\"\",J=q+U;u5t.setClipUrl(L,J?\"clip\"+r._uid+J:null,t)}function g(S){return[S.xref,S.x,S.sizex,S.yref,S.y,S.sizey].join(\"_\")}function p(S,L){return S._index-L._index}var x=r._imageLowerLayer.selectAll(\"image\").data(i,g),_=r._imageUpperLayer.selectAll(\"image\").data(n,g);x.enter().append(\"image\"),_.enter().append(\"image\"),x.exit().remove(),_.exit().remove(),x.each(function(S){f.bind(this)(S),v.bind(this)(S)}),_.each(function(S){f.bind(this)(S),v.bind(this)(S)}),x.sort(p),_.sort(p);var y=Object.keys(r._plots);for(s=0;s<y.length;s++){o=y[s];var C=r._plots[o];if(C.imagelayer){var k=C.imagelayer.selectAll(\"image\").data(a[o]||[],g);k.enter().append(\"image\"),k.exit().remove(),k.each(function(S){f.bind(this)(S),v.bind(this)(S)}),k.sort(p)}}}});var Wme=fe((Opr,jme)=>{\"use strict\";var Hme=aa(),f5t=rE();jme.exports=function(t,r,n,a){r=r||{};var i=n===\"log\"&&r.type===\"linear\",o=n===\"linear\"&&r.type===\"log\";if(i||o){for(var s=t._fullLayout.images,l=r._id.charAt(0),u,c,f=0;f<s.length;f++)if(u=s[f],c=\"images[\"+f+\"].\",u[l+\"ref\"]===r._id){var v=u[l],g=u[\"size\"+l],p=null,x=null;if(i){p=f5t(v,r.range);var _=g/Math.pow(10,p)/2;x=2*Math.log(_+Math.sqrt(1+_*_))/Math.LN10}else p=Math.pow(10,v),x=p*(Math.pow(10,g/2)-Math.pow(10,-g/2));Hme(p)?Hme(x)||(x=null):(p=null,x=null),a(c+l,p),a(c+\"size\"+l,x)}}}});var Xme=fe((Npr,Yme)=>{\"use strict\";Yme.exports={moduleType:\"component\",name:\"images\",layoutAttributes:WU(),supplyLayoutDefaults:Ome(),includeBasePlot:KT()(\"images\"),draw:Gme(),convertCoords:Wme()}});var SC=fe((Upr,Zme)=>{\"use strict\";Zme.exports={name:\"updatemenus\",containerClassName:\"updatemenu-container\",headerGroupClassName:\"updatemenu-header-group\",headerClassName:\"updatemenu-header\",headerArrowClassName:\"updatemenu-header-arrow\",dropdownButtonGroupClassName:\"updatemenu-dropdown-button-group\",dropdownButtonClassName:\"updatemenu-dropdown-button\",buttonClassName:\"updatemenu-button\",itemRectClassName:\"updatemenu-item-rect\",itemTextClassName:\"updatemenu-item-text\",menuIndexAttrName:\"updatemenu-active-index\",autoMarginIdRoot:\"updatemenu-\",blankHeaderOpts:{label:\"  \"},minWidth:30,minHeight:30,textPadX:24,arrowPadX:16,rx:2,ry:2,textOffsetX:12,textOffsetY:3,arrowOffsetX:4,gapButtonHeader:5,gapButton:2,activeColor:\"#F4FAFF\",hoverColor:\"#F4FAFF\",arrowSymbol:{left:\"\\u25C4\",right:\"\\u25BA\",up:\"\\u25B2\",down:\"\\u25BC\"}}});var XU=fe((Vpr,Kme)=>{\"use strict\";var h5t=Ds(),d5t=tc(),v5t=ta().extendFlat,p5t=rl().overrideAll,g5t=zE(),$me=Mo().templatedArray,m5t=$me(\"button\",{visible:{valType:\"boolean\"},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},args2:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\",dflt:\"\"},execute:{valType:\"boolean\",dflt:!0}});Kme.exports=p5t($me(\"updatemenu\",{_arrayAttrRegexps:[/^updatemenus\\[(0|[1-9][0-9]+)\\]\\.buttons/],visible:{valType:\"boolean\"},type:{valType:\"enumerated\",values:[\"dropdown\",\"buttons\"],dflt:\"dropdown\"},direction:{valType:\"enumerated\",values:[\"left\",\"right\",\"up\",\"down\"],dflt:\"down\"},active:{valType:\"integer\",min:-1,dflt:0},showactive:{valType:\"boolean\",dflt:!0},buttons:m5t,x:{valType:\"number\",min:-2,max:3,dflt:-.05},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"right\"},y:{valType:\"number\",min:-2,max:3,dflt:1},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},pad:v5t(g5t({editType:\"arraydraw\"}),{}),font:h5t({}),bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\",dflt:d5t.borderLine},borderwidth:{valType:\"number\",min:0,dflt:1,editType:\"arraydraw\"}}),\"arraydraw\",\"from-root\")});var tye=fe((Gpr,eye)=>{\"use strict\";var EC=Xt(),Jme=Ah(),Qme=XU(),y5t=SC(),x5t=y5t.name,_5t=Qme.buttons;eye.exports=function(t,r){var n={name:x5t,handleItemDefaults:b5t};Jme(t,r,n)};function b5t(e,t,r){function n(o,s){return EC.coerce(e,t,Qme,o,s)}var a=Jme(e,t,{name:\"buttons\",handleItemDefaults:w5t}),i=n(\"visible\",a.length>0);i&&(n(\"active\"),n(\"direction\"),n(\"type\"),n(\"showactive\"),n(\"x\"),n(\"y\"),EC.noneOrAll(e,t,[\"x\",\"y\"]),n(\"xanchor\"),n(\"yanchor\"),n(\"pad.t\"),n(\"pad.r\"),n(\"pad.b\"),n(\"pad.l\"),EC.coerceFont(n,\"font\",r.font),n(\"bgcolor\",r.paper_bgcolor),n(\"bordercolor\"),n(\"borderwidth\"))}function w5t(e,t){function r(a,i){return EC.coerce(e,t,_5t,a,i)}var n=r(\"visible\",e.method===\"skip\"||Array.isArray(e.args));n&&(r(\"method\"),r(\"args\"),r(\"args2\"),r(\"label\"),r(\"execute\"))}});var iye=fe((Hpr,nye)=>{\"use strict\";nye.exports=Nl;var vp=xi(),rye=ni(),S3=ea(),kC=Xt();function Nl(e,t,r){this.gd=e,this.container=t,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll(\"rect.scrollbox-bg\").data([0]),this.bg.exit().on(\".drag\",null).on(\"wheel\",null).remove(),this.bg.enter().append(\"rect\").classed(\"scrollbox-bg\",!0).style(\"pointer-events\",\"all\").attr({opacity:0,x:0,y:0,width:0,height:0})}Nl.barWidth=2;Nl.barLength=20;Nl.barRadius=2;Nl.barPad=1;Nl.barColor=\"#808BA4\";Nl.prototype.enable=function(t,r,n){var a=this.gd._fullLayout,i=a.width,o=a.height;this.position=t;var s=this.position.l,l=this.position.w,u=this.position.t,c=this.position.h,f=this.position.direction,v=f===\"down\",g=f===\"left\",p=f===\"right\",x=f===\"up\",_=l,y=c,C,k,S,L;!v&&!g&&!p&&!x&&(this.position.direction=\"down\",v=!0);var w=v||x;w?(C=s,k=C+_,v?(S=u,L=Math.min(S+y,o),y=L-S):(L=u+y,S=Math.max(L-y,0),y=L-S)):(S=u,L=S+y,g?(k=s+_,C=Math.max(k-_,0),_=k-C):(C=s,k=Math.min(C+_,i),_=k-C)),this._box={l:C,t:S,w:_,h:y};var A=l>_,T=Nl.barLength+2*Nl.barPad,m=Nl.barWidth+2*Nl.barPad,F=s,E=u+c;E+m>o&&(E=o-m);var I=this.container.selectAll(\"rect.scrollbar-horizontal\").data(A?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call(rye.fill,Nl.barColor),A?(this.hbar=I.attr({rx:Nl.barRadius,ry:Nl.barRadius,x:F,y:E,width:T,height:m}),this._hbarXMin=F+T/2,this._hbarTranslateMax=_-T):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var R=c>y,B=Nl.barWidth+2*Nl.barPad,O=Nl.barLength+2*Nl.barPad,V=s+l,j=u;V+B>i&&(V=i-B);var q=this.container.selectAll(\"rect.scrollbar-vertical\").data(R?[0]:[]);q.exit().on(\".drag\",null).remove(),q.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(rye.fill,Nl.barColor),R?(this.vbar=q.attr({rx:Nl.barRadius,ry:Nl.barRadius,x:V,y:j,width:B,height:O}),this._vbarYMin=j+O/2,this._vbarTranslateMax=y-O):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var U=this.id,J=C-.5,ee=R?k+B+.5:k+.5,ce=S-.5,ye=A?L+m+.5:L+.5,xe=a._topdefs.selectAll(\"#\"+U).data(A||R?[0]:[]);if(xe.exit().remove(),xe.enter().append(\"clipPath\").attr(\"id\",U).append(\"rect\"),A||R?(this._clipRect=xe.select(\"rect\").attr({x:Math.floor(J),y:Math.floor(ce),width:Math.ceil(ee)-Math.floor(J),height:Math.ceil(ye)-Math.floor(ce)}),this.container.call(S3.setClipUrl,U,this.gd),this.bg.attr({x:s,y:u,width:l,height:c})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(S3.setClipUrl,null),delete this._clipRect),A||R){var ue=vp.behavior.drag().on(\"dragstart\",function(){vp.event.sourceEvent.preventDefault()}).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(ue);var K=vp.behavior.drag().on(\"dragstart\",function(){vp.event.sourceEvent.preventDefault(),vp.event.sourceEvent.stopPropagation()}).on(\"drag\",this._onBarDrag.bind(this));A&&this.hbar.on(\".drag\",null).call(K),R&&this.vbar.on(\".drag\",null).call(K)}this.setTranslate(r,n)};Nl.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(S3.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)};Nl.prototype._onBoxDrag=function(){var t=this.translateX,r=this.translateY;this.hbar&&(t-=vp.event.dx),this.vbar&&(r-=vp.event.dy),this.setTranslate(t,r)};Nl.prototype._onBoxWheel=function(){var t=this.translateX,r=this.translateY;this.hbar&&(t+=vp.event.deltaY),this.vbar&&(r+=vp.event.deltaY),this.setTranslate(t,r)};Nl.prototype._onBarDrag=function(){var t=this.translateX,r=this.translateY;if(this.hbar){var n=t+this._hbarXMin,a=n+this._hbarTranslateMax,i=kC.constrain(vp.event.x,n,a),o=(i-n)/(a-n),s=this.position.w-this._box.w;t=o*s}if(this.vbar){var l=r+this._vbarYMin,u=l+this._vbarTranslateMax,c=kC.constrain(vp.event.y,l,u),f=(c-l)/(u-l),v=this.position.h-this._box.h;r=f*v}this.setTranslate(t,r)};Nl.prototype.setTranslate=function(t,r){var n=this.position.w-this._box.w,a=this.position.h-this._box.h;if(t=kC.constrain(t||0,0,n),r=kC.constrain(r||0,0,a),this.translateX=t,this.translateY=r,this.container.call(S3.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-r),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+r-.5)}),this.hbar){var i=t/n;this.hbar.call(S3.setTranslate,t+i*this._hbarTranslateMax,r)}if(this.vbar){var o=r/a;this.vbar.call(S3.setTranslate,t,r+o*this._vbarTranslateMax)}}});var vye=fe((jpr,dye)=>{\"use strict\";var E3=xi(),yA=Hs(),xA=ni(),k3=ea(),R0=Xt(),CC=as(),T5t=Mo().arrayEditor,oye=nc().LINE_SPACING,ba=SC(),A5t=iye();dye.exports=function(t){var r=t._fullLayout,n=R0.filterVisible(r[ba.name]);function a(v){yA.autoMargin(t,fye(v))}var i=r._menulayer.selectAll(\"g.\"+ba.containerClassName).data(n.length>0?[0]:[]);if(i.enter().append(\"g\").classed(ba.containerClassName,!0).style(\"cursor\",\"pointer\"),i.exit().each(function(){E3.select(this).selectAll(\"g.\"+ba.headerGroupClassName).each(a)}).remove(),n.length!==0){var o=i.selectAll(\"g.\"+ba.headerGroupClassName).data(n,M5t);o.enter().append(\"g\").classed(ba.headerGroupClassName,!0);for(var s=R0.ensureSingle(i,\"g\",ba.dropdownButtonGroupClassName,function(v){v.style(\"pointer-events\",\"all\")}),l=0;l<n.length;l++){var u=n[l];P5t(t,u)}var c=\"updatemenus\"+r._uid,f=new A5t(t,s,c);o.enter().size()&&(s.node().parentNode.appendChild(s.node()),s.call($U)),o.exit().each(function(v){s.call($U),a(v)}).remove(),o.each(function(v){var g=E3.select(this),p=v.type===\"dropdown\"?s:null;yA.manageCommandObserver(t,v,v.buttons,function(x){ZU(t,v,v.buttons[x.index],g,p,f,x.index,!0)}),v.type===\"dropdown\"?(lye(t,g,s,f,v),sye(s,v)&&_A(t,g,s,f,v)):_A(t,g,null,null,v)})}};function M5t(e){return e._index}function S5t(e){return+e.attr(ba.menuIndexAttrName)==-1}function sye(e,t){return+e.attr(ba.menuIndexAttrName)===t._index}function ZU(e,t,r,n,a,i,o,s){t.active=o,T5t(e.layout,ba.name,t).applyUpdate(\"active\",o),t.type===\"buttons\"?_A(e,n,null,null,t):t.type===\"dropdown\"&&(a.attr(ba.menuIndexAttrName,\"-1\"),lye(e,n,a,i,t),s||_A(e,n,a,i,t))}function lye(e,t,r,n,a){var i=R0.ensureSingle(t,\"g\",ba.headerClassName,function(v){v.style(\"pointer-events\",\"all\")}),o=a._dims,s=a.active,l=a.buttons[s]||ba.blankHeaderOpts,u={y:a.pad.t,yPad:0,x:a.pad.l,xPad:0,index:0},c={width:o.headerWidth,height:o.headerHeight};i.call(KU,a,l,e).call(hye,a,u,c);var f=R0.ensureSingle(t,\"text\",ba.headerArrowClassName,function(v){v.attr(\"text-anchor\",\"end\").call(k3.font,a.font).text(ba.arrowSymbol[a.direction])});f.attr({x:o.headerWidth-ba.arrowOffsetX+a.pad.l,y:o.headerHeight/2+ba.textOffsetY+a.pad.t}),i.on(\"click\",function(){r.call($U,String(sye(r,a)?-1:a._index)),_A(e,t,r,n,a)}),i.on(\"mouseover\",function(){i.call(uye)}),i.on(\"mouseout\",function(){i.call(cye,a)}),k3.setTranslate(t,o.lx,o.ly)}function _A(e,t,r,n,a){r||(r=t,r.attr(\"pointer-events\",\"all\"));var i=!S5t(r)||a.type===\"buttons\"?a.buttons:[],o=a.type===\"dropdown\"?ba.dropdownButtonClassName:ba.buttonClassName,s=r.selectAll(\"g.\"+o).data(R0.filterVisible(i)),l=s.enter().append(\"g\").classed(o,!0),u=s.exit();a.type===\"dropdown\"?(l.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),u.transition().attr(\"opacity\",\"0\").remove()):u.remove();var c=0,f=0,v=a._dims,g=[\"up\",\"down\"].indexOf(a.direction)!==-1;a.type===\"dropdown\"&&(g?f=v.headerHeight+ba.gapButtonHeader:c=v.headerWidth+ba.gapButtonHeader),a.type===\"dropdown\"&&a.direction===\"up\"&&(f=-ba.gapButtonHeader+ba.gapButton-v.openHeight),a.type===\"dropdown\"&&a.direction===\"left\"&&(c=-ba.gapButtonHeader+ba.gapButton-v.openWidth);var p={x:v.lx+c+a.pad.l,y:v.ly+f+a.pad.t,yPad:ba.gapButton,xPad:ba.gapButton,index:0},x={l:p.x+a.borderwidth,t:p.y+a.borderwidth};s.each(function(_,y){var C=E3.select(this);C.call(KU,a,_,e).call(hye,a,p),C.on(\"click\",function(){E3.event.defaultPrevented||(_.execute&&(_.args2&&a.active===y?(ZU(e,a,_,t,r,n,-1),yA.executeAPICommand(e,_.method,_.args2)):(ZU(e,a,_,t,r,n,y),yA.executeAPICommand(e,_.method,_.args))),e.emit(\"plotly_buttonclicked\",{menu:a,button:_,active:a.active}))}),C.on(\"mouseover\",function(){C.call(uye)}),C.on(\"mouseout\",function(){C.call(cye,a),s.call(aye,a)})}),s.call(aye,a),g?(x.w=Math.max(v.openWidth,v.headerWidth),x.h=p.y-x.t):(x.w=p.x-x.l,x.h=Math.max(v.openHeight,v.headerHeight)),x.direction=a.direction,n&&(s.size()?E5t(e,t,r,n,a,x):k5t(n))}function E5t(e,t,r,n,a,i){var o=a.direction,s=o===\"up\"||o===\"down\",l=a._dims,u=a.active,c,f,v;if(s)for(f=0,v=0;v<u;v++)f+=l.heights[v]+ba.gapButton;else for(c=0,v=0;v<u;v++)c+=l.widths[v]+ba.gapButton;n.enable(i,c,f),n.hbar&&n.hbar.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),n.vbar&&n.vbar.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\")}function k5t(e){var t=!!e.hbar,r=!!e.vbar;t&&e.hbar.transition().attr(\"opacity\",\"0\").each(\"end\",function(){t=!1,r||e.disable()}),r&&e.vbar.transition().attr(\"opacity\",\"0\").each(\"end\",function(){r=!1,t||e.disable()})}function KU(e,t,r,n){e.call(C5t,t).call(L5t,t,r,n)}function C5t(e,t){var r=R0.ensureSingle(e,\"rect\",ba.itemRectClassName,function(n){n.attr({rx:ba.rx,ry:ba.ry,\"shape-rendering\":\"crispEdges\"})});r.call(xA.stroke,t.bordercolor).call(xA.fill,t.bgcolor).style(\"stroke-width\",t.borderwidth+\"px\")}function L5t(e,t,r,n){var a=R0.ensureSingle(e,\"text\",ba.itemTextClassName,function(s){s.attr({\"text-anchor\":\"start\",\"data-notex\":1})}),i=r.label,o=n._fullLayout._meta;o&&(i=R0.templateString(i,o)),a.call(k3.font,t.font).text(i).call(CC.convertToTspans,n)}function aye(e,t){var r=t.active;e.each(function(n,a){var i=E3.select(this);a===r&&t.showactive&&i.select(\"rect.\"+ba.itemRectClassName).call(xA.fill,ba.activeColor)})}function uye(e){e.select(\"rect.\"+ba.itemRectClassName).call(xA.fill,ba.hoverColor)}function cye(e,t){e.select(\"rect.\"+ba.itemRectClassName).call(xA.fill,t.bgcolor)}function P5t(e,t){var r=t._dims={width1:0,height1:0,heights:[],widths:[],totalWidth:0,totalHeight:0,openWidth:0,openHeight:0,lx:0,ly:0},n=k3.tester.selectAll(\"g.\"+ba.dropdownButtonClassName).data(R0.filterVisible(t.buttons));n.enter().append(\"g\").classed(ba.dropdownButtonClassName,!0);var a=[\"up\",\"down\"].indexOf(t.direction)!==-1;n.each(function(c,f){var v=E3.select(this);v.call(KU,t,c,e);var g=v.select(\".\"+ba.itemTextClassName),p=g.node()&&k3.bBox(g.node()).width,x=Math.max(p+ba.textPadX,ba.minWidth),_=t.font.size*oye,y=CC.lineCount(g),C=Math.max(_*y,ba.minHeight)+ba.textOffsetY;C=Math.ceil(C),x=Math.ceil(x),r.widths[f]=x,r.heights[f]=C,r.height1=Math.max(r.height1,C),r.width1=Math.max(r.width1,x),a?(r.totalWidth=Math.max(r.totalWidth,x),r.openWidth=r.totalWidth,r.totalHeight+=C+ba.gapButton,r.openHeight+=C+ba.gapButton):(r.totalWidth+=x+ba.gapButton,r.openWidth+=x+ba.gapButton,r.totalHeight=Math.max(r.totalHeight,C),r.openHeight=r.totalHeight)}),a?r.totalHeight-=ba.gapButton:r.totalWidth-=ba.gapButton,r.headerWidth=r.width1+ba.arrowPadX,r.headerHeight=r.height1,t.type===\"dropdown\"&&(a?(r.width1+=ba.arrowPadX,r.totalHeight=r.height1):r.totalWidth=r.width1,r.totalWidth+=ba.arrowPadX),n.remove();var i=r.totalWidth+t.pad.l+t.pad.r,o=r.totalHeight+t.pad.t+t.pad.b,s=e._fullLayout._size;r.lx=s.l+s.w*t.x,r.ly=s.t+s.h*(1-t.y);var l=\"left\";R0.isRightAnchor(t)&&(r.lx-=i,l=\"right\"),R0.isCenterAnchor(t)&&(r.lx-=i/2,l=\"center\");var u=\"top\";R0.isBottomAnchor(t)&&(r.ly-=o,u=\"bottom\"),R0.isMiddleAnchor(t)&&(r.ly-=o/2,u=\"middle\"),r.totalWidth=Math.ceil(r.totalWidth),r.totalHeight=Math.ceil(r.totalHeight),r.lx=Math.round(r.lx),r.ly=Math.round(r.ly),yA.autoMargin(e,fye(t),{x:t.x,y:t.y,l:i*({right:1,center:.5}[l]||0),r:i*({left:1,center:.5}[l]||0),b:o*({top:1,middle:.5}[u]||0),t:o*({bottom:1,middle:.5}[u]||0)})}function fye(e){return ba.autoMarginIdRoot+e._index}function hye(e,t,r,n){n=n||{};var a=e.select(\".\"+ba.itemRectClassName),i=e.select(\".\"+ba.itemTextClassName),o=t.borderwidth,s=r.index,l=t._dims;k3.setTranslate(e,o+r.x,o+r.y);var u=[\"up\",\"down\"].indexOf(t.direction)!==-1,c=n.height||(u?l.heights[s]:l.height1);a.attr({x:0,y:0,width:n.width||(u?l.width1:l.widths[s]),height:c});var f=t.font.size*oye,v=CC.lineCount(i),g=(v-1)*f/2;CC.positionText(i,ba.textOffsetX,c/2-g+ba.textOffsetY),u?r.y+=l.heights[s]+r.yPad:r.x+=l.widths[s]+r.xPad,r.index++}function $U(e,t){e.attr(ba.menuIndexAttrName,t||\"-1\").selectAll(\"g.\"+ba.dropdownButtonClassName).remove()}});var gye=fe((Wpr,pye)=>{\"use strict\";var F5t=SC();pye.exports={moduleType:\"component\",name:F5t.name,layoutAttributes:XU(),supplyLayoutDefaults:tye(),draw:vye()}});var bA=fe((Ypr,mye)=>{\"use 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JU=fe((Xpr,_ye)=>{\"use strict\";var yye=Ds(),D5t=zE(),R5t=ta().extendDeepAll,I5t=rl().overrideAll,z5t=Z4(),xye=Mo().templatedArray,h2=bA(),q5t=xye(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});_ye.exports=I5t(xye(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:q5t,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:R5t(D5t({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:z5t.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:yye({})},font:yye({}),activebgcolor:{valType:\"color\",dflt:h2.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:h2.railBgColor},bordercolor:{valType:\"color\",dflt:h2.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:h2.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:h2.tickLength},tickcolor:{valType:\"color\",dflt:h2.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:h2.minorTickLength}}),\"arraydraw\",\"from-root\")});var Aye=fe((Zpr,Tye)=>{\"use strict\";var C3=Xt(),bye=Ah(),wye=JU(),B5t=bA(),O5t=B5t.name,N5t=wye.steps;Tye.exports=function(t,r){bye(t,r,{name:O5t,handleItemDefaults:U5t})};function U5t(e,t,r){function n(f,v){return C3.coerce(e,t,wye,f,v)}for(var a=bye(e,t,{name:\"steps\",handleItemDefaults:V5t}),i=0,o=0;o<a.length;o++)a[o].visible&&i++;var s;if(i<2?s=t.visible=!1:s=n(\"visible\"),!!s){t._stepCount=i;var l=t._visibleSteps=C3.filterVisible(a),u=n(\"active\");(a[u]||{}).visible||(t.active=l[0]._index),n(\"x\"),n(\"y\"),C3.noneOrAll(e,t,[\"x\",\"y\"]),n(\"xanchor\"),n(\"yanchor\"),n(\"len\"),n(\"lenmode\"),n(\"pad.t\"),n(\"pad.r\"),n(\"pad.b\"),n(\"pad.l\"),C3.coerceFont(n,\"font\",r.font);var c=n(\"currentvalue.visible\");c&&(n(\"currentvalue.xanchor\"),n(\"currentvalue.prefix\"),n(\"currentvalue.suffix\"),n(\"currentvalue.offset\"),C3.coerceFont(n,\"currentvalue.font\",t.font)),n(\"transition.duration\"),n(\"transition.easing\"),n(\"bgcolor\"),n(\"activebgcolor\"),n(\"bordercolor\"),n(\"borderwidth\"),n(\"ticklen\"),n(\"tickwidth\"),n(\"tickcolor\"),n(\"minorticklen\")}}function V5t(e,t){function r(i,o){return C3.coerce(e,t,N5t,i,o)}var n;if(e.method!==\"skip\"&&!Array.isArray(e.args)?n=t.visible=!1:n=r(\"visible\"),n){r(\"method\"),r(\"args\");var a=r(\"label\",\"step-\"+t._index);r(\"value\",a),r(\"execute\")}}});var Rye=fe(($pr,Dye)=>{\"use strict\";var pp=xi(),LC=Hs(),Om=ni(),gp=ea(),I0=Xt(),G5t=I0.strTranslate,wA=as(),H5t=Mo().arrayEditor,Xa=bA(),tV=nc(),Eye=tV.LINE_SPACING,QU=tV.FROM_TL,eV=tV.FROM_BR;Dye.exports=function(t){var r=t._context.staticPlot,n=t._fullLayout,a=j5t(n,t),i=n._infolayer.selectAll(\"g.\"+Xa.containerClassName).data(a.length>0?[0]:[]);i.enter().append(\"g\").classed(Xa.containerClassName,!0).style(\"cursor\",r?null:\"ew-resize\");function o(c){c._commandObserver&&(c._commandObserver.remove(),delete c._commandObserver),LC.autoMargin(t,kye(c))}if(i.exit().each(function(){pp.select(this).selectAll(\"g.\"+Xa.groupClassName).each(o)}).remove(),a.length!==0){var s=i.selectAll(\"g.\"+Xa.groupClassName).data(a,W5t);s.enter().append(\"g\").classed(Xa.groupClassName,!0),s.exit().each(o).remove();for(var l=0;l<a.length;l++){var u=a[l];Y5t(t,u)}s.each(function(c){var f=pp.select(this);J5t(c),LC.manageCommandObserver(t,c,c._visibleSteps,function(v){var g=f.data()[0];g.active!==v.index&&(g._dragging||Lye(t,f,g,v.index,!1,!0))}),X5t(t,pp.select(this),c)})}};function kye(e){return Xa.autoMarginIdRoot+e._index}function j5t(e,t){for(var r=e[Xa.name],n=[],a=0;a<r.length;a++){var i=r[a];i.visible&&(i._gd=t,n.push(i))}return n}function W5t(e){return e._index}function Y5t(e,t){var r=gp.tester.selectAll(\"g.\"+Xa.labelGroupClass).data(t._visibleSteps);r.enter().append(\"g\").classed(Xa.labelGroupClass,!0);var n=0,a=0;r.each(function(p){var x=pp.select(this),_=Cye(x,{step:p},t),y=_.node();if(y){var C=gp.bBox(y);a=Math.max(a,C.height),n=Math.max(n,C.width)}}),r.remove();var i=t._dims={};i.inputAreaWidth=Math.max(Xa.railWidth,Xa.gripHeight);var o=e._fullLayout._size;i.lx=o.l+o.w*t.x,i.ly=o.t+o.h*(1-t.y),t.lenmode===\"fraction\"?i.outerLength=Math.round(o.w*t.len):i.outerLength=t.len,i.inputAreaStart=0,i.inputAreaLength=Math.round(i.outerLength-t.pad.l-t.pad.r);var s=i.inputAreaLength-2*Xa.stepInset,l=s/(t._stepCount-1),u=n+Xa.labelPadding;if(i.labelStride=Math.max(1,Math.ceil(u/l)),i.labelHeight=a,i.currentValueMaxWidth=0,i.currentValueHeight=0,i.currentValueTotalHeight=0,i.currentValueMaxLines=1,t.currentvalue.visible){var c=gp.tester.append(\"g\");r.each(function(p){var x=PC(c,t,p.label),_=x.node()&&gp.bBox(x.node())||{width:0,height:0},y=wA.lineCount(x);i.currentValueMaxWidth=Math.max(i.currentValueMaxWidth,Math.ceil(_.width)),i.currentValueHeight=Math.max(i.currentValueHeight,Math.ceil(_.height)),i.currentValueMaxLines=Math.max(i.currentValueMaxLines,y)}),i.currentValueTotalHeight=i.currentValueHeight+t.currentvalue.offset,c.remove()}i.height=i.currentValueTotalHeight+Xa.tickOffset+t.ticklen+Xa.labelOffset+i.labelHeight+t.pad.t+t.pad.b;var f=\"left\";I0.isRightAnchor(t)&&(i.lx-=i.outerLength,f=\"right\"),I0.isCenterAnchor(t)&&(i.lx-=i.outerLength/2,f=\"center\");var v=\"top\";I0.isBottomAnchor(t)&&(i.ly-=i.height,v=\"bottom\"),I0.isMiddleAnchor(t)&&(i.ly-=i.height/2,v=\"middle\"),i.outerLength=Math.ceil(i.outerLength),i.height=Math.ceil(i.height),i.lx=Math.round(i.lx),i.ly=Math.round(i.ly);var g={y:t.y,b:i.height*eV[v],t:i.height*QU[v]};t.lenmode===\"fraction\"?(g.l=0,g.xl=t.x-t.len*QU[f],g.r=0,g.xr=t.x+t.len*eV[f]):(g.x=t.x,g.l=i.outerLength*QU[f],g.r=i.outerLength*eV[f]),LC.autoMargin(e,kye(t),g)}function X5t(e,t,r){(r.steps[r.active]||{}).visible||(r.active=r._visibleSteps[0]._index),t.call(PC,r).call(e4t,r).call($5t,r).call(K5t,r).call(Q5t,e,r).call(Z5t,e,r);var n=r._dims;gp.setTranslate(t,n.lx+r.pad.l,n.ly+r.pad.t),t.call(Fye,r,!1),t.call(PC,r)}function PC(e,t,r){if(t.currentvalue.visible){var n=t._dims,a,i;switch(t.currentvalue.xanchor){case\"right\":a=n.inputAreaLength-Xa.currentValueInset-n.currentValueMaxWidth,i=\"left\";break;case\"center\":a=n.inputAreaLength*.5,i=\"middle\";break;default:a=Xa.currentValueInset,i=\"left\"}var o=I0.ensureSingle(e,\"text\",Xa.labelClass,function(v){v.attr({\"text-anchor\":i,\"data-notex\":1})}),s=t.currentvalue.prefix?t.currentvalue.prefix:\"\";if(typeof r==\"string\")s+=r;else{var l=t.steps[t.active].label,u=t._gd._fullLayout._meta;u&&(l=I0.templateString(l,u)),s+=l}t.currentvalue.suffix&&(s+=t.currentvalue.suffix),o.call(gp.font,t.currentvalue.font).text(s).call(wA.convertToTspans,t._gd);var c=wA.lineCount(o),f=(n.currentValueMaxLines+1-c)*t.currentvalue.font.size*Eye;return wA.positionText(o,a,f),o}}function Z5t(e,t,r){var n=I0.ensureSingle(e,\"rect\",Xa.gripRectClass,function(a){a.call(Pye,t,e,r).style(\"pointer-events\",\"all\")});n.attr({width:Xa.gripWidth,height:Xa.gripHeight,rx:Xa.gripRadius,ry:Xa.gripRadius}).call(Om.stroke,r.bordercolor).call(Om.fill,r.bgcolor).style(\"stroke-width\",r.borderwidth+\"px\")}function Cye(e,t,r){var n=I0.ensureSingle(e,\"text\",Xa.labelClass,function(o){o.attr({\"text-anchor\":\"middle\",\"data-notex\":1})}),a=t.step.label,i=r._gd._fullLayout._meta;return i&&(a=I0.templateString(a,i)),n.call(gp.font,r.font).text(a).call(wA.convertToTspans,r._gd),n}function $5t(e,t){var r=I0.ensureSingle(e,\"g\",Xa.labelsClass),n=t._dims,a=r.selectAll(\"g.\"+Xa.labelGroupClass).data(n.labelSteps);a.enter().append(\"g\").classed(Xa.labelGroupClass,!0),a.exit().remove(),a.each(function(i){var o=pp.select(this);o.call(Cye,i,t),gp.setTranslate(o,rV(t,i.fraction),Xa.tickOffset+t.ticklen+t.font.size*Eye+Xa.labelOffset+n.currentValueTotalHeight)})}function Mye(e,t,r,n,a){var i=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[i]._index;o!==r.active&&Lye(e,t,r,o,!0,a)}function Lye(e,t,r,n,a,i){var o=r.active;r.active=n,H5t(e.layout,Xa.name,r).applyUpdate(\"active\",n);var s=r.steps[r.active];t.call(Fye,r,i),t.call(PC,r),e.emit(\"plotly_sliderchange\",{slider:r,step:r.steps[r.active],interaction:a,previousActive:o}),s&&s.method&&a&&(t._nextMethod?(t._nextMethod.step=s,t._nextMethod.doCallback=a,t._nextMethod.doTransition=i):(t._nextMethod={step:s,doCallback:a,doTransition:i},t._nextMethodRaf=window.requestAnimationFrame(function(){var l=t._nextMethod.step;l.method&&(l.execute&&LC.executeAPICommand(e,l.method,l.args),t._nextMethod=null,t._nextMethodRaf=null)})))}function Pye(e,t,r){if(t._context.staticPlot)return;var n=r.node(),a=pp.select(t);function i(){return r.data()[0]}function o(){var s=i();t.emit(\"plotly_sliderstart\",{slider:s});var l=r.select(\".\"+Xa.gripRectClass);pp.event.stopPropagation(),pp.event.preventDefault(),l.call(Om.fill,s.activebgcolor);var u=Sye(s,pp.mouse(n)[0]);Mye(t,r,s,u,!0),s._dragging=!0;function c(){var v=i(),g=Sye(v,pp.mouse(n)[0]);Mye(t,r,v,g,!1)}a.on(\"mousemove\",c),a.on(\"touchmove\",c);function f(){var v=i();v._dragging=!1,l.call(Om.fill,v.bgcolor),a.on(\"mouseup\",null),a.on(\"mousemove\",null),a.on(\"touchend\",null),a.on(\"touchmove\",null),t.emit(\"plotly_sliderend\",{slider:v,step:v.steps[v.active]})}a.on(\"mouseup\",f),a.on(\"touchend\",f)}e.on(\"mousedown\",o),e.on(\"touchstart\",o)}function K5t(e,t){var r=e.selectAll(\"rect.\"+Xa.tickRectClass).data(t._visibleSteps),n=t._dims;r.enter().append(\"rect\").classed(Xa.tickRectClass,!0),r.exit().remove(),r.attr({width:t.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each(function(a,i){var o=i%n.labelStride===0,s=pp.select(this);s.attr({height:o?t.ticklen:t.minorticklen}).call(Om.fill,t.tickcolor),gp.setTranslate(s,rV(t,i/(t._stepCount-1))-.5*t.tickwidth,(o?Xa.tickOffset:Xa.minorTickOffset)+n.currentValueTotalHeight)})}function J5t(e){var t=e._dims;t.labelSteps=[];for(var r=e._stepCount,n=0;n<r;n+=t.labelStride)t.labelSteps.push({fraction:n/(r-1),step:e._visibleSteps[n]})}function Fye(e,t,r){for(var n=e.select(\"rect.\"+Xa.gripRectClass),a=0,i=0;i<t._stepCount;i++)if(t._visibleSteps[i]._index===t.active){a=i;break}var o=rV(t,a/(t._stepCount-1));if(!t._invokingCommand){var s=n;r&&t.transition.duration>0&&(s=s.transition().duration(t.transition.duration).ease(t.transition.easing)),s.attr(\"transform\",G5t(o-Xa.gripWidth*.5,t._dims.currentValueTotalHeight))}}function rV(e,t){var r=e._dims;return r.inputAreaStart+Xa.stepInset+(r.inputAreaLength-2*Xa.stepInset)*Math.min(1,Math.max(0,t))}function Sye(e,t){var r=e._dims;return Math.min(1,Math.max(0,(t-Xa.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*Xa.stepInset-2*r.inputAreaStart)))}function Q5t(e,t,r){var n=r._dims,a=I0.ensureSingle(e,\"rect\",Xa.railTouchRectClass,function(i){i.call(Pye,t,e,r).style(\"pointer-events\",\"all\")});a.attr({width:n.inputAreaLength,height:Math.max(n.inputAreaWidth,Xa.tickOffset+r.ticklen+n.labelHeight)}).call(Om.fill,r.bgcolor).attr(\"opacity\",0),gp.setTranslate(a,0,n.currentValueTotalHeight)}function e4t(e,t){var r=t._dims,n=r.inputAreaLength-Xa.railInset*2,a=I0.ensureSingle(e,\"rect\",Xa.railRectClass);a.attr({width:n,height:Xa.railWidth,rx:Xa.railRadius,ry:Xa.railRadius,\"shape-rendering\":\"crispEdges\"}).call(Om.stroke,t.bordercolor).call(Om.fill,t.bgcolor).style(\"stroke-width\",t.borderwidth+\"px\"),gp.setTranslate(a,Xa.railInset,(r.inputAreaWidth-Xa.railWidth)*.5+r.currentValueTotalHeight)}});var zye=fe((Kpr,Iye)=>{\"use strict\";var t4t=bA();Iye.exports={moduleType:\"component\",name:t4t.name,layoutAttributes:JU(),supplyLayoutDefaults:Aye(),draw:Rye()}});var FC=fe((Jpr,Bye)=>{\"use strict\";var qye=tc();Bye.exports={bgcolor:{valType:\"color\",dflt:qye.background,editType:\"plot\"},bordercolor:{valType:\"color\",dflt:qye.defaultLine,editType:\"plot\"},borderwidth:{valType:\"integer\",dflt:0,min:0,editType:\"plot\"},autorange:{valType:\"boolean\",dflt:!0,editType:\"calc\",impliedEdits:{\"range[0]\":void 0,\"range[1]\":void 0}},range:{valType:\"info_array\",items:[{valType:\"any\",editType:\"calc\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"calc\",impliedEdits:{\"^autorange\":!1}}],editType:\"calc\",impliedEdits:{autorange:!1}},thickness:{valType:\"number\",dflt:.15,min:0,max:1,editType:\"plot\"},visible:{valType:\"boolean\",dflt:!0,editType:\"calc\"},editType:\"calc\"}});var nV=fe((Qpr,Oye)=>{\"use strict\";Oye.exports={_isSubplotObj:!0,rangemode:{valType:\"enumerated\",values:[\"auto\",\"fixed\",\"match\"],dflt:\"match\",editType:\"calc\"},range:{valType:\"info_array\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},editType:\"calc\"}});var DC=fe((e1r,Nye)=>{\"use strict\";Nye.exports={name:\"rangeslider\",containerClassName:\"rangeslider-container\",bgClassName:\"rangeslider-bg\",rangePlotClassName:\"rangeslider-rangeplot\",maskMinClassName:\"rangeslider-mask-min\",maskMaxClassName:\"rangeslider-mask-max\",slideBoxClassName:\"rangeslider-slidebox\",grabberMinClassName:\"rangeslider-grabber-min\",grabAreaMinClassName:\"rangeslider-grabarea-min\",handleMinClassName:\"rangeslider-handle-min\",grabberMaxClassName:\"rangeslider-grabber-max\",grabAreaMaxClassName:\"rangeslider-grabarea-max\",handleMaxClassName:\"rangeslider-handle-max\",maskMinOppAxisClassName:\"rangeslider-mask-min-opp-axis\",maskMaxOppAxisClassName:\"rangeslider-mask-max-opp-axis\",maskColor:\"rgba(0,0,0,0.4)\",maskOppAxisColor:\"rgba(0,0,0,0.2)\",slideBoxFill:\"transparent\",slideBoxCursor:\"ew-resize\",grabAreaFill:\"transparent\",grabAreaCursor:\"col-resize\",grabAreaWidth:10,handleWidth:4,handleRadius:1,handleStrokeWidth:1,extraPad:15}});var Gye=fe(IC=>{\"use strict\";var r4t=El(),n4t=as(),Uye=DC(),i4t=nc().LINE_SPACING,RC=Uye.name;function Vye(e){var t=e&&e[RC];return t&&t.visible}IC.isVisible=Vye;IC.makeData=function(e){for(var t=r4t.list({_fullLayout:e},\"x\",!0),r=e.margin,n=[],a=0;a<t.length;a++){var i=t[a];if(Vye(i)){n.push(i);var o=i[RC];o._id=RC+i._id,o._height=(e.height-r.b-r.t)*o.thickness,o._offsetShift=Math.floor(o.borderwidth/2)}}e._rangeSliderData=n};IC.autoMarginOpts=function(e,t){var r=e._fullLayout,n=t[RC],a=t._id.charAt(0),i=0,o=0;if(t.side===\"bottom\"&&(i=t._depth,t.title.text!==r._dfltTitle[a])){o=1.5*t.title.font.size+10+n._offsetShift;var s=(t.title.text.match(n4t.BR_TAG_ALL)||[]).length;o+=s*t.title.font.size*i4t}return{x:0,y:t._counterDomainMin,l:0,r:0,t:0,b:n._height+i+Math.max(r.margin.b,o),pad:Uye.extraPad+n._offsetShift*2}}});var Yye=fe((r1r,Wye)=>{\"use strict\";var zC=Xt(),Hye=Mo(),jye=El(),a4t=FC(),o4t=nV();Wye.exports=function(t,r,n){var a=t[n],i=r[n];if(!(a.rangeslider||r._requestRangeslider[i._id]))return;zC.isPlainObject(a.rangeslider)||(a.rangeslider={});var o=a.rangeslider,s=Hye.newContainer(i,\"rangeslider\");function l(L,w){return zC.coerce(o,s,a4t,L,w)}var u,c;function f(L,w){return zC.coerce(u,c,o4t,L,w)}var v=l(\"visible\");if(v){l(\"bgcolor\",r.plot_bgcolor),l(\"bordercolor\"),l(\"borderwidth\"),l(\"thickness\"),l(\"autorange\",!i.isValidRange(o.range)),l(\"range\");var g=r._subplots;if(g)for(var p=g.cartesian.filter(function(L){return L.slice(0,Math.max(0,L.indexOf(\"y\")))===jye.name2id(n)}).map(function(L){return L.slice(L.indexOf(\"y\"),L.length)}),x=zC.simpleMap(p,jye.id2name),_=0;_<x.length;_++){var y=x[_];u=o[y]||{},c=Hye.newContainer(s,y,\"yaxis\");var C=r[y],k;u.range&&C.isValidRange(u.range)&&(k=\"fixed\");var S=f(\"rangemode\",k);S!==\"match\"&&f(\"range\",C.range.slice())}s._input=o}}});var Zye=fe((n1r,Xye)=>{\"use strict\";var s4t=El().list,l4t=rp().getAutoRange,u4t=DC();Xye.exports=function(t){for(var r=s4t(t,\"x\",!0),n=0;n<r.length;n++){var a=r[n],i=a[u4t.name];i&&i.visible&&i.autorange&&(i._input.autorange=!0,i._input.range=i.range=l4t(t,a))}}});var Qye=fe((i1r,Jye)=>{\"use strict\";var qC=xi(),c4t=bi(),f4t=Hs(),mu=Xt(),BC=mu.strTranslate,Kye=ea(),Nm=ni(),h4t=qx(),d4t=gu(),iV=El(),v4t=ed(),p4t=np(),fo=DC();Jye.exports=function(e){for(var t=e._fullLayout,r=t._rangeSliderData,n=0;n<r.length;n++){var a=r[n][fo.name];a._clipId=a._id+\"-\"+t._uid}function i(s){return s._name}var o=t._infolayer.selectAll(\"g.\"+fo.containerClassName).data(r,i);o.exit().each(function(s){var l=s[fo.name];t._topdefs.select(\"#\"+l._clipId).remove()}).remove(),r.length!==0&&(o.enter().append(\"g\").classed(fo.containerClassName,!0).attr(\"pointer-events\",\"all\"),o.each(function(s){var l=qC.select(this),u=s[fo.name],c=t[iV.id2name(s.anchor)],f=u[iV.id2name(s.anchor)];if(u.range){var v=mu.simpleMap(u.range,s.r2l),g=mu.simpleMap(s.range,s.r2l),p;g[0]<g[1]?p=[Math.min(v[0],g[0]),Math.max(v[1],g[1])]:p=[Math.max(v[0],g[0]),Math.min(v[1],g[1])],u.range=u._input.range=mu.simpleMap(p,s.l2r)}s.cleanRange(\"rangeslider.range\");var x=t._size,_=s.domain;u._width=x.w*(_[1]-_[0]);var y=Math.round(x.l+x.w*_[0]),C=Math.round(x.t+x.h*(1-s._counterDomainMin)+(s.side===\"bottom\"?s._depth:0)+u._offsetShift+fo.extraPad);l.attr(\"transform\",BC(y,C)),u._rl=mu.simpleMap(u.range,s.r2l);var k=u._rl[0],S=u._rl[1],L=S-k;if(u.p2d=function(O){return O/u._width*L+k},u.d2p=function(O){return(O-k)/L*u._width},s.rangebreaks){var w=s.locateBreaks(k,S);if(w.length){var A,T,m=0;for(A=0;A<w.length;A++)T=w[A],m+=T.max-T.min;var F=u._width/(S-k-m),E=[-F*k];for(A=0;A<w.length;A++)T=w[A],E.push(E[E.length-1]-F*(T.max-T.min));for(u.d2p=function(O){for(var V=E[0],j=0;j<w.length;j++){var q=w[j];if(O>=q.max)V=E[j+1];else if(O<q.min)break}return V+F*O},A=0;A<w.length;A++)T=w[A],T.pmin=u.d2p(T.min),T.pmax=u.d2p(T.max);u.p2d=function(O){for(var V=E[0],j=0;j<w.length;j++){var q=w[j];if(O>=q.pmax)V=E[j+1];else if(O<q.pmin)break}return(O-V)/F}}}if(f.rangemode!==\"match\"){var I=c.r2l(f.range[0]),R=c.r2l(f.range[1]),B=R-I;u.d2pOppAxis=function(O){return(O-I)/B*u._height}}l.call(x4t,e,s,u).call(_4t,e,s,u).call(b4t,e,s,u).call(T4t,e,s,u,f).call(A4t,e,s,u).call(M4t,e,s,u),g4t(l,e,s,u),y4t(l,e,s,u,c,f),s.side===\"bottom\"&&h4t.draw(e,s._id+\"title\",{propContainer:s,propName:s._name+\".title.text\",placeholder:t._dfltTitle.x,attributes:{x:s._offset+s._length/2,y:C+u._height+u._offsetShift+10+1.5*s.title.font.size,\"text-anchor\":\"middle\"}})}))};function $ye(e){return typeof e.clientX==\"number\"?e.clientX:e.touches&&e.touches.length>0?e.touches[0].clientX:0}function g4t(e,t,r,n){if(t._context.staticPlot)return;var a=e.select(\"rect.\"+fo.slideBoxClassName).node(),i=e.select(\"rect.\"+fo.grabAreaMinClassName).node(),o=e.select(\"rect.\"+fo.grabAreaMaxClassName).node();function s(){var l=qC.event,u=l.target,c=$ye(l),f=c-e.node().getBoundingClientRect().left,v=n.d2p(r._rl[0]),g=n.d2p(r._rl[1]),p=v4t.coverSlip();this.addEventListener(\"touchmove\",x),this.addEventListener(\"touchend\",_),p.addEventListener(\"mousemove\",x),p.addEventListener(\"mouseup\",_);function x(y){var C=$ye(y),k=+C-c,S,L,w;switch(u){case a:if(w=\"ew-resize\",v+k>r._length||g+k<0)return;S=v+k,L=g+k;break;case i:if(w=\"col-resize\",v+k>r._length)return;S=v+k,L=g;break;case o:if(w=\"col-resize\",g+k<0)return;S=v,L=g+k;break;default:w=\"ew-resize\",S=f,L=f+k;break}if(L<S){var A=L;L=S,S=A}n._pixelMin=S,n._pixelMax=L,p4t(qC.select(p),w),m4t(e,t,r,n)}function _(){p.removeEventListener(\"mousemove\",x),p.removeEventListener(\"mouseup\",_),this.removeEventListener(\"touchmove\",x),this.removeEventListener(\"touchend\",_),mu.removeElement(p)}}e.on(\"mousedown\",s),e.on(\"touchstart\",s)}function m4t(e,t,r,n){function a(s){return r.l2r(mu.constrain(s,n._rl[0],n._rl[1]))}var i=a(n.p2d(n._pixelMin)),o=a(n.p2d(n._pixelMax));window.requestAnimationFrame(function(){c4t.call(\"_guiRelayout\",t,r._name+\".range\",[i,o])})}function y4t(e,t,r,n,a,i){var o=fo.handleWidth/2;function s(y){return mu.constrain(y,0,n._width)}function l(y){return mu.constrain(y,0,n._height)}function u(y){return mu.constrain(y,-o,n._width+o)}var c=s(n.d2p(r._rl[0])),f=s(n.d2p(r._rl[1]));if(e.select(\"rect.\"+fo.slideBoxClassName).attr(\"x\",c).attr(\"width\",f-c),e.select(\"rect.\"+fo.maskMinClassName).attr(\"width\",c),e.select(\"rect.\"+fo.maskMaxClassName).attr(\"x\",f).attr(\"width\",n._width-f),i.rangemode!==\"match\"){var v=n._height-l(n.d2pOppAxis(a._rl[1])),g=n._height-l(n.d2pOppAxis(a._rl[0]));e.select(\"rect.\"+fo.maskMinOppAxisClassName).attr(\"x\",c).attr(\"height\",v).attr(\"width\",f-c),e.select(\"rect.\"+fo.maskMaxOppAxisClassName).attr(\"x\",c).attr(\"y\",g).attr(\"height\",n._height-g).attr(\"width\",f-c),e.select(\"rect.\"+fo.slideBoxClassName).attr(\"y\",v).attr(\"height\",g-v)}var p=.5,x=Math.round(u(c-o))-p,_=Math.round(u(f-o))+p;e.select(\"g.\"+fo.grabberMinClassName).attr(\"transform\",BC(x,p)),e.select(\"g.\"+fo.grabberMaxClassName).attr(\"transform\",BC(_,p))}function x4t(e,t,r,n){var a=mu.ensureSingle(e,\"rect\",fo.bgClassName,function(l){l.attr({x:0,y:0,\"shape-rendering\":\"crispEdges\"})}),i=n.borderwidth%2===0?n.borderwidth:n.borderwidth-1,o=-n._offsetShift,s=Kye.crispRound(t,n.borderwidth);a.attr({width:n._width+i,height:n._height+i,transform:BC(o,o),\"stroke-width\":s}).call(Nm.stroke,n.bordercolor).call(Nm.fill,n.bgcolor)}function _4t(e,t,r,n){var a=t._fullLayout,i=mu.ensureSingleById(a._topdefs,\"clipPath\",n._clipId,function(o){o.append(\"rect\").attr({x:0,y:0})});i.select(\"rect\").attr({width:n._width,height:n._height})}function b4t(e,t,r,n){var a=t.calcdata,i=e.selectAll(\"g.\"+fo.rangePlotClassName).data(r._subplotsWith,mu.identity);i.enter().append(\"g\").attr(\"class\",function(s){return fo.rangePlotClassName+\" \"+s}).call(Kye.setClipUrl,n._clipId,t),i.order(),i.exit().remove();var o;i.each(function(s,l){var u=qC.select(this),c=l===0,f=iV.getFromId(t,s,\"y\"),v=f._name,g=n[v],p={data:[],layout:{xaxis:{type:r.type,domain:[0,1],range:n.range.slice(),calendar:r.calendar},width:n._width,height:n._height,margin:{t:0,b:0,l:0,r:0}},_context:t._context};r.rangebreaks&&(p.layout.xaxis.rangebreaks=r.rangebreaks),p.layout[v]={type:f.type,domain:[0,1],range:g.rangemode!==\"match\"?g.range.slice():f.range.slice(),calendar:f.calendar},f.rangebreaks&&(p.layout[v].rangebreaks=f.rangebreaks),f4t.supplyDefaults(p);var x=p._fullLayout.xaxis,_=p._fullLayout[v];x.clearCalc(),x.setScale(),_.clearCalc(),_.setScale();var y={id:s,plotgroup:u,xaxis:x,yaxis:_,isRangePlot:!0};c?o=y:(y.mainplot=\"xy\",y.mainplotinfo=o),d4t.rangePlot(t,y,w4t(a,s))})}function w4t(e,t){for(var r=[],n=0;n<e.length;n++){var a=e[n],i=a[0].trace;i.xaxis+i.yaxis===t&&r.push(a)}return r}function T4t(e,t,r,n,a){var i=mu.ensureSingle(e,\"rect\",fo.maskMinClassName,function(u){u.attr({x:0,y:0,\"shape-rendering\":\"crispEdges\"})});i.attr(\"height\",n._height).call(Nm.fill,fo.maskColor);var o=mu.ensureSingle(e,\"rect\",fo.maskMaxClassName,function(u){u.attr({y:0,\"shape-rendering\":\"crispEdges\"})});if(o.attr(\"height\",n._height).call(Nm.fill,fo.maskColor),a.rangemode!==\"match\"){var s=mu.ensureSingle(e,\"rect\",fo.maskMinOppAxisClassName,function(u){u.attr({y:0,\"shape-rendering\":\"crispEdges\"})});s.attr(\"width\",n._width).call(Nm.fill,fo.maskOppAxisColor);var 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c={width:fo.grabAreaWidth,x:0,y:0,fill:fo.grabAreaFill,cursor:t._context.staticPlot?void 0:fo.grabAreaCursor},f=mu.ensureSingle(a,\"rect\",fo.grabAreaMinClassName,function(g){g.attr(c)});f.attr(\"height\",n._height);var v=mu.ensureSingle(i,\"rect\",fo.grabAreaMaxClassName,function(g){g.attr(c)});v.attr(\"height\",n._height)}});var txe=fe((a1r,exe)=>{\"use strict\";var S4t=Xt(),E4t=FC(),k4t=nV(),aV=Gye();exe.exports={moduleType:\"component\",name:\"rangeslider\",schema:{subplots:{xaxis:{rangeslider:S4t.extendFlat({},E4t,{yaxis:k4t})}}},layoutAttributes:FC(),handleDefaults:Yye(),calcAutorange:Zye(),draw:Qye(),isVisible:aV.isVisible,makeData:aV.makeData,autoMarginOpts:aV.autoMarginOpts}});var OC=fe((o1r,nxe)=>{\"use strict\";var 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y=_.enter().append(\"g\").classed(\"errorbar\",!0);l&&y.style(\"opacity\",0).transition().duration(a.duration).style(\"opacity\",1),cTt.setClipUrl(_,n.layerClipId,t),_.each(function(C){var k=Rxe.select(this),S=hTt(C,o,s);if(!(x&&!C.vis)){var L,w=k.select(\"path.yerror\");if(g.visible&&Um(S.x)&&Um(S.yh)&&Um(S.ys)){var A=g.width;L=\"M\"+(S.x-A)+\",\"+S.yh+\"h\"+2*A+\"m-\"+A+\",0V\"+S.ys,S.noYS||(L+=\"m-\"+A+\",0h\"+2*A),i=!w.size(),i?w=k.append(\"path\").style(\"vector-effect\",u?\"none\":\"non-scaling-stroke\").classed(\"yerror\",!0):l&&(w=w.transition().duration(a.duration).ease(a.easing)),w.attr(\"d\",L)}else w.remove();var T=k.select(\"path.xerror\");if(v.visible&&Um(S.y)&&Um(S.xh)&&Um(S.xs)){var 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Vxe=fe((_1r,Uxe)=>{\"use strict\";var TA=Xt(),Nxe=rl().overrideAll,AA=vV(),d2={error_x:TA.extendFlat({},AA),error_y:TA.extendFlat({},AA)};delete d2.error_x.copy_zstyle;delete d2.error_y.copy_zstyle;delete d2.error_y.copy_ystyle;var MA={error_x:TA.extendFlat({},AA),error_y:TA.extendFlat({},AA),error_z:TA.extendFlat({},AA)};delete MA.error_x.copy_ystyle;delete MA.error_y.copy_ystyle;delete MA.error_z.copy_ystyle;delete MA.error_z.copy_zstyle;Uxe.exports={moduleType:\"component\",name:\"errorbars\",schema:{traces:{scatter:d2,bar:d2,histogram:d2,scatter3d:Nxe(MA,\"calc\",\"nested\"),scattergl:Nxe(d2,\"calc\",\"nested\")}},supplyDefaults:kxe(),calc:Dxe(),makeComputeError:pV(),plot:zxe(),style:Oxe(),hoverInfo:vTt};function vTt(e,t,r){(t.error_y||{}).visible&&(r.yerr=e.yh-e.y,t.error_y.symmetric||(r.yerrneg=e.y-e.ys)),(t.error_x||{}).visible&&(r.xerr=e.xh-e.x,t.error_x.symmetric||(r.xerrneg=e.x-e.xs))}});var Hxe=fe((b1r,Gxe)=>{\"use 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ae=[\"top\",\"bottom\"].indexOf(T)!==-1;if(n&&ae&&(ne.title.side=T,ne.titlex=x+g/S.w,ne.titley=K+(A.side===\"top\"?U-p/S.h:p/S.h)),!n&&!ae&&(ne.title.side=T,ne.titley=_+p/S.h,ne.titlex=K+g/S.w),w.color&&t.tickmode===\"auto\"){ne.tickmode=\"linear\",ne.tick0=I.start;var pe=I.size,ie=xv.constrain(q/50,4,15)+1,le=(m[1]-m[0])/((t.nticks||ie)*pe);if(le>1){var me=Math.pow(10,Math.floor(Math.log(le)/Math.LN10));pe*=me*xv.roundUp(le/me,[2,5,10]),(Math.abs(I.start)/I.size+1e-6)%1<2e-6&&(ne.tick0=0)}ne.dtick=pe}var be=n?p/S.h:g/S.w;ne.domain=Zxe(xV([K,K+U],be),be),ne.setScale(),e.attr(\"transform\",yp(Math.round(S.l),Math.round(S.t)));var De=e.select(\".\"+Cl.cbtitleunshift).attr(\"transform\",yp(-Math.round(S.l),-Math.round(S.t))),Ge=ne.ticklabelposition,ht=ne.title.font.size,it=e.select(\".\"+Cl.cbaxis),Oe,ze=0,ct=0;function et(Te,Ve){var $e={propContainer:ne,propName:t._propPrefix+\"title.text\",traceIndex:t._traceIndex,_meta:t._meta,placeholder:k._dfltTitle.colorbar,containerGroup:e.select(\".\"+Cl.cbtitle)},ot=Te.charAt(0)===\"h\"?Te.slice(1):\"h\"+Te;e.selectAll(\".\"+ot+\",.\"+ot+\"-math-group\").remove(),pTt.draw(r,Te,$xe($e,Ve||{}))}function qe(){if(n&&ae||!n&&!ae){var Te,Ve;T===\"top\"&&(Te=g+S.l+J*x,Ve=p+S.t+ee*(1-K-U)+3+ht*.75),T===\"bottom\"&&(Te=g+S.l+J*x,Ve=p+S.t+ee*(1-K)-3-ht*.25),T===\"right\"&&(Ve=p+S.t+ee*_+3+ht*.75,Te=g+S.l+J*K),et(ne._id+\"title\",{attributes:{x:Te,y:Ve,\"text-anchor\":n?\"start\":\"middle\"}})}}function we(){if(n&&!ae||!n&&ae){var Te=ne.position||0,Ve=ne._offset+ne._length/2,$e,ot;if(T===\"right\")ot=Ve,$e=S.l+J*Te+10+ht*(ne.showticklabels?1:.5);else if($e=Ve,T===\"bottom\"&&(ot=S.t+ee*Te+10+(Ge.indexOf(\"inside\")===-1?ne.tickfont.size:0)+(ne.ticks!==\"inside\"&&t.ticklen||0)),T===\"top\"){var tt=A.text.split(\"<br>\").length;ot=S.t+ee*Te+10-V-Wxe*ht*tt}et((n?\"h\":\"v\")+ne._id+\"title\",{avoid:{selection:Vm.select(r).selectAll(\"g.\"+ne._id+\"tick\"),side:T,offsetTop:n?0:S.t,offsetLeft:n?S.l:0,maxShift:n?k.width:k.height},attributes:{x:$e,y:ot,\"text-anchor\":\"middle\"},transform:{rotate:n?-90:0,offset:0}})}}function Le(){if(!n&&!ae||n&&ae){var Te=e.select(\".\"+Cl.cbtitle),Ve=Te.select(\"text\"),$e=[-l/2,l/2],ot=Te.select(\".h\"+ne._id+\"title-math-group\").node(),tt=15.6;Ve.node()&&(tt=parseInt(Ve.node().style.fontSize,10)*Wxe);var nt;if(ot?(nt=mp.bBox(ot),ct=nt.width,ze=nt.height,ze>tt&&($e[1]-=(ze-tt)/2)):Ve.node()&&!Ve.classed(Cl.jsPlaceholder)&&(nt=mp.bBox(Ve.node()),ct=nt.width,ze=nt.height),n){if(ze){if(ze+=5,T===\"top\")ne.domain=Zxe(ne.domain,ze/S.h),$e[1]*=-1;else{ne.domain=xV(ne.domain,ze/S.h);var We=gTt.lineCount(Ve);$e[1]+=(1-We)*tt}Te.attr(\"transform\",yp($e[0],$e[1])),ne.setScale()}}else ct&&(T===\"right\"&&(ne.domain=xV(ne.domain,(ct+ht/2)/S.w)),Te.attr(\"transform\",yp($e[0],$e[1])),ne.setScale())}e.selectAll(\".\"+Cl.cbfills+\",.\"+Cl.cblines).attr(\"transform\",n?yp(0,Math.round(S.h*(1-ne.domain[1]))):yp(Math.round(S.w*ne.domain[0]),0)),it.attr(\"transform\",n?yp(0,Math.round(-S.t)):yp(Math.round(-S.l),0));var mt=e.select(\".\"+Cl.cbfills).selectAll(\"rect.\"+Cl.cbfill).attr(\"style\",\"\").data(B);mt.enter().append(\"rect\").classed(Cl.cbfill,!0).attr(\"style\",\"\"),mt.exit().remove();var bt=m.map(ne.c2p).map(Math.round).sort(function(fr,Ot){return fr-Ot});mt.each(function(fr,Ot){var cr=[Ot===0?m[0]:(B[Ot]+B[Ot-1])/2,Ot===B.length-1?m[1]:(B[Ot]+B[Ot+1])/2].map(ne.c2p).map(Math.round);n&&(cr[1]=xv.constrain(cr[1]+(cr[1]>cr[0])?1:-1,bt[0],bt[1]));var Ct=Vm.select(this).attr(n?\"x\":\"y\",ce).attr(n?\"y\":\"x\",Vm.min(cr)).attr(n?\"width\":\"height\",Math.max(V,2)).attr(n?\"height\":\"width\",Math.max(Vm.max(cr)-Vm.min(cr),2));if(t._fillgradient)mp.gradient(Ct,r,t._id,n?\"vertical\":\"horizontalreversed\",t._fillgradient,\"fill\");else{var wt=E(fr).replace(\"e-\",\"\");Ct.attr(\"fill\",L3.hexString(wt))}});var Yt=e.select(\".\"+Cl.cblines).selectAll(\"path.\"+Cl.cbline).data(w.color&&w.width?O:[]);Yt.enter().append(\"path\").classed(Cl.cbline,!0),Yt.exit().remove(),Yt.each(function(fr){var Ot=ce,cr=Math.round(ne.c2p(fr))+w.width/2%1;Vm.select(this).attr(\"d\",\"M\"+(n?Ot+\",\"+cr:cr+\",\"+Ot)+(n?\"h\":\"v\")+V).call(mp.lineGroupStyle,w.width,F(fr),w.dash)}),it.selectAll(\"g.\"+ne._id+\"tick,path\").remove();var jt=ce+V+(l||0)/2-(t.ticks===\"outside\"?1:0),mr=ug.calcTicks(ne),or=ug.getTickSigns(ne)[2];return ug.drawTicks(r,ne,{vals:ne.ticks===\"inside\"?ug.clipEnds(ne,mr):mr,layer:it,path:ug.makeTickPath(ne,jt,or),transFn:ug.makeTransTickFn(ne)}),ug.drawLabels(r,ne,{vals:mr,layer:it,transFn:ug.makeTransTickLabelFn(ne),labelFns:ug.makeLabelFns(ne,jt)})}function Ke(){var Te,Ve=V+l/2;Ge.indexOf(\"inside\")===-1&&(Te=mp.bBox(it.node()),Ve+=n?Te.width:Te.height),Oe=De.select(\"text\");var $e=0,ot=n&&T===\"top\",tt=!n&&T===\"right\",nt=0;if(Oe.node()&&!Oe.classed(Cl.jsPlaceholder)){var We,mt=De.select(\".h\"+ne._id+\"title-math-group\").node();mt&&(n&&ae||!n&&!ae)?(Te=mp.bBox(mt),$e=Te.width,We=Te.height):(Te=mp.bBox(De.node()),$e=Te.right-S.l-(n?ce:_e),We=Te.bottom-S.t-(n?_e:ce),!n&&T===\"top\"&&(Ve+=Te.height,nt=Te.height)),tt&&(Oe.attr(\"transform\",yp($e/2+ht/2,0)),$e*=2),Ve=Math.max(Ve,n?$e:We)}var bt=(n?g:p)*2+Ve+u+l/2,Yt=0;!n&&A.text&&v===\"bottom\"&&_<=0&&(Yt=bt/2,bt+=Yt,nt+=Yt),k._hColorbarMoveTitle=Yt,k._hColorbarMoveCBTitle=nt;var jt=u+l,mr=(n?ce:_e)-jt/2-(n?g:0),or=(n?_e:ce)-(n?q:p+nt-Yt);e.select(\".\"+Cl.cbbg).attr(\"x\",mr).attr(\"y\",or).attr(n?\"width\":\"height\",Math.max(bt-Yt,2)).attr(n?\"height\":\"width\",Math.max(q+jt,2)).call(L3.fill,c).call(L3.stroke,t.bordercolor).style(\"stroke-width\",u);var fr=tt?Math.max($e-10,0):0;e.selectAll(\".\"+Cl.cboutline).attr(\"x\",(n?ce:_e+g)+fr).attr(\"y\",(n?_e+p-q:ce)+(ot?ze:0)).attr(n?\"width\":\"height\",Math.max(V,2)).attr(n?\"height\":\"width\",Math.max(q-(n?2*p+ze:2*g+fr),2)).call(L3.stroke,t.outlinecolor).style({fill:\"none\",\"stroke-width\":l});var Ot=n?ye*bt:0,cr=n?0:(1-xe)*bt-nt;if(Ot=C?S.l-Ot:-Ot,cr=y?S.t-cr:-cr,e.attr(\"transform\",yp(Ot,cr)),!n&&(u||L3.opacity(c)&&!L3.equals(k.paper_bgcolor,c))){var Ct=it.selectAll(\"text\"),wt=Ct[0].length,Tt=e.select(\".\"+Cl.cbbg).node(),Mt=mp.bBox(Tt),Lt=mp.getTranslate(e),ft=2;Ct.each(function(Cn,Dt){var At=0,Vt=wt-1;if(Dt===At||Dt===Vt){var Qt=mp.bBox(this),Ut=mp.getTranslate(this),_r;if(Dt===Vt){var 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Zr={r:k.width-mr-Ot,l:mr+kt.r,b:k.height-or-cr,t:or+kt.b};C&&y?jC.autoMargin(r,t._id,kt):C?r._fullLayout._reservedMargin[t._id][xr]=Zr[xr]:y||n?r._fullLayout._reservedMargin[t._id][Fr]=Zr[Fr]:r._fullLayout._reservedMargin[t._id][xr]=Zr[xr]}return xv.syncOrAsync([jC.previousPromises,qe,Le,we,jC.previousPromises,Ke],r)}function ATt(e,t,r){var n=t.orientation===\"v\",a=r._fullLayout,i=a._size,o,s,l;HC.init({element:e.node(),gd:r,prepFn:function(){o=e.attr(\"transform\"),yV(e)},moveFn:function(u,c){e.attr(\"transform\",o+yp(u,c)),s=HC.align((n?t._uFrac:t._vFrac)+u/i.w,n?t._thickFrac:t._lenFrac,0,1,t.xanchor),l=HC.align((n?t._vFrac:1-t._uFrac)-c/i.h,n?t._lenFrac:t._thickFrac,0,1,t.yanchor);var f=HC.getCursor(s,l,t.xanchor,t.yanchor);yV(e,f)},doneFn:function(){if(yV(e),s!==void 0&&l!==void 0){var u={};u[t._propPrefix+\"x\"]=s,u[t._propPrefix+\"y\"]=l,t._traceIndex!==void 0?jxe.call(\"_guiRestyle\",r,u,t._traceIndex):jxe.call(\"_guiRelayout\",r,u)}}})}function MTt(e,t,r){var 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qTt=bi(),SA=cg.register=qTt.register,AV=qpe(),m2e=Object.keys(AV);for(WC=0;WC<m2e.length;WC++)P3=m2e[WC],P3.charAt(0)!==\"_\"&&(cg[P3]=AV[P3]),SA({moduleType:\"apiMethod\",name:P3,fn:AV[P3]});var P3,WC;SA(Fge());SA([nme(),gme(),Ac(),Ime(),Xme(),gye(),zye(),txe(),xxe(),dV(),Vxe(),js(),e2e(),r2e(),kl(),uN()]);SA([i2e(),o2e()]);window.PlotlyLocales&&Array.isArray(window.PlotlyLocales)&&(SA(window.PlotlyLocales),delete window.PlotlyLocales);cg.Icons=Jk();var YC=kl(),TV=Hs();cg.Plots={resize:TV.resize,graphJson:TV.graphJson,sendDataToCloud:TV.sendDataToCloud};cg.Fx={hover:YC.hover,unhover:YC.unhover,loneHover:YC.loneHover,loneUnhover:YC.loneUnhover};cg.Snapshot=g2e();cg.PlotSchema=qb()});var _2e=fe((P1r,x2e)=>{\"use strict\";x2e.exports=y2e()});var u1=fe((F1r,M2e)=>{\"use strict\";var nu=Ol(),b2e=pl().axisHoverFormat,{hovertemplateAttrs:BTt,texttemplateAttrs:OTt,templatefallbackAttrs:w2e}=Oo(),A2e=_s(),NTt=Ds(),T2e=Im(),UTt=Uc().pattern,v2=ta().extendFlat,MV=NTt({editType:\"calc\",arrayOk:!0,colorEditType:\"style\"}),VTt=nu.marker,GTt=VTt.line,HTt=v2({},GTt.width,{dflt:0}),jTt=v2({width:HTt,editType:\"calc\"},A2e(\"marker.line\")),WTt=v2({line:jTt,editType:\"calc\"},A2e(\"marker\"),{opacity:{valType:\"number\",arrayOk:!0,dflt:1,min:0,max:1,editType:\"style\"},pattern:UTt,cornerradius:{valType:\"any\",editType:\"calc\"}});M2e.exports={x:nu.x,x0:nu.x0,dx:nu.dx,y:nu.y,y0:nu.y0,dy:nu.dy,xperiod:nu.xperiod,yperiod:nu.yperiod,xperiod0:nu.xperiod0,yperiod0:nu.yperiod0,xperiodalignment:nu.xperiodalignment,yperiodalignment:nu.yperiodalignment,xhoverformat:b2e(\"x\"),yhoverformat:b2e(\"y\"),text:nu.text,texttemplate:OTt({editType:\"plot\"},{keys:T2e.eventDataKeys}),texttemplatefallback:w2e({editType:\"plot\"}),hovertext:nu.hovertext,hovertemplate:BTt({},{keys:T2e.eventDataKeys}),hovertemplatefallback:w2e(),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"calc\"},insidetextanchor:{valType:\"enumerated\",values:[\"end\",\"middle\",\"start\"],dflt:\"end\",editType:\"plot\"},textangle:{valType:\"angle\",dflt:\"auto\",editType:\"plot\"},textfont:v2({},MV,{}),insidetextfont:v2({},MV,{}),outsidetextfont:v2({},MV,{}),constraintext:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"both\",\"none\"],dflt:\"both\",editType:\"calc\"},cliponaxis:v2({},nu.cliponaxis,{}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],editType:\"calc+clearAxisTypes\"},base:{valType:\"any\",dflt:null,arrayOk:!0,editType:\"calc\"},offset:{valType:\"number\",dflt:null,arrayOk:!0,editType:\"calc\"},width:{valType:\"number\",dflt:null,min:0,arrayOk:!0,editType:\"calc\"},marker:WTt,offsetgroup:nu.offsetgroup,alignmentgroup:nu.alignmentgroup,selected:{marker:{opacity:nu.selected.marker.opacity,color:nu.selected.marker.color,editType:\"style\"},textfont:nu.selected.textfont,editType:\"style\"},unselected:{marker:{opacity:nu.unselected.marker.opacity,color:nu.unselected.marker.color,editType:\"style\"},textfont:nu.unselected.textfont,editType:\"style\"},zorder:nu.zorder}});var XC=fe((D1r,S2e)=>{\"use strict\";S2e.exports={barmode:{valType:\"enumerated\",values:[\"stack\",\"group\",\"overlay\",\"relative\"],dflt:\"group\",editType:\"calc\"},barnorm:{valType:\"enumerated\",values:[\"\",\"fraction\",\"percent\"],dflt:\"\",editType:\"calc\"},bargap:{valType:\"number\",min:0,max:1,editType:\"calc\"},bargroupgap:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},barcornerradius:{valType:\"any\",editType:\"calc\"}}});var ZC=fe((R1r,C2e)=>{\"use strict\";var YTt=ni(),E2e=Wf().hasColorscale,k2e=Vc(),XTt=Xt().coercePattern;C2e.exports=function(t,r,n,a,i){var o=n(\"marker.color\",a),s=E2e(t,\"marker\");s&&k2e(t,r,i,n,{prefix:\"marker.\",cLetter:\"c\"}),n(\"marker.line.color\",YTt.defaultLine),E2e(t,\"marker.line\")&&k2e(t,r,i,n,{prefix:\"marker.line.\",cLetter:\"c\"}),n(\"marker.line.width\"),n(\"marker.opacity\"),XTt(n,\"marker.pattern\",o,s),n(\"selected.marker.color\"),n(\"unselected.marker.color\")}});var z0=fe((I1r,I2e)=>{\"use strict\";var L2e=aa(),F3=Xt(),P2e=ni(),ZTt=bi(),$Tt=r2(),KTt=sp(),JTt=ZC(),QTt=n2(),F2e=u1(),$C=F3.coerceFont;function eAt(e,t,r,n){function a(u,c){return F3.coerce(e,t,F2e,u,c)}var i=$Tt(e,t,n,a);if(!i){t.visible=!1;return}KTt(e,t,n,a),a(\"xhoverformat\"),a(\"yhoverformat\"),a(\"zorder\"),a(\"orientation\",t.x&&!t.y?\"h\":\"v\"),a(\"base\"),a(\"offset\"),a(\"width\"),a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var o=a(\"textposition\");R2e(e,t,n,a,o,{moduleHasSelected:!0,moduleHasUnselected:!0,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),JTt(e,t,a,r,n);var s=(t.marker.line||{}).color,l=ZTt.getComponentMethod(\"errorbars\",\"supplyDefaults\");l(e,t,s||P2e.defaultLine,{axis:\"y\"}),l(e,t,s||P2e.defaultLine,{axis:\"x\",inherit:\"y\"}),F3.coerceSelectionMarkerOpacity(t,a)}function tAt(e,t){var r,n;function a(s,l){return F3.coerce(n._input,n,F2e,s,l)}for(var i=0;i<e.length;i++)if(n=e[i],n.type===\"bar\"){r=n._input;var o=a(\"marker.cornerradius\",t.barcornerradius);n.marker&&(n.marker.cornerradius=D2e(o)),QTt(r,n,t,a,t.barmode)}}function D2e(e){if(L2e(e)){if(e=+e,e>=0)return e}else if(typeof e==\"string\"&&(e=e.trim(),e.slice(-1)===\"%\"&&L2e(e.slice(0,-1))&&(e=+e.slice(0,-1),e>=0)))return e+\"%\"}function R2e(e,t,r,n,a,i){i=i||{};var o=i.moduleHasSelected!==!1,s=i.moduleHasUnselected!==!1,l=i.moduleHasConstrain!==!1,u=i.moduleHasCliponaxis!==!1,c=i.moduleHasTextangle!==!1,f=i.moduleHasInsideanchor!==!1,v=!!i.hasPathbar,g=Array.isArray(a)||a===\"auto\",p=g||a===\"inside\",x=g||a===\"outside\";if(p||x){var _=$C(n,\"textfont\",r.font),y=F3.extendFlat({},_),C=e.textfont&&e.textfont.color,k=!C;if(k&&delete y.color,$C(n,\"insidetextfont\",y),v){var S=F3.extendFlat({},_);k&&delete S.color,$C(n,\"pathbar.textfont\",S)}x&&$C(n,\"outsidetextfont\",_),o&&n(\"selected.textfont.color\"),s&&n(\"unselected.textfont.color\"),l&&n(\"constraintext\"),u&&n(\"cliponaxis\"),c&&n(\"textangle\"),n(\"texttemplate\"),n(\"texttemplatefallback\")}p&&f&&n(\"insidetextanchor\")}I2e.exports={supplyDefaults:eAt,crossTraceDefaults:tAt,handleText:R2e,validateCornerradius:D2e}});var SV=fe((z1r,z2e)=>{\"use strict\";var rAt=bi(),nAt=Yi(),iAt=Xt(),aAt=XC(),oAt=z0().validateCornerradius;z2e.exports=function(e,t,r){function n(x,_){return iAt.coerce(e,t,aAt,x,_)}for(var a=!1,i=!1,o=!1,s={},l=n(\"barmode\"),u=l===\"group\",c=0;c<r.length;c++){var f=r[c];if(rAt.traceIs(f,\"bar\")&&f.visible)a=!0;else continue;var v=f.xaxis+f.yaxis;if(u?(s[v]&&(o=!0),s[v]=!0):(v+=f._input.offsetgroup,s.length>0&&!s[v]&&(o=!0),s[v]=!0),f.visible&&f.type===\"histogram\"){var g=nAt.getFromId({_fullLayout:t},f[f.orientation===\"v\"?\"xaxis\":\"yaxis\"]);g.type!==\"category\"&&(i=!0)}}if(!a){delete t.barmode;return}l!==\"overlay\"&&n(\"barnorm\"),n(\"bargap\",i&&!o?0:.2),n(\"bargroupgap\");var p=n(\"barcornerradius\");t.barcornerradius=oAt(p)}});var EA=fe((q1r,q2e)=>{\"use strict\";var D3=Xt();q2e.exports=function(t,r){for(var n=0;n<t.length;n++)t[n].i=n;D3.mergeArray(r.text,t,\"tx\"),D3.mergeArray(r.hovertext,t,\"htx\");var a=r.marker;if(a){D3.mergeArray(a.opacity,t,\"mo\",!0),D3.mergeArray(a.color,t,\"mc\");var i=a.line;i&&(D3.mergeArray(i.color,t,\"mlc\"),D3.mergeArrayCastPositive(i.width,t,\"mlw\"))}}});var G2e=fe((B1r,V2e)=>{\"use strict\";var B2e=Yi(),O2e=cp(),N2e=Wf().hasColorscale,U2e=Mh(),sAt=EA(),lAt=P0();V2e.exports=function(t,r){var n=B2e.getFromId(t,r.xaxis||\"x\"),a=B2e.getFromId(t,r.yaxis||\"y\"),i,o,s,l,u,c,f={msUTC:!!(r.base||r.base===0)};r.orientation===\"h\"?(i=n.makeCalcdata(r,\"x\",f),s=a.makeCalcdata(r,\"y\"),l=O2e(r,a,\"y\",s),u=!!r.yperiodalignment,c=\"y\"):(i=a.makeCalcdata(r,\"y\",f),s=n.makeCalcdata(r,\"x\"),l=O2e(r,n,\"x\",s),u=!!r.xperiodalignment,c=\"x\"),o=l.vals;for(var v=Math.min(o.length,i.length),g=new Array(v),p=0;p<v;p++)g[p]={p:o[p],s:i[p]},u&&(g[p].orig_p=s[p],g[p][c+\"End\"]=l.ends[p],g[p][c+\"Start\"]=l.starts[p]),r.ids&&(g[p].id=String(r.ids[p]));return N2e(r,\"marker\")&&U2e(t,r,{vals:r.marker.color,containerStr:\"marker\",cLetter:\"c\"}),N2e(r,\"marker.line\")&&U2e(t,r,{vals:r.marker.line.color,containerStr:\"marker.line\",cLetter:\"c\"}),sAt(g,r),lAt(g,r),g}});var kh=fe((O1r,j2e)=>{\"use strict\";var uAt=xi(),cAt=Xt();function fAt(e,t,r){var n=e._fullLayout,a=n[\"_\"+r+\"Text_minsize\"];if(a){var i=n.uniformtext.mode===\"hide\",o;switch(r){case\"funnelarea\":case\"pie\":case\"sunburst\":o=\"g.slice\";break;case\"treemap\":case\"icicle\":o=\"g.slice, g.pathbar\";break;default:o=\"g.points > g.point\"}t.selectAll(o).each(function(s){var l=s.transform;if(l){l.scale=i&&l.hide?0:a/l.fontSize;var u=uAt.select(this).select(\"text\");cAt.setTransormAndDisplay(u,l)}})}}function hAt(e,t,r){if(r.uniformtext.mode){var n=H2e(e),a=r.uniformtext.minsize,i=t.scale*t.fontSize;t.hide=i<a,r[n]=r[n]||1/0,t.hide||(r[n]=Math.min(r[n],Math.max(i,a)))}}function dAt(e,t){var r=H2e(e);t[r]=void 0}function H2e(e){return\"_\"+e+\"Text_minsize\"}j2e.exports={recordMinTextSize:hAt,clearMinTextSize:dAt,resizeText:fAt}});var KC=fe(p2=>{\"use strict\";var vAt=ni(),pAt=aa(),W2e=Xt().isArrayOrTypedArray;p2.coerceString=function(e,t,r){if(typeof t==\"string\"){if(t||!e.noBlank)return t}else if((typeof t==\"number\"||t===!0)&&!e.strict)return String(t);return r!==void 0?r:e.dflt};p2.coerceNumber=function(e,t,r){if(pAt(t)){t=+t;var n=e.min,a=e.max,i=n!==void 0&&t<n||a!==void 0&&t>a;if(!i)return t}return r!==void 0?r:e.dflt};p2.coerceColor=function(e,t,r){return vAt.isValid(t)?t:r!==void 0?r:e.dflt};p2.coerceEnumerated=function(e,t,r){return e.coerceNumber&&(t=+t),e.values.indexOf(t)!==-1?t:r!==void 0?r:e.dflt};p2.getValue=function(e,t){var r;return W2e(e)?t<e.length&&(r=e[t]):r=e,r};p2.getLineWidth=function(e,t){var r=0<t.mlw?t.mlw:W2e(e.marker.line.width)?0:e.marker.line.width;return r}});var _v=fe((U1r,n_e)=>{\"use strict\";var kA=xi(),gAt=ni(),CA=ea(),Y2e=Xt(),X2e=bi(),Z2e=kh().resizeText,EV=u1(),mAt=EV.textfont,yAt=EV.insidetextfont,xAt=EV.outsidetextfont,$f=KC();function _At(e){var t=kA.select(e).selectAll('g[class^=\"barlayer\"]').selectAll(\"g.trace\");Z2e(e,t,\"bar\");var r=t.size(),n=e._fullLayout;t.style(\"opacity\",function(a){return a[0].trace.opacity}).each(function(a){(n.barmode===\"stack\"&&r>1||n.bargap===0&&n.bargroupgap===0&&!a[0].trace.marker.line.width)&&kA.select(this).attr(\"shape-rendering\",\"crispEdges\")}),t.selectAll(\"g.points\").each(function(a){var i=kA.select(this),o=a[0].trace;$2e(i,o,e)}),X2e.getComponentMethod(\"errorbars\",\"style\")(t)}function $2e(e,t,r){CA.pointStyle(e.selectAll(\"path\"),t,r),K2e(e,t,r)}function K2e(e,t,r){e.selectAll(\"text\").each(function(n){var a=kA.select(this),i=Y2e.ensureUniformFontSize(r,J2e(a,n,t,r));CA.font(a,i)})}function bAt(e,t,r){var n=t[0].trace;n.selectedpoints?wAt(r,n,e):($2e(r,n,e),X2e.getComponentMethod(\"errorbars\",\"style\")(r))}function wAt(e,t,r){CA.selectedPointStyle(e.selectAll(\"path\"),t),TAt(e.selectAll(\"text\"),t,r)}function TAt(e,t,r){e.each(function(n){var a=kA.select(this),i;if(n.selected){i=Y2e.ensureUniformFontSize(r,J2e(a,n,t,r));var o=t.selected.textfont&&t.selected.textfont.color;o&&(i.color=o),CA.font(a,i)}else CA.selectedTextStyle(a,t)})}function J2e(e,t,r,n){var a=n._fullLayout.font,i=r.textfont;if(e.classed(\"bartext-inside\")){var o=r_e(t,r);i=e_e(r,t.i,a,o)}else e.classed(\"bartext-outside\")&&(i=t_e(r,t.i,a));return i}function Q2e(e,t,r){return kV(mAt,e.textfont,t,r)}function e_e(e,t,r,n){var a=Q2e(e,t,r),i=e._input.textfont===void 0||e._input.textfont.color===void 0||Array.isArray(e.textfont.color)&&e.textfont.color[t]===void 0;return i&&(a={color:gAt.contrast(n),family:a.family,size:a.size,weight:a.weight,style:a.style,variant:a.variant,textcase:a.textcase,lineposition:a.lineposition,shadow:a.shadow}),kV(yAt,e.insidetextfont,t,a)}function t_e(e,t,r){var n=Q2e(e,t,r);return kV(xAt,e.outsidetextfont,t,n)}function kV(e,t,r,n){t=t||{};var a=$f.getValue(t.family,r),i=$f.getValue(t.size,r),o=$f.getValue(t.color,r),s=$f.getValue(t.weight,r),l=$f.getValue(t.style,r),u=$f.getValue(t.variant,r),c=$f.getValue(t.textcase,r),f=$f.getValue(t.lineposition,r),v=$f.getValue(t.shadow,r);return{family:$f.coerceString(e.family,a,n.family),size:$f.coerceNumber(e.size,i,n.size),color:$f.coerceColor(e.color,o,n.color),weight:$f.coerceString(e.weight,s,n.weight),style:$f.coerceString(e.style,l,n.style),variant:$f.coerceString(e.variant,u,n.variant),textcase:$f.coerceString(e.variant,c,n.textcase),lineposition:$f.coerceString(e.variant,f,n.lineposition),shadow:$f.coerceString(e.variant,v,n.shadow)}}function r_e(e,t){return t.type===\"waterfall\"?t[e.dir].marker.color:e.mcc||e.mc||t.marker.color}n_e.exports={style:_At,styleTextPoints:K2e,styleOnSelect:bAt,getInsideTextFont:e_e,getOutsideTextFont:t_e,getBarColor:r_e,resizeText:Z2e}});var m2=fe((V1r,f_e)=>{\"use strict\";var JC=xi(),QC=aa(),mf=Xt(),AAt=as(),MAt=ni(),Gm=ea(),SAt=bi(),e7=Yi().tickText,i_e=kh(),EAt=i_e.recordMinTextSize,kAt=i_e.clearMinTextSize,CV=_v(),R3=KC(),CAt=Im(),a_e=u1(),LAt=a_e.text,PAt=a_e.textposition,FAt=Sd().appendArrayPointValue,od=CAt.TEXTPAD;function DAt(e){return e.id}function RAt(e){if(e.ids)return DAt}function LV(e){return(e>0)-(e<0)}function xp(e,t){return e<t?1:-1}function IAt(e,t,r,n){var a=[],i=[],o=n?t:r,s=n?r:t;return a[0]=o.c2p(e.s0,!0),i[0]=s.c2p(e.p0,!0),a[1]=o.c2p(e.s1,!0),i[1]=s.c2p(e.p1,!0),n?[a,i]:[i,a]}function o_e(e,t,r,n){if(!t.uniformtext.mode&&s_e(r)){var a;return n&&(a=n()),e.transition().duration(r.duration).ease(r.easing).each(\"end\",function(){a&&a()}).each(\"interrupt\",function(){a&&a()})}else return e}function s_e(e){return e&&e.duration>0}function zAt(e,t,r,n,a,i){var o=t.xaxis,s=t.yaxis,l=e._fullLayout,u=e._context.staticPlot;a||(a={mode:l.barmode,norm:l.barmode,gap:l.bargap,groupgap:l.bargroupgap},kAt(\"bar\",l));var c=mf.makeTraceGroups(n,r,\"trace bars\").each(function(f){var v=JC.select(this),g=f[0].trace,p=f[0].t,x=g.type===\"waterfall\",_=g.type===\"funnel\",y=g.type===\"histogram\",C=g.type===\"bar\",k=C||_,S=0;x&&g.connector.visible&&g.connector.mode===\"between\"&&(S=g.connector.line.width/2);var L=g.orientation===\"h\",w=s_e(a),A=mf.ensureSingle(v,\"g\",\"points\"),T=RAt(g),m=A.selectAll(\"g.point\").data(mf.identity,T);m.enter().append(\"g\").classed(\"point\",!0),m.exit().remove(),m.each(function(E,I){var R=JC.select(this),B=IAt(E,o,s,L),O=B[0][0],V=B[0][1],j=B[1][0],q=B[1][1],U=(L?V-O:q-j)===0;U&&k&&R3.getLineWidth(g,E)&&(U=!1),U||(U=!QC(O)||!QC(V)||!QC(j)||!QC(q)),E.isBlank=U,U&&(L?V=O:q=j),S&&!U&&(L?(O-=xp(O,V)*S,V+=xp(O,V)*S):(j-=xp(j,q)*S,q+=xp(j,q)*S));var J,ee;if(g.type===\"waterfall\"){if(!U){var ce=g[E.dir].marker;J=ce.line.width,ee=ce.color}}else J=R3.getLineWidth(g,E),ee=E.mc||g.marker.color;function ye(Te){var Ve=JC.round(J/2%1,2);return a.gap===0&&a.groupgap===0?JC.round(Math.round(Te)-Ve,2):Te}function xe(Te,Ve,$e){return $e&&Te===Ve?Te:Math.abs(Te-Ve)>=2?ye(Te):Te>Ve?Math.ceil(Te):Math.floor(Te)}var ue=MAt.opacity(ee),K=ue<1||J>.01?ye:xe;e._context.staticPlot||(O=K(O,V,L),V=K(V,O,L),j=K(j,q,!L),q=K(q,j,!L));var _e=L?o.c2p:s.c2p,ne;E.s0>0?ne=E._sMax:E.s0<0?ne=E._sMin:ne=E.s1>0?E._sMax:E._sMin;function ae(Te,Ve){if(!Te)return 0;var $e=Math.abs(L?q-j:V-O),ot=Math.abs(L?V-O:q-j),tt=K(Math.abs(_e(ne,!0)-_e(0,!0))),nt=E.hasB?Math.min($e/2,ot/2):Math.min($e/2,tt),We;if(Ve===\"%\"){var mt=Math.min(50,Te);We=$e*(mt/100)}else We=Te;return K(Math.max(Math.min(We,nt),0))}var pe=C||y?ae(p.cornerradiusvalue,p.cornerradiusform):0,ie,le,me=\"M\"+O+\",\"+j+\"V\"+q+\"H\"+V+\"V\"+j+\"Z\",be=0;if(pe&&E.s){var De=LV(E.s0)===0||LV(E.s)===LV(E.s0)?E.s1:E.s0;if(be=K(E.hasB?0:Math.abs(_e(ne,!0)-_e(De,!0))),be<pe){var Ge=xp(O,V),ht=xp(j,q),it=Ge===-ht?1:0;if(L)if(E.hasB)ie=\"M\"+(O+pe*Ge)+\",\"+j+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+O+\",\"+(j+pe*ht)+\"V\"+(q-pe*ht)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+(O+pe*Ge)+\",\"+q+\"H\"+(V-pe*Ge)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+V+\",\"+(q-pe*ht)+\"V\"+(j+pe*ht)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+(V-pe*Ge)+\",\"+j+\"Z\";else{le=Math.abs(V-O)+be;var Oe=le<pe?pe-Math.sqrt(le*(2*pe-le)):0,ze=be>0?Math.sqrt(be*(2*pe-be)):0,ct=Ge>0?Math.max:Math.min;ie=\"M\"+O+\",\"+j+\"V\"+(q-Oe*ht)+\"H\"+ct(V-(pe-be)*Ge,O)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+V+\",\"+(q-pe*ht-ze)+\"V\"+(j+pe*ht+ze)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+ct(V-(pe-be)*Ge,O)+\",\"+(j+Oe*ht)+\"Z\"}else if(E.hasB)ie=\"M\"+(O+pe*Ge)+\",\"+j+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+O+\",\"+(j+pe*ht)+\"V\"+(q-pe*ht)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+(O+pe*Ge)+\",\"+q+\"H\"+(V-pe*Ge)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+V+\",\"+(q-pe*ht)+\"V\"+(j+pe*ht)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+(V-pe*Ge)+\",\"+j+\"Z\";else{le=Math.abs(q-j)+be;var et=le<pe?pe-Math.sqrt(le*(2*pe-le)):0,qe=be>0?Math.sqrt(be*(2*pe-be)):0,we=ht>0?Math.max:Math.min;ie=\"M\"+(O+et*Ge)+\",\"+j+\"V\"+we(q-(pe-be)*ht,j)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+(O+pe*Ge-qe)+\",\"+q+\"H\"+(V-pe*Ge+qe)+\"A \"+pe+\",\"+pe+\" 0 0 \"+it+\" \"+(V-et*Ge)+\",\"+we(q-(pe-be)*ht,j)+\"V\"+j+\"Z\"}}else ie=me}else ie=me;var Le=o_e(mf.ensureSingle(R,\"path\"),l,a,i);if(Le.style(\"vector-effect\",u?\"none\":\"non-scaling-stroke\").attr(\"d\",isNaN((V-O)*(q-j))||U&&e._context.staticPlot?\"M0,0Z\":ie).call(Gm.setClipUrl,t.layerClipId,e),!l.uniformtext.mode&&w){var Ke=Gm.makePointStyleFns(g);Gm.singlePointStyle(E,Le,g,Ke,e)}qAt(e,t,R,f,I,O,V,j,q,pe,be,a,i),t.layerClipId&&Gm.hideOutsideRangePoint(E,R.select(\"text\"),o,s,g.xcalendar,g.ycalendar)});var F=g.cliponaxis===!1;Gm.setClipUrl(v,F?null:t.layerClipId,e)});SAt.getComponentMethod(\"errorbars\",\"plot\")(e,c,t,a)}function qAt(e,t,r,n,a,i,o,s,l,u,c,f,v){var g=t.xaxis,p=t.yaxis,x=e._fullLayout,_;function y(me,be,De){var Ge=mf.ensureSingle(me,\"text\").text(be).attr({class:\"bartext bartext-\"+_,\"text-anchor\":\"middle\",\"data-notex\":1}).call(Gm.font,De).call(AAt.convertToTspans,e);return Ge}var C=n[0].trace,k=C.orientation===\"h\",S=VAt(n,k,g,p),L=NAt(x,n,a,g,p);_=UAt(C,a);var w=f.mode===\"stack\"||f.mode===\"relative\",A=n[a],T=!w||A._outmost,m=A.hasB,F=u&&u-c>od;if(!L||_===\"none\"||(A.isBlank||i===o||s===l)&&(_===\"auto\"||_===\"inside\")){r.select(\"text\").remove();return}var E=x.font,I=CV.getBarColor(n[a],C),R=CV.getInsideTextFont(C,a,E,I),B=CV.getOutsideTextFont(C,a,E),O=C.insidetextanchor||\"end\",V=r.datum();k?g.type===\"log\"&&V.s0<=0&&(g.range[0]<g.range[1]?i=0:i=g._length):p.type===\"log\"&&V.s0<=0&&(p.range[0]<p.range[1]?s=p._length:s=0);var j=Math.abs(o-i),q=Math.abs(l-s),U=j-2*od,J=q-2*od,ee,ce,ye,xe,ue;if(_===\"outside\"&&!T&&!A.hasB&&(_=\"inside\"),_===\"auto\")if(T){_=\"inside\",ue=mf.ensureUniformFontSize(e,R),ee=y(r,L,ue),ce=Gm.bBox(ee.node()),ye=ce.width,xe=ce.height;var K=ye>0&&xe>0,_e;F?m?_e=g2(U-2*u,J,ye,xe,k)||g2(U,J-2*u,ye,xe,k):k?_e=g2(U-(u-c),J,ye,xe,k)||g2(U,J-2*(u-c),ye,xe,k):_e=g2(U,J-(u-c),ye,xe,k)||g2(U-2*(u-c),J,ye,xe,k):_e=g2(U,J,ye,xe,k),K&&_e?_=\"inside\":(_=\"outside\",ee.remove(),ee=null)}else _=\"inside\";if(!ee){ue=mf.ensureUniformFontSize(e,_===\"outside\"?B:R),ee=y(r,L,ue);var ne=ee.attr(\"transform\");if(ee.attr(\"transform\",\"\"),ce=Gm.bBox(ee.node()),ye=ce.width,xe=ce.height,ee.attr(\"transform\",ne),ye<=0||xe<=0){ee.remove();return}}var ae=C.textangle,pe,ie;_===\"outside\"?(ie=C.constraintext===\"both\"||C.constraintext===\"outside\",pe=OAt(i,o,s,l,ce,{isHorizontal:k,constrained:ie,angle:ae,zeroBarDir:S})):(ie=C.constraintext===\"both\"||C.constraintext===\"inside\",pe=c_e(i,o,s,l,ce,{isHorizontal:k,constrained:ie,angle:ae,anchor:O,hasB:m,r:u,overhead:c})),pe.fontSize=ue.size,EAt(C.type===\"histogram\"?\"bar\":C.type,pe,x),A.transform=pe;var le=o_e(ee,x,f,v);mf.setTransormAndDisplay(le,pe)}function 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m=t6t.getComponentMethod(\"calendars\",\"handleTraceDefaults\");m(e,t,[\"x\",\"y\"],n)}function L_e(e,t,r,n){var a=n.prefix,i=wv.coerce2(e,t,DA,\"marker.outliercolor\"),o=r(\"marker.line.outliercolor\"),s=\"outliers\";t._hasPreCompStats?s=\"all\":(i||o)&&(s=\"suspectedoutliers\");var l=r(a+\"points\",s);l?(r(\"jitter\",l===\"all\"?.3:0),r(\"pointpos\",l===\"all\"?-1.5:0),r(\"marker.symbol\"),r(\"marker.opacity\"),r(\"marker.size\"),r(\"marker.angle\"),r(\"marker.color\",t.line.color),r(\"marker.line.color\"),r(\"marker.line.width\"),l===\"suspectedoutliers\"&&(r(\"marker.line.outliercolor\",t.marker.color),r(\"marker.line.outlierwidth\")),r(\"selected.marker.color\"),r(\"unselected.marker.color\"),r(\"selected.marker.size\"),r(\"unselected.marker.size\"),r(\"text\"),r(\"hovertext\")):delete t.marker;var u=r(\"hoveron\");(u===\"all\"||u.indexOf(\"points\")!==-1)&&(r(\"hovertemplate\"),r(\"hovertemplatefallback\")),wv.coerceSelectionMarkerOpacity(t,r)}function o6t(e,t){var r,n;function a(l){return wv.coerce(n._input,n,DA,l)}for(var i=0;i<e.length;i++){n=e[i];var o=n.type;if(o===\"box\"||o===\"violin\"){r=n._input;var s=t[o+\"mode\"];s===\"group\"&&i6t(r,n,t,a,s)}}}P_e.exports={supplyDefaults:a6t,crossTraceDefaults:o6t,handleSampleDefaults:C_e,handlePointsDefaults:L_e}});var t7=fe((K1r,D_e)=>{\"use strict\";var s6t=bi(),l6t=Xt(),u6t=FA();function F_e(e,t,r,n,a){for(var i=a+\"Layout\",o=!1,s=0;s<r.length;s++){var l=r[s];if(s6t.traceIs(l,i)){o=!0;break}}o&&(n(a+\"mode\"),n(a+\"gap\"),n(a+\"groupgap\"))}function c6t(e,t,r){function n(a,i){return l6t.coerce(e,t,u6t,a,i)}F_e(e,t,r,n,\"box\")}D_e.exports={supplyLayoutDefaults:c6t,_supply:F_e}});var RV=fe((J1r,V_e)=>{\"use strict\";var DV=aa(),r7=Yi(),f6t=cp(),qu=Xt(),q0=Wa().BADNUM,hg=qu._;V_e.exports=function(t,r){var n=t._fullLayout,a=r7.getFromId(t,r.xaxis||\"x\"),i=r7.getFromId(t,r.yaxis||\"y\"),o=[],s=r.type===\"violin\"?\"_numViolins\":\"_numBoxes\",l,u,c,f,v,g,p;r.orientation===\"h\"?(c=a,f=\"x\",v=i,g=\"y\",p=!!r.yperiodalignment):(c=i,f=\"y\",v=a,g=\"x\",p=!!r.xperiodalignment);var x=h6t(r,g,v,n[s]),_=x[0],y=x[1],C=qu.distinctVals(_,v),k=C.vals,S=C.minDiff/2,L,w,A,T,m,F,E=(r.boxpoints||r.points)===\"all\"?qu.identity:function(it){return it.v<L.lf||it.v>L.uf};if(r._hasPreCompStats){var I=r[f],R=function(it){return c.d2c((r[it]||[])[l])},B=1/0,O=-1/0;for(l=0;l<r._length;l++){var V=_[l];if(DV(V)){if(L={},L.pos=L[g]=V,p&&y&&(L.orig_p=y[l]),L.q1=R(\"q1\"),L.med=R(\"median\"),L.q3=R(\"q3\"),w=[],I&&qu.isArrayOrTypedArray(I[l]))for(u=0;u<I[l].length;u++)F=c.d2c(I[l][u]),F!==q0&&(m={v:F,i:[l,u]},R_e(m,r,[l,u]),w.push(m));if(L.pts=w.sort(I_e),A=L[f]=w.map(z_e),T=A.length,L.med!==q0&&L.q1!==q0&&L.q3!==q0&&L.med>=L.q1&&L.q3>=L.med){var j=R(\"lowerfence\");L.lf=j!==q0&&j<=L.q1?j:q_e(L,A,T);var q=R(\"upperfence\");L.uf=q!==q0&&q>=L.q3?q:B_e(L,A,T);var U=R(\"mean\");L.mean=U!==q0?U:T?qu.mean(A,T):(L.q1+L.q3)/2;var J=R(\"sd\");L.sd=U!==q0&&J>=0?J:T?qu.stdev(A,T,L.mean):L.q3-L.q1,L.lo=O_e(L),L.uo=N_e(L);var ee=R(\"notchspan\");ee=ee!==q0&&ee>0?ee:U_e(L,T),L.ln=L.med-ee,L.un=L.med+ee;var ce=L.lf,ye=L.uf;r.boxpoints&&A.length&&(ce=Math.min(ce,A[0]),ye=Math.max(ye,A[T-1])),r.notched&&(ce=Math.min(ce,L.ln),ye=Math.max(ye,L.un)),L.min=ce,L.max=ye}else{qu.warn([\"Invalid input - make sure that q1 <= median <= q3\",\"q1 = \"+L.q1,\"median = \"+L.med,\"q3 = \"+L.q3].join(`\n",
+       "`));var xe;L.med!==q0?xe=L.med:L.q1!==q0?L.q3!==q0?xe=(L.q1+L.q3)/2:xe=L.q1:L.q3!==q0?xe=L.q3:xe=0,L.med=xe,L.q1=L.q3=xe,L.lf=L.uf=xe,L.mean=L.sd=xe,L.ln=L.un=xe,L.min=L.max=xe}B=Math.min(B,L.min),O=Math.max(O,L.max),L.pts2=w.filter(E),o.push(L)}}r._extremes[c._id]=r7.findExtremes(c,[B,O],{padded:!0})}else{var ue=c.makeCalcdata(r,f),K=d6t(k,S),_e=k.length,ne=v6t(_e);for(l=0;l<r._length;l++)if(F=ue[l],!!DV(F)){var ae=qu.findBin(_[l],K);ae>=0&&ae<_e&&(m={v:F,i:l},R_e(m,r,l),ne[ae].push(m))}var pe=1/0,ie=-1/0,le=r.quartilemethod,me=le===\"exclusive\",be=le===\"inclusive\";for(l=0;l<_e;l++)if(ne[l].length>0){if(L={},L.pos=L[g]=k[l],w=L.pts=ne[l].sort(I_e),A=L[f]=w.map(z_e),T=A.length,L.min=A[0],L.max=A[T-1],L.mean=qu.mean(A,T),L.sd=qu.stdev(A,T,L.mean)*r.sdmultiple,L.med=qu.interp(A,.5),T%2&&(me||be)){var De,Ge;me?(De=A.slice(0,T/2),Ge=A.slice(T/2+1)):be&&(De=A.slice(0,T/2+1),Ge=A.slice(T/2)),L.q1=qu.interp(De,.5),L.q3=qu.interp(Ge,.5)}else L.q1=qu.interp(A,.25),L.q3=qu.interp(A,.75);L.lf=q_e(L,A,T),L.uf=B_e(L,A,T),L.lo=O_e(L),L.uo=N_e(L);var ht=U_e(L,T);L.ln=L.med-ht,L.un=L.med+ht,pe=Math.min(pe,L.ln),ie=Math.max(ie,L.un),L.pts2=w.filter(E),o.push(L)}r.notched&&qu.isTypedArray(ue)&&(ue=Array.from(ue)),r._extremes[c._id]=r7.findExtremes(c,r.notched?ue.concat([pe,ie]):ue,{padded:!0})}return p6t(o,r),o.length>0?(o[0].t={num:n[s],dPos:S,posLetter:g,valLetter:f,labels:{med:hg(t,\"median:\"),min:hg(t,\"min:\"),q1:hg(t,\"q1:\"),q3:hg(t,\"q3:\"),max:hg(t,\"max:\"),mean:r.boxmean===\"sd\"||r.sizemode===\"sd\"?hg(t,\"mean \\xB1 \\u03C3:\").replace(\"\\u03C3\",r.sdmultiple===1?\"\\u03C3\":r.sdmultiple+\"\\u03C3\"):hg(t,\"mean:\"),lf:hg(t,\"lower fence:\"),uf:hg(t,\"upper fence:\")}},n[s]++,o):[{t:{empty:!0}}]};function h6t(e,t,r,n){var a=t in e,i=t+\"0\"in e,o=\"d\"+t in e;if(a||i&&o){var s=r.makeCalcdata(e,t),l=f6t(e,r,t,s).vals;return[l,s]}var u;i?u=e[t+\"0\"]:\"name\"in 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g=g6t.distinctVals(f);(n.type===\"category\"||n.type===\"multicategory\")&&(g.minDiff=1);var p=g.minDiff/2;G_e.minDtick(n,g.minDiff,g.vals[0],!0);var x=e===\"violin\"?\"_numViolins\":\"_numBoxes\",_=i[x],y=i[e+\"mode\"]===\"group\"&&_>1,C=1-i[e+\"gap\"],k=1-i[e+\"groupgap\"];for(l=0;l<r.length;l++){c=a[r[l]];var S=c[0].trace,L=c[0].t,w=S.width,A=S.side,T,m,F,E;if(w)T=m=E=w/2,F=0;else if(T=p,y){var I=m6t(i,n._id)+S.orientation,R=i._alignmentOpts[I]||{},B=R[S.alignmentgroup]||{},O=Object.keys(B.offsetGroups||{}).length,V=O||_,j=O?S._offsetIndex:L.num;m=T*C*k/V,F=2*T*(-.5+(j+.5)/V)*C,E=T*C/V}else m=T*C*k,F=0,E=T;L.dPos=T,L.bPos=F,L.bdPos=m,L.wHover=E;var q,U,J=F+m,ee,ce,ye,xe,ue,K,_e=!!w,ne=(S.boxpoints||S.points)&&v>0;if(A===\"positive\"?(q=T*(w?1:.5),ee=J,U=ee=F):A===\"negative\"?(q=ee=F,U=T*(w?1:.5),ce=J):(q=U=T,ee=ce=J),ne){var ae=S.pointpos,pe=S.jitter,ie=S.marker.size/2,le=0;ae+pe>=0&&(le=J*(ae+pe),le>q?(_e=!0,ue=ie,ye=le):le>ee&&(ue=ie,ye=q)),le<=q&&(ye=q);var me=0;ae-pe<=0&&(me=-J*(ae-pe),me>U?(_e=!0,K=ie,xe=me):me>ce&&(K=ie,xe=U)),me<=U&&(xe=U)}else ye=q,xe=U;var be=new Array(c.length);for(u=0;u<c.length;u++)be[u]=c[u].pos;S._extremes[o]=G_e.findExtremes(n,be,{padded:_e,vpadminus:xe,vpadplus:ye,vpadLinearized:!0,ppadminus:{x:K,y:ue}[s],ppadplus:{x:ue,y:K}[s]})}}}W_e.exports={crossTraceCalc:y6t,setPositionOffset:j_e}});var i7=fe((egr,K_e)=>{\"use strict\";var B3=xi(),y2=Xt(),x6t=ea(),Y_e=5,_6t=.01;function b6t(e,t,r,n){var a=e._context.staticPlot,i=t.xaxis,o=t.yaxis;y2.makeTraceGroups(n,r,\"trace boxes\").each(function(s){var l=B3.select(this),u=s[0],c=u.t,f=u.trace;if(c.wdPos=c.bdPos*f.whiskerwidth,f.visible!==!0||c.empty){l.remove();return}var v,g;f.orientation===\"h\"?(v=o,g=i):(v=i,g=o),X_e(l,{pos:v,val:g},f,c,a),Z_e(l,{x:i,y:o},f,c),$_e(l,{pos:v,val:g},f,c)})}function X_e(e,t,r,n,a){var i=r.orientation===\"h\",o=t.val,s=t.pos,l=!!s.rangebreaks,u=n.bPos,c=n.wdPos||0,f=n.bPosPxOffset||0,v=r.whiskerwidth||0,g=r.showwhiskers!==!1,p=r.notched||!1,x=p?1-2*r.notchwidth:1,_,y;Array.isArray(n.bdPos)?(_=n.bdPos[0],y=n.bdPos[1]):(_=n.bdPos,y=n.bdPos);var C=e.selectAll(\"path.box\").data(r.type!==\"violin\"||r.box.visible?y2.identity:[]);C.enter().append(\"path\").style(\"vector-effect\",a?\"none\":\"non-scaling-stroke\").attr(\"class\",\"box\"),C.exit().remove(),C.each(function(k){if(k.empty)return B3.select(this).attr(\"d\",\"M0,0Z\");var S=s.c2l(k.pos+u,!0),L=s.l2p(S-_)+f,w=s.l2p(S+y)+f,A=l?(L+w)/2:s.l2p(S)+f,T=r.whiskerwidth,m=l?L*T+(1-T)*A:s.l2p(S-c)+f,F=l?w*T+(1-T)*A:s.l2p(S+c)+f,E=s.l2p(S-_*x)+f,I=s.l2p(S+y*x)+f,R=r.sizemode===\"sd\",B=o.c2p(R?k.mean-k.sd:k.q1,!0),O=R?o.c2p(k.mean+k.sd,!0):o.c2p(k.q3,!0),V=y2.constrain(R?o.c2p(k.mean,!0):o.c2p(k.med,!0),Math.min(B,O)+1,Math.max(B,O)-1),j=k.lf===void 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w5e=fe((wmr,b5e)=>{\"use strict\";var x5e=Xt(),eEt=r1(),X3=Is(),tEt=vv(),rEt=lp(),_5e=f3(),nEt=up(),iEt=a1(),aEt=CG();b5e.exports=function(t,r,n,a){function i(v,g){return x5e.coerce(t,r,aEt,v,g)}var o=i(\"a\"),s=i(\"b\"),l=i(\"c\"),u;if(o?(u=o.length,s?(u=Math.min(u,s.length),l&&(u=Math.min(u,l.length))):l?u=Math.min(u,l.length):u=0):s&&l&&(u=Math.min(s.length,l.length)),!u){r.visible=!1;return}r._length=u,i(\"sum\"),i(\"text\"),i(\"hovertext\"),r.hoveron!==\"fills\"&&(i(\"hovertemplate\"),i(\"hovertemplatefallback\"));var c=u<eEt.PTS_LINESONLY?\"lines+markers\":\"lines\";i(\"mode\",c),X3.hasMarkers(r)&&tEt(t,r,n,a,i,{gradient:!0}),X3.hasLines(r)&&(rEt(t,r,n,a,i,{backoff:!0}),_5e(t,r,i),i(\"connectgaps\")),X3.hasText(r)&&(i(\"texttemplate\"),i(\"texttemplatefallback\"),nEt(t,r,a,i));var f=[];(X3.hasMarkers(r)||X3.hasText(r))&&(i(\"cliponaxis\"),i(\"marker.maxdisplayed\"),f.push(\"points\")),i(\"fill\"),r.fill!==\"none\"&&(iEt(t,r,n,i),X3.hasLines(r)||_5e(t,r,i)),(r.fill===\"tonext\"||r.fill===\"toself\")&&f.push(\"fills\"),i(\"hoveron\",f.join(\"+\")||\"points\"),x5e.coerceSelectionMarkerOpacity(r,i)}});var A5e=fe((Tmr,T5e)=>{\"use strict\";var LG=Yi();T5e.exports=function(t,r,n){var a={},i=n[r.subplot]._subplot;return a.aLabel=LG.tickText(i.aaxis,t.a,!0).text,a.bLabel=LG.tickText(i.baxis,t.b,!0).text,a.cLabel=LG.tickText(i.caxis,t.c,!0).text,a}});var k5e=fe((Amr,E5e)=>{\"use strict\";var PG=aa(),oEt=pv(),sEt=o1(),lEt=P0(),uEt=gv().calcMarkerSize,M5e=[\"a\",\"b\",\"c\"],S5e={a:[\"b\",\"c\"],b:[\"a\",\"c\"],c:[\"a\",\"b\"]};E5e.exports=function(t,r){var n=t._fullLayout[r.subplot],a=n.sum,i=r.sum||a,o={a:r.a,b:r.b,c:r.c},s=r.ids,l,u,c,f,v,g;for(l=0;l<M5e.length;l++)if(c=M5e[l],!o[c]){for(v=o[S5e[c][0]],g=o[S5e[c][1]],f=new Array(v.length),u=0;u<v.length;u++)f[u]=i-v[u]-g[u];o[c]=f}var p=r._length,x=new Array(p),_,y,C,k,S,L;for(l=0;l<p;l++)_=o.a[l],y=o.b[l],C=o.c[l],PG(_)&&PG(y)&&PG(C)?(_=+_,y=+y,C=+C,k=a/(_+y+C),k!==1&&(_*=k,y*=k,C*=k),L=_,S=C-y,x[l]={x:S,y:L,a:_,b:y,c:C},s&&(x[l].id=s[l])):x[l]={x:!1,y:!1};return uEt(r,p),oEt(t,r),sEt(x,r),lEt(x,r),x}});var L5e=fe((Mmr,C5e)=>{\"use strict\";var cEt=m3();C5e.exports=function(t,r,n){var a=r.plotContainer;a.select(\".scatterlayer\").selectAll(\"*\").remove();for(var i=r.xaxis,o=r.yaxis,s={xaxis:i,yaxis:o,plot:a,layerClipId:r._hasClipOnAxisFalse?r.clipIdRelative:null},l=r.layers.frontplot.select(\"g.scatterlayer\"),u=0;u<n.length;u++){var c=n[u];c.length&&(c[0].trace._xA=i,c[0].trace._yA=o)}cEt(t,s,n,l)}});var F5e=fe((Smr,P5e)=>{\"use strict\";var fEt=b3();P5e.exports=function(t,r,n,a){var i=fEt(t,r,n,a);if(!i||i[0].index===!1)return;var o=i[0];if(o.index===void 0){var s=1-o.y0/t.ya._length,l=t.xa._length,u=l*s/2,c=l-u;return o.x0=Math.max(Math.min(o.x0,c),u),o.x1=Math.max(Math.min(o.x1,c),u),i}var f=o.cd[o.index],v=o.trace,g=o.subplot;o.a=f.a,o.b=f.b,o.c=f.c,o.xLabelVal=void 0,o.yLabelVal=void 0;var p={};p[v.subplot]={_subplot:g};var x=v._module.formatLabels(f,v,p);o.aLabel=x.aLabel,o.bLabel=x.bLabel,o.cLabel=x.cLabel;var _=f.hi||v.hoverinfo,y=[];function C(S,L){y.push(S._hovertitle+\": \"+L)}if(!v.hovertemplate){var k=_.split(\"+\");k.indexOf(\"all\")!==-1&&(k=[\"a\",\"b\",\"c\"]),k.indexOf(\"a\")!==-1&&C(g.aaxis,o.aLabel),k.indexOf(\"b\")!==-1&&C(g.baxis,o.bLabel),k.indexOf(\"c\")!==-1&&C(g.caxis,o.cLabel)}return o.extraText=y.join(\"<br>\"),o.hovertemplate=v.hovertemplate,i}});var R5e=fe((Emr,D5e)=>{\"use strict\";D5e.exports=function(t,r,n,a,i){if(r.xa&&(t.xaxis=r.xa),r.ya&&(t.yaxis=r.ya),a[i]){var o=a[i];t.a=o.a,t.b=o.b,t.c=o.c}else t.a=r.a,t.b=r.b,t.c=r.c;return t}});var j5e=fe((kmr,H5e)=>{\"use strict\";var N5e=xi(),FG=bi(),vg=Xt(),d1=vg.strTranslate,N7=vg._,_2=ni(),U7=ea(),YA=Hp(),DG=ta().extendFlat,hEt=Hs(),Wm=Yi(),I5e=ed(),z5e=kl(),U5e=$p(),q5e=U5e.freeMode,dEt=U5e.rectMode,RG=qx(),vEt=Ac().prepSelect,pEt=Ac().selectOnClick,gEt=Ac().clearOutline,mEt=Ac().clearSelectionsCache,V5e=rc();function G5e(e,t){this.id=e.id,this.graphDiv=e.graphDiv,this.init(t),this.makeFramework(t),this.updateFx(t),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}H5e.exports=G5e;var v1=G5e.prototype;v1.init=function(e){this.container=e._ternarylayer,this.defs=e._defs,this.layoutId=e._uid,this.traceHash={},this.layers={}};v1.plot=function(e,t){var r=this,n=t[r.id],a=t._size;r._hasClipOnAxisFalse=!1;for(var i=0;i<e.length;i++){var o=e[i][0].trace;if(o.cliponaxis===!1){r._hasClipOnAxisFalse=!0;break}}r.updateLayers(n),r.adjustLayout(n,a),hEt.generalUpdatePerTraceModule(r.graphDiv,r,e,n),r.layers.plotbg.select(\"path\").call(_2.fill,n.bgcolor)};v1.makeFramework=function(e){var t=this,r=t.graphDiv,n=e[t.id],a=t.clipId=\"clip\"+t.layoutId+t.id,i=t.clipIdRelative=\"clip-relative\"+t.layoutId+t.id;t.clipDef=vg.ensureSingleById(e._clips,\"clipPath\",a,function(o){o.append(\"path\").attr(\"d\",\"M0,0Z\")}),t.clipDefRelative=vg.ensureSingleById(e._clips,\"clipPath\",i,function(o){o.append(\"path\").attr(\"d\",\"M0,0Z\")}),t.plotContainer=vg.ensureSingle(t.container,\"g\",t.id),t.updateLayers(n),U7.setClipUrl(t.layers.backplot,a,r),U7.setClipUrl(t.layers.grids,a,r)};v1.updateFx=function(e){e._ternarylayer.selectAll(\"g.toplevel\").style(\"cursor\",e.dragmode===\"pan\"?\"move\":\"crosshair\")};v1.updateLayers=function(e){var t=this,r=t.layers,n=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];e.aaxis.layer===\"below traces\"&&n.push(\"aaxis\",\"aline\"),e.baxis.layer===\"below traces\"&&n.push(\"baxis\",\"bline\"),e.caxis.layer===\"below traces\"&&n.push(\"caxis\",\"cline\"),n.push(\"frontplot\"),e.aaxis.layer===\"above traces\"&&n.push(\"aaxis\",\"aline\"),e.baxis.layer===\"above traces\"&&n.push(\"baxis\",\"bline\"),e.caxis.layer===\"above traces\"&&n.push(\"caxis\",\"cline\");var a=t.plotContainer.selectAll(\"g.toplevel\").data(n,String),i=[\"agrid\",\"bgrid\",\"cgrid\"];a.enter().append(\"g\").attr(\"class\",function(o){return\"toplevel \"+o}).each(function(o){var s=N5e.select(this);r[o]=s,o===\"frontplot\"?s.append(\"g\").classed(\"scatterlayer\",!0):o===\"backplot\"?s.append(\"g\").classed(\"maplayer\",!0):o===\"plotbg\"?s.append(\"path\").attr(\"d\",\"M0,0Z\"):o===\"aline\"||o===\"bline\"||o===\"cline\"?s.append(\"path\"):o===\"grids\"&&i.forEach(function(l){r[l]=s.append(\"g\").classed(\"grid \"+l,!0)})}),a.order()};var Z3=Math.sqrt(4/3);v1.adjustLayout=function(e,t){var r=this,n=e.domain,a=(n.x[0]+n.x[1])/2,i=(n.y[0]+n.y[1])/2,o=n.x[1]-n.x[0],s=n.y[1]-n.y[0],l=o*t.w,u=s*t.h,c=e.sum,f=e.aaxis.min,v=e.baxis.min,g=e.caxis.min,p,x,_,y,C,k;l>Z3*u?(y=u,_=y*Z3):(_=l,y=_/Z3),C=o*_/l,k=s*y/u,p=t.l+t.w*a-_/2,x=t.t+t.h*(1-i)-y/2,r.x0=p,r.y0=x,r.w=_,r.h=y,r.sum=c,r.xaxis={type:\"linear\",range:[f+2*g-c,c-f-2*v],domain:[a-C/2,a+C/2],_id:\"x\"},YA(r.xaxis,r.graphDiv._fullLayout),r.xaxis.setScale(),r.xaxis.isPtWithinRange=function(B){return B.a>=r.aaxis.range[0]&&B.a<=r.aaxis.range[1]&&B.b>=r.baxis.range[1]&&B.b<=r.baxis.range[0]&&B.c>=r.caxis.range[1]&&B.c<=r.caxis.range[0]},r.yaxis={type:\"linear\",range:[f,c-v-g],domain:[i-k/2,i+k/2],_id:\"y\"},YA(r.yaxis,r.graphDiv._fullLayout),r.yaxis.setScale(),r.yaxis.isPtWithinRange=function(){return!0};var S=r.yaxis.domain[0],L=r.aaxis=DG({},e.aaxis,{range:[f,c-v-g],side:\"left\",tickangle:(+e.aaxis.tickangle||0)-30,domain:[S,S+k*Z3],anchor:\"free\",position:0,_id:\"y\",_length:_});YA(L,r.graphDiv._fullLayout),L.setScale();var w=r.baxis=DG({},e.baxis,{range:[c-f-g,v],side:\"bottom\",domain:r.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:_});YA(w,r.graphDiv._fullLayout),w.setScale();var A=r.caxis=DG({},e.caxis,{range:[c-f-v,g],side:\"right\",tickangle:(+e.caxis.tickangle||0)+30,domain:[S,S+k*Z3],anchor:\"free\",position:0,_id:\"y\",_length:_});YA(A,r.graphDiv._fullLayout),A.setScale();var T=\"M\"+p+\",\"+(x+y)+\"h\"+_+\"l-\"+_/2+\",-\"+y+\"Z\";r.clipDef.select(\"path\").attr(\"d\",T),r.layers.plotbg.select(\"path\").attr(\"d\",T);var m=\"M0,\"+y+\"h\"+_+\"l-\"+_/2+\",-\"+y+\"Z\";r.clipDefRelative.select(\"path\").attr(\"d\",m);var F=d1(p,x);r.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",F),r.clipDefRelative.select(\"path\").attr(\"transform\",null);var E=d1(p-w._offset,x+y);r.layers.baxis.attr(\"transform\",E),r.layers.bgrid.attr(\"transform\",E);var I=d1(p+_/2,x)+\"rotate(30)\"+d1(0,-L._offset);r.layers.aaxis.attr(\"transform\",I),r.layers.agrid.attr(\"transform\",I);var R=d1(p+_/2,x)+\"rotate(-30)\"+d1(0,-A._offset);r.layers.caxis.attr(\"transform\",R),r.layers.cgrid.attr(\"transform\",R),r.drawAxes(!0),r.layers.aline.select(\"path\").attr(\"d\",L.showline?\"M\"+p+\",\"+(x+y)+\"l\"+_/2+\",-\"+y:\"M0,0\").call(_2.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),r.layers.bline.select(\"path\").attr(\"d\",w.showline?\"M\"+p+\",\"+(x+y)+\"h\"+_:\"M0,0\").call(_2.stroke,w.linecolor||\"#000\").style(\"stroke-width\",(w.linewidth||0)+\"px\"),r.layers.cline.select(\"path\").attr(\"d\",A.showline?\"M\"+(p+_/2)+\",\"+x+\"l\"+_/2+\",\"+y:\"M0,0\").call(_2.stroke,A.linecolor||\"#000\").style(\"stroke-width\",(A.linewidth||0)+\"px\"),r.graphDiv._context.staticPlot||r.initInteractions(),U7.setClipUrl(r.layers.frontplot,r._hasClipOnAxisFalse?null:r.clipId,r.graphDiv)};v1.drawAxes=function(e){var t=this,r=t.graphDiv,n=t.id.slice(7)+\"title\",a=t.layers,i=t.aaxis,o=t.baxis,s=t.caxis;if(t.drawAx(i),t.drawAx(o),t.drawAx(s),e){var l=Math.max(i.showticklabels?i.tickfont.size/2:0,(s.showticklabels?s.tickfont.size*.75:0)+(s.ticks===\"outside\"?s.ticklen*.87:0)),u=(o.showticklabels?o.tickfont.size:0)+(o.ticks===\"outside\"?o.ticklen:0)+3;a[\"a-title\"]=RG.draw(r,\"a\"+n,{propContainer:i,propName:t.id+\".aaxis.title.text\",placeholder:N7(r,\"Click to enter Component A title\"),attributes:{x:t.x0+t.w/2,y:t.y0-i.title.font.size/3-l,\"text-anchor\":\"middle\"}}),a[\"b-title\"]=RG.draw(r,\"b\"+n,{propContainer:o,propName:t.id+\".baxis.title.text\",placeholder:N7(r,\"Click to enter Component B title\"),attributes:{x:t.x0-u,y:t.y0+t.h+o.title.font.size*.83+u,\"text-anchor\":\"middle\"}}),a[\"c-title\"]=RG.draw(r,\"c\"+n,{propContainer:s,propName:t.id+\".caxis.title.text\",placeholder:N7(r,\"Click to enter Component C title\"),attributes:{x:t.x0+t.w+u,y:t.y0+t.h+s.title.font.size*.83+u,\"text-anchor\":\"middle\"}})}};v1.drawAx=function(e){var t=this,r=t.graphDiv,n=e._name,a=n.charAt(0),i=e._id,o=t.layers[n],s=30,l=a+\"tickLayout\",u=yEt(e);t[l]!==u&&(o.selectAll(\".\"+i+\"tick\").remove(),t[l]=u),e.setScale();var c=Wm.calcTicks(e),f=Wm.clipEnds(e,c),v=Wm.makeTransTickFn(e),g=Wm.getTickSigns(e)[2],p=vg.deg2rad(s),x=g*(e.linewidth||1)/2,_=g*e.ticklen,y=t.w,C=t.h,k=a===\"b\"?\"M0,\"+x+\"l\"+Math.sin(p)*_+\",\"+Math.cos(p)*_:\"M\"+x+\",0l\"+Math.cos(p)*_+\",\"+-Math.sin(p)*_,S={a:\"M0,0l\"+C+\",-\"+y/2,b:\"M0,0l-\"+y/2+\",-\"+C,c:\"M0,0l-\"+C+\",\"+y/2}[a];Wm.drawTicks(r,e,{vals:e.ticks===\"inside\"?f:c,layer:o,path:k,transFn:v,crisp:!1}),Wm.drawGrid(r,e,{vals:f,layer:t.layers[a+\"grid\"],path:S,transFn:v,crisp:!1}),Wm.drawLabels(r,e,{vals:c,layer:o,transFn:v,labelFns:Wm.makeLabelFns(e,0,s)})};function yEt(e){return e.ticks+String(e.ticklen)+String(e.showticklabels)}var Yc=V5e.MINZOOM/2+.87,xEt=\"m-0.87,.5h\"+Yc+\"v3h-\"+(Yc+5.2)+\"l\"+(Yc/2+2.6)+\",-\"+(Yc*.87+4.5)+\"l2.6,1.5l-\"+Yc/2+\",\"+Yc*.87+\"Z\",_Et=\"m0.87,.5h-\"+Yc+\"v3h\"+(Yc+5.2)+\"l-\"+(Yc/2+2.6)+\",-\"+(Yc*.87+4.5)+\"l-2.6,1.5l\"+Yc/2+\",\"+Yc*.87+\"Z\",bEt=\"m0,1l\"+Yc/2+\",\"+Yc*.87+\"l2.6,-1.5l-\"+(Yc/2+2.6)+\",-\"+(Yc*.87+4.5)+\"l-\"+(Yc/2+2.6)+\",\"+(Yc*.87+4.5)+\"l2.6,1.5l\"+Yc/2+\",-\"+Yc*.87+\"Z\",wEt=\"m0.5,0.5h5v-2h-5v-5h-2v5h-5v2h5v5h2Z\",B5e=!0;v1.clearOutline=function(){mEt(this.dragOptions),gEt(this.dragOptions.gd)};v1.initInteractions=function(){var e=this,t=e.layers.plotbg.select(\"path\").node(),r=e.graphDiv,n=r._fullLayout._zoomlayer,a,i;this.dragOptions={element:t,gd:r,plotinfo:{id:e.id,domain:r._fullLayout[e.id].domain,xaxis:e.xaxis,yaxis:e.yaxis},subplot:e.id,prepFn:function(E,I,R){e.dragOptions.xaxes=[e.xaxis],e.dragOptions.yaxes=[e.yaxis],a=r._fullLayout._invScaleX,i=r._fullLayout._invScaleY;var 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ae=[U,y.c2p(V[0].t)],pe=[U,y.c2p(V[j-1].t)];p.orientation===\"h\"&&(ae.reverse(),pe.reverse()),S?xe=\"M\"+ae+\"L\"+ce.slice(1)+\"L\"+pe:xe=\"M\"+pe+\"L\"+ye.slice(1)+\"L\"+ae}O.attr(\"d\",xe),B.posCenterPx=U,B.posDensityScale=J*_,B.path=O.node(),B.pathLength=B.path.getTotalLength()/(k?2:1)});var A=p.box,T=A.width,m=(A.line||{}).width,F,E;k?(F=_*T,E=0):S?(F=[0,_*T/2],E=m*{x:1,y:-1}[g.posLetter]):(F=[_*T/2,0],E=m*{x:-1,y:1}[g.posLetter]),WG.plotBoxAndWhiskers(f,{pos:C,val:y},p,{bPos:x,bdPos:F,bPosPxOffset:E}),WG.plotBoxMean(f,{pos:C,val:y},p,{bPos:x,bdPos:F,bPosPxOffset:E});var I;!p.box.visible&&p.meanline.visible&&(I=jG.identity);var R=f.selectAll(\"path.meanline\").data(I||[]);R.enter().append(\"path\").attr(\"class\",\"meanline\").style(\"fill\",\"none\").style(\"vector-effect\",i?\"none\":\"non-scaling-stroke\"),R.exit().remove(),R.each(function(B){var O=y.c2p(B.mean,!0),V=ikt.getPositionOnKdePath(B,p,O);HG.select(this).attr(\"d\",p.orientation===\"h\"?\"M\"+O+\",\"+V[0]+\"V\"+V[1]:\"M\"+V[0]+\",\"+O+\"H\"+V[1])}),WG.plotPoints(f,{x:s,y:l},p,g)})}});var T4e=fe((Gmr,w4e)=>{\"use strict\";var b4e=xi(),J3=ni(),akt=Ld().stylePoints;w4e.exports=function(t){var r=b4e.select(t).selectAll(\"g.trace.violins\");r.style(\"opacity\",function(n){return n[0].trace.opacity}),r.each(function(n){var a=n[0].trace,i=b4e.select(this),o=a.box||{},s=o.line||{},l=a.meanline||{},u=l.width;i.selectAll(\"path.violin\").style(\"stroke-width\",a.line.width+\"px\").call(J3.stroke,a.line.color).call(J3.fill,a.fillcolor),i.selectAll(\"path.box\").style(\"stroke-width\",s.width+\"px\").call(J3.stroke,s.color).call(J3.fill,o.fillcolor);var c={\"stroke-width\":u+\"px\",\"stroke-dasharray\":2*u+\"px,\"+u+\"px\"};i.selectAll(\"path.mean\").style(c).call(J3.stroke,l.color),i.selectAll(\"path.meanline\").style(c).call(J3.stroke,l.color),akt(i,a,t)})}});var E4e=fe((Hmr,S4e)=>{\"use strict\";var okt=ni(),YG=Xt(),skt=Yi(),A4e=OV(),M4e=H7();S4e.exports=function(t,r,n,a,i){i||(i={});var o=i.hoverLayer,s=t.cd,l=s[0].trace,u=l.hoveron,c=u.indexOf(\"violins\")!==-1,f=u.indexOf(\"kde\")!==-1,v=[],g,p;if(c||f){var x=A4e.hoverOnBoxes(t,r,n,a);if(f&&x.length>0){var _=t.xa,y=t.ya,C,k,S,L,w;l.orientation===\"h\"?(w=r,C=\"y\",S=y,k=\"x\",L=_):(w=n,C=\"x\",S=_,k=\"y\",L=y);var A=s[t.index];if(w>=A.span[0]&&w<=A.span[1]){var T=YG.extendFlat({},t),m=L.c2p(w,!0),F=M4e.getKdeValue(A,l,w),E=M4e.getPositionOnKdePath(A,l,m),I=S._offset,R=S._length;T[C+\"0\"]=E[0],T[C+\"1\"]=E[1],T[k+\"0\"]=T[k+\"1\"]=m,T[k+\"Label\"]=k+\": \"+skt.hoverLabelText(L,w,l[k+\"hoverformat\"])+\", \"+s[0].t.labels.kde+\" \"+F.toFixed(3);for(var B=0,O=0;O<x.length;O++)if(x[O].attr===\"med\"){B=O;break}T.spikeDistance=x[B].spikeDistance;var V=C+\"Spike\";T[V]=x[B][V],x[B].spikeDistance=void 0,x[B][V]=void 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s=a(\"textposition\");vkt(e,t,n,a,s,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!0,moduleHasCliponaxis:!0,moduleHasTextangle:!0,moduleHasInsideanchor:!0}),t.textposition!==\"none\"&&!t.texttemplate&&a(\"textinfo\",j7.isArrayOrTypedArray(o)?\"text+value\":\"value\");var l=a(\"marker.color\",r);a(\"marker.line.color\",KG.defaultLine),a(\"marker.line.width\");var u=a(\"connector.visible\");if(u){a(\"connector.fillcolor\",ykt(l));var c=a(\"connector.line.width\");c&&(a(\"connector.line.color\"),a(\"connector.line.dash\"))}a(\"zorder\")}function ykt(e){var t=j7.isArrayOrTypedArray(e)?\"#000\":e;return KG.addOpacity(t,.5*KG.opacity(t))}function xkt(e,t){var r,n;function a(o){return j7.coerce(n._input,n,O4e,o)}for(var i=0;i<e.length;i++)n=e[i],n.type===\"funnel\"&&(r=n._input,dkt(r,n,t,a,t.funnelmode))}N4e.exports={supplyDefaults:mkt,crossTraceDefaults:xkt}});var V4e=fe((Kmr,U4e)=>{\"use strict\";var _kt=Xt(),bkt=$G();U4e.exports=function(e,t,r){var n=!1;function 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n=j4e.getFromId(t,r.xaxis||\"x\"),a=j4e.getFromId(t,r.yaxis||\"y\"),i,o,s,l,u,c,f,v;r.orientation===\"h\"?(i=n.makeCalcdata(r,\"x\"),s=a.makeCalcdata(r,\"y\"),l=W4e(r,a,\"y\",s),u=!!r.yperiodalignment,c=\"y\"):(i=a.makeCalcdata(r,\"y\"),s=n.makeCalcdata(r,\"x\"),l=W4e(r,n,\"x\",s),u=!!r.xperiodalignment,c=\"x\"),o=l.vals;var g=Math.min(o.length,i.length),p=new Array(g);for(r._base=[],f=0;f<g;f++){i[f]<0&&(i[f]=XA);var x=!1;i[f]!==XA&&f+1<g&&i[f+1]!==XA&&(x=!0),v=p[f]={p:o[f],s:i[f],cNext:x},r._base[f]=-.5*v.s,u&&(p[f].orig_p=s[f],p[f][c+\"End\"]=l.ends[f],p[f][c+\"Start\"]=l.starts[f]),r.ids&&(v.id=String(r.ids[f])),f===0&&(p[0].vTotal=0),p[0].vTotal+=QG(v.s),v.begR=QG(v.s)/QG(p[0].s)}var _;for(f=0;f<g;f++)v=p[f],v.s!==XA&&(v.sumR=v.s/p[0].vTotal,v.difR=_!==void 0?v.s/_:1,_=v.s);return wkt(p,r),Tkt(p,r),p};function QG(e){return e===XA?0:e}});var K4e=fe((eyr,$4e)=>{\"use strict\";var Z4e=i2().setGroupPositions;$4e.exports=function(t,r){var n=t._fullLayout,a=t._fullData,i=t.calcdata,o=r.xaxis,s=r.yaxis,l=[],u=[],c=[],f,v;for(v=0;v<a.length;v++){var g=a[v],p=g.orientation===\"h\";g.visible===!0&&g.xaxis===o._id&&g.yaxis===s._id&&g.type===\"funnel\"&&(f=i[v],p?c.push(f):u.push(f),l.push(f))}var x={mode:n.funnelmode,norm:n.funnelnorm,gap:n.funnelgap,groupgap:n.funnelgroupgap};for(Z4e(t,o,s,u,x),Z4e(t,s,o,c,x),v=0;v<l.length;v++){f=l[v];for(var _=0;_<f.length;_++)_+1<f.length&&(f[_].nextP0=f[_+1].p0,f[_].nextS0=f[_+1].s0,f[_].nextP1=f[_+1].p1,f[_].nextS1=f[_+1].s1)}}});var tTe=fe((tyr,eTe)=>{\"use strict\";var W7=xi(),Xm=Xt(),J4e=ea(),Ym=Wa().BADNUM,Akt=m2(),Mkt=kh().clearMinTextSize;eTe.exports=function(t,r,n,a){var i=t._fullLayout;Mkt(\"funnel\",i),Skt(t,r,n,a),Ekt(t,r,n,a),Akt.plot(t,r,n,a,{mode:i.funnelmode,norm:i.funnelmode,gap:i.funnelgap,groupgap:i.funnelgroupgap})};function Skt(e,t,r,n){var a=t.xaxis,i=t.yaxis;Xm.makeTraceGroups(n,r,\"trace bars\").each(function(o){var s=W7.select(this),l=o[0].trace,u=Xm.ensureSingle(s,\"g\",\"regions\");if(!l.connector||!l.connector.visible){u.remove();return}var c=l.orientation===\"h\",f=u.selectAll(\"g.region\").data(Xm.identity);f.enter().append(\"g\").classed(\"region\",!0),f.exit().remove();var v=f.size();f.each(function(g,p){if(!(p!==v-1&&!g.cNext)){var x=Q4e(g,a,i,c),_=x[0],y=x[1],C=\"\";_[0]!==Ym&&y[0]!==Ym&&_[1]!==Ym&&y[1]!==Ym&&_[2]!==Ym&&y[2]!==Ym&&_[3]!==Ym&&y[3]!==Ym&&(c?C+=\"M\"+_[0]+\",\"+y[1]+\"L\"+_[2]+\",\"+y[2]+\"H\"+_[3]+\"L\"+_[1]+\",\"+y[1]+\"Z\":C+=\"M\"+_[1]+\",\"+y[1]+\"L\"+_[2]+\",\"+y[3]+\"V\"+y[2]+\"L\"+_[1]+\",\"+y[0]+\"Z\"),C===\"\"&&(C=\"M0,0Z\"),Xm.ensureSingle(W7.select(this),\"path\").attr(\"d\",C).call(J4e.setClipUrl,t.layerClipId,e)}})})}function Ekt(e,t,r,n){var a=t.xaxis,i=t.yaxis;Xm.makeTraceGroups(n,r,\"trace bars\").each(function(o){var s=W7.select(this),l=o[0].trace,u=Xm.ensureSingle(s,\"g\",\"lines\");if(!l.connector||!l.connector.visible||!l.connector.line.width){u.remove();return}var c=l.orientation===\"h\",f=u.selectAll(\"g.line\").data(Xm.identity);f.enter().append(\"g\").classed(\"line\",!0),f.exit().remove();var v=f.size();f.each(function(g,p){if(!(p!==v-1&&!g.cNext)){var x=Q4e(g,a,i,c),_=x[0],y=x[1],C=\"\";_[3]!==void 0&&y[3]!==void 0&&(c?(C+=\"M\"+_[0]+\",\"+y[1]+\"L\"+_[2]+\",\"+y[2],C+=\"M\"+_[1]+\",\"+y[1]+\"L\"+_[3]+\",\"+y[2]):(C+=\"M\"+_[1]+\",\"+y[1]+\"L\"+_[2]+\",\"+y[3],C+=\"M\"+_[1]+\",\"+y[0]+\"L\"+_[2]+\",\"+y[2])),C===\"\"&&(C=\"M0,0Z\"),Xm.ensureSingle(W7.select(this),\"path\").attr(\"d\",C).call(J4e.setClipUrl,t.layerClipId,e)}})})}function Q4e(e,t,r,n){var a=[],i=[],o=n?t:r,s=n?r:t;return a[0]=o.c2p(e.s0,!0),i[0]=s.c2p(e.p0,!0),a[1]=o.c2p(e.s1,!0),i[1]=s.c2p(e.p1,!0),a[2]=o.c2p(e.nextS0,!0),i[2]=s.c2p(e.nextP0,!0),a[3]=o.c2p(e.nextS1,!0),i[3]=s.c2p(e.nextP1,!0),n?[a,i]:[i,a]}});var iTe=fe((ryr,nTe)=>{\"use strict\";var ZA=xi(),rTe=ea(),eH=ni(),kkt=Y1().DESELECTDIM,Ckt=_v(),Lkt=kh().resizeText,Pkt=Ckt.styleTextPoints;function Fkt(e,t,r){var n=r||ZA.select(e).selectAll('g[class^=\"funnellayer\"]').selectAll(\"g.trace\");Lkt(e,n,\"funnel\"),n.style(\"opacity\",function(a){return a[0].trace.opacity}),n.each(function(a){var i=ZA.select(this),o=a[0].trace;i.selectAll(\".point > path\").each(function(s){if(!s.isBlank){var l=o.marker;ZA.select(this).call(eH.fill,s.mc||l.color).call(eH.stroke,s.mlc||l.line.color).call(rTe.dashLine,l.line.dash,s.mlw||l.line.width).style(\"opacity\",o.selectedpoints&&!s.selected?kkt:1)}}),Pkt(i,o,e),i.selectAll(\".regions\").each(function(){ZA.select(this).selectAll(\"path\").style(\"stroke-width\",0).call(eH.fill,o.connector.fillcolor)}),i.selectAll(\".lines\").each(function(){var s=o.connector.line;rTe.lineGroupStyle(ZA.select(this).selectAll(\"path\"),s.width,s.color,s.dash)})})}nTe.exports={style:Fkt}});var sTe=fe((nyr,oTe)=>{\"use 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qs=u1(),rH=Ol().line,Ikt=Ho(),gTe=pl().axisHoverFormat,{hovertemplateAttrs:zkt,texttemplateAttrs:qkt,templatefallbackAttrs:mTe}=Oo(),yTe=pTe(),ew=ta().extendFlat,Bkt=ni();function nH(e){return{marker:{color:ew({},qs.marker.color,{arrayOk:!1,editType:\"style\"}),line:{color:ew({},qs.marker.line.color,{arrayOk:!1,editType:\"style\"}),width:ew({},qs.marker.line.width,{arrayOk:!1,editType:\"style\"}),editType:\"style\"},editType:\"style\"},editType:\"style\"}}xTe.exports={measure:{valType:\"data_array\",dflt:[],editType:\"calc\"},base:{valType:\"number\",dflt:null,arrayOk:!1,editType:\"calc\"},x:qs.x,x0:qs.x0,dx:qs.dx,y:qs.y,y0:qs.y0,dy:qs.dy,xperiod:qs.xperiod,yperiod:qs.yperiod,xperiod0:qs.xperiod0,yperiod0:qs.yperiod0,xperiodalignment:qs.xperiodalignment,yperiodalignment:qs.yperiodalignment,xhoverformat:gTe(\"x\"),yhoverformat:gTe(\"y\"),hovertext:qs.hovertext,hovertemplate:zkt({},{keys:yTe.eventDataKeys}),hovertemplatefallback:mTe(),hoverinfo:ew({},Ikt.hoverinfo,{flags:[\"name\",\"x\",\"y\",\"text\",\"initial\",\"delta\",\"final\"]}),textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"initial\",\"delta\",\"final\"],extras:[\"none\"],editType:\"plot\",arrayOk:!1},texttemplate:qkt({editType:\"plot\"},{keys:yTe.eventDataKeys.concat([\"label\"])}),texttemplatefallback:mTe({editType:\"plot\"}),text:qs.text,textposition:qs.textposition,insidetextanchor:qs.insidetextanchor,textangle:qs.textangle,textfont:qs.textfont,insidetextfont:qs.insidetextfont,outsidetextfont:qs.outsidetextfont,constraintext:qs.constraintext,cliponaxis:qs.cliponaxis,orientation:qs.orientation,offset:qs.offset,width:qs.width,increasing:nH(\"increasing\"),decreasing:nH(\"decreasing\"),totals:nH(\"intermediate 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s=a(\"connector.visible\");if(s){a(\"connector.mode\");var l=a(\"connector.line.width\");l&&(a(\"connector.line.color\"),a(\"connector.line.dash\"))}a(\"zorder\")}function Xkt(e,t){var r,n;function a(o){return wTe.coerce(n._input,n,TTe,o)}if(t.waterfallmode===\"group\")for(var i=0;i<e.length;i++)n=e[i],r=n._input,Okt(r,n,t,a,t.waterfallmode)}MTe.exports={supplyDefaults:Ykt,crossTraceDefaults:Xkt}});var ETe=fe((hyr,STe)=>{\"use strict\";var Zkt=Xt(),$kt=aH();STe.exports=function(e,t,r){var n=!1;function a(s,l){return Zkt.coerce(e,t,$kt,s,l)}for(var i=0;i<r.length;i++){var o=r[i];if(o.visible&&o.type===\"waterfall\"){n=!0;break}}n&&(a(\"waterfallmode\"),a(\"waterfallgap\",.2),a(\"waterfallgroupgap\"))}});var DTe=fe((dyr,FTe)=>{\"use strict\";var kTe=Yi(),CTe=cp(),LTe=Xt().mergeArray,Kkt=P0(),PTe=Wa().BADNUM;function lH(e){return e===\"a\"||e===\"absolute\"}function uH(e){return e===\"t\"||e===\"total\"}FTe.exports=function(t,r){var n=kTe.getFromId(t,r.xaxis||\"x\"),a=kTe.getFromId(t,r.yaxis||\"y\"),i,o,s,l,u,c;r.orientation===\"h\"?(i=n.makeCalcdata(r,\"x\"),s=a.makeCalcdata(r,\"y\"),l=CTe(r,a,\"y\",s),u=!!r.yperiodalignment,c=\"y\"):(i=a.makeCalcdata(r,\"y\"),s=n.makeCalcdata(r,\"x\"),l=CTe(r,n,\"x\",s),u=!!r.xperiodalignment,c=\"x\"),o=l.vals;for(var f=Math.min(o.length,i.length),v=new Array(f),g=0,p,x=!1,_=0;_<f;_++){var y=i[_]||0,C=!1;(i[_]!==PTe||uH(r.measure[_])||lH(r.measure[_]))&&_+1<f&&(i[_+1]!==PTe||uH(r.measure[_+1])||lH(r.measure[_+1]))&&(C=!0);var k=v[_]={i:_,p:o[_],s:y,rawS:y,cNext:C};lH(r.measure[_])?(g=k.s,k.isSum=!0,k.dir=\"totals\",k.s=g):uH(r.measure[_])?(k.isSum=!0,k.dir=\"totals\",k.s=g):(k.isSum=!1,k.dir=k.rawS<0?\"decreasing\":\"increasing\",p=k.s,k.s=g+p,g+=p),k.dir===\"totals\"&&(x=!0),u&&(v[_].orig_p=s[_],v[_][c+\"End\"]=l.ends[_],v[_][c+\"Start\"]=l.starts[_]),r.ids&&(k.id=String(r.ids[_])),k.v=(r.base||0)+g}return v.length&&(v[0].hasTotals=x),LTe(r.text,v,\"tx\"),LTe(r.hovertext,v,\"htx\"),Kkt(v,r),v}});var zTe=fe((vyr,ITe)=>{\"use strict\";var RTe=i2().setGroupPositions;ITe.exports=function(t,r){var n=t._fullLayout,a=t._fullData,i=t.calcdata,o=r.xaxis,s=r.yaxis,l=[],u=[],c=[],f,v;for(v=0;v<a.length;v++){var g=a[v];g.visible===!0&&g.xaxis===o._id&&g.yaxis===s._id&&g.type===\"waterfall\"&&(f=i[v],g.orientation===\"h\"?c.push(f):u.push(f),l.push(f))}var p={mode:n.waterfallmode,norm:n.waterfallnorm,gap:n.waterfallgap,groupgap:n.waterfallgroupgap};for(RTe(t,o,s,u,p),RTe(t,s,o,c,p),v=0;v<l.length;v++){f=l[v];for(var x=0;x<f.length;x++){var _=f[x];_.isSum===!1&&(_.s0+=x===0?0:f[x-1].s),x+1<f.length&&(f[x].nextP0=f[x+1].p0,f[x].nextS0=f[x+1].s0)}}}});var OTe=fe((pyr,BTe)=>{\"use strict\";var qTe=xi(),Y7=Xt(),Jkt=ea(),rw=Wa().BADNUM,Qkt=m2(),e8t=kh().clearMinTextSize;BTe.exports=function(t,r,n,a){var i=t._fullLayout;e8t(\"waterfall\",i),Qkt.plot(t,r,n,a,{mode:i.waterfallmode,norm:i.waterfallmode,gap:i.waterfallgap,groupgap:i.waterfallgroupgap}),t8t(t,r,n,a)};function t8t(e,t,r,n){var a=t.xaxis,i=t.yaxis;Y7.makeTraceGroups(n,r,\"trace bars\").each(function(o){var s=qTe.select(this),l=o[0].trace,u=Y7.ensureSingle(s,\"g\",\"lines\");if(!l.connector||!l.connector.visible){u.remove();return}var c=l.orientation===\"h\",f=l.connector.mode,v=u.selectAll(\"g.line\").data(Y7.identity);v.enter().append(\"g\").classed(\"line\",!0),v.exit().remove();var g=v.size();v.each(function(p,x){if(!(x!==g-1&&!p.cNext)){var 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n=r||X7.select(e).selectAll('g[class^=\"waterfalllayer\"]').selectAll(\"g.trace\");a8t(e,n,\"waterfall\"),n.style(\"opacity\",function(a){return a[0].trace.opacity}),n.each(function(a){var i=X7.select(this),o=a[0].trace;i.selectAll(\".point > path\").each(function(s){if(!s.isBlank){var l=o[s.dir].marker;X7.select(this).call(UTe.fill,l.color).call(UTe.stroke,l.line.color).call(NTe.dashLine,l.line.dash,l.line.width).style(\"opacity\",o.selectedpoints&&!s.selected?n8t:1)}}),o8t(i,o,e),i.selectAll(\".lines\").each(function(){var s=o.connector.line;NTe.lineGroupStyle(X7.select(this).selectAll(\"path\"),s.width,s.color,s.dash)})})}VTe.exports={style:s8t}});var XTe=fe((myr,YTe)=>{\"use strict\";var l8t=Yi().hoverLabelText,HTe=ni().opacity,u8t=I3().hoverOnBars,jTe=tw(),WTe={increasing:jTe.INCREASING.SYMBOL,decreasing:jTe.DECREASING.SYMBOL};YTe.exports=function(t,r,n,a,i){var o=u8t(t,r,n,a,i);if(!o)return;var s=o.cd,l=s[0].trace,u=l.orientation===\"h\",c=u?\"x\":\"y\",f=u?t.xa:t.ya;function v(w){return l8t(f,w,l[c+\"hoverformat\"])}var g=o.index,p=s[g],x=p.isSum?p.b+p.s:p.rawS;o.initial=p.b+p.s-x,o.delta=x,o.final=o.initial+o.delta;var _=v(Math.abs(o.delta));o.deltaLabel=x<0?\"(\"+_+\")\":_,o.finalLabel=v(o.final),o.initialLabel=v(o.initial);var y=p.hi||l.hoverinfo,C=[];if(y&&y!==\"none\"&&y!==\"skip\"){var k=y===\"all\",S=y.split(\"+\"),L=function(w){return k||S.indexOf(w)!==-1};p.isSum||(L(\"final\")&&(u?!L(\"x\"):!L(\"y\"))&&C.push(o.finalLabel),L(\"delta\")&&(x<0?C.push(o.deltaLabel+\" \"+WTe.decreasing):C.push(o.deltaLabel+\" \"+WTe.increasing)),L(\"initial\")&&C.push(\"Initial: \"+o.initialLabel))}return C.length&&(o.extraText=C.join(\"<br>\")),o.color=c8t(l,p),[o]};function c8t(e,t){var r=e[t.dir].marker,n=r.color,a=r.line.color,i=r.line.width;if(HTe(n))return n;if(HTe(a)&&i)return a}});var $Te=fe((yyr,ZTe)=>{\"use strict\";ZTe.exports=function(t,r){return t.x=\"xVal\"in r?r.xVal:r.x,t.y=\"yVal\"in r?r.yVal:r.y,\"initial\"in r&&(t.initial=r.initial),\"delta\"in r&&(t.delta=r.delta),\"final\"in r&&(t.final=r.final),r.xa&&(t.xaxis=r.xa),r.ya&&(t.yaxis=r.ya),t}});var JTe=fe((xyr,KTe)=>{\"use strict\";KTe.exports={attributes:iH(),layoutAttributes:aH(),supplyDefaults:sH().supplyDefaults,crossTraceDefaults:sH().crossTraceDefaults,supplyLayoutDefaults:ETe(),calc:DTe(),crossTraceCalc:zTe(),plot:OTe(),style:GTe().style,hoverPoints:XTe(),eventData:$Te(),selectPoints:z3(),moduleType:\"trace\",name:\"waterfall\",basePlotModule:gu(),categories:[\"bar-like\",\"cartesian\",\"svg\",\"oriented\",\"showLegend\",\"zoomScale\"],meta:{}}});var eAe=fe((_yr,QTe)=>{\"use strict\";QTe.exports=JTe()});var nw=fe((byr,tAe)=>{\"use strict\";tAe.exports={colormodel:{rgb:{min:[0,0,0],max:[255,255,255],fmt:function(e){return e.slice(0,3)},suffix:[\"\",\"\",\"\"]},rgba:{min:[0,0,0,0],max:[255,255,255,1],fmt:function(e){return e.slice(0,4)},suffix:[\"\",\"\",\"\",\"\"]},rgba256:{colormodel:\"rgba\",zminDflt:[0,0,0,0],zmaxDflt:[255,255,255,255],min:[0,0,0,0],max:[255,255,255,1],fmt:function(e){return e.slice(0,4)},suffix:[\"\",\"\",\"\",\"\"]},hsl:{min:[0,0,0],max:[360,100,100],fmt:function(e){var t=e.slice(0,3);return t[1]=t[1]+\"%\",t[2]=t[2]+\"%\",t},suffix:[\"\\xB0\",\"%\",\"%\"]},hsla:{min:[0,0,0,0],max:[360,100,100,1],fmt:function(e){var t=e.slice(0,4);return t[1]=t[1]+\"%\",t[2]=t[2]+\"%\",t},suffix:[\"\\xB0\",\"%\",\"%\",\"\"]}}}});var cH={};zp(cH,{default:()=>h8t});var rAe,nAe,Z7,iAe,aAe,oAe,sAe,lAe,f8t,h8t,fH=Co(()=>{\"use strict\";rAe=$0(ta()),nAe=$0(Ho()),Z7=$0(Oo()),iAe=$0(Ol()),aAe=$0(nw()),oAe=[\"rgb\",\"rgba\",\"rgba256\",\"hsl\",\"hsla\"],sAe=[],lAe=[];for(let e of oAe){let t=aAe.colormodel[e];sAe.push(`For the \\`${e}\\` colormodel, it is [${(t.zminDflt||t.min).join(\", \")}].`),lAe.push(`For the \\`${e}\\` colormodel, it is [${(t.zmaxDflt||t.max).join(\", 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*(milliseconds?|msecs?|ms|seconds?|secs?|s|minutes?|mins?|m|hours?|hrs?|h|days?|d|years?|yrs?|y)?$/i.exec(e);if(t){var r=parseFloat(t[1]),n=(t[2]||\"ms\").toLowerCase();switch(n){case\"years\":case\"year\":case\"yrs\":case\"yr\":case\"y\":return r*fkt;case\"days\":case\"day\":case\"d\":return r*FE;case\"hours\":case\"hour\":case\"hrs\":case\"hr\":case\"h\":return r*DE;case\"minutes\":case\"minute\":case\"mins\":case\"min\":case\"m\":return r*RE;case\"seconds\":case\"second\":case\"secs\":case\"sec\":case\"s\":return r*IE;case\"milliseconds\":case\"millisecond\":case\"msecs\":case\"msec\":case\"ms\":return r;default:return}}}}function dkt(e){return e>=FE?Math.round(e/FE)+\"d\":e>=DE?Math.round(e/DE)+\"h\":e>=RE?Math.round(e/RE)+\"m\":e>=IE?Math.round(e/IE)+\"s\":e+\"ms\"}function vkt(e){return fD(e,FE,\"day\")||fD(e,DE,\"hour\")||fD(e,RE,\"minute\")||fD(e,IE,\"second\")||e+\" ms\"}function fD(e,t,r){if(!(e<t))return e<t*1.5?Math.floor(e/t)+\" \"+r:Math.ceil(e/t)+\" \"+r+\"s\"}});var V4e=ye((Lc,U4e)=>{Lc=U4e.exports=pW.debug=pW.default=pW;Lc.coerce=_kt;Lc.disable=mkt;Lc.enable=gkt;Lc.enabled=ykt;Lc.humanize=N4e();Lc.names=[];Lc.skips=[];Lc.formatters={};var vW;function pkt(e){var t=0,r;for(r in e)t=(t<<5)-t+e.charCodeAt(r),t|=0;return Lc.colors[Math.abs(t)%Lc.colors.length]}function pW(e){function t(){if(t.enabled){var r=t,n=+new Date,i=n-(vW||n);r.diff=i,r.prev=vW,r.curr=n,vW=n;for(var a=new Array(arguments.length),o=0;o<a.length;o++)a[o]=arguments[o];a[0]=Lc.coerce(a[0]),typeof a[0]!=\"string\"&&a.unshift(\"%O\");var s=0;a[0]=a[0].replace(/%([a-zA-Z%])/g,function(u,c){if(u===\"%%\")return u;s++;var f=Lc.formatters[c];if(typeof f==\"function\"){var h=a[s];u=f.call(r,h),a.splice(s,1),s--}return u}),Lc.formatArgs.call(r,a);var l=t.log||Lc.log||console.log.bind(console);l.apply(r,a)}}return t.namespace=e,t.enabled=Lc.enabled(e),t.useColors=Lc.useColors(),t.color=pkt(e),typeof Lc.init==\"function\"&&Lc.init(t),t}function gkt(e){Lc.save(e),Lc.names=[],Lc.skips=[];for(var t=(typeof e==\"string\"?e:\"\").split(/[\\s,]+/),r=t.length,n=0;n<r;n++)t[n]&&(e=t[n].replace(/\\*/g,\".*?\"),e[0]===\"-\"?Lc.skips.push(new RegExp(\"^\"+e.substr(1)+\"$\")):Lc.names.push(new RegExp(\"^\"+e+\"$\")))}function mkt(){Lc.enable(\"\")}function ykt(e){var t,r;for(t=0,r=Lc.skips.length;t<r;t++)if(Lc.skips[t].test(e))return!1;for(t=0,r=Lc.names.length;t<r;t++)if(Lc.names[t].test(e))return!0;return!1}function _kt(e){return e instanceof Error?e.stack||e.message:e}});var j4e=ye((up,H4e)=>{up=H4e.exports=V4e();up.log=wkt;up.formatArgs=bkt;up.save=Tkt;up.load=G4e;up.useColors=xkt;up.storage=typeof chrome!=\"undefined\"&&typeof chrome.storage!=\"undefined\"?chrome.storage.local:Akt();up.colors=[\"lightseagreen\",\"forestgreen\",\"goldenrod\",\"dodgerblue\",\"darkorchid\",\"crimson\"];function xkt(){return typeof window!=\"undefined\"&&window.process&&window.process.type===\"renderer\"?!0:typeof document!=\"undefined\"&&document.documentElement&&document.documentElement.style&&document.documentElement.style.WebkitAppearance||typeof window!=\"undefined\"&&window.console&&(window.console.firebug||window.console.exception&&window.console.table)||typeof navigator!=\"undefined\"&&navigator.userAgent&&navigator.userAgent.toLowerCase().match(/firefox\\/(\\d+)/)&&parseInt(RegExp.$1,10)>=31||typeof navigator!=\"undefined\"&&navigator.userAgent&&navigator.userAgent.toLowerCase().match(/applewebkit\\/(\\d+)/)}up.formatters.j=function(e){try{return JSON.stringify(e)}catch(t){return\"[UnexpectedJSONParseError]: \"+t.message}};function bkt(e){var t=this.useColors;if(e[0]=(t?\"%c\":\"\")+this.namespace+(t?\" %c\":\" \")+e[0]+(t?\"%c \":\" \")+\"+\"+up.humanize(this.diff),!!t){var r=\"color: \"+this.color;e.splice(1,0,r,\"color: inherit\");var n=0,i=0;e[0].replace(/%[a-zA-Z%]/g,function(a){a!==\"%%\"&&(n++,a===\"%c\"&&(i=n))}),e.splice(i,0,r)}}function wkt(){return typeof console==\"object\"&&console.log&&Function.prototype.apply.call(console.log,console,arguments)}function Tkt(e){try{e==null?up.storage.removeItem(\"debug\"):up.storage.debug=e}catch(t){}}function G4e(){var e;try{e=up.storage.debug}catch(t){}return!e&&typeof process!=\"undefined\"&&\"env\"in process&&(e=process.env.DEBUG),e}up.enable(G4e());function Akt(){try{return window.localStorage}catch(e){}}});var Q4e=ye((avr,$4e)=>{var kA=_E(),$_=j4e()(\"stream-parser\");$4e.exports=Mkt;var X4e=-1,hD=0,Skt=1,Z4e=2;function Mkt(e){var t=e&&typeof e._transform==\"function\",r=e&&typeof e._write==\"function\";if(!t&&!r)throw new Error(\"must pass a Writable or Transform stream in\");$_(\"extending Parser into stream\"),e._bytes=Ekt,e._skipBytes=kkt,t&&(e._passthrough=Ckt),t?e._transform=Pkt:e._write=Lkt}function zE(e){$_(\"initializing parser stream\"),e._parserBytesLeft=0,e._parserBuffers=[],e._parserBuffered=0,e._parserState=X4e,e._parserCallback=null,typeof e.push==\"function\"&&(e._parserOutput=e.push.bind(e)),e._parserInit=!0}function Ekt(e,t){kA(!this._parserCallback,'there is already a \"callback\" set!'),kA(isFinite(e)&&e>0,'can only buffer a finite number of bytes > 0, got \"'+e+'\"'),this._parserInit||zE(this),$_(\"buffering %o bytes\",e),this._parserBytesLeft=e,this._parserCallback=t,this._parserState=hD}function kkt(e,t){kA(!this._parserCallback,'there is already a \"callback\" set!'),kA(e>0,'can only skip > 0 bytes, got \"'+e+'\"'),this._parserInit||zE(this),$_(\"skipping %o bytes\",e),this._parserBytesLeft=e,this._parserCallback=t,this._parserState=Skt}function Ckt(e,t){kA(!this._parserCallback,'There is already a \"callback\" set!'),kA(e>0,'can only pass through > 0 bytes, got \"'+e+'\"'),this._parserInit||zE(this),$_(\"passing through %o bytes\",e),this._parserBytesLeft=e,this._parserCallback=t,this._parserState=Z4e}function Lkt(e,t,r){this._parserInit||zE(this),$_(\"write(%o bytes)\",e.length),typeof t==\"function\"&&(r=t),K4e(this,e,null,r)}function Pkt(e,t,r){this._parserInit||zE(this),$_(\"transform(%o bytes)\",e.length),typeof t!=\"function\"&&(t=this._parserOutput),K4e(this,e,t,r)}function Y4e(e,t,r,n){return e._parserBytesLeft<=0?n(new Error(\"got data but not currently parsing anything\")):t.length<=e._parserBytesLeft?function(){return W4e(e,t,r,n)}:function(){var i=t.slice(0,e._parserBytesLeft);return W4e(e,i,r,function(a){if(a)return n(a);if(t.length>i.length)return function(){return Y4e(e,t.slice(i.length),r,n)}})}}function W4e(e,t,r,n){if(e._parserBytesLeft-=t.length,$_(\"%o bytes left for stream piece\",e._parserBytesLeft),e._parserState===hD?(e._parserBuffers.push(t),e._parserBuffered+=t.length):e._parserState===Z4e&&r(t),e._parserBytesLeft===0){var i=e._parserCallback;if(i&&e._parserState===hD&&e._parserBuffers.length>1&&(t=Buffer.concat(e._parserBuffers,e._parserBuffered)),e._parserState!==hD&&(t=null),e._parserCallback=null,e._parserBuffered=0,e._parserState=X4e,e._parserBuffers.splice(0),i){var a=[];t&&a.push(t),r&&a.push(r);var o=i.length>a.length;o&&a.push(J4e(n));var s=i.apply(e,a);if(!o||n===s)return n}}else return n}var K4e=J4e(Y4e);function J4e(e){return function(){for(var t=e.apply(this,arguments);typeof t==\"function\";)t=t();return t}}});var rc=ye($y=>{\"use strict\";var eEe=WSe().Transform,Ikt=Q4e();function qE(){eEe.call(this,{readableObjectMode:!0})}qE.prototype=Object.create(eEe.prototype);qE.prototype.constructor=qE;Ikt(qE.prototype);$y.ParserStream=qE;$y.sliceEq=function(e,t,r){for(var n=t,i=0;i<r.length;)if(e[n++]!==r[i++])return!1;return!0};$y.str2arr=function(e,t){var r=[],n=0;if(t&&t===\"hex\")for(;n<e.length;)r.push(parseInt(e.slice(n,n+2),16)),n+=2;else for(;n<e.length;n++)r.push(e.charCodeAt(n)&255);return r};$y.readUInt16LE=function(e,t){return e[t]|e[t+1]<<8};$y.readUInt16BE=function(e,t){return e[t+1]|e[t]<<8};$y.readUInt32LE=function(e,t){return e[t]|e[t+1]<<8|e[t+2]<<16|e[t+3]*16777216};$y.readUInt32BE=function(e,t){return e[t+3]|e[t+2]<<8|e[t+1]<<16|e[t]*16777216};function dD(e,t,r){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=new Error().stack||\"\",this.name=this.constructor.name,this.message=e,t&&(this.code=t),r&&(this.statusCode=r)}dD.prototype=Object.create(Error.prototype);dD.prototype.constructor=dD;$y.ProbeError=dD});var tEe=ye((svr,vD)=>{\"use strict\";var CA=rc().readUInt16BE,mW=rc().readUInt32BE;function OE(e,t){if(e.length<4+t)return null;var r=mW(e,t);return e.length<r+t||r<8?null:{boxtype:String.fromCharCode.apply(null,e.slice(t+4,t+8)),data:e.slice(t+8,t+r),end:t+r}}vD.exports.unbox=OE;function Rkt(e,t){for(var r=0;;){var n=OE(e,r);if(!n)break;switch(n.boxtype){case\"ispe\":t.sizes.push({width:mW(n.data,4),height:mW(n.data,8)});break;case\"irot\":t.transforms.push({type:\"irot\",value:n.data[0]&3});break;case\"imir\":t.transforms.push({type:\"imir\",value:n.data[0]&1});break}r=n.end}}function gW(e,t,r){for(var n=0,i=0;i<r;i++)n=n*256+(e[t+i]||0);return n}function Dkt(e,t){for(var r=e[4]>>4&15,n=e[4]&15,i=e[5]>>4&15,a=CA(e,6),o=8,s=0;s<a;s++){var l=CA(e,o);o+=2;var u=CA(e,o);o+=2;var c=gW(e,o,i);o+=i;var f=CA(e,o);if(o+=2,u===0&&f===1){var h=gW(e,o,r),d=gW(e,o+r,n);t.item_loc[l]={length:d,offset:h+c}}o+=f*(r+n)}}function Fkt(e,t){for(var r=CA(e,4),n=6,i=0;i<r;i++){var a=OE(e,n);if(!a)break;if(a.boxtype===\"infe\"){for(var o=CA(a.data,4),s=\"\",l=8;l<a.data.length&&a.data[l];l++)s+=String.fromCharCode(a.data[l]);t.item_inf[s]=o}n=a.end}}function zkt(e,t){for(var r=0;;){var n=OE(e,r);if(!n)break;n.boxtype===\"ipco\"&&Rkt(n.data,t),r=n.end}}function qkt(e,t){for(var r=4;;){var n=OE(e,r);if(!n)break;n.boxtype===\"iprp\"&&zkt(n.data,t),n.boxtype===\"iloc\"&&Dkt(n.data,t),n.boxtype===\"iinf\"&&Fkt(n.data,t),r=n.end}}function Okt(e){var t=e.reduce(function(i,a){return i.width>a.width||i.width===a.width&&i.height>a.height?i:a}),r=e.reduce(function(i,a){return i.height>a.height||i.height===a.height&&i.width>a.width?i:a}),n;return t.width>r.height||t.width===r.height&&t.height>r.width?n=t:n=r,n}vD.exports.readSizeFromMeta=function(e){var t={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(qkt(e,t),!!t.sizes.length){var r=Okt(t.sizes),n=1;t.transforms.forEach(function(a){var o={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},s={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(a.type===\"imir\"&&(a.value===0?n=s[n]:(n=s[n],n=o[n],n=o[n])),a.type===\"irot\")for(var l=0;l<a.value;l++)n=o[n]});var i=null;return t.item_inf.Exif&&(i=t.item_loc[t.item_inf.Exif]),{width:r.width,height:r.height,orientation:t.transforms.length?n:null,variants:t.sizes,exif_location:i}}};vD.exports.getMimeType=function(e){var t=String.fromCharCode.apply(null,e.slice(0,4)),r={};r[t]=!0;for(var n=8;n<e.length;n+=4)r[String.fromCharCode.apply(null,e.slice(n,n+4))]=!0;if(!(!r.mif1&&!r.msf1&&!r.miaf))return t===\"avif\"||t===\"avis\"||t===\"avio\"?{type:\"avif\",mime:\"image/avif\"}:t===\"heic\"||t===\"heix\"?{type:\"heic\",mime:\"image/heic\"}:t===\"hevc\"||t===\"hevx\"?{type:\"heic\",mime:\"image/heic-sequence\"}:r.avif||r.avis?{type:\"avif\",mime:\"image/avif\"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:\"heif\",mime:\"image/heif-sequence\"}:{type:\"heif\",mime:\"image/heif\"}:{type:\"avif\",mime:\"image/avif\"}}});var gD=ye((lvr,yW)=>{\"use strict\";function pD(e,t){var r=new Error(e);return r.code=t,r}function Bkt(e){try{return decodeURIComponent(escape(e))}catch(t){return e}}function Qy(e,t,r){this.input=e.subarray(t,r),this.start=t;var n=String.fromCharCode.apply(null,this.input.subarray(0,4));if(n!==\"II*\\0\"&&n!==\"MM\\0*\")throw pD(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=n[0]===\"M\"}Qy.prototype.each=function(e){this.aborted=!1;var t=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:t}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,e)}};Qy.prototype.read_uint16=function(e){var t=this.input;if(e+2>t.length)throw pD(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?t[e]*256+t[e+1]:t[e]+t[e+1]*256};Qy.prototype.read_uint32=function(e){var t=this.input;if(e+4>t.length)throw pD(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?t[e]*16777216+t[e+1]*65536+t[e+2]*256+t[e+3]:t[e]+t[e+1]*256+t[e+2]*65536+t[e+3]*16777216};Qy.prototype.is_subifd_link=function(e,t){return e===0&&t===34665||e===0&&t===34853||e===34665&&t===40965};Qy.prototype.exif_format_length=function(e){switch(e){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}};Qy.prototype.exif_format_read=function(e,t){var r;switch(e){case 1:case 2:return r=this.input[t],r;case 6:return r=this.input[t],r|(r&128)*33554430;case 3:return r=this.read_uint16(t),r;case 8:return r=this.read_uint16(t),r|(r&32768)*131070;case 4:return r=this.read_uint32(t),r;case 9:return r=this.read_uint32(t),r|0;case 5:case 10:case 11:case 12:return null;case 7:return null;default:return null}};Qy.prototype.scan_ifd=function(e,t,r){var n=this.read_uint16(t);t+=2;for(var i=0;i<n;i++){var a=this.read_uint16(t),o=this.read_uint16(t+2),s=this.read_uint32(t+4),l=this.exif_format_length(o),u=s*l,c=u<=4?t+8:this.read_uint32(t+8),f=!1;if(c+u>this.input.length)throw pD(\"unexpected EOF\",\"EBADDATA\");for(var h=[],d=c,v=0;v<s;v++,d+=l){var m=this.exif_format_read(o,d);if(m===null){h=null;break}h.push(m)}Array.isArray(h)&&o===2&&(h=Bkt(String.fromCharCode.apply(null,h)),h&&h[h.length-1]===\"\\0\"&&(h=h.slice(0,-1))),this.is_subifd_link(e,a)&&Array.isArray(h)&&Number.isInteger(h[0])&&h[0]>0&&(this.ifds_to_read.push({id:a,offset:h[0]}),f=!0);var b={is_big_endian:this.big_endian,ifd:e,tag:a,format:o,count:s,entry_offset:t+this.start,data_length:u,data_offset:c+this.start,value:h,is_subifd_link:f};if(r(b)===!1){this.aborted=!0;return}t+=12}e===0&&this.ifds_to_read.push({id:1,offset:this.read_uint32(t)})};yW.exports.ExifParser=Qy;yW.exports.get_orientation=function(e){var t=0;try{return new Qy(e,0,e.length).each(function(r){if(r.ifd===0&&r.tag===274&&Array.isArray(r.value))return t=r.value[0],!1}),t}catch(r){return-1}}});var iEe=ye((uvr,rEe)=>{\"use strict\";var Nkt=rc().str2arr,Ukt=rc().sliceEq,Vkt=rc().readUInt32BE,mD=tEe(),Gkt=gD(),Hkt=Nkt(\"ftyp\");rEe.exports=function(e){if(Ukt(e,4,Hkt)){var t=mD.unbox(e,0);if(t){var r=mD.getMimeType(t.data);if(r){for(var n,i=t.end;;){var a=mD.unbox(e,i);if(!a)break;if(i=a.end,a.boxtype===\"mdat\")return;if(a.boxtype===\"meta\"){n=a.data;break}}if(n){var o=mD.readSizeFromMeta(n);if(o){var s={width:o.width,height:o.height,type:r.type,mime:r.mime,wUnits:\"px\",hUnits:\"px\"};if(o.variants.length>1&&(s.variants=o.variants),o.orientation&&(s.orientation=o.orientation),o.exif_location&&o.exif_location.offset+o.exif_location.length<=e.length){var l=Vkt(e,o.exif_location.offset),u=e.slice(o.exif_location.offset+l+4,o.exif_location.offset+o.exif_location.length),c=Gkt.get_orientation(u);c>0&&(s.orientation=c)}return s}}}}}}});var oEe=ye((cvr,aEe)=>{\"use strict\";var jkt=rc().str2arr,Wkt=rc().sliceEq,nEe=rc().readUInt16LE,Xkt=jkt(\"BM\");aEe.exports=function(e){if(!(e.length<26)&&Wkt(e,0,Xkt))return{width:nEe(e,18),height:nEe(e,22),type:\"bmp\",mime:\"image/bmp\",wUnits:\"px\",hUnits:\"px\"}}});var fEe=ye((fvr,cEe)=>{\"use strict\";var uEe=rc().str2arr,sEe=rc().sliceEq,lEe=rc().readUInt16LE,Zkt=uEe(\"GIF87a\"),Ykt=uEe(\"GIF89a\");cEe.exports=function(e){if(!(e.length<10)&&!(!sEe(e,0,Zkt)&&!sEe(e,0,Ykt)))return{width:lEe(e,6),height:lEe(e,8),type:\"gif\",mime:\"image/gif\",wUnits:\"px\",hUnits:\"px\"}}});var vEe=ye((hvr,dEe)=>{\"use strict\";var _W=rc().readUInt16LE,Kkt=0,Jkt=1,hEe=16;dEe.exports=function(e){var t=_W(e,0),r=_W(e,2),n=_W(e,4);if(!(t!==Kkt||r!==Jkt||!n)){for(var i=[],a={width:0,height:0},o=0;o<n;o++){var s=e[6+hEe*o]||256,l=e[6+hEe*o+1]||256,u={width:s,height:l};i.push(u),(s>a.width||l>a.height)&&(a=u)}return{width:a.width,height:a.height,variants:i,type:\"ico\",mime:\"image/x-icon\",wUnits:\"px\",hUnits:\"px\"}}}});var gEe=ye((dvr,pEe)=>{\"use strict\";var xW=rc().readUInt16BE,$kt=rc().str2arr,Qkt=rc().sliceEq,eCt=gD(),tCt=$kt(\"Exif\\0\\0\");pEe.exports=function(e){if(!(e.length<2)&&!(e[0]!==255||e[1]!==216||e[2]!==255))for(var t=2;;){for(;;){if(e.length-t<2)return;if(e[t++]===255)break}for(var r=e[t++],n;r===255;)r=e[t++];if(208<=r&&r<=217||r===1)n=0;else if(192<=r&&r<=254){if(e.length-t<2)return;n=xW(e,t)-2,t+=2}else return;if(r===217||r===218)return;var i;if(r===225&&n>=10&&Qkt(e,t,tCt)&&(i=eCt.get_orientation(e.slice(t+6,t+n))),n>=5&&192<=r&&r<=207&&r!==196&&r!==200&&r!==204){if(e.length-t<n)return;var a={width:xW(e,t+3),height:xW(e,t+1),type:\"jpg\",mime:\"image/jpeg\",wUnits:\"px\",hUnits:\"px\"};return i>0&&(a.orientation=i),a}t+=n}}});var bEe=ye((vvr,xEe)=>{\"use strict\";var _Ee=rc().str2arr,mEe=rc().sliceEq,yEe=rc().readUInt32BE,rCt=_Ee(`\\x89PNG\\r\n",
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l8e=fe((Xs,s8e)=>{Xs=s8e.exports=nW.debug=nW.default=nW;Xs.coerce=IDt;Xs.disable=DDt;Xs.enable=FDt;Xs.enabled=RDt;Xs.humanize=o8e();Xs.names=[];Xs.skips=[];Xs.formatters={};var rW;function PDt(e){var t=0,r;for(r in e)t=(t<<5)-t+e.charCodeAt(r),t|=0;return Xs.colors[Math.abs(t)%Xs.colors.length]}function nW(e){function t(){if(t.enabled){var r=t,n=+new Date,a=n-(rW||n);r.diff=a,r.prev=rW,r.curr=n,rW=n;for(var i=new Array(arguments.length),o=0;o<i.length;o++)i[o]=arguments[o];i[0]=Xs.coerce(i[0]),typeof i[0]!=\"string\"&&i.unshift(\"%O\");var s=0;i[0]=i[0].replace(/%([a-zA-Z%])/g,function(u,c){if(u===\"%%\")return u;s++;var f=Xs.formatters[c];if(typeof f==\"function\"){var v=i[s];u=f.call(r,v),i.splice(s,1),s--}return u}),Xs.formatArgs.call(r,i);var l=t.log||Xs.log||console.log.bind(console);l.apply(r,i)}}return t.namespace=e,t.enabled=Xs.enabled(e),t.useColors=Xs.useColors(),t.color=PDt(e),typeof Xs.init==\"function\"&&Xs.init(t),t}function FDt(e){Xs.save(e),Xs.names=[],Xs.skips=[];for(var t=(typeof e==\"string\"?e:\"\").split(/[\\s,]+/),r=t.length,n=0;n<r;n++)t[n]&&(e=t[n].replace(/\\*/g,\".*?\"),e[0]===\"-\"?Xs.skips.push(new RegExp(\"^\"+e.substr(1)+\"$\")):Xs.names.push(new RegExp(\"^\"+e+\"$\")))}function DDt(){Xs.enable(\"\")}function RDt(e){var t,r;for(t=0,r=Xs.skips.length;t<r;t++)if(Xs.skips[t].test(e))return!1;for(t=0,r=Xs.names.length;t<r;t++)if(Xs.names[t].test(e))return!0;return!1}function IDt(e){return e instanceof Error?e.stack||e.message:e}});var f8e=fe((Fd,c8e)=>{Fd=c8e.exports=l8e();Fd.log=BDt;Fd.formatArgs=qDt;Fd.save=ODt;Fd.load=u8e;Fd.useColors=zDt;Fd.storage=typeof chrome!=\"undefined\"&&typeof chrome.storage!=\"undefined\"?chrome.storage.local:NDt();Fd.colors=[\"lightseagreen\",\"forestgreen\",\"goldenrod\",\"dodgerblue\",\"darkorchid\",\"crimson\"];function zDt(){return typeof window!=\"undefined\"&&window.process&&window.process.type===\"renderer\"?!0:typeof document!=\"undefined\"&&document.documentElement&&document.documentElement.style&&document.documentElement.style.WebkitAppearance||typeof window!=\"undefined\"&&window.console&&(window.console.firebug||window.console.exception&&window.console.table)||typeof navigator!=\"undefined\"&&navigator.userAgent&&navigator.userAgent.toLowerCase().match(/firefox\\/(\\d+)/)&&parseInt(RegExp.$1,10)>=31||typeof navigator!=\"undefined\"&&navigator.userAgent&&navigator.userAgent.toLowerCase().match(/applewebkit\\/(\\d+)/)}Fd.formatters.j=function(e){try{return JSON.stringify(e)}catch(t){return\"[UnexpectedJSONParseError]: \"+t.message}};function qDt(e){var t=this.useColors;if(e[0]=(t?\"%c\":\"\")+this.namespace+(t?\" %c\":\" \")+e[0]+(t?\"%c \":\" \")+\"+\"+Fd.humanize(this.diff),!!t){var r=\"color: \"+this.color;e.splice(1,0,r,\"color: inherit\");var n=0,a=0;e[0].replace(/%[a-zA-Z%]/g,function(i){i!==\"%%\"&&(n++,i===\"%c\"&&(a=n))}),e.splice(a,0,r)}}function BDt(){return typeof console==\"object\"&&console.log&&Function.prototype.apply.call(console.log,console,arguments)}function ODt(e){try{e==null?Fd.storage.removeItem(\"debug\"):Fd.storage.debug=e}catch(t){}}function u8e(){var e;try{e=Fd.storage.debug}catch(t){}return!e&&typeof process!=\"undefined\"&&\"env\"in process&&(e=process.env.DEBUG),e}Fd.enable(u8e());function NDt(){try{return window.localStorage}catch(e){}}});var x8e=fe((v2r,y8e)=>{var Sw=e9(),oy=f8e()(\"stream-parser\");y8e.exports=VDt;var d8e=-1,x9=0,UDt=1,v8e=2;function VDt(e){var t=e&&typeof e._transform==\"function\",r=e&&typeof e._write==\"function\";if(!t&&!r)throw new Error(\"must pass a Writable or Transform stream in\");oy(\"extending Parser into stream\"),e._bytes=GDt,e._skipBytes=HDt,t&&(e._passthrough=jDt),t?e._transform=YDt:e._write=WDt}function D6(e){oy(\"initializing parser stream\"),e._parserBytesLeft=0,e._parserBuffers=[],e._parserBuffered=0,e._parserState=d8e,e._parserCallback=null,typeof 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t==\"function\"&&(r=t),g8e(this,e,null,r)}function YDt(e,t,r){this._parserInit||D6(this),oy(\"transform(%o bytes)\",e.length),typeof t!=\"function\"&&(t=this._parserOutput),g8e(this,e,t,r)}function p8e(e,t,r,n){return e._parserBytesLeft<=0?n(new Error(\"got data but not currently parsing anything\")):t.length<=e._parserBytesLeft?function(){return h8e(e,t,r,n)}:function(){var a=t.slice(0,e._parserBytesLeft);return h8e(e,a,r,function(i){if(i)return n(i);if(t.length>a.length)return function(){return p8e(e,t.slice(a.length),r,n)}})}}function h8e(e,t,r,n){if(e._parserBytesLeft-=t.length,oy(\"%o bytes left for stream piece\",e._parserBytesLeft),e._parserState===x9?(e._parserBuffers.push(t),e._parserBuffered+=t.length):e._parserState===v8e&&r(t),e._parserBytesLeft===0){var a=e._parserCallback;if(a&&e._parserState===x9&&e._parserBuffers.length>1&&(t=Buffer.concat(e._parserBuffers,e._parserBuffered)),e._parserState!==x9&&(t=null),e._parserCallback=null,e._parserBuffered=0,e._parserState=d8e,e._parserBuffers.splice(0),a){var i=[];t&&i.push(t),r&&i.push(r);var o=a.length>i.length;o&&i.push(m8e(n));var s=a.apply(e,i);if(!o||n===s)return n}}else return n}var g8e=m8e(p8e);function m8e(e){return function(){for(var t=e.apply(this,arguments);typeof t==\"function\";)t=t();return t}}});var ds=fe(Ph=>{\"use strict\";var _8e=uEe().Transform,XDt=x8e();function R6(){_8e.call(this,{readableObjectMode:!0})}R6.prototype=Object.create(_8e.prototype);R6.prototype.constructor=R6;XDt(R6.prototype);Ph.ParserStream=R6;Ph.sliceEq=function(e,t,r){for(var n=t,a=0;a<r.length;)if(e[n++]!==r[a++])return!1;return!0};Ph.str2arr=function(e,t){var r=[],n=0;if(t&&t===\"hex\")for(;n<e.length;)r.push(parseInt(e.slice(n,n+2),16)),n+=2;else for(;n<e.length;n++)r.push(e.charCodeAt(n)&255);return r};Ph.readUInt16LE=function(e,t){return e[t]|e[t+1]<<8};Ph.readUInt16BE=function(e,t){return e[t]<<8|e[t+1]};Ph.readInt16LE=function(e,t){return Ph.readUInt16LE(e,t)<<16>>16};Ph.readInt16BE=function(e,t){return Ph.readUInt16BE(e,t)<<16>>16};Ph.readUInt32LE=function(e,t){return(e[t]|e[t+1]<<8|e[t+2]<<16)+e[t+3]*16777216};Ph.readUInt32BE=function(e,t){return e[t]*16777216+(e[t+1]<<16|e[t+2]<<8|e[t+3])};Ph.readInt32LE=function(e,t){return Ph.readUInt32LE(e,t)|0};Ph.readInt32BE=function(e,t){return Ph.readUInt32BE(e,t)|0};function _9(e,t,r){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=new Error().stack||\"\",this.name=this.constructor.name,this.message=e,t&&(this.code=t),r&&(this.statusCode=r)}_9.prototype=Object.create(Error.prototype);_9.prototype.constructor=_9;Ph.ProbeError=_9});var b8e=fe((g2r,b9)=>{\"use strict\";var Ew=ds().readUInt16BE,aW=ds().readUInt32BE;function I6(e,t){if(e.length<4+t)return null;var r=aW(e,t);return e.length<r+t||r<8?null:{boxtype:String.fromCharCode.apply(null,e.slice(t+4,t+8)),data:e.slice(t+8,t+r),end:t+r}}b9.exports.unbox=I6;function ZDt(e,t){for(var r=0;;){var n=I6(e,r);if(!n)break;switch(n.boxtype){case\"ispe\":t.sizes.push({width:aW(n.data,4),height:aW(n.data,8)});break;case\"irot\":t.transforms.push({type:\"irot\",value:n.data[0]&3});break;case\"imir\":t.transforms.push({type:\"imir\",value:n.data[0]&1});break}r=n.end}}function iW(e,t,r){for(var n=0,a=0;a<r;a++)n=n*256+(e[t+a]||0);return n}function $Dt(e,t){for(var r=e[4]>>4&15,n=e[4]&15,a=e[5]>>4&15,i=Ew(e,6),o=8,s=0;s<i;s++){var l=Ew(e,o);o+=2;var u=Ew(e,o);o+=2;var c=iW(e,o,a);o+=a;var f=Ew(e,o);if(o+=2,u===0&&f===1){var v=iW(e,o,r),g=iW(e,o+r,n);t.item_loc[l]={length:g,offset:v+c}}o+=f*(r+n)}}function KDt(e,t){for(var r=Ew(e,4),n=6,a=0;a<r;a++){var i=I6(e,n);if(!i)break;if(i.boxtype===\"infe\"){for(var o=Ew(i.data,4),s=\"\",l=8;l<i.data.length&&i.data[l];l++)s+=String.fromCharCode(i.data[l]);t.item_inf[s]=o}n=i.end}}function JDt(e,t){for(var r=0;;){var n=I6(e,r);if(!n)break;n.boxtype===\"ipco\"&&ZDt(n.data,t),r=n.end}}function QDt(e,t){for(var r=4;;){var n=I6(e,r);if(!n)break;n.boxtype===\"iprp\"&&JDt(n.data,t),n.boxtype===\"iloc\"&&$Dt(n.data,t),n.boxtype===\"iinf\"&&KDt(n.data,t),r=n.end}}function eRt(e){var t=e.reduce(function(a,i){return a.width>i.width||a.width===i.width&&a.height>i.height?a:i}),r=e.reduce(function(a,i){return a.height>i.height||a.height===i.height&&a.width>i.width?a:i}),n;return t.width>r.height||t.width===r.height&&t.height>r.width?n=t:n=r,n}b9.exports.readSizeFromMeta=function(e){var t={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(QDt(e,t),!!t.sizes.length){var r=eRt(t.sizes),n=1;t.transforms.forEach(function(i){var o={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},s={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(i.type===\"imir\"&&(i.value===0?n=s[n]:(n=s[n],n=o[n],n=o[n])),i.type===\"irot\")for(var l=0;l<i.value;l++)n=o[n]});var a=null;return t.item_inf.Exif&&(a=t.item_loc[t.item_inf.Exif]),{width:r.width,height:r.height,orientation:t.transforms.length?n:null,variants:t.sizes,exif_location:a}}};b9.exports.getMimeType=function(e){var t=String.fromCharCode.apply(null,e.slice(0,4)),r={};r[t]=!0;for(var n=8;n<e.length;n+=4)r[String.fromCharCode.apply(null,e.slice(n,n+4))]=!0;if(!(!r.mif1&&!r.msf1&&!r.miaf))return t===\"avif\"||t===\"avis\"||t===\"avio\"?{type:\"avif\",mime:\"image/avif\"}:t===\"heic\"||t===\"heix\"?{type:\"heic\",mime:\"image/heic\"}:t===\"hevc\"||t===\"hevx\"?{type:\"heic\",mime:\"image/heic-sequence\"}:r.avif||r.avis?{type:\"avif\",mime:\"image/avif\"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:\"heif\",mime:\"image/heif-sequence\"}:{type:\"heif\",mime:\"image/heif\"}:{type:\"avif\",mime:\"image/avif\"}}});var T9=fe((m2r,oW)=>{\"use strict\";function w9(e,t){var r=new Error(e);return r.code=t,r}function tRt(e){try{return decodeURIComponent(escape(e))}catch(t){return e}}function xg(e,t,r){this.input=e.subarray(t,r),this.start=t;var n=String.fromCharCode.apply(null,this.input.subarray(0,4));if(n!==\"II*\\0\"&&n!==\"MM\\0*\")throw w9(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=n[0]===\"M\"}xg.prototype.each=function(e){this.aborted=!1;var t=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:t}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,e)}};xg.prototype.read_uint16=function(e){var t=this.input;if(e+2>t.length)throw w9(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?t[e]*256+t[e+1]:t[e]+t[e+1]*256};xg.prototype.read_uint32=function(e){var t=this.input;if(e+4>t.length)throw w9(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?t[e]*16777216+t[e+1]*65536+t[e+2]*256+t[e+3]:t[e]+t[e+1]*256+t[e+2]*65536+t[e+3]*16777216};xg.prototype.is_subifd_link=function(e,t){return e===0&&t===34665||e===0&&t===34853||e===34665&&t===40965};xg.prototype.exif_format_length=function(e){switch(e){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}};xg.prototype.exif_format_read=function(e,t){var r;switch(e){case 1:case 2:return r=this.input[t],r;case 6:return r=this.input[t],r|(r&128)*33554430;case 3:return r=this.read_uint16(t),r;case 8:return r=this.read_uint16(t),r|(r&32768)*131070;case 4:return r=this.read_uint32(t),r;case 9:return r=this.read_uint32(t),r|0;case 5:case 10:case 11:case 12:return null;case 7:return null;default:return null}};xg.prototype.scan_ifd=function(e,t,r){var n=this.read_uint16(t);t+=2;for(var a=0;a<n;a++){var i=this.read_uint16(t),o=this.read_uint16(t+2),s=this.read_uint32(t+4),l=this.exif_format_length(o),u=s*l,c=u<=4?t+8:this.read_uint32(t+8),f=!1;if(c+u>this.input.length)throw w9(\"unexpected EOF\",\"EBADDATA\");for(var v=[],g=c,p=0;p<s;p++,g+=l){var x=this.exif_format_read(o,g);if(x===null){v=null;break}v.push(x)}Array.isArray(v)&&o===2&&(v=tRt(String.fromCharCode.apply(null,v)),v&&v[v.length-1]===\"\\0\"&&(v=v.slice(0,-1))),this.is_subifd_link(e,i)&&Array.isArray(v)&&Number.isInteger(v[0])&&v[0]>0&&(this.ifds_to_read.push({id:i,offset:v[0]}),f=!0);var _={is_big_endian:this.big_endian,ifd:e,tag:i,format:o,count:s,entry_offset:t+this.start,data_length:u,data_offset:c+this.start,value:v,is_subifd_link:f};if(r(_)===!1){this.aborted=!0;return}t+=12}e===0&&this.ifds_to_read.push({id:1,offset:this.read_uint32(t)})};oW.exports.ExifParser=xg;oW.exports.get_orientation=function(e){var t=0;try{return new xg(e,0,e.length).each(function(r){if(r.ifd===0&&r.tag===274&&Array.isArray(r.value))return t=r.value[0],!1}),t}catch(r){return-1}}});var T8e=fe((y2r,w8e)=>{\"use strict\";var rRt=ds().str2arr,nRt=ds().sliceEq,iRt=ds().readUInt32BE,A9=b8e(),aRt=T9(),oRt=rRt(\"ftyp\");w8e.exports=function(e){if(nRt(e,4,oRt)){var t=A9.unbox(e,0);if(t){var r=A9.getMimeType(t.data);if(r){for(var n,a=t.end;;){var i=A9.unbox(e,a);if(!i)break;if(a=i.end,i.boxtype===\"mdat\")return;if(i.boxtype===\"meta\"){n=i.data;break}}if(n){var o=A9.readSizeFromMeta(n);if(o){var 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L8e=ds().str2arr,k8e=ds().sliceEq,C8e=ds().readUInt16LE,fRt=L8e(\"GIF87a\"),hRt=L8e(\"GIF89a\");P8e.exports=function(e){if(!(e.length<10)&&!(!k8e(e,0,fRt)&&!k8e(e,0,hRt)))return{width:C8e(e,6),height:C8e(e,8),type:\"gif\",mime:\"image/gif\",wUnits:\"px\",hUnits:\"px\"}}});var R8e=fe((b2r,D8e)=>{\"use strict\";var sW=ds().readUInt16LE,dRt=0,vRt=1,lW=16;D8e.exports=function(e){var t=sW(e,0),r=sW(e,2),n=sW(e,4);if(!(t!==dRt||r!==vRt||!n)&&!(e.length<6+n*lW)){for(var a=[],i={width:0,height:0},o=0;o<n;o++){var s=e[6+lW*o]||256,l=e[6+lW*o+1]||256,u={width:s,height:l};a.push(u),(s>i.width||l>i.height)&&(i=u)}return{width:i.width,height:i.height,variants:a,type:\"ico\",mime:\"image/x-icon\",wUnits:\"px\",hUnits:\"px\"}}}});var z8e=fe((w2r,I8e)=>{\"use strict\";var uW=ds().readUInt16BE,pRt=ds().str2arr,gRt=ds().sliceEq,mRt=T9(),yRt=pRt(\"Exif\\0\\0\");I8e.exports=function(e){if(!(e.length<2)&&!(e[0]!==255||e[1]!==216||e[2]!==255))for(var t=2;;){for(;;){if(e.length-t<2)return;if(e[t++]===255)break}for(var r=e[t++],n;r===255;)r=e[t++];if(r>=208&&r<=217||r===1)n=0;else if(r>=192&&r<=254){if(e.length-t<2)return;n=uW(e,t)-2,t+=2}else return;if(r===217||r===218)return;var a;if(r===225&&n>=10&&gRt(e,t,yRt)&&(a=mRt.get_orientation(e.slice(t+6,t+n))),n>=5&&r>=192&&r<=207&&r!==196&&r!==200&&r!==204){if(e.length-t<n)return;var i={width:uW(e,t+3),height:uW(e,t+1),type:\"jpg\",mime:\"image/jpeg\",wUnits:\"px\",hUnits:\"px\"};return a>0&&(i.orientation=a),i}t+=n}}});var U8e=fe((T2r,N8e)=>{\"use strict\";var O8e=ds().str2arr,q8e=ds().sliceEq,B8e=ds().readUInt32BE,xRt=O8e(`\\x89PNG\\r\n",
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l=Math.floor((r-i.x0)/a.dx),u=Math.floor(Math.abs(n-i.y0)/a.dy),c;if(a._hasZ?c=i.z[u][l]:a._hasSource&&(c=a._canvas.el.getContext(\"2d\",{willReadFrequently:!0}).getImageData(l,u,1,1).data),!!c){var f=i.hi||a.hoverinfo,h;if(f){var d=f.split(\"+\");d.indexOf(\"all\")!==-1&&(d=[\"color\"]),d.indexOf(\"color\")!==-1&&(h=!0)}var v=HCt.colormodel[a.colormodel],m=v.colormodel||a.colormodel,b=m.length,p=a._scaler(c),k=v.suffix,M=[];(a.hovertemplate||h)&&(M.push(\"[\"+[p[0]+k[0],p[1]+k[1],p[2]+k[2]].join(\", \")),b===4&&M.push(\", \"+p[3]+k[3]),M.push(\"]\"),M=M.join(\"\"),t.extraText=m.toUpperCase()+\": \"+M);var T;xD(a.hovertext)&&xD(a.hovertext[u])?T=a.hovertext[u][l]:xD(a.text)&&xD(a.text[u])&&(T=a.text[u][l]);var L=s.c2p(i.y0+(u+.5)*a.dy),x=i.x0+(l+.5)*a.dx,C=i.y0+(u+.5)*a.dy,S=\"[\"+c.slice(0,a.colormodel.length).join(\", 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bD=Vl(),jCt=Cc().attributes,WCt=ec(),XCt=Ih(),{hovertemplateAttrs:ZCt,texttemplateAttrs:YCt,templatefallbackAttrs:ske}=Pl(),Q_=Ao().extendFlat,KCt=Pd().pattern,wD=WCt({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});lke.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:XCt.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},pattern:KCt,editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:Q_({},bD.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:ZCt({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),hovertemplatefallback:ske(),texttemplate:YCt({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplatefallback:ske({editType:\"plot\"}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:Q_({},wD,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:Q_({},wD,{}),outsidetextfont:Q_({},wD,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},showlegend:Q_({},bD.showlegend,{arrayOk:!0}),legend:Q_({},bD.legend,{arrayOk:!0}),legendrank:Q_({},bD.legendrank,{arrayOk:!0}),title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:Q_({},wD,{}),position:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"plot\"},editType:\"plot\"},domain:jCt({name:\"pie\",trace:!0,editType:\"calc\"}),hole:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},sort:{valType:\"boolean\",dflt:!0,editType:\"calc\"},direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"],dflt:\"counterclockwise\",editType:\"calc\"},rotation:{valType:\"angle\",dflt:0,editType:\"calc\"},pull:{valType:\"number\",min:0,max:1,dflt:0,arrayOk:!0,editType:\"calc\"}}});var O2=ye((Lvr,fke)=>{\"use strict\";var JCt=Eo(),BE=Pr(),$Ct=q2(),QCt=Cc().defaults,e6t=i0().handleText,t6t=Pr().coercePattern;function uke(e,t){var r=BE.isArrayOrTypedArray(e),n=BE.isArrayOrTypedArray(t),i=Math.min(r?e.length:1/0,n?t.length:1/0);if(isFinite(i)||(i=0),i&&n){for(var a,o=0;o<i;o++){var s=t[o];if(JCt(s)&&s>0){a=!0;break}}a||(i=0)}return{hasLabels:r,hasValues:n,len:i}}function cke(e,t,r,n,i){var a=n(\"marker.line.width\");a&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var o=n(\"marker.colors\");t6t(n,\"marker.pattern\",o),e.marker&&!t.marker.pattern.fgcolor&&(t.marker.pattern.fgcolor=e.marker.colors),t.marker.pattern.bgcolor||(t.marker.pattern.bgcolor=r.paper_bgcolor)}function r6t(e,t,r,n){function i(k,M){return BE.coerce(e,t,$Ct,k,M)}var a=i(\"labels\"),o=i(\"values\"),s=uke(a,o),l=s.len;if(t._hasLabels=s.hasLabels,t._hasValues=s.hasValues,!t._hasLabels&&t._hasValues&&(i(\"label0\"),i(\"dlabel\")),!l){t.visible=!1;return}t._length=l,cke(e,t,n,i,!0),i(\"scalegroup\");var u=i(\"text\"),c=i(\"texttemplate\");i(\"texttemplatefallback\");var f;if(c||(f=i(\"textinfo\",BE.isArrayOrTypedArray(u)?\"text+percent\":\"percent\")),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\"),c||f&&f!==\"none\"){var h=i(\"textposition\");e6t(e,t,n,i,h,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1});var d=Array.isArray(h)||h===\"auto\",v=d||h===\"outside\";v&&i(\"automargin\"),(h===\"inside\"||h===\"auto\"||Array.isArray(h))&&i(\"insidetextorientation\")}else f===\"none\"&&i(\"textposition\",\"none\");QCt(t,n,i);var m=i(\"hole\"),b=i(\"title.text\");if(b){var p=i(\"title.position\",m?\"middle center\":\"top center\");!m&&p===\"middle center\"&&(t.title.position=\"top center\"),BE.coerceFont(i,\"title.font\",n.font)}i(\"sort\"),i(\"direction\"),i(\"rotation\"),i(\"pull\")}fke.exports={handleLabelsAndValues:uke,handleMarkerDefaults:cke,supplyDefaults:r6t}});var TD=ye((Pvr,hke)=>{\"use strict\";hke.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}});var vke=ye((Ivr,dke)=>{\"use 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x.v-L.v}),r[0]&&(r[0].vTotal=v),r}function pke(e){return function(r,n){return!r||(r=SW(r),!r.isValid())?!1:(r=o6t.addOpacity(r,r.getAlpha()),e[n]||(e[n]=r),r)}}function u6t(e,t){var r=(t||{}).type;r||(r=\"pie\");var n=e._fullLayout,i=e.calcdata,a=n[r+\"colorway\"],o=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=gke(a,s6t));for(var s=0,l=0;l<i.length;l++){var u=i[l],c=u[0].trace.type;if(c===r)for(var f=0;f<u.length;f++){var h=u[f];h.color===!1&&(o[h.label]?h.color=o[h.label]:(o[h.label]=h.color=a[s%a.length],s++))}}}function gke(e,t){var r,n=JSON.stringify(e),i=t[n];if(!i){for(i=e.slice(),r=0;r<e.length;r++)i.push(SW(e[r]).lighten(20).toHexString());for(r=0;r<e.length;r++)i.push(SW(e[r]).darken(20).toHexString());t[n]=i}return i}mke.exports={calc:l6t,crossTraceCalc:u6t,makePullColorFn:pke,generateExtendedColors:gke}});var _ke=ye((Dvr,yke)=>{\"use strict\";var c6t=ip().appendArrayMultiPointValues;yke.exports=function(t,r){var n={curveNumber:r.index,pointNumbers:t.pts,data:r._input,fullData:r,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return t.pts.length===1&&(n.pointNumber=n.i=t.pts[0]),c6t(n,r,t.pts),r.type===\"funnelarea\"&&(delete n.v,delete n.i),n}});var ED=ye((Fvr,Nke)=>{\"use strict\";var Fp=Fa(),f6t=Mc(),AD=ef(),Ske=ka(),e1=So(),iv=Pr(),h6t=iv.strScale,xke=iv.strTranslate,MW=Zl(),Mke=bv(),d6t=Mke.recordMinTextSize,v6t=Mke.clearMinTextSize,Eke=S_().TEXTPAD,ns=m_(),SD=_ke(),bke=Pr().isValidTextValue;function p6t(e,t){var r=e._context.staticPlot,n=e._fullLayout,i=n._size;v6t(\"pie\",n),Lke(t,e),qke(t,i);var a=iv.makeTraceGroups(n._pielayer,t,\"trace\").each(function(o){var s=Fp.select(this),l=o[0],u=l.trace;A6t(o),s.attr(\"stroke-linejoin\",\"round\"),s.each(function(){var c=Fp.select(this).selectAll(\"g.slice\").data(o);c.enter().append(\"g\").classed(\"slice\",!0),c.exit().remove();var f=[[[],[]],[[],[]]],h=!1;c.each(function(T,L){if(T.hidden){Fp.select(this).selectAll(\"path,g\").remove();return}T.pointNumber=T.i,T.curveNumber=u.index,f[T.pxmid[1]<0?0:1][T.pxmid[0]<0?0:1].push(T);var x=l.cx,C=l.cy,S=Fp.select(this),g=S.selectAll(\"path.surface\").data([T]);if(g.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),S.call(kke,e,o),u.pull){var P=+ns.castOption(u.pull,T.pts)||0;P>0&&(x+=P*T.pxmid[0],C+=P*T.pxmid[1])}T.cxFinal=x,T.cyFinal=C;function E(N,H,re,ae){var _e=ae*(H[0]-N[0]),Ee=ae*(H[1]-N[1]);return\"a\"+ae*l.r+\",\"+ae*l.r+\" 0 \"+T.largeArc+(re?\" 1 \":\" 0 \")+_e+\",\"+Ee}var z=u.hole;if(T.v===l.vTotal){var q=\"M\"+(x+T.px0[0])+\",\"+(C+T.px0[1])+E(T.px0,T.pxmid,!0,1)+E(T.pxmid,T.px0,!0,1)+\"Z\";z?g.attr(\"d\",\"M\"+(x+z*T.px0[0])+\",\"+(C+z*T.px0[1])+E(T.px0,T.pxmid,!1,z)+E(T.pxmid,T.px0,!1,z)+\"Z\"+q):g.attr(\"d\",q)}else{var V=E(T.px0,T.px1,!0,1);if(z){var G=1-z;g.attr(\"d\",\"M\"+(x+z*T.px1[0])+\",\"+(C+z*T.px1[1])+E(T.px1,T.px0,!1,z)+\"l\"+G*T.px0[0]+\",\"+G*T.px0[1]+V+\"Z\")}else g.attr(\"d\",\"M\"+x+\",\"+C+\"l\"+T.px0[0]+\",\"+T.px0[1]+V+\"Z\")}Oke(e,T,l);var Z=ns.castOption(u.textposition,T.pts),j=S.selectAll(\"g.slicetext\").data(T.text&&Z!==\"none\"?[0]:[]);j.enter().append(\"g\").classed(\"slicetext\",!0),j.exit().remove(),j.each(function(){var N=iv.ensureSingle(Fp.select(this),\"text\",\"\",function(ie){ie.attr(\"data-notex\",1)}),H=iv.ensureUniformFontSize(e,Z===\"outside\"?m6t(u,T,n.font):Cke(u,T,n.font));N.text(T.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(e1.font,H).call(MW.convertToTspans,e);var re=e1.bBox(N.node()),ae;if(Z===\"outside\")ae=Ake(re,T);else if(ae=Pke(re,T,l),Z===\"auto\"&&ae.scale<1){var _e=iv.ensureUniformFontSize(e,u.outsidetextfont);N.call(e1.font,_e),re=e1.bBox(N.node()),ae=Ake(re,T)}var Ee=ae.textPosAngle,Ce=Ee===void 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v=e1.bBox(s.node()),m=u.domain,b=i.w*(m.x[1]-m.x[0]),p=i.h*(m.y[1]-m.y[0]),k=(.5*b-l.r)/i.w,M=(.5*p-l.r)/i.h;f6t.autoMargin(e,\"pie.\"+u.uid+\".automargin\",{xl:m.x[0]-k,xr:m.x[1]+k,yb:m.y[0]-M,yt:m.y[1]+M,l:Math.max(l.cx-l.r-v.left,0),r:Math.max(v.right-(l.cx+l.r),0),b:Math.max(v.bottom-(l.cy+l.r),0),t:Math.max(l.cy-l.r-v.top,0),pad:5})}})});setTimeout(function(){a.selectAll(\"tspan\").each(function(){var o=Fp.select(this);o.attr(\"dy\")&&o.attr(\"dy\",o.attr(\"dy\"))})},0)}function g6t(e,t){e.each(function(r){var n=Fp.select(this);if(!r.labelExtraX&&!r.labelExtraY){n.select(\"path.textline\").remove();return}var i=n.select(\"g.slicetext text\");r.transform.targetX+=r.labelExtraX,r.transform.targetY+=r.labelExtraY,iv.setTransormAndDisplay(i,r.transform);var a=r.cxFinal+r.pxmid[0],o=r.cyFinal+r.pxmid[1],s=\"M\"+a+\",\"+o,l=(r.yLabelMax-r.yLabelMin)*(r.pxmid[0]<0?-1:1)/4;if(r.labelExtraX){var u=r.labelExtraX*r.pxmid[1]/r.pxmid[0],c=r.yLabelMid+r.labelExtraY-(r.cyFinal+r.pxmid[1]);Math.abs(u)>Math.abs(c)?s+=\"l\"+c*r.pxmid[0]/r.pxmid[1]+\",\"+c+\"H\"+(a+r.labelExtraX+l):s+=\"l\"+r.labelExtraX+\",\"+u+\"v\"+(c-u)+\"h\"+l}else s+=\"V\"+(r.yLabelMid+r.labelExtraY)+\"h\"+l;iv.ensureSingle(n,\"path\",\"textline\").call(Ske.stroke,t.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,t.outsidetextfont.size/8),d:s,fill:\"none\"})})}function kke(e,t,r){var n=r[0],i=n.cx,a=n.cy,o=n.trace,s=o.type===\"funnelarea\";\"_hasHoverLabel\"in o||(o._hasHoverLabel=!1),\"_hasHoverEvent\"in o||(o._hasHoverEvent=!1),e.on(\"mouseover\",function(l){var u=t._fullLayout,c=t._fullData[o.index];if(!(t._dragging||u.hovermode===!1)){var f=c.hoverinfo;if(Array.isArray(f)&&(f=AD.castHoverinfo({hoverinfo:[ns.castOption(f,l.pts)],_module:o._module},u,0)),f===\"all\"&&(f=\"label+text+value+percent+name\"),c.hovertemplate||f!==\"none\"&&f!==\"skip\"&&f){var h=l.rInscribed||0,d=i+l.pxmid[0]*(1-h),v=a+l.pxmid[1]*(1-h),m=u.separators,b=[];if(f&&f.indexOf(\"label\")!==-1&&b.push(l.label),l.text=ns.castOption(c.hovertext||c.text,l.pts),f&&f.indexOf(\"text\")!==-1){var p=l.text;iv.isValidTextValue(p)&&b.push(p)}l.value=l.v,l.valueLabel=ns.formatPieValue(l.v,m),f&&f.indexOf(\"value\")!==-1&&b.push(l.valueLabel),l.percent=l.v/n.vTotal,l.percentLabel=ns.formatPiePercent(l.percent,m),f&&f.indexOf(\"percent\")!==-1&&b.push(l.percentLabel);var k=c.hoverlabel,M=k.font,T=[];AD.loneHover({trace:o,x0:d-h*n.r,x1:d+h*n.r,y:v,_x0:s?i+l.TL[0]:d-h*n.r,_x1:s?i+l.TR[0]:d+h*n.r,_y0:s?a+l.TL[1]:v-h*n.r,_y1:s?a+l.BL[1]:v+h*n.r,text:b.join(\"<br>\"),name:c.hovertemplate||f.indexOf(\"name\")!==-1?c.name:void 0,idealAlign:l.pxmid[0]<0?\"left\":\"right\",color:ns.castOption(k.bgcolor,l.pts)||l.color,borderColor:ns.castOption(k.bordercolor,l.pts),fontFamily:ns.castOption(M.family,l.pts),fontSize:ns.castOption(M.size,l.pts),fontColor:ns.castOption(M.color,l.pts),nameLength:ns.castOption(k.namelength,l.pts),textAlign:ns.castOption(k.align,l.pts),hovertemplate:ns.castOption(c.hovertemplate,l.pts),hovertemplateLabels:l,eventData:[SD(l,c)]},{container:u._hoverlayer.node(),outerContainer:u._paper.node(),gd:t,inOut_bbox:T}),l.bbox=T[0],o._hasHoverLabel=!0}o._hasHoverEvent=!0,t.emit(\"plotly_hover\",{points:[SD(l,c)],event:Fp.event})}}),e.on(\"mouseout\",function(l){var u=t._fullLayout,c=t._fullData[o.index],f=Fp.select(this).datum();o._hasHoverEvent&&(l.originalEvent=Fp.event,t.emit(\"plotly_unhover\",{points:[SD(f,c)],event:Fp.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(AD.loneUnhover(u._hoverlayer.node()),o._hasHoverLabel=!1)}),e.on(\"click\",function(l){var u=t._fullLayout,c=t._fullData[o.index];t._dragging||u.hovermode===!1||(t._hoverdata=[SD(l,c)],AD.click(t,Fp.event))})}function m6t(e,t,r){var n=ns.castOption(e.outsidetextfont.color,t.pts)||ns.castOption(e.textfont.color,t.pts)||r.color,i=ns.castOption(e.outsidetextfont.family,t.pts)||ns.castOption(e.textfont.family,t.pts)||r.family,a=ns.castOption(e.outsidetextfont.size,t.pts)||ns.castOption(e.textfont.size,t.pts)||r.size,o=ns.castOption(e.outsidetextfont.weight,t.pts)||ns.castOption(e.textfont.weight,t.pts)||r.weight,s=ns.castOption(e.outsidetextfont.style,t.pts)||ns.castOption(e.textfont.style,t.pts)||r.style,l=ns.castOption(e.outsidetextfont.variant,t.pts)||ns.castOption(e.textfont.variant,t.pts)||r.variant,u=ns.castOption(e.outsidetextfont.textcase,t.pts)||ns.castOption(e.textfont.textcase,t.pts)||r.textcase,c=ns.castOption(e.outsidetextfont.lineposition,t.pts)||ns.castOption(e.textfont.lineposition,t.pts)||r.lineposition,f=ns.castOption(e.outsidetextfont.shadow,t.pts)||ns.castOption(e.textfont.shadow,t.pts)||r.shadow;return{color:n,family:i,size:a,weight:o,style:s,variant:l,textcase:u,lineposition:c,shadow:f}}function Cke(e,t,r){var n=ns.castOption(e.insidetextfont.color,t.pts);!n&&e._input.textfont&&(n=ns.castOption(e._input.textfont.color,t.pts));var i=ns.castOption(e.insidetextfont.family,t.pts)||ns.castOption(e.textfont.family,t.pts)||r.family,a=ns.castOption(e.insidetextfont.size,t.pts)||ns.castOption(e.textfont.size,t.pts)||r.size,o=ns.castOption(e.insidetextfont.weight,t.pts)||ns.castOption(e.textfont.weight,t.pts)||r.weight,s=ns.castOption(e.insidetextfont.style,t.pts)||ns.castOption(e.textfont.style,t.pts)||r.style,l=ns.castOption(e.insidetextfont.variant,t.pts)||ns.castOption(e.textfont.variant,t.pts)||r.variant,u=ns.castOption(e.insidetextfont.textcase,t.pts)||ns.castOption(e.textfont.textcase,t.pts)||r.textcase,c=ns.castOption(e.insidetextfont.lineposition,t.pts)||ns.castOption(e.textfont.lineposition,t.pts)||r.lineposition,f=ns.castOption(e.insidetextfont.shadow,t.pts)||ns.castOption(e.textfont.shadow,t.pts)||r.shadow;return{color:n||Ske.contrast(t.color),family:i,size:a,weight:o,style:s,variant:l,textcase:u,lineposition:c,shadow:f}}function Lke(e,t){for(var r,n,i=0;i<e.length;i++)if(r=e[i][0],n=r.trace,n.title.text){var a=n.title.text;n._meta&&(a=iv.templateString(a,n._meta));var o=e1.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(e1.font,n.title.font).call(MW.convertToTspans,t),s=e1.bBox(o.node(),!0);r.titleBox={width:s.width,height:s.height},o.remove()}}function Pke(e,t,r){var n=r.r||t.rpx1,i=t.rInscribed,a=t.startangle===t.stopangle;if(a)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var o=t.ring,s=o===1&&Math.abs(t.startangle-t.stopangle)===Math.PI*2,l=t.halfangle,u=t.midangle,c=r.trace.insidetextorientation,f=c===\"horizontal\",h=c===\"tangential\",d=c===\"radial\",v=c===\"auto\",m=[],b;if(!v){var p=function(S,g){if(y6t(t,S)){var 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s=o.split(\"+\"),l=function(T){return s.indexOf(T)!==-1},u=l(\"label\"),c=l(\"text\"),f=l(\"value\"),h=l(\"percent\"),d=n.separators,v;if(v=u?[t.label]:[],c){var m=ns.getFirstFilled(i.text,t.pts);bke(m)&&v.push(m)}f&&v.push(ns.formatPieValue(t.v,d)),h&&v.push(ns.formatPiePercent(t.v/r.vTotal,d)),t.text=v.join(\"<br>\")}function b(T){return{label:T.label,value:T.v,valueLabel:ns.formatPieValue(T.v,n.separators),percent:T.v/r.vTotal,percentLabel:ns.formatPiePercent(T.v/r.vTotal,n.separators),color:T.color,text:T.text,customdata:iv.castOption(i,T.i,\"customdata\")}}if(a){var p=iv.castOption(i,t.i,\"texttemplate\");if(!p)t.text=\"\";else{var k=b(t),M=ns.getFirstFilled(i.text,t.pts);(bke(M)||M===\"\")&&(k.text=M),t.text=iv.texttemplateString({data:[k,i._meta],fallback:i.texttemplatefallback,labels:k,locale:e._fullLayout._d3locale,template:p})}}}function Bke(e,t){var r=e.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(t.left+t.right)/2,o=(t.top+t.bottom)/2;e.textX=a*n-o*i,e.textY=a*i+o*n,e.noCenter=!0}Nke.exports={plot:p6t,formatSliceLabel:Oke,transformInsideText:Pke,determineInsideTextFont:Cke,positionTitleOutside:Fke,prerenderTitles:Lke,layoutAreas:qke,attachFxHandlers:kke,computeTransform:Bke}});var Gke=ye((zvr,Vke)=>{\"use strict\";var Uke=Fa(),S6t=J3(),M6t=bv().resizeText;Vke.exports=function(t){var r=t._fullLayout._pielayer.selectAll(\".trace\");M6t(t,r,\"pie\"),r.each(function(n){var i=n[0],a=i.trace,o=Uke.select(this);o.style({opacity:a.opacity}),o.selectAll(\"path.surface\").each(function(s){Uke.select(this).call(S6t,s,a,t)})})}});var jke=ye(IA=>{\"use strict\";var Hke=Mc();IA.name=\"pie\";IA.plot=function(e,t,r,n){Hke.plotBasePlot(IA.name,e,t,r,n)};IA.clean=function(e,t,r,n){Hke.cleanBasePlot(IA.name,e,t,r,n)}});var Xke=ye((Ovr,Wke)=>{\"use 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E6t=Vl(),{hovertemplateAttrs:k6t,texttemplateAttrs:C6t,templatefallbackAttrs:Qke}=Pl(),L6t=Tu(),P6t=Cc().attributes,t1=q2(),eCe=kW(),NE=Ao().extendFlat,I6t=Pd().pattern;tCe.exports={labels:{valType:\"data_array\",editType:\"calc\"},parents:{valType:\"data_array\",editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},branchvalues:{valType:\"enumerated\",values:[\"remainder\",\"total\"],dflt:\"remainder\",editType:\"calc\"},count:{valType:\"flaglist\",flags:[\"branches\",\"leaves\"],dflt:\"leaves\",editType:\"calc\"},level:{valType:\"any\",editType:\"plot\",anim:!0},maxdepth:{valType:\"integer\",editType:\"plot\",dflt:-1},marker:NE({colors:{valType:\"data_array\",editType:\"calc\"},line:{color:NE({},t1.marker.line.color,{dflt:null}),width:NE({},t1.marker.line.width,{dflt:1}),editType:\"calc\"},pattern:I6t,editType:\"calc\"},L6t(\"marker\",{colorAttr:\"colors\",anim:!1})),leaf:{opacity:{valType:\"number\",editType:\"style\",min:0,max:1},editType:\"plot\"},text:t1.text,textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"current path\",\"percent root\",\"percent entry\",\"percent parent\"],extras:[\"none\"],editType:\"plot\"},texttemplate:C6t({editType:\"plot\"},{keys:eCe.eventDataKeys.concat([\"label\",\"value\"])}),texttemplatefallback:Qke({editType:\"plot\"}),hovertext:t1.hovertext,hoverinfo:NE({},E6t.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"name\",\"current path\",\"percent root\",\"percent entry\",\"percent parent\"],dflt:\"label+text+value+name\"}),hovertemplate:k6t({},{keys:eCe.eventDataKeys}),hovertemplatefallback:Qke(),textfont:t1.textfont,insidetextorientation:t1.insidetextorientation,insidetextfont:t1.insidetextfont,outsidetextfont:NE({},t1.outsidetextfont,{}),rotation:{valType:\"angle\",dflt:0,editType:\"plot\"},sort:t1.sort,root:{color:{valType:\"color\",editType:\"calc\",dflt:\"rgba(0,0,0,0)\"},editType:\"calc\"},domain:P6t({name:\"sunburst\",trace:!0,editType:\"calc\"})}});var CW=ye((Gvr,rCe)=>{\"use 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ie=rk.select(this),Se=Av.ensureSingle(ie,\"path\",\"surface\",function(Pe){Pe.style(\"pointer-events\",i?\"none\":\"all\")});ge.rpx0=q(ge.y0),ge.rpx1=q(ge.y1),ge.xmid=(ge.x0+ge.x1)/2,ge.pxmid=V(ge.rpx1,ge.xmid),ge.midangle=-(ge.xmid-Math.PI/2),ge.startangle=-(ge.x0-Math.PI/2),ge.stopangle=-(ge.x1-Math.PI/2),ge.halfangle=.5*Math.min(Av.angleDelta(ge.x0,ge.x1)||Math.PI,Math.PI),ge.ring=1-ge.rpx0/ge.rpx1,ge.rInscribed=OLt(ge,c),o?Se.transition().attrTween(\"d\",function(Pe){var ce=_e(Pe);return function(He){return G(ce(He))}}):Se.attr(\"d\",G),ie.call(zLt,h,e,t,{eventDataKeys:QW.eventDataKeys,transitionTime:QW.CLICK_TRANSITION_TIME,transitionEasing:QW.CLICK_TRANSITION_EASING}).call(Il.setSliceCursor,e,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:e._transitioning}),Se.call(DLt,ge,c,e);var ke=Av.ensureSingle(ie,\"g\",\"slicetext\"),be=Av.ensureSingle(ke,\"text\",\"\",function(Pe){Pe.attr(\"data-notex\",1)}),Be=Av.ensureUniformFontSize(e,Il.determineTextFont(c,ge,a.font));be.text(YD.formatSliceLabel(ge,h,c,t,a)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(P6e.font,Be).call(CLt.convertToTspans,e);var Le=P6e.bBox(be.node());ge.transform=RLt(Le,ge,u),ge.transform.targetX=Z(ge),ge.transform.targetY=j(ge);var me=function(Pe,ce){var He=Pe.transform;return ILt(He,ce),He.fontSize=Be.size,I6e(c.type,He,a),Av.getTextTransform(He)};o?be.transition().attrTween(\"transform\",function(Pe){var ce=Ee(Pe);return function(He){return me(ce(He),Le)}}):be.attr(\"transform\",me(ge,Le))});function ae(ge){var ie=Il.getPtId(ge),Se=x[ie],ke=x[Il.getPtId(h)],be;if(ke){var Be=(ge.x1>ke.x1?2*Math.PI:0)+E;be=ge.rpx1<ke.rpx1?{x0:ge.x0,x1:ge.x1,rpx0:0,rpx1:0}:{x0:Be,x1:Be,rpx0:ge.rpx0,rpx1:ge.rpx1}}else{var Le,me=Il.getPtId(ge.parent);l.each(function(mt){if(Il.getPtId(mt)===me)return Le=mt});var Pe=Le.children,ce;Pe.forEach(function(mt,Ht){if(Il.getPtId(mt)===ie)return ce=Ht});var He=Pe.length,lt=Jg(Le.x0,Le.x1);be={rpx0:k,rpx1:k,x0:lt(ce/He),x1:lt((ce+1)/He)}}return Jg(Se,be)}function _e(ge){var ie=x[Il.getPtId(ge)],Se,ke={x0:ge.x0,x1:ge.x1,rpx0:ge.rpx0,rpx1:ge.rpx1};if(ie)Se=ie;else if(L)if(ge.parent)if(N){var be=(ge.x1>N?2*Math.PI:0)+E;Se={x0:be,x1:be}}else Se={rpx0:k,rpx1:k},Av.extendFlat(Se,Ce(ge));else Se={rpx0:0,rpx1:0};else Se={x0:E,x1:E};return Jg(Se,ke)}function Ee(ge){var ie=x[Il.getPtId(ge)],Se,ke=ge.transform;if(ie)Se=ie;else if(Se={rpx1:ge.rpx1,transform:{textPosAngle:ke.textPosAngle,scale:0,rotate:ke.rotate,rCenter:ke.rCenter,x:ke.x,y:ke.y}},L)if(ge.parent)if(N){var be=ge.x1>N?2*Math.PI:0;Se.x0=Se.x1=be}else Av.extendFlat(Se,Ce(ge));else Se.x0=Se.x1=E;else Se.x0=Se.x1=E;var 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C={};u.label&&(C.label=u.label),u.hasOwnProperty(\"v\")&&(C.value=u.v,C.valueLabel=Il.formatValue(u.v,s)),C.currentPath=Il.getPath(e.data),f||(C.percentParent=d/Il.getValue(h),C.percentParentLabel=Il.formatPercent(C.percentParent,s),C.parent=Il.getPtLabel(h)),C.percentEntry=d/Il.getValue(t),C.percentEntryLabel=Il.formatPercent(C.percentEntry,s),C.entry=Il.getPtLabel(t),C.percentRoot=d/Il.getValue(c),C.percentRootLabel=Il.formatPercent(C.percentRoot,s),C.root=Il.getPtLabel(c),u.hasOwnProperty(\"color\")&&(C.color=u.color);var S=Av.castOption(r,u.i,\"text\");return(Av.isValidTextValue(S)||S===\"\")&&(C.text=S),C.customdata=Av.castOption(r,u.i,\"customdata\"),Av.texttemplateString({data:[C,r._meta],fallback:r.texttemplatefallback,labels:C,locale:i._d3locale,template:x})};function OLt(e){return e.rpx0===0&&Av.isFullCircle([e.x0,e.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(e.halfangle)),e.ring/2))}function D6e(e){return BLt(e.rpx1,e.transform.textPosAngle)}function 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u=a(\"tiling.packing\");u===\"squarify\"&&a(\"tiling.squarifyratio\"),a(\"tiling.flip\"),a(\"tiling.pad\");var c=a(\"text\");a(\"texttemplate\"),a(\"texttemplatefallback\"),r.texttemplate||a(\"textinfo\",Z6e.isArrayOrTypedArray(c)?\"text+label\":\"label\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var f=a(\"pathbar.visible\"),h=\"auto\";ZLt(t,r,i,a,h,{hasPathbar:f,moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),a(\"textposition\");var d=r.textposition.indexOf(\"bottom\")!==-1;KLt(t,r,i,a);var v=r._hasColorscale=JLt(t,\"marker\",\"colors\")||(t.marker||{}).coloraxis;v?$Lt(t,r,i,a,{prefix:\"marker.\",cLetter:\"c\"}):a(\"marker.depthfade\",!(r.marker.colors||[]).length);var m=r.textfont.size*2;a(\"marker.pad.t\",d?m/4:m),a(\"marker.pad.l\",m/4),a(\"marker.pad.r\",m/4),a(\"marker.pad.b\",d?m:m/4),a(\"marker.cornerradius\"),r._hovered={marker:{line:{width:2,color:WLt.contrast(i.paper_bgcolor)}}},f&&(a(\"pathbar.thickness\",r.pathbar.textfont.size+2*YLt),a(\"pathbar.side\"),a(\"pathbar.edgeshape\")),a(\"sort\"),a(\"root.color\"),XLt(r,i,a),r._length=null}});var Q6e=ye((X0r,$6e)=>{\"use strict\";var QLt=Pr(),ePt=tX();$6e.exports=function(t,r){function n(i,a){return QLt.coerce(t,r,ePt,i,a)}n(\"treemapcolorway\",r.colorway),n(\"extendtreemapcolors\")}});var iX=ye(rX=>{\"use strict\";var eLe=HE();rX.calc=function(e,t){return eLe.calc(e,t)};rX.crossTraceCalc=function(e){return eLe._runCrossTraceCalc(\"treemap\",e)}});var nX=ye((Y0r,tLe)=>{\"use strict\";tLe.exports=function e(t,r,n){var i;n.swapXY&&(i=t.x0,t.x0=t.y0,t.y0=i,i=t.x1,t.x1=t.y1,t.y1=i),n.flipX&&(i=t.x0,t.x0=r[0]-t.x1,t.x1=r[0]-i),n.flipY&&(i=t.y0,t.y0=r[1]-t.y1,t.y1=r[1]-i);var a=t.children;if(a)for(var o=0;o<a.length;o++)e(a[o],r,n)}});var aX=ye((K0r,rLe)=>{\"use strict\";var HA=VE(),tPt=nX();rLe.exports=function(t,r,n){var i=n.flipX,a=n.flipY,o=n.packing===\"dice-slice\",s=n.pad[a?\"bottom\":\"top\"],l=n.pad[i?\"right\":\"left\"],u=n.pad[i?\"left\":\"right\"],c=n.pad[a?\"top\":\"bottom\"],f;o&&(f=l,l=s,s=f,f=u,u=c,c=f);var h=HA.treemap().tile(rPt(n.packing,n.squarifyratio)).paddingInner(n.pad.inner).paddingLeft(l).paddingRight(u).paddingTop(s).paddingBottom(c).size(o?[r[1],r[0]]:r)(t);return(o||i||a)&&tPt(h,r,{swapXY:o,flipX:i,flipY:a}),h};function rPt(e,t){switch(e){case\"squarify\":return HA.treemapSquarify.ratio(t);case\"binary\":return HA.treemapBinary;case\"dice\":return HA.treemapDice;case\"slice\":return HA.treemapSlice;default:return HA.treemapSliceDice}}});var $D=ye((J0r,oLe)=>{\"use strict\";var iLe=Fa(),jA=ka(),nLe=Pr(),oX=n1(),iPt=bv().resizeText,nPt=ZD();function aPt(e){var 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pX=ye((sgr,DLe)=>{\"use strict\";DLe.exports={iciclecolorway:{valType:\"colorlist\",editType:\"calc\"},extendiciclecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}});var OLe=ye((lgr,qLe)=>{\"use strict\";var FLe=Pr(),PPt=vX(),IPt=ka(),RPt=Cc().defaults,DPt=i0().handleText,FPt=S_().TEXTPAD,zPt=O2().handleMarkerDefaults,zLe=tc(),qPt=zLe.hasColorscale,OPt=zLe.handleDefaults;qLe.exports=function(t,r,n,i){function a(d,v){return FLe.coerce(t,r,PPt,d,v)}var o=a(\"labels\"),s=a(\"parents\");if(!o||!o.length||!s||!s.length){r.visible=!1;return}var l=a(\"values\");l&&l.length?a(\"branchvalues\"):a(\"count\"),a(\"level\"),a(\"maxdepth\"),a(\"tiling.orientation\"),a(\"tiling.flip\"),a(\"tiling.pad\");var u=a(\"text\");a(\"texttemplate\"),a(\"texttemplatefallback\"),r.texttemplate||a(\"textinfo\",FLe.isArrayOrTypedArray(u)?\"text+label\":\"label\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var 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GLe=ye((fgr,VLe)=>{\"use strict\";var UPt=VE(),VPt=nX();VLe.exports=function(t,r,n){var i=n.flipX,a=n.flipY,o=n.orientation===\"h\",s=n.maxDepth,l=r[0],u=r[1];s&&(l=(t.height+1)*r[0]/Math.min(t.height+1,s),u=(t.height+1)*r[1]/Math.min(t.height+1,s));var c=UPt.partition().padding(n.pad.inner).size(o?[r[1],l]:[r[0],u])(t);return(o||i||a)&&VPt(c,r,{swapXY:o,flipX:i,flipY:a}),c}});var yX=ye((hgr,ZLe)=>{\"use strict\";var HLe=Fa(),jLe=ka(),WLe=Pr(),GPt=bv().resizeText,HPt=ZD();function jPt(e){var t=e._fullLayout._iciclelayer.selectAll(\".trace\");GPt(e,t,\"icicle\"),t.each(function(r){var n=HLe.select(this),i=r[0],a=i.trace;n.style(\"opacity\",a.opacity),n.selectAll(\"path.surface\").each(function(o){HLe.select(this).call(XLe,o,a,e)})})}function XLe(e,t,r,n){var 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je=+je,Ue=Ue>>>0,Ae||Be(this,je,Ue,4,4294967295,0),this[Ue]=je>>>24,this[Ue+1]=je>>>16,this[Ue+2]=je>>>8,this[Ue+3]=je&255,Ue+4},m.prototype.writeIntLE=function(je,Ue,Ae,rt){if(je=+je,Ue=Ue>>>0,!rt){var St=Math.pow(2,8*Ae-1);Be(this,je,Ue,Ae,St-1,-St)}var Tt=0,dt=1,Et=0;for(this[Ue]=je&255;++Tt<Ae&&(dt*=256);)je<0&&Et===0&&this[Ue+Tt-1]!==0&&(Et=1),this[Ue+Tt]=(je/dt>>0)-Et&255;return Ue+Ae},m.prototype.writeIntBE=function(je,Ue,Ae,rt){if(je=+je,Ue=Ue>>>0,!rt){var St=Math.pow(2,8*Ae-1);Be(this,je,Ue,Ae,St-1,-St)}var Tt=Ae-1,dt=1,Et=0;for(this[Ue+Tt]=je&255;--Tt>=0&&(dt*=256);)je<0&&Et===0&&this[Ue+Tt+1]!==0&&(Et=1),this[Ue+Tt]=(je/dt>>0)-Et&255;return Ue+Ae},m.prototype.writeInt8=function(je,Ue,Ae){return je=+je,Ue=Ue>>>0,Ae||Be(this,je,Ue,1,127,-128),je<0&&(je=255+je+1),this[Ue]=je&255,Ue+1},m.prototype.writeInt16LE=function(je,Ue,Ae){return je=+je,Ue=Ue>>>0,Ae||Be(this,je,Ue,2,32767,-32768),this[Ue]=je&255,this[Ue+1]=je>>>8,Ue+2},m.prototype.writeInt16BE=function(je,Ue,Ae){return 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H,re;this.length>N.length?(H=this,re=N):(H=N,re=this);for(var ae=0;ae<re.length;ae++)this.words[ae]=H.words[ae]^re.words[ae];if(this!==H)for(;ae<H.length;ae++)this.words[ae]=H.words[ae];return this.length=H.length,this.strip()},f.prototype.ixor=function(N){return u((this.negative|N.negative)===0),this.iuxor(N)},f.prototype.xor=function(N){return this.length>N.length?this.clone().ixor(N):N.clone().ixor(this)},f.prototype.uxor=function(N){return this.length>N.length?this.clone().iuxor(N):N.clone().iuxor(this)},f.prototype.inotn=function(N){u(typeof N==\"number\"&&N>=0);var H=Math.ceil(N/26)|0,re=N%26;this._expand(H),re>0&&H--;for(var ae=0;ae<H;ae++)this.words[ae]=~this.words[ae]&67108863;return re>0&&(this.words[ae]=~this.words[ae]&67108863>>26-re),this.strip()},f.prototype.notn=function(N){return this.clone().inotn(N)},f.prototype.setn=function(N,H){u(typeof N==\"number\"&&N>=0);var re=N/26|0,ae=N%26;return 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N.negative!==0&&this.negative===0?(N.negative=0,H=this.sub(N),N.negative^=1,H):N.negative===0&&this.negative!==0?(this.negative=0,H=N.sub(this),this.negative=1,H):this.length>N.length?this.clone().iadd(N):N.clone().iadd(this)},f.prototype.isub=function(N){if(N.negative!==0){N.negative=0;var H=this.iadd(N);return N.negative=1,H._normSign()}else if(this.negative!==0)return this.negative=0,this.iadd(N),this.negative=1,this._normSign();var re=this.cmp(N);if(re===0)return this.negative=0,this.length=1,this.words[0]=0,this;var ae,_e;re>0?(ae=this,_e=N):(ae=N,_e=this);for(var Ee=0,Ce=0;Ce<_e.length;Ce++)H=(ae.words[Ce]|0)-(_e.words[Ce]|0)+Ee,Ee=H>>26,this.words[Ce]=H&67108863;for(;Ee!==0&&Ce<ae.length;Ce++)H=(ae.words[Ce]|0)+Ee,Ee=H>>26,this.words[Ce]=H&67108863;if(Ee===0&&Ce<ae.length&&ae!==this)for(;Ce<ae.length;Ce++)this.words[Ce]=ae.words[Ce];return this.length=Math.max(this.length,Ce),ae!==this&&(this.negative=1),this.strip()},f.prototype.sub=function(N){return this.clone().isub(N)};function T(j,N,H){H.negative=N.negative^j.negative;var re=j.length+N.length|0;H.length=re,re=re-1|0;var ae=j.words[0]|0,_e=N.words[0]|0,Ee=ae*_e,Ce=Ee&67108863,ge=Ee/67108864|0;H.words[0]=Ce;for(var ie=1;ie<re;ie++){for(var Se=ge>>>26,ke=ge&67108863,be=Math.min(ie,N.length-1),Be=Math.max(0,ie-j.length+1);Be<=be;Be++){var Le=ie-Be|0;ae=j.words[Le]|0,_e=N.words[Be]|0,Ee=ae*_e+ke,Se+=Ee/67108864|0,ke=Ee&67108863}H.words[ie]=ke|0,ge=Se|0}return ge!==0?H.words[ie]=ge|0:H.length--,H.strip()}var L=function(N,H,re){var ae=N.words,_e=H.words,Ee=re.words,Ce=0,ge,ie,Se,ke=ae[0]|0,be=ke&8191,Be=ke>>>13,Le=ae[1]|0,me=Le&8191,Pe=Le>>>13,ce=ae[2]|0,He=ce&8191,lt=ce>>>13,mt=ae[3]|0,Ht=mt&8191,at=mt>>>13,ct=ae[4]|0,ar=ct&8191,Vt=ct>>>13,rr=ae[5]|0,tt=rr&8191,je=rr>>>13,Ue=ae[6]|0,Ae=Ue&8191,rt=Ue>>>13,St=ae[7]|0,Tt=St&8191,dt=St>>>13,Et=ae[8]|0,pt=Et&8191,jt=Et>>>13,or=ae[9]|0,mr=or&8191,Ar=or>>>13,ei=_e[0]|0,qr=ei&8191,jr=ei>>>13,gt=_e[1]|0,Ge=gt&8191,Je=gt>>>13,We=_e[2]|0,et=We&8191,xt=We>>>13,At=_e[3]|0,Kt=At&8191,Qt=At>>>13,Mr=_e[4]|0,Gr=Mr&8191,Ir=Mr>>>13,Yr=_e[5]|0,_i=Yr&8191,Pi=Yr>>>13,ai=_e[6]|0,mi=ai&8191,un=ai>>>13,Fn=_e[7]|0,An=Fn&8191,Hn=Fn>>>13,Qn=_e[8]|0,Vi=Qn&8191,Jn=Qn>>>13,Yn=_e[9]|0,Gt=Yn&8191,wt=Yn>>>13;re.negative=N.negative^H.negative,re.length=19,ge=Math.imul(be,qr),ie=Math.imul(be,jr),ie=ie+Math.imul(Be,qr)|0,Se=Math.imul(Be,jr);var tr=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(tr>>>26)|0,tr&=67108863,ge=Math.imul(me,qr),ie=Math.imul(me,jr),ie=ie+Math.imul(Pe,qr)|0,Se=Math.imul(Pe,jr),ge=ge+Math.imul(be,Ge)|0,ie=ie+Math.imul(be,Je)|0,ie=ie+Math.imul(Be,Ge)|0,Se=Se+Math.imul(Be,Je)|0;var ir=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ir>>>26)|0,ir&=67108863,ge=Math.imul(He,qr),ie=Math.imul(He,jr),ie=ie+Math.imul(lt,qr)|0,Se=Math.imul(lt,jr),ge=ge+Math.imul(me,Ge)|0,ie=ie+Math.imul(me,Je)|0,ie=ie+Math.imul(Pe,Ge)|0,Se=Se+Math.imul(Pe,Je)|0,ge=ge+Math.imul(be,et)|0,ie=ie+Math.imul(be,xt)|0,ie=ie+Math.imul(Be,et)|0,Se=Se+Math.imul(Be,xt)|0;var wr=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(wr>>>26)|0,wr&=67108863,ge=Math.imul(Ht,qr),ie=Math.imul(Ht,jr),ie=ie+Math.imul(at,qr)|0,Se=Math.imul(at,jr),ge=ge+Math.imul(He,Ge)|0,ie=ie+Math.imul(He,Je)|0,ie=ie+Math.imul(lt,Ge)|0,Se=Se+Math.imul(lt,Je)|0,ge=ge+Math.imul(me,et)|0,ie=ie+Math.imul(me,xt)|0,ie=ie+Math.imul(Pe,et)|0,Se=Se+Math.imul(Pe,xt)|0,ge=ge+Math.imul(be,Kt)|0,ie=ie+Math.imul(be,Qt)|0,ie=ie+Math.imul(Be,Kt)|0,Se=Se+Math.imul(Be,Qt)|0;var Xr=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Xr>>>26)|0,Xr&=67108863,ge=Math.imul(ar,qr),ie=Math.imul(ar,jr),ie=ie+Math.imul(Vt,qr)|0,Se=Math.imul(Vt,jr),ge=ge+Math.imul(Ht,Ge)|0,ie=ie+Math.imul(Ht,Je)|0,ie=ie+Math.imul(at,Ge)|0,Se=Se+Math.imul(at,Je)|0,ge=ge+Math.imul(He,et)|0,ie=ie+Math.imul(He,xt)|0,ie=ie+Math.imul(lt,et)|0,Se=Se+Math.imul(lt,xt)|0,ge=ge+Math.imul(me,Kt)|0,ie=ie+Math.imul(me,Qt)|0,ie=ie+Math.imul(Pe,Kt)|0,Se=Se+Math.imul(Pe,Qt)|0,ge=ge+Math.imul(be,Gr)|0,ie=ie+Math.imul(be,Ir)|0,ie=ie+Math.imul(Be,Gr)|0,Se=Se+Math.imul(Be,Ir)|0;var ti=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ti>>>26)|0,ti&=67108863,ge=Math.imul(tt,qr),ie=Math.imul(tt,jr),ie=ie+Math.imul(je,qr)|0,Se=Math.imul(je,jr),ge=ge+Math.imul(ar,Ge)|0,ie=ie+Math.imul(ar,Je)|0,ie=ie+Math.imul(Vt,Ge)|0,Se=Se+Math.imul(Vt,Je)|0,ge=ge+Math.imul(Ht,et)|0,ie=ie+Math.imul(Ht,xt)|0,ie=ie+Math.imul(at,et)|0,Se=Se+Math.imul(at,xt)|0,ge=ge+Math.imul(He,Kt)|0,ie=ie+Math.imul(He,Qt)|0,ie=ie+Math.imul(lt,Kt)|0,Se=Se+Math.imul(lt,Qt)|0,ge=ge+Math.imul(me,Gr)|0,ie=ie+Math.imul(me,Ir)|0,ie=ie+Math.imul(Pe,Gr)|0,Se=Se+Math.imul(Pe,Ir)|0,ge=ge+Math.imul(be,_i)|0,ie=ie+Math.imul(be,Pi)|0,ie=ie+Math.imul(Be,_i)|0,Se=Se+Math.imul(Be,Pi)|0;var $r=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+($r>>>26)|0,$r&=67108863,ge=Math.imul(Ae,qr),ie=Math.imul(Ae,jr),ie=ie+Math.imul(rt,qr)|0,Se=Math.imul(rt,jr),ge=ge+Math.imul(tt,Ge)|0,ie=ie+Math.imul(tt,Je)|0,ie=ie+Math.imul(je,Ge)|0,Se=Se+Math.imul(je,Je)|0,ge=ge+Math.imul(ar,et)|0,ie=ie+Math.imul(ar,xt)|0,ie=ie+Math.imul(Vt,et)|0,Se=Se+Math.imul(Vt,xt)|0,ge=ge+Math.imul(Ht,Kt)|0,ie=ie+Math.imul(Ht,Qt)|0,ie=ie+Math.imul(at,Kt)|0,Se=Se+Math.imul(at,Qt)|0,ge=ge+Math.imul(He,Gr)|0,ie=ie+Math.imul(He,Ir)|0,ie=ie+Math.imul(lt,Gr)|0,Se=Se+Math.imul(lt,Ir)|0,ge=ge+Math.imul(me,_i)|0,ie=ie+Math.imul(me,Pi)|0,ie=ie+Math.imul(Pe,_i)|0,Se=Se+Math.imul(Pe,Pi)|0,ge=ge+Math.imul(be,mi)|0,ie=ie+Math.imul(be,un)|0,ie=ie+Math.imul(Be,mi)|0,Se=Se+Math.imul(Be,un)|0;var Ri=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Ri>>>26)|0,Ri&=67108863,ge=Math.imul(Tt,qr),ie=Math.imul(Tt,jr),ie=ie+Math.imul(dt,qr)|0,Se=Math.imul(dt,jr),ge=ge+Math.imul(Ae,Ge)|0,ie=ie+Math.imul(Ae,Je)|0,ie=ie+Math.imul(rt,Ge)|0,Se=Se+Math.imul(rt,Je)|0,ge=ge+Math.imul(tt,et)|0,ie=ie+Math.imul(tt,xt)|0,ie=ie+Math.imul(je,et)|0,Se=Se+Math.imul(je,xt)|0,ge=ge+Math.imul(ar,Kt)|0,ie=ie+Math.imul(ar,Qt)|0,ie=ie+Math.imul(Vt,Kt)|0,Se=Se+Math.imul(Vt,Qt)|0,ge=ge+Math.imul(Ht,Gr)|0,ie=ie+Math.imul(Ht,Ir)|0,ie=ie+Math.imul(at,Gr)|0,Se=Se+Math.imul(at,Ir)|0,ge=ge+Math.imul(He,_i)|0,ie=ie+Math.imul(He,Pi)|0,ie=ie+Math.imul(lt,_i)|0,Se=Se+Math.imul(lt,Pi)|0,ge=ge+Math.imul(me,mi)|0,ie=ie+Math.imul(me,un)|0,ie=ie+Math.imul(Pe,mi)|0,Se=Se+Math.imul(Pe,un)|0,ge=ge+Math.imul(be,An)|0,ie=ie+Math.imul(be,Hn)|0,ie=ie+Math.imul(Be,An)|0,Se=Se+Math.imul(Be,Hn)|0;var Hi=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Hi>>>26)|0,Hi&=67108863,ge=Math.imul(pt,qr),ie=Math.imul(pt,jr),ie=ie+Math.imul(jt,qr)|0,Se=Math.imul(jt,jr),ge=ge+Math.imul(Tt,Ge)|0,ie=ie+Math.imul(Tt,Je)|0,ie=ie+Math.imul(dt,Ge)|0,Se=Se+Math.imul(dt,Je)|0,ge=ge+Math.imul(Ae,et)|0,ie=ie+Math.imul(Ae,xt)|0,ie=ie+Math.imul(rt,et)|0,Se=Se+Math.imul(rt,xt)|0,ge=ge+Math.imul(tt,Kt)|0,ie=ie+Math.imul(tt,Qt)|0,ie=ie+Math.imul(je,Kt)|0,Se=Se+Math.imul(je,Qt)|0,ge=ge+Math.imul(ar,Gr)|0,ie=ie+Math.imul(ar,Ir)|0,ie=ie+Math.imul(Vt,Gr)|0,Se=Se+Math.imul(Vt,Ir)|0,ge=ge+Math.imul(Ht,_i)|0,ie=ie+Math.imul(Ht,Pi)|0,ie=ie+Math.imul(at,_i)|0,Se=Se+Math.imul(at,Pi)|0,ge=ge+Math.imul(He,mi)|0,ie=ie+Math.imul(He,un)|0,ie=ie+Math.imul(lt,mi)|0,Se=Se+Math.imul(lt,un)|0,ge=ge+Math.imul(me,An)|0,ie=ie+Math.imul(me,Hn)|0,ie=ie+Math.imul(Pe,An)|0,Se=Se+Math.imul(Pe,Hn)|0,ge=ge+Math.imul(be,Vi)|0,ie=ie+Math.imul(be,Jn)|0,ie=ie+Math.imul(Be,Vi)|0,Se=Se+Math.imul(Be,Jn)|0;var en=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(en>>>26)|0,en&=67108863,ge=Math.imul(mr,qr),ie=Math.imul(mr,jr),ie=ie+Math.imul(Ar,qr)|0,Se=Math.imul(Ar,jr),ge=ge+Math.imul(pt,Ge)|0,ie=ie+Math.imul(pt,Je)|0,ie=ie+Math.imul(jt,Ge)|0,Se=Se+Math.imul(jt,Je)|0,ge=ge+Math.imul(Tt,et)|0,ie=ie+Math.imul(Tt,xt)|0,ie=ie+Math.imul(dt,et)|0,Se=Se+Math.imul(dt,xt)|0,ge=ge+Math.imul(Ae,Kt)|0,ie=ie+Math.imul(Ae,Qt)|0,ie=ie+Math.imul(rt,Kt)|0,Se=Se+Math.imul(rt,Qt)|0,ge=ge+Math.imul(tt,Gr)|0,ie=ie+Math.imul(tt,Ir)|0,ie=ie+Math.imul(je,Gr)|0,Se=Se+Math.imul(je,Ir)|0,ge=ge+Math.imul(ar,_i)|0,ie=ie+Math.imul(ar,Pi)|0,ie=ie+Math.imul(Vt,_i)|0,Se=Se+Math.imul(Vt,Pi)|0,ge=ge+Math.imul(Ht,mi)|0,ie=ie+Math.imul(Ht,un)|0,ie=ie+Math.imul(at,mi)|0,Se=Se+Math.imul(at,un)|0,ge=ge+Math.imul(He,An)|0,ie=ie+Math.imul(He,Hn)|0,ie=ie+Math.imul(lt,An)|0,Se=Se+Math.imul(lt,Hn)|0,ge=ge+Math.imul(me,Vi)|0,ie=ie+Math.imul(me,Jn)|0,ie=ie+Math.imul(Pe,Vi)|0,Se=Se+Math.imul(Pe,Jn)|0,ge=ge+Math.imul(be,Gt)|0,ie=ie+Math.imul(be,wt)|0,ie=ie+Math.imul(Be,Gt)|0,Se=Se+Math.imul(Be,wt)|0;var fn=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(fn>>>26)|0,fn&=67108863,ge=Math.imul(mr,Ge),ie=Math.imul(mr,Je),ie=ie+Math.imul(Ar,Ge)|0,Se=Math.imul(Ar,Je),ge=ge+Math.imul(pt,et)|0,ie=ie+Math.imul(pt,xt)|0,ie=ie+Math.imul(jt,et)|0,Se=Se+Math.imul(jt,xt)|0,ge=ge+Math.imul(Tt,Kt)|0,ie=ie+Math.imul(Tt,Qt)|0,ie=ie+Math.imul(dt,Kt)|0,Se=Se+Math.imul(dt,Qt)|0,ge=ge+Math.imul(Ae,Gr)|0,ie=ie+Math.imul(Ae,Ir)|0,ie=ie+Math.imul(rt,Gr)|0,Se=Se+Math.imul(rt,Ir)|0,ge=ge+Math.imul(tt,_i)|0,ie=ie+Math.imul(tt,Pi)|0,ie=ie+Math.imul(je,_i)|0,Se=Se+Math.imul(je,Pi)|0,ge=ge+Math.imul(ar,mi)|0,ie=ie+Math.imul(ar,un)|0,ie=ie+Math.imul(Vt,mi)|0,Se=Se+Math.imul(Vt,un)|0,ge=ge+Math.imul(Ht,An)|0,ie=ie+Math.imul(Ht,Hn)|0,ie=ie+Math.imul(at,An)|0,Se=Se+Math.imul(at,Hn)|0,ge=ge+Math.imul(He,Vi)|0,ie=ie+Math.imul(He,Jn)|0,ie=ie+Math.imul(lt,Vi)|0,Se=Se+Math.imul(lt,Jn)|0,ge=ge+Math.imul(me,Gt)|0,ie=ie+Math.imul(me,wt)|0,ie=ie+Math.imul(Pe,Gt)|0,Se=Se+Math.imul(Pe,wt)|0;var yn=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(yn>>>26)|0,yn&=67108863,ge=Math.imul(mr,et),ie=Math.imul(mr,xt),ie=ie+Math.imul(Ar,et)|0,Se=Math.imul(Ar,xt),ge=ge+Math.imul(pt,Kt)|0,ie=ie+Math.imul(pt,Qt)|0,ie=ie+Math.imul(jt,Kt)|0,Se=Se+Math.imul(jt,Qt)|0,ge=ge+Math.imul(Tt,Gr)|0,ie=ie+Math.imul(Tt,Ir)|0,ie=ie+Math.imul(dt,Gr)|0,Se=Se+Math.imul(dt,Ir)|0,ge=ge+Math.imul(Ae,_i)|0,ie=ie+Math.imul(Ae,Pi)|0,ie=ie+Math.imul(rt,_i)|0,Se=Se+Math.imul(rt,Pi)|0,ge=ge+Math.imul(tt,mi)|0,ie=ie+Math.imul(tt,un)|0,ie=ie+Math.imul(je,mi)|0,Se=Se+Math.imul(je,un)|0,ge=ge+Math.imul(ar,An)|0,ie=ie+Math.imul(ar,Hn)|0,ie=ie+Math.imul(Vt,An)|0,Se=Se+Math.imul(Vt,Hn)|0,ge=ge+Math.imul(Ht,Vi)|0,ie=ie+Math.imul(Ht,Jn)|0,ie=ie+Math.imul(at,Vi)|0,Se=Se+Math.imul(at,Jn)|0,ge=ge+Math.imul(He,Gt)|0,ie=ie+Math.imul(He,wt)|0,ie=ie+Math.imul(lt,Gt)|0,Se=Se+Math.imul(lt,wt)|0;var Mn=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Mn>>>26)|0,Mn&=67108863,ge=Math.imul(mr,Kt),ie=Math.imul(mr,Qt),ie=ie+Math.imul(Ar,Kt)|0,Se=Math.imul(Ar,Qt),ge=ge+Math.imul(pt,Gr)|0,ie=ie+Math.imul(pt,Ir)|0,ie=ie+Math.imul(jt,Gr)|0,Se=Se+Math.imul(jt,Ir)|0,ge=ge+Math.imul(Tt,_i)|0,ie=ie+Math.imul(Tt,Pi)|0,ie=ie+Math.imul(dt,_i)|0,Se=Se+Math.imul(dt,Pi)|0,ge=ge+Math.imul(Ae,mi)|0,ie=ie+Math.imul(Ae,un)|0,ie=ie+Math.imul(rt,mi)|0,Se=Se+Math.imul(rt,un)|0,ge=ge+Math.imul(tt,An)|0,ie=ie+Math.imul(tt,Hn)|0,ie=ie+Math.imul(je,An)|0,Se=Se+Math.imul(je,Hn)|0,ge=ge+Math.imul(ar,Vi)|0,ie=ie+Math.imul(ar,Jn)|0,ie=ie+Math.imul(Vt,Vi)|0,Se=Se+Math.imul(Vt,Jn)|0,ge=ge+Math.imul(Ht,Gt)|0,ie=ie+Math.imul(Ht,wt)|0,ie=ie+Math.imul(at,Gt)|0,Se=Se+Math.imul(at,wt)|0;var Ba=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Ba>>>26)|0,Ba&=67108863,ge=Math.imul(mr,Gr),ie=Math.imul(mr,Ir),ie=ie+Math.imul(Ar,Gr)|0,Se=Math.imul(Ar,Ir),ge=ge+Math.imul(pt,_i)|0,ie=ie+Math.imul(pt,Pi)|0,ie=ie+Math.imul(jt,_i)|0,Se=Se+Math.imul(jt,Pi)|0,ge=ge+Math.imul(Tt,mi)|0,ie=ie+Math.imul(Tt,un)|0,ie=ie+Math.imul(dt,mi)|0,Se=Se+Math.imul(dt,un)|0,ge=ge+Math.imul(Ae,An)|0,ie=ie+Math.imul(Ae,Hn)|0,ie=ie+Math.imul(rt,An)|0,Se=Se+Math.imul(rt,Hn)|0,ge=ge+Math.imul(tt,Vi)|0,ie=ie+Math.imul(tt,Jn)|0,ie=ie+Math.imul(je,Vi)|0,Se=Se+Math.imul(je,Jn)|0,ge=ge+Math.imul(ar,Gt)|0,ie=ie+Math.imul(ar,wt)|0,ie=ie+Math.imul(Vt,Gt)|0,Se=Se+Math.imul(Vt,wt)|0;var ua=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ua>>>26)|0,ua&=67108863,ge=Math.imul(mr,_i),ie=Math.imul(mr,Pi),ie=ie+Math.imul(Ar,_i)|0,Se=Math.imul(Ar,Pi),ge=ge+Math.imul(pt,mi)|0,ie=ie+Math.imul(pt,un)|0,ie=ie+Math.imul(jt,mi)|0,Se=Se+Math.imul(jt,un)|0,ge=ge+Math.imul(Tt,An)|0,ie=ie+Math.imul(Tt,Hn)|0,ie=ie+Math.imul(dt,An)|0,Se=Se+Math.imul(dt,Hn)|0,ge=ge+Math.imul(Ae,Vi)|0,ie=ie+Math.imul(Ae,Jn)|0,ie=ie+Math.imul(rt,Vi)|0,Se=Se+Math.imul(rt,Jn)|0,ge=ge+Math.imul(tt,Gt)|0,ie=ie+Math.imul(tt,wt)|0,ie=ie+Math.imul(je,Gt)|0,Se=Se+Math.imul(je,wt)|0;var ma=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(ma>>>26)|0,ma&=67108863,ge=Math.imul(mr,mi),ie=Math.imul(mr,un),ie=ie+Math.imul(Ar,mi)|0,Se=Math.imul(Ar,un),ge=ge+Math.imul(pt,An)|0,ie=ie+Math.imul(pt,Hn)|0,ie=ie+Math.imul(jt,An)|0,Se=Se+Math.imul(jt,Hn)|0,ge=ge+Math.imul(Tt,Vi)|0,ie=ie+Math.imul(Tt,Jn)|0,ie=ie+Math.imul(dt,Vi)|0,Se=Se+Math.imul(dt,Jn)|0,ge=ge+Math.imul(Ae,Gt)|0,ie=ie+Math.imul(Ae,wt)|0,ie=ie+Math.imul(rt,Gt)|0,Se=Se+Math.imul(rt,wt)|0;var Wa=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Wa>>>26)|0,Wa&=67108863,ge=Math.imul(mr,An),ie=Math.imul(mr,Hn),ie=ie+Math.imul(Ar,An)|0,Se=Math.imul(Ar,Hn),ge=ge+Math.imul(pt,Vi)|0,ie=ie+Math.imul(pt,Jn)|0,ie=ie+Math.imul(jt,Vi)|0,Se=Se+Math.imul(jt,Jn)|0,ge=ge+Math.imul(Tt,Gt)|0,ie=ie+Math.imul(Tt,wt)|0,ie=ie+Math.imul(dt,Gt)|0,Se=Se+Math.imul(dt,wt)|0;var za=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(za>>>26)|0,za&=67108863,ge=Math.imul(mr,Vi),ie=Math.imul(mr,Jn),ie=ie+Math.imul(Ar,Vi)|0,Se=Math.imul(Ar,Jn),ge=ge+Math.imul(pt,Gt)|0,ie=ie+Math.imul(pt,wt)|0,ie=ie+Math.imul(jt,Gt)|0,Se=Se+Math.imul(jt,wt)|0;var Xo=(Ce+ge|0)+((ie&8191)<<13)|0;Ce=(Se+(ie>>>13)|0)+(Xo>>>26)|0,Xo&=67108863,ge=Math.imul(mr,Gt),ie=Math.imul(mr,wt),ie=ie+Math.imul(Ar,Gt)|0,Se=Math.imul(Ar,wt);var da=(Ce+ge|0)+((ie&8191)<<13)|0;return Ce=(Se+(ie>>>13)|0)+(da>>>26)|0,da&=67108863,Ee[0]=tr,Ee[1]=ir,Ee[2]=wr,Ee[3]=Xr,Ee[4]=ti,Ee[5]=$r,Ee[6]=Ri,Ee[7]=Hi,Ee[8]=en,Ee[9]=fn,Ee[10]=yn,Ee[11]=Mn,Ee[12]=Ba,Ee[13]=ua,Ee[14]=ma,Ee[15]=Wa,Ee[16]=za,Ee[17]=Xo,Ee[18]=da,Ce!==0&&(Ee[19]=Ce,re.length++),re};Math.imul||(L=T);function x(j,N,H){H.negative=N.negative^j.negative,H.length=j.length+N.length;for(var re=0,ae=0,_e=0;_e<H.length-1;_e++){var Ee=ae;ae=0;for(var Ce=re&67108863,ge=Math.min(_e,N.length-1),ie=Math.max(0,_e-j.length+1);ie<=ge;ie++){var Se=_e-ie,ke=j.words[Se]|0,be=N.words[ie]|0,Be=ke*be,Le=Be&67108863;Ee=Ee+(Be/67108864|0)|0,Le=Le+Ce|0,Ce=Le&67108863,Ee=Ee+(Le>>>26)|0,ae+=Ee>>>26,Ee&=67108863}H.words[_e]=Ce,re=Ee,Ee=ae}return re!==0?H.words[_e]=re:H.length--,H.strip()}function C(j,N,H){var re=new S;return re.mulp(j,N,H)}f.prototype.mulTo=function(N,H){var re,ae=this.length+N.length;return this.length===10&&N.length===10?re=L(this,N,H):ae<63?re=T(this,N,H):ae<1024?re=x(this,N,H):re=C(this,N,H),re};function S(j,N){this.x=j,this.y=N}S.prototype.makeRBT=function(N){for(var H=new Array(N),re=f.prototype._countBits(N)-1,ae=0;ae<N;ae++)H[ae]=this.revBin(ae,re,N);return H},S.prototype.revBin=function(N,H,re){if(N===0||N===re-1)return N;for(var ae=0,_e=0;_e<H;_e++)ae|=(N&1)<<H-_e-1,N>>=1;return ae},S.prototype.permute=function(N,H,re,ae,_e,Ee){for(var Ce=0;Ce<Ee;Ce++)ae[Ce]=H[N[Ce]],_e[Ce]=re[N[Ce]]},S.prototype.transform=function(N,H,re,ae,_e,Ee){this.permute(Ee,N,H,re,ae,_e);for(var Ce=1;Ce<_e;Ce<<=1)for(var ge=Ce<<1,ie=Math.cos(2*Math.PI/ge),Se=Math.sin(2*Math.PI/ge),ke=0;ke<_e;ke+=ge)for(var be=ie,Be=Se,Le=0;Le<Ce;Le++){var me=re[ke+Le],Pe=ae[ke+Le],ce=re[ke+Le+Ce],He=ae[ke+Le+Ce],lt=be*ce-Be*He;He=be*He+Be*ce,ce=lt,re[ke+Le]=me+ce,ae[ke+Le]=Pe+He,re[ke+Le+Ce]=me-ce,ae[ke+Le+Ce]=Pe-He,Le!==ge&&(lt=ie*be-Se*Be,Be=ie*Be+Se*be,be=lt)}},S.prototype.guessLen13b=function(N,H){var re=Math.max(H,N)|1,ae=re&1,_e=0;for(re=re/2|0;re;re=re>>>1)_e++;return 1<<_e+1+ae},S.prototype.conjugate=function(N,H,re){if(!(re<=1))for(var ae=0;ae<re/2;ae++){var _e=N[ae];N[ae]=N[re-ae-1],N[re-ae-1]=_e,_e=H[ae],H[ae]=-H[re-ae-1],H[re-ae-1]=-_e}},S.prototype.normalize13b=function(N,H){for(var re=0,ae=0;ae<H/2;ae++){var _e=Math.round(N[2*ae+1]/H)*8192+Math.round(N[2*ae]/H)+re;N[ae]=_e&67108863,_e<67108864?re=0:re=_e/67108864|0}return N},S.prototype.convert13b=function(N,H,re,ae){for(var _e=0,Ee=0;Ee<H;Ee++)_e=_e+(N[Ee]|0),re[2*Ee]=_e&8191,_e=_e>>>13,re[2*Ee+1]=_e&8191,_e=_e>>>13;for(Ee=2*H;Ee<ae;++Ee)re[Ee]=0;u(_e===0),u((_e&-8192)===0)},S.prototype.stub=function(N){for(var H=new Array(N),re=0;re<N;re++)H[re]=0;return H},S.prototype.mulp=function(N,H,re){var ae=2*this.guessLen13b(N.length,H.length),_e=this.makeRBT(ae),Ee=this.stub(ae),Ce=new Array(ae),ge=new Array(ae),ie=new Array(ae),Se=new Array(ae),ke=new Array(ae),be=new Array(ae),Be=re.words;Be.length=ae,this.convert13b(N.words,N.length,Ce,ae),this.convert13b(H.words,H.length,Se,ae),this.transform(Ce,Ee,ge,ie,ae,_e),this.transform(Se,Ee,ke,be,ae,_e);for(var Le=0;Le<ae;Le++){var me=ge[Le]*ke[Le]-ie[Le]*be[Le];ie[Le]=ge[Le]*be[Le]+ie[Le]*ke[Le],ge[Le]=me}return this.conjugate(ge,ie,ae),this.transform(ge,ie,Be,Ee,ae,_e),this.conjugate(Be,Ee,ae),this.normalize13b(Be,ae),re.negative=N.negative^H.negative,re.length=N.length+H.length,re.strip()},f.prototype.mul=function(N){var H=new f(null);return H.words=new Array(this.length+N.length),this.mulTo(N,H)},f.prototype.mulf=function(N){var H=new f(null);return H.words=new Array(this.length+N.length),C(this,N,H)},f.prototype.imul=function(N){return this.clone().mulTo(N,this)},f.prototype.imuln=function(N){u(typeof N==\"number\"),u(N<67108864);for(var H=0,re=0;re<this.length;re++){var ae=(this.words[re]|0)*N,_e=(ae&67108863)+(H&67108863);H>>=26,H+=ae/67108864|0,H+=_e>>>26,this.words[re]=_e&67108863}return H!==0&&(this.words[re]=H,this.length++),this.length=N===0?1:this.length,this},f.prototype.muln=function(N){return this.clone().imuln(N)},f.prototype.sqr=function(){return this.mul(this)},f.prototype.isqr=function(){return this.imul(this.clone())},f.prototype.pow=function(N){var H=M(N);if(H.length===0)return new f(1);for(var re=this,ae=0;ae<H.length&&H[ae]===0;ae++,re=re.sqr());if(++ae<H.length)for(var _e=re.sqr();ae<H.length;ae++,_e=_e.sqr())H[ae]!==0&&(re=re.mul(_e));return re},f.prototype.iushln=function(N){u(typeof N==\"number\"&&N>=0);var H=N%26,re=(N-H)/26,ae=67108863>>>26-H<<26-H,_e;if(H!==0){var Ee=0;for(_e=0;_e<this.length;_e++){var Ce=this.words[_e]&ae,ge=(this.words[_e]|0)-Ce<<H;this.words[_e]=ge|Ee,Ee=Ce>>>26-H}Ee&&(this.words[_e]=Ee,this.length++)}if(re!==0){for(_e=this.length-1;_e>=0;_e--)this.words[_e+re]=this.words[_e];for(_e=0;_e<re;_e++)this.words[_e]=0;this.length+=re}return this.strip()},f.prototype.ishln=function(N){return u(this.negative===0),this.iushln(N)},f.prototype.iushrn=function(N,H,re){u(typeof N==\"number\"&&N>=0);var ae;H?ae=(H-H%26)/26:ae=0;var _e=N%26,Ee=Math.min((N-_e)/26,this.length),Ce=67108863^67108863>>>_e<<_e,ge=re;if(ae-=Ee,ae=Math.max(0,ae),ge){for(var ie=0;ie<Ee;ie++)ge.words[ie]=this.words[ie];ge.length=Ee}if(Ee!==0)if(this.length>Ee)for(this.length-=Ee,ie=0;ie<this.length;ie++)this.words[ie]=this.words[ie+Ee];else this.words[0]=0,this.length=1;var Se=0;for(ie=this.length-1;ie>=0&&(Se!==0||ie>=ae);ie--){var ke=this.words[ie]|0;this.words[ie]=Se<<26-_e|ke>>>_e,Se=ke&Ce}return ge&&Se!==0&&(ge.words[ge.length++]=Se),this.length===0&&(this.words[0]=0,this.length=1),this.strip()},f.prototype.ishrn=function(N,H,re){return u(this.negative===0),this.iushrn(N,H,re)},f.prototype.shln=function(N){return this.clone().ishln(N)},f.prototype.ushln=function(N){return this.clone().iushln(N)},f.prototype.shrn=function(N){return this.clone().ishrn(N)},f.prototype.ushrn=function(N){return this.clone().iushrn(N)},f.prototype.testn=function(N){u(typeof N==\"number\"&&N>=0);var H=N%26,re=(N-H)/26,ae=1<<H;if(this.length<=re)return!1;var _e=this.words[re];return!!(_e&ae)},f.prototype.imaskn=function(N){u(typeof N==\"number\"&&N>=0);var H=N%26,re=(N-H)/26;if(u(this.negative===0,\"imaskn works only with positive numbers\"),this.length<=re)return this;if(H!==0&&re++,this.length=Math.min(re,this.length),H!==0){var ae=67108863^67108863>>>H<<H;this.words[this.length-1]&=ae}return this.length===0&&(this.words[0]=0,this.length=1),this.strip()},f.prototype.maskn=function(N){return this.clone().imaskn(N)},f.prototype.iaddn=function(N){return u(typeof N==\"number\"),u(N<67108864),N<0?this.isubn(-N):this.negative!==0?this.length===1&&(this.words[0]|0)<N?(this.words[0]=N-(this.words[0]|0),this.negative=0,this):(this.negative=0,this.isubn(N),this.negative=1,this):this._iaddn(N)},f.prototype._iaddn=function(N){this.words[0]+=N;for(var H=0;H<this.length&&this.words[H]>=67108864;H++)this.words[H]-=67108864,H===this.length-1?this.words[H+1]=1:this.words[H+1]++;return this.length=Math.max(this.length,H+1),this},f.prototype.isubn=function(N){if(u(typeof N==\"number\"),u(N<67108864),N<0)return this.iaddn(-N);if(this.negative!==0)return this.negative=0,this.iaddn(N),this.negative=1,this;if(this.words[0]-=N,this.length===1&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var H=0;H<this.length&&this.words[H]<0;H++)this.words[H]+=67108864,this.words[H+1]-=1;return this.strip()},f.prototype.addn=function(N){return this.clone().iaddn(N)},f.prototype.subn=function(N){return this.clone().isubn(N)},f.prototype.iabs=function(){return this.negative=0,this},f.prototype.abs=function(){return this.clone().iabs()},f.prototype._ishlnsubmul=function(N,H,re){var ae=N.length+re,_e;this._expand(ae);var Ee,Ce=0;for(_e=0;_e<N.length;_e++){Ee=(this.words[_e+re]|0)+Ce;var ge=(N.words[_e]|0)*H;Ee-=ge&67108863,Ce=(Ee>>26)-(ge/67108864|0),this.words[_e+re]=Ee&67108863}for(;_e<this.length-re;_e++)Ee=(this.words[_e+re]|0)+Ce,Ce=Ee>>26,this.words[_e+re]=Ee&67108863;if(Ce===0)return this.strip();for(u(Ce===-1),Ce=0,_e=0;_e<this.length;_e++)Ee=-(this.words[_e]|0)+Ce,Ce=Ee>>26,this.words[_e]=Ee&67108863;return this.negative=1,this.strip()},f.prototype._wordDiv=function(N,H){var re=this.length-N.length,ae=this.clone(),_e=N,Ee=_e.words[_e.length-1]|0,Ce=this._countBits(Ee);re=26-Ce,re!==0&&(_e=_e.ushln(re),ae.iushln(re),Ee=_e.words[_e.length-1]|0);var ge=ae.length-_e.length,ie;if(H!==\"mod\"){ie=new f(null),ie.length=ge+1,ie.words=new Array(ie.length);for(var Se=0;Se<ie.length;Se++)ie.words[Se]=0}var ke=ae.clone()._ishlnsubmul(_e,1,ge);ke.negative===0&&(ae=ke,ie&&(ie.words[ge]=1));for(var be=ge-1;be>=0;be--){var Be=(ae.words[_e.length+be]|0)*67108864+(ae.words[_e.length+be-1]|0);for(Be=Math.min(Be/Ee|0,67108863),ae._ishlnsubmul(_e,Be,be);ae.negative!==0;)Be--,ae.negative=0,ae._ishlnsubmul(_e,1,be),ae.isZero()||(ae.negative^=1);ie&&(ie.words[be]=Be)}return ie&&ie.strip(),ae.strip(),H!==\"div\"&&re!==0&&ae.iushrn(re),{div:ie||null,mod:ae}},f.prototype.divmod=function(N,H,re){if(u(!N.isZero()),this.isZero())return{div:new f(0),mod:new f(0)};var ae,_e,Ee;return this.negative!==0&&N.negative===0?(Ee=this.neg().divmod(N,H),H!==\"mod\"&&(ae=Ee.div.neg()),H!==\"div\"&&(_e=Ee.mod.neg(),re&&_e.negative!==0&&_e.iadd(N)),{div:ae,mod:_e}):this.negative===0&&N.negative!==0?(Ee=this.divmod(N.neg(),H),H!==\"mod\"&&(ae=Ee.div.neg()),{div:ae,mod:Ee.mod}):(this.negative&N.negative)!==0?(Ee=this.neg().divmod(N.neg(),H),H!==\"div\"&&(_e=Ee.mod.neg(),re&&_e.negative!==0&&_e.isub(N)),{div:Ee.div,mod:_e}):N.length>this.length||this.cmp(N)<0?{div:new f(0),mod:this}:N.length===1?H===\"div\"?{div:this.divn(N.words[0]),mod:null}:H===\"mod\"?{div:null,mod:new f(this.modn(N.words[0]))}:{div:this.divn(N.words[0]),mod:new f(this.modn(N.words[0]))}:this._wordDiv(N,H)},f.prototype.div=function(N){return this.divmod(N,\"div\",!1).div},f.prototype.mod=function(N){return this.divmod(N,\"mod\",!1).mod},f.prototype.umod=function(N){return this.divmod(N,\"mod\",!0).mod},f.prototype.divRound=function(N){var H=this.divmod(N);if(H.mod.isZero())return H.div;var re=H.div.negative!==0?H.mod.isub(N):H.mod,ae=N.ushrn(1),_e=N.andln(1),Ee=re.cmp(ae);return Ee<0||_e===1&&Ee===0?H.div:H.div.negative!==0?H.div.isubn(1):H.div.iaddn(1)},f.prototype.modn=function(N){u(N<=67108863);for(var H=(1<<26)%N,re=0,ae=this.length-1;ae>=0;ae--)re=(H*re+(this.words[ae]|0))%N;return re},f.prototype.idivn=function(N){u(N<=67108863);for(var H=0,re=this.length-1;re>=0;re--){var ae=(this.words[re]|0)+H*67108864;this.words[re]=ae/N|0,H=ae%N}return this.strip()},f.prototype.divn=function(N){return this.clone().idivn(N)},f.prototype.egcd=function(N){u(N.negative===0),u(!N.isZero());var H=this,re=N.clone();H.negative!==0?H=H.umod(N):H=H.clone();for(var ae=new f(1),_e=new f(0),Ee=new f(0),Ce=new f(1),ge=0;H.isEven()&&re.isEven();)H.iushrn(1),re.iushrn(1),++ge;for(var ie=re.clone(),Se=H.clone();!H.isZero();){for(var ke=0,be=1;(H.words[0]&be)===0&&ke<26;++ke,be<<=1);if(ke>0)for(H.iushrn(ke);ke-- >0;)(ae.isOdd()||_e.isOdd())&&(ae.iadd(ie),_e.isub(Se)),ae.iushrn(1),_e.iushrn(1);for(var Be=0,Le=1;(re.words[0]&Le)===0&&Be<26;++Be,Le<<=1);if(Be>0)for(re.iushrn(Be);Be-- >0;)(Ee.isOdd()||Ce.isOdd())&&(Ee.iadd(ie),Ce.isub(Se)),Ee.iushrn(1),Ce.iushrn(1);H.cmp(re)>=0?(H.isub(re),ae.isub(Ee),_e.isub(Ce)):(re.isub(H),Ee.isub(ae),Ce.isub(_e))}return{a:Ee,b:Ce,gcd:re.iushln(ge)}},f.prototype._invmp=function(N){u(N.negative===0),u(!N.isZero());var H=this,re=N.clone();H.negative!==0?H=H.umod(N):H=H.clone();for(var ae=new f(1),_e=new f(0),Ee=re.clone();H.cmpn(1)>0&&re.cmpn(1)>0;){for(var Ce=0,ge=1;(H.words[0]&ge)===0&&Ce<26;++Ce,ge<<=1);if(Ce>0)for(H.iushrn(Ce);Ce-- >0;)ae.isOdd()&&ae.iadd(Ee),ae.iushrn(1);for(var ie=0,Se=1;(re.words[0]&Se)===0&&ie<26;++ie,Se<<=1);if(ie>0)for(re.iushrn(ie);ie-- >0;)_e.isOdd()&&_e.iadd(Ee),_e.iushrn(1);H.cmp(re)>=0?(H.isub(re),ae.isub(_e)):(re.isub(H),_e.isub(ae))}var ke;return H.cmpn(1)===0?ke=ae:ke=_e,ke.cmpn(0)<0&&ke.iadd(N),ke},f.prototype.gcd=function(N){if(this.isZero())return N.abs();if(N.isZero())return this.abs();var H=this.clone(),re=N.clone();H.negative=0,re.negative=0;for(var ae=0;H.isEven()&&re.isEven();ae++)H.iushrn(1),re.iushrn(1);do{for(;H.isEven();)H.iushrn(1);for(;re.isEven();)re.iushrn(1);var _e=H.cmp(re);if(_e<0){var Ee=H;H=re,re=Ee}else if(_e===0||re.cmpn(1)===0)break;H.isub(re)}while(!0);return re.iushln(ae)},f.prototype.invm=function(N){return this.egcd(N).a.umod(N)},f.prototype.isEven=function(){return(this.words[0]&1)===0},f.prototype.isOdd=function(){return(this.words[0]&1)===1},f.prototype.andln=function(N){return this.words[0]&N},f.prototype.bincn=function(N){u(typeof N==\"number\");var H=N%26,re=(N-H)/26,ae=1<<H;if(this.length<=re)return this._expand(re+1),this.words[re]|=ae,this;for(var _e=ae,Ee=re;_e!==0&&Ee<this.length;Ee++){var Ce=this.words[Ee]|0;Ce+=_e,_e=Ce>>>26,Ce&=67108863,this.words[Ee]=Ce}return _e!==0&&(this.words[Ee]=_e,this.length++),this},f.prototype.isZero=function(){return this.length===1&&this.words[0]===0},f.prototype.cmpn=function(N){var H=N<0;if(this.negative!==0&&!H)return-1;if(this.negative===0&&H)return 1;this.strip();var re;if(this.length>1)re=1;else{H&&(N=-N),u(N<=67108863,\"Number is too big\");var ae=this.words[0]|0;re=ae===N?0:ae<N?-1:1}return this.negative!==0?-re|0:re},f.prototype.cmp=function(N){if(this.negative!==0&&N.negative===0)return-1;if(this.negative===0&&N.negative!==0)return 1;var H=this.ucmp(N);return this.negative!==0?-H|0:H},f.prototype.ucmp=function(N){if(this.length>N.length)return 1;if(this.length<N.length)return-1;for(var H=0,re=this.length-1;re>=0;re--){var ae=this.words[re]|0,_e=N.words[re]|0;if(ae!==_e){ae<_e?H=-1:ae>_e&&(H=1);break}}return H},f.prototype.gtn=function(N){return this.cmpn(N)===1},f.prototype.gt=function(N){return this.cmp(N)===1},f.prototype.gten=function(N){return this.cmpn(N)>=0},f.prototype.gte=function(N){return this.cmp(N)>=0},f.prototype.ltn=function(N){return this.cmpn(N)===-1},f.prototype.lt=function(N){return this.cmp(N)===-1},f.prototype.lten=function(N){return this.cmpn(N)<=0},f.prototype.lte=function(N){return this.cmp(N)<=0},f.prototype.eqn=function(N){return this.cmpn(N)===0},f.prototype.eq=function(N){return this.cmp(N)===0},f.red=function(N){return new G(N)},f.prototype.toRed=function(N){return u(!this.red,\"Already a number in reduction context\"),u(this.negative===0,\"red works only with positives\"),N.convertTo(this)._forceRed(N)},f.prototype.fromRed=function(){return u(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},f.prototype._forceRed=function(N){return this.red=N,this},f.prototype.forceRed=function(N){return u(!this.red,\"Already a number in reduction context\"),this._forceRed(N)},f.prototype.redAdd=function(N){return u(this.red,\"redAdd works only with red numbers\"),this.red.add(this,N)},f.prototype.redIAdd=function(N){return u(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,N)},f.prototype.redSub=function(N){return u(this.red,\"redSub works only with red numbers\"),this.red.sub(this,N)},f.prototype.redISub=function(N){return u(this.red,\"redISub works only with red numbers\"),this.red.isub(this,N)},f.prototype.redShl=function(N){return u(this.red,\"redShl works only with red numbers\"),this.red.shl(this,N)},f.prototype.redMul=function(N){return u(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,N),this.red.mul(this,N)},f.prototype.redIMul=function(N){return u(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,N),this.red.imul(this,N)},f.prototype.redSqr=function(){return u(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},f.prototype.redISqr=function(){return u(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},f.prototype.redSqrt=function(){return u(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},f.prototype.redInvm=function(){return u(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},f.prototype.redNeg=function(){return u(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},f.prototype.redPow=function(N){return u(this.red&&!N.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,N)};var g={k256:null,p224:null,p192:null,p25519:null};function P(j,N){this.name=j,this.p=new f(N,16),this.n=this.p.bitLength(),this.k=new f(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}P.prototype._tmp=function(){var N=new f(null);return N.words=new Array(Math.ceil(this.n/13)),N},P.prototype.ireduce=function(N){var H=N,re;do this.split(H,this.tmp),H=this.imulK(H),H=H.iadd(this.tmp),re=H.bitLength();while(re>this.n);var ae=re<this.n?-1:H.ucmp(this.p);return ae===0?(H.words[0]=0,H.length=1):ae>0?H.isub(this.p):H.strip!==void 0?H.strip():H._strip(),H},P.prototype.split=function(N,H){N.iushrn(this.n,0,H)},P.prototype.imulK=function(N){return N.imul(this.k)};function E(){P.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}c(E,P),E.prototype.split=function(N,H){for(var re=4194303,ae=Math.min(N.length,9),_e=0;_e<ae;_e++)H.words[_e]=N.words[_e];if(H.length=ae,N.length<=9){N.words[0]=0,N.length=1;return}var Ee=N.words[9];for(H.words[H.length++]=Ee&re,_e=10;_e<N.length;_e++){var Ce=N.words[_e]|0;N.words[_e-10]=(Ce&re)<<4|Ee>>>22,Ee=Ce}Ee>>>=22,N.words[_e-10]=Ee,Ee===0&&N.length>10?N.length-=10:N.length-=9},E.prototype.imulK=function(N){N.words[N.length]=0,N.words[N.length+1]=0,N.length+=2;for(var H=0,re=0;re<N.length;re++){var ae=N.words[re]|0;H+=ae*977,N.words[re]=H&67108863,H=ae*64+(H/67108864|0)}return N.words[N.length-1]===0&&(N.length--,N.words[N.length-1]===0&&N.length--),N};function z(){P.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}c(z,P);function q(){P.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}c(q,P);function V(){P.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}c(V,P),V.prototype.imulK=function(N){for(var H=0,re=0;re<N.length;re++){var ae=(N.words[re]|0)*19+H,_e=ae&67108863;ae>>>=26,N.words[re]=_e,H=ae}return H!==0&&(N.words[N.length++]=H),N},f._prime=function(N){if(g[N])return g[N];var H;if(N===\"k256\")H=new E;else if(N===\"p224\")H=new z;else if(N===\"p192\")H=new q;else if(N===\"p25519\")H=new V;else throw new Error(\"Unknown prime \"+N);return g[N]=H,H};function G(j){if(typeof j==\"string\"){var N=f._prime(j);this.m=N.p,this.prime=N}else u(j.gtn(1),\"modulus must be greater than 1\"),this.m=j,this.prime=null}G.prototype._verify1=function(N){u(N.negative===0,\"red works only with positives\"),u(N.red,\"red works only with red 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H.negative!==0?(H.negative=0,this.imod(H).redNeg()):this.imod(H)},G.prototype.pow=function(N,H){if(H.isZero())return new f(1).toRed(this);if(H.cmpn(1)===0)return N.clone();var re=4,ae=new Array(1<<re);ae[0]=new f(1).toRed(this),ae[1]=N;for(var _e=2;_e<ae.length;_e++)ae[_e]=this.mul(ae[_e-1],N);var Ee=ae[0],Ce=0,ge=0,ie=H.bitLength()%26;for(ie===0&&(ie=26),_e=H.length-1;_e>=0;_e--){for(var Se=H.words[_e],ke=ie-1;ke>=0;ke--){var be=Se>>ke&1;if(Ee!==ae[0]&&(Ee=this.sqr(Ee)),be===0&&Ce===0){ge=0;continue}Ce<<=1,Ce|=be,ge++,!(ge!==re&&(_e!==0||ke!==0))&&(Ee=this.mul(Ee,ae[Ce]),ge=0,Ce=0)}ie=26}return Ee},G.prototype.convertTo=function(N){var H=N.umod(this.m);return H===N?H.clone():H},G.prototype.convertFrom=function(N){var H=N.clone();return H.red=null,H},f.mont=function(N){return new Z(N)};function Z(j){G.call(this,j),this.shift=this.m.bitLength(),this.shift%26!==0&&(this.shift+=26-this.shift%26),this.r=new f(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}c(Z,G),Z.prototype.convertTo=function(N){return this.imod(N.ushln(this.shift))},Z.prototype.convertFrom=function(N){var H=this.imod(N.mul(this.rinv));return H.red=null,H},Z.prototype.imul=function(N,H){if(N.isZero()||H.isZero())return N.words[0]=0,N.length=1,N;var re=N.imul(H),ae=re.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),_e=re.isub(ae).iushrn(this.shift),Ee=_e;return _e.cmp(this.m)>=0?Ee=_e.isub(this.m):_e.cmpn(0)<0&&(Ee=_e.iadd(this.m)),Ee._forceRed(this)},Z.prototype.mul=function(N,H){if(N.isZero()||H.isZero())return new f(0)._forceRed(this);var re=N.mul(H),ae=re.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),_e=re.isub(ae).iushrn(this.shift),Ee=_e;return 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ct}}}function Z(N,H,re,ae,_e,Ee,Ce,ge,ie,Se){var ke=2*N,be=ge*ke,Be=ie[be+H];e:for(var Le=ae,me=ae*ke;Le<_e;++Le,me+=ke){var Pe=Ce[Le];if(Pe!==Se){var ce=Ee[me+H],He=Ee[me+H+N];if(!(Be<ce||He<Be)){for(var lt=H+1;lt<N;++lt){var ce=Ee[me+lt],He=Ee[me+lt+N],mt=ie[be+lt],Ht=ie[be+lt+N];if(He<mt||Ht<ce)continue e}var at=re(Pe,Se);if(at!==void 0)return at}}}}function j(N,H,re,ae,_e,Ee,Ce,ge,ie){q(N,ae+Ce);var Se=0,ke=2*N,be;for(V(Se++,0,0,ae,0,Ce,re?16:0,-1/0,1/0),re||V(Se++,0,0,Ce,0,ae,1,-1/0,1/0);Se>0;){Se-=1;var Be=Se*S,Le=E[Be],me=E[Be+1],Pe=E[Be+2],ce=E[Be+3],He=E[Be+4],lt=E[Be+5],mt=Se*g,Ht=z[mt],at=z[mt+1],ct=lt&1,ar=!!(lt&16),Vt=_e,rr=Ee,tt=ge,je=ie;if(ct&&(Vt=ge,rr=ie,tt=_e,je=Ee),!(lt&2&&(Pe=T(N,Le,me,Pe,Vt,rr,at),me>=Pe))&&!(lt&4&&(me=L(N,Le,me,Pe,Vt,rr,Ht),me>=Pe))){var Ue=Pe-me,Ae=He-ce;if(ar){if(N*Ue*(Ue+Ae)<p){if(be=h.scanComplete(N,Le,H,me,Pe,Vt,rr,ce,He,tt,je),be!==void 0)return be;continue}}else if(N*Math.min(Ue,Ae)<m){if(be=c(N,Le,H,ct,me,Pe,Vt,rr,ce,He,tt,je),be!==void 0)return be;continue}else if(N*Ue*Ae<b){if(be=h.scanBipartite(N,Le,H,ct,me,Pe,Vt,rr,ce,He,tt,je),be!==void 0)return be;continue}var rt=k(N,Le,me,Pe,Vt,rr,Ht,at);if(me<rt)if(N*(rt-me)<m){if(be=f(N,Le+1,H,me,rt,Vt,rr,ce,He,tt,je),be!==void 0)return be}else if(Le===N-2){if(ct?be=h.sweepBipartite(N,H,ce,He,tt,je,me,rt,Vt,rr):be=h.sweepBipartite(N,H,me,rt,Vt,rr,ce,He,tt,je),be!==void 0)return be}else V(Se++,Le+1,me,rt,ce,He,ct,-1/0,1/0),V(Se++,Le+1,ce,He,me,rt,ct^1,-1/0,1/0);if(rt<Pe){var St=d(N,Le,ce,He,tt,je),Tt=tt[ke*St+Le],dt=M(N,Le,St,He,tt,je,Tt);if(dt<He&&V(Se++,Le,rt,Pe,dt,He,(ct|4)+(ar?16:0),Tt,at),ce<St&&V(Se++,Le,rt,Pe,ce,St,(ct|2)+(ar?16:0),Ht,Tt),St+1===dt){if(ar?be=Z(N,Le,H,rt,Pe,Vt,rr,St,tt,je[St]):be=G(N,Le,H,ct,rt,Pe,Vt,rr,St,tt,je[St]),be!==void 0)return be}else if(St<dt){var Et;if(ar){if(Et=x(N,Le,rt,Pe,Vt,rr,Tt),rt<Et){var pt=M(N,Le,rt,Et,Vt,rr,Tt);if(Le===N-2){if(rt<pt&&(be=h.sweepComplete(N,H,rt,pt,Vt,rr,St,dt,tt,je),be!==void 0)||pt<Et&&(be=h.sweepBipartite(N,H,pt,Et,Vt,rr,St,dt,tt,je),be!==void 0))return be}else rt<pt&&V(Se++,Le+1,rt,pt,St,dt,16,-1/0,1/0),pt<Et&&(V(Se++,Le+1,pt,Et,St,dt,0,-1/0,1/0),V(Se++,Le+1,St,dt,pt,Et,1,-1/0,1/0))}}else ct?Et=C(N,Le,rt,Pe,Vt,rr,Tt):Et=x(N,Le,rt,Pe,Vt,rr,Tt),rt<Et&&(Le===N-2?ct?be=h.sweepBipartite(N,H,St,dt,tt,je,rt,Et,Vt,rr):be=h.sweepBipartite(N,H,rt,Et,Vt,rr,St,dt,tt,je):(V(Se++,Le+1,rt,Et,St,dt,ct,-1/0,1/0),V(Se++,Le+1,St,dt,rt,Et,ct^1,-1/0,1/0)))}}}}}}),3545:(function(i,a,o){\"use strict\";i.exports=f;var s=o(8105),l=s(\"lo<p0\"),u=8;function c(h,d,v,m,b,p){for(var k=2*h,M=k*(v+1)+d,T=v+1;T<m;++T,M+=k)for(var L=b[M],x=T,C=k*(T-1);x>v&&b[C+d]>L;--x,C-=k){for(var S=C,g=C+k,P=0;P<k;++P,++S,++g){var E=b[S];b[S]=b[g],b[g]=E}var z=p[x];p[x]=p[x-1],p[x-1]=z}}function f(h,d,v,m,b,p){if(m<=v+1)return v;for(var k=v,M=m,T=m+v>>>1,L=2*h,x=T,C=b[L*T+d];k<M;){if(M-k<u){c(h,d,k,M,b,p),C=b[L*T+d];break}var S=M-k,g=Math.random()*S+k|0,P=b[L*g+d],E=Math.random()*S+k|0,z=b[L*E+d],q=Math.random()*S+k|0,V=b[L*q+d];P<=z?V>=z?(x=E,C=z):P>=V?(x=g,C=P):(x=q,C=V):z>=V?(x=E,C=z):V>=P?(x=g,C=P):(x=q,C=V);for(var j=L*(M-1),N=L*x,G=0;G<L;++G,++j,++N){var Z=b[j];b[j]=b[N],b[N]=Z}var H=p[M-1];p[M-1]=p[x],p[x]=H,x=l(h,d,k,M-1,b,p,C);for(var j=L*(M-1),N=L*x,G=0;G<L;++G,++j,++N){var Z=b[j];b[j]=b[N],b[N]=Z}var H=p[M-1];if(p[M-1]=p[x],p[x]=H,T<x){for(M=x-1;k<M&&b[L*(M-1)+d]===C;)M-=1;M+=1}else if(x<T)for(k=x+1;k<M&&b[L*k+d]===C;)k+=1;else break}return l(h,d,v,T,b,p,b[L*T+d])}}),8105:(function(i){\"use strict\";i.exports=o;var a={\"lo===p0\":s,\"lo<p0\":l,\"lo<=p0\":u,\"hi<=p0\":c,\"lo<p0&&p0<=hi\":h,\"lo<=p0&&p0<=hi\":f,\"!(lo>=p0)&&!(p1>=hi)\":d};function o(v){return a[v]}function s(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+g];if(z===T)if(S===E)S+=1,C+=L;else{for(var q=0;L>q;++q){var V=k[x+q];k[x+q]=k[C],k[C++]=V}var G=M[E];M[E]=M[S],M[S++]=G}}return S}function l(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+g];if(z<T)if(S===E)S+=1,C+=L;else{for(var q=0;L>q;++q){var V=k[x+q];k[x+q]=k[C],k[C++]=V}var G=M[E];M[E]=M[S],M[S++]=G}}return S}function u(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+P];if(z<=T)if(S===E)S+=1,C+=L;else{for(var q=0;L>q;++q){var V=k[x+q];k[x+q]=k[C],k[C++]=V}var G=M[E];M[E]=M[S],M[S++]=G}}return S}function c(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+P];if(z<=T)if(S===E)S+=1,C+=L;else{for(var q=0;L>q;++q){var V=k[x+q];k[x+q]=k[C],k[C++]=V}var G=M[E];M[E]=M[S],M[S++]=G}}return S}function f(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+g],q=k[x+P];if(z<=T&&T<=q)if(S===E)S+=1,C+=L;else{for(var V=0;L>V;++V){var G=k[x+V];k[x+V]=k[C],k[C++]=G}var Z=M[E];M[E]=M[S],M[S++]=Z}}return S}function h(v,m,b,p,k,M,T){for(var L=2*v,x=L*b,C=x,S=b,g=m,P=v+m,E=b;p>E;++E,x+=L){var z=k[x+g],q=k[x+P];if(z<T&&T<=q)if(S===E)S+=1,C+=L;else{for(var V=0;L>V;++V){var G=k[x+V];k[x+V]=k[C],k[C++]=G}var Z=M[E];M[E]=M[S],M[S++]=Z}}return S}function d(v,m,b,p,k,M,T,L){for(var x=2*v,C=x*b,S=C,g=b,P=m,E=v+m,z=b;p>z;++z,C+=x){var q=k[C+P],V=k[C+E];if(!(q>=T)&&!(L>=V))if(g===z)g+=1,S+=x;else{for(var G=0;x>G;++G){var Z=k[C+G];k[C+G]=k[S],k[S++]=Z}var j=M[z];M[z]=M[g],M[g++]=j}}return g}}),4192:(function(i){\"use strict\";i.exports=o;var a=32;function o(m,b){b<=4*a?s(0,b-1,m):v(0,b-1,m)}function s(m,b,p){for(var k=2*(m+1),M=m+1;M<=b;++M){for(var T=p[k++],L=p[k++],x=M,C=k-2;x-- >m;){var S=p[C-2],g=p[C-1];if(S<T)break;if(S===T&&g<L)break;p[C]=S,p[C+1]=g,C-=2}p[C]=T,p[C+1]=L}}function l(m,b,p){m*=2,b*=2;var k=p[m],M=p[m+1];p[m]=p[b],p[m+1]=p[b+1],p[b]=k,p[b+1]=M}function u(m,b,p){m*=2,b*=2,p[m]=p[b],p[m+1]=p[b+1]}function c(m,b,p,k){m*=2,b*=2,p*=2;var M=k[m],T=k[m+1];k[m]=k[b],k[m+1]=k[b+1],k[b]=k[p],k[b+1]=k[p+1],k[p]=M,k[p+1]=T}function f(m,b,p,k,M){m*=2,b*=2,M[m]=M[b],M[b]=p,M[m+1]=M[b+1],M[b+1]=k}function h(m,b,p){m*=2,b*=2;var k=p[m],M=p[b];return k<M?!1:k===M?p[m+1]>p[b+1]:!0}function d(m,b,p,k){m*=2;var M=k[m];return M<b?!0:M===b?k[m+1]<p:!1}function v(m,b,p){var k=(b-m+1)/6|0,M=m+k,T=b-k,L=m+b>>1,x=L-k,C=L+k,S=M,g=x,P=L,E=C,z=T,q=m+1,V=b-1,G=0;h(S,g,p)&&(G=S,S=g,g=G),h(E,z,p)&&(G=E,E=z,z=G),h(S,P,p)&&(G=S,S=P,P=G),h(g,P,p)&&(G=g,g=P,P=G),h(S,E,p)&&(G=S,S=E,E=G),h(P,E,p)&&(G=P,P=E,E=G),h(g,z,p)&&(G=g,g=z,z=G),h(g,P,p)&&(G=g,g=P,P=G),h(E,z,p)&&(G=E,E=z,z=G);for(var Z=p[2*g],j=p[2*g+1],N=p[2*E],H=p[2*E+1],re=2*S,ae=2*P,_e=2*z,Ee=2*M,Ce=2*L,ge=2*T,ie=0;ie<2;++ie){var Se=p[re+ie],ke=p[ae+ie],be=p[_e+ie];p[Ee+ie]=Se,p[Ce+ie]=ke,p[ge+ie]=be}u(x,m,p),u(C,b,p);for(var Be=q;Be<=V;++Be)if(d(Be,Z,j,p))Be!==q&&l(Be,q,p),++q;else 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T(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}var L=[new T,new T,new T];function x(V,G,Z,j,N){for(var H=V.primalOffset,re=V.primalMinor,ae=V.mirrorOffset,_e=V.mirrorMinor,Ee=j[G],Ce=0;Ce<3;++Ce)if(G!==Ce){var ge=H,ie=ae,Se=re,ke=_e;Ee&1<<Ce&&(ge=ae,ie=H,Se=_e,ke=re),ge[Ce]=Z[0][Ce],ie[Ce]=Z[1][Ce],N[Ce]>0?(Se[Ce]=-1,ke[Ce]=0):(Se[Ce]=0,ke[Ce]=1)}}var C=[0,0,0],S={model:h,view:h,projection:h,_ortho:!1};M.isOpaque=function(){return!0},M.isTransparent=function(){return!1},M.drawTransparent=function(V){};var g=0,P=[0,0,0],E=[0,0,0],z=[0,0,0];M.draw=function(V){V=V||S;for(var Le=this.gl,G=V.model||h,Z=V.view||h,j=V.projection||h,N=this.bounds,H=V._ortho||!1,re=c(G,Z,j,N,H),ae=re.cubeEdges,_e=re.axis,Ee=Z[12],Ce=Z[13],ge=Z[14],ie=Z[15],Se=H?2:1,ke=Se*this.pixelRatio*(j[3]*Ee+j[7]*Ce+j[11]*ge+j[15]*ie)/Le.drawingBufferHeight,be=0;be<3;++be)this.lastCubeProps.cubeEdges[be]=ae[be],this.lastCubeProps.axis[be]=_e[be];for(var Be=L,be=0;be<3;++be)x(L[be],be,this.bounds,ae,_e);for(var Le=this.gl,me=C,be=0;be<3;++be)this.backgroundEnable[be]?me[be]=_e[be]:me[be]=0;this._background.draw(G,Z,j,N,me,this.backgroundColor),this._lines.bind(G,Z,j,this);for(var be=0;be<3;++be){var Pe=[0,0,0];_e[be]>0?Pe[be]=N[1][be]:Pe[be]=N[0][be];for(var ce=0;ce<2;++ce){var He=(be+1+ce)%3,lt=(be+1+(ce^1))%3;this.gridEnable[He]&&this._lines.drawGrid(He,lt,this.bounds,Pe,this.gridColor[He],this.gridWidth[He]*this.pixelRatio)}for(var ce=0;ce<2;++ce){var He=(be+1+ce)%3,lt=(be+1+(ce^1))%3;this.zeroEnable[lt]&&Math.min(N[0][lt],N[1][lt])<=0&&Math.max(N[0][lt],N[1][lt])>=0&&this._lines.drawZero(He,lt,this.bounds,Pe,this.zeroLineColor[lt],this.zeroLineWidth[lt]*this.pixelRatio)}}for(var be=0;be<3;++be){this.lineEnable[be]&&this._lines.drawAxisLine(be,this.bounds,Be[be].primalOffset,this.lineColor[be],this.lineWidth[be]*this.pixelRatio),this.lineMirror[be]&&this._lines.drawAxisLine(be,this.bounds,Be[be].mirrorOffset,this.lineColor[be],this.lineWidth[be]*this.pixelRatio);for(var mt=p(P,Be[be].primalMinor),Ht=p(E,Be[be].mirrorMinor),at=this.lineTickLength,ce=0;ce<3;++ce){var ct=ke/G[5*ce];mt[ce]*=at[ce]*ct,Ht[ce]*=at[ce]*ct}this.lineTickEnable[be]&&this._lines.drawAxisTicks(be,Be[be].primalOffset,mt,this.lineTickColor[be],this.lineTickWidth[be]*this.pixelRatio),this.lineTickMirror[be]&&this._lines.drawAxisTicks(be,Be[be].mirrorOffset,Ht,this.lineTickColor[be],this.lineTickWidth[be]*this.pixelRatio)}this._lines.unbind(),this._text.bind(G,Z,j,this.pixelRatio);var ar,Vt=.5,rr,tt;function je(dt){tt=[0,0,0],tt[dt]=1}function Ue(dt,Et,pt){var jt=(dt+1)%3,or=(dt+2)%3,mr=Et[jt],Ar=Et[or],ei=pt[jt],qr=pt[or];if(mr>0&&qr>0){je(jt);return}else if(mr>0&&qr<0){je(jt);return}else 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ce=0;ce<3;++ce)St[ce]+=ke*Ae[ce]*this.labelPad[ce]/G[5*ce];St[be]+=.5*(N[0][be]+N[1][be]),this._text.drawLabel(be,this.labelSize[be],this.labelAngle[be],St,this.labelColor[be],[0,0,0],tt,ar)}}this._text.unbind()},M.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null};function q(V,G){var Z=new k(V);return Z.update(G),Z}}),5304:(function(i,a,o){\"use strict\";i.exports=h;var s=o(2762),l=o(8116),u=o(1879).bg;function c(d,v,m,b){this.gl=d,this.buffer=v,this.vao=m,this.shader=b}var f=c.prototype;f.draw=function(d,v,m,b,p,k){for(var M=!1,T=0;T<3;++T)M=M||p[T];if(M){var L=this.gl;L.enable(L.POLYGON_OFFSET_FILL),L.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:d,view:v,projection:m,bounds:b,enable:p,colors:k},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),L.disable(L.POLYGON_OFFSET_FILL)}},f.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()};function h(d){for(var v=[],m=[],b=0,p=0;p<3;++p)for(var k=(p+1)%3,M=(p+2)%3,T=[0,0,0],L=[0,0,0],x=-1;x<=1;x+=2){m.push(b,b+2,b+1,b+1,b+2,b+3),T[p]=x,L[p]=x;for(var C=-1;C<=1;C+=2){T[k]=C;for(var S=-1;S<=1;S+=2)T[M]=S,v.push(T[0],T[1],T[2],L[0],L[1],L[2]),b+=1}var g=k;k=M,M=g}var P=s(d,new Float32Array(v)),E=s(d,new Uint16Array(m),d.ELEMENT_ARRAY_BUFFER),z=l(d,[{buffer:P,type:d.FLOAT,size:3,offset:0,stride:24},{buffer:P,type:d.FLOAT,size:3,offset:12,stride:24}],E),q=u(d);return q.attributes.position.location=0,q.attributes.normal.location=1,new c(d,P,z,q)}}),6429:(function(i,a,o){\"use strict\";i.exports=x;var s=o(8828),l=o(6760),u=o(5202),c=o(3250),f=new Array(16),h=new Array(8),d=new Array(8),v=new Array(3),m=[0,0,0];(function(){for(var C=0;C<8;++C)h[C]=[1,1,1,1],d[C]=[1,1,1]})();function b(C,S,g){for(var P=0;P<4;++P){C[P]=g[12+P];for(var E=0;E<3;++E)C[P]+=S[E]*g[4*E+P]}}var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function k(C){for(var S=0;S<p.length;++S)if(C=u.positive(C,p[S]),C.length<3)return 0;for(var g=C[0],P=g[0]/g[3],E=g[1]/g[3],z=0,S=1;S+1<C.length;++S){var q=C[S],V=C[S+1],G=q[0]/q[3],Z=q[1]/q[3],j=V[0]/V[3],N=V[1]/V[3],H=G-P,re=Z-E,ae=j-P,_e=N-E;z+=Math.abs(H*_e-re*ae)}return z}var M=[1,1,1],T=[0,0,0],L={cubeEdges:M,axis:T};function x(C,S,g,P,E){l(f,S,C),l(f,g,f);for(var z=0,q=0;q<2;++q){v[2]=P[q][2];for(var V=0;V<2;++V){v[1]=P[V][1];for(var G=0;G<2;++G)v[0]=P[G][0],b(h[z],v,f),z+=1}}for(var Z=-1,q=0;q<8;++q){for(var j=h[q][3],N=0;N<3;++N)d[q][N]=h[q][N]/j;E&&(d[q][2]*=-1),j<0&&(Z<0||d[q][2]<d[Z][2])&&(Z=q)}if(Z<0){Z=0;for(var H=0;H<3;++H){for(var re=(H+2)%3,ae=(H+1)%3,_e=-1,Ee=-1,Ce=0;Ce<2;++Ce){var ge=Ce<<H,ie=ge+(Ce<<re)+(1-Ce<<ae),Se=ge+(1-Ce<<re)+(Ce<<ae);c(d[ge],d[ie],d[Se],m)<0||(Ce?_e=1:Ee=1)}if(_e<0||Ee<0){Ee>_e&&(Z|=1<<H);continue}for(var Ce=0;Ce<2;++Ce){var ge=Ce<<H,ie=ge+(Ce<<re)+(1-Ce<<ae),Se=ge+(1-Ce<<re)+(Ce<<ae),ke=k([h[ge],h[ie],h[Se],h[ge+(1<<re)+(1<<ae)]]);Ce?_e=ke:Ee=ke}if(Ee>_e){Z|=1<<H;continue}}}for(var be=7^Z,Be=-1,q=0;q<8;++q)q===Z||q===be||(Be<0||d[Be][1]>d[q][1])&&(Be=q);for(var Le=-1,q=0;q<3;++q){var me=Be^1<<q;if(!(me===Z||me===be)){Le<0&&(Le=me);var ae=d[me];ae[0]<d[Le][0]&&(Le=me)}}for(var Pe=-1,q=0;q<3;++q){var me=Be^1<<q;if(!(me===Z||me===be||me===Le)){Pe<0&&(Pe=me);var ae=d[me];ae[0]>d[Pe][0]&&(Pe=me)}}var ce=M;ce[0]=ce[1]=ce[2]=0,ce[s.log2(Le^Be)]=Be&Le,ce[s.log2(Be^Pe)]=Be&Pe;var He=Pe^7;He===Z||He===be?(He=Le^7,ce[s.log2(Pe^He)]=He&Pe):ce[s.log2(Le^He)]=He&Le;for(var lt=T,mt=Z,H=0;H<3;++H)mt&1<<H?lt[H]=-1:lt[H]=1;return L}}),501:(function(i,a,o){\"use strict\";i.exports=M;var s=o(2762),l=o(8116),u=o(1879).n,c=[0,0,0],f=[0,0,0],h=[0,0,0],d=[0,0,0],v=[1,1];function m(T){return T[0]=T[1]=T[2]=0,T}function b(T,L){return T[0]=L[0],T[1]=L[1],T[2]=L[2],T}function p(T,L,x,C,S,g,P,E){this.gl=T,this.vertBuffer=L,this.vao=x,this.shader=C,this.tickCount=S,this.tickOffset=g,this.gridCount=P,this.gridOffset=E}var k=p.prototype;k.bind=function(T,L,x){this.shader.bind(),this.shader.uniforms.model=T,this.shader.uniforms.view=L,this.shader.uniforms.projection=x,v[0]=this.gl.drawingBufferWidth,v[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=v,this.vao.bind()},k.unbind=function(){this.vao.unbind()},k.drawAxisLine=function(T,L,x,C,S){var g=m(f);this.shader.uniforms.majorAxis=f,g[T]=L[1][T]-L[0][T],this.shader.uniforms.minorAxis=g;var P=b(d,x);P[T]+=L[0][T],this.shader.uniforms.offset=P,this.shader.uniforms.lineWidth=S,this.shader.uniforms.color=C;var E=m(h);E[(T+2)%3]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,6);var E=m(h);E[(T+1)%3]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,6)},k.drawAxisTicks=function(T,L,x,C,S){if(this.tickCount[T]){var g=m(c);g[T]=1,this.shader.uniforms.majorAxis=g,this.shader.uniforms.offset=L,this.shader.uniforms.minorAxis=x,this.shader.uniforms.color=C,this.shader.uniforms.lineWidth=S;var P=m(h);P[T]=1,this.shader.uniforms.screenAxis=P,this.vao.draw(this.gl.TRIANGLES,this.tickCount[T],this.tickOffset[T])}},k.drawGrid=function(T,L,x,C,S,g){if(this.gridCount[T]){var P=m(f);P[L]=x[1][L]-x[0][L],this.shader.uniforms.minorAxis=P;var E=b(d,C);E[L]+=x[0][L],this.shader.uniforms.offset=E;var z=m(c);z[T]=1,this.shader.uniforms.majorAxis=z;var q=m(h);q[T]=1,this.shader.uniforms.screenAxis=q,this.shader.uniforms.lineWidth=g,this.shader.uniforms.color=S,this.vao.draw(this.gl.TRIANGLES,this.gridCount[T],this.gridOffset[T])}},k.drawZero=function(T,L,x,C,S,g){var P=m(f);this.shader.uniforms.majorAxis=P,P[T]=x[1][T]-x[0][T],this.shader.uniforms.minorAxis=P;var E=b(d,C);E[T]+=x[0][T],this.shader.uniforms.offset=E;var z=m(h);z[L]=1,this.shader.uniforms.screenAxis=z,this.shader.uniforms.lineWidth=g,this.shader.uniforms.color=S,this.vao.draw(this.gl.TRIANGLES,6)},k.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()};function M(T,L,x){var C=[],S=[0,0,0],g=[0,0,0],P=[0,0,0],E=[0,0,0];C.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var z=0;z<3;++z){for(var G=C.length/3|0,q=0;q<x[z].length;++q){var V=+x[z][q].x;C.push(V,0,1,V,1,1,V,0,-1,V,0,-1,V,1,1,V,1,-1)}var j=C.length/3|0;S[z]=G,g[z]=j-G;for(var G=C.length/3|0,Z=0;Z<x[z].length;++Z){var V=+x[z][Z].x;C.push(V,0,1,V,1,1,V,0,-1,V,0,-1,V,1,1,V,1,-1)}var j=C.length/3|0;P[z]=G,E[z]=j-G}var N=s(T,new Float32Array(C)),H=l(T,[{buffer:N,type:T.FLOAT,size:3,stride:0,offset:0}]),re=u(T);return re.attributes.position.location=0,new p(T,N,H,re,g,S,E,P)}}),1879:(function(i,a,o){\"use strict\";var s=o(3236),l=o(9405),u=s([`precision highp float;\n",
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w.v-L.v}),r[0]&&(r[0].vTotal=p),r}function ICe(e){return function(r,n){if(!r||!Cw.isValid(r))return!1;let a=Cw.rgbaString(r);return e[n]||(e[n]=a),a}}function MIt(e,t){var r=(t||{}).type;r||(r=\"pie\");var n=e._fullLayout,a=e.calcdata,i=n[r+\"colorway\"],o=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(i=zCe(i,TIt));for(var s=0,l=0;l<a.length;l++){var u=a[l],c=u[0].trace.type;if(c===r)for(var f=0;f<u.length;f++){var v=u[f];v.color===!1&&(o[v.label]?v.color=o[v.label]:(o[v.label]=v.color=i[s%i.length],s++))}}}function zCe(e,t){var r,n=JSON.stringify(e),a=t[n];if(!a){for(a=e.slice(),r=0;r<e.length;r++)a.push(Cw.hexString(Cw.adjustLightness(e[r],20)));for(r=0;r<e.length;r++)a.push(Cw.hexString(Cw.adjustLightness(e[r],-20)));t[n]=a}return a}qCe.exports={calc:AIt,crossTraceCalc:MIt,makePullColorFn:ICe,generateExtendedColors:zCe}});var OCe=fe((G2r,BCe)=>{\"use strict\";var SIt=Sd().appendArrayMultiPointValues;BCe.exports=function(t,r){var 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f=[[[],[]],[[],[]]],v=!1;c.each(function(S,L){if(S.hidden){u0.select(this).selectAll(\"path,g\").remove();return}S.pointNumber=S.i,S.curveNumber=u.index,f[S.pxmid[1]<0?0:1][S.pxmid[0]<0?0:1].push(S);var w=l.cx,A=l.cy,T=u0.select(this),m=T.selectAll(\"path.surface\").data([S]);if(m.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),T.call(XCe,e,o),u.pull){var F=+Aa.castOption(u.pull,S.pts)||0;F>0&&(w+=F*S.pxmid[0],A+=F*S.pxmid[1])}S.cxFinal=w,S.cyFinal=A;function E(q,U,J,ee){var ce=ee*(U[0]-q[0]),ye=ee*(U[1]-q[1]);return\"a\"+ee*l.r+\",\"+ee*l.r+\" 0 \"+S.largeArc+(J?\" 1 \":\" 0 \")+ce+\",\"+ye}var I=u.hole;if(S.v===l.vTotal){var R=\"M\"+(w+S.px0[0])+\",\"+(A+S.px0[1])+E(S.px0,S.pxmid,!0,1)+E(S.pxmid,S.px0,!0,1)+\"Z\";I?m.attr(\"d\",\"M\"+(w+I*S.px0[0])+\",\"+(A+I*S.px0[1])+E(S.px0,S.pxmid,!1,I)+E(S.pxmid,S.px0,!1,I)+\"Z\"+R):m.attr(\"d\",R)}else{var B=E(S.px0,S.px1,!0,1);if(I){var O=1-I;m.attr(\"d\",\"M\"+(w+I*S.px1[0])+\",\"+(A+I*S.px1[1])+E(S.px1,S.px0,!1,I)+\"l\"+O*S.px0[0]+\",\"+O*S.px0[1]+B+\"Z\")}else m.attr(\"d\",\"M\"+w+\",\"+A+\"l\"+S.px0[0]+\",\"+S.px0[1]+B+\"Z\")}i7e(e,S,l);var V=Aa.castOption(u.textposition,S.pts),j=T.selectAll(\"g.slicetext\").data(S.text&&V!==\"none\"?[0]:[]);j.enter().append(\"g\").classed(\"slicetext\",!0),j.exit().remove(),j.each(function(){var q=Qf.ensureSingle(u0.select(this),\"text\",\"\",function(K){K.attr(\"data-notex\",1)}),U=Qf.ensureUniformFontSize(e,V===\"outside\"?DIt(u,S,n.font):ZCe(u,S,n.font));q.text(S.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(_g.font,U).call(vW.convertToTspans,e);var J=_g.bBox(q.node()),ee;if(V===\"outside\")ee=HCe(J,S);else if(ee=KCe(J,S,l),V===\"auto\"&&ee.scale<1){var ce=Qf.ensureUniformFontSize(e,u.outsidetextfont);q.call(_g.font,ce),J=_g.bBox(q.node()),ee=HCe(J,S)}var ye=ee.textPosAngle,xe=ye===void 0?S.pxmid:D9(l.r,ye);if(ee.targetX=w+xe[0]*ee.rCenter+(ee.x||0),ee.targetY=A+xe[1]*ee.rCenter+(ee.y||0),a7e(ee,J),ee.outside){var ue=ee.targetY;S.yLabelMin=ue-J.height/2,S.yLabelMid=ue,S.yLabelMax=ue+J.height/2,S.labelExtraX=0,S.labelExtraY=0,v=!0}ee.fontSize=U.size,CIt(u.type,ee,n),o[L].transform=ee,Qf.setTransormAndDisplay(q,ee)})});var g=u0.select(this).selectAll(\"g.titletext\").data(u.title.text?[0]:[]);if(g.enter().append(\"g\").classed(\"titletext\",!0),g.exit().remove(),g.each(function(){var S=Qf.ensureSingle(u0.select(this),\"text\",\"\",function(A){A.attr(\"data-notex\",1)}),L=u.title.text;u._meta&&(L=Qf.templateString(L,u._meta)),S.text(L).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(_g.font,u.title.font).call(vW.convertToTspans,e);var w;u.title.position===\"middle center\"?w=zIt(l):w=t7e(l,a),S.attr(\"transform\",NCe(w.x,w.y)+kIt(Math.min(1,w.scale))+NCe(w.tx,w.ty))}),v&&BIt(f,u),FIt(c,u),v&&u.automargin){var p=_g.bBox(s.node()),x=u.domain,_=a.w*(x.x[1]-x.x[0]),y=a.h*(x.y[1]-x.y[0]),C=(.5*_-l.r)/a.w,k=(.5*y-l.r)/a.h;EIt.autoMargin(e,\"pie.\"+u.uid+\".automargin\",{xl:x.x[0]-C,xr:x.x[1]+C,yb:x.y[0]-k,yt:x.y[1]+k,l:Math.max(l.cx-l.r-p.left,0),r:Math.max(p.right-(l.cx+l.r),0),b:Math.max(p.bottom-(l.cy+l.r),0),t:Math.max(l.cy-l.r-p.top,0),pad:5})}})});setTimeout(function(){i.selectAll(\"tspan\").each(function(){var o=u0.select(this);o.attr(\"dy\")&&o.attr(\"dy\",o.attr(\"dy\"))})},0)}function FIt(e,t){e.each(function(r){var n=u0.select(this);if(!r.labelExtraX&&!r.labelExtraY){n.select(\"path.textline\").remove();return}var a=n.select(\"g.slicetext text\");r.transform.targetX+=r.labelExtraX,r.transform.targetY+=r.labelExtraY,Qf.setTransormAndDisplay(a,r.transform);var i=r.cxFinal+r.pxmid[0],o=r.cyFinal+r.pxmid[1],s=\"M\"+i+\",\"+o,l=(r.yLabelMax-r.yLabelMin)*(r.pxmid[0]<0?-1:1)/4;if(r.labelExtraX){var u=r.labelExtraX*r.pxmid[1]/r.pxmid[0],c=r.yLabelMid+r.labelExtraY-(r.cyFinal+r.pxmid[1]);Math.abs(u)>Math.abs(c)?s+=\"l\"+c*r.pxmid[0]/r.pxmid[1]+\",\"+c+\"H\"+(i+r.labelExtraX+l):s+=\"l\"+r.labelExtraX+\",\"+u+\"v\"+(c-u)+\"h\"+l}else s+=\"V\"+(r.yLabelMid+r.labelExtraY)+\"h\"+l;Qf.ensureSingle(n,\"path\",\"textline\").call(jCe.stroke,t.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,t.outsidetextfont.size/8),d:s,fill:\"none\"})})}function XCe(e,t,r){var n=r[0],a=n.cx,i=n.cy,o=n.trace,s=o.type===\"funnelarea\";\"_hasHoverLabel\"in o||(o._hasHoverLabel=!1),\"_hasHoverEvent\"in o||(o._hasHoverEvent=!1),e.on(\"mouseover\",function(l){var u=t._fullLayout,c=t._fullData[o.index];if(!(t._dragging||u.hovermode===!1)){var f=c.hoverinfo;if(Array.isArray(f)&&(f=P9.castHoverinfo({hoverinfo:[Aa.castOption(f,l.pts)],_module:o._module},u,0)),f===\"all\"&&(f=\"label+text+value+percent+name\"),c.hovertemplate||f!==\"none\"&&f!==\"skip\"&&f){var v=l.rInscribed||0,g=a+l.pxmid[0]*(1-v),p=i+l.pxmid[1]*(1-v),x=u.separators,_=[];if(f&&f.indexOf(\"label\")!==-1&&_.push(l.label),l.text=Aa.castOption(c.hovertext||c.text,l.pts),f&&f.indexOf(\"text\")!==-1){var y=l.text;Qf.isValidTextValue(y)&&_.push(y)}l.value=l.v,l.valueLabel=Aa.formatPieValue(l.v,x),f&&f.indexOf(\"value\")!==-1&&_.push(l.valueLabel),l.percent=l.v/n.vTotal,l.percentLabel=Aa.formatPiePercent(l.percent,x),f&&f.indexOf(\"percent\")!==-1&&_.push(l.percentLabel);var C=c.hoverlabel,k=C.font,S=[];P9.loneHover({trace:o,x0:g-v*n.r,x1:g+v*n.r,y:p,_x0:s?a+l.TL[0]:g-v*n.r,_x1:s?a+l.TR[0]:g+v*n.r,_y0:s?i+l.TL[1]:p-v*n.r,_y1:s?i+l.BL[1]:p+v*n.r,text:_.join(\"<br>\"),name:c.hovertemplate||f.indexOf(\"name\")!==-1?c.name:void 0,idealAlign:l.pxmid[0]<0?\"left\":\"right\",color:Aa.castOption(C.bgcolor,l.pts)||l.color,borderColor:Aa.castOption(C.bordercolor,l.pts),fontFamily:Aa.castOption(k.family,l.pts),fontSize:Aa.castOption(k.size,l.pts),fontColor:Aa.castOption(k.color,l.pts),nameLength:Aa.castOption(C.namelength,l.pts),textAlign:Aa.castOption(C.align,l.pts),hovertemplate:Aa.castOption(c.hovertemplate,l.pts),hovertemplateLabels:l,eventData:[F9(l,c)]},{container:u._hoverlayer.node(),outerContainer:u._paper.node(),gd:t,inOut_bbox:S}),l.bbox=S[0],o._hasHoverLabel=!0}o._hasHoverEvent=!0,t.emit(\"plotly_hover\",{points:[F9(l,c)],event:u0.event})}}),e.on(\"mouseout\",function(l){var u=t._fullLayout,c=t._fullData[o.index],f=u0.select(this).datum();o._hasHoverEvent&&(l.originalEvent=u0.event,t.emit(\"plotly_unhover\",{points:[F9(f,c)],event:u0.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(P9.loneUnhover(u._hoverlayer.node()),o._hasHoverLabel=!1)}),e.on(\"click\",function(l){var u=t._fullLayout,c=t._fullData[o.index];t._dragging||u.hovermode===!1||(t._hoverdata=[F9(l,c)],P9.click(t,u0.event))})}function DIt(e,t,r){var n=Aa.castOption(e.outsidetextfont.color,t.pts)||Aa.castOption(e.textfont.color,t.pts)||r.color,a=Aa.castOption(e.outsidetextfont.family,t.pts)||Aa.castOption(e.textfont.family,t.pts)||r.family,i=Aa.castOption(e.outsidetextfont.size,t.pts)||Aa.castOption(e.textfont.size,t.pts)||r.size,o=Aa.castOption(e.outsidetextfont.weight,t.pts)||Aa.castOption(e.textfont.weight,t.pts)||r.weight,s=Aa.castOption(e.outsidetextfont.style,t.pts)||Aa.castOption(e.textfont.style,t.pts)||r.style,l=Aa.castOption(e.outsidetextfont.variant,t.pts)||Aa.castOption(e.textfont.variant,t.pts)||r.variant,u=Aa.castOption(e.outsidetextfont.textcase,t.pts)||Aa.castOption(e.textfont.textcase,t.pts)||r.textcase,c=Aa.castOption(e.outsidetextfont.lineposition,t.pts)||Aa.castOption(e.textfont.lineposition,t.pts)||r.lineposition,f=Aa.castOption(e.outsidetextfont.shadow,t.pts)||Aa.castOption(e.textfont.shadow,t.pts)||r.shadow;return{color:n,family:a,size:i,weight:o,style:s,variant:l,textcase:u,lineposition:c,shadow:f}}function ZCe(e,t,r){var n=Aa.castOption(e.insidetextfont.color,t.pts);!n&&e._input.textfont&&(n=Aa.castOption(e._input.textfont.color,t.pts));var a=Aa.castOption(e.insidetextfont.family,t.pts)||Aa.castOption(e.textfont.family,t.pts)||r.family,i=Aa.castOption(e.insidetextfont.size,t.pts)||Aa.castOption(e.textfont.size,t.pts)||r.size,o=Aa.castOption(e.insidetextfont.weight,t.pts)||Aa.castOption(e.textfont.weight,t.pts)||r.weight,s=Aa.castOption(e.insidetextfont.style,t.pts)||Aa.castOption(e.textfont.style,t.pts)||r.style,l=Aa.castOption(e.insidetextfont.variant,t.pts)||Aa.castOption(e.textfont.variant,t.pts)||r.variant,u=Aa.castOption(e.insidetextfont.textcase,t.pts)||Aa.castOption(e.textfont.textcase,t.pts)||r.textcase,c=Aa.castOption(e.insidetextfont.lineposition,t.pts)||Aa.castOption(e.textfont.lineposition,t.pts)||r.lineposition,f=Aa.castOption(e.insidetextfont.shadow,t.pts)||Aa.castOption(e.textfont.shadow,t.pts)||r.shadow;return{color:n||jCe.contrast(t.color),family:a,size:i,weight:o,style:s,variant:l,textcase:u,lineposition:c,shadow:f}}function $Ce(e,t){for(var r,n,a=0;a<e.length;a++)if(r=e[a][0],n=r.trace,n.title.text){var i=n.title.text;n._meta&&(i=Qf.templateString(i,n._meta));var o=_g.tester.append(\"text\").attr(\"data-notex\",1).text(i).call(_g.font,n.title.font).call(vW.convertToTspans,t),s=_g.bBox(o.node(),!0);r.titleBox={width:s.width,height:s.height},o.remove()}}function KCe(e,t,r){var n=r.r||t.rpx1,a=t.rInscribed,i=t.startangle===t.stopangle;if(i)return{rCenter:1-a,scale:0,rotate:0,textPosAngle:0};var o=t.ring,s=o===1&&Math.abs(t.startangle-t.stopangle)===Math.PI*2,l=t.halfangle,u=t.midangle,c=r.trace.insidetextorientation,f=c===\"horizontal\",v=c===\"tangential\",g=c===\"radial\",p=c===\"auto\",x=[],_;if(!p){var y=function(T,m){if(RIt(t,T)){var F=Math.abs(T-t.startangle),E=Math.abs(T-t.stopangle),I=F<E?F:E;m===\"tan\"?_=GCe(e,n,o,I,0):_=VCe(e,n,o,I,Math.PI/2),_.textPosAngle=T,x.push(_)}},C;if(f||v){for(C=4;C>=-4;C-=2)y(Math.PI*C,\"tan\");for(C=4;C>=-4;C-=2)y(Math.PI*(C+1),\"tan\")}if(f||g){for(C=4;C>=-4;C-=2)y(Math.PI*(C+1.5),\"rad\");for(C=4;C>=-4;C-=2)y(Math.PI*(C+.5),\"rad\")}}if(s||p||f){var k=Math.sqrt(e.width*e.width+e.height*e.height);if(_={scale:a*n*2/k,rCenter:1-a,rotate:0},_.textPosAngle=(t.startangle+t.stopangle)/2,_.scale>=1)return _;x.push(_)}(p||g)&&(_=VCe(e,n,o,l,u),_.textPosAngle=(t.startangle+t.stopangle)/2,x.push(_)),(p||v)&&(_=GCe(e,n,o,l,u),_.textPosAngle=(t.startangle+t.stopangle)/2,x.push(_));for(var S=0,L=0,w=0;w<x.length;w++){var A=x[w].scale;if(L<A&&(L=A,S=w),!p&&L>=1)break}return x[S]}function RIt(e,t){var r=e.startangle,n=e.stopangle;return r>t&&t>n||r<t&&t<n}function VCe(e,t,r,n,a){t=Math.max(0,t-2*YCe);var i=e.width/e.height,o=e7e(i,n,t,r);return{scale:o*2/e.height,rCenter:JCe(i,o/t),rotate:QCe(a)}}function GCe(e,t,r,n,a){t=Math.max(0,t-2*YCe);var i=e.height/e.width,o=e7e(i,n,t,r);return{scale:o*2/e.width,rCenter:JCe(i,o/t),rotate:QCe(a+Math.PI/2)}}function JCe(e,t){return Math.cos(t)-e*t}function QCe(e){return(180/Math.PI*e+720)%180-90}function e7e(e,t,r,n){var a=e+1/(2*Math.tan(t));return r*Math.min(1/(Math.sqrt(a*a+.5)+a),n/(Math.sqrt(e*e+n/2)+e))}function IIt(e,t){return e.v===t.vTotal&&!t.trace.hole?1:Math.min(1/(1+1/Math.sin(e.halfangle)),e.ring/2)}function HCe(e,t){var r=t.pxmid[0],n=t.pxmid[1],a=e.width/2,i=e.height/2;return r<0&&(a*=-1),n<0&&(i*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(i)*(a>0?1:-1)/2,y:i/(1+r*r/(n*n)),outside:!0}}function zIt(e){var t=Math.sqrt(e.titleBox.width*e.titleBox.width+e.titleBox.height*e.titleBox.height);return{x:e.cx,y:e.cy,scale:e.trace.hole*e.r*2/t,tx:0,ty:-e.titleBox.height/2+e.trace.title.font.size}}function t7e(e,t){var 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\"+R.root,U()),R.text=w(\"hovertext\")||w(\"text\"),V(\"text\")&&(j=R.text,ZLe.isValidTextValue(j)&&O.push(j)),F=[K6(p,_,i.eventDataKeys)];var J={trace:_,y:I,_x0:p._x0,_x1:p._x1,_y0:p._y0,_y1:p._y1,text:O.join(\"<br>\"),name:A||V(\"name\")?_.name:void 0,color:w(\"hoverlabel.bgcolor\")||y.color,borderColor:w(\"hoverlabel.bordercolor\"),fontFamily:w(\"hoverlabel.font.family\"),fontSize:w(\"hoverlabel.font.size\"),fontColor:w(\"hoverlabel.font.color\"),fontWeight:w(\"hoverlabel.font.weight\"),fontStyle:w(\"hoverlabel.font.style\"),fontVariant:w(\"hoverlabel.font.variant\"),nameLength:w(\"hoverlabel.namelength\"),textAlign:w(\"hoverlabel.align\"),hovertemplate:A,hovertemplateLabels:R,eventData:F};u&&(J.x0=E-p.rInscribed*p.rpx1,J.x1=E+p.rInscribed*p.rpx1,J.idealAlign=p.pxmid[0]<0?\"left\":\"right\"),c&&(J.x=E,J.idealAlign=E<0?\"left\":\"right\");var ee=[];$6.loneHover(J,{container:x._hoverlayer.node(),outerContainer:x._paper.node(),gd:n,inOut_bbox:ee}),F[0].bbox=ee[0],s._hasHoverLabel=!0}if(c){var ce=t.select(\"path.surface\");i.styleOne(ce,p,_,n,{hovered:!0})}s._hasHoverEvent=!0,n.emit(\"plotly_hover\",{points:F||[K6(p,_,i.eventDataKeys)],event:Uw.event})}},v=function(p){var x=n._fullLayout,_=n._fullData[s.index],y=Uw.select(this).datum();if(s._hasHoverEvent&&(p.originalEvent=Uw.event,n.emit(\"plotly_unhover\",{points:[K6(y,_,i.eventDataKeys)],event:Uw.event}),s._hasHoverEvent=!1),s._hasHoverLabel&&($6.loneUnhover(x._hoverlayer.node()),s._hasHoverLabel=!1),c){var C=t.select(\"path.surface\");i.styleOne(C,y,_,n,{hovered:!1})}},g=function(p){var x=n._fullLayout,_=n._fullData[s.index],y=u&&(Sc.isHierarchyRoot(p)||Sc.isLeaf(p)),C=Sc.getPtId(p),k=Sc.isEntry(p)?Sc.findEntryWithChild(l,C):Sc.findEntryWithLevel(l,C),S=Sc.getPtId(k),L={points:[K6(p,_,i.eventDataKeys)],event:Uw.event};y||(L.nextLevel=S);var w=Uzt.triggerHandler(n,\"plotly_\"+s.type+\"click\",L);if(w!==!1&&x.hovermode&&(n._hoverdata=[K6(p,_,i.eventDataKeys)],$6.click(n,Uw.event)),!y&&w!==!1&&!n._dragging&&!n._transitioning){XLe.call(\"_storeDirectGUIEdit\",_,x._tracePreGUI[_.uid],{level:_.level});var A={data:[{level:S}],traces:[s.index]},T={frame:{redraw:!1,duration:i.transitionTime},transition:{duration:i.transitionTime,easing:i.transitionEasing},mode:\"immediate\",fromcurrent:!0};$6.loneUnhover(x._hoverlayer.node()),XLe.call(\"animate\",n,A,T)}};t.on(\"mouseover\",f),t.on(\"mouseout\",v),t.on(\"click\",g)};function K6(e,t,r){for(var n=e.data.data,a={curveNumber:t.index,pointNumber:n.i,data:t._input,fullData:t},i=0;i<r.length;i++){var o=r[i];o in e&&(a[o]=e[o])}return\"parentString\"in e&&!Sc.isHierarchyRoot(e)&&(a.parent=e.parentString),Nzt(a,t,n.i),a}});var nP=fe(rP=>{\"use strict\";var Q6=xi(),Hzt=O6(),Sp=($2(),Gs(Z2)).interpolate,KLe=ea(),Fh=Xt(),jzt=as(),t9e=kh(),JLe=t9e.recordMinTextSize,Wzt=t9e.clearMinTextSize,r9e=R9(),Yzt=Sm().getRotationAngle,Xzt=r9e.computeTransform,Zzt=r9e.transformInsideText,$zt=NW().styleOne,Kzt=_v().resizeText,Jzt=J6(),UW=gW(),Po=Ag();rP.plot=function(e,t,r,n){var a=e._fullLayout,i=a._sunburstlayer,o,s,l=!r,u=!a.uniformtext.mode&&Po.hasTransition(r);if(Wzt(\"sunburst\",a),o=i.selectAll(\"g.trace.sunburst\").data(t,function(f){return f[0].trace.uid}),o.enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),o.order(),u){n&&(s=n());var c=Q6.transition().duration(r.duration).ease(r.easing).each(\"end\",function(){s&&s()}).each(\"interrupt\",function(){s&&s()});c.each(function(){i.selectAll(\"g.trace\").each(function(f){QLe(e,f,this,r)})})}else o.each(function(f){QLe(e,f,this,r)}),a.uniformtext.mode&&Kzt(e,a._sunburstlayer.selectAll(\".trace\"),\"sunburst\");l&&o.exit().remove()};function QLe(e,t,r,n){var a=e._context.staticPlot,i=e._fullLayout,o=!i.uniformtext.mode&&Po.hasTransition(n),s=Q6.select(r),l=s.selectAll(\"g.slice\"),u=t[0],c=u.trace,f=u.hierarchy,v=Po.findEntryWithLevel(f,c.level),g=Po.getMaxDepth(c),p=i._size,x=c.domain,_=p.w*(x.x[1]-x.x[0]),y=p.h*(x.y[1]-x.y[0]),C=.5*Math.min(_,y),k=u.cx=p.l+p.w*(x.x[1]+x.x[0])/2,S=u.cy=p.t+p.h*(1-x.y[0])-y/2;if(!v)return l.remove();var L=null,w={};o&&l.each(function(ue){w[Po.getPtId(ue)]={rpx0:ue.rpx0,rpx1:ue.rpx1,x0:ue.x0,x1:ue.x1,transform:ue.transform},!L&&Po.isEntry(ue)&&(L=ue)});var A=Qzt(v).descendants(),T=v.height+1,m=0,F=g;u.hasMultipleRoots&&Po.isHierarchyRoot(v)&&(A=A.slice(1),T-=1,m=1,F+=1),A=A.filter(function(ue){return ue.y1<=F});var E=Yzt(c.rotation);E&&A.forEach(function(ue){ue.x0+=E,ue.x1+=E});var I=Math.min(T,g),R=function(ue){return(ue-m)/I*C},B=function(ue,K){return[ue*Math.cos(K),-ue*Math.sin(K)]},O=function(ue){return Fh.pathAnnulus(ue.rpx0,ue.rpx1,ue.x0,ue.x1,k,S)},V=function(ue){return k+e9e(ue)[0]*(ue.transform.rCenter||0)+(ue.transform.x||0)},j=function(ue){return S+e9e(ue)[1]*(ue.transform.rCenter||0)+(ue.transform.y||0)};l=l.data(A,Po.getPtId),l.enter().append(\"g\").classed(\"slice\",!0),o?l.exit().transition().each(function(){var ue=Q6.select(this),K=ue.select(\"path.surface\");K.transition().attrTween(\"d\",function(ne){var ae=ee(ne);return function(pe){return O(ae(pe))}});var _e=ue.select(\"g.slicetext\");_e.attr(\"opacity\",0)}).remove():l.exit().remove(),l.order();var q=null;if(o&&L){var U=Po.getPtId(L);l.each(function(ue){q===null&&Po.getPtId(ue)===U&&(q=ue.x1)})}var J=l;o&&(J=J.transition().each(\"end\",function(){var ue=Q6.select(this);Po.setSliceCursor(ue,e,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})})),J.each(function(ue){var K=Q6.select(this),_e=Fh.ensureSingle(K,\"path\",\"surface\",function(me){me.style(\"pointer-events\",a?\"none\":\"all\")});ue.rpx0=R(ue.y0),ue.rpx1=R(ue.y1),ue.xmid=(ue.x0+ue.x1)/2,ue.pxmid=B(ue.rpx1,ue.xmid),ue.midangle=-(ue.xmid-Math.PI/2),ue.startangle=-(ue.x0-Math.PI/2),ue.stopangle=-(ue.x1-Math.PI/2),ue.halfangle=.5*Math.min(Fh.angleDelta(ue.x0,ue.x1)||Math.PI,Math.PI),ue.ring=1-ue.rpx0/ue.rpx1,ue.rInscribed=eqt(ue,c),o?_e.transition().attrTween(\"d\",function(me){var be=ce(me);return function(De){return O(be(De))}}):_e.attr(\"d\",O),K.call(Jzt,v,e,t,{eventDataKeys:UW.eventDataKeys,transitionTime:UW.CLICK_TRANSITION_TIME,transitionEasing:UW.CLICK_TRANSITION_EASING}).call(Po.setSliceCursor,e,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:e._transitioning}),_e.call($zt,ue,c,e);var ne=Fh.ensureSingle(K,\"g\",\"slicetext\"),ae=Fh.ensureSingle(ne,\"text\",\"\",function(me){me.attr(\"data-notex\",1)}),pe=Fh.ensureUniformFontSize(e,Po.determineTextFont(c,ue,i.font));ae.text(rP.formatSliceLabel(ue,v,c,t,i)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(KLe.font,pe).call(jzt.convertToTspans,e);var ie=KLe.bBox(ae.node());ue.transform=Zzt(ie,ue,u),ue.transform.targetX=V(ue),ue.transform.targetY=j(ue);var le=function(me,be){var De=me.transform;return Xzt(De,be),De.fontSize=pe.size,JLe(c.type,De,i),Fh.getTextTransform(De)};o?ae.transition().attrTween(\"transform\",function(me){var be=ye(me);return function(De){return le(be(De),ie)}}):ae.attr(\"transform\",le(ue,ie))});function ee(ue){var K=Po.getPtId(ue),_e=w[K],ne=w[Po.getPtId(v)],ae;if(ne){var pe=(ue.x1>ne.x1?2*Math.PI:0)+E;ae=ue.rpx1<ne.rpx1?{x0:ue.x0,x1:ue.x1,rpx0:0,rpx1:0}:{x0:pe,x1:pe,rpx0:ue.rpx0,rpx1:ue.rpx1}}else{var ie,le=Po.getPtId(ue.parent);l.each(function(ht){if(Po.getPtId(ht)===le)return ie=ht});var me=ie.children,be;me.forEach(function(ht,it){if(Po.getPtId(ht)===K)return be=it});var De=me.length,Ge=Sp(ie.x0,ie.x1);ae={rpx0:C,rpx1:C,x0:Ge(be/De),x1:Ge((be+1)/De)}}return Sp(_e,ae)}function ce(ue){var K=w[Po.getPtId(ue)],_e,ne={x0:ue.x0,x1:ue.x1,rpx0:ue.rpx0,rpx1:ue.rpx1};if(K)_e=K;else if(L)if(ue.parent)if(q){var ae=(ue.x1>q?2*Math.PI:0)+E;_e={x0:ae,x1:ae}}else _e={rpx0:C,rpx1:C},Fh.extendFlat(_e,xe(ue));else _e={rpx0:0,rpx1:0};else _e={x0:E,x1:E};return Sp(_e,ne)}function ye(ue){var K=w[Po.getPtId(ue)],_e,ne=ue.transform;if(K)_e=K;else if(_e={rpx1:ue.rpx1,transform:{textPosAngle:ne.textPosAngle,scale:0,rotate:ne.rotate,rCenter:ne.rCenter,x:ne.x,y:ne.y}},L)if(ue.parent)if(q){var ae=ue.x1>q?2*Math.PI:0;_e.x0=_e.x1=ae}else Fh.extendFlat(_e,xe(ue));else _e.x0=_e.x1=E;else _e.x0=_e.x1=E;var pe=Sp(_e.transform.textPosAngle,ue.transform.textPosAngle),ie=Sp(_e.rpx1,ue.rpx1),le=Sp(_e.x0,ue.x0),me=Sp(_e.x1,ue.x1),be=Sp(_e.transform.scale,ne.scale),De=Sp(_e.transform.rotate,ne.rotate),Ge=ne.rCenter===0?3:_e.transform.rCenter===0?1/3:1,ht=Sp(_e.transform.rCenter,ne.rCenter),it=function(Oe){return ht(Math.pow(Oe,Ge))};return function(Oe){var ze=ie(Oe),ct=le(Oe),et=me(Oe),qe=it(Oe),we=B(ze,(ct+et)/2),Le=pe(Oe),Ke={pxmid:we,rpx1:ze,transform:{textPosAngle:Le,rCenter:qe,x:ne.x,y:ne.y}};return JLe(c.type,ne,i),{transform:{targetX:V(Ke),targetY:j(Ke),scale:be(Oe),rotate:De(Oe),rCenter:qe}}}}function xe(ue){var K=ue.parent,_e=w[Po.getPtId(K)],ne={};if(_e){var ae=K.children,pe=ae.indexOf(ue),ie=ae.length,le=Sp(_e.x0,_e.x1);ne.x0=le(pe/ie),ne.x1=le(pe/ie)}else ne.x0=ne.x1=0;return ne}}function Qzt(e){return Hzt.partition().size([2*Math.PI,e.height+1])(e)}rP.formatSliceLabel=function(e,t,r,n,a){var i=r.texttemplate,o=r.textinfo;if(!i&&(!o||o===\"none\"))return\"\";var s=a.separators,l=n[0],u=e.data.data,c=l.hierarchy,f=Po.isHierarchyRoot(e),v=Po.getParent(c,e),g=Po.getValue(e);if(!i){var p=o.split(\"+\"),x=function(m){return p.indexOf(m)!==-1},_=[],y;if(x(\"label\")&&u.label&&_.push(u.label),u.hasOwnProperty(\"v\")&&x(\"value\")&&_.push(Po.formatValue(u.v,s)),!f){x(\"current path\")&&_.push(Po.getPath(e.data));var C=0;x(\"percent parent\")&&C++,x(\"percent entry\")&&C++,x(\"percent root\")&&C++;var k=C>1;if(C){var S,L=function(m){y=Po.formatPercent(S,s),k&&(y+=\" of \"+m),_.push(y)};x(\"percent parent\")&&!f&&(S=g/Po.getValue(v),L(\"parent\")),x(\"percent entry\")&&(S=g/Po.getValue(t),L(\"entry\")),x(\"percent root\")&&(S=g/Po.getValue(c),L(\"root\"))}}return x(\"text\")&&(y=Fh.castOption(r,u.i,\"text\"),Fh.isValidTextValue(y)&&_.push(y)),_.join(\"<br>\")}var w=Fh.castOption(r,u.i,\"texttemplate\");if(!w)return\"\";var A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=Po.formatValue(u.v,s)),A.currentPath=Po.getPath(e.data),f||(A.percentParent=g/Po.getValue(v),A.percentParentLabel=Po.formatPercent(A.percentParent,s),A.parent=Po.getPtLabel(v)),A.percentEntry=g/Po.getValue(t),A.percentEntryLabel=Po.formatPercent(A.percentEntry,s),A.entry=Po.getPtLabel(t),A.percentRoot=g/Po.getValue(c),A.percentRootLabel=Po.formatPercent(A.percentRoot,s),A.root=Po.getPtLabel(c),u.hasOwnProperty(\"color\")&&(A.color=u.color);var T=Fh.castOption(r,u.i,\"text\");return(Fh.isValidTextValue(T)||T===\"\")&&(A.text=T),A.customdata=Fh.castOption(r,u.i,\"customdata\"),Fh.texttemplateString({data:[A,r._meta],fallback:r.texttemplatefallback,labels:A,locale:a._d3locale,template:w})};function eqt(e){return e.rpx0===0&&Fh.isFullCircle([e.x0,e.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(e.halfangle)),e.ring/2))}function e9e(e){return tqt(e.rpx1,e.transform.textPosAngle)}function tqt(e,t){return[e*Math.sin(t),-e*Math.cos(t)]}});var i9e=fe((Zbr,n9e)=>{\"use strict\";n9e.exports={moduleType:\"trace\",name:\"sunburst\",basePlotModule:m7e(),categories:[],animatable:!0,attributes:B6(),layoutAttributes:mW(),supplyDefaults:S7e(),supplyLayoutDefaults:k7e(),calc:U6().calc,crossTraceCalc:U6().crossTraceCalc,plot:nP().plot,style:NW().style,colorbar:Zf(),meta:{}}});var o9e=fe(($br,a9e)=>{\"use strict\";a9e.exports=i9e()});var l9e=fe(Vw=>{\"use strict\";var s9e=Hs();Vw.name=\"treemap\";Vw.plot=function(e,t,r,n){s9e.plotBasePlot(Vw.name,e,t,r,n)};Vw.clean=function(e,t,r,n){s9e.cleanBasePlot(Vw.name,e,t,r,n)}});var J2=fe((Jbr,u9e)=>{\"use strict\";u9e.exports={CLICK_TRANSITION_TIME:750,CLICK_TRANSITION_EASING:\"poly\",eventDataKeys:[\"currentPath\",\"root\",\"entry\",\"percentRoot\",\"percentEntry\",\"percentParent\"],gapWithPathbar:1}});var iP=fe((Qbr,h9e)=>{\"use strict\";var{hovertemplateAttrs:rqt,texttemplateAttrs:nqt,templatefallbackAttrs:c9e}=Oo(),iqt=_s(),aqt=nl().attributes,Q2=U2(),Dv=B6(),f9e=J2(),VW=ta().extendFlat,oqt=Uc().pattern;h9e.exports={labels:Dv.labels,parents:Dv.parents,values:Dv.values,branchvalues:Dv.branchvalues,count:Dv.count,level:Dv.level,maxdepth:Dv.maxdepth,tiling:{packing:{valType:\"enumerated\",values:[\"squarify\",\"binary\",\"dice\",\"slice\",\"slice-dice\",\"dice-slice\"],dflt:\"squarify\",editType:\"plot\"},squarifyratio:{valType:\"number\",min:1,dflt:1,editType:\"plot\"},flip:{valType:\"flaglist\",flags:[\"x\",\"y\"],dflt:\"\",editType:\"plot\"},pad:{valType:\"number\",min:0,dflt:3,editType:\"plot\"},editType:\"calc\"},marker:VW({pad:{t:{valType:\"number\",min:0,editType:\"plot\"},l:{valType:\"number\",min:0,editType:\"plot\"},r:{valType:\"number\",min:0,editType:\"plot\"},b:{valType:\"number\",min:0,editType:\"plot\"},editType:\"calc\"},colors:Dv.marker.colors,pattern:oqt,depthfade:{valType:\"enumerated\",values:[!0,!1,\"reversed\"],editType:\"style\"},line:Dv.marker.line,cornerradius:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"calc\"},iqt(\"marker\",{colorAttr:\"colors\",anim:!1})),pathbar:{visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},side:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},edgeshape:{valType:\"enumerated\",values:[\">\",\"<\",\"|\",\"/\",\"\\\\\"],dflt:\">\",editType:\"plot\"},thickness:{valType:\"number\",min:12,editType:\"plot\"},textfont:VW({},Q2.textfont,{}),editType:\"calc\"},text:Q2.text,textinfo:Dv.textinfo,texttemplate:nqt({editType:\"plot\"},{keys:f9e.eventDataKeys.concat([\"label\",\"value\"])}),texttemplatefallback:c9e({editType:\"plot\"}),hovertext:Q2.hovertext,hoverinfo:Dv.hoverinfo,hovertemplate:rqt({},{keys:f9e.eventDataKeys}),hovertemplatefallback:c9e(),textfont:Q2.textfont,insidetextfont:Q2.insidetextfont,outsidetextfont:VW({},Q2.outsidetextfont,{}),textposition:{valType:\"enumerated\",values:[\"top 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u=i(\"tiling.packing\");u===\"squarify\"&&i(\"tiling.squarifyratio\"),i(\"tiling.flip\"),i(\"tiling.pad\");var c=i(\"text\");i(\"texttemplate\"),i(\"texttemplatefallback\"),r.texttemplate||i(\"textinfo\",v9e.isArrayOrTypedArray(c)?\"text+label\":\"label\"),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\");var f=i(\"pathbar.visible\"),v=\"auto\";cqt(t,r,a,i,v,{hasPathbar:f,moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),i(\"textposition\");var g=r.textposition.indexOf(\"bottom\")!==-1;hqt(t,r,a,i);var p=r._hasColorscale=dqt(t,\"marker\",\"colors\")||(t.marker||{}).coloraxis;p?vqt(t,r,a,i,{prefix:\"marker.\",cLetter:\"c\"}):i(\"marker.depthfade\",!(r.marker.colors||[]).length);var 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t=e._fullLayout._treemaplayer.selectAll(\".trace\");xqt(e,t,\"treemap\"),t.each(function(r){var n=T9e.select(this),a=r[0],i=a.trace;n.style(\"opacity\",i.opacity),n.selectAll(\"path.surface\").each(function(o){T9e.select(this).call(M9e,o,i,e,{hovered:!1})})})}function M9e(e,t,r,n,a){var i=(a||{}).hovered,o=t.data.data,s=o.i,l,u,c=o.color,f=XW.isHierarchyRoot(t),v=1;if(i)l=r._hovered.marker.line.color,u=r._hovered.marker.line.width;else if(f&&c===r.root.color)v=100,l=\"rgba(0,0,0,0)\",u=0;else if(l=A9e.castOption(r,s,\"marker.line.color\")||Hw.defaultLine,u=A9e.castOption(r,s,\"marker.line.width\")||0,!r._hasColorscale&&!t.onPathbar){var g=r.marker.depthfade;if(g){var p=Hw.combine(Hw.addOpacity(r._backgroundColor,.75),c),x;if(g===!0){var _=XW.getMaxDepth(r);isFinite(_)?XW.isLeaf(t)?x=0:x=r._maxVisibleLayers-(t.data.depth-r._entryDepth):x=t.data.height+1}else x=t.data.depth-r._entryDepth,r._atRootLevel||x++;if(x>0)for(var y=0;y<x;y++){var 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Te.x0!==Te.x1&&Te.y0!==Te.y1&&JW(l.type,Ve,o),eM.getTextTransform({textX:Ve.textX,textY:Ve.textY,anchorX:Ve.anchorX,anchorY:Ve.anchorY,targetX:Ve.targetX,targetY:Ve.targetY,scale:Ve.scale,rotate:Ve.rotate})};y&&(p.each(function(Te){O[e_(Te)]={x0:Te.x0,x1:Te.x1,y0:Te.y0,y1:Te.y1},Te.transform&&(O[e_(Te)].transform={textX:Te.transform.textX,textY:Te.transform.textY,anchorX:Te.transform.anchorX,anchorY:Te.transform.anchorY,targetX:Te.transform.targetX,targetY:Te.transform.targetY,scale:Te.transform.scale,rotate:Te.transform.rotate})}),x.each(function(Te){V[e_(Te)]={x0:Te.x0,x1:Te.x1,y0:Te.y0,y1:Te.y1},Te.transform&&(V[e_(Te)].transform={textX:Te.transform.textX,textY:Te.transform.textY,anchorX:Te.transform.anchorX,anchorY:Te.transform.anchorY,targetX:Te.transform.targetX,targetY:Te.transform.targetY,scale:Te.transform.scale,rotate:Te.transform.rotate}),!B&&dy.isEntry(Te)&&(B=Te)})),j=i(t,r,v,x,{width:w,height:A,viewX:ce,viewY:ye,pathSlice:Oe,toMoveInsideSlice:ze,prevEntry:B,makeUpdateSliceInterpolator:qe,makeUpdateTextInterpolator:we,handleSlicesExit:Le,hasTransition:y,strTransform:Ke}),l.pathbar.visible?Cqt(t,r,v,p,{barDifY:E,width:T,height:m,viewX:K,viewY:_e,pathSlice:le,toMoveInsideSlice:ze,makeUpdateSliceInterpolator:qe,makeUpdateTextInterpolator:we,handleSlicesExit:Le,hasTransition:y,strTransform:Ke}):p.remove()}});var 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g[x]}),this._mode=p,this._active=g[p],this._active||(this._mode=\"turntable\",this._active=g.turntable),this.modes=this._controllerNames,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}var f=c.prototype;f.flush=function(g){for(var p=this._controllerList,x=0;x<p.length;++x)p[x].flush(g)},f.idle=function(g){for(var p=this._controllerList,x=0;x<p.length;++x)p[x].idle(g)},f.lookAt=function(g,p,x,_){for(var y=this._controllerList,C=0;C<y.length;++C)y[C].lookAt(g,p,x,_)},f.rotate=function(g,p,x,_){for(var y=this._controllerList,C=0;C<y.length;++C)y[C].rotate(g,p,x,_)},f.pan=function(g,p,x,_){for(var y=this._controllerList,C=0;C<y.length;++C)y[C].pan(g,p,x,_)},f.translate=function(g,p,x,_){for(var y=this._controllerList,C=0;C<y.length;++C)y[C].translate(g,p,x,_)},f.setMatrix=function(g,p){for(var x=this._controllerList,_=0;_<x.length;++_)x[_].setMatrix(g,p)},f.setDistanceLimits=function(g,p){for(var x=this._controllerList,_=0;_<x.length;++_)x[_].setDistanceLimits(g,p)},f.setDistance=function(g,p){for(var x=this._controllerList,_=0;_<x.length;++_)x[_].setDistance(g,p)},f.recalcMatrix=function(g){this._active.recalcMatrix(g)},f.getDistance=function(g){return this._active.getDistance(g)},f.getDistanceLimits=function(g){return this._active.getDistanceLimits(g)},f.lastT=function(){return this._active.lastT()},f.setMode=function(g){if(g!==this._mode){var p=this._controllerNames.indexOf(g);if(!(p<0)){var 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_=[\"\",\"0\",\"00\",\"000\",\"0000\",\"00000\",\"000000\",\"0000000\",\"00000000\",\"000000000\",\"0000000000\",\"00000000000\",\"000000000000\",\"0000000000000\",\"00000000000000\",\"000000000000000\",\"0000000000000000\",\"00000000000000000\",\"000000000000000000\",\"0000000000000000000\",\"00000000000000000000\",\"000000000000000000000\",\"0000000000000000000000\",\"00000000000000000000000\",\"000000000000000000000000\",\"0000000000000000000000000\"],y=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],C=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];f.prototype.toString=function(q,U){q=q||10,U=U|0||1;var J;if(q===16||q===\"hex\"){J=\"\";for(var ee=0,ce=0,ye=0;ye<this.length;ye++){var xe=this.words[ye],ue=((xe<<ee|ce)&16777215).toString(16);ce=xe>>>24-ee&16777215,ee+=2,ee>=26&&(ee-=26,ye--),ce!==0||ye!==this.length-1?J=_[6-ue.length]+ue+J:J=ue+J}for(ce!==0&&(J=ce.toString(16)+J);J.length%U!==0;)J=\"0\"+J;return this.negative!==0&&(J=\"-\"+J),J}if(q===(q|0)&&q>=2&&q<=36){var K=y[q],_e=C[q];J=\"\";var ne=this.clone();for(ne.negative=0;!ne.isZero();){var ae=ne.modn(_e).toString(q);ne=ne.idivn(_e),ne.isZero()?J=ae+J:J=_[K-ae.length]+ae+J}for(this.isZero()&&(J=\"0\"+J);J.length%U!==0;)J=\"0\"+J;return this.negative!==0&&(J=\"-\"+J),J}u(!1,\"Base should be between 2 and 36\")},f.prototype.toNumber=function(){var q=this.words[0];return this.length===2?q+=this.words[1]*67108864:this.length===3&&this.words[2]===1?q+=4503599627370496+this.words[1]*67108864:this.length>2&&u(!1,\"Number can only safely store up to 53 bits\"),this.negative!==0?-q:q},f.prototype.toJSON=function(){return this.toString(16)},f.prototype.toBuffer=function(q,U){return u(typeof v!=\"undefined\"),this.toArrayLike(v,q,U)},f.prototype.toArray=function(q,U){return this.toArrayLike(Array,q,U)},f.prototype.toArrayLike=function(q,U,J){var ee=this.byteLength(),ce=J||Math.max(1,ee);u(ee<=ce,\"byte array longer than desired length\"),u(ce>0,\"Requested array length <= 0\"),this.strip();var ye=U===\"le\",xe=new q(ce),ue,K,_e=this.clone();if(ye){for(K=0;!_e.isZero();K++)ue=_e.andln(255),_e.iushrn(8),xe[K]=ue;for(;K<ce;K++)xe[K]=0}else{for(K=0;K<ce-ee;K++)xe[K]=0;for(K=0;!_e.isZero();K++)ue=_e.andln(255),_e.iushrn(8),xe[ce-K-1]=ue}return xe},Math.clz32?f.prototype._countBits=function(q){return 32-Math.clz32(q)}:f.prototype._countBits=function(q){var U=q,J=0;return U>=4096&&(J+=13,U>>>=13),U>=64&&(J+=7,U>>>=7),U>=8&&(J+=4,U>>>=4),U>=2&&(J+=2,U>>>=2),J+U},f.prototype._zeroBits=function(q){if(q===0)return 26;var U=q,J=0;return(U&8191)===0&&(J+=13,U>>>=13),(U&127)===0&&(J+=7,U>>>=7),(U&15)===0&&(J+=4,U>>>=4),(U&3)===0&&(J+=2,U>>>=2),(U&1)===0&&J++,J},f.prototype.bitLength=function(){var q=this.words[this.length-1],U=this._countBits(q);return(this.length-1)*26+U};function k(j){for(var q=new Array(j.bitLength()),U=0;U<q.length;U++){var J=U/26|0,ee=U%26;q[U]=(j.words[J]&1<<ee)>>>ee}return q}f.prototype.zeroBits=function(){if(this.isZero())return 0;for(var q=0,U=0;U<this.length;U++){var J=this._zeroBits(this.words[U]);if(q+=J,J!==26)break}return q},f.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},f.prototype.toTwos=function(q){return this.negative!==0?this.abs().inotn(q).iaddn(1):this.clone()},f.prototype.fromTwos=function(q){return this.testn(q-1)?this.notn(q).iaddn(1).ineg():this.clone()},f.prototype.isNeg=function(){return this.negative!==0},f.prototype.neg=function(){return this.clone().ineg()},f.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},f.prototype.iuor=function(q){for(;this.length<q.length;)this.words[this.length++]=0;for(var U=0;U<q.length;U++)this.words[U]=this.words[U]|q.words[U];return this.strip()},f.prototype.ior=function(q){return u((this.negative|q.negative)===0),this.iuor(q)},f.prototype.or=function(q){return this.length>q.length?this.clone().ior(q):q.clone().ior(this)},f.prototype.uor=function(q){return this.length>q.length?this.clone().iuor(q):q.clone().iuor(this)},f.prototype.iuand=function(q){var U;this.length>q.length?U=q:U=this;for(var J=0;J<U.length;J++)this.words[J]=this.words[J]&q.words[J];return this.length=U.length,this.strip()},f.prototype.iand=function(q){return u((this.negative|q.negative)===0),this.iuand(q)},f.prototype.and=function(q){return this.length>q.length?this.clone().iand(q):q.clone().iand(this)},f.prototype.uand=function(q){return this.length>q.length?this.clone().iuand(q):q.clone().iuand(this)},f.prototype.iuxor=function(q){var U,J;this.length>q.length?(U=this,J=q):(U=q,J=this);for(var ee=0;ee<J.length;ee++)this.words[ee]=U.words[ee]^J.words[ee];if(this!==U)for(;ee<U.length;ee++)this.words[ee]=U.words[ee];return this.length=U.length,this.strip()},f.prototype.ixor=function(q){return u((this.negative|q.negative)===0),this.iuxor(q)},f.prototype.xor=function(q){return this.length>q.length?this.clone().ixor(q):q.clone().ixor(this)},f.prototype.uxor=function(q){return this.length>q.length?this.clone().iuxor(q):q.clone().iuxor(this)},f.prototype.inotn=function(q){u(typeof q==\"number\"&&q>=0);var U=Math.ceil(q/26)|0,J=q%26;this._expand(U),J>0&&U--;for(var ee=0;ee<U;ee++)this.words[ee]=~this.words[ee]&67108863;return J>0&&(this.words[ee]=~this.words[ee]&67108863>>26-J),this.strip()},f.prototype.notn=function(q){return this.clone().inotn(q)},f.prototype.setn=function(q,U){u(typeof q==\"number\"&&q>=0);var J=q/26|0,ee=q%26;return this._expand(J+1),U?this.words[J]=this.words[J]|1<<ee:this.words[J]=this.words[J]&~(1<<ee),this.strip()},f.prototype.iadd=function(q){var U;if(this.negative!==0&&q.negative===0)return this.negative=0,U=this.isub(q),this.negative^=1,this._normSign();if(this.negative===0&&q.negative!==0)return q.negative=0,U=this.isub(q),q.negative=1,U._normSign();var J,ee;this.length>q.length?(J=this,ee=q):(J=q,ee=this);for(var ce=0,ye=0;ye<ee.length;ye++)U=(J.words[ye]|0)+(ee.words[ye]|0)+ce,this.words[ye]=U&67108863,ce=U>>>26;for(;ce!==0&&ye<J.length;ye++)U=(J.words[ye]|0)+ce,this.words[ye]=U&67108863,ce=U>>>26;if(this.length=J.length,ce!==0)this.words[this.length]=ce,this.length++;else if(J!==this)for(;ye<J.length;ye++)this.words[ye]=J.words[ye];return this},f.prototype.add=function(q){var U;return q.negative!==0&&this.negative===0?(q.negative=0,U=this.sub(q),q.negative^=1,U):q.negative===0&&this.negative!==0?(this.negative=0,U=q.sub(this),this.negative=1,U):this.length>q.length?this.clone().iadd(q):q.clone().iadd(this)},f.prototype.isub=function(q){if(q.negative!==0){q.negative=0;var U=this.iadd(q);return q.negative=1,U._normSign()}else if(this.negative!==0)return this.negative=0,this.iadd(q),this.negative=1,this._normSign();var J=this.cmp(q);if(J===0)return this.negative=0,this.length=1,this.words[0]=0,this;var ee,ce;J>0?(ee=this,ce=q):(ee=q,ce=this);for(var ye=0,xe=0;xe<ce.length;xe++)U=(ee.words[xe]|0)-(ce.words[xe]|0)+ye,ye=U>>26,this.words[xe]=U&67108863;for(;ye!==0&&xe<ee.length;xe++)U=(ee.words[xe]|0)+ye,ye=U>>26,this.words[xe]=U&67108863;if(ye===0&&xe<ee.length&&ee!==this)for(;xe<ee.length;xe++)this.words[xe]=ee.words[xe];return this.length=Math.max(this.length,xe),ee!==this&&(this.negative=1),this.strip()},f.prototype.sub=function(q){return this.clone().isub(q)};function S(j,q,U){U.negative=q.negative^j.negative;var J=j.length+q.length|0;U.length=J,J=J-1|0;var ee=j.words[0]|0,ce=q.words[0]|0,ye=ee*ce,xe=ye&67108863,ue=ye/67108864|0;U.words[0]=xe;for(var K=1;K<J;K++){for(var _e=ue>>>26,ne=ue&67108863,ae=Math.min(K,q.length-1),pe=Math.max(0,K-j.length+1);pe<=ae;pe++){var ie=K-pe|0;ee=j.words[ie]|0,ce=q.words[pe]|0,ye=ee*ce+ne,_e+=ye/67108864|0,ne=ye&67108863}U.words[K]=ne|0,ue=_e|0}return ue!==0?U.words[K]=ue|0:U.length--,U.strip()}var L=function(q,U,J){var ee=q.words,ce=U.words,ye=J.words,xe=0,ue,K,_e,ne=ee[0]|0,ae=ne&8191,pe=ne>>>13,ie=ee[1]|0,le=ie&8191,me=ie>>>13,be=ee[2]|0,De=be&8191,Ge=be>>>13,ht=ee[3]|0,it=ht&8191,Oe=ht>>>13,ze=ee[4]|0,ct=ze&8191,et=ze>>>13,qe=ee[5]|0,we=qe&8191,Le=qe>>>13,Ke=ee[6]|0,Te=Ke&8191,Ve=Ke>>>13,$e=ee[7]|0,ot=$e&8191,tt=$e>>>13,nt=ee[8]|0,We=nt&8191,mt=nt>>>13,bt=ee[9]|0,Yt=bt&8191,jt=bt>>>13,mr=ce[0]|0,or=mr&8191,fr=mr>>>13,Ot=ce[1]|0,cr=Ot&8191,Ct=Ot>>>13,wt=ce[2]|0,Tt=wt&8191,Mt=wt>>>13,Lt=ce[3]|0,ft=Lt&8191,kt=Lt>>>13,rr=ce[4]|0,pr=rr&8191,Kr=rr>>>13,Nr=ce[5]|0,dn=Nr&8191,xr=Nr>>>13,Fr=ce[6]|0,Zr=Fr&8191,Cn=Fr>>>13,Dt=ce[7]|0,At=Dt&8191,Vt=Dt>>>13,Qt=ce[8]|0,Ut=Qt&8191,_r=Qt>>>13,rn=ce[9]|0,Jr=rn&8191,qn=rn>>>13;J.negative=q.negative^U.negative,J.length=19,ue=Math.imul(ae,or),K=Math.imul(ae,fr),K=K+Math.imul(pe,or)|0,_e=Math.imul(pe,fr);var ri=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(ri>>>26)|0,ri&=67108863,ue=Math.imul(le,or),K=Math.imul(le,fr),K=K+Math.imul(me,or)|0,_e=Math.imul(me,fr),ue=ue+Math.imul(ae,cr)|0,K=K+Math.imul(ae,Ct)|0,K=K+Math.imul(pe,cr)|0,_e=_e+Math.imul(pe,Ct)|0;var Jn=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(Jn>>>26)|0,Jn&=67108863,ue=Math.imul(De,or),K=Math.imul(De,fr),K=K+Math.imul(Ge,or)|0,_e=Math.imul(Ge,fr),ue=ue+Math.imul(le,cr)|0,K=K+Math.imul(le,Ct)|0,K=K+Math.imul(me,cr)|0,_e=_e+Math.imul(me,Ct)|0,ue=ue+Math.imul(ae,Tt)|0,K=K+Math.imul(ae,Mt)|0,K=K+Math.imul(pe,Tt)|0,_e=_e+Math.imul(pe,Mt)|0;var Ii=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(Ii>>>26)|0,Ii&=67108863,ue=Math.imul(it,or),K=Math.imul(it,fr),K=K+Math.imul(Oe,or)|0,_e=Math.imul(Oe,fr),ue=ue+Math.imul(De,cr)|0,K=K+Math.imul(De,Ct)|0,K=K+Math.imul(Ge,cr)|0,_e=_e+Math.imul(Ge,Ct)|0,ue=ue+Math.imul(le,Tt)|0,K=K+Math.imul(le,Mt)|0,K=K+Math.imul(me,Tt)|0,_e=_e+Math.imul(me,Mt)|0,ue=ue+Math.imul(ae,ft)|0,K=K+Math.imul(ae,kt)|0,K=K+Math.imul(pe,ft)|0,_e=_e+Math.imul(pe,kt)|0;var Vi=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(Vi>>>26)|0,Vi&=67108863,ue=Math.imul(ct,or),K=Math.imul(ct,fr),K=K+Math.imul(et,or)|0,_e=Math.imul(et,fr),ue=ue+Math.imul(it,cr)|0,K=K+Math.imul(it,Ct)|0,K=K+Math.imul(Oe,cr)|0,_e=_e+Math.imul(Oe,Ct)|0,ue=ue+Math.imul(De,Tt)|0,K=K+Math.imul(De,Mt)|0,K=K+Math.imul(Ge,Tt)|0,_e=_e+Math.imul(Ge,Mt)|0,ue=ue+Math.imul(le,ft)|0,K=K+Math.imul(le,kt)|0,K=K+Math.imul(me,ft)|0,_e=_e+Math.imul(me,kt)|0,ue=ue+Math.imul(ae,pr)|0,K=K+Math.imul(ae,Kr)|0,K=K+Math.imul(pe,pr)|0,_e=_e+Math.imul(pe,Kr)|0;var ra=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(ra>>>26)|0,ra&=67108863,ue=Math.imul(we,or),K=Math.imul(we,fr),K=K+Math.imul(Le,or)|0,_e=Math.imul(Le,fr),ue=ue+Math.imul(ct,cr)|0,K=K+Math.imul(ct,Ct)|0,K=K+Math.imul(et,cr)|0,_e=_e+Math.imul(et,Ct)|0,ue=ue+Math.imul(it,Tt)|0,K=K+Math.imul(it,Mt)|0,K=K+Math.imul(Oe,Tt)|0,_e=_e+Math.imul(Oe,Mt)|0,ue=ue+Math.imul(De,ft)|0,K=K+Math.imul(De,kt)|0,K=K+Math.imul(Ge,ft)|0,_e=_e+Math.imul(Ge,kt)|0,ue=ue+Math.imul(le,pr)|0,K=K+Math.imul(le,Kr)|0,K=K+Math.imul(me,pr)|0,_e=_e+Math.imul(me,Kr)|0,ue=ue+Math.imul(ae,dn)|0,K=K+Math.imul(ae,xr)|0,K=K+Math.imul(pe,dn)|0,_e=_e+Math.imul(pe,xr)|0;var ha=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(ha>>>26)|0,ha&=67108863,ue=Math.imul(Te,or),K=Math.imul(Te,fr),K=K+Math.imul(Ve,or)|0,_e=Math.imul(Ve,fr),ue=ue+Math.imul(we,cr)|0,K=K+Math.imul(we,Ct)|0,K=K+Math.imul(Le,cr)|0,_e=_e+Math.imul(Le,Ct)|0,ue=ue+Math.imul(ct,Tt)|0,K=K+Math.imul(ct,Mt)|0,K=K+Math.imul(et,Tt)|0,_e=_e+Math.imul(et,Mt)|0,ue=ue+Math.imul(it,ft)|0,K=K+Math.imul(it,kt)|0,K=K+Math.imul(Oe,ft)|0,_e=_e+Math.imul(Oe,kt)|0,ue=ue+Math.imul(De,pr)|0,K=K+Math.imul(De,Kr)|0,K=K+Math.imul(Ge,pr)|0,_e=_e+Math.imul(Ge,Kr)|0,ue=ue+Math.imul(le,dn)|0,K=K+Math.imul(le,xr)|0,K=K+Math.imul(me,dn)|0,_e=_e+Math.imul(me,xr)|0,ue=ue+Math.imul(ae,Zr)|0,K=K+Math.imul(ae,Cn)|0,K=K+Math.imul(pe,Zr)|0,_e=_e+Math.imul(pe,Cn)|0;var va=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(va>>>26)|0,va&=67108863,ue=Math.imul(ot,or),K=Math.imul(ot,fr),K=K+Math.imul(tt,or)|0,_e=Math.imul(tt,fr),ue=ue+Math.imul(Te,cr)|0,K=K+Math.imul(Te,Ct)|0,K=K+Math.imul(Ve,cr)|0,_e=_e+Math.imul(Ve,Ct)|0,ue=ue+Math.imul(we,Tt)|0,K=K+Math.imul(we,Mt)|0,K=K+Math.imul(Le,Tt)|0,_e=_e+Math.imul(Le,Mt)|0,ue=ue+Math.imul(ct,ft)|0,K=K+Math.imul(ct,kt)|0,K=K+Math.imul(et,ft)|0,_e=_e+Math.imul(et,kt)|0,ue=ue+Math.imul(it,pr)|0,K=K+Math.imul(it,Kr)|0,K=K+Math.imul(Oe,pr)|0,_e=_e+Math.imul(Oe,Kr)|0,ue=ue+Math.imul(De,dn)|0,K=K+Math.imul(De,xr)|0,K=K+Math.imul(Ge,dn)|0,_e=_e+Math.imul(Ge,xr)|0,ue=ue+Math.imul(le,Zr)|0,K=K+Math.imul(le,Cn)|0,K=K+Math.imul(me,Zr)|0,_e=_e+Math.imul(me,Cn)|0,ue=ue+Math.imul(ae,At)|0,K=K+Math.imul(ae,Vt)|0,K=K+Math.imul(pe,At)|0,_e=_e+Math.imul(pe,Vt)|0;var Xi=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(Xi>>>26)|0,Xi&=67108863,ue=Math.imul(We,or),K=Math.imul(We,fr),K=K+Math.imul(mt,or)|0,_e=Math.imul(mt,fr),ue=ue+Math.imul(ot,cr)|0,K=K+Math.imul(ot,Ct)|0,K=K+Math.imul(tt,cr)|0,_e=_e+Math.imul(tt,Ct)|0,ue=ue+Math.imul(Te,Tt)|0,K=K+Math.imul(Te,Mt)|0,K=K+Math.imul(Ve,Tt)|0,_e=_e+Math.imul(Ve,Mt)|0,ue=ue+Math.imul(we,ft)|0,K=K+Math.imul(we,kt)|0,K=K+Math.imul(Le,ft)|0,_e=_e+Math.imul(Le,kt)|0,ue=ue+Math.imul(ct,pr)|0,K=K+Math.imul(ct,Kr)|0,K=K+Math.imul(et,pr)|0,_e=_e+Math.imul(et,Kr)|0,ue=ue+Math.imul(it,dn)|0,K=K+Math.imul(it,xr)|0,K=K+Math.imul(Oe,dn)|0,_e=_e+Math.imul(Oe,xr)|0,ue=ue+Math.imul(De,Zr)|0,K=K+Math.imul(De,Cn)|0,K=K+Math.imul(Ge,Zr)|0,_e=_e+Math.imul(Ge,Cn)|0,ue=ue+Math.imul(le,At)|0,K=K+Math.imul(le,Vt)|0,K=K+Math.imul(me,At)|0,_e=_e+Math.imul(me,Vt)|0,ue=ue+Math.imul(ae,Ut)|0,K=K+Math.imul(ae,_r)|0,K=K+Math.imul(pe,Ut)|0,_e=_e+Math.imul(pe,_r)|0;var oa=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(oa>>>26)|0,oa&=67108863,ue=Math.imul(Yt,or),K=Math.imul(Yt,fr),K=K+Math.imul(jt,or)|0,_e=Math.imul(jt,fr),ue=ue+Math.imul(We,cr)|0,K=K+Math.imul(We,Ct)|0,K=K+Math.imul(mt,cr)|0,_e=_e+Math.imul(mt,Ct)|0,ue=ue+Math.imul(ot,Tt)|0,K=K+Math.imul(ot,Mt)|0,K=K+Math.imul(tt,Tt)|0,_e=_e+Math.imul(tt,Mt)|0,ue=ue+Math.imul(Te,ft)|0,K=K+Math.imul(Te,kt)|0,K=K+Math.imul(Ve,ft)|0,_e=_e+Math.imul(Ve,kt)|0,ue=ue+Math.imul(we,pr)|0,K=K+Math.imul(we,Kr)|0,K=K+Math.imul(Le,pr)|0,_e=_e+Math.imul(Le,Kr)|0,ue=ue+Math.imul(ct,dn)|0,K=K+Math.imul(ct,xr)|0,K=K+Math.imul(et,dn)|0,_e=_e+Math.imul(et,xr)|0,ue=ue+Math.imul(it,Zr)|0,K=K+Math.imul(it,Cn)|0,K=K+Math.imul(Oe,Zr)|0,_e=_e+Math.imul(Oe,Cn)|0,ue=ue+Math.imul(De,At)|0,K=K+Math.imul(De,Vt)|0,K=K+Math.imul(Ge,At)|0,_e=_e+Math.imul(Ge,Vt)|0,ue=ue+Math.imul(le,Ut)|0,K=K+Math.imul(le,_r)|0,K=K+Math.imul(me,Ut)|0,_e=_e+Math.imul(me,_r)|0,ue=ue+Math.imul(ae,Jr)|0,K=K+Math.imul(ae,qn)|0,K=K+Math.imul(pe,Jr)|0,_e=_e+Math.imul(pe,qn)|0;var Ma=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(Ma>>>26)|0,Ma&=67108863,ue=Math.imul(Yt,cr),K=Math.imul(Yt,Ct),K=K+Math.imul(jt,cr)|0,_e=Math.imul(jt,Ct),ue=ue+Math.imul(We,Tt)|0,K=K+Math.imul(We,Mt)|0,K=K+Math.imul(mt,Tt)|0,_e=_e+Math.imul(mt,Mt)|0,ue=ue+Math.imul(ot,ft)|0,K=K+Math.imul(ot,kt)|0,K=K+Math.imul(tt,ft)|0,_e=_e+Math.imul(tt,kt)|0,ue=ue+Math.imul(Te,pr)|0,K=K+Math.imul(Te,Kr)|0,K=K+Math.imul(Ve,pr)|0,_e=_e+Math.imul(Ve,Kr)|0,ue=ue+Math.imul(we,dn)|0,K=K+Math.imul(we,xr)|0,K=K+Math.imul(Le,dn)|0,_e=_e+Math.imul(Le,xr)|0,ue=ue+Math.imul(ct,Zr)|0,K=K+Math.imul(ct,Cn)|0,K=K+Math.imul(et,Zr)|0,_e=_e+Math.imul(et,Cn)|0,ue=ue+Math.imul(it,At)|0,K=K+Math.imul(it,Vt)|0,K=K+Math.imul(Oe,At)|0,_e=_e+Math.imul(Oe,Vt)|0,ue=ue+Math.imul(De,Ut)|0,K=K+Math.imul(De,_r)|0,K=K+Math.imul(Ge,Ut)|0,_e=_e+Math.imul(Ge,_r)|0,ue=ue+Math.imul(le,Jr)|0,K=K+Math.imul(le,qn)|0,K=K+Math.imul(me,Jr)|0,_e=_e+Math.imul(me,qn)|0;var Ga=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(Ga>>>26)|0,Ga&=67108863,ue=Math.imul(Yt,Tt),K=Math.imul(Yt,Mt),K=K+Math.imul(jt,Tt)|0,_e=Math.imul(jt,Mt),ue=ue+Math.imul(We,ft)|0,K=K+Math.imul(We,kt)|0,K=K+Math.imul(mt,ft)|0,_e=_e+Math.imul(mt,kt)|0,ue=ue+Math.imul(ot,pr)|0,K=K+Math.imul(ot,Kr)|0,K=K+Math.imul(tt,pr)|0,_e=_e+Math.imul(tt,Kr)|0,ue=ue+Math.imul(Te,dn)|0,K=K+Math.imul(Te,xr)|0,K=K+Math.imul(Ve,dn)|0,_e=_e+Math.imul(Ve,xr)|0,ue=ue+Math.imul(we,Zr)|0,K=K+Math.imul(we,Cn)|0,K=K+Math.imul(Le,Zr)|0,_e=_e+Math.imul(Le,Cn)|0,ue=ue+Math.imul(ct,At)|0,K=K+Math.imul(ct,Vt)|0,K=K+Math.imul(et,At)|0,_e=_e+Math.imul(et,Vt)|0,ue=ue+Math.imul(it,Ut)|0,K=K+Math.imul(it,_r)|0,K=K+Math.imul(Oe,Ut)|0,_e=_e+Math.imul(Oe,_r)|0,ue=ue+Math.imul(De,Jr)|0,K=K+Math.imul(De,qn)|0,K=K+Math.imul(Ge,Jr)|0,_e=_e+Math.imul(Ge,qn)|0;var ss=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(ss>>>26)|0,ss&=67108863,ue=Math.imul(Yt,ft),K=Math.imul(Yt,kt),K=K+Math.imul(jt,ft)|0,_e=Math.imul(jt,kt),ue=ue+Math.imul(We,pr)|0,K=K+Math.imul(We,Kr)|0,K=K+Math.imul(mt,pr)|0,_e=_e+Math.imul(mt,Kr)|0,ue=ue+Math.imul(ot,dn)|0,K=K+Math.imul(ot,xr)|0,K=K+Math.imul(tt,dn)|0,_e=_e+Math.imul(tt,xr)|0,ue=ue+Math.imul(Te,Zr)|0,K=K+Math.imul(Te,Cn)|0,K=K+Math.imul(Ve,Zr)|0,_e=_e+Math.imul(Ve,Cn)|0,ue=ue+Math.imul(we,At)|0,K=K+Math.imul(we,Vt)|0,K=K+Math.imul(Le,At)|0,_e=_e+Math.imul(Le,Vt)|0,ue=ue+Math.imul(ct,Ut)|0,K=K+Math.imul(ct,_r)|0,K=K+Math.imul(et,Ut)|0,_e=_e+Math.imul(et,_r)|0,ue=ue+Math.imul(it,Jr)|0,K=K+Math.imul(it,qn)|0,K=K+Math.imul(Oe,Jr)|0,_e=_e+Math.imul(Oe,qn)|0;var mo=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(mo>>>26)|0,mo&=67108863,ue=Math.imul(Yt,pr),K=Math.imul(Yt,Kr),K=K+Math.imul(jt,pr)|0,_e=Math.imul(jt,Kr),ue=ue+Math.imul(We,dn)|0,K=K+Math.imul(We,xr)|0,K=K+Math.imul(mt,dn)|0,_e=_e+Math.imul(mt,xr)|0,ue=ue+Math.imul(ot,Zr)|0,K=K+Math.imul(ot,Cn)|0,K=K+Math.imul(tt,Zr)|0,_e=_e+Math.imul(tt,Cn)|0,ue=ue+Math.imul(Te,At)|0,K=K+Math.imul(Te,Vt)|0,K=K+Math.imul(Ve,At)|0,_e=_e+Math.imul(Ve,Vt)|0,ue=ue+Math.imul(we,Ut)|0,K=K+Math.imul(we,_r)|0,K=K+Math.imul(Le,Ut)|0,_e=_e+Math.imul(Le,_r)|0,ue=ue+Math.imul(ct,Jr)|0,K=K+Math.imul(ct,qn)|0,K=K+Math.imul(et,Jr)|0,_e=_e+Math.imul(et,qn)|0;var sl=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(sl>>>26)|0,sl&=67108863,ue=Math.imul(Yt,dn),K=Math.imul(Yt,xr),K=K+Math.imul(jt,dn)|0,_e=Math.imul(jt,xr),ue=ue+Math.imul(We,Zr)|0,K=K+Math.imul(We,Cn)|0,K=K+Math.imul(mt,Zr)|0,_e=_e+Math.imul(mt,Cn)|0,ue=ue+Math.imul(ot,At)|0,K=K+Math.imul(ot,Vt)|0,K=K+Math.imul(tt,At)|0,_e=_e+Math.imul(tt,Vt)|0,ue=ue+Math.imul(Te,Ut)|0,K=K+Math.imul(Te,_r)|0,K=K+Math.imul(Ve,Ut)|0,_e=_e+Math.imul(Ve,_r)|0,ue=ue+Math.imul(we,Jr)|0,K=K+Math.imul(we,qn)|0,K=K+Math.imul(Le,Jr)|0,_e=_e+Math.imul(Le,qn)|0;var Eo=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(Eo>>>26)|0,Eo&=67108863,ue=Math.imul(Yt,Zr),K=Math.imul(Yt,Cn),K=K+Math.imul(jt,Zr)|0,_e=Math.imul(jt,Cn),ue=ue+Math.imul(We,At)|0,K=K+Math.imul(We,Vt)|0,K=K+Math.imul(mt,At)|0,_e=_e+Math.imul(mt,Vt)|0,ue=ue+Math.imul(ot,Ut)|0,K=K+Math.imul(ot,_r)|0,K=K+Math.imul(tt,Ut)|0,_e=_e+Math.imul(tt,_r)|0,ue=ue+Math.imul(Te,Jr)|0,K=K+Math.imul(Te,qn)|0,K=K+Math.imul(Ve,Jr)|0,_e=_e+Math.imul(Ve,qn)|0;var ao=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(ao>>>26)|0,ao&=67108863,ue=Math.imul(Yt,At),K=Math.imul(Yt,Vt),K=K+Math.imul(jt,At)|0,_e=Math.imul(jt,Vt),ue=ue+Math.imul(We,Ut)|0,K=K+Math.imul(We,_r)|0,K=K+Math.imul(mt,Ut)|0,_e=_e+Math.imul(mt,_r)|0,ue=ue+Math.imul(ot,Jr)|0,K=K+Math.imul(ot,qn)|0,K=K+Math.imul(tt,Jr)|0,_e=_e+Math.imul(tt,qn)|0;var Da=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(Da>>>26)|0,Da&=67108863,ue=Math.imul(Yt,Ut),K=Math.imul(Yt,_r),K=K+Math.imul(jt,Ut)|0,_e=Math.imul(jt,_r),ue=ue+Math.imul(We,Jr)|0,K=K+Math.imul(We,qn)|0,K=K+Math.imul(mt,Jr)|0,_e=_e+Math.imul(mt,qn)|0;var bl=(xe+ue|0)+((K&8191)<<13)|0;xe=(_e+(K>>>13)|0)+(bl>>>26)|0,bl&=67108863,ue=Math.imul(Yt,Jr),K=Math.imul(Yt,qn),K=K+Math.imul(jt,Jr)|0,_e=Math.imul(jt,qn);var bo=(xe+ue|0)+((K&8191)<<13)|0;return xe=(_e+(K>>>13)|0)+(bo>>>26)|0,bo&=67108863,ye[0]=ri,ye[1]=Jn,ye[2]=Ii,ye[3]=Vi,ye[4]=ra,ye[5]=ha,ye[6]=va,ye[7]=Xi,ye[8]=oa,ye[9]=Ma,ye[10]=Ga,ye[11]=ss,ye[12]=mo,ye[13]=sl,ye[14]=Eo,ye[15]=ao,ye[16]=Da,ye[17]=bl,ye[18]=bo,xe!==0&&(ye[19]=xe,J.length++),J};Math.imul||(L=S);function w(j,q,U){U.negative=q.negative^j.negative,U.length=j.length+q.length;for(var J=0,ee=0,ce=0;ce<U.length-1;ce++){var ye=ee;ee=0;for(var xe=J&67108863,ue=Math.min(ce,q.length-1),K=Math.max(0,ce-j.length+1);K<=ue;K++){var _e=ce-K,ne=j.words[_e]|0,ae=q.words[K]|0,pe=ne*ae,ie=pe&67108863;ye=ye+(pe/67108864|0)|0,ie=ie+xe|0,xe=ie&67108863,ye=ye+(ie>>>26)|0,ee+=ye>>>26,ye&=67108863}U.words[ce]=xe,J=ye,ye=ee}return J!==0?U.words[ce]=J:U.length--,U.strip()}function A(j,q,U){var J=new T;return J.mulp(j,q,U)}f.prototype.mulTo=function(q,U){var J,ee=this.length+q.length;return this.length===10&&q.length===10?J=L(this,q,U):ee<63?J=S(this,q,U):ee<1024?J=w(this,q,U):J=A(this,q,U),J};function T(j,q){this.x=j,this.y=q}T.prototype.makeRBT=function(q){for(var U=new Array(q),J=f.prototype._countBits(q)-1,ee=0;ee<q;ee++)U[ee]=this.revBin(ee,J,q);return U},T.prototype.revBin=function(q,U,J){if(q===0||q===J-1)return q;for(var ee=0,ce=0;ce<U;ce++)ee|=(q&1)<<U-ce-1,q>>=1;return ee},T.prototype.permute=function(q,U,J,ee,ce,ye){for(var xe=0;xe<ye;xe++)ee[xe]=U[q[xe]],ce[xe]=J[q[xe]]},T.prototype.transform=function(q,U,J,ee,ce,ye){this.permute(ye,q,U,J,ee,ce);for(var xe=1;xe<ce;xe<<=1)for(var ue=xe<<1,K=Math.cos(2*Math.PI/ue),_e=Math.sin(2*Math.PI/ue),ne=0;ne<ce;ne+=ue)for(var ae=K,pe=_e,ie=0;ie<xe;ie++){var le=J[ne+ie],me=ee[ne+ie],be=J[ne+ie+xe],De=ee[ne+ie+xe],Ge=ae*be-pe*De;De=ae*De+pe*be,be=Ge,J[ne+ie]=le+be,ee[ne+ie]=me+De,J[ne+ie+xe]=le-be,ee[ne+ie+xe]=me-De,ie!==ue&&(Ge=K*ae-_e*pe,pe=K*pe+_e*ae,ae=Ge)}},T.prototype.guessLen13b=function(q,U){var J=Math.max(U,q)|1,ee=J&1,ce=0;for(J=J/2|0;J;J=J>>>1)ce++;return 1<<ce+1+ee},T.prototype.conjugate=function(q,U,J){if(!(J<=1))for(var ee=0;ee<J/2;ee++){var ce=q[ee];q[ee]=q[J-ee-1],q[J-ee-1]=ce,ce=U[ee],U[ee]=-U[J-ee-1],U[J-ee-1]=-ce}},T.prototype.normalize13b=function(q,U){for(var J=0,ee=0;ee<U/2;ee++){var ce=Math.round(q[2*ee+1]/U)*8192+Math.round(q[2*ee]/U)+J;q[ee]=ce&67108863,ce<67108864?J=0:J=ce/67108864|0}return q},T.prototype.convert13b=function(q,U,J,ee){for(var ce=0,ye=0;ye<U;ye++)ce=ce+(q[ye]|0),J[2*ye]=ce&8191,ce=ce>>>13,J[2*ye+1]=ce&8191,ce=ce>>>13;for(ye=2*U;ye<ee;++ye)J[ye]=0;u(ce===0),u((ce&-8192)===0)},T.prototype.stub=function(q){for(var U=new Array(q),J=0;J<q;J++)U[J]=0;return U},T.prototype.mulp=function(q,U,J){var ee=2*this.guessLen13b(q.length,U.length),ce=this.makeRBT(ee),ye=this.stub(ee),xe=new Array(ee),ue=new Array(ee),K=new Array(ee),_e=new Array(ee),ne=new Array(ee),ae=new Array(ee),pe=J.words;pe.length=ee,this.convert13b(q.words,q.length,xe,ee),this.convert13b(U.words,U.length,_e,ee),this.transform(xe,ye,ue,K,ee,ce),this.transform(_e,ye,ne,ae,ee,ce);for(var ie=0;ie<ee;ie++){var le=ue[ie]*ne[ie]-K[ie]*ae[ie];K[ie]=ue[ie]*ae[ie]+K[ie]*ne[ie],ue[ie]=le}return this.conjugate(ue,K,ee),this.transform(ue,K,pe,ye,ee,ce),this.conjugate(pe,ye,ee),this.normalize13b(pe,ee),J.negative=q.negative^U.negative,J.length=q.length+U.length,J.strip()},f.prototype.mul=function(q){var U=new f(null);return U.words=new Array(this.length+q.length),this.mulTo(q,U)},f.prototype.mulf=function(q){var U=new f(null);return U.words=new Array(this.length+q.length),A(this,q,U)},f.prototype.imul=function(q){return this.clone().mulTo(q,this)},f.prototype.imuln=function(q){u(typeof q==\"number\"),u(q<67108864);for(var U=0,J=0;J<this.length;J++){var ee=(this.words[J]|0)*q,ce=(ee&67108863)+(U&67108863);U>>=26,U+=ee/67108864|0,U+=ce>>>26,this.words[J]=ce&67108863}return U!==0&&(this.words[J]=U,this.length++),this.length=q===0?1:this.length,this},f.prototype.muln=function(q){return this.clone().imuln(q)},f.prototype.sqr=function(){return this.mul(this)},f.prototype.isqr=function(){return this.imul(this.clone())},f.prototype.pow=function(q){var U=k(q);if(U.length===0)return new f(1);for(var J=this,ee=0;ee<U.length&&U[ee]===0;ee++,J=J.sqr());if(++ee<U.length)for(var ce=J.sqr();ee<U.length;ee++,ce=ce.sqr())U[ee]!==0&&(J=J.mul(ce));return J},f.prototype.iushln=function(q){u(typeof q==\"number\"&&q>=0);var U=q%26,J=(q-U)/26,ee=67108863>>>26-U<<26-U,ce;if(U!==0){var ye=0;for(ce=0;ce<this.length;ce++){var xe=this.words[ce]&ee,ue=(this.words[ce]|0)-xe<<U;this.words[ce]=ue|ye,ye=xe>>>26-U}ye&&(this.words[ce]=ye,this.length++)}if(J!==0){for(ce=this.length-1;ce>=0;ce--)this.words[ce+J]=this.words[ce];for(ce=0;ce<J;ce++)this.words[ce]=0;this.length+=J}return this.strip()},f.prototype.ishln=function(q){return u(this.negative===0),this.iushln(q)},f.prototype.iushrn=function(q,U,J){u(typeof q==\"number\"&&q>=0);var ee;U?ee=(U-U%26)/26:ee=0;var ce=q%26,ye=Math.min((q-ce)/26,this.length),xe=67108863^67108863>>>ce<<ce,ue=J;if(ee-=ye,ee=Math.max(0,ee),ue){for(var K=0;K<ye;K++)ue.words[K]=this.words[K];ue.length=ye}if(ye!==0)if(this.length>ye)for(this.length-=ye,K=0;K<this.length;K++)this.words[K]=this.words[K+ye];else this.words[0]=0,this.length=1;var _e=0;for(K=this.length-1;K>=0&&(_e!==0||K>=ee);K--){var ne=this.words[K]|0;this.words[K]=_e<<26-ce|ne>>>ce,_e=ne&xe}return ue&&_e!==0&&(ue.words[ue.length++]=_e),this.length===0&&(this.words[0]=0,this.length=1),this.strip()},f.prototype.ishrn=function(q,U,J){return u(this.negative===0),this.iushrn(q,U,J)},f.prototype.shln=function(q){return this.clone().ishln(q)},f.prototype.ushln=function(q){return this.clone().iushln(q)},f.prototype.shrn=function(q){return this.clone().ishrn(q)},f.prototype.ushrn=function(q){return this.clone().iushrn(q)},f.prototype.testn=function(q){u(typeof q==\"number\"&&q>=0);var U=q%26,J=(q-U)/26,ee=1<<U;if(this.length<=J)return!1;var ce=this.words[J];return!!(ce&ee)},f.prototype.imaskn=function(q){u(typeof q==\"number\"&&q>=0);var U=q%26,J=(q-U)/26;if(u(this.negative===0,\"imaskn works only with positive numbers\"),this.length<=J)return this;if(U!==0&&J++,this.length=Math.min(J,this.length),U!==0){var ee=67108863^67108863>>>U<<U;this.words[this.length-1]&=ee}return this.length===0&&(this.words[0]=0,this.length=1),this.strip()},f.prototype.maskn=function(q){return this.clone().imaskn(q)},f.prototype.iaddn=function(q){return u(typeof q==\"number\"),u(q<67108864),q<0?this.isubn(-q):this.negative!==0?this.length===1&&(this.words[0]|0)<q?(this.words[0]=q-(this.words[0]|0),this.negative=0,this):(this.negative=0,this.isubn(q),this.negative=1,this):this._iaddn(q)},f.prototype._iaddn=function(q){this.words[0]+=q;for(var U=0;U<this.length&&this.words[U]>=67108864;U++)this.words[U]-=67108864,U===this.length-1?this.words[U+1]=1:this.words[U+1]++;return this.length=Math.max(this.length,U+1),this},f.prototype.isubn=function(q){if(u(typeof q==\"number\"),u(q<67108864),q<0)return this.iaddn(-q);if(this.negative!==0)return this.negative=0,this.iaddn(q),this.negative=1,this;if(this.words[0]-=q,this.length===1&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var U=0;U<this.length&&this.words[U]<0;U++)this.words[U]+=67108864,this.words[U+1]-=1;return this.strip()},f.prototype.addn=function(q){return this.clone().iaddn(q)},f.prototype.subn=function(q){return this.clone().isubn(q)},f.prototype.iabs=function(){return this.negative=0,this},f.prototype.abs=function(){return this.clone().iabs()},f.prototype._ishlnsubmul=function(q,U,J){var ee=q.length+J,ce;this._expand(ee);var ye,xe=0;for(ce=0;ce<q.length;ce++){ye=(this.words[ce+J]|0)+xe;var ue=(q.words[ce]|0)*U;ye-=ue&67108863,xe=(ye>>26)-(ue/67108864|0),this.words[ce+J]=ye&67108863}for(;ce<this.length-J;ce++)ye=(this.words[ce+J]|0)+xe,xe=ye>>26,this.words[ce+J]=ye&67108863;if(xe===0)return this.strip();for(u(xe===-1),xe=0,ce=0;ce<this.length;ce++)ye=-(this.words[ce]|0)+xe,xe=ye>>26,this.words[ce]=ye&67108863;return this.negative=1,this.strip()},f.prototype._wordDiv=function(q,U){var J=this.length-q.length,ee=this.clone(),ce=q,ye=ce.words[ce.length-1]|0,xe=this._countBits(ye);J=26-xe,J!==0&&(ce=ce.ushln(J),ee.iushln(J),ye=ce.words[ce.length-1]|0);var ue=ee.length-ce.length,K;if(U!==\"mod\"){K=new f(null),K.length=ue+1,K.words=new Array(K.length);for(var _e=0;_e<K.length;_e++)K.words[_e]=0}var ne=ee.clone()._ishlnsubmul(ce,1,ue);ne.negative===0&&(ee=ne,K&&(K.words[ue]=1));for(var ae=ue-1;ae>=0;ae--){var pe=(ee.words[ce.length+ae]|0)*67108864+(ee.words[ce.length+ae-1]|0);for(pe=Math.min(pe/ye|0,67108863),ee._ishlnsubmul(ce,pe,ae);ee.negative!==0;)pe--,ee.negative=0,ee._ishlnsubmul(ce,1,ae),ee.isZero()||(ee.negative^=1);K&&(K.words[ae]=pe)}return K&&K.strip(),ee.strip(),U!==\"div\"&&J!==0&&ee.iushrn(J),{div:K||null,mod:ee}},f.prototype.divmod=function(q,U,J){if(u(!q.isZero()),this.isZero())return{div:new f(0),mod:new f(0)};var ee,ce,ye;return this.negative!==0&&q.negative===0?(ye=this.neg().divmod(q,U),U!==\"mod\"&&(ee=ye.div.neg()),U!==\"div\"&&(ce=ye.mod.neg(),J&&ce.negative!==0&&ce.iadd(q)),{div:ee,mod:ce}):this.negative===0&&q.negative!==0?(ye=this.divmod(q.neg(),U),U!==\"mod\"&&(ee=ye.div.neg()),{div:ee,mod:ye.mod}):(this.negative&q.negative)!==0?(ye=this.neg().divmod(q.neg(),U),U!==\"div\"&&(ce=ye.mod.neg(),J&&ce.negative!==0&&ce.isub(q)),{div:ye.div,mod:ce}):q.length>this.length||this.cmp(q)<0?{div:new f(0),mod:this}:q.length===1?U===\"div\"?{div:this.divn(q.words[0]),mod:null}:U===\"mod\"?{div:null,mod:new f(this.modn(q.words[0]))}:{div:this.divn(q.words[0]),mod:new f(this.modn(q.words[0]))}:this._wordDiv(q,U)},f.prototype.div=function(q){return this.divmod(q,\"div\",!1).div},f.prototype.mod=function(q){return this.divmod(q,\"mod\",!1).mod},f.prototype.umod=function(q){return this.divmod(q,\"mod\",!0).mod},f.prototype.divRound=function(q){var U=this.divmod(q);if(U.mod.isZero())return U.div;var J=U.div.negative!==0?U.mod.isub(q):U.mod,ee=q.ushrn(1),ce=q.andln(1),ye=J.cmp(ee);return ye<0||ce===1&&ye===0?U.div:U.div.negative!==0?U.div.isubn(1):U.div.iaddn(1)},f.prototype.modn=function(q){u(q<=67108863);for(var U=(1<<26)%q,J=0,ee=this.length-1;ee>=0;ee--)J=(U*J+(this.words[ee]|0))%q;return J},f.prototype.idivn=function(q){u(q<=67108863);for(var U=0,J=this.length-1;J>=0;J--){var ee=(this.words[J]|0)+U*67108864;this.words[J]=ee/q|0,U=ee%q}return this.strip()},f.prototype.divn=function(q){return this.clone().idivn(q)},f.prototype.egcd=function(q){u(q.negative===0),u(!q.isZero());var U=this,J=q.clone();U.negative!==0?U=U.umod(q):U=U.clone();for(var ee=new f(1),ce=new f(0),ye=new f(0),xe=new f(1),ue=0;U.isEven()&&J.isEven();)U.iushrn(1),J.iushrn(1),++ue;for(var K=J.clone(),_e=U.clone();!U.isZero();){for(var ne=0,ae=1;(U.words[0]&ae)===0&&ne<26;++ne,ae<<=1);if(ne>0)for(U.iushrn(ne);ne-- >0;)(ee.isOdd()||ce.isOdd())&&(ee.iadd(K),ce.isub(_e)),ee.iushrn(1),ce.iushrn(1);for(var pe=0,ie=1;(J.words[0]&ie)===0&&pe<26;++pe,ie<<=1);if(pe>0)for(J.iushrn(pe);pe-- >0;)(ye.isOdd()||xe.isOdd())&&(ye.iadd(K),xe.isub(_e)),ye.iushrn(1),xe.iushrn(1);U.cmp(J)>=0?(U.isub(J),ee.isub(ye),ce.isub(xe)):(J.isub(U),ye.isub(ee),xe.isub(ce))}return{a:ye,b:xe,gcd:J.iushln(ue)}},f.prototype._invmp=function(q){u(q.negative===0),u(!q.isZero());var U=this,J=q.clone();U.negative!==0?U=U.umod(q):U=U.clone();for(var ee=new f(1),ce=new f(0),ye=J.clone();U.cmpn(1)>0&&J.cmpn(1)>0;){for(var xe=0,ue=1;(U.words[0]&ue)===0&&xe<26;++xe,ue<<=1);if(xe>0)for(U.iushrn(xe);xe-- >0;)ee.isOdd()&&ee.iadd(ye),ee.iushrn(1);for(var K=0,_e=1;(J.words[0]&_e)===0&&K<26;++K,_e<<=1);if(K>0)for(J.iushrn(K);K-- >0;)ce.isOdd()&&ce.iadd(ye),ce.iushrn(1);U.cmp(J)>=0?(U.isub(J),ee.isub(ce)):(J.isub(U),ce.isub(ee))}var ne;return U.cmpn(1)===0?ne=ee:ne=ce,ne.cmpn(0)<0&&ne.iadd(q),ne},f.prototype.gcd=function(q){if(this.isZero())return q.abs();if(q.isZero())return this.abs();var U=this.clone(),J=q.clone();U.negative=0,J.negative=0;for(var ee=0;U.isEven()&&J.isEven();ee++)U.iushrn(1),J.iushrn(1);do{for(;U.isEven();)U.iushrn(1);for(;J.isEven();)J.iushrn(1);var ce=U.cmp(J);if(ce<0){var ye=U;U=J,J=ye}else if(ce===0||J.cmpn(1)===0)break;U.isub(J)}while(!0);return J.iushln(ee)},f.prototype.invm=function(q){return this.egcd(q).a.umod(q)},f.prototype.isEven=function(){return(this.words[0]&1)===0},f.prototype.isOdd=function(){return(this.words[0]&1)===1},f.prototype.andln=function(q){return this.words[0]&q},f.prototype.bincn=function(q){u(typeof q==\"number\");var U=q%26,J=(q-U)/26,ee=1<<U;if(this.length<=J)return this._expand(J+1),this.words[J]|=ee,this;for(var ce=ee,ye=J;ce!==0&&ye<this.length;ye++){var xe=this.words[ye]|0;xe+=ce,ce=xe>>>26,xe&=67108863,this.words[ye]=xe}return ce!==0&&(this.words[ye]=ce,this.length++),this},f.prototype.isZero=function(){return this.length===1&&this.words[0]===0},f.prototype.cmpn=function(q){var U=q<0;if(this.negative!==0&&!U)return-1;if(this.negative===0&&U)return 1;this.strip();var J;if(this.length>1)J=1;else{U&&(q=-q),u(q<=67108863,\"Number is too big\");var ee=this.words[0]|0;J=ee===q?0:ee<q?-1:1}return this.negative!==0?-J|0:J},f.prototype.cmp=function(q){if(this.negative!==0&&q.negative===0)return-1;if(this.negative===0&&q.negative!==0)return 1;var U=this.ucmp(q);return this.negative!==0?-U|0:U},f.prototype.ucmp=function(q){if(this.length>q.length)return 1;if(this.length<q.length)return-1;for(var U=0,J=this.length-1;J>=0;J--){var ee=this.words[J]|0,ce=q.words[J]|0;if(ee!==ce){ee<ce?U=-1:ee>ce&&(U=1);break}}return U},f.prototype.gtn=function(q){return this.cmpn(q)===1},f.prototype.gt=function(q){return this.cmp(q)===1},f.prototype.gten=function(q){return this.cmpn(q)>=0},f.prototype.gte=function(q){return this.cmp(q)>=0},f.prototype.ltn=function(q){return this.cmpn(q)===-1},f.prototype.lt=function(q){return this.cmp(q)===-1},f.prototype.lten=function(q){return this.cmpn(q)<=0},f.prototype.lte=function(q){return this.cmp(q)<=0},f.prototype.eqn=function(q){return this.cmpn(q)===0},f.prototype.eq=function(q){return this.cmp(q)===0},f.red=function(q){return new O(q)},f.prototype.toRed=function(q){return u(!this.red,\"Already a number in reduction context\"),u(this.negative===0,\"red works only with positives\"),q.convertTo(this)._forceRed(q)},f.prototype.fromRed=function(){return u(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},f.prototype._forceRed=function(q){return this.red=q,this},f.prototype.forceRed=function(q){return u(!this.red,\"Already a number in reduction context\"),this._forceRed(q)},f.prototype.redAdd=function(q){return u(this.red,\"redAdd works only with red numbers\"),this.red.add(this,q)},f.prototype.redIAdd=function(q){return u(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,q)},f.prototype.redSub=function(q){return u(this.red,\"redSub works only with red numbers\"),this.red.sub(this,q)},f.prototype.redISub=function(q){return u(this.red,\"redISub works only with red numbers\"),this.red.isub(this,q)},f.prototype.redShl=function(q){return u(this.red,\"redShl works only with red numbers\"),this.red.shl(this,q)},f.prototype.redMul=function(q){return u(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,q),this.red.mul(this,q)},f.prototype.redIMul=function(q){return u(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,q),this.red.imul(this,q)},f.prototype.redSqr=function(){return u(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},f.prototype.redISqr=function(){return u(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},f.prototype.redSqrt=function(){return u(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},f.prototype.redInvm=function(){return u(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},f.prototype.redNeg=function(){return u(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},f.prototype.redPow=function(q){return u(this.red&&!q.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,q)};var m={k256:null,p224:null,p192:null,p25519:null};function F(j,q){this.name=j,this.p=new f(q,16),this.n=this.p.bitLength(),this.k=new f(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}F.prototype._tmp=function(){var q=new f(null);return q.words=new Array(Math.ceil(this.n/13)),q},F.prototype.ireduce=function(q){var U=q,J;do this.split(U,this.tmp),U=this.imulK(U),U=U.iadd(this.tmp),J=U.bitLength();while(J>this.n);var ee=J<this.n?-1:U.ucmp(this.p);return ee===0?(U.words[0]=0,U.length=1):ee>0?U.isub(this.p):U.strip!==void 0?U.strip():U._strip(),U},F.prototype.split=function(q,U){q.iushrn(this.n,0,U)},F.prototype.imulK=function(q){return q.imul(this.k)};function E(){F.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}c(E,F),E.prototype.split=function(q,U){for(var J=4194303,ee=Math.min(q.length,9),ce=0;ce<ee;ce++)U.words[ce]=q.words[ce];if(U.length=ee,q.length<=9){q.words[0]=0,q.length=1;return}var ye=q.words[9];for(U.words[U.length++]=ye&J,ce=10;ce<q.length;ce++){var xe=q.words[ce]|0;q.words[ce-10]=(xe&J)<<4|ye>>>22,ye=xe}ye>>>=22,q.words[ce-10]=ye,ye===0&&q.length>10?q.length-=10:q.length-=9},E.prototype.imulK=function(q){q.words[q.length]=0,q.words[q.length+1]=0,q.length+=2;for(var U=0,J=0;J<q.length;J++){var ee=q.words[J]|0;U+=ee*977,q.words[J]=U&67108863,U=ee*64+(U/67108864|0)}return q.words[q.length-1]===0&&(q.length--,q.words[q.length-1]===0&&q.length--),q};function I(){F.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}c(I,F);function R(){F.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}c(R,F);function B(){F.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}c(B,F),B.prototype.imulK=function(q){for(var U=0,J=0;J<q.length;J++){var ee=(q.words[J]|0)*19+U,ce=ee&67108863;ee>>>=26,q.words[J]=ce,U=ee}return U!==0&&(q.words[q.length++]=U),q},f._prime=function(q){if(m[q])return m[q];var U;if(q===\"k256\")U=new E;else if(q===\"p224\")U=new I;else if(q===\"p192\")U=new R;else if(q===\"p25519\")U=new B;else throw new Error(\"Unknown prime \"+q);return m[q]=U,U};function O(j){if(typeof j==\"string\"){var q=f._prime(j);this.m=q.p,this.prime=q}else u(j.gtn(1),\"modulus must be greater than 1\"),this.m=j,this.prime=null}O.prototype._verify1=function(q){u(q.negative===0,\"red works only with positives\"),u(q.red,\"red works only with red numbers\")},O.prototype._verify2=function(q,U){u((q.negative|U.negative)===0,\"red works only with positives\"),u(q.red&&q.red===U.red,\"red works only with red numbers\")},O.prototype.imod=function(q){return this.prime?this.prime.ireduce(q)._forceRed(this):q.umod(this.m)._forceRed(this)},O.prototype.neg=function(q){return q.isZero()?q.clone():this.m.sub(q)._forceRed(this)},O.prototype.add=function(q,U){this._verify2(q,U);var J=q.add(U);return J.cmp(this.m)>=0&&J.isub(this.m),J._forceRed(this)},O.prototype.iadd=function(q,U){this._verify2(q,U);var J=q.iadd(U);return J.cmp(this.m)>=0&&J.isub(this.m),J},O.prototype.sub=function(q,U){this._verify2(q,U);var J=q.sub(U);return J.cmpn(0)<0&&J.iadd(this.m),J._forceRed(this)},O.prototype.isub=function(q,U){this._verify2(q,U);var J=q.isub(U);return J.cmpn(0)<0&&J.iadd(this.m),J},O.prototype.shl=function(q,U){return this._verify1(q),this.imod(q.ushln(U))},O.prototype.imul=function(q,U){return this._verify2(q,U),this.imod(q.imul(U))},O.prototype.mul=function(q,U){return this._verify2(q,U),this.imod(q.mul(U))},O.prototype.isqr=function(q){return this.imul(q,q.clone())},O.prototype.sqr=function(q){return this.mul(q,q)},O.prototype.sqrt=function(q){if(q.isZero())return q.clone();var U=this.m.andln(3);if(u(U%2===1),U===3){var J=this.m.add(new f(1)).iushrn(2);return this.pow(q,J)}for(var ee=this.m.subn(1),ce=0;!ee.isZero()&&ee.andln(1)===0;)ce++,ee.iushrn(1);u(!ee.isZero());var ye=new f(1).toRed(this),xe=ye.redNeg(),ue=this.m.subn(1).iushrn(1),K=this.m.bitLength();for(K=new f(2*K*K).toRed(this);this.pow(K,ue).cmp(xe)!==0;)K.redIAdd(xe);for(var _e=this.pow(K,ee),ne=this.pow(q,ee.addn(1).iushrn(1)),ae=this.pow(q,ee),pe=ce;ae.cmp(ye)!==0;){for(var ie=ae,le=0;ie.cmp(ye)!==0;le++)ie=ie.redSqr();u(le<pe);var me=this.pow(_e,new f(1).iushln(pe-le-1));ne=ne.redMul(me),_e=me.redSqr(),ae=ae.redMul(_e),pe=le}return ne},O.prototype.invm=function(q){var U=q._invmp(this.m);return U.negative!==0?(U.negative=0,this.imod(U).redNeg()):this.imod(U)},O.prototype.pow=function(q,U){if(U.isZero())return new f(1).toRed(this);if(U.cmpn(1)===0)return q.clone();var J=4,ee=new Array(1<<J);ee[0]=new f(1).toRed(this),ee[1]=q;for(var ce=2;ce<ee.length;ce++)ee[ce]=this.mul(ee[ce-1],q);var ye=ee[0],xe=0,ue=0,K=U.bitLength()%26;for(K===0&&(K=26),ce=U.length-1;ce>=0;ce--){for(var _e=U.words[ce],ne=K-1;ne>=0;ne--){var ae=_e>>ne&1;if(ye!==ee[0]&&(ye=this.sqr(ye)),ae===0&&xe===0){ue=0;continue}xe<<=1,xe|=ae,ue++,!(ue!==J&&(ce!==0||ne!==0))&&(ye=this.mul(ye,ee[xe]),ue=0,xe=0)}K=26}return ye},O.prototype.convertTo=function(q){var U=q.umod(this.m);return U===q?U.clone():U},O.prototype.convertFrom=function(q){var U=q.clone();return U.red=null,U},f.mont=function(q){return new V(q)};function V(j){O.call(this,j),this.shift=this.m.bitLength(),this.shift%26!==0&&(this.shift+=26-this.shift%26),this.r=new f(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}c(V,O),V.prototype.convertTo=function(q){return 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C=(_-x+1)/6|0,k=x+C,S=_-C,L=x+_>>1,w=L-C,A=L+C,T=k,m=w,F=L,E=A,I=S,R=x+1,B=_-1,O=0;v(T,m,y)&&(O=T,T=m,m=O),v(E,I,y)&&(O=E,E=I,I=O),v(T,F,y)&&(O=T,T=F,F=O),v(m,F,y)&&(O=m,m=F,F=O),v(T,E,y)&&(O=T,T=E,E=O),v(F,E,y)&&(O=F,F=E,E=O),v(m,I,y)&&(O=m,m=I,I=O),v(m,F,y)&&(O=m,m=F,F=O),v(E,I,y)&&(O=E,E=I,I=O);for(var V=y[2*m],j=y[2*m+1],q=y[2*E],U=y[2*E+1],J=2*T,ee=2*F,ce=2*I,ye=2*k,xe=2*L,ue=2*S,K=0;K<2;++K){var _e=y[J+K],ne=y[ee+K],ae=y[ce+K];y[ye+K]=_e,y[xe+K]=ne,y[ue+K]=ae}u(w,x,y),u(A,_,y);for(var pe=R;pe<=B;++pe)if(g(pe,V,j,y))pe!==R&&l(pe,R,y),++R;else if(!g(pe,q,U,y))for(;;)if(g(B,q,U,y)){g(B,V,j,y)?(c(pe,R,B,y),++R,--B):(l(pe,B,y),--B);break}else{if(--B<pe)break;continue}f(x,R-1,V,j,y),f(_,B+1,q,U,y),R-2-x<=i?s(x,R-2,y):p(x,R-2,y),_-(B+2)<=i?s(B+2,_,y):p(B+2,_,y),B-R<=i?s(R,B,y):p(R,B,y)}}),855:(function(a,i,o){\"use strict\";a.exports={init:k,sweepBipartite:w,sweepComplete:A,scanBipartite:T,scanComplete:m};var 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if(m.right&&m.right._color===i){if(m=T.left=l(m),F=m.right=l(m.right),T.left=F.right,m.right=F.left,F.right=T,F.left=m,F._color=T._color,T._color=o,m._color=o,A._color=o,c(T),c(m),c(F),E>1){var I=w[E-2];I.right===T?I.right=F:I.left=F}w[E-1]=F;return}if(m._color===o)if(T._color===i){T._color=o,T.left=u(i,m);return}else{T.left=u(i,m);continue}else{if(m=l(m),T.left=m.right,m.right=T,m._color=T._color,T._color=i,c(T),c(m),E>1){var I=w[E-2];I.right===T?I.right=m:I.left=m}w[E-1]=m,w[E]=T,E+1<w.length?w[E+1]=A:w.push(A),E=E+2}}}}y.remove=function(){var w=this._stack;if(w.length===0)return this.tree;var A=new Array(w.length),T=w[w.length-1];A[A.length-1]=new s(T._color,T.key,T.value,T.left,T.right,T._count);for(var m=w.length-2;m>=0;--m){var T=w[m];T.left===w[m+1]?A[m]=new s(T._color,T.key,T.value,A[m+1],T.right,T._count):A[m]=new s(T._color,T.key,T.value,T.left,A[m+1],T._count)}if(T=A[A.length-1],T.left&&T.right){var F=A.length;for(T=T.left;T.right;)A.push(T),T=T.right;var E=A[F-1];A.push(new s(T._color,E.key,E.value,T.left,T.right,T._count)),A[F-1].key=T.key,A[F-1].value=T.value;for(var m=A.length-2;m>=F;--m)T=A[m],A[m]=new s(T._color,T.key,T.value,T.left,A[m+1],T._count);A[F-1].left=A[F]}if(T=A[A.length-1],T._color===i){var I=A[A.length-2];I.left===T?I.left=null:I.right===T&&(I.right=null),A.pop();for(var m=0;m<A.length;++m)A[m]._count--;return new f(this.tree._compare,A[0])}else if(T.left||T.right){T.left?C(T,T.left):T.right&&C(T,T.right),T._color=o;for(var m=0;m<A.length-1;++m)A[m]._count--;return new f(this.tree._compare,A[0])}else{if(A.length===1)return new f(this.tree._compare,null);for(var m=0;m<A.length;++m)A[m]._count--;var R=A[A.length-2];k(A),R.left===T?R.left=null:R.right=null}return new f(this.tree._compare,A[0])},Object.defineProperty(y,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(y,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(y,\"index\",{get:function(){var w=0,A=this._stack;if(A.length===0){var T=this.tree.root;return T?T._count:0}else A[A.length-1].left&&(w=A[A.length-1].left._count);for(var m=A.length-2;m>=0;--m)A[m+1]===A[m].right&&(++w,A[m].left&&(w+=A[m].left._count));return w},enumerable:!0}),y.next=function(){var w=this._stack;if(w.length!==0){var A=w[w.length-1];if(A.right)for(A=A.right;A;)w.push(A),A=A.left;else for(w.pop();w.length>0&&w[w.length-1].right===A;)A=w[w.length-1],w.pop()}},Object.defineProperty(y,\"hasNext\",{get:function(){var w=this._stack;if(w.length===0)return!1;if(w[w.length-1].right)return!0;for(var A=w.length-1;A>0;--A)if(w[A-1].left===w[A])return!0;return!1}}),y.update=function(w){var A=this._stack;if(A.length===0)throw new Error(\"Can't update empty node!\");var T=new Array(A.length),m=A[A.length-1];T[T.length-1]=new s(m._color,m.key,w,m.left,m.right,m._count);for(var F=A.length-2;F>=0;--F)m=A[F],m.left===A[F+1]?T[F]=new s(m._color,m.key,m.value,T[F+1],m.right,m._count):T[F]=new s(m._color,m.key,m.value,m.left,T[F+1],m._count);return new f(this.tree._compare,T[0])},y.prev=function(){var w=this._stack;if(w.length!==0){var A=w[w.length-1];if(A.left)for(A=A.left;A;)w.push(A),A=A.right;else for(w.pop();w.length>0&&w[w.length-1].left===A;)A=w[w.length-1],w.pop()}},Object.defineProperty(y,\"hasPrev\",{get:function(){var w=this._stack;if(w.length===0)return!1;if(w[w.length-1].left)return!0;for(var A=w.length-1;A>0;--A)if(w[A-1].right===w[A])return!0;return!1}});function S(w,A){return w<A?-1:w>A?1:0}function L(w){return new f(w||S,null)}}),3837:(function(a,i,o){\"use strict\";a.exports=R;var s=o(4935),l=o(501),u=o(5304),c=o(6429),f=o(6444),v=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),g=ArrayBuffer,p=DataView;function x(B){return g.isView(B)&&!(B instanceof p)}function _(B){return Array.isArray(B)||x(B)}function y(B,O){return B[0]=O[0],B[1]=O[1],B[2]=O[2],B}function C(B){this.gl=B,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\"],this.tickFontWeight=[\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.labelFontStyle=[\"normal\",\"normal\",\"normal\"],this.labelFontWeight=[\"normal\",\"normal\",\"normal\"],this.labelFontVariant=[\"normal\",\"normal\",\"normal\"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=u(B)}var k=C.prototype;k.update=function(B){B=B||{};function O(ae,pe,ie){if(ie in B){var le=B[ie],me=this[ie],be;(ae?_(le)&&_(le[0]):_(le))?this[ie]=be=[pe(le[0]),pe(le[1]),pe(le[2])]:this[ie]=be=[pe(le),pe(le),pe(le)];for(var De=0;De<3;++De)if(be[De]!==me[De])return!0}return!1}var V=O.bind(this,!1,Number),j=O.bind(this,!1,Boolean),q=O.bind(this,!1,String),U=O.bind(this,!0,function(ae){if(_(ae)){if(ae.length===3)return[+ae[0],+ae[1],+ae[2],1];if(ae.length===4)return[+ae[0],+ae[1],+ae[2],+ae[3]]}return[0,0,0,1]}),J,ee=!1,ce=!1;if(\"bounds\"in B)for(var ye=B.bounds,xe=0;xe<2;++xe)for(var ue=0;ue<3;++ue)ye[xe][ue]!==this.bounds[xe][ue]&&(ce=!0),this.bounds[xe][ue]=ye[xe][ue];if(\"ticks\"in B){J=B.ticks,ee=!0,this.autoTicks=!1;for(var xe=0;xe<3;++xe)this.tickSpacing[xe]=0}else V(\"tickSpacing\")&&(this.autoTicks=!0,ce=!0);if(this._firstInit&&(\"ticks\"in B||\"tickSpacing\"in B||(this.autoTicks=!0),ce=!0,ee=!0,this._firstInit=!1),ce&&this.autoTicks&&(J=f.create(this.bounds,this.tickSpacing),ee=!0),ee){for(var xe=0;xe<3;++xe)J[xe].sort(function(pe,ie){return pe.x-ie.x});f.equal(J,this.ticks)?ee=!1:this.ticks=J}j(\"tickEnable\"),q(\"tickFont\")&&(ee=!0),q(\"tickFontStyle\")&&(ee=!0),q(\"tickFontWeight\")&&(ee=!0),q(\"tickFontVariant\")&&(ee=!0),V(\"tickSize\"),V(\"tickAngle\"),V(\"tickPad\"),U(\"tickColor\");var K=q(\"labels\");q(\"labelFont\")&&(K=!0),q(\"labelFontStyle\")&&(K=!0),q(\"labelFontWeight\")&&(K=!0),q(\"labelFontVariant\")&&(K=!0),j(\"labelEnable\"),V(\"labelSize\"),V(\"labelPad\"),U(\"labelColor\"),j(\"lineEnable\"),j(\"lineMirror\"),V(\"lineWidth\"),U(\"lineColor\"),j(\"lineTickEnable\"),j(\"lineTickMirror\"),V(\"lineTickLength\"),V(\"lineTickWidth\"),U(\"lineTickColor\"),j(\"gridEnable\"),V(\"gridWidth\"),U(\"gridColor\"),j(\"zeroEnable\"),U(\"zeroLineColor\"),V(\"zeroLineWidth\"),j(\"backgroundEnable\"),U(\"backgroundColor\");var _e=[{family:this.labelFont[0],style:this.labelFontStyle[0],weight:this.labelFontWeight[0],variant:this.labelFontVariant[0]},{family:this.labelFont[1],style:this.labelFontStyle[1],weight:this.labelFontWeight[1],variant:this.labelFontVariant[1]},{family:this.labelFont[2],style:this.labelFontStyle[2],weight:this.labelFontWeight[2],variant:this.labelFontVariant[2]}],ne=[{family:this.tickFont[0],style:this.tickFontStyle[0],weight:this.tickFontWeight[0],variant:this.tickFontVariant[0]},{family:this.tickFont[1],style:this.tickFontStyle[1],weight:this.tickFontWeight[1],variant:this.tickFontVariant[1]},{family:this.tickFont[2],style:this.tickFontStyle[2],weight:this.tickFontWeight[2],variant:this.tickFontVariant[2]}];this._text?this._text&&(K||ee)&&this._text.update(this.bounds,this.labels,_e,this.ticks,ne):this._text=s(this.gl,this.bounds,this.labels,_e,this.ticks,ne),this._lines&&ee&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=l(this.gl,this.bounds,this.ticks))};function S(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}var L=[new S,new S,new S];function w(B,O,V,j,q){for(var U=B.primalOffset,J=B.primalMinor,ee=B.mirrorOffset,ce=B.mirrorMinor,ye=j[O],xe=0;xe<3;++xe)if(O!==xe){var ue=U,K=ee,_e=J,ne=ce;ye&1<<xe&&(ue=ee,K=U,_e=ce,ne=J),ue[xe]=V[0][xe],K[xe]=V[1][xe],q[xe]>0?(_e[xe]=-1,ne[xe]=0):(_e[xe]=0,ne[xe]=1)}}var A=[0,0,0],T={model:v,view:v,projection:v,_ortho:!1};k.isOpaque=function(){return!0},k.isTransparent=function(){return!1},k.drawTransparent=function(B){};var m=0,F=[0,0,0],E=[0,0,0],I=[0,0,0];k.draw=function(B){B=B||T;for(var ie=this.gl,O=B.model||v,V=B.view||v,j=B.projection||v,q=this.bounds,U=B._ortho||!1,J=c(O,V,j,q,U),ee=J.cubeEdges,ce=J.axis,ye=V[12],xe=V[13],ue=V[14],K=V[15],_e=U?2:1,ne=_e*this.pixelRatio*(j[3]*ye+j[7]*xe+j[11]*ue+j[15]*K)/ie.drawingBufferHeight,ae=0;ae<3;++ae)this.lastCubeProps.cubeEdges[ae]=ee[ae],this.lastCubeProps.axis[ae]=ce[ae];for(var pe=L,ae=0;ae<3;++ae)w(L[ae],ae,this.bounds,ee,ce);for(var ie=this.gl,le=A,ae=0;ae<3;++ae)this.backgroundEnable[ae]?le[ae]=ce[ae]:le[ae]=0;this._background.draw(O,V,j,q,le,this.backgroundColor),this._lines.bind(O,V,j,this);for(var ae=0;ae<3;++ae){var me=[0,0,0];ce[ae]>0?me[ae]=q[1][ae]:me[ae]=q[0][ae];for(var be=0;be<2;++be){var De=(ae+1+be)%3,Ge=(ae+1+(be^1))%3;this.gridEnable[De]&&this._lines.drawGrid(De,Ge,this.bounds,me,this.gridColor[De],this.gridWidth[De]*this.pixelRatio)}for(var be=0;be<2;++be){var De=(ae+1+be)%3,Ge=(ae+1+(be^1))%3;this.zeroEnable[Ge]&&Math.min(q[0][Ge],q[1][Ge])<=0&&Math.max(q[0][Ge],q[1][Ge])>=0&&this._lines.drawZero(De,Ge,this.bounds,me,this.zeroLineColor[Ge],this.zeroLineWidth[Ge]*this.pixelRatio)}}for(var ae=0;ae<3;++ae){this.lineEnable[ae]&&this._lines.drawAxisLine(ae,this.bounds,pe[ae].primalOffset,this.lineColor[ae],this.lineWidth[ae]*this.pixelRatio),this.lineMirror[ae]&&this._lines.drawAxisLine(ae,this.bounds,pe[ae].mirrorOffset,this.lineColor[ae],this.lineWidth[ae]*this.pixelRatio);for(var ht=y(F,pe[ae].primalMinor),it=y(E,pe[ae].mirrorMinor),Oe=this.lineTickLength,be=0;be<3;++be){var ze=ne/O[5*be];ht[be]*=Oe[be]*ze,it[be]*=Oe[be]*ze}this.lineTickEnable[ae]&&this._lines.drawAxisTicks(ae,pe[ae].primalOffset,ht,this.lineTickColor[ae],this.lineTickWidth[ae]*this.pixelRatio),this.lineTickMirror[ae]&&this._lines.drawAxisTicks(ae,pe[ae].mirrorOffset,it,this.lineTickColor[ae],this.lineTickWidth[ae]*this.pixelRatio)}this._lines.unbind(),this._text.bind(O,V,j,this.pixelRatio);var ct,et=.5,qe,we;function Le(tt){we=[0,0,0],we[tt]=1}function Ke(tt,nt,We){var mt=(tt+1)%3,bt=(tt+2)%3,Yt=nt[mt],jt=nt[bt],mr=We[mt],or=We[bt];if(Yt>0&&or>0){Le(mt);return}else if(Yt>0&&or<0){Le(mt);return}else if(Yt<0&&or>0){Le(mt);return}else if(Yt<0&&or<0){Le(mt);return}else if(jt>0&&mr>0){Le(bt);return}else if(jt>0&&mr<0){Le(bt);return}else if(jt<0&&mr>0){Le(bt);return}else if(jt<0&&mr<0){Le(bt);return}}for(var ae=0;ae<3;++ae){for(var Te=pe[ae].primalMinor,Ve=pe[ae].mirrorMinor,$e=y(I,pe[ae].primalOffset),be=0;be<3;++be)this.lineTickEnable[ae]&&($e[be]+=ne*Te[be]*Math.max(this.lineTickLength[be],0)/O[5*be]);var ot=[0,0,0];if(ot[ae]=1,this.tickEnable[ae]){this.tickAngle[ae]===-3600?(this.tickAngle[ae]=0,this.tickAlign[ae]=\"auto\"):this.tickAlign[ae]=-1,qe=1,ct=[this.tickAlign[ae],et,qe],ct[0]===\"auto\"?ct[0]=m:ct[0]=parseInt(\"\"+ct[0]),we=[0,0,0],Ke(ae,Te,Ve);for(var be=0;be<3;++be)$e[be]+=ne*Te[be]*this.tickPad[be]/O[5*be];this._text.drawTicks(ae,this.tickSize[ae],this.tickAngle[ae],$e,this.tickColor[ae],ot,we,ct)}if(this.labelEnable[ae]){qe=0,we=[0,0,0],this.labels[ae].length>4&&(Le(ae),qe=1),ct=[this.labelAlign[ae],et,qe],ct[0]===\"auto\"?ct[0]=m:ct[0]=parseInt(\"\"+ct[0]);for(var be=0;be<3;++be)$e[be]+=ne*Te[be]*this.labelPad[be]/O[5*be];$e[ae]+=.5*(q[0][ae]+q[1][ae]),this._text.drawLabel(ae,this.labelSize[ae],this.labelAngle[ae],$e,this.labelColor[ae],[0,0,0],we,ct)}}this._text.unbind()},k.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null};function R(B,O){var V=new C(B);return V.update(O),V}}),5304:(function(a,i,o){\"use strict\";a.exports=v;var s=o(2762),l=o(8116),u=o(1879).bg;function c(g,p,x,_){this.gl=g,this.buffer=p,this.vao=x,this.shader=_}var f=c.prototype;f.draw=function(g,p,x,_,y,C){for(var k=!1,S=0;S<3;++S)k=k||y[S];if(k){var L=this.gl;L.enable(L.POLYGON_OFFSET_FILL),L.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:g,view:p,projection:x,bounds:_,enable:y,colors:C},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),L.disable(L.POLYGON_OFFSET_FILL)}},f.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()};function v(g){for(var p=[],x=[],_=0,y=0;y<3;++y)for(var C=(y+1)%3,k=(y+2)%3,S=[0,0,0],L=[0,0,0],w=-1;w<=1;w+=2){x.push(_,_+2,_+1,_+1,_+2,_+3),S[y]=w,L[y]=w;for(var A=-1;A<=1;A+=2){S[C]=A;for(var T=-1;T<=1;T+=2)S[k]=T,p.push(S[0],S[1],S[2],L[0],L[1],L[2]),_+=1}var m=C;C=k,k=m}var F=s(g,new Float32Array(p)),E=s(g,new Uint16Array(x),g.ELEMENT_ARRAY_BUFFER),I=l(g,[{buffer:F,type:g.FLOAT,size:3,offset:0,stride:24},{buffer:F,type:g.FLOAT,size:3,offset:12,stride:24}],E),R=u(g);return R.attributes.position.location=0,R.attributes.normal.location=1,new c(g,F,I,R)}}),6429:(function(a,i,o){\"use strict\";a.exports=w;var s=o(8828),l=o(6760),u=o(5202),c=o(3250),f=new Array(16),v=new Array(8),g=new Array(8),p=new Array(3),x=[0,0,0];(function(){for(var A=0;A<8;++A)v[A]=[1,1,1,1],g[A]=[1,1,1]})();function _(A,T,m){for(var F=0;F<4;++F){A[F]=m[12+F];for(var E=0;E<3;++E)A[F]+=T[E]*m[4*E+F]}}var y=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function C(A){for(var T=0;T<y.length;++T)if(A=u.positive(A,y[T]),A.length<3)return 0;for(var m=A[0],F=m[0]/m[3],E=m[1]/m[3],I=0,T=1;T+1<A.length;++T){var R=A[T],B=A[T+1],O=R[0]/R[3],V=R[1]/R[3],j=B[0]/B[3],q=B[1]/B[3],U=O-F,J=V-E,ee=j-F,ce=q-E;I+=Math.abs(U*ce-J*ee)}return I}var k=[1,1,1],S=[0,0,0],L={cubeEdges:k,axis:S};function w(A,T,m,F,E){l(f,T,A),l(f,m,f);for(var I=0,R=0;R<2;++R){p[2]=F[R][2];for(var B=0;B<2;++B){p[1]=F[B][1];for(var O=0;O<2;++O)p[0]=F[O][0],_(v[I],p,f),I+=1}}for(var V=-1,R=0;R<8;++R){for(var j=v[R][3],q=0;q<3;++q)g[R][q]=v[R][q]/j;E&&(g[R][2]*=-1),j<0&&(V<0||g[R][2]<g[V][2])&&(V=R)}if(V<0){V=0;for(var U=0;U<3;++U){for(var J=(U+2)%3,ee=(U+1)%3,ce=-1,ye=-1,xe=0;xe<2;++xe){var ue=xe<<U,K=ue+(xe<<J)+(1-xe<<ee),_e=ue+(1-xe<<J)+(xe<<ee);c(g[ue],g[K],g[_e],x)<0||(xe?ce=1:ye=1)}if(ce<0||ye<0){ye>ce&&(V|=1<<U);continue}for(var xe=0;xe<2;++xe){var ue=xe<<U,K=ue+(xe<<J)+(1-xe<<ee),_e=ue+(1-xe<<J)+(xe<<ee),ne=C([v[ue],v[K],v[_e],v[ue+(1<<J)+(1<<ee)]]);xe?ce=ne:ye=ne}if(ye>ce){V|=1<<U;continue}}}for(var ae=7^V,pe=-1,R=0;R<8;++R)R===V||R===ae||(pe<0||g[pe][1]>g[R][1])&&(pe=R);for(var ie=-1,R=0;R<3;++R){var le=pe^1<<R;if(!(le===V||le===ae)){ie<0&&(ie=le);var ee=g[le];ee[0]<g[ie][0]&&(ie=le)}}for(var me=-1,R=0;R<3;++R){var le=pe^1<<R;if(!(le===V||le===ae||le===ie)){me<0&&(me=le);var ee=g[le];ee[0]>g[me][0]&&(me=le)}}var be=k;be[0]=be[1]=be[2]=0,be[s.log2(ie^pe)]=pe&ie,be[s.log2(pe^me)]=pe&me;var De=me^7;De===V||De===ae?(De=ie^7,be[s.log2(me^De)]=De&me):be[s.log2(ie^De)]=De&ie;for(var Ge=S,ht=V,U=0;U<3;++U)ht&1<<U?Ge[U]=-1:Ge[U]=1;return L}}),501:(function(a,i,o){\"use strict\";a.exports=k;var s=o(2762),l=o(8116),u=o(1879).n,c=[0,0,0],f=[0,0,0],v=[0,0,0],g=[0,0,0],p=[1,1];function x(S){return S[0]=S[1]=S[2]=0,S}function _(S,L){return S[0]=L[0],S[1]=L[1],S[2]=L[2],S}function y(S,L,w,A,T,m,F,E){this.gl=S,this.vertBuffer=L,this.vao=w,this.shader=A,this.tickCount=T,this.tickOffset=m,this.gridCount=F,this.gridOffset=E}var C=y.prototype;C.bind=function(S,L,w){this.shader.bind(),this.shader.uniforms.model=S,this.shader.uniforms.view=L,this.shader.uniforms.projection=w,p[0]=this.gl.drawingBufferWidth,p[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=p,this.vao.bind()},C.unbind=function(){this.vao.unbind()},C.drawAxisLine=function(S,L,w,A,T){var m=x(f);this.shader.uniforms.majorAxis=f,m[S]=L[1][S]-L[0][S],this.shader.uniforms.minorAxis=m;var F=_(g,w);F[S]+=L[0][S],this.shader.uniforms.offset=F,this.shader.uniforms.lineWidth=T,this.shader.uniforms.color=A;var E=x(v);E[(S+2)%3]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,6);var E=x(v);E[(S+1)%3]=1,this.shader.uniforms.screenAxis=E,this.vao.draw(this.gl.TRIANGLES,6)},C.drawAxisTicks=function(S,L,w,A,T){if(this.tickCount[S]){var m=x(c);m[S]=1,this.shader.uniforms.majorAxis=m,this.shader.uniforms.offset=L,this.shader.uniforms.minorAxis=w,this.shader.uniforms.color=A,this.shader.uniforms.lineWidth=T;var F=x(v);F[S]=1,this.shader.uniforms.screenAxis=F,this.vao.draw(this.gl.TRIANGLES,this.tickCount[S],this.tickOffset[S])}},C.drawGrid=function(S,L,w,A,T,m){if(this.gridCount[S]){var F=x(f);F[L]=w[1][L]-w[0][L],this.shader.uniforms.minorAxis=F;var E=_(g,A);E[L]+=w[0][L],this.shader.uniforms.offset=E;var I=x(c);I[S]=1,this.shader.uniforms.majorAxis=I;var R=x(v);R[S]=1,this.shader.uniforms.screenAxis=R,this.shader.uniforms.lineWidth=m,this.shader.uniforms.color=T,this.vao.draw(this.gl.TRIANGLES,this.gridCount[S],this.gridOffset[S])}},C.drawZero=function(S,L,w,A,T,m){var F=x(f);this.shader.uniforms.majorAxis=F,F[S]=w[1][S]-w[0][S],this.shader.uniforms.minorAxis=F;var E=_(g,A);E[S]+=w[0][S],this.shader.uniforms.offset=E;var I=x(v);I[L]=1,this.shader.uniforms.screenAxis=I,this.shader.uniforms.lineWidth=m,this.shader.uniforms.color=T,this.vao.draw(this.gl.TRIANGLES,6)},C.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()};function k(S,L,w){var A=[],T=[0,0,0],m=[0,0,0],F=[0,0,0],E=[0,0,0];A.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var I=0;I<3;++I){for(var O=A.length/3|0,R=0;R<w[I].length;++R){var B=+w[I][R].x;A.push(B,0,1,B,1,1,B,0,-1,B,0,-1,B,1,1,B,1,-1)}var j=A.length/3|0;T[I]=O,m[I]=j-O;for(var O=A.length/3|0,V=0;V<w[I].length;++V){var B=+w[I][V].x;A.push(B,0,1,B,1,1,B,0,-1,B,0,-1,B,1,1,B,1,-1)}var j=A.length/3|0;F[I]=O,E[I]=j-O}var q=s(S,new Float32Array(A)),U=l(S,[{buffer:q,type:S.FLOAT,size:3,stride:0,offset:0}]),J=u(S);return J.attributes.position.location=0,new y(S,q,U,J,m,T,E,F)}}),1879:(function(a,i,o){\"use strict\";var s=o(3236),l=o(9405),u=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position;\n",
@@ -177,7 +180,7 @@
        "uniform vec4 color;\n",
        "void main() {\n",
        "  gl_FragColor = color;\n",
-       "}`]);a.n=function(m){return l(m,u,c,null,[{name:\"position\",type:\"vec3\"}])};var f=s([`precision highp float;\n",
+       "}`]);i.n=function(x){return l(x,u,c,null,[{name:\"position\",type:\"vec3\"}])};var f=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position;\n",
@@ -314,13 +317,13 @@
        "  //Done\n",
        "  gl_Position = vec4(clipPosition, 1.0);\n",
        "}\n",
-       "`]),h=s([`precision highp float;\n",
+       "`]),v=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "uniform vec4 color;\n",
        "void main() {\n",
        "  gl_FragColor = color;\n",
-       "}`]);a.Q=function(m){return l(m,f,h,null,[{name:\"position\",type:\"vec3\"}])};var d=s([`precision highp float;\n",
+       "}`]);i.Q=function(x){return l(x,f,v,null,[{name:\"position\",type:\"vec3\"}])};var g=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position;\n",
@@ -349,7 +352,7 @@
        "\n",
        "  colorChannel = abs(realNormal);\n",
        "}\n",
-       "`]),v=s([`precision highp float;\n",
+       "`]),p=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "uniform vec4 colors[3];\n",
@@ -360,7 +363,7 @@
        "  gl_FragColor = colorChannel.x * colors[0] +\n",
        "                 colorChannel.y * colors[1] +\n",
        "                 colorChannel.z * colors[2];\n",
-       "}`]);a.bg=function(m){return l(m,d,v,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}}),4935:(function(i,a,o){\"use strict\";i.exports=k;var s=o(2762),l=o(8116),u=o(4359),c=o(1879).Q,f=window||process.global||{},h=f.__TEXT_CACHE||{};f.__TEXT_CACHE={};var d=3;function v(M,T,L,x){this.gl=M,this.shader=T,this.buffer=L,this.vao=x,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var m=v.prototype,b=[0,0];m.bind=function(M,T,L,x){this.vao.bind(),this.shader.bind();var C=this.shader.uniforms;C.model=M,C.view=T,C.projection=L,C.pixelScale=x,b[0]=this.gl.drawingBufferWidth,b[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=b},m.unbind=function(){this.vao.unbind()},m.update=function(M,T,L,x,C){var S=[];function g(H,re,ae,_e,Ee,Ce){var ge=[ae.style,ae.weight,ae.variant,ae.family].join(\"_\"),ie=h[ge];ie||(ie=h[ge]={});var Se=ie[re];Se||(Se=ie[re]=p(re,{triangles:!0,font:ae.family,fontStyle:ae.style,fontWeight:ae.weight,fontVariant:ae.variant,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:Ee,styletags:Ce}));for(var ke=(_e||12)/12,be=Se.positions,Be=Se.cells,Le=0,me=Be.length;Le<me;++Le)for(var Pe=Be[Le],ce=2;ce>=0;--ce){var He=be[Pe[ce]];S.push(ke*He[0],-ke*He[1],H)}}for(var P=[0,0,0],E=[0,0,0],z=[0,0,0],q=[0,0,0],V=1.25,G={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},Z=0;Z<3;++Z){z[Z]=S.length/d|0,g(.5*(M[0][Z]+M[1][Z]),T[Z],L[Z],12,V,G),q[Z]=(S.length/d|0)-z[Z],P[Z]=S.length/d|0;for(var j=0;j<x[Z].length;++j)if(x[Z][j].text){var N={family:x[Z][j].font||C[Z].family,style:C[Z].fontStyle||C[Z].style,weight:C[Z].fontWeight||C[Z].weight,variant:C[Z].fontVariant||C[Z].variant};g(x[Z][j].x,x[Z][j].text,N,x[Z][j].fontSize||12,V,G)}E[Z]=(S.length/d|0)-P[Z]}this.buffer.update(S),this.tickOffset=P,this.tickCount=E,this.labelOffset=z,this.labelCount=q},m.drawTicks=function(M,T,L,x,C,S,g,P){this.tickCount[M]&&(this.shader.uniforms.axis=S,this.shader.uniforms.color=C,this.shader.uniforms.angle=L,this.shader.uniforms.scale=T,this.shader.uniforms.offset=x,this.shader.uniforms.alignDir=g,this.shader.uniforms.alignOpt=P,this.vao.draw(this.gl.TRIANGLES,this.tickCount[M],this.tickOffset[M]))},m.drawLabel=function(M,T,L,x,C,S,g,P){this.labelCount[M]&&(this.shader.uniforms.axis=S,this.shader.uniforms.color=C,this.shader.uniforms.angle=L,this.shader.uniforms.scale=T,this.shader.uniforms.offset=x,this.shader.uniforms.alignDir=g,this.shader.uniforms.alignOpt=P,this.vao.draw(this.gl.TRIANGLES,this.labelCount[M],this.labelOffset[M]))},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()};function p(M,T){try{return u(M,T)}catch(L){return console.warn('error vectorizing text:\"'+M+'\" error:',L),{cells:[],positions:[]}}}function k(M,T,L,x,C,S){var g=s(M),P=l(M,[{buffer:g,size:3}]),E=c(M);E.attributes.position.location=0;var z=new v(M,E,g,P);return z.update(T,L,x,C,S),z}}),6444:(function(i,a){\"use strict\";a.create=s,a.equal=l;function o(u,c){var f=u+\"\",h=f.indexOf(\".\"),d=0;h>=0&&(d=f.length-h-1);var v=Math.pow(10,d),m=Math.round(u*c*v),b=m+\"\";if(b.indexOf(\"e\")>=0)return b;var p=m/v,k=m%v;m<0?(p=-Math.ceil(p)|0,k=-k|0):(p=Math.floor(p)|0,k=k|0);var M=\"\"+p;if(m<0&&(M=\"-\"+M),d){for(var T=\"\"+k;T.length<d;)T=\"0\"+T;return M+\".\"+T}else return M}function s(u,c){for(var f=[],h=0;h<3;++h){for(var d=[],v=.5*(u[0][h]+u[1][h]),m=0;m*c[h]<=u[1][h];++m)d.push({x:m*c[h],text:o(c[h],m)});for(var m=-1;m*c[h]>=u[0][h];--m)d.push({x:m*c[h],text:o(c[h],m)});f.push(d)}return f}function l(u,c){for(var f=0;f<3;++f){if(u[f].length!==c[f].length)return!1;for(var h=0;h<u[f].length;++h){var d=u[f][h],v=c[f][h];if(d.x!==v.x||d.text!==v.text||d.font!==v.font||d.fontColor!==v.fontColor||d.fontSize!==v.fontSize||d.dx!==v.dx||d.dy!==v.dy)return!1}}return!0}}),5445:(function(i,a,o){\"use strict\";i.exports=L;var s=o(5033),l=o(5202),u=o(6429),c=o(6760),f=o(5665),h=o(5352),d=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),v=new Float32Array(16);function m(x,C,S){this.lo=x,this.hi=C,this.pixelsPerDataUnit=S}var b=[0,0,0,1],p=[0,0,0,1];function k(x,C,S,g,P){for(var E=0;E<3;++E){for(var z=b,q=p,V=0;V<3;++V)q[V]=z[V]=S[V];q[3]=z[3]=1,q[E]+=1,h(q,q,C),q[3]<0&&(x[E]=1/0),z[E]-=1,h(z,z,C),z[3]<0&&(x[E]=1/0);var G=(z[0]/z[3]-q[0]/q[3])*g,Z=(z[1]/z[3]-q[1]/q[3])*P;x[E]=.25*Math.sqrt(G*G+Z*Z)}return x}var M=[new m(1/0,-1/0,1/0),new m(1/0,-1/0,1/0),new m(1/0,-1/0,1/0)],T=[0,0,0];function L(x,C,S,g,Z){var E=C.model||d,z=C.view||d,q=C.projection||d,V=C._ortho||!1,G=x.bounds,Z=Z||u(E,z,q,G,V),j=Z.axis;c(v,z,E),c(v,q,v);for(var N=M,H=0;H<3;++H)N[H].lo=1/0,N[H].hi=-1/0,N[H].pixelsPerDataUnit=1/0;var re=s(f(v,v));f(v,v);for(var ae=0;ae<3;++ae){var _e=(ae+1)%3,Ee=(ae+2)%3,Ce=T;e:for(var H=0;H<2;++H){var ge=[];if(j[ae]<0!=!!H){Ce[ae]=G[H][ae];for(var ie=0;ie<2;++ie){Ce[_e]=G[ie^H][_e];for(var Se=0;Se<2;++Se)Ce[Ee]=G[Se^ie^H][Ee],ge.push(Ce.slice())}for(var ke=V?5:4,ie=ke;ie===ke;++ie){if(ge.length===0)continue e;ge=l.positive(ge,re[ie])}for(var ie=0;ie<ge.length;++ie)for(var Ee=ge[ie],be=k(T,v,Ee,S,g),Se=0;Se<3;++Se)N[Se].lo=Math.min(N[Se].lo,Ee[Se]),N[Se].hi=Math.max(N[Se].hi,Ee[Se]),Se!==ae&&(N[Se].pixelsPerDataUnit=Math.min(N[Se].pixelsPerDataUnit,Math.abs(be[Se])))}}}return N}}),2762:(function(i,a,o){\"use strict\";var s=o(1888),l=o(5298),u=o(9618),c=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function f(p,k,M,T,L){this.gl=p,this.type=k,this.handle=M,this.length=T,this.usage=L}var h=f.prototype;h.bind=function(){this.gl.bindBuffer(this.type,this.handle)},h.unbind=function(){this.gl.bindBuffer(this.type,null)},h.dispose=function(){this.gl.deleteBuffer(this.handle)};function d(p,k,M,T,L,x){var C=L.length*L.BYTES_PER_ELEMENT;if(x<0)return p.bufferData(k,L,T),C;if(C+x>M)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return p.bufferSubData(k,x,L),M}function v(p,k){for(var M=s.malloc(p.length,k),T=p.length,L=0;L<T;++L)M[L]=p[L];return M}function m(p,k){for(var M=1,T=k.length-1;T>=0;--T){if(k[T]!==M)return!1;M*=p[T]}return!0}h.update=function(p,k){if(typeof k!=\"number\"&&(k=-1),this.bind(),typeof p==\"object\"&&typeof p.shape!=\"undefined\"){var M=p.dtype;if(c.indexOf(M)<0&&(M=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER){var T=gl.getExtension(\"OES_element_index_uint\");T&&M!==\"uint16\"?M=\"uint32\":M=\"uint16\"}if(M===p.dtype&&m(p.shape,p.stride))p.offset===0&&p.data.length===p.shape[0]?this.length=d(this.gl,this.type,this.length,this.usage,p.data,k):this.length=d(this.gl,this.type,this.length,this.usage,p.data.subarray(p.offset,p.shape[0]),k);else{var L=s.malloc(p.size,M),x=u(L,p.shape);l.assign(x,p),k<0?this.length=d(this.gl,this.type,this.length,this.usage,L,k):this.length=d(this.gl,this.type,this.length,this.usage,L.subarray(0,p.size),k),s.free(L)}}else if(Array.isArray(p)){var C;this.type===this.gl.ELEMENT_ARRAY_BUFFER?C=v(p,\"uint16\"):C=v(p,\"float32\"),k<0?this.length=d(this.gl,this.type,this.length,this.usage,C,k):this.length=d(this.gl,this.type,this.length,this.usage,C.subarray(0,p.length),k),s.free(C)}else if(typeof p==\"object\"&&typeof p.length==\"number\")this.length=d(this.gl,this.type,this.length,this.usage,p,k);else if(typeof p==\"number\"||p===void 0){if(k>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");p=p|0,p<=0&&(p=1),this.gl.bufferData(this.type,p|0,this.usage),this.length=p}else throw new Error(\"gl-buffer: Invalid data type\")};function b(p,k,M,T){if(M=M||p.ARRAY_BUFFER,T=T||p.DYNAMIC_DRAW,M!==p.ARRAY_BUFFER&&M!==p.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(T!==p.DYNAMIC_DRAW&&T!==p.STATIC_DRAW&&T!==p.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var L=p.createBuffer(),x=new f(p,M,L,0,T);return x.update(k),x}i.exports=b}),6405:(function(i,a,o){\"use strict\";var s=o(2931);i.exports=function(u,c){var f=u.positions,h=u.vectors,d={positions:[],vertexIntensity:[],vertexIntensityBounds:u.vertexIntensityBounds,vectors:[],cells:[],coneOffset:u.coneOffset,colormap:u.colormap};if(u.positions.length===0)return c&&(c[0]=[0,0,0],c[1]=[0,0,0]),d;for(var v=0,m=1/0,b=-1/0,p=1/0,k=-1/0,M=1/0,T=-1/0,L=null,x=null,C=[],S=1/0,g=!1,P=u.coneSizemode===\"raw\",E=0;E<f.length;E++){var z=f[E];m=Math.min(z[0],m),b=Math.max(z[0],b),p=Math.min(z[1],p),k=Math.max(z[1],k),M=Math.min(z[2],M),T=Math.max(z[2],T);var q=h[E];if(s.length(q)>v&&(v=s.length(q)),E&&!P){var V=2*s.distance(L,z)/(s.length(x)+s.length(q));V?(S=Math.min(S,V),g=!1):g=!0}g||(L=z,x=q),C.push(q)}var G=[m,p,M],Z=[b,k,T];c&&(c[0]=G,c[1]=Z),v===0&&(v=1);var j=1/v;isFinite(S)||(S=1),d.vectorScale=S;var N=u.coneSize||(P?1:.5);u.absoluteConeSize&&(N=u.absoluteConeSize*j),d.coneScale=N;for(var E=0,H=0;E<f.length;E++)for(var z=f[E],re=z[0],ae=z[1],_e=z[2],Ee=C[E],Ce=s.length(Ee)*j,ge=0,ie=8;ge<ie;ge++){d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.positions.push([re,ae,_e,H++]),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vectors.push(Ee),d.vertexIntensity.push(Ce,Ce,Ce),d.vertexIntensity.push(Ce,Ce,Ce);var Se=d.positions.length;d.cells.push([Se-6,Se-5,Se-4],[Se-3,Se-2,Se-1])}return d};var l=o(614);i.exports.createMesh=o(9060),i.exports.createConeMesh=function(u,c){return i.exports.createMesh(u,c,{shaders:l,traceType:\"cone\"})}}),9060:(function(i,a,o){\"use strict\";var s=o(9405),l=o(2762),u=o(8116),c=o(7766),f=o(6760),h=o(7608),d=o(9618),v=o(6729),m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function b(C,S,g,P,E,z,q,V,G,Z,j){this.gl=C,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=S,this.dirty=!0,this.triShader=g,this.pickShader=P,this.trianglePositions=E,this.triangleVectors=z,this.triangleColors=V,this.triangleUVs=G,this.triangleIds=q,this.triangleVAO=Z,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=j,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1]}var p=b.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(C){this.pickId=C};function k(C){for(var S=v({colormap:C,nshades:256,format:\"rgba\"}),g=new Uint8Array(256*4),P=0;P<256;++P){for(var E=S[P],z=0;z<3;++z)g[4*P+z]=E[z];g[4*P+3]=E[3]*255}return d(g,[256,256,4],[4,0,1])}function M(C){for(var S=C.length,g=new Array(S),P=0;P<S;++P)g[P]=C[P][2];return g}p.update=function(C){C=C||{};var S=this.gl;this.dirty=!0,\"lightPosition\"in C&&(this.lightPosition=C.lightPosition),\"opacity\"in C&&(this.opacity=C.opacity),\"ambient\"in C&&(this.ambientLight=C.ambient),\"diffuse\"in C&&(this.diffuseLight=C.diffuse),\"specular\"in C&&(this.specularLight=C.specular),\"roughness\"in C&&(this.roughness=C.roughness),\"fresnel\"in C&&(this.fresnel=C.fresnel),C.tubeScale!==void 0&&(this.tubeScale=C.tubeScale),C.vectorScale!==void 0&&(this.vectorScale=C.vectorScale),C.coneScale!==void 0&&(this.coneScale=C.coneScale),C.coneOffset!==void 0&&(this.coneOffset=C.coneOffset),C.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=S.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=S.LINEAR,this.texture.setPixels(k(C.colormap)),this.texture.generateMipmap());var g=C.cells,P=C.positions,E=C.vectors;if(!(!P||!g||!E)){var z=[],q=[],V=[],G=[],Z=[];this.cells=g,this.positions=P,this.vectors=E;var j=C.meshColor||[1,1,1,1],N=C.vertexIntensity,H=1/0,re=-1/0;if(N)if(C.vertexIntensityBounds)H=+C.vertexIntensityBounds[0],re=+C.vertexIntensityBounds[1];else for(var ae=0;ae<N.length;++ae){var _e=N[ae];H=Math.min(H,_e),re=Math.max(re,_e)}else for(var ae=0;ae<P.length;++ae){var _e=P[ae][2];H=Math.min(H,_e),re=Math.max(re,_e)}N?this.intensity=N:this.intensity=M(P),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var ae=0;ae<P.length;++ae)for(var Ee=P[ae],Ce=0;Ce<3;++Ce)isNaN(Ee[Ce])||!isFinite(Ee[Ce])||(this.bounds[0][Ce]=Math.min(this.bounds[0][Ce],Ee[Ce]),this.bounds[1][Ce]=Math.max(this.bounds[1][Ce],Ee[Ce]));var ge=0;e:for(var ae=0;ae<g.length;++ae){var ie=g[ae];switch(ie.length){case 3:for(var Ce=0;Ce<3;++Ce)for(var Se=ie[Ce],Ee=P[Se],ke=0;ke<3;++ke)if(isNaN(Ee[ke])||!isFinite(Ee[ke]))continue e;for(var Ce=0;Ce<3;++Ce){var Se=ie[2-Ce],Ee=P[Se];z.push(Ee[0],Ee[1],Ee[2],Ee[3]);var be=E[Se];q.push(be[0],be[1],be[2],be[3]||0);var Be=j;Be.length===3?V.push(Be[0],Be[1],Be[2],1):V.push(Be[0],Be[1],Be[2],Be[3]);var Le;N?Le=[(N[Se]-H)/(re-H),0]:Le=[(Ee[2]-H)/(re-H),0],G.push(Le[0],Le[1]),Z.push(ae)}ge+=1;break;default:break}}this.triangleCount=ge,this.trianglePositions.update(z),this.triangleVectors.update(q),this.triangleColors.update(V),this.triangleUVs.update(G),this.triangleIds.update(new Uint32Array(Z))}},p.drawTransparent=p.draw=function(C){C=C||{};for(var S=this.gl,g=C.model||m,P=C.view||m,E=C.projection||m,z=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],q=0;q<3;++q)z[0][q]=Math.max(z[0][q],this.clipBounds[0][q]),z[1][q]=Math.min(z[1][q],this.clipBounds[1][q]);var V={model:g,view:P,projection:E,inverseModel:m.slice(),clipBounds:z,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};V.inverseModel=h(V.inverseModel,V.model),S.disable(S.CULL_FACE),this.texture.bind(0);var G=new Array(16);f(G,V.view,V.model),f(G,V.projection,G),h(G,G);for(var q=0;q<3;++q)V.eyePosition[q]=G[12+q]/G[15];for(var Z=G[15],q=0;q<3;++q)Z+=this.lightPosition[q]*G[4*q+3];for(var q=0;q<3;++q){for(var j=G[12+q],N=0;N<3;++N)j+=G[4*N+q]*this.lightPosition[N];V.lightPosition[q]=j/Z}if(this.triangleCount>0){var H=this.triShader;H.bind(),H.uniforms=V,this.triangleVAO.bind(),S.drawArrays(S.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}},p.drawPick=function(C){C=C||{};for(var S=this.gl,g=C.model||m,P=C.view||m,E=C.projection||m,z=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],q=0;q<3;++q)z[0][q]=Math.max(z[0][q],this.clipBounds[0][q]),z[1][q]=Math.min(z[1][q],this.clipBounds[1][q]);this._model=[].slice.call(g),this._view=[].slice.call(P),this._projection=[].slice.call(E),this._resolution=[S.drawingBufferWidth,S.drawingBufferHeight];var V={model:g,view:P,projection:E,clipBounds:z,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},G=this.pickShader;G.bind(),G.uniforms=V,this.triangleCount>0&&(this.triangleVAO.bind(),S.drawArrays(S.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind())},p.pick=function(C){if(!C||C.id!==this.pickId)return null;var S=C.value[0]+256*C.value[1]+65536*C.value[2],g=this.cells[S],P=this.positions[g[1]].slice(0,3),E={position:P,dataCoordinate:P,index:Math.floor(g[1]/48)};return this.traceType===\"cone\"?E.index=Math.floor(g[1]/48):this.traceType===\"streamtube\"&&(E.intensity=this.intensity[g[1]],E.velocity=this.vectors[g[1]].slice(0,3),E.divergence=this.vectors[g[1]][3],E.index=S),E},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()};function T(C,S){var g=s(C,S.meshShader.vertex,S.meshShader.fragment,null,S.meshShader.attributes);return g.attributes.position.location=0,g.attributes.color.location=2,g.attributes.uv.location=3,g.attributes.vector.location=4,g}function L(C,S){var g=s(C,S.pickShader.vertex,S.pickShader.fragment,null,S.pickShader.attributes);return g.attributes.position.location=0,g.attributes.id.location=1,g.attributes.vector.location=4,g}function x(C,S,g){var P=g.shaders;arguments.length===1&&(S=C,C=S.gl);var E=T(C,P),z=L(C,P),q=c(C,d(new Uint8Array([255,255,255,255]),[1,1,4]));q.generateMipmap(),q.minFilter=C.LINEAR_MIPMAP_LINEAR,q.magFilter=C.LINEAR;var V=l(C),G=l(C),Z=l(C),j=l(C),N=l(C),H=u(C,[{buffer:V,type:C.FLOAT,size:4},{buffer:N,type:C.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Z,type:C.FLOAT,size:4},{buffer:j,type:C.FLOAT,size:2},{buffer:G,type:C.FLOAT,size:4}]),re=new b(C,q,E,z,V,G,N,Z,j,H,g.traceType||\"cone\");return re.update(S),re}i.exports=x}),614:(function(i,a,o){var s=o(3236),l=s([`precision highp float;\n",
+       "}`]);i.bg=function(x){return l(x,g,p,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}}),4935:(function(a,i,o){\"use strict\";a.exports=C;var s=o(2762),l=o(8116),u=o(4359),c=o(1879).Q,f=window||process.global||{},v=f.__TEXT_CACHE||{};f.__TEXT_CACHE={};var g=3;function p(k,S,L,w){this.gl=k,this.shader=S,this.buffer=L,this.vao=w,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var x=p.prototype,_=[0,0];x.bind=function(k,S,L,w){this.vao.bind(),this.shader.bind();var A=this.shader.uniforms;A.model=k,A.view=S,A.projection=L,A.pixelScale=w,_[0]=this.gl.drawingBufferWidth,_[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=_},x.unbind=function(){this.vao.unbind()},x.update=function(k,S,L,w,A){var T=[];function m(U,J,ee,ce,ye,xe){var ue=[ee.style,ee.weight,ee.variant,ee.family].join(\"_\"),K=v[ue];K||(K=v[ue]={});var _e=K[J];_e||(_e=K[J]=y(J,{triangles:!0,font:ee.family,fontStyle:ee.style,fontWeight:ee.weight,fontVariant:ee.variant,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:ye,styletags:xe}));for(var ne=(ce||12)/12,ae=_e.positions,pe=_e.cells,ie=0,le=pe.length;ie<le;++ie)for(var me=pe[ie],be=2;be>=0;--be){var De=ae[me[be]];T.push(ne*De[0],-ne*De[1],U)}}for(var F=[0,0,0],E=[0,0,0],I=[0,0,0],R=[0,0,0],B=1.25,O={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},V=0;V<3;++V){I[V]=T.length/g|0,m(.5*(k[0][V]+k[1][V]),S[V],L[V],12,B,O),R[V]=(T.length/g|0)-I[V],F[V]=T.length/g|0;for(var j=0;j<w[V].length;++j)if(w[V][j].text){var q={family:w[V][j].font||A[V].family,style:A[V].fontStyle||A[V].style,weight:A[V].fontWeight||A[V].weight,variant:A[V].fontVariant||A[V].variant};m(w[V][j].x,w[V][j].text,q,w[V][j].fontSize||12,B,O)}E[V]=(T.length/g|0)-F[V]}this.buffer.update(T),this.tickOffset=F,this.tickCount=E,this.labelOffset=I,this.labelCount=R},x.drawTicks=function(k,S,L,w,A,T,m,F){this.tickCount[k]&&(this.shader.uniforms.axis=T,this.shader.uniforms.color=A,this.shader.uniforms.angle=L,this.shader.uniforms.scale=S,this.shader.uniforms.offset=w,this.shader.uniforms.alignDir=m,this.shader.uniforms.alignOpt=F,this.vao.draw(this.gl.TRIANGLES,this.tickCount[k],this.tickOffset[k]))},x.drawLabel=function(k,S,L,w,A,T,m,F){this.labelCount[k]&&(this.shader.uniforms.axis=T,this.shader.uniforms.color=A,this.shader.uniforms.angle=L,this.shader.uniforms.scale=S,this.shader.uniforms.offset=w,this.shader.uniforms.alignDir=m,this.shader.uniforms.alignOpt=F,this.vao.draw(this.gl.TRIANGLES,this.labelCount[k],this.labelOffset[k]))},x.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()};function y(k,S){try{return u(k,S)}catch(L){return console.warn('error vectorizing text:\"'+k+'\" error:',L),{cells:[],positions:[]}}}function C(k,S,L,w,A,T){var m=s(k),F=l(k,[{buffer:m,size:3}]),E=c(k);E.attributes.position.location=0;var I=new p(k,E,m,F);return I.update(S,L,w,A,T),I}}),6444:(function(a,i){\"use strict\";i.create=s,i.equal=l;function o(u,c){var f=u+\"\",v=f.indexOf(\".\"),g=0;v>=0&&(g=f.length-v-1);var p=Math.pow(10,g),x=Math.round(u*c*p),_=x+\"\";if(_.indexOf(\"e\")>=0)return _;var y=x/p,C=x%p;x<0?(y=-Math.ceil(y)|0,C=-C|0):(y=Math.floor(y)|0,C=C|0);var k=\"\"+y;if(x<0&&(k=\"-\"+k),g){for(var S=\"\"+C;S.length<g;)S=\"0\"+S;return k+\".\"+S}else return k}function s(u,c){for(var f=[],v=0;v<3;++v){for(var g=[],p=.5*(u[0][v]+u[1][v]),x=0;x*c[v]<=u[1][v];++x)g.push({x:x*c[v],text:o(c[v],x)});for(var x=-1;x*c[v]>=u[0][v];--x)g.push({x:x*c[v],text:o(c[v],x)});f.push(g)}return f}function l(u,c){for(var f=0;f<3;++f){if(u[f].length!==c[f].length)return!1;for(var v=0;v<u[f].length;++v){var g=u[f][v],p=c[f][v];if(g.x!==p.x||g.text!==p.text||g.font!==p.font||g.fontColor!==p.fontColor||g.fontSize!==p.fontSize||g.dx!==p.dx||g.dy!==p.dy)return!1}}return!0}}),5445:(function(a,i,o){\"use strict\";a.exports=L;var s=o(5033),l=o(5202),u=o(6429),c=o(6760),f=o(5665),v=o(5352),g=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),p=new Float32Array(16);function x(w,A,T){this.lo=w,this.hi=A,this.pixelsPerDataUnit=T}var _=[0,0,0,1],y=[0,0,0,1];function C(w,A,T,m,F){for(var E=0;E<3;++E){for(var I=_,R=y,B=0;B<3;++B)R[B]=I[B]=T[B];R[3]=I[3]=1,R[E]+=1,v(R,R,A),R[3]<0&&(w[E]=1/0),I[E]-=1,v(I,I,A),I[3]<0&&(w[E]=1/0);var O=(I[0]/I[3]-R[0]/R[3])*m,V=(I[1]/I[3]-R[1]/R[3])*F;w[E]=.25*Math.sqrt(O*O+V*V)}return w}var k=[new x(1/0,-1/0,1/0),new x(1/0,-1/0,1/0),new x(1/0,-1/0,1/0)],S=[0,0,0];function L(w,A,T,m,V){var E=A.model||g,I=A.view||g,R=A.projection||g,B=A._ortho||!1,O=w.bounds,V=V||u(E,I,R,O,B),j=V.axis;c(p,I,E),c(p,R,p);for(var q=k,U=0;U<3;++U)q[U].lo=1/0,q[U].hi=-1/0,q[U].pixelsPerDataUnit=1/0;var J=s(f(p,p));f(p,p);for(var ee=0;ee<3;++ee){var ce=(ee+1)%3,ye=(ee+2)%3,xe=S;e:for(var U=0;U<2;++U){var ue=[];if(j[ee]<0!=!!U){xe[ee]=O[U][ee];for(var K=0;K<2;++K){xe[ce]=O[K^U][ce];for(var _e=0;_e<2;++_e)xe[ye]=O[_e^K^U][ye],ue.push(xe.slice())}for(var ne=B?5:4,K=ne;K===ne;++K){if(ue.length===0)continue e;ue=l.positive(ue,J[K])}for(var K=0;K<ue.length;++K)for(var ye=ue[K],ae=C(S,p,ye,T,m),_e=0;_e<3;++_e)q[_e].lo=Math.min(q[_e].lo,ye[_e]),q[_e].hi=Math.max(q[_e].hi,ye[_e]),_e!==ee&&(q[_e].pixelsPerDataUnit=Math.min(q[_e].pixelsPerDataUnit,Math.abs(ae[_e])))}}}return q}}),2762:(function(a,i,o){\"use strict\";var s=o(1888),l=o(5298),u=o(9618),c=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function f(y,C,k,S,L){this.gl=y,this.type=C,this.handle=k,this.length=S,this.usage=L}var v=f.prototype;v.bind=function(){this.gl.bindBuffer(this.type,this.handle)},v.unbind=function(){this.gl.bindBuffer(this.type,null)},v.dispose=function(){this.gl.deleteBuffer(this.handle)};function g(y,C,k,S,L,w){var A=L.length*L.BYTES_PER_ELEMENT;if(w<0)return y.bufferData(C,L,S),A;if(A+w>k)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return y.bufferSubData(C,w,L),k}function p(y,C){for(var k=s.malloc(y.length,C),S=y.length,L=0;L<S;++L)k[L]=y[L];return k}function x(y,C){for(var k=1,S=C.length-1;S>=0;--S){if(C[S]!==k)return!1;k*=y[S]}return!0}v.update=function(y,C){if(typeof C!=\"number\"&&(C=-1),this.bind(),typeof y==\"object\"&&typeof y.shape!=\"undefined\"){var k=y.dtype;if(c.indexOf(k)<0&&(k=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER){var S=gl.getExtension(\"OES_element_index_uint\");S&&k!==\"uint16\"?k=\"uint32\":k=\"uint16\"}if(k===y.dtype&&x(y.shape,y.stride))y.offset===0&&y.data.length===y.shape[0]?this.length=g(this.gl,this.type,this.length,this.usage,y.data,C):this.length=g(this.gl,this.type,this.length,this.usage,y.data.subarray(y.offset,y.shape[0]),C);else{var L=s.malloc(y.size,k),w=u(L,y.shape);l.assign(w,y),C<0?this.length=g(this.gl,this.type,this.length,this.usage,L,C):this.length=g(this.gl,this.type,this.length,this.usage,L.subarray(0,y.size),C),s.free(L)}}else if(Array.isArray(y)){var A;this.type===this.gl.ELEMENT_ARRAY_BUFFER?A=p(y,\"uint16\"):A=p(y,\"float32\"),C<0?this.length=g(this.gl,this.type,this.length,this.usage,A,C):this.length=g(this.gl,this.type,this.length,this.usage,A.subarray(0,y.length),C),s.free(A)}else if(typeof y==\"object\"&&typeof y.length==\"number\")this.length=g(this.gl,this.type,this.length,this.usage,y,C);else if(typeof y==\"number\"||y===void 0){if(C>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");y=y|0,y<=0&&(y=1),this.gl.bufferData(this.type,y|0,this.usage),this.length=y}else throw new Error(\"gl-buffer: Invalid data type\")};function _(y,C,k,S){if(k=k||y.ARRAY_BUFFER,S=S||y.DYNAMIC_DRAW,k!==y.ARRAY_BUFFER&&k!==y.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(S!==y.DYNAMIC_DRAW&&S!==y.STATIC_DRAW&&S!==y.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var L=y.createBuffer(),w=new f(y,k,L,0,S);return w.update(C),w}a.exports=_}),6405:(function(a,i,o){\"use strict\";var s=o(2931);a.exports=function(u,c){var f=u.positions,v=u.vectors,g={positions:[],vertexIntensity:[],vertexIntensityBounds:u.vertexIntensityBounds,vectors:[],cells:[],coneOffset:u.coneOffset,colormap:u.colormap};if(u.positions.length===0)return c&&(c[0]=[0,0,0],c[1]=[0,0,0]),g;for(var p=0,x=1/0,_=-1/0,y=1/0,C=-1/0,k=1/0,S=-1/0,L=null,w=null,A=[],T=1/0,m=!1,F=u.coneSizemode===\"raw\",E=0;E<f.length;E++){var I=f[E];x=Math.min(I[0],x),_=Math.max(I[0],_),y=Math.min(I[1],y),C=Math.max(I[1],C),k=Math.min(I[2],k),S=Math.max(I[2],S);var R=v[E];if(s.length(R)>p&&(p=s.length(R)),E&&!F){var B=2*s.distance(L,I)/(s.length(w)+s.length(R));B?(T=Math.min(T,B),m=!1):m=!0}m||(L=I,w=R),A.push(R)}var O=[x,y,k],V=[_,C,S];c&&(c[0]=O,c[1]=V),p===0&&(p=1);var j=1/p;isFinite(T)||(T=1),g.vectorScale=T;var q=u.coneSize||(F?1:.5);u.absoluteConeSize&&(q=u.absoluteConeSize*j),g.coneScale=q;for(var E=0,U=0;E<f.length;E++)for(var I=f[E],J=I[0],ee=I[1],ce=I[2],ye=A[E],xe=s.length(ye)*j,ue=0,K=8;ue<K;ue++){g.positions.push([J,ee,ce,U++]),g.positions.push([J,ee,ce,U++]),g.positions.push([J,ee,ce,U++]),g.positions.push([J,ee,ce,U++]),g.positions.push([J,ee,ce,U++]),g.positions.push([J,ee,ce,U++]),g.vectors.push(ye),g.vectors.push(ye),g.vectors.push(ye),g.vectors.push(ye),g.vectors.push(ye),g.vectors.push(ye),g.vertexIntensity.push(xe,xe,xe),g.vertexIntensity.push(xe,xe,xe);var _e=g.positions.length;g.cells.push([_e-6,_e-5,_e-4],[_e-3,_e-2,_e-1])}return g};var l=o(614);a.exports.createMesh=o(9060),a.exports.createConeMesh=function(u,c){return a.exports.createMesh(u,c,{shaders:l,traceType:\"cone\"})}}),9060:(function(a,i,o){\"use strict\";var s=o(9405),l=o(2762),u=o(8116),c=o(7766),f=o(6760),v=o(7608),g=o(9618),p=o(6729),x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function _(A,T,m,F,E,I,R,B,O,V,j){this.gl=A,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=T,this.dirty=!0,this.triShader=m,this.pickShader=F,this.trianglePositions=E,this.triangleVectors=I,this.triangleColors=B,this.triangleUVs=O,this.triangleIds=R,this.triangleVAO=V,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=j,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=x,this._view=x,this._projection=x,this._resolution=[1,1]}var y=_.prototype;y.isOpaque=function(){return this.opacity>=1},y.isTransparent=function(){return this.opacity<1},y.pickSlots=1,y.setPickBase=function(A){this.pickId=A};function C(A){for(var T=p({colormap:A,nshades:256,format:\"rgba\"}),m=new Uint8Array(256*4),F=0;F<256;++F){for(var E=T[F],I=0;I<3;++I)m[4*F+I]=E[I];m[4*F+3]=E[3]*255}return g(m,[256,256,4],[4,0,1])}function k(A){for(var T=A.length,m=new Array(T),F=0;F<T;++F)m[F]=A[F][2];return m}y.update=function(A){A=A||{};var T=this.gl;this.dirty=!0,\"lightPosition\"in A&&(this.lightPosition=A.lightPosition),\"opacity\"in A&&(this.opacity=A.opacity),\"ambient\"in A&&(this.ambientLight=A.ambient),\"diffuse\"in A&&(this.diffuseLight=A.diffuse),\"specular\"in A&&(this.specularLight=A.specular),\"roughness\"in A&&(this.roughness=A.roughness),\"fresnel\"in A&&(this.fresnel=A.fresnel),A.tubeScale!==void 0&&(this.tubeScale=A.tubeScale),A.vectorScale!==void 0&&(this.vectorScale=A.vectorScale),A.coneScale!==void 0&&(this.coneScale=A.coneScale),A.coneOffset!==void 0&&(this.coneOffset=A.coneOffset),A.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=T.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=T.LINEAR,this.texture.setPixels(C(A.colormap)),this.texture.generateMipmap());var m=A.cells,F=A.positions,E=A.vectors;if(!(!F||!m||!E)){var I=[],R=[],B=[],O=[],V=[];this.cells=m,this.positions=F,this.vectors=E;var j=A.meshColor||[1,1,1,1],q=A.vertexIntensity,U=1/0,J=-1/0;if(q)if(A.vertexIntensityBounds)U=+A.vertexIntensityBounds[0],J=+A.vertexIntensityBounds[1];else for(var ee=0;ee<q.length;++ee){var ce=q[ee];U=Math.min(U,ce),J=Math.max(J,ce)}else for(var ee=0;ee<F.length;++ee){var ce=F[ee][2];U=Math.min(U,ce),J=Math.max(J,ce)}q?this.intensity=q:this.intensity=k(F),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var ee=0;ee<F.length;++ee)for(var ye=F[ee],xe=0;xe<3;++xe)isNaN(ye[xe])||!isFinite(ye[xe])||(this.bounds[0][xe]=Math.min(this.bounds[0][xe],ye[xe]),this.bounds[1][xe]=Math.max(this.bounds[1][xe],ye[xe]));var ue=0;e:for(var ee=0;ee<m.length;++ee){var K=m[ee];switch(K.length){case 3:for(var xe=0;xe<3;++xe)for(var _e=K[xe],ye=F[_e],ne=0;ne<3;++ne)if(isNaN(ye[ne])||!isFinite(ye[ne]))continue e;for(var xe=0;xe<3;++xe){var _e=K[2-xe],ye=F[_e];I.push(ye[0],ye[1],ye[2],ye[3]);var ae=E[_e];R.push(ae[0],ae[1],ae[2],ae[3]||0);var pe=j;pe.length===3?B.push(pe[0],pe[1],pe[2],1):B.push(pe[0],pe[1],pe[2],pe[3]);var ie;q?ie=[(q[_e]-U)/(J-U),0]:ie=[(ye[2]-U)/(J-U),0],O.push(ie[0],ie[1]),V.push(ee)}ue+=1;break;default:break}}this.triangleCount=ue,this.trianglePositions.update(I),this.triangleVectors.update(R),this.triangleColors.update(B),this.triangleUVs.update(O),this.triangleIds.update(new Uint32Array(V))}},y.drawTransparent=y.draw=function(A){A=A||{};for(var T=this.gl,m=A.model||x,F=A.view||x,E=A.projection||x,I=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],R=0;R<3;++R)I[0][R]=Math.max(I[0][R],this.clipBounds[0][R]),I[1][R]=Math.min(I[1][R],this.clipBounds[1][R]);var B={model:m,view:F,projection:E,inverseModel:x.slice(),clipBounds:I,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};B.inverseModel=v(B.inverseModel,B.model),T.disable(T.CULL_FACE),this.texture.bind(0);var O=new Array(16);f(O,B.view,B.model),f(O,B.projection,O),v(O,O);for(var R=0;R<3;++R)B.eyePosition[R]=O[12+R]/O[15];for(var V=O[15],R=0;R<3;++R)V+=this.lightPosition[R]*O[4*R+3];for(var R=0;R<3;++R){for(var j=O[12+R],q=0;q<3;++q)j+=O[4*q+R]*this.lightPosition[q];B.lightPosition[R]=j/V}if(this.triangleCount>0){var U=this.triShader;U.bind(),U.uniforms=B,this.triangleVAO.bind(),T.drawArrays(T.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}},y.drawPick=function(A){A=A||{};for(var T=this.gl,m=A.model||x,F=A.view||x,E=A.projection||x,I=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],R=0;R<3;++R)I[0][R]=Math.max(I[0][R],this.clipBounds[0][R]),I[1][R]=Math.min(I[1][R],this.clipBounds[1][R]);this._model=[].slice.call(m),this._view=[].slice.call(F),this._projection=[].slice.call(E),this._resolution=[T.drawingBufferWidth,T.drawingBufferHeight];var B={model:m,view:F,projection:E,clipBounds:I,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},O=this.pickShader;O.bind(),O.uniforms=B,this.triangleCount>0&&(this.triangleVAO.bind(),T.drawArrays(T.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind())},y.pick=function(A){if(!A||A.id!==this.pickId)return null;var T=A.value[0]+256*A.value[1]+65536*A.value[2],m=this.cells[T],F=this.positions[m[1]].slice(0,3),E={position:F,dataCoordinate:F,index:Math.floor(m[1]/48)};return this.traceType===\"cone\"?E.index=Math.floor(m[1]/48):this.traceType===\"streamtube\"&&(E.intensity=this.intensity[m[1]],E.velocity=this.vectors[m[1]].slice(0,3),E.divergence=this.vectors[m[1]][3],E.index=T),E},y.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()};function S(A,T){var m=s(A,T.meshShader.vertex,T.meshShader.fragment,null,T.meshShader.attributes);return m.attributes.position.location=0,m.attributes.color.location=2,m.attributes.uv.location=3,m.attributes.vector.location=4,m}function L(A,T){var m=s(A,T.pickShader.vertex,T.pickShader.fragment,null,T.pickShader.attributes);return m.attributes.position.location=0,m.attributes.id.location=1,m.attributes.vector.location=4,m}function w(A,T,m){var F=m.shaders;arguments.length===1&&(T=A,A=T.gl);var E=S(A,F),I=L(A,F),R=c(A,g(new Uint8Array([255,255,255,255]),[1,1,4]));R.generateMipmap(),R.minFilter=A.LINEAR_MIPMAP_LINEAR,R.magFilter=A.LINEAR;var B=l(A),O=l(A),V=l(A),j=l(A),q=l(A),U=u(A,[{buffer:B,type:A.FLOAT,size:4},{buffer:q,type:A.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:V,type:A.FLOAT,size:4},{buffer:j,type:A.FLOAT,size:2},{buffer:O,type:A.FLOAT,size:4}]),J=new _(A,R,E,I,B,O,q,V,j,U,m.traceType||\"cone\");return J.update(T),J}a.exports=w}),614:(function(a,i,o){var s=o(3236),l=s([`precision highp float;\n",
        "\n",
        "precision highp float;\n",
        "#define GLSLIFY 1\n",
@@ -699,7 +702,7 @@
        "  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
        "\n",
        "  gl_FragColor = vec4(pickId, f_id.xyz);\n",
-       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        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position, offset;\n",
@@ -750,13 +753,13 @@
        "  ) discard;\n",
        "\n",
        "  gl_FragColor = opacity * fragColor;\n",
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x(this,g,P),[g,P]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(S){return S=S|0,x(this,S,this._shape[1]),S},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(S){return S=S|0,x(this,this._shape[0],S),S},enumerable:!1}}),L.bind=function(){if(!this._destroyed){var S=this.gl;S.bindFramebuffer(S.FRAMEBUFFER,this.handle),S.viewport(0,0,this._shape[0],this._shape[1])}},L.dispose=function(){if(!this._destroyed){this._destroyed=!0;var S=this.gl;S.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(S.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var g=0;g<this.color.length;++g)this.color[g].dispose(),this.color[g]=null;this._color_rb&&(S.deleteRenderbuffer(this._color_rb),this._color_rb=null)}};function C(S,g,P,E){u||(u=S.FRAMEBUFFER_UNSUPPORTED,c=S.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,f=S.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,h=S.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var z=S.getExtension(\"WEBGL_draw_buffers\");if(!l&&z&&m(S,z),Array.isArray(g)&&(E=P,P=g[1]|0,g=g[0]|0),typeof g!=\"number\")throw new Error(\"gl-fbo: Missing shape parameter\");var q=S.getParameter(S.MAX_RENDERBUFFER_SIZE);if(g<0||g>q||P<0||P>q)throw new Error(\"gl-fbo: Parameters are too large for FBO\");E=E||{};var V=1;if(\"color\"in E){if(V=Math.max(E.color|0,0),V<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(V>1)if(z){if(V>S.getParameter(z.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+V+\" draw buffers\")}else throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\")}var G=S.UNSIGNED_BYTE,Z=S.getExtension(\"OES_texture_float\");if(E.float&&V>0){if(!Z)throw new Error(\"gl-fbo: Context does not support floating point textures\");G=S.FLOAT}else E.preferFloat&&V>0&&Z&&(G=S.FLOAT);var j=!0;\"depth\"in E&&(j=!!E.depth);var N=!1;return\"stencil\"in E&&(N=!!E.stencil),new T(S,g,P,G,V,j,N,z)}}),2992:(function(i,a,o){var s=o(3387).sprintf,l=o(5171),u=o(1848),c=o(1085);i.exports=f;function f(h,d,v){\"use strict\";var m=u(d)||\"of unknown name (see npm glsl-shader-name)\",b=\"unknown type\";v!==void 0&&(b=v===l.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var p=s(`Error compiling %s shader %s:\n",
-       "`,b,m),k=s(\"%s%s\",p,h),M=h.split(`\n",
-       "`),T={},L=0;L<M.length;L++){var x=M[L];if(!(x===\"\"||x===\"\\0\")){var C=parseInt(x.split(\":\")[2]);if(isNaN(C))throw new Error(s(\"Could not parse error: %s\",x));T[C]=x}}for(var S=c(d).split(`\n",
-       "`),L=0;L<S.length;L++)if(!(!T[L+3]&&!T[L+2]&&!T[L+1])){var g=S[L];if(p+=g+`\n",
-       "`,T[L+1]){var P=T[L+1];P=P.substr(P.split(\":\",3).join(\":\").length+1).trim(),p+=s(`^^^ %s\n",
+       "}`]);a.exports=function(f){return l(f,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}}),2260:(function(a,i,o){\"use strict\";var s=o(7766);a.exports=A;var l=null,u,c,f,v;function g(T){var m=T.getParameter(T.FRAMEBUFFER_BINDING),F=T.getParameter(T.RENDERBUFFER_BINDING),E=T.getParameter(T.TEXTURE_BINDING_2D);return[m,F,E]}function p(T,m){T.bindFramebuffer(T.FRAMEBUFFER,m[0]),T.bindRenderbuffer(T.RENDERBUFFER,m[1]),T.bindTexture(T.TEXTURE_2D,m[2])}function x(T,m){var F=T.getParameter(m.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(F+1);for(var E=0;E<=F;++E){for(var I=new Array(F),R=0;R<E;++R)I[R]=T.COLOR_ATTACHMENT0+R;for(var R=E;R<F;++R)I[R]=T.NONE;l[E]=I}}function _(T){switch(T){case u:throw new Error(\"gl-fbo: Framebuffer unsupported\");case c:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case f:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case v:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function y(T,m,F,E,I,R){if(!E)return null;var B=s(T,m,F,I,E);return B.magFilter=T.NEAREST,B.minFilter=T.NEAREST,B.mipSamples=1,B.bind(),T.framebufferTexture2D(T.FRAMEBUFFER,R,T.TEXTURE_2D,B.handle,0),B}function C(T,m,F,E,I){var R=T.createRenderbuffer();return T.bindRenderbuffer(T.RENDERBUFFER,R),T.renderbufferStorage(T.RENDERBUFFER,E,m,F),T.framebufferRenderbuffer(T.FRAMEBUFFER,I,T.RENDERBUFFER,R),R}function k(T){var m=g(T.gl),F=T.gl,E=T.handle=F.createFramebuffer(),I=T._shape[0],R=T._shape[1],B=T.color.length,O=T._ext,V=T._useStencil,j=T._useDepth,q=T._colorType;F.bindFramebuffer(F.FRAMEBUFFER,E);for(var U=0;U<B;++U)T.color[U]=y(F,I,R,q,F.RGBA,F.COLOR_ATTACHMENT0+U);B===0?(T._color_rb=C(F,I,R,F.RGBA4,F.COLOR_ATTACHMENT0),O&&O.drawBuffersWEBGL(l[0])):B>1&&O.drawBuffersWEBGL(l[B]);var J=F.getExtension(\"WEBGL_depth_texture\");J?V?T.depth=y(F,I,R,J.UNSIGNED_INT_24_8_WEBGL,F.DEPTH_STENCIL,F.DEPTH_STENCIL_ATTACHMENT):j&&(T.depth=y(F,I,R,F.UNSIGNED_SHORT,F.DEPTH_COMPONENT,F.DEPTH_ATTACHMENT)):j&&V?T._depth_rb=C(F,I,R,F.DEPTH_STENCIL,F.DEPTH_STENCIL_ATTACHMENT):j?T._depth_rb=C(F,I,R,F.DEPTH_COMPONENT16,F.DEPTH_ATTACHMENT):V&&(T._depth_rb=C(F,I,R,F.STENCIL_INDEX,F.STENCIL_ATTACHMENT));var ee=F.checkFramebufferStatus(F.FRAMEBUFFER);if(ee!==F.FRAMEBUFFER_COMPLETE){T._destroyed=!0,F.bindFramebuffer(F.FRAMEBUFFER,null),F.deleteFramebuffer(T.handle),T.handle=null,T.depth&&(T.depth.dispose(),T.depth=null),T._depth_rb&&(F.deleteRenderbuffer(T._depth_rb),T._depth_rb=null);for(var U=0;U<T.color.length;++U)T.color[U].dispose(),T.color[U]=null;T._color_rb&&(F.deleteRenderbuffer(T._color_rb),T._color_rb=null),p(F,m),_(ee)}p(F,m)}function S(T,m,F,E,I,R,B,O){this.gl=T,this._shape=[m|0,F|0],this._destroyed=!1,this._ext=O,this.color=new Array(I);for(var V=0;V<I;++V)this.color[V]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=E,this._useDepth=R,this._useStencil=B;var j=this,q=[m|0,F|0];Object.defineProperties(q,{0:{get:function(){return j._shape[0]},set:function(U){return j.width=U}},1:{get:function(){return j._shape[1]},set:function(U){return j.height=U}}}),this._shapeVector=q,k(this)}var L=S.prototype;function w(T,m,F){if(T._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(!(T._shape[0]===m&&T._shape[1]===F)){var E=T.gl,I=E.getParameter(E.MAX_RENDERBUFFER_SIZE);if(m<0||m>I||F<0||F>I)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");T._shape[0]=m,T._shape[1]=F;for(var R=g(E),B=0;B<T.color.length;++B)T.color[B].shape=T._shape;T._color_rb&&(E.bindRenderbuffer(E.RENDERBUFFER,T._color_rb),E.renderbufferStorage(E.RENDERBUFFER,E.RGBA4,T._shape[0],T._shape[1])),T.depth&&(T.depth.shape=T._shape),T._depth_rb&&(E.bindRenderbuffer(E.RENDERBUFFER,T._depth_rb),T._useDepth&&T._useStencil?E.renderbufferStorage(E.RENDERBUFFER,E.DEPTH_STENCIL,T._shape[0],T._shape[1]):T._useDepth?E.renderbufferStorage(E.RENDERBUFFER,E.DEPTH_COMPONENT16,T._shape[0],T._shape[1]):T._useStencil&&E.renderbufferStorage(E.RENDERBUFFER,E.STENCIL_INDEX,T._shape[0],T._shape[1])),E.bindFramebuffer(E.FRAMEBUFFER,T.handle);var O=E.checkFramebufferStatus(E.FRAMEBUFFER);O!==E.FRAMEBUFFER_COMPLETE&&(T.dispose(),p(E,R),_(O)),p(E,R)}}Object.defineProperties(L,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(T){if(Array.isArray(T)||(T=[T|0,T|0]),T.length!==2)throw new Error(\"gl-fbo: Shape vector must be length 2\");var m=T[0]|0,F=T[1]|0;return w(this,m,F),[m,F]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(T){return T=T|0,w(this,T,this._shape[1]),T},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(T){return T=T|0,w(this,this._shape[0],T),T},enumerable:!1}}),L.bind=function(){if(!this._destroyed){var T=this.gl;T.bindFramebuffer(T.FRAMEBUFFER,this.handle),T.viewport(0,0,this._shape[0],this._shape[1])}},L.dispose=function(){if(!this._destroyed){this._destroyed=!0;var T=this.gl;T.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(T.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var m=0;m<this.color.length;++m)this.color[m].dispose(),this.color[m]=null;this._color_rb&&(T.deleteRenderbuffer(this._color_rb),this._color_rb=null)}};function A(T,m,F,E){u||(u=T.FRAMEBUFFER_UNSUPPORTED,c=T.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,f=T.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,v=T.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var I=T.getExtension(\"WEBGL_draw_buffers\");if(!l&&I&&x(T,I),Array.isArray(m)&&(E=F,F=m[1]|0,m=m[0]|0),typeof m!=\"number\")throw new Error(\"gl-fbo: Missing shape parameter\");var R=T.getParameter(T.MAX_RENDERBUFFER_SIZE);if(m<0||m>R||F<0||F>R)throw new Error(\"gl-fbo: Parameters are too large for FBO\");E=E||{};var B=1;if(\"color\"in E){if(B=Math.max(E.color|0,0),B<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(B>1)if(I){if(B>T.getParameter(I.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+B+\" draw buffers\")}else throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\")}var O=T.UNSIGNED_BYTE,V=T.getExtension(\"OES_texture_float\");if(E.float&&B>0){if(!V)throw new Error(\"gl-fbo: Context does not support floating point textures\");O=T.FLOAT}else E.preferFloat&&B>0&&V&&(O=T.FLOAT);var j=!0;\"depth\"in E&&(j=!!E.depth);var q=!1;return\"stencil\"in E&&(q=!!E.stencil),new S(T,m,F,O,B,j,q,I)}}),2992:(function(a,i,o){var s=o(3387).sprintf,l=o(5171),u=o(1848),c=o(1085);a.exports=f;function f(v,g,p){\"use strict\";var x=u(g)||\"of unknown name (see npm glsl-shader-name)\",_=\"unknown type\";p!==void 0&&(_=p===l.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var y=s(`Error compiling %s shader %s:\n",
+       "`,_,x),C=s(\"%s%s\",y,v),k=v.split(`\n",
+       "`),S={},L=0;L<k.length;L++){var w=k[L];if(!(w===\"\"||w===\"\\0\")){var A=parseInt(w.split(\":\")[2]);if(isNaN(A))throw new Error(s(\"Could not parse error: %s\",w));S[A]=w}}for(var T=c(g).split(`\n",
+       "`),L=0;L<T.length;L++)if(!(!S[L+3]&&!S[L+2]&&!S[L+1])){var m=T[L];if(y+=m+`\n",
+       "`,S[L+1]){var F=S[L+1];F=F.substr(F.split(\":\",3).join(\":\").length+1).trim(),y+=s(`^^^ %s\n",
        "\n",
-       "`,P)}}return{long:p.trim(),short:k.trim()}}}),7319:(function(i,a,o){var s=o(3236),l=o(9405),u=s([`precision highp float;\n",
+       "`,F)}}return{long:y.trim(),short:C.trim()}}}),7319:(function(a,i,o){var s=o(3236),l=o(9405),u=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position, nextPosition;\n",
@@ -918,7 +921,7 @@
        "  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n",
        "\n",
        "  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n",
-       "}`]),h=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];a.createShader=function(d){return l(d,u,c,null,h)},a.createPickShader=function(d){return l(d,u,f,null,h)}}),5714:(function(i,a,o){\"use strict\";i.exports=S;var s=o(2762),l=o(8116),u=o(7766),c=new Uint8Array(4),f=new Float32Array(c.buffer);function h(g,P,E,z){return c[0]=z,c[1]=E,c[2]=P,c[3]=g,f[0]}var d=o(2478),v=o(9618),m=o(7319),b=m.createShader,p=m.createPickShader,k=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function M(g,P){for(var E=0,z=0;z<3;++z){var q=g[z]-P[z];E+=q*q}return Math.sqrt(E)}function T(g){for(var P=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],E=0;E<3;++E)P[0][E]=Math.max(g[0][E],P[0][E]),P[1][E]=Math.min(g[1][E],P[1][E]);return P}function L(g,P,E,z){this.arcLength=g,this.position=P,this.index=E,this.dataCoordinate=z}function x(g,P,E,z,q,V){this.gl=g,this.shader=P,this.pickShader=E,this.buffer=z,this.vao=q,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=V,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var C=x.prototype;C.isTransparent=function(){return this.hasAlpha},C.isOpaque=function(){return!this.hasAlpha},C.pickSlots=1,C.setPickBase=function(g){this.pickId=g},C.drawTransparent=C.draw=function(g){if(this.vertexCount){var P=this.gl,E=this.shader,z=this.vao;E.bind(),E.uniforms={model:g.model||k,view:g.view||k,projection:g.projection||k,clipBounds:T(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[P.drawingBufferWidth,P.drawingBufferHeight],pixelRatio:this.pixelRatio},z.bind(),z.draw(P.TRIANGLE_STRIP,this.vertexCount),z.unbind()}},C.drawPick=function(g){if(this.vertexCount){var P=this.gl,E=this.pickShader,z=this.vao;E.bind(),E.uniforms={model:g.model||k,view:g.view||k,projection:g.projection||k,pickId:this.pickId,clipBounds:T(this.clipBounds),screenShape:[P.drawingBufferWidth,P.drawingBufferHeight],pixelRatio:this.pixelRatio},z.bind(),z.draw(P.TRIANGLE_STRIP,this.vertexCount),z.unbind()}},C.update=function(g){var P,E;this.dirty=!0;var z=!!g.connectGaps;\"dashScale\"in g&&(this.dashScale=g.dashScale),this.hasAlpha=!1,\"opacity\"in g&&(this.opacity=+g.opacity,this.opacity<1&&(this.hasAlpha=!0));var q=[],V=[],G=[],Z=0,j=0,N=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],H=g.position||g.positions;if(H){var re=g.color||g.colors||[0,0,0,1],ae=g.lineWidth||1,_e=!1;e:for(P=1;P<H.length;++P){var Ee=H[P-1],Ce=H[P];for(V.push(Z),G.push(Ee.slice()),E=0;E<3;++E){if(isNaN(Ee[E])||isNaN(Ce[E])||!isFinite(Ee[E])||!isFinite(Ce[E])){if(!z&&q.length>0){for(var ge=0;ge<24;++ge)q.push(q[q.length-12]);j+=2,_e=!0}continue e}N[0][E]=Math.min(N[0][E],Ee[E],Ce[E]),N[1][E]=Math.max(N[1][E],Ee[E],Ce[E])}var ie,Se;Array.isArray(re[0])?(ie=re.length>P-1?re[P-1]:re.length>0?re[re.length-1]:[0,0,0,1],Se=re.length>P?re[P]:re.length>0?re[re.length-1]:[0,0,0,1]):ie=Se=re,ie.length===3&&(ie=[ie[0],ie[1],ie[2],1]),Se.length===3&&(Se=[Se[0],Se[1],Se[2],1]),!this.hasAlpha&&ie[3]<1&&(this.hasAlpha=!0);var ke;Array.isArray(ae)?ke=ae.length>P-1?ae[P-1]:ae.length>0?ae[ae.length-1]:[0,0,0,1]:ke=ae;var be=Z;if(Z+=M(Ee,Ce),_e){for(E=0;E<2;++E)q.push(Ee[0],Ee[1],Ee[2],Ce[0],Ce[1],Ce[2],be,ke,ie[0],ie[1],ie[2],ie[3]);j+=2,_e=!1}q.push(Ee[0],Ee[1],Ee[2],Ce[0],Ce[1],Ce[2],be,ke,ie[0],ie[1],ie[2],ie[3],Ee[0],Ee[1],Ee[2],Ce[0],Ce[1],Ce[2],be,-ke,ie[0],ie[1],ie[2],ie[3],Ce[0],Ce[1],Ce[2],Ee[0],Ee[1],Ee[2],Z,-ke,Se[0],Se[1],Se[2],Se[3],Ce[0],Ce[1],Ce[2],Ee[0],Ee[1],Ee[2],Z,ke,Se[0],Se[1],Se[2],Se[3]),j+=4}}if(this.buffer.update(q),V.push(Z),G.push(H[H.length-1].slice()),this.bounds=N,this.vertexCount=j,this.points=G,this.arcLength=V,\"dashes\"in g){var Be=g.dashes,Le=Be.slice();for(Le.unshift(0),P=1;P<Le.length;++P)Le[P]=Le[P-1]+Le[P];var me=v(new Array(256*4),[256,1,4]);for(P=0;P<256;++P){for(E=0;E<4;++E)me.set(P,0,E,0);d.le(Le,Le[Le.length-1]*P/255)&1?me.set(P,0,0,0):me.set(P,0,0,255)}this.texture.setPixels(me)}},C.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},C.pick=function(g){if(!g||g.id!==this.pickId)return null;var 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F=this.gl,E=this.shader,I=this.vao;E.bind(),E.uniforms={model:m.model||C,view:m.view||C,projection:m.projection||C,clipBounds:S(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[F.drawingBufferWidth,F.drawingBufferHeight],pixelRatio:this.pixelRatio},I.bind(),I.draw(F.TRIANGLE_STRIP,this.vertexCount),I.unbind()}},A.drawPick=function(m){if(this.vertexCount){var F=this.gl,E=this.pickShader,I=this.vao;E.bind(),E.uniforms={model:m.model||C,view:m.view||C,projection:m.projection||C,pickId:this.pickId,clipBounds:S(this.clipBounds),screenShape:[F.drawingBufferWidth,F.drawingBufferHeight],pixelRatio:this.pixelRatio},I.bind(),I.draw(F.TRIANGLE_STRIP,this.vertexCount),I.unbind()}},A.update=function(m){var F,E;this.dirty=!0;var I=!!m.connectGaps;\"dashScale\"in m&&(this.dashScale=m.dashScale),this.hasAlpha=!1,\"opacity\"in m&&(this.opacity=+m.opacity,this.opacity<1&&(this.hasAlpha=!0));var R=[],B=[],O=[],V=0,j=0,q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],U=m.position||m.positions;if(U){var J=m.color||m.colors||[0,0,0,1],ee=m.lineWidth||1,ce=!1;e:for(F=1;F<U.length;++F){var ye=U[F-1],xe=U[F];for(B.push(V),O.push(ye.slice()),E=0;E<3;++E){if(isNaN(ye[E])||isNaN(xe[E])||!isFinite(ye[E])||!isFinite(xe[E])){if(!I&&R.length>0){for(var ue=0;ue<24;++ue)R.push(R[R.length-12]);j+=2,ce=!0}continue e}q[0][E]=Math.min(q[0][E],ye[E],xe[E]),q[1][E]=Math.max(q[1][E],ye[E],xe[E])}var K,_e;Array.isArray(J[0])?(K=J.length>F-1?J[F-1]:J.length>0?J[J.length-1]:[0,0,0,1],_e=J.length>F?J[F]:J.length>0?J[J.length-1]:[0,0,0,1]):K=_e=J,K.length===3&&(K=[K[0],K[1],K[2],1]),_e.length===3&&(_e=[_e[0],_e[1],_e[2],1]),!this.hasAlpha&&K[3]<1&&(this.hasAlpha=!0);var ne;Array.isArray(ee)?ne=ee.length>F-1?ee[F-1]:ee.length>0?ee[ee.length-1]:[0,0,0,1]:ne=ee;var ae=V;if(V+=k(ye,xe),ce){for(E=0;E<2;++E)R.push(ye[0],ye[1],ye[2],xe[0],xe[1],xe[2],ae,ne,K[0],K[1],K[2],K[3]);j+=2,ce=!1}R.push(ye[0],ye[1],ye[2],xe[0],xe[1],xe[2],ae,ne,K[0],K[1],K[2],K[3],ye[0],ye[1],ye[2],xe[0],xe[1],xe[2],ae,-ne,K[0],K[1],K[2],K[3],xe[0],xe[1],xe[2],ye[0],ye[1],ye[2],V,-ne,_e[0],_e[1],_e[2],_e[3],xe[0],xe[1],xe[2],ye[0],ye[1],ye[2],V,ne,_e[0],_e[1],_e[2],_e[3]),j+=4}}if(this.buffer.update(R),B.push(V),O.push(U[U.length-1].slice()),this.bounds=q,this.vertexCount=j,this.points=O,this.arcLength=B,\"dashes\"in m){var pe=m.dashes,ie=pe.slice();for(ie.unshift(0),F=1;F<ie.length;++F)ie[F]=ie[F-1]+ie[F];var le=p(new Array(256*4),[256,1,4]);for(F=0;F<256;++F){for(E=0;E<4;++E)le.set(F,0,E,0);g.le(ie,ie[ie.length-1]*F/255)&1?le.set(F,0,0,0):le.set(F,0,0,255)}this.texture.setPixels(le)}},A.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},A.pick=function(m){if(!m||m.id!==this.pickId)return null;var F=v(m.value[0],m.value[1],m.value[2],0),E=g.le(this.arcLength,F);if(E<0)return null;if(E===this.arcLength.length-1)return new L(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),E);for(var I=this.points[E],R=this.points[Math.min(E+1,this.points.length-1)],B=(F-this.arcLength[E])/(this.arcLength[E+1]-this.arcLength[E]),O=1-B,V=[0,0,0],j=0;j<3;++j)V[j]=O*I[j]+B*R[j];var q=Math.min(B<.5?E:E+1,this.points.length-1);return new L(F,V,q,this.points[q])};function T(m){var F=m.gl||m.scene&&m.scene.gl,E=_(F);E.attributes.position.location=0,E.attributes.nextPosition.location=1,E.attributes.arcLength.location=2,E.attributes.lineWidth.location=3,E.attributes.color.location=4;var I=y(F);I.attributes.position.location=0,I.attributes.nextPosition.location=1,I.attributes.arcLength.location=2,I.attributes.lineWidth.location=3,I.attributes.color.location=4;for(var R=s(F),B=l(F,[{buffer:R,size:3,offset:0,stride:48},{buffer:R,size:3,offset:12,stride:48},{buffer:R,size:1,offset:24,stride:48},{buffer:R,size:1,offset:28,stride:48},{buffer:R,size:4,offset:32,stride:48}]),O=p(new Array(256*4),[256,1,4]),V=0;V<1024;++V)O.data[V]=255;var j=u(F,O);j.wrap=F.REPEAT;var q=new w(F,E,I,R,B,j);return q.update(m),q}}),1903:(function(a){a.exports=i;function i(o){var s=new Float32Array(16);return s[0]=o[0],s[1]=o[1],s[2]=o[2],s[3]=o[3],s[4]=o[4],s[5]=o[5],s[6]=o[6],s[7]=o[7],s[8]=o[8],s[9]=o[9],s[10]=o[10],s[11]=o[11],s[12]=o[12],s[13]=o[13],s[14]=o[14],s[15]=o[15],s}}),6864:(function(a){a.exports=i;function i(){var o=new Float32Array(16);return o[0]=1,o[1]=0,o[2]=0,o[3]=0,o[4]=0,o[5]=1,o[6]=0,o[7]=0,o[8]=0,o[9]=0,o[10]=1,o[11]=0,o[12]=0,o[13]=0,o[14]=0,o[15]=1,o}}),9921:(function(a){a.exports=i;function i(o){var s=o[0],l=o[1],u=o[2],c=o[3],f=o[4],v=o[5],g=o[6],p=o[7],x=o[8],_=o[9],y=o[10],C=o[11],k=o[12],S=o[13],L=o[14],w=o[15],A=s*v-l*f,T=s*g-u*f,m=s*p-c*f,F=l*g-u*v,E=l*p-c*v,I=u*p-c*g,R=x*S-_*k,B=x*L-y*k,O=x*w-C*k,V=_*L-y*S,j=_*w-C*S,q=y*w-C*L;return A*q-T*j+m*V+F*O-E*B+I*R}}),7399:(function(a){a.exports=i;function i(o,s){var l=s[0],u=s[1],c=s[2],f=s[3],v=l+l,g=u+u,p=c+c,x=l*v,_=u*v,y=u*g,C=c*v,k=c*g,S=c*p,L=f*v,w=f*g,A=f*p;return o[0]=1-y-S,o[1]=_+A,o[2]=C-w,o[3]=0,o[4]=_-A,o[5]=1-x-S,o[6]=k+L,o[7]=0,o[8]=C+w,o[9]=k-L,o[10]=1-x-y,o[11]=0,o[12]=0,o[13]=0,o[14]=0,o[15]=1,o}}),6743:(function(a){a.exports=i;function i(o,s,l){var u=s[0],c=s[1],f=s[2],v=s[3],g=u+u,p=c+c,x=f+f,_=u*g,y=u*p,C=u*x,k=c*p,S=c*x,L=f*x,w=v*g,A=v*p,T=v*x;return o[0]=1-(k+L),o[1]=y+T,o[2]=C-A,o[3]=0,o[4]=y-T,o[5]=1-(_+L),o[6]=S+w,o[7]=0,o[8]=C+A,o[9]=S-w,o[10]=1-(_+k),o[11]=0,o[12]=l[0],o[13]=l[1],o[14]=l[2],o[15]=1,o}}),7894:(function(a){a.exports=i;function i(o){return 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Math.abs(A-R)<1e-6&&Math.abs(T-B)<1e-6&&Math.abs(m-O)<1e-6?s(u):(k=A-R,S=T-B,L=m-O,w=1/Math.sqrt(k*k+S*S+L*L),k*=w,S*=w,L*=w,g=E*L-I*S,p=I*k-F*L,x=F*S-E*k,w=Math.sqrt(g*g+p*p+x*x),w?(w=1/w,g*=w,p*=w,x*=w):(g=0,p=0,x=0),_=S*x-L*p,y=L*g-k*x,C=k*p-S*g,w=Math.sqrt(_*_+y*y+C*C),w?(w=1/w,_*=w,y*=w,C*=w):(_=0,y=0,C=0),u[0]=g,u[1]=_,u[2]=k,u[3]=0,u[4]=p,u[5]=y,u[6]=S,u[7]=0,u[8]=x,u[9]=C,u[10]=L,u[11]=0,u[12]=-(g*A+p*T+x*m),u[13]=-(_*A+y*T+C*m),u[14]=-(k*A+S*T+L*m),u[15]=1,u)}}),6760:(function(a){a.exports=i;function i(o,s,l){var u=s[0],c=s[1],f=s[2],v=s[3],g=s[4],p=s[5],x=s[6],_=s[7],y=s[8],C=s[9],k=s[10],S=s[11],L=s[12],w=s[13],A=s[14],T=s[15],m=l[0],F=l[1],E=l[2],I=l[3];return o[0]=m*u+F*g+E*y+I*L,o[1]=m*c+F*p+E*C+I*w,o[2]=m*f+F*x+E*k+I*A,o[3]=m*v+F*_+E*S+I*T,m=l[4],F=l[5],E=l[6],I=l[7],o[4]=m*u+F*g+E*y+I*L,o[5]=m*c+F*p+E*C+I*w,o[6]=m*f+F*x+E*k+I*A,o[7]=m*v+F*_+E*S+I*T,m=l[8],F=l[9],E=l[10],I=l[11],o[8]=m*u+F*g+E*y+I*L,o[9]=m*c+F*p+E*C+I*w,o[10]=m*f+F*x+E*k+I*A,o[11]=m*v+F*_+E*S+I*T,m=l[12],F=l[13],E=l[14],I=l[15],o[12]=m*u+F*g+E*y+I*L,o[13]=m*c+F*p+E*C+I*w,o[14]=m*f+F*x+E*k+I*A,o[15]=m*v+F*_+E*S+I*T,o}}),4040:(function(a){a.exports=i;function i(o,s,l,u,c,f,v){var g=1/(s-l),p=1/(u-c),x=1/(f-v);return o[0]=-2*g,o[1]=0,o[2]=0,o[3]=0,o[4]=0,o[5]=-2*p,o[6]=0,o[7]=0,o[8]=0,o[9]=0,o[10]=2*x,o[11]=0,o[12]=(s+l)*g,o[13]=(c+u)*p,o[14]=(v+f)*x,o[15]=1,o}}),4772:(function(a){a.exports=i;function i(o,s,l,u,c){var f=1/Math.tan(s/2),v=1/(u-c);return o[0]=f/l,o[1]=0,o[2]=0,o[3]=0,o[4]=0,o[5]=f,o[6]=0,o[7]=0,o[8]=0,o[9]=0,o[10]=(c+u)*v,o[11]=-1,o[12]=0,o[13]=0,o[14]=2*c*u*v,o[15]=0,o}}),6079:(function(a){a.exports=i;function i(o,s,l,u){var c=u[0],f=u[1],v=u[2],g=Math.sqrt(c*c+f*f+v*v),p,x,_,y,C,k,S,L,w,A,T,m,F,E,I,R,B,O,V,j,q,U,J,ee;return Math.abs(g)<1e-6?null:(g=1/g,c*=g,f*=g,v*=g,p=Math.sin(l),x=Math.cos(l),_=1-x,y=s[0],C=s[1],k=s[2],S=s[3],L=s[4],w=s[5],A=s[6],T=s[7],m=s[8],F=s[9],E=s[10],I=s[11],R=c*c*_+x,B=f*c*_+v*p,O=v*c*_-f*p,V=c*f*_-v*p,j=f*f*_+x,q=v*f*_+c*p,U=c*v*_+f*p,J=f*v*_-c*p,ee=v*v*_+x,o[0]=y*R+L*B+m*O,o[1]=C*R+w*B+F*O,o[2]=k*R+A*B+E*O,o[3]=S*R+T*B+I*O,o[4]=y*V+L*j+m*q,o[5]=C*V+w*j+F*q,o[6]=k*V+A*j+E*q,o[7]=S*V+T*j+I*q,o[8]=y*U+L*J+m*ee,o[9]=C*U+w*J+F*ee,o[10]=k*U+A*J+E*ee,o[11]=S*U+T*J+I*ee,s!==o&&(o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15]),o)}}),5567:(function(a){a.exports=i;function i(o,s,l){var u=Math.sin(l),c=Math.cos(l),f=s[4],v=s[5],g=s[6],p=s[7],x=s[8],_=s[9],y=s[10],C=s[11];return s!==o&&(o[0]=s[0],o[1]=s[1],o[2]=s[2],o[3]=s[3],o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15]),o[4]=f*c+x*u,o[5]=v*c+_*u,o[6]=g*c+y*u,o[7]=p*c+C*u,o[8]=x*c-f*u,o[9]=_*c-v*u,o[10]=y*c-g*u,o[11]=C*c-p*u,o}}),2408:(function(a){a.exports=i;function i(o,s,l){var u=Math.sin(l),c=Math.cos(l),f=s[0],v=s[1],g=s[2],p=s[3],x=s[8],_=s[9],y=s[10],C=s[11];return s!==o&&(o[4]=s[4],o[5]=s[5],o[6]=s[6],o[7]=s[7],o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15]),o[0]=f*c-x*u,o[1]=v*c-_*u,o[2]=g*c-y*u,o[3]=p*c-C*u,o[8]=f*u+x*c,o[9]=v*u+_*c,o[10]=g*u+y*c,o[11]=p*u+C*c,o}}),7089:(function(a){a.exports=i;function i(o,s,l){var u=Math.sin(l),c=Math.cos(l),f=s[0],v=s[1],g=s[2],p=s[3],x=s[4],_=s[5],y=s[6],C=s[7];return s!==o&&(o[8]=s[8],o[9]=s[9],o[10]=s[10],o[11]=s[11],o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15]),o[0]=f*c+x*u,o[1]=v*c+_*u,o[2]=g*c+y*u,o[3]=p*c+C*u,o[4]=x*c-f*u,o[5]=_*c-v*u,o[6]=y*c-g*u,o[7]=C*c-p*u,o}}),2504:(function(a){a.exports=i;function i(o,s,l){var u=l[0],c=l[1],f=l[2];return o[0]=s[0]*u,o[1]=s[1]*u,o[2]=s[2]*u,o[3]=s[3]*u,o[4]=s[4]*c,o[5]=s[5]*c,o[6]=s[6]*c,o[7]=s[7]*c,o[8]=s[8]*f,o[9]=s[9]*f,o[10]=s[10]*f,o[11]=s[11]*f,o[12]=s[12],o[13]=s[13],o[14]=s[14],o[15]=s[15],o}}),7656:(function(a){a.exports=i;function i(o,s,l){var u=l[0],c=l[1],f=l[2],v,g,p,x,_,y,C,k,S,L,w,A;return s===o?(o[12]=s[0]*u+s[4]*c+s[8]*f+s[12],o[13]=s[1]*u+s[5]*c+s[9]*f+s[13],o[14]=s[2]*u+s[6]*c+s[10]*f+s[14],o[15]=s[3]*u+s[7]*c+s[11]*f+s[15]):(v=s[0],g=s[1],p=s[2],x=s[3],_=s[4],y=s[5],C=s[6],k=s[7],S=s[8],L=s[9],w=s[10],A=s[11],o[0]=v,o[1]=g,o[2]=p,o[3]=x,o[4]=_,o[5]=y,o[6]=C,o[7]=k,o[8]=S,o[9]=L,o[10]=w,o[11]=A,o[12]=v*u+_*c+S*f+s[12],o[13]=g*u+y*c+L*f+s[13],o[14]=p*u+C*c+w*f+s[14],o[15]=x*u+k*c+A*f+s[15]),o}}),5665:(function(a){a.exports=i;function i(o,s){if(o===s){var l=s[1],u=s[2],c=s[3],f=s[6],v=s[7],g=s[11];o[1]=s[4],o[2]=s[8],o[3]=s[12],o[4]=l,o[6]=s[9],o[7]=s[13],o[8]=u,o[9]=f,o[11]=s[14],o[12]=c,o[13]=v,o[14]=g}else o[0]=s[0],o[1]=s[4],o[2]=s[8],o[3]=s[12],o[4]=s[1],o[5]=s[5],o[6]=s[9],o[7]=s[13],o[8]=s[2],o[9]=s[6],o[10]=s[10],o[11]=s[14],o[12]=s[3],o[13]=s[7],o[14]=s[11],o[15]=s[15];return o}}),7626:(function(a,i,o){\"use strict\";var s=o(2642),l=o(9346);a.exports=g;function u(p,x){for(var _=[0,0,0,0],y=0;y<4;++y)for(var C=0;C<4;++C)_[C]+=p[4*y+C]*x[y];return _}function c(p,x,_,y,C){for(var k=u(y,u(_,u(x,[p[0],p[1],p[2],1]))),S=0;S<3;++S)k[S]/=k[3];return[.5*C[0]*(1+k[0]),.5*C[1]*(1-k[1])]}function f(p,x){if(p.length===2){for(var _=0,y=0,C=0;C<2;++C)_+=Math.pow(x[C]-p[0][C],2),y+=Math.pow(x[C]-p[1][C],2);return _=Math.sqrt(_),y=Math.sqrt(y),_+y<1e-6?[1,0]:[y/(_+y),_/(y+_)]}else if(p.length===3){var k=[0,0];return l(p[0],p[1],p[2],x,k),s(p,k)}return[]}function v(p,x){for(var _=[0,0,0],y=0;y<p.length;++y)for(var C=p[y],k=x[y],S=0;S<3;++S)_[S]+=k*C[S];return _}function g(p,x,_,y,C,k){if(p.length===1)return[0,p[0].slice()];for(var S=new Array(p.length),L=0;L<p.length;++L)S[L]=c(p[L],_,y,C,k);for(var w=0,A=1/0,L=0;L<S.length;++L){for(var T=0,m=0;m<2;++m)T+=Math.pow(S[L][m]-x[m],2);T<A&&(A=T,w=L)}for(var F=f(S,x),E=0,L=0;L<3;++L){if(F[L]<-.001||F[L]>1.0001)return null;E+=F[L]}return Math.abs(E-1)>.001?null:[w,v(p,F),F]}}),840:(function(a,i,o){var s=o(3236),l=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position, normal;\n",
@@ -1115,7 +1118,7 @@
        "  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n",
        "\n",
        "  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n",
-       "}`]),h=s([`precision highp float;\n",
+       "}`]),v=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "bool outOfRange(float a, float b, float p) {\n",
@@ -1159,7 +1162,7 @@
        "  gl_PointSize = pointSize;\n",
        "  f_color = color;\n",
        "  f_uv = uv;\n",
-       "}`]),d=s([`precision highp float;\n",
+       "}`]),g=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "uniform sampler2D texture;\n",
@@ -1174,7 +1177,7 @@
        "    discard;\n",
        "  }\n",
        "  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n",
-       "}`]),v=s([`precision highp float;\n",
+       "}`]),p=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position;\n",
@@ -1189,7 +1192,7 @@
        "  gl_Position = projection * (view * (model * vec4(position, 1.0)));\n",
        "  f_id        = id;\n",
        "  f_position  = position;\n",
-       "}`]),m=s([`precision highp float;\n",
+       "}`]),x=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "bool outOfRange(float a, float b, float p) {\n",
@@ -1222,7 +1225,7 @@
        "  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
        "\n",
        "  gl_FragColor = vec4(pickId, f_id.xyz);\n",
-       "}`]),b=s([`precision highp float;\n",
+       "}`]),_=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "bool outOfRange(float a, float b, float p) {\n",
@@ -1265,7 +1268,7 @@
        "  }\n",
        "  f_id         = id;\n",
        "  f_position   = position;\n",
-       "}`]),p=s([`precision highp float;\n",
+       "}`]),y=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position;\n",
@@ -1274,7 +1277,7 @@
        "\n",
        "void main() {\n",
        "  gl_Position = projection * (view * (model * vec4(position, 1.0)));\n",
-       "}`]),k=s([`precision highp float;\n",
+       "}`]),C=s([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "uniform vec3 contourColor;\n",
@@ -1282,7 +1285,7 @@
        "void main() {\n",
        "  gl_FragColor = vec4(contourColor, 1.0);\n",
        "}\n",
-       "`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},a.wireShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},a.pointShader={vertex:h,fragment:d,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},a.pickShader={vertex:v,fragment:m,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},a.pointPickShader={vertex:b,fragment:m,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},a.contourShader={vertex:p,fragment:k,attributes:[{name:\"position\",type:\"vec3\"}]}}),7201:(function(i,a,o){\"use strict\";var s=1e-6,l=1e-6,u=o(9405),c=o(2762),f=o(8116),h=o(7766),d=o(8406),v=o(6760),m=o(7608),b=o(9618),p=o(6729),k=o(7765),M=o(1888),T=o(840),L=o(7626),x=T.meshShader,C=T.wireShader,S=T.pointShader,g=T.pickShader,P=T.pointPickShader,E=T.contourShader,z=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function q(ge,ie,Se,ke,be,Be,Le,me,Pe,ce,He,lt,mt,Ht,at,ct,ar,Vt,rr,tt,je,Ue,Ae,rt,St,Tt,dt){this.gl=ge,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=ie,this.dirty=!0,this.triShader=Se,this.lineShader=ke,this.pointShader=be,this.pickShader=Be,this.pointPickShader=Le,this.contourShader=me,this.trianglePositions=Pe,this.triangleColors=He,this.triangleNormals=mt,this.triangleUVs=lt,this.triangleIds=ce,this.triangleVAO=Ht,this.triangleCount=0,this.lineWidth=1,this.edgePositions=at,this.edgeColors=ar,this.edgeUVs=Vt,this.edgeIds=ct,this.edgeVAO=rr,this.edgeCount=0,this.pointPositions=tt,this.pointColors=Ue,this.pointUVs=Ae,this.pointSizes=rt,this.pointIds=je,this.pointVAO=St,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=Tt,this.contourVAO=dt,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=z,this._view=z,this._projection=z,this._resolution=[1,1]}var V=q.prototype;V.isOpaque=function(){return!this.hasAlpha},V.isTransparent=function(){return this.hasAlpha},V.pickSlots=1,V.setPickBase=function(ge){this.pickId=ge};function G(ge,ie){if(!ie||!ie.length)return 1;for(var Se=0;Se<ie.length;++Se){if(ie.length<2)return 1;if(ie[Se][0]===ge)return ie[Se][1];if(ie[Se][0]>ge&&Se>0){var ke=(ie[Se][0]-ge)/(ie[Se][0]-ie[Se-1][0]);return ie[Se][1]*(1-ke)+ke*ie[Se-1][1]}}return 1}function Z(ge,ie){for(var Se=p({colormap:ge,nshades:256,format:\"rgba\"}),ke=new Uint8Array(256*4),be=0;be<256;++be){for(var Be=Se[be],Le=0;Le<3;++Le)ke[4*be+Le]=Be[Le];ie?ke[4*be+3]=255*G(be/255,ie):ke[4*be+3]=255*Be[3]}return b(ke,[256,256,4],[4,0,1])}function j(ge){for(var ie=ge.length,Se=new Array(ie),ke=0;ke<ie;++ke)Se[ke]=ge[ke][2];return Se}V.highlight=function(ge){if(!ge||!this.contourEnable){this.contourCount=0;return}for(var ie=k(this.cells,this.intensity,ge.intensity),Se=ie.cells,ke=ie.vertexIds,be=ie.vertexWeights,Be=Se.length,Le=M.mallocFloat32(6*Be),me=0,Pe=0;Pe<Be;++Pe)for(var ce=Se[Pe],He=0;He<2;++He){var lt=ce[0];ce.length===2&&(lt=ce[He]);for(var mt=ke[lt][0],Ht=ke[lt][1],at=be[lt],ct=1-at,ar=this.positions[mt],Vt=this.positions[Ht],rr=0;rr<3;++rr)Le[me++]=at*ar[rr]+ct*Vt[rr]}this.contourCount=me/3|0,this.contourPositions.update(Le.subarray(0,me)),M.free(Le)},V.update=function(ge){ge=ge||{};var ie=this.gl;this.dirty=!0,\"contourEnable\"in ge&&(this.contourEnable=ge.contourEnable),\"contourColor\"in ge&&(this.contourColor=ge.contourColor),\"lineWidth\"in ge&&(this.lineWidth=ge.lineWidth),\"lightPosition\"in ge&&(this.lightPosition=ge.lightPosition),this.hasAlpha=!1,\"opacity\"in ge&&(this.opacity=ge.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in ge&&(this.opacityscale=ge.opacityscale,this.hasAlpha=!0),\"ambient\"in ge&&(this.ambientLight=ge.ambient),\"diffuse\"in ge&&(this.diffuseLight=ge.diffuse),\"specular\"in ge&&(this.specularLight=ge.specular),\"roughness\"in ge&&(this.roughness=ge.roughness),\"fresnel\"in ge&&(this.fresnel=ge.fresnel),ge.texture?(this.texture.dispose(),this.texture=h(ie,ge.texture)):ge.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=ie.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=ie.LINEAR,this.texture.setPixels(Z(ge.colormap,this.opacityscale)),this.texture.generateMipmap());var Se=ge.cells,ke=ge.positions;if(!(!ke||!Se)){var be=[],Be=[],Le=[],me=[],Pe=[],ce=[],He=[],lt=[],mt=[],Ht=[],at=[],ct=[],ar=[],Vt=[];this.cells=Se,this.positions=ke;var rr=ge.vertexNormals,tt=ge.cellNormals,je=ge.vertexNormalsEpsilon===void 0?s:ge.vertexNormalsEpsilon,Ue=ge.faceNormalsEpsilon===void 0?l:ge.faceNormalsEpsilon;ge.useFacetNormals&&!tt&&(tt=d.faceNormals(Se,ke,Ue)),!tt&&!rr&&(rr=d.vertexNormals(Se,ke,je));var Ae=ge.vertexColors,rt=ge.cellColors,St=ge.meshColor||[1,1,1,1],Tt=ge.vertexUVs,dt=ge.vertexIntensity,Et=ge.cellUVs,pt=ge.cellIntensity,jt=1/0,or=-1/0;if(!Tt&&!Et)if(dt)if(ge.vertexIntensityBounds)jt=+ge.vertexIntensityBounds[0],or=+ge.vertexIntensityBounds[1];else for(var mr=0;mr<dt.length;++mr){var Ar=dt[mr];jt=Math.min(jt,Ar),or=Math.max(or,Ar)}else if(pt)if(ge.cellIntensityBounds)jt=+ge.cellIntensityBounds[0],or=+ge.cellIntensityBounds[1];else for(var mr=0;mr<pt.length;++mr){var Ar=pt[mr];jt=Math.min(jt,Ar),or=Math.max(or,Ar)}else for(var mr=0;mr<ke.length;++mr){var Ar=ke[mr][2];jt=Math.min(jt,Ar),or=Math.max(or,Ar)}dt?this.intensity=dt:pt?this.intensity=pt:this.intensity=j(ke),this.pickVertex=!(pt||rt);var ei=ge.pointSizes,qr=ge.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var mr=0;mr<ke.length;++mr)for(var jr=ke[mr],gt=0;gt<3;++gt)isNaN(jr[gt])||!isFinite(jr[gt])||(this.bounds[0][gt]=Math.min(this.bounds[0][gt],jr[gt]),this.bounds[1][gt]=Math.max(this.bounds[1][gt],jr[gt]));var Ge=0,Je=0,We=0;e:for(var mr=0;mr<Se.length;++mr){var et=Se[mr];switch(et.length){case 1:for(var xt=et[0],jr=ke[xt],gt=0;gt<3;++gt)if(isNaN(jr[gt])||!isFinite(jr[gt]))continue e;Ht.push(jr[0],jr[1],jr[2]);var At;Ae?At=Ae[xt]:rt?At=rt[mr]:At=St,this.opacityscale&&dt?Be.push(At[0],At[1],At[2],this.opacity*G((dt[xt]-jt)/(or-jt),this.opacityscale)):At.length===3?at.push(At[0],At[1],At[2],this.opacity):(at.push(At[0],At[1],At[2],At[3]*this.opacity),At[3]<1&&(this.hasAlpha=!0));var Kt;Tt?Kt=Tt[xt]:dt?Kt=[(dt[xt]-jt)/(or-jt),0]:Et?Kt=Et[mr]:pt?Kt=[(pt[mr]-jt)/(or-jt),0]:Kt=[(jr[2]-jt)/(or-jt),0],ct.push(Kt[0],Kt[1]),ei?ar.push(ei[xt]):ar.push(qr),Vt.push(mr),We+=1;break;case 2:for(var gt=0;gt<2;++gt)for(var xt=et[gt],jr=ke[xt],Qt=0;Qt<3;++Qt)if(isNaN(jr[Qt])||!isFinite(jr[Qt]))continue e;for(var gt=0;gt<2;++gt){var xt=et[gt],jr=ke[xt];ce.push(jr[0],jr[1],jr[2]);var At;Ae?At=Ae[xt]:rt?At=rt[mr]:At=St,this.opacityscale&&dt?Be.push(At[0],At[1],At[2],this.opacity*G((dt[xt]-jt)/(or-jt),this.opacityscale)):At.length===3?He.push(At[0],At[1],At[2],this.opacity):(He.push(At[0],At[1],At[2],At[3]*this.opacity),At[3]<1&&(this.hasAlpha=!0));var Kt;Tt?Kt=Tt[xt]:dt?Kt=[(dt[xt]-jt)/(or-jt),0]:Et?Kt=Et[mr]:pt?Kt=[(pt[mr]-jt)/(or-jt),0]:Kt=[(jr[2]-jt)/(or-jt),0],lt.push(Kt[0],Kt[1]),mt.push(mr)}Je+=1;break;case 3:for(var gt=0;gt<3;++gt)for(var xt=et[gt],jr=ke[xt],Qt=0;Qt<3;++Qt)if(isNaN(jr[Qt])||!isFinite(jr[Qt]))continue e;for(var gt=0;gt<3;++gt){var xt=et[2-gt],jr=ke[xt];be.push(jr[0],jr[1],jr[2]);var At;Ae?At=Ae[xt]:rt?At=rt[mr]:At=St,At?this.opacityscale&&dt?Be.push(At[0],At[1],At[2],this.opacity*G((dt[xt]-jt)/(or-jt),this.opacityscale)):At.length===3?Be.push(At[0],At[1],At[2],this.opacity):(Be.push(At[0],At[1],At[2],At[3]*this.opacity),At[3]<1&&(this.hasAlpha=!0)):Be.push(.5,.5,.5,1);var Kt;Tt?Kt=Tt[xt]:dt?Kt=[(dt[xt]-jt)/(or-jt),0]:Et?Kt=Et[mr]:pt?Kt=[(pt[mr]-jt)/(or-jt),0]:Kt=[(jr[2]-jt)/(or-jt),0],me.push(Kt[0],Kt[1]);var Mr;rr?Mr=rr[xt]:Mr=tt[mr],Le.push(Mr[0],Mr[1],Mr[2]),Pe.push(mr)}Ge+=1;break;default:break}}this.pointCount=We,this.edgeCount=Je,this.triangleCount=Ge,this.pointPositions.update(Ht),this.pointColors.update(at),this.pointUVs.update(ct),this.pointSizes.update(ar),this.pointIds.update(new Uint32Array(Vt)),this.edgePositions.update(ce),this.edgeColors.update(He),this.edgeUVs.update(lt),this.edgeIds.update(new Uint32Array(mt)),this.trianglePositions.update(be),this.triangleColors.update(Be),this.triangleUVs.update(me),this.triangleNormals.update(Le),this.triangleIds.update(new Uint32Array(Pe))}},V.drawTransparent=V.draw=function(ge){ge=ge||{};for(var ie=this.gl,Se=ge.model||z,ke=ge.view||z,be=ge.projection||z,Be=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],Le=0;Le<3;++Le)Be[0][Le]=Math.max(Be[0][Le],this.clipBounds[0][Le]),Be[1][Le]=Math.min(Be[1][Le],this.clipBounds[1][Le]);var me={model:Se,view:ke,projection:be,inverseModel:z.slice(),clipBounds:Be,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};me.inverseModel=m(me.inverseModel,me.model),ie.disable(ie.CULL_FACE),this.texture.bind(0);var Pe=new Array(16);v(Pe,me.view,me.model),v(Pe,me.projection,Pe),m(Pe,Pe);for(var Le=0;Le<3;++Le)me.eyePosition[Le]=Pe[12+Le]/Pe[15];for(var ce=Pe[15],Le=0;Le<3;++Le)ce+=this.lightPosition[Le]*Pe[4*Le+3];for(var Le=0;Le<3;++Le){for(var He=Pe[12+Le],lt=0;lt<3;++lt)He+=Pe[4*lt+Le]*this.lightPosition[lt];me.lightPosition[Le]=He/ce}if(this.triangleCount>0){var mt=this.triShader;mt.bind(),mt.uniforms=me,this.triangleVAO.bind(),ie.drawArrays(ie.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var mt=this.lineShader;mt.bind(),mt.uniforms=me,this.edgeVAO.bind(),ie.lineWidth(this.lineWidth*this.pixelRatio),ie.drawArrays(ie.LINES,0,this.edgeCount*2),this.edgeVAO.unbind()}if(this.pointCount>0){var mt=this.pointShader;mt.bind(),mt.uniforms=me,this.pointVAO.bind(),ie.drawArrays(ie.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var mt=this.contourShader;mt.bind(),mt.uniforms=me,this.contourVAO.bind(),ie.drawArrays(ie.LINES,0,this.contourCount),this.contourVAO.unbind()}},V.drawPick=function(ge){ge=ge||{};for(var ie=this.gl,Se=ge.model||z,ke=ge.view||z,be=ge.projection||z,Be=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],Le=0;Le<3;++Le)Be[0][Le]=Math.max(Be[0][Le],this.clipBounds[0][Le]),Be[1][Le]=Math.min(Be[1][Le],this.clipBounds[1][Le]);this._model=[].slice.call(Se),this._view=[].slice.call(ke),this._projection=[].slice.call(be),this._resolution=[ie.drawingBufferWidth,ie.drawingBufferHeight];var me={model:Se,view:ke,projection:be,clipBounds:Be,pickId:this.pickId/255},Pe=this.pickShader;if(Pe.bind(),Pe.uniforms=me,this.triangleCount>0&&(this.triangleVAO.bind(),ie.drawArrays(ie.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),ie.lineWidth(this.lineWidth*this.pixelRatio),ie.drawArrays(ie.LINES,0,this.edgeCount*2),this.edgeVAO.unbind()),this.pointCount>0){var Pe=this.pointPickShader;Pe.bind(),Pe.uniforms=me,this.pointVAO.bind(),ie.drawArrays(ie.POINTS,0,this.pointCount),this.pointVAO.unbind()}},V.pick=function(ge){if(!ge||ge.id!==this.pickId)return null;for(var ie=ge.value[0]+256*ge.value[1]+65536*ge.value[2],Se=this.cells[ie],ke=this.positions,be=new Array(Se.length),Be=0;Be<Se.length;++Be)be[Be]=ke[Se[Be]];var Le=ge.coord[0],me=ge.coord[1];if(!this.pickVertex){var Pe=this.positions[Se[0]],ce=this.positions[Se[1]],He=this.positions[Se[2]],lt=[(Pe[0]+ce[0]+He[0])/3,(Pe[1]+ce[1]+He[1])/3,(Pe[2]+ce[2]+He[2])/3];return{_cellCenter:!0,position:[Le,me],index:ie,cell:Se,cellId:ie,intensity:this.intensity[ie],dataCoordinate:lt}}var mt=L(be,[Le*this.pixelRatio,this._resolution[1]-me*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!mt)return null;for(var Ht=mt[2],at=0,Be=0;Be<Se.length;++Be)at+=Ht[Be]*this.intensity[Se[Be]];return{position:mt[1],index:Se[mt[0]],cell:Se,cellId:ie,intensity:at,dataCoordinate:this.positions[Se[mt[0]]]}},V.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()};function N(ge){var ie=u(ge,x.vertex,x.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie.attributes.normal.location=4,ie}function H(ge){var ie=u(ge,C.vertex,C.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie}function re(ge){var ie=u(ge,S.vertex,S.fragment);return ie.attributes.position.location=0,ie.attributes.color.location=2,ie.attributes.uv.location=3,ie.attributes.pointSize.location=4,ie}function ae(ge){var ie=u(ge,g.vertex,g.fragment);return ie.attributes.position.location=0,ie.attributes.id.location=1,ie}function _e(ge){var ie=u(ge,P.vertex,P.fragment);return ie.attributes.position.location=0,ie.attributes.id.location=1,ie.attributes.pointSize.location=4,ie}function Ee(ge){var ie=u(ge,E.vertex,E.fragment);return ie.attributes.position.location=0,ie}function Ce(ge,ie){arguments.length===1&&(ie=ge,ge=ie.gl);var Se=ge.getExtension(\"OES_standard_derivatives\")||ge.getExtension(\"MOZ_OES_standard_derivatives\")||ge.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!Se)throw new Error(\"derivatives not supported\");var ke=N(ge),be=H(ge),Be=re(ge),Le=ae(ge),me=_e(ge),Pe=Ee(ge),ce=h(ge,b(new Uint8Array([255,255,255,255]),[1,1,4]));ce.generateMipmap(),ce.minFilter=ge.LINEAR_MIPMAP_LINEAR,ce.magFilter=ge.LINEAR;var He=c(ge),lt=c(ge),mt=c(ge),Ht=c(ge),at=c(ge),ct=f(ge,[{buffer:He,type:ge.FLOAT,size:3},{buffer:at,type:ge.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:lt,type:ge.FLOAT,size:4},{buffer:mt,type:ge.FLOAT,size:2},{buffer:Ht,type:ge.FLOAT,size:3}]),ar=c(ge),Vt=c(ge),rr=c(ge),tt=c(ge),je=f(ge,[{buffer:ar,type:ge.FLOAT,size:3},{buffer:tt,type:ge.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Vt,type:ge.FLOAT,size:4},{buffer:rr,type:ge.FLOAT,size:2}]),Ue=c(ge),Ae=c(ge),rt=c(ge),St=c(ge),Tt=c(ge),dt=f(ge,[{buffer:Ue,type:ge.FLOAT,size:3},{buffer:Tt,type:ge.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Ae,type:ge.FLOAT,size:4},{buffer:rt,type:ge.FLOAT,size:2},{buffer:St,type:ge.FLOAT,size:1}]),Et=c(ge),pt=f(ge,[{buffer:Et,type:ge.FLOAT,size:3}]),jt=new q(ge,ce,ke,be,Be,Le,me,Pe,He,at,lt,mt,Ht,ct,ar,tt,Vt,rr,je,Ue,Tt,Ae,rt,St,dt,Et,pt);return jt.update(ie),jt}i.exports=Ce}),4437:(function(i,a,o){\"use strict\";i.exports=d;var s=o(3025),l=o(6296),u=o(351),c=o(8512),f=o(24),h=o(7520);function d(v,m){v=v||document.body,m=m||{};var b=[.01,1/0];\"distanceLimits\"in m&&(b[0]=m.distanceLimits[0],b[1]=m.distanceLimits[1]),\"zoomMin\"in m&&(b[0]=m.zoomMin),\"zoomMax\"in m&&(b[1]=m.zoomMax);var p=l({center:m.center||[0,0,0],up:m.up||[0,1,0],eye:m.eye||[0,0,10],mode:m.mode||\"orbit\",distanceLimits:b}),k=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],M=0,T=v.clientWidth,L=v.clientHeight,x={keyBindingMode:\"rotate\",enableWheel:!0,view:p,element:v,delay:m.delay||16,rotateSpeed:m.rotateSpeed||1,zoomSpeed:m.zoomSpeed||1,translateSpeed:m.translateSpeed||1,flipX:!!m.flipX,flipY:!!m.flipY,modes:p.modes,_ortho:m._ortho||m.projection&&m.projection.type===\"orthographic\"||!1,tick:function(){var C=s(),S=this.delay,g=C-2*S;p.idle(C-S),p.recalcMatrix(g),p.flush(C-(100+S*2));for(var P=!0,E=p.computedMatrix,z=0;z<16;++z)P=P&&k[z]===E[z],k[z]=E[z];var q=v.clientWidth===T&&v.clientHeight===L;return T=v.clientWidth,L=v.clientHeight,P?!q:(M=Math.exp(p.computedRadius[0]),!0)},lookAt:function(C,S,g){p.lookAt(p.lastT(),C,S,g)},rotate:function(C,S,g){p.rotate(p.lastT(),C,S,g)},pan:function(C,S,g){p.pan(p.lastT(),C,S,g)},translate:function(C,S,g){p.translate(p.lastT(),C,S,g)}};return Object.defineProperties(x,{matrix:{get:function(){return p.computedMatrix},set:function(C){return p.setMatrix(p.lastT(),C),p.computedMatrix},enumerable:!0},mode:{get:function(){return p.getMode()},set:function(C){var S=p.computedUp.slice(),g=p.computedEye.slice(),P=p.computedCenter.slice();if(p.setMode(C),C===\"turntable\"){var E=s();p._active.lookAt(E,g,P,S),p._active.lookAt(E+500,g,P,[0,0,1]),p._active.flush(E)}return p.getMode()},enumerable:!0},center:{get:function(){return p.computedCenter},set:function(C){return p.lookAt(p.lastT(),null,C),p.computedCenter},enumerable:!0},eye:{get:function(){return p.computedEye},set:function(C){return p.lookAt(p.lastT(),C),p.computedEye},enumerable:!0},up:{get:function(){return p.computedUp},set:function(C){return p.lookAt(p.lastT(),null,null,C),p.computedUp},enumerable:!0},distance:{get:function(){return M},set:function(C){return p.setDistance(p.lastT(),C),C},enumerable:!0},distanceLimits:{get:function(){return p.getDistanceLimits(b)},set:function(C){return p.setDistanceLimits(C),C},enumerable:!0}}),v.addEventListener(\"contextmenu\",function(C){return C.preventDefault(),!1}),x._lastX=-1,x._lastY=-1,x._lastMods={shift:!1,control:!1,alt:!1,meta:!1},x.enableMouseListeners=function(){x.mouseListener=u(v,C),v.addEventListener(\"touchstart\",function(S){var g=f(S.changedTouches[0],v);C(0,g[0],g[1],x._lastMods),C(1,g[0],g[1],x._lastMods)},h?{passive:!0}:!1),v.addEventListener(\"touchmove\",function(S){var g=f(S.changedTouches[0],v);C(1,g[0],g[1],x._lastMods),S.preventDefault()},h?{passive:!1}:!1),v.addEventListener(\"touchend\",function(S){C(0,x._lastX,x._lastY,x._lastMods)},h?{passive:!0}:!1);function C(S,g,P,E){var z=x.keyBindingMode;if(z!==!1){var q=z===\"rotate\",V=z===\"pan\",G=z===\"zoom\",Z=!!E.control,j=!!E.alt,N=!!E.shift,H=!!(S&1),re=!!(S&2),ae=!!(S&4),_e=1/v.clientHeight,Ee=_e*(g-x._lastX),Ce=_e*(P-x._lastY),ge=x.flipX?1:-1,ie=x.flipY?1:-1,Se=Math.PI*x.rotateSpeed,ke=s();if(x._lastX!==-1&&x._lastY!==-1&&((q&&H&&!Z&&!j&&!N||H&&!Z&&!j&&N)&&p.rotate(ke,ge*Se*Ee,-ie*Se*Ce,0),(V&&H&&!Z&&!j&&!N||re||H&&Z&&!j&&!N)&&p.pan(ke,-x.translateSpeed*Ee*M,x.translateSpeed*Ce*M,0),G&&H&&!Z&&!j&&!N||ae||H&&!Z&&j&&!N)){var be=-x.zoomSpeed*Ce/window.innerHeight*(ke-p.lastT())*100;p.pan(ke,0,0,M*(Math.exp(be)-1))}return x._lastX=g,x._lastY=P,x._lastMods=E,!0}}x.wheelListener=c(v,function(S,g){if(x.keyBindingMode!==!1&&x.enableWheel){var P=x.flipX?1:-1,E=x.flipY?1:-1,z=s();if(Math.abs(S)>Math.abs(g))p.rotate(z,0,0,-S*P*Math.PI*x.rotateSpeed/window.innerWidth);else if(!x._ortho){var q=-x.zoomSpeed*E*g/window.innerHeight*(z-p.lastT())/20;p.pan(z,0,0,M*(Math.exp(q)-1))}}},!0)},x.enableMouseListeners(),x}}),799:(function(i,a,o){var s=o(3236),l=o(9405),u=s([`precision mediump float;\n",
+       "`]);i.meshShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},i.wireShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},i.pointShader={vertex:v,fragment:g,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},i.pickShader={vertex:p,fragment:x,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},i.pointPickShader={vertex:_,fragment:x,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},i.contourShader={vertex:y,fragment:C,attributes:[{name:\"position\",type:\"vec3\"}]}}),7201:(function(a,i,o){\"use strict\";var s=1e-6,l=1e-6,u=o(9405),c=o(2762),f=o(8116),v=o(7766),g=o(8406),p=o(6760),x=o(7608),_=o(9618),y=o(6729),C=o(7765),k=o(1888),S=o(840),L=o(7626),w=S.meshShader,A=S.wireShader,T=S.pointShader,m=S.pickShader,F=S.pointPickShader,E=S.contourShader,I=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function R(ue,K,_e,ne,ae,pe,ie,le,me,be,De,Ge,ht,it,Oe,ze,ct,et,qe,we,Le,Ke,Te,Ve,$e,ot,tt){this.gl=ue,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=K,this.dirty=!0,this.triShader=_e,this.lineShader=ne,this.pointShader=ae,this.pickShader=pe,this.pointPickShader=ie,this.contourShader=le,this.trianglePositions=me,this.triangleColors=De,this.triangleNormals=ht,this.triangleUVs=Ge,this.triangleIds=be,this.triangleVAO=it,this.triangleCount=0,this.lineWidth=1,this.edgePositions=Oe,this.edgeColors=ct,this.edgeUVs=et,this.edgeIds=ze,this.edgeVAO=qe,this.edgeCount=0,this.pointPositions=we,this.pointColors=Ke,this.pointUVs=Te,this.pointSizes=Ve,this.pointIds=Le,this.pointVAO=$e,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=ot,this.contourVAO=tt,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=I,this._view=I,this._projection=I,this._resolution=[1,1]}var B=R.prototype;B.isOpaque=function(){return!this.hasAlpha},B.isTransparent=function(){return this.hasAlpha},B.pickSlots=1,B.setPickBase=function(ue){this.pickId=ue};function O(ue,K){if(!K||!K.length)return 1;for(var _e=0;_e<K.length;++_e){if(K.length<2)return 1;if(K[_e][0]===ue)return K[_e][1];if(K[_e][0]>ue&&_e>0){var ne=(K[_e][0]-ue)/(K[_e][0]-K[_e-1][0]);return K[_e][1]*(1-ne)+ne*K[_e-1][1]}}return 1}function V(ue,K){for(var _e=y({colormap:ue,nshades:256,format:\"rgba\"}),ne=new Uint8Array(256*4),ae=0;ae<256;++ae){for(var pe=_e[ae],ie=0;ie<3;++ie)ne[4*ae+ie]=pe[ie];K?ne[4*ae+3]=255*O(ae/255,K):ne[4*ae+3]=255*pe[3]}return _(ne,[256,256,4],[4,0,1])}function j(ue){for(var K=ue.length,_e=new Array(K),ne=0;ne<K;++ne)_e[ne]=ue[ne][2];return _e}B.highlight=function(ue){if(!ue||!this.contourEnable){this.contourCount=0;return}for(var K=C(this.cells,this.intensity,ue.intensity),_e=K.cells,ne=K.vertexIds,ae=K.vertexWeights,pe=_e.length,ie=k.mallocFloat32(6*pe),le=0,me=0;me<pe;++me)for(var be=_e[me],De=0;De<2;++De){var Ge=be[0];be.length===2&&(Ge=be[De]);for(var ht=ne[Ge][0],it=ne[Ge][1],Oe=ae[Ge],ze=1-Oe,ct=this.positions[ht],et=this.positions[it],qe=0;qe<3;++qe)ie[le++]=Oe*ct[qe]+ze*et[qe]}this.contourCount=le/3|0,this.contourPositions.update(ie.subarray(0,le)),k.free(ie)},B.update=function(ue){ue=ue||{};var K=this.gl;this.dirty=!0,\"contourEnable\"in ue&&(this.contourEnable=ue.contourEnable),\"contourColor\"in ue&&(this.contourColor=ue.contourColor),\"lineWidth\"in ue&&(this.lineWidth=ue.lineWidth),\"lightPosition\"in ue&&(this.lightPosition=ue.lightPosition),this.hasAlpha=!1,\"opacity\"in ue&&(this.opacity=ue.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in ue&&(this.opacityscale=ue.opacityscale,this.hasAlpha=!0),\"ambient\"in ue&&(this.ambientLight=ue.ambient),\"diffuse\"in ue&&(this.diffuseLight=ue.diffuse),\"specular\"in ue&&(this.specularLight=ue.specular),\"roughness\"in ue&&(this.roughness=ue.roughness),\"fresnel\"in ue&&(this.fresnel=ue.fresnel),ue.texture?(this.texture.dispose(),this.texture=v(K,ue.texture)):ue.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=K.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=K.LINEAR,this.texture.setPixels(V(ue.colormap,this.opacityscale)),this.texture.generateMipmap());var _e=ue.cells,ne=ue.positions;if(!(!ne||!_e)){var ae=[],pe=[],ie=[],le=[],me=[],be=[],De=[],Ge=[],ht=[],it=[],Oe=[],ze=[],ct=[],et=[];this.cells=_e,this.positions=ne;var qe=ue.vertexNormals,we=ue.cellNormals,Le=ue.vertexNormalsEpsilon===void 0?s:ue.vertexNormalsEpsilon,Ke=ue.faceNormalsEpsilon===void 0?l:ue.faceNormalsEpsilon;ue.useFacetNormals&&!we&&(we=g.faceNormals(_e,ne,Ke)),!we&&!qe&&(qe=g.vertexNormals(_e,ne,Le));var Te=ue.vertexColors,Ve=ue.cellColors,$e=ue.meshColor||[1,1,1,1],ot=ue.vertexUVs,tt=ue.vertexIntensity,nt=ue.cellUVs,We=ue.cellIntensity,mt=1/0,bt=-1/0;if(!ot&&!nt)if(tt)if(ue.vertexIntensityBounds)mt=+ue.vertexIntensityBounds[0],bt=+ue.vertexIntensityBounds[1];else for(var Yt=0;Yt<tt.length;++Yt){var jt=tt[Yt];mt=Math.min(mt,jt),bt=Math.max(bt,jt)}else if(We)if(ue.cellIntensityBounds)mt=+ue.cellIntensityBounds[0],bt=+ue.cellIntensityBounds[1];else for(var Yt=0;Yt<We.length;++Yt){var jt=We[Yt];mt=Math.min(mt,jt),bt=Math.max(bt,jt)}else for(var Yt=0;Yt<ne.length;++Yt){var jt=ne[Yt][2];mt=Math.min(mt,jt),bt=Math.max(bt,jt)}tt?this.intensity=tt:We?this.intensity=We:this.intensity=j(ne),this.pickVertex=!(We||Ve);var mr=ue.pointSizes,or=ue.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var Yt=0;Yt<ne.length;++Yt)for(var fr=ne[Yt],Ot=0;Ot<3;++Ot)isNaN(fr[Ot])||!isFinite(fr[Ot])||(this.bounds[0][Ot]=Math.min(this.bounds[0][Ot],fr[Ot]),this.bounds[1][Ot]=Math.max(this.bounds[1][Ot],fr[Ot]));var cr=0,Ct=0,wt=0;e:for(var Yt=0;Yt<_e.length;++Yt){var Tt=_e[Yt];switch(Tt.length){case 1:for(var Mt=Tt[0],fr=ne[Mt],Ot=0;Ot<3;++Ot)if(isNaN(fr[Ot])||!isFinite(fr[Ot]))continue e;it.push(fr[0],fr[1],fr[2]);var Lt;Te?Lt=Te[Mt]:Ve?Lt=Ve[Yt]:Lt=$e,this.opacityscale&&tt?pe.push(Lt[0],Lt[1],Lt[2],this.opacity*O((tt[Mt]-mt)/(bt-mt),this.opacityscale)):Lt.length===3?Oe.push(Lt[0],Lt[1],Lt[2],this.opacity):(Oe.push(Lt[0],Lt[1],Lt[2],Lt[3]*this.opacity),Lt[3]<1&&(this.hasAlpha=!0));var ft;ot?ft=ot[Mt]:tt?ft=[(tt[Mt]-mt)/(bt-mt),0]:nt?ft=nt[Yt]:We?ft=[(We[Yt]-mt)/(bt-mt),0]:ft=[(fr[2]-mt)/(bt-mt),0],ze.push(ft[0],ft[1]),mr?ct.push(mr[Mt]):ct.push(or),et.push(Yt),wt+=1;break;case 2:for(var Ot=0;Ot<2;++Ot)for(var Mt=Tt[Ot],fr=ne[Mt],kt=0;kt<3;++kt)if(isNaN(fr[kt])||!isFinite(fr[kt]))continue e;for(var Ot=0;Ot<2;++Ot){var Mt=Tt[Ot],fr=ne[Mt];be.push(fr[0],fr[1],fr[2]);var Lt;Te?Lt=Te[Mt]:Ve?Lt=Ve[Yt]:Lt=$e,this.opacityscale&&tt?pe.push(Lt[0],Lt[1],Lt[2],this.opacity*O((tt[Mt]-mt)/(bt-mt),this.opacityscale)):Lt.length===3?De.push(Lt[0],Lt[1],Lt[2],this.opacity):(De.push(Lt[0],Lt[1],Lt[2],Lt[3]*this.opacity),Lt[3]<1&&(this.hasAlpha=!0));var ft;ot?ft=ot[Mt]:tt?ft=[(tt[Mt]-mt)/(bt-mt),0]:nt?ft=nt[Yt]:We?ft=[(We[Yt]-mt)/(bt-mt),0]:ft=[(fr[2]-mt)/(bt-mt),0],Ge.push(ft[0],ft[1]),ht.push(Yt)}Ct+=1;break;case 3:for(var Ot=0;Ot<3;++Ot)for(var Mt=Tt[Ot],fr=ne[Mt],kt=0;kt<3;++kt)if(isNaN(fr[kt])||!isFinite(fr[kt]))continue e;for(var Ot=0;Ot<3;++Ot){var Mt=Tt[2-Ot],fr=ne[Mt];ae.push(fr[0],fr[1],fr[2]);var Lt;Te?Lt=Te[Mt]:Ve?Lt=Ve[Yt]:Lt=$e,Lt?this.opacityscale&&tt?pe.push(Lt[0],Lt[1],Lt[2],this.opacity*O((tt[Mt]-mt)/(bt-mt),this.opacityscale)):Lt.length===3?pe.push(Lt[0],Lt[1],Lt[2],this.opacity):(pe.push(Lt[0],Lt[1],Lt[2],Lt[3]*this.opacity),Lt[3]<1&&(this.hasAlpha=!0)):pe.push(.5,.5,.5,1);var ft;ot?ft=ot[Mt]:tt?ft=[(tt[Mt]-mt)/(bt-mt),0]:nt?ft=nt[Yt]:We?ft=[(We[Yt]-mt)/(bt-mt),0]:ft=[(fr[2]-mt)/(bt-mt),0],le.push(ft[0],ft[1]);var rr;qe?rr=qe[Mt]:rr=we[Yt],ie.push(rr[0],rr[1],rr[2]),me.push(Yt)}cr+=1;break;default:break}}this.pointCount=wt,this.edgeCount=Ct,this.triangleCount=cr,this.pointPositions.update(it),this.pointColors.update(Oe),this.pointUVs.update(ze),this.pointSizes.update(ct),this.pointIds.update(new Uint32Array(et)),this.edgePositions.update(be),this.edgeColors.update(De),this.edgeUVs.update(Ge),this.edgeIds.update(new Uint32Array(ht)),this.trianglePositions.update(ae),this.triangleColors.update(pe),this.triangleUVs.update(le),this.triangleNormals.update(ie),this.triangleIds.update(new Uint32Array(me))}},B.drawTransparent=B.draw=function(ue){ue=ue||{};for(var K=this.gl,_e=ue.model||I,ne=ue.view||I,ae=ue.projection||I,pe=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],ie=0;ie<3;++ie)pe[0][ie]=Math.max(pe[0][ie],this.clipBounds[0][ie]),pe[1][ie]=Math.min(pe[1][ie],this.clipBounds[1][ie]);var le={model:_e,view:ne,projection:ae,inverseModel:I.slice(),clipBounds:pe,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};le.inverseModel=x(le.inverseModel,le.model),K.disable(K.CULL_FACE),this.texture.bind(0);var me=new Array(16);p(me,le.view,le.model),p(me,le.projection,me),x(me,me);for(var ie=0;ie<3;++ie)le.eyePosition[ie]=me[12+ie]/me[15];for(var be=me[15],ie=0;ie<3;++ie)be+=this.lightPosition[ie]*me[4*ie+3];for(var ie=0;ie<3;++ie){for(var De=me[12+ie],Ge=0;Ge<3;++Ge)De+=me[4*Ge+ie]*this.lightPosition[Ge];le.lightPosition[ie]=De/be}if(this.triangleCount>0){var ht=this.triShader;ht.bind(),ht.uniforms=le,this.triangleVAO.bind(),K.drawArrays(K.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var ht=this.lineShader;ht.bind(),ht.uniforms=le,this.edgeVAO.bind(),K.lineWidth(this.lineWidth*this.pixelRatio),K.drawArrays(K.LINES,0,this.edgeCount*2),this.edgeVAO.unbind()}if(this.pointCount>0){var ht=this.pointShader;ht.bind(),ht.uniforms=le,this.pointVAO.bind(),K.drawArrays(K.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var ht=this.contourShader;ht.bind(),ht.uniforms=le,this.contourVAO.bind(),K.drawArrays(K.LINES,0,this.contourCount),this.contourVAO.unbind()}},B.drawPick=function(ue){ue=ue||{};for(var K=this.gl,_e=ue.model||I,ne=ue.view||I,ae=ue.projection||I,pe=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],ie=0;ie<3;++ie)pe[0][ie]=Math.max(pe[0][ie],this.clipBounds[0][ie]),pe[1][ie]=Math.min(pe[1][ie],this.clipBounds[1][ie]);this._model=[].slice.call(_e),this._view=[].slice.call(ne),this._projection=[].slice.call(ae),this._resolution=[K.drawingBufferWidth,K.drawingBufferHeight];var le={model:_e,view:ne,projection:ae,clipBounds:pe,pickId:this.pickId/255},me=this.pickShader;if(me.bind(),me.uniforms=le,this.triangleCount>0&&(this.triangleVAO.bind(),K.drawArrays(K.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),K.lineWidth(this.lineWidth*this.pixelRatio),K.drawArrays(K.LINES,0,this.edgeCount*2),this.edgeVAO.unbind()),this.pointCount>0){var me=this.pointPickShader;me.bind(),me.uniforms=le,this.pointVAO.bind(),K.drawArrays(K.POINTS,0,this.pointCount),this.pointVAO.unbind()}},B.pick=function(ue){if(!ue||ue.id!==this.pickId)return null;for(var K=ue.value[0]+256*ue.value[1]+65536*ue.value[2],_e=this.cells[K],ne=this.positions,ae=new Array(_e.length),pe=0;pe<_e.length;++pe)ae[pe]=ne[_e[pe]];var ie=ue.coord[0],le=ue.coord[1];if(!this.pickVertex){var me=this.positions[_e[0]],be=this.positions[_e[1]],De=this.positions[_e[2]],Ge=[(me[0]+be[0]+De[0])/3,(me[1]+be[1]+De[1])/3,(me[2]+be[2]+De[2])/3];return{_cellCenter:!0,position:[ie,le],index:K,cell:_e,cellId:K,intensity:this.intensity[K],dataCoordinate:Ge}}var ht=L(ae,[ie*this.pixelRatio,this._resolution[1]-le*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!ht)return null;for(var it=ht[2],Oe=0,pe=0;pe<_e.length;++pe)Oe+=it[pe]*this.intensity[_e[pe]];return{position:ht[1],index:_e[ht[0]],cell:_e,cellId:K,intensity:Oe,dataCoordinate:this.positions[_e[ht[0]]]}},B.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()};function q(ue){var K=u(ue,w.vertex,w.fragment);return K.attributes.position.location=0,K.attributes.color.location=2,K.attributes.uv.location=3,K.attributes.normal.location=4,K}function U(ue){var K=u(ue,A.vertex,A.fragment);return K.attributes.position.location=0,K.attributes.color.location=2,K.attributes.uv.location=3,K}function J(ue){var K=u(ue,T.vertex,T.fragment);return K.attributes.position.location=0,K.attributes.color.location=2,K.attributes.uv.location=3,K.attributes.pointSize.location=4,K}function ee(ue){var K=u(ue,m.vertex,m.fragment);return K.attributes.position.location=0,K.attributes.id.location=1,K}function ce(ue){var K=u(ue,F.vertex,F.fragment);return K.attributes.position.location=0,K.attributes.id.location=1,K.attributes.pointSize.location=4,K}function ye(ue){var K=u(ue,E.vertex,E.fragment);return K.attributes.position.location=0,K}function xe(ue,K){arguments.length===1&&(K=ue,ue=K.gl);var _e=ue.getExtension(\"OES_standard_derivatives\")||ue.getExtension(\"MOZ_OES_standard_derivatives\")||ue.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!_e)throw new Error(\"derivatives not supported\");var ne=q(ue),ae=U(ue),pe=J(ue),ie=ee(ue),le=ce(ue),me=ye(ue),be=v(ue,_(new Uint8Array([255,255,255,255]),[1,1,4]));be.generateMipmap(),be.minFilter=ue.LINEAR_MIPMAP_LINEAR,be.magFilter=ue.LINEAR;var De=c(ue),Ge=c(ue),ht=c(ue),it=c(ue),Oe=c(ue),ze=f(ue,[{buffer:De,type:ue.FLOAT,size:3},{buffer:Oe,type:ue.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Ge,type:ue.FLOAT,size:4},{buffer:ht,type:ue.FLOAT,size:2},{buffer:it,type:ue.FLOAT,size:3}]),ct=c(ue),et=c(ue),qe=c(ue),we=c(ue),Le=f(ue,[{buffer:ct,type:ue.FLOAT,size:3},{buffer:we,type:ue.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:et,type:ue.FLOAT,size:4},{buffer:qe,type:ue.FLOAT,size:2}]),Ke=c(ue),Te=c(ue),Ve=c(ue),$e=c(ue),ot=c(ue),tt=f(ue,[{buffer:Ke,type:ue.FLOAT,size:3},{buffer:ot,type:ue.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Te,type:ue.FLOAT,size:4},{buffer:Ve,type:ue.FLOAT,size:2},{buffer:$e,type:ue.FLOAT,size:1}]),nt=c(ue),We=f(ue,[{buffer:nt,type:ue.FLOAT,size:3}]),mt=new R(ue,be,ne,ae,pe,ie,le,me,De,Oe,Ge,ht,it,ze,ct,we,et,qe,Le,Ke,ot,Te,Ve,$e,tt,nt,We);return mt.update(K),mt}a.exports=xe}),4437:(function(a,i,o){\"use strict\";a.exports=g;var s=o(3025),l=o(6296),u=o(351),c=o(8512),f=o(24),v=o(7520);function g(p,x){p=p||document.body,x=x||{};var _=[.01,1/0];\"distanceLimits\"in x&&(_[0]=x.distanceLimits[0],_[1]=x.distanceLimits[1]),\"zoomMin\"in x&&(_[0]=x.zoomMin),\"zoomMax\"in x&&(_[1]=x.zoomMax);var y=l({center:x.center||[0,0,0],up:x.up||[0,1,0],eye:x.eye||[0,0,10],mode:x.mode||\"orbit\",distanceLimits:_}),C=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],k=0,S=p.clientWidth,L=p.clientHeight,w={keyBindingMode:\"rotate\",enableWheel:!0,view:y,element:p,delay:x.delay||16,rotateSpeed:x.rotateSpeed||1,zoomSpeed:x.zoomSpeed||1,translateSpeed:x.translateSpeed||1,flipX:!!x.flipX,flipY:!!x.flipY,modes:y.modes,_ortho:x._ortho||x.projection&&x.projection.type===\"orthographic\"||!1,tick:function(){var A=s(),T=this.delay,m=A-2*T;y.idle(A-T),y.recalcMatrix(m),y.flush(A-(100+T*2));for(var F=!0,E=y.computedMatrix,I=0;I<16;++I)F=F&&C[I]===E[I],C[I]=E[I];var R=p.clientWidth===S&&p.clientHeight===L;return S=p.clientWidth,L=p.clientHeight,F?!R:(k=Math.exp(y.computedRadius[0]),!0)},lookAt:function(A,T,m){y.lookAt(y.lastT(),A,T,m)},rotate:function(A,T,m){y.rotate(y.lastT(),A,T,m)},pan:function(A,T,m){y.pan(y.lastT(),A,T,m)},translate:function(A,T,m){y.translate(y.lastT(),A,T,m)}};return Object.defineProperties(w,{matrix:{get:function(){return y.computedMatrix},set:function(A){return y.setMatrix(y.lastT(),A),y.computedMatrix},enumerable:!0},mode:{get:function(){return y.getMode()},set:function(A){var T=y.computedUp.slice(),m=y.computedEye.slice(),F=y.computedCenter.slice();if(y.setMode(A),A===\"turntable\"){var E=s();y._active.lookAt(E,m,F,T),y._active.lookAt(E+500,m,F,[0,0,1]),y._active.flush(E)}return y.getMode()},enumerable:!0},center:{get:function(){return y.computedCenter},set:function(A){return y.lookAt(y.lastT(),null,A),y.computedCenter},enumerable:!0},eye:{get:function(){return y.computedEye},set:function(A){return y.lookAt(y.lastT(),A),y.computedEye},enumerable:!0},up:{get:function(){return y.computedUp},set:function(A){return y.lookAt(y.lastT(),null,null,A),y.computedUp},enumerable:!0},distance:{get:function(){return k},set:function(A){return y.setDistance(y.lastT(),A),A},enumerable:!0},distanceLimits:{get:function(){return y.getDistanceLimits(_)},set:function(A){return y.setDistanceLimits(A),A},enumerable:!0}}),p.addEventListener(\"contextmenu\",function(A){return A.preventDefault(),!1}),w._lastX=-1,w._lastY=-1,w._lastMods={shift:!1,control:!1,alt:!1,meta:!1},w.enableMouseListeners=function(){w.mouseListener=u(p,A),p.addEventListener(\"touchstart\",function(T){var m=f(T.changedTouches[0],p);A(0,m[0],m[1],w._lastMods),A(1,m[0],m[1],w._lastMods)},v?{passive:!0}:!1),p.addEventListener(\"touchmove\",function(T){var m=f(T.changedTouches[0],p);A(1,m[0],m[1],w._lastMods),T.preventDefault()},v?{passive:!1}:!1),p.addEventListener(\"touchend\",function(T){A(0,w._lastX,w._lastY,w._lastMods)},v?{passive:!0}:!1);function A(T,m,F,E){var I=w.keyBindingMode;if(I!==!1){var R=I===\"rotate\",B=I===\"pan\",O=I===\"zoom\",V=!!E.control,j=!!E.alt,q=!!E.shift,U=!!(T&1),J=!!(T&2),ee=!!(T&4),ce=1/p.clientHeight,ye=ce*(m-w._lastX),xe=ce*(F-w._lastY),ue=w.flipX?1:-1,K=w.flipY?1:-1,_e=Math.PI*w.rotateSpeed,ne=s();if(w._lastX!==-1&&w._lastY!==-1&&((R&&U&&!V&&!j&&!q||U&&!V&&!j&&q)&&y.rotate(ne,ue*_e*ye,-K*_e*xe,0),(B&&U&&!V&&!j&&!q||J||U&&V&&!j&&!q)&&y.pan(ne,-w.translateSpeed*ye*k,w.translateSpeed*xe*k,0),O&&U&&!V&&!j&&!q||ee||U&&!V&&j&&!q)){var ae=-w.zoomSpeed*xe/window.innerHeight*(ne-y.lastT())*100;y.pan(ne,0,0,k*(Math.exp(ae)-1))}return w._lastX=m,w._lastY=F,w._lastMods=E,!0}}w.wheelListener=c(p,function(T,m){if(w.keyBindingMode!==!1&&w.enableWheel){var F=w.flipX?1:-1,E=w.flipY?1:-1,I=s();if(Math.abs(T)>Math.abs(m))y.rotate(I,0,0,-T*F*Math.PI*w.rotateSpeed/window.innerWidth);else if(!w._ortho){var R=-w.zoomSpeed*E*m/window.innerHeight*(I-y.lastT())/20;y.pan(I,0,0,k*(Math.exp(R)-1))}}},!0)},w.enableMouseListeners(),w}}),799:(function(a,i,o){var s=o(3236),l=o(9405),u=s([`precision mediump float;\n",
        "#define GLSLIFY 1\n",
        "attribute vec2 position;\n",
        "varying vec2 uv;\n",
@@ -1298,7 +1301,7 @@
        "void main() {\n",
        "  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n",
        "  gl_FragColor = min(vec4(1,1,1,1), accum);\n",
-       "}`]);i.exports=function(f){return l(f,u,c,null,[{name:\"position\",type:\"vec2\"}])}}),4100:(function(i,a,o){\"use strict\";var s=o(4437),l=o(3837),u=o(5445),c=o(4449),f=o(3589),h=o(2260),d=o(7169),v=o(351),m=o(4772),b=o(4040),p=o(799),k=o(9216)({tablet:!0,featureDetect:!0});i.exports={createScene:C,createCamera:s};function M(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function T(g,P){var E=null;try{E=g.getContext(\"webgl\",P),E||(E=g.getContext(\"experimental-webgl\",P))}catch(z){return null}return E}function L(g){var P=Math.round(Math.log(Math.abs(g))/Math.log(10));if(P<0){var E=Math.round(Math.pow(10,-P));return Math.ceil(g*E)/E}else if(P>0){var E=Math.round(Math.pow(10,P));return Math.ceil(g/E)*E}return Math.ceil(g)}function x(g){return typeof g==\"boolean\"?g:!0}function C(g){g=g||{},g.camera=g.camera||{};var P=g.canvas;if(!P)if(P=document.createElement(\"canvas\"),g.container){var E=g.container;E.appendChild(P)}else document.body.appendChild(P);var z=g.gl;if(z||(g.glOptions&&(k=!!g.glOptions.preserveDrawingBuffer),z=T(P,g.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:k})),!z)throw new Error(\"webgl not supported\");var q=g.bounds||[[-10,-10,-10],[10,10,10]],V=new M,G=h(z,z.drawingBufferWidth,z.drawingBufferHeight,{preferFloat:!k}),Z=p(z),j=g.cameraObject&&g.cameraObject._ortho===!0||g.camera.projection&&g.camera.projection.type===\"orthographic\"||!1,N={eye:g.camera.eye||[2,0,0],center:g.camera.center||[0,0,0],up:g.camera.up||[0,1,0],zoomMin:g.camera.zoomMax||.1,zoomMax:g.camera.zoomMin||100,mode:g.camera.mode||\"turntable\",_ortho:j},H=g.axes||{},re=l(z,H);re.enable=!H.disable;var ae=g.spikes||{},_e=c(z,ae),Ee=[],Ce=[],ge=[],ie=[],Se=!0,Le=!0,ke=new Array(16),be=new Array(16),Be={view:null,projection:ke,model:be,_ortho:!1},Le=!0,me=[z.drawingBufferWidth,z.drawingBufferHeight],Pe=g.cameraObject||s(P,N),ce={gl:z,contextLost:!1,pixelRatio:g.pixelRatio||1,canvas:P,selection:V,camera:Pe,axes:re,axesPixels:null,spikes:_e,bounds:q,objects:Ee,shape:me,aspect:g.aspectRatio||[1,1,1],pickRadius:g.pickRadius||10,zNear:g.zNear||.01,zFar:g.zFar||1e3,fovy:g.fovy||Math.PI/4,clearColor:g.clearColor||[0,0,0,0],autoResize:x(g.autoResize),autoBounds:x(g.autoBounds),autoScale:!!g.autoScale,autoCenter:x(g.autoCenter),clipToBounds:x(g.clipToBounds),snapToData:!!g.snapToData,onselect:g.onselect||null,onrender:g.onrender||null,onclick:g.onclick||null,cameraParams:Be,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(tt){this.aspect[0]=tt.x,this.aspect[1]=tt.y,this.aspect[2]=tt.z,Le=!0},setBounds:function(tt,je){this.bounds[0][tt]=je.min,this.bounds[1][tt]=je.max},setClearColor:function(tt){this.clearColor=tt},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},He=[z.drawingBufferWidth/ce.pixelRatio|0,z.drawingBufferHeight/ce.pixelRatio|0];function lt(){if(!ce._stopped&&ce.autoResize){var tt=P.parentNode,je=1,Ue=1;tt&&tt!==document.body?(je=tt.clientWidth,Ue=tt.clientHeight):(je=window.innerWidth,Ue=window.innerHeight);var Ae=Math.ceil(je*ce.pixelRatio)|0,rt=Math.ceil(Ue*ce.pixelRatio)|0;if(Ae!==P.width||rt!==P.height){P.width=Ae,P.height=rt;var St=P.style;St.position=St.position||\"absolute\",St.left=\"0px\",St.top=\"0px\",St.width=je+\"px\",St.height=Ue+\"px\",Se=!0}}}ce.autoResize&&lt(),window.addEventListener(\"resize\",lt);function mt(){for(var tt=Ee.length,je=ie.length,Ue=0;Ue<je;++Ue)ge[Ue]=0;e:for(var Ue=0;Ue<tt;++Ue){var Ae=Ee[Ue],rt=Ae.pickSlots;if(!rt){Ce[Ue]=-1;continue}for(var St=0;St<je;++St)if(ge[St]+rt<255){Ce[Ue]=St,Ae.setPickBase(ge[St]+1),ge[St]+=rt;continue e}var Tt=f(z,me);Ce[Ue]=je,ie.push(Tt),ge.push(rt),Ae.setPickBase(1),je+=1}for(;je>0&&ge[je-1]===0;)ge.pop(),ie.pop().dispose()}ce.update=function(tt){ce._stopped||(tt=tt||{},Se=!0,Le=!0)},ce.add=function(tt){ce._stopped||(tt.axes=re,Ee.push(tt),Ce.push(-1),Se=!0,Le=!0,mt())},ce.remove=function(tt){if(!ce._stopped){var je=Ee.indexOf(tt);je<0||(Ee.splice(je,1),Ce.pop(),Se=!0,Le=!0,mt())}},ce.dispose=function(){if(!ce._stopped&&(ce._stopped=!0,window.removeEventListener(\"resize\",lt),P.removeEventListener(\"webglcontextlost\",Ht),ce.mouseListener.enabled=!1,!ce.contextLost)){re.dispose(),_e.dispose();for(var tt=0;tt<Ee.length;++tt)Ee[tt].dispose();G.dispose();for(var tt=0;tt<ie.length;++tt)ie[tt].dispose();Z.dispose(),z=null,re=null,_e=null,Ee=[]}},ce._mouseRotating=!1,ce._prevButtons=0,ce.enableMouseListeners=function(){ce.mouseListener=v(P,function(tt,je,Ue){if(!ce._stopped){var Ae=ie.length,rt=Ee.length,St=V.object;V.distance=1/0,V.mouse[0]=je,V.mouse[1]=Ue,V.object=null,V.screen=null,V.dataCoordinate=V.dataPosition=null;var Tt=!1;if(tt&&ce._prevButtons)ce._mouseRotating=!0;else{ce._mouseRotating&&(Le=!0),ce._mouseRotating=!1;for(var dt=0;dt<Ae;++dt){var Et=ie[dt].query(je,He[1]-Ue-1,ce.pickRadius);if(Et){if(Et.distance>V.distance)continue;for(var pt=0;pt<rt;++pt){var jt=Ee[pt];if(Ce[pt]===dt){var or=jt.pick(Et);or&&(V.buttons=tt,V.screen=Et.coord,V.distance=Et.distance,V.object=jt,V.index=or.distance,V.dataPosition=or.position,V.dataCoordinate=or.dataCoordinate,V.data=or,Tt=!0)}}}}}St&&St!==V.object&&(St.highlight&&St.highlight(null),Se=!0),V.object&&(V.object.highlight&&V.object.highlight(V.data),Se=!0),Tt=Tt||V.object!==St,Tt&&ce.onselect&&ce.onselect(V),tt&1&&!(ce._prevButtons&1)&&ce.onclick&&ce.onclick(V),ce._prevButtons=tt}})};function 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St=Ee[rt];St.axesBounds=jt,ce.clipToBounds&&(St.clipBounds=jt)}V.object&&(ce.snapToData?_e.position=V.dataCoordinate:_e.position=V.dataPosition,_e.bounds=jt),Le&&(Le=!1,at()),ce.axesPixels=u(ce.axes,Be,ei,qr),ce.onrender&&ce.onrender(),z.bindFramebuffer(z.FRAMEBUFFER,null),z.viewport(0,0,ei,qr),ce.clearRGBA(),z.depthMask(!0),z.colorMask(!0,!0,!0,!0),z.enable(z.DEPTH_TEST),z.depthFunc(z.LEQUAL),z.disable(z.BLEND),z.disable(z.CULL_FACE);var jr=!1;re.enable&&(jr=jr||re.isTransparent(),re.draw(Be)),_e.axes=re,V.object&&_e.draw(Be),z.disable(z.CULL_FACE);for(var rt=0;rt<je;++rt){var St=Ee[rt];St.axes=re,St.pixelRatio=ce.pixelRatio,St.isOpaque&&St.isOpaque()&&St.draw(Be),St.isTransparent&&St.isTransparent()&&(jr=!0)}if(jr){G.shape=me,G.bind(),z.clear(z.DEPTH_BUFFER_BIT),z.colorMask(!1,!1,!1,!1),z.depthMask(!0),z.depthFunc(z.LESS),re.enable&&re.isTransparent()&&re.drawTransparent(Be);for(var rt=0;rt<je;++rt){var St=Ee[rt];St.isOpaque&&St.isOpaque()&&St.draw(Be)}z.enable(z.BLEND),z.blendEquation(z.FUNC_ADD),z.blendFunc(z.ONE,z.ONE_MINUS_SRC_ALPHA),z.colorMask(!0,!0,!0,!0),z.depthMask(!1),z.clearColor(0,0,0,0),z.clear(z.COLOR_BUFFER_BIT),re.isTransparent()&&re.drawTransparent(Be);for(var rt=0;rt<je;++rt){var St=Ee[rt];St.isTransparent&&St.isTransparent()&&St.drawTransparent(Be)}z.bindFramebuffer(z.FRAMEBUFFER,null),z.blendFunc(z.ONE,z.ONE_MINUS_SRC_ALPHA),z.disable(z.DEPTH_TEST),Z.bind(),G.color[0].bind(0),Z.uniforms.accumBuffer=0,d(z),z.disable(z.BLEND)}Se=!1;for(var rt=0;rt<je;++rt)Ee[rt].dirty=!1}}}function rr(){ce._stopped||ce.contextLost||(Vt(),requestAnimationFrame(rr))}return ce.enableMouseListeners(),rr(),ce.redraw=function(){ce._stopped||(Se=!0,Vt())},ce}function S(g,P){var E=g.bounds,z=g.cameraParams,q=z.projection,V=z.model,G=g.gl.drawingBufferWidth,Z=g.gl.drawingBufferHeight,j=g.zNear,N=g.zFar,H=g.fovy,re=G/Z;P?(b(q,-re,re,-1,1,j,N),z._ortho=!0):(m(q,H,re,j,N),z._ortho=!1);for(var 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Ge(){if(!be._stopped&&be.autoResize){var we=F.parentNode,Le=1,Ke=1;we&&we!==document.body?(Le=we.clientWidth,Ke=we.clientHeight):(Le=window.innerWidth,Ke=window.innerHeight);var Te=Math.ceil(Le*be.pixelRatio)|0,Ve=Math.ceil(Ke*be.pixelRatio)|0;if(Te!==F.width||Ve!==F.height){F.width=Te,F.height=Ve;var $e=F.style;$e.position=$e.position||\"absolute\",$e.left=\"0px\",$e.top=\"0px\",$e.width=Le+\"px\",$e.height=Ke+\"px\",_e=!0}}}be.autoResize&&Ge(),window.addEventListener(\"resize\",Ge);function ht(){for(var we=ye.length,Le=K.length,Ke=0;Ke<Le;++Ke)ue[Ke]=0;e:for(var Ke=0;Ke<we;++Ke){var Te=ye[Ke],Ve=Te.pickSlots;if(!Ve){xe[Ke]=-1;continue}for(var $e=0;$e<Le;++$e)if(ue[$e]+Ve<255){xe[Ke]=$e,Te.setPickBase(ue[$e]+1),ue[$e]+=Ve;continue e}var ot=f(I,le);xe[Ke]=Le,K.push(ot),ue.push(Ve),Te.setPickBase(1),Le+=1}for(;Le>0&&ue[Le-1]===0;)ue.pop(),K.pop().dispose()}be.update=function(we){be._stopped||(we=we||{},_e=!0,ie=!0)},be.add=function(we){be._stopped||(we.axes=J,ye.push(we),xe.push(-1),_e=!0,ie=!0,ht())},be.remove=function(we){if(!be._stopped){var Le=ye.indexOf(we);Le<0||(ye.splice(Le,1),xe.pop(),_e=!0,ie=!0,ht())}},be.dispose=function(){if(!be._stopped&&(be._stopped=!0,window.removeEventListener(\"resize\",Ge),F.removeEventListener(\"webglcontextlost\",it),be.mouseListener.enabled=!1,!be.contextLost)){J.dispose(),ce.dispose();for(var we=0;we<ye.length;++we)ye[we].dispose();O.dispose();for(var we=0;we<K.length;++we)K[we].dispose();V.dispose(),I=null,J=null,ce=null,ye=[]}},be._mouseRotating=!1,be._prevButtons=0,be.enableMouseListeners=function(){be.mouseListener=p(F,function(we,Le,Ke){if(!be._stopped){var Te=K.length,Ve=ye.length,$e=B.object;B.distance=1/0,B.mouse[0]=Le,B.mouse[1]=Ke,B.object=null,B.screen=null,B.dataCoordinate=B.dataPosition=null;var ot=!1;if(we&&be._prevButtons)be._mouseRotating=!0;else{be._mouseRotating&&(ie=!0),be._mouseRotating=!1;for(var tt=0;tt<Te;++tt){var nt=K[tt].query(Le,De[1]-Ke-1,be.pickRadius);if(nt){if(nt.distance>B.distance)continue;for(var We=0;We<Ve;++We){var mt=ye[We];if(xe[We]===tt){var bt=mt.pick(nt);bt&&(B.buttons=we,B.screen=nt.coord,B.distance=nt.distance,B.object=mt,B.index=bt.distance,B.dataPosition=bt.position,B.dataCoordinate=bt.dataCoordinate,B.data=bt,ot=!0)}}}}}$e&&$e!==B.object&&($e.highlight&&$e.highlight(null),_e=!0),B.object&&(B.object.highlight&&B.object.highlight(B.data),_e=!0),ot=ot||B.object!==$e,ot&&be.onselect&&be.onselect(B),we&1&&!(be._prevButtons&1)&&be.onclick&&be.onclick(B),be._prevButtons=we}})};function it(){if(be.contextLost)return!0;I.isContextLost()&&(be.contextLost=!0,be.mouseListener.enabled=!1,be.selection.object=null,be.oncontextloss&&be.oncontextloss())}F.addEventListener(\"webglcontextlost\",it);function Oe(){if(!it()){I.colorMask(!0,!0,!0,!0),I.depthMask(!0),I.disable(I.BLEND),I.enable(I.DEPTH_TEST),I.depthFunc(I.LEQUAL);for(var we=ye.length,Le=K.length,Ke=0;Ke<Le;++Ke){var Te=K[Ke];Te.shape=De,Te.begin();for(var Ve=0;Ve<we;++Ve)if(xe[Ve]===Ke){var $e=ye[Ve];$e.drawPick&&($e.pixelRatio=1,$e.drawPick(pe))}Te.end()}}}var ze=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],ct=[ze[0].slice(),ze[1].slice()];function et(){if(!it()){Ge();var we=be.camera.tick();pe.view=be.camera.matrix,_e=_e||we,ie=ie||we,J.pixelRatio=be.pixelRatio,ce.pixelRatio=be.pixelRatio;var Le=ye.length,Ke=ze[0],Te=ze[1];Ke[0]=Ke[1]=Ke[2]=1/0,Te[0]=Te[1]=Te[2]=-1/0;for(var Ve=0;Ve<Le;++Ve){var $e=ye[Ve];$e.pixelRatio=be.pixelRatio,$e.axes=be.axes,_e=_e||!!$e.dirty,ie=ie||!!$e.dirty;var ot=$e.bounds;if(ot)for(var tt=ot[0],nt=ot[1],We=0;We<3;++We)Ke[We]=Math.min(Ke[We],tt[We]),Te[We]=Math.max(Te[We],nt[We])}var mt=be.bounds;if(be.autoBounds)for(var We=0;We<3;++We){if(Te[We]<Ke[We])Ke[We]=-1,Te[We]=1;else{Ke[We]===Te[We]&&(Ke[We]-=1,Te[We]+=1);var bt=.05*(Te[We]-Ke[We]);Ke[We]=Ke[We]-bt,Te[We]=Te[We]+bt}mt[0][We]=Ke[We],mt[1][We]=Te[We]}for(var Yt=!1,We=0;We<3;++We)Yt=Yt||ct[0][We]!==mt[0][We]||ct[1][We]!==mt[1][We],ct[0][We]=mt[0][We],ct[1][We]=mt[1][We];if(ie=ie||Yt,_e=_e||Yt,!!_e){if(Yt){for(var jt=[0,0,0],Ve=0;Ve<3;++Ve)jt[Ve]=L((mt[1][Ve]-mt[0][Ve])/10);J.autoTicks?J.update({bounds:mt,tickSpacing:jt}):J.update({bounds:mt})}var mr=I.drawingBufferWidth,or=I.drawingBufferHeight;le[0]=mr,le[1]=or,De[0]=Math.max(mr/be.pixelRatio,1)|0,De[1]=Math.max(or/be.pixelRatio,1)|0,T(be,j);for(var Ve=0;Ve<Le;++Ve){var $e=ye[Ve];$e.axesBounds=mt,be.clipToBounds&&($e.clipBounds=mt)}B.object&&(be.snapToData?ce.position=B.dataCoordinate:ce.position=B.dataPosition,ce.bounds=mt),ie&&(ie=!1,Oe()),be.axesPixels=u(be.axes,pe,mr,or),be.onrender&&be.onrender(),I.bindFramebuffer(I.FRAMEBUFFER,null),I.viewport(0,0,mr,or),be.clearRGBA(),I.depthMask(!0),I.colorMask(!0,!0,!0,!0),I.enable(I.DEPTH_TEST),I.depthFunc(I.LEQUAL),I.disable(I.BLEND),I.disable(I.CULL_FACE);var fr=!1;J.enable&&(fr=fr||J.isTransparent(),J.draw(pe)),ce.axes=J,B.object&&ce.draw(pe),I.disable(I.CULL_FACE);for(var Ve=0;Ve<Le;++Ve){var $e=ye[Ve];$e.axes=J,$e.pixelRatio=be.pixelRatio,$e.isOpaque&&$e.isOpaque()&&$e.draw(pe),$e.isTransparent&&$e.isTransparent()&&(fr=!0)}if(fr){O.shape=le,O.bind(),I.clear(I.DEPTH_BUFFER_BIT),I.colorMask(!1,!1,!1,!1),I.depthMask(!0),I.depthFunc(I.LESS),J.enable&&J.isTransparent()&&J.drawTransparent(pe);for(var Ve=0;Ve<Le;++Ve){var $e=ye[Ve];$e.isOpaque&&$e.isOpaque()&&$e.draw(pe)}I.enable(I.BLEND),I.blendEquation(I.FUNC_ADD),I.blendFunc(I.ONE,I.ONE_MINUS_SRC_ALPHA),I.colorMask(!0,!0,!0,!0),I.depthMask(!1),I.clearColor(0,0,0,0),I.clear(I.COLOR_BUFFER_BIT),J.isTransparent()&&J.drawTransparent(pe);for(var Ve=0;Ve<Le;++Ve){var $e=ye[Ve];$e.isTransparent&&$e.isTransparent()&&$e.drawTransparent(pe)}I.bindFramebuffer(I.FRAMEBUFFER,null),I.blendFunc(I.ONE,I.ONE_MINUS_SRC_ALPHA),I.disable(I.DEPTH_TEST),V.bind(),O.color[0].bind(0),V.uniforms.accumBuffer=0,g(I),I.disable(I.BLEND)}_e=!1;for(var Ve=0;Ve<Le;++Ve)ye[Ve].dirty=!1}}}function qe(){be._stopped||be.contextLost||(et(),requestAnimationFrame(qe))}return be.enableMouseListeners(),qe(),be.redraw=function(){be._stopped||(_e=!0,et())},be}function T(m,F){var E=m.bounds,I=m.cameraParams,R=I.projection,B=I.model,O=m.gl.drawingBufferWidth,V=m.gl.drawingBufferHeight,j=m.zNear,q=m.zFar,U=m.fovy,J=O/V;F?(_(R,-J,J,-1,1,j,q),I._ortho=!0):(x(R,U,J,j,q),I._ortho=!1);for(var ee=0;ee<16;++ee)B[ee]=0;B[15]=1;for(var ce=0,ee=0;ee<3;++ee)ce=Math.max(ce,E[1][ee]-E[0][ee]);for(var ee=0;ee<3;++ee)m.autoScale?B[5*ee]=m.aspect[ee]/(E[1][ee]-E[0][ee]):B[5*ee]=1/ce,m.autoCenter&&(B[12+ee]=-B[5*ee]*.5*(E[0][ee]+E[1][ee]))}}),783:(function(a){a.exports=i;function i(o,s,l,u){var c=s[0],f=s[1],v=s[2],g=s[3],p=l[0],x=l[1],_=l[2],y=l[3],C,k,S,L,w;return k=c*p+f*x+v*_+g*y,k<0&&(k=-k,p=-p,x=-x,_=-_,y=-y),1-k>1e-6?(C=Math.acos(k),S=Math.sin(C),L=Math.sin((1-u)*C)/S,w=Math.sin(u*C)/S):(L=1-u,w=u),o[0]=L*c+w*p,o[1]=L*f+w*x,o[2]=L*v+w*_,o[3]=L*g+w*y,o}}),5964:(function(a){\"use strict\";a.exports=function(i){return!i&&i!==0?\"\":i.toString()}}),9366:(function(a,i,o){\"use strict\";var s=o(4359);a.exports=u;var l={};function u(c,f,v){var g=[f.style,f.weight,f.variant,f.family].join(\"_\"),p=l[g];if(p||(p=l[g]={}),c in p)return p[c];var x={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:f.family,fontStyle:f.style,fontWeight:f.weight,fontVariant:f.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0}};x.triangles=!0;var _=s(c,x);x.triangles=!1;var y=s(c,x),C,k;if(v&&v!==1){for(C=0;C<_.positions.length;++C)for(k=0;k<_.positions[C].length;++k)_.positions[C][k]/=v;for(C=0;C<y.positions.length;++C)for(k=0;k<y.positions[C].length;++k)y.positions[C][k]/=v}var S=[[1/0,1/0],[-1/0,-1/0]],L=y.positions.length;for(C=0;C<L;++C){var w=y.positions[C];for(k=0;k<2;++k)S[0][k]=Math.min(S[0][k],w[k]),S[1][k]=Math.max(S[1][k],w[k])}return p[c]=[_,y,S]}}),1283:(function(a,i,o){var s=o(9405),l=o(3236),u=l([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "bool outOfRange(float a, float b, float p) {\n",
@@ -1473,7 +1476,7 @@
        "    dataCoordinate = dataPosition;\n",
        "  }\n",
        "}\n",
-       "`]),h=l([`precision highp float;\n",
+       "`]),v=l([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "bool outOfRange(float a, float b, float p) {\n",
@@ -1509,7 +1512,7 @@
        "  ) discard;\n",
        "  gl_FragColor = interpColor * opacity;\n",
        "}\n",
-       "`]),d=l([`precision highp float;\n",
+       "`]),g=l([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "bool outOfRange(float a, float b, float p) {\n",
@@ -1542,9 +1545,9 @@
        "  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n",
        "\n",
        "  gl_FragColor = vec4(pickGroup, pickId.bgr);\n",
-       "}`]),v=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],m={vertex:u,fragment:h,attributes:v},b={vertex:c,fragment:h,attributes:v},p={vertex:f,fragment:h,attributes:v},k={vertex:u,fragment:d,attributes:v},M={vertex:c,fragment:d,attributes:v},T={vertex:f,fragment:d,attributes:v};function L(x,C){var S=s(x,C),g=S.attributes;return g.position.location=0,g.color.location=1,g.glyph.location=2,g.id.location=3,S}a.createPerspective=function(x){return L(x,m)},a.createOrtho=function(x){return L(x,b)},a.createProject=function(x){return L(x,p)},a.createPickPerspective=function(x){return L(x,k)},a.createPickOrtho=function(x){return L(x,M)},a.createPickProject=function(x){return L(x,T)}}),8418:(function(i,a,o){\"use strict\";var s=o(5219),l=o(2762),u=o(8116),c=o(1888),f=o(6760),h=o(1283),d=o(9366),v=o(5964),m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],b=ArrayBuffer,p=DataView;function k(be){return b.isView(be)&&!(be instanceof p)}function M(be){return Array.isArray(be)||k(be)}i.exports=ke;function T(be,Be){var Le=be[0],me=be[1],Pe=be[2],ce=be[3];return be[0]=Be[0]*Le+Be[4]*me+Be[8]*Pe+Be[12]*ce,be[1]=Be[1]*Le+Be[5]*me+Be[9]*Pe+Be[13]*ce,be[2]=Be[2]*Le+Be[6]*me+Be[10]*Pe+Be[14]*ce,be[3]=Be[3]*Le+Be[7]*me+Be[11]*Pe+Be[15]*ce,be}function L(be,Be,Le,me){return T(me,me,Le),T(me,me,Be),T(me,me,be)}function x(be,Be){this.index=be,this.dataCoordinate=this.position=Be}function C(be){return be===!0||be>1?1:be}function S(be,Be,Le,me,Pe,ce,He,lt,mt,Ht,at,ct){this.gl=be,this.pixelRatio=1,this.shader=Be,this.orthoShader=Le,this.projectShader=me,this.pointBuffer=Pe,this.colorBuffer=ce,this.glyphBuffer=He,this.idBuffer=lt,this.vao=mt,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[.6666666666666666,.6666666666666666,.6666666666666666],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=Ht,this.pickOrthoShader=at,this.pickProjectShader=ct,this.points=[],this._selectResult=new x(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}var g=S.prototype;g.pickSlots=1,g.setPickBase=function(be){this.pickId=be},g.isTransparent=function(){if(this.hasAlpha)return!0;for(var be=0;be<3;++be)if(this.axesProject[be]&&this.projectHasAlpha)return!0;return!1},g.isOpaque=function(){if(!this.hasAlpha)return!0;for(var be=0;be<3;++be)if(this.axesProject[be]&&!this.projectHasAlpha)return!0;return!1};var P=[0,0],E=[0,0,0],z=[0,0,0],q=[0,0,0,1],V=[0,0,0,1],G=m.slice(),Z=[0,0,0],j=[[0,0,0],[0,0,0]];function N(be){return be[0]=be[1]=be[2]=0,be}function H(be,Be){return be[0]=Be[0],be[1]=Be[1],be[2]=Be[2],be[3]=1,be}function re(be,Be,Le,me){return be[0]=Be[0],be[1]=Be[1],be[2]=Be[2],be[Le]=me,be}function ae(be){for(var Be=j,Le=0;Le<2;++Le)for(var me=0;me<3;++me)Be[Le][me]=Math.max(Math.min(be[Le][me],1e8),-1e8);return Be}function _e(be,Be,Le,me){var Pe=Be.axesProject,ce=Be.gl,He=be.uniforms,lt=Le.model||m,mt=Le.view||m,Ht=Le.projection||m,at=Be.axesBounds,ct=ae(Be.clipBounds),ar;Be.axes&&Be.axes.lastCubeProps?ar=Be.axes.lastCubeProps.axis:ar=[1,1,1],P[0]=2/ce.drawingBufferWidth,P[1]=2/ce.drawingBufferHeight,be.bind(),He.view=mt,He.projection=Ht,He.screenSize=P,He.highlightId=Be.highlightId,He.highlightScale=Be.highlightScale,He.clipBounds=ct,He.pickGroup=Be.pickId/255,He.pixelRatio=me;for(var Vt=0;Vt<3;++Vt)if(Pe[Vt]){He.scale=Be.projectScale[Vt],He.opacity=Be.projectOpacity[Vt];for(var rr=G,tt=0;tt<16;++tt)rr[tt]=0;for(var tt=0;tt<4;++tt)rr[5*tt]=1;rr[5*Vt]=0,ar[Vt]<0?rr[12+Vt]=at[0][Vt]:rr[12+Vt]=at[1][Vt],f(rr,lt,rr),He.model=rr;var je=(Vt+1)%3,Ue=(Vt+2)%3,Ae=N(E),rt=N(z);Ae[je]=1,rt[Ue]=1;var St=L(Ht,mt,lt,H(q,Ae)),Tt=L(Ht,mt,lt,H(V,rt));if(Math.abs(St[1])>Math.abs(Tt[1])){var dt=St;St=Tt,Tt=dt,dt=Ae,Ae=rt,rt=dt;var Et=je;je=Ue,Ue=Et}St[0]<0&&(Ae[je]=-1),Tt[1]>0&&(rt[Ue]=-1);for(var pt=0,jt=0,tt=0;tt<4;++tt)pt+=Math.pow(lt[4*je+tt],2),jt+=Math.pow(lt[4*Ue+tt],2);Ae[je]/=Math.sqrt(pt),rt[Ue]/=Math.sqrt(jt),He.axes[0]=Ae,He.axes[1]=rt,He.fragClipBounds[0]=re(Z,ct[0],Vt,-1e8),He.fragClipBounds[1]=re(Z,ct[1],Vt,1e8),Be.vao.bind(),Be.vao.draw(ce.TRIANGLES,Be.vertexCount),Be.lineWidth>0&&(ce.lineWidth(Be.lineWidth*me),Be.vao.draw(ce.LINES,Be.lineVertexCount,Be.vertexCount)),Be.vao.unbind()}}var Ee=[-1e8,-1e8,-1e8],Ce=[1e8,1e8,1e8],ge=[Ee,Ce];function ie(be,Be,Le,me,Pe,ce,He){var lt=Le.gl;if((ce===Le.projectHasAlpha||He)&&_e(Be,Le,me,Pe),ce===Le.hasAlpha||He){be.bind();var mt=be.uniforms;mt.model=me.model||m,mt.view=me.view||m,mt.projection=me.projection||m,P[0]=2/lt.drawingBufferWidth,P[1]=2/lt.drawingBufferHeight,mt.screenSize=P,mt.highlightId=Le.highlightId,mt.highlightScale=Le.highlightScale,mt.fragClipBounds=ge,mt.clipBounds=Le.axes.bounds,mt.opacity=Le.opacity,mt.pickGroup=Le.pickId/255,mt.pixelRatio=Pe,Le.vao.bind(),Le.vao.draw(lt.TRIANGLES,Le.vertexCount),Le.lineWidth>0&&(lt.lineWidth(Le.lineWidth*Pe),Le.vao.draw(lt.LINES,Le.lineVertexCount,Le.vertexCount)),Le.vao.unbind()}}g.draw=function(be){var Be=this.useOrtho?this.orthoShader:this.shader;ie(Be,this.projectShader,this,be,this.pixelRatio,!1,!1)},g.drawTransparent=function(be){var Be=this.useOrtho?this.orthoShader:this.shader;ie(Be,this.projectShader,this,be,this.pixelRatio,!0,!1)},g.drawPick=function(be){var Be=this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader;ie(Be,this.pickProjectShader,this,be,1,!0,!0)},g.pick=function(be){if(!be||be.id!==this.pickId)return null;var Be=be.value[2]+(be.value[1]<<8)+(be.value[0]<<16);if(Be>=this.pointCount||Be<0)return null;var Le=this.points[Be],me=this._selectResult;me.index=Be;for(var Pe=0;Pe<3;++Pe)me.position[Pe]=me.dataCoordinate[Pe]=Le[Pe];return me},g.highlight=function(be){if(!be)this.highlightId=[1,1,1,1];else{var Be=be.index,Le=Be&255,me=Be>>8&255,Pe=Be>>16&255;this.highlightId=[Le/255,me/255,Pe/255,0]}};function Se(be,Be,Le,me){var Pe;M(be)?Be<be.length?Pe=be[Be]:Pe=void 0:Pe=be,Pe=v(Pe);var ce=!0;s(Pe)&&(Pe=\"\\u25BC\",ce=!1),Le||(Le={});var He=Le.family;M(He)&&(He=He[Be]),He||(He=\"normal\");var lt=Le.weight;M(lt)&&(lt=lt[Be]),lt||(lt=\"normal\");var mt=Le.style;M(mt)&&(mt=mt[Be]),mt||(mt=\"normal\");var Ht=Le.variant;M(Ht)&&(Ht=Ht[Be]),Ht||(Ht=\"normal\");var at=d(Pe,{family:He,weight:lt,style:mt,variant:Ht},me),at=d(Pe,Le,me);return{mesh:at[0],lines:at[1],bounds:at[2],visible:ce}}g.update=function(be){if(be=be||{},\"perspective\"in be&&(this.useOrtho=!be.perspective),\"orthographic\"in be&&(this.useOrtho=!!be.orthographic),\"lineWidth\"in be&&(this.lineWidth=be.lineWidth),\"project\"in be)if(M(be.project))this.axesProject=be.project;else{var Be=!!be.project;this.axesProject=[Be,Be,Be]}if(\"projectScale\"in be)if(M(be.projectScale))this.projectScale=be.projectScale.slice();else{var Le=+be.projectScale;this.projectScale=[Le,Le,Le]}if(this.projectHasAlpha=!1,\"projectOpacity\"in be){if(M(be.projectOpacity))this.projectOpacity=be.projectOpacity.slice();else{var Le=+be.projectOpacity;this.projectOpacity=[Le,Le,Le]}for(var me=0;me<3;++me)this.projectOpacity[me]=C(this.projectOpacity[me]),this.projectOpacity[me]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in be&&(this.opacity=C(be.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var Pe=be.position,ce={family:be.font||\"normal\",style:be.fontStyle||\"normal\",weight:be.fontWeight||\"normal\",variant:be.fontVariant||\"normal\"},He=be.alignment||[0,0],lt,mt;if(He.length===2)lt=He[0],mt=He[1];else{lt=[],mt=[];for(var me=0;me<He.length;++me)lt[me]=He[me][0],mt[me]=He[me][1]}var Ht=[1/0,1/0,1/0],at=[-1/0,-1/0,-1/0],ct=be.glyph,ar=be.color,Vt=be.size,rr=be.angle,tt=be.lineColor,je=-1,Ue=0,Ae=0,rt=0;if(Pe.length){rt=Pe.length;e:for(var me=0;me<rt;++me){for(var St=Pe[me],Tt=0;Tt<3;++Tt)if(isNaN(St[Tt])||!isFinite(St[Tt]))continue e;var dt=Se(ct,me,ce,this.pixelRatio),Et=dt.mesh,pt=dt.lines,jt=dt.bounds;Ue+=Et.cells.length*3,Ae+=pt.edges.length*2}}var or=Ue+Ae,mr=c.mallocFloat(3*or),Ar=c.mallocFloat(4*or),ei=c.mallocFloat(2*or),qr=c.mallocUint32(or);if(or>0){var jr=0,gt=Ue,Ge=[0,0,0,1],Je=[0,0,0,1],We=M(ar)&&M(ar[0]),et=M(tt)&&M(tt[0]);e:for(var me=0;me<rt;++me){je+=1;for(var St=Pe[me],Tt=0;Tt<3;++Tt){if(isNaN(St[Tt])||!isFinite(St[Tt]))continue e;at[Tt]=Math.max(at[Tt],St[Tt]),Ht[Tt]=Math.min(Ht[Tt],St[Tt])}var dt=Se(ct,me,ce,this.pixelRatio),Et=dt.mesh,pt=dt.lines,jt=dt.bounds,xt=dt.visible;if(!xt)Ge=[1,1,1,0];else if(M(ar)){var At;if(We?me<ar.length?At=ar[me]:At=[0,0,0,0]:At=ar,At.length===3){for(var Tt=0;Tt<3;++Tt)Ge[Tt]=At[Tt];Ge[3]=1}else if(At.length===4){for(var Tt=0;Tt<4;++Tt)Ge[Tt]=At[Tt];!this.hasAlpha&&At[3]<1&&(this.hasAlpha=!0)}}else Ge[0]=Ge[1]=Ge[2]=0,Ge[3]=1;if(!xt)Je=[1,1,1,0];else if(M(tt)){var At;if(et?me<tt.length?At=tt[me]:At=[0,0,0,0]:At=tt,At.length===3){for(var Tt=0;Tt<3;++Tt)Je[Tt]=At[Tt];Je[Tt]=1}else if(At.length===4){for(var Tt=0;Tt<4;++Tt)Je[Tt]=At[Tt];!this.hasAlpha&&At[3]<1&&(this.hasAlpha=!0)}}else Je[0]=Je[1]=Je[2]=0,Je[3]=1;var Kt=.5;xt?M(Vt)?me<Vt.length?Kt=+Vt[me]:Kt=12:Vt?Kt=+Vt:this.useOrtho&&(Kt=12):Kt=0;var Qt=0;M(rr)?me<rr.length?Qt=+rr[me]:Qt=0:rr&&(Qt=+rr);for(var Mr=Math.cos(Qt),Gr=Math.sin(Qt),St=Pe[me],Tt=0;Tt<3;++Tt)at[Tt]=Math.max(at[Tt],St[Tt]),Ht[Tt]=Math.min(Ht[Tt],St[Tt]);var Ir=lt,Yr=mt,Ir=0;M(lt)?me<lt.length?Ir=lt[me]:Ir=0:lt&&(Ir=lt);var Yr=0;M(mt)?me<mt.length?Yr=mt[me]:Yr=0:mt&&(Yr=mt),Ir*=Ir>0?1-jt[0][0]:Ir<0?1+jt[1][0]:1,Yr*=Yr>0?1-jt[0][1]:Yr<0?1+jt[1][1]:1;for(var _i=[Ir,Yr],Fn=Et.cells||[],An=Et.positions||[],Tt=0;Tt<Fn.length;++Tt)for(var Pi=Fn[Tt],ai=0;ai<3;++ai){for(var mi=0;mi<3;++mi)mr[3*jr+mi]=St[mi];for(var mi=0;mi<4;++mi)Ar[4*jr+mi]=Ge[mi];qr[jr]=je;var un=An[Pi[ai]];ei[2*jr]=Kt*(Mr*un[0]-Gr*un[1]+_i[0]),ei[2*jr+1]=Kt*(Gr*un[0]+Mr*un[1]+_i[1]),jr+=1}for(var Fn=pt.edges,An=pt.positions,Tt=0;Tt<Fn.length;++Tt)for(var Pi=Fn[Tt],ai=0;ai<2;++ai){for(var mi=0;mi<3;++mi)mr[3*gt+mi]=St[mi];for(var mi=0;mi<4;++mi)Ar[4*gt+mi]=Je[mi];qr[gt]=je;var 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me=pe.axesProject,be=pe.gl,De=ae.uniforms,Ge=ie.model||x,ht=ie.view||x,it=ie.projection||x,Oe=pe.axesBounds,ze=ee(pe.clipBounds),ct;pe.axes&&pe.axes.lastCubeProps?ct=pe.axes.lastCubeProps.axis:ct=[1,1,1],F[0]=2/be.drawingBufferWidth,F[1]=2/be.drawingBufferHeight,ae.bind(),De.view=ht,De.projection=it,De.screenSize=F,De.highlightId=pe.highlightId,De.highlightScale=pe.highlightScale,De.clipBounds=ze,De.pickGroup=pe.pickId/255,De.pixelRatio=le;for(var et=0;et<3;++et)if(me[et]){De.scale=pe.projectScale[et],De.opacity=pe.projectOpacity[et];for(var qe=O,we=0;we<16;++we)qe[we]=0;for(var we=0;we<4;++we)qe[5*we]=1;qe[5*et]=0,ct[et]<0?qe[12+et]=Oe[0][et]:qe[12+et]=Oe[1][et],f(qe,Ge,qe),De.model=qe;var Le=(et+1)%3,Ke=(et+2)%3,Te=q(E),Ve=q(I);Te[Le]=1,Ve[Ke]=1;var $e=L(it,ht,Ge,U(R,Te)),ot=L(it,ht,Ge,U(B,Ve));if(Math.abs($e[1])>Math.abs(ot[1])){var tt=$e;$e=ot,ot=tt,tt=Te,Te=Ve,Ve=tt;var nt=Le;Le=Ke,Ke=nt}$e[0]<0&&(Te[Le]=-1),ot[1]>0&&(Ve[Ke]=-1);for(var 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Cn=At[xr[Fr]];mr[2*Ot]=ft*(rr*Cn[0]-pr*Cn[1]+dn[0]),mr[2*Ot+1]=ft*(pr*Cn[0]+rr*Cn[1]+dn[1]),Ot+=1}}}this.bounds=[it,Oe],this.points=me,this.pointCount=me.length,this.vertexCount=Ke,this.lineVertexCount=Te,this.pointBuffer.update(Yt),this.colorBuffer.update(jt),this.glyphBuffer.update(mr),this.idBuffer.update(or),c.free(Yt),c.free(jt),c.free(mr),c.free(or)},m.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()};function ne(ae){var 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+       "`+T)}throw new s(T,m.short,m.long)}return A}p.getShaderReference=function(S,L){var w=this.gl,A=this.shaders[S===w.FRAGMENT_SHADER|0],T=A[L];if(!T||!w.isShader(T.shader)){var m=x(w,S,L);T=A[L]=new v(f++,L,S,m,[],1,this)}else T.count+=1;return T};function _(S,L,w,A,T){var m=S.createProgram();S.attachShader(m,L),S.attachShader(m,w);for(var F=0;F<A.length;++F)S.bindAttribLocation(m,T[F],A[F]);if(S.linkProgram(m),!S.getProgramParameter(m,S.LINK_STATUS)){var E=S.getProgramInfoLog(m);throw new s(E,\"Error linking program: \"+E)}return m}p.getProgram=function(S,L,w,A){var T=[S.id,L.id,w.join(\":\"),A.join(\":\")].join(\"@\"),m=this.programs[T];return(!m||!this.gl.isProgram(m))&&(this.programs[T]=m=_(this.gl,S.shader,L.shader,w,A),S.programs.push(T),L.programs.push(T)),m};function y(S){var L=c.get(S);return L||(L=new g(S),c.set(S,L)),L}function C(S,L,w){return y(S).getShaderReference(L,w)}function k(S,L,w,A,T){return y(S).getProgram(L,w,A,T)}}),1493:(function(a,i,o){\"use strict\";var s=o(3236),l=o(9405),u=s([`precision mediump float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec3 position, color;\n",
@@ -1577,7 +1580,7 @@
        "\n",
        "void main() {\n",
        "  gl_FragColor = fragColor;\n",
-       "}`]);i.exports=function(f){return l(f,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}}),4449:(function(i,a,o){\"use strict\";var s=o(2762),l=o(8116),u=o(1493);i.exports=b;var c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(p,k,M,T){this.gl=p,this.buffer=k,this.vao=M,this.shader=T,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var h=f.prototype,d=[0,0,0],v=[0,0,0],m=[0,0];h.isTransparent=function(){return!1},h.drawTransparent=function(p){},h.draw=function(p){var k=this.gl,M=this.vao,T=this.shader;M.bind(),T.bind();var L=p.model||c,x=p.view||c,C=p.projection||c,S;this.axes&&(S=this.axes.lastCubeProps.axis);for(var g=d,P=v,E=0;E<3;++E)S&&S[E]<0?(g[E]=this.bounds[0][E],P[E]=this.bounds[1][E]):(g[E]=this.bounds[1][E],P[E]=this.bounds[0][E]);m[0]=k.drawingBufferWidth,m[1]=k.drawingBufferHeight,T.uniforms.model=L,T.uniforms.view=x,T.uniforms.projection=C,T.uniforms.coordinates=[this.position,g,P],T.uniforms.colors=this.colors,T.uniforms.screenShape=m;for(var E=0;E<3;++E)T.uniforms.lineWidth=this.lineWidth[E]*this.pixelRatio,this.enabled[E]&&(M.draw(k.TRIANGLES,6,6*E),this.drawSides[E]&&M.draw(k.TRIANGLES,12,18+12*E));M.unbind()},h.update=function(p){p&&(\"bounds\"in p&&(this.bounds=p.bounds),\"position\"in p&&(this.position=p.position),\"lineWidth\"in p&&(this.lineWidth=p.lineWidth),\"colors\"in p&&(this.colors=p.colors),\"enabled\"in p&&(this.enabled=p.enabled),\"drawSides\"in p&&(this.drawSides=p.drawSides))},h.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()};function b(p,k){var M=[];function T(g,P,E,z,q,V){var G=[g,P,E,0,0,0,1];G[z+3]=1,G[z]=q,M.push.apply(M,G),G[6]=-1,M.push.apply(M,G),G[z]=V,M.push.apply(M,G),M.push.apply(M,G),G[6]=1,M.push.apply(M,G),G[z]=q,M.push.apply(M,G)}T(0,0,0,0,0,1),T(0,0,0,1,0,1),T(0,0,0,2,0,1),T(1,0,0,1,-1,1),T(1,0,0,2,-1,1),T(0,1,0,0,-1,1),T(0,1,0,2,-1,1),T(0,0,1,0,-1,1),T(0,0,1,1,-1,1);var L=s(p,M),x=l(p,[{type:p.FLOAT,buffer:L,size:3,offset:0,stride:28},{type:p.FLOAT,buffer:L,size:3,offset:12,stride:28},{type:p.FLOAT,buffer:L,size:1,offset:24,stride:28}]),C=u(p);C.attributes.position.location=0,C.attributes.color.location=1,C.attributes.weight.location=2;var S=new f(p,L,x,C);return S.update(k),S}}),6740:(function(i,a,o){var s=o(3236),l=s([`precision highp float;\n",
+       "}`]);a.exports=function(f){return l(f,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}}),4449:(function(a,i,o){\"use strict\";var s=o(2762),l=o(8116),u=o(1493);a.exports=_;var c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(y,C,k,S){this.gl=y,this.buffer=C,this.vao=k,this.shader=S,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var v=f.prototype,g=[0,0,0],p=[0,0,0],x=[0,0];v.isTransparent=function(){return!1},v.drawTransparent=function(y){},v.draw=function(y){var C=this.gl,k=this.vao,S=this.shader;k.bind(),S.bind();var L=y.model||c,w=y.view||c,A=y.projection||c,T;this.axes&&(T=this.axes.lastCubeProps.axis);for(var m=g,F=p,E=0;E<3;++E)T&&T[E]<0?(m[E]=this.bounds[0][E],F[E]=this.bounds[1][E]):(m[E]=this.bounds[1][E],F[E]=this.bounds[0][E]);x[0]=C.drawingBufferWidth,x[1]=C.drawingBufferHeight,S.uniforms.model=L,S.uniforms.view=w,S.uniforms.projection=A,S.uniforms.coordinates=[this.position,m,F],S.uniforms.colors=this.colors,S.uniforms.screenShape=x;for(var E=0;E<3;++E)S.uniforms.lineWidth=this.lineWidth[E]*this.pixelRatio,this.enabled[E]&&(k.draw(C.TRIANGLES,6,6*E),this.drawSides[E]&&k.draw(C.TRIANGLES,12,18+12*E));k.unbind()},v.update=function(y){y&&(\"bounds\"in y&&(this.bounds=y.bounds),\"position\"in y&&(this.position=y.position),\"lineWidth\"in y&&(this.lineWidth=y.lineWidth),\"colors\"in y&&(this.colors=y.colors),\"enabled\"in y&&(this.enabled=y.enabled),\"drawSides\"in y&&(this.drawSides=y.drawSides))},v.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()};function _(y,C){var k=[];function S(m,F,E,I,R,B){var O=[m,F,E,0,0,0,1];O[I+3]=1,O[I]=R,k.push.apply(k,O),O[6]=-1,k.push.apply(k,O),O[I]=B,k.push.apply(k,O),k.push.apply(k,O),O[6]=1,k.push.apply(k,O),O[I]=R,k.push.apply(k,O)}S(0,0,0,0,0,1),S(0,0,0,1,0,1),S(0,0,0,2,0,1),S(1,0,0,1,-1,1),S(1,0,0,2,-1,1),S(0,1,0,0,-1,1),S(0,1,0,2,-1,1),S(0,0,1,0,-1,1),S(0,0,1,1,-1,1);var L=s(y,k),w=l(y,[{type:y.FLOAT,buffer:L,size:3,offset:0,stride:28},{type:y.FLOAT,buffer:L,size:3,offset:12,stride:28},{type:y.FLOAT,buffer:L,size:1,offset:24,stride:28}]),A=u(y);A.attributes.position.location=0,A.attributes.color.location=1,A.attributes.weight.location=2;var T=new f(y,L,w,A);return T.update(C),T}}),6740:(function(a,i,o){var s=o(3236),l=s([`precision highp float;\n",
        "\n",
        "precision highp float;\n",
        "#define GLSLIFY 1\n",
@@ -1841,7 +1844,7 @@
        "  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
        "\n",
        "  gl_FragColor = vec4(pickId, f_id.xyz);\n",
-       "}`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},a.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}}),7815:(function(i,a,o){\"use strict\";var s=o(2931),l=o(9970),u=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],c=function(T,L,x,C){for(var S=T.points,g=T.velocities,P=T.divergences,E=[],z=[],q=[],V=[],G=[],Z=[],j=0,N=0,H=l.create(),re=l.create(),ae=8,_e=0;_e<S.length;_e++){var Ee=S[_e],Ce=g[_e],ge=P[_e];L===0&&(ge=x*.05),N=s.length(Ce)/C,H=l.create(),s.copy(H,Ce),H[3]=ge;for(var ie=0;ie<ae;ie++)G[ie]=[Ee[0],Ee[1],Ee[2],ie];if(V.length>0)for(var ie=0;ie<ae;ie++){var Se=(ie+1)%ae;E.push(V[ie],G[ie],G[Se],G[Se],V[Se],V[ie]),q.push(re,H,H,H,re,re),Z.push(j,N,N,N,j,j);var ke=E.length;z.push([ke-6,ke-5,ke-4],[ke-3,ke-2,ke-1])}var be=V;V=G,G=be;var Be=re;re=H,H=Be;var Le=j;j=N,N=Le}return{positions:E,cells:z,vectors:q,vertexIntensity:Z}},f=function(T,L,x,C){for(var S=0,g=0;g<T.length;g++)for(var P=T[g].velocities,E=0;E<P.length;E++)S=Math.max(S,s.length(P[E]));for(var z=T.map(function(_e){return c(_e,x,C,S)}),q=[],V=[],G=[],Z=[],g=0;g<z.length;g++){var j=z[g],N=q.length;q=q.concat(j.positions),G=G.concat(j.vectors),Z=Z.concat(j.vertexIntensity);for(var E=0;E<j.cells.length;E++){var H=j.cells[E],re=[];V.push(re);for(var ae=0;ae<H.length;ae++)re.push(H[ae]+N)}}return{positions:q,cells:V,vectors:G,vertexIntensity:Z,colormap:L}},h=function(T,L){var x=T.length,C;for(C=0;C<x;C++){var S=T[C];if(S===L)return C;if(S>L)return C-1}return C},d=function(T,L,x){return T<L?L:T>x?x:T},v=function(T,L,x){var C=L.vectors,S=L.meshgrid,g=T[0],P=T[1],E=T[2],z=S[0].length,q=S[1].length,V=S[2].length,G=h(S[0],g),Z=h(S[1],P),j=h(S[2],E),N=G+1,H=Z+1,re=j+1;if(G=d(G,0,z-1),N=d(N,0,z-1),Z=d(Z,0,q-1),H=d(H,0,q-1),j=d(j,0,V-1),re=d(re,0,V-1),G<0||Z<0||j<0||N>z-1||H>q-1||re>V-1)return s.create();var ae=S[0][G],_e=S[0][N],Ee=S[1][Z],Ce=S[1][H],ge=S[2][j],ie=S[2][re],Se=(g-ae)/(_e-ae),ke=(P-Ee)/(Ce-Ee),be=(E-ge)/(ie-ge);isFinite(Se)||(Se=.5),isFinite(ke)||(ke=.5),isFinite(be)||(be=.5);var Be,Le,me,Pe,ce,He;switch(x.reversedX&&(G=z-1-G,N=z-1-N),x.reversedY&&(Z=q-1-Z,H=q-1-H),x.reversedZ&&(j=V-1-j,re=V-1-re),x.filled){case 5:ce=j,He=re,me=Z*V,Pe=H*V,Be=G*V*q,Le=N*V*q;break;case 4:ce=j,He=re,Be=G*V,Le=N*V,me=Z*V*z,Pe=H*V*z;break;case 3:me=Z,Pe=H,ce=j*q,He=re*q,Be=G*q*V,Le=N*q*V;break;case 2:me=Z,Pe=H,Be=G*q,Le=N*q,ce=j*q*z,He=re*q*z;break;case 1:Be=G,Le=N,ce=j*z,He=re*z,me=Z*z*V,Pe=H*z*V;break;default:Be=G,Le=N,me=Z*z,Pe=H*z,ce=j*z*q,He=re*z*q;break}var lt=C[Be+me+ce],mt=C[Be+me+He],Ht=C[Be+Pe+ce],at=C[Be+Pe+He],ct=C[Le+me+ce],ar=C[Le+me+He],Vt=C[Le+Pe+ce],rr=C[Le+Pe+He],tt=s.create(),je=s.create(),Ue=s.create(),Ae=s.create();s.lerp(tt,lt,ct,Se),s.lerp(je,mt,ar,Se),s.lerp(Ue,Ht,Vt,Se),s.lerp(Ae,at,rr,Se);var rt=s.create(),St=s.create();s.lerp(rt,tt,Ue,ke),s.lerp(St,je,Ae,ke);var Tt=s.create();return s.lerp(Tt,rt,St,be),Tt},m=function(T,L){var x=L[0],C=L[1],S=L[2];return T[0]=x<0?-x:x,T[1]=C<0?-C:C,T[2]=S<0?-S:S,T},b=function(T){var L=1/0;T.sort(function(g,P){return g-P});for(var x=T.length,C=1;C<x;C++){var S=Math.abs(T[C]-T[C-1]);S<L&&(L=S)}return L},p=function(T){for(var L=[],x=[],C=[],S={},g={},P={},E=T.length,z=0;z<E;z++){var q=T[z],V=q[0],G=q[1],Z=q[2];S[V]||(L.push(V),S[V]=!0),g[G]||(x.push(G),g[G]=!0),P[Z]||(C.push(Z),P[Z]=!0)}var j=b(L),N=b(x),H=b(C),re=Math.min(j,N,H);return isFinite(re)?re:1};i.exports=function(T,L){var x=T.startingPositions,C=T.maxLength||1e3,S=T.tubeSize||1,g=T.absoluteTubeSize,P=T.gridFill||\"+x+y+z\",E={};P.indexOf(\"-x\")!==-1&&(E.reversedX=!0),P.indexOf(\"-y\")!==-1&&(E.reversedY=!0),P.indexOf(\"-z\")!==-1&&(E.reversedZ=!0),E.filled=u.indexOf(P.replace(/-/g,\"\").replace(/\\+/g,\"\"));var z=T.getVelocity||function(ar){return v(ar,T,E)},q=T.getDivergence||function(ar,Vt){var rr=s.create(),tt=1e-4;s.add(rr,ar,[tt,0,0]);var je=z(rr);s.subtract(je,je,Vt),s.scale(je,je,1/tt),s.add(rr,ar,[0,tt,0]);var Ue=z(rr);s.subtract(Ue,Ue,Vt),s.scale(Ue,Ue,1/tt),s.add(rr,ar,[0,0,tt]);var Ae=z(rr);return s.subtract(Ae,Ae,Vt),s.scale(Ae,Ae,1/tt),s.add(rr,je,Ue),s.add(rr,rr,Ae),rr},V=[],G=L[0][0],Z=L[0][1],j=L[0][2],N=L[1][0],H=L[1][1],re=L[1][2],ae=function(ar){var Vt=ar[0],rr=ar[1],tt=ar[2];return!(Vt<G||Vt>N||rr<Z||rr>H||tt<j||tt>re)},_e=s.distance(L[0],L[1]),Ee=10*_e/C,Ce=Ee*Ee,ge=1,ie=0,Se=x.length;Se>1&&(ge=p(x));for(var ke=0;ke<Se;ke++){var be=s.create();s.copy(be,x[ke]);var Be=[be],Le=[],me=z(be),Pe=be;Le.push(me);var ce=[],He=q(be,me),lt=s.length(He);isFinite(lt)&&lt>ie&&(ie=lt),ce.push(lt),V.push({points:Be,velocities:Le,divergences:ce});for(var mt=0;mt<C*100&&Be.length<C&&ae(be);){mt++;var Ht=s.clone(me),at=s.squaredLength(Ht);if(at===0)break;if(at>Ce&&s.scale(Ht,Ht,Ee/Math.sqrt(at)),s.add(Ht,Ht,be),me=z(Ht),s.squaredDistance(Pe,Ht)-Ce>-1e-4*Ce){Be.push(Ht),Pe=Ht,Le.push(me);var He=q(Ht,me),lt=s.length(He);isFinite(lt)&&lt>ie&&(ie=lt),ce.push(lt)}be=Ht}}var ct=f(V,T.colormap,ie,ge);return g?ct.tubeScale=g:(ie===0&&(ie=1),ct.tubeScale=S*.5*ge/ie),ct};var k=o(6740),M=o(6405).createMesh;i.exports.createTubeMesh=function(T,L){return M(T,L,{shaders:k,traceType:\"streamtube\"})}}),990:(function(i,a,o){var s=o(9405),l=o(3236),u=l([`precision highp float;\n",
+       "}`]);i.meshShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},i.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}}),7815:(function(a,i,o){\"use strict\";var s=o(2931),l=o(9970),u=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],c=function(S,L,w,A){for(var T=S.points,m=S.velocities,F=S.divergences,E=[],I=[],R=[],B=[],O=[],V=[],j=0,q=0,U=l.create(),J=l.create(),ee=8,ce=0;ce<T.length;ce++){var ye=T[ce],xe=m[ce],ue=F[ce];L===0&&(ue=w*.05),q=s.length(xe)/A,U=l.create(),s.copy(U,xe),U[3]=ue;for(var K=0;K<ee;K++)O[K]=[ye[0],ye[1],ye[2],K];if(B.length>0)for(var K=0;K<ee;K++){var _e=(K+1)%ee;E.push(B[K],O[K],O[_e],O[_e],B[_e],B[K]),R.push(J,U,U,U,J,J),V.push(j,q,q,q,j,j);var ne=E.length;I.push([ne-6,ne-5,ne-4],[ne-3,ne-2,ne-1])}var ae=B;B=O,O=ae;var pe=J;J=U,U=pe;var ie=j;j=q,q=ie}return{positions:E,cells:I,vectors:R,vertexIntensity:V}},f=function(S,L,w,A){for(var T=0,m=0;m<S.length;m++)for(var F=S[m].velocities,E=0;E<F.length;E++)T=Math.max(T,s.length(F[E]));for(var I=S.map(function(ce){return c(ce,w,A,T)}),R=[],B=[],O=[],V=[],m=0;m<I.length;m++){var j=I[m],q=R.length;R=R.concat(j.positions),O=O.concat(j.vectors),V=V.concat(j.vertexIntensity);for(var E=0;E<j.cells.length;E++){var U=j.cells[E],J=[];B.push(J);for(var ee=0;ee<U.length;ee++)J.push(U[ee]+q)}}return{positions:R,cells:B,vectors:O,vertexIntensity:V,colormap:L}},v=function(S,L){var w=S.length,A;for(A=0;A<w;A++){var T=S[A];if(T===L)return A;if(T>L)return A-1}return A},g=function(S,L,w){return S<L?L:S>w?w:S},p=function(S,L,w){var A=L.vectors,T=L.meshgrid,m=S[0],F=S[1],E=S[2],I=T[0].length,R=T[1].length,B=T[2].length,O=v(T[0],m),V=v(T[1],F),j=v(T[2],E),q=O+1,U=V+1,J=j+1;if(O=g(O,0,I-1),q=g(q,0,I-1),V=g(V,0,R-1),U=g(U,0,R-1),j=g(j,0,B-1),J=g(J,0,B-1),O<0||V<0||j<0||q>I-1||U>R-1||J>B-1)return s.create();var ee=T[0][O],ce=T[0][q],ye=T[1][V],xe=T[1][U],ue=T[2][j],K=T[2][J],_e=(m-ee)/(ce-ee),ne=(F-ye)/(xe-ye),ae=(E-ue)/(K-ue);isFinite(_e)||(_e=.5),isFinite(ne)||(ne=.5),isFinite(ae)||(ae=.5);var pe,ie,le,me,be,De;switch(w.reversedX&&(O=I-1-O,q=I-1-q),w.reversedY&&(V=R-1-V,U=R-1-U),w.reversedZ&&(j=B-1-j,J=B-1-J),w.filled){case 5:be=j,De=J,le=V*B,me=U*B,pe=O*B*R,ie=q*B*R;break;case 4:be=j,De=J,pe=O*B,ie=q*B,le=V*B*I,me=U*B*I;break;case 3:le=V,me=U,be=j*R,De=J*R,pe=O*R*B,ie=q*R*B;break;case 2:le=V,me=U,pe=O*R,ie=q*R,be=j*R*I,De=J*R*I;break;case 1:pe=O,ie=q,be=j*I,De=J*I,le=V*I*B,me=U*I*B;break;default:pe=O,ie=q,le=V*I,me=U*I,be=j*I*R,De=J*I*R;break}var Ge=A[pe+le+be],ht=A[pe+le+De],it=A[pe+me+be],Oe=A[pe+me+De],ze=A[ie+le+be],ct=A[ie+le+De],et=A[ie+me+be],qe=A[ie+me+De],we=s.create(),Le=s.create(),Ke=s.create(),Te=s.create();s.lerp(we,Ge,ze,_e),s.lerp(Le,ht,ct,_e),s.lerp(Ke,it,et,_e),s.lerp(Te,Oe,qe,_e);var Ve=s.create(),$e=s.create();s.lerp(Ve,we,Ke,ne),s.lerp($e,Le,Te,ne);var ot=s.create();return s.lerp(ot,Ve,$e,ae),ot},x=function(S,L){var w=L[0],A=L[1],T=L[2];return S[0]=w<0?-w:w,S[1]=A<0?-A:A,S[2]=T<0?-T:T,S},_=function(S){var L=1/0;S.sort(function(m,F){return m-F});for(var w=S.length,A=1;A<w;A++){var T=Math.abs(S[A]-S[A-1]);T<L&&(L=T)}return L},y=function(S){for(var L=[],w=[],A=[],T={},m={},F={},E=S.length,I=0;I<E;I++){var R=S[I],B=R[0],O=R[1],V=R[2];T[B]||(L.push(B),T[B]=!0),m[O]||(w.push(O),m[O]=!0),F[V]||(A.push(V),F[V]=!0)}var j=_(L),q=_(w),U=_(A),J=Math.min(j,q,U);return isFinite(J)?J:1};a.exports=function(S,L){var w=S.startingPositions,A=S.maxLength||1e3,T=S.tubeSize||1,m=S.absoluteTubeSize,F=S.gridFill||\"+x+y+z\",E={};F.indexOf(\"-x\")!==-1&&(E.reversedX=!0),F.indexOf(\"-y\")!==-1&&(E.reversedY=!0),F.indexOf(\"-z\")!==-1&&(E.reversedZ=!0),E.filled=u.indexOf(F.replace(/-/g,\"\").replace(/\\+/g,\"\"));var I=S.getVelocity||function(ct){return p(ct,S,E)},R=S.getDivergence||function(ct,et){var qe=s.create(),we=1e-4;s.add(qe,ct,[we,0,0]);var Le=I(qe);s.subtract(Le,Le,et),s.scale(Le,Le,1/we),s.add(qe,ct,[0,we,0]);var Ke=I(qe);s.subtract(Ke,Ke,et),s.scale(Ke,Ke,1/we),s.add(qe,ct,[0,0,we]);var Te=I(qe);return s.subtract(Te,Te,et),s.scale(Te,Te,1/we),s.add(qe,Le,Ke),s.add(qe,qe,Te),qe},B=[],O=L[0][0],V=L[0][1],j=L[0][2],q=L[1][0],U=L[1][1],J=L[1][2],ee=function(ct){var et=ct[0],qe=ct[1],we=ct[2];return!(et<O||et>q||qe<V||qe>U||we<j||we>J)},ce=s.distance(L[0],L[1]),ye=10*ce/A,xe=ye*ye,ue=1,K=0,_e=w.length;_e>1&&(ue=y(w));for(var ne=0;ne<_e;ne++){var ae=s.create();s.copy(ae,w[ne]);var pe=[ae],ie=[],le=I(ae),me=ae;ie.push(le);var be=[],De=R(ae,le),Ge=s.length(De);isFinite(Ge)&&Ge>K&&(K=Ge),be.push(Ge),B.push({points:pe,velocities:ie,divergences:be});for(var ht=0;ht<A*100&&pe.length<A&&ee(ae);){ht++;var it=s.clone(le),Oe=s.squaredLength(it);if(Oe===0)break;if(Oe>xe&&s.scale(it,it,ye/Math.sqrt(Oe)),s.add(it,it,ae),le=I(it),s.squaredDistance(me,it)-xe>-1e-4*xe){pe.push(it),me=it,ie.push(le);var De=R(it,le),Ge=s.length(De);isFinite(Ge)&&Ge>K&&(K=Ge),be.push(Ge)}ae=it}}var ze=f(B,S.colormap,K,ue);return m?ze.tubeScale=m:(K===0&&(K=1),ze.tubeScale=T*.5*ue/K),ze};var C=o(6740),k=o(6405).createMesh;a.exports.createTubeMesh=function(S,L){return k(S,L,{shaders:C,traceType:\"streamtube\"})}}),990:(function(a,i,o){var s=o(9405),l=o(3236),u=l([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute vec4 uv;\n",
@@ -1998,7 +2001,7 @@
        "  eyeDirection    = vec3(0,1,0);\n",
        "  lightDirection  = vec3(0,0,1);\n",
        "}\n",
-       "`]),h=l([`precision highp float;\n",
+       "`]),v=l([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "bool outOfRange(float a, float b, float p) {\n",
@@ -2045,15 +2048,13 @@
        "  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n",
        "  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n",
        "}\n",
-       "`]);a.createShader=function(d){var v=s(d,u,c,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v.attributes.normal.location=2,v},a.createPickShader=function(d){var v=s(d,u,h,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v.attributes.normal.location=2,v},a.createContourShader=function(d){var v=s(d,f,c,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v},a.createPickContourShader=function(d){var v=s(d,f,h,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return v.attributes.uv.location=0,v.attributes.f.location=1,v}}),9499:(function(i,a,o){\"use strict\";i.exports=Be;var s=o(8828),l=o(2762),u=o(8116),c=o(7766),f=o(1888),h=o(6729),d=o(5298),v=o(9994),m=o(9618),b=o(3711),p=o(6760),k=o(7608),M=o(2478),T=o(6199),L=o(990),x=L.createShader,C=L.createContourShader,S=L.createPickShader,g=L.createPickContourShader,P=40,E=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],z=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],q=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];(function(){for(var Le=0;Le<3;++Le){var me=q[Le],Pe=(Le+1)%3,ce=(Le+2)%3;me[Pe+0]=1,me[ce+3]=1,me[Le+6]=1}})();function V(Le,me,Pe,ce,He){this.position=Le,this.index=me,this.uv=Pe,this.level=ce,this.dataCoordinate=He}var G=256;function Z(Le,me,Pe,ce,He,lt,mt,Ht,at,ct,ar,Vt,rr,tt,je){this.gl=Le,this.shape=me,this.bounds=Pe,this.objectOffset=je,this.intensityBounds=[],this._shader=ce,this._pickShader=He,this._coordinateBuffer=lt,this._vao=mt,this._colorMap=Ht,this._contourShader=at,this._contourPickShader=ct,this._contourBuffer=ar,this._contourVAO=Vt,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new V([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=rr,this._dynamicVAO=tt,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[m(f.mallocFloat(1024),[0,0]),m(f.mallocFloat(1024),[0,0]),m(f.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var j=Z.prototype;j.genColormap=function(Le,me){var Pe=!1,ce=v([h({colormap:Le,nshades:G,format:\"rgba\"}).map(function(He,lt){var mt=me?N(lt/255,me):He[3];return mt<1&&(Pe=!0),[He[0],He[1],He[2],255*mt]})]);return d.divseq(ce,255),this.hasAlphaScale=Pe,ce},j.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},j.isOpaque=function(){return!this.isTransparent()},j.pickSlots=1,j.setPickBase=function(Le){this.pickId=Le};function N(Le,me){if(!me||!me.length)return 1;for(var Pe=0;Pe<me.length;++Pe){if(me.length<2)return 1;if(me[Pe][0]===Le)return me[Pe][1];if(me[Pe][0]>Le&&Pe>0){var ce=(me[Pe][0]-Le)/(me[Pe][0]-me[Pe-1][0]);return me[Pe][1]*(1-ce)+ce*me[Pe-1][1]}}return 1}var H=[0,0,0],re={showSurface:!1,showContour:!1,projections:[E.slice(),E.slice(),E.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function ae(Le,me){var Pe,ce,He,lt=me.axes&&me.axes.lastCubeProps.axis||H,mt=me.showSurface,Ht=me.showContour;for(Pe=0;Pe<3;++Pe)for(mt=mt||me.surfaceProject[Pe],ce=0;ce<3;++ce)Ht=Ht||me.contourProject[Pe][ce];for(Pe=0;Pe<3;++Pe){var at=re.projections[Pe];for(ce=0;ce<16;++ce)at[ce]=0;for(ce=0;ce<4;++ce)at[5*ce]=1;at[5*Pe]=0,at[12+Pe]=me.axesBounds[+(lt[Pe]>0)][Pe],p(at,Le.model,at);var ct=re.clipBounds[Pe];for(He=0;He<2;++He)for(ce=0;ce<3;++ce)ct[He][ce]=Le.clipBounds[He][ce];ct[0][Pe]=-1e8,ct[1][Pe]=1e8}return re.showSurface=mt,re.showContour=Ht,re}var _e={model:E,view:E,projection:E,inverseModel:E.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},Ee=E.slice(),Ce=[1,0,0,0,1,0,0,0,1];function ge(Le,me){Le=Le||{};var Pe=this.gl;Pe.disable(Pe.CULL_FACE),this._colorMap.bind(0);var ce=_e;ce.model=Le.model||E,ce.view=Le.view||E,ce.projection=Le.projection||E,ce.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],ce.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],ce.objectOffset=this.objectOffset,ce.contourColor=this.contourColor[0],ce.inverseModel=k(ce.inverseModel,ce.model);for(var He=0;He<2;++He)for(var lt=ce.clipBounds[He],mt=0;mt<3;++mt)lt[mt]=Math.min(Math.max(this.clipBounds[He][mt],-1e8),1e8);ce.kambient=this.ambientLight,ce.kdiffuse=this.diffuseLight,ce.kspecular=this.specularLight,ce.roughness=this.roughness,ce.fresnel=this.fresnel,ce.opacity=this.opacity,ce.height=0,ce.permutation=Ce,ce.vertexColor=this.vertexColor;var Ht=Ee;for(p(Ht,ce.view,ce.model),p(Ht,ce.projection,Ht),k(Ht,Ht),He=0;He<3;++He)ce.eyePosition[He]=Ht[12+He]/Ht[15];var at=Ht[15];for(He=0;He<3;++He)at+=this.lightPosition[He]*Ht[4*He+3];for(He=0;He<3;++He){var ct=Ht[12+He];for(mt=0;mt<3;++mt)ct+=Ht[4*mt+He]*this.lightPosition[mt];ce.lightPosition[He]=ct/at}var ar=ae(ce,this);if(ar.showSurface){for(this._shader.bind(),this._shader.uniforms=ce,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(Pe.TRIANGLES,this._vertexCount),He=0;He<3;++He)!this.surfaceProject[He]||!this.vertexCount||(this._shader.uniforms.model=ar.projections[He],this._shader.uniforms.clipBounds=ar.clipBounds[He],this._vao.draw(Pe.TRIANGLES,this._vertexCount));this._vao.unbind()}if(ar.showContour){var Vt=this._contourShader;ce.kambient=1,ce.kdiffuse=0,ce.kspecular=0,ce.opacity=1,Vt.bind(),Vt.uniforms=ce;var rr=this._contourVAO;for(rr.bind(),He=0;He<3;++He)for(Vt.uniforms.permutation=q[He],Pe.lineWidth(this.contourWidth[He]*this.pixelRatio),mt=0;mt<this.contourLevels[He].length;++mt)mt===this.highlightLevel[He]?(Vt.uniforms.contourColor=this.highlightColor[He],Vt.uniforms.contourTint=this.highlightTint[He]):(mt===0||mt-1===this.highlightLevel[He])&&(Vt.uniforms.contourColor=this.contourColor[He],Vt.uniforms.contourTint=this.contourTint[He]),this._contourCounts[He][mt]&&(Vt.uniforms.height=this.contourLevels[He][mt],rr.draw(Pe.LINES,this._contourCounts[He][mt],this._contourOffsets[He][mt]));for(He=0;He<3;++He)for(Vt.uniforms.model=ar.projections[He],Vt.uniforms.clipBounds=ar.clipBounds[He],mt=0;mt<3;++mt)if(this.contourProject[He][mt]){Vt.uniforms.permutation=q[mt],Pe.lineWidth(this.contourWidth[mt]*this.pixelRatio);for(var tt=0;tt<this.contourLevels[mt].length;++tt)tt===this.highlightLevel[mt]?(Vt.uniforms.contourColor=this.highlightColor[mt],Vt.uniforms.contourTint=this.highlightTint[mt]):(tt===0||tt-1===this.highlightLevel[mt])&&(Vt.uniforms.contourColor=this.contourColor[mt],Vt.uniforms.contourTint=this.contourTint[mt]),this._contourCounts[mt][tt]&&(Vt.uniforms.height=this.contourLevels[mt][tt],rr.draw(Pe.LINES,this._contourCounts[mt][tt],this._contourOffsets[mt][tt]))}for(rr.unbind(),rr=this._dynamicVAO,rr.bind(),He=0;He<3;++He)if(this._dynamicCounts[He]!==0)for(Vt.uniforms.model=ce.model,Vt.uniforms.clipBounds=ce.clipBounds,Vt.uniforms.permutation=q[He],Pe.lineWidth(this.dynamicWidth[He]*this.pixelRatio),Vt.uniforms.contourColor=this.dynamicColor[He],Vt.uniforms.contourTint=this.dynamicTint[He],Vt.uniforms.height=this.dynamicLevel[He],rr.draw(Pe.LINES,this._dynamicCounts[He],this._dynamicOffsets[He]),mt=0;mt<3;++mt)this.contourProject[mt][He]&&(Vt.uniforms.model=ar.projections[mt],Vt.uniforms.clipBounds=ar.clipBounds[mt],rr.draw(Pe.LINES,this._dynamicCounts[He],this._dynamicOffsets[He]));rr.unbind()}}j.draw=function(Le){return ge.call(this,Le,!1)},j.drawTransparent=function(Le){return ge.call(this,Le,!0)};var ie={model:E,view:E,projection:E,inverseModel:E,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};j.drawPick=function(Le){Le=Le||{};var me=this.gl;me.disable(me.CULL_FACE);var Pe=ie;Pe.model=Le.model||E,Pe.view=Le.view||E,Pe.projection=Le.projection||E,Pe.shape=this._field[2].shape,Pe.pickId=this.pickId/255,Pe.lowerBound=this.bounds[0],Pe.upperBound=this.bounds[1],Pe.objectOffset=this.objectOffset,Pe.permutation=Ce;for(var ce=0;ce<2;++ce)for(var He=Pe.clipBounds[ce],lt=0;lt<3;++lt)He[lt]=Math.min(Math.max(this.clipBounds[ce][lt],-1e8),1e8);var mt=ae(Pe,this);if(mt.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=Pe,this._vao.bind(),this._vao.draw(me.TRIANGLES,this._vertexCount),ce=0;ce<3;++ce)this.surfaceProject[ce]&&(this._pickShader.uniforms.model=mt.projections[ce],this._pickShader.uniforms.clipBounds=mt.clipBounds[ce],this._vao.draw(me.TRIANGLES,this._vertexCount));this._vao.unbind()}if(mt.showContour){var Ht=this._contourPickShader;Ht.bind(),Ht.uniforms=Pe;var at=this._contourVAO;for(at.bind(),lt=0;lt<3;++lt)for(me.lineWidth(this.contourWidth[lt]*this.pixelRatio),Ht.uniforms.permutation=q[lt],ce=0;ce<this.contourLevels[lt].length;++ce)this._contourCounts[lt][ce]&&(Ht.uniforms.height=this.contourLevels[lt][ce],at.draw(me.LINES,this._contourCounts[lt][ce],this._contourOffsets[lt][ce]));for(ce=0;ce<3;++ce)for(Ht.uniforms.model=mt.projections[ce],Ht.uniforms.clipBounds=mt.clipBounds[ce],lt=0;lt<3;++lt)if(this.contourProject[ce][lt]){Ht.uniforms.permutation=q[lt],me.lineWidth(this.contourWidth[lt]*this.pixelRatio);for(var ct=0;ct<this.contourLevels[lt].length;++ct)this._contourCounts[lt][ct]&&(Ht.uniforms.height=this.contourLevels[lt][ct],at.draw(me.LINES,this._contourCounts[lt][ct],this._contourOffsets[lt][ct]))}at.unbind()}},j.pick=function(Le){if(!Le||Le.id!==this.pickId)return null;var me=this._field[2].shape,Pe=this._pickResult,ce=me[0]*(Le.value[0]+(Le.value[2]>>4)/16)/255,He=Math.floor(ce),lt=ce-He,mt=me[1]*(Le.value[1]+(Le.value[2]&15)/16)/255,Ht=Math.floor(mt),at=mt-Ht;He+=1,Ht+=1;var ct=Pe.position;ct[0]=ct[1]=ct[2]=0;for(var ar=0;ar<2;++ar)for(var Vt=ar?lt:1-lt,rr=0;rr<2;++rr)for(var tt=rr?at:1-at,je=He+ar,Ue=Ht+rr,Ae=Vt*tt,rt=0;rt<3;++rt)ct[rt]+=this._field[rt].get(je,Ue)*Ae;for(var St=this._pickResult.level,Tt=0;Tt<3;++Tt)if(St[Tt]=M.le(this.contourLevels[Tt],ct[Tt]),St[Tt]<0)this.contourLevels[Tt].length>0&&(St[Tt]=0);else if(St[Tt]<this.contourLevels[Tt].length-1){var dt=this.contourLevels[Tt][St[Tt]],Et=this.contourLevels[Tt][St[Tt]+1];Math.abs(dt-ct[Tt])>Math.abs(Et-ct[Tt])&&(St[Tt]+=1)}for(Pe.index[0]=lt<.5?He:He+1,Pe.index[1]=at<.5?Ht:Ht+1,Pe.uv[0]=ce/me[0],Pe.uv[1]=mt/me[1],rt=0;rt<3;++rt)Pe.dataCoordinate[rt]=this._field[rt].get(Pe.index[0],Pe.index[1]);return Pe},j.padField=function(Le,me){var Pe=me.shape.slice(),ce=Le.shape.slice();d.assign(Le.lo(1,1).hi(Pe[0],Pe[1]),me),d.assign(Le.lo(1).hi(Pe[0],1),me.hi(Pe[0],1)),d.assign(Le.lo(1,ce[1]-1).hi(Pe[0],1),me.lo(0,Pe[1]-1).hi(Pe[0],1)),d.assign(Le.lo(0,1).hi(1,Pe[1]),me.hi(1)),d.assign(Le.lo(ce[0]-1,1).hi(1,Pe[1]),me.lo(Pe[0]-1)),Le.set(0,0,me.get(0,0)),Le.set(0,ce[1]-1,me.get(0,Pe[1]-1)),Le.set(ce[0]-1,0,me.get(Pe[0]-1,0)),Le.set(ce[0]-1,ce[1]-1,me.get(Pe[0]-1,Pe[1]-1))};function Se(Le,me){return Array.isArray(Le)?[me(Le[0]),me(Le[1]),me(Le[2])]:[me(Le),me(Le),me(Le)]}function ke(Le){return Array.isArray(Le)?Le.length===3?[Le[0],Le[1],Le[2],1]:[Le[0],Le[1],Le[2],Le[3]]:[0,0,0,1]}function be(Le){if(Array.isArray(Le)){if(Array.isArray(Le))return[ke(Le[0]),ke(Le[1]),ke(Le[2])];var me=ke(Le);return[me.slice(),me.slice(),me.slice()]}}j.update=function(Le){Le=Le||{},this.objectOffset=Le.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in Le&&(this.contourWidth=Se(Le.contourWidth,Number)),\"showContour\"in Le&&(this.showContour=Se(Le.showContour,Boolean)),\"showSurface\"in Le&&(this.showSurface=!!Le.showSurface),\"contourTint\"in Le&&(this.contourTint=Se(Le.contourTint,Boolean)),\"contourColor\"in Le&&(this.contourColor=be(Le.contourColor)),\"contourProject\"in Le&&(this.contourProject=Se(Le.contourProject,function(yn){return Se(yn,Boolean)})),\"surfaceProject\"in Le&&(this.surfaceProject=Le.surfaceProject),\"dynamicColor\"in Le&&(this.dynamicColor=be(Le.dynamicColor)),\"dynamicTint\"in Le&&(this.dynamicTint=Se(Le.dynamicTint,Number)),\"dynamicWidth\"in Le&&(this.dynamicWidth=Se(Le.dynamicWidth,Number)),\"opacity\"in Le&&(this.opacity=Le.opacity),\"opacityscale\"in Le&&(this.opacityscale=Le.opacityscale),\"colorBounds\"in Le&&(this.colorBounds=Le.colorBounds),\"vertexColor\"in Le&&(this.vertexColor=Le.vertexColor?1:0),\"colormap\"in Le&&this._colorMap.setPixels(this.genColormap(Le.colormap,this.opacityscale));var me=Le.field||Le.coords&&Le.coords[2]||null,Pe=!1;if(me||(this._field[2].shape[0]||this._field[2].shape[2]?me=this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):me=this._field[2].hi(0,0)),\"field\"in Le||\"coords\"in Le){var ce=(me.shape[0]+2)*(me.shape[1]+2);ce>this._field[2].data.length&&(f.freeFloat(this._field[2].data),this._field[2].data=f.mallocFloat(s.nextPow2(ce))),this._field[2]=m(this._field[2].data,[me.shape[0]+2,me.shape[1]+2]),this.padField(this._field[2],me),this.shape=me.shape.slice();for(var He=this.shape,lt=0;lt<2;++lt)this._field[2].size>this._field[lt].data.length&&(f.freeFloat(this._field[lt].data),this._field[lt].data=f.mallocFloat(this._field[2].size)),this._field[lt]=m(this._field[lt].data,[He[0]+2,He[1]+2]);if(Le.coords){var mt=Le.coords;if(!Array.isArray(mt)||mt.length!==3)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(lt=0;lt<2;++lt){var Ht=mt[lt];for(rr=0;rr<2;++rr)if(Ht.shape[rr]!==He[rr])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[lt],Ht)}}else if(Le.ticks){var at=Le.ticks;if(!Array.isArray(at)||at.length!==2)throw new Error(\"gl-surface: invalid ticks\");for(lt=0;lt<2;++lt){var ct=at[lt];if((Array.isArray(ct)||ct.length)&&(ct=m(ct)),ct.shape[0]!==He[lt])throw new Error(\"gl-surface: invalid tick length\");var ar=m(ct.data,He);ar.stride[lt]=ct.stride[0],ar.stride[lt^1]=0,this.padField(this._field[lt],ar)}}else{for(lt=0;lt<2;++lt){var Vt=[0,0];Vt[lt]=1,this._field[lt]=m(this._field[lt].data,[He[0]+2,He[1]+2],Vt,0)}this._field[0].set(0,0,0);for(var rr=0;rr<He[0];++rr)this._field[0].set(rr+1,0,rr);for(this._field[0].set(He[0]+1,0,He[0]-1),this._field[1].set(0,0,0),rr=0;rr<He[1];++rr)this._field[1].set(0,rr+1,rr);this._field[1].set(0,He[1]+1,He[1]-1)}var tt=this._field,je=m(f.mallocFloat(tt[2].size*3*2),[3,He[0]+2,He[1]+2,2]);for(lt=0;lt<3;++lt)T(je.pick(lt),tt[lt],\"mirror\");var Ue=m(f.mallocFloat(tt[2].size*3),[He[0]+2,He[1]+2,3]);for(lt=0;lt<He[0]+2;++lt)for(rr=0;rr<He[1]+2;++rr){var Ae=je.get(0,lt,rr,0),rt=je.get(0,lt,rr,1),St=je.get(1,lt,rr,0),Tt=je.get(1,lt,rr,1),dt=je.get(2,lt,rr,0),Et=je.get(2,lt,rr,1),pt=St*Et-Tt*dt,jt=dt*rt-Et*Ae,or=Ae*Tt-rt*St,mr=Math.sqrt(pt*pt+jt*jt+or*or);mr<1e-8?(mr=Math.max(Math.abs(pt),Math.abs(jt),Math.abs(or)),mr<1e-8?(or=1,jt=pt=0,mr=1):mr=1/mr):mr=1/Math.sqrt(mr),Ue.set(lt,rr,0,pt*mr),Ue.set(lt,rr,1,jt*mr),Ue.set(lt,rr,2,or*mr)}f.free(je.data);var Ar=[1/0,1/0,1/0],ei=[-1/0,-1/0,-1/0],qr=1/0,jr=-1/0,gt=(He[0]-1)*(He[1]-1)*6,Ge=f.mallocFloat(s.nextPow2(10*gt)),Je=0,We=0;for(lt=0;lt<He[0]-1;++lt)e:for(rr=0;rr<He[1]-1;++rr){for(var et=0;et<2;++et)for(var xt=0;xt<2;++xt)for(var At=0;At<3;++At){var Kt=this._field[At].get(1+lt+et,1+rr+xt);if(isNaN(Kt)||!isFinite(Kt))continue e}for(At=0;At<6;++At){var Qt=lt+z[At][0],Mr=rr+z[At][1],Gr=this._field[0].get(Qt+1,Mr+1),Ir=this._field[1].get(Qt+1,Mr+1);Kt=this._field[2].get(Qt+1,Mr+1),pt=Ue.get(Qt+1,Mr+1,0),jt=Ue.get(Qt+1,Mr+1,1),or=Ue.get(Qt+1,Mr+1,2),Le.intensity&&(Yr=Le.intensity.get(Qt,Mr));var Yr=Le.intensity?Le.intensity.get(Qt,Mr):Kt+this.objectOffset[2];Ge[Je++]=Qt,Ge[Je++]=Mr,Ge[Je++]=Gr,Ge[Je++]=Ir,Ge[Je++]=Kt,Ge[Je++]=0,Ge[Je++]=Yr,Ge[Je++]=pt,Ge[Je++]=jt,Ge[Je++]=or,Ar[0]=Math.min(Ar[0],Gr+this.objectOffset[0]),Ar[1]=Math.min(Ar[1],Ir+this.objectOffset[1]),Ar[2]=Math.min(Ar[2],Kt+this.objectOffset[2]),qr=Math.min(qr,Yr),ei[0]=Math.max(ei[0],Gr+this.objectOffset[0]),ei[1]=Math.max(ei[1],Ir+this.objectOffset[1]),ei[2]=Math.max(ei[2],Kt+this.objectOffset[2]),jr=Math.max(jr,Yr),We+=1}}for(Le.intensityBounds&&(qr=+Le.intensityBounds[0],jr=+Le.intensityBounds[1]),lt=6;lt<Je;lt+=10)Ge[lt]=(Ge[lt]-qr)/(jr-qr);this._vertexCount=We,this._coordinateBuffer.update(Ge.subarray(0,Je)),f.freeFloat(Ge),f.free(Ue.data),this.bounds=[Ar,ei],this.intensity=Le.intensity||this._field[2],(this.intensityBounds[0]!==qr||this.intensityBounds[1]!==jr)&&(Pe=!0),this.intensityBounds=[qr,jr]}if(\"levels\"in Le){var _i=Le.levels;for(Array.isArray(_i[0])?_i=_i.slice():_i=[[],[],_i],lt=0;lt<3;++lt)_i[lt]=_i[lt].slice(),_i[lt].sort(function(yn,Mn){return yn-Mn});for(lt=0;lt<3;++lt)for(rr=0;rr<_i[lt].length;++rr)_i[lt][rr]-=this.objectOffset[lt];e:for(lt=0;lt<3;++lt){if(_i[lt].length!==this.contourLevels[lt].length){Pe=!0;break}for(rr=0;rr<_i[lt].length;++rr)if(_i[lt][rr]!==this.contourLevels[lt][rr]){Pe=!0;break e}}this.contourLevels=_i}if(Pe){tt=this._field,He=this.shape;for(var Pi=[],ai=0;ai<3;++ai){var mi=this.contourLevels[ai],un=[],Fn=[],An=[0,0,0];for(lt=0;lt<mi.length;++lt){var Hn=b(this._field[ai],mi[lt]);un.push(Pi.length/5|0),We=0;e:for(rr=0;rr<Hn.cells.length;++rr){var Qn=Hn.cells[rr];for(At=0;At<2;++At){var Vi=Hn.positions[Qn[At]],Jn=Vi[0],Yn=Math.floor(Jn)|0,Gt=Jn-Yn,wt=Vi[1],tr=Math.floor(wt)|0,ir=wt-tr,wr=!1;t:for(var Xr=0;Xr<3;++Xr){An[Xr]=0;var ti=(ai+Xr+1)%3;for(et=0;et<2;++et){var $r=et?Gt:1-Gt;for(Qt=Math.min(Math.max(Yn+et,0),He[0])|0,xt=0;xt<2;++xt){var Ri=xt?ir:1-ir;if(Mr=Math.min(Math.max(tr+xt,0),He[1])|0,Xr<2?Kt=this._field[ti].get(Qt,Mr):Kt=(this.intensity.get(Qt,Mr)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(Kt)||isNaN(Kt)){wr=!0;break t}var Hi=$r*Ri;An[Xr]+=Hi*Kt}}}if(!wr)Pi.push(An[0],An[1],Vi[0],Vi[1],An[2]),We+=1;else{if(At>0){for(var en=0;en<5;++en)Pi.pop();We-=1}continue e}}}Fn.push(We)}this._contourOffsets[ai]=un,this._contourCounts[ai]=Fn}var fn=f.mallocFloat(Pi.length);for(lt=0;lt<Pi.length;++lt)fn[lt]=Pi[lt];this._contourBuffer.update(fn),f.freeFloat(fn)}},j.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var Le=0;Le<3;++Le)f.freeFloat(this._field[Le].data)},j.highlight=function(Le){var me;if(!Le){this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],this.highlightLevel=[-1,-1,-1];return}for(me=0;me<3;++me)this.enableHighlight[me]?this.highlightLevel[me]=Le.level[me]:this.highlightLevel[me]=-1;var Pe;for(this.snapToData?Pe=Le.dataCoordinate:Pe=Le.position,me=0;me<3;++me)Pe[me]-=this.objectOffset[me];if(!((!this.enableDynamic[0]||Pe[0]===this.dynamicLevel[0])&&(!this.enableDynamic[1]||Pe[1]===this.dynamicLevel[1])&&(!this.enableDynamic[2]||Pe[2]===this.dynamicLevel[2]))){for(var ce=0,He=this.shape,lt=f.mallocFloat(12*He[0]*He[1]),mt=0;mt<3;++mt){if(!this.enableDynamic[mt]){this.dynamicLevel[mt]=NaN,this._dynamicCounts[mt]=0;continue}this.dynamicLevel[mt]=Pe[mt];var Ht=(mt+1)%3,at=(mt+2)%3,ct=this._field[mt],ar=this._field[Ht],Vt=this._field[at],rr=b(ct,Pe[mt]),tt=rr.cells,je=rr.positions;for(this._dynamicOffsets[mt]=ce,me=0;me<tt.length;++me)for(var Ue=tt[me],Ae=0;Ae<2;++Ae){var rt=je[Ue[Ae]],St=+rt[0],Tt=St|0,dt=Math.min(Tt+1,He[0])|0,Et=St-Tt,pt=1-Et,jt=+rt[1],or=jt|0,mr=Math.min(or+1,He[1])|0,Ar=jt-or,ei=1-Ar,qr=pt*ei,jr=pt*Ar,gt=Et*ei,Ge=Et*Ar,Je=qr*ar.get(Tt,or)+jr*ar.get(Tt,mr)+gt*ar.get(dt,or)+Ge*ar.get(dt,mr),We=qr*Vt.get(Tt,or)+jr*Vt.get(Tt,mr)+gt*Vt.get(dt,or)+Ge*Vt.get(dt,mr);if(isNaN(Je)||isNaN(We)){Ae&&(ce-=1);break}lt[2*ce+0]=Je,lt[2*ce+1]=We,ce+=1}this._dynamicCounts[mt]=ce-this._dynamicOffsets[mt]}this._dynamicBuffer.update(lt.subarray(0,2*ce)),f.freeFloat(lt)}};function Be(Le){var me=Le.gl,Pe=x(me),ce=S(me),He=C(me),lt=g(me),mt=l(me),Ht=u(me,[{buffer:mt,size:4,stride:P,offset:0},{buffer:mt,size:3,stride:P,offset:16},{buffer:mt,size:3,stride:P,offset:28}]),at=l(me),ct=u(me,[{buffer:at,size:4,stride:20,offset:0},{buffer:at,size:1,stride:20,offset:16}]),ar=l(me),Vt=u(me,[{buffer:ar,size:2,type:me.FLOAT}]),rr=c(me,1,G,me.RGBA,me.UNSIGNED_BYTE);rr.minFilter=me.LINEAR,rr.magFilter=me.LINEAR;var tt=new Z(me,[0,0],[[0,0,0],[0,0,0]],Pe,ce,mt,Ht,rr,He,lt,at,ct,ar,Vt,[0,0,0]),je={levels:[[],[],[]]};for(var Ue in Le)je[Ue]=Le[Ue];return je.colormap=je.colormap||\"jet\",tt.update(je),tt}}),7766:(function(i,a,o){\"use strict\";var s=o(9618),l=o(5298),u=o(1888);i.exports=g;var c=null,f=null,h=null;function d(P){c=[P.LINEAR,P.NEAREST_MIPMAP_LINEAR,P.LINEAR_MIPMAP_NEAREST,P.LINEAR_MIPMAP_NEAREST],f=[P.NEAREST,P.LINEAR,P.NEAREST_MIPMAP_NEAREST,P.NEAREST_MIPMAP_LINEAR,P.LINEAR_MIPMAP_NEAREST,P.LINEAR_MIPMAP_LINEAR],h=[P.REPEAT,P.CLAMP_TO_EDGE,P.MIRRORED_REPEAT]}function v(P){return typeof HTMLCanvasElement!=\"undefined\"&&P instanceof HTMLCanvasElement||typeof HTMLImageElement!=\"undefined\"&&P instanceof HTMLImageElement||typeof HTMLVideoElement!=\"undefined\"&&P instanceof HTMLVideoElement||typeof ImageData!=\"undefined\"&&P instanceof ImageData}var m=function(P,E){l.muls(P,E,255)};function b(P,E,z){var q=P.gl,V=q.getParameter(q.MAX_TEXTURE_SIZE);if(E<0||E>V||z<0||z>V)throw new Error(\"gl-texture2d: Invalid texture size\");return P._shape=[E,z],P.bind(),q.texImage2D(q.TEXTURE_2D,0,P.format,E,z,0,P.format,P.type,null),P._mipLevels=[0],P}function p(P,E,z,q,V,G){this.gl=P,this.handle=E,this.format=V,this.type=G,this._shape=[z,q],this._mipLevels=[0],this._magFilter=P.NEAREST,this._minFilter=P.NEAREST,this._wrapS=P.CLAMP_TO_EDGE,this._wrapT=P.CLAMP_TO_EDGE,this._anisoSamples=1;var Z=this,j=[this._wrapS,this._wrapT];Object.defineProperties(j,[{get:function(){return Z._wrapS},set:function(H){return Z.wrapS=H}},{get:function(){return Z._wrapT},set:function(H){return Z.wrapT=H}}]),this._wrapVector=j;var N=[this._shape[0],this._shape[1]];Object.defineProperties(N,[{get:function(){return Z._shape[0]},set:function(H){return Z.width=H}},{get:function(){return Z._shape[1]},set:function(H){return Z.height=H}}]),this._shapeVector=N}var k=p.prototype;Object.defineProperties(k,{minFilter:{get:function(){return this._minFilter},set:function(P){this.bind();var E=this.gl;if(this.type===E.FLOAT&&c.indexOf(P)>=0&&(E.getExtension(\"OES_texture_float_linear\")||(P=E.NEAREST)),f.indexOf(P)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+P);return E.texParameteri(E.TEXTURE_2D,E.TEXTURE_MIN_FILTER,P),this._minFilter=P}},magFilter:{get:function(){return this._magFilter},set:function(P){this.bind();var E=this.gl;if(this.type===E.FLOAT&&c.indexOf(P)>=0&&(E.getExtension(\"OES_texture_float_linear\")||(P=E.NEAREST)),f.indexOf(P)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+P);return E.texParameteri(E.TEXTURE_2D,E.TEXTURE_MAG_FILTER,P),this._magFilter=P}},mipSamples:{get:function(){return this._anisoSamples},set:function(P){var E=this._anisoSamples;if(this._anisoSamples=Math.max(P,1)|0,E!==this._anisoSamples){var z=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");z&&this.gl.texParameterf(this.gl.TEXTURE_2D,z.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(P){if(this.bind(),h.indexOf(P)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+P);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,P),this._wrapS=P}},wrapT:{get:function(){return this._wrapT},set:function(P){if(this.bind(),h.indexOf(P)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+P);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,P),this._wrapT=P}},wrap:{get:function(){return this._wrapVector},set:function(P){if(Array.isArray(P)||(P=[P,P]),P.length!==2)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var E=0;E<2;++E)if(h.indexOf(P[E])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+P);this._wrapS=P[0],this._wrapT=P[1];var z=this.gl;return this.bind(),z.texParameteri(z.TEXTURE_2D,z.TEXTURE_WRAP_S,this._wrapS),z.texParameteri(z.TEXTURE_2D,z.TEXTURE_WRAP_T,this._wrapT),P}},shape:{get:function(){return this._shapeVector},set:function(P){if(!Array.isArray(P))P=[P|0,P|0];else if(P.length!==2)throw new Error(\"gl-texture2d: Invalid texture shape\");return b(this,P[0]|0,P[1]|0),[P[0]|0,P[1]|0]}},width:{get:function(){return this._shape[0]},set:function(P){return P=P|0,b(this,P,this._shape[1]),P}},height:{get:function(){return this._shape[1]},set:function(P){return P=P|0,b(this,this._shape[0],P),P}}}),k.bind=function(P){var E=this.gl;return P!==void 0&&E.activeTexture(E.TEXTURE0+(P|0)),E.bindTexture(E.TEXTURE_2D,this.handle),P!==void 0?P|0:E.getParameter(E.ACTIVE_TEXTURE)-E.TEXTURE0},k.dispose=function(){this.gl.deleteTexture(this.handle)},k.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var P=Math.min(this._shape[0],this._shape[1]),E=0;P>0;++E,P>>>=1)this._mipLevels.indexOf(E)<0&&this._mipLevels.push(E)},k.setPixels=function(P,E,z,q){var V=this.gl;this.bind(),Array.isArray(E)?(q=z,z=E[1]|0,E=E[0]|0):(E=E||0,z=z||0),q=q||0;var G=v(P)?P:P.raw;if(G){var Z=this._mipLevels.indexOf(q)<0;Z?(V.texImage2D(V.TEXTURE_2D,0,this.format,this.format,this.type,G),this._mipLevels.push(q)):V.texSubImage2D(V.TEXTURE_2D,q,E,z,this.format,this.type,G)}else if(P.shape&&P.stride&&P.data){if(P.shape.length<2||E+P.shape[1]>this._shape[1]>>>q||z+P.shape[0]>this._shape[0]>>>q||E<0||z<0)throw new Error(\"gl-texture2d: Texture dimensions are out of bounds\");T(V,E,z,q,this.format,this.type,this._mipLevels,P)}else throw new Error(\"gl-texture2d: Unsupported data type\")};function M(P,E){return P.length===3?E[2]===1&&E[1]===P[0]*P[2]&&E[0]===P[2]:E[0]===1&&E[1]===P[0]}function T(P,E,z,q,V,G,Z,j){var N=j.dtype,H=j.shape.slice();if(H.length<2||H.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var re=0,ae=0,_e=M(H,j.stride.slice());N===\"float32\"?re=P.FLOAT:N===\"float64\"?(re=P.FLOAT,_e=!1,N=\"float32\"):N===\"uint8\"?re=P.UNSIGNED_BYTE:(re=P.UNSIGNED_BYTE,_e=!1,N=\"uint8\");var Ee=1;if(H.length===2)ae=P.LUMINANCE,H=[H[0],H[1],1],j=s(j.data,H,[j.stride[0],j.stride[1],1],j.offset);else if(H.length===3){if(H[2]===1)ae=P.ALPHA;else if(H[2]===2)ae=P.LUMINANCE_ALPHA;else if(H[2]===3)ae=P.RGB;else if(H[2]===4)ae=P.RGBA;else throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");Ee=H[2]}else throw new Error(\"gl-texture2d: Invalid shape for texture\");if((ae===P.LUMINANCE||ae===P.ALPHA)&&(V===P.LUMINANCE||V===P.ALPHA)&&(ae=V),ae!==V)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var Ce=j.size,ge=Z.indexOf(q)<0;if(ge&&Z.push(q),re===G&&_e)j.offset===0&&j.data.length===Ce?ge?P.texImage2D(P.TEXTURE_2D,q,V,H[0],H[1],0,V,G,j.data):P.texSubImage2D(P.TEXTURE_2D,q,E,z,H[0],H[1],V,G,j.data):ge?P.texImage2D(P.TEXTURE_2D,q,V,H[0],H[1],0,V,G,j.data.subarray(j.offset,j.offset+Ce)):P.texSubImage2D(P.TEXTURE_2D,q,E,z,H[0],H[1],V,G,j.data.subarray(j.offset,j.offset+Ce));else{var ie;G===P.FLOAT?ie=u.mallocFloat32(Ce):ie=u.mallocUint8(Ce);var Se=s(ie,H,[H[2],H[2]*H[0],1]);re===P.FLOAT&&G===P.UNSIGNED_BYTE?m(Se,j):l.assign(Se,j),ge?P.texImage2D(P.TEXTURE_2D,q,V,H[0],H[1],0,V,G,ie.subarray(0,Ce)):P.texSubImage2D(P.TEXTURE_2D,q,E,z,H[0],H[1],V,G,ie.subarray(0,Ce)),G===P.FLOAT?u.freeFloat32(ie):u.freeUint8(ie)}}function L(P){var E=P.createTexture();return P.bindTexture(P.TEXTURE_2D,E),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_MIN_FILTER,P.NEAREST),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_MAG_FILTER,P.NEAREST),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_WRAP_S,P.CLAMP_TO_EDGE),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_WRAP_T,P.CLAMP_TO_EDGE),E}function x(P,E,z,q,V){var G=P.getParameter(P.MAX_TEXTURE_SIZE);if(E<0||E>G||z<0||z>G)throw new Error(\"gl-texture2d: Invalid texture shape\");if(V===P.FLOAT&&!P.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var Z=L(P);return P.texImage2D(P.TEXTURE_2D,0,q,E,z,0,q,V,null),new p(P,Z,E,z,q,V)}function C(P,E,z,q,V,G){var Z=L(P);return P.texImage2D(P.TEXTURE_2D,0,V,V,G,E),new p(P,Z,z,q,V,G)}function S(P,E){var z=E.dtype,q=E.shape.slice(),V=P.getParameter(P.MAX_TEXTURE_SIZE);if(q[0]<0||q[0]>V||q[1]<0||q[1]>V)throw new Error(\"gl-texture2d: Invalid texture size\");var G=M(q,E.stride.slice()),Z=0;z===\"float32\"?Z=P.FLOAT:z===\"float64\"?(Z=P.FLOAT,G=!1,z=\"float32\"):z===\"uint8\"?Z=P.UNSIGNED_BYTE:(Z=P.UNSIGNED_BYTE,G=!1,z=\"uint8\");var j=0;if(q.length===2)j=P.LUMINANCE,q=[q[0],q[1],1],E=s(E.data,q,[E.stride[0],E.stride[1],1],E.offset);else if(q.length===3)if(q[2]===1)j=P.ALPHA;else if(q[2]===2)j=P.LUMINANCE_ALPHA;else if(q[2]===3)j=P.RGB;else if(q[2]===4)j=P.RGBA;else throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");else throw new Error(\"gl-texture2d: Invalid shape for texture\");Z===P.FLOAT&&!P.getExtension(\"OES_texture_float\")&&(Z=P.UNSIGNED_BYTE,G=!1);var N,H,re=E.size;if(G)E.offset===0&&E.data.length===re?N=E.data:N=E.data.subarray(E.offset,E.offset+re);else{var ae=[q[2],q[2]*q[0],1];H=u.malloc(re,z);var _e=s(H,q,ae,0);(z===\"float32\"||z===\"float64\")&&Z===P.UNSIGNED_BYTE?m(_e,E):l.assign(_e,E),N=H.subarray(0,re)}var Ee=L(P);return P.texImage2D(P.TEXTURE_2D,0,j,q[0],q[1],0,j,Z,N),G||u.free(H),new p(P,Ee,q[0],q[1],j,Z)}function g(P){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");if(c||d(P),typeof arguments[1]==\"number\")return x(P,arguments[1],arguments[2],arguments[3]||P.RGBA,arguments[4]||P.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return x(P,arguments[1][0]|0,arguments[1][1]|0,arguments[2]||P.RGBA,arguments[3]||P.UNSIGNED_BYTE);if(typeof arguments[1]==\"object\"){var E=arguments[1],z=v(E)?E:E.raw;if(z)return C(P,z,E.width|0,E.height|0,arguments[2]||P.RGBA,arguments[3]||P.UNSIGNED_BYTE);if(E.shape&&E.data&&E.stride)return S(P,E)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")}}),1433:(function(i){\"use strict\";function a(o,s,l){s?s.bind():o.bindBuffer(o.ELEMENT_ARRAY_BUFFER,null);var u=o.getParameter(o.MAX_VERTEX_ATTRIBS)|0;if(l){if(l.length>u)throw new Error(\"gl-vao: Too many vertex attributes\");for(var c=0;c<l.length;++c){var f=l[c];if(f.buffer){var h=f.buffer,d=f.size||4,v=f.type||o.FLOAT,m=!!f.normalized,b=f.stride||0,p=f.offset||0;h.bind(),o.enableVertexAttribArray(c),o.vertexAttribPointer(c,d,v,m,b,p)}else{if(typeof f==\"number\")o.vertexAttrib1f(c,f);else if(f.length===1)o.vertexAttrib1f(c,f[0]);else if(f.length===2)o.vertexAttrib2f(c,f[0],f[1]);else if(f.length===3)o.vertexAttrib3f(c,f[0],f[1],f[2]);else if(f.length===4)o.vertexAttrib4f(c,f[0],f[1],f[2],f[3]);else throw new Error(\"gl-vao: Invalid vertex attribute\");o.disableVertexAttribArray(c)}}for(;c<u;++c)o.disableVertexAttribArray(c)}else{o.bindBuffer(o.ARRAY_BUFFER,null);for(var c=0;c<u;++c)o.disableVertexAttribArray(c)}}i.exports=a}),870:(function(i,a,o){\"use strict\";var s=o(1433);function 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g=0;g<3;++g){for(var P=0,E=0;E<3;++E)P+=T[g+4*E]*x[E];T[12+g]=-P}},b.getMatrix=function(k,M){this.recalcMatrix(k);var T=this.computedMatrix;if(M){for(var L=0;L<16;++L)M[L]=T[L];return M}return T},b.idle=function(k){this.center.idle(k),this.radius.idle(k),this.rotation.idle(k)},b.flush=function(k){this.center.flush(k),this.radius.flush(k),this.rotation.flush(k)},b.pan=function(k,M,T,L){M=M||0,T=T||0,L=L||0,this.recalcMatrix(k);var x=this.computedMatrix,C=x[1],S=x[5],g=x[9],P=h(C,S,g);C/=P,S/=P,g/=P;var E=x[0],z=x[4],q=x[8],V=E*C+z*S+q*g;E-=C*V,z-=S*V,q-=g*V;var G=h(E,z,q);E/=G,z/=G,q/=G;var Z=x[2],j=x[6],N=x[10],H=Z*C+j*S+N*g,re=Z*E+j*z+N*q;Z-=H*C+re*E,j-=H*S+re*z,N-=H*g+re*q;var ae=h(Z,j,N);Z/=ae,j/=ae,N/=ae;var _e=E*M+C*T,Ee=z*M+S*T,Ce=q*M+g*T;this.center.move(k,_e,Ee,Ce);var ge=Math.exp(this.computedRadius[0]);ge=Math.max(1e-4,ge+L),this.radius.set(k,Math.log(ge))},b.rotate=function(k,M,T,L){this.recalcMatrix(k),M=M||0,T=T||0;var x=this.computedMatrix,C=x[0],S=x[4],g=x[8],P=x[1],E=x[5],z=x[9],q=x[2],V=x[6],G=x[10],Z=M*C+T*P,j=M*S+T*E,N=M*g+T*z,H=-(V*N-G*j),re=-(G*Z-q*N),ae=-(q*j-V*Z),_e=Math.sqrt(Math.max(0,1-Math.pow(H,2)-Math.pow(re,2)-Math.pow(ae,2))),Ee=d(H,re,ae,_e);Ee>1e-6?(H/=Ee,re/=Ee,ae/=Ee,_e/=Ee):(H=re=ae=0,_e=1);var Ce=this.computedRotation,ge=Ce[0],ie=Ce[1],Se=Ce[2],ke=Ce[3],be=ge*_e+ke*H+ie*ae-Se*re,Be=ie*_e+ke*re+Se*H-ge*ae,Le=Se*_e+ke*ae+ge*re-ie*H,me=ke*_e-ge*H-ie*re-Se*ae;if(L){H=q,re=V,ae=G;var Pe=Math.sin(L)/h(H,re,ae);H*=Pe,re*=Pe,ae*=Pe,_e=Math.cos(M),be=be*_e+me*H+Be*ae-Le*re,Be=Be*_e+me*re+Le*H-be*ae,Le=Le*_e+me*ae+be*re-Be*H,me=me*_e-be*H-Be*re-Le*ae}var ce=d(be,Be,Le,me);ce>1e-6?(be/=ce,Be/=ce,Le/=ce,me/=ce):(be=Be=Le=0,me=1),this.rotation.set(k,be,Be,Le,me)},b.lookAt=function(k,M,T,L){this.recalcMatrix(k),T=T||this.computedCenter,M=M||this.computedEye,L=L||this.computedUp;var x=this.computedMatrix;l(x,M,T,L);var C=this.computedRotation;f(C,x[0],x[1],x[2],x[4],x[5],x[6],x[8],x[9],x[10]),v(C,C),this.rotation.set(k,C[0],C[1],C[2],C[3]);for(var S=0,g=0;g<3;++g)S+=Math.pow(T[g]-M[g],2);this.radius.set(k,.5*Math.log(Math.max(S,1e-6))),this.center.set(k,T[0],T[1],T[2])},b.translate=function(k,M,T,L){this.center.move(k,M||0,T||0,L||0)},b.setMatrix=function(k,M){var T=this.computedRotation;f(T,M[0],M[1],M[2],M[4],M[5],M[6],M[8],M[9],M[10]),v(T,T),this.rotation.set(k,T[0],T[1],T[2],T[3]);var L=this.computedMatrix;c(L,M);var x=L[15];if(Math.abs(x)>1e-6){var C=L[12]/x,S=L[13]/x,g=L[14]/x;this.recalcMatrix(k);var P=Math.exp(this.computedRadius[0]);this.center.set(k,C-L[2]*P,S-L[6]*P,g-L[10]*P),this.radius.idle(k)}else 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M=k.center||[0,0,0],T=k.rotation||[0,0,0,1],L=k.radius||1;M=[].slice.call(M,0,3),T=[].slice.call(T,0,4),v(T,T);var x=new m(T,M,Math.log(L));return x.setDistanceLimits(k.zoomMin,k.zoomMax),(\"eye\"in k||\"up\"in k)&&x.lookAt(0,k.eye,k.center,k.up),x}}),1371:(function(i,a,o){\"use strict\";var s=o(3233);i.exports=function(u,c,f){return f=typeof f!=\"undefined\"?f+\"\":\" \",s(f,c)+u}}),3202:(function(i){i.exports=function(o,s){s||(s=[0,\"\"]),o=String(o);var l=parseFloat(o,10);return s[0]=l,s[1]=o.match(/[\\d.\\-\\+]*\\s*(.*)/)[1]||\"\",s}}),3088:(function(i,a,o){\"use strict\";i.exports=l;var s=o(3140);function l(u,c){for(var f=c.length|0,h=u.length,d=[new Array(f),new Array(f)],v=0;v<f;++v)d[0][v]=[],d[1][v]=[];for(var v=0;v<h;++v){var m=u[v];d[0][m[0]].push(m),d[1][m[1]].push(m)}for(var b=[],v=0;v<f;++v)d[0][v].length+d[1][v].length===0&&b.push([v]);function p(g,P){var E=d[P][g[P]];E.splice(E.indexOf(g),1)}function k(g,P,E){for(var 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k=f[p],m=0;m<k.length;++m){var M=k[m];--d[M]===0&&v.push(M)}}for(var T=new Array(c.length),L=[],m=0;m<c.length;++m)if(h[m]){var p=L.length;T[m]=p,L.push(c[m])}else T[m]=-1;for(var x=[],m=0;m<u.length;++m){var C=u[m];h[C[0]]&&h[C[1]]&&x.push([T[C[0]],T[C[1]]])}return[x,L]}}),2095:(function(i,a,o){\"use strict\";i.exports=b;var s=o(3134),l=o(3088),u=o(5085),c=o(5250),f=o(8210),h=o(1682),d=o(5609);function v(p,k){for(var M=new Array(p),T=0;T<p;++T)M[T]=k;return M}function m(p){for(var k=new Array(p),M=0;M<p;++M)k[M]=[];return k}function b(p,k){var Pe=d(p,k);p=Pe[0],k=Pe[1];for(var M=k.length,T=p.length,L=s(p,k.length),x=0;x<M;++x)if(L[x].length%2===1)throw new Error(\"planar-graph-to-polyline: graph must be manifold\");var C=l(p,k);function S(ct){for(var ar=ct.length,Vt=[0],rr=0;rr<ar;++rr){var tt=k[ct[rr]],je=k[ct[(rr+1)%ar]],Ue=c(-tt[0],tt[1]),Ae=c(-tt[0],je[1]),rt=c(je[0],tt[1]),St=c(je[0],je[1]);Vt=f(Vt,f(f(Ue,Ae),f(rt,St)))}return Vt[Vt.length-1]>0}C=C.filter(S);for(var g=C.length,P=new Array(g),E=new Array(g),x=0;x<g;++x){P[x]=x;var z=new Array(g),q=C[x].map(function(ar){return k[ar]}),V=u([q]),G=0;e:for(var Z=0;Z<g;++Z)if(z[Z]=0,x!==Z){for(var j=C[Z],N=j.length,H=0;H<N;++H){var re=V(k[j[H]]);if(re!==0){re<0&&(z[Z]=1,G+=1);continue e}}z[Z]=1,G+=1}E[x]=[G,x,z]}E.sort(function(ct,ar){return ar[0]-ct[0]});for(var x=0;x<g;++x)for(var z=E[x],ae=z[1],_e=z[2],Z=0;Z<g;++Z)_e[Z]&&(P[Z]=ae);for(var Ee=m(g),x=0;x<g;++x)Ee[x].push(P[x]),Ee[P[x]].push(x);for(var Ce={},ge=v(M,!1),x=0;x<g;++x)for(var j=C[x],N=j.length,Z=0;Z<N;++Z){var ie=j[Z],Se=j[(Z+1)%N],ke=Math.min(ie,Se)+\":\"+Math.max(ie,Se);if(ke in Ce){var be=Ce[ke];Ee[be].push(x),Ee[x].push(be),ge[ie]=ge[Se]=!0}else Ce[ke]=x}function Be(ct){for(var ar=ct.length,Vt=0;Vt<ar;++Vt)if(!ge[ct[Vt]])return!1;return!0}for(var Le=[],me=v(g,-1),x=0;x<g;++x)P[x]===x&&!Be(C[x])?(Le.push(x),me[x]=0):me[x]=-1;for(var Pe=[];Le.length>0;){var ce=Le.pop(),He=Ee[ce];h(He,function(ct,ar){return ct-ar});var lt=He.length,mt=me[ce],Ht;if(mt===0){var j=C[ce];Ht=[j]}for(var x=0;x<lt;++x){var at=He[x];if(!(me[at]>=0)&&(me[at]=mt^1,Le.push(at),mt===0)){var j=C[at];Be(j)||(j.reverse(),Ht.push(j))}}mt===0&&Pe.push(Ht)}return Pe}}),5085:(function(i,a,o){i.exports=k;var s=o(3250)[3],l=o(4209),u=o(3352),c=o(2478);function f(){return!0}function h(M){return function(T,L){var x=M[T];return x?!!x.queryPoint(L,f):!1}}function d(M){for(var T={},L=0;L<M.length;++L){var x=M[L],C=x[0][0],S=x[0][1],g=x[1][1],P=[Math.min(S,g),Math.max(S,g)];C in T?T[C].push(P):T[C]=[P]}for(var E={},z=Object.keys(T),L=0;L<z.length;++L){var q=T[z[L]];E[z[L]]=u(q)}return h(E)}function v(M,T){return function(L){var x=c.le(T,L[0]);if(x<0)return 1;var C=M[x];if(!C)if(x>0&&T[x]===L[0])C=M[x-1];else return 1;for(var S=1;C;){var g=C.key,P=s(L,g[0],g[1]);if(g[0][0]<g[1][0])if(P<0)C=C.left;else if(P>0)S=-1,C=C.right;else return 0;else if(P>0)C=C.left;else if(P<0)S=1,C=C.right;else return 0}return S}}function m(M){return 1}function 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9:m.push([l(h,f,p[3],p[1]),l(h,f,p[0],p[1]),l(h,f,p[0],p[2])],[l(h,f,p[3],p[2]),l(h,f,p[3],p[1]),l(h,f,p[0],p[2])]);break;case 10:m.push([l(h,f,p[1],p[0]),l(h,f,p[3],p[0]),l(h,f,p[1],p[2])],[l(h,f,p[3],p[0]),l(h,f,p[3],p[2]),l(h,f,p[1],p[2])]);break;case 11:m.push([l(h,f,p[1],p[2]),l(h,f,p[0],p[2]),l(h,f,p[3],p[2])]);break;case 12:m.push([l(h,f,p[3],p[0]),l(h,f,p[2],p[0]),l(h,f,p[2],p[1])],[l(h,f,p[3],p[1]),l(h,f,p[3],p[0]),l(h,f,p[2],p[1])]);break;case 13:m.push([l(h,f,p[0],p[1]),l(h,f,p[2],p[1]),l(h,f,p[3],p[1])]);break;case 14:m.push([l(h,f,p[2],p[0]),l(h,f,p[1],p[0]),l(h,f,p[3],p[0])]);break;case 15:break}}else if(k===3){var M=(d[p[0]]<<0)+(d[p[1]]<<1)+(d[p[2]]<<2);if(M===0||M===7)continue;switch(M){case 0:break;case 1:m.push([l(h,f,p[0],p[2]),l(h,f,p[0],p[1])]);break;case 2:m.push([l(h,f,p[1],p[0]),l(h,f,p[1],p[2])]);break;case 3:m.push([l(h,f,p[0],p[2]),l(h,f,p[1],p[2])]);break;case 4:m.push([l(h,f,p[2],p[1]),l(h,f,p[2],p[0])]);break;case 5:m.push([l(h,f,p[2],p[1]),l(h,f,p[0],p[1])]);break;case 6:m.push([l(h,f,p[1],p[0]),l(h,f,p[2],p[0])]);break;case 7:break}}else if(k===2){var M=(d[p[0]]<<0)+(d[p[1]]<<1);if(M===0||M===3)continue;switch(M){case 0:break;case 1:m.push([l(h,f,p[0],p[1])]);break;case 2:m.push([l(h,f,p[1],p[0])]);break;case 3:break}}}return m}return u}];function o(s){return a[s]()}}),6803:(function(i,a,o){\"use strict\";var s,l=o(8828),u=o(1755);function c(P){for(var E=0,z=Math.max,q=0,V=P.length;q<V;++q)E=z(E,P[q].length);return E-1}s=c;function f(P){for(var E=-1,z=Math.max,q=0,V=P.length;q<V;++q)for(var G=P[q],Z=0,j=G.length;Z<j;++Z)E=z(E,G[Z]);return E+1}s=f;function h(P){for(var E=new Array(P.length),z=0,q=P.length;z<q;++z)E[z]=P[z].slice(0);return E}s=h;function d(P,E){var z=P.length,q=P.length-E.length,V=Math.min;if(q)return q;switch(z){case 0:return 0;case 1:return P[0]-E[0];case 2:var H=P[0]+P[1]-E[0]-E[1];return H||V(P[0],P[1])-V(E[0],E[1]);case 3:var 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V=P[q],G=0;G<V.length;++G)for(var Z=G+1;Z<V.length;++Z)z.link(V[G],V[Z]);for(var j=[],N=z.ranks,q=0;q<N.length;++q)N[q]=-1;for(var q=0;q<P.length;++q){var H=z.find(P[q][0]);N[H]<0?(N[H]=j.length,j.push([P[q].slice(0)])):j[N[H]].push(P[q].slice(0))}return j}function S(P){for(var E=b(m(L(P,0))),z=new u(E.length),q=0;q<P.length;++q)for(var V=P[q],G=0;G<V.length;++G)for(var Z=p(E,[V[G]]),j=G+1;j<V.length;++j)z.link(Z,p(E,[V[j]]));for(var N=[],H=z.ranks,q=0;q<H.length;++q)H[q]=-1;for(var q=0;q<P.length;++q){var re=z.find(p(E,[P[q][0]]));H[re]<0?(H[re]=N.length,N.push([P[q].slice(0)])):N[H[re]].push(P[q].slice(0))}return N}function g(P,E){return E?C(P,E):S(P)}s=g}),3105:(function(i,a){\"use strict\";\"use restrict\";var o=32;a.INT_BITS=o,a.INT_MAX=2147483647,a.INT_MIN=-1<<o-1,a.sign=function(u){return(u>0)-(u<0)},a.abs=function(u){var c=u>>o-1;return(u^c)-c},a.min=function(u,c){return c^(u^c)&-(u<c)},a.max=function(u,c){return u^(u^c)&-(u<c)},a.isPow2=function(u){return!(u&u-1)&&!!u},a.log2=function(u){var c,f;return c=(u>65535)<<4,u>>>=c,f=(u>255)<<3,u>>>=f,c|=f,f=(u>15)<<2,u>>>=f,c|=f,f=(u>3)<<1,u>>>=f,c|=f,c|u>>1},a.log10=function(u){return u>=1e9?9:u>=1e8?8:u>=1e7?7:u>=1e6?6:u>=1e5?5:u>=1e4?4:u>=1e3?3:u>=100?2:u>=10?1:0},a.popCount=function(u){return u=u-(u>>>1&1431655765),u=(u&858993459)+(u>>>2&858993459),(u+(u>>>4)&252645135)*16843009>>>24};function s(u){var c=32;return u&=-u,u&&c--,u&65535&&(c-=16),u&16711935&&(c-=8),u&252645135&&(c-=4),u&858993459&&(c-=2),u&1431655765&&(c-=1),c}a.countTrailingZeros=s,a.nextPow2=function(u){return u+=u===0,--u,u|=u>>>1,u|=u>>>2,u|=u>>>4,u|=u>>>8,u|=u>>>16,u+1},a.prevPow2=function(u){return u|=u>>>1,u|=u>>>2,u|=u>>>4,u|=u>>>8,u|=u>>>16,u-(u>>>1)},a.parity=function(u){return u^=u>>>16,u^=u>>>8,u^=u>>>4,u&=15,27030>>>u&1};var l=new Array(256);(function(u){for(var c=0;c<256;++c){var 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u=u>>>c&1227133513,u=(u|u>>>2)&3272356035,u=(u|u>>>4)&251719695,u=(u|u>>>8)&4278190335,u=(u|u>>>16)&1023,u<<22>>22},a.nextCombination=function(u){var c=u|u-1;return c+1|(~c&-~c)-1>>>s(u)+1}}),2014:(function(i,a,o){\"use strict\";\"use restrict\";var s=o(3105),l=o(4623);function u(g){for(var P=0,E=Math.max,z=0,q=g.length;z<q;++z)P=E(P,g[z].length);return P-1}a.dimension=u;function c(g){for(var P=-1,E=Math.max,z=0,q=g.length;z<q;++z)for(var V=g[z],G=0,Z=V.length;G<Z;++G)P=E(P,V[G]);return P+1}a.countVertices=c;function f(g){for(var P=new Array(g.length),E=0,z=g.length;E<z;++E)P[E]=g[E].slice(0);return P}a.cloneCells=f;function h(g,P){var E=g.length,z=g.length-P.length,q=Math.min;if(z)return z;switch(E){case 0:return 0;case 1:return g[0]-P[0];case 2:var N=g[0]+g[1]-P[0]-P[1];return N||q(g[0],g[1])-q(P[0],P[1]);case 3:var V=g[0]+g[1],G=P[0]+P[1];if(N=V+g[2]-(G+P[2]),N)return N;var Z=q(g[0],g[1]),j=q(P[0],P[1]),N=q(Z,g[2])-q(j,P[2]);return N||q(Z+g[2],V)-q(j+P[2],G);default:var 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b(T,L,x,C,S,g,P,E){this.center=s(x),this.up=s(C),this.right=s(S),this.radius=s([g]),this.angle=s([P,E]),this.angle.bounds=[[-1/0,-Math.PI/2],[1/0,Math.PI/2]],this.setDistanceLimits(T,L),this.computedCenter=this.center.curve(0),this.computedUp=this.up.curve(0),this.computedRight=this.right.curve(0),this.computedRadius=this.radius.curve(0),this.computedAngle=this.angle.curve(0),this.computedToward=[0,0,0],this.computedEye=[0,0,0],this.computedMatrix=new Array(16);for(var z=0;z<16;++z)this.computedMatrix[z]=.5;this.recalcMatrix(0)}var p=b.prototype;p.setDistanceLimits=function(T,L){T>0?T=Math.log(T):T=-1/0,L>0?L=Math.log(L):L=1/0,L=Math.max(L,T),this.radius.bounds[0][0]=T,this.radius.bounds[1][0]=L},p.getDistanceLimits=function(T){var L=this.radius.bounds[0];return T?(T[0]=Math.exp(L[0][0]),T[1]=Math.exp(L[1][0]),T):[Math.exp(L[0][0]),Math.exp(L[1][0])]},p.recalcMatrix=function(T){this.center.curve(T),this.up.curve(T),this.right.curve(T),this.radius.curve(T),this.angle.curve(T);for(var L=this.computedUp,x=this.computedRight,C=0,S=0,g=0;g<3;++g)S+=L[g]*x[g],C+=L[g]*L[g];for(var P=Math.sqrt(C),E=0,g=0;g<3;++g)x[g]-=L[g]*S/C,E+=x[g]*x[g],L[g]/=P;for(var z=Math.sqrt(E),g=0;g<3;++g)x[g]/=z;var q=this.computedToward;c(q,L,x),f(q,q);for(var V=Math.exp(this.computedRadius[0]),G=this.computedAngle[0],Z=this.computedAngle[1],j=Math.cos(G),N=Math.sin(G),H=Math.cos(Z),re=Math.sin(Z),ae=this.computedCenter,_e=j*H,Ee=N*H,Ce=re,ge=-j*re,ie=-N*re,Se=H,ke=this.computedEye,be=this.computedMatrix,g=0;g<3;++g){var Be=_e*x[g]+Ee*q[g]+Ce*L[g];be[4*g+1]=ge*x[g]+ie*q[g]+Se*L[g],be[4*g+2]=Be,be[4*g+3]=0}var Le=be[1],me=be[5],Pe=be[9],ce=be[2],He=be[6],lt=be[10],mt=me*lt-Pe*He,Ht=Pe*ce-Le*lt,at=Le*He-me*ce,ct=d(mt,Ht,at);mt/=ct,Ht/=ct,at/=ct,be[0]=mt,be[4]=Ht,be[8]=at;for(var g=0;g<3;++g)ke[g]=ae[g]+be[2+4*g]*V;for(var g=0;g<3;++g){for(var E=0,ar=0;ar<3;++ar)E+=be[g+4*ar]*ke[ar];be[12+g]=-E}be[15]=1},p.getMatrix=function(T,L){this.recalcMatrix(T);var x=this.computedMatrix;if(L){for(var C=0;C<16;++C)L[C]=x[C];return L}return x};var k=[0,0,0];p.rotate=function(T,L,x,C){if(this.angle.move(T,L,x),C){this.recalcMatrix(T);var S=this.computedMatrix;k[0]=S[2],k[1]=S[6],k[2]=S[10];for(var g=this.computedUp,P=this.computedRight,E=this.computedToward,z=0;z<3;++z)S[4*z]=g[z],S[4*z+1]=P[z],S[4*z+2]=E[z];u(S,S,C,k);for(var z=0;z<3;++z)g[z]=S[4*z],P[z]=S[4*z+1];this.up.set(T,g[0],g[1],g[2]),this.right.set(T,P[0],P[1],P[2])}},p.pan=function(T,L,x,C){L=L||0,x=x||0,C=C||0,this.recalcMatrix(T);var S=this.computedMatrix,g=Math.exp(this.computedRadius[0]),P=S[1],E=S[5],z=S[9],q=d(P,E,z);P/=q,E/=q,z/=q;var V=S[0],G=S[4],Z=S[8],j=V*P+G*E+Z*z;V-=P*j,G-=E*j,Z-=z*j;var N=d(V,G,Z);V/=N,G/=N,Z/=N;var H=V*L+P*x,re=G*L+E*x,ae=Z*L+z*x;this.center.move(T,H,re,ae);var _e=Math.exp(this.computedRadius[0]);_e=Math.max(1e-4,_e+C),this.radius.set(T,Math.log(_e))},p.translate=function(T,L,x,C){this.center.move(T,L||0,x||0,C||0)},p.setMatrix=function(T,L,x,C){var S=1;typeof x==\"number\"&&(S=x|0),(S<0||S>3)&&(S=1);var g=(S+2)%3,P=(S+1)%3;L||(this.recalcMatrix(T),L=this.computedMatrix);var E=L[S],z=L[S+4],q=L[S+8];if(C){var G=Math.abs(E),Z=Math.abs(z),j=Math.abs(q),N=Math.max(G,Z,j);G===N?(E=E<0?-1:1,z=q=0):j===N?(q=q<0?-1:1,E=z=0):(z=z<0?-1:1,E=q=0)}else{var V=d(E,z,q);E/=V,z/=V,q/=V}var H=L[g],re=L[g+4],ae=L[g+8],_e=H*E+re*z+ae*q;H-=E*_e,re-=z*_e,ae-=q*_e;var Ee=d(H,re,ae);H/=Ee,re/=Ee,ae/=Ee;var Ce=z*ae-q*re,ge=q*H-E*ae,ie=E*re-z*H,Se=d(Ce,ge,ie);Ce/=Se,ge/=Se,ie/=Se,this.center.jump(T,Ue,Ae,rt),this.radius.idle(T),this.up.jump(T,E,z,q),this.right.jump(T,H,re,ae);var ke,be;if(S===2){var Be=L[1],Le=L[5],me=L[9],Pe=Be*H+Le*re+me*ae,ce=Be*Ce+Le*ge+me*ie;Ht<0?ke=-Math.PI/2:ke=Math.PI/2,be=Math.atan2(ce,Pe)}else{var He=L[2],lt=L[6],mt=L[10],Ht=He*E+lt*z+mt*q,at=He*H+lt*re+mt*ae,ct=He*Ce+lt*ge+mt*ie;ke=Math.asin(v(Ht)),be=Math.atan2(ct,at)}this.angle.jump(T,be,ke),this.recalcMatrix(T);var ar=L[2],Vt=L[6],rr=L[10],tt=this.computedMatrix;l(tt,L);var je=tt[15],Ue=tt[12]/je,Ae=tt[13]/je,rt=tt[14]/je,St=Math.exp(this.computedRadius[0]);this.center.jump(T,Ue-ar*St,Ae-Vt*St,rt-rr*St)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(T){this.center.idle(T),this.up.idle(T),this.right.idle(T),this.radius.idle(T),this.angle.idle(T)},p.flush=function(T){this.center.flush(T),this.up.flush(T),this.right.flush(T),this.radius.flush(T),this.angle.flush(T)},p.setDistance=function(T,L){L>0&&this.radius.set(T,Math.log(L))},p.lookAt=function(T,L,x,C){this.recalcMatrix(T),L=L||this.computedEye,x=x||this.computedCenter,C=C||this.computedUp;var S=C[0],g=C[1],P=C[2],E=d(S,g,P);if(!(E<1e-6)){S/=E,g/=E,P/=E;var z=L[0]-x[0],q=L[1]-x[1],V=L[2]-x[2],G=d(z,q,V);if(!(G<1e-6)){z/=G,q/=G,V/=G;var Z=this.computedRight,j=Z[0],N=Z[1],H=Z[2],re=S*j+g*N+P*H;j-=re*S,N-=re*g,H-=re*P;var 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Uint16Array;case\"uint32\":return Uint32Array;case\"float32\":return Float32Array;case\"float64\":return Float64Array;case\"array\":return Array;case\"uint8_clamped\":return Uint8ClampedArray}}});var lx=ye((Rgr,NPe)=>{\"use strict\";var IIt=qPe(),nF=ak(),RIt=iF();NPe.exports=function(t,r){(r===\"float\"||!r)&&(r=\"array\"),r===\"uint\"&&(r=\"uint8\"),r===\"uint_clamped\"&&(r=\"uint8_clamped\");var n=RIt(r),i=new n(4),a=r!==\"uint8\"&&r!==\"uint8_clamped\";return(!t.length||typeof t==\"string\")&&(t=IIt(t),t[0]/=255,t[1]/=255,t[2]/=255),DIt(t)?(i[0]=t[0],i[1]=t[1],i[2]=t[2],i[3]=t[3]!=null?t[3]:255,a&&(i[0]/=255,i[1]/=255,i[2]/=255,i[3]/=255),i):(a?(i[0]=t[0],i[1]=t[1],i[2]=t[2],i[3]=t[3]!=null?t[3]:1):(i[0]=nF(Math.floor(t[0]*255),0,255),i[1]=nF(Math.floor(t[1]*255),0,255),i[2]=nF(Math.floor(t[2]*255),0,255),i[3]=t[3]==null?255:nF(Math.floor(t[3]*255),0,255)),i)};function DIt(e){return!!(e instanceof Uint8Array||e instanceof Uint8ClampedArray||Array.isArray(e)&&(e[0]>1||e[0]===0)&&(e[1]>1||e[1]===0)&&(e[2]>1||e[2]===0)&&(!e[3]||e[3]>1))}});var a1=ye((Dgr,UPe)=>{\"use strict\";var FIt=lx();function zIt(e){return e?FIt(e):[0,0,0,1]}UPe.exports=zIt});var o1=ye((Fgr,ZPe)=>{\"use strict\";var WPe=Eo(),qIt=fd(),aF=lx(),oF=tc(),OIt=Ih().defaultLine,VPe=Yd().isArrayOrTypedArray,EX=aF(OIt),XPe=1;function GPe(e,t){var r=e;return r[3]*=t,r}function HPe(e){if(WPe(e))return EX;var t=aF(e);return t.length?t:EX}function jPe(e){return WPe(e)?e:XPe}function BIt(e,t,r){var n=e.color;n&&n._inputArray&&(n=n._inputArray);var i=VPe(n),a=VPe(t),o=oF.extractOpts(e),s=[],l,u,c,f,h;if(o.colorscale!==void 0?l=oF.makeColorScaleFuncFromTrace(e):l=HPe,i?u=function(v,m){return v[m]===void 0?EX:aF(l(v[m]))}:u=HPe,a?c=function(v,m){return v[m]===void 0?XPe:jPe(v[m])}:c=jPe,i||a)for(var d=0;d<r;d++)f=u(n,d),h=c(t,d),s[d]=GPe(f,h);else s=GPe(aF(n),t);return s}function NIt(e){var t=oF.extractOpts(e),r=t.colorscale;return t.reversescale&&(r=oF.flipScale(t.colorscale)),r.map(function(n){var i=n[0],a=qIt(n[1]),o=a.toRgb();return{index:i,rgb:[o.r,o.g,o.b,o.a]}})}ZPe.exports={formatColor:BIt,parseColorScale:NIt}});var kX=ye((zgr,YPe)=>{\"use strict\";YPe.exports={solid:[[],0],dot:[[.5,1],200],dash:[[.5,1],50],longdash:[[.5,1],10],dashdot:[[.5,.625,.875,1],50],longdashdot:[[.5,.7,.8,1],10]}});var sF=ye((qgr,KPe)=>{\"use strict\";KPe.exports={circle:\"\\u25CF\",\"circle-open\":\"\\u25CB\",square:\"\\u25A0\",\"square-open\":\"\\u25A1\",diamond:\"\\u25C6\",\"diamond-open\":\"\\u25C7\",cross:\"+\",x:\"\\u274C\"}});var $Pe=ye((Ogr,JPe)=>{\"use strict\";var UIt=Oa();function CX(e,t,r,n){if(!t||!t.visible)return null;for(var i=UIt.getComponentMethod(\"errorbars\",\"makeComputeError\")(t),a=new Array(e.length),o=0;o<e.length;o++){var s=i(+e[o],o);if(n.type===\"log\"){var l=n.c2l(e[o]),u=e[o]-s[0],c=e[o]+s[1];if(a[o]=[(n.c2l(u,!0)-l)*r,(n.c2l(c,!0)-l)*r],u>0){var f=n.c2l(u);n._lowerLogErrorBound||(n._lowerLogErrorBound=f),n._lowerErrorBound=Math.min(n._lowerLogErrorBound,f)}}else a[o]=[-s[0]*r,s[1]*r]}return a}function VIt(e){for(var t=0;t<e.length;t++)if(e[t])return e[t].length;return 0}function GIt(e,t,r){var n=[CX(e.x,e.error_x,t[0],r.xaxis),CX(e.y,e.error_y,t[1],r.yaxis),CX(e.z,e.error_z,t[2],r.zaxis)],i=VIt(n);if(i===0)return null;for(var a=new Array(i),o=0;o<i;o++){for(var s=[[0,0,0],[0,0,0]],l=0;l<3;l++)if(n[l])for(var u=0;u<2;u++)s[u][l]=n[l][o][u];a[o]=s}return a}JPe.exports=GIt});var sIe=ye((Bgr,oIe)=>{\"use strict\";var HIt=qd().gl_line3d,QPe=qd().gl_scatter3d,jIt=qd().gl_error3d,WIt=qd().gl_mesh3d,XIt=qd().delaunay_triangulate,s1=Pr(),nIe=a1(),lF=o1().formatColor,ZIt=U3(),LX=kX(),YIt=sF(),KIt=ho(),JIt=ip().appendArrayPointValue,$It=$Pe();function aIe(e,t){this.scene=e,this.uid=t,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode=\"\",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var IX=aIe.prototype;IX.handlePick=function(e){if(e.object&&(e.object===this.linePlot||e.object===this.delaunayMesh||e.object===this.textMarkers||e.object===this.scatterPlot)){var t=e.index=e.data.index;return e.object.highlight&&e.object.highlight(null),this.scatterPlot&&(e.object=this.scatterPlot,this.scatterPlot.highlight(e.data)),e.textLabel=\"\",this.textLabels&&(s1.isArrayOrTypedArray(this.textLabels)?(this.textLabels[t]||this.textLabels[t]===0)&&(e.textLabel=this.textLabels[t]):e.textLabel=this.textLabels),e.traceCoordinate=[this.data.x[t],this.data.y[t],this.data.z[t]],!0}};function QIt(e,t,r){var n=(r+1)%3,i=(r+2)%3,a=[],o=[],s;for(s=0;s<e.length;++s){var 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0&&(l*=e.marker.opacity),n={gl:this.scene.glplot.gl,position:s.position,color:s.scatterColor,size:s.scatterSize,glyph:s.scatterMarker,opacity:l,orthographic:!0,lineWidth:s.scatterLineWidth,lineColor:s.scatterLineColor,project:s.project,projectScale:s.projectScale,projectOpacity:s.projectOpacity},this.mode.indexOf(\"markers\")!==-1?this.scatterPlot?this.scatterPlot.update(n):(this.scatterPlot=QPe(n),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),a={gl:this.scene.glplot.gl,position:s.position,glyph:s.text,color:s.textColor,size:s.textSize,angle:s.textAngle,alignment:s.textOffset,font:s.textFontFamily,fontWeight:s.textFontWeight,fontStyle:s.textFontStyle,fontVariant:s.textFontVariant,orthographic:!0,lineWidth:0,project:!1,opacity:e.opacity},this.textLabels=e.hovertext||e.text,this.mode.indexOf(\"text\")!==-1?this.textMarkers?this.textMarkers.update(a):(this.textMarkers=QPe(a),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),i={gl:this.scene.glplot.gl,position:s.position,color:s.errorColor,error:s.errorBounds,lineWidth:s.errorLineWidth,capSize:s.errorCapSize,opacity:e.opacity},this.errorBars?s.errorBounds?this.errorBars.update(i):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):s.errorBounds&&(this.errorBars=jIt(i),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),s.delaunayAxis>=0){var u=QIt(s.position,s.delaunayColor,s.delaunayAxis);u.opacity=e.opacity,this.delaunayMesh?this.delaunayMesh.update(u):(u.gl=t,this.delaunayMesh=WIt(u),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)};IX.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())};function a8t(e,t){var r=new aIe(e,t.uid);return r.update(t),r}oIe.exports=a8t});var qX=ye((Ngr,fIe)=>{\"use strict\";var l1=pf(),o8t=ec(),zX=Tu(),RX=vf().axisHoverFormat,{hovertemplateAttrs:s8t,texttemplateAttrs:l8t,templatefallbackAttrs:lIe}=Pl(),uIe=Vl(),u8t=kX(),c8t=sF(),$g=Ao().extendFlat,f8t=mc().overrideAll,cIe=r_(),h8t=l1.line,Q2=l1.marker,d8t=Q2.line,v8t=$g({width:h8t.width,dash:{valType:\"enumerated\",values:cIe(u8t),dflt:\"solid\"}},zX(\"line\"));function DX(e){return{show:{valType:\"boolean\",dflt:!1},opacity:{valType:\"number\",min:0,max:1,dflt:1},scale:{valType:\"number\",min:0,max:10,dflt:2/3}}}var FX=fIe.exports=f8t({x:l1.x,y:l1.y,z:{valType:\"data_array\"},text:$g({},l1.text,{}),texttemplate:l8t(),texttemplatefallback:lIe({editType:\"calc\"}),hovertext:$g({},l1.hovertext,{}),hovertemplate:s8t(),hovertemplatefallback:lIe(),xhoverformat:RX(\"x\"),yhoverformat:RX(\"y\"),zhoverformat:RX(\"z\"),mode:$g({},l1.mode,{dflt:\"lines+markers\"}),surfaceaxis:{valType:\"enumerated\",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:\"color\"},projection:{x:DX(\"x\"),y:DX(\"y\"),z:DX(\"z\")},connectgaps:l1.connectgaps,line:v8t,marker:$g({symbol:{valType:\"enumerated\",values:cIe(c8t),dflt:\"circle\",arrayOk:!0},size:$g({},Q2.size,{dflt:8}),sizeref:Q2.sizeref,sizemin:Q2.sizemin,sizemode:Q2.sizemode,opacity:$g({},Q2.opacity,{arrayOk:!1}),colorbar:Q2.colorbar,line:$g({width:$g({},d8t.width,{arrayOk:!1})},zX(\"marker.line\"))},zX(\"marker\")),textposition:$g({},l1.textposition,{dflt:\"top center\"}),textfont:o8t({noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0,editType:\"calc\",colorEditType:\"style\",arrayOk:!0,variantValues:[\"normal\",\"small-caps\"]}),opacity:uIe.opacity,hoverinfo:$g({},uIe.hoverinfo)},\"calc\",\"nested\");FX.x.editType=FX.y.editType=FX.z.editType=\"calc+clearAxisTypes\"});var vIe=ye((Ugr,dIe)=>{\"use strict\";var hIe=Oa(),p8t=Pr(),OX=Ru(),g8t=$p(),m8t=D0(),y8t=F0(),_8t=qX();dIe.exports=function(t,r,n,i){function a(d,v){return p8t.coerce(t,r,_8t,d,v)}var o=x8t(t,r,a,i);if(!o){r.visible=!1;return}a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),a(\"xhoverformat\"),a(\"yhoverformat\"),a(\"zhoverformat\"),a(\"mode\"),OX.hasMarkers(r)&&g8t(t,r,n,i,a,{noAngle:!0,noLineDash:!0,noSelect:!0}),OX.hasLines(r)&&(a(\"connectgaps\"),m8t(t,r,n,i,a)),OX.hasText(r)&&(a(\"texttemplate\"),a(\"texttemplatefallback\"),y8t(t,r,i,a,{noSelect:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}));var s=(r.line||{}).color,l=(r.marker||{}).color;a(\"surfaceaxis\")>=0&&a(\"surfacecolor\",s||l);for(var u=[\"x\",\"y\",\"z\"],c=0;c<3;++c){var f=\"projection.\"+u[c];a(f+\".show\")&&(a(f+\".opacity\"),a(f+\".scale\"))}var h=hIe.getComponentMethod(\"errorbars\",\"supplyDefaults\");h(t,r,s||l||n,{axis:\"z\"}),h(t,r,s||l||n,{axis:\"y\",inherit:\"z\"}),h(t,r,s||l||n,{axis:\"x\",inherit:\"z\"})};function x8t(e,t,r,n){var i=0,a=r(\"x\"),o=r(\"y\"),s=r(\"z\"),l=hIe.getComponentMethod(\"calendars\",\"handleTraceDefaults\");return l(e,t,[\"x\",\"y\",\"z\"],n),a&&o&&s&&(i=Math.min(a.length,o.length,s.length),t._length=t._xlength=t._ylength=t._zlength=i),i}});var gIe=ye((Vgr,pIe)=>{\"use strict\";var b8t=Dm(),w8t=z0();pIe.exports=function(t,r){var n=[{x:!1,y:!1,trace:r,t:{}}];return b8t(n,r),w8t(t,r),n}});var yIe=ye((Ggr,mIe)=>{mIe.exports=T8t;function T8t(e,t){if(typeof e!=\"string\")throw new TypeError(\"must specify type string\");if(t=t||{},typeof document==\"undefined\"&&!t.canvas)return null;var r=t.canvas||document.createElement(\"canvas\");typeof t.width==\"number\"&&(r.width=t.width),typeof t.height==\"number\"&&(r.height=t.height);var n=t,i;try{var a=[e];e.indexOf(\"webgl\")===0&&a.push(\"experimental-\"+e);for(var o=0;o<a.length;o++)if(i=r.getContext(a[o],n),i)return i}catch(s){i=null}return i||null}});var xIe=ye((Hgr,_Ie)=>{var A8t=yIe();_Ie.exports=function(t){return A8t(\"webgl\",t)}});var BX=ye((jgr,wIe)=>{\"use strict\";var bIe=ka(),S8t=function(){};wIe.exports=function(t){for(var r in t)typeof t[r]==\"function\"&&(t[r]=S8t);t.destroy=function(){t.container.parentNode.removeChild(t.container)};var n=document.createElement(\"div\");n.className=\"no-webgl\",n.style.cursor=\"pointer\",n.style.fontSize=\"24px\",n.style.color=bIe.defaults[0],n.style.position=\"absolute\",n.style.left=n.style.top=\"0px\",n.style.width=n.style.height=\"100%\",n.style[\"background-color\"]=bIe.lightLine,n.style[\"z-index\"]=30;var i=document.createElement(\"p\");return i.textContent=\"WebGL is not supported by your browser - visit https://get.webgl.org for more info\",i.style.position=\"relative\",i.style.top=\"50%\",i.style.left=\"50%\",i.style.height=\"30%\",i.style.width=\"50%\",i.style.margin=\"-15% 0 0 -25%\",n.appendChild(i),t.container.appendChild(n),t.container.style.background=\"#FFFFFF\",t.container.onclick=function(){window.open(\"https://get.webgl.org\")},!1}});var SIe=ye((Wgr,AIe)=>{\"use strict\";var ew=a1(),M8t=Pr(),E8t=[\"xaxis\",\"yaxis\",\"zaxis\"];function TIe(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickFontWeight=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open 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k8t=TIe.prototype;k8t.merge=function(e,t){for(var r=this,n=0;n<3;++n){var i=t[E8t[n]];if(!i.visible){r.tickEnable[n]=!1,r.labelEnable[n]=!1,r.lineEnable[n]=!1,r.lineTickEnable[n]=!1,r.gridEnable[n]=!1,r.zeroEnable[n]=!1,r.backgroundEnable[n]=!1;continue}r.labels[n]=e._meta?M8t.templateString(i.title.text,e._meta):i.title.text,\"font\"in i.title&&(i.title.font.color&&(r.labelColor[n]=ew(i.title.font.color)),i.title.font.family&&(r.labelFont[n]=i.title.font.family),i.title.font.size&&(r.labelSize[n]=i.title.font.size),i.title.font.weight&&(r.labelFontWeight[n]=i.title.font.weight),i.title.font.style&&(r.labelFontStyle[n]=i.title.font.style),i.title.font.variant&&(r.labelFontVariant[n]=i.title.font.variant)),\"showline\"in i&&(r.lineEnable[n]=i.showline),\"linecolor\"in i&&(r.lineColor[n]=ew(i.linecolor)),\"linewidth\"in i&&(r.lineWidth[n]=i.linewidth),\"showgrid\"in i&&(r.gridEnable[n]=i.showgrid),\"gridcolor\"in i&&(r.gridColor[n]=ew(i.gridcolor)),\"gridwidth\"in i&&(r.gridWidth[n]=i.gridwidth),i.type===\"log\"?r.zeroEnable[n]=!1:\"zeroline\"in i&&(r.zeroEnable[n]=i.zeroline),\"zerolinecolor\"in i&&(r.zeroLineColor[n]=ew(i.zerolinecolor)),\"zerolinewidth\"in i&&(r.zeroLineWidth[n]=i.zerolinewidth),\"ticks\"in i&&i.ticks?r.lineTickEnable[n]=!0:r.lineTickEnable[n]=!1,\"ticklen\"in i&&(r.lineTickLength[n]=r._defaultLineTickLength[n]=i.ticklen),\"tickcolor\"in i&&(r.lineTickColor[n]=ew(i.tickcolor)),\"tickwidth\"in i&&(r.lineTickWidth[n]=i.tickwidth),\"tickangle\"in i&&(r.tickAngle[n]=i.tickangle===\"auto\"?-3600:Math.PI*-i.tickangle/180),\"showticklabels\"in i&&(r.tickEnable[n]=i.showticklabels),\"tickfont\"in i&&(i.tickfont.color&&(r.tickColor[n]=ew(i.tickfont.color)),i.tickfont.family&&(r.tickFont[n]=i.tickfont.family),i.tickfont.size&&(r.tickSize[n]=i.tickfont.size),i.tickfont.weight&&(r.tickFontWeight[n]=i.tickfont.weight),i.tickfont.style&&(r.tickFontStyle[n]=i.tickfont.style),i.tickfont.variant&&(r.tickFontVariant[n]=i.tickfont.variant)),\"mirror\"in i?[\"ticks\",\"all\",\"allticks\"].indexOf(i.mirror)!==-1?(r.lineTickMirror[n]=!0,r.lineMirror[n]=!0):i.mirror===!0?(r.lineTickMirror[n]=!1,r.lineMirror[n]=!0):(r.lineTickMirror[n]=!1,r.lineMirror[n]=!1):r.lineMirror[n]=!1,\"showbackground\"in i&&i.showbackground!==!1?(r.backgroundEnable[n]=!0,r.backgroundColor[n]=ew(i.backgroundcolor)):r.backgroundEnable[n]=!1}};function C8t(e,t){var r=new TIe;return r.merge(e,t),r}AIe.exports=C8t});var kIe=ye((Xgr,EIe)=>{\"use strict\";var L8t=a1(),P8t=[\"xaxis\",\"yaxis\",\"zaxis\"];function MIe(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}var I8t=MIe.prototype;I8t.merge=function(e){for(var t=0;t<3;++t){var r=e[P8t[t]];if(!r.visible){this.enabled[t]=!1,this.drawSides[t]=!1;continue}this.enabled[t]=r.showspikes,this.colors[t]=L8t(r.spikecolor),this.drawSides[t]=r.spikesides,this.lineWidth[t]=r.spikethickness}};function R8t(e){var t=new MIe;return t.merge(e),t}EIe.exports=R8t});var PIe=ye((Zgr,LIe)=>{\"use strict\";LIe.exports=O8t;var CIe=ho(),D8t=Pr(),F8t=[\"xaxis\",\"yaxis\",\"zaxis\"],z8t=[0,0,0];function q8t(e){for(var t=new Array(3),r=0;r<3;++r){for(var n=e[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;t[r]=i}return t}function O8t(e){for(var t=e.axesOptions,r=e.glplot.axesPixels,n=e.fullSceneLayout,i=[[],[],[]],a=0;a<3;++a){var o=n[F8t[a]];if(o._length=(r[a].hi-r[a].lo)*r[a].pixelsPerDataUnit/e.dataScale[a],Math.abs(o._length)===1/0||isNaN(o._length))i[a]=[];else{o._input_range=o.range.slice(),o.range[0]=r[a].lo/e.dataScale[a],o.range[1]=r[a].hi/e.dataScale[a],o._m=1/(e.dataScale[a]*r[a].pixelsPerDataUnit),o.range[0]===o.range[1]&&(o.range[0]-=1,o.range[1]+=1);var s=o.tickmode;if(o.tickmode===\"auto\"){o.tickmode=\"linear\";var l=o.nticks||D8t.constrain(o._length/40,4,9);CIe.autoTicks(o,Math.abs(o.range[1]-o.range[0])/l)}for(var u=CIe.calcTicks(o,{msUTC:!0}),c=0;c<u.length;++c)u[c].x=u[c].x*e.dataScale[a],o.type===\"date\"&&(u[c].text=u[c].text.replace(/\\<br\\>/g,\" \"));i[a]=u,o.tickmode=s}}t.ticks=i;for(var a=0;a<3;++a){z8t[a]=.5*(e.glplot.bounds[0][a]+e.glplot.bounds[1][a]);for(var c=0;c<2;++c)t.bounds[c][a]=e.glplot.bounds[c][a]}e.contourLevels=q8t(i)}});var OIe=ye((Ygr,qIe)=>{\"use strict\";var DIe=qd().gl_plot3d,B8t=DIe.createCamera,IIe=DIe.createScene,N8t=xIe(),U8t=GL(),fF=Oa(),cp=Pr(),cF=cp.preserveDrawingBuffer(),hF=ho(),Qg=ef(),V8t=a1(),G8t=BX(),H8t=JU(),j8t=SIe(),W8t=kIe(),X8t=PIe(),Z8t=Mg().applyAutorangeOptions,ok,uF,FIe=!1;function zIe(e,t){var r=document.createElement(\"div\"),n=e.container;this.graphDiv=e.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=e.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=t,this.id=e.id||\"scene\",this.fullSceneLayout=t[this.id],this.plotArgs=[[],{},{}],this.axesOptions=j8t(t,t[this.id]),this.spikeOptions=W8t(t[this.id]),this.container=r,this.staticMode=!!e.staticPlot,this.pixelRatio=this.pixelRatio||e.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=fF.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=fF.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var Sv=zIe.prototype;Sv.prepareOptions=function(){var e=this,t={canvas:e.canvas,gl:e.gl,glOptions:{preserveDrawingBuffer:cF,premultipliedAlpha:!0,antialias:!0},container:e.container,axes:e.axesOptions,spikes:e.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:e.camera,pixelRatio:e.pixelRatio};if(e.staticMode){if(!uF&&(ok=document.createElement(\"canvas\"),uF=N8t({canvas:ok,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}),!uF))throw new Error(\"error creating static canvas/context for image server\");t.gl=uF,t.canvas=ok}return t};var RIe=!0;Sv.tryCreatePlot=function(){var e=this,t=e.prepareOptions(),r=!0;try{e.glplot=IIe(t)}catch(n){if(e.staticMode||!RIe||cF)r=!1;else{cp.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{cF=t.glOptions.preserveDrawingBuffer=!0,e.glplot=IIe(t)}catch(i){cF=t.glOptions.preserveDrawingBuffer=!1,r=!1}}}return RIe=!1,r};Sv.initializeGLCamera=function(){var e=this,t=e.fullSceneLayout.camera,r=t.projection.type===\"orthographic\";e.camera=B8t(e.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:\"orbit\"})};Sv.initializeGLPlot=function(){var e=this;e.initializeGLCamera();var t=e.tryCreatePlot();if(!t)return G8t(e);e.traces={},e.make4thDimension();var r=e.graphDiv,n=r.layout,i=function(){var o={};return e.isCameraChanged(n)&&(o[e.id+\".camera\"]=e.getCamera()),e.isAspectChanged(n)&&(o[e.id+\".aspectratio\"]=e.glplot.getAspectratio(),n[e.id].aspectmode!==\"manual\"&&(e.fullSceneLayout.aspectmode=n[e.id].aspectmode=o[e.id+\".aspectmode\"]=\"manual\")),o},a=function(o){if(o.fullSceneLayout.dragmode!==!1){var s=i();o.saveLayout(n),o.graphDiv.emit(\"plotly_relayout\",s)}};return e.glplot.canvas&&(e.glplot.canvas.addEventListener(\"mouseup\",function(){a(e)}),e.glplot.canvas.addEventListener(\"touchstart\",function(){FIe=!0}),e.glplot.canvas.addEventListener(\"wheel\",function(o){if(r._context._scrollZoom.gl3d){if(e.camera._ortho){var s=o.deltaX>o.deltaY?1.1:.9090909090909091,l=e.glplot.getAspectratio();e.glplot.setAspectratio({x:s*l.x,y:s*l.y,z:s*l.z})}a(e)}},U8t?{passive:!1}:!1),e.glplot.canvas.addEventListener(\"mousemove\",function(){if(e.fullSceneLayout.dragmode!==!1&&e.camera.mouseListener.buttons!==0){var o=i();e.graphDiv.emit(\"plotly_relayouting\",o)}}),e.staticMode||e.glplot.canvas.addEventListener(\"webglcontextlost\",function(o){r&&r.emit&&r.emit(\"plotly_webglcontextlost\",{event:o,layer:e.id})},!1)),e.glplot.oncontextloss=function(){e.recoverContext()},e.glplot.onrender=function(){e.render()},!0};Sv.render=function(){var e=this,t=e.graphDiv,r,n=e.svgContainer,i=e.container.getBoundingClientRect();t._fullLayout._calcInverseTransform(t);var a=t._fullLayout._invScaleX,o=t._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),X8t(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,f=e.glplot.selection,h=0;h<u.length;++h)r=e.traces[u[h]],r.data.hoverinfo!==\"skip\"&&r.handlePick(f)&&(c=r),r.setContourLevels&&r.setContourLevels();function d(P,E,z){var q=e.fullSceneLayout[P+\"axis\"];return q.type!==\"log\"&&(E=q.d2l(E)),hF.hoverLabelText(q,E,z)}if(c!==null){var v=H8t(e.glplot.cameraParams,f.dataCoordinate);r=c.data;var m=t._fullData[r.index],b=f.index,p={xLabel:d(\"x\",f.traceCoordinate[0],r.xhoverformat),yLabel:d(\"y\",f.traceCoordinate[1],r.yhoverformat),zLabel:d(\"z\",f.traceCoordinate[2],r.zhoverformat)},k=Qg.castHoverinfo(m,e.fullLayout,b),M=(k||\"\").split(\"+\"),T=k&&k===\"all\";!m.hovertemplate&&!T&&(M.indexOf(\"x\")===-1&&(p.xLabel=void 0),M.indexOf(\"y\")===-1&&(p.yLabel=void 0),M.indexOf(\"z\")===-1&&(p.zLabel=void 0),M.indexOf(\"text\")===-1&&(f.textLabel=void 0),M.indexOf(\"name\")===-1&&(c.name=void 0));var L,x=[];r.type===\"cone\"||r.type===\"streamtube\"?(p.uLabel=d(\"x\",f.traceCoordinate[3],r.uhoverformat),(T||M.indexOf(\"u\")!==-1)&&x.push(\"u: \"+p.uLabel),p.vLabel=d(\"y\",f.traceCoordinate[4],r.vhoverformat),(T||M.indexOf(\"v\")!==-1)&&x.push(\"v: \"+p.vLabel),p.wLabel=d(\"z\",f.traceCoordinate[5],r.whoverformat),(T||M.indexOf(\"w\")!==-1)&&x.push(\"w: \"+p.wLabel),p.normLabel=f.traceCoordinate[6].toPrecision(3),(T||M.indexOf(\"norm\")!==-1)&&x.push(\"norm: \"+p.normLabel),r.type===\"streamtube\"&&(p.divergenceLabel=f.traceCoordinate[7].toPrecision(3),(T||M.indexOf(\"divergence\")!==-1)&&x.push(\"divergence: \"+p.divergenceLabel)),f.textLabel&&x.push(f.textLabel),L=x.join(\"<br>\")):r.type===\"isosurface\"||r.type===\"volume\"?(p.valueLabel=hF.hoverLabelText(e._mockAxis,e._mockAxis.d2l(f.traceCoordinate[3]),r.valuehoverformat),x.push(\"value: \"+p.valueLabel),f.textLabel&&x.push(f.textLabel),L=x.join(\"<br>\")):L=f.textLabel;var C={x:f.traceCoordinate[0],y:f.traceCoordinate[1],z:f.traceCoordinate[2],data:m._input,fullData:m,curveNumber:m.index,pointNumber:b};Qg.appendArrayPointValue(C,m,b),r._module.eventData&&(C=m._module.eventData(C,f,m,{},b));var S={points:[C]};if(e.fullSceneLayout.hovermode){var g=[];Qg.loneHover({trace:m,x:(.5+.5*v[0]/v[3])*s,y:(.5-.5*v[1]/v[3])*l,xLabel:p.xLabel,yLabel:p.yLabel,zLabel:p.zLabel,text:L,name:c.name,color:Qg.castHoverOption(m,b,\"bgcolor\")||c.color,borderColor:Qg.castHoverOption(m,b,\"bordercolor\"),fontFamily:Qg.castHoverOption(m,b,\"font.family\"),fontSize:Qg.castHoverOption(m,b,\"font.size\"),fontColor:Qg.castHoverOption(m,b,\"font.color\"),nameLength:Qg.castHoverOption(m,b,\"namelength\"),textAlign:Qg.castHoverOption(m,b,\"align\"),hovertemplate:cp.castOption(m,b,\"hovertemplate\"),hovertemplateLabels:cp.extendFlat({},C,p),eventData:[C]},{container:n,gd:t,inOut_bbox:g}),C.bbox=g[0]}f.distance<5&&(f.buttons||FIe)?t.emit(\"plotly_click\",S):t.emit(\"plotly_hover\",S),this.oldEventData=S}else Qg.loneUnhover(n),this.oldEventData&&t.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)};Sv.recoverContext=function(){var e=this;e.glplot.dispose();var t=function(){if(e.glplot.gl.isContextLost()){requestAnimationFrame(t);return}if(!e.initializeGLPlot()){cp.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\");return}e.plot.apply(e,e.plotArgs)};requestAnimationFrame(t)};var sk=[\"xaxis\",\"yaxis\",\"zaxis\"];function Y8t(e,t,r){for(var n=e.fullSceneLayout,i=0;i<3;i++){var a=sk[i],o=a.charAt(0),s=n[a],l=t[o],u=t[o+\"calendar\"],c=t[\"_\"+o+\"length\"];if(!cp.isArrayOrTypedArray(l))r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1);else for(var f,h=0;h<(c||l.length);h++)if(cp.isArrayOrTypedArray(l[h]))for(var d=0;d<l[h].length;++d)f=s.d2l(l[h][d],0,u),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f));else f=s.d2l(l[h],0,u),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f))}}function K8t(e,t){for(var r=e.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=sk[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(t[0][i]=Math.min(t[0][i],c),t[1][i]=Math.max(t[1][i],c))}}}Sv.plot=function(e,t,r){var n=this;if(n.plotArgs=[e,t,r],!n.glplot.contextLost){var i,a,o,s,l,u,c=t[n.id],f=r[n.id];n.fullLayout=t,n.fullSceneLayout=c,n.axesOptions.merge(t,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(V8t(c.bgcolor)),n.setConvert(l),e?Array.isArray(e)||(e=[e]):e=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<e.length;++o)i=e[o],!(i.visible!==!0||i._length===0)&&Y8t(this,i,h);K8t(this,h);var d=[1,1,1];for(s=0;s<3;++s)h[1][s]===h[0][s]?d[s]=1:d[s]=1/(h[1][s]-h[0][s]);for(n.dataScale=d,n.convertAnnotations(this),o=0;o<e.length;++o)i=e[o],!(i.visible!==!0||i._length===0)&&(a=n.traces[i.uid],a?a.data.type===i.type?a.update(i):(a.dispose(),a=i._module.plot(this,i),n.traces[i.uid]=a):(a=i._module.plot(this,i),n.traces[i.uid]=a),a.name=i.name);var v=Object.keys(n.traces);e:for(o=0;o<v.length;++o){for(s=0;s<e.length;++s)if(e[s].uid===v[o]&&e[s].visible===!0&&e[s]._length!==0)continue e;a=n.traces[v[o]],a.dispose(),delete n.traces[v[o]]}n.glplot.objects.sort(function(ae,_e){return ae._trace.data.index-_e._trace.data.index});var m=[[0,0,0],[0,0,0]],b=[],p={};for(o=0;o<3;++o){l=c[sk[o]],u=l.type,u in p?(p[u].acc*=d[o],p[u].count+=1):p[u]={acc:d[o],count:1};var k;if(l.autorange){m[0][o]=1/0,m[1][o]=-1/0;var M=n.glplot.objects,T=n.fullSceneLayout.annotations||[],L=l._name.charAt(0);for(s=0;s<M.length;s++){var x=M[s],C=x.bounds,S=x._trace.data._pad||0;x.constructor.name===\"ErrorBars\"&&l._lowerLogErrorBound?m[0][o]=Math.min(m[0][o],l._lowerLogErrorBound):m[0][o]=Math.min(m[0][o],C[0][o]/d[o]-S),m[1][o]=Math.max(m[1][o],C[1][o]/d[o]+S)}for(s=0;s<T.length;s++){var g=T[s];if(g.visible){var P=l.r2l(g[L]);m[0][o]=Math.min(m[0][o],P),m[1][o]=Math.max(m[1][o],P)}}if(\"rangemode\"in l&&l.rangemode===\"tozero\"&&(m[0][o]=Math.min(m[0][o],0),m[1][o]=Math.max(m[1][o],0)),m[0][o]>m[1][o])m[0][o]=-1,m[1][o]=1;else{var E=m[1][o]-m[0][o];m[0][o]-=E/32,m[1][o]+=E/32}if(k=[m[0][o],m[1][o]],k=Z8t(k,l),m[0][o]=k[0],m[1][o]=k[1],l.isReversed()){var z=m[0][o];m[0][o]=m[1][o],m[1][o]=z}}else k=l.range,m[0][o]=l.r2l(k[0]),m[1][o]=l.r2l(k[1]);m[0][o]===m[1][o]&&(m[0][o]-=1,m[1][o]+=1),b[o]=m[1][o]-m[0][o],l.range=[m[0][o],m[1][o]],l.limitRange(),n.glplot.setBounds(o,{min:l.range[0]*d[o],max:l.range[1]*d[o]})}var q,V=c.aspectmode;if(V===\"cube\")q=[1,1,1];else if(V===\"manual\"){var G=c.aspectratio;q=[G.x,G.y,G.z]}else if(V===\"auto\"||V===\"data\"){var Z=[1,1,1];for(o=0;o<3;++o){l=c[sk[o]],u=l.type;var j=p[u];Z[o]=Math.pow(j.acc,1/j.count)/d[o]}V===\"data\"||Math.max.apply(null,Z)/Math.min.apply(null,Z)<=4?q=Z:q=[1,1,1]}else throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");c.aspectratio.x=f.aspectratio.x=q[0],c.aspectratio.y=f.aspectratio.y=q[1],c.aspectratio.z=f.aspectratio.z=q[2],n.glplot.setAspectratio(c.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=c.aspectmode);var N=c.domain||null,H=t._size||null;if(N&&H){var re=n.container.style;re.position=\"absolute\",re.left=H.l+N.x[0]*H.w+\"px\",re.top=H.t+(1-N.y[1])*H.h+\"px\",re.width=H.w*(N.x[1]-N.x[0])+\"px\",re.height=H.h*(N.y[1]-N.y[0])+\"px\"}n.glplot.redraw()}};Sv.destroy=function(){var e=this;e.glplot&&(e.camera.mouseListener.enabled=!1,e.container.removeEventListener(\"wheel\",e.camera.wheelListener),e.camera=null,e.glplot.dispose(),e.container.parentNode.removeChild(e.container),e.glplot=null)};function J8t(e){return[[e.eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]}function $8t(e){return{up:{x:e.up[0],y:e.up[1],z:e.up[2]},center:{x:e.center[0],y:e.center[1],z:e.center[2]},eye:{x:e.eye[0],y:e.eye[1],z:e.eye[2]},projection:{type:e._ortho===!0?\"orthographic\":\"perspective\"}}}Sv.getCamera=function(){var e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),$8t(e.camera)};Sv.setViewport=function(e){var t=this,r=e.camera;t.camera.lookAt.apply(this,J8t(r)),t.glplot.setAspectratio(e.aspectratio);var n=r.projection.type===\"orthographic\",i=t.camera._ortho;n!==i&&(t.glplot.redraw(),t.glplot.clearRGBA(),t.glplot.dispose(),t.initializeGLPlot())};Sv.isCameraChanged=function(e){var t=this,r=t.getCamera(),n=cp.nestedProperty(e,t.id+\".camera\"),i=n.get();function a(u,c,f,h){var d=[\"up\",\"center\",\"eye\"],v=[\"x\",\"y\",\"z\"];return c[d[f]]&&u[d[f]][v[h]]===c[d[f]][v[h]]}var o=!1;if(i===void 0)o=!0;else{for(var s=0;s<3;s++)for(var l=0;l<3;l++)if(!a(r,i,s,l)){o=!0;break}(!i.projection||r.projection&&r.projection.type!==i.projection.type)&&(o=!0)}return o};Sv.isAspectChanged=function(e){var t=this,r=t.glplot.getAspectratio(),n=cp.nestedProperty(e,t.id+\".aspectratio\"),i=n.get();return i===void 0||i.x!==r.x||i.y!==r.y||i.z!==r.z};Sv.saveLayout=function(e){var t=this,r=t.fullLayout,n,i,a,o,s,l,u=t.isCameraChanged(e),c=t.isAspectChanged(e),f=u||c;if(f){var h={};if(u&&(n=t.getCamera(),i=cp.nestedProperty(e,t.id+\".camera\"),a=i.get(),h[t.id+\".camera\"]=a),c&&(o=t.glplot.getAspectratio(),s=cp.nestedProperty(e,t.id+\".aspectratio\"),l=s.get(),h[t.id+\".aspectratio\"]=l),fF.call(\"_storeDirectGUIEdit\",e,r._preGUI,h),u){i.set(n);var d=cp.nestedProperty(r,t.id+\".camera\");d.set(n)}if(c){s.set(o);var v=cp.nestedProperty(r,t.id+\".aspectratio\");v.set(o),t.glplot.redraw()}}return f};Sv.updateFx=function(e,t){var r=this,n=r.camera;if(n)if(e===\"orbit\")n.mode=\"orbit\",n.keyBindingMode=\"rotate\";else if(e===\"turntable\"){n.up=[0,0,1],n.mode=\"turntable\",n.keyBindingMode=\"rotate\";var i=r.graphDiv,a=i._fullLayout,o=r.fullSceneLayout.camera,s=o.up.x,l=o.up.y,u=o.up.z;if(u/Math.sqrt(s*s+l*l+u*u)<.999){var c=r.id+\".camera.up\",f={x:0,y:0,z:1},h={};h[c]=f;var d=i.layout;fF.call(\"_storeDirectGUIEdit\",d,a._preGUI,h),o.up=f,cp.nestedProperty(d,c).set(f)}}else n.keyBindingMode=e;r.fullSceneLayout.hovermode=t};function Q8t(e,t,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<t;++a)for(var o=0;o<4;++o){var s=4*(t*n+a)+o,l=4*(t*i+a)+o,u=e[s];e[s]=e[l],e[l]=u}}function eRt(e,t,r){for(var n=0;n<r;++n)for(var i=0;i<t;++i){var a=4*(t*n+i),o=e[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)e[a+l]=Math.min(s*e[a+l],255)}}Sv.toImage=function(e){var t=this;e||(e=\"png\"),t.staticMode&&t.container.appendChild(ok),t.glplot.redraw();var r=t.glplot.gl,n=r.drawingBufferWidth,i=r.drawingBufferHeight;r.bindFramebuffer(r.FRAMEBUFFER,null);var a=new Uint8Array(n*i*4);r.readPixels(0,0,n,i,r.RGBA,r.UNSIGNED_BYTE,a),Q8t(a,n,i),eRt(a,n,i);var o=document.createElement(\"canvas\");o.width=n,o.height=i;var s=o.getContext(\"2d\",{willReadFrequently:!0}),l=s.createImageData(n,i);l.data.set(a),s.putImageData(l,0,0);var u;switch(e){case\"jpeg\":u=o.toDataURL(\"image/jpeg\");break;case\"webp\":u=o.toDataURL(\"image/webp\");break;default:u=o.toDataURL(\"image/png\")}return t.staticMode&&t.container.removeChild(ok),u};Sv.setConvert=function(){for(var e=this,t=0;t<3;t++){var r=e.fullSceneLayout[sk[t]];hF.setConvert(r,e.fullLayout),r.setScale=cp.noop}};Sv.make4thDimension=function(){var e=this,t=e.graphDiv,r=t._fullLayout;e._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},hF.setConvert(e._mockAxis,r)};qIe.exports=zIe});var NIe=ye((Kgr,BIe)=>{\"use strict\";BIe.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}});var UX=ye((Jgr,UIe)=>{\"use strict\";var tRt=ka(),xs=Rd(),NX=Ao().extendFlat,rRt=mc().overrideAll;UIe.exports=rRt({visible:xs.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:tRt.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:xs.color,categoryorder:xs.categoryorder,categoryarray:xs.categoryarray,title:{text:xs.title.text,font:xs.title.font},type:NX({},xs.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:xs.autotypenumbers,autorange:xs.autorange,autorangeoptions:{minallowed:xs.autorangeoptions.minallowed,maxallowed:xs.autorangeoptions.maxallowed,clipmin:xs.autorangeoptions.clipmin,clipmax:xs.autorangeoptions.clipmax,include:xs.autorangeoptions.include,editType:\"plot\"},rangemode:xs.rangemode,minallowed:xs.minallowed,maxallowed:xs.maxallowed,range:NX({},xs.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:xs.minor.tickmode,nticks:xs.nticks,tick0:xs.tick0,dtick:xs.dtick,tickvals:xs.tickvals,ticktext:xs.ticktext,ticks:xs.ticks,mirror:xs.mirror,ticklen:xs.ticklen,tickwidth:xs.tickwidth,tickcolor:xs.tickcolor,showticklabels:xs.showticklabels,labelalias:xs.labelalias,tickfont:xs.tickfont,tickangle:xs.tickangle,tickprefix:xs.tickprefix,showtickprefix:xs.showtickprefix,ticksuffix:xs.ticksuffix,showticksuffix:xs.showticksuffix,showexponent:xs.showexponent,exponentformat:xs.exponentformat,minexponent:xs.minexponent,separatethousands:xs.separatethousands,tickformat:xs.tickformat,tickformatstops:xs.tickformatstops,hoverformat:xs.hoverformat,showline:xs.showline,linecolor:xs.linecolor,linewidth:xs.linewidth,showgrid:xs.showgrid,gridcolor:NX({},xs.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:xs.gridwidth,zeroline:xs.zeroline,zerolinecolor:xs.zerolinecolor,zerolinewidth:xs.zerolinewidth},\"plot\",\"from-root\")});var jX=ye(($gr,VIe)=>{\"use strict\";var VX=UX(),iRt=Cc().attributes,GX=Ao().extendFlat,nRt=Pr().counterRegex;function HX(e,t,r){return{x:{valType:\"number\",dflt:e,editType:\"camera\"},y:{valType:\"number\",dflt:t,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}VIe.exports={_arrayAttrRegexps:[nRt(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:GX(HX(0,0,1),{}),center:GX(HX(0,0,0),{}),eye:GX(HX(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:iRt({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:VX,yaxis:VX,zaxis:VX,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\"}});var WIe=ye((Qgr,jIe)=>{\"use strict\";var aRt=fd().mix,GIe=Pr(),oRt=vl(),sRt=UX(),lRt=FU(),uRt=v4(),HIe=[\"xaxis\",\"yaxis\",\"zaxis\"],cRt=13600/187;jIe.exports=function(t,r,n){var i,a;function o(u,c){return GIe.coerce(i,a,sRt,u,c)}for(var s=0;s<HIe.length;s++){var l=HIe[s];i=t[l]||{},a=oRt.newContainer(r,l),a._id=l[0]+n.scene,a._name=l,lRt(i,a,o,n),uRt(i,a,o,{font:n.font,letter:l[0],data:n.data,showGrid:!0,noAutotickangles:!0,noMinorloglabels:!0,noTicklabelindex:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelshift:!0,noTicklabelstandoff:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,noUnifiedhovertitle:!0,bgColor:n.bgColor,calendar:n.calendar},n.fullLayout),o(\"gridcolor\",aRt(a.color,n.bgColor,cRt).toRgbString()),o(\"title.text\",l[0]),a.setScale=GIe.noop,o(\"showspikes\")&&(o(\"spikesides\"),o(\"spikethickness\"),o(\"spikecolor\",a.color)),o(\"showaxeslabels\"),o(\"showbackground\")&&o(\"backgroundcolor\")}}});var KIe=ye((emr,YIe)=>{\"use strict\";var fRt=Pr(),hRt=ka(),dRt=Oa(),vRt=O_(),pRt=WIe(),XIe=jX(),gRt=Id().getSubplotData,ZIe=\"gl3d\";YIe.exports=function(t,r,n){var i=r._basePlotModules.length>1;function a(o){if(!i){var s=fRt.validate(t[o],XIe[o]);if(s)return t[o]}}vRt(t,r,n,{type:ZIe,attributes:XIe,handleDefaults:mRt,fullLayout:r,font:r.font,fullData:n,getDfltFromLayout:a,autotypenumbersDflt:r.autotypenumbers,paper_bgcolor:r.paper_bgcolor,calendar:r.calendar})};function mRt(e,t,r,n){for(var i=r(\"bgcolor\"),a=hRt.combine(i,n.paper_bgcolor),o=[\"up\",\"center\",\"eye\"],s=0;s<o.length;s++)r(\"camera.\"+o[s]+\".x\"),r(\"camera.\"+o[s]+\".y\"),r(\"camera.\"+o[s]+\".z\");r(\"camera.projection.type\");var l=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),u=l?\"manual\":\"auto\",c=r(\"aspectmode\",u);l||(e.aspectratio=t.aspectratio={x:1,y:1,z:1},c===\"manual\"&&(t.aspectmode=\"auto\"),e.aspectmode=t.aspectmode);var f=gRt(n.fullData,ZIe,n.id);pRt(e,t,{font:n.font,scene:n.id,data:f,bgColor:a,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),dRt.getComponentMethod(\"annotations3d\",\"handleDefaults\")(e,t,n);var h=n.getDfltFromLayout(\"dragmode\");if(h!==!1&&!h)if(h=\"orbit\",e.camera&&e.camera.up){var d=e.camera.up.x,v=e.camera.up.y,m=e.camera.up.z;m!==0&&(!d||!v||!m||m/Math.sqrt(d*d+v*v+m*m)>.999)&&(h=\"turntable\")}else h=\"turntable\";r(\"dragmode\",h),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}});var ux=ye(fp=>{\"use strict\";var yRt=mc().overrideAll,_Rt=X1(),xRt=OIe(),bRt=Id().getSubplotData,wRt=Pr(),TRt=Wp(),JA=\"gl3d\",WX=\"scene\";fp.name=JA;fp.attr=WX;fp.idRoot=WX;fp.idRegex=fp.attrRegex=wRt.counterRegex(\"scene\");fp.attributes=NIe();fp.layoutAttributes=jX();fp.baseLayoutAttrOverrides=yRt({hoverlabel:_Rt.hoverlabel},\"plot\",\"nested\");fp.supplyLayoutDefaults=KIe();fp.plot=function(t){for(var r=t._fullLayout,n=t._fullData,i=r._subplots[JA],a=0;a<i.length;a++){var o=i[a],s=bRt(n,JA,o),l=r[o],u=l.camera,c=l._scene;c||(c=new xRt({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},r),l._scene=c),c.viewInitial||(c.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),c.plot(s,r,t.layout)}};fp.clean=function(e,t,r,n){for(var i=n._subplots[JA]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}};fp.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[JA],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._scene,l=s.toImage(\"png\"),u=t._glimages.append(\"svg:image\");u.attr({xmlns:TRt.svg,\"xlink:href\":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}};fp.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var r=t.slice(5);return r===\"1\"&&(r=\"\"),WX+r}};fp.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[JA],n=0;n<r.length;n++){var i=t[r[n]]._scene;i.updateFx(t.dragmode,t.hovermode)}}});var $Ie=ye((rmr,JIe)=>{\"use strict\";JIe.exports={plot:sIe(),attributes:qX(),markerSymbols:sF(),supplyDefaults:vIe(),colorbar:[{container:\"marker\",min:\"cmin\",max:\"cmax\"},{container:\"line\",min:\"cmin\",max:\"cmax\"}],calc:gIe(),moduleType:\"trace\",name:\"scatter3d\",basePlotModule:ux(),categories:[\"gl3d\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}});var e8e=ye((imr,QIe)=>{\"use strict\";QIe.exports=$Ie()});var lk=ye((nmr,i8e)=>{\"use strict\";var t8e=ka(),ARt=Tu(),XX=vf().axisHoverFormat,{hovertemplateAttrs:SRt,templatefallbackAttrs:MRt}=Pl(),r8e=Vl(),ZX=Ao().extendFlat,ERt=mc().overrideAll;function YX(e){return{valType:\"boolean\",dflt:!1}}function KX(e){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:YX(\"x\"),y:YX(\"y\"),z:YX(\"z\")},color:{valType:\"color\",dflt:t8e.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:t8e.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var JX=i8e.exports=ERt(ZX({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:SRt(),hovertemplatefallback:MRt(),xhoverformat:XX(\"x\"),yhoverformat:XX(\"y\"),zhoverformat:XX(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},ARt(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:KX(\"x\"),y:KX(\"y\"),z:KX(\"z\")},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05,description:\"Represents the level that incident rays are reflected in a single direction, causing shine.\"},roughness:{valType:\"number\",min:0,max:1,dflt:.5,description:\"Alters specular reflection; the rougher the surface, the wider and less contrasty the shine.\"},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},hoverinfo:ZX({},r8e.hoverinfo),showlegend:ZX({},r8e.showlegend,{dflt:!1})}),\"calc\",\"nested\");JX.x.editType=JX.y.editType=JX.z.editType=\"calc+clearAxisTypes\"});var QX=ye((amr,o8e)=>{\"use strict\";var kRt=Oa(),n8e=Pr(),CRt=td(),LRt=lk(),$X=.1;function PRt(e,t){for(var r=[],n=32,i=0;i<n;i++){var a=i/(n-1),o=t+(1-t)*(1-Math.pow(Math.sin(e*a*Math.PI),2));r.push([a,Math.max(0,Math.min(1,o))])}return r}function IRt(e){var t=0;if(!Array.isArray(e)||e.length<2||!e[0]||!e[e.length-1]||+e[0][0]!=0||+e[e.length-1][0]!=1)return!1;for(var r=0;r<e.length;r++){var n=e[r];if(n.length!==2||+n[0]<t)return!1;t=+n[0]}return!0}function RRt(e,t,r,n){var i,a;function o(b,p){return n8e.coerce(e,t,LRt,b,p)}var s=o(\"x\"),l=o(\"y\"),u=o(\"z\");if(!u||!u.length||s&&s.length<1||l&&l.length<1){t.visible=!1;return}t._xlength=Array.isArray(s)&&n8e.isArrayOrTypedArray(s[0])?u.length:u[0].length,t._ylength=u.length;var c=kRt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");c(e,t,[\"x\",\"y\",\"z\"],n),o(\"text\"),o(\"hovertext\"),o(\"hovertemplate\"),o(\"hovertemplatefallback\"),o(\"xhoverformat\"),o(\"yhoverformat\"),o(\"zhoverformat\"),[\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"hidesurface\",\"connectgaps\",\"opacity\"].forEach(function(b){o(b)});var f=o(\"surfacecolor\"),h=[\"x\",\"y\",\"z\"];for(i=0;i<3;++i){var d=\"contours.\"+h[i],v=o(d+\".show\"),m=o(d+\".highlight\");if(v||m)for(a=0;a<3;++a)o(d+\".project.\"+h[a]);v&&(o(d+\".color\"),o(d+\".width\"),o(d+\".usecolormap\")),m&&(o(d+\".highlightcolor\"),o(d+\".highlightwidth\")),o(d+\".start\"),o(d+\".end\"),o(d+\".size\")}CRt(e,t,n,o,{prefix:\"\",cLetter:\"c\"}),a8e(e,t,n,o),t._length=null}function a8e(e,t,r,n){var i=n(\"opacityscale\");i===\"max\"?t.opacityscale=[[0,$X],[1,1]]:i===\"min\"?t.opacityscale=[[0,1],[1,$X]]:i===\"extremes\"?t.opacityscale=PRt(1,$X):IRt(i)||(t.opacityscale=void 0)}o8e.exports={supplyDefaults:RRt,opacityscaleDefaults:a8e}});var u8e=ye((omr,l8e)=>{\"use strict\";var s8e=gv();l8e.exports=function(t,r){r.surfacecolor?s8e(t,r,{vals:r.surfacecolor,containerStr:\"\",cLetter:\"c\"}):s8e(t,r,{vals:r.z,containerStr:\"\",cLetter:\"c\"})}});var p8e=ye((smr,v8e)=>{\"use strict\";var DRt=qd().gl_surface3d,$A=qd().ndarray,FRt=qd().ndarray_linear_interpolate.d2,zRt=d8(),qRt=v8(),uk=Pr().isArrayOrTypedArray,ORt=o1().parseColorScale,c8e=a1(),BRt=tc().extractOpts;function h8e(e,t,r){this.scene=e,this.uid=r,this.surface=t,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var em=h8e.prototype;em.getXat=function(e,t,r,n){var i=uk(this.data.x)?uk(this.data.x[0])?this.data.x[t][e]:this.data.x[e]:e;return r===void 0?i:n.d2l(i,0,r)};em.getYat=function(e,t,r,n){var i=uk(this.data.y)?uk(this.data.y[0])?this.data.y[t][e]:this.data.y[t]:t;return r===void 0?i:n.d2l(i,0,r)};em.getZat=function(e,t,r,n){var i=this.data.z[t][e];return i===null&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[t][e]),r===void 0?i:n.d2l(i,0,r)};em.handlePick=function(e){if(e.object===this.surface){var t=(e.data.index[0]-1)/this.dataScaleX-1,r=(e.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(t),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);e.index=[n,i],e.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],e.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=e.dataCoordinate[a];o!=null&&(e.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return uk(s)&&s[i]&&s[i][n]!==void 0?e.textLabel=s[i][n]:s?e.textLabel=s:e.textLabel=\"\",e.data.dataCoordinate=e.dataCoordinate.slice(),this.surface.highlight(e.data),this.scene.glplot.spikes.position=e.dataCoordinate,!0}};function NRt(e){var t=e[0].rgb,r=e[e.length-1].rgb;return t[0]===r[0]&&t[1]===r[1]&&t[2]===r[2]&&t[3]===r[3]}var QA=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function URt(e,t){if(e<t)return 0;for(var r=0;Math.floor(e%t)===0;)e/=t,r++;return r}function eZ(e){for(var t=[],r=0;r<QA.length;r++){var n=QA[r];t.push(URt(e,n))}return t}function VRt(e){for(var t=eZ(e),r=e,n=0;n<QA.length;n++)if(t[n]>0){r=QA[n];break}return r}function GRt(e,t){if(!(e<1||t<1)){for(var r=eZ(e),n=eZ(t),i=1,a=0;a<QA.length;a++)i*=Math.pow(QA[a],Math.max(r[a],n[a]));return i}}function HRt(e){if(e.length!==0){for(var t=1,r=0;r<e.length;r++)t=GRt(t,e[r]);return t}}em.calcXnums=function(e){var t,r=[];for(t=1;t<e;t++){var n=this.getXat(t-1,0),i=this.getXat(t,0);i!==n&&n!==void 0&&n!==null&&i!==void 0&&i!==null?r[t-1]=Math.abs(i-n):r[t-1]=0}var a=0;for(t=1;t<e;t++)a+=r[t-1];for(t=1;t<e;t++)r[t-1]===0?r[t-1]=1:r[t-1]=Math.round(a/r[t-1]);return r};em.calcYnums=function(e){var t,r=[];for(t=1;t<e;t++){var n=this.getYat(0,t-1),i=this.getYat(0,t);i!==n&&n!==void 0&&n!==null&&i!==void 0&&i!==null?r[t-1]=Math.abs(i-n):r[t-1]=0}var a=0;for(t=1;t<e;t++)a+=r[t-1];for(t=1;t<e;t++)r[t-1]===0?r[t-1]=1:r[t-1]=Math.round(a/r[t-1]);return r};var d8e=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],f8e=d8e[9],dF=d8e[13];em.estimateScale=function(e,t){for(var r=t===0?this.calcXnums(e):this.calcYnums(e),n=1+HRt(r);n<f8e;)n*=2;for(;n>dF;)n--,n/=VRt(n),n++,n<f8e&&(n=dF);var i=Math.round(n/e);return i>1?i:1};function jRt(e,t,r){var n=r[8]+r[2]*t[0]+r[5]*t[1];return e[0]=(r[6]+r[0]*t[0]+r[3]*t[1])/n,e[1]=(r[7]+r[1]*t[0]+r[4]*t[1])/n,e}function WRt(e,t,r){return XRt(e,t,jRt,r),e}function XRt(e,t,r,n){for(var i=[0,0],a=e.shape[0],o=e.shape[1],s=0;s<a;s++)for(var l=0;l<o;l++)r(i,[s,l],n),e.set(s,l,FRt(t,i[0],i[1]));return e}em.refineCoords=function(e){for(var t=this.dataScaleX,r=this.dataScaleY,n=e[0].shape[0],i=e[0].shape[1],a=Math.floor(e[0].shape[0]*t+1)|0,o=Math.floor(e[0].shape[1]*r+1)|0,s=1+n+1,l=1+i+1,u=$A(new Float32Array(s*l),[s,l]),c=[1/t,0,0,0,1/r,0,0,0,1],f=0;f<e.length;++f){this.surface.padField(u,e[f]);var h=$A(new Float32Array(a*o),[a,o]);WRt(h,u,c),e[f]=h}};function ZRt(e,t){for(var r=!1,n=0;n<e.length;n++)if(t===e[n]){r=!0;break}r===!1&&e.push(t)}em.setContourLevels=function(){var e=[[],[],[]],t=[!1,!1,!1],r=!1,n,i,a;for(n=0;n<3;++n)if(this.showContour[n]&&(r=!0,this.contourSize[n]>0&&this.contourStart[n]!==null&&this.contourEnd[n]!==null&&this.contourEnd[n]>this.contourStart[n]))for(t[n]=!0,i=this.contourStart[n];i<this.contourEnd[n];i+=this.contourSize[n])a=i*this.scene.dataScale[n],ZRt(e[n],a);if(r){var o=[[],[],[]];for(n=0;n<3;++n)this.showContour[n]&&(o[n]=t[n]?e[n]:this.scene.contourLevels[n]);this.surface.update({levels:o})}};em.update=function(e){var t=this.scene,r=t.fullSceneLayout,n=this.surface,i=ORt(e),a=t.dataScale,o=e.z[0].length,s=e._ylength,l=t.contourLevels;this.data=e;var u,c,f,h,d=[];for(u=0;u<3;u++)for(d[u]=[],c=0;c<o;c++)d[u][c]=[];for(c=0;c<o;c++)for(f=0;f<s;f++)d[0][c][f]=this.getXat(c,f,e.xcalendar,r.xaxis),d[1][c][f]=this.getYat(c,f,e.ycalendar,r.yaxis),d[2][c][f]=this.getZat(c,f,e.zcalendar,r.zaxis);if(e.connectgaps)for(e._emptypoints=qRt(d[2]),zRt(d[2],e._emptypoints),e._interpolatedZ=[],c=0;c<o;c++)for(e._interpolatedZ[c]=[],f=0;f<s;f++)e._interpolatedZ[c][f]=d[2][c][f];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h==null?d[u][c][f]=NaN:h=d[u][c][f]*=a[u];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h!=null&&(this.minValues[u]>h&&(this.minValues[u]=h),this.maxValues[u]<h&&(this.maxValues[u]=h));for(u=0;u<3;u++)this.objectOffset[u]=.5*(this.minValues[u]+this.maxValues[u]);for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)h=d[u][c][f],h!=null&&(d[u][c][f]-=this.objectOffset[u]);var v=[$A(new Float32Array(o*s),[o,s]),$A(new Float32Array(o*s),[o,s]),$A(new Float32Array(o*s),[o,s])];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)v[u].set(c,f,d[u][c][f]);d=[];var m={colormap:i,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!e.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:e.opacityscale,opacity:e.opacity},b=BRt(e);if(m.intensityBounds=[b.min,b.max],e.surfacecolor){var p=$A(new Float32Array(o*s),[o,s]);for(c=0;c<o;c++)for(f=0;f<s;f++)p.set(c,f,e.surfacecolor[f][c]);v.push(p)}else m.intensityBounds[0]*=a[2],m.intensityBounds[1]*=a[2];(dF<v[0].shape[0]||dF<v[0].shape[1])&&(this.refineData=!1),this.refineData===!0&&(this.dataScaleX=this.estimateScale(v[0].shape[0],0),this.dataScaleY=this.estimateScale(v[0].shape[1],1),(this.dataScaleX!==1||this.dataScaleY!==1)&&this.refineCoords(v)),e.surfacecolor&&(m.intensity=v.pop());var k=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(u=0;u<3;++u){var T=e.contours[M[u]];k[u]=T.highlight,m.showContour[u]=T.show||T.highlight,m.showContour[u]&&(m.contourProject[u]=[T.project.x,T.project.y,T.project.z],T.show?(this.showContour[u]=!0,m.levels[u]=l[u],n.highlightColor[u]=m.contourColor[u]=c8e(T.color),T.usecolormap?n.highlightTint[u]=m.contourTint[u]=0:n.highlightTint[u]=m.contourTint[u]=1,m.contourWidth[u]=T.width,this.contourStart[u]=T.start,this.contourEnd[u]=T.end,this.contourSize[u]=T.size):(this.showContour[u]=!1,this.contourStart[u]=null,this.contourEnd[u]=null,this.contourSize[u]=0),T.highlight&&(m.dynamicColor[u]=c8e(T.highlightcolor),m.dynamicWidth[u]=T.highlightwidth))}NRt(i)&&(m.vertexColor=!0),m.objectOffset=this.objectOffset,m.coords=v,n.update(m),n.visible=e.visible,n.enableDynamic=k,n.enableHighlight=k,n.snapToData=!0,\"lighting\"in e&&(n.ambientLight=e.lighting.ambient,n.diffuseLight=e.lighting.diffuse,n.specularLight=e.lighting.specular,n.roughness=e.lighting.roughness,n.fresnel=e.lighting.fresnel),\"lightposition\"in e&&(n.lightPosition=[e.lightposition.x,e.lightposition.y,e.lightposition.z])};em.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()};function YRt(e,t){var r=e.glplot.gl,n=DRt({gl:r}),i=new h8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}v8e.exports=YRt});var m8e=ye((lmr,g8e)=>{\"use strict\";g8e.exports={attributes:lk(),supplyDefaults:QX().supplyDefaults,colorbar:{min:\"cmin\",max:\"cmax\"},calc:u8e(),plot:p8e(),moduleType:\"trace\",name:\"surface\",basePlotModule:ux(),categories:[\"gl3d\",\"2dMap\",\"showLegend\"],meta:{}}});var _8e=ye((umr,y8e)=>{\"use strict\";y8e.exports=m8e()});var e5=ye((cmr,b8e)=>{\"use strict\";var KRt=Tu(),tZ=vf().axisHoverFormat,{hovertemplateAttrs:JRt,templatefallbackAttrs:$Rt}=Pl(),cx=lk(),x8e=Vl(),fx=Ao().extendFlat;b8e.exports=fx({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:JRt({editType:\"calc\"}),hovertemplatefallback:$Rt({editType:\"calc\"}),xhoverformat:tZ(\"x\"),yhoverformat:tZ(\"y\"),zhoverformat:tZ(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"}},KRt(\"\",{colorAttr:\"`intensity`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:cx.opacity,flatshading:{valType:\"boolean\",dflt:!1,editType:\"calc\"},contour:{show:fx({},cx.contours.x.show,{}),color:cx.contours.x.color,width:cx.contours.x.width,editType:\"calc\"},lightposition:{x:fx({},cx.lightposition.x,{dflt:1e5}),y:fx({},cx.lightposition.y,{dflt:1e5}),z:fx({},cx.lightposition.z,{dflt:0}),editType:\"calc\"},lighting:fx({vertexnormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-12,editType:\"calc\",description:\"Epsilon for vertex normals calculation avoids math issues arising from degenerate geometry.\"},facenormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-6,editType:\"calc\",description:\"Epsilon for face normals calculation avoids math issues arising from degenerate geometry.\"},editType:\"calc\"},cx.lighting),hoverinfo:fx({},x8e.hoverinfo,{editType:\"calc\"}),showlegend:fx({},x8e.showlegend,{dflt:!1})})});var pF=ye((fmr,T8e)=>{\"use strict\";var QRt=Tu(),vF=vf().axisHoverFormat,{hovertemplateAttrs:eDt,templatefallbackAttrs:tDt}=Pl(),ck=e5(),w8e=Vl(),rZ=Ao().extendFlat,rDt=mc().overrideAll;function iZ(e){return{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}}function nZ(e){return{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}}var t5=T8e.exports=rDt(rZ({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:iZ(\"x\"),y:iZ(\"y\"),z:iZ(\"z\")},caps:{x:nZ(\"x\"),y:nZ(\"y\"),z:nZ(\"z\")},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:eDt(),hovertemplatefallback:tDt(),xhoverformat:vF(\"x\"),yhoverformat:vF(\"y\"),zhoverformat:vF(\"z\"),valuehoverformat:vF(\"value\",1),showlegend:rZ({},w8e.showlegend,{dflt:!1})},QRt(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:ck.opacity,lightposition:ck.lightposition,lighting:ck.lighting,flatshading:ck.flatshading,contour:ck.contour,hoverinfo:rZ({},w8e.hoverinfo)}),\"calc\",\"nested\");t5.flatshading.dflt=!0;t5.lighting.facenormalsepsilon.dflt=0;t5.x.editType=t5.y.editType=t5.z.editType=t5.value.editType=\"calc+clearAxisTypes\"});var aZ=ye((hmr,S8e)=>{\"use strict\";var iDt=Pr(),nDt=Oa(),aDt=pF(),oDt=td();function sDt(e,t,r,n){function i(a,o){return iDt.coerce(e,t,aDt,a,o)}A8e(e,t,r,n,i)}function A8e(e,t,r,n,i){var a=i(\"isomin\"),o=i(\"isomax\");o!=null&&a!==void 0&&a!==null&&a>o&&(t.isomin=null,t.isomax=null);var s=i(\"x\"),l=i(\"y\"),u=i(\"z\"),c=i(\"value\");if(!s||!s.length||!l||!l.length||!u||!u.length||!c||!c.length){t.visible=!1;return}var f=nDt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");f(e,t,[\"x\",\"y\",\"z\"],n),i(\"valuehoverformat\"),[\"x\",\"y\",\"z\"].forEach(function(m){i(m+\"hoverformat\");var b=\"caps.\"+m,p=i(b+\".show\");p&&i(b+\".fill\");var k=\"slices.\"+m,M=i(k+\".show\");M&&(i(k+\".fill\"),i(k+\".locations\"))});var h=i(\"spaceframe.show\");h&&i(\"spaceframe.fill\");var d=i(\"surface.show\");d&&(i(\"surface.count\"),i(\"surface.fill\"),i(\"surface.pattern\"));var v=i(\"contour.show\");v&&(i(\"contour.color\"),i(\"contour.width\")),[\"text\",\"hovertext\",\"hovertemplate\",\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lighting.vertexnormalsepsilon\",\"lighting.facenormalsepsilon\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"flatshading\",\"opacity\"].forEach(function(m){i(m)}),oDt(e,t,n,i,{prefix:\"\",cLetter:\"c\"}),t._length=null}S8e.exports={supplyDefaults:sDt,supplyIsoDefaults:A8e}});var gF=ye((dmr,E8e)=>{\"use strict\";var sZ=Pr(),lDt=gv();function uDt(e,t){t._len=Math.min(t.u.length,t.v.length,t.w.length,t.x.length,t.y.length,t.z.length),t._u=$m(t.u,t._len),t._v=$m(t.v,t._len),t._w=$m(t.w,t._len),t._x=$m(t.x,t._len),t._y=$m(t.y,t._len),t._z=$m(t.z,t._len);var r=M8e(t);t._gridFill=r.fill,t._Xs=r.Xs,t._Ys=r.Ys,t._Zs=r.Zs,t._len=r.len;var n=0,i,a,o;t.starts&&(i=$m(t.starts.x||[]),a=$m(t.starts.y||[]),o=$m(t.starts.z||[]),n=Math.min(i.length,a.length,o.length)),t._startsX=i||[],t._startsY=a||[],t._startsZ=o||[];var s=0,l=1/0,u;for(u=0;u<t._len;u++){var c=t._u[u],f=t._v[u],h=t._w[u],d=Math.sqrt(c*c+f*f+h*h);s=Math.max(s,d),l=Math.min(l,d)}for(lDt(e,t,{vals:[l,s],containerStr:\"\",cLetter:\"c\"}),u=0;u<n;u++){var v=i[u];r.xMax=Math.max(r.xMax,v),r.xMin=Math.min(r.xMin,v);var m=a[u];r.yMax=Math.max(r.yMax,m),r.yMin=Math.min(r.yMin,m);var b=o[u];r.zMax=Math.max(r.zMax,b),r.zMin=Math.min(r.zMin,b)}t._slen=n,t._normMax=s,t._xbnds=[r.xMin,r.xMax],t._ybnds=[r.yMin,r.yMax],t._zbnds=[r.zMin,r.zMax]}function M8e(e){var t=e._x,r=e._y,n=e._z,i=e._len,a,o,s,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,d=1/0,v=\"\",m,b,p,k,M,T,L,x,C;for(i&&(k=t[0],T=r[0],x=n[0]),i>1&&(M=t[i-1],L=r[i-1],C=n[i-1]),a=0;a<i;a++)l=Math.max(l,t[a]),u=Math.min(u,t[a]),c=Math.max(c,r[a]),f=Math.min(f,r[a]),h=Math.max(h,n[a]),d=Math.min(d,n[a]),!m&&t[a]!==k&&(m=!0,v+=\"x\"),!b&&r[a]!==T&&(b=!0,v+=\"y\"),!p&&n[a]!==x&&(p=!0,v+=\"z\");m||(v+=\"x\"),b||(v+=\"y\"),p||(v+=\"z\");var S=oZ(e._x),g=oZ(e._y),P=oZ(e._z);v=v.replace(\"x\",(k>M?\"-\":\"+\")+\"x\"),v=v.replace(\"y\",(T>L?\"-\":\"+\")+\"y\"),v=v.replace(\"z\",(x>C?\"-\":\"+\")+\"z\");var E=function(){i=0,S=[],g=[],P=[]};(!i||i<S.length*g.length*P.length)&&E();var z=function(Be){return Be===\"x\"?t:Be===\"y\"?r:n},q=function(Be){return Be===\"x\"?S:Be===\"y\"?g:P},V=function(Be){return Be[i-1]<Be[0]?-1:1},G=z(v[1]),Z=z(v[3]),j=z(v[5]),N=q(v[1]).length,H=q(v[3]).length,re=q(v[5]).length,ae=!1,_e=function(Be,Le,me){return N*(H*Be+Le)+me},Ee=V(z(v[1])),Ce=V(z(v[3])),ge=V(z(v[5]));for(a=0;a<re-1;a++){for(o=0;o<H-1;o++){for(s=0;s<N-1;s++){var ie=_e(a,o,s),Se=_e(a,o,s+1),ke=_e(a,o+1,s),be=_e(a+1,o,s);if((!(G[ie]*Ee<G[Se]*Ee)||!(Z[ie]*Ce<Z[ke]*Ce)||!(j[ie]*ge<j[be]*ge))&&(ae=!0),ae)break}if(ae)break}if(ae)break}return ae&&(sZ.warn(\"Encountered arbitrary coordinates! Unable to input data grid.\"),E()),{xMin:u,yMin:f,zMin:d,xMax:l,yMax:c,zMax:h,Xs:S,Ys:g,Zs:P,len:i,fill:v}}function oZ(e){return sZ.distinctVals(e).vals}function $m(e,t){if(t===void 0&&(t=e.length),sZ.isTypedArray(e))return e.subarray(0,t);for(var r=[],n=0;n<t;n++)r[n]=+e[n];return r}E8e.exports={calc:uDt,filter:$m,processGrid:M8e}});var lZ=ye((vmr,k8e)=>{\"use strict\";var cDt=gv(),fDt=gF().processGrid,mF=gF().filter;k8e.exports=function(t,r){r._len=Math.min(r.x.length,r.y.length,r.z.length,r.value.length),r._x=mF(r.x,r._len),r._y=mF(r.y,r._len),r._z=mF(r.z,r._len),r._value=mF(r.value,r._len);var n=fDt(r);r._gridFill=n.fill,r._Xs=n.Xs,r._Ys=n.Ys,r._Zs=n.Zs,r._len=n.len;for(var i=1/0,a=-1/0,o=0;o<r._len;o++){var s=r._value[o];i=Math.min(i,s),a=Math.max(a,s)}r._minValues=i,r._maxValues=a,r._vMin=r.isomin===void 0||r.isomin===null?i:r.isomin,r._vMax=r.isomax===void 0||r.isomax===null?a:r.isomax,cDt(t,r,{vals:[r._vMin,r._vMax],containerStr:\"\",cLetter:\"c\"})}});var r5=ye((pmr,C8e)=>{\"use strict\";C8e.exports=function(t,r,n,i){i=i||t.length;for(var a=new Array(i),o=0;o<i;o++)a[o]=[t[o],r[o],n[o]];return a}});var yF=ye((gmr,R8e)=>{\"use strict\";var hDt=qd().gl_mesh3d,dDt=o1().parseColorScale,vDt=Pr().isArrayOrTypedArray,pDt=a1(),gDt=tc().extractOpts,L8e=r5(),fk=function(e,t){for(var r=t.length-1;r>0;r--){var n=Math.min(t[r],t[r-1]),i=Math.max(t[r],t[r-1]);if(i>n&&n<e&&e<=i)return{id:r,distRatio:(i-e)/(i-n)}}return{id:0,distRatio:0}};function P8e(e,t,r){this.scene=e,this.uid=r,this.mesh=t,this.name=\"\",this.data=null,this.showContour=!1}var uZ=P8e.prototype;uZ.handlePick=function(e){if(e.object===this.mesh){var t=e.data.index,r=this.data._meshX[t],n=this.data._meshY[t],i=this.data._meshZ[t],a=this.data._Ys.length,o=this.data._Zs.length,s=fk(r,this.data._Xs).id,l=fk(n,this.data._Ys).id,u=fk(i,this.data._Zs).id,c=e.index=u+o*l+o*a*s;e.traceCoordinate=[this.data._meshX[c],this.data._meshY[c],this.data._meshZ[c],this.data._value[c]];var f=this.data.hovertext||this.data.text;return vDt(f)&&f[c]!==void 0?e.textLabel=f[c]:f&&(e.textLabel=f),!0}};uZ.update=function(e){var t=this.scene,r=t.fullSceneLayout;this.data=I8e(e);function n(l,u,c,f){return u.map(function(h){return l.d2l(h,0,f)*c})}var i=L8e(n(r.xaxis,e._meshX,t.dataScale[0],e.xcalendar),n(r.yaxis,e._meshY,t.dataScale[1],e.ycalendar),n(r.zaxis,e._meshZ,t.dataScale[2],e.zcalendar)),a=L8e(e._meshI,e._meshJ,e._meshK),o={positions:i,cells:a,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,contourEnable:e.contour.show,contourColor:pDt(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading},s=gDt(e);o.vertexIntensity=e._meshIntensity,o.vertexIntensityBounds=[s.min,s.max],o.colormap=dDt(e),this.mesh.update(o)};uZ.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var mDt=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"];function I8e(e){e._meshI=[],e._meshJ=[],e._meshK=[];var t=e.surface.show,r=e.spaceframe.show,n=e.surface.fill,i=e.spaceframe.fill,a=!1,o=!1,s=0,l,u,c=e._Xs,f=e._Ys,h=e._Zs,d=c.length,v=f.length,m=h.length,b=mDt.indexOf(e._gridFill.replace(/-/g,\"\").replace(/\\+/g,\"\")),p=function(St,Tt,dt){switch(b){case 5:return dt+m*Tt+m*v*St;case 4:return dt+m*St+m*d*Tt;case 3:return Tt+v*dt+v*m*St;case 2:return Tt+v*St+v*d*dt;case 1:return St+d*dt+d*m*Tt;default:return St+d*Tt+d*v*dt}},k=e._minValues,M=e._maxValues,T=e._vMin,L=e._vMax,x,C,S,g;function P(St,Tt,dt){for(var Et=g.length,pt=u;pt<Et;pt++)if(St===x[pt]&&Tt===C[pt]&&dt===S[pt])return pt;return-1}function E(){u=l}function z(){x=[],C=[],S=[],g=[],l=0,E()}function q(St,Tt,dt,Et){return x.push(St),C.push(Tt),S.push(dt),g.push(Et),l++,l-1}function V(St,Tt,dt){return e._meshI.push(St),e._meshJ.push(Tt),e._meshK.push(dt),s++,s-1}function G(St,Tt,dt){for(var Et=[],pt=0;pt<St.length;pt++)Et[pt]=(St[pt]+Tt[pt]+dt[pt])/3;return Et}function Z(St,Tt,dt){for(var Et=[],pt=0;pt<St.length;pt++)Et[pt]=St[pt]*(1-dt)+dt*Tt[pt];return Et}var j;function N(St){j=St}function H(St,Tt){var dt=St[0],Et=St[1],pt=St[2],jt=G(dt,Et,pt),or=Math.sqrt(1-j),mr=Z(jt,dt,or),Ar=Z(jt,Et,or),ei=Z(jt,pt,or),qr=Tt[0],jr=Tt[1],gt=Tt[2];return{xyzv:[[dt,Et,Ar],[Ar,mr,dt],[Et,pt,ei],[ei,Ar,Et],[pt,dt,mr],[mr,ei,pt]],abc:[[qr,jr,-1],[-1,-1,qr],[jr,gt,-1],[-1,-1,jr],[gt,qr,-1],[-1,-1,gt]]}}function re(St,Tt){return St===\"all\"||St===null?!0:St.indexOf(Tt)>-1}function ae(St,Tt){return St===null?Tt:St}function _e(St,Tt,dt){E();var Et=[Tt],pt=[dt];if(j>=1)Et=[Tt],pt=[dt];else if(j>0){var jt=H(Tt,dt);Et=jt.xyzv,pt=jt.abc}for(var or=0;or<Et.length;or++){Tt=Et[or],dt=pt[or];for(var mr=[],Ar=0;Ar<3;Ar++){var ei=Tt[Ar][0],qr=Tt[Ar][1],jr=Tt[Ar][2],gt=Tt[Ar][3],Ge=dt[Ar]>-1?dt[Ar]:P(ei,qr,jr);Ge>-1?mr[Ar]=Ge:mr[Ar]=q(ei,qr,jr,ae(St,gt))}V(mr[0],mr[1],mr[2])}}function Ee(St,Tt,dt){var Et=function(pt,jt,or){_e(St,[Tt[pt],Tt[jt],Tt[or]],[dt[pt],dt[jt],dt[or]])};Et(0,1,2),Et(2,3,0)}function Ce(St,Tt,dt){var Et=function(pt,jt,or){_e(St,[Tt[pt],Tt[jt],Tt[or]],[dt[pt],dt[jt],dt[or]])};Et(0,1,2),Et(3,0,1),Et(2,3,0),Et(1,2,3)}function ge(St,Tt,dt,Et){var pt=St[3];pt<dt&&(pt=dt),pt>Et&&(pt=Et);for(var jt=(St[3]-pt)/(St[3]-Tt[3]+1e-9),or=[],mr=0;mr<4;mr++)or[mr]=(1-jt)*St[mr]+jt*Tt[mr];return or}function ie(St,Tt,dt){return St>=Tt&&St<=dt}function Se(St){var Tt=.001*(L-T);return St>=T-Tt&&St<=L+Tt}function ke(St){for(var Tt=[],dt=0;dt<4;dt++){var Et=St[dt];Tt.push([e._x[Et],e._y[Et],e._z[Et],e._value[Et]])}return Tt}var be=3;function Be(St,Tt,dt,Et,pt,jt){jt||(jt=1),dt=[-1,-1,-1];var or=!1,mr=[ie(Tt[0][3],Et,pt),ie(Tt[1][3],Et,pt),ie(Tt[2][3],Et,pt)];if(!mr[0]&&!mr[1]&&!mr[2])return!1;var Ar=function(qr,jr,gt){return Se(jr[0][3])&&Se(jr[1][3])&&Se(jr[2][3])?(_e(qr,jr,gt),!0):jt<be?Be(qr,jr,gt,T,L,++jt):!1};if(mr[0]&&mr[1]&&mr[2])return Ar(St,Tt,dt)||or;var ei=!1;return[[0,1,2],[2,0,1],[1,2,0]].forEach(function(qr){if(mr[qr[0]]&&mr[qr[1]]&&!mr[qr[2]]){var jr=Tt[qr[0]],gt=Tt[qr[1]],Ge=Tt[qr[2]],Je=ge(Ge,jr,Et,pt),We=ge(Ge,gt,Et,pt);or=Ar(St,[We,Je,jr],[-1,-1,dt[qr[0]]])||or,or=Ar(St,[jr,gt,We],[dt[qr[0]],dt[qr[1]],-1])||or,ei=!0}}),ei||[[0,1,2],[1,2,0],[2,0,1]].forEach(function(qr){if(mr[qr[0]]&&!mr[qr[1]]&&!mr[qr[2]]){var jr=Tt[qr[0]],gt=Tt[qr[1]],Ge=Tt[qr[2]],Je=ge(gt,jr,Et,pt),We=ge(Ge,jr,Et,pt);or=Ar(St,[We,Je,jr],[-1,-1,dt[qr[0]]])||or,ei=!0}}),or}function Le(St,Tt,dt,Et){var pt=!1,jt=ke(Tt),or=[ie(jt[0][3],dt,Et),ie(jt[1][3],dt,Et),ie(jt[2][3],dt,Et),ie(jt[3][3],dt,Et)];if(!or[0]&&!or[1]&&!or[2]&&!or[3])return pt;if(or[0]&&or[1]&&or[2]&&or[3])return o&&(pt=Ce(St,jt,Tt)||pt),pt;var mr=!1;return[[0,1,2,3],[3,0,1,2],[2,3,0,1],[1,2,3,0]].forEach(function(Ar){if(or[Ar[0]]&&or[Ar[1]]&&or[Ar[2]]&&!or[Ar[3]]){var ei=jt[Ar[0]],qr=jt[Ar[1]],jr=jt[Ar[2]],gt=jt[Ar[3]];if(o)pt=_e(St,[ei,qr,jr],[Tt[Ar[0]],Tt[Ar[1]],Tt[Ar[2]]])||pt;else{var Ge=ge(gt,ei,dt,Et),Je=ge(gt,qr,dt,Et),We=ge(gt,jr,dt,Et);pt=_e(null,[Ge,Je,We],[-1,-1,-1])||pt}mr=!0}}),mr||([[0,1,2,3],[1,2,3,0],[2,3,0,1],[3,0,1,2],[0,2,3,1],[1,3,2,0]].forEach(function(Ar){if(or[Ar[0]]&&or[Ar[1]]&&!or[Ar[2]]&&!or[Ar[3]]){var ei=jt[Ar[0]],qr=jt[Ar[1]],jr=jt[Ar[2]],gt=jt[Ar[3]],Ge=ge(jr,ei,dt,Et),Je=ge(jr,qr,dt,Et),We=ge(gt,qr,dt,Et),et=ge(gt,ei,dt,Et);o?(pt=_e(St,[ei,et,Ge],[Tt[Ar[0]],-1,-1])||pt,pt=_e(St,[qr,Je,We],[Tt[Ar[1]],-1,-1])||pt):pt=Ee(null,[Ge,Je,We,et],[-1,-1,-1,-1])||pt,mr=!0}}),mr)||[[0,1,2,3],[1,2,3,0],[2,3,0,1],[3,0,1,2]].forEach(function(Ar){if(or[Ar[0]]&&!or[Ar[1]]&&!or[Ar[2]]&&!or[Ar[3]]){var ei=jt[Ar[0]],qr=jt[Ar[1]],jr=jt[Ar[2]],gt=jt[Ar[3]],Ge=ge(qr,ei,dt,Et),Je=ge(jr,ei,dt,Et),We=ge(gt,ei,dt,Et);o?(pt=_e(St,[ei,Ge,Je],[Tt[Ar[0]],-1,-1])||pt,pt=_e(St,[ei,Je,We],[Tt[Ar[0]],-1,-1])||pt,pt=_e(St,[ei,We,Ge],[Tt[Ar[0]],-1,-1])||pt):pt=_e(null,[Ge,Je,We],[-1,-1,-1])||pt,mr=!0}}),pt}function me(St,Tt,dt,Et,pt,jt,or,mr,Ar,ei,qr){var jr=!1;return a&&(re(St,\"A\")&&(jr=Le(null,[Tt,dt,Et,jt],ei,qr)||jr),re(St,\"B\")&&(jr=Le(null,[dt,Et,pt,Ar],ei,qr)||jr),re(St,\"C\")&&(jr=Le(null,[dt,jt,or,Ar],ei,qr)||jr),re(St,\"D\")&&(jr=Le(null,[Et,jt,mr,Ar],ei,qr)||jr),re(St,\"E\")&&(jr=Le(null,[dt,Et,jt,Ar],ei,qr)||jr)),o&&(jr=Le(St,[dt,Et,jt,Ar],ei,qr)||jr),jr}function Pe(St,Tt,dt,Et,pt,jt,or,mr){return[mr[0]===!0?!0:Be(St,ke([Tt,dt,Et]),[Tt,dt,Et],jt,or),mr[1]===!0?!0:Be(St,ke([Et,pt,Tt]),[Et,pt,Tt],jt,or)]}function ce(St,Tt,dt,Et,pt,jt,or,mr,Ar){return mr?Pe(St,Tt,dt,pt,Et,jt,or,Ar):Pe(St,dt,pt,Et,Tt,jt,or,Ar)}function He(St,Tt,dt,Et,pt,jt,or){var mr=!1,Ar,ei,qr,jr,gt=function(){mr=Be(St,[Ar,ei,qr],[-1,-1,-1],pt,jt)||mr,mr=Be(St,[qr,jr,Ar],[-1,-1,-1],pt,jt)||mr},Ge=or[0],Je=or[1],We=or[2];return Ge&&(Ar=Z(ke([p(Tt,dt-0,Et-0)])[0],ke([p(Tt-1,dt-0,Et-0)])[0],Ge),ei=Z(ke([p(Tt,dt-0,Et-1)])[0],ke([p(Tt-1,dt-0,Et-1)])[0],Ge),qr=Z(ke([p(Tt,dt-1,Et-1)])[0],ke([p(Tt-1,dt-1,Et-1)])[0],Ge),jr=Z(ke([p(Tt,dt-1,Et-0)])[0],ke([p(Tt-1,dt-1,Et-0)])[0],Ge),gt()),Je&&(Ar=Z(ke([p(Tt-0,dt,Et-0)])[0],ke([p(Tt-0,dt-1,Et-0)])[0],Je),ei=Z(ke([p(Tt-0,dt,Et-1)])[0],ke([p(Tt-0,dt-1,Et-1)])[0],Je),qr=Z(ke([p(Tt-1,dt,Et-1)])[0],ke([p(Tt-1,dt-1,Et-1)])[0],Je),jr=Z(ke([p(Tt-1,dt,Et-0)])[0],ke([p(Tt-1,dt-1,Et-0)])[0],Je),gt()),We&&(Ar=Z(ke([p(Tt-0,dt-0,Et)])[0],ke([p(Tt-0,dt-0,Et-1)])[0],We),ei=Z(ke([p(Tt-0,dt-1,Et)])[0],ke([p(Tt-0,dt-1,Et-1)])[0],We),qr=Z(ke([p(Tt-1,dt-1,Et)])[0],ke([p(Tt-1,dt-1,Et-1)])[0],We),jr=Z(ke([p(Tt-1,dt-0,Et)])[0],ke([p(Tt-1,dt-0,Et-1)])[0],We),gt()),mr}function lt(St,Tt,dt,Et,pt,jt,or,mr,Ar,ei,qr,jr){var gt=St;return jr?(a&&St===\"even\"&&(gt=null),me(gt,Tt,dt,Et,pt,jt,or,mr,Ar,ei,qr)):(a&&St===\"odd\"&&(gt=null),me(gt,Ar,mr,or,jt,pt,Et,dt,Tt,ei,qr))}function mt(St,Tt,dt,Et,pt){for(var jt=[],or=0,mr=0;mr<Tt.length;mr++)for(var Ar=Tt[mr],ei=1;ei<m;ei++)for(var qr=1;qr<v;qr++)jt.push(ce(St,p(Ar,qr-1,ei-1),p(Ar,qr-1,ei),p(Ar,qr,ei-1),p(Ar,qr,ei),dt,Et,(Ar+qr+ei)%2,pt&&pt[or]?pt[or]:[])),or++;return jt}function Ht(St,Tt,dt,Et,pt){for(var jt=[],or=0,mr=0;mr<Tt.length;mr++)for(var Ar=Tt[mr],ei=1;ei<d;ei++)for(var qr=1;qr<m;qr++)jt.push(ce(St,p(ei-1,Ar,qr-1),p(ei,Ar,qr-1),p(ei-1,Ar,qr),p(ei,Ar,qr),dt,Et,(ei+Ar+qr)%2,pt&&pt[or]?pt[or]:[])),or++;return jt}function at(St,Tt,dt,Et,pt){for(var jt=[],or=0,mr=0;mr<Tt.length;mr++)for(var Ar=Tt[mr],ei=1;ei<v;ei++)for(var qr=1;qr<d;qr++)jt.push(ce(St,p(qr-1,ei-1,Ar),p(qr-1,ei,Ar),p(qr,ei-1,Ar),p(qr,ei,Ar),dt,Et,(qr+ei+Ar)%2,pt&&pt[or]?pt[or]:[])),or++;return jt}function ct(St,Tt,dt){for(var Et=1;Et<m;Et++)for(var pt=1;pt<v;pt++)for(var jt=1;jt<d;jt++)lt(St,p(jt-1,pt-1,Et-1),p(jt-1,pt-1,Et),p(jt-1,pt,Et-1),p(jt-1,pt,Et),p(jt,pt-1,Et-1),p(jt,pt-1,Et),p(jt,pt,Et-1),p(jt,pt,Et),Tt,dt,(jt+pt+Et)%2)}function ar(St,Tt,dt){o=!0,ct(St,Tt,dt),o=!1}function Vt(St,Tt,dt){a=!0,ct(St,Tt,dt),a=!1}function rr(St,Tt,dt,Et,pt,jt){for(var or=[],mr=0,Ar=0;Ar<Tt.length;Ar++)for(var ei=Tt[Ar],qr=1;qr<m;qr++)for(var jr=1;jr<v;jr++)or.push(He(St,ei,jr,qr,dt,Et,pt[Ar],jt&&jt[mr]?jt[mr]:[])),mr++;return or}function tt(St,Tt,dt,Et,pt,jt){for(var or=[],mr=0,Ar=0;Ar<Tt.length;Ar++)for(var ei=Tt[Ar],qr=1;qr<d;qr++)for(var jr=1;jr<m;jr++)or.push(He(St,qr,ei,jr,dt,Et,pt[Ar],jt&&jt[mr]?jt[mr]:[])),mr++;return or}function je(St,Tt,dt,Et,pt,jt){for(var or=[],mr=0,Ar=0;Ar<Tt.length;Ar++)for(var ei=Tt[Ar],qr=1;qr<v;qr++)for(var jr=1;jr<d;jr++)or.push(He(St,jr,qr,ei,dt,Et,pt[Ar],jt&&jt[mr]?jt[mr]:[])),mr++;return or}function Ue(St,Tt){for(var dt=[],Et=St;Et<Tt;Et++)dt.push(Et);return dt}function Ae(){for(var St=0;St<d;St++)for(var Tt=0;Tt<v;Tt++)for(var dt=0;dt<m;dt++){var Et=p(St,Tt,dt);q(e._x[Et],e._y[Et],e._z[Et],e._value[Et])}}function rt(){z(),Ae();var St=null;if(r&&i&&(N(i),ar(St,T,L)),t&&n){N(n);for(var Tt=e.surface.pattern,dt=e.surface.count,Et=0;Et<dt;Et++){var pt=dt===1?.5:Et/(dt-1),jt=(1-pt)*T+pt*L,or=Math.abs(jt-k),mr=Math.abs(jt-M),Ar=or>mr?[k,jt]:[jt,M];Vt(Tt,Ar[0],Ar[1])}}var ei=[[Math.min(T,M),Math.max(T,M)],[Math.min(k,L),Math.max(k,L)]];[\"x\",\"y\",\"z\"].forEach(function(qr){for(var jr=[],gt=0;gt<ei.length;gt++){var Ge=0,Je=ei[gt][0],We=ei[gt][1],et=e.slices[qr];if(et.show&&et.fill){N(et.fill);var xt=[],At=[],Kt=[];if(et.locations.length)for(var Qt=0;Qt<et.locations.length;Qt++){var Mr=fk(et.locations[Qt],qr===\"x\"?c:qr===\"y\"?f:h);Mr.distRatio===0?xt.push(Mr.id):Mr.id>0&&(At.push(Mr.id),qr===\"x\"?Kt.push([Mr.distRatio,0,0]):qr===\"y\"?Kt.push([0,Mr.distRatio,0]):Kt.push([0,0,Mr.distRatio]))}else qr===\"x\"?xt=Ue(1,d-1):qr===\"y\"?xt=Ue(1,v-1):xt=Ue(1,m-1);At.length>0&&(qr===\"x\"?jr[Ge]=rr(St,At,Je,We,Kt,jr[Ge]):qr===\"y\"?jr[Ge]=tt(St,At,Je,We,Kt,jr[Ge]):jr[Ge]=je(St,At,Je,We,Kt,jr[Ge]),Ge++),xt.length>0&&(qr===\"x\"?jr[Ge]=mt(St,xt,Je,We,jr[Ge]):qr===\"y\"?jr[Ge]=Ht(St,xt,Je,We,jr[Ge]):jr[Ge]=at(St,xt,Je,We,jr[Ge]),Ge++)}var Gr=e.caps[qr];Gr.show&&Gr.fill&&(N(Gr.fill),qr===\"x\"?jr[Ge]=mt(St,[0,d-1],Je,We,jr[Ge]):qr===\"y\"?jr[Ge]=Ht(St,[0,v-1],Je,We,jr[Ge]):jr[Ge]=at(St,[0,m-1],Je,We,jr[Ge]),Ge++)}}),s===0&&z(),e._meshX=x,e._meshY=C,e._meshZ=S,e._meshIntensity=g,e._Xs=c,e._Ys=f,e._Zs=h}return rt(),e}function yDt(e,t){var r=e.glplot.gl,n=hDt({gl:r}),i=new P8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}R8e.exports={findNearestOnAxis:fk,generateIsoMeshes:I8e,createIsosurfaceTrace:yDt}});var F8e=ye((mmr,D8e)=>{\"use strict\";D8e.exports={attributes:pF(),supplyDefaults:aZ().supplyDefaults,calc:lZ(),colorbar:{min:\"cmin\",max:\"cmax\"},plot:yF().createIsosurfaceTrace,moduleType:\"trace\",name:\"isosurface\",basePlotModule:ux(),categories:[\"gl3d\",\"showLegend\"],meta:{}}});var q8e=ye((ymr,z8e)=>{\"use strict\";z8e.exports=F8e()});var fZ=ye((_mr,B8e)=>{\"use strict\";var _Dt=Tu(),xh=pF(),xDt=lk(),O8e=Vl(),cZ=Ao().extendFlat,bDt=mc().overrideAll,_F=B8e.exports=bDt(cZ({x:xh.x,y:xh.y,z:xh.z,value:xh.value,isomin:xh.isomin,isomax:xh.isomax,surface:xh.surface,spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:1}},slices:xh.slices,caps:xh.caps,text:xh.text,hovertext:xh.hovertext,xhoverformat:xh.xhoverformat,yhoverformat:xh.yhoverformat,zhoverformat:xh.zhoverformat,valuehoverformat:xh.valuehoverformat,hovertemplate:xh.hovertemplate,hovertemplatefallback:xh.hovertemplatefallback},_Dt(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{colorbar:xh.colorbar,opacity:xh.opacity,opacityscale:xDt.opacityscale,lightposition:xh.lightposition,lighting:xh.lighting,flatshading:xh.flatshading,contour:xh.contour,hoverinfo:cZ({},O8e.hoverinfo),showlegend:cZ({},O8e.showlegend,{dflt:!1})}),\"calc\",\"nested\");_F.x.editType=_F.y.editType=_F.z.editType=_F.value.editType=\"calc+clearAxisTypes\"});var U8e=ye((xmr,N8e)=>{\"use strict\";var wDt=Pr(),TDt=fZ(),ADt=aZ().supplyIsoDefaults,SDt=QX().opacityscaleDefaults;N8e.exports=function(t,r,n,i){function a(o,s){return wDt.coerce(t,r,TDt,o,s)}ADt(t,r,n,i,a),SDt(t,r,i,a)}});var j8e=ye((bmr,H8e)=>{\"use strict\";var MDt=qd().gl_mesh3d,EDt=o1().parseColorScale,kDt=Pr().isArrayOrTypedArray,CDt=a1(),LDt=tc().extractOpts,V8e=r5(),hZ=yF().findNearestOnAxis,PDt=yF().generateIsoMeshes;function G8e(e,t,r){this.scene=e,this.uid=r,this.mesh=t,this.name=\"\",this.data=null,this.showContour=!1}var dZ=G8e.prototype;dZ.handlePick=function(e){if(e.object===this.mesh){var t=e.data.index,r=this.data._meshX[t],n=this.data._meshY[t],i=this.data._meshZ[t],a=this.data._Ys.length,o=this.data._Zs.length,s=hZ(r,this.data._Xs).id,l=hZ(n,this.data._Ys).id,u=hZ(i,this.data._Zs).id,c=e.index=u+o*l+o*a*s;e.traceCoordinate=[this.data._meshX[c],this.data._meshY[c],this.data._meshZ[c],this.data._value[c]];var f=this.data.hovertext||this.data.text;return kDt(f)&&f[c]!==void 0?e.textLabel=f[c]:f&&(e.textLabel=f),!0}};dZ.update=function(e){var t=this.scene,r=t.fullSceneLayout;this.data=PDt(e);function n(l,u,c,f){return u.map(function(h){return l.d2l(h,0,f)*c})}var i=V8e(n(r.xaxis,e._meshX,t.dataScale[0],e.xcalendar),n(r.yaxis,e._meshY,t.dataScale[1],e.ycalendar),n(r.zaxis,e._meshZ,t.dataScale[2],e.zcalendar)),a=V8e(e._meshI,e._meshJ,e._meshK),o={positions:i,cells:a,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,opacityscale:e.opacityscale,contourEnable:e.contour.show,contourColor:CDt(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading},s=LDt(e);o.vertexIntensity=e._meshIntensity,o.vertexIntensityBounds=[s.min,s.max],o.colormap=EDt(e),this.mesh.update(o)};dZ.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};function IDt(e,t){var r=e.glplot.gl,n=MDt({gl:r}),i=new G8e(e,n,t.uid);return n._trace=i,i.update(t),e.glplot.add(n),i}H8e.exports=IDt});var X8e=ye((wmr,W8e)=>{\"use strict\";W8e.exports={attributes:fZ(),supplyDefaults:U8e(),calc:lZ(),colorbar:{min:\"cmin\",max:\"cmax\"},plot:j8e(),moduleType:\"trace\",name:\"volume\",basePlotModule:ux(),categories:[\"gl3d\",\"showLegend\"],meta:{}}});var Y8e=ye((Tmr,Z8e)=>{\"use strict\";Z8e.exports=X8e()});var $8e=ye((Amr,J8e)=>{\"use strict\";var RDt=Oa(),K8e=Pr(),DDt=td(),FDt=e5();J8e.exports=function(t,r,n,i){function a(c,f){return K8e.coerce(t,r,FDt,c,f)}function o(c){var f=c.map(function(h){var d=a(h);return d&&K8e.isArrayOrTypedArray(d)?d:null});return f.every(function(h){return h&&h.length===f[0].length})&&f}var s=o([\"x\",\"y\",\"z\"]);if(!s){r.visible=!1;return}if(o([\"i\",\"j\",\"k\"]),r.i&&(!r.j||!r.k)||r.j&&(!r.k||!r.i)||r.k&&(!r.i||!r.j)){r.visible=!1;return}var 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Ie=2-n(xe)/G(2*M/3);return[ve*G(6*M)/(2*Ie),v(xe)*q((4-Ie*Ie)/3)]};function An(){return t.geoProjection(Fn).scale(165.664)}function Hn(ve,xe){var Ie=G(M*(4+M));return[2/Ie*ve*(1+G(1-4*xe*xe/(M*M))),4/Ie*xe]}Hn.invert=function(ve,xe){var Ie=G(M*(4+M))/2;return[ve*Ie/(1+G(1-xe*xe*(4+M)/(4*M))),xe*Ie/2]};function Qn(){return t.geoProjection(Hn).scale(180.739)}function Vi(ve,xe){var Ie=(2+T)*m(xe);xe/=2;for(var ze=0,Qe=1/0;ze<10&&n(Qe)>p;ze++){var Xe=o(xe);xe-=Qe=(xe+m(xe)*(Xe+2)-Ie)/(2*Xe*(1+Xe))}return[2/G(M*(4+M))*ve*(1+o(xe)),2*G(M/(4+M))*m(xe)]}Vi.invert=function(ve,xe){var Ie=xe*G((4+M)/M)/2,ze=q(Ie),Qe=o(ze);return[ve/(2/G(M*(4+M))*(1+Qe)),q((ze+Ie*(Qe+2))/(2+T))]};function Jn(){return t.geoProjection(Vi).scale(180.739)}function Yn(ve,xe){return[ve*(1+o(xe))/G(2+M),2*xe/G(2+M)]}Yn.invert=function(ve,xe){var Ie=G(2+M),ze=xe*Ie/2;return[Ie*ve/(1+o(ze)),ze]};function Gt(){return t.geoProjection(Yn).scale(173.044)}function wt(ve,xe){for(var 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t.geoProjection(vo).scale(153.93)}var Ct=da(5/6*M,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Sr(){return t.geoProjection(Ct).scale(130.945)}function Jr(ve,xe){var Ie=ve*ve,ze=xe*xe;return[ve*(1-.162388*ze)*(.87-952426e-9*Ie*Ie),xe*(1+ze/12)]}Jr.invert=function(ve,xe){var Ie=ve,ze=xe,Qe=50,Xe;do{var nt=ze*ze;ze-=Xe=(ze*(1+nt/12)-xe)/(1+nt/4)}while(n(Xe)>p&&--Qe>0);Qe=50,ve/=1-.162388*nt;do{var qt=(qt=Ie*Ie)*qt;Ie-=Xe=(Ie*(.87-952426e-9*qt)-ve)/(.87-.00476213*qt)}while(n(Xe)>p&&--Qe>0);return[Ie,ze]};function hi(){return t.geoProjection(Jr).scale(131.747)}var hn=da(2.6516,-.76534,.19123,-.047094,1.36289,-.13965,.031762);function Sn(){return t.geoProjection(hn).scale(131.087)}function En(ve){var xe=ve(T,0)[0]-ve(-T,0)[0];function Ie(ze,Qe){var Xe=ze>0?-.5:.5,nt=ve(ze+Xe*M,Qe);return nt[0]-=Xe*xe,nt}return ve.invert&&(Ie.invert=function(ze,Qe){var Xe=ze>0?-.5:.5,nt=ve.invert(ze+Xe*xe,Qe),qt=nt[0]-Xe*M;return qt<-M?qt+=2*M:qt>M&&(qt-=2*M),nt[0]=qt,nt}),Ie}function ki(ve,xe){var Ie=v(ve),ze=v(xe),Qe=o(xe),Xe=o(ve)*Qe,nt=m(ve)*Qe,qt=m(ze*xe);ve=n(a(nt,qt)),xe=q(Xe),n(ve-T)>p&&(ve%=T);var Wt=_n(ve>M/4?T-ve:ve,xe);return ve>M/4&&(qt=Wt[0],Wt[0]=-Wt[1],Wt[1]=-qt),Wt[0]*=Ie,Wt[1]*=-ze,Wt}ki.invert=function(ve,xe){n(ve)>1&&(ve=v(ve)*2-ve),n(xe)>1&&(xe=v(xe)*2-xe);var Ie=v(ve),ze=v(xe),Qe=-Ie*ve,Xe=-ze*xe,nt=Xe/Qe<1,qt=ya(nt?Xe:Qe,nt?Qe:Xe),Wt=qt[0],nr=qt[1],fr=o(nr);return nt&&(Wt=-T-Wt),[Ie*(a(m(Wt)*fr,-m(nr))+M),ze*q(o(Wt)*fr)]};function _n(ve,xe){if(xe===T)return[0,0];var Ie=m(xe),ze=Ie*Ie,Qe=ze*ze,Xe=1+Qe,nt=1+3*Qe,qt=1-Qe,Wt=q(1/G(Xe)),nr=qt+ze*Xe*Wt,fr=(1-Ie)/nr,br=G(fr),Fr=fr*Xe,Hr=G(Fr),ri=br*qt,Ci,cn;if(ve===0)return[0,-(ri+ze*Hr)];var sn=o(xe),Ln=1/sn,Kn=2*Ie*sn,Aa=(-3*ze+Wt*nt)*Kn,fa=(-nr*sn-(1-Ie)*Aa)/(nr*nr),$a=.5*fa/br,Co=qt*$a-2*ze*br*Kn,Qa=ze*Xe*fa+fr*nt*Kn,mo=-Ln*Kn,Bo=-Ln*Qa,Ps=-2*Ln*Co,Ts=4*ve/M,wo;if(ve>.222*M||xe<M/4&&ve>.175*M){if(Ci=(ri+ze*G(Fr*(1+Qe)-ri*ri))/(1+Qe),ve>M/4)return[Ci,Ci];var To=Ci,hl=.5*Ci;Ci=.5*(hl+To),cn=50;do{var 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ve?(ze=_o(ve,Ie),xe?(Qe=_o(xe,1-Ie),Xe=Qe[1]*Qe[1]+Ie*ze[0]*ze[0]*Qe[0]*Qe[0],[[ze[0]*Qe[2]/Xe,ze[1]*ze[2]*Qe[0]*Qe[1]/Xe],[ze[1]*Qe[1]/Xe,-ze[0]*ze[2]*Qe[0]*Qe[2]/Xe],[ze[2]*Qe[1]*Qe[2]/Xe,-Ie*ze[0]*ze[1]*Qe[0]/Xe]]):[[ze[0],0],[ze[1],0],[ze[2],0]]):(Qe=_o(xe,1-Ie),[[0,Qe[0]/Qe[1]],[1/Qe[1],0],[Qe[2]/Qe[1],0]])}function _o(ve,xe){var Ie,ze,Qe,Xe,nt;if(xe<p)return Xe=m(ve),ze=o(ve),Ie=xe*(ve-Xe*ze)/4,[Xe-Ie*ze,ze+Ie*Xe,1-xe*Xe*Xe/2,ve-Ie];if(xe>=1-p)return Ie=(1-xe)/4,ze=N(ve),Xe=Z(ve),Qe=1/ze,nt=ze*j(ve),[Xe+Ie*(nt-ve)/(ze*ze),Qe-Ie*Xe*Qe*(nt-ve),Qe+Ie*Xe*Qe*(nt+ve),2*i(s(ve))-T+Ie*(nt-ve)/ze];var qt=[1,0,0,0,0,0,0,0,0],Wt=[G(xe),0,0,0,0,0,0,0,0],nr=0;for(ze=G(1-xe),nt=1;n(Wt[nr]/qt[nr])>p&&nr<8;)Ie=qt[nr++],Wt[nr]=(Ie-ze)/2,qt[nr]=(Ie+ze)/2,ze=G(Ie*ze),nt*=2;Qe=nt*qt[nr]*ve;do Xe=Wt[nr]*m(ze=Qe)/qt[nr],Qe=(q(Xe)+Qe)/2;while(--nr);return[m(Qe),Xe=o(Qe),Xe/o(Qe-ze),Qe]}function No(ve,xe,Ie){var ze=n(ve),Qe=n(xe),Xe=j(Qe);if(ze){var nt=1/m(ze),qt=1/(b(ze)*b(ze)),Wt=-(qt+Ie*(Xe*Xe*nt*nt)-1+Ie),nr=(Ie-1)*qt,fr=(-Wt+G(Wt*Wt-4*nr))/2;return[po(i(1/G(fr)),Ie)*v(ve),po(i(G((fr/qt-1)/Ie)),1-Ie)*v(xe)]}return[0,po(i(Xe),1-Ie)*v(xe)]}function po(ve,xe){if(!xe)return ve;if(xe===1)return u(b(ve/2+L));for(var Ie=1,ze=G(1-xe),Qe=G(xe),Xe=0;n(Qe)>p;Xe++){if(ve%M){var nt=i(ze*b(ve)/Ie);nt<0&&(nt+=M),ve+=nt+~~(ve/M)*M}else ve+=ve;Qe=(Ie+ze)/2,ze=G(Ie*ze),Qe=((Ie=Qe)-ze)/2}return ve/(h(2,Xe)*Ie)}function Lo(ve,xe){var Ie=(C-1)/(C+1),ze=G(1-Ie*Ie),Qe=po(T,ze*ze),Xe=-1,nt=u(b(M/4+n(xe)/2)),qt=s(Xe*nt)/G(Ie),Wt=ko(qt*o(Xe*ve),qt*m(Xe*ve)),nr=No(Wt[0],Wt[1],ze*ze);return[-nr[1],(xe>=0?1:-1)*(.5*Qe-nr[0])]}function ko(ve,xe){var Ie=ve*ve,ze=xe+1,Qe=1-Ie-xe*xe;return[.5*((ve>=0?T:-T)-a(Qe,2*ve)),-.25*u(Qe*Qe+4*Ie)+.5*u(ze*ze+Ie)]}function Ds(ve,xe){var Ie=xe[0]*xe[0]+xe[1]*xe[1];return[(ve[0]*xe[0]+ve[1]*xe[1])/Ie,(ve[1]*xe[0]-ve[0]*xe[1])/Ie]}Lo.invert=function(ve,xe){var Ie=(C-1)/(C+1),ze=G(1-Ie*Ie),Qe=po(T,ze*ze),Xe=-1,nt=Ma(.5*Qe-xe,-ve,ze*ze),qt=Ds(nt[0],nt[1]),Wt=a(qt[1],qt[0])/Xe;return[Wt,2*i(s(.5/Xe*u(Ie*qt[0]*qt[0]+Ie*qt[1]*qt[1])))-T]};function Fs(){return t.geoProjection(En(Lo)).scale(151.496)}function ll(ve){var xe=m(ve),Ie=o(ve),ze=ul(ve);ze.invert=ul(-ve);function Qe(Xe,nt){var qt=ze(Xe,nt);Xe=qt[0],nt=qt[1];var Wt=m(nt),nr=o(nt),fr=o(Xe),br=V(xe*Wt+Ie*nr*fr),Fr=m(br),Hr=n(Fr)>p?br/Fr:1;return[Hr*Ie*m(Xe),(n(Xe)>T?Hr:-Hr)*(xe*nr-Ie*Wt*fr)]}return Qe.invert=function(Xe,nt){var qt=G(Xe*Xe+nt*nt),Wt=-m(qt),nr=o(qt),fr=qt*nr,br=-nt*Wt,Fr=qt*xe,Hr=G(fr*fr+br*br-Fr*Fr),ri=a(fr*Fr+br*Hr,br*Fr-fr*Hr),Ci=(qt>T?-1:1)*a(Xe*Wt,qt*o(ri)*nr+nt*m(ri)*Wt);return ze.invert(Ci,ri)},Qe}function ul(ve){var xe=m(ve),Ie=o(ve);return function(ze,Qe){var Xe=o(Qe),nt=o(ze)*Xe,qt=m(ze)*Xe,Wt=m(Qe);return[a(qt,nt*Ie-Wt*xe),q(Wt*Ie+nt*xe)]}}function Fl(){var ve=0,xe=t.geoProjectionMutator(ll),Ie=xe(ve),ze=Ie.rotate,Qe=Ie.stream,Xe=t.geoCircle();return Ie.parallel=function(nt){if(!arguments.length)return ve*P;var qt=Ie.rotate();return xe(ve=nt*E).rotate(qt)},Ie.rotate=function(nt){return arguments.length?(ze.call(Ie,[nt[0],nt[1]-ve*P]),Xe.center([-nt[0],-nt[1]]),Ie):(nt=ze.call(Ie),nt[1]+=ve*P,nt)},Ie.stream=function(nt){return nt=Qe(nt),nt.sphere=function(){nt.polygonStart();var qt=.01,Wt=Xe.radius(90-qt)().coordinates[0],nr=Wt.length-1,fr=-1,br;for(nt.lineStart();++fr<nr;)nt.point((br=Wt[fr])[0],br[1]);for(nt.lineEnd(),Wt=Xe.radius(90+qt)().coordinates[0],nr=Wt.length-1,nt.lineStart();--fr>=0;)nt.point((br=Wt[fr])[0],br[1]);nt.lineEnd(),nt.polygonEnd()},nt},Ie.scale(79.4187).parallel(45).clipAngle(180-.001)}var us=3,rl=q(1-1/us)*P,As=Yr(0);function cl(ve){var xe=rl*E,Ie=xt(M,xe)[0]-xt(-M,xe)[0],ze=As(0,xe)[1],Qe=xt(0,xe)[1],Xe=S-Qe,nt=g/ve,qt=4/g,Wt=ze+Xe*Xe*4/g;function nr(fr,br){var Fr,Hr=n(br);if(Hr>xe){var 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JDe=ye((syr,KDe)=>{\"use strict\";var nd=Fa(),jZ=Pr(),s7t=Oa(),a5=Math.PI/180,nw=180/Math.PI,XZ={cursor:\"pointer\"},ZZ={cursor:\"auto\"};function l7t(e,t){var r=e.projection,n;return t._isScoped?n=u7t:t._isClipped?n=f7t:n=c7t,n(e,r)}KDe.exports=l7t;function YZ(e,t){return nd.behavior.zoom().translate(t.translate()).scale(t.scale())}function KZ(e,t,r){var n=e.id,i=e.graphDiv,a=i.layout,o=a[n],s=i._fullLayout,l=s[n],u={},c={};function f(h,d){u[n+\".\"+h]=jZ.nestedProperty(o,h).get(),s7t.call(\"_storeDirectGUIEdit\",a,s._preGUI,u);var v=jZ.nestedProperty(l,h);v.get()!==d&&(v.set(d),jZ.nestedProperty(o,h).set(d),c[n+\".\"+h]=d)}r(f),f(\"projection.scale\",t.scale()/e.fitScale),f(\"fitbounds\",!1),i.emit(\"plotly_relayout\",c)}function u7t(e,t){var r=YZ(e,t);function n(){nd.select(this).style(XZ)}function i(){t.scale(nd.event.scale).translate(nd.event.translate),e.render(!0);var 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a=t[this.id],o=[],s=!1;for(var l in hp.layerNameToAdjective)if(l!==\"frame\"&&a[\"show\"+l]){s=!0;break}for(var u=!1,c=0;c<e.length;c++){var f=e[0][0].trace;f._geo=i,f.locationmode&&(s=!0);var h=f.marker;if(h){var d=h.angle,v=h.angleref;(d||v===\"north\"||v===\"previous\")&&(u=!0)}}if(this._hasMarkerAngles=u,s){var m=rFe.getTopojsonName(a);(i.topojson===null||m!==i.topojsonName)&&(i.topojsonName=m,PlotlyGeoAssets.topojson[i.topojsonName]===void 0&&o.push(i.fetchTopojson()))}o=o.concat(tFe.fetchTraceGeoData(e)),r.push(new Promise(function(b,p){Promise.all(o).then(function(){i.topojson=PlotlyGeoAssets.topojson[i.topojsonName],i.update(e,t),b()}).catch(p)}))};tm.fetchTopojson=function(){var e=this,t=rFe.getTopojsonPath(e.topojsonURL,e.topojsonName);return new Promise(function(r,n){u1.json(t,function(i,a){if(i)return i.status===404?n(new Error([\"plotly.js could not find topojson file at\",t+\".\",\"Make sure the *topojsonURL* plot config option\",\"is set properly.\"].join(\" \"))):n(new 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fFe=ye((cyr,cFe)=>{\"use strict\";var VF=Pr(),B7t=O_(),N7t=Id().getSubplotData,{unwrapLonRange:U7t}=ey(),GF=hk(),V7t=eY(),uFe=GF.axesNames;cFe.exports=function(t,r,n){B7t(t,r,n,{type:\"geo\",attributes:V7t,handleDefaults:G7t,fullData:n,partition:\"y\"})};function G7t(e,t,r,n){var i=N7t(n.fullData,\"geo\",n.id),a=i.map(function(ae){return ae.index}),o=r(\"resolution\"),s=r(\"scope\"),l=GF.scopeDefaults[s],u=r(\"projection.type\",l.projType),c=t._isAlbersUsa=u===\"albers usa\";c&&(s=t.scope=\"usa\");var f=t._isScoped=s!==\"world\",h=t._isSatellite=u===\"satellite\",d=t._isConic=u.indexOf(\"conic\")!==-1||u===\"albers\",v=t._isClipped=!!GF.lonaxisSpan[u];if(e.visible===!1){var m=VF.extendDeep({},t._template);m.showcoastlines=!1,m.showcountries=!1,m.showframe=!1,m.showlakes=!1,m.showland=!1,m.showocean=!1,m.showrivers=!1,m.showsubunits=!1,m.lonaxis&&(m.lonaxis.showgrid=!1),m.lataxis&&(m.lataxis.showgrid=!1),t._template=m}for(var b=r(\"visible\"),p,k=0;k<uFe.length;k++){var M=uFe[k],T=[30,10][k],L;if(f)L=l[M+\"Range\"];else{var x=GF[M+\"Span\"],C=(x[u]||x[\"*\"])/2,S=r(\"projection.rotation.\"+M.slice(0,3),l.projRotate[k]);L=[S-C,S+C]}var g=r(M+\".range\",L);r(M+\".tick0\"),r(M+\".dtick\",T),p=r(M+\".showgrid\",b?void 0:!1),p&&(r(M+\".gridcolor\"),r(M+\".gridwidth\"),r(M+\".griddash\")),t[M]._ax={type:\"linear\",_id:M.slice(0,3),_traceIndices:a,setScale:VF.identity,c2l:VF.identity,r2l:VF.identity,autorange:!0,range:g.slice(),_m:1,_input:{}}}var P=t.lonaxis.range,E=t.lataxis.range;let[z,q]=U7t(P);var V=(z+q)/2,G;if(!c){var Z=f?l.projRotate:[V,0,0];G=r(\"projection.rotation.lon\",Z[0]),r(\"projection.rotation.lat\",Z[1]),r(\"projection.rotation.roll\",Z[2]),p=r(\"showcoastlines\",!f&&b),p&&(r(\"coastlinecolor\"),r(\"coastlinewidth\")),p=r(\"showocean\",b?void 0:!1),p&&r(\"oceancolor\")}var j,N;if(c?(j=-96.6,N=38.7):(j=f?V:G,N=(E[0]+E[1])/2),r(\"center.lon\",j),r(\"center.lat\",N),h&&(r(\"projection.tilt\"),r(\"projection.distance\")),d){var 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wqt=Pze(),Tqt=lM(),Aqt=Bze(),Sqt=cY(),Mqt=Gze(),hY=document.createElement(\"canvas\"),dp=hY.getContext(\"2d\");Hze.exports=Eqt;function Eqt(e,t){if(!Sqt(e))throw Error(\"Argument should be valid svg path string\");t||(t={});var r,n;t.shape?(r=t.shape[0],n=t.shape[1]):(r=hY.width=t.w||t.width||200,n=hY.height=t.h||t.height||200);var i=Math.min(r,n),a=t.stroke||0,o=t.viewbox||t.viewBox||wqt(e),s=[r/(o[2]-o[0]),n/(o[3]-o[1])],l=Math.min(s[0]||0,s[1]||0)/2;if(dp.fillStyle=\"black\",dp.fillRect(0,0,r,n),dp.fillStyle=\"white\",a&&(typeof a!=\"number\"&&(a=1),a>0?dp.strokeStyle=\"white\":dp.strokeStyle=\"black\",dp.lineWidth=Math.abs(a)),dp.translate(r*.5,n*.5),dp.scale(l,l),kqt()){var u=new Path2D(e);dp.fill(u),a&&dp.stroke(u)}else{var c=Tqt(e);Aqt(dp,c),dp.fill(),a&&dp.stroke()}dp.setTransform(1,0,0,1,0,0);var f=Mqt(dp,{cutoff:t.cutoff!=null?t.cutoff:.5,radius:t.radius!=null?t.radius:i*.5});return f}var rz;function kqt(){if(rz!=null)return rz;var e=document.createElement(\"canvas\").getContext(\"2d\");if(e.canvas.width=e.canvas.height=1,!window.Path2D)return rz=!1;var t=new Path2D(\"M0,0h1v1h-1v-1Z\");e.fillStyle=\"black\",e.fill(t);var r=e.getImageData(0,0,1,1);return rz=r&&r.data&&r.data[3]===255}});var cw=ye((Zyr,r7e)=>{\"use strict\";var nz=Eo(),Cqt=jze(),iz=lx(),Lqt=Oa(),d5=Pr(),bh=d5.isArrayOrTypedArray,f5=So(),Wze=df(),Xze=o1().formatColor,h5=Ru(),Pqt=U3(),vY=KF(),Tk=gx(),Iqt=Z1().DESELECTDIM,Zze={start:1,left:1,end:-1,right:-1,middle:0,center:0,bottom:1,top:-1},Rqt=ip().appendArrayPointValue;function Dqt(e,t){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=e._context.plotGlPixelRatio;if(t.visible!==!0)return n;if(h5.hasText(t)&&(n.text=t7e(e,t),n.textSel=Kze(e,t,t.selected),n.textUnsel=Kze(e,t,t.unselected)),h5.hasMarkers(t)&&(n.marker=gY(e,t),n.markerSel=pY(e,t,t.selected),n.markerUnsel=pY(e,t,t.unselected),!t.unselected&&bh(t.marker.opacity))){var a=t.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=Iqt*a[r]}if(h5.hasLines(t)){n.line={overlay:!0,thickness:t.line.width*i,color:t.line.color,opacity:t.opacity};var o=(Tk.DASHES[t.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=t.line.width*i;n.line.dashes=o}return t.error_x&&t.error_x.visible&&(n.errorX=Jze(t,t.error_x,i)),t.error_y&&t.error_y.visible&&(n.errorY=Jze(t,t.error_y,i)),t.fill&&t.fill!==\"none\"&&(n.fill={closed:!0,fill:t.fillcolor,thickness:0}),n}function t7e(e,t){var r=e._fullLayout,n=t._length,i=t.textfont,a=t.textposition,o=bh(a)?a:[a],s=i.color,l=i.size,u=i.family,c=i.weight,f=i.style,h=i.variant,d={},v,m=e._context.plotGlPixelRatio,b=t.texttemplate;if(b){d.text=[];var p=r._d3locale,k=Array.isArray(b),M=k?Math.min(b.length,n):n,T=k?function(P){return b[P]}:function(){return b};for(v=0;v<M;v++){var L={i:v},x=t._module.formatLabels(L,t,r),C={};Rqt(C,t,v),d.text.push(d5.texttemplateString({data:[C,L,t._meta],fallback:t.texttemplatefallback,labels:x,locale:p,template:T(v)}))}}else bh(t.text)&&t.text.length<n?d.text=t.text.slice():d.text=t.text;if(bh(d.text))for(v=d.text.length;v<n;v++)d.text[v]=\"\";for(d.opacity=t.opacity,d.font={},d.align=[],d.baseline=[],v=0;v<o.length;v++){var S=o[v].split(/\\s+/);switch(S[1]){case\"left\":d.align.push(\"right\");break;case\"right\":d.align.push(\"left\");break;default:d.align.push(S[1])}switch(S[0]){case\"top\":d.baseline.push(\"bottom\");break;case\"bottom\":d.baseline.push(\"top\");break;default:d.baseline.push(S[0])}}if(bh(s))for(d.color=new Array(n),v=0;v<n;v++)d.color[v]=s[v];else d.color=s;if(bh(l)||Array.isArray(u)||bh(c)||Array.isArray(f)||Array.isArray(h))for(d.font=new Array(n),v=0;v<n;v++){var g=d.font[v]={};g.size=(d5.isTypedArray(l)?l[v]:bh(l)?nz(l[v])?l[v]:0:l)*m,g.family=Array.isArray(u)?u[v]:u,g.weight=Yze(bh(c)?c[v]:c),g.style=Array.isArray(f)?f[v]:f,g.variant=Array.isArray(h)?h[v]:h}else d.font={size:l*m,family:u,weight:Yze(c),style:f,variant:h};return d}function Yze(e){return e<=1e3?e>500?\"bold\":\"normal\":e}function gY(e,t){var r=t._length,n=t.marker,i={},a,o=bh(n.symbol),s=bh(n.angle),l=bh(n.color),u=bh(n.line.color),c=bh(n.opacity),f=bh(n.size),h=bh(n.line.width),d;if(o||(d=vY.isOpenSymbol(n.symbol)),o||l||u||c||s){i.symbols=new Array(r),i.angles=new Array(r),i.colors=new Array(r),i.borderColors=new Array(r);var v=n.symbol,m=n.angle,b=Xze(n,n.opacity,r),p=Xze(n.line,n.opacity,r);if(!bh(p[0])){var k=p;for(p=Array(r),a=0;a<r;a++)p[a]=k}if(!bh(b[0])){var M=b;for(b=Array(r),a=0;a<r;a++)b[a]=M}if(!bh(v)){var T=v;for(v=Array(r),a=0;a<r;a++)v[a]=T}if(!bh(m)){var L=m;for(m=Array(r),a=0;a<r;a++)m[a]=L}for(i.symbols=v,i.angles=m,i.colors=b,i.borderColors=p,a=0;a<r;a++)o&&(d=vY.isOpenSymbol(n.symbol[a])),d&&(p[a]=b[a].slice(),b[a]=b[a].slice(),b[a][3]=0);for(i.opacity=t.opacity,i.markers=new Array(r),a=0;a<r;a++)i.markers[a]=e7e({mx:i.symbols[a],ma:i.angles[a]},t)}else d?(i.color=iz(n.color,\"uint8\"),i.color[3]=0,i.borderColor=iz(n.color,\"uint8\")):(i.color=iz(n.color,\"uint8\"),i.borderColor=iz(n.line.color,\"uint8\")),i.opacity=t.opacity*n.opacity,i.marker=e7e({mx:n.symbol,ma:n.angle},t);var x=1,C=Pqt(t,x),S;if(f||h){var g=i.sizes=new Array(r),P=i.borderSizes=new Array(r),E=0,z;if(f){for(a=0;a<r;a++)g[a]=C(n.size[a]),E+=g[a];z=E/r}else for(S=C(n.size),a=0;a<r;a++)g[a]=S;if(h)for(a=0;a<r;a++)P[a]=n.line.width[a];else for(S=n.line.width,a=0;a<r;a++)P[a]=S;i.sizeAvg=z}else i.size=C(n&&n.size||10),i.borderSizes=C(n.line.width);return i}function pY(e,t,r){var n=t.marker,i={};return r&&(r.marker&&r.marker.symbol?i=gY(e,d5.extendFlat({},n,r.marker)):r.marker&&(r.marker.size&&(i.size=r.marker.size),r.marker.color&&(i.colors=r.marker.color),r.marker.opacity!==void 0&&(i.opacity=r.marker.opacity))),i}function Kze(e,t,r){var n={};if(!r)return n;if(r.textfont){var i={opacity:1,text:t.text,texttemplate:t.texttemplate,textposition:t.textposition,textfont:d5.extendFlat({},t.textfont)};r.textfont&&d5.extendFlat(i.textfont,r.textfont),n=t7e(e,i)}return n}function Jze(e,t,r){var n={capSize:t.width*2*r,lineWidth:t.thickness*r,color:t.color};return t.copy_ystyle&&(n=e.error_y),n}var $ze=Tk.SYMBOL_SDF_SIZE,uw=Tk.SYMBOL_SIZE,Qze=Tk.SYMBOL_STROKE,dY={},Fqt=f5.symbolFuncs[0](uw*.05);function e7e(e,t){var r=e.mx;if(r===\"circle\")return null;var n,i,a=f5.symbolNumber(r),o=f5.symbolFuncs[a%100],s=!!f5.symbolNoDot[a%100],l=!!f5.symbolNoFill[a%100],u=vY.isDotSymbol(r);if(e.ma&&(r+=\"_\"+e.ma),dY[r])return dY[r];var c=f5.getMarkerAngle(e,t);return u&&!s?n=o(uw*1.1,c)+Fqt:n=o(uw,c),i=Cqt(n,{w:$ze,h:$ze,viewBox:[-uw,-uw,uw,uw],stroke:l?Qze:-Qze}),dY[r]=i,i||null}function zqt(e,t,r){var n=r.length,i=n/2,a,o;if(h5.hasLines(t)&&i)if(t.line.shape===\"hv\"){for(a=[],o=0;o<i-1;o++)isNaN(r[o*2])||isNaN(r[o*2+1])?a.push(NaN,NaN,NaN,NaN):(a.push(r[o*2],r[o*2+1]),!isNaN(r[o*2+2])&&!isNaN(r[o*2+3])?a.push(r[o*2+2],r[o*2+1]):a.push(NaN,NaN));a.push(r[n-2],r[n-1])}else if(t.line.shape===\"hvh\"){for(a=[],o=0;o<i-1;o++)if(isNaN(r[o*2])||isNaN(r[o*2+1])||isNaN(r[o*2+2])||isNaN(r[o*2+3]))!isNaN(r[o*2])&&!isNaN(r[o*2+1])?a.push(r[o*2],r[o*2+1]):a.push(NaN,NaN),a.push(NaN,NaN);else{var s=(r[o*2]+r[o*2+2])/2;a.push(r[o*2],r[o*2+1],s,r[o*2+1],s,r[o*2+3])}a.push(r[n-2],r[n-1])}else if(t.line.shape===\"vhv\"){for(a=[],o=0;o<i-1;o++)if(isNaN(r[o*2])||isNaN(r[o*2+1])||isNaN(r[o*2+2])||isNaN(r[o*2+3]))!isNaN(r[o*2])&&!isNaN(r[o*2+1])?a.push(r[o*2],r[o*2+1]):a.push(NaN,NaN),a.push(NaN,NaN);else{var l=(r[o*2+1]+r[o*2+3])/2;a.push(r[o*2],r[o*2+1],r[o*2],l,r[o*2+2],l)}a.push(r[n-2],r[n-1])}else if(t.line.shape===\"vh\"){for(a=[],o=0;o<i-1;o++)isNaN(r[o*2])||isNaN(r[o*2+1])?a.push(NaN,NaN,NaN,NaN):(a.push(r[o*2],r[o*2+1]),!isNaN(r[o*2+2])&&!isNaN(r[o*2+3])?a.push(r[o*2],r[o*2+3]):a.push(NaN,NaN));a.push(r[n-2],r[n-1])}else a=r;var u=!1;for(o=0;o<a.length;o++)if(isNaN(a[o])){u=!0;break}var c=u||a.length>Tk.TOO_MANY_POINTS||h5.hasMarkers(t)?\"rect\":\"round\";if(u&&t.connectgaps){var f=a[0],h=a[1];for(o=0;o<a.length;o+=2)isNaN(a[o])||isNaN(a[o+1])?(a[o]=f,a[o+1]=h):(f=a[o],h=a[o+1])}return{join:c,positions:a}}function qqt(e,t,r,n,i){var a=Lqt.getComponentMethod(\"errorbars\",\"makeComputeError\"),o=Wze.getFromId(e,t.xaxis,\"x\"),s=Wze.getFromId(e,t.yaxis,\"y\"),l=r.length/2,u={};function c(f,h){var d=h._id.charAt(0),v=t[\"error_\"+d];if(v&&v.visible&&(h.type===\"linear\"||h.type===\"log\")){for(var m=a(v),b={x:0,y:1}[d],p={x:[0,1,2,3],y:[2,3,0,1]}[d],k=new Float64Array(4*l),M=1/0,T=-1/0,L=0,x=0;L<l;L++,x+=4){var C=f[L];if(nz(C)){var S=r[L*2+b],g=m(C,L),P=g[0],E=g[1];if(nz(P)&&nz(E)){var z=C-P,q=C+E;k[x+p[0]]=S-h.c2l(z),k[x+p[1]]=h.c2l(q)-S,k[x+p[2]]=0,k[x+p[3]]=0,M=Math.min(M,C-P),T=Math.max(T,C+E)}}}u[d]={positions:r,errors:k,_bnds:[M,T]}}}return c(n,o),c(i,s),u}function Oqt(e,t,r,n){var i=t._length,a={},o;if(h5.hasMarkers(t)){var s=r.font,l=r.align,u=r.baseline;for(a.offset=new Array(i),o=0;o<i;o++){var c=n.sizes?n.sizes[o]:n.size,f=bh(s)?s[o].size:s.size,h=bh(l)?l.length>1?l[o]:l[0]:l,d=bh(u)?u.length>1?u[o]:u[0]:u,v=Zze[h],m=Zze[d],b=c?c/.8+1:0,p=-m*b-m*.5;a.offset[o]=[v*b/f,p/f]}}return a}r7e.exports={style:Dqt,markerStyle:gY,markerSelection:pY,linePositions:zqt,errorBarPositions:qqt,textPosition:Oqt}});var mY=ye((Yyr,i7e)=>{\"use strict\";var az=Pr();i7e.exports=function(t,r){var n=r._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return r._scene||(n=r._scene={},n.init=function(){az.extendFlat(n,a,i)},n.init(),n.update=function(s){var l=az.repeat(s,n.count);if(n.fill2d&&n.fill2d.update(l),n.scatter2d&&n.scatter2d.update(l),n.line2d&&n.line2d.update(l),n.error2d&&n.error2d.update(l.concat(l)),n.select2d&&n.select2d.update(l),n.glText)for(var u=0;u<n.count;u++)n.glText[u].update(s)},n.draw=function(){for(var s=n.count,l=n.fill2d,u=n.error2d,c=n.line2d,f=n.scatter2d,h=n.glText,d=n.select2d,v=n.selectBatch,m=n.unselectBatch,b=0;b<s;b++){if(l&&n.fillOrder[b]&&l.draw(n.fillOrder[b]),c&&n.lineOptions[b]&&c.draw(b),u&&(n.errorXOptions[b]&&u.draw(b),n.errorYOptions[b]&&u.draw(b+s)),f&&n.markerOptions[b])if(m[b].length){var p=az.repeat([],n.count);p[b]=m[b],f.draw(p)}else v[b].length||f.draw(b);h[b]&&n.textOptions[b]&&h[b].render()}d&&d.draw(v),n.dirty=!1},n.destroy=function(){n.fill2d&&n.fill2d.destroy&&n.fill2d.destroy(),n.scatter2d&&n.scatter2d.destroy&&n.scatter2d.destroy(),n.error2d&&n.error2d.destroy&&n.error2d.destroy(),n.line2d&&n.line2d.destroy&&n.line2d.destroy(),n.select2d&&n.select2d.destroy&&n.select2d.destroy(),n.glText&&n.glText.forEach(function(s){s.destroy&&s.destroy()}),n.lineOptions=null,n.fillOptions=null,n.markerOptions=null,n.markerSelectedOptions=null,n.markerUnselectedOptions=null,n.errorXOptions=null,n.errorYOptions=null,n.textOptions=null,n.textSelectedOptions=null,n.textUnselectedOptions=null,n.selectBatch=null,n.unselectBatch=null,r._scene=null}),n.dirty||az.extendFlat(n,i),n}});var u7e=ye((Kyr,l7e)=>{\"use strict\";var Bqt=$F(),v5=Pr(),n7e=df(),Nqt=Mg().findExtremes,a7e=zg(),yY=O0(),Uqt=yY.calcMarkerSize,Vqt=yY.calcAxisExpansion,Gqt=yY.setFirstScatter,Hqt=z0(),p5=cw(),jqt=mY(),o7e=fs().BADNUM,Wqt=gx().TOO_MANY_POINTS;l7e.exports=function(t,r){var n=t._fullLayout,i=r._xA=n7e.getFromId(t,r.xaxis,\"x\"),a=r._yA=n7e.getFromId(t,r.yaxis,\"y\"),o=n._plots[r.xaxis+r.yaxis],s=r._length,l=s>=Wqt,u=s*2,c={},f,h=i.makeCalcdata(r,\"x\"),d=a.makeCalcdata(r,\"y\"),v=a7e(r,i,\"x\",h),m=a7e(r,a,\"y\",d),b=v.vals,p=m.vals;r._x=b,r._y=p,r.xperiodalignment&&(r._origX=h,r._xStarts=v.starts,r._xEnds=v.ends),r.yperiodalignment&&(r._origY=d,r._yStarts=m.starts,r._yEnds=m.ends);var k=new Array(u),M=new Array(s);for(f=0;f<s;f++)k[f*2]=b[f]===o7e?NaN:b[f],k[f*2+1]=p[f]===o7e?NaN:p[f],M[f]=f;if(i.type===\"log\")for(f=0;f<u;f+=2)k[f]=i.c2l(k[f]);if(a.type===\"log\")for(f=1;f<u;f+=2)k[f]=a.c2l(k[f]);l&&i.type!==\"log\"&&a.type!==\"log\"?c.tree=Bqt(k):c.ids=M,Hqt(t,r);var T=Xqt(t,o,r,k,b,p),L=jqt(t,o);Gqt(n,r);var x;return l?T.marker&&(x=T.marker.sizeAvg||Math.max(T.marker.size,3)):x=Uqt(r,s),Vqt(t,r,i,a,b,p,x),T.errorX&&s7e(r,i,T.errorX),T.errorY&&s7e(r,a,T.errorY),T.fill&&!L.fill2d&&(L.fill2d=!0),T.marker&&!L.scatter2d&&(L.scatter2d=!0),T.line&&!L.line2d&&(L.line2d=!0),(T.errorX||T.errorY)&&!L.error2d&&(L.error2d=!0),T.text&&!L.glText&&(L.glText=!0),T.marker&&(T.marker.snap=s),L.lineOptions.push(T.line),L.errorXOptions.push(T.errorX),L.errorYOptions.push(T.errorY),L.fillOptions.push(T.fill),L.markerOptions.push(T.marker),L.markerSelectedOptions.push(T.markerSel),L.markerUnselectedOptions.push(T.markerUnsel),L.textOptions.push(T.text),L.textSelectedOptions.push(T.textSel),L.textUnselectedOptions.push(T.textUnsel),L.selectBatch.push([]),L.unselectBatch.push([]),c._scene=L,c.index=L.count,c.x=b,c.y=p,c.positions=k,L.count++,[{x:!1,y:!1,t:c,trace:r}]};function s7e(e,t,r){var n=e._extremes[t._id],i=Nqt(t,r._bnds,{padded:!0});n.min=n.min.concat(i.min),n.max=n.max.concat(i.max)}function Xqt(e,t,r,n,i,a){var 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me,be,De,Ge=le.axes&&le.axes.lastCubeProps.axis||U,ht=le.showSurface,it=le.showContour;for(me=0;me<3;++me)for(ht=ht||le.surfaceProject[me],be=0;be<3;++be)it=it||le.contourProject[me][be];for(me=0;me<3;++me){var Oe=J.projections[me];for(be=0;be<16;++be)Oe[be]=0;for(be=0;be<4;++be)Oe[5*be]=1;Oe[5*me]=0,Oe[12+me]=le.axesBounds[+(Ge[me]>0)][me],y(Oe,ie.model,Oe);var ze=J.clipBounds[me];for(De=0;De<2;++De)for(be=0;be<3;++be)ze[De][be]=ie.clipBounds[De][be];ze[0][me]=-1e8,ze[1][me]=1e8}return J.showSurface=ht,J.showContour=it,J}var ce={model:E,view:E,projection:E,inverseModel:E.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},ye=E.slice(),xe=[1,0,0,0,1,0,0,0,1];function ue(ie,le){ie=ie||{};var me=this.gl;me.disable(me.CULL_FACE),this._colorMap.bind(0);var be=ce;be.model=ie.model||E,be.view=ie.view||E,be.projection=ie.projection||E,be.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],be.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],be.objectOffset=this.objectOffset,be.contourColor=this.contourColor[0],be.inverseModel=C(be.inverseModel,be.model);for(var De=0;De<2;++De)for(var Ge=be.clipBounds[De],ht=0;ht<3;++ht)Ge[ht]=Math.min(Math.max(this.clipBounds[De][ht],-1e8),1e8);be.kambient=this.ambientLight,be.kdiffuse=this.diffuseLight,be.kspecular=this.specularLight,be.roughness=this.roughness,be.fresnel=this.fresnel,be.opacity=this.opacity,be.height=0,be.permutation=xe,be.vertexColor=this.vertexColor;var it=ye;for(y(it,be.view,be.model),y(it,be.projection,it),C(it,it),De=0;De<3;++De)be.eyePosition[De]=it[12+De]/it[15];var Oe=it[15];for(De=0;De<3;++De)Oe+=this.lightPosition[De]*it[4*De+3];for(De=0;De<3;++De){var ze=it[12+De];for(ht=0;ht<3;++ht)ze+=it[4*ht+De]*this.lightPosition[ht];be.lightPosition[De]=ze/Oe}var ct=ee(be,this);if(ct.showSurface){for(this._shader.bind(),this._shader.uniforms=be,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(me.TRIANGLES,this._vertexCount),De=0;De<3;++De)!this.surfaceProject[De]||!this.vertexCount||(this._shader.uniforms.model=ct.projections[De],this._shader.uniforms.clipBounds=ct.clipBounds[De],this._vao.draw(me.TRIANGLES,this._vertexCount));this._vao.unbind()}if(ct.showContour){var et=this._contourShader;be.kambient=1,be.kdiffuse=0,be.kspecular=0,be.opacity=1,et.bind(),et.uniforms=be;var qe=this._contourVAO;for(qe.bind(),De=0;De<3;++De)for(et.uniforms.permutation=R[De],me.lineWidth(this.contourWidth[De]*this.pixelRatio),ht=0;ht<this.contourLevels[De].length;++ht)ht===this.highlightLevel[De]?(et.uniforms.contourColor=this.highlightColor[De],et.uniforms.contourTint=this.highlightTint[De]):(ht===0||ht-1===this.highlightLevel[De])&&(et.uniforms.contourColor=this.contourColor[De],et.uniforms.contourTint=this.contourTint[De]),this._contourCounts[De][ht]&&(et.uniforms.height=this.contourLevels[De][ht],qe.draw(me.LINES,this._contourCounts[De][ht],this._contourOffsets[De][ht]));for(De=0;De<3;++De)for(et.uniforms.model=ct.projections[De],et.uniforms.clipBounds=ct.clipBounds[De],ht=0;ht<3;++ht)if(this.contourProject[De][ht]){et.uniforms.permutation=R[ht],me.lineWidth(this.contourWidth[ht]*this.pixelRatio);for(var we=0;we<this.contourLevels[ht].length;++we)we===this.highlightLevel[ht]?(et.uniforms.contourColor=this.highlightColor[ht],et.uniforms.contourTint=this.highlightTint[ht]):(we===0||we-1===this.highlightLevel[ht])&&(et.uniforms.contourColor=this.contourColor[ht],et.uniforms.contourTint=this.contourTint[ht]),this._contourCounts[ht][we]&&(et.uniforms.height=this.contourLevels[ht][we],qe.draw(me.LINES,this._contourCounts[ht][we],this._contourOffsets[ht][we]))}for(qe.unbind(),qe=this._dynamicVAO,qe.bind(),De=0;De<3;++De)if(this._dynamicCounts[De]!==0)for(et.uniforms.model=be.model,et.uniforms.clipBounds=be.clipBounds,et.uniforms.permutation=R[De],me.lineWidth(this.dynamicWidth[De]*this.pixelRatio),et.uniforms.contourColor=this.dynamicColor[De],et.uniforms.contourTint=this.dynamicTint[De],et.uniforms.height=this.dynamicLevel[De],qe.draw(me.LINES,this._dynamicCounts[De],this._dynamicOffsets[De]),ht=0;ht<3;++ht)this.contourProject[ht][De]&&(et.uniforms.model=ct.projections[ht],et.uniforms.clipBounds=ct.clipBounds[ht],qe.draw(me.LINES,this._dynamicCounts[De],this._dynamicOffsets[De]));qe.unbind()}}j.draw=function(ie){return ue.call(this,ie,!1)},j.drawTransparent=function(ie){return ue.call(this,ie,!0)};var K={model:E,view:E,projection:E,inverseModel:E,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};j.drawPick=function(ie){ie=ie||{};var le=this.gl;le.disable(le.CULL_FACE);var me=K;me.model=ie.model||E,me.view=ie.view||E,me.projection=ie.projection||E,me.shape=this._field[2].shape,me.pickId=this.pickId/255,me.lowerBound=this.bounds[0],me.upperBound=this.bounds[1],me.objectOffset=this.objectOffset,me.permutation=xe;for(var be=0;be<2;++be)for(var De=me.clipBounds[be],Ge=0;Ge<3;++Ge)De[Ge]=Math.min(Math.max(this.clipBounds[be][Ge],-1e8),1e8);var ht=ee(me,this);if(ht.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=me,this._vao.bind(),this._vao.draw(le.TRIANGLES,this._vertexCount),be=0;be<3;++be)this.surfaceProject[be]&&(this._pickShader.uniforms.model=ht.projections[be],this._pickShader.uniforms.clipBounds=ht.clipBounds[be],this._vao.draw(le.TRIANGLES,this._vertexCount));this._vao.unbind()}if(ht.showContour){var it=this._contourPickShader;it.bind(),it.uniforms=me;var Oe=this._contourVAO;for(Oe.bind(),Ge=0;Ge<3;++Ge)for(le.lineWidth(this.contourWidth[Ge]*this.pixelRatio),it.uniforms.permutation=R[Ge],be=0;be<this.contourLevels[Ge].length;++be)this._contourCounts[Ge][be]&&(it.uniforms.height=this.contourLevels[Ge][be],Oe.draw(le.LINES,this._contourCounts[Ge][be],this._contourOffsets[Ge][be]));for(be=0;be<3;++be)for(it.uniforms.model=ht.projections[be],it.uniforms.clipBounds=ht.clipBounds[be],Ge=0;Ge<3;++Ge)if(this.contourProject[be][Ge]){it.uniforms.permutation=R[Ge],le.lineWidth(this.contourWidth[Ge]*this.pixelRatio);for(var ze=0;ze<this.contourLevels[Ge].length;++ze)this._contourCounts[Ge][ze]&&(it.uniforms.height=this.contourLevels[Ge][ze],Oe.draw(le.LINES,this._contourCounts[Ge][ze],this._contourOffsets[Ge][ze]))}Oe.unbind()}},j.pick=function(ie){if(!ie||ie.id!==this.pickId)return null;var le=this._field[2].shape,me=this._pickResult,be=le[0]*(ie.value[0]+(ie.value[2]>>4)/16)/255,De=Math.floor(be),Ge=be-De,ht=le[1]*(ie.value[1]+(ie.value[2]&15)/16)/255,it=Math.floor(ht),Oe=ht-it;De+=1,it+=1;var ze=me.position;ze[0]=ze[1]=ze[2]=0;for(var ct=0;ct<2;++ct)for(var et=ct?Ge:1-Ge,qe=0;qe<2;++qe)for(var we=qe?Oe:1-Oe,Le=De+ct,Ke=it+qe,Te=et*we,Ve=0;Ve<3;++Ve)ze[Ve]+=this._field[Ve].get(Le,Ke)*Te;for(var $e=this._pickResult.level,ot=0;ot<3;++ot)if($e[ot]=k.le(this.contourLevels[ot],ze[ot]),$e[ot]<0)this.contourLevels[ot].length>0&&($e[ot]=0);else if($e[ot]<this.contourLevels[ot].length-1){var tt=this.contourLevels[ot][$e[ot]],nt=this.contourLevels[ot][$e[ot]+1];Math.abs(tt-ze[ot])>Math.abs(nt-ze[ot])&&($e[ot]+=1)}for(me.index[0]=Ge<.5?De:De+1,me.index[1]=Oe<.5?it:it+1,me.uv[0]=be/le[0],me.uv[1]=ht/le[1],Ve=0;Ve<3;++Ve)me.dataCoordinate[Ve]=this._field[Ve].get(me.index[0],me.index[1]);return me},j.padField=function(ie,le){var me=le.shape.slice(),be=ie.shape.slice();g.assign(ie.lo(1,1).hi(me[0],me[1]),le),g.assign(ie.lo(1).hi(me[0],1),le.hi(me[0],1)),g.assign(ie.lo(1,be[1]-1).hi(me[0],1),le.lo(0,me[1]-1).hi(me[0],1)),g.assign(ie.lo(0,1).hi(1,me[1]),le.hi(1)),g.assign(ie.lo(be[0]-1,1).hi(1,me[1]),le.lo(me[0]-1)),ie.set(0,0,le.get(0,0)),ie.set(0,be[1]-1,le.get(0,me[1]-1)),ie.set(be[0]-1,0,le.get(me[0]-1,0)),ie.set(be[0]-1,be[1]-1,le.get(me[0]-1,me[1]-1))};function _e(ie,le){return Array.isArray(ie)?[le(ie[0]),le(ie[1]),le(ie[2])]:[le(ie),le(ie),le(ie)]}function ne(ie){return Array.isArray(ie)?ie.length===3?[ie[0],ie[1],ie[2],1]:[ie[0],ie[1],ie[2],ie[3]]:[0,0,0,1]}function ae(ie){if(Array.isArray(ie)){if(Array.isArray(ie))return[ne(ie[0]),ne(ie[1]),ne(ie[2])];var le=ne(ie);return[le.slice(),le.slice(),le.slice()]}}j.update=function(ie){ie=ie||{},this.objectOffset=ie.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in ie&&(this.contourWidth=_e(ie.contourWidth,Number)),\"showContour\"in ie&&(this.showContour=_e(ie.showContour,Boolean)),\"showSurface\"in ie&&(this.showSurface=!!ie.showSurface),\"contourTint\"in ie&&(this.contourTint=_e(ie.contourTint,Boolean)),\"contourColor\"in ie&&(this.contourColor=ae(ie.contourColor)),\"contourProject\"in ie&&(this.contourProject=_e(ie.contourProject,function(Ga){return _e(Ga,Boolean)})),\"surfaceProject\"in ie&&(this.surfaceProject=ie.surfaceProject),\"dynamicColor\"in ie&&(this.dynamicColor=ae(ie.dynamicColor)),\"dynamicTint\"in ie&&(this.dynamicTint=_e(ie.dynamicTint,Number)),\"dynamicWidth\"in ie&&(this.dynamicWidth=_e(ie.dynamicWidth,Number)),\"opacity\"in ie&&(this.opacity=ie.opacity),\"opacityscale\"in ie&&(this.opacityscale=ie.opacityscale),\"colorBounds\"in ie&&(this.colorBounds=ie.colorBounds),\"vertexColor\"in ie&&(this.vertexColor=ie.vertexColor?1:0),\"colormap\"in ie&&this._colorMap.setPixels(this.genColormap(ie.colormap,this.opacityscale));var le=ie.field||ie.coords&&ie.coords[2]||null,me=!1;if(le||(this._field[2].shape[0]||this._field[2].shape[2]?le=this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):le=this._field[2].hi(0,0)),\"field\"in ie||\"coords\"in ie){var be=(le.shape[0]+2)*(le.shape[1]+2);be>this._field[2].data.length&&(f.freeFloat(this._field[2].data),this._field[2].data=f.mallocFloat(s.nextPow2(be))),this._field[2]=x(this._field[2].data,[le.shape[0]+2,le.shape[1]+2]),this.padField(this._field[2],le),this.shape=le.shape.slice();for(var De=this.shape,Ge=0;Ge<2;++Ge)this._field[2].size>this._field[Ge].data.length&&(f.freeFloat(this._field[Ge].data),this._field[Ge].data=f.mallocFloat(this._field[2].size)),this._field[Ge]=x(this._field[Ge].data,[De[0]+2,De[1]+2]);if(ie.coords){var ht=ie.coords;if(!Array.isArray(ht)||ht.length!==3)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(Ge=0;Ge<2;++Ge){var it=ht[Ge];for(qe=0;qe<2;++qe)if(it.shape[qe]!==De[qe])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[Ge],it)}}else if(ie.ticks){var Oe=ie.ticks;if(!Array.isArray(Oe)||Oe.length!==2)throw new Error(\"gl-surface: invalid ticks\");for(Ge=0;Ge<2;++Ge){var ze=Oe[Ge];if((Array.isArray(ze)||ze.length)&&(ze=x(ze)),ze.shape[0]!==De[Ge])throw new Error(\"gl-surface: invalid tick length\");var ct=x(ze.data,De);ct.stride[Ge]=ze.stride[0],ct.stride[Ge^1]=0,this.padField(this._field[Ge],ct)}}else{for(Ge=0;Ge<2;++Ge){var et=[0,0];et[Ge]=1,this._field[Ge]=x(this._field[Ge].data,[De[0]+2,De[1]+2],et,0)}this._field[0].set(0,0,0);for(var qe=0;qe<De[0];++qe)this._field[0].set(qe+1,0,qe);for(this._field[0].set(De[0]+1,0,De[0]-1),this._field[1].set(0,0,0),qe=0;qe<De[1];++qe)this._field[1].set(0,qe+1,qe);this._field[1].set(0,De[1]+1,De[1]-1)}var we=this._field,Le=x(f.mallocFloat(we[2].size*3*2),[3,De[0]+2,De[1]+2,2]);for(Ge=0;Ge<3;++Ge)S(Le.pick(Ge),we[Ge],\"mirror\");var Ke=x(f.mallocFloat(we[2].size*3),[De[0]+2,De[1]+2,3]);for(Ge=0;Ge<De[0]+2;++Ge)for(qe=0;qe<De[1]+2;++qe){var Te=Le.get(0,Ge,qe,0),Ve=Le.get(0,Ge,qe,1),$e=Le.get(1,Ge,qe,0),ot=Le.get(1,Ge,qe,1),tt=Le.get(2,Ge,qe,0),nt=Le.get(2,Ge,qe,1),We=$e*nt-ot*tt,mt=tt*Ve-nt*Te,bt=Te*ot-Ve*$e,Yt=Math.sqrt(We*We+mt*mt+bt*bt);Yt<1e-8?(Yt=Math.max(Math.abs(We),Math.abs(mt),Math.abs(bt)),Yt<1e-8?(bt=1,mt=We=0,Yt=1):Yt=1/Yt):Yt=1/Math.sqrt(Yt),Ke.set(Ge,qe,0,We*Yt),Ke.set(Ge,qe,1,mt*Yt),Ke.set(Ge,qe,2,bt*Yt)}f.free(Le.data);var jt=[1/0,1/0,1/0],mr=[-1/0,-1/0,-1/0],or=1/0,fr=-1/0,Ot=(De[0]-1)*(De[1]-1)*6,cr=f.mallocFloat(s.nextPow2(10*Ot)),Ct=0,wt=0;for(Ge=0;Ge<De[0]-1;++Ge)e:for(qe=0;qe<De[1]-1;++qe){for(var Tt=0;Tt<2;++Tt)for(var Mt=0;Mt<2;++Mt)for(var Lt=0;Lt<3;++Lt){var ft=this._field[Lt].get(1+Ge+Tt,1+qe+Mt);if(isNaN(ft)||!isFinite(ft))continue e}for(Lt=0;Lt<6;++Lt){var kt=Ge+I[Lt][0],rr=qe+I[Lt][1],pr=this._field[0].get(kt+1,rr+1),Kr=this._field[1].get(kt+1,rr+1);ft=this._field[2].get(kt+1,rr+1),We=Ke.get(kt+1,rr+1,0),mt=Ke.get(kt+1,rr+1,1),bt=Ke.get(kt+1,rr+1,2),ie.intensity&&(Nr=ie.intensity.get(kt,rr));var Nr=ie.intensity?ie.intensity.get(kt,rr):ft+this.objectOffset[2];cr[Ct++]=kt,cr[Ct++]=rr,cr[Ct++]=pr,cr[Ct++]=Kr,cr[Ct++]=ft,cr[Ct++]=0,cr[Ct++]=Nr,cr[Ct++]=We,cr[Ct++]=mt,cr[Ct++]=bt,jt[0]=Math.min(jt[0],pr+this.objectOffset[0]),jt[1]=Math.min(jt[1],Kr+this.objectOffset[1]),jt[2]=Math.min(jt[2],ft+this.objectOffset[2]),or=Math.min(or,Nr),mr[0]=Math.max(mr[0],pr+this.objectOffset[0]),mr[1]=Math.max(mr[1],Kr+this.objectOffset[1]),mr[2]=Math.max(mr[2],ft+this.objectOffset[2]),fr=Math.max(fr,Nr),wt+=1}}for(ie.intensityBounds&&(or=+ie.intensityBounds[0],fr=+ie.intensityBounds[1]),Ge=6;Ge<Ct;Ge+=10)cr[Ge]=(cr[Ge]-or)/(fr-or);this._vertexCount=wt,this._coordinateBuffer.update(cr.subarray(0,Ct)),f.freeFloat(cr),f.free(Ke.data),this.bounds=[jt,mr],this.intensity=ie.intensity||this._field[2],(this.intensityBounds[0]!==or||this.intensityBounds[1]!==fr)&&(me=!0),this.intensityBounds=[or,fr]}if(\"levels\"in ie){var dn=ie.levels;for(Array.isArray(dn[0])?dn=dn.slice():dn=[[],[],dn],Ge=0;Ge<3;++Ge)dn[Ge]=dn[Ge].slice(),dn[Ge].sort(function(Ga,ss){return Ga-ss});for(Ge=0;Ge<3;++Ge)for(qe=0;qe<dn[Ge].length;++qe)dn[Ge][qe]-=this.objectOffset[Ge];e:for(Ge=0;Ge<3;++Ge){if(dn[Ge].length!==this.contourLevels[Ge].length){me=!0;break}for(qe=0;qe<dn[Ge].length;++qe)if(dn[Ge][qe]!==this.contourLevels[Ge][qe]){me=!0;break e}}this.contourLevels=dn}if(me){we=this._field,De=this.shape;for(var xr=[],Fr=0;Fr<3;++Fr){var Zr=this.contourLevels[Fr],Cn=[],Dt=[],At=[0,0,0];for(Ge=0;Ge<Zr.length;++Ge){var Vt=_(this._field[Fr],Zr[Ge]);Cn.push(xr.length/5|0),wt=0;e:for(qe=0;qe<Vt.cells.length;++qe){var Qt=Vt.cells[qe];for(Lt=0;Lt<2;++Lt){var Ut=Vt.positions[Qt[Lt]],_r=Ut[0],rn=Math.floor(_r)|0,Jr=_r-rn,qn=Ut[1],ri=Math.floor(qn)|0,Jn=qn-ri,Ii=!1;t:for(var Vi=0;Vi<3;++Vi){At[Vi]=0;var ra=(Fr+Vi+1)%3;for(Tt=0;Tt<2;++Tt){var ha=Tt?Jr:1-Jr;for(kt=Math.min(Math.max(rn+Tt,0),De[0])|0,Mt=0;Mt<2;++Mt){var va=Mt?Jn:1-Jn;if(rr=Math.min(Math.max(ri+Mt,0),De[1])|0,Vi<2?ft=this._field[ra].get(kt,rr):ft=(this.intensity.get(kt,rr)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(ft)||isNaN(ft)){Ii=!0;break t}var Xi=ha*va;At[Vi]+=Xi*ft}}}if(!Ii)xr.push(At[0],At[1],Ut[0],Ut[1],At[2]),wt+=1;else{if(Lt>0){for(var oa=0;oa<5;++oa)xr.pop();wt-=1}continue e}}}Dt.push(wt)}this._contourOffsets[Fr]=Cn,this._contourCounts[Fr]=Dt}var Ma=f.mallocFloat(xr.length);for(Ge=0;Ge<xr.length;++Ge)Ma[Ge]=xr[Ge];this._contourBuffer.update(Ma),f.freeFloat(Ma)}},j.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var ie=0;ie<3;++ie)f.freeFloat(this._field[ie].data)},j.highlight=function(ie){var le;if(!ie){this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],this.highlightLevel=[-1,-1,-1];return}for(le=0;le<3;++le)this.enableHighlight[le]?this.highlightLevel[le]=ie.level[le]:this.highlightLevel[le]=-1;var me;for(this.snapToData?me=ie.dataCoordinate:me=ie.position,le=0;le<3;++le)me[le]-=this.objectOffset[le];if(!((!this.enableDynamic[0]||me[0]===this.dynamicLevel[0])&&(!this.enableDynamic[1]||me[1]===this.dynamicLevel[1])&&(!this.enableDynamic[2]||me[2]===this.dynamicLevel[2]))){for(var be=0,De=this.shape,Ge=f.mallocFloat(12*De[0]*De[1]),ht=0;ht<3;++ht){if(!this.enableDynamic[ht]){this.dynamicLevel[ht]=NaN,this._dynamicCounts[ht]=0;continue}this.dynamicLevel[ht]=me[ht];var it=(ht+1)%3,Oe=(ht+2)%3,ze=this._field[ht],ct=this._field[it],et=this._field[Oe],qe=_(ze,me[ht]),we=qe.cells,Le=qe.positions;for(this._dynamicOffsets[ht]=be,le=0;le<we.length;++le)for(var Ke=we[le],Te=0;Te<2;++Te){var Ve=Le[Ke[Te]],$e=+Ve[0],ot=$e|0,tt=Math.min(ot+1,De[0])|0,nt=$e-ot,We=1-nt,mt=+Ve[1],bt=mt|0,Yt=Math.min(bt+1,De[1])|0,jt=mt-bt,mr=1-jt,or=We*mr,fr=We*jt,Ot=nt*mr,cr=nt*jt,Ct=or*ct.get(ot,bt)+fr*ct.get(ot,Yt)+Ot*ct.get(tt,bt)+cr*ct.get(tt,Yt),wt=or*et.get(ot,bt)+fr*et.get(ot,Yt)+Ot*et.get(tt,bt)+cr*et.get(tt,Yt);if(isNaN(Ct)||isNaN(wt)){Te&&(be-=1);break}Ge[2*be+0]=Ct,Ge[2*be+1]=wt,be+=1}this._dynamicCounts[ht]=be-this._dynamicOffsets[ht]}this._dynamicBuffer.update(Ge.subarray(0,2*be)),f.freeFloat(Ge)}};function pe(ie){var le=ie.gl,me=w(le),be=T(le),De=A(le),Ge=m(le),ht=l(le),it=u(le,[{buffer:ht,size:4,stride:F,offset:0},{buffer:ht,size:3,stride:F,offset:16},{buffer:ht,size:3,stride:F,offset:28}]),Oe=l(le),ze=u(le,[{buffer:Oe,size:4,stride:20,offset:0},{buffer:Oe,size:1,stride:20,offset:16}]),ct=l(le),et=u(le,[{buffer:ct,size:2,type:le.FLOAT}]),qe=c(le,1,O,le.RGBA,le.UNSIGNED_BYTE);qe.minFilter=le.LINEAR,qe.magFilter=le.LINEAR;var we=new V(le,[0,0],[[0,0,0],[0,0,0]],me,be,ht,it,qe,De,Ge,Oe,ze,ct,et,[0,0,0]),Le={levels:[[],[],[]]};for(var Ke in ie)Le[Ke]=ie[Ke];return Le.colormap=Le.colormap||\"jet\",we.update(Le),we}}),7766:(function(a,i,o){\"use strict\";var s=o(9618),l=o(5298),u=o(1888);a.exports=m;var c=null,f=null,v=null;function g(F){c=[F.LINEAR,F.NEAREST_MIPMAP_LINEAR,F.LINEAR_MIPMAP_NEAREST,F.LINEAR_MIPMAP_NEAREST],f=[F.NEAREST,F.LINEAR,F.NEAREST_MIPMAP_NEAREST,F.NEAREST_MIPMAP_LINEAR,F.LINEAR_MIPMAP_NEAREST,F.LINEAR_MIPMAP_LINEAR],v=[F.REPEAT,F.CLAMP_TO_EDGE,F.MIRRORED_REPEAT]}function p(F){return typeof HTMLCanvasElement!=\"undefined\"&&F instanceof HTMLCanvasElement||typeof HTMLImageElement!=\"undefined\"&&F instanceof HTMLImageElement||typeof HTMLVideoElement!=\"undefined\"&&F instanceof HTMLVideoElement||typeof ImageData!=\"undefined\"&&F instanceof ImageData}var x=function(F,E){l.muls(F,E,255)};function _(F,E,I){var R=F.gl,B=R.getParameter(R.MAX_TEXTURE_SIZE);if(E<0||E>B||I<0||I>B)throw new Error(\"gl-texture2d: Invalid texture size\");return F._shape=[E,I],F.bind(),R.texImage2D(R.TEXTURE_2D,0,F.format,E,I,0,F.format,F.type,null),F._mipLevels=[0],F}function y(F,E,I,R,B,O){this.gl=F,this.handle=E,this.format=B,this.type=O,this._shape=[I,R],this._mipLevels=[0],this._magFilter=F.NEAREST,this._minFilter=F.NEAREST,this._wrapS=F.CLAMP_TO_EDGE,this._wrapT=F.CLAMP_TO_EDGE,this._anisoSamples=1;var V=this,j=[this._wrapS,this._wrapT];Object.defineProperties(j,[{get:function(){return V._wrapS},set:function(U){return V.wrapS=U}},{get:function(){return V._wrapT},set:function(U){return V.wrapT=U}}]),this._wrapVector=j;var q=[this._shape[0],this._shape[1]];Object.defineProperties(q,[{get:function(){return V._shape[0]},set:function(U){return V.width=U}},{get:function(){return V._shape[1]},set:function(U){return V.height=U}}]),this._shapeVector=q}var C=y.prototype;Object.defineProperties(C,{minFilter:{get:function(){return this._minFilter},set:function(F){this.bind();var E=this.gl;if(this.type===E.FLOAT&&c.indexOf(F)>=0&&(E.getExtension(\"OES_texture_float_linear\")||(F=E.NEAREST)),f.indexOf(F)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+F);return E.texParameteri(E.TEXTURE_2D,E.TEXTURE_MIN_FILTER,F),this._minFilter=F}},magFilter:{get:function(){return this._magFilter},set:function(F){this.bind();var E=this.gl;if(this.type===E.FLOAT&&c.indexOf(F)>=0&&(E.getExtension(\"OES_texture_float_linear\")||(F=E.NEAREST)),f.indexOf(F)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+F);return E.texParameteri(E.TEXTURE_2D,E.TEXTURE_MAG_FILTER,F),this._magFilter=F}},mipSamples:{get:function(){return this._anisoSamples},set:function(F){var E=this._anisoSamples;if(this._anisoSamples=Math.max(F,1)|0,E!==this._anisoSamples){var I=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");I&&this.gl.texParameterf(this.gl.TEXTURE_2D,I.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(F){if(this.bind(),v.indexOf(F)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+F);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,F),this._wrapS=F}},wrapT:{get:function(){return this._wrapT},set:function(F){if(this.bind(),v.indexOf(F)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+F);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,F),this._wrapT=F}},wrap:{get:function(){return this._wrapVector},set:function(F){if(Array.isArray(F)||(F=[F,F]),F.length!==2)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var E=0;E<2;++E)if(v.indexOf(F[E])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+F);this._wrapS=F[0],this._wrapT=F[1];var I=this.gl;return this.bind(),I.texParameteri(I.TEXTURE_2D,I.TEXTURE_WRAP_S,this._wrapS),I.texParameteri(I.TEXTURE_2D,I.TEXTURE_WRAP_T,this._wrapT),F}},shape:{get:function(){return this._shapeVector},set:function(F){if(!Array.isArray(F))F=[F|0,F|0];else if(F.length!==2)throw new Error(\"gl-texture2d: Invalid texture shape\");return _(this,F[0]|0,F[1]|0),[F[0]|0,F[1]|0]}},width:{get:function(){return this._shape[0]},set:function(F){return F=F|0,_(this,F,this._shape[1]),F}},height:{get:function(){return this._shape[1]},set:function(F){return F=F|0,_(this,this._shape[0],F),F}}}),C.bind=function(F){var E=this.gl;return F!==void 0&&E.activeTexture(E.TEXTURE0+(F|0)),E.bindTexture(E.TEXTURE_2D,this.handle),F!==void 0?F|0:E.getParameter(E.ACTIVE_TEXTURE)-E.TEXTURE0},C.dispose=function(){this.gl.deleteTexture(this.handle)},C.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var F=Math.min(this._shape[0],this._shape[1]),E=0;F>0;++E,F>>>=1)this._mipLevels.indexOf(E)<0&&this._mipLevels.push(E)},C.setPixels=function(F,E,I,R){var B=this.gl;this.bind(),Array.isArray(E)?(R=I,I=E[1]|0,E=E[0]|0):(E=E||0,I=I||0),R=R||0;var O=p(F)?F:F.raw;if(O){var V=this._mipLevels.indexOf(R)<0;V?(B.texImage2D(B.TEXTURE_2D,0,this.format,this.format,this.type,O),this._mipLevels.push(R)):B.texSubImage2D(B.TEXTURE_2D,R,E,I,this.format,this.type,O)}else if(F.shape&&F.stride&&F.data){if(F.shape.length<2||E+F.shape[1]>this._shape[1]>>>R||I+F.shape[0]>this._shape[0]>>>R||E<0||I<0)throw new Error(\"gl-texture2d: Texture dimensions are out of bounds\");S(B,E,I,R,this.format,this.type,this._mipLevels,F)}else throw new Error(\"gl-texture2d: Unsupported data type\")};function k(F,E){return F.length===3?E[2]===1&&E[1]===F[0]*F[2]&&E[0]===F[2]:E[0]===1&&E[1]===F[0]}function S(F,E,I,R,B,O,V,j){var q=j.dtype,U=j.shape.slice();if(U.length<2||U.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var J=0,ee=0,ce=k(U,j.stride.slice());q===\"float32\"?J=F.FLOAT:q===\"float64\"?(J=F.FLOAT,ce=!1,q=\"float32\"):q===\"uint8\"?J=F.UNSIGNED_BYTE:(J=F.UNSIGNED_BYTE,ce=!1,q=\"uint8\");var ye=1;if(U.length===2)ee=F.LUMINANCE,U=[U[0],U[1],1],j=s(j.data,U,[j.stride[0],j.stride[1],1],j.offset);else if(U.length===3){if(U[2]===1)ee=F.ALPHA;else if(U[2]===2)ee=F.LUMINANCE_ALPHA;else if(U[2]===3)ee=F.RGB;else if(U[2]===4)ee=F.RGBA;else throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");ye=U[2]}else throw new Error(\"gl-texture2d: Invalid shape for texture\");if((ee===F.LUMINANCE||ee===F.ALPHA)&&(B===F.LUMINANCE||B===F.ALPHA)&&(ee=B),ee!==B)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var xe=j.size,ue=V.indexOf(R)<0;if(ue&&V.push(R),J===O&&ce)j.offset===0&&j.data.length===xe?ue?F.texImage2D(F.TEXTURE_2D,R,B,U[0],U[1],0,B,O,j.data):F.texSubImage2D(F.TEXTURE_2D,R,E,I,U[0],U[1],B,O,j.data):ue?F.texImage2D(F.TEXTURE_2D,R,B,U[0],U[1],0,B,O,j.data.subarray(j.offset,j.offset+xe)):F.texSubImage2D(F.TEXTURE_2D,R,E,I,U[0],U[1],B,O,j.data.subarray(j.offset,j.offset+xe));else{var K;O===F.FLOAT?K=u.mallocFloat32(xe):K=u.mallocUint8(xe);var _e=s(K,U,[U[2],U[2]*U[0],1]);J===F.FLOAT&&O===F.UNSIGNED_BYTE?x(_e,j):l.assign(_e,j),ue?F.texImage2D(F.TEXTURE_2D,R,B,U[0],U[1],0,B,O,K.subarray(0,xe)):F.texSubImage2D(F.TEXTURE_2D,R,E,I,U[0],U[1],B,O,K.subarray(0,xe)),O===F.FLOAT?u.freeFloat32(K):u.freeUint8(K)}}function L(F){var E=F.createTexture();return F.bindTexture(F.TEXTURE_2D,E),F.texParameteri(F.TEXTURE_2D,F.TEXTURE_MIN_FILTER,F.NEAREST),F.texParameteri(F.TEXTURE_2D,F.TEXTURE_MAG_FILTER,F.NEAREST),F.texParameteri(F.TEXTURE_2D,F.TEXTURE_WRAP_S,F.CLAMP_TO_EDGE),F.texParameteri(F.TEXTURE_2D,F.TEXTURE_WRAP_T,F.CLAMP_TO_EDGE),E}function w(F,E,I,R,B){var O=F.getParameter(F.MAX_TEXTURE_SIZE);if(E<0||E>O||I<0||I>O)throw new Error(\"gl-texture2d: Invalid texture shape\");if(B===F.FLOAT&&!F.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var V=L(F);return F.texImage2D(F.TEXTURE_2D,0,R,E,I,0,R,B,null),new y(F,V,E,I,R,B)}function A(F,E,I,R,B,O){var V=L(F);return F.texImage2D(F.TEXTURE_2D,0,B,B,O,E),new y(F,V,I,R,B,O)}function T(F,E){var I=E.dtype,R=E.shape.slice(),B=F.getParameter(F.MAX_TEXTURE_SIZE);if(R[0]<0||R[0]>B||R[1]<0||R[1]>B)throw new Error(\"gl-texture2d: Invalid texture size\");var O=k(R,E.stride.slice()),V=0;I===\"float32\"?V=F.FLOAT:I===\"float64\"?(V=F.FLOAT,O=!1,I=\"float32\"):I===\"uint8\"?V=F.UNSIGNED_BYTE:(V=F.UNSIGNED_BYTE,O=!1,I=\"uint8\");var j=0;if(R.length===2)j=F.LUMINANCE,R=[R[0],R[1],1],E=s(E.data,R,[E.stride[0],E.stride[1],1],E.offset);else if(R.length===3)if(R[2]===1)j=F.ALPHA;else if(R[2]===2)j=F.LUMINANCE_ALPHA;else if(R[2]===3)j=F.RGB;else if(R[2]===4)j=F.RGBA;else throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");else throw new Error(\"gl-texture2d: Invalid shape for texture\");V===F.FLOAT&&!F.getExtension(\"OES_texture_float\")&&(V=F.UNSIGNED_BYTE,O=!1);var q,U,J=E.size;if(O)E.offset===0&&E.data.length===J?q=E.data:q=E.data.subarray(E.offset,E.offset+J);else{var ee=[R[2],R[2]*R[0],1];U=u.malloc(J,I);var ce=s(U,R,ee,0);(I===\"float32\"||I===\"float64\")&&V===F.UNSIGNED_BYTE?x(ce,E):l.assign(ce,E),q=U.subarray(0,J)}var ye=L(F);return F.texImage2D(F.TEXTURE_2D,0,j,R[0],R[1],0,j,V,q),O||u.free(U),new y(F,ye,R[0],R[1],j,V)}function m(F){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");if(c||g(F),typeof arguments[1]==\"number\")return w(F,arguments[1],arguments[2],arguments[3]||F.RGBA,arguments[4]||F.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return w(F,arguments[1][0]|0,arguments[1][1]|0,arguments[2]||F.RGBA,arguments[3]||F.UNSIGNED_BYTE);if(typeof arguments[1]==\"object\"){var E=arguments[1],I=p(E)?E:E.raw;if(I)return A(F,I,E.width|0,E.height|0,arguments[2]||F.RGBA,arguments[3]||F.UNSIGNED_BYTE);if(E.shape&&E.data&&E.stride)return T(F,E)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")}}),1433:(function(a){\"use strict\";function i(o,s,l){s?s.bind():o.bindBuffer(o.ELEMENT_ARRAY_BUFFER,null);var u=o.getParameter(o.MAX_VERTEX_ATTRIBS)|0;if(l){if(l.length>u)throw new Error(\"gl-vao: Too many vertex attributes\");for(var c=0;c<l.length;++c){var f=l[c];if(f.buffer){var v=f.buffer,g=f.size||4,p=f.type||o.FLOAT,x=!!f.normalized,_=f.stride||0,y=f.offset||0;v.bind(),o.enableVertexAttribArray(c),o.vertexAttribPointer(c,g,p,x,_,y)}else{if(typeof f==\"number\")o.vertexAttrib1f(c,f);else if(f.length===1)o.vertexAttrib1f(c,f[0]);else if(f.length===2)o.vertexAttrib2f(c,f[0],f[1]);else if(f.length===3)o.vertexAttrib3f(c,f[0],f[1],f[2]);else if(f.length===4)o.vertexAttrib4f(c,f[0],f[1],f[2],f[3]);else throw new Error(\"gl-vao: Invalid vertex attribute\");o.disableVertexAttribArray(c)}}for(;c<u;++c)o.disableVertexAttribArray(c)}else{o.bindBuffer(o.ARRAY_BUFFER,null);for(var c=0;c<u;++c)o.disableVertexAttribArray(c)}}a.exports=i}),870:(function(a,i,o){\"use strict\";var s=o(1433);function 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A=this.computedRotation;f(A,w[0],w[1],w[2],w[4],w[5],w[6],w[8],w[9],w[10]),p(A,A),this.rotation.set(C,A[0],A[1],A[2],A[3]);for(var T=0,m=0;m<3;++m)T+=Math.pow(S[m]-k[m],2);this.radius.set(C,.5*Math.log(Math.max(T,1e-6))),this.center.set(C,S[0],S[1],S[2])},_.translate=function(C,k,S,L){this.center.move(C,k||0,S||0,L||0)},_.setMatrix=function(C,k){var S=this.computedRotation;f(S,k[0],k[1],k[2],k[4],k[5],k[6],k[8],k[9],k[10]),p(S,S),this.rotation.set(C,S[0],S[1],S[2],S[3]);var L=this.computedMatrix;c(L,k);var w=L[15];if(Math.abs(w)>1e-6){var A=L[12]/w,T=L[13]/w,m=L[14]/w;this.recalcMatrix(C);var F=Math.exp(this.computedRadius[0]);this.center.set(C,A-L[2]*F,T-L[6]*F,m-L[10]*F),this.radius.idle(C)}else 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w==\"number\"&&(T=w|0),(T<0||T>3)&&(T=1);var m=(T+2)%3,F=(T+1)%3;L||(this.recalcMatrix(S),L=this.computedMatrix);var E=L[T],I=L[T+4],R=L[T+8];if(A){var O=Math.abs(E),V=Math.abs(I),j=Math.abs(R),q=Math.max(O,V,j);O===q?(E=E<0?-1:1,I=R=0):j===q?(R=R<0?-1:1,E=I=0):(I=I<0?-1:1,E=R=0)}else{var B=g(E,I,R);E/=B,I/=B,R/=B}var U=L[m],J=L[m+4],ee=L[m+8],ce=U*E+J*I+ee*R;U-=E*ce,J-=I*ce,ee-=R*ce;var ye=g(U,J,ee);U/=ye,J/=ye,ee/=ye;var xe=I*ee-R*J,ue=R*U-E*ee,K=E*J-I*U,_e=g(xe,ue,K);xe/=_e,ue/=_e,K/=_e,this.center.jump(S,Ke,Te,Ve),this.radius.idle(S),this.up.jump(S,E,I,R),this.right.jump(S,U,J,ee);var ne,ae;if(T===2){var pe=L[1],ie=L[5],le=L[9],me=pe*U+ie*J+le*ee,be=pe*xe+ie*ue+le*K;it<0?ne=-Math.PI/2:ne=Math.PI/2,ae=Math.atan2(be,me)}else{var De=L[2],Ge=L[6],ht=L[10],it=De*E+Ge*I+ht*R,Oe=De*U+Ge*J+ht*ee,ze=De*xe+Ge*ue+ht*K;ne=Math.asin(p(it)),ae=Math.atan2(ze,Oe)}this.angle.jump(S,ae,ne),this.recalcMatrix(S);var ct=L[2],et=L[6],qe=L[10],we=this.computedMatrix;l(we,L);var 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s=(r.line||{}).color,l=(r.marker||{}).color;i(\"surfaceaxis\")>=0&&i(\"surfacecolor\",s||l);for(var u=[\"x\",\"y\",\"z\"],c=0;c<3;++c){var f=\"projection.\"+u[c];i(f+\".show\")&&(i(f+\".opacity\"),i(f+\".scale\"))}var v=bFe.getComponentMethod(\"errorbars\",\"supplyDefaults\");v(t,r,s||l||n,{axis:\"z\"}),v(t,r,s||l||n,{axis:\"y\",inherit:\"z\"}),v(t,r,s||l||n,{axis:\"x\",inherit:\"z\"})};function kOt(e,t,r,n){var a=0,i=r(\"x\"),o=r(\"y\"),s=r(\"z\"),l=bFe.getComponentMethod(\"calendars\",\"handleTraceDefaults\");return l(e,t,[\"x\",\"y\",\"z\"],n),i&&o&&s&&(a=Math.min(i.length,o.length,s.length),t._length=t._xlength=t._ylength=t._zlength=a),a}});var MFe=fe((j3r,AFe)=>{\"use strict\";var COt=o1(),LOt=pv();AFe.exports=function(t,r){var n=[{x:!1,y:!1,trace:r,t:{}}];return COt(n,r),LOt(t,r),n}});var EFe=fe((W3r,SFe)=>{SFe.exports=POt;function POt(e,t){if(typeof e!=\"string\")throw new TypeError(\"must specify type string\");if(t=t||{},typeof document==\"undefined\"&&!t.canvas)return 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a.textContent=\"WebGL is not supported by your browser - visit https://get.webgl.org for more info\",a.style.position=\"relative\",a.style.top=\"50%\",a.style.left=\"50%\",a.style.height=\"30%\",a.style.width=\"50%\",a.style.margin=\"-15% 0 0 -25%\",n.appendChild(a),t.container.appendChild(n),t.container.style.background=\"#FFFFFF\",t.container.onclick=function(){window.open(\"https://get.webgl.org\")},!1}});var RFe=fe((Z3r,DFe)=>{\"use strict\";var i_=Mg(),ROt=Xt(),IOt=[\"xaxis\",\"yaxis\",\"zaxis\"];function FFe(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickFontWeight=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelFontWeight=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelFontStyle=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelFontVariant=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}var zOt=FFe.prototype;zOt.merge=function(e,t){for(var r=this,n=0;n<3;++n){var a=t[IOt[n]];if(!a.visible){r.tickEnable[n]=!1,r.labelEnable[n]=!1,r.lineEnable[n]=!1,r.lineTickEnable[n]=!1,r.gridEnable[n]=!1,r.zeroEnable[n]=!1,r.backgroundEnable[n]=!1;continue}r.labels[n]=e._meta?ROt.templateString(a.title.text,e._meta):a.title.text,\"font\"in a.title&&(a.title.font.color&&(r.labelColor[n]=i_(a.title.font.color)),a.title.font.family&&(r.labelFont[n]=a.title.font.family),a.title.font.size&&(r.labelSize[n]=a.title.font.size),a.title.font.weight&&(r.labelFontWeight[n]=a.title.font.weight),a.title.font.style&&(r.labelFontStyle[n]=a.title.font.style),a.title.font.variant&&(r.labelFontVariant[n]=a.title.font.variant)),\"showline\"in a&&(r.lineEnable[n]=a.showline),\"linecolor\"in a&&(r.lineColor[n]=i_(a.linecolor)),\"linewidth\"in a&&(r.lineWidth[n]=a.linewidth),\"showgrid\"in a&&(r.gridEnable[n]=a.showgrid),\"gridcolor\"in a&&(r.gridColor[n]=i_(a.gridcolor)),\"gridwidth\"in a&&(r.gridWidth[n]=a.gridwidth),a.type===\"log\"?r.zeroEnable[n]=!1:\"zeroline\"in a&&(r.zeroEnable[n]=a.zeroline),\"zerolinecolor\"in a&&(r.zeroLineColor[n]=i_(a.zerolinecolor)),\"zerolinewidth\"in a&&(r.zeroLineWidth[n]=a.zerolinewidth),\"ticks\"in a&&a.ticks?r.lineTickEnable[n]=!0:r.lineTickEnable[n]=!1,\"ticklen\"in a&&(r.lineTickLength[n]=r._defaultLineTickLength[n]=a.ticklen),\"tickcolor\"in a&&(r.lineTickColor[n]=i_(a.tickcolor)),\"tickwidth\"in a&&(r.lineTickWidth[n]=a.tickwidth),\"tickangle\"in a&&(r.tickAngle[n]=a.tickangle===\"auto\"?-3600:Math.PI*-a.tickangle/180),\"showticklabels\"in a&&(r.tickEnable[n]=a.showticklabels),\"tickfont\"in a&&(a.tickfont.color&&(r.tickColor[n]=i_(a.tickfont.color)),a.tickfont.family&&(r.tickFont[n]=a.tickfont.family),a.tickfont.size&&(r.tickSize[n]=a.tickfont.size),a.tickfont.weight&&(r.tickFontWeight[n]=a.tickfont.weight),a.tickfont.style&&(r.tickFontStyle[n]=a.tickfont.style),a.tickfont.variant&&(r.tickFontVariant[n]=a.tickfont.variant)),\"mirror\"in a?[\"ticks\",\"all\",\"allticks\"].indexOf(a.mirror)!==-1?(r.lineTickMirror[n]=!0,r.lineMirror[n]=!0):a.mirror===!0?(r.lineTickMirror[n]=!1,r.lineMirror[n]=!0):(r.lineTickMirror[n]=!1,r.lineMirror[n]=!1):r.lineMirror[n]=!1,\"showbackground\"in a&&a.showbackground!==!1?(r.backgroundEnable[n]=!0,r.backgroundColor[n]=i_(a.backgroundcolor)):r.backgroundEnable[n]=!1}};function qOt(e,t){var r=new FFe;return r.merge(e,t),r}DFe.exports=qOt});var qFe=fe(($3r,zFe)=>{\"use strict\";var BOt=Mg(),OOt=[\"xaxis\",\"yaxis\",\"zaxis\"];function IFe(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}var NOt=IFe.prototype;NOt.merge=function(e){for(var t=0;t<3;++t){var r=e[OOt[t]];if(!r.visible){this.enabled[t]=!1,this.drawSides[t]=!1;continue}this.enabled[t]=r.showspikes,this.colors[t]=BOt(r.spikecolor),this.drawSides[t]=r.spikesides,this.lineWidth[t]=r.spikethickness}};function UOt(e){var t=new IFe;return t.merge(e),t}zFe.exports=UOt});var NFe=fe((K3r,OFe)=>{\"use strict\";OFe.exports=WOt;var BFe=Yi(),VOt=Xt(),GOt=[\"xaxis\",\"yaxis\",\"zaxis\"],HOt=[0,0,0];function jOt(e){for(var t=new Array(3),r=0;r<3;++r){for(var n=e[r],a=new Array(n.length),i=0;i<n.length;++i)a[i]=n[i].x;t[r]=a}return t}function WOt(e){for(var t=e.axesOptions,r=e.glplot.axesPixels,n=e.fullSceneLayout,a=[[],[],[]],i=0;i<3;++i){var o=n[GOt[i]];if(o._length=(r[i].hi-r[i].lo)*r[i].pixelsPerDataUnit/e.dataScale[i],Math.abs(o._length)===1/0||isNaN(o._length))a[i]=[];else{o._input_range=o.range.slice(),o.range[0]=r[i].lo/e.dataScale[i],o.range[1]=r[i].hi/e.dataScale[i],o._m=1/(e.dataScale[i]*r[i].pixelsPerDataUnit),o.range[0]===o.range[1]&&(o.range[0]-=1,o.range[1]+=1);var s=o.tickmode;if(o.tickmode===\"auto\"){o.tickmode=\"linear\";var l=o.nticks||VOt.constrain(o._length/40,4,9);BFe.autoTicks(o,Math.abs(o.range[1]-o.range[0])/l)}for(var u=BFe.calcTicks(o,{msUTC:!0}),c=0;c<u.length;++c)u[c].x=u[c].x*e.dataScale[i],o.type===\"date\"&&(u[c].text=u[c].text.replace(/\\<br\\>/g,\" \"));a[i]=u,o.tickmode=s}}t.ticks=a;for(var i=0;i<3;++i){HOt[i]=.5*(e.glplot.bounds[0][i]+e.glplot.bounds[1][i]);for(var c=0;c<2;++c)t.bounds[c][i]=e.glplot.bounds[c][i]}e.contourLevels=jOt(a)}});var YFe=fe((J3r,WFe)=>{\"use strict\";var GFe=xf().gl_plot3d,YOt=GFe.createCamera,UFe=GFe.createScene,XOt=CFe(),ZOt=Uk(),pP=bi(),Dd=Xt(),vP=Dd.preserveDrawingBuffer(),gP=Yi(),kp=kl(),$Ot=Mg(),KOt=EY(),JOt=VU(),QOt=RFe(),eNt=qFe(),tNt=NFe(),rNt=rp().applyAutorangeOptions,rM,dP,HFe=!1;function jFe(e,t){var r=document.createElement(\"div\"),n=e.container;this.graphDiv=e.graphDiv;var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\",r.appendChild(a),this.svgContainer=a,r.id=e.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=t,this.id=e.id||\"scene\",this.fullSceneLayout=t[this.id],this.plotArgs=[[],{},{}],this.axesOptions=QOt(t,t[this.id]),this.spikeOptions=eNt(t[this.id]),this.container=r,this.staticMode=!!e.staticPlot,this.pixelRatio=this.pixelRatio||e.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=pP.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=pP.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var Dh=jFe.prototype;Dh.prepareOptions=function(){var e=this,t={canvas:e.canvas,gl:e.gl,glOptions:{preserveDrawingBuffer:vP,premultipliedAlpha:!0,antialias:!0},container:e.container,axes:e.axesOptions,spikes:e.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:e.camera,pixelRatio:e.pixelRatio};if(e.staticMode){if(!dP&&(rM=document.createElement(\"canvas\"),dP=XOt({canvas:rM,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}),!dP))throw new Error(\"error creating static canvas/context for image server\");t.gl=dP,t.canvas=rM}return t};var VFe=!0;Dh.tryCreatePlot=function(){var e=this,t=e.prepareOptions(),r=!0;try{e.glplot=UFe(t)}catch(n){if(e.staticMode||!VFe||vP)r=!1;else{Dd.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{vP=t.glOptions.preserveDrawingBuffer=!0,e.glplot=UFe(t)}catch(a){vP=t.glOptions.preserveDrawingBuffer=!1,r=!1}}}return VFe=!1,r};Dh.initializeGLCamera=function(){var e=this,t=e.fullSceneLayout.camera,r=t.projection.type===\"orthographic\";e.camera=YOt(e.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:\"orbit\"})};Dh.initializeGLPlot=function(){var e=this;e.initializeGLCamera();var t=e.tryCreatePlot();if(!t)return KOt(e);e.traces={},e.make4thDimension();var r=e.graphDiv,n=r.layout,a=function(){var o={};return e.isCameraChanged(n)&&(o[e.id+\".camera\"]=e.getCamera()),e.isAspectChanged(n)&&(o[e.id+\".aspectratio\"]=e.glplot.getAspectratio(),n[e.id].aspectmode!==\"manual\"&&(e.fullSceneLayout.aspectmode=n[e.id].aspectmode=o[e.id+\".aspectmode\"]=\"manual\")),o},i=function(o){if(o.fullSceneLayout.dragmode!==!1){var s=a();o.saveLayout(n),o.graphDiv.emit(\"plotly_relayout\",s)}};return e.glplot.canvas&&(e.glplot.canvas.addEventListener(\"mouseup\",function(){i(e)}),e.glplot.canvas.addEventListener(\"touchstart\",function(){HFe=!0}),e.glplot.canvas.addEventListener(\"wheel\",function(o){if(r._context._scrollZoom.gl3d){if(e.camera._ortho){var s=o.deltaX>o.deltaY?1.1:.9090909090909091,l=e.glplot.getAspectratio();e.glplot.setAspectratio({x:s*l.x,y:s*l.y,z:s*l.z})}i(e)}},ZOt?{passive:!1}:!1),e.glplot.canvas.addEventListener(\"mousemove\",function(){if(e.fullSceneLayout.dragmode!==!1&&e.camera.mouseListener.buttons!==0){var o=a();e.graphDiv.emit(\"plotly_relayouting\",o)}}),e.staticMode||e.glplot.canvas.addEventListener(\"webglcontextlost\",function(o){r&&r.emit&&r.emit(\"plotly_webglcontextlost\",{event:o,layer:e.id})},!1)),e.glplot.oncontextloss=function(){e.recoverContext()},e.glplot.onrender=function(){e.render()},!0};Dh.render=function(){var e=this,t=e.graphDiv,r,n=e.svgContainer,a=e.container.getBoundingClientRect();t._fullLayout._calcInverseTransform(t);var i=t._fullLayout._invScaleX,o=t._fullLayout._invScaleY,s=a.width*i,l=a.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),tNt(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,f=e.glplot.selection,v=0;v<u.length;++v)r=e.traces[u[v]],r.data.hoverinfo!==\"skip\"&&r.handlePick(f)&&(c=r),r.setContourLevels&&r.setContourLevels();function g(F,E,I){var R=e.fullSceneLayout[F+\"axis\"];return R.type!==\"log\"&&(E=R.d2l(E)),gP.hoverLabelText(R,E,I)}if(c!==null){var p=JOt(e.glplot.cameraParams,f.dataCoordinate);r=c.data;var x=t._fullData[r.index],_=f.index,y={xLabel:g(\"x\",f.traceCoordinate[0],r.xhoverformat),yLabel:g(\"y\",f.traceCoordinate[1],r.yhoverformat),zLabel:g(\"z\",f.traceCoordinate[2],r.zhoverformat)},C=kp.castHoverinfo(x,e.fullLayout,_),k=(C||\"\").split(\"+\"),S=C&&C===\"all\";!x.hovertemplate&&!S&&(k.indexOf(\"x\")===-1&&(y.xLabel=void 0),k.indexOf(\"y\")===-1&&(y.yLabel=void 0),k.indexOf(\"z\")===-1&&(y.zLabel=void 0),k.indexOf(\"text\")===-1&&(f.textLabel=void 0),k.indexOf(\"name\")===-1&&(c.name=void 0));var L,w=[];r.type===\"cone\"||r.type===\"streamtube\"?(y.uLabel=g(\"x\",f.traceCoordinate[3],r.uhoverformat),(S||k.indexOf(\"u\")!==-1)&&w.push(\"u: \"+y.uLabel),y.vLabel=g(\"y\",f.traceCoordinate[4],r.vhoverformat),(S||k.indexOf(\"v\")!==-1)&&w.push(\"v: \"+y.vLabel),y.wLabel=g(\"z\",f.traceCoordinate[5],r.whoverformat),(S||k.indexOf(\"w\")!==-1)&&w.push(\"w: \"+y.wLabel),y.normLabel=f.traceCoordinate[6].toPrecision(3),(S||k.indexOf(\"norm\")!==-1)&&w.push(\"norm: \"+y.normLabel),r.type===\"streamtube\"&&(y.divergenceLabel=f.traceCoordinate[7].toPrecision(3),(S||k.indexOf(\"divergence\")!==-1)&&w.push(\"divergence: \"+y.divergenceLabel)),f.textLabel&&w.push(f.textLabel),L=w.join(\"<br>\")):r.type===\"isosurface\"||r.type===\"volume\"?(y.valueLabel=gP.hoverLabelText(e._mockAxis,e._mockAxis.d2l(f.traceCoordinate[3]),r.valuehoverformat),w.push(\"value: \"+y.valueLabel),f.textLabel&&w.push(f.textLabel),L=w.join(\"<br>\")):L=f.textLabel;var A={x:f.traceCoordinate[0],y:f.traceCoordinate[1],z:f.traceCoordinate[2],data:x._input,fullData:x,curveNumber:x.index,pointNumber:_};kp.appendArrayPointValue(A,x,_),r._module.eventData&&(A=x._module.eventData(A,f,x,{},_));var T={points:[A]};if(e.fullSceneLayout.hovermode){var m=[];kp.loneHover({trace:x,x:(.5+.5*p[0]/p[3])*s,y:(.5-.5*p[1]/p[3])*l,xLabel:y.xLabel,yLabel:y.yLabel,zLabel:y.zLabel,text:L,name:c.name,color:kp.castHoverOption(x,_,\"bgcolor\")||c.color,borderColor:kp.castHoverOption(x,_,\"bordercolor\"),fontFamily:kp.castHoverOption(x,_,\"font.family\"),fontSize:kp.castHoverOption(x,_,\"font.size\"),fontColor:kp.castHoverOption(x,_,\"font.color\"),nameLength:kp.castHoverOption(x,_,\"namelength\"),textAlign:kp.castHoverOption(x,_,\"align\"),hovertemplate:Dd.castOption(x,_,\"hovertemplate\"),hovertemplateLabels:Dd.extendFlat({},A,y),eventData:[A]},{container:n,gd:t,inOut_bbox:m}),A.bbox=m[0]}f.distance<5&&(f.buttons||HFe)?t.emit(\"plotly_click\",T):t.emit(\"plotly_hover\",T),this.oldEventData=T}else kp.loneUnhover(n),this.oldEventData&&t.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)};Dh.recoverContext=function(){var e=this;e.glplot.dispose();var t=function(){if(e.glplot.gl.isContextLost()){requestAnimationFrame(t);return}if(!e.initializeGLPlot()){Dd.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\");return}e.plot.apply(e,e.plotArgs)};requestAnimationFrame(t)};var nM=[\"xaxis\",\"yaxis\",\"zaxis\"];function nNt(e,t,r){for(var n=e.fullSceneLayout,a=0;a<3;a++){var i=nM[a],o=i.charAt(0),s=n[i],l=t[o],u=t[o+\"calendar\"],c=t[\"_\"+o+\"length\"];if(!Dd.isArrayOrTypedArray(l))r[0][a]=Math.min(r[0][a],0),r[1][a]=Math.max(r[1][a],c-1);else for(var f,v=0;v<(c||l.length);v++)if(Dd.isArrayOrTypedArray(l[v]))for(var g=0;g<l[v].length;++g)f=s.d2l(l[v][g],0,u),!isNaN(f)&&isFinite(f)&&(r[0][a]=Math.min(r[0][a],f),r[1][a]=Math.max(r[1][a],f));else f=s.d2l(l[v],0,u),!isNaN(f)&&isFinite(f)&&(r[0][a]=Math.min(r[0][a],f),r[1][a]=Math.max(r[1][a],f))}}function iNt(e,t){for(var r=e.fullSceneLayout,n=r.annotations||[],a=0;a<3;a++)for(var i=nM[a],o=i.charAt(0),s=r[i],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(t[0][a]=Math.min(t[0][a],c),t[1][a]=Math.max(t[1][a],c))}}}Dh.plot=function(e,t,r){var n=this;if(n.plotArgs=[e,t,r],!n.glplot.contextLost){var a,i,o,s,l,u,c=t[n.id],f=r[n.id];n.fullLayout=t,n.fullSceneLayout=c,n.axesOptions.merge(t,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor($Ot(c.bgcolor)),n.setConvert(l),e?Array.isArray(e)||(e=[e]):e=[];var v=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<e.length;++o)a=e[o],!(a.visible!==!0||a._length===0)&&nNt(this,a,v);iNt(this,v);var g=[1,1,1];for(s=0;s<3;++s)v[1][s]===v[0][s]?g[s]=1:g[s]=1/(v[1][s]-v[0][s]);for(n.dataScale=g,n.convertAnnotations(this),o=0;o<e.length;++o)a=e[o],!(a.visible!==!0||a._length===0)&&(i=n.traces[a.uid],i?i.data.type===a.type?i.update(a):(i.dispose(),i=a._module.plot(this,a),n.traces[a.uid]=i):(i=a._module.plot(this,a),n.traces[a.uid]=i),i.name=a.name);var p=Object.keys(n.traces);e:for(o=0;o<p.length;++o){for(s=0;s<e.length;++s)if(e[s].uid===p[o]&&e[s].visible===!0&&e[s]._length!==0)continue e;i=n.traces[p[o]],i.dispose(),delete n.traces[p[o]]}n.glplot.objects.sort(function(ee,ce){return ee._trace.data.index-ce._trace.data.index});var x=[[0,0,0],[0,0,0]],_=[],y={};for(o=0;o<3;++o){l=c[nM[o]],u=l.type,u in y?(y[u].acc*=g[o],y[u].count+=1):y[u]={acc:g[o],count:1};var C;if(l.autorange){x[0][o]=1/0,x[1][o]=-1/0;var k=n.glplot.objects,S=n.fullSceneLayout.annotations||[],L=l._name.charAt(0);for(s=0;s<k.length;s++){var w=k[s],A=w.bounds,T=w._trace.data._pad||0;w.constructor.name===\"ErrorBars\"&&l._lowerLogErrorBound?x[0][o]=Math.min(x[0][o],l._lowerLogErrorBound):x[0][o]=Math.min(x[0][o],A[0][o]/g[o]-T),x[1][o]=Math.max(x[1][o],A[1][o]/g[o]+T)}for(s=0;s<S.length;s++){var m=S[s];if(m.visible){var F=l.r2l(m[L]);x[0][o]=Math.min(x[0][o],F),x[1][o]=Math.max(x[1][o],F)}}if(\"rangemode\"in l&&l.rangemode===\"tozero\"&&(x[0][o]=Math.min(x[0][o],0),x[1][o]=Math.max(x[1][o],0)),x[0][o]>x[1][o])x[0][o]=-1,x[1][o]=1;else{var E=x[1][o]-x[0][o];x[0][o]-=E/32,x[1][o]+=E/32}if(C=[x[0][o],x[1][o]],C=rNt(C,l),x[0][o]=C[0],x[1][o]=C[1],l.isReversed()){var I=x[0][o];x[0][o]=x[1][o],x[1][o]=I}}else C=l.range,x[0][o]=l.r2l(C[0]),x[1][o]=l.r2l(C[1]);x[0][o]===x[1][o]&&(x[0][o]-=1,x[1][o]+=1),_[o]=x[1][o]-x[0][o],l.range=[x[0][o],x[1][o]],l.limitRange(),n.glplot.setBounds(o,{min:l.range[0]*g[o],max:l.range[1]*g[o]})}var R,B=c.aspectmode;if(B===\"cube\")R=[1,1,1];else if(B===\"manual\"){var O=c.aspectratio;R=[O.x,O.y,O.z]}else if(B===\"auto\"||B===\"data\"){var V=[1,1,1];for(o=0;o<3;++o){l=c[nM[o]],u=l.type;var j=y[u];V[o]=Math.pow(j.acc,1/j.count)/g[o]}B===\"data\"||Math.max.apply(null,V)/Math.min.apply(null,V)<=4?R=V:R=[1,1,1]}else throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");c.aspectratio.x=f.aspectratio.x=R[0],c.aspectratio.y=f.aspectratio.y=R[1],c.aspectratio.z=f.aspectratio.z=R[2],n.glplot.setAspectratio(c.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=c.aspectmode);var q=c.domain||null,U=t._size||null;if(q&&U){var J=n.container.style;J.position=\"absolute\",J.left=U.l+q.x[0]*U.w+\"px\",J.top=U.t+(1-q.y[1])*U.h+\"px\",J.width=U.w*(q.x[1]-q.x[0])+\"px\",J.height=U.h*(q.y[1]-q.y[0])+\"px\"}n.glplot.redraw()}};Dh.destroy=function(){var e=this;e.glplot&&(e.camera.mouseListener.enabled=!1,e.container.removeEventListener(\"wheel\",e.camera.wheelListener),e.camera=null,e.glplot.dispose(),e.container.parentNode.removeChild(e.container),e.glplot=null)};function aNt(e){return[[e.eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]}function oNt(e){return{up:{x:e.up[0],y:e.up[1],z:e.up[2]},center:{x:e.center[0],y:e.center[1],z:e.center[2]},eye:{x:e.eye[0],y:e.eye[1],z:e.eye[2]},projection:{type:e._ortho===!0?\"orthographic\":\"perspective\"}}}Dh.getCamera=function(){var e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),oNt(e.camera)};Dh.setViewport=function(e){var t=this,r=e.camera;t.camera.lookAt.apply(this,aNt(r)),t.glplot.setAspectratio(e.aspectratio);var n=r.projection.type===\"orthographic\",a=t.camera._ortho;n!==a&&(t.glplot.redraw(),t.glplot.clearRGBA(),t.glplot.dispose(),t.initializeGLPlot())};Dh.isCameraChanged=function(e){var t=this,r=t.getCamera(),n=Dd.nestedProperty(e,t.id+\".camera\"),a=n.get();function i(u,c,f,v){var g=[\"up\",\"center\",\"eye\"],p=[\"x\",\"y\",\"z\"];return c[g[f]]&&u[g[f]][p[v]]===c[g[f]][p[v]]}var o=!1;if(a===void 0)o=!0;else{for(var s=0;s<3;s++)for(var l=0;l<3;l++)if(!i(r,a,s,l)){o=!0;break}(!a.projection||r.projection&&r.projection.type!==a.projection.type)&&(o=!0)}return o};Dh.isAspectChanged=function(e){var t=this,r=t.glplot.getAspectratio(),n=Dd.nestedProperty(e,t.id+\".aspectratio\"),a=n.get();return a===void 0||a.x!==r.x||a.y!==r.y||a.z!==r.z};Dh.saveLayout=function(e){var t=this,r=t.fullLayout,n,a,i,o,s,l,u=t.isCameraChanged(e),c=t.isAspectChanged(e),f=u||c;if(f){var v={};if(u&&(n=t.getCamera(),a=Dd.nestedProperty(e,t.id+\".camera\"),i=a.get(),v[t.id+\".camera\"]=i),c&&(o=t.glplot.getAspectratio(),s=Dd.nestedProperty(e,t.id+\".aspectratio\"),l=s.get(),v[t.id+\".aspectratio\"]=l),pP.call(\"_storeDirectGUIEdit\",e,r._preGUI,v),u){a.set(n);var g=Dd.nestedProperty(r,t.id+\".camera\");g.set(n)}if(c){s.set(o);var p=Dd.nestedProperty(r,t.id+\".aspectratio\");p.set(o),t.glplot.redraw()}}return f};Dh.updateFx=function(e,t){var r=this,n=r.camera;if(n)if(e===\"orbit\")n.mode=\"orbit\",n.keyBindingMode=\"rotate\";else if(e===\"turntable\"){n.up=[0,0,1],n.mode=\"turntable\",n.keyBindingMode=\"rotate\";var a=r.graphDiv,i=a._fullLayout,o=r.fullSceneLayout.camera,s=o.up.x,l=o.up.y,u=o.up.z;if(u/Math.sqrt(s*s+l*l+u*u)<.999){var c=r.id+\".camera.up\",f={x:0,y:0,z:1},v={};v[c]=f;var g=a.layout;pP.call(\"_storeDirectGUIEdit\",g,i._preGUI,v),o.up=f,Dd.nestedProperty(g,c).set(f)}}else n.keyBindingMode=e;r.fullSceneLayout.hovermode=t};function sNt(e,t,r){for(var n=0,a=r-1;n<a;++n,--a)for(var i=0;i<t;++i)for(var o=0;o<4;++o){var s=4*(t*n+i)+o,l=4*(t*a+i)+o,u=e[s];e[s]=e[l],e[l]=u}}function lNt(e,t,r){for(var n=0;n<r;++n)for(var a=0;a<t;++a){var i=4*(t*n+a),o=e[i+3];if(o>0)for(var s=255/o,l=0;l<3;++l)e[i+l]=Math.min(s*e[i+l],255)}}Dh.toImage=function(e){var t=this;e||(e=\"png\"),t.staticMode&&t.container.appendChild(rM),t.glplot.redraw();var r=t.glplot.gl,n=r.drawingBufferWidth,a=r.drawingBufferHeight;r.bindFramebuffer(r.FRAMEBUFFER,null);var i=new Uint8Array(n*a*4);r.readPixels(0,0,n,a,r.RGBA,r.UNSIGNED_BYTE,i),sNt(i,n,a),lNt(i,n,a);var o=document.createElement(\"canvas\");o.width=n,o.height=a;var s=o.getContext(\"2d\",{willReadFrequently:!0}),l=s.createImageData(n,a);l.data.set(i),s.putImageData(l,0,0);var u;switch(e){case\"jpeg\":u=o.toDataURL(\"image/jpeg\");break;case\"webp\":u=o.toDataURL(\"image/webp\");break;default:u=o.toDataURL(\"image/png\")}return t.staticMode&&t.container.removeChild(rM),u};Dh.setConvert=function(){for(var e=this,t=0;t<3;t++){var r=e.fullSceneLayout[nM[t]];gP.setConvert(r,e.fullLayout),r.setScale=Dd.noop}};Dh.make4thDimension=function(){var e=this,t=e.graphDiv,r=t._fullLayout;e._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},gP.setConvert(e._mockAxis,r)};WFe.exports=jFe});var ZFe=fe((Q3r,XFe)=>{\"use strict\";XFe.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}});var CY=fe((ewr,$Fe)=>{\"use strict\";var uNt=ni(),Va=pf(),kY=ta().extendFlat,cNt=rl().overrideAll;$Fe.exports=cNt({visible:Va.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:uNt.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:Va.color,categoryorder:Va.categoryorder,categoryarray:Va.categoryarray,title:{text:Va.title.text,font:Va.title.font},type:kY({},Va.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:Va.autotypenumbers,autorange:Va.autorange,autorangeoptions:{minallowed:Va.autorangeoptions.minallowed,maxallowed:Va.autorangeoptions.maxallowed,clipmin:Va.autorangeoptions.clipmin,clipmax:Va.autorangeoptions.clipmax,include:Va.autorangeoptions.include,editType:\"plot\"},rangemode:Va.rangemode,minallowed:Va.minallowed,maxallowed:Va.maxallowed,range:kY({},Va.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:Va.minor.tickmode,nticks:Va.nticks,tick0:Va.tick0,dtick:Va.dtick,tickvals:Va.tickvals,ticktext:Va.ticktext,ticks:Va.ticks,mirror:Va.mirror,ticklen:Va.ticklen,tickwidth:Va.tickwidth,tickcolor:Va.tickcolor,showticklabels:Va.showticklabels,labelalias:Va.labelalias,tickfont:Va.tickfont,tickangle:Va.tickangle,tickprefix:Va.tickprefix,showtickprefix:Va.showtickprefix,ticksuffix:Va.ticksuffix,showticksuffix:Va.showticksuffix,showexponent:Va.showexponent,exponentformat:Va.exponentformat,minexponent:Va.minexponent,separatethousands:Va.separatethousands,tickformat:Va.tickformat,tickformatstops:Va.tickformatstops,hoverformat:Va.hoverformat,showline:Va.showline,linecolor:Va.linecolor,linewidth:Va.linewidth,showgrid:Va.showgrid,gridcolor:kY({},Va.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:Va.gridwidth,zeroline:Va.zeroline,zerolinecolor:Va.zerolinecolor,zerolinewidth:Va.zerolinewidth},\"plot\",\"from-root\")});var DY=fe((twr,KFe)=>{\"use strict\";var LY=CY(),fNt=nl().attributes,PY=ta().extendFlat,hNt=Xt().counterRegex;function FY(e,t,r){return{x:{valType:\"number\",dflt:e,editType:\"camera\"},y:{valType:\"number\",dflt:t,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}KFe.exports={_arrayAttrRegexps:[hNt(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:PY(FY(0,0,1),{}),center:PY(FY(0,0,0),{}),eye:PY(FY(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:fNt({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:LY,yaxis:LY,zaxis:LY,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\"}});var tDe=fe((rwr,eDe)=>{\"use strict\";var dNt=ni(),JFe=Xt(),vNt=Mo(),pNt=CY(),gNt=SU(),mNt=vA(),QFe=[\"xaxis\",\"yaxis\",\"zaxis\"],yNt=13600/187;eDe.exports=function(t,r,n){var a,i;function o(u,c){return JFe.coerce(a,i,pNt,u,c)}for(var s=0;s<QFe.length;s++){var l=QFe[s];a=t[l]||{},i=vNt.newContainer(r,l),i._id=l[0]+n.scene,i._name=l,gNt(a,i,o,n),mNt(a,i,o,{font:n.font,letter:l[0],data:n.data,showGrid:!0,noAutotickangles:!0,noMinorloglabels:!0,noTicklabelindex:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelshift:!0,noTicklabelstandoff:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,noUnifiedhovertitle:!0,bgColor:n.bgColor,calendar:n.calendar},n.fullLayout),o(\"gridcolor\",dNt.mix(i.color,n.bgColor,yNt)),o(\"title.text\",l[0]),i.setScale=JFe.noop,o(\"showspikes\")&&(o(\"spikesides\"),o(\"spikethickness\"),o(\"spikecolor\",i.color)),o(\"showaxeslabels\"),o(\"showbackground\")&&o(\"backgroundcolor\")}}});var aDe=fe((nwr,iDe)=>{\"use strict\";var xNt=Xt(),_Nt=ni(),bNt=bi(),wNt=b2(),TNt=tDe(),rDe=DY(),ANt=jf().getSubplotData,nDe=\"gl3d\";iDe.exports=function(t,r,n){var a=r._basePlotModules.length>1;function i(o){if(!a){var s=xNt.validate(t[o],rDe[o]);if(s)return t[o]}}wNt(t,r,n,{type:nDe,attributes:rDe,handleDefaults:MNt,fullLayout:r,font:r.font,fullData:n,getDfltFromLayout:i,autotypenumbersDflt:r.autotypenumbers,paper_bgcolor:r.paper_bgcolor,calendar:r.calendar})};function MNt(e,t,r,n){for(var a=r(\"bgcolor\"),i=_Nt.combine(a,n.paper_bgcolor),o=[\"up\",\"center\",\"eye\"],s=0;s<o.length;s++)r(\"camera.\"+o[s]+\".x\"),r(\"camera.\"+o[s]+\".y\"),r(\"camera.\"+o[s]+\".z\");r(\"camera.projection.type\");var l=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),u=l?\"manual\":\"auto\",c=r(\"aspectmode\",u);l||(e.aspectratio=t.aspectratio={x:1,y:1,z:1},c===\"manual\"&&(t.aspectmode=\"auto\"),e.aspectmode=t.aspectmode);var f=ANt(n.fullData,nDe,n.id);TNt(e,t,{font:n.font,scene:n.id,data:f,bgColor:i,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),bNt.getComponentMethod(\"annotations3d\",\"handleDefaults\")(e,t,n);var v=n.getDfltFromLayout(\"dragmode\");if(v!==!1&&!v)if(v=\"orbit\",e.camera&&e.camera.up){var g=e.camera.up.x,p=e.camera.up.y,x=e.camera.up.z;x!==0&&(!g||!p||!x||x/Math.sqrt(g*g+p*p+x*x)>.999)&&(v=\"turntable\")}else v=\"turntable\";r(\"dragmode\",v),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}});var gy=fe(Rd=>{\"use strict\";var SNt=rl().overrideAll,ENt=tm(),kNt=YFe(),CNt=jf().getSubplotData,LNt=Xt(),PNt=Q0(),Kw=\"gl3d\",RY=\"scene\";Rd.name=Kw;Rd.attr=RY;Rd.idRoot=RY;Rd.idRegex=Rd.attrRegex=LNt.counterRegex(\"scene\");Rd.attributes=ZFe();Rd.layoutAttributes=DY();Rd.baseLayoutAttrOverrides=SNt({hoverlabel:ENt.hoverlabel},\"plot\",\"nested\");Rd.supplyLayoutDefaults=aDe();Rd.plot=function(t){for(var r=t._fullLayout,n=t._fullData,a=r._subplots[Kw],i=0;i<a.length;i++){var o=a[i],s=CNt(n,Kw,o),l=r[o],u=l.camera,c=l._scene;c||(c=new kNt({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},r),l._scene=c),c.viewInitial||(c.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),c.plot(s,r,t.layout)}};Rd.clean=function(e,t,r,n){for(var a=n._subplots[Kw]||[],i=0;i<a.length;i++){var o=a[i];!t[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}};Rd.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[Kw],n=t._size,a=0;a<r.length;a++){var i=t[r[a]],o=i.domain,s=i._scene,l=s.toImage(\"png\"),u=t._glimages.append(\"svg:image\");u.attr({xmlns:PNt.svg,\"xlink:href\":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}};Rd.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var r=t.slice(5);return r===\"1\"&&(r=\"\"),RY+r}};Rd.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[Kw],n=0;n<r.length;n++){var a=t[r[n]]._scene;a.updateFx(t.dragmode,t.hovermode)}}});var sDe=fe((awr,oDe)=>{\"use strict\";oDe.exports={plot:gFe(),attributes:MY(),markerSymbols:fP(),supplyDefaults:TFe(),colorbar:[{container:\"marker\",min:\"cmin\",max:\"cmax\"},{container:\"line\",min:\"cmin\",max:\"cmax\"}],calc:MFe(),moduleType:\"trace\",name:\"scatter3d\",basePlotModule:gy(),categories:[\"gl3d\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}});var uDe=fe((owr,lDe)=>{\"use strict\";lDe.exports=sDe()});var iM=fe((swr,hDe)=>{\"use strict\";var cDe=ni(),FNt=_s(),IY=pl().axisHoverFormat,{hovertemplateAttrs:DNt,templatefallbackAttrs:RNt}=Oo(),fDe=Ho(),zY=ta().extendFlat,INt=rl().overrideAll;function qY(e){return{valType:\"boolean\",dflt:!1}}function BY(e){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:qY(\"x\"),y:qY(\"y\"),z:qY(\"z\")},color:{valType:\"color\",dflt:cDe.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:cDe.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var OY=hDe.exports=INt(zY({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:DNt(),hovertemplatefallback:RNt(),xhoverformat:IY(\"x\"),yhoverformat:IY(\"y\"),zhoverformat:IY(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},FNt(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:BY(\"x\"),y:BY(\"y\"),z:BY(\"z\")},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05,description:\"Represents the level that incident rays are reflected in a single direction, causing shine.\"},roughness:{valType:\"number\",min:0,max:1,dflt:.5,description:\"Alters specular reflection; the rougher the surface, the wider and less contrasty the shine.\"},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},hoverinfo:zY({},fDe.hoverinfo),showlegend:zY({},fDe.showlegend,{dflt:!1})}),\"calc\",\"nested\");OY.x.editType=OY.y.editType=OY.z.editType=\"calc+clearAxisTypes\"});var UY=fe((lwr,pDe)=>{\"use strict\";var zNt=bi(),dDe=Xt(),qNt=Vc(),BNt=iM(),NY=.1;function ONt(e,t){for(var r=[],n=32,a=0;a<n;a++){var i=a/(n-1),o=t+(1-t)*(1-Math.pow(Math.sin(e*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,o))])}return r}function NNt(e){var t=0;if(!Array.isArray(e)||e.length<2||!e[0]||!e[e.length-1]||+e[0][0]!=0||+e[e.length-1][0]!=1)return!1;for(var r=0;r<e.length;r++){var n=e[r];if(n.length!==2||+n[0]<t)return!1;t=+n[0]}return!0}function UNt(e,t,r,n){var a,i;function o(_,y){return dDe.coerce(e,t,BNt,_,y)}var s=o(\"x\"),l=o(\"y\"),u=o(\"z\");if(!u||!u.length||s&&s.length<1||l&&l.length<1){t.visible=!1;return}t._xlength=Array.isArray(s)&&dDe.isArrayOrTypedArray(s[0])?u.length:u[0].length,t._ylength=u.length;var c=zNt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");c(e,t,[\"x\",\"y\",\"z\"],n),o(\"text\"),o(\"hovertext\"),o(\"hovertemplate\"),o(\"hovertemplatefallback\"),o(\"xhoverformat\"),o(\"yhoverformat\"),o(\"zhoverformat\"),[\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"hidesurface\",\"connectgaps\",\"opacity\"].forEach(function(_){o(_)});var f=o(\"surfacecolor\"),v=[\"x\",\"y\",\"z\"];for(a=0;a<3;++a){var g=\"contours.\"+v[a],p=o(g+\".show\"),x=o(g+\".highlight\");if(p||x)for(i=0;i<3;++i)o(g+\".project.\"+v[i]);p&&(o(g+\".color\"),o(g+\".width\"),o(g+\".usecolormap\")),x&&(o(g+\".highlightcolor\"),o(g+\".highlightwidth\")),o(g+\".start\"),o(g+\".end\"),o(g+\".size\")}qNt(e,t,n,o,{prefix:\"\",cLetter:\"c\"}),vDe(e,t,n,o),t._length=null}function vDe(e,t,r,n){var a=n(\"opacityscale\");a===\"max\"?t.opacityscale=[[0,NY],[1,1]]:a===\"min\"?t.opacityscale=[[0,1],[1,NY]]:a===\"extremes\"?t.opacityscale=ONt(1,NY):NNt(a)||(t.opacityscale=void 0)}pDe.exports={supplyDefaults:UNt,opacityscaleDefaults:vDe}});var yDe=fe((uwr,mDe)=>{\"use strict\";var gDe=Mh();mDe.exports=function(t,r){r.surfacecolor?gDe(t,r,{vals:r.surfacecolor,containerStr:\"\",cLetter:\"c\"}):gDe(t,r,{vals:r.z,containerStr:\"\",cLetter:\"c\"})}});var ADe=fe((cwr,TDe)=>{\"use strict\";var VNt=xf().gl_surface3d,Jw=xf().ndarray,GNt=xf().ndarray_linear_interpolate.d2,HNt=d7(),jNt=v7(),aM=Xt().isArrayOrTypedArray,WNt=Sg().parseColorScale,xDe=Mg(),YNt=js().extractOpts;function bDe(e,t,r){this.scene=e,this.uid=r,this.surface=t,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var Cp=bDe.prototype;Cp.getXat=function(e,t,r,n){var a=aM(this.data.x)?aM(this.data.x[0])?this.data.x[t][e]:this.data.x[e]:e;return r===void 0?a:n.d2l(a,0,r)};Cp.getYat=function(e,t,r,n){var a=aM(this.data.y)?aM(this.data.y[0])?this.data.y[t][e]:this.data.y[t]:t;return r===void 0?a:n.d2l(a,0,r)};Cp.getZat=function(e,t,r,n){var a=this.data.z[t][e];return a===null&&this.data.connectgaps&&this.data._interpolatedZ&&(a=this.data._interpolatedZ[t][e]),r===void 0?a:n.d2l(a,0,r)};Cp.handlePick=function(e){if(e.object===this.surface){var t=(e.data.index[0]-1)/this.dataScaleX-1,r=(e.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(t),this.data.z[0].length-1),0),a=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);e.index=[n,a],e.traceCoordinate=[this.getXat(n,a),this.getYat(n,a),this.getZat(n,a)],e.dataCoordinate=[this.getXat(n,a,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,a,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,a,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var i=0;i<3;i++){var o=e.dataCoordinate[i];o!=null&&(e.dataCoordinate[i]*=this.scene.dataScale[i])}var s=this.data.hovertext||this.data.text;return aM(s)&&s[a]&&s[a][n]!==void 0?e.textLabel=s[a][n]:s?e.textLabel=s:e.textLabel=\"\",e.data.dataCoordinate=e.dataCoordinate.slice(),this.surface.highlight(e.data),this.scene.glplot.spikes.position=e.dataCoordinate,!0}};function XNt(e){var t=e[0].rgb,r=e[e.length-1].rgb;return t[0]===r[0]&&t[1]===r[1]&&t[2]===r[2]&&t[3]===r[3]}var Qw=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function ZNt(e,t){if(e<t)return 0;for(var r=0;Math.floor(e%t)===0;)e/=t,r++;return r}function VY(e){for(var t=[],r=0;r<Qw.length;r++){var n=Qw[r];t.push(ZNt(e,n))}return t}function $Nt(e){for(var t=VY(e),r=e,n=0;n<Qw.length;n++)if(t[n]>0){r=Qw[n];break}return r}function KNt(e,t){if(!(e<1||t<1)){for(var r=VY(e),n=VY(t),a=1,i=0;i<Qw.length;i++)a*=Math.pow(Qw[i],Math.max(r[i],n[i]));return a}}function JNt(e){if(e.length!==0){for(var t=1,r=0;r<e.length;r++)t=KNt(t,e[r]);return t}}Cp.calcXnums=function(e){var t,r=[];for(t=1;t<e;t++){var n=this.getXat(t-1,0),a=this.getXat(t,0);a!==n&&n!==void 0&&n!==null&&a!==void 0&&a!==null?r[t-1]=Math.abs(a-n):r[t-1]=0}var i=0;for(t=1;t<e;t++)i+=r[t-1];for(t=1;t<e;t++)r[t-1]===0?r[t-1]=1:r[t-1]=Math.round(i/r[t-1]);return r};Cp.calcYnums=function(e){var t,r=[];for(t=1;t<e;t++){var n=this.getYat(0,t-1),a=this.getYat(0,t);a!==n&&n!==void 0&&n!==null&&a!==void 0&&a!==null?r[t-1]=Math.abs(a-n):r[t-1]=0}var i=0;for(t=1;t<e;t++)i+=r[t-1];for(t=1;t<e;t++)r[t-1]===0?r[t-1]=1:r[t-1]=Math.round(i/r[t-1]);return r};var wDe=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],_De=wDe[9],mP=wDe[13];Cp.estimateScale=function(e,t){for(var r=t===0?this.calcXnums(e):this.calcYnums(e),n=1+JNt(r);n<_De;)n*=2;for(;n>mP;)n--,n/=$Nt(n),n++,n<_De&&(n=mP);var a=Math.round(n/e);return a>1?a:1};function QNt(e,t,r){var n=r[8]+r[2]*t[0]+r[5]*t[1];return e[0]=(r[6]+r[0]*t[0]+r[3]*t[1])/n,e[1]=(r[7]+r[1]*t[0]+r[4]*t[1])/n,e}function eUt(e,t,r){return tUt(e,t,QNt,r),e}function tUt(e,t,r,n){for(var a=[0,0],i=e.shape[0],o=e.shape[1],s=0;s<i;s++)for(var l=0;l<o;l++)r(a,[s,l],n),e.set(s,l,GNt(t,a[0],a[1]));return e}Cp.refineCoords=function(e){for(var t=this.dataScaleX,r=this.dataScaleY,n=e[0].shape[0],a=e[0].shape[1],i=Math.floor(e[0].shape[0]*t+1)|0,o=Math.floor(e[0].shape[1]*r+1)|0,s=1+n+1,l=1+a+1,u=Jw(new Float32Array(s*l),[s,l]),c=[1/t,0,0,0,1/r,0,0,0,1],f=0;f<e.length;++f){this.surface.padField(u,e[f]);var v=Jw(new Float32Array(i*o),[i,o]);eUt(v,u,c),e[f]=v}};function rUt(e,t){for(var r=!1,n=0;n<e.length;n++)if(t===e[n]){r=!0;break}r===!1&&e.push(t)}Cp.setContourLevels=function(){var e=[[],[],[]],t=[!1,!1,!1],r=!1,n,a,i;for(n=0;n<3;++n)if(this.showContour[n]&&(r=!0,this.contourSize[n]>0&&this.contourStart[n]!==null&&this.contourEnd[n]!==null&&this.contourEnd[n]>this.contourStart[n]))for(t[n]=!0,a=this.contourStart[n];a<this.contourEnd[n];a+=this.contourSize[n])i=a*this.scene.dataScale[n],rUt(e[n],i);if(r){var o=[[],[],[]];for(n=0;n<3;++n)this.showContour[n]&&(o[n]=t[n]?e[n]:this.scene.contourLevels[n]);this.surface.update({levels:o})}};Cp.update=function(e){var t=this.scene,r=t.fullSceneLayout,n=this.surface,a=WNt(e),i=t.dataScale,o=e.z[0].length,s=e._ylength,l=t.contourLevels;this.data=e;var u,c,f,v,g=[];for(u=0;u<3;u++)for(g[u]=[],c=0;c<o;c++)g[u][c]=[];for(c=0;c<o;c++)for(f=0;f<s;f++)g[0][c][f]=this.getXat(c,f,e.xcalendar,r.xaxis),g[1][c][f]=this.getYat(c,f,e.ycalendar,r.yaxis),g[2][c][f]=this.getZat(c,f,e.zcalendar,r.zaxis);if(e.connectgaps)for(e._emptypoints=jNt(g[2]),HNt(g[2],e._emptypoints),e._interpolatedZ=[],c=0;c<o;c++)for(e._interpolatedZ[c]=[],f=0;f<s;f++)e._interpolatedZ[c][f]=g[2][c][f];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)v=g[u][c][f],v==null?g[u][c][f]=NaN:v=g[u][c][f]*=i[u];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)v=g[u][c][f],v!=null&&(this.minValues[u]>v&&(this.minValues[u]=v),this.maxValues[u]<v&&(this.maxValues[u]=v));for(u=0;u<3;u++)this.objectOffset[u]=.5*(this.minValues[u]+this.maxValues[u]);for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)v=g[u][c][f],v!=null&&(g[u][c][f]-=this.objectOffset[u]);var p=[Jw(new Float32Array(o*s),[o,s]),Jw(new Float32Array(o*s),[o,s]),Jw(new Float32Array(o*s),[o,s])];for(u=0;u<3;u++)for(c=0;c<o;c++)for(f=0;f<s;f++)p[u].set(c,f,g[u][c][f]);g=[];var x={colormap:a,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!e.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:e.opacityscale,opacity:e.opacity},_=YNt(e);if(x.intensityBounds=[_.min,_.max],e.surfacecolor){var y=Jw(new Float32Array(o*s),[o,s]);for(c=0;c<o;c++)for(f=0;f<s;f++)y.set(c,f,e.surfacecolor[f][c]);p.push(y)}else x.intensityBounds[0]*=i[2],x.intensityBounds[1]*=i[2];(mP<p[0].shape[0]||mP<p[0].shape[1])&&(this.refineData=!1),this.refineData===!0&&(this.dataScaleX=this.estimateScale(p[0].shape[0],0),this.dataScaleY=this.estimateScale(p[0].shape[1],1),(this.dataScaleX!==1||this.dataScaleY!==1)&&this.refineCoords(p)),e.surfacecolor&&(x.intensity=p.pop());var C=[!0,!0,!0],k=[\"x\",\"y\",\"z\"];for(u=0;u<3;++u){var S=e.contours[k[u]];C[u]=S.highlight,x.showContour[u]=S.show||S.highlight,x.showContour[u]&&(x.contourProject[u]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[u]=!0,x.levels[u]=l[u],n.highlightColor[u]=x.contourColor[u]=xDe(S.color),S.usecolormap?n.highlightTint[u]=x.contourTint[u]=0:n.highlightTint[u]=x.contourTint[u]=1,x.contourWidth[u]=S.width,this.contourStart[u]=S.start,this.contourEnd[u]=S.end,this.contourSize[u]=S.size):(this.showContour[u]=!1,this.contourStart[u]=null,this.contourEnd[u]=null,this.contourSize[u]=0),S.highlight&&(x.dynamicColor[u]=xDe(S.highlightcolor),x.dynamicWidth[u]=S.highlightwidth))}XNt(a)&&(x.vertexColor=!0),x.objectOffset=this.objectOffset,x.coords=p,n.update(x),n.visible=e.visible,n.enableDynamic=C,n.enableHighlight=C,n.snapToData=!0,\"lighting\"in e&&(n.ambientLight=e.lighting.ambient,n.diffuseLight=e.lighting.diffuse,n.specularLight=e.lighting.specular,n.roughness=e.lighting.roughness,n.fresnel=e.lighting.fresnel),\"lightposition\"in e&&(n.lightPosition=[e.lightposition.x,e.lightposition.y,e.lightposition.z])};Cp.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()};function nUt(e,t){var r=e.glplot.gl,n=VNt({gl:r}),a=new bDe(e,n,t.uid);return n._trace=a,a.update(t),e.glplot.add(n),a}TDe.exports=nUt});var SDe=fe((fwr,MDe)=>{\"use strict\";MDe.exports={attributes:iM(),supplyDefaults:UY().supplyDefaults,colorbar:{min:\"cmin\",max:\"cmax\"},calc:yDe(),plot:ADe(),moduleType:\"trace\",name:\"surface\",basePlotModule:gy(),categories:[\"gl3d\",\"2dMap\",\"showLegend\"],meta:{}}});var kDe=fe((hwr,EDe)=>{\"use strict\";EDe.exports=SDe()});var e5=fe((dwr,LDe)=>{\"use strict\";var iUt=_s(),GY=pl().axisHoverFormat,{hovertemplateAttrs:aUt,templatefallbackAttrs:oUt}=Oo(),my=iM(),CDe=Ho(),yy=ta().extendFlat;LDe.exports=yy({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:aUt({editType:\"calc\"}),hovertemplatefallback:oUt({editType:\"calc\"}),xhoverformat:GY(\"x\"),yhoverformat:GY(\"y\"),zhoverformat:GY(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"}},iUt(\"\",{colorAttr:\"`intensity`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:my.opacity,flatshading:{valType:\"boolean\",dflt:!1,editType:\"calc\"},contour:{show:yy({},my.contours.x.show,{}),color:my.contours.x.color,width:my.contours.x.width,editType:\"calc\"},lightposition:{x:yy({},my.lightposition.x,{dflt:1e5}),y:yy({},my.lightposition.y,{dflt:1e5}),z:yy({},my.lightposition.z,{dflt:0}),editType:\"calc\"},lighting:yy({vertexnormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-12,editType:\"calc\",description:\"Epsilon for vertex normals calculation avoids math issues arising from degenerate geometry.\"},facenormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-6,editType:\"calc\",description:\"Epsilon for face normals calculation avoids math issues arising from degenerate geometry.\"},editType:\"calc\"},my.lighting),hoverinfo:yy({},CDe.hoverinfo,{editType:\"calc\"}),showlegend:yy({},CDe.showlegend,{dflt:!1})})});var xP=fe((vwr,FDe)=>{\"use strict\";var sUt=_s(),yP=pl().axisHoverFormat,{hovertemplateAttrs:lUt,templatefallbackAttrs:uUt}=Oo(),oM=e5(),PDe=Ho(),HY=ta().extendFlat,cUt=rl().overrideAll;function jY(e){return{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}}function WY(e){return{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}}var t5=FDe.exports=cUt(HY({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:jY(\"x\"),y:jY(\"y\"),z:jY(\"z\")},caps:{x:WY(\"x\"),y:WY(\"y\"),z:WY(\"z\")},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:lUt(),hovertemplatefallback:uUt(),xhoverformat:yP(\"x\"),yhoverformat:yP(\"y\"),zhoverformat:yP(\"z\"),valuehoverformat:yP(\"value\",1),showlegend:HY({},PDe.showlegend,{dflt:!1})},sUt(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:oM.opacity,lightposition:oM.lightposition,lighting:oM.lighting,flatshading:oM.flatshading,contour:oM.contour,hoverinfo:HY({},PDe.hoverinfo)}),\"calc\",\"nested\");t5.flatshading.dflt=!0;t5.lighting.facenormalsepsilon.dflt=0;t5.x.editType=t5.y.editType=t5.z.editType=t5.value.editType=\"calc+clearAxisTypes\"});var YY=fe((pwr,RDe)=>{\"use strict\";var fUt=Xt(),hUt=bi(),dUt=xP(),vUt=Vc();function pUt(e,t,r,n){function a(i,o){return fUt.coerce(e,t,dUt,i,o)}DDe(e,t,r,n,a)}function DDe(e,t,r,n,a){var i=a(\"isomin\"),o=a(\"isomax\");o!=null&&i!==void 0&&i!==null&&i>o&&(t.isomin=null,t.isomax=null);var s=a(\"x\"),l=a(\"y\"),u=a(\"z\"),c=a(\"value\");if(!s||!s.length||!l||!l.length||!u||!u.length||!c||!c.length){t.visible=!1;return}var f=hUt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");f(e,t,[\"x\",\"y\",\"z\"],n),a(\"valuehoverformat\"),[\"x\",\"y\",\"z\"].forEach(function(x){a(x+\"hoverformat\");var _=\"caps.\"+x,y=a(_+\".show\");y&&a(_+\".fill\");var C=\"slices.\"+x,k=a(C+\".show\");k&&(a(C+\".fill\"),a(C+\".locations\"))});var v=a(\"spaceframe.show\");v&&a(\"spaceframe.fill\");var g=a(\"surface.show\");g&&(a(\"surface.count\"),a(\"surface.fill\"),a(\"surface.pattern\"));var p=a(\"contour.show\");p&&(a(\"contour.color\"),a(\"contour.width\")),[\"text\",\"hovertext\",\"hovertemplate\",\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lighting.vertexnormalsepsilon\",\"lighting.facenormalsepsilon\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"flatshading\",\"opacity\"].forEach(function(x){a(x)}),vUt(e,t,n,a,{prefix:\"\",cLetter:\"c\"}),t._length=null}RDe.exports={supplyDefaults:pUt,supplyIsoDefaults:DDe}});var _P=fe((gwr,zDe)=>{\"use strict\";var ZY=Xt(),gUt=Mh();function mUt(e,t){t._len=Math.min(t.u.length,t.v.length,t.w.length,t.x.length,t.y.length,t.z.length),t._u=T1(t.u,t._len),t._v=T1(t.v,t._len),t._w=T1(t.w,t._len),t._x=T1(t.x,t._len),t._y=T1(t.y,t._len),t._z=T1(t.z,t._len);var r=IDe(t);t._gridFill=r.fill,t._Xs=r.Xs,t._Ys=r.Ys,t._Zs=r.Zs,t._len=r.len;var n=0,a,i,o;t.starts&&(a=T1(t.starts.x||[]),i=T1(t.starts.y||[]),o=T1(t.starts.z||[]),n=Math.min(a.length,i.length,o.length)),t._startsX=a||[],t._startsY=i||[],t._startsZ=o||[];var s=0,l=1/0,u;for(u=0;u<t._len;u++){var c=t._u[u],f=t._v[u],v=t._w[u],g=Math.sqrt(c*c+f*f+v*v);s=Math.max(s,g),l=Math.min(l,g)}for(gUt(e,t,{vals:[l,s],containerStr:\"\",cLetter:\"c\"}),u=0;u<n;u++){var p=a[u];r.xMax=Math.max(r.xMax,p),r.xMin=Math.min(r.xMin,p);var x=i[u];r.yMax=Math.max(r.yMax,x),r.yMin=Math.min(r.yMin,x);var _=o[u];r.zMax=Math.max(r.zMax,_),r.zMin=Math.min(r.zMin,_)}t._slen=n,t._normMax=s,t._xbnds=[r.xMin,r.xMax],t._ybnds=[r.yMin,r.yMax],t._zbnds=[r.zMin,r.zMax]}function IDe(e){var t=e._x,r=e._y,n=e._z,a=e._len,i,o,s,l=-1/0,u=1/0,c=-1/0,f=1/0,v=-1/0,g=1/0,p=\"\",x,_,y,C,k,S,L,w,A;for(a&&(C=t[0],S=r[0],w=n[0]),a>1&&(k=t[a-1],L=r[a-1],A=n[a-1]),i=0;i<a;i++)l=Math.max(l,t[i]),u=Math.min(u,t[i]),c=Math.max(c,r[i]),f=Math.min(f,r[i]),v=Math.max(v,n[i]),g=Math.min(g,n[i]),!x&&t[i]!==C&&(x=!0,p+=\"x\"),!_&&r[i]!==S&&(_=!0,p+=\"y\"),!y&&n[i]!==w&&(y=!0,p+=\"z\");x||(p+=\"x\"),_||(p+=\"y\"),y||(p+=\"z\");var T=XY(e._x),m=XY(e._y),F=XY(e._z);p=p.replace(\"x\",(C>k?\"-\":\"+\")+\"x\"),p=p.replace(\"y\",(S>L?\"-\":\"+\")+\"y\"),p=p.replace(\"z\",(w>A?\"-\":\"+\")+\"z\");var E=function(){a=0,T=[],m=[],F=[]};(!a||a<T.length*m.length*F.length)&&E();var I=function(pe){return pe===\"x\"?t:pe===\"y\"?r:n},R=function(pe){return pe===\"x\"?T:pe===\"y\"?m:F},B=function(pe){return pe[a-1]<pe[0]?-1:1},O=I(p[1]),V=I(p[3]),j=I(p[5]),q=R(p[1]).length,U=R(p[3]).length,J=R(p[5]).length,ee=!1,ce=function(pe,ie,le){return q*(U*pe+ie)+le},ye=B(I(p[1])),xe=B(I(p[3])),ue=B(I(p[5]));for(i=0;i<J-1;i++){for(o=0;o<U-1;o++){for(s=0;s<q-1;s++){var K=ce(i,o,s),_e=ce(i,o,s+1),ne=ce(i,o+1,s),ae=ce(i+1,o,s);if((!(O[K]*ye<O[_e]*ye)||!(V[K]*xe<V[ne]*xe)||!(j[K]*ue<j[ae]*ue))&&(ee=!0),ee)break}if(ee)break}if(ee)break}return ee&&(ZY.warn(\"Encountered arbitrary coordinates! Unable to input data grid.\"),E()),{xMin:u,yMin:f,zMin:g,xMax:l,yMax:c,zMax:v,Xs:T,Ys:m,Zs:F,len:a,fill:p}}function XY(e){return ZY.distinctVals(e).vals}function T1(e,t){if(t===void 0&&(t=e.length),ZY.isTypedArray(e))return e.subarray(0,t);for(var r=[],n=0;n<t;n++)r[n]=+e[n];return r}zDe.exports={calc:mUt,filter:T1,processGrid:IDe}});var $Y=fe((mwr,qDe)=>{\"use strict\";var yUt=Mh(),xUt=_P().processGrid,bP=_P().filter;qDe.exports=function(t,r){r._len=Math.min(r.x.length,r.y.length,r.z.length,r.value.length),r._x=bP(r.x,r._len),r._y=bP(r.y,r._len),r._z=bP(r.z,r._len),r._value=bP(r.value,r._len);var n=xUt(r);r._gridFill=n.fill,r._Xs=n.Xs,r._Ys=n.Ys,r._Zs=n.Zs,r._len=n.len;for(var a=1/0,i=-1/0,o=0;o<r._len;o++){var s=r._value[o];a=Math.min(a,s),i=Math.max(i,s)}r._minValues=a,r._maxValues=i,r._vMin=r.isomin===void 0||r.isomin===null?a:r.isomin,r._vMax=r.isomax===void 0||r.isomax===null?i:r.isomax,yUt(t,r,{vals:[r._vMin,r._vMax],containerStr:\"\",cLetter:\"c\"})}});var r5=fe((ywr,BDe)=>{\"use strict\";BDe.exports=function(t,r,n,a){a=a||t.length;for(var i=new Array(a),o=0;o<a;o++)i[o]=[t[o],r[o],n[o]];return i}});var wP=fe((xwr,VDe)=>{\"use strict\";var _Ut=xf().gl_mesh3d,bUt=Sg().parseColorScale,wUt=Xt().isArrayOrTypedArray,TUt=Mg(),AUt=js().extractOpts,ODe=r5(),sM=function(e,t){for(var r=t.length-1;r>0;r--){var n=Math.min(t[r],t[r-1]),a=Math.max(t[r],t[r-1]);if(a>n&&n<e&&e<=a)return{id:r,distRatio:(a-e)/(a-n)}}return{id:0,distRatio:0}};function NDe(e,t,r){this.scene=e,this.uid=r,this.mesh=t,this.name=\"\",this.data=null,this.showContour=!1}var KY=NDe.prototype;KY.handlePick=function(e){if(e.object===this.mesh){var t=e.data.index,r=this.data._meshX[t],n=this.data._meshY[t],a=this.data._meshZ[t],i=this.data._Ys.length,o=this.data._Zs.length,s=sM(r,this.data._Xs).id,l=sM(n,this.data._Ys).id,u=sM(a,this.data._Zs).id,c=e.index=u+o*l+o*i*s;e.traceCoordinate=[this.data._meshX[c],this.data._meshY[c],this.data._meshZ[c],this.data._value[c]];var f=this.data.hovertext||this.data.text;return wUt(f)&&f[c]!==void 0?e.textLabel=f[c]:f&&(e.textLabel=f),!0}};KY.update=function(e){var t=this.scene,r=t.fullSceneLayout;this.data=UDe(e);function n(l,u,c,f){return u.map(function(v){return l.d2l(v,0,f)*c})}var a=ODe(n(r.xaxis,e._meshX,t.dataScale[0],e.xcalendar),n(r.yaxis,e._meshY,t.dataScale[1],e.ycalendar),n(r.zaxis,e._meshZ,t.dataScale[2],e.zcalendar)),i=ODe(e._meshI,e._meshJ,e._meshK),o={positions:a,cells:i,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,contourEnable:e.contour.show,contourColor:TUt(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading},s=AUt(e);o.vertexIntensity=e._meshIntensity,o.vertexIntensityBounds=[s.min,s.max],o.colormap=bUt(e),this.mesh.update(o)};KY.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var MUt=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"];function UDe(e){e._meshI=[],e._meshJ=[],e._meshK=[];var t=e.surface.show,r=e.spaceframe.show,n=e.surface.fill,a=e.spaceframe.fill,i=!1,o=!1,s=0,l,u,c=e._Xs,f=e._Ys,v=e._Zs,g=c.length,p=f.length,x=v.length,_=MUt.indexOf(e._gridFill.replace(/-/g,\"\").replace(/\\+/g,\"\")),y=function($e,ot,tt){switch(_){case 5:return tt+x*ot+x*p*$e;case 4:return tt+x*$e+x*g*ot;case 3:return ot+p*tt+p*x*$e;case 2:return ot+p*$e+p*g*tt;case 1:return $e+g*tt+g*x*ot;default:return $e+g*ot+g*p*tt}},C=e._minValues,k=e._maxValues,S=e._vMin,L=e._vMax,w,A,T,m;function F($e,ot,tt){for(var nt=m.length,We=u;We<nt;We++)if($e===w[We]&&ot===A[We]&&tt===T[We])return We;return-1}function E(){u=l}function I(){w=[],A=[],T=[],m=[],l=0,E()}function R($e,ot,tt,nt){return w.push($e),A.push(ot),T.push(tt),m.push(nt),l++,l-1}function B($e,ot,tt){return e._meshI.push($e),e._meshJ.push(ot),e._meshK.push(tt),s++,s-1}function O($e,ot,tt){for(var nt=[],We=0;We<$e.length;We++)nt[We]=($e[We]+ot[We]+tt[We])/3;return nt}function V($e,ot,tt){for(var 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ht($e,ot,tt,nt,We){for(var mt=[],bt=0,Yt=0;Yt<ot.length;Yt++)for(var jt=ot[Yt],mr=1;mr<x;mr++)for(var or=1;or<p;or++)mt.push(be($e,y(jt,or-1,mr-1),y(jt,or-1,mr),y(jt,or,mr-1),y(jt,or,mr),tt,nt,(jt+or+mr)%2,We&&We[bt]?We[bt]:[])),bt++;return mt}function it($e,ot,tt,nt,We){for(var mt=[],bt=0,Yt=0;Yt<ot.length;Yt++)for(var jt=ot[Yt],mr=1;mr<g;mr++)for(var or=1;or<x;or++)mt.push(be($e,y(mr-1,jt,or-1),y(mr,jt,or-1),y(mr-1,jt,or),y(mr,jt,or),tt,nt,(mr+jt+or)%2,We&&We[bt]?We[bt]:[])),bt++;return mt}function Oe($e,ot,tt,nt,We){for(var mt=[],bt=0,Yt=0;Yt<ot.length;Yt++)for(var jt=ot[Yt],mr=1;mr<p;mr++)for(var or=1;or<g;or++)mt.push(be($e,y(or-1,mr-1,jt),y(or-1,mr,jt),y(or,mr-1,jt),y(or,mr,jt),tt,nt,(or+mr+jt)%2,We&&We[bt]?We[bt]:[])),bt++;return mt}function ze($e,ot,tt){for(var nt=1;nt<x;nt++)for(var We=1;We<p;We++)for(var mt=1;mt<g;mt++)Ge($e,y(mt-1,We-1,nt-1),y(mt-1,We-1,nt),y(mt-1,We,nt-1),y(mt-1,We,nt),y(mt,We-1,nt-1),y(mt,We-1,nt),y(mt,We,nt-1),y(mt,We,nt),ot,tt,(mt+We+nt)%2)}function 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ot=e.surface.pattern,tt=e.surface.count,nt=0;nt<tt;nt++){var We=tt===1?.5:nt/(tt-1),mt=(1-We)*S+We*L,bt=Math.abs(mt-C),Yt=Math.abs(mt-k),jt=bt>Yt?[C,mt]:[mt,k];et(ot,jt[0],jt[1])}}var mr=[[Math.min(S,k),Math.max(S,k)],[Math.min(C,L),Math.max(C,L)]];[\"x\",\"y\",\"z\"].forEach(function(or){for(var fr=[],Ot=0;Ot<mr.length;Ot++){var cr=0,Ct=mr[Ot][0],wt=mr[Ot][1],Tt=e.slices[or];if(Tt.show&&Tt.fill){q(Tt.fill);var Mt=[],Lt=[],ft=[];if(Tt.locations.length)for(var kt=0;kt<Tt.locations.length;kt++){var rr=sM(Tt.locations[kt],or===\"x\"?c:or===\"y\"?f:v);rr.distRatio===0?Mt.push(rr.id):rr.id>0&&(Lt.push(rr.id),or===\"x\"?ft.push([rr.distRatio,0,0]):or===\"y\"?ft.push([0,rr.distRatio,0]):ft.push([0,0,rr.distRatio]))}else or===\"x\"?Mt=Ke(1,g-1):or===\"y\"?Mt=Ke(1,p-1):Mt=Ke(1,x-1);Lt.length>0&&(or===\"x\"?fr[cr]=qe($e,Lt,Ct,wt,ft,fr[cr]):or===\"y\"?fr[cr]=we($e,Lt,Ct,wt,ft,fr[cr]):fr[cr]=Le($e,Lt,Ct,wt,ft,fr[cr]),cr++),Mt.length>0&&(or===\"x\"?fr[cr]=ht($e,Mt,Ct,wt,fr[cr]):or===\"y\"?fr[cr]=it($e,Mt,Ct,wt,fr[cr]):fr[cr]=Oe($e,Mt,Ct,wt,fr[cr]),cr++)}var pr=e.caps[or];pr.show&&pr.fill&&(q(pr.fill),or===\"x\"?fr[cr]=ht($e,[0,g-1],Ct,wt,fr[cr]):or===\"y\"?fr[cr]=it($e,[0,p-1],Ct,wt,fr[cr]):fr[cr]=Oe($e,[0,x-1],Ct,wt,fr[cr]),cr++)}}),s===0&&I(),e._meshX=w,e._meshY=A,e._meshZ=T,e._meshIntensity=m,e._Xs=c,e._Ys=f,e._Zs=v}return Ve(),e}function SUt(e,t){var r=e.glplot.gl,n=_Ut({gl:r}),a=new NDe(e,n,t.uid);return n._trace=a,a.update(t),e.glplot.add(n),a}VDe.exports={findNearestOnAxis:sM,generateIsoMeshes:UDe,createIsosurfaceTrace:SUt}});var HDe=fe((_wr,GDe)=>{\"use 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EUt=_s(),Ou=xP(),kUt=iM(),YDe=Ho(),JY=ta().extendFlat,CUt=rl().overrideAll,TP=XDe.exports=CUt(JY({x:Ou.x,y:Ou.y,z:Ou.z,value:Ou.value,isomin:Ou.isomin,isomax:Ou.isomax,surface:Ou.surface,spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:1}},slices:Ou.slices,caps:Ou.caps,text:Ou.text,hovertext:Ou.hovertext,xhoverformat:Ou.xhoverformat,yhoverformat:Ou.yhoverformat,zhoverformat:Ou.zhoverformat,valuehoverformat:Ou.valuehoverformat,hovertemplate:Ou.hovertemplate,hovertemplatefallback:Ou.hovertemplatefallback},EUt(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{colorbar:Ou.colorbar,opacity:Ou.opacity,opacityscale:kUt.opacityscale,lightposition:Ou.lightposition,lighting:Ou.lighting,flatshading:Ou.flatshading,contour:Ou.contour,hoverinfo:JY({},YDe.hoverinfo),showlegend:JY({},YDe.showlegend,{dflt:!1})}),\"calc\",\"nested\");TP.x.editType=TP.y.editType=TP.z.editType=TP.value.editType=\"calc+clearAxisTypes\"});var $De=fe((Twr,ZDe)=>{\"use strict\";var LUt=Xt(),PUt=QY(),FUt=YY().supplyIsoDefaults,DUt=UY().opacityscaleDefaults;ZDe.exports=function(t,r,n,a){function i(o,s){return LUt.coerce(t,r,PUt,o,s)}FUt(t,r,n,a,i),DUt(t,r,a,i)}});var eRe=fe((Awr,QDe)=>{\"use strict\";var RUt=xf().gl_mesh3d,IUt=Sg().parseColorScale,zUt=Xt().isArrayOrTypedArray,qUt=Mg(),BUt=js().extractOpts,KDe=r5(),eX=wP().findNearestOnAxis,OUt=wP().generateIsoMeshes;function JDe(e,t,r){this.scene=e,this.uid=r,this.mesh=t,this.name=\"\",this.data=null,this.showContour=!1}var tX=JDe.prototype;tX.handlePick=function(e){if(e.object===this.mesh){var t=e.data.index,r=this.data._meshX[t],n=this.data._meshY[t],a=this.data._meshZ[t],i=this.data._Ys.length,o=this.data._Zs.length,s=eX(r,this.data._Xs).id,l=eX(n,this.data._Ys).id,u=eX(a,this.data._Zs).id,c=e.index=u+o*l+o*i*s;e.traceCoordinate=[this.data._meshX[c],this.data._meshY[c],this.data._meshZ[c],this.data._value[c]];var f=this.data.hovertext||this.data.text;return zUt(f)&&f[c]!==void 0?e.textLabel=f[c]:f&&(e.textLabel=f),!0}};tX.update=function(e){var t=this.scene,r=t.fullSceneLayout;this.data=OUt(e);function n(l,u,c,f){return u.map(function(v){return l.d2l(v,0,f)*c})}var a=KDe(n(r.xaxis,e._meshX,t.dataScale[0],e.xcalendar),n(r.yaxis,e._meshY,t.dataScale[1],e.ycalendar),n(r.zaxis,e._meshZ,t.dataScale[2],e.zcalendar)),i=KDe(e._meshI,e._meshJ,e._meshK),o={positions:a,cells:i,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,opacityscale:e.opacityscale,contourEnable:e.contour.show,contourColor:qUt(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading},s=BUt(e);o.vertexIntensity=e._meshIntensity,o.vertexIntensityBounds=[s.min,s.max],o.colormap=IUt(e),this.mesh.update(o)};tX.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};function NUt(e,t){var r=e.glplot.gl,n=RUt({gl:r}),a=new JDe(e,n,t.uid);return n._trace=a,a.update(t),e.glplot.add(n),a}QDe.exports=NUt});var rRe=fe((Mwr,tRe)=>{\"use strict\";tRe.exports={attributes:QY(),supplyDefaults:$De(),calc:$Y(),colorbar:{min:\"cmin\",max:\"cmax\"},plot:eRe(),moduleType:\"trace\",name:\"volume\",basePlotModule:gy(),categories:[\"gl3d\",\"showLegend\"],meta:{}}});var iRe=fe((Swr,nRe)=>{\"use strict\";nRe.exports=rRe()});var sRe=fe((Ewr,oRe)=>{\"use strict\";var UUt=bi(),aRe=Xt(),VUt=Vc(),GUt=e5();oRe.exports=function(t,r,n,a){function i(c,f){return aRe.coerce(t,r,GUt,c,f)}function o(c){var f=c.map(function(v){var g=i(v);return g&&aRe.isArrayOrTypedArray(g)?g:null});return f.every(function(v){return v&&v.length===f[0].length})&&f}var s=o([\"x\",\"y\",\"z\"]);if(!s){r.visible=!1;return}if(o([\"i\",\"j\",\"k\"]),r.i&&(!r.j||!r.k)||r.j&&(!r.k||!r.i)||r.k&&(!r.i||!r.j)){r.visible=!1;return}var l=UUt.getComponentMethod(\"calendars\",\"handleTraceDefaults\");l(t,r,[\"x\",\"y\",\"z\"],a),[\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lighting.vertexnormalsepsilon\",\"lighting.facenormalsepsilon\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"flatshading\",\"alphahull\",\"delaunayaxis\",\"opacity\"].forEach(function(c){i(c)});var u=i(\"contour.show\");u&&(i(\"contour.color\"),i(\"contour.width\")),\"intensity\"in t?(i(\"intensity\"),i(\"intensitymode\"),VUt(t,r,a,i,{prefix:\"\",cLetter:\"c\"})):(r.showscale=!1,\"facecolor\"in t?i(\"facecolor\"):\"vertexcolor\"in t?i(\"vertexcolor\"):i(\"color\",n)),i(\"text\"),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\"),i(\"xhoverformat\"),i(\"yhoverformat\"),i(\"zhoverformat\"),r._length=null}});var uRe=fe((kwr,lRe)=>{\"use strict\";var 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t=[],r=e.length,n=0;n<r;n++)t[n]=Math.round(e[n]);return t}function JUt(e,t){for(var r=[\"x\",\"y\",\"z\"].indexOf(e),n=[],a=t.length,i=0;i<a;i++)n[i]=[t[i][(r+1)%3],t[i][(r+2)%3]];return WUt(n)}function iX(e,t){for(var r=e.length,n=0;n<r;n++)if(e[n]<=-.5||e[n]>=t-.5)return!1;return!0}oX.update=function(e){var t=this.scene,r=t.fullSceneLayout;this.data=e;var n=e.x.length,a=cRe(rX(r.xaxis,e.x,t.dataScale[0],e.xcalendar),rX(r.yaxis,e.y,t.dataScale[1],e.ycalendar),rX(r.zaxis,e.z,t.dataScale[2],e.zcalendar)),i;if(e.i&&e.j&&e.k){if(e.i.length!==e.j.length||e.j.length!==e.k.length||!iX(e.i,n)||!iX(e.j,n)||!iX(e.k,n))return;i=cRe(nX(e.i),nX(e.j),nX(e.k))}else e.alphahull===0?i=XUt(a):e.alphahull>0?i=YUt(e.alphahull,a):i=JUt(e.delaunayaxis,a);var o={positions:a,cells:i,lightPosition:[e.lightposition.x,e.lightposition.y,e.lightposition.z],ambient:e.lighting.ambient,diffuse:e.lighting.diffuse,specular:e.lighting.specular,roughness:e.lighting.roughness,fresnel:e.lighting.fresnel,vertexNormalsEpsilon:e.lighting.vertexnormalsepsilon,faceNormalsEpsilon:e.lighting.facenormalsepsilon,opacity:e.opacity,contourEnable:e.contour.show,contourColor:aX(e.contour.color).slice(0,3),contourWidth:e.contour.width,useFacetNormals:e.flatshading};if(e.intensity){var s=KUt(e);this.color=\"#fff\";var l=e.intensitymode;o[l+\"Intensity\"]=e.intensity,o[l+\"IntensityBounds\"]=[s.min,s.max],o.colormap=ZUt(e)}else e.vertexcolor?(this.color=e.vertexcolor[0],o.vertexColors=fRe(e.vertexcolor)):e.facecolor?(this.color=e.facecolor[0],o.cellColors=fRe(e.facecolor)):(this.color=e.color,o.meshColor=aX(e.color));this.mesh.update(o)};oX.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};function QUt(e,t){var r=e.glplot.gl,n=jUt({gl:r}),a=new hRe(e,n,t.uid);return n._trace=a,a.update(t),e.glplot.add(n),a}dRe.exports=QUt});var gRe=fe((Lwr,pRe)=>{\"use strict\";pRe.exports={attributes:e5(),supplyDefaults:sRe(),calc:uRe(),colorbar:{min:\"cmin\",max:\"cmax\"},plot:vRe(),moduleType:\"trace\",name:\"mesh3d\",basePlotModule:gy(),categories:[\"gl3d\",\"showLegend\"],meta:{}}});var yRe=fe((Pwr,mRe)=>{\"use strict\";mRe.exports=gRe()});var lX=fe((Fwr,_Re)=>{\"use strict\";var eVt=_s(),n5=pl().axisHoverFormat,{hovertemplateAttrs:tVt,templatefallbackAttrs:rVt}=Oo(),nVt=e5(),xRe=Ho(),sX=ta().extendFlat,AP={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},sizemode:{valType:\"enumerated\",values:[\"scaled\",\"absolute\",\"raw\"],editType:\"calc\",dflt:\"scaled\"},sizeref:{valType:\"number\",editType:\"calc\",min:0},anchor:{valType:\"enumerated\",editType:\"calc\",values:[\"tip\",\"tail\",\"cm\",\"center\"],dflt:\"cm\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:tVt({editType:\"calc\"},{keys:[\"norm\"]}),hovertemplatefallback:rVt({editType:\"calc\"}),uhoverformat:n5(\"u\",1),vhoverformat:n5(\"v\",1),whoverformat:n5(\"w\",1),xhoverformat:n5(\"x\"),yhoverformat:n5(\"y\"),zhoverformat:n5(\"z\"),showlegend:sX({},xRe.showlegend,{dflt:!1})};sX(AP,eVt(\"\",{colorAttr:\"u/v/w 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v=i(\"sizemode\");i(\"sizeref\",v===\"raw\"?1:.5),i(\"anchor\"),i(\"lighting.ambient\"),i(\"lighting.diffuse\"),i(\"lighting.specular\"),i(\"lighting.roughness\"),i(\"lighting.fresnel\"),i(\"lightposition.x\"),i(\"lightposition.y\"),i(\"lightposition.z\"),oVt(t,r,a,i,{prefix:\"\",cLetter:\"c\"}),i(\"text\"),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\"),i(\"uhoverformat\"),i(\"vhoverformat\"),i(\"whoverformat\"),i(\"xhoverformat\"),i(\"yhoverformat\"),i(\"zhoverformat\"),r._length=null}});var ARe=fe((Rwr,TRe)=>{\"use strict\";var lVt=Mh();TRe.exports=function(t,r){for(var n=r.u,a=r.v,i=r.w,o=Math.min(r.x.length,r.y.length,r.z.length,n.length,a.length,i.length),s=-1/0,l=1/0,u=0;u<o;u++){var c=n[u],f=a[u],v=i[u],g=Math.sqrt(c*c+f*f+v*v);s=Math.max(s,g),l=Math.min(l,g)}r._len=o,r._normMax=s,lVt(t,r,{vals:[l,s],containerStr:\"\",cLetter:\"c\"})}});var CRe=fe((Iwr,kRe)=>{\"use strict\";var uVt=xf().gl_cone3d,cVt=xf().gl_cone3d.createConeMesh,fVt=Xt().simpleMap,hVt=Sg().parseColorScale,dVt=js().extractOpts,vVt=Xt().isArrayOrTypedArray,MRe=r5();function SRe(e,t){this.scene=e,this.uid=t,this.mesh=null,this.data=null}var uX=SRe.prototype;uX.handlePick=function(e){if(e.object===this.mesh){var t=e.index=e.data.index,r=this.data.x[t],n=this.data.y[t],a=this.data.z[t],i=this.data.u[t],o=this.data.v[t],s=this.data.w[t];e.traceCoordinate=[r,n,a,i,o,s,Math.sqrt(i*i+o*o+s*s)];var l=this.data.hovertext||this.data.text;return vVt(l)&&l[t]!==void 0?e.textLabel=l[t]:l&&(e.textLabel=l),!0}};var pVt={xaxis:0,yaxis:1,zaxis:2},gVt={tip:1,tail:0,cm:.25,center:.5},mVt={tip:1,tail:1,cm:.75,center:.5};function ERe(e,t){var r=e.fullSceneLayout,n=e.dataScale,a={};function i(c,f){var v=r[f],g=n[pVt[f]];return fVt(c,function(p){return v.d2l(p)*g})}a.vectors=MRe(i(t.u,\"xaxis\"),i(t.v,\"yaxis\"),i(t.w,\"zaxis\"),t._len),a.positions=MRe(i(t.x,\"xaxis\"),i(t.y,\"yaxis\"),i(t.z,\"zaxis\"),t._len);var o=dVt(t);a.colormap=hVt(t),a.vertexIntensityBounds=[o.min/t._normMax,o.max/t._normMax],a.coneOffset=gVt[t.anchor];var s=t.sizemode;s===\"scaled\"?a.coneSize=t.sizeref||.5:s===\"absolute\"?a.coneSize=t.sizeref&&t._normMax?t.sizeref/t._normMax:.5:s===\"raw\"&&(a.coneSize=t.sizeref),a.coneSizemode=s;var l=uVt(a),u=t.lightposition;return l.lightPosition=[u.x,u.y,u.z],l.ambient=t.lighting.ambient,l.diffuse=t.lighting.diffuse,l.specular=t.lighting.specular,l.roughness=t.lighting.roughness,l.fresnel=t.lighting.fresnel,l.opacity=t.opacity,t._pad=mVt[t.anchor]*l.vectorScale*l.coneScale*t._normMax,l}uX.update=function(e){this.data=e;var t=ERe(this.scene,e);this.mesh.update(t)};uX.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};function yVt(e,t){var r=e.glplot.gl,n=ERe(e,t),a=cVt(r,n),i=new SRe(e,t.uid);return i.mesh=a,i.data=t,a._trace=i,e.glplot.add(a),i}kRe.exports=yVt});var PRe=fe((zwr,LRe)=>{\"use 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xVt=_s(),i5=pl().axisHoverFormat,{hovertemplateAttrs:_Vt,templatefallbackAttrs:bVt}=Oo(),wVt=e5(),RRe=Ho(),cX=ta().extendFlat,MP={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},starts:{x:{valType:\"data_array\",editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"},maxdisplayed:{valType:\"integer\",min:0,dflt:1e3,editType:\"calc\"},sizeref:{valType:\"number\",editType:\"calc\",min:0,dflt:1},text:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertemplate:_Vt({editType:\"calc\"},{keys:[\"tubex\",\"tubey\",\"tubez\",\"tubeu\",\"tubev\",\"tubew\",\"norm\",\"divergence\"]}),hovertemplatefallback:bVt({editType:\"calc\"}),uhoverformat:i5(\"u\",1),vhoverformat:i5(\"v\",1),whoverformat:i5(\"w\",1),xhoverformat:i5(\"x\"),yhoverformat:i5(\"y\"),zhoverformat:i5(\"z\"),showlegend:cX({},RRe.showlegend,{dflt:!1})};cX(MP,xVt(\"\",{colorAttr:\"u/v/w 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dX=VRe.prototype;dX.handlePick=function(e){var t=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(o,s){var l=t[s],u=r[URe[s]];return l.l2c(o)/u}if(e.object===this.mesh){var a=e.data.position,i=e.data.velocity;return e.traceCoordinate=[n(a[0],\"xaxis\"),n(a[1],\"yaxis\"),n(a[2],\"zaxis\"),n(i[0],\"xaxis\"),n(i[1],\"yaxis\"),n(i[2],\"zaxis\"),e.data.intensity*this.data._normMax,e.data.divergence],e.textLabel=this.data.hovertext||this.data.text,!0}};function ORe(e){var t=e.length,r;return t>2?r=e.slice(1,t-1):t===2?r=[(e[0]+e[1])/2]:r=e,r}function hX(e){var t=e.length;return t===1?[.5,.5]:[e[1]-e[0],e[t-1]-e[t-2]]}function GRe(e,t){var r=e.fullSceneLayout,n=e.dataScale,a=t._len,i={};function o(I,R){var B=r[R],O=n[URe[R]];return kVt.simpleMap(I,function(V){return B.d2l(V)*O})}if(i.vectors=BRe(o(t._u,\"xaxis\"),o(t._v,\"yaxis\"),o(t._w,\"zaxis\"),a),!a)return{positions:[],cells:[]};var s=o(t._Xs,\"xaxis\"),l=o(t._Ys,\"yaxis\"),u=o(t._Zs,\"zaxis\");i.meshgrid=[s,l,u],i.gridFill=t._gridFill;var c=t._slen;if(c)i.startingPositions=BRe(o(t._startsX,\"xaxis\"),o(t._startsY,\"yaxis\"),o(t._startsZ,\"zaxis\"));else{for(var f=l[0],v=ORe(s),g=ORe(u),p=new Array(v.length*g.length),x=0,_=0;_<v.length;_++)for(var y=0;y<g.length;y++)p[x++]=[v[_],f,g[y]];i.startingPositions=p}i.colormap=CVt(t),i.tubeSize=t.sizeref,i.maxLength=t.maxdisplayed;var C=o(t._xbnds,\"xaxis\"),k=o(t._ybnds,\"yaxis\"),S=o(t._zbnds,\"zaxis\"),L=hX(s),w=hX(l),A=hX(u),T=[[C[0]-L[0],k[0]-w[0],S[0]-A[0]],[C[1]+L[1],k[1]+w[1],S[1]+A[1]]],m=NRe(i,T),F=LVt(t);m.vertexIntensityBounds=[F.min/t._normMax,F.max/t._normMax];var E=t.lightposition;return m.lightPosition=[E.x,E.y,E.z],m.ambient=t.lighting.ambient,m.diffuse=t.lighting.diffuse,m.specular=t.lighting.specular,m.roughness=t.lighting.roughness,m.fresnel=t.lighting.fresnel,m.opacity=t.opacity,t._pad=m.tubeScale*t.sizeref*2,m}dX.update=function(e){this.data=e;var t=GRe(this.scene,e);this.mesh.update(t)};dX.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};function PVt(e,t){var r=e.glplot.gl,n=GRe(e,t),a=EVt(r,n),i=new VRe(e,t.uid);return i.mesh=a,i.data=t,a._trace=i,e.glplot.add(a),i}HRe.exports=PVt});var YRe=fe((Uwr,WRe)=>{\"use strict\";WRe.exports={moduleType:\"trace\",name:\"streamtube\",basePlotModule:gy(),categories:[\"gl3d\",\"showLegend\"],attributes:fX(),supplyDefaults:qRe(),colorbar:{min:\"cmin\",max:\"cmax\"},calc:_P().calc,plot:jRe(),eventData:function(e,t){return e.tubex=e.x,e.tubey=e.y,e.tubez=e.z,e.tubeu=t.traceCoordinate[3],e.tubev=t.traceCoordinate[4],e.tubew=t.traceCoordinate[5],e.norm=t.traceCoordinate[6],e.divergence=t.traceCoordinate[7],delete e.x,delete e.y,delete e.z,e},meta:{}}});var ZRe=fe((Vwr,XRe)=>{\"use strict\";XRe.exports=YRe()});var a5=fe((Gwr,eIe)=>{\"use 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lt=Be/2,Gt=Ne/2,Zt=x(lt),Nt=o(lt),Kt=x(Gt),ur=o(Gt),Sr=o(Ne),Br=O(Sr),ln=Kt/(ur+A*Nt*Br),wn=ln*ln,In=O(2/(1+wn)),gi=A*ur+(Nt+Zt)*Br,Bi=A*ur+(Nt-Zt)*Br,mi=gi/Bi,ia=O(mi),ua=ia-1/ia,lo=ia+1/ia,Bo=In*ua-2*u(ia)-Me,Ua=In*ln*lo-2*a(ln)-Ce,ls=Kt&&w*Br*Zt*wn/Kt,cl=(A*Nt*ur+Br)/(2*(ur+A*Nt*Br)*(ur+A*Nt*Br)*Br),Eu=-.5*ln*In*In*In,el=Eu*ls,yc=Eu*cl,wi=(wi=2*ur+A*Br*(Nt-Zt))*wi*ia,Di=(A*Nt*ur*Br+Sr)/wi,fl=-(A*Zt*Kt)/(Br*wi),Fc=ua*el-2*Di/ia+In*(Di+Di/mi),du=ua*yc-2*fl/ia+In*(fl+fl/mi),ms=ln*lo*el-2*ls/(1+wn)+In*lo*ls+In*ln*(Di-Di/mi),ff=ln*lo*yc-2*cl/(1+wn)+In*lo*cl+In*ln*(fl-fl/mi),ku=du*ms-ff*Fc;if(!ku)break;var hl=(Ua*du-Bo*ff)/ku,mh=(Bo*ms-Ua*Fc)/ku;Be-=hl,Ne=c(-S,f(S,Ne-mh))}while((n(hl)>y||n(mh)>y)&&--Qe>0);return n(n(Ne)-S)<y?[0,Ne]:Qe&&[Be,Ne]};function Vi(){return t.geoProjection(Ii).scale(62.5271)}var ra=o(35*E);function ha(Me,Ce){var Be=_(Ce/2);return[Me*ra*O(1-Be*Be),(1+ra)*Be]}ha.invert=function(Me,Ce){var Be=Ce/(1+ra);return[Me&&Me/(ra*O(1-Be*Be)),2*a(Be)]};function va(){return t.geoProjection(ha).scale(137.152)}function Xi(Me,Ce){var Be=Ce/2,Ne=o(Be);return[2*Me/T*o(Ce)*Ne*Ne,T*_(Be)]}Xi.invert=function(Me,Ce){var Be=a(Ce/T),Ne=o(Be),Qe=2*Be;return[Me*T/2/(o(Qe)*Ne*Ne),Qe]};function oa(){return t.geoProjection(Xi).scale(135.264)}function Ma(Me){var Ce=1-Me,Be=Je(k,0)[0]-Je(-k,0)[0],Ne=Je(0,S)[1]-Je(0,-S)[1],Qe=O(2*Ne/Be);function Je(Zt,Nt){var Kt=o(Nt),ur=x(Nt);return[Kt/(Ce+Me*Kt)*Zt,Ce*Nt+Me*ur]}function lt(Zt,Nt){var Kt=Je(Zt,Nt);return[Kt[0]*Qe,Kt[1]/Qe]}function Gt(Zt){return lt(0,Zt)[1]}return lt.invert=function(Zt,Nt){var Kt=it(Gt,Nt),ur=Zt/Qe*(Me+Ce/o(Kt));return[ur,Kt]},lt}function Ga(){var Me=.5,Ce=t.geoProjectionMutator(Ma),Be=Ce(Me);return Be.alpha=function(Ne){return arguments.length?Ce(Me=+Ne):Me},Be.scale(168.725)}function ss(Me){return[Me[0]/2,R(_(Me[1]/2*E))*F]}function mo(Me){return[Me[0]*2,2*a(x(Me[1]*E))*F]}function sl(Me){Me==null&&(Me=t.geoOrthographic);var Ce=Me(),Be=t.geoEquirectangular().scale(F).precision(0).clipAngle(null).translate([0,0]);function Ne(Je){return Ce(ss(Je))}Ce.invert&&(Ne.invert=function(Je){return mo(Ce.invert(Je))}),Ne.stream=function(Je){var lt=Ce.stream(Je),Gt=Be.stream({point:function(Zt,Nt){lt.point(Zt/2,R(_(-Nt/2*E))*F)},lineStart:function(){lt.lineStart()},lineEnd:function(){lt.lineEnd()},polygonStart:function(){lt.polygonStart()},polygonEnd:function(){lt.polygonEnd()}});return Gt.sphere=lt.sphere,Gt};function Qe(Je){Ne[Je]=function(){return arguments.length?(Ce[Je].apply(Ce,arguments),Ne):Ce[Je]()}}return Ne.rotate=function(Je){return arguments.length?(Be.rotate(Je),Ne):Be.rotate()},Ne.center=function(Je){return arguments.length?(Ce.center(ss(Je)),Ne):mo(Ce.center())},Qe(\"angle\"),Qe(\"clipAngle\"),Qe(\"clipExtent\"),Qe(\"fitExtent\"),Qe(\"fitHeight\"),Qe(\"fitSize\"),Qe(\"fitWidth\"),Qe(\"scale\"),Qe(\"translate\"),Qe(\"precision\"),Ne.scale(249.5)}function Eo(Me,Ce){var Be=2*k/Ce,Ne=Me*Me;function Qe(Je,lt){var Gt=t.geoAzimuthalEquidistantRaw(Je,lt),Zt=Gt[0],Nt=Gt[1],Kt=Zt*Zt+Nt*Nt;if(Kt>Ne){var ur=O(Kt),Sr=i(Nt,Zt),Br=Be*g(Sr/Be),ln=Sr-Br,wn=Me*o(ln),In=(Me*x(ln)-ln*x(wn))/(S-wn),gi=ao(ln,In),Bi=(k-Me)/Da(gi,wn,k);Zt=ur;var mi=50,ia;do Zt-=ia=(Me+Da(gi,wn,Zt)*Bi-ur)/(gi(Zt)*Bi);while(n(ia)>y&&--mi>0);Nt=ln*x(Zt),Zt<S&&(Nt-=In*(Zt-S));var ua=x(Br),lo=o(Br);Gt[0]=Zt*lo-Nt*ua,Gt[1]=Zt*ua+Nt*lo}return Gt}return Qe.invert=function(Je,lt){var Gt=Je*Je+lt*lt;if(Gt>Ne){var Zt=O(Gt),Nt=i(lt,Je),Kt=Be*g(Nt/Be),ur=Nt-Kt;Je=Zt*o(ur),lt=Zt*x(ur);for(var Sr=Je-S,Br=x(Je),ln=lt/Br,wn=Je<S?1/0:0,In=10;;){var gi=Me*x(ln),Bi=Me*o(ln),mi=x(Bi),ia=S-Bi,ua=(gi-ln*mi)/ia,lo=ao(ln,ua);if(n(wn)<C||!--In)break;ln-=wn=(ln*Br-ua*Sr-lt)/(Br-Sr*2*(ia*(Bi+ln*gi*o(Bi)-mi)-gi*(gi-ln*mi))/(ia*ia))}Zt=Me+Da(lo,Bi,Je)*(k-Me)/Da(lo,Bi,k),Nt=Kt+ln,Je=Zt*o(Nt),lt=Zt*x(Nt)}return t.geoAzimuthalEquidistantRaw.invert(Je,lt)},Qe}function ao(Me,Ce){return function(Be){var Ne=Me*o(Be);return Be<S&&(Ne-=Ce),O(1+Ne*Ne)}}function Da(Me,Ce,Be){for(var Ne=50,Qe=(Be-Ce)/Ne,Je=Me(Ce)+Me(Be),lt=1,Gt=Ce;lt<Ne;++lt)Je+=2*Me(Gt+=Qe);return Je*.5*Qe}function bl(){var Me=6,Ce=30*E,Be=o(Ce),Ne=x(Ce),Qe=t.geoProjectionMutator(Eo),Je=Qe(Ce,Me),lt=Je.stream,Gt=.01,Zt=-o(Gt*E),Nt=x(Gt*E);return Je.radius=function(Kt){return arguments.length?(Be=o(Ce=Kt*E),Ne=x(Ce),Qe(Ce,Me)):Ce*F},Je.lobes=function(Kt){return arguments.length?Qe(Ce,Me=+Kt):Me},Je.stream=function(Kt){var ur=Je.rotate(),Sr=lt(Kt),Br=(Je.rotate([0,0]),lt(Kt));return Je.rotate(ur),Sr.sphere=function(){Br.polygonStart(),Br.lineStart();for(var ln=0,wn=2*k/Me,In=0;ln<Me;++ln,In-=wn)Br.point(i(Nt*o(In),Zt)*F,R(Nt*x(In))*F),Br.point(i(Ne*o(In-wn/2),Be)*F,R(Ne*x(In-wn/2))*F);Br.lineEnd(),Br.polygonEnd()},Sr},Je.rotate([90,-40]).scale(91.7095).clipAngle(180-.001)}function bo(Me,Ce,Be,Ne,Qe,Je,lt,Gt){arguments.length<8&&(Gt=0);function Zt(Nt,Kt){if(!Kt)return[Me*Nt/k,0];var ur=Kt*Kt,Sr=Me+ur*(Ce+ur*(Be+ur*Ne)),Br=Kt*(Qe-1+ur*(Je-Gt+ur*lt)),ln=(Sr*Sr+Br*Br)/(2*Br),wn=Nt*R(Sr/ln)/k;return[ln*x(wn),Kt*(1+ur*Gt)+ln*(1-o(wn))]}return Zt.invert=function(Nt,Kt){var ur=k*Nt/Me,Sr=Kt,Br,ln,wn=50;do{var In=Sr*Sr,gi=Me+In*(Ce+In*(Be+In*Ne)),Bi=Sr*(Qe-1+In*(Je-Gt+In*lt)),mi=gi*gi+Bi*Bi,ia=2*Bi,ua=mi/ia,lo=ua*ua,Bo=R(gi/ua)/k,Ua=ur*Bo,ls=gi*gi,cl=(2*Ce+In*(4*Be+In*6*Ne))*Sr,Eu=Qe+In*(3*Je+In*5*lt),el=2*(gi*cl+Bi*(Eu-1)),yc=2*(Eu-1),wi=(el*ia-mi*yc)/(ia*ia),Di=o(Ua),fl=x(Ua),Fc=ua*Di,du=ua*fl,ms=ur/k*(1/O(1-ls/lo))*(cl*ua-gi*wi)/lo,ff=du-Nt,ku=Sr*(1+In*Gt)+ua-Fc-Kt,hl=wi*fl+Fc*ms,mh=Fc*Bo,W=1+wi-(wi*Di-du*ms),te=du*Bo,ve=hl*te-W*mh;if(!ve)break;ur-=Br=(ku*hl-ff*W)/ve,Sr-=ln=(ff*te-ku*mh)/ve}while((n(Br)>y||n(ln)>y)&&--wn>0);return[ur,Sr]},Zt}var Zs=bo(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);function su(){return t.geoProjection(Zs).scale(149.995)}var fc=bo(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);function Af(){return t.geoProjection(fc).scale(153.93)}var ut=bo(5/6*k,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Jt(){return t.geoProjection(ut).scale(130.945)}function er(Me,Ce){var Be=Me*Me,Ne=Ce*Ce;return[Me*(1-.162388*Ne)*(.87-952426e-9*Be*Be),Ce*(1+Ne/12)]}er.invert=function(Me,Ce){var Be=Me,Ne=Ce,Qe=50,Je;do{var lt=Ne*Ne;Ne-=Je=(Ne*(1+lt/12)-Ce)/(1+lt/4)}while(n(Je)>y&&--Qe>0);Qe=50,Me/=1-.162388*lt;do{var Gt=(Gt=Be*Be)*Gt;Be-=Je=(Be*(.87-952426e-9*Gt)-Me)/(.87-.00476213*Gt)}while(n(Je)>y&&--Qe>0);return[Be,Ne]};function wr(){return t.geoProjection(er).scale(131.747)}var nn=bo(2.6516,-.76534,.19123,-.047094,1.36289,-.13965,.031762);function Dr(){return t.geoProjection(nn).scale(131.087)}function bn(Me){var Ce=Me(S,0)[0]-Me(-S,0)[0];function Be(Ne,Qe){var Je=Ne>0?-.5:.5,lt=Me(Ne+Je*k,Qe);return lt[0]-=Je*Ce,lt}return Me.invert&&(Be.invert=function(Ne,Qe){var Je=Ne>0?-.5:.5,lt=Me.invert(Ne+Je*Ce,Qe),Gt=lt[0]-Je*k;return Gt<-k?Gt+=2*k:Gt>k&&(Gt-=2*k),lt[0]=Gt,lt}),Be}function Yr(Me,Ce){var Be=p(Me),Ne=p(Ce),Qe=o(Ce),Je=o(Me)*Qe,lt=x(Me)*Qe,Gt=x(Ne*Ce);Me=n(i(lt,Gt)),Ce=R(Je),n(Me-S)>y&&(Me%=S);var Zt=Tn(Me>k/4?S-Me:Me,Ce);return Me>k/4&&(Gt=Zt[0],Zt[0]=-Zt[1],Zt[1]=-Gt),Zt[0]*=Be,Zt[1]*=-Ne,Zt}Yr.invert=function(Me,Ce){n(Me)>1&&(Me=p(Me)*2-Me),n(Ce)>1&&(Ce=p(Ce)*2-Ce);var Be=p(Me),Ne=p(Ce),Qe=-Be*Me,Je=-Ne*Ce,lt=Je/Qe<1,Gt=Kn(lt?Je:Qe,lt?Qe:Je),Zt=Gt[0],Nt=Gt[1],Kt=o(Nt);return lt&&(Zt=-S-Zt),[Be*(i(x(Zt)*Kt,-x(Nt))+k),Ne*R(o(Zt)*Kt)]};function Tn(Me,Ce){if(Ce===S)return[0,0];var Be=x(Ce),Ne=Be*Be,Qe=Ne*Ne,Je=1+Qe,lt=1+3*Qe,Gt=1-Qe,Zt=R(1/O(Je)),Nt=Gt+Ne*Je*Zt,Kt=(1-Be)/Nt,ur=O(Kt),Sr=Kt*Je,Br=O(Sr),ln=ur*Gt,wn,In;if(Me===0)return[0,-(ln+Ne*Br)];var gi=o(Ce),Bi=1/gi,mi=2*Be*gi,ia=(-3*Ne+Zt*lt)*mi,ua=(-Nt*gi-(1-Be)*ia)/(Nt*Nt),lo=.5*ua/ur,Bo=Gt*lo-2*Ne*ur*mi,Ua=Ne*Je*ua+Kt*lt*mi,ls=-Bi*mi,cl=-Bi*Ua,Eu=-2*Bi*Bo,el=4*Me/k,yc;if(Me>.222*k||Ce<k/4&&Me>.175*k){if(wn=(ln+Ne*O(Sr*(1+Qe)-ln*ln))/(1+Qe),Me>k/4)return[wn,wn];var wi=wn,Di=.5*wn;wn=.5*(Di+wi),In=50;do{var fl=O(Sr-wn*wn),Fc=wn*(Eu+ls*fl)+cl*R(wn/Br)-el;if(!Fc)break;Fc<0?Di=wn:wi=wn,wn=.5*(Di+wi)}while(n(wi-Di)>y&&--In>0)}else{wn=y,In=25;do{var du=wn*wn,ms=O(Sr-du),ff=Eu+ls*ms,ku=wn*ff+cl*R(wn/Br)-el,hl=ff+(cl-ls*du)/ms;wn-=yc=ms?ku/hl:0}while(n(yc)>y&&--In>0)}return[wn,-ln-Ne*O(Sr-wn*wn)]}function Kn(Me,Ce){for(var Be=0,Ne=1,Qe=.5,Je=50;;){var lt=Qe*Qe,Gt=O(Qe),Zt=R(1/O(1+lt)),Nt=1-lt+Qe*(1+lt)*Zt,Kt=(1-Gt)/Nt,ur=O(Kt),Sr=Kt*(1+lt),Br=ur*(1-lt),ln=Sr-Me*Me,wn=O(ln),In=Ce+Br+Qe*wn;if(n(Ne-Be)<C||--Je===0||In===0)break;In>0?Be=Qe:Ne=Qe,Qe=.5*(Be+Ne)}if(!Je)return null;var gi=R(Gt),Bi=o(gi),mi=1/Bi,ia=2*Gt*Bi,ua=(-3*Qe+Zt*(1+3*lt))*ia,lo=(-Nt*Bi-(1-Gt)*ua)/(Nt*Nt),Bo=.5*lo/ur,Ua=(1-lt)*Bo-2*Qe*ur*ia,ls=-2*mi*Ua,cl=-mi*ia,Eu=-mi*(Qe*(1+lt)*lo+Kt*(1+3*lt)*ia);return[k/4*(Me*(ls+cl*wn)+Eu*R(Me/O(Sr))),gi]}function Qn(){return t.geoProjection(bn(Yr)).scale(239.75)}function Ai(Me,Ce,Be){var Ne,Qe,Je;return Me?(Ne=Ji(Me,Be),Ce?(Qe=Ji(Ce,1-Be),Je=Qe[1]*Qe[1]+Be*Ne[0]*Ne[0]*Qe[0]*Qe[0],[[Ne[0]*Qe[2]/Je,Ne[1]*Ne[2]*Qe[0]*Qe[1]/Je],[Ne[1]*Qe[1]/Je,-Ne[0]*Ne[2]*Qe[0]*Qe[2]/Je],[Ne[2]*Qe[1]*Qe[2]/Je,-Be*Ne[0]*Ne[1]*Qe[0]/Je]]):[[Ne[0],0],[Ne[1],0],[Ne[2],0]]):(Qe=Ji(Ce,1-Be),[[0,Qe[0]/Qe[1]],[1/Qe[1],0],[Qe[2]/Qe[1],0]])}function Ji(Me,Ce){var Be,Ne,Qe,Je,lt;if(Ce<y)return Je=x(Me),Ne=o(Me),Be=Ce*(Me-Je*Ne)/4,[Je-Be*Ne,Ne+Be*Je,1-Ce*Je*Je/2,Me-Be];if(Ce>=1-y)return Be=(1-Ce)/4,Ne=q(Me),Je=V(Me),Qe=1/Ne,lt=Ne*j(Me),[Je+Be*(lt-Me)/(Ne*Ne),Qe-Be*Je*Qe*(lt-Me),Qe+Be*Je*Qe*(lt+Me),2*a(s(Me))-S+Be*(lt-Me)/Ne];var Gt=[1,0,0,0,0,0,0,0,0],Zt=[O(Ce),0,0,0,0,0,0,0,0],Nt=0;for(Ne=O(1-Ce),lt=1;n(Zt[Nt]/Gt[Nt])>y&&Nt<8;)Be=Gt[Nt++],Zt[Nt]=(Be-Ne)/2,Gt[Nt]=(Be+Ne)/2,Ne=O(Be*Ne),lt*=2;Qe=lt*Gt[Nt]*Me;do Je=Zt[Nt]*x(Ne=Qe)/Gt[Nt],Qe=(R(Je)+Qe)/2;while(--Nt);return[x(Qe),Je=o(Qe),Je/o(Qe-Ne),Qe]}function Ra(Me,Ce,Be){var Ne=n(Me),Qe=n(Ce),Je=j(Qe);if(Ne){var lt=1/x(Ne),Gt=1/(_(Ne)*_(Ne)),Zt=-(Gt+Be*(Je*Je*lt*lt)-1+Be),Nt=(Be-1)*Gt,Kt=(-Zt+O(Zt*Zt-4*Nt))/2;return[ii(a(1/O(Kt)),Be)*p(Me),ii(a(O((Kt/Gt-1)/Be)),1-Be)*p(Ce)]}return[0,ii(a(Je),1-Be)*p(Ce)]}function ii(Me,Ce){if(!Ce)return Me;if(Ce===1)return u(_(Me/2+L));for(var Be=1,Ne=O(1-Ce),Qe=O(Ce),Je=0;n(Qe)>y;Je++){if(Me%k){var lt=a(Ne*_(Me)/Be);lt<0&&(lt+=k),Me+=lt+~~(Me/k)*k}else Me+=Me;Qe=(Be+Ne)/2,Ne=O(Be*Ne),Qe=((Be=Qe)-Ne)/2}return Me/(v(2,Je)*Be)}function Za(Me,Ce){var Be=(A-1)/(A+1),Ne=O(1-Be*Be),Qe=ii(S,Ne*Ne),Je=-1,lt=u(_(k/4+n(Ce)/2)),Gt=s(Je*lt)/O(Be),Zt=ja(Gt*o(Je*Me),Gt*x(Je*Me)),Nt=Ra(Zt[0],Zt[1],Ne*Ne);return[-Nt[1],(Ce>=0?1:-1)*(.5*Qe-Nt[0])]}function ja(Me,Ce){var Be=Me*Me,Ne=Ce+1,Qe=1-Be-Ce*Ce;return[.5*((Me>=0?S:-S)-i(Qe,2*Me)),-.25*u(Qe*Qe+4*Be)+.5*u(Ne*Ne+Be)]}function qo(Me,Ce){var Be=Ce[0]*Ce[0]+Ce[1]*Ce[1];return[(Me[0]*Ce[0]+Me[1]*Ce[1])/Be,(Me[1]*Ce[0]-Me[0]*Ce[1])/Be]}Za.invert=function(Me,Ce){var Be=(A-1)/(A+1),Ne=O(1-Be*Be),Qe=ii(S,Ne*Ne),Je=-1,lt=Ai(.5*Qe-Ce,-Me,Ne*Ne),Gt=qo(lt[0],lt[1]),Zt=i(Gt[1],Gt[0])/Je;return[Zt,2*a(s(.5/Je*u(Be*Gt[0]*Gt[0]+Be*Gt[1]*Gt[1])))-S]};function $a(){return t.geoProjection(bn(Za)).scale(151.496)}function Ka(Me){var Ce=x(Me),Be=o(Me),Ne=Oa(Me);Ne.invert=Oa(-Me);function Qe(Je,lt){var Gt=Ne(Je,lt);Je=Gt[0],lt=Gt[1];var Zt=x(lt),Nt=o(lt),Kt=o(Je),ur=B(Ce*Zt+Be*Nt*Kt),Sr=x(ur),Br=n(Sr)>y?ur/Sr:1;return[Br*Be*x(Je),(n(Je)>S?Br:-Br)*(Ce*Nt-Be*Zt*Kt)]}return Qe.invert=function(Je,lt){var Gt=O(Je*Je+lt*lt),Zt=-x(Gt),Nt=o(Gt),Kt=Gt*Nt,ur=-lt*Zt,Sr=Gt*Ce,Br=O(Kt*Kt+ur*ur-Sr*Sr),ln=i(Kt*Sr+ur*Br,ur*Sr-Kt*Br),wn=(Gt>S?-1:1)*i(Je*Zt,Gt*o(ln)*Nt+lt*x(ln)*Zt);return Ne.invert(wn,ln)},Qe}function Oa(Me){var Ce=x(Me),Be=o(Me);return function(Ne,Qe){var Je=o(Qe),lt=o(Ne)*Je,Gt=x(Ne)*Je,Zt=x(Qe);return[i(Gt,lt*Be-Zt*Ce),R(Zt*Be+lt*Ce)]}}function Ni(){var Me=0,Ce=t.geoProjectionMutator(Ka),Be=Ce(Me),Ne=Be.rotate,Qe=Be.stream,Je=t.geoCircle();return Be.parallel=function(lt){if(!arguments.length)return Me*F;var Gt=Be.rotate();return Ce(Me=lt*E).rotate(Gt)},Be.rotate=function(lt){return arguments.length?(Ne.call(Be,[lt[0],lt[1]-Me*F]),Je.center([-lt[0],-lt[1]]),Be):(lt=Ne.call(Be),lt[1]+=Me*F,lt)},Be.stream=function(lt){return lt=Qe(lt),lt.sphere=function(){lt.polygonStart();var Gt=.01,Zt=Je.radius(90-Gt)().coordinates[0],Nt=Zt.length-1,Kt=-1,ur;for(lt.lineStart();++Kt<Nt;)lt.point((ur=Zt[Kt])[0],ur[1]);for(lt.lineEnd(),Zt=Je.radius(90+Gt)().coordinates[0],Nt=Zt.length-1,lt.lineStart();--Kt>=0;)lt.point((ur=Zt[Kt])[0],ur[1]);lt.lineEnd(),lt.polygonEnd()},lt},Be.scale(79.4187).parallel(45).clipAngle(180-.001)}var ka=3,vs=R(1-1/ka)*F,yo=Nr(0);function so(Me){var Ce=vs*E,Be=Mt(k,Ce)[0]-Mt(-k,Ce)[0],Ne=yo(0,Ce)[1],Qe=Mt(0,Ce)[1],Je=T-Qe,lt=m/Me,Gt=4/m,Zt=Ne+Je*Je*4/m;function Nt(Kt,ur){var Sr,Br=n(ur);if(Br>Ce){var ln=f(Me-1,c(0,l((Kt+k)/lt)));Kt+=k*(Me-1)/Me-ln*lt,Sr=Mt(Kt,Br),Sr[0]=Sr[0]*m/Be-m*(Me-1)/(2*Me)+ln*m/Me,Sr[1]=Ne+(Sr[1]-Qe)*4*Je/m,ur<0&&(Sr[1]=-Sr[1])}else Sr=yo(Kt,ur);return Sr[0]*=Gt,Sr[1]/=Zt,Sr}return Nt.invert=function(Kt,ur){Kt/=Gt,ur*=Zt;var Sr=n(ur);if(Sr>Ne){var Br=f(Me-1,c(0,l((Kt+k)/lt)));Kt=(Kt+k*(Me-1)/Me-Br*lt)*Be/m;var ln=Mt.invert(Kt,.25*(Sr-Ne)*m/Je+Qe);return ln[0]-=k*(Me-1)/Me-Br*lt,ur<0&&(ln[1]=-ln[1]),ln}return yo.invert(Kt,ur)},Nt}function oo(Me,Ce){return[Me,Ce&1?90-y:vs]}function ko(Me,Ce){return[Me,Ce&1?-90+y:-vs]}function pa(Me){return[Me[0]*(1-y),Me[1]]}function la(Me){var Ce=[].concat(r.range(-180,180+Me/2,Me).map(oo),r.range(180,-180-Me/2,-Me).map(ko));return{type:\"Polygon\",coordinates:[Me===180?Ce.map(pa):Ce]}}function As(){var Me=4,Ce=t.geoProjectionMutator(so),Be=Ce(Me),Ne=Be.stream;return Be.lobes=function(Qe){return arguments.length?Ce(Me=+Qe):Me},Be.stream=function(Qe){var Je=Be.rotate(),lt=Ne(Qe),Gt=(Be.rotate([0,0]),Ne(Qe));return Be.rotate(Je),lt.sphere=function(){t.geoStream(la(180/Me),Gt)},lt},Be.scale(239.75)}function Ms(Me){var Ce=1+Me,Be=x(1/Ce),Ne=R(Be),Qe=2*O(k/(Je=k+4*Ne*Ce)),Je,lt=.5*Qe*(Ce+O(Me*(2+Me))),Gt=Me*Me,Zt=Ce*Ce;function Nt(Kt,ur){var Sr=1-x(ur),Br,ln;if(Sr&&Sr<2){var wn=S-ur,In=25,gi;do{var Bi=x(wn),mi=o(wn),ia=Ne+i(Bi,Ce-mi),ua=1+Zt-2*Ce*mi;wn-=gi=(wn-Gt*Ne-Ce*Bi+ua*ia-.5*Sr*Je)/(2*Ce*Bi*ia)}while(n(gi)>C&&--In>0);Br=Qe*O(ua),ln=Kt*ia/k}else Br=Qe*(Me+Sr),ln=Kt*Ne/k;return[Br*x(ln),lt-Br*o(ln)]}return Nt.invert=function(Kt,ur){var Sr=Kt*Kt+(ur-=lt)*ur,Br=(1+Zt-Sr/(Qe*Qe))/(2*Ce),ln=B(Br),wn=x(ln),In=Ne+i(wn,Ce-Br);return[R(Kt/O(Sr))*k/In,R(1-2*(ln-Gt*Ne-Ce*wn+(1+Zt-2*Ce*Br)*In)/Je)]},Nt}function xu(){var Me=1,Ce=t.geoProjectionMutator(Ms),Be=Ce(Me);return Be.ratio=function(Ne){return arguments.length?Ce(Me=+Ne):Me},Be.scale(167.774).center([0,18.67])}var rs=.7109889596207567,Ls=.0528035274542;function $s(Me,Ce){return Ce>-rs?(Me=we(Me,Ce),Me[1]+=Ls,Me):tt(Me,Ce)}$s.invert=function(Me,Ce){return Ce>-rs?we.invert(Me,Ce-Ls):tt.invert(Me,Ce)};function Kc(){return t.geoProjection($s).rotate([-20,-55]).scale(164.263).center([0,-5.4036])}function _u(Me,Ce){return n(Ce)>rs?(Me=we(Me,Ce),Me[1]-=Ce>0?Ls:-Ls,Me):tt(Me,Ce)}_u.invert=function(Me,Ce){return n(Ce)>rs?we.invert(Me,Ce+(Ce>0?Ls:-Ls)):tt.invert(Me,Ce)};function Gu(){return t.geoProjection(_u).scale(152.63)}function Ja(Me,Ce,Be,Ne){var Qe=O(4*k/(2*Be+(1+Me-Ce/2)*x(2*Be)+(Me+Ce)/2*x(4*Be)+Ce/2*x(6*Be))),Je=O(Ne*x(Be)*O((1+Me*o(2*Be)+Ce*o(4*Be))/(1+Me+Ce))),lt=Be*Zt(1);function Gt(ur){return O(1+Me*o(2*ur)+Ce*o(4*ur))}function Zt(ur){var Sr=ur*Be;return(2*Sr+(1+Me-Ce/2)*x(2*Sr)+(Me+Ce)/2*x(4*Sr)+Ce/2*x(6*Sr))/Be}function Nt(ur){return Gt(ur)*x(ur)}var Kt=function(ur,Sr){var Br=Be*it(Zt,lt*x(Sr)/Be,Sr/k);isNaN(Br)&&(Br=Be*p(Sr));var ln=Qe*Gt(Br);return[ln*Je*ur/k*o(Br),ln/Je*x(Br)]};return Kt.invert=function(ur,Sr){var Br=it(Nt,Sr*Je/Qe);return[ur*k/(o(Br)*Qe*Je*Gt(Br)),R(Be*Zt(Br/Be)/lt)]},Be===0&&(Qe=O(Ne/k),Kt=function(ur,Sr){return[ur*Qe,x(Sr)/Qe]},Kt.invert=function(ur,Sr){return[ur/Qe,R(Sr*Qe)]}),Kt}function ps(){var Me=1,Ce=0,Be=45*E,Ne=2,Qe=t.geoProjectionMutator(Ja),Je=Qe(Me,Ce,Be,Ne);return Je.a=function(lt){return arguments.length?Qe(Me=+lt,Ce,Be,Ne):Me},Je.b=function(lt){return arguments.length?Qe(Me,Ce=+lt,Be,Ne):Ce},Je.psiMax=function(lt){return arguments.length?Qe(Me,Ce,Be=+lt*E,Ne):Be*F},Je.ratio=function(lt){return arguments.length?Qe(Me,Ce,Be,Ne=+lt):Ne},Je.scale(180.739)}function Sa(Me,Ce,Be,Ne,Qe,Je,lt,Gt,Zt,Nt,Kt){if(Kt.nanEncountered)return NaN;var ur,Sr,Br,ln,wn,In,gi,Bi,mi,ia;if(ur=Be-Ce,Sr=Me(Ce+ur*.25),Br=Me(Be-ur*.25),isNaN(Sr)){Kt.nanEncountered=!0;return}if(isNaN(Br)){Kt.nanEncountered=!0;return}return ln=ur*(Ne+4*Sr+Qe)/12,wn=ur*(Qe+4*Br+Je)/12,In=ln+wn,ia=(In-lt)/15,Nt>Zt?(Kt.maxDepthCount++,In+ia):Math.abs(ia)<Gt?In+ia:(gi=Ce+ur*.5,Bi=Sa(Me,Ce,gi,Ne,Sr,Qe,ln,Gt*.5,Zt,Nt+1,Kt),isNaN(Bi)?(Kt.nanEncountered=!0,NaN):(mi=Sa(Me,gi,Be,Qe,Br,Je,wn,Gt*.5,Zt,Nt+1,Kt),isNaN(mi)?(Kt.nanEncountered=!0,NaN):Bi+mi))}function ma(Me,Ce,Be,Ne,Qe){var Je={maxDepthCount:0,nanEncountered:!1};Ne===void 0&&(Ne=1e-8),Qe===void 0&&(Qe=20);var lt=Me(Ce),Gt=Me(.5*(Ce+Be)),Zt=Me(Be),Nt=(lt+4*Gt+Zt)*(Be-Ce)/6,Kt=Sa(Me,Ce,Be,lt,Gt,Zt,Nt,Ne,Qe,1,Je);return Kt}function Ps(Me,Ce,Be){function Ne(Br){return Me+(1-Me)*v(1-v(Br,Ce),1/Ce)}function Qe(Br){return ma(Ne,0,Br,1e-4)}for(var Je=1/Qe(1),lt=1e3,Gt=(1+1e-8)*Je,Zt=[],Nt=0;Nt<=lt;Nt++)Zt.push(Qe(Nt/lt)*Gt);function Kt(Br){var ln=0,wn=lt,In=lt>>1;do Zt[In]>Br?wn=In:ln=In,In=ln+wn>>1;while(In>ln);var gi=Zt[In+1]-Zt[In];return gi&&(gi=(Br-Zt[In+1])/gi),(In+1+gi)/lt}var ur=2*Kt(1)/k*Je/Be,Sr=function(Br,ln){var wn=Kt(n(x(ln))),In=Ne(wn)*Br;return wn/=ur,[In,ln>=0?wn:-wn]};return Sr.invert=function(Br,ln){var wn;return ln*=ur,n(ln)<1&&(wn=p(ln)*R(Qe(n(ln))*Je)),[Br/Ne(n(ln)),wn]},Sr}function Ca(){var Me=0,Ce=2.5,Be=1.183136,Ne=t.geoProjectionMutator(Ps),Qe=Ne(Me,Ce,Be);return Qe.alpha=function(Je){return arguments.length?Ne(Me=+Je,Ce,Be):Me},Qe.k=function(Je){return arguments.length?Ne(Me,Ce=+Je,Be):Ce},Qe.gamma=function(Je){return arguments.length?Ne(Me,Ce,Be=+Je):Be},Qe.scale(152.63)}function Pl(Me,Ce){return n(Me[0]-Ce[0])<y&&n(Me[1]-Ce[1])<y}function ll(Me,Ce){for(var Be=-1,Ne=Me.length,Qe=Me[0],Je,lt,Gt,Zt=[];++Be<Ne;){Je=Me[Be],lt=(Je[0]-Qe[0])/Ce,Gt=(Je[1]-Qe[1])/Ce;for(var Nt=0;Nt<Ce;++Nt)Zt.push([Qe[0]+Nt*lt,Qe[1]+Nt*Gt]);Qe=Je}return Zt.push(Je),Zt}function Jc(Me){var Ce=[],Be,Ne,Qe,Je,lt,Gt,Zt,Nt=Me[0].length;for(Zt=0;Zt<Nt;++Zt)Be=Me[0][Zt],Ne=Be[0][0],Qe=Be[0][1],Je=Be[1][1],lt=Be[2][0],Gt=Be[2][1],Ce.push(ll([[Ne+y,Qe+y],[Ne+y,Je-y],[lt-y,Je-y],[lt-y,Gt+y]],30));for(Zt=Me[1].length-1;Zt>=0;--Zt)Be=Me[1][Zt],Ne=Be[0][0],Qe=Be[0][1],Je=Be[1][1],lt=Be[2][0],Gt=Be[2][1],Ce.push(ll([[lt-y,Gt-y],[lt-y,Je+y],[Ne+y,Je+y],[Ne+y,Qe-y]],30));return{type:\"Polygon\",coordinates:[r.merge(Ce)]}}function lu(Me,Ce,Be){var Ne,Qe;function Je(Zt,Nt){for(var Kt=Nt<0?-1:1,ur=Ce[+(Nt<0)],Sr=0,Br=ur.length-1;Sr<Br&&Zt>ur[Sr][2][0];++Sr);var ln=Me(Zt-ur[Sr][1][0],Nt);return ln[0]+=Me(ur[Sr][1][0],Kt*Nt>Kt*ur[Sr][0][1]?ur[Sr][0][1]:Nt)[0],ln}Be?Je.invert=Be(Je):Me.invert&&(Je.invert=function(Zt,Nt){for(var Kt=Qe[+(Nt<0)],ur=Ce[+(Nt<0)],Sr=0,Br=Kt.length;Sr<Br;++Sr){var ln=Kt[Sr];if(ln[0][0]<=Zt&&Zt<ln[1][0]&&ln[0][1]<=Nt&&Nt<ln[1][1]){var wn=Me.invert(Zt-Me(ur[Sr][1][0],0)[0],Nt);return wn[0]+=ur[Sr][1][0],Pl(Je(wn[0],wn[1]),[Zt,Nt])?wn:null}}});var lt=t.geoProjection(Je),Gt=lt.stream;return lt.stream=function(Zt){var Nt=lt.rotate(),Kt=Gt(Zt),ur=(lt.rotate([0,0]),Gt(Zt));return lt.rotate(Nt),Kt.sphere=function(){t.geoStream(Ne,ur)},Kt},lt.lobes=function(Zt){return arguments.length?(Ne=Jc(Zt),Ce=Zt.map(function(Nt){return Nt.map(function(Kt){return[[Kt[0][0]*E,Kt[0][1]*E],[Kt[1][0]*E,Kt[1][1]*E],[Kt[2][0]*E,Kt[2][1]*E]]})}),Qe=Ce.map(function(Nt){return Nt.map(function(Kt){var ur=Me(Kt[0][0],Kt[0][1])[0],Sr=Me(Kt[2][0],Kt[2][1])[0],Br=Me(Kt[1][0],Kt[0][1])[1],ln=Me(Kt[1][0],Kt[1][1])[1],wn;return Br>ln&&(wn=Br,Br=ln,ln=wn),[[ur,Br],[Sr,ln]]})}),lt):Ce.map(function(Nt){return Nt.map(function(Kt){return[[Kt[0][0]*F,Kt[0][1]*F],[Kt[1][0]*F,Kt[1][1]*F],[Kt[2][0]*F,Kt[2][1]*F]]})})},Ce!=null&&lt.lobes(Ce),lt}var Mf=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]];function Bd(){return lu(Ve,Mf).scale(160.857)}var Hu=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]];function Sf(){return lu(_u,Hu).scale(152.63)}var kc=[[[[-180,0],[-100,90],[-40,0]],[[-40,0],[30,90],[180,0]]],[[[-180,0],[-160,-90],[-100,0]],[[-100,0],[-60,-90],[-20,0]],[[-20,0],[20,-90],[80,0]],[[80,0],[140,-90],[180,0]]]];function Uo(){return lu(we,kc).scale(169.529)}var ns=[[[[-180,0],[-90,90],[0,0]],[[0,0],[90,90],[180,0]]],[[[-180,0],[-90,-90],[0,0]],[[0,0],[90,-90],[180,0]]]];function Ks(){return lu(we,ns).scale(169.529).rotate([20,0])}var Ef=[[[[-180,35],[-30,90],[0,35]],[[0,35],[30,90],[180,35]]],[[[-180,-10],[-102,-90],[-65,-10]],[[-65,-10],[5,-90],[77,-10]],[[77,-10],[103,-90],[180,-10]]]];function ju(){return lu($s,Ef,Oe).rotate([-20,-55]).scale(164.263).center([0,-5.4036])}var gs=[[[[-180,0],[-110,90],[-40,0]],[[-40,0],[0,90],[40,0]],[[40,0],[110,90],[180,0]]],[[[-180,0],[-110,-90],[-40,0]],[[-40,0],[0,-90],[40,0]],[[40,0],[110,-90],[180,0]]]];function bu(){return lu(tt,gs).scale(152.63).rotate([-20,0])}function kf(Me,Ce){return[3/m*Me*O(k*k/3-Ce*Ce),Ce]}kf.invert=function(Me,Ce){return[m/3*Me/O(k*k/3-Ce*Ce),Ce]};function ah(){return t.geoProjection(kf).scale(158.837)}function Od(Me){function Ce(Be,Ne){if(n(n(Ne)-S)<y)return[0,Ne<0?-2:2];var Qe=x(Ne),Je=v((1+Qe)/(1-Qe),Me/2),lt=.5*(Je+1/Je)+o(Be*=Me);return[2*x(Be)/lt,(Je-1/Je)/lt]}return Ce.invert=function(Be,Ne){var Qe=n(Ne);if(n(Qe-2)<y)return Be?null:[0,p(Ne)*S];if(Qe>2)return null;Be/=2,Ne/=2;var Je=Be*Be,lt=Ne*Ne,Gt=2*Ne/(1+Je+lt);return Gt=v((1+Gt)/(1-Gt),1/Me),[i(2*Be,1-Je-lt)/Me,R((Gt-1)/(Gt+1))]},Ce}function Qc(){var Me=.5,Ce=t.geoProjectionMutator(Od),Be=Ce(Me);return Be.spacing=function(Ne){return arguments.length?Ce(Me=+Ne):Me},Be.scale(124.75)}var uu=k/A;function oh(Me,Ce){return[Me*(1+O(o(Ce)))/2,Ce/(o(Ce/2)*o(Me/6))]}oh.invert=function(Me,Ce){var Be=n(Me),Ne=n(Ce),Qe=y,Je=S;Ne<uu?Je*=Ne/uu:Qe+=6*B(uu/Ne);for(var lt=0;lt<25;lt++){var Gt=x(Je),Zt=O(o(Je)),Nt=x(Je/2),Kt=o(Je/2),ur=x(Qe/6),Sr=o(Qe/6),Br=.5*Qe*(1+Zt)-Be,ln=Je/(Kt*Sr)-Ne,wn=Zt?-.25*Qe*Gt/Zt:0,In=.5*(1+Zt),gi=(1+.5*Je*Nt/Kt)/(Kt*Sr),Bi=Je/Kt*(ur/6)/(Sr*Sr),mi=wn*Bi-gi*In,ia=(Br*Bi-ln*In)/mi,ua=(ln*wn-Br*gi)/mi;if(Je-=ia,Qe-=ua,n(ia)<y&&n(ua)<y)break}return[Me<0?-Qe:Qe,Ce<0?-Je:Je]};function hc(){return t.geoProjection(oh).scale(97.2672)}function Fl(Me,Ce){var Be=Me*Me,Ne=Ce*Ce;return[Me*(.975534+Ne*(-.119161+Be*-.0143059+Ne*-.0547009)),Ce*(1.00384+Be*(.0802894+Ne*-.02855+Be*199025e-9)+Ne*(.0998909+Ne*-.0491032))]}Fl.invert=function(Me,Ce){var Be=p(Me)*k,Ne=Ce/2,Qe=50;do{var Je=Be*Be,lt=Ne*Ne,Gt=Be*Ne,Zt=Be*(.975534+lt*(-.119161+Je*-.0143059+lt*-.0547009))-Me,Nt=Ne*(1.00384+Je*(.0802894+lt*-.02855+Je*199025e-9)+lt*(.0998909+lt*-.0491032))-Ce,Kt=.975534-lt*(.119161+3*Je*.0143059+lt*.0547009),ur=-Gt*(2*.119161+4*.0547009*lt+2*.0143059*Je),Sr=Gt*(2*.0802894+4*199025e-9*Je+2*-.02855*lt),Br=1.00384+Je*(.0802894+199025e-9*Je)+lt*(3*(.0998909-.02855*Je)-5*.0491032*lt),ln=ur*Sr-Br*Kt,wn=(Nt*ur-Zt*Br)/ln,In=(Zt*Sr-Nt*Kt)/ln;Be-=wn,Ne-=In}while((n(wn)>y||n(In)>y)&&--Qe>0);return Qe&&[Be,Ne]};function Yo(){return t.geoProjection(Fl).scale(139.98)}function Ro(Me,Ce){return[x(Me)/o(Ce),_(Ce)*o(Me)]}Ro.invert=function(Me,Ce){var Be=Me*Me,Ne=Ce*Ce,Qe=Ne+1,Je=Be+Qe,lt=Me?w*O((Je-O(Je*Je-4*Be))/Be):1/O(Qe);return[R(Me*lt),p(Ce)*B(lt)]};function is(){return t.geoProjection(Ro).scale(144.049).clipAngle(90-.001)}function Dl(Me){var Ce=o(Me),Be=_(L+Me/2);function Ne(Qe,Je){var lt=Je-Me,Gt=n(lt)<y?Qe*Ce:n(Gt=L+Je/2)<y||n(n(Gt)-S)<y?0:Qe*lt/u(_(Gt)/Be);return[Gt,lt]}return Ne.invert=function(Qe,Je){var lt,Gt=Je+Me;return[n(Je)<y?Qe/Ce:n(lt=L+Gt/2)<y||n(n(lt)-S)<y?0:Qe*u(_(lt)/Be)/Je,Gt]},Ne}function dc(){return ot(Dl).parallel(40).scale(158.837)}function ef(Me,Ce){return[Me,1.25*u(_(L+.4*Ce))]}ef.invert=function(Me,Ce){return[Me,2.5*a(s(.8*Ce))-.625*k]};function Cc(){return t.geoProjection(ef).scale(108.318)}function sh(Me){var Ce=Me.length-1;function Be(Ne,Qe){for(var Je=o(Qe),lt=2/(1+Je*o(Ne)),Gt=lt*Je*x(Ne),Zt=lt*x(Qe),Nt=Ce,Kt=Me[Nt],ur=Kt[0],Sr=Kt[1],Br;--Nt>=0;)Kt=Me[Nt],ur=Kt[0]+Gt*(Br=ur)-Zt*Sr,Sr=Kt[1]+Gt*Sr+Zt*Br;return ur=Gt*(Br=ur)-Zt*Sr,Sr=Gt*Sr+Zt*Br,[ur,Sr]}return Be.invert=function(Ne,Qe){var Je=20,lt=Ne,Gt=Qe;do{for(var Zt=Ce,Nt=Me[Zt],Kt=Nt[0],ur=Nt[1],Sr=0,Br=0,ln;--Zt>=0;)Nt=Me[Zt],Sr=Kt+lt*(ln=Sr)-Gt*Br,Br=ur+lt*Br+Gt*ln,Kt=Nt[0]+lt*(ln=Kt)-Gt*ur,ur=Nt[1]+lt*ur+Gt*ln;Sr=Kt+lt*(ln=Sr)-Gt*Br,Br=ur+lt*Br+Gt*ln,Kt=lt*(ln=Kt)-Gt*ur-Ne,ur=lt*ur+Gt*ln-Qe;var wn=Sr*Sr+Br*Br,In,gi;lt-=In=(Kt*Sr+ur*Br)/wn,Gt-=gi=(ur*Sr-Kt*Br)/wn}while(n(In)+n(gi)>y*y&&--Je>0);if(Je){var Bi=O(lt*lt+Gt*Gt),mi=2*a(Bi*.5),ia=x(mi);return[i(lt*ia,Bi*o(mi)),Bi?R(Gt*ia/Bi):0]}},Be}var Y0=[[.9972523,0],[.0052513,-.0041175],[.0074606,.0048125],[-.0153783,-.1968253],[.0636871,-.1408027],[.3660976,-.2937382]],Nd=[[.98879,0],[0,0],[-.050909,0],[0,0],[.075528,0]],Cf=[[.984299,0],[.0211642,.0037608],[-.1036018,-.0575102],[-.0329095,-.0320119],[.0499471,.1223335],[.026046,.0899805],[7388e-7,-.1435792],[.0075848,-.1334108],[-.0216473,.0776645],[-.0225161,.0853673]],Vo=[[.9245,0],[0,0],[.01943,0]],Ud=[[.721316,0],[0,0],[-.00881625,-.00617325]];function zh(){return vc(Y0,[152,-64]).scale(1400).center([-160.908,62.4864]).clipAngle(30).angle(7.8)}function tf(){return vc(Nd,[95,-38]).scale(1e3).clipAngle(55).center([-96.5563,38.8675])}function Vd(){return vc(Cf,[120,-45]).scale(359.513).clipAngle(55).center([-117.474,53.0628])}function rf(){return vc(Vo,[-20,-18]).scale(209.091).center([20,16.7214]).clipAngle(82)}function qh(){return vc(Ud,[165,10]).scale(250).clipAngle(130).center([-165,-10])}function vc(Me,Ce){var Be=t.geoProjection(sh(Me)).rotate(Ce).clipAngle(90),Ne=t.geoRotation(Ce),Qe=Be.center;return delete Be.rotate,Be.center=function(Je){return arguments.length?Qe(Ne(Je)):Ne.invert(Qe())},Be}var dd=O(6),Bh=O(7);function Wu(Me,Ce){var Be=R(7*x(Ce)/(3*dd));return[dd*Me*(2*o(2*Be/3)-1)/Bh,9*x(Be/3)/Bh]}Wu.invert=function(Me,Ce){var Be=3*R(Ce*Bh/9);return[Me*Bh/(dd*(2*o(2*Be/3)-1)),R(x(Be)*3*dd/7)]};function vd(){return t.geoProjection(Wu).scale(164.859)}function pd(Me,Ce){for(var Be=(1+w)*x(Ce),Ne=Ce,Qe=0,Je;Qe<25&&(Ne-=Je=(x(Ne/2)+x(Ne)-Be)/(.5*o(Ne/2)+o(Ne)),!(n(Je)<y));Qe++);return[Me*(1+2*o(Ne)/o(Ne/2))/(3*A),2*O(3)*x(Ne/2)/O(2+A)]}pd.invert=function(Me,Ce){var Be=Ce*O(2+A)/(2*O(3)),Ne=2*R(Be);return[3*A*Me/(1+2*o(Ne)/o(Ne/2)),R((Be+x(Ne))/(1+w))]};function lh(){return t.geoProjection(pd).scale(188.209)}function nf(Me,Ce){for(var Be=O(6/(4+k)),Ne=(1+k/4)*x(Ce),Qe=Ce/2,Je=0,lt;Je<25&&(Qe-=lt=(Qe/2+x(Qe)-Ne)/(.5+o(Qe)),!(n(lt)<y));Je++);return[Be*(.5+o(Qe))*Me/1.5,Be*Qe]}nf.invert=function(Me,Ce){var Be=O(6/(4+k)),Ne=Ce/Be;return n(n(Ne)-S)<y&&(Ne=Ne<0?-S:S),[1.5*Me/(Be*(.5+o(Ne))),R((Ne/2+x(Ne))/(1+k/4))]};function uh(){return t.geoProjection(nf).scale(166.518)}function af(Me,Ce){var Be=Ce*Ce,Ne=Be*Be,Qe=Be*Ne;return[Me*(.84719-.13063*Be+Qe*Qe*(-.04515+.05494*Be-.02326*Ne+.00331*Qe)),Ce*(1.01183+Ne*Ne*(-.02625+.01926*Be-.00396*Ne))]}af.invert=function(Me,Ce){var Be=Ce,Ne=25,Qe,Je,lt,Gt;do Je=Be*Be,lt=Je*Je,Be-=Qe=(Be*(1.01183+lt*lt*(-.02625+.01926*Je-.00396*lt))-Ce)/(1.01183+lt*lt*(9*-.02625+11*.01926*Je+13*-.00396*lt));while(n(Qe)>C&&--Ne>0);return Je=Be*Be,lt=Je*Je,Gt=Je*lt,[Me/(.84719-.13063*Je+Gt*Gt*(-.04515+.05494*Je-.02326*lt+.00331*Gt)),Be]};function gd(){return t.geoProjection(af).scale(175.295)}function Rl(Me,Ce){return[Me*(1+o(Ce))/2,2*(Ce-_(Ce/2))]}Rl.invert=function(Me,Ce){for(var Be=Ce/2,Ne=0,Qe=1/0;Ne<10&&n(Qe)>y;++Ne){var Je=o(Ce/2);Ce-=Qe=(Ce-_(Ce/2)-Be)/(1-.5/(Je*Je))}return[2*Me/(1+o(Ce)),Ce]};function ch(){return t.geoProjection(Rl).scale(152.63)}var Lc=[[[[-180,0],[-90,90],[0,0]],[[0,0],[90,90],[180,0]]],[[[-180,0],[-90,-90],[0,0]],[[0,0],[90,-90],[180,0]]]];function md(){return lu(De(1/0),Lc).rotate([20,0]).scale(152.63)}function yd(Me,Ce){var Be=x(Ce),Ne=o(Ce),Qe=p(Me);if(Me===0||n(Ce)===S)return[0,Ce];if(Ce===0)return[Me,0];if(n(Me)===S)return[Me*Ne,S*Be];var Je=k/(2*Me)-2*Me/k,lt=2*Ce/k,Gt=(1-lt*lt)/(Be-lt),Zt=Je*Je,Nt=Gt*Gt,Kt=1+Zt/Nt,ur=1+Nt/Zt,Sr=(Je*Be/Gt-Je/2)/Kt,Br=(Nt*Be/Zt+Gt/2)/ur,ln=Sr*Sr+Ne*Ne/Kt,wn=Br*Br-(Nt*Be*Be/Zt+Gt*Be-1)/ur;return[S*(Sr+O(ln)*Qe),S*(Br+O(wn<0?0:wn)*p(-Ce*Je)*Qe)]}yd.invert=function(Me,Ce){Me/=S,Ce/=S;var Be=Me*Me,Ne=Ce*Ce,Qe=Be+Ne,Je=k*k;return[Me?(Qe-1+O((1-Qe)*(1-Qe)+4*Be))/(2*Me)*S:0,it(function(lt){return Qe*(k*x(lt)-2*lt)*k+4*lt*lt*(Ce-x(lt))+2*k*lt-Je*Ce},0)]};function Lf(){return t.geoProjection(yd).scale(127.267)}var wu=1.0148,fh=.23185,Oh=-.14499,hh=.02406,Gd=wu,Gl=5*fh,wl=7*Oh,xd=9*hh,Hl=1.790857183;function _d(Me,Ce){var Be=Ce*Ce;return[Me,Ce*(wu+Be*Be*(fh+Be*(Oh+hh*Be)))]}_d.invert=function(Me,Ce){Ce>Hl?Ce=Hl:Ce<-Hl&&(Ce=-Hl);var Be=Ce,Ne;do{var Qe=Be*Be;Be-=Ne=(Be*(wu+Qe*Qe*(fh+Qe*(Oh+hh*Qe)))-Ce)/(Gd+Qe*Qe*(Gl+Qe*(wl+xd*Qe)))}while(n(Ne)>y);return[Me,Be]};function dh(){return t.geoProjection(_d).scale(139.319)}function Pf(Me,Ce){if(n(Ce)<y)return[Me,0];var Be=_(Ce),Ne=Me*x(Ce);return[x(Ne)/Be,Ce+(1-o(Ne))/Be]}Pf.invert=function(Me,Ce){if(n(Ce)<y)return[Me,0];var Be=Me*Me+Ce*Ce,Ne=Ce*.5,Qe=10,Je;do{var lt=_(Ne),Gt=1/o(Ne),Zt=Be-2*Ce*Ne+Ne*Ne;Ne-=Je=(lt*Zt+2*(Ne-Ce))/(2+Zt*Gt*Gt+2*(Ne-Ce)*lt)}while(n(Je)>y&&--Qe>0);return lt=_(Ne),[(n(Ce)<n(Ne+1/lt)?R(Me*lt):p(Ce)*p(Me)*(B(n(Me*lt))+S))/x(Ne),Ne]};function Hd(){return t.geoProjection(Pf).scale(103.74)}function xo(Me,Ce){var Be=Au(Me[1],Me[0]),Ne=Au(Ce[1],Ce[0]),Qe=g0(Be,Ne),Je=Il(Be)/Il(Ne);return Tu([1,0,Me[0][0],0,1,Me[0][1]],Tu([Je,0,0,0,Je,0],Tu([o(Qe),x(Qe),0,-x(Qe),o(Qe),0],[1,0,-Ce[0][0],0,1,-Ce[0][1]])))}function p0(Me){var Ce=1/(Me[0]*Me[4]-Me[1]*Me[3]);return[Ce*Me[4],-Ce*Me[1],Ce*(Me[1]*Me[5]-Me[2]*Me[4]),-Ce*Me[3],Ce*Me[0],Ce*(Me[2]*Me[3]-Me[0]*Me[5])]}function Tu(Me,Ce){return[Me[0]*Ce[0]+Me[1]*Ce[3],Me[0]*Ce[1]+Me[1]*Ce[4],Me[0]*Ce[2]+Me[1]*Ce[5]+Me[2],Me[3]*Ce[0]+Me[4]*Ce[3],Me[3]*Ce[1]+Me[4]*Ce[4],Me[3]*Ce[2]+Me[4]*Ce[5]+Me[5]]}function Au(Me,Ce){return[Me[0]-Ce[0],Me[1]-Ce[1]]}function Il(Me){return O(Me[0]*Me[0]+Me[1]*Me[1])}function g0(Me,Ce){return i(Me[0]*Ce[1]-Me[1]*Ce[0],Me[0]*Ce[0]+Me[1]*Ce[1])}function Yu(Me,Ce,Be){Ne(Me,{transform:null});function Ne(Nt,Kt){if(Nt.edges=Uh(Nt.face),Kt.face){var ur=Nt.shared=jl(Nt.face,Kt.face),Sr=xo(ur.map(Kt.project),ur.map(Nt.project));Nt.transform=Kt.transform?Tu(Kt.transform,Sr):Sr;for(var Br=Kt.edges,ln=0,wn=Br.length;ln<wn;++ln)Tl(ur[0],Br[ln][1])&&Tl(ur[1],Br[ln][0])&&(Br[ln]=Nt),Tl(ur[0],Br[ln][0])&&Tl(ur[1],Br[ln][1])&&(Br[ln]=Nt);for(Br=Nt.edges,ln=0,wn=Br.length;ln<wn;++ln)Tl(ur[0],Br[ln][0])&&Tl(ur[1],Br[ln][1])&&(Br[ln]=Kt),Tl(ur[0],Br[ln][1])&&Tl(ur[1],Br[ln][0])&&(Br[ln]=Kt)}else Nt.transform=Kt.transform;return Nt.children&&Nt.children.forEach(function(In){Ne(In,Nt)}),Nt}function Qe(Nt,Kt){var ur=Ce(Nt,Kt),Sr=ur.project([Nt*F,Kt*F]),Br;return(Br=ur.transform)?[Br[0]*Sr[0]+Br[1]*Sr[1]+Br[2],-(Br[3]*Sr[0]+Br[4]*Sr[1]+Br[5])]:(Sr[1]=-Sr[1],Sr)}Vh(Me)&&(Qe.invert=function(Nt,Kt){var ur=Je(Me,[Nt,-Kt]);return ur&&(ur[0]*=E,ur[1]*=E,ur)});function Je(Nt,Kt){var ur=Nt.project.invert,Sr=Nt.transform,Br=Kt;if(Sr&&(Sr=p0(Sr),Br=[Sr[0]*Br[0]+Sr[1]*Br[1]+Sr[2],Sr[3]*Br[0]+Sr[4]*Br[1]+Sr[5]]),ur&&Nt===lt(ln=ur(Br)))return ln;for(var ln,wn=Nt.children,In=0,gi=wn&&wn.length;In<gi;++In)if(ln=Je(wn[In],Kt))return ln}function lt(Nt){return Ce(Nt[0]*E,Nt[1]*E)}var Gt=t.geoProjection(Qe),Zt=Gt.stream;return Gt.stream=function(Nt){var Kt=Gt.rotate(),ur=Zt(Nt),Sr=(Gt.rotate([0,0]),Zt(Nt));return Gt.rotate(Kt),ur.sphere=function(){Sr.polygonStart(),Sr.lineStart(),Nh(Sr,Me),Sr.lineEnd(),Sr.polygonEnd()},ur},Gt.angle(Be==null?-30:Be*F)}function Nh(Me,Ce,Be){var Ne,Qe=Ce.edges,Je=Qe.length,lt,Gt={type:\"MultiPoint\",coordinates:Ce.face},Zt=Ce.face.filter(function(wn){return n(wn[1])!==90}),Nt=t.geoBounds({type:\"MultiPoint\",coordinates:Zt}),Kt=!1,ur=-1,Sr=Nt[1][0]-Nt[0][0],Br=Sr===180||Sr===360?[(Nt[0][0]+Nt[1][0])/2,(Nt[0][1]+Nt[1][1])/2]:t.geoCentroid(Gt);if(Be)for(;++ur<Je&&Qe[ur]!==Be;);++ur;for(var ln=0;ln<Je;++ln)lt=Qe[(ln+ur)%Je],Array.isArray(lt)?(Kt||(Me.point((Ne=t.geoInterpolate(lt[0],Br)(y))[0],Ne[1]),Kt=!0),Me.point((Ne=t.geoInterpolate(lt[1],Br)(y))[0],Ne[1])):(Kt=!1,lt!==Be&&Nh(Me,lt,Ce))}function Tl(Me,Ce){return Me&&Ce&&Me[0]===Ce[0]&&Me[1]===Ce[1]}function jl(Me,Ce){for(var Be,Ne,Qe=Me.length,Je=null,lt=0;lt<Qe;++lt){Be=Me[lt];for(var Gt=Ce.length;--Gt>=0;)if(Ne=Ce[Gt],Be[0]===Ne[0]&&Be[1]===Ne[1]){if(Je)return[Je,Be];Je=Be}}}function Uh(Me){for(var Ce=Me.length,Be=[],Ne=Me[Ce-1],Qe=0;Qe<Ce;++Qe)Be.push([Ne,Ne=Me[Qe]]);return Be}function Vh(Me){return Me.project.invert||Me.children&&Me.children.some(Vh)}var jd=[[0,90],[-90,0],[0,0],[90,0],[180,0],[0,-90]],pc=[[0,2,1],[0,3,2],[5,1,2],[5,2,3],[0,1,4],[0,4,3],[5,4,1],[5,3,4]].map(function(Me){return Me.map(function(Ce){return jd[Ce]})});function gc(Me){Me=Me||function(Be){var Ne=t.geoCentroid({type:\"MultiPoint\",coordinates:Be});return t.geoGnomonic().scale(1).translate([0,0]).rotate([-Ne[0],-Ne[1]])};var Ce=pc.map(function(Be){return{face:Be,project:Me(Be)}});return[-1,0,0,1,0,1,4,5].forEach(function(Be,Ne){var Qe=Ce[Be];Qe&&(Qe.children||(Qe.children=[])).push(Ce[Ne])}),Yu(Ce[0],function(Be,Ne){return Ce[Be<-k/2?Ne<0?6:4:Be<0?Ne<0?2:0:Be<k/2?Ne<0?3:1:Ne<0?7:5]}).angle(-30).scale(101.858).center([0,45])}var of=2/O(3);function sf(Me,Ce){var Be=Mt(Me,Ce);return[Be[0]*of,Be[1]]}sf.invert=function(Me,Ce){return Mt.invert(Me/of,Ce)};function bd(Me){Me=Me||function(Be){var Ne=t.geoCentroid({type:\"MultiPoint\",coordinates:Be});return t.geoProjection(sf).translate([0,0]).scale(1).rotate(Ne[1]>0?[-Ne[0],0]:[180-Ne[0],180])};var Ce=pc.map(function(Be){return{face:Be,project:Me(Be)}});return[-1,0,0,1,0,1,4,5].forEach(function(Be,Ne){var Qe=Ce[Be];Qe&&(Qe.children||(Qe.children=[])).push(Ce[Ne])}),Yu(Ce[0],function(Be,Ne){return Ce[Be<-k/2?Ne<0?6:4:Be<0?Ne<0?2:0:Be<k/2?Ne<0?3:1:Ne<0?7:5]}).angle(-30).scale(121.906).center([0,48.5904])}function Gh(Me){Me=Me||function(lt){var Gt=lt.length===6?t.geoCentroid({type:\"MultiPoint\",coordinates:lt}):lt[0];return t.geoGnomonic().scale(1).translate([0,0]).rotate([-Gt[0],-Gt[1]])};var Ce=pc.map(function(lt){for(var Gt=lt.map(Df),Zt=Gt.length,Nt=Gt[Zt-1],Kt,ur=[],Sr=0;Sr<Zt;++Sr)Kt=Gt[Sr],ur.push(Ff([Nt[0]*.9486832980505138+Kt[0]*.31622776601683794,Nt[1]*.9486832980505138+Kt[1]*.31622776601683794,Nt[2]*.9486832980505138+Kt[2]*.31622776601683794]),Ff([Kt[0]*.9486832980505138+Nt[0]*.31622776601683794,Kt[1]*.9486832980505138+Nt[1]*.31622776601683794,Kt[2]*.9486832980505138+Nt[2]*.31622776601683794])),Nt=Kt;return ur}),Be=[],Ne=[-1,0,0,1,0,1,4,5];Ce.forEach(function(lt,Gt){for(var Zt=pc[Gt],Nt=Zt.length,Kt=Be[Gt]=[],ur=0;ur<Nt;++ur)Ce.push([Zt[ur],lt[(ur*2+2)%(2*Nt)],lt[(ur*2+1)%(2*Nt)]]),Ne.push(Gt),Kt.push(Pc(Df(lt[(ur*2+2)%(2*Nt)]),Df(lt[(ur*2+1)%(2*Nt)])))});var Qe=Ce.map(function(lt){return{project:Me(lt),face:lt}});Ne.forEach(function(lt,Gt){var Zt=Qe[lt];Zt&&(Zt.children||(Zt.children=[])).push(Qe[Gt])});function Je(lt,Gt){var Zt=o(Gt),Nt=[Zt*o(lt),Zt*x(lt),x(Gt)],Kt=lt<-k/2?Gt<0?6:4:lt<0?Gt<0?2:0:lt<k/2?Gt<0?3:1:Gt<0?7:5,ur=Be[Kt];return 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t.geoProjection(lf).scale(79.4183)}function ph(Me,Ce){if(n(Ce)<y)return[Me,0];var Be=n(Ce/S),Ne=R(Be);if(n(Me)<y||n(n(Ce)-S)<y)return[0,p(Ce)*k*_(Ne/2)];var Qe=o(Ne),Je=n(k/Me-Me/k)/2,lt=Je*Je,Gt=Qe*(O(1+lt)-Je*Qe)/(1+lt*Be*Be);return[p(Me)*k*Gt,p(Ce)*k*O(1-Gt*(2*Je+Gt))]}ph.invert=function(Me,Ce){if(!Me)return[0,S*x(2*a(Ce/k))];var Be=n(Me/k),Ne=(1-Be*Be-(Ce/=k)*Ce)/(2*Be),Qe=Ne*Ne,Je=O(Qe+1);return[p(Me)*k*(Je-Ne),p(Ce)*S*x(2*i(O((1-2*Ne*Be)*(Ne+Je)-Be),O(Je+Ne+Be)))]};function gh(){return t.geoProjection(ph).scale(79.4183)}function uf(Me,Ce){if(n(Ce)<y)return[Me,0];var Be=Ce/S,Ne=R(Be);if(n(Me)<y||n(n(Ce)-S)<y)return[0,k*_(Ne/2)];var Qe=(k/Me-Me/k)/2,Je=Be/(1+o(Ne));return[k*(p(Me)*O(Qe*Qe+1-Je*Je)-Qe),k*Je]}uf.invert=function(Me,Ce){if(!Ce)return[Me,0];var Be=Ce/k,Ne=(k*k*(1-Be*Be)-Me*Me)/(2*k*Me);return[Me?k*(p(Me)*O(Ne*Ne+1)-Ne):0,S*x(2*a(Be))]};function Ku(){return t.geoProjection(uf).scale(79.4183)}function fu(Me,Ce){if(!Ce)return[Me,0];var 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t.geoProjection(fu).scale(127.16)}function Qu(Me,Ce,Be,Ne,Qe){function Je(lt,Gt){var Zt=Be*x(Ne*Gt),Nt=O(1-Zt*Zt),Kt=O(2/(1+Nt*o(lt*=Qe)));return[Me*Nt*Kt*x(lt),Ce*Zt*Kt]}return Je.invert=function(lt,Gt){var Zt=lt/Me,Nt=Gt/Ce,Kt=O(Zt*Zt+Nt*Nt),ur=2*R(Kt/2);return[i(lt*_(ur),Me*Kt)/Qe,Kt&&R(Gt*x(ur)/(Ce*Be*Kt))/Ne]},Je}function Wd(Me,Ce,Be,Ne){var Qe=k/3;Me=c(Me,y),Ce=c(Ce,y),Me=f(Me,S),Ce=f(Ce,k-y),Be=c(Be,0),Be=f(Be,100-y),Ne=c(Ne,y);var Je=Be/100+1,lt=Ne/100,Gt=B(Je*o(Qe))/Qe,Zt=x(Me)/x(Gt*S),Nt=Ce/k,Kt=O(lt*x(Me/2)/x(Ce/2)),ur=Kt/O(Nt*Zt*Gt),Sr=1/(Kt*O(Nt*Zt*Gt));return Qu(ur,Sr,Zt,Gt,Nt)}function x0(){var Me=65*E,Ce=60*E,Be=20,Ne=200,Qe=t.geoProjectionMutator(Wd),Je=Qe(Me,Ce,Be,Ne);return Je.poleline=function(lt){return arguments.length?Qe(Me=+lt*E,Ce,Be,Ne):Me*F},Je.parallels=function(lt){return arguments.length?Qe(Me,Ce=+lt*E,Be,Ne):Ce*F},Je.inflation=function(lt){return arguments.length?Qe(Me,Ce,Be=+lt,Ne):Be},Je.ratio=function(lt){return 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i=t[this.id],o=[],s=!1;for(var l in Id.layerNameToAdjective)if(l!==\"frame\"&&i[\"show\"+l]){s=!0;break}for(var u=!1,c=0;c<e.length;c++){var f=e[0][0].trace;f._geo=a,f.locationmode&&(s=!0);var v=f.marker;if(v){var g=v.angle,p=v.angleref;(g||p===\"north\"||p===\"previous\")&&(u=!0)}}if(this._hasMarkerAngles=u,s){var x=gze.getTopojsonName(i);(a.topojson===null||x!==a.topojsonName)&&(a.topojsonName=x,PlotlyGeoAssets.topojson[a.topojsonName]===void 0&&o.push(a.fetchTopojson()))}o=o.concat(YX.fetchTraceGeoData(e)),r.push(new Promise(function(_,y){Promise.all(o).then(function(){a.topojson=PlotlyGeoAssets.topojson[a.topojsonName],a.update(e,t),_()}).catch(y)}))};Lp.fetchTopojson=function(){var e=this,t=gze.getTopojsonPath(e.topojsonURL,e.topojsonName);return new Promise(function(r,n){M1.json(t,function(a,i){if(a)return a.status===404?n(new Error([\"plotly.js could not find topojson file at\",t+\".\",\"Make sure the *topojsonURL* plot config option\",\"is set properly.\"].join(\" \"))):n(new Error([\"unexpected error while fetching topojson file at\",t].join(\" \")));PlotlyGeoAssets.topojson[e.topojsonName]=i,r()})})};Lp.update=function(e,t,r){var n=t[this.id];this.hasChoropleth=!1;for(var a=0;a<e.length;a++){var i=e[a],o=i[0].trace;o.type===\"choropleth\"&&(this.hasChoropleth=!0),o.visible===!0&&o._length>0&&o._module.calcGeoJSON(i,t)}if(!r){var s=this.updateProjection(e,t);if(s)return;(!this.viewInitial||this.scope!==n.scope)&&this.saveViewInitial(n)}this.scope=n.scope,this.updateBaseLayers(t,n),this.updateDims(t,n),this.updateFx(t,n),XHt.generalUpdatePerTraceModule(this.graphDiv,this,e,n);var l=this.layers.frontplot.select(\".scatterlayer\");this.dataPoints.point=l.selectAll(\".point\"),this.dataPoints.text=l.selectAll(\"text\"),this.dataPaths.line=l.selectAll(\".js-line\");var u=this.layers.backplot.select(\".choroplethlayer\");this.dataPaths.choropleth=u.selectAll(\"path\"),this._render()};Lp.updateProjection=function(e,t){var r=this.graphDiv,n=t[this.id],a=t._size,i=n.domain,o=n.projection,s=n.lonaxis,l=n.lataxis,u=s._ax,c=l._ax,f=this.projection=rjt(n),v=[[a.l+a.w*i.x[0],a.t+a.h*(1-i.y[1])],[a.l+a.w*i.x[1],a.t+a.h*(1-i.y[0])]],g=n.center||{},p=o.rotation||{},x=s.range||[],_=l.range||[];if(n.fitbounds){u._length=v[1][0]-v[0][0],c._length=v[1][1]-v[0][1],u.range=dze(r,u),c.range=dze(r,c);let O=[];for(let ue of e){let K=ue[0].trace;K.visible===!0&&K._module.fitCoords&&O.push(K._module.fitCoords(ue,n))}let V=ZHt(r,u),j=V.min.reduce((ue,{val:K})=>Math.min(ue,K),1/0),U=V.max.reduce((ue,{val:K})=>Math.max(ue,K),-1/0)-j,J=YX.boundsOfCoords(O.flat()),[ee,,ce]=J||[],ye=ce-ee;if(!!J&&(U>360||U<360&&ye<U)){let ue=vze(t,u,0),K=vze(t,u,1),_e=V.min.reduce((le,me)=>Math.max(le,ue(me)),0),ne=V.max.reduce((le,me)=>Math.max(le,K(me)),0),ae=u._length-_e-ne,pe=ae>u._length/10?ye*u._length/ae:ye,ie=(ee+ce)/2;u.range=[ie-pe/2,ie+pe/2]}var y=(u.range[0]+u.range[1])/2,C=(c.range[0]+c.range[1])/2;if(n._isScoped)g={lon:y,lat:C};else if(n._isClipped){g={lon:y,lat:C},p={lon:y,lat:C,roll:p.roll};var k=o.type,S=Id.lonaxisSpan[k]/2||180,L=Id.lataxisSpan[k]/2||90;x=[y-S,y+S],_=[C-L,C+L]}else g={lon:y,lat:C},p={lon:y,lat:p.lat,roll:p.roll}}f.center([g.lon-p.lon,g.lat-p.lat]).rotate([-p.lon,-p.lat,p.roll]).parallels(o.parallels);var w=pze(x,_);f.fitExtent(v,w);var A=this.bounds=f.getBounds(w),T=this.fitScale=f.scale(),m=f.translate();if(f.scaleExtent=()=>{var j;let{minscale:O}=o,V=(j=o.maxscale)!=null?j:1/0;return[T*Math.min(O,V),T*Math.max(O,V)]},n.fitbounds){var F=f.getBounds(pze(u.range,c.range)),E=Math.min((A[1][0]-A[0][0])/(F[1][0]-F[0][0]),(A[1][1]-A[0][1])/(F[1][1]-F[0][1]));isFinite(E)?f.scale(E*T):vM.warn(\"Something went wrong during\"+this.id+\"fitbounds computations.\")}else f.scale(o.scale*T);var I=this.midPt=[(A[0][0]+A[1][0])/2,(A[0][1]+A[1][1])/2];if(f.translate([m[0]+(I[0]-m[0]),m[1]+(I[1]-m[1])]).clipExtent(A),n._isAlbersUsa){var R=f([g.lon,g.lat]);if(R){var B=f.translate();f.translate([B[0]-(R[0]-B[0]),B[1]-(R[1]-B[1])])}}};Lp.updateBaseLayers=function(e,t){var r=this,n=r.topojson,a=r.layers,i=r.basePaths;function o(v){return v===\"lonaxis\"||v===\"lataxis\"}function s(v){return!!Id.lineLayers[v]}function l(v){return!!Id.fillLayers[v]}var u=this.hasChoropleth?Id.layersForChoropleth:Id.layers,c=u.filter(function(v){return s(v)||l(v)?t[\"show\"+v]:o(v)?t[v].showgrid:!0}),f=r.framework.selectAll(\".layer\").data(c,String);f.exit().each(function(v){delete a[v],delete i[v],M1.select(this).remove()}),f.enter().append(\"g\").attr(\"class\",function(v){return\"layer \"+v}).each(function(v){var g=a[v]=M1.select(this);v===\"bg\"?r.bgRect=g.append(\"rect\").style(\"pointer-events\",\"all\"):o(v)?i[v]=g.append(\"path\").style(\"fill\",\"none\"):v===\"backplot\"?g.append(\"g\").classed(\"choroplethlayer\",!0):v===\"frontplot\"?g.append(\"g\").classed(\"scatterlayer\",!0):s(v)?i[v]=g.append(\"path\").style(\"fill\",\"none\").style(\"stroke-miterlimit\",2):l(v)&&(i[v]=g.append(\"path\").style(\"stroke\",\"none\"))}),f.order(),f.each(function(v){var g=i[v],p=Id.layerNameToAdjective[v];v===\"frame\"?g.datum(Id.sphereSVG):s(v)||l(v)?g.datum(tjt(n,n.objects[v])):o(v)&&g.datum(njt(v,t,e)).call(WP.stroke,t[v].gridcolor).call(dM.dashLine,t[v].griddash,t[v].gridwidth),s(v)?g.call(WP.stroke,t[p+\"color\"]).call(dM.dashLine,\"\",t[p+\"width\"]):l(v)&&g.call(WP.fill,t[p+\"color\"])})};Lp.updateDims=function(e,t){var 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f=QHt(r,t);if(a.call(f),!M1.event){let v=r.projection.scale(),[g,p]=r.projection.scaleExtent();(v<g||v>p)&&f.event(a)}a.on(\"dblclick.zoom\",s),n._context._scrollZoom.geo||a.on(\"wheel.zoom\",null)}else(i===\"select\"||i===\"lasso\")&&(a.on(\".zoom\",null),c.prepFn=function(f,v,g){$Ht(f,v,g,c,i)},HX.init(c));a.on(\"mousemove\",function(){var f=r.projection.invert(vM.getPositionFromD3Event());if(!f)return HX.unhover(n,M1.event);r.xaxis.p2c=function(){return f[0]},r.yaxis.p2c=function(){return f[1]},hze.hover(n,M1.event,r.id)}),a.on(\"mouseout\",function(){n._dragging||HX.unhover(n,M1.event)}),a.on(\"click\",function(){i!==\"select\"&&i!==\"lasso\"&&(o.indexOf(\"select\")>-1&&JHt(M1.event,n,[r.xaxis],[r.yaxis],r.id,c),o.indexOf(\"event\")>-1&&hze.click(n,M1.event))})};Lp.makeFramework=function(){var e=this,t=e.graphDiv,r=t._fullLayout,n=\"clip\"+r._uid+e.id;e.clipDef=r._clips.append(\"clipPath\").attr(\"id\",n),e.clipRect=e.clipDef.append(\"rect\"),e.framework=M1.select(e.container).append(\"g\").attr(\"class\",\"geo \"+e.id).call(dM.setClipUrl,n,t),e.project=function(a){var i=e.projection(a);return i?[i[0]-e.xaxis._offset,i[1]-e.yaxis._offset]:[null,null]},e.xaxis={_id:\"x\",c2p:function(a){return e.project(a)[0]}},e.yaxis={_id:\"y\",c2p:function(a){return e.project(a)[1]}},e.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},jX.setConvert(e.mockAxis,r)};Lp.saveViewInitial=function(e){var t=e.center||{},r=e.projection,n=r.rotation||{};this.viewInitial={fitbounds:e.fitbounds,\"projection.scale\":r.scale};var a;e._isScoped?a={\"center.lon\":t.lon,\"center.lat\":t.lat}:e._isClipped?a={\"projection.rotation.lon\":n.lon,\"projection.rotation.lat\":n.lat,\"center.lon\":t.lon,\"center.lat\":t.lat}:a={\"center.lon\":t.lon,\"center.lat\":t.lat,\"projection.rotation.lon\":n.lon},vM.extendFlat(this.viewInitial,a)};Lp.render=function(e){this._hasMarkerAngles&&e?this.plot(this._geoCalcData,this._fullLayout,[],!0):this._render()};Lp._render=function(){var e=this.projection,t=e.getPath(),r;function n(i){var o=e(i.lonlat);return o?YHt(o[0],o[1]):null}function a(i){return e.isLonLatOverEdges(i.lonlat)?\"none\":null}for(r in this.basePaths)this.basePaths[r].attr(\"d\",t);for(r in this.dataPaths)this.dataPaths[r].attr(\"d\",function(i){return t(i.geojson)});for(r in this.dataPoints)this.dataPoints[r].attr(\"display\",a).attr(\"transform\",n)};function rjt(e){var t=e.projection,r=t.type,n=Id.projNames[r];n=\"geo\"+vM.titleCase(n);for(var a=WX[n]||jHt[n],i=a(),o=e._isSatellite?Math.acos(1/t.distance)*180/Math.PI:e._isClipped?Id.lonaxisSpan[r]/2:null,s=[\"center\",\"rotate\",\"parallels\",\"clipExtent\"],l=function(f){return f?i:[]},u=0;u<s.length;u++){var c=s[u];typeof i[c]!=\"function\"&&(i[c]=l)}return i.isLonLatOverEdges=function(f){if(i(f)===null)return!0;if(o){var v=i.rotate(),g=HHt(f,[-v[0],-v[1]]),p=o*Math.PI/180;return g>p}else return!1},i.getPath=function(){return GHt().projection(i)},i.getBounds=function(f){return i.getPath().bounds(f)},i.precision(Id.precision),e._isSatellite&&i.tilt(t.tilt).distance(t.distance),o&&i.clipAngle(o-Id.clipPad),i}function njt(e,t,r){var n=1e-6,a=2.5,i=t[e],o=Id.scopeDefaults[t.scope],s,l,u;e===\"lonaxis\"?(s=o.lonaxisRange,l=o.lataxisRange,u=function(C,k){return[C,k]}):e===\"lataxis\"&&(s=o.lataxisRange,l=o.lonaxisRange,u=function(C,k){return[k,C]});var c={type:\"linear\",range:[s[0],s[1]-n],tick0:i.tick0,dtick:i.dtick};jX.setConvert(c,r);var f=jX.calcTicks(c);!t.isScoped&&e===\"lonaxis\"&&f.pop();for(var v=f.length,g=new Array(v),p=0;p<v;p++)for(var x=f[p].x,_=g[p]=[],y=l[0];y<l[1]+a;y+=a)_.push(u(x,y));return{type:\"MultiLineString\",coordinates:g}}function pze(e,t){var r=Id.clipPad;let[n,a]=ejt([e[0]+r,e[1]-r]);var i=t[0]+r,o=t[1]-r,s=(a-n)/4;return{type:\"Polygon\",coordinates:[[[n,i],[n,o],[n+s,o],[n+2*s,o],[n+3*s,o],[a,o],[a,i],[a-s,i],[a-2*s,i],[a-3*s,i],[n,i]]]}}});var XX=fe((c5r,wze)=>{\"use strict\";var l5=tc(),ijt=nl().attributes,ajt=Uc().dash,s5=lM(),ojt=rl().overrideAll,_ze=(bx(),Gs(_x)).default,bze={range:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},showgrid:{valType:\"boolean\",dflt:!1},tick0:{valType:\"number\",dflt:0},dtick:{valType:\"number\"},gridcolor:{valType:\"color\",dflt:l5.lightLine},gridwidth:{valType:\"number\",min:0,dflt:1},griddash:ajt},sjt=wze.exports=ojt({domain:ijt({name:\"geo\"},{}),fitbounds:{valType:\"enumerated\",values:[!1,\"locations\",\"geojson\"],dflt:\"locations\",editType:\"plot\"},resolution:{valType:\"enumerated\",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:\"enumerated\",values:_ze(s5.scopeDefaults),dflt:\"world\"},projection:{type:{valType:\"enumerated\",values:_ze(s5.projNames)},rotation:{lon:{valType:\"number\"},lat:{valType:\"number\"},roll:{valType:\"number\"}},tilt:{valType:\"number\",dflt:0},distance:{valType:\"number\",min:1.001,dflt:2},parallels:{valType:\"info_array\",items:[{valType:\"number\"},{valType:\"number\"}]},scale:{valType:\"number\",min:0,dflt:1},minscale:{valType:\"number\",min:0,dflt:0},maxscale:{valType:\"number\",min:0,dflt:null}},center:{lon:{valType:\"number\"},lat:{valType:\"number\"}},visible:{valType:\"boolean\",dflt:!0},showcoastlines:{valType:\"boolean\"},coastlinecolor:{valType:\"color\",dflt:l5.defaultLine},coastlinewidth:{valType:\"number\",min:0,dflt:1},showland:{valType:\"boolean\",dflt:!1},landcolor:{valType:\"color\",dflt:s5.landColor},showocean:{valType:\"boolean\",dflt:!1},oceancolor:{valType:\"color\",dflt:s5.waterColor},showlakes:{valType:\"boolean\",dflt:!1},lakecolor:{valType:\"color\",dflt:s5.waterColor},showrivers:{valType:\"boolean\",dflt:!1},rivercolor:{valType:\"color\",dflt:s5.waterColor},riverwidth:{valType:\"number\",min:0,dflt:1},showcountries:{valType:\"boolean\"},countrycolor:{valType:\"color\",dflt:l5.defaultLine},countrywidth:{valType:\"number\",min:0,dflt:1},showsubunits:{valType:\"boolean\"},subunitcolor:{valType:\"color\",dflt:l5.defaultLine},subunitwidth:{valType:\"number\",min:0,dflt:1},showframe:{valType:\"boolean\"},framecolor:{valType:\"color\",dflt:l5.defaultLine},framewidth:{valType:\"number\",min:0,dflt:1},bgcolor:{valType:\"color\",dflt:l5.background},lonaxis:bze,lataxis:bze},\"plot\",\"from-root\");sjt.uirevision={valType:\"any\",editType:\"none\"}});var Mze=fe((f5r,Aze)=>{\"use strict\";var YP=Xt(),ljt=b2(),ujt=jf().getSubplotData,{unwrapLonRange:cjt}=Cg(),pM=lM(),fjt=XX(),Tze=pM.axesNames;Aze.exports=function(t,r,n){ljt(t,r,n,{type:\"geo\",attributes:fjt,handleDefaults:hjt,fullData:n,partition:\"y\"})};function hjt(e,t,r,n){var a=ujt(n.fullData,\"geo\",n.id),i=a.map(function(J){return J.index}),o=r(\"resolution\"),s=r(\"scope\"),l=pM.scopeDefaults[s],u=r(\"projection.type\",l.projType),c=t._isAlbersUsa=u===\"albers usa\";c&&(s=t.scope=\"usa\");var f=t._isScoped=s!==\"world\",v=t._isSatellite=u===\"satellite\",g=t._isConic=u.indexOf(\"conic\")!==-1||u===\"albers\",p=t._isClipped=!!pM.lonaxisSpan[u];if(e.visible===!1){var x=YP.extendDeep({},t._template);x.showcoastlines=!1,x.showcountries=!1,x.showframe=!1,x.showlakes=!1,x.showland=!1,x.showocean=!1,x.showrivers=!1,x.showsubunits=!1,x.lonaxis&&(x.lonaxis.showgrid=!1),x.lataxis&&(x.lataxis.showgrid=!1),t._template=x}for(var _=r(\"visible\"),y,C=0;C<Tze.length;C++){var 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tYt=tF(),d5=Xt(),yBe=El(),rYt=rp().findExtremes,xBe=cp(),pZ=gv(),nYt=pZ.calcMarkerSize,iYt=pZ.calcAxisExpansion,aYt=pZ.setFirstScatter,oYt=pv(),v5=f_(),sYt=vZ(),_Be=Wa().BADNUM,lYt=Ty().TOO_MANY_POINTS;wBe.exports=function(t,r){var n=t._fullLayout,a=r._xA=yBe.getFromId(t,r.xaxis,\"x\"),i=r._yA=yBe.getFromId(t,r.yaxis,\"y\"),o=n._plots[r.xaxis+r.yaxis],s=r._length,l=s>=lYt,u=s*2,c={},f,v=a.makeCalcdata(r,\"x\"),g=i.makeCalcdata(r,\"y\"),p=xBe(r,a,\"x\",v),x=xBe(r,i,\"y\",g),_=p.vals,y=x.vals;r._x=_,r._y=y,r.xperiodalignment&&(r._origX=v,r._xStarts=p.starts,r._xEnds=p.ends),r.yperiodalignment&&(r._origY=g,r._yStarts=x.starts,r._yEnds=x.ends);var C=new Array(u),k=new Array(s);for(f=0;f<s;f++)C[f*2]=_[f]===_Be?NaN:_[f],C[f*2+1]=y[f]===_Be?NaN:y[f],k[f]=f;if(a.type===\"log\")for(f=0;f<u;f+=2)C[f]=a.c2l(C[f]);if(i.type===\"log\")for(f=1;f<u;f+=2)C[f]=i.c2l(C[f]);l&&a.type!==\"log\"&&i.type!==\"log\"?c.tree=tYt(C):c.ids=k,oYt(t,r);var S=uYt(t,o,r,C,_,y),L=sYt(t,o);aYt(n,r);var w;return 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f={uniforms:{constPointSize:!!t.constPointSize,opacity:e.prop(\"opacity\"),paletteSize:(p,x)=>[this.tooManyColors?0:u,n.height],pixelRatio:e.context(\"pixelRatio\"),scale:e.prop(\"scale\"),scaleFract:e.prop(\"scaleFract\"),translate:e.prop(\"translate\"),translateFract:e.prop(\"translateFract\"),markerTexture:e.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:(p,x)=>x.xAttr||{buffer:x.positionBuffer,stride:8,offset:0},y:(p,x)=>x.yAttr||{buffer:x.positionBuffer,stride:8,offset:4},xFract:(p,x)=>x.xAttr?{constant:[0,0]}:{buffer:x.positionFractBuffer,stride:8,offset:0},yFract:(p,x)=>x.yAttr?{constant:[0,0]}:{buffer:x.positionFractBuffer,stride:8,offset:4},size:(p,x)=>x.size.length?{buffer:x.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(x.size*255/this.maxSize)]},borderSize:(p,x)=>x.borderSize.length?{buffer:x.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(x.borderSize*255/this.maxSize)]},colorId:(p,x)=>x.color.length?{buffer:x.colorBuffer,stride:this.tooManyColors?8:4,offset:0}:{constant:this.tooManyColors?a.slice(x.color*4,x.color*4+4):[x.color]},borderColorId:(p,x)=>x.borderColor.length?{buffer:x.colorBuffer,stride:this.tooManyColors?8:4,offset:this.tooManyColors?4:2}:{constant:this.tooManyColors?a.slice(x.borderColor*4,x.borderColor*4+4):[x.borderColor]},isActive:(p,x)=>x.activation===!0?{constant:[1]}:x.activation?x.activation:{constant:[0]}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:e.prop(\"viewport\")},viewport:e.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:e.prop(\"elements\"),count:e.prop(\"count\"),offset:e.prop(\"offset\"),primitive:\"points\"},v=Object.assign({},f);v.frag=cF([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "uniform float opacity;\n",
@@ -2090,7 +2091,7 @@
        "  }\n",
        "\n",
        "}\n",
-       "`]),d.vert=uz([`precision highp float;\n",
+       "`]),v.vert=cF([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute float x, y, xFract, yFract;\n",
@@ -2158,7 +2159,7 @@
        "  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n",
        "  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n",
        "}\n",
-       "`]),this.drawMarker=e(d);var v=fw({},h);v.frag=uz([`precision highp float;\n",
+       "`]),this.drawMarker=e(v);let g=Object.assign({},f);g.frag=cF([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "varying vec4 fragColor, fragBorderColor;\n",
@@ -2189,7 +2190,7 @@
        "\tcolor.a *= alpha * opacity;\n",
        "\tgl_FragColor = color;\n",
        "}\n",
-       "`]),v.vert=uz([`precision highp float;\n",
+       "`]),g.vert=cF([`precision highp float;\n",
        "#define GLSLIFY 1\n",
        "\n",
        "attribute float x, y, xFract, yFract;\n",
@@ -2253,8 +2254,8 @@
        "  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n",
        "  fragWidth = 1. / gl_PointSize;\n",
        "}\n",
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k=oOt(f[p],2),M=k[0],T=k[1];c.push(fw({},t,{markerTexture:this.markerTextures[e],activation:r?s:o[e],offset:M,count:T-M}))}return c};av.prototype.update=function(){for(var e=this,t=arguments.length,r=new Array(t),n=0;n<t;n++)r[n]=arguments[n];if(r.length){r.length===1&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,l=this.maxColors,u=this.palette;this.groups=i=r.map(function(c,f){var h=i[f];if(c===void 0)return h;c===null?c={positions:null}:typeof c==\"function\"?c={ondraw:c}:typeof c[0]==\"number\"&&(c={positions:c}),c=mOt(c,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox 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d=0,v=0;if(yOt(h,c,[{snap:!0,size:function(j,N){return j==null&&(j=av.defaults.size),d+=j&&j.length?1:0,j},borderSize:function(j,N){return j==null&&(j=av.defaults.borderSize),d+=j&&j.length?1:0,j},opacity:parseFloat,color:function(j,N){return j==null&&(j=av.defaults.color),j=e.updateColor(j),v++,j},borderColor:function(j,N){return j==null&&(j=av.defaults.borderColor),j=e.updateColor(j),v++,j},bounds:function(j,N,H){return\"range\"in H||(H.range=null),j},positions:function(j,N,H){var re=N.snap,ae=N.positionBuffer,_e=N.positionFractBuffer,Ee=N.selectionBuffer;if(j.x||j.y)return j.x.length?N.xAttr={buffer:o.buffer(j.x),offset:0,stride:4,count:j.x.length}:N.xAttr={buffer:j.x.buffer,offset:j.x.offset*4||0,stride:(j.x.stride||1)*4,count:j.x.count},j.y.length?N.yAttr={buffer:o.buffer(j.y),offset:0,stride:4,count:j.y.length}:N.yAttr={buffer:j.y.buffer,offset:j.y.offset*4||0,stride:(j.y.stride||1)*4,count:j.y.count},N.count=Math.max(N.xAttr.count,N.yAttr.count),j;j=_Ot(j,\"float64\");var Ce=N.count=Math.floor(j.length/2),ge=N.bounds=Ce?vOt(j,2):null;if(!H.range&&!N.range&&(delete N.range,H.range=ge),!H.marker&&!N.marker&&(delete N.marker,H.marker=null),re&&(re===!0||Ce>re)?N.tree=gOt(j,{bounds:ge}):re&&re.length&&(N.tree=re),N.tree){var ie={primitive:\"points\",usage:\"static\",data:N.tree,type:\"uint32\"};N.elements?N.elements(ie):N.elements=o.elements(ie)}var Se=cz.float32(j);ae({data:Se,usage:\"dynamic\"});var ke=cz.fract32(j,Se);return _e({data:ke,usage:\"dynamic\"}),Ee({data:new Uint8Array(Ce),type:\"uint8\",usage:\"stream\"}),j}},{marker:function(j,N,H){var re=N.activation;if(re.forEach(function(ke){return ke&&ke.destroy&&ke.destroy()}),re.length=0,!j||typeof j[0]==\"number\"){var ae=e.addMarker(j);re[ae]=!0}else{for(var _e=[],Ee=0,Ce=Math.min(j.length,N.count);Ee<Ce;Ee++){var ge=e.addMarker(j[Ee]);_e[ge]||(_e[ge]=new Uint8Array(N.count)),_e[ge][Ee]=1}for(var ie=0;ie<_e.length;ie++)if(_e[ie]){var Se={data:_e[ie],type:\"uint8\",usage:\"static\"};re[ie]?re[ie](Se):re[ie]=o.buffer(Se),re[ie].data=_e[ie]}}return j},range:function(j,N,H){var re=N.bounds;if(re)return j||(j=re),N.scale=[1/(j[2]-j[0]),1/(j[3]-j[1])],N.translate=[-j[0],-j[1]],N.scaleFract=cz.fract(N.scale),N.translateFract=cz.fract(N.translate),j},viewport:function(j){var N=xOt(j||[a.drawingBufferWidth,a.drawingBufferHeight]);return N}}]),d){var m=h,b=m.count,p=m.size,k=m.borderSize,M=m.sizeBuffer,T=new Uint8Array(b*2);if(p.length||k.length)for(var L=0;L<b;L++)T[L*2]=Math.round((p[L]==null?p:p[L])*255/s),T[L*2+1]=Math.round((k[L]==null?k:k[L])*255/s);M({data:T,usage:\"dynamic\"})}if(v){var x=h,C=x.count,S=x.color,g=x.borderColor,P=x.colorBuffer,E;if(e.tooManyColors){if(S.length||g.length){E=new Uint8Array(C*8);for(var z=0;z<C;z++){var q=S[z];E[z*8]=u[q*4],E[z*8+1]=u[q*4+1],E[z*8+2]=u[q*4+2],E[z*8+3]=u[q*4+3];var V=g[z];E[z*8+4]=u[V*4],E[z*8+5]=u[V*4+1],E[z*8+6]=u[V*4+2],E[z*8+7]=u[V*4+3]}}}else if(S.length||g.length){E=new Uint8Array(C*4+2);for(var G=0;G<C;G++)S[G]!=null&&(E[G*4]=S[G]%l,E[G*4+1]=Math.floor(S[G]/l)),g[G]!=null&&(E[G*4+2]=g[G]%l,E[G*4+3]=Math.floor(g[G]/l))}P({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return h})}};av.prototype.addMarker=function(e){var t=this.markerTextures,r=this.regl,n=this.markerCache,i=e==null?0:n.indexOf(e);if(i>=0)return i;var a;if(e instanceof Uint8Array||e instanceof Uint8ClampedArray)a=e;else{a=new Uint8Array(e.length);for(var o=0,s=e.length;o<s;o++)a[o]=e[o]*255}var l=Math.floor(Math.sqrt(a.length));return i=t.length,n.push(e),t.push(r.texture({channels:1,data:a,radius:l,mag:\"linear\",min:\"linear\"})),i};av.prototype.updateColor=function(e){var t=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(e)||(e=[e]);var i=[];if(typeof e[0]==\"number\"){var a=[];if(Array.isArray(e))for(var o=0;o<e.length;o+=4)a.push(e.slice(o,o+4));else for(var s=0;s<e.length;s+=4)a.push(e.subarray(s,s+4));e=a}for(var l=0;l<e.length;l++){var u=e[l];u=dOt(u,\"uint8\");var c=pOt(u,!1);if(t[c]==null){var f=r.length;t[c]=Math.floor(f/4),r[f]=u[0],r[f+1]=u[1],r[f+2]=u[2],r[f+3]=u[3]}i[l]=t[c]}return!this.tooManyColors&&r.length>n*4&&(this.tooManyColors=!0),this.updatePalette(r),i.length===1?i[0]:i};av.prototype.updatePalette=function(e){if(!this.tooManyColors){var t=this.maxColors,r=this.paletteTexture,n=Math.ceil(e.length*.25/t);if(n>1){e=e.slice();for(var i=e.length*.25%t;i<n*t;i++)e.push(0,0,0,0)}r.height<n&&r.resize(t,n),r.subimage({width:Math.min(e.length*.25,t),height:n,data:e},0,0)}};av.prototype.destroy=function(){return this.groups.forEach(function(e){e.sizeBuffer.destroy(),e.positionBuffer.destroy(),e.positionFractBuffer.destroy(),e.colorBuffer.destroy(),e.activation.forEach(function(t){return t&&t.destroy&&t.destroy()}),e.selectionBuffer.destroy(),e.elements&&e.elements.destroy()}),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach(function(e){return 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v=0,g=0;if(kYt(f,u,[{snap:!0,size:(p,x)=>(p==null&&(p=rh.defaults.size),v+=p&&p.length?1:0,p),borderSize:(p,x)=>(p==null&&(p=rh.defaults.borderSize),v+=p&&p.length?1:0,p),opacity:parseFloat,color:(p,x)=>(p==null&&(p=rh.defaults.color),p=this.updateColor(p),g++,p),borderColor:(p,x)=>(p==null&&(p=rh.defaults.borderColor),p=this.updateColor(p),g++,p),bounds:(p,x,_)=>(\"range\"in _||(_.range=null),p),positions:(p,x,_)=>{let{snap:y}=x,{positionBuffer:C,positionFractBuffer:k,selectionBuffer:S}=x;if(p.x||p.y)return p.x.length?x.xAttr={buffer:i.buffer(p.x),offset:0,stride:4,count:p.x.length}:x.xAttr={buffer:p.x.buffer,offset:p.x.offset*4||0,stride:(p.x.stride||1)*4,count:p.x.count},p.y.length?x.yAttr={buffer:i.buffer(p.y),offset:0,stride:4,count:p.y.length}:x.yAttr={buffer:p.y.buffer,offset:p.y.offset*4||0,stride:(p.y.stride||1)*4,count:p.y.count},x.count=Math.max(x.xAttr.count,x.yAttr.count),p;p=CYt(p,\"float64\");let L=x.count=Math.floor(p.length/2),w=x.bounds=L?AYt(p,2):null;if(!_.range&&!x.range&&(delete x.range,_.range=w),!_.marker&&!x.marker&&(delete x.marker,_.marker=null),y&&(y===!0||L>y)?x.tree=SYt(p,{bounds:w}):y&&y.length&&(x.tree=y),x.tree){let m={primitive:\"points\",usage:\"static\",data:x.tree,type:\"uint32\"};x.elements?x.elements(m):x.elements=i.elements(m)}var A=fF.float32(p);C({data:A,usage:\"dynamic\"});var T=fF.fract32(p,A);return k({data:T,usage:\"dynamic\"}),S({data:new Uint8Array(L),type:\"uint8\",usage:\"stream\"}),p}},{marker:(p,x,_)=>{let{activation:y}=x;if(y.forEach(C=>C&&C.destroy&&C.destroy()),y.length=0,!p||typeof p[0]==\"number\"){let C=this.addMarker(p);y[C]=!0}else{let C=[];for(let k=0,S=Math.min(p.length,x.count);k<S;k++){let L=this.addMarker(p[k]);C[L]||(C[L]=new Uint8Array(x.count)),C[L][k]=1}for(let k=0;k<C.length;k++){if(!C[k])continue;let S={data:C[k],type:\"uint8\",usage:\"static\"};y[k]?y[k](S):y[k]=i.buffer(S),y[k].data=C[k]}}return p},range:(p,x,_)=>{let 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        "precision highp float;\n",
        "\n",
        "attribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\n",
@@ -2293,7 +2294,7 @@
        "\n",
        "\tfragColor = color / 255.;\n",
        "}\n",
-       "`,lUt=`\n",
+       "`,w$t=`\n",
        "precision highp float;\n",
        "\n",
        "uniform float dashLength, pixelRatio, thickness, opacity, id;\n",
@@ -2311,7 +2312,7 @@
        "\tgl_FragColor = fragColor;\n",
        "\tgl_FragColor.a *= alpha * opacity * dash;\n",
        "}\n",
-       "`,uUt=`\n",
+       "`,T$t=`\n",
        "precision highp float;\n",
        "\n",
        "attribute vec2 position, positionFract;\n",
@@ -2339,14 +2340,14 @@
        "\tfragColor = color / 255.;\n",
        "\tfragColor.a *= opacity;\n",
        "}\n",
-       "`,cUt=`\n",
+       "`,A$t=`\n",
        "precision highp float;\n",
        "varying vec4 fragColor;\n",
        "\n",
        "void main() {\n",
        "\tgl_FragColor = fragColor;\n",
        "}\n",
-       "`,fUt=`\n",
+       "`,M$t=`\n",
        "precision highp float;\n",
        "\n",
        "attribute vec2 aCoord, bCoord, nextCoord, prevCoord;\n",
@@ -2550,7 +2551,7 @@
        "\t\t}\n",
        "\t}\n",
        "}\n",
-       "`,hUt=`\n",
+       "`,S$t=`\n",
        "precision highp float;\n",
        "\n",
        "uniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\n",
@@ -2630,7 +2631,7 @@
        "\tgl_FragColor = fragColor;\n",
        "\tgl_FragColor.a *= alpha * opacity * dash;\n",
        "}\n",
-       "`;COe.exports=Wc;function Wc(e,t){if(!(this instanceof Wc))return new Wc(e,t);if(typeof e==\"function\"?(t||(t={}),t.regl=e):t=e,t.length&&(t.positions=t),e=t.regl,!e.hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=e._gl,this.regl=e,this.passes=[],this.shaders=Wc.shaders.has(e)?Wc.shaders.get(e):Wc.shaders.set(e,Wc.createShaders(e)).get(e),this.update(t)}Wc.dashMult=2;Wc.maxPatternLength=256;Wc.precisionThreshold=3e6;Wc.maxPoints=1e4;Wc.maxLines=2048;Wc.shaders=new aUt;Wc.createShaders=function(e){let t=e.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),r={primitive:\"triangle strip\",instances:e.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:(o,s)=>s.join===\"round\"?2:1,miterLimit:e.prop(\"miterLimit\"),scale:e.prop(\"scale\"),scaleFract:e.prop(\"scaleFract\"),translateFract:e.prop(\"translateFract\"),translate:e.prop(\"translate\"),thickness:e.prop(\"thickness\"),dashTexture:e.prop(\"dashTexture\"),opacity:e.prop(\"opacity\"),pixelRatio:e.context(\"pixelRatio\"),id:e.prop(\"id\"),dashLength:e.prop(\"dashLength\"),viewport:(o,s)=>[s.viewport.x,s.viewport.y,o.viewportWidth,o.viewportHeight],depth:e.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:(o,s)=>!s.overlay},stencil:{enable:!1},scissor:{enable:!0,box:e.prop(\"viewport\")},viewport:e.prop(\"viewport\")},n=e(QY({vert:sUt,frag:lUt,attributes:{lineEnd:{buffer:t,divisor:0,stride:8,offset:0},lineTop:{buffer:t,divisor:0,stride:8,offset:4},aCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},r)),i;try{i=e(QY({cull:{enable:!0,face:\"back\"},vert:fUt,frag:hUt,attributes:{lineEnd:{buffer:t,divisor:0,stride:8,offset:0},lineTop:{buffer:t,divisor:0,stride:8,offset:4},aColor:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},r))}catch(o){i=n}return{fill:e({primitive:\"triangle\",elements:(o,s)=>s.triangles,offset:0,vert:uUt,frag:cUt,uniforms:{scale:e.prop(\"scale\"),color:e.prop(\"fill\"),scaleFract:e.prop(\"scaleFract\"),translateFract:e.prop(\"translateFract\"),translate:e.prop(\"translate\"),opacity:e.prop(\"opacity\"),pixelRatio:e.context(\"pixelRatio\"),id:e.prop(\"id\"),viewport:(o,s)=>[s.viewport.x,s.viewport.y,o.viewportWidth,o.viewportHeight]},attributes:{position:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:r.blend,depth:{enable:!1},scissor:r.scissor,stencil:r.stencil,viewport:r.viewport}),rect:n,miter:i}};Wc.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null};Wc.prototype.render=function(...e){e.length&&this.update(...e),this.draw()};Wc.prototype.draw=function(...e){return(e.length?e:this.passes).forEach((t,r)=>{if(t&&Array.isArray(t))return this.draw(...t);typeof t==\"number\"&&(t=this.passes[t]),t&&t.count>1&&t.opacity&&(this.regl._refresh(),t.fill&&t.triangles&&t.triangles.length>2&&this.shaders.fill(t),t.thickness&&(t.scale[0]*t.viewport.width>Wc.precisionThreshold||t.scale[1]*t.viewport.height>Wc.precisionThreshold?this.shaders.rect(t):t.join===\"rect\"||!t.join&&(t.thickness<=2||t.count>=Wc.maxPoints)?this.shaders.rect(t):this.shaders.miter(t)))}),this};Wc.prototype.update=function(e){if(!e)return;e.length!=null?typeof e[0]==\"number\"&&(e=[{positions:e}]):Array.isArray(e)||(e=[e]);let{regl:t,gl:r}=this;if(e.forEach((i,a)=>{let o=this.passes[a];if(i!==void 0){if(i===null){this.passes[a]=null;return}if(typeof i[0]==\"number\"&&(i={positions:i}),i=tUt(i,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin join type mode\",miterLimit:\"miterlimit miterLimit\",dashes:\"dash dashes dasharray dash-array dashArray\",color:\"color colour stroke colors colours stroke-color strokeColor\",fill:\"fill fill-color fillColor\",opacity:\"alpha opacity\",overlay:\"overlay crease overlap intersect\",close:\"closed close closed-path closePath\",range:\"range dataBox\",viewport:\"viewport viewBox\",hole:\"holes hole hollow\",splitNull:\"splitNull\"}),o||(this.passes[a]=o={id:a,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:t.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:\"linear\",min:\"linear\"}),colorBuffer:t.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array}),positionBuffer:t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),positionFractBuffer:t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array})},i=QY({},Wc.defaults,i)),i.thickness!=null&&(o.thickness=parseFloat(i.thickness)),i.opacity!=null&&(o.opacity=parseFloat(i.opacity)),i.miterLimit!=null&&(o.miterLimit=parseFloat(i.miterLimit)),i.overlay!=null&&(o.overlay=!!i.overlay,a<Wc.maxLines&&(o.depth=2*(Wc.maxLines-1-a%Wc.maxLines)/Wc.maxLines-1)),i.join!=null&&(o.join=i.join),i.hole!=null&&(o.hole=i.hole),i.fill!=null&&(o.fill=i.fill?Mz(i.fill,\"uint8\"):null),i.viewport!=null&&(o.viewport=kOe(i.viewport)),o.viewport||(o.viewport=kOe([r.drawingBufferWidth,r.drawingBufferHeight])),i.close!=null&&(o.close=i.close),i.positions===null&&(i.positions=[]),i.positions){let u,c;if(i.positions.x&&i.positions.y){let v=i.positions.x,m=i.positions.y;c=o.count=Math.max(v.length,m.length),u=new Float64Array(c*2);for(let b=0;b<c;b++)u[b*2]=v[b],u[b*2+1]=m[b]}else u=rUt(i.positions,\"float64\"),c=o.count=Math.floor(u.length/2);let f=o.bounds=eUt(u,2);if(o.fill){let v=[],m={},b=0;for(let p=0,k=0,M=o.count;p<M;p++){let T=u[p*2],L=u[p*2+1];isNaN(T)||isNaN(L)||T==null||L==null?(T=u[b*2],L=u[b*2+1],m[p]=b):b=p,v[k++]=T,v[k++]=L}if(i.splitNull){o.count-1 in m||(m[o.count]=o.count-1);let p=Object.keys(m).map(Number).sort((L,x)=>L-x),k=[],M=0,T=o.hole!=null?o.hole[0]:null;if(T!=null){let L=oUt(p,x=>x>=T);p=p.slice(0,L),p.push(T)}for(let L=0;L<p.length;L++){let x=v.slice(M*2,p[L]*2).concat(T?v.slice(T*2):[]),C=(o.hole||[]).map(g=>g-T+(p[L]-M)),S=EOe(x,C);S=S.map(g=>g+M+(g+M<p[L]?0:T-p[L])),k.push(...S),M=p[L]+1}for(let L=0,x=k.length;L<x;L++)m[k[L]]!=null&&(k[L]=m[k[L]]);o.triangles=k}else{let p=EOe(v,o.hole||[]);for(let k=0,M=p.length;k<M;k++)m[p[k]]!=null&&(p[k]=m[p[k]]);o.triangles=p}}let h=new Float64Array(u);iUt(h,2,f);let d=new Float64Array(c*2+6);o.close?u[0]===u[c*2-2]&&u[1]===u[c*2-1]?(d[0]=h[c*2-4],d[1]=h[c*2-3]):(d[0]=h[c*2-2],d[1]=h[c*2-1]):(d[0]=h[0],d[1]=h[1]),d.set(h,2),o.close?u[0]===u[c*2-2]&&u[1]===u[c*2-1]?(d[c*2+2]=h[2],d[c*2+3]=h[3],o.count-=1):(d[c*2+2]=h[0],d[c*2+3]=h[1],d[c*2+4]=h[2],d[c*2+5]=h[3]):(d[c*2+2]=h[c*2-2],d[c*2+3]=h[c*2-1],d[c*2+4]=h[c*2-2],d[c*2+5]=h[c*2-1]);var s=nUt(d);o.positionBuffer(s);var l=$Y(d,s);o.positionFractBuffer(l)}if(i.range?o.range=i.range:o.range||(o.range=o.bounds),(i.range||i.positions)&&o.count){let u=o.bounds,c=u[2]-u[0],f=u[3]-u[1],h=o.range[2]-o.range[0],d=o.range[3]-o.range[1];o.scale=[c/h,f/d],o.translate=[-o.range[0]/h+u[0]/h||0,-o.range[1]/d+u[1]/d||0],o.scaleFract=$Y(o.scale),o.translateFract=$Y(o.translate)}if(i.dashes){let u=0,c;if(!i.dashes||i.dashes.length<2)u=1,c=new Uint8Array([255,255,255,255,255,255,255,255]);else{u=0;for(let d=0;d<i.dashes.length;++d)u+=i.dashes[d];c=new Uint8Array(u*Wc.dashMult);let f=0,h=255;for(let d=0;d<2;d++)for(let v=0;v<i.dashes.length;++v){for(let m=0,b=i.dashes[v]*Wc.dashMult*.5;m<b;++m)c[f++]=h;h^=255}}o.dashLength=u,o.dashTexture({channels:1,data:c,width:c.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(i.color){let u=o.count,c=i.color;c||(c=\"transparent\");let f=new Uint8Array(u*4+4);if(!Array.isArray(c)||typeof c[0]==\"number\"){let h=Mz(c,\"uint8\");for(let d=0;d<u+1;d++)f.set(h,d*4)}else{for(let h=0;h<u;h++){let d=Mz(c[h],\"uint8\");f.set(d,h*4)}f.set(Mz(c[0],\"uint8\"),u*4)}o.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:f})}}}),e.length<this.passes.length){for(let i=e.length;i<this.passes.length;i++){let a=this.passes[i];a&&(a.colorBuffer.destroy(),a.positionBuffer.destroy(),a.dashTexture.destroy())}this.passes.length=e.length}let n=[];for(let i=0;i<this.passes.length;i++)this.passes[i]!==null&&n.push(this.passes[i]);return this.passes=n,this};Wc.prototype.destroy=function(){return this.passes.forEach(e=>{e.colorBuffer.destroy(),e.positionBuffer.destroy(),e.dashTexture.destroy()}),this.passes.length=0,this}});var DOe=ye((C_r,ROe)=>{\"use strict\";var dUt=ow(),vUt=lx(),pUt=bY(),gUt=ry(),LOe=Nh(),POe=sw(),{float32:mUt,fract32:tK}=lz();ROe.exports=yUt;var IOe=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]];function yUt(e,t){if(typeof e==\"function\"?(t||(t={}),t.regl=e):t=e,t.length&&(t.positions=t),e=t.regl,!e.hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");let r=e._gl,n,i,a,o,s,l,u={color:\"black\",capSize:5,lineWidth:1,opacity:1,viewport:null,range:null,offset:0,count:0,bounds:null,positions:[],errors:[]},c=[];return o=e.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array(0)}),i=e.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),a=e.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),s=e.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),l=e.buffer({usage:\"static\",type:\"float\",data:IOe}),v(t),n=e({vert:`\n",
+       "`;XUe.exports=al;function al(e,t){if(!(this instanceof al))return new al(e,t);if(typeof e==\"function\"?(t||(t={}),t.regl=e):t=e,t.length&&(t.positions=t),e=t.regl,!e.hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=e._gl,this.regl=e,this.passes=[],this.shaders=al.shaders.has(e)?al.shaders.get(e):al.shaders.set(e,al.createShaders(e)).get(e),this.update(t)}al.dashMult=2;al.maxPatternLength=256;al.precisionThreshold=3e6;al.maxPoints=1e4;al.maxLines=2048;al.shaders=new x$t;al.createShaders=function(e){let t=e.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),r={primitive:\"triangle strip\",instances:e.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:(o,s)=>s.join===\"round\"?2:1,miterLimit:e.prop(\"miterLimit\"),scale:e.prop(\"scale\"),scaleFract:e.prop(\"scaleFract\"),translateFract:e.prop(\"translateFract\"),translate:e.prop(\"translate\"),thickness:e.prop(\"thickness\"),dashTexture:e.prop(\"dashTexture\"),opacity:e.prop(\"opacity\"),pixelRatio:e.context(\"pixelRatio\"),id:e.prop(\"id\"),dashLength:e.prop(\"dashLength\"),viewport:(o,s)=>[s.viewport.x,s.viewport.y,o.viewportWidth,o.viewportHeight],depth:e.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:(o,s)=>!s.overlay},stencil:{enable:!1},scissor:{enable:!0,box:e.prop(\"viewport\")},viewport:e.prop(\"viewport\")},n=e($Z({vert:b$t,frag:w$t,attributes:{lineEnd:{buffer:t,divisor:0,stride:8,offset:0},lineTop:{buffer:t,divisor:0,stride:8,offset:4},aCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},r)),a;try{a=e($Z({cull:{enable:!0,face:\"back\"},vert:M$t,frag:S$t,attributes:{lineEnd:{buffer:t,divisor:0,stride:8,offset:0},lineTop:{buffer:t,divisor:0,stride:8,offset:4},aColor:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:e.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},r))}catch(o){a=n}return{fill:e({primitive:\"triangle\",elements:(o,s)=>s.triangles,offset:0,vert:T$t,frag:A$t,uniforms:{scale:e.prop(\"scale\"),color:e.prop(\"fill\"),scaleFract:e.prop(\"scaleFract\"),translateFract:e.prop(\"translateFract\"),translate:e.prop(\"translate\"),opacity:e.prop(\"opacity\"),pixelRatio:e.context(\"pixelRatio\"),id:e.prop(\"id\"),viewport:(o,s)=>[s.viewport.x,s.viewport.y,o.viewportWidth,o.viewportHeight]},attributes:{position:{buffer:e.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:e.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:r.blend,depth:{enable:!1},scissor:r.scissor,stencil:r.stencil,viewport:r.viewport}),rect:n,miter:a}};al.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null};al.prototype.render=function(...e){e.length&&this.update(...e),this.draw()};al.prototype.draw=function(...e){return(e.length?e:this.passes).forEach((t,r)=>{if(t&&Array.isArray(t))return this.draw(...t);typeof t==\"number\"&&(t=this.passes[t]),t&&t.count>1&&t.opacity&&(this.regl._refresh(),t.fill&&t.triangles&&t.triangles.length>2&&this.shaders.fill(t),t.thickness&&(t.scale[0]*t.viewport.width>al.precisionThreshold||t.scale[1]*t.viewport.height>al.precisionThreshold?this.shaders.rect(t):t.join===\"rect\"||!t.join&&(t.thickness<=2||t.count>=al.maxPoints)?this.shaders.rect(t):this.shaders.miter(t)))}),this};al.prototype.update=function(e){if(!e)return;e.length!=null?typeof e[0]==\"number\"&&(e=[{positions:e}]):Array.isArray(e)||(e=[e]);let{regl:t,gl:r}=this;if(e.forEach((a,i)=>{let o=this.passes[i];if(a!==void 0){if(a===null){this.passes[i]=null;return}if(typeof a[0]==\"number\"&&(a={positions:a}),a=p$t(a,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin join type mode\",miterLimit:\"miterlimit miterLimit\",dashes:\"dash dashes dasharray dash-array dashArray\",color:\"color colour stroke colors colours stroke-color strokeColor\",fill:\"fill fill-color fillColor\",opacity:\"alpha opacity\",overlay:\"overlay crease overlap intersect\",close:\"closed close closed-path closePath\",range:\"range dataBox\",viewport:\"viewport viewBox\",hole:\"holes hole hollow\",splitNull:\"splitNull\"}),o||(this.passes[i]=o={id:i,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:t.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:\"linear\",min:\"linear\"}),colorBuffer:t.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array}),positionBuffer:t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),positionFractBuffer:t.buffer({usage:\"dynamic\",type:\"float\",data:new 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        "\t\tprecision highp float;\n",
        "\n",
        "\t\tattribute vec2 position, positionFract;\n",
@@ -2674,10 +2675,10 @@
        "\t\t\tgl_FragColor = fragColor;\n",
        "\t\t\tgl_FragColor.a *= opacity;\n",
        "\t\t}\n",
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si(Ut,Oe),Ut.width=0,Ut.height=0,Bi(Ut,Nt),Ut.width=Ut.width||(Oe.width>>Ft)-It,Ut.height=Ut.height||(Oe.height>>Ft)-Bt,al(Oe),Nn(Ut,_n,It,Bt,Ft),io(),Cn(Ut),ht}function cr(Nt,Ne){var $e=Nt|0,st=Ne|0||$e;if($e===Oe.width&&st===Oe.height)return ht;ht.width=Oe.width=$e,ht.height=Oe.height=st,al(Oe);for(var It=0;Oe.mipmask>>It;++It){var Bt=$e>>It,Ft=st>>It;if(!Bt||!Ft)break;Me.texImage2D(_n,It,Oe.format,Bt,Ft,0,Oe.format,Oe.type,null)}return io(),Nr.profile&&(Oe.stats.size=cc(Oe.internalformat,Oe.type,$e,st,!1,!1)),ht}return ht(ne,we),ht.subimage=Yt,ht.resize=cr,ht._reglType=\"texture2d\",ht._texture=Oe,Nr.profile&&(ht.stats=Oe.stats),ht.destroy=function(){Oe.decRef()},ht}function J(ne,we,Oe,ht,Yt,cr){var Nt=new wa(ya);ss[Nt.id]=Nt,Or.cubeCount++;var Ne=new Array(6);function $e(Bt,Ft,Ut,zt,_t,Dt){var vt,lr=Nt.texInfo;for(zo.call(lr),vt=0;vt<6;++vt)Ne[vt]=Ga();if(typeof Bt==\"number\"||!Bt){var _r=Bt|0||1;for(vt=0;vt<6;++vt)sa(Ne[vt],_r,_r)}else if(typeof 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It(Bt){var Ft=Bt|0;if(Ft!==Nt.width){$e.width=Nt.width=Ft,$e.height=Nt.height=Ft,al(Nt);for(var Ut=0;Ut<6;++Ut)for(var zt=0;Nt.mipmask>>zt;++zt)Me.texImage2D(ea+Ut,zt,Nt.format,Ft>>zt,Ft>>zt,0,Nt.format,Nt.type,null);return io(),Nr.profile&&(Nt.stats.size=cc(Nt.internalformat,Nt.type,$e.width,$e.height,!1,!0)),$e}}return $e(ne,we,Oe,ht,Yt,cr),$e.subimage=st,$e.resize=It,$e._reglType=\"textureCube\",$e._texture=Nt,Nr.profile&&($e.stats=Nt.stats),$e.destroy=function(){Nt.decRef()},$e}function O(){for(var ne=0;ne<Vs;++ne)Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Ys[ne]=null;gt(ss).forEach(Y),Or.cubeCount=0,Or.textureCount=0}Nr.profile&&(Or.getTotalTextureSize=function(){var ne=0;return Object.keys(ss).forEach(function(we){ne+=ss[we].stats.size}),ne});function K(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null)}gt(ss).forEach(function(Oe){Oe.texture=Me.createTexture(),Me.bindTexture(Oe.target,Oe.texture);for(var ht=0;ht<32;++ht)if((Oe.mipmask&1<<ht)!==0)if(Oe.target===_n)Me.texImage2D(_n,ht,Oe.internalformat,Oe.width>>ht,Oe.height>>ht,0,Oe.internalformat,Oe.type,null);else for(var Yt=0;Yt<6;++Yt)Me.texImage2D(ea+Yt,ht,Oe.internalformat,Oe.width>>ht,Oe.height>>ht,0,Oe.internalformat,Oe.type,null);nl(Oe.texInfo,Oe.target)})}function de(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null),Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Me.bindTexture(ya,null)}}return{create2D:D,createCube:J,clear:O,getTexture:function(ne){return null},restore:K,refresh:de}}var Bl=36161,Kc=32854,Rc=32855,gs=36194,Wf=33189,Wh=36168,rh=34041,lf=35907,Sh=34836,Mu=34842,ih=34843,js=[];js[Kc]=2,js[Rc]=2,js[gs]=2,js[Wf]=2,js[Wh]=1,js[rh]=4,js[lf]=4,js[Sh]=16,js[Mu]=8,js[ih]=6;function Eu(Me,bt,Ot){return js[Me]*bt*Ot}var Dc=function(Me,bt,Ot,Lr,Vr){var Or={rgba4:Kc,rgb565:gs,\"rgb5 a1\":Rc,depth:Wf,stencil:Wh,\"depth 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Qi(){gt(Ni).forEach(function(Wi){Wi.renderbuffer=Me.createRenderbuffer(),Me.bindRenderbuffer(Bl,Wi.renderbuffer),Me.renderbufferStorage(Bl,Wi.format,Wi.width,Wi.height)}),Me.bindRenderbuffer(Bl,null)}return{create:Vn,clear:function(){gt(Ni).forEach(Si)},restore:Qi}},ks=36160,bc=36161,hu=3553,_u=34069,il=36064,nh=36096,Mh=36128,zu=33306,Fc=36053,wc=36193,bd=5121,xf=5126,Pf=6407,qu=6408,bf=[];bf[qu]=4,bf[Pf]=3;var jl=[];jl[bd]=1,jl[xf]=4,jl[wc]=2;function uf(Me,bt,Ot,Lr,Vr,Or){var Nr={cur:null,next:null,dirty:!1,setFBO:null},vi=[\"rgba\"],Ni=[\"rgba4\",\"rgb565\",\"rgb5 a1\"];bt.ext_srgb&&Ni.push(\"srgba\"),bt.ext_color_buffer_half_float&&Ni.push(\"rgba16f\",\"rgb16f\"),bt.webgl_color_buffer_float&&Ni.push(\"rgba32f\");var qi=[\"uint8\"];bt.oes_texture_half_float&&qi.push(\"half float\",\"float16\"),bt.oes_texture_float&&qi.push(\"float\",\"float32\");function Si(Ui,Bi,pn){this.target=Ui,this.texture=Bi,this.renderbuffer=pn;var Nn=0,aa=0;Bi?(Nn=Bi.width,aa=Bi.height):pn&&(Nn=pn.width,aa=pn.height),this.width=Nn,this.height=aa}function Vn(Ui){Ui&&(Ui.texture&&Ui.texture._texture.decRef(),Ui.renderbuffer&&Ui.renderbuffer._renderbuffer.decRef())}function Qi(Ui,Bi,pn){if(Ui)if(Ui.texture){var Nn=Ui.texture._texture,aa=Math.max(1,Nn.width),Ki=Math.max(1,Nn.height);Nn.refCount+=1}else{var Cn=Ui.renderbuffer._renderbuffer;Cn.refCount+=1}}function Wi(Ui,Bi){Bi&&(Bi.texture?Me.framebufferTexture2D(ks,Ui,Bi.target,Bi.texture._texture.texture,0):Me.framebufferRenderbuffer(ks,Ui,bc,Bi.renderbuffer._renderbuffer.renderbuffer))}function oi(Ui){var Bi=hu,pn=null,Nn=null,aa=Ui;typeof Ui==\"object\"&&(aa=Ui.data,\"target\"in Ui&&(Bi=Ui.target|0));var Ki=aa._reglType;return Ki===\"texture2d\"||Ki===\"textureCube\"?pn=aa:Ki===\"renderbuffer\"&&(Nn=aa,Bi=bc),new Si(Bi,pn,Nn)}function Tr(Ui,Bi,pn,Nn,aa){if(pn){var Ki=Lr.create2D({width:Ui,height:Bi,format:Nn,type:aa});return Ki._texture.refCount=0,new Si(hu,Ki,null)}else{var Cn=Vr.create({width:Ui,height:Bi,format:Nn});return Cn._renderbuffer.refCount=0,new Si(bc,null,Cn)}}function Zr(Ui){return Ui&&(Ui.texture||Ui.renderbuffer)}function yi(Ui,Bi,pn){Ui&&(Ui.texture?Ui.texture.resize(Bi,pn):Ui.renderbuffer&&Ui.renderbuffer.resize(Bi,pn),Ui.width=Bi,Ui.height=pn)}var Ii=0,ui={};function nn(){this.id=Ii++,ui[this.id]=this,this.framebuffer=Me.createFramebuffer(),this.width=0,this.height=0,this.colorAttachments=[],this.depthAttachment=null,this.stencilAttachment=null,this.depthStencilAttachment=null}function Zi(Ui){Ui.colorAttachments.forEach(Vn),Vn(Ui.depthAttachment),Vn(Ui.stencilAttachment),Vn(Ui.depthStencilAttachment)}function qn(Ui){var Bi=Ui.framebuffer;Me.deleteFramebuffer(Bi),Ui.framebuffer=null,Or.framebufferCount--,delete ui[Ui.id]}function fi(Ui){var Bi;Me.bindFramebuffer(ks,Ui.framebuffer);var pn=Ui.colorAttachments;for(Bi=0;Bi<pn.length;++Bi)Wi(il+Bi,pn[Bi]);for(Bi=pn.length;Bi<Ot.maxColorAttachments;++Bi)Me.framebufferTexture2D(ks,il+Bi,hu,null,0);Me.framebufferTexture2D(ks,zu,hu,null,0),Me.framebufferTexture2D(ks,nh,hu,null,0),Me.framebufferTexture2D(ks,Mh,hu,null,0),Wi(nh,Ui.depthAttachment),Wi(Mh,Ui.stencilAttachment),Wi(zu,Ui.depthStencilAttachment);var Nn=Me.checkFramebufferStatus(ks);Me.isContextLost(),Me.bindFramebuffer(ks,Nr.next?Nr.next.framebuffer:null),Nr.cur=Nr.next,Me.getError()}function si(Ui,Bi){var pn=new nn;Or.framebufferCount++;function Nn(Ki,Cn){var ia,sa=0,ba=0,is=!0,Zs=!0,Ga=null,El=!0,zo=\"rgba\",Qs=\"uint8\",nl=1,Ul=null,ss=null,Vs=null,Ys=!1;if(typeof Ki==\"number\")sa=Ki|0,ba=Cn|0||sa;else if(!Ki)sa=ba=1;else{var wa=Ki;if(\"shape\"in wa){var al=wa.shape;sa=al[0],ba=al[1]}else\"radius\"in wa&&(sa=ba=wa.radius),\"width\"in wa&&(sa=wa.width),\"height\"in wa&&(ba=wa.height);(\"color\"in wa||\"colors\"in wa)&&(Ga=wa.color||wa.colors,Array.isArray(Ga)),Ga||(\"colorCount\"in wa&&(nl=wa.colorCount|0),\"colorTexture\"in wa&&(El=!!wa.colorTexture,zo=\"rgba4\"),\"colorType\"in wa&&(Qs=wa.colorType,El||(Qs===\"half float\"||Qs===\"float16\"?zo=\"rgba16f\":(Qs===\"float\"||Qs===\"float32\")&&(zo=\"rgba32f\"))),\"colorFormat\"in wa&&(zo=wa.colorFormat,vi.indexOf(zo)>=0?El=!0:Ni.indexOf(zo)>=0&&(El=!1))),(\"depthTexture\"in wa||\"depthStencilTexture\"in wa)&&(Ys=!!(wa.depthTexture||wa.depthStencilTexture)),\"depth\"in wa&&(typeof wa.depth==\"boolean\"?is=wa.depth:(Ul=wa.depth,Zs=!1)),\"stencil\"in wa&&(typeof wa.stencil==\"boolean\"?Zs=wa.stencil:(ss=wa.stencil,is=!1)),\"depthStencil\"in wa&&(typeof wa.depthStencil==\"boolean\"?is=Zs=wa.depthStencil:(Vs=wa.depthStencil,is=!1,Zs=!1))}var io=null,Y=null,D=null,J=null;if(Array.isArray(Ga))io=Ga.map(oi);else if(Ga)io=[oi(Ga)];else for(io=new Array(nl),ia=0;ia<nl;++ia)io[ia]=Tr(sa,ba,El,zo,Qs);sa=sa||io[0].width,ba=ba||io[0].height,Ul?Y=oi(Ul):is&&!Zs&&(Y=Tr(sa,ba,Ys,\"depth\",\"uint32\")),ss?D=oi(ss):Zs&&!is&&(D=Tr(sa,ba,!1,\"stencil\",\"uint8\")),Vs?J=oi(Vs):!Ul&&!ss&&Zs&&is&&(J=Tr(sa,ba,Ys,\"depth stencil\",\"depth stencil\"));var O=null;for(ia=0;ia<io.length;++ia)if(Qi(io[ia],sa,ba),io[ia]&&io[ia].texture){var K=bf[io[ia].texture._texture.format]*jl[io[ia].texture._texture.type];O===null&&(O=K)}return Qi(Y,sa,ba),Qi(D,sa,ba),Qi(J,sa,ba),Zi(pn),pn.width=sa,pn.height=ba,pn.colorAttachments=io,pn.depthAttachment=Y,pn.stencilAttachment=D,pn.depthStencilAttachment=J,Nn.color=io.map(Zr),Nn.depth=Zr(Y),Nn.stencil=Zr(D),Nn.depthStencil=Zr(J),Nn.width=pn.width,Nn.height=pn.height,fi(pn),Nn}function aa(Ki,Cn){var ia=Math.max(Ki|0,1),sa=Math.max(Cn|0||ia,1);if(ia===pn.width&&sa===pn.height)return Nn;for(var ba=pn.colorAttachments,is=0;is<ba.length;++is)yi(ba[is],ia,sa);return yi(pn.depthAttachment,ia,sa),yi(pn.stencilAttachment,ia,sa),yi(pn.depthStencilAttachment,ia,sa),pn.width=Nn.width=ia,pn.height=Nn.height=sa,fi(pn),Nn}return Nn(Ui,Bi),e(Nn,{resize:aa,_reglType:\"framebuffer\",_framebuffer:pn,destroy:function(){qn(pn),Zi(pn)},use:function(Ki){Nr.setFBO({framebuffer:Nn},Ki)}})}function mn(Ui){var Bi=Array(6);function pn(aa){var Ki,Cn={color:null},ia=0,sa=null,ba=\"rgba\",is=\"uint8\",Zs=1;if(typeof aa==\"number\")ia=aa|0;else if(!aa)ia=1;else{var Ga=aa;if(\"shape\"in Ga){var El=Ga.shape;ia=El[0]}else\"radius\"in Ga&&(ia=Ga.radius|0),\"width\"in Ga?(ia=Ga.width|0,\"height\"in Ga):\"height\"in Ga&&(ia=Ga.height|0);(\"color\"in Ga||\"colors\"in Ga)&&(sa=Ga.color||Ga.colors,Array.isArray(sa)),sa||(\"colorCount\"in Ga&&(Zs=Ga.colorCount|0),\"colorType\"in Ga&&(is=Ga.colorType),\"colorFormat\"in Ga&&(ba=Ga.colorFormat)),\"depth\"in Ga&&(Cn.depth=Ga.depth),\"stencil\"in Ga&&(Cn.stencil=Ga.stencil),\"depthStencil\"in Ga&&(Cn.depthStencil=Ga.depthStencil)}var zo;if(sa)if(Array.isArray(sa))for(zo=[],Ki=0;Ki<sa.length;++Ki)zo[Ki]=sa[Ki];else zo=[sa];else{zo=Array(Zs);var Qs={radius:ia,format:ba,type:is};for(Ki=0;Ki<Zs;++Ki)zo[Ki]=Lr.createCube(Qs)}for(Cn.color=Array(zo.length),Ki=0;Ki<zo.length;++Ki){var nl=zo[Ki];ia=ia||nl.width,Cn.color[Ki]={target:_u,data:zo[Ki]}}for(Ki=0;Ki<6;++Ki){for(var Ul=0;Ul<zo.length;++Ul)Cn.color[Ul].target=_u+Ki;Ki>0&&(Cn.depth=Bi[0].depth,Cn.stencil=Bi[0].stencil,Cn.depthStencil=Bi[0].depthStencil),Bi[Ki]?Bi[Ki](Cn):Bi[Ki]=si(Cn)}return e(pn,{width:ia,height:ia,color:zo})}function Nn(aa){var Ki,Cn=aa|0;if(Cn===pn.width)return pn;var ia=pn.color;for(Ki=0;Ki<ia.length;++Ki)ia[Ki].resize(Cn);for(Ki=0;Ki<6;++Ki)Bi[Ki].resize(Cn);return pn.width=pn.height=Cn,pn}return pn(Ui),e(pn,{faces:Bi,resize:Nn,_reglType:\"framebufferCube\",destroy:function(){Bi.forEach(function(aa){aa.destroy()})}})}function Ji(){Nr.cur=null,Nr.next=null,Nr.dirty=!0,gt(ui).forEach(function(Ui){Ui.framebuffer=Me.createFramebuffer(),fi(Ui)})}return e(Nr,{getFramebuffer:function(Ui){if(typeof Ui==\"function\"&&Ui._reglType===\"framebuffer\"){var Bi=Ui._framebuffer;if(Bi instanceof nn)return Bi}return null},create:si,createCube:mn,clear:function(){gt(ui).forEach(qn)},restore:Ji})}var Xh=5126,If=34962,Cs=34963;function du(){this.state=0,this.x=0,this.y=0,this.z=0,this.w=0,this.buffer=null,this.size=0,this.normalized=!1,this.type=Xh,this.offset=0,this.stride=0,this.divisor=0}function ku(Me,bt,Ot,Lr,Vr,Or,Nr){for(var vi=Ot.maxAttributes,Ni=new Array(vi),qi=0;qi<vi;++qi)Ni[qi]=new du;var Si=0,Vn={},Qi={Record:du,scope:{},state:Ni,currentVAO:null,targetVAO:null,restore:oi()?Zi:function(){},createVAO:qn,getVAO:Zr,destroyBuffer:Wi,setVAO:oi()?yi:Ii,clear:oi()?ui:function(){}};function Wi(fi){for(var si=0;si<Ni.length;++si){var mn=Ni[si];mn.buffer===fi&&(Me.disableVertexAttribArray(si),mn.buffer=null)}}function oi(){return bt.oes_vertex_array_object}function Tr(){return bt.angle_instanced_arrays}function Zr(fi){return typeof fi==\"function\"&&fi._vao?fi._vao:null}function yi(fi){if(fi!==Qi.currentVAO){var si=oi();fi?si.bindVertexArrayOES(fi.vao):si.bindVertexArrayOES(null),Qi.currentVAO=fi}}function Ii(fi){if(fi!==Qi.currentVAO){if(fi)fi.bindAttrs();else{for(var si=Tr(),mn=0;mn<Ni.length;++mn){var Ji=Ni[mn];Ji.buffer?(Me.enableVertexAttribArray(mn),Ji.buffer.bind(),Me.vertexAttribPointer(mn,Ji.size,Ji.type,Ji.normalized,Ji.stride,Ji.offfset),si&&Ji.divisor&&si.vertexAttribDivisorANGLE(mn,Ji.divisor)):(Me.disableVertexAttribArray(mn),Me.vertexAttrib4f(mn,Ji.x,Ji.y,Ji.z,Ji.w))}Nr.elements?Me.bindBuffer(Cs,Nr.elements.buffer.buffer):Me.bindBuffer(Cs,null)}Qi.currentVAO=fi}}function ui(){gt(Vn).forEach(function(fi){fi.destroy()})}function nn(){this.id=++Si,this.attributes=[],this.elements=null,this.ownsElements=!1,this.count=0,this.offset=0,this.instances=-1,this.primitive=4;var fi=oi();fi?this.vao=fi.createVertexArrayOES():this.vao=null,Vn[this.id]=this,this.buffers=[]}nn.prototype.bindAttrs=function(){for(var fi=Tr(),si=this.attributes,mn=0;mn<si.length;++mn){var Ji=si[mn];Ji.buffer?(Me.enableVertexAttribArray(mn),Me.bindBuffer(If,Ji.buffer.buffer),Me.vertexAttribPointer(mn,Ji.size,Ji.type,Ji.normalized,Ji.stride,Ji.offset),fi&&Ji.divisor&&fi.vertexAttribDivisorANGLE(mn,Ji.divisor)):(Me.disableVertexAttribArray(mn),Me.vertexAttrib4f(mn,Ji.x,Ji.y,Ji.z,Ji.w))}for(var Ui=si.length;Ui<vi;++Ui)Me.disableVertexAttribArray(Ui);var Bi=Or.getElements(this.elements);Bi?Me.bindBuffer(Cs,Bi.buffer.buffer):Me.bindBuffer(Cs,null)},nn.prototype.refresh=function(){var fi=oi();fi&&(fi.bindVertexArrayOES(this.vao),this.bindAttrs(),Qi.currentVAO=null,fi.bindVertexArrayOES(null))},nn.prototype.destroy=function(){if(this.vao){var fi=oi();this===Qi.currentVAO&&(Qi.currentVAO=null,fi.bindVertexArrayOES(null)),fi.deleteVertexArrayOES(this.vao),this.vao=null}this.ownsElements&&(this.elements.destroy(),this.elements=null,this.ownsElements=!1),Vn[this.id]&&(delete Vn[this.id],Lr.vaoCount-=1)};function Zi(){var fi=oi();fi&&gt(Vn).forEach(function(si){si.refresh()})}function qn(fi){var si=new nn;Lr.vaoCount+=1;function mn(Ji){var Ui;if(Array.isArray(Ji))Ui=Ji,si.elements&&si.ownsElements&&si.elements.destroy(),si.elements=null,si.ownsElements=!1,si.offset=0,si.count=0,si.instances=-1,si.primitive=4;else{if(Ji.elements){var Bi=Ji.elements;si.ownsElements?typeof Bi==\"function\"&&Bi._reglType===\"elements\"?(si.elements.destroy(),si.ownsElements=!1):(si.elements(Bi),si.ownsElements=!1):Or.getElements(Ji.elements)?(si.elements=Ji.elements,si.ownsElements=!1):(si.elements=Or.create(Ji.elements),si.ownsElements=!0)}else 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Tr=Me.deleteShader.bind(Me);gt(Vr).forEach(Tr),Vr={},gt(Or).forEach(Tr),Or={},Si.forEach(function(Zr){Me.deleteProgram(Zr.program)}),Si.length=0,qi={},Ot.shaderCount=0},program:function(Tr,Zr,yi,Ii){var ui=qi[Zr];ui||(ui=qi[Zr]={});var nn=ui[Tr];if(nn&&(nn.refCount++,!Ii))return nn;var Zi=new Qi(Zr,Tr);return Ot.shaderCount++,Wi(Zi,yi,Ii),nn||(ui[Tr]=Zi),Si.push(Zi),e(Zi,{destroy:function(){if(Zi.refCount--,Zi.refCount<=0){Me.deleteProgram(Zi.program);var qn=Si.indexOf(Zi);Si.splice(qn,1),Ot.shaderCount--}ui[Zi.vertId].refCount<=0&&(Me.deleteShader(Or[Zi.vertId]),delete Or[Zi.vertId],delete qi[Zi.fragId][Zi.vertId]),Object.keys(qi[Zi.fragId]).length||(Me.deleteShader(Vr[Zi.fragId]),delete Vr[Zi.fragId],delete qi[Zi.fragId])}})},restore:oi,shader:Ni,frag:-1,vert:-1}}var Rf=6408,Xu=5121,cf=3333,Zf=5126;function Wl(Me,bt,Ot,Lr,Vr,Or,Nr){function vi(Si){var Vn;bt.next===null?Vn=Xu:Vn=bt.next.colorAttachments[0].texture._texture.type;var 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Hr=vr.width||xn[0].length,Mr=vr.height||xn.length,zr=vr.channels;bn(xn[0][0])?zr=zr||xn[0][0].length:zr=zr||1;for(var yr=cr.shape(xn),hn=1,kn=0;kn<yr.length;++kn)hn*=yr[kn];var Xn=lh(vr,hn);cr.flatten(xn,yr,\"\",Xn),nf(vr,Xn),vr.alignment=1,vr.width=Hr,vr.height=Mr,vr.channels=zr,vr.format=vr.internalformat=Yo[zr],vr.needsFree=!0}vr.type===Ps||vr.type}function si(vr,gn,xn){var Gi=vr.element,wo=vr.data,xs=vr.internalformat,Hi=vr.format,ar=vr.type,gr=vr.width,Pr=vr.height;Vn(vr),Gi?Pe.texImage2D(gn,xn,Hi,Hi,ar,Gi):vr.compressed?Pe.compressedTexImage2D(gn,xn,xs,gr,Pr,0,wo):vr.needsCopy?(xt(),Pe.copyTexImage2D(gn,xn,Hi,vr.xOffset,vr.yOffset,gr,Pr,0)):Pe.texImage2D(gn,xn,Hi,gr,Pr,0,Hi,ar,wo||null)}function zi(vr,gn,xn,Gi,wo){var xs=vr.element,Hi=vr.data,ar=vr.internalformat,gr=vr.format,Pr=vr.type,pn=vr.width,Mn=vr.height;Vn(vr),xs?Pe.texSubImage2D(gn,wo,xn,Gi,gr,Pr,xs):vr.compressed?Pe.compressedTexSubImage2D(gn,wo,xn,Gi,ar,pn,Mn,Hi):vr.needsCopy?(xt(),Pe.copyTexSubImage2D(gn,wo,xn,Gi,vr.xOffset,vr.yOffset,pn,Mn)):Pe.texSubImage2D(gn,wo,xn,Gi,pn,Mn,gr,Pr,Hi)}var da=[];function ui(){return da.pop()||new jn}function Li(vr){vr.needsFree&&U.freeType(vr.data),jn.call(vr),da.push(vr)}function Ki(){Sn.call(this),this.genMipmaps=!1,this.mipmapHint=ju,this.mipmask=0,this.images=Array(16)}function ca(vr,gn,xn){var Gi=vr.images[0]=ui();vr.mipmask=1,Gi.width=vr.width=gn,Gi.height=vr.height=xn,Gi.channels=vr.channels=4}function Ia(vr,gn){var xn=null;if(Wu(gn))xn=vr.images[0]=ui(),An(xn,vr),Wn(xn,gn),vr.mipmask=1;else if(li(vr,gn),Array.isArray(gn.mipmap))for(var Gi=gn.mipmap,wo=0;wo<Gi.length;++wo)xn=vr.images[wo]=ui(),An(xn,vr),xn.width>>=wo,xn.height>>=wo,Wn(xn,Gi[wo]),vr.mipmask|=1<<wo;else xn=vr.images[0]=ui(),An(xn,vr),Wn(xn,gn),vr.mipmask=1;An(vr,vr.images[0]),vr.compressed&&(vr.internalformat===pa||vr.internalformat===la||vr.internalformat===As||vr.internalformat)}function Os(vr,gn){for(var xn=vr.images,Gi=0;Gi<xn.length;++Gi){if(!xn[Gi])return;si(xn[Gi],gn,Gi)}}var Ss=[];function Na(){var vr=Ss.pop()||new Ki;Sn.call(vr),vr.mipmask=0;for(var gn=0;gn<16;++gn)vr.images[gn]=null;return vr}function Ns(vr){for(var gn=vr.images,xn=0;xn<gn.length;++xn)gn[xn]&&Li(gn[xn]),gn[xn]=null;Ss.push(vr)}function vo(){this.minFilter=Hu,this.magFilter=Hu,this.wrapS=Jc,this.wrapT=Jc,this.anisotropic=1,this.genMipmaps=!1,this.mipmapHint=ju}function _o(vr,gn){if(\"min\"in gn){var xn=gn.min;vr.minFilter=dr[xn],Fl.indexOf(vr.minFilter)>=0&&!(\"faces\"in gn)&&(vr.genMipmaps=!0)}if(\"mag\"in gn){var Gi=gn.mag;vr.magFilter=Ht[Gi]}var wo=vr.wrapS,xs=vr.wrapT;if(\"wrap\"in gn){var Hi=gn.wrap;typeof Hi==\"string\"?wo=xs=$t[Hi]:Array.isArray(Hi)&&(wo=$t[Hi[0]],xs=$t[Hi[1]])}else{if(\"wrapS\"in gn){var ar=gn.wrapS;wo=$t[ar]}if(\"wrapT\"in gn){var gr=gn.wrapT;xs=$t[gr]}}if(vr.wrapS=wo,vr.wrapT=xs,\"anisotropic\"in gn){var Pr=gn.anisotropic;vr.anisotropic=gn.anisotropic}if(\"mipmap\"in gn){var pn=!1;switch(typeof gn.mipmap){case\"string\":vr.mipmapHint=Pt[gn.mipmap],vr.genMipmaps=!0,pn=!0;break;case\"boolean\":pn=vr.genMipmaps=gn.mipmap;break;case\"object\":vr.genMipmaps=!1,pn=!0;break;default:}pn&&!(\"min\"in gn)&&(vr.minFilter=kc)}}function po(vr,gn){Pe.texParameteri(gn,Bd,vr.minFilter),Pe.texParameteri(gn,Mf,vr.magFilter),Pe.texParameteri(gn,Ca,vr.wrapS),Pe.texParameteri(gn,Pl,vr.wrapT),Xe.ext_texture_filter_anisotropic&&Pe.texParameteri(gn,kf,vr.anisotropic),vr.genMipmaps&&(Pe.hint(Ef,vr.mipmapHint),Pe.generateMipmap(gn))}var Xo=0,no={},Us=at.maxTextureUnits,ql=Array(Us).map(function(){return null});function Ta(vr){Sn.call(this),this.mipmask=0,this.internalformat=Ai,this.id=Xo++,this.refCount=1,this.target=vr,this.texture=Pe.createTexture(),this.unit=-1,this.bindCount=0,this.texInfo=new vo,St.profile&&(this.stats={size:0})}function vu(vr){Pe.activeTexture(hc),Pe.bindTexture(vr.target,vr.texture)}function ys(){var vr=ql[0];vr?Pe.bindTexture(vr.target,vr.texture):Pe.bindTexture(Tn,null)}function xc(vr){var gn=vr.texture,xn=vr.unit,Gi=vr.target;xn>=0&&(Pe.activeTexture(hc+xn),Pe.bindTexture(Gi,null),ql[xn]=null),Pe.deleteTexture(gn),vr.texture=null,vr.params=null,vr.pixels=null,vr.refCount=0,delete no[vr.id],Bt.textureCount--}e(Ta.prototype,{bind:function(){var vr=this;vr.bindCount+=1;var gn=vr.unit;if(gn<0){for(var xn=0;xn<Us;++xn){var Gi=ql[xn];if(Gi){if(Gi.bindCount>0)continue;Gi.unit=-1}ql[xn]=vr,gn=xn;break}gn>=Us,St.profile&&Bt.maxTextureUnits<gn+1&&(Bt.maxTextureUnits=gn+1),vr.unit=gn,Pe.activeTexture(hc+gn),Pe.bindTexture(vr.target,vr.texture)}return gn},unbind:function(){this.bindCount-=1},decRef:function(){--this.refCount<=0&&xc(this)}});function Zl(vr,gn){var xn=new Ta(Tn);no[xn.id]=xn,Bt.textureCount++;function Gi(Hi,ar){var gr=xn.texInfo;vo.call(gr);var Pr=Na();return typeof Hi==\"number\"?typeof ar==\"number\"?ca(Pr,Hi|0,ar|0):ca(Pr,Hi|0,Hi|0):Hi?(_o(gr,Hi),Ia(Pr,Hi)):ca(Pr,1,1),gr.genMipmaps&&(Pr.mipmask=(Pr.width<<1)-1),xn.mipmask=Pr.mipmask,An(xn,Pr),xn.internalformat=Pr.internalformat,Gi.width=Pr.width,Gi.height=Pr.height,vu(xn),Os(Pr,Tn),po(gr,Tn),ys(),Ns(Pr),St.profile&&(xn.stats.size=af(xn.internalformat,xn.type,Pr.width,Pr.height,gr.genMipmaps,!1)),Gi.format=Lr[xn.internalformat],Gi.type=an[xn.type],Gi.mag=on[gr.magFilter],Gi.min=Nn[gr.minFilter],Gi.wrapS=Yn[gr.wrapS],Gi.wrapT=Yn[gr.wrapT],Gi}function wo(Hi,ar,gr,Pr){var pn=ar|0,Mn=gr|0,sn=Pr|0,tn=ui();return An(tn,xn),tn.width=0,tn.height=0,Wn(tn,Hi),tn.width=tn.width||(xn.width>>sn)-pn,tn.height=tn.height||(xn.height>>sn)-Mn,vu(xn),zi(tn,Tn,pn,Mn,sn),ys(),Li(tn),Gi}function xs(Hi,ar){var gr=Hi|0,Pr=ar|0||gr;if(gr===xn.width&&Pr===xn.height)return Gi;Gi.width=xn.width=gr,Gi.height=xn.height=Pr,vu(xn);for(var pn=0;xn.mipmask>>pn;++pn){var Mn=gr>>pn,sn=Pr>>pn;if(!Mn||!sn)break;Pe.texImage2D(Tn,pn,xn.format,Mn,sn,0,xn.format,xn.type,null)}return ys(),St.profile&&(xn.stats.size=af(xn.internalformat,xn.type,gr,Pr,!1,!1)),Gi}return Gi(vr,gn),Gi.subimage=wo,Gi.resize=xs,Gi._reglType=\"texture2d\",Gi._texture=xn,St.profile&&(Gi.stats=xn.stats),Gi.destroy=function(){xn.decRef()},Gi}function $l(vr,gn,xn,Gi,wo,xs){var Hi=new Ta(Kn);no[Hi.id]=Hi,Bt.cubeCount++;var ar=new Array(6);function gr(Mn,sn,tn,Hr,Mr,zr){var yr,hn=Hi.texInfo;for(vo.call(hn),yr=0;yr<6;++yr)ar[yr]=Na();if(typeof Mn==\"number\"||!Mn){var kn=Mn|0||1;for(yr=0;yr<6;++yr)ca(ar[yr],kn,kn)}else if(typeof Mn==\"object\")if(sn)Ia(ar[0],Mn),Ia(ar[1],sn),Ia(ar[2],tn),Ia(ar[3],Hr),Ia(ar[4],Mr),Ia(ar[5],zr);else if(_o(hn,Mn),li(Hi,Mn),\"faces\"in Mn){var Xn=Mn.faces;for(yr=0;yr<6;++yr)An(ar[yr],Hi),Ia(ar[yr],Xn[yr])}else for(yr=0;yr<6;++yr)Ia(ar[yr],Mn);for(An(Hi,ar[0]),hn.genMipmaps?Hi.mipmask=(ar[0].width<<1)-1:Hi.mipmask=ar[0].mipmask,Hi.internalformat=ar[0].internalformat,gr.width=ar[0].width,gr.height=ar[0].height,vu(Hi),yr=0;yr<6;++yr)Os(ar[yr],Qn+yr);for(po(hn,Kn),ys(),St.profile&&(Hi.stats.size=af(Hi.internalformat,Hi.type,gr.width,gr.height,hn.genMipmaps,!0)),gr.format=Lr[Hi.internalformat],gr.type=an[Hi.type],gr.mag=on[hn.magFilter],gr.min=Nn[hn.minFilter],gr.wrapS=Yn[hn.wrapS],gr.wrapT=Yn[hn.wrapT],yr=0;yr<6;++yr)Ns(ar[yr]);return gr}function Pr(Mn,sn,tn,Hr,Mr){var zr=tn|0,yr=Hr|0,hn=Mr|0,kn=ui();return An(kn,Hi),kn.width=0,kn.height=0,Wn(kn,sn),kn.width=kn.width||(Hi.width>>hn)-zr,kn.height=kn.height||(Hi.height>>hn)-yr,vu(Hi),zi(kn,Qn+Mn,zr,yr,hn),ys(),Li(kn),gr}function pn(Mn){var sn=Mn|0;if(sn!==Hi.width){gr.width=Hi.width=sn,gr.height=Hi.height=sn,vu(Hi);for(var tn=0;tn<6;++tn)for(var Hr=0;Hi.mipmask>>Hr;++Hr)Pe.texImage2D(Qn+tn,Hr,Hi.format,sn>>Hr,sn>>Hr,0,Hi.format,Hi.type,null);return ys(),St.profile&&(Hi.stats.size=af(Hi.internalformat,Hi.type,gr.width,gr.height,!1,!0)),gr}}return gr(vr,gn,xn,Gi,wo,xs),gr.subimage=Pr,gr.resize=pn,gr._reglType=\"textureCube\",gr._texture=Hi,St.profile&&(gr.stats=Hi.stats),gr.destroy=function(){Hi.decRef()},gr}function xa(){for(var vr=0;vr<Us;++vr)Pe.activeTexture(hc+vr),Pe.bindTexture(Tn,null),ql[vr]=null;Ot(no).forEach(xc),Bt.cubeCount=0,Bt.textureCount=0}St.profile&&(Bt.getTotalTextureSize=function(){var vr=0;return Object.keys(no).forEach(function(gn){vr+=no[gn].stats.size}),vr});function uo(){for(var vr=0;vr<Us;++vr){var gn=ql[vr];gn&&(gn.bindCount=0,gn.unit=-1,ql[vr]=null)}Ot(no).forEach(function(xn){xn.texture=Pe.createTexture(),Pe.bindTexture(xn.target,xn.texture);for(var Gi=0;Gi<32;++Gi)if((xn.mipmask&1<<Gi)!==0)if(xn.target===Tn)Pe.texImage2D(Tn,Gi,xn.internalformat,xn.width>>Gi,xn.height>>Gi,0,xn.internalformat,xn.type,null);else for(var wo=0;wo<6;++wo)Pe.texImage2D(Qn+wo,Gi,xn.internalformat,xn.width>>Gi,xn.height>>Gi,0,xn.internalformat,xn.type,null);po(xn.texInfo,xn.target)})}function Es(){for(var vr=0;vr<Us;++vr){var gn=ql[vr];gn&&(gn.bindCount=0,gn.unit=-1,ql[vr]=null),Pe.activeTexture(hc+vr),Pe.bindTexture(Tn,null),Pe.bindTexture(Kn,null)}}return{create2D:Zl,createCube:$l,clear:xa,getTexture:function(vr){return null},restore:uo,refresh:Es}}var Rl=36161,ch=32854,Lc=32855,md=36194,yd=33189,Lf=36168,wu=34041,fh=35907,Oh=34836,hh=34842,Gd=34843,Gl=[];Gl[ch]=2,Gl[Lc]=2,Gl[md]=2,Gl[yd]=2,Gl[Lf]=1,Gl[wu]=4,Gl[fh]=4,Gl[Oh]=16,Gl[hh]=8,Gl[Gd]=6;function wl(Pe,Xe,at){return Gl[Pe]*Xe*at}var xd=function(Pe,Xe,at,xt,Et){var Bt={rgba4:ch,rgb565:md,\"rgb5 a1\":Lc,depth:yd,stencil:Lf,\"depth stencil\":wu};Xe.ext_srgb&&(Bt.srgba=fh),Xe.ext_color_buffer_half_float&&(Bt.rgba16f=hh,Bt.rgb16f=Gd),Xe.webgl_color_buffer_float&&(Bt.rgba32f=Oh);var St=[];Object.keys(Bt).forEach(function($r){var Er=Bt[$r];St[Er]=$r});var Pt=0,$t={};function Ht($r){this.id=Pt++,this.refCount=1,this.renderbuffer=$r,this.format=ch,this.width=0,this.height=0,Et.profile&&(this.stats={size:0})}Ht.prototype.decRef=function(){--this.refCount<=0&&dr(this)};function dr($r){var Er=$r.renderbuffer;Pe.bindRenderbuffer(Rl,null),Pe.deleteRenderbuffer(Er),$r.renderbuffer=null,$r.refCount=0,delete $t[$r.id],xt.renderbufferCount--}function Xr($r,Er){var Tr=new Ht(Pe.createRenderbuffer());$t[Tr.id]=Tr,xt.renderbufferCount++;function Rr(an,on){var Nn=0,Yn=0,yi=ch;if(typeof an==\"object\"&&an){var Sn=an;if(\"shape\"in Sn){var An=Sn.shape;Nn=An[0]|0,Yn=An[1]|0}else\"radius\"in Sn&&(Nn=Yn=Sn.radius|0),\"width\"in Sn&&(Nn=Sn.width|0),\"height\"in Sn&&(Yn=Sn.height|0);\"format\"in Sn&&(yi=Bt[Sn.format])}else typeof an==\"number\"?(Nn=an|0,typeof on==\"number\"?Yn=on|0:Yn=Nn):an||(Nn=Yn=1);if(!(Nn===Tr.width&&Yn===Tr.height&&yi===Tr.format))return Rr.width=Tr.width=Nn,Rr.height=Tr.height=Yn,Tr.format=yi,Pe.bindRenderbuffer(Rl,Tr.renderbuffer),Pe.renderbufferStorage(Rl,yi,Nn,Yn),Et.profile&&(Tr.stats.size=wl(Tr.format,Tr.width,Tr.height)),Rr.format=St[Tr.format],Rr}function Lr(an,on){var Nn=an|0,Yn=on|0||Nn;return Nn===Tr.width&&Yn===Tr.height||(Rr.width=Tr.width=Nn,Rr.height=Tr.height=Yn,Pe.bindRenderbuffer(Rl,Tr.renderbuffer),Pe.renderbufferStorage(Rl,Tr.format,Nn,Yn),Et.profile&&(Tr.stats.size=wl(Tr.format,Tr.width,Tr.height))),Rr}return Rr($r,Er),Rr.resize=Lr,Rr._reglType=\"renderbuffer\",Rr._renderbuffer=Tr,Et.profile&&(Rr.stats=Tr.stats),Rr.destroy=function(){Tr.decRef()},Rr}Et.profile&&(xt.getTotalRenderbufferSize=function(){var $r=0;return Object.keys($t).forEach(function(Er){$r+=$t[Er].stats.size}),$r});function Or(){Ot($t).forEach(function($r){$r.renderbuffer=Pe.createRenderbuffer(),Pe.bindRenderbuffer(Rl,$r.renderbuffer),Pe.renderbufferStorage(Rl,$r.format,$r.width,$r.height)}),Pe.bindRenderbuffer(Rl,null)}return{create:Xr,clear:function(){Ot($t).forEach(dr)},restore:Or}},Hl=36160,_d=36161,dh=3553,Pf=34069,Hd=36064,xo=36096,p0=36128,Tu=33306,Au=36053,Il=36193,g0=5121,Yu=5126,Nh=6407,Tl=6408,jl=[];jl[Tl]=4,jl[Nh]=3;var Uh=[];Uh[g0]=1,Uh[Yu]=4,Uh[Il]=2;function Vh(Pe,Xe,at,xt,Et,Bt){var St={cur:null,next:null,dirty:!1,setFBO:null},Pt=[\"rgba\"],$t=[\"rgba4\",\"rgb565\",\"rgb5 a1\"];Xe.ext_srgb&&$t.push(\"srgba\"),Xe.ext_color_buffer_half_float&&$t.push(\"rgba16f\",\"rgb16f\"),Xe.webgl_color_buffer_float&&$t.push(\"rgba32f\");var Ht=[\"uint8\"];Xe.oes_texture_half_float&&Ht.push(\"half float\",\"float16\"),Xe.oes_texture_float&&Ht.push(\"float\",\"float32\");function dr(jn,Wn,si){this.target=jn,this.texture=Wn,this.renderbuffer=si;var zi=0,da=0;Wn?(zi=Wn.width,da=Wn.height):si&&(zi=si.width,da=si.height),this.width=zi,this.height=da}function Xr(jn){jn&&(jn.texture&&jn.texture._texture.decRef(),jn.renderbuffer&&jn.renderbuffer._renderbuffer.decRef())}function Or(jn,Wn,si){if(jn)if(jn.texture){var zi=jn.texture._texture,da=Math.max(1,zi.width),ui=Math.max(1,zi.height);zi.refCount+=1}else{var Li=jn.renderbuffer._renderbuffer;Li.refCount+=1}}function $r(jn,Wn){Wn&&(Wn.texture?Pe.framebufferTexture2D(Hl,jn,Wn.target,Wn.texture._texture.texture,0):Pe.framebufferRenderbuffer(Hl,jn,_d,Wn.renderbuffer._renderbuffer.renderbuffer))}function Er(jn){var Wn=dh,si=null,zi=null,da=jn;typeof jn==\"object\"&&(da=jn.data,\"target\"in jn&&(Wn=jn.target|0));var ui=da._reglType;return ui===\"texture2d\"||ui===\"textureCube\"?si=da:ui===\"renderbuffer\"&&(zi=da,Wn=_d),new dr(Wn,si,zi)}function Tr(jn,Wn,si,zi,da){if(si){var ui=xt.create2D({width:jn,height:Wn,format:zi,type:da});return ui._texture.refCount=0,new dr(dh,ui,null)}else{var Li=Et.create({width:jn,height:Wn,format:zi});return Li._renderbuffer.refCount=0,new dr(_d,null,Li)}}function Rr(jn){return jn&&(jn.texture||jn.renderbuffer)}function Lr(jn,Wn,si){jn&&(jn.texture?jn.texture.resize(Wn,si):jn.renderbuffer&&jn.renderbuffer.resize(Wn,si),jn.width=Wn,jn.height=si)}var an=0,on={};function Nn(){this.id=an++,on[this.id]=this,this.framebuffer=Pe.createFramebuffer(),this.width=0,this.height=0,this.colorAttachments=[],this.depthAttachment=null,this.stencilAttachment=null,this.depthStencilAttachment=null}function Yn(jn){jn.colorAttachments.forEach(Xr),Xr(jn.depthAttachment),Xr(jn.stencilAttachment),Xr(jn.depthStencilAttachment)}function yi(jn){var Wn=jn.framebuffer;Pe.deleteFramebuffer(Wn),jn.framebuffer=null,Bt.framebufferCount--,delete on[jn.id]}function Sn(jn){var Wn;Pe.bindFramebuffer(Hl,jn.framebuffer);var si=jn.colorAttachments;for(Wn=0;Wn<si.length;++Wn)$r(Hd+Wn,si[Wn]);for(Wn=si.length;Wn<at.maxColorAttachments;++Wn)Pe.framebufferTexture2D(Hl,Hd+Wn,dh,null,0);Pe.framebufferTexture2D(Hl,Tu,dh,null,0),Pe.framebufferTexture2D(Hl,xo,dh,null,0),Pe.framebufferTexture2D(Hl,p0,dh,null,0),$r(xo,jn.depthAttachment),$r(p0,jn.stencilAttachment),$r(Tu,jn.depthStencilAttachment);var zi=Pe.checkFramebufferStatus(Hl);Pe.isContextLost(),Pe.bindFramebuffer(Hl,St.next?St.next.framebuffer:null),St.cur=St.next,Pe.getError()}function An(jn,Wn){var si=new Nn;Bt.framebufferCount++;function zi(ui,Li){var Ki,ca=0,Ia=0,Os=!0,Ss=!0,Na=null,Ns=!0,vo=\"rgba\",_o=\"uint8\",po=1,Xo=null,no=null,Us=null,ql=!1;if(typeof ui==\"number\")ca=ui|0,Ia=Li|0||ca;else if(!ui)ca=Ia=1;else{var Ta=ui;if(\"shape\"in Ta){var vu=Ta.shape;ca=vu[0],Ia=vu[1]}else\"radius\"in Ta&&(ca=Ia=Ta.radius),\"width\"in Ta&&(ca=Ta.width),\"height\"in Ta&&(Ia=Ta.height);(\"color\"in Ta||\"colors\"in Ta)&&(Na=Ta.color||Ta.colors,Array.isArray(Na)),Na||(\"colorCount\"in Ta&&(po=Ta.colorCount|0),\"colorTexture\"in Ta&&(Ns=!!Ta.colorTexture,vo=\"rgba4\"),\"colorType\"in Ta&&(_o=Ta.colorType,Ns||(_o===\"half float\"||_o===\"float16\"?vo=\"rgba16f\":(_o===\"float\"||_o===\"float32\")&&(vo=\"rgba32f\"))),\"colorFormat\"in Ta&&(vo=Ta.colorFormat,Pt.indexOf(vo)>=0?Ns=!0:$t.indexOf(vo)>=0&&(Ns=!1))),(\"depthTexture\"in Ta||\"depthStencilTexture\"in Ta)&&(ql=!!(Ta.depthTexture||Ta.depthStencilTexture)),\"depth\"in Ta&&(typeof Ta.depth==\"boolean\"?Os=Ta.depth:(Xo=Ta.depth,Ss=!1)),\"stencil\"in Ta&&(typeof Ta.stencil==\"boolean\"?Ss=Ta.stencil:(no=Ta.stencil,Os=!1)),\"depthStencil\"in Ta&&(typeof Ta.depthStencil==\"boolean\"?Os=Ss=Ta.depthStencil:(Us=Ta.depthStencil,Os=!1,Ss=!1))}var ys=null,xc=null,Zl=null,$l=null;if(Array.isArray(Na))ys=Na.map(Er);else if(Na)ys=[Er(Na)];else for(ys=new 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Lr(si.depthAttachment,Ki,ca),Lr(si.stencilAttachment,Ki,ca),Lr(si.depthStencilAttachment,Ki,ca),si.width=zi.width=Ki,si.height=zi.height=ca,Sn(si),zi}return zi(jn,Wn),e(zi,{resize:da,_reglType:\"framebuffer\",_framebuffer:si,destroy:function(){yi(si),Yn(si)},use:function(ui){St.setFBO({framebuffer:zi},ui)}})}function li(jn){var Wn=Array(6);function si(da){var ui,Li={color:null},Ki=0,ca=null,Ia=\"rgba\",Os=\"uint8\",Ss=1;if(typeof da==\"number\")Ki=da|0;else if(!da)Ki=1;else{var Na=da;if(\"shape\"in Na){var Ns=Na.shape;Ki=Ns[0]}else\"radius\"in Na&&(Ki=Na.radius|0),\"width\"in Na?(Ki=Na.width|0,\"height\"in Na):\"height\"in Na&&(Ki=Na.height|0);(\"color\"in Na||\"colors\"in Na)&&(ca=Na.color||Na.colors,Array.isArray(ca)),ca||(\"colorCount\"in Na&&(Ss=Na.colorCount|0),\"colorType\"in Na&&(Os=Na.colorType),\"colorFormat\"in Na&&(Ia=Na.colorFormat)),\"depth\"in Na&&(Li.depth=Na.depth),\"stencil\"in Na&&(Li.stencil=Na.stencil),\"depthStencil\"in Na&&(Li.depthStencil=Na.depthStencil)}var vo;if(ca)if(Array.isArray(ca))for(vo=[],ui=0;ui<ca.length;++ui)vo[ui]=ca[ui];else vo=[ca];else{vo=Array(Ss);var _o={radius:Ki,format:Ia,type:Os};for(ui=0;ui<Ss;++ui)vo[ui]=xt.createCube(_o)}for(Li.color=Array(vo.length),ui=0;ui<vo.length;++ui){var po=vo[ui];Ki=Ki||po.width,Li.color[ui]={target:Pf,data:vo[ui]}}for(ui=0;ui<6;++ui){for(var Xo=0;Xo<vo.length;++Xo)Li.color[Xo].target=Pf+ui;ui>0&&(Li.depth=Wn[0].depth,Li.stencil=Wn[0].stencil,Li.depthStencil=Wn[0].depthStencil),Wn[ui]?Wn[ui](Li):Wn[ui]=An(Li)}return e(si,{width:Ki,height:Ki,color:vo})}function zi(da){var ui,Li=da|0;if(Li===si.width)return si;var Ki=si.color;for(ui=0;ui<Ki.length;++ui)Ki[ui].resize(Li);for(ui=0;ui<6;++ui)Wn[ui].resize(Li);return si.width=si.height=Li,si}return si(jn),e(si,{faces:Wn,resize:zi,_reglType:\"framebufferCube\",destroy:function(){Wn.forEach(function(da){da.destroy()})}})}function Vn(){St.cur=null,St.next=null,St.dirty=!0,Ot(on).forEach(function(jn){jn.framebuffer=Pe.createFramebuffer(),Sn(jn)})}return e(St,{getFramebuffer:function(jn){if(typeof jn==\"function\"&&jn._reglType===\"framebuffer\"){var Wn=jn._framebuffer;if(Wn instanceof Nn)return Wn}return null},create:An,createCube:li,clear:function(){Ot(on).forEach(yi)},restore:Vn})}var jd=5126,pc=34962,gc=34963;function of(){this.state=0,this.x=0,this.y=0,this.z=0,this.w=0,this.buffer=null,this.size=0,this.normalized=!1,this.type=jd,this.offset=0,this.stride=0,this.divisor=0}function sf(Pe,Xe,at,xt,Et,Bt,St){for(var Pt=at.maxAttributes,$t=new Array(Pt),Ht=0;Ht<Pt;++Ht)$t[Ht]=new of;var dr=0,Xr={},Or={Record:of,scope:{},state:$t,currentVAO:null,targetVAO:null,restore:Er()?Yn:function(){},createVAO:yi,getVAO:Rr,destroyBuffer:$r,setVAO:Er()?Lr:an,clear:Er()?on:function(){}};function $r(Sn){for(var An=0;An<$t.length;++An){var li=$t[An];li.buffer===Sn&&(Pe.disableVertexAttribArray(An),li.buffer=null)}}function Er(){return Xe.oes_vertex_array_object}function Tr(){return Xe.angle_instanced_arrays}function Rr(Sn){return typeof Sn==\"function\"&&Sn._vao?Sn._vao:null}function Lr(Sn){if(Sn!==Or.currentVAO){var An=Er();Sn?An.bindVertexArrayOES(Sn.vao):An.bindVertexArrayOES(null),Or.currentVAO=Sn}}function an(Sn){if(Sn!==Or.currentVAO){if(Sn)Sn.bindAttrs();else{for(var An=Tr(),li=0;li<$t.length;++li){var Vn=$t[li];Vn.buffer?(Pe.enableVertexAttribArray(li),Vn.buffer.bind(),Pe.vertexAttribPointer(li,Vn.size,Vn.type,Vn.normalized,Vn.stride,Vn.offfset),An&&Vn.divisor&&An.vertexAttribDivisorANGLE(li,Vn.divisor)):(Pe.disableVertexAttribArray(li),Pe.vertexAttrib4f(li,Vn.x,Vn.y,Vn.z,Vn.w))}St.elements?Pe.bindBuffer(gc,St.elements.buffer.buffer):Pe.bindBuffer(gc,null)}Or.currentVAO=Sn}}function on(){Ot(Xr).forEach(function(Sn){Sn.destroy()})}function Nn(){this.id=++dr,this.attributes=[],this.elements=null,this.ownsElements=!1,this.count=0,this.offset=0,this.instances=-1,this.primitive=4;var Sn=Er();Sn?this.vao=Sn.createVertexArrayOES():this.vao=null,Xr[this.id]=this,this.buffers=[]}Nn.prototype.bindAttrs=function(){for(var Sn=Tr(),An=this.attributes,li=0;li<An.length;++li){var Vn=An[li];Vn.buffer?(Pe.enableVertexAttribArray(li),Pe.bindBuffer(pc,Vn.buffer.buffer),Pe.vertexAttribPointer(li,Vn.size,Vn.type,Vn.normalized,Vn.stride,Vn.offset),Sn&&Vn.divisor&&Sn.vertexAttribDivisorANGLE(li,Vn.divisor)):(Pe.disableVertexAttribArray(li),Pe.vertexAttrib4f(li,Vn.x,Vn.y,Vn.z,Vn.w))}for(var jn=An.length;jn<Pt;++jn)Pe.disableVertexAttribArray(jn);var Wn=Bt.getElements(this.elements);Wn?Pe.bindBuffer(gc,Wn.buffer.buffer):Pe.bindBuffer(gc,null)},Nn.prototype.refresh=function(){var Sn=Er();Sn&&(Sn.bindVertexArrayOES(this.vao),this.bindAttrs(),Or.currentVAO=null,Sn.bindVertexArrayOES(null))},Nn.prototype.destroy=function(){if(this.vao){var Sn=Er();this===Or.currentVAO&&(Or.currentVAO=null,Sn.bindVertexArrayOES(null)),Sn.deleteVertexArrayOES(this.vao),this.vao=null}this.ownsElements&&(this.elements.destroy(),this.elements=null,this.ownsElements=!1),Xr[this.id]&&(delete Xr[this.id],xt.vaoCount-=1)};function Yn(){var Sn=Er();Sn&&Ot(Xr).forEach(function(An){An.refresh()})}function yi(Sn){var An=new Nn;xt.vaoCount+=1;function li(Vn){var jn;if(Array.isArray(Vn))jn=Vn,An.elements&&An.ownsElements&&An.elements.destroy(),An.elements=null,An.ownsElements=!1,An.offset=0,An.count=0,An.instances=-1,An.primitive=4;else{if(Vn.elements){var Wn=Vn.elements;An.ownsElements?typeof Wn==\"function\"&&Wn._reglType===\"elements\"?(An.elements.destroy(),An.ownsElements=!1):(An.elements(Wn),An.ownsElements=!1):Bt.getElements(Vn.elements)?(An.elements=Vn.elements,An.ownsElements=!1):(An.elements=Bt.create(Vn.elements),An.ownsElements=!0)}else An.elements=null,An.ownsElements=!1;jn=Vn.attributes,An.offset=0,An.count=-1,An.instances=-1,An.primitive=4,An.elements&&(An.count=An.elements._elements.vertCount,An.primitive=An.elements._elements.primType),\"offset\"in Vn&&(An.offset=Vn.offset|0),\"count\"in Vn&&(An.count=Vn.count|0),\"instances\"in Vn&&(An.instances=Vn.instances|0),\"primitive\"in Vn&&(An.primitive=ss[Vn.primitive])}var si={},zi=An.attributes;zi.length=jn.length;for(var da=0;da<jn.length;++da){var ui=jn[da],Li=zi[da]=new of,Ki=ui.data||ui;if(Array.isArray(Ki)||or(Ki)||fr(Ki)){var ca;An.buffers[da]&&(ca=An.buffers[da],or(Ki)&&ca._buffer.byteLength>=Ki.byteLength?ca.subdata(Ki):(ca.destroy(),An.buffers[da]=null)),An.buffers[da]||(ca=An.buffers[da]=Et.create(ui,pc,!1,!0)),Li.buffer=Et.getBuffer(ca),Li.size=Li.buffer.dimension|0,Li.normalized=!1,Li.type=Li.buffer.dtype,Li.offset=0,Li.stride=0,Li.divisor=0,Li.state=1,si[da]=1}else Et.getBuffer(ui)?(Li.buffer=Et.getBuffer(ui),Li.size=Li.buffer.dimension|0,Li.normalized=!1,Li.type=Li.buffer.dtype,Li.offset=0,Li.stride=0,Li.divisor=0,Li.state=1):Et.getBuffer(ui.buffer)?(Li.buffer=Et.getBuffer(ui.buffer),Li.size=(+ui.size||Li.buffer.dimension)|0,Li.normalized=!!ui.normalized||!1,\"type\"in ui?Li.type=Cn[ui.type]:Li.type=Li.buffer.dtype,Li.offset=(ui.offset||0)|0,Li.stride=(ui.stride||0)|0,Li.divisor=(ui.divisor||0)|0,Li.state=1):\"x\"in ui&&(Li.x=+ui.x||0,Li.y=+ui.y||0,Li.z=+ui.z||0,Li.w=+ui.w||0,Li.state=2)}for(var Ia=0;Ia<An.buffers.length;++Ia)!si[Ia]&&An.buffers[Ia]&&(An.buffers[Ia].destroy(),An.buffers[Ia]=null);return An.refresh(),li}return li.destroy=function(){for(var Vn=0;Vn<An.buffers.length;++Vn)An.buffers[Vn]&&An.buffers[Vn].destroy();An.buffers.length=0,An.ownsElements&&(An.elements.destroy(),An.elements=null,An.ownsElements=!1),An.destroy()},li._vao=An,li._reglType=\"vao\",li(Sn)}return Or}var bd=35632,Gh=35633,Xu=35718,Pc=35721;function Ff(Pe,Xe,at,xt){var Et={},Bt={};function St(Tr,Rr,Lr,an){this.name=Tr,this.id=Rr,this.location=Lr,this.info=an}function Pt(Tr,Rr){for(var Lr=0;Lr<Tr.length;++Lr)if(Tr[Lr].id===Rr.id){Tr[Lr].location=Rr.location;return}Tr.push(Rr)}function $t(Tr,Rr,Lr){var an=Tr===bd?Et:Bt,on=an[Rr];if(!on){var Nn=Xe.str(Rr);on=Pe.createShader(Tr),Pe.shaderSource(on,Nn),Pe.compileShader(on),an[Rr]=on}return on}var Ht={},dr=[],Xr=0;function Or(Tr,Rr){this.id=Xr++,this.fragId=Tr,this.vertId=Rr,this.program=null,this.uniforms=[],this.attributes=[],this.refCount=1,xt.profile&&(this.stats={uniformsCount:0,attributesCount:0})}function $r(Tr,Rr,Lr){var an,on,Nn=$t(bd,Tr.fragId),Yn=$t(Gh,Tr.vertId),yi=Tr.program=Pe.createProgram();if(Pe.attachShader(yi,Nn),Pe.attachShader(yi,Yn),Lr)for(an=0;an<Lr.length;++an){var Sn=Lr[an];Pe.bindAttribLocation(yi,Sn[0],Sn[1])}Pe.linkProgram(yi);var An=Pe.getProgramParameter(yi,Xu);xt.profile&&(Tr.stats.uniformsCount=An);var li=Tr.uniforms;for(an=0;an<An;++an)if(on=Pe.getActiveUniform(yi,an),on)if(on.size>1)for(var Vn=0;Vn<on.size;++Vn){var jn=on.name.replace(\"[0]\",\"[\"+Vn+\"]\");Pt(li,new St(jn,Xe.id(jn),Pe.getUniformLocation(yi,jn),on))}else Pt(li,new St(on.name,Xe.id(on.name),Pe.getUniformLocation(yi,on.name),on));var Wn=Pe.getProgramParameter(yi,Pc);xt.profile&&(Tr.stats.attributesCount=Wn);var si=Tr.attributes;for(an=0;an<Wn;++an)on=Pe.getActiveAttrib(yi,an),on&&Pt(si,new St(on.name,Xe.id(on.name),Pe.getAttribLocation(yi,on.name),on))}xt.profile&&(at.getMaxUniformsCount=function(){var Tr=0;return dr.forEach(function(Rr){Rr.stats.uniformsCount>Tr&&(Tr=Rr.stats.uniformsCount)}),Tr},at.getMaxAttributesCount=function(){var Tr=0;return dr.forEach(function(Rr){Rr.stats.attributesCount>Tr&&(Tr=Rr.stats.attributesCount)}),Tr});function Er(){Et={},Bt={};for(var Tr=0;Tr<dr.length;++Tr)$r(dr[Tr],null,dr[Tr].attributes.map(function(Rr){return[Rr.location,Rr.name]}))}return{clear:function(){var Tr=Pe.deleteShader.bind(Pe);Ot(Et).forEach(Tr),Et={},Ot(Bt).forEach(Tr),Bt={},dr.forEach(function(Rr){Pe.deleteProgram(Rr.program)}),dr.length=0,Ht={},at.shaderCount=0},program:function(Tr,Rr,Lr,an){var on=Ht[Rr];on||(on=Ht[Rr]={});var Nn=on[Tr];if(Nn&&(Nn.refCount++,!an))return Nn;var Yn=new Or(Rr,Tr);return at.shaderCount++,$r(Yn,Lr,an),Nn||(on[Tr]=Yn),dr.push(Yn),e(Yn,{destroy:function(){if(Yn.refCount--,Yn.refCount<=0){Pe.deleteProgram(Yn.program);var yi=dr.indexOf(Yn);dr.splice(yi,1),at.shaderCount--}on[Yn.vertId].refCount<=0&&(Pe.deleteShader(Bt[Yn.vertId]),delete Bt[Yn.vertId],delete Ht[Yn.fragId][Yn.vertId]),Object.keys(Ht[Yn.fragId]).length||(Pe.deleteShader(Et[Yn.fragId]),delete Et[Yn.fragId],delete Ht[Yn.fragId])}})},restore:Er,shader:$t,frag:-1,vert:-1}}var Df=6408,La=5121,Rf=3333,m0=5126;function wd(Pe,Xe,at,xt,Et,Bt,St){function Pt(dr){var Xr;Xe.next===null?Xr=La:Xr=Xe.next.colorAttachments[0].texture._texture.type;var Or=0,$r=0,Er=xt.framebufferWidth,Tr=xt.framebufferHeight,Rr=null;or(dr)?Rr=dr:dr&&(Or=dr.x|0,$r=dr.y|0,Er=(dr.width||xt.framebufferWidth-Or)|0,Tr=(dr.height||xt.framebufferHeight-$r)|0,Rr=dr.data||null),at();var Lr=Er*Tr*4;return Rr||(Xr===La?Rr=new Uint8Array(Lr):Xr===m0&&(Rr=Rr||new Float32Array(Lr))),Pe.pixelStorei(Rf,4),Pe.readPixels(Or,$r,Er,Tr,Df,Xr,Rr),Rr}function $t(dr){var Xr;return Xe.setFBO({framebuffer:dr.framebuffer},function(){Xr=Pt(dr)}),Xr}function Ht(dr){return!dr||!(\"framebuffer\"in dr)?Pt(dr):$t(dr)}return Ht}var Hh=0,X0=\"\";function y0(Pe){return Wl(Td(zf(Pe)))}function Td(Pe){return Ze(en(qf(Pe),Pe.length*8))}function ul(Pe,Xe){var at=qf(Pe);at.length>16&&(at=en(at,Pe.length*8));for(var xt=Array(16),Et=Array(16),Bt=0;Bt<16;Bt++)xt[Bt]=at[Bt]^909522486,Et[Bt]=at[Bt]^1549556828;var St=en(xt.concat(qf(Xe)),512+Xe.length*8);return Ze(en(Et.concat(St),768))}function Wl(Pe){for(var 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on=Oi.ELEMENTS;return Gi.def(on,\"?\",on,\".primType:\",Sl)});if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(Oi,Gi){return Gi.def(Oi.shared.vao+\".currentVAO?\"+Oi.shared.vao+\".currentVAO.primitive:\"+Sl)})}return null}function _r(tn,Ei){if(tn in st){var Oi=st[tn]|0;return Ei?Bt.offset=Oi:Bt.instances=Oi,kn(function(on,zn){return Ei&&(on.OFFSET=Oi),Oi})}else if(tn in It){var Gi=It[tn];return Bn(Gi,function(on,zn){var Ja=on.invoke(zn,Gi);return Ei&&(on.OFFSET=Ja),Ja})}else if(Ei){if(_t)return kn(function(on,zn){return on.OFFSET=0,0});if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(on,zn){return zn.def(on.shared.vao+\".currentVAO?\"+on.shared.vao+\".currentVAO.offset:0\")})}else if(Ft)return new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(on,zn){return zn.def(on.shared.vao+\".currentVAO?\"+on.shared.vao+\".currentVAO.instances:-1\")});return null}var Br=_r(yr,!0);function Di(){if($t in st){var tn=st[$t]|0;return Bt.count=tn,kn(function(){return tn})}else if($t in It){var Ei=It[$t];return Bn(Ei,function(zn,Ja){var co=zn.invoke(Ja,Ei);return co})}else if(_t)if(Pn(vt)){if(vt)return Br?new Qr(Br.thisDep,Br.contextDep,Br.propDep,function(zn,Ja){var co=Ja.def(zn.ELEMENTS,\".vertCount-\",zn.OFFSET);return co}):kn(function(zn,Ja){return Ja.def(zn.ELEMENTS,\".vertCount\")});var Oi=kn(function(){return-1});return Oi}else{var Gi=new Qr(vt.thisDep||Br.thisDep,vt.contextDep||Br.contextDep,vt.propDep||Br.propDep,function(zn,Ja){var co=zn.ELEMENTS;return zn.OFFSET?Ja.def(co,\"?\",co,\".vertCount-\",zn.OFFSET,\":-1\"):Ja.def(co,\"?\",co,\".vertCount:-1\")});return Gi}else if(Ft){var on=new Qr(zt.thisDep,zt.contextDep,zt.propDep,function(zn,Ja){return Ja.def(zn.shared.vao,\".currentVAO?\",zn.shared.vao,\".currentVAO.count:-1\")});return on}return null}var ci=lr(),bi=Di(),gn=_r(Cr,!1);return{elements:vt,primitive:ci,count:bi,instances:gn,offset:Br,vao:zt,vaoActive:Ft,elementsActive:_t,static:Bt}}function El(Ne,$e){var st=Ne.static,It=Ne.dynamic,Bt={};return fi.forEach(function(Ft){var Ut=Ji(Ft);function zt(_t,Dt){if(Ft in st){var vt=_t(st[Ft]);Bt[Ut]=kn(function(){return vt})}else if(Ft in It){var lr=It[Ft];Bt[Ut]=Bn(lr,function(_r,Br){return Dt(_r,Br,_r.invoke(Br,lr))})}}switch(Ft){case Cu:case Ho:case ws:case Vc:case va:case B:case Uc:case Ac:case Va:case rs:return zt(function(_t){return _t},function(_t,Dt,vt){return vt});case no:return zt(function(_t){return Wr[_t]},function(_t,Dt,vt){var lr=_t.constants.compareFuncs;return Dt.def(lr,\"[\",vt,\"]\")});case ys:return zt(function(_t){return _t},function(_t,Dt,vt){var lr=Dt.def(\"+\",vt,\"[0]\"),_r=Dt.def(\"+\",vt,\"[1]\");return[lr,_r]});case Ls:return zt(function(_t){var Dt=\"srcRGB\"in _t?_t.srcRGB:_t.src,vt=\"srcAlpha\"in _t?_t.srcAlpha:_t.src,lr=\"dstRGB\"in _t?_t.dstRGB:_t.dst,_r=\"dstAlpha\"in _t?_t.dstAlpha:_t.dst;return[xi[Dt],xi[lr],xi[vt],xi[_r]]},function(_t,Dt,vt){var lr=_t.constants.blendFuncs;function _r(Ei,Oi){var Gi=Dt.def('\"',Ei,Oi,'\" in ',vt,\"?\",vt,\".\",Ei,Oi,\":\",vt,\".\",Ei);return Gi}var Br=_r(\"src\",\"RGB\"),Di=_r(\"dst\",\"RGB\"),ci=Dt.def(lr,\"[\",Br,\"]\"),bi=Dt.def(lr,\"[\",_r(\"src\",\"Alpha\"),\"]\"),gn=Dt.def(lr,\"[\",Di,\"]\"),tn=Dt.def(lr,\"[\",_r(\"dst\",\"Alpha\"),\"]\");return[ci,gn,bi,tn]});case Ws:return zt(function(_t){if(typeof _t==\"string\")return[yi[_t],yi[_t]];if(typeof _t==\"object\")return[yi[_t.rgb],yi[_t.alpha]]},function(_t,Dt,vt){var lr=_t.constants.blendEquations,_r=Dt.def(),Br=Dt.def(),Di=_t.cond(\"typeof \",vt,'===\"string\"');return Di.then(_r,\"=\",Br,\"=\",lr,\"[\",vt,\"];\"),Di.else(_r,\"=\",lr,\"[\",vt,\".rgb];\",Br,\"=\",lr,\"[\",vt,\".alpha];\"),Dt(Di),[_r,Br]});case ml:return zt(function(_t){return S(4,function(Dt){return+_t[Dt]})},function(_t,Dt,vt){return S(4,function(lr){return Dt.def(\"+\",vt,\"[\",lr,\"]\")})});case hc:return zt(function(_t){return _t|0},function(_t,Dt,vt){return Dt.def(vt,\"|0\")});case Ku:return zt(function(_t){var Dt=_t.cmp||\"keep\",vt=_t.ref||0,lr=\"mask\"in _t?_t.mask:-1;return[Wr[Dt],vt,lr]},function(_t,Dt,vt){var lr=_t.constants.compareFuncs,_r=Dt.def('\"cmp\" in ',vt,\"?\",lr,\"[\",vt,\".cmp]\",\":\",ot),Br=Dt.def(vt,\".ref|0\"),Di=Dt.def('\"mask\" in ',vt,\"?\",vt,\".mask|0:-1\");return[_r,Br,Di]});case ue:case w:return zt(function(_t){var Dt=_t.fail||\"keep\",vt=_t.zfail||\"keep\",lr=_t.zpass||\"keep\";return[Ft===w?se:X,ii[Dt],ii[vt],ii[lr]]},function(_t,Dt,vt){var lr=_t.constants.stencilOps;function _r(Br){return Dt.def('\"',Br,'\" in ',vt,\"?\",lr,\"[\",vt,\".\",Br,\"]:\",ot)}return[Ft===w?se:X,_r(\"fail\"),_r(\"zfail\"),_r(\"zpass\")]});case xu:return zt(function(_t){var Dt=_t.factor|0,vt=_t.units|0;return[Dt,vt]},function(_t,Dt,vt){var lr=Dt.def(vt,\".factor|0\"),_r=Dt.def(vt,\".units|0\");return[lr,_r]});case Yu:return zt(function(_t){var Dt=0;return _t===\"front\"?Dt=X:_t===\"back\"&&(Dt=se),Dt},function(_t,Dt,vt){return Dt.def(vt,'===\"front\"?',X,\":\",se)});case pu:return zt(function(_t){return _t},function(_t,Dt,vt){return vt});case Nc:return zt(function(_t){return di[_t]},function(_t,Dt,vt){return Dt.def(vt+'===\"cw\"?'+Te+\":\"+qe)});case Ql:return zt(function(_t){return _t.map(function(Dt){return!!Dt})},function(_t,Dt,vt){return S(4,function(lr){return\"!!\"+vt+\"[\"+lr+\"]\"})});case oo:return zt(function(_t){var Dt=\"value\"in _t?_t.value:1,vt=!!_t.invert;return[Dt,vt]},function(_t,Dt,vt){var lr=Dt.def('\"value\" in ',vt,\"?+\",vt,\".value:1\"),_r=Dt.def(\"!!\",vt,\".invert\");return[lr,_r]})}}),Bt}function zo(Ne,$e){var st=Ne.static,It=Ne.dynamic,Bt={};return Object.keys(st).forEach(function(Ft){var Ut=st[Ft],zt;if(typeof Ut==\"number\"||typeof Ut==\"boolean\")zt=kn(function(){return Ut});else if(typeof Ut==\"function\"){var _t=Ut._reglType;_t===\"texture2d\"||_t===\"textureCube\"?zt=kn(function(Dt){return Dt.link(Ut)}):(_t===\"framebuffer\"||_t===\"framebufferCube\")&&(zt=kn(function(Dt){return Dt.link(Ut.color[0])}))}else 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bi=Ut.divisor|0;_t.buffer=Dt,_t.state=La,_t.size=Br,_t.normalized=Di,_t.type=ci||Dt.dtype,_t.offset=lr,_t.stride=_r,_t.divisor=bi}}Bt[Ft]=kn(function(gn,tn){var Ei=gn.attribCache;if(zt in Ei)return Ei[zt];var Oi={isStream:!1};return Object.keys(_t).forEach(function(Gi){Oi[Gi]=_t[Gi]}),_t.buffer&&(Oi.buffer=gn.link(_t.buffer),Oi.type=Oi.type||Oi.buffer+\".dtype\"),Ei[zt]=Oi,Oi})}),Object.keys(It).forEach(function(Ft){var Ut=It[Ft];function zt(_t,Dt){var vt=_t.invoke(Dt,Ut),lr=_t.shared,_r=_t.constants,Br=lr.isBufferArgs,Di=lr.buffer,ci={isStream:Dt.def(!1)},bi=new Zr;bi.state=La,Object.keys(bi).forEach(function(Oi){ci[Oi]=Dt.def(\"\"+bi[Oi])});var gn=ci.buffer,tn=ci.type;Dt(\"if(\",Br,\"(\",vt,\")){\",ci.isStream,\"=true;\",gn,\"=\",Di,\".createStream(\",br,\",\",vt,\");\",tn,\"=\",gn,\".dtype;\",\"}else{\",gn,\"=\",Di,\".getBuffer(\",vt,\");\",\"if(\",gn,\"){\",tn,\"=\",gn,\".dtype;\",'}else if(\"constant\" in ',vt,\"){\",ci.state,\"=\",Ua,\";\",\"if(typeof \"+vt+'.constant === 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Ft==\"boolean\"?\"\"+Ft:Ut.link(Ft)})}),Object.keys(st).forEach(function(Bt){var Ft=st[Bt];It[Bt]=Bn(Ft,function(Ut,zt){return Ut.invoke(zt,Ft)})}),It}function Ul(Ne,$e,st,It,Bt){var Ft=Ne.static,Ut=Ne.dynamic,zt=is(Ne,$e),_t=sa(Ne,Bt),Dt=ba(Ne,_t,Bt),vt=Ga(Ne,Bt),lr=El(Ne,Bt),_r=Zs(Ne,Bt,zt);function Br(Ei){var Oi=Dt[Ei];Oi&&(lr[Ei]=Oi)}Br(ee),Br(Ji(Q));var Di=Object.keys(lr).length>0,ci={framebuffer:_t,draw:vt,shader:_r,state:lr,dirty:Di,scopeVAO:null,drawVAO:null,useVAO:!1,attributes:{}};if(ci.profile=ia(Ne,Bt),ci.uniforms=zo(st,Bt),ci.drawVAO=ci.scopeVAO=vt.vao,!ci.drawVAO&&_r.program&&!zt&&Ot.angle_instanced_arrays&&vt.static.elements){var bi=!0,gn=_r.program.attributes.map(function(Ei){var Oi=$e.static[Ei];return bi=bi&&!!Oi,Oi});if(bi&&gn.length>0){var tn=qi.getVAO(qi.createVAO({attributes:gn,elements:vt.static.elements}));ci.drawVAO=new Qr(null,null,null,function(Ei,Oi){return Ei.link(tn)}),ci.useVAO=!0}}return zt?ci.useVAO=!0:ci.attributes=Qs($e,Bt),ci.context=nl(It,Bt),ci}function ss(Ne,$e,st){var It=Ne.shared,Bt=It.context,Ft=Ne.scope();Object.keys(st).forEach(function(Ut){$e.save(Bt,\".\"+Ut);var zt=st[Ut],_t=zt.append(Ne,$e);Array.isArray(_t)?Ft(Bt,\".\",Ut,\"=[\",_t.join(),\"];\"):Ft(Bt,\".\",Ut,\"=\",_t,\";\")}),$e(Ft)}function Vs(Ne,$e,st,It){var Bt=Ne.shared,Ft=Bt.gl,Ut=Bt.framebuffer,zt;ui&&(zt=$e.def(Bt.extensions,\".webgl_draw_buffers\"));var _t=Ne.constants,Dt=_t.drawBuffer,vt=_t.backBuffer,lr;st?lr=st.append(Ne,$e):lr=$e.def(Ut,\".next\"),It||$e(\"if(\",lr,\"!==\",Ut,\".cur){\"),$e(\"if(\",lr,\"){\",Ft,\".bindFramebuffer(\",zr,\",\",lr,\".framebuffer);\"),ui&&$e(zt,\".drawBuffersWEBGL(\",Dt,\"[\",lr,\".colorAttachments.length]);\"),$e(\"}else{\",Ft,\".bindFramebuffer(\",zr,\",null);\"),ui&&$e(zt,\".drawBuffersWEBGL(\",vt,\");\"),$e(\"}\",Ut,\".cur=\",lr,\";\"),It||$e(\"}\")}function Ys(Ne,$e,st){var It=Ne.shared,Bt=It.gl,Ft=Ne.current,Ut=Ne.next,zt=It.current,_t=It.next,Dt=Ne.cond(zt,\".dirty\");fi.forEach(function(vt){var lr=Ji(vt);if(!(lr in st.state)){var _r,Br;if(lr in Ut){_r=Ut[lr],Br=Ft[lr];var Di=S(Zi[lr].length,function(bi){return Dt.def(_r,\"[\",bi,\"]\")});Dt(Ne.cond(Di.map(function(bi,gn){return bi+\"!==\"+Br+\"[\"+gn+\"]\"}).join(\"||\")).then(Bt,\".\",mn[lr],\"(\",Di,\");\",Di.map(function(bi,gn){return Br+\"[\"+gn+\"]=\"+bi}).join(\";\"),\";\"))}else{_r=Dt.def(_t,\".\",lr);var ci=Ne.cond(_r,\"!==\",zt,\".\",lr);Dt(ci),lr in si?ci(Ne.cond(_r).then(Bt,\".enable(\",si[lr],\");\").else(Bt,\".disable(\",si[lr],\");\"),zt,\".\",lr,\"=\",_r,\";\"):ci(Bt,\".\",mn[lr],\"(\",_r,\");\",zt,\".\",lr,\"=\",_r,\";\")}}}),Object.keys(st.state).length===0&&Dt(zt,\".dirty=false;\"),$e(Dt)}function wa(Ne,$e,st,It){var Bt=Ne.shared,Ft=Ne.current,Ut=Bt.current,zt=Bt.gl,_t;Ti(Object.keys(st)).forEach(function(Dt){var vt=st[Dt];if(!(It&&!It(vt))){var lr=vt.append(Ne,$e);if(si[Dt]){var 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Bt==\"string\"?Ei(Ut,\".count+=\",Bt,\";\"):Ei(Ut,\".count++;\"),Wi&&(It?(_r=$e.def(),Ei(_r,\"=\",_t,\".getNumPendingQueries();\")):Ei(_t,\".beginQuery(\",Ut,\");\"))}function Di(Ei){Ei(Ut,\".cpuTime+=\",vt(),\"-\",lr,\";\"),Wi&&(It?Ei(_t,\".pushScopeStats(\",_r,\",\",_t,\".getNumPendingQueries(),\",Ut,\");\"):Ei(_t,\".endQuery();\"))}function ci(Ei){var Oi=$e.def(zt,\".profile\");$e(zt,\".profile=\",Ei,\";\"),$e.exit(zt,\".profile=\",Oi,\";\")}var bi;if(Dt){if(Pn(Dt)){Dt.enable?(Br($e),Di($e.exit),ci(\"true\")):ci(\"false\");return}bi=Dt.append(Ne,$e),ci(bi)}else bi=$e.def(zt,\".profile\");var gn=Ne.block();Br(gn),$e(\"if(\",bi,\"){\",gn,\"}\");var tn=Ne.block();Di(tn),$e.exit(\"if(\",bi,\"){\",tn,\"}\")}function Y(Ne,$e,st,It,Bt){var Ft=Ne.shared;function Ut(_t){switch(_t){case Co:case Ps:case hl:return 2;case Qa:case Ts:case Nl:return 3;case mo:case wo:case Lu:return 4;default:return 1}}function zt(_t,Dt,vt){var 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fa=yr+\".type\",Qi=Hr.elements&&En(Hr.elements)&&!Hr.vaoActive;function $i(){function ji(){Pr(Pi,\".drawElementsInstancedANGLE(\",[kn,sa,fa,Xn+\"<<((\"+fa+\"-\"+Ci+\")>>1)\",Oi],\");\")}function Wi(){Pr(Pi,\".drawArraysInstancedANGLE(\",[kn,Xn,sa,Oi],\");\")}yr&&yr!==\"null\"?Qi?ji():(Pr(\"if(\",yr,\"){\"),ji(),Pr(\"}else{\"),Wi(),Pr(\"}\")):Wi()}function Fi(){function ji(){Pr(sn+\".drawElements(\"+[kn,sa,fa,Xn+\"<<((\"+fa+\"-\"+Ci+\")>>1)\"]+\");\")}function Wi(){Pr(sn+\".drawArrays(\"+[kn,Xn,sa]+\");\")}yr&&yr!==\"null\"?Qi?ji():(Pr(\"if(\",yr,\"){\"),ji(),Pr(\"}else{\"),Wi(),Pr(\"}\")):Wi()}an&&(typeof Oi!=\"number\"||Oi>=0)?typeof Oi==\"string\"?(Pr(\"if(\",Oi,\">0){\"),$i(),Pr(\"}else if(\",Oi,\"<0){\"),Fi(),Pr(\"}\")):$i():Fi()}function xa(ar,gr,Pr,pn,Mn){var sn=Li(),tn=sn.proc(\"body\",Mn);return an&&(sn.instancing=tn.def(sn.shared.extensions,\".angle_instanced_arrays\")),ar(sn,tn,Pr,pn),sn.compile().body}function 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pn=!1,Mn=!0;Object.keys(gr.context).forEach(function(Xn){pn=pn||gr.context[Xn].propDep}),pn||(no(ar,Pr,gr.context),Mn=!1);var sn=gr.framebuffer,tn=!1;sn?(sn.propDep?pn=tn=!0:sn.contextDep&&pn&&(tn=!0),tn||Us(ar,Pr,sn)):Us(ar,Pr,null),gr.state.viewport&&gr.state.viewport.propDep&&(pn=!0);function Hr(Xn){return Xn.contextDep&&pn||Xn.propDep}ql(ar,Pr,gr),Ta(ar,Pr,gr.state,function(Xn){return!Hr(Xn)}),(!gr.profile||!Hr(gr.profile))&&ys(ar,Pr,gr,!1,\"a1\"),gr.contextDep=pn,gr.needsContext=Mn,gr.needsFramebuffer=tn;var Mr=gr.shader.progVar;if(Mr.contextDep&&pn||Mr.propDep)gn(ar,Pr,gr,null);else{var zr=Mr.append(ar,Pr);if(Pr(ar.shared.gl,\".useProgram(\",zr,\".program);\"),gr.shader.program)gn(ar,Pr,gr,gr.shader.program);else{Pr(ar.shared.vao,\".setVAO(null);\");var yr=ar.global.def(\"{}\"),hn=Pr.def(zr,\".id\"),kn=Pr.def(yr,\"[\",hn,\"]\");Pr(ar.cond(kn).then(kn,\".call(this,a0,a1);\").else(kn,\"=\",yr,\"[\",hn,\"]=\",ar.link(function(Xn){return 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Mn=ar.global,sn=Object.keys(pn),tn=!1,Hr=!1,Mr=!1,zr=ar.global.def(\"{}\");sn.forEach(function(hn){var kn=pn[hn];if(v.isDynamic(kn)){typeof kn==\"function\"&&(kn=pn[hn]=v.unbox(kn));var Xn=Zn(kn,null);tn=tn||Xn.thisDep,Mr=Mr||Xn.propDep,Hr=Hr||Xn.contextDep}else{switch(Mn(zr,\".\",hn,\"=\"),typeof kn){case\"number\":Mn(kn);break;case\"string\":Mn('\"',kn,'\"');break;case\"object\":Array.isArray(kn)&&Mn(\"[\",kn.join(),\"]\");break;default:Mn(ar.link(kn));break}Mn(\";\")}});function yr(hn,kn){sn.forEach(function(Xn){var sa=pn[Xn];if(v.isDynamic(sa)){var Oi=hn.invoke(kn,sa);kn(zr,\".\",Xn,\"=\",Oi,\";\")}})}gr.dynamic[Pr]=new v.DynamicVariable(Ha,{thisDep:tn,contextDep:Hr,propDep:Mr,ref:zr,append:yr}),delete gr.static[Pr]}function Hi(ar,gr,Pr,pn,Mn){var sn=Li();sn.stats=sn.link(Mn),Object.keys(gr.static).forEach(function(Hr){xs(sn,gr,Hr)}),Kt.forEach(function(Hr){xs(sn,ar,Hr)});var tn=Xo(ar,gr,Pr,pn,sn);return tn.shader.program&&(tn.shader.program.attributes.sort(function(Hr,Mr){return 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GVe(e){e=ky.nextPow2(e);var t=ky.log2(e),r=kF[t];return r.length>0?r.pop():new LVe(e)}os.mallocBuffer=GVe;os.clearCache=function(){for(var t=0;t<32;++t)Ec.UINT8[t].length=0,Ec.UINT16[t].length=0,Ec.UINT32[t].length=0,Ec.INT8[t].length=0,Ec.INT16[t].length=0,Ec.INT32[t].length=0,Ec.FLOAT[t].length=0,Ec.DOUBLE[t].length=0,Ec.BIGUINT64[t].length=0,Ec.BIGINT64[t].length=0,Ec.UINT8C[t].length=0,EF[t].length=0,kF[t].length=0}});var WVe=fe((QTr,jVe)=>{\"use strict\";var vKt=Object.prototype.toString;jVe.exports=function(e){var t;return vKt.call(e)===\"[object Object]\"&&(t=Object.getPrototypeOf(e),t===null||t===Object.getPrototypeOf({}))}});var p$=fe((eAr,YVe)=>{YVe.exports=function(t,r){r||(r=[0,\"\"]),t=String(t);var n=parseFloat(t,10);return r[0]=n,r[1]=t.match(/[\\d.\\-\\+]*\\s*(.*)/)[1]||\"\",r}});var $Ve=fe((tAr,ZVe)=>{\"use strict\";var pKt=p$();ZVe.exports=XVe;var qM=96;function g$(e,t){var r=pKt(getComputedStyle(e).getPropertyValue(t));return r[0]*XVe(r[1],e)}function gKt(e,t){var r=document.createElement(\"div\");r.style[\"font-size\"]=\"128\"+e,t.appendChild(r);var n=g$(r,\"font-size\")/128;return t.removeChild(r),n}function XVe(e,t){switch(t=t||document.body,e=(e||\"px\").trim().toLowerCase(),(t===window||t===document)&&(t=document.body),e){case\"%\":return t.clientHeight/100;case\"ch\":case\"ex\":return gKt(e,t);case\"em\":return g$(t,\"font-size\");case\"rem\":return g$(document.body,\"font-size\");case\"vw\":return window.innerWidth/100;case\"vh\":return window.innerHeight/100;case\"vmin\":return Math.min(window.innerWidth,window.innerHeight)/100;case\"vmax\":return Math.max(window.innerWidth,window.innerHeight)/100;case\"in\":return qM;case\"cm\":return qM/2.54;case\"mm\":return qM/25.4;case\"pt\":return qM/72;case\"pc\":return qM/6}return 1}});var QVe=fe((rAr,JVe)=>{\"use strict\";JVe.exports=PF;var mKt=PF.canvas=document.createElement(\"canvas\"),CF=mKt.getContext(\"2d\"),KVe=LF([32,126]);PF.createPairs=LF;PF.ascii=KVe;function PF(e,t){Array.isArray(e)&&(e=e.join(\", \"));var r={},n,a=16,i=.05;t&&(t.length===2&&typeof t[0]==\"number\"?n=LF(t):Array.isArray(t)?n=t:(t.o?n=LF(t.o):t.pairs&&(n=t.pairs),t.fontSize&&(a=t.fontSize),t.threshold!=null&&(i=t.threshold))),n||(n=KVe),CF.font=a+\"px \"+e;for(var o=0;o<n.length;o++){var s=n[o],l=CF.measureText(s[0]).width+CF.measureText(s[1]).width,u=CF.measureText(s).width;if(Math.abs(l-u)>a*i){var c=(u-l)/a;r[s]=c*1e3}}return r}function LF(e){for(var t=[],r=e[0];r<=e[1];r++)for(var n=String.fromCharCode(r),a=e[0];a<e[1];a++){var i=String.fromCharCode(a),o=n+i;t.push(o)}return t}});var nGe=fe((nAr,rGe)=>{\"use strict\";rGe.exports=Cy;Cy.canvas=document.createElement(\"canvas\");Cy.cache={};function Cy(o,t){t||(t={}),(typeof o==\"string\"||Array.isArray(o))&&(t.family=o);var r=Array.isArray(t.family)?t.family.join(\", \"):t.family;if(!r)throw Error(\"`family` must be defined\");var n=t.size||t.fontSize||t.em||48,a=t.weight||t.fontWeight||\"\",i=t.style||t.fontStyle||\"\",o=[i,a,n].join(\" \")+\"px \"+r,s=t.origin||\"top\";if(Cy.cache[r]&&n<=Cy.cache[r].em)return eGe(Cy.cache[r],s);var l=t.canvas||Cy.canvas,u=l.getContext(\"2d\"),c={upper:t.upper!==void 0?t.upper:\"H\",lower:t.lower!==void 0?t.lower:\"x\",descent:t.descent!==void 0?t.descent:\"p\",ascent:t.ascent!==void 0?t.ascent:\"h\",tittle:t.tittle!==void 0?t.tittle:\"i\",overshoot:t.overshoot!==void 0?t.overshoot:\"O\"},f=Math.ceil(n*1.5);l.height=f,l.width=f*.5,u.font=o;var v=\"H\",g={top:0};u.clearRect(0,0,f,f),u.textBaseline=\"top\",u.fillStyle=\"black\",u.fillText(v,0,0);var p=C1(u.getImageData(0,0,f,f));u.clearRect(0,0,f,f),u.textBaseline=\"bottom\",u.fillText(v,0,f);var x=C1(u.getImageData(0,0,f,f));g.lineHeight=g.bottom=f-x+p,u.clearRect(0,0,f,f),u.textBaseline=\"alphabetic\",u.fillText(v,0,f);var _=C1(u.getImageData(0,0,f,f)),y=f-_-1+p;g.baseline=g.alphabetic=y,u.clearRect(0,0,f,f),u.textBaseline=\"middle\",u.fillText(v,0,f*.5);var C=C1(u.getImageData(0,0,f,f));g.median=g.middle=f-C-1+p-f*.5,u.clearRect(0,0,f,f),u.textBaseline=\"hanging\",u.fillText(v,0,f*.5);var k=C1(u.getImageData(0,0,f,f));g.hanging=f-k-1+p-f*.5,u.clearRect(0,0,f,f),u.textBaseline=\"ideographic\",u.fillText(v,0,f);var S=C1(u.getImageData(0,0,f,f));if(g.ideographic=f-S-1+p,c.upper&&(u.clearRect(0,0,f,f),u.textBaseline=\"top\",u.fillText(c.upper,0,0),g.upper=C1(u.getImageData(0,0,f,f)),g.capHeight=g.baseline-g.upper),c.lower&&(u.clearRect(0,0,f,f),u.textBaseline=\"top\",u.fillText(c.lower,0,0),g.lower=C1(u.getImageData(0,0,f,f)),g.xHeight=g.baseline-g.lower),c.tittle&&(u.clearRect(0,0,f,f),u.textBaseline=\"top\",u.fillText(c.tittle,0,0),g.tittle=C1(u.getImageData(0,0,f,f))),c.ascent&&(u.clearRect(0,0,f,f),u.textBaseline=\"top\",u.fillText(c.ascent,0,0),g.ascent=C1(u.getImageData(0,0,f,f))),c.descent&&(u.clearRect(0,0,f,f),u.textBaseline=\"top\",u.fillText(c.descent,0,0),g.descent=tGe(u.getImageData(0,0,f,f))),c.overshoot){u.clearRect(0,0,f,f),u.textBaseline=\"top\",u.fillText(c.overshoot,0,0);var L=tGe(u.getImageData(0,0,f,f));g.overshoot=L-y}for(var w in g)g[w]/=n;return g.em=n,Cy.cache[r]=g,eGe(g,s)}function eGe(e,t){var r={};typeof t==\"string\"&&(t=e[t]);for(var n in e)n!==\"em\"&&(r[n]=e[n]-t);return r}function C1(e){for(var t=e.height,r=e.data,n=3;n<r.length;n+=4)if(r[n]!==0)return Math.floor((n-3)*.25/t)}function tGe(e){for(var t=e.height,r=e.data,n=r.length-1;n>0;n-=4)if(r[n]!==0)return Math.floor((n-3)*.25/t)}});var sGe=fe((iAr,oGe)=>{\"use strict\";var b5=yVe(),yKt=E1(),xKt=f$(),_Kt=TVe(),bKt=XZ(),m$=wM(),wKt=MVe(),Ly=HVe(),TKt=u5(),AKt=WVe(),MKt=p$(),SKt=$Ve(),EKt=QVe(),kKt=Zc(),CKt=nGe(),LKt=u_(),PKt=d$(),iGe=PKt.nextPow2,aGe=new bKt,DF=!1;document.body&&(FF=document.body.appendChild(document.createElement(\"div\")),FF.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(FF).fontStretch&&(DF=!0),document.body.removeChild(FF));var FF,Bs=function(t){FKt(t)?(t={regl:t},this.gl=t.regl._gl):this.gl=_Kt(t),this.shader=aGe.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||xKt({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),aGe.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(AKt(t)?t:{})};Bs.prototype.createShader=function(){var t=this.regl,r=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(a,i){return[i.atlas.width,i.atlas.height]},atlasDim:function(a,i){return[i.atlas.cols,i.atlas.rows]},atlas:function(a,i){return i.atlas.texture},charStep:function(a,i){return i.atlas.step},em:function(a,i){return i.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:`\n",
        "\t\t\tprecision highp float;\n",
        "\t\t\tattribute float width, charOffset, char;\n",
        "\t\t\tattribute vec2 position;\n",
@@ -2751,513 +2752,9 @@
        "\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n",
        "\n",
        "\t\t\t\tgl_FragColor = color;\n",
-       "\t\t\t}`}),n={};return{regl:t,draw:r,atlas:n}};xc.prototype.update=function(t){var r=this;if(typeof t==\"string\")t={text:t};else if(!t)return;t=nVt(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0),t.opacity!=null&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map(function(Ce){return parseFloat(Ce)}):this.opacity=parseFloat(t.opacity)),t.viewport!=null&&(this.viewport=uVt(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),this.viewport==null&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),t.kerning!=null&&(this.kerning=t.kerning),t.offset!=null&&(typeof t.offset==\"number\"&&(t.offset=[t.offset,0]),this.positionOffset=gVt(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),!this.font.length&&!t.font&&(t.font=xc.baseFontSize+\"px sans-serif\");var n=!1,i=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach(function(Ce,ge){if(typeof Ce==\"string\")try{Ce=w5.parse(Ce)}catch(He){Ce=w5.parse(xc.baseFontSize+\"px \"+Ce)}else{var ie=Ce.style,Se=Ce.weight,ke=Ce.stretch,be=Ce.variant;Ce=w5.parse(w5.stringify(Ce)),ie&&(Ce.style=ie),Se&&(Ce.weight=Se),ke&&(Ce.stretch=ke),be&&(Ce.variant=be)}var Be=w5.stringify({size:xc.baseFontSize,family:Ce.family,stretch:Rz?Ce.stretch:void 0,variant:Ce.variant,weight:Ce.weight,style:Ce.style}),Le=fVt(Ce.size),me=Math.round(Le[0]*hVt(Le[1]));if(me!==r.fontSize[ge]&&(i=!0,r.fontSize[ge]=me),(!r.font[ge]||Be!=r.font[ge].baseString)&&(n=!0,r.font[ge]=xc.fonts[Be],!r.font[ge])){var Pe=Ce.family.join(\", \"),ce=[Ce.style];Ce.style!=Ce.variant&&ce.push(Ce.variant),Ce.variant!=Ce.weight&&ce.push(Ce.weight),Rz&&Ce.weight!=Ce.stretch&&ce.push(Ce.stretch),r.font[ge]={baseString:Be,family:Pe,weight:Ce.weight,stretch:Ce.stretch,style:Ce.style,variant:Ce.variant,width:{},kerning:{},metrics:pVt(Pe,{origin:\"top\",fontSize:xc.baseFontSize,fontStyle:ce.join(\" \")})},xc.fonts[Be]=r.font[ge]}}),(n||i)&&this.font.forEach(function(Ce,ge){var ie=w5.stringify({size:r.fontSize[ge],family:Ce.family,stretch:Rz?Ce.stretch:void 0,variant:Ce.variant,weight:Ce.weight,style:Ce.style});if(r.fontAtlas[ge]=r.shader.atlas[ie],!r.fontAtlas[ge]){var Se=Ce.metrics;r.shader.atlas[ie]=r.fontAtlas[ge]={fontString:ie,step:Math.ceil(r.fontSize[ge]*Se.bottom*.5)*2,em:r.fontSize[ge],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:r.regl.texture()}}t.text==null&&(t.text=r.text)}),typeof t.text==\"string\"&&t.position&&t.position.length>2){for(var a=Array(t.position.length*.5),o=0;o<a.length;o++)a[o]=t.text;t.text=a}var s;if(t.text!=null||n){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var l=1;l<t.text.length;l++)this.textOffsets[l]=this.textOffsets[l-1]+t.text[l-1].length,this.count+=t.text[l].length,this.counts.push(t.text[l].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];s=[],this.font.forEach(function(Ce,ge){xc.atlasContext.font=Ce.baseString;for(var ie=r.fontAtlas[ge],Se=0;Se<r.text.length;Se++){var ke=r.text.charAt(Se);if(ie.ids[ke]==null&&(ie.ids[ke]=ie.chars.length,ie.chars.push(ke),s.push(ke)),Ce.width[ke]==null&&(Ce.width[ke]=xc.atlasContext.measureText(ke).width/xc.baseFontSize,r.kerning)){var be=[];for(var Be in Ce.width)be.push(Be+ke,ke+Be);vVt(Ce.kerning,dVt(Ce.family,{pairs:be}))}}})}if(t.position)if(t.position.length>2){for(var u=!t.position[0].length,c=Tx.mallocFloat(this.count*2),f=0,h=0;f<this.counts.length;f++){var d=this.counts[f];if(u)for(var v=0;v<d;v++)c[h++]=t.position[f*2],c[h++]=t.position[f*2+1];else for(var m=0;m<d;m++)c[h++]=t.position[f][0],c[h++]=t.position[f][1]}this.position.call?this.position({type:\"float\",data:c}):this.position=this.regl.buffer({type:\"float\",data:c}),Tx.freeFloat(c)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||n){var b=Tx.mallocUint8(this.count),p=Tx.mallocFloat(this.count*2);this.textWidth=[];for(var k=0,M=0;k<this.counts.length;k++){for(var T=this.counts[k],L=this.font[k]||this.font[0],x=this.fontAtlas[k]||this.fontAtlas[0],C=0;C<T;C++){var S=this.text.charAt(M),g=this.text.charAt(M-1);if(b[M]=x.ids[S],p[M*2]=L.width[S],C){var P=p[M*2-2],E=p[M*2],z=p[M*2-1],q=z+P*.5+E*.5;if(this.kerning){var V=L.kerning[g+S];V&&(q+=V*.001)}p[M*2+1]=q}else p[M*2+1]=p[M*2]*.5;M++}this.textWidth.push(p.length?p[M*2-2]*.5+p[M*2-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:b,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:p,type:\"float\",usage:\"stream\"}),Tx.freeUint8(b),Tx.freeFloat(p),s.length&&this.font.forEach(function(Ce,ge){var ie=r.fontAtlas[ge],Se=ie.step,ke=Math.floor(xc.maxAtlasSize/Se),be=Math.min(ke,ie.chars.length),Be=Math.ceil(ie.chars.length/be),Le=NBe(be*Se),me=NBe(Be*Se);ie.width=Le,ie.height=me,ie.rows=Be,ie.cols=be,ie.em&&ie.texture({data:lVt({canvas:xc.atlasCanvas,font:ie.fontString,chars:ie.chars,shape:[Le,me],step:[Se,Se]})})})}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map(function(Ce,ge){var ie=Array.isArray(r.align)?r.align.length>1?r.align[ge]:r.align[0]:r.align;if(typeof ie==\"number\")return ie;switch(ie){case\"right\":case\"end\":return-Ce;case\"center\":case\"centre\":case\"middle\":return-Ce*.5}return 0})),this.baseline==null&&t.baseline==null&&(t.baseline=0),t.baseline!=null&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map(function(Ce,ge){var ie=(r.font[ge]||r.font[0]).metrics,Se=0;return Se+=ie.bottom*.5,typeof Ce==\"number\"?Se+=Ce-ie.baseline:Se+=-ie[Ce],Se*=-1,Se})),t.color!=null)if(t.color||(t.color=\"transparent\"),typeof t.color==\"string\"||!isNaN(t.color))this.color=_K(t.color,\"uint8\");else{var G;if(typeof t.color[0]==\"number\"&&t.color.length>this.counts.length){var Z=t.color.length;G=Tx.mallocUint8(Z);for(var j=(t.color.subarray||t.color.slice).bind(t.color),N=0;N<Z;N+=4)G.set(_K(j(N,N+4),\"uint8\"),N)}else{var H=t.color.length;G=Tx.mallocUint8(H*4);for(var re=0;re<H;re++)G.set(_K(t.color[re]||0,\"uint8\"),re*4)}this.color=G}if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity){var ae=this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2;if(ae){var _e=Math.max(this.position.length*.5||0,this.color.length*.25||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,this.positionOffset.length*.5||0);this.batch=Array(_e);for(var Ee=0;Ee<this.batch.length;Ee++)this.batch[Ee]={count:this.counts.length>1?this.counts[Ee]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[Ee]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(Ee*4,Ee*4+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[Ee]:this.opacity,baseline:this.baselineOffset[Ee]!=null?this.baselineOffset[Ee]:this.baselineOffset[0],align:this.align?this.alignOffset[Ee]!=null?this.alignOffset[Ee]:this.alignOffset[0]:0,atlas:this.fontAtlas[Ee]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(Ee*2,Ee*2+2):this.positionOffset}}else 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si(Ut,Oe),Ut.width=0,Ut.height=0,Bi(Ut,Nt),Ut.width=Ut.width||(Oe.width>>Ft)-It,Ut.height=Ut.height||(Oe.height>>Ft)-Bt,al(Oe),Nn(Ut,_n,It,Bt,Ft),io(),Cn(Ut),ht}function cr(Nt,Ne){var $e=Nt|0,st=Ne|0||$e;if($e===Oe.width&&st===Oe.height)return ht;ht.width=Oe.width=$e,ht.height=Oe.height=st,al(Oe);for(var It=0;Oe.mipmask>>It;++It){var Bt=$e>>It,Ft=st>>It;if(!Bt||!Ft)break;Me.texImage2D(_n,It,Oe.format,Bt,Ft,0,Oe.format,Oe.type,null)}return io(),Nr.profile&&(Oe.stats.size=cc(Oe.internalformat,Oe.type,$e,st,!1,!1)),ht}return ht(ne,we),ht.subimage=Yt,ht.resize=cr,ht._reglType=\"texture2d\",ht._texture=Oe,Nr.profile&&(ht.stats=Oe.stats),ht.destroy=function(){Oe.decRef()},ht}function J(ne,we,Oe,ht,Yt,cr){var Nt=new wa(ya);ss[Nt.id]=Nt,Or.cubeCount++;var Ne=new Array(6);function $e(Bt,Ft,Ut,zt,_t,Dt){var vt,lr=Nt.texInfo;for(zo.call(lr),vt=0;vt<6;++vt)Ne[vt]=Ga();if(typeof Bt==\"number\"||!Bt){var _r=Bt|0||1;for(vt=0;vt<6;++vt)sa(Ne[vt],_r,_r)}else if(typeof 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It(Bt){var Ft=Bt|0;if(Ft!==Nt.width){$e.width=Nt.width=Ft,$e.height=Nt.height=Ft,al(Nt);for(var Ut=0;Ut<6;++Ut)for(var zt=0;Nt.mipmask>>zt;++zt)Me.texImage2D(ea+Ut,zt,Nt.format,Ft>>zt,Ft>>zt,0,Nt.format,Nt.type,null);return io(),Nr.profile&&(Nt.stats.size=cc(Nt.internalformat,Nt.type,$e.width,$e.height,!1,!0)),$e}}return $e(ne,we,Oe,ht,Yt,cr),$e.subimage=st,$e.resize=It,$e._reglType=\"textureCube\",$e._texture=Nt,Nr.profile&&($e.stats=Nt.stats),$e.destroy=function(){Nt.decRef()},$e}function O(){for(var ne=0;ne<Vs;++ne)Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Ys[ne]=null;gt(ss).forEach(Y),Or.cubeCount=0,Or.textureCount=0}Nr.profile&&(Or.getTotalTextureSize=function(){var ne=0;return Object.keys(ss).forEach(function(we){ne+=ss[we].stats.size}),ne});function K(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null)}gt(ss).forEach(function(Oe){Oe.texture=Me.createTexture(),Me.bindTexture(Oe.target,Oe.texture);for(var ht=0;ht<32;++ht)if((Oe.mipmask&1<<ht)!==0)if(Oe.target===_n)Me.texImage2D(_n,ht,Oe.internalformat,Oe.width>>ht,Oe.height>>ht,0,Oe.internalformat,Oe.type,null);else for(var Yt=0;Yt<6;++Yt)Me.texImage2D(ea+Yt,ht,Oe.internalformat,Oe.width>>ht,Oe.height>>ht,0,Oe.internalformat,Oe.type,null);nl(Oe.texInfo,Oe.target)})}function de(){for(var ne=0;ne<Vs;++ne){var we=Ys[ne];we&&(we.bindCount=0,we.unit=-1,Ys[ne]=null),Me.activeTexture(Fu+ne),Me.bindTexture(_n,null),Me.bindTexture(ya,null)}}return{create2D:D,createCube:J,clear:O,getTexture:function(ne){return null},restore:K,refresh:de}}var Bl=36161,Kc=32854,Rc=32855,gs=36194,Wf=33189,Wh=36168,rh=34041,lf=35907,Sh=34836,Mu=34842,ih=34843,js=[];js[Kc]=2,js[Rc]=2,js[gs]=2,js[Wf]=2,js[Wh]=1,js[rh]=4,js[lf]=4,js[Sh]=16,js[Mu]=8,js[ih]=6;function Eu(Me,bt,Ot){return js[Me]*bt*Ot}var Dc=function(Me,bt,Ot,Lr,Vr){var Or={rgba4:Kc,rgb565:gs,\"rgb5 a1\":Rc,depth:Wf,stencil:Wh,\"depth 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Qi(){gt(Ni).forEach(function(Wi){Wi.renderbuffer=Me.createRenderbuffer(),Me.bindRenderbuffer(Bl,Wi.renderbuffer),Me.renderbufferStorage(Bl,Wi.format,Wi.width,Wi.height)}),Me.bindRenderbuffer(Bl,null)}return{create:Vn,clear:function(){gt(Ni).forEach(Si)},restore:Qi}},ks=36160,bc=36161,hu=3553,_u=34069,il=36064,nh=36096,Mh=36128,zu=33306,Fc=36053,wc=36193,bd=5121,xf=5126,Pf=6407,qu=6408,bf=[];bf[qu]=4,bf[Pf]=3;var jl=[];jl[bd]=1,jl[xf]=4,jl[wc]=2;function uf(Me,bt,Ot,Lr,Vr,Or){var Nr={cur:null,next:null,dirty:!1,setFBO:null},vi=[\"rgba\"],Ni=[\"rgba4\",\"rgb565\",\"rgb5 a1\"];bt.ext_srgb&&Ni.push(\"srgba\"),bt.ext_color_buffer_half_float&&Ni.push(\"rgba16f\",\"rgb16f\"),bt.webgl_color_buffer_float&&Ni.push(\"rgba32f\");var qi=[\"uint8\"];bt.oes_texture_half_float&&qi.push(\"half float\",\"float16\"),bt.oes_texture_float&&qi.push(\"float\",\"float32\");function Si(Ui,Bi,pn){this.target=Ui,this.texture=Bi,this.renderbuffer=pn;var Nn=0,aa=0;Bi?(Nn=Bi.width,aa=Bi.height):pn&&(Nn=pn.width,aa=pn.height),this.width=Nn,this.height=aa}function Vn(Ui){Ui&&(Ui.texture&&Ui.texture._texture.decRef(),Ui.renderbuffer&&Ui.renderbuffer._renderbuffer.decRef())}function Qi(Ui,Bi,pn){if(Ui)if(Ui.texture){var Nn=Ui.texture._texture,aa=Math.max(1,Nn.width),Ki=Math.max(1,Nn.height);Nn.refCount+=1}else{var Cn=Ui.renderbuffer._renderbuffer;Cn.refCount+=1}}function Wi(Ui,Bi){Bi&&(Bi.texture?Me.framebufferTexture2D(ks,Ui,Bi.target,Bi.texture._texture.texture,0):Me.framebufferRenderbuffer(ks,Ui,bc,Bi.renderbuffer._renderbuffer.renderbuffer))}function oi(Ui){var Bi=hu,pn=null,Nn=null,aa=Ui;typeof Ui==\"object\"&&(aa=Ui.data,\"target\"in Ui&&(Bi=Ui.target|0));var Ki=aa._reglType;return Ki===\"texture2d\"||Ki===\"textureCube\"?pn=aa:Ki===\"renderbuffer\"&&(Nn=aa,Bi=bc),new Si(Bi,pn,Nn)}function Tr(Ui,Bi,pn,Nn,aa){if(pn){var Ki=Lr.create2D({width:Ui,height:Bi,format:Nn,type:aa});return Ki._texture.refCount=0,new Si(hu,Ki,null)}else{var Cn=Vr.create({width:Ui,height:Bi,format:Nn});return Cn._renderbuffer.refCount=0,new Si(bc,null,Cn)}}function Zr(Ui){return Ui&&(Ui.texture||Ui.renderbuffer)}function yi(Ui,Bi,pn){Ui&&(Ui.texture?Ui.texture.resize(Bi,pn):Ui.renderbuffer&&Ui.renderbuffer.resize(Bi,pn),Ui.width=Bi,Ui.height=pn)}var Ii=0,ui={};function nn(){this.id=Ii++,ui[this.id]=this,this.framebuffer=Me.createFramebuffer(),this.width=0,this.height=0,this.colorAttachments=[],this.depthAttachment=null,this.stencilAttachment=null,this.depthStencilAttachment=null}function Zi(Ui){Ui.colorAttachments.forEach(Vn),Vn(Ui.depthAttachment),Vn(Ui.stencilAttachment),Vn(Ui.depthStencilAttachment)}function qn(Ui){var Bi=Ui.framebuffer;Me.deleteFramebuffer(Bi),Ui.framebuffer=null,Or.framebufferCount--,delete ui[Ui.id]}function fi(Ui){var Bi;Me.bindFramebuffer(ks,Ui.framebuffer);var pn=Ui.colorAttachments;for(Bi=0;Bi<pn.length;++Bi)Wi(il+Bi,pn[Bi]);for(Bi=pn.length;Bi<Ot.maxColorAttachments;++Bi)Me.framebufferTexture2D(ks,il+Bi,hu,null,0);Me.framebufferTexture2D(ks,zu,hu,null,0),Me.framebufferTexture2D(ks,nh,hu,null,0),Me.framebufferTexture2D(ks,Mh,hu,null,0),Wi(nh,Ui.depthAttachment),Wi(Mh,Ui.stencilAttachment),Wi(zu,Ui.depthStencilAttachment);var Nn=Me.checkFramebufferStatus(ks);Me.isContextLost(),Me.bindFramebuffer(ks,Nr.next?Nr.next.framebuffer:null),Nr.cur=Nr.next,Me.getError()}function si(Ui,Bi){var pn=new nn;Or.framebufferCount++;function Nn(Ki,Cn){var ia,sa=0,ba=0,is=!0,Zs=!0,Ga=null,El=!0,zo=\"rgba\",Qs=\"uint8\",nl=1,Ul=null,ss=null,Vs=null,Ys=!1;if(typeof Ki==\"number\")sa=Ki|0,ba=Cn|0||sa;else if(!Ki)sa=ba=1;else{var wa=Ki;if(\"shape\"in wa){var al=wa.shape;sa=al[0],ba=al[1]}else\"radius\"in wa&&(sa=ba=wa.radius),\"width\"in wa&&(sa=wa.width),\"height\"in wa&&(ba=wa.height);(\"color\"in wa||\"colors\"in wa)&&(Ga=wa.color||wa.colors,Array.isArray(Ga)),Ga||(\"colorCount\"in wa&&(nl=wa.colorCount|0),\"colorTexture\"in wa&&(El=!!wa.colorTexture,zo=\"rgba4\"),\"colorType\"in wa&&(Qs=wa.colorType,El||(Qs===\"half float\"||Qs===\"float16\"?zo=\"rgba16f\":(Qs===\"float\"||Qs===\"float32\")&&(zo=\"rgba32f\"))),\"colorFormat\"in wa&&(zo=wa.colorFormat,vi.indexOf(zo)>=0?El=!0:Ni.indexOf(zo)>=0&&(El=!1))),(\"depthTexture\"in wa||\"depthStencilTexture\"in wa)&&(Ys=!!(wa.depthTexture||wa.depthStencilTexture)),\"depth\"in wa&&(typeof wa.depth==\"boolean\"?is=wa.depth:(Ul=wa.depth,Zs=!1)),\"stencil\"in wa&&(typeof wa.stencil==\"boolean\"?Zs=wa.stencil:(ss=wa.stencil,is=!1)),\"depthStencil\"in wa&&(typeof wa.depthStencil==\"boolean\"?is=Zs=wa.depthStencil:(Vs=wa.depthStencil,is=!1,Zs=!1))}var io=null,Y=null,D=null,J=null;if(Array.isArray(Ga))io=Ga.map(oi);else if(Ga)io=[oi(Ga)];else for(io=new Array(nl),ia=0;ia<nl;++ia)io[ia]=Tr(sa,ba,El,zo,Qs);sa=sa||io[0].width,ba=ba||io[0].height,Ul?Y=oi(Ul):is&&!Zs&&(Y=Tr(sa,ba,Ys,\"depth\",\"uint32\")),ss?D=oi(ss):Zs&&!is&&(D=Tr(sa,ba,!1,\"stencil\",\"uint8\")),Vs?J=oi(Vs):!Ul&&!ss&&Zs&&is&&(J=Tr(sa,ba,Ys,\"depth stencil\",\"depth stencil\"));var O=null;for(ia=0;ia<io.length;++ia)if(Qi(io[ia],sa,ba),io[ia]&&io[ia].texture){var K=bf[io[ia].texture._texture.format]*jl[io[ia].texture._texture.type];O===null&&(O=K)}return Qi(Y,sa,ba),Qi(D,sa,ba),Qi(J,sa,ba),Zi(pn),pn.width=sa,pn.height=ba,pn.colorAttachments=io,pn.depthAttachment=Y,pn.stencilAttachment=D,pn.depthStencilAttachment=J,Nn.color=io.map(Zr),Nn.depth=Zr(Y),Nn.stencil=Zr(D),Nn.depthStencil=Zr(J),Nn.width=pn.width,Nn.height=pn.height,fi(pn),Nn}function aa(Ki,Cn){var ia=Math.max(Ki|0,1),sa=Math.max(Cn|0||ia,1);if(ia===pn.width&&sa===pn.height)return Nn;for(var ba=pn.colorAttachments,is=0;is<ba.length;++is)yi(ba[is],ia,sa);return yi(pn.depthAttachment,ia,sa),yi(pn.stencilAttachment,ia,sa),yi(pn.depthStencilAttachment,ia,sa),pn.width=Nn.width=ia,pn.height=Nn.height=sa,fi(pn),Nn}return Nn(Ui,Bi),e(Nn,{resize:aa,_reglType:\"framebuffer\",_framebuffer:pn,destroy:function(){qn(pn),Zi(pn)},use:function(Ki){Nr.setFBO({framebuffer:Nn},Ki)}})}function mn(Ui){var Bi=Array(6);function pn(aa){var Ki,Cn={color:null},ia=0,sa=null,ba=\"rgba\",is=\"uint8\",Zs=1;if(typeof aa==\"number\")ia=aa|0;else if(!aa)ia=1;else{var Ga=aa;if(\"shape\"in Ga){var El=Ga.shape;ia=El[0]}else\"radius\"in Ga&&(ia=Ga.radius|0),\"width\"in Ga?(ia=Ga.width|0,\"height\"in Ga):\"height\"in Ga&&(ia=Ga.height|0);(\"color\"in Ga||\"colors\"in Ga)&&(sa=Ga.color||Ga.colors,Array.isArray(sa)),sa||(\"colorCount\"in Ga&&(Zs=Ga.colorCount|0),\"colorType\"in Ga&&(is=Ga.colorType),\"colorFormat\"in Ga&&(ba=Ga.colorFormat)),\"depth\"in Ga&&(Cn.depth=Ga.depth),\"stencil\"in Ga&&(Cn.stencil=Ga.stencil),\"depthStencil\"in Ga&&(Cn.depthStencil=Ga.depthStencil)}var zo;if(sa)if(Array.isArray(sa))for(zo=[],Ki=0;Ki<sa.length;++Ki)zo[Ki]=sa[Ki];else zo=[sa];else{zo=Array(Zs);var Qs={radius:ia,format:ba,type:is};for(Ki=0;Ki<Zs;++Ki)zo[Ki]=Lr.createCube(Qs)}for(Cn.color=Array(zo.length),Ki=0;Ki<zo.length;++Ki){var nl=zo[Ki];ia=ia||nl.width,Cn.color[Ki]={target:_u,data:zo[Ki]}}for(Ki=0;Ki<6;++Ki){for(var Ul=0;Ul<zo.length;++Ul)Cn.color[Ul].target=_u+Ki;Ki>0&&(Cn.depth=Bi[0].depth,Cn.stencil=Bi[0].stencil,Cn.depthStencil=Bi[0].depthStencil),Bi[Ki]?Bi[Ki](Cn):Bi[Ki]=si(Cn)}return e(pn,{width:ia,height:ia,color:zo})}function Nn(aa){var Ki,Cn=aa|0;if(Cn===pn.width)return pn;var ia=pn.color;for(Ki=0;Ki<ia.length;++Ki)ia[Ki].resize(Cn);for(Ki=0;Ki<6;++Ki)Bi[Ki].resize(Cn);return pn.width=pn.height=Cn,pn}return pn(Ui),e(pn,{faces:Bi,resize:Nn,_reglType:\"framebufferCube\",destroy:function(){Bi.forEach(function(aa){aa.destroy()})}})}function 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oi(){return bt.oes_vertex_array_object}function Tr(){return bt.angle_instanced_arrays}function Zr(fi){return typeof fi==\"function\"&&fi._vao?fi._vao:null}function yi(fi){if(fi!==Qi.currentVAO){var si=oi();fi?si.bindVertexArrayOES(fi.vao):si.bindVertexArrayOES(null),Qi.currentVAO=fi}}function Ii(fi){if(fi!==Qi.currentVAO){if(fi)fi.bindAttrs();else{for(var si=Tr(),mn=0;mn<Ni.length;++mn){var Ji=Ni[mn];Ji.buffer?(Me.enableVertexAttribArray(mn),Ji.buffer.bind(),Me.vertexAttribPointer(mn,Ji.size,Ji.type,Ji.normalized,Ji.stride,Ji.offfset),si&&Ji.divisor&&si.vertexAttribDivisorANGLE(mn,Ji.divisor)):(Me.disableVertexAttribArray(mn),Me.vertexAttrib4f(mn,Ji.x,Ji.y,Ji.z,Ji.w))}Nr.elements?Me.bindBuffer(Cs,Nr.elements.buffer.buffer):Me.bindBuffer(Cs,null)}Qi.currentVAO=fi}}function ui(){gt(Vn).forEach(function(fi){fi.destroy()})}function nn(){this.id=++Si,this.attributes=[],this.elements=null,this.ownsElements=!1,this.count=0,this.offset=0,this.instances=-1,this.primitive=4;var 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fi=oi();this===Qi.currentVAO&&(Qi.currentVAO=null,fi.bindVertexArrayOES(null)),fi.deleteVertexArrayOES(this.vao),this.vao=null}this.ownsElements&&(this.elements.destroy(),this.elements=null,this.ownsElements=!1),Vn[this.id]&&(delete Vn[this.id],Lr.vaoCount-=1)};function Zi(){var fi=oi();fi&&gt(Vn).forEach(function(si){si.refresh()})}function qn(fi){var si=new nn;Lr.vaoCount+=1;function mn(Ji){var Ui;if(Array.isArray(Ji))Ui=Ji,si.elements&&si.ownsElements&&si.elements.destroy(),si.elements=null,si.ownsElements=!1,si.offset=0,si.count=0,si.instances=-1,si.primitive=4;else{if(Ji.elements){var Bi=Ji.elements;si.ownsElements?typeof Bi==\"function\"&&Bi._reglType===\"elements\"?(si.elements.destroy(),si.ownsElements=!1):(si.elements(Bi),si.ownsElements=!1):Or.getElements(Ji.elements)?(si.elements=Ji.elements,si.ownsElements=!1):(si.elements=Or.create(Ji.elements),si.ownsElements=!0)}else 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Wu(Me,bt,Ot,Lr){var Vr={},Or={};function Nr(Tr,Zr,yi,Ii){this.name=Tr,this.id=Zr,this.location=yi,this.info=Ii}function vi(Tr,Zr){for(var yi=0;yi<Tr.length;++yi)if(Tr[yi].id===Zr.id){Tr[yi].location=Zr.location;return}Tr.push(Zr)}function Ni(Tr,Zr,yi){var Ii=Tr===Xf?Vr:Or,ui=Ii[Zr];if(!ui){var nn=bt.str(Zr);ui=Me.createShader(Tr),Me.shaderSource(ui,nn),Me.compileShader(ui),Ii[Zr]=ui}return ui}var qi={},Si=[],Vn=0;function Qi(Tr,Zr){this.id=Vn++,this.fragId=Tr,this.vertId=Zr,this.program=null,this.uniforms=[],this.attributes=[],this.refCount=1,Lr.profile&&(this.stats={uniformsCount:0,attributesCount:0})}function Wi(Tr,Zr,yi){var Ii,ui,nn=Ni(Xf,Tr.fragId),Zi=Ni(Us,Tr.vertId),qn=Tr.program=Me.createProgram();if(Me.attachShader(qn,nn),Me.attachShader(qn,Zi),yi)for(Ii=0;Ii<yi.length;++Ii){var fi=yi[Ii];Me.bindAttribLocation(qn,fi[0],fi[1])}Me.linkProgram(qn);var si=Me.getProgramParameter(qn,wf);Lr.profile&&(Tr.stats.uniformsCount=si);var mn=Tr.uniforms;for(Ii=0;Ii<si;++Ii)if(ui=Me.getActiveUniform(qn,Ii),ui)if(ui.size>1)for(var Ji=0;Ji<ui.size;++Ji){var Ui=ui.name.replace(\"[0]\",\"[\"+Ji+\"]\");vi(mn,new Nr(Ui,bt.id(Ui),Me.getUniformLocation(qn,Ui),ui))}else vi(mn,new Nr(ui.name,bt.id(ui.name),Me.getUniformLocation(qn,ui.name),ui));var Bi=Me.getProgramParameter(qn,zc);Lr.profile&&(Tr.stats.attributesCount=Bi);var pn=Tr.attributes;for(Ii=0;Ii<Bi;++Ii)ui=Me.getActiveAttrib(qn,Ii),ui&&vi(pn,new Nr(ui.name,bt.id(ui.name),Me.getAttribLocation(qn,ui.name),ui))}Lr.profile&&(Ot.getMaxUniformsCount=function(){var Tr=0;return Si.forEach(function(Zr){Zr.stats.uniformsCount>Tr&&(Tr=Zr.stats.uniformsCount)}),Tr},Ot.getMaxAttributesCount=function(){var Tr=0;return Si.forEach(function(Zr){Zr.stats.attributesCount>Tr&&(Tr=Zr.stats.attributesCount)}),Tr});function oi(){Vr={},Or={};for(var Tr=0;Tr<Si.length;++Tr)Wi(Si[Tr],null,Si[Tr].attributes.map(function(Zr){return[Zr.location,Zr.name]}))}return{clear:function(){var 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st=Ne.static,It=Ne.dynamic,Bt={};return fi.forEach(function(Ft){var Ut=Ji(Ft);function zt(_t,Dt){if(Ft in st){var vt=_t(st[Ft]);Bt[Ut]=kn(function(){return vt})}else if(Ft in It){var lr=It[Ft];Bt[Ut]=Bn(lr,function(_r,Br){return Dt(_r,Br,_r.invoke(Br,lr))})}}switch(Ft){case Cu:case Ho:case ws:case Vc:case va:case B:case Uc:case Ac:case Va:case rs:return zt(function(_t){return _t},function(_t,Dt,vt){return vt});case no:return zt(function(_t){return Wr[_t]},function(_t,Dt,vt){var lr=_t.constants.compareFuncs;return Dt.def(lr,\"[\",vt,\"]\")});case ys:return zt(function(_t){return _t},function(_t,Dt,vt){var lr=Dt.def(\"+\",vt,\"[0]\"),_r=Dt.def(\"+\",vt,\"[1]\");return[lr,_r]});case Ls:return zt(function(_t){var Dt=\"srcRGB\"in _t?_t.srcRGB:_t.src,vt=\"srcAlpha\"in _t?_t.srcAlpha:_t.src,lr=\"dstRGB\"in _t?_t.dstRGB:_t.dst,_r=\"dstAlpha\"in _t?_t.dstAlpha:_t.dst;return[xi[Dt],xi[lr],xi[vt],xi[_r]]},function(_t,Dt,vt){var lr=_t.constants.blendFuncs;function _r(Ei,Oi){var 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lr=Ft.gl,_r=$e.def(_t,\".location\"),Br=$e.def(Ft.attributes,\"[\",_r,\"]\"),Di=vt.state,ci=vt.buffer,bi=[vt.x,vt.y,vt.z,vt.w],gn=[\"buffer\",\"normalized\",\"offset\",\"stride\"];function tn(){$e(\"if(!\",Br,\".buffer){\",lr,\".enableVertexAttribArray(\",_r,\");}\");var Oi=vt.type,Gi;if(vt.size?Gi=$e.def(vt.size,\"||\",Dt):Gi=Dt,$e(\"if(\",Br,\".type!==\",Oi,\"||\",Br,\".size!==\",Gi,\"||\",gn.map(function(zn){return Br+\".\"+zn+\"!==\"+vt[zn]}).join(\"||\"),\"){\",lr,\".bindBuffer(\",br,\",\",ci,\".buffer);\",lr,\".vertexAttribPointer(\",[_r,Gi,Oi,vt.normalized,vt.stride,vt.offset],\");\",Br,\".type=\",Oi,\";\",Br,\".size=\",Gi,\";\",gn.map(function(zn){return Br+\".\"+zn+\"=\"+vt[zn]+\";\"}).join(\"\"),\"}\"),Ii){var on=vt.divisor;$e(\"if(\",Br,\".divisor!==\",on,\"){\",Ne.instancing,\".vertexAttribDivisorANGLE(\",[_r,on],\");\",Br,\".divisor=\",on,\";}\")}}function 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Gi?((Gi.contextDep&&It.contextDynamic||Gi.propDep)&&(zn=st),on=Gi.append(Ne,zn),zt.elementsActive&&zn(\"if(\"+on+\")\"+Ft+\".bindBuffer(\"+Fr+\",\"+on+\".buffer.buffer);\")):(on=zn.def(),zn(on,\"=\",Ut,\".\",Mt,\";\",\"if(\",on,\"){\",Ft,\".bindBuffer(\",Fr,\",\",on,\".buffer.buffer);}\",\"else if(\",Bt.vao,\".currentVAO){\",on,\"=\",Ne.shared.elements+\".getElements(\"+Bt.vao,\".currentVAO.elements);\",nn?\"\":\"if(\"+on+\")\"+Ft+\".bindBuffer(\"+Fr+\",\"+on+\".buffer.buffer);\",\"}\")),on}function Dt(){var Gi=zt.count,on,zn=$e;return Gi?((Gi.contextDep&&It.contextDynamic||Gi.propDep)&&(zn=st),on=Gi.append(Ne,zn)):on=zn.def(Ut,\".\",$t),on}var vt=_t();function lr(Gi){var on=zt[Gi];return on?on.contextDep&&It.contextDynamic||on.propDep?on.append(Ne,st):on.append(Ne,$e):$e.def(Ut,\".\",Gi)}var _r=lr(Jt),Br=lr(yr),Di=Dt();if(typeof Di==\"number\"){if(Di===0)return}else st(\"if(\",Di,\"){\"),st.exit(\"}\");var ci,bi;Ii&&(ci=lr(Cr),bi=Ne.instancing);var 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m=0;m<i.columns;m++)i.bounds[m]=$Vt(n.data[m],1)}let a;n.range&&(i.range=n.range,a=i.range&&typeof i.range[0]!=\"number\"),n.domain&&(i.domain=n.domain);let o=!1;n.padding!=null&&(Array.isArray(n.padding)&&n.padding.length===i.columns&&typeof n.padding[n.padding.length-1]==\"number\"?(i.padding=n.padding.map(TNe),o=!0):i.padding=TNe(n.padding));let s=i.columns,l=i.count,u=i.viewport.width,c=i.viewport.height,f=i.viewport.x,h=i.viewport.y,d=u/s,v=c/s;i.passes=[];for(let m=0;m<s;m++)for(let b=0;b<s;b++){if(!i.diagonal&&b===m||!i.upper&&m>b||!i.lower&&m<b)continue;let p=tGt(i.id,m,b),k=this.passes[p]||(this.passes[p]={});if(n.data&&(n.transpose?k.positions={x:{buffer:i.buffer,offset:b,count:l,stride:s},y:{buffer:i.buffer,offset:m,count:l,stride:s}}:k.positions={x:{buffer:i.buffer,offset:b*l,count:l},y:{buffer:i.buffer,offset:m*l,count:l}},k.bounds=Uz(i.bounds,m,b)),n.domain||n.viewport||n.data){let M=o?Uz(i.padding,m,b):i.padding;if(i.domain){let[T,L,x,C]=Uz(i.domain,m,b);k.viewport=[f+T*u+M[0],h+L*c+M[1],f+x*u-M[2],h+C*c-M[3]]}else k.viewport=[f+b*d+d*M[0],h+m*v+v*M[1],f+(b+1)*d-d*M[2],h+(m+1)*v-v*M[3]]}n.color&&(k.color=i.color),n.size&&(k.size=i.size),n.marker&&(k.marker=i.marker),n.borderSize&&(k.borderSize=i.borderSize),n.borderColor&&(k.borderColor=i.borderColor),n.opacity&&(k.opacity=i.opacity),n.range&&(k.range=a?Uz(i.range,m,b):i.range||k.bounds),i.passes.push(p)}return this};Sx.prototype.draw=function(...e){if(!e.length)this.scatter.draw();else{let t=[];for(let r=0;r<e.length;r++)if(typeof e[r]==\"number\"){let{passes:n,passOffset:i}=this.traces[e[r]];t.push(...QVt(i,i+n.length))}else if(e[r].length){let n=e[r],{passes:i,passOffset:a}=this.traces[r];i=i.map((o,s)=>{t[a+s]=n})}this.scatter.draw(...t)}return this};Sx.prototype.destroy=function(){return 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r=e._fullLayout,n=r._size,i=t.trace,a=t.t,o=r._splomScenes[i.uid],s=o.matrixOptions,l=s.cdata,u=r._glcanvas.data()[0].regl,c=r.dragmode,f,h,d,v,m;if(l.length!==0){s.lower=i.showupperhalf,s.upper=i.showlowerhalf,s.diagonal=i.diagonal.visible;var b=i._visibleDims,p=l.length,k=o.viewOpts={};for(k.ranges=new Array(p),k.domains=new Array(p),m=0;m<b.length;m++){d=b[m];var M=k.ranges[m]=new Array(4),T=k.domains[m]=new Array(4);f=Vz.getFromId(e,i._diag[d][0]),f&&(M[0]=f._rl[0],M[2]=f._rl[1],T[0]=f.domain[0],T[2]=f.domain[1]),h=Vz.getFromId(e,i._diag[d][1]),h&&(M[1]=h._rl[0],M[3]=h._rl[1],T[1]=h.domain[0],T[3]=h.domain[1])}var L=e._context.plotGlPixelRatio,x=n.l*L,C=n.b*L,S=n.w*L,g=n.h*L;k.viewport=[x,C,S+x,g+C],o.matrix===!0&&(o.matrix=rGt(u));var P=r.clickmode.indexOf(\"select\")>-1,E=iGt(c)||!!i.selectedpoints||P,z=!0;if(E){var q=i._length;if(i.selectedpoints){o.selectBatch=i.selectedpoints;var V=i.selectedpoints,G={};for(d=0;d<V.length;d++)G[V[d]]=!0;var Z=[];for(d=0;d<q;d++)G[d]||Z.push(d);o.unselectBatch=Z}var j=a.xpx=new Array(p),N=a.ypx=new Array(p);for(m=0;m<b.length;m++){if(d=b[m],f=Vz.getFromId(e,i._diag[d][0]),f)for(j[m]=new Array(q),v=0;v<q;v++)j[m][v]=f.c2p(l[m][v]);if(h=Vz.getFromId(e,i._diag[d][1]),h)for(N[m]=new Array(q),v=0;v<q;v++)N[m][v]=h.c2p(l[m][v])}if(o.selectBatch.length||o.unselectBatch.length){var H=LK.extendFlat({},s,o.unselectedOptions,k),re=LK.extendFlat({},s,o.selectedOptions,k);o.matrix.update(H,re),z=!1}}else a.xpx=a.ypx=null;if(z){var ae=LK.extendFlat({},s,k);o.matrix.update(ae,null)}}}});var PK=ye(kNe=>{\"use strict\";kNe.getDimIndex=function(t,r){for(var n=r._id,i=n.charAt(0),a={x:0,y:1}[i],o=t._visibleDims,s=0;s<o.length;s++){var l=o[s];if(t._diag[l][a]===n)return s}return!1}});var RNe=ye((dxr,INe)=>{\"use strict\";var CNe=PK(),aGt=YF().calcHover,LNe=ho().getFromId,oGt=Ao().extendFlat;function sGt(e,t,r,n,i){i||(i={});var 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n=t.cd,i=n[0].trace,a=n[0].t,o=t.scene,s=o.matrixOptions.cdata,l=t.xaxis,u=t.yaxis,c=[];if(!o)return c;var f=!FNe.hasMarkers(i)&&!FNe.hasText(i);if(i.visible!==!0||f)return c;var h=zNe.getDimIndex(i,l),d=zNe.getDimIndex(i,u);if(h===!1||d===!1)return c;var v=a.xpx[h],m=a.ypx[d],b=s[h],p=s[d],k=(t.scene.selectBatch||[]).slice(),M=[];if(r!==!1&&!r.degenerate)for(var T=0;T<b.length;T++)r.contains([v[T],m[T]],null,T,t)?(c.push({pointNumber:T,x:b[T],y:p[T]}),DNe(k,T)):k.indexOf(T)!==-1?DNe(k,T):M.push(T);var L=o.matrixOptions;return!k.length&&!M.length?o.matrix.update(L,null):!o.selectBatch.length&&!o.unselectBatch.length&&o.matrix.update(o.unselectedOptions,qNe.extendFlat({},L,o.selectedOptions,o.viewOpts)),o.selectBatch=k,o.unselectBatch=M,c}});var VNe=ye((pxr,UNe)=>{\"use strict\";var NNe=Pr(),lGt=z0(),uGt=cw().markerStyle;UNe.exports=function(t,r){var n=r.trace,i=t._fullLayout._splomScenes[n.uid];if(i){lGt(t,n),NNe.extendFlat(i.matrixOptions,uGt(t,n));var a=NNe.extendFlat({},i.matrixOptions,i.viewOpts);i.matrix.update(a,null)}}});var HNe=ye((gxr,GNe)=>{\"use strict\";var cGt=Oa(),fGt=wV();GNe.exports={moduleType:\"trace\",name:\"splom\",categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"showLegend\",\"scatter-like\"],attributes:SK(),supplyDefaults:uNe(),colorbar:Qd(),calc:pNe(),plot:ENe(),hoverPoints:RNe().hoverPoints,selectPoints:BNe(),editStyle:VNe(),meta:{}};cGt.register(fGt)});var KNe=ye((mxr,YNe)=>{\"use strict\";var hGt=eK(),dGt=Oa(),vGt=Dz(),pGt=Id().getModuleCalcData,Mx=mh(),jNe=df().getFromId,WNe=ho().shouldShowZeroLine,XNe=\"splom\",ZNe={};function gGt(e){var t=e._fullLayout,r=dGt.getModule(XNe),n=pGt(e.calcdata,r)[0],i=vGt(e,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"],ZNe);i&&(t._hasOnlyLargeSploms&&IK(e),r.plot(e,{},n))}function mGt(e){var t=e.calcdata,r=e._fullLayout;r._hasOnlyLargeSploms&&IK(e);for(var n=0;n<t.length;n++){var 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l=r._plots[o],u=l.xaxis,c=l.yaxis,f=u._gridVals,h=c._gridVals,d=u._offset,v=u._length,m=c._length,b=n.b+c.domain[0]*n.h,p=-c._m,k=-p*c.r2l(c.range[0],c.calendar),M,T;if(u.showgrid)for(o=0;o<f.length;o++)M=d+u.l2p(f[o].x),s(\"grid\",u,M,b,M,b+m);if(c.showgrid)for(o=0;o<h.length;o++)T=b+k+p*h[o].x,s(\"grid\",c,d,T,d+v,T);WNe(e,u,c)&&(M=d+u.l2p(0),s(\"zeroline\",u,M,b,M,b+m)),WNe(e,c,u)&&(T=b+k+0,s(\"zeroline\",c,d,T,d+v,T))}var L=[];for(o in a)L.push(a[o]);return L}function xGt(e,t,r,n){var i={},a;if(n._splomScenes){for(a=0;a<e.length;a++){var o=e[a];o.type===\"splom\"&&(i[o.uid]=1)}for(a=0;a<r.length;a++){var s=r[a];if(!i[s.uid]){var l=n._splomScenes[s.uid];l&&l.destroy&&l.destroy(),n._splomScenes[s.uid]=null,delete n._splomScenes[s.uid]}}}Object.keys(n._splomScenes||{}).length===0&&delete n._splomScenes,n._splomGrid&&!t._hasOnlyLargeSploms&&n._hasOnlyLargeSploms&&(n._splomGrid.destroy(),n._splomGrid=null,delete 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bGt=Tu(),RK=Rd(),DK=ec(),wGt=Cc().attributes,Gz=Ao().extendFlat,TGt=vl().templatedArray;rUe.exports={domain:wGt({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:DK({editType:\"plot\"}),tickfont:DK({autoShadowDflt:!0,editType:\"plot\"}),rangefont:DK({editType:\"plot\"}),dimensions:TGt(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:Gz({},RK.tickvals,{editType:\"plot\"}),ticktext:Gz({},RK.ticktext,{editType:\"plot\"}),tickformat:Gz({},RK.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:Gz({editType:\"calc\"},bGt(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"})),unselected:{line:{color:{valType:\"color\",dflt:\"#7f7f7f\",editType:\"plot\"},opacity:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"plot\"},editType:\"plot\"},editType:\"plot\"}}});var Vk=ye((bxr,iUe)=>{\"use 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ay=ye((wxr,aUe)=>{\"use strict\";var AGt=$S();function nUe(e){return[e]}aUe.exports={keyFun:function(e){return e.key},repeat:nUe,descend:AGt,wrap:nUe,unwrap:function(e){return e[0]}}});var OK=ye((Txr,gUe)=>{\"use strict\";var Th=Vk(),im=Fa(),SGt=ay().keyFun,Hz=ay().repeat,E5=Pr().sorterAsc,MGt=Pr().strTranslate,oUe=Th.bar.snapRatio;function sUe(e,t){return e*(1-oUe)+t*oUe}var lUe=Th.bar.snapClose;function EGt(e,t){return e*(1-lUe)+t*lUe}function Wz(e,t,r,n){if(kGt(r,n))return r;var i=e?-1:1,a=0,o=t.length-1;if(i<0){var s=a;a=o,o=s}for(var l=t[a],u=l,c=a;i*c<i*o;c+=i){var f=c+i,h=t[f];if(i*r<i*EGt(l,h))return sUe(l,u);if(i*r<i*h||f===o)return sUe(h,l);u=l,l=h}}function kGt(e,t){for(var r=0;r<t.length;r++)if(e>=t[r][0]&&e<=t[r][1])return!0;return!1}function CGt(e){e.attr(\"x\",-Th.bar.captureWidth/2).attr(\"width\",Th.bar.captureWidth)}function LGt(e){e.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function PGt(e){if(!e.brush.filterSpecified)return\"0,\"+e.height;for(var t=uUe(e.brush.filter.getConsolidated(),e.height),r=[0],n,i,a,o=t.length?t[0][0]:null,s=0;s<t.length;s++)n=t[s],i=n[1]-n[0],r.push(o),r.push(i),a=s+1,a<t.length&&(o=t[a][0]-n[1]);return r.push(e.height),r}function uUe(e,t){return e.map(function(r){return r.map(function(n){return Math.max(0,n*t)}).sort(E5)})}function IGt(e,t){var r=Th.bar.handleHeight;if(!(t>e[1]+r||t<e[0]-r))return t>=.9*e[1]+.1*e[0]?\"n\":t<=.9*e[0]+.1*e[1]?\"s\":\"ns\"}function cUe(){im.select(document.body).style(\"cursor\",null)}function qK(e){e.attr(\"stroke-dasharray\",PGt)}function jz(e,t){var r=im.select(e).selectAll(\".highlight, .highlight-shadow\"),n=t?r.transition().duration(Th.bar.snapDuration).each(\"end\",t):r;qK(n)}function fUe(e,t){var r=e.brush,n=r.filterSpecified,i=NaN,a={},o;if(n){var s=e.height,l=r.filter.getConsolidated(),u=uUe(l,s),c=NaN,f=NaN,h=NaN;for(o=0;o<=u.length;o++){var d=u[o];if(d&&d[0]<=t&&t<=d[1]){c=o;break}else if(f=o?o-1:NaN,d&&d[0]>t){h=o;break}}if(i=c,isNaN(i)&&(isNaN(f)||isNaN(h)?i=isNaN(f)?h:f:i=t-u[f][1]<u[h][0]-t?f:h),!isNaN(i)){var v=u[i],m=IGt(v,t);m&&(a.interval=l[i],a.intervalPix=v,a.region=m)}}if(e.ordinal&&!a.region){var b=e.unitTickvals,p=e.unitToPaddedPx.invert(t);for(o=0;o<b.length;o++){var k=[b[Math.max(o-1,0)]*.25+b[o]*.75,b[Math.min(o+1,b.length-1)]*.25+b[o]*.75];if(p>=k[0]&&p<=k[1]){a.clickableOrdinalRange=k;break}}}return a}function RGt(e,t){im.event.sourceEvent.stopPropagation();var r=t.height-im.mouse(e)[1]-2*Th.verticalPadding,n=t.unitToPaddedPx.invert(r),i=t.brush,a=fUe(t,r),o=a.interval,s=i.svgBrush;if(s.wasDragged=!1,s.grabbingBar=a.region===\"ns\",s.grabbingBar){var l=o.map(t.unitToPaddedPx);s.grabPoint=r-l[0]-Th.verticalPadding,s.barLength=l[1]-l[0]}s.clickableOrdinalRange=a.clickableOrdinalRange,s.stayingIntervals=t.multiselect&&i.filterSpecified?i.filter.getConsolidated():[],o&&(s.stayingIntervals=s.stayingIntervals.filter(function(u){return u[0]!==o[0]&&u[1]!==o[1]})),s.startExtent=a.region?o[a.region===\"s\"?1:0]:n,t.parent.inBrushDrag=!0,s.brushStartCallback()}function hUe(e,t){im.event.sourceEvent.stopPropagation();var r=t.height-im.mouse(e)[1]-2*Th.verticalPadding,n=t.brush.svgBrush;n.wasDragged=!0,n._dragging=!0,n.grabbingBar?n.newExtent=[r-n.grabPoint,r+n.barLength-n.grabPoint].map(t.unitToPaddedPx.invert):n.newExtent=[n.startExtent,t.unitToPaddedPx.invert(r)].sort(E5),t.brush.filterSpecified=!0,n.extent=n.stayingIntervals.concat([n.newExtent]),n.brushCallback(t),jz(e.parentNode)}function DGt(e,t){var r=t.brush,n=r.filter,i=r.svgBrush;i._dragging||(dUe(e,t),hUe(e,t),t.brush.svgBrush.wasDragged=!1),i._dragging=!1;var a=im.event;a.sourceEvent.stopPropagation();var o=i.grabbingBar;if(i.grabbingBar=!1,i.grabLocation=void 0,t.parent.inBrushDrag=!1,cUe(),!i.wasDragged){i.wasDragged=void 0,i.clickableOrdinalRange?r.filterSpecified&&t.multiselect?i.extent.push(i.clickableOrdinalRange):(i.extent=[i.clickableOrdinalRange],r.filterSpecified=!0):o?(i.extent=i.stayingIntervals,i.extent.length===0&&zK(r)):zK(r),i.brushCallback(t),jz(e.parentNode),i.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);return}var s=function(){n.set(n.getConsolidated())};if(t.ordinal){var l=t.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),i.newExtent=[Wz(0,l,i.newExtent[0],i.stayingIntervals),Wz(1,l,i.newExtent[1],i.stayingIntervals)];var u=i.newExtent[1]>i.newExtent[0];i.extent=i.stayingIntervals.concat(u?[i.newExtent]:[]),i.extent.length||zK(r),i.brushCallback(t),u?jz(e.parentNode,s):(s(),jz(e.parentNode))}else s();i.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}function dUe(e,t){var r=t.height-im.mouse(e)[1]-2*Th.verticalPadding,n=fUe(t,r),i=\"crosshair\";n.clickableOrdinalRange?i=\"pointer\":n.region&&(i=n.region+\"-resize\"),im.select(document.body).style(\"cursor\",i)}function FGt(e){e.on(\"mousemove\",function(t){im.event.preventDefault(),t.parent.inBrushDrag||dUe(this,t)}).on(\"mouseleave\",function(t){t.parent.inBrushDrag||cUe()}).call(im.behavior.drag().on(\"dragstart\",function(t){RGt(this,t)}).on(\"drag\",function(t){hUe(this,t)}).on(\"dragend\",function(t){DGt(this,t)}))}function vUe(e,t){return e[0]-t[0]}function zGt(e,t,r){var n=r._context.staticPlot,i=e.selectAll(\".background\").data(Hz);i.enter().append(\"rect\").classed(\"background\",!0).call(CGt).call(LGt).style(\"pointer-events\",n?\"none\":\"auto\").attr(\"transform\",MGt(0,Th.verticalPadding)),i.call(FGt).attr(\"height\",function(s){return s.height-Th.verticalPadding});var a=e.selectAll(\".highlight-shadow\").data(Hz);a.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-Th.bar.width/2).attr(\"stroke-width\",Th.bar.width+Th.bar.strokeWidth).attr(\"stroke\",t).attr(\"opacity\",Th.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),a.attr(\"y1\",function(s){return s.height}).call(qK);var o=e.selectAll(\".highlight\").data(Hz);o.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-Th.bar.width/2).attr(\"stroke-width\",Th.bar.width-Th.bar.strokeWidth).attr(\"stroke\",Th.bar.fillColor).attr(\"opacity\",Th.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),o.attr(\"y1\",function(s){return s.height}).call(qK)}function qGt(e,t,r){var n=e.selectAll(\".\"+Th.cn.axisBrush).data(Hz,SGt);n.enter().append(\"g\").classed(Th.cn.axisBrush,!0),zGt(n,t,r)}function OGt(e){return e.svgBrush.extent.map(function(t){return t.slice()})}function zK(e){e.filterSpecified=!1,e.svgBrush.extent=[[-1/0,1/0]]}function BGt(e){return function(r){var n=r.brush,i=OGt(n),a=i.slice();n.filter.set(a),e()}}function pUe(e){for(var t=e.slice(),r=[],n,i=t.shift();i;){for(n=i.slice();(i=t.shift())&&i[0]<=n[1];)n[1]=Math.max(n[1],i[1]);r.push(n)}return r.length===1&&r[0][0]>r[0][1]&&(r=[]),r}function NGt(){var e=[],t,r;return{set:function(n){e=n.map(function(i){return i.slice().sort(E5)}).sort(vUe),e.length===1&&e[0][0]===-1/0&&e[0][1]===1/0&&(e=[[0,-1]]),t=pUe(e),r=e.reduce(function(i,a){return[Math.min(i[0],a[0]),Math.max(i[1],a[1])]},[1/0,-1/0])},get:function(){return e.slice()},getConsolidated:function(){return t},getBounds:function(){return r}}}function UGt(e,t,r,n,i,a){var o=NGt();return o.set(r),{filter:o,filterSpecified:t,svgBrush:{extent:[],brushStartCallback:n,brushCallback:BGt(i),brushEndCallback:a}}}function VGt(e,t){if(Array.isArray(e[0])?(e=e.map(function(n){return n.sort(E5)}),t.multiselect?e=pUe(e.sort(vUe)):e=[e[0]]):e=[e.sort(E5)],t.tickvals){var r=t.tickvals.slice().sort(E5);if(e=e.map(function(n){var i=[Wz(0,r,n[0],[]),Wz(1,r,n[1],[])];if(i[1]>i[0])return i}).filter(function(n){return n}),!e.length)return}return e.length>1?e:e[0]}gUe.exports={makeBrush:UGt,ensureAxisBrush:qGt,cleanRanges:VGt}});var _Ue=ye((Axr,yUe)=>{\"use strict\";var Ex=Pr(),GGt=pv().hasColorscale,HGt=td(),jGt=Cc().defaults,WGt=Kd(),XGt=ho(),mUe=FK(),ZGt=OK(),BK=Vk().maxDimensionCount,YGt=zz();function KGt(e,t,r,n,i){var a=i(\"line.color\",r);if(GGt(e,\"line\")&&Ex.isArrayOrTypedArray(a)){if(a.length)return i(\"line.colorscale\"),HGt(e,t,n,i,{prefix:\"line.\",cLetter:\"c\"}),a.length;t.line.color=r}return 1/0}function JGt(e,t,r,n){function i(u,c){return Ex.coerce(e,t,mUe.dimensions,u,c)}var a=i(\"values\"),o=i(\"visible\");if(a&&a.length||(o=t.visible=!1),o){i(\"label\"),i(\"tickvals\"),i(\"ticktext\"),i(\"tickformat\");var s=i(\"range\");t._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:s},XGt.setConvert(t._ax,n.layout),i(\"multiselect\");var l=i(\"constraintrange\");l&&(t.constraintrange=ZGt.cleanRanges(l,t))}}yUe.exports=function(t,r,n,i){function a(c,f){return Ex.coerce(t,r,mUe,c,f)}var o=t.dimensions;Array.isArray(o)&&o.length>BK&&(Ex.log(\"parcoords traces support up to \"+BK+\" dimensions at the moment\"),o.splice(BK));var s=WGt(t,r,{name:\"dimensions\",layout:i,handleItemDefaults:JGt}),l=KGt(t,r,n,i,a);jGt(r,i,a),(!Array.isArray(s)||!s.length)&&(r.visible=!1),YGt(r,s,\"values\",l);var u=Ex.extendFlat({},i.font,{size:Math.round(i.font.size/1.2)});Ex.coerceFont(a,\"labelfont\",u),Ex.coerceFont(a,\"tickfont\",u,{autoShadowDflt:!0}),Ex.coerceFont(a,\"rangefont\",u),a(\"labelangle\"),a(\"labelside\"),a(\"unselected.line.color\"),a(\"unselected.line.opacity\")}});var bUe=ye((Sxr,xUe)=>{\"use strict\";var $Gt=Pr().isArrayOrTypedArray,NK=tc(),QGt=ay().wrap;xUe.exports=function(t,r){var n,i;return NK.hasColorscale(r,\"line\")&&$Gt(r.line.color)?(n=r.line.color,i=NK.extractOpts(r.line).colorscale,NK.calc(t,r,{vals:n,containerStr:\"line\",cLetter:\"c\"})):(n=eHt(r._length),i=[[0,r.line.color],[1,r.line.color]]),QGt({lineColor:n,cscale:i})};function eHt(e){for(var t=new Array(e),r=0;r<e;r++)t[r]=.5;return t}});function tHt(e){var c,f;var t,r=[],n=1,i;if(typeof e==\"number\")return{space:\"rgb\",values:[e>>>16,(e&65280)>>>8,e&255],alpha:1};if(typeof e==\"number\")return{space:\"rgb\",values:[e>>>16,(e&65280)>>>8,e&255],alpha:1};if(e=String(e).toLowerCase(),UK.default[e])r=UK.default[e].slice(),i=\"rgb\";else if(e===\"transparent\")n=0,i=\"rgb\",r=[0,0,0];else if(e[0]===\"#\"){var a=e.slice(1),o=a.length,s=o<=4;n=1,s?(r=[parseInt(a[0]+a[0],16),parseInt(a[1]+a[1],16),parseInt(a[2]+a[2],16)],o===4&&(n=parseInt(a[3]+a[3],16)/255)):(r=[parseInt(a[0]+a[1],16),parseInt(a[2]+a[3],16),parseInt(a[4]+a[5],16)],o===8&&(n=parseInt(a[6]+a[7],16)/255)),r[0]||(r[0]=0),r[1]||(r[1]=0),r[2]||(r[2]=0),i=\"rgb\"}else if(t=/^((?:rgba?|hs[lvb]a?|hwba?|cmyk?|xy[zy]|gray|lab|lchu?v?|[ly]uv|lms|oklch|oklab|color))\\s*\\(([^\\)]*)\\)/.exec(e)){var l=t[1];i=l.replace(/a$/,\"\");var u=i===\"cmyk\"?4:i===\"gray\"?1:3;r=t[2].trim().split(/\\s*[,\\/]\\s*|\\s+/),i===\"color\"&&(i=r.shift()),r=r.map(function(h,d){if(h[h.length-1]===\"%\")return h=parseFloat(h)/100,d===3?h:i===\"rgb\"?h*255:i[0]===\"h\"||i[0]===\"l\"&&!d?h*100:i===\"lab\"?h*125:i===\"lch\"?d<2?h*150:h*360:i[0]===\"o\"&&!d?h:i===\"oklab\"?h*.4:i===\"oklch\"?d<2?h*.4:h*360:h;if(i[d]===\"h\"||d===2&&i[i.length-1]===\"h\"){if(wUe[h]!==void 0)return wUe[h];if(h.endsWith(\"deg\"))return parseFloat(h);if(h.endsWith(\"turn\"))return parseFloat(h)*360;if(h.endsWith(\"grad\"))return parseFloat(h)*360/400;if(h.endsWith(\"rad\"))return parseFloat(h)*180/Math.PI}return h===\"none\"?0:parseFloat(h)}),n=r.length>u?r.pop():1}else/[0-9](?:\\s|\\/|,)/.test(e)&&(r=e.match(/([0-9]+)/g).map(function(h){return parseFloat(h)}),i=((f=(c=e.match(/([a-z])/ig))==null?void 0:c.join(\"\"))==null?void 0:f.toLowerCase())||\"rgb\");return{space:i,values:r,alpha:n}}var UK,TUe,wUe,AUe=gu(()=>{UK=Rtt(MX(),1),TUe=tHt,wUe={red:0,orange:60,yellow:120,green:180,blue:240,purple:300}});var Gk,VK=gu(()=>{Gk={name:\"rgb\",min:[0,0,0],max:[255,255,255],channel:[\"red\",\"green\",\"blue\"],alias:[\"RGB\"]}});var Xz,SUe=gu(()=>{VK();Xz={name:\"hsl\",min:[0,0,0],max:[360,100,100],channel:[\"hue\",\"saturation\",\"lightness\"],alias:[\"HSL\"],rgb:function(e){var t=e[0]/360,r=e[1]/100,n=e[2]/100,i,a,o,s,l,u=0;if(r===0)return l=n*255,[l,l,l];for(a=n<.5?n*(1+r):n+r-n*r,i=2*n-a,s=[0,0,0];u<3;)o=t+1/3*-(u-1),o<0?o++:o>1&&o--,l=6*o<1?i+(a-i)*6*o:2*o<1?a:3*o<2?i+(a-i)*(2/3-o)*6:i,s[u++]=l*255;return s}};Gk.hsl=function(e){var t=e[0]/255,r=e[1]/255,n=e[2]/255,i=Math.min(t,r,n),a=Math.max(t,r,n),o=a-i,s,l,u;return a===i?s=0:t===a?s=(r-n)/o:r===a?s=2+(n-t)/o:n===a&&(s=4+(t-r)/o),s=Math.min(s*60,360),s<0&&(s+=360),u=(i+a)/2,a===i?l=0:u<=.5?l=o/(a+i):l=o/(2-a-i),[s,l*100,u*100]}});var EUe={};bee(EUe,{default:()=>MUe});function MUe(e){Array.isArray(e)&&e.raw&&(e=String.raw(...arguments)),e instanceof Number&&(e=+e);var t,r,n,i=TUe(e);if(!i.space)return[];let a=i.space[0]===\"h\"?Xz.min:Gk.min,o=i.space[0]===\"h\"?Xz.max:Gk.max;return t=Array(3),t[0]=Math.min(Math.max(i.values[0],a[0]),o[0]),t[1]=Math.min(Math.max(i.values[1],a[1]),o[1]),t[2]=Math.min(Math.max(i.values[2],a[2]),o[2]),i.space[0]===\"h\"&&(t=Xz.rgb(t)),t.push(Math.min(Math.max(i.alpha,0),1)),t}var kUe=gu(()=>{AUe();VK();SUe()});var GK=ye(Zz=>{\"use strict\";var rHt=Pr().isTypedArray;Zz.convertTypedArray=function(e){return rHt(e)?Array.prototype.slice.call(e):e};Zz.isOrdinal=function(e){return!!e.tickvals};Zz.isVisible=function(e){return e.visible||!(\"visible\"in e)}});var OUe=ye((Dxr,qUe)=>{\"use strict\";var iHt=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"attribute vec4 p01_04, p05_08, p09_12, p13_16,\",\"               p17_20, p21_24, p25_28, p29_32,\",\"               p33_36, p37_40, p41_44, p45_48,\",\"               p49_52, p53_56, p57_60, colors;\",\"\",\"uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\",\"             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\",\"\",\"uniform vec2 resolution, viewBoxPos, viewBoxSize;\",\"uniform float maskHeight;\",\"uniform float drwLayer; // 0: context, 1: focus, 2: pick\",\"uniform vec4 contextColor;\",\"uniform sampler2D maskTexture, palette;\",\"\",\"bool isPick    = (drwLayer > 1.5);\",\"bool isContext = (drwLayer < 0.5);\",\"\",\"const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\",\"const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\",\"\",\"float val(mat4 p, mat4 v) {\",\"    return dot(matrixCompMult(p, v) * UNITS, UNITS);\",\"}\",\"\",\"float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\",\"    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\",\"    return y1 * (1.0 - ratio) + y2 * ratio;\",\"}\",\"\",\"int iMod(int a, int b) {\",\"    return a - b * (a / b);\",\"}\",\"\",\"bool fOutside(float p, float lo, float hi) {\",\"    return (lo < hi) && (lo > p || p > hi);\",\"}\",\"\",\"bool vOutside(vec4 p, vec4 lo, vec4 hi) {\",\"    return (\",\"        fOutside(p[0], lo[0], hi[0]) ||\",\"        fOutside(p[1], lo[1], hi[1]) ||\",\"        fOutside(p[2], lo[2], hi[2]) ||\",\"        fOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool mOutside(mat4 p, mat4 lo, mat4 hi) {\",\"    return (\",\"        vOutside(p[0], lo[0], hi[0]) ||\",\"        vOutside(p[1], lo[1], hi[1]) ||\",\"        vOutside(p[2], lo[2], hi[2]) ||\",\"        vOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    return mOutside(A, loA, hiA) ||\",\"           mOutside(B, loB, hiB) ||\",\"           mOutside(C, loC, hiC) ||\",\"           mOutside(D, loD, hiD);\",\"}\",\"\",\"bool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    mat4 pnts[4];\",\"    pnts[0] = A;\",\"    pnts[1] = B;\",\"    pnts[2] = C;\",\"    pnts[3] = D;\",\"\",\"    for(int i = 0; i < 4; ++i) {\",\"        for(int j = 0; j < 4; ++j) {\",\"            for(int k = 0; k < 4; ++k) {\",\"                if(0 == iMod(\",\"                    int(255.0 * texture2D(maskTexture,\",\"                        vec2(\",\"                            (float(i * 2 + j / 2) + 0.5) / 8.0,\",\"                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\",\"                        ))[3]\",\"                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\",\"                    2\",\"                )) return true;\",\"            }\",\"        }\",\"    }\",\"    return false;\",\"}\",\"\",\"vec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float x = 0.5 * sign(v) + 0.5;\",\"    float y = axisY(x, A, B, C, D);\",\"    float z = 1.0 - abs(v);\",\"\",\"    z += isContext ? 0.0 : 2.0 * float(\",\"        outsideBoundingBox(A, B, C, D) ||\",\"        outsideRasterMask(A, B, C, D)\",\"    );\",\"\",\"    return vec4(\",\"        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\",\"        z,\",\"        1.0\",\"    );\",\"}\",\"\",\"void main() {\",\"    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\",\"    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\",\"    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\",\"    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\",\"\",\"    float v = colors[3];\",\"\",\"    gl_Position = position(isContext, v, A, B, C, D);\",\"\",\"    fragColor =\",\"        isContext ? vec4(contextColor) :\",\"        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\",\"}\"].join(`\n",
-       "`),nHt=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"void main() {\",\"    gl_FragColor = fragColor;\",\"}\"].join(`\n",
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b=e.lines.canvasOverdrag*d,p=e.domain,k=e.canvasWidth*d,M=e.canvasHeight*d,T=e.pad.l*d,L=e.pad.b*d,x=e.layoutHeight*d,C=e.layoutWidth*d,S=e.deselectedLines.color,g=e.deselectedLines.opacity,P=DUe.extendFlat({key:c,resolution:[k,M],viewBoxPos:[o+b,s],viewBoxSize:[l,u],i0:i,i1:a,dim0A:v[0].slice(0,16),dim0B:v[0].slice(16,32),dim0C:v[0].slice(32,48),dim0D:v[0].slice(48,64),dim1A:v[1].slice(0,16),dim1B:v[1].slice(16,32),dim1C:v[1].slice(32,48),dim1D:v[1].slice(48,64),drwLayer:f,contextColor:[S[0]/255,S[1]/255,S[2]/255,g!==\"auto\"?S[3]*g:Math.max(1/255,Math.pow(1/e.lines.color.length,1/3))],scissorX:(n===t?0:o+b)+(T-b)+C*p.x[0],scissorWidth:(n===r?k-o+b:l+.5)+(n===t?o+b:0),scissorY:s+L+x*p.y[0],scissorHeight:u,viewportX:T-b+C*p.x[0],viewportY:L+x*p.y[0],viewportWidth:k,viewportHeight:M},h);return P}function RUe(e){var t=Yz-1,r=Math.max(0,Math.floor(e[0]*t),0),n=Math.min(t,Math.ceil(e[1]*t),t);return[Math.min(r,n),Math.max(r,n)]}qUe.exports=function(e,t){var r=t.context,n=t.pick,i=t.regl,a=i._gl,o=a.getParameter(a.ALIASED_LINE_WIDTH_RANGE),s=Math.max(o[0],Math.min(o[1],t.viewModel.plotGlPixelRatio)),l={currentRafs:{},drawCompleted:!0,clearOnly:!1},u,c,f,h,d=dHt(i),v,m=i.texture(PUe),b=[];k(t);var p=i({profile:!1,blend:{enable:r,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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k(S){u=S.model,c=S.viewModel,f=c.dimensions.slice(),h=f[0]?f[0].values.length:0;var g=u.lines,P=n?g.color.map(function(z,q){return q/g.color.length}):g.color,E=cHt(h,f,P);hHt(d,h,E),!r&&!n&&(m=i.texture(DUe.extendFlat({data:uHt(u.unitToColor,255)},PUe)))}function M(S){var g,P,E,z=[[],[]];for(E=0;E<64;E++){var q=!S&&E<f.length?f[E].brush.filter.getBounds():[-1/0,1/0];z[0][E]=q[0],z[1][E]=q[1]}var V=Yz*8,G=new Array(V);for(g=0;g<V;g++)G[g]=255;if(!S)for(g=0;g<f.length;g++){var Z=g%8,j=(g-Z)/8,N=Math.pow(2,Z),H=f[g],re=H.brush.filter.get();if(!(re.length<2)){var ae=RUe(re[0])[1];for(P=1;P<re.length;P++){var _e=RUe(re[P]);for(E=ae+1;E<_e[0];E++)G[E*8+j]&=~N;ae=Math.max(ae,_e[1])}}}var Ee={shape:[8,Yz],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:G};return v?v(Ee):v=i.texture(Ee),{maskTexture:v,maskHeight:Yz,loA:z[0].slice(0,16),loB:z[0].slice(16,32),loC:z[0].slice(32,48),loD:z[0].slice(48,64),hiA:z[1].slice(0,16),hiB:z[1].slice(16,32),hiC:z[1].slice(32,48),hiD:z[1].slice(48,64)}}function T(S,g,P){var E=S.length,z,q,V,G=1/0,Z=-1/0;for(z=0;z<E;z++)S[z].dim0.canvasX<G&&(G=S[z].dim0.canvasX,q=z),S[z].dim1.canvasX>Z&&(Z=S[z].dim1.canvasX,V=z);E===0&&FUe(i,0,0,u.canvasWidth,u.canvasHeight);var j=M(r);for(z=0;z<E;z++){var N=S[z],H=N.dim0.crossfilterDimensionIndex,re=N.dim1.crossfilterDimensionIndex,ae=N.canvasX,_e=N.canvasY,Ee=ae+N.panelSizeX,Ce=N.plotGlPixelRatio;if(g||!b[H]||b[H][0]!==ae||b[H][1]!==Ee){b[H]=[ae,Ee];var ge=vHt(u,q,V,z,H,re,ae,_e,N.panelSizeX,N.panelSizeY,N.dim0.crossfilterDimensionIndex,r?0:n?2:1,j,Ce);l.clearOnly=P;var ie=g?u.lines.blockLineCount:h;sHt(i,p,l,ie,h,ge)}}}function L(S,g){return i.read({x:S,y:g,width:1,height:1,data:LUe}),LUe}function x(S,g,P,E){var z=new Uint8Array(4*P*E);return i.read({x:S,y:g,width:P,height:E,data:z}),z}function C(){e.style[\"pointer-events\"]=\"none\",m.destroy(),v&&v.destroy();for(var S in d)d[S].destroy()}return{render:T,readPixel:L,readPixels:x,destroy:C,update:k}}});var $Ue=ye((Fxr,JUe)=>{\"use strict\";var Bd=Fa(),g1=Pr(),jK=g1.isArrayOrTypedArray,HUe=g1.numberFormat,jUe=(kUe(),gb(EUe)).default,WUe=ho(),pHt=g1.strRotate,oy=g1.strTranslate,gHt=Zl(),Kz=So(),BUe=tc(),ZK=ay(),ig=ZK.keyFun,sy=ZK.repeat,XUe=ZK.unwrap,k5=GK(),Rl=Vk(),ZUe=OK(),mHt=OUe();function NUe(e,t,r){return g1.aggNums(e,null,t,r)}function YUe(e,t){return YK(NUe(Math.min,e,t),NUe(Math.max,e,t))}function Jz(e){var t=e.range;return t?YK(t[0],t[1]):YUe(e.values,e._length)}function YK(e,t){return(isNaN(e)||!isFinite(e))&&(e=0),(isNaN(t)||!isFinite(t))&&(t=0),e===t&&(e===0?(e-=1,t+=1):(e*=.9,t*=1.1)),[e,t]}function yHt(e,t){return t?function(r,n){var i=t[n];return i==null?e(r):i}:e}function _Ht(e,t,r,n,i){var a=Jz(r);return n?Bd.scale.ordinal().domain(n.map(yHt(HUe(r.tickformat),i))).range(n.map(function(o){var s=(o-a[0])/(a[1]-a[0]);return e-t+s*(2*t-e)})):Bd.scale.linear().domain(a).range([e-t,t])}function xHt(e,t){return Bd.scale.linear().range([t,e-t])}function bHt(e,t){return Bd.scale.linear().domain(Jz(e)).range([t,1-t])}function wHt(e){if(e.tickvals){var t=Jz(e);return Bd.scale.ordinal().domain(e.tickvals).range(e.tickvals.map(function(r){return(r-t[0])/(t[1]-t[0])}))}}function THt(e){var t=e.map(function(a){return a[0]}),r=e.map(function(a){var o=jUe(a[1]);return Bd.rgb(\"rgb(\"+o[0]+\",\"+o[1]+\",\"+o[2]+\")\")}),n=function(a){return function(o){return o[a]}},i=\"rgb\".split(\"\").map(function(a){return Bd.scale.linear().clamp(!0).domain(t).range(r.map(n(a)))});return function(a){return i.map(function(o){return o(a)})}}function XK(e){return e.dimensions.some(function(t){return t.brush.filterSpecified})}function AHt(e,t,r){var n=XUe(t),i=n.trace,a=k5.convertTypedArray(n.lineColor),o=i.line,s={color:jUe(i.unselected.line.color),opacity:i.unselected.line.opacity},l=BUe.extractOpts(o),u=l.reversescale?BUe.flipScale(n.cscale):n.cscale,c=i.domain,f=i.dimensions,h=e.width,d=i.labelangle,v=i.labelside,m=i.labelfont,b=i.tickfont,p=i.rangefont,k=g1.extendDeepNoArrays({},o,{color:a.map(Bd.scale.linear().domain(Jz({values:a,range:[l.min,l.max],_length:i._length}))),blockLineCount:Rl.blockLineCount,canvasOverdrag:Rl.overdrag*Rl.canvasPixelRatio}),M=Math.floor(h*(c.x[1]-c.x[0])),T=Math.floor(e.height*(c.y[1]-c.y[0])),L=e.margin||{l:80,r:80,t:100,b:80},x=M,C=T;return{key:r,colCount:f.filter(k5.isVisible).length,dimensions:f,tickDistance:Rl.tickDistance,unitToColor:THt(u),lines:k,deselectedLines:s,labelAngle:d,labelSide:v,labelFont:m,tickFont:b,rangeFont:p,layoutWidth:h,layoutHeight:e.height,domain:c,translateX:c.x[0]*h,translateY:e.height-c.y[1]*e.height,pad:L,canvasWidth:x*Rl.canvasPixelRatio+2*k.canvasOverdrag,canvasHeight:C*Rl.canvasPixelRatio,width:x,height:C,canvasPixelRatio:Rl.canvasPixelRatio}}function 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q.val-V.val}if(jK(x)&&x.length){g1.isTypedArray(x)&&(x=Array.from(x)),C=h.ticktext,!jK(C)||!C.length?C=x.map(HUe(h.tickformat)):C.length>x.length?C=C.slice(0,x.length):x.length>C.length&&(x=x.slice(0,C.length));for(var P=1;P<x.length;P++)if(x[P]<x[P-1]){for(var E=x.map(S).sort(g),z=0;z<x.length;z++)x[z]=E[z].val,C[z]=E[z].text;break}}else x=void 0;return L=k5.convertTypedArray(L),{key:b,label:h.label,tickFormat:h.tickformat,tickvals:x,ticktext:C,ordinal:k5.isOrdinal(h),multiselect:h.multiselect,xIndex:d,crossfilterDimensionIndex:d,visibleIndex:h._index,height:i,values:L,paddedUnitValues:L.map(v),unitTickvals:x&&x.map(v),xScale:s,x:s(d),canvasX:s(d)*o,unitToPaddedPx:u,domainScale:_Ht(i,Rl.verticalPadding,h,x,C),ordinalScale:wHt(h),parent:c,model:r,brush:ZUe.makeBrush(e,k,M,function(){e.linePickActive(!1)},T,function(q){if(c.focusLayer.render(c.panels,!0),c.pickLayer&&c.pickLayer.render(c.panels,!0),e.linePickActive(!0),t&&t.filterChanged){var V=v.invert,G=q.map(function(Z){return 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o=i[a].values,s=i[a]._ax;s&&(s.range?s.range=YK(s.range[0],s.range[1]):s.range=YUe(o,n._length),s.dtick||(s.dtick=.01*(Math.abs(s.range[1]-s.range[0])||1)),s.tickformat=i[a].tickformat,WUe.calcTicks(s),s.cleanRange())}}function KUe(e,t){return WUe.tickText(e._ax,t,!1).text}function GUe(e,t){if(e.ordinal)return\"\";var r=e.domainScale.domain(),n=r[t?r.length-1:0];return KUe(e.model.dimensions[e.visibleIndex],n)}JUe.exports=function(t,r,n,i){var a=t._context.staticPlot,o=t._fullLayout,s=o._toppaper,l=o._glcontainer,u=t._context.plotGlPixelRatio,c=t._fullLayout.paper_bgcolor;EHt(r);var f=MHt(),h=r.filter(function(z){return XUe(z).trace.visible}).map(AHt.bind(0,n)).map(SHt.bind(0,f,i));l.each(function(z,q){return g1.extendFlat(z,h[q])});var d=l.selectAll(\".gl-canvas\").each(function(z){z.viewModel=h[0],z.viewModel.plotGlPixelRatio=u,z.viewModel.paperColor=c,z.model=z.viewModel?z.viewModel.model:null}),v=null,m=d.filter(function(z){return 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l=i.font;C5.coerceFont(a,\"labelfont\",l,{overrideDflt:{size:Math.round(l.size)}}),C5.coerceFont(a,\"tickfont\",l,{autoShadowDflt:!0,overrideDflt:{size:Math.round(l.size/1.2)}})}});var bVe=ye((Vxr,xVe)=>{\"use strict\";var WHt=ay().wrap,XHt=pv().hasColorscale,ZHt=gv(),YHt=aO(),KHt=So(),Wk=Pr(),JHt=Eo();xVe.exports=function(t,r){var n=Wk.filterVisible(r.dimensions);if(n.length===0)return[];var i=n.map(function(g){var P;if(g.categoryorder===\"trace\")P=null;else if(g.categoryorder===\"array\")P=g.categoryarray;else{P=YHt(g.values);for(var E=!0,z=0;z<P.length;z++)if(!JHt(P[z])){E=!1;break}P.sort(E?Wk.sorterAsc:void 0),g.categoryorder===\"category descending\"&&(P=P.reverse())}return njt(g.values,P)}),a,o,s;Wk.isArrayOrTypedArray(r.counts)?a=r.counts:a=[r.counts],ajt(n),n.forEach(function(g,P){ojt(g,i[P])});var l=r.line,u;l?(XHt(r,\"line\")&&ZHt(t,r,{vals:r.line.color,containerStr:\"line\",cLetter:\"c\"}),u=KHt.tryColorscale(l)):u=Wk.identity;function c(g){var P,E;return 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k.width/2}).attr(\"y\",-5).text(function(k,M){return M===0?k.parcatsViewModel.model.dimensions[k.model.dimensionInd].dimensionLabel:null}).each(function(k){wVe.font(Dl.select(this),k.parcatsViewModel.labelfont)}),v.selectAll(\"rect.bandrect\").on(\"mouseover\",bjt).on(\"mouseout\",wjt),v.exit().remove(),d.call(Dl.behavior.drag().origin(function(k){return{x:k.x,y:0}}).on(\"dragstart\",Tjt).on(\"drag\",Ajt).on(\"dragend\",Sjt)),s.each(function(k){k.traceSelection=Dl.select(this),k.pathSelection=Dl.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),k.dimensionSelection=Dl.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")}),s.exit().remove()}CVe.exports=function(e,t,r,n){fjt(r,e,n,t)};function m1(e){return e.key}function Zk(e){var t=e.parcatsViewModel.dimensions.length,r=e.parcatsViewModel.dimensions[t-1].model.dimensionInd;return e.model.dimensionInd===r}function eJ(e,t){return e.model.rawColor>t.model.rawColor?1:e.model.rawColor<t.model.rawColor?-1:0}function hjt(e){if(!e.parcatsViewModel.dragDimension&&e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1){kx.raiseToTop(this),iJ(Dl.select(this));var t=Kk(e),r=tJ(e);if(e.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:t,event:Dl.event,constraints:r}),e.parcatsViewModel.hoverinfoItems.indexOf(\"none\")===-1){var n=Dl.mouse(this)[0],i=e.parcatsViewModel.graphDiv,a=e.parcatsViewModel.trace,o=i._fullLayout,s=o._paperdiv.node().getBoundingClientRect(),l=e.parcatsViewModel.graphDiv.getBoundingClientRect(),u,c,f;for(f=0;f<e.leftXs.length-1;f++)if(e.leftXs[f]+e.dimWidths[f]-2<=n&&n<=e.leftXs[f+1]+2){var h=e.parcatsViewModel.dimensions[f],d=e.parcatsViewModel.dimensions[f+1];u=(h.x+h.width+d.x)/2,c=(e.topYs[f]+e.topYs[f+1]+e.height)/2;break}var v=e.parcatsViewModel.x+u,m=e.parcatsViewModel.y+c,b=QK.mostReadable(e.model.color,[\"black\",\"white\"]),p=e.model.count,k=p/e.parcatsViewModel.model.count,M={countLabel:p,probabilityLabel:k.toFixed(3)},T=[];e.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&T.push([\"Count:\",M.countLabel].join(\" \")),e.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&T.push([\"P:\",M.probabilityLabel].join(\" \"));var L=T.join(\"<br>\"),x=Dl.mouse(i)[0];Yk.loneHover({trace:a,x:v-s.left+l.left,y:m-s.top+l.top,text:L,color:e.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:b,idealAlign:x<v?\"right\":\"left\",hovertemplate:(a.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:a._input,fullData:a,count:p,probability:k}]},{container:o._hoverlayer.node(),outerContainer:o._paper.node(),gd:i})}}}function djt(e){if(!e.parcatsViewModel.dragDimension&&(rJ(Dl.select(this)),Yk.loneUnhover(e.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),e.parcatsViewModel.pathSelection.sort(eJ),e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1)){var t=Kk(e),r=tJ(e);e.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:t,event:Dl.event,constraints:r})}}function Kk(e){for(var t=[],r=MVe(e.parcatsViewModel),n=0;n<e.model.valueInds.length;n++){var i=e.model.valueInds[n];t.push({curveNumber:r,pointNumber:i})}return t}function tJ(e){for(var t={},r=e.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[e.model.categoryInds[n]];t[i.containerInd]=a.categoryValue}return e.model.rawColor!==void 0&&(t.color=e.model.rawColor),t}function vjt(e){if(e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1){var t=Kk(e),r=tJ(e);e.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:t,event:Dl.event,constraints:r})}}function rJ(e){e.attr(\"fill\",function(t){return t.model.color}).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function iJ(e){e.attr(\"fill-opacity\",.8).attr(\"stroke\",function(t){return QK.mostReadable(t.model.color,[\"black\",\"white\"])}).attr(\"stroke-width\",.3)}function pjt(e){e.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}function AVe(e){e.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function gjt(e){e.attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}function nJ(e){e.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function $z(e){var t=e.parcatsViewModel.pathSelection,r=e.categoryViewModel.model.dimensionInd,n=e.categoryViewModel.model.categoryInd;return t.filter(function(i){return i.model.categoryInds[r]===n&&i.model.color===e.color})}function mjt(e){var t=Dl.select(e.parentNode).selectAll(\"rect.bandrect\");t.each(function(r){var n=$z(r);iJ(n),n.each(function(){kx.raiseToTop(this)})}),pjt(Dl.select(e.parentNode))}function yjt(e){var t=Dl.select(e).datum(),r=$z(t);iJ(r),r.each(function(){kx.raiseToTop(this)}),Dl.select(e.parentNode).selectAll(\"rect.bandrect\").filter(function(n){return n.color===t.color}).each(function(){kx.raiseToTop(this),gjt(Dl.select(this))})}function aJ(e,t,r){var n=Dl.select(e).datum(),i=n.categoryViewModel.model,a=n.parcatsViewModel.graphDiv,o=Dl.select(e.parentNode).selectAll(\"rect.bandrect\"),s=[];o.each(function(u){var c=$z(u);c.each(function(f){Array.prototype.push.apply(s,Kk(f))})});var l={};l[i.dimensionInd]=i.categoryValue,a.emit(t,{points:s,event:r,constraints:l})}function oJ(e,t,r){var n=Dl.select(e).datum(),i=n.categoryViewModel.model,a=n.parcatsViewModel.graphDiv,o=$z(n),s=[];o.each(function(u){Array.prototype.push.apply(s,Kk(u))});var l={};l[i.dimensionInd]=i.categoryValue,n.rawColor!==void 0&&(l.color=n.rawColor),a.emit(t,{points:s,event:r,constraints:l})}function SVe(e,t,r){e._fullLayout._calcInverseTransform(e);var n=e._fullLayout._invScaleX,i=e._fullLayout._invScaleY,a=Dl.select(r.parentNode).select(\"rect.catrect\"),o=a.node().getBoundingClientRect(),s=a.datum(),l=s.parcatsViewModel,u=l.model.dimensions[s.model.dimensionInd],c=l.trace,f=o.top+o.height/2,h,d;l.dimensions.length>1&&u.displayInd===l.dimensions.length-1?(h=o.left,d=\"left\"):(h=o.left+o.width,d=\"right\");var v=s.model.count,m=s.model.categoryLabel,b=v/s.parcatsViewModel.model.count,p={countLabel:v,categoryLabel:m,probabilityLabel:b.toFixed(3)},k=[];s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&k.push([\"Count:\",p.countLabel].join(\" \")),s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&k.push([\"P(\"+p.categoryLabel+\"):\",p.probabilityLabel].join(\" \"));var M=k.join(\"<br>\");return{trace:c,x:n*(h-t.left),y:i*(f-t.top),text:M,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:d,hovertemplate:c.hovertemplate,hovertemplateLabels:p,eventData:[{data:c._input,fullData:c,count:v,category:m,probability:b}]}}function _jt(e,t,r){var n=[];return Dl.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each(function(){var i=this;n.push(SVe(e,t,i))}),n}function xjt(e,t,r){e._fullLayout._calcInverseTransform(e);var n=e._fullLayout._invScaleX,i=e._fullLayout._invScaleY,a=r.getBoundingClientRect(),o=Dl.select(r).datum(),s=o.categoryViewModel,l=s.parcatsViewModel,u=l.model.dimensions[s.model.dimensionInd],c=l.trace,f=a.y+a.height/2,h,d;l.dimensions.length>1&&u.displayInd===l.dimensions.length-1?(h=a.left,d=\"left\"):(h=a.left+a.width,d=\"right\");var v=s.model.categoryLabel,m=o.parcatsViewModel.model.count,b=0;o.categoryViewModel.bands.forEach(function(P){P.color===o.color&&(b+=P.count)});var p=s.model.count,k=0;l.pathSelection.each(function(P){P.model.color===o.color&&(k+=P.model.count)});var M=b/m,T=b/k,L=b/p,x={countLabel:b,categoryLabel:v,probabilityLabel:M.toFixed(3)},C=[];s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&C.push([\"Count:\",x.countLabel].join(\" \")),s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&(C.push(\"P(color \\u2229 \"+v+\"): \"+x.probabilityLabel),C.push(\"P(\"+v+\" | color): \"+T.toFixed(3)),C.push(\"P(color | \"+v+\"): \"+L.toFixed(3)));var S=C.join(\"<br>\"),g=QK.mostReadable(o.color,[\"black\",\"white\"]);return{trace:c,x:n*(h-t.left),y:i*(f-t.top),text:S,color:o.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:g,fontSize:10,idealAlign:d,hovertemplate:c.hovertemplate,hovertemplateLabels:x,eventData:[{data:c._input,fullData:c,category:v,count:m,probability:M,categorycount:p,colorcount:k,bandcolorcount:b}]}}function bjt(e){if(!e.parcatsViewModel.dragDimension&&e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1){var t=Dl.mouse(this)[1];if(t<-1)return;var r=e.parcatsViewModel.graphDiv,n=r._fullLayout,i=n._paperdiv.node().getBoundingClientRect(),a=e.parcatsViewModel.hoveron,o=this;if(a===\"color\"?(yjt(o),oJ(o,\"plotly_hover\",Dl.event)):(mjt(o),aJ(o,\"plotly_hover\",Dl.event)),e.parcatsViewModel.hoverinfoItems.indexOf(\"none\")===-1){var s;a===\"category\"?s=SVe(r,i,o):a===\"color\"?s=xjt(r,i,o):a===\"dimension\"&&(s=_jt(r,i,o)),s&&Yk.loneHover(s,{container:n._hoverlayer.node(),outerContainer:n._paper.node(),gd:r})}}}function wjt(e){var t=e.parcatsViewModel;if(!t.dragDimension&&(rJ(t.pathSelection),AVe(t.dimensionSelection.selectAll(\"g.category\")),nJ(t.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),Yk.loneUnhover(t.graphDiv._fullLayout._hoverlayer.node()),t.pathSelection.sort(eJ),t.hoverinfoItems.indexOf(\"skip\")===-1)){var r=e.parcatsViewModel.hoveron,n=this;r===\"color\"?oJ(n,\"plotly_unhover\",Dl.event):aJ(n,\"plotly_unhover\",Dl.event)}}function Tjt(e){e.parcatsViewModel.arrangement!==\"fixed\"&&(e.dragDimensionDisplayInd=e.model.displayInd,e.initialDragDimensionDisplayInds=e.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),e.dragHasMoved=!1,e.dragCategoryDisplayInd=null,Dl.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each(function(t){var r=Dl.mouse(this)[0],n=Dl.mouse(this)[1];-2<=r&&r<=t.width+2&&-2<=n&&n<=t.height+2&&(e.dragCategoryDisplayInd=t.model.displayInd,e.initialDragCategoryDisplayInds=e.model.categories.map(function(i){return i.displayInd}),t.model.dragY=t.y,kx.raiseToTop(this.parentNode),Dl.select(this.parentNode).selectAll(\"rect.bandrect\").each(function(i){i.y<n&&n<=i.y+i.height&&(e.potentialClickBand=this)}))}),e.parcatsViewModel.dragDimension=e,Yk.loneUnhover(e.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function Ajt(e){if(e.parcatsViewModel.arrangement!==\"fixed\"&&(e.dragHasMoved=!0,e.dragDimensionDisplayInd!==null)){var t=e.dragDimensionDisplayInd,r=t-1,n=t+1,i=e.parcatsViewModel.dimensions[t];if(e.dragCategoryDisplayInd!==null){var a=i.categories[e.dragCategoryDisplayInd];a.model.dragY+=Dl.event.dy;var o=a.model.dragY,s=a.model.displayInd,l=i.categories,u=l[s-1],c=l[s+1];u!==void 0&&o<u.y+u.height/2&&(a.model.displayInd=u.model.displayInd,u.model.displayInd=s),c!==void 0&&o+a.height>c.y+c.height/2&&(a.model.displayInd=c.model.displayInd,c.model.displayInd=s),e.dragCategoryDisplayInd=a.model.displayInd}if(e.dragCategoryDisplayInd===null||e.parcatsViewModel.arrangement===\"freeform\"){i.model.dragX=Dl.event.x;var f=e.parcatsViewModel.dimensions[r],h=e.parcatsViewModel.dimensions[n];f!==void 0&&i.model.dragX<f.x+f.width&&(i.model.displayInd=f.model.displayInd,f.model.displayInd=t),h!==void 0&&i.model.dragX+i.width>h.x&&(i.model.displayInd=h.model.displayInd,h.model.displayInd=e.dragDimensionDisplayInd),e.dragDimensionDisplayInd=i.model.displayInd}lJ(e.parcatsViewModel),sJ(e.parcatsViewModel),kVe(e.parcatsViewModel),EVe(e.parcatsViewModel)}}function Sjt(e){if(e.parcatsViewModel.arrangement!==\"fixed\"&&e.dragDimensionDisplayInd!==null){Dl.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var t={},r=MVe(e.parcatsViewModel),n=e.parcatsViewModel.model.dimensions.map(function(h){return h.displayInd}),i=e.initialDragDimensionDisplayInds.some(function(h,d){return h!==n[d]});i&&n.forEach(function(h,d){var v=e.parcatsViewModel.model.dimensions[d].containerInd;t[\"dimensions[\"+v+\"].displayindex\"]=h});var a=!1;if(e.dragCategoryDisplayInd!==null){var o=e.model.categories.map(function(h){return h.displayInd});if(a=e.initialDragCategoryDisplayInds.some(function(h,d){return h!==o[d]}),a){var s=e.model.categories.slice().sort(function(h,d){return h.displayInd-d.displayInd}),l=s.map(function(h){return h.categoryValue}),u=s.map(function(h){return h.categoryLabel});t[\"dimensions[\"+e.model.containerInd+\"].categoryarray\"]=[l],t[\"dimensions[\"+e.model.containerInd+\"].ticktext\"]=[u],t[\"dimensions[\"+e.model.containerInd+\"].categoryorder\"]=\"array\"}}if(e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1&&!e.dragHasMoved&&e.potentialClickBand&&(e.parcatsViewModel.hoveron===\"color\"?oJ(e.potentialClickBand,\"plotly_click\",Dl.event.sourceEvent):aJ(e.potentialClickBand,\"plotly_click\",Dl.event.sourceEvent)),e.model.dragX=null,e.dragCategoryDisplayInd!==null){var c=e.parcatsViewModel.dimensions[e.dragDimensionDisplayInd].categories[e.dragCategoryDisplayInd];c.model.dragY=null,e.dragCategoryDisplayInd=null}e.dragDimensionDisplayInd=null,e.parcatsViewModel.dragDimension=null,e.dragHasMoved=null,e.potentialClickBand=null,lJ(e.parcatsViewModel),sJ(e.parcatsViewModel);var f=Dl.transition().duration(300).ease(\"cubic-in-out\");f.each(function(){kVe(e.parcatsViewModel,!0),EVe(e.parcatsViewModel,!0)}).each(\"end\",function(){(i||a)&&ujt.restyle(e.parcatsViewModel.graphDiv,t,[r])})}}function MVe(e){for(var t,r=e.graphDiv._fullData,n=0;n<r.length;n++)if(e.key===r[n].uid){t=n;break}return t}function EVe(e,t){t===void 0&&(t=!1);function r(n){return t?n.transition():n}e.pathSelection.data(function(n){return n.paths},m1),r(e.pathSelection).attr(\"d\",function(n){return n.svgD})}function kVe(e,t){t===void 0&&(t=!1);function r(l){return t?l.transition():l}e.dimensionSelection.data(function(l){return l.dimensions},m1);var n=e.dimensionSelection.selectAll(\"g.category\").data(function(l){return l.categories},m1);r(e.dimensionSelection).attr(\"transform\",function(l){return Xk(l.x,0)}),r(n).attr(\"transform\",function(l){return Xk(0,l.y)});var i=n.select(\".dimlabel\");i.text(function(l,u){return u===0?l.parcatsViewModel.model.dimensions[l.model.dimensionInd].dimensionLabel:null});var a=n.select(\".catlabel\");a.attr(\"text-anchor\",function(l){return Zk(l)?\"start\":\"end\"}).attr(\"x\",function(l){return Zk(l)?l.width+5:-5}).each(function(l){var u,c;Zk(l)?(u=l.width+5,c=\"start\"):(u=-5,c=\"end\"),Dl.select(this).selectAll(\"tspan\").attr(\"x\",u).attr(\"text-anchor\",c)});var o=n.selectAll(\"rect.bandrect\").data(function(l){return l.bands},m1),s=o.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",function(l){return l.color}).attr(\"fill-opacity\",0);o.attr(\"fill\",function(l){return l.color}).attr(\"width\",function(l){return l.width}).attr(\"height\",function(l){return l.height}).attr(\"y\",function(l){return l.y}),nJ(s),o.each(function(){kx.raiseToTop(this)}),o.exit().remove()}function Mjt(e,t,r){var n=r[0],i=t.margin||{l:80,r:80,t:100,b:80},a=n.trace,o=a.domain,s=t.width,l=t.height,u=Math.floor(s*(o.x[1]-o.x[0])),c=Math.floor(l*(o.y[1]-o.y[0])),f=o.x[0]*s+i.l,h=t.height-o.y[1]*t.height+i.t,d=a.line.shape,v;a.hoverinfo===\"all\"?v=[\"count\",\"probability\"]:v=(a.hoverinfo||\"\").split(\"+\");var m={trace:a,key:a.uid,model:n,x:f,y:h,width:u,height:c,hoveron:a.hoveron,hoverinfoItems:v,arrangement:a.arrangement,bundlecolors:a.bundlecolors,sortpaths:a.sortpaths,labelfont:a.labelfont,categorylabelfont:a.tickfont,pathShape:d,dragDimension:null,margin:i,paths:[],dimensions:[],graphDiv:e,traceSelection:null,pathSelection:null,dimensionSelection:null};return n.dimensions&&(lJ(m),sJ(m)),m}function TVe(e,t,r,n,i){var a=[],o=[],s,l;for(l=0;l<r.length-1;l++)s=ljt(r[l]+e[l],e[l+1]),a.push(s(i)),o.push(s(1-i));var u=\"M \"+e[0]+\",\"+t[0];for(u+=\"l\"+r[0]+\",0 \",l=1;l<r.length;l++)u+=\"C\"+a[l-1]+\",\"+t[l-1]+\" \"+o[l-1]+\",\"+t[l]+\" \"+e[l]+\",\"+t[l],u+=\"l\"+r[l]+\",0 \";for(u+=\"l0,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",l=r.length-2;l>=0;l--)u+=\"C\"+o[l]+\",\"+(t[l+1]+n)+\" \"+a[l]+\",\"+(t[l]+n)+\" \"+(e[l]+r[l])+\",\"+(t[l]+n),u+=\"l-\"+r[l]+\",0 \";return u+=\"Z\",u}function sJ(e){var t=e.dimensions,r=e.model,n=t.map(function(q){return q.categories.map(function(V){return V.y})}),i=e.model.dimensions.map(function(q){return q.categories.map(function(V){return V.displayInd})}),a=e.model.dimensions.map(function(q){return q.displayInd}),o=e.dimensions.map(function(q){return q.model.dimensionInd}),s=t.map(function(q){return q.x}),l=t.map(function(q){return q.width}),u=[];for(var c in r.paths)r.paths.hasOwnProperty(c)&&u.push(r.paths[c]);function f(q){var V=q.categoryInds.map(function(Z,j){return i[j][Z]}),G=o.map(function(Z){return V[Z]});return G}u.sort(function(q,V){var G=f(q),Z=f(V);return e.sortpaths===\"backward\"&&(G.reverse(),Z.reverse()),G.push(q.valueInds[0]),Z.push(V.valueInds[0]),e.bundlecolors&&(G.unshift(q.rawColor),Z.unshift(V.rawColor)),G<Z?-1:G>Z?1:0});for(var h=new Array(u.length),d=t[0].model.count,v=t[0].categories.map(function(q){return q.height}).reduce(function(q,V){return q+V}),m=0;m<u.length;m++){var b=u[m],p;d>0?p=v*(b.count/d):p=0;for(var k=new Array(n.length),M=0;M<b.categoryInds.length;M++){var T=b.categoryInds[M],L=i[M][T],x=a[M];k[x]=n[x][L],n[x][L]+=p;var C=e.dimensions[x].categories[L],S=C.bands.length,g=C.bands[S-1];if(g===void 0||b.rawColor!==g.rawColor){var P=g===void 0?0:g.y+g.height;C.bands.push({key:P,color:b.color,rawColor:b.rawColor,height:p,width:C.width,count:b.count,y:P,categoryViewModel:C,parcatsViewModel:e})}else{var E=C.bands[S-1];E.height+=p,E.count+=b.count}}var z;e.pathShape===\"hspline\"?z=TVe(s,k,l,p,.5):z=TVe(s,k,l,p,0),h[m]={key:b.valueInds[0],model:b,height:p,leftXs:s,topYs:k,dimWidths:l,svgD:z,parcatsViewModel:e}}e.paths=h}function lJ(e){var t=e.model.dimensions.map(function(o){return{displayInd:o.displayInd,dimensionInd:o.dimensionInd}});t.sort(function(o,s){return o.displayInd-s.displayInd});var r=[];for(var n in t){var i=t[n].dimensionInd,a=e.model.dimensions[i];r.push(Ejt(e,a))}e.dimensions=r}function Ejt(e,t){var r=40,n=16,i=e.model.dimensions.length,a=t.displayInd,o,s,l;i>1?o=(e.width-2*r-n)/(i-1):o=0,s=r,l=s+o*a;var u=[],c=e.model.maxCats,f=t.categories.length,h=8,d=t.count,v=e.height-h*(c-1),m,b,p,k,M,T=(c-f)*h/2,L=t.categories.map(function(x){return{displayInd:x.displayInd,categoryInd:x.categoryInd}});for(L.sort(function(x,C){return 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target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">ODbL</a>'].join(\" \"),Ijt=['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by-sa/3.0\">CC BY SA</a>'].join(\" 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custom plotly.js bundle is not using the correct mapbox-gl version\",\"Please install @plotly/mapbox-gl@\"+OVe+\".\"].join(`\n",
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jVe=Pr(),WVe=ka().defaultLine,Rjt=Cc().attributes,Djt=ec(),Fjt=pf().textposition,zjt=mc().overrideAll,qjt=vl().templatedArray,cJ=y1(),XVe=Djt({noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0});XVe.family.dflt=\"Open Sans Regular, Arial Unicode MS Regular\";var Ojt=ZVe.exports=zjt({_arrayAttrRegexps:[jVe.counterRegex(\"mapbox\",\".layers\",!0)],domain:Rjt({name:\"mapbox\"}),accesstoken:{valType:\"string\",noBlank:!0,strict:!0},style:{valType:\"any\",values:cJ.styleValuesMapbox.concat(cJ.styleValuesNonMapbox),dflt:cJ.styleValueDflt},center:{lon:{valType:\"number\",dflt:0},lat:{valType:\"number\",dflt:0}},zoom:{valType:\"number\",dflt:1},bearing:{valType:\"number\",dflt:0},pitch:{valType:\"number\",dflt:0},bounds:{west:{valType:\"number\"},east:{valType:\"number\"},south:{valType:\"number\"},north:{valType:\"number\"}},layers:qjt(\"layer\",{visible:{valType:\"boolean\",dflt:!0},sourcetype:{valType:\"enumerated\",values:[\"geojson\",\"vector\",\"raster\",\"image\"],dflt:\"geojson\"},source:{valType:\"any\"},sourcelayer:{valType:\"string\",dflt:\"\"},sourceattribution:{valType:\"string\"},type:{valType:\"enumerated\",values:[\"circle\",\"line\",\"fill\",\"symbol\",\"raster\"],dflt:\"circle\"},coordinates:{valType:\"any\"},below:{valType:\"string\"},color:{valType:\"color\",dflt:WVe},opacity:{valType:\"number\",min:0,max:1,dflt:1},minzoom:{valType:\"number\",min:0,max:24,dflt:0},maxzoom:{valType:\"number\",min:0,max:24,dflt:24},circle:{radius:{valType:\"number\",dflt:15}},line:{width:{valType:\"number\",dflt:2},dash:{valType:\"data_array\"}},fill:{outlinecolor:{valType:\"color\",dflt:WVe}},symbol:{icon:{valType:\"string\",dflt:\"marker\"},iconsize:{valType:\"number\",dflt:10},text:{valType:\"string\",dflt:\"\"},placement:{valType:\"enumerated\",values:[\"point\",\"line\",\"line-center\"],dflt:\"point\"},textfont:XVe,textposition:jVe.extendFlat({},Fjt,{arrayOk:!1})}})},\"plot\",\"from-root\");Ojt.uirevision={valType:\"any\",editType:\"none\"}});var 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strict\";var{hovertemplateAttrs:Bjt,texttemplateAttrs:Njt,templatefallbackAttrs:YVe}=Pl(),Ujt=Pg(),$k=rw(),L5=pf(),KVe=Jk(),Vjt=Vl(),Gjt=Tu(),pw=Ao().extendFlat,Hjt=mc().overrideAll,jjt=Jk(),JVe=$k.line,P5=$k.marker;$Ve.exports=Hjt({lon:$k.lon,lat:$k.lat,cluster:{enabled:{valType:\"boolean\"},maxzoom:pw({},jjt.layers.maxzoom,{}),step:{valType:\"number\",arrayOk:!0,dflt:-1,min:-1},size:{valType:\"number\",arrayOk:!0,dflt:20,min:0},color:{valType:\"color\",arrayOk:!0},opacity:pw({},P5.opacity,{dflt:1})},mode:pw({},L5.mode,{dflt:\"markers\"}),text:pw({},L5.text,{}),texttemplate:Njt({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),texttemplatefallback:YVe({editType:\"plot\"}),hovertext:pw({},L5.hovertext,{}),line:{color:JVe.color,width:JVe.width},connectgaps:L5.connectgaps,marker:pw({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:P5.opacity,size:P5.size,sizeref:P5.sizeref,sizemin:P5.sizemin,sizemode:P5.sizemode},Gjt(\"marker\")),fill:$k.fill,fillcolor:Ujt(),textfont:KVe.layers.symbol.textfont,textposition:KVe.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:L5.selected.marker},unselected:{marker:L5.unselected.marker},hoverinfo:pw({},Vjt.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:Bjt(),hovertemplatefallback:YVe()},\"calc\",\"nested\")});var fJ=ye((Jxr,QVe)=>{\"use strict\";var Wjt=[\"Metropolis Black Italic\",\"Metropolis Black\",\"Metropolis Bold Italic\",\"Metropolis Bold\",\"Metropolis Extra Bold Italic\",\"Metropolis Extra Bold\",\"Metropolis Extra Light Italic\",\"Metropolis Extra Light\",\"Metropolis Light Italic\",\"Metropolis Light\",\"Metropolis Medium Italic\",\"Metropolis Medium\",\"Metropolis Regular Italic\",\"Metropolis Regular\",\"Metropolis Semi Bold Italic\",\"Metropolis Semi Bold\",\"Metropolis Thin Italic\",\"Metropolis Thin\",\"Open Sans Bold Italic\",\"Open Sans Bold\",\"Open Sans Extrabold Italic\",\"Open Sans Extrabold\",\"Open Sans Italic\",\"Open Sans Light Italic\",\"Open Sans Light\",\"Open Sans Regular\",\"Open Sans Semibold Italic\",\"Open Sans Semibold\",\"Klokantech Noto Sans Bold\",\"Klokantech Noto Sans CJK Bold\",\"Klokantech Noto Sans CJK Regular\",\"Klokantech Noto Sans Italic\",\"Klokantech Noto Sans Regular\"];QVe.exports={isSupportedFont:function(e){return Wjt.indexOf(e)!==-1}}});var rGe=ye(($xr,tGe)=>{\"use strict\";var Qk=Pr(),hJ=Ru(),Xjt=$p(),Zjt=D0(),Yjt=F0(),Kjt=Fg(),eGe=e7(),Jjt=fJ().isSupportedFont;tGe.exports=function(t,r,n,i){function a(p,k){return Qk.coerce(t,r,eGe,p,k)}function o(p,k){return Qk.coerce2(t,r,eGe,p,k)}var s=$jt(t,r,a);if(!s){r.visible=!1;return}if(a(\"text\"),a(\"texttemplate\"),a(\"texttemplatefallback\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),a(\"mode\"),a(\"below\"),hJ.hasMarkers(r)){Xjt(t,r,n,i,a,{noLine:!0,noAngle:!0}),a(\"marker.allowoverlap\"),a(\"marker.angle\");var l=r.marker;l.symbol!==\"circle\"&&(Qk.isArrayOrTypedArray(l.size)&&(l.size=l.size[0]),Qk.isArrayOrTypedArray(l.color)&&(l.color=l.color[0]))}hJ.hasLines(r)&&(Zjt(t,r,n,i,a,{noDash:!0}),a(\"connectgaps\"));var u=o(\"cluster.maxzoom\"),c=o(\"cluster.step\"),f=o(\"cluster.color\",r.marker&&r.marker.color||n),h=o(\"cluster.size\"),d=o(\"cluster.opacity\"),v=u!==!1||c!==!1||f!==!1||h!==!1||d!==!1,m=a(\"cluster.enabled\",v);if(m||hJ.hasText(r)){var b=i.font.family;Yjt(t,r,i,a,{noSelect:!0,noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0,font:{family:Jjt(b)?b:\"Open Sans Regular\",weight:i.font.weight,style:i.font.style,size:i.font.size,color:i.font.color}})}a(\"fill\"),r.fill!==\"none\"&&Kjt(t,r,n,a),Qk.coerceSelectionMarkerOpacity(r,a)};function $jt(e,t,r){var n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length);return t._length=a,a}});var dJ=ye((Qxr,nGe)=>{\"use strict\";var iGe=ho();nGe.exports=function(t,r,n){var i={},a=n[r.subplot]._subplot,o=a.mockAxis,s=t.lonlat;return i.lonLabel=iGe.tickText(o,o.c2l(s[0]),!0).text,i.latLabel=iGe.tickText(o,o.c2l(s[1]),!0).text,i}});var vJ=ye((ebr,oGe)=>{\"use strict\";var aGe=Pr();oGe.exports=function(t,r){var n=t.split(\" \"),i=n[0],a=n[1],o=aGe.isArrayOrTypedArray(r)?aGe.mean(r):r,s=.5+o/100,l=1.5+o/100,u=[\"\",\"\"],c=[0,0];switch(i){case\"top\":u[0]=\"top\",c[1]=-l;break;case\"bottom\":u[0]=\"bottom\",c[1]=l;break}switch(a){case\"left\":u[1]=\"right\",c[0]=-s;break;case\"right\":u[1]=\"left\",c[0]=s;break}var f;return u[0]&&u[1]?f=u.join(\"-\"):u[0]?f=u[0]:u[1]?f=u[1]:f=\"center\",{anchor:f,offset:c}}});var hGe=ye((tbr,fGe)=>{\"use strict\";var uGe=Eo(),ov=Pr(),Qjt=fs().BADNUM,r7=hx(),sGe=tc(),eWt=So(),tWt=U3(),i7=Ru(),rWt=fJ().isSupportedFont,iWt=vJ(),nWt=ip().appendArrayPointValue,aWt=Zl().NEWLINES,oWt=Zl().BR_TAG_ALL;fGe.exports=function(t,r){var n=r[0].trace,i=n.visible===!0&&n._length!==0,a=n.fill!==\"none\",o=i7.hasLines(n),s=i7.hasMarkers(n),l=i7.hasText(n),u=s&&n.marker.symbol===\"circle\",c=s&&n.marker.symbol!==\"circle\",f=n.cluster&&n.cluster.enabled,h=t7(\"fill\"),d=t7(\"line\"),v=t7(\"circle\"),m=t7(\"symbol\"),b={fill:h,line:d,circle:v,symbol:m};if(!i)return b;var p;if((a||o)&&(p=r7.calcTraceToLineCoords(r)),a&&(h.geojson=r7.makePolygon(p),h.layout.visibility=\"visible\",ov.extendFlat(h.paint,{\"fill-color\":n.fillcolor})),o&&(d.geojson=r7.makeLine(p),d.layout.visibility=\"visible\",ov.extendFlat(d.paint,{\"line-width\":n.line.width,\"line-color\":n.line.color,\"line-opacity\":n.opacity})),u){var k=sWt(r);v.geojson=k.geojson,v.layout.visibility=\"visible\",f&&(v.filter=[\"!\",[\"has\",\"point_count\"]],b.cluster={type:\"circle\",filter:[\"has\",\"point_count\"],layout:{visibility:\"visible\"},paint:{\"circle-color\":gJ(n.cluster.color,n.cluster.step),\"circle-radius\":gJ(n.cluster.size,n.cluster.step),\"circle-opacity\":gJ(n.cluster.opacity,n.cluster.step)}},b.clusterCount={type:\"symbol\",filter:[\"has\",\"point_count\"],paint:{},layout:{\"text-field\":\"{point_count_abbreviated}\",\"text-font\":lGe(n),\"text-size\":12}}),ov.extendFlat(v.paint,{\"circle-color\":k.mcc,\"circle-radius\":k.mrc,\"circle-opacity\":k.mo})}if(u&&f&&(v.filter=[\"!\",[\"has\",\"point_count\"]]),(c||l)&&(m.geojson=lWt(r,t),ov.extendFlat(m.layout,{visibility:\"visible\",\"icon-image\":\"{symbol}-15\",\"text-field\":\"{text}\"}),c&&(ov.extendFlat(m.layout,{\"icon-size\":n.marker.size/10}),\"angle\"in n.marker&&n.marker.angle!==\"auto\"&&ov.extendFlat(m.layout,{\"icon-rotate\":{type:\"identity\",property:\"angle\"},\"icon-rotation-alignment\":\"map\"}),m.layout[\"icon-allow-overlap\"]=n.marker.allowoverlap,ov.extendFlat(m.paint,{\"icon-opacity\":n.opacity*n.marker.opacity,\"icon-color\":n.marker.color})),l)){var M=(n.marker||{}).size,T=iWt(n.textposition,M);ov.extendFlat(m.layout,{\"text-size\":n.textfont.size,\"text-anchor\":T.anchor,\"text-offset\":T.offset,\"text-font\":lGe(n)}),ov.extendFlat(m.paint,{\"text-color\":n.textfont.color,\"text-opacity\":n.opacity})}return b};function t7(e){return{type:e,geojson:r7.makeBlank(),layout:{visibility:\"none\"},filter:null,paint:{}}}function sWt(e){var t=e[0].trace,r=t.marker,n=t.selectedpoints,i=ov.isArrayOrTypedArray(r.color),a=ov.isArrayOrTypedArray(r.size),o=ov.isArrayOrTypedArray(r.opacity),s;function l(M){return t.opacity*M}function u(M){return M/2}var c;i&&(sGe.hasColorscale(t,\"marker\")?c=sGe.makeColorScaleFuncFromTrace(r):c=ov.identity);var f;a&&(f=tWt(t));var h;o&&(h=function(M){var T=uGe(M)?+ov.constrain(M,0,1):0;return l(T)});var d=[];for(s=0;s<e.length;s++){var v=e[s],m=v.lonlat;if(!cGe(m)){var b={};c&&(b.mcc=v.mcc=c(v.mc)),f&&(b.mrc=v.mrc=f(v.ms)),h&&(b.mo=h(v.mo)),n&&(b.selected=v.selected||0),d.push({type:\"Feature\",id:s+1,geometry:{type:\"Point\",coordinates:m},properties:b})}}var p;if(n)for(p=eWt.makeSelectedPointStyleFns(t),s=0;s<d.length;s++){var k=d[s].properties;p.selectedOpacityFn&&(k.mo=l(p.selectedOpacityFn(k))),p.selectedColorFn&&(k.mcc=p.selectedColorFn(k)),p.selectedSizeFn&&(k.mrc=p.selectedSizeFn(k))}return{geojson:{type:\"FeatureCollection\",features:d},mcc:i||p&&p.selectedColorFn?{type:\"identity\",property:\"mcc\"}:r.color,mrc:a||p&&p.selectedSizeFn?{type:\"identity\",property:\"mrc\"}:u(r.size),mo:o||p&&p.selectedOpacityFn?{type:\"identity\",property:\"mo\"}:l(r.opacity)}}function lWt(e,t){for(var r=t._fullLayout,n=e[0].trace,i=n.marker||{},a=i.symbol,o=i.angle,s=a!==\"circle\"?pJ(a):n7,l=o!==\"auto\"?pJ(o,!0):n7,u=i7.hasText(n)?pJ(n.text):n7,c=[],f=0;f<e.length;f++){var h=e[f];if(!cGe(h.lonlat)){var d=n.texttemplate,v;if(d){var m=Array.isArray(d)?d[f]||\"\":d,b=n._module.formatLabels(h,n,r),p={};nWt(p,n,h.i),v=ov.texttemplateString({data:[p,h,n._meta],fallback:n.texttemplatefallback,labels:b,locale:r._d3locale,template:m})}else v=u(f);v&&(v=v.replace(aWt,\"\").replace(oWt,`\n",
-       "`)),c.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:h.lonlat},properties:{symbol:s(f),angle:l(f),text:v}})}}return{type:\"FeatureCollection\",features:c}}function pJ(e,t){return ov.isArrayOrTypedArray(e)?t?function(r){return uGe(e[r])?+e[r]:0}:function(r){return e[r]}:e?function(){return e}:n7}function n7(){return\"\"}function cGe(e){return e[0]===Qjt}function gJ(e,t){var r;if(ov.isArrayOrTypedArray(e)&&ov.isArrayOrTypedArray(t)){r=[\"step\",[\"get\",\"point_count\"],e[0]];for(var n=1;n<e.length;n++)r.push(t[n-1],e[n])}else r=e;return r}function lGe(e){var t=e.textfont,r=t.family,n=t.style,i=t.weight,a=r.split(\" \"),o=a[a.length-1]===\"Italic\";o&&a.pop(),o=o||n===\"italic\";var s=a.join(\" \");i===\"bold\"&&a.indexOf(\"Bold\")===-1?s+=\" Bold\":i<=1e3&&(a[0]===\"Metropolis\"?(s=\"Metropolis\",i>850?s+=\" Black\":i>750?s+=\" Extra Bold\":i>650?s+=\" Bold\":i>550?s+=\" Semi Bold\":i>450?s+=\" Medium\":i>350?s+=\" Regular\":i>250?s+=\" Light\":i>150?s+=\" Extra Light\":s+=\" Thin\"):a.slice(0,2).join(\" \")===\"Open Sans\"?(s=\"Open Sans\",i>750?s+=\" Extrabold\":i>650?s+=\" Bold\":i>550?s+=\" Semibold\":i>350?s+=\" Regular\":s+=\" Light\"):a.slice(0,3).join(\" \")===\"Klokantech Noto Sans\"&&(s=\"Klokantech Noto Sans\",a[3]===\"CJK\"&&(s+=\" CJK\"),s+=i>500?\" Bold\":\" Regular\")),o&&(s+=\" Italic\"),s===\"Open Sans Regular Italic\"?s=\"Open Sans Italic\":s===\"Open Sans Regular Bold\"?s=\"Open Sans Bold\":s===\"Open Sans Regular Bold Italic\"?s=\"Open Sans Bold Italic\":s===\"Klokantech Noto Sans Regular Italic\"&&(s=\"Klokantech Noto Sans Italic\"),rWt(s)||(s=r);var l=s.split(\", \");return l}});var gGe=ye((rbr,pGe)=>{\"use strict\";var uWt=Pr(),dGe=hGe(),I5=y1().traceLayerPrefix,ng={cluster:[\"cluster\",\"clusterCount\",\"circle\"],nonCluster:[\"fill\",\"line\",\"circle\",\"symbol\"]};function vGe(e,t,r,n){this.type=\"scattermapbox\",this.subplot=e,this.uid=t,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+t+\"-fill\",line:\"source-\"+t+\"-line\",circle:\"source-\"+t+\"-circle\",symbol:\"source-\"+t+\"-symbol\",cluster:\"source-\"+t+\"-circle\",clusterCount:\"source-\"+t+\"-circle\"},this.layerIds={fill:I5+t+\"-fill\",line:I5+t+\"-line\",circle:I5+t+\"-circle\",symbol:I5+t+\"-symbol\",cluster:I5+t+\"-cluster\",clusterCount:I5+t+\"-cluster-count\"},this.below=null}var eC=vGe.prototype;eC.addSource=function(e,t,r){var n={type:\"geojson\",data:t.geojson};r&&r.enabled&&uWt.extendFlat(n,{cluster:!0,clusterMaxZoom:r.maxzoom});var i=this.subplot.map.getSource(this.sourceIds[e]);i?i.setData(t.geojson):this.subplot.map.addSource(this.sourceIds[e],n)};eC.setSourceData=function(e,t){this.subplot.map.getSource(this.sourceIds[e]).setData(t.geojson)};eC.addLayer=function(e,t,r){var n={type:t.type,id:this.layerIds[e],source:this.sourceIds[e],layout:t.layout,paint:t.paint};t.filter&&(n.filter=t.filter);for(var i=this.layerIds[e],a,o=this.subplot.getMapLayers(),s=0;s<o.length;s++)if(o[s].id===i){a=!0;break}a?(this.subplot.setOptions(i,\"setLayoutProperty\",n.layout),n.layout.visibility===\"visible\"&&this.subplot.setOptions(i,\"setPaintProperty\",n.paint)):this.subplot.addLayer(n,r)};eC.update=function(t){var r=t[0].trace,n=this.subplot,i=n.map,a=dGe(n.gd,t),o=n.belowLookup[\"trace-\"+this.uid],s=!!(r.cluster&&r.cluster.enabled),l=!!this.clusterEnabled,u=this;function c(M){M||u.addSource(\"circle\",a.circle,r.cluster);for(var T=ng.cluster,L=0;L<T.length;L++){var x=T[L],C=a[x];u.addLayer(x,C,o)}}function f(M){for(var T=ng.cluster,L=T.length-1;L>=0;L--){var x=T[L];i.removeLayer(u.layerIds[x])}M||i.removeSource(u.sourceIds.circle)}function h(M){for(var T=ng.nonCluster,L=0;L<T.length;L++){var x=T[L],C=a[x];M||u.addSource(x,C),u.addLayer(x,C,o)}}function d(M){for(var T=ng.nonCluster,L=T.length-1;L>=0;L--){var x=T[L];i.removeLayer(u.layerIds[x]),M||i.removeSource(u.sourceIds[x])}}function v(M){l?f(M):d(M)}function m(M){s?c(M):h(M)}function b(){for(var M=s?ng.cluster:ng.nonCluster,T=0;T<M.length;T++){var L=M[T],x=a[L];x&&(n.setOptions(u.layerIds[L],\"setLayoutProperty\",x.layout),x.layout.visibility===\"visible\"&&(L!==\"cluster\"&&u.setSourceData(L,x),n.setOptions(u.layerIds[L],\"setPaintProperty\",x.paint)))}}var p=this.isHidden,k=r.visible!==!0;k?p||v():p?k||m():l!==s?(v(),m()):(this.below!==o&&(v(!0),m(!0)),b()),this.clusterEnabled=s,this.isHidden=k,this.below=o,t[0].trace._glTrace=this};eC.dispose=function(){for(var t=this.subplot.map,r=this.clusterEnabled?ng.cluster:ng.nonCluster,n=r.length-1;n>=0;n--){var i=r[n];t.removeLayer(this.layerIds[i]),t.removeSource(this.sourceIds[i])}};pGe.exports=function(t,r){var n=r[0].trace,i=n.cluster&&n.cluster.enabled,a=n.visible!==!0,o=new vGe(t,n.uid,i,a),s=dGe(t.gd,r),l=o.below=t.belowLookup[\"trace-\"+n.uid],u,c,f;if(i)for(o.addSource(\"circle\",s.circle,n.cluster),u=0;u<ng.cluster.length;u++)c=ng.cluster[u],f=s[c],o.addLayer(c,f,l);else for(u=0;u<ng.nonCluster.length;u++)c=ng.nonCluster[u],f=s[c],o.addSource(c,f,n.cluster),o.addLayer(c,f,l);return r[0].trace._glTrace=o,o}});var a7=ye((ibr,yGe)=>{\"use strict\";var cWt=ef(),mJ=Pr(),fWt=xT(),hWt=mJ.fillText,dWt=fs().BADNUM,vWt=y1().traceLayerPrefix;function pWt(e,t,r){var n=e.cd,i=n[0].trace,a=e.xa,o=e.ya,s=e.subplot,l=[],u=vWt+i.uid+\"-circle\",c=i.cluster&&i.cluster.enabled;if(c){var f=s.map.queryRenderedFeatures(null,{layers:[u]});l=f.map(function(S){return S.id})}var h=t>=0?Math.floor((t+180)/360):Math.ceil((t-180)/360),d=h*360,v=t-d;function m(S){var g=S.lonlat;if(g[0]===dWt||c&&l.indexOf(S.i+1)===-1)return 1/0;var P=mJ.modHalf(g[0],360),E=g[1],z=s.project([P,E]),q=z.x-a.c2p([v,E]),V=z.y-o.c2p([P,r]),G=Math.max(3,S.mrc||0);return Math.max(Math.sqrt(q*q+V*V)-G,1-3/G)}if(cWt.getClosest(n,m,e),e.index!==!1){var b=n[e.index],p=b.lonlat,k=[mJ.modHalf(p[0],360)+d,p[1]],M=a.c2p(k),T=o.c2p(k),L=b.mrc||1;e.x0=M-L,e.x1=M+L,e.y0=T-L,e.y1=T+L;var x={};x[i.subplot]={_subplot:s};var C=i._module.formatLabels(b,i,x);return e.lonLabel=C.lonLabel,e.latLabel=C.latLabel,e.color=fWt(i,b),e.extraText=mGe(i,b,n[0].t.labels),e.hovertemplate=i.hovertemplate,[e]}}function mGe(e,t,r){if(e.hovertemplate)return;var n=t.hi||e.hoverinfo,i=n.split(\"+\"),a=i.indexOf(\"all\")!==-1,o=i.indexOf(\"lon\")!==-1,s=i.indexOf(\"lat\")!==-1,l=t.lonlat,u=[];function c(f){return f+\"\\xB0\"}return a||o&&s?u.push(\"(\"+c(l[1])+\", \"+c(l[0])+\")\"):o?u.push(r.lon+c(l[0])):s&&u.push(r.lat+c(l[1])),(a||i.indexOf(\"text\")!==-1)&&hWt(t,e,u),u.join(\"<br>\")}yGe.exports={hoverPoints:pWt,getExtraText:mGe}});var xGe=ye((nbr,_Ge)=>{\"use strict\";_Ge.exports=function(t,r){return t.lon=r.lon,t.lat=r.lat,t}});var wGe=ye((abr,bGe)=>{\"use strict\";var gWt=Pr(),mWt=Ru(),yWt=fs().BADNUM;bGe.exports=function(t,r){var n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].trace,l;if(!mWt.hasMarkers(s))return[];if(r===!1)for(l=0;l<n.length;l++)n[l].selected=0;else for(l=0;l<n.length;l++){var u=n[l],c=u.lonlat;if(c[0]!==yWt){var f=[gWt.modHalf(c[0],360),c[1]],h=[i.c2p(f),a.c2p(f)];r.contains(h,null,l,t)?(o.push({pointNumber:l,lon:c[0],lat:c[1]}),u.selected=1):u.selected=0}}return o}});var xJ=ye((yJ,_J)=>{(function(e,t){typeof yJ==\"object\"&&typeof _J!=\"undefined\"?_J.exports=t():(e=e||self,e.mapboxgl=t())})(yJ,(function(){\"use strict\";var e,t,r;function n(i,a){if(!e)e=a;else if(!t)t=a;else{var o=\"var sharedChunk = {}; (\"+e+\")(sharedChunk); (\"+t+\")(sharedChunk);\",s={};e(s),r=a(s),typeof window!=\"undefined\"&&(r.workerUrl=window.URL.createObjectURL(new Blob([o],{type:\"text/javascript\"})))}}return n([\"exports\"],function(i){\"use strict\";function a(y,_){return _={exports:{}},y(_,_.exports),_.exports}var o=\"1.13.4\",s=l;function l(y,_,I,U){this.cx=3*y,this.bx=3*(I-y)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*_,this.by=3*(U-_)-this.cy,this.ay=1-this.cy-this.by,this.p1x=y,this.p1y=U,this.p2x=I,this.p2y=U}l.prototype.sampleCurveX=function(y){return((this.ax*y+this.bx)*y+this.cx)*y},l.prototype.sampleCurveY=function(y){return((this.ay*y+this.by)*y+this.cy)*y},l.prototype.sampleCurveDerivativeX=function(y){return(3*this.ax*y+2*this.bx)*y+this.cx},l.prototype.solveCurveX=function(y,_){typeof _==\"undefined\"&&(_=1e-6);var I,U,$,oe,he;for($=y,he=0;he<8;he++){if(oe=this.sampleCurveX($)-y,Math.abs(oe)<_)return $;var De=this.sampleCurveDerivativeX($);if(Math.abs(De)<1e-6)break;$=$-oe/De}if(I=0,U=1,$=y,$<I)return I;if($>U)return U;for(;I<U;){if(oe=this.sampleCurveX($),Math.abs(oe-y)<_)return $;y>oe?I=$:U=$,$=(U-I)*.5+I}return $},l.prototype.solve=function(y,_){return this.sampleCurveY(this.solveCurveX(y,_))};var u=c;function c(y,_){this.x=y,this.y=_}c.prototype={clone:function(){return new 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I=this.index.evaluate(_),U=this.input.evaluate(_);if(I<0)throw new ps(\"Array index out of bounds: \"+I+\" < 0.\");if(I>=U.length)throw new ps(\"Array index out of bounds: \"+I+\" > \"+(U.length-1)+\".\");if(I!==Math.floor(I))throw new ps(\"Array index must be an integer, but found \"+I+\" instead.\");return U[I]},Tc.prototype.eachChild=function(_){_(this.index),_(this.input)},Tc.prototype.outputDefined=function(){return!1},Tc.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var Tl=function(_,I){this.type=Oo,this.needle=_,this.haystack=I};Tl.parse=function(_,I){if(_.length!==3)return I.error(\"Expected 2 arguments, but found \"+(_.length-1)+\" instead.\");var U=I.parse(_[1],1,Do),$=I.parse(_[2],2,Do);return!U||!$?null:Cf(U.type,[Oo,qo,oa,ac,Do])?new Tl(U,$):I.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+qs(U.type)+\" instead\")},Tl.prototype.evaluate=function(_){var I=this.needle.evaluate(_),U=this.haystack.evaluate(_);if(!U)return!1;if(!sc(I,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ps(\"Expected first argument to be of type boolean, string, number or null, but found \"+qs(Es(I))+\" instead.\");if(!sc(U,[\"string\",\"array\"]))throw new ps(\"Expected second argument to be of type array or string, but found \"+qs(Es(U))+\" instead.\");return U.indexOf(I)>=0},Tl.prototype.eachChild=function(_){_(this.needle),_(this.haystack)},Tl.prototype.outputDefined=function(){return!0},Tl.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var vu=function(_,I,U){this.type=oa,this.needle=_,this.haystack=I,this.fromIndex=U};vu.parse=function(_,I){if(_.length<=2||_.length>=5)return I.error(\"Expected 3 or 4 arguments, but found \"+(_.length-1)+\" instead.\");var U=I.parse(_[1],1,Do),$=I.parse(_[2],2,Do);if(!U||!$)return null;if(!Cf(U.type,[Oo,qo,oa,ac,Do]))return I.error(\"Expected first argument to be of 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ji=[],wi=ni.value,bn=ni.key;if(no(wi)!==\"array\")return[new _a(bn,wi,\"array expected, \"+no(wi)+\" found\")];if(wi.length!==2)return[new _a(bn,wi,\"array length 2 expected, length \"+wi.length+\" found\")];if(yt){if(no(wi[0])!==\"object\")return[new _a(bn,wi,\"object expected, \"+no(wi[0])+\" found\")];if(wi[0].zoom===void 0)return[new _a(bn,wi,\"object stop key must have zoom\")];if(wi[0].value===void 0)return[new _a(bn,wi,\"object stop key must have value\")];if(he&&he>xo(wi[0].zoom))return[new _a(bn,wi[0].zoom,\"stop zoom values must appear in ascending order\")];xo(wi[0].zoom)!==he&&(he=xo(wi[0].zoom),oe=void 0,$={}),ji=ji.concat(le({key:bn+\"[0]\",value:wi[0],valueSpec:{zoom:{}},style:ni.style,styleSpec:ni.styleSpec,objectElementValidators:{zoom:Ze,value:hr}}))}else ji=ji.concat(hr({key:bn+\"[0]\",value:wi[0],valueSpec:{},style:ni.style,styleSpec:ni.styleSpec},wi));return Va(Kl(wi[1]))?ji.concat([new _a(bn+\"[1]\",wi[1],\"expressions are not allowed in function stops.\")]):ji.concat(Qa({key:bn+\"[1]\",value:wi[1],valueSpec:_,style:ni.style,styleSpec:ni.styleSpec}))}function hr(ni,ji){var wi=no(ni.value),bn=xo(ni.value),rn=ni.value!==null?ni.value:ji;if(!U)U=wi;else if(wi!==U)return[new _a(ni.key,rn,wi+\" stop domain type must match previous stop domain type \"+U)];if(wi!==\"number\"&&wi!==\"string\"&&wi!==\"boolean\")return[new _a(ni.key,rn,\"stop domain value must be a number, string, or boolean\")];if(wi!==\"number\"&&I!==\"categorical\"){var xn=\"number expected, \"+wi+\" found\";return Ws(_)&&I===void 0&&(xn+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new _a(ni.key,rn,xn)]}return I===\"categorical\"&&wi===\"number\"&&(!isFinite(bn)||Math.floor(bn)!==bn)?[new _a(ni.key,rn,\"integer expected, found \"+bn)]:I!==\"categorical\"&&wi===\"number\"&&oe!==void 0&&bn<oe?[new _a(ni.key,rn,\"stop domain values must appear in ascending order\")]:(oe=bn,I===\"categorical\"&&bn in $?[new _a(ni.key,rn,\"stop domain values must be unique\")]:($[bn]=!0,[]))}function Rr(ni){return Qa({key:ni.key,value:ni.value,valueSpec:_,style:ni.style,styleSpec:ni.styleSpec})}}function Mt(y){var _=(y.expressionContext===\"property\"?Ku:oo)(Kl(y.value),y.valueSpec);if(_.result===\"error\")return _.value.map(function(U){return new _a(\"\"+y.key+U.key,y.value,U.message)});var I=_.value.expression||_.value._styleExpression.expression;if(y.expressionContext===\"property\"&&y.propertyKey===\"text-font\"&&!I.outputDefined())return[new _a(y.key,y.value,'Invalid data expression for \"'+y.propertyKey+'\". 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U!==\"string\"?[new _a(_,I,\"color expected, \"+U+\" found\")]:Lf(I)===null?[new _a(_,I,'color expected, \"'+I+'\" found')]:[]}function yr(y){var _=y.key,I=y.value,U=y.valueSpec,$=[];return Array.isArray(U.values)?U.values.indexOf(xo(I))===-1&&$.push(new _a(_,I,\"expected one of [\"+U.values.join(\", \")+\"], \"+JSON.stringify(I)+\" found\")):Object.keys(U.values).indexOf(xo(I))===-1&&$.push(new _a(_,I,\"expected one of [\"+Object.keys(U.values).join(\", \")+\"], \"+JSON.stringify(I)+\" found\")),$}function Cr(y){if(y===!0||y===!1)return!0;if(!Array.isArray(y)||y.length===0)return!1;switch(y[0]){case\"has\":return y.length>=2&&y[1]!==\"$id\"&&y[1]!==\"$type\";case\"in\":return y.length>=3&&(typeof y[1]!=\"string\"||Array.isArray(y[2]));case\"!in\":case\"!has\":case\"none\":return!1;case\"==\":case\"!=\":case\">\":case\">=\":case\"<\":case\"<=\":return y.length!==3||Array.isArray(y[1])||Array.isArray(y[2]);case\"any\":case\"all\":for(var _=0,I=y.slice(1);_<I.length;_+=1){var U=I[_];if(!Cr(U)&&typeof U!=\"boolean\")return!1}return!0;default:return!0}}var ve={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function xe(y){if(y==null)return{filter:function(){return!0},needGeometry:!1};Cr(y)||(y=Qe(y));var _=oo(y,ve);if(_.result===\"error\")throw new Error(_.value.map(function(U){return U.key+\": \"+U.message}).join(\", \"));var I=ze(y);return{filter:function(U,$,oe){return _.value.evaluate(U,$,{},oe)},needGeometry:I}}function Ie(y,_){return y<_?-1:y>_?1:0}function ze(y){if(!Array.isArray(y))return!1;if(y[0]===\"within\")return!0;for(var _=1;_<y.length;_++)if(ze(y[_]))return!0;return!1}function Qe(y){if(!y)return!0;var _=y[0];if(y.length<=1)return _!==\"any\";var I=_===\"==\"?Xe(y[1],y[2],\"==\"):_===\"!=\"?nr(Xe(y[1],y[2],\"==\")):_===\"<\"||_===\">\"||_===\"<=\"||_===\">=\"?Xe(y[1],y[2],_):_===\"any\"?nt(y.slice(1)):_===\"all\"?[\"all\"].concat(y.slice(1).map(Qe)):_===\"none\"?[\"all\"].concat(y.slice(1).map(Qe).map(nr)):_===\"in\"?qt(y[1],y.slice(2)):_===\"!in\"?nr(qt(y[1],y.slice(2))):_===\"has\"?Wt(y[1]):_===\"!has\"?nr(Wt(y[1])):_===\"within\"?y:!0;return I}function Xe(y,_,I){switch(y){case\"$type\":return[\"filter-type-\"+I,_];case\"$id\":return[\"filter-id-\"+I,_];default:return[\"filter-\"+I,y,_]}}function nt(y){return[\"any\"].concat(y.map(Qe))}function qt(y,_){if(_.length===0)return!1;switch(y){case\"$type\":return[\"filter-type-in\",[\"literal\",_]];case\"$id\":return[\"filter-id-in\",[\"literal\",_]];default:return _.length>200&&!_.some(function(I){return typeof I!=typeof _[0]})?[\"filter-in-large\",y,[\"literal\",_.sort(Ie)]]:[\"filter-in-small\",y,[\"literal\",_]]}}function 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I=y.key,U=y.style,$=y.styleSpec,oe=y.value,he=y.objectKey,De=$[_+\"_\"+y.layerType];if(!De)return[];var it=he.match(/^(.*)-transition$/);if(_===\"paint\"&&it&&De[it[1]]&&De[it[1]].transition)return Qa({key:I,value:oe,valueSpec:$.transition,style:U,styleSpec:$});var yt=y.valueSpec||De[he];if(!yt)return[new _a(I,oe,'unknown property \"'+he+'\"')];var Lt;if(no(oe)===\"string\"&&Ws(yt)&&!yt.tokens&&(Lt=/^{([^}]+)}$/.exec(oe)))return[new _a(I,oe,'\"'+he+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(Lt[1])+\" }`.\")];var sr=[];return y.layerType===\"symbol\"&&(he===\"text-field\"&&U&&!U.glyphs&&sr.push(new _a(I,oe,'use of \"text-field\" requires a style \"glyphs\" property')),he===\"text-font\"&&ys(Kl(oe))&&xo(oe.type)===\"identity\"&&sr.push(new _a(I,oe,'\"text-font\" does not support identity 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if(he!==\"background\")if(!I.source)_.push(new _a(U,I,'missing required property \"source\"'));else{var gr=$.sources&&$.sources[I.source],hr=gr&&xo(gr.type);gr?hr===\"vector\"&&he===\"raster\"?_.push(new _a(U,I.source,'layer \"'+I.id+'\" requires a raster source')):hr===\"raster\"&&he!==\"raster\"?_.push(new _a(U,I.source,'layer \"'+I.id+'\" requires a vector source')):hr===\"vector\"&&!I[\"source-layer\"]?_.push(new _a(U,I,'layer \"'+I.id+'\" must specify a \"source-layer\"')):hr===\"raster-dem\"&&he!==\"hillshade\"?_.push(new _a(U,I.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):he===\"line\"&&I.paint&&I.paint[\"line-gradient\"]&&(hr!==\"geojson\"||!gr.lineMetrics)&&_.push(new _a(U,I,'layer \"'+I.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):_.push(new _a(U,I.source,'source \"'+I.source+'\" not found'))}return 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it=oe[De],yt=oe[De+1],Lt=he[De],sr=he[De+1],gr=[it,yt,sr,Lt,it];so(I,gr)&&(U=Math.min(U,SS(I,gr)))}return U===1/0?!1:U}function MS(y,_,I,U){for(var $=[],oe=[],he=U[8]*_,De=U[9]*_,it=U[10]*_,yt=U[11]*_,Lt=U[8]*I,sr=U[9]*I,gr=U[10]*I,hr=U[11]*I,Rr=0,ni=y;Rr<ni.length;Rr+=1){for(var ji=ni[Rr],wi=[],bn=[],rn=0,xn=ji;rn<xn.length;rn+=1){var In=xn[rn],Xn=In.x,ga=In.y,ha=U[0]*Xn+U[4]*ga+U[12],eo=U[1]*Xn+U[5]*ga+U[13],qa=U[2]*Xn+U[6]*ga+U[14],Za=U[3]*Xn+U[7]*ga+U[15],Jo=ha+he,to=eo+De,ao=qa+it,_s=Za+yt,jo=ha+Lt,kl=eo+sr,Iu=qa+gr,Cl=Za+hr,Ll=new u(Jo/_s,to/_s);Ll.z=ao/_s,wi.push(Ll);var yl=new u(jo/Cl,kl/Cl);yl.z=Iu/Cl,bn.push(yl)}$.push(wi),oe.push(bn)}return[$,oe]}function fq(y,_,I,U){for(var $=[],oe=0,he=y;oe<he.length;oe+=1){var De=he[oe],it=[De.x,De.y,U,1];my(it,it,_),$.push(new u(it[0]/it[3],it[1]/it[3]))}return $}var e6=Wi([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4),t6=e6.members,hq=Wi([{name:\"a_uv_x\",components:1,type:\"Float32\"},{name:\"a_split_index\",components:1,type:\"Float32\"}]),r6=hq.members,ES=mg.VectorTileFeature.types,Qw=63,i6=Math.cos(75/2*(Math.PI/180)),cb=15,kS=20,n6=15,e3=1/2,fb=Math.pow(2,n6-1)/e3,sh=function(_){var I=this;this.zoom=_.zoom,this.overscaling=_.overscaling,this.layers=_.layers,this.layerIds=this.layers.map(function(U){return U.id}),this.index=_.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach(function(U){I.gradients[U.id]={}}),this.layoutVertexArray=new ui,this.layoutVertexArray2=new nn,this.indexArray=new pn,this.programConfigurations=new ci(_.layers,_.zoom),this.segments=new io,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter(function(U){return U.isStateDependent()}).map(function(U){return U.id})};sh.prototype.populate=function(_,I,U){this.hasPattern=Jw(\"line\",this.layers,I);for(var $=this.layers[0].layout.get(\"line-sort-key\"),oe=[],he=0,De=_;he<De.length;he+=1){var it=De[he],yt=it.feature,Lt=it.id,sr=it.index,gr=it.sourceLayerIndex,hr=this.layers[0]._featureFilter.needGeometry,Rr=Ja(yt,hr);if(this.layers[0]._featureFilter.filter(new Un(this.zoom),Rr,U)){var ni=$?$.evaluate(Rr,{},U):void 0,ji={id:Lt,properties:yt.properties,type:yt.type,sourceLayerIndex:gr,index:sr,geometry:hr?Rr.geometry:zn(yt),patterns:{},sortKey:ni};oe.push(ji)}}$&&oe.sort(function(qa,Za){return qa.sortKey-Za.sortKey});for(var wi=0,bn=oe;wi<bn.length;wi+=1){var rn=bn[wi],xn=rn,In=xn.geometry,Xn=xn.index,ga=xn.sourceLayerIndex;if(this.hasPattern){var ha=sb(\"line\",this.layers,rn,this.zoom,I);this.patternFeatures.push(ha)}else this.addFeature(rn,In,Xn,U,{});var eo=_[Xn].feature;I.featureIndex.insert(eo,In,Xn,ga,this.index)}},sh.prototype.update=function(_,I,U){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(_,I,this.stateDependentLayers,U)},sh.prototype.addFeatures=function(_,I,U){for(var $=0,oe=this.patternFeatures;$<oe.length;$+=1){var he=oe[$];this.addFeature(he,he.geometry,he.index,I,U)}},sh.prototype.isEmpty=function(){return this.layoutVertexArray.length===0},sh.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},sh.prototype.upload=function(_){this.uploaded||(this.layoutVertexArray2.length!==0&&(this.layoutVertexBuffer2=_.createVertexBuffer(this.layoutVertexArray2,r6)),this.layoutVertexBuffer=_.createVertexBuffer(this.layoutVertexArray,t6),this.indexBuffer=_.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(_),this.uploaded=!0},sh.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},sh.prototype.lineFeatureClips=function(_){if(_.properties&&_.properties.hasOwnProperty(\"mapbox_clip_start\")&&_.properties.hasOwnProperty(\"mapbox_clip_end\")){var I=+_.properties.mapbox_clip_start,U=+_.properties.mapbox_clip_end;return{start:I,end:U}}},sh.prototype.addFeature=function(_,I,U,$,oe){var he=this.layers[0].layout,De=he.get(\"line-join\").evaluate(_,{}),it=he.get(\"line-cap\"),yt=he.get(\"line-miter-limit\"),Lt=he.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(_);for(var sr=0,gr=I;sr<gr.length;sr+=1){var hr=gr[sr];this.addLine(hr,_,De,it,yt,Lt)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,_,U,oe,$)},sh.prototype.addLine=function(_,I,U,$,oe,he){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(var De=0;De<_.length-1;De++)this.totalDistance+=_[De].dist(_[De+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}for(var it=ES[I.type]===\"Polygon\",yt=_.length;yt>=2&&_[yt-1].equals(_[yt-2]);)yt--;for(var Lt=0;Lt<yt-1&&_[Lt].equals(_[Lt+1]);)Lt++;if(!(yt<(it?3:2))){U===\"bevel\"&&(oe=1.05);var sr=this.overscaling<=16?cb*Ei/(512*this.overscaling):0,gr=this.segments.prepareSegment(yt*10,this.layoutVertexArray,this.indexArray),hr,Rr=void 0,ni=void 0,ji=void 0,wi=void 0;this.e1=this.e2=-1,it&&(hr=_[yt-2],wi=_[Lt].sub(hr)._unit()._perp());for(var bn=Lt;bn<yt;bn++)if(ni=bn===yt-1?it?_[Lt+1]:void 0:_[bn+1],!(ni&&_[bn].equals(ni))){wi&&(ji=wi),hr&&(Rr=hr),hr=_[bn],wi=ni?ni.sub(hr)._unit()._perp():ji,ji=ji||wi;var rn=ji.add(wi);(rn.x!==0||rn.y!==0)&&rn._unit();var xn=ji.x*wi.x+ji.y*wi.y,In=rn.x*wi.x+rn.y*wi.y,Xn=In!==0?1/In:1/0,ga=2*Math.sqrt(2-2*In),ha=In<i6&&Rr&&ni,eo=ji.x*wi.y-ji.y*wi.x>0;if(ha&&bn>Lt){var qa=hr.dist(Rr);if(qa>2*sr){var Za=hr.sub(hr.sub(Rr)._mult(sr/qa)._round());this.updateDistance(Rr,Za),this.addCurrentVertex(Za,ji,0,0,gr),Rr=Za}}var Jo=Rr&&ni,to=Jo?U:it?\"butt\":$;if(Jo&&to===\"round\"&&(Xn<he?to=\"miter\":Xn<=2&&(to=\"fakeround\")),to===\"miter\"&&Xn>oe&&(to=\"bevel\"),to===\"bevel\"&&(Xn>2&&(to=\"flipbevel\"),Xn<oe&&(to=\"miter\")),Rr&&this.updateDistance(Rr,hr),to===\"miter\")rn._mult(Xn),this.addCurrentVertex(hr,rn,0,0,gr);else if(to===\"flipbevel\"){if(Xn>100)rn=wi.mult(-1);else{var ao=Xn*ji.add(wi).mag()/ji.sub(wi).mag();rn._perp()._mult(ao*(eo?-1:1))}this.addCurrentVertex(hr,rn,0,0,gr),this.addCurrentVertex(hr,rn.mult(-1),0,0,gr)}else if(to===\"bevel\"||to===\"fakeround\"){var _s=-Math.sqrt(Xn*Xn-1),jo=eo?_s:0,kl=eo?0:_s;if(Rr&&this.addCurrentVertex(hr,ji,jo,kl,gr),to===\"fakeround\")for(var Iu=Math.round(ga*180/Math.PI/kS),Cl=1;Cl<Iu;Cl++){var Ll=Cl/Iu;if(Ll!==.5){var yl=Ll-.5,Qu=1.0904+xn*(-3.2452+xn*(3.55645-xn*1.43519)),gc=.848013+xn*(-1.06021+xn*.215638);Ll=Ll+Ll*yl*(Ll-1)*(Qu*yl*yl+gc)}var 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De=I.x+I.y*U,it=I.y-I.x*U,yt=-I.x+I.y*$,Lt=-I.y-I.x*$;this.addHalfVertex(_,De,it,he,!1,U,oe),this.addHalfVertex(_,yt,Lt,he,!0,-$,oe),this.distance>fb/2&&this.totalDistance===0&&(this.distance=0,this.addCurrentVertex(_,I,U,$,oe,he))},sh.prototype.addHalfVertex=function(_,I,U,$,oe,he,De){var it=_.x,yt=_.y,Lt=this.lineClips?this.scaledDistance*(fb-1):this.scaledDistance,sr=Lt*e3;if(this.layoutVertexArray.emplaceBack((it<<1)+($?1:0),(yt<<1)+(oe?1:0),Math.round(Qw*I)+128,Math.round(Qw*U)+128,(he===0?0:he<0?-1:1)+1|(sr&63)<<2,sr>>6),this.lineClips){var gr=this.scaledDistance-this.lineClips.start,hr=this.lineClips.end-this.lineClips.start,Rr=gr/hr;this.layoutVertexArray2.emplaceBack(Rr,this.lineClipsArray.length)}var ni=De.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,ni),De.primitiveLength++),oe?this.e2=ni:this.e1=ni},sh.prototype.updateScaledDistance=function(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance},sh.prototype.updateDistance=function(_,I){this.distance+=_.dist(I),this.updateScaledDistance()},X(\"LineBucket\",sh,{omit:[\"layers\",\"patternFeatures\"]});var CS=new Or({\"line-cap\":new Me(Rn.layout_line[\"line-cap\"]),\"line-join\":new bt(Rn.layout_line[\"line-join\"]),\"line-miter-limit\":new Me(Rn.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Me(Rn.layout_line[\"line-round-limit\"]),\"line-sort-key\":new bt(Rn.layout_line[\"line-sort-key\"])}),LS=new Or({\"line-opacity\":new bt(Rn.paint_line[\"line-opacity\"]),\"line-color\":new bt(Rn.paint_line[\"line-color\"]),\"line-translate\":new Me(Rn.paint_line[\"line-translate\"]),\"line-translate-anchor\":new Me(Rn.paint_line[\"line-translate-anchor\"]),\"line-width\":new bt(Rn.paint_line[\"line-width\"]),\"line-gap-width\":new bt(Rn.paint_line[\"line-gap-width\"]),\"line-offset\":new bt(Rn.paint_line[\"line-offset\"]),\"line-blur\":new bt(Rn.paint_line[\"line-blur\"]),\"line-dasharray\":new Lr(Rn.paint_line[\"line-dasharray\"]),\"line-pattern\":new Ot(Rn.paint_line[\"line-pattern\"]),\"line-gradient\":new Vr(Rn.paint_line[\"line-gradient\"])}),t3={paint:LS,layout:CS},dq=(function(y){function _(){y.apply(this,arguments)}return y&&(_.__proto__=y),_.prototype=Object.create(y&&y.prototype),_.prototype.constructor=_,_.prototype.possiblyEvaluate=function(U,$){return $=new Un(Math.floor($.zoom),{now:$.now,fadeDuration:$.fadeDuration,zoomHistory:$.zoomHistory,transition:$.transition}),y.prototype.possiblyEvaluate.call(this,U,$)},_.prototype.evaluate=function(U,$,oe,he){return $=x({},$,{zoom:Math.floor($.zoom)}),y.prototype.evaluate.call(this,U,$,oe,he)},_})(bt),R=new dq(t3.paint.properties[\"line-width\"].specification);R.useIntegerZoom=!0;var A=(function(y){function _(I){y.call(this,I,t3),this.gradientVersion=0}return y&&(_.__proto__=y),_.prototype=Object.create(y&&y.prototype),_.prototype.constructor=_,_.prototype._handleSpecialPaintPropertyUpdate=function(U){if(U===\"line-gradient\"){var $=this._transitionablePaint._values[\"line-gradient\"].value.expression;this.stepInterpolant=$._styleExpression.expression instanceof _u,this.gradientVersion=(this.gradientVersion+1)%d}},_.prototype.gradientExpression=function(){return this._transitionablePaint._values[\"line-gradient\"].value.expression},_.prototype.recalculate=function(U,$){y.prototype.recalculate.call(this,U,$),this.paint._values[\"line-floorwidth\"]=R.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,U)},_.prototype.createBucket=function(U){return new 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it=oe[De-1],yt=oe[De],Lt=oe[De+1],sr=De===0?U:yt.sub(it)._unit()._perp(),gr=De===oe.length-1?U:Lt.sub(yt)._unit()._perp(),hr=sr._add(gr)._unit(),Rr=hr.x*gr.x+hr.y*gr.y;hr._mult(1/Rr),he.push(hr._mult(_)._add(yt))}I.push(he)}return I}var te=Wi([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"},{name:\"a_pixeloffset\",components:4,type:\"Int16\"}],4),fe=Wi([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4),pe=Wi([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4),Re=Wi([{name:\"a_placed\",components:2,type:\"Uint8\"},{name:\"a_shift\",components:2,type:\"Float32\"}]),Ke=Wi([{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]),ft=Wi([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4),Rt=Wi([{name:\"a_pos\",components:2,type:\"Float32\"},{name:\"a_radius\",components:1,type:\"Float32\"},{name:\"a_flags\",components:2,type:\"Int16\"}],4),er=Wi([{name:\"triangle\",components:3,type:\"Uint16\"}]),ur=Wi([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Uint16\",name:\"glyphStartIndex\"},{type:\"Uint16\",name:\"numGlyphs\"},{type:\"Uint32\",name:\"vertexStartIndex\"},{type:\"Uint32\",name:\"lineStartIndex\"},{type:\"Uint32\",name:\"lineLength\"},{type:\"Uint16\",name:\"segment\"},{type:\"Uint16\",name:\"lowerSize\"},{type:\"Uint16\",name:\"upperSize\"},{type:\"Float32\",name:\"lineOffsetX\"},{type:\"Float32\",name:\"lineOffsetY\"},{type:\"Uint8\",name:\"writingMode\"},{type:\"Uint8\",name:\"placedOrientation\"},{type:\"Uint8\",name:\"hidden\"},{type:\"Uint32\",name:\"crossTileID\"},{type:\"Int16\",name:\"associatedIconIndex\"}]),vr=Wi([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Int16\",name:\"rightJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"centerJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"leftJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"verticalPlacedTextSymbolIndex\"},{type:\"Int16\",name:\"placedIconSymbolIndex\"},{type:\"Int16\",name:\"verticalPlacedIconSymbolIndex\"},{type:\"Uint16\",name:\"key\"},{type:\"Uint16\",name:\"textBoxStartIndex\"},{type:\"Uint16\",name:\"textBoxEndIndex\"},{type:\"Uint16\",name:\"verticalTextBoxStartIndex\"},{type:\"Uint16\",name:\"verticalTextBoxEndIndex\"},{type:\"Uint16\",name:\"iconBoxStartIndex\"},{type:\"Uint16\",name:\"iconBoxEndIndex\"},{type:\"Uint16\",name:\"verticalIconBoxStartIndex\"},{type:\"Uint16\",name:\"verticalIconBoxEndIndex\"},{type:\"Uint16\",name:\"featureIndex\"},{type:\"Uint16\",name:\"numHorizontalGlyphVertices\"},{type:\"Uint16\",name:\"numVerticalGlyphVertices\"},{type:\"Uint16\",name:\"numIconVertices\"},{type:\"Uint16\",name:\"numVerticalIconVertices\"},{type:\"Uint16\",name:\"useRuntimeCollisionCircles\"},{type:\"Uint32\",name:\"crossTileID\"},{type:\"Float32\",name:\"textBoxScale\"},{type:\"Float32\",components:2,name:\"textOffset\"},{type:\"Float32\",name:\"collisionCircleDiameter\"}]),kr=Wi([{type:\"Float32\",name:\"offsetX\"}]),Dr=Wi([{type:\"Int16\",name:\"x\"},{type:\"Int16\",name:\"y\"},{type:\"Int16\",name:\"tileUnitDistanceFromAnchor\"}]);function 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m0={};m0[9]=!0,m0[10]=!0,m0[11]=!0,m0[12]=!0,m0[13]=!0,m0[32]=!0;var fv={};fv[10]=!0,fv[32]=!0,fv[38]=!0,fv[40]=!0,fv[41]=!0,fv[43]=!0,fv[45]=!0,fv[47]=!0,fv[173]=!0,fv[183]=!0,fv[8203]=!0,fv[8208]=!0,fv[8211]=!0,fv[8231]=!0;function a6(y,_,I,U,$,oe){if(_.imageName){var it=U[_.imageName];return it?it.displaySize[0]*_.scale*Tn/oe+$:0}else{var he=I[_.fontStack],De=he&&he[y];return De?De.metrics.advance*_.scale+$:0}}function pq(y,_,I,U,$,oe){for(var he=0,De=0;De<y.length();De++){var it=y.getSection(De);he+=a6(y.getCharCode(De),it,U,$,_,oe)}var yt=Math.max(1,Math.ceil(he/I));return he/yt}function o6(y,_,I,U){var $=Math.pow(y-_,2);return U?y<_?$/2:$*2:$+Math.abs(I)*I}function gq(y,_,I){var U=0;return y===10&&(U-=1e4),I&&(U+=150),(y===40||y===65288)&&(U+=50),(_===41||_===65289)&&(U+=50),U}function _p(y,_,I,U,$,oe){for(var he=null,De=o6(_,I,$,oe),it=0,yt=U;it<yt.length;it+=1){var Lt=yt[it],sr=_-Lt.x,gr=o6(sr,I,$,oe)+Lt.badness;gr<=De&&(he=Lt,De=gr)}return{index:y,x:_,priorBreak:he,badness:De}}function hb(y){return y?hb(y.priorBreak).concat(y.index):[]}function n3(y,_,I,U,$,oe,he){if(oe!==\"point\")return[];if(!y)return[];for(var De=[],it=pq(y,_,I,U,$,he),yt=y.text.indexOf(\"\\u200B\")>=0,Lt=0,sr=0;sr<y.length();sr++){var gr=y.getSection(sr),hr=y.getCharCode(sr);if(m0[hr]||(Lt+=a6(hr,gr,U,$,_,he)),sr<y.length()-1){var Rr=Ur(hr);(fv[hr]||Rr||gr.imageName)&&De.push(_p(sr+1,Lt,it,De,gq(hr,y.getCharCode(sr+1),Rr&&yt),!1))}}return hb(_p(y.length(),Lt,it,De,0,!0))}function IS(y){var _=.5,I=.5;switch(y){case\"right\":case\"top-right\":case\"bottom-right\":_=1;break;case\"left\":case\"top-left\":case\"bottom-left\":_=0;break}switch(y){case\"bottom\":case\"bottom-right\":case\"bottom-left\":I=1;break;case\"top\":case\"top-right\":case\"top-left\":I=0;break}return{horizontalAlign:_,verticalAlign:I}}function mq(y,_,I,U,$,oe,he,De,it,yt,Lt,sr){for(var gr=0,hr=N1,Rr=0,ni=0,ji=De===\"right\"?1:De===\"left\"?0:.5,wi=0,bn=0,rn=$;bn<rn.length;bn+=1){var xn=rn[bn];xn.trim();var In=xn.getMaxScale(),Xn=(In-1)*Tn,ga={positionedGlyphs:[],lineOffset:0};y.positionedLines[wi]=ga;var ha=ga.positionedGlyphs,eo=0;if(!xn.length()){hr+=oe,++wi;continue}for(var qa=0;qa<xn.length();qa++){var Za=xn.getSection(qa),Jo=xn.getSectionIndex(qa),to=xn.getCharCode(qa),ao=0,_s=null,jo=null,kl=null,Iu=Tn,Cl=!(it===cv.horizontal||!Lt&&!zr(to)||Lt&&(m0[to]||Wr(to)));if(Za.imageName){var Sf=U[Za.imageName];if(!Sf)continue;kl=Za.imageName,y.iconsInText=y.iconsInText||!0,jo=Sf.paddedRect;var Ff=Sf.displaySize;Za.scale=Za.scale*Tn/sr,_s={width:Ff[0],height:Ff[1],left:Gc,top:-Gp,advance:Cl?Ff[1]:Ff[0]};var $h=Tn-Ff[1]*Za.scale;ao=Xn+$h,Iu=_s.advance;var ch=Cl?Ff[0]*Za.scale-Tn*In:Ff[1]*Za.scale-Tn*In;ch>0&&ch>eo&&(eo=ch)}else{var Ll=I[Za.fontStack],yl=Ll&&Ll[to];if(yl&&yl.rect)jo=yl.rect,_s=yl.metrics;else{var 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Reduce your \"text-size\".')):ji.kind===\"composite\"&&(wi=[Cd*hr.compositeTextSizes[0].evaluate(he,{},Rr),Cd*hr.compositeTextSizes[1].evaluate(he,{},Rr)],(wi[0]>U1||wi[1]>U1)&&re(y.layerIds[0]+': Value for \"text-size\" is >= '+RS+'. 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Reduce your \"icon-size\".')):dv.kind===\"composite\"&&(Ph=[Cd*In.compositeIconSizes[0].evaluate(xn,{},ga),Cd*In.compositeIconSizes[1].evaluate(xn,{},ga)],(Ph[0]>U1||Ph[1]>U1)&&re(y.layerIds[0]+': Value for \"icon-size\" is >= '+RS+'. Reduce your \"icon-size\".')),y.addSymbols(y.icon,Qh,Ph,rn,bn,xn,!1,_,qa.lineStartIndex,qa.lineLength,-1,ga),Cl=y.icon.placedSymbolArray.length-1,hv&&(jo=hv.length*4,y.addSymbols(y.icon,hv,Ph,rn,bn,xn,cv.vertical,_,qa.lineStartIndex,qa.lineLength,-1,ga),Ll=y.icon.placedSymbolArray.length-1)}for(var vv in U.horizontal){var bp=U.horizontal[vv];if(!Za){Qu=K(bp.text);var Ay=De.layout.get(\"text-rotate\").evaluate(xn,{},ga);Za=new c6(it,_,yt,Lt,sr,bp,gr,hr,Rr,Ay)}var G1=bp.positionedLines.length===1;if(kl+=eee(y,_,bp,oe,De,Rr,xn,ni,qa,U.vertical?cv.horizontal:cv.horizontalOnly,G1?Object.keys(U.horizontal):[vv],yl,Cl,In,ga),G1)break}U.vertical&&(Iu+=eee(y,_,U.vertical,oe,De,Rr,xn,ni,qa,cv.vertical,[\"vertical\"],yl,Ll,In,ga));var H1=Za?Za.boxStartIndex:y.collisionBoxArray.length,j1=Za?Za.boxEndIndex:y.collisionBoxArray.length,d3=to?to.boxStartIndex:y.collisionBoxArray.length,y0=to?to.boxEndIndex:y.collisionBoxArray.length,p6=Jo?Jo.boxStartIndex:y.collisionBoxArray.length,Mq=Jo?Jo.boxEndIndex:y.collisionBoxArray.length,g6=ao?ao.boxStartIndex:y.collisionBoxArray.length,Eq=ao?ao.boxEndIndex:y.collisionBoxArray.length,Hp=-1,v3=function(zS,mee){return zS&&zS.circleDiameter?Math.max(zS.circleDiameter,mee):mee};Hp=v3(Za,Hp),Hp=v3(to,Hp),Hp=v3(Jo,Hp),Hp=v3(ao,Hp);var m6=Hp>-1?1:0;m6&&(Hp*=ha/Tn),y.glyphOffsetArray.length>=Pu.MAX_GLYPHS&&re(\"Too many glyphs being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),xn.sortKey!==void 0&&y.addToSortKeyRanges(y.symbolInstances.length,xn.sortKey),y.symbolInstances.emplaceBack(_.x,_.y,yl.right>=0?yl.right:-1,yl.center>=0?yl.center:-1,yl.left>=0?yl.left:-1,yl.vertical||-1,Cl,Ll,Qu,H1,j1,d3,y0,p6,Mq,g6,Eq,yt,kl,Iu,_s,jo,m6,0,gr,gc,Sf,Hp)}function ett(y,_,I,U){var $=y.compareText;if(!(_ in $))$[_]=[];else for(var oe=$[_],he=oe.length-1;he>=0;he--)if(U.dist(oe[he])<I)return!0;return $[_].push(U),!1}var ttt=mg.VectorTileFeature.types,rtt=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function f6(y,_,I,U,$,oe,he,De,it,yt,Lt,sr,gr){var hr=De?Math.min(U1,Math.round(De[0])):0,Rr=De?Math.min(U1,Math.round(De[1])):0;y.emplaceBack(_,I,Math.round(U*32),Math.round($*32),oe,he,(hr<<1)+(it?1:0),Rr,yt*16,Lt*16,sr*256,gr*256)}function bq(y,_,I){y.emplaceBack(_.x,_.y,I),y.emplaceBack(_.x,_.y,I),y.emplaceBack(_.x,_.y,I),y.emplaceBack(_.x,_.y,I)}function itt(y){for(var _=0,I=y.sections;_<I.length;_+=1){var U=I[_];if(Li(U.text))return!0}return!1}var u3=function(_){this.layoutVertexArray=new qn,this.indexArray=new pn,this.programConfigurations=_,this.segments=new io,this.dynamicLayoutVertexArray=new fi,this.opacityVertexArray=new si,this.placedSymbolArray=new zo};u3.prototype.isEmpty=function(){return this.layoutVertexArray.length===0&&this.indexArray.length===0&&this.dynamicLayoutVertexArray.length===0&&this.opacityVertexArray.length===0},u3.prototype.upload=function(_,I,U,$){this.isEmpty()||(U&&(this.layoutVertexBuffer=_.createVertexBuffer(this.layoutVertexArray,te.members),this.indexBuffer=_.createIndexBuffer(this.indexArray,I),this.dynamicLayoutVertexBuffer=_.createVertexBuffer(this.dynamicLayoutVertexArray,fe.members,!0),this.opacityVertexBuffer=_.createVertexBuffer(this.opacityVertexArray,rtt,!0),this.opacityVertexBuffer.itemSize=1),(U||$)&&this.programConfigurations.upload(_))},u3.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},X(\"SymbolBuffers\",u3);var DS=function(_,I,U){this.layoutVertexArray=new _,this.layoutAttributes=I,this.indexArray=new U,this.segments=new io,this.collisionVertexArray=new Bi};DS.prototype.upload=function(_){this.layoutVertexBuffer=_.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=_.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=_.createVertexBuffer(this.collisionVertexArray,Re.members,!0)},DS.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},X(\"CollisionBuffers\",DS);var Pu=function(_){this.collisionBoxArray=_.collisionBoxArray,this.zoom=_.zoom,this.overscaling=_.overscaling,this.layers=_.layers,this.layerIds=this.layers.map(function(it){return it.id}),this.index=_.index,this.pixelRatio=_.pixelRatio,this.sourceLayerIndex=_.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=py([]),this.placementViewportMatrix=py([]);var I=this.layers[0],U=I._unevaluatedLayout._values;this.textSizeData=xp(this.zoom,U[\"text-size\"]),this.iconSizeData=xp(this.zoom,U[\"icon-size\"]);var $=this.layers[0].layout,oe=$.get(\"symbol-sort-key\"),he=$.get(\"symbol-z-order\");this.canOverlap=$.get(\"text-allow-overlap\")||$.get(\"icon-allow-overlap\")||$.get(\"text-ignore-placement\")||$.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=he!==\"viewport-y\"&&oe.constantOr(1)!==void 0;var De=he===\"viewport-y\"||he===\"auto\"&&!this.sortFeaturesByKey;this.sortFeaturesByY=De&&this.canOverlap,$.get(\"symbol-placement\")===\"point\"&&(this.writingModes=$.get(\"text-writing-mode\").map(function(it){return cv[it]})),this.stateDependentLayerIds=this.layers.filter(function(it){return it.isStateDependent()}).map(function(it){return it.id}),this.sourceID=_.sourceID};Pu.prototype.createArrays=function(){this.text=new u3(new ci(this.layers,this.zoom,function(_){return/^text/.test(_)})),this.icon=new u3(new ci(this.layers,this.zoom,function(_){return/^icon/.test(_)})),this.glyphOffsetArray=new Ul,this.lineVertexArray=new ss,this.symbolInstances=new nl},Pu.prototype.calculateGlyphDependencies=function(_,I,U,$,oe){for(var he=0;he<_.length;he++)if(I[_.charCodeAt(he)]=!0,(U||$)&&oe){var De=zi[_.charAt(he)];De&&(I[De.charCodeAt(0)]=!0)}},Pu.prototype.populate=function(_,I,U){var $=this.layers[0],oe=$.layout,he=oe.get(\"text-font\"),De=oe.get(\"text-field\"),it=oe.get(\"icon-image\"),yt=(De.value.kind!==\"constant\"||De.value.value instanceof $l&&!De.value.value.isEmpty()||De.value.value.toString().length>0)&&(he.value.kind!==\"constant\"||he.value.value.length>0),Lt=it.value.kind!==\"constant\"||!!it.value.value||Object.keys(it.parameters).length>0,sr=oe.get(\"symbol-sort-key\");if(this.features=[],!(!yt&&!Lt)){for(var gr=I.iconDependencies,hr=I.glyphDependencies,Rr=I.availableImages,ni=new Un(this.zoom),ji=0,wi=_;ji<wi.length;ji+=1){var bn=wi[ji],rn=bn.feature,xn=bn.id,In=bn.index,Xn=bn.sourceLayerIndex,ga=$._featureFilter.needGeometry,ha=Ja(rn,ga);if($._featureFilter.filter(ni,ha,U)){ga||(ha.geometry=zn(rn));var eo=void 0;if(yt){var qa=$.getValueAndResolveTokens(\"text-field\",ha,U,Rr),Za=$l.factory(qa);itt(Za)&&(this.hasRTLText=!0),(!this.hasRTLText||yo()===\"unavailable\"||this.hasRTLText&&Ms.isParsed())&&(eo=pi(Za,$,ha))}var Jo=void 0;if(Lt){var to=$.getValueAndResolveTokens(\"icon-image\",ha,U,Rr);to instanceof fl?Jo=to:Jo=fl.fromString(to)}if(!(!eo&&!Jo)){var ao=this.sortFeaturesByKey?sr.evaluate(ha,{},U):void 0,_s={id:xn,text:eo,icon:Jo,index:In,sourceLayerIndex:Xn,geometry:ha.geometry,properties:rn.properties,type:ttt[rn.type],sortKey:ao};if(this.features.push(_s),Jo&&(gr[Jo.name]=!0),eo){var jo=he.evaluate(ha,{},U).join(\",\"),kl=oe.get(\"text-rotation-alignment\")===\"map\"&&oe.get(\"symbol-placement\")!==\"point\";this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(cv.vertical)>=0;for(var Iu=0,Cl=eo.sections;Iu<Cl.length;Iu+=1){var Ll=Cl[Iu];if(Ll.image)gr[Ll.image.name]=!0;else{var yl=Xt(eo.toString()),Qu=Ll.fontStack||jo,gc=hr[Qu]=hr[Qu]||{};this.calculateGlyphDependencies(Ll.text,gc,kl,this.allowVerticalPlacement,yl)}}}}}}oe.get(\"symbol-placement\")===\"line\"&&(this.features=vn(this.features)),this.sortFeaturesByKey&&this.features.sort(function(Sf,Ff){return Sf.sortKey-Ff.sortKey})}},Pu.prototype.update=function(_,I,U){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(_,I,this.layers,U),this.icon.programConfigurations.updatePaintArrays(_,I,this.layers,U))},Pu.prototype.isEmpty=function(){return this.symbolInstances.length===0&&!this.hasRTLText},Pu.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Pu.prototype.upload=function(_){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(_),this.iconCollisionBox.upload(_)),this.text.upload(_,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(_,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Pu.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Pu.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Pu.prototype.addToLineVertexArray=function(_,I){var U=this.lineVertexArray.length;if(_.segment!==void 0){for(var $=_.dist(I[_.segment+1]),oe=_.dist(I[_.segment]),he={},De=_.segment+1;De<I.length;De++)he[De]={x:I[De].x,y:I[De].y,tileUnitDistanceFromAnchor:$},De<I.length-1&&($+=I[De+1].dist(I[De]));for(var it=_.segment||0;it>=0;it--)he[it]={x:I[it].x,y:I[it].y,tileUnitDistanceFromAnchor:oe},it>0&&(oe+=I[it-1].dist(I[it]));for(var yt=0;yt<I.length;yt++){var Lt=he[yt];this.lineVertexArray.emplaceBack(Lt.x,Lt.y,Lt.tileUnitDistanceFromAnchor)}}return{lineStartIndex:U,lineLength:this.lineVertexArray.length-U}},Pu.prototype.addSymbols=function(_,I,U,$,oe,he,De,it,yt,Lt,sr,gr){for(var hr=_.indexArray,Rr=_.layoutVertexArray,ni=_.segments.prepareSegment(4*I.length,Rr,hr,this.canOverlap?he.sortKey:void 0),ji=this.glyphOffsetArray.length,wi=ni.vertexLength,bn=this.allowVerticalPlacement&&De===cv.vertical?Math.PI/2:0,rn=he.text&&he.text.sections,xn=0;xn<I.length;xn++){var In=I[xn],Xn=In.tl,ga=In.tr,ha=In.bl,eo=In.br,qa=In.tex,Za=In.pixelOffsetTL,Jo=In.pixelOffsetBR,to=In.minFontScaleX,ao=In.minFontScaleY,_s=In.glyphOffset,jo=In.isSDF,kl=In.sectionIndex,Iu=ni.vertexLength,Cl=_s[1];f6(Rr,it.x,it.y,Xn.x,Cl+Xn.y,qa.x,qa.y,U,jo,Za.x,Za.y,to,ao),f6(Rr,it.x,it.y,ga.x,Cl+ga.y,qa.x+qa.w,qa.y,U,jo,Jo.x,Za.y,to,ao),f6(Rr,it.x,it.y,ha.x,Cl+ha.y,qa.x,qa.y+qa.h,U,jo,Za.x,Jo.y,to,ao),f6(Rr,it.x,it.y,eo.x,Cl+eo.y,qa.x+qa.w,qa.y+qa.h,U,jo,Jo.x,Jo.y,to,ao),bq(_.dynamicLayoutVertexArray,it,bn),hr.emplaceBack(Iu,Iu+1,Iu+2),hr.emplaceBack(Iu+1,Iu+2,Iu+3),ni.vertexLength+=4,ni.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(_s[0]),(xn===I.length-1||kl!==I[xn+1].sectionIndex)&&_.programConfigurations.populatePaintArrays(Rr.length,he,he.index,{},gr,rn&&rn[kl])}_.placedSymbolArray.emplaceBack(it.x,it.y,ji,this.glyphOffsetArray.length-ji,wi,yt,Lt,it.segment,U?U[0]:0,U?U[1]:0,$[0],$[1],De,0,!1,0,sr)},Pu.prototype._addCollisionDebugVertex=function(_,I,U,$,oe,he){return I.emplaceBack(0,0),_.emplaceBack(U.x,U.y,$,oe,Math.round(he.x),Math.round(he.y))},Pu.prototype.addCollisionDebugVertices=function(_,I,U,$,oe,he,De){var it=oe.segments.prepareSegment(4,oe.layoutVertexArray,oe.indexArray),yt=it.vertexLength,Lt=oe.layoutVertexArray,sr=oe.collisionVertexArray,gr=De.anchorX,hr=De.anchorY;this._addCollisionDebugVertex(Lt,sr,he,gr,hr,new u(_,I)),this._addCollisionDebugVertex(Lt,sr,he,gr,hr,new u(U,I)),this._addCollisionDebugVertex(Lt,sr,he,gr,hr,new u(U,$)),this._addCollisionDebugVertex(Lt,sr,he,gr,hr,new u(_,$)),it.vertexLength+=4;var Rr=oe.indexArray;Rr.emplaceBack(yt,yt+1),Rr.emplaceBack(yt+1,yt+2),Rr.emplaceBack(yt+2,yt+3),Rr.emplaceBack(yt+3,yt),it.primitiveLength+=4},Pu.prototype.addDebugCollisionBoxes=function(_,I,U,$){for(var oe=_;oe<I;oe++){var he=this.collisionBoxArray.get(oe),De=he.x1,it=he.y1,yt=he.x2,Lt=he.y2;this.addCollisionDebugVertices(De,it,yt,Lt,$?this.textCollisionBox:this.iconCollisionBox,he.anchorPoint,U)}},Pu.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new DS(Ji,ft.members,sa),this.iconCollisionBox=new DS(Ji,ft.members,sa);for(var _=0;_<this.symbolInstances.length;_++){var I=this.symbolInstances.get(_);this.addDebugCollisionBoxes(I.textBoxStartIndex,I.textBoxEndIndex,I,!0),this.addDebugCollisionBoxes(I.verticalTextBoxStartIndex,I.verticalTextBoxEndIndex,I,!0),this.addDebugCollisionBoxes(I.iconBoxStartIndex,I.iconBoxEndIndex,I,!1),this.addDebugCollisionBoxes(I.verticalIconBoxStartIndex,I.verticalIconBoxEndIndex,I,!1)}},Pu.prototype._deserializeCollisionBoxesForSymbol=function(_,I,U,$,oe,he,De,it,yt){for(var Lt={},sr=I;sr<U;sr++){var gr=_.get(sr);Lt.textBox={x1:gr.x1,y1:gr.y1,x2:gr.x2,y2:gr.y2,anchorPointX:gr.anchorPointX,anchorPointY:gr.anchorPointY},Lt.textFeatureIndex=gr.featureIndex;break}for(var hr=$;hr<oe;hr++){var Rr=_.get(hr);Lt.verticalTextBox={x1:Rr.x1,y1:Rr.y1,x2:Rr.x2,y2:Rr.y2,anchorPointX:Rr.anchorPointX,anchorPointY:Rr.anchorPointY},Lt.verticalTextFeatureIndex=Rr.featureIndex;break}for(var ni=he;ni<De;ni++){var ji=_.get(ni);Lt.iconBox={x1:ji.x1,y1:ji.y1,x2:ji.x2,y2:ji.y2,anchorPointX:ji.anchorPointX,anchorPointY:ji.anchorPointY},Lt.iconFeatureIndex=ji.featureIndex;break}for(var wi=it;wi<yt;wi++){var bn=_.get(wi);Lt.verticalIconBox={x1:bn.x1,y1:bn.y1,x2:bn.x2,y2:bn.y2,anchorPointX:bn.anchorPointX,anchorPointY:bn.anchorPointY},Lt.verticalIconFeatureIndex=bn.featureIndex;break}return Lt},Pu.prototype.deserializeCollisionBoxes=function(_){this.collisionArrays=[];for(var I=0;I<this.symbolInstances.length;I++){var U=this.symbolInstances.get(I);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(_,U.textBoxStartIndex,U.textBoxEndIndex,U.verticalTextBoxStartIndex,U.verticalTextBoxEndIndex,U.iconBoxStartIndex,U.iconBoxEndIndex,U.verticalIconBoxStartIndex,U.verticalIconBoxEndIndex))}},Pu.prototype.hasTextData=function(){return this.text.segments.get().length>0},Pu.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Pu.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Pu.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Pu.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Pu.prototype.addIndicesForPlacedSymbol=function(_,I){for(var U=_.placedSymbolArray.get(I),$=U.vertexStartIndex+U.numGlyphs*4,oe=U.vertexStartIndex;oe<$;oe+=4)_.indexArray.emplaceBack(oe,oe+1,oe+2),_.indexArray.emplaceBack(oe+1,oe+2,oe+3)},Pu.prototype.getSortedSymbolIndexes=function(_){if(this.sortedAngle===_&&this.symbolInstanceIndexes!==void 0)return this.symbolInstanceIndexes;for(var I=Math.sin(_),U=Math.cos(_),$=[],oe=[],he=[],De=0;De<this.symbolInstances.length;++De){he.push(De);var it=this.symbolInstances.get(De);$.push(Math.round(I*it.anchorX+U*it.anchorY)|0),oe.push(it.featureIndex)}return he.sort(function(yt,Lt){return $[yt]-$[Lt]||oe[Lt]-oe[yt]}),he},Pu.prototype.addToSortKeyRanges=function(_,I){var U=this.sortKeyRanges[this.sortKeyRanges.length-1];U&&U.sortKey===I?U.symbolInstanceEnd=_+1:this.sortKeyRanges.push({sortKey:I,symbolInstanceStart:_,symbolInstanceEnd:_+1})},Pu.prototype.sortFeatures=function(_){var I=this;if(this.sortFeaturesByY&&this.sortedAngle!==_&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(_),this.sortedAngle=_,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var U=0,$=this.symbolInstanceIndexes;U<$.length;U+=1){var oe=$[U],he=this.symbolInstances.get(oe);this.featureSortOrder.push(he.featureIndex),[he.rightJustifiedTextSymbolIndex,he.centerJustifiedTextSymbolIndex,he.leftJustifiedTextSymbolIndex].forEach(function(De,it,yt){De>=0&&yt.indexOf(De)===it&&I.addIndicesForPlacedSymbol(I.text,De)}),he.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,he.verticalPlacedTextSymbolIndex),he.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,he.placedIconSymbolIndex),he.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,he.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},X(\"SymbolBucket\",Pu,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Pu.MAX_GLYPHS=65535,Pu.addDynamicAttributes=bq;function ntt(y,_){return _.replace(/{([^{}]+)}/g,function(I,U){return U in y?String(y[U]):\"\"})}var att=new Or({\"symbol-placement\":new Me(Rn.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Me(Rn.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Me(Rn.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new bt(Rn.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new Me(Rn.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new Me(Rn.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new Me(Rn.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Me(Rn.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Me(Rn.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new bt(Rn.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Me(Rn.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Me(Rn.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new bt(Rn.layout_symbol[\"icon-image\"]),\"icon-rotate\":new bt(Rn.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new 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y&&(_.__proto__=y),_.prototype=Object.create(y&&y.prototype),_.prototype.constructor=_,_.prototype.recalculate=function(U,$){if(y.prototype.recalculate.call(this,U,$),this.layout.get(\"icon-rotation-alignment\")===\"auto\"&&(this.layout.get(\"symbol-placement\")!==\"point\"?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),this.layout.get(\"text-rotation-alignment\")===\"auto\"&&(this.layout.get(\"symbol-placement\")!==\"point\"?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),this.layout.get(\"text-pitch-alignment\")===\"auto\"&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),this.layout.get(\"icon-pitch-alignment\")===\"auto\"&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),this.layout.get(\"symbol-placement\")===\"point\"){var 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lh=function(_,I){_&&(I?this.setSouthWest(_).setNorthEast(I):_.length===4?this.setSouthWest([_[0],_[1]]).setNorthEast([_[2],_[3]]):this.setSouthWest(_[0]).setNorthEast(_[1]))};lh.prototype.setNorthEast=function(_){return this._ne=_ instanceof Hc?new Hc(_.lng,_.lat):Hc.convert(_),this},lh.prototype.setSouthWest=function(_){return this._sw=_ instanceof Hc?new Hc(_.lng,_.lat):Hc.convert(_),this},lh.prototype.extend=function(_){var I=this._sw,U=this._ne,$,oe;if(_ instanceof Hc)$=_,oe=_;else if(_ instanceof lh){if($=_._sw,oe=_._ne,!$||!oe)return this}else{if(Array.isArray(_))if(_.length===4||_.every(Array.isArray)){var he=_;return this.extend(lh.convert(he))}else{var De=_;return this.extend(Hc.convert(De))}return this}return!I&&!U?(this._sw=new Hc($.lng,$.lat),this._ne=new Hc(oe.lng,oe.lat)):(I.lng=Math.min($.lng,I.lng),I.lat=Math.min($.lat,I.lat),U.lng=Math.max(oe.lng,U.lng),U.lat=Math.max(oe.lat,U.lat)),this},lh.prototype.getCenter=function(){return new 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_.overscaledZ===0||_.overscaledZ<this.overscaledZ&&_.canonical.x===this.canonical.x>>I&&_.canonical.y===this.canonical.y>>I},uh.prototype.children=function(_){if(this.overscaledZ>=_)return[new uh(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var I=this.canonical.z+1,U=this.canonical.x*2,$=this.canonical.y*2;return[new uh(I,this.wrap,I,U,$),new uh(I,this.wrap,I,U+1,$),new uh(I,this.wrap,I,U,$+1),new uh(I,this.wrap,I,U+1,$+1)]},uh.prototype.isLessThan=function(_){return this.wrap<_.wrap?!0:this.wrap>_.wrap?!1:this.overscaledZ<_.overscaledZ?!0:this.overscaledZ>_.overscaledZ?!1:this.canonical.x<_.canonical.x?!0:this.canonical.x>_.canonical.x?!1:this.canonical.y<_.canonical.y},uh.prototype.wrapped=function(){return new uh(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},uh.prototype.unwrapTo=function(_){return new uh(this.overscaledZ,_,this.canonical.z,this.canonical.x,this.canonical.y)},uh.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},uh.prototype.toUnwrapped=function(){return new dee(this.wrap,this.canonical)},uh.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},uh.prototype.getTilePoint=function(_){return this.canonical.getTilePoint(new vb(_.x-this.wrap,_.y))};function FS(y,_,I,U,$){y*=2,y<0&&(y=y*-1-1);var oe=1<<I;return(oe*oe*y+oe*$+U).toString(36)+I.toString(36)+_.toString(36)}function wtt(y,_,I){for(var U=\"\",$,oe=y;oe>0;oe--)$=1<<oe-1,U+=(_&$?1:0)+(I&$?2:0);return U}X(\"CanonicalTileID\",pb),X(\"OverscaledTileID\",uh,{omit:[\"posMatrix\"]});var wy=function(_,I,U){if(this.uid=_,I.height!==I.width)throw new RangeError(\"DEM tiles must be square\");if(U&&U!==\"mapbox\"&&U!==\"terrarium\")return re('\"'+U+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=I.height;var $=this.dim=I.height-2;this.data=new Uint32Array(I.data.buffer),this.encoding=U||\"mapbox\";for(var oe=0;oe<$;oe++)this.data[this._idx(-1,oe)]=this.data[this._idx(0,oe)],this.data[this._idx($,oe)]=this.data[this._idx($-1,oe)],this.data[this._idx(oe,-1)]=this.data[this._idx(oe,0)],this.data[this._idx(oe,$)]=this.data[this._idx(oe,$-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx($,-1)]=this.data[this._idx($-1,0)],this.data[this._idx(-1,$)]=this.data[this._idx(0,$-1)],this.data[this._idx($,$)]=this.data[this._idx($-1,$-1)]};wy.prototype.get=function(_,I){var U=new Uint8Array(this.data.buffer),$=this._idx(_,I)*4,oe=this.encoding===\"terrarium\"?this._unpackTerrarium:this._unpackMapbox;return oe(U[$],U[$+1],U[$+2])},wy.prototype.getUnpackVector=function(){return this.encoding===\"terrarium\"?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},wy.prototype._idx=function(_,I){if(_<-1||_>=this.dim+1||I<-1||I>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(I+1)*this.stride+(_+1)},wy.prototype._unpackMapbox=function(_,I,U){return(_*256*256+I*256+U)/10-1e4},wy.prototype._unpackTerrarium=function(_,I,U){return _*256+I+U/256-32768},wy.prototype.getPixels=function(){return new Eh({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},wy.prototype.backfillBorder=function(_,I,U){if(this.dim!==_.dim)throw new Error(\"dem dimension mismatch\");var $=I*this.dim,oe=I*this.dim+this.dim,he=U*this.dim,De=U*this.dim+this.dim;switch(I){case-1:$=oe-1;break;case 1:oe=$+1;break}switch(U){case-1:he=De-1;break;case 1:De=he+1;break}for(var it=-I*this.dim,yt=-U*this.dim,Lt=he;Lt<De;Lt++)for(var sr=$;sr<oe;sr++)this.data[this._idx(sr,Lt)]=_.data[this._idx(sr+it,Lt+yt)]},X(\"DEMData\",wy);function Ttt(y,_){var I={};if(!_)return I;for(var U=function(){var he=oe[$],De=he.layerIds.map(function(sr){return _.getLayer(sr)}).filter(Boolean);if(De.length!==0){he.layers=De,he.stateDependentLayerIds&&(he.stateDependentLayers=he.stateDependentLayerIds.map(function(sr){return De.filter(function(gr){return gr.id===sr})[0]}));for(var it=0,yt=De;it<yt.length;it+=1){var Lt=yt[it];I[Lt.id]=he}}},$=0,oe=y;$<oe.length;$+=1)U();return I}var d6=function(_){this._stringToNumber={},this._numberToString=[];for(var I=0;I<_.length;I++){var U=_[I];this._stringToNumber[U]=I,this._numberToString[I]=U}};d6.prototype.encode=function(_){return this._stringToNumber[_]},d6.prototype.decode=function(_){return this._numberToString[_]};var v6=function(_,I,U,$,oe){this.type=\"Feature\",this._vectorTileFeature=_,_._z=I,_._x=U,_._y=$,this.properties=_.properties,this.id=oe},Sq={geometry:{configurable:!0}};Sq.geometry.get=function(){return this._geometry===void 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yt=this.deletedStates[_]&&this.deletedStates[_][$]&&this.deletedStates[_][$][it]===null;yt&&delete this.deletedStates[_][$][it]}}},h3.prototype.removeFeatureState=function(_,I,U){var $=this.deletedStates[_]===null;if(!$){var oe=String(I);if(this.deletedStates[_]=this.deletedStates[_]||{},U&&I!==void 0)this.deletedStates[_][oe]!==null&&(this.deletedStates[_][oe]=this.deletedStates[_][oe]||{},this.deletedStates[_][oe][U]=null);else if(I!==void 0){var he=this.stateChanges[_]&&this.stateChanges[_][oe];if(he){this.deletedStates[_][oe]={};for(U in this.stateChanges[_][oe])this.deletedStates[_][oe][U]=null}else this.deletedStates[_][oe]=null}else this.deletedStates[_]=null}},h3.prototype.getState=function(_,I){var U=String(I),$=this.state[_]||{},oe=this.stateChanges[_]||{},he=x({},$[U],oe[U]);if(this.deletedStates[_]===null)return{};if(this.deletedStates[_]){var De=this.deletedStates[_][I];if(De===null)return{};for(var it in De)delete he[it]}return he},h3.prototype.initializeTileState=function(_,I){_.setFeatureState(this.state,I)},h3.prototype.coalesceChanges=function(_,I){var U={};for(var $ in this.stateChanges){this.state[$]=this.state[$]||{};var oe={};for(var he in this.stateChanges[$])this.state[$][he]||(this.state[$][he]={}),x(this.state[$][he],this.stateChanges[$][he]),oe[he]=this.state[$][he];U[$]=oe}for(var De in this.deletedStates){this.state[De]=this.state[De]||{};var it={};if(this.deletedStates[De]===null)for(var yt in this.state[De])it[yt]={},this.state[De][yt]={};else for(var Lt in this.deletedStates[De]){var sr=this.deletedStates[De][Lt]===null;if(sr)this.state[De][Lt]={};else for(var gr=0,hr=Object.keys(this.deletedStates[De][Lt]);gr<hr.length;gr+=1){var Rr=hr[gr];delete this.state[De][Lt][Rr]}it[Lt]=this.state[De][Lt]}U[De]=U[De]||{},x(U[De],it)}if(this.stateChanges={},this.deletedStates={},Object.keys(U).length!==0)for(var ni in _){var ji=_[ni];ji.setFeatureState(U,I)}};var Ty=function(_,I){this.tileID=_,this.x=_.canonical.x,this.y=_.canonical.y,this.z=_.canonical.z,this.grid=new au(Ei,16,0),this.grid3D=new au(Ei,16,0),this.featureIndexArray=new Ys,this.promoteId=I};Ty.prototype.insert=function(_,I,U,$,oe,he){var De=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(U,$,oe);for(var it=he?this.grid3D:this.grid,yt=0;yt<I.length;yt++){for(var Lt=I[yt],sr=[1/0,1/0,-1/0,-1/0],gr=0;gr<Lt.length;gr++){var hr=Lt[gr];sr[0]=Math.min(sr[0],hr.x),sr[1]=Math.min(sr[1],hr.y),sr[2]=Math.max(sr[2],hr.x),sr[3]=Math.max(sr[3],hr.y)}sr[0]<Ei&&sr[1]<Ei&&sr[2]>=0&&sr[3]>=0&&it.insert(De,sr[0],sr[1],sr[2],sr[3])}},Ty.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new mg.VectorTile(new Xa(this.rawTileData)).layers,this.sourceLayerCoder=new d6(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Ty.prototype.query=function(_,I,U,$){var oe=this;this.loadVTLayers();for(var he=_.params||{},De=Ei/_.tileSize/_.scale,it=xe(he.filter),yt=_.queryGeometry,Lt=_.queryPadding*De,sr=pee(yt),gr=this.grid.query(sr.minX-Lt,sr.minY-Lt,sr.maxX+Lt,sr.maxY+Lt),hr=pee(_.cameraQueryGeometry),Rr=this.grid3D.query(hr.minX-Lt,hr.minY-Lt,hr.maxX+Lt,hr.maxY+Lt,function(Xn,ga,ha,eo){return mp(_.cameraQueryGeometry,Xn-Lt,ga-Lt,ha+Lt,eo+Lt)}),ni=0,ji=Rr;ni<ji.length;ni+=1){var wi=ji[ni];gr.push(wi)}gr.sort(Att);for(var bn={},rn,xn=function(Xn){var ga=gr[Xn];if(ga!==rn){rn=ga;var ha=oe.featureIndexArray.get(ga),eo=null;oe.loadMatchingFeature(bn,ha.bucketIndex,ha.sourceLayerIndex,ha.featureIndex,it,he.layers,he.availableImages,I,U,$,function(qa,Za,Jo){return eo||(eo=zn(qa)),Za.queryIntersectsFeature(yt,qa,Jo,eo,oe.z,_.transform,De,_.pixelPosMatrix)})}},In=0;In<gr.length;In++)xn(In);return bn},Ty.prototype.loadMatchingFeature=function(_,I,U,$,oe,he,De,it,yt,Lt,sr){var gr=this.bucketLayerIDs[I];if(!(he&&!N(he,gr))){var hr=this.sourceLayerCoder.decode(U),Rr=this.vtLayers[hr],ni=Rr.feature($);if(oe.needGeometry){var ji=Ja(ni,!0);if(!oe.filter(new Un(this.tileID.overscaledZ),ji,this.tileID.canonical))return}else if(!oe.filter(new Un(this.tileID.overscaledZ),ni))return;for(var wi=this.getId(ni,hr),bn=0;bn<gr.length;bn++){var rn=gr[bn];if(!(he&&he.indexOf(rn)<0)){var xn=it[rn];if(xn){var In={};wi!==void 0&&Lt&&(In=Lt.getState(xn.sourceLayer||\"_geojsonTileLayer\",wi));var Xn=x({},yt[rn]);Xn.paint=vee(Xn.paint,xn.paint,ni,In,De),Xn.layout=vee(Xn.layout,xn.layout,ni,In,De);var ga=!sr||sr(ni,xn,In);if(ga){var ha=new v6(ni,this.z,this.x,this.y,wi);ha.layer=Xn;var eo=_[rn];eo===void 0&&(eo=_[rn]=[]),eo.push({featureIndex:$,feature:ha,intersectionZ:ga})}}}}}},Ty.prototype.lookupSymbolFeatures=function(_,I,U,$,oe,he,De,it){var yt={};this.loadVTLayers();for(var Lt=xe(oe),sr=0,gr=_;sr<gr.length;sr+=1){var hr=gr[sr];this.loadMatchingFeature(yt,U,$,hr,Lt,he,De,it,I)}return yt},Ty.prototype.hasLayer=function(_){for(var I=0,U=this.bucketLayerIDs;I<U.length;I+=1)for(var $=U[I],oe=0,he=$;oe<he.length;oe+=1){var De=he[oe];if(_===De)return!0}return!1},Ty.prototype.getId=function(_,I){var U=_.id;if(this.promoteId){var $=typeof this.promoteId==\"string\"?this.promoteId:this.promoteId[I];U=_.properties[$],typeof U==\"boolean\"&&(U=Number(U))}return U},X(\"FeatureIndex\",Ty,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});function vee(y,_,I,U,$){return G(y,function(oe,he){var De=_ instanceof Sc?_.get(he):null;return De&&De.evaluate?De.evaluate(I,U,$):De})}function pee(y){for(var _=1/0,I=1/0,U=-1/0,$=-1/0,oe=0,he=y;oe<he.length;oe+=1){var De=he[oe];_=Math.min(_,De.x),I=Math.min(I,De.y),U=Math.max(U,De.x),$=Math.max($,De.y)}return{minX:_,minY:I,maxX:U,maxY:$}}function Att(y,_){return _-y}var Stt=3e4,Jh=function(_,I){this.tileID=_,this.uid=g(),this.uses=0,this.tileSize=I,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};Jh.prototype.registerFadeDuration=function(_){var I=_+this.timeAdded;I<lt.now()||this.fadeEndTime&&I<this.fadeEndTime||(this.fadeEndTime=I)},Jh.prototype.wasRequested=function(){return this.state===\"errored\"||this.state===\"loaded\"||this.state===\"reloading\"},Jh.prototype.loadVectorData=function(_,I,U){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",!_){this.collisionBoxArray=new Ga;return}_.featureIndex&&(this.latestFeatureIndex=_.featureIndex,_.rawTileData?(this.latestRawTileData=_.rawTileData,this.latestFeatureIndex.rawTileData=_.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=_.collisionBoxArray,this.buckets=Ttt(_.buckets,I.style),this.hasSymbolBuckets=!1;for(var $ in this.buckets){var oe=this.buckets[$];if(oe instanceof Pu)if(this.hasSymbolBuckets=!0,U)oe.justReloaded=!0;else break}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var he in this.buckets){var De=this.buckets[he];if(De instanceof Pu&&De.hasRTLText){this.hasRTLText=!0,Xs();break}}this.queryPadding=0;for(var it in this.buckets){var yt=this.buckets[it];this.queryPadding=Math.max(this.queryPadding,I.style.getLayer(it).queryRadius(yt))}_.imageAtlas&&(this.imageAtlas=_.imageAtlas),_.glyphAtlasImage&&(this.glyphAtlasImage=_.glyphAtlasImage)},Jh.prototype.unloadVectorData=function(){for(var _ in this.buckets)this.buckets[_].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},Jh.prototype.getBucket=function(_){return this.buckets[_.id]},Jh.prototype.upload=function(_){for(var I in this.buckets){var U=this.buckets[I];U.uploadPending()&&U.upload(_)}var $=_.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new db(_,this.imageAtlas.image,$.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new db(_,this.glyphAtlasImage,$.ALPHA),this.glyphAtlasImage=null)},Jh.prototype.prepare=function(_){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(_,this.imageAtlasTexture)},Jh.prototype.queryRenderedFeatures=function(_,I,U,$,oe,he,De,it,yt,Lt){return!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData?{}:this.latestFeatureIndex.query({queryGeometry:$,cameraQueryGeometry:oe,scale:he,tileSize:this.tileSize,pixelPosMatrix:Lt,transform:it,params:De,queryPadding:this.queryPadding*yt},_,I,U)},Jh.prototype.querySourceFeatures=function(_,I){var U=this.latestFeatureIndex;if(!(!U||!U.rawTileData)){var $=U.loadVTLayers(),oe=I?I.sourceLayer:\"\",he=$._geojsonTileLayer||$[oe];if(he)for(var De=xe(I&&I.filter),it=this.tileID.canonical,yt=it.z,Lt=it.x,sr=it.y,gr={z:yt,x:Lt,y:sr},hr=0;hr<he.length;hr++){var Rr=he.feature(hr);if(De.needGeometry){var ni=Ja(Rr,!0);if(!De.filter(new Un(this.tileID.overscaledZ),ni,this.tileID.canonical))continue}else if(!De.filter(new Un(this.tileID.overscaledZ),Rr))continue;var ji=U.getId(Rr,oe),wi=new v6(Rr,yt,Lt,sr,ji);wi.tile=gr,_.push(wi)}}},Jh.prototype.hasData=function(){return this.state===\"loaded\"||this.state===\"reloading\"||this.state===\"expired\"},Jh.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},Jh.prototype.setExpiryData=function(_){var I=this.expirationTime;if(_.cacheControl){var U=ge(_.cacheControl);U[\"max-age\"]&&(this.expirationTime=Date.now()+U[\"max-age\"]*1e3)}else _.expires&&(this.expirationTime=new Date(_.expires).getTime());if(this.expirationTime){var $=Date.now(),oe=!1;if(this.expirationTime>$)oe=!1;else if(!I)oe=!0;else if(this.expirationTime<I)oe=!0;else{var he=this.expirationTime-I;he?this.expirationTime=$+Math.max(he,Stt):oe=!0}oe?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},Jh.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-new Date().getTime(),Math.pow(2,31)-1)},Jh.prototype.setFeatureState=function(_,I){if(!(!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData||Object.keys(_).length===0)){var U=this.latestFeatureIndex.loadVTLayers();for(var $ in this.buckets)if(I.style.hasLayer($)){var oe=this.buckets[$],he=oe.layers[0].sourceLayer||\"_geojsonTileLayer\",De=U[he],it=_[he];if(!(!De||!it||Object.keys(it).length===0)){oe.update(it,De,this.imageAtlas&&this.imageAtlas.patternPositions||{});var yt=I&&I.style&&I.style.getLayer($);yt&&(this.queryPadding=Math.max(this.queryPadding,yt.queryRadius(oe)))}}}},Jh.prototype.holdingForFade=function(){return this.symbolFadeHoldUntil!==void 0},Jh.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<lt.now()},Jh.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},Jh.prototype.setHoldDuration=function(_){this.symbolFadeHoldUntil=lt.now()+_},Jh.prototype.setDependencies=function(_,I){for(var U={},$=0,oe=I;$<oe.length;$+=1){var he=oe[$];U[he]=!0}this.dependencies[_]=U},Jh.prototype.hasDependency=function(_,I){for(var U=0,$=_;U<$.length;U+=1){var oe=$[U],he=this.dependencies[oe];if(he)for(var De=0,it=I;De<it.length;De+=1){var yt=it[De];if(he[yt])return!0}}return!1};var Mtt=[\"type\",\"source\",\"source-layer\",\"minzoom\",\"maxzoom\",\"filter\",\"layout\"],V1=f.performance,gee=function(_){this._marks={start:[_.url,\"start\"].join(\"#\"),end:[_.url,\"end\"].join(\"#\"),measure:_.url.toString()},V1.mark(this._marks.start)};gee.prototype.finish=function(){V1.mark(this._marks.end);var _=V1.getEntriesByName(this._marks.measure);return _.length===0&&(V1.measure(this._marks.measure,this._marks.start,this._marks.end),_=V1.getEntriesByName(this._marks.measure),V1.clearMarks(this._marks.start),V1.clearMarks(this._marks.end),V1.clearMeasures(this._marks.measure)),_},i.Actor=f3,i.AlphaImage=Dv,i.CanonicalTileID=pb,i.CollisionBoxArray=Ga,i.Color=cs,i.DEMData=wy,i.DataConstantProperty=Me,i.DictionaryCoder=d6,i.EXTENT=Ei,i.ErrorEvent=da,i.EvaluationParameters=Un,i.Event=Xo,i.Evented=jn,i.FeatureIndex=Ty,i.FillBucket=yp,i.FillExtrusionBucket=Vp,i.ImageAtlas=yg,i.ImagePosition=Yf,i.LineBucket=sh,i.LngLat=Hc,i.LngLatBounds=lh,i.MercatorCoordinate=vb,i.ONE_EM=Tn,i.OverscaledTileID=uh,i.Point=u,i.Point$1=u,i.Properties=Or,i.Protobuf=Xa,i.RGBAImage=Eh,i.RequestManager=Ae,i.RequestPerformance=gee,i.ResourceType=Jn,i.SegmentVector=io,i.SourceFeatureState=h3,i.StructArrayLayout1ui2=ba,i.StructArrayLayout2f1f2i16=Ui,i.StructArrayLayout2i4=Zr,i.StructArrayLayout3ui6=pn,i.StructArrayLayout4i8=yi,i.SymbolBucket=Pu,i.Texture=db,i.Tile=Jh,i.Transitionable=Uo,i.Uniform1f=$e,i.Uniform1i=Ne,i.Uniform2f=st,i.Uniform3f=It,i.Uniform4f=Bt,i.UniformColor=Ft,i.UniformMatrix4f=zt,i.UnwrappedTileID=dee,i.ValidationError=_a,i.WritingMode=cv,i.ZoomHistory=Pt,i.add=Rv,i.addDynamicAttributes=bq,i.asyncAll=M,i.bezier=m,i.bindAll=q,i.browser=lt,i.cacheEntryPossiblyAdded=un,i.clamp=p,i.clearTileCache=An,i.clipLine=KQ,i.clone=L1,i.clone$1=j,i.clone$2=Uw,i.collisionCircleLayout=Rt,i.config=mt,i.create=C1,i.create$1=Zh,i.create$2=vg,i.createCommonjsModule=a,i.createExpression=oo,i.createLayout=Wi,i.createStyleLayer=mtt,i.cross=X9,i.deepEqual=h,i.dot=W9,i.dot$1=$9,i.ease=b,i.emitValidationErrors=Lu,i.endsWith=V,i.enforceCacheSizeLimit=Fn,i.evaluateSizeForFeature=GQ,i.evaluateSizeForZoom=HQ,i.evaluateVariableOffset=QQ,i.evented=Ia,i.extend=x,i.featureFilter=xe,i.filterObject=Z,i.fromRotation=am,i.getAnchorAlignment=IS,i.getAnchorJustification=xq,i.getArrayBuffer=ti,i.getImage=ua,i.getJSON=Xr,i.getRTLTextPluginStatus=yo,i.getReferrer=Gt,i.getVideo=ma,i.identity=py,i.invert=om,i.isChar=ot,i.isMapboxURL=rt,i.keysDifference=L,i.makeRequest=wr,i.mapObject=G,i.mercatorXfromLng=cee,i.mercatorYfromLat=fee,i.mercatorZfromAltitude=hee,i.mul=Kx,i.multiply=sm,i.mvt=mg,i.nextPowerOfTwo=E,i.normalize=Jx,i.number=il,i.offscreenCanvasSupported=Vi,i.ortho=wu,i.parseGlyphPBF=p0,i.pbf=Xa,i.performSymbolLayout=Jet,i.perspective=P1,i.pick=C,i.plugin=Ms,i.polygonIntersectsPolygon=so,i.postMapLoadEvent=We,i.postTurnstileEvent=Ge,i.potpack=ep,i.refProperties=Mtt,i.register=X,i.registerForPluginStateChange=Da,i.renderColorRamp=eb,i.rotate=vy,i.rotateX=Sd,i.rotateZ=gy,i.scale=tu,i.scale$1=J9,i.scale$2=Vw,i.setCacheLimits=Hn,i.setRTLTextPlugin=go,i.sphericalToCartesian=Ee,i.sqrLen=hS,i.styleSpec=Rn,i.sub=Y9,i.symbolSize=Vet,i.transformMat3=Z9,i.transformMat4=my,i.translate=vc,i.triggerPluginCompletionEvent=na,i.uniqueId=g,i.validateCustomStyleLayer=vtt,i.validateLight=To,i.validateStyle=wo,i.values=T,i.vectorTile=mg,i.version=o,i.warnOnce=re,i.webpSupported=Ht,i.window=f,i.wrap=k}),n([\"./shared\"],function(i){\"use strict\";function a(Gt){var wt=typeof Gt;if(wt===\"number\"||wt===\"boolean\"||wt===\"string\"||Gt===void 0||Gt===null)return JSON.stringify(Gt);if(Array.isArray(Gt)){for(var tr=\"[\",ir=0,wr=Gt;ir<wr.length;ir+=1){var Xr=wr[ir];tr+=a(Xr)+\",\"}return tr+\"]\"}for(var ti=Object.keys(Gt).sort(),$r=\"{\",Ri=0;Ri<ti.length;Ri++)$r+=JSON.stringify(ti[Ri])+\":\"+a(Gt[ti[Ri]])+\",\";return $r+\"}\"}function o(Gt){for(var wt=\"\",tr=0,ir=i.refProperties;tr<ir.length;tr+=1){var wr=ir[tr];wt+=\"/\"+a(Gt[wr])}return wt}function s(Gt,wt){for(var tr={},ir=0;ir<Gt.length;ir++){var wr=wt&&wt[Gt[ir].id]||o(Gt[ir]);wt&&(wt[Gt[ir].id]=wr);var Xr=tr[wr];Xr||(Xr=tr[wr]=[]),Xr.push(Gt[ir])}var ti=[];for(var $r in tr)ti.push(tr[$r]);return ti}var l=function(wt){this.keyCache={},wt&&this.replace(wt)};l.prototype.replace=function(wt){this._layerConfigs={},this._layers={},this.update(wt,[])},l.prototype.update=function(wt,tr){for(var ir=this,wr=0,Xr=wt;wr<Xr.length;wr+=1){var ti=Xr[wr];this._layerConfigs[ti.id]=ti;var $r=this._layers[ti.id]=i.createStyleLayer(ti);$r._featureFilter=i.featureFilter($r.filter),this.keyCache[ti.id]&&delete this.keyCache[ti.id]}for(var Ri=0,Hi=tr;Ri<Hi.length;Ri+=1){var en=Hi[Ri];delete this.keyCache[en],delete this._layerConfigs[en],delete this._layers[en]}this.familiesBySource={};for(var fn=s(i.values(this._layerConfigs),this.keyCache),yn=0,Mn=fn;yn<Mn.length;yn+=1){var Ba=Mn[yn],ua=Ba.map(function(jn){return ir._layers[jn.id]}),ma=ua[0];if(ma.visibility!==\"none\"){var Wa=ma.source||\"\",za=this.familiesBySource[Wa];za||(za=this.familiesBySource[Wa]={});var Xo=ma.sourceLayer||\"_geojsonTileLayer\",da=za[Xo];da||(da=za[Xo]=[]),da.push(ua)}}};var u=1,c=function(wt){var tr={},ir=[];for(var wr in wt){var Xr=wt[wr],ti=tr[wr]={};for(var $r in Xr){var Ri=Xr[+$r];if(!(!Ri||Ri.bitmap.width===0||Ri.bitmap.height===0)){var Hi={x:0,y:0,w:Ri.bitmap.width+2*u,h:Ri.bitmap.height+2*u};ir.push(Hi),ti[$r]={rect:Hi,metrics:Ri.metrics}}}}var en=i.potpack(ir),fn=en.w,yn=en.h,Mn=new i.AlphaImage({width:fn||1,height:yn||1});for(var Ba in wt){var ua=wt[Ba];for(var ma in ua){var Wa=ua[+ma];if(!(!Wa||Wa.bitmap.width===0||Wa.bitmap.height===0)){var za=tr[Ba][ma].rect;i.AlphaImage.copy(Wa.bitmap,Mn,{x:0,y:0},{x:za.x+u,y:za.y+u},Wa.bitmap)}}}this.image=Mn,this.positions=tr};i.register(\"GlyphAtlas\",c);var f=function(wt){this.tileID=new i.OverscaledTileID(wt.tileID.overscaledZ,wt.tileID.wrap,wt.tileID.canonical.z,wt.tileID.canonical.x,wt.tileID.canonical.y),this.uid=wt.uid,this.zoom=wt.zoom,this.pixelRatio=wt.pixelRatio,this.tileSize=wt.tileSize,this.source=wt.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=wt.showCollisionBoxes,this.collectResourceTiming=!!wt.collectResourceTiming,this.returnDependencies=!!wt.returnDependencies,this.promoteId=wt.promoteId};f.prototype.parse=function(wt,tr,ir,wr,Xr){var ti=this;this.status=\"parsing\",this.data=wt,this.collisionBoxArray=new i.CollisionBoxArray;var $r=new i.DictionaryCoder(Object.keys(wt.layers).sort()),Ri=new i.FeatureIndex(this.tileID,this.promoteId);Ri.bucketLayerIDs=[];var Hi={},en={featureIndex:Ri,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:ir},fn=tr.familiesBySource[this.source];for(var yn in fn){var Mn=wt.layers[yn];if(Mn){Mn.version===1&&i.warnOnce('Vector tile source \"'+this.source+'\" layer \"'+yn+'\" does not use vector tile spec v2 and therefore may have some rendering errors.');for(var Ba=$r.encode(yn),ua=[],ma=0;ma<Mn.length;ma++){var Wa=Mn.feature(ma),za=Ri.getId(Wa,yn);ua.push({feature:Wa,id:za,index:ma,sourceLayerIndex:Ba})}for(var Xo=0,da=fn[yn];Xo<da.length;Xo+=1){var jn=da[Xo],Ha=jn[0];if(!(Ha.minzoom&&this.zoom<Math.floor(Ha.minzoom))&&!(Ha.maxzoom&&this.zoom>=Ha.maxzoom)&&Ha.visibility!==\"none\"){h(jn,this.zoom,ir);var vo=Hi[Ha.id]=Ha.createBucket({index:Ri.bucketLayerIDs.length,layers:jn,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:Ba,sourceID:this.source});vo.populate(ua,en,this.tileID.canonical),Ri.bucketLayerIDs.push(jn.map(function(ki){return ki.id}))}}}}var Gn,Ct,Sr,Jr,hi=i.mapObject(en.glyphDependencies,function(ki){return Object.keys(ki).map(Number)});Object.keys(hi).length?wr.send(\"getGlyphs\",{uid:this.uid,stacks:hi},function(ki,_n){Gn||(Gn=ki,Ct=_n,En.call(ti))}):Ct={};var hn=Object.keys(en.iconDependencies);hn.length?wr.send(\"getImages\",{icons:hn,source:this.source,tileID:this.tileID,type:\"icons\"},function(ki,_n){Gn||(Gn=ki,Sr=_n,En.call(ti))}):Sr={};var Sn=Object.keys(en.patternDependencies);Sn.length?wr.send(\"getImages\",{icons:Sn,source:this.source,tileID:this.tileID,type:\"patterns\"},function(ki,_n){Gn||(Gn=ki,Jr=_n,En.call(ti))}):Jr={},En.call(this);function En(){if(Gn)return Xr(Gn);if(Ct&&Sr&&Jr){var ki=new c(Ct),_n=new i.ImageAtlas(Sr,Jr);for(var ya in Hi){var ea=Hi[ya];ea instanceof i.SymbolBucket?(h(ea.layers,this.zoom,ir),i.performSymbolLayout(ea,Ct,ki.positions,Sr,_n.iconPositions,this.showCollisionBoxes,this.tileID.canonical)):ea.hasPattern&&(ea instanceof i.LineBucket||ea instanceof i.FillBucket||ea instanceof i.FillExtrusionBucket)&&(h(ea.layers,this.zoom,ir),ea.addFeatures(en,this.tileID.canonical,_n.patternPositions))}this.status=\"done\",Xr(null,{buckets:i.values(Hi).filter(function(Ma){return!Ma.isEmpty()}),featureIndex:Ri,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:ki.image,imageAtlas:_n,glyphMap:this.returnDependencies?Ct:null,iconMap:this.returnDependencies?Sr:null,glyphPositions:this.returnDependencies?ki.positions:null})}}};function h(Gt,wt,tr){for(var ir=new i.EvaluationParameters(wt),wr=0,Xr=Gt;wr<Xr.length;wr+=1){var ti=Xr[wr];ti.recalculate(ir,tr)}}function d(Gt,wt){var tr=i.getArrayBuffer(Gt.request,function(ir,wr,Xr,ti){ir?wt(ir):wr&&wt(null,{vectorTile:new i.vectorTile.VectorTile(new i.pbf(wr)),rawData:wr,cacheControl:Xr,expires:ti})});return function(){tr.cancel(),wt()}}var v=function(wt,tr,ir,wr){this.actor=wt,this.layerIndex=tr,this.availableImages=ir,this.loadVectorData=wr||d,this.loading={},this.loaded={}};v.prototype.loadTile=function(wt,tr){var ir=this,wr=wt.uid;this.loading||(this.loading={});var Xr=wt&&wt.request&&wt.request.collectResourceTiming?new i.RequestPerformance(wt.request):!1,ti=this.loading[wr]=new f(wt);ti.abort=this.loadVectorData(wt,function($r,Ri){if(delete ir.loading[wr],$r||!Ri)return ti.status=\"done\",ir.loaded[wr]=ti,tr($r);var Hi=Ri.rawData,en={};Ri.expires&&(en.expires=Ri.expires),Ri.cacheControl&&(en.cacheControl=Ri.cacheControl);var fn={};if(Xr){var yn=Xr.finish();yn&&(fn.resourceTiming=JSON.parse(JSON.stringify(yn)))}ti.vectorTile=Ri.vectorTile,ti.parse(Ri.vectorTile,ir.layerIndex,ir.availableImages,ir.actor,function(Mn,Ba){if(Mn||!Ba)return tr(Mn);tr(null,i.extend({rawTileData:Hi.slice(0)},Ba,en,fn))}),ir.loaded=ir.loaded||{},ir.loaded[wr]=ti})},v.prototype.reloadTile=function(wt,tr){var ir=this,wr=this.loaded,Xr=wt.uid,ti=this;if(wr&&wr[Xr]){var $r=wr[Xr];$r.showCollisionBoxes=wt.showCollisionBoxes;var Ri=function(Hi,en){var fn=$r.reloadCallback;fn&&(delete $r.reloadCallback,$r.parse($r.vectorTile,ti.layerIndex,ir.availableImages,ti.actor,fn)),tr(Hi,en)};$r.status===\"parsing\"?$r.reloadCallback=Ri:$r.status===\"done\"&&($r.vectorTile?$r.parse($r.vectorTile,this.layerIndex,this.availableImages,this.actor,Ri):Ri())}},v.prototype.abortTile=function(wt,tr){var ir=this.loading,wr=wt.uid;ir&&ir[wr]&&ir[wr].abort&&(ir[wr].abort(),delete ir[wr]),tr()},v.prototype.removeTile=function(wt,tr){var ir=this.loaded,wr=wt.uid;ir&&ir[wr]&&delete ir[wr],tr()};var m=i.window.ImageBitmap,b=function(){this.loaded={}};b.prototype.loadTile=function(wt,tr){var ir=wt.uid,wr=wt.encoding,Xr=wt.rawImageData,ti=m&&Xr instanceof m?this.getImageData(Xr):Xr,$r=new i.DEMData(ir,ti,wr);this.loaded=this.loaded||{},this.loaded[ir]=$r,tr(null,$r)},b.prototype.getImageData=function(wt){(!this.offscreenCanvas||!this.offscreenCanvasContext)&&(this.offscreenCanvas=new OffscreenCanvas(wt.width,wt.height),this.offscreenCanvasContext=this.offscreenCanvas.getContext(\"2d\")),this.offscreenCanvas.width=wt.width,this.offscreenCanvas.height=wt.height,this.offscreenCanvasContext.drawImage(wt,0,0,wt.width,wt.height);var tr=this.offscreenCanvasContext.getImageData(-1,-1,wt.width+2,wt.height+2);return this.offscreenCanvasContext.clearRect(0,0,this.offscreenCanvas.width,this.offscreenCanvas.height),new i.RGBAImage({width:tr.width,height:tr.height},tr.data)},b.prototype.removeTile=function(wt){var tr=this.loaded,ir=wt.uid;tr&&tr[ir]&&delete tr[ir]};var p=k;function k(Gt,wt){var tr=Gt&&Gt.type,ir;if(tr===\"FeatureCollection\")for(ir=0;ir<Gt.features.length;ir++)k(Gt.features[ir],wt);else if(tr===\"GeometryCollection\")for(ir=0;ir<Gt.geometries.length;ir++)k(Gt.geometries[ir],wt);else if(tr===\"Feature\")k(Gt.geometry,wt);else if(tr===\"Polygon\")M(Gt.coordinates,wt);else if(tr===\"MultiPolygon\")for(ir=0;ir<Gt.coordinates.length;ir++)M(Gt.coordinates[ir],wt);return Gt}function M(Gt,wt){if(Gt.length!==0){T(Gt[0],wt);for(var tr=1;tr<Gt.length;tr++)T(Gt[tr],!wt)}}function T(Gt,wt){for(var tr=0,ir=0,wr=Gt.length,Xr=wr-1;ir<wr;Xr=ir++)tr+=(Gt[ir][0]-Gt[Xr][0])*(Gt[Xr][1]+Gt[ir][1]);tr>=0!=!!wt&&Gt.reverse()}var L=i.vectorTile.VectorTileFeature.prototype.toGeoJSON,x=function(wt){this._feature=wt,this.extent=i.EXTENT,this.type=wt.type,this.properties=wt.tags,\"id\"in wt&&!isNaN(wt.id)&&(this.id=parseInt(wt.id,10))};x.prototype.loadGeometry=function(){if(this._feature.type===1){for(var wt=[],tr=0,ir=this._feature.geometry;tr<ir.length;tr+=1){var wr=ir[tr];wt.push([new i.Point$1(wr[0],wr[1])])}return wt}else{for(var Xr=[],ti=0,$r=this._feature.geometry;ti<$r.length;ti+=1){for(var Ri=$r[ti],Hi=[],en=0,fn=Ri;en<fn.length;en+=1){var yn=fn[en];Hi.push(new i.Point$1(yn[0],yn[1]))}Xr.push(Hi)}return Xr}},x.prototype.toGeoJSON=function(wt,tr,ir){return L.call(this,wt,tr,ir)};var C=function(wt){this.layers={_geojsonTileLayer:this},this.name=\"_geojsonTileLayer\",this.extent=i.EXTENT,this.length=wt.length,this._features=wt};C.prototype.feature=function(wt){return new x(this._features[wt])};var S=i.vectorTile.VectorTileFeature,g=P;function P(Gt,wt){this.options=wt||{},this.features=Gt,this.length=Gt.length}P.prototype.feature=function(Gt){return new E(this.features[Gt],this.options.extent)};function E(Gt,wt){this.id=typeof Gt.id==\"number\"?Gt.id:void 0,this.type=Gt.type,this.rawGeometry=Gt.type===1?[Gt.geometry]:Gt.geometry,this.properties=Gt.tags,this.extent=wt||4096}E.prototype.loadGeometry=function(){var Gt=this.rawGeometry;this.geometry=[];for(var wt=0;wt<Gt.length;wt++){for(var tr=Gt[wt],ir=[],wr=0;wr<tr.length;wr++)ir.push(new i.Point$1(tr[wr][0],tr[wr][1]));this.geometry.push(ir)}return this.geometry},E.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var Gt=this.geometry,wt=1/0,tr=-1/0,ir=1/0,wr=-1/0,Xr=0;Xr<Gt.length;Xr++)for(var ti=Gt[Xr],$r=0;$r<ti.length;$r++){var Ri=ti[$r];wt=Math.min(wt,Ri.x),tr=Math.max(tr,Ri.x),ir=Math.min(ir,Ri.y),wr=Math.max(wr,Ri.y)}return[wt,ir,tr,wr]},E.prototype.toGeoJSON=S.prototype.toGeoJSON;var z=Z,q=Z,V=j,G=g;function Z(Gt){var wt=new i.pbf;return N(Gt,wt),wt.finish()}function j(Gt,wt){wt=wt||{};var tr={};for(var ir in Gt)tr[ir]=new g(Gt[ir].features,wt),tr[ir].name=ir,tr[ir].version=wt.version,tr[ir].extent=wt.extent;return Z({layers:tr})}function N(Gt,wt){for(var tr in Gt.layers)wt.writeMessage(3,H,Gt.layers[tr])}function H(Gt,wt){wt.writeVarintField(15,Gt.version||1),wt.writeStringField(1,Gt.name||\"\"),wt.writeVarintField(5,Gt.extent||4096);var tr,ir={keys:[],values:[],keycache:{},valuecache:{}};for(tr=0;tr<Gt.length;tr++)ir.feature=Gt.feature(tr),wt.writeMessage(2,re,ir);var wr=ir.keys;for(tr=0;tr<wr.length;tr++)wt.writeStringField(3,wr[tr]);var Xr=ir.values;for(tr=0;tr<Xr.length;tr++)wt.writeMessage(4,ge,Xr[tr])}function re(Gt,wt){var tr=Gt.feature;tr.id!==void 0&&wt.writeVarintField(1,tr.id),wt.writeMessage(2,ae,Gt),wt.writeVarintField(3,tr.type),wt.writeMessage(4,Ce,tr)}function ae(Gt,wt){var tr=Gt.feature,ir=Gt.keys,wr=Gt.values,Xr=Gt.keycache,ti=Gt.valuecache;for(var $r in tr.properties){var Ri=Xr[$r];typeof Ri==\"undefined\"&&(ir.push($r),Ri=ir.length-1,Xr[$r]=Ri),wt.writeVarint(Ri);var Hi=tr.properties[$r],en=typeof Hi;en!==\"string\"&&en!==\"boolean\"&&en!==\"number\"&&(Hi=JSON.stringify(Hi));var fn=en+\":\"+Hi,yn=ti[fn];typeof yn==\"undefined\"&&(wr.push(Hi),yn=wr.length-1,ti[fn]=yn),wt.writeVarint(yn)}}function _e(Gt,wt){return(wt<<3)+(Gt&7)}function Ee(Gt){return Gt<<1^Gt>>31}function Ce(Gt,wt){for(var tr=Gt.loadGeometry(),ir=Gt.type,wr=0,Xr=0,ti=tr.length,$r=0;$r<ti;$r++){var Ri=tr[$r],Hi=1;ir===1&&(Hi=Ri.length),wt.writeVarint(_e(1,Hi));for(var en=ir===3?Ri.length-1:Ri.length,fn=0;fn<en;fn++){fn===1&&ir!==1&&wt.writeVarint(_e(2,en-1));var yn=Ri[fn].x-wr,Mn=Ri[fn].y-Xr;wt.writeVarint(Ee(yn)),wt.writeVarint(Ee(Mn)),wr+=yn,Xr+=Mn}ir===3&&wt.writeVarint(_e(7,1))}}function ge(Gt,wt){var tr=typeof Gt;tr===\"string\"?wt.writeStringField(1,Gt):tr===\"boolean\"?wt.writeBooleanField(7,Gt):tr===\"number\"&&(Gt%1!==0?wt.writeDoubleField(3,Gt):Gt<0?wt.writeSVarintField(6,Gt):wt.writeVarintField(5,Gt))}z.fromVectorTileJs=q,z.fromGeojsonVt=V,z.GeoJSONWrapper=G;function ie(Gt,wt,tr,ir,wr,Xr){if(!(wr-ir<=tr)){var ti=ir+wr>>1;Se(Gt,wt,ti,ir,wr,Xr%2),ie(Gt,wt,tr,ir,ti-1,Xr+1),ie(Gt,wt,tr,ti+1,wr,Xr+1)}}function Se(Gt,wt,tr,ir,wr,Xr){for(;wr>ir;){if(wr-ir>600){var ti=wr-ir+1,$r=tr-ir+1,Ri=Math.log(ti),Hi=.5*Math.exp(2*Ri/3),en=.5*Math.sqrt(Ri*Hi*(ti-Hi)/ti)*($r-ti/2<0?-1:1),fn=Math.max(ir,Math.floor(tr-$r*Hi/ti+en)),yn=Math.min(wr,Math.floor(tr+(ti-$r)*Hi/ti+en));Se(Gt,wt,tr,fn,yn,Xr)}var Mn=wt[2*tr+Xr],Ba=ir,ua=wr;for(ke(Gt,wt,ir,tr),wt[2*wr+Xr]>Mn&&ke(Gt,wt,ir,wr);Ba<ua;){for(ke(Gt,wt,Ba,ua),Ba++,ua--;wt[2*Ba+Xr]<Mn;)Ba++;for(;wt[2*ua+Xr]>Mn;)ua--}wt[2*ir+Xr]===Mn?ke(Gt,wt,ir,ua):(ua++,ke(Gt,wt,ua,wr)),ua<=tr&&(ir=ua+1),tr<=ua&&(wr=ua-1)}}function ke(Gt,wt,tr,ir){be(Gt,tr,ir),be(wt,2*tr,2*ir),be(wt,2*tr+1,2*ir+1)}function be(Gt,wt,tr){var ir=Gt[wt];Gt[wt]=Gt[tr],Gt[tr]=ir}function Be(Gt,wt,tr,ir,wr,Xr,ti){for(var $r=[0,Gt.length-1,0],Ri=[],Hi,en;$r.length;){var fn=$r.pop(),yn=$r.pop(),Mn=$r.pop();if(yn-Mn<=ti){for(var Ba=Mn;Ba<=yn;Ba++)Hi=wt[2*Ba],en=wt[2*Ba+1],Hi>=tr&&Hi<=wr&&en>=ir&&en<=Xr&&Ri.push(Gt[Ba]);continue}var ua=Math.floor((Mn+yn)/2);Hi=wt[2*ua],en=wt[2*ua+1],Hi>=tr&&Hi<=wr&&en>=ir&&en<=Xr&&Ri.push(Gt[ua]);var ma=(fn+1)%2;(fn===0?tr<=Hi:ir<=en)&&($r.push(Mn),$r.push(ua-1),$r.push(ma)),(fn===0?wr>=Hi:Xr>=en)&&($r.push(ua+1),$r.push(yn),$r.push(ma))}return Ri}function Le(Gt,wt,tr,ir,wr,Xr){for(var ti=[0,Gt.length-1,0],$r=[],Ri=wr*wr;ti.length;){var Hi=ti.pop(),en=ti.pop(),fn=ti.pop();if(en-fn<=Xr){for(var yn=fn;yn<=en;yn++)me(wt[2*yn],wt[2*yn+1],tr,ir)<=Ri&&$r.push(Gt[yn]);continue}var Mn=Math.floor((fn+en)/2),Ba=wt[2*Mn],ua=wt[2*Mn+1];me(Ba,ua,tr,ir)<=Ri&&$r.push(Gt[Mn]);var ma=(Hi+1)%2;(Hi===0?tr-wr<=Ba:ir-wr<=ua)&&(ti.push(fn),ti.push(Mn-1),ti.push(ma)),(Hi===0?tr+wr>=Ba:ir+wr>=ua)&&(ti.push(Mn+1),ti.push(en),ti.push(ma))}return $r}function me(Gt,wt,tr,ir){var wr=Gt-tr,Xr=wt-ir;return wr*wr+Xr*Xr}var Pe=function(Gt){return Gt[0]},ce=function(Gt){return Gt[1]},He=function(wt,tr,ir,wr,Xr){tr===void 0&&(tr=Pe),ir===void 0&&(ir=ce),wr===void 0&&(wr=64),Xr===void 0&&(Xr=Float64Array),this.nodeSize=wr,this.points=wt;for(var ti=wt.length<65536?Uint16Array:Uint32Array,$r=this.ids=new ti(wt.length),Ri=this.coords=new Xr(wt.length*2),Hi=0;Hi<wt.length;Hi++)$r[Hi]=Hi,Ri[2*Hi]=tr(wt[Hi]),Ri[2*Hi+1]=ir(wt[Hi]);ie($r,Ri,wr,0,$r.length-1,0)};He.prototype.range=function(wt,tr,ir,wr){return Be(this.ids,this.coords,wt,tr,ir,wr,this.nodeSize)},He.prototype.within=function(wt,tr,ir){return Le(this.ids,this.coords,wt,tr,ir,this.nodeSize)};var lt={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(Gt){return Gt}},mt=function(wt){this.options=Ue(Object.create(lt),wt),this.trees=new Array(this.options.maxZoom+1)};mt.prototype.load=function(wt){var tr=this.options,ir=tr.log,wr=tr.minZoom,Xr=tr.maxZoom,ti=tr.nodeSize;ir&&console.time(\"total time\");var $r=\"prepare \"+wt.length+\" points\";ir&&console.time($r),this.points=wt;for(var Ri=[],Hi=0;Hi<wt.length;Hi++)wt[Hi].geometry&&Ri.push(at(wt[Hi],Hi));this.trees[Xr+1]=new He(Ri,Ae,rt,ti,Float32Array),ir&&console.timeEnd($r);for(var en=Xr;en>=wr;en--){var fn=+Date.now();Ri=this._cluster(Ri,en),this.trees[en]=new He(Ri,Ae,rt,ti,Float32Array),ir&&console.log(\"z%d: %d clusters in %dms\",en,Ri.length,+Date.now()-fn)}return ir&&console.timeEnd(\"total time\"),this},mt.prototype.getClusters=function(wt,tr){var ir=((wt[0]+180)%360+360)%360-180,wr=Math.max(-90,Math.min(90,wt[1])),Xr=wt[2]===180?180:((wt[2]+180)%360+360)%360-180,ti=Math.max(-90,Math.min(90,wt[3]));if(wt[2]-wt[0]>=360)ir=-180,Xr=180;else if(ir>Xr){var $r=this.getClusters([ir,wr,180,ti],tr),Ri=this.getClusters([-180,wr,Xr,ti],tr);return $r.concat(Ri)}for(var Hi=this.trees[this._limitZoom(tr)],en=Hi.range(Vt(ir),rr(ti),Vt(Xr),rr(wr)),fn=[],yn=0,Mn=en;yn<Mn.length;yn+=1){var Ba=Mn[yn],ua=Hi.points[Ba];fn.push(ua.numPoints?ct(ua):this.points[ua.index])}return fn},mt.prototype.getChildren=function(wt){var tr=this._getOriginId(wt),ir=this._getOriginZoom(wt),wr=\"No cluster with the specified id.\",Xr=this.trees[ir];if(!Xr)throw new Error(wr);var ti=Xr.points[tr];if(!ti)throw new Error(wr);for(var $r=this.options.radius/(this.options.extent*Math.pow(2,ir-1)),Ri=Xr.within(ti.x,ti.y,$r),Hi=[],en=0,fn=Ri;en<fn.length;en+=1){var yn=fn[en],Mn=Xr.points[yn];Mn.parentId===wt&&Hi.push(Mn.numPoints?ct(Mn):this.points[Mn.index])}if(Hi.length===0)throw new Error(wr);return Hi},mt.prototype.getLeaves=function(wt,tr,ir){tr=tr||10,ir=ir||0;var wr=[];return this._appendLeaves(wr,wt,tr,ir,0),wr},mt.prototype.getTile=function(wt,tr,ir){var wr=this.trees[this._limitZoom(wt)],Xr=Math.pow(2,wt),ti=this.options,$r=ti.extent,Ri=ti.radius,Hi=Ri/$r,en=(ir-Hi)/Xr,fn=(ir+1+Hi)/Xr,yn={features:[]};return this._addTileFeatures(wr.range((tr-Hi)/Xr,en,(tr+1+Hi)/Xr,fn),wr.points,tr,ir,Xr,yn),tr===0&&this._addTileFeatures(wr.range(1-Hi/Xr,en,1,fn),wr.points,Xr,ir,Xr,yn),tr===Xr-1&&this._addTileFeatures(wr.range(0,en,Hi/Xr,fn),wr.points,-1,ir,Xr,yn),yn.features.length?yn:null},mt.prototype.getClusterExpansionZoom=function(wt){for(var tr=this._getOriginZoom(wt)-1;tr<=this.options.maxZoom;){var ir=this.getChildren(wt);if(tr++,ir.length!==1)break;wt=ir[0].properties.cluster_id}return tr},mt.prototype._appendLeaves=function(wt,tr,ir,wr,Xr){for(var ti=this.getChildren(tr),$r=0,Ri=ti;$r<Ri.length;$r+=1){var Hi=Ri[$r],en=Hi.properties;if(en&&en.cluster?Xr+en.point_count<=wr?Xr+=en.point_count:Xr=this._appendLeaves(wt,en.cluster_id,ir,wr,Xr):Xr<wr?Xr++:wt.push(Hi),wt.length===ir)break}return Xr},mt.prototype._addTileFeatures=function(wt,tr,ir,wr,Xr,ti){for(var $r=0,Ri=wt;$r<Ri.length;$r+=1){var Hi=Ri[$r],en=tr[Hi],fn=en.numPoints,yn={type:1,geometry:[[Math.round(this.options.extent*(en.x*Xr-ir)),Math.round(this.options.extent*(en.y*Xr-wr))]],tags:fn?ar(en):this.points[en.index].properties},Mn=void 0;fn?Mn=en.id:this.options.generateId?Mn=en.index:this.points[en.index].id&&(Mn=this.points[en.index].id),Mn!==void 0&&(yn.id=Mn),ti.features.push(yn)}},mt.prototype._limitZoom=function(wt){return Math.max(this.options.minZoom,Math.min(+wt,this.options.maxZoom+1))},mt.prototype._cluster=function(wt,tr){for(var ir=[],wr=this.options,Xr=wr.radius,ti=wr.extent,$r=wr.reduce,Ri=wr.minPoints,Hi=Xr/(ti*Math.pow(2,tr)),en=0;en<wt.length;en++){var fn=wt[en];if(!(fn.zoom<=tr)){fn.zoom=tr;for(var yn=this.trees[tr+1],Mn=yn.within(fn.x,fn.y,Hi),Ba=fn.numPoints||1,ua=Ba,ma=0,Wa=Mn;ma<Wa.length;ma+=1){var za=Wa[ma],Xo=yn.points[za];Xo.zoom>tr&&(ua+=Xo.numPoints||1)}if(ua>=Ri){for(var da=fn.x*Ba,jn=fn.y*Ba,Ha=$r&&Ba>1?this._map(fn,!0):null,vo=(en<<5)+(tr+1)+this.points.length,Gn=0,Ct=Mn;Gn<Ct.length;Gn+=1){var Sr=Ct[Gn],Jr=yn.points[Sr];if(!(Jr.zoom<=tr)){Jr.zoom=tr;var hi=Jr.numPoints||1;da+=Jr.x*hi,jn+=Jr.y*hi,Jr.parentId=vo,$r&&(Ha||(Ha=this._map(fn,!0)),$r(Ha,this._map(Jr)))}}fn.parentId=vo,ir.push(Ht(da/ua,jn/ua,vo,ua,Ha))}else if(ir.push(fn),ua>1)for(var hn=0,Sn=Mn;hn<Sn.length;hn+=1){var En=Sn[hn],ki=yn.points[En];ki.zoom<=tr||(ki.zoom=tr,ir.push(ki))}}}return ir},mt.prototype._getOriginId=function(wt){return wt-this.points.length>>5},mt.prototype._getOriginZoom=function(wt){return(wt-this.points.length)%32},mt.prototype._map=function(wt,tr){if(wt.numPoints)return tr?Ue({},wt.properties):wt.properties;var ir=this.points[wt.index].properties,wr=this.options.map(ir);return tr&&wr===ir?Ue({},wr):wr};function Ht(Gt,wt,tr,ir,wr){return{x:Gt,y:wt,zoom:1/0,id:tr,parentId:-1,numPoints:ir,properties:wr}}function at(Gt,wt){var tr=Gt.geometry.coordinates,ir=tr[0],wr=tr[1];return{x:Vt(ir),y:rr(wr),zoom:1/0,index:wt,parentId:-1}}function ct(Gt){return{type:\"Feature\",id:Gt.id,properties:ar(Gt),geometry:{type:\"Point\",coordinates:[tt(Gt.x),je(Gt.y)]}}}function ar(Gt){var wt=Gt.numPoints,tr=wt>=1e4?Math.round(wt/1e3)+\"k\":wt>=1e3?Math.round(wt/100)/10+\"k\":wt;return Ue(Ue({},Gt.properties),{cluster:!0,cluster_id:Gt.id,point_count:wt,point_count_abbreviated:tr})}function Vt(Gt){return Gt/360+.5}function rr(Gt){var wt=Math.sin(Gt*Math.PI/180),tr=.5-.25*Math.log((1+wt)/(1-wt))/Math.PI;return tr<0?0:tr>1?1:tr}function tt(Gt){return(Gt-.5)*360}function je(Gt){var wt=(180-Gt*360)*Math.PI/180;return 360*Math.atan(Math.exp(wt))/Math.PI-90}function Ue(Gt,wt){for(var tr in wt)Gt[tr]=wt[tr];return Gt}function Ae(Gt){return Gt.x}function rt(Gt){return Gt.y}function St(Gt,wt,tr,ir){for(var wr=ir,Xr=tr-wt>>1,ti=tr-wt,$r,Ri=Gt[wt],Hi=Gt[wt+1],en=Gt[tr],fn=Gt[tr+1],yn=wt+3;yn<tr;yn+=3){var Mn=Tt(Gt[yn],Gt[yn+1],Ri,Hi,en,fn);if(Mn>wr)$r=yn,wr=Mn;else if(Mn===wr){var Ba=Math.abs(yn-Xr);Ba<ti&&($r=yn,ti=Ba)}}wr>ir&&($r-wt>3&&St(Gt,wt,$r,ir),Gt[$r+2]=wr,tr-$r>3&&St(Gt,$r,tr,ir))}function Tt(Gt,wt,tr,ir,wr,Xr){var ti=wr-tr,$r=Xr-ir;if(ti!==0||$r!==0){var Ri=((Gt-tr)*ti+(wt-ir)*$r)/(ti*ti+$r*$r);Ri>1?(tr=wr,ir=Xr):Ri>0&&(tr+=ti*Ri,ir+=$r*Ri)}return ti=Gt-tr,$r=wt-ir,ti*ti+$r*$r}function dt(Gt,wt,tr,ir){var wr={id:typeof Gt==\"undefined\"?null:Gt,type:wt,geometry:tr,tags:ir,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return Et(wr),wr}function Et(Gt){var wt=Gt.geometry,tr=Gt.type;if(tr===\"Point\"||tr===\"MultiPoint\"||tr===\"LineString\")pt(Gt,wt);else if(tr===\"Polygon\"||tr===\"MultiLineString\")for(var ir=0;ir<wt.length;ir++)pt(Gt,wt[ir]);else if(tr===\"MultiPolygon\")for(ir=0;ir<wt.length;ir++)for(var wr=0;wr<wt[ir].length;wr++)pt(Gt,wt[ir][wr])}function pt(Gt,wt){for(var tr=0;tr<wt.length;tr+=3)Gt.minX=Math.min(Gt.minX,wt[tr]),Gt.minY=Math.min(Gt.minY,wt[tr+1]),Gt.maxX=Math.max(Gt.maxX,wt[tr]),Gt.maxY=Math.max(Gt.maxY,wt[tr+1])}function jt(Gt,wt){var tr=[];if(Gt.type===\"FeatureCollection\")for(var ir=0;ir<Gt.features.length;ir++)or(tr,Gt.features[ir],wt,ir);else Gt.type===\"Feature\"?or(tr,Gt,wt):or(tr,{geometry:Gt},wt);return tr}function or(Gt,wt,tr,ir){if(wt.geometry){var wr=wt.geometry.coordinates,Xr=wt.geometry.type,ti=Math.pow(tr.tolerance/((1<<tr.maxZoom)*tr.extent),2),$r=[],Ri=wt.id;if(tr.promoteId?Ri=wt.properties[tr.promoteId]:tr.generateId&&(Ri=ir||0),Xr===\"Point\")mr(wr,$r);else if(Xr===\"MultiPoint\")for(var Hi=0;Hi<wr.length;Hi++)mr(wr[Hi],$r);else if(Xr===\"LineString\")Ar(wr,$r,ti,!1);else if(Xr===\"MultiLineString\")if(tr.lineMetrics){for(Hi=0;Hi<wr.length;Hi++)$r=[],Ar(wr[Hi],$r,ti,!1),Gt.push(dt(Ri,\"LineString\",$r,wt.properties));return}else ei(wr,$r,ti,!1);else if(Xr===\"Polygon\")ei(wr,$r,ti,!0);else if(Xr===\"MultiPolygon\")for(Hi=0;Hi<wr.length;Hi++){var en=[];ei(wr[Hi],en,ti,!0),$r.push(en)}else if(Xr===\"GeometryCollection\"){for(Hi=0;Hi<wt.geometry.geometries.length;Hi++)or(Gt,{id:Ri,geometry:wt.geometry.geometries[Hi],properties:wt.properties},tr,ir);return}else throw new Error(\"Input data is not a valid GeoJSON object.\");Gt.push(dt(Ri,Xr,$r,wt.properties))}}function mr(Gt,wt){wt.push(qr(Gt[0])),wt.push(jr(Gt[1])),wt.push(0)}function Ar(Gt,wt,tr,ir){for(var wr,Xr,ti=0,$r=0;$r<Gt.length;$r++){var Ri=qr(Gt[$r][0]),Hi=jr(Gt[$r][1]);wt.push(Ri),wt.push(Hi),wt.push(0),$r>0&&(ir?ti+=(wr*Hi-Ri*Xr)/2:ti+=Math.sqrt(Math.pow(Ri-wr,2)+Math.pow(Hi-Xr,2))),wr=Ri,Xr=Hi}var en=wt.length-3;wt[2]=1,St(wt,0,en,tr),wt[en+2]=1,wt.size=Math.abs(ti),wt.start=0,wt.end=wt.size}function ei(Gt,wt,tr,ir){for(var wr=0;wr<Gt.length;wr++){var Xr=[];Ar(Gt[wr],Xr,tr,ir),wt.push(Xr)}}function qr(Gt){return Gt/360+.5}function jr(Gt){var wt=Math.sin(Gt*Math.PI/180),tr=.5-.25*Math.log((1+wt)/(1-wt))/Math.PI;return tr<0?0:tr>1?1:tr}function gt(Gt,wt,tr,ir,wr,Xr,ti,$r){if(tr/=wt,ir/=wt,Xr>=tr&&ti<ir)return Gt;if(ti<tr||Xr>=ir)return null;for(var Ri=[],Hi=0;Hi<Gt.length;Hi++){var en=Gt[Hi],fn=en.geometry,yn=en.type,Mn=wr===0?en.minX:en.minY,Ba=wr===0?en.maxX:en.maxY;if(Mn>=tr&&Ba<ir){Ri.push(en);continue}else if(Ba<tr||Mn>=ir)continue;var ua=[];if(yn===\"Point\"||yn===\"MultiPoint\")Ge(fn,ua,tr,ir,wr);else if(yn===\"LineString\")Je(fn,ua,tr,ir,wr,!1,$r.lineMetrics);else if(yn===\"MultiLineString\")et(fn,ua,tr,ir,wr,!1);else if(yn===\"Polygon\")et(fn,ua,tr,ir,wr,!0);else if(yn===\"MultiPolygon\")for(var ma=0;ma<fn.length;ma++){var Wa=[];et(fn[ma],Wa,tr,ir,wr,!0),Wa.length&&ua.push(Wa)}if(ua.length){if($r.lineMetrics&&yn===\"LineString\"){for(ma=0;ma<ua.length;ma++)Ri.push(dt(en.id,yn,ua[ma],en.tags));continue}(yn===\"LineString\"||yn===\"MultiLineString\")&&(ua.length===1?(yn=\"LineString\",ua=ua[0]):yn=\"MultiLineString\"),(yn===\"Point\"||yn===\"MultiPoint\")&&(yn=ua.length===3?\"Point\":\"MultiPoint\"),Ri.push(dt(en.id,yn,ua,en.tags))}}return Ri.length?Ri:null}function Ge(Gt,wt,tr,ir,wr){for(var Xr=0;Xr<Gt.length;Xr+=3){var ti=Gt[Xr+wr];ti>=tr&&ti<=ir&&(wt.push(Gt[Xr]),wt.push(Gt[Xr+1]),wt.push(Gt[Xr+2]))}}function Je(Gt,wt,tr,ir,wr,Xr,ti){for(var $r=We(Gt),Ri=wr===0?At:Kt,Hi=Gt.start,en,fn,yn=0;yn<Gt.length-3;yn+=3){var Mn=Gt[yn],Ba=Gt[yn+1],ua=Gt[yn+2],ma=Gt[yn+3],Wa=Gt[yn+4],za=wr===0?Mn:Ba,Xo=wr===0?ma:Wa,da=!1;ti&&(en=Math.sqrt(Math.pow(Mn-ma,2)+Math.pow(Ba-Wa,2))),za<tr?Xo>tr&&(fn=Ri($r,Mn,Ba,ma,Wa,tr),ti&&($r.start=Hi+en*fn)):za>ir?Xo<ir&&(fn=Ri($r,Mn,Ba,ma,Wa,ir),ti&&($r.start=Hi+en*fn)):xt($r,Mn,Ba,ua),Xo<tr&&za>=tr&&(fn=Ri($r,Mn,Ba,ma,Wa,tr),da=!0),Xo>ir&&za<=ir&&(fn=Ri($r,Mn,Ba,ma,Wa,ir),da=!0),!Xr&&da&&(ti&&($r.end=Hi+en*fn),wt.push($r),$r=We(Gt)),ti&&(Hi+=en)}var jn=Gt.length-3;Mn=Gt[jn],Ba=Gt[jn+1],ua=Gt[jn+2],za=wr===0?Mn:Ba,za>=tr&&za<=ir&&xt($r,Mn,Ba,ua),jn=$r.length-3,Xr&&jn>=3&&($r[jn]!==$r[0]||$r[jn+1]!==$r[1])&&xt($r,$r[0],$r[1],$r[2]),$r.length&&wt.push($r)}function We(Gt){var wt=[];return wt.size=Gt.size,wt.start=Gt.start,wt.end=Gt.end,wt}function et(Gt,wt,tr,ir,wr,Xr){for(var ti=0;ti<Gt.length;ti++)Je(Gt[ti],wt,tr,ir,wr,Xr,!1)}function xt(Gt,wt,tr,ir){Gt.push(wt),Gt.push(tr),Gt.push(ir)}function At(Gt,wt,tr,ir,wr,Xr){var ti=(Xr-wt)/(ir-wt);return Gt.push(Xr),Gt.push(tr+(wr-tr)*ti),Gt.push(1),ti}function Kt(Gt,wt,tr,ir,wr,Xr){var ti=(Xr-tr)/(wr-tr);return Gt.push(wt+(ir-wt)*ti),Gt.push(Xr),Gt.push(1),ti}function Qt(Gt,wt){var tr=wt.buffer/wt.extent,ir=Gt,wr=gt(Gt,1,-1-tr,tr,0,-1,2,wt),Xr=gt(Gt,1,1-tr,2+tr,0,-1,2,wt);return(wr||Xr)&&(ir=gt(Gt,1,-tr,1+tr,0,-1,2,wt)||[],wr&&(ir=Mr(wr,1).concat(ir)),Xr&&(ir=ir.concat(Mr(Xr,-1)))),ir}function Mr(Gt,wt){for(var tr=[],ir=0;ir<Gt.length;ir++){var wr=Gt[ir],Xr=wr.type,ti;if(Xr===\"Point\"||Xr===\"MultiPoint\"||Xr===\"LineString\")ti=Gr(wr.geometry,wt);else if(Xr===\"MultiLineString\"||Xr===\"Polygon\"){ti=[];for(var $r=0;$r<wr.geometry.length;$r++)ti.push(Gr(wr.geometry[$r],wt))}else if(Xr===\"MultiPolygon\")for(ti=[],$r=0;$r<wr.geometry.length;$r++){for(var Ri=[],Hi=0;Hi<wr.geometry[$r].length;Hi++)Ri.push(Gr(wr.geometry[$r][Hi],wt));ti.push(Ri)}tr.push(dt(wr.id,Xr,ti,wr.tags))}return tr}function Gr(Gt,wt){var tr=[];tr.size=Gt.size,Gt.start!==void 0&&(tr.start=Gt.start,tr.end=Gt.end);for(var ir=0;ir<Gt.length;ir+=3)tr.push(Gt[ir]+wt,Gt[ir+1],Gt[ir+2]);return tr}function Ir(Gt,wt){if(Gt.transformed)return Gt;var tr=1<<Gt.z,ir=Gt.x,wr=Gt.y,Xr,ti,$r;for(Xr=0;Xr<Gt.features.length;Xr++){var Ri=Gt.features[Xr],Hi=Ri.geometry,en=Ri.type;if(Ri.geometry=[],en===1)for(ti=0;ti<Hi.length;ti+=2)Ri.geometry.push(Yr(Hi[ti],Hi[ti+1],wt,tr,ir,wr));else for(ti=0;ti<Hi.length;ti++){var fn=[];for($r=0;$r<Hi[ti].length;$r+=2)fn.push(Yr(Hi[ti][$r],Hi[ti][$r+1],wt,tr,ir,wr));Ri.geometry.push(fn)}}return Gt.transformed=!0,Gt}function Yr(Gt,wt,tr,ir,wr,Xr){return[Math.round(tr*(Gt*ir-wr)),Math.round(tr*(wt*ir-Xr))]}function _i(Gt,wt,tr,ir,wr){for(var Xr=wt===wr.maxZoom?0:wr.tolerance/((1<<wt)*wr.extent),ti={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:tr,y:ir,z:wt,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},$r=0;$r<Gt.length;$r++){ti.numFeatures++,Pi(ti,Gt[$r],Xr,wr);var Ri=Gt[$r].minX,Hi=Gt[$r].minY,en=Gt[$r].maxX,fn=Gt[$r].maxY;Ri<ti.minX&&(ti.minX=Ri),Hi<ti.minY&&(ti.minY=Hi),en>ti.maxX&&(ti.maxX=en),fn>ti.maxY&&(ti.maxY=fn)}return ti}function Pi(Gt,wt,tr,ir){var wr=wt.geometry,Xr=wt.type,ti=[];if(Xr===\"Point\"||Xr===\"MultiPoint\")for(var $r=0;$r<wr.length;$r+=3)ti.push(wr[$r]),ti.push(wr[$r+1]),Gt.numPoints++,Gt.numSimplified++;else if(Xr===\"LineString\")ai(ti,wr,Gt,tr,!1,!1);else if(Xr===\"MultiLineString\"||Xr===\"Polygon\")for($r=0;$r<wr.length;$r++)ai(ti,wr[$r],Gt,tr,Xr===\"Polygon\",$r===0);else if(Xr===\"MultiPolygon\")for(var Ri=0;Ri<wr.length;Ri++){var Hi=wr[Ri];for($r=0;$r<Hi.length;$r++)ai(ti,Hi[$r],Gt,tr,!0,$r===0)}if(ti.length){var en=wt.tags||null;if(Xr===\"LineString\"&&ir.lineMetrics){en={};for(var fn in wt.tags)en[fn]=wt.tags[fn];en.mapbox_clip_start=wr.start/wr.size,en.mapbox_clip_end=wr.end/wr.size}var yn={geometry:ti,type:Xr===\"Polygon\"||Xr===\"MultiPolygon\"?3:Xr===\"LineString\"||Xr===\"MultiLineString\"?2:1,tags:en};wt.id!==null&&(yn.id=wt.id),Gt.features.push(yn)}}function ai(Gt,wt,tr,ir,wr,Xr){var ti=ir*ir;if(ir>0&&wt.size<(wr?ti:ir)){tr.numPoints+=wt.length/3;return}for(var $r=[],Ri=0;Ri<wt.length;Ri+=3)(ir===0||wt[Ri+2]>ti)&&(tr.numSimplified++,$r.push(wt[Ri]),$r.push(wt[Ri+1])),tr.numPoints++;wr&&mi($r,Xr),Gt.push($r)}function mi(Gt,wt){for(var tr=0,ir=0,wr=Gt.length,Xr=wr-2;ir<wr;Xr=ir,ir+=2)tr+=(Gt[ir]-Gt[Xr])*(Gt[ir+1]+Gt[Xr+1]);if(tr>0===wt)for(ir=0,wr=Gt.length;ir<wr/2;ir+=2){var ti=Gt[ir],$r=Gt[ir+1];Gt[ir]=Gt[wr-2-ir],Gt[ir+1]=Gt[wr-1-ir],Gt[wr-2-ir]=ti,Gt[wr-1-ir]=$r}}function un(Gt,wt){return new Fn(Gt,wt)}function Fn(Gt,wt){wt=this.options=Hn(Object.create(this.options),wt);var tr=wt.debug;if(tr&&console.time(\"preprocess data\"),wt.maxZoom<0||wt.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(wt.promoteId&&wt.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var ir=jt(Gt,wt);this.tiles={},this.tileCoords=[],tr&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",wt.indexMaxZoom,wt.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),ir=Qt(ir,wt),ir.length&&this.splitTile(ir,0,0,0),tr&&(ir.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}Fn.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Fn.prototype.splitTile=function(Gt,wt,tr,ir,wr,Xr,ti){for(var $r=[Gt,wt,tr,ir],Ri=this.options,Hi=Ri.debug;$r.length;){ir=$r.pop(),tr=$r.pop(),wt=$r.pop(),Gt=$r.pop();var en=1<<wt,fn=An(wt,tr,ir),yn=this.tiles[fn];if(!yn&&(Hi>1&&console.time(\"creation\"),yn=this.tiles[fn]=_i(Gt,wt,tr,ir,Ri),this.tileCoords.push({z:wt,x:tr,y:ir}),Hi)){Hi>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",wt,tr,ir,yn.numFeatures,yn.numPoints,yn.numSimplified),console.timeEnd(\"creation\"));var Mn=\"z\"+wt;this.stats[Mn]=(this.stats[Mn]||0)+1,this.total++}if(yn.source=Gt,wr){if(wt===Ri.maxZoom||wt===wr)continue;var Ba=1<<wr-wt;if(tr!==Math.floor(Xr/Ba)||ir!==Math.floor(ti/Ba))continue}else if(wt===Ri.indexMaxZoom||yn.numPoints<=Ri.indexMaxPoints)continue;if(yn.source=null,Gt.length!==0){Hi>1&&console.time(\"clipping\");var ua=.5*Ri.buffer/Ri.extent,ma=.5-ua,Wa=.5+ua,za=1+ua,Xo,da,jn,Ha,vo,Gn;Xo=da=jn=Ha=null,vo=gt(Gt,en,tr-ua,tr+Wa,0,yn.minX,yn.maxX,Ri),Gn=gt(Gt,en,tr+ma,tr+za,0,yn.minX,yn.maxX,Ri),Gt=null,vo&&(Xo=gt(vo,en,ir-ua,ir+Wa,1,yn.minY,yn.maxY,Ri),da=gt(vo,en,ir+ma,ir+za,1,yn.minY,yn.maxY,Ri),vo=null),Gn&&(jn=gt(Gn,en,ir-ua,ir+Wa,1,yn.minY,yn.maxY,Ri),Ha=gt(Gn,en,ir+ma,ir+za,1,yn.minY,yn.maxY,Ri),Gn=null),Hi>1&&console.timeEnd(\"clipping\"),$r.push(Xo||[],wt+1,tr*2,ir*2),$r.push(da||[],wt+1,tr*2,ir*2+1),$r.push(jn||[],wt+1,tr*2+1,ir*2),$r.push(Ha||[],wt+1,tr*2+1,ir*2+1)}}},Fn.prototype.getTile=function(Gt,wt,tr){var ir=this.options,wr=ir.extent,Xr=ir.debug;if(Gt<0||Gt>24)return null;var ti=1<<Gt;wt=(wt%ti+ti)%ti;var $r=An(Gt,wt,tr);if(this.tiles[$r])return Ir(this.tiles[$r],wr);Xr>1&&console.log(\"drilling down to z%d-%d-%d\",Gt,wt,tr);for(var Ri=Gt,Hi=wt,en=tr,fn;!fn&&Ri>0;)Ri--,Hi=Math.floor(Hi/2),en=Math.floor(en/2),fn=this.tiles[An(Ri,Hi,en)];return!fn||!fn.source?null:(Xr>1&&console.log(\"found parent tile z%d-%d-%d\",Ri,Hi,en),Xr>1&&console.time(\"drilling down\"),this.splitTile(fn.source,Ri,Hi,en,Gt,wt,tr),Xr>1&&console.timeEnd(\"drilling down\"),this.tiles[$r]?Ir(this.tiles[$r],wr):null)};function An(Gt,wt,tr){return((1<<Gt)*tr+wt)*32+Gt}function Hn(Gt,wt){for(var tr in wt)Gt[tr]=wt[tr];return Gt}function Qn(Gt,wt){var tr=Gt.tileID.canonical;if(!this._geoJSONIndex)return wt(null,null);var ir=this._geoJSONIndex.getTile(tr.z,tr.x,tr.y);if(!ir)return wt(null,null);var wr=new C(ir.features),Xr=z(wr);(Xr.byteOffset!==0||Xr.byteLength!==Xr.buffer.byteLength)&&(Xr=new Uint8Array(Xr)),wt(null,{vectorTile:wr,rawData:Xr.buffer})}var Vi=(function(Gt){function wt(tr,ir,wr,Xr){Gt.call(this,tr,ir,wr,Qn),Xr&&(this.loadGeoJSON=Xr)}return Gt&&(wt.__proto__=Gt),wt.prototype=Object.create(Gt&&Gt.prototype),wt.prototype.constructor=wt,wt.prototype.loadData=function(ir,wr){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=wr,this._pendingLoadDataParams=ir,this._state&&this._state!==\"Idle\"?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},wt.prototype._loadData=function(){var ir=this;if(!(!this._pendingCallback||!this._pendingLoadDataParams)){var wr=this._pendingCallback,Xr=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var ti=Xr&&Xr.request&&Xr.request.collectResourceTiming?new i.RequestPerformance(Xr.request):!1;this.loadGeoJSON(Xr,function($r,Ri){if($r||!Ri)return wr($r);if(typeof Ri!=\"object\")return wr(new Error(\"Input data given to '\"+Xr.source+\"' is not a valid GeoJSON object.\"));p(Ri,!0);try{if(Xr.filter){var Hi=i.createExpression(Xr.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(Hi.result===\"error\")throw new Error(Hi.value.map(function(Mn){return Mn.key+\": \"+Mn.message}).join(\", \"));var en=Ri.features.filter(function(Mn){return Hi.value.evaluate({zoom:0},Mn)});Ri={type:\"FeatureCollection\",features:en}}ir._geoJSONIndex=Xr.cluster?new mt(Jn(Xr)).load(Ri.features):un(Ri,Xr.geojsonVtOptions)}catch(Mn){return wr(Mn)}ir.loaded={};var fn={};if(ti){var yn=ti.finish();yn&&(fn.resourceTiming={},fn.resourceTiming[Xr.source]=JSON.parse(JSON.stringify(yn)))}wr(null,fn)})}},wt.prototype.coalesce=function(){this._state===\"Coalescing\"?this._state=\"Idle\":this._state===\"NeedsLoadData\"&&(this._state=\"Coalescing\",this._loadData())},wt.prototype.reloadTile=function(ir,wr){var Xr=this.loaded,ti=ir.uid;return Xr&&Xr[ti]?Gt.prototype.reloadTile.call(this,ir,wr):this.loadTile(ir,wr)},wt.prototype.loadGeoJSON=function(ir,wr){if(ir.request)i.getJSON(ir.request,wr);else if(typeof ir.data==\"string\")try{return wr(null,JSON.parse(ir.data))}catch(Xr){return wr(new Error(\"Input data given to '\"+ir.source+\"' is not a valid GeoJSON object.\"))}else return wr(new Error(\"Input data given to '\"+ir.source+\"' is not a valid GeoJSON object.\"))},wt.prototype.removeSource=function(ir,wr){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),wr()},wt.prototype.getClusterExpansionZoom=function(ir,wr){try{wr(null,this._geoJSONIndex.getClusterExpansionZoom(ir.clusterId))}catch(Xr){wr(Xr)}},wt.prototype.getClusterChildren=function(ir,wr){try{wr(null,this._geoJSONIndex.getChildren(ir.clusterId))}catch(Xr){wr(Xr)}},wt.prototype.getClusterLeaves=function(ir,wr){try{wr(null,this._geoJSONIndex.getLeaves(ir.clusterId,ir.limit,ir.offset))}catch(Xr){wr(Xr)}},wt})(v);function Jn(Gt){var wt=Gt.superclusterOptions,tr=Gt.clusterProperties;if(!tr||!wt)return wt;for(var ir={},wr={},Xr={accumulated:null,zoom:0},ti={properties:null},$r=Object.keys(tr),Ri=0,Hi=$r;Ri<Hi.length;Ri+=1){var en=Hi[Ri],fn=tr[en],yn=fn[0],Mn=fn[1],Ba=i.createExpression(Mn),ua=i.createExpression(typeof yn==\"string\"?[yn,[\"accumulated\"],[\"get\",en]]:yn);ir[en]=Ba.value,wr[en]=ua.value}return wt.map=function(ma){ti.properties=ma;for(var Wa={},za=0,Xo=$r;za<Xo.length;za+=1){var da=Xo[za];Wa[da]=ir[da].evaluate(Xr,ti)}return Wa},wt.reduce=function(ma,Wa){ti.properties=Wa;for(var za=0,Xo=$r;za<Xo.length;za+=1){var da=Xo[za];Xr.accumulated=ma[da],ma[da]=wr[da].evaluate(Xr,ti)}},wt}var Yn=function(wt){var tr=this;this.self=wt,this.actor=new i.Actor(wt,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:v,geojson:Vi},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(ir,wr){if(tr.workerSourceTypes[ir])throw new Error('Worker source with name \"'+ir+'\" already registered.');tr.workerSourceTypes[ir]=wr},this.self.registerRTLTextPlugin=function(ir){if(i.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");i.plugin.applyArabicShaping=ir.applyArabicShaping,i.plugin.processBidirectionalText=ir.processBidirectionalText,i.plugin.processStyledBidirectionalText=ir.processStyledBidirectionalText}};return Yn.prototype.setReferrer=function(wt,tr){this.referrer=tr},Yn.prototype.setImages=function(wt,tr,ir){this.availableImages[wt]=tr;for(var wr in this.workerSources[wt]){var Xr=this.workerSources[wt][wr];for(var ti in Xr)Xr[ti].availableImages=tr}ir()},Yn.prototype.setLayers=function(wt,tr,ir){this.getLayerIndex(wt).replace(tr),ir()},Yn.prototype.updateLayers=function(wt,tr,ir){this.getLayerIndex(wt).update(tr.layers,tr.removedIds),ir()},Yn.prototype.loadTile=function(wt,tr,ir){this.getWorkerSource(wt,tr.type,tr.source).loadTile(tr,ir)},Yn.prototype.loadDEMTile=function(wt,tr,ir){this.getDEMWorkerSource(wt,tr.source).loadTile(tr,ir)},Yn.prototype.reloadTile=function(wt,tr,ir){this.getWorkerSource(wt,tr.type,tr.source).reloadTile(tr,ir)},Yn.prototype.abortTile=function(wt,tr,ir){this.getWorkerSource(wt,tr.type,tr.source).abortTile(tr,ir)},Yn.prototype.removeTile=function(wt,tr,ir){this.getWorkerSource(wt,tr.type,tr.source).removeTile(tr,ir)},Yn.prototype.removeDEMTile=function(wt,tr){this.getDEMWorkerSource(wt,tr.source).removeTile(tr)},Yn.prototype.removeSource=function(wt,tr,ir){if(!(!this.workerSources[wt]||!this.workerSources[wt][tr.type]||!this.workerSources[wt][tr.type][tr.source])){var wr=this.workerSources[wt][tr.type][tr.source];delete this.workerSources[wt][tr.type][tr.source],wr.removeSource!==void 0?wr.removeSource(tr,ir):ir()}},Yn.prototype.loadWorkerSource=function(wt,tr,ir){try{this.self.importScripts(tr.url),ir()}catch(wr){ir(wr.toString())}},Yn.prototype.syncRTLPluginState=function(wt,tr,ir){try{i.plugin.setState(tr);var wr=i.plugin.getPluginURL();if(i.plugin.isLoaded()&&!i.plugin.isParsed()&&wr!=null){this.self.importScripts(wr);var Xr=i.plugin.isParsed(),ti=Xr?void 0:new Error(\"RTL Text Plugin failed to import scripts from \"+wr);ir(ti,Xr)}}catch($r){ir($r.toString())}},Yn.prototype.getAvailableImages=function(wt){var tr=this.availableImages[wt];return tr||(tr=[]),tr},Yn.prototype.getLayerIndex=function(wt){var tr=this.layerIndexes[wt];return tr||(tr=this.layerIndexes[wt]=new l),tr},Yn.prototype.getWorkerSource=function(wt,tr,ir){var wr=this;if(this.workerSources[wt]||(this.workerSources[wt]={}),this.workerSources[wt][tr]||(this.workerSources[wt][tr]={}),!this.workerSources[wt][tr][ir]){var Xr={send:function(ti,$r,Ri){wr.actor.send(ti,$r,Ri,wt)}};this.workerSources[wt][tr][ir]=new this.workerSourceTypes[tr](Xr,this.getLayerIndex(wt),this.getAvailableImages(wt))}return this.workerSources[wt][tr][ir]},Yn.prototype.getDEMWorkerSource=function(wt,tr){return this.demWorkerSources[wt]||(this.demWorkerSources[wt]={}),this.demWorkerSources[wt][tr]||(this.demWorkerSources[wt][tr]=new b),this.demWorkerSources[wt][tr]},Yn.prototype.enforceCacheSizeLimit=function(wt,tr){i.enforceCacheSizeLimit(tr)},typeof WorkerGlobalScope!=\"undefined\"&&typeof self!=\"undefined\"&&self instanceof WorkerGlobalScope&&(self.worker=new Yn(self)),Yn}),n([\"./shared\"],function(i){\"use strict\";var a=i.createCommonjsModule(function(Y){Y.exports?Y.exports=D:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=D,window.mapboxgl.notSupportedReason=J);function D(It){return!J(It)}function J(It){if(!O())return\"not a browser\";if(!K())return\"insufficent Array support\";if(!de())return\"insufficient Function support\";if(!ne())return\"insufficient Object support\";if(!we())return\"insufficient JSON support\";if(!Oe())return\"insufficient worker support\";if(!ht())return\"insufficient Uint8ClampedArray support\";if(!Yt())return\"insufficient ArrayBuffer support\";if(!cr())return\"insufficient Canvas/getImageData support\";if(!Ne(It&&It.failIfMajorPerformanceCaveat))return\"insufficient WebGL support\"}function O(){return typeof window!=\"undefined\"&&typeof document!=\"undefined\"}function K(){return 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ht=this.images[Oe];ht?de[Oe]={data:ht.data.clone(),pixelRatio:ht.pixelRatio,sdf:ht.sdf,version:ht.version,stretchX:ht.stretchX,stretchY:ht.stretchY,content:ht.content,hasRenderCallback:!!(ht.userImage&&ht.userImage.render)}:i.warnOnce('Image \"'+Oe+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. 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Oe=0;Oe<J;Oe++)O[Oe]=Y[Oe*D+we];for(g(O,K,de,ne,J),Oe=0;Oe<J;Oe++)Y[Oe*D+we]=K[Oe]}for(Oe=0;Oe<J;Oe++){for(we=0;we<D;we++)O[we]=Y[Oe*D+we];for(g(O,K,de,ne,D),we=0;we<D;we++)Y[Oe*D+we]=Math.sqrt(K[we])}}function g(Y,D,J,O,K){J[0]=0,O[0]=-x,O[1]=+x;for(var de=1,ne=0;de<K;de++){for(var we=(Y[de]+de*de-(Y[J[ne]]+J[ne]*J[ne]))/(2*de-2*J[ne]);we<=O[ne];)ne--,we=(Y[de]+de*de-(Y[J[ne]]+J[ne]*J[ne]))/(2*de-2*J[ne]);ne++,J[ne]=de,O[ne]=we,O[ne+1]=+x}for(de=0,ne=0;de<K;de++){for(;O[ne+1]<de;)ne++;D[de]=(de-J[ne])*(de-J[ne])+Y[J[ne]]}}T.default=L;var P=function(D,J){this.requestManager=D,this.localIdeographFontFamily=J,this.entries={}};P.prototype.setURL=function(D){this.url=D},P.prototype.getGlyphs=function(D,J){var O=this,K=[];for(var de in D)for(var ne=0,we=D[de];ne<we.length;ne+=1){var Oe=we[ne];K.push({stack:de,id:Oe})}i.asyncAll(K,function(ht,Yt){var cr=ht.stack,Nt=ht.id,Ne=O.entries[cr];Ne||(Ne=O.entries[cr]={glyphs:{},requests:{},ranges:{}});var $e=Ne.glyphs[Nt];if($e!==void 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$e=Ne[Nt],st=$e.stack,It=$e.id,Bt=$e.glyph;(cr[st]||(cr[st]={}))[It]=Bt&&{id:Bt.id,bitmap:Bt.bitmap.clone(),metrics:Bt.metrics}}J(null,cr)}})},P.prototype._doesCharSupportLocalGlyph=function(D){return!!this.localIdeographFontFamily&&(i.isChar[\"CJK Unified Ideographs\"](D)||i.isChar[\"Hangul Syllables\"](D)||i.isChar.Hiragana(D)||i.isChar.Katakana(D))},P.prototype._tinySDF=function(D,J,O){var K=this.localIdeographFontFamily;if(K&&this._doesCharSupportLocalGlyph(O)){var de=D.tinySDF;if(!de){var ne=\"400\";/bold/i.test(J)?ne=\"900\":/medium/i.test(J)?ne=\"500\":/light/i.test(J)&&(ne=\"200\"),de=D.tinySDF=new P.TinySDF(24,3,8,.25,K,ne)}return{id:O,bitmap:new i.AlphaImage({width:30,height:30},de.draw(String.fromCharCode(O))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},P.loadGlyphRange=M,P.TinySDF=T;var E=function(){this.specification=i.styleSpec.light.position};E.prototype.possiblyEvaluate=function(D,J){return i.sphericalToCartesian(D.expression.evaluate(J))},E.prototype.interpolate=function(D,J,O){return{x:i.number(D.x,J.x,O),y:i.number(D.y,J.y,O),z:i.number(D.z,J.z,O)}};var z=new i.Properties({anchor:new i.DataConstantProperty(i.styleSpec.light.anchor),position:new E,color:new i.DataConstantProperty(i.styleSpec.light.color),intensity:new i.DataConstantProperty(i.styleSpec.light.intensity)}),q=\"-transition\",V=(function(Y){function D(J){Y.call(this),this._transitionable=new i.Transitionable(z),this.setLight(J),this._transitioning=this._transitionable.untransitioned()}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getLight=function(){return this._transitionable.serialize()},D.prototype.setLight=function(O,K){if(K===void 0&&(K={}),!this._validate(i.validateLight,O,K))for(var de in O){var ne=O[de];i.endsWith(de,q)?this._transitionable.setTransition(de.slice(0,-q.length),ne):this._transitionable.setValue(de,ne)}},D.prototype.updateTransitions=function(O){this._transitioning=this._transitionable.transitioned(O,this._transitioning)},D.prototype.hasTransition=function(){return this._transitioning.hasTransition()},D.prototype.recalculate=function(O){this.properties=this._transitioning.possiblyEvaluate(O)},D.prototype._validate=function(O,K,de){return de&&de.validate===!1?!1:i.emitValidationErrors(this,O.call(i.validateStyle,i.extend({value:K,style:{glyphs:!0,sprite:!0},styleSpec:i.styleSpec})))},D})(i.Evented),G=function(D,J){this.width=D,this.height=J,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};G.prototype.getDash=function(D,J){var O=D.join(\",\")+String(J);return this.dashEntry[O]||(this.dashEntry[O]=this.addDash(D,J)),this.dashEntry[O]},G.prototype.getDashRanges=function(D,J,O){var K=D.length%2===1,de=[],ne=K?-D[D.length-1]*O:0,we=D[0]*O,Oe=!0;de.push({left:ne,right:we,isDash:Oe,zeroLength:D[0]===0});for(var ht=D[0],Yt=1;Yt<D.length;Yt++){Oe=!Oe;var cr=D[Yt];ne=ht*O,ht+=cr,we=ht*O,de.push({left:ne,right:we,isDash:Oe,zeroLength:cr===0})}return de},G.prototype.addRoundDash=function(D,J,O){for(var K=J/2,de=-O;de<=O;de++)for(var ne=this.nextRow+O+de,we=this.width*ne,Oe=0,ht=D[Oe],Yt=0;Yt<this.width;Yt++){Yt/ht.right>1&&(ht=D[++Oe]);var cr=Math.abs(Yt-ht.left),Nt=Math.abs(Yt-ht.right),Ne=Math.min(cr,Nt),$e=void 0,st=de/O*(K+1);if(ht.isDash){var It=K-Math.abs(st);$e=Math.sqrt(Ne*Ne+It*It)}else $e=K-Math.sqrt(Ne*Ne+st*st);this.data[we+Yt]=Math.max(0,Math.min(255,$e+128))}},G.prototype.addRegularDash=function(D){for(var J=D.length-1;J>=0;--J){var O=D[J],K=D[J+1];O.zeroLength?D.splice(J,1):K&&K.isDash===O.isDash&&(K.left=O.left,D.splice(J,1))}var de=D[0],ne=D[D.length-1];de.isDash===ne.isDash&&(de.left=ne.left-this.width,ne.right=de.right+this.width);for(var we=this.width*this.nextRow,Oe=0,ht=D[Oe],Yt=0;Yt<this.width;Yt++){Yt/ht.right>1&&(ht=D[++Oe]);var cr=Math.abs(Yt-ht.left),Nt=Math.abs(Yt-ht.right),Ne=Math.min(cr,Nt),$e=ht.isDash?Ne:-Ne;this.data[we+Yt]=Math.max(0,Math.min(255,$e+128))}},G.prototype.addDash=function(D,J){var O=J?7:0,K=2*O+1;if(this.nextRow+K>this.height)return i.warnOnce(\"LineAtlas out of space\"),null;for(var de=0,ne=0;ne<D.length;ne++)de+=D[ne];if(de!==0){var we=this.width/de,Oe=this.getDashRanges(D,this.width,we);J?this.addRoundDash(Oe,we,O):this.addRegularDash(Oe)}var ht={y:(this.nextRow+O+.5)/this.height,height:2*O/this.height,width:de};return this.nextRow+=K,this.dirty=!0,ht},G.prototype.bind=function(D){var J=D.gl;this.texture?(J.bindTexture(J.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,J.texSubImage2D(J.TEXTURE_2D,0,0,0,this.width,this.height,J.ALPHA,J.UNSIGNED_BYTE,this.data))):(this.texture=J.createTexture(),J.bindTexture(J.TEXTURE_2D,this.texture),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_WRAP_S,J.REPEAT),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_WRAP_T,J.REPEAT),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_MIN_FILTER,J.LINEAR),J.texParameteri(J.TEXTURE_2D,J.TEXTURE_MAG_FILTER,J.LINEAR),J.texImage2D(J.TEXTURE_2D,0,J.ALPHA,this.width,this.height,0,J.ALPHA,J.UNSIGNED_BYTE,this.data))};var Z=function Y(D,J){this.workerPool=D,this.actors=[],this.currentActor=0,this.id=i.uniqueId();for(var O=this.workerPool.acquire(this.id),K=0;K<O.length;K++){var de=O[K],ne=new Y.Actor(de,J,this.id);ne.name=\"Worker \"+K,this.actors.push(ne)}};Z.prototype.broadcast=function(D,J,O){O=O||function(){},i.asyncAll(this.actors,function(K,de){K.send(D,J,de)},O)},Z.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},Z.prototype.remove=function(){this.actors.forEach(function(D){D.remove()}),this.actors=[],this.workerPool.release(this.id)},Z.Actor=i.Actor;function j(Y,D,J){var O=function(K,de){if(K)return J(K);if(de){var ne=i.pick(i.extend(de,Y),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);de.vector_layers&&(ne.vectorLayers=de.vector_layers,ne.vectorLayerIds=ne.vectorLayers.map(function(we){return we.id})),ne.tiles=D.canonicalizeTileset(ne,Y.url),J(null,ne)}};return Y.url?i.getJSON(D.transformRequest(D.normalizeSourceURL(Y.url),i.ResourceType.Source),O):i.browser.frame(function(){return O(null,Y)})}var N=function(D,J,O){this.bounds=i.LngLatBounds.convert(this.validateBounds(D)),this.minzoom=J||0,this.maxzoom=O||24};N.prototype.validateBounds=function(D){return!Array.isArray(D)||D.length!==4?[-180,-90,180,90]:[Math.max(-180,D[0]),Math.max(-90,D[1]),Math.min(180,D[2]),Math.min(90,D[3])]},N.prototype.contains=function(D){var J=Math.pow(2,D.z),O={minX:Math.floor(i.mercatorXfromLng(this.bounds.getWest())*J),minY:Math.floor(i.mercatorYfromLat(this.bounds.getNorth())*J),maxX:Math.ceil(i.mercatorXfromLng(this.bounds.getEast())*J),maxY:Math.ceil(i.mercatorYfromLat(this.bounds.getSouth())*J)},K=D.x>=O.minX&&D.x<O.maxX&&D.y>=O.minY&&D.y<O.maxY;return K};var H=(function(Y){function D(J,O,K,de){if(Y.call(this),this.id=J,this.dispatcher=K,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,i.extend(this,i.pick(O,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=i.extend({type:\"vector\"},O),this._collectResourceTiming=O.collectResourceTiming,this.tileSize!==512)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(de)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var O=this;this._loaded=!1,this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=j(this._options,this.map._requestManager,function(K,de){O._tileJSONRequest=null,O._loaded=!0,K?O.fire(new i.ErrorEvent(K)):de&&(i.extend(O,de),de.bounds&&(O.tileBounds=new N(de.bounds,O.minzoom,O.maxzoom)),i.postTurnstileEvent(de.tiles,O.map._requestManager._customAccessToken),i.postMapLoadEvent(de.tiles,O.map._getMapId(),O.map._requestManager._skuToken,O.map._requestManager._customAccessToken),O.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),O.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},D.prototype.loaded=function(){return this._loaded},D.prototype.hasTile=function(O){return!this.tileBounds||this.tileBounds.contains(O.canonical)},D.prototype.onAdd=function(O){this.map=O,this.load()},D.prototype.setSourceProperty=function(O){this._tileJSONRequest&&this._tileJSONRequest.cancel(),O();var K=this.map.style.sourceCaches[this.id];K.clearTiles(),this.load()},D.prototype.setTiles=function(O){var K=this;return this.setSourceProperty(function(){K._options.tiles=O}),this},D.prototype.setUrl=function(O){var K=this;return this.setSourceProperty(function(){K.url=O,K._options.url=O}),this},D.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},D.prototype.serialize=function(){return i.extend({},this._options)},D.prototype.loadTile=function(O,K){var de=this.map._requestManager.normalizeTileURL(O.tileID.canonical.url(this.tiles,this.scheme)),ne={request:this.map._requestManager.transformRequest(de,i.ResourceType.Tile),uid:O.uid,tileID:O.tileID,zoom:O.tileID.overscaledZ,tileSize:this.tileSize*O.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:i.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};ne.request.collectResourceTiming=this._collectResourceTiming,!O.actor||O.state===\"expired\"?(O.actor=this.dispatcher.getActor(),O.request=O.actor.send(\"loadTile\",ne,we.bind(this))):O.state===\"loading\"?O.reloadCallback=K:O.request=O.actor.send(\"reloadTile\",ne,we.bind(this));function we(Oe,ht){if(delete O.request,O.aborted)return K(null);if(Oe&&Oe.status!==404)return K(Oe);ht&&ht.resourceTiming&&(O.resourceTiming=ht.resourceTiming),this.map._refreshExpiredTiles&&ht&&O.setExpiryData(ht),O.loadVectorData(ht,this.map.painter),i.cacheEntryPossiblyAdded(this.dispatcher),K(null),O.reloadCallback&&(this.loadTile(O,O.reloadCallback),O.reloadCallback=null)}},D.prototype.abortTile=function(O){O.request&&(O.request.cancel(),delete O.request),O.actor&&O.actor.send(\"abortTile\",{uid:O.uid,type:this.type,source:this.id},void 0)},D.prototype.unloadTile=function(O){O.unloadVectorData(),O.actor&&O.actor.send(\"removeTile\",{uid:O.uid,type:this.type,source:this.id},void 0)},D.prototype.hasTransition=function(){return!1},D})(i.Evented),re=(function(Y){function D(J,O,K,de){Y.call(this),this.id=J,this.dispatcher=K,this.setEventedParent(de),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=i.extend({type:\"raster\"},O),i.extend(this,i.pick(O,[\"url\",\"scheme\",\"tileSize\"]))}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var O=this;this._loaded=!1,this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=j(this._options,this.map._requestManager,function(K,de){O._tileJSONRequest=null,O._loaded=!0,K?O.fire(new i.ErrorEvent(K)):de&&(i.extend(O,de),de.bounds&&(O.tileBounds=new N(de.bounds,O.minzoom,O.maxzoom)),i.postTurnstileEvent(de.tiles),i.postMapLoadEvent(de.tiles,O.map._getMapId(),O.map._requestManager._skuToken),O.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),O.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},D.prototype.loaded=function(){return this._loaded},D.prototype.onAdd=function(O){this.map=O,this.load()},D.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},D.prototype.serialize=function(){return i.extend({},this._options)},D.prototype.hasTile=function(O){return!this.tileBounds||this.tileBounds.contains(O.canonical)},D.prototype.loadTile=function(O,K){var de=this,ne=this.map._requestManager.normalizeTileURL(O.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);O.request=i.getImage(this.map._requestManager.transformRequest(ne,i.ResourceType.Tile),function(we,Oe){if(delete O.request,O.aborted)O.state=\"unloaded\",K(null);else if(we)O.state=\"errored\",K(we);else if(Oe){de.map._refreshExpiredTiles&&O.setExpiryData(Oe),delete Oe.cacheControl,delete Oe.expires;var ht=de.map.painter.context,Yt=ht.gl;O.texture=de.map.painter.getTileTexture(Oe.width),O.texture?O.texture.update(Oe,{useMipmap:!0}):(O.texture=new i.Texture(ht,Oe,Yt.RGBA,{useMipmap:!0}),O.texture.bind(Yt.LINEAR,Yt.CLAMP_TO_EDGE,Yt.LINEAR_MIPMAP_NEAREST),ht.extTextureFilterAnisotropic&&Yt.texParameterf(Yt.TEXTURE_2D,ht.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,ht.extTextureFilterAnisotropicMax)),O.state=\"loaded\",i.cacheEntryPossiblyAdded(de.dispatcher),K(null)}})},D.prototype.abortTile=function(O,K){O.request&&(O.request.cancel(),delete O.request),K()},D.prototype.unloadTile=function(O,K){O.texture&&this.map.painter.saveTileTexture(O.texture),K()},D.prototype.hasTransition=function(){return!1},D})(i.Evented),ae=(function(Y){function D(J,O,K,de){Y.call(this,J,O,K,de),this.type=\"raster-dem\",this.maxzoom=22,this._options=i.extend({type:\"raster-dem\"},O),this.encoding=O.encoding||\"mapbox\"}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},D.prototype.loadTile=function(O,K){var de=this.map._requestManager.normalizeTileURL(O.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);O.request=i.getImage(this.map._requestManager.transformRequest(de,i.ResourceType.Tile),ne.bind(this)),O.neighboringTiles=this._getNeighboringTiles(O.tileID);function ne(Oe,ht){if(delete O.request,O.aborted)O.state=\"unloaded\",K(null);else if(Oe)O.state=\"errored\",K(Oe);else if(ht){this.map._refreshExpiredTiles&&O.setExpiryData(ht),delete ht.cacheControl,delete ht.expires;var Yt=i.window.ImageBitmap&&ht instanceof i.window.ImageBitmap&&i.offscreenCanvasSupported(),cr=Yt?ht:i.browser.getImageData(ht,1),Nt={uid:O.uid,coord:O.tileID,source:this.id,rawImageData:cr,encoding:this.encoding};(!O.actor||O.state===\"expired\")&&(O.actor=this.dispatcher.getActor(),O.actor.send(\"loadDEMTile\",Nt,we.bind(this)))}}function we(Oe,ht){Oe&&(O.state=\"errored\",K(Oe)),ht&&(O.dem=ht,O.needsHillshadePrepare=!0,O.state=\"loaded\",K(null))}},D.prototype._getNeighboringTiles=function(O){var K=O.canonical,de=Math.pow(2,K.z),ne=(K.x-1+de)%de,we=K.x===0?O.wrap-1:O.wrap,Oe=(K.x+1+de)%de,ht=K.x+1===de?O.wrap+1:O.wrap,Yt={};return Yt[new i.OverscaledTileID(O.overscaledZ,we,K.z,ne,K.y).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,ht,K.z,Oe,K.y).key]={backfilled:!1},K.y>0&&(Yt[new i.OverscaledTileID(O.overscaledZ,we,K.z,ne,K.y-1).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,O.wrap,K.z,K.x,K.y-1).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,ht,K.z,Oe,K.y-1).key]={backfilled:!1}),K.y+1<de&&(Yt[new i.OverscaledTileID(O.overscaledZ,we,K.z,ne,K.y+1).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,O.wrap,K.z,K.x,K.y+1).key]={backfilled:!1},Yt[new i.OverscaledTileID(O.overscaledZ,ht,K.z,Oe,K.y+1).key]={backfilled:!1}),Yt},D.prototype.unloadTile=function(O){O.demTexture&&this.map.painter.saveTileTexture(O.demTexture),O.fbo&&(O.fbo.destroy(),delete O.fbo),O.dem&&delete O.dem,delete O.neighboringTiles,O.state=\"unloaded\",O.actor&&O.actor.send(\"removeDEMTile\",{uid:O.uid,source:this.id})},D})(re),_e=(function(Y){function D(J,O,K,de){Y.call(this),this.id=J,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=K.getActor(),this.setEventedParent(de),this._data=O.data,this._options=i.extend({},O),this._collectResourceTiming=O.collectResourceTiming,this._resourceTiming=[],O.maxzoom!==void 0&&(this.maxzoom=O.maxzoom),O.type&&(this.type=O.type),O.attribution&&(this.attribution=O.attribution),this.promoteId=O.promoteId;var ne=i.EXTENT/this.tileSize;this.workerOptions=i.extend({source:this.id,cluster:O.cluster||!1,geojsonVtOptions:{buffer:(O.buffer!==void 0?O.buffer:128)*ne,tolerance:(O.tolerance!==void 0?O.tolerance:.375)*ne,extent:i.EXTENT,maxZoom:this.maxzoom,lineMetrics:O.lineMetrics||!1,generateId:O.generateId||!1},superclusterOptions:{maxZoom:O.clusterMaxZoom!==void 0?Math.min(O.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,minPoints:Math.max(2,O.clusterMinPoints||2),extent:i.EXTENT,radius:(O.clusterRadius||50)*ne,log:!1,generateId:O.generateId||!1},clusterProperties:O.clusterProperties,filter:O.filter},O.workerOptions)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var O=this;this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData(function(K){if(K){O.fire(new i.ErrorEvent(K));return}var de={dataType:\"source\",sourceDataType:\"metadata\"};O._collectResourceTiming&&O._resourceTiming&&O._resourceTiming.length>0&&(de.resourceTiming=O._resourceTiming,O._resourceTiming=[]),O.fire(new i.Event(\"data\",de))})},D.prototype.onAdd=function(O){this.map=O,this.load()},D.prototype.setData=function(O){var K=this;return this._data=O,this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData(function(de){if(de){K.fire(new i.ErrorEvent(de));return}var ne={dataType:\"source\",sourceDataType:\"content\"};K._collectResourceTiming&&K._resourceTiming&&K._resourceTiming.length>0&&(ne.resourceTiming=K._resourceTiming,K._resourceTiming=[]),K.fire(new i.Event(\"data\",ne))}),this},D.prototype.getClusterExpansionZoom=function(O,K){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:O,source:this.id},K),this},D.prototype.getClusterChildren=function(O,K){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:O,source:this.id},K),this},D.prototype.getClusterLeaves=function(O,K,de,ne){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:O,limit:K,offset:de},ne),this},D.prototype._updateWorkerData=function(O){var K=this;this._loaded=!1;var de=i.extend({},this.workerOptions),ne=this._data;typeof ne==\"string\"?(de.request=this.map._requestManager.transformRequest(i.browser.resolveURL(ne),i.ResourceType.Source),de.request.collectResourceTiming=this._collectResourceTiming):de.data=JSON.stringify(ne),this.actor.send(this.type+\".loadData\",de,function(we,Oe){K._removed||Oe&&Oe.abandoned||(K._loaded=!0,Oe&&Oe.resourceTiming&&Oe.resourceTiming[K.id]&&(K._resourceTiming=Oe.resourceTiming[K.id].slice(0)),K.actor.send(K.type+\".coalesce\",{source:de.source},null),O(we))})},D.prototype.loaded=function(){return this._loaded},D.prototype.loadTile=function(O,K){var de=this,ne=O.actor?\"reloadTile\":\"loadTile\";O.actor=this.actor;var we={type:this.type,uid:O.uid,tileID:O.tileID,zoom:O.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:i.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};O.request=this.actor.send(ne,we,function(Oe,ht){return delete O.request,O.unloadVectorData(),O.aborted?K(null):Oe?K(Oe):(O.loadVectorData(ht,de.map.painter,ne===\"reloadTile\"),K(null))})},D.prototype.abortTile=function(O){O.request&&(O.request.cancel(),delete O.request),O.aborted=!0},D.prototype.unloadTile=function(O){O.unloadVectorData(),this.actor.send(\"removeTile\",{uid:O.uid,type:this.type,source:this.id})},D.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},D.prototype.serialize=function(){return i.extend({},this._options,{type:this.type,data:this._data})},D.prototype.hasTransition=function(){return!1},D})(i.Evented),Ee=i.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),Ce=(function(Y){function D(J,O,K,de){Y.call(this),this.id=J,this.dispatcher=K,this.coordinates=O.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(de),this.options=O}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(O,K){var de=this;this._loaded=!1,this.fire(new i.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,i.getImage(this.map._requestManager.transformRequest(this.url,i.ResourceType.Image),function(ne,we){de._loaded=!0,ne?de.fire(new i.ErrorEvent(ne)):we&&(de.image=we,O&&(de.coordinates=O),K&&K(),de._finishLoading())})},D.prototype.loaded=function(){return this._loaded},D.prototype.updateImage=function(O){var K=this;return!this.image||!O.url?this:(this.options.url=O.url,this.load(O.coordinates,function(){K.texture=null}),this)},D.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},D.prototype.onAdd=function(O){this.map=O,this.load()},D.prototype.setCoordinates=function(O){var K=this;this.coordinates=O;var de=O.map(i.MercatorCoordinate.fromLngLat);this.tileID=ge(de),this.minzoom=this.maxzoom=this.tileID.z;var ne=de.map(function(we){return K.tileID.getTilePoint(we)._round()});return this._boundsArray=new i.StructArrayLayout4i8,this._boundsArray.emplaceBack(ne[0].x,ne[0].y,0,0),this._boundsArray.emplaceBack(ne[1].x,ne[1].y,i.EXTENT,0),this._boundsArray.emplaceBack(ne[3].x,ne[3].y,0,i.EXTENT),this._boundsArray.emplaceBack(ne[2].x,ne[2].y,i.EXTENT,i.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new i.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},D.prototype.prepare=function(){if(!(Object.keys(this.tiles).length===0||!this.image)){var O=this.map.painter.context,K=O.gl;this.boundsBuffer||(this.boundsBuffer=O.createVertexBuffer(this._boundsArray,Ee.members)),this.boundsSegments||(this.boundsSegments=i.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new i.Texture(O,this.image,K.RGBA),this.texture.bind(K.LINEAR,K.CLAMP_TO_EDGE));for(var de in this.tiles){var ne=this.tiles[de];ne.state!==\"loaded\"&&(ne.state=\"loaded\",ne.texture=this.texture)}}},D.prototype.loadTile=function(O,K){this.tileID&&this.tileID.equals(O.tileID.canonical)?(this.tiles[String(O.tileID.wrap)]=O,O.buckets={},K(null)):(O.state=\"errored\",K(null))},D.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},D.prototype.hasTransition=function(){return!1},D})(i.Evented);function ge(Y){for(var D=1/0,J=1/0,O=-1/0,K=-1/0,de=0,ne=Y;de<ne.length;de+=1){var we=ne[de];D=Math.min(D,we.x),J=Math.min(J,we.y),O=Math.max(O,we.x),K=Math.max(K,we.y)}var Oe=O-D,ht=K-J,Yt=Math.max(Oe,ht),cr=Math.max(0,Math.floor(-Math.log(Yt)/Math.LN2)),Nt=Math.pow(2,cr);return new i.CanonicalTileID(cr,Math.floor((D+O)/2*Nt),Math.floor((J+K)/2*Nt))}var ie=(function(Y){function D(J,O,K,de){Y.call(this,J,O,K,de),this.roundZoom=!0,this.type=\"video\",this.options=O}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.load=function(){var O=this;this._loaded=!1;var K=this.options;this.urls=[];for(var de=0,ne=K.urls;de<ne.length;de+=1){var we=ne[de];this.urls.push(this.map._requestManager.transformRequest(we,i.ResourceType.Source).url)}i.getVideo(this.urls,function(Oe,ht){O._loaded=!0,Oe?O.fire(new i.ErrorEvent(Oe)):ht&&(O.video=ht,O.video.loop=!0,O.video.addEventListener(\"playing\",function(){O.map.triggerRepaint()}),O.map&&O.video.play(),O._finishLoading())})},D.prototype.pause=function(){this.video&&this.video.pause()},D.prototype.play=function(){this.video&&this.video.play()},D.prototype.seek=function(O){if(this.video){var K=this.video.seekable;O<K.start(0)||O>K.end(0)?this.fire(new i.ErrorEvent(new i.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+K.start(0)+\" and \"+K.end(0)+\"-second mark.\"))):this.video.currentTime=O}},D.prototype.getVideo=function(){return this.video},D.prototype.onAdd=function(O){this.map||(this.map=O,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},D.prototype.prepare=function(){if(!(Object.keys(this.tiles).length===0||this.video.readyState<2)){var 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Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(O){if(!(O===this.current&&!this.dirty)){var K=this.gl;K.bindTexture(K.TEXTURE_2D,O),this.current=O,this.dirty=!1}},D})(rr),Qt=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(O){if(!(O===this.current&&!this.dirty)){var K=this.gl;K.bindBuffer(K.ARRAY_BUFFER,O),this.current=O,this.dirty=!1}},D})(rr),Mr=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(O){var K=this.gl;K.bindBuffer(K.ELEMENT_ARRAY_BUFFER,O),this.current=O,this.dirty=!1},D})(rr),Gr=(function(Y){function D(J){Y.call(this,J),this.vao=J.extVertexArrayObject}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D.prototype.set=function(O){!this.vao||O===this.current&&!this.dirty||(this.vao.bindVertexArrayOES(O),this.current=O,this.dirty=!1)},D})(rr),Ir=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return 4},D.prototype.set=function(O){if(!(O===this.current&&!this.dirty)){var K=this.gl;K.pixelStorei(K.UNPACK_ALIGNMENT,O),this.current=O,this.dirty=!1}},D})(rr),Yr=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return!1},D.prototype.set=function(O){if(!(O===this.current&&!this.dirty)){var K=this.gl;K.pixelStorei(K.UNPACK_PREMULTIPLY_ALPHA_WEBGL,O),this.current=O,this.dirty=!1}},D})(rr),_i=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return!1},D.prototype.set=function(O){if(!(O===this.current&&!this.dirty)){var K=this.gl;K.pixelStorei(K.UNPACK_FLIP_Y_WEBGL,O),this.current=O,this.dirty=!1}},D})(rr),Pi=(function(Y){function D(J,O){Y.call(this,J),this.context=J,this.parent=O}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getDefault=function(){return null},D})(rr),ai=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.setDirty=function(){this.dirty=!0},D.prototype.set=function(O){if(!(O===this.current&&!this.dirty)){this.context.bindFramebuffer.set(this.parent);var K=this.gl;K.framebufferTexture2D(K.FRAMEBUFFER,K.COLOR_ATTACHMENT0,K.TEXTURE_2D,O,0),this.current=O,this.dirty=!1}},D})(Pi),mi=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.set=function(O){if(!(O===this.current&&!this.dirty)){this.context.bindFramebuffer.set(this.parent);var K=this.gl;K.framebufferRenderbuffer(K.FRAMEBUFFER,K.DEPTH_ATTACHMENT,K.RENDERBUFFER,O),this.current=O,this.dirty=!1}},D})(Pi),un=function(D,J,O,K){this.context=D,this.width=J,this.height=O;var de=D.gl,ne=this.framebuffer=de.createFramebuffer();this.colorAttachment=new ai(D,ne),K&&(this.depthAttachment=new mi(D,ne))};un.prototype.destroy=function(){var D=this.context.gl,J=this.colorAttachment.get();if(J&&D.deleteTexture(J),this.depthAttachment){var O=this.depthAttachment.get();O&&D.deleteRenderbuffer(O)}D.deleteFramebuffer(this.framebuffer)};var Fn=519,An=function(D,J,O){this.func=D,this.mask=J,this.range=O};An.ReadOnly=!1,An.ReadWrite=!0,An.disabled=new An(Fn,An.ReadOnly,[0,1]);var Hn=519,Qn=7680,Vi=function(D,J,O,K,de,ne){this.test=D,this.ref=J,this.mask=O,this.fail=K,this.depthFail=de,this.pass=ne};Vi.disabled=new Vi({func:Hn,mask:0},0,0,Qn,Qn,Qn);var Jn=0,Yn=1,Gt=771,wt=function(D,J,O){this.blendFunction=D,this.blendColor=J,this.mask=O};wt.Replace=[Yn,Jn],wt.disabled=new wt(wt.Replace,i.Color.transparent,[!1,!1,!1,!1]),wt.unblended=new wt(wt.Replace,i.Color.transparent,[!0,!0,!0,!0]),wt.alphaBlended=new wt([Yn,Gt],i.Color.transparent,[!0,!0,!0,!0]);var tr=1029,ir=2305,wr=function(D,J,O){this.enable=D,this.mode=J,this.frontFace=O};wr.disabled=new wr(!1,tr,ir),wr.backCCW=new wr(!0,tr,ir);var Xr=function(D){this.gl=D,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new tt(this),this.clearDepth=new je(this),this.clearStencil=new Ue(this),this.colorMask=new Ae(this),this.depthMask=new rt(this),this.stencilMask=new St(this),this.stencilFunc=new Tt(this),this.stencilOp=new dt(this),this.stencilTest=new Et(this),this.depthRange=new pt(this),this.depthTest=new jt(this),this.depthFunc=new or(this),this.blend=new mr(this),this.blendFunc=new Ar(this),this.blendColor=new ei(this),this.blendEquation=new qr(this),this.cullFace=new jr(this),this.cullFaceSide=new gt(this),this.frontFace=new Ge(this),this.program=new Je(this),this.activeTexture=new We(this),this.viewport=new et(this),this.bindFramebuffer=new xt(this),this.bindRenderbuffer=new At(this),this.bindTexture=new Kt(this),this.bindVertexBuffer=new Qt(this),this.bindElementBuffer=new Mr(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new Gr(this),this.pixelStoreUnpack=new Ir(this),this.pixelStoreUnpackPremultiplyAlpha=new Yr(this),this.pixelStoreUnpackFlipY=new _i(this),this.extTextureFilterAnisotropic=D.getExtension(\"EXT_texture_filter_anisotropic\")||D.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||D.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=D.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=D.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(D.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=D.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=D.getExtension(\"EXT_disjoint_timer_query\"),this.maxTextureSize=D.getParameter(D.MAX_TEXTURE_SIZE)};Xr.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Xr.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Xr.prototype.createIndexBuffer=function(D,J){return new ct(this,D,J)},Xr.prototype.createVertexBuffer=function(D,J,O){return new Vt(this,D,J,O)},Xr.prototype.createRenderbuffer=function(D,J,O){var K=this.gl,de=K.createRenderbuffer();return this.bindRenderbuffer.set(de),K.renderbufferStorage(K.RENDERBUFFER,D,J,O),this.bindRenderbuffer.set(null),de},Xr.prototype.createFramebuffer=function(D,J,O){return new un(this,D,J,O)},Xr.prototype.clear=function(D){var J=D.color,O=D.depth,K=this.gl,de=0;J&&(de|=K.COLOR_BUFFER_BIT,this.clearColor.set(J),this.colorMask.set([!0,!0,!0,!0])),typeof O!=\"undefined\"&&(de|=K.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(O),this.depthMask.set(!0)),K.clear(de)},Xr.prototype.setCullFace=function(D){D.enable===!1?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(D.mode),this.frontFace.set(D.frontFace))},Xr.prototype.setDepthMode=function(D){D.func===this.gl.ALWAYS&&!D.mask?this.depthTest.set(!1):(this.depthTest.set(!0),this.depthFunc.set(D.func),this.depthMask.set(D.mask),this.depthRange.set(D.range))},Xr.prototype.setStencilMode=function(D){D.test.func===this.gl.ALWAYS&&!D.mask?this.stencilTest.set(!1):(this.stencilTest.set(!0),this.stencilMask.set(D.mask),this.stencilOp.set([D.fail,D.depthFail,D.pass]),this.stencilFunc.set({func:D.test.func,ref:D.ref,mask:D.test.mask}))},Xr.prototype.setColorMode=function(D){i.deepEqual(D.blendFunction,wt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(D.blendFunction),this.blendColor.set(D.blendColor)),this.colorMask.set(D.mask)},Xr.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var ti=(function(Y){function D(J,O,K){var de=this;Y.call(this),this.id=J,this.dispatcher=K,this.on(\"data\",function(ne){ne.dataType===\"source\"&&ne.sourceDataType===\"metadata\"&&(de._sourceLoaded=!0),de._sourceLoaded&&!de._paused&&ne.dataType===\"source\"&&ne.sourceDataType===\"content\"&&(de.reload(),de.transform&&de.update(de.transform))}),this.on(\"error\",function(){de._sourceErrored=!0}),this._source=be(J,O,K,this),this._tiles={},this._cache=new at(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new i.SourceFeatureState}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.onAdd=function(O){this.map=O,this._maxTileCacheSize=O?O._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(O)},D.prototype.onRemove=function(O){this._source&&this._source.onRemove&&this._source.onRemove(O)},D.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;for(var O in this._tiles){var K=this._tiles[O];if(K.state!==\"loaded\"&&K.state!==\"errored\")return!1}return!0},D.prototype.getSource=function(){return this._source},D.prototype.pause=function(){this._paused=!0},D.prototype.resume=function(){if(this._paused){var O=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,O&&this.reload(),this.transform&&this.update(this.transform)}},D.prototype._loadTile=function(O,K){return this._source.loadTile(O,K)},D.prototype._unloadTile=function(O){if(this._source.unloadTile)return this._source.unloadTile(O,function(){})},D.prototype._abortTile=function(O){if(this._source.abortTile)return this._source.abortTile(O,function(){})},D.prototype.serialize=function(){return this._source.serialize()},D.prototype.prepare=function(O){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(var K in this._tiles){var de=this._tiles[K];de.upload(O),de.prepare(this.map.style.imageManager)}},D.prototype.getIds=function(){return i.values(this._tiles).map(function(O){return O.tileID}).sort($r).map(function(O){return O.key})},D.prototype.getRenderableIds=function(O){var K=this,de=[];for(var ne in this._tiles)this._isIdRenderable(ne,O)&&de.push(this._tiles[ne]);return O?de.sort(function(we,Oe){var ht=we.tileID,Yt=Oe.tileID,cr=new i.Point(ht.canonical.x,ht.canonical.y)._rotate(K.transform.angle),Nt=new i.Point(Yt.canonical.x,Yt.canonical.y)._rotate(K.transform.angle);return ht.overscaledZ-Yt.overscaledZ||Nt.y-cr.y||Nt.x-cr.x}).map(function(we){return we.tileID.key}):de.map(function(we){return we.tileID}).sort($r).map(function(we){return we.key})},D.prototype.hasRenderableParent=function(O){var K=this.findLoadedParent(O,0);return K?this._isIdRenderable(K.tileID.key):!1},D.prototype._isIdRenderable=function(O,K){return this._tiles[O]&&this._tiles[O].hasData()&&!this._coveredTiles[O]&&(K||!this._tiles[O].holdingForFade())},D.prototype.reload=function(){if(this._paused){this._shouldReloadOnResume=!0;return}this._cache.reset();for(var O in this._tiles)this._tiles[O].state!==\"errored\"&&this._reloadTile(O,\"reloading\")},D.prototype._reloadTile=function(O,K){var de=this._tiles[O];de&&(de.state!==\"loading\"&&(de.state=K),this._loadTile(de,this._tileLoaded.bind(this,de,O,K)))},D.prototype._tileLoaded=function(O,K,de,ne){if(ne){O.state=\"errored\",ne.status!==404?this._source.fire(new i.ErrorEvent(ne,{tile:O})):this.update(this.transform);return}O.timeAdded=i.browser.now(),de===\"expired\"&&(O.refreshedUponExpiration=!0),this._setTileReloadTimer(K,O),this.getSource().type===\"raster-dem\"&&O.dem&&this._backfillDEM(O),this._state.initializeTileState(O,this.map?this.map.painter:null),this._source.fire(new i.Event(\"data\",{dataType:\"source\",tile:O,coord:O.tileID}))},D.prototype._backfillDEM=function(O){for(var K=this.getRenderableIds(),de=0;de<K.length;de++){var ne=K[de];if(O.neighboringTiles&&O.neighboringTiles[ne]){var we=this.getTileByID(ne);Oe(O,we),Oe(we,O)}}function Oe(ht,Yt){ht.needsHillshadePrepare=!0;var cr=Yt.tileID.canonical.x-ht.tileID.canonical.x,Nt=Yt.tileID.canonical.y-ht.tileID.canonical.y,Ne=Math.pow(2,ht.tileID.canonical.z),$e=Yt.tileID.key;cr===0&&Nt===0||Math.abs(Nt)>1||(Math.abs(cr)>1&&(Math.abs(cr+Ne)===1?cr+=Ne:Math.abs(cr-Ne)===1&&(cr-=Ne)),!(!Yt.dem||!ht.dem)&&(ht.dem.backfillBorder(Yt.dem,cr,Nt),ht.neighboringTiles&&ht.neighboringTiles[$e]&&(ht.neighboringTiles[$e].backfilled=!0)))}},D.prototype.getTile=function(O){return this.getTileByID(O.key)},D.prototype.getTileByID=function(O){return this._tiles[O]},D.prototype._retainLoadedChildren=function(O,K,de,ne){for(var we in this._tiles){var Oe=this._tiles[we];if(!(ne[we]||!Oe.hasData()||Oe.tileID.overscaledZ<=K||Oe.tileID.overscaledZ>de)){for(var ht=Oe.tileID;Oe&&Oe.tileID.overscaledZ>K+1;){var Yt=Oe.tileID.scaledTo(Oe.tileID.overscaledZ-1);Oe=this._tiles[Yt.key],Oe&&Oe.hasData()&&(ht=Yt)}for(var cr=ht;cr.overscaledZ>K;)if(cr=cr.scaledTo(cr.overscaledZ-1),O[cr.key]){ne[ht.key]=ht;break}}}},D.prototype.findLoadedParent=function(O,K){if(O.key in this._loadedParentTiles){var de=this._loadedParentTiles[O.key];return de&&de.tileID.overscaledZ>=K?de:null}for(var ne=O.overscaledZ-1;ne>=K;ne--){var we=O.scaledTo(ne),Oe=this._getLoadedTile(we);if(Oe)return Oe}},D.prototype._getLoadedTile=function(O){var K=this._tiles[O.key];if(K&&K.hasData())return K;var de=this._cache.getByKey(O.wrapped().key);return de},D.prototype.updateCacheSize=function(O){var K=Math.ceil(O.width/this._source.tileSize)+1,de=Math.ceil(O.height/this._source.tileSize)+1,ne=K*de,we=5,Oe=Math.floor(ne*we),ht=typeof this._maxTileCacheSize==\"number\"?Math.min(this._maxTileCacheSize,Oe):Oe;this._cache.setMaxSize(ht)},D.prototype.handleWrapJump=function(O){var K=this._prevLng===void 0?O:this._prevLng,de=O-K,ne=de/360,we=Math.round(ne);if(this._prevLng=O,we){var Oe={};for(var ht in this._tiles){var Yt=this._tiles[ht];Yt.tileID=Yt.tileID.unwrapTo(Yt.tileID.wrap+we),Oe[Yt.tileID.key]=Yt}this._tiles=Oe;for(var cr in this._timers)clearTimeout(this._timers[cr]),delete this._timers[cr];for(var Nt in this._tiles){var Ne=this._tiles[Nt];this._setTileReloadTimer(Nt,Ne)}}},D.prototype.update=function(O){var K=this;if(this.transform=O,!(!this._sourceLoaded||this._paused)){this.updateCacheSize(O),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={};var de;this.used?this._source.tileID?de=O.getVisibleUnwrappedCoordinates(this._source.tileID).map(function(Br){return new i.OverscaledTileID(Br.canonical.z,Br.wrap,Br.canonical.z,Br.canonical.x,Br.canonical.y)}):(de=O.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(de=de.filter(function(Br){return K._source.hasTile(Br)}))):de=[];var ne=O.coveringZoomLevel(this._source),we=Math.max(ne-D.maxOverzooming,this._source.minzoom),Oe=Math.max(ne+D.maxUnderzooming,this._source.minzoom),ht=this._updateRetainedTiles(de,ne);if(Ri(this._source.type)){for(var Yt={},cr={},Nt=Object.keys(ht),Ne=0,$e=Nt;Ne<$e.length;Ne+=1){var st=$e[Ne],It=ht[st],Bt=this._tiles[st];if(!(!Bt||Bt.fadeEndTime&&Bt.fadeEndTime<=i.browser.now())){var Ft=this.findLoadedParent(It,we);Ft&&(this._addTile(Ft.tileID),Yt[Ft.tileID.key]=Ft.tileID),cr[st]=It}}this._retainLoadedChildren(cr,ne,Oe,ht);for(var Ut in Yt)ht[Ut]||(this._coveredTiles[Ut]=!0,ht[Ut]=Yt[Ut])}for(var zt in ht)this._tiles[zt].clearFadeHold();for(var _t=i.keysDifference(this._tiles,ht),Dt=0,vt=_t;Dt<vt.length;Dt+=1){var lr=vt[Dt],_r=this._tiles[lr];_r.hasSymbolBuckets&&!_r.holdingForFade()?_r.setHoldDuration(this.map._fadeDuration):(!_r.hasSymbolBuckets||_r.symbolFadeFinished())&&this._removeTile(lr)}this._updateLoadedParentTileCache()}},D.prototype.releaseSymbolFadeTiles=function(){for(var O in this._tiles)this._tiles[O].holdingForFade()&&this._removeTile(O)},D.prototype._updateRetainedTiles=function(O,K){for(var de={},ne={},we=Math.max(K-D.maxOverzooming,this._source.minzoom),Oe=Math.max(K+D.maxUnderzooming,this._source.minzoom),ht={},Yt=0,cr=O;Yt<cr.length;Yt+=1){var Nt=cr[Yt],Ne=this._addTile(Nt);de[Nt.key]=Nt,!Ne.hasData()&&K<this._source.maxzoom&&(ht[Nt.key]=Nt)}this._retainLoadedChildren(ht,K,Oe,de);for(var $e=0,st=O;$e<st.length;$e+=1){var It=st[$e],Bt=this._tiles[It.key];if(!Bt.hasData()){if(K+1>this._source.maxzoom){var Ft=It.children(this._source.maxzoom)[0],Ut=this.getTile(Ft);if(Ut&&Ut.hasData()){de[Ft.key]=Ft;continue}}else{var zt=It.children(this._source.maxzoom);if(de[zt[0].key]&&de[zt[1].key]&&de[zt[2].key]&&de[zt[3].key])continue}for(var _t=Bt.wasRequested(),Dt=It.overscaledZ-1;Dt>=we;--Dt){var vt=It.scaledTo(Dt);if(ne[vt.key]||(ne[vt.key]=!0,Bt=this.getTile(vt),!Bt&&_t&&(Bt=this._addTile(vt)),Bt&&(de[vt.key]=vt,_t=Bt.wasRequested(),Bt.hasData())))break}}}return de},D.prototype._updateLoadedParentTileCache=function(){this._loadedParentTiles={};for(var O in this._tiles){for(var K=[],de=void 0,ne=this._tiles[O].tileID;ne.overscaledZ>0;){if(ne.key in this._loadedParentTiles){de=this._loadedParentTiles[ne.key];break}K.push(ne.key);var we=ne.scaledTo(ne.overscaledZ-1);if(de=this._getLoadedTile(we),de)break;ne=we}for(var Oe=0,ht=K;Oe<ht.length;Oe+=1){var Yt=ht[Oe];this._loadedParentTiles[Yt]=de}}},D.prototype._addTile=function(O){var K=this._tiles[O.key];if(K)return K;K=this._cache.getAndRemove(O),K&&(this._setTileReloadTimer(O.key,K),K.tileID=O,this._state.initializeTileState(K,this.map?this.map.painter:null),this._cacheTimers[O.key]&&(clearTimeout(this._cacheTimers[O.key]),delete this._cacheTimers[O.key],this._setTileReloadTimer(O.key,K)));var de=!!K;return de||(K=new i.Tile(O,this._source.tileSize*O.overscaleFactor()),this._loadTile(K,this._tileLoaded.bind(this,K,O.key,K.state))),K?(K.uses++,this._tiles[O.key]=K,de||this._source.fire(new i.Event(\"dataloading\",{tile:K,coord:K.tileID,dataType:\"source\"})),K):null},D.prototype._setTileReloadTimer=function(O,K){var de=this;O in this._timers&&(clearTimeout(this._timers[O]),delete this._timers[O]);var ne=K.getExpiryTimeout();ne&&(this._timers[O]=setTimeout(function(){de._reloadTile(O,\"expired\"),delete de._timers[O]},ne))},D.prototype._removeTile=function(O){var K=this._tiles[O];K&&(K.uses--,delete this._tiles[O],this._timers[O]&&(clearTimeout(this._timers[O]),delete this._timers[O]),!(K.uses>0)&&(K.hasData()&&K.state!==\"reloading\"?this._cache.add(K.tileID,K,K.getExpiryTimeout()):(K.aborted=!0,this._abortTile(K),this._unloadTile(K))))},D.prototype.clearTiles=function(){this._shouldReloadOnResume=!1,this._paused=!1;for(var O in this._tiles)this._removeTile(O);this._cache.reset()},D.prototype.tilesIn=function(O,K,de){var ne=this,we=[],Oe=this.transform;if(!Oe)return we;for(var ht=de?Oe.getCameraQueryGeometry(O):O,Yt=O.map(function(Dt){return Oe.pointCoordinate(Dt)}),cr=ht.map(function(Dt){return Oe.pointCoordinate(Dt)}),Nt=this.getIds(),Ne=1/0,$e=1/0,st=-1/0,It=-1/0,Bt=0,Ft=cr;Bt<Ft.length;Bt+=1){var Ut=Ft[Bt];Ne=Math.min(Ne,Ut.x),$e=Math.min($e,Ut.y),st=Math.max(st,Ut.x),It=Math.max(It,Ut.y)}for(var zt=function(Dt){var vt=ne._tiles[Nt[Dt]];if(!vt.holdingForFade()){var lr=vt.tileID,_r=Math.pow(2,Oe.zoom-vt.tileID.overscaledZ),Br=K*vt.queryPadding*i.EXTENT/vt.tileSize/_r,Di=[lr.getTilePoint(new i.MercatorCoordinate(Ne,$e)),lr.getTilePoint(new i.MercatorCoordinate(st,It))];if(Di[0].x-Br<i.EXTENT&&Di[0].y-Br<i.EXTENT&&Di[1].x+Br>=0&&Di[1].y+Br>=0){var ci=Yt.map(function(gn){return lr.getTilePoint(gn)}),bi=cr.map(function(gn){return lr.getTilePoint(gn)});we.push({tile:vt,tileID:lr,queryGeometry:ci,cameraQueryGeometry:bi,scale:_r})}}},_t=0;_t<Nt.length;_t++)zt(_t);return we},D.prototype.getVisibleCoordinates=function(O){for(var K=this,de=this.getRenderableIds(O).map(function(ht){return K._tiles[ht].tileID}),ne=0,we=de;ne<we.length;ne+=1){var Oe=we[ne];Oe.posMatrix=this.transform.calculatePosMatrix(Oe.toUnwrapped())}return de},D.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Ri(this._source.type))for(var O in this._tiles){var K=this._tiles[O];if(K.fadeEndTime!==void 0&&K.fadeEndTime>=i.browser.now())return!0}return!1},D.prototype.setFeatureState=function(O,K,de){O=O||\"_geojsonTileLayer\",this._state.updateState(O,K,de)},D.prototype.removeFeatureState=function(O,K,de){O=O||\"_geojsonTileLayer\",this._state.removeFeatureState(O,K,de)},D.prototype.getFeatureState=function(O,K){return O=O||\"_geojsonTileLayer\",this._state.getState(O,K)},D.prototype.setDependencies=function(O,K,de){var ne=this._tiles[O];ne&&ne.setDependencies(K,de)},D.prototype.reloadTilesForDependencies=function(O,K){for(var de in this._tiles){var ne=this._tiles[de];ne.hasDependency(O,K)&&this._reloadTile(de,\"reloading\")}this._cache.filter(function(we){return!we.hasDependency(O,K)})},D})(i.Evented);ti.maxOverzooming=10,ti.maxUnderzooming=3;function $r(Y,D){var J=Math.abs(Y.wrap*2)-+(Y.wrap<0),O=Math.abs(D.wrap*2)-+(D.wrap<0);return Y.overscaledZ-D.overscaledZ||O-J||D.canonical.y-Y.canonical.y||D.canonical.x-Y.canonical.x}function Ri(Y){return Y===\"raster\"||Y===\"image\"||Y===\"video\"}function Hi(){return new i.window.Worker(io.workerUrl)}var en=\"mapboxgl_preloaded_worker_pool\",fn=function(){this.active={}};fn.prototype.acquire=function(D){if(!this.workers)for(this.workers=[];this.workers.length<fn.workerCount;)this.workers.push(new Hi);return this.active[D]=!0,this.workers.slice()},fn.prototype.release=function(D){delete this.active[D],this.numActive()===0&&(this.workers.forEach(function(J){J.terminate()}),this.workers=null)},fn.prototype.isPreloaded=function(){return!!this.active[en]},fn.prototype.numActive=function(){return Object.keys(this.active).length};var yn=Math.floor(i.browser.hardwareConcurrency/2);fn.workerCount=Math.max(Math.min(yn,6),1);var Mn;function Ba(){return Mn||(Mn=new fn),Mn}function ua(){var Y=Ba();Y.acquire(en)}function ma(){var Y=Mn;Y&&(Y.isPreloaded()&&Y.numActive()===1?(Y.release(en),Mn=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))}function Wa(Y,D){var J={};for(var O in Y)O!==\"ref\"&&(J[O]=Y[O]);return i.refProperties.forEach(function(K){K in D&&(J[K]=D[K])}),J}function za(Y){Y=Y.slice();for(var D=Object.create(null),J=0;J<Y.length;J++)D[Y[J].id]=Y[J];for(var O=0;O<Y.length;O++)\"ref\"in Y[O]&&(Y[O]=Wa(Y[O],D[Y[O].ref]));return Y}function Xo(){var Y={},D=i.styleSpec.$version;for(var J in i.styleSpec.$root){var O=i.styleSpec.$root[J];if(O.required){var K=null;J===\"version\"?K=D:O.type===\"array\"?K=[]:K={},K!=null&&(Y[J]=K)}}return Y}var da={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function jn(Y,D,J){J.push({command:da.addSource,args:[Y,D[Y]]})}function Ha(Y,D,J){D.push({command:da.removeSource,args:[Y]}),J[Y]=!0}function vo(Y,D,J,O){Ha(Y,J,O),jn(Y,D,J)}function Gn(Y,D,J){var O;for(O in Y[J])if(Y[J].hasOwnProperty(O)&&O!==\"data\"&&!i.deepEqual(Y[J][O],D[J][O]))return!1;for(O in D[J])if(D[J].hasOwnProperty(O)&&O!==\"data\"&&!i.deepEqual(Y[J][O],D[J][O]))return!1;return!0}function Ct(Y,D,J,O){Y=Y||{},D=D||{};var K;for(K in Y)Y.hasOwnProperty(K)&&(D.hasOwnProperty(K)||Ha(K,J,O));for(K in D)D.hasOwnProperty(K)&&(Y.hasOwnProperty(K)?i.deepEqual(Y[K],D[K])||(Y[K].type===\"geojson\"&&D[K].type===\"geojson\"&&Gn(Y,D,K)?J.push({command:da.setGeoJSONSourceData,args:[K,D[K].data]}):vo(K,D,J,O)):jn(K,D,J))}function Sr(Y,D,J,O,K,de){Y=Y||{},D=D||{};var ne;for(ne in Y)Y.hasOwnProperty(ne)&&(i.deepEqual(Y[ne],D[ne])||J.push({command:de,args:[O,ne,D[ne],K]}));for(ne in D)!D.hasOwnProperty(ne)||Y.hasOwnProperty(ne)||i.deepEqual(Y[ne],D[ne])||J.push({command:de,args:[O,ne,D[ne],K]})}function Jr(Y){return Y.id}function hi(Y,D){return Y[D.id]=D,Y}function hn(Y,D,J){Y=Y||[],D=D||[];var O=Y.map(Jr),K=D.map(Jr),de=Y.reduce(hi,{}),ne=D.reduce(hi,{}),we=O.slice(),Oe=Object.create(null),ht,Yt,cr,Nt,Ne,$e,st;for(ht=0,Yt=0;ht<O.length;ht++)cr=O[ht],ne.hasOwnProperty(cr)?Yt++:(J.push({command:da.removeLayer,args:[cr]}),we.splice(we.indexOf(cr,Yt),1));for(ht=0,Yt=0;ht<K.length;ht++)cr=K[K.length-1-ht],we[we.length-1-ht]!==cr&&(de.hasOwnProperty(cr)?(J.push({command:da.removeLayer,args:[cr]}),we.splice(we.lastIndexOf(cr,we.length-Yt),1)):Yt++,$e=we[we.length-ht],J.push({command:da.addLayer,args:[ne[cr],$e]}),we.splice(we.length-ht,0,cr),Oe[cr]=!0);for(ht=0;ht<K.length;ht++)if(cr=K[ht],Nt=de[cr],Ne=ne[cr],!(Oe[cr]||i.deepEqual(Nt,Ne))){if(!i.deepEqual(Nt.source,Ne.source)||!i.deepEqual(Nt[\"source-layer\"],Ne[\"source-layer\"])||!i.deepEqual(Nt.type,Ne.type)){J.push({command:da.removeLayer,args:[cr]}),$e=we[we.lastIndexOf(cr)+1],J.push({command:da.addLayer,args:[Ne,$e]});continue}Sr(Nt.layout,Ne.layout,J,cr,null,da.setLayoutProperty),Sr(Nt.paint,Ne.paint,J,cr,null,da.setPaintProperty),i.deepEqual(Nt.filter,Ne.filter)||J.push({command:da.setFilter,args:[cr,Ne.filter]}),(!i.deepEqual(Nt.minzoom,Ne.minzoom)||!i.deepEqual(Nt.maxzoom,Ne.maxzoom))&&J.push({command:da.setLayerZoomRange,args:[cr,Ne.minzoom,Ne.maxzoom]});for(st in Nt)Nt.hasOwnProperty(st)&&(st===\"layout\"||st===\"paint\"||st===\"filter\"||st===\"metadata\"||st===\"minzoom\"||st===\"maxzoom\"||(st.indexOf(\"paint.\")===0?Sr(Nt[st],Ne[st],J,cr,st.slice(6),da.setPaintProperty):i.deepEqual(Nt[st],Ne[st])||J.push({command:da.setLayerProperty,args:[cr,st,Ne[st]]})));for(st in Ne)!Ne.hasOwnProperty(st)||Nt.hasOwnProperty(st)||st===\"layout\"||st===\"paint\"||st===\"filter\"||st===\"metadata\"||st===\"minzoom\"||st===\"maxzoom\"||(st.indexOf(\"paint.\")===0?Sr(Nt[st],Ne[st],J,cr,st.slice(6),da.setPaintProperty):i.deepEqual(Nt[st],Ne[st])||J.push({command:da.setLayerProperty,args:[cr,st,Ne[st]]}))}}function Sn(Y,D){if(!Y)return[{command:da.setStyle,args:[D]}];var J=[];try{if(!i.deepEqual(Y.version,D.version))return[{command:da.setStyle,args:[D]}];i.deepEqual(Y.center,D.center)||J.push({command:da.setCenter,args:[D.center]}),i.deepEqual(Y.zoom,D.zoom)||J.push({command:da.setZoom,args:[D.zoom]}),i.deepEqual(Y.bearing,D.bearing)||J.push({command:da.setBearing,args:[D.bearing]}),i.deepEqual(Y.pitch,D.pitch)||J.push({command:da.setPitch,args:[D.pitch]}),i.deepEqual(Y.sprite,D.sprite)||J.push({command:da.setSprite,args:[D.sprite]}),i.deepEqual(Y.glyphs,D.glyphs)||J.push({command:da.setGlyphs,args:[D.glyphs]}),i.deepEqual(Y.transition,D.transition)||J.push({command:da.setTransition,args:[D.transition]}),i.deepEqual(Y.light,D.light)||J.push({command:da.setLight,args:[D.light]});var O={},K=[];Ct(Y.sources,D.sources,K,O);var de=[];Y.layers&&Y.layers.forEach(function(ne){O[ne.source]?J.push({command:da.removeLayer,args:[ne.id]}):de.push(ne)}),J=J.concat(K),hn(de,D.layers,J)}catch(ne){console.warn(\"Unable to compute style diff:\",ne),J=[{command:da.setStyle,args:[D]}]}return J}var En=function(D,J){this.reset(D,J)};En.prototype.reset=function(D,J){this.points=D||[],this._distances=[0];for(var O=1;O<this.points.length;O++)this._distances[O]=this._distances[O-1]+this.points[O].dist(this.points[O-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(J||0,this.length*.5),this.paddedLength=this.length-this.padding*2},En.prototype.lerp=function(D){if(this.points.length===1)return this.points[0];D=i.clamp(D,0,1);for(var J=1,O=this._distances[J],K=D*this.paddedLength+this.padding;O<K&&J<this._distances.length;)O=this._distances[++J];var de=J-1,ne=this._distances[de],we=O-ne,Oe=we>0?(K-ne)/we:0;return this.points[de].mult(1-Oe).add(this.points[J].mult(Oe))};var ki=function(D,J,O){var K=this.boxCells=[],de=this.circleCells=[];this.xCellCount=Math.ceil(D/O),this.yCellCount=Math.ceil(J/O);for(var ne=0;ne<this.xCellCount*this.yCellCount;ne++)K.push([]),de.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=D,this.height=J,this.xScale=this.xCellCount/D,this.yScale=this.yCellCount/J,this.boxUid=0,this.circleUid=0};ki.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},ki.prototype.insert=function(D,J,O,K,de){this._forEachCell(J,O,K,de,this._insertBoxCell,this.boxUid++),this.boxKeys.push(D),this.bboxes.push(J),this.bboxes.push(O),this.bboxes.push(K),this.bboxes.push(de)},ki.prototype.insertCircle=function(D,J,O,K){this._forEachCell(J-K,O-K,J+K,O+K,this._insertCircleCell,this.circleUid++),this.circleKeys.push(D),this.circles.push(J),this.circles.push(O),this.circles.push(K)},ki.prototype._insertBoxCell=function(D,J,O,K,de,ne){this.boxCells[de].push(ne)},ki.prototype._insertCircleCell=function(D,J,O,K,de,ne){this.circleCells[de].push(ne)},ki.prototype._query=function(D,J,O,K,de,ne){if(O<0||D>this.width||K<0||J>this.height)return de?!1:[];var we=[];if(D<=0&&J<=0&&this.width<=O&&this.height<=K){if(de)return!0;for(var Oe=0;Oe<this.boxKeys.length;Oe++)we.push({key:this.boxKeys[Oe],x1:this.bboxes[Oe*4],y1:this.bboxes[Oe*4+1],x2:this.bboxes[Oe*4+2],y2:this.bboxes[Oe*4+3]});for(var ht=0;ht<this.circleKeys.length;ht++){var Yt=this.circles[ht*3],cr=this.circles[ht*3+1],Nt=this.circles[ht*3+2];we.push({key:this.circleKeys[ht],x1:Yt-Nt,y1:cr-Nt,x2:Yt+Nt,y2:cr+Nt})}return ne?we.filter(ne):we}else{var Ne={hitTest:de,seenUids:{box:{},circle:{}}};return this._forEachCell(D,J,O,K,this._queryCell,we,Ne,ne),de?we.length>0:we}},ki.prototype._queryCircle=function(D,J,O,K,de){var ne=D-O,we=D+O,Oe=J-O,ht=J+O;if(we<0||ne>this.width||ht<0||Oe>this.height)return K?!1:[];var Yt=[],cr={hitTest:K,circle:{x:D,y:J,radius:O},seenUids:{box:{},circle:{}}};return this._forEachCell(ne,Oe,we,ht,this._queryCellCircle,Yt,cr,de),K?Yt.length>0:Yt},ki.prototype.query=function(D,J,O,K,de){return this._query(D,J,O,K,!1,de)},ki.prototype.hitTest=function(D,J,O,K,de){return this._query(D,J,O,K,!0,de)},ki.prototype.hitTestCircle=function(D,J,O,K){return this._queryCircle(D,J,O,!0,K)},ki.prototype._queryCell=function(D,J,O,K,de,ne,we,Oe){var ht=we.seenUids,Yt=this.boxCells[de];if(Yt!==null)for(var cr=this.bboxes,Nt=0,Ne=Yt;Nt<Ne.length;Nt+=1){var $e=Ne[Nt];if(!ht.box[$e]){ht.box[$e]=!0;var st=$e*4;if(D<=cr[st+2]&&J<=cr[st+3]&&O>=cr[st+0]&&K>=cr[st+1]&&(!Oe||Oe(this.boxKeys[$e]))){if(we.hitTest)return ne.push(!0),!0;ne.push({key:this.boxKeys[$e],x1:cr[st],y1:cr[st+1],x2:cr[st+2],y2:cr[st+3]})}}}var It=this.circleCells[de];if(It!==null)for(var Bt=this.circles,Ft=0,Ut=It;Ft<Ut.length;Ft+=1){var zt=Ut[Ft];if(!ht.circle[zt]){ht.circle[zt]=!0;var _t=zt*3;if(this._circleAndRectCollide(Bt[_t],Bt[_t+1],Bt[_t+2],D,J,O,K)&&(!Oe||Oe(this.circleKeys[zt]))){if(we.hitTest)return ne.push(!0),!0;var Dt=Bt[_t],vt=Bt[_t+1],lr=Bt[_t+2];ne.push({key:this.circleKeys[zt],x1:Dt-lr,y1:vt-lr,x2:Dt+lr,y2:vt+lr})}}}},ki.prototype._queryCellCircle=function(D,J,O,K,de,ne,we,Oe){var ht=we.circle,Yt=we.seenUids,cr=this.boxCells[de];if(cr!==null)for(var Nt=this.bboxes,Ne=0,$e=cr;Ne<$e.length;Ne+=1){var st=$e[Ne];if(!Yt.box[st]){Yt.box[st]=!0;var It=st*4;if(this._circleAndRectCollide(ht.x,ht.y,ht.radius,Nt[It+0],Nt[It+1],Nt[It+2],Nt[It+3])&&(!Oe||Oe(this.boxKeys[st])))return ne.push(!0),!0}}var Bt=this.circleCells[de];if(Bt!==null)for(var Ft=this.circles,Ut=0,zt=Bt;Ut<zt.length;Ut+=1){var _t=zt[Ut];if(!Yt.circle[_t]){Yt.circle[_t]=!0;var Dt=_t*3;if(this._circlesCollide(Ft[Dt],Ft[Dt+1],Ft[Dt+2],ht.x,ht.y,ht.radius)&&(!Oe||Oe(this.circleKeys[_t])))return ne.push(!0),!0}}},ki.prototype._forEachCell=function(D,J,O,K,de,ne,we,Oe){for(var ht=this._convertToXCellCoord(D),Yt=this._convertToYCellCoord(J),cr=this._convertToXCellCoord(O),Nt=this._convertToYCellCoord(K),Ne=ht;Ne<=cr;Ne++)for(var $e=Yt;$e<=Nt;$e++){var st=this.xCellCount*$e+Ne;if(de.call(this,D,J,O,K,st,ne,we,Oe))return}},ki.prototype._convertToXCellCoord=function(D){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(D*this.xScale)))},ki.prototype._convertToYCellCoord=function(D){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(D*this.yScale)))},ki.prototype._circlesCollide=function(D,J,O,K,de,ne){var we=K-D,Oe=de-J,ht=O+ne;return ht*ht>we*we+Oe*Oe},ki.prototype._circleAndRectCollide=function(D,J,O,K,de,ne,we){var Oe=(ne-K)/2,ht=Math.abs(D-(K+Oe));if(ht>Oe+O)return!1;var Yt=(we-de)/2,cr=Math.abs(J-(de+Yt));if(cr>Yt+O)return!1;if(ht<=Oe||cr<=Yt)return!0;var Nt=ht-Oe,Ne=cr-Yt;return Nt*Nt+Ne*Ne<=O*O};function _n(Y,D,J,O,K){var de=i.create();return D?(i.scale(de,de,[1/K,1/K,1]),J||i.rotateZ(de,de,O.angle)):i.multiply(de,O.labelPlaneMatrix,Y),de}function ya(Y,D,J,O,K){if(D){var de=i.clone(Y);return i.scale(de,de,[K,K,1]),J||i.rotateZ(de,de,-O.angle),de}else return O.glCoordMatrix}function ea(Y,D){var J=[Y.x,Y.y,0,1];Fl(J,J,D);var O=J[3];return{point:new i.Point(J[0]/O,J[1]/O),signedDistanceFromCamera:O}}function Ma(Y,D){return .5+.5*(Y/D)}function _o(Y,D){var J=Y[0]/Y[3],O=Y[1]/Y[3],K=J>=-D[0]&&J<=D[0]&&O>=-D[1]&&O<=D[1];return K}function No(Y,D,J,O,K,de,ne,we){var Oe=O?Y.textSizeData:Y.iconSizeData,ht=i.evaluateSizeForZoom(Oe,J.transform.zoom),Yt=[256/J.width*2+1,256/J.height*2+1],cr=O?Y.text.dynamicLayoutVertexArray:Y.icon.dynamicLayoutVertexArray;cr.clear();for(var Nt=Y.lineVertexArray,Ne=O?Y.text.placedSymbolArray:Y.icon.placedSymbolArray,$e=J.transform.width/J.transform.height,st=!1,It=0;It<Ne.length;It++){var Bt=Ne.get(It);if(Bt.hidden||Bt.writingMode===i.WritingMode.vertical&&!st){ul(Bt.numGlyphs,cr);continue}st=!1;var Ft=[Bt.anchorX,Bt.anchorY,0,1];if(i.transformMat4(Ft,Ft,D),!_o(Ft,Yt)){ul(Bt.numGlyphs,cr);continue}var Ut=Ft[3],zt=Ma(J.transform.cameraToCenterDistance,Ut),_t=i.evaluateSizeForFeature(Oe,ht,Bt),Dt=ne?_t/zt:_t*zt,vt=new i.Point(Bt.anchorX,Bt.anchorY),lr=ea(vt,K).point,_r={},Br=ko(Bt,Dt,!1,we,D,K,de,Y.glyphOffsetArray,Nt,cr,lr,vt,_r,$e);st=Br.useVertical,(Br.notEnoughRoom||st||Br.needsFlipping&&ko(Bt,Dt,!0,we,D,K,de,Y.glyphOffsetArray,Nt,cr,lr,vt,_r,$e).notEnoughRoom)&&ul(Bt.numGlyphs,cr)}O?Y.text.dynamicLayoutVertexBuffer.updateData(cr):Y.icon.dynamicLayoutVertexBuffer.updateData(cr)}function po(Y,D,J,O,K,de,ne,we,Oe,ht,Yt){var cr=we.glyphStartIndex+we.numGlyphs,Nt=we.lineStartIndex,Ne=we.lineStartIndex+we.lineLength,$e=D.getoffsetX(we.glyphStartIndex),st=D.getoffsetX(cr-1),It=Fs(Y*$e,J,O,K,de,ne,we.segment,Nt,Ne,Oe,ht,Yt);if(!It)return null;var Bt=Fs(Y*st,J,O,K,de,ne,we.segment,Nt,Ne,Oe,ht,Yt);return Bt?{first:It,last:Bt}:null}function Lo(Y,D,J,O){if(Y===i.WritingMode.horizontal){var K=Math.abs(J.y-D.y),de=Math.abs(J.x-D.x)*O;if(K>de)return{useVertical:!0}}return(Y===i.WritingMode.vertical?D.y<J.y:D.x>J.x)?{needsFlipping:!0}:null}function ko(Y,D,J,O,K,de,ne,we,Oe,ht,Yt,cr,Nt,Ne){var $e=D/24,st=Y.lineOffsetX*$e,It=Y.lineOffsetY*$e,Bt;if(Y.numGlyphs>1){var Ft=Y.glyphStartIndex+Y.numGlyphs,Ut=Y.lineStartIndex,zt=Y.lineStartIndex+Y.lineLength,_t=po($e,we,st,It,J,Yt,cr,Y,Oe,de,Nt);if(!_t)return{notEnoughRoom:!0};var Dt=ea(_t.first.point,ne).point,vt=ea(_t.last.point,ne).point;if(O&&!J){var lr=Lo(Y.writingMode,Dt,vt,Ne);if(lr)return lr}Bt=[_t.first];for(var _r=Y.glyphStartIndex+1;_r<Ft-1;_r++)Bt.push(Fs($e*we.getoffsetX(_r),st,It,J,Yt,cr,Y.segment,Ut,zt,Oe,de,Nt));Bt.push(_t.last)}else{if(O&&!J){var Br=ea(cr,K).point,Di=Y.lineStartIndex+Y.segment+1,ci=new i.Point(Oe.getx(Di),Oe.gety(Di)),bi=ea(ci,K),gn=bi.signedDistanceFromCamera>0?bi.point:Ds(cr,ci,Br,1,K),tn=Lo(Y.writingMode,Br,gn,Ne);if(tn)return tn}var Ei=Fs($e*we.getoffsetX(Y.glyphStartIndex),st,It,J,Yt,cr,Y.segment,Y.lineStartIndex,Y.lineStartIndex+Y.lineLength,Oe,de,Nt);if(!Ei)return{notEnoughRoom:!0};Bt=[Ei]}for(var Oi=0,Gi=Bt;Oi<Gi.length;Oi+=1){var on=Gi[Oi];i.addDynamicAttributes(ht,on.point,on.angle)}return{}}function Ds(Y,D,J,O,K){var de=ea(Y.add(Y.sub(D)._unit()),K).point,ne=J.sub(de);return J.add(ne._mult(O/ne.mag()))}function Fs(Y,D,J,O,K,de,ne,we,Oe,ht,Yt,cr){var Nt=O?Y-D:Y+D,Ne=Nt>0?1:-1,$e=0;O&&(Ne*=-1,$e=Math.PI),Ne<0&&($e+=Math.PI);for(var st=Ne>0?we+ne:we+ne+1,It=K,Bt=K,Ft=0,Ut=0,zt=Math.abs(Nt),_t=[];Ft+Ut<=zt;){if(st+=Ne,st<we||st>=Oe)return null;if(Bt=It,_t.push(It),It=cr[st],It===void 0){var Dt=new i.Point(ht.getx(st),ht.gety(st)),vt=ea(Dt,Yt);if(vt.signedDistanceFromCamera>0)It=cr[st]=vt.point;else{var lr=st-Ne,_r=Ft===0?de:new i.Point(ht.getx(lr),ht.gety(lr));It=Ds(_r,Dt,Bt,zt-Ft+1,Yt)}}Ft+=Ut,Ut=Bt.dist(It)}var Br=(zt-Ft)/Ut,Di=It.sub(Bt),ci=Di.mult(Br)._add(Bt);ci._add(Di._unit()._perp()._mult(J*Ne));var bi=$e+Math.atan2(It.y-Bt.y,It.x-Bt.x);return _t.push(ci),{point:ci,angle:bi,path:_t}}var ll=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ul(Y,D){for(var J=0;J<Y;J++){var O=D.length;D.resize(O+4),D.float32.set(ll,O*3)}}function Fl(Y,D,J){var O=D[0],K=D[1];return Y[0]=J[0]*O+J[4]*K+J[12],Y[1]=J[1]*O+J[5]*K+J[13],Y[3]=J[3]*O+J[7]*K+J[15],Y}var us=100,rl=function(D,J,O){J===void 0&&(J=new ki(D.width+2*us,D.height+2*us,25)),O===void 0&&(O=new ki(D.width+2*us,D.height+2*us,25)),this.transform=D,this.grid=J,this.ignoredGrid=O,this.pitchfactor=Math.cos(D._pitch)*D.cameraToCenterDistance,this.screenRightBoundary=D.width+us,this.screenBottomBoundary=D.height+us,this.gridRightBoundary=D.width+2*us,this.gridBottomBoundary=D.height+2*us};rl.prototype.placeCollisionBox=function(D,J,O,K,de){var ne=this.projectAndGetPerspectiveRatio(K,D.anchorPointX,D.anchorPointY),we=O*ne.perspectiveRatio,Oe=D.x1*we+ne.point.x,ht=D.y1*we+ne.point.y,Yt=D.x2*we+ne.point.x,cr=D.y2*we+ne.point.y;return!this.isInsideGrid(Oe,ht,Yt,cr)||!J&&this.grid.hitTest(Oe,ht,Yt,cr,de)?{box:[],offscreen:!1}:{box:[Oe,ht,Yt,cr],offscreen:this.isOffscreen(Oe,ht,Yt,cr)}},rl.prototype.placeCollisionCircles=function(D,J,O,K,de,ne,we,Oe,ht,Yt,cr,Nt,Ne){var $e=[],st=new i.Point(J.anchorX,J.anchorY),It=ea(st,ne),Bt=Ma(this.transform.cameraToCenterDistance,It.signedDistanceFromCamera),Ft=Yt?de/Bt:de*Bt,Ut=Ft/i.ONE_EM,zt=ea(st,we).point,_t={},Dt=J.lineOffsetX*Ut,vt=J.lineOffsetY*Ut,lr=po(Ut,K,Dt,vt,!1,zt,st,J,O,we,_t),_r=!1,Br=!1,Di=!0;if(lr){for(var ci=Nt*.5*Bt+Ne,bi=new i.Point(-us,-us),gn=new i.Point(this.screenRightBoundary,this.screenBottomBoundary),tn=new En,Ei=lr.first,Oi=lr.last,Gi=[],on=Ei.path.length-1;on>=1;on--)Gi.push(Ei.path[on]);for(var zn=1;zn<Oi.path.length;zn++)Gi.push(Oi.path[zn]);var Ja=ci*2.5;if(Oe){var co=Gi.map(function(Iv){return ea(Iv,Oe)});co.some(function(Iv){return Iv.signedDistanceFromCamera<=0})?Gi=[]:Gi=co.map(function(Iv){return Iv.point})}var ts=[];if(Gi.length>0){for(var so=Gi[0].clone(),Yo=Gi[0].clone(),ms=1;ms<Gi.length;ms++)so.x=Math.min(so.x,Gi[ms].x),so.y=Math.min(so.y,Gi[ms].y),Yo.x=Math.max(Yo.x,Gi[ms].x),Yo.y=Math.max(Yo.y,Gi[ms].y);so.x>=bi.x&&Yo.x<=gn.x&&so.y>=bi.y&&Yo.y<=gn.y?ts=[Gi]:Yo.x<bi.x||so.x>gn.x||Yo.y<bi.y||so.y>gn.y?ts=[]:ts=i.clipLine([Gi],bi.x,bi.y,gn.x,gn.y)}for(var ou=0,Cv=ts;ou<Cv.length;ou+=1){var Lv=Cv[ou];tn.reset(Lv,ci*.25);var wd=0;tn.length<=.5*ci?wd=1:wd=Math.ceil(tn.paddedLength/Ja)+1;for(var Kv=0;Kv<wd;Kv++){var hg=Kv/Math.max(wd-1,1),gp=tn.lerp(hg),Td=gp.x+us,mp=gp.y+us;$e.push(Td,mp,ci,0);var Vd=Td-ci,Ad=mp-ci,Pv=Td+ci,Jv=mp+ci;if(Di=Di&&this.isOffscreen(Vd,Ad,Pv,Jv),Br=Br||this.isInsideGrid(Vd,Ad,Pv,Jv),!D&&this.grid.hitTestCircle(Td,mp,ci,cr)&&(_r=!0,!ht))return{circles:[],offscreen:!1,collisionDetected:_r}}}}return{circles:!ht&&_r||!Br?[]:$e,offscreen:Di,collisionDetected:_r}},rl.prototype.queryRenderedSymbols=function(D){if(D.length===0||this.grid.keysLength()===0&&this.ignoredGrid.keysLength()===0)return{};for(var J=[],O=1/0,K=1/0,de=-1/0,ne=-1/0,we=0,Oe=D;we<Oe.length;we+=1){var ht=Oe[we],Yt=new i.Point(ht.x+us,ht.y+us);O=Math.min(O,Yt.x),K=Math.min(K,Yt.y),de=Math.max(de,Yt.x),ne=Math.max(ne,Yt.y),J.push(Yt)}for(var cr=this.grid.query(O,K,de,ne).concat(this.ignoredGrid.query(O,K,de,ne)),Nt={},Ne={},$e=0,st=cr;$e<st.length;$e+=1){var It=st[$e],Bt=It.key;if(Nt[Bt.bucketInstanceId]===void 0&&(Nt[Bt.bucketInstanceId]={}),!Nt[Bt.bucketInstanceId][Bt.featureIndex]){var Ft=[new i.Point(It.x1,It.y1),new i.Point(It.x2,It.y1),new i.Point(It.x2,It.y2),new i.Point(It.x1,It.y2)];i.polygonIntersectsPolygon(J,Ft)&&(Nt[Bt.bucketInstanceId][Bt.featureIndex]=!0,Ne[Bt.bucketInstanceId]===void 0&&(Ne[Bt.bucketInstanceId]=[]),Ne[Bt.bucketInstanceId].push(Bt.featureIndex))}}return Ne},rl.prototype.insertCollisionBox=function(D,J,O,K,de){var ne=J?this.ignoredGrid:this.grid,we={bucketInstanceId:O,featureIndex:K,collisionGroupID:de};ne.insert(we,D[0],D[1],D[2],D[3])},rl.prototype.insertCollisionCircles=function(D,J,O,K,de){for(var ne=J?this.ignoredGrid:this.grid,we={bucketInstanceId:O,featureIndex:K,collisionGroupID:de},Oe=0;Oe<D.length;Oe+=4)ne.insertCircle(we,D[Oe],D[Oe+1],D[Oe+2])},rl.prototype.projectAndGetPerspectiveRatio=function(D,J,O){var K=[J,O,0,1];Fl(K,K,D);var de=new i.Point((K[0]/K[3]+1)/2*this.transform.width+us,(-K[1]/K[3]+1)/2*this.transform.height+us);return{point:de,perspectiveRatio:.5+.5*(this.transform.cameraToCenterDistance/K[3])}},rl.prototype.isOffscreen=function(D,J,O,K){return O<us||D>=this.screenRightBoundary||K<us||J>this.screenBottomBoundary},rl.prototype.isInsideGrid=function(D,J,O,K){return O>=0&&D<this.gridRightBoundary&&K>=0&&J<this.gridBottomBoundary},rl.prototype.getViewportMatrix=function(){var D=i.identity([]);return i.translate(D,D,[-us,-us,0]),D};function As(Y,D,J){return D*(i.EXTENT/(Y.tileSize*Math.pow(2,J-Y.tileID.overscaledZ)))}var cl=function(D,J,O,K){D?this.opacity=Math.max(0,Math.min(1,D.opacity+(D.placed?J:-J))):this.opacity=K&&O?1:0,this.placed=O};cl.prototype.isHidden=function(){return this.opacity===0&&!this.placed};var Ks=function(D,J,O,K,de){this.text=new cl(D?D.text:null,J,O,de),this.icon=new cl(D?D.icon:null,J,K,de)};Ks.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var zs=function(D,J,O){this.text=D,this.icon=J,this.skipFade=O},Io=function(){this.invProjMatrix=i.create(),this.viewportMatrix=i.create(),this.circles=[]},ls=function(D,J,O,K,de){this.bucketInstanceId=D,this.featureIndex=J,this.sourceLayerIndex=O,this.bucketIndex=K,this.tileID=de},Yl=function(D){this.crossSourceCollisions=D,this.maxGroupID=0,this.collisionGroups={}};Yl.prototype.get=function(D){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[D]){var J=++this.maxGroupID;this.collisionGroups[D]={ID:J,predicate:function(O){return O.collisionGroupID===J}}}return this.collisionGroups[D]};function Su(Y,D,J,O,K){var de=i.getAnchorAlignment(Y),ne=de.horizontalAlign,we=de.verticalAlign,Oe=-(ne-.5)*D,ht=-(we-.5)*J,Yt=i.evaluateVariableOffset(Y,O);return new i.Point(Oe+Yt[0]*K,ht+Yt[1]*K)}function nc(Y,D,J,O,K,de){var ne=Y.x1,we=Y.x2,Oe=Y.y1,ht=Y.y2,Yt=Y.anchorPointX,cr=Y.anchorPointY,Nt=new i.Point(D,J);return O&&Nt._rotate(K?de:-de),{x1:ne+Nt.x,y1:Oe+Nt.y,x2:we+Nt.x,y2:ht+Nt.y,anchorPointX:Yt,anchorPointY:cr}}var bs=function(D,J,O,K){this.transform=D.clone(),this.collisionIndex=new rl(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=J,this.retainedQueryData={},this.collisionGroups=new Yl(O),this.collisionCircleArrays={},this.prevPlacement=K,K&&(K.prevPlacement=void 0),this.placedOrientations={}};bs.prototype.getBucketParts=function(D,J,O,K){var de=O.getBucket(J),ne=O.latestFeatureIndex;if(!(!de||!ne||J.id!==de.layerIds[0])){var we=O.collisionBoxArray,Oe=de.layers[0].layout,ht=Math.pow(2,this.transform.zoom-O.tileID.overscaledZ),Yt=O.tileSize/i.EXTENT,cr=this.transform.calculatePosMatrix(O.tileID.toUnwrapped()),Nt=Oe.get(\"text-pitch-alignment\")===\"map\",Ne=Oe.get(\"text-rotation-alignment\")===\"map\",$e=As(O,1,this.transform.zoom),st=_n(cr,Nt,Ne,this.transform,$e),It=null;if(Nt){var Bt=ya(cr,Nt,Ne,this.transform,$e);It=i.multiply([],this.transform.labelPlaneMatrix,Bt)}this.retainedQueryData[de.bucketInstanceId]=new ls(de.bucketInstanceId,ne,de.sourceLayerIndex,de.index,O.tileID);var Ft={bucket:de,layout:Oe,posMatrix:cr,textLabelPlaneMatrix:st,labelToScreenMatrix:It,scale:ht,textPixelRatio:Yt,holdingForFade:O.holdingForFade(),collisionBoxArray:we,partiallyEvaluatedTextSize:i.evaluateSizeForZoom(de.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(de.sourceID)};if(K)for(var Ut=0,zt=de.sortKeyRanges;Ut<zt.length;Ut+=1){var _t=zt[Ut],Dt=_t.sortKey,vt=_t.symbolInstanceStart,lr=_t.symbolInstanceEnd;D.push({sortKey:Dt,symbolInstanceStart:vt,symbolInstanceEnd:lr,parameters:Ft})}else D.push({symbolInstanceStart:0,symbolInstanceEnd:de.symbolInstances.length,parameters:Ft})}},bs.prototype.attemptAnchorPlacement=function(D,J,O,K,de,ne,we,Oe,ht,Yt,cr,Nt,Ne,$e,st){var It=[Nt.textOffset0,Nt.textOffset1],Bt=Su(D,O,K,It,de),Ft=this.collisionIndex.placeCollisionBox(nc(J,Bt.x,Bt.y,ne,we,this.transform.angle),cr,Oe,ht,Yt.predicate);if(st){var Ut=this.collisionIndex.placeCollisionBox(nc(st,Bt.x,Bt.y,ne,we,this.transform.angle),cr,Oe,ht,Yt.predicate);if(Ut.box.length===0)return}if(Ft.box.length>0){var zt;return this.prevPlacement&&this.prevPlacement.variableOffsets[Nt.crossTileID]&&this.prevPlacement.placements[Nt.crossTileID]&&this.prevPlacement.placements[Nt.crossTileID].text&&(zt=this.prevPlacement.variableOffsets[Nt.crossTileID].anchor),this.variableOffsets[Nt.crossTileID]={textOffset:It,width:O,height:K,anchor:D,textBoxScale:de,prevAnchor:zt},this.markUsedJustification(Ne,D,Nt,$e),Ne.allowVerticalPlacement&&(this.markUsedOrientation(Ne,$e,Nt),this.placedOrientations[Nt.crossTileID]=$e),{shift:Bt,placedGlyphBoxes:Ft}}},bs.prototype.placeLayerBucketPart=function(D,J,O){var K=this,de=D.parameters,ne=de.bucket,we=de.layout,Oe=de.posMatrix,ht=de.textLabelPlaneMatrix,Yt=de.labelToScreenMatrix,cr=de.textPixelRatio,Nt=de.holdingForFade,Ne=de.collisionBoxArray,$e=de.partiallyEvaluatedTextSize,st=de.collisionGroup,It=we.get(\"text-optional\"),Bt=we.get(\"icon-optional\"),Ft=we.get(\"text-allow-overlap\"),Ut=we.get(\"icon-allow-overlap\"),zt=we.get(\"text-rotation-alignment\")===\"map\",_t=we.get(\"text-pitch-alignment\")===\"map\",Dt=we.get(\"icon-text-fit\")!==\"none\",vt=we.get(\"symbol-z-order\")===\"viewport-y\",lr=Ft&&(Ut||!ne.hasIconData()||Bt),_r=Ut&&(Ft||!ne.hasTextData()||It);!ne.collisionArrays&&Ne&&ne.deserializeCollisionBoxes(Ne);var Br=function(Ei,Oi){if(!J[Ei.crossTileID]){if(Nt){K.placements[Ei.crossTileID]=new zs(!1,!1,!1);return}var Gi=!1,on=!1,zn=!0,Ja=null,co={box:null,offscreen:null},ts={box:null,offscreen:null},so=null,Yo=null,ms=null,ou=0,Cv=0,Lv=0;Oi.textFeatureIndex?ou=Oi.textFeatureIndex:Ei.useRuntimeCollisionCircles&&(ou=Ei.featureIndex),Oi.verticalTextFeatureIndex&&(Cv=Oi.verticalTextFeatureIndex);var wd=Oi.textBox;if(wd){var Kv=function(vc){var tu=i.WritingMode.horizontal;if(ne.allowVerticalPlacement&&!vc&&K.prevPlacement){var Sd=K.prevPlacement.placedOrientations[Ei.crossTileID];Sd&&(K.placedOrientations[Ei.crossTileID]=Sd,tu=Sd,K.markUsedOrientation(ne,tu,Ei))}return tu},hg=function(vc,tu){if(ne.allowVerticalPlacement&&Ei.numVerticalGlyphVertices>0&&Oi.verticalTextBox)for(var Sd=0,gy=ne.writingModes;Sd<gy.length;Sd+=1){var P1=gy[Sd];if(P1===i.WritingMode.vertical?(co=tu(),ts=co):co=vc(),co&&co.box&&co.box.length)break}else co=vc()};if(we.get(\"text-variable-anchor\")){var Vd=we.get(\"text-variable-anchor\");if(K.prevPlacement&&K.prevPlacement.variableOffsets[Ei.crossTileID]){var Ad=K.prevPlacement.variableOffsets[Ei.crossTileID];Vd.indexOf(Ad.anchor)>0&&(Vd=Vd.filter(function(vc){return vc!==Ad.anchor}),Vd.unshift(Ad.anchor))}var Pv=function(vc,tu,Sd){for(var gy=vc.x2-vc.x1,P1=vc.y2-vc.y1,wu=Ei.textBoxScale,Kx=Dt&&!Ut?tu:null,lm={box:[],offscreen:!1},Uw=Ft?Vd.length*2:Vd.length,Rv=0;Rv<Uw;++Rv){var um=Vd[Rv%Vd.length],Vw=Rv>=Vd.length,Jx=K.attemptAnchorPlacement(um,vc,gy,P1,wu,zt,_t,cr,Oe,st,Vw,Ei,ne,Sd,Kx);if(Jx&&(lm=Jx.placedGlyphBoxes,lm&&lm.box&&lm.box.length)){Gi=!0,Ja=Jx.shift;break}}return lm},Jv=function(){return Pv(wd,Oi.iconBox,i.WritingMode.horizontal)},Iv=function(){var vc=Oi.verticalTextBox,tu=co&&co.box&&co.box.length;return ne.allowVerticalPlacement&&!tu&&Ei.numVerticalGlyphVertices>0&&vc?Pv(vc,Oi.verticalIconBox,i.WritingMode.vertical):{box:null,offscreen:null}};hg(Jv,Iv),co&&(Gi=co.box,zn=co.offscreen);var dy=Kv(co&&co.box);if(!Gi&&K.prevPlacement){var dg=K.prevPlacement.variableOffsets[Ei.crossTileID];dg&&(K.variableOffsets[Ei.crossTileID]=dg,K.markUsedJustification(ne,dg.anchor,Ei,dy))}}else{var gp=function(vc,tu){var Sd=K.collisionIndex.placeCollisionBox(vc,Ft,cr,Oe,st.predicate);return Sd&&Sd.box&&Sd.box.length&&(K.markUsedOrientation(ne,tu,Ei),K.placedOrientations[Ei.crossTileID]=tu),Sd},Td=function(){return gp(wd,i.WritingMode.horizontal)},mp=function(){var vc=Oi.verticalTextBox;return ne.allowVerticalPlacement&&Ei.numVerticalGlyphVertices>0&&vc?gp(vc,i.WritingMode.vertical):{box:null,offscreen:null}};hg(Td,mp),Kv(co&&co.box&&co.box.length)}}if(so=co,Gi=so&&so.box&&so.box.length>0,zn=so&&so.offscreen,Ei.useRuntimeCollisionCircles){var oh=ne.text.placedSymbolArray.get(Ei.centerJustifiedTextSymbolIndex),vg=i.evaluateSizeForFeature(ne.textSizeData,$e,oh),vy=we.get(\"text-padding\"),Zh=Ei.collisionCircleDiameter;Yo=K.collisionIndex.placeCollisionCircles(Ft,oh,ne.lineVertexArray,ne.glyphOffsetArray,vg,Oe,ht,Yt,O,_t,st.predicate,Zh,vy),Gi=Ft||Yo.circles.length>0&&!Yo.collisionDetected,zn=zn&&Yo.offscreen}if(Oi.iconFeatureIndex&&(Lv=Oi.iconFeatureIndex),Oi.iconBox){var am=function(vc){var tu=Dt&&Ja?nc(vc,Ja.x,Ja.y,zt,_t,K.transform.angle):vc;return K.collisionIndex.placeCollisionBox(tu,Ut,cr,Oe,st.predicate)};ts&&ts.box&&ts.box.length&&Oi.verticalIconBox?(ms=am(Oi.verticalIconBox),on=ms.box.length>0):(ms=am(Oi.iconBox),on=ms.box.length>0),zn=zn&&ms.offscreen}var C1=It||Ei.numHorizontalGlyphVertices===0&&Ei.numVerticalGlyphVertices===0,L1=Bt||Ei.numIconVertices===0;if(!C1&&!L1?on=Gi=on&&Gi:L1?C1||(on=on&&Gi):Gi=on&&Gi,Gi&&so&&so.box&&(ts&&ts.box&&Cv?K.collisionIndex.insertCollisionBox(so.box,we.get(\"text-ignore-placement\"),ne.bucketInstanceId,Cv,st.ID):K.collisionIndex.insertCollisionBox(so.box,we.get(\"text-ignore-placement\"),ne.bucketInstanceId,ou,st.ID)),on&&ms&&K.collisionIndex.insertCollisionBox(ms.box,we.get(\"icon-ignore-placement\"),ne.bucketInstanceId,Lv,st.ID),Yo&&(Gi&&K.collisionIndex.insertCollisionCircles(Yo.circles,we.get(\"text-ignore-placement\"),ne.bucketInstanceId,ou,st.ID),O)){var py=ne.bucketInstanceId,om=K.collisionCircleArrays[py];om===void 0&&(om=K.collisionCircleArrays[py]=new Io);for(var sm=0;sm<Yo.circles.length;sm+=4)om.circles.push(Yo.circles[sm+0]),om.circles.push(Yo.circles[sm+1]),om.circles.push(Yo.circles[sm+2]),om.circles.push(Yo.collisionDetected?1:0)}K.placements[Ei.crossTileID]=new zs(Gi||lr,on||_r,zn||ne.justReloaded),J[Ei.crossTileID]=!0}};if(vt)for(var Di=ne.getSortedSymbolIndexes(this.transform.angle),ci=Di.length-1;ci>=0;--ci){var bi=Di[ci];Br(ne.symbolInstances.get(bi),ne.collisionArrays[bi])}else for(var gn=D.symbolInstanceStart;gn<D.symbolInstanceEnd;gn++)Br(ne.symbolInstances.get(gn),ne.collisionArrays[gn]);if(O&&ne.bucketInstanceId in this.collisionCircleArrays){var tn=this.collisionCircleArrays[ne.bucketInstanceId];i.invert(tn.invProjMatrix,Oe),tn.viewportMatrix=this.collisionIndex.getViewportMatrix()}ne.justReloaded=!1},bs.prototype.markUsedJustification=function(D,J,O,K){var de={left:O.leftJustifiedTextSymbolIndex,center:O.centerJustifiedTextSymbolIndex,right:O.rightJustifiedTextSymbolIndex},ne;K===i.WritingMode.vertical?ne=O.verticalPlacedTextSymbolIndex:ne=de[i.getAnchorJustification(J)];for(var we=[O.leftJustifiedTextSymbolIndex,O.centerJustifiedTextSymbolIndex,O.rightJustifiedTextSymbolIndex,O.verticalPlacedTextSymbolIndex],Oe=0,ht=we;Oe<ht.length;Oe+=1){var Yt=ht[Oe];Yt>=0&&(ne>=0&&Yt!==ne?D.text.placedSymbolArray.get(Yt).crossTileID=0:D.text.placedSymbolArray.get(Yt).crossTileID=O.crossTileID)}},bs.prototype.markUsedOrientation=function(D,J,O){for(var K=J===i.WritingMode.horizontal||J===i.WritingMode.horizontalOnly?J:0,de=J===i.WritingMode.vertical?J:0,ne=[O.leftJustifiedTextSymbolIndex,O.centerJustifiedTextSymbolIndex,O.rightJustifiedTextSymbolIndex],we=0,Oe=ne;we<Oe.length;we+=1){var ht=Oe[we];D.text.placedSymbolArray.get(ht).placedOrientation=K}O.verticalPlacedTextSymbolIndex&&(D.text.placedSymbolArray.get(O.verticalPlacedTextSymbolIndex).placedOrientation=de)},bs.prototype.commit=function(D){this.commitTime=D,this.zoomAtLastRecencyCheck=this.transform.zoom;var J=this.prevPlacement,O=!1;this.prevZoomAdjustment=J?J.zoomAdjustment(this.transform.zoom):0;var K=J?J.symbolFadeChange(D):1,de=J?J.opacities:{},ne=J?J.variableOffsets:{},we=J?J.placedOrientations:{};for(var Oe in this.placements){var ht=this.placements[Oe],Yt=de[Oe];Yt?(this.opacities[Oe]=new Ks(Yt,K,ht.text,ht.icon),O=O||ht.text!==Yt.text.placed||ht.icon!==Yt.icon.placed):(this.opacities[Oe]=new Ks(null,K,ht.text,ht.icon,ht.skipFade),O=O||ht.text||ht.icon)}for(var cr in de){var Nt=de[cr];if(!this.opacities[cr]){var Ne=new Ks(Nt,K,!1,!1);Ne.isHidden()||(this.opacities[cr]=Ne,O=O||Nt.text.placed||Nt.icon.placed)}}for(var $e in ne)!this.variableOffsets[$e]&&this.opacities[$e]&&!this.opacities[$e].isHidden()&&(this.variableOffsets[$e]=ne[$e]);for(var st in we)!this.placedOrientations[st]&&this.opacities[st]&&!this.opacities[st].isHidden()&&(this.placedOrientations[st]=we[st]);O?this.lastPlacementChangeTime=D:typeof this.lastPlacementChangeTime!=\"number\"&&(this.lastPlacementChangeTime=J?J.lastPlacementChangeTime:D)},bs.prototype.updateLayerOpacities=function(D,J){for(var O={},K=0,de=J;K<de.length;K+=1){var ne=de[K],we=ne.getBucket(D);we&&ne.latestFeatureIndex&&D.id===we.layerIds[0]&&this.updateBucketOpacities(we,O,ne.collisionBoxArray)}},bs.prototype.updateBucketOpacities=function(D,J,O){var K=this;D.hasTextData()&&D.text.opacityVertexArray.clear(),D.hasIconData()&&D.icon.opacityVertexArray.clear(),D.hasIconCollisionBoxData()&&D.iconCollisionBox.collisionVertexArray.clear(),D.hasTextCollisionBoxData()&&D.textCollisionBox.collisionVertexArray.clear();var de=D.layers[0].layout,ne=new Ks(null,0,!1,!1,!0),we=de.get(\"text-allow-overlap\"),Oe=de.get(\"icon-allow-overlap\"),ht=de.get(\"text-variable-anchor\"),Yt=de.get(\"text-rotation-alignment\")===\"map\",cr=de.get(\"text-pitch-alignment\")===\"map\",Nt=de.get(\"icon-text-fit\")!==\"none\",Ne=new Ks(null,0,we&&(Oe||!D.hasIconData()||de.get(\"icon-optional\")),Oe&&(we||!D.hasTextData()||de.get(\"text-optional\")),!0);!D.collisionArrays&&O&&(D.hasIconCollisionBoxData()||D.hasTextCollisionBoxData())&&D.deserializeCollisionBoxes(O);for(var $e=function(Ft,Ut,zt){for(var _t=0;_t<Ut/4;_t++)Ft.opacityVertexArray.emplaceBack(zt)},st=function(Ft){var Ut=D.symbolInstances.get(Ft),zt=Ut.numHorizontalGlyphVertices,_t=Ut.numVerticalGlyphVertices,Dt=Ut.crossTileID,vt=J[Dt],lr=K.opacities[Dt];vt?lr=ne:lr||(lr=Ne,K.opacities[Dt]=lr),J[Dt]=!0;var _r=zt>0||_t>0,Br=Ut.numIconVertices>0,Di=K.placedOrientations[Ut.crossTileID],ci=Di===i.WritingMode.vertical,bi=Di===i.WritingMode.horizontal||Di===i.WritingMode.horizontalOnly;if(_r){var gn=ac(lr.text),tn=ci?oa:gn;$e(D.text,zt,tn);var Ei=bi?oa:gn;$e(D.text,_t,Ei);var Oi=lr.text.isHidden();[Ut.rightJustifiedTextSymbolIndex,Ut.centerJustifiedTextSymbolIndex,Ut.leftJustifiedTextSymbolIndex].forEach(function(Lv){Lv>=0&&(D.text.placedSymbolArray.get(Lv).hidden=Oi||ci?1:0)}),Ut.verticalPlacedTextSymbolIndex>=0&&(D.text.placedSymbolArray.get(Ut.verticalPlacedTextSymbolIndex).hidden=Oi||bi?1:0);var Gi=K.variableOffsets[Ut.crossTileID];Gi&&K.markUsedJustification(D,Gi.anchor,Ut,Di);var on=K.placedOrientations[Ut.crossTileID];on&&(K.markUsedJustification(D,\"left\",Ut,on),K.markUsedOrientation(D,on,Ut))}if(Br){var zn=ac(lr.icon),Ja=!(Nt&&Ut.verticalPlacedIconSymbolIndex&&ci);if(Ut.placedIconSymbolIndex>=0){var co=Ja?zn:oa;$e(D.icon,Ut.numIconVertices,co),D.icon.placedSymbolArray.get(Ut.placedIconSymbolIndex).hidden=lr.icon.isHidden()}if(Ut.verticalPlacedIconSymbolIndex>=0){var ts=Ja?oa:zn;$e(D.icon,Ut.numVerticalIconVertices,ts),D.icon.placedSymbolArray.get(Ut.verticalPlacedIconSymbolIndex).hidden=lr.icon.isHidden()}}if(D.hasIconCollisionBoxData()||D.hasTextCollisionBoxData()){var so=D.collisionArrays[Ft];if(so){var Yo=new i.Point(0,0);if(so.textBox||so.verticalTextBox){var ms=!0;if(ht){var ou=K.variableOffsets[Dt];ou?(Yo=Su(ou.anchor,ou.width,ou.height,ou.textOffset,ou.textBoxScale),Yt&&Yo._rotate(cr?K.transform.angle:-K.transform.angle)):ms=!1}so.textBox&&Rn(D.textCollisionBox.collisionVertexArray,lr.text.placed,!ms||ci,Yo.x,Yo.y),so.verticalTextBox&&Rn(D.textCollisionBox.collisionVertexArray,lr.text.placed,!ms||bi,Yo.x,Yo.y)}var Cv=!!(!bi&&so.verticalIconBox);so.iconBox&&Rn(D.iconCollisionBox.collisionVertexArray,lr.icon.placed,Cv,Nt?Yo.x:0,Nt?Yo.y:0),so.verticalIconBox&&Rn(D.iconCollisionBox.collisionVertexArray,lr.icon.placed,!Cv,Nt?Yo.x:0,Nt?Yo.y:0)}}},It=0;It<D.symbolInstances.length;It++)st(It);if(D.sortFeatures(this.transform.angle),this.retainedQueryData[D.bucketInstanceId]&&(this.retainedQueryData[D.bucketInstanceId].featureSortOrder=D.featureSortOrder),D.hasTextData()&&D.text.opacityVertexBuffer&&D.text.opacityVertexBuffer.updateData(D.text.opacityVertexArray),D.hasIconData()&&D.icon.opacityVertexBuffer&&D.icon.opacityVertexBuffer.updateData(D.icon.opacityVertexArray),D.hasIconCollisionBoxData()&&D.iconCollisionBox.collisionVertexBuffer&&D.iconCollisionBox.collisionVertexBuffer.updateData(D.iconCollisionBox.collisionVertexArray),D.hasTextCollisionBoxData()&&D.textCollisionBox.collisionVertexBuffer&&D.textCollisionBox.collisionVertexBuffer.updateData(D.textCollisionBox.collisionVertexArray),D.bucketInstanceId in this.collisionCircleArrays){var Bt=this.collisionCircleArrays[D.bucketInstanceId];D.placementInvProjMatrix=Bt.invProjMatrix,D.placementViewportMatrix=Bt.viewportMatrix,D.collisionCircleArray=Bt.circles,delete this.collisionCircleArrays[D.bucketInstanceId]}},bs.prototype.symbolFadeChange=function(D){return this.fadeDuration===0?1:(D-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},bs.prototype.zoomAdjustment=function(D){return Math.max(0,(this.transform.zoom-D)/1.5)},bs.prototype.hasTransitions=function(D){return this.stale||D-this.lastPlacementChangeTime<this.fadeDuration},bs.prototype.stillRecent=function(D,J){var O=this.zoomAtLastRecencyCheck===J?1-this.zoomAdjustment(J):1;return this.zoomAtLastRecencyCheck=J,this.commitTime+this.fadeDuration*O>D},bs.prototype.setStale=function(){this.stale=!0};function Rn(Y,D,J,O,K){Y.emplaceBack(D?1:0,J?1:0,O||0,K||0),Y.emplaceBack(D?1:0,J?1:0,O||0,K||0),Y.emplaceBack(D?1:0,J?1:0,O||0,K||0),Y.emplaceBack(D?1:0,J?1:0,O||0,K||0)}var _a=Math.pow(2,25),Vu=Math.pow(2,24),zl=Math.pow(2,17),xo=Math.pow(2,16),Kl=Math.pow(2,9),Ns=Math.pow(2,8),Hl=Math.pow(2,1);function ac(Y){if(Y.opacity===0&&!Y.placed)return 0;if(Y.opacity===1&&Y.placed)return 4294967295;var D=Y.placed?1:0,J=Math.floor(Y.opacity*127);return J*_a+D*Vu+J*zl+D*xo+J*Kl+D*Ns+J*Hl+D}var oa=0,qo=function(D){this._sortAcrossTiles=D.layout.get(\"symbol-z-order\")!==\"viewport-y\"&&D.layout.get(\"symbol-sort-key\").constantOr(1)!==void 0,this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};qo.prototype.continuePlacement=function(D,J,O,K,de){for(var ne=this._bucketParts;this._currentTileIndex<D.length;){var we=D[this._currentTileIndex];if(J.getBucketParts(ne,K,we,this._sortAcrossTiles),this._currentTileIndex++,de())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,ne.sort(function(ht,Yt){return ht.sortKey-Yt.sortKey}));this._currentPartIndex<ne.length;){var Oe=ne[this._currentPartIndex];if(J.placeLayerBucketPart(Oe,this._seenCrossTileIDs,O),this._currentPartIndex++,de())return!0}return!1};var Oo=function(D,J,O,K,de,ne,we){this.placement=new bs(D,de,ne,we),this._currentPlacementIndex=J.length-1,this._forceFullPlacement=O,this._showCollisionBoxes=K,this._done=!1};Oo.prototype.isDone=function(){return this._done},Oo.prototype.continuePlacement=function(D,J,O){for(var K=this,de=i.browser.now(),ne=function(){var cr=i.browser.now()-de;return K._forceFullPlacement?!1:cr>2};this._currentPlacementIndex>=0;){var we=D[this._currentPlacementIndex],Oe=J[we],ht=this.placement.collisionIndex.transform.zoom;if(Oe.type===\"symbol\"&&(!Oe.minzoom||Oe.minzoom<=ht)&&(!Oe.maxzoom||Oe.maxzoom>ht)){this._inProgressLayer||(this._inProgressLayer=new qo(Oe));var Yt=this._inProgressLayer.continuePlacement(O[Oe.source],this.placement,this._showCollisionBoxes,Oe,ne);if(Yt)return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Oo.prototype.commit=function(D){return this.placement.commit(D),this.placement};var ql=512/i.EXTENT/2,Pc=function(D,J,O){this.tileID=D,this.indexedSymbolInstances={},this.bucketInstanceId=O;for(var K=0;K<J.length;K++){var de=J.get(K),ne=de.key;this.indexedSymbolInstances[ne]||(this.indexedSymbolInstances[ne]=[]),this.indexedSymbolInstances[ne].push({crossTileID:de.crossTileID,coord:this.getScaledCoordinates(de,D)})}};Pc.prototype.getScaledCoordinates=function(D,J){var O=J.canonical.z-this.tileID.canonical.z,K=ql/Math.pow(2,O);return{x:Math.floor((J.canonical.x*i.EXTENT+D.anchorX)*K),y:Math.floor((J.canonical.y*i.EXTENT+D.anchorY)*K)}},Pc.prototype.findMatches=function(D,J,O){for(var K=this.tileID.canonical.z<J.canonical.z?1:Math.pow(2,this.tileID.canonical.z-J.canonical.z),de=0;de<D.length;de++){var ne=D.get(de);if(!ne.crossTileID){var we=this.indexedSymbolInstances[ne.key];if(we)for(var Oe=this.getScaledCoordinates(ne,J),ht=0,Yt=we;ht<Yt.length;ht+=1){var cr=Yt[ht];if(Math.abs(cr.coord.x-Oe.x)<=K&&Math.abs(cr.coord.y-Oe.y)<=K&&!O[cr.crossTileID]){O[cr.crossTileID]=!0,ne.crossTileID=cr.crossTileID;break}}}}};var Do=function(){this.maxCrossTileID=0};Do.prototype.generate=function(){return++this.maxCrossTileID};var nf=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};nf.prototype.handleWrapJump=function(D){var J=Math.round((D-this.lng)/360);if(J!==0)for(var O in this.indexes){var K=this.indexes[O],de={};for(var ne in K){var we=K[ne];we.tileID=we.tileID.unwrapTo(we.tileID.wrap+J),de[we.tileID.key]=we}this.indexes[O]=de}this.lng=D},nf.prototype.addBucket=function(D,J,O){if(this.indexes[D.overscaledZ]&&this.indexes[D.overscaledZ][D.key]){if(this.indexes[D.overscaledZ][D.key].bucketInstanceId===J.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(D.overscaledZ,this.indexes[D.overscaledZ][D.key])}for(var K=0;K<J.symbolInstances.length;K++){var de=J.symbolInstances.get(K);de.crossTileID=0}this.usedCrossTileIDs[D.overscaledZ]||(this.usedCrossTileIDs[D.overscaledZ]={});var ne=this.usedCrossTileIDs[D.overscaledZ];for(var we in this.indexes){var Oe=this.indexes[we];if(Number(we)>D.overscaledZ)for(var ht in Oe){var Yt=Oe[ht];Yt.tileID.isChildOf(D)&&Yt.findMatches(J.symbolInstances,D,ne)}else{var cr=D.scaledTo(Number(we)),Nt=Oe[cr.key];Nt&&Nt.findMatches(J.symbolInstances,D,ne)}}for(var Ne=0;Ne<J.symbolInstances.length;Ne++){var $e=J.symbolInstances.get(Ne);$e.crossTileID||($e.crossTileID=O.generate(),ne[$e.crossTileID]=!0)}return this.indexes[D.overscaledZ]===void 0&&(this.indexes[D.overscaledZ]={}),this.indexes[D.overscaledZ][D.key]=new Pc(D,J.symbolInstances,J.bucketInstanceId),!0},nf.prototype.removeBucketCrossTileIDs=function(D,J){for(var O in J.indexedSymbolInstances)for(var K=0,de=J.indexedSymbolInstances[O];K<de.length;K+=1){var ne=de[K];delete this.usedCrossTileIDs[D][ne.crossTileID]}},nf.prototype.removeStaleBuckets=function(D){var J=!1;for(var O in this.indexes){var K=this.indexes[O];for(var de in K)D[K[de].bucketInstanceId]||(this.removeBucketCrossTileIDs(O,K[de]),delete K[de],J=!0)}return J};var Vf=function(){this.layerIndexes={},this.crossTileIDs=new Do,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Vf.prototype.addLayer=function(D,J,O){var K=this.layerIndexes[D.id];K===void 0&&(K=this.layerIndexes[D.id]=new nf);var de=!1,ne={};K.handleWrapJump(O);for(var we=0,Oe=J;we<Oe.length;we+=1){var ht=Oe[we],Yt=ht.getBucket(D);!Yt||D.id!==Yt.layerIds[0]||(Yt.bucketInstanceId||(Yt.bucketInstanceId=++this.maxBucketInstanceId),K.addBucket(ht.tileID,Yt,this.crossTileIDs)&&(de=!0),ne[Yt.bucketInstanceId]=!0)}return K.removeStaleBuckets(ne)&&(de=!0),de},Vf.prototype.pruneUnusedLayers=function(D){var J={};D.forEach(function(K){J[K]=!0});for(var O in this.layerIndexes)J[O]||delete this.layerIndexes[O]};var pl=function(Y,D){return i.emitValidationErrors(Y,D&&D.filter(function(J){return J.identifier!==\"source.canvas\"}))},Zc=i.pick(da,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Jl=i.pick(da,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),qs=Xo(),yu=(function(Y){function D(J,O){var K=this;O===void 0&&(O={}),Y.call(this),this.map=J,this.dispatcher=new Z(Ba(),this),this.imageManager=new k,this.imageManager.setEventedParent(this),this.glyphManager=new P(J._requestManager,O.localIdeographFontFamily),this.lineAtlas=new G(256,512),this.crossTileSymbolIndex=new Vf,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new i.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",i.getReferrer());var de=this;this._rtlTextPluginCallback=D.registerForPluginStateChange(function(ne){var we={pluginStatus:ne.pluginStatus,pluginURL:ne.pluginURL};de.dispatcher.broadcast(\"syncRTLPluginState\",we,function(Oe,ht){if(i.triggerPluginCompletionEvent(Oe),ht){var Yt=ht.every(function(Nt){return Nt});if(Yt)for(var cr in de.sourceCaches)de.sourceCaches[cr].reload()}})}),this.on(\"data\",function(ne){if(!(ne.dataType!==\"source\"||ne.sourceDataType!==\"metadata\")){var we=K.sourceCaches[ne.sourceId];if(we){var Oe=we.getSource();if(!(!Oe||!Oe.vectorLayerIds))for(var ht in K._layers){var Yt=K._layers[ht];Yt.source===Oe.id&&K._validateLayer(Yt)}}}})}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.loadURL=function(O,K){var de=this;K===void 0&&(K={}),this.fire(new i.Event(\"dataloading\",{dataType:\"style\"}));var ne=typeof K.validate==\"boolean\"?K.validate:!i.isMapboxURL(O);O=this.map._requestManager.normalizeStyleURL(O,K.accessToken);var we=this.map._requestManager.transformRequest(O,i.ResourceType.Style);this._request=i.getJSON(we,function(Oe,ht){de._request=null,Oe?de.fire(new i.ErrorEvent(Oe)):ht&&de._load(ht,ne)})},D.prototype.loadJSON=function(O,K){var de=this;K===void 0&&(K={}),this.fire(new i.Event(\"dataloading\",{dataType:\"style\"})),this._request=i.browser.frame(function(){de._request=null,de._load(O,K.validate!==!1)})},D.prototype.loadEmpty=function(){this.fire(new i.Event(\"dataloading\",{dataType:\"style\"})),this._load(qs,!1)},D.prototype._load=function(O,K){if(!(K&&pl(this,i.validateStyle(O)))){this._loaded=!0,this.stylesheet=O;for(var de in O.sources)this.addSource(de,O.sources[de],{validate:!1});O.sprite?this._loadSprite(O.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(O.glyphs);var ne=za(this.stylesheet.layers);this._order=ne.map(function(Yt){return Yt.id}),this._layers={},this._serializedLayers={};for(var we=0,Oe=ne;we<Oe.length;we+=1){var ht=Oe[we];ht=i.createStyleLayer(ht),ht.setEventedParent(this,{layer:{id:ht.id}}),this._layers[ht.id]=ht,this._serializedLayers[ht.id]=ht.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new V(this.stylesheet.light),this.fire(new i.Event(\"data\",{dataType:\"style\"})),this.fire(new i.Event(\"style.load\"))}},D.prototype._loadSprite=function(O){var K=this;this._spriteRequest=m(O,this.map._requestManager,function(de,ne){if(K._spriteRequest=null,de)K.fire(new i.ErrorEvent(de));else if(ne)for(var we in ne)K.imageManager.addImage(we,ne[we]);K.imageManager.setLoaded(!0),K._availableImages=K.imageManager.listImages(),K.dispatcher.broadcast(\"setImages\",K._availableImages),K.fire(new i.Event(\"data\",{dataType:\"style\"}))})},D.prototype._validateLayer=function(O){var K=this.sourceCaches[O.source];if(K){var de=O.sourceLayer;if(de){var ne=K.getSource();(ne.type===\"geojson\"||ne.vectorLayerIds&&ne.vectorLayerIds.indexOf(de)===-1)&&this.fire(new i.ErrorEvent(new Error('Source layer \"'+de+'\" does not exist on source \"'+ne.id+'\" as specified by style layer \"'+O.id+'\"')))}}},D.prototype.loaded=function(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(var O in this.sourceCaches)if(!this.sourceCaches[O].loaded())return!1;return!!this.imageManager.isLoaded()},D.prototype._serializeLayers=function(O){for(var K=[],de=0,ne=O;de<ne.length;de+=1){var we=ne[de],Oe=this._layers[we];Oe.type!==\"custom\"&&K.push(Oe.serialize())}return K},D.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var O in this.sourceCaches)if(this.sourceCaches[O].hasTransition())return!0;for(var K in this._layers)if(this._layers[K].hasTransition())return!0;return!1},D.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},D.prototype.update=function(O){if(this._loaded){var K=this._changed;if(this._changed){var de=Object.keys(this._updatedLayers),ne=Object.keys(this._removedLayers);(de.length||ne.length)&&this._updateWorkerLayers(de,ne);for(var we in this._updatedSources){var Oe=this._updatedSources[we];Oe===\"reload\"?this._reloadSource(we):Oe===\"clear\"&&this._clearSource(we)}this._updateTilesForChangedImages();for(var ht in this._updatedPaintProps)this._layers[ht].updateTransitions(O);this.light.updateTransitions(O),this._resetUpdates()}var Yt={};for(var cr in this.sourceCaches){var Nt=this.sourceCaches[cr];Yt[cr]=Nt.used,Nt.used=!1}for(var Ne=0,$e=this._order;Ne<$e.length;Ne+=1){var st=$e[Ne],It=this._layers[st];It.recalculate(O,this._availableImages),!It.isHidden(O.zoom)&&It.source&&(this.sourceCaches[It.source].used=!0)}for(var Bt in Yt){var Ft=this.sourceCaches[Bt];Yt[Bt]!==Ft.used&&Ft.fire(new i.Event(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:Bt}))}this.light.recalculate(O),this.z=O.zoom,K&&this.fire(new i.Event(\"data\",{dataType:\"style\"}))}},D.prototype._updateTilesForChangedImages=function(){var O=Object.keys(this._changedImages);if(O.length){for(var K in this.sourceCaches)this.sourceCaches[K].reloadTilesForDependencies([\"icons\",\"patterns\"],O);this._changedImages={}}},D.prototype._updateWorkerLayers=function(O,K){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(O),removedIds:K})},D.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},D.prototype.setState=function(O){var K=this;if(this._checkLoaded(),pl(this,i.validateStyle(O)))return!1;O=i.clone$1(O),O.layers=za(O.layers);var de=Sn(this.serialize(),O).filter(function(we){return!(we.command in Jl)});if(de.length===0)return!1;var ne=de.filter(function(we){return!(we.command in Zc)});if(ne.length>0)throw new Error(\"Unimplemented: \"+ne.map(function(we){return we.command}).join(\", \")+\".\");return de.forEach(function(we){we.command!==\"setTransition\"&&K[we.command].apply(K,we.args)}),this.stylesheet=O,!0},D.prototype.addImage=function(O,K){if(this.getImage(O))return this.fire(new i.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(O,K),this._afterImageUpdated(O)},D.prototype.updateImage=function(O,K){this.imageManager.updateImage(O,K)},D.prototype.getImage=function(O){return this.imageManager.getImage(O)},D.prototype.removeImage=function(O){if(!this.getImage(O))return this.fire(new i.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(O),this._afterImageUpdated(O)},D.prototype._afterImageUpdated=function(O){this._availableImages=this.imageManager.listImages(),this._changedImages[O]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new i.Event(\"data\",{dataType:\"style\"}))},D.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},D.prototype.addSource=function(O,K,de){var ne=this;if(de===void 0&&(de={}),this._checkLoaded(),this.sourceCaches[O]!==void 0)throw new Error(\"There is already a source with this ID\");if(!K.type)throw new Error(\"The type property must be defined, but only the following properties were given: \"+Object.keys(K).join(\", \")+\".\");var we=[\"vector\",\"raster\",\"geojson\",\"video\",\"image\"],Oe=we.indexOf(K.type)>=0;if(!(Oe&&this._validate(i.validateStyle.source,\"sources.\"+O,K,null,de))){this.map&&this.map._collectResourceTiming&&(K.collectResourceTiming=!0);var ht=this.sourceCaches[O]=new ti(O,K,this.dispatcher);ht.style=this,ht.setEventedParent(this,function(){return{isSourceLoaded:ne.loaded(),source:ht.serialize(),sourceId:O}}),ht.onAdd(this.map),this._changed=!0}},D.prototype.removeSource=function(O){if(this._checkLoaded(),this.sourceCaches[O]===void 0)throw new Error(\"There is no source with this ID\");for(var K in this._layers)if(this._layers[K].source===O)return this.fire(new i.ErrorEvent(new Error('Source \"'+O+'\" cannot be removed while layer \"'+K+'\" is using it.')));var de=this.sourceCaches[O];delete this.sourceCaches[O],delete this._updatedSources[O],de.fire(new i.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:O})),de.setEventedParent(null),de.clearTiles(),de.onRemove&&de.onRemove(this.map),this._changed=!0},D.prototype.setGeoJSONSourceData=function(O,K){this._checkLoaded();var de=this.sourceCaches[O].getSource();de.setData(K),this._changed=!0},D.prototype.getSource=function(O){return this.sourceCaches[O]&&this.sourceCaches[O].getSource()},D.prototype.addLayer=function(O,K,de){de===void 0&&(de={}),this._checkLoaded();var ne=O.id;if(this.getLayer(ne)){this.fire(new i.ErrorEvent(new Error('Layer with id \"'+ne+'\" already exists on this map')));return}var we;if(O.type===\"custom\"){if(pl(this,i.validateCustomStyleLayer(O)))return;we=i.createStyleLayer(O)}else{if(typeof O.source==\"object\"&&(this.addSource(ne,O.source),O=i.clone$1(O),O=i.extend(O,{source:ne})),this._validate(i.validateStyle.layer,\"layers.\"+ne,O,{arrayIndex:-1},de))return;we=i.createStyleLayer(O),this._validateLayer(we),we.setEventedParent(this,{layer:{id:ne}}),this._serializedLayers[we.id]=we.serialize()}var Oe=K?this._order.indexOf(K):this._order.length;if(K&&Oe===-1){this.fire(new i.ErrorEvent(new Error('Layer with id \"'+K+'\" does not exist on this map.')));return}if(this._order.splice(Oe,0,ne),this._layerOrderChanged=!0,this._layers[ne]=we,this._removedLayers[ne]&&we.source&&we.type!==\"custom\"){var ht=this._removedLayers[ne];delete this._removedLayers[ne],ht.type!==we.type?this._updatedSources[we.source]=\"clear\":(this._updatedSources[we.source]=\"reload\",this.sourceCaches[we.source].pause())}this._updateLayer(we),we.onAdd&&we.onAdd(this.map)},D.prototype.moveLayer=function(O,K){this._checkLoaded(),this._changed=!0;var de=this._layers[O];if(!de){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be moved.\")));return}if(O!==K){var ne=this._order.indexOf(O);this._order.splice(ne,1);var we=K?this._order.indexOf(K):this._order.length;if(K&&we===-1){this.fire(new i.ErrorEvent(new Error('Layer with id \"'+K+'\" does not exist on this map.')));return}this._order.splice(we,0,O),this._layerOrderChanged=!0}},D.prototype.removeLayer=function(O){this._checkLoaded();var K=this._layers[O];if(!K){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be removed.\")));return}K.setEventedParent(null);var de=this._order.indexOf(O);this._order.splice(de,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[O]=K,delete this._layers[O],delete this._serializedLayers[O],delete this._updatedLayers[O],delete this._updatedPaintProps[O],K.onRemove&&K.onRemove(this.map)},D.prototype.getLayer=function(O){return this._layers[O]},D.prototype.hasLayer=function(O){return O in this._layers},D.prototype.setLayerZoomRange=function(O,K,de){this._checkLoaded();var ne=this.getLayer(O);if(!ne){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot have zoom extent.\")));return}ne.minzoom===K&&ne.maxzoom===de||(K!=null&&(ne.minzoom=K),de!=null&&(ne.maxzoom=de),this._updateLayer(ne))},D.prototype.setFilter=function(O,K,de){de===void 0&&(de={}),this._checkLoaded();var ne=this.getLayer(O);if(!ne){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be filtered.\")));return}if(!i.deepEqual(ne.filter,K)){if(K==null){ne.filter=void 0,this._updateLayer(ne);return}this._validate(i.validateStyle.filter,\"layers.\"+ne.id+\".filter\",K,null,de)||(ne.filter=i.clone$1(K),this._updateLayer(ne))}},D.prototype.getFilter=function(O){return i.clone$1(this.getLayer(O).filter)},D.prototype.setLayoutProperty=function(O,K,de,ne){ne===void 0&&(ne={}),this._checkLoaded();var we=this.getLayer(O);if(!we){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be styled.\")));return}i.deepEqual(we.getLayoutProperty(K),de)||(we.setLayoutProperty(K,de,ne),this._updateLayer(we))},D.prototype.getLayoutProperty=function(O,K){var de=this.getLayer(O);if(!de){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style.\")));return}return de.getLayoutProperty(K)},D.prototype.setPaintProperty=function(O,K,de,ne){ne===void 0&&(ne={}),this._checkLoaded();var we=this.getLayer(O);if(!we){this.fire(new i.ErrorEvent(new Error(\"The layer '\"+O+\"' does not exist in the map's style and cannot be styled.\")));return}if(!i.deepEqual(we.getPaintProperty(K),de)){var Oe=we.setPaintProperty(K,de,ne);Oe&&this._updateLayer(we),this._changed=!0,this._updatedPaintProps[O]=!0}},D.prototype.getPaintProperty=function(O,K){return this.getLayer(O).getPaintProperty(K)},D.prototype.setFeatureState=function(O,K){this._checkLoaded();var de=O.source,ne=O.sourceLayer,we=this.sourceCaches[de];if(we===void 0){this.fire(new i.ErrorEvent(new Error(\"The source '\"+de+\"' does not exist in the map's style.\")));return}var Oe=we.getSource().type;if(Oe===\"geojson\"&&ne){this.fire(new i.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\")));return}if(Oe===\"vector\"&&!ne){this.fire(new i.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));return}O.id===void 0&&this.fire(new i.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),we.setFeatureState(ne,O.id,K)},D.prototype.removeFeatureState=function(O,K){this._checkLoaded();var de=O.source,ne=this.sourceCaches[de];if(ne===void 0){this.fire(new i.ErrorEvent(new Error(\"The source '\"+de+\"' does not exist in the map's style.\")));return}var we=ne.getSource().type,Oe=we===\"vector\"?O.sourceLayer:void 0;if(we===\"vector\"&&!Oe){this.fire(new i.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));return}if(K&&typeof O.id!=\"string\"&&typeof O.id!=\"number\"){this.fire(new i.ErrorEvent(new Error(\"A feature id is required to remove its specific state property.\")));return}ne.removeFeatureState(Oe,O.id,K)},D.prototype.getFeatureState=function(O){this._checkLoaded();var K=O.source,de=O.sourceLayer,ne=this.sourceCaches[K];if(ne===void 0){this.fire(new i.ErrorEvent(new Error(\"The source '\"+K+\"' does not exist in the map's style.\")));return}var we=ne.getSource().type;if(we===\"vector\"&&!de){this.fire(new i.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));return}return O.id===void 0&&this.fire(new i.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),ne.getFeatureState(de,O.id)},D.prototype.getTransition=function(){return i.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},D.prototype.serialize=function(){return i.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:i.mapObject(this.sourceCaches,function(O){return O.serialize()}),layers:this._serializeLayers(this._order)},function(O){return O!==void 0})},D.prototype._updateLayer=function(O){this._updatedLayers[O.id]=!0,O.source&&!this._updatedSources[O.source]&&this.sourceCaches[O.source].getSource().type!==\"raster\"&&(this._updatedSources[O.source]=\"reload\",this.sourceCaches[O.source].pause()),this._changed=!0},D.prototype._flattenAndSortRenderedFeatures=function(O){for(var K=this,de=function(bi){return K._layers[bi].type===\"fill-extrusion\"},ne={},we=[],Oe=this._order.length-1;Oe>=0;Oe--){var ht=this._order[Oe];if(de(ht)){ne[ht]=Oe;for(var Yt=0,cr=O;Yt<cr.length;Yt+=1){var Nt=cr[Yt],Ne=Nt[ht];if(Ne)for(var $e=0,st=Ne;$e<st.length;$e+=1){var It=st[$e];we.push(It)}}}}we.sort(function(bi,gn){return gn.intersectionZ-bi.intersectionZ});for(var Bt=[],Ft=this._order.length-1;Ft>=0;Ft--){var Ut=this._order[Ft];if(de(Ut))for(var zt=we.length-1;zt>=0;zt--){var _t=we[zt].feature;if(ne[_t.layer.id]<Ft)break;Bt.push(_t),we.pop()}else for(var Dt=0,vt=O;Dt<vt.length;Dt+=1){var lr=vt[Dt],_r=lr[Ut];if(_r)for(var Br=0,Di=_r;Br<Di.length;Br+=1){var ci=Di[Br];Bt.push(ci.feature)}}}return Bt},D.prototype.queryRenderedFeatures=function(O,K,de){K&&K.filter&&this._validate(i.validateStyle.filter,\"queryRenderedFeatures.filter\",K.filter,null,K);var ne={};if(K&&K.layers){if(!Array.isArray(K.layers))return this.fire(new i.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var we=0,Oe=K.layers;we<Oe.length;we+=1){var ht=Oe[we],Yt=this._layers[ht];if(!Yt)return this.fire(new i.ErrorEvent(new Error(\"The layer '\"+ht+\"' does not exist in the map's style and cannot be queried for features.\"))),[];ne[Yt.source]=!0}}var cr=[];K.availableImages=this._availableImages;for(var Nt in this.sourceCaches)K.layers&&!ne[Nt]||cr.push(ce(this.sourceCaches[Nt],this._layers,this._serializedLayers,O,K,de));return this.placement&&cr.push(He(this._layers,this._serializedLayers,this.sourceCaches,O,K,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(cr)},D.prototype.querySourceFeatures=function(O,K){K&&K.filter&&this._validate(i.validateStyle.filter,\"querySourceFeatures.filter\",K.filter,null,K);var de=this.sourceCaches[O];return de?lt(de,K):[]},D.prototype.addSourceType=function(O,K,de){if(D.getSourceType(O))return de(new Error('A source type called \"'+O+'\" already exists.'));if(D.setSourceType(O,K),!K.workerSourceURL)return de(null,null);this.dispatcher.broadcast(\"loadWorkerSource\",{name:O,url:K.workerSourceURL},de)},D.prototype.getLight=function(){return this.light.getLight()},D.prototype.setLight=function(O,K){K===void 0&&(K={}),this._checkLoaded();var de=this.light.getLight(),ne=!1;for(var we in O)if(!i.deepEqual(O[we],de[we])){ne=!0;break}if(ne){var Oe={now:i.browser.now(),transition:i.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(O,K),this.light.updateTransitions(Oe)}},D.prototype._validate=function(O,K,de,ne,we){return we===void 0&&(we={}),we&&we.validate===!1?!1:pl(this,O.call(i.validateStyle,i.extend({key:K,style:this.serialize(),value:de,styleSpec:i.styleSpec},ne)))},D.prototype._remove=function(){this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),i.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback);for(var O in this._layers){var K=this._layers[O];K.setEventedParent(null)}for(var de in this.sourceCaches)this.sourceCaches[de].clearTiles(),this.sourceCaches[de].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},D.prototype._clearSource=function(O){this.sourceCaches[O].clearTiles()},D.prototype._reloadSource=function(O){this.sourceCaches[O].resume(),this.sourceCaches[O].reload()},D.prototype._updateSources=function(O){for(var K in this.sourceCaches)this.sourceCaches[K].update(O)},D.prototype._generateCollisionBoxes=function(){for(var O in this.sourceCaches)this._reloadSource(O)},D.prototype._updatePlacement=function(O,K,de,ne,we){we===void 0&&(we=!1);for(var Oe=!1,ht=!1,Yt={},cr=0,Nt=this._order;cr<Nt.length;cr+=1){var Ne=Nt[cr],$e=this._layers[Ne];if($e.type===\"symbol\"){if(!Yt[$e.source]){var st=this.sourceCaches[$e.source];Yt[$e.source]=st.getRenderableIds(!0).map(function(Dt){return st.getTileByID(Dt)}).sort(function(Dt,vt){return vt.tileID.overscaledZ-Dt.tileID.overscaledZ||(Dt.tileID.isLessThan(vt.tileID)?-1:1)})}var It=this.crossTileSymbolIndex.addLayer($e,Yt[$e.source],O.center.lng);Oe=Oe||It}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),we=we||this._layerOrderChanged||de===0,(we||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(i.browser.now(),O.zoom))&&(this.pauseablePlacement=new Oo(O,this._order,we,K,de,ne,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,Yt),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(i.browser.now()),ht=!0),Oe&&this.pauseablePlacement.placement.setStale()),ht||Oe)for(var Bt=0,Ft=this._order;Bt<Ft.length;Bt+=1){var Ut=Ft[Bt],zt=this._layers[Ut];zt.type===\"symbol\"&&this.placement.updateLayerOpacities(zt,Yt[zt.source])}var _t=!this.pauseablePlacement.isDone()||this.placement.hasTransitions(i.browser.now());return _t},D.prototype._releaseSymbolFadeTiles=function(){for(var O in this.sourceCaches)this.sourceCaches[O].releaseSymbolFadeTiles()},D.prototype.getImages=function(O,K,de){this.imageManager.getImages(K.icons,de),this._updateTilesForChangedImages();var ne=this.sourceCaches[K.source];ne&&ne.setDependencies(K.tileID.key,K.type,K.icons)},D.prototype.getGlyphs=function(O,K,de){this.glyphManager.getGlyphs(K.stacks,de)},D.prototype.getResource=function(O,K,de){return i.makeRequest(K,de)},D})(i.Evented);yu.getSourceType=Be,yu.setSourceType=Le,yu.registerForPluginStateChange=i.registerForPluginStateChange;var oc=i.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Cf=`#ifdef GL_ES\n",
-       "precision mediump float;\n",
-       "#else\n",
-       "#if !defined(lowp)\n",
-       "#define lowp\n",
-       "#endif\n",
-       "#if !defined(mediump)\n",
-       "#define mediump\n",
-       "#endif\n",
-       "#if !defined(highp)\n",
-       "#define highp\n",
-       "#endif\n",
-       "#endif`,sc=`#ifdef GL_ES\n",
-       "precision highp float;\n",
-       "#else\n",
-       "#if !defined(lowp)\n",
-       "#define lowp\n",
-       "#endif\n",
-       "#if !defined(mediump)\n",
-       "#define mediump\n",
-       "#endif\n",
-       "#if !defined(highp)\n",
-       "#define highp\n",
-       "#endif\n",
-       "#endif\n",
-       "vec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n",
-       ");}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}`,jh=`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Lf=\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\",cs=`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,af=\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\",Gf=`varying vec3 v_data;\n",
-       "#pragma mapbox: define highp vec4 color\n",
-       "#pragma mapbox: define mediump float radius\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define highp vec4 stroke_color\n",
-       "#pragma mapbox: define mediump float stroke_width\n",
-       "#pragma mapbox: define lowp float stroke_opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "#pragma mapbox: initialize mediump float radius\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize highp vec4 stroke_color\n",
-       "#pragma mapbox: initialize mediump float stroke_width\n",
-       "#pragma mapbox: initialize lowp float stroke_opacity\n",
-       "vec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,$l=`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n",
-       "#pragma mapbox: define highp vec4 color\n",
-       "#pragma mapbox: define mediump float radius\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define highp vec4 stroke_color\n",
-       "#pragma mapbox: define mediump float stroke_width\n",
-       "#pragma mapbox: define lowp float stroke_opacity\n",
-       "void main(void) {\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "#pragma mapbox: initialize mediump float radius\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize highp vec4 stroke_color\n",
-       "#pragma mapbox: initialize mediump float stroke_width\n",
-       "#pragma mapbox: initialize lowp float stroke_opacity\n",
-       "vec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`,fl=\"void main() {gl_FragColor=vec4(1.0);}\",lc=\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\",Fu=`uniform highp float u_intensity;varying vec2 v_extrude;\n",
-       "#pragma mapbox: define highp float weight\n",
-       "#define GAUSS_COEF 0.3989422804014327\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp float weight\n",
-       "float d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Es=`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n",
-       "#pragma mapbox: define highp float weight\n",
-       "#pragma mapbox: define mediump float radius\n",
-       "const highp float ZERO=1.0/255.0/16.0;\n",
-       "#define GAUSS_COEF 0.3989422804014327\n",
-       "void main(void) {\n",
-       "#pragma mapbox: initialize highp float weight\n",
-       "#pragma mapbox: initialize mediump float radius\n",
-       "vec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}`,Hs=`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(0.0);\n",
-       "#endif\n",
-       "}`,Go=\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\",ps=\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",uc=\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\",bl=\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",Gu=\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\",Os=\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",od=\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\",Po=`#pragma mapbox: define highp vec4 color\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "gl_FragColor=color*opacity;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,sd=`attribute vec2 a_pos;uniform mat4 u_matrix;\n",
-       "#pragma mapbox: define highp vec4 color\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "gl_Position=u_matrix*vec4(a_pos,0,1);}`,Ko=`varying vec2 v_pos;\n",
-       "#pragma mapbox: define highp vec4 outline_color\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 outline_color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Pa=`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n",
-       "#pragma mapbox: define highp vec4 outline_color\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 outline_color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`,of=`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp vec4 pattern_from\n",
-       "#pragma mapbox: define lowp vec4 pattern_to\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Hu=`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp vec4 pattern_from\n",
-       "#pragma mapbox: define lowp vec4 pattern_to\n",
-       "#pragma mapbox: define lowp float pixel_ratio_from\n",
-       "#pragma mapbox: define lowp float pixel_ratio_to\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`,wl=`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp vec4 pattern_from\n",
-       "#pragma mapbox: define lowp vec4 pattern_to\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Hf=`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp vec4 pattern_from\n",
-       "#pragma mapbox: define lowp vec4 pattern_to\n",
-       "#pragma mapbox: define lowp float pixel_ratio_from\n",
-       "#pragma mapbox: define lowp float pixel_ratio_to\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`,Ic=`varying vec4 v_color;void main() {gl_FragColor=v_color;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,yf=`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n",
-       "#pragma mapbox: define highp float base\n",
-       "#pragma mapbox: define highp float height\n",
-       "#pragma mapbox: define highp vec4 color\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp float base\n",
-       "#pragma mapbox: initialize highp float height\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "vec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`,Ol=`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n",
-       "#pragma mapbox: define lowp float base\n",
-       "#pragma mapbox: define lowp float height\n",
-       "#pragma mapbox: define lowp vec4 pattern_from\n",
-       "#pragma mapbox: define lowp vec4 pattern_to\n",
-       "#pragma mapbox: define lowp float pixel_ratio_from\n",
-       "#pragma mapbox: define lowp float pixel_ratio_to\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float base\n",
-       "#pragma mapbox: initialize lowp float height\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Ah=`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n",
-       "#pragma mapbox: define lowp float base\n",
-       "#pragma mapbox: define lowp float height\n",
-       "#pragma mapbox: define lowp vec4 pattern_from\n",
-       "#pragma mapbox: define lowp vec4 pattern_to\n",
-       "#pragma mapbox: define lowp float pixel_ratio_from\n",
-       "#pragma mapbox: define lowp float pixel_ratio_to\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float base\n",
-       "#pragma mapbox: initialize lowp float height\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n",
-       "? a_pos\n",
-       ": vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`,Qf=`#ifdef GL_ES\n",
-       "precision highp float;\n",
-       "#endif\n",
-       "uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,_f=\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\",Yc=`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n",
-       "#define PI 3.141592653589793\n",
-       "void main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,eh=\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\",th=`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n",
-       "#pragma mapbox: define highp vec4 color\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,ju=`\n",
-       "#define scale 0.015873016\n",
-       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n",
-       "#pragma mapbox: define highp vec4 color\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define mediump float gapwidth\n",
-       "#pragma mapbox: define lowp float offset\n",
-       "#pragma mapbox: define mediump float width\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize mediump float gapwidth\n",
-       "#pragma mapbox: initialize lowp float offset\n",
-       "#pragma mapbox: initialize mediump float width\n",
-       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}`,jf=`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,cc=`\n",
-       "#define scale 0.015873016\n",
-       "attribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define mediump float gapwidth\n",
-       "#pragma mapbox: define lowp float offset\n",
-       "#pragma mapbox: define mediump float width\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize mediump float gapwidth\n",
-       "#pragma mapbox: initialize lowp float offset\n",
-       "#pragma mapbox: initialize mediump float width\n",
-       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}`,sf=`uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n",
-       "#pragma mapbox: define lowp vec4 pattern_from\n",
-       "#pragma mapbox: define lowp vec4 pattern_to\n",
-       "#pragma mapbox: define lowp float pixel_ratio_from\n",
-       "#pragma mapbox: define lowp float pixel_ratio_to\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Bl=`\n",
-       "#define scale 0.015873016\n",
-       "#define LINE_DISTANCE_SCALE 2.0\n",
-       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp float offset\n",
-       "#pragma mapbox: define mediump float gapwidth\n",
-       "#pragma mapbox: define mediump float width\n",
-       "#pragma mapbox: define lowp float floorwidth\n",
-       "#pragma mapbox: define lowp vec4 pattern_from\n",
-       "#pragma mapbox: define lowp vec4 pattern_to\n",
-       "#pragma mapbox: define lowp float pixel_ratio_from\n",
-       "#pragma mapbox: define lowp float pixel_ratio_to\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize lowp float offset\n",
-       "#pragma mapbox: initialize mediump float gapwidth\n",
-       "#pragma mapbox: initialize mediump float width\n",
-       "#pragma mapbox: initialize lowp float floorwidth\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_from\n",
-       "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
-       "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`,Kc=`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n",
-       "#pragma mapbox: define highp vec4 color\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define mediump float width\n",
-       "#pragma mapbox: define lowp float floorwidth\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize mediump float width\n",
-       "#pragma mapbox: initialize lowp float floorwidth\n",
-       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Rc=`\n",
-       "#define scale 0.015873016\n",
-       "#define LINE_DISTANCE_SCALE 2.0\n",
-       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n",
-       "#pragma mapbox: define highp vec4 color\n",
-       "#pragma mapbox: define lowp float blur\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define mediump float gapwidth\n",
-       "#pragma mapbox: define lowp float offset\n",
-       "#pragma mapbox: define mediump float width\n",
-       "#pragma mapbox: define lowp float floorwidth\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 color\n",
-       "#pragma mapbox: initialize lowp float blur\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize mediump float gapwidth\n",
-       "#pragma mapbox: initialize lowp float offset\n",
-       "#pragma mapbox: initialize mediump float width\n",
-       "#pragma mapbox: initialize lowp float floorwidth\n",
-       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`,gs=`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Wf=\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\",Wh=`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "lowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,rh=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
-       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
-       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}`,lf=`#define SDF_PX 8.0\n",
-       "uniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n",
-       "#pragma mapbox: define highp vec4 fill_color\n",
-       "#pragma mapbox: define highp vec4 halo_color\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp float halo_width\n",
-       "#pragma mapbox: define lowp float halo_blur\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 fill_color\n",
-       "#pragma mapbox: initialize highp vec4 halo_color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize lowp float halo_width\n",
-       "#pragma mapbox: initialize lowp float halo_blur\n",
-       "float EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,Sh=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n",
-       "#pragma mapbox: define highp vec4 fill_color\n",
-       "#pragma mapbox: define highp vec4 halo_color\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp float halo_width\n",
-       "#pragma mapbox: define lowp float halo_blur\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 fill_color\n",
-       "#pragma mapbox: initialize highp vec4 halo_color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize lowp float halo_width\n",
-       "#pragma mapbox: initialize lowp float halo_blur\n",
-       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
-       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
-       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`,Mu=`#define SDF_PX 8.0\n",
-       "#define SDF 1.0\n",
-       "#define ICON 0.0\n",
-       "uniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n",
-       "#pragma mapbox: define highp vec4 fill_color\n",
-       "#pragma mapbox: define highp vec4 halo_color\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp float halo_width\n",
-       "#pragma mapbox: define lowp float halo_blur\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 fill_color\n",
-       "#pragma mapbox: initialize highp vec4 halo_color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize lowp float halo_width\n",
-       "#pragma mapbox: initialize lowp float halo_blur\n",
-       "float fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "return;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n",
-       "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
-       "#endif\n",
-       "}`,ih=`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n",
-       "#pragma mapbox: define highp vec4 fill_color\n",
-       "#pragma mapbox: define highp vec4 halo_color\n",
-       "#pragma mapbox: define lowp float opacity\n",
-       "#pragma mapbox: define lowp float halo_width\n",
-       "#pragma mapbox: define lowp float halo_blur\n",
-       "void main() {\n",
-       "#pragma mapbox: initialize highp vec4 fill_color\n",
-       "#pragma mapbox: initialize highp vec4 halo_color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
-       "#pragma mapbox: initialize lowp float halo_width\n",
-       "#pragma mapbox: initialize lowp float halo_blur\n",
-       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
-       "camera_to_anchor_distance/u_camera_to_center_distance :\n",
-       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`,js=Us(Cf,sc),Eu=Us(jh,Lf),Dc=Us(cs,af),ks=Us(Gf,$l),bc=Us(fl,lc),hu=Us(Fu,Es),_u=Us(Hs,Go),il=Us(ps,uc),nh=Us(bl,Gu),Mh=Us(Os,od),zu=Us(Po,sd),Fc=Us(Ko,Pa),wc=Us(of,Hu),bd=Us(wl,Hf),xf=Us(Ic,yf),Pf=Us(Ol,Ah),qu=Us(Qf,_f),bf=Us(Yc,eh),jl=Us(th,ju),uf=Us(jf,cc),Xh=Us(sf,Bl),If=Us(Kc,Rc),Cs=Us(gs,Wf),du=Us(Wh,rh),ku=Us(lf,Sh),Xf=Us(Mu,ih);function Us(Y,D){var J=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,O=D.match(/attribute ([\\w]+) ([\\w]+)/g),K=Y.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),de=D.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),ne=de?de.concat(K):K,we={};return Y=Y.replace(J,function(Oe,ht,Yt,cr,Nt){return we[Nt]=!0,ht===\"define\"?`\n",
-       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
-       "varying `+Yt+\" \"+cr+\" \"+Nt+`;\n",
-       "#else\n",
-       "uniform `+Yt+\" \"+cr+\" u_\"+Nt+`;\n",
-       "#endif\n",
-       "`:`\n",
-       "#ifdef HAS_UNIFORM_u_`+Nt+`\n",
-       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
-       "#endif\n",
-       "`}),D=D.replace(J,function(Oe,ht,Yt,cr,Nt){var Ne=cr===\"float\"?\"vec2\":\"vec4\",$e=Nt.match(/color/)?\"color\":Ne;return we[Nt]?ht===\"define\"?`\n",
-       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
-       "uniform lowp float u_`+Nt+`_t;\n",
-       "attribute `+Yt+\" \"+Ne+\" a_\"+Nt+`;\n",
-       "varying `+Yt+\" \"+cr+\" \"+Nt+`;\n",
-       "#else\n",
-       "uniform `+Yt+\" \"+cr+\" u_\"+Nt+`;\n",
-       "#endif\n",
-       "`:$e===\"vec4\"?`\n",
-       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
-       "    `+Nt+\" = a_\"+Nt+`;\n",
-       "#else\n",
-       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
-       "#endif\n",
-       "`:`\n",
-       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
-       "    `+Nt+\" = unpack_mix_\"+$e+\"(a_\"+Nt+\", u_\"+Nt+`_t);\n",
-       "#else\n",
-       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
-       "#endif\n",
-       "`:ht===\"define\"?`\n",
-       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
-       "uniform lowp float u_`+Nt+`_t;\n",
-       "attribute `+Yt+\" \"+Ne+\" a_\"+Nt+`;\n",
-       "#else\n",
-       "uniform `+Yt+\" \"+cr+\" u_\"+Nt+`;\n",
-       "#endif\n",
-       "`:$e===\"vec4\"?`\n",
-       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
-       "    `+Yt+\" \"+cr+\" \"+Nt+\" = a_\"+Nt+`;\n",
-       "#else\n",
-       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
-       "#endif\n",
-       "`:`\n",
-       "#ifndef HAS_UNIFORM_u_`+Nt+`\n",
-       "    `+Yt+\" \"+cr+\" \"+Nt+\" = unpack_mix_\"+$e+\"(a_\"+Nt+\", u_\"+Nt+`_t);\n",
-       "#else\n",
-       "    `+Yt+\" \"+cr+\" \"+Nt+\" = u_\"+Nt+`;\n",
-       "#endif\n",
-       "`}),{fragmentSource:Y,vertexSource:D,staticAttributes:O,staticUniforms:ne}}var wf=Object.freeze({__proto__:null,prelude:js,background:Eu,backgroundPattern:Dc,circle:ks,clippingMask:bc,heatmap:hu,heatmapTexture:_u,collisionBox:il,collisionCircle:nh,debug:Mh,fill:zu,fillOutline:Fc,fillOutlinePattern:wc,fillPattern:bd,fillExtrusion:xf,fillExtrusionPattern:Pf,hillshadePrepare:qu,hillshade:bf,line:jl,lineGradient:uf,linePattern:Xh,lineSDF:If,raster:Cs,symbolIcon:du,symbolSDF:ku,symbolTextAndIcon:Xf}),zc=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};zc.prototype.bind=function(D,J,O,K,de,ne,we,Oe){this.context=D;for(var ht=this.boundPaintVertexBuffers.length!==K.length,Yt=0;!ht&&Yt<K.length;Yt++)this.boundPaintVertexBuffers[Yt]!==K[Yt]&&(ht=!0);var cr=!this.vao||this.boundProgram!==J||this.boundLayoutVertexBuffer!==O||ht||this.boundIndexBuffer!==de||this.boundVertexOffset!==ne||this.boundDynamicVertexBuffer!==we||this.boundDynamicVertexBuffer2!==Oe;!D.extVertexArrayObject||cr?this.freshBind(J,O,K,de,ne,we,Oe):(D.bindVertexArrayOES.set(this.vao),we&&we.bind(),de&&de.dynamicDraw&&de.bind(),Oe&&Oe.bind())},zc.prototype.freshBind=function(D,J,O,K,de,ne,we){var Oe,ht=D.numAttributes,Yt=this.context,cr=Yt.gl;if(Yt.extVertexArrayObject)this.vao&&this.destroy(),this.vao=Yt.extVertexArrayObject.createVertexArrayOES(),Yt.bindVertexArrayOES.set(this.vao),Oe=0,this.boundProgram=D,this.boundLayoutVertexBuffer=J,this.boundPaintVertexBuffers=O,this.boundIndexBuffer=K,this.boundVertexOffset=de,this.boundDynamicVertexBuffer=ne,this.boundDynamicVertexBuffer2=we;else{Oe=Yt.currentNumAttributes||0;for(var Nt=ht;Nt<Oe;Nt++)cr.disableVertexAttribArray(Nt)}J.enableAttributes(cr,D);for(var Ne=0,$e=O;Ne<$e.length;Ne+=1){var st=$e[Ne];st.enableAttributes(cr,D)}ne&&ne.enableAttributes(cr,D),we&&we.enableAttributes(cr,D),J.bind(),J.setVertexAttribPointers(cr,D,de);for(var It=0,Bt=O;It<Bt.length;It+=1){var Ft=Bt[It];Ft.bind(),Ft.setVertexAttribPointers(cr,D,de)}ne&&(ne.bind(),ne.setVertexAttribPointers(cr,D,de)),K&&K.bind(),we&&(we.bind(),we.setVertexAttribPointers(cr,D,de)),Yt.currentNumAttributes=ht},zc.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};function Wu(Y){for(var D=[],J=0;J<Y.length;J++)if(Y[J]!==null){var O=Y[J].split(\" \");D.push(O.pop())}return D}var Rf=function(D,J,O,K,de,ne){var we=D.gl;this.program=we.createProgram();for(var Oe=Wu(O.staticAttributes),ht=K?K.getBinderAttributes():[],Yt=Oe.concat(ht),cr=O.staticUniforms?Wu(O.staticUniforms):[],Nt=K?K.getBinderUniforms():[],Ne=cr.concat(Nt),$e=[],st=0,It=Ne;st<It.length;st+=1){var Bt=It[st];$e.indexOf(Bt)<0&&$e.push(Bt)}var Ft=K?K.defines():[];ne&&Ft.push(\"#define OVERDRAW_INSPECTOR;\");var Ut=Ft.concat(js.fragmentSource,O.fragmentSource).join(`\n",
-       "`),zt=Ft.concat(js.vertexSource,O.vertexSource).join(`\n",
-       "`),_t=we.createShader(we.FRAGMENT_SHADER);if(we.isContextLost()){this.failedToCreate=!0;return}we.shaderSource(_t,Ut),we.compileShader(_t),we.attachShader(this.program,_t);var Dt=we.createShader(we.VERTEX_SHADER);if(we.isContextLost()){this.failedToCreate=!0;return}we.shaderSource(Dt,zt),we.compileShader(Dt),we.attachShader(this.program,Dt),this.attributes={};var vt={};this.numAttributes=Yt.length;for(var lr=0;lr<this.numAttributes;lr++)Yt[lr]&&(we.bindAttribLocation(this.program,lr,Yt[lr]),this.attributes[Yt[lr]]=lr);we.linkProgram(this.program),we.deleteShader(Dt),we.deleteShader(_t);for(var _r=0;_r<$e.length;_r++){var Br=$e[_r];if(Br&&!vt[Br]){var Di=we.getUniformLocation(this.program,Br);Di&&(vt[Br]=Di)}}this.fixedUniforms=de(D,vt),this.binderUniforms=K?K.getUniforms(D,vt):[]};Rf.prototype.draw=function(D,J,O,K,de,ne,we,Oe,ht,Yt,cr,Nt,Ne,$e,st,It){var Bt,Ft=D.gl;if(!this.failedToCreate){D.program.set(this.program),D.setDepthMode(O),D.setStencilMode(K),D.setColorMode(de),D.setCullFace(ne);for(var Ut in this.fixedUniforms)this.fixedUniforms[Ut].set(we[Ut]);$e&&$e.setUniforms(D,this.binderUniforms,Nt,{zoom:Ne});for(var zt=(Bt={},Bt[Ft.LINES]=2,Bt[Ft.TRIANGLES]=3,Bt[Ft.LINE_STRIP]=1,Bt)[J],_t=0,Dt=cr.get();_t<Dt.length;_t+=1){var vt=Dt[_t],lr=vt.vaos||(vt.vaos={}),_r=lr[Oe]||(lr[Oe]=new zc);_r.bind(D,this,ht,$e?$e.getPaintVertexBuffers():[],Yt,vt.vertexOffset,st,It),Ft.drawElements(J,vt.primitiveLength*zt,Ft.UNSIGNED_SHORT,vt.primitiveOffset*zt*2)}}};function Xu(Y,D,J){var O=1/As(J,1,D.transform.tileZoom),K=Math.pow(2,J.tileID.overscaledZ),de=J.tileSize*Math.pow(2,D.transform.tileZoom)/K,ne=de*(J.tileID.canonical.x+J.tileID.wrap*K),we=de*J.tileID.canonical.y;return{u_image:0,u_texsize:J.imageAtlasTexture.size,u_scale:[O,Y.fromScale,Y.toScale],u_fade:Y.t,u_pixel_coord_upper:[ne>>16,we>>16],u_pixel_coord_lower:[ne&65535,we&65535]}}function cf(Y,D,J,O){var K=J.imageManager.getPattern(Y.from.toString()),de=J.imageManager.getPattern(Y.to.toString()),ne=J.imageManager.getPixelSize(),we=ne.width,Oe=ne.height,ht=Math.pow(2,O.tileID.overscaledZ),Yt=O.tileSize*Math.pow(2,J.transform.tileZoom)/ht,cr=Yt*(O.tileID.canonical.x+O.tileID.wrap*ht),Nt=Yt*O.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:K.tl,u_pattern_br_a:K.br,u_pattern_tl_b:de.tl,u_pattern_br_b:de.br,u_texsize:[we,Oe],u_mix:D.t,u_pattern_size_a:K.displaySize,u_pattern_size_b:de.displaySize,u_scale_a:D.fromScale,u_scale_b:D.toScale,u_tile_units_to_pixels:1/As(O,1,J.transform.tileZoom),u_pixel_coord_upper:[cr>>16,Nt>>16],u_pixel_coord_lower:[cr&65535,Nt&65535]}}var Zf=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_lightpos:new i.Uniform3f(Y,D.u_lightpos),u_lightintensity:new i.Uniform1f(Y,D.u_lightintensity),u_lightcolor:new i.Uniform3f(Y,D.u_lightcolor),u_vertical_gradient:new i.Uniform1f(Y,D.u_vertical_gradient),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},Wl=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_lightpos:new i.Uniform3f(Y,D.u_lightpos),u_lightintensity:new i.Uniform1f(Y,D.u_lightintensity),u_lightcolor:new i.Uniform3f(Y,D.u_lightcolor),u_vertical_gradient:new i.Uniform1f(Y,D.u_vertical_gradient),u_height_factor:new i.Uniform1f(Y,D.u_height_factor),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},ah=function(Y,D,J,O){var K=D.style.light,de=K.properties.get(\"position\"),ne=[de.x,de.y,de.z],we=i.create$1();K.properties.get(\"anchor\")===\"viewport\"&&i.fromRotation(we,-D.transform.angle),i.transformMat3(ne,ne,we);var Oe=K.properties.get(\"color\");return{u_matrix:Y,u_lightpos:ne,u_lightintensity:K.properties.get(\"intensity\"),u_lightcolor:[Oe.r,Oe.g,Oe.b],u_vertical_gradient:+J,u_opacity:O}},Zu=function(Y,D,J,O,K,de,ne){return i.extend(ah(Y,D,J,O),Xu(de,D,ne),{u_height_factor:-Math.pow(2,K.overscaledZ)/ne.tileSize/8})},qc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},Tc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},Tl=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world)}},vu=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world),u_image:new i.Uniform1i(Y,D.u_image),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},Oc=function(Y){return{u_matrix:Y}},ff=function(Y,D,J,O){return i.extend(Oc(Y),Xu(J,D,O))},fc=function(Y,D){return{u_matrix:Y,u_world:D}},Bc=function(Y,D,J,O,K){return i.extend(ff(Y,D,J,O),{u_world:K})},kt=function(Y,D){return{u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_scale_with_map:new i.Uniform1i(Y,D.u_scale_with_map),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_extrude_scale:new i.Uniform2f(Y,D.u_extrude_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},Zt=function(Y,D,J,O){var K=Y.transform,de,ne;if(O.paint.get(\"circle-pitch-alignment\")===\"map\"){var we=As(J,1,K.zoom);de=!0,ne=[we,we]}else de=!1,ne=K.pixelsToGLUnits;return{u_camera_to_center_distance:K.cameraToCenterDistance,u_scale_with_map:+(O.paint.get(\"circle-pitch-scale\")===\"map\"),u_matrix:Y.translatePosMatrix(D.posMatrix,J,O.paint.get(\"circle-translate\"),O.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+de,u_device_pixel_ratio:i.browser.devicePixelRatio,u_extrude_scale:ne}},Er=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pixels_to_tile_units:new i.Uniform1f(Y,D.u_pixels_to_tile_units),u_extrude_scale:new i.Uniform2f(Y,D.u_extrude_scale),u_overscale_factor:new i.Uniform1f(Y,D.u_overscale_factor)}},xr=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_inv_matrix:new i.UniformMatrix4f(Y,D.u_inv_matrix),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_viewport_size:new i.Uniform2f(Y,D.u_viewport_size)}},Kr=function(Y,D,J){var O=As(J,1,D.zoom),K=Math.pow(2,D.zoom-J.tileID.overscaledZ),de=J.tileID.overscaleFactor();return{u_matrix:Y,u_camera_to_center_distance:D.cameraToCenterDistance,u_pixels_to_tile_units:O,u_extrude_scale:[D.pixelsToGLUnits[0]/(O*K),D.pixelsToGLUnits[1]/(O*K)],u_overscale_factor:de}},Mi=function(Y,D,J){return{u_matrix:Y,u_inv_matrix:D,u_camera_to_center_distance:J.cameraToCenterDistance,u_viewport_size:[J.width,J.height]}},Xi=function(Y,D){return{u_color:new i.UniformColor(Y,D.u_color),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_overlay:new i.Uniform1i(Y,D.u_overlay),u_overlay_scale:new i.Uniform1f(Y,D.u_overlay_scale)}},dn=function(Y,D,J){return J===void 0&&(J=1),{u_matrix:Y,u_color:D,u_overlay:0,u_overlay_scale:J}},wn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},On=function(Y){return{u_matrix:Y}},Yi=function(Y,D){return{u_extrude_scale:new i.Uniform1f(Y,D.u_extrude_scale),u_intensity:new i.Uniform1f(Y,D.u_intensity),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix)}},$i=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_world:new i.Uniform2f(Y,D.u_world),u_image:new i.Uniform1i(Y,D.u_image),u_color_ramp:new i.Uniform1i(Y,D.u_color_ramp),u_opacity:new i.Uniform1f(Y,D.u_opacity)}},an=function(Y,D,J,O){return{u_matrix:Y,u_extrude_scale:As(D,1,J),u_intensity:O}},Fi=function(Y,D,J,O){var K=i.create();i.ortho(K,0,Y.width,Y.height,0,0,1);var de=Y.context.gl;return{u_matrix:K,u_world:[de.drawingBufferWidth,de.drawingBufferHeight],u_image:J,u_color_ramp:O,u_opacity:D.paint.get(\"heatmap-opacity\")}},ta=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_latrange:new i.Uniform2f(Y,D.u_latrange),u_light:new i.Uniform2f(Y,D.u_light),u_shadow:new i.UniformColor(Y,D.u_shadow),u_highlight:new i.UniformColor(Y,D.u_highlight),u_accent:new i.UniformColor(Y,D.u_accent)}},Ca=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_image:new i.Uniform1i(Y,D.u_image),u_dimension:new i.Uniform2f(Y,D.u_dimension),u_zoom:new i.Uniform1f(Y,D.u_zoom),u_unpack:new i.Uniform4f(Y,D.u_unpack)}},Ra=function(Y,D,J){var O=J.paint.get(\"hillshade-shadow-color\"),K=J.paint.get(\"hillshade-highlight-color\"),de=J.paint.get(\"hillshade-accent-color\"),ne=J.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);J.paint.get(\"hillshade-illumination-anchor\")===\"viewport\"&&(ne-=Y.transform.angle);var we=!Y.options.moving;return{u_matrix:Y.transform.calculatePosMatrix(D.tileID.toUnwrapped(),we),u_image:0,u_latrange:Ua(Y,D.tileID),u_light:[J.paint.get(\"hillshade-exaggeration\"),ne],u_shadow:O,u_highlight:K,u_accent:de}},La=function(Y,D){var J=D.stride,O=i.create();return i.ortho(O,0,i.EXTENT,-i.EXTENT,0,0,1),i.translate(O,O,[0,-i.EXTENT,0]),{u_matrix:O,u_image:1,u_dimension:[J,J],u_zoom:Y.overscaledZ,u_unpack:D.getUnpackVector()}};function Ua(Y,D){var J=Math.pow(2,D.canonical.z),O=D.canonical.y;return[new i.MercatorCoordinate(0,O/J).toLngLat().lat,new i.MercatorCoordinate(0,(O+1)/J).toLngLat().lat]}var Zn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels)}},Dn=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_image:new i.Uniform1i(Y,D.u_image),u_image_height:new i.Uniform1f(Y,D.u_image_height)}},Ka=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_image:new i.Uniform1i(Y,D.u_image),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_scale:new i.Uniform3f(Y,D.u_scale),u_fade:new i.Uniform1f(Y,D.u_fade)}},bo=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_ratio:new i.Uniform1f(Y,D.u_ratio),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_units_to_pixels:new i.Uniform2f(Y,D.u_units_to_pixels),u_patternscale_a:new i.Uniform2f(Y,D.u_patternscale_a),u_patternscale_b:new i.Uniform2f(Y,D.u_patternscale_b),u_sdfgamma:new i.Uniform1f(Y,D.u_sdfgamma),u_image:new i.Uniform1i(Y,D.u_image),u_tex_y_a:new i.Uniform1f(Y,D.u_tex_y_a),u_tex_y_b:new i.Uniform1f(Y,D.u_tex_y_b),u_mix:new i.Uniform1f(Y,D.u_mix)}},Zo=function(Y,D,J){var O=Y.transform;return{u_matrix:ml(Y,D,J),u_ratio:1/As(D,1,O.zoom),u_device_pixel_ratio:i.browser.devicePixelRatio,u_units_to_pixels:[1/O.pixelsToGLUnits[0],1/O.pixelsToGLUnits[1]]}},Ss=function(Y,D,J,O){return i.extend(Zo(Y,D,J),{u_image:0,u_image_height:O})},as=function(Y,D,J,O){var K=Y.transform,de=Ho(D,K);return{u_matrix:ml(Y,D,J),u_texsize:D.imageAtlasTexture.size,u_ratio:1/As(D,1,K.zoom),u_device_pixel_ratio:i.browser.devicePixelRatio,u_image:0,u_scale:[de,O.fromScale,O.toScale],u_fade:O.t,u_units_to_pixels:[1/K.pixelsToGLUnits[0],1/K.pixelsToGLUnits[1]]}},ws=function(Y,D,J,O,K){var de=Y.transform,ne=Y.lineAtlas,we=Ho(D,de),Oe=J.layout.get(\"line-cap\")===\"round\",ht=ne.getDash(O.from,Oe),Yt=ne.getDash(O.to,Oe),cr=ht.width*K.fromScale,Nt=Yt.width*K.toScale;return i.extend(Zo(Y,D,J),{u_patternscale_a:[we/cr,-ht.height/2],u_patternscale_b:[we/Nt,-Yt.height/2],u_sdfgamma:ne.width/(Math.min(cr,Nt)*256*i.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:ht.y,u_tex_y_b:Yt.y,u_mix:K.t})};function Ho(Y,D){return 1/As(Y,1,D.tileZoom)}function ml(Y,D,J){return Y.translatePosMatrix(D.tileID.posMatrix,D,J.paint.get(\"line-translate\"),J.paint.get(\"line-translate-anchor\"))}var Ws=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_tl_parent:new i.Uniform2f(Y,D.u_tl_parent),u_scale_parent:new i.Uniform1f(Y,D.u_scale_parent),u_buffer_scale:new i.Uniform1f(Y,D.u_buffer_scale),u_fade_t:new i.Uniform1f(Y,D.u_fade_t),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_image0:new i.Uniform1i(Y,D.u_image0),u_image1:new i.Uniform1i(Y,D.u_image1),u_brightness_low:new i.Uniform1f(Y,D.u_brightness_low),u_brightness_high:new i.Uniform1f(Y,D.u_brightness_high),u_saturation_factor:new i.Uniform1f(Y,D.u_saturation_factor),u_contrast_factor:new i.Uniform1f(Y,D.u_contrast_factor),u_spin_weights:new i.Uniform3f(Y,D.u_spin_weights)}},Ls=function(Y,D,J,O,K){return{u_matrix:Y,u_tl_parent:D,u_scale_parent:J,u_buffer_scale:1,u_fade_t:O.mix,u_opacity:O.opacity*K.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:K.paint.get(\"raster-brightness-min\"),u_brightness_high:K.paint.get(\"raster-brightness-max\"),u_saturation_factor:ys(K.paint.get(\"raster-saturation\")),u_contrast_factor:no(K.paint.get(\"raster-contrast\")),u_spin_weights:va(K.paint.get(\"raster-hue-rotate\"))}};function va(Y){Y*=Math.PI/180;var D=Math.sin(Y),J=Math.cos(Y);return[(2*J+1)/3,(-Math.sqrt(3)*D-J+1)/3,(Math.sqrt(3)*D-J+1)/3]}function no(Y){return Y>0?1/(1-Y):1+Y}function ys(Y){return Y>0?1-1/(1.001-Y):-Y}var rs=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texture:new i.Uniform1i(Y,D.u_texture)}},Ql=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texture:new i.Uniform1i(Y,D.u_texture),u_gamma_scale:new i.Uniform1f(Y,D.u_gamma_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_is_halo:new i.Uniform1i(Y,D.u_is_halo)}},Cu=function(Y,D){return{u_is_size_zoom_constant:new i.Uniform1i(Y,D.u_is_size_zoom_constant),u_is_size_feature_constant:new i.Uniform1i(Y,D.u_is_size_feature_constant),u_size_t:new i.Uniform1f(Y,D.u_size_t),u_size:new i.Uniform1f(Y,D.u_size),u_camera_to_center_distance:new i.Uniform1f(Y,D.u_camera_to_center_distance),u_pitch:new i.Uniform1f(Y,D.u_pitch),u_rotate_symbol:new i.Uniform1i(Y,D.u_rotate_symbol),u_aspect_ratio:new i.Uniform1f(Y,D.u_aspect_ratio),u_fade_change:new i.Uniform1f(Y,D.u_fade_change),u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_label_plane_matrix:new i.UniformMatrix4f(Y,D.u_label_plane_matrix),u_coord_matrix:new i.UniformMatrix4f(Y,D.u_coord_matrix),u_is_text:new i.Uniform1i(Y,D.u_is_text),u_pitch_with_map:new i.Uniform1i(Y,D.u_pitch_with_map),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_texsize_icon:new i.Uniform2f(Y,D.u_texsize_icon),u_texture:new i.Uniform1i(Y,D.u_texture),u_texture_icon:new i.Uniform1i(Y,D.u_texture_icon),u_gamma_scale:new i.Uniform1f(Y,D.u_gamma_scale),u_device_pixel_ratio:new i.Uniform1f(Y,D.u_device_pixel_ratio),u_is_halo:new i.Uniform1i(Y,D.u_is_halo)}},Yu=function(Y,D,J,O,K,de,ne,we,Oe,ht){var Yt=K.transform;return{u_is_size_zoom_constant:+(Y===\"constant\"||Y===\"source\"),u_is_size_feature_constant:+(Y===\"constant\"||Y===\"camera\"),u_size_t:D?D.uSizeT:0,u_size:D?D.uSize:0,u_camera_to_center_distance:Yt.cameraToCenterDistance,u_pitch:Yt.pitch/360*2*Math.PI,u_rotate_symbol:+J,u_aspect_ratio:Yt.width/Yt.height,u_fade_change:K.options.fadeDuration?K.symbolFadeChange:1,u_matrix:de,u_label_plane_matrix:ne,u_coord_matrix:we,u_is_text:+Oe,u_pitch_with_map:+O,u_texsize:ht,u_texture:0}},Nc=function(Y,D,J,O,K,de,ne,we,Oe,ht,Yt){var cr=K.transform;return i.extend(Yu(Y,D,J,O,K,de,ne,we,Oe,ht),{u_gamma_scale:O?Math.cos(cr._pitch)*cr.cameraToCenterDistance:1,u_device_pixel_ratio:i.browser.devicePixelRatio,u_is_halo:+Yt})},pu=function(Y,D,J,O,K,de,ne,we,Oe,ht){return i.extend(Nc(Y,D,J,O,K,de,ne,we,!0,Oe,!0),{u_texsize_icon:ht,u_texture_icon:1})},Uc=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_color:new i.UniformColor(Y,D.u_color)}},xu=function(Y,D){return{u_matrix:new i.UniformMatrix4f(Y,D.u_matrix),u_opacity:new i.Uniform1f(Y,D.u_opacity),u_image:new i.Uniform1i(Y,D.u_image),u_pattern_tl_a:new i.Uniform2f(Y,D.u_pattern_tl_a),u_pattern_br_a:new i.Uniform2f(Y,D.u_pattern_br_a),u_pattern_tl_b:new i.Uniform2f(Y,D.u_pattern_tl_b),u_pattern_br_b:new i.Uniform2f(Y,D.u_pattern_br_b),u_texsize:new i.Uniform2f(Y,D.u_texsize),u_mix:new i.Uniform1f(Y,D.u_mix),u_pattern_size_a:new i.Uniform2f(Y,D.u_pattern_size_a),u_pattern_size_b:new i.Uniform2f(Y,D.u_pattern_size_b),u_scale_a:new i.Uniform1f(Y,D.u_scale_a),u_scale_b:new i.Uniform1f(Y,D.u_scale_b),u_pixel_coord_upper:new i.Uniform2f(Y,D.u_pixel_coord_upper),u_pixel_coord_lower:new i.Uniform2f(Y,D.u_pixel_coord_lower),u_tile_units_to_pixels:new i.Uniform1f(Y,D.u_tile_units_to_pixels)}},Ac=function(Y,D,J){return{u_matrix:Y,u_opacity:D,u_color:J}},Va=function(Y,D,J,O,K,de){return i.extend(cf(O,de,J,K),{u_matrix:Y,u_opacity:D})},oo={fillExtrusion:Zf,fillExtrusionPattern:Wl,fill:qc,fillPattern:Tc,fillOutline:Tl,fillOutlinePattern:vu,circle:kt,collisionBox:Er,collisionCircle:xr,debug:Xi,clippingMask:wn,heatmap:Yi,heatmapTexture:$i,hillshade:ta,hillshadePrepare:Ca,line:Zn,lineGradient:Dn,linePattern:Ka,lineSDF:bo,raster:Ws,symbolIcon:rs,symbolSDF:Ql,symbolTextAndIcon:Cu,background:Uc,backgroundPattern:xu},Vc;function hc(Y,D,J,O,K,de,ne){for(var we=Y.context,Oe=we.gl,ht=Y.useProgram(\"collisionBox\"),Yt=[],cr=0,Nt=0,Ne=0;Ne<O.length;Ne++){var $e=O[Ne],st=D.getTile($e),It=st.getBucket(J);if(It){var Bt=$e.posMatrix;(K[0]!==0||K[1]!==0)&&(Bt=Y.translatePosMatrix($e.posMatrix,st,K,de));var Ft=ne?It.textCollisionBox:It.iconCollisionBox,Ut=It.collisionCircleArray;if(Ut.length>0){var zt=i.create(),_t=Bt;i.mul(zt,It.placementInvProjMatrix,Y.transform.glCoordMatrix),i.mul(zt,zt,It.placementViewportMatrix),Yt.push({circleArray:Ut,circleOffset:Nt,transform:_t,invTransform:zt}),cr+=Ut.length/4,Nt=cr}Ft&&ht.draw(we,Oe.LINES,An.disabled,Vi.disabled,Y.colorModeForRenderPass(),wr.disabled,Kr(Bt,Y.transform,st),J.id,Ft.layoutVertexBuffer,Ft.indexBuffer,Ft.segments,null,Y.transform.zoom,null,null,Ft.collisionVertexBuffer)}}if(!(!ne||!Yt.length)){var Dt=Y.useProgram(\"collisionCircle\"),vt=new i.StructArrayLayout2f1f2i16;vt.resize(cr*4),vt._trim();for(var lr=0,_r=0,Br=Yt;_r<Br.length;_r+=1)for(var Di=Br[_r],ci=0;ci<Di.circleArray.length/4;ci++){var bi=ci*4,gn=Di.circleArray[bi+0],tn=Di.circleArray[bi+1],Ei=Di.circleArray[bi+2],Oi=Di.circleArray[bi+3];vt.emplace(lr++,gn,tn,Ei,Oi,0),vt.emplace(lr++,gn,tn,Ei,Oi,1),vt.emplace(lr++,gn,tn,Ei,Oi,2),vt.emplace(lr++,gn,tn,Ei,Oi,3)}(!Vc||Vc.length<cr*2)&&(Vc=Ku(cr));for(var Gi=we.createIndexBuffer(Vc,!0),on=we.createVertexBuffer(vt,i.collisionCircleLayout.members,!0),zn=0,Ja=Yt;zn<Ja.length;zn+=1){var co=Ja[zn],ts=Mi(co.transform,co.invTransform,Y.transform);Dt.draw(we,Oe.TRIANGLES,An.disabled,Vi.disabled,Y.colorModeForRenderPass(),wr.disabled,ts,J.id,on,Gi,i.SegmentVector.simpleSegment(0,co.circleOffset*2,co.circleArray.length,co.circleArray.length/2),null,Y.transform.zoom,null,null,null)}on.destroy(),Gi.destroy()}}function Ku(Y){var D=Y*2,J=new i.StructArrayLayout3ui6;J.resize(D),J._trim();for(var O=0;O<D;O++){var K=O*6;J.uint16[K+0]=O*4+0,J.uint16[K+1]=O*4+1,J.uint16[K+2]=O*4+2,J.uint16[K+3]=O*4+2,J.uint16[K+4]=O*4+3,J.uint16[K+5]=O*4+0}return J}var ue=i.identity(new Float32Array(16));function w(Y,D,J,O,K){if(Y.renderPass===\"translucent\"){var de=Vi.disabled,ne=Y.colorModeForRenderPass(),we=J.layout.get(\"text-variable-anchor\");we&&Q(O,Y,J,D,J.layout.get(\"text-rotation-alignment\"),J.layout.get(\"text-pitch-alignment\"),K),J.paint.get(\"icon-opacity\").constantOr(1)!==0&&Fe(Y,D,J,O,!1,J.paint.get(\"icon-translate\"),J.paint.get(\"icon-translate-anchor\"),J.layout.get(\"icon-rotation-alignment\"),J.layout.get(\"icon-pitch-alignment\"),J.layout.get(\"icon-keep-upright\"),de,ne),J.paint.get(\"text-opacity\").constantOr(1)!==0&&Fe(Y,D,J,O,!0,J.paint.get(\"text-translate\"),J.paint.get(\"text-translate-anchor\"),J.layout.get(\"text-rotation-alignment\"),J.layout.get(\"text-pitch-alignment\"),J.layout.get(\"text-keep-upright\"),de,ne),D.map.showCollisionBoxes&&(hc(Y,D,J,O,J.paint.get(\"text-translate\"),J.paint.get(\"text-translate-anchor\"),!0),hc(Y,D,J,O,J.paint.get(\"icon-translate\"),J.paint.get(\"icon-translate-anchor\"),!1))}}function B(Y,D,J,O,K,de){var ne=i.getAnchorAlignment(Y),we=ne.horizontalAlign,Oe=ne.verticalAlign,ht=-(we-.5)*D,Yt=-(Oe-.5)*J,cr=i.evaluateVariableOffset(Y,O);return new i.Point((ht/K+cr[0])*de,(Yt/K+cr[1])*de)}function Q(Y,D,J,O,K,de,ne){for(var we=D.transform,Oe=K===\"map\",ht=de===\"map\",Yt=0,cr=Y;Yt<cr.length;Yt+=1){var Nt=cr[Yt],Ne=O.getTile(Nt),$e=Ne.getBucket(J);if(!(!$e||!$e.text||!$e.text.segments.get().length)){var st=$e.textSizeData,It=i.evaluateSizeForZoom(st,we.zoom),Bt=As(Ne,1,D.transform.zoom),Ft=_n(Nt.posMatrix,ht,Oe,D.transform,Bt),Ut=J.layout.get(\"icon-text-fit\")!==\"none\"&&$e.hasIconData();if(It){var zt=Math.pow(2,we.zoom-Ne.tileID.overscaledZ);ee($e,Oe,ht,ne,i.symbolSize,we,Ft,Nt.posMatrix,zt,It,Ut)}}}}function ee(Y,D,J,O,K,de,ne,we,Oe,ht,Yt){var cr=Y.text.placedSymbolArray,Nt=Y.text.dynamicLayoutVertexArray,Ne=Y.icon.dynamicLayoutVertexArray,$e={};Nt.clear();for(var st=0;st<cr.length;st++){var It=cr.get(st),Bt=Y.allowVerticalPlacement&&!It.placedOrientation,Ft=!It.hidden&&It.crossTileID&&!Bt?O[It.crossTileID]:null;if(!Ft)ul(It.numGlyphs,Nt);else{var Ut=new i.Point(It.anchorX,It.anchorY),zt=ea(Ut,J?we:ne),_t=Ma(de.cameraToCenterDistance,zt.signedDistanceFromCamera),Dt=K.evaluateSizeForFeature(Y.textSizeData,ht,It)*_t/i.ONE_EM;J&&(Dt*=Y.tilePixelRatio/Oe);for(var vt=Ft.width,lr=Ft.height,_r=Ft.anchor,Br=Ft.textOffset,Di=Ft.textBoxScale,ci=B(_r,vt,lr,Br,Di,Dt),bi=J?ea(Ut.add(ci),ne).point:zt.point.add(D?ci.rotate(-de.angle):ci),gn=Y.allowVerticalPlacement&&It.placedOrientation===i.WritingMode.vertical?Math.PI/2:0,tn=0;tn<It.numGlyphs;tn++)i.addDynamicAttributes(Nt,bi,gn);Yt&&It.associatedIconIndex>=0&&($e[It.associatedIconIndex]={shiftedAnchor:bi,angle:gn})}}if(Yt){Ne.clear();for(var Ei=Y.icon.placedSymbolArray,Oi=0;Oi<Ei.length;Oi++){var Gi=Ei.get(Oi);if(Gi.hidden)ul(Gi.numGlyphs,Ne);else{var on=$e[Oi];if(!on)ul(Gi.numGlyphs,Ne);else for(var 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0;tn?ci.iconsInText?Ad=pu(Ei.kind,on,Ft,It,Y,gp,Td,mp,zn,Ja):Ad=Nc(Ei.kind,on,Ft,It,Y,gp,Td,mp,K,zn,!0):Ad=Yu(Ei.kind,on,Ft,It,Y,gp,Td,mp,K,zn);var Pv={program:Gi,buffers:bi,uniformValues:Ad,atlasTexture:co,atlasTextureIcon:so,atlasInterpolation:ts,atlasInterpolationIcon:Yo,isSDF:tn,hasHalo:Vd};if(Ut&&ci.canOverlap){zt=!0;for(var Jv=bi.segments.get(),Iv=0,dy=Jv;Iv<dy.length;Iv+=1){var dg=dy[Iv];vt.push({segments:new i.SegmentVector([dg]),sortKey:dg.sortKey,state:Pv})}}else vt.push({segments:bi.segments,sortKey:0,state:Pv})}}}zt&&vt.sort(function(C1,L1){return C1.sortKey-L1.sortKey});for(var oh=0,vg=vt;oh<vg.length;oh+=1){var vy=vg[oh],Zh=vy.state;if(Nt.activeTexture.set(Ne.TEXTURE0),Zh.atlasTexture.bind(Zh.atlasInterpolation,Ne.CLAMP_TO_EDGE),Zh.atlasTextureIcon&&(Nt.activeTexture.set(Ne.TEXTURE1),Zh.atlasTextureIcon&&Zh.atlasTextureIcon.bind(Zh.atlasInterpolationIcon,Ne.CLAMP_TO_EDGE)),Zh.isSDF){var am=Zh.uniformValues;Zh.hasHalo&&(am.u_is_halo=1,Ze(Zh.buffers,vy.segments,J,Y,Zh.program,_t,Yt,cr,am)),am.u_is_halo=0}Ze(Zh.buffers,vy.segments,J,Y,Zh.program,_t,Yt,cr,Zh.uniformValues)}}function Ze(Y,D,J,O,K,de,ne,we,Oe){var ht=O.context,Yt=ht.gl;K.draw(ht,Yt.TRIANGLES,de,ne,we,wr.disabled,Oe,J.id,Y.layoutVertexBuffer,Y.indexBuffer,D,J.paint,O.transform.zoom,Y.programConfigurations.get(J.id),Y.dynamicLayoutVertexBuffer,Y.opacityVertexBuffer)}function ut(Y,D,J,O){if(Y.renderPass===\"translucent\"){var K=J.paint.get(\"circle-opacity\"),de=J.paint.get(\"circle-stroke-width\"),ne=J.paint.get(\"circle-stroke-opacity\"),we=J.layout.get(\"circle-sort-key\").constantOr(1)!==void 0;if(!(K.constantOr(1)===0&&(de.constantOr(1)===0||ne.constantOr(1)===0))){for(var Oe=Y.context,ht=Oe.gl,Yt=Y.depthModeForSublayer(0,An.ReadOnly),cr=Vi.disabled,Nt=Y.colorModeForRenderPass(),Ne=[],$e=0;$e<O.length;$e++){var st=O[$e],It=D.getTile(st),Bt=It.getBucket(J);if(Bt){var Ft=Bt.programConfigurations.get(J.id),Ut=Y.useProgram(\"circle\",Ft),zt=Bt.layoutVertexBuffer,_t=Bt.indexBuffer,Dt=Zt(Y,st,It,J),vt={programConfiguration:Ft,program:Ut,layoutVertexBuffer:zt,indexBuffer:_t,uniformValues:Dt};if(we)for(var lr=Bt.segments.get(),_r=0,Br=lr;_r<Br.length;_r+=1){var Di=Br[_r];Ne.push({segments:new i.SegmentVector([Di]),sortKey:Di.sortKey,state:vt})}else Ne.push({segments:Bt.segments,sortKey:0,state:vt})}}we&&Ne.sort(function(co,ts){return co.sortKey-ts.sortKey});for(var ci=0,bi=Ne;ci<bi.length;ci+=1){var gn=bi[ci],tn=gn.state,Ei=tn.programConfiguration,Oi=tn.program,Gi=tn.layoutVertexBuffer,on=tn.indexBuffer,zn=tn.uniformValues,Ja=gn.segments;Oi.draw(Oe,ht.TRIANGLES,Yt,cr,Nt,wr.disabled,zn,J.id,Gi,on,Ja,J.paint,Y.transform.zoom,Ei)}}}}function Mt(Y,D,J,O){if(J.paint.get(\"heatmap-opacity\")!==0)if(Y.renderPass===\"offscreen\"){var K=Y.context,de=K.gl,ne=Vi.disabled,we=new wt([de.ONE,de.ONE],i.Color.transparent,[!0,!0,!0,!0]);Jt(K,Y,J),K.clear({color:i.Color.transparent});for(var Oe=0;Oe<O.length;Oe++){var ht=O[Oe];if(!D.hasRenderableParent(ht)){var Yt=D.getTile(ht),cr=Yt.getBucket(J);if(cr){var Nt=cr.programConfigurations.get(J.id),Ne=Y.useProgram(\"heatmap\",Nt),$e=Y.transform,st=$e.zoom;Ne.draw(K,de.TRIANGLES,An.disabled,ne,we,wr.disabled,an(ht.posMatrix,Yt,st,J.paint.get(\"heatmap-intensity\")),J.id,cr.layoutVertexBuffer,cr.indexBuffer,cr.segments,J.paint,Y.transform.zoom,Nt)}}}K.viewport.set([0,0,Y.width,Y.height])}else Y.renderPass===\"translucent\"&&(Y.context.setColorMode(Y.colorModeForRenderPass()),yr(Y,J))}function Jt(Y,D,J){var O=Y.gl;Y.activeTexture.set(O.TEXTURE1),Y.viewport.set([0,0,D.width/4,D.height/4]);var K=J.heatmapFbo;if(K)O.bindTexture(O.TEXTURE_2D,K.colorAttachment.get()),Y.bindFramebuffer.set(K.framebuffer);else{var de=O.createTexture();O.bindTexture(O.TEXTURE_2D,de),O.texParameteri(O.TEXTURE_2D,O.TEXTURE_WRAP_S,O.CLAMP_TO_EDGE),O.texParameteri(O.TEXTURE_2D,O.TEXTURE_WRAP_T,O.CLAMP_TO_EDGE),O.texParameteri(O.TEXTURE_2D,O.TEXTURE_MIN_FILTER,O.LINEAR),O.texParameteri(O.TEXTURE_2D,O.TEXTURE_MAG_FILTER,O.LINEAR),K=J.heatmapFbo=Y.createFramebuffer(D.width/4,D.height/4,!1),$t(Y,D,de,K)}}function $t(Y,D,J,O){var K=Y.gl,de=Y.extRenderToTextureHalfFloat?Y.extTextureHalfFloat.HALF_FLOAT_OES:K.UNSIGNED_BYTE;K.texImage2D(K.TEXTURE_2D,0,K.RGBA,D.width/4,D.height/4,0,K.RGBA,de,null),O.colorAttachment.set(J)}function yr(Y,D){var J=Y.context,O=J.gl,K=D.heatmapFbo;if(K){J.activeTexture.set(O.TEXTURE0),O.bindTexture(O.TEXTURE_2D,K.colorAttachment.get()),J.activeTexture.set(O.TEXTURE1);var de=D.colorRampTexture;de||(de=D.colorRampTexture=new i.Texture(J,D.colorRamp,O.RGBA)),de.bind(O.LINEAR,O.CLAMP_TO_EDGE),Y.useProgram(\"heatmapTexture\").draw(J,O.TRIANGLES,An.disabled,Vi.disabled,Y.colorModeForRenderPass(),wr.disabled,Fi(Y,D,0,1),D.id,Y.viewportBuffer,Y.quadTriangleIndexBuffer,Y.viewportSegments,D.paint,Y.transform.zoom)}}function Cr(Y,D,J,O){if(Y.renderPass===\"translucent\"){var K=J.paint.get(\"line-opacity\"),de=J.paint.get(\"line-width\");if(!(K.constantOr(1)===0||de.constantOr(1)===0))for(var ne=Y.depthModeForSublayer(0,An.ReadOnly),we=Y.colorModeForRenderPass(),Oe=J.paint.get(\"line-dasharray\"),ht=J.paint.get(\"line-pattern\"),Yt=ht.constantOr(1),cr=J.paint.get(\"line-gradient\"),Nt=J.getCrossfadeParameters(),Ne=Yt?\"linePattern\":Oe?\"lineSDF\":cr?\"lineGradient\":\"line\",$e=Y.context,st=$e.gl,It=!0,Bt=0,Ft=O;Bt<Ft.length;Bt+=1){var Ut=Ft[Bt],zt=D.getTile(Ut);if(!(Yt&&!zt.patternsLoaded())){var _t=zt.getBucket(J);if(_t){var Dt=_t.programConfigurations.get(J.id),vt=Y.context.program.get(),lr=Y.useProgram(Ne,Dt),_r=It||lr.program!==vt,Br=ht.constantOr(null);if(Br&&zt.imageAtlas){var Di=zt.imageAtlas,ci=Di.patternPositions[Br.to.toString()],bi=Di.patternPositions[Br.from.toString()];ci&&bi&&Dt.setConstantPatternPositions(ci,bi)}var gn=Yt?as(Y,zt,J,Nt):Oe?ws(Y,zt,J,Oe,Nt):cr?Ss(Y,zt,J,_t.lineClipsArray.length):Zo(Y,zt,J);if(Yt)$e.activeTexture.set(st.TEXTURE0),zt.imageAtlasTexture.bind(st.LINEAR,st.CLAMP_TO_EDGE),Dt.updatePaintBuffers(Nt);else if(Oe&&(_r||Y.lineAtlas.dirty))$e.activeTexture.set(st.TEXTURE0),Y.lineAtlas.bind($e);else if(cr){var tn=_t.gradients[J.id],Ei=tn.texture;if(J.gradientVersion!==tn.version){var Oi=256;if(J.stepInterpolant){var Gi=D.getSource().maxzoom,on=Ut.canonical.z===Gi?Math.ceil(1<<Y.transform.maxZoom-Ut.canonical.z):1,zn=_t.maxLineLength/i.EXTENT,Ja=1024,co=zn*Ja*on;Oi=i.clamp(i.nextPowerOfTwo(co),256,$e.maxTextureSize)}tn.gradient=i.renderColorRamp({expression:J.gradientExpression(),evaluationKey:\"lineProgress\",resolution:Oi,image:tn.gradient||void 0,clips:_t.lineClipsArray}),tn.texture?tn.texture.update(tn.gradient):tn.texture=new i.Texture($e,tn.gradient,st.RGBA),tn.version=J.gradientVersion,Ei=tn.texture}$e.activeTexture.set(st.TEXTURE0),Ei.bind(J.stepInterpolant?st.NEAREST:st.LINEAR,st.CLAMP_TO_EDGE)}lr.draw($e,st.TRIANGLES,ne,Y.stencilModeForClipping(Ut),we,wr.disabled,gn,J.id,_t.layoutVertexBuffer,_t.indexBuffer,_t.segments,J.paint,Y.transform.zoom,Dt,_t.layoutVertexBuffer2),It=!1}}}}}function ve(Y,D,J,O){var K=J.paint.get(\"fill-color\"),de=J.paint.get(\"fill-opacity\");if(de.constantOr(1)!==0){var ne=Y.colorModeForRenderPass(),we=J.paint.get(\"fill-pattern\"),Oe=Y.opaquePassEnabledForLayer()&&!we.constantOr(1)&&K.constantOr(i.Color.transparent).a===1&&de.constantOr(0)===1?\"opaque\":\"translucent\";if(Y.renderPass===Oe){var ht=Y.depthModeForSublayer(1,Y.renderPass===\"opaque\"?An.ReadWrite:An.ReadOnly);xe(Y,D,J,O,ht,ne,!1)}if(Y.renderPass===\"translucent\"&&J.paint.get(\"fill-antialias\")){var Yt=Y.depthModeForSublayer(J.getPaintProperty(\"fill-outline-color\")?2:0,An.ReadOnly);xe(Y,D,J,O,Yt,ne,!0)}}}function xe(Y,D,J,O,K,de,ne){var we=Y.context.gl,Oe=J.paint.get(\"fill-pattern\"),ht=Oe&&Oe.constantOr(1),Yt=J.getCrossfadeParameters(),cr,Nt,Ne,$e,st;ne?(Nt=ht&&!J.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",cr=we.LINES):(Nt=ht?\"fillPattern\":\"fill\",cr=we.TRIANGLES);for(var It=0,Bt=O;It<Bt.length;It+=1){var Ft=Bt[It],Ut=D.getTile(Ft);if(!(ht&&!Ut.patternsLoaded())){var zt=Ut.getBucket(J);if(zt){var _t=zt.programConfigurations.get(J.id),Dt=Y.useProgram(Nt,_t);ht&&(Y.context.activeTexture.set(we.TEXTURE0),Ut.imageAtlasTexture.bind(we.LINEAR,we.CLAMP_TO_EDGE),_t.updatePaintBuffers(Yt));var vt=Oe.constantOr(null);if(vt&&Ut.imageAtlas){var lr=Ut.imageAtlas,_r=lr.patternPositions[vt.to.toString()],Br=lr.patternPositions[vt.from.toString()];_r&&Br&&_t.setConstantPatternPositions(_r,Br)}var Di=Y.translatePosMatrix(Ft.posMatrix,Ut,J.paint.get(\"fill-translate\"),J.paint.get(\"fill-translate-anchor\"));if(!ne)$e=zt.indexBuffer,st=zt.segments,Ne=ht?ff(Di,Y,Yt,Ut):Oc(Di);else{$e=zt.indexBuffer2,st=zt.segments2;var ci=[we.drawingBufferWidth,we.drawingBufferHeight];Ne=Nt===\"fillOutlinePattern\"&&ht?Bc(Di,Y,Yt,Ut,ci):fc(Di,ci)}Dt.draw(Y.context,cr,K,Y.stencilModeForClipping(Ft),de,wr.disabled,Ne,J.id,zt.layoutVertexBuffer,$e,st,J.paint,Y.transform.zoom,_t)}}}}function Ie(Y,D,J,O){var K=J.paint.get(\"fill-extrusion-opacity\");if(K!==0&&Y.renderPass===\"translucent\"){var de=new An(Y.context.gl.LEQUAL,An.ReadWrite,Y.depthRangeFor3D);if(K===1&&!J.paint.get(\"fill-extrusion-pattern\").constantOr(1)){var ne=Y.colorModeForRenderPass();ze(Y,D,J,O,de,Vi.disabled,ne)}else ze(Y,D,J,O,de,Vi.disabled,wt.disabled),ze(Y,D,J,O,de,Y.stencilModeFor3D(),Y.colorModeForRenderPass())}}function ze(Y,D,J,O,K,de,ne){for(var we=Y.context,Oe=we.gl,ht=J.paint.get(\"fill-extrusion-pattern\"),Yt=ht.constantOr(1),cr=J.getCrossfadeParameters(),Nt=J.paint.get(\"fill-extrusion-opacity\"),Ne=0,$e=O;Ne<$e.length;Ne+=1){var st=$e[Ne],It=D.getTile(st),Bt=It.getBucket(J);if(Bt){var Ft=Bt.programConfigurations.get(J.id),Ut=Y.useProgram(Yt?\"fillExtrusionPattern\":\"fillExtrusion\",Ft);Yt&&(Y.context.activeTexture.set(Oe.TEXTURE0),It.imageAtlasTexture.bind(Oe.LINEAR,Oe.CLAMP_TO_EDGE),Ft.updatePaintBuffers(cr));var zt=ht.constantOr(null);if(zt&&It.imageAtlas){var _t=It.imageAtlas,Dt=_t.patternPositions[zt.to.toString()],vt=_t.patternPositions[zt.from.toString()];Dt&&vt&&Ft.setConstantPatternPositions(Dt,vt)}var lr=Y.translatePosMatrix(st.posMatrix,It,J.paint.get(\"fill-extrusion-translate\"),J.paint.get(\"fill-extrusion-translate-anchor\")),_r=J.paint.get(\"fill-extrusion-vertical-gradient\"),Br=Yt?Zu(lr,Y,_r,Nt,st,cr,It):ah(lr,Y,_r,Nt);Ut.draw(we,we.gl.TRIANGLES,K,de,ne,wr.backCCW,Br,J.id,Bt.layoutVertexBuffer,Bt.indexBuffer,Bt.segments,J.paint,Y.transform.zoom,Ft)}}}function Qe(Y,D,J,O){if(!(Y.renderPass!==\"offscreen\"&&Y.renderPass!==\"translucent\")){for(var K=Y.context,de=Y.depthModeForSublayer(0,An.ReadOnly),ne=Y.colorModeForRenderPass(),we=Y.renderPass===\"translucent\"?Y.stencilConfigForOverlap(O):[{},O],Oe=we[0],ht=we[1],Yt=0,cr=ht;Yt<cr.length;Yt+=1){var Nt=cr[Yt],Ne=D.getTile(Nt);Ne.needsHillshadePrepare&&Y.renderPass===\"offscreen\"?nt(Y,Ne,J,de,Vi.disabled,ne):Y.renderPass===\"translucent\"&&Xe(Y,Ne,J,de,Oe[Nt.overscaledZ],ne)}K.viewport.set([0,0,Y.width,Y.height])}}function Xe(Y,D,J,O,K,de){var ne=Y.context,we=ne.gl,Oe=D.fbo;if(Oe){var ht=Y.useProgram(\"hillshade\");ne.activeTexture.set(we.TEXTURE0),we.bindTexture(we.TEXTURE_2D,Oe.colorAttachment.get());var Yt=Ra(Y,D,J);ht.draw(ne,we.TRIANGLES,O,K,de,wr.disabled,Yt,J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments)}}function nt(Y,D,J,O,K,de){var ne=Y.context,we=ne.gl,Oe=D.dem;if(Oe&&Oe.data){var ht=Oe.dim,Yt=Oe.stride,cr=Oe.getPixels();if(ne.activeTexture.set(we.TEXTURE1),ne.pixelStoreUnpackPremultiplyAlpha.set(!1),D.demTexture=D.demTexture||Y.getTileTexture(Yt),D.demTexture){var Nt=D.demTexture;Nt.update(cr,{premultiply:!1}),Nt.bind(we.NEAREST,we.CLAMP_TO_EDGE)}else D.demTexture=new i.Texture(ne,cr,we.RGBA,{premultiply:!1}),D.demTexture.bind(we.NEAREST,we.CLAMP_TO_EDGE);ne.activeTexture.set(we.TEXTURE0);var Ne=D.fbo;if(!Ne){var $e=new i.Texture(ne,{width:ht,height:ht,data:null},we.RGBA);$e.bind(we.LINEAR,we.CLAMP_TO_EDGE),Ne=D.fbo=ne.createFramebuffer(ht,ht,!0),Ne.colorAttachment.set($e.texture)}ne.bindFramebuffer.set(Ne.framebuffer),ne.viewport.set([0,0,ht,ht]),Y.useProgram(\"hillshadePrepare\").draw(ne,we.TRIANGLES,O,K,de,wr.disabled,La(D.tileID,Oe),J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments),D.needsHillshadePrepare=!1}}function qt(Y,D,J,O){if(Y.renderPass===\"translucent\"&&J.paint.get(\"raster-opacity\")!==0&&O.length)for(var K=Y.context,de=K.gl,ne=D.getSource(),we=Y.useProgram(\"raster\"),Oe=Y.colorModeForRenderPass(),ht=ne instanceof Ce?[{},O]:Y.stencilConfigForOverlap(O),Yt=ht[0],cr=ht[1],Nt=cr[cr.length-1].overscaledZ,Ne=!Y.options.moving,$e=0,st=cr;$e<st.length;$e+=1){var It=st[$e],Bt=Y.depthModeForSublayer(It.overscaledZ-Nt,J.paint.get(\"raster-opacity\")===1?An.ReadWrite:An.ReadOnly,de.LESS),Ft=D.getTile(It),Ut=Y.transform.calculatePosMatrix(It.toUnwrapped(),Ne);Ft.registerFadeDuration(J.paint.get(\"raster-fade-duration\"));var zt=D.findLoadedParent(It,0),_t=Wt(Ft,zt,D,J,Y.transform),Dt=void 0,vt=void 0,lr=J.paint.get(\"raster-resampling\")===\"nearest\"?de.NEAREST:de.LINEAR;K.activeTexture.set(de.TEXTURE0),Ft.texture.bind(lr,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST),K.activeTexture.set(de.TEXTURE1),zt?(zt.texture.bind(lr,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST),Dt=Math.pow(2,zt.tileID.overscaledZ-Ft.tileID.overscaledZ),vt=[Ft.tileID.canonical.x*Dt%1,Ft.tileID.canonical.y*Dt%1]):Ft.texture.bind(lr,de.CLAMP_TO_EDGE,de.LINEAR_MIPMAP_NEAREST);var _r=Ls(Ut,vt||[0,0],Dt||1,_t,J);ne instanceof Ce?we.draw(K,de.TRIANGLES,Bt,Vi.disabled,Oe,wr.disabled,_r,J.id,ne.boundsBuffer,Y.quadTriangleIndexBuffer,ne.boundsSegments):we.draw(K,de.TRIANGLES,Bt,Yt[It.overscaledZ],Oe,wr.disabled,_r,J.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments)}}function Wt(Y,D,J,O,K){var de=O.paint.get(\"raster-fade-duration\");if(de>0){var ne=i.browser.now(),we=(ne-Y.timeAdded)/de,Oe=D?(ne-D.timeAdded)/de:-1,ht=J.getSource(),Yt=K.coveringZoomLevel({tileSize:ht.tileSize,roundZoom:ht.roundZoom}),cr=!D||Math.abs(D.tileID.overscaledZ-Yt)>Math.abs(Y.tileID.overscaledZ-Yt),Nt=cr&&Y.refreshedUponExpiration?1:i.clamp(cr?we:1-Oe,0,1);return Y.refreshedUponExpiration&&we>=1&&(Y.refreshedUponExpiration=!1),D?{opacity:1,mix:1-Nt}:{opacity:Nt,mix:0}}else return{opacity:1,mix:0}}function nr(Y,D,J){var O=J.paint.get(\"background-color\"),K=J.paint.get(\"background-opacity\");if(K!==0){var de=Y.context,ne=de.gl,we=Y.transform,Oe=we.tileSize,ht=J.paint.get(\"background-pattern\");if(!Y.isPatternMissing(ht)){var Yt=!ht&&O.a===1&&K===1&&Y.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(Y.renderPass===Yt){var cr=Vi.disabled,Nt=Y.depthModeForSublayer(0,Yt===\"opaque\"?An.ReadWrite:An.ReadOnly),Ne=Y.colorModeForRenderPass(),$e=Y.useProgram(ht?\"backgroundPattern\":\"background\"),st=we.coveringTiles({tileSize:Oe});ht&&(de.activeTexture.set(ne.TEXTURE0),Y.imageManager.bind(Y.context));for(var It=J.getCrossfadeParameters(),Bt=0,Ft=st;Bt<Ft.length;Bt+=1){var Ut=Ft[Bt],zt=Y.transform.calculatePosMatrix(Ut.toUnwrapped()),_t=ht?Va(zt,K,Y,ht,{tileID:Ut,tileSize:Oe},It):Ac(zt,K,O);$e.draw(de,ne.TRIANGLES,Nt,cr,Ne,wr.disabled,_t,J.id,Y.tileExtentBuffer,Y.quadTriangleIndexBuffer,Y.tileExtentSegments)}}}}}var fr=new i.Color(1,0,0,1),br=new i.Color(0,1,0,1),Fr=new i.Color(0,0,1,1),Hr=new i.Color(1,0,1,1),ri=new i.Color(0,1,1,1);function Ci(Y){var D=Y.transform.padding,J=3;sn(Y,Y.transform.height-(D.top||0),J,fr),sn(Y,D.bottom||0,J,br),Ln(Y,D.left||0,J,Fr),Ln(Y,Y.transform.width-(D.right||0),J,Hr);var O=Y.transform.centerPoint;cn(Y,O.x,Y.transform.height-O.y,ri)}function cn(Y,D,J,O){var K=20,de=2;Kn(Y,D-de/2,J-K/2,de,K,O),Kn(Y,D-K/2,J-de/2,K,de,O)}function sn(Y,D,J,O){Kn(Y,0,D+J/2,Y.transform.width,J,O)}function Ln(Y,D,J,O){Kn(Y,D-J/2,0,J,Y.transform.height,O)}function Kn(Y,D,J,O,K,de){var ne=Y.context,we=ne.gl;we.enable(we.SCISSOR_TEST),we.scissor(D*i.browser.devicePixelRatio,J*i.browser.devicePixelRatio,O*i.browser.devicePixelRatio,K*i.browser.devicePixelRatio),ne.clear({color:de}),we.disable(we.SCISSOR_TEST)}function Aa(Y,D,J){for(var O=0;O<J.length;O++)fa(Y,D,J[O])}function fa(Y,D,J){var O=Y.context,K=O.gl,de=J.posMatrix,ne=Y.useProgram(\"debug\"),we=An.disabled,Oe=Vi.disabled,ht=Y.colorModeForRenderPass(),Yt=\"$debug\";O.activeTexture.set(K.TEXTURE0),Y.emptyTexture.bind(K.LINEAR,K.CLAMP_TO_EDGE),ne.draw(O,K.LINE_STRIP,we,Oe,ht,wr.disabled,dn(de,i.Color.red),Yt,Y.debugBuffer,Y.tileBorderIndexBuffer,Y.debugSegments);var cr=D.getTileByID(J.key).latestRawTileData,Nt=cr&&cr.byteLength||0,Ne=Math.floor(Nt/1024),$e=D.getTile(J).tileSize,st=512/Math.min($e,512)*(J.overscaledZ/Y.transform.zoom)*.5,It=J.canonical.toString();J.overscaledZ!==J.canonical.z&&(It+=\" => \"+J.overscaledZ);var Bt=It+\" \"+Ne+\"kb\";$a(Y,Bt),ne.draw(O,K.TRIANGLES,we,Oe,wt.alphaBlended,wr.disabled,dn(de,i.Color.transparent,st),Yt,Y.debugBuffer,Y.quadTriangleIndexBuffer,Y.debugSegments)}function $a(Y,D){Y.initDebugOverlayCanvas();var J=Y.debugOverlayCanvas,O=Y.context.gl,K=Y.debugOverlayCanvas.getContext(\"2d\");K.clearRect(0,0,J.width,J.height),K.shadowColor=\"white\",K.shadowBlur=2,K.lineWidth=1.5,K.strokeStyle=\"white\",K.textBaseline=\"top\",K.font=\"bold 36px Open Sans, sans-serif\",K.fillText(D,5,5),K.strokeText(D,5,5),Y.debugOverlayTexture.update(J),Y.debugOverlayTexture.bind(O.LINEAR,O.CLAMP_TO_EDGE)}function Co(Y,D,J){var O=Y.context,K=J.implementation;if(Y.renderPass===\"offscreen\"){var de=K.prerender;de&&(Y.setCustomLayerDefaults(),O.setColorMode(Y.colorModeForRenderPass()),de.call(K,O.gl,Y.transform.customLayerMatrix()),O.setDirty(),Y.setBaseState())}else if(Y.renderPass===\"translucent\"){Y.setCustomLayerDefaults(),O.setColorMode(Y.colorModeForRenderPass()),O.setStencilMode(Vi.disabled);var ne=K.renderingMode===\"3d\"?new An(Y.context.gl.LEQUAL,An.ReadWrite,Y.depthRangeFor3D):Y.depthModeForSublayer(0,An.ReadOnly);O.setDepthMode(ne),K.render(O.gl,Y.transform.customLayerMatrix()),O.setDirty(),Y.setBaseState(),O.bindFramebuffer.set(null)}}var Qa={symbol:w,circle:ut,heatmap:Mt,line:Cr,fill:ve,\"fill-extrusion\":Ie,hillshade:Qe,raster:qt,background:nr,debug:Aa,custom:Co},mo=function(D,J){this.context=new Xr(D),this.transform=J,this._tileTextures={},this.setup(),this.numSublayers=ti.maxUnderzooming+ti.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Vf,this.gpuTimers={}};mo.prototype.resize=function(D,J){if(this.width=D*i.browser.devicePixelRatio,this.height=J*i.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var O=0,K=this.style._order;O<K.length;O+=1){var de=K[O];this.style._layers[de].resize()}},mo.prototype.setup=function(){var D=this.context,J=new i.StructArrayLayout2i4;J.emplaceBack(0,0),J.emplaceBack(i.EXTENT,0),J.emplaceBack(0,i.EXTENT),J.emplaceBack(i.EXTENT,i.EXTENT),this.tileExtentBuffer=D.createVertexBuffer(J,oc.members),this.tileExtentSegments=i.SegmentVector.simpleSegment(0,0,4,2);var O=new i.StructArrayLayout2i4;O.emplaceBack(0,0),O.emplaceBack(i.EXTENT,0),O.emplaceBack(0,i.EXTENT),O.emplaceBack(i.EXTENT,i.EXTENT),this.debugBuffer=D.createVertexBuffer(O,oc.members),this.debugSegments=i.SegmentVector.simpleSegment(0,0,4,5);var K=new i.StructArrayLayout4i8;K.emplaceBack(0,0,0,0),K.emplaceBack(i.EXTENT,0,i.EXTENT,0),K.emplaceBack(0,i.EXTENT,0,i.EXTENT),K.emplaceBack(i.EXTENT,i.EXTENT,i.EXTENT,i.EXTENT),this.rasterBoundsBuffer=D.createVertexBuffer(K,Ee.members),this.rasterBoundsSegments=i.SegmentVector.simpleSegment(0,0,4,2);var de=new i.StructArrayLayout2i4;de.emplaceBack(0,0),de.emplaceBack(1,0),de.emplaceBack(0,1),de.emplaceBack(1,1),this.viewportBuffer=D.createVertexBuffer(de,oc.members),this.viewportSegments=i.SegmentVector.simpleSegment(0,0,4,2);var ne=new i.StructArrayLayout1ui2;ne.emplaceBack(0),ne.emplaceBack(1),ne.emplaceBack(3),ne.emplaceBack(2),ne.emplaceBack(0),this.tileBorderIndexBuffer=D.createIndexBuffer(ne);var we=new i.StructArrayLayout3ui6;we.emplaceBack(0,1,2),we.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=D.createIndexBuffer(we),this.emptyTexture=new i.Texture(D,{width:1,height:1,data:new Uint8Array([0,0,0,0])},D.gl.RGBA);var Oe=this.context.gl;this.stencilClearMode=new Vi({func:Oe.ALWAYS,mask:0},0,255,Oe.ZERO,Oe.ZERO,Oe.ZERO)},mo.prototype.clearStencil=function(){var D=this.context,J=D.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var O=i.create();i.ortho(O,0,this.width,this.height,0,0,1),i.scale(O,O,[J.drawingBufferWidth,J.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(D,J.TRIANGLES,An.disabled,this.stencilClearMode,wt.disabled,wr.disabled,On(O),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},mo.prototype._renderTileClippingMasks=function(D,J){if(!(this.currentStencilSource===D.source||!D.isTileClipped()||!J||!J.length)){this.currentStencilSource=D.source;var O=this.context,K=O.gl;this.nextStencilID+J.length>256&&this.clearStencil(),O.setColorMode(wt.disabled),O.setDepthMode(An.disabled);var de=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var ne=0,we=J;ne<we.length;ne+=1){var Oe=we[ne],ht=this._tileClippingMaskIDs[Oe.key]=this.nextStencilID++;de.draw(O,K.TRIANGLES,An.disabled,new Vi({func:K.ALWAYS,mask:0},ht,255,K.KEEP,K.KEEP,K.REPLACE),wt.disabled,wr.disabled,On(Oe.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},mo.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var D=this.nextStencilID++,J=this.context.gl;return new Vi({func:J.NOTEQUAL,mask:255},D,255,J.KEEP,J.KEEP,J.REPLACE)},mo.prototype.stencilModeForClipping=function(D){var J=this.context.gl;return new Vi({func:J.EQUAL,mask:255},this._tileClippingMaskIDs[D.key],0,J.KEEP,J.KEEP,J.REPLACE)},mo.prototype.stencilConfigForOverlap=function(D){var J,O=this.context.gl,K=D.sort(function(ht,Yt){return Yt.overscaledZ-ht.overscaledZ}),de=K[K.length-1].overscaledZ,ne=K[0].overscaledZ-de+1;if(ne>1){this.currentStencilSource=void 0,this.nextStencilID+ne>256&&this.clearStencil();for(var we={},Oe=0;Oe<ne;Oe++)we[Oe+de]=new Vi({func:O.GEQUAL,mask:255},Oe+this.nextStencilID,255,O.KEEP,O.KEEP,O.REPLACE);return this.nextStencilID+=ne,[we,K]}return[(J={},J[de]=Vi.disabled,J),K]},mo.prototype.colorModeForRenderPass=function(){var D=this.context.gl;if(this._showOverdrawInspector){var J=8,O=1/J;return new wt([D.CONSTANT_COLOR,D.ONE],new i.Color(O,O,O,0),[!0,!0,!0,!0])}else return this.renderPass===\"opaque\"?wt.unblended:wt.alphaBlended},mo.prototype.depthModeForSublayer=function(D,J,O){if(!this.opaquePassEnabledForLayer())return An.disabled;var K=1-((1+this.currentLayer)*this.numSublayers+D)*this.depthEpsilon;return new An(O||this.context.gl.LEQUAL,J,[K,K])},mo.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},mo.prototype.render=function(D,J){var O=this;this.style=D,this.options=J,this.lineAtlas=D.lineAtlas,this.imageManager=D.imageManager,this.glyphManager=D.glyphManager,this.symbolFadeChange=D.placement.symbolFadeChange(i.browser.now()),this.imageManager.beginFrame();var K=this.style._order,de=this.style.sourceCaches;for(var ne in de){var we=de[ne];we.used&&we.prepare(this.context)}var Oe={},ht={},Yt={};for(var cr in de){var Nt=de[cr];Oe[cr]=Nt.getVisibleCoordinates(),ht[cr]=Oe[cr].slice().reverse(),Yt[cr]=Nt.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var Ne=0;Ne<K.length;Ne++){var $e=K[Ne];if(this.style._layers[$e].is3D()){this.opaquePassCutoff=Ne;break}}this.renderPass=\"offscreen\";for(var st=0,It=K;st<It.length;st+=1){var Bt=It[st],Ft=this.style._layers[Bt];if(!(!Ft.hasOffscreenPass()||Ft.isHidden(this.transform.zoom))){var Ut=ht[Ft.source];Ft.type!==\"custom\"&&!Ut.length||this.renderLayer(this,de[Ft.source],Ft,Ut)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:J.showOverdrawInspector?i.Color.black:i.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=J.showOverdrawInspector,this.depthRangeFor3D=[0,1-(D._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=K.length-1;this.currentLayer>=0;this.currentLayer--){var zt=this.style._layers[K[this.currentLayer]],_t=de[zt.source],Dt=Oe[zt.source];this._renderTileClippingMasks(zt,Dt),this.renderLayer(this,_t,zt,Dt)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<K.length;this.currentLayer++){var vt=this.style._layers[K[this.currentLayer]],lr=de[vt.source],_r=(vt.type===\"symbol\"?Yt:ht)[vt.source];this._renderTileClippingMasks(vt,Oe[vt.source]),this.renderLayer(this,lr,vt,_r)}if(this.options.showTileBoundaries){var Br,Di,ci=i.values(this.style._layers);ci.forEach(function(bi){bi.source&&!bi.isHidden(O.transform.zoom)&&(bi.source!==(Di&&Di.id)&&(Di=O.style.sourceCaches[bi.source]),(!Br||Br.getSource().maxzoom<Di.getSource().maxzoom)&&(Br=Di))}),Br&&Qa.debug(this,Br,Br.getVisibleCoordinates())}this.options.showPadding&&Ci(this),this.context.setDefault()},mo.prototype.renderLayer=function(D,J,O,K){O.isHidden(this.transform.zoom)||O.type!==\"background\"&&O.type!==\"custom\"&&!K.length||(this.id=O.id,this.gpuTimingStart(O),Qa[O.type](D,J,O,K,this.style.placement.variableOffsets),this.gpuTimingEnd())},mo.prototype.gpuTimingStart=function(D){if(this.options.gpuTiming){var J=this.context.extTimerQuery,O=this.gpuTimers[D.id];O||(O=this.gpuTimers[D.id]={calls:0,cpuTime:0,query:J.createQueryEXT()}),O.calls++,J.beginQueryEXT(J.TIME_ELAPSED_EXT,O.query)}},mo.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var D=this.context.extTimerQuery;D.endQueryEXT(D.TIME_ELAPSED_EXT)}},mo.prototype.collectGpuTimers=function(){var D=this.gpuTimers;return this.gpuTimers={},D},mo.prototype.queryGpuTimers=function(D){var J={};for(var O in D){var K=D[O],de=this.context.extTimerQuery,ne=de.getQueryObjectEXT(K.query,de.QUERY_RESULT_EXT)/(1e3*1e3);de.deleteQueryEXT(K.query),J[O]=ne}return J},mo.prototype.translatePosMatrix=function(D,J,O,K,de){if(!O[0]&&!O[1])return D;var ne=de?K===\"map\"?this.transform.angle:0:K===\"viewport\"?-this.transform.angle:0;if(ne){var we=Math.sin(ne),Oe=Math.cos(ne);O=[O[0]*Oe-O[1]*we,O[0]*we+O[1]*Oe]}var ht=[de?O[0]:As(J,O[0],this.transform.zoom),de?O[1]:As(J,O[1],this.transform.zoom),0],Yt=new Float32Array(16);return i.translate(Yt,D,ht),Yt},mo.prototype.saveTileTexture=function(D){var J=this._tileTextures[D.size[0]];J?J.push(D):this._tileTextures[D.size[0]]=[D]},mo.prototype.getTileTexture=function(D){var J=this._tileTextures[D];return J&&J.length>0?J.pop():null},mo.prototype.isPatternMissing=function(D){if(!D)return!1;if(!D.from||!D.to)return!0;var J=this.imageManager.getPattern(D.from.toString()),O=this.imageManager.getPattern(D.to.toString());return!J||!O},mo.prototype.useProgram=function(D,J){this.cache=this.cache||{};var O=\"\"+D+(J?J.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[O]||(this.cache[O]=new Rf(this.context,D,wf[D],J,oo[D],this._showOverdrawInspector)),this.cache[O]},mo.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},mo.prototype.setBaseState=function(){var D=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(D.FUNC_ADD)},mo.prototype.initDebugOverlayCanvas=function(){if(this.debugOverlayCanvas==null){this.debugOverlayCanvas=i.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var D=this.context.gl;this.debugOverlayTexture=new i.Texture(this.context,this.debugOverlayCanvas,D.RGBA)}},mo.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var Bo=function(D,J){this.points=D,this.planes=J};Bo.fromInvProjectionMatrix=function(D,J,O){var K=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]],de=Math.pow(2,O),ne=K.map(function(ht){return i.transformMat4([],ht,D)}).map(function(ht){return i.scale$1([],ht,1/ht[3]/J*de)}),we=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]],Oe=we.map(function(ht){var Yt=i.sub([],ne[ht[0]],ne[ht[1]]),cr=i.sub([],ne[ht[2]],ne[ht[1]]),Nt=i.normalize([],i.cross([],Yt,cr)),Ne=-i.dot(Nt,ne[ht[1]]);return Nt.concat(Ne)});return new Bo(ne,Oe)};var Ps=function(D,J){this.min=D,this.max=J,this.center=i.scale$2([],i.add([],this.min,this.max),.5)};Ps.prototype.quadrant=function(D){for(var J=[D%2===0,D<2],O=i.clone$2(this.min),K=i.clone$2(this.max),de=0;de<J.length;de++)O[de]=J[de]?this.min[de]:this.center[de],K[de]=J[de]?this.center[de]:this.max[de];return K[2]=this.max[2],new Ps(O,K)},Ps.prototype.distanceX=function(D){var J=Math.max(Math.min(this.max[0],D[0]),this.min[0]);return J-D[0]},Ps.prototype.distanceY=function(D){var J=Math.max(Math.min(this.max[1],D[1]),this.min[1]);return J-D[1]},Ps.prototype.intersects=function(D){for(var J=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],O=!0,K=0;K<D.planes.length;K++){for(var de=D.planes[K],ne=0,we=0;we<J.length;we++)ne+=i.dot$1(de,J[we])>=0;if(ne===0)return 0;ne!==J.length&&(O=!1)}if(O)return 2;for(var Oe=0;Oe<3;Oe++){for(var ht=Number.MAX_VALUE,Yt=-Number.MAX_VALUE,cr=0;cr<D.points.length;cr++){var Nt=D.points[cr][Oe]-this.min[Oe];ht=Math.min(ht,Nt),Yt=Math.max(Yt,Nt)}if(Yt<0||ht>this.max[Oe]-this.min[Oe])return 0}return 1};var Ts=function(D,J,O,K){if(D===void 0&&(D=0),J===void 0&&(J=0),O===void 0&&(O=0),K===void 0&&(K=0),isNaN(D)||D<0||isNaN(J)||J<0||isNaN(O)||O<0||isNaN(K)||K<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=D,this.bottom=J,this.left=O,this.right=K};Ts.prototype.interpolate=function(D,J,O){return J.top!=null&&D.top!=null&&(this.top=i.number(D.top,J.top,O)),J.bottom!=null&&D.bottom!=null&&(this.bottom=i.number(D.bottom,J.bottom,O)),J.left!=null&&D.left!=null&&(this.left=i.number(D.left,J.left,O)),J.right!=null&&D.right!=null&&(this.right=i.number(D.right,J.right,O)),this},Ts.prototype.getCenter=function(D,J){var O=i.clamp((this.left+D-this.right)/2,0,D),K=i.clamp((this.top+J-this.bottom)/2,0,J);return new i.Point(O,K)},Ts.prototype.equals=function(D){return this.top===D.top&&this.bottom===D.bottom&&this.left===D.left&&this.right===D.right},Ts.prototype.clone=function(){return new Ts(this.top,this.bottom,this.left,this.right)},Ts.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var wo=function(D,J,O,K,de){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=de===void 0?!0:de,this._minZoom=D||0,this._maxZoom=J||22,this._minPitch=O==null?0:O,this._maxPitch=K==null?60:K,this.setMaxBounds(),this.width=0,this.height=0,this._center=new i.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Ts,this._posMatrixCache={},this._alignedPosMatrixCache={}},To={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};wo.prototype.clone=function(){var D=new wo(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return D.tileSize=this.tileSize,D.latRange=this.latRange,D.width=this.width,D.height=this.height,D._center=this._center,D.zoom=this.zoom,D.angle=this.angle,D._fov=this._fov,D._pitch=this._pitch,D._unmodified=this._unmodified,D._edgeInsets=this._edgeInsets.clone(),D._calcMatrices(),D},To.minZoom.get=function(){return this._minZoom},To.minZoom.set=function(Y){this._minZoom!==Y&&(this._minZoom=Y,this.zoom=Math.max(this.zoom,Y))},To.maxZoom.get=function(){return this._maxZoom},To.maxZoom.set=function(Y){this._maxZoom!==Y&&(this._maxZoom=Y,this.zoom=Math.min(this.zoom,Y))},To.minPitch.get=function(){return this._minPitch},To.minPitch.set=function(Y){this._minPitch!==Y&&(this._minPitch=Y,this.pitch=Math.max(this.pitch,Y))},To.maxPitch.get=function(){return this._maxPitch},To.maxPitch.set=function(Y){this._maxPitch!==Y&&(this._maxPitch=Y,this.pitch=Math.min(this.pitch,Y))},To.renderWorldCopies.get=function(){return this._renderWorldCopies},To.renderWorldCopies.set=function(Y){Y===void 0?Y=!0:Y===null&&(Y=!1),this._renderWorldCopies=Y},To.worldSize.get=function(){return this.tileSize*this.scale},To.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},To.size.get=function(){return new i.Point(this.width,this.height)},To.bearing.get=function(){return-this.angle/Math.PI*180},To.bearing.set=function(Y){var D=-i.wrap(Y,-180,180)*Math.PI/180;this.angle!==D&&(this._unmodified=!1,this.angle=D,this._calcMatrices(),this.rotationMatrix=i.create$2(),i.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},To.pitch.get=function(){return this._pitch/Math.PI*180},To.pitch.set=function(Y){var D=i.clamp(Y,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==D&&(this._unmodified=!1,this._pitch=D,this._calcMatrices())},To.fov.get=function(){return this._fov/Math.PI*180},To.fov.set=function(Y){Y=Math.max(.01,Math.min(60,Y)),this._fov!==Y&&(this._unmodified=!1,this._fov=Y/180*Math.PI,this._calcMatrices())},To.zoom.get=function(){return this._zoom},To.zoom.set=function(Y){var D=Math.min(Math.max(Y,this.minZoom),this.maxZoom);this._zoom!==D&&(this._unmodified=!1,this._zoom=D,this.scale=this.zoomScale(D),this.tileZoom=Math.floor(D),this.zoomFraction=D-this.tileZoom,this._constrain(),this._calcMatrices())},To.center.get=function(){return this._center},To.center.set=function(Y){Y.lat===this._center.lat&&Y.lng===this._center.lng||(this._unmodified=!1,this._center=Y,this._constrain(),this._calcMatrices())},To.padding.get=function(){return this._edgeInsets.toJSON()},To.padding.set=function(Y){this._edgeInsets.equals(Y)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,Y,1),this._calcMatrices())},To.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},wo.prototype.isPaddingEqual=function(D){return this._edgeInsets.equals(D)},wo.prototype.interpolatePadding=function(D,J,O){this._unmodified=!1,this._edgeInsets.interpolate(D,J,O),this._constrain(),this._calcMatrices()},wo.prototype.coveringZoomLevel=function(D){var J=(D.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/D.tileSize));return Math.max(0,J)},wo.prototype.getVisibleUnwrappedCoordinates=function(D){var J=[new i.UnwrappedTileID(0,D)];if(this._renderWorldCopies)for(var O=this.pointCoordinate(new i.Point(0,0)),K=this.pointCoordinate(new i.Point(this.width,0)),de=this.pointCoordinate(new i.Point(this.width,this.height)),ne=this.pointCoordinate(new i.Point(0,this.height)),we=Math.floor(Math.min(O.x,K.x,de.x,ne.x)),Oe=Math.floor(Math.max(O.x,K.x,de.x,ne.x)),ht=1,Yt=we-ht;Yt<=Oe+ht;Yt++)Yt!==0&&J.push(new i.UnwrappedTileID(Yt,D));return J},wo.prototype.coveringTiles=function(D){var J=this.coveringZoomLevel(D),O=J;if(D.minzoom!==void 0&&J<D.minzoom)return[];D.maxzoom!==void 0&&J>D.maxzoom&&(J=D.maxzoom);var K=i.MercatorCoordinate.fromLngLat(this.center),de=Math.pow(2,J),ne=[de*K.x,de*K.y,0],we=Bo.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,J),Oe=D.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(Oe=J);var ht=3,Yt=function(ci){return{aabb:new Ps([ci*de,0,0],[(ci+1)*de,de,0]),zoom:0,x:0,y:0,wrap:ci,fullyVisible:!1}},cr=[],Nt=[],Ne=J,$e=D.reparseOverscaled?O:J;if(this._renderWorldCopies)for(var st=1;st<=3;st++)cr.push(Yt(-st)),cr.push(Yt(st));for(cr.push(Yt(0));cr.length>0;){var It=cr.pop(),Bt=It.x,Ft=It.y,Ut=It.fullyVisible;if(!Ut){var zt=It.aabb.intersects(we);if(zt===0)continue;Ut=zt===2}var _t=It.aabb.distanceX(ne),Dt=It.aabb.distanceY(ne),vt=Math.max(Math.abs(_t),Math.abs(Dt)),lr=ht+(1<<Ne-It.zoom)-2;if(It.zoom===Ne||vt>lr&&It.zoom>=Oe){Nt.push({tileID:new i.OverscaledTileID(It.zoom===Ne?$e:It.zoom,It.wrap,It.zoom,Bt,Ft),distanceSq:i.sqrLen([ne[0]-.5-Bt,ne[1]-.5-Ft])});continue}for(var _r=0;_r<4;_r++){var Br=(Bt<<1)+_r%2,Di=(Ft<<1)+(_r>>1);cr.push({aabb:It.aabb.quadrant(_r),zoom:It.zoom+1,x:Br,y:Di,wrap:It.wrap,fullyVisible:Ut})}}return Nt.sort(function(ci,bi){return ci.distanceSq-bi.distanceSq}).map(function(ci){return ci.tileID})},wo.prototype.resize=function(D,J){this.width=D,this.height=J,this.pixelsToGLUnits=[2/D,-2/J],this._constrain(),this._calcMatrices()},To.unmodified.get=function(){return this._unmodified},wo.prototype.zoomScale=function(D){return Math.pow(2,D)},wo.prototype.scaleZoom=function(D){return Math.log(D)/Math.LN2},wo.prototype.project=function(D){var J=i.clamp(D.lat,-this.maxValidLatitude,this.maxValidLatitude);return new i.Point(i.mercatorXfromLng(D.lng)*this.worldSize,i.mercatorYfromLat(J)*this.worldSize)},wo.prototype.unproject=function(D){return new i.MercatorCoordinate(D.x/this.worldSize,D.y/this.worldSize).toLngLat()},To.point.get=function(){return this.project(this.center)},wo.prototype.setLocationAtPoint=function(D,J){var O=this.pointCoordinate(J),K=this.pointCoordinate(this.centerPoint),de=this.locationCoordinate(D),ne=new i.MercatorCoordinate(de.x-(O.x-K.x),de.y-(O.y-K.y));this.center=this.coordinateLocation(ne),this._renderWorldCopies&&(this.center=this.center.wrap())},wo.prototype.locationPoint=function(D){return this.coordinatePoint(this.locationCoordinate(D))},wo.prototype.pointLocation=function(D){return this.coordinateLocation(this.pointCoordinate(D))},wo.prototype.locationCoordinate=function(D){return i.MercatorCoordinate.fromLngLat(D)},wo.prototype.coordinateLocation=function(D){return D.toLngLat()},wo.prototype.pointCoordinate=function(D){var J=0,O=[D.x,D.y,0,1],K=[D.x,D.y,1,1];i.transformMat4(O,O,this.pixelMatrixInverse),i.transformMat4(K,K,this.pixelMatrixInverse);var de=O[3],ne=K[3],we=O[0]/de,Oe=K[0]/ne,ht=O[1]/de,Yt=K[1]/ne,cr=O[2]/de,Nt=K[2]/ne,Ne=cr===Nt?0:(J-cr)/(Nt-cr);return new i.MercatorCoordinate(i.number(we,Oe,Ne)/this.worldSize,i.number(ht,Yt,Ne)/this.worldSize)},wo.prototype.coordinatePoint=function(D){var J=[D.x*this.worldSize,D.y*this.worldSize,0,1];return i.transformMat4(J,J,this.pixelMatrix),new i.Point(J[0]/J[3],J[1]/J[3])},wo.prototype.getBounds=function(){return new i.LngLatBounds().extend(this.pointLocation(new i.Point(0,0))).extend(this.pointLocation(new i.Point(this.width,0))).extend(this.pointLocation(new i.Point(this.width,this.height))).extend(this.pointLocation(new i.Point(0,this.height)))},wo.prototype.getMaxBounds=function(){return!this.latRange||this.latRange.length!==2||!this.lngRange||this.lngRange.length!==2?null:new i.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]])},wo.prototype.setMaxBounds=function(D){D?(this.lngRange=[D.getWest(),D.getEast()],this.latRange=[D.getSouth(),D.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},wo.prototype.calculatePosMatrix=function(D,J){J===void 0&&(J=!1);var O=D.key,K=J?this._alignedPosMatrixCache:this._posMatrixCache;if(K[O])return K[O];var de=D.canonical,ne=this.worldSize/this.zoomScale(de.z),we=de.x+Math.pow(2,de.z)*D.wrap,Oe=i.identity(new Float64Array(16));return i.translate(Oe,Oe,[we*ne,de.y*ne,0]),i.scale(Oe,Oe,[ne/i.EXTENT,ne/i.EXTENT,1]),i.multiply(Oe,J?this.alignedProjMatrix:this.projMatrix,Oe),K[O]=new Float32Array(Oe),K[O]},wo.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},wo.prototype._constrain=function(){if(!(!this.center||!this.width||!this.height||this._constraining)){this._constraining=!0;var D=-90,J=90,O=-180,K=180,de,ne,we,Oe,ht=this.size,Yt=this._unmodified;if(this.latRange){var cr=this.latRange;D=i.mercatorYfromLat(cr[1])*this.worldSize,J=i.mercatorYfromLat(cr[0])*this.worldSize,de=J-D<ht.y?ht.y/(J-D):0}if(this.lngRange){var Nt=this.lngRange;O=i.mercatorXfromLng(Nt[0])*this.worldSize,K=i.mercatorXfromLng(Nt[1])*this.worldSize,ne=K-O<ht.x?ht.x/(K-O):0}var Ne=this.point,$e=Math.max(ne||0,de||0);if($e){this.center=this.unproject(new i.Point(ne?(K+O)/2:Ne.x,de?(J+D)/2:Ne.y)),this.zoom+=this.scaleZoom($e),this._unmodified=Yt,this._constraining=!1;return}if(this.latRange){var st=Ne.y,It=ht.y/2;st-It<D&&(Oe=D+It),st+It>J&&(Oe=J-It)}if(this.lngRange){var Bt=Ne.x,Ft=ht.x/2;Bt-Ft<O&&(we=O+Ft),Bt+Ft>K&&(we=K-Ft)}(we!==void 0||Oe!==void 0)&&(this.center=this.unproject(new i.Point(we!==void 0?we:Ne.x,Oe!==void 0?Oe:Ne.y))),this._unmodified=Yt,this._constraining=!1}},wo.prototype._calcMatrices=function(){if(this.height){var D=this._fov/2,J=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(D)*this.height;var O=Math.PI/2+this._pitch,K=this._fov*(.5+J.y/this.height),de=Math.sin(K)*this.cameraToCenterDistance/Math.sin(i.clamp(Math.PI-O-K,.01,Math.PI-.01)),ne=this.point,we=ne.x,Oe=ne.y,ht=Math.cos(Math.PI/2-this._pitch)*de+this.cameraToCenterDistance,Yt=ht*1.01,cr=this.height/50,Nt=new Float64Array(16);i.perspective(Nt,this._fov,this.width/this.height,cr,Yt),Nt[8]=-J.x*2/this.width,Nt[9]=J.y*2/this.height,i.scale(Nt,Nt,[1,-1,1]),i.translate(Nt,Nt,[0,0,-this.cameraToCenterDistance]),i.rotateX(Nt,Nt,this._pitch),i.rotateZ(Nt,Nt,this.angle),i.translate(Nt,Nt,[-we,-Oe,0]),this.mercatorMatrix=i.scale([],Nt,[this.worldSize,this.worldSize,this.worldSize]),i.scale(Nt,Nt,[1,1,i.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=Nt,this.invProjMatrix=i.invert([],this.projMatrix);var Ne=this.width%2/2,$e=this.height%2/2,st=Math.cos(this.angle),It=Math.sin(this.angle),Bt=we-Math.round(we)+st*Ne+It*$e,Ft=Oe-Math.round(Oe)+st*$e+It*Ne,Ut=new Float64Array(Nt);if(i.translate(Ut,Ut,[Bt>.5?Bt-1:Bt,Ft>.5?Ft-1:Ft,0]),this.alignedProjMatrix=Ut,Nt=i.create(),i.scale(Nt,Nt,[this.width/2,-this.height/2,1]),i.translate(Nt,Nt,[1,-1,0]),this.labelPlaneMatrix=Nt,Nt=i.create(),i.scale(Nt,Nt,[1,-1,1]),i.translate(Nt,Nt,[-1,-1,0]),i.scale(Nt,Nt,[2/this.width,2/this.height,1]),this.glCoordMatrix=Nt,this.pixelMatrix=i.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),Nt=i.invert(new Float64Array(16),this.pixelMatrix),!Nt)throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=Nt,this._posMatrixCache={},this._alignedPosMatrixCache={}}},wo.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var D=this.pointCoordinate(new i.Point(0,0)),J=[D.x*this.worldSize,D.y*this.worldSize,0,1],O=i.transformMat4(J,J,this.pixelMatrix);return O[3]/this.cameraToCenterDistance},wo.prototype.getCameraPoint=function(){var D=this._pitch,J=Math.tan(D)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new i.Point(0,J))},wo.prototype.getCameraQueryGeometry=function(D){var J=this.getCameraPoint();if(D.length===1)return[D[0],J];for(var O=J.x,K=J.y,de=J.x,ne=J.y,we=0,Oe=D;we<Oe.length;we+=1){var ht=Oe[we];O=Math.min(O,ht.x),K=Math.min(K,ht.y),de=Math.max(de,ht.x),ne=Math.max(ne,ht.y)}return[new i.Point(O,K),new i.Point(de,K),new i.Point(de,ne),new i.Point(O,ne),new i.Point(O,K)]},Object.defineProperties(wo.prototype,To);function hl(Y,D){var J=!1,O=null,K=function(){O=null,J&&(Y(),O=setTimeout(K,D),J=!1)};return function(){return J=!0,O||K(),O}}var Nl=function(D){this._hashName=D&&encodeURIComponent(D),i.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=hl(this._updateHashUnthrottled.bind(this),30*1e3/100)};Nl.prototype.addTo=function(D){return this._map=D,i.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Nl.prototype.remove=function(){return i.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Nl.prototype.getHashString=function(D){var J=this._map.getCenter(),O=Math.round(this._map.getZoom()*100)/100,K=Math.ceil((O*Math.LN2+Math.log(512/360/.5))/Math.LN10),de=Math.pow(10,K),ne=Math.round(J.lng*de)/de,we=Math.round(J.lat*de)/de,Oe=this._map.getBearing(),ht=this._map.getPitch(),Yt=\"\";if(D?Yt+=\"/\"+ne+\"/\"+we+\"/\"+O:Yt+=O+\"/\"+we+\"/\"+ne,(Oe||ht)&&(Yt+=\"/\"+Math.round(Oe*10)/10),ht&&(Yt+=\"/\"+Math.round(ht)),this._hashName){var cr=this._hashName,Nt=!1,Ne=i.window.location.hash.slice(1).split(\"&\").map(function($e){var st=$e.split(\"=\")[0];return st===cr?(Nt=!0,st+\"=\"+Yt):$e}).filter(function($e){return $e});return Nt||Ne.push(cr+\"=\"+Yt),\"#\"+Ne.join(\"&\")}return\"#\"+Yt},Nl.prototype._getCurrentHash=function(){var D=this,J=i.window.location.hash.replace(\"#\",\"\");if(this._hashName){var O;return J.split(\"&\").map(function(K){return K.split(\"=\")}).forEach(function(K){K[0]===D._hashName&&(O=K)}),(O&&O[1]||\"\").split(\"/\")}return J.split(\"/\")},Nl.prototype._onHashChange=function(){var D=this._getCurrentHash();if(D.length>=3&&!D.some(function(O){return isNaN(O)})){var J=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(D[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+D[2],+D[1]],zoom:+D[0],bearing:J,pitch:+(D[4]||0)}),!0}return!1},Nl.prototype._updateHashUnthrottled=function(){var D=i.window.location.href.replace(/(#.+)?$/,this.getHashString());try{i.window.history.replaceState(i.window.history.state,null,D)}catch(J){}};var Lu={linearity:.3,easing:i.bezier(0,0,.3,1)},au=i.extend({deceleration:2500,maxSpeed:1400},Lu),Js=i.extend({deceleration:20,maxSpeed:1400},Lu),eu=i.extend({deceleration:1e3,maxSpeed:360},Lu),dc=i.extend({deceleration:1e3,maxSpeed:90},Lu),Al=function(D){this._map=D,this.clear()};Al.prototype.clear=function(){this._inertiaBuffer=[]},Al.prototype.record=function(D){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:i.browser.now(),settings:D})},Al.prototype._drainInertiaBuffer=function(){for(var D=this._inertiaBuffer,J=i.browser.now(),O=160;D.length>0&&J-D[0].time>O;)D.shift()},Al.prototype._onMoveEnd=function(D){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var J={zoom:0,bearing:0,pitch:0,pan:new i.Point(0,0),pinchAround:void 0,around:void 0},O=0,K=this._inertiaBuffer;O<K.length;O+=1){var de=K[O],ne=de.settings;J.zoom+=ne.zoomDelta||0,J.bearing+=ne.bearingDelta||0,J.pitch+=ne.pitchDelta||0,ne.panDelta&&J.pan._add(ne.panDelta),ne.around&&(J.around=ne.around),ne.pinchAround&&(J.pinchAround=ne.pinchAround)}var we=this._inertiaBuffer[this._inertiaBuffer.length-1],Oe=we.time-this._inertiaBuffer[0].time,ht={};if(J.pan.mag()){var Yt=X(J.pan.mag(),Oe,i.extend({},au,D||{}));ht.offset=J.pan.mult(Yt.amount/J.pan.mag()),ht.center=this._map.transform.center,Sl(ht,Yt)}if(J.zoom){var cr=X(J.zoom,Oe,Js);ht.zoom=this._map.transform.zoom+cr.amount,Sl(ht,cr)}if(J.bearing){var Nt=X(J.bearing,Oe,eu);ht.bearing=this._map.transform.bearing+i.clamp(Nt.amount,-179,179),Sl(ht,Nt)}if(J.pitch){var Ne=X(J.pitch,Oe,dc);ht.pitch=this._map.transform.pitch+Ne.amount,Sl(ht,Ne)}if(ht.zoom||ht.bearing){var $e=J.pinchAround===void 0?J.around:J.pinchAround;ht.around=$e?this._map.unproject($e):this._map.getCenter()}return this.clear(),i.extend(ht,{noMoveStart:!0})}};function Sl(Y,D){(!Y.duration||Y.duration<D.duration)&&(Y.duration=D.duration,Y.easing=D.easing)}function X(Y,D,J){var O=J.maxSpeed,K=J.linearity,de=J.deceleration,ne=i.clamp(Y*K/(D/1e3),-O,O),we=Math.abs(ne)/(de*K);return{easing:J.easing,duration:we*1e3,amount:ne*(we/2)}}var se=(function(Y){function D(O,K,de,ne){ne===void 0&&(ne={});var we=o.mousePos(K.getCanvasContainer(),de),Oe=K.unproject(we);Y.call(this,O,i.extend({point:we,lngLat:Oe,originalEvent:de},ne)),this._defaultPrevented=!1,this.target=K}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D})(i.Event),Te=(function(Y){function D(O,K,de){var ne=O===\"touchend\"?de.changedTouches:de.touches,we=o.touchPos(K.getCanvasContainer(),ne),Oe=we.map(function(cr){return K.unproject(cr)}),ht=we.reduce(function(cr,Nt,Ne,$e){return cr.add(Nt.div($e.length))},new i.Point(0,0)),Yt=K.unproject(ht);Y.call(this,O,{points:we,point:ht,lngLats:Oe,lngLat:Yt,originalEvent:de}),this._defaultPrevented=!1}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D})(i.Event),qe=(function(Y){function D(O,K,de){Y.call(this,O,{originalEvent:de}),this._defaultPrevented=!1}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={defaultPrevented:{configurable:!0}};return D.prototype.preventDefault=function(){this._defaultPrevented=!0},J.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(D.prototype,J),D})(i.Event),Ve=function(D,J){this._map=D,this._clickTolerance=J.clickTolerance};Ve.prototype.reset=function(){delete this._mousedownPos},Ve.prototype.wheel=function(D){return this._firePreventable(new qe(D.type,this._map,D))},Ve.prototype.mousedown=function(D,J){return this._mousedownPos=J,this._firePreventable(new se(D.type,this._map,D))},Ve.prototype.mouseup=function(D){this._map.fire(new se(D.type,this._map,D))},Ve.prototype.click=function(D,J){this._mousedownPos&&this._mousedownPos.dist(J)>=this._clickTolerance||this._map.fire(new se(D.type,this._map,D))},Ve.prototype.dblclick=function(D){return this._firePreventable(new se(D.type,this._map,D))},Ve.prototype.mouseover=function(D){this._map.fire(new se(D.type,this._map,D))},Ve.prototype.mouseout=function(D){this._map.fire(new se(D.type,this._map,D))},Ve.prototype.touchstart=function(D){return this._firePreventable(new Te(D.type,this._map,D))},Ve.prototype.touchmove=function(D){this._map.fire(new Te(D.type,this._map,D))},Ve.prototype.touchend=function(D){this._map.fire(new Te(D.type,this._map,D))},Ve.prototype.touchcancel=function(D){this._map.fire(new Te(D.type,this._map,D))},Ve.prototype._firePreventable=function(D){if(this._map.fire(D),D.defaultPrevented)return{}},Ve.prototype.isEnabled=function(){return!0},Ve.prototype.isActive=function(){return!1},Ve.prototype.enable=function(){},Ve.prototype.disable=function(){};var Ye=function(D){this._map=D};Ye.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Ye.prototype.mousemove=function(D){this._map.fire(new se(D.type,this._map,D))},Ye.prototype.mousedown=function(){this._delayContextMenu=!0},Ye.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new se(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Ye.prototype.contextmenu=function(D){this._delayContextMenu?this._contextMenuEvent=D:this._map.fire(new se(D.type,this._map,D)),this._map.listens(\"contextmenu\")&&D.preventDefault()},Ye.prototype.isEnabled=function(){return!0},Ye.prototype.isActive=function(){return!1},Ye.prototype.enable=function(){},Ye.prototype.disable=function(){};var Pt=function(D,J){this._map=D,this._el=D.getCanvasContainer(),this._container=D.getContainer(),this._clickTolerance=J.clickTolerance||1};Pt.prototype.isEnabled=function(){return!!this._enabled},Pt.prototype.isActive=function(){return!!this._active},Pt.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Pt.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Pt.prototype.mousedown=function(D,J){this.isEnabled()&&D.shiftKey&&D.button===0&&(o.disableDrag(),this._startPos=this._lastPos=J,this._active=!0)},Pt.prototype.mousemoveWindow=function(D,J){if(this._active){var O=J;if(!(this._lastPos.equals(O)||!this._box&&O.dist(this._startPos)<this._clickTolerance)){var K=this._startPos;this._lastPos=O,this._box||(this._box=o.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",D));var de=Math.min(K.x,O.x),ne=Math.max(K.x,O.x),we=Math.min(K.y,O.y),Oe=Math.max(K.y,O.y);o.setTransform(this._box,\"translate(\"+de+\"px,\"+we+\"px)\"),this._box.style.width=ne-de+\"px\",this._box.style.height=Oe-we+\"px\"}}},Pt.prototype.mouseupWindow=function(D,J){var O=this;if(this._active&&D.button===0){var K=this._startPos,de=J;if(this.reset(),o.suppressClick(),K.x===de.x&&K.y===de.y)this._fireEvent(\"boxzoomcancel\",D);else return this._map.fire(new i.Event(\"boxzoomend\",{originalEvent:D})),{cameraAnimation:function(ne){return ne.fitScreenCoordinates(K,de,O._map.getBearing(),{linear:!0})}}}},Pt.prototype.keydown=function(D){this._active&&D.keyCode===27&&(this.reset(),this._fireEvent(\"boxzoomcancel\",D))},Pt.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(o.remove(this._box),this._box=null),o.enableDrag(),delete this._startPos,delete this._lastPos},Pt.prototype._fireEvent=function(D,J){return this._map.fire(new i.Event(D,{originalEvent:J}))};function ot(Y,D){for(var J={},O=0;O<Y.length;O++)J[Y[O].identifier]=D[O];return J}function Xt(Y){for(var D=new i.Point(0,0),J=0,O=Y;J<O.length;J+=1){var K=O[J];D._add(K)}return D.div(Y.length)}var dr=500,pr=500,Ur=30,zr=function(D){this.reset(),this.numTouches=D.numTouches};zr.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},zr.prototype.touchstart=function(D,J,O){(this.centroid||O.length>this.numTouches)&&(this.aborted=!0),!this.aborted&&(this.startTime===void 0&&(this.startTime=D.timeStamp),O.length===this.numTouches&&(this.centroid=Xt(J),this.touches=ot(O,J)))},zr.prototype.touchmove=function(D,J,O){if(!(this.aborted||!this.centroid)){var K=ot(O,J);for(var de in this.touches){var ne=this.touches[de],we=K[de];(!we||we.dist(ne)>Ur)&&(this.aborted=!0)}}},zr.prototype.touchend=function(D,J,O){if((!this.centroid||D.timeStamp-this.startTime>pr)&&(this.aborted=!0),O.length===0){var K=!this.aborted&&this.centroid;if(this.reset(),K)return K}};var gi=function(D){this.singleTap=new zr(D),this.numTaps=D.numTaps,this.reset()};gi.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},gi.prototype.touchstart=function(D,J,O){this.singleTap.touchstart(D,J,O)},gi.prototype.touchmove=function(D,J,O){this.singleTap.touchmove(D,J,O)},gi.prototype.touchend=function(D,J,O){var K=this.singleTap.touchend(D,J,O);if(K){var de=D.timeStamp-this.lastTime<dr,ne=!this.lastTap||this.lastTap.dist(K)<Ur;if((!de||!ne)&&this.reset(),this.count++,this.lastTime=D.timeStamp,this.lastTap=K,this.count===this.numTaps)return this.reset(),K}};var xi=function(){this._zoomIn=new gi({numTouches:1,numTaps:2}),this._zoomOut=new gi({numTouches:2,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},xi.prototype.touchstart=function(D,J,O){this._zoomIn.touchstart(D,J,O),this._zoomOut.touchstart(D,J,O)},xi.prototype.touchmove=function(D,J,O){this._zoomIn.touchmove(D,J,O),this._zoomOut.touchmove(D,J,O)},xi.prototype.touchend=function(D,J,O){var K=this,de=this._zoomIn.touchend(D,J,O),ne=this._zoomOut.touchend(D,J,O);if(de)return this._active=!0,D.preventDefault(),setTimeout(function(){return K.reset()},0),{cameraAnimation:function(we){return we.easeTo({duration:300,zoom:we.getZoom()+1,around:we.unproject(de)},{originalEvent:D})}};if(ne)return this._active=!0,D.preventDefault(),setTimeout(function(){return K.reset()},0),{cameraAnimation:function(we){return we.easeTo({duration:300,zoom:we.getZoom()-1,around:we.unproject(ne)},{originalEvent:D})}}},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var Wr=0,ii=2,di={};di[Wr]=1,di[ii]=2;function Li(Y,D){var J=di[D];return Y.buttons===void 0||(Y.buttons&J)!==J}var Ti=function(D){this.reset(),this._clickTolerance=D.clickTolerance||1};Ti.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Ti.prototype._correctButton=function(D,J){return!1},Ti.prototype._move=function(D,J){return{}},Ti.prototype.mousedown=function(D,J){if(!this._lastPoint){var O=o.mouseButton(D);this._correctButton(D,O)&&(this._lastPoint=J,this._eventButton=O)}},Ti.prototype.mousemoveWindow=function(D,J){var O=this._lastPoint;if(O){if(D.preventDefault(),Li(D,this._eventButton)){this.reset();return}if(!(!this._moved&&J.dist(O)<this._clickTolerance))return this._moved=!0,this._lastPoint=J,this._move(O,J)}},Ti.prototype.mouseupWindow=function(D){if(this._lastPoint){var J=o.mouseButton(D);J===this._eventButton&&(this._moved&&o.suppressClick(),this.reset())}},Ti.prototype.enable=function(){this._enabled=!0},Ti.prototype.disable=function(){this._enabled=!1,this.reset()},Ti.prototype.isEnabled=function(){return this._enabled},Ti.prototype.isActive=function(){return this._active};var Qr=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.mousedown=function(O,K){Y.prototype.mousedown.call(this,O,K),this._lastPoint&&(this._active=!0)},D.prototype._correctButton=function(O,K){return K===Wr&&!O.ctrlKey},D.prototype._move=function(O,K){return{around:K,panDelta:K.sub(O)}},D})(Ti),Pn=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype._correctButton=function(O,K){return K===Wr&&O.ctrlKey||K===ii},D.prototype._move=function(O,K){var de=.8,ne=(K.x-O.x)*de;if(ne)return this._active=!0,{bearingDelta:ne}},D.prototype.contextmenu=function(O){O.preventDefault()},D})(Ti),kn=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype._correctButton=function(O,K){return K===Wr&&O.ctrlKey||K===ii},D.prototype._move=function(O,K){var de=-.5,ne=(K.y-O.y)*de;if(ne)return this._active=!0,{pitchDelta:ne}},D.prototype.contextmenu=function(O){O.preventDefault()},D})(Ti),Bn=function(D){this._minTouches=1,this._clickTolerance=D.clickTolerance||1,this.reset()};Bn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new i.Point(0,0)},Bn.prototype.touchstart=function(D,J,O){return this._calculateTransform(D,J,O)},Bn.prototype.touchmove=function(D,J,O){if(!(!this._active||O.length<this._minTouches))return D.preventDefault(),this._calculateTransform(D,J,O)},Bn.prototype.touchend=function(D,J,O){this._calculateTransform(D,J,O),this._active&&O.length<this._minTouches&&this.reset()},Bn.prototype.touchcancel=function(){this.reset()},Bn.prototype._calculateTransform=function(D,J,O){O.length>0&&(this._active=!0);var K=ot(O,J),de=new i.Point(0,0),ne=new i.Point(0,0),we=0;for(var Oe in K){var ht=K[Oe],Yt=this._touches[Oe];Yt&&(de._add(ht),ne._add(ht.sub(Yt)),we++,K[Oe]=ht)}if(this._touches=K,!(we<this._minTouches||!ne.mag())){var cr=ne.div(we);if(this._sum._add(cr),!(this._sum.mag()<this._clickTolerance)){var Nt=de.div(we);return{around:Nt,panDelta:cr}}}},Bn.prototype.enable=function(){this._enabled=!0},Bn.prototype.disable=function(){this._enabled=!1,this.reset()},Bn.prototype.isEnabled=function(){return this._enabled},Bn.prototype.isActive=function(){return this._active};var na=function(){this.reset()};na.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},na.prototype._start=function(D){},na.prototype._move=function(D,J,O){return{}},na.prototype.touchstart=function(D,J,O){this._firstTwoTouches||O.length<2||(this._firstTwoTouches=[O[0].identifier,O[1].identifier],this._start([J[0],J[1]]))},na.prototype.touchmove=function(D,J,O){if(this._firstTwoTouches){D.preventDefault();var K=this._firstTwoTouches,de=K[0],ne=K[1],we=Ea(O,J,de),Oe=Ea(O,J,ne);if(!(!we||!Oe)){var ht=this._aroundCenter?null:we.add(Oe).div(2);return this._move([we,Oe],ht,D)}}},na.prototype.touchend=function(D,J,O){if(this._firstTwoTouches){var K=this._firstTwoTouches,de=K[0],ne=K[1],we=Ea(O,J,de),Oe=Ea(O,J,ne);we&&Oe||(this._active&&o.suppressClick(),this.reset())}},na.prototype.touchcancel=function(){this.reset()},na.prototype.enable=function(D){this._enabled=!0,this._aroundCenter=!!D&&D.around===\"center\"},na.prototype.disable=function(){this._enabled=!1,this.reset()},na.prototype.isEnabled=function(){return this._enabled},na.prototype.isActive=function(){return this._active};function Ea(Y,D,J){for(var O=0;O<Y.length;O++)if(Y[O].identifier===J)return D[O]}var Ia=.1;function yo(Y,D){return Math.log(Y/D)/Math.LN2}var Da=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),delete this._distance,delete this._startDistance},D.prototype._start=function(O){this._startDistance=this._distance=O[0].dist(O[1])},D.prototype._move=function(O,K){var de=this._distance;if(this._distance=O[0].dist(O[1]),!(!this._active&&Math.abs(yo(this._distance,this._startDistance))<Ia))return this._active=!0,{zoomDelta:yo(this._distance,de),pinchAround:K}},D})(na),go=25;function Is(Y,D){return Y.angleWith(D)*180/Math.PI}var Ms=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},D.prototype._start=function(O){this._startVector=this._vector=O[0].sub(O[1]),this._minDiameter=O[0].dist(O[1])},D.prototype._move=function(O,K){var de=this._vector;if(this._vector=O[0].sub(O[1]),!(!this._active&&this._isBelowThreshold(this._vector)))return this._active=!0,{bearingDelta:Is(this._vector,de),pinchAround:K}},D.prototype._isBelowThreshold=function(O){this._minDiameter=Math.min(this._minDiameter,O.mag());var K=Math.PI*this._minDiameter,de=go/K*360,ne=Is(O,this._startVector);return Math.abs(ne)<de},D})(na);function Xs(Y){return Math.abs(Y.y)>Math.abs(Y.x)}var Un=100,ja=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.reset=function(){Y.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},D.prototype._start=function(O){this._lastPoints=O,Xs(O[0].sub(O[1]))&&(this._valid=!1)},D.prototype._move=function(O,K,de){var ne=O[0].sub(this._lastPoints[0]),we=O[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(ne,we,de.timeStamp),!!this._valid){this._lastPoints=O,this._active=!0;var Oe=(ne.y+we.y)/2,ht=-.5;return{pitchDelta:Oe*ht}}},D.prototype.gestureBeginsVertically=function(O,K,de){if(this._valid!==void 0)return this._valid;var ne=2,we=O.mag()>=ne,Oe=K.mag()>=ne;if(!(!we&&!Oe)){if(!we||!Oe)return this._firstMove===void 0&&(this._firstMove=de),de-this._firstMove<Un?void 0:!1;var ht=O.y>0==K.y>0;return Xs(O)&&Xs(K)&&ht}},D})(na),Fo={panStep:100,bearingStep:15,pitchStep:10},Uo=function(){var D=Fo;this._panStep=D.panStep,this._bearingStep=D.bearingStep,this._pitchStep=D.pitchStep,this._rotationDisabled=!1};Uo.prototype.reset=function(){this._active=!1},Uo.prototype.keydown=function(D){var J=this;if(!(D.altKey||D.ctrlKey||D.metaKey)){var O=0,K=0,de=0,ne=0,we=0;switch(D.keyCode){case 61:case 107:case 171:case 187:O=1;break;case 189:case 109:case 173:O=-1;break;case 37:D.shiftKey?K=-1:(D.preventDefault(),ne=-1);break;case 39:D.shiftKey?K=1:(D.preventDefault(),ne=1);break;case 38:D.shiftKey?de=1:(D.preventDefault(),we=-1);break;case 40:D.shiftKey?de=-1:(D.preventDefault(),we=1);break;default:return}return this._rotationDisabled&&(K=0,de=0),{cameraAnimation:function(Oe){var ht=Oe.getZoom();Oe.easeTo({duration:300,easeId:\"keyboardHandler\",easing:$s,zoom:O?Math.round(ht)+O*(D.shiftKey?2:1):ht,bearing:Oe.getBearing()+K*J._bearingStep,pitch:Oe.getPitch()+de*J._pitchStep,offset:[-ne*J._panStep,-we*J._panStep],center:Oe.getCenter()},{originalEvent:D})}}}},Uo.prototype.enable=function(){this._enabled=!0},Uo.prototype.disable=function(){this._enabled=!1,this.reset()},Uo.prototype.isEnabled=function(){return this._enabled},Uo.prototype.isActive=function(){return this._active},Uo.prototype.disableRotation=function(){this._rotationDisabled=!0},Uo.prototype.enableRotation=function(){this._rotationDisabled=!1};function $s(Y){return Y*(2-Y)}var Ml=4.000244140625,bu=1/100,dl=1/450,Sc=2,Me=function(D,J){this._map=D,this._el=D.getCanvasContainer(),this._handler=J,this._delta=0,this._defaultZoomRate=bu,this._wheelZoomRate=dl,i.bindAll([\"_onTimeout\"],this)};Me.prototype.setZoomRate=function(D){this._defaultZoomRate=D},Me.prototype.setWheelZoomRate=function(D){this._wheelZoomRate=D},Me.prototype.isEnabled=function(){return!!this._enabled},Me.prototype.isActive=function(){return!!this._active||this._finishTimeout!==void 0},Me.prototype.isZooming=function(){return!!this._zooming},Me.prototype.enable=function(D){this.isEnabled()||(this._enabled=!0,this._aroundCenter=D&&D.around===\"center\")},Me.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Me.prototype.wheel=function(D){if(this.isEnabled()){var J=D.deltaMode===i.window.WheelEvent.DOM_DELTA_LINE?D.deltaY*40:D.deltaY,O=i.browser.now(),K=O-(this._lastWheelEventTime||0);this._lastWheelEventTime=O,J!==0&&J%Ml===0?this._type=\"wheel\":J!==0&&Math.abs(J)<4?this._type=\"trackpad\":K>400?(this._type=null,this._lastValue=J,this._timeout=setTimeout(this._onTimeout,40,D)):this._type||(this._type=Math.abs(K*J)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,J+=this._lastValue)),D.shiftKey&&J&&(J=J/4),this._type&&(this._lastWheelEvent=D,this._delta-=J,this._active||this._start(D)),D.preventDefault()}},Me.prototype._onTimeout=function(D){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(D)},Me.prototype._start=function(D){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var J=o.mousePos(this._el,D);this._around=i.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(J)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},Me.prototype.renderFrame=function(){var D=this;if(this._frameId&&(this._frameId=null,!!this.isActive())){var J=this._map.transform;if(this._delta!==0){var O=this._type===\"wheel\"&&Math.abs(this._delta)>Ml?this._wheelZoomRate:this._defaultZoomRate,K=Sc/(1+Math.exp(-Math.abs(this._delta*O)));this._delta<0&&K!==0&&(K=1/K);var de=typeof this._targetZoom==\"number\"?J.zoomScale(this._targetZoom):J.scale;this._targetZoom=Math.min(J.maxZoom,Math.max(J.minZoom,J.scaleZoom(de*K))),this._type===\"wheel\"&&(this._startZoom=J.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var ne=typeof this._targetZoom==\"number\"?this._targetZoom:J.zoom,we=this._startZoom,Oe=this._easing,ht=!1,Yt;if(this._type===\"wheel\"&&we&&Oe){var cr=Math.min((i.browser.now()-this._lastWheelEventTime)/200,1),Nt=Oe(cr);Yt=i.number(we,ne,Nt),cr<1?this._frameId||(this._frameId=!0):ht=!0}else Yt=ne,ht=!0;return this._active=!0,ht&&(this._active=!1,this._finishTimeout=setTimeout(function(){D._zooming=!1,D._handler._triggerRenderFrame(),delete D._targetZoom,delete D._finishTimeout},200)),{noInertia:!0,needsRenderFrame:!ht,zoomDelta:Yt-J.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},Me.prototype._smoothOutEasing=function(D){var J=i.ease;if(this._prevEase){var O=this._prevEase,K=(i.browser.now()-O.start)/O.duration,de=O.easing(K+.01)-O.easing(K),ne=.27/Math.sqrt(de*de+1e-4)*.01,we=Math.sqrt(.27*.27-ne*ne);J=i.bezier(ne,we,.25,1)}return this._prevEase={start:i.browser.now(),duration:D,easing:J},J},Me.prototype.reset=function(){this._active=!1};var bt=function(D,J){this._clickZoom=D,this._tapZoom=J};bt.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},bt.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},bt.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},bt.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var Ot=function(){this.reset()};Ot.prototype.reset=function(){this._active=!1},Ot.prototype.dblclick=function(D,J){return D.preventDefault(),{cameraAnimation:function(O){O.easeTo({duration:300,zoom:O.getZoom()+(D.shiftKey?-1:1),around:O.unproject(J)},{originalEvent:D})}}},Ot.prototype.enable=function(){this._enabled=!0},Ot.prototype.disable=function(){this._enabled=!1,this.reset()},Ot.prototype.isEnabled=function(){return this._enabled},Ot.prototype.isActive=function(){return this._active};var Lr=function(){this._tap=new gi({numTouches:1,numTaps:1}),this.reset()};Lr.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},Lr.prototype.touchstart=function(D,J,O){this._swipePoint||(this._tapTime&&D.timeStamp-this._tapTime>dr&&this.reset(),this._tapTime?O.length>0&&(this._swipePoint=J[0],this._swipeTouch=O[0].identifier):this._tap.touchstart(D,J,O))},Lr.prototype.touchmove=function(D,J,O){if(!this._tapTime)this._tap.touchmove(D,J,O);else if(this._swipePoint){if(O[0].identifier!==this._swipeTouch)return;var K=J[0],de=K.y-this._swipePoint.y;return this._swipePoint=K,D.preventDefault(),this._active=!0,{zoomDelta:de/128}}},Lr.prototype.touchend=function(D,J,O){if(this._tapTime)this._swipePoint&&O.length===0&&this.reset();else{var K=this._tap.touchend(D,J,O);K&&(this._tapTime=D.timeStamp)}},Lr.prototype.touchcancel=function(){this.reset()},Lr.prototype.enable=function(){this._enabled=!0},Lr.prototype.disable=function(){this._enabled=!1,this.reset()},Lr.prototype.isEnabled=function(){return this._enabled},Lr.prototype.isActive=function(){return this._active};var Vr=function(D,J,O){this._el=D,this._mousePan=J,this._touchPan=O};Vr.prototype.enable=function(D){this._inertiaOptions=D||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},Vr.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},Vr.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},Vr.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var Or=function(D,J,O){this._pitchWithRotate=D.pitchWithRotate,this._mouseRotate=J,this._mousePitch=O};Or.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},Or.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},Or.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},Or.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var Nr=function(D,J,O,K){this._el=D,this._touchZoom=J,this._touchRotate=O,this._tapDragZoom=K,this._rotationDisabled=!1,this._enabled=!0};Nr.prototype.enable=function(D){this._touchZoom.enable(D),this._rotationDisabled||this._touchRotate.enable(D),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},Nr.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},Nr.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},Nr.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},Nr.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},Nr.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var vi=function(Y){return Y.zoom||Y.drag||Y.pitch||Y.rotate},Ni=(function(Y){function D(){Y.apply(this,arguments)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D})(i.Event);function qi(Y){return Y.panDelta&&Y.panDelta.mag()||Y.zoomDelta||Y.bearingDelta||Y.pitchDelta}var Si=function(D,J){this._map=D,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Al(D),this._bearingSnap=J.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(J),i.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var O=this._el;this._listeners=[[O,\"touchstart\",{passive:!0}],[O,\"touchmove\",{passive:!1}],[O,\"touchend\",void 0],[O,\"touchcancel\",void 0],[O,\"mousedown\",void 0],[O,\"mousemove\",void 0],[O,\"mouseup\",void 0],[i.window.document,\"mousemove\",{capture:!0}],[i.window.document,\"mouseup\",void 0],[O,\"mouseover\",void 0],[O,\"mouseout\",void 0],[O,\"dblclick\",void 0],[O,\"click\",void 0],[O,\"keydown\",{capture:!1}],[O,\"keyup\",void 0],[O,\"wheel\",{passive:!1}],[O,\"contextmenu\",void 0],[i.window,\"blur\",void 0]];for(var K=0,de=this._listeners;K<de.length;K+=1){var ne=de[K],we=ne[0],Oe=ne[1],ht=ne[2];o.addEventListener(we,Oe,we===i.window.document?this.handleWindowEvent:this.handleEvent,ht)}};Si.prototype.destroy=function(){for(var D=0,J=this._listeners;D<J.length;D+=1){var O=J[D],K=O[0],de=O[1],ne=O[2];o.removeEventListener(K,de,K===i.window.document?this.handleWindowEvent:this.handleEvent,ne)}},Si.prototype._addDefaultHandlers=function(D){var J=this._map,O=J.getCanvasContainer();this._add(\"mapEvent\",new Ve(J,D));var K=J.boxZoom=new Pt(J,D);this._add(\"boxZoom\",K);var de=new xi,ne=new Ot;J.doubleClickZoom=new bt(ne,de),this._add(\"tapZoom\",de),this._add(\"clickZoom\",ne);var we=new Lr;this._add(\"tapDragZoom\",we);var Oe=J.touchPitch=new ja;this._add(\"touchPitch\",Oe);var ht=new Pn(D),Yt=new kn(D);J.dragRotate=new Or(D,ht,Yt),this._add(\"mouseRotate\",ht,[\"mousePitch\"]),this._add(\"mousePitch\",Yt,[\"mouseRotate\"]);var cr=new Qr(D),Nt=new Bn(D);J.dragPan=new Vr(O,cr,Nt),this._add(\"mousePan\",cr),this._add(\"touchPan\",Nt,[\"touchZoom\",\"touchRotate\"]);var Ne=new Ms,$e=new Da;J.touchZoomRotate=new Nr(O,$e,Ne,we),this._add(\"touchRotate\",Ne,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",$e,[\"touchPan\",\"touchRotate\"]);var st=J.scrollZoom=new Me(J,this);this._add(\"scrollZoom\",st,[\"mousePan\"]);var It=J.keyboard=new Uo;this._add(\"keyboard\",It),this._add(\"blockableMapEvent\",new Ye(J));for(var Bt=0,Ft=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];Bt<Ft.length;Bt+=1){var Ut=Ft[Bt];D.interactive&&D[Ut]&&J[Ut].enable(D[Ut])}},Si.prototype._add=function(D,J,O){this._handlers.push({handlerName:D,handler:J,allowed:O}),this._handlersById[D]=J},Si.prototype.stop=function(D){if(!this._updatingCamera){for(var J=0,O=this._handlers;J<O.length;J+=1){var K=O[J],de=K.handler;de.reset()}this._inertia.clear(),this._fireEvents({},{},D),this._changes=[]}},Si.prototype.isActive=function(){for(var D=0,J=this._handlers;D<J.length;D+=1){var O=J[D],K=O.handler;if(K.isActive())return!0}return!1},Si.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Si.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Si.prototype.isMoving=function(){return!!vi(this._eventsInProgress)||this.isZooming()},Si.prototype._blockedByActive=function(D,J,O){for(var K in D)if(K!==O&&(!J||J.indexOf(K)<0))return!0;return!1},Si.prototype.handleWindowEvent=function(D){this.handleEvent(D,D.type+\"Window\")},Si.prototype._getMapTouches=function(D){for(var J=[],O=0,K=D;O<K.length;O+=1){var de=K[O],ne=de.target;this._el.contains(ne)&&J.push(de)}return J},Si.prototype.handleEvent=function(D,J){if(D.type===\"blur\"){this.stop(!0);return}this._updatingCamera=!0;for(var O=D.type===\"renderFrame\"?void 0:D,K={needsRenderFrame:!1},de={},ne={},we=D.touches?this._getMapTouches(D.touches):void 0,Oe=we?o.touchPos(this._el,we):o.mousePos(this._el,D),ht=0,Yt=this._handlers;ht<Yt.length;ht+=1){var cr=Yt[ht],Nt=cr.handlerName,Ne=cr.handler,$e=cr.allowed;if(Ne.isEnabled()){var st=void 0;this._blockedByActive(ne,$e,Nt)?Ne.reset():Ne[J||D.type]&&(st=Ne[J||D.type](D,Oe,we),this.mergeHandlerResult(K,de,st,Nt,O),st&&st.needsRenderFrame&&this._triggerRenderFrame()),(st||Ne.isActive())&&(ne[Nt]=Ne)}}var It={};for(var Bt in this._previousActiveHandlers)ne[Bt]||(It[Bt]=O);this._previousActiveHandlers=ne,(Object.keys(It).length||qi(K))&&(this._changes.push([K,de,It]),this._triggerRenderFrame()),(Object.keys(ne).length||qi(K))&&this._map._stop(!0),this._updatingCamera=!1;var Ft=K.cameraAnimation;Ft&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],Ft(this._map))},Si.prototype.mergeHandlerResult=function(D,J,O,K,de){if(O){i.extend(D,O);var ne={handlerName:K,originalEvent:O.originalEvent||de};O.zoomDelta!==void 0&&(J.zoom=ne),O.panDelta!==void 0&&(J.drag=ne),O.pitchDelta!==void 0&&(J.pitch=ne),O.bearingDelta!==void 0&&(J.rotate=ne)}},Si.prototype._applyChanges=function(){for(var D={},J={},O={},K=0,de=this._changes;K<de.length;K+=1){var ne=de[K],we=ne[0],Oe=ne[1],ht=ne[2];we.panDelta&&(D.panDelta=(D.panDelta||new i.Point(0,0))._add(we.panDelta)),we.zoomDelta&&(D.zoomDelta=(D.zoomDelta||0)+we.zoomDelta),we.bearingDelta&&(D.bearingDelta=(D.bearingDelta||0)+we.bearingDelta),we.pitchDelta&&(D.pitchDelta=(D.pitchDelta||0)+we.pitchDelta),we.around!==void 0&&(D.around=we.around),we.pinchAround!==void 0&&(D.pinchAround=we.pinchAround),we.noInertia&&(D.noInertia=we.noInertia),i.extend(J,Oe),i.extend(O,ht)}this._updateMapTransform(D,J,O),this._changes=[]},Si.prototype._updateMapTransform=function(D,J,O){var K=this._map,de=K.transform;if(!qi(D))return this._fireEvents(J,O,!0);var ne=D.panDelta,we=D.zoomDelta,Oe=D.bearingDelta,ht=D.pitchDelta,Yt=D.around,cr=D.pinchAround;cr!==void 0&&(Yt=cr),K._stop(!0),Yt=Yt||K.transform.centerPoint;var Nt=de.pointLocation(ne?Yt.sub(ne):Yt);Oe&&(de.bearing+=Oe),ht&&(de.pitch+=ht),we&&(de.zoom+=we),de.setLocationAtPoint(Nt,Yt),this._map._update(),D.noInertia||this._inertia.record(D),this._fireEvents(J,O,!0)},Si.prototype._fireEvents=function(D,J,O){var K=this,de=vi(this._eventsInProgress),ne=vi(D),we={};for(var Oe in D){var ht=D[Oe],Yt=ht.originalEvent;this._eventsInProgress[Oe]||(we[Oe+\"start\"]=Yt),this._eventsInProgress[Oe]=D[Oe]}!de&&ne&&this._fireEvent(\"movestart\",ne.originalEvent);for(var cr in we)this._fireEvent(cr,we[cr]);ne&&this._fireEvent(\"move\",ne.originalEvent);for(var Nt in D){var Ne=D[Nt],$e=Ne.originalEvent;this._fireEvent(Nt,$e)}var st={},It;for(var Bt in this._eventsInProgress){var Ft=this._eventsInProgress[Bt],Ut=Ft.handlerName,zt=Ft.originalEvent;this._handlersById[Ut].isActive()||(delete this._eventsInProgress[Bt],It=J[Ut]||zt,st[Bt+\"end\"]=It)}for(var _t in st)this._fireEvent(_t,st[_t]);var Dt=vi(this._eventsInProgress);if(O&&(de||ne)&&!Dt){this._updatingCamera=!0;var vt=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),lr=function(_r){return _r!==0&&-K._bearingSnap<_r&&_r<K._bearingSnap};vt?(lr(vt.bearing||this._map.getBearing())&&(vt.bearing=0),this._map.easeTo(vt,{originalEvent:It})):(this._map.fire(new i.Event(\"moveend\",{originalEvent:It})),lr(this._map.getBearing())&&this._map.resetNorth()),this._updatingCamera=!1}},Si.prototype._fireEvent=function(D,J){this._map.fire(new i.Event(D,J?{originalEvent:J}:{}))},Si.prototype._requestFrame=function(){var D=this;return this._map.triggerRepaint(),this._map._renderTaskQueue.add(function(J){delete D._frameId,D.handleEvent(new Ni(\"renderFrame\",{timeStamp:J})),D._applyChanges()})},Si.prototype._triggerRenderFrame=function(){this._frameId===void 0&&(this._frameId=this._requestFrame())};var Vn=(function(Y){function D(J,O){Y.call(this),this._moving=!1,this._zooming=!1,this.transform=J,this._bearingSnap=O.bearingSnap,i.bindAll([\"_renderFrameCallback\"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.getCenter=function(){return new i.LngLat(this.transform.center.lng,this.transform.center.lat)},D.prototype.setCenter=function(O,K){return this.jumpTo({center:O},K)},D.prototype.panBy=function(O,K,de){return O=i.Point.convert(O).mult(-1),this.panTo(this.transform.center,i.extend({offset:O},K),de)},D.prototype.panTo=function(O,K,de){return this.easeTo(i.extend({center:O},K),de)},D.prototype.getZoom=function(){return this.transform.zoom},D.prototype.setZoom=function(O,K){return this.jumpTo({zoom:O},K),this},D.prototype.zoomTo=function(O,K,de){return this.easeTo(i.extend({zoom:O},K),de)},D.prototype.zoomIn=function(O,K){return this.zoomTo(this.getZoom()+1,O,K),this},D.prototype.zoomOut=function(O,K){return this.zoomTo(this.getZoom()-1,O,K),this},D.prototype.getBearing=function(){return this.transform.bearing},D.prototype.setBearing=function(O,K){return this.jumpTo({bearing:O},K),this},D.prototype.getPadding=function(){return this.transform.padding},D.prototype.setPadding=function(O,K){return this.jumpTo({padding:O},K),this},D.prototype.rotateTo=function(O,K,de){return this.easeTo(i.extend({bearing:O},K),de)},D.prototype.resetNorth=function(O,K){return this.rotateTo(0,i.extend({duration:1e3},O),K),this},D.prototype.resetNorthPitch=function(O,K){return this.easeTo(i.extend({bearing:0,pitch:0,duration:1e3},O),K),this},D.prototype.snapToNorth=function(O,K){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(O,K):this},D.prototype.getPitch=function(){return this.transform.pitch},D.prototype.setPitch=function(O,K){return this.jumpTo({pitch:O},K),this},D.prototype.cameraForBounds=function(O,K){O=i.LngLatBounds.convert(O);var de=K&&K.bearing||0;return this._cameraForBoxAndBearing(O.getNorthWest(),O.getSouthEast(),de,K)},D.prototype._cameraForBoxAndBearing=function(O,K,de,ne){var we={top:0,bottom:0,right:0,left:0};if(ne=i.extend({padding:we,offset:[0,0],maxZoom:this.transform.maxZoom},ne),typeof ne.padding==\"number\"){var Oe=ne.padding;ne.padding={top:Oe,bottom:Oe,right:Oe,left:Oe}}ne.padding=i.extend(we,ne.padding);var ht=this.transform,Yt=ht.padding,cr=ht.project(i.LngLat.convert(O)),Nt=ht.project(i.LngLat.convert(K)),Ne=cr.rotate(-de*Math.PI/180),$e=Nt.rotate(-de*Math.PI/180),st=new i.Point(Math.max(Ne.x,$e.x),Math.max(Ne.y,$e.y)),It=new i.Point(Math.min(Ne.x,$e.x),Math.min(Ne.y,$e.y)),Bt=st.sub(It),Ft=(ht.width-(Yt.left+Yt.right+ne.padding.left+ne.padding.right))/Bt.x,Ut=(ht.height-(Yt.top+Yt.bottom+ne.padding.top+ne.padding.bottom))/Bt.y;if(Ut<0||Ft<0){i.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\");return}var zt=Math.min(ht.scaleZoom(ht.scale*Math.min(Ft,Ut)),ne.maxZoom),_t=typeof ne.offset.x==\"number\"?new i.Point(ne.offset.x,ne.offset.y):i.Point.convert(ne.offset),Dt=(ne.padding.left-ne.padding.right)/2,vt=(ne.padding.top-ne.padding.bottom)/2,lr=new i.Point(Dt,vt),_r=lr.rotate(de*Math.PI/180),Br=_t.add(_r),Di=Br.mult(ht.scale/ht.zoomScale(zt)),ci=ht.unproject(cr.add(Nt).div(2).sub(Di));return{center:ci,zoom:zt,bearing:de}},D.prototype.fitBounds=function(O,K,de){return this._fitInternal(this.cameraForBounds(O,K),K,de)},D.prototype.fitScreenCoordinates=function(O,K,de,ne,we){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(i.Point.convert(O)),this.transform.pointLocation(i.Point.convert(K)),de,ne),ne,we)},D.prototype._fitInternal=function(O,K,de){return O?(K=i.extend(O,K),delete K.padding,K.linear?this.easeTo(K,de):this.flyTo(K,de)):this},D.prototype.jumpTo=function(O,K){this.stop();var de=this.transform,ne=!1,we=!1,Oe=!1;return\"zoom\"in O&&de.zoom!==+O.zoom&&(ne=!0,de.zoom=+O.zoom),O.center!==void 0&&(de.center=i.LngLat.convert(O.center)),\"bearing\"in O&&de.bearing!==+O.bearing&&(we=!0,de.bearing=+O.bearing),\"pitch\"in O&&de.pitch!==+O.pitch&&(Oe=!0,de.pitch=+O.pitch),O.padding!=null&&!de.isPaddingEqual(O.padding)&&(de.padding=O.padding),this.fire(new i.Event(\"movestart\",K)).fire(new i.Event(\"move\",K)),ne&&this.fire(new i.Event(\"zoomstart\",K)).fire(new i.Event(\"zoom\",K)).fire(new i.Event(\"zoomend\",K)),we&&this.fire(new i.Event(\"rotatestart\",K)).fire(new i.Event(\"rotate\",K)).fire(new i.Event(\"rotateend\",K)),Oe&&this.fire(new i.Event(\"pitchstart\",K)).fire(new i.Event(\"pitch\",K)).fire(new i.Event(\"pitchend\",K)),this.fire(new i.Event(\"moveend\",K))},D.prototype.easeTo=function(O,K){var de=this;this._stop(!1,O.easeId),O=i.extend({offset:[0,0],duration:500,easing:i.ease},O),(O.animate===!1||!O.essential&&i.browser.prefersReducedMotion)&&(O.duration=0);var ne=this.transform,we=this.getZoom(),Oe=this.getBearing(),ht=this.getPitch(),Yt=this.getPadding(),cr=\"zoom\"in O?+O.zoom:we,Nt=\"bearing\"in O?this._normalizeBearing(O.bearing,Oe):Oe,Ne=\"pitch\"in O?+O.pitch:ht,$e=\"padding\"in O?O.padding:ne.padding,st=i.Point.convert(O.offset),It=ne.centerPoint.add(st),Bt=ne.pointLocation(It),Ft=i.LngLat.convert(O.center||Bt);this._normalizeCenter(Ft);var Ut=ne.project(Bt),zt=ne.project(Ft).sub(Ut),_t=ne.zoomScale(cr-we),Dt,vt;O.around&&(Dt=i.LngLat.convert(O.around),vt=ne.locationPoint(Dt));var lr={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||cr!==we,this._rotating=this._rotating||Oe!==Nt,this._pitching=this._pitching||Ne!==ht,this._padding=!ne.isPaddingEqual($e),this._easeId=O.easeId,this._prepareEase(K,O.noMoveStart,lr),this._ease(function(_r){if(de._zooming&&(ne.zoom=i.number(we,cr,_r)),de._rotating&&(ne.bearing=i.number(Oe,Nt,_r)),de._pitching&&(ne.pitch=i.number(ht,Ne,_r)),de._padding&&(ne.interpolatePadding(Yt,$e,_r),It=ne.centerPoint.add(st)),Dt)ne.setLocationAtPoint(Dt,vt);else{var Br=ne.zoomScale(ne.zoom-we),Di=cr>we?Math.min(2,_t):Math.max(.5,_t),ci=Math.pow(Di,1-_r),bi=ne.unproject(Ut.add(zt.mult(_r*ci)).mult(Br));ne.setLocationAtPoint(ne.renderWorldCopies?bi.wrap():bi,It)}de._fireMoveEvents(K)},function(_r){de._afterEase(K,_r)},O),this},D.prototype._prepareEase=function(O,K,de){de===void 0&&(de={}),this._moving=!0,!K&&!de.moving&&this.fire(new i.Event(\"movestart\",O)),this._zooming&&!de.zooming&&this.fire(new i.Event(\"zoomstart\",O)),this._rotating&&!de.rotating&&this.fire(new i.Event(\"rotatestart\",O)),this._pitching&&!de.pitching&&this.fire(new i.Event(\"pitchstart\",O))},D.prototype._fireMoveEvents=function(O){this.fire(new i.Event(\"move\",O)),this._zooming&&this.fire(new i.Event(\"zoom\",O)),this._rotating&&this.fire(new i.Event(\"rotate\",O)),this._pitching&&this.fire(new i.Event(\"pitch\",O))},D.prototype._afterEase=function(O,K){if(!(this._easeId&&K&&this._easeId===K)){delete this._easeId;var de=this._zooming,ne=this._rotating,we=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,de&&this.fire(new i.Event(\"zoomend\",O)),ne&&this.fire(new i.Event(\"rotateend\",O)),we&&this.fire(new i.Event(\"pitchend\",O)),this.fire(new i.Event(\"moveend\",O))}},D.prototype.flyTo=function(O,K){var de=this;if(!O.essential&&i.browser.prefersReducedMotion){var ne=i.pick(O,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(ne,K)}this.stop(),O=i.extend({offset:[0,0],speed:1.2,curve:1.42,easing:i.ease},O);var we=this.transform,Oe=this.getZoom(),ht=this.getBearing(),Yt=this.getPitch(),cr=this.getPadding(),Nt=\"zoom\"in O?i.clamp(+O.zoom,we.minZoom,we.maxZoom):Oe,Ne=\"bearing\"in O?this._normalizeBearing(O.bearing,ht):ht,$e=\"pitch\"in O?+O.pitch:Yt,st=\"padding\"in O?O.padding:we.padding,It=we.zoomScale(Nt-Oe),Bt=i.Point.convert(O.offset),Ft=we.centerPoint.add(Bt),Ut=we.pointLocation(Ft),zt=i.LngLat.convert(O.center||Ut);this._normalizeCenter(zt);var _t=we.project(Ut),Dt=we.project(zt).sub(_t),vt=O.curve,lr=Math.max(we.width,we.height),_r=lr/It,Br=Dt.mag();if(\"minZoom\"in O){var Di=i.clamp(Math.min(O.minZoom,Oe,Nt),we.minZoom,we.maxZoom),ci=lr/we.zoomScale(Di-Oe);vt=Math.sqrt(ci/Br*2)}var bi=vt*vt;function gn(so){var Yo=(_r*_r-lr*lr+(so?-1:1)*bi*bi*Br*Br)/(2*(so?_r:lr)*bi*Br);return Math.log(Math.sqrt(Yo*Yo+1)-Yo)}function tn(so){return(Math.exp(so)-Math.exp(-so))/2}function Ei(so){return(Math.exp(so)+Math.exp(-so))/2}function Oi(so){return tn(so)/Ei(so)}var Gi=gn(0),on=function(so){return Ei(Gi)/Ei(Gi+vt*so)},zn=function(so){return lr*((Ei(Gi)*Oi(Gi+vt*so)-tn(Gi))/bi)/Br},Ja=(gn(1)-Gi)/vt;if(Math.abs(Br)<1e-6||!isFinite(Ja)){if(Math.abs(lr-_r)<1e-6)return this.easeTo(O,K);var co=_r<lr?-1:1;Ja=Math.abs(Math.log(_r/lr))/vt,zn=function(){return 0},on=function(so){return Math.exp(co*vt*so)}}if(\"duration\"in O)O.duration=+O.duration;else{var ts=\"screenSpeed\"in O?+O.screenSpeed/vt:+O.speed;O.duration=1e3*Ja/ts}return O.maxDuration&&O.duration>O.maxDuration&&(O.duration=0),this._zooming=!0,this._rotating=ht!==Ne,this._pitching=$e!==Yt,this._padding=!we.isPaddingEqual(st),this._prepareEase(K,!1),this._ease(function(so){var Yo=so*Ja,ms=1/on(Yo);we.zoom=so===1?Nt:Oe+we.scaleZoom(ms),de._rotating&&(we.bearing=i.number(ht,Ne,so)),de._pitching&&(we.pitch=i.number(Yt,$e,so)),de._padding&&(we.interpolatePadding(cr,st,so),Ft=we.centerPoint.add(Bt));var ou=so===1?zt:we.unproject(_t.add(Dt.mult(zn(Yo))).mult(ms));we.setLocationAtPoint(we.renderWorldCopies?ou.wrap():ou,Ft),de._fireMoveEvents(K)},function(){return de._afterEase(K)},O),this},D.prototype.isEasing=function(){return!!this._easeFrameId},D.prototype.stop=function(){return this._stop()},D.prototype._stop=function(O,K){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var de=this._onEaseEnd;delete this._onEaseEnd,de.call(this,K)}if(!O){var ne=this.handlers;ne&&ne.stop(!1)}return this},D.prototype._ease=function(O,K,de){de.animate===!1||de.duration===0?(O(1),K()):(this._easeStart=i.browser.now(),this._easeOptions=de,this._onEaseFrame=O,this._onEaseEnd=K,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},D.prototype._renderFrameCallback=function(){var O=Math.min((i.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(O)),O<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},D.prototype._normalizeBearing=function(O,K){O=i.wrap(O,-180,180);var de=Math.abs(O-K);return Math.abs(O-360-K)<de&&(O-=360),Math.abs(O+360-K)<de&&(O+=360),O},D.prototype._normalizeCenter=function(O){var K=this.transform;if(!(!K.renderWorldCopies||K.lngRange)){var de=O.lng-K.center.lng;O.lng+=de>180?-360:de<-180?360:0}},D})(i.Evented),Qi=function(D){D===void 0&&(D={}),this.options=D,i.bindAll([\"_toggleAttribution\",\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Qi.prototype.getDefaultPosition=function(){return\"bottom-right\"},Qi.prototype.onAdd=function(D){var J=this.options&&this.options.compact;return this._map=D,this._container=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._compactButton=o.create(\"button\",\"mapboxgl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=o.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),this._innerContainer.setAttribute(\"role\",\"list\"),J&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),J===void 0&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Qi.prototype.onRemove=function(){o.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Qi.prototype._setElementTitle=function(D,J){var O=this._map._getUIString(\"AttributionControl.\"+J);D.title=O,D.setAttribute(\"aria-label\",O)},Qi.prototype._toggleAttribution=function(){this._container.classList.contains(\"mapboxgl-compact-show\")?(this._container.classList.remove(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"false\")):(this._container.classList.add(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"true\"))},Qi.prototype._updateEditLink=function(){var D=this._editLink;D||(D=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var J=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||i.config.ACCESS_TOKEN}];if(D){var O=J.reduce(function(K,de,ne){return de.value&&(K+=de.key+\"=\"+de.value+(ne<J.length-1?\"&\":\"\")),K},\"?\");D.href=i.config.FEEDBACK_URL+\"/\"+O+(this._map._hash?this._map._hash.getHashString(!0):\"\"),D.rel=\"noopener nofollow\",this._setElementTitle(D,\"MapFeedback\")}},Qi.prototype._updateData=function(D){D&&(D.sourceDataType===\"metadata\"||D.sourceDataType===\"visibility\"||D.dataType===\"style\")&&(this._updateAttributions(),this._updateEditLink())},Qi.prototype._updateAttributions=function(){if(this._map.style){var D=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?D=D.concat(this.options.customAttribution.map(function(Oe){return typeof Oe!=\"string\"?\"\":Oe})):typeof this.options.customAttribution==\"string\"&&D.push(this.options.customAttribution)),this._map.style.stylesheet){var J=this._map.style.stylesheet;this.styleOwner=J.owner,this.styleId=J.id}var O=this._map.style.sourceCaches;for(var K in O){var de=O[K];if(de.used){var ne=de.getSource();ne.attribution&&D.indexOf(ne.attribution)<0&&D.push(ne.attribution)}}D.sort(function(Oe,ht){return Oe.length-ht.length}),D=D.filter(function(Oe,ht){for(var Yt=ht+1;Yt<D.length;Yt++)if(D[Yt].indexOf(Oe)>=0)return!1;return!0});var we=D.join(\" | \");we!==this._attribHTML&&(this._attribHTML=we,D.length?(this._innerContainer.innerHTML=we,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Qi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\",\"mapboxgl-compact-show\")};var Wi=function(){i.bindAll([\"_updateLogo\"],this),i.bindAll([\"_updateCompact\"],this)};Wi.prototype.onAdd=function(D){this._map=D,this._container=o.create(\"div\",\"mapboxgl-ctrl\");var J=o.create(\"a\",\"mapboxgl-ctrl-logo\");return J.target=\"_blank\",J.rel=\"noopener nofollow\",J.href=\"https://www.mapbox.com/\",J.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),J.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(J),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Wi.prototype.onRemove=function(){o.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Wi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Wi.prototype._updateLogo=function(D){(!D||D.sourceDataType===\"metadata\")&&(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Wi.prototype._logoRequired=function(){if(this._map.style){var D=this._map.style.sourceCaches;for(var J in D){var O=D[J].getSource();if(O.mapbox_logo)return!0}return!1}},Wi.prototype._updateCompact=function(){var D=this._container.children;if(D.length){var J=D[0];this._map.getCanvasContainer().offsetWidth<250?J.classList.add(\"mapboxgl-compact\"):J.classList.remove(\"mapboxgl-compact\")}};var oi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};oi.prototype.add=function(D){var J=++this._id,O=this._queue;return O.push({callback:D,id:J,cancelled:!1}),J},oi.prototype.remove=function(D){for(var J=this._currentlyRunning,O=J?this._queue.concat(J):this._queue,K=0,de=O;K<de.length;K+=1){var ne=de[K];if(ne.id===D){ne.cancelled=!0;return}}},oi.prototype.run=function(D){D===void 0&&(D=0);var J=this._currentlyRunning=this._queue;this._queue=[];for(var O=0,K=J;O<K.length;O+=1){var de=K[O];if(!de.cancelled&&(de.callback(D),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},oi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Tr={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},Zr=i.window.HTMLImageElement,yi=i.window.HTMLElement,Ii=i.window.ImageBitmap,ui=-2,nn=22,Zi=0,qn=60,fi={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:ui,maxZoom:nn,minPitch:Zi,maxPitch:qn,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},si=(function(Y){function D(O){var K=this;if(O=i.extend({},fi,O),O.minZoom!=null&&O.maxZoom!=null&&O.minZoom>O.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(O.minPitch!=null&&O.maxPitch!=null&&O.minPitch>O.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(O.minPitch!=null&&O.minPitch<Zi)throw new Error(\"minPitch must be greater than or equal to \"+Zi);if(O.maxPitch!=null&&O.maxPitch>qn)throw new Error(\"maxPitch must be less than or equal to \"+qn);var de=new wo(O.minZoom,O.maxZoom,O.minPitch,O.maxPitch,O.renderWorldCopies);if(Y.call(this,de,O),this._interactive=O.interactive,this._maxTileCacheSize=O.maxTileCacheSize,this._failIfMajorPerformanceCaveat=O.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=O.preserveDrawingBuffer,this._antialias=O.antialias,this._trackResize=O.trackResize,this._bearingSnap=O.bearingSnap,this._refreshExpiredTiles=O.refreshExpiredTiles,this._fadeDuration=O.fadeDuration,this._crossSourceCollisions=O.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=O.collectResourceTiming,this._renderTaskQueue=new oi,this._controls=[],this._mapId=i.uniqueId(),this._locale=i.extend({},Tr,O.locale),this._clickTolerance=O.clickTolerance,this._requestManager=new i.RequestManager(O.transformRequest,O.accessToken),typeof O.container==\"string\"){if(this._container=i.window.document.getElementById(O.container),!this._container)throw new Error(\"Container '\"+O.container+\"' not found.\")}else if(O.container instanceof yi)this._container=O.container;else throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");if(O.maxBounds&&this.setMaxBounds(O.maxBounds),i.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_onMapScroll\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),this.painter===void 0)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",function(){return K._update(!1)}),this.on(\"moveend\",function(){return K._update(!1)}),this.on(\"zoom\",function(){return K._update(!0)}),typeof i.window!=\"undefined\"&&(i.window.addEventListener(\"online\",this._onWindowOnline,!1),i.window.addEventListener(\"resize\",this._onWindowResize,!1),i.window.addEventListener(\"orientationchange\",this._onWindowResize,!1)),this.handlers=new Si(this,O);var ne=typeof O.hash==\"string\"&&O.hash||void 0;this._hash=O.hash&&new Nl(ne).addTo(this),(!this._hash||!this._hash._onHashChange())&&(this.jumpTo({center:O.center,zoom:O.zoom,bearing:O.bearing,pitch:O.pitch}),O.bounds&&(this.resize(),this.fitBounds(O.bounds,i.extend({},O.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=O.localIdeographFontFamily,O.style&&this.setStyle(O.style,{localIdeographFontFamily:O.localIdeographFontFamily}),O.attributionControl&&this.addControl(new Qi({customAttribution:O.customAttribution})),this.addControl(new Wi,O.logoPosition),this.on(\"style.load\",function(){K.transform.unmodified&&K.jumpTo(K.style.stylesheet)}),this.on(\"data\",function(we){K._update(we.dataType===\"style\"),K.fire(new i.Event(we.dataType+\"data\",we))}),this.on(\"dataloading\",function(we){K.fire(new i.Event(we.dataType+\"dataloading\",we))})}Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D;var J={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return D.prototype._getMapId=function(){return this._mapId},D.prototype.addControl=function(K,de){if(de===void 0&&(K.getDefaultPosition?de=K.getDefaultPosition():de=\"top-right\"),!K||!K.onAdd)return this.fire(new i.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var ne=K.onAdd(this);this._controls.push(K);var we=this._controlPositions[de];return de.indexOf(\"bottom\")!==-1?we.insertBefore(ne,we.firstChild):we.appendChild(ne),this},D.prototype.removeControl=function(K){if(!K||!K.onRemove)return this.fire(new i.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var de=this._controls.indexOf(K);return de>-1&&this._controls.splice(de,1),K.onRemove(this),this},D.prototype.hasControl=function(K){return this._controls.indexOf(K)>-1},D.prototype.resize=function(K){var de=this._containerDimensions(),ne=de[0],we=de[1];this._resizeCanvas(ne,we),this.transform.resize(ne,we),this.painter.resize(ne,we);var Oe=!this._moving;return Oe&&(this.stop(),this.fire(new i.Event(\"movestart\",K)).fire(new i.Event(\"move\",K))),this.fire(new i.Event(\"resize\",K)),Oe&&this.fire(new i.Event(\"moveend\",K)),this},D.prototype.getBounds=function(){return this.transform.getBounds()},D.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},D.prototype.setMaxBounds=function(K){return this.transform.setMaxBounds(i.LngLatBounds.convert(K)),this._update()},D.prototype.setMinZoom=function(K){if(K=K==null?ui:K,K>=ui&&K<=this.transform.maxZoom)return this.transform.minZoom=K,this._update(),this.getZoom()<K&&this.setZoom(K),this;throw new Error(\"minZoom must be between \"+ui+\" and the current maxZoom, inclusive\")},D.prototype.getMinZoom=function(){return this.transform.minZoom},D.prototype.setMaxZoom=function(K){if(K=K==null?nn:K,K>=this.transform.minZoom)return this.transform.maxZoom=K,this._update(),this.getZoom()>K&&this.setZoom(K),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},D.prototype.getMaxZoom=function(){return this.transform.maxZoom},D.prototype.setMinPitch=function(K){if(K=K==null?Zi:K,K<Zi)throw new Error(\"minPitch must be greater than or equal to \"+Zi);if(K>=Zi&&K<=this.transform.maxPitch)return this.transform.minPitch=K,this._update(),this.getPitch()<K&&this.setPitch(K),this;throw new Error(\"minPitch must be between \"+Zi+\" and the current maxPitch, inclusive\")},D.prototype.getMinPitch=function(){return this.transform.minPitch},D.prototype.setMaxPitch=function(K){if(K=K==null?qn:K,K>qn)throw new Error(\"maxPitch must be less than or equal to \"+qn);if(K>=this.transform.minPitch)return this.transform.maxPitch=K,this._update(),this.getPitch()>K&&this.setPitch(K),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},D.prototype.getMaxPitch=function(){return this.transform.maxPitch},D.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},D.prototype.setRenderWorldCopies=function(K){return this.transform.renderWorldCopies=K,this._update()},D.prototype.project=function(K){return this.transform.locationPoint(i.LngLat.convert(K))},D.prototype.unproject=function(K){return this.transform.pointLocation(i.Point.convert(K))},D.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},D.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},D.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},D.prototype._createDelegatedListener=function(K,de,ne){var we=this,Oe;if(K===\"mouseenter\"||K===\"mouseover\"){var ht=!1,Yt=function(It){var Bt=we.getLayer(de)?we.queryRenderedFeatures(It.point,{layers:[de]}):[];Bt.length?ht||(ht=!0,ne.call(we,new se(K,we,It.originalEvent,{features:Bt}))):ht=!1},cr=function(){ht=!1};return{layer:de,listener:ne,delegates:{mousemove:Yt,mouseout:cr}}}else if(K===\"mouseleave\"||K===\"mouseout\"){var Nt=!1,Ne=function(It){var Bt=we.getLayer(de)?we.queryRenderedFeatures(It.point,{layers:[de]}):[];Bt.length?Nt=!0:Nt&&(Nt=!1,ne.call(we,new se(K,we,It.originalEvent)))},$e=function(It){Nt&&(Nt=!1,ne.call(we,new se(K,we,It.originalEvent)))};return{layer:de,listener:ne,delegates:{mousemove:Ne,mouseout:$e}}}else{var st=function(It){var Bt=we.getLayer(de)?we.queryRenderedFeatures(It.point,{layers:[de]}):[];Bt.length&&(It.features=Bt,ne.call(we,It),delete It.features)};return{layer:de,listener:ne,delegates:(Oe={},Oe[K]=st,Oe)}}},D.prototype.on=function(K,de,ne){if(ne===void 0)return Y.prototype.on.call(this,K,de);var we=this._createDelegatedListener(K,de,ne);this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[K]=this._delegatedListeners[K]||[],this._delegatedListeners[K].push(we);for(var Oe in we.delegates)this.on(Oe,we.delegates[Oe]);return this},D.prototype.once=function(K,de,ne){if(ne===void 0)return Y.prototype.once.call(this,K,de);var we=this._createDelegatedListener(K,de,ne);for(var Oe in we.delegates)this.once(Oe,we.delegates[Oe]);return this},D.prototype.off=function(K,de,ne){var we=this;if(ne===void 0)return Y.prototype.off.call(this,K,de);var Oe=function(ht){for(var Yt=ht[K],cr=0;cr<Yt.length;cr++){var Nt=Yt[cr];if(Nt.layer===de&&Nt.listener===ne){for(var Ne in Nt.delegates)we.off(Ne,Nt.delegates[Ne]);return Yt.splice(cr,1),we}}};return this._delegatedListeners&&this._delegatedListeners[K]&&Oe(this._delegatedListeners),this},D.prototype.queryRenderedFeatures=function(K,de){if(!this.style)return[];de===void 0&&K!==void 0&&!(K instanceof i.Point)&&!Array.isArray(K)&&(de=K,K=void 0),de=de||{},K=K||[[0,0],[this.transform.width,this.transform.height]];var ne;if(K instanceof i.Point||typeof K[0]==\"number\")ne=[i.Point.convert(K)];else{var we=i.Point.convert(K[0]),Oe=i.Point.convert(K[1]);ne=[we,new i.Point(Oe.x,we.y),Oe,new i.Point(we.x,Oe.y),we]}return this.style.queryRenderedFeatures(ne,de,this.transform)},D.prototype.querySourceFeatures=function(K,de){return this.style.querySourceFeatures(K,de)},D.prototype.setStyle=function(K,de){return de=i.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},de),de.diff!==!1&&de.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&K?(this._diffStyle(K,de),this):(this._localIdeographFontFamily=de.localIdeographFontFamily,this._updateStyle(K,de))},D.prototype._getUIString=function(K){var de=this._locale[K];if(de==null)throw new Error(\"Missing UI string '\"+K+\"'\");return de},D.prototype._updateStyle=function(K,de){if(this.style&&(this.style.setEventedParent(null),this.style._remove()),K)this.style=new yu(this,de||{});else return delete this.style,this;return this.style.setEventedParent(this,{style:this.style}),typeof K==\"string\"?this.style.loadURL(K):this.style.loadJSON(K),this},D.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new yu(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},D.prototype._diffStyle=function(K,de){var ne=this;if(typeof K==\"string\"){var we=this._requestManager.normalizeStyleURL(K),Oe=this._requestManager.transformRequest(we,i.ResourceType.Style);i.getJSON(Oe,function(ht,Yt){ht?ne.fire(new i.ErrorEvent(ht)):Yt&&ne._updateDiff(Yt,de)})}else typeof K==\"object\"&&this._updateDiff(K,de)},D.prototype._updateDiff=function(K,de){try{this.style.setState(K)&&this._update(!0)}catch(ne){i.warnOnce(\"Unable to perform style diff: \"+(ne.message||ne.error||ne)+\".  Rebuilding the style from scratch.\"),this._updateStyle(K,de)}},D.prototype.getStyle=function(){if(this.style)return this.style.serialize()},D.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():i.warnOnce(\"There is no style added to the map.\")},D.prototype.addSource=function(K,de){return this._lazyInitEmptyStyle(),this.style.addSource(K,de),this._update(!0)},D.prototype.isSourceLoaded=function(K){var de=this.style&&this.style.sourceCaches[K];if(de===void 0){this.fire(new i.ErrorEvent(new Error(\"There is no source with ID '\"+K+\"'\")));return}return de.loaded()},D.prototype.areTilesLoaded=function(){var K=this.style&&this.style.sourceCaches;for(var de in K){var ne=K[de],we=ne._tiles;for(var Oe in we){var ht=we[Oe];if(!(ht.state===\"loaded\"||ht.state===\"errored\"))return!1}}return!0},D.prototype.addSourceType=function(K,de,ne){return this._lazyInitEmptyStyle(),this.style.addSourceType(K,de,ne)},D.prototype.removeSource=function(K){return this.style.removeSource(K),this._update(!0)},D.prototype.getSource=function(K){return this.style.getSource(K)},D.prototype.addImage=function(K,de,ne){ne===void 0&&(ne={});var we=ne.pixelRatio;we===void 0&&(we=1);var Oe=ne.sdf;Oe===void 0&&(Oe=!1);var ht=ne.stretchX,Yt=ne.stretchY,cr=ne.content;this._lazyInitEmptyStyle();var Nt=0;if(de instanceof Zr||Ii&&de instanceof Ii){var Ne=i.browser.getImageData(de),$e=Ne.width,st=Ne.height,It=Ne.data;this.style.addImage(K,{data:new i.RGBAImage({width:$e,height:st},It),pixelRatio:we,stretchX:ht,stretchY:Yt,content:cr,sdf:Oe,version:Nt})}else{if(de.width===void 0||de.height===void 0)return this.fire(new i.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var Bt=de.width,Ft=de.height,Ut=de.data,zt=de;this.style.addImage(K,{data:new i.RGBAImage({width:Bt,height:Ft},new Uint8Array(Ut)),pixelRatio:we,stretchX:ht,stretchY:Yt,content:cr,sdf:Oe,version:Nt,userImage:zt}),zt.onAdd&&zt.onAdd(this,K)}},D.prototype.updateImage=function(K,de){var ne=this.style.getImage(K);if(!ne)return this.fire(new i.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var we=de instanceof Zr||Ii&&de instanceof Ii?i.browser.getImageData(de):de,Oe=we.width,ht=we.height,Yt=we.data;if(Oe===void 0||ht===void 0)return this.fire(new i.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(Oe!==ne.data.width||ht!==ne.data.height)return this.fire(new i.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var cr=!(de instanceof Zr||Ii&&de instanceof Ii);ne.data.replace(Yt,cr),this.style.updateImage(K,ne)},D.prototype.hasImage=function(K){return K?!!this.style.getImage(K):(this.fire(new i.ErrorEvent(new Error(\"Missing required image id\"))),!1)},D.prototype.removeImage=function(K){this.style.removeImage(K)},D.prototype.loadImage=function(K,de){i.getImage(this._requestManager.transformRequest(K,i.ResourceType.Image),de)},D.prototype.listImages=function(){return this.style.listImages()},D.prototype.addLayer=function(K,de){return this._lazyInitEmptyStyle(),this.style.addLayer(K,de),this._update(!0)},D.prototype.moveLayer=function(K,de){return this.style.moveLayer(K,de),this._update(!0)},D.prototype.removeLayer=function(K){return this.style.removeLayer(K),this._update(!0)},D.prototype.getLayer=function(K){return this.style.getLayer(K)},D.prototype.setLayerZoomRange=function(K,de,ne){return this.style.setLayerZoomRange(K,de,ne),this._update(!0)},D.prototype.setFilter=function(K,de,ne){return ne===void 0&&(ne={}),this.style.setFilter(K,de,ne),this._update(!0)},D.prototype.getFilter=function(K){return this.style.getFilter(K)},D.prototype.setPaintProperty=function(K,de,ne,we){return we===void 0&&(we={}),this.style.setPaintProperty(K,de,ne,we),this._update(!0)},D.prototype.getPaintProperty=function(K,de){return this.style.getPaintProperty(K,de)},D.prototype.setLayoutProperty=function(K,de,ne,we){return we===void 0&&(we={}),this.style.setLayoutProperty(K,de,ne,we),this._update(!0)},D.prototype.getLayoutProperty=function(K,de){return this.style.getLayoutProperty(K,de)},D.prototype.setLight=function(K,de){return de===void 0&&(de={}),this._lazyInitEmptyStyle(),this.style.setLight(K,de),this._update(!0)},D.prototype.getLight=function(){return this.style.getLight()},D.prototype.setFeatureState=function(K,de){return this.style.setFeatureState(K,de),this._update()},D.prototype.removeFeatureState=function(K,de){return this.style.removeFeatureState(K,de),this._update()},D.prototype.getFeatureState=function(K){return this.style.getFeatureState(K)},D.prototype.getContainer=function(){return this._container},D.prototype.getCanvasContainer=function(){return this._canvasContainer},D.prototype.getCanvas=function(){return this._canvas},D.prototype._containerDimensions=function(){var K=0,de=0;return this._container&&(K=this._container.clientWidth||400,de=this._container.clientHeight||300),[K,de]},D.prototype._detectMissingCSS=function(){var K=i.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\");K!==\"rgb(250, 128, 114)\"&&i.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},D.prototype._setupContainer=function(){var K=this._container;K.classList.add(\"mapboxgl-map\");var de=this._missingCSSCanary=o.create(\"div\",\"mapboxgl-canary\",K);de.style.visibility=\"hidden\",this._detectMissingCSS();var ne=this._canvasContainer=o.create(\"div\",\"mapboxgl-canvas-container\",K);this._interactive&&ne.classList.add(\"mapboxgl-interactive\"),this._canvas=o.create(\"canvas\",\"mapboxgl-canvas\",ne),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\"),this._canvas.setAttribute(\"role\",\"region\");var we=this._containerDimensions();this._resizeCanvas(we[0],we[1]);var Oe=this._controlContainer=o.create(\"div\",\"mapboxgl-control-container\",K),ht=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(Yt){ht[Yt]=o.create(\"div\",\"mapboxgl-ctrl-\"+Yt,Oe)}),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)},D.prototype._resizeCanvas=function(K,de){var ne=i.browser.devicePixelRatio||1;this._canvas.width=ne*K,this._canvas.height=ne*de,this._canvas.style.width=K+\"px\",this._canvas.style.height=de+\"px\"},D.prototype._setupPainter=function(){var K=i.extend({},a.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),de=this._canvas.getContext(\"webgl\",K)||this._canvas.getContext(\"experimental-webgl\",K);if(!de){this.fire(new i.ErrorEvent(new Error(\"Failed to initialize WebGL\")));return}this.painter=new mo(de,this.transform),i.webpSupported.testSupport(de)},D.prototype._contextLost=function(K){K.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new i.Event(\"webglcontextlost\",{originalEvent:K}))},D.prototype._contextRestored=function(K){this._setupPainter(),this.resize(),this._update(),this.fire(new i.Event(\"webglcontextrestored\",{originalEvent:K}))},D.prototype._onMapScroll=function(K){if(K.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},D.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},D.prototype._update=function(K){return this.style?(this._styleDirty=this._styleDirty||K,this._sourcesDirty=!0,this.triggerRepaint(),this):this},D.prototype._requestRenderFrame=function(K){return this._update(),this._renderTaskQueue.add(K)},D.prototype._cancelRenderFrame=function(K){this._renderTaskQueue.remove(K)},D.prototype._render=function(K){var de=this,ne,we=0,Oe=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(ne=Oe.createQueryEXT(),Oe.beginQueryEXT(Oe.TIME_ELAPSED_EXT,ne),we=i.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(K),!this._removed){var ht=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var Yt=this.transform.zoom,cr=i.browser.now();this.style.zoomHistory.update(Yt,cr);var Nt=new i.EvaluationParameters(Yt,{now:cr,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),Ne=Nt.crossFadingFactor();(Ne!==1||Ne!==this._crossFadingFactor)&&(ht=!0,this._crossFadingFactor=Ne),this.style.update(Nt)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new i.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new i.Event(\"load\"))),this.style&&(this.style.hasTransitions()||ht)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var $e=i.browser.now()-we;Oe.endQueryEXT(Oe.TIME_ELAPSED_EXT,ne),setTimeout(function(){var Bt=Oe.getQueryObjectEXT(ne,Oe.QUERY_RESULT_EXT)/1e6;Oe.deleteQueryEXT(ne),de.fire(new i.Event(\"gpu-timing-frame\",{cpuTime:$e,gpuTime:Bt}))},50)}if(this.listens(\"gpu-timing-layer\")){var st=this.painter.collectGpuTimers();setTimeout(function(){var Bt=de.painter.queryGpuTimers(st);de.fire(new i.Event(\"gpu-timing-layer\",{layerTimes:Bt}))},50)}var It=this._sourcesDirty||this._styleDirty||this._placementDirty;return It||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new i.Event(\"idle\")),this._loaded&&!this._fullyLoaded&&!It&&(this._fullyLoaded=!0),this}},D.prototype.remove=function(){this._hash&&this._hash.remove();for(var K=0,de=this._controls;K<de.length;K+=1){var ne=de[K];ne.onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof i.window!=\"undefined\"&&(i.window.removeEventListener(\"resize\",this._onWindowResize,!1),i.window.removeEventListener(\"orientationchange\",this._onWindowResize,!1),i.window.removeEventListener(\"online\",this._onWindowOnline,!1));var we=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");we&&we.loseContext&&we.loseContext(),mn(this._canvasContainer),mn(this._controlContainer),mn(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new i.Event(\"remove\"))},D.prototype.triggerRepaint=function(){var K=this;this.style&&!this._frame&&(this._frame=i.browser.frame(function(de){K._frame=null,K._render(de)}))},D.prototype._onWindowOnline=function(){this._update()},D.prototype._onWindowResize=function(K){this._trackResize&&this.resize({originalEvent:K})._update()},J.showTileBoundaries.get=function(){return!!this._showTileBoundaries},J.showTileBoundaries.set=function(O){this._showTileBoundaries!==O&&(this._showTileBoundaries=O,this._update())},J.showPadding.get=function(){return!!this._showPadding},J.showPadding.set=function(O){this._showPadding!==O&&(this._showPadding=O,this._update())},J.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},J.showCollisionBoxes.set=function(O){this._showCollisionBoxes!==O&&(this._showCollisionBoxes=O,O?this.style._generateCollisionBoxes():this._update())},J.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},J.showOverdrawInspector.set=function(O){this._showOverdrawInspector!==O&&(this._showOverdrawInspector=O,this._update())},J.repaint.get=function(){return!!this._repaint},J.repaint.set=function(O){this._repaint!==O&&(this._repaint=O,this.triggerRepaint())},J.vertices.get=function(){return!!this._vertices},J.vertices.set=function(O){this._vertices=O,this._update()},D.prototype._setCacheLimits=function(K,de){i.setCacheLimits(K,de)},J.version.get=function(){return i.version},Object.defineProperties(D.prototype,J),D})(Vn);function mn(Y){Y.parentNode&&Y.parentNode.removeChild(Y)}var Ji={showCompass:!0,showZoom:!0,visualizePitch:!1},Ui=function(D){var J=this;this.options=i.extend({},Ji,D),this._container=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",function(O){return O.preventDefault()}),this.options.showZoom&&(i.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",function(O){return J._map.zoomIn({},{originalEvent:O})}),o.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",function(O){return J._map.zoomOut({},{originalEvent:O})}),o.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(i.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",function(O){J.options.visualizePitch?J._map.resetNorthPitch({},{originalEvent:O}):J._map.resetNorth({},{originalEvent:O})}),this._compassIcon=o.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Ui.prototype._updateZoomButtons=function(){var D=this._map.getZoom(),J=D===this._map.getMaxZoom(),O=D===this._map.getMinZoom();this._zoomInButton.disabled=J,this._zoomOutButton.disabled=O,this._zoomInButton.setAttribute(\"aria-disabled\",J.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",O.toString())},Ui.prototype._rotateCompassArrow=function(){var D=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=D},Ui.prototype.onAdd=function(D){return this._map=D,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Bi(this._map,this._compass,this.options.visualizePitch)),this._container},Ui.prototype.onRemove=function(){o.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Ui.prototype._createButton=function(D,J){var O=o.create(\"button\",D,this._container);return O.type=\"button\",O.addEventListener(\"click\",J),O},Ui.prototype._setButtonTitle=function(D,J){var O=this._map._getUIString(\"NavigationControl.\"+J);D.title=O,D.setAttribute(\"aria-label\",O)};var Bi=function(D,J,O){O===void 0&&(O=!1),this._clickTolerance=10,this.element=J,this.mouseRotate=new Pn({clickTolerance:D.dragRotate._mouseRotate._clickTolerance}),this.map=D,O&&(this.mousePitch=new kn({clickTolerance:D.dragRotate._mousePitch._clickTolerance})),i.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),o.addEventListener(J,\"mousedown\",this.mousedown),o.addEventListener(J,\"touchstart\",this.touchstart,{passive:!1}),o.addEventListener(J,\"touchmove\",this.touchmove),o.addEventListener(J,\"touchend\",this.touchend),o.addEventListener(J,\"touchcancel\",this.reset)};Bi.prototype.down=function(D,J){this.mouseRotate.mousedown(D,J),this.mousePitch&&this.mousePitch.mousedown(D,J),o.disableDrag()},Bi.prototype.move=function(D,J){var O=this.map,K=this.mouseRotate.mousemoveWindow(D,J);if(K&&K.bearingDelta&&O.setBearing(O.getBearing()+K.bearingDelta),this.mousePitch){var de=this.mousePitch.mousemoveWindow(D,J);de&&de.pitchDelta&&O.setPitch(O.getPitch()+de.pitchDelta)}},Bi.prototype.off=function(){var D=this.element;o.removeEventListener(D,\"mousedown\",this.mousedown),o.removeEventListener(D,\"touchstart\",this.touchstart,{passive:!1}),o.removeEventListener(D,\"touchmove\",this.touchmove),o.removeEventListener(D,\"touchend\",this.touchend),o.removeEventListener(D,\"touchcancel\",this.reset),this.offTemp()},Bi.prototype.offTemp=function(){o.enableDrag(),o.removeEventListener(i.window,\"mousemove\",this.mousemove),o.removeEventListener(i.window,\"mouseup\",this.mouseup)},Bi.prototype.mousedown=function(D){this.down(i.extend({},D,{ctrlKey:!0,preventDefault:function(){return D.preventDefault()}}),o.mousePos(this.element,D)),o.addEventListener(i.window,\"mousemove\",this.mousemove),o.addEventListener(i.window,\"mouseup\",this.mouseup)},Bi.prototype.mousemove=function(D){this.move(D,o.mousePos(this.element,D))},Bi.prototype.mouseup=function(D){this.mouseRotate.mouseupWindow(D),this.mousePitch&&this.mousePitch.mouseupWindow(D),this.offTemp()},Bi.prototype.touchstart=function(D){D.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=o.touchPos(this.element,D.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return D.preventDefault()}},this._startPos))},Bi.prototype.touchmove=function(D){D.targetTouches.length!==1?this.reset():(this._lastPos=o.touchPos(this.element,D.targetTouches)[0],this.move({preventDefault:function(){return D.preventDefault()}},this._lastPos))},Bi.prototype.touchend=function(D){D.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Bi.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};function pn(Y,D,J){if(Y=new i.LngLat(Y.lng,Y.lat),D){var O=new i.LngLat(Y.lng-360,Y.lat),K=new i.LngLat(Y.lng+360,Y.lat),de=J.locationPoint(Y).distSqr(D);J.locationPoint(O).distSqr(D)<de?Y=O:J.locationPoint(K).distSqr(D)<de&&(Y=K)}for(;Math.abs(Y.lng-J.center.lng)>180;){var ne=J.locationPoint(Y);if(ne.x>=0&&ne.y>=0&&ne.x<=J.width&&ne.y<=J.height)break;Y.lng>J.center.lng?Y.lng-=360:Y.lng+=360}return Y}var Nn={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function aa(Y,D,J){var O=Y.classList;for(var K in Nn)O.remove(\"mapboxgl-\"+J+\"-anchor-\"+K);O.add(\"mapboxgl-\"+J+\"-anchor-\"+D)}var Ki=(function(Y){function D(J,O){if(Y.call(this),(J instanceof i.window.HTMLElement||O)&&(J=i.extend({element:J},O)),i.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=J&&J.anchor||\"center\",this._color=J&&J.color||\"#3FB1CE\",this._scale=J&&J.scale||1,this._draggable=J&&J.draggable||!1,this._clickTolerance=J&&J.clickTolerance||0,this._isDragging=!1,this._state=\"inactive\",this._rotation=J&&J.rotation||0,this._rotationAlignment=J&&J.rotationAlignment||\"auto\",this._pitchAlignment=J&&J.pitchAlignment&&J.pitchAlignment!==\"auto\"?J.pitchAlignment:this._rotationAlignment,!J||!J.element){this._defaultMarker=!0,this._element=o.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var K=o.createNS(\"http://www.w3.org/2000/svg\",\"svg\"),de=41,ne=27;K.setAttributeNS(null,\"display\",\"block\"),K.setAttributeNS(null,\"height\",de+\"px\"),K.setAttributeNS(null,\"width\",ne+\"px\"),K.setAttributeNS(null,\"viewBox\",\"0 0 \"+ne+\" \"+de);var we=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");we.setAttributeNS(null,\"stroke\",\"none\"),we.setAttributeNS(null,\"stroke-width\",\"1\"),we.setAttributeNS(null,\"fill\",\"none\"),we.setAttributeNS(null,\"fill-rule\",\"evenodd\");var Oe=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");Oe.setAttributeNS(null,\"fill-rule\",\"nonzero\");var ht=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");ht.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),ht.setAttributeNS(null,\"fill\",\"#000000\");for(var Yt=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}],cr=0,Nt=Yt;cr<Nt.length;cr+=1){var Ne=Nt[cr],$e=o.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");$e.setAttributeNS(null,\"opacity\",\"0.04\"),$e.setAttributeNS(null,\"cx\",\"10.5\"),$e.setAttributeNS(null,\"cy\",\"5.80029008\"),$e.setAttributeNS(null,\"rx\",Ne.rx),$e.setAttributeNS(null,\"ry\",Ne.ry),ht.appendChild($e)}var st=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");st.setAttributeNS(null,\"fill\",this._color);var It=o.createNS(\"http://www.w3.org/2000/svg\",\"path\");It.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),st.appendChild(It);var Bt=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");Bt.setAttributeNS(null,\"opacity\",\"0.25\"),Bt.setAttributeNS(null,\"fill\",\"#000000\");var Ft=o.createNS(\"http://www.w3.org/2000/svg\",\"path\");Ft.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),Bt.appendChild(Ft);var Ut=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");Ut.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),Ut.setAttributeNS(null,\"fill\",\"#FFFFFF\");var zt=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");zt.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var _t=o.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_t.setAttributeNS(null,\"fill\",\"#000000\"),_t.setAttributeNS(null,\"opacity\",\"0.25\"),_t.setAttributeNS(null,\"cx\",\"5.5\"),_t.setAttributeNS(null,\"cy\",\"5.5\"),_t.setAttributeNS(null,\"r\",\"5.4999962\");var Dt=o.createNS(\"http://www.w3.org/2000/svg\",\"circle\");Dt.setAttributeNS(null,\"fill\",\"#FFFFFF\"),Dt.setAttributeNS(null,\"cx\",\"5.5\"),Dt.setAttributeNS(null,\"cy\",\"5.5\"),Dt.setAttributeNS(null,\"r\",\"5.4999962\"),zt.appendChild(_t),zt.appendChild(Dt),Oe.appendChild(ht),Oe.appendChild(st),Oe.appendChild(Bt),Oe.appendChild(Ut),Oe.appendChild(zt),K.appendChild(Oe),K.setAttributeNS(null,\"height\",de*this._scale+\"px\"),K.setAttributeNS(null,\"width\",ne*this._scale+\"px\"),this._element.appendChild(K),this._offset=i.Point.convert(J&&J.offset||[0,-14])}else this._element=J.element,this._offset=i.Point.convert(J&&J.offset||[0,0]);this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",function(vt){vt.preventDefault()}),this._element.addEventListener(\"mousedown\",function(vt){vt.preventDefault()}),aa(this._element,this._anchor,\"marker\"),this._popup=null}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.addTo=function(O){return this.remove(),this._map=O,O.getCanvasContainer().appendChild(this._element),O.on(\"move\",this._update),O.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},D.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),o.remove(this._element),this._popup&&this._popup.remove(),this},D.prototype.getLngLat=function(){return this._lngLat},D.prototype.setLngLat=function(O){return this._lngLat=i.LngLat.convert(O),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},D.prototype.getElement=function(){return this._element},D.prototype.setPopup=function(O){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),O){if(!(\"offset\"in O.options)){var K=38.1,de=13.5,ne=Math.sqrt(Math.pow(de,2)/2);O.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-K],\"bottom-left\":[ne,(K-de+ne)*-1],\"bottom-right\":[-ne,(K-de+ne)*-1],left:[de,(K-de)*-1],right:[-de,(K-de)*-1]}:this._offset}this._popup=O,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},D.prototype._onKeyPress=function(O){var K=O.code,de=O.charCode||O.keyCode;(K===\"Space\"||K===\"Enter\"||de===32||de===13)&&this.togglePopup()},D.prototype._onMapClick=function(O){var K=O.originalEvent.target,de=this._element;this._popup&&(K===de||de.contains(K))&&this.togglePopup()},D.prototype.getPopup=function(){return this._popup},D.prototype.togglePopup=function(){var O=this._popup;if(O)O.isOpen()?O.remove():O.addTo(this._map);else return this;return this},D.prototype._update=function(O){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=pn(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var K=\"\";this._rotationAlignment===\"viewport\"||this._rotationAlignment===\"auto\"?K=\"rotateZ(\"+this._rotation+\"deg)\":this._rotationAlignment===\"map\"&&(K=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var de=\"\";this._pitchAlignment===\"viewport\"||this._pitchAlignment===\"auto\"?de=\"rotateX(0deg)\":this._pitchAlignment===\"map\"&&(de=\"rotateX(\"+this._map.getPitch()+\"deg)\"),(!O||O.type===\"moveend\")&&(this._pos=this._pos.round()),o.setTransform(this._element,Nn[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+de+\" \"+K)}},D.prototype.getOffset=function(){return this._offset},D.prototype.setOffset=function(O){return this._offset=i.Point.convert(O),this._update(),this},D.prototype._onMove=function(O){if(!this._isDragging){var K=this._clickTolerance||this._map._clickTolerance;this._isDragging=O.point.dist(this._pointerdownPos)>=K}this._isDragging&&(this._pos=O.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",this._state===\"pending\"&&(this._state=\"active\",this.fire(new i.Event(\"dragstart\"))),this.fire(new i.Event(\"drag\")))},D.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),this._state===\"active\"&&this.fire(new i.Event(\"dragend\")),this._state=\"inactive\"},D.prototype._addDragHandler=function(O){this._element.contains(O.originalEvent.target)&&(O.preventDefault(),this._positionDelta=O.point.sub(this._pos).add(this._offset),this._pointerdownPos=O.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},D.prototype.setDraggable=function(O){return this._draggable=!!O,this._map&&(O?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},D.prototype.isDraggable=function(){return this._draggable},D.prototype.setRotation=function(O){return this._rotation=O||0,this._update(),this},D.prototype.getRotation=function(){return this._rotation},D.prototype.setRotationAlignment=function(O){return this._rotationAlignment=O||\"auto\",this._update(),this},D.prototype.getRotationAlignment=function(){return this._rotationAlignment},D.prototype.setPitchAlignment=function(O){return this._pitchAlignment=O&&O!==\"auto\"?O:this._rotationAlignment,this._update(),this},D.prototype.getPitchAlignment=function(){return this._pitchAlignment},D})(i.Evented),Cn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},ia;function sa(Y){ia!==void 0?Y(ia):i.window.navigator.permissions!==void 0?i.window.navigator.permissions.query({name:\"geolocation\"}).then(function(D){ia=D.state!==\"denied\",Y(ia)}):(ia=!!i.window.navigator.geolocation,Y(ia))}var ba=0,is=!1,Zs=(function(Y){function D(J){Y.call(this),this.options=i.extend({},Cn,J),i.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.onAdd=function(O){return this._map=O,this._container=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),sa(this._setupUI),this._container},D.prototype.onRemove=function(){this._geolocationWatchID!==void 0&&(i.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),o.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,ba=0,is=!1},D.prototype._isOutOfMapMaxBounds=function(O){var K=this._map.getMaxBounds(),de=O.coords;return K&&(de.longitude<K.getWest()||de.longitude>K.getEast()||de.latitude<K.getSouth()||de.latitude>K.getNorth())},D.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break}},D.prototype._onSuccess=function(O){if(this._map){if(this._isOutOfMapMaxBounds(O)){this._setErrorState(),this.fire(new i.Event(\"outofmaxbounds\",O)),this._updateMarker(),this._finish();return}if(this.options.trackUserLocation)switch(this._lastKnownPosition=O,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break}this.options.showUserLocation&&this._watchState!==\"OFF\"&&this._updateMarker(O),(!this.options.trackUserLocation||this._watchState===\"ACTIVE_LOCK\")&&this._updateCamera(O),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new i.Event(\"geolocate\",O)),this._finish()}},D.prototype._updateCamera=function(O){var K=new i.LngLat(O.coords.longitude,O.coords.latitude),de=O.coords.accuracy,ne=this._map.getBearing(),we=i.extend({bearing:ne},this.options.fitBoundsOptions);this._map.fitBounds(K.toBounds(de),we,{geolocateSource:!0})},D.prototype._updateMarker=function(O){if(O){var K=new i.LngLat(O.coords.longitude,O.coords.latitude);this._accuracyCircleMarker.setLngLat(K).addTo(this._map),this._userLocationDotMarker.setLngLat(K).addTo(this._map),this._accuracy=O.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},D.prototype._updateCircleRadius=function(){var O=this._map._container.clientHeight/2,K=this._map.unproject([0,O]),de=this._map.unproject([1,O]),ne=K.distanceTo(de),we=Math.ceil(2*this._accuracy/ne);this._circleElement.style.width=we+\"px\",this._circleElement.style.height=we+\"px\"},D.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},D.prototype._onError=function(O){if(this._map){if(this.options.trackUserLocation)if(O.code===1){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var K=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=K,this._geolocateButton.setAttribute(\"aria-label\",K),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(O.code===3&&is)return;this._setErrorState()}this._watchState!==\"OFF\"&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new i.Event(\"error\",O)),this._finish()}},D.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},D.prototype._setupUI=function(O){var K=this;if(this._container.addEventListener(\"contextmenu\",function(we){return we.preventDefault()}),this._geolocateButton=o.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),o.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",O===!1){i.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var de=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=de,this._geolocateButton.setAttribute(\"aria-label\",de)}else{var ne=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=ne,this._geolocateButton.setAttribute(\"aria-label\",ne)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=o.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Ki(this._dotElement),this._circleElement=o.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Ki({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",function(we){var Oe=we.originalEvent&&we.originalEvent.type===\"resize\";!we.geolocateSource&&K._watchState===\"ACTIVE_LOCK\"&&!Oe&&(K._watchState=\"BACKGROUND\",K._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),K._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),K.fire(new i.Event(\"trackuserlocationend\")))})},D.prototype.trigger=function(){if(!this._setup)return i.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new i.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":ba--,is=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new i.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new i.Event(\"trackuserlocationstart\"));break}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\");break}if(this._watchState===\"OFF\"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),ba++;var O;ba>1?(O={maximumAge:6e5,timeout:0},is=!0):(O=this.options.positionOptions,is=!1),this._geolocationWatchID=i.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,O)}}else i.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},D.prototype._clearWatch=function(){i.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},D})(i.Evented),Ga={maxWidth:100,unit:\"metric\"},El=function(D){this.options=i.extend({},Ga,D),i.bindAll([\"_onMove\",\"setUnit\"],this)};El.prototype.getDefaultPosition=function(){return\"bottom-left\"},El.prototype._onMove=function(){zo(this._map,this._container,this.options)},El.prototype.onAdd=function(D){return this._map=D,this._container=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",D.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},El.prototype.onRemove=function(){o.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},El.prototype.setUnit=function(D){this.options.unit=D,zo(this._map,this._container,this.options)};function zo(Y,D,J){var O=J&&J.maxWidth||100,K=Y._container.clientHeight/2,de=Y.unproject([0,K]),ne=Y.unproject([O,K]),we=de.distanceTo(ne);if(J&&J.unit===\"imperial\"){var Oe=3.2808*we;if(Oe>5280){var ht=Oe/5280;Qs(D,O,ht,Y._getUIString(\"ScaleControl.Miles\"))}else Qs(D,O,Oe,Y._getUIString(\"ScaleControl.Feet\"))}else if(J&&J.unit===\"nautical\"){var Yt=we/1852;Qs(D,O,Yt,Y._getUIString(\"ScaleControl.NauticalMiles\"))}else we>=1e3?Qs(D,O,we/1e3,Y._getUIString(\"ScaleControl.Kilometers\")):Qs(D,O,we,Y._getUIString(\"ScaleControl.Meters\"))}function Qs(Y,D,J,O){var K=Ul(J),de=K/J;Y.style.width=D*de+\"px\",Y.innerHTML=K+\"&nbsp;\"+O}function nl(Y){var D=Math.pow(10,Math.ceil(-Math.log(Y)/Math.LN10));return Math.round(Y*D)/D}function Ul(Y){var D=Math.pow(10,(\"\"+Math.floor(Y)).length-1),J=Y/D;return J=J>=10?10:J>=5?5:J>=3?3:J>=2?2:J>=1?1:nl(J),D*J}var ss=function(D){this._fullscreen=!1,D&&D.container&&(D.container instanceof i.window.HTMLElement?this._container=D.container:i.warnOnce(\"Full screen control 'container' must be a DOM element.\")),i.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in i.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in i.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in i.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in i.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};ss.prototype.onAdd=function(D){return this._map=D,this._container||(this._container=this._map.getContainer()),this._controlContainer=o.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",i.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},ss.prototype.onRemove=function(){o.remove(this._controlContainer),this._map=null,i.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ss.prototype._checkFullscreenSupport=function(){return!!(i.window.document.fullscreenEnabled||i.window.document.mozFullScreenEnabled||i.window.document.msFullscreenEnabled||i.window.document.webkitFullscreenEnabled)},ss.prototype._setupUI=function(){var D=this._fullscreenButton=o.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);o.create(\"span\",\"mapboxgl-ctrl-icon\",D).setAttribute(\"aria-hidden\",!0),D.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),i.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ss.prototype._updateTitle=function(){var D=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",D),this._fullscreenButton.title=D},ss.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},ss.prototype._isFullscreen=function(){return this._fullscreen},ss.prototype._changeIcon=function(){var D=i.window.document.fullscreenElement||i.window.document.mozFullScreenElement||i.window.document.webkitFullscreenElement||i.window.document.msFullscreenElement;D===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},ss.prototype._onClickFullscreen=function(){this._isFullscreen()?i.window.document.exitFullscreen?i.window.document.exitFullscreen():i.window.document.mozCancelFullScreen?i.window.document.mozCancelFullScreen():i.window.document.msExitFullscreen?i.window.document.msExitFullscreen():i.window.document.webkitCancelFullScreen&&i.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Vs={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},Ys=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \"),wa=(function(Y){function D(J){Y.call(this),this.options=i.extend(Object.create(Vs),J),i.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return Y&&(D.__proto__=Y),D.prototype=Object.create(Y&&Y.prototype),D.prototype.constructor=D,D.prototype.addTo=function(O){return this._map&&this.remove(),this._map=O,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new i.Event(\"open\")),this},D.prototype.isOpen=function(){return!!this._map},D.prototype.remove=function(){return this._content&&o.remove(this._content),this._container&&(o.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new i.Event(\"close\")),this},D.prototype.getLngLat=function(){return this._lngLat},D.prototype.setLngLat=function(O){return this._lngLat=i.LngLat.convert(O),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},D.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},D.prototype.getElement=function(){return this._container},D.prototype.setText=function(O){return this.setDOMContent(i.window.document.createTextNode(O))},D.prototype.setHTML=function(O){var K=i.window.document.createDocumentFragment(),de=i.window.document.createElement(\"body\"),ne;for(de.innerHTML=O;ne=de.firstChild,!!ne;)K.appendChild(ne);return this.setDOMContent(K)},D.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},D.prototype.setMaxWidth=function(O){return this.options.maxWidth=O,this._update(),this},D.prototype.setDOMContent=function(O){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=o.create(\"div\",\"mapboxgl-popup-content\",this._container);return this._content.appendChild(O),this._createCloseButton(),this._update(),this._focusFirstElement(),this},D.prototype.addClassName=function(O){this._container&&this._container.classList.add(O)},D.prototype.removeClassName=function(O){this._container&&this._container.classList.remove(O)},D.prototype.setOffset=function(O){return this.options.offset=O,this._update(),this},D.prototype.toggleClassName=function(O){if(this._container)return this._container.classList.toggle(O)},D.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=o.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},D.prototype._onMouseUp=function(O){this._update(O.point)},D.prototype._onMouseMove=function(O){this._update(O.point)},D.prototype._onDrag=function(O){this._update(O.point)},D.prototype._update=function(O){var K=this,de=this._lngLat||this._trackPointer;if(!(!this._map||!de||!this._content)&&(this._container||(this._container=o.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=o.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach(function(Ne){return K._container.classList.add(Ne)}),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=pn(this._lngLat,this._pos,this._map.transform)),!(this._trackPointer&&!O))){var ne=this._pos=this._trackPointer&&O?O:this._map.project(this._lngLat),we=this.options.anchor,Oe=al(this.options.offset);if(!we){var ht=this._container.offsetWidth,Yt=this._container.offsetHeight,cr;ne.y+Oe.bottom.y<Yt?cr=[\"top\"]:ne.y>this._map.transform.height-Yt?cr=[\"bottom\"]:cr=[],ne.x<ht/2?cr.push(\"left\"):ne.x>this._map.transform.width-ht/2&&cr.push(\"right\"),cr.length===0?we=\"bottom\":we=cr.join(\"-\")}var Nt=ne.add(Oe[we]).round();o.setTransform(this._container,Nn[we]+\" translate(\"+Nt.x+\"px,\"+Nt.y+\"px)\"),aa(this._container,we,\"popup\")}},D.prototype._focusFirstElement=function(){if(!(!this.options.focusAfterOpen||!this._container)){var O=this._container.querySelector(Ys);O&&O.focus()}},D.prototype._onClose=function(){this.remove()},D})(i.Evented);function al(Y){if(Y)if(typeof Y==\"number\"){var D=Math.round(Math.sqrt(.5*Math.pow(Y,2)));return{center:new i.Point(0,0),top:new i.Point(0,Y),\"top-left\":new i.Point(D,D),\"top-right\":new i.Point(-D,D),bottom:new i.Point(0,-Y),\"bottom-left\":new i.Point(D,-D),\"bottom-right\":new i.Point(-D,-D),left:new i.Point(Y,0),right:new i.Point(-Y,0)}}else if(Y instanceof i.Point||Array.isArray(Y)){var J=i.Point.convert(Y);return{center:J,top:J,\"top-left\":J,\"top-right\":J,bottom:J,\"bottom-left\":J,\"bottom-right\":J,left:J,right:J}}else return{center:i.Point.convert(Y.center||[0,0]),top:i.Point.convert(Y.top||[0,0]),\"top-left\":i.Point.convert(Y[\"top-left\"]||[0,0]),\"top-right\":i.Point.convert(Y[\"top-right\"]||[0,0]),bottom:i.Point.convert(Y.bottom||[0,0]),\"bottom-left\":i.Point.convert(Y[\"bottom-left\"]||[0,0]),\"bottom-right\":i.Point.convert(Y[\"bottom-right\"]||[0,0]),left:i.Point.convert(Y.left||[0,0]),right:i.Point.convert(Y.right||[0,0])};else return al(new i.Point(0,0))}var io={version:i.version,supported:a,setRTLTextPlugin:i.setRTLTextPlugin,getRTLTextPluginStatus:i.getRTLTextPluginStatus,Map:si,NavigationControl:Ui,GeolocateControl:Zs,AttributionControl:Qi,ScaleControl:El,FullscreenControl:ss,Popup:wa,Marker:Ki,Style:yu,LngLat:i.LngLat,LngLatBounds:i.LngLatBounds,Point:i.Point,MercatorCoordinate:i.MercatorCoordinate,Evented:i.Evented,config:i.config,prewarm:ua,clearPrewarmedResources:ma,get accessToken(){return i.config.ACCESS_TOKEN},set accessToken(Y){i.config.ACCESS_TOKEN=Y},get baseApiUrl(){return i.config.API_URL},set baseApiUrl(Y){i.config.API_URL=Y},get workerCount(){return fn.workerCount},set workerCount(Y){fn.workerCount=Y},get maxParallelImageRequests(){return i.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(Y){i.config.MAX_PARALLEL_IMAGE_REQUESTS=Y},clearStorage:function(D){i.clearTileCache(D)},workerUrl:\"\"};return io}),r}))});var EGe=ye((obr,MGe)=>{\"use strict\";var gw=Pr(),_Wt=Zl().sanitizeHTML,xWt=vJ(),TGe=y1();function AGe(e,t){this.subplot=e,this.uid=e.uid+\"-\"+t,this.index=t,this.idSource=\"source-\"+this.uid,this.idLayer=TGe.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var ag=AGe.prototype;ag.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=o7(t)};ag.needsNewImage=function(e){var t=this.subplot.map;return t.getSource(this.idSource)&&this.sourceType===\"image\"&&e.sourcetype===\"image\"&&(this.source!==e.source||JSON.stringify(this.coordinates)!==JSON.stringify(e.coordinates))};ag.needsNewSource=function(e){return this.sourceType!==e.sourcetype||JSON.stringify(this.source)!==JSON.stringify(e.source)||this.layerType!==e.type};ag.needsNewLayer=function(e){return this.layerType!==e.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]};ag.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]};ag.updateImage=function(e){var t=this.subplot.map;t.getSource(this.idSource).updateImage({url:e.source,coordinates:e.coordinates});var r=this.findFollowingMapboxLayerId(this.lookupBelow());r!==null&&this.subplot.map.moveLayer(this.idLayer,r)};ag.updateSource=function(e){var t=this.subplot.map;if(t.getSource(this.idSource)&&t.removeSource(this.idSource),this.sourceType=e.sourcetype,this.source=e.source,!!o7(e)){var r=bWt(e);t.addSource(this.idSource,r)}};ag.findFollowingMapboxLayerId=function(e){if(e===\"traces\")for(var t=this.subplot.getMapLayers(),r=0;r<t.length;r++){var n=t[r].id;if(typeof n==\"string\"&&n.indexOf(TGe.traceLayerPrefix)===0){e=n;break}}return e};ag.updateLayer=function(e){var t=this.subplot,r=SGe(e),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),o7(e)&&t.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":e.sourcelayer||\"\",type:e.type,minzoom:e.minzoom,maxzoom:e.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=e.type,this.below=n};ag.updateStyle=function(e){if(o7(e)){var t=SGe(e);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",t.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",t.paint)}};ag.removeLayer=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer)};ag.dispose=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),e.getSource(this.idSource)&&e.removeSource(this.idSource)};function o7(e){if(!e.visible)return!1;var t=e.source;if(Array.isArray(t)&&t.length>0){for(var r=0;r<t.length;r++)if(typeof t[r]!=\"string\"||t[r].length===0)return!1;return!0}return gw.isPlainObject(t)||typeof t==\"string\"&&t.length>0}function SGe(e){var t={},r={};switch(e.type){case\"circle\":gw.extendFlat(r,{\"circle-radius\":e.circle.radius,\"circle-color\":e.color,\"circle-opacity\":e.opacity});break;case\"line\":gw.extendFlat(r,{\"line-width\":e.line.width,\"line-color\":e.color,\"line-opacity\":e.opacity,\"line-dasharray\":e.line.dash});break;case\"fill\":gw.extendFlat(r,{\"fill-color\":e.color,\"fill-outline-color\":e.fill.outlinecolor,\"fill-opacity\":e.opacity});break;case\"symbol\":var n=e.symbol,i=xWt(n.textposition,n.iconsize);gw.extendFlat(t,{\"icon-image\":n.icon+\"-15\",\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":i.anchor,\"text-offset\":i.offset,\"symbol-placement\":n.placement}),gw.extendFlat(r,{\"icon-color\":e.color,\"text-color\":n.textfont.color,\"text-opacity\":e.opacity});break;case\"raster\":gw.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":e.opacity});break}return{layout:t,paint:r}}function bWt(e){var t=e.sourcetype,r=e.source,n={type:t},i;return t===\"geojson\"?i=\"data\":t===\"vector\"?i=typeof r==\"string\"?\"url\":\"tiles\":t===\"raster\"?(i=\"tiles\",n.tileSize=256):t===\"image\"&&(i=\"url\",n.coordinates=e.coordinates),n[i]=r,e.sourceattribution&&(n.attribution=_Wt(e.sourceattribution)),n}MGe.exports=function(t,r,n){var i=new AGe(t,r);return i.update(n),i}});var zGe=ye((sbr,FGe)=>{\"use strict\";var bJ=xJ(),wJ=Pr(),PGe=ey(),kGe=Oa(),wWt=ho(),TWt=yv(),s7=ef(),IGe=Cg(),AWt=IGe.drawMode,SWt=IGe.selectMode,MWt=qf().prepSelect,EWt=qf().clearOutline,kWt=qf().clearSelectionsCache,CWt=qf().selectOnClick,Cx=y1(),LWt=EGe();function RGe(e,t){this.id=t,this.gd=e;var r=e._fullLayout,n=e._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+\"-\"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var Gh=RGe.prototype;Gh.plot=function(e,t,r){var n=this,i=t[n.id];n.map&&i.accesstoken!==n.accessToken&&(n.map.remove(),n.map=null,n.styleObj=null,n.traceHash={},n.layerList=[]);var a;n.map?a=new Promise(function(o,s){n.updateMap(e,t,o,s)}):a=new Promise(function(o,s){n.createMap(e,t,o,s)}),r.push(a)};Gh.createMap=function(e,t,r,n){var i=this,a=t[i.id],o=i.styleObj=DGe(a.style,t);i.accessToken=a.accesstoken;var s=a.bounds,l=s?[[s.west,s.south],[s.east,s.north]]:null,u=i.map=new bJ.Map({container:i.div,style:o.style,center:TJ(a.center),zoom:a.zoom,bearing:a.bearing,pitch:a.pitch,maxBounds:l,interactive:!i.isStatic,preserveDrawingBuffer:i.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new bJ.AttributionControl({compact:!0}));u._canvas.style.left=\"0px\",u._canvas.style.top=\"0px\",i.rejectOnError(n),i.isStatic||i.initFx(e,t);var c=[];c.push(new Promise(function(f){u.once(\"load\",f)})),c=c.concat(PGe.fetchTraceGeoData(e)),Promise.all(c).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Gh.updateMap=function(e,t,r,n){var i=this,a=i.map,o=t[this.id];i.rejectOnError(n);var s=[],l=DGe(o.style,t);JSON.stringify(i.styleObj)!==JSON.stringify(l)&&(i.styleObj=l,a.setStyle(l.style),i.traceHash={},s.push(new Promise(function(u){a.once(\"styledata\",u)}))),s=s.concat(PGe.fetchTraceGeoData(e)),Promise.all(s).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Gh.fillBelowLookup=function(e,t){var r=t[this.id],n=r.layers,i,a,o=this.belowLookup={},s=!1;for(i=0;i<e.length;i++){var l=e[i][0].trace,u=l._module;typeof l.below==\"string\"?a=l.below:u.getBelow&&(a=u.getBelow(l,this)),a===\"\"&&(s=!0),o[\"trace-\"+l.uid]=a||\"\"}for(i=0;i<n.length;i++){var c=n[i];typeof c.below==\"string\"?a=c.below:s?a=\"traces\":a=\"\",o[\"layout-\"+i]=a}var f={},h,d;for(h in o)a=o[h],f[a]?f[a].push(h):f[a]=[h];for(a in f){var v=f[a];if(v.length>1)for(i=0;i<v.length;i++)h=v[i],h.indexOf(\"trace-\")===0?(d=h.split(\"trace-\")[1],this.traceHash[d]&&(this.traceHash[d].below=null)):h.indexOf(\"layout-\")===0&&(d=h.split(\"layout-\")[1],this.layerList[d]&&(this.layerList[d].below=null))}};var CGe={choroplethmapbox:0,densitymapbox:1,scattermapbox:2};Gh.updateData=function(e){var t=this.traceHash,r,n,i,a,o=e.slice().sort(function(f,h){return CGe[f[0].trace.type]-CGe[h[0].trace.type]});for(i=0;i<o.length;i++){var s=o[i];n=s[0].trace,r=t[n.uid];var l=!1;r&&(r.type===n.type?(r.update(s),l=!0):r.dispose()),!l&&n._module&&(t[n.uid]=n._module.plot(this,s))}var u=Object.keys(t);e:for(i=0;i<u.length;i++){var c=u[i];for(a=0;a<e.length;a++)if(n=e[a][0].trace,c===n.uid)continue e;r=t[c],r.dispose(),delete t[c]}};Gh.updateLayout=function(e){var t=this.map,r=e[this.id];!this.dragging&&!this.wheeling&&(t.setCenter(TJ(r.center)),t.setZoom(r.zoom),t.setBearing(r.bearing),t.setPitch(r.pitch)),this.updateLayers(e),this.updateFramework(e),this.updateFx(e),this.map.resize(),this.gd._context._scrollZoom.mapbox?t.scrollZoom.enable():t.scrollZoom.disable()};Gh.resolveOnRender=function(e){var t=this.map;t.on(\"render\",function r(){t.loaded()&&(t.off(\"render\",r),setTimeout(e,10))})};Gh.rejectOnError=function(e){var t=this.map;function r(){e(new Error(Cx.mapOnErrorMsg))}t.once(\"error\",r),t.once(\"style.error\",r),t.once(\"source.error\",r),t.once(\"tile.error\",r),t.once(\"layer.error\",r)};Gh.createFramework=function(e){var t=this,r=t.div=document.createElement(\"div\");r.id=t.uid,r.style.position=\"absolute\",t.container.appendChild(r),t.xaxis={_id:\"x\",c2p:function(n){return t.project(n).x}},t.yaxis={_id:\"y\",c2p:function(n){return t.project(n).y}},t.updateFramework(e),t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},wWt.setConvert(t.mockAxis,e)};Gh.initFx=function(e,t){var r=this,n=r.gd,i=r.map;i.on(\"moveend\",function(s){if(r.map){var l=n._fullLayout;if(s.originalEvent||r.wheeling){var u=l[r.id];kGe.call(\"_storeDirectGUIEdit\",n.layout,l._preGUI,r.getViewEdits(u));var c=r.getView();u._input.center=u.center=c.center,u._input.zoom=u.zoom=c.zoom,u._input.bearing=u.bearing=c.bearing,u._input.pitch=u.pitch=c.pitch,n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(c))}s.originalEvent&&s.originalEvent.type===\"mouseup\"?r.dragging=!1:r.wheeling&&(r.wheeling=!1),l._rehover&&l._rehover()}}),i.on(\"wheel\",function(){r.wheeling=!0}),i.on(\"mousemove\",function(s){var l=r.div.getBoundingClientRect(),u=[s.originalEvent.offsetX,s.originalEvent.offsetY];s.target.getBoundingClientRect=function(){return l},r.xaxis.p2c=function(){return i.unproject(u).lng},r.yaxis.p2c=function(){return i.unproject(u).lat},n._fullLayout._rehover=function(){n._fullLayout._hoversubplot===r.id&&n._fullLayout[r.id]&&s7.hover(n,s,r.id)},s7.hover(n,s,r.id),n._fullLayout._hoversubplot=r.id});function a(){s7.loneUnhover(t._hoverlayer)}i.on(\"dragstart\",function(){r.dragging=!0,a()}),i.on(\"zoomstart\",a),i.on(\"mouseout\",function(){n._fullLayout._hoversubplot=null});function o(){var s=r.getView();n.emit(\"plotly_relayouting\",r.getViewEditsWithDerived(s))}i.on(\"drag\",o),i.on(\"zoom\",o),i.on(\"dblclick\",function(){var s=n._fullLayout[r.id];kGe.call(\"_storeDirectGUIEdit\",n.layout,n._fullLayout._preGUI,r.getViewEdits(s));var l=r.viewInitial;i.setCenter(TJ(l.center)),i.setZoom(l.zoom),i.setBearing(l.bearing),i.setPitch(l.pitch);var u=r.getView();s._input.center=s.center=u.center,s._input.zoom=s.zoom=u.zoom,s._input.bearing=s.bearing=u.bearing,s._input.pitch=s.pitch=u.pitch,n.emit(\"plotly_doubleclick\",null),n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(u))}),r.clearOutline=function(){kWt(r.dragOptions),EWt(r.dragOptions.gd)},r.onClickInPanFn=function(s){return function(l){var u=n._fullLayout.clickmode;u.indexOf(\"select\")>-1&&CWt(l.originalEvent,n,[r.xaxis],[r.yaxis],r.id,s),u.indexOf(\"event\")>-1&&s7.click(n,l.originalEvent)}}};Gh.updateFx=function(e){var t=this,r=t.map,n=t.gd;if(t.isStatic)return;function i(l){var u=t.map.unproject(l);return[u.lng,u.lat]}var a=e.dragmode,o;o=function(l,u){if(u.isRect){var c=l.range={};c[t.id]=[i([u.xmin,u.ymin]),i([u.xmax,u.ymax])]}else{var f=l.lassoPoints={};f[t.id]=u.map(i)}};var s=t.dragOptions;t.dragOptions=wJ.extendDeep(s||{},{dragmode:e.dragmode,element:t.div,gd:n,plotinfo:{id:t.id,domain:e[t.id].domain,xaxis:t.xaxis,yaxis:t.yaxis,fillRangeItems:o},xaxes:[t.xaxis],yaxes:[t.yaxis],subplot:t.id}),r.off(\"click\",t.onClickInPanHandler),SWt(a)||AWt(a)?(r.dragPan.disable(),r.on(\"zoomstart\",t.clearOutline),t.dragOptions.prepFn=function(l,u,c){MWt(l,u,c,t.dragOptions,a)},TWt.init(t.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",t.clearOutline),t.div.onmousedown=null,t.div.ontouchstart=null,t.div.removeEventListener(\"touchstart\",t.div._ontouchstart),t.onClickInPanHandler=t.onClickInPanFn(t.dragOptions),r.on(\"click\",t.onClickInPanHandler))};Gh.updateFramework=function(e){var t=e[this.id].domain,r=e._size,n=this.div.style;n.width=r.w*(t.x[1]-t.x[0])+\"px\",n.height=r.h*(t.y[1]-t.y[0])+\"px\",n.left=r.l+t.x[0]*r.w+\"px\",n.top=r.t+(1-t.y[1])*r.h+\"px\",this.xaxis._offset=r.l+t.x[0]*r.w,this.xaxis._length=r.w*(t.x[1]-t.x[0]),this.yaxis._offset=r.t+(1-t.y[1])*r.h,this.yaxis._length=r.h*(t.y[1]-t.y[0])};Gh.updateLayers=function(e){var t=e[this.id],r=t.layers,n=this.layerList,i;if(r.length!==n.length){for(i=0;i<n.length;i++)n[i].dispose();for(n=this.layerList=[],i=0;i<r.length;i++)n.push(LWt(this,i,r[i]))}else for(i=0;i<r.length;i++)n[i].update(r[i])};Gh.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))};Gh.toImage=function(){return this.map.stop(),this.map.getCanvas().toDataURL()};Gh.setOptions=function(e,t,r){for(var n in r)this.map[t](e,n,r[n])};Gh.getMapLayers=function(){return this.map.getStyle().layers};Gh.addLayer=function(e,t){var r=this.map;if(typeof t==\"string\"){if(t===\"\"){r.addLayer(e,t);return}for(var n=this.getMapLayers(),i=0;i<n.length;i++)if(t===n[i].id){r.addLayer(e,t);return}wJ.warn([\"Trying to add layer with *below* value\",t,\"referencing a layer that does not exist\",\"or that does not yet exist.\"].join(\" \"))}r.addLayer(e)};Gh.project=function(e){return this.map.project(new bJ.LngLat(e[0],e[1]))};Gh.getView=function(){var e=this.map,t=e.getCenter(),r=t.lng,n=t.lat,i={lon:r,lat:n},a=e.getCanvas(),o=parseInt(a.style.width),s=parseInt(a.style.height);return{center:i,zoom:e.getZoom(),bearing:e.getBearing(),pitch:e.getPitch(),_derived:{coordinates:[e.unproject([0,0]).toArray(),e.unproject([o,0]).toArray(),e.unproject([o,s]).toArray(),e.unproject([0,s]).toArray()]}}};Gh.getViewEdits=function(e){for(var t=this.id,r=[\"center\",\"zoom\",\"bearing\",\"pitch\"],n={},i=0;i<r.length;i++){var a=r[i];n[t+\".\"+a]=e[a]}return n};Gh.getViewEditsWithDerived=function(e){var t=this.id,r=this.getViewEdits(e);return r[t+\"._derived\"]=e._derived,r};function DGe(e,t){var r={};if(wJ.isPlainObject(e))r.id=e.id,r.style=e;else if(typeof e==\"string\")if(r.id=e,Cx.styleValuesMapbox.indexOf(e)!==-1)r.style=LGe(e);else if(Cx.stylesNonMapbox[e]){r.style=Cx.stylesNonMapbox[e];var n=r.style.sources[\"plotly-\"+e],i=n?n.tiles:void 0;i&&i[0]&&i[0].slice(-9)===\"?api_key=\"&&(i[0]+=t._mapboxAccessToken)}else r.style=e;else r.id=Cx.styleValueDflt,r.style=LGe(Cx.styleValueDflt);return r.transition={duration:0,delay:0},r}function LGe(e){return Cx.styleUrlPrefix+e+\"-\"+Cx.styleUrlSuffix}function TJ(e){return[e.lon,e.lat]}FGe.exports=RGe});var BGe=ye((lbr,OGe)=>{\"use strict\";var AJ=Pr(),PWt=O_(),IWt=Kd(),qGe=Jk();OGe.exports=function(t,r,n){PWt(t,r,n,{type:\"mapbox\",attributes:qGe,handleDefaults:RWt,partition:\"y\",accessToken:r._mapboxAccessToken})};function RWt(e,t,r,n){r(\"accesstoken\",n.accessToken),r(\"style\"),r(\"center.lon\"),r(\"center.lat\"),r(\"zoom\"),r(\"bearing\"),r(\"pitch\");var i=r(\"bounds.west\"),a=r(\"bounds.east\"),o=r(\"bounds.south\"),s=r(\"bounds.north\");(i===void 0||a===void 0||o===void 0||s===void 0)&&delete t.bounds,IWt(e,t,{name:\"layers\",handleItemDefaults:DWt}),t._input=e}function DWt(e,t){function r(l,u){return AJ.coerce(e,t,qGe.layers,l,u)}var n=r(\"visible\");if(n){var i=r(\"sourcetype\"),a=i===\"raster\"||i===\"image\";r(\"source\"),r(\"sourceattribution\"),i===\"vector\"&&r(\"sourcelayer\"),i===\"image\"&&r(\"coordinates\");var o;a&&(o=\"raster\");var s=r(\"type\",o);a&&s!==\"raster\"&&(s=t.type=\"raster\",AJ.log(\"Source types *raster* and *image* must drawn *raster* layer type.\")),r(\"below\"),r(\"color\"),r(\"opacity\"),r(\"minzoom\"),r(\"maxzoom\"),s===\"circle\"&&r(\"circle.radius\"),s===\"line\"&&(r(\"line.width\"),r(\"line.dash\")),s===\"fill\"&&r(\"fill.outlinecolor\"),s===\"symbol\"&&(r(\"symbol.icon\"),r(\"symbol.iconsize\"),r(\"symbol.text\"),AJ.coerceFont(r,\"symbol.textfont\",void 0,{noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}),r(\"symbol.textposition\"),r(\"symbol.placement\"))}}});var l7=ye(Np=>{\"use strict\";var NGe=xJ(),nm=Pr(),SJ=nm.strTranslate,FWt=nm.strScale,zWt=Id().getSubplotCalcData,qWt=Wp(),OWt=Fa(),UGe=So(),BWt=Zl(),NWt=zGe(),Lx=\"mapbox\",ly=Np.constants=y1();Np.name=Lx;Np.attr=\"subplot\";Np.idRoot=Lx;Np.idRegex=Np.attrRegex=nm.counterRegex(Lx);var UWt=[\"mapbox subplots and traces are deprecated!\",\"Please consider switching to `map` subplots and traces.\",\"Learn more at: https://plotly.com/python/maplibre-migration/\",\"as well as https://plotly.com/javascript/maplibre-migration/\"].join(\" \");Np.attributes={subplot:{valType:\"subplotid\",dflt:\"mapbox\",editType:\"calc\"}};Np.layoutAttributes=Jk();Np.supplyLayoutDefaults=BGe();var VGe=!0;Np.plot=function(t){VGe&&(VGe=!1,nm.warn(UWt));var r=t._fullLayout,n=t.calcdata,i=r._subplots[Lx];if(NGe.version!==ly.requiredVersion)throw new Error(ly.wrongVersionErrorMsg);var a=VWt(t,i);NGe.accessToken=a;for(var o=0;o<i.length;o++){var s=i[o],l=zWt(n,Lx,s),u=r[s],c=u._subplot;c||(c=new NWt(t,s),r[s]._subplot=c),c.viewInitial||(c.viewInitial={center:nm.extendFlat({},u.center),zoom:u.zoom,bearing:u.bearing,pitch:u.pitch}),c.plot(l,r,t._promises)}};Np.clean=function(e,t,r,n){for(var i=n._subplots[Lx]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._subplot&&n[o]._subplot.destroy()}};Np.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[Lx],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._subplot,l=s.toImage(\"png\"),u=t._glimages.append(\"svg:image\");u.attr({xmlns:qWt.svg,\"xlink:href\":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"});var c=OWt.select(a._subplot.div),f=c.select(\".mapboxgl-ctrl-logo\").node().offsetParent===null;if(!f){var h=t._glimages.append(\"g\");h.attr(\"transform\",SJ(n.l+n.w*o.x[0]+10,n.t+n.h*(1-o.y[0])-31)),h.append(\"path\").attr(\"d\",ly.mapboxLogo.path0).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"}),h.append(\"path\").attr(\"d\",ly.mapboxLogo.path1).style(\"opacity\",.35).style(\"enable-background\",\"new\"),h.append(\"path\").attr(\"d\",ly.mapboxLogo.path2).style(\"opacity\",.35).style(\"enable-background\",\"new\"),h.append(\"polygon\").attr(\"points\",ly.mapboxLogo.polygon).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"})}var d=c.select(\".mapboxgl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),v=t._glimages.append(\"g\"),m=v.append(\"text\");m.text(d).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":d});var b=UGe.bBox(m.node()),p=n.w*(o.x[1]-o.x[0]);if(b.width>p/2){var k=d.split(\"|\").join(\"<br>\");m.text(k).attr(\"data-unformatted\",k).call(BWt.convertToTspans,e),b=UGe.bBox(m.node())}m.attr(\"transform\",SJ(-3,-b.height+8)),v.insert(\"rect\",\".static-attribution\").attr({x:-b.width-6,y:-b.height-3,width:b.width+6,height:b.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var M=1;b.width+6>p&&(M=p/(b.width+6));var T=[n.l+n.w*o.x[1],n.t+n.h*(1-o.y[0])];v.attr(\"transform\",SJ(T[0],T[1])+FWt(M))}};function VWt(e,t){var r=e._fullLayout,n=e._context;if(n.mapboxAccessToken===\"\")return\"\";for(var i=[],a=[],o=!1,s=!1,l=0;l<t.length;l++){var u=r[t[l]],c=u.accesstoken;GGe(u.style)&&(c?nm.pushUnique(i,c):(GGe(u._input.style)&&(nm.error(\"Uses Mapbox map style, but did not set an access token.\"),o=!0),s=!0)),c&&nm.pushUnique(a,c)}if(s){var f=o?ly.noAccessTokenErrorMsg:ly.missingStyleErrorMsg;throw nm.error(f),new Error(f)}return i.length?(i.length>1&&nm.warn(ly.multipleTokensErrorMsg),i[0]):(a.length&&nm.log([\"Listed mapbox access token(s)\",a.join(\",\"),\"but did not use a Mapbox map style, ignoring token(s).\"].join(\" \")),\"\")}function GGe(e){return typeof e==\"string\"&&(ly.styleValuesMapbox.indexOf(e)!==-1||e.indexOf(\"mapbox://\")===0||e.indexOf(\"stamen\")===0)}Np.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[Lx],n=0;n<r.length;n++){var i=t[r[n]]._subplot;i.updateFx(t)}}});var jGe=ye((fbr,HGe)=>{\"use strict\";var cbr=[\"*scattermapbox* trace is deprecated!\",\"Please consider switching to the *scattermap* trace type and `map` subplots.\",\"Learn more at: https://plotly.com/python/maplibre-migration/\",\"as well as https://plotly.com/javascript/maplibre-migration/\"].join(\" \");HGe.exports={attributes:e7(),supplyDefaults:rGe(),colorbar:Qd(),formatLabels:dJ(),calc:TF(),plot:gGe(),hoverPoints:a7().hoverPoints,eventData:xGe(),selectPoints:wGe(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.update(t)}},moduleType:\"trace\",name:\"scattermapbox\",basePlotModule:l7(),categories:[\"mapbox\",\"gl\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}});var XGe=ye((hbr,WGe)=>{\"use strict\";WGe.exports=jGe()});var MJ=ye((dbr,ZGe)=>{\"use strict\";var _1=l5(),GWt=Tu(),{hovertemplateAttrs:HWt,templatefallbackAttrs:jWt}=Pl(),WWt=Vl(),Px=Ao().extendFlat;ZGe.exports=Px({locations:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},geojson:{valType:\"any\",editType:\"calc\"},featureidkey:Px({},_1.featureidkey,{}),below:{valType:\"string\",editType:\"plot\"},text:_1.text,hovertext:_1.hovertext,marker:{line:{color:Px({},_1.marker.line.color,{editType:\"plot\"}),width:Px({},_1.marker.line.width,{editType:\"plot\"}),editType:\"calc\"},opacity:Px({},_1.marker.opacity,{editType:\"plot\"}),editType:\"calc\"},selected:{marker:{opacity:Px({},_1.selected.marker.opacity,{editType:\"plot\"}),editType:\"plot\"},editType:\"plot\"},unselected:{marker:{opacity:Px({},_1.unselected.marker.opacity,{editType:\"plot\"}),editType:\"plot\"},editType:\"plot\"},hoverinfo:_1.hoverinfo,hovertemplate:HWt({},{keys:[\"properties\"]}),hovertemplatefallback:jWt(),showlegend:Px({},WWt.showlegend,{dflt:!1})},GWt(\"\",{cLetter:\"z\",editTypeOverride:\"calc\"}))});var KGe=ye((vbr,YGe)=>{\"use strict\";var tC=Pr(),XWt=td(),ZWt=MJ();YGe.exports=function(t,r,n,i){function a(c,f){return tC.coerce(t,r,ZWt,c,f)}var o=a(\"locations\"),s=a(\"z\"),l=a(\"geojson\");if(!tC.isArrayOrTypedArray(o)||!o.length||!tC.isArrayOrTypedArray(s)||!s.length||!(typeof l==\"string\"&&l!==\"\"||tC.isPlainObject(l))){r.visible=!1;return}a(\"featureidkey\"),r._length=Math.min(o.length,s.length),a(\"below\"),a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var u=a(\"marker.line.width\");u&&a(\"marker.line.color\"),a(\"marker.opacity\"),XWt(t,r,i,a,{prefix:\"\",cLetter:\"z\"}),tC.coerceSelectionMarkerOpacity(r,a)}});var EJ=ye((pbr,QGe)=>{\"use strict\";var YWt=Eo(),x1=Pr(),KWt=tc(),JWt=So(),$Wt=hx().makeBlank,JGe=ey();function QWt(e){var t=e[0].trace,r=t.visible===!0&&t._length!==0,n={layout:{visibility:\"none\"},paint:{}},i={layout:{visibility:\"none\"},paint:{}},a=t._opts={fill:n,line:i,geojson:$Wt()};if(!r)return a;var o=JGe.extractTraceFeature(e);if(!o)return a;var s=KWt.makeColorScaleFuncFromTrace(t),l=t.marker,u=l.line||{},c;x1.isArrayOrTypedArray(l.opacity)&&(c=function(k){var M=k.mo;return YWt(M)?+x1.constrain(M,0,1):0});var f;x1.isArrayOrTypedArray(u.color)&&(f=function(k){return k.mlc});var h;x1.isArrayOrTypedArray(u.width)&&(h=function(k){return k.mlw});for(var d=0;d<e.length;d++){var v=e[d],m=v.fOut;if(m){var b=m.properties;b.fc=s(v.z),c&&(b.mo=c(v)),f&&(b.mlc=f(v)),h&&(b.mlw=h(v)),v.ct=b.ct,v._polygons=JGe.feature2polygons(m)}}var p=c?{type:\"identity\",property:\"mo\"}:l.opacity;return x1.extendFlat(n.paint,{\"fill-color\":{type:\"identity\",property:\"fc\"},\"fill-opacity\":p}),x1.extendFlat(i.paint,{\"line-color\":f?{type:\"identity\",property:\"mlc\"}:u.color,\"line-width\":h?{type:\"identity\",property:\"mlw\"}:u.width,\"line-opacity\":p}),n.layout.visibility=\"visible\",i.layout.visibility=\"visible\",a.geojson={type:\"FeatureCollection\",features:o},$Ge(e),a}function $Ge(e){var t=e[0].trace,r=t._opts,n;if(t.selectedpoints){for(var i=JWt.makeSelectedPointStyleFns(t),a=0;a<e.length;a++){var o=e[a];o.fOut&&(o.fOut.properties.mo2=i.selectedOpacityFn(o))}n={type:\"identity\",property:\"mo2\"}}else n=x1.isArrayOrTypedArray(t.marker.opacity)?{type:\"identity\",property:\"mo\"}:t.marker.opacity;return x1.extendFlat(r.fill.paint,{\"fill-opacity\":n}),x1.extendFlat(r.line.paint,{\"line-opacity\":n}),r}QGe.exports={convert:QWt,convertOnSelect:$Ge}});var nHe=ye((gbr,iHe)=>{\"use strict\";var tHe=EJ().convert,eXt=EJ().convertOnSelect,eHe=y1().traceLayerPrefix;function rHe(e,t){this.type=\"choroplethmapbox\",this.subplot=e,this.uid=t,this.sourceId=\"source-\"+t,this.layerList=[[\"fill\",eHe+t+\"-fill\"],[\"line\",eHe+t+\"-line\"]],this.below=null}var R5=rHe.prototype;R5.update=function(e){this._update(tHe(e)),e[0].trace._glTrace=this};R5.updateOnSelect=function(e){this._update(eXt(e))};R5._update=function(e){var t=this.subplot,r=this.layerList,n=t.belowLookup[\"trace-\"+this.uid];t.map.getSource(this.sourceId).setData(e.geojson),n!==this.below&&(this._removeLayers(),this._addLayers(e,n),this.below=n);for(var i=0;i<r.length;i++){var a=r[i],o=a[0],s=a[1],l=e[o];t.setOptions(s,\"setLayoutProperty\",l.layout),l.layout.visibility===\"visible\"&&t.setOptions(s,\"setPaintProperty\",l.paint)}};R5._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},t)}};R5._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};R5.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};iHe.exports=function(t,r){var n=r[0].trace,i=new rHe(t,n.uid),a=i.sourceId,o=tHe(r),s=i.below=t.belowLookup[\"trace-\"+n.uid];return t.map.addSource(a,{type:\"geojson\",data:o.geojson}),i._addLayers(o,s),r[0].trace._glTrace=i,i}});var oHe=ye((ybr,aHe)=>{\"use strict\";var mbr=[\"*choroplethmapbox* trace is deprecated!\",\"Please consider switching to the *choroplethmap* trace type and `map` subplots.\",\"Learn more at: https://plotly.com/python/maplibre-migration/\",\"as well as https://plotly.com/javascript/maplibre-migration/\"].join(\" \");aHe.exports={attributes:MJ(),supplyDefaults:KGe(),colorbar:F_(),calc:HF(),plot:nHe(),hoverPoints:WF(),eventData:XF(),selectPoints:ZF(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.updateOnSelect(t)}},getBelow:function(e,t){for(var r=t.getMapLayers(),n=r.length-2;n>=0;n--){var i=r[n].id;if(typeof i==\"string\"&&i.indexOf(\"water\")===0){for(var a=n+1;a<r.length;a++)if(i=r[a].id,typeof i==\"string\"&&i.indexOf(\"plotly-\")===-1)return i}}},moduleType:\"trace\",name:\"choroplethmapbox\",basePlotModule:l7(),categories:[\"mapbox\",\"gl\",\"noOpacity\",\"showLegend\"],meta:{hr_name:\"choropleth_mapbox\"}}});var lHe=ye((_br,sHe)=>{\"use strict\";sHe.exports=oHe()});var CJ=ye((xbr,cHe)=>{\"use strict\";var tXt=Tu(),{hovertemplateAttrs:rXt,templatefallbackAttrs:iXt}=Pl(),uHe=Vl(),u7=e7(),kJ=Ao().extendFlat;cHe.exports=kJ({lon:u7.lon,lat:u7.lat,z:{valType:\"data_array\",editType:\"calc\"},radius:{valType:\"number\",editType:\"plot\",arrayOk:!0,min:1,dflt:30},below:{valType:\"string\",editType:\"plot\"},text:u7.text,hovertext:u7.hovertext,hoverinfo:kJ({},uHe.hoverinfo,{flags:[\"lon\",\"lat\",\"z\",\"text\",\"name\"]}),hovertemplate:rXt(),hovertemplatefallback:iXt(),showlegend:kJ({},uHe.showlegend,{dflt:!1})},tXt(\"\",{cLetter:\"z\",editTypeOverride:\"calc\"}))});var hHe=ye((bbr,fHe)=>{\"use strict\";var nXt=Pr(),aXt=td(),oXt=CJ();fHe.exports=function(t,r,n,i){function a(u,c){return nXt.coerce(t,r,oXt,u,c)}var o=a(\"lon\")||[],s=a(\"lat\")||[],l=Math.min(o.length,s.length);if(!l){r.visible=!1;return}r._length=l,a(\"z\"),a(\"radius\"),a(\"below\"),a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),aXt(t,r,i,a,{prefix:\"\",cLetter:\"z\"})}});var pHe=ye((wbr,vHe)=>{\"use strict\";var LJ=Eo(),sXt=Pr().isArrayOrTypedArray,PJ=fs().BADNUM,lXt=gv(),dHe=Pr()._;vHe.exports=function(t,r){for(var n=r._length,i=new Array(n),a=r.z,o=sXt(a)&&a.length,s=0;s<n;s++){var l=i[s]={},u=r.lon[s],c=r.lat[s];if(l.lonlat=LJ(u)&&LJ(c)?[+u,+c]:[PJ,PJ],o){var f=a[s];l.z=LJ(f)?f:PJ}}return lXt(t,r,{vals:o?a:[0,1],containerStr:\"\",cLetter:\"z\"}),n&&(i[0].t={labels:{lat:dHe(t,\"lat:\")+\" \",lon:dHe(t,\"lon:\")+\" \"}}),i}});var xHe=ye((Tbr,_He)=>{\"use strict\";var uXt=Eo(),IJ=Pr(),gHe=ka(),mHe=tc(),yHe=fs().BADNUM,cXt=hx().makeBlank;_He.exports=function(t){var r=t[0].trace,n=r.visible===!0&&r._length!==0,i={layout:{visibility:\"none\"},paint:{}},a=r._opts={heatmap:i,geojson:cXt()};if(!n)return a;var o=[],s,l=r.z,u=r.radius,c=IJ.isArrayOrTypedArray(l)&&l.length,f=IJ.isArrayOrTypedArray(u);for(s=0;s<t.length;s++){var h=t[s],d=h.lonlat;if(d[0]!==yHe){var v={};if(c){var m=h.z;v.z=m!==yHe?m:0}f&&(v.r=uXt(u[s])&&u[s]>0?+u[s]:0),o.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:d},properties:v})}}var b=mHe.extractOpts(r),p=b.reversescale?mHe.flipScale(b.colorscale):b.colorscale,k=p[0][1],M=gHe.opacity(k)<1?k:gHe.addOpacity(k,0),T=[\"interpolate\",[\"linear\"],[\"heatmap-density\"],0,M];for(s=1;s<p.length;s++)T.push(p[s][0],p[s][1]);var L=[\"interpolate\",[\"linear\"],[\"get\",\"z\"],b.min,0,b.max,1];return IJ.extendFlat(a.heatmap.paint,{\"heatmap-weight\":c?L:1/(b.max-b.min),\"heatmap-color\":T,\"heatmap-radius\":f?{type:\"identity\",property:\"r\"}:r.radius,\"heatmap-opacity\":r.opacity}),a.geojson={type:\"FeatureCollection\",features:o},a.heatmap.layout.visibility=\"visible\",a}});var AHe=ye((Abr,THe)=>{\"use strict\";var bHe=xHe(),fXt=y1().traceLayerPrefix;function wHe(e,t){this.type=\"densitymapbox\",this.subplot=e,this.uid=t,this.sourceId=\"source-\"+t,this.layerList=[[\"heatmap\",fXt+t+\"-heatmap\"]],this.below=null}var c7=wHe.prototype;c7.update=function(e){var t=this.subplot,r=this.layerList,n=bHe(e),i=t.belowLookup[\"trace-\"+this.uid];t.map.getSource(this.sourceId).setData(n.geojson),i!==this.below&&(this._removeLayers(),this._addLayers(n,i),this.below=i);for(var a=0;a<r.length;a++){var o=r[a],s=o[0],l=o[1],u=n[s];t.setOptions(l,\"setLayoutProperty\",u.layout),u.layout.visibility===\"visible\"&&t.setOptions(l,\"setPaintProperty\",u.paint)}};c7._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},t)}};c7._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};c7.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};THe.exports=function(t,r){var n=r[0].trace,i=new wHe(t,n.uid),a=i.sourceId,o=bHe(r),s=i.below=t.belowLookup[\"trace-\"+n.uid];return t.map.addSource(a,{type:\"geojson\",data:o.geojson}),i._addLayers(o,s),i}});var MHe=ye((Sbr,SHe)=>{\"use strict\";var hXt=ho(),dXt=a7().hoverPoints,vXt=a7().getExtraText;SHe.exports=function(t,r,n){var i=dXt(t,r,n);if(i){var a=i[0],o=a.cd,s=o[0].trace,l=o[a.index];if(delete a.color,\"z\"in l){var u=a.subplot.mockAxis;a.z=l.z,a.zLabel=hXt.tickText(u,u.c2l(l.z),\"hover\").text}return a.extraText=vXt(s,l,o[0].t.labels),[a]}}});var kHe=ye((Mbr,EHe)=>{\"use strict\";EHe.exports=function(t,r){return t.lon=r.lon,t.lat=r.lat,t.z=r.z,t}});var LHe=ye((kbr,CHe)=>{\"use strict\";var Ebr=[\"*densitymapbox* trace is deprecated!\",\"Please consider switching to the *densitymap* trace type and `map` subplots.\",\"Learn more at: https://plotly.com/python/maplibre-migration/\",\"as well as https://plotly.com/javascript/maplibre-migration/\"].join(\" \");CHe.exports={attributes:CJ(),supplyDefaults:hHe(),colorbar:F_(),formatLabels:dJ(),calc:pHe(),plot:AHe(),hoverPoints:MHe(),eventData:kHe(),getBelow:function(e,t){for(var r=t.getMapLayers(),n=0;n<r.length;n++){var i=r[n],a=i.id;if(i.type===\"symbol\"&&typeof a==\"string\"&&a.indexOf(\"plotly-\")===-1)return a}},moduleType:\"trace\",name:\"densitymapbox\",basePlotModule:l7(),categories:[\"mapbox\",\"gl\",\"showLegend\"],meta:{hr_name:\"density_mapbox\"}}});var IHe=ye((Cbr,PHe)=>{\"use strict\";PHe.exports=LHe()});var DHe=ye((Lbr,RHe)=>{RHe.exports={version:8,name:\"orto\",metadata:{\"maputnik:renderer\":\"mlgljs\"},center:[1.537786,41.837539],zoom:12,bearing:0,pitch:0,light:{anchor:\"viewport\",color:\"white\",intensity:.4,position:[1.15,45,30]},sources:{ortoEsri:{type:\"raster\",tiles:[\"https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}\"],tileSize:256,maxzoom:18,attribution:\"ESRI &copy; <a 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0.68)\",\"line-dasharray\":[.2,8],\"line-width\":{base:1.4,stops:[[14.5,0],[15,2],[20,6]]}}},{id:\"railway\",type:\"line\",metadata:{\"mapbox:group\":\"1444849345966.4436\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"$type\",\"LineString\"],[\"all\",[\"!has\",\"service\"],[\"!in\",\"brunnel\",\"bridge\",\"tunnel\"],[\"==\",\"class\",\"rail\"]]],paint:{\"line-color\":\"#bbb\",\"line-width\":{base:1.4,stops:[[14,.4],[15,.75],[20,2]]}}},{id:\"railway-hatching\",type:\"line\",metadata:{\"mapbox:group\":\"1444849345966.4436\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"$type\",\"LineString\"],[\"all\",[\"!has\",\"service\"],[\"!in\",\"brunnel\",\"bridge\",\"tunnel\"],[\"==\",\"class\",\"rail\"]]],paint:{\"line-color\":\"#bbb\",\"line-dasharray\":[.2,8],\"line-width\":{base:1.4,stops:[[14.5,0],[15,3],[20,8]]}}},{id:\"bridge-motorway-link-casing\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"==\",\"class\",\"motorway_link\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#e9ac77\",\"line-opacity\":1,\"line-width\":{base:1.2,stops:[[12,1],[13,3],[14,4],[20,15]]}}},{id:\"bridge-link-casing\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"in\",\"class\",\"primary_link\",\"secondary_link\",\"tertiary_link\",\"trunk_link\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#e9ac77\",\"line-opacity\":1,\"line-width\":{base:1.2,stops:[[12,1],[13,3],[14,4],[20,15]]}}},{id:\"bridge-secondary-tertiary-casing\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"in\",\"class\",\"secondary\",\"tertiary\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#e9ac77\",\"line-opacity\":1,\"line-width\":{base:1.2,stops:[[8,1.5],[20,28]]}}},{id:\"bridge-trunk-primary-casing\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"in\",\"class\",\"primary\",\"trunk\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"hsl(28, 76%, 67%)\",\"line-width\":{base:1.2,stops:[[5,.4],[6,.6],[7,1.5],[20,26]]}}},{id:\"bridge-motorway-casing\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"==\",\"class\",\"motorway\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#e9ac77\",\"line-width\":{base:1.2,stops:[[5,.4],[6,.6],[7,1.5],[20,22]]},\"line-opacity\":.5}},{id:\"bridge-path-casing\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"$type\",\"LineString\"],[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"==\",\"class\",\"path\"]]],paint:{\"line-color\":\"#f8f4f0\",\"line-width\":{base:1.2,stops:[[15,1.2],[20,18]]}}},{id:\"bridge-path\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"$type\",\"LineString\"],[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"==\",\"class\",\"path\"]]],paint:{\"line-color\":\"#cba\",\"line-width\":{base:1.2,stops:[[15,1.2],[20,4]]},\"line-dasharray\":[1.5,.75]}},{id:\"bridge-motorway-link\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"==\",\"class\",\"motorway_link\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#fc8\",\"line-width\":{base:1.2,stops:[[12.5,0],[13,1.5],[14,2.5],[20,11.5]]}}},{id:\"bridge-link\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"in\",\"class\",\"primary_link\",\"secondary_link\",\"tertiary_link\",\"trunk_link\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#fea\",\"line-width\":{base:1.2,stops:[[12.5,0],[13,1.5],[14,2.5],[20,11.5]]}}},{id:\"bridge-secondary-tertiary\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"in\",\"class\",\"secondary\",\"tertiary\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#fea\",\"line-width\":{base:1.2,stops:[[6.5,0],[7,.5],[20,20]]}}},{id:\"bridge-trunk-primary\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"in\",\"class\",\"primary\",\"trunk\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#fea\",\"line-width\":{base:1.2,stops:[[6.5,0],[7,.5],[20,18]]}}},{id:\"bridge-motorway\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"==\",\"class\",\"motorway\"]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#fc8\",\"line-width\":{base:1.2,stops:[[6.5,0],[7,.5],[20,18]]},\"line-opacity\":.5}},{id:\"bridge-railway\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"==\",\"class\",\"rail\"]],paint:{\"line-color\":\"#bbb\",\"line-width\":{base:1.4,stops:[[14,.4],[15,.75],[20,2]]}}},{id:\"bridge-railway-hatching\",type:\"line\",metadata:{\"mapbox:group\":\"1444849334699.1902\"},source:\"openmaptiles\",\"source-layer\":\"transportation\",filter:[\"all\",[\"==\",\"brunnel\",\"bridge\"],[\"==\",\"class\",\"rail\"]],paint:{\"line-color\":\"#bbb\",\"line-dasharray\":[.2,8],\"line-width\":{base:1.4,stops:[[14.5,0],[15,3],[20,8]]}}},{id:\"cablecar\",type:\"line\",source:\"openmaptiles\",\"source-layer\":\"transportation\",minzoom:13,filter:[\"==\",\"class\",\"cable_car\"],layout:{visibility:\"visible\",\"line-cap\":\"round\"},paint:{\"line-color\":\"hsl(0, 0%, 70%)\",\"line-width\":{base:1,stops:[[11,1],[19,2.5]]}}},{id:\"cablecar-dash\",type:\"line\",source:\"openmaptiles\",\"source-layer\":\"transportation\",minzoom:13,filter:[\"==\",\"class\",\"cable_car\"],layout:{visibility:\"visible\",\"line-cap\":\"round\"},paint:{\"line-color\":\"hsl(0, 0%, 70%)\",\"line-width\":{base:1,stops:[[11,3],[19,5.5]]},\"line-dasharray\":[2,3]}},{id:\"boundary-land-level-4\",type:\"line\",source:\"openmaptiles\",\"source-layer\":\"boundary\",filter:[\"all\",[\">=\",\"admin_level\",4],[\"<=\",\"admin_level\",8],[\"!=\",\"maritime\",1]],layout:{\"line-join\":\"round\"},paint:{\"line-color\":\"#9e9cab\",\"line-dasharray\":[3,1,1,1],\"line-width\":{base:1.4,stops:[[4,.4],[5,1],[12,3]]},\"line-opacity\":.6}},{id:\"boundary-land-level-2\",type:\"line\",source:\"openmaptiles\",\"source-layer\":\"boundary\",filter:[\"all\",[\"==\",\"admin_level\",2],[\"!=\",\"maritime\",1],[\"!=\",\"disputed\",1]],layout:{\"line-cap\":\"round\",\"line-join\":\"round\"},paint:{\"line-color\":\"hsl(248, 7%, 66%)\",\"line-width\":{base:1,stops:[[0,.6],[4,1.4],[5,2],[12,2]]}}},{id:\"boundary-land-disputed\",type:\"line\",source:\"openmaptiles\",\"source-layer\":\"boundary\",filter:[\"all\",[\"!=\",\"maritime\",1],[\"==\",\"disputed\",1]],layout:{\"line-cap\":\"round\",\"line-join\":\"round\"},paint:{\"line-color\":\"hsl(248, 7%, 70%)\",\"line-dasharray\":[1,3],\"line-width\":{base:1,stops:[[0,.6],[4,1.4],[5,2],[12,8]]}}},{id:\"boundary-water\",type:\"line\",source:\"openmaptiles\",\"source-layer\":\"boundary\",filter:[\"all\",[\"in\",\"admin_level\",2,4],[\"==\",\"maritime\",1]],layout:{\"line-cap\":\"round\",\"line-join\":\"round\"},paint:{\"line-color\":\"rgba(154, 189, 214, 1)\",\"line-width\":{base:1,stops:[[0,.6],[4,1],[5,1],[12,1]]},\"line-opacity\":{stops:[[6,0],[10,0]]}}},{id:\"waterway-name\",type:\"symbol\",source:\"openmaptiles\",\"source-layer\":\"waterway\",minzoom:13,filter:[\"all\",[\"==\",\"$type\",\"LineString\"],[\"has\",\"name\"]],layout:{\"text-font\":[\"Noto Sans Italic\"],\"text-size\":14,\"text-field\":\"{name:latin} {name:nonlatin}\",\"text-max-width\":5,\"text-rotation-alignment\":\"map\",\"symbol-placement\":\"line\",\"text-letter-spacing\":.2,\"symbol-spacing\":350},paint:{\"text-color\":\"#74aee9\",\"text-halo-width\":1.5,\"text-halo-color\":\"rgba(255,255,255,0.7)\"}},{id:\"water-name-lakeline\",type:\"symbol\",source:\"openmaptiles\",\"source-layer\":\"water_name\",filter:[\"==\",\"$type\",\"LineString\"],layout:{\"text-font\":[\"Noto Sans Italic\"],\"text-size\":14,\"text-field\":`{name:latin}\n",
+       "\t\t\t}`}),n={};return{regl:t,draw:r,atlas:n}};Bs.prototype.update=function(t){var r=this;if(typeof t==\"string\")t={text:t};else if(!t)return;t=yKt(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0),t.opacity!=null&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map(function(xe){return parseFloat(xe)}):this.opacity=parseFloat(t.opacity)),t.viewport!=null&&(this.viewport=TKt(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),this.viewport==null&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),t.kerning!=null&&(this.kerning=t.kerning),t.offset!=null&&(typeof t.offset==\"number\"&&(t.offset=[t.offset,0]),this.positionOffset=LKt(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),!this.font.length&&!t.font&&(t.font=Bs.baseFontSize+\"px sans-serif\");var n=!1,a=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach(function(xe,ue){if(typeof xe==\"string\")try{xe=b5.parse(xe)}catch(De){xe=b5.parse(Bs.baseFontSize+\"px \"+xe)}else{var K=xe.style,_e=xe.weight,ne=xe.stretch,ae=xe.variant;xe=b5.parse(b5.stringify(xe)),K&&(xe.style=K),_e&&(xe.weight=_e),ne&&(xe.stretch=ne),ae&&(xe.variant=ae)}var pe=b5.stringify({size:Bs.baseFontSize,family:xe.family,stretch:DF?xe.stretch:void 0,variant:xe.variant,weight:xe.weight,style:xe.style}),ie=MKt(xe.size),le=Math.round(ie[0]*SKt(ie[1]));if(le!==r.fontSize[ue]&&(a=!0,r.fontSize[ue]=le),(!r.font[ue]||pe!=r.font[ue].baseString)&&(n=!0,r.font[ue]=Bs.fonts[pe],!r.font[ue])){var me=xe.family.join(\", \"),be=[xe.style];xe.style!=xe.variant&&be.push(xe.variant),xe.variant!=xe.weight&&be.push(xe.weight),DF&&xe.weight!=xe.stretch&&be.push(xe.stretch),r.font[ue]={baseString:pe,family:me,weight:xe.weight,stretch:xe.stretch,style:xe.style,variant:xe.variant,width:{},kerning:{},metrics:CKt(me,{origin:\"top\",fontSize:Bs.baseFontSize,fontStyle:be.join(\" \")})},Bs.fonts[pe]=r.font[ue]}}),(n||a)&&this.font.forEach(function(xe,ue){var K=b5.stringify({size:r.fontSize[ue],family:xe.family,stretch:DF?xe.stretch:void 0,variant:xe.variant,weight:xe.weight,style:xe.style});if(r.fontAtlas[ue]=r.shader.atlas[K],!r.fontAtlas[ue]){var _e=xe.metrics;r.shader.atlas[K]=r.fontAtlas[ue]={fontString:K,step:Math.ceil(r.fontSize[ue]*_e.bottom*.5)*2,em:r.fontSize[ue],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:r.regl.texture()}}t.text==null&&(t.text=r.text)}),typeof t.text==\"string\"&&t.position&&t.position.length>2){for(var i=Array(t.position.length*.5),o=0;o<i.length;o++)i[o]=t.text;t.text=i}var s;if(t.text!=null||n){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var l=1;l<t.text.length;l++)this.textOffsets[l]=this.textOffsets[l-1]+t.text[l-1].length,this.count+=t.text[l].length,this.counts.push(t.text[l].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];s=[],this.font.forEach(function(xe,ue){Bs.atlasContext.font=xe.baseString;for(var K=r.fontAtlas[ue],_e=0;_e<r.text.length;_e++){var ne=r.text.charAt(_e);if(K.ids[ne]==null&&(K.ids[ne]=K.chars.length,K.chars.push(ne),s.push(ne)),xe.width[ne]==null&&(xe.width[ne]=Bs.atlasContext.measureText(ne).width/Bs.baseFontSize,r.kerning)){var ae=[];for(var pe in xe.width)ae.push(pe+ne,ne+pe);kKt(xe.kerning,EKt(xe.family,{pairs:ae}))}}})}if(t.position)if(t.position.length>2){for(var u=!t.position[0].length,c=Ly.mallocFloat(this.count*2),f=0,v=0;f<this.counts.length;f++){var g=this.counts[f];if(u)for(var p=0;p<g;p++)c[v++]=t.position[f*2],c[v++]=t.position[f*2+1];else for(var x=0;x<g;x++)c[v++]=t.position[f][0],c[v++]=t.position[f][1]}this.position.call?this.position({type:\"float\",data:c}):this.position=this.regl.buffer({type:\"float\",data:c}),Ly.freeFloat(c)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||n){var _=Ly.mallocUint8(this.count),y=Ly.mallocFloat(this.count*2);this.textWidth=[];for(var C=0,k=0;C<this.counts.length;C++){for(var S=this.counts[C],L=this.font[C]||this.font[0],w=this.fontAtlas[C]||this.fontAtlas[0],A=0;A<S;A++){var T=this.text.charAt(k),m=this.text.charAt(k-1);if(_[k]=w.ids[T],y[k*2]=L.width[T],A){var F=y[k*2-2],E=y[k*2],I=y[k*2-1],R=I+F*.5+E*.5;if(this.kerning){var B=L.kerning[m+T];B&&(R+=B*.001)}y[k*2+1]=R}else y[k*2+1]=y[k*2]*.5;k++}this.textWidth.push(y.length?y[k*2-2]*.5+y[k*2-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:_,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:y,type:\"float\",usage:\"stream\"}),Ly.freeUint8(_),Ly.freeFloat(y),s.length&&this.font.forEach(function(xe,ue){var K=r.fontAtlas[ue],_e=K.step,ne=Math.floor(Bs.maxAtlasSize/_e),ae=Math.min(ne,K.chars.length),pe=Math.ceil(K.chars.length/ae),ie=iGe(ae*_e),le=iGe(pe*_e);K.width=ie,K.height=le,K.rows=pe,K.cols=ae,K.em&&K.texture({data:wKt({canvas:Bs.atlasCanvas,font:K.fontString,chars:K.chars,shape:[ie,le],step:[_e,_e]})})})}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map(function(xe,ue){var K=Array.isArray(r.align)?r.align.length>1?r.align[ue]:r.align[0]:r.align;if(typeof K==\"number\")return K;switch(K){case\"right\":case\"end\":return-xe;case\"center\":case\"centre\":case\"middle\":return-xe*.5}return 0})),this.baseline==null&&t.baseline==null&&(t.baseline=0),t.baseline!=null&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map(function(xe,ue){var K=(r.font[ue]||r.font[0]).metrics,_e=0;return _e+=K.bottom*.5,typeof xe==\"number\"?_e+=xe-K.baseline:_e+=-K[xe],_e*=-1,_e})),t.color!=null)if(t.color||(t.color=\"transparent\"),typeof t.color==\"string\"||!isNaN(t.color))this.color=m$(t.color,\"uint8\");else{var O;if(typeof t.color[0]==\"number\"&&t.color.length>this.counts.length){var V=t.color.length;O=Ly.mallocUint8(V);for(var j=(t.color.subarray||t.color.slice).bind(t.color),q=0;q<V;q+=4)O.set(m$(j(q,q+4),\"uint8\"),q)}else{var U=t.color.length;O=Ly.mallocUint8(U*4);for(var J=0;J<U;J++)O.set(m$(t.color[J]||0,\"uint8\"),J*4)}this.color=O}if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity){var ee=this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2;if(ee){var ce=Math.max(this.position.length*.5||0,this.color.length*.25||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,this.positionOffset.length*.5||0);this.batch=Array(ce);for(var ye=0;ye<this.batch.length;ye++)this.batch[ye]={count:this.counts.length>1?this.counts[ye]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[ye]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(ye*4,ye*4+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[ye]:this.opacity,baseline:this.baselineOffset[ye]!=null?this.baselineOffset[ye]:this.baselineOffset[0],align:this.align?this.alignOffset[ye]!=null?this.alignOffset[ye]:this.alignOffset[0]:0,atlas:this.fontAtlas[ye]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(ye*2,ye*2+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]}};Bs.prototype.destroy=function(){};Bs.prototype.kerning=!0;Bs.prototype.position={constant:new Float32Array(2)};Bs.prototype.translate=null;Bs.prototype.scale=null;Bs.prototype.font=null;Bs.prototype.text=\"\";Bs.prototype.positionOffset=[0,0];Bs.prototype.opacity=1;Bs.prototype.color=new Uint8Array([0,0,0,255]);Bs.prototype.alignOffset=[0,0];Bs.maxAtlasSize=1024;Bs.atlasCanvas=document.createElement(\"canvas\");Bs.atlasContext=Bs.atlasCanvas.getContext(\"2d\",{alpha:!1});Bs.baseFontSize=64;Bs.fonts={};function FKt(e){return typeof e==\"function\"&&e._gl&&e.prop&&e.texture&&e.buffer}oGe.exports=Bs});var RF=fe((aAr,lGe)=>{\"use strict\";var DKt=EY(),RKt=f$();lGe.exports=function(t,r,n){var 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bGe(e){return{valType:\"info_array\",freeLength:!0,editType:\"calc\",items:{valType:\"subplotid\",regex:jKt[e],editType:\"plot\"}}}TGe.exports={dimensions:WKt(\"dimension\",{visible:{valType:\"boolean\",dflt:!0,editType:\"calc\"},label:{valType:\"string\",editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},axis:{type:{valType:\"enumerated\",values:[\"linear\",\"log\",\"date\",\"category\"],editType:\"calc+clearAxisTypes\"},matches:{valType:\"boolean\",dflt:!0,editType:\"calc\"},editType:\"calc+clearAxisTypes\"},editType:\"calc+clearAxisTypes\"}),text:w5({},BM.text,{}),hovertext:w5({},BM.hovertext,{}),hovertemplate:GKt(),hovertemplatefallback:HKt(),xhoverformat:_Ge(\"x\"),yhoverformat:_Ge(\"y\"),marker:IF,xaxes:bGe(\"x\"),yaxes:bGe(\"y\"),diagonal:{visible:{valType:\"boolean\",dflt:!0,editType:\"calc\"},editType:\"calc\"},showupperhalf:{valType:\"boolean\",dflt:!0,editType:\"calc\"},showlowerhalf:{valType:\"boolean\",dflt:!0,editType:\"calc\"},selected:{marker:BM.selected.marker,editType:\"calc\"},unselected:{marker:BM.unselected.marker,editType:\"calc\"},opacity:BM.opacity}});var zF=fe((uAr,AGe)=>{\"use strict\";AGe.exports=function(e,t,r,n){n||(n=1/0);var a,i;for(a=0;a<t.length;a++)i=t[a],i.visible&&(n=Math.min(n,i[r].length));for(n===1/0&&(n=0),e._length=n,a=0;a<t.length;a++)i=t[a],i.visible&&(i._length=n);return n}});var EGe=fe((cAr,SGe)=>{\"use strict\";var b$=Xt(),ZKt=Ah(),MGe=_$(),$Kt=Is(),KKt=vv(),JKt=zF(),QKt=JP().isOpenSymbol;SGe.exports=function(t,r,n,a){function i(g,p){return b$.coerce(t,r,MGe,g,p)}var o=ZKt(t,r,{name:\"dimensions\",handleItemDefaults:eJt}),s=i(\"diagonal.visible\"),l=i(\"showupperhalf\"),u=i(\"showlowerhalf\"),c=JKt(r,o,\"values\");if(!c||!s&&!l&&!u){r.visible=!1;return}i(\"text\"),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\"),i(\"xhoverformat\"),i(\"yhoverformat\"),KKt(t,r,n,a,i,{noAngleRef:!0,noLineDash:!0,noStandOff:!0});var f=QKt(r.marker.symbol),v=$Kt.isBubble(r);i(\"marker.line.width\",f||v?1:0),tJt(t,r,a,i),b$.coerceSelectionMarkerOpacity(r,i)};function eJt(e,t){function r(a,i){return 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k=o?UF(a.padding,x,_):a.padding;if(a.domain){let[S,L,w,A]=UF(a.domain,x,_);C.viewport=[f+S*u+k[0],v+L*c+k[1],f+w*u-k[2],v+A*c-k[3]]}else C.viewport=[f+_*g+g*k[0],v+x*p+p*k[1],f+(_+1)*g-g*k[2],v+(x+1)*p-p*k[3]]}n.color&&(C.color=a.color),n.size&&(C.size=a.size),n.marker&&(C.marker=a.marker),n.borderSize&&(C.borderSize=a.borderSize),n.borderColor&&(C.borderColor=a.borderColor),n.opacity&&(C.opacity=a.opacity),n.range&&(C.range=i?UF(a.range,x,_):a.range||C.bounds),a.passes.push(y)}return this};Fy.prototype.draw=function(...e){if(!e.length)this.scatter.draw();else{let t=[];for(let r=0;r<e.length;r++)if(typeof e[r]==\"number\"){let{passes:n,passOffset:a}=this.traces[e[r]];t.push(...dJt(a,a+n.length))}else if(e[r].length){let n=e[r],{passes:a,passOffset:i}=this.traces[r];a=a.map((o,s)=>{t[i+s]=n})}this.scatter.draw(...t)}return this};Fy.prototype.destroy=function(){return 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L1=fe((EAr,THe)=>{\"use strict\";var BJt=J4();function wHe(e){return[e]}THe.exports={keyFun:function(e){return e.key},repeat:wHe,descend:BJt,wrap:wHe,unwrap:function(e){return e[0]}}});var D$=fe((kAr,RHe)=>{\"use strict\";var Vu=UM(),Dp=xi(),OJt=L1().keyFun,HF=L1().repeat,S5=Xt().sorterAsc,NJt=Xt().strTranslate,AHe=Vu.bar.snapRatio;function MHe(e,t){return e*(1-AHe)+t*AHe}var SHe=Vu.bar.snapClose;function UJt(e,t){return e*(1-SHe)+t*SHe}function WF(e,t,r,n){if(VJt(r,n))return r;var a=e?-1:1,i=0,o=t.length-1;if(a<0){var s=i;i=o,o=s}for(var l=t[i],u=l,c=i;a*c<a*o;c+=a){var f=c+a,v=t[f];if(a*r<a*UJt(l,v))return MHe(l,u);if(a*r<a*v||f===o)return MHe(v,l);u=l,l=v}}function VJt(e,t){for(var r=0;r<t.length;r++)if(e>=t[r][0]&&e<=t[r][1])return!0;return!1}function GJt(e){e.attr(\"x\",-Vu.bar.captureWidth/2).attr(\"width\",Vu.bar.captureWidth)}function HJt(e){e.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function jJt(e){if(!e.brush.filterSpecified)return\"0,\"+e.height;for(var t=EHe(e.brush.filter.getConsolidated(),e.height),r=[0],n,a,i,o=t.length?t[0][0]:null,s=0;s<t.length;s++)n=t[s],a=n[1]-n[0],r.push(o),r.push(a),i=s+1,i<t.length&&(o=t[i][0]-n[1]);return r.push(e.height),r}function EHe(e,t){return e.map(function(r){return r.map(function(n){return Math.max(0,n*t)}).sort(S5)})}function WJt(e,t){var r=Vu.bar.handleHeight;if(!(t>e[1]+r||t<e[0]-r))return t>=.9*e[1]+.1*e[0]?\"n\":t<=.9*e[0]+.1*e[1]?\"s\":\"ns\"}function kHe(){Dp.select(document.body).style(\"cursor\",null)}function F$(e){e.attr(\"stroke-dasharray\",jJt)}function jF(e,t){var r=Dp.select(e).selectAll(\".highlight, .highlight-shadow\"),n=t?r.transition().duration(Vu.bar.snapDuration).each(\"end\",t):r;F$(n)}function CHe(e,t){var r=e.brush,n=r.filterSpecified,a=NaN,i={},o;if(n){var s=e.height,l=r.filter.getConsolidated(),u=EHe(l,s),c=NaN,f=NaN,v=NaN;for(o=0;o<=u.length;o++){var g=u[o];if(g&&g[0]<=t&&t<=g[1]){c=o;break}else if(f=o?o-1:NaN,g&&g[0]>t){v=o;break}}if(a=c,isNaN(a)&&(isNaN(f)||isNaN(v)?a=isNaN(f)?v:f:a=t-u[f][1]<u[v][0]-t?f:v),!isNaN(a)){var p=u[a],x=WJt(p,t);x&&(i.interval=l[a],i.intervalPix=p,i.region=x)}}if(e.ordinal&&!i.region){var _=e.unitTickvals,y=e.unitToPaddedPx.invert(t);for(o=0;o<_.length;o++){var C=[_[Math.max(o-1,0)]*.25+_[o]*.75,_[Math.min(o+1,_.length-1)]*.25+_[o]*.75];if(y>=C[0]&&y<=C[1]){i.clickableOrdinalRange=C;break}}}return i}function YJt(e,t){Dp.event.sourceEvent.stopPropagation();var r=t.height-Dp.mouse(e)[1]-2*Vu.verticalPadding,n=t.unitToPaddedPx.invert(r),a=t.brush,i=CHe(t,r),o=i.interval,s=a.svgBrush;if(s.wasDragged=!1,s.grabbingBar=i.region===\"ns\",s.grabbingBar){var l=o.map(t.unitToPaddedPx);s.grabPoint=r-l[0]-Vu.verticalPadding,s.barLength=l[1]-l[0]}s.clickableOrdinalRange=i.clickableOrdinalRange,s.stayingIntervals=t.multiselect&&a.filterSpecified?a.filter.getConsolidated():[],o&&(s.stayingIntervals=s.stayingIntervals.filter(function(u){return u[0]!==o[0]&&u[1]!==o[1]})),s.startExtent=i.region?o[i.region===\"s\"?1:0]:n,t.parent.inBrushDrag=!0,s.brushStartCallback()}function LHe(e,t){Dp.event.sourceEvent.stopPropagation();var r=t.height-Dp.mouse(e)[1]-2*Vu.verticalPadding,n=t.brush.svgBrush;n.wasDragged=!0,n._dragging=!0,n.grabbingBar?n.newExtent=[r-n.grabPoint,r+n.barLength-n.grabPoint].map(t.unitToPaddedPx.invert):n.newExtent=[n.startExtent,t.unitToPaddedPx.invert(r)].sort(S5),t.brush.filterSpecified=!0,n.extent=n.stayingIntervals.concat([n.newExtent]),n.brushCallback(t),jF(e.parentNode)}function XJt(e,t){var r=t.brush,n=r.filter,a=r.svgBrush;a._dragging||(PHe(e,t),LHe(e,t),t.brush.svgBrush.wasDragged=!1),a._dragging=!1;var i=Dp.event;i.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,t.parent.inBrushDrag=!1,kHe(),!a.wasDragged){a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&t.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,a.extent.length===0&&P$(r)):P$(r),a.brushCallback(t),jF(e.parentNode),a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);return}var s=function(){n.set(n.getConsolidated())};if(t.ordinal){var l=t.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[WF(0,l,a.newExtent[0],a.stayingIntervals),WF(1,l,a.newExtent[1],a.stayingIntervals)];var u=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(u?[a.newExtent]:[]),a.extent.length||P$(r),a.brushCallback(t),u?jF(e.parentNode,s):(s(),jF(e.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}function PHe(e,t){var r=t.height-Dp.mouse(e)[1]-2*Vu.verticalPadding,n=CHe(t,r),a=\"crosshair\";n.clickableOrdinalRange?a=\"pointer\":n.region&&(a=n.region+\"-resize\"),Dp.select(document.body).style(\"cursor\",a)}function ZJt(e){e.on(\"mousemove\",function(t){Dp.event.preventDefault(),t.parent.inBrushDrag||PHe(this,t)}).on(\"mouseleave\",function(t){t.parent.inBrushDrag||kHe()}).call(Dp.behavior.drag().on(\"dragstart\",function(t){YJt(this,t)}).on(\"drag\",function(t){LHe(this,t)}).on(\"dragend\",function(t){XJt(this,t)}))}function FHe(e,t){return e[0]-t[0]}function $Jt(e,t,r){var n=r._context.staticPlot,a=e.selectAll(\".background\").data(HF);a.enter().append(\"rect\").classed(\"background\",!0).call(GJt).call(HJt).style(\"pointer-events\",n?\"none\":\"auto\").attr(\"transform\",NJt(0,Vu.verticalPadding)),a.call(ZJt).attr(\"height\",function(s){return s.height-Vu.verticalPadding});var i=e.selectAll(\".highlight-shadow\").data(HF);i.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-Vu.bar.width/2).attr(\"stroke-width\",Vu.bar.width+Vu.bar.strokeWidth).attr(\"stroke\",t).attr(\"opacity\",Vu.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",function(s){return s.height}).call(F$);var o=e.selectAll(\".highlight\").data(HF);o.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-Vu.bar.width/2).attr(\"stroke-width\",Vu.bar.width-Vu.bar.strokeWidth).attr(\"stroke\",Vu.bar.fillColor).attr(\"opacity\",Vu.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),o.attr(\"y1\",function(s){return s.height}).call(F$)}function KJt(e,t,r){var n=e.selectAll(\".\"+Vu.cn.axisBrush).data(HF,OJt);n.enter().append(\"g\").classed(Vu.cn.axisBrush,!0),$Jt(n,t,r)}function JJt(e){return e.svgBrush.extent.map(function(t){return t.slice()})}function P$(e){e.filterSpecified=!1,e.svgBrush.extent=[[-1/0,1/0]]}function QJt(e){return function(r){var n=r.brush,a=JJt(n),i=a.slice();n.filter.set(i),e()}}function DHe(e){for(var t=e.slice(),r=[],n,a=t.shift();a;){for(n=a.slice();(a=t.shift())&&a[0]<=n[1];)n[1]=Math.max(n[1],a[1]);r.push(n)}return r.length===1&&r[0][0]>r[0][1]&&(r=[]),r}function eQt(){var e=[],t,r;return{set:function(n){e=n.map(function(a){return a.slice().sort(S5)}).sort(FHe),e.length===1&&e[0][0]===-1/0&&e[0][1]===1/0&&(e=[[0,-1]]),t=DHe(e),r=e.reduce(function(a,i){return[Math.min(a[0],i[0]),Math.max(a[1],i[1])]},[1/0,-1/0])},get:function(){return e.slice()},getConsolidated:function(){return t},getBounds:function(){return r}}}function tQt(e,t,r,n,a,i){var o=eQt();return o.set(r),{filter:o,filterSpecified:t,svgBrush:{extent:[],brushStartCallback:n,brushCallback:QJt(a),brushEndCallback:i}}}function rQt(e,t){if(Array.isArray(e[0])?(e=e.map(function(n){return n.sort(S5)}),t.multiselect?e=DHe(e.sort(FHe)):e=[e[0]]):e=[e.sort(S5)],t.tickvals){var r=t.tickvals.slice().sort(S5);if(e=e.map(function(n){var a=[WF(0,r,n[0],[]),WF(1,r,n[1],[])];if(a[1]>a[0])return a}).filter(function(n){return n}),!e.length)return}return e.length>1?e:e[0]}RHe.exports={makeBrush:tQt,ensureAxisBrush:KJt,cleanRanges:rQt}});var qHe=fe((CAr,zHe)=>{\"use strict\";var Ry=Xt(),nQt=Wf().hasColorscale,iQt=Vc(),aQt=nl().defaults,oQt=Ah(),sQt=Yi(),IHe=L$(),lQt=D$(),R$=UM().maxDimensionCount,uQt=zF();function cQt(e,t,r,n,a){var i=a(\"line.color\",r);if(nQt(e,\"line\")&&Ry.isArrayOrTypedArray(i)){if(i.length)return a(\"line.colorscale\"),iQt(e,t,n,a,{prefix:\"line.\",cLetter:\"c\"}),i.length;t.line.color=r}return 1/0}function fQt(e,t,r,n){function a(u,c){return Ry.coerce(e,t,IHe.dimensions,u,c)}var i=a(\"values\"),o=a(\"visible\");if(i&&i.length||(o=t.visible=!1),o){a(\"label\"),a(\"tickvals\"),a(\"ticktext\"),a(\"tickformat\");var s=a(\"range\");t._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:s},sQt.setConvert(t._ax,n.layout),a(\"multiselect\");var l=a(\"constraintrange\");l&&(t.constraintrange=lQt.cleanRanges(l,t))}}zHe.exports=function(t,r,n,a){function i(c,f){return Ry.coerce(t,r,IHe,c,f)}var o=t.dimensions;Array.isArray(o)&&o.length>R$&&(Ry.log(\"parcoords traces support up to \"+R$+\" dimensions at the moment\"),o.splice(R$));var s=oQt(t,r,{name:\"dimensions\",layout:a,handleItemDefaults:fQt}),l=cQt(t,r,n,a,i);aQt(r,a,i),(!Array.isArray(s)||!s.length)&&(r.visible=!1),uQt(r,s,\"values\",l);var u=Ry.extendFlat({},a.font,{size:Math.round(a.font.size/1.2)});Ry.coerceFont(i,\"labelfont\",u),Ry.coerceFont(i,\"tickfont\",u,{autoShadowDflt:!0}),Ry.coerceFont(i,\"rangefont\",u),i(\"labelangle\"),i(\"labelside\"),i(\"unselected.line.color\"),i(\"unselected.line.opacity\")}});var OHe=fe((LAr,BHe)=>{\"use strict\";var hQt=Xt().isArrayOrTypedArray,I$=js(),dQt=L1().wrap;BHe.exports=function(t,r){var n,a;return I$.hasColorscale(r,\"line\")&&hQt(r.line.color)?(n=r.line.color,a=I$.extractOpts(r.line).colorscale,I$.calc(t,r,{vals:n,containerStr:\"line\",cLetter:\"c\"})):(n=vQt(r._length),a=[[0,r.line.color],[1,r.line.color]]),dQt({lineColor:n,cscale:a})};function vQt(e){for(var t=new Array(e),r=0;r<e;r++)t[r]=.5;return t}});var z$=fe(YF=>{\"use strict\";var pQt=Xt().isTypedArray;YF.convertTypedArray=function(e){return pQt(e)?Array.prototype.slice.call(e):e};YF.isOrdinal=function(e){return!!e.tickvals};YF.isVisible=function(e){return e.visible||!(\"visible\"in e)}});var ZHe=fe((FAr,XHe)=>{\"use strict\";var gQt=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"attribute vec4 p01_04, p05_08, p09_12, p13_16,\",\"               p17_20, p21_24, p25_28, p29_32,\",\"               p33_36, p37_40, p41_44, p45_48,\",\"               p49_52, p53_56, p57_60, colors;\",\"\",\"uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\",\"             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\",\"\",\"uniform vec2 resolution, viewBoxPos, viewBoxSize;\",\"uniform float maskHeight;\",\"uniform float drwLayer; // 0: context, 1: focus, 2: pick\",\"uniform vec4 contextColor;\",\"uniform sampler2D maskTexture, palette;\",\"\",\"bool isPick    = (drwLayer > 1.5);\",\"bool isContext = (drwLayer < 0.5);\",\"\",\"const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\",\"const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\",\"\",\"float val(mat4 p, mat4 v) {\",\"    return dot(matrixCompMult(p, v) * UNITS, UNITS);\",\"}\",\"\",\"float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\",\"    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\",\"    return y1 * (1.0 - ratio) + y2 * ratio;\",\"}\",\"\",\"int iMod(int a, int b) {\",\"    return a - b * (a / b);\",\"}\",\"\",\"bool fOutside(float p, float lo, float hi) {\",\"    return (lo < hi) && (lo > p || p > hi);\",\"}\",\"\",\"bool vOutside(vec4 p, vec4 lo, vec4 hi) {\",\"    return (\",\"        fOutside(p[0], lo[0], hi[0]) ||\",\"        fOutside(p[1], lo[1], hi[1]) ||\",\"        fOutside(p[2], lo[2], hi[2]) ||\",\"        fOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool mOutside(mat4 p, mat4 lo, mat4 hi) {\",\"    return (\",\"        vOutside(p[0], lo[0], hi[0]) ||\",\"        vOutside(p[1], lo[1], hi[1]) ||\",\"        vOutside(p[2], lo[2], hi[2]) ||\",\"        vOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    return mOutside(A, loA, hiA) ||\",\"           mOutside(B, loB, hiB) ||\",\"           mOutside(C, loC, hiC) ||\",\"           mOutside(D, loD, hiD);\",\"}\",\"\",\"bool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    mat4 pnts[4];\",\"    pnts[0] = A;\",\"    pnts[1] = B;\",\"    pnts[2] = C;\",\"    pnts[3] = D;\",\"\",\"    for(int i = 0; i < 4; ++i) {\",\"        for(int j = 0; j < 4; ++j) {\",\"            for(int k = 0; k < 4; ++k) {\",\"                if(0 == iMod(\",\"                    int(255.0 * texture2D(maskTexture,\",\"                        vec2(\",\"                            (float(i * 2 + j / 2) + 0.5) / 8.0,\",\"                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\",\"                        ))[3]\",\"                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\",\"                    2\",\"                )) return true;\",\"            }\",\"        }\",\"    }\",\"    return false;\",\"}\",\"\",\"vec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float x = 0.5 * sign(v) + 0.5;\",\"    float y = axisY(x, A, B, C, D);\",\"    float z = 1.0 - abs(v);\",\"\",\"    z += isContext ? 0.0 : 2.0 * float(\",\"        outsideBoundingBox(A, B, C, D) ||\",\"        outsideRasterMask(A, B, C, D)\",\"    );\",\"\",\"    return vec4(\",\"        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\",\"        z,\",\"        1.0\",\"    );\",\"}\",\"\",\"void main() {\",\"    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\",\"    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\",\"    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\",\"    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\",\"\",\"    float v = colors[3];\",\"\",\"    gl_Position = position(isContext, v, A, B, C, D);\",\"\",\"    fragColor =\",\"        isContext ? vec4(contextColor) :\",\"        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\",\"}\"].join(`\n",
+       "`),mQt=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"void main() {\",\"    gl_FragColor = fragColor;\",\"}\"].join(`\n",
+       "`),VM=UM().maxDimensionCount,jHe=Xt(),NHe=1e-6,XF=2048,yQt=new Uint8Array(4),UHe=new Uint8Array(4),VHe={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function xQt(e){e.read({x:0,y:0,width:1,height:1,data:yQt})}function WHe(e,t,r,n,a){var i=e._gl;i.enable(i.SCISSOR_TEST),i.scissor(t,r,n,a),e.clear({color:[0,0,0,0],depth:1})}function _Qt(e,t,r,n,a,i){var o=i.key;function s(l){var u=Math.min(n,a-l*n);l===0&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],WHe(e,i.scissorX,i.scissorY,i.scissorWidth,i.viewBoxSize[1])),!r.clearOnly&&(i.count=2*u,i.offset=2*l*n,t(i),l*n+u<a&&(r.currentRafs[o]=window.requestAnimationFrame(function(){s(l+1)})),r.drawCompleted=!1)}r.drawCompleted||(xQt(e),r.drawCompleted=!0),s(0)}function bQt(e){return Math.max(NHe,Math.min(1-NHe,e))}function wQt(e,t){for(var r=new Array(256),n=0;n<256;n++)r[n]=e(n/255).concat(t);return r}function q$(e,t){return(e>>>8*t)%256/255}function TQt(e,t,r){for(var n=new Array(e*(VM+4)),a=0,i=0;i<e;i++){for(var o=0;o<VM;o++)n[a++]=o<t.length?t[o].paddedUnitValues[i]:.5;n[a++]=q$(i,2),n[a++]=q$(i,1),n[a++]=q$(i,0),n[a++]=bQt(r[i])}return n}function AQt(e,t,r){for(var n=new Array(t*8),a=0,i=0;i<t;i++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=e*4+s,u=r[i*64+l];l===63&&o===0&&(u*=-1),n[a++]=u}return n}function GHe(e){var t=\"0\"+e;return t.slice(-2)}function YHe(e){return e<VM?\"p\"+GHe(e+1)+\"_\"+GHe(e+4):\"colors\"}function MQt(e,t,r){for(var n=0;n<=VM;n+=4)e[YHe(n)](AQt(n/4,t,r))}function SQt(e){for(var t={},r=0;r<=VM;r+=4)t[YHe(r)]=e.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return t}function EQt(e,t,r,n,a,i,o,s,l,u,c,f,v,g){for(var p=[[],[]],x=0;x<64;x++)p[0][x]=x===a?1:0,p[1][x]=x===i?1:0;o*=g,s*=g,l*=g,u*=g;var _=e.lines.canvasOverdrag*g,y=e.domain,C=e.canvasWidth*g,k=e.canvasHeight*g,S=e.pad.l*g,L=e.pad.b*g,w=e.layoutHeight*g,A=e.layoutWidth*g,T=e.deselectedLines.color,m=e.deselectedLines.opacity,F=jHe.extendFlat({key:c,resolution:[C,k],viewBoxPos:[o+_,s],viewBoxSize:[l,u],i0:a,i1:i,dim0A:p[0].slice(0,16),dim0B:p[0].slice(16,32),dim0C:p[0].slice(32,48),dim0D:p[0].slice(48,64),dim1A:p[1].slice(0,16),dim1B:p[1].slice(16,32),dim1C:p[1].slice(32,48),dim1D:p[1].slice(48,64),drwLayer:f,contextColor:[T[0],T[1],T[2],m!==\"auto\"?T[3]*m:Math.max(1/255,Math.pow(1/e.lines.color.length,1/3))],scissorX:(n===t?0:o+_)+(S-_)+A*y.x[0],scissorWidth:(n===r?C-o+_:l+.5)+(n===t?o+_:0),scissorY:s+L+w*y.y[0],scissorHeight:u,viewportX:S-_+A*y.x[0],viewportY:L+w*y.y[0],viewportWidth:C,viewportHeight:k},v);return F}function HHe(e){var t=XF-1,r=Math.max(0,Math.floor(e[0]*t),0),n=Math.min(t,Math.ceil(e[1]*t),t);return[Math.min(r,n),Math.max(r,n)]}XHe.exports=function(e,t){var r=t.context,n=t.pick,a=t.regl,i=a._gl,o=i.getParameter(i.ALIASED_LINE_WIDTH_RANGE),s=Math.max(o[0],Math.min(o[1],t.viewModel.plotGlPixelRatio)),l={currentRafs:{},drawCompleted:!0,clearOnly:!1},u,c,f,v,g=SQt(a),p,x=a.texture(VHe),_=[];C(t);var y=a({profile:!1,blend:{enable:r,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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C(T){u=T.model,c=T.viewModel,f=c.dimensions.slice(),v=f[0]?f[0].values.length:0;var m=u.lines,F=n?m.color.map(function(I,R){return R/m.color.length}):m.color,E=TQt(v,f,F);MQt(g,v,E),!r&&!n&&(x=a.texture(jHe.extendFlat({data:wQt(u.unitToColor,255)},VHe)))}function k(T){var m,F,E,I=[[],[]];for(E=0;E<64;E++){var R=!T&&E<f.length?f[E].brush.filter.getBounds():[-1/0,1/0];I[0][E]=R[0],I[1][E]=R[1]}var B=XF*8,O=new Array(B);for(m=0;m<B;m++)O[m]=255;if(!T)for(m=0;m<f.length;m++){var V=m%8,j=(m-V)/8,q=Math.pow(2,V),U=f[m],J=U.brush.filter.get();if(!(J.length<2)){var ee=HHe(J[0])[1];for(F=1;F<J.length;F++){var ce=HHe(J[F]);for(E=ee+1;E<ce[0];E++)O[E*8+j]&=~q;ee=Math.max(ee,ce[1])}}}var ye={shape:[8,XF],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:O};return 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n=ije(t),a=n.trace,i=E5.convertTypedArray(n.lineColor),o=a.line,s={color:rje.normalize(a.unselected.line.color),opacity:a.unselected.line.opacity},l=$He.extractOpts(o),u=l.reversescale?$He.flipScale(n.cscale):n.cscale,c=a.domain,f=a.dimensions,v=e.width,g=a.labelangle,p=a.labelside,x=a.labelfont,_=a.tickfont,y=a.rangefont,C=zg.extendDeepNoArrays({},o,{color:i.map(bf.scale.linear().domain($F({values:i,range:[l.min,l.max],_length:a._length}))),blockLineCount:Fo.blockLineCount,canvasOverdrag:Fo.overdrag*Fo.canvasPixelRatio}),k=Math.floor(v*(c.x[1]-c.x[0])),S=Math.floor(e.height*(c.y[1]-c.y[0])),L=e.margin||{l:80,r:80,t:100,b:80},w=k,A=S;return{key:r,colCount:f.filter(E5.isVisible).length,dimensions:f,tickDistance:Fo.tickDistance,unitToColor:zQt(u),lines:C,deselectedLines:s,labelAngle:g,labelSide:p,labelFont:x,tickFont:_,rangeFont:y,layoutWidth:v,layoutHeight:e.height,domain:c,translateX:c.x[0]*v,translateY:e.height-c.y[1]*e.height,pad:L,canvasWidth:w*Fo.canvasPixelRatio+2*C.canvasOverdrag,canvasHeight:A*Fo.canvasPixelRatio,width:w,height:A,canvasPixelRatio:Fo.canvasPixelRatio}}function 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y=_.selectAll(\".\"+Fo.cn.parcoordsControlView).data(F1,Iv);y.enter().append(\"g\").classed(Fo.cn.parcoordsControlView,!0),y.attr(\"transform\",function(I){return P1(I.model.pad.l,I.model.pad.t)});var C=y.selectAll(\".\"+Fo.cn.yAxis).data(function(I){return I.dimensions},Iv);C.enter().append(\"g\").classed(Fo.cn.yAxis,!0),y.each(function(I){O$(C,I,u)}),g.each(function(I){if(I.viewModel){!I.lineLayer||a?I.lineLayer=LQt(this,I):I.lineLayer.update(I),(I.key||I.key===0)&&(I.viewModel[I.key]=I.lineLayer);var R=!I.context||a;I.lineLayer.render(I.viewModel.panels,R)}}),C.attr(\"transform\",function(I){return P1(I.xScale(I.xIndex),0)}),C.call(bf.behavior.drag().origin(function(I){return I}).on(\"drag\",function(I){var R=I.parent;f.linePickActive(!1),I.x=Math.max(-Fo.overdrag,Math.min(I.model.width+Fo.overdrag,bf.event.x)),I.canvasX=I.x*I.model.canvasPixelRatio,C.sort(function(B,O){return 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k=C.selectAll(\".\"+Fo.cn.axisOverlays).data(F1,Iv);k.enter().append(\"g\").classed(Fo.cn.axisOverlays,!0),k.selectAll(\".\"+Fo.cn.axis).remove();var S=k.selectAll(\".\"+Fo.cn.axis).data(F1,Iv);S.enter().append(\"g\").classed(Fo.cn.axis,!0),S.each(function(I){var R=I.model.height/I.model.tickDistance,B=I.domainScale,O=B.domain();bf.select(this).call(bf.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(R,I.tickFormat).tickValues(I.ordinal?O:null).tickFormat(function(V){return E5.isOrdinal(I)?V:sje(I.model.dimensions[I.visibleIndex],V)}).scale(B)),ZF.font(S.selectAll(\"text\"),I.model.tickFont)}),S.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),S.selectAll(\"text\").style(\"cursor\",\"default\");var L=k.selectAll(\".\"+Fo.cn.axisHeading).data(F1,Iv);L.enter().append(\"g\").classed(Fo.cn.axisHeading,!0);var 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m=T.selectAll(\".\"+Fo.cn.axisExtentTopText).data(F1,Iv);m.enter().append(\"text\").classed(Fo.cn.axisExtentTopText,!0).call(JHe),m.text(function(I){return eje(I,!0)}).each(function(I){ZF.font(bf.select(this),I.model.rangeFont)});var F=A.selectAll(\".\"+Fo.cn.axisExtentBottom).data(F1,Iv);F.enter().append(\"g\").classed(Fo.cn.axisExtentBottom,!0),F.attr(\"transform\",function(I){return P1(0,I.model.height+Fo.axisExtentOffset)});var E=F.selectAll(\".\"+Fo.cn.axisExtentBottomText).data(F1,Iv);E.enter().append(\"text\").classed(Fo.cn.axisExtentBottomText,!0).attr(\"dy\",\"0.75em\").call(JHe),E.text(function(I){return eje(I,!1)}).each(function(I){ZF.font(bf.select(this),I.model.rangeFont)}),aje.ensureAxisBrush(k,c,t)}});var H$=fe((G$,dje)=>{\"use strict\";var UQt=uje(),VQt=RF(),cje=z$().isVisible,hje={};function fje(e,t,r){var n=t.indexOf(r),a=e.indexOf(n);return a===-1&&(a+=t.length),a}function GQt(e,t){return function(n,a){return fje(e,t,n)-fje(e,t,a)}}var 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p=e.parcatsViewModel.x+u,x=e.parcatsViewModel.y+c,_=W$.mostReadable(e.model.color),y=e.model.count,C=y/e.parcatsViewModel.model.count,k={countLabel:y,probabilityLabel:C.toFixed(3)},S=[];e.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&S.push([\"Count:\",k.countLabel].join(\" \")),e.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&S.push([\"P:\",k.probabilityLabel].join(\" \"));var L=S.join(\"<br>\"),w=Do.mouse(a)[0];YM.loneHover({trace:i,x:p-s.left+l.left,y:x-s.top+l.top,text:L,color:e.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:_,idealAlign:w<p?\"right\":\"left\",hovertemplate:(i.line||{}).hovertemplate,hovertemplateLabels:k,eventData:[{data:i._input,fullData:i,count:y,probability:C}]},{container:o._hoverlayer.node(),outerContainer:o._paper.node(),gd:a})}}}function Eer(e){if(!e.parcatsViewModel.dragDimension&&(Z$(Do.select(this)),YM.loneUnhover(e.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),e.parcatsViewModel.pathSelection.sort(Y$),e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1)){var t=XM(e),r=X$(e);e.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:t,event:Do.event,constraints:r})}}function XM(e){for(var t=[],r=qje(e.parcatsViewModel),n=0;n<e.model.valueInds.length;n++){var a=e.model.valueInds[n];t.push({curveNumber:r,pointNumber:a})}return t}function X$(e){for(var t={},r=e.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var a=r[n],i=a.categories[e.model.categoryInds[n]];t[a.containerInd]=i.categoryValue}return e.model.rawColor!==void 0&&(t.color=e.model.rawColor),t}function ker(e){if(e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1){var t=XM(e),r=X$(e);e.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:t,event:Do.event,constraints:r})}}function Z$(e){e.attr(\"fill\",function(t){return t.model.color}).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function $$(e){e.attr(\"fill-opacity\",.8).attr(\"stroke\",t=>W$.mostReadable(t.model.color)).attr(\"stroke-width\",.3)}function Cer(e){e.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}function Ije(e){e.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function Ler(e){e.attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}function K$(e){e.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function KF(e){var t=e.parcatsViewModel.pathSelection,r=e.categoryViewModel.model.dimensionInd,n=e.categoryViewModel.model.categoryInd;return t.filter(function(a){return a.model.categoryInds[r]===n&&a.model.color===e.color})}function Per(e){var t=Do.select(e.parentNode).selectAll(\"rect.bandrect\");t.each(function(r){var n=KF(r);$$(n),n.each(function(){Iy.raiseToTop(this)})}),Cer(Do.select(e.parentNode))}function Fer(e){var t=Do.select(e).datum(),r=KF(t);$$(r),r.each(function(){Iy.raiseToTop(this)}),Do.select(e.parentNode).selectAll(\"rect.bandrect\").filter(function(n){return n.color===t.color}).each(function(){Iy.raiseToTop(this),Ler(Do.select(this))})}function J$(e,t,r){var n=Do.select(e).datum(),a=n.categoryViewModel.model,i=n.parcatsViewModel.graphDiv,o=Do.select(e.parentNode).selectAll(\"rect.bandrect\"),s=[];o.each(function(u){var c=KF(u);c.each(function(f){Array.prototype.push.apply(s,XM(f))})});var l={};l[a.dimensionInd]=a.categoryValue,i.emit(t,{points:s,event:r,constraints:l})}function Q$(e,t,r){var n=Do.select(e).datum(),a=n.categoryViewModel.model,i=n.parcatsViewModel.graphDiv,o=KF(n),s=[];o.each(function(u){Array.prototype.push.apply(s,XM(u))});var l={};l[a.dimensionInd]=a.categoryValue,n.rawColor!==void 0&&(l.color=n.rawColor),i.emit(t,{points:s,event:r,constraints:l})}function zje(e,t,r){e._fullLayout._calcInverseTransform(e);var n=e._fullLayout._invScaleX,a=e._fullLayout._invScaleY,i=Do.select(r.parentNode).select(\"rect.catrect\"),o=i.node().getBoundingClientRect(),s=i.datum(),l=s.parcatsViewModel,u=l.model.dimensions[s.model.dimensionInd],c=l.trace,f=o.top+o.height/2,v,g;l.dimensions.length>1&&u.displayInd===l.dimensions.length-1?(v=o.left,g=\"left\"):(v=o.left+o.width,g=\"right\");var p=s.model.count,x=s.model.categoryLabel,_=p/s.parcatsViewModel.model.count,y={countLabel:p,categoryLabel:x,probabilityLabel:_.toFixed(3)},C=[];s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&C.push([\"Count:\",y.countLabel].join(\" \")),s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&C.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var k=C.join(\"<br>\");return{trace:c,x:n*(v-t.left),y:a*(f-t.top),text:k,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", 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y=s.model.count,C=0;l.pathSelection.each(function(F){F.model.color===o.color&&(C+=F.model.count)});var k=_/x,S=_/C,L=_/y,w={countLabel:_,categoryLabel:p,probabilityLabel:k.toFixed(3)},A=[];s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")!==-1&&A.push([\"Count:\",w.countLabel].join(\" \")),s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")!==-1&&(A.push(\"P(color \\u2229 \"+p+\"): \"+w.probabilityLabel),A.push(\"P(\"+p+\" | color): \"+S.toFixed(3)),A.push(\"P(color | \"+p+\"): \"+L.toFixed(3)));var T=A.join(\"<br>\"),m=W$.mostReadable(o.color);return{trace:c,x:n*(v-t.left),y:a*(f-t.top),text:T,color:o.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:m,fontSize:10,idealAlign:g,hovertemplate:c.hovertemplate,hovertemplateLabels:w,eventData:[{data:c._input,fullData:c,category:p,count:x,probability:k,categorycount:y,colorcount:C,bandcolorcount:_}]}}function Ier(e){if(!e.parcatsViewModel.dragDimension&&e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1){var t=Do.mouse(this)[1];if(t<-1)return;var r=e.parcatsViewModel.graphDiv,n=r._fullLayout,a=n._paperdiv.node().getBoundingClientRect(),i=e.parcatsViewModel.hoveron,o=this;if(i===\"color\"?(Fer(o),Q$(o,\"plotly_hover\",Do.event)):(Per(o),J$(o,\"plotly_hover\",Do.event)),e.parcatsViewModel.hoverinfoItems.indexOf(\"none\")===-1){var s;i===\"category\"?s=zje(r,a,o):i===\"color\"?s=Rer(r,a,o):i===\"dimension\"&&(s=Der(r,a,o)),s&&YM.loneHover(s,{container:n._hoverlayer.node(),outerContainer:n._paper.node(),gd:r})}}}function zer(e){var t=e.parcatsViewModel;if(!t.dragDimension&&(Z$(t.pathSelection),Ije(t.dimensionSelection.selectAll(\"g.category\")),K$(t.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),YM.loneUnhover(t.graphDiv._fullLayout._hoverlayer.node()),t.pathSelection.sort(Y$),t.hoverinfoItems.indexOf(\"skip\")===-1)){var r=e.parcatsViewModel.hoveron,n=this;r===\"color\"?Q$(n,\"plotly_unhover\",Do.event):J$(n,\"plotly_unhover\",Do.event)}}function qer(e){e.parcatsViewModel.arrangement!==\"fixed\"&&(e.dragDimensionDisplayInd=e.model.displayInd,e.initialDragDimensionDisplayInds=e.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),e.dragHasMoved=!1,e.dragCategoryDisplayInd=null,Do.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each(function(t){var r=Do.mouse(this)[0],n=Do.mouse(this)[1];-2<=r&&r<=t.width+2&&-2<=n&&n<=t.height+2&&(e.dragCategoryDisplayInd=t.model.displayInd,e.initialDragCategoryDisplayInds=e.model.categories.map(function(a){return a.displayInd}),t.model.dragY=t.y,Iy.raiseToTop(this.parentNode),Do.select(this.parentNode).selectAll(\"rect.bandrect\").each(function(a){a.y<n&&n<=a.y+a.height&&(e.potentialClickBand=this)}))}),e.parcatsViewModel.dragDimension=e,YM.loneUnhover(e.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function Ber(e){if(e.parcatsViewModel.arrangement!==\"fixed\"&&(e.dragHasMoved=!0,e.dragDimensionDisplayInd!==null)){var t=e.dragDimensionDisplayInd,r=t-1,n=t+1,a=e.parcatsViewModel.dimensions[t];if(e.dragCategoryDisplayInd!==null){var i=a.categories[e.dragCategoryDisplayInd];i.model.dragY+=Do.event.dy;var o=i.model.dragY,s=i.model.displayInd,l=a.categories,u=l[s-1],c=l[s+1];u!==void 0&&o<u.y+u.height/2&&(i.model.displayInd=u.model.displayInd,u.model.displayInd=s),c!==void 0&&o+i.height>c.y+c.height/2&&(i.model.displayInd=c.model.displayInd,c.model.displayInd=s),e.dragCategoryDisplayInd=i.model.displayInd}if(e.dragCategoryDisplayInd===null||e.parcatsViewModel.arrangement===\"freeform\"){a.model.dragX=Do.event.x;var f=e.parcatsViewModel.dimensions[r],v=e.parcatsViewModel.dimensions[n];f!==void 0&&a.model.dragX<f.x+f.width&&(a.model.displayInd=f.model.displayInd,f.model.displayInd=t),v!==void 0&&a.model.dragX+a.width>v.x&&(a.model.displayInd=v.model.displayInd,v.model.displayInd=e.dragDimensionDisplayInd),e.dragDimensionDisplayInd=a.model.displayInd}tK(e.parcatsViewModel),eK(e.parcatsViewModel),Oje(e.parcatsViewModel),Bje(e.parcatsViewModel)}}function Oer(e){if(e.parcatsViewModel.arrangement!==\"fixed\"&&e.dragDimensionDisplayInd!==null){Do.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var t={},r=qje(e.parcatsViewModel),n=e.parcatsViewModel.model.dimensions.map(function(v){return v.displayInd}),a=e.initialDragDimensionDisplayInds.some(function(v,g){return v!==n[g]});a&&n.forEach(function(v,g){var p=e.parcatsViewModel.model.dimensions[g].containerInd;t[\"dimensions[\"+p+\"].displayindex\"]=v});var i=!1;if(e.dragCategoryDisplayInd!==null){var o=e.model.categories.map(function(v){return v.displayInd});if(i=e.initialDragCategoryDisplayInds.some(function(v,g){return v!==o[g]}),i){var s=e.model.categories.slice().sort(function(v,g){return v.displayInd-g.displayInd}),l=s.map(function(v){return v.categoryValue}),u=s.map(function(v){return v.categoryLabel});t[\"dimensions[\"+e.model.containerInd+\"].categoryarray\"]=[l],t[\"dimensions[\"+e.model.containerInd+\"].ticktext\"]=[u],t[\"dimensions[\"+e.model.containerInd+\"].categoryorder\"]=\"array\"}}if(e.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")===-1&&!e.dragHasMoved&&e.potentialClickBand&&(e.parcatsViewModel.hoveron===\"color\"?Q$(e.potentialClickBand,\"plotly_click\",Do.event.sourceEvent):J$(e.potentialClickBand,\"plotly_click\",Do.event.sourceEvent)),e.model.dragX=null,e.dragCategoryDisplayInd!==null){var c=e.parcatsViewModel.dimensions[e.dragDimensionDisplayInd].categories[e.dragCategoryDisplayInd];c.model.dragY=null,e.dragCategoryDisplayInd=null}e.dragDimensionDisplayInd=null,e.parcatsViewModel.dragDimension=null,e.dragHasMoved=null,e.potentialClickBand=null,tK(e.parcatsViewModel),eK(e.parcatsViewModel);var f=Do.transition().duration(300).ease(\"cubic-in-out\");f.each(function(){Oje(e.parcatsViewModel,!0),Bje(e.parcatsViewModel,!0)}).each(\"end\",function(){(a||i)&&wer.restyle(e.parcatsViewModel.graphDiv,t,[r])})}}function qje(e){for(var t,r=e.graphDiv._fullData,n=0;n<r.length;n++)if(e.key===r[n].uid){t=n;break}return t}function Bje(e,t){t===void 0&&(t=!1);function r(n){return t?n.transition():n}e.pathSelection.data(function(n){return n.paths},qg),r(e.pathSelection).attr(\"d\",function(n){return n.svgD})}function Oje(e,t){t===void 0&&(t=!1);function r(l){return t?l.transition():l}e.dimensionSelection.data(function(l){return l.dimensions},qg);var n=e.dimensionSelection.selectAll(\"g.category\").data(function(l){return l.categories},qg);r(e.dimensionSelection).attr(\"transform\",function(l){return jM(l.x,0)}),r(n).attr(\"transform\",function(l){return jM(0,l.y)});var a=n.select(\".dimlabel\");a.text(function(l,u){return u===0?l.parcatsViewModel.model.dimensions[l.model.dimensionInd].dimensionLabel:null});var i=n.select(\".catlabel\");i.attr(\"text-anchor\",function(l){return WM(l)?\"start\":\"end\"}).attr(\"x\",function(l){return WM(l)?l.width+5:-5}).each(function(l){var u,c;WM(l)?(u=l.width+5,c=\"start\"):(u=-5,c=\"end\"),Do.select(this).selectAll(\"tspan\").attr(\"x\",u).attr(\"text-anchor\",c)});var o=n.selectAll(\"rect.bandrect\").data(function(l){return l.bands},qg),s=o.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",function(l){return l.color}).attr(\"fill-opacity\",0);o.attr(\"fill\",function(l){return l.color}).attr(\"width\",function(l){return l.width}).attr(\"height\",function(l){return l.height}).attr(\"y\",function(l){return l.y}),K$(s),o.each(function(){Iy.raiseToTop(this)}),o.exit().remove()}function Ner(e,t,r){var n=r[0],a=t.margin||{l:80,r:80,t:100,b:80},i=n.trace,o=i.domain,s=t.width,l=t.height,u=Math.floor(s*(o.x[1]-o.x[0])),c=Math.floor(l*(o.y[1]-o.y[0])),f=o.x[0]*s+a.l,v=t.height-o.y[1]*t.height+a.t,g=i.line.shape,p;i.hoverinfo===\"all\"?p=[\"count\",\"probability\"]:p=(i.hoverinfo||\"\").split(\"+\");var x={trace:i,key:i.uid,model:n,x:f,y:v,width:u,height:c,hoveron:i.hoveron,hoverinfoItems:p,arrangement:i.arrangement,bundlecolors:i.bundlecolors,sortpaths:i.sortpaths,labelfont:i.labelfont,categorylabelfont:i.tickfont,pathShape:g,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:e,traceSelection:null,pathSelection:null,dimensionSelection:null};return n.dimensions&&(tK(x),eK(x)),x}function Rje(e,t,r,n,a){var i=[],o=[],s,l;for(l=0;l<r.length-1;l++)s=ber(r[l]+e[l],e[l+1]),i.push(s(a)),o.push(s(1-a));var u=\"M \"+e[0]+\",\"+t[0];for(u+=\"l\"+r[0]+\",0 \",l=1;l<r.length;l++)u+=\"C\"+i[l-1]+\",\"+t[l-1]+\" \"+o[l-1]+\",\"+t[l]+\" \"+e[l]+\",\"+t[l],u+=\"l\"+r[l]+\",0 \";for(u+=\"l0,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",l=r.length-2;l>=0;l--)u+=\"C\"+o[l]+\",\"+(t[l+1]+n)+\" \"+i[l]+\",\"+(t[l]+n)+\" \"+(e[l]+r[l])+\",\"+(t[l]+n),u+=\"l-\"+r[l]+\",0 \";return u+=\"Z\",u}function eK(e){var t=e.dimensions,r=e.model,n=t.map(function(R){return R.categories.map(function(B){return B.y})}),a=e.model.dimensions.map(function(R){return R.categories.map(function(B){return B.displayInd})}),i=e.model.dimensions.map(function(R){return R.displayInd}),o=e.dimensions.map(function(R){return R.model.dimensionInd}),s=t.map(function(R){return R.x}),l=t.map(function(R){return R.width}),u=[];for(var c in r.paths)r.paths.hasOwnProperty(c)&&u.push(r.paths[c]);function f(R){var B=R.categoryInds.map(function(V,j){return a[j][V]}),O=o.map(function(V){return B[V]});return O}u.sort(function(R,B){var O=f(R),V=f(B);return e.sortpaths===\"backward\"&&(O.reverse(),V.reverse()),O.push(R.valueInds[0]),V.push(B.valueInds[0]),e.bundlecolors&&(O.unshift(R.rawColor),V.unshift(B.rawColor)),Mer(O,V)});for(var v=new Array(u.length),g=t[0].model.count,p=t[0].categories.map(function(R){return R.height}).reduce(function(R,B){return R+B}),x=0;x<u.length;x++){var _=u[x],y;g>0?y=p*(_.count/g):y=0;for(var C=new Array(n.length),k=0;k<_.categoryInds.length;k++){var S=_.categoryInds[k],L=a[k][S],w=i[k];C[w]=n[w][L],n[w][L]+=y;var A=e.dimensions[w].categories[L],T=A.bands.length,m=A.bands[T-1];if(m===void 0||_.rawColor!==m.rawColor){var F=m===void 0?0:m.y+m.height;A.bands.push({key:F,color:_.color,rawColor:_.rawColor,height:y,width:A.width,count:_.count,y:F,categoryViewModel:A,parcatsViewModel:e})}else{var E=A.bands[T-1];E.height+=y,E.count+=_.count}}var I;e.pathShape===\"hspline\"?I=Rje(s,C,l,y,.5):I=Rje(s,C,l,y,0),v[x]={key:_.valueInds[0],model:_,height:y,leftXs:s,topYs:C,dimWidths:l,svgD:I,parcatsViewModel:e}}e.paths=v}function tK(e){var t=e.model.dimensions.map(function(o){return{displayInd:o.displayInd,dimensionInd:o.dimensionInd}});t.sort(function(o,s){return o.displayInd-s.displayInd});var r=[];for(var n in t){var a=t[n].dimensionInd,i=e.model.dimensions[a];r.push(Uer(e,i))}e.dimensions=r}function Uer(e,t){var r=40,n=16,a=e.model.dimensions.length,i=t.displayInd,o,s,l;a>1?o=(e.width-2*r-n)/(a-1):o=0,s=r,l=s+o*i;var u=[],c=e.model.maxCats,f=t.categories.length,v=8,g=t.count,p=e.height-v*(c-1),x,_,y,C,k,S=(c-f)*v/2,L=t.categories.map(function(w){return{displayInd:w.displayInd,categoryInd:w.categoryInd}});for(L.sort(function(w,A){return 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EXt=WHe.exports=SXt({_arrayAttrRegexps:[VHe.counterRegex(\"map\",\".layers\",!0)],domain:wXt({name:\"map\"}),style:{valType:\"any\",values:HHe.styleValuesMap,dflt:HHe.styleValueDflt},center:{lon:{valType:\"number\",dflt:0},lat:{valType:\"number\",dflt:0}},zoom:{valType:\"number\",dflt:1},bearing:{valType:\"number\",dflt:0},pitch:{valType:\"number\",dflt:0},bounds:{west:{valType:\"number\"},east:{valType:\"number\"},south:{valType:\"number\"},north:{valType:\"number\"}},layers:MXt(\"layer\",{visible:{valType:\"boolean\",dflt:!0},sourcetype:{valType:\"enumerated\",values:[\"geojson\",\"vector\",\"raster\",\"image\"],dflt:\"geojson\"},source:{valType:\"any\"},sourcelayer:{valType:\"string\",dflt:\"\"},sourceattribution:{valType:\"string\"},type:{valType:\"enumerated\",values:[\"circle\",\"line\",\"fill\",\"symbol\",\"raster\"],dflt:\"circle\"},coordinates:{valType:\"any\"},below:{valType:\"string\"},color:{valType:\"color\",dflt:GHe},opacity:{valType:\"number\",min:0,max:1,dflt:1},minzoom:{valType:\"number\",min:0,max:24,dflt:0},maxzoom:{valType:\"number\",min:0,max:24,dflt:24},circle:{radius:{valType:\"number\",dflt:15}},line:{width:{valType:\"number\",dflt:2},dash:{valType:\"data_array\"}},fill:{outlinecolor:{valType:\"color\",dflt:GHe}},symbol:{icon:{valType:\"string\",dflt:\"marker\"},iconsize:{valType:\"number\",dflt:10},text:{valType:\"string\",dflt:\"\"},placement:{valType:\"enumerated\",values:[\"point\",\"line\",\"line-center\"],dflt:\"point\"},textfont:jHe,textposition:VHe.extendFlat({},AXt,{arrayOk:!1})}})},\"plot\",\"from-root\");EXt.uirevision={valType:\"any\",editType:\"none\"}});var h7=ye((Dbr,KHe)=>{\"use 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RJ=ye((Fbr,JHe)=>{\"use strict\";var FXt=[\"Metropolis Black Italic\",\"Metropolis Black\",\"Metropolis Bold Italic\",\"Metropolis Bold\",\"Metropolis Extra Bold Italic\",\"Metropolis Extra Bold\",\"Metropolis Extra Light Italic\",\"Metropolis Extra Light\",\"Metropolis Light Italic\",\"Metropolis Light\",\"Metropolis Medium Italic\",\"Metropolis Medium\",\"Metropolis Regular Italic\",\"Metropolis Regular\",\"Metropolis Semi Bold Italic\",\"Metropolis Semi Bold\",\"Metropolis Thin Italic\",\"Metropolis Thin\",\"Open Sans Bold Italic\",\"Open Sans Bold\",\"Open Sans Extrabold Italic\",\"Open Sans Extrabold\",\"Open Sans Italic\",\"Open Sans Light Italic\",\"Open Sans Light\",\"Open Sans Regular\",\"Open Sans Semibold Italic\",\"Open Sans Semibold\",\"Klokantech Noto Sans Bold\",\"Klokantech Noto Sans CJK Bold\",\"Klokantech Noto Sans CJK Regular\",\"Klokantech Noto Sans Italic\",\"Klokantech Noto Sans Regular\"];JHe.exports={isSupportedFont:function(e){return FXt.indexOf(e)!==-1}}});var eje=ye((zbr,QHe)=>{\"use strict\";var nC=Pr(),DJ=Ru(),zXt=$p(),qXt=D0(),OXt=F0(),BXt=Fg(),$He=h7(),NXt=RJ().isSupportedFont;QHe.exports=function(t,r,n,i){function a(p,k){return nC.coerce(t,r,$He,p,k)}function o(p,k){return nC.coerce2(t,r,$He,p,k)}var s=UXt(t,r,a);if(!s){r.visible=!1;return}if(a(\"text\"),a(\"texttemplate\"),a(\"texttemplatefallback\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),a(\"mode\"),a(\"below\"),DJ.hasMarkers(r)){zXt(t,r,n,i,a,{noLine:!0,noAngle:!0}),a(\"marker.allowoverlap\"),a(\"marker.angle\");var l=r.marker;l.symbol!==\"circle\"&&(nC.isArrayOrTypedArray(l.size)&&(l.size=l.size[0]),nC.isArrayOrTypedArray(l.color)&&(l.color=l.color[0]))}DJ.hasLines(r)&&(qXt(t,r,n,i,a,{noDash:!0}),a(\"connectgaps\"));var u=o(\"cluster.maxzoom\"),c=o(\"cluster.step\"),f=o(\"cluster.color\",r.marker&&r.marker.color||n),h=o(\"cluster.size\"),d=o(\"cluster.opacity\"),v=u!==!1||c!==!1||f!==!1||h!==!1||d!==!1,m=a(\"cluster.enabled\",v);if(m||DJ.hasText(r)){var b=i.font.family;OXt(t,r,i,a,{noSelect:!0,noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0,font:{family:NXt(b)?b:\"Open Sans Regular\",weight:i.font.weight,style:i.font.style,size:i.font.size,color:i.font.color}})}a(\"fill\"),r.fill!==\"none\"&&BXt(t,r,n,a),nC.coerceSelectionMarkerOpacity(r,a)};function UXt(e,t,r){var n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length);return t._length=a,a}});var FJ=ye((qbr,rje)=>{\"use strict\";var tje=ho();rje.exports=function(t,r,n){var i={},a=n[r.subplot]._subplot,o=a.mockAxis,s=t.lonlat;return i.lonLabel=tje.tickText(o,o.c2l(s[0]),!0).text,i.latLabel=tje.tickText(o,o.c2l(s[1]),!0).text,i}});var zJ=ye((Obr,nje)=>{\"use strict\";var ije=Pr();nje.exports=function(t,r){var n=t.split(\" 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p;if((a||o)&&(p=v7.calcTraceToLineCoords(r)),a&&(h.geojson=v7.makePolygon(p),h.layout.visibility=\"visible\",sv.extendFlat(h.paint,{\"fill-color\":n.fillcolor})),o&&(d.geojson=v7.makeLine(p),d.layout.visibility=\"visible\",sv.extendFlat(d.paint,{\"line-width\":n.line.width,\"line-color\":n.line.color,\"line-opacity\":n.opacity})),u){var k=KXt(r);v.geojson=k.geojson,v.layout.visibility=\"visible\",f&&(v.filter=[\"!\",[\"has\",\"point_count\"]],b.cluster={type:\"circle\",filter:[\"has\",\"point_count\"],layout:{visibility:\"visible\"},paint:{\"circle-color\":OJ(n.cluster.color,n.cluster.step),\"circle-radius\":OJ(n.cluster.size,n.cluster.step),\"circle-opacity\":OJ(n.cluster.opacity,n.cluster.step)}},b.clusterCount={type:\"symbol\",filter:[\"has\",\"point_count\"],paint:{},layout:{\"text-field\":\"{point_count_abbreviated}\",\"text-font\":oje(n),\"text-size\":12}}),sv.extendFlat(v.paint,{\"circle-color\":k.mcc,\"circle-radius\":k.mrc,\"circle-opacity\":k.mo})}if(u&&f&&(v.filter=[\"!\",[\"has\",\"point_count\"]]),(c||l)&&(m.geojson=JXt(r,t),sv.extendFlat(m.layout,{visibility:\"visible\",\"icon-image\":\"{symbol}-15\",\"text-field\":\"{text}\"}),c&&(sv.extendFlat(m.layout,{\"icon-size\":n.marker.size/10}),\"angle\"in 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c;i&&(aje.hasColorscale(t,\"marker\")?c=aje.makeColorScaleFuncFromTrace(r):c=sv.identity);var f;a&&(f=HXt(t));var h;o&&(h=function(M){var T=sje(M)?+sv.constrain(M,0,1):0;return l(T)});var d=[];for(s=0;s<e.length;s++){var v=e[s],m=v.lonlat;if(!lje(m)){var b={};c&&(b.mcc=v.mcc=c(v.mc)),f&&(b.mrc=v.mrc=f(v.ms)),h&&(b.mo=h(v.mo)),n&&(b.selected=v.selected||0),d.push({type:\"Feature\",id:s+1,geometry:{type:\"Point\",coordinates:m},properties:b})}}var p;if(n)for(p=GXt.makeSelectedPointStyleFns(t),s=0;s<d.length;s++){var k=d[s].properties;p.selectedOpacityFn&&(k.mo=l(p.selectedOpacityFn(k))),p.selectedColorFn&&(k.mcc=p.selectedColorFn(k)),p.selectedSizeFn&&(k.mrc=p.selectedSizeFn(k))}return{geojson:{type:\"FeatureCollection\",features:d},mcc:i||p&&p.selectedColorFn?{type:\"identity\",property:\"mcc\"}:r.color,mrc:a||p&&p.selectedSizeFn?{type:\"identity\",property:\"mrc\"}:u(r.size),mo:o||p&&p.selectedOpacityFn?{type:\"identity\",property:\"mo\"}:l(r.opacity)}}function JXt(e,t){for(var r=t._fullLayout,n=e[0].trace,i=n.marker||{},a=i.symbol,o=i.angle,s=a!==\"circle\"?qJ(a):g7,l=o!==\"auto\"?qJ(o,!0):g7,u=p7.hasText(n)?qJ(n.text):g7,c=[],f=0;f<e.length;f++){var h=e[f];if(!lje(h.lonlat)){var d=n.texttemplate,v;if(d){var m=Array.isArray(d)?d[f]||\"\":d,b=n._module.formatLabels(h,n,r),p={};XXt(p,n,h.i),v=sv.texttemplateString({data:[p,h,n._meta],fallback:n.texttemplatefallback,labels:b,locale:r._d3locale,template:m})}else v=u(f);v&&(v=v.replace(ZXt,\"\").replace(YXt,`\n",
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hje(e,t,r,n){this.type=\"scattermap\",this.subplot=e,this.uid=t,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+t+\"-fill\",line:\"source-\"+t+\"-line\",circle:\"source-\"+t+\"-circle\",symbol:\"source-\"+t+\"-symbol\",cluster:\"source-\"+t+\"-circle\",clusterCount:\"source-\"+t+\"-circle\"},this.layerIds={fill:z5+t+\"-fill\",line:z5+t+\"-line\",circle:z5+t+\"-circle\",symbol:z5+t+\"-symbol\",cluster:z5+t+\"-cluster\",clusterCount:z5+t+\"-cluster-count\"},this.below=null}var aC=hje.prototype;aC.addSource=function(e,t,r){var n={type:\"geojson\",data:t.geojson};r&&r.enabled&&$Xt.extendFlat(n,{cluster:!0,clusterMaxZoom:r.maxzoom});var i=this.subplot.map.getSource(this.sourceIds[e]);i?i.setData(t.geojson):this.subplot.map.addSource(this.sourceIds[e],n)};aC.setSourceData=function(e,t){this.subplot.map.getSource(this.sourceIds[e]).setData(t.geojson)};aC.addLayer=function(e,t,r){var n={type:t.type,id:this.layerIds[e],source:this.sourceIds[e],layout:t.layout,paint:t.paint};t.filter&&(n.filter=t.filter);for(var i=this.layerIds[e],a,o=this.subplot.getMapLayers(),s=0;s<o.length;s++)if(o[s].id===i){a=!0;break}a?(this.subplot.setOptions(i,\"setLayoutProperty\",n.layout),n.layout.visibility===\"visible\"&&this.subplot.setOptions(i,\"setPaintProperty\",n.paint)):this.subplot.addLayer(n,r)};aC.update=function(t){var r=t[0].trace,n=this.subplot,i=n.map,a=fje(n.gd,t),o=n.belowLookup[\"trace-\"+this.uid],s=!!(r.cluster&&r.cluster.enabled),l=!!this.clusterEnabled,u=this;function c(M){M||u.addSource(\"circle\",a.circle,r.cluster);for(var T=og.cluster,L=0;L<T.length;L++){var x=T[L],C=a[x];u.addLayer(x,C,o)}}function f(M){for(var T=og.cluster,L=T.length-1;L>=0;L--){var x=T[L];i.removeLayer(u.layerIds[x])}M||i.removeSource(u.sourceIds.circle)}function h(M){for(var T=og.nonCluster,L=0;L<T.length;L++){var x=T[L],C=a[x];M||u.addSource(x,C),u.addLayer(x,C,o)}}function d(M){for(var 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Math.max(Math.sqrt(q*q+V*V)-G,1-3/G)}if(QXt.getClosest(n,m,e),e.index!==!1){var b=n[e.index],p=b.lonlat,k=[BJ.modHalf(p[0],360)+d,p[1]],M=a.c2p(k),T=o.c2p(k),L=b.mrc||1;e.x0=M-L,e.x1=M+L,e.y0=T-L,e.y1=T+L;var x={};x[i.subplot]={_subplot:s};var C=i._module.formatLabels(b,i,x);return e.lonLabel=C.lonLabel,e.latLabel=C.latLabel,e.color=eZt(i,b),e.extraText=pje(i,b,n[0].t.labels),e.hovertemplate=i.hovertemplate,[e]}}function pje(e,t,r){if(e.hovertemplate)return;var n=t.hi||e.hoverinfo,i=n.split(\"+\"),a=i.indexOf(\"all\")!==-1,o=i.indexOf(\"lon\")!==-1,s=i.indexOf(\"lat\")!==-1,l=t.lonlat,u=[];function c(f){return f+\"\\xB0\"}return a||o&&s?u.push(\"(\"+c(l[1])+\", \"+c(l[0])+\")\"):o?u.push(r.lon+c(l[0])):s&&u.push(r.lat+c(l[1])),(a||i.indexOf(\"text\")!==-1)&&tZt(t,e,u),u.join(\"<br>\")}gje.exports={hoverPoints:nZt,getExtraText:pje}});var yje=ye((Vbr,mje)=>{\"use strict\";mje.exports=function(t,r){return t.lon=r.lon,t.lat=r.lat,t}});var xje=ye((Gbr,_je)=>{\"use strict\";var aZt=Pr(),oZt=Ru(),sZt=fs().BADNUM;_je.exports=function(t,r){var n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].trace,l;if(!oZt.hasMarkers(s))return[];if(r===!1)for(l=0;l<n.length;l++)n[l].selected=0;else for(l=0;l<n.length;l++){var u=n[l],c=u.lonlat;if(c[0]!==sZt){var f=[aZt.modHalf(c[0],360),c[1]],h=[i.c2p(f),a.c2p(f)];r.contains(h,null,l,t)?(o.push({pointNumber:l,lon:c[0],lat:c[1]}),u.selected=1):u.selected=0}}return o}});var wje=ye((NJ,UJ)=>{(function(e,t){typeof NJ==\"object\"&&typeof UJ!=\"undefined\"?UJ.exports=t():(e=typeof globalThis!=\"undefined\"?globalThis:e||self,e.maplibregl=t())})(NJ,(function(){\"use strict\";var e={},t={};function r(i,a,o){if(t[i]=o,i===\"index\"){var s=\"var sharedModule = {}; (\"+t.shared+\")(sharedModule); (\"+t.worker+\")(sharedModule);\",l={};return t.shared(l),t.index(e,l),typeof window!=\"undefined\"&&e.setWorkerUrl(window.URL.createObjectURL(new Blob([s],{type:\"text/javascript\"}))),e}}r(\"shared\",[\"exports\"],(function(i){\"use strict\";function 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W;F.expectedType&&F.expectedType.kind!==\"value\"&&(W=F.expectedType);let te=[];for(let pe=1;pe<A.length-1;pe+=2){let Re=F.parse(A[pe],pe,pt);if(!Re)return null;let Ke=F.parse(A[pe+1],pe+1,W);if(!Ke)return null;te.push([Re,Ke]),W=W||Ke.type}let fe=F.parse(A[A.length-1],A.length-1,W);if(!fe)return null;if(!W)throw new Error(\"Can't infer output type\");return new Sn(W,te,fe)}evaluate(A){for(let[F,W]of this.branches)if(F.evaluate(A))return W.evaluate(A);return this.otherwise.evaluate(A)}eachChild(A){for(let[F,W]of this.branches)A(F),A(W);A(this.otherwise)}outputDefined(){return this.branches.every((([A,F])=>F.outputDefined()))&&this.otherwise.outputDefined()}}class En{constructor(A,F,W,te){this.type=A,this.input=F,this.beginIndex=W,this.endIndex=te}static parse(A,F){if(A.length<=2||A.length>=5)return F.error(`Expected 3 or 4 arguments, but found ${A.length-1} instead.`);let W=F.parse(A[1],1,mr),te=F.parse(A[2],2,dt);if(!W||!te)return null;if(!xt(W.type,[Ge(mr),Et,mr]))return 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this._warningHistory[Re.message]||(this._warningHistory[Re.message]=!0,typeof console!=\"undefined\"&&console.warn(Re.message)),this._defaultValue}}}function Dc(R){return Array.isArray(R)&&R.length>0&&typeof R[0]==\"string\"&&R[0]in yf}function ks(R,A){let F=new vo(yf,eh,[],A?(function(te){let fe={color:jt,string:Et,number:dt,enum:Et,boolean:pt,formatted:ei,padding:qr,resolvedImage:jr,variableAnchorOffsetCollection:gt};return te.type===\"array\"?Ge(fe[te.value]||mr,te.length):fe[te.type]})(A):void 0),W=F.parse(R,void 0,void 0,void 0,A&&A.type===\"string\"?{typeAnnotation:\"coerce\"}:void 0);return W?cc(new Eu(W,A)):sf(F.errors)}class bc{constructor(A,F){this.kind=A,this._styleExpression=F,this.isStateDependent=A!==\"constant\"&&!ju(F.expression)}evaluateWithoutErrorHandling(A,F,W,te,fe,pe){return this._styleExpression.evaluateWithoutErrorHandling(A,F,W,te,fe,pe)}evaluate(A,F,W,te,fe,pe){return this._styleExpression.evaluate(A,F,W,te,fe,pe)}}class hu{constructor(A,F,W,te){this.kind=A,this.zoomStops=W,this._styleExpression=F,this.isStateDependent=A!==\"camera\"&&!ju(F.expression),this.interpolationType=te}evaluateWithoutErrorHandling(A,F,W,te,fe,pe){return this._styleExpression.evaluateWithoutErrorHandling(A,F,W,te,fe,pe)}evaluate(A,F,W,te,fe,pe){return this._styleExpression.evaluate(A,F,W,te,fe,pe)}interpolationFactor(A,F,W){return this.interpolationType?ko.interpolationFactor(this.interpolationType,A,F,W):0}}function _u(R,A){let F=ks(R,A);if(F.result===\"error\")return F;let W=F.value.expression,te=th(W);if(!te&&!Bl(A))return sf([new rt(\"\",\"data expressions not supported\")]);let fe=jf(W,[\"zoom\"]);if(!fe&&!Kc(A))return sf([new rt(\"\",\"zoom expressions not supported\")]);let pe=nh(W);return pe||fe?pe instanceof rt?sf([pe]):pe instanceof ko&&!Rc(A)?sf([new rt(\"\",'\"interpolate\" expressions cannot be used with this property')]):cc(pe?new hu(te?\"camera\":\"composite\",F.value,pe.labels,pe instanceof ko?pe.interpolation:void 0):new bc(te?\"constant\":\"source\",F.value)):sf([new rt(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.')])}class il{constructor(A,F){this._parameters=A,this._specification=F,Ae(this,rh(this._parameters,this._specification))}static deserialize(A){return new il(A._parameters,A._specification)}static serialize(A){return{_parameters:A._parameters,_specification:A._specification}}}function nh(R){let A=null;if(R instanceof Gn)A=nh(R.result);else if(R instanceof Fs){for(let F of R.args)if(A=nh(F),A)break}else(R instanceof _n||R instanceof ko)&&R.input instanceof Ol&&R.input.name===\"zoom\"&&(A=R);return A instanceof rt||R.eachChild((F=>{let W=nh(F);W instanceof rt?A=W:!A&&W?A=new rt(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.'):A&&W&&A!==W&&(A=new rt(\"\",'Only one zoom-based \"step\" or \"interpolate\" subexpression may be used in an expression.'))})),A}function Mh(R){if(R===!0||R===!1)return!0;if(!Array.isArray(R)||R.length===0)return!1;switch(R[0]){case\"has\":return R.length>=2&&R[1]!==\"$id\"&&R[1]!==\"$type\";case\"in\":return R.length>=3&&(typeof R[1]!=\"string\"||Array.isArray(R[2]));case\"!in\":case\"!has\":case\"none\":return!1;case\"==\":case\"!=\":case\">\":case\">=\":case\"<\":case\"<=\":return R.length!==3||Array.isArray(R[1])||Array.isArray(R[2]);case\"any\":case\"all\":for(let A of R.slice(1))if(!Mh(A)&&typeof A!=\"boolean\")return!1;return!0;default:return!0}}let zu={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function Fc(R){if(R==null)return{filter:()=>!0,needGeometry:!1};Mh(R)||(R=xf(R));let A=ks(R,zu);if(A.result===\"error\")throw new Error(A.value.map((F=>`${F.key}: ${F.message}`)).join(\", \"));return{filter:(F,W,te)=>A.value.evaluate(F,W,{},te),needGeometry:bd(R)}}function wc(R,A){return R<A?-1:R>A?1:0}function bd(R){if(!Array.isArray(R))return!1;if(R[0]===\"within\"||R[0]===\"distance\")return!0;for(let A=1;A<R.length;A++)if(bd(R[A]))return!0;return!1}function xf(R){if(!R)return!0;let A=R[0];return R.length<=1?A!==\"any\":A===\"==\"?Pf(R[1],R[2],\"==\"):A===\"!=\"?jl(Pf(R[1],R[2],\"==\")):A===\"<\"||A===\">\"||A===\"<=\"||A===\">=\"?Pf(R[1],R[2],A):A===\"any\"?(F=R.slice(1),[\"any\"].concat(F.map(xf))):A===\"all\"?[\"all\"].concat(R.slice(1).map(xf)):A===\"none\"?[\"all\"].concat(R.slice(1).map(xf).map(jl)):A===\"in\"?qu(R[1],R.slice(2)):A===\"!in\"?jl(qu(R[1],R.slice(2))):A===\"has\"?bf(R[1]):A!==\"!has\"||jl(bf(R[1]));var F}function Pf(R,A,F){switch(R){case\"$type\":return[`filter-type-${F}`,A];case\"$id\":return[`filter-id-${F}`,A];default:return[`filter-${F}`,R,A]}}function qu(R,A){if(A.length===0)return!1;switch(R){case\"$type\":return[\"filter-type-in\",[\"literal\",A]];case\"$id\":return[\"filter-id-in\",[\"literal\",A]];default:return A.length>200&&!A.some((F=>typeof F!=typeof A[0]))?[\"filter-in-large\",R,[\"literal\",A.sort(wc)]]:[\"filter-in-small\",R,[\"literal\",A]]}}function bf(R){switch(R){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",R]}}function jl(R){return[\"!\",R]}function uf(R){let A=typeof R;if(A===\"number\"||A===\"boolean\"||A===\"string\"||R==null)return JSON.stringify(R);if(Array.isArray(R)){let te=\"[\";for(let fe of R)te+=`${uf(fe)},`;return`${te}]`}let F=Object.keys(R).sort(),W=\"{\";for(let te=0;te<F.length;te++)W+=`${JSON.stringify(F[te])}:${uf(R[F[te]])},`;return`${W}}`}function Xh(R){let A=\"\";for(let F of He)A+=`/${uf(R[F])}`;return A}function If(R){let A=R.value;return A?[new Ue(R.key,A,\"constants have been deprecated as of v8\")]:[]}function Cs(R){return R instanceof Number||R instanceof String||R instanceof Boolean?R.valueOf():R}function du(R){if(Array.isArray(R))return R.map(du);if(R instanceof Object&&!(R instanceof Number||R instanceof String||R instanceof Boolean)){let A={};for(let F in R)A[F]=du(R[F]);return A}return Cs(R)}function ku(R){let A=R.key,F=R.value,W=R.valueSpec||{},te=R.objectElementValidators||{},fe=R.style,pe=R.styleSpec,Re=R.validateSpec,Ke=[],ft=gs(F);if(ft!==\"object\")return[new Ue(A,F,`object expected, ${ft} found`)];for(let Rt in F){let er=Rt.split(\".\")[0],ur=W[er]||W[\"*\"],vr;if(te[er])vr=te[er];else if(W[er])vr=Re;else if(te[\"*\"])vr=te[\"*\"];else{if(!W[\"*\"]){Ke.push(new Ue(A,F[Rt],`unknown property \"${Rt}\"`));continue}vr=Re}Ke=Ke.concat(vr({key:(A&&`${A}.`)+Rt,value:F[Rt],valueSpec:ur,style:fe,styleSpec:pe,object:F,objectKey:Rt,validateSpec:Re},F))}for(let Rt in W)te[Rt]||W[Rt].required&&W[Rt].default===void 0&&F[Rt]===void 0&&Ke.push(new Ue(A,F,`missing required property \"${Rt}\"`));return Ke}function Xf(R){let A=R.value,F=R.valueSpec,W=R.style,te=R.styleSpec,fe=R.key,pe=R.arrayElementValidator||R.validateSpec;if(gs(A)!==\"array\")return[new Ue(fe,A,`array expected, ${gs(A)} found`)];if(F.length&&A.length!==F.length)return[new Ue(fe,A,`array length ${F.length} expected, length ${A.length} found`)];if(F[\"min-length\"]&&A.length<F[\"min-length\"])return[new Ue(fe,A,`array length at least ${F[\"min-length\"]} expected, length ${A.length} found`)];let Re={type:F.value,values:F.values};te.$version<7&&(Re.function=F.function),gs(F.value)===\"object\"&&(Re=F.value);let Ke=[];for(let ft=0;ft<A.length;ft++)Ke=Ke.concat(pe({array:A,arrayIndex:ft,value:A[ft],valueSpec:Re,validateSpec:R.validateSpec,style:W,styleSpec:te,key:`${fe}[${ft}]`}));return Ke}function Us(R){let A=R.key,F=R.value,W=R.valueSpec,te=gs(F);return te===\"number\"&&F!=F&&(te=\"NaN\"),te!==\"number\"?[new Ue(A,F,`number expected, ${te} found`)]:\"minimum\"in W&&F<W.minimum?[new Ue(A,F,`${F} is less than the minimum value ${W.minimum}`)]:\"maximum\"in W&&F>W.maximum?[new Ue(A,F,`${F} is greater than the maximum value ${W.maximum}`)]:[]}function wf(R){let A=R.valueSpec,F=Cs(R.value.type),W,te,fe,pe={},Re=F!==\"categorical\"&&R.value.property===void 0,Ke=!Re,ft=gs(R.value.stops)===\"array\"&&gs(R.value.stops[0])===\"array\"&&gs(R.value.stops[0][0])===\"object\",Rt=ku({key:R.key,value:R.value,valueSpec:R.styleSpec.function,validateSpec:R.validateSpec,style:R.style,styleSpec:R.styleSpec,objectElementValidators:{stops:function(vr){if(F===\"identity\")return[new Ue(vr.key,vr.value,'identity function may not have a \"stops\" property')];let kr=[],Dr=vr.value;return kr=kr.concat(Xf({key:vr.key,value:Dr,valueSpec:vr.valueSpec,validateSpec:vr.validateSpec,style:vr.style,styleSpec:vr.styleSpec,arrayElementValidator:er})),gs(Dr)===\"array\"&&Dr.length===0&&kr.push(new Ue(vr.key,Dr,\"array must have at least one stop\")),kr},default:function(vr){return vr.validateSpec({key:vr.key,value:vr.value,valueSpec:A,validateSpec:vr.validateSpec,style:vr.style,styleSpec:vr.styleSpec})}}});return F===\"identity\"&&Re&&Rt.push(new Ue(R.key,R.value,'missing required property \"property\"')),F===\"identity\"||R.value.stops||Rt.push(new Ue(R.key,R.value,'missing required property \"stops\"')),F===\"exponential\"&&R.valueSpec.expression&&!Rc(R.valueSpec)&&Rt.push(new Ue(R.key,R.value,\"exponential functions not supported\")),R.styleSpec.$version>=8&&(Ke&&!Bl(R.valueSpec)?Rt.push(new Ue(R.key,R.value,\"property functions not supported\")):Re&&!Kc(R.valueSpec)&&Rt.push(new Ue(R.key,R.value,\"zoom functions not supported\"))),F!==\"categorical\"&&!ft||R.value.property!==void 0||Rt.push(new Ue(R.key,R.value,'\"property\" property is required')),Rt;function er(vr){let kr=[],Dr=vr.value,li=vr.key;if(gs(Dr)!==\"array\")return[new Ue(li,Dr,`array expected, ${gs(Dr)} found`)];if(Dr.length!==2)return[new Ue(li,Dr,`array length 2 expected, length ${Dr.length} found`)];if(ft){if(gs(Dr[0])!==\"object\")return[new Ue(li,Dr,`object expected, ${gs(Dr[0])} found`)];if(Dr[0].zoom===void 0)return[new Ue(li,Dr,\"object stop key must have 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F=A.value.expression||A.value._styleExpression.expression;if(R.expressionContext===\"property\"&&R.propertyKey===\"text-font\"&&!F.outputDefined())return[new Ue(R.key,R.value,`Invalid data expression for \"${R.propertyKey}\". 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-       "Use an identity property function instead: \\`{ \"type\": \"identity\", \"property\": ${JSON.stringify(er[1])} }\\`.`)];let ur=[];return R.layerType===\"symbol\"&&(Re===\"text-field\"&&te&&!te.glyphs&&ur.push(new Ue(F,pe,'use of \"text-field\" requires a style \"glyphs\" property')),Re===\"text-font\"&&Wf(du(pe))&&Cs(pe.type)===\"identity\"&&ur.push(new Ue(F,pe,'\"text-font\" does not support identity functions'))),ur.concat(W({key:R.key,value:pe,valueSpec:Rt,style:te,styleSpec:fe,expressionContext:\"property\",propertyType:A,propertyKey:Re}))}function Zf(R){return cf(R,\"paint\")}function Wl(R){return cf(R,\"layout\")}function ah(R){let A=[],F=R.value,W=R.key,te=R.style,fe=R.styleSpec;F.type||F.ref||A.push(new Ue(W,F,'either \"type\" or \"ref\" is required'));let pe=Cs(F.type),Re=Cs(F.ref);if(F.id){let Ke=Cs(F.id);for(let ft=0;ft<R.arrayIndex;ft++){let Rt=te.layers[ft];Cs(Rt.id)===Ke&&A.push(new Ue(W,F.id,`duplicate layer id \"${F.id}\", previously used at line 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ws=Zo([\"Bopo\",\"Hani\",\"Hira\",\"Kana\",\"Kits\",\"Nshu\",\"Tang\",\"Yiii\"]);function Ho(R){return!(R!==746&&R!==747&&(R<4352||!(Dn[\"CJK Compatibility Forms\"](R)&&!(R>=65097&&R<=65103)||Dn[\"CJK Compatibility\"](R)||Dn[\"CJK Strokes\"](R)||!(!Dn[\"CJK Symbols and Punctuation\"](R)||R>=12296&&R<=12305||R>=12308&&R<=12319||R===12336)||Dn[\"Enclosed CJK Letters and Months\"](R)||Dn[\"Ideographic Description Characters\"](R)||Dn.Kanbun(R)||Dn.Katakana(R)&&R!==12540||!(!Dn[\"Halfwidth and Fullwidth Forms\"](R)||R===65288||R===65289||R===65293||R>=65306&&R<=65310||R===65339||R===65341||R===65343||R>=65371&&R<=65503||R===65507||R>=65512&&R<=65519)||!(!Dn[\"Small Form Variants\"](R)||R>=65112&&R<=65118||R>=65123&&R<=65126)||Dn[\"Vertical Forms\"](R)||Dn[\"Yijing Hexagram Symbols\"](R)||new RegExp(\"\\\\p{sc=Cans}\",\"u\").test(String.fromCodePoint(R))||new RegExp(\"\\\\p{sc=Hang}\",\"u\").test(String.fromCodePoint(R))||ws.test(String.fromCodePoint(R)))))}function ml(R){return!(Ho(R)||(function(A){return!!(Dn[\"Latin-1 Supplement\"](A)&&(A===167||A===169||A===174||A===177||A===188||A===189||A===190||A===215||A===247)||Dn[\"General Punctuation\"](A)&&(A===8214||A===8224||A===8225||A===8240||A===8241||A===8251||A===8252||A===8258||A===8263||A===8264||A===8265||A===8273)||Dn[\"Letterlike Symbols\"](A)||Dn[\"Number Forms\"](A)||Dn[\"Miscellaneous Technical\"](A)&&(A>=8960&&A<=8967||A>=8972&&A<=8991||A>=8996&&A<=9e3||A===9003||A>=9085&&A<=9114||A>=9150&&A<=9165||A===9167||A>=9169&&A<=9179||A>=9186&&A<=9215)||Dn[\"Control Pictures\"](A)&&A!==9251||Dn[\"Optical Character Recognition\"](A)||Dn[\"Enclosed Alphanumerics\"](A)||Dn[\"Geometric Shapes\"](A)||Dn[\"Miscellaneous Symbols\"](A)&&!(A>=9754&&A<=9759)||Dn[\"Miscellaneous Symbols and Arrows\"](A)&&(A>=11026&&A<=11055||A>=11088&&A<=11097||A>=11192&&A<=11243)||Dn[\"CJK Symbols and Punctuation\"](A)||Dn.Katakana(A)||Dn[\"Private Use Area\"](A)||Dn[\"CJK Compatibility Forms\"](A)||Dn[\"Small Form Variants\"](A)||Dn[\"Halfwidth and Fullwidth Forms\"](A)||A===8734||A===8756||A===8757||A>=9984&&A<=10087||A>=10102&&A<=10131||A===65532||A===65533)})(R))}let Ws=Zo([\"Adlm\",\"Arab\",\"Armi\",\"Avst\",\"Chrs\",\"Cprt\",\"Egyp\",\"Elym\",\"Gara\",\"Hatr\",\"Hebr\",\"Hung\",\"Khar\",\"Lydi\",\"Mand\",\"Mani\",\"Mend\",\"Merc\",\"Mero\",\"Narb\",\"Nbat\",\"Nkoo\",\"Orkh\",\"Palm\",\"Phli\",\"Phlp\",\"Phnx\",\"Prti\",\"Rohg\",\"Samr\",\"Sarb\",\"Sogo\",\"Syrc\",\"Thaa\",\"Todr\",\"Yezi\"]);function Ls(R){return Ws.test(String.fromCodePoint(R))}function va(R,A){return!(!A&&Ls(R)||R>=2304&&R<=3583||R>=3840&&R<=4255||Dn.Khmer(R))}function no(R){for(let A of R)if(Ls(A.charCodeAt(0)))return!0;return!1}let ys=new class{constructor(){this.applyArabicShaping=null,this.processBidirectionalText=null,this.processStyledBidirectionalText=null,this.pluginStatus=\"unavailable\",this.pluginURL=null}setState(R){this.pluginStatus=R.pluginStatus,this.pluginURL=R.pluginURL}getState(){return{pluginStatus:this.pluginStatus,pluginURL:this.pluginURL}}setMethods(R){this.applyArabicShaping=R.applyArabicShaping,this.processBidirectionalText=R.processBidirectionalText,this.processStyledBidirectionalText=R.processStyledBidirectionalText}isParsed(){return this.applyArabicShaping!=null&&this.processBidirectionalText!=null&&this.processStyledBidirectionalText!=null}getPluginURL(){return this.pluginURL}getRTLTextPluginStatus(){return this.pluginStatus}};class rs{constructor(A,F){this.zoom=A,F?(this.now=F.now,this.fadeDuration=F.fadeDuration,this.zoomHistory=F.zoomHistory,this.transition=F.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Zn,this.transition={})}isSupportedScript(A){return(function(F,W){for(let te of F)if(!va(te.charCodeAt(0),W))return!1;return!0})(A,ys.getRTLTextPluginStatus()===\"loaded\")}crossFadingFactor(){return this.fadeDuration===0?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)}getCrossfadeParameters(){let A=this.zoom,F=A-Math.floor(A),W=this.crossFadingFactor();return A>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:F+(1-F)*W}:{fromScale:.5,toScale:1,t:1-(1-W)*F}}}class Ql{constructor(A,F){this.property=A,this.value=F,this.expression=(function(W,te){if(Wf(W))return new il(W,te);if(Dc(W)){let fe=_u(W,te);if(fe.result===\"error\")throw new Error(fe.value.map((pe=>`${pe.key}: ${pe.message}`)).join(\", \"));return fe.value}{let fe=W;return te.type===\"color\"&&typeof W==\"string\"?fe=ir.parse(W):te.type!==\"padding\"||typeof W!=\"number\"&&!Array.isArray(W)?te.type===\"variableAnchorOffsetCollection\"&&Array.isArray(W)&&(fe=Hi.parse(W)):fe=$r.parse(W),{kind:\"constant\",evaluate:()=>fe}}})(F===void 0?A.specification.default:F,A.specification)}isDataDriven(){return this.expression.kind===\"source\"||this.expression.kind===\"composite\"}possiblyEvaluate(A,F,W){return this.property.possiblyEvaluate(this,A,F,W)}}class Cu{constructor(A){this.property=A,this.value=new Ql(A,void 0)}transitioned(A,F){return new Nc(this.property,this.value,F,L({},A.transition,this.transition),A.now)}untransitioned(){return new Nc(this.property,this.value,null,{},0)}}class Yu{constructor(A){this._properties=A,this._values=Object.create(A.defaultTransitionablePropertyValues)}getValue(A){return g(this._values[A].value.value)}setValue(A,F){Object.prototype.hasOwnProperty.call(this._values,A)||(this._values[A]=new Cu(this._values[A].property)),this._values[A].value=new Ql(this._values[A].property,F===null?void 0:g(F))}getTransition(A){return g(this._values[A].transition)}setTransition(A,F){Object.prototype.hasOwnProperty.call(this._values,A)||(this._values[A]=new Cu(this._values[A].property)),this._values[A].transition=g(F)||void 0}serialize(){let A={};for(let F of Object.keys(this._values)){let W=this.getValue(F);W!==void 0&&(A[F]=W);let te=this.getTransition(F);te!==void 0&&(A[`${F}-transition`]=te)}return A}transitioned(A,F){let W=new pu(this._properties);for(let te of Object.keys(this._values))W._values[te]=this._values[te].transitioned(A,F._values[te]);return W}untransitioned(){let A=new pu(this._properties);for(let F of Object.keys(this._values))A._values[F]=this._values[F].untransitioned();return A}}class Nc{constructor(A,F,W,te,fe){this.property=A,this.value=F,this.begin=fe+te.delay||0,this.end=this.begin+te.duration||0,A.specification.transition&&(te.delay||te.duration)&&(this.prior=W)}possiblyEvaluate(A,F,W){let te=A.now||0,fe=this.value.possiblyEvaluate(A,F,W),pe=this.prior;if(pe){if(te>this.end)return this.prior=null,fe;if(this.value.isDataDriven())return this.prior=null,fe;if(te<this.begin)return pe.possiblyEvaluate(A,F,W);{let Re=(te-this.begin)/(this.end-this.begin);return this.property.interpolate(pe.possiblyEvaluate(A,F,W),fe,(function(Ke){if(Ke<=0)return 0;if(Ke>=1)return 1;let ft=Ke*Ke,Rt=ft*Ke;return 4*(Ke<.5?Rt:3*(Ke-ft)+Rt-.75)})(Re))}}return fe}}class pu{constructor(A){this._properties=A,this._values=Object.create(A.defaultTransitioningPropertyValues)}possiblyEvaluate(A,F,W){let te=new Ac(this._properties);for(let fe of Object.keys(this._values))te._values[fe]=this._values[fe].possiblyEvaluate(A,F,W);return te}hasTransition(){for(let A of Object.keys(this._values))if(this._values[A].prior)return!0;return!1}}class Uc{constructor(A){this._properties=A,this._values=Object.create(A.defaultPropertyValues)}hasValue(A){return this._values[A].value!==void 0}getValue(A){return g(this._values[A].value)}setValue(A,F){this._values[A]=new Ql(this._values[A].property,F===null?void 0:g(F))}serialize(){let A={};for(let F of Object.keys(this._values)){let W=this.getValue(F);W!==void 0&&(A[F]=W)}return A}possiblyEvaluate(A,F,W){let te=new Ac(this._properties);for(let fe of Object.keys(this._values))te._values[fe]=this._values[fe].possiblyEvaluate(A,F,W);return te}}class xu{constructor(A,F,W){this.property=A,this.value=F,this.parameters=W}isConstant(){return this.value.kind===\"constant\"}constantOr(A){return this.value.kind===\"constant\"?this.value.value:A}evaluate(A,F,W,te){return this.property.evaluate(this.value,this.parameters,A,F,W,te)}}class Ac{constructor(A){this._properties=A,this._values=Object.create(A.defaultPossiblyEvaluatedValues)}get(A){return this._values[A]}}class Va{constructor(A){this.specification=A}possiblyEvaluate(A,F){if(A.isDataDriven())throw new Error(\"Value should not be data driven\");return A.expression.evaluate(F)}interpolate(A,F,W){let te=Lo[this.specification.type];return te?te(A,F,W):A}}class oo{constructor(A,F){this.specification=A,this.overrides=F}possiblyEvaluate(A,F,W,te){return new xu(this,A.expression.kind===\"constant\"||A.expression.kind===\"camera\"?{kind:\"constant\",value:A.expression.evaluate(F,null,{},W,te)}:A.expression,F)}interpolate(A,F,W){if(A.value.kind!==\"constant\"||F.value.kind!==\"constant\")return A;if(A.value.value===void 0||F.value.value===void 0)return new xu(this,{kind:\"constant\",value:void 0},A.parameters);let te=Lo[this.specification.type];if(te){let fe=te(A.value.value,F.value.value,W);return new xu(this,{kind:\"constant\",value:fe},A.parameters)}return A}evaluate(A,F,W,te,fe,pe){return A.kind===\"constant\"?A.value:A.evaluate(F,W,te,fe,pe)}}class Vc extends oo{possiblyEvaluate(A,F,W,te){if(A.value===void 0)return new xu(this,{kind:\"constant\",value:void 0},F);if(A.expression.kind===\"constant\"){let fe=A.expression.evaluate(F,null,{},W,te),pe=A.property.specification.type===\"resolvedImage\"&&typeof fe!=\"string\"?fe.name:fe,Re=this._calculate(pe,pe,pe,F);return new xu(this,{kind:\"constant\",value:Re},F)}if(A.expression.kind===\"camera\"){let fe=this._calculate(A.expression.evaluate({zoom:F.zoom-1}),A.expression.evaluate({zoom:F.zoom}),A.expression.evaluate({zoom:F.zoom+1}),F);return new xu(this,{kind:\"constant\",value:fe},F)}return new xu(this,A.expression,F)}evaluate(A,F,W,te,fe,pe){if(A.kind===\"source\"){let Re=A.evaluate(F,W,te,fe,pe);return this._calculate(Re,Re,Re,F)}return A.kind===\"composite\"?this._calculate(A.evaluate({zoom:Math.floor(F.zoom)-1},W,te),A.evaluate({zoom:Math.floor(F.zoom)},W,te),A.evaluate({zoom:Math.floor(F.zoom)+1},W,te),F):A.value}_calculate(A,F,W,te){return te.zoom>te.zoomHistory.lastIntegerZoom?{from:A,to:F}:{from:W,to:F}}interpolate(A){return A}}class hc{constructor(A){this.specification=A}possiblyEvaluate(A,F,W,te){if(A.value!==void 0){if(A.expression.kind===\"constant\"){let fe=A.expression.evaluate(F,null,{},W,te);return this._calculate(fe,fe,fe,F)}return this._calculate(A.expression.evaluate(new rs(Math.floor(F.zoom-1),F)),A.expression.evaluate(new rs(Math.floor(F.zoom),F)),A.expression.evaluate(new rs(Math.floor(F.zoom+1),F)),F)}}_calculate(A,F,W,te){return te.zoom>te.zoomHistory.lastIntegerZoom?{from:A,to:F}:{from:W,to:F}}interpolate(A){return A}}class Ku{constructor(A){this.specification=A}possiblyEvaluate(A,F,W,te){return!!A.expression.evaluate(F,null,{},W,te)}interpolate(){return!1}}class ue{constructor(A){this.properties=A,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[];for(let F in A){let W=A[F];W.specification.overridable&&this.overridableProperties.push(F);let te=this.defaultPropertyValues[F]=new Ql(W,void 0),fe=this.defaultTransitionablePropertyValues[F]=new Cu(W);this.defaultTransitioningPropertyValues[F]=fe.untransitioned(),this.defaultPossiblyEvaluatedValues[F]=te.possiblyEvaluate({})}}}Fi(\"DataDrivenProperty\",oo),Fi(\"DataConstantProperty\",Va),Fi(\"CrossFadedDataDrivenProperty\",Vc),Fi(\"CrossFadedProperty\",hc),Fi(\"ColorRampProperty\",Ku);let w=\"-transition\";class B extends Pe{constructor(A,F){if(super(),this.id=A.id,this.type=A.type,this._featureFilter={filter:()=>!0,needGeometry:!1},A.type!==\"custom\"&&(this.metadata=A.metadata,this.minzoom=A.minzoom,this.maxzoom=A.maxzoom,A.type!==\"background\"&&(this.source=A.source,this.sourceLayer=A[\"source-layer\"],this.filter=A.filter),F.layout&&(this._unevaluatedLayout=new Uc(F.layout)),F.paint)){this._transitionablePaint=new Yu(F.paint);for(let W in A.paint)this.setPaintProperty(W,A.paint[W],{validate:!1});for(let W in A.layout)this.setLayoutProperty(W,A.layout[W],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Ac(F.paint)}}getCrossfadeParameters(){return this._crossfadeParameters}getLayoutProperty(A){return A===\"visibility\"?this.visibility:this._unevaluatedLayout.getValue(A)}setLayoutProperty(A,F,W={}){F!=null&&this._validate(On,`layers.${this.id}.layout.${A}`,A,F,W)||(A!==\"visibility\"?this._unevaluatedLayout.setValue(A,F):this.visibility=F)}getPaintProperty(A){return A.endsWith(w)?this._transitionablePaint.getTransition(A.slice(0,-11)):this._transitionablePaint.getValue(A)}setPaintProperty(A,F,W={}){if(F!=null&&this._validate(wn,`layers.${this.id}.paint.${A}`,A,F,W))return!1;if(A.endsWith(w))return this._transitionablePaint.setTransition(A.slice(0,-11),F||void 0),!1;{let te=this._transitionablePaint._values[A],fe=te.property.specification[\"property-type\"]===\"cross-faded-data-driven\",pe=te.value.isDataDriven(),Re=te.value;this._transitionablePaint.setValue(A,F),this._handleSpecialPaintPropertyUpdate(A);let Ke=this._transitionablePaint._values[A].value;return Ke.isDataDriven()||pe||fe||this._handleOverridablePaintPropertyUpdate(A,Re,Ke)}}_handleSpecialPaintPropertyUpdate(A){}_handleOverridablePaintPropertyUpdate(A,F,W){return!1}isHidden(A){return!!(this.minzoom&&A<this.minzoom)||!!(this.maxzoom&&A>=this.maxzoom)||this.visibility===\"none\"}updateTransitions(A){this._transitioningPaint=this._transitionablePaint.transitioned(A,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculate(A,F){A.getCrossfadeParameters&&(this._crossfadeParameters=A.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(A,void 0,F)),this.paint=this._transitioningPaint.possiblyEvaluate(A,void 0,F)}serialize(){let A={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(A.layout=A.layout||{},A.layout.visibility=this.visibility),S(A,((F,W)=>!(F===void 0||W===\"layout\"&&!Object.keys(F).length||W===\"paint\"&&!Object.keys(F).length)))}_validate(A,F,W,te,fe={}){return(!fe||fe.validate!==!1)&&Yi(this,A.call(Mi,{key:F,layerType:this.type,objectKey:W,value:te,styleSpec:ce,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(let A in this.paint._values){let F=this.paint.get(A);if(F instanceof xu&&Bl(F.property.specification)&&(F.value.kind===\"source\"||F.value.kind===\"composite\")&&F.value.isStateDependent)return!0}return!1}}let Q={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class ee{constructor(A,F){this._structArray=A,this._pos1=F*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class le{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(A,F){return A._trim(),F&&(A.isTransferred=!0,F.push(A.arrayBuffer)),{length:A.length,arrayBuffer:A.arrayBuffer}}static deserialize(A){let F=Object.create(this.prototype);return F.arrayBuffer=A.arrayBuffer,F.length=A.length,F.capacity=A.arrayBuffer.byteLength/F.bytesPerElement,F._refreshViews(),F}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(A){this.reserve(A),this.length=A}reserve(A){if(A>this.capacity){this.capacity=Math.max(A,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);let F=this.uint8;this._refreshViews(),F&&this.uint8.set(F)}}_refreshViews(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")}}function Fe(R,A=1){let F=0,W=0;return{members:R.map((te=>{let fe=Q[te.type].BYTES_PER_ELEMENT,pe=F=Ze(F,Math.max(A,fe)),Re=te.components||1;return W=Math.max(W,fe),F+=fe*Re,{name:te.name,type:te.type,components:Re,offset:pe}})),size:Ze(F,Math.max(W,A)),alignment:A}}function Ze(R,A){return Math.ceil(R/A)*A}class ut extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let te=2*A;return this.int16[te+0]=F,this.int16[te+1]=W,A}}ut.prototype.bytesPerElement=4,Fi(\"StructArrayLayout2i4\",ut);class Mt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=3*A;return this.int16[fe+0]=F,this.int16[fe+1]=W,this.int16[fe+2]=te,A}}Mt.prototype.bytesPerElement=6,Fi(\"StructArrayLayout3i6\",Mt);class Jt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te){let fe=this.length;return this.resize(fe+1),this.emplace(fe,A,F,W,te)}emplace(A,F,W,te,fe){let pe=4*A;return this.int16[pe+0]=F,this.int16[pe+1]=W,this.int16[pe+2]=te,this.int16[pe+3]=fe,A}}Jt.prototype.bytesPerElement=8,Fi(\"StructArrayLayout4i8\",Jt);class $t extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe){let Re=this.length;return this.resize(Re+1),this.emplace(Re,A,F,W,te,fe,pe)}emplace(A,F,W,te,fe,pe,Re){let Ke=6*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.int16[Ke+2]=te,this.int16[Ke+3]=fe,this.int16[Ke+4]=pe,this.int16[Ke+5]=Re,A}}$t.prototype.bytesPerElement=12,Fi(\"StructArrayLayout2i4i12\",$t);class yr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe){let Re=this.length;return this.resize(Re+1),this.emplace(Re,A,F,W,te,fe,pe)}emplace(A,F,W,te,fe,pe,Re){let Ke=4*A,ft=8*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.uint8[ft+4]=te,this.uint8[ft+5]=fe,this.uint8[ft+6]=pe,this.uint8[ft+7]=Re,A}}yr.prototype.bytesPerElement=8,Fi(\"StructArrayLayout2i4ub8\",yr);class Cr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let te=2*A;return this.float32[te+0]=F,this.float32[te+1]=W,A}}Cr.prototype.bytesPerElement=8,Fi(\"StructArrayLayout2f8\",Cr);class ve extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft,Rt){let er=this.length;return this.resize(er+1),this.emplace(er,A,F,W,te,fe,pe,Re,Ke,ft,Rt)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er){let ur=10*A;return this.uint16[ur+0]=F,this.uint16[ur+1]=W,this.uint16[ur+2]=te,this.uint16[ur+3]=fe,this.uint16[ur+4]=pe,this.uint16[ur+5]=Re,this.uint16[ur+6]=Ke,this.uint16[ur+7]=ft,this.uint16[ur+8]=Rt,this.uint16[ur+9]=er,A}}ve.prototype.bytesPerElement=20,Fi(\"StructArrayLayout10ui20\",ve);class xe extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur){let vr=this.length;return this.resize(vr+1),this.emplace(vr,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr){let kr=12*A;return this.int16[kr+0]=F,this.int16[kr+1]=W,this.int16[kr+2]=te,this.int16[kr+3]=fe,this.uint16[kr+4]=pe,this.uint16[kr+5]=Re,this.uint16[kr+6]=Ke,this.uint16[kr+7]=ft,this.int16[kr+8]=Rt,this.int16[kr+9]=er,this.int16[kr+10]=ur,this.int16[kr+11]=vr,A}}xe.prototype.bytesPerElement=24,Fi(\"StructArrayLayout4i4ui4i24\",xe);class Ie extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=3*A;return this.float32[fe+0]=F,this.float32[fe+1]=W,this.float32[fe+2]=te,A}}Ie.prototype.bytesPerElement=12,Fi(\"StructArrayLayout3f12\",Ie);class ze extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.uint32[1*A+0]=F,A}}ze.prototype.bytesPerElement=4,Fi(\"StructArrayLayout1ul4\",ze);class Qe extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft){let Rt=this.length;return this.resize(Rt+1),this.emplace(Rt,A,F,W,te,fe,pe,Re,Ke,ft)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt){let er=10*A,ur=5*A;return this.int16[er+0]=F,this.int16[er+1]=W,this.int16[er+2]=te,this.int16[er+3]=fe,this.int16[er+4]=pe,this.int16[er+5]=Re,this.uint32[ur+3]=Ke,this.uint16[er+8]=ft,this.uint16[er+9]=Rt,A}}Qe.prototype.bytesPerElement=20,Fi(\"StructArrayLayout6i1ul2ui20\",Qe);class Xe extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe){let Re=this.length;return this.resize(Re+1),this.emplace(Re,A,F,W,te,fe,pe)}emplace(A,F,W,te,fe,pe,Re){let Ke=6*A;return this.int16[Ke+0]=F,this.int16[Ke+1]=W,this.int16[Ke+2]=te,this.int16[Ke+3]=fe,this.int16[Ke+4]=pe,this.int16[Ke+5]=Re,A}}Xe.prototype.bytesPerElement=12,Fi(\"StructArrayLayout2i2i2i12\",Xe);class nt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe){let pe=this.length;return this.resize(pe+1),this.emplace(pe,A,F,W,te,fe)}emplace(A,F,W,te,fe,pe){let Re=4*A,Ke=8*A;return this.float32[Re+0]=F,this.float32[Re+1]=W,this.float32[Re+2]=te,this.int16[Ke+6]=fe,this.int16[Ke+7]=pe,A}}nt.prototype.bytesPerElement=16,Fi(\"StructArrayLayout2f1f2i16\",nt);class qt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe){let Re=this.length;return this.resize(Re+1),this.emplace(Re,A,F,W,te,fe,pe)}emplace(A,F,W,te,fe,pe,Re){let Ke=16*A,ft=4*A,Rt=8*A;return this.uint8[Ke+0]=F,this.uint8[Ke+1]=W,this.float32[ft+1]=te,this.float32[ft+2]=fe,this.int16[Rt+6]=pe,this.int16[Rt+7]=Re,A}}qt.prototype.bytesPerElement=16,Fi(\"StructArrayLayout2ub2f2i16\",qt);class Wt extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=3*A;return this.uint16[fe+0]=F,this.uint16[fe+1]=W,this.uint16[fe+2]=te,A}}Wt.prototype.bytesPerElement=6,Fi(\"StructArrayLayout3ui6\",Wt);class nr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi){let vn=this.length;return this.resize(vn+1),this.emplace(vn,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi,vn){let zi=24*A,ln=12*A,Tn=48*A;return this.int16[zi+0]=F,this.int16[zi+1]=W,this.uint16[zi+2]=te,this.uint16[zi+3]=fe,this.uint32[ln+2]=pe,this.uint32[ln+3]=Re,this.uint32[ln+4]=Ke,this.uint16[zi+10]=ft,this.uint16[zi+11]=Rt,this.uint16[zi+12]=er,this.float32[ln+7]=ur,this.float32[ln+8]=vr,this.uint8[Tn+36]=kr,this.uint8[Tn+37]=Dr,this.uint8[Tn+38]=li,this.uint32[ln+10]=pi,this.int16[zi+22]=vn,A}}nr.prototype.bytesPerElement=48,Fi(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",nr);class fr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi,vn,zi,ln,Tn,pa,ro,Vo,Xa,la,Mo,fo){let lo=this.length;return this.resize(lo+1),this.emplace(lo,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi,vn,zi,ln,Tn,pa,ro,Vo,Xa,la,Mo,fo)}emplace(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr,Dr,li,pi,vn,zi,ln,Tn,pa,ro,Vo,Xa,la,Mo,fo,lo){let Wn=32*A,Ro=16*A;return this.int16[Wn+0]=F,this.int16[Wn+1]=W,this.int16[Wn+2]=te,this.int16[Wn+3]=fe,this.int16[Wn+4]=pe,this.int16[Wn+5]=Re,this.int16[Wn+6]=Ke,this.int16[Wn+7]=ft,this.uint16[Wn+8]=Rt,this.uint16[Wn+9]=er,this.uint16[Wn+10]=ur,this.uint16[Wn+11]=vr,this.uint16[Wn+12]=kr,this.uint16[Wn+13]=Dr,this.uint16[Wn+14]=li,this.uint16[Wn+15]=pi,this.uint16[Wn+16]=vn,this.uint16[Wn+17]=zi,this.uint16[Wn+18]=ln,this.uint16[Wn+19]=Tn,this.uint16[Wn+20]=pa,this.uint16[Wn+21]=ro,this.uint16[Wn+22]=Vo,this.uint32[Ro+12]=Xa,this.float32[Ro+13]=la,this.float32[Ro+14]=Mo,this.uint16[Wn+30]=fo,this.uint16[Wn+31]=lo,A}}fr.prototype.bytesPerElement=64,Fi(\"StructArrayLayout8i15ui1ul2f2ui64\",fr);class br extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.float32[1*A+0]=F,A}}br.prototype.bytesPerElement=4,Fi(\"StructArrayLayout1f4\",br);class Fr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=3*A;return this.uint16[6*A+0]=F,this.float32[fe+1]=W,this.float32[fe+2]=te,A}}Fr.prototype.bytesPerElement=12,Fi(\"StructArrayLayout1ui2f12\",Fr);class Hr extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F,W){let te=this.length;return this.resize(te+1),this.emplace(te,A,F,W)}emplace(A,F,W,te){let fe=4*A;return this.uint32[2*A+0]=F,this.uint16[fe+2]=W,this.uint16[fe+3]=te,A}}Hr.prototype.bytesPerElement=8,Fi(\"StructArrayLayout1ul2ui8\",Hr);class ri extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A,F){let W=this.length;return this.resize(W+1),this.emplace(W,A,F)}emplace(A,F,W){let te=2*A;return this.uint16[te+0]=F,this.uint16[te+1]=W,A}}ri.prototype.bytesPerElement=4,Fi(\"StructArrayLayout2ui4\",ri);class Ci extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(A){let F=this.length;return this.resize(F+1),this.emplace(F,A)}emplace(A,F){return this.uint16[1*A+0]=F,A}}Ci.prototype.bytesPerElement=2,Fi(\"StructArrayLayout1ui2\",Ci);class cn extends le{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(A,F,W,te){let fe=this.length;return this.resize(fe+1),this.emplace(fe,A,F,W,te)}emplace(A,F,W,te,fe){let pe=4*A;return this.float32[pe+0]=F,this.float32[pe+1]=W,this.float32[pe+2]=te,this.float32[pe+3]=fe,A}}cn.prototype.bytesPerElement=16,Fi(\"StructArrayLayout4f16\",cn);class sn extends ee{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new u(this.anchorPointX,this.anchorPointY)}}sn.prototype.size=20;class Ln extends Qe{get(A){return new sn(this,A)}}Fi(\"CollisionBoxArray\",Ln);class Kn extends ee{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(A){this._structArray.uint8[this._pos1+37]=A}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(A){this._structArray.uint8[this._pos1+38]=A}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(A){this._structArray.uint32[this._pos4+10]=A}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}Kn.prototype.size=48;class Aa extends nr{get(A){return new Kn(this,A)}}Fi(\"PlacedSymbolArray\",Aa);class fa extends ee{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(A){this._structArray.uint32[this._pos4+12]=A}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}fa.prototype.size=64;class $a extends fr{get(A){return new fa(this,A)}}Fi(\"SymbolInstanceArray\",$a);class Co extends br{getoffsetX(A){return this.float32[1*A+0]}}Fi(\"GlyphOffsetArray\",Co);class Qa extends Mt{getx(A){return this.int16[3*A+0]}gety(A){return this.int16[3*A+1]}gettileUnitDistanceFromAnchor(A){return this.int16[3*A+2]}}Fi(\"SymbolLineVertexArray\",Qa);class mo extends ee{get textAnchor(){return this._structArray.uint16[this._pos2+0]}get textOffset0(){return this._structArray.float32[this._pos4+1]}get textOffset1(){return this._structArray.float32[this._pos4+2]}}mo.prototype.size=12;class Bo extends Fr{get(A){return new mo(this,A)}}Fi(\"TextAnchorOffsetArray\",Bo);class Ps extends ee{get featureIndex(){return this._structArray.uint32[this._pos4+0]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+2]}get bucketIndex(){return this._structArray.uint16[this._pos2+3]}}Ps.prototype.size=8;class Ts extends Hr{get(A){return new Ps(this,A)}}Fi(\"FeatureIndexArray\",Ts);class wo extends ut{}class To extends ut{}class hl extends ut{}class Nl extends $t{}class Lu extends yr{}class au extends Cr{}class Js extends ve{}class eu extends xe{}class dc extends Ie{}class Al extends ze{}class Sl extends Xe{}class X extends qt{}class se extends Wt{}class Te extends ri{}let qe=Fe([{name:\"a_pos\",components:2,type:\"Int16\"}],4),{members:Ve}=qe;class Ye{constructor(A=[]){this.segments=A}prepareSegment(A,F,W,te){let fe=this.segments[this.segments.length-1];return A>Ye.MAX_VERTEX_ARRAY_LENGTH&&E(`Max vertices per segment is ${Ye.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${A}`),(!fe||fe.vertexLength+A>Ye.MAX_VERTEX_ARRAY_LENGTH||fe.sortKey!==te)&&(fe={vertexOffset:F.length,primitiveOffset:W.length,vertexLength:0,primitiveLength:0},te!==void 0&&(fe.sortKey=te),this.segments.push(fe)),fe}get(){return this.segments}destroy(){for(let A of this.segments)for(let F in A.vaos)A.vaos[F].destroy()}static simpleSegment(A,F,W,te){return new Ye([{vertexOffset:A,primitiveOffset:F,vertexLength:W,primitiveLength:te,vaos:{},sortKey:0}])}}function Pt(R,A){return 256*(R=M(Math.floor(R),0,255))+M(Math.floor(A),0,255)}Ye.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Fi(\"SegmentVector\",Ye);let ot=Fe([{name:\"a_pattern_from\",components:4,type:\"Uint16\"},{name:\"a_pattern_to\",components:4,type:\"Uint16\"},{name:\"a_pixel_ratio_from\",components:1,type:\"Uint16\"},{name:\"a_pixel_ratio_to\",components:1,type:\"Uint16\"}]);var Xt={exports:{}},dr={exports:{}};dr.exports=function(R,A){var F,W,te,fe,pe,Re,Ke,ft;for(W=R.length-(F=3&R.length),te=A,pe=3432918353,Re=461845907,ft=0;ft<W;)Ke=255&R.charCodeAt(ft)|(255&R.charCodeAt(++ft))<<8|(255&R.charCodeAt(++ft))<<16|(255&R.charCodeAt(++ft))<<24,++ft,te=27492+(65535&(fe=5*(65535&(te=(te^=Ke=(65535&(Ke=(Ke=(65535&Ke)*pe+(((Ke>>>16)*pe&65535)<<16)&4294967295)<<15|Ke>>>17))*Re+(((Ke>>>16)*Re&65535)<<16)&4294967295)<<13|te>>>19))+((5*(te>>>16)&65535)<<16)&4294967295))+((58964+(fe>>>16)&65535)<<16);switch(Ke=0,F){case 3:Ke^=(255&R.charCodeAt(ft+2))<<16;case 2:Ke^=(255&R.charCodeAt(ft+1))<<8;case 1:te^=Ke=(65535&(Ke=(Ke=(65535&(Ke^=255&R.charCodeAt(ft)))*pe+(((Ke>>>16)*pe&65535)<<16)&4294967295)<<15|Ke>>>17))*Re+(((Ke>>>16)*Re&65535)<<16)&4294967295}return te^=R.length,te=2246822507*(65535&(te^=te>>>16))+((2246822507*(te>>>16)&65535)<<16)&4294967295,te=3266489909*(65535&(te^=te>>>13))+((3266489909*(te>>>16)&65535)<<16)&4294967295,(te^=te>>>16)>>>0};var pr=dr.exports,Ur={exports:{}};Ur.exports=function(R,A){for(var F,W=R.length,te=A^W,fe=0;W>=4;)F=1540483477*(65535&(F=255&R.charCodeAt(fe)|(255&R.charCodeAt(++fe))<<8|(255&R.charCodeAt(++fe))<<16|(255&R.charCodeAt(++fe))<<24))+((1540483477*(F>>>16)&65535)<<16),te=1540483477*(65535&te)+((1540483477*(te>>>16)&65535)<<16)^(F=1540483477*(65535&(F^=F>>>24))+((1540483477*(F>>>16)&65535)<<16)),W-=4,++fe;switch(W){case 3:te^=(255&R.charCodeAt(fe+2))<<16;case 2:te^=(255&R.charCodeAt(fe+1))<<8;case 1:te=1540483477*(65535&(te^=255&R.charCodeAt(fe)))+((1540483477*(te>>>16)&65535)<<16)}return te=1540483477*(65535&(te^=te>>>13))+((1540483477*(te>>>16)&65535)<<16),(te^=te>>>15)>>>0};var zr=pr,gi=Ur.exports;Xt.exports=zr,Xt.exports.murmur3=zr,Xt.exports.murmur2=gi;var xi=o(Xt.exports);class Wr{constructor(){this.ids=[],this.positions=[],this.indexed=!1}add(A,F,W,te){this.ids.push(ii(A)),this.positions.push(F,W,te)}getPositions(A){if(!this.indexed)throw new Error(\"Trying to get index, but feature positions are not indexed\");let F=ii(A),W=0,te=this.ids.length-1;for(;W<te;){let pe=W+te>>1;this.ids[pe]>=F?te=pe:W=pe+1}let fe=[];for(;this.ids[W]===F;)fe.push({index:this.positions[3*W],start:this.positions[3*W+1],end:this.positions[3*W+2]}),W++;return fe}static serialize(A,F){let W=new Float64Array(A.ids),te=new Uint32Array(A.positions);return di(W,te,0,W.length-1),F&&F.push(W.buffer,te.buffer),{ids:W,positions:te}}static deserialize(A){let F=new Wr;return F.ids=A.ids,F.positions=A.positions,F.indexed=!0,F}}function ii(R){let A=+R;return!isNaN(A)&&A<=Number.MAX_SAFE_INTEGER?A:xi(String(R))}function di(R,A,F,W){for(;F<W;){let te=R[F+W>>1],fe=F-1,pe=W+1;for(;;){do fe++;while(R[fe]<te);do pe--;while(R[pe]>te);if(fe>=pe)break;Li(R,fe,pe),Li(A,3*fe,3*pe),Li(A,3*fe+1,3*pe+1),Li(A,3*fe+2,3*pe+2)}pe-F<W-pe?(di(R,A,F,pe),F=pe+1):(di(R,A,pe+1,W),W=pe)}}function Li(R,A,F){let W=R[A];R[A]=R[F],R[F]=W}Fi(\"FeaturePositionMap\",Wr);class Ti{constructor(A,F){this.gl=A.gl,this.location=F}}class Qr extends Ti{constructor(A,F){super(A,F),this.current=0}set(A){this.current!==A&&(this.current=A,this.gl.uniform1f(this.location,A))}}class Pn extends Ti{constructor(A,F){super(A,F),this.current=[0,0,0,0]}set(A){A[0]===this.current[0]&&A[1]===this.current[1]&&A[2]===this.current[2]&&A[3]===this.current[3]||(this.current=A,this.gl.uniform4f(this.location,A[0],A[1],A[2],A[3]))}}class kn extends Ti{constructor(A,F){super(A,F),this.current=ir.transparent}set(A){A.r===this.current.r&&A.g===this.current.g&&A.b===this.current.b&&A.a===this.current.a||(this.current=A,this.gl.uniform4f(this.location,A.r,A.g,A.b,A.a))}}let Bn=new Float32Array(16);function na(R){return[Pt(255*R.r,255*R.g),Pt(255*R.b,255*R.a)]}class Ea{constructor(A,F,W){this.value=A,this.uniformNames=F.map((te=>`u_${te}`)),this.type=W}setUniform(A,F,W){A.set(W.constantOr(this.value))}getBinding(A,F,W){return this.type===\"color\"?new kn(A,F):new Qr(A,F)}}class Ia{constructor(A,F){this.uniformNames=F.map((W=>`u_${W}`)),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1}setConstantPatternPositions(A,F){this.pixelRatioFrom=F.pixelRatio,this.pixelRatioTo=A.pixelRatio,this.patternFrom=F.tlbr,this.patternTo=A.tlbr}setUniform(A,F,W,te){let fe=te===\"u_pattern_to\"?this.patternTo:te===\"u_pattern_from\"?this.patternFrom:te===\"u_pixel_ratio_to\"?this.pixelRatioTo:te===\"u_pixel_ratio_from\"?this.pixelRatioFrom:null;fe&&A.set(fe)}getBinding(A,F,W){return W.substr(0,9)===\"u_pattern\"?new Pn(A,F):new Qr(A,F)}}class yo{constructor(A,F,W,te){this.expression=A,this.type=W,this.maxValue=0,this.paintVertexAttributes=F.map((fe=>({name:`a_${fe}`,type:\"Float32\",components:W===\"color\"?2:1,offset:0}))),this.paintVertexArray=new te}populatePaintArray(A,F,W,te,fe){let pe=this.paintVertexArray.length,Re=this.expression.evaluate(new rs(0),F,{},te,[],fe);this.paintVertexArray.resize(A),this._setPaintValue(pe,A,Re)}updatePaintArray(A,F,W,te){let fe=this.expression.evaluate({zoom:0},W,te);this._setPaintValue(A,F,fe)}_setPaintValue(A,F,W){if(this.type===\"color\"){let te=na(W);for(let fe=A;fe<F;fe++)this.paintVertexArray.emplace(fe,te[0],te[1])}else{for(let te=A;te<F;te++)this.paintVertexArray.emplace(te,W);this.maxValue=Math.max(this.maxValue,Math.abs(W))}}upload(A){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=A.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}}class Da{constructor(A,F,W,te,fe,pe){this.expression=A,this.uniformNames=F.map((Re=>`u_${Re}_t`)),this.type=W,this.useIntegerZoom=te,this.zoom=fe,this.maxValue=0,this.paintVertexAttributes=F.map((Re=>({name:`a_${Re}`,type:\"Float32\",components:W===\"color\"?4:2,offset:0}))),this.paintVertexArray=new pe}populatePaintArray(A,F,W,te,fe){let pe=this.expression.evaluate(new rs(this.zoom),F,{},te,[],fe),Re=this.expression.evaluate(new rs(this.zoom+1),F,{},te,[],fe),Ke=this.paintVertexArray.length;this.paintVertexArray.resize(A),this._setPaintValue(Ke,A,pe,Re)}updatePaintArray(A,F,W,te){let fe=this.expression.evaluate({zoom:this.zoom},W,te),pe=this.expression.evaluate({zoom:this.zoom+1},W,te);this._setPaintValue(A,F,fe,pe)}_setPaintValue(A,F,W,te){if(this.type===\"color\"){let fe=na(W),pe=na(te);for(let Re=A;Re<F;Re++)this.paintVertexArray.emplace(Re,fe[0],fe[1],pe[0],pe[1])}else{for(let fe=A;fe<F;fe++)this.paintVertexArray.emplace(fe,W,te);this.maxValue=Math.max(this.maxValue,Math.abs(W),Math.abs(te))}}upload(A){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=A.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}setUniform(A,F){let W=this.useIntegerZoom?Math.floor(F.zoom):F.zoom,te=M(this.expression.interpolationFactor(W,this.zoom,this.zoom+1),0,1);A.set(te)}getBinding(A,F,W){return new Qr(A,F)}}class go{constructor(A,F,W,te,fe,pe){this.expression=A,this.type=F,this.useIntegerZoom=W,this.zoom=te,this.layerId=pe,this.zoomInPaintVertexArray=new fe,this.zoomOutPaintVertexArray=new fe}populatePaintArray(A,F,W){let te=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(A),this.zoomOutPaintVertexArray.resize(A),this._setPaintValues(te,A,F.patterns&&F.patterns[this.layerId],W)}updatePaintArray(A,F,W,te,fe){this._setPaintValues(A,F,W.patterns&&W.patterns[this.layerId],fe)}_setPaintValues(A,F,W,te){if(!te||!W)return;let{min:fe,mid:pe,max:Re}=W,Ke=te[fe],ft=te[pe],Rt=te[Re];if(Ke&&ft&&Rt)for(let er=A;er<F;er++)this.zoomInPaintVertexArray.emplace(er,ft.tl[0],ft.tl[1],ft.br[0],ft.br[1],Ke.tl[0],Ke.tl[1],Ke.br[0],Ke.br[1],ft.pixelRatio,Ke.pixelRatio),this.zoomOutPaintVertexArray.emplace(er,ft.tl[0],ft.tl[1],ft.br[0],ft.br[1],Rt.tl[0],Rt.tl[1],Rt.br[0],Rt.br[1],ft.pixelRatio,Rt.pixelRatio)}upload(A){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=A.createVertexBuffer(this.zoomInPaintVertexArray,ot.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=A.createVertexBuffer(this.zoomOutPaintVertexArray,ot.members,this.expression.isStateDependent))}destroy(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()}}class Is{constructor(A,F,W){this.binders={},this._buffers=[];let te=[];for(let fe in A.paint._values){if(!W(fe))continue;let pe=A.paint.get(fe);if(!(pe instanceof xu&&Bl(pe.property.specification)))continue;let Re=Xs(fe,A.type),Ke=pe.value,ft=pe.property.specification.type,Rt=pe.property.useIntegerZoom,er=pe.property.specification[\"property-type\"],ur=er===\"cross-faded\"||er===\"cross-faded-data-driven\";if(Ke.kind===\"constant\")this.binders[fe]=ur?new Ia(Ke.value,Re):new Ea(Ke.value,Re,ft),te.push(`/u_${fe}`);else if(Ke.kind===\"source\"||ur){let vr=Un(fe,ft,\"source\");this.binders[fe]=ur?new go(Ke,ft,Rt,F,vr,A.id):new yo(Ke,Re,ft,vr),te.push(`/a_${fe}`)}else{let vr=Un(fe,ft,\"composite\");this.binders[fe]=new Da(Ke,Re,ft,Rt,F,vr),te.push(`/z_${fe}`)}}this.cacheKey=te.sort().join(\"\")}getMaxValue(A){let F=this.binders[A];return F instanceof yo||F instanceof Da?F.maxValue:0}populatePaintArrays(A,F,W,te,fe){for(let pe in this.binders){let Re=this.binders[pe];(Re instanceof yo||Re instanceof Da||Re instanceof go)&&Re.populatePaintArray(A,F,W,te,fe)}}setConstantPatternPositions(A,F){for(let W in this.binders){let te=this.binders[W];te instanceof Ia&&te.setConstantPatternPositions(A,F)}}updatePaintArrays(A,F,W,te,fe){let pe=!1;for(let Re in A){let Ke=F.getPositions(Re);for(let ft of Ke){let Rt=W.feature(ft.index);for(let er in this.binders){let ur=this.binders[er];if((ur instanceof yo||ur instanceof Da||ur instanceof go)&&ur.expression.isStateDependent===!0){let vr=te.paint.get(er);ur.expression=vr.value,ur.updatePaintArray(ft.start,ft.end,Rt,A[Re],fe),pe=!0}}}}return pe}defines(){let A=[];for(let F in this.binders){let W=this.binders[F];(W instanceof Ea||W instanceof Ia)&&A.push(...W.uniformNames.map((te=>`#define HAS_UNIFORM_${te}`)))}return A}getBinderAttributes(){let A=[];for(let F in this.binders){let W=this.binders[F];if(W instanceof yo||W instanceof Da)for(let te=0;te<W.paintVertexAttributes.length;te++)A.push(W.paintVertexAttributes[te].name);else if(W instanceof go)for(let te=0;te<ot.members.length;te++)A.push(ot.members[te].name)}return A}getBinderUniforms(){let A=[];for(let F in this.binders){let W=this.binders[F];if(W instanceof Ea||W instanceof Ia||W instanceof Da)for(let te of W.uniformNames)A.push(te)}return A}getPaintVertexBuffers(){return this._buffers}getUniforms(A,F){let W=[];for(let te in this.binders){let fe=this.binders[te];if(fe instanceof Ea||fe instanceof Ia||fe instanceof Da){for(let pe of fe.uniformNames)if(F[pe]){let Re=fe.getBinding(A,F[pe],pe);W.push({name:pe,property:te,binding:Re})}}}return W}setUniforms(A,F,W,te){for(let{name:fe,property:pe,binding:Re}of F)this.binders[pe].setUniform(Re,te,W.get(pe),fe)}updatePaintBuffers(A){this._buffers=[];for(let F in this.binders){let W=this.binders[F];if(A&&W instanceof go){let te=A.fromScale===2?W.zoomInPaintVertexBuffer:W.zoomOutPaintVertexBuffer;te&&this._buffers.push(te)}else(W instanceof yo||W instanceof Da)&&W.paintVertexBuffer&&this._buffers.push(W.paintVertexBuffer)}}upload(A){for(let F in this.binders){let W=this.binders[F];(W instanceof yo||W instanceof Da||W instanceof go)&&W.upload(A)}this.updatePaintBuffers()}destroy(){for(let A in this.binders){let F=this.binders[A];(F instanceof yo||F instanceof Da||F instanceof go)&&F.destroy()}}}class Ms{constructor(A,F,W=(()=>!0)){this.programConfigurations={};for(let te of A)this.programConfigurations[te.id]=new Is(te,F,W);this.needsUpload=!1,this._featureMap=new Wr,this._bufferOffset=0}populatePaintArrays(A,F,W,te,fe,pe){for(let Re in this.programConfigurations)this.programConfigurations[Re].populatePaintArrays(A,F,te,fe,pe);F.id!==void 0&&this._featureMap.add(F.id,W,this._bufferOffset,A),this._bufferOffset=A,this.needsUpload=!0}updatePaintArrays(A,F,W,te){for(let fe of W)this.needsUpload=this.programConfigurations[fe.id].updatePaintArrays(A,this._featureMap,F,fe,te)||this.needsUpload}get(A){return this.programConfigurations[A]}upload(A){if(this.needsUpload){for(let F in this.programConfigurations)this.programConfigurations[F].upload(A);this.needsUpload=!1}}destroy(){for(let A in this.programConfigurations)this.programConfigurations[A].destroy()}}function Xs(R,A){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[R]||[R.replace(`${A}-`,\"\").replace(/-/g,\"_\")]}function Un(R,A,F){let W={color:{source:Cr,composite:cn},number:{source:br,composite:Cr}},te=(function(fe){return{\"line-pattern\":{source:Js,composite:Js},\"fill-pattern\":{source:Js,composite:Js},\"fill-extrusion-pattern\":{source:Js,composite:Js}}[fe]})(R);return te&&te[F]||W[A][F]}Fi(\"ConstantBinder\",Ea),Fi(\"CrossFadedConstantBinder\",Ia),Fi(\"SourceExpressionBinder\",yo),Fi(\"CrossFadedCompositeBinder\",go),Fi(\"CompositeExpressionBinder\",Da),Fi(\"ProgramConfiguration\",Is,{omit:[\"_buffers\"]}),Fi(\"ProgramConfigurationSet\",Ms);let ja=8192,Fo=Math.pow(2,14)-1,Uo=-Fo-1;function $s(R){let A=ja/R.extent,F=R.loadGeometry();for(let W=0;W<F.length;W++){let te=F[W];for(let fe=0;fe<te.length;fe++){let pe=te[fe],Re=Math.round(pe.x*A),Ke=Math.round(pe.y*A);pe.x=M(Re,Uo,Fo),pe.y=M(Ke,Uo,Fo),(Re<pe.x||Re>pe.x+1||Ke<pe.y||Ke>pe.y+1)&&E(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\")}}return F}function Ml(R,A){return{type:R.type,id:R.id,properties:R.properties,geometry:A?$s(R):[]}}function bu(R,A,F,W,te){R.emplaceBack(2*A+(W+1)/2,2*F+(te+1)/2)}class dl{constructor(A){this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map((F=>F.id)),this.index=A.index,this.hasPattern=!1,this.layoutVertexArray=new To,this.indexArray=new se,this.segments=new Ye,this.programConfigurations=new Ms(A.layers,A.zoom),this.stateDependentLayerIds=this.layers.filter((F=>F.isStateDependent())).map((F=>F.id))}populate(A,F,W){let te=this.layers[0],fe=[],pe=null,Re=!1;te.type===\"circle\"&&(pe=te.layout.get(\"circle-sort-key\"),Re=!pe.isConstant());for(let{feature:Ke,id:ft,index:Rt,sourceLayerIndex:er}of A){let ur=this.layers[0]._featureFilter.needGeometry,vr=Ml(Ke,ur);if(!this.layers[0]._featureFilter.filter(new rs(this.zoom),vr,W))continue;let kr=Re?pe.evaluate(vr,{},W):void 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te=F,fe=!1,pe=(F.x+W.x)/2,Re=(F.y+W.y)/2;do te.y>Re!=te.next.y>Re&&te.next.y!==te.y&&pe<(te.next.x-te.x)*(Re-te.y)/(te.next.y-te.y)+te.x&&(fe=!fe),te=te.next;while(te!==F);return fe})(R,A)&&(de(R.prev,R,A.prev)||de(R,A.prev,A))||ne(R,A)&&de(R.prev,R,R.next)>0&&de(A.prev,A,A.next)>0)}function de(R,A,F){return(A.y-R.y)*(F.x-A.x)-(A.x-R.x)*(F.y-A.y)}function ne(R,A){return R.x===A.x&&R.y===A.y}function we(R,A,F,W){let te=ht(de(R,A,F)),fe=ht(de(R,A,W)),pe=ht(de(F,W,R)),Re=ht(de(F,W,A));return te!==fe&&pe!==Re||!(te!==0||!Oe(R,F,A))||!(fe!==0||!Oe(R,W,A))||!(pe!==0||!Oe(F,R,W))||!(Re!==0||!Oe(F,A,W))}function Oe(R,A,F){return A.x<=Math.max(R.x,F.x)&&A.x>=Math.min(R.x,F.x)&&A.y<=Math.max(R.y,F.y)&&A.y>=Math.min(R.y,F.y)}function ht(R){return R>0?1:R<0?-1:0}function Yt(R,A){return de(R.prev,R,R.next)<0?de(R,A,R.next)>=0&&de(R,R.prev,A)>=0:de(R,A,R.prev)<0||de(R,R.next,A)<0}function cr(R,A){let F=$e(R.i,R.x,R.y),W=$e(A.i,A.x,A.y),te=R.next,fe=A.prev;return R.next=A,A.prev=R,F.next=te,te.prev=F,W.next=F,F.prev=W,fe.next=W,W.prev=fe,W}function Nt(R,A,F,W){let te=$e(R,A,F);return W?(te.next=W.next,te.prev=W,W.next.prev=te,W.next=te):(te.prev=te,te.next=te),te}function Ne(R){R.next.prev=R.prev,R.prev.next=R.next,R.prevZ&&(R.prevZ.nextZ=R.nextZ),R.nextZ&&(R.nextZ.prevZ=R.prevZ)}function $e(R,A,F){return{i:R,x:A,y:F,prev:null,next:null,z:0,prevZ:null,nextZ:null,steiner:!1}}function st(R,A,F){let W=F.patternDependencies,te=!1;for(let fe of A){let pe=fe.paint.get(`${R}-pattern`);pe.isConstant()||(te=!0);let Re=pe.constantOr(null);Re&&(te=!0,W[Re.to]=!0,W[Re.from]=!0)}return te}function It(R,A,F,W,te){let fe=te.patternDependencies;for(let pe of A){let Re=pe.paint.get(`${R}-pattern`).value;if(Re.kind!==\"constant\"){let Ke=Re.evaluate({zoom:W-1},F,{},te.availableImages),ft=Re.evaluate({zoom:W},F,{},te.availableImages),Rt=Re.evaluate({zoom:W+1},F,{},te.availableImages);Ke=Ke&&Ke.name?Ke.name:Ke,ft=ft&&ft.name?ft.name:ft,Rt=Rt&&Rt.name?Rt.name:Rt,fe[Ke]=!0,fe[ft]=!0,fe[Rt]=!0,F.patterns[pe.id]={min:Ke,mid:ft,max:Rt}}}return F}class Bt{constructor(A){this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map((F=>F.id)),this.index=A.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new hl,this.indexArray=new se,this.indexArray2=new Te,this.programConfigurations=new Ms(A.layers,A.zoom),this.segments=new Ye,this.segments2=new Ye,this.stateDependentLayerIds=this.layers.filter((F=>F.isStateDependent())).map((F=>F.id))}populate(A,F,W){this.hasPattern=st(\"fill\",this.layers,F);let te=this.layers[0].layout.get(\"fill-sort-key\"),fe=!te.isConstant(),pe=[];for(let{feature:Re,id:Ke,index:ft,sourceLayerIndex:Rt}of A){let er=this.layers[0]._featureFilter.needGeometry,ur=Ml(Re,er);if(!this.layers[0]._featureFilter.filter(new rs(this.zoom),ur,W))continue;let vr=fe?te.evaluate(ur,{},W,F.availableImages):void 0,kr={id:Ke,properties:Re.properties,type:Re.type,sourceLayerIndex:Rt,index:ft,geometry:er?ur.geometry:$s(Re),patterns:{},sortKey:vr};pe.push(kr)}fe&&pe.sort(((Re,Ke)=>Re.sortKey-Ke.sortKey));for(let Re of pe){let{geometry:Ke,index:ft,sourceLayerIndex:Rt}=Re;if(this.hasPattern){let er=It(\"fill\",this.layers,Re,this.zoom,F);this.patternFeatures.push(er)}else this.addFeature(Re,Ke,ft,W,{});F.featureIndex.insert(A[ft].feature,Ke,ft,Rt,this.index)}}update(A,F,W){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(A,F,this.stateDependentLayers,W)}addFeatures(A,F,W){for(let te of this.patternFeatures)this.addFeature(te,te.geometry,te.index,F,W)}isEmpty(){return this.layoutVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(A){this.uploaded||(this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,El),this.indexBuffer=A.createIndexBuffer(this.indexArray),this.indexBuffer2=A.createIndexBuffer(this.indexArray2)),this.programConfigurations.upload(A),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.indexBuffer2.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.segments2.destroy())}addFeature(A,F,W,te,fe){for(let pe of Cf(F,500)){let Re=0;for(let vr of pe)Re+=vr.length;let Ke=this.segments.prepareSegment(Re,this.layoutVertexArray,this.indexArray),ft=Ke.vertexLength,Rt=[],er=[];for(let vr of pe){if(vr.length===0)continue;vr!==pe[0]&&er.push(Rt.length/2);let kr=this.segments2.prepareSegment(vr.length,this.layoutVertexArray,this.indexArray2),Dr=kr.vertexLength;this.layoutVertexArray.emplaceBack(vr[0].x,vr[0].y),this.indexArray2.emplaceBack(Dr+vr.length-1,Dr),Rt.push(vr[0].x),Rt.push(vr[0].y);for(let li=1;li<vr.length;li++)this.layoutVertexArray.emplaceBack(vr[li].x,vr[li].y),this.indexArray2.emplaceBack(Dr+li-1,Dr+li),Rt.push(vr[li].x),Rt.push(vr[li].y);kr.vertexLength+=vr.length,kr.primitiveLength+=vr.length}let ur=zo(Rt,er);for(let vr=0;vr<ur.length;vr+=3)this.indexArray.emplaceBack(ft+ur[vr],ft+ur[vr+1],ft+ur[vr+2]);Ke.vertexLength+=Re,Ke.primitiveLength+=ur.length/3}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,A,W,fe,te)}}let Ft,Ut;Fi(\"FillBucket\",Bt,{omit:[\"layers\",\"patternFeatures\"]});var zt={get paint(){return Ut=Ut||new ue({\"fill-antialias\":new Va(ce.paint_fill[\"fill-antialias\"]),\"fill-opacity\":new oo(ce.paint_fill[\"fill-opacity\"]),\"fill-color\":new oo(ce.paint_fill[\"fill-color\"]),\"fill-outline-color\":new oo(ce.paint_fill[\"fill-outline-color\"]),\"fill-translate\":new Va(ce.paint_fill[\"fill-translate\"]),\"fill-translate-anchor\":new Va(ce.paint_fill[\"fill-translate-anchor\"]),\"fill-pattern\":new Vc(ce.paint_fill[\"fill-pattern\"])})},get layout(){return Ft=Ft||new ue({\"fill-sort-key\":new oo(ce.layout_fill[\"fill-sort-key\"])})}};class _t extends B{constructor(A){super(A,zt)}recalculate(A,F){super.recalculate(A,F);let W=this.paint._values[\"fill-outline-color\"];W.value.kind===\"constant\"&&W.value.value===void 0&&(this.paint._values[\"fill-outline-color\"]=this.paint._values[\"fill-color\"])}createBucket(A){return new Bt(A)}queryRadius(){return Vn(this.paint.get(\"fill-translate\"))}queryIntersectsFeature(A,F,W,te,fe,pe,Re){return bt(Qi(A,this.paint.get(\"fill-translate\"),this.paint.get(\"fill-translate-anchor\"),pe.angle,Re),te)}isTileClipped(){return!0}}let Dt=Fe([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_normal_ed\",components:4,type:\"Int16\"}],4),vt=Fe([{name:\"a_centroid\",components:2,type:\"Int16\"}],4),{members:lr}=Dt;var _r={},Br=s,Di=ci;function ci(R,A,F,W,te){this.properties={},this.extent=F,this.type=0,this._pbf=R,this._geometry=-1,this._keys=W,this._values=te,R.readFields(bi,this,A)}function bi(R,A,F){R==1?A.id=F.readVarint():R==2?(function(W,te){for(var fe=W.readVarint()+W.pos;W.pos<fe;){var pe=te._keys[W.readVarint()],Re=te._values[W.readVarint()];te.properties[pe]=Re}})(F,A):R==3?A.type=F.readVarint():R==4&&(A._geometry=F.pos)}function gn(R){for(var A,F,W=0,te=0,fe=R.length,pe=fe-1;te<fe;pe=te++)W+=((F=R[pe]).x-(A=R[te]).x)*(A.y+F.y);return W}ci.types=[\"Unknown\",\"Point\",\"LineString\",\"Polygon\"],ci.prototype.loadGeometry=function(){var R=this._pbf;R.pos=this._geometry;for(var A,F=R.readVarint()+R.pos,W=1,te=0,fe=0,pe=0,Re=[];R.pos<F;){if(te<=0){var Ke=R.readVarint();W=7&Ke,te=Ke>>3}if(te--,W===1||W===2)fe+=R.readSVarint(),pe+=R.readSVarint(),W===1&&(A&&Re.push(A),A=[]),A.push(new Br(fe,pe));else{if(W!==7)throw new Error(\"unknown command \"+W);A&&A.push(A[0].clone())}}return A&&Re.push(A),Re},ci.prototype.bbox=function(){var R=this._pbf;R.pos=this._geometry;for(var A=R.readVarint()+R.pos,F=1,W=0,te=0,fe=0,pe=1/0,Re=-1/0,Ke=1/0,ft=-1/0;R.pos<A;){if(W<=0){var Rt=R.readVarint();F=7&Rt,W=Rt>>3}if(W--,F===1||F===2)(te+=R.readSVarint())<pe&&(pe=te),te>Re&&(Re=te),(fe+=R.readSVarint())<Ke&&(Ke=fe),fe>ft&&(ft=fe);else if(F!==7)throw new Error(\"unknown command \"+F)}return[pe,Ke,Re,ft]},ci.prototype.toGeoJSON=function(R,A,F){var W,te,fe=this.extent*Math.pow(2,F),pe=this.extent*R,Re=this.extent*A,Ke=this.loadGeometry(),ft=ci.types[this.type];function Rt(vr){for(var kr=0;kr<vr.length;kr++){var Dr=vr[kr];vr[kr]=[360*(Dr.x+pe)/fe-180,360/Math.PI*Math.atan(Math.exp((180-360*(Dr.y+Re)/fe)*Math.PI/180))-90]}}switch(this.type){case 1:var er=[];for(W=0;W<Ke.length;W++)er[W]=Ke[W][0];Rt(Ke=er);break;case 2:for(W=0;W<Ke.length;W++)Rt(Ke[W]);break;case 3:for(Ke=(function(vr){var kr=vr.length;if(kr<=1)return[vr];for(var Dr,li,pi=[],vn=0;vn<kr;vn++){var zi=gn(vr[vn]);zi!==0&&(li===void 0&&(li=zi<0),li===zi<0?(Dr&&pi.push(Dr),Dr=[vr[vn]]):Dr.push(vr[vn]))}return Dr&&pi.push(Dr),pi})(Ke),W=0;W<Ke.length;W++)for(te=0;te<Ke[W].length;te++)Rt(Ke[W][te])}Ke.length===1?Ke=Ke[0]:ft=\"Multi\"+ft;var ur={type:\"Feature\",geometry:{type:ft,coordinates:Ke},properties:this.properties};return\"id\"in this&&(ur.id=this.id),ur};var tn=Di,Ei=Oi;function Oi(R,A){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=R,this._keys=[],this._values=[],this._features=[],R.readFields(Gi,this,A),this.length=this._features.length}function Gi(R,A,F){R===15?A.version=F.readVarint():R===1?A.name=F.readString():R===5?A.extent=F.readVarint():R===2?A._features.push(F.pos):R===3?A._keys.push(F.readString()):R===4&&A._values.push((function(W){for(var te=null,fe=W.readVarint()+W.pos;W.pos<fe;){var pe=W.readVarint()>>3;te=pe===1?W.readString():pe===2?W.readFloat():pe===3?W.readDouble():pe===4?W.readVarint64():pe===5?W.readVarint():pe===6?W.readSVarint():pe===7?W.readBoolean():null}return te})(F))}Oi.prototype.feature=function(R){if(R<0||R>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[R];var A=this._pbf.readVarint()+this._pbf.pos;return new tn(this._pbf,A,this.extent,this._keys,this._values)};var on=Ei;function zn(R,A,F){if(R===3){var W=new on(F,F.readVarint()+F.pos);W.length&&(A[W.name]=W)}}_r.VectorTile=function(R,A){this.layers=R.readFields(zn,{},A)},_r.VectorTileFeature=Di,_r.VectorTileLayer=Ei;let Ja=_r.VectorTileFeature.types,co=Math.pow(2,13);function ts(R,A,F,W,te,fe,pe,Re){R.emplaceBack(A,F,2*Math.floor(W*co)+pe,te*co*2,fe*co*2,Math.round(Re))}class so{constructor(A){this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map((F=>F.id)),this.index=A.index,this.hasPattern=!1,this.layoutVertexArray=new Nl,this.centroidVertexArray=new wo,this.indexArray=new se,this.programConfigurations=new Ms(A.layers,A.zoom),this.segments=new Ye,this.stateDependentLayerIds=this.layers.filter((F=>F.isStateDependent())).map((F=>F.id))}populate(A,F,W){this.features=[],this.hasPattern=st(\"fill-extrusion\",this.layers,F);for(let{feature:te,id:fe,index:pe,sourceLayerIndex:Re}of A){let Ke=this.layers[0]._featureFilter.needGeometry,ft=Ml(te,Ke);if(!this.layers[0]._featureFilter.filter(new rs(this.zoom),ft,W))continue;let Rt={id:fe,sourceLayerIndex:Re,index:pe,geometry:Ke?ft.geometry:$s(te),properties:te.properties,type:te.type,patterns:{}};this.hasPattern?this.features.push(It(\"fill-extrusion\",this.layers,Rt,this.zoom,F)):this.addFeature(Rt,Rt.geometry,pe,W,{}),F.featureIndex.insert(te,Rt.geometry,pe,Re,this.index,!0)}}addFeatures(A,F,W){for(let te of this.features){let{geometry:fe}=te;this.addFeature(te,fe,te.index,F,W)}}update(A,F,W){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(A,F,this.stateDependentLayers,W)}isEmpty(){return this.layoutVertexArray.length===0&&this.centroidVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(A){this.uploaded||(this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,lr),this.centroidVertexBuffer=A.createVertexBuffer(this.centroidVertexArray,vt.members,!0),this.indexBuffer=A.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(A),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.centroidVertexBuffer.destroy())}addFeature(A,F,W,te,fe){for(let pe of Cf(F,500)){let Re={x:0,y:0,vertexCount:0},Ke=0;for(let kr of pe)Ke+=kr.length;let ft=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray);for(let kr of pe){if(kr.length===0||ms(kr))continue;let Dr=0;for(let li=0;li<kr.length;li++){let pi=kr[li];if(li>=1){let vn=kr[li-1];if(!Yo(pi,vn)){ft.vertexLength+4>Ye.MAX_VERTEX_ARRAY_LENGTH&&(ft=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));let zi=pi.sub(vn)._perp()._unit(),ln=vn.dist(pi);Dr+ln>32768&&(Dr=0),ts(this.layoutVertexArray,pi.x,pi.y,zi.x,zi.y,0,0,Dr),ts(this.layoutVertexArray,pi.x,pi.y,zi.x,zi.y,0,1,Dr),Re.x+=2*pi.x,Re.y+=2*pi.y,Re.vertexCount+=2,Dr+=ln,ts(this.layoutVertexArray,vn.x,vn.y,zi.x,zi.y,0,0,Dr),ts(this.layoutVertexArray,vn.x,vn.y,zi.x,zi.y,0,1,Dr),Re.x+=2*vn.x,Re.y+=2*vn.y,Re.vertexCount+=2;let Tn=ft.vertexLength;this.indexArray.emplaceBack(Tn,Tn+2,Tn+1),this.indexArray.emplaceBack(Tn+1,Tn+2,Tn+3),ft.vertexLength+=4,ft.primitiveLength+=2}}}}if(ft.vertexLength+Ke>Ye.MAX_VERTEX_ARRAY_LENGTH&&(ft=this.segments.prepareSegment(Ke,this.layoutVertexArray,this.indexArray)),Ja[A.type]!==\"Polygon\")continue;let Rt=[],er=[],ur=ft.vertexLength;for(let kr of pe)if(kr.length!==0){kr!==pe[0]&&er.push(Rt.length/2);for(let Dr=0;Dr<kr.length;Dr++){let li=kr[Dr];ts(this.layoutVertexArray,li.x,li.y,0,0,1,1,0),Re.x+=li.x,Re.y+=li.y,Re.vertexCount+=1,Rt.push(li.x),Rt.push(li.y)}}let vr=zo(Rt,er);for(let kr=0;kr<vr.length;kr+=3)this.indexArray.emplaceBack(ur+vr[kr],ur+vr[kr+2],ur+vr[kr+1]);ft.primitiveLength+=vr.length/3,ft.vertexLength+=Ke;for(let kr=0;kr<Re.vertexCount;kr++){let Dr=Math.floor(Re.x/Re.vertexCount),li=Math.floor(Re.y/Re.vertexCount);this.centroidVertexArray.emplaceBack(Dr,li)}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,A,W,fe,te)}}function Yo(R,A){return R.x===A.x&&(R.x<0||R.x>ja)||R.y===A.y&&(R.y<0||R.y>ja)}function ms(R){return R.every((A=>A.x<0))||R.every((A=>A.x>ja))||R.every((A=>A.y<0))||R.every((A=>A.y>ja))}let ou;Fi(\"FillExtrusionBucket\",so,{omit:[\"layers\",\"features\"]});var Cv={get paint(){return ou=ou||new ue({\"fill-extrusion-opacity\":new Va(ce[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new oo(ce[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new Va(ce[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new Va(ce[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new Vc(ce[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new oo(ce[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new oo(ce[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new Va(ce[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})}};class Lv extends B{constructor(A){super(A,Cv)}createBucket(A){return new so(A)}queryRadius(){return Vn(this.paint.get(\"fill-extrusion-translate\"))}is3D(){return!0}queryIntersectsFeature(A,F,W,te,fe,pe,Re,Ke){let ft=Qi(A,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),pe.angle,Re),Rt=this.paint.get(\"fill-extrusion-height\").evaluate(F,W),er=this.paint.get(\"fill-extrusion-base\").evaluate(F,W),ur=(function(kr,Dr,li,pi){let vn=[];for(let zi of kr){let ln=[zi.x,zi.y,0,1];qn(ln,ln,Dr),vn.push(new u(ln[0]/ln[3],ln[1]/ln[3]))}return vn})(ft,Ke),vr=(function(kr,Dr,li,pi){let vn=[],zi=[],ln=pi[8]*Dr,Tn=pi[9]*Dr,pa=pi[10]*Dr,ro=pi[11]*Dr,Vo=pi[8]*li,Xa=pi[9]*li,la=pi[10]*li,Mo=pi[11]*li;for(let fo of kr){let lo=[],Wn=[];for(let Ro of fo){let uo=Ro.x,$o=Ro.y,Ju=pi[0]*uo+pi[4]*$o+pi[12],Ou=pi[1]*uo+pi[5]*$o+pi[13],kh=pi[2]*uo+pi[6]*$o+pi[14],Qv=pi[3]*uo+pi[7]*$o+pi[15],ud=kh+pa,Ch=Qv+ro,Gd=Ju+Vo,Hd=Ou+Xa,jd=kh+la,Af=Qv+Mo,Lh=new u((Ju+ln)/Ch,(Ou+Tn)/Ch);Lh.z=ud/Ch,lo.push(Lh);let Ed=new u(Gd/Af,Hd/Af);Ed.z=jd/Af,Wn.push(Ed)}vn.push(lo),zi.push(Wn)}return[vn,zi]})(te,er,Rt,Ke);return(function(kr,Dr,li){let pi=1/0;bt(li,Dr)&&(pi=Kv(li,Dr[0]));for(let vn=0;vn<Dr.length;vn++){let zi=Dr[vn],ln=kr[vn];for(let Tn=0;Tn<zi.length-1;Tn++){let pa=zi[Tn],ro=[pa,zi[Tn+1],ln[Tn+1],ln[Tn],pa];Sc(li,ro)&&(pi=Math.min(pi,Kv(li,ro)))}}return pi!==1/0&&pi})(vr[0],vr[1],ur)}}function wd(R,A){return R.x*A.x+R.y*A.y}function Kv(R,A){if(R.length===1){let F=0,W=A[F++],te;for(;!te||W.equals(te);)if(te=A[F++],!te)return 1/0;for(;F<A.length;F++){let fe=A[F],pe=R[0],Re=te.sub(W),Ke=fe.sub(W),ft=pe.sub(W),Rt=wd(Re,Re),er=wd(Re,Ke),ur=wd(Ke,Ke),vr=wd(ft,Re),kr=wd(ft,Ke),Dr=Rt*ur-er*er,li=(ur*vr-er*kr)/Dr,pi=(Rt*kr-er*vr)/Dr,vn=W.z*(1-li-pi)+te.z*li+fe.z*pi;if(isFinite(vn))return vn}return 1/0}{let F=1/0;for(let W of A)F=Math.min(F,W.z);return F}}let hg=Fe([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4),{members:gp}=hg,Td=Fe([{name:\"a_uv_x\",components:1,type:\"Float32\"},{name:\"a_split_index\",components:1,type:\"Float32\"}]),{members:mp}=Td,Vd=_r.VectorTileFeature.types,Ad=Math.cos(Math.PI/180*37.5),Pv=Math.pow(2,14)/.5;class Jv{constructor(A){this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map((F=>F.id)),this.index=A.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((F=>{this.gradients[F.id]={}})),this.layoutVertexArray=new Lu,this.layoutVertexArray2=new au,this.indexArray=new se,this.programConfigurations=new Ms(A.layers,A.zoom),this.segments=new Ye,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((F=>F.isStateDependent())).map((F=>F.id))}populate(A,F,W){this.hasPattern=st(\"line\",this.layers,F);let te=this.layers[0].layout.get(\"line-sort-key\"),fe=!te.isConstant(),pe=[];for(let{feature:Re,id:Ke,index:ft,sourceLayerIndex:Rt}of A){let er=this.layers[0]._featureFilter.needGeometry,ur=Ml(Re,er);if(!this.layers[0]._featureFilter.filter(new rs(this.zoom),ur,W))continue;let vr=fe?te.evaluate(ur,{},W):void 0,kr={id:Ke,properties:Re.properties,type:Re.type,sourceLayerIndex:Rt,index:ft,geometry:er?ur.geometry:$s(Re),patterns:{},sortKey:vr};pe.push(kr)}fe&&pe.sort(((Re,Ke)=>Re.sortKey-Ke.sortKey));for(let Re of pe){let{geometry:Ke,index:ft,sourceLayerIndex:Rt}=Re;if(this.hasPattern){let er=It(\"line\",this.layers,Re,this.zoom,F);this.patternFeatures.push(er)}else this.addFeature(Re,Ke,ft,W,{});F.featureIndex.insert(A[ft].feature,Ke,ft,Rt,this.index)}}update(A,F,W){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(A,F,this.stateDependentLayers,W)}addFeatures(A,F,W){for(let te of this.patternFeatures)this.addFeature(te,te.geometry,te.index,F,W)}isEmpty(){return this.layoutVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(A){this.uploaded||(this.layoutVertexArray2.length!==0&&(this.layoutVertexBuffer2=A.createVertexBuffer(this.layoutVertexArray2,mp)),this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,gp),this.indexBuffer=A.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(A),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}lineFeatureClips(A){if(A.properties&&Object.prototype.hasOwnProperty.call(A.properties,\"mapbox_clip_start\")&&Object.prototype.hasOwnProperty.call(A.properties,\"mapbox_clip_end\"))return{start:+A.properties.mapbox_clip_start,end:+A.properties.mapbox_clip_end}}addFeature(A,F,W,te,fe){let pe=this.layers[0].layout,Re=pe.get(\"line-join\").evaluate(A,{}),Ke=pe.get(\"line-cap\"),ft=pe.get(\"line-miter-limit\"),Rt=pe.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(A);for(let er of F)this.addLine(er,A,Re,Ke,ft,Rt);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,A,W,fe,te)}addLine(A,F,W,te,fe,pe){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(let pi=0;pi<A.length-1;pi++)this.totalDistance+=A[pi].dist(A[pi+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}let Re=Vd[F.type]===\"Polygon\",Ke=A.length;for(;Ke>=2&&A[Ke-1].equals(A[Ke-2]);)Ke--;let ft=0;for(;ft<Ke-1&&A[ft].equals(A[ft+1]);)ft++;if(Ke<(Re?3:2))return;W===\"bevel\"&&(fe=1.05);let Rt=this.overscaling<=16?15*ja/(512*this.overscaling):0,er=this.segments.prepareSegment(10*Ke,this.layoutVertexArray,this.indexArray),ur,vr,kr,Dr,li;this.e1=this.e2=-1,Re&&(ur=A[Ke-2],li=A[ft].sub(ur)._unit()._perp());for(let pi=ft;pi<Ke;pi++){if(kr=pi===Ke-1?Re?A[ft+1]:void 0:A[pi+1],kr&&A[pi].equals(kr))continue;li&&(Dr=li),ur&&(vr=ur),ur=A[pi],li=kr?kr.sub(ur)._unit()._perp():Dr,Dr=Dr||li;let vn=Dr.add(li);vn.x===0&&vn.y===0||vn._unit();let zi=Dr.x*li.x+Dr.y*li.y,ln=vn.x*li.x+vn.y*li.y,Tn=ln!==0?1/ln:1/0,pa=2*Math.sqrt(2-2*ln),ro=ln<Ad&&vr&&kr,Vo=Dr.x*li.y-Dr.y*li.x>0;if(ro&&pi>ft){let Mo=ur.dist(vr);if(Mo>2*Rt){let fo=ur.sub(ur.sub(vr)._mult(Rt/Mo)._round());this.updateDistance(vr,fo),this.addCurrentVertex(fo,Dr,0,0,er),vr=fo}}let Xa=vr&&kr,la=Xa?W:Re?\"butt\":te;if(Xa&&la===\"round\"&&(Tn<pe?la=\"miter\":Tn<=2&&(la=\"fakeround\")),la===\"miter\"&&Tn>fe&&(la=\"bevel\"),la===\"bevel\"&&(Tn>2&&(la=\"flipbevel\"),Tn<fe&&(la=\"miter\")),vr&&this.updateDistance(vr,ur),la===\"miter\")vn._mult(Tn),this.addCurrentVertex(ur,vn,0,0,er);else if(la===\"flipbevel\"){if(Tn>100)vn=li.mult(-1);else{let Mo=Tn*Dr.add(li).mag()/Dr.sub(li).mag();vn._perp()._mult(Mo*(Vo?-1:1))}this.addCurrentVertex(ur,vn,0,0,er),this.addCurrentVertex(ur,vn.mult(-1),0,0,er)}else if(la===\"bevel\"||la===\"fakeround\"){let Mo=-Math.sqrt(Tn*Tn-1),fo=Vo?Mo:0,lo=Vo?0:Mo;if(vr&&this.addCurrentVertex(ur,Dr,fo,lo,er),la===\"fakeround\"){let Wn=Math.round(180*pa/Math.PI/20);for(let Ro=1;Ro<Wn;Ro++){let uo=Ro/Wn;if(uo!==.5){let Ju=uo-.5;uo+=uo*Ju*(uo-1)*((1.0904+zi*(zi*(3.55645-1.43519*zi)-3.2452))*Ju*Ju+(.848013+zi*(.215638*zi-1.06021)))}let $o=li.sub(Dr)._mult(uo)._add(Dr)._unit()._mult(Vo?-1:1);this.addHalfVertex(ur,$o.x,$o.y,!1,Vo,0,er)}}kr&&this.addCurrentVertex(ur,li,-fo,-lo,er)}else if(la===\"butt\")this.addCurrentVertex(ur,vn,0,0,er);else if(la===\"square\"){let Mo=vr?1:-1;this.addCurrentVertex(ur,vn,Mo,Mo,er)}else la===\"round\"&&(vr&&(this.addCurrentVertex(ur,Dr,0,0,er),this.addCurrentVertex(ur,Dr,1,1,er,!0)),kr&&(this.addCurrentVertex(ur,li,-1,-1,er,!0),this.addCurrentVertex(ur,li,0,0,er)));if(ro&&pi<Ke-1){let Mo=ur.dist(kr);if(Mo>2*Rt){let fo=ur.add(kr.sub(ur)._mult(Rt/Mo)._round());this.updateDistance(ur,fo),this.addCurrentVertex(fo,li,0,0,er),ur=fo}}}}addCurrentVertex(A,F,W,te,fe,pe=!1){let Re=F.y*te-F.x,Ke=-F.y-F.x*te;this.addHalfVertex(A,F.x+F.y*W,F.y-F.x*W,pe,!1,W,fe),this.addHalfVertex(A,Re,Ke,pe,!0,-te,fe),this.distance>Pv/2&&this.totalDistance===0&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(A,F,W,te,fe,pe))}addHalfVertex({x:A,y:F},W,te,fe,pe,Re,Ke){let ft=.5*(this.lineClips?this.scaledDistance*(Pv-1):this.scaledDistance);this.layoutVertexArray.emplaceBack((A<<1)+(fe?1:0),(F<<1)+(pe?1:0),Math.round(63*W)+128,Math.round(63*te)+128,1+(Re===0?0:Re<0?-1:1)|(63&ft)<<2,ft>>6),this.lineClips&&this.layoutVertexArray2.emplaceBack((this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start),this.lineClipsArray.length);let Rt=Ke.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,Rt),Ke.primitiveLength++),pe?this.e2=Rt:this.e1=Rt}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(A,F){this.distance+=A.dist(F),this.updateScaledDistance()}}let Iv,dy;Fi(\"LineBucket\",Jv,{omit:[\"layers\",\"patternFeatures\"]});var dg={get paint(){return dy=dy||new ue({\"line-opacity\":new oo(ce.paint_line[\"line-opacity\"]),\"line-color\":new oo(ce.paint_line[\"line-color\"]),\"line-translate\":new Va(ce.paint_line[\"line-translate\"]),\"line-translate-anchor\":new Va(ce.paint_line[\"line-translate-anchor\"]),\"line-width\":new oo(ce.paint_line[\"line-width\"]),\"line-gap-width\":new oo(ce.paint_line[\"line-gap-width\"]),\"line-offset\":new oo(ce.paint_line[\"line-offset\"]),\"line-blur\":new oo(ce.paint_line[\"line-blur\"]),\"line-dasharray\":new hc(ce.paint_line[\"line-dasharray\"]),\"line-pattern\":new Vc(ce.paint_line[\"line-pattern\"]),\"line-gradient\":new Ku(ce.paint_line[\"line-gradient\"])})},get layout(){return Iv=Iv||new ue({\"line-cap\":new Va(ce.layout_line[\"line-cap\"]),\"line-join\":new oo(ce.layout_line[\"line-join\"]),\"line-miter-limit\":new Va(ce.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Va(ce.layout_line[\"line-round-limit\"]),\"line-sort-key\":new oo(ce.layout_line[\"line-sort-key\"])})}};class oh extends oo{possiblyEvaluate(A,F){return F=new rs(Math.floor(F.zoom),{now:F.now,fadeDuration:F.fadeDuration,zoomHistory:F.zoomHistory,transition:F.transition}),super.possiblyEvaluate(A,F)}evaluate(A,F,W,te){return F=L({},F,{zoom:Math.floor(F.zoom)}),super.evaluate(A,F,W,te)}}let vg;class vy extends B{constructor(A){super(A,dg),this.gradientVersion=0,vg||(vg=new oh(dg.paint.properties[\"line-width\"].specification),vg.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(A){if(A===\"line-gradient\"){let F=this.gradientExpression();this.stepInterpolant=!!(function(W){return W._styleExpression!==void 0})(F)&&F._styleExpression.expression instanceof _n,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values[\"line-gradient\"].value.expression}recalculate(A,F){super.recalculate(A,F),this.paint._values[\"line-floorwidth\"]=vg.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,A)}createBucket(A){return new Jv(A)}queryRadius(A){let F=A,W=Zh(Si(\"line-width\",this,F),Si(\"line-gap-width\",this,F)),te=Si(\"line-offset\",this,F);return W/2+Math.abs(te)+Vn(this.paint.get(\"line-translate\"))}queryIntersectsFeature(A,F,W,te,fe,pe,Re){let Ke=Qi(A,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),pe.angle,Re),ft=Re/2*Zh(this.paint.get(\"line-width\").evaluate(F,W),this.paint.get(\"line-gap-width\").evaluate(F,W)),Rt=this.paint.get(\"line-offset\").evaluate(F,W);return Rt&&(te=(function(er,ur){let vr=[];for(let kr=0;kr<er.length;kr++){let Dr=er[kr],li=[];for(let pi=0;pi<Dr.length;pi++){let vn=Dr[pi-1],zi=Dr[pi],ln=Dr[pi+1],Tn=pi===0?new u(0,0):zi.sub(vn)._unit()._perp(),pa=pi===Dr.length-1?new u(0,0):ln.sub(zi)._unit()._perp(),ro=Tn._add(pa)._unit(),Vo=ro.x*pa.x+ro.y*pa.y;Vo!==0&&ro._mult(1/Vo),li.push(ro._mult(ur)._add(zi))}vr.push(li)}return vr})(te,Rt*Re)),(function(er,ur,vr){for(let kr=0;kr<ur.length;kr++){let Dr=ur[kr];if(er.length>=3){for(let li=0;li<Dr.length;li++)if(Ni(er,Dr[li]))return!0}if(Ot(er,Dr,vr))return!0}return!1})(Ke,te,ft)}isTileClipped(){return!0}}function Zh(R,A){return A>0?A+2*R:R}let am=Fe([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"},{name:\"a_pixeloffset\",components:4,type:\"Int16\"}],4),C1=Fe([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4);Fe([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4);let L1=Fe([{name:\"a_placed\",components:2,type:\"Uint8\"},{name:\"a_shift\",components:2,type:\"Float32\"},{name:\"a_box_real\",components:2,type:\"Int16\"}]);Fe([{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]);let py=Fe([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4),om=Fe([{name:\"a_pos\",components:2,type:\"Float32\"},{name:\"a_radius\",components:1,type:\"Float32\"},{name:\"a_flags\",components:2,type:\"Int16\"}],4);function sm(R,A,F){return R.sections.forEach((W=>{W.text=(function(te,fe,pe){let Re=fe.layout.get(\"text-transform\").evaluate(pe,{});return Re===\"uppercase\"?te=te.toLocaleUpperCase():Re===\"lowercase\"&&(te=te.toLocaleLowerCase()),ys.applyArabicShaping&&(te=ys.applyArabicShaping(te)),te})(W.text,A,F)})),R}Fe([{name:\"triangle\",components:3,type:\"Uint16\"}]),Fe([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Uint16\",name:\"glyphStartIndex\"},{type:\"Uint16\",name:\"numGlyphs\"},{type:\"Uint32\",name:\"vertexStartIndex\"},{type:\"Uint32\",name:\"lineStartIndex\"},{type:\"Uint32\",name:\"lineLength\"},{type:\"Uint16\",name:\"segment\"},{type:\"Uint16\",name:\"lowerSize\"},{type:\"Uint16\",name:\"upperSize\"},{type:\"Float32\",name:\"lineOffsetX\"},{type:\"Float32\",name:\"lineOffsetY\"},{type:\"Uint8\",name:\"writingMode\"},{type:\"Uint8\",name:\"placedOrientation\"},{type:\"Uint8\",name:\"hidden\"},{type:\"Uint32\",name:\"crossTileID\"},{type:\"Int16\",name:\"associatedIconIndex\"}]),Fe([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Int16\",name:\"rightJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"centerJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"leftJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"verticalPlacedTextSymbolIndex\"},{type:\"Int16\",name:\"placedIconSymbolIndex\"},{type:\"Int16\",name:\"verticalPlacedIconSymbolIndex\"},{type:\"Uint16\",name:\"key\"},{type:\"Uint16\",name:\"textBoxStartIndex\"},{type:\"Uint16\",name:\"textBoxEndIndex\"},{type:\"Uint16\",name:\"verticalTextBoxStartIndex\"},{type:\"Uint16\",name:\"verticalTextBoxEndIndex\"},{type:\"Uint16\",name:\"iconBoxStartIndex\"},{type:\"Uint16\",name:\"iconBoxEndIndex\"},{type:\"Uint16\",name:\"verticalIconBoxStartIndex\"},{type:\"Uint16\",name:\"verticalIconBoxEndIndex\"},{type:\"Uint16\",name:\"featureIndex\"},{type:\"Uint16\",name:\"numHorizontalGlyphVertices\"},{type:\"Uint16\",name:\"numVerticalGlyphVertices\"},{type:\"Uint16\",name:\"numIconVertices\"},{type:\"Uint16\",name:\"numVerticalIconVertices\"},{type:\"Uint16\",name:\"useRuntimeCollisionCircles\"},{type:\"Uint32\",name:\"crossTileID\"},{type:\"Float32\",name:\"textBoxScale\"},{type:\"Float32\",name:\"collisionCircleDiameter\"},{type:\"Uint16\",name:\"textAnchorOffsetStartIndex\"},{type:\"Uint16\",name:\"textAnchorOffsetEndIndex\"}]),Fe([{type:\"Float32\",name:\"offsetX\"}]),Fe([{type:\"Int16\",name:\"x\"},{type:\"Int16\",name:\"y\"},{type:\"Int16\",name:\"tileUnitDistanceFromAnchor\"}]),Fe([{type:\"Uint16\",name:\"textAnchor\"},{type:\"Float32\",components:2,name:\"textOffset\"}]);let 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pe.h===Ke.h?(Ke.x+=pe.w,Ke.w-=pe.w):pe.w===Ke.w?(Ke.y+=pe.h,Ke.h-=pe.h):(W.push({x:Ke.x+pe.w,y:Ke.y,w:Ke.w-pe.w,h:pe.h}),Ke.y+=pe.h,Ke.h-=pe.h);break}}return{w:te,h:fe,fill:A/(te*fe)||0}}let Md=1;class Gw{constructor(A,{pixelRatio:F,version:W,stretchX:te,stretchY:fe,content:pe,textFitWidth:Re,textFitHeight:Ke}){this.paddedRect=A,this.pixelRatio=F,this.stretchX=te,this.stretchY=fe,this.content=pe,this.version=W,this.textFitWidth=Re,this.textFitHeight=Ke}get tl(){return[this.paddedRect.x+Md,this.paddedRect.y+Md]}get br(){return[this.paddedRect.x+this.paddedRect.w-Md,this.paddedRect.y+this.paddedRect.h-Md]}get tlbr(){return this.tl.concat(this.br)}get displaySize(){return[(this.paddedRect.w-2*Md)/this.pixelRatio,(this.paddedRect.h-2*Md)/this.pixelRatio]}}class Hw{constructor(A,F){let W={},te={};this.haveRenderCallbacks=[];let fe=[];this.addImages(A,W,fe),this.addImages(F,te,fe);let{w:pe,h:Re}=dS(fe),Ke=new Ki({width:pe||1,height:Re||1});for(let ft in A){let Rt=A[ft],er=W[ft].paddedRect;Ki.copy(Rt.data,Ke,{x:0,y:0},{x:er.x+Md,y:er.y+Md},Rt.data)}for(let ft in F){let Rt=F[ft],er=te[ft].paddedRect,ur=er.x+Md,vr=er.y+Md,kr=Rt.data.width,Dr=Rt.data.height;Ki.copy(Rt.data,Ke,{x:0,y:0},{x:ur,y:vr},Rt.data),Ki.copy(Rt.data,Ke,{x:0,y:Dr-1},{x:ur,y:vr-1},{width:kr,height:1}),Ki.copy(Rt.data,Ke,{x:0,y:0},{x:ur,y:vr+Dr},{width:kr,height:1}),Ki.copy(Rt.data,Ke,{x:kr-1,y:0},{x:ur-1,y:vr},{width:1,height:Dr}),Ki.copy(Rt.data,Ke,{x:0,y:0},{x:ur+kr,y:vr},{width:1,height:Dr})}this.image=Ke,this.iconPositions=W,this.patternPositions=te}addImages(A,F,W){for(let te in A){let fe=A[te],pe={x:0,y:0,w:fe.data.width+2*Md,h:fe.data.height+2*Md};W.push(pe),F[te]=new Gw(pe,fe),fe.hasRenderCallback&&this.haveRenderCallbacks.push(te)}}patchUpdatedImages(A,F){A.dispatchRenderCallbacks(this.haveRenderCallbacks);for(let W in A.updatedImages)this.patchUpdatedImage(this.iconPositions[W],A.getImage(W),F),this.patchUpdatedImage(this.patternPositions[W],A.getImage(W),F)}patchUpdatedImage(A,F,W){if(!A||!F||A.version===F.version)return;A.version=F.version;let[te,fe]=A.tl;W.update(F.data,void 0,{x:te,y:fe})}}var Dv;Fi(\"ImagePosition\",Gw),Fi(\"ImageAtlas\",Hw),i.ah=void 0,(Dv=i.ah||(i.ah={}))[Dv.none=0]=\"none\",Dv[Dv.horizontal=1]=\"horizontal\",Dv[Dv.vertical=2]=\"vertical\",Dv[Dv.horizontalOnly=3]=\"horizontalOnly\";let Eh=-17;class Qx{constructor(){this.scale=1,this.fontStack=\"\",this.imageName=null}static forText(A,F){let W=new Qx;return W.scale=A||1,W.fontStack=F,W}static forImage(A){let F=new Qx;return F.imageName=A,F}}class I1{constructor(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null}static fromFeature(A,F){let W=new I1;for(let te=0;te<A.sections.length;te++){let fe=A.sections[te];fe.image?W.addImageSection(fe):W.addTextSection(fe,F)}return W}length(){return this.text.length}getSection(A){return this.sections[this.sectionIndex[A]]}getSectionIndex(A){return this.sectionIndex[A]}getCharCode(A){return this.text.charCodeAt(A)}verticalizePunctuation(){this.text=(function(A){let F=\"\";for(let W=0;W<A.length;W++){let te=A.charCodeAt(W+1)||null,fe=A.charCodeAt(W-1)||null;F+=te&&ml(te)&&!vc[A[W+1]]||fe&&ml(fe)&&!vc[A[W-1]]||!vc[A[W]]?A[W]:vc[A[W]]}return F})(this.text)}trim(){let A=0;for(let W=0;W<this.text.length&&jw[this.text.charCodeAt(W)];W++)A++;let F=this.text.length;for(let W=this.text.length-1;W>=0&&W>=A&&jw[this.text.charCodeAt(W)];W--)F--;this.text=this.text.substring(A,F),this.sectionIndex=this.sectionIndex.slice(A,F)}substring(A,F){let W=new I1;return W.text=this.text.substring(A,F),W.sectionIndex=this.sectionIndex.slice(A,F),W.sections=this.sections,W}toString(){return this.text}getMaxScale(){return this.sectionIndex.reduce(((A,F)=>Math.max(A,this.sections[F].scale)),0)}addTextSection(A,F){this.text+=A.text,this.sections.push(Qx.forText(A.scale,A.fontStack||F));let W=this.sections.length-1;for(let te=0;te<A.text.length;++te)this.sectionIndex.push(W)}addImageSection(A){let F=A.image?A.image.name:\"\";if(F.length===0)return void E(\"Can't add FormattedSection with an empty image.\");let W=this.getNextImageSectionCharCode();W?(this.text+=String.fromCharCode(W),this.sections.push(Qx.forImage(F)),this.sectionIndex.push(this.sections.length-1)):E(\"Reached maximum number of images 6401\")}getNextImageSectionCharCode(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)}}function eb(R,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur,vr,kr){let Dr=I1.fromFeature(R,te),li;er===i.ah.vertical&&Dr.verticalizePunctuation();let{processBidirectionalText:pi,processStyledBidirectionalText:vn}=ys;if(pi&&Dr.sections.length===1){li=[];let Tn=pi(Dr.toString(),R1(Dr,ft,fe,A,W,vr));for(let pa of Tn){let ro=new I1;ro.text=pa,ro.sections=Dr.sections;for(let Vo=0;Vo<pa.length;Vo++)ro.sectionIndex.push(0);li.push(ro)}}else if(vn){li=[];let Tn=vn(Dr.text,Dr.sectionIndex,R1(Dr,ft,fe,A,W,vr));for(let pa of Tn){let ro=new I1;ro.text=pa[0],ro.sectionIndex=pa[1],ro.sections=Dr.sections,li.push(ro)}}else li=(function(Tn,pa){let ro=[],Vo=Tn.text,Xa=0;for(let la of pa)ro.push(Tn.substring(Xa,la)),Xa=la;return Xa<Vo.length&&ro.push(Tn.substring(Xa,Vo.length)),ro})(Dr,R1(Dr,ft,fe,A,W,vr));let zi=[],ln={positionedLines:zi,text:Dr.toString(),top:Rt[1],bottom:Rt[1],left:Rt[0],right:Rt[0],writingMode:er,iconsInText:!1,verticalizable:!1};return(function(Tn,pa,ro,Vo,Xa,la,Mo,fo,lo,Wn,Ro,uo){let $o=0,Ju=Eh,Ou=0,kh=0,Qv=fo===\"right\"?1:fo===\"left\"?0:.5,ud=0;for(let Af of Xa){Af.trim();let Lh=Af.getMaxScale(),Ed=(Lh-1)*tu,cd={positionedGlyphs:[],lineOffset:0};Tn.positionedLines[ud]=cd;let Yh=cd.positionedGlyphs,Df=0;if(!Af.length()){Ju+=la,++ud;continue}for(let uv=0;uv<Af.length();uv++){let ru=Af.getSection(uv),pc=Af.getSectionIndex(uv),$u=Af.getCharCode(uv),zv=0,hf=null,B1=null,p0=null,Gp=tu,ep=!(lo===i.ah.horizontal||!Ro&&!Ho($u)||Ro&&(jw[$u]||(Ch=$u,new RegExp(\"\\\\p{sc=Arab}\",\"u\").test(String.fromCodePoint(Ch)))));if(ru.imageName){let Gc=Vo[ru.imageName];if(!Gc)continue;p0=ru.imageName,Tn.iconsInText=Tn.iconsInText||!0,B1=Gc.paddedRect;let Yf=Gc.displaySize;ru.scale=ru.scale*tu/uo,hf={width:Yf[0],height:Yf[1],left:Md,top:-LC,advance:ep?Yf[1]:Yf[0]},zv=Ed+(tu-Yf[1]*ru.scale),Gp=hf.advance;let tp=ep?Yf[0]*ru.scale-tu*Lh:Yf[1]*ru.scale-tu*Lh;tp>0&&tp>Df&&(Df=tp)}else{let Gc=ro[ru.fontStack],Yf=Gc&&Gc[$u];if(Yf&&Yf.rect)B1=Yf.rect,hf=Yf.metrics;else{let tp=pa[ru.fontStack],yg=tp&&tp[$u];if(!yg)continue;hf=yg.metrics}zv=(Lh-ru.scale)*tu}ep?(Tn.verticalizable=!0,Yh.push({glyph:$u,imageName:p0,x:$o,y:Ju+zv,vertical:ep,scale:ru.scale,fontStack:ru.fontStack,sectionIndex:pc,metrics:hf,rect:B1}),$o+=Gp*ru.scale+Wn):(Yh.push({glyph:$u,imageName:p0,x:$o,y:Ju+zv,vertical:ep,scale:ru.scale,fontStack:ru.fontStack,sectionIndex:pc,metrics:hf,rect:B1}),$o+=hf.advance*ru.scale+Wn)}Yh.length!==0&&(Ou=Math.max($o-Wn,Ou),cm(Yh,0,Yh.length-1,Qv,Df)),$o=0;let Fv=la*Lh+Df;cd.lineOffset=Math.max(Df,Ed),Ju+=Fv,kh=Math.max(Fv,kh),++ud}var Ch;let Gd=Ju-Eh,{horizontalAlign:Hd,verticalAlign:jd}=Xw(Mo);(function(Af,Lh,Ed,cd,Yh,Df,Fv,uv,ru){let pc=(Lh-Ed)*Yh,$u=0;$u=Df!==Fv?-uv*cd-Eh:(-cd*ru+.5)*Fv;for(let zv of Af)for(let hf of zv.positionedGlyphs)hf.x+=pc,hf.y+=$u})(Tn.positionedLines,Qv,Hd,jd,Ou,kh,la,Gd,Xa.length),Tn.top+=-jd*Gd,Tn.bottom=Tn.top+Gd,Tn.left+=-Hd*Ou,Tn.right=Tn.left+Ou})(ln,A,F,W,li,pe,Re,Ke,er,ft,ur,kr),!(function(Tn){for(let pa of Tn)if(pa.positionedGlyphs.length!==0)return!1;return!0})(zi)&&ln}let jw={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},eq={10:!0,32:!0,38:!0,41:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0,8231:!0},tq={40:!0};function PC(R,A,F,W,te,fe){if(A.imageName){let pe=W[A.imageName];return pe?pe.displaySize[0]*A.scale*tu/fe+te:0}{let pe=F[A.fontStack],Re=pe&&pe[R];return Re?Re.metrics.advance*A.scale+te:0}}function IC(R,A,F,W){let te=Math.pow(R-A,2);return W?R<A?te/2:2*te:te+Math.abs(F)*F}function rq(R,A,F){let W=0;return R===10&&(W-=1e4),F&&(W+=150),R!==40&&R!==65288||(W+=50),A!==41&&A!==65289||(W+=50),W}function Ww(R,A,F,W,te,fe){let pe=null,Re=IC(A,F,te,fe);for(let Ke of W){let ft=IC(A-Ke.x,F,te,fe)+Ke.badness;ft<=Re&&(pe=Ke,Re=ft)}return{index:R,x:A,priorBreak:pe,badness:Re}}function RC(R){return R?RC(R.priorBreak).concat(R.index):[]}function R1(R,A,F,W,te,fe){if(!R)return[];let pe=[],Re=(function(er,ur,vr,kr,Dr,li){let pi=0;for(let vn=0;vn<er.length();vn++){let zi=er.getSection(vn);pi+=PC(er.getCharCode(vn),zi,kr,Dr,ur,li)}return pi/Math.max(1,Math.ceil(pi/vr))})(R,A,F,W,te,fe),Ke=R.text.indexOf(\"\\u200B\")>=0,ft=0;for(let er=0;er<R.length();er++){let ur=R.getSection(er),vr=R.getCharCode(er);if(jw[vr]||(ft+=PC(vr,ur,W,te,A,fe)),er<R.length()-1){let kr=!((Rt=vr)<11904)&&(!!Dn[\"CJK Compatibility Forms\"](Rt)||!!Dn[\"CJK Compatibility\"](Rt)||!!Dn[\"CJK Strokes\"](Rt)||!!Dn[\"CJK Symbols and Punctuation\"](Rt)||!!Dn[\"Enclosed CJK Letters and Months\"](Rt)||!!Dn[\"Halfwidth and Fullwidth Forms\"](Rt)||!!Dn[\"Ideographic Description Characters\"](Rt)||!!Dn[\"Vertical Forms\"](Rt)||ws.test(String.fromCodePoint(Rt)));(eq[vr]||kr||ur.imageName||er!==R.length()-2&&tq[R.getCharCode(er+1)])&&pe.push(Ww(er+1,ft,Re,pe,rq(vr,R.getCharCode(er+1),kr&&Ke),!1))}}var Rt;return RC(Ww(R.length(),ft,Re,pe,0,!0))}function Xw(R){let A=.5,F=.5;switch(R){case\"right\":case\"top-right\":case\"bottom-right\":A=1;break;case\"left\":case\"top-left\":case\"bottom-left\":A=0}switch(R){case\"bottom\":case\"bottom-right\":case\"bottom-left\":F=1;break;case\"top\":case\"top-right\":case\"top-left\":F=0}return{horizontalAlign:A,verticalAlign:F}}function cm(R,A,F,W,te){if(!W&&!te)return;let fe=R[F],pe=(R[F].x+fe.metrics.advance*fe.scale)*W;for(let Re=A;Re<=F;Re++)R[Re].x-=pe,R[Re].y+=te}function tb(R,A,F){let{horizontalAlign:W,verticalAlign:te}=Xw(F),fe=A[0]-R.displaySize[0]*W,pe=A[1]-R.displaySize[1]*te;return{image:R,top:pe,bottom:pe+R.displaySize[1],left:fe,right:fe+R.displaySize[0]}}function DC(R){var A,F;let W=R.left,te=R.top,fe=R.right-W,pe=R.bottom-te,Re=(A=R.image.textFitWidth)!==null&&A!==void 0?A:\"stretchOrShrink\",Ke=(F=R.image.textFitHeight)!==null&&F!==void 0?F:\"stretchOrShrink\",ft=(R.image.content[2]-R.image.content[0])/(R.image.content[3]-R.image.content[1]);if(Ke===\"proportional\"){if(Re===\"stretchOnly\"&&fe/pe<ft||Re===\"proportional\"){let Rt=Math.ceil(pe*ft);W*=Rt/fe,fe=Rt}}else if(Re===\"proportional\"&&Ke===\"stretchOnly\"&&ft!==0&&fe/pe>ft){let Rt=Math.ceil(fe/ft);te*=Rt/pe,pe=Rt}return{x1:W,y1:te,x2:W+fe,y2:te+pe}}function FC(R,A,F,W,te,fe){let pe=R.image,Re;if(pe.content){let li=pe.content,pi=pe.pixelRatio||1;Re=[li[0]/pi,li[1]/pi,pe.displaySize[0]-li[2]/pi,pe.displaySize[1]-li[3]/pi]}let Ke=A.left*fe,ft=A.right*fe,Rt,er,ur,vr;F===\"width\"||F===\"both\"?(vr=te[0]+Ke-W[3],er=te[0]+ft+W[1]):(vr=te[0]+(Ke+ft-pe.displaySize[0])/2,er=vr+pe.displaySize[0]);let kr=A.top*fe,Dr=A.bottom*fe;return F===\"height\"||F===\"both\"?(Rt=te[1]+kr-W[0],ur=te[1]+Dr+W[2]):(Rt=te[1]+(kr+Dr-pe.displaySize[1])/2,ur=Rt+pe.displaySize[1]),{image:pe,top:Rt,right:er,bottom:ur,left:vr,collisionPadding:Re}}let rb=255,v0=128,fm=rb*v0;function zC(R,A){let{expression:F}=A;if(F.kind===\"constant\")return{kind:\"constant\",layoutSize:F.evaluate(new rs(R+1))};if(F.kind===\"source\")return{kind:\"source\"};{let{zoomStops:W,interpolationType:te}=F,fe=0;for(;fe<W.length&&W[fe]<=R;)fe++;fe=Math.max(0,fe-1);let pe=fe;for(;pe<W.length&&W[pe]<R+1;)pe++;pe=Math.min(W.length-1,pe);let Re=W[fe],Ke=W[pe];return F.kind===\"composite\"?{kind:\"composite\",minZoom:Re,maxZoom:Ke,interpolationType:te}:{kind:\"camera\",minZoom:Re,maxZoom:Ke,minSize:F.evaluate(new rs(Re)),maxSize:F.evaluate(new rs(Ke)),interpolationType:te}}}function vS(R,A,F){let W=\"never\",te=R.get(A);return te?W=te:R.get(F)&&(W=\"always\"),W}let iq=_r.VectorTileFeature.types,nq=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function Zw(R,A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur){let vr=Re?Math.min(fm,Math.round(Re[0])):0,kr=Re?Math.min(fm,Math.round(Re[1])):0;R.emplaceBack(A,F,Math.round(32*W),Math.round(32*te),fe,pe,(vr<<1)+(Ke?1:0),kr,16*ft,16*Rt,256*er,256*ur)}function pS(R,A,F){R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F),R.emplaceBack(A.x,A.y,F)}function gS(R){for(let A of R.sections)if(no(A.text))return!0;return!1}class mS{constructor(A){this.layoutVertexArray=new eu,this.indexArray=new se,this.programConfigurations=A,this.segments=new Ye,this.dynamicLayoutVertexArray=new dc,this.opacityVertexArray=new Al,this.hasVisibleVertices=!1,this.placedSymbolArray=new Aa}isEmpty(){return this.layoutVertexArray.length===0&&this.indexArray.length===0&&this.dynamicLayoutVertexArray.length===0&&this.opacityVertexArray.length===0}upload(A,F,W,te){this.isEmpty()||(W&&(this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,am.members),this.indexBuffer=A.createIndexBuffer(this.indexArray,F),this.dynamicLayoutVertexBuffer=A.createVertexBuffer(this.dynamicLayoutVertexArray,C1.members,!0),this.opacityVertexBuffer=A.createVertexBuffer(this.opacityVertexArray,nq,!0),this.opacityVertexBuffer.itemSize=1),(W||te)&&this.programConfigurations.upload(A))}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())}}Fi(\"SymbolBuffers\",mS);class hm{constructor(A,F,W){this.layoutVertexArray=new A,this.layoutAttributes=F,this.indexArray=new W,this.segments=new Ye,this.collisionVertexArray=new X}upload(A){this.layoutVertexBuffer=A.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=A.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=A.createVertexBuffer(this.collisionVertexArray,L1.members,!0)}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())}}Fi(\"CollisionBuffers\",hm);class D1{constructor(A){this.collisionBoxArray=A.collisionBoxArray,this.zoom=A.zoom,this.overscaling=A.overscaling,this.layers=A.layers,this.layerIds=this.layers.map((pe=>pe.id)),this.index=A.index,this.pixelRatio=A.pixelRatio,this.sourceLayerIndex=A.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Ii([]),this.placementViewportMatrix=Ii([]);let F=this.layers[0]._unevaluatedLayout._values;this.textSizeData=zC(this.zoom,F[\"text-size\"]),this.iconSizeData=zC(this.zoom,F[\"icon-size\"]);let W=this.layers[0].layout,te=W.get(\"symbol-sort-key\"),fe=W.get(\"symbol-z-order\");this.canOverlap=vS(W,\"text-overlap\",\"text-allow-overlap\")!==\"never\"||vS(W,\"icon-overlap\",\"icon-allow-overlap\")!==\"never\"||W.get(\"text-ignore-placement\")||W.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=fe!==\"viewport-y\"&&!te.isConstant(),this.sortFeaturesByY=(fe===\"viewport-y\"||fe===\"auto\"&&!this.sortFeaturesByKey)&&this.canOverlap,W.get(\"symbol-placement\")===\"point\"&&(this.writingModes=W.get(\"text-writing-mode\").map((pe=>i.ah[pe]))),this.stateDependentLayerIds=this.layers.filter((pe=>pe.isStateDependent())).map((pe=>pe.id)),this.sourceID=A.sourceID}createArrays(){this.text=new mS(new Ms(this.layers,this.zoom,(A=>/^text/.test(A)))),this.icon=new mS(new Ms(this.layers,this.zoom,(A=>/^icon/.test(A)))),this.glyphOffsetArray=new Co,this.lineVertexArray=new Qa,this.symbolInstances=new $a,this.textAnchorOffsets=new Bo}calculateGlyphDependencies(A,F,W,te,fe){for(let pe=0;pe<A.length;pe++)if(F[A.charCodeAt(pe)]=!0,(W||te)&&fe){let Re=vc[A.charAt(pe)];Re&&(F[Re.charCodeAt(0)]=!0)}}populate(A,F,W){let te=this.layers[0],fe=te.layout,pe=fe.get(\"text-font\"),Re=fe.get(\"text-field\"),Ke=fe.get(\"icon-image\"),ft=(Re.value.kind!==\"constant\"||Re.value.value instanceof ti&&!Re.value.value.isEmpty()||Re.value.value.toString().length>0)&&(pe.value.kind!==\"constant\"||pe.value.value.length>0),Rt=Ke.value.kind!==\"constant\"||!!Ke.value.value||Object.keys(Ke.parameters).length>0,er=fe.get(\"symbol-sort-key\");if(this.features=[],!ft&&!Rt)return;let ur=F.iconDependencies,vr=F.glyphDependencies,kr=F.availableImages,Dr=new rs(this.zoom);for(let{feature:li,id:pi,index:vn,sourceLayerIndex:zi}of A){let ln=te._featureFilter.needGeometry,Tn=Ml(li,ln);if(!te._featureFilter.filter(Dr,Tn,W))continue;let pa,ro;if(ln||(Tn.geometry=$s(li)),ft){let Xa=te.getValueAndResolveTokens(\"text-field\",Tn,W,kr),la=ti.factory(Xa),Mo=this.hasRTLText=this.hasRTLText||gS(la);(!Mo||ys.getRTLTextPluginStatus()===\"unavailable\"||Mo&&ys.isParsed())&&(pa=sm(la,te,Tn))}if(Rt){let Xa=te.getValueAndResolveTokens(\"icon-image\",Tn,W,kr);ro=Xa instanceof en?Xa:en.fromString(Xa)}if(!pa&&!ro)continue;let Vo=this.sortFeaturesByKey?er.evaluate(Tn,{},W):void 0;if(this.features.push({id:pi,text:pa,icon:ro,index:vn,sourceLayerIndex:zi,geometry:Tn.geometry,properties:li.properties,type:iq[li.type],sortKey:Vo}),ro&&(ur[ro.name]=!0),pa){let Xa=pe.evaluate(Tn,{},W).join(\",\"),la=fe.get(\"text-rotation-alignment\")!==\"viewport\"&&fe.get(\"symbol-placement\")!==\"point\";this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(i.ah.vertical)>=0;for(let Mo of pa.sections)if(Mo.image)ur[Mo.image.name]=!0;else{let fo=Ka(pa.toString()),lo=Mo.fontStack||Xa,Wn=vr[lo]=vr[lo]||{};this.calculateGlyphDependencies(Mo.text,Wn,la,this.allowVerticalPlacement,fo)}}}fe.get(\"symbol-placement\")===\"line\"&&(this.features=(function(li){let pi={},vn={},zi=[],ln=0;function Tn(Xa){zi.push(li[Xa]),ln++}function pa(Xa,la,Mo){let fo=vn[Xa];return delete vn[Xa],vn[la]=fo,zi[fo].geometry[0].pop(),zi[fo].geometry[0]=zi[fo].geometry[0].concat(Mo[0]),fo}function ro(Xa,la,Mo){let fo=pi[la];return delete pi[la],pi[Xa]=fo,zi[fo].geometry[0].shift(),zi[fo].geometry[0]=Mo[0].concat(zi[fo].geometry[0]),fo}function Vo(Xa,la,Mo){let fo=Mo?la[0][la[0].length-1]:la[0][0];return`${Xa}:${fo.x}:${fo.y}`}for(let Xa=0;Xa<li.length;Xa++){let la=li[Xa],Mo=la.geometry,fo=la.text?la.text.toString():null;if(!fo){Tn(Xa);continue}let lo=Vo(fo,Mo),Wn=Vo(fo,Mo,!0);if(lo in vn&&Wn in pi&&vn[lo]!==pi[Wn]){let Ro=ro(lo,Wn,Mo),uo=pa(lo,Wn,zi[Ro].geometry);delete pi[lo],delete vn[Wn],vn[Vo(fo,zi[uo].geometry,!0)]=uo,zi[Ro].geometry=null}else lo in vn?pa(lo,Wn,Mo):Wn in pi?ro(lo,Wn,Mo):(Tn(Xa),pi[lo]=ln-1,vn[Wn]=ln-1)}return zi.filter((Xa=>Xa.geometry))})(this.features)),this.sortFeaturesByKey&&this.features.sort(((li,pi)=>li.sortKey-pi.sortKey))}update(A,F,W){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(A,F,this.layers,W),this.icon.programConfigurations.updatePaintArrays(A,F,this.layers,W))}isEmpty(){return this.symbolInstances.length===0&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(A){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(A),this.iconCollisionBox.upload(A)),this.text.upload(A,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(A,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(A,F){let W=this.lineVertexArray.length;if(A.segment!==void 0){let te=A.dist(F[A.segment+1]),fe=A.dist(F[A.segment]),pe={};for(let Re=A.segment+1;Re<F.length;Re++)pe[Re]={x:F[Re].x,y:F[Re].y,tileUnitDistanceFromAnchor:te},Re<F.length-1&&(te+=F[Re+1].dist(F[Re]));for(let Re=A.segment||0;Re>=0;Re--)pe[Re]={x:F[Re].x,y:F[Re].y,tileUnitDistanceFromAnchor:fe},Re>0&&(fe+=F[Re-1].dist(F[Re]));for(let Re=0;Re<F.length;Re++){let Ke=pe[Re];this.lineVertexArray.emplaceBack(Ke.x,Ke.y,Ke.tileUnitDistanceFromAnchor)}}return{lineStartIndex:W,lineLength:this.lineVertexArray.length-W}}addSymbols(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er,ur){let vr=A.indexArray,kr=A.layoutVertexArray,Dr=A.segments.prepareSegment(4*F.length,kr,vr,this.canOverlap?pe.sortKey:void 0),li=this.glyphOffsetArray.length,pi=Dr.vertexLength,vn=this.allowVerticalPlacement&&Re===i.ah.vertical?Math.PI/2:0,zi=pe.text&&pe.text.sections;for(let ln=0;ln<F.length;ln++){let{tl:Tn,tr:pa,bl:ro,br:Vo,tex:Xa,pixelOffsetTL:la,pixelOffsetBR:Mo,minFontScaleX:fo,minFontScaleY:lo,glyphOffset:Wn,isSDF:Ro,sectionIndex:uo}=F[ln],$o=Dr.vertexLength,Ju=Wn[1];Zw(kr,Ke.x,Ke.y,Tn.x,Ju+Tn.y,Xa.x,Xa.y,W,Ro,la.x,la.y,fo,lo),Zw(kr,Ke.x,Ke.y,pa.x,Ju+pa.y,Xa.x+Xa.w,Xa.y,W,Ro,Mo.x,la.y,fo,lo),Zw(kr,Ke.x,Ke.y,ro.x,Ju+ro.y,Xa.x,Xa.y+Xa.h,W,Ro,la.x,Mo.y,fo,lo),Zw(kr,Ke.x,Ke.y,Vo.x,Ju+Vo.y,Xa.x+Xa.w,Xa.y+Xa.h,W,Ro,Mo.x,Mo.y,fo,lo),pS(A.dynamicLayoutVertexArray,Ke,vn),vr.emplaceBack($o,$o+1,$o+2),vr.emplaceBack($o+1,$o+2,$o+3),Dr.vertexLength+=4,Dr.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(Wn[0]),ln!==F.length-1&&uo===F[ln+1].sectionIndex||A.programConfigurations.populatePaintArrays(kr.length,pe,pe.index,{},ur,zi&&zi[uo])}A.placedSymbolArray.emplaceBack(Ke.x,Ke.y,li,this.glyphOffsetArray.length-li,pi,ft,Rt,Ke.segment,W?W[0]:0,W?W[1]:0,te[0],te[1],Re,0,!1,0,er)}_addCollisionDebugVertex(A,F,W,te,fe,pe){return F.emplaceBack(0,0),A.emplaceBack(W.x,W.y,te,fe,Math.round(pe.x),Math.round(pe.y))}addCollisionDebugVertices(A,F,W,te,fe,pe,Re){let Ke=fe.segments.prepareSegment(4,fe.layoutVertexArray,fe.indexArray),ft=Ke.vertexLength,Rt=fe.layoutVertexArray,er=fe.collisionVertexArray,ur=Re.anchorX,vr=Re.anchorY;this._addCollisionDebugVertex(Rt,er,pe,ur,vr,new u(A,F)),this._addCollisionDebugVertex(Rt,er,pe,ur,vr,new u(W,F)),this._addCollisionDebugVertex(Rt,er,pe,ur,vr,new u(W,te)),this._addCollisionDebugVertex(Rt,er,pe,ur,vr,new u(A,te)),Ke.vertexLength+=4;let kr=fe.indexArray;kr.emplaceBack(ft,ft+1),kr.emplaceBack(ft+1,ft+2),kr.emplaceBack(ft+2,ft+3),kr.emplaceBack(ft+3,ft),Ke.primitiveLength+=4}addDebugCollisionBoxes(A,F,W,te){for(let fe=A;fe<F;fe++){let pe=this.collisionBoxArray.get(fe);this.addCollisionDebugVertices(pe.x1,pe.y1,pe.x2,pe.y2,te?this.textCollisionBox:this.iconCollisionBox,pe.anchorPoint,W)}}generateCollisionDebugBuffers(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new hm(Sl,py.members,Te),this.iconCollisionBox=new hm(Sl,py.members,Te);for(let A=0;A<this.symbolInstances.length;A++){let F=this.symbolInstances.get(A);this.addDebugCollisionBoxes(F.textBoxStartIndex,F.textBoxEndIndex,F,!0),this.addDebugCollisionBoxes(F.verticalTextBoxStartIndex,F.verticalTextBoxEndIndex,F,!0),this.addDebugCollisionBoxes(F.iconBoxStartIndex,F.iconBoxEndIndex,F,!1),this.addDebugCollisionBoxes(F.verticalIconBoxStartIndex,F.verticalIconBoxEndIndex,F,!1)}}_deserializeCollisionBoxesForSymbol(A,F,W,te,fe,pe,Re,Ke,ft){let Rt={};for(let er=F;er<W;er++){let ur=A.get(er);Rt.textBox={x1:ur.x1,y1:ur.y1,x2:ur.x2,y2:ur.y2,anchorPointX:ur.anchorPointX,anchorPointY:ur.anchorPointY},Rt.textFeatureIndex=ur.featureIndex;break}for(let er=te;er<fe;er++){let ur=A.get(er);Rt.verticalTextBox={x1:ur.x1,y1:ur.y1,x2:ur.x2,y2:ur.y2,anchorPointX:ur.anchorPointX,anchorPointY:ur.anchorPointY},Rt.verticalTextFeatureIndex=ur.featureIndex;break}for(let er=pe;er<Re;er++){let ur=A.get(er);Rt.iconBox={x1:ur.x1,y1:ur.y1,x2:ur.x2,y2:ur.y2,anchorPointX:ur.anchorPointX,anchorPointY:ur.anchorPointY},Rt.iconFeatureIndex=ur.featureIndex;break}for(let er=Ke;er<ft;er++){let ur=A.get(er);Rt.verticalIconBox={x1:ur.x1,y1:ur.y1,x2:ur.x2,y2:ur.y2,anchorPointX:ur.anchorPointX,anchorPointY:ur.anchorPointY},Rt.verticalIconFeatureIndex=ur.featureIndex;break}return Rt}deserializeCollisionBoxes(A){this.collisionArrays=[];for(let F=0;F<this.symbolInstances.length;F++){let W=this.symbolInstances.get(F);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(A,W.textBoxStartIndex,W.textBoxEndIndex,W.verticalTextBoxStartIndex,W.verticalTextBoxEndIndex,W.iconBoxStartIndex,W.iconBoxEndIndex,W.verticalIconBoxStartIndex,W.verticalIconBoxEndIndex))}}hasTextData(){return this.text.segments.get().length>0}hasIconData(){return this.icon.segments.get().length>0}hasDebugData(){return this.textCollisionBox&&this.iconCollisionBox}hasTextCollisionBoxData(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0}hasIconCollisionBoxData(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0}addIndicesForPlacedSymbol(A,F){let W=A.placedSymbolArray.get(F),te=W.vertexStartIndex+4*W.numGlyphs;for(let fe=W.vertexStartIndex;fe<te;fe+=4)A.indexArray.emplaceBack(fe,fe+1,fe+2),A.indexArray.emplaceBack(fe+1,fe+2,fe+3)}getSortedSymbolIndexes(A){if(this.sortedAngle===A&&this.symbolInstanceIndexes!==void 0)return this.symbolInstanceIndexes;let F=Math.sin(A),W=Math.cos(A),te=[],fe=[],pe=[];for(let Re=0;Re<this.symbolInstances.length;++Re){pe.push(Re);let Ke=this.symbolInstances.get(Re);te.push(0|Math.round(F*Ke.anchorX+W*Ke.anchorY)),fe.push(Ke.featureIndex)}return pe.sort(((Re,Ke)=>te[Re]-te[Ke]||fe[Ke]-fe[Re])),pe}addToSortKeyRanges(A,F){let W=this.sortKeyRanges[this.sortKeyRanges.length-1];W&&W.sortKey===F?W.symbolInstanceEnd=A+1:this.sortKeyRanges.push({sortKey:F,symbolInstanceStart:A,symbolInstanceEnd:A+1})}sortFeatures(A){if(this.sortFeaturesByY&&this.sortedAngle!==A&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(A),this.sortedAngle=A,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(let F of this.symbolInstanceIndexes){let W=this.symbolInstances.get(F);this.featureSortOrder.push(W.featureIndex),[W.rightJustifiedTextSymbolIndex,W.centerJustifiedTextSymbolIndex,W.leftJustifiedTextSymbolIndex].forEach(((te,fe,pe)=>{te>=0&&pe.indexOf(te)===fe&&this.addIndicesForPlacedSymbol(this.text,te)})),W.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,W.verticalPlacedTextSymbolIndex),W.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,W.placedIconSymbolIndex),W.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,W.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let Tf,ib;Fi(\"SymbolBucket\",D1,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),D1.MAX_GLYPHS=65535,D1.addDynamicAttributes=pS;var Yw={get paint(){return ib=ib||new ue({\"icon-opacity\":new oo(ce.paint_symbol[\"icon-opacity\"]),\"icon-color\":new oo(ce.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new oo(ce.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new oo(ce.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new oo(ce.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Va(ce.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Va(ce.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new oo(ce.paint_symbol[\"text-opacity\"]),\"text-color\":new oo(ce.paint_symbol[\"text-color\"],{runtimeType:jt,getOverride:R=>R.textColor,hasOverride:R=>!!R.textColor}),\"text-halo-color\":new oo(ce.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new oo(ce.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new oo(ce.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Va(ce.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Va(ce.paint_symbol[\"text-translate-anchor\"])})},get layout(){return Tf=Tf||new ue({\"symbol-placement\":new Va(ce.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Va(ce.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Va(ce.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new oo(ce.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new Va(ce.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new Va(ce.layout_symbol[\"icon-allow-overlap\"]),\"icon-overlap\":new Va(ce.layout_symbol[\"icon-overlap\"]),\"icon-ignore-placement\":new Va(ce.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Va(ce.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Va(ce.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new oo(ce.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Va(ce.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Va(ce.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new oo(ce.layout_symbol[\"icon-image\"]),\"icon-rotate\":new oo(ce.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new oo(ce.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Va(ce.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new oo(ce.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new oo(ce.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Va(ce.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Va(ce.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Va(ce.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new oo(ce.layout_symbol[\"text-field\"]),\"text-font\":new oo(ce.layout_symbol[\"text-font\"]),\"text-size\":new oo(ce.layout_symbol[\"text-size\"]),\"text-max-width\":new oo(ce.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Va(ce.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new oo(ce.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new oo(ce.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new oo(ce.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new Va(ce.layout_symbol[\"text-variable-anchor\"]),\"text-variable-anchor-offset\":new oo(ce.layout_symbol[\"text-variable-anchor-offset\"]),\"text-anchor\":new oo(ce.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Va(ce.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new Va(ce.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new oo(ce.layout_symbol[\"text-rotate\"]),\"text-padding\":new Va(ce.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Va(ce.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new oo(ce.layout_symbol[\"text-transform\"]),\"text-offset\":new oo(ce.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Va(ce.layout_symbol[\"text-allow-overlap\"]),\"text-overlap\":new Va(ce.layout_symbol[\"text-overlap\"]),\"text-ignore-placement\":new Va(ce.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Va(ce.layout_symbol[\"text-optional\"])})}};class nb{constructor(A){if(A.property.overrides===void 0)throw new Error(\"overrides must be provided to instantiate FormatSectionOverride class\");this.type=A.property.overrides?A.property.overrides.runtimeType:Tt,this.defaultValue=A}evaluate(A){if(A.formattedSection){let F=this.defaultValue.property.overrides;if(F&&F.hasOverride(A.formattedSection))return F.getOverride(A.formattedSection)}return A.feature&&A.featureState?this.defaultValue.evaluate(A.feature,A.featureState):this.defaultValue.property.specification.default}eachChild(A){this.defaultValue.isConstant()||A(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}Fi(\"FormatSectionOverride\",nb,{omit:[\"defaultValue\"]});class yy extends B{constructor(A){super(A,Yw)}recalculate(A,F){if(super.recalculate(A,F),this.layout.get(\"icon-rotation-alignment\")===\"auto\"&&(this.layout._values[\"icon-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-rotation-alignment\")===\"auto\"&&(this.layout._values[\"text-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-pitch-alignment\")===\"auto\"&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")===\"map\"?\"map\":\"viewport\"),this.layout.get(\"icon-pitch-alignment\")===\"auto\"&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),this.layout.get(\"symbol-placement\")===\"point\"){let W=this.layout.get(\"text-writing-mode\");if(W){let te=[];for(let fe of W)te.indexOf(fe)<0&&te.push(fe);this.layout._values[\"text-writing-mode\"]=te}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()}getValueAndResolveTokens(A,F,W,te){let fe=this.layout.get(A).evaluate(F,{},W,te),pe=this._unevaluatedLayout._values[A];return pe.isDataDriven()||Dc(pe.value)||!fe?fe:(function(Re,Ke){return Ke.replace(/{([^{}]+)}/g,((ft,Rt)=>Re&&Rt in Re?String(Re[Rt]):\"\"))})(F.properties,fe)}createBucket(A){return new D1(A)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error(\"Should take a different path in FeatureIndex\")}_setPaintOverrides(){for(let A of Yw.paint.overridableProperties){if(!yy.hasPaintOverride(this.layout,A))continue;let F=this.paint.get(A),W=new nb(F),te=new Eu(W,F.property.specification),fe=null;fe=F.value.kind===\"constant\"||F.value.kind===\"source\"?new bc(\"source\",te):new hu(\"composite\",te,F.value.zoomStops),this.paint._values[A]=new xu(F.property,fe,F.parameters)}}_handleOverridablePaintPropertyUpdate(A,F,W){return!(!this.layout||F.isDataDriven()||W.isDataDriven())&&yy.hasPaintOverride(this.layout,A)}static hasPaintOverride(A,F){let W=A.get(\"text-field\"),te=Yw.paint.properties[F],fe=!1,pe=Re=>{for(let Ke of Re)if(te.overrides&&te.overrides.hasOverride(Ke))return void(fe=!0)};if(W.value.kind===\"constant\"&&W.value.value instanceof ti)pe(W.value.value.sections);else if(W.value.kind===\"source\"){let Re=ft=>{fe||(ft instanceof ua&&Mn(ft.value)===ei?pe(ft.value.sections):ft instanceof Yl?pe(ft.sections):ft.eachChild(Re))},Ke=W.value;Ke._styleExpression&&Re(Ke._styleExpression.expression)}return fe}}let qC;var ab={get paint(){return qC=qC||new ue({\"background-color\":new Va(ce.paint_background[\"background-color\"]),\"background-pattern\":new hc(ce.paint_background[\"background-pattern\"]),\"background-opacity\":new Va(ce.paint_background[\"background-opacity\"])})}};class aq extends B{constructor(A){super(A,ab)}}let yS;var OC={get paint(){return yS=yS||new ue({\"raster-opacity\":new Va(ce.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new Va(ce.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new Va(ce.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new Va(ce.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new Va(ce.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new Va(ce.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new Va(ce.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new Va(ce.paint_raster[\"raster-fade-duration\"])})}};class ob extends B{constructor(A){super(A,OC)}}class _S extends B{constructor(A){super(A,{}),this.onAdd=F=>{this.implementation.onAdd&&this.implementation.onAdd(F,F.painter.context.gl)},this.onRemove=F=>{this.implementation.onRemove&&this.implementation.onRemove(F,F.painter.context.gl)},this.implementation=A}is3D(){return this.implementation.renderingMode===\"3d\"}hasOffscreenPass(){return this.implementation.prerender!==void 0}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error(\"Custom layers cannot be serialized\")}}class xS{constructor(A){this._methodToThrottle=A,this._triggered=!1,typeof MessageChannel!=\"undefined\"&&(this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._methodToThrottle()})}trigger(){this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((()=>{this._triggered=!1,this._methodToThrottle()}),0))}remove(){delete this._channel,this._methodToThrottle=()=>{}}}let bS=63710088e-1;class pg{constructor(A,F){if(isNaN(A)||isNaN(F))throw new Error(`Invalid LngLat object: (${A}, ${F})`);if(this.lng=+A,this.lat=+F,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}wrap(){return new pg(T(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(A){let F=Math.PI/180,W=this.lat*F,te=A.lat*F,fe=Math.sin(W)*Math.sin(te)+Math.cos(W)*Math.cos(te)*Math.cos((A.lng-this.lng)*F);return bS*Math.acos(Math.min(fe,1))}static convert(A){if(A instanceof pg)return A;if(Array.isArray(A)&&(A.length===2||A.length===3))return new pg(Number(A[0]),Number(A[1]));if(!Array.isArray(A)&&typeof A==\"object\"&&A!==null)return new pg(Number(\"lng\"in A?A.lng:A.lon),Number(A.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")}}let F1=2*Math.PI*bS;function BC(R){return F1*Math.cos(R*Math.PI/180)}function Kw(R){return(180+R)/360}function NC(R){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+R*Math.PI/360)))/360}function Jw(R,A){return R/BC(A)}function sb(R){return 360/Math.PI*Math.atan(Math.exp((180-360*R)*Math.PI/180))-90}class lb{constructor(A,F,W=0){this.x=+A,this.y=+F,this.z=+W}static fromLngLat(A,F=0){let W=pg.convert(A);return new lb(Kw(W.lng),NC(W.lat),Jw(F,W.lat))}toLngLat(){return new pg(360*this.x-180,sb(this.y))}toAltitude(){return this.z*BC(sb(this.y))}meterInMercatorCoordinateUnits(){return 1/F1*(A=sb(this.y),1/Math.cos(A*Math.PI/180));var A}}function yp(R,A,F){var W=2*Math.PI*6378137/256/Math.pow(2,F);return[R*W-2*Math.PI*6378137/2,A*W-2*Math.PI*6378137/2]}class wS{constructor(A,F,W){if(!(function(te,fe,pe){return!(te<0||te>25||pe<0||pe>=Math.pow(2,te)||fe<0||fe>=Math.pow(2,te))})(A,F,W))throw new Error(`x=${F}, y=${W}, z=${A} outside of bounds. 0<=x<${Math.pow(2,A)}, 0<=y<${Math.pow(2,A)} 0<=z<=25 `);this.z=A,this.x=F,this.y=W,this.key=ub(0,A,A,F,W)}equals(A){return this.z===A.z&&this.x===A.x&&this.y===A.y}url(A,F,W){let te=(pe=this.y,Re=this.z,Ke=yp(256*(fe=this.x),256*(pe=Math.pow(2,Re)-pe-1),Re),ft=yp(256*(fe+1),256*(pe+1),Re),Ke[0]+\",\"+Ke[1]+\",\"+ft[0]+\",\"+ft[1]);var fe,pe,Re,Ke,ft;let Rt=(function(er,ur,vr){let kr,Dr=\"\";for(let li=er;li>0;li--)kr=1<<li-1,Dr+=(ur&kr?1:0)+(vr&kr?2:0);return Dr})(this.z,this.x,this.y);return A[(this.x+this.y)%A.length].replace(/{prefix}/g,(this.x%16).toString(16)+(this.y%16).toString(16)).replace(/{z}/g,String(this.z)).replace(/{x}/g,String(this.x)).replace(/{y}/g,String(W===\"tms\"?Math.pow(2,this.z)-this.y-1:this.y)).replace(/{ratio}/g,F>1?\"@2x\":\"\").replace(/{quadkey}/g,Rt).replace(/{bbox-epsg-3857}/g,te)}isChildOf(A){let F=this.z-A.z;return F>0&&A.x===this.x>>F&&A.y===this.y>>F}getTilePoint(A){let F=Math.pow(2,this.z);return new u((A.x*F-this.x)*ja,(A.y*F-this.y)*ja)}toString(){return`${this.z}/${this.x}/${this.y}`}}class UC{constructor(A,F){this.wrap=A,this.canonical=F,this.key=ub(A,F.z,F.z,F.x,F.y)}}class $v{constructor(A,F,W,te,fe){if(A<W)throw new Error(`overscaledZ should be >= z; overscaledZ = ${A}; z = ${W}`);this.overscaledZ=A,this.wrap=F,this.canonical=new wS(W,+te,+fe),this.key=ub(F,A,W,te,fe)}clone(){return new $v(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(A){return this.overscaledZ===A.overscaledZ&&this.wrap===A.wrap&&this.canonical.equals(A.canonical)}scaledTo(A){if(A>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${A}; overscaledZ = ${this.overscaledZ}`);let F=this.canonical.z-A;return A>this.canonical.z?new $v(A,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new $v(A,this.wrap,A,this.canonical.x>>F,this.canonical.y>>F)}calculateScaledKey(A,F){if(A>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${A}; overscaledZ = ${this.overscaledZ}`);let W=this.canonical.z-A;return A>this.canonical.z?ub(this.wrap*+F,A,this.canonical.z,this.canonical.x,this.canonical.y):ub(this.wrap*+F,A,A,this.canonical.x>>W,this.canonical.y>>W)}isChildOf(A){if(A.wrap!==this.wrap)return!1;let F=this.canonical.z-A.canonical.z;return A.overscaledZ===0||A.overscaledZ<this.overscaledZ&&A.canonical.x===this.canonical.x>>F&&A.canonical.y===this.canonical.y>>F}children(A){if(this.overscaledZ>=A)return[new $v(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];let F=this.canonical.z+1,W=2*this.canonical.x,te=2*this.canonical.y;return[new $v(F,this.wrap,F,W,te),new $v(F,this.wrap,F,W+1,te),new $v(F,this.wrap,F,W,te+1),new $v(F,this.wrap,F,W+1,te+1)]}isLessThan(A){return this.wrap<A.wrap||!(this.wrap>A.wrap)&&(this.overscaledZ<A.overscaledZ||!(this.overscaledZ>A.overscaledZ)&&(this.canonical.x<A.canonical.x||!(this.canonical.x>A.canonical.x)&&this.canonical.y<A.canonical.y))}wrapped(){return new $v(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)}unwrapTo(A){return new $v(this.overscaledZ,A,this.canonical.z,this.canonical.x,this.canonical.y)}overscaleFactor(){return Math.pow(2,this.overscaledZ-this.canonical.z)}toUnwrapped(){return new UC(this.wrap,this.canonical)}toString(){return`${this.overscaledZ}/${this.canonical.x}/${this.canonical.y}`}getTilePoint(A){return this.canonical.getTilePoint(new lb(A.x-this.wrap,A.y))}}function ub(R,A,F,W,te){(R*=2)<0&&(R=-1*R-1);let fe=1<<F;return(fe*fe*R+fe*te+W).toString(36)+F.toString(36)+A.toString(36)}Fi(\"CanonicalTileID\",wS),Fi(\"OverscaledTileID\",$v,{omit:[\"posMatrix\"]});class VC{constructor(A,F,W,te=1,fe=1,pe=1,Re=0){if(this.uid=A,F.height!==F.width)throw new RangeError(\"DEM tiles must be square\");if(W&&![\"mapbox\",\"terrarium\",\"custom\"].includes(W))return void E(`\"${W}\" is not a valid encoding type. Valid types include \"mapbox\", \"terrarium\" and \"custom\".`);this.stride=F.height;let Ke=this.dim=F.height-2;switch(this.data=new Uint32Array(F.data.buffer),W){case\"terrarium\":this.redFactor=256,this.greenFactor=1,this.blueFactor=1/256,this.baseShift=32768;break;case\"custom\":this.redFactor=te,this.greenFactor=fe,this.blueFactor=pe,this.baseShift=Re;break;default:this.redFactor=6553.6,this.greenFactor=25.6,this.blueFactor=.1,this.baseShift=1e4}for(let ft=0;ft<Ke;ft++)this.data[this._idx(-1,ft)]=this.data[this._idx(0,ft)],this.data[this._idx(Ke,ft)]=this.data[this._idx(Ke-1,ft)],this.data[this._idx(ft,-1)]=this.data[this._idx(ft,0)],this.data[this._idx(ft,Ke)]=this.data[this._idx(ft,Ke-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(Ke,-1)]=this.data[this._idx(Ke-1,0)],this.data[this._idx(-1,Ke)]=this.data[this._idx(0,Ke-1)],this.data[this._idx(Ke,Ke)]=this.data[this._idx(Ke-1,Ke-1)],this.min=Number.MAX_SAFE_INTEGER,this.max=Number.MIN_SAFE_INTEGER;for(let ft=0;ft<Ke;ft++)for(let Rt=0;Rt<Ke;Rt++){let er=this.get(ft,Rt);er>this.max&&(this.max=er),er<this.min&&(this.min=er)}}get(A,F){let W=new Uint8Array(this.data.buffer),te=4*this._idx(A,F);return this.unpack(W[te],W[te+1],W[te+2])}getUnpackVector(){return[this.redFactor,this.greenFactor,this.blueFactor,this.baseShift]}_idx(A,F){if(A<-1||A>=this.dim+1||F<-1||F>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(F+1)*this.stride+(A+1)}unpack(A,F,W){return A*this.redFactor+F*this.greenFactor+W*this.blueFactor-this.baseShift}getPixels(){return new Ki({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(A,F,W){if(this.dim!==A.dim)throw new Error(\"dem dimension mismatch\");let te=F*this.dim,fe=F*this.dim+this.dim,pe=W*this.dim,Re=W*this.dim+this.dim;switch(F){case-1:te=fe-1;break;case 1:fe=te+1}switch(W){case-1:pe=Re-1;break;case 1:Re=pe+1}let Ke=-F*this.dim,ft=-W*this.dim;for(let Rt=pe;Rt<Re;Rt++)for(let er=te;er<fe;er++)this.data[this._idx(er,Rt)]=A.data[this._idx(er+Ke,Rt+ft)]}}Fi(\"DEMData\",VC);class GC{constructor(A){this._stringToNumber={},this._numberToString=[];for(let F=0;F<A.length;F++){let W=A[F];this._stringToNumber[W]=F,this._numberToString[F]=W}}encode(A){return this._stringToNumber[A]}decode(A){if(A>=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${A} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[A]}}class TS{constructor(A,F,W,te,fe){this.type=\"Feature\",this._vectorTileFeature=A,A._z=F,A._x=W,A._y=te,this.properties=A.properties,this.id=fe}get geometry(){return this._geometry===void 0&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry}set geometry(A){this._geometry=A}toJSON(){let A={geometry:this.geometry};for(let F in this)F!==\"_geometry\"&&F!==\"_vectorTileFeature\"&&(A[F]=this[F]);return A}}class _y{constructor(A,F){this.tileID=A,this.x=A.canonical.x,this.y=A.canonical.y,this.z=A.canonical.z,this.grid=new $i(ja,16,0),this.grid3D=new $i(ja,16,0),this.featureIndexArray=new Ts,this.promoteId=F}insert(A,F,W,te,fe,pe){let Re=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(W,te,fe);let Ke=pe?this.grid3D:this.grid;for(let ft=0;ft<F.length;ft++){let Rt=F[ft],er=[1/0,1/0,-1/0,-1/0];for(let ur=0;ur<Rt.length;ur++){let vr=Rt[ur];er[0]=Math.min(er[0],vr.x),er[1]=Math.min(er[1],vr.y),er[2]=Math.max(er[2],vr.x),er[3]=Math.max(er[3],vr.y)}er[0]<ja&&er[1]<ja&&er[2]>=0&&er[3]>=0&&Ke.insert(Re,er[0],er[1],er[2],er[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=new _r.VectorTile(new fS(this.rawTileData)).layers,this.sourceLayerCoder=new GC(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers}query(A,F,W,te){this.loadVTLayers();let fe=A.params||{},pe=ja/A.tileSize/A.scale,Re=Fc(fe.filter),Ke=A.queryGeometry,ft=A.queryPadding*pe,Rt=jC(Ke),er=this.grid.query(Rt.minX-ft,Rt.minY-ft,Rt.maxX+ft,Rt.maxY+ft),ur=jC(A.cameraQueryGeometry),vr=this.grid3D.query(ur.minX-ft,ur.minY-ft,ur.maxX+ft,ur.maxY+ft,((li,pi,vn,zi)=>(function(ln,Tn,pa,ro,Vo){for(let la of ln)if(Tn<=la.x&&pa<=la.y&&ro>=la.x&&Vo>=la.y)return!0;let Xa=[new u(Tn,pa),new u(Tn,Vo),new u(ro,Vo),new u(ro,pa)];if(ln.length>2){for(let la of Xa)if(Ni(ln,la))return!0}for(let la=0;la<ln.length-1;la++)if(qi(ln[la],ln[la+1],Xa))return!0;return!1})(A.cameraQueryGeometry,li-ft,pi-ft,vn+ft,zi+ft)));for(let li of vr)er.push(li);er.sort(oq);let kr={},Dr;for(let li=0;li<er.length;li++){let pi=er[li];if(pi===Dr)continue;Dr=pi;let vn=this.featureIndexArray.get(pi),zi=null;this.loadMatchingFeature(kr,vn.bucketIndex,vn.sourceLayerIndex,vn.featureIndex,Re,fe.layers,fe.availableImages,F,W,te,((ln,Tn,pa)=>(zi||(zi=$s(ln)),Tn.queryIntersectsFeature(Ke,ln,pa,zi,this.z,A.transform,pe,A.pixelPosMatrix))))}return kr}loadMatchingFeature(A,F,W,te,fe,pe,Re,Ke,ft,Rt,er){let ur=this.bucketLayerIDs[F];if(pe&&!(function(li,pi){for(let vn=0;vn<li.length;vn++)if(pi.indexOf(li[vn])>=0)return!0;return!1})(pe,ur))return;let vr=this.sourceLayerCoder.decode(W),kr=this.vtLayers[vr].feature(te);if(fe.needGeometry){let li=Ml(kr,!0);if(!fe.filter(new rs(this.tileID.overscaledZ),li,this.tileID.canonical))return}else if(!fe.filter(new rs(this.tileID.overscaledZ),kr))return;let Dr=this.getId(kr,vr);for(let li=0;li<ur.length;li++){let pi=ur[li];if(pe&&pe.indexOf(pi)<0)continue;let vn=Ke[pi];if(!vn)continue;let zi={};Dr&&Rt&&(zi=Rt.getState(vn.sourceLayer||\"_geojsonTileLayer\",Dr));let ln=L({},ft[pi]);ln.paint=HC(ln.paint,vn.paint,kr,zi,Re),ln.layout=HC(ln.layout,vn.layout,kr,zi,Re);let Tn=!er||er(kr,vn,zi);if(!Tn)continue;let pa=new TS(kr,this.z,this.x,this.y,Dr);pa.layer=ln;let ro=A[pi];ro===void 0&&(ro=A[pi]=[]),ro.push({featureIndex:te,feature:pa,intersectionZ:Tn})}}lookupSymbolFeatures(A,F,W,te,fe,pe,Re,Ke){let ft={};this.loadVTLayers();let Rt=Fc(fe);for(let er of A)this.loadMatchingFeature(ft,W,te,er,Rt,pe,Re,Ke,F);return ft}hasLayer(A){for(let F of this.bucketLayerIDs)for(let W of F)if(A===W)return!0;return!1}getId(A,F){let W=A.id;return this.promoteId&&(W=A.properties[typeof this.promoteId==\"string\"?this.promoteId:this.promoteId[F]],typeof W==\"boolean\"&&(W=Number(W))),W}}function HC(R,A,F,W,te){return C(R,((fe,pe)=>{let Re=A instanceof Ac?A.get(pe):null;return Re&&Re.evaluate?Re.evaluate(F,W,te):Re}))}function jC(R){let A=1/0,F=1/0,W=-1/0,te=-1/0;for(let fe of R)A=Math.min(A,fe.x),F=Math.min(F,fe.y),W=Math.max(W,fe.x),te=Math.max(te,fe.y);return{minX:A,minY:F,maxX:W,maxY:te}}function oq(R,A){return A-R}function WC(R,A,F,W,te){let fe=[];for(let pe=0;pe<R.length;pe++){let Re=R[pe],Ke;for(let ft=0;ft<Re.length-1;ft++){let Rt=Re[ft],er=Re[ft+1];Rt.x<A&&er.x<A||(Rt.x<A?Rt=new u(A,Rt.y+(A-Rt.x)/(er.x-Rt.x)*(er.y-Rt.y))._round():er.x<A&&(er=new u(A,Rt.y+(A-Rt.x)/(er.x-Rt.x)*(er.y-Rt.y))._round()),Rt.y<F&&er.y<F||(Rt.y<F?Rt=new u(Rt.x+(F-Rt.y)/(er.y-Rt.y)*(er.x-Rt.x),F)._round():er.y<F&&(er=new u(Rt.x+(F-Rt.y)/(er.y-Rt.y)*(er.x-Rt.x),F)._round()),Rt.x>=W&&er.x>=W||(Rt.x>=W?Rt=new u(W,Rt.y+(W-Rt.x)/(er.x-Rt.x)*(er.y-Rt.y))._round():er.x>=W&&(er=new u(W,Rt.y+(W-Rt.x)/(er.x-Rt.x)*(er.y-Rt.y))._round()),Rt.y>=te&&er.y>=te||(Rt.y>=te?Rt=new u(Rt.x+(te-Rt.y)/(er.y-Rt.y)*(er.x-Rt.x),te)._round():er.y>=te&&(er=new u(Rt.x+(te-Rt.y)/(er.y-Rt.y)*(er.x-Rt.x),te)._round()),Ke&&Rt.equals(Ke[Ke.length-1])||(Ke=[Rt],fe.push(Ke)),Ke.push(er)))))}}return fe}Fi(\"FeatureIndex\",_y,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});class gg extends u{constructor(A,F,W,te){super(A,F),this.angle=W,te!==void 0&&(this.segment=te)}clone(){return new gg(this.x,this.y,this.angle,this.segment)}}function AS(R,A,F,W,te){if(A.segment===void 0||F===0)return!0;let fe=A,pe=A.segment+1,Re=0;for(;Re>-F/2;){if(pe--,pe<0)return!1;Re-=R[pe].dist(fe),fe=R[pe]}Re+=R[pe].dist(R[pe+1]),pe++;let Ke=[],ft=0;for(;Re<F/2;){let Rt=R[pe],er=R[pe+1];if(!er)return!1;let ur=R[pe-1].angleTo(Rt)-Rt.angleTo(er);for(ur=Math.abs((ur+3*Math.PI)%(2*Math.PI)-Math.PI),Ke.push({distance:Re,angleDelta:ur}),ft+=ur;Re-Ke[0].distance>W;)ft-=Ke.shift().angleDelta;if(ft>te)return!1;pe++,Re+=Rt.dist(er)}return!0}function XC(R){let A=0;for(let 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zi=(ur-er)/pi,ln=Lo.number(Dr.x,li.x,zi),Tn=Lo.number(Dr.y,li.y,zi);if(ln>=0&&ln<Ke&&Tn>=0&&Tn<Ke&&ur-ft>=0&&ur+ft<=Rt){let pa=new gg(ln,Tn,vn,kr);pa._round(),W&&!AS(R,pa,fe,W,te)||vr.push(pa)}}er+=pi}return Re||vr.length||pe||(vr=KC(R,er/2,F,W,te,fe,pe,!0,Ke)),vr}Fi(\"Anchor\",gg);let z1=Md;function JC(R,A,F,W){let te=[],fe=R.image,pe=fe.pixelRatio,Re=fe.paddedRect.w-2*z1,Ke=fe.paddedRect.h-2*z1,ft={x1:R.left,y1:R.top,x2:R.right,y2:R.bottom},Rt=fe.stretchX||[[0,Re]],er=fe.stretchY||[[0,Ke]],ur=(Wn,Ro)=>Wn+Ro[1]-Ro[0],vr=Rt.reduce(ur,0),kr=er.reduce(ur,0),Dr=Re-vr,li=Ke-kr,pi=0,vn=vr,zi=0,ln=kr,Tn=0,pa=Dr,ro=0,Vo=li;if(fe.content&&W){let Wn=fe.content,Ro=Wn[2]-Wn[0],uo=Wn[3]-Wn[1];(fe.textFitWidth||fe.textFitHeight)&&(ft=DC(R)),pi=mg(Rt,0,Wn[0]),zi=mg(er,0,Wn[1]),vn=mg(Rt,Wn[0],Wn[2]),ln=mg(er,Wn[1],Wn[3]),Tn=Wn[0]-pi,ro=Wn[1]-zi,pa=Ro-vn,Vo=uo-ln}let Xa=ft.x1,la=ft.y1,Mo=ft.x2-Xa,fo=ft.y2-la,lo=(Wn,Ro,uo,$o)=>{let 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ld;i.aq=void 0,(ld=i.aq||(i.aq={}))[ld.center=1]=\"center\",ld[ld.left=2]=\"left\",ld[ld.right=3]=\"right\",ld[ld.top=4]=\"top\",ld[ld.bottom=5]=\"bottom\",ld[ld[\"top-left\"]=6]=\"top-left\",ld[ld[\"top-right\"]=7]=\"top-right\",ld[ld[\"bottom-left\"]=8]=\"bottom-left\",ld[ld[\"bottom-right\"]=9]=\"bottom-right\";let vm=7,xy=Number.POSITIVE_INFINITY;function SS(R,A){return A[1]!==xy?(function(F,W,te){let fe=0,pe=0;switch(W=Math.abs(W),te=Math.abs(te),F){case\"top-right\":case\"top-left\":case\"top\":pe=te-vm;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":pe=-te+vm}switch(F){case\"top-right\":case\"bottom-right\":case\"right\":fe=-W;break;case\"top-left\":case\"bottom-left\":case\"left\":fe=W}return[fe,pe]})(R,A[0],A[1]):(function(F,W){let te=0,fe=0;W<0&&(W=0);let pe=W/Math.SQRT2;switch(F){case\"top-right\":case\"top-left\":fe=pe-vm;break;case\"bottom-right\":case\"bottom-left\":fe=-pe+vm;break;case\"bottom\":fe=-W+vm;break;case\"top\":fe=W-vm}switch(F){case\"top-right\":case\"bottom-right\":te=-pe;break;case\"top-left\":case\"bottom-left\":te=pe;break;case\"left\":te=W;break;case\"right\":te=-W}return[te,fe]})(R,A[0])}function QC(R,A,F){var W;let te=R.layout,fe=(W=te.get(\"text-variable-anchor-offset\"))===null||W===void 0?void 0:W.evaluate(A,{},F);if(fe){let Re=fe.values,Ke=[];for(let ft=0;ft<Re.length;ft+=2){let Rt=Ke[ft]=Re[ft],er=Re[ft+1].map((ur=>ur*tu));Rt.startsWith(\"top\")?er[1]-=vm:Rt.startsWith(\"bottom\")&&(er[1]+=vm),Ke[ft+1]=er}return new Hi(Ke)}let pe=te.get(\"text-variable-anchor\");if(pe){let Re;Re=R._unevaluatedLayout.getValue(\"text-radial-offset\")!==void 0?[te.get(\"text-radial-offset\").evaluate(A,{},F)*tu,xy]:te.get(\"text-offset\").evaluate(A,{},F).map((ft=>ft*tu));let Ke=[];for(let ft of pe)Ke.push(ft,SS(ft,Re));return 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Ju=Wn.get(\"icon-padding\").evaluate(Ro,{},uo),Ou=Ju&&Ju.values;return[Ou[0]*$o,Ou[1]*$o,Ou[2]*$o,Ou[3]*$o]})(ur,A,Rt,R.tilePixelRatio),pa=ur.get(\"text-max-angle\")/180*Math.PI,ro=ur.get(\"text-rotation-alignment\")!==\"viewport\"&&ur.get(\"symbol-placement\")!==\"point\",Vo=ur.get(\"icon-rotation-alignment\")===\"map\"&&ur.get(\"symbol-placement\")!==\"point\",Xa=ur.get(\"symbol-placement\"),la=zi/2,Mo=ur.get(\"icon-text-fit\"),fo;W&&Mo!==\"none\"&&(R.allowVerticalPlacement&&F.vertical&&(fo=FC(W,F.vertical,Mo,ur.get(\"icon-text-fit-padding\"),vr,Dr)),kr&&(W=FC(W,kr,Mo,ur.get(\"icon-text-fit-padding\"),vr,Dr)));let lo=(Wn,Ro)=>{Ro.x<0||Ro.x>=ja||Ro.y<0||Ro.y>=ja||(function(uo,$o,Ju,Ou,kh,Qv,ud,Ch,Gd,Hd,jd,Af,Lh,Ed,cd,Yh,Df,Fv,uv,ru,pc,$u,zv,hf,B1){let p0=uo.addToLineVertexArray($o,Ju),Gp,ep,Gc,Yf,tp=0,yg=0,cv=0,N1=0,PS=-1,r3=-1,g0={},by=xi(\"\");if(uo.allowVerticalPlacement&&Ou.vertical){let kd=Ch.layout.get(\"text-rotate\").evaluate(pc,{},hf)+90;Gc=new 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this._layers[et];this.familiesBySource={};let We=i.bk(Object.values(this._layerConfigs),this.keyCache);for(let et of We){let xt=et.map((Ir=>this._layers[Ir.id])),At=xt[0];if(At.visibility===\"none\")continue;let Kt=At.source||\"\",Qt=this.familiesBySource[Kt];Qt||(Qt=this.familiesBySource[Kt]={});let Mr=At.sourceLayer||\"_geojsonTileLayer\",Gr=Qt[Mr];Gr||(Gr=Qt[Mr]=[]),Gr.push(xt)}}}class o{constructor(Ge){let Je={},We=[];for(let Kt in Ge){let Qt=Ge[Kt],Mr=Je[Kt]={};for(let Gr in Qt){let Ir=Qt[+Gr];if(!Ir||Ir.bitmap.width===0||Ir.bitmap.height===0)continue;let Yr={x:0,y:0,w:Ir.bitmap.width+2,h:Ir.bitmap.height+2};We.push(Yr),Mr[Gr]={rect:Yr,metrics:Ir.metrics}}}let{w:et,h:xt}=i.p(We),At=new i.o({width:et||1,height:xt||1});for(let Kt in Ge){let Qt=Ge[Kt];for(let Mr in Qt){let Gr=Qt[+Mr];if(!Gr||Gr.bitmap.width===0||Gr.bitmap.height===0)continue;let Ir=Je[Kt][Mr].rect;i.o.copy(Gr.bitmap,At,{x:0,y:0},{x:Ir.x+1,y:Ir.y+1},Gr.bitmap)}}this.image=At,this.positions=Je}}i.bl(\"GlyphAtlas\",o);class s{constructor(Ge){this.tileID=new i.S(Ge.tileID.overscaledZ,Ge.tileID.wrap,Ge.tileID.canonical.z,Ge.tileID.canonical.x,Ge.tileID.canonical.y),this.uid=Ge.uid,this.zoom=Ge.zoom,this.pixelRatio=Ge.pixelRatio,this.tileSize=Ge.tileSize,this.source=Ge.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=Ge.showCollisionBoxes,this.collectResourceTiming=!!Ge.collectResourceTiming,this.returnDependencies=!!Ge.returnDependencies,this.promoteId=Ge.promoteId,this.inFlightDependencies=[]}parse(Ge,Je,We,et){return i._(this,void 0,void 0,(function*(){this.status=\"parsing\",this.data=Ge,this.collisionBoxArray=new i.a5;let xt=new i.bm(Object.keys(Ge.layers).sort()),At=new i.bn(this.tileID,this.promoteId);At.bucketLayerIDs=[];let Kt={},Qt={featureIndex:At,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:We},Mr=Je.familiesBySource[this.source];for(let Qn in Mr){let Vi=Ge.layers[Qn];if(!Vi)continue;Vi.version===1&&i.w(`Vector tile source \"${this.source}\" layer \"${Qn}\" does not use vector tile spec v2 and therefore may have some rendering errors.`);let Jn=xt.encode(Qn),Yn=[];for(let Gt=0;Gt<Vi.length;Gt++){let wt=Vi.feature(Gt),tr=At.getId(wt,Qn);Yn.push({feature:wt,id:tr,index:Gt,sourceLayerIndex:Jn})}for(let Gt of Mr[Qn]){let wt=Gt[0];wt.source!==this.source&&i.w(`layer.source = ${wt.source} does not equal this.source = ${this.source}`),wt.minzoom&&this.zoom<Math.floor(wt.minzoom)||wt.maxzoom&&this.zoom>=wt.maxzoom||wt.visibility!==\"none\"&&(l(Gt,this.zoom,We),(Kt[wt.id]=wt.createBucket({index:At.bucketLayerIDs.length,layers:Gt,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:Jn,sourceID:this.source})).populate(Yn,Qt,this.tileID.canonical),At.bucketLayerIDs.push(Gt.map((tr=>tr.id))))}}let Gr=i.aF(Qt.glyphDependencies,(Qn=>Object.keys(Qn).map(Number)));this.inFlightDependencies.forEach((Qn=>Qn==null?void 0:Qn.abort())),this.inFlightDependencies=[];let Ir=Promise.resolve({});if(Object.keys(Gr).length){let Qn=new AbortController;this.inFlightDependencies.push(Qn),Ir=et.sendAsync({type:\"GG\",data:{stacks:Gr,source:this.source,tileID:this.tileID,type:\"glyphs\"}},Qn)}let Yr=Object.keys(Qt.iconDependencies),_i=Promise.resolve({});if(Yr.length){let Qn=new AbortController;this.inFlightDependencies.push(Qn),_i=et.sendAsync({type:\"GI\",data:{icons:Yr,source:this.source,tileID:this.tileID,type:\"icons\"}},Qn)}let Pi=Object.keys(Qt.patternDependencies),ai=Promise.resolve({});if(Pi.length){let Qn=new AbortController;this.inFlightDependencies.push(Qn),ai=et.sendAsync({type:\"GI\",data:{icons:Pi,source:this.source,tileID:this.tileID,type:\"patterns\"}},Qn)}let[mi,un,Fn]=yield Promise.all([Ir,_i,ai]),An=new o(mi),Hn=new i.bo(un,Fn);for(let Qn in Kt){let Vi=Kt[Qn];Vi instanceof i.a6?(l(Vi.layers,this.zoom,We),i.bp({bucket:Vi,glyphMap:mi,glyphPositions:An.positions,imageMap:un,imagePositions:Hn.iconPositions,showCollisionBoxes:this.showCollisionBoxes,canonical:this.tileID.canonical})):Vi.hasPattern&&(Vi instanceof i.bq||Vi instanceof i.br||Vi instanceof i.bs)&&(l(Vi.layers,this.zoom,We),Vi.addFeatures(Qt,this.tileID.canonical,Hn.patternPositions))}return this.status=\"done\",{buckets:Object.values(Kt).filter((Qn=>!Qn.isEmpty())),featureIndex:At,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:An.image,imageAtlas:Hn,glyphMap:this.returnDependencies?mi:null,iconMap:this.returnDependencies?un:null,glyphPositions:this.returnDependencies?An.positions:null}}))}}function l(gt,Ge,Je){let We=new i.z(Ge);for(let et of gt)et.recalculate(We,Je)}class u{constructor(Ge,Je,We){this.actor=Ge,this.layerIndex=Je,this.availableImages=We,this.fetching={},this.loading={},this.loaded={}}loadVectorTile(Ge,Je){return i._(this,void 0,void 0,(function*(){let We=yield i.l(Ge.request,Je);try{return{vectorTile:new i.bt.VectorTile(new i.bu(We.data)),rawData:We.data,cacheControl:We.cacheControl,expires:We.expires}}catch(et){let xt=new Uint8Array(We.data),At=`Unable to parse the tile at ${Ge.request.url}, `;throw At+=xt[0]===31&&xt[1]===139?\"please make sure the data is not gzipped and that you have configured the relevant header in the server\":`got error: ${et.message}`,new Error(At)}}))}loadTile(Ge){return i._(this,void 0,void 0,(function*(){let Je=Ge.uid,We=!!(Ge&&Ge.request&&Ge.request.collectResourceTiming)&&new i.bv(Ge.request),et=new s(Ge);this.loading[Je]=et;let xt=new AbortController;et.abort=xt;try{let At=yield this.loadVectorTile(Ge,xt);if(delete this.loading[Je],!At)return null;let Kt=At.rawData,Qt={};At.expires&&(Qt.expires=At.expires),At.cacheControl&&(Qt.cacheControl=At.cacheControl);let Mr={};if(We){let Ir=We.finish();Ir&&(Mr.resourceTiming=JSON.parse(JSON.stringify(Ir)))}et.vectorTile=At.vectorTile;let Gr=et.parse(At.vectorTile,this.layerIndex,this.availableImages,this.actor);this.loaded[Je]=et,this.fetching[Je]={rawTileData:Kt,cacheControl:Qt,resourceTiming:Mr};try{let Ir=yield Gr;return i.e({rawTileData:Kt.slice(0)},Ir,Qt,Mr)}finally{delete this.fetching[Je]}}catch(At){throw delete this.loading[Je],et.status=\"done\",this.loaded[Je]=et,At}}))}reloadTile(Ge){return i._(this,void 0,void 0,(function*(){let Je=Ge.uid;if(!this.loaded||!this.loaded[Je])throw new Error(\"Should not be trying to reload a tile that was never loaded or has been removed\");let We=this.loaded[Je];if(We.showCollisionBoxes=Ge.showCollisionBoxes,We.status===\"parsing\"){let et=yield We.parse(We.vectorTile,this.layerIndex,this.availableImages,this.actor),xt;if(this.fetching[Je]){let{rawTileData:At,cacheControl:Kt,resourceTiming:Qt}=this.fetching[Je];delete this.fetching[Je],xt=i.e({rawTileData:At.slice(0)},et,Kt,Qt)}else xt=et;return xt}if(We.status===\"done\"&&We.vectorTile)return We.parse(We.vectorTile,this.layerIndex,this.availableImages,this.actor)}))}abortTile(Ge){return i._(this,void 0,void 0,(function*(){let Je=this.loading,We=Ge.uid;Je&&Je[We]&&Je[We].abort&&(Je[We].abort.abort(),delete Je[We])}))}removeTile(Ge){return i._(this,void 0,void 0,(function*(){this.loaded&&this.loaded[Ge.uid]&&delete this.loaded[Ge.uid]}))}}class c{constructor(){this.loaded={}}loadTile(Ge){return i._(this,void 0,void 0,(function*(){let{uid:Je,encoding:We,rawImageData:et,redFactor:xt,greenFactor:At,blueFactor:Kt,baseShift:Qt}=Ge,Mr=et.width+2,Gr=et.height+2,Ir=i.b(et)?new i.R({width:Mr,height:Gr},yield i.bw(et,-1,-1,Mr,Gr)):et,Yr=new i.bx(Je,Ir,We,xt,At,Kt,Qt);return this.loaded=this.loaded||{},this.loaded[Je]=Yr,Yr}))}removeTile(Ge){let Je=this.loaded,We=Ge.uid;Je&&Je[We]&&delete Je[We]}}function f(gt,Ge){if(gt.length!==0){h(gt[0],Ge);for(var Je=1;Je<gt.length;Je++)h(gt[Je],!Ge)}}function h(gt,Ge){for(var Je=0,We=0,et=0,xt=gt.length,At=xt-1;et<xt;At=et++){var Kt=(gt[et][0]-gt[At][0])*(gt[At][1]+gt[et][1]),Qt=Je+Kt;We+=Math.abs(Je)>=Math.abs(Kt)?Je-Qt+Kt:Kt-Qt+Je,Je=Qt}Je+We>=0!=!!Ge&&gt.reverse()}var d=i.by((function gt(Ge,Je){var We,et=Ge&&Ge.type;if(et===\"FeatureCollection\")for(We=0;We<Ge.features.length;We++)gt(Ge.features[We],Je);else if(et===\"GeometryCollection\")for(We=0;We<Ge.geometries.length;We++)gt(Ge.geometries[We],Je);else if(et===\"Feature\")gt(Ge.geometry,Je);else if(et===\"Polygon\")f(Ge.coordinates,Je);else if(et===\"MultiPolygon\")for(We=0;We<Ge.coordinates.length;We++)f(Ge.coordinates[We],Je);return Ge}));let v=i.bt.VectorTileFeature.prototype.toGeoJSON;var m={exports:{}},b=i.bz,p=i.bt.VectorTileFeature,k=M;function M(gt,Ge){this.options=Ge||{},this.features=gt,this.length=gt.length}function T(gt,Ge){this.id=typeof gt.id==\"number\"?gt.id:void 0,this.type=gt.type,this.rawGeometry=gt.type===1?[gt.geometry]:gt.geometry,this.properties=gt.tags,this.extent=Ge||4096}M.prototype.feature=function(gt){return new T(this.features[gt],this.options.extent)},T.prototype.loadGeometry=function(){var gt=this.rawGeometry;this.geometry=[];for(var Ge=0;Ge<gt.length;Ge++){for(var Je=gt[Ge],We=[],et=0;et<Je.length;et++)We.push(new b(Je[et][0],Je[et][1]));this.geometry.push(We)}return this.geometry},T.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var gt=this.geometry,Ge=1/0,Je=-1/0,We=1/0,et=-1/0,xt=0;xt<gt.length;xt++)for(var At=gt[xt],Kt=0;Kt<At.length;Kt++){var Qt=At[Kt];Ge=Math.min(Ge,Qt.x),Je=Math.max(Je,Qt.x),We=Math.min(We,Qt.y),et=Math.max(et,Qt.y)}return[Ge,We,Je,et]},T.prototype.toGeoJSON=p.prototype.toGeoJSON;var L=i.bA,x=k;function C(gt){var Ge=new L;return(function(Je,We){for(var et in Je.layers)We.writeMessage(3,S,Je.layers[et])})(gt,Ge),Ge.finish()}function S(gt,Ge){var Je;Ge.writeVarintField(15,gt.version||1),Ge.writeStringField(1,gt.name||\"\"),Ge.writeVarintField(5,gt.extent||4096);var We={keys:[],values:[],keycache:{},valuecache:{}};for(Je=0;Je<gt.length;Je++)We.feature=gt.feature(Je),Ge.writeMessage(2,g,We);var et=We.keys;for(Je=0;Je<et.length;Je++)Ge.writeStringField(3,et[Je]);var xt=We.values;for(Je=0;Je<xt.length;Je++)Ge.writeMessage(4,V,xt[Je])}function g(gt,Ge){var Je=gt.feature;Je.id!==void 0&&Ge.writeVarintField(1,Je.id),Ge.writeMessage(2,P,gt),Ge.writeVarintField(3,Je.type),Ge.writeMessage(4,q,Je)}function P(gt,Ge){var Je=gt.feature,We=gt.keys,et=gt.values,xt=gt.keycache,At=gt.valuecache;for(var Kt in Je.properties){var Qt=Je.properties[Kt],Mr=xt[Kt];if(Qt!==null){Mr===void 0&&(We.push(Kt),xt[Kt]=Mr=We.length-1),Ge.writeVarint(Mr);var Gr=typeof Qt;Gr!==\"string\"&&Gr!==\"boolean\"&&Gr!==\"number\"&&(Qt=JSON.stringify(Qt));var Ir=Gr+\":\"+Qt,Yr=At[Ir];Yr===void 0&&(et.push(Qt),At[Ir]=Yr=et.length-1),Ge.writeVarint(Yr)}}}function E(gt,Ge){return(Ge<<3)+(7&gt)}function z(gt){return gt<<1^gt>>31}function q(gt,Ge){for(var Je=gt.loadGeometry(),We=gt.type,et=0,xt=0,At=Je.length,Kt=0;Kt<At;Kt++){var Qt=Je[Kt],Mr=1;We===1&&(Mr=Qt.length),Ge.writeVarint(E(1,Mr));for(var Gr=We===3?Qt.length-1:Qt.length,Ir=0;Ir<Gr;Ir++){Ir===1&&We!==1&&Ge.writeVarint(E(2,Gr-1));var Yr=Qt[Ir].x-et,_i=Qt[Ir].y-xt;Ge.writeVarint(z(Yr)),Ge.writeVarint(z(_i)),et+=Yr,xt+=_i}We===3&&Ge.writeVarint(E(7,1))}}function V(gt,Ge){var Je=typeof gt;Je===\"string\"?Ge.writeStringField(1,gt):Je===\"boolean\"?Ge.writeBooleanField(7,gt):Je===\"number\"&&(gt%1!=0?Ge.writeDoubleField(3,gt):gt<0?Ge.writeSVarintField(6,gt):Ge.writeVarintField(5,gt))}m.exports=C,m.exports.fromVectorTileJs=C,m.exports.fromGeojsonVt=function(gt,Ge){Ge=Ge||{};var Je={};for(var We in gt)Je[We]=new x(gt[We].features,Ge),Je[We].name=We,Je[We].version=Ge.version,Je[We].extent=Ge.extent;return C({layers:Je})},m.exports.GeoJSONWrapper=x;var G=i.by(m.exports);let Z={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:gt=>gt},j=Math.fround||(N=new Float32Array(1),gt=>(N[0]=+gt,N[0]));var N;let H=3,re=5,ae=6;class _e{constructor(Ge){this.options=Object.assign(Object.create(Z),Ge),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[]}load(Ge){let{log:Je,minZoom:We,maxZoom:et}=this.options;Je&&console.time(\"total time\");let xt=`prepare ${Ge.length} points`;Je&&console.time(xt),this.points=Ge;let At=[];for(let Qt=0;Qt<Ge.length;Qt++){let Mr=Ge[Qt];if(!Mr.geometry)continue;let[Gr,Ir]=Mr.geometry.coordinates,Yr=j(ge(Gr)),_i=j(ie(Ir));At.push(Yr,_i,1/0,Qt,-1,1),this.options.reduce&&At.push(0)}let Kt=this.trees[et+1]=this._createTree(At);Je&&console.timeEnd(xt);for(let Qt=et;Qt>=We;Qt--){let Mr=+Date.now();Kt=this.trees[Qt]=this._createTree(this._cluster(Kt,Qt)),Je&&console.log(\"z%d: %d clusters in %dms\",Qt,Kt.numItems,+Date.now()-Mr)}return Je&&console.timeEnd(\"total time\"),this}getClusters(Ge,Je){let We=((Ge[0]+180)%360+360)%360-180,et=Math.max(-90,Math.min(90,Ge[1])),xt=Ge[2]===180?180:((Ge[2]+180)%360+360)%360-180,At=Math.max(-90,Math.min(90,Ge[3]));if(Ge[2]-Ge[0]>=360)We=-180,xt=180;else if(We>xt){let Ir=this.getClusters([We,et,180,At],Je),Yr=this.getClusters([-180,et,xt,At],Je);return Ir.concat(Yr)}let Kt=this.trees[this._limitZoom(Je)],Qt=Kt.range(ge(We),ie(At),ge(xt),ie(et)),Mr=Kt.data,Gr=[];for(let Ir of Qt){let Yr=this.stride*Ir;Gr.push(Mr[Yr+re]>1?Ee(Mr,Yr,this.clusterProps):this.points[Mr[Yr+H]])}return Gr}getChildren(Ge){let Je=this._getOriginId(Ge),We=this._getOriginZoom(Ge),et=\"No cluster with the specified id.\",xt=this.trees[We];if(!xt)throw new Error(et);let At=xt.data;if(Je*this.stride>=At.length)throw new Error(et);let Kt=this.options.radius/(this.options.extent*Math.pow(2,We-1)),Qt=xt.within(At[Je*this.stride],At[Je*this.stride+1],Kt),Mr=[];for(let Gr of Qt){let Ir=Gr*this.stride;At[Ir+4]===Ge&&Mr.push(At[Ir+re]>1?Ee(At,Ir,this.clusterProps):this.points[At[Ir+H]])}if(Mr.length===0)throw new Error(et);return Mr}getLeaves(Ge,Je,We){let et=[];return this._appendLeaves(et,Ge,Je=Je||10,We=We||0,0),et}getTile(Ge,Je,We){let et=this.trees[this._limitZoom(Ge)],xt=Math.pow(2,Ge),{extent:At,radius:Kt}=this.options,Qt=Kt/At,Mr=(We-Qt)/xt,Gr=(We+1+Qt)/xt,Ir={features:[]};return this._addTileFeatures(et.range((Je-Qt)/xt,Mr,(Je+1+Qt)/xt,Gr),et.data,Je,We,xt,Ir),Je===0&&this._addTileFeatures(et.range(1-Qt/xt,Mr,1,Gr),et.data,xt,We,xt,Ir),Je===xt-1&&this._addTileFeatures(et.range(0,Mr,Qt/xt,Gr),et.data,-1,We,xt,Ir),Ir.features.length?Ir:null}getClusterExpansionZoom(Ge){let Je=this._getOriginZoom(Ge)-1;for(;Je<=this.options.maxZoom;){let We=this.getChildren(Ge);if(Je++,We.length!==1)break;Ge=We[0].properties.cluster_id}return Je}_appendLeaves(Ge,Je,We,et,xt){let At=this.getChildren(Je);for(let Kt of At){let Qt=Kt.properties;if(Qt&&Qt.cluster?xt+Qt.point_count<=et?xt+=Qt.point_count:xt=this._appendLeaves(Ge,Qt.cluster_id,We,et,xt):xt<et?xt++:Ge.push(Kt),Ge.length===We)break}return xt}_createTree(Ge){let Je=new i.av(Ge.length/this.stride|0,this.options.nodeSize,Float32Array);for(let We=0;We<Ge.length;We+=this.stride)Je.add(Ge[We],Ge[We+1]);return Je.finish(),Je.data=Ge,Je}_addTileFeatures(Ge,Je,We,et,xt,At){for(let Kt of Ge){let Qt=Kt*this.stride,Mr=Je[Qt+re]>1,Gr,Ir,Yr;if(Mr)Gr=Ce(Je,Qt,this.clusterProps),Ir=Je[Qt],Yr=Je[Qt+1];else{let ai=this.points[Je[Qt+H]];Gr=ai.properties;let[mi,un]=ai.geometry.coordinates;Ir=ge(mi),Yr=ie(un)}let _i={type:1,geometry:[[Math.round(this.options.extent*(Ir*xt-We)),Math.round(this.options.extent*(Yr*xt-et))]],tags:Gr},Pi;Pi=Mr||this.options.generateId?Je[Qt+H]:this.points[Je[Qt+H]].id,Pi!==void 0&&(_i.id=Pi),At.features.push(_i)}}_limitZoom(Ge){return Math.max(this.options.minZoom,Math.min(Math.floor(+Ge),this.options.maxZoom+1))}_cluster(Ge,Je){let{radius:We,extent:et,reduce:xt,minPoints:At}=this.options,Kt=We/(et*Math.pow(2,Je)),Qt=Ge.data,Mr=[],Gr=this.stride;for(let Ir=0;Ir<Qt.length;Ir+=Gr){if(Qt[Ir+2]<=Je)continue;Qt[Ir+2]=Je;let Yr=Qt[Ir],_i=Qt[Ir+1],Pi=Ge.within(Qt[Ir],Qt[Ir+1],Kt),ai=Qt[Ir+re],mi=ai;for(let un of Pi){let Fn=un*Gr;Qt[Fn+2]>Je&&(mi+=Qt[Fn+re])}if(mi>ai&&mi>=At){let un,Fn=Yr*ai,An=_i*ai,Hn=-1,Qn=((Ir/Gr|0)<<5)+(Je+1)+this.points.length;for(let Vi of Pi){let Jn=Vi*Gr;if(Qt[Jn+2]<=Je)continue;Qt[Jn+2]=Je;let Yn=Qt[Jn+re];Fn+=Qt[Jn]*Yn,An+=Qt[Jn+1]*Yn,Qt[Jn+4]=Qn,xt&&(un||(un=this._map(Qt,Ir,!0),Hn=this.clusterProps.length,this.clusterProps.push(un)),xt(un,this._map(Qt,Jn)))}Qt[Ir+4]=Qn,Mr.push(Fn/mi,An/mi,1/0,Qn,-1,mi),xt&&Mr.push(Hn)}else{for(let un=0;un<Gr;un++)Mr.push(Qt[Ir+un]);if(mi>1)for(let un of Pi){let Fn=un*Gr;if(!(Qt[Fn+2]<=Je)){Qt[Fn+2]=Je;for(let An=0;An<Gr;An++)Mr.push(Qt[Fn+An])}}}}return Mr}_getOriginId(Ge){return Ge-this.points.length>>5}_getOriginZoom(Ge){return(Ge-this.points.length)%32}_map(Ge,Je,We){if(Ge[Je+re]>1){let At=this.clusterProps[Ge[Je+ae]];return We?Object.assign({},At):At}let et=this.points[Ge[Je+H]].properties,xt=this.options.map(et);return We&&xt===et?Object.assign({},xt):xt}}function Ee(gt,Ge,Je){return{type:\"Feature\",id:gt[Ge+H],properties:Ce(gt,Ge,Je),geometry:{type:\"Point\",coordinates:[(We=gt[Ge],360*(We-.5)),Se(gt[Ge+1])]}};var We}function Ce(gt,Ge,Je){let We=gt[Ge+re],et=We>=1e4?`${Math.round(We/1e3)}k`:We>=1e3?Math.round(We/100)/10+\"k\":We,xt=gt[Ge+ae],At=xt===-1?{}:Object.assign({},Je[xt]);return Object.assign(At,{cluster:!0,cluster_id:gt[Ge+H],point_count:We,point_count_abbreviated:et})}function ge(gt){return gt/360+.5}function ie(gt){let Ge=Math.sin(gt*Math.PI/180),Je=.5-.25*Math.log((1+Ge)/(1-Ge))/Math.PI;return Je<0?0:Je>1?1:Je}function Se(gt){let Ge=(180-360*gt)*Math.PI/180;return 360*Math.atan(Math.exp(Ge))/Math.PI-90}function ke(gt,Ge,Je,We){let et=We,xt=Ge+(Je-Ge>>1),At,Kt=Je-Ge,Qt=gt[Ge],Mr=gt[Ge+1],Gr=gt[Je],Ir=gt[Je+1];for(let Yr=Ge+3;Yr<Je;Yr+=3){let _i=be(gt[Yr],gt[Yr+1],Qt,Mr,Gr,Ir);if(_i>et)At=Yr,et=_i;else if(_i===et){let Pi=Math.abs(Yr-xt);Pi<Kt&&(At=Yr,Kt=Pi)}}et>We&&(At-Ge>3&&ke(gt,Ge,At,We),gt[At+2]=et,Je-At>3&&ke(gt,At,Je,We))}function be(gt,Ge,Je,We,et,xt){let At=et-Je,Kt=xt-We;if(At!==0||Kt!==0){let Qt=((gt-Je)*At+(Ge-We)*Kt)/(At*At+Kt*Kt);Qt>1?(Je=et,We=xt):Qt>0&&(Je+=At*Qt,We+=Kt*Qt)}return At=gt-Je,Kt=Ge-We,At*At+Kt*Kt}function Be(gt,Ge,Je,We){let et={id:gt==null?null:gt,type:Ge,geometry:Je,tags:We,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};if(Ge===\"Point\"||Ge===\"MultiPoint\"||Ge===\"LineString\")Le(et,Je);else if(Ge===\"Polygon\")Le(et,Je[0]);else if(Ge===\"MultiLineString\")for(let xt of Je)Le(et,xt);else if(Ge===\"MultiPolygon\")for(let xt of Je)Le(et,xt[0]);return et}function Le(gt,Ge){for(let Je=0;Je<Ge.length;Je+=3)gt.minX=Math.min(gt.minX,Ge[Je]),gt.minY=Math.min(gt.minY,Ge[Je+1]),gt.maxX=Math.max(gt.maxX,Ge[Je]),gt.maxY=Math.max(gt.maxY,Ge[Je+1])}function me(gt,Ge,Je,We){if(!Ge.geometry)return;let et=Ge.geometry.coordinates;if(et&&et.length===0)return;let xt=Ge.geometry.type,At=Math.pow(Je.tolerance/((1<<Je.maxZoom)*Je.extent),2),Kt=[],Qt=Ge.id;if(Je.promoteId?Qt=Ge.properties[Je.promoteId]:Je.generateId&&(Qt=We||0),xt===\"Point\")Pe(et,Kt);else if(xt===\"MultiPoint\")for(let Mr of et)Pe(Mr,Kt);else if(xt===\"LineString\")ce(et,Kt,At,!1);else if(xt===\"MultiLineString\"){if(Je.lineMetrics){for(let Mr of et)Kt=[],ce(Mr,Kt,At,!1),gt.push(Be(Qt,\"LineString\",Kt,Ge.properties));return}He(et,Kt,At,!1)}else if(xt===\"Polygon\")He(et,Kt,At,!0);else{if(xt!==\"MultiPolygon\"){if(xt===\"GeometryCollection\"){for(let Mr of Ge.geometry.geometries)me(gt,{id:Qt,geometry:Mr,properties:Ge.properties},Je,We);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(let Mr of et){let Gr=[];He(Mr,Gr,At,!0),Kt.push(Gr)}}gt.push(Be(Qt,xt,Kt,Ge.properties))}function Pe(gt,Ge){Ge.push(lt(gt[0]),mt(gt[1]),0)}function ce(gt,Ge,Je,We){let et,xt,At=0;for(let Qt=0;Qt<gt.length;Qt++){let Mr=lt(gt[Qt][0]),Gr=mt(gt[Qt][1]);Ge.push(Mr,Gr,0),Qt>0&&(At+=We?(et*Gr-Mr*xt)/2:Math.sqrt(Math.pow(Mr-et,2)+Math.pow(Gr-xt,2))),et=Mr,xt=Gr}let Kt=Ge.length-3;Ge[2]=1,ke(Ge,0,Kt,Je),Ge[Kt+2]=1,Ge.size=Math.abs(At),Ge.start=0,Ge.end=Ge.size}function He(gt,Ge,Je,We){for(let et=0;et<gt.length;et++){let xt=[];ce(gt[et],xt,Je,We),Ge.push(xt)}}function lt(gt){return gt/360+.5}function mt(gt){let Ge=Math.sin(gt*Math.PI/180),Je=.5-.25*Math.log((1+Ge)/(1-Ge))/Math.PI;return Je<0?0:Je>1?1:Je}function Ht(gt,Ge,Je,We,et,xt,At,Kt){if(We/=Ge,xt>=(Je/=Ge)&&At<We)return gt;if(At<Je||xt>=We)return null;let Qt=[];for(let Mr of gt){let Gr=Mr.geometry,Ir=Mr.type,Yr=et===0?Mr.minX:Mr.minY,_i=et===0?Mr.maxX:Mr.maxY;if(Yr>=Je&&_i<We){Qt.push(Mr);continue}if(_i<Je||Yr>=We)continue;let Pi=[];if(Ir===\"Point\"||Ir===\"MultiPoint\")at(Gr,Pi,Je,We,et);else if(Ir===\"LineString\")ct(Gr,Pi,Je,We,et,!1,Kt.lineMetrics);else if(Ir===\"MultiLineString\")Vt(Gr,Pi,Je,We,et,!1);else if(Ir===\"Polygon\")Vt(Gr,Pi,Je,We,et,!0);else if(Ir===\"MultiPolygon\")for(let ai of Gr){let mi=[];Vt(ai,mi,Je,We,et,!0),mi.length&&Pi.push(mi)}if(Pi.length){if(Kt.lineMetrics&&Ir===\"LineString\"){for(let ai of Pi)Qt.push(Be(Mr.id,Ir,ai,Mr.tags));continue}Ir!==\"LineString\"&&Ir!==\"MultiLineString\"||(Pi.length===1?(Ir=\"LineString\",Pi=Pi[0]):Ir=\"MultiLineString\"),Ir!==\"Point\"&&Ir!==\"MultiPoint\"||(Ir=Pi.length===3?\"Point\":\"MultiPoint\"),Qt.push(Be(Mr.id,Ir,Pi,Mr.tags))}}return Qt.length?Qt:null}function at(gt,Ge,Je,We,et){for(let xt=0;xt<gt.length;xt+=3){let At=gt[xt+et];At>=Je&&At<=We&&rr(Ge,gt[xt],gt[xt+1],gt[xt+2])}}function ct(gt,Ge,Je,We,et,xt,At){let Kt=ar(gt),Qt=et===0?tt:je,Mr,Gr,Ir=gt.start;for(let mi=0;mi<gt.length-3;mi+=3){let un=gt[mi],Fn=gt[mi+1],An=gt[mi+2],Hn=gt[mi+3],Qn=gt[mi+4],Vi=et===0?un:Fn,Jn=et===0?Hn:Qn,Yn=!1;At&&(Mr=Math.sqrt(Math.pow(un-Hn,2)+Math.pow(Fn-Qn,2))),Vi<Je?Jn>Je&&(Gr=Qt(Kt,un,Fn,Hn,Qn,Je),At&&(Kt.start=Ir+Mr*Gr)):Vi>We?Jn<We&&(Gr=Qt(Kt,un,Fn,Hn,Qn,We),At&&(Kt.start=Ir+Mr*Gr)):rr(Kt,un,Fn,An),Jn<Je&&Vi>=Je&&(Gr=Qt(Kt,un,Fn,Hn,Qn,Je),Yn=!0),Jn>We&&Vi<=We&&(Gr=Qt(Kt,un,Fn,Hn,Qn,We),Yn=!0),!xt&&Yn&&(At&&(Kt.end=Ir+Mr*Gr),Ge.push(Kt),Kt=ar(gt)),At&&(Ir+=Mr)}let Yr=gt.length-3,_i=gt[Yr],Pi=gt[Yr+1],ai=et===0?_i:Pi;ai>=Je&&ai<=We&&rr(Kt,_i,Pi,gt[Yr+2]),Yr=Kt.length-3,xt&&Yr>=3&&(Kt[Yr]!==Kt[0]||Kt[Yr+1]!==Kt[1])&&rr(Kt,Kt[0],Kt[1],Kt[2]),Kt.length&&Ge.push(Kt)}function ar(gt){let Ge=[];return Ge.size=gt.size,Ge.start=gt.start,Ge.end=gt.end,Ge}function Vt(gt,Ge,Je,We,et,xt){for(let At of gt)ct(At,Ge,Je,We,et,xt,!1)}function rr(gt,Ge,Je,We){gt.push(Ge,Je,We)}function tt(gt,Ge,Je,We,et,xt){let At=(xt-Ge)/(We-Ge);return rr(gt,xt,Je+(et-Je)*At,1),At}function je(gt,Ge,Je,We,et,xt){let At=(xt-Je)/(et-Je);return rr(gt,Ge+(We-Ge)*At,xt,1),At}function Ue(gt,Ge){let Je=[];for(let We=0;We<gt.length;We++){let et=gt[We],xt=et.type,At;if(xt===\"Point\"||xt===\"MultiPoint\"||xt===\"LineString\")At=Ae(et.geometry,Ge);else if(xt===\"MultiLineString\"||xt===\"Polygon\"){At=[];for(let Kt of et.geometry)At.push(Ae(Kt,Ge))}else if(xt===\"MultiPolygon\"){At=[];for(let Kt of et.geometry){let Qt=[];for(let Mr of Kt)Qt.push(Ae(Mr,Ge));At.push(Qt)}}Je.push(Be(et.id,xt,At,et.tags))}return Je}function Ae(gt,Ge){let Je=[];Je.size=gt.size,gt.start!==void 0&&(Je.start=gt.start,Je.end=gt.end);for(let We=0;We<gt.length;We+=3)Je.push(gt[We]+Ge,gt[We+1],gt[We+2]);return Je}function rt(gt,Ge){if(gt.transformed)return gt;let Je=1<<gt.z,We=gt.x,et=gt.y;for(let xt of gt.features){let At=xt.geometry,Kt=xt.type;if(xt.geometry=[],Kt===1)for(let Qt=0;Qt<At.length;Qt+=2)xt.geometry.push(St(At[Qt],At[Qt+1],Ge,Je,We,et));else for(let Qt=0;Qt<At.length;Qt++){let Mr=[];for(let Gr=0;Gr<At[Qt].length;Gr+=2)Mr.push(St(At[Qt][Gr],At[Qt][Gr+1],Ge,Je,We,et));xt.geometry.push(Mr)}}return gt.transformed=!0,gt}function St(gt,Ge,Je,We,et,xt){return[Math.round(Je*(gt*We-et)),Math.round(Je*(Ge*We-xt))]}function Tt(gt,Ge,Je,We,et){let xt=Ge===et.maxZoom?0:et.tolerance/((1<<Ge)*et.extent),At={features:[],numPoints:0,numSimplified:0,numFeatures:gt.length,source:null,x:Je,y:We,z:Ge,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0};for(let Kt of gt)dt(At,Kt,xt,et);return At}function dt(gt,Ge,Je,We){let et=Ge.geometry,xt=Ge.type,At=[];if(gt.minX=Math.min(gt.minX,Ge.minX),gt.minY=Math.min(gt.minY,Ge.minY),gt.maxX=Math.max(gt.maxX,Ge.maxX),gt.maxY=Math.max(gt.maxY,Ge.maxY),xt===\"Point\"||xt===\"MultiPoint\")for(let Kt=0;Kt<et.length;Kt+=3)At.push(et[Kt],et[Kt+1]),gt.numPoints++,gt.numSimplified++;else if(xt===\"LineString\")Et(At,et,gt,Je,!1,!1);else if(xt===\"MultiLineString\"||xt===\"Polygon\")for(let Kt=0;Kt<et.length;Kt++)Et(At,et[Kt],gt,Je,xt===\"Polygon\",Kt===0);else if(xt===\"MultiPolygon\")for(let Kt=0;Kt<et.length;Kt++){let Qt=et[Kt];for(let Mr=0;Mr<Qt.length;Mr++)Et(At,Qt[Mr],gt,Je,!0,Mr===0)}if(At.length){let Kt=Ge.tags||null;if(xt===\"LineString\"&&We.lineMetrics){Kt={};for(let Mr in Ge.tags)Kt[Mr]=Ge.tags[Mr];Kt.mapbox_clip_start=et.start/et.size,Kt.mapbox_clip_end=et.end/et.size}let Qt={geometry:At,type:xt===\"Polygon\"||xt===\"MultiPolygon\"?3:xt===\"LineString\"||xt===\"MultiLineString\"?2:1,tags:Kt};Ge.id!==null&&(Qt.id=Ge.id),gt.features.push(Qt)}}function Et(gt,Ge,Je,We,et,xt){let At=We*We;if(We>0&&Ge.size<(et?At:We))return void(Je.numPoints+=Ge.length/3);let Kt=[];for(let Qt=0;Qt<Ge.length;Qt+=3)(We===0||Ge[Qt+2]>At)&&(Je.numSimplified++,Kt.push(Ge[Qt],Ge[Qt+1])),Je.numPoints++;et&&(function(Qt,Mr){let Gr=0;for(let Ir=0,Yr=Qt.length,_i=Yr-2;Ir<Yr;_i=Ir,Ir+=2)Gr+=(Qt[Ir]-Qt[_i])*(Qt[Ir+1]+Qt[_i+1]);if(Gr>0===Mr)for(let Ir=0,Yr=Qt.length;Ir<Yr/2;Ir+=2){let _i=Qt[Ir],Pi=Qt[Ir+1];Qt[Ir]=Qt[Yr-2-Ir],Qt[Ir+1]=Qt[Yr-1-Ir],Qt[Yr-2-Ir]=_i,Qt[Yr-1-Ir]=Pi}})(Kt,xt),gt.push(Kt)}let pt={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0};class jt{constructor(Ge,Je){let We=(Je=this.options=(function(xt,At){for(let Kt in At)xt[Kt]=At[Kt];return xt})(Object.create(pt),Je)).debug;if(We&&console.time(\"preprocess data\"),Je.maxZoom<0||Je.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(Je.promoteId&&Je.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");let et=(function(xt,At){let Kt=[];if(xt.type===\"FeatureCollection\")for(let Qt=0;Qt<xt.features.length;Qt++)me(Kt,xt.features[Qt],At,Qt);else me(Kt,xt.type===\"Feature\"?xt:{geometry:xt},At);return Kt})(Ge,Je);this.tiles={},this.tileCoords=[],We&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",Je.indexMaxZoom,Je.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),et=(function(xt,At){let Kt=At.buffer/At.extent,Qt=xt,Mr=Ht(xt,1,-1-Kt,Kt,0,-1,2,At),Gr=Ht(xt,1,1-Kt,2+Kt,0,-1,2,At);return(Mr||Gr)&&(Qt=Ht(xt,1,-Kt,1+Kt,0,-1,2,At)||[],Mr&&(Qt=Ue(Mr,1).concat(Qt)),Gr&&(Qt=Qt.concat(Ue(Gr,-1)))),Qt})(et,Je),et.length&&this.splitTile(et,0,0,0),We&&(et.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}splitTile(Ge,Je,We,et,xt,At,Kt){let Qt=[Ge,Je,We,et],Mr=this.options,Gr=Mr.debug;for(;Qt.length;){et=Qt.pop(),We=Qt.pop(),Je=Qt.pop(),Ge=Qt.pop();let Ir=1<<Je,Yr=or(Je,We,et),_i=this.tiles[Yr];if(!_i&&(Gr>1&&console.time(\"creation\"),_i=this.tiles[Yr]=Tt(Ge,Je,We,et,Mr),this.tileCoords.push({z:Je,x:We,y:et}),Gr)){Gr>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",Je,We,et,_i.numFeatures,_i.numPoints,_i.numSimplified),console.timeEnd(\"creation\"));let Yn=`z${Je}`;this.stats[Yn]=(this.stats[Yn]||0)+1,this.total++}if(_i.source=Ge,xt==null){if(Je===Mr.indexMaxZoom||_i.numPoints<=Mr.indexMaxPoints)continue}else{if(Je===Mr.maxZoom||Je===xt)continue;if(xt!=null){let Yn=xt-Je;if(We!==At>>Yn||et!==Kt>>Yn)continue}}if(_i.source=null,Ge.length===0)continue;Gr>1&&console.time(\"clipping\");let Pi=.5*Mr.buffer/Mr.extent,ai=.5-Pi,mi=.5+Pi,un=1+Pi,Fn=null,An=null,Hn=null,Qn=null,Vi=Ht(Ge,Ir,We-Pi,We+mi,0,_i.minX,_i.maxX,Mr),Jn=Ht(Ge,Ir,We+ai,We+un,0,_i.minX,_i.maxX,Mr);Ge=null,Vi&&(Fn=Ht(Vi,Ir,et-Pi,et+mi,1,_i.minY,_i.maxY,Mr),An=Ht(Vi,Ir,et+ai,et+un,1,_i.minY,_i.maxY,Mr),Vi=null),Jn&&(Hn=Ht(Jn,Ir,et-Pi,et+mi,1,_i.minY,_i.maxY,Mr),Qn=Ht(Jn,Ir,et+ai,et+un,1,_i.minY,_i.maxY,Mr),Jn=null),Gr>1&&console.timeEnd(\"clipping\"),Qt.push(Fn||[],Je+1,2*We,2*et),Qt.push(An||[],Je+1,2*We,2*et+1),Qt.push(Hn||[],Je+1,2*We+1,2*et),Qt.push(Qn||[],Je+1,2*We+1,2*et+1)}}getTile(Ge,Je,We){Ge=+Ge,Je=+Je,We=+We;let et=this.options,{extent:xt,debug:At}=et;if(Ge<0||Ge>24)return null;let Kt=1<<Ge,Qt=or(Ge,Je=Je+Kt&Kt-1,We);if(this.tiles[Qt])return rt(this.tiles[Qt],xt);At>1&&console.log(\"drilling down to z%d-%d-%d\",Ge,Je,We);let Mr,Gr=Ge,Ir=Je,Yr=We;for(;!Mr&&Gr>0;)Gr--,Ir>>=1,Yr>>=1,Mr=this.tiles[or(Gr,Ir,Yr)];return Mr&&Mr.source?(At>1&&(console.log(\"found parent tile z%d-%d-%d\",Gr,Ir,Yr),console.time(\"drilling down\")),this.splitTile(Mr.source,Gr,Ir,Yr,Ge,Je,We),At>1&&console.timeEnd(\"drilling down\"),this.tiles[Qt]?rt(this.tiles[Qt],xt):null):null}}function or(gt,Ge,Je){return 32*((1<<gt)*Je+Ge)+gt}function mr(gt,Ge){return Ge?gt.properties[Ge]:gt.id}function Ar(gt,Ge){if(gt==null)return!0;if(gt.type===\"Feature\")return mr(gt,Ge)!=null;if(gt.type===\"FeatureCollection\"){let Je=new Set;for(let We of gt.features){let et=mr(We,Ge);if(et==null||Je.has(et))return!1;Je.add(et)}return!0}return!1}function ei(gt,Ge){let Je=new Map;if(gt!=null)if(gt.type===\"Feature\")Je.set(mr(gt,Ge),gt);else for(let We of gt.features)Je.set(mr(We,Ge),We);return Je}class qr extends u{constructor(){super(...arguments),this._dataUpdateable=new Map}loadVectorTile(Ge,Je){return i._(this,void 0,void 0,(function*(){let We=Ge.tileID.canonical;if(!this._geoJSONIndex)throw new Error(\"Unable to parse the data into a cluster or geojson\");let et=this._geoJSONIndex.getTile(We.z,We.x,We.y);if(!et)return null;let xt=new class{constructor(Kt){this.layers={_geojsonTileLayer:this},this.name=\"_geojsonTileLayer\",this.extent=i.X,this.length=Kt.length,this._features=Kt}feature(Kt){return new class{constructor(Qt){this._feature=Qt,this.extent=i.X,this.type=Qt.type,this.properties=Qt.tags,\"id\"in Qt&&!isNaN(Qt.id)&&(this.id=parseInt(Qt.id,10))}loadGeometry(){if(this._feature.type===1){let Qt=[];for(let Mr of this._feature.geometry)Qt.push([new i.P(Mr[0],Mr[1])]);return Qt}{let Qt=[];for(let Mr of this._feature.geometry){let Gr=[];for(let Ir of Mr)Gr.push(new i.P(Ir[0],Ir[1]));Qt.push(Gr)}return Qt}}toGeoJSON(Qt,Mr,Gr){return v.call(this,Qt,Mr,Gr)}}(this._features[Kt])}}(et.features),At=G(xt);return At.byteOffset===0&&At.byteLength===At.buffer.byteLength||(At=new Uint8Array(At)),{vectorTile:xt,rawData:At.buffer}}))}loadData(Ge){return i._(this,void 0,void 0,(function*(){var Je;(Je=this._pendingRequest)===null||Je===void 0||Je.abort();let We=!!(Ge&&Ge.request&&Ge.request.collectResourceTiming)&&new i.bv(Ge.request);this._pendingRequest=new AbortController;try{this._pendingData=this.loadAndProcessGeoJSON(Ge,this._pendingRequest),this._geoJSONIndex=Ge.cluster?new _e((function({superclusterOptions:At,clusterProperties:Kt}){if(!Kt||!At)return At;let Qt={},Mr={},Gr={accumulated:null,zoom:0},Ir={properties:null},Yr=Object.keys(Kt);for(let _i of Yr){let[Pi,ai]=Kt[_i],mi=i.bC(ai),un=i.bC(typeof Pi==\"string\"?[Pi,[\"accumulated\"],[\"get\",_i]]:Pi);Qt[_i]=mi.value,Mr[_i]=un.value}return At.map=_i=>{Ir.properties=_i;let Pi={};for(let ai of Yr)Pi[ai]=Qt[ai].evaluate(Gr,Ir);return Pi},At.reduce=(_i,Pi)=>{Ir.properties=Pi;for(let ai of Yr)Gr.accumulated=_i[ai],_i[ai]=Mr[ai].evaluate(Gr,Ir)},At})(Ge)).load((yield this._pendingData).features):(et=yield this._pendingData,new jt(et,Ge.geojsonVtOptions)),this.loaded={};let xt={};if(We){let At=We.finish();At&&(xt.resourceTiming={},xt.resourceTiming[Ge.source]=JSON.parse(JSON.stringify(At)))}return xt}catch(xt){if(delete this._pendingRequest,i.bB(xt))return{abandoned:!0};throw xt}var et}))}getData(){return i._(this,void 0,void 0,(function*(){return this._pendingData}))}reloadTile(Ge){let Je=this.loaded;return Je&&Je[Ge.uid]?super.reloadTile(Ge):this.loadTile(Ge)}loadAndProcessGeoJSON(Ge,Je){return i._(this,void 0,void 0,(function*(){let We=yield this.loadGeoJSON(Ge,Je);if(delete this._pendingRequest,typeof We!=\"object\")throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`);if(d(We,!0),Ge.filter){let et=i.bC(Ge.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(et.result===\"error\")throw new Error(et.value.map((At=>`${At.key}: ${At.message}`)).join(\", \"));We={type:\"FeatureCollection\",features:We.features.filter((At=>et.value.evaluate({zoom:0},At)))}}return We}))}loadGeoJSON(Ge,Je){return i._(this,void 0,void 0,(function*(){let{promoteId:We}=Ge;if(Ge.request){let et=yield i.h(Ge.request,Je);return this._dataUpdateable=Ar(et.data,We)?ei(et.data,We):void 0,et.data}if(typeof Ge.data==\"string\")try{let et=JSON.parse(Ge.data);return this._dataUpdateable=Ar(et,We)?ei(et,We):void 0,et}catch(et){throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`)}if(!Ge.dataDiff)throw new Error(`Input data given to '${Ge.source}' is not a valid GeoJSON object.`);if(!this._dataUpdateable)throw new Error(`Cannot update existing geojson data in ${Ge.source}`);return(function(et,xt,At){var Kt,Qt,Mr,Gr;if(xt.removeAll&&et.clear(),xt.remove)for(let Ir of xt.remove)et.delete(Ir);if(xt.add)for(let Ir of xt.add){let Yr=mr(Ir,At);Yr!=null&&et.set(Yr,Ir)}if(xt.update)for(let Ir of xt.update){let Yr=et.get(Ir.id);if(Yr==null)continue;let _i=!Ir.removeAllProperties&&(((Kt=Ir.removeProperties)===null||Kt===void 0?void 0:Kt.length)>0||((Qt=Ir.addOrUpdateProperties)===null||Qt===void 0?void 0:Qt.length)>0);if((Ir.newGeometry||Ir.removeAllProperties||_i)&&(Yr=Object.assign({},Yr),et.set(Ir.id,Yr),_i&&(Yr.properties=Object.assign({},Yr.properties))),Ir.newGeometry&&(Yr.geometry=Ir.newGeometry),Ir.removeAllProperties)Yr.properties={};else if(((Mr=Ir.removeProperties)===null||Mr===void 0?void 0:Mr.length)>0)for(let Pi of Ir.removeProperties)Object.prototype.hasOwnProperty.call(Yr.properties,Pi)&&delete Yr.properties[Pi];if(((Gr=Ir.addOrUpdateProperties)===null||Gr===void 0?void 0:Gr.length)>0)for(let{key:Pi,value:ai}of Ir.addOrUpdateProperties)Yr.properties[Pi]=ai}})(this._dataUpdateable,Ge.dataDiff,We),{type:\"FeatureCollection\",features:Array.from(this._dataUpdateable.values())}}))}removeSource(Ge){return i._(this,void 0,void 0,(function*(){this._pendingRequest&&this._pendingRequest.abort()}))}getClusterExpansionZoom(Ge){return this._geoJSONIndex.getClusterExpansionZoom(Ge.clusterId)}getClusterChildren(Ge){return this._geoJSONIndex.getChildren(Ge.clusterId)}getClusterLeaves(Ge){return this._geoJSONIndex.getLeaves(Ge.clusterId,Ge.limit,Ge.offset)}}class jr{constructor(Ge){this.self=Ge,this.actor=new i.F(Ge),this.layerIndexes={},this.availableImages={},this.workerSources={},this.demWorkerSources={},this.externalWorkerSourceTypes={},this.self.registerWorkerSource=(Je,We)=>{if(this.externalWorkerSourceTypes[Je])throw new Error(`Worker source with name \"${Je}\" already registered.`);this.externalWorkerSourceTypes[Je]=We},this.self.addProtocol=i.bi,this.self.removeProtocol=i.bj,this.self.registerRTLTextPlugin=Je=>{if(i.bD.isParsed())throw new Error(\"RTL text plugin already registered.\");i.bD.setMethods(Je)},this.actor.registerMessageHandler(\"LDT\",((Je,We)=>this._getDEMWorkerSource(Je,We.source).loadTile(We))),this.actor.registerMessageHandler(\"RDT\",((Je,We)=>i._(this,void 0,void 0,(function*(){this._getDEMWorkerSource(Je,We.source).removeTile(We)})))),this.actor.registerMessageHandler(\"GCEZ\",((Je,We)=>i._(this,void 0,void 0,(function*(){return this._getWorkerSource(Je,We.type,We.source).getClusterExpansionZoom(We)})))),this.actor.registerMessageHandler(\"GCC\",((Je,We)=>i._(this,void 0,void 0,(function*(){return this._getWorkerSource(Je,We.type,We.source).getClusterChildren(We)})))),this.actor.registerMessageHandler(\"GCL\",((Je,We)=>i._(this,void 0,void 0,(function*(){return this._getWorkerSource(Je,We.type,We.source).getClusterLeaves(We)})))),this.actor.registerMessageHandler(\"LD\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).loadData(We))),this.actor.registerMessageHandler(\"GD\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).getData())),this.actor.registerMessageHandler(\"LT\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).loadTile(We))),this.actor.registerMessageHandler(\"RT\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).reloadTile(We))),this.actor.registerMessageHandler(\"AT\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).abortTile(We))),this.actor.registerMessageHandler(\"RMT\",((Je,We)=>this._getWorkerSource(Je,We.type,We.source).removeTile(We))),this.actor.registerMessageHandler(\"RS\",((Je,We)=>i._(this,void 0,void 0,(function*(){if(!this.workerSources[Je]||!this.workerSources[Je][We.type]||!this.workerSources[Je][We.type][We.source])return;let et=this.workerSources[Je][We.type][We.source];delete this.workerSources[Je][We.type][We.source],et.removeSource!==void 0&&et.removeSource(We)})))),this.actor.registerMessageHandler(\"RM\",(Je=>i._(this,void 0,void 0,(function*(){delete this.layerIndexes[Je],delete this.availableImages[Je],delete this.workerSources[Je],delete this.demWorkerSources[Je]})))),this.actor.registerMessageHandler(\"SR\",((Je,We)=>i._(this,void 0,void 0,(function*(){this.referrer=We})))),this.actor.registerMessageHandler(\"SRPS\",((Je,We)=>this._syncRTLPluginState(Je,We))),this.actor.registerMessageHandler(\"IS\",((Je,We)=>i._(this,void 0,void 0,(function*(){this.self.importScripts(We)})))),this.actor.registerMessageHandler(\"SI\",((Je,We)=>this._setImages(Je,We))),this.actor.registerMessageHandler(\"UL\",((Je,We)=>i._(this,void 0,void 0,(function*(){this._getLayerIndex(Je).update(We.layers,We.removedIds)})))),this.actor.registerMessageHandler(\"SL\",((Je,We)=>i._(this,void 0,void 0,(function*(){this._getLayerIndex(Je).replace(We)}))))}_setImages(Ge,Je){return i._(this,void 0,void 0,(function*(){this.availableImages[Ge]=Je;for(let We in this.workerSources[Ge]){let et=this.workerSources[Ge][We];for(let xt in et)et[xt].availableImages=Je}}))}_syncRTLPluginState(Ge,Je){return i._(this,void 0,void 0,(function*(){if(i.bD.isParsed())return i.bD.getState();if(Je.pluginStatus!==\"loading\")return i.bD.setState(Je),Je;let We=Je.pluginURL;if(this.self.importScripts(We),i.bD.isParsed()){let et={pluginStatus:\"loaded\",pluginURL:We};return i.bD.setState(et),et}throw i.bD.setState({pluginStatus:\"error\",pluginURL:\"\"}),new Error(`RTL Text Plugin failed to import scripts from ${We}`)}))}_getAvailableImages(Ge){let Je=this.availableImages[Ge];return Je||(Je=[]),Je}_getLayerIndex(Ge){let Je=this.layerIndexes[Ge];return Je||(Je=this.layerIndexes[Ge]=new a),Je}_getWorkerSource(Ge,Je,We){if(this.workerSources[Ge]||(this.workerSources[Ge]={}),this.workerSources[Ge][Je]||(this.workerSources[Ge][Je]={}),!this.workerSources[Ge][Je][We]){let et={sendAsync:(xt,At)=>(xt.targetMapId=Ge,this.actor.sendAsync(xt,At))};switch(Je){case\"vector\":this.workerSources[Ge][Je][We]=new u(et,this._getLayerIndex(Ge),this._getAvailableImages(Ge));break;case\"geojson\":this.workerSources[Ge][Je][We]=new qr(et,this._getLayerIndex(Ge),this._getAvailableImages(Ge));break;default:this.workerSources[Ge][Je][We]=new this.externalWorkerSourceTypes[Je](et,this._getLayerIndex(Ge),this._getAvailableImages(Ge))}}return this.workerSources[Ge][Je][We]}_getDEMWorkerSource(Ge,Je){return this.demWorkerSources[Ge]||(this.demWorkerSources[Ge]={}),this.demWorkerSources[Ge][Je]||(this.demWorkerSources[Ge][Je]=new c),this.demWorkerSources[Ge][Je]}}return i.i(self)&&(self.worker=new jr(self)),jr})),r(\"index\",[\"exports\",\"./shared\"],(function(i,a){\"use strict\";var o=\"4.7.1\";let s,l,u={now:typeof performance!=\"undefined\"&&performance&&performance.now?performance.now.bind(performance):Date.now.bind(Date),frameAsync:ue=>new Promise(((w,B)=>{let 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0&&(ee.className=B),Q&&Q.appendChild(ee),ee}static createNS(w,B){return window.document.createElementNS(w,B)}static disableDrag(){c.docStyle&&c.selectProp&&(c.userSelect=c.docStyle[c.selectProp],c.docStyle[c.selectProp]=\"none\")}static enableDrag(){c.docStyle&&c.selectProp&&(c.docStyle[c.selectProp]=c.userSelect)}static setTransform(w,B){w.style[c.transformProp]=B}static addEventListener(w,B,Q,ee={}){w.addEventListener(B,Q,\"passive\"in ee?ee:ee.capture)}static removeEventListener(w,B,Q,ee={}){w.removeEventListener(B,Q,\"passive\"in ee?ee:ee.capture)}static suppressClickInternal(w){w.preventDefault(),w.stopPropagation(),window.removeEventListener(\"click\",c.suppressClickInternal,!0)}static suppressClick(){window.addEventListener(\"click\",c.suppressClickInternal,!0),window.setTimeout((()=>{window.removeEventListener(\"click\",c.suppressClickInternal,!0)}),0)}static getScale(w){let B=w.getBoundingClientRect();return{x:B.width/w.offsetWidth||1,y:B.height/w.offsetHeight||1,boundingClientRect:B}}static getPoint(w,B,Q){let ee=B.boundingClientRect;return new a.P((Q.clientX-ee.left)/B.x-w.clientLeft,(Q.clientY-ee.top)/B.y-w.clientTop)}static mousePos(w,B){let Q=c.getScale(w);return c.getPoint(w,Q,B)}static touchPos(w,B){let Q=[],ee=c.getScale(w);for(let le=0;le<B.length;le++)Q.push(c.getPoint(w,ee,B[le]));return Q}static mouseButton(w){return w.button}static remove(w){w.parentNode&&w.parentNode.removeChild(w)}}c.docStyle=typeof window!=\"undefined\"&&window.document&&window.document.documentElement.style,c.selectProp=c.testProp([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]),c.transformProp=c.testProp([\"transform\",\"WebkitTransform\"]);let f={supported:!1,testSupport:function(ue){!v&&d&&(m?b(ue):h=ue)}},h,d,v=!1,m=!1;function b(ue){let w=ue.createTexture();ue.bindTexture(ue.TEXTURE_2D,w);try{if(ue.texImage2D(ue.TEXTURE_2D,0,ue.RGBA,ue.RGBA,ue.UNSIGNED_BYTE,d),ue.isContextLost())return;f.supported=!0}catch(B){}ue.deleteTexture(w),v=!0}var p;typeof document!=\"undefined\"&&(d=document.createElement(\"img\"),d.onload=()=>{h&&b(h),h=null,m=!0},d.onerror=()=>{v=!0,h=null},d.src=\"data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=\"),(function(ue){let w,B,Q,ee;ue.resetRequestQueue=()=>{w=[],B=0,Q=0,ee={}},ue.addThrottleControl=ut=>{let Mt=Q++;return ee[Mt]=ut,Mt},ue.removeThrottleControl=ut=>{delete ee[ut],Fe()},ue.getImage=(ut,Mt,Jt=!0)=>new Promise((($t,yr)=>{f.supported&&(ut.headers||(ut.headers={}),ut.headers.accept=\"image/webp,*/*\"),a.e(ut,{type:\"image\"}),w.push({abortController:Mt,requestParameters:ut,supportImageRefresh:Jt,state:\"queued\",onError:Cr=>{yr(Cr)},onSuccess:Cr=>{$t(Cr)}}),Fe()}));let le=ut=>a._(this,void 0,void 0,(function*(){ut.state=\"running\";let{requestParameters:Mt,supportImageRefresh:Jt,onError:$t,onSuccess:yr,abortController:Cr}=ut,ve=Jt===!1&&!a.i(self)&&!a.g(Mt.url)&&(!Mt.headers||Object.keys(Mt.headers).reduce(((ze,Qe)=>ze&&Qe===\"accept\"),!0));B++;let xe=ve?Ze(Mt,Cr):a.m(Mt,Cr);try{let ze=yield xe;delete ut.abortController,ut.state=\"completed\",ze.data instanceof HTMLImageElement||a.b(ze.data)?yr(ze):ze.data&&yr({data:yield(Ie=ze.data,typeof createImageBitmap==\"function\"?a.d(Ie):a.f(Ie)),cacheControl:ze.cacheControl,expires:ze.expires})}catch(ze){delete ut.abortController,$t(ze)}finally{B--,Fe()}var Ie})),Fe=()=>{let ut=(()=>{for(let Mt of Object.keys(ee))if(ee[Mt]())return!0;return!1})()?a.a.MAX_PARALLEL_IMAGE_REQUESTS_PER_FRAME:a.a.MAX_PARALLEL_IMAGE_REQUESTS;for(let Mt=B;Mt<ut&&w.length>0;Mt++){let Jt=w.shift();Jt.abortController.signal.aborted?Mt--:le(Jt)}},Ze=(ut,Mt)=>new Promise(((Jt,$t)=>{let yr=new Image,Cr=ut.url,ve=ut.credentials;ve&&ve===\"include\"?yr.crossOrigin=\"use-credentials\":(ve&&ve===\"same-origin\"||!a.s(Cr))&&(yr.crossOrigin=\"anonymous\"),Mt.signal.addEventListener(\"abort\",(()=>{yr.src=\"\",$t(a.c())})),yr.fetchPriority=\"high\",yr.onload=()=>{yr.onerror=yr.onload=null,Jt({data:yr})},yr.onerror=()=>{yr.onerror=yr.onload=null,Mt.signal.aborted||$t(new Error(\"Could not load image. Please make sure to use a supported image type such as PNG or JPEG. Note that SVGs are not supported.\"))},yr.src=Cr}))})(p||(p={})),p.resetRequestQueue();class k{constructor(w){this._transformRequestFn=w}transformRequest(w,B){return this._transformRequestFn&&this._transformRequestFn(w,B)||{url:w}}setTransformRequest(w){this._transformRequestFn=w}}function M(ue){var w=new a.A(3);return w[0]=ue[0],w[1]=ue[1],w[2]=ue[2],w}var T,L=function(ue,w,B){return ue[0]=w[0]-B[0],ue[1]=w[1]-B[1],ue[2]=w[2]-B[2],ue};T=new a.A(3),a.A!=Float32Array&&(T[0]=0,T[1]=0,T[2]=0);var x=function(ue){var w=ue[0],B=ue[1];return w*w+B*B};function C(ue){let w=[];if(typeof ue==\"string\")w.push({id:\"default\",url:ue});else if(ue&&ue.length>0){let B=[];for(let{id:Q,url:ee}of ue){let le=`${Q}${ee}`;B.indexOf(le)===-1&&(B.push(le),w.push({id:Q,url:ee}))}}return w}function S(ue,w,B){let Q=ue.split(\"?\");return Q[0]+=`${w}${B}`,Q.join(\"?\")}(function(){var ue=new a.A(2);a.A!=Float32Array&&(ue[0]=0,ue[1]=0)})();class g{constructor(w,B,Q,ee){this.context=w,this.format=Q,this.texture=w.gl.createTexture(),this.update(B,ee)}update(w,B,Q){let{width:ee,height:le}=w,Fe=!(this.size&&this.size[0]===ee&&this.size[1]===le||Q),{context:Ze}=this,{gl:ut}=Ze;if(this.useMipmap=!!(B&&B.useMipmap),ut.bindTexture(ut.TEXTURE_2D,this.texture),Ze.pixelStoreUnpackFlipY.set(!1),Ze.pixelStoreUnpack.set(1),Ze.pixelStoreUnpackPremultiplyAlpha.set(this.format===ut.RGBA&&(!B||B.premultiply!==!1)),Fe)this.size=[ee,le],w instanceof HTMLImageElement||w instanceof HTMLCanvasElement||w instanceof HTMLVideoElement||w instanceof ImageData||a.b(w)?ut.texImage2D(ut.TEXTURE_2D,0,this.format,this.format,ut.UNSIGNED_BYTE,w):ut.texImage2D(ut.TEXTURE_2D,0,this.format,ee,le,0,this.format,ut.UNSIGNED_BYTE,w.data);else{let{x:Mt,y:Jt}=Q||{x:0,y:0};w instanceof HTMLImageElement||w instanceof HTMLCanvasElement||w instanceof HTMLVideoElement||w instanceof ImageData||a.b(w)?ut.texSubImage2D(ut.TEXTURE_2D,0,Mt,Jt,ut.RGBA,ut.UNSIGNED_BYTE,w):ut.texSubImage2D(ut.TEXTURE_2D,0,Mt,Jt,ee,le,ut.RGBA,ut.UNSIGNED_BYTE,w.data)}this.useMipmap&&this.isSizePowerOfTwo()&&ut.generateMipmap(ut.TEXTURE_2D)}bind(w,B,Q){let{context:ee}=this,{gl:le}=ee;le.bindTexture(le.TEXTURE_2D,this.texture),Q!==le.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(Q=le.LINEAR),w!==this.filter&&(le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MAG_FILTER,w),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MIN_FILTER,Q||w),this.filter=w),B!==this.wrap&&(le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_S,B),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_T,B),this.wrap=B)}isSizePowerOfTwo(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0}destroy(){let{gl:w}=this.context;w.deleteTexture(this.texture),this.texture=null}}function P(ue){let{userImage:w}=ue;return!!(w&&w.render&&w.render())&&(ue.data.replace(new Uint8Array(w.data.buffer)),!0)}class E extends a.E{constructor(){super(),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new a.R({width:1,height:1}),this.dirty=!0}isLoaded(){return this.loaded}setLoaded(w){if(this.loaded!==w&&(this.loaded=w,w)){for(let{ids:B,promiseResolve:Q}of this.requestors)Q(this._getImagesForIds(B));this.requestors=[]}}getImage(w){let B=this.images[w];if(B&&!B.data&&B.spriteData){let Q=B.spriteData;B.data=new a.R({width:Q.width,height:Q.height},Q.context.getImageData(Q.x,Q.y,Q.width,Q.height).data),B.spriteData=null}return B}addImage(w,B){if(this.images[w])throw new Error(`Image id ${w} already exist, use updateImage instead`);this._validate(w,B)&&(this.images[w]=B)}_validate(w,B){let Q=!0,ee=B.data||B.spriteData;return this._validateStretch(B.stretchX,ee&&ee.width)||(this.fire(new a.j(new Error(`Image \"${w}\" has invalid \"stretchX\" value`))),Q=!1),this._validateStretch(B.stretchY,ee&&ee.height)||(this.fire(new a.j(new Error(`Image \"${w}\" has invalid \"stretchY\" value`))),Q=!1),this._validateContent(B.content,B)||(this.fire(new a.j(new Error(`Image \"${w}\" has invalid \"content\" value`))),Q=!1),Q}_validateStretch(w,B){if(!w)return!0;let Q=0;for(let ee of w){if(ee[0]<Q||ee[1]<ee[0]||B<ee[1])return!1;Q=ee[1]}return!0}_validateContent(w,B){if(!w)return!0;if(w.length!==4)return!1;let Q=B.spriteData,ee=Q&&Q.width||B.data.width,le=Q&&Q.height||B.data.height;return!(w[0]<0||ee<w[0]||w[1]<0||le<w[1]||w[2]<0||ee<w[2]||w[3]<0||le<w[3]||w[2]<w[0]||w[3]<w[1])}updateImage(w,B,Q=!0){let ee=this.getImage(w);if(Q&&(ee.data.width!==B.data.width||ee.data.height!==B.data.height))throw new Error(`size mismatch between old image (${ee.data.width}x${ee.data.height}) and new image (${B.data.width}x${B.data.height}).`);B.version=ee.version+1,this.images[w]=B,this.updatedImages[w]=!0}removeImage(w){let B=this.images[w];delete this.images[w],delete this.patterns[w],B.userImage&&B.userImage.onRemove&&B.userImage.onRemove()}listImages(){return Object.keys(this.images)}getImages(w){return new Promise(((B,Q)=>{let ee=!0;if(!this.isLoaded())for(let le of w)this.images[le]||(ee=!1);this.isLoaded()||ee?B(this._getImagesForIds(w)):this.requestors.push({ids:w,promiseResolve:B})}))}_getImagesForIds(w){let B={};for(let Q of w){let ee=this.getImage(Q);ee||(this.fire(new a.k(\"styleimagemissing\",{id:Q})),ee=this.getImage(Q)),ee?B[Q]={data:ee.data.clone(),pixelRatio:ee.pixelRatio,sdf:ee.sdf,version:ee.version,stretchX:ee.stretchX,stretchY:ee.stretchY,content:ee.content,textFitWidth:ee.textFitWidth,textFitHeight:ee.textFitHeight,hasRenderCallback:!!(ee.userImage&&ee.userImage.render)}:a.w(`Image \"${Q}\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.`)}return B}getPixelSize(){let{width:w,height:B}=this.atlasImage;return{width:w,height:B}}getPattern(w){let B=this.patterns[w],Q=this.getImage(w);if(!Q)return null;if(B&&B.position.version===Q.version)return B.position;if(B)B.position.version=Q.version;else{let ee={w:Q.data.width+2,h:Q.data.height+2,x:0,y:0},le=new a.I(ee,Q);this.patterns[w]={bin:ee,position:le}}return this._updatePatternAtlas(),this.patterns[w].position}bind(w){let B=w.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new g(w,this.atlasImage,B.RGBA),this.atlasTexture.bind(B.LINEAR,B.CLAMP_TO_EDGE)}_updatePatternAtlas(){let w=[];for(let le in this.patterns)w.push(this.patterns[le].bin);let{w:B,h:Q}=a.p(w),ee=this.atlasImage;ee.resize({width:B||1,height:Q||1});for(let le in this.patterns){let{bin:Fe}=this.patterns[le],Ze=Fe.x+1,ut=Fe.y+1,Mt=this.getImage(le).data,Jt=Mt.width,$t=Mt.height;a.R.copy(Mt,ee,{x:0,y:0},{x:Ze,y:ut},{width:Jt,height:$t}),a.R.copy(Mt,ee,{x:0,y:$t-1},{x:Ze,y:ut-1},{width:Jt,height:1}),a.R.copy(Mt,ee,{x:0,y:0},{x:Ze,y:ut+$t},{width:Jt,height:1}),a.R.copy(Mt,ee,{x:Jt-1,y:0},{x:Ze-1,y:ut},{width:1,height:$t}),a.R.copy(Mt,ee,{x:0,y:0},{x:Ze+Jt,y:ut},{width:1,height:$t})}this.dirty=!0}beginFrame(){this.callbackDispatchedThisFrame={}}dispatchRenderCallbacks(w){for(let B of w){if(this.callbackDispatchedThisFrame[B])continue;this.callbackDispatchedThisFrame[B]=!0;let Q=this.getImage(B);Q||a.w(`Image with ID: \"${B}\" was not found`),P(Q)&&this.updateImage(B,Q)}}}let z=1e20;function q(ue,w,B,Q,ee,le,Fe,Ze,ut){for(let Mt=w;Mt<w+Q;Mt++)V(ue,B*le+Mt,le,ee,Fe,Ze,ut);for(let Mt=B;Mt<B+ee;Mt++)V(ue,Mt*le+w,1,Q,Fe,Ze,ut)}function V(ue,w,B,Q,ee,le,Fe){le[0]=0,Fe[0]=-z,Fe[1]=z,ee[0]=ue[w];for(let Ze=1,ut=0,Mt=0;Ze<Q;Ze++){ee[Ze]=ue[w+Ze*B];let Jt=Ze*Ze;do{let $t=le[ut];Mt=(ee[Ze]-ee[$t]+Jt-$t*$t)/(Ze-$t)/2}while(Mt<=Fe[ut]&&--ut>-1);ut++,le[ut]=Ze,Fe[ut]=Mt,Fe[ut+1]=z}for(let Ze=0,ut=0;Ze<Q;Ze++){for(;Fe[ut+1]<Ze;)ut++;let Mt=le[ut],Jt=Ze-Mt;ue[w+Ze*B]=ee[Mt]+Jt*Jt}}class G{constructor(w,B){this.requestManager=w,this.localIdeographFontFamily=B,this.entries={}}setURL(w){this.url=w}getGlyphs(w){return a._(this,void 0,void 0,(function*(){let B=[];for(let le in w)for(let Fe of w[le])B.push(this._getAndCacheGlyphsPromise(le,Fe));let Q=yield Promise.all(B),ee={};for(let{stack:le,id:Fe,glyph:Ze}of Q)ee[le]||(ee[le]={}),ee[le][Fe]=Ze&&{id:Ze.id,bitmap:Ze.bitmap.clone(),metrics:Ze.metrics};return ee}))}_getAndCacheGlyphsPromise(w,B){return a._(this,void 0,void 0,(function*(){let Q=this.entries[w];Q||(Q=this.entries[w]={glyphs:{},requests:{},ranges:{}});let ee=Q.glyphs[B];if(ee!==void 0)return{stack:w,id:B,glyph:ee};if(ee=this._tinySDF(Q,w,B),ee)return Q.glyphs[B]=ee,{stack:w,id:B,glyph:ee};let le=Math.floor(B/256);if(256*le>65535)throw new Error(\"glyphs > 65535 not supported\");if(Q.ranges[le])return{stack:w,id:B,glyph:ee};if(!this.url)throw new Error(\"glyphsUrl is not set\");if(!Q.requests[le]){let Ze=G.loadGlyphRange(w,le,this.url,this.requestManager);Q.requests[le]=Ze}let Fe=yield Q.requests[le];for(let Ze in Fe)this._doesCharSupportLocalGlyph(+Ze)||(Q.glyphs[+Ze]=Fe[+Ze]);return Q.ranges[le]=!0,{stack:w,id:B,glyph:Fe[B]||null}}))}_doesCharSupportLocalGlyph(w){return!!this.localIdeographFontFamily&&new RegExp(\"\\\\p{Ideo}|\\\\p{sc=Hang}|\\\\p{sc=Hira}|\\\\p{sc=Kana}\",\"u\").test(String.fromCodePoint(w))}_tinySDF(w,B,Q){let ee=this.localIdeographFontFamily;if(!ee||!this._doesCharSupportLocalGlyph(Q))return;let le=w.tinySDF;if(!le){let Ze=\"400\";/bold/i.test(B)?Ze=\"900\":/medium/i.test(B)?Ze=\"500\":/light/i.test(B)&&(Ze=\"200\"),le=w.tinySDF=new G.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:ee,fontWeight:Ze})}let Fe=le.draw(String.fromCharCode(Q));return{id:Q,bitmap:new a.o({width:Fe.width||60,height:Fe.height||60},Fe.data),metrics:{width:Fe.glyphWidth/2||24,height:Fe.glyphHeight/2||24,left:Fe.glyphLeft/2+.5||0,top:Fe.glyphTop/2-27.5||-8,advance:Fe.glyphAdvance/2||24,isDoubleResolution:!0}}}}G.loadGlyphRange=function(ue,w,B,Q){return a._(this,void 0,void 0,(function*(){let ee=256*w,le=ee+255,Fe=Q.transformRequest(B.replace(\"{fontstack}\",ue).replace(\"{range}\",`${ee}-${le}`),\"Glyphs\"),Ze=yield a.l(Fe,new AbortController);if(!Ze||!Ze.data)throw new Error(`Could not load glyph range. range: ${w}, ${ee}-${le}`);let ut={};for(let Mt of a.n(Ze.data))ut[Mt.id]=Mt;return ut}))},G.TinySDF=class{constructor({fontSize:ue=24,buffer:w=3,radius:B=8,cutoff:Q=.25,fontFamily:ee=\"sans-serif\",fontWeight:le=\"normal\",fontStyle:Fe=\"normal\"}={}){this.buffer=w,this.cutoff=Q,this.radius=B;let Ze=this.size=ue+4*w,ut=this._createCanvas(Ze),Mt=this.ctx=ut.getContext(\"2d\",{willReadFrequently:!0});Mt.font=`${Fe} ${le} ${ue}px ${ee}`,Mt.textBaseline=\"alphabetic\",Mt.textAlign=\"left\",Mt.fillStyle=\"black\",this.gridOuter=new Float64Array(Ze*Ze),this.gridInner=new Float64Array(Ze*Ze),this.f=new Float64Array(Ze),this.z=new Float64Array(Ze+1),this.v=new Uint16Array(Ze)}_createCanvas(ue){let w=document.createElement(\"canvas\");return w.width=w.height=ue,w}draw(ue){let{width:w,actualBoundingBoxAscent:B,actualBoundingBoxDescent:Q,actualBoundingBoxLeft:ee,actualBoundingBoxRight:le}=this.ctx.measureText(ue),Fe=Math.ceil(B),Ze=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(le-ee))),ut=Math.min(this.size-this.buffer,Fe+Math.ceil(Q)),Mt=Ze+2*this.buffer,Jt=ut+2*this.buffer,$t=Math.max(Mt*Jt,0),yr=new Uint8ClampedArray($t),Cr={data:yr,width:Mt,height:Jt,glyphWidth:Ze,glyphHeight:ut,glyphTop:Fe,glyphLeft:0,glyphAdvance:w};if(Ze===0||ut===0)return Cr;let{ctx:ve,buffer:xe,gridInner:Ie,gridOuter:ze}=this;ve.clearRect(xe,xe,Ze,ut),ve.fillText(ue,xe,xe+Fe);let Qe=ve.getImageData(xe,xe,Ze,ut);ze.fill(z,0,$t),Ie.fill(0,0,$t);for(let Xe=0;Xe<ut;Xe++)for(let nt=0;nt<Ze;nt++){let qt=Qe.data[4*(Xe*Ze+nt)+3]/255;if(qt===0)continue;let Wt=(Xe+xe)*Mt+nt+xe;if(qt===1)ze[Wt]=0,Ie[Wt]=z;else{let nr=.5-qt;ze[Wt]=nr>0?nr*nr:0,Ie[Wt]=nr<0?nr*nr:0}}q(ze,0,0,Mt,Jt,Mt,this.f,this.v,this.z),q(Ie,xe,xe,Ze,ut,Mt,this.f,this.v,this.z);for(let Xe=0;Xe<$t;Xe++){let nt=Math.sqrt(ze[Xe])-Math.sqrt(Ie[Xe]);yr[Xe]=Math.round(255-255*(nt/this.radius+this.cutoff))}return Cr}};class Z{constructor(){this.specification=a.v.light.position}possiblyEvaluate(w,B){return a.x(w.expression.evaluate(B))}interpolate(w,B,Q){return{x:a.y.number(w.x,B.x,Q),y:a.y.number(w.y,B.y,Q),z:a.y.number(w.z,B.z,Q)}}}let j;class N extends a.E{constructor(w){super(),j=j||new a.q({anchor:new a.D(a.v.light.anchor),position:new Z,color:new a.D(a.v.light.color),intensity:new a.D(a.v.light.intensity)}),this._transitionable=new a.T(j),this.setLight(w),this._transitioning=this._transitionable.untransitioned()}getLight(){return this._transitionable.serialize()}setLight(w,B={}){if(!this._validate(a.r,w,B))for(let Q in w){let ee=w[Q];Q.endsWith(\"-transition\")?this._transitionable.setTransition(Q.slice(0,-11),ee):this._transitionable.setValue(Q,ee)}}updateTransitions(w){this._transitioning=this._transitionable.transitioned(w,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(w){this.properties=this._transitioning.possiblyEvaluate(w)}_validate(w,B,Q){return(!Q||Q.validate!==!1)&&a.t(this,w.call(a.u,{value:B,style:{glyphs:!0,sprite:!0},styleSpec:a.v}))}}let H=new a.q({\"sky-color\":new a.D(a.v.sky[\"sky-color\"]),\"horizon-color\":new a.D(a.v.sky[\"horizon-color\"]),\"fog-color\":new a.D(a.v.sky[\"fog-color\"]),\"fog-ground-blend\":new a.D(a.v.sky[\"fog-ground-blend\"]),\"horizon-fog-blend\":new a.D(a.v.sky[\"horizon-fog-blend\"]),\"sky-horizon-blend\":new a.D(a.v.sky[\"sky-horizon-blend\"]),\"atmosphere-blend\":new a.D(a.v.sky[\"atmosphere-blend\"])});class re extends a.E{constructor(w){super(),this._transitionable=new a.T(H),this.setSky(w),this._transitioning=this._transitionable.untransitioned(),this.recalculate(new a.z(0))}setSky(w,B={}){if(!this._validate(a.B,w,B)){w||(w={\"sky-color\":\"transparent\",\"horizon-color\":\"transparent\",\"fog-color\":\"transparent\",\"fog-ground-blend\":1,\"atmosphere-blend\":0});for(let Q in w){let ee=w[Q];Q.endsWith(\"-transition\")?this._transitionable.setTransition(Q.slice(0,-11),ee):this._transitionable.setValue(Q,ee)}}}getSky(){return this._transitionable.serialize()}updateTransitions(w){this._transitioning=this._transitionable.transitioned(w,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(w){this.properties=this._transitioning.possiblyEvaluate(w)}_validate(w,B,Q={}){return(Q==null?void 0:Q.validate)!==!1&&a.t(this,w.call(a.u,a.e({value:B,style:{glyphs:!0,sprite:!0},styleSpec:a.v})))}calculateFogBlendOpacity(w){return w<60?0:w<70?(w-60)/10:1}}class ae{constructor(w,B){this.width=w,this.height=B,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}}getDash(w,B){let Q=w.join(\",\")+String(B);return this.dashEntry[Q]||(this.dashEntry[Q]=this.addDash(w,B)),this.dashEntry[Q]}getDashRanges(w,B,Q){let ee=[],le=w.length%2==1?-w[w.length-1]*Q:0,Fe=w[0]*Q,Ze=!0;ee.push({left:le,right:Fe,isDash:Ze,zeroLength:w[0]===0});let ut=w[0];for(let Mt=1;Mt<w.length;Mt++){Ze=!Ze;let Jt=w[Mt];le=ut*Q,ut+=Jt,Fe=ut*Q,ee.push({left:le,right:Fe,isDash:Ze,zeroLength:Jt===0})}return ee}addRoundDash(w,B,Q){let ee=B/2;for(let le=-Q;le<=Q;le++){let Fe=this.width*(this.nextRow+Q+le),Ze=0,ut=w[Ze];for(let Mt=0;Mt<this.width;Mt++){Mt/ut.right>1&&(ut=w[++Ze]);let Jt=Math.abs(Mt-ut.left),$t=Math.abs(Mt-ut.right),yr=Math.min(Jt,$t),Cr,ve=le/Q*(ee+1);if(ut.isDash){let xe=ee-Math.abs(ve);Cr=Math.sqrt(yr*yr+xe*xe)}else Cr=ee-Math.sqrt(yr*yr+ve*ve);this.data[Fe+Mt]=Math.max(0,Math.min(255,Cr+128))}}}addRegularDash(w){for(let Ze=w.length-1;Ze>=0;--Ze){let ut=w[Ze],Mt=w[Ze+1];ut.zeroLength?w.splice(Ze,1):Mt&&Mt.isDash===ut.isDash&&(Mt.left=ut.left,w.splice(Ze,1))}let B=w[0],Q=w[w.length-1];B.isDash===Q.isDash&&(B.left=Q.left-this.width,Q.right=B.right+this.width);let ee=this.width*this.nextRow,le=0,Fe=w[le];for(let Ze=0;Ze<this.width;Ze++){Ze/Fe.right>1&&(Fe=w[++le]);let ut=Math.abs(Ze-Fe.left),Mt=Math.abs(Ze-Fe.right),Jt=Math.min(ut,Mt);this.data[ee+Ze]=Math.max(0,Math.min(255,(Fe.isDash?Jt:-Jt)+128))}}addDash(w,B){let Q=B?7:0,ee=2*Q+1;if(this.nextRow+ee>this.height)return a.w(\"LineAtlas out of space\"),null;let le=0;for(let Ze=0;Ze<w.length;Ze++)le+=w[Ze];if(le!==0){let Ze=this.width/le,ut=this.getDashRanges(w,this.width,Ze);B?this.addRoundDash(ut,Ze,Q):this.addRegularDash(ut)}let Fe={y:(this.nextRow+Q+.5)/this.height,height:2*Q/this.height,width:le};return this.nextRow+=ee,this.dirty=!0,Fe}bind(w){let B=w.gl;this.texture?(B.bindTexture(B.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,B.texSubImage2D(B.TEXTURE_2D,0,0,0,this.width,this.height,B.ALPHA,B.UNSIGNED_BYTE,this.data))):(this.texture=B.createTexture(),B.bindTexture(B.TEXTURE_2D,this.texture),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_WRAP_S,B.REPEAT),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_WRAP_T,B.REPEAT),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_MIN_FILTER,B.LINEAR),B.texParameteri(B.TEXTURE_2D,B.TEXTURE_MAG_FILTER,B.LINEAR),B.texImage2D(B.TEXTURE_2D,0,B.ALPHA,this.width,this.height,0,B.ALPHA,B.UNSIGNED_BYTE,this.data))}}let _e=\"maplibre_preloaded_worker_pool\";class Ee{constructor(){this.active={}}acquire(w){if(!this.workers)for(this.workers=[];this.workers.length<Ee.workerCount;)this.workers.push(new Worker(a.a.WORKER_URL));return this.active[w]=!0,this.workers.slice()}release(w){delete this.active[w],this.numActive()===0&&(this.workers.forEach((B=>{B.terminate()})),this.workers=null)}isPreloaded(){return!!this.active[_e]}numActive(){return Object.keys(this.active).length}}let Ce=Math.floor(u.hardwareConcurrency/2),ge,ie;function Se(){return ge||(ge=new Ee),ge}Ee.workerCount=a.C(globalThis)?Math.max(Math.min(Ce,3),1):1;class ke{constructor(w,B){this.workerPool=w,this.actors=[],this.currentActor=0,this.id=B;let Q=this.workerPool.acquire(B);for(let ee=0;ee<Q.length;ee++){let le=new a.F(Q[ee],B);le.name=`Worker ${ee}`,this.actors.push(le)}if(!this.actors.length)throw new Error(\"No actors found\")}broadcast(w,B){let Q=[];for(let ee of this.actors)Q.push(ee.sendAsync({type:w,data:B}));return Promise.all(Q)}getActor(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]}remove(w=!0){this.actors.forEach((B=>{B.remove()})),this.actors=[],w&&this.workerPool.release(this.id)}registerMessageHandler(w,B){for(let Q of this.actors)Q.registerMessageHandler(w,B)}}function be(){return ie||(ie=new ke(Se(),a.G),ie.registerMessageHandler(\"GR\",((ue,w,B)=>a.m(w,B)))),ie}function Be(ue,w){let B=a.H();return a.J(B,B,[1,1,0]),a.K(B,B,[.5*ue.width,.5*ue.height,1]),a.L(B,B,ue.calculatePosMatrix(w.toUnwrapped()))}function Le(ue,w,B,Q,ee,le){let Fe=(function($t,yr,Cr){if($t)for(let ve of $t){let xe=yr[ve];if(xe&&xe.source===Cr&&xe.type===\"fill-extrusion\")return!0}else for(let ve in yr){let xe=yr[ve];if(xe.source===Cr&&xe.type===\"fill-extrusion\")return!0}return!1})(ee&&ee.layers,w,ue.id),Ze=le.maxPitchScaleFactor(),ut=ue.tilesIn(Q,Ze,Fe);ut.sort(me);let Mt=[];for(let $t of ut)Mt.push({wrappedTileID:$t.tileID.wrapped().key,queryResults:$t.tile.queryRenderedFeatures(w,B,ue._state,$t.queryGeometry,$t.cameraQueryGeometry,$t.scale,ee,le,Ze,Be(ue.transform,$t.tileID))});let Jt=(function($t){let yr={},Cr={};for(let ve of $t){let xe=ve.queryResults,Ie=ve.wrappedTileID,ze=Cr[Ie]=Cr[Ie]||{};for(let Qe in xe){let Xe=xe[Qe],nt=ze[Qe]=ze[Qe]||{},qt=yr[Qe]=yr[Qe]||[];for(let Wt of Xe)nt[Wt.featureIndex]||(nt[Wt.featureIndex]=!0,qt.push(Wt))}}return yr})(Mt);for(let $t in Jt)Jt[$t].forEach((yr=>{let Cr=yr.feature,ve=ue.getFeatureState(Cr.layer[\"source-layer\"],Cr.id);Cr.source=Cr.layer.source,Cr.layer[\"source-layer\"]&&(Cr.sourceLayer=Cr.layer[\"source-layer\"]),Cr.state=ve}));return Jt}function me(ue,w){let B=ue.tileID,Q=w.tileID;return B.overscaledZ-Q.overscaledZ||B.canonical.y-Q.canonical.y||B.wrap-Q.wrap||B.canonical.x-Q.canonical.x}function Pe(ue,w,B){return a._(this,void 0,void 0,(function*(){let Q=ue;if(ue.url?Q=(yield a.h(w.transformRequest(ue.url,\"Source\"),B)).data:yield u.frameAsync(B),!Q)return null;let ee=a.M(a.e(Q,ue),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);return\"vector_layers\"in Q&&Q.vector_layers&&(ee.vectorLayerIds=Q.vector_layers.map((le=>le.id))),ee}))}class ce{constructor(w,B){w&&(B?this.setSouthWest(w).setNorthEast(B):Array.isArray(w)&&(w.length===4?this.setSouthWest([w[0],w[1]]).setNorthEast([w[2],w[3]]):this.setSouthWest(w[0]).setNorthEast(w[1])))}setNorthEast(w){return this._ne=w instanceof a.N?new a.N(w.lng,w.lat):a.N.convert(w),this}setSouthWest(w){return this._sw=w instanceof a.N?new a.N(w.lng,w.lat):a.N.convert(w),this}extend(w){let B=this._sw,Q=this._ne,ee,le;if(w instanceof a.N)ee=w,le=w;else{if(!(w instanceof ce))return Array.isArray(w)?w.length===4||w.every(Array.isArray)?this.extend(ce.convert(w)):this.extend(a.N.convert(w)):w&&(\"lng\"in w||\"lon\"in w)&&\"lat\"in w?this.extend(a.N.convert(w)):this;if(ee=w._sw,le=w._ne,!ee||!le)return this}return B||Q?(B.lng=Math.min(ee.lng,B.lng),B.lat=Math.min(ee.lat,B.lat),Q.lng=Math.max(le.lng,Q.lng),Q.lat=Math.max(le.lat,Q.lat)):(this._sw=new a.N(ee.lng,ee.lat),this._ne=new a.N(le.lng,le.lat)),this}getCenter(){return new a.N((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new a.N(this.getWest(),this.getNorth())}getSouthEast(){return new a.N(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(w){let{lng:B,lat:Q}=a.N.convert(w),ee=this._sw.lng<=B&&B<=this._ne.lng;return this._sw.lng>this._ne.lng&&(ee=this._sw.lng>=B&&B>=this._ne.lng),this._sw.lat<=Q&&Q<=this._ne.lat&&ee}static convert(w){return w instanceof ce?w:w&&new ce(w)}static fromLngLat(w,B=0){let Q=360*B/40075017,ee=Q/Math.cos(Math.PI/180*w.lat);return new ce(new a.N(w.lng-ee,w.lat-Q),new a.N(w.lng+ee,w.lat+Q))}adjustAntiMeridian(){let w=new a.N(this._sw.lng,this._sw.lat),B=new a.N(this._ne.lng,this._ne.lat);return new ce(w,w.lng>B.lng?new a.N(B.lng+360,B.lat):B)}}class He{constructor(w,B,Q){this.bounds=ce.convert(this.validateBounds(w)),this.minzoom=B||0,this.maxzoom=Q||24}validateBounds(w){return Array.isArray(w)&&w.length===4?[Math.max(-180,w[0]),Math.max(-90,w[1]),Math.min(180,w[2]),Math.min(90,w[3])]:[-180,-90,180,90]}contains(w){let B=Math.pow(2,w.z),Q=Math.floor(a.O(this.bounds.getWest())*B),ee=Math.floor(a.Q(this.bounds.getNorth())*B),le=Math.ceil(a.O(this.bounds.getEast())*B),Fe=Math.ceil(a.Q(this.bounds.getSouth())*B);return w.x>=Q&&w.x<le&&w.y>=ee&&w.y<Fe}}class lt extends a.E{constructor(w,B,Q,ee){if(super(),this.id=w,this.dispatcher=Q,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,a.e(this,a.M(B,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=a.e({type:\"vector\"},B),this._collectResourceTiming=B.collectResourceTiming,this.tileSize!==512)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(ee)}load(){return a._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new a.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=new AbortController;try{let w=yield Pe(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,this.map.style.sourceCaches[this.id].clearTiles(),w&&(a.e(this,w),w.bounds&&(this.tileBounds=new He(w.bounds,this.minzoom,this.maxzoom)),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}catch(w){this._tileJSONRequest=null,this.fire(new a.j(w))}}))}loaded(){return this._loaded}hasTile(w){return!this.tileBounds||this.tileBounds.contains(w.canonical)}onAdd(w){this.map=w,this.load()}setSourceProperty(w){this._tileJSONRequest&&this._tileJSONRequest.abort(),w(),this.load()}setTiles(w){return this.setSourceProperty((()=>{this._options.tiles=w})),this}setUrl(w){return this.setSourceProperty((()=>{this.url=w,this._options.url=w})),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}serialize(){return a.e({},this._options)}loadTile(w){return a._(this,void 0,void 0,(function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),Q={request:this.map._requestManager.transformRequest(B,\"Tile\"),uid:w.uid,tileID:w.tileID,zoom:w.tileID.overscaledZ,tileSize:this.tileSize*w.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};Q.request.collectResourceTiming=this._collectResourceTiming;let ee=\"RT\";if(w.actor&&w.state!==\"expired\"){if(w.state===\"loading\")return new Promise(((le,Fe)=>{w.reloadPromise={resolve:le,reject:Fe}}))}else w.actor=this.dispatcher.getActor(),ee=\"LT\";w.abortController=new AbortController;try{let le=yield w.actor.sendAsync({type:ee,data:Q},w.abortController);if(delete w.abortController,w.aborted)return;this._afterTileLoadWorkerResponse(w,le)}catch(le){if(delete w.abortController,w.aborted)return;if(le&&le.status!==404)throw le;this._afterTileLoadWorkerResponse(w,null)}}))}_afterTileLoadWorkerResponse(w,B){if(B&&B.resourceTiming&&(w.resourceTiming=B.resourceTiming),B&&this.map._refreshExpiredTiles&&w.setExpiryData(B),w.loadVectorData(B,this.map.painter),w.reloadPromise){let Q=w.reloadPromise;w.reloadPromise=null,this.loadTile(w).then(Q.resolve).catch(Q.reject)}}abortTile(w){return a._(this,void 0,void 0,(function*(){w.abortController&&(w.abortController.abort(),delete w.abortController),w.actor&&(yield w.actor.sendAsync({type:\"AT\",data:{uid:w.uid,type:this.type,source:this.id}}))}))}unloadTile(w){return a._(this,void 0,void 0,(function*(){w.unloadVectorData(),w.actor&&(yield w.actor.sendAsync({type:\"RMT\",data:{uid:w.uid,type:this.type,source:this.id}}))}))}hasTransition(){return!1}}class mt extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.dispatcher=Q,this.setEventedParent(ee),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=a.e({type:\"raster\"},B),a.e(this,a.M(B,[\"url\",\"scheme\",\"tileSize\"]))}load(){return a._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new a.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=new AbortController;try{let w=yield Pe(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,w&&(a.e(this,w),w.bounds&&(this.tileBounds=new He(w.bounds,this.minzoom,this.maxzoom)),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}catch(w){this._tileJSONRequest=null,this.fire(new a.j(w))}}))}loaded(){return this._loaded}onAdd(w){this.map=w,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}setSourceProperty(w){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null),w(),this.load()}setTiles(w){return this.setSourceProperty((()=>{this._options.tiles=w})),this}setUrl(w){return this.setSourceProperty((()=>{this.url=w,this._options.url=w})),this}serialize(){return a.e({},this._options)}hasTile(w){return!this.tileBounds||this.tileBounds.contains(w.canonical)}loadTile(w){return a._(this,void 0,void 0,(function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);w.abortController=new AbortController;try{let Q=yield p.getImage(this.map._requestManager.transformRequest(B,\"Tile\"),w.abortController,this.map._refreshExpiredTiles);if(delete w.abortController,w.aborted)return void(w.state=\"unloaded\");if(Q&&Q.data){this.map._refreshExpiredTiles&&Q.cacheControl&&Q.expires&&w.setExpiryData({cacheControl:Q.cacheControl,expires:Q.expires});let ee=this.map.painter.context,le=ee.gl,Fe=Q.data;w.texture=this.map.painter.getTileTexture(Fe.width),w.texture?w.texture.update(Fe,{useMipmap:!0}):(w.texture=new g(ee,Fe,le.RGBA,{useMipmap:!0}),w.texture.bind(le.LINEAR,le.CLAMP_TO_EDGE,le.LINEAR_MIPMAP_NEAREST)),w.state=\"loaded\"}}catch(Q){if(delete w.abortController,w.aborted)w.state=\"unloaded\";else if(Q)throw w.state=\"errored\",Q}}))}abortTile(w){return a._(this,void 0,void 0,(function*(){w.abortController&&(w.abortController.abort(),delete w.abortController)}))}unloadTile(w){return a._(this,void 0,void 0,(function*(){w.texture&&this.map.painter.saveTileTexture(w.texture)}))}hasTransition(){return!1}}class Ht extends mt{constructor(w,B,Q,ee){super(w,B,Q,ee),this.type=\"raster-dem\",this.maxzoom=22,this._options=a.e({type:\"raster-dem\"},B),this.encoding=B.encoding||\"mapbox\",this.redFactor=B.redFactor,this.greenFactor=B.greenFactor,this.blueFactor=B.blueFactor,this.baseShift=B.baseShift}loadTile(w){return a._(this,void 0,void 0,(function*(){let B=w.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),Q=this.map._requestManager.transformRequest(B,\"Tile\");w.neighboringTiles=this._getNeighboringTiles(w.tileID),w.abortController=new AbortController;try{let ee=yield p.getImage(Q,w.abortController,this.map._refreshExpiredTiles);if(delete w.abortController,w.aborted)return void(w.state=\"unloaded\");if(ee&&ee.data){let le=ee.data;this.map._refreshExpiredTiles&&ee.cacheControl&&ee.expires&&w.setExpiryData({cacheControl:ee.cacheControl,expires:ee.expires});let Fe=a.b(le)&&a.U()?le:yield this.readImageNow(le),Ze={type:this.type,uid:w.uid,source:this.id,rawImageData:Fe,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};if(!w.actor||w.state===\"expired\"){w.actor=this.dispatcher.getActor();let ut=yield w.actor.sendAsync({type:\"LDT\",data:Ze});w.dem=ut,w.needsHillshadePrepare=!0,w.needsTerrainPrepare=!0,w.state=\"loaded\"}}}catch(ee){if(delete w.abortController,w.aborted)w.state=\"unloaded\";else if(ee)throw w.state=\"errored\",ee}}))}readImageNow(w){return a._(this,void 0,void 0,(function*(){if(typeof VideoFrame!=\"undefined\"&&a.V()){let B=w.width+2,Q=w.height+2;try{return new a.R({width:B,height:Q},yield a.W(w,-1,-1,B,Q))}catch(ee){}}return u.getImageData(w,1)}))}_getNeighboringTiles(w){let B=w.canonical,Q=Math.pow(2,B.z),ee=(B.x-1+Q)%Q,le=B.x===0?w.wrap-1:w.wrap,Fe=(B.x+1+Q)%Q,Ze=B.x+1===Q?w.wrap+1:w.wrap,ut={};return ut[new a.S(w.overscaledZ,le,B.z,ee,B.y).key]={backfilled:!1},ut[new a.S(w.overscaledZ,Ze,B.z,Fe,B.y).key]={backfilled:!1},B.y>0&&(ut[new a.S(w.overscaledZ,le,B.z,ee,B.y-1).key]={backfilled:!1},ut[new a.S(w.overscaledZ,w.wrap,B.z,B.x,B.y-1).key]={backfilled:!1},ut[new a.S(w.overscaledZ,Ze,B.z,Fe,B.y-1).key]={backfilled:!1}),B.y+1<Q&&(ut[new a.S(w.overscaledZ,le,B.z,ee,B.y+1).key]={backfilled:!1},ut[new a.S(w.overscaledZ,w.wrap,B.z,B.x,B.y+1).key]={backfilled:!1},ut[new a.S(w.overscaledZ,Ze,B.z,Fe,B.y+1).key]={backfilled:!1}),ut}unloadTile(w){return a._(this,void 0,void 0,(function*(){w.demTexture&&this.map.painter.saveTileTexture(w.demTexture),w.fbo&&(w.fbo.destroy(),delete w.fbo),w.dem&&delete w.dem,delete w.neighboringTiles,w.state=\"unloaded\",w.actor&&(yield w.actor.sendAsync({type:\"RDT\",data:{type:this.type,uid:w.uid,source:this.id}}))}))}}class at extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._pendingLoads=0,this.actor=Q.getActor(),this.setEventedParent(ee),this._data=B.data,this._options=a.e({},B),this._collectResourceTiming=B.collectResourceTiming,B.maxzoom!==void 0&&(this.maxzoom=B.maxzoom),B.type&&(this.type=B.type),B.attribution&&(this.attribution=B.attribution),this.promoteId=B.promoteId;let le=a.X/this.tileSize;B.clusterMaxZoom!==void 0&&this.maxzoom<=B.clusterMaxZoom&&a.w(`The maxzoom value \"${this.maxzoom}\" is expected to be greater than the clusterMaxZoom value \"${B.clusterMaxZoom}\".`),this.workerOptions=a.e({source:this.id,cluster:B.cluster||!1,geojsonVtOptions:{buffer:(B.buffer!==void 0?B.buffer:128)*le,tolerance:(B.tolerance!==void 0?B.tolerance:.375)*le,extent:a.X,maxZoom:this.maxzoom,lineMetrics:B.lineMetrics||!1,generateId:B.generateId||!1},superclusterOptions:{maxZoom:B.clusterMaxZoom!==void 0?B.clusterMaxZoom:this.maxzoom-1,minPoints:Math.max(2,B.clusterMinPoints||2),extent:a.X,radius:(B.clusterRadius||50)*le,log:!1,generateId:B.generateId||!1},clusterProperties:B.clusterProperties,filter:B.filter},B.workerOptions),typeof this.promoteId==\"string\"&&(this.workerOptions.promoteId=this.promoteId)}load(){return a._(this,void 0,void 0,(function*(){yield this._updateWorkerData()}))}onAdd(w){this.map=w,this.load()}setData(w){return this._data=w,this._updateWorkerData(),this}updateData(w){return this._updateWorkerData(w),this}getData(){return a._(this,void 0,void 0,(function*(){let w=a.e({type:this.type},this.workerOptions);return this.actor.sendAsync({type:\"GD\",data:w})}))}setClusterOptions(w){return this.workerOptions.cluster=w.cluster,w&&(w.clusterRadius!==void 0&&(this.workerOptions.superclusterOptions.radius=w.clusterRadius),w.clusterMaxZoom!==void 0&&(this.workerOptions.superclusterOptions.maxZoom=w.clusterMaxZoom)),this._updateWorkerData(),this}getClusterExpansionZoom(w){return this.actor.sendAsync({type:\"GCEZ\",data:{type:this.type,clusterId:w,source:this.id}})}getClusterChildren(w){return this.actor.sendAsync({type:\"GCC\",data:{type:this.type,clusterId:w,source:this.id}})}getClusterLeaves(w,B,Q){return this.actor.sendAsync({type:\"GCL\",data:{type:this.type,source:this.id,clusterId:w,limit:B,offset:Q}})}_updateWorkerData(w){return a._(this,void 0,void 0,(function*(){let B=a.e({type:this.type},this.workerOptions);w?B.dataDiff=w:typeof this._data==\"string\"?(B.request=this.map._requestManager.transformRequest(u.resolveURL(this._data),\"Source\"),B.request.collectResourceTiming=this._collectResourceTiming):B.data=JSON.stringify(this._data),this._pendingLoads++,this.fire(new a.k(\"dataloading\",{dataType:\"source\"}));try{let Q=yield this.actor.sendAsync({type:\"LD\",data:B});if(this._pendingLoads--,this._removed||Q.abandoned)return void this.fire(new a.k(\"dataabort\",{dataType:\"source\"}));let ee=null;Q.resourceTiming&&Q.resourceTiming[this.id]&&(ee=Q.resourceTiming[this.id].slice(0));let le={dataType:\"source\"};this._collectResourceTiming&&ee&&ee.length>0&&a.e(le,{resourceTiming:ee}),this.fire(new a.k(\"data\",Object.assign(Object.assign({},le),{sourceDataType:\"metadata\"}))),this.fire(new a.k(\"data\",Object.assign(Object.assign({},le),{sourceDataType:\"content\"})))}catch(Q){if(this._pendingLoads--,this._removed)return void this.fire(new a.k(\"dataabort\",{dataType:\"source\"}));this.fire(new a.j(Q))}}))}loaded(){return this._pendingLoads===0}loadTile(w){return a._(this,void 0,void 0,(function*(){let B=w.actor?\"RT\":\"LT\";w.actor=this.actor;let Q={type:this.type,uid:w.uid,tileID:w.tileID,zoom:w.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};w.abortController=new AbortController;let ee=yield this.actor.sendAsync({type:B,data:Q},w.abortController);delete w.abortController,w.unloadVectorData(),w.aborted||w.loadVectorData(ee,this.map.painter,B===\"RT\")}))}abortTile(w){return a._(this,void 0,void 0,(function*(){w.abortController&&(w.abortController.abort(),delete w.abortController),w.aborted=!0}))}unloadTile(w){return a._(this,void 0,void 0,(function*(){w.unloadVectorData(),yield this.actor.sendAsync({type:\"RMT\",data:{uid:w.uid,type:this.type,source:this.id}})}))}onRemove(){this._removed=!0,this.actor.sendAsync({type:\"RS\",data:{type:this.type,source:this.id}})}serialize(){return a.e({},this._options,{type:this.type,data:this._data})}hasTransition(){return!1}}var ct=a.Y([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]);class ar extends a.E{constructor(w,B,Q,ee){super(),this.id=w,this.dispatcher=Q,this.coordinates=B.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(ee),this.options=B}load(w){return a._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new a.k(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,this._request=new AbortController;try{let B=yield p.getImage(this.map._requestManager.transformRequest(this.url,\"Image\"),this._request);this._request=null,this._loaded=!0,B&&B.data&&(this.image=B.data,w&&(this.coordinates=w),this._finishLoading())}catch(B){this._request=null,this._loaded=!0,this.fire(new a.j(B))}}))}loaded(){return this._loaded}updateImage(w){return w.url?(this._request&&(this._request.abort(),this._request=null),this.options.url=w.url,this.load(w.coordinates).finally((()=>{this.texture=null})),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))}onAdd(w){this.map=w,this.load()}onRemove(){this._request&&(this._request.abort(),this._request=null)}setCoordinates(w){this.coordinates=w;let B=w.map(a.Z.fromLngLat);this.tileID=(function(ee){let le=1/0,Fe=1/0,Ze=-1/0,ut=-1/0;for(let yr of ee)le=Math.min(le,yr.x),Fe=Math.min(Fe,yr.y),Ze=Math.max(Ze,yr.x),ut=Math.max(ut,yr.y);let Mt=Math.max(Ze-le,ut-Fe),Jt=Math.max(0,Math.floor(-Math.log(Mt)/Math.LN2)),$t=Math.pow(2,Jt);return new a.a1(Jt,Math.floor((le+Ze)/2*$t),Math.floor((Fe+ut)/2*$t))})(B),this.minzoom=this.maxzoom=this.tileID.z;let Q=B.map((ee=>this.tileID.getTilePoint(ee)._round()));return this._boundsArray=new a.$,this._boundsArray.emplaceBack(Q[0].x,Q[0].y,0,0),this._boundsArray.emplaceBack(Q[1].x,Q[1].y,a.X,0),this._boundsArray.emplaceBack(Q[3].x,Q[3].y,0,a.X),this._boundsArray.emplaceBack(Q[2].x,Q[2].y,a.X,a.X),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this}prepare(){if(Object.keys(this.tiles).length===0||!this.image)return;let w=this.map.painter.context,B=w.gl;this.boundsBuffer||(this.boundsBuffer=w.createVertexBuffer(this._boundsArray,ct.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture||(this.texture=new g(w,this.image,B.RGBA),this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE));let Q=!1;for(let ee in this.tiles){let le=this.tiles[ee];le.state!==\"loaded\"&&(le.state=\"loaded\",le.texture=this.texture,Q=!0)}Q&&this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}loadTile(w){return a._(this,void 0,void 0,(function*(){this.tileID&&this.tileID.equals(w.tileID.canonical)?(this.tiles[String(w.tileID.wrap)]=w,w.buckets={}):w.state=\"errored\"}))}serialize(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}}hasTransition(){return!1}}class Vt extends ar{constructor(w,B,Q,ee){super(w,B,Q,ee),this.roundZoom=!0,this.type=\"video\",this.options=B}load(){return a._(this,void 0,void 0,(function*(){this._loaded=!1;let w=this.options;this.urls=[];for(let B of w.urls)this.urls.push(this.map._requestManager.transformRequest(B,\"Source\").url);try{let B=yield a.a3(this.urls);if(this._loaded=!0,!B)return;this.video=B,this.video.loop=!0,this.video.addEventListener(\"playing\",(()=>{this.map.triggerRepaint()})),this.map&&this.video.play(),this._finishLoading()}catch(B){this.fire(new a.j(B))}}))}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(w){if(this.video){let B=this.video.seekable;w<B.start(0)||w>B.end(0)?this.fire(new a.j(new a.a2(`sources.${this.id}`,null,`Playback for this video can be set only between the ${B.start(0)} and ${B.end(0)}-second mark.`))):this.video.currentTime=w}}getVideo(){return this.video}onAdd(w){this.map||(this.map=w,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}prepare(){if(Object.keys(this.tiles).length===0||this.video.readyState<2)return;let w=this.map.painter.context,B=w.gl;this.boundsBuffer||(this.boundsBuffer=w.createVertexBuffer(this._boundsArray,ct.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE),B.texSubImage2D(B.TEXTURE_2D,0,0,0,B.RGBA,B.UNSIGNED_BYTE,this.video)):(this.texture=new g(w,this.video,B.RGBA),this.texture.bind(B.LINEAR,B.CLAMP_TO_EDGE));let Q=!1;for(let ee in this.tiles){let le=this.tiles[ee];le.state!==\"loaded\"&&(le.state=\"loaded\",le.texture=this.texture,Q=!0)}Q&&this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}hasTransition(){return this.video&&!this.video.paused}}class rr extends ar{constructor(w,B,Q,ee){super(w,B,Q,ee),B.coordinates?Array.isArray(B.coordinates)&&B.coordinates.length===4&&!B.coordinates.some((le=>!Array.isArray(le)||le.length!==2||le.some((Fe=>typeof Fe!=\"number\"))))||this.fire(new a.j(new a.a2(`sources.${w}`,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new a.j(new a.a2(`sources.${w}`,null,'missing required property \"coordinates\"'))),B.animate&&typeof B.animate!=\"boolean\"&&this.fire(new a.j(new a.a2(`sources.${w}`,null,'optional \"animate\" property must be a boolean value'))),B.canvas?typeof B.canvas==\"string\"||B.canvas instanceof HTMLCanvasElement||this.fire(new a.j(new a.a2(`sources.${w}`,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new a.j(new a.a2(`sources.${w}`,null,'missing required property \"canvas\"'))),this.options=B,this.animate=B.animate===void 0||B.animate}load(){return a._(this,void 0,void 0,(function*(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new a.j(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())}))}getCanvas(){return this.canvas}onAdd(w){this.map=w,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}prepare(){let w=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,w=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,w=!0),this._hasInvalidDimensions()||Object.keys(this.tiles).length===0)return;let B=this.map.painter.context,Q=B.gl;this.boundsBuffer||(this.boundsBuffer=B.createVertexBuffer(this._boundsArray,ct.members)),this.boundsSegments||(this.boundsSegments=a.a0.simpleSegment(0,0,4,2)),this.texture?(w||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new g(B,this.canvas,Q.RGBA,{premultiply:!0});let ee=!1;for(let le in this.tiles){let Fe=this.tiles[le];Fe.state!==\"loaded\"&&(Fe.state=\"loaded\",Fe.texture=this.texture,ee=!0)}ee&&this.fire(new a.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"canvas\",coordinates:this.coordinates}}hasTransition(){return this._playing}_hasInvalidDimensions(){for(let w of[this.canvas.width,this.canvas.height])if(isNaN(w)||w<=0)return!0;return!1}}let tt={},je=ue=>{switch(ue){case\"geojson\":return at;case\"image\":return ar;case\"raster\":return mt;case\"raster-dem\":return Ht;case\"vector\":return lt;case\"video\":return Vt;case\"canvas\":return rr}return tt[ue]},Ue=\"RTLPluginLoaded\";class Ae extends a.E{constructor(){super(...arguments),this.status=\"unavailable\",this.url=null,this.dispatcher=be()}_syncState(w){return this.status=w,this.dispatcher.broadcast(\"SRPS\",{pluginStatus:w,pluginURL:this.url}).catch((B=>{throw this.status=\"error\",B}))}getRTLTextPluginStatus(){return this.status}clearRTLTextPlugin(){this.status=\"unavailable\",this.url=null}setRTLTextPlugin(w){return a._(this,arguments,void 0,(function*(B,Q=!1){if(this.url)throw new Error(\"setRTLTextPlugin cannot be called multiple times.\");if(this.url=u.resolveURL(B),!this.url)throw new Error(`requested url ${B} is invalid`);if(this.status===\"unavailable\"){if(!Q)return this._requestImport();this.status=\"deferred\",this._syncState(this.status)}else if(this.status===\"requested\")return this._requestImport()}))}_requestImport(){return a._(this,void 0,void 0,(function*(){yield this._syncState(\"loading\"),this.status=\"loaded\",this.fire(new a.k(Ue))}))}lazyLoad(){this.status===\"unavailable\"?this.status=\"requested\":this.status===\"deferred\"&&this._requestImport()}}let rt=null;function St(){return rt||(rt=new Ae),rt}class Tt{constructor(w,B){this.timeAdded=0,this.fadeEndTime=0,this.tileID=w,this.uid=a.a4(),this.uses=0,this.tileSize=B,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttCoords={},this.expiredRequestCount=0,this.state=\"loading\"}registerFadeDuration(w){let B=w+this.timeAdded;B<this.fadeEndTime||(this.fadeEndTime=B)}wasRequested(){return this.state===\"errored\"||this.state===\"loaded\"||this.state===\"reloading\"}clearTextures(w){this.demTexture&&w.saveTileTexture(this.demTexture),this.demTexture=null}loadVectorData(w,B,Q){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",w){w.featureIndex&&(this.latestFeatureIndex=w.featureIndex,w.rawTileData?(this.latestRawTileData=w.rawTileData,this.latestFeatureIndex.rawTileData=w.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=w.collisionBoxArray,this.buckets=(function(ee,le){let Fe={};if(!le)return Fe;for(let Ze of ee){let ut=Ze.layerIds.map((Mt=>le.getLayer(Mt))).filter(Boolean);if(ut.length!==0){Ze.layers=ut,Ze.stateDependentLayerIds&&(Ze.stateDependentLayers=Ze.stateDependentLayerIds.map((Mt=>ut.filter((Jt=>Jt.id===Mt))[0])));for(let Mt of ut)Fe[Mt.id]=Ze}}return Fe})(w.buckets,B.style),this.hasSymbolBuckets=!1;for(let ee in this.buckets){let le=this.buckets[ee];if(le instanceof a.a6){if(this.hasSymbolBuckets=!0,!Q)break;le.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(let ee in this.buckets){let le=this.buckets[ee];if(le instanceof a.a6&&le.hasRTLText){this.hasRTLText=!0,St().lazyLoad();break}}this.queryPadding=0;for(let ee in this.buckets){let le=this.buckets[ee];this.queryPadding=Math.max(this.queryPadding,B.style.getLayer(ee).queryRadius(le))}w.imageAtlas&&(this.imageAtlas=w.imageAtlas),w.glyphAtlasImage&&(this.glyphAtlasImage=w.glyphAtlasImage)}else this.collisionBoxArray=new a.a5}unloadVectorData(){for(let w in this.buckets)this.buckets[w].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"}getBucket(w){return this.buckets[w.id]}upload(w){for(let Q in this.buckets){let ee=this.buckets[Q];ee.uploadPending()&&ee.upload(w)}let B=w.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new g(w,this.imageAtlas.image,B.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new g(w,this.glyphAtlasImage,B.ALPHA),this.glyphAtlasImage=null)}prepare(w){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(w,this.imageAtlasTexture)}queryRenderedFeatures(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:ee,cameraQueryGeometry:le,scale:Fe,tileSize:this.tileSize,pixelPosMatrix:Jt,transform:ut,params:Ze,queryPadding:this.queryPadding*Mt},w,B,Q):{}}querySourceFeatures(w,B){let Q=this.latestFeatureIndex;if(!Q||!Q.rawTileData)return;let ee=Q.loadVTLayers(),le=B&&B.sourceLayer?B.sourceLayer:\"\",Fe=ee._geojsonTileLayer||ee[le];if(!Fe)return;let Ze=a.a7(B&&B.filter),{z:ut,x:Mt,y:Jt}=this.tileID.canonical,$t={z:ut,x:Mt,y:Jt};for(let yr=0;yr<Fe.length;yr++){let Cr=Fe.feature(yr);if(Ze.needGeometry){let Ie=a.a8(Cr,!0);if(!Ze.filter(new a.z(this.tileID.overscaledZ),Ie,this.tileID.canonical))continue}else if(!Ze.filter(new a.z(this.tileID.overscaledZ),Cr))continue;let ve=Q.getId(Cr,le),xe=new a.a9(Cr,ut,Mt,Jt,ve);xe.tile=$t,w.push(xe)}}hasData(){return this.state===\"loaded\"||this.state===\"reloading\"||this.state===\"expired\"}patternsLoaded(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length}setExpiryData(w){let B=this.expirationTime;if(w.cacheControl){let Q=a.aa(w.cacheControl);Q[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*Q[\"max-age\"])}else w.expires&&(this.expirationTime=new Date(w.expires).getTime());if(this.expirationTime){let Q=Date.now(),ee=!1;if(this.expirationTime>Q)ee=!1;else if(B)if(this.expirationTime<B)ee=!0;else{let le=this.expirationTime-B;le?this.expirationTime=Q+Math.max(le,3e4):ee=!0}else ee=!0;ee?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}}getExpiryTimeout(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-new Date().getTime(),Math.pow(2,31)-1)}setFeatureState(w,B){if(!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData||Object.keys(w).length===0)return;let Q=this.latestFeatureIndex.loadVTLayers();for(let ee in this.buckets){if(!B.style.hasLayer(ee))continue;let le=this.buckets[ee],Fe=le.layers[0].sourceLayer||\"_geojsonTileLayer\",Ze=Q[Fe],ut=w[Fe];if(!Ze||!ut||Object.keys(ut).length===0)continue;le.update(ut,Ze,this.imageAtlas&&this.imageAtlas.patternPositions||{});let Mt=B&&B.style&&B.style.getLayer(ee);Mt&&(this.queryPadding=Math.max(this.queryPadding,Mt.queryRadius(le)))}}holdingForFade(){return this.symbolFadeHoldUntil!==void 0}symbolFadeFinished(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<u.now()}clearFadeHold(){this.symbolFadeHoldUntil=void 0}setHoldDuration(w){this.symbolFadeHoldUntil=u.now()+w}setDependencies(w,B){let Q={};for(let ee of B)Q[ee]=!0;this.dependencies[w]=Q}hasDependency(w,B){for(let Q of w){let ee=this.dependencies[Q];if(ee){for(let le of B)if(ee[le])return!0}}return!1}}class dt{constructor(w,B){this.max=w,this.onRemove=B,this.reset()}reset(){for(let w in this.data)for(let B of this.data[w])B.timeout&&clearTimeout(B.timeout),this.onRemove(B.value);return this.data={},this.order=[],this}add(w,B,Q){let ee=w.wrapped().key;this.data[ee]===void 0&&(this.data[ee]=[]);let le={value:B,timeout:void 0};if(Q!==void 0&&(le.timeout=setTimeout((()=>{this.remove(w,le)}),Q)),this.data[ee].push(le),this.order.push(ee),this.order.length>this.max){let Fe=this._getAndRemoveByKey(this.order[0]);Fe&&this.onRemove(Fe)}return this}has(w){return w.wrapped().key in this.data}getAndRemove(w){return this.has(w)?this._getAndRemoveByKey(w.wrapped().key):null}_getAndRemoveByKey(w){let B=this.data[w].shift();return B.timeout&&clearTimeout(B.timeout),this.data[w].length===0&&delete this.data[w],this.order.splice(this.order.indexOf(w),1),B.value}getByKey(w){let B=this.data[w];return B?B[0].value:null}get(w){return this.has(w)?this.data[w.wrapped().key][0].value:null}remove(w,B){if(!this.has(w))return this;let Q=w.wrapped().key,ee=B===void 0?0:this.data[Q].indexOf(B),le=this.data[Q][ee];return this.data[Q].splice(ee,1),le.timeout&&clearTimeout(le.timeout),this.data[Q].length===0&&delete this.data[Q],this.onRemove(le.value),this.order.splice(this.order.indexOf(Q),1),this}setMaxSize(w){for(this.max=w;this.order.length>this.max;){let B=this._getAndRemoveByKey(this.order[0]);B&&this.onRemove(B)}return this}filter(w){let B=[];for(let Q in this.data)for(let ee of this.data[Q])w(ee.value)||B.push(ee);for(let Q of B)this.remove(Q.value.tileID,Q)}}class Et{constructor(){this.state={},this.stateChanges={},this.deletedStates={}}updateState(w,B,Q){let ee=String(B);if(this.stateChanges[w]=this.stateChanges[w]||{},this.stateChanges[w][ee]=this.stateChanges[w][ee]||{},a.e(this.stateChanges[w][ee],Q),this.deletedStates[w]===null){this.deletedStates[w]={};for(let le in this.state[w])le!==ee&&(this.deletedStates[w][le]=null)}else if(this.deletedStates[w]&&this.deletedStates[w][ee]===null){this.deletedStates[w][ee]={};for(let le in this.state[w][ee])Q[le]||(this.deletedStates[w][ee][le]=null)}else for(let le in Q)this.deletedStates[w]&&this.deletedStates[w][ee]&&this.deletedStates[w][ee][le]===null&&delete this.deletedStates[w][ee][le]}removeFeatureState(w,B,Q){if(this.deletedStates[w]===null)return;let ee=String(B);if(this.deletedStates[w]=this.deletedStates[w]||{},Q&&B!==void 0)this.deletedStates[w][ee]!==null&&(this.deletedStates[w][ee]=this.deletedStates[w][ee]||{},this.deletedStates[w][ee][Q]=null);else if(B!==void 0)if(this.stateChanges[w]&&this.stateChanges[w][ee])for(Q in this.deletedStates[w][ee]={},this.stateChanges[w][ee])this.deletedStates[w][ee][Q]=null;else this.deletedStates[w][ee]=null;else this.deletedStates[w]=null}getState(w,B){let Q=String(B),ee=a.e({},(this.state[w]||{})[Q],(this.stateChanges[w]||{})[Q]);if(this.deletedStates[w]===null)return{};if(this.deletedStates[w]){let le=this.deletedStates[w][B];if(le===null)return{};for(let Fe in le)delete ee[Fe]}return ee}initializeTileState(w,B){w.setFeatureState(this.state,B)}coalesceChanges(w,B){let Q={};for(let ee in this.stateChanges){this.state[ee]=this.state[ee]||{};let le={};for(let Fe in this.stateChanges[ee])this.state[ee][Fe]||(this.state[ee][Fe]={}),a.e(this.state[ee][Fe],this.stateChanges[ee][Fe]),le[Fe]=this.state[ee][Fe];Q[ee]=le}for(let ee in this.deletedStates){this.state[ee]=this.state[ee]||{};let le={};if(this.deletedStates[ee]===null)for(let Fe in this.state[ee])le[Fe]={},this.state[ee][Fe]={};else for(let Fe in this.deletedStates[ee]){if(this.deletedStates[ee][Fe]===null)this.state[ee][Fe]={};else for(let Ze of Object.keys(this.deletedStates[ee][Fe]))delete this.state[ee][Fe][Ze];le[Fe]=this.state[ee][Fe]}Q[ee]=Q[ee]||{},a.e(Q[ee],le)}if(this.stateChanges={},this.deletedStates={},Object.keys(Q).length!==0)for(let ee in w)w[ee].setFeatureState(Q,B)}}class pt extends a.E{constructor(w,B,Q){super(),this.id=w,this.dispatcher=Q,this.on(\"data\",(ee=>this._dataHandler(ee))),this.on(\"dataloading\",(()=>{this._sourceErrored=!1})),this.on(\"error\",(()=>{this._sourceErrored=this._source.loaded()})),this._source=((ee,le,Fe,Ze)=>{let ut=new(je(le.type))(ee,le,Fe,Ze);if(ut.id!==ee)throw new Error(`Expected Source id to be ${ee} instead of ${ut.id}`);return ut})(w,B,Q,this),this._tiles={},this._cache=new dt(0,(ee=>this._unloadTile(ee))),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new Et,this._didEmitContent=!1,this._updated=!1}onAdd(w){this.map=w,this._maxTileCacheSize=w?w._maxTileCacheSize:null,this._maxTileCacheZoomLevels=w?w._maxTileCacheZoomLevels:null,this._source&&this._source.onAdd&&this._source.onAdd(w)}onRemove(w){this.clearTiles(),this._source&&this._source.onRemove&&this._source.onRemove(w)}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;if(!(this.used===void 0&&this.usedForTerrain===void 0||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(let w in this._tiles){let B=this._tiles[w];if(B.state!==\"loaded\"&&B.state!==\"errored\")return!1}return!0}getSource(){return this._source}pause(){this._paused=!0}resume(){if(!this._paused)return;let w=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,w&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(w,B,Q){return a._(this,void 0,void 0,(function*(){try{yield this._source.loadTile(w),this._tileLoaded(w,B,Q)}catch(ee){w.state=\"errored\",ee.status!==404?this._source.fire(new a.j(ee,{tile:w})):this.update(this.transform,this.terrain)}}))}_unloadTile(w){this._source.unloadTile&&this._source.unloadTile(w)}_abortTile(w){this._source.abortTile&&this._source.abortTile(w),this._source.fire(new a.k(\"dataabort\",{tile:w,coord:w.tileID,dataType:\"source\"}))}serialize(){return this._source.serialize()}prepare(w){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(let B in this._tiles){let Q=this._tiles[B];Q.upload(w),Q.prepare(this.map.style.imageManager)}}getIds(){return Object.values(this._tiles).map((w=>w.tileID)).sort(jt).map((w=>w.key))}getRenderableIds(w){let B=[];for(let Q in this._tiles)this._isIdRenderable(Q,w)&&B.push(this._tiles[Q]);return w?B.sort(((Q,ee)=>{let le=Q.tileID,Fe=ee.tileID,Ze=new a.P(le.canonical.x,le.canonical.y)._rotate(this.transform.angle),ut=new a.P(Fe.canonical.x,Fe.canonical.y)._rotate(this.transform.angle);return le.overscaledZ-Fe.overscaledZ||ut.y-Ze.y||ut.x-Ze.x})).map((Q=>Q.tileID.key)):B.map((Q=>Q.tileID)).sort(jt).map((Q=>Q.key))}hasRenderableParent(w){let B=this.findLoadedParent(w,0);return!!B&&this._isIdRenderable(B.tileID.key)}_isIdRenderable(w,B){return this._tiles[w]&&this._tiles[w].hasData()&&!this._coveredTiles[w]&&(B||!this._tiles[w].holdingForFade())}reload(){if(this._paused)this._shouldReloadOnResume=!0;else{this._cache.reset();for(let w in this._tiles)this._tiles[w].state!==\"errored\"&&this._reloadTile(w,\"reloading\")}}_reloadTile(w,B){return a._(this,void 0,void 0,(function*(){let Q=this._tiles[w];Q&&(Q.state!==\"loading\"&&(Q.state=B),yield this._loadTile(Q,w,B))}))}_tileLoaded(w,B,Q){w.timeAdded=u.now(),Q===\"expired\"&&(w.refreshedUponExpiration=!0),this._setTileReloadTimer(B,w),this.getSource().type===\"raster-dem\"&&w.dem&&this._backfillDEM(w),this._state.initializeTileState(w,this.map?this.map.painter:null),w.aborted||this._source.fire(new a.k(\"data\",{dataType:\"source\",tile:w,coord:w.tileID}))}_backfillDEM(w){let B=this.getRenderableIds();for(let ee=0;ee<B.length;ee++){let le=B[ee];if(w.neighboringTiles&&w.neighboringTiles[le]){let Fe=this.getTileByID(le);Q(w,Fe),Q(Fe,w)}}function Q(ee,le){ee.needsHillshadePrepare=!0,ee.needsTerrainPrepare=!0;let Fe=le.tileID.canonical.x-ee.tileID.canonical.x,Ze=le.tileID.canonical.y-ee.tileID.canonical.y,ut=Math.pow(2,ee.tileID.canonical.z),Mt=le.tileID.key;Fe===0&&Ze===0||Math.abs(Ze)>1||(Math.abs(Fe)>1&&(Math.abs(Fe+ut)===1?Fe+=ut:Math.abs(Fe-ut)===1&&(Fe-=ut)),le.dem&&ee.dem&&(ee.dem.backfillBorder(le.dem,Fe,Ze),ee.neighboringTiles&&ee.neighboringTiles[Mt]&&(ee.neighboringTiles[Mt].backfilled=!0)))}}getTile(w){return this.getTileByID(w.key)}getTileByID(w){return this._tiles[w]}_retainLoadedChildren(w,B,Q,ee){for(let le in this._tiles){let Fe=this._tiles[le];if(ee[le]||!Fe.hasData()||Fe.tileID.overscaledZ<=B||Fe.tileID.overscaledZ>Q)continue;let Ze=Fe.tileID;for(;Fe&&Fe.tileID.overscaledZ>B+1;){let Mt=Fe.tileID.scaledTo(Fe.tileID.overscaledZ-1);Fe=this._tiles[Mt.key],Fe&&Fe.hasData()&&(Ze=Mt)}let ut=Ze;for(;ut.overscaledZ>B;)if(ut=ut.scaledTo(ut.overscaledZ-1),w[ut.key]){ee[Ze.key]=Ze;break}}}findLoadedParent(w,B){if(w.key in this._loadedParentTiles){let Q=this._loadedParentTiles[w.key];return Q&&Q.tileID.overscaledZ>=B?Q:null}for(let Q=w.overscaledZ-1;Q>=B;Q--){let ee=w.scaledTo(Q),le=this._getLoadedTile(ee);if(le)return le}}findLoadedSibling(w){return this._getLoadedTile(w)}_getLoadedTile(w){let B=this._tiles[w.key];return B&&B.hasData()?B:this._cache.getByKey(w.wrapped().key)}updateCacheSize(w){let B=Math.ceil(w.width/this._source.tileSize)+1,Q=Math.ceil(w.height/this._source.tileSize)+1,ee=Math.floor(B*Q*(this._maxTileCacheZoomLevels===null?a.a.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels)),le=typeof this._maxTileCacheSize==\"number\"?Math.min(this._maxTileCacheSize,ee):ee;this._cache.setMaxSize(le)}handleWrapJump(w){let B=Math.round((w-(this._prevLng===void 0?w:this._prevLng))/360);if(this._prevLng=w,B){let Q={};for(let ee in this._tiles){let le=this._tiles[ee];le.tileID=le.tileID.unwrapTo(le.tileID.wrap+B),Q[le.tileID.key]=le}this._tiles=Q;for(let ee in this._timers)clearTimeout(this._timers[ee]),delete this._timers[ee];for(let ee in this._tiles)this._setTileReloadTimer(ee,this._tiles[ee])}}_updateCoveredAndRetainedTiles(w,B,Q,ee,le,Fe){let Ze={},ut={},Mt=Object.keys(w),Jt=u.now();for(let $t of Mt){let yr=w[$t],Cr=this._tiles[$t];if(!Cr||Cr.fadeEndTime!==0&&Cr.fadeEndTime<=Jt)continue;let ve=this.findLoadedParent(yr,B),xe=this.findLoadedSibling(yr),Ie=ve||xe||null;Ie&&(this._addTile(Ie.tileID),Ze[Ie.tileID.key]=Ie.tileID),ut[$t]=yr}this._retainLoadedChildren(ut,ee,Q,w);for(let $t in Ze)w[$t]||(this._coveredTiles[$t]=!0,w[$t]=Ze[$t]);if(Fe){let $t={},yr={};for(let Cr of le)this._tiles[Cr.key].hasData()?$t[Cr.key]=Cr:yr[Cr.key]=Cr;for(let Cr in yr){let ve=yr[Cr].children(this._source.maxzoom);this._tiles[ve[0].key]&&this._tiles[ve[1].key]&&this._tiles[ve[2].key]&&this._tiles[ve[3].key]&&($t[ve[0].key]=w[ve[0].key]=ve[0],$t[ve[1].key]=w[ve[1].key]=ve[1],$t[ve[2].key]=w[ve[2].key]=ve[2],$t[ve[3].key]=w[ve[3].key]=ve[3],delete yr[Cr])}for(let Cr in yr){let ve=yr[Cr],xe=this.findLoadedParent(ve,this._source.minzoom),Ie=this.findLoadedSibling(ve),ze=xe||Ie||null;if(ze){$t[ze.tileID.key]=w[ze.tileID.key]=ze.tileID;for(let Qe in $t)$t[Qe].isChildOf(ze.tileID)&&delete $t[Qe]}}for(let Cr in this._tiles)$t[Cr]||(this._coveredTiles[Cr]=!0)}}update(w,B){if(!this._sourceLoaded||this._paused)return;let Q;this.transform=w,this.terrain=B,this.updateCacheSize(w),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used||this.usedForTerrain?this._source.tileID?Q=w.getVisibleUnwrappedCoordinates(this._source.tileID).map((Jt=>new a.S(Jt.canonical.z,Jt.wrap,Jt.canonical.z,Jt.canonical.x,Jt.canonical.y))):(Q=w.coveringTiles({tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:B}),this._source.hasTile&&(Q=Q.filter((Jt=>this._source.hasTile(Jt))))):Q=[];let ee=w.coveringZoomLevel(this._source),le=Math.max(ee-pt.maxOverzooming,this._source.minzoom),Fe=Math.max(ee+pt.maxUnderzooming,this._source.minzoom);if(this.usedForTerrain){let Jt={};for(let $t of Q)if($t.canonical.z>this._source.minzoom){let yr=$t.scaledTo($t.canonical.z-1);Jt[yr.key]=yr;let Cr=$t.scaledTo(Math.max(this._source.minzoom,Math.min($t.canonical.z,5)));Jt[Cr.key]=Cr}Q=Q.concat(Object.values(Jt))}let Ze=Q.length===0&&!this._updated&&this._didEmitContent;this._updated=!0,Ze&&this.fire(new a.k(\"data\",{sourceDataType:\"idle\",dataType:\"source\",sourceId:this.id}));let ut=this._updateRetainedTiles(Q,ee);or(this._source.type)&&this._updateCoveredAndRetainedTiles(ut,le,Fe,ee,Q,B);for(let Jt in ut)this._tiles[Jt].clearFadeHold();let Mt=a.ab(this._tiles,ut);for(let Jt of Mt){let $t=this._tiles[Jt];$t.hasSymbolBuckets&&!$t.holdingForFade()?$t.setHoldDuration(this.map._fadeDuration):$t.hasSymbolBuckets&&!$t.symbolFadeFinished()||this._removeTile(Jt)}this._updateLoadedParentTileCache(),this._updateLoadedSiblingTileCache()}releaseSymbolFadeTiles(){for(let w in this._tiles)this._tiles[w].holdingForFade()&&this._removeTile(w)}_updateRetainedTiles(w,B){var Q;let ee={},le={},Fe=Math.max(B-pt.maxOverzooming,this._source.minzoom),Ze=Math.max(B+pt.maxUnderzooming,this._source.minzoom),ut={};for(let Mt of w){let Jt=this._addTile(Mt);ee[Mt.key]=Mt,Jt.hasData()||B<this._source.maxzoom&&(ut[Mt.key]=Mt)}this._retainLoadedChildren(ut,B,Ze,ee);for(let Mt of w){let Jt=this._tiles[Mt.key];if(Jt.hasData())continue;if(B+1>this._source.maxzoom){let yr=Mt.children(this._source.maxzoom)[0],Cr=this.getTile(yr);if(Cr&&Cr.hasData()){ee[yr.key]=yr;continue}}else{let yr=Mt.children(this._source.maxzoom);if(ee[yr[0].key]&&ee[yr[1].key]&&ee[yr[2].key]&&ee[yr[3].key])continue}let $t=Jt.wasRequested();for(let yr=Mt.overscaledZ-1;yr>=Fe;--yr){let Cr=Mt.scaledTo(yr);if(le[Cr.key])break;if(le[Cr.key]=!0,Jt=this.getTile(Cr),!Jt&&$t&&(Jt=this._addTile(Cr)),Jt){let ve=Jt.hasData();if((ve||!(!((Q=this.map)===null||Q===void 0)&&Q.cancelPendingTileRequestsWhileZooming)||$t)&&(ee[Cr.key]=Cr),$t=Jt.wasRequested(),ve)break}}}return ee}_updateLoadedParentTileCache(){this._loadedParentTiles={};for(let w in this._tiles){let B=[],Q,ee=this._tiles[w].tileID;for(;ee.overscaledZ>0;){if(ee.key in this._loadedParentTiles){Q=this._loadedParentTiles[ee.key];break}B.push(ee.key);let le=ee.scaledTo(ee.overscaledZ-1);if(Q=this._getLoadedTile(le),Q)break;ee=le}for(let le of B)this._loadedParentTiles[le]=Q}}_updateLoadedSiblingTileCache(){this._loadedSiblingTiles={};for(let w in this._tiles){let B=this._tiles[w].tileID,Q=this._getLoadedTile(B);this._loadedSiblingTiles[B.key]=Q}}_addTile(w){let B=this._tiles[w.key];if(B)return B;B=this._cache.getAndRemove(w),B&&(this._setTileReloadTimer(w.key,B),B.tileID=w,this._state.initializeTileState(B,this.map?this.map.painter:null),this._cacheTimers[w.key]&&(clearTimeout(this._cacheTimers[w.key]),delete this._cacheTimers[w.key],this._setTileReloadTimer(w.key,B)));let Q=B;return B||(B=new Tt(w,this._source.tileSize*w.overscaleFactor()),this._loadTile(B,w.key,B.state)),B.uses++,this._tiles[w.key]=B,Q||this._source.fire(new a.k(\"dataloading\",{tile:B,coord:B.tileID,dataType:\"source\"})),B}_setTileReloadTimer(w,B){w in this._timers&&(clearTimeout(this._timers[w]),delete this._timers[w]);let Q=B.getExpiryTimeout();Q&&(this._timers[w]=setTimeout((()=>{this._reloadTile(w,\"expired\"),delete this._timers[w]}),Q))}_removeTile(w){let B=this._tiles[w];B&&(B.uses--,delete this._tiles[w],this._timers[w]&&(clearTimeout(this._timers[w]),delete this._timers[w]),B.uses>0||(B.hasData()&&B.state!==\"reloading\"?this._cache.add(B.tileID,B,B.getExpiryTimeout()):(B.aborted=!0,this._abortTile(B),this._unloadTile(B))))}_dataHandler(w){let B=w.sourceDataType;w.dataType===\"source\"&&B===\"metadata\"&&(this._sourceLoaded=!0),this._sourceLoaded&&!this._paused&&w.dataType===\"source\"&&B===\"content\"&&(this.reload(),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0)}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(let w in this._tiles)this._removeTile(w);this._cache.reset()}tilesIn(w,B,Q){let ee=[],le=this.transform;if(!le)return ee;let Fe=Q?le.getCameraQueryGeometry(w):w,Ze=w.map((ve=>le.pointCoordinate(ve,this.terrain))),ut=Fe.map((ve=>le.pointCoordinate(ve,this.terrain))),Mt=this.getIds(),Jt=1/0,$t=1/0,yr=-1/0,Cr=-1/0;for(let ve of ut)Jt=Math.min(Jt,ve.x),$t=Math.min($t,ve.y),yr=Math.max(yr,ve.x),Cr=Math.max(Cr,ve.y);for(let ve=0;ve<Mt.length;ve++){let xe=this._tiles[Mt[ve]];if(xe.holdingForFade())continue;let Ie=xe.tileID,ze=Math.pow(2,le.zoom-xe.tileID.overscaledZ),Qe=B*xe.queryPadding*a.X/xe.tileSize/ze,Xe=[Ie.getTilePoint(new a.Z(Jt,$t)),Ie.getTilePoint(new a.Z(yr,Cr))];if(Xe[0].x-Qe<a.X&&Xe[0].y-Qe<a.X&&Xe[1].x+Qe>=0&&Xe[1].y+Qe>=0){let nt=Ze.map((Wt=>Ie.getTilePoint(Wt))),qt=ut.map((Wt=>Ie.getTilePoint(Wt)));ee.push({tile:xe,tileID:Ie,queryGeometry:nt,cameraQueryGeometry:qt,scale:ze})}}return ee}getVisibleCoordinates(w){let B=this.getRenderableIds(w).map((Q=>this._tiles[Q].tileID));for(let Q of B)Q.posMatrix=this.transform.calculatePosMatrix(Q.toUnwrapped());return B}hasTransition(){if(this._source.hasTransition())return!0;if(or(this._source.type)){let w=u.now();for(let B in this._tiles)if(this._tiles[B].fadeEndTime>=w)return!0}return!1}setFeatureState(w,B,Q){this._state.updateState(w=w||\"_geojsonTileLayer\",B,Q)}removeFeatureState(w,B,Q){this._state.removeFeatureState(w=w||\"_geojsonTileLayer\",B,Q)}getFeatureState(w,B){return this._state.getState(w=w||\"_geojsonTileLayer\",B)}setDependencies(w,B,Q){let ee=this._tiles[w];ee&&ee.setDependencies(B,Q)}reloadTilesForDependencies(w,B){for(let Q in this._tiles)this._tiles[Q].hasDependency(w,B)&&this._reloadTile(Q,\"reloading\");this._cache.filter((Q=>!Q.hasDependency(w,B)))}}function jt(ue,w){let B=Math.abs(2*ue.wrap)-+(ue.wrap<0),Q=Math.abs(2*w.wrap)-+(w.wrap<0);return ue.overscaledZ-w.overscaledZ||Q-B||w.canonical.y-ue.canonical.y||w.canonical.x-ue.canonical.x}function or(ue){return ue===\"raster\"||ue===\"image\"||ue===\"video\"}pt.maxOverzooming=10,pt.maxUnderzooming=3;class mr{constructor(w,B){this.reset(w,B)}reset(w,B){this.points=w||[],this._distances=[0];for(let Q=1;Q<this.points.length;Q++)this._distances[Q]=this._distances[Q-1]+this.points[Q].dist(this.points[Q-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(B||0,.5*this.length),this.paddedLength=this.length-2*this.padding}lerp(w){if(this.points.length===1)return this.points[0];w=a.ac(w,0,1);let B=1,Q=this._distances[B],ee=w*this.paddedLength+this.padding;for(;Q<ee&&B<this._distances.length;)Q=this._distances[++B];let le=B-1,Fe=this._distances[le],Ze=Q-Fe,ut=Ze>0?(ee-Fe)/Ze:0;return this.points[le].mult(1-ut).add(this.points[B].mult(ut))}}function Ar(ue,w){let B=!0;return ue===\"always\"||ue!==\"never\"&&w!==\"never\"||(B=!1),B}class ei{constructor(w,B,Q){let ee=this.boxCells=[],le=this.circleCells=[];this.xCellCount=Math.ceil(w/Q),this.yCellCount=Math.ceil(B/Q);for(let Fe=0;Fe<this.xCellCount*this.yCellCount;Fe++)ee.push([]),le.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=w,this.height=B,this.xScale=this.xCellCount/w,this.yScale=this.yCellCount/B,this.boxUid=0,this.circleUid=0}keysLength(){return this.boxKeys.length+this.circleKeys.length}insert(w,B,Q,ee,le){this._forEachCell(B,Q,ee,le,this._insertBoxCell,this.boxUid++),this.boxKeys.push(w),this.bboxes.push(B),this.bboxes.push(Q),this.bboxes.push(ee),this.bboxes.push(le)}insertCircle(w,B,Q,ee){this._forEachCell(B-ee,Q-ee,B+ee,Q+ee,this._insertCircleCell,this.circleUid++),this.circleKeys.push(w),this.circles.push(B),this.circles.push(Q),this.circles.push(ee)}_insertBoxCell(w,B,Q,ee,le,Fe){this.boxCells[le].push(Fe)}_insertCircleCell(w,B,Q,ee,le,Fe){this.circleCells[le].push(Fe)}_query(w,B,Q,ee,le,Fe,Ze){if(Q<0||w>this.width||ee<0||B>this.height)return[];let ut=[];if(w<=0&&B<=0&&this.width<=Q&&this.height<=ee){if(le)return[{key:null,x1:w,y1:B,x2:Q,y2:ee}];for(let Mt=0;Mt<this.boxKeys.length;Mt++)ut.push({key:this.boxKeys[Mt],x1:this.bboxes[4*Mt],y1:this.bboxes[4*Mt+1],x2:this.bboxes[4*Mt+2],y2:this.bboxes[4*Mt+3]});for(let Mt=0;Mt<this.circleKeys.length;Mt++){let Jt=this.circles[3*Mt],$t=this.circles[3*Mt+1],yr=this.circles[3*Mt+2];ut.push({key:this.circleKeys[Mt],x1:Jt-yr,y1:$t-yr,x2:Jt+yr,y2:$t+yr})}}else this._forEachCell(w,B,Q,ee,this._queryCell,ut,{hitTest:le,overlapMode:Fe,seenUids:{box:{},circle:{}}},Ze);return ut}query(w,B,Q,ee){return this._query(w,B,Q,ee,!1,null)}hitTest(w,B,Q,ee,le,Fe){return this._query(w,B,Q,ee,!0,le,Fe).length>0}hitTestCircle(w,B,Q,ee,le){let Fe=w-Q,Ze=w+Q,ut=B-Q,Mt=B+Q;if(Ze<0||Fe>this.width||Mt<0||ut>this.height)return!1;let Jt=[];return this._forEachCell(Fe,ut,Ze,Mt,this._queryCellCircle,Jt,{hitTest:!0,overlapMode:ee,circle:{x:w,y:B,radius:Q},seenUids:{box:{},circle:{}}},le),Jt.length>0}_queryCell(w,B,Q,ee,le,Fe,Ze,ut){let{seenUids:Mt,hitTest:Jt,overlapMode:$t}=Ze,yr=this.boxCells[le];if(yr!==null){let ve=this.bboxes;for(let xe of yr)if(!Mt.box[xe]){Mt.box[xe]=!0;let Ie=4*xe,ze=this.boxKeys[xe];if(w<=ve[Ie+2]&&B<=ve[Ie+3]&&Q>=ve[Ie+0]&&ee>=ve[Ie+1]&&(!ut||ut(ze))&&(!Jt||!Ar($t,ze.overlapMode))&&(Fe.push({key:ze,x1:ve[Ie],y1:ve[Ie+1],x2:ve[Ie+2],y2:ve[Ie+3]}),Jt))return!0}}let Cr=this.circleCells[le];if(Cr!==null){let ve=this.circles;for(let xe of Cr)if(!Mt.circle[xe]){Mt.circle[xe]=!0;let Ie=3*xe,ze=this.circleKeys[xe];if(this._circleAndRectCollide(ve[Ie],ve[Ie+1],ve[Ie+2],w,B,Q,ee)&&(!ut||ut(ze))&&(!Jt||!Ar($t,ze.overlapMode))){let Qe=ve[Ie],Xe=ve[Ie+1],nt=ve[Ie+2];if(Fe.push({key:ze,x1:Qe-nt,y1:Xe-nt,x2:Qe+nt,y2:Xe+nt}),Jt)return!0}}}return!1}_queryCellCircle(w,B,Q,ee,le,Fe,Ze,ut){let{circle:Mt,seenUids:Jt,overlapMode:$t}=Ze,yr=this.boxCells[le];if(yr!==null){let ve=this.bboxes;for(let xe of yr)if(!Jt.box[xe]){Jt.box[xe]=!0;let Ie=4*xe,ze=this.boxKeys[xe];if(this._circleAndRectCollide(Mt.x,Mt.y,Mt.radius,ve[Ie+0],ve[Ie+1],ve[Ie+2],ve[Ie+3])&&(!ut||ut(ze))&&!Ar($t,ze.overlapMode))return Fe.push(!0),!0}}let Cr=this.circleCells[le];if(Cr!==null){let ve=this.circles;for(let xe of Cr)if(!Jt.circle[xe]){Jt.circle[xe]=!0;let Ie=3*xe,ze=this.circleKeys[xe];if(this._circlesCollide(ve[Ie],ve[Ie+1],ve[Ie+2],Mt.x,Mt.y,Mt.radius)&&(!ut||ut(ze))&&!Ar($t,ze.overlapMode))return Fe.push(!0),!0}}}_forEachCell(w,B,Q,ee,le,Fe,Ze,ut){let Mt=this._convertToXCellCoord(w),Jt=this._convertToYCellCoord(B),$t=this._convertToXCellCoord(Q),yr=this._convertToYCellCoord(ee);for(let Cr=Mt;Cr<=$t;Cr++)for(let ve=Jt;ve<=yr;ve++)if(le.call(this,w,B,Q,ee,this.xCellCount*ve+Cr,Fe,Ze,ut))return}_convertToXCellCoord(w){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(w*this.xScale)))}_convertToYCellCoord(w){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(w*this.yScale)))}_circlesCollide(w,B,Q,ee,le,Fe){let Ze=ee-w,ut=le-B,Mt=Q+Fe;return Mt*Mt>Ze*Ze+ut*ut}_circleAndRectCollide(w,B,Q,ee,le,Fe,Ze){let ut=(Fe-ee)/2,Mt=Math.abs(w-(ee+ut));if(Mt>ut+Q)return!1;let Jt=(Ze-le)/2,$t=Math.abs(B-(le+Jt));if($t>Jt+Q)return!1;if(Mt<=ut||$t<=Jt)return!0;let yr=Mt-ut,Cr=$t-Jt;return yr*yr+Cr*Cr<=Q*Q}}function qr(ue,w,B,Q,ee){let le=a.H();return w?(a.K(le,le,[1/ee,1/ee,1]),B||a.ad(le,le,Q.angle)):a.L(le,Q.labelPlaneMatrix,ue),le}function jr(ue,w,B,Q,ee){if(w){let le=a.ae(ue);return a.K(le,le,[ee,ee,1]),B||a.ad(le,le,-Q.angle),le}return Q.glCoordMatrix}function gt(ue,w,B,Q){let ee;Q?(ee=[ue,w,Q(ue,w),1],a.af(ee,ee,B)):(ee=[ue,w,0,1],ai(ee,ee,B));let le=ee[3];return{point:new a.P(ee[0]/le,ee[1]/le),signedDistanceFromCamera:le,isOccluded:!1}}function Ge(ue,w){return .5+ue/w*.5}function Je(ue,w){return ue.x>=-w[0]&&ue.x<=w[0]&&ue.y>=-w[1]&&ue.y<=w[1]}function We(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve){let xe=Q?ue.textSizeData:ue.iconSizeData,Ie=a.ag(xe,B.transform.zoom),ze=[256/B.width*2+1,256/B.height*2+1],Qe=Q?ue.text.dynamicLayoutVertexArray:ue.icon.dynamicLayoutVertexArray;Qe.clear();let Xe=ue.lineVertexArray,nt=Q?ue.text.placedSymbolArray:ue.icon.placedSymbolArray,qt=B.transform.width/B.transform.height,Wt=!1;for(let nr=0;nr<nt.length;nr++){let fr=nt.get(nr);if(fr.hidden||fr.writingMode===a.ah.vertical&&!Wt){Pi(fr.numGlyphs,Qe);continue}Wt=!1;let br=gt(fr.anchorX,fr.anchorY,w,ve);if(!Je(br.point,ze)){Pi(fr.numGlyphs,Qe);continue}let Fr=Ge(B.transform.cameraToCenterDistance,br.signedDistanceFromCamera),Hr=a.ai(xe,Ie,fr),ri=Fe?Hr/Fr:Hr*Fr,Ci={getElevation:ve,labelPlaneMatrix:ee,lineVertexArray:Xe,pitchWithMap:Fe,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},projection:Mt,tileAnchorPoint:new a.P(fr.anchorX,fr.anchorY),unwrappedTileID:Jt,width:$t,height:yr,translation:Cr},cn=At(Ci,fr,ri,!1,Ze,w,le,ue.glyphOffsetArray,Qe,qt,ut);Wt=cn.useVertical,(cn.notEnoughRoom||Wt||cn.needsFlipping&&At(Ci,fr,ri,!0,Ze,w,le,ue.glyphOffsetArray,Qe,qt,ut).notEnoughRoom)&&Pi(fr.numGlyphs,Qe)}Q?ue.text.dynamicLayoutVertexBuffer.updateData(Qe):ue.icon.dynamicLayoutVertexBuffer.updateData(Qe)}function et(ue,w,B,Q,ee,le,Fe,Ze){let ut=le.glyphStartIndex+le.numGlyphs,Mt=le.lineStartIndex,Jt=le.lineStartIndex+le.lineLength,$t=w.getoffsetX(le.glyphStartIndex),yr=w.getoffsetX(ut-1),Cr=Yr(ue*$t,B,Q,ee,le.segment,Mt,Jt,Ze,Fe);if(!Cr)return null;let ve=Yr(ue*yr,B,Q,ee,le.segment,Mt,Jt,Ze,Fe);return ve?Ze.projectionCache.anyProjectionOccluded?null:{first:Cr,last:ve}:null}function xt(ue,w,B,Q){return ue===a.ah.horizontal&&Math.abs(B.y-w.y)>Math.abs(B.x-w.x)*Q?{useVertical:!0}:(ue===a.ah.vertical?w.y<B.y:w.x>B.x)?{needsFlipping:!0}:null}function At(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt){let $t=B/24,yr=w.lineOffsetX*$t,Cr=w.lineOffsetY*$t,ve;if(w.numGlyphs>1){let xe=w.glyphStartIndex+w.numGlyphs,Ie=w.lineStartIndex,ze=w.lineStartIndex+w.lineLength,Qe=et($t,Ze,yr,Cr,Q,w,Jt,ue);if(!Qe)return{notEnoughRoom:!0};let Xe=gt(Qe.first.point.x,Qe.first.point.y,Fe,ue.getElevation).point,nt=gt(Qe.last.point.x,Qe.last.point.y,Fe,ue.getElevation).point;if(ee&&!Q){let qt=xt(w.writingMode,Xe,nt,Mt);if(qt)return qt}ve=[Qe.first];for(let qt=w.glyphStartIndex+1;qt<xe-1;qt++)ve.push(Yr($t*Ze.getoffsetX(qt),yr,Cr,Q,w.segment,Ie,ze,ue,Jt));ve.push(Qe.last)}else{if(ee&&!Q){let Ie=gt(ue.tileAnchorPoint.x,ue.tileAnchorPoint.y,le,ue.getElevation).point,ze=w.lineStartIndex+w.segment+1,Qe=new a.P(ue.lineVertexArray.getx(ze),ue.lineVertexArray.gety(ze)),Xe=gt(Qe.x,Qe.y,le,ue.getElevation),nt=Xe.signedDistanceFromCamera>0?Xe.point:(function(Wt,nr,fr,br,Fr,Hr){return Kt(Wt,nr,fr,1,Fr,Hr)})(ue.tileAnchorPoint,Qe,Ie,0,le,ue),qt=xt(w.writingMode,Ie,nt,Mt);if(qt)return qt}let xe=Yr($t*Ze.getoffsetX(w.glyphStartIndex),yr,Cr,Q,w.segment,w.lineStartIndex,w.lineStartIndex+w.lineLength,ue,Jt);if(!xe||ue.projectionCache.anyProjectionOccluded)return{notEnoughRoom:!0};ve=[xe]}for(let xe of ve)a.aj(ut,xe.point,xe.angle);return{}}function Kt(ue,w,B,Q,ee,le){let Fe=ue.add(ue.sub(w)._unit()),Ze=ee!==void 0?gt(Fe.x,Fe.y,ee,le.getElevation).point:Mr(Fe.x,Fe.y,le).point,ut=B.sub(Ze);return B.add(ut._mult(Q/ut.mag()))}function Qt(ue,w,B){let Q=w.projectionCache;if(Q.projections[ue])return Q.projections[ue];let ee=new a.P(w.lineVertexArray.getx(ue),w.lineVertexArray.gety(ue)),le=Mr(ee.x,ee.y,w);if(le.signedDistanceFromCamera>0)return Q.projections[ue]=le.point,Q.anyProjectionOccluded=Q.anyProjectionOccluded||le.isOccluded,le.point;let Fe=ue-B.direction;return(function(Ze,ut,Mt,Jt,$t){return Kt(Ze,ut,Mt,Jt,void 0,$t)})(B.distanceFromAnchor===0?w.tileAnchorPoint:new a.P(w.lineVertexArray.getx(Fe),w.lineVertexArray.gety(Fe)),ee,B.previousVertex,B.absOffsetX-B.distanceFromAnchor+1,w)}function Mr(ue,w,B){let Q=ue+B.translation[0],ee=w+B.translation[1],le;return!B.pitchWithMap&&B.projection.useSpecialProjectionForSymbols?(le=B.projection.projectTileCoordinates(Q,ee,B.unwrappedTileID,B.getElevation),le.point.x=(.5*le.point.x+.5)*B.width,le.point.y=(.5*-le.point.y+.5)*B.height):(le=gt(Q,ee,B.labelPlaneMatrix,B.getElevation),le.isOccluded=!1),le}function Gr(ue,w,B){return ue._unit()._perp()._mult(w*B)}function Ir(ue,w,B,Q,ee,le,Fe,Ze,ut){if(Ze.projectionCache.offsets[ue])return Ze.projectionCache.offsets[ue];let Mt=B.add(w);if(ue+ut.direction<Q||ue+ut.direction>=ee)return Ze.projectionCache.offsets[ue]=Mt,Mt;let Jt=Qt(ue+ut.direction,Ze,ut),$t=Gr(Jt.sub(B),Fe,ut.direction),yr=B.add($t),Cr=Jt.add($t);return Ze.projectionCache.offsets[ue]=a.ak(le,Mt,yr,Cr)||Mt,Ze.projectionCache.offsets[ue]}function Yr(ue,w,B,Q,ee,le,Fe,Ze,ut){let Mt=Q?ue-w:ue+w,Jt=Mt>0?1:-1,$t=0;Q&&(Jt*=-1,$t=Math.PI),Jt<0&&($t+=Math.PI);let yr,Cr=Jt>0?le+ee:le+ee+1;Ze.projectionCache.cachedAnchorPoint?yr=Ze.projectionCache.cachedAnchorPoint:(yr=Mr(Ze.tileAnchorPoint.x,Ze.tileAnchorPoint.y,Ze).point,Ze.projectionCache.cachedAnchorPoint=yr);let ve,xe,Ie=yr,ze=yr,Qe=0,Xe=0,nt=Math.abs(Mt),qt=[],Wt;for(;Qe+Xe<=nt;){if(Cr+=Jt,Cr<le||Cr>=Fe)return null;Qe+=Xe,ze=Ie,xe=ve;let br={absOffsetX:nt,direction:Jt,distanceFromAnchor:Qe,previousVertex:ze};if(Ie=Qt(Cr,Ze,br),B===0)qt.push(ze),Wt=Ie.sub(ze);else{let Fr,Hr=Ie.sub(ze);Fr=Hr.mag()===0?Gr(Qt(Cr+Jt,Ze,br).sub(Ie),B,Jt):Gr(Hr,B,Jt),xe||(xe=ze.add(Fr)),ve=Ir(Cr,Fr,Ie,le,Fe,xe,B,Ze,br),qt.push(xe),Wt=ve.sub(xe)}Xe=Wt.mag()}let nr=Wt._mult((nt-Qe)/Xe)._add(xe||ze),fr=$t+Math.atan2(Ie.y-ze.y,Ie.x-ze.x);return qt.push(nr),{point:nr,angle:ut?fr:0,path:qt}}let _i=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function Pi(ue,w){for(let B=0;B<ue;B++){let Q=w.length;w.resize(Q+4),w.float32.set(_i,3*Q)}}function ai(ue,w,B){let Q=w[0],ee=w[1];return ue[0]=B[0]*Q+B[4]*ee+B[12],ue[1]=B[1]*Q+B[5]*ee+B[13],ue[3]=B[3]*Q+B[7]*ee+B[15],ue}let mi=100;class un{constructor(w,B,Q=new ei(w.width+200,w.height+200,25),ee=new ei(w.width+200,w.height+200,25)){this.transform=w,this.mapProjection=B,this.grid=Q,this.ignoredGrid=ee,this.pitchFactor=Math.cos(w._pitch)*w.cameraToCenterDistance,this.screenRightBoundary=w.width+mi,this.screenBottomBoundary=w.height+mi,this.gridRightBoundary=w.width+200,this.gridBottomBoundary=w.height+200,this.perspectiveRatioCutoff=.6}placeCollisionBox(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t){let yr=w.anchorPointX+ut[0],Cr=w.anchorPointY+ut[1],ve=this.projectAndGetPerspectiveRatio(ee,yr,Cr,le,Jt),xe=Q*ve.perspectiveRatio,Ie;if(Fe||Ze)Ie=this._projectCollisionBox(w,xe,ee,le,Fe,Ze,ut,ve,Jt,$t);else{let qt=ve.point.x+($t?$t.x*xe:0),Wt=ve.point.y+($t?$t.y*xe:0);Ie={allPointsOccluded:!1,box:[qt+w.x1*xe,Wt+w.y1*xe,qt+w.x2*xe,Wt+w.y2*xe]}}let[ze,Qe,Xe,nt]=Ie.box;return this.mapProjection.useSpecialProjectionForSymbols&&(Fe?Ie.allPointsOccluded:this.mapProjection.isOccluded(yr,Cr,le))||ve.perspectiveRatio<this.perspectiveRatioCutoff||!this.isInsideGrid(ze,Qe,Xe,nt)||B!==\"always\"&&this.grid.hitTest(ze,Qe,Xe,nt,B,Mt)?{box:[ze,Qe,Xe,nt],placeable:!1,offscreen:!1}:{box:[ze,Qe,Xe,nt],placeable:!0,offscreen:this.isOffscreen(ze,Qe,Xe,nt)}}placeCollisionCircles(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve,xe,Ie){let ze=[],Qe=new a.P(B.anchorX,B.anchorY),Xe=this.getPerspectiveRatio(Fe,Qe.x,Qe.y,Ze,Ie),nt=($t?le/Xe:le*Xe)/a.ap,qt={getElevation:Ie,labelPlaneMatrix:ut,lineVertexArray:Q,pitchWithMap:$t,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},projection:this.mapProjection,tileAnchorPoint:Qe,unwrappedTileID:Ze,width:this.transform.width,height:this.transform.height,translation:xe},Wt=et(nt,ee,B.lineOffsetX*nt,B.lineOffsetY*nt,!1,B,!1,qt),nr=!1,fr=!1,br=!0;if(Wt){let Fr=.5*Cr*Xe+ve,Hr=new a.P(-100,-100),ri=new a.P(this.screenRightBoundary,this.screenBottomBoundary),Ci=new mr,cn=Wt.first,sn=Wt.last,Ln=[];for(let fa=cn.path.length-1;fa>=1;fa--)Ln.push(cn.path[fa]);for(let fa=1;fa<sn.path.length;fa++)Ln.push(sn.path[fa]);let Kn=2.5*Fr;if(Mt){let fa=this.projectPathToScreenSpace(Ln,qt,Mt);Ln=fa.some(($a=>$a.signedDistanceFromCamera<=0))?[]:fa.map(($a=>$a.point))}let Aa=[];if(Ln.length>0){let fa=Ln[0].clone(),$a=Ln[0].clone();for(let Co=1;Co<Ln.length;Co++)fa.x=Math.min(fa.x,Ln[Co].x),fa.y=Math.min(fa.y,Ln[Co].y),$a.x=Math.max($a.x,Ln[Co].x),$a.y=Math.max($a.y,Ln[Co].y);Aa=fa.x>=Hr.x&&$a.x<=ri.x&&fa.y>=Hr.y&&$a.y<=ri.y?[Ln]:$a.x<Hr.x||fa.x>ri.x||$a.y<Hr.y||fa.y>ri.y?[]:a.al([Ln],Hr.x,Hr.y,ri.x,ri.y)}for(let fa of Aa){Ci.reset(fa,.25*Fr);let $a=0;$a=Ci.length<=.5*Fr?1:Math.ceil(Ci.paddedLength/Kn)+1;for(let Co=0;Co<$a;Co++){let Qa=Co/Math.max($a-1,1),mo=Ci.lerp(Qa),Bo=mo.x+mi,Ps=mo.y+mi;ze.push(Bo,Ps,Fr,0);let Ts=Bo-Fr,wo=Ps-Fr,To=Bo+Fr,hl=Ps+Fr;if(br=br&&this.isOffscreen(Ts,wo,To,hl),fr=fr||this.isInsideGrid(Ts,wo,To,hl),w!==\"always\"&&this.grid.hitTestCircle(Bo,Ps,Fr,w,yr)&&(nr=!0,!Jt))return{circles:[],offscreen:!1,collisionDetected:nr}}}}return{circles:!Jt&&nr||!fr||Xe<this.perspectiveRatioCutoff?[]:ze,offscreen:br,collisionDetected:nr}}projectPathToScreenSpace(w,B,Q){return w.map((ee=>gt(ee.x,ee.y,Q,B.getElevation)))}queryRenderedSymbols(w){if(w.length===0||this.grid.keysLength()===0&&this.ignoredGrid.keysLength()===0)return{};let B=[],Q=1/0,ee=1/0,le=-1/0,Fe=-1/0;for(let Jt of w){let $t=new a.P(Jt.x+mi,Jt.y+mi);Q=Math.min(Q,$t.x),ee=Math.min(ee,$t.y),le=Math.max(le,$t.x),Fe=Math.max(Fe,$t.y),B.push($t)}let Ze=this.grid.query(Q,ee,le,Fe).concat(this.ignoredGrid.query(Q,ee,le,Fe)),ut={},Mt={};for(let Jt of Ze){let $t=Jt.key;if(ut[$t.bucketInstanceId]===void 0&&(ut[$t.bucketInstanceId]={}),ut[$t.bucketInstanceId][$t.featureIndex])continue;let yr=[new a.P(Jt.x1,Jt.y1),new a.P(Jt.x2,Jt.y1),new a.P(Jt.x2,Jt.y2),new a.P(Jt.x1,Jt.y2)];a.am(B,yr)&&(ut[$t.bucketInstanceId][$t.featureIndex]=!0,Mt[$t.bucketInstanceId]===void 0&&(Mt[$t.bucketInstanceId]=[]),Mt[$t.bucketInstanceId].push($t.featureIndex))}return Mt}insertCollisionBox(w,B,Q,ee,le,Fe){(Q?this.ignoredGrid:this.grid).insert({bucketInstanceId:ee,featureIndex:le,collisionGroupID:Fe,overlapMode:B},w[0],w[1],w[2],w[3])}insertCollisionCircles(w,B,Q,ee,le,Fe){let Ze=Q?this.ignoredGrid:this.grid,ut={bucketInstanceId:ee,featureIndex:le,collisionGroupID:Fe,overlapMode:B};for(let Mt=0;Mt<w.length;Mt+=4)Ze.insertCircle(ut,w[Mt],w[Mt+1],w[Mt+2])}projectAndGetPerspectiveRatio(w,B,Q,ee,le){let Fe;le?(Fe=[B,Q,le(B,Q),1],a.af(Fe,Fe,w)):(Fe=[B,Q,0,1],ai(Fe,Fe,w));let Ze=Fe[3];return{point:new a.P((Fe[0]/Ze+1)/2*this.transform.width+mi,(-Fe[1]/Ze+1)/2*this.transform.height+mi),perspectiveRatio:.5+this.transform.cameraToCenterDistance/Ze*.5,isOccluded:!1,signedDistanceFromCamera:Ze}}getPerspectiveRatio(w,B,Q,ee,le){let Fe=this.mapProjection.useSpecialProjectionForSymbols?this.mapProjection.projectTileCoordinates(B,Q,ee,le):gt(B,Q,w,le);return .5+this.transform.cameraToCenterDistance/Fe.signedDistanceFromCamera*.5}isOffscreen(w,B,Q,ee){return Q<mi||w>=this.screenRightBoundary||ee<mi||B>this.screenBottomBoundary}isInsideGrid(w,B,Q,ee){return Q>=0&&w<this.gridRightBoundary&&ee>=0&&B<this.gridBottomBoundary}getViewportMatrix(){let w=a.an([]);return a.J(w,w,[-100,-100,0]),w}_projectCollisionBox(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt){let $t=new a.P(1,0),yr=new a.P(0,1),Cr=new a.P(w.anchorPointX+Ze[0],w.anchorPointY+Ze[1]);if(Fe&&!le){let br=this.projectAndGetPerspectiveRatio(Q,Cr.x+1,Cr.y,ee,Mt).point.sub(ut.point).unit(),Fr=Math.atan(br.y/br.x)+(br.x<0?Math.PI:0),Hr=Math.sin(Fr),ri=Math.cos(Fr);$t=new a.P(ri,Hr),yr=new a.P(-Hr,ri)}else if(!Fe&&le){let br=-this.transform.angle,Fr=Math.sin(br),Hr=Math.cos(br);$t=new a.P(Hr,Fr),yr=new a.P(-Fr,Hr)}let ve=ut.point,xe=B;if(le){ve=Cr;let br=this.transform.zoom-Math.floor(this.transform.zoom);xe=Math.pow(2,-br),xe*=this.mapProjection.getPitchedTextCorrection(this.transform,Cr,ee),Jt||(xe*=a.ac(.5+ut.signedDistanceFromCamera/this.transform.cameraToCenterDistance*.5,0,4))}Jt&&(ve=ve.add($t.mult(Jt.x*xe)).add(yr.mult(Jt.y*xe)));let Ie=w.x1*xe,ze=w.x2*xe,Qe=(Ie+ze)/2,Xe=w.y1*xe,nt=w.y2*xe,qt=(Xe+nt)/2,Wt=[{offsetX:Ie,offsetY:Xe},{offsetX:Qe,offsetY:Xe},{offsetX:ze,offsetY:Xe},{offsetX:ze,offsetY:qt},{offsetX:ze,offsetY:nt},{offsetX:Qe,offsetY:nt},{offsetX:Ie,offsetY:nt},{offsetX:Ie,offsetY:qt}],nr=[];for(let{offsetX:br,offsetY:Fr}of Wt)nr.push(new a.P(ve.x+$t.x*br+yr.x*Fr,ve.y+$t.y*br+yr.y*Fr));let fr=!1;if(le){let br=nr.map((Fr=>this.projectAndGetPerspectiveRatio(Q,Fr.x,Fr.y,ee,Mt)));fr=br.some((Fr=>!Fr.isOccluded)),nr=br.map((Fr=>Fr.point))}else fr=!0;return{box:a.ao(nr),allPointsOccluded:!fr}}}function Fn(ue,w,B){return w*(a.X/(ue.tileSize*Math.pow(2,B-ue.tileID.overscaledZ)))}class An{constructor(w,B,Q,ee){this.opacity=w?Math.max(0,Math.min(1,w.opacity+(w.placed?B:-B))):ee&&Q?1:0,this.placed=Q}isHidden(){return this.opacity===0&&!this.placed}}class Hn{constructor(w,B,Q,ee,le){this.text=new An(w?w.text:null,B,Q,le),this.icon=new An(w?w.icon:null,B,ee,le)}isHidden(){return this.text.isHidden()&&this.icon.isHidden()}}class Qn{constructor(w,B,Q){this.text=w,this.icon=B,this.skipFade=Q}}class Vi{constructor(){this.invProjMatrix=a.H(),this.viewportMatrix=a.H(),this.circles=[]}}class Jn{constructor(w,B,Q,ee,le){this.bucketInstanceId=w,this.featureIndex=B,this.sourceLayerIndex=Q,this.bucketIndex=ee,this.tileID=le}}class Yn{constructor(w){this.crossSourceCollisions=w,this.maxGroupID=0,this.collisionGroups={}}get(w){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[w]){let B=++this.maxGroupID;this.collisionGroups[w]={ID:B,predicate:Q=>Q.collisionGroupID===B}}return this.collisionGroups[w]}}function Gt(ue,w,B,Q,ee){let{horizontalAlign:le,verticalAlign:Fe}=a.au(ue);return new a.P(-(le-.5)*w+Q[0]*ee,-(Fe-.5)*B+Q[1]*ee)}class wt{constructor(w,B,Q,ee,le,Fe){this.transform=w.clone(),this.terrain=Q,this.collisionIndex=new un(this.transform,B),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=ee,this.retainedQueryData={},this.collisionGroups=new Yn(le),this.collisionCircleArrays={},this.collisionBoxArrays=new Map,this.prevPlacement=Fe,Fe&&(Fe.prevPlacement=void 0),this.placedOrientations={}}_getTerrainElevationFunc(w){let B=this.terrain;return B?(Q,ee)=>B.getElevation(w,Q,ee):null}getBucketParts(w,B,Q,ee){let le=Q.getBucket(B),Fe=Q.latestFeatureIndex;if(!le||!Fe||B.id!==le.layerIds[0])return;let Ze=Q.collisionBoxArray,ut=le.layers[0].layout,Mt=le.layers[0].paint,Jt=Math.pow(2,this.transform.zoom-Q.tileID.overscaledZ),$t=Q.tileSize/a.X,yr=Q.tileID.toUnwrapped(),Cr=this.transform.calculatePosMatrix(yr),ve=ut.get(\"text-pitch-alignment\")===\"map\",xe=ut.get(\"text-rotation-alignment\")===\"map\",Ie=Fn(Q,1,this.transform.zoom),ze=this.collisionIndex.mapProjection.translatePosition(this.transform,Q,Mt.get(\"text-translate\"),Mt.get(\"text-translate-anchor\")),Qe=this.collisionIndex.mapProjection.translatePosition(this.transform,Q,Mt.get(\"icon-translate\"),Mt.get(\"icon-translate-anchor\")),Xe=qr(Cr,ve,xe,this.transform,Ie),nt=null;if(ve){let Wt=jr(Cr,ve,xe,this.transform,Ie);nt=a.L([],this.transform.labelPlaneMatrix,Wt)}this.retainedQueryData[le.bucketInstanceId]=new Jn(le.bucketInstanceId,Fe,le.sourceLayerIndex,le.index,Q.tileID);let qt={bucket:le,layout:ut,translationText:ze,translationIcon:Qe,posMatrix:Cr,unwrappedTileID:yr,textLabelPlaneMatrix:Xe,labelToScreenMatrix:nt,scale:Jt,textPixelRatio:$t,holdingForFade:Q.holdingForFade(),collisionBoxArray:Ze,partiallyEvaluatedTextSize:a.ag(le.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(le.sourceID)};if(ee)for(let Wt of le.sortKeyRanges){let{sortKey:nr,symbolInstanceStart:fr,symbolInstanceEnd:br}=Wt;w.push({sortKey:nr,symbolInstanceStart:fr,symbolInstanceEnd:br,parameters:qt})}else w.push({symbolInstanceStart:0,symbolInstanceEnd:le.symbolInstances.length,parameters:qt})}attemptAnchorPlacement(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve,xe,Ie,ze,Qe,Xe){let nt=a.aq[w.textAnchor],qt=[w.textOffset0,w.textOffset1],Wt=Gt(nt,Q,ee,qt,le),nr=this.collisionIndex.placeCollisionBox(B,yr,ut,Mt,Jt,Ze,Fe,Ie,$t.predicate,Xe,Wt);if((!Qe||this.collisionIndex.placeCollisionBox(Qe,yr,ut,Mt,Jt,Ze,Fe,ze,$t.predicate,Xe,Wt).placeable)&&nr.placeable){let fr;if(this.prevPlacement&&this.prevPlacement.variableOffsets[Cr.crossTileID]&&this.prevPlacement.placements[Cr.crossTileID]&&this.prevPlacement.placements[Cr.crossTileID].text&&(fr=this.prevPlacement.variableOffsets[Cr.crossTileID].anchor),Cr.crossTileID===0)throw new Error(\"symbolInstance.crossTileID can't be 0\");return this.variableOffsets[Cr.crossTileID]={textOffset:qt,width:Q,height:ee,anchor:nt,textBoxScale:le,prevAnchor:fr},this.markUsedJustification(ve,nt,Cr,xe),ve.allowVerticalPlacement&&(this.markUsedOrientation(ve,xe,Cr),this.placedOrientations[Cr.crossTileID]=xe),{shift:Wt,placedGlyphBoxes:nr}}}placeLayerBucketPart(w,B,Q){let{bucket:ee,layout:le,translationText:Fe,translationIcon:Ze,posMatrix:ut,unwrappedTileID:Mt,textLabelPlaneMatrix:Jt,labelToScreenMatrix:$t,textPixelRatio:yr,holdingForFade:Cr,collisionBoxArray:ve,partiallyEvaluatedTextSize:xe,collisionGroup:Ie}=w.parameters,ze=le.get(\"text-optional\"),Qe=le.get(\"icon-optional\"),Xe=a.ar(le,\"text-overlap\",\"text-allow-overlap\"),nt=Xe===\"always\",qt=a.ar(le,\"icon-overlap\",\"icon-allow-overlap\"),Wt=qt===\"always\",nr=le.get(\"text-rotation-alignment\")===\"map\",fr=le.get(\"text-pitch-alignment\")===\"map\",br=le.get(\"icon-text-fit\")!==\"none\",Fr=le.get(\"symbol-z-order\")===\"viewport-y\",Hr=nt&&(Wt||!ee.hasIconData()||Qe),ri=Wt&&(nt||!ee.hasTextData()||ze);!ee.collisionArrays&&ve&&ee.deserializeCollisionBoxes(ve);let Ci=this._getTerrainElevationFunc(this.retainedQueryData[ee.bucketInstanceId].tileID),cn=(sn,Ln,Kn)=>{var Aa,fa;if(B[sn.crossTileID])return;if(Cr)return void(this.placements[sn.crossTileID]=new Qn(!1,!1,!1));let $a=!1,Co=!1,Qa=!0,mo=null,Bo={box:null,placeable:!1,offscreen:null},Ps={box:null,placeable:!1,offscreen:null},Ts=null,wo=null,To=null,hl=0,Nl=0,Lu=0;Ln.textFeatureIndex?hl=Ln.textFeatureIndex:sn.useRuntimeCollisionCircles&&(hl=sn.featureIndex),Ln.verticalTextFeatureIndex&&(Nl=Ln.verticalTextFeatureIndex);let au=Ln.textBox;if(au){let Al=Te=>{let qe=a.ah.horizontal;if(ee.allowVerticalPlacement&&!Te&&this.prevPlacement){let Ve=this.prevPlacement.placedOrientations[sn.crossTileID];Ve&&(this.placedOrientations[sn.crossTileID]=Ve,qe=Ve,this.markUsedOrientation(ee,qe,sn))}return qe},Sl=(Te,qe)=>{if(ee.allowVerticalPlacement&&sn.numVerticalGlyphVertices>0&&Ln.verticalTextBox){for(let Ve of ee.writingModes)if(Ve===a.ah.vertical?(Bo=qe(),Ps=Bo):Bo=Te(),Bo&&Bo.placeable)break}else Bo=Te()},X=sn.textAnchorOffsetStartIndex,se=sn.textAnchorOffsetEndIndex;if(se===X){let Te=(qe,Ve)=>{let Ye=this.collisionIndex.placeCollisionBox(qe,Xe,yr,ut,Mt,fr,nr,Fe,Ie.predicate,Ci);return Ye&&Ye.placeable&&(this.markUsedOrientation(ee,Ve,sn),this.placedOrientations[sn.crossTileID]=Ve),Ye};Sl((()=>Te(au,a.ah.horizontal)),(()=>{let qe=Ln.verticalTextBox;return ee.allowVerticalPlacement&&sn.numVerticalGlyphVertices>0&&qe?Te(qe,a.ah.vertical):{box:null,offscreen:null}})),Al(Bo&&Bo.placeable)}else{let Te=a.aq[(fa=(Aa=this.prevPlacement)===null||Aa===void 0?void 0:Aa.variableOffsets[sn.crossTileID])===null||fa===void 0?void 0:fa.anchor],qe=(Ye,Pt,ot)=>{let Xt=Ye.x2-Ye.x1,dr=Ye.y2-Ye.y1,pr=sn.textBoxScale,Ur=br&&qt===\"never\"?Pt:null,zr=null,gi=Xe===\"never\"?1:2,xi=\"never\";Te&&gi++;for(let Wr=0;Wr<gi;Wr++){for(let ii=X;ii<se;ii++){let di=ee.textAnchorOffsets.get(ii);if(Te&&di.textAnchor!==Te)continue;let Li=this.attemptAnchorPlacement(di,Ye,Xt,dr,pr,nr,fr,yr,ut,Mt,Ie,xi,sn,ee,ot,Fe,Ze,Ur,Ci);if(Li&&(zr=Li.placedGlyphBoxes,zr&&zr.placeable))return $a=!0,mo=Li.shift,zr}Te?Te=null:xi=Xe}return Q&&!zr&&(zr={box:this.collisionIndex.placeCollisionBox(au,\"always\",yr,ut,Mt,fr,nr,Fe,Ie.predicate,Ci,new a.P(0,0)).box,offscreen:!1,placeable:!1}),zr};Sl((()=>qe(au,Ln.iconBox,a.ah.horizontal)),(()=>{let Ye=Ln.verticalTextBox;return ee.allowVerticalPlacement&&(!Bo||!Bo.placeable)&&sn.numVerticalGlyphVertices>0&&Ye?qe(Ye,Ln.verticalIconBox,a.ah.vertical):{box:null,occluded:!0,offscreen:null}})),Bo&&($a=Bo.placeable,Qa=Bo.offscreen);let Ve=Al(Bo&&Bo.placeable);if(!$a&&this.prevPlacement){let Ye=this.prevPlacement.variableOffsets[sn.crossTileID];Ye&&(this.variableOffsets[sn.crossTileID]=Ye,this.markUsedJustification(ee,Ye.anchor,sn,Ve))}}}if(Ts=Bo,$a=Ts&&Ts.placeable,Qa=Ts&&Ts.offscreen,sn.useRuntimeCollisionCircles){let Al=ee.text.placedSymbolArray.get(sn.centerJustifiedTextSymbolIndex),Sl=a.ai(ee.textSizeData,xe,Al),X=le.get(\"text-padding\");wo=this.collisionIndex.placeCollisionCircles(Xe,Al,ee.lineVertexArray,ee.glyphOffsetArray,Sl,ut,Mt,Jt,$t,Q,fr,Ie.predicate,sn.collisionCircleDiameter,X,Fe,Ci),wo.circles.length&&wo.collisionDetected&&!Q&&a.w(\"Collisions detected, but collision boxes are not shown\"),$a=nt||wo.circles.length>0&&!wo.collisionDetected,Qa=Qa&&wo.offscreen}if(Ln.iconFeatureIndex&&(Lu=Ln.iconFeatureIndex),Ln.iconBox){let Al=Sl=>this.collisionIndex.placeCollisionBox(Sl,qt,yr,ut,Mt,fr,nr,Ze,Ie.predicate,Ci,br&&mo?mo:void 0);Ps&&Ps.placeable&&Ln.verticalIconBox?(To=Al(Ln.verticalIconBox),Co=To.placeable):(To=Al(Ln.iconBox),Co=To.placeable),Qa=Qa&&To.offscreen}let Js=ze||sn.numHorizontalGlyphVertices===0&&sn.numVerticalGlyphVertices===0,eu=Qe||sn.numIconVertices===0;Js||eu?eu?Js||(Co=Co&&$a):$a=Co&&$a:Co=$a=Co&&$a;let dc=Co&&To.placeable;if($a&&Ts.placeable&&this.collisionIndex.insertCollisionBox(Ts.box,Xe,le.get(\"text-ignore-placement\"),ee.bucketInstanceId,Ps&&Ps.placeable&&Nl?Nl:hl,Ie.ID),dc&&this.collisionIndex.insertCollisionBox(To.box,qt,le.get(\"icon-ignore-placement\"),ee.bucketInstanceId,Lu,Ie.ID),wo&&$a&&this.collisionIndex.insertCollisionCircles(wo.circles,Xe,le.get(\"text-ignore-placement\"),ee.bucketInstanceId,hl,Ie.ID),Q&&this.storeCollisionData(ee.bucketInstanceId,Kn,Ln,Ts,To,wo),sn.crossTileID===0)throw new Error(\"symbolInstance.crossTileID can't be 0\");if(ee.bucketInstanceId===0)throw new Error(\"bucket.bucketInstanceId can't be 0\");this.placements[sn.crossTileID]=new Qn($a||Hr,Co||ri,Qa||ee.justReloaded),B[sn.crossTileID]=!0};if(Fr){if(w.symbolInstanceStart!==0)throw new Error(\"bucket.bucketInstanceId should be 0\");let sn=ee.getSortedSymbolIndexes(this.transform.angle);for(let Ln=sn.length-1;Ln>=0;--Ln){let Kn=sn[Ln];cn(ee.symbolInstances.get(Kn),ee.collisionArrays[Kn],Kn)}}else for(let sn=w.symbolInstanceStart;sn<w.symbolInstanceEnd;sn++)cn(ee.symbolInstances.get(sn),ee.collisionArrays[sn],sn);if(Q&&ee.bucketInstanceId in this.collisionCircleArrays){let sn=this.collisionCircleArrays[ee.bucketInstanceId];a.as(sn.invProjMatrix,ut),sn.viewportMatrix=this.collisionIndex.getViewportMatrix()}ee.justReloaded=!1}storeCollisionData(w,B,Q,ee,le,Fe){if(Q.textBox||Q.iconBox){let Ze,ut;this.collisionBoxArrays.has(w)?Ze=this.collisionBoxArrays.get(w):(Ze=new Map,this.collisionBoxArrays.set(w,Ze)),Ze.has(B)?ut=Ze.get(B):(ut={text:null,icon:null},Ze.set(B,ut)),Q.textBox&&(ut.text=ee.box),Q.iconBox&&(ut.icon=le.box)}if(Fe){let Ze=this.collisionCircleArrays[w];Ze===void 0&&(Ze=this.collisionCircleArrays[w]=new Vi);for(let ut=0;ut<Fe.circles.length;ut+=4)Ze.circles.push(Fe.circles[ut+0]),Ze.circles.push(Fe.circles[ut+1]),Ze.circles.push(Fe.circles[ut+2]),Ze.circles.push(Fe.collisionDetected?1:0)}}markUsedJustification(w,B,Q,ee){let le;le=ee===a.ah.vertical?Q.verticalPlacedTextSymbolIndex:{left:Q.leftJustifiedTextSymbolIndex,center:Q.centerJustifiedTextSymbolIndex,right:Q.rightJustifiedTextSymbolIndex}[a.at(B)];let Fe=[Q.leftJustifiedTextSymbolIndex,Q.centerJustifiedTextSymbolIndex,Q.rightJustifiedTextSymbolIndex,Q.verticalPlacedTextSymbolIndex];for(let Ze of Fe)Ze>=0&&(w.text.placedSymbolArray.get(Ze).crossTileID=le>=0&&Ze!==le?0:Q.crossTileID)}markUsedOrientation(w,B,Q){let ee=B===a.ah.horizontal||B===a.ah.horizontalOnly?B:0,le=B===a.ah.vertical?B:0,Fe=[Q.leftJustifiedTextSymbolIndex,Q.centerJustifiedTextSymbolIndex,Q.rightJustifiedTextSymbolIndex];for(let Ze of Fe)w.text.placedSymbolArray.get(Ze).placedOrientation=ee;Q.verticalPlacedTextSymbolIndex&&(w.text.placedSymbolArray.get(Q.verticalPlacedTextSymbolIndex).placedOrientation=le)}commit(w){this.commitTime=w,this.zoomAtLastRecencyCheck=this.transform.zoom;let B=this.prevPlacement,Q=!1;this.prevZoomAdjustment=B?B.zoomAdjustment(this.transform.zoom):0;let ee=B?B.symbolFadeChange(w):1,le=B?B.opacities:{},Fe=B?B.variableOffsets:{},Ze=B?B.placedOrientations:{};for(let ut in this.placements){let Mt=this.placements[ut],Jt=le[ut];Jt?(this.opacities[ut]=new Hn(Jt,ee,Mt.text,Mt.icon),Q=Q||Mt.text!==Jt.text.placed||Mt.icon!==Jt.icon.placed):(this.opacities[ut]=new Hn(null,ee,Mt.text,Mt.icon,Mt.skipFade),Q=Q||Mt.text||Mt.icon)}for(let ut in le){let Mt=le[ut];if(!this.opacities[ut]){let Jt=new Hn(Mt,ee,!1,!1);Jt.isHidden()||(this.opacities[ut]=Jt,Q=Q||Mt.text.placed||Mt.icon.placed)}}for(let ut in Fe)this.variableOffsets[ut]||!this.opacities[ut]||this.opacities[ut].isHidden()||(this.variableOffsets[ut]=Fe[ut]);for(let ut in Ze)this.placedOrientations[ut]||!this.opacities[ut]||this.opacities[ut].isHidden()||(this.placedOrientations[ut]=Ze[ut]);if(B&&B.lastPlacementChangeTime===void 0)throw new Error(\"Last placement time for previous placement is not defined\");Q?this.lastPlacementChangeTime=w:typeof this.lastPlacementChangeTime!=\"number\"&&(this.lastPlacementChangeTime=B?B.lastPlacementChangeTime:w)}updateLayerOpacities(w,B){let Q={};for(let ee of B){let le=ee.getBucket(w);le&&ee.latestFeatureIndex&&w.id===le.layerIds[0]&&this.updateBucketOpacities(le,ee.tileID,Q,ee.collisionBoxArray)}}updateBucketOpacities(w,B,Q,ee){w.hasTextData()&&(w.text.opacityVertexArray.clear(),w.text.hasVisibleVertices=!1),w.hasIconData()&&(w.icon.opacityVertexArray.clear(),w.icon.hasVisibleVertices=!1),w.hasIconCollisionBoxData()&&w.iconCollisionBox.collisionVertexArray.clear(),w.hasTextCollisionBoxData()&&w.textCollisionBox.collisionVertexArray.clear();let le=w.layers[0],Fe=le.layout,Ze=new Hn(null,0,!1,!1,!0),ut=Fe.get(\"text-allow-overlap\"),Mt=Fe.get(\"icon-allow-overlap\"),Jt=le._unevaluatedLayout.hasValue(\"text-variable-anchor\")||le._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),$t=Fe.get(\"text-rotation-alignment\")===\"map\",yr=Fe.get(\"text-pitch-alignment\")===\"map\",Cr=Fe.get(\"icon-text-fit\")!==\"none\",ve=new Hn(null,0,ut&&(Mt||!w.hasIconData()||Fe.get(\"icon-optional\")),Mt&&(ut||!w.hasTextData()||Fe.get(\"text-optional\")),!0);!w.collisionArrays&&ee&&(w.hasIconCollisionBoxData()||w.hasTextCollisionBoxData())&&w.deserializeCollisionBoxes(ee);let xe=(ze,Qe,Xe)=>{for(let nt=0;nt<Qe/4;nt++)ze.opacityVertexArray.emplaceBack(Xe);ze.hasVisibleVertices=ze.hasVisibleVertices||Xe!==fn},Ie=this.collisionBoxArrays.get(w.bucketInstanceId);for(let ze=0;ze<w.symbolInstances.length;ze++){let Qe=w.symbolInstances.get(ze),{numHorizontalGlyphVertices:Xe,numVerticalGlyphVertices:nt,crossTileID:qt}=Qe,Wt=this.opacities[qt];Q[qt]?Wt=Ze:Wt||(Wt=ve,this.opacities[qt]=Wt),Q[qt]=!0;let nr=Qe.numIconVertices>0,fr=this.placedOrientations[Qe.crossTileID],br=fr===a.ah.vertical,Fr=fr===a.ah.horizontal||fr===a.ah.horizontalOnly;if(Xe>0||nt>0){let ri=en(Wt.text);xe(w.text,Xe,br?fn:ri),xe(w.text,nt,Fr?fn:ri);let Ci=Wt.text.isHidden();[Qe.rightJustifiedTextSymbolIndex,Qe.centerJustifiedTextSymbolIndex,Qe.leftJustifiedTextSymbolIndex].forEach((Ln=>{Ln>=0&&(w.text.placedSymbolArray.get(Ln).hidden=Ci||br?1:0)})),Qe.verticalPlacedTextSymbolIndex>=0&&(w.text.placedSymbolArray.get(Qe.verticalPlacedTextSymbolIndex).hidden=Ci||Fr?1:0);let cn=this.variableOffsets[Qe.crossTileID];cn&&this.markUsedJustification(w,cn.anchor,Qe,fr);let sn=this.placedOrientations[Qe.crossTileID];sn&&(this.markUsedJustification(w,\"left\",Qe,sn),this.markUsedOrientation(w,sn,Qe))}if(nr){let ri=en(Wt.icon),Ci=!(Cr&&Qe.verticalPlacedIconSymbolIndex&&br);Qe.placedIconSymbolIndex>=0&&(xe(w.icon,Qe.numIconVertices,Ci?ri:fn),w.icon.placedSymbolArray.get(Qe.placedIconSymbolIndex).hidden=Wt.icon.isHidden()),Qe.verticalPlacedIconSymbolIndex>=0&&(xe(w.icon,Qe.numVerticalIconVertices,Ci?fn:ri),w.icon.placedSymbolArray.get(Qe.verticalPlacedIconSymbolIndex).hidden=Wt.icon.isHidden())}let Hr=Ie&&Ie.has(ze)?Ie.get(ze):{text:null,icon:null};if(w.hasIconCollisionBoxData()||w.hasTextCollisionBoxData()){let ri=w.collisionArrays[ze];if(ri){let Ci=new a.P(0,0);if(ri.textBox||ri.verticalTextBox){let cn=!0;if(Jt){let sn=this.variableOffsets[qt];sn?(Ci=Gt(sn.anchor,sn.width,sn.height,sn.textOffset,sn.textBoxScale),$t&&Ci._rotate(yr?this.transform.angle:-this.transform.angle)):cn=!1}if(ri.textBox||ri.verticalTextBox){let sn;ri.textBox&&(sn=br),ri.verticalTextBox&&(sn=Fr),tr(w.textCollisionBox.collisionVertexArray,Wt.text.placed,!cn||sn,Hr.text,Ci.x,Ci.y)}}if(ri.iconBox||ri.verticalIconBox){let cn=!!(!Fr&&ri.verticalIconBox),sn;ri.iconBox&&(sn=cn),ri.verticalIconBox&&(sn=!cn),tr(w.iconCollisionBox.collisionVertexArray,Wt.icon.placed,sn,Hr.icon,Cr?Ci.x:0,Cr?Ci.y:0)}}}}if(w.sortFeatures(this.transform.angle),this.retainedQueryData[w.bucketInstanceId]&&(this.retainedQueryData[w.bucketInstanceId].featureSortOrder=w.featureSortOrder),w.hasTextData()&&w.text.opacityVertexBuffer&&w.text.opacityVertexBuffer.updateData(w.text.opacityVertexArray),w.hasIconData()&&w.icon.opacityVertexBuffer&&w.icon.opacityVertexBuffer.updateData(w.icon.opacityVertexArray),w.hasIconCollisionBoxData()&&w.iconCollisionBox.collisionVertexBuffer&&w.iconCollisionBox.collisionVertexBuffer.updateData(w.iconCollisionBox.collisionVertexArray),w.hasTextCollisionBoxData()&&w.textCollisionBox.collisionVertexBuffer&&w.textCollisionBox.collisionVertexBuffer.updateData(w.textCollisionBox.collisionVertexArray),w.text.opacityVertexArray.length!==w.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${w.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${w.text.layoutVertexArray.length}) / 4`);if(w.icon.opacityVertexArray.length!==w.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${w.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${w.icon.layoutVertexArray.length}) / 4`);if(w.bucketInstanceId in this.collisionCircleArrays){let ze=this.collisionCircleArrays[w.bucketInstanceId];w.placementInvProjMatrix=ze.invProjMatrix,w.placementViewportMatrix=ze.viewportMatrix,w.collisionCircleArray=ze.circles,delete this.collisionCircleArrays[w.bucketInstanceId]}}symbolFadeChange(w){return this.fadeDuration===0?1:(w-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(w){return Math.max(0,(this.transform.zoom-w)/1.5)}hasTransitions(w){return this.stale||w-this.lastPlacementChangeTime<this.fadeDuration}stillRecent(w,B){let Q=this.zoomAtLastRecencyCheck===B?1-this.zoomAdjustment(B):1;return this.zoomAtLastRecencyCheck=B,this.commitTime+this.fadeDuration*Q>w}setStale(){this.stale=!0}}function tr(ue,w,B,Q,ee,le){Q&&Q.length!==0||(Q=[0,0,0,0]);let Fe=Q[0]-mi,Ze=Q[1]-mi,ut=Q[2]-mi,Mt=Q[3]-mi;ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,Fe,Ze),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,ut,Ze),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,ut,Mt),ue.emplaceBack(w?1:0,B?1:0,ee||0,le||0,Fe,Mt)}let ir=Math.pow(2,25),wr=Math.pow(2,24),Xr=Math.pow(2,17),ti=Math.pow(2,16),$r=Math.pow(2,9),Ri=Math.pow(2,8),Hi=Math.pow(2,1);function en(ue){if(ue.opacity===0&&!ue.placed)return 0;if(ue.opacity===1&&ue.placed)return 4294967295;let w=ue.placed?1:0,B=Math.floor(127*ue.opacity);return B*ir+w*wr+B*Xr+w*ti+B*$r+w*Ri+B*Hi+w}let fn=0;function yn(){return{isOccluded:(ue,w,B)=>!1,getPitchedTextCorrection:(ue,w,B)=>1,get useSpecialProjectionForSymbols(){return!1},projectTileCoordinates(ue,w,B,Q){throw new Error(\"Not implemented.\")},translatePosition:(ue,w,B,Q)=>(function(ee,le,Fe,Ze,ut=!1){if(!Fe[0]&&!Fe[1])return[0,0];let Mt=ut?Ze===\"map\"?ee.angle:0:Ze===\"viewport\"?-ee.angle:0;if(Mt){let Jt=Math.sin(Mt),$t=Math.cos(Mt);Fe=[Fe[0]*$t-Fe[1]*Jt,Fe[0]*Jt+Fe[1]*$t]}return[ut?Fe[0]:Fn(le,Fe[0],ee.zoom),ut?Fe[1]:Fn(le,Fe[1],ee.zoom)]})(ue,w,B,Q),getCircleRadiusCorrection:ue=>1}}class Mn{constructor(w){this._sortAcrossTiles=w.layout.get(\"symbol-z-order\")!==\"viewport-y\"&&!w.layout.get(\"symbol-sort-key\").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(w,B,Q,ee,le){let Fe=this._bucketParts;for(;this._currentTileIndex<w.length;)if(B.getBucketParts(Fe,ee,w[this._currentTileIndex],this._sortAcrossTiles),this._currentTileIndex++,le())return!0;for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,Fe.sort(((Ze,ut)=>Ze.sortKey-ut.sortKey)));this._currentPartIndex<Fe.length;)if(B.placeLayerBucketPart(Fe[this._currentPartIndex],this._seenCrossTileIDs,Q),this._currentPartIndex++,le())return!0;return!1}}class Ba{constructor(w,B,Q,ee,le,Fe,Ze,ut){this.placement=new wt(w,yn(),B,Fe,Ze,ut),this._currentPlacementIndex=Q.length-1,this._forceFullPlacement=ee,this._showCollisionBoxes=le,this._done=!1}isDone(){return this._done}continuePlacement(w,B,Q){let ee=u.now(),le=()=>!this._forceFullPlacement&&u.now()-ee>2;for(;this._currentPlacementIndex>=0;){let Fe=B[w[this._currentPlacementIndex]],Ze=this.placement.collisionIndex.transform.zoom;if(Fe.type===\"symbol\"&&(!Fe.minzoom||Fe.minzoom<=Ze)&&(!Fe.maxzoom||Fe.maxzoom>Ze)){if(this._inProgressLayer||(this._inProgressLayer=new Mn(Fe)),this._inProgressLayer.continuePlacement(Q[Fe.source],this.placement,this._showCollisionBoxes,Fe,le))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(w){return this.placement.commit(w),this.placement}}let ua=512/a.X/2;class ma{constructor(w,B,Q){this.tileID=w,this.bucketInstanceId=Q,this._symbolsByKey={};let ee=new Map;for(let le=0;le<B.length;le++){let Fe=B.get(le),Ze=Fe.key,ut=ee.get(Ze);ut?ut.push(Fe):ee.set(Ze,[Fe])}for(let[le,Fe]of ee){let Ze={positions:Fe.map((ut=>({x:Math.floor(ut.anchorX*ua),y:Math.floor(ut.anchorY*ua)}))),crossTileIDs:Fe.map((ut=>ut.crossTileID))};if(Ze.positions.length>128){let ut=new a.av(Ze.positions.length,16,Uint16Array);for(let{x:Mt,y:Jt}of Ze.positions)ut.add(Mt,Jt);ut.finish(),delete Ze.positions,Ze.index=ut}this._symbolsByKey[le]=Ze}}getScaledCoordinates(w,B){let{x:Q,y:ee,z:le}=this.tileID.canonical,{x:Fe,y:Ze,z:ut}=B.canonical,Mt=ua/Math.pow(2,ut-le),Jt=(Ze*a.X+w.anchorY)*Mt,$t=ee*a.X*ua;return{x:Math.floor((Fe*a.X+w.anchorX)*Mt-Q*a.X*ua),y:Math.floor(Jt-$t)}}findMatches(w,B,Q){let ee=this.tileID.canonical.z<B.canonical.z?1:Math.pow(2,this.tileID.canonical.z-B.canonical.z);for(let le=0;le<w.length;le++){let Fe=w.get(le);if(Fe.crossTileID)continue;let Ze=this._symbolsByKey[Fe.key];if(!Ze)continue;let ut=this.getScaledCoordinates(Fe,B);if(Ze.index){let Mt=Ze.index.range(ut.x-ee,ut.y-ee,ut.x+ee,ut.y+ee).sort();for(let Jt of Mt){let $t=Ze.crossTileIDs[Jt];if(!Q[$t]){Q[$t]=!0,Fe.crossTileID=$t;break}}}else if(Ze.positions)for(let Mt=0;Mt<Ze.positions.length;Mt++){let Jt=Ze.positions[Mt],$t=Ze.crossTileIDs[Mt];if(Math.abs(Jt.x-ut.x)<=ee&&Math.abs(Jt.y-ut.y)<=ee&&!Q[$t]){Q[$t]=!0,Fe.crossTileID=$t;break}}}}getCrossTileIDsLists(){return Object.values(this._symbolsByKey).map((({crossTileIDs:w})=>w))}}class Wa{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class za{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(w){let B=Math.round((w-this.lng)/360);if(B!==0)for(let Q in this.indexes){let ee=this.indexes[Q],le={};for(let Fe in ee){let Ze=ee[Fe];Ze.tileID=Ze.tileID.unwrapTo(Ze.tileID.wrap+B),le[Ze.tileID.key]=Ze}this.indexes[Q]=le}this.lng=w}addBucket(w,B,Q){if(this.indexes[w.overscaledZ]&&this.indexes[w.overscaledZ][w.key]){if(this.indexes[w.overscaledZ][w.key].bucketInstanceId===B.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(w.overscaledZ,this.indexes[w.overscaledZ][w.key])}for(let le=0;le<B.symbolInstances.length;le++)B.symbolInstances.get(le).crossTileID=0;this.usedCrossTileIDs[w.overscaledZ]||(this.usedCrossTileIDs[w.overscaledZ]={});let ee=this.usedCrossTileIDs[w.overscaledZ];for(let le in this.indexes){let Fe=this.indexes[le];if(Number(le)>w.overscaledZ)for(let Ze in Fe){let ut=Fe[Ze];ut.tileID.isChildOf(w)&&ut.findMatches(B.symbolInstances,w,ee)}else{let Ze=Fe[w.scaledTo(Number(le)).key];Ze&&Ze.findMatches(B.symbolInstances,w,ee)}}for(let le=0;le<B.symbolInstances.length;le++){let Fe=B.symbolInstances.get(le);Fe.crossTileID||(Fe.crossTileID=Q.generate(),ee[Fe.crossTileID]=!0)}return this.indexes[w.overscaledZ]===void 0&&(this.indexes[w.overscaledZ]={}),this.indexes[w.overscaledZ][w.key]=new ma(w,B.symbolInstances,B.bucketInstanceId),!0}removeBucketCrossTileIDs(w,B){for(let Q of B.getCrossTileIDsLists())for(let ee of Q)delete this.usedCrossTileIDs[w][ee]}removeStaleBuckets(w){let B=!1;for(let Q in this.indexes){let ee=this.indexes[Q];for(let le in ee)w[ee[le].bucketInstanceId]||(this.removeBucketCrossTileIDs(Q,ee[le]),delete ee[le],B=!0)}return B}}class Xo{constructor(){this.layerIndexes={},this.crossTileIDs=new Wa,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}}addLayer(w,B,Q){let ee=this.layerIndexes[w.id];ee===void 0&&(ee=this.layerIndexes[w.id]=new za);let le=!1,Fe={};ee.handleWrapJump(Q);for(let Ze of B){let ut=Ze.getBucket(w);ut&&w.id===ut.layerIds[0]&&(ut.bucketInstanceId||(ut.bucketInstanceId=++this.maxBucketInstanceId),ee.addBucket(Ze.tileID,ut,this.crossTileIDs)&&(le=!0),Fe[ut.bucketInstanceId]=!0)}return ee.removeStaleBuckets(Fe)&&(le=!0),le}pruneUnusedLayers(w){let B={};w.forEach((Q=>{B[Q]=!0}));for(let Q in this.layerIndexes)B[Q]||delete this.layerIndexes[Q]}}let da=(ue,w)=>a.t(ue,w&&w.filter((B=>B.identifier!==\"source.canvas\"))),jn=a.aw();class Ha extends a.E{constructor(w,B={}){super(),this._rtlPluginLoaded=()=>{for(let Q in this.sourceCaches){let ee=this.sourceCaches[Q].getSource().type;ee!==\"vector\"&&ee!==\"geojson\"||this.sourceCaches[Q].reload()}},this.map=w,this.dispatcher=new ke(Se(),w._getMapId()),this.dispatcher.registerMessageHandler(\"GG\",((Q,ee)=>this.getGlyphs(Q,ee))),this.dispatcher.registerMessageHandler(\"GI\",((Q,ee)=>this.getImages(Q,ee))),this.imageManager=new E,this.imageManager.setEventedParent(this),this.glyphManager=new G(w._requestManager,B.localIdeographFontFamily),this.lineAtlas=new ae(256,512),this.crossTileSymbolIndex=new Xo,this._spritesImagesIds={},this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new a.ax,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"SR\",a.ay()),St().on(Ue,this._rtlPluginLoaded),this.on(\"data\",(Q=>{if(Q.dataType!==\"source\"||Q.sourceDataType!==\"metadata\")return;let ee=this.sourceCaches[Q.sourceId];if(!ee)return;let le=ee.getSource();if(le&&le.vectorLayerIds)for(let Fe in this._layers){let Ze=this._layers[Fe];Ze.source===le.id&&this._validateLayer(Ze)}}))}loadURL(w,B={},Q){this.fire(new a.k(\"dataloading\",{dataType:\"style\"})),B.validate=typeof B.validate!=\"boolean\"||B.validate;let ee=this.map._requestManager.transformRequest(w,\"Style\");this._loadStyleRequest=new AbortController;let le=this._loadStyleRequest;a.h(ee,this._loadStyleRequest).then((Fe=>{this._loadStyleRequest=null,this._load(Fe.data,B,Q)})).catch((Fe=>{this._loadStyleRequest=null,Fe&&!le.signal.aborted&&this.fire(new a.j(Fe))}))}loadJSON(w,B={},Q){this.fire(new a.k(\"dataloading\",{dataType:\"style\"})),this._frameRequest=new AbortController,u.frameAsync(this._frameRequest).then((()=>{this._frameRequest=null,B.validate=B.validate!==!1,this._load(w,B,Q)})).catch((()=>{}))}loadEmpty(){this.fire(new a.k(\"dataloading\",{dataType:\"style\"})),this._load(jn,{validate:!1})}_load(w,B,Q){var ee;let le=B.transformStyle?B.transformStyle(Q,w):w;if(!B.validate||!da(this,a.u(le))){this._loaded=!0,this.stylesheet=le;for(let Fe in le.sources)this.addSource(Fe,le.sources[Fe],{validate:!1});le.sprite?this._loadSprite(le.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(le.glyphs),this._createLayers(),this.light=new N(this.stylesheet.light),this.sky=new re(this.stylesheet.sky),this.map.setTerrain((ee=this.stylesheet.terrain)!==null&&ee!==void 0?ee:null),this.fire(new a.k(\"data\",{dataType:\"style\"})),this.fire(new a.k(\"style.load\"))}}_createLayers(){let w=a.az(this.stylesheet.layers);this.dispatcher.broadcast(\"SL\",w),this._order=w.map((B=>B.id)),this._layers={},this._serializedLayers=null;for(let B of w){let Q=a.aA(B);Q.setEventedParent(this,{layer:{id:B.id}}),this._layers[B.id]=Q}}_loadSprite(w,B=!1,Q=void 0){let ee;this.imageManager.setLoaded(!1),this._spriteRequest=new AbortController,(function(le,Fe,Ze,ut){return a._(this,void 0,void 0,(function*(){let Mt=C(le),Jt=Ze>1?\"@2x\":\"\",$t={},yr={};for(let{id:Cr,url:ve}of Mt){let xe=Fe.transformRequest(S(ve,Jt,\".json\"),\"SpriteJSON\");$t[Cr]=a.h(xe,ut);let Ie=Fe.transformRequest(S(ve,Jt,\".png\"),\"SpriteImage\");yr[Cr]=p.getImage(Ie,ut)}return yield Promise.all([...Object.values($t),...Object.values(yr)]),(function(Cr,ve){return a._(this,void 0,void 0,(function*(){let xe={};for(let Ie in Cr){xe[Ie]={};let ze=u.getImageCanvasContext((yield ve[Ie]).data),Qe=(yield Cr[Ie]).data;for(let Xe in Qe){let{width:nt,height:qt,x:Wt,y:nr,sdf:fr,pixelRatio:br,stretchX:Fr,stretchY:Hr,content:ri,textFitWidth:Ci,textFitHeight:cn}=Qe[Xe];xe[Ie][Xe]={data:null,pixelRatio:br,sdf:fr,stretchX:Fr,stretchY:Hr,content:ri,textFitWidth:Ci,textFitHeight:cn,spriteData:{width:nt,height:qt,x:Wt,y:nr,context:ze}}}}return xe}))})($t,yr)}))})(w,this.map._requestManager,this.map.getPixelRatio(),this._spriteRequest).then((le=>{if(this._spriteRequest=null,le)for(let Fe in le){this._spritesImagesIds[Fe]=[];let Ze=this._spritesImagesIds[Fe]?this._spritesImagesIds[Fe].filter((ut=>!(ut in le))):[];for(let ut of Ze)this.imageManager.removeImage(ut),this._changedImages[ut]=!0;for(let ut in le[Fe]){let Mt=Fe===\"default\"?ut:`${Fe}:${ut}`;this._spritesImagesIds[Fe].push(Mt),Mt in this.imageManager.images?this.imageManager.updateImage(Mt,le[Fe][ut],!1):this.imageManager.addImage(Mt,le[Fe][ut]),B&&(this._changedImages[Mt]=!0)}}})).catch((le=>{this._spriteRequest=null,ee=le,this.fire(new a.j(ee))})).finally((()=>{this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),B&&(this._changed=!0),this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new a.k(\"data\",{dataType:\"style\"})),Q&&Q(ee)}))}_unloadSprite(){for(let w of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(w),this._changedImages[w]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new a.k(\"data\",{dataType:\"style\"}))}_validateLayer(w){let B=this.sourceCaches[w.source];if(!B)return;let Q=w.sourceLayer;if(!Q)return;let ee=B.getSource();(ee.type===\"geojson\"||ee.vectorLayerIds&&ee.vectorLayerIds.indexOf(Q)===-1)&&this.fire(new a.j(new Error(`Source layer \"${Q}\" does not exist on source \"${ee.id}\" as specified by style layer \"${w.id}\".`)))}loaded(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(let w in this.sourceCaches)if(!this.sourceCaches[w].loaded())return!1;return!!this.imageManager.isLoaded()}_serializeByIds(w,B=!1){let Q=this._serializedAllLayers();if(!w||w.length===0)return Object.values(B?a.aB(Q):Q);let ee=[];for(let le of w)if(Q[le]){let Fe=B?a.aB(Q[le]):Q[le];ee.push(Fe)}return ee}_serializedAllLayers(){let w=this._serializedLayers;if(w)return w;w=this._serializedLayers={};let B=Object.keys(this._layers);for(let Q of B){let ee=this._layers[Q];ee.type!==\"custom\"&&(w[Q]=ee.serialize())}return w}hasTransitions(){if(this.light&&this.light.hasTransition()||this.sky&&this.sky.hasTransition())return!0;for(let w in this.sourceCaches)if(this.sourceCaches[w].hasTransition())return!0;for(let w in this._layers)if(this._layers[w].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error(\"Style is not done loading.\")}update(w){if(!this._loaded)return;let B=this._changed;if(B){let ee=Object.keys(this._updatedLayers),le=Object.keys(this._removedLayers);(ee.length||le.length)&&this._updateWorkerLayers(ee,le);for(let Fe in this._updatedSources){let Ze=this._updatedSources[Fe];if(Ze===\"reload\")this._reloadSource(Fe);else{if(Ze!==\"clear\")throw new Error(`Invalid action ${Ze}`);this._clearSource(Fe)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(let Fe in this._updatedPaintProps)this._layers[Fe].updateTransitions(w);this.light.updateTransitions(w),this.sky.updateTransitions(w),this._resetUpdates()}let Q={};for(let ee in this.sourceCaches){let le=this.sourceCaches[ee];Q[ee]=le.used,le.used=!1}for(let ee of this._order){let le=this._layers[ee];le.recalculate(w,this._availableImages),!le.isHidden(w.zoom)&&le.source&&(this.sourceCaches[le.source].used=!0)}for(let ee in Q){let le=this.sourceCaches[ee];!!Q[ee]!=!!le.used&&le.fire(new a.k(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:ee}))}this.light.recalculate(w),this.sky.recalculate(w),this.z=w.zoom,B&&this.fire(new a.k(\"data\",{dataType:\"style\"}))}_updateTilesForChangedImages(){let w=Object.keys(this._changedImages);if(w.length){for(let B in this.sourceCaches)this.sourceCaches[B].reloadTilesForDependencies([\"icons\",\"patterns\"],w);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(let w in this.sourceCaches)this.sourceCaches[w].reloadTilesForDependencies([\"glyphs\"],[\"\"]);this._glyphsDidChange=!1}}_updateWorkerLayers(w,B){this.dispatcher.broadcast(\"UL\",{layers:this._serializeByIds(w,!1),removedIds:B})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(w,B={}){var Q;this._checkLoaded();let ee=this.serialize();if(w=B.transformStyle?B.transformStyle(ee,w):w,((Q=B.validate)===null||Q===void 0||Q)&&da(this,a.u(w)))return!1;(w=a.aB(w)).layers=a.az(w.layers);let le=a.aC(ee,w),Fe=this._getOperationsToPerform(le);if(Fe.unimplemented.length>0)throw new Error(`Unimplemented: ${Fe.unimplemented.join(\", \")}.`);if(Fe.operations.length===0)return!1;for(let Ze of Fe.operations)Ze();return this.stylesheet=w,this._serializedLayers=null,!0}_getOperationsToPerform(w){let B=[],Q=[];for(let ee of w)switch(ee.command){case\"setCenter\":case\"setZoom\":case\"setBearing\":case\"setPitch\":continue;case\"addLayer\":B.push((()=>this.addLayer.apply(this,ee.args)));break;case\"removeLayer\":B.push((()=>this.removeLayer.apply(this,ee.args)));break;case\"setPaintProperty\":B.push((()=>this.setPaintProperty.apply(this,ee.args)));break;case\"setLayoutProperty\":B.push((()=>this.setLayoutProperty.apply(this,ee.args)));break;case\"setFilter\":B.push((()=>this.setFilter.apply(this,ee.args)));break;case\"addSource\":B.push((()=>this.addSource.apply(this,ee.args)));break;case\"removeSource\":B.push((()=>this.removeSource.apply(this,ee.args)));break;case\"setLayerZoomRange\":B.push((()=>this.setLayerZoomRange.apply(this,ee.args)));break;case\"setLight\":B.push((()=>this.setLight.apply(this,ee.args)));break;case\"setGeoJSONSourceData\":B.push((()=>this.setGeoJSONSourceData.apply(this,ee.args)));break;case\"setGlyphs\":B.push((()=>this.setGlyphs.apply(this,ee.args)));break;case\"setSprite\":B.push((()=>this.setSprite.apply(this,ee.args)));break;case\"setSky\":B.push((()=>this.setSky.apply(this,ee.args)));break;case\"setTerrain\":B.push((()=>this.map.setTerrain.apply(this,ee.args)));break;case\"setTransition\":B.push((()=>{}));break;default:Q.push(ee.command)}return{operations:B,unimplemented:Q}}addImage(w,B){if(this.getImage(w))return this.fire(new a.j(new Error(`An image named \"${w}\" already exists.`)));this.imageManager.addImage(w,B),this._afterImageUpdated(w)}updateImage(w,B){this.imageManager.updateImage(w,B)}getImage(w){return this.imageManager.getImage(w)}removeImage(w){if(!this.getImage(w))return this.fire(new a.j(new Error(`An image named \"${w}\" does not exist.`)));this.imageManager.removeImage(w),this._afterImageUpdated(w)}_afterImageUpdated(w){this._availableImages=this.imageManager.listImages(),this._changedImages[w]=!0,this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new a.k(\"data\",{dataType:\"style\"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(w,B,Q={}){if(this._checkLoaded(),this.sourceCaches[w]!==void 0)throw new Error(`Source \"${w}\" already exists.`);if(!B.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(B).join(\", \")}.`);if([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(B.type)>=0&&this._validate(a.u.source,`sources.${w}`,B,null,Q))return;this.map&&this.map._collectResourceTiming&&(B.collectResourceTiming=!0);let ee=this.sourceCaches[w]=new pt(w,B,this.dispatcher);ee.style=this,ee.setEventedParent(this,(()=>({isSourceLoaded:ee.loaded(),source:ee.serialize(),sourceId:w}))),ee.onAdd(this.map),this._changed=!0}removeSource(w){if(this._checkLoaded(),this.sourceCaches[w]===void 0)throw new Error(\"There is no source with this ID\");for(let Q in this._layers)if(this._layers[Q].source===w)return this.fire(new a.j(new Error(`Source \"${w}\" cannot be removed while layer \"${Q}\" is using it.`)));let B=this.sourceCaches[w];delete this.sourceCaches[w],delete this._updatedSources[w],B.fire(new a.k(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:w})),B.setEventedParent(null),B.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(w,B){if(this._checkLoaded(),this.sourceCaches[w]===void 0)throw new Error(`There is no source with this ID=${w}`);let Q=this.sourceCaches[w].getSource();if(Q.type!==\"geojson\")throw new Error(`geojsonSource.type is ${Q.type}, which is !== 'geojson`);Q.setData(B),this._changed=!0}getSource(w){return this.sourceCaches[w]&&this.sourceCaches[w].getSource()}addLayer(w,B,Q={}){this._checkLoaded();let ee=w.id;if(this.getLayer(ee))return void this.fire(new a.j(new Error(`Layer \"${ee}\" already exists on this map.`)));let le;if(w.type===\"custom\"){if(da(this,a.aD(w)))return;le=a.aA(w)}else{if(\"source\"in w&&typeof w.source==\"object\"&&(this.addSource(ee,w.source),w=a.aB(w),w=a.e(w,{source:ee})),this._validate(a.u.layer,`layers.${ee}`,w,{arrayIndex:-1},Q))return;le=a.aA(w),this._validateLayer(le),le.setEventedParent(this,{layer:{id:ee}})}let Fe=B?this._order.indexOf(B):this._order.length;if(B&&Fe===-1)this.fire(new a.j(new Error(`Cannot add layer \"${ee}\" before non-existing layer \"${B}\".`)));else{if(this._order.splice(Fe,0,ee),this._layerOrderChanged=!0,this._layers[ee]=le,this._removedLayers[ee]&&le.source&&le.type!==\"custom\"){let Ze=this._removedLayers[ee];delete this._removedLayers[ee],Ze.type!==le.type?this._updatedSources[le.source]=\"clear\":(this._updatedSources[le.source]=\"reload\",this.sourceCaches[le.source].pause())}this._updateLayer(le),le.onAdd&&le.onAdd(this.map)}}moveLayer(w,B){if(this._checkLoaded(),this._changed=!0,!this._layers[w])return void this.fire(new a.j(new Error(`The layer '${w}' does not exist in the map's style and cannot be moved.`)));if(w===B)return;let Q=this._order.indexOf(w);this._order.splice(Q,1);let ee=B?this._order.indexOf(B):this._order.length;B&&ee===-1?this.fire(new a.j(new Error(`Cannot move layer \"${w}\" before non-existing layer \"${B}\".`))):(this._order.splice(ee,0,w),this._layerOrderChanged=!0)}removeLayer(w){this._checkLoaded();let B=this._layers[w];if(!B)return void this.fire(new a.j(new Error(`Cannot remove non-existing layer \"${w}\".`)));B.setEventedParent(null);let Q=this._order.indexOf(w);this._order.splice(Q,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[w]=B,delete this._layers[w],this._serializedLayers&&delete this._serializedLayers[w],delete this._updatedLayers[w],delete this._updatedPaintProps[w],B.onRemove&&B.onRemove(this.map)}getLayer(w){return this._layers[w]}getLayersOrder(){return[...this._order]}hasLayer(w){return w in this._layers}setLayerZoomRange(w,B,Q){this._checkLoaded();let ee=this.getLayer(w);ee?ee.minzoom===B&&ee.maxzoom===Q||(B!=null&&(ee.minzoom=B),Q!=null&&(ee.maxzoom=Q),this._updateLayer(ee)):this.fire(new a.j(new Error(`Cannot set the zoom range of non-existing layer \"${w}\".`)))}setFilter(w,B,Q={}){this._checkLoaded();let ee=this.getLayer(w);if(ee){if(!a.aE(ee.filter,B))return B==null?(ee.filter=void 0,void this._updateLayer(ee)):void(this._validate(a.u.filter,`layers.${ee.id}.filter`,B,null,Q)||(ee.filter=a.aB(B),this._updateLayer(ee)))}else this.fire(new a.j(new Error(`Cannot filter non-existing layer \"${w}\".`)))}getFilter(w){return a.aB(this.getLayer(w).filter)}setLayoutProperty(w,B,Q,ee={}){this._checkLoaded();let le=this.getLayer(w);le?a.aE(le.getLayoutProperty(B),Q)||(le.setLayoutProperty(B,Q,ee),this._updateLayer(le)):this.fire(new a.j(new Error(`Cannot style non-existing layer \"${w}\".`)))}getLayoutProperty(w,B){let Q=this.getLayer(w);if(Q)return Q.getLayoutProperty(B);this.fire(new a.j(new Error(`Cannot get style of non-existing layer \"${w}\".`)))}setPaintProperty(w,B,Q,ee={}){this._checkLoaded();let le=this.getLayer(w);le?a.aE(le.getPaintProperty(B),Q)||(le.setPaintProperty(B,Q,ee)&&this._updateLayer(le),this._changed=!0,this._updatedPaintProps[w]=!0,this._serializedLayers=null):this.fire(new a.j(new Error(`Cannot style non-existing layer \"${w}\".`)))}getPaintProperty(w,B){return this.getLayer(w).getPaintProperty(B)}setFeatureState(w,B){this._checkLoaded();let Q=w.source,ee=w.sourceLayer,le=this.sourceCaches[Q];if(le===void 0)return void this.fire(new a.j(new Error(`The source '${Q}' does not exist in the map's style.`)));let Fe=le.getSource().type;Fe===\"geojson\"&&ee?this.fire(new a.j(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):Fe!==\"vector\"||ee?(w.id===void 0&&this.fire(new a.j(new Error(\"The feature id parameter must be provided.\"))),le.setFeatureState(ee,w.id,B)):this.fire(new a.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}removeFeatureState(w,B){this._checkLoaded();let Q=w.source,ee=this.sourceCaches[Q];if(ee===void 0)return void this.fire(new a.j(new Error(`The source '${Q}' does not exist in the map's style.`)));let le=ee.getSource().type,Fe=le===\"vector\"?w.sourceLayer:void 0;le!==\"vector\"||Fe?B&&typeof w.id!=\"string\"&&typeof w.id!=\"number\"?this.fire(new a.j(new Error(\"A feature id is required to remove its specific state property.\"))):ee.removeFeatureState(Fe,w.id,B):this.fire(new a.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}getFeatureState(w){this._checkLoaded();let B=w.source,Q=w.sourceLayer,ee=this.sourceCaches[B];if(ee!==void 0)return ee.getSource().type!==\"vector\"||Q?(w.id===void 0&&this.fire(new a.j(new Error(\"The feature id parameter must be provided.\"))),ee.getFeatureState(Q,w.id)):void this.fire(new a.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));this.fire(new a.j(new Error(`The source '${B}' does not exist in the map's style.`)))}getTransition(){return a.e({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)}serialize(){if(!this._loaded)return;let w=a.aF(this.sourceCaches,(le=>le.serialize())),B=this._serializeByIds(this._order,!0),Q=this.map.getTerrain()||void 0,ee=this.stylesheet;return a.aG({version:ee.version,name:ee.name,metadata:ee.metadata,light:ee.light,sky:ee.sky,center:ee.center,zoom:ee.zoom,bearing:ee.bearing,pitch:ee.pitch,sprite:ee.sprite,glyphs:ee.glyphs,transition:ee.transition,sources:w,layers:B,terrain:Q},(le=>le!==void 0))}_updateLayer(w){this._updatedLayers[w.id]=!0,w.source&&!this._updatedSources[w.source]&&this.sourceCaches[w.source].getSource().type!==\"raster\"&&(this._updatedSources[w.source]=\"reload\",this.sourceCaches[w.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(w){let B=Fe=>this._layers[Fe].type===\"fill-extrusion\",Q={},ee=[];for(let Fe=this._order.length-1;Fe>=0;Fe--){let Ze=this._order[Fe];if(B(Ze)){Q[Ze]=Fe;for(let ut of w){let Mt=ut[Ze];if(Mt)for(let Jt of Mt)ee.push(Jt)}}}ee.sort(((Fe,Ze)=>Ze.intersectionZ-Fe.intersectionZ));let le=[];for(let Fe=this._order.length-1;Fe>=0;Fe--){let Ze=this._order[Fe];if(B(Ze))for(let ut=ee.length-1;ut>=0;ut--){let Mt=ee[ut].feature;if(Q[Mt.layer.id]<Fe)break;le.push(Mt),ee.pop()}else for(let ut of w){let Mt=ut[Ze];if(Mt)for(let Jt of Mt)le.push(Jt.feature)}}return le}queryRenderedFeatures(w,B,Q){B&&B.filter&&this._validate(a.u.filter,\"queryRenderedFeatures.filter\",B.filter,null,B);let ee={};if(B&&B.layers){if(!Array.isArray(B.layers))return this.fire(new a.j(new Error(\"parameters.layers must be an Array.\"))),[];for(let Ze of B.layers){let ut=this._layers[Ze];if(!ut)return this.fire(new a.j(new Error(`The layer '${Ze}' does not exist in the map's style and cannot be queried for features.`))),[];ee[ut.source]=!0}}let le=[];B.availableImages=this._availableImages;let Fe=this._serializedAllLayers();for(let Ze in this.sourceCaches)B.layers&&!ee[Ze]||le.push(Le(this.sourceCaches[Ze],this._layers,Fe,w,B,Q));return this.placement&&le.push((function(Ze,ut,Mt,Jt,$t,yr,Cr){let ve={},xe=yr.queryRenderedSymbols(Jt),Ie=[];for(let ze of Object.keys(xe).map(Number))Ie.push(Cr[ze]);Ie.sort(me);for(let ze of Ie){let Qe=ze.featureIndex.lookupSymbolFeatures(xe[ze.bucketInstanceId],ut,ze.bucketIndex,ze.sourceLayerIndex,$t.filter,$t.layers,$t.availableImages,Ze);for(let Xe in Qe){let nt=ve[Xe]=ve[Xe]||[],qt=Qe[Xe];qt.sort(((Wt,nr)=>{let fr=ze.featureSortOrder;if(fr){let br=fr.indexOf(Wt.featureIndex);return fr.indexOf(nr.featureIndex)-br}return nr.featureIndex-Wt.featureIndex}));for(let Wt of qt)nt.push(Wt)}}for(let ze in ve)ve[ze].forEach((Qe=>{let Xe=Qe.feature,nt=Mt[Ze[ze].source].getFeatureState(Xe.layer[\"source-layer\"],Xe.id);Xe.source=Xe.layer.source,Xe.layer[\"source-layer\"]&&(Xe.sourceLayer=Xe.layer[\"source-layer\"]),Xe.state=nt}));return ve})(this._layers,Fe,this.sourceCaches,w,B,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(le)}querySourceFeatures(w,B){B&&B.filter&&this._validate(a.u.filter,\"querySourceFeatures.filter\",B.filter,null,B);let Q=this.sourceCaches[w];return Q?(function(ee,le){let Fe=ee.getRenderableIds().map((Mt=>ee.getTileByID(Mt))),Ze=[],ut={};for(let Mt=0;Mt<Fe.length;Mt++){let Jt=Fe[Mt],$t=Jt.tileID.canonical.key;ut[$t]||(ut[$t]=!0,Jt.querySourceFeatures(Ze,le))}return Ze})(Q,B):[]}getLight(){return this.light.getLight()}setLight(w,B={}){this._checkLoaded();let Q=this.light.getLight(),ee=!1;for(let Fe in w)if(!a.aE(w[Fe],Q[Fe])){ee=!0;break}if(!ee)return;let le={now:u.now(),transition:a.e({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(w,B),this.light.updateTransitions(le)}getSky(){var w;return(w=this.stylesheet)===null||w===void 0?void 0:w.sky}setSky(w,B={}){let Q=this.getSky(),ee=!1;if(!w&&!Q)return;if(w&&!Q)ee=!0;else if(!w&&Q)ee=!0;else for(let Fe in w)if(!a.aE(w[Fe],Q[Fe])){ee=!0;break}if(!ee)return;let le={now:u.now(),transition:a.e({duration:300,delay:0},this.stylesheet.transition)};this.stylesheet.sky=w,this.sky.setSky(w,B),this.sky.updateTransitions(le)}_validate(w,B,Q,ee,le={}){return(!le||le.validate!==!1)&&da(this,w.call(a.u,a.e({key:B,style:this.serialize(),value:Q,styleSpec:a.v},ee)))}_remove(w=!0){this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._loadStyleRequest&&(this._loadStyleRequest.abort(),this._loadStyleRequest=null),this._spriteRequest&&(this._spriteRequest.abort(),this._spriteRequest=null),St().off(Ue,this._rtlPluginLoaded);for(let B in this._layers)this._layers[B].setEventedParent(null);for(let B in this.sourceCaches){let Q=this.sourceCaches[B];Q.setEventedParent(null),Q.onRemove(this.map)}this.imageManager.setEventedParent(null),this.setEventedParent(null),w&&this.dispatcher.broadcast(\"RM\",void 0),this.dispatcher.remove(w)}_clearSource(w){this.sourceCaches[w].clearTiles()}_reloadSource(w){this.sourceCaches[w].resume(),this.sourceCaches[w].reload()}_updateSources(w){for(let B in this.sourceCaches)this.sourceCaches[B].update(w,this.map.terrain)}_generateCollisionBoxes(){for(let w in this.sourceCaches)this._reloadSource(w)}_updatePlacement(w,B,Q,ee,le=!1){let Fe=!1,Ze=!1,ut={};for(let Mt of this._order){let Jt=this._layers[Mt];if(Jt.type!==\"symbol\")continue;if(!ut[Jt.source]){let yr=this.sourceCaches[Jt.source];ut[Jt.source]=yr.getRenderableIds(!0).map((Cr=>yr.getTileByID(Cr))).sort(((Cr,ve)=>ve.tileID.overscaledZ-Cr.tileID.overscaledZ||(Cr.tileID.isLessThan(ve.tileID)?-1:1)))}let $t=this.crossTileSymbolIndex.addLayer(Jt,ut[Jt.source],w.center.lng);Fe=Fe||$t}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((le=le||this._layerOrderChanged||Q===0)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(u.now(),w.zoom))&&(this.pauseablePlacement=new Ba(w,this.map.terrain,this._order,le,B,Q,ee,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,ut),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(u.now()),Ze=!0),Fe&&this.pauseablePlacement.placement.setStale()),Ze||Fe)for(let Mt of this._order){let Jt=this._layers[Mt];Jt.type===\"symbol\"&&this.placement.updateLayerOpacities(Jt,ut[Jt.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(u.now())}_releaseSymbolFadeTiles(){for(let w in this.sourceCaches)this.sourceCaches[w].releaseSymbolFadeTiles()}getImages(w,B){return a._(this,void 0,void 0,(function*(){let Q=yield this.imageManager.getImages(B.icons);this._updateTilesForChangedImages();let ee=this.sourceCaches[B.source];return ee&&ee.setDependencies(B.tileID.key,B.type,B.icons),Q}))}getGlyphs(w,B){return a._(this,void 0,void 0,(function*(){let Q=yield this.glyphManager.getGlyphs(B.stacks),ee=this.sourceCaches[B.source];return ee&&ee.setDependencies(B.tileID.key,B.type,[\"\"]),Q}))}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(w,B={}){this._checkLoaded(),w&&this._validate(a.u.glyphs,\"glyphs\",w,null,B)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=w,this.glyphManager.entries={},this.glyphManager.setURL(w))}addSprite(w,B,Q={},ee){this._checkLoaded();let le=[{id:w,url:B}],Fe=[...C(this.stylesheet.sprite),...le];this._validate(a.u.sprite,\"sprite\",Fe,null,Q)||(this.stylesheet.sprite=Fe,this._loadSprite(le,!0,ee))}removeSprite(w){this._checkLoaded();let B=C(this.stylesheet.sprite);if(B.find((Q=>Q.id===w))){if(this._spritesImagesIds[w])for(let Q of this._spritesImagesIds[w])this.imageManager.removeImage(Q),this._changedImages[Q]=!0;B.splice(B.findIndex((Q=>Q.id===w)),1),this.stylesheet.sprite=B.length>0?B:void 0,delete this._spritesImagesIds[w],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new a.k(\"data\",{dataType:\"style\"}))}else this.fire(new a.j(new Error(`Sprite \"${w}\" doesn't exists on this map.`)))}getSprite(){return C(this.stylesheet.sprite)}setSprite(w,B={},Q){this._checkLoaded(),w&&this._validate(a.u.sprite,\"sprite\",w,null,B)||(this.stylesheet.sprite=w,w?this._loadSprite(w,!0,Q):(this._unloadSprite(),Q&&Q(null)))}}var vo=a.Y([{name:\"a_pos\",type:\"Int16\",components:2}]);let Gn={prelude:Ct(`#ifdef GL_ES\n",
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ii(G,X,Z,re,!1)}}}}ii.black=new ii(0,0,0,1),ii.white=new ii(1,1,1,1),ii.transparent=new ii(0,0,0,0),ii.red=new ii(1,0,0,1);class Za{constructor(h,b,D){this.sensitivity=h?b?\"variant\":\"case\":b?\"accent\":\"base\",this.locale=D,this.collator=new Intl.Collator(this.locale?this.locale:[],{sensitivity:this.sensitivity,usage:\"search\"})}compare(h,b){return this.collator.compare(h,b)}resolvedLocale(){return new Intl.Collator(this.locale?this.locale:[]).resolvedOptions().locale}}let ja=[\"bottom\",\"center\",\"top\"];class qo{constructor(h,b,D,z,G,X){this.text=h,this.image=b,this.scale=D,this.fontStack=z,this.textColor=G,this.verticalAlign=X}}class $a{constructor(h){this.sections=h}static fromString(h){return new $a([new qo(h,null,null,null,null,null)])}isEmpty(){return this.sections.length===0||!this.sections.some((h=>h.text.length!==0||h.image&&h.image.name.length!==0))}static factory(h){return h instanceof $a?h:$a.fromString(h)}toString(){return this.sections.length===0?\"\":this.sections.map((h=>h.text)).join(\"\")}}class Ka{constructor(h){this.values=h.slice()}static parse(h){if(h instanceof Ka)return h;if(typeof h==\"number\")return new Ka([h,h,h,h]);if(Array.isArray(h)&&!(h.length<1||h.length>4)){for(let b of h)if(typeof b!=\"number\")return;switch(h.length){case 1:h=[h[0],h[0],h[0],h[0]];break;case 2:h=[h[0],h[1],h[0],h[1]];break;case 3:h=[h[0],h[1],h[2],h[1]]}return new Ka(h)}}toString(){return JSON.stringify(this.values)}static interpolate(h,b,D){return new Ka(Ra(h.values,b.values,D))}}class Oa{constructor(h){this.values=h.slice()}static parse(h){if(h instanceof Oa)return h;if(typeof h==\"number\")return new Oa([h]);if(Array.isArray(h)){for(let b of h)if(typeof b!=\"number\")return;return new Oa(h)}}toString(){return JSON.stringify(this.values)}static interpolate(h,b,D){return new Oa(Ra(h.values,b.values,D))}}class Ni{constructor(h){this.values=h.slice()}static parse(h){if(h instanceof Ni)return h;if(typeof h==\"string\"){let D=ii.parse(h);return D?new Ni([D]):void 0}if(!Array.isArray(h))return;let b=[];for(let D of h){if(typeof D!=\"string\")return;let z=ii.parse(D);if(!z)return;b.push(z)}return new Ni(b)}toString(){return JSON.stringify(this.values)}static interpolate(h,b,D,z=\"rgb\"){let G=[];if(h.values.length!=b.values.length)throw new Error(`colorArray: Arrays have mismatched length (${h.values.length} vs. ${b.values.length}), cannot interpolate.`);for(let X=0;X<h.values.length;X++)G.push(ii.interpolate(h.values[X],b.values[X],D,z));return new Ni(G)}}class ka extends Error{constructor(h){super(h),this.name=\"RuntimeError\"}toJSON(){return this.message}}let vs=new Set([\"center\",\"left\",\"right\",\"top\",\"bottom\",\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"]);class yo{constructor(h){this.values=h.slice()}static parse(h){if(h instanceof yo)return h;if(Array.isArray(h)&&!(h.length<1)&&h.length%2==0){for(let b=0;b<h.length;b+=2){let D=h[b],z=h[b+1];if(typeof D!=\"string\"||!vs.has(D)||!Array.isArray(z)||z.length!==2||typeof z[0]!=\"number\"||typeof z[1]!=\"number\")return}return new yo(h)}}toString(){return JSON.stringify(this.values)}static interpolate(h,b,D){let z=h.values,G=b.values;if(z.length!==G.length)throw new ka(`Cannot interpolate values of different length. from: ${h.toString()}, to: ${b.toString()}`);let X=[];for(let Z=0;Z<z.length;Z+=2){if(z[Z]!==G[Z])throw new ka(`Cannot interpolate values containing mismatched anchors. from[${Z}]: ${z[Z]}, to[${Z}]: ${G[Z]}`);X.push(z[Z]);let[re,Q]=z[Z+1],[he,oe]=G[Z+1];X.push([Ji(re,he,D),Ji(Q,oe,D)])}return new yo(X)}}class so{constructor(h){this.name=h.name,this.available=h.available}toString(){return this.name}static fromString(h){return h?new so({name:h,available:!1}):null}}class oo{constructor(h,b,D){this.from=h,this.to=b,this.transition=D}static interpolate(h,b,D){return new oo(h,b,D)}static parse(h){return h instanceof oo?h:Array.isArray(h)&&h.length===3&&typeof h[0]==\"string\"&&typeof h[1]==\"string\"&&typeof h[2]==\"number\"?new oo(h[0],h[1],h[2]):typeof h==\"object\"&&typeof h.from==\"string\"&&typeof h.to==\"string\"&&typeof h.transition==\"number\"?new oo(h.from,h.to,h.transition):typeof h==\"string\"?new oo(h,h,1):void 0}}function ko(M,h,b,D){return typeof M==\"number\"&&M>=0&&M<=255&&typeof h==\"number\"&&h>=0&&h<=255&&typeof b==\"number\"&&b>=0&&b<=255?D===void 0||typeof D==\"number\"&&D>=0&&D<=1?null:`Invalid rgba value [${[M,h,b,D].join(\", \")}]: 'a' must be between 0 and 1.`:`Invalid rgba value [${(typeof D==\"number\"?[M,h,b,D]:[M,h,b]).join(\", \")}]: 'r', 'g', and 'b' must be between 0 and 255.`}function pa(M){if(M===null||typeof M==\"string\"||typeof M==\"boolean\"||typeof M==\"number\"||M instanceof oo||M instanceof ii||M instanceof Za||M instanceof $a||M instanceof Ka||M instanceof Oa||M instanceof Ni||M instanceof yo||M instanceof so)return!0;if(Array.isArray(M)){for(let h of M)if(!pa(h))return!1;return!0}if(typeof M==\"object\"){for(let h in M)if(!pa(M[h]))return!1;return!0}return!1}function la(M){if(M===null)return Dt;if(typeof M==\"string\")return Vt;if(typeof M==\"boolean\")return Qt;if(typeof M==\"number\")return At;if(M instanceof ii)return Ut;if(M instanceof oo)return _r;if(M instanceof Za)return qn;if(M instanceof $a)return ri;if(M instanceof Ka)return Jn;if(M instanceof Oa)return Vi;if(M instanceof Ni)return Ii;if(M instanceof yo)return ha;if(M instanceof so)return ra;if(Array.isArray(M)){let h=M.length,b;for(let D of M){let z=la(D);if(b){if(b===z)continue;b=Jr;break}b=z}return va(b||Jr,h)}return rn}function As(M){let h=typeof M;return M===null?\"\":h===\"string\"||h===\"number\"||h===\"boolean\"?String(M):M instanceof ii||M instanceof oo||M instanceof $a||M instanceof Ka||M instanceof Oa||M instanceof Ni||M instanceof yo||M instanceof so?M.toString():JSON.stringify(M)}class Ms{constructor(h,b){this.type=h,this.value=b}static parse(h,b){if(h.length!==2)return b.error(`'literal' 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b;for(let D of this.args){b=D.evaluate(h);let z=Ni.parse(b);if(z)return z}throw new ka(`Could not parse colorArray from value '${typeof b==\"string\"?b:JSON.stringify(b)}'`)}case\"variableAnchorOffsetCollection\":{let b;for(let D of this.args){b=D.evaluate(h);let z=yo.parse(b);if(z)return z}throw new ka(`Could not parse variableAnchorOffsetCollection from value '${typeof b==\"string\"?b:JSON.stringify(b)}'`)}case\"number\":{let b=null;for(let D of this.args){if(b=D.evaluate(h),b===null)return 0;let z=Number(b);if(!isNaN(z))return z}throw new ka(`Could not convert ${JSON.stringify(b)} to number.`)}case\"formatted\":return $a.fromString(As(this.args[0].evaluate(h)));case\"resolvedImage\":return so.fromString(As(this.args[0].evaluate(h)));case\"projectionDefinition\":return this.args[0].evaluate(h);default:return As(this.args[0].evaluate(h))}}eachChild(h){this.args.forEach(h)}outputDefined(){return this.args.every((h=>h.outputDefined()))}}let Kc=[\"Unknown\",\"Point\",\"LineString\",\"Polygon\"];class _u{constructor(){this.globals=null,this.feature=null,this.featureState=null,this.formattedSection=null,this._parseColorCache=new Map,this.availableImages=null,this.canonical=null}id(){return this.feature&&\"id\"in this.feature?this.feature.id:null}geometryType(){return this.feature?typeof this.feature.type==\"number\"?Kc[this.feature.type]:this.feature.type:null}geometry(){return this.feature&&\"geometry\"in this.feature?this.feature.geometry:null}canonicalID(){return this.canonical}properties(){return this.feature&&this.feature.properties||{}}parseColor(h){let b=this._parseColorCache.get(h);return b||(b=ii.parse(h),this._parseColorCache.set(h,b)),b}}class Gu{constructor(h,b,D=[],z,G=new Cn,X=[]){this.registry=h,this.path=D,this.key=D.map((Z=>`[${Z}]`)).join(\"\"),this.scope=G,this.errors=X,this.expectedType=z,this._isConstant=b}parse(h,b,D,z,G={}){return b?this.concat(b,D,z)._parse(h,G):this._parse(h,G)}_parse(h,b){function 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D=b.parse(h[1],1,Jr),z=b.parse(h[2],2,Jr);if(!D||!z)return null;if(!Ga(D.type,[Qt,Vt,At,Dt,Jr]))return b.error(`Expected first argument to be of type boolean, string, number or null, but found ${Xi(D.type)} instead`);if(h.length===4){let G=b.parse(h[3],3,At);return G?new Ps(D,z,G):null}return new Ps(D,z)}evaluate(h){let b=this.needle.evaluate(h),D=this.haystack.evaluate(h);if(!ss(b,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ka(`Expected first argument to be of type boolean, string, number or null, but found ${Xi(la(b))} instead.`);let z;if(this.fromIndex&&(z=this.fromIndex.evaluate(h)),ss(D,[\"string\"])){let G=D.indexOf(b,z);return G===-1?-1:[...D.slice(0,G)].length}if(ss(D,[\"array\"]))return D.indexOf(b,z);throw new ka(`Expected second argument to be of type array or string, but found ${Xi(la(D))} instead.`)}eachChild(h){h(this.needle),h(this.haystack),this.fromIndex&&h(this.fromIndex)}outputDefined(){return!1}}class Ca{constructor(h,b,D,z,G,X){this.inputType=h,this.type=b,this.input=D,this.cases=z,this.outputs=G,this.otherwise=X}static parse(h,b){if(h.length<5)return b.error(`Expected at least 4 arguments, but found only ${h.length-1}.`);if(h.length%2!=1)return b.error(\"Expected an even number of arguments.\");let D,z;b.expectedType&&b.expectedType.kind!==\"value\"&&(z=b.expectedType);let G={},X=[];for(let Q=2;Q<h.length-1;Q+=2){let he=h[Q],oe=h[Q+1];Array.isArray(he)||(he=[he]);let ke=b.concat(Q);if(he.length===0)return ke.error(\"Expected at least one branch label.\");for(let Ue of he){if(typeof Ue!=\"number\"&&typeof Ue!=\"string\")return ke.error(\"Branch labels must be numbers or strings.\");if(typeof Ue==\"number\"&&Math.abs(Ue)>Number.MAX_SAFE_INTEGER)return ke.error(`Branch labels must be integers no larger than ${Number.MAX_SAFE_INTEGER}.`);if(typeof Ue==\"number\"&&Math.floor(Ue)!==Ue)return ke.error(\"Numeric branch labels must be integer 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D;b.expectedType&&b.expectedType.kind!==\"value\"&&(D=b.expectedType);let z=[];for(let X=1;X<h.length-1;X+=2){let Z=b.parse(h[X],X,Qt);if(!Z)return null;let re=b.parse(h[X+1],X+1,D);if(!re)return null;z.push([Z,re]),D=D||re.type}let G=b.parse(h[h.length-1],h.length-1,D);if(!G)return null;if(!D)throw new Error(\"Can't infer output type\");return new Pl(D,z,G)}evaluate(h){for(let[b,D]of this.branches)if(b.evaluate(h))return D.evaluate(h);return this.otherwise.evaluate(h)}eachChild(h){for(let[b,D]of this.branches)h(b),h(D);h(this.otherwise)}outputDefined(){return this.branches.every((([h,b])=>b.outputDefined()))&&this.otherwise.outputDefined()}}class ll{constructor(h,b,D,z){this.type=h,this.input=b,this.beginIndex=D,this.endIndex=z}static parse(h,b){if(h.length<=2||h.length>=5)return b.error(`Expected 2 or 3 arguments, but found ${h.length-1} instead.`);let D=b.parse(h[1],1,Jr),z=b.parse(h[2],2,At);if(!D||!z)return null;if(!Ga(D.type,[va(Jr),Vt,Jr]))return b.error(`Expected first argument 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z>=b[G-1]?D[G-1].evaluate(h):D[Jc(b,z)].evaluate(h)}eachChild(h){h(this.input);for(let b of this.outputs)h(b)}outputDefined(){return this.outputs.every((h=>h.outputDefined()))}}function Mf(M){return M&&M.__esModule&&Object.prototype.hasOwnProperty.call(M,\"default\")?M.default:M}var Bd,Hu,Sf=(function(){if(Hu)return Bd;function M(h,b,D,z){this.cx=3*h,this.bx=3*(D-h)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*b,this.by=3*(z-b)-this.cy,this.ay=1-this.cy-this.by,this.p1x=h,this.p1y=b,this.p2x=D,this.p2y=z}return Hu=1,Bd=M,M.prototype={sampleCurveX:function(h){return((this.ax*h+this.bx)*h+this.cx)*h},sampleCurveY:function(h){return((this.ay*h+this.by)*h+this.cy)*h},sampleCurveDerivativeX:function(h){return(3*this.ax*h+2*this.bx)*h+this.cx},solveCurveX:function(h,b){if(b===void 0&&(b=1e-6),h<0)return 0;if(h>1)return 1;for(var D=h,z=0;z<8;z++){var G=this.sampleCurveX(D)-h;if(Math.abs(G)<b)return D;var X=this.sampleCurveDerivativeX(D);if(Math.abs(X)<1e-6)break;D-=G/X}var Z=0,re=1;for(D=h,z=0;z<20&&(G=this.sampleCurveX(D),!(Math.abs(G-h)<b));z++)h>G?Z=D:re=D,D=.5*(re-Z)+Z;return D},solve:function(h,b){return this.sampleCurveY(this.solveCurveX(h,b))}},Bd})(),kc=Mf(Sf);class Uo{constructor(h,b,D,z,G){this.type=h,this.operator=b,this.interpolation=D,this.input=z,this.labels=[],this.outputs=[];for(let[X,Z]of G)this.labels.push(X),this.outputs.push(Z)}static interpolationFactor(h,b,D,z){let G=0;if(h.name===\"exponential\")G=ns(b,h.base,D,z);else if(h.name===\"linear\")G=ns(b,1,D,z);else if(h.name===\"cubic-bezier\"){let X=h.controlPoints;G=new kc(X[0],X[1],X[2],X[3]).solve(ns(b,1,D,z))}return G}static parse(h,b){let[D,z,G,...X]=h;if(!Array.isArray(z)||z.length===0)return b.error(\"Expected an interpolation type expression.\",1);if(z[0]===\"linear\")z={name:\"linear\"};else if(z[0]===\"exponential\"){let Q=z[1];if(typeof Q!=\"number\")return b.error(\"Exponential interpolation requires a numeric 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re=null;if(z[\"min-fraction-digits\"]&&(re=b.parse(z[\"min-fraction-digits\"],1,At),!re))return null;let Q=null;return z[\"max-fraction-digits\"]&&(Q=b.parse(z[\"max-fraction-digits\"],1,At),!Q)?null:new Fl(D,G,X,Z,re,Q)}evaluate(h){return new Intl.NumberFormat(this.locale?this.locale.evaluate(h):[],{style:this.currency?\"currency\":this.unit?\"unit\":\"decimal\",currency:this.currency?this.currency.evaluate(h):void 0,unit:this.unit?this.unit.evaluate(h):void 0,minimumFractionDigits:this.minFractionDigits?this.minFractionDigits.evaluate(h):void 0,maximumFractionDigits:this.maxFractionDigits?this.maxFractionDigits.evaluate(h):void 0}).format(this.number.evaluate(h))}eachChild(h){h(this.number),this.locale&&h(this.locale),this.currency&&h(this.currency),this.unit&&h(this.unit),this.minFractionDigits&&h(this.minFractionDigits),this.maxFractionDigits&&h(this.maxFractionDigits)}outputDefined(){return!1}}class Yo{constructor(h){this.type=ri,this.sections=h}static parse(h,b){if(h.length<2)return b.error(\"Expected at least one argument.\");let D=h[1];if(!Array.isArray(D)&&typeof D==\"object\")return b.error(\"First argument must be an image or text section.\");let z=[],G=!1;for(let X=1;X<=h.length-1;++X){let Z=h[X];if(G&&typeof Z==\"object\"&&!Array.isArray(Z)){G=!1;let re=null;if(Z[\"font-scale\"]&&(re=b.parse(Z[\"font-scale\"],1,At),!re))return null;let Q=null;if(Z[\"text-font\"]&&(Q=b.parse(Z[\"text-font\"],1,va(Vt)),!Q))return null;let he=null;if(Z[\"text-color\"]&&(he=b.parse(Z[\"text-color\"],1,Ut),!he))return null;let oe=null;if(Z[\"vertical-align\"]){if(typeof Z[\"vertical-align\"]==\"string\"&&!ja.includes(Z[\"vertical-align\"]))return b.error(`'vertical-align' must be one of: 'bottom', 'center', 'top' but found '${Z[\"vertical-align\"]}' instead.`);if(oe=b.parse(Z[\"vertical-align\"],1,Vt),!oe)return null}let ke=z[z.length-1];ke.scale=re,ke.font=Q,ke.textColor=he,ke.verticalAlign=oe}else{let re=b.parse(h[X],1,Jr);if(!re)return null;let 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lt(M){return/[\\u02EA\\u02EB\\u1100-\\u11FF\\u1400-\\u167F\\u18B0-\\u18F5\\u2E80-\\u2E99\\u2E9B-\\u2EF3\\u2F00-\\u2FD5\\u2FF0-\\u3007\\u3012\\u3013\\u3020-\\u302F\\u3031-\\u303F\\u3041-\\u3096\\u309D-\\u30FB\\u30FD-\\u30FF\\u3105-\\u312F\\u3131-\\u318E\\u3190-\\uA48C\\uA490-\\uA4C6\\uA960-\\uA97C\\uAC00-\\uD7A3\\uD7B0-\\uD7C6\\uD7CB-\\uD7FB\\uF900-\\uFA6D\\uFA70-\\uFAD9\\uFE10-\\uFE1F\\uFE30-\\uFE48\\uFE50-\\uFE57\\uFE5F-\\uFE62\\uFE67-\\uFE6F\\uFF00-\\uFF07\\uFF0A-\\uFF0C\\uFF0E-\\uFF19\\uFF1F-\\uFF3A\\uFF3C\\uFF3E\\uFF40-\\uFF5A\\uFFE0-\\uFFE2\\uFFE4-\\uFFE7]|\\uD802[\\uDD80-\\uDD9F]|\\uD805[\\uDD80-\\uDDFF]|\\uD806[\\uDE00-\\uDEBF]|\\uD811[\\uDC00-\\uDE7F]|\\uD81B[\\uDFE0-\\uDFE4\\uDFF0-\\uDFF6]|[\\uD81C-\\uD822\\uD83D\\uD840-\\uD868\\uD86A-\\uD86D\\uD86F-\\uD872\\uD874-\\uD879\\uD880-\\uD883\\uD885-\\uD88C][\\uDC00-\\uDFFF]|\\uD823[\\uDC00-\\uDCD5\\uDCFF-\\uDD1E\\uDD80-\\uDDF2]|\\uD82B[\\uDFF0-\\uDFF3\\uDFF5-\\uDFFB\\uDFFD\\uDFFE]|\\uD82C[\\uDC00-\\uDD22\\uDD30-\\uDEFB]|\\uD833[\\uDEC0-\\uDFCF]|\\uD834[\\uDC00-\\uDDFF\\uDEE0-\\uDF7F]|\\uD836[\\uDC00-\\uDEAF]|\\uD83C[\\uDC00-\\uDE00\\uDF00-\\uDFFF]|\\uD83E[\\uDD00-\\uDEFF]|\\uD869[\\uDC00-\\uDEDF\\uDF00-\\uDFFF]|\\uD86E[\\uDC00-\\uDC1D\\uDC20-\\uDFFF]|\\uD873[\\uDC00-\\uDEAD\\uDEB0-\\uDFFF]|\\uD87A[\\uDC00-\\uDFE0\\uDFF0-\\uDFFF]|\\uD87B[\\uDC00-\\uDE5D]|\\uD87E[\\uDC00-\\uDE1D]|\\uD884[\\uDC00-\\uDF4A\\uDF50-\\uDFFF]|\\uD88D[\\uDC00-\\uDC79]/gim.test(String.fromCodePoint(M))}function Gt(M){return/\\s/u.test(String.fromCodePoint(M))}function Zt(M){for(let h of M)if(lt(h.codePointAt(0)))return!0;return!1}function Nt(M){for(let h of M)if(!Sr(h.codePointAt(0)))return!1;return!0}function Kt(M){let h=M.map((b=>{try{return new RegExp(`\\\\p{sc=${b}}`,\"u\").source}catch(D){return null}})).filter((b=>b));return new RegExp(h.join(\"|\"),\"u\")}let ur=Kt([\"Arab\",\"Dupl\",\"Mong\",\"Ougr\",\"Syrc\"]);function Sr(M){return!ur.test(String.fromCodePoint(M))}function Br(M){return!(lt(M)||(h=M,/[\\xA7\\xA9\\xAE\\xB1\\xBC-\\xBE\\xD7\\xF7\\u2016\\u2020\\u2021\\u2030\\u2031\\u203B\\u203C\\u2042\\u2047-\\u2049\\u2051\\u2100-\\u218F\\u221E\\u2234\\u2235\\u2300-\\u2307\\u230C-\\u231F\\u2324-\\u2328\\u232B\\u237D-\\u239A\\u23BE-\\u23CD\\u23CF\\u23D1-\\u23DB\\u23E2-\\u2422\\u2424-\\u24FF\\u25A0-\\u2619\\u2620-\\u2767\\u2776-\\u2793\\u2B12-\\u2B2F\\u2B50-\\u2B59\\u2BB8-\\u2BEB\\u3000-\\u303F\\u30A0-\\u30FF\\uE000-\\uF8FF\\uFE30-\\uFE6F\\uFF00-\\uFFEF\\uFFFC\\uFFFD]|[\\uDB80-\\uDBFF][\\uDC00-\\uDFFF]/gim.test(String.fromCodePoint(h))));var h}let ln=Kt([\"Adlm\",\"Arab\",\"Armi\",\"Avst\",\"Chrs\",\"Cprt\",\"Egyp\",\"Elym\",\"Gara\",\"Hatr\",\"Hebr\",\"Hung\",\"Khar\",\"Lydi\",\"Mand\",\"Mani\",\"Mend\",\"Merc\",\"Mero\",\"Narb\",\"Nbat\",\"Nkoo\",\"Orkh\",\"Palm\",\"Phli\",\"Phlp\",\"Phnx\",\"Prti\",\"Rohg\",\"Samr\",\"Sarb\",\"Sogo\",\"Syrc\",\"Thaa\",\"Todr\",\"Yezi\"]);function wn(M){return ln.test(String.fromCodePoint(M))}function In(M,h){return!(!h&&wn(M)||/[\\u0900-\\u0DFF\\u0F00-\\u109F\\u1780-\\u17FF]/gim.test(String.fromCodePoint(M)))}function gi(M){for(let h of M)if(wn(h.codePointAt(0)))return!0;return!1}let Bi=new class{constructor(){this.TIMEOUT=5e3,this.applyArabicShaping=null,this.processBidirectionalText=null,this.processStyledBidirectionalText=null,this.pluginStatus=\"unavailable\",this.pluginURL=null,this.loadScriptResolve=()=>{}}setState(M){this.pluginStatus=M.pluginStatus,this.pluginURL=M.pluginURL}getState(){return{pluginStatus:this.pluginStatus,pluginURL:this.pluginURL}}setMethods(M){if(Bi.isParsed())throw new Error(\"RTL text plugin already registered.\");this.applyArabicShaping=M.applyArabicShaping,this.processBidirectionalText=M.processBidirectionalText,this.processStyledBidirectionalText=M.processStyledBidirectionalText,this.loadScriptResolve()}isParsed(){return this.applyArabicShaping!=null&&this.processBidirectionalText!=null&&this.processStyledBidirectionalText!=null}getRTLTextPluginStatus(){return this.pluginStatus}syncState(M,h){return i(this,void 0,void 0,(function*(){if(this.isParsed())return this.getState();if(M.pluginStatus!==\"loading\")return this.setState(M),M;let b=M.pluginURL,D=new Promise((G=>{this.loadScriptResolve=G}));h(b);let z=new Promise((G=>setTimeout((()=>G()),this.TIMEOUT)));if(yield Promise.race([D,z]),this.isParsed()){let G={pluginStatus:\"loaded\",pluginURL:b};return this.setState(G),G}throw this.setState({pluginStatus:\"error\",pluginURL:\"\"}),new Error(`RTL Text Plugin failed to import scripts from ${b}`)}))}};class mi{constructor(h,b){this.isSupportedScript=ia,this.zoom=h,b?(this.now=b.now||0,this.fadeDuration=b.fadeDuration||0,this.zoomHistory=b.zoomHistory||new Qe,this.transition=b.transition||{}):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Qe,this.transition={})}crossFadingFactor(){return this.fadeDuration===0?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)}getCrossfadeParameters(){let h=this.zoom,b=h-Math.floor(h),D=this.crossFadingFactor();return h>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:b+(1-b)*D}:{fromScale:.5,toScale:1,t:1-(1-D)*b}}}function ia(M){return(function(h,b){for(let D of h)if(!In(D.codePointAt(0),b))return!1;return!0})(M,Bi.getRTLTextPluginStatus()===\"loaded\")}let ua=\"-transition\";class lo{constructor(h,b,D){this.property=h,this.value=b,this.expression=(function(z,G,X){if(Rf(z))return new yt(z,G);if(If(z)){let Z=Ze(z,G,X);if(Z.result===\"error\")throw new Error(Z.value.map((re=>`${re.key}: ${re.message}`)).join(\", \"));return Z.value}{let Z=z;return G.type===\"color\"&&typeof z==\"string\"?Z=ii.parse(z):G.type!==\"padding\"||typeof z!=\"number\"&&!Array.isArray(z)?G.type!==\"numberArray\"||typeof z!=\"number\"&&!Array.isArray(z)?G.type!==\"colorArray\"||typeof z!=\"string\"&&!Array.isArray(z)?G.type===\"variableAnchorOffsetCollection\"&&Array.isArray(z)?Z=yo.parse(z):G.type===\"projectionDefinition\"&&typeof z==\"string\"&&(Z=oo.parse(z)):Z=Ni.parse(z):Z=Oa.parse(z):Z=Ka.parse(z),{globalStateRefs:new Set,_globalState:null,kind:\"constant\",evaluate:()=>Z}}})(b===void 0?h.specification.default:b,h.specification,D)}isDataDriven(){return this.expression.kind===\"source\"||this.expression.kind===\"composite\"}getGlobalStateRefs(){return this.expression.globalStateRefs||new Set}possiblyEvaluate(h,b,D){return this.property.possiblyEvaluate(this,h,b,D)}}class Bo{constructor(h,b){this.property=h,this.value=new lo(h,void 0,b)}transitioned(h,b){return new ls(this.property,this.value,b,_e({},h.transition,this.transition),h.now)}untransitioned(){return new ls(this.property,this.value,null,{},0)}}class Ua{constructor(h,b){this._properties=h,this._values=Object.create(h.defaultTransitionablePropertyValues),this._globalState=b}hasProperty(h){return h in this._properties.defaultTransitionablePropertyValues}getValue(h){return ie(this._values[h].value.value)}setValue(h,b){Object.hasOwn(this._values,h)||(this._values[h]=new Bo(this._values[h].property,this._globalState)),this._values[h].value=new lo(this._values[h].property,b===null?void 0:ie(b),this._globalState)}getTransition(h){return ie(this._values[h].transition)}setTransition(h,b){Object.hasOwn(this._values,h)||(this._values[h]=new Bo(this._values[h].property,this._globalState)),this._values[h].transition=ie(b)||void 0}serialize(){let h={};for(let b of Object.keys(this._values)){let D=this.getValue(b);D!==void 0&&(h[b]=D);let z=this.getTransition(b);z!==void 0&&(h[`${b}${ua}`]=z)}return h}transitioned(h,b){let D=new cl(this._properties);for(let z of Object.keys(this._values))D._values[z]=this._values[z].transitioned(h,b._values[z]);return D}untransitioned(){let h=new cl(this._properties);for(let b of Object.keys(this._values))h._values[b]=this._values[b].untransitioned();return h}}class ls{constructor(h,b,D,z,G){this.property=h,this.value=b,this.begin=G+z.delay||0,this.end=this.begin+z.duration||0,h.specification.transition&&(z.delay||z.duration)&&(this.prior=D)}possiblyEvaluate(h,b,D){let z=h.now||0,G=this.value.possiblyEvaluate(h,b,D),X=this.prior;if(X){if(z>this.end)return this.prior=null,G;if(this.value.isDataDriven())return this.prior=null,G;if(z<this.begin)return X.possiblyEvaluate(h,b,D);{let Z=(z-this.begin)/(this.end-this.begin);return this.property.interpolate(X.possiblyEvaluate(h,b,D),G,ce(Z))}}return G}}class cl{constructor(h){this._properties=h,this._values=Object.create(h.defaultTransitioningPropertyValues)}possiblyEvaluate(h,b,D){let z=new yc(this._properties);for(let G of Object.keys(this._values))z._values[G]=this._values[G].possiblyEvaluate(h,b,D);return z}hasTransition(){for(let h of Object.keys(this._values))if(this._values[h].prior)return!0;return!1}}class Eu{constructor(h,b){this._properties=h,this._values=Object.create(h.defaultPropertyValues),this._globalState=b}hasValue(h){return this._values[h].value!==void 0}hasProperty(h){return h in this._properties.defaultPropertyValues}getValue(h){return ie(this._values[h].value)}setValue(h,b){this._values[h]=new lo(this._values[h].property,b===null?void 0:ie(b),this._globalState)}serialize(){let h={};for(let b of Object.keys(this._values)){let D=this.getValue(b);D!==void 0&&(h[b]=D)}return h}possiblyEvaluate(h,b,D){let z=new yc(this._properties);for(let G of Object.keys(this._values))z._values[G]=this._values[G].possiblyEvaluate(h,b,D);return z}}class el{constructor(h,b,D){this.property=h,this.value=b,this.parameters=D}isConstant(){return this.value.kind===\"constant\"}constantOr(h){return this.value.kind===\"constant\"?this.value.value:h}evaluate(h,b,D,z){return this.property.evaluate(this.value,this.parameters,h,b,D,z)}}class yc{constructor(h){this._properties=h,this._values=Object.create(h.defaultPossiblyEvaluatedValues)}get(h){return this._values[h]}}class wi{constructor(h){this.specification=h}possiblyEvaluate(h,b){if(h.isDataDriven())throw new Error(\"Value should not be data driven\");return h.expression.evaluate(b)}interpolate(h,b,D){let z=Ks[this.specification.type];return z?z(h,b,D):h}}class Di{constructor(h,b){this.specification=h,this.overrides=b}possiblyEvaluate(h,b,D,z){return new el(this,h.expression.kind===\"constant\"||h.expression.kind===\"camera\"?{kind:\"constant\",value:h.expression.evaluate(b,null,{},D,z)}:h.expression,b)}interpolate(h,b,D){if(h.value.kind!==\"constant\"||b.value.kind!==\"constant\")return h;if(h.value.value===void 0||b.value.value===void 0)return new el(this,{kind:\"constant\",value:void 0},h.parameters);let z=Ks[this.specification.type];if(z){let G=z(h.value.value,b.value.value,D);return new el(this,{kind:\"constant\",value:G},h.parameters)}return h}evaluate(h,b,D,z,G,X){return h.kind===\"constant\"?h.value:h.evaluate(b,D,z,G,X)}}class fl extends Di{possiblyEvaluate(h,b,D,z){if(h.value===void 0)return new el(this,{kind:\"constant\",value:void 0},b);if(h.expression.kind===\"constant\"){let G=h.expression.evaluate(b,null,{},D,z),X=h.property.specification.type===\"resolvedImage\"&&typeof G!=\"string\"?G.name:G,Z=this._calculate(X,X,X,b);return new el(this,{kind:\"constant\",value:Z},b)}if(h.expression.kind===\"camera\"){let G=this._calculate(h.expression.evaluate({zoom:b.zoom-1}),h.expression.evaluate({zoom:b.zoom}),h.expression.evaluate({zoom:b.zoom+1}),b);return new el(this,{kind:\"constant\",value:G},b)}return new el(this,h.expression,b)}evaluate(h,b,D,z,G,X){if(h.kind===\"source\"){let Z=h.evaluate(b,D,z,G,X);return this._calculate(Z,Z,Z,b)}return h.kind===\"composite\"?this._calculate(h.evaluate({zoom:Math.floor(b.zoom)-1},D,z),h.evaluate({zoom:Math.floor(b.zoom)},D,z),h.evaluate({zoom:Math.floor(b.zoom)+1},D,z),b):h.value}_calculate(h,b,D,z){return z.zoom>z.zoomHistory.lastIntegerZoom?{from:h,to:b}:{from:D,to:b}}interpolate(h){return h}}class Fc{constructor(h){this.specification=h}possiblyEvaluate(h,b,D,z){if(h.value!==void 0){if(h.expression.kind===\"constant\"){let G=h.expression.evaluate(b,null,{},D,z);return this._calculate(G,G,G,b)}return this._calculate(h.expression.evaluate(new mi(Math.floor(b.zoom-1),b)),h.expression.evaluate(new mi(Math.floor(b.zoom),b)),h.expression.evaluate(new mi(Math.floor(b.zoom+1),b)),b)}}_calculate(h,b,D,z){return z.zoom>z.zoomHistory.lastIntegerZoom?{from:h,to:b}:{from:D,to:b}}interpolate(h){return h}}class du{constructor(h){this.specification=h}possiblyEvaluate(h,b,D,z){return!!h.expression.evaluate(b,null,{},D,z)}interpolate(){return!1}}class ms{constructor(h){this.properties=h,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[];for(let b in h){let D=h[b];D.specification.overridable&&this.overridableProperties.push(b);let z=this.defaultPropertyValues[b]=new lo(D,void 0,void 0),G=this.defaultTransitionablePropertyValues[b]=new Bo(D,void 0);this.defaultTransitioningPropertyValues[b]=G.untransitioned(),this.defaultPossiblyEvaluatedValues[b]=z.possiblyEvaluate({})}}}_i(\"DataDrivenProperty\",Di),_i(\"DataConstantProperty\",wi),_i(\"CrossFadedDataDrivenProperty\",fl),_i(\"CrossFadedProperty\",Fc),_i(\"ColorRampProperty\",du);let ff=\" is a PAINT property not a LAYOUT property. Use get/setPaintProperty instead?\",ku=\" is a LAYOUT property not a PAINT property. Use get/setLayoutProperty instead?\";class hl extends cr{constructor(h,b,D){if(super(),this.id=h.id,this.type=h.type,this._globalState=D,this._featureFilter={filter:()=>!0,needGeometry:!1,getGlobalStateRefs:()=>new Set},this._visibilityExpression=(function(z,G){return new x0(z,G)})(this.visibility,D),h.type!==\"custom\"&&(this.metadata=h.metadata,this.minzoom=h.minzoom,this.maxzoom=h.maxzoom,h.type!==\"background\"&&(this.source=h.source,this.sourceLayer=h[\"source-layer\"],this.filter=h.filter,this._featureFilter=cn(h.filter,D)),b.layout&&(this._unevaluatedLayout=new Eu(b.layout,D)),b.paint)){this._transitionablePaint=new Ua(b.paint,D);for(let z in h.paint)this.setPaintProperty(z,h.paint[z],{validate:!1});for(let z in h.layout)this.setLayoutProperty(z,h.layout[z],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new yc(b.paint)}}setFilter(h){this.filter=h,this._featureFilter=cn(h,this._globalState)}getCrossfadeParameters(){return this._crossfadeParameters}getLayoutProperty(h){var b;if(h===\"visibility\")return this.visibility;if(!((b=this._transitionablePaint)===null||b===void 0)&&b.hasProperty(h))throw new Error(h+ff);if(!this._unevaluatedLayout)throw new Error(`Cannot get layout property \"${h}\" on layer type \"${this.type}\" which has no layout properties.`);return this._unevaluatedLayout.getValue(h)}getLayoutAffectingGlobalStateRefs(){let h=new Set;for(let b of this._visibilityExpression.getGlobalStateRefs())h.add(b);if(this._unevaluatedLayout)for(let b in this._unevaluatedLayout._values){let D=this._unevaluatedLayout._values[b];for(let z of D.getGlobalStateRefs())h.add(z)}for(let b of this._featureFilter.getGlobalStateRefs())h.add(b);return h}getPaintAffectingGlobalStateRefs(){var h;let b=new globalThis.Map;if(this._transitionablePaint)for(let D in this._transitionablePaint._values){let z=this._transitionablePaint._values[D].value;for(let G of z.getGlobalStateRefs()){let X=(h=b.get(G))!==null&&h!==void 0?h:[];X.push({name:D,value:z.value}),b.set(G,X)}}return b}getVisibilityAffectingGlobalStateRefs(){return this._visibilityExpression.getGlobalStateRefs()}setLayoutProperty(h,b,D={}){var z;if(h===\"visibility\")return this.visibility=b,this._visibilityExpression.setValue(b),void this.recalculateVisibility();!((z=this._transitionablePaint)===null||z===void 0)&&z.hasProperty(h)?this.fire(new Ot(new Error(h+ff))):b!=null&&this._validate(Wh,`layers.${this.id}.layout.${h}`,h,b,D)||this._unevaluatedLayout.setValue(h,b)}getPaintProperty(h){var b,D;if(h.endsWith(ua)){let z=h.slice(0,-11);if(z===\"visibility\"||!((b=this._unevaluatedLayout)===null||b===void 0)&&b.hasProperty(z))throw new Error(h+ku);return this._transitionablePaint.getTransition(z)}if(h===\"visibility\"||!((D=this._unevaluatedLayout)===null||D===void 0)&&D.hasProperty(h))throw new Error(h+ku);return this._transitionablePaint.getValue(h)}setPaintProperty(h,b,D={}){var z;if(h===\"visibility\"||!((z=this._unevaluatedLayout)===null||z===void 0)&&z.hasProperty(h))return this.fire(new Ot(new Error(h+ku))),!1;if(b!=null&&this._validate(jh,`layers.${this.id}.paint.${h}`,h,b,D))return!1;if(h.endsWith(ua))return this._transitionablePaint.setTransition(h.slice(0,-11),b||void 0),!1;{let G=this._transitionablePaint._values[h],X=G.property.specification[\"property-type\"]===\"cross-faded-data-driven\",Z=G.value.isDataDriven(),re=G.value;this._transitionablePaint.setValue(h,b),this._handleSpecialPaintPropertyUpdate(h);let Q=this._transitionablePaint._values[h].value;return Q.isDataDriven()||Z||X||this._handleOverridablePaintPropertyUpdate(h,re,Q)}}_handleSpecialPaintPropertyUpdate(h){}_handleOverridablePaintPropertyUpdate(h,b,D){return!1}isHidden(h=this.minzoom,b=!1){return!!(this.minzoom&&h<(b?Math.floor(this.minzoom):this.minzoom))||!!(this.maxzoom&&h>=this.maxzoom)||this._evaluatedVisibility===\"none\"}updateTransitions(h){this._transitioningPaint=this._transitionablePaint.transitioned(h,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculateVisibility(){this._evaluatedVisibility=this._visibilityExpression.evaluate()}recalculate(h,b){h.getCrossfadeParameters&&(this._crossfadeParameters=h.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(h,void 0,b)),this.paint=this._transitioningPaint.possiblyEvaluate(h,void 0,b)}serialize(){var h,b;let D={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:(h=this._unevaluatedLayout)===null||h===void 0?void 0:h.serialize(),paint:(b=this._transitionablePaint)===null||b===void 0?void 0:b.serialize()};return this.visibility&&(D.layout||(D.layout={}),D.layout.visibility=this.visibility),pe(D,((z,G)=>!(z===void 0||G===\"layout\"&&!Object.keys(z).length||G===\"paint\"&&!Object.keys(z).length)))}_validate(h,b,D,z,G={}){return(G==null?void 0:G.validate)!==!1&&Yh(this,h.call(Al,{key:b,layerType:this.type,objectKey:D,value:z,styleSpec:Ct,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(let h in this.paint._values){let b=this.paint.get(h);if(b instanceof el&&Pc(b.property.specification)&&(b.value.kind===\"source\"||b.value.kind===\"composite\")&&b.value.isStateDependent)return!0}return!1}}let mh;var W={get paint(){return mh=mh||new ms({\"raster-opacity\":new wi(Ct.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Ct.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Ct.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Ct.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Ct.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Ct.paint_raster[\"raster-contrast\"]),resampling:new wi(Ct.paint_raster.resampling),\"raster-resampling\":new wi(Ct.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Ct.paint_raster[\"raster-fade-duration\"])})}};class te extends hl{constructor(h,b){super(h,W,b)}}let ve={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class Se{constructor(h,b){this._structArray=h,this._pos1=b*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class Ee{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(h,b){return h._trim(),b&&(h.isTransferred=!0,b.push(h.arrayBuffer)),{length:h.length,arrayBuffer:h.arrayBuffer}}static deserialize(h){let b=Object.create(this.prototype);return b.arrayBuffer=h.arrayBuffer,b.length=h.length,b.capacity=h.arrayBuffer.byteLength/b.bytesPerElement,b._refreshViews(),b}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(h){this.reserve(h),this.length=h}reserve(h){if(h>this.capacity){this.capacity=Math.max(h,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);let b=this.uint8;this._refreshViews(),b&&this.uint8.set(b)}}_refreshViews(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")}freeBufferAfterUpload(){this.arrayBuffer=new ArrayBuffer(0),this._refreshViews()}}function Re(M,h=1){let b=0,D=0;return{members:M.map((z=>{let G=ve[z.type].BYTES_PER_ELEMENT,X=b=rt(b,Math.max(h,G)),Z=z.components||1;return D=Math.max(D,G),b+=G*Z,{name:z.name,type:z.type,components:Z,offset:X}})),size:rt(b,Math.max(D,h)),alignment:h}}function rt(M,h){return Math.ceil(M/h)*h}class He extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(h,b){let D=this.length;return this.resize(D+1),this.emplace(D,h,b)}emplace(h,b,D){let z=2*h;return this.int16[z+0]=b,this.int16[z+1]=D,h}}He.prototype.bytesPerElement=4,_i(\"StructArrayLayout2i4\",He);class pt extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(h,b,D){let z=this.length;return this.resize(z+1),this.emplace(z,h,b,D)}emplace(h,b,D,z){let G=3*h;return this.int16[G+0]=b,this.int16[G+1]=D,this.int16[G+2]=z,h}}pt.prototype.bytesPerElement=6,_i(\"StructArrayLayout3i6\",pt);class Ft extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(h,b,D,z){let G=this.length;return this.resize(G+1),this.emplace(G,h,b,D,z)}emplace(h,b,D,z,G){let X=4*h;return this.int16[X+0]=b,this.int16[X+1]=D,this.int16[X+2]=z,this.int16[X+3]=G,h}}Ft.prototype.bytesPerElement=8,_i(\"StructArrayLayout4i8\",Ft);class Rt extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X){let Z=this.length;return this.resize(Z+1),this.emplace(Z,h,b,D,z,G,X)}emplace(h,b,D,z,G,X,Z){let re=6*h;return this.int16[re+0]=b,this.int16[re+1]=D,this.int16[re+2]=z,this.int16[re+3]=G,this.int16[re+4]=X,this.int16[re+5]=Z,h}}Rt.prototype.bytesPerElement=12,_i(\"StructArrayLayout2i4i12\",Rt);class ir extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X){let Z=this.length;return this.resize(Z+1),this.emplace(Z,h,b,D,z,G,X)}emplace(h,b,D,z,G,X,Z){let re=4*h,Q=8*h;return this.int16[re+0]=b,this.int16[re+1]=D,this.uint8[Q+4]=z,this.uint8[Q+5]=G,this.uint8[Q+6]=X,this.uint8[Q+7]=Z,h}}ir.prototype.bytesPerElement=8,_i(\"StructArrayLayout2i4ub8\",ir);class nr extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(h,b){let D=this.length;return this.resize(D+1),this.emplace(D,h,b)}emplace(h,b,D){let z=2*h;return this.float32[z+0]=b,this.float32[z+1]=D,h}}nr.prototype.bytesPerElement=8,_i(\"StructArrayLayout2f8\",nr);class Cr extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X,Z,re,Q,he){let oe=this.length;return this.resize(oe+1),this.emplace(oe,h,b,D,z,G,X,Z,re,Q,he)}emplace(h,b,D,z,G,X,Z,re,Q,he,oe){let ke=10*h;return this.uint16[ke+0]=b,this.uint16[ke+1]=D,this.uint16[ke+2]=z,this.uint16[ke+3]=G,this.uint16[ke+4]=X,this.uint16[ke+5]=Z,this.uint16[ke+6]=re,this.uint16[ke+7]=Q,this.uint16[ke+8]=he,this.uint16[ke+9]=oe,h}}Cr.prototype.bytesPerElement=20,_i(\"StructArrayLayout10ui20\",Cr);class Vr extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X,Z,re){let Q=this.length;return this.resize(Q+1),this.emplace(Q,h,b,D,z,G,X,Z,re)}emplace(h,b,D,z,G,X,Z,re,Q){let he=8*h;return this.uint16[he+0]=b,this.uint16[he+1]=D,this.uint16[he+2]=z,this.uint16[he+3]=G,this.uint16[he+4]=X,this.uint16[he+5]=Z,this.uint16[he+6]=re,this.uint16[he+7]=Q,h}}Vr.prototype.bytesPerElement=16,_i(\"StructArrayLayout8ui16\",Vr);class sr extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X,Z,re,Q,he,oe,ke){let ge=this.length;return this.resize(ge+1),this.emplace(ge,h,b,D,z,G,X,Z,re,Q,he,oe,ke)}emplace(h,b,D,z,G,X,Z,re,Q,he,oe,ke,ge){let Ue=12*h;return this.int16[Ue+0]=b,this.int16[Ue+1]=D,this.int16[Ue+2]=z,this.int16[Ue+3]=G,this.uint16[Ue+4]=X,this.uint16[Ue+5]=Z,this.uint16[Ue+6]=re,this.uint16[Ue+7]=Q,this.int16[Ue+8]=he,this.int16[Ue+9]=oe,this.int16[Ue+10]=ke,this.int16[Ue+11]=ge,h}}sr.prototype.bytesPerElement=24,_i(\"StructArrayLayout4i4ui4i24\",sr);class br extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(h,b,D){let z=this.length;return this.resize(z+1),this.emplace(z,h,b,D)}emplace(h,b,D,z){let G=3*h;return this.float32[G+0]=b,this.float32[G+1]=D,this.float32[G+2]=z,h}}br.prototype.bytesPerElement=12,_i(\"StructArrayLayout3f12\",br);class kr extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(h){let b=this.length;return this.resize(b+1),this.emplace(b,h)}emplace(h,b){return this.uint32[1*h+0]=b,h}}kr.prototype.bytesPerElement=4,_i(\"StructArrayLayout1ul4\",kr);class Gr extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X,Z,re,Q){let he=this.length;return this.resize(he+1),this.emplace(he,h,b,D,z,G,X,Z,re,Q)}emplace(h,b,D,z,G,X,Z,re,Q,he){let oe=10*h,ke=5*h;return this.int16[oe+0]=b,this.int16[oe+1]=D,this.int16[oe+2]=z,this.int16[oe+3]=G,this.int16[oe+4]=X,this.int16[oe+5]=Z,this.uint32[ke+3]=re,this.uint16[oe+8]=Q,this.uint16[oe+9]=he,h}}Gr.prototype.bytesPerElement=20,_i(\"StructArrayLayout6i1ul2ui20\",Gr);class Wr extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X){let Z=this.length;return this.resize(Z+1),this.emplace(Z,h,b,D,z,G,X)}emplace(h,b,D,z,G,X,Z){let re=6*h;return this.int16[re+0]=b,this.int16[re+1]=D,this.int16[re+2]=z,this.int16[re+3]=G,this.int16[re+4]=X,this.int16[re+5]=Z,h}}Wr.prototype.bytesPerElement=12,_i(\"StructArrayLayout2i2i2i12\",Wr);class Ar extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G){let X=this.length;return this.resize(X+1),this.emplace(X,h,b,D,z,G)}emplace(h,b,D,z,G,X){let Z=4*h,re=8*h;return this.float32[Z+0]=b,this.float32[Z+1]=D,this.float32[Z+2]=z,this.int16[re+6]=G,this.int16[re+7]=X,h}}Ar.prototype.bytesPerElement=16,_i(\"StructArrayLayout2f1f2i16\",Ar);class En extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X){let Z=this.length;return this.resize(Z+1),this.emplace(Z,h,b,D,z,G,X)}emplace(h,b,D,z,G,X,Z){let re=16*h,Q=4*h,he=8*h;return this.uint8[re+0]=b,this.uint8[re+1]=D,this.float32[Q+1]=z,this.float32[Q+2]=G,this.int16[he+6]=X,this.int16[he+7]=Z,h}}En.prototype.bytesPerElement=16,_i(\"StructArrayLayout2ub2f2i16\",En);class zn extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(h,b,D){let z=this.length;return this.resize(z+1),this.emplace(z,h,b,D)}emplace(h,b,D,z){let G=3*h;return this.uint16[G+0]=b,this.uint16[G+1]=D,this.uint16[G+2]=z,h}}zn.prototype.bytesPerElement=6,_i(\"StructArrayLayout3ui6\",zn);class Zn extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X,Z,re,Q,he,oe,ke,ge,Ue,je,vt,gt){let It=this.length;return this.resize(It+1),this.emplace(It,h,b,D,z,G,X,Z,re,Q,he,oe,ke,ge,Ue,je,vt,gt)}emplace(h,b,D,z,G,X,Z,re,Q,he,oe,ke,ge,Ue,je,vt,gt,It){let _t=24*h,qt=12*h,tr=48*h;return this.int16[_t+0]=b,this.int16[_t+1]=D,this.uint16[_t+2]=z,this.uint16[_t+3]=G,this.uint32[qt+2]=X,this.uint32[qt+3]=Z,this.uint32[qt+4]=re,this.uint16[_t+10]=Q,this.uint16[_t+11]=he,this.uint16[_t+12]=oe,this.float32[qt+7]=ke,this.float32[qt+8]=ge,this.uint8[tr+36]=Ue,this.uint8[tr+37]=je,this.uint8[tr+38]=vt,this.uint32[qt+10]=gt,this.int16[_t+22]=It,h}}Zn.prototype.bytesPerElement=48,_i(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Zn);class ai extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(h,b,D,z,G,X,Z,re,Q,he,oe,ke,ge,Ue,je,vt,gt,It,_t,qt,tr,lr,qr,jr,_n,fn,Dn,Hn){let Ln=this.length;return this.resize(Ln+1),this.emplace(Ln,h,b,D,z,G,X,Z,re,Q,he,oe,ke,ge,Ue,je,vt,gt,It,_t,qt,tr,lr,qr,jr,_n,fn,Dn,Hn)}emplace(h,b,D,z,G,X,Z,re,Q,he,oe,ke,ge,Ue,je,vt,gt,It,_t,qt,tr,lr,qr,jr,_n,fn,Dn,Hn,Ln){let un=32*h,ei=16*h;return this.int16[un+0]=b,this.int16[un+1]=D,this.int16[un+2]=z,this.int16[un+3]=G,this.int16[un+4]=X,this.int16[un+5]=Z,this.int16[un+6]=re,this.int16[un+7]=Q,this.uint16[un+8]=he,this.uint16[un+9]=oe,this.uint16[un+10]=ke,this.uint16[un+11]=ge,this.uint16[un+12]=Ue,this.uint16[un+13]=je,this.uint16[un+14]=vt,this.uint16[un+15]=gt,this.uint16[un+16]=It,this.uint16[un+17]=_t,this.uint16[un+18]=qt,this.uint16[un+19]=tr,this.uint16[un+20]=lr,this.uint16[un+21]=qr,this.uint16[un+22]=jr,this.uint32[ei+12]=_n,this.float32[ei+13]=fn,this.float32[ei+14]=Dn,this.uint16[un+30]=Hn,this.uint16[un+31]=Ln,h}}ai.prototype.bytesPerElement=64,_i(\"StructArrayLayout8i15ui1ul2f2ui64\",ai);class Mi extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(h){let b=this.length;return this.resize(b+1),this.emplace(b,h)}emplace(h,b){return this.float32[1*h+0]=b,h}}Mi.prototype.bytesPerElement=4,_i(\"StructArrayLayout1f4\",Mi);class Si extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(h,b,D){let z=this.length;return this.resize(z+1),this.emplace(z,h,b,D)}emplace(h,b,D,z){let G=3*h;return this.uint16[6*h+0]=b,this.float32[G+1]=D,this.float32[G+2]=z,h}}Si.prototype.bytesPerElement=12,_i(\"StructArrayLayout1ui2f12\",Si);class ya extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(h,b,D){let z=this.length;return this.resize(z+1),this.emplace(z,h,b,D)}emplace(h,b,D,z){let G=4*h;return this.uint32[2*h+0]=b,this.uint16[G+2]=D,this.uint16[G+3]=z,h}}ya.prototype.bytesPerElement=8,_i(\"StructArrayLayout1ul2ui8\",ya);class Y extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(h,b){let D=this.length;return this.resize(D+1),this.emplace(D,h,b)}emplace(h,b,D){let z=2*h;return this.uint16[z+0]=b,this.uint16[z+1]=D,h}}Y.prototype.bytesPerElement=4,_i(\"StructArrayLayout2ui4\",Y);class d extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(h){let b=this.length;return this.resize(b+1),this.emplace(b,h)}emplace(h,b){return this.uint16[1*h+0]=b,h}}d.prototype.bytesPerElement=2,_i(\"StructArrayLayout1ui2\",d);class P extends Ee{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(h,b,D,z){let G=this.length;return this.resize(G+1),this.emplace(G,h,b,D,z)}emplace(h,b,D,z,G){let X=4*h;return this.float32[X+0]=b,this.float32[X+1]=D,this.float32[X+2]=z,this.float32[X+3]=G,h}}P.prototype.bytesPerElement=16,_i(\"StructArrayLayout4f16\",P);class N extends Se{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new o(this.anchorPointX,this.anchorPointY)}}N.prototype.size=20;class H extends Gr{get(h){return new N(this,h)}}_i(\"CollisionBoxArray\",H);class $ extends Se{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(h){this._structArray.uint8[this._pos1+37]=h}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(h){this._structArray.uint8[this._pos1+38]=h}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(h){this._structArray.uint32[this._pos4+10]=h}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}$.prototype.size=48;class se extends Zn{get(h){return new $(this,h)}}_i(\"PlacedSymbolArray\",se);class de extends Se{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(h){this._structArray.uint32[this._pos4+12]=h}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}de.prototype.size=64;class Ae extends ai{get(h){return new de(this,h)}}_i(\"SymbolInstanceArray\",Ae);class Fe extends Mi{getoffsetX(h){return this.float32[1*h+0]}}_i(\"GlyphOffsetArray\",Fe);class Ie extends pt{getx(h){return this.int16[3*h+0]}gety(h){return this.int16[3*h+1]}gettileUnitDistanceFromAnchor(h){return this.int16[3*h+2]}}_i(\"SymbolLineVertexArray\",Ie);class Ye extends Se{get textAnchor(){return this._structArray.uint16[this._pos2+0]}get textOffset0(){return this._structArray.float32[this._pos4+1]}get textOffset1(){return this._structArray.float32[this._pos4+2]}}Ye.prototype.size=12;class st extends Si{get(h){return new Ye(this,h)}}_i(\"TextAnchorOffsetArray\",st);class dt extends Se{get featureIndex(){return this._structArray.uint32[this._pos4+0]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+2]}get bucketIndex(){return this._structArray.uint16[this._pos2+3]}}dt.prototype.size=8;class Pe extends ya{get(h){return new dt(this,h)}}_i(\"FeatureIndexArray\",Pe);class Xe extends He{}class at extends He{}class xt extends He{}class Et extends Rt{}class Bt extends ir{}class St extends nr{}class Pt extends Cr{}class $t extends Vr{}class Ht extends sr{}class dr extends br{}class Xr extends kr{}class Or extends Wr{}class $r extends En{}class Er extends zn{}class Tr extends Y{}let Rr=Re([{name:\"a_pos\",components:2,type:\"Int16\"}],4),{members:Lr}=Rr;class an{constructor(h=[]){this._forceNewSegmentOnNextPrepare=!1,this.segments=h}prepareSegment(h,b,D,z){let G=this.segments[this.segments.length-1];return h>an.MAX_VERTEX_ARRAY_LENGTH&&me(`Max vertices per segment is ${an.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${h}. Consider using the \\`fillLargeMeshArrays\\` function if you require meshes with more than ${an.MAX_VERTEX_ARRAY_LENGTH} vertices.`),this._forceNewSegmentOnNextPrepare||!G||G.vertexLength+h>an.MAX_VERTEX_ARRAY_LENGTH||G.sortKey!==z?this.createNewSegment(b,D,z):G}createNewSegment(h,b,D){let z={vertexOffset:h.length,primitiveOffset:b.length,vertexLength:0,primitiveLength:0,vaos:{}};return D!==void 0&&(z.sortKey=D),this._forceNewSegmentOnNextPrepare=!1,this.segments.push(z),z}getOrCreateLatestSegment(h,b,D){return this.prepareSegment(0,h,b,D)}forceNewSegmentOnNextPrepare(){this._forceNewSegmentOnNextPrepare=!0}get(){return this.segments}destroy(){for(let h of this.segments)for(let b in h.vaos)h.vaos[b].destroy()}static simpleSegment(h,b,D,z){return new an([{vertexOffset:h,primitiveOffset:b,vertexLength:D,primitiveLength:z,vaos:{},sortKey:0}])}}function on(M,h){return 256*(M=ue(Math.floor(M),0,255))+ue(Math.floor(h),0,255)}an.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,_i(\"SegmentVector\",an);let Nn=Re([{name:\"a_pattern_from\",components:4,type:\"Uint16\"},{name:\"a_pattern_to\",components:4,type:\"Uint16\"},{name:\"a_pixel_ratio_from\",components:1,type:\"Uint16\"},{name:\"a_pixel_ratio_to\",components:1,type:\"Uint16\"}]),Yn=Re([{name:\"a_dasharray_from\",components:4,type:\"Uint16\"},{name:\"a_dasharray_to\",components:4,type:\"Uint16\"}]);var yi,Sn,An,li={exports:{}},Vn={exports:{}},jn={exports:{}},Wn=(function(){if(An)return li.exports;An=1;var M=(yi||(yi=1,Vn.exports=function(b,D){var z,G,X,Z,re,Q,he,oe;for(G=b.length-(z=3&b.length),X=D,re=3432918353,Q=461845907,oe=0;oe<G;)he=255&b.charCodeAt(oe)|(255&b.charCodeAt(++oe))<<8|(255&b.charCodeAt(++oe))<<16|(255&b.charCodeAt(++oe))<<24,++oe,X=27492+(65535&(Z=5*(65535&(X=(X^=he=(65535&(he=(he=(65535&he)*re+(((he>>>16)*re&65535)<<16)&4294967295)<<15|he>>>17))*Q+(((he>>>16)*Q&65535)<<16)&4294967295)<<13|X>>>19))+((5*(X>>>16)&65535)<<16)&4294967295))+((58964+(Z>>>16)&65535)<<16);switch(he=0,z){case 3:he^=(255&b.charCodeAt(oe+2))<<16;case 2:he^=(255&b.charCodeAt(oe+1))<<8;case 1:X^=he=(65535&(he=(he=(65535&(he^=255&b.charCodeAt(oe)))*re+(((he>>>16)*re&65535)<<16)&4294967295)<<15|he>>>17))*Q+(((he>>>16)*Q&65535)<<16)&4294967295}return X^=b.length,X=2246822507*(65535&(X^=X>>>16))+((2246822507*(X>>>16)&65535)<<16)&4294967295,X=3266489909*(65535&(X^=X>>>13))+((3266489909*(X>>>16)&65535)<<16)&4294967295,(X^=X>>>16)>>>0}),Vn.exports),h=(Sn||(Sn=1,jn.exports=function(b,D){for(var z,G=b.length,X=D^G,Z=0;G>=4;)z=1540483477*(65535&(z=255&b.charCodeAt(Z)|(255&b.charCodeAt(++Z))<<8|(255&b.charCodeAt(++Z))<<16|(255&b.charCodeAt(++Z))<<24))+((1540483477*(z>>>16)&65535)<<16),X=1540483477*(65535&X)+((1540483477*(X>>>16)&65535)<<16)^(z=1540483477*(65535&(z^=z>>>24))+((1540483477*(z>>>16)&65535)<<16)),G-=4,++Z;switch(G){case 3:X^=(255&b.charCodeAt(Z+2))<<16;case 2:X^=(255&b.charCodeAt(Z+1))<<8;case 1:X=1540483477*(65535&(X^=255&b.charCodeAt(Z)))+((1540483477*(X>>>16)&65535)<<16)}return X=1540483477*(65535&(X^=X>>>13))+((1540483477*(X>>>16)&65535)<<16),(X^=X>>>15)>>>0}),jn.exports);return li.exports=M,li.exports.murmur3=M,li.exports.murmur2=h,li.exports})(),si=s(Wn);class zi{constructor(){this.ids=[],this.positions=[],this.indexed=!1}add(h,b,D,z){this.ids.push(da(h)),this.positions.push(b,D,z)}getPositions(h){if(!this.indexed)throw new Error(\"Trying to get index, but feature positions are not indexed\");let b=da(h),D=0,z=this.ids.length-1;for(;D<z;){let X=D+z>>1;this.ids[X]>=b?z=X:D=X+1}let G=[];for(;this.ids[D]===b;)G.push({index:this.positions[3*D],start:this.positions[3*D+1],end:this.positions[3*D+2]}),D++;return G}static serialize(h,b){let D=new Float64Array(h.ids),z=new Uint32Array(h.positions);return ui(D,z,0,D.length-1),b&&b.push(D.buffer,z.buffer),{ids:D,positions:z}}static deserialize(h){let b=new zi;return b.ids=h.ids,b.positions=h.positions,b.indexed=!0,b}}function da(M){let h=+M;return!isNaN(h)&&h<=Number.MAX_SAFE_INTEGER?h:si(String(M))}function ui(M,h,b,D){for(;b<D;){let z=M[b+D>>1],G=b-1,X=D+1;for(;;){do G++;while(M[G]<z);do X--;while(M[X]>z);if(G>=X)break;Li(M,G,X),Li(h,3*G,3*X),Li(h,3*G+1,3*X+1),Li(h,3*G+2,3*X+2)}X-b<D-X?(ui(M,h,b,X),b=X+1):(ui(M,h,X+1,D),D=X)}}function Li(M,h,b){let D=M[h];M[h]=M[b],M[b]=D}_i(\"FeaturePositionMap\",zi);class Ki{constructor(h,b){this.gl=h.gl,this.location=b}}class ca extends Ki{constructor(h,b){super(h,b),this.current=0}set(h){this.current!==h&&(this.current=h,this.gl.uniform1f(this.location,h))}}class Ia extends Ki{constructor(h,b){super(h,b),this.current=[0,0,0,0]}set(h){h[0]===this.current[0]&&h[1]===this.current[1]&&h[2]===this.current[2]&&h[3]===this.current[3]||(this.current=h,this.gl.uniform4f(this.location,h[0],h[1],h[2],h[3]))}}class Os extends Ki{constructor(h,b){super(h,b),this.current=ii.transparent}set(h){h.r===this.current.r&&h.g===this.current.g&&h.b===this.current.b&&h.a===this.current.a||(this.current=h,this.gl.uniform4f(this.location,h.r,h.g,h.b,h.a))}}let Ss=new Float32Array(16);function Na(M){return[on(255*M.r,255*M.g),on(255*M.b,255*M.a)]}class Ns{constructor(h,b,D){this.value=h,this.uniformNames=b.map((z=>`u_${z}`)),this.type=D}setUniform(h,b,D){h.set(D.constantOr(this.value))}getBinding(h,b,D){return this.type===\"color\"?new Os(h,b):new ca(h,b)}}class vo{constructor(h,b){this.uniformNames=b.map((D=>`u_${D}`)),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1}setConstantPatternPositions(h,b){this.pixelRatioFrom=b.pixelRatio,this.pixelRatioTo=h.pixelRatio,this.patternFrom=b.tlbr,this.patternTo=h.tlbr}setConstantDashPositions(h,b){this.dashTo=[0,h.y,h.height,h.width],this.dashFrom=[0,b.y,b.height,b.width]}setUniform(h,b,D,z){let G=null;z===\"u_pattern_to\"?G=this.patternTo:z===\"u_pattern_from\"?G=this.patternFrom:z===\"u_dasharray_to\"?G=this.dashTo:z===\"u_dasharray_from\"?G=this.dashFrom:z===\"u_pixel_ratio_to\"?G=this.pixelRatioTo:z===\"u_pixel_ratio_from\"&&(G=this.pixelRatioFrom),G!==null&&h.set(G)}getBinding(h,b,D){return D.startsWith(\"u_pattern\")||D.startsWith(\"u_dasharray_\")?new Ia(h,b):new ca(h,b)}}class _o{constructor(h,b,D,z){this.expression=h,this.type=D,this.maxValue=0,this.paintVertexAttributes=b.map((G=>({name:`a_${G}`,type:\"Float32\",components:D===\"color\"?2:1,offset:0}))),this.paintVertexArray=new z}populatePaintArray(h,b,D){let z=this.paintVertexArray.length,G=this.expression.evaluate(new mi(0,D),b,{},D.canonical,[],D.formattedSection);this.paintVertexArray.resize(h),this._setPaintValue(z,h,G)}updatePaintArray(h,b,D,z,G){let X=this.expression.evaluate(new mi(0,G),D,z);this._setPaintValue(h,b,X)}_setPaintValue(h,b,D){if(this.type===\"color\"){let z=Na(D);for(let G=h;G<b;G++)this.paintVertexArray.emplace(G,z[0],z[1])}else{for(let z=h;z<b;z++)this.paintVertexArray.emplace(z,D);this.maxValue=Math.max(this.maxValue,Math.abs(D))}}upload(h){var b,D;!((b=this.paintVertexArray)===null||b===void 0)&&b.arrayBuffer.byteLength&&(!((D=this.paintVertexBuffer)===null||D===void 0)&&D.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=h.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}}class po{constructor(h,b,D,z,G,X){this.expression=h,this.uniformNames=b.map((Z=>`u_${Z}_t`)),this.type=D,this.useIntegerZoom=z,this.zoom=G,this.maxValue=0,this.paintVertexAttributes=b.map((Z=>({name:`a_${Z}`,type:\"Float32\",components:D===\"color\"?4:2,offset:0}))),this.paintVertexArray=new X}populatePaintArray(h,b,D){let z=this.expression.evaluate(new mi(this.zoom,D),b,{},D.canonical,[],D.formattedSection),G=this.expression.evaluate(new mi(this.zoom+1,D),b,{},D.canonical,[],D.formattedSection),X=this.paintVertexArray.length;this.paintVertexArray.resize(h),this._setPaintValue(X,h,z,G)}updatePaintArray(h,b,D,z,G){let X=this.expression.evaluate(new mi(this.zoom,G),D,z),Z=this.expression.evaluate(new mi(this.zoom+1,G),D,z);this._setPaintValue(h,b,X,Z)}_setPaintValue(h,b,D,z){if(this.type===\"color\"){let G=Na(D),X=Na(z);for(let Z=h;Z<b;Z++)this.paintVertexArray.emplace(Z,G[0],G[1],X[0],X[1])}else{for(let G=h;G<b;G++)this.paintVertexArray.emplace(G,D,z);this.maxValue=Math.max(this.maxValue,Math.abs(D),Math.abs(z))}}upload(h){var b,D;!((b=this.paintVertexArray)===null||b===void 0)&&b.arrayBuffer.byteLength&&(!((D=this.paintVertexBuffer)===null||D===void 0)&&D.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=h.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}setUniform(h,b){let D=this.useIntegerZoom?Math.floor(b.zoom):b.zoom,z=ue(this.expression.interpolationFactor(D,this.zoom,this.zoom+1),0,1);h.set(z)}getBinding(h,b,D){return new ca(h,b)}}class Xo{constructor(h,b,D,z,G,X){this.expression=h,this.type=b,this.useIntegerZoom=D,this.zoom=z,this.layerId=X,this.zoomInPaintVertexArray=new G,this.zoomOutPaintVertexArray=new G}populatePaintArray(h,b,D){let z=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(h),this.zoomOutPaintVertexArray.resize(h),this._setPaintValues(z,h,this.getPositionIds(b),D)}updatePaintArray(h,b,D,z,G){this._setPaintValues(h,b,this.getPositionIds(D),G)}_setPaintValues(h,b,D,z){let G=this.getPositions(z);if(!G||!D)return;let X=G[D.min],Z=G[D.mid],re=G[D.max];if(X&&Z&&re)for(let Q=h;Q<b;Q++)this.emplace(this.zoomInPaintVertexArray,Q,Z,X),this.emplace(this.zoomOutPaintVertexArray,Q,Z,re)}upload(h){var b,D;if(!((b=this.zoomInPaintVertexArray)===null||b===void 0)&&b.arrayBuffer.byteLength&&(!((D=this.zoomOutPaintVertexArray)===null||D===void 0)&&D.arrayBuffer.byteLength)){let z=this.getVertexAttributes();this.zoomInPaintVertexBuffer=h.createVertexBuffer(this.zoomInPaintVertexArray,z,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=h.createVertexBuffer(this.zoomOutPaintVertexArray,z,this.expression.isStateDependent)}}destroy(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()}}class no extends Xo{getPositions(h){return h.imagePositions}getPositionIds(h){var b;return(b=h.patterns)===null||b===void 0?void 0:b[this.layerId]}getVertexAttributes(){return Nn.members}emplace(h,b,D,z){h.emplace(b,D.tlbr[0],D.tlbr[1],D.tlbr[2],D.tlbr[3],z.tlbr[0],z.tlbr[1],z.tlbr[2],z.tlbr[3],D.pixelRatio,z.pixelRatio)}}class Us extends Xo{getPositions(h){return h.dashPositions}getPositionIds(h){var b;return(b=h.dashes)===null||b===void 0?void 0:b[this.layerId]}getVertexAttributes(){return Yn.members}emplace(h,b,D,z){h.emplace(b,0,D.y,D.height,D.width,0,z.y,z.height,z.width)}}class ql{constructor(h,b,D){this.binders={},this._buffers=[];let z=[];for(let G in h.paint._values){if(!D(G))continue;let X=h.paint.get(G);if(!(X instanceof el&&Pc(X.property.specification)))continue;let Z=vu(G,h.type),re=X.value,Q=X.property.specification.type,he=X.property.useIntegerZoom,oe=X.property.specification[\"property-type\"],ke=oe===\"cross-faded\"||oe===\"cross-faded-data-driven\";if(re.kind===\"constant\")this.binders[G]=ke?new vo(re.value,Z):new Ns(re.value,Z,Q),z.push(`/u_${G}`);else if(re.kind===\"source\"||ke){let ge=ys(G,Q,\"source\");this.binders[G]=ke?G===\"line-dasharray\"?new Us(re,Q,he,b,ge,h.id):new no(re,Q,he,b,ge,h.id):new _o(re,Z,Q,ge),z.push(`/a_${G}`)}else{let ge=ys(G,Q,\"composite\");this.binders[G]=new po(re,Z,Q,he,b,ge),z.push(`/z_${G}`)}}this.cacheKey=z.sort().join(\"\")}getMaxValue(h){let b=this.binders[h];return b instanceof _o||b instanceof po?b.maxValue:0}populatePaintArrays(h,b,D){for(let z in this.binders){let G=this.binders[z];(G instanceof _o||G instanceof po||G instanceof Xo)&&G.populatePaintArray(h,b,D)}}setConstantPatternPositions(h,b){for(let D in this.binders){let z=this.binders[D];z instanceof vo&&z.setConstantPatternPositions(h,b)}}setConstantDashPositions(h,b){for(let D in this.binders){let z=this.binders[D];z instanceof vo&&z.setConstantDashPositions(h,b)}}updatePaintArrays(h,b,D,z,G){let X=!1;for(let Z in h){let re=b.getPositions(Z);for(let Q of re){let he=D.feature(Q.index);for(let oe in this.binders){let ke=this.binders[oe];if((ke instanceof _o||ke instanceof po||ke instanceof Xo)&&ke.expression.isStateDependent===!0){let ge=z.paint.get(oe);ke.expression=ge.value,ke.updatePaintArray(Q.start,Q.end,he,h[Z],G),X=!0}}}}return X}defines(){let h=[];for(let b in this.binders){let D=this.binders[b];(D instanceof Ns||D instanceof vo)&&h.push(...D.uniformNames.map((z=>`#define HAS_UNIFORM_${z}`)))}return h}getBinderAttributes(){let h=[];for(let b in this.binders){let D=this.binders[b];if(D instanceof _o||D instanceof po)for(let z of D.paintVertexAttributes)h.push(z.name);else if(D instanceof Xo){let z=D.getVertexAttributes();for(let G of z)h.push(G.name)}}return h}getBinderUniforms(){let h=[];for(let b in this.binders){let D=this.binders[b];if(D instanceof Ns||D instanceof vo||D instanceof po)for(let z of D.uniformNames)h.push(z)}return h}getPaintVertexBuffers(){return this._buffers}getUniforms(h,b){let D=[];for(let z in this.binders){let G=this.binders[z];if(G instanceof Ns||G instanceof vo||G instanceof po){for(let X of G.uniformNames)if(b[X]){let Z=G.getBinding(h,b[X],X);D.push({name:X,property:z,binding:Z})}}}return D}setUniforms(h,b,D,z){for(let{name:G,property:X,binding:Z}of b)this.binders[X].setUniform(Z,z,D.get(X),G)}updatePaintBuffers(h){this._buffers=[];for(let b in this.binders){let D=this.binders[b];if(h&&D instanceof Xo){let z=h.fromScale===2?D.zoomInPaintVertexBuffer:D.zoomOutPaintVertexBuffer;z&&this._buffers.push(z)}else(D instanceof _o||D instanceof po)&&D.paintVertexBuffer&&this._buffers.push(D.paintVertexBuffer)}}upload(h){for(let b in this.binders){let D=this.binders[b];(D instanceof _o||D instanceof po||D instanceof Xo)&&D.upload(h)}this.updatePaintBuffers()}destroy(){for(let h in this.binders){let b=this.binders[h];(b instanceof _o||b instanceof po||b instanceof Xo)&&b.destroy()}}}class Ta{constructor(h,b,D=()=>!0){this.programConfigurations={};for(let z of h)this.programConfigurations[z.id]=new ql(z,b,D);this.needsUpload=!1,this._featureMap=new zi,this._bufferOffset=0}populatePaintArrays(h,b,D,z){for(let G in this.programConfigurations)this.programConfigurations[G].populatePaintArrays(h,b,z);b.id!==void 0&&this._featureMap.add(b.id,D,this._bufferOffset,h),this._bufferOffset=h,this.needsUpload=!0}updatePaintArrays(h,b,D,z){for(let G of D)this.needsUpload=this.programConfigurations[G.id].updatePaintArrays(h,this._featureMap,b,G,z)||this.needsUpload}get(h){return this.programConfigurations[h]}upload(h){if(this.needsUpload){for(let b in this.programConfigurations)this.programConfigurations[b].upload(h);this.needsUpload=!1}}destroy(){for(let h in this.programConfigurations)this.programConfigurations[h].destroy()}}function 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NR{constructor(h,b,D,z){this.context=h,this.format=D,this.texture=h.gl.createTexture(),this._ownedHandle=this.texture,this.update(b,z)}update(h,b,D){var z;let{width:G,height:X}=h,Z=(((z=this.size)===null||z===void 0?void 0:z[0])!==G||this.size[1]!==X)&&!D,{context:re}=this,{gl:Q}=re;this.useMipmap=!!(b!=null&&b.useMipmap),Q.bindTexture(Q.TEXTURE_2D,this.texture),re.pixelStoreUnpackFlipY.set(!1),re.pixelStoreUnpack.set(1);let he=this.format===Q.RGBA&&(b==null?void 0:b.premultiply)!==!1;if(Z)this.size=[G,X],gQ(h)?(re.pixelStoreUnpackPremultiplyAlpha.set(!1),this._uploadRawData(h,he,G,X,Q)):(re.pixelStoreUnpackPremultiplyAlpha.set(he),this._uploadDomImage(h,Q));else{let{x:oe,y:ke}=D||{x:0,y:0};gQ(h)?(re.pixelStoreUnpackPremultiplyAlpha.set(!1),this._updateRawData(h,he,oe,ke,G,X,Q)):(re.pixelStoreUnpackPremultiplyAlpha.set(he),this._updateDomImage(h,oe,ke,Q))}this.useMipmap&&this.isSizePowerOfTwo()&&Q.generateMipmap(Q.TEXTURE_2D),re.pixelStoreUnpackFlipY.setDefault(),re.pixelStoreUnpack.setDefault(),re.pixelStoreUnpackPremultiplyAlpha.setDefault()}_uploadDomImage(h,b){b.texImage2D(b.TEXTURE_2D,0,this.format,this.format,b.UNSIGNED_BYTE,h)}_uploadRawData(h,b,D,z,G){let{data:X}=h;b&&X&&(X=Vv(X)),G.texImage2D(G.TEXTURE_2D,0,this.format,D,z,0,this.format,G.UNSIGNED_BYTE,X)}_updateDomImage(h,b,D,z){z.texSubImage2D(z.TEXTURE_2D,0,b,D,z.RGBA,z.UNSIGNED_BYTE,h)}_updateRawData(h,b,D,z,G,X,Z){let{data:re}=h;b&&re&&(re=Vv(re)),Z.texSubImage2D(Z.TEXTURE_2D,0,D,z,G,X,Z.RGBA,Z.UNSIGNED_BYTE,re)}bind(h,b,D){let{context:z}=this,{gl:G}=z;this.texture!==this._ownedHandle&&(this.texture=this._ownedHandle),G.bindTexture(G.TEXTURE_2D,this.texture),D!==G.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(D=G.LINEAR),h!==this.filter&&(G.texParameteri(G.TEXTURE_2D,G.TEXTURE_MAG_FILTER,h),G.texParameteri(G.TEXTURE_2D,G.TEXTURE_MIN_FILTER,D||h),this.filter=h),b!==this.wrap&&(G.texParameteri(G.TEXTURE_2D,G.TEXTURE_WRAP_S,b),G.texParameteri(G.TEXTURE_2D,G.TEXTURE_WRAP_T,b),this.wrap=b)}isSizePowerOfTwo(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0}destroy(){let{gl:h}=this.context;h.deleteTexture(this.texture),this.texture=null,this._ownedHandle=null}}class mQ{constructor(h,b,D,z=1,G=1,X=1,Z=0){if(this.uid=h,b.height!==b.width)throw new RangeError(\"DEM tiles must be square\");if(D&&![\"mapbox\",\"terrarium\",\"custom\"].includes(D))return void me(`\"${D}\" is not a valid encoding type. Valid types include \"mapbox\", \"terrarium\" and \"custom\".`);this.stride=b.height;let re=this.dim=b.height-2;switch(this.data=new Uint32Array(b.data.buffer),D){case\"terrarium\":this.redFactor=256,this.greenFactor=1,this.blueFactor=1/256,this.baseShift=32768;break;case\"custom\":this.redFactor=z,this.greenFactor=G,this.blueFactor=X,this.baseShift=Z;break;default:this.redFactor=6553.6,this.greenFactor=25.6,this.blueFactor=.1,this.baseShift=1e4}for(let Q=0;Q<re;Q++)this.data[this._idx(-1,Q)]=this.data[this._idx(0,Q)],this.data[this._idx(re,Q)]=this.data[this._idx(re-1,Q)],this.data[this._idx(Q,-1)]=this.data[this._idx(Q,0)],this.data[this._idx(Q,re)]=this.data[this._idx(Q,re-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(re,-1)]=this.data[this._idx(re-1,0)],this.data[this._idx(-1,re)]=this.data[this._idx(0,re-1)],this.data[this._idx(re,re)]=this.data[this._idx(re-1,re-1)],this.min=Number.MAX_SAFE_INTEGER,this.max=Number.MIN_SAFE_INTEGER;for(let Q=0;Q<re;Q++)for(let he=0;he<re;he++){let oe=this.get(Q,he);oe>this.max&&(this.max=oe),oe<this.min&&(this.min=oe)}}get(h,b){let D=new Uint8Array(this.data.buffer),z=4*this._idx(h,b);return this.unpack(D[z],D[z+1],D[z+2])}getUnpackVector(){return[this.redFactor,this.greenFactor,this.blueFactor,this.baseShift]}_idx(h,b){if(h<-1||h>=this.dim+1||b<-1||b>=this.dim+1)throw new RangeError(`Out of range source coordinates for DEM data. x: ${h}, y: ${b}, dim: ${this.dim}`);return(b+1)*this.stride+(h+1)}unpack(h,b,D){return h*this.redFactor+b*this.greenFactor+D*this.blueFactor-this.baseShift}pack(h){return yQ(h,this.getUnpackVector())}getPixels(){return new Kl({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(h,b,D){if(this.dim!==h.dim)throw new Error(\"dem dimension mismatch\");let z=b*this.dim,G=b*this.dim+this.dim,X=D*this.dim,Z=D*this.dim+this.dim;switch(b){case-1:z=G-1;break;case 1:G=z+1}switch(D){case-1:X=Z-1;break;case 1:Z=X+1}let re=-b*this.dim,Q=-D*this.dim;for(let he=X;he<Z;he++)for(let oe=z;oe<G;oe++)this.data[this._idx(oe,he)]=h.data[this._idx(oe+re,he+Q)]}}function yQ(M,h){let b=h[0],D=h[1],z=h[2],G=h[3],X=Math.min(b,D,z),Z=Math.round((M+G)/X);return{r:Math.floor(Z*X/b)%256,g:Math.floor(Z*X/D)%256,b:Math.floor(Z*X/z)%256}}_i(\"DEMData\",mQ);class Vrt extends hl{constructor(h,b){super(h,Urt,b)}_createColorRamp(h){let b={elevationStops:[],colorStops:[]},D=this._transitionablePaint._values[\"color-relief-color\"].value.expression;if(D instanceof zf&&D._styleExpression.expression instanceof Uo){this.colorRampExpression=D;let X=D._styleExpression.expression;b.elevationStops=X.labels,b.colorStops=[];for(let Z of b.elevationStops)b.colorStops.push(X.evaluate({globals:{elevation:Z}}))}if(b.elevationStops.length<1&&(b.elevationStops=[0],b.colorStops=[ii.transparent]),b.elevationStops.length<2&&(b.elevationStops.push(b.elevationStops[0]+1),b.colorStops.push(b.colorStops[0])),b.elevationStops.length<=h)return b;let z={elevationStops:[],colorStops:[]},G=(b.elevationStops.length-1)/(h-1);for(let X=0;X<b.elevationStops.length-.5;X+=G)z.elevationStops.push(b.elevationStops[Math.round(X)]),z.colorStops.push(b.colorStops[Math.round(X)]);return me(`Too many colors in specification of ${this.id} color-relief layer, may not render properly. Max possible colors: ${h}, provided: ${b.elevationStops.length}`),z}_colorRampChanged(){return this.colorRampExpression!=this._transitionablePaint._values[\"color-relief-color\"].value.expression}getColorRampTextures(h,b,D){if(this.colorRampTextures&&!this._colorRampChanged())return this.colorRampTextures;let z=this._createColorRamp(b),G=new Kl({width:z.colorStops.length,height:1}),X=new Kl({width:z.colorStops.length,height:1});for(let Z=0;Z<z.elevationStops.length;Z++){let re=yQ(z.elevationStops[Z],D);X.setPixel(0,Z,new ii(re.r/255,re.g/255,re.b/255,1)),G.setPixel(0,Z,z.colorStops[Z])}return this.colorRampTextures={elevationTexture:new NR(h,X,h.gl.RGBA),colorTexture:new NR(h,G,h.gl.RGBA)},this.colorRampTextures}hasOffscreenPass(){return!this.isHidden()&&!!this.colorRampTextures}}let Grt=Re([{name:\"a_pos\",components:2,type:\"Int16\"}],4),{members:Hrt}=Grt;function MS(M,h,b){let D=b.patternDependencies,z=!1;for(let G of h){let X=G.paint.get(`${M}-pattern`);X.isConstant()||(z=!0);let Z=X.constantOr(null);Z&&(z=!0,D[Z.to]=!0,D[Z.from]=!0)}return z}function UR(M,h,b,D,z){let{zoom:G}=D,X=z.patternDependencies;for(let Z of h){let re=Z.paint.get(`${M}-pattern`).value;if(re.kind!==\"constant\"){let Q=re.evaluate({zoom:G-1},b,{},z.availableImages),he=re.evaluate({zoom:G},b,{},z.availableImages),oe=re.evaluate({zoom:G+1},b,{},z.availableImages);Q=Q!=null&&Q.name?Q.name:Q,he=he!=null&&he.name?he.name:he,oe=oe!=null&&oe.name?oe.name:oe,X[Q]=!0,X[he]=!0,X[oe]=!0,b.patterns[Z.id]={min:Q,mid:he,max:oe}}}return b}function xQ(M,h,b,D,z){let G;if(z===(function(X,Z,re,Q){let he=0;for(let oe=Z,ke=re-Q;oe<re;oe+=Q)he+=(X[ke]-X[oe])*(X[oe+1]+X[ke+1]),ke=oe;return he})(M,h,b,D)>0)for(let X=h;X<b;X+=D)G=TQ(X/D|0,M[X],M[X+1],G);else for(let X=b-D;X>=h;X-=D)G=TQ(X/D|0,M[X],M[X+1],G);return G&&N_(G,G.next)&&(u4(G),G=G.next),G}function rx(M,h){if(!M)return M;h||(h=M);let b,D=M;do if(b=!1,D.steiner||!N_(D,D.next)&&Cu(D.prev,D,D.next)!==0)D=D.next;else{if(u4(D),D=h=D.prev,D===D.next)break;b=!0}while(b||D!==h);return h}function o4(M,h,b,D,z,G,X){if(!M)return;!X&&G&&(function(re,Q,he,oe){let ke=re;do ke.z===0&&(ke.z=VR(ke.x,ke.y,Q,he,oe)),ke.prevZ=ke.prev,ke.nextZ=ke.next,ke=ke.next;while(ke!==re);ke.prevZ.nextZ=null,ke.prevZ=null,(function(ge){let Ue,je=1;do{let vt,gt=ge;ge=null;let It=null;for(Ue=0;gt;){Ue++;let _t=gt,qt=0;for(let lr=0;lr<je&&(qt++,_t=_t.nextZ,_t);lr++);let tr=je;for(;qt>0||tr>0&&_t;)qt!==0&&(tr===0||!_t||gt.z<=_t.z)?(vt=gt,gt=gt.nextZ,qt--):(vt=_t,_t=_t.nextZ,tr--),It?It.nextZ=vt:ge=vt,vt.prevZ=It,It=vt;gt=_t}It.nextZ=null,je*=2}while(Ue>1)})(ke)})(M,D,z,G);let Z=M;for(;M.prev!==M.next;){let re=M.prev,Q=M.next;if(G?Wrt(M,D,z,G):jrt(M))h.push(re.i,M.i,Q.i),u4(M),M=Q.next,Z=Q.next;else if((M=Q)===Z){X?X===1?o4(M=Yrt(rx(M),h),h,b,D,z,G,2):X===2&&Xrt(M,h,b,D,z,G):o4(rx(M),h,b,D,z,G,1);break}}}function jrt(M){let h=M.prev,b=M,D=M.next;if(Cu(h,b,D)>=0)return!1;let z=h.x,G=b.x,X=D.x,Z=h.y,re=b.y,Q=D.y,he=Math.min(z,G,X),oe=Math.min(Z,re,Q),ke=Math.max(z,G,X),ge=Math.max(Z,re,Q),Ue=D.next;for(;Ue!==h;){if(Ue.x>=he&&Ue.x<=ke&&Ue.y>=oe&&Ue.y<=ge&&s4(z,Z,G,re,X,Q,Ue.x,Ue.y)&&Cu(Ue.prev,Ue,Ue.next)>=0)return!1;Ue=Ue.next}return!0}function Wrt(M,h,b,D){let z=M.prev,G=M,X=M.next;if(Cu(z,G,X)>=0)return!1;let Z=z.x,re=G.x,Q=X.x,he=z.y,oe=G.y,ke=X.y,ge=Math.min(Z,re,Q),Ue=Math.min(he,oe,ke),je=Math.max(Z,re,Q),vt=Math.max(he,oe,ke),gt=VR(ge,Ue,h,b,D),It=VR(je,vt,h,b,D),_t=M.prevZ,qt=M.nextZ;for(;_t&&_t.z>=gt&&qt&&qt.z<=It;){if(_t.x>=ge&&_t.x<=je&&_t.y>=Ue&&_t.y<=vt&&_t!==z&&_t!==X&&s4(Z,he,re,oe,Q,ke,_t.x,_t.y)&&Cu(_t.prev,_t,_t.next)>=0||(_t=_t.prevZ,qt.x>=ge&&qt.x<=je&&qt.y>=Ue&&qt.y<=vt&&qt!==z&&qt!==X&&s4(Z,he,re,oe,Q,ke,qt.x,qt.y)&&Cu(qt.prev,qt,qt.next)>=0))return!1;qt=qt.nextZ}for(;_t&&_t.z>=gt;){if(_t.x>=ge&&_t.x<=je&&_t.y>=Ue&&_t.y<=vt&&_t!==z&&_t!==X&&s4(Z,he,re,oe,Q,ke,_t.x,_t.y)&&Cu(_t.prev,_t,_t.next)>=0)return!1;_t=_t.prevZ}for(;qt&&qt.z<=It;){if(qt.x>=ge&&qt.x<=je&&qt.y>=Ue&&qt.y<=vt&&qt!==z&&qt!==X&&s4(Z,he,re,oe,Q,ke,qt.x,qt.y)&&Cu(qt.prev,qt,qt.next)>=0)return!1;qt=qt.nextZ}return!0}function Yrt(M,h){let b=M;do{let D=b.prev,z=b.next.next;!N_(D,z)&&bQ(D,b,b.next,z)&&l4(D,z)&&l4(z,D)&&(h.push(D.i,b.i,z.i),u4(b),u4(b.next),b=M=z),b=b.next}while(b!==M);return rx(b)}function Xrt(M,h,b,D,z,G){let X=M;do{let Z=X.next.next;for(;Z!==X.prev;){if(X.i!==Z.i&&Qrt(X,Z)){let re=wQ(X,Z);return X=rx(X,X.next),re=rx(re,re.next),o4(X,h,b,D,z,G,0),void o4(re,h,b,D,z,G,0)}Z=Z.next}X=X.next}while(X!==M)}function Zrt(M,h){let b=M.x-h.x;return b===0&&(b=M.y-h.y,b===0)&&(b=(M.next.y-M.y)/(M.next.x-M.x)-(h.next.y-h.y)/(h.next.x-h.x)),b}function $rt(M,h){let b=(function(z,G){let X=G,Z=z.x,re=z.y,Q,he=-1/0;if(N_(z,X))return X;do{if(N_(z,X.next))return X.next;if(re<=X.y&&re>=X.next.y&&X.next.y!==X.y){let je=X.x+(re-X.y)*(X.next.x-X.x)/(X.next.y-X.y);if(je<=Z&&je>he&&(he=je,Q=X.x<X.next.x?X:X.next,je===Z))return Q}X=X.next}while(X!==G);if(!Q)return null;let oe=Q,ke=Q.x,ge=Q.y,Ue=1/0;X=Q;do{if(Z>=X.x&&X.x>=ke&&Z!==X.x&&_Q(re<ge?Z:he,re,ke,ge,re<ge?he:Z,re,X.x,X.y)){let je=Math.abs(re-X.y)/(Z-X.x);l4(X,z)&&(je<Ue||je===Ue&&(X.x>Q.x||X.x===Q.x&&Krt(Q,X)))&&(Q=X,Ue=je)}X=X.next}while(X!==oe);return Q})(M,h);if(!b)return h;let D=wQ(b,M);return rx(D,D.next),rx(b,b.next)}function Krt(M,h){return Cu(M.prev,M,h.prev)<0&&Cu(h.next,M,M.next)<0}function VR(M,h,b,D,z){return(M=1431655765&((M=858993459&((M=252645135&((M=16711935&((M=(M-b)*z|0)|M<<8))|M<<4))|M<<2))|M<<1))|(h=1431655765&((h=858993459&((h=252645135&((h=16711935&((h=(h-D)*z|0)|h<<8))|h<<4))|h<<2))|h<<1))<<1}function Jrt(M){let h=M,b=M;do(h.x<b.x||h.x===b.x&&h.y<b.y)&&(b=h),h=h.next;while(h!==M);return b}function _Q(M,h,b,D,z,G,X,Z){return(z-X)*(h-Z)>=(M-X)*(G-Z)&&(M-X)*(D-Z)>=(b-X)*(h-Z)&&(b-X)*(G-Z)>=(z-X)*(D-Z)}function s4(M,h,b,D,z,G,X,Z){return!(M===X&&h===Z)&&_Q(M,h,b,D,z,G,X,Z)}function Qrt(M,h){return M.next.i!==h.i&&M.prev.i!==h.i&&!(function(b,D){let z=b;do{if(z.i!==b.i&&z.next.i!==b.i&&z.i!==D.i&&z.next.i!==D.i&&bQ(z,z.next,b,D))return!0;z=z.next}while(z!==b);return!1})(M,h)&&(l4(M,h)&&l4(h,M)&&(function(b,D){let z=b,G=!1,X=(b.x+D.x)/2,Z=(b.y+D.y)/2;do z.y>Z!=z.next.y>Z&&z.next.y!==z.y&&X<(z.next.x-z.x)*(Z-z.y)/(z.next.y-z.y)+z.x&&(G=!G),z=z.next;while(z!==b);return G})(M,h)&&(Cu(M.prev,M,h.prev)||Cu(M,h.prev,h))||N_(M,h)&&Cu(M.prev,M,M.next)>0&&Cu(h.prev,h,h.next)>0)}function Cu(M,h,b){return(h.y-M.y)*(b.x-h.x)-(h.x-M.x)*(b.y-h.y)}function N_(M,h){return M.x===h.x&&M.y===h.y}function bQ(M,h,b,D){let z=ES(Cu(M,h,b)),G=ES(Cu(M,h,D)),X=ES(Cu(b,D,M)),Z=ES(Cu(b,D,h));return z!==G&&X!==Z||!(z!==0||!SS(M,b,h))||!(G!==0||!SS(M,D,h))||!(X!==0||!SS(b,M,D))||!(Z!==0||!SS(b,h,D))}function SS(M,h,b){return h.x<=Math.max(M.x,b.x)&&h.x>=Math.min(M.x,b.x)&&h.y<=Math.max(M.y,b.y)&&h.y>=Math.min(M.y,b.y)}function ES(M){return M>0?1:M<0?-1:0}function l4(M,h){return Cu(M.prev,M,M.next)<0?Cu(M,h,M.next)>=0&&Cu(M,M.prev,h)>=0:Cu(M,h,M.prev)<0||Cu(M,M.next,h)<0}function wQ(M,h){let b=GR(M.i,M.x,M.y),D=GR(h.i,h.x,h.y),z=M.next,G=h.prev;return M.next=h,h.prev=M,b.next=z,z.prev=b,D.next=b,b.prev=D,G.next=D,D.prev=G,D}function TQ(M,h,b,D){let z=GR(M,h,b);return D?(z.next=D.next,z.prev=D,D.next.prev=z,D.next=z):(z.prev=z,z.next=z),z}function u4(M){M.next.prev=M.prev,M.prev.next=M.next,M.prevZ&&(M.prevZ.nextZ=M.nextZ),M.nextZ&&(M.nextZ.prevZ=M.prevZ)}function GR(M,h,b){return{i:M,x:h,y:b,prev:null,next:null,z:0,prevZ:null,nextZ:null,steiner:!1}}class U_{constructor(h,b){if(b>h)throw new Error(\"Min granularity must not be greater than base granularity.\");this._baseZoomGranularity=h,this._minGranularity=b}getGranularityForZoomLevel(h){return Math.max(Math.floor(this._baseZoomGranularity/(1<<h)),this._minGranularity,1)}}class kS{constructor(h){this.fill=h.fill,this.line=h.line,this.tile=h.tile,this.stencil=h.stencil,this.circle=h.circle}}kS.noSubdivision=new kS({fill:new U_(0,0),line:new U_(0,0),tile:new U_(0,0),stencil:new U_(0,0),circle:1}),_i(\"SubdivisionGranularityExpression\",U_),_i(\"SubdivisionGranularitySetting\",kS);let V_=-32768,c4=32767;class ent{constructor(h,b){this._vertexBuffer=[],this._vertexDictionary=new Map,this._used=!1,this._granularity=h,this._granularityCellSize=j/h,this._canonical=b}_getKey(h,b){return(h+=32768)<<16|b+32768}_vertexToIndex(h,b){if(h<-32768||b<-32768||h>32767||b>32767)throw new Error(\"Vertex coordinates are out of signed 16 bit integer range.\");let D=0|Math.round(h),z=0|Math.round(b),G=this._getKey(D,z);if(this._vertexDictionary.has(G))return this._vertexDictionary.get(G);let X=this._vertexBuffer.length/2;return this._vertexDictionary.set(G,X),this._vertexBuffer.push(D,z),X}_subdivideTrianglesScanline(h){if(this._granularity<2)return(function(z,G){let X=[];for(let Z=0;Z<G.length;Z+=3){let re=G[Z],Q=G[Z+1],he=G[Z+2],oe=z[2*re],ke=z[2*re+1];(z[2*Q]-oe)*(z[2*he+1]-ke)-(z[2*Q+1]-ke)*(z[2*he]-oe)>0?(X.push(re),X.push(he),X.push(Q)):(X.push(re),X.push(Q),X.push(he))}return X})(this._vertexBuffer,h);let b=[],D=h.length;for(let z=0;z<D;z+=3){let G=[h[z+0],h[z+1],h[z+2]],X=[this._vertexBuffer[2*h[z+0]+0],this._vertexBuffer[2*h[z+0]+1],this._vertexBuffer[2*h[z+1]+0],this._vertexBuffer[2*h[z+1]+1],this._vertexBuffer[2*h[z+2]+0],this._vertexBuffer[2*h[z+2]+1]],Z=1/0,re=1/0,Q=-1/0,he=-1/0;for(let je=0;je<3;je++){let vt=X[2*je],gt=X[2*je+1];Z=Math.min(Z,vt),Q=Math.max(Q,vt),re=Math.min(re,gt),he=Math.max(he,gt)}if(Z===Q||re===he)continue;let oe=Math.floor(Z/this._granularityCellSize),ke=Math.ceil(Q/this._granularityCellSize),ge=Math.floor(re/this._granularityCellSize),Ue=Math.ceil(he/this._granularityCellSize);if(oe!==ke||ge!==Ue)for(let je=ge;je<Ue;je++){let vt=this._scanlineGenerateVertexRingForCellRow(je,X,G);tnt(this._vertexBuffer,vt,b)}else b.push(...G)}return b}_scanlineGenerateVertexRingForCellRow(h,b,D){let z=h*this._granularityCellSize,G=z+this._granularityCellSize,X=[];for(let Z=0;Z<3;Z++){let re=b[2*Z],Q=b[2*Z+1],he=b[2*(Z+1)%6],oe=b[(2*(Z+1)+1)%6],ke=b[2*(Z+2)%6],ge=b[(2*(Z+2)+1)%6],Ue=he-re,je=oe-Q,vt=Ue===0,gt=je===0,It=(z-Q)/je,_t=(G-Q)/je,qt=Math.min(It,_t),tr=Math.max(It,_t);if(!gt&&(qt>=1||tr<=0)||gt&&(Q<z||Q>G)){oe>=z&&oe<=G&&X.push(D[(Z+1)%3]);continue}!gt&&qt>0&&X.push(this._vertexToIndex(re+Ue*qt,Q+je*qt));let lr=re+Ue*Math.max(qt,0),qr=re+Ue*Math.min(tr,1);vt||this._generateIntraEdgeVertices(X,re,Q,he,oe,lr,qr),!gt&&tr<1&&X.push(this._vertexToIndex(re+Ue*tr,Q+je*tr)),(gt||oe>=z&&oe<=G)&&X.push(D[(Z+1)%3]),!gt&&(oe<=z||oe>=G)&&this._generateInterEdgeVertices(X,re,Q,he,oe,ke,ge,qr,z,G)}return X}_generateIntraEdgeVertices(h,b,D,z,G,X,Z){let re=z-b,Q=G-D,he=Q===0,oe=he?Math.min(b,z):Math.min(X,Z),ke=he?Math.max(b,z):Math.max(X,Z),ge=Math.floor(oe/this._granularityCellSize)+1,Ue=Math.ceil(ke/this._granularityCellSize)-1;if(he?b<z:X<Z)for(let je=ge;je<=Ue;je++){let vt=je*this._granularityCellSize;h.push(this._vertexToIndex(vt,D+Q*(vt-b)/re))}else for(let je=Ue;je>=ge;je--){let vt=je*this._granularityCellSize;h.push(this._vertexToIndex(vt,D+Q*(vt-b)/re))}}_generateInterEdgeVertices(h,b,D,z,G,X,Z,re,Q,he){let oe=G-D,ke=X-z,ge=Z-G,Ue=(Q-G)/ge,je=(he-G)/ge,vt=Math.min(Ue,je),gt=Math.max(Ue,je),It=z+ke*vt,_t=Math.floor(Math.min(It,re)/this._granularityCellSize)+1,qt=Math.ceil(Math.max(It,re)/this._granularityCellSize)-1,tr=re<It,lr=ge===0;if(lr&&(Z===Q||Z===he))return;if(lr||vt>=1||gt<=0){let jr=D-Z,_n=X+(b-X)*Math.min((Q-Z)/jr,(he-Z)/jr);_t=Math.floor(Math.min(_n,re)/this._granularityCellSize)+1,qt=Math.ceil(Math.max(_n,re)/this._granularityCellSize)-1,tr=re<_n}let qr=oe>0?he:Q;if(tr)for(let jr=_t;jr<=qt;jr++)h.push(this._vertexToIndex(jr*this._granularityCellSize,qr));else for(let jr=qt;jr>=_t;jr--)h.push(this._vertexToIndex(jr*this._granularityCellSize,qr))}_generateOutline(h){let b=[];for(let D of h){let z=nx(D,this._granularity,!0),G=this._pointArrayToIndices(z),X=[];for(let Z=1;Z<G.length;Z++)X.push(G[Z-1]),X.push(G[Z]);b.push(X)}return b}_handlePoles(h){let b=!1,D=!1;this._canonical&&(this._canonical.y===0&&(b=!0),this._canonical.y===(1<<this._canonical.z)-1&&(D=!0)),(b||D)&&this._fillPoles(h,b,D)}_ensureNoPoleVertices(){let h=this._vertexBuffer;for(let b=0;b<h.length;b+=2){let D=h[b+1];D===V_&&(h[b+1]=-32767),D===c4&&(h[b+1]=32766)}}_generatePoleQuad(h,b,D,z,G,X){z>G!=(X===V_)?(h.push(b),h.push(D),h.push(this._vertexToIndex(z,X)),h.push(D),h.push(this._vertexToIndex(G,X)),h.push(this._vertexToIndex(z,X))):(h.push(D),h.push(b),h.push(this._vertexToIndex(z,X)),h.push(this._vertexToIndex(G,X)),h.push(D),h.push(this._vertexToIndex(z,X)))}_fillPoles(h,b,D){let z=this._vertexBuffer,G=j,X=h.length;for(let Z=2;Z<X;Z+=3){let re=h[Z-2],Q=h[Z-1],he=h[Z],oe=z[2*re],ke=z[2*re+1],ge=z[2*Q],Ue=z[2*Q+1],je=z[2*he],vt=z[2*he+1];b&&(ke===0&&Ue===0&&this._generatePoleQuad(h,re,Q,oe,ge,V_),Ue===0&&vt===0&&this._generatePoleQuad(h,Q,he,ge,je,V_),vt===0&&ke===0&&this._generatePoleQuad(h,he,re,je,oe,V_)),D&&(ke===G&&Ue===G&&this._generatePoleQuad(h,re,Q,oe,ge,c4),Ue===G&&vt===G&&this._generatePoleQuad(h,Q,he,ge,je,c4),vt===G&&ke===G&&this._generatePoleQuad(h,he,re,je,oe,c4))}}_initializeVertices(h){for(let b=0;b<h.length;b+=2)this._vertexToIndex(h[b],h[b+1])}subdividePolygonInternal(h,b){if(this._used)throw new Error(\"Subdivision: multiple use not allowed.\");this._used=!0;let{flattened:D,holeIndices:z}=(function(Z){let re=[],Q=[];for(let he of Z)if(he.length!==0){he!==Z[0]&&re.push(Q.length/2);for(let oe of he)Q.push(oe.x),Q.push(oe.y)}return{flattened:Q,holeIndices:re}})(h),G;this._initializeVertices(D);try{let Z=(function(Q,he,oe=2){let ke=he&&he.length,ge=ke?he[0]*oe:Q.length,Ue=xQ(Q,0,ge,oe,!0),je=[];if(!Ue||Ue.next===Ue.prev)return je;let vt,gt,It;if(ke&&(Ue=(function(_t,qt,tr,lr){let qr=[];for(let jr=0,_n=qt.length;jr<_n;jr++){let fn=xQ(_t,qt[jr]*lr,jr<_n-1?qt[jr+1]*lr:_t.length,lr,!1);fn===fn.next&&(fn.steiner=!0),qr.push(Jrt(fn))}qr.sort(Zrt);for(let jr=0;jr<qr.length;jr++)tr=$rt(qr[jr],tr);return tr})(Q,he,Ue,oe)),Q.length>80*oe){vt=Q[0],gt=Q[1];let _t=vt,qt=gt;for(let tr=oe;tr<ge;tr+=oe){let lr=Q[tr],qr=Q[tr+1];lr<vt&&(vt=lr),qr<gt&&(gt=qr),lr>_t&&(_t=lr),qr>qt&&(qt=qr)}It=Math.max(_t-vt,qt-gt),It=It!==0?32767/It:0}return o4(Ue,je,oe,vt,gt,It,0),je})(D,z),re=this._convertIndices(D,Z);G=this._subdivideTrianglesScanline(re)}catch(Z){console.error(Z)}let X=[];return b&&(X=this._generateOutline(h)),this._ensureNoPoleVertices(),this._handlePoles(G),{verticesFlattened:this._vertexBuffer,indicesTriangles:G,indicesLineList:X}}_convertIndices(h,b){let D=[];for(let z of b)D.push(this._vertexToIndex(h[2*z],h[2*z+1]));return D}_pointArrayToIndices(h){let b=[];for(let D of h)b.push(this._vertexToIndex(D.x,D.y));return b}}function AQ(M,h,b,D=!0){return new ent(b,h).subdividePolygonInternal(M,D)}function nx(M,h,b=!1){if(!M||M.length<1)return[];if(M.length<2)return[];let D=M[0],z=M[M.length-1],G=b&&(D.x!==z.x||D.y!==z.y);if(h<2)return G?[...M,M[0]]:[...M];let X=Math.floor(j/h),Z=[];Z.push(new o(M[0].x,M[0].y));let re=M.length,Q=G?re:re-1;for(let he=0;he<Q;he++){let oe=M[he],ke=he<re-1?M[he+1]:M[0],ge=oe.x,Ue=oe.y,je=ke.x,vt=ke.y,gt=ge!==je,It=Ue!==vt;if(!gt&&!It)continue;let _t=je-ge,qt=vt-Ue,tr=Math.abs(_t),lr=Math.abs(qt),qr=ge,jr=Ue;for(;;){let fn=_t>0?(Math.floor(qr/X)+1)*X:(Math.ceil(qr/X)-1)*X,Dn=qt>0?(Math.floor(jr/X)+1)*X:(Math.ceil(jr/X)-1)*X,Hn=Math.abs(qr-fn),Ln=Math.abs(jr-Dn),un=Math.abs(qr-je),ei=Math.abs(jr-vt),ci=gt?Hn/tr:Number.POSITIVE_INFINITY,ti=It?Ln/lr:Number.POSITIVE_INFINITY;if((un<=Hn||!gt)&&(ei<=Ln||!It))break;if(ci<ti&&gt||!It){qr=fn,jr+=qt*ci;let di=new o(qr,Math.round(jr));Z[Z.length-1].x===di.x&&Z[Z.length-1].y===di.y||Z.push(di)}else{qr+=_t*ti,jr=Dn;let di=new o(Math.round(qr),jr);Z[Z.length-1].x===di.x&&Z[Z.length-1].y===di.y||Z.push(di)}}let _n=new o(je,vt);Z[Z.length-1].x===_n.x&&Z[Z.length-1].y===_n.y||Z.push(_n)}return Z}function tnt(M,h,b){if(h.length===0)throw new Error(\"Subdivision vertex ring is empty.\");let D=0,z=M[2*h[0]];for(let re=1;re<h.length;re++){let Q=M[2*h[re]];Q<z&&(z=Q,D=re)}let G=h.length,X=D,Z=(X+1)%G;for(;;){let re=X-1>=0?X-1:G-1,Q=(Z+1)%G,he=M[2*h[re]],oe=M[2*h[Q]],ke=M[2*h[X]],ge=M[2*h[X]+1],Ue=M[2*h[Z]+1],je=!1;if(he<oe)je=!0;else if(he>oe)je=!1;else{let vt=Ue-ge,gt=-(M[2*h[Z]]-ke),It=ge<Ue?1:-1;((he-ke)*vt+(M[2*h[re]+1]-ge)*gt)*It>((oe-ke)*vt+(M[2*h[Q]+1]-ge)*gt)*It&&(je=!0)}if(je){let vt=h[re],gt=h[X],It=h[Z];vt!==gt&&vt!==It&&gt!==It&&b.push(It,gt,vt),X--,X<0&&(X=G-1)}else{let vt=h[Q],gt=h[X],It=h[Z];vt!==gt&&vt!==It&&gt!==It&&b.push(It,gt,vt),Z++,Z>=G&&(Z=0)}if(re===Q)break}}function MQ(M,h,b,D,z,G,X,Z,re){let Q=z.length/2,he=X&&Z&&re;if(Q<an.MAX_VERTEX_ARRAY_LENGTH){let oe=h.prepareSegment(Q,b,D),ke=oe.vertexLength;for(let je=0;je<G.length;je+=3)D.emplaceBack(ke+G[je],ke+G[je+1],ke+G[je+2]);let ge,Ue;oe.vertexLength+=Q,oe.primitiveLength+=G.length/3,he&&(Ue=X.prepareSegment(Q,b,Z),ge=Ue.vertexLength,Ue.vertexLength+=Q);for(let je=0;je<z.length;je+=2)M(z[je],z[je+1]);if(he)for(let je of re){for(let vt=1;vt<je.length;vt+=2)Z.emplaceBack(ge+je[vt-1],ge+je[vt]);Ue.primitiveLength+=je.length/2}}else(function(oe,ke,ge,Ue,je,vt){let gt=[];for(let lr=0;lr<Ue.length/2;lr++)gt.push(-1);let It={count:0},_t=0,qt=oe.getOrCreateLatestSegment(ke,ge),tr=qt.vertexLength;for(let lr=2;lr<je.length;lr+=3){let qr=je[lr-2],jr=je[lr-1],_n=je[lr],fn=gt[qr]<_t,Dn=gt[jr]<_t,Hn=gt[_n]<_t;qt.vertexLength+((fn?1:0)+(Dn?1:0)+(Hn?1:0))>an.MAX_VERTEX_ARRAY_LENGTH&&(qt=oe.createNewSegment(ke,ge),_t=It.count,fn=!0,Dn=!0,Hn=!0,tr=0);let Ln=f4(gt,Ue,vt,It,qr,fn,qt),un=f4(gt,Ue,vt,It,jr,Dn,qt),ei=f4(gt,Ue,vt,It,_n,Hn,qt);ge.emplaceBack(tr+Ln-_t,tr+un-_t,tr+ei-_t),qt.primitiveLength++}})(h,b,D,z,G,M),he&&(function(oe,ke,ge,Ue,je,vt){let gt=[];for(let lr=0;lr<Ue.length/2;lr++)gt.push(-1);let It={count:0},_t=0,qt=oe.getOrCreateLatestSegment(ke,ge),tr=qt.vertexLength;for(let lr of je)for(let qr=1;qr<lr.length;qr+=2){let jr=lr[qr-1],_n=lr[qr],fn=gt[jr]<_t,Dn=gt[_n]<_t;qt.vertexLength+((fn?1:0)+(Dn?1:0))>an.MAX_VERTEX_ARRAY_LENGTH&&(qt=oe.createNewSegment(ke,ge),_t=It.count,fn=!0,Dn=!0,tr=0);let Hn=f4(gt,Ue,vt,It,jr,fn,qt),Ln=f4(gt,Ue,vt,It,_n,Dn,qt);ge.emplaceBack(tr+Hn-_t,tr+Ln-_t),qt.primitiveLength++}})(X,b,Z,z,re,M),h.forceNewSegmentOnNextPrepare(),X==null||X.forceNewSegmentOnNextPrepare()}function f4(M,h,b,D,z,G,X){if(G){let Z=D.count;return b(h[2*z],h[2*z+1]),M[z]=D.count,D.count++,X.vertexLength++,Z}return M[z]}class HR{constructor(h){this.zoom=h.zoom,this.overscaling=h.overscaling,this.layers=h.layers,this.layerIds=this.layers.map((b=>b.id)),this.index=h.index,this.hasDependencies=!1,this.patternFeatures=[],this.layoutVertexArray=new xt,this.indexArray=new Er,this.indexArray2=new Tr,this.programConfigurations=new Ta(h.layers,h.zoom),this.segments=new an,this.segments2=new an,this.stateDependentLayerIds=this.layers.filter((b=>b.isStateDependent())).map((b=>b.id))}populate(h,b,D){this.hasDependencies=MS(\"fill\",this.layers,b);let z=this.layers[0].layout.get(\"fill-sort-key\"),G=!z.isConstant(),X=[];for(let{feature:Z,id:re,index:Q,sourceLayerIndex:he}of h){let oe=this.layers[0]._featureFilter.needGeometry,ke=xa(Z,oe);if(!this.layers[0]._featureFilter.filter(new mi(this.zoom),ke,D))continue;let ge=G?z.evaluate(ke,{},D,b.availableImages):void 0,Ue={id:re,properties:Z.properties,type:Z.type,sourceLayerIndex:he,index:Q,geometry:oe?ke.geometry:$l(Z),patterns:{},sortKey:ge};X.push(Ue)}G&&X.sort(((Z,re)=>Z.sortKey-re.sortKey));for(let Z of X){let{geometry:re,index:Q,sourceLayerIndex:he}=Z;if(this.hasDependencies){let oe=UR(\"fill\",this.layers,Z,{zoom:this.zoom},b);this.patternFeatures.push(oe)}else this.addFeature(Z,re,Q,D,{},b.subdivisionGranularity);b.featureIndex.insert(h[Q].feature,re,Q,he,this.index)}}update(h,b,D){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(h,b,this.stateDependentLayers,{imagePositions:D})}addFeatures(h,b,D){for(let z of this.patternFeatures)this.addFeature(z,z.geometry,z.index,b,D,h.subdivisionGranularity)}isEmpty(){return this.layoutVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(h){this.uploaded||(this.layoutVertexBuffer=h.createVertexBuffer(this.layoutVertexArray,Hrt),this.indexBuffer=h.createIndexBuffer(this.indexArray),this.indexBuffer2=h.createIndexBuffer(this.indexArray2)),this.programConfigurations.upload(h),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.indexBuffer2.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.segments2.destroy())}addFeature(h,b,D,z,G,X){for(let Z of uh(b,500)){let re=AQ(Z,z,X.fill.getGranularityForZoomLevel(z.z)),Q=this.layoutVertexArray;MQ(((he,oe)=>{Q.emplaceBack(he,oe)}),this.segments,this.layoutVertexArray,this.indexArray,re.verticesFlattened,re.indicesTriangles,this.segments2,this.indexArray2,re.indicesLineList)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,h,D,{imagePositions:G,canonical:z})}}let SQ,EQ;_i(\"FillBucket\",HR,{omit:[\"layers\",\"patternFeatures\"]});var rnt={get paint(){return EQ=EQ||new ms({\"fill-antialias\":new wi(Ct.paint_fill[\"fill-antialias\"]),\"fill-opacity\":new Di(Ct.paint_fill[\"fill-opacity\"]),\"fill-color\":new Di(Ct.paint_fill[\"fill-color\"]),\"fill-outline-color\":new Di(Ct.paint_fill[\"fill-outline-color\"]),\"fill-translate\":new wi(Ct.paint_fill[\"fill-translate\"]),\"fill-translate-anchor\":new wi(Ct.paint_fill[\"fill-translate-anchor\"]),\"fill-pattern\":new fl(Ct.paint_fill[\"fill-pattern\"])})},get layout(){return SQ=SQ||new ms({\"fill-sort-key\":new 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0,this._pbf=h,this._geometry=-1,this._keys=z,this._values=G,h.readFields(snt,this,b)}loadGeometry(){let h=this._pbf;h.pos=this._geometry;let b=h.readVarint()+h.pos,D=[],z,G=1,X=0,Z=0,re=0;for(;h.pos<b;){if(X<=0){let Q=h.readVarint();G=7&Q,X=Q>>3}if(X--,G===1||G===2)Z+=h.readSVarint(),re+=h.readSVarint(),G===1&&(z&&D.push(z),z=[]),z&&z.push(new o(Z,re));else{if(G!==7)throw new Error(`unknown command ${G}`);z&&z.push(z[0].clone())}}return z&&D.push(z),D}bbox(){let h=this._pbf;h.pos=this._geometry;let b=h.readVarint()+h.pos,D=1,z=0,G=0,X=0,Z=1/0,re=-1/0,Q=1/0,he=-1/0;for(;h.pos<b;){if(z<=0){let oe=h.readVarint();D=7&oe,z=oe>>3}if(z--,D===1||D===2)G+=h.readSVarint(),X+=h.readSVarint(),G<Z&&(Z=G),G>re&&(re=G),X<Q&&(Q=X),X>he&&(he=X);else if(D!==7)throw new Error(`unknown command ${D}`)}return[Z,Q,re,he]}toGeoJSON(h,b,D){let z=this.extent*Math.pow(2,D),G=this.extent*h,X=this.extent*b,Z=this.loadGeometry();function re(ke){return[360*(ke.x+G)/z-180,360/Math.PI*Math.atan(Math.exp((1-2*(ke.y+X)/z)*Math.PI))-90]}function Q(ke){return ke.map(re)}let he;if(this.type===1){let ke=[];for(let Ue of Z)ke.push(Ue[0]);let ge=Q(ke);he=ke.length===1?{type:\"Point\",coordinates:ge[0]}:{type:\"MultiPoint\",coordinates:ge}}else if(this.type===2){let ke=Z.map(Q);he=ke.length===1?{type:\"LineString\",coordinates:ke[0]}:{type:\"MultiLineString\",coordinates:ke}}else{if(this.type!==3)throw new Error(\"unknown feature type\");{let ke=kQ(Z),ge=[];for(let Ue of ke)ge.push(Ue.map(Q));he=ge.length===1?{type:\"Polygon\",coordinates:ge[0]}:{type:\"MultiPolygon\",coordinates:ge}}}let oe={type:\"Feature\",geometry:he,properties:this.properties};return this.id!=null&&(oe.id=this.id),oe}}function snt(M,h,b){M===1?h.id=b.readVarint():M===2?(function(D,z){let G=D.readVarint()+D.pos;for(;D.pos<G;){let X=z._keys[D.readVarint()],Z=z._values[D.readVarint()];z.properties[X]=Z}})(b,h):M===3?h.type=b.readVarint():M===4&&(h._geometry=b.pos)}function kQ(M){let h=M.length;if(h<=1)return[M];let b=[],D,z;for(let G=0;G<h;G++){let X=lnt(M[G]);X!==0&&(z===void 0&&(z=X<0),z===X<0?(D&&b.push(D),D=[M[G]]):D&&D.push(M[G]))}return D&&b.push(D),b}function lnt(M){let h=0;for(let b,D,z=0,G=M.length,X=G-1;z<G;X=z++)b=M[z],D=M[X],h+=(D.x-b.x)*(b.y+D.y);return h}h4.types=[\"Unknown\",\"Point\",\"LineString\",\"Polygon\"];class unt{constructor(h,b){this.version=1,this.name=\"\",this.extent=4096,this.length=0,this._pbf=h,this._keys=[],this._values=[],this._features=[],h.readFields(cnt,this,b),this.length=this._features.length}feature(h){if(h<0||h>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[h];let b=this._pbf.readVarint()+this._pbf.pos;return new h4(this._pbf,b,this.extent,this._keys,this._values)}}function cnt(M,h,b){M===15?h.version=b.readVarint():M===1?h.name=b.readString():M===5?h.extent=b.readVarint():M===2?h._features.push(b.pos):M===3?h._keys.push(b.readString()):M===4&&h._values.push((function(D){let z=null,G=D.readVarint()+D.pos;for(;D.pos<G;){let X=D.readVarint()>>3;z=X===1?D.readString():X===2?D.readFloat():X===3?D.readDouble():X===4?D.readVarint64():X===5?D.readVarint():X===6?D.readSVarint():X===7?D.readBoolean():null}if(z==null)throw new Error(\"unknown feature value\");return z})(b))}class CQ{constructor(h,b){this.layers=h.readFields(fnt,{},b)}}function fnt(M,h,b){if(M===3){let D=new unt(b,b.readVarint()+b.pos);D.length&&(h[D.name]=D)}}let jR=Math.pow(2,13);function d4(M,h,b,D,z,G,X,Z){M.emplaceBack(h,b,2*Math.floor(D*jR)+X,z*jR*2,G*jR*2,Math.round(Z))}class WR{constructor(h){this.zoom=h.zoom,this.overscaling=h.overscaling,this.layers=h.layers,this.layerIds=this.layers.map((b=>b.id)),this.index=h.index,this.hasDependencies=!1,this.layoutVertexArray=new Et,this.centroidVertexArray=new Xe,this.indexArray=new Er,this.programConfigurations=new Ta(h.layers,h.zoom),this.segments=new an,this.stateDependentLayerIds=this.layers.filter((b=>b.isStateDependent())).map((b=>b.id))}populate(h,b,D){this.features=[],this.hasDependencies=MS(\"fill-extrusion\",this.layers,b);for(let{feature:z,id:G,index:X,sourceLayerIndex:Z}of h){let re=this.layers[0]._featureFilter.needGeometry,Q=xa(z,re);if(!this.layers[0]._featureFilter.filter(new mi(this.zoom),Q,D))continue;let he={id:G,sourceLayerIndex:Z,index:X,geometry:re?Q.geometry:$l(z),properties:z.properties,type:z.type,patterns:{}};this.hasDependencies?this.features.push(UR(\"fill-extrusion\",this.layers,he,{zoom:this.zoom},b)):this.addFeature(he,he.geometry,X,D,{},b.subdivisionGranularity),b.featureIndex.insert(z,he.geometry,X,Z,this.index,!0)}}addFeatures(h,b,D){for(let z of this.features){let{geometry:G}=z;this.addFeature(z,G,z.index,b,D,h.subdivisionGranularity)}}update(h,b,D){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(h,b,this.stateDependentLayers,{imagePositions:D})}isEmpty(){return this.layoutVertexArray.length===0&&this.centroidVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(h){this.uploaded||(this.layoutVertexBuffer=h.createVertexBuffer(this.layoutVertexArray,ont),this.centroidVertexBuffer=h.createVertexBuffer(this.centroidVertexArray,ant.members,!0),this.indexBuffer=h.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(h),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.centroidVertexBuffer.destroy())}addFeature(h,b,D,z,G,X){for(let Z of uh(b,500)){let re={x:0,y:0,sampleCount:0},Q=this.layoutVertexArray.length;this.processPolygon(re,z,h,Z,X);let he=this.layoutVertexArray.length-Q,oe=Math.floor(re.x/re.sampleCount),ke=Math.floor(re.y/re.sampleCount);for(let ge=0;ge<he;ge++)this.centroidVertexArray.emplaceBack(oe,ke)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,h,D,{imagePositions:G,canonical:z})}processPolygon(h,b,D,z,G){if(z.length<1||LQ(z[0]))return;for(let oe of z)oe.length!==0&&hnt(h,oe);let X={segment:this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray)},Z=G.fill.getGranularityForZoomLevel(b.z),re=h4.types[D.type]===\"Polygon\";for(let oe of z){if(oe.length===0||LQ(oe))continue;let ke=nx(oe,Z,re);this._generateSideFaces(ke,X)}if(!re)return;let Q=AQ(z,b,Z,!1),he=this.layoutVertexArray;MQ(((oe,ke)=>{d4(he,oe,ke,0,0,1,1,0)}),this.segments,this.layoutVertexArray,this.indexArray,Q.verticesFlattened,Q.indicesTriangles)}_generateSideFaces(h,b){let D=0;for(let z=1;z<h.length;z++){let G=h[z],X=h[z-1];if(dnt(G,X))continue;b.segment.vertexLength+4>an.MAX_VERTEX_ARRAY_LENGTH&&(b.segment=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));let Z=G.sub(X)._perp()._unit(),re=X.dist(G);D+re>32768&&(D=0),d4(this.layoutVertexArray,G.x,G.y,Z.x,Z.y,0,0,D),d4(this.layoutVertexArray,G.x,G.y,Z.x,Z.y,0,1,D),D+=re,d4(this.layoutVertexArray,X.x,X.y,Z.x,Z.y,0,0,D),d4(this.layoutVertexArray,X.x,X.y,Z.x,Z.y,0,1,D);let Q=b.segment.vertexLength;this.indexArray.emplaceBack(Q,Q+2,Q+1),this.indexArray.emplaceBack(Q+1,Q+2,Q+3),b.segment.vertexLength+=4,b.segment.primitiveLength+=2}}}function hnt(M,h){for(let b=0;b<h.length;b++){let D=h[b];b===h.length-1&&h[0].x===D.x&&h[0].y===D.y||(M.x+=D.x,M.y+=D.y,M.sampleCount++)}}function dnt(M,h){return M.x===h.x&&(M.x<0||M.x>j)||M.y===h.y&&(M.y<0||M.y>j)}function LQ(M){return M.every((h=>h.x<0))||M.every((h=>h.x>j))||M.every((h=>h.y<0))||M.every((h=>h.y>j))}let PQ;_i(\"FillExtrusionBucket\",WR,{omit:[\"layers\",\"features\"]});var vnt={get paint(){return PQ=PQ||new ms({\"fill-extrusion-opacity\":new wi(Ct[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Di(Ct[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Ct[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Ct[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new fl(Ct[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Di(Ct[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Di(Ct[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Ct[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})}};class pnt extends hl{constructor(h,b){super(h,vnt,b)}createBucket(h){return new WR(h)}queryRadius(){return 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o((ti+_t)/ks,(di+qt)/ks);Sl.z=us/ks,Hn.push(Sl);let df=new o(Uf/zc,Jl/zc);df.z=_c/zc,Ln.push(df)}gt.push(Hn),It.push(Ln)}return[gt,It]})(z,he,Q,Z);return(function(ge,Ue,je){let vt=1/0;Gi(je,Ue)&&(vt=FQ(je,Ue[0]));for(let gt=0;gt<Ue.length;gt++){let It=Ue[gt],_t=ge[gt];for(let qt=0;qt<It.length-1;qt++){let tr=It[qt],lr=[tr,It[qt+1],_t[qt+1],_t[qt],tr];gn(je,lr)&&(vt=Math.min(vt,FQ(je,lr)))}}return vt!==1/0&&vt})(ke[0],ke[1],oe)}}function v4(M,h){return M.x*h.x+M.y*h.y}function FQ(M,h){if(M.length===1){let b=0,D=h[b++],z;for(;!z||D.equals(z);)if(z=h[b++],!z)return 1/0;for(;b<h.length;b++){let G=h[b],X=M[0],Z=z.sub(D),re=G.sub(D),Q=X.sub(D),he=v4(Z,Z),oe=v4(Z,re),ke=v4(re,re),ge=v4(Q,Z),Ue=v4(Q,re),je=he*ke-oe*oe,vt=(ke*ge-oe*Ue)/je,gt=(he*Ue-oe*ge)/je,It=D.z*(1-vt-gt)+z.z*vt+G.z*gt;if(isFinite(It))return It}return 1/0}{let b=1/0;for(let D of h)b=Math.min(b,D.z);return b}}let DQ=[Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array];class CS{static from(h){if(!(h instanceof ArrayBuffer))throw new Error(\"Data must be an instance of ArrayBuffer.\");let[b,D]=new Uint8Array(h,0,2);if(b!==219)throw new Error(\"Data does not appear to be in a KDBush format.\");let z=D>>4;if(z!==1)throw new Error(`Got v${z} data when expected v1.`);let G=DQ[15&D];if(!G)throw new Error(\"Unrecognized array type.\");let[X]=new Uint16Array(h,2,1),[Z]=new Uint32Array(h,4,1);return new CS(Z,X,G,h)}constructor(h,b=64,D=Float64Array,z){if(isNaN(h)||h<0)throw new Error(`Unpexpected numItems value: ${h}.`);this.numItems=+h,this.nodeSize=Math.min(Math.max(+b,2),65535),this.ArrayType=D,this.IndexArrayType=h<65536?Uint16Array:Uint32Array;let G=DQ.indexOf(this.ArrayType),X=2*h*this.ArrayType.BYTES_PER_ELEMENT,Z=h*this.IndexArrayType.BYTES_PER_ELEMENT,re=(8-Z%8)%8;if(G<0)throw new Error(`Unexpected typed array class: ${D}.`);z&&z 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Q.coordinates)oe.push(G_(ke[0]),H_(ke[1]),0);Z.push(hf(re,\"MultiPoint\",oe,he))})(M,G,h.geometry,h.properties);case\"LineString\":return void(function(Z,re,Q,he,oe){let ke={points:[]};JR(Q.coordinates,ke,he,!1),Z.push(hf(re,\"LineString\",ke,oe))})(M,G,h.geometry,X,h.properties);case\"MultiLineString\":return void(function(Z,re,Q,he,oe,ke){if(oe.lineMetrics)for(let ge of Q.coordinates){let Ue={points:[]};JR(ge,Ue,he,!1),Z.push(hf(re,\"LineString\",Ue,ke))}else{let ge=[];QR(Q.coordinates,ge,he,!1),Z.push(hf(re,\"MultiLineString\",ge,ke))}})(M,G,h.geometry,X,b,h.properties);case\"Polygon\":return void(function(Z,re,Q,he,oe){let ke=[];QR(Q.coordinates,ke,he,!0),Z.push(hf(re,\"Polygon\",ke,oe))})(M,G,h.geometry,X,h.properties);case\"MultiPolygon\":return void(function(Z,re,Q,he,oe){let ke=[];for(let ge of Q.coordinates){let Ue=[];QR(ge,Ue,he,!0),ke.push(Ue)}Z.push(hf(re,\"MultiPolygon\",ke,oe))})(M,G,h.geometry,X,h.properties);default:throw new Error(\"Input data is not a valid GeoJSON object.\")}}function JR(M,h,b,D){let z,G,X=0;for(let re=0;re<M.length;re++){let Q=G_(M[re][0]),he=H_(M[re][1]);h.points.push(Q,he,0),re>0&&(X+=D?(z*he-Q*G)/2:Math.sqrt(Math.pow(Q-z,2)+Math.pow(he-G,2))),z=Q,G=he}let Z=h.points.length-3;h.points[2]=1,b>0&&ZR(h.points,0,Z,b),h.points[Z+2]=1,$R(h),h.size=Math.abs(X),h.start=0,h.end=h.size}function QR(M,h,b,D){for(let z=0;z<M.length;z++){let G={points:[]};JR(M[z],G,b,D),h.push(G)}}function G_(M){return M/360+.5}function H_(M){let h=Math.sin(M*Math.PI/180),b=.5-.25*Math.log((1+h)/(1-h))/Math.PI;return b<0?0:b>1?1:b}function PS(M){let h={type:\"Feature\",geometry:mnt(M),properties:M.tags};return M.id!=null&&(h.id=M.id),h}function mnt(M){let{type:h,geometry:b}=M;switch(h){case\"Point\":return{type:h,coordinates:zQ(b[0],b[1])};case\"MultiPoint\":return{type:h,coordinates:FS(b)};case\"LineString\":return{type:h,coordinates:FS(b.points)};case\"MultiLineString\":case\"Polygon\":return{type:h,coordinates:b.map((D=>FS(D.points)))};case\"MultiPolygon\":return{type:h,coordinates:b.map((D=>D.map((z=>FS(z.points)))))}}}function FS(M){let h=[];for(let b=0;b<M.length;b+=3)h.push(zQ(M[b],M[b+1]));return h}function zQ(M,h){return[qQ(M),BQ(h)]}function qQ(M){return 360*(M-.5)}function BQ(M){let h=(180-360*M)*Math.PI/180;return 360*Math.atan(Math.exp(h))/Math.PI-90}var yh;function O1(M,h,b,D,z,G,X,Z){if(D/=h,G>=(b/=h)&&X<D)return M;if(X<b||G>=D)return null;let re=[];for(let Q of M){let he=z===yh.X?Q.minX:Q.minY,oe=z===yh.X?Q.maxX:Q.maxY;if(he>=b&&oe<D)re.push(Q);else if(!(oe<b||he>=D))switch(Q.type){case\"Point\":case\"MultiPoint\":ynt(Q,re,b,D,z);continue;case\"LineString\":xnt(Q,re,b,D,z,Z);continue;case\"MultiLineString\":_nt(Q,re,b,D,z);continue;case\"Polygon\":bnt(Q,re,b,D,z);continue;case\"MultiPolygon\":wnt(Q,re,b,D,z);continue}}return re.length?re:null}function ynt(M,h,b,D,z){let G=[];(function(X,Z,re,Q,he){for(let oe=0;oe<X.length;oe+=3){let ke=X[oe+he];ke>=re&&ke<=Q&&j_(Z,X[oe],X[oe+1],X[oe+2])}})(M.geometry,G,b,D,z),G.length&&h.push(hf(M.id,G.length===3?\"Point\":\"MultiPoint\",G,M.tags))}function xnt(M,h,b,D,z,G){let X=[];if(OQ(M.geometry,X,b,D,z,!1,G.lineMetrics),X.length)if(G.lineMetrics)for(let Z of X)h.push(hf(M.id,\"LineString\",Z,M.tags));else h.push(X.length>1?hf(M.id,\"MultiLineString\",X,M.tags):hf(M.id,\"LineString\",X[0],M.tags))}function _nt(M,h,b,D,z){let G=[];eI(M.geometry,G,b,D,z,!1),G.length&&h.push(G.length!==1?hf(M.id,\"MultiLineString\",G,M.tags):hf(M.id,\"LineString\",G[0],M.tags))}function bnt(M,h,b,D,z){let G=[];eI(M.geometry,G,b,D,z,!0),G.length&&h.push(hf(M.id,\"Polygon\",G,M.tags))}function wnt(M,h,b,D,z){let G=[];for(let X of M.geometry){let Z=[];eI(X,Z,b,D,z,!0),Z.length&&G.push(Z)}G.length&&h.push(hf(M.id,\"MultiPolygon\",G,M.tags))}function OQ(M,h,b,D,z,G,X){let Z=NQ(M),re=z===yh.X?Tnt:Ant,Q,he,oe=M.start;for(let vt=0;vt<M.points.length-3;vt+=3){let gt=M.points[vt],It=M.points[vt+1],_t=M.points[vt+2],qt=M.points[vt+3],tr=M.points[vt+4],lr=z===yh.X?gt:It,qr=z===yh.X?qt:tr,jr=!1;X&&(Q=Math.sqrt(Math.pow(gt-qt,2)+Math.pow(It-tr,2))),lr<b?qr>b&&(he=re(Z,gt,It,qt,tr,b),X&&(Z.start=oe+Q*he)):lr>D?qr<D&&(he=re(Z,gt,It,qt,tr,D),X&&(Z.start=oe+Q*he)):j_(Z.points,gt,It,_t),qr<b&&lr>=b&&(he=re(Z,gt,It,qt,tr,b),jr=!0),qr>D&&lr<=D&&(he=re(Z,gt,It,qt,tr,D),jr=!0),!G&&jr&&(X&&(Z.end=oe+Q*he),h.push(Z),Z=NQ(M)),X&&(oe+=Q)}let ke=M.points.length-3,ge=M.points[ke],Ue=M.points[ke+1],je=z===yh.X?ge:Ue;je>=b&&je<=D&&j_(Z.points,ge,Ue,M.points[ke+2]),ke=Z.points.length-3,G&&ke>=3&&(Z.points[ke]!==Z.points[0]||Z.points[ke+1]!==Z.points[1])&&j_(Z.points,Z.points[0],Z.points[1],Z.points[2]),Z.points.length&&($R(Z),h.push(Z))}function NQ(M){return{points:[],size:M.size,start:M.start,end:M.end}}function eI(M,h,b,D,z,G){for(let X of M)OQ(X,h,b,D,z,G,!1)}function j_(M,h,b,D){M.push(h,b,D)}function Tnt(M,h,b,D,z,G){let X=(G-h)/(D-h);return j_(M.points,G,b+(z-b)*X,1),X}function Ant(M,h,b,D,z,G){let X=(G-b)/(z-b);return j_(M.points,h+(D-h)*X,G,1),X}function tI(M,h){let b=h.buffer/h.extent,D=M,z=O1(M,1,-1-b,b,yh.X,-1,2,h),G=O1(M,1,1-b,2+b,yh.X,-1,2,h);return(z||G)&&(D=O1(M,1,-b,1+b,yh.X,-1,2,h)||[],z&&(D=UQ(z,1).concat(D)),G&&(D=D.concat(UQ(G,-1)))),D}function UQ(M,h){let b=[];for(let D of M)switch(D.type){case\"Point\":case\"MultiPoint\":{let z=Mnt(D.geometry,h);b.push(hf(D.id,D.type,z,D.tags));continue}case\"LineString\":{let z=rI(D.geometry,h);b.push(hf(D.id,D.type,z,D.tags));continue}case\"MultiLineString\":case\"Polygon\":{let z=[];for(let G of D.geometry)z.push(rI(G,h));b.push(hf(D.id,D.type,z,D.tags));continue}case\"MultiPolygon\":{let z=[];for(let G of D.geometry){let X=[];for(let Z of G)X.push(rI(Z,h));z.push(X)}b.push(hf(D.id,D.type,z,D.tags));continue}}return b}function Mnt(M,h){let b=[];for(let D=0;D<M.length;D+=3)b.push(M[D]+h,M[D+1],M[D+2]);return b}function rI(M,h){let b={points:[],size:M.size};M.start!==void 0&&(b.start=M.start,b.end=M.end);for(let D=0;D<M.points.length;D+=3)b.points.push(M.points[D]+h,M.points[D+1],M.points[D+2]);return $R(b),b}function Snt(M,h,b){var G,X;let D=!!h.newGeometry,z=h.removeAllProperties||((G=h.removeProperties)==null?void 0:G.length)>0||((X=h.addOrUpdateProperties)==null?void 0:X.length)>0;if(D){let Z=M[0],re=KR({type:\"FeatureCollection\",features:[{type:\"Feature\",id:Z.id,geometry:h.newGeometry,properties:z?VQ(Z.tags,h):Z.tags}]},b);return re=tI(re,b),re}if(z){let Z=[];for(let re of M){let Q=zo({},re);Q.tags=VQ(Q.tags,h),Z.push(Q)}return Z}return M}function VQ(M,h){if(h.removeAllProperties)return{};let b=zo({},M||{});if(h.removeProperties)for(let D of h.removeProperties)delete b[D];if(h.addOrUpdateProperties)for(let{key:D,value:z}of h.addOrUpdateProperties)b[D]=z;return b}(function(M){M[M.X=0]=\"X\",M[M.Y=1]=\"Y\"})(yh||(yh={}));let nI={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:M=>M};class Ent{constructor(h){this.options=Object.assign(Object.create(nI),h),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[],this.points=[]}load(h){let b=[];for(let D of h){if(!D.geometry)continue;let[z,G]=D.geometry.coordinates,[X,Z]=[G_(z),H_(G)];b.push({id:D.id,type:\"Point\",geometry:[X,Z],tags:D.properties})}this.createIndex(b)}initialize(h){let b=[];for(let D of h)D.type===\"Point\"&&b.push(D);this.createIndex(b)}updateIndex(h,b,D){this.options=Object.assign(Object.create(nI),D.clusterOptions),this.initialize(h)}createIndex(h){let{log:b,minZoom:D,maxZoom:z}=this.options;b&&console.time(\"total time\");let G=`prepare ${h.length} points`;b&&console.time(G),this.points=h;let X=[];for(let re=0;re<h.length;re++){let Q=h[re];if(!(Q!=null&&Q.geometry))continue;let[he,oe]=Q.geometry;he=Math.fround(he),oe=Math.fround(oe),X.push(he,oe,1/0,re,-1,1),this.options.reduce&&X.push(0)}let Z=this.trees[z+1]=this.createTree(X);b&&console.timeEnd(G);for(let re=z;re>=D;re--){let Q=Date.now();Z=this.trees[re]=this.createTree(this.cluster(Z,re)),b&&console.log(\"z%d: %d clusters in %dms\",re,Z.numItems,Date.now()-Q)}b&&console.timeEnd(\"total time\")}getClusters(h,b){return this.getClustersInternal(h,b).map((D=>PS(D)))}getClustersInternal(h,b){let D=((h[0]+180)%360+360)%360-180,z=Math.max(-90,Math.min(90,h[1])),G=h[2]===180?180:((h[2]+180)%360+360)%360-180,X=Math.max(-90,Math.min(90,h[3]));if(h[2]-h[0]>=360)D=-180,G=180;else if(D>G){let oe=this.getClustersInternal([D,z,180,X],b),ke=this.getClustersInternal([-180,z,G,X],b);return oe.concat(ke)}let Z=this.trees[this.limitZoom(b)],re=Z.range(G_(D),H_(X),G_(G),H_(z)),Q=Z.flatData,he=[];for(let oe of re){let ke=this.stride*oe;he.push(Q[ke+5]>1?knt(Q,ke,this.clusterProps):this.points[Q[ke+3]])}return he}getChildren(h){let b=this.getOriginId(h),D=this.getOriginZoom(h),z=new Error(\"No cluster with the specified id: \"+h),G=this.trees[D];if(!G)throw z;let X=G.flatData;if(b*this.stride>=X.length)throw z;let Z=this.options.radius/(this.options.extent*Math.pow(2,D-1)),re=G.within(X[b*this.stride],X[b*this.stride+1],Z),Q=[];for(let he of re){let oe=he*this.stride;X[oe+4]===h&&Q.push(X[oe+5]>1?Cnt(X,oe,this.clusterProps):PS(this.points[X[oe+3]]))}if(Q.length===0)throw z;return Q}getLeaves(h,b,D){let z=[];return this.appendLeaves(z,h,b=b||10,D=D||0,0),z}getTile(h,b,D){let z=this.trees[this.limitZoom(h)];if(!z)return null;let G=Math.pow(2,h),{extent:X,radius:Z}=this.options,re=Z/X,Q=(D-re)/G,he=(D+1+re)/G,oe={transformed:!0,features:[],source:null,x:b,y:D,z:h};return this.addTileFeatures(z.range((b-re)/G,Q,(b+1+re)/G,he),z.flatData,b,D,G,oe),b===0&&this.addTileFeatures(z.range(1-re/G,Q,1,he),z.flatData,G,D,G,oe),b===G-1&&this.addTileFeatures(z.range(0,Q,re/G,he),z.flatData,-1,D,G,oe),oe}getClusterExpansionZoom(h){return this.getOriginZoom(h)}appendLeaves(h,b,D,z,G){let X=this.getChildren(b);for(let Z of X){let re=Z.properties;if(re!=null&&re.cluster?G+re.point_count<=z?G+=re.point_count:G=this.appendLeaves(h,re.cluster_id,D,z,G):G<z?G++:h.push(Z),h.length===D)break}return G}createTree(h){let b=new CS(h.length/this.stride|0,this.options.nodeSize,Float32Array);for(let D=0;D<h.length;D+=this.stride)b.add(h[D],h[D+1]);return b.finish(),b.flatData=h,b.data=null,b}addTileFeatures(h,b,D,z,G,X){for(let Z of h){let re=Z*this.stride,Q=b[re+5]>1,he,oe,ke;if(Q)he=iI(b,re,this.clusterProps),oe=b[re],ke=b[re+1];else{let je=this.points[b[re+3]];he=je.tags,[oe,ke]=je.geometry}let ge={type:1,geometry:[[Math.round(this.options.extent*(oe*G-D)),Math.round(this.options.extent*(ke*G-z))]],tags:he},Ue;Ue=Q||this.options.generateId?b[re+3]:this.points[b[re+3]].id,Ue!==void 0&&(ge.id=Ue),X.features.push(ge)}}limitZoom(h){return Math.max(this.options.minZoom,Math.min(Math.floor(+h),this.options.maxZoom+1))}cluster(h,b){let{radius:D,extent:z,reduce:G,minPoints:X}=this.options,Z=D/(z*Math.pow(2,b)),re=h.flatData,Q=[],he=this.stride;for(let oe=0;oe<re.length;oe+=he){if(re[oe+2]<=b)continue;re[oe+2]=b;let ke=re[oe],ge=re[oe+1],Ue=h.within(re[oe],re[oe+1],Z),je=re[oe+5],vt=je;for(let gt of Ue){let It=gt*he;re[It+2]>b&&(vt+=re[It+5])}if(vt>je&&vt>=X){let gt,It=ke*je,_t=ge*je,qt=-1,tr=(oe/he<<5)+(b+1)+this.points.length;for(let lr of Ue){let qr=lr*he;if(re[qr+2]<=b)continue;re[qr+2]=b;let jr=re[qr+5];It+=re[qr]*jr,_t+=re[qr+1]*jr,re[qr+4]=tr,G&&(gt||(gt=this.map(re,oe,!0),qt=this.clusterProps.length,this.clusterProps.push(gt)),G(gt,this.map(re,qr)))}re[oe+4]=tr,Q.push(It/vt,_t/vt,1/0,tr,-1,vt),G&&Q.push(qt)}else{for(let gt=0;gt<he;gt++)Q.push(re[oe+gt]);if(vt>1)for(let gt of Ue){let It=gt*he;if(!(re[It+2]<=b)){re[It+2]=b;for(let _t=0;_t<he;_t++)Q.push(re[It+_t])}}}}return Q}getOriginId(h){return h-this.points.length>>5}getOriginZoom(h){return(h-this.points.length)%32}map(h,b,D){if(h[b+5]>1){let X=this.clusterProps[h[b+6]];return D?Object.assign({},X):X}let z=this.points[h[b+3]].tags,G=this.options.map(z);return D&&G===z?Object.assign({},G):G}}function knt(M,h,b){return{id:M[h+3],type:\"Point\",tags:iI(M,h,b),geometry:[M[h],M[h+1]]}}function Cnt(M,h,b){return{type:\"Feature\",id:M[h+3],properties:iI(M,h,b),geometry:{type:\"Point\",coordinates:[qQ(M[h]),BQ(M[h+1])]}}}function iI(M,h,b){let D=M[h+5],z=D>=1e4?`${Math.round(D/1e3)}k`:D>=1e3?Math.round(D/100)/10+\"k\":D,G=M[h+6],X=G===-1?{}:Object.assign({},b[G]);return Object.assign(X,{cluster:!0,cluster_id:M[h+3],point_count:D,point_count_abbreviated:z})}let aI=\"geojsonvt_clip_start\",oI=\"geojsonvt_clip_end\";function GQ(M,h,b,D,z){let G=h===z.maxZoom?0:z.tolerance/((1<<h)*z.extent),X={transformed:!1,features:[],source:null,x:b,y:D,z:h,minX:2,minY:1,maxX:-1,maxY:0,numPoints:0,numSimplified:0,numFeatures:M.length};for(let Z of M)Lnt(X,Z,G,z);return X}function Lnt(M,h,b,D){switch(M.minX=Math.min(M.minX,h.minX),M.minY=Math.min(M.minY,h.minY),M.maxX=Math.max(M.maxX,h.maxX),M.maxY=Math.max(M.maxY,h.maxY),h.type){case\"Point\":case\"MultiPoint\":return void(function(z,G){let X=[];for(let re=0;re<G.geometry.length;re+=3)X.push(G.geometry[re],G.geometry[re+1]),z.numPoints++,z.numSimplified++;if(!X.length)return;let Z={type:1,tags:G.tags||null,geometry:X};G.id!==null&&(Z.id=G.id),z.features.push(Z)})(M,h);case\"LineString\":return void(function(z,G,X,Z){let re=[];if(sI(re,G.geometry,z,X,!1,!1),!re.length)return;let Q=G.tags||null;if(Z.lineMetrics){Q={};for(let oe in G.tags)Q[oe]=G.tags[oe];Q[aI]=G.geometry.start/G.geometry.size,Q[oI]=G.geometry.end/G.geometry.size}let he={type:2,tags:Q,geometry:re};G.id!==null&&(he.id=G.id),z.features.push(he)})(M,h,b,D);case\"MultiLineString\":case\"Polygon\":return void(function(z,G,X){let Z=[];for(let Q=0;Q<G.geometry.length;Q++)sI(Z,G.geometry[Q],z,X,G.type===\"Polygon\",Q===0);if(!Z.length)return;let re={type:G.type===\"Polygon\"?3:2,tags:G.tags||null,geometry:Z};G.id!==null&&(re.id=G.id),z.features.push(re)})(M,h,b);case\"MultiPolygon\":return void(function(z,G,X){let Z=[];for(let Q=0;Q<G.geometry.length;Q++){let he=G.geometry[Q];for(let oe=0;oe<he.length;oe++)sI(Z,he[oe],z,X,!0,oe===0)}if(!Z.length)return;let re={type:3,tags:G.tags||null,geometry:Z};G.id!==null&&(re.id=G.id),z.features.push(re)})(M,h,b)}}function sI(M,h,b,D,z,G){let X=D*D;if(D>0&&h.size<(z?X:D))return void(b.numPoints+=h.points.length/3);let Z=[];for(let re=0;re<h.points.length;re+=3)(D===0||h.points[re+2]>X)&&(b.numSimplified++,Z.push(h.points[re],h.points[re+1])),b.numPoints++;z&&(function(re,Q){let he=0;for(let oe=0,ke=re.length,ge=ke-2;oe<ke;ge=oe,oe+=2)he+=(re[oe]-re[ge])*(re[oe+1]+re[ge+1]);if(he>0===Q)for(let oe=0,ke=re.length;oe<ke/2;oe+=2){let ge=re[oe],Ue=re[oe+1];re[oe]=re[ke-2-oe],re[oe+1]=re[ke-1-oe],re[ke-2-oe]=ge,re[ke-1-oe]=Ue}})(Z,G),M.push(Z)}function HQ(M,h){if(M.transformed)return M;let b=1<<M.z,D=M.x,z=M.y;for(let G of M.features)G.type===1?Pnt(G,h,b,D,z):Fnt(G,h,b,D,z);return M.transformed=!0,M}function Pnt(M,h,b,D,z){let G=M,X=M.geometry,Z=[];for(let re=0;re<X.length;re+=2)Z.push(jQ(X[re],X[re+1],h,b,D,z));return G.geometry=Z,G}function Fnt(M,h,b,D,z){let G=M,X=M.geometry,Z=[];for(let re of X){let Q=[];for(let he=0;he<re.length;he+=2)Q.push(jQ(re[he],re[he+1],h,b,D,z));Z.push(Q)}return G.geometry=Z,G}function jQ(M,h,b,D,z,G){return[Math.round(b*(M*D-z)),Math.round(b*(h*D-G))]}class Dnt{constructor(h){this.options=h,this.total=0,this.stats={},this.tiles={},this.tileCoords=[],this.stats={},this.total=0}initialize(h){this.splitTile(h,0,0,0),this.options.debug&&(h.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}updateIndex(h,b,D){D.debug>1&&(console.log(\"invalidating tiles\"),console.time(\"invalidating\")),this.invalidateTiles(b),D.debug>1&&console.timeEnd(\"invalidating\");let[z,G,X]=[0,0,0],Z=GQ(h,z,G,X,D);Z.source=h;let re=DS(z,G,X);if(this.tiles[re]=Z,this.tileCoords.push({z,x:G,y:X,id:re}),D.debug){let Q=`z${z}`;this.stats[Q]=(this.stats[Q]||0)+1,this.total++}}getClusterExpansionZoom(h){return null}getChildren(h){return null}getLeaves(h,b,D){return null}getTile(h,b,D){let{extent:z,debug:G}=this.options,X=1<<h,Z=DS(h,b=b+X&X-1,D);if(this.tiles[Z])return HQ(this.tiles[Z],z);G>1&&console.log(\"drilling down to z%d-%d-%d\",h,b,D);let re,Q=h,he=b,oe=D;for(;!re&&Q>0;)Q--,he>>=1,oe>>=1,re=this.tiles[DS(Q,he,oe)];return re!=null&&re.source?(G>1&&(console.log(\"found parent tile z%d-%d-%d\",Q,he,oe),console.time(\"drilling down\")),this.splitTile(re.source,Q,he,oe,h,b,D),G>1&&console.timeEnd(\"drilling down\"),this.tiles[Z]?HQ(this.tiles[Z],z):null):null}splitTile(h,b,D,z,G,X,Z){let re=[h,b,D,z],Q=this.options,he=Q.debug;for(;re.length;){z=re.pop(),D=re.pop(),b=re.pop(),h=re.pop();let oe=1<<b,ke=DS(b,D,z),ge=this.tiles[ke];if(!ge&&(he>1&&console.time(\"creation\"),ge=this.tiles[ke]=GQ(h,b,D,z,Q),this.tileCoords.push({z:b,x:D,y:z,id:ke}),he)){he>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",b,D,z,ge.numFeatures,ge.numPoints,ge.numSimplified),console.timeEnd(\"creation\"));let jr=`z${b}`;this.stats[jr]=(this.stats[jr]||0)+1,this.total++}if(ge.source=h,G==null){if(b===Q.indexMaxZoom||ge.numPoints<=Q.indexMaxPoints)continue}else{if(b===Q.maxZoom||b===G)continue;if(G!=null){let jr=G-b;if(D!==X>>jr||z!==Z>>jr)continue}}if(ge.source=null,!h.length)continue;he>1&&console.time(\"clipping\");let Ue=.5*Q.buffer/Q.extent,je=.5-Ue,vt=.5+Ue,gt=1+Ue,It=null,_t=null,qt=null,tr=null,lr=O1(h,oe,D-Ue,D+vt,yh.X,ge.minX,ge.maxX,Q),qr=O1(h,oe,D+je,D+gt,yh.X,ge.minX,ge.maxX,Q);lr&&(It=O1(lr,oe,z-Ue,z+vt,yh.Y,ge.minY,ge.maxY,Q),_t=O1(lr,oe,z+je,z+gt,yh.Y,ge.minY,ge.maxY,Q)),qr&&(qt=O1(qr,oe,z-Ue,z+vt,yh.Y,ge.minY,ge.maxY,Q),tr=O1(qr,oe,z+je,z+gt,yh.Y,ge.minY,ge.maxY,Q)),he>1&&console.timeEnd(\"clipping\"),re.push(It||[],b+1,2*D,2*z),re.push(_t||[],b+1,2*D,2*z+1),re.push(qt||[],b+1,2*D+1,2*z),re.push(tr||[],b+1,2*D+1,2*z+1)}}invalidateTiles(h){if(!h.length)return;let b=this.options,{debug:D}=b,z=1/0,G=-1/0,X=1/0,Z=-1/0;for(let he of h)z=Math.min(z,he.minX),G=Math.max(G,he.maxX),X=Math.min(X,he.minY),Z=Math.max(Z,he.maxY);let re=b.buffer/b.extent,Q=new Set;for(let he in this.tiles){let oe=this.tiles[he],ke=1<<oe.z,ge=(oe.x-re)/ke,Ue=(oe.x+1+re)/ke,je=(oe.y-re)/ke,vt=(oe.y+1+re)/ke;if(G<ge||z>=Ue||Z<je||X>=vt)continue;let gt=!1;for(let It of h)if(It.maxX>=ge&&It.minX<Ue&&It.maxY>=je&&It.minY<vt){gt=!0;break}if(gt){if(D){D>1&&console.log(\"invalidate tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",oe.z,oe.x,oe.y,oe.numFeatures,oe.numPoints,oe.numSimplified);let It=`z${oe.z}`;this.stats[It]=(this.stats[It]||0)-1,this.total--}delete this.tiles[he],Q.add(he)}}Q.size&&(this.tileCoords=this.tileCoords.filter((he=>!Q.has(he.id))))}}function DS(M,h,b){return 32*((1<<M)*b+h)+M}let Rnt={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,updateable:!1,cluster:!1,clusterOptions:nI,debug:0},Int=Re([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4),{members:znt}=Int,qnt=Re([{name:\"a_uv_x\",components:1,type:\"Float32\"},{name:\"a_split_index\",components:1,type:\"Float32\"}]),{members:Bnt}=qnt,Ont=Math.cos(Math.PI/180*37.5),WQ=Math.pow(2,14)/.5;class lI{constructor(h){this.zoom=h.zoom,this.overscaling=h.overscaling,this.layers=h.layers,this.layerIds=this.layers.map((b=>b.id)),this.index=h.index,this.hasDependencies=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={};for(let b of this.layers)this.gradients[b.id]={};this.layoutVertexArray=new Bt,this.layoutVertexArray2=new St,this.indexArray=new Er,this.programConfigurations=new Ta(h.layers,h.zoom),this.segments=new an,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((b=>b.isStateDependent())).map((b=>b.id))}populate(h,b,D){this.hasDependencies=MS(\"line\",this.layers,b)||this.hasLineDasharray(this.layers);let z=this.layers[0].layout.get(\"line-sort-key\"),G=!z.isConstant(),X=[];for(let{feature:Z,id:re,index:Q,sourceLayerIndex:he}of h){let oe=this.layers[0]._featureFilter.needGeometry,ke=xa(Z,oe);if(!this.layers[0]._featureFilter.filter(new mi(this.zoom),ke,D))continue;let ge=G?z.evaluate(ke,{},D):void 0,Ue={id:re,properties:Z.properties,type:Z.type,sourceLayerIndex:he,index:Q,geometry:oe?ke.geometry:$l(Z),patterns:{},dashes:{},sortKey:ge};X.push(Ue)}G&&X.sort(((Z,re)=>Z.sortKey-re.sortKey));for(let Z of X){let{geometry:re,index:Q,sourceLayerIndex:he}=Z;this.hasDependencies?(MS(\"line\",this.layers,b)?UR(\"line\",this.layers,Z,{zoom:this.zoom},b):this.hasLineDasharray(this.layers)&&this.addLineDashDependencies(this.layers,Z,this.zoom,b),this.patternFeatures.push(Z)):this.addFeature(Z,re,Q,D,{},{},b.subdivisionGranularity),b.featureIndex.insert(h[Q].feature,re,Q,he,this.index)}}update(h,b,D,z){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(h,b,this.stateDependentLayers,{imagePositions:D,dashPositions:z})}addFeatures(h,b,D,z){for(let G of this.patternFeatures)this.addFeature(G,G.geometry,G.index,b,D,z,h.subdivisionGranularity)}isEmpty(){return this.layoutVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(h){this.uploaded||(this.layoutVertexArray2.length!==0&&(this.layoutVertexBuffer2=h.createVertexBuffer(this.layoutVertexArray2,Bnt)),this.layoutVertexBuffer=h.createVertexBuffer(this.layoutVertexArray,znt),this.indexBuffer=h.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(h),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}lineFeatureClips(h){if(h.properties&&Object.hasOwn(h.properties,aI)&&Object.hasOwn(h.properties,oI))return{start:+h.properties[aI],end:+h.properties[oI]}}addFeature(h,b,D,z,G,X,Z){let re=this.layers[0].layout,Q=re.get(\"line-join\").evaluate(h,{}),he=re.get(\"line-cap\").evaluate(h,{}),oe=re.get(\"line-miter-limit\").evaluate(h,{}),ke=re.get(\"line-round-limit\").evaluate(h,{});this.lineClips=this.lineFeatureClips(h);for(let ge of b)this.addLine(ge,h,Q,he,oe,ke,z,Z);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,h,D,{imagePositions:G,dashPositions:X,canonical:z})}addLine(h,b,D,z,G,X,Z,re){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,h=nx(h,Z?re.line.getGranularityForZoomLevel(Z.z):1),this.lineClips){this.lineClipsArray.push(this.lineClips);for(let _t=0;_t<h.length-1;_t++)this.totalDistance+=h[_t].dist(h[_t+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}let Q=h4.types[b.type]===\"Polygon\",he=h.length;for(;he>=2&&h[he-1].equals(h[he-2]);)he--;let oe=0;for(;oe<he-1&&h[oe].equals(h[oe+1]);)oe++;if(he<(Q?3:2))return;D===\"bevel\"&&(G=1.05);let ke=this.overscaling<=16?122880/(512*this.overscaling):0,ge=this.segments.prepareSegment(10*he,this.layoutVertexArray,this.indexArray),Ue,je,vt,gt,It;this.e1=this.e2=-1,Q&&(Ue=h[he-2],It=h[oe].sub(Ue)._unit()._perp());for(let _t=oe;_t<he;_t++){if(vt=_t===he-1?Q?h[oe+1]:void 0:h[_t+1],vt&&h[_t].equals(vt))continue;It&&(gt=It),Ue&&(je=Ue),Ue=h[_t],It=vt?vt.sub(Ue)._unit()._perp():gt,gt||(gt=It);let qt=gt.add(It);qt.x===0&&qt.y===0||qt._unit();let tr=gt.x*It.x+gt.y*It.y,lr=qt.x*It.x+qt.y*It.y,qr=lr!==0?1/lr:1/0,jr=2*Math.sqrt(2-2*lr),_n=lr<Ont&&je&&vt,fn=gt.x*It.y-gt.y*It.x>0;if(_n&&_t>oe){let Ln=Ue.dist(je);if(Ln>2*ke){let un=Ue.sub(Ue.sub(je)._mult(ke/Ln)._round());this.updateDistance(je,un),this.addCurrentVertex(un,gt,0,0,ge),je=un}}let Dn=je&&vt,Hn=Dn?D:Q?\"butt\":z;if(Dn&&Hn===\"round\"&&(qr<X?Hn=\"miter\":qr<=2&&(Hn=\"fakeround\")),Hn===\"miter\"&&qr>G&&(Hn=\"bevel\"),Hn===\"bevel\"&&(qr>2&&(Hn=\"flipbevel\"),qr<G&&(Hn=\"miter\")),je&&this.updateDistance(je,Ue),Hn===\"miter\")qt._mult(qr),this.addCurrentVertex(Ue,qt,0,0,ge);else if(Hn===\"flipbevel\"){if(qr>100)qt=It.mult(-1);else{let Ln=qr*gt.add(It).mag()/gt.sub(It).mag();qt._perp()._mult(Ln*(fn?-1:1))}this.addCurrentVertex(Ue,qt,0,0,ge),this.addCurrentVertex(Ue,qt.mult(-1),0,0,ge)}else if(Hn===\"bevel\"||Hn===\"fakeround\"){let Ln=-Math.sqrt(qr*qr-1),un=fn?Ln:0,ei=fn?0:Ln;if(je&&this.addCurrentVertex(Ue,gt,un,ei,ge),Hn===\"fakeround\"){let ci=Math.round(180*jr/Math.PI/20);for(let ti=1;ti<ci;ti++){let di=ti/ci;if(di!==.5){let Ao=di-.5;di+=di*Ao*(di-1)*((1.0904+tr*(tr*(3.55645-1.43519*tr)-3.2452))*Ao*Ao+(.848013+tr*(.215638*tr-1.06021)))}let _a=It.sub(gt)._mult(di)._add(gt)._unit()._mult(fn?-1:1);this.addHalfVertex(Ue,_a.x,_a.y,!1,fn,0,ge)}}vt&&this.addCurrentVertex(Ue,It,-un,-ei,ge)}else if(Hn===\"butt\")this.addCurrentVertex(Ue,qt,0,0,ge);else if(Hn===\"square\"){let Ln=je?1:-1;this.addCurrentVertex(Ue,qt,Ln,Ln,ge)}else Hn===\"round\"&&(je&&(this.addCurrentVertex(Ue,gt,0,0,ge),this.addCurrentVertex(Ue,gt,1,1,ge,!0)),vt&&(this.addCurrentVertex(Ue,It,-1,-1,ge,!0),this.addCurrentVertex(Ue,It,0,0,ge)));if(_n&&_t<he-1){let Ln=Ue.dist(vt);if(Ln>2*ke){let un=Ue.add(vt.sub(Ue)._mult(ke/Ln)._round());this.updateDistance(Ue,un),this.addCurrentVertex(un,It,0,0,ge),Ue=un}}}}addCurrentVertex(h,b,D,z,G,X=!1){let Z=b.y*z-b.x,re=-b.y-b.x*z;this.addHalfVertex(h,b.x+b.y*D,b.y-b.x*D,X,!1,D,G),this.addHalfVertex(h,Z,re,X,!0,-z,G),this.distance>WQ/2&&this.totalDistance===0&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(h,b,D,z,G,X))}addHalfVertex({x:h,y:b},D,z,G,X,Z,re){let Q=.5*(this.lineClips?this.scaledDistance*(WQ-1):this.scaledDistance);this.layoutVertexArray.emplaceBack((h<<1)+(G?1:0),(b<<1)+(X?1:0),Math.round(63*D)+128,Math.round(63*z)+128,1+(Z===0?0:Z<0?-1:1)|(63&Q)<<2,Q>>6),this.lineClips&&this.layoutVertexArray2.emplaceBack((this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start),this.lineClipsArray.length);let he=re.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,he,this.e2),re.primitiveLength++),X?this.e2=he:this.e1=he}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(h,b){this.distance+=h.dist(b),this.updateScaledDistance()}hasLineDasharray(h){for(let b of h){let D=b.paint.get(\"line-dasharray\");if(D&&!D.isConstant())return!0}return!1}addLineDashDependencies(h,b,D,z){for(let G of h){let X=G.paint.get(\"line-dasharray\");if(!X||X.value.kind===\"constant\")continue;let Z=G.layout.get(\"line-cap\").evaluate(b,{})===\"round\",re={dasharray:X.value.evaluate({zoom:D-1},b,{}),round:Z},Q={dasharray:X.value.evaluate({zoom:D},b,{}),round:Z},he={dasharray:X.value.evaluate({zoom:D+1},b,{}),round:Z},oe=`${re.dasharray.join(\",\")},${re.round}`,ke=`${Q.dasharray.join(\",\")},${Q.round}`,ge=`${he.dasharray.join(\",\")},${he.round}`;z.dashDependencies[oe]=re,z.dashDependencies[ke]=Q,z.dashDependencies[ge]=he,b.dashes[G.id]={min:oe,mid:ke,max:ge}}}}let YQ,XQ;_i(\"LineBucket\",lI,{omit:[\"layers\",\"patternFeatures\"]});var ZQ={get paint(){return XQ=XQ||new ms({\"line-opacity\":new Di(Ct.paint_line[\"line-opacity\"]),\"line-color\":new Di(Ct.paint_line[\"line-color\"]),\"line-translate\":new wi(Ct.paint_line[\"line-translate\"]),\"line-translate-anchor\":new wi(Ct.paint_line[\"line-translate-anchor\"]),\"line-width\":new Di(Ct.paint_line[\"line-width\"]),\"line-gap-width\":new Di(Ct.paint_line[\"line-gap-width\"]),\"line-offset\":new Di(Ct.paint_line[\"line-offset\"]),\"line-blur\":new Di(Ct.paint_line[\"line-blur\"]),\"line-dasharray\":new fl(Ct.paint_line[\"line-dasharray\"]),\"line-pattern\":new fl(Ct.paint_line[\"line-pattern\"]),\"line-gradient\":new du(Ct.paint_line[\"line-gradient\"])})},get layout(){return YQ=YQ||new ms({\"line-cap\":new Di(Ct.layout_line[\"line-cap\"]),\"line-join\":new Di(Ct.layout_line[\"line-join\"]),\"line-miter-limit\":new Di(Ct.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Di(Ct.layout_line[\"line-round-limit\"]),\"line-sort-key\":new Di(Ct.layout_line[\"line-sort-key\"])})}};class Nnt extends Di{possiblyEvaluate(h,b){return b=new mi(Math.floor(b.zoom),{now:b.now,fadeDuration:b.fadeDuration,zoomHistory:b.zoomHistory,transition:b.transition}),super.possiblyEvaluate(h,b)}evaluate(h,b,D,z){return b=_e({},b,{zoom:Math.floor(b.zoom)}),super.evaluate(h,b,D,z)}}let RS;class Unt extends hl{constructor(h,b){super(h,ZQ,b),this.gradientVersion=0,RS||(RS=new Nnt(ZQ.paint.properties[\"line-width\"].specification),RS.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(h){if(h===\"line-gradient\"){let b=this.gradientExpression();this.stepInterpolant=!!(function(D){return D._styleExpression!==void 0})(b)&&b._styleExpression.expression instanceof lu,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values[\"line-gradient\"].value.expression}recalculate(h,b){super.recalculate(h,b),this.paint._values[\"line-floorwidth\"]=RS.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,h)}createBucket(h){return new lI(h)}queryRadius(h){let b=h,D=$Q(sn(\"line-width\",this,b),sn(\"line-gap-width\",this,b)),z=sn(\"line-offset\",this,b);return D/2+Math.abs(z)+tn(this.paint.get(\"line-translate\"))}queryIntersectsFeature({queryGeometry:h,feature:b,featureState:D,geometry:z,transform:G,pixelsToTileUnits:X}){let Z=Hr(h,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),-G.bearingInRadians,X),re=X/2*$Q(this.paint.get(\"line-width\").evaluate(b,D),this.paint.get(\"line-gap-width\").evaluate(b,D)),Q=this.paint.get(\"line-offset\").evaluate(b,D);return Q&&(z=(function(he,oe){let ke=[];for(let ge of he){let Ue=Mr(ge),je=[];for(let vt=0;vt<Ue.length;vt++){let gt=Ue[vt],It=Ue[vt-1],_t=Ue[vt+1],qt=vt===0?new o(0,0):gt.sub(It)._unit()._perp(),tr=vt===Ue.length-1?new o(0,0):_t.sub(gt)._unit()._perp(),lr=qt._add(tr)._unit(),qr=lr.x*tr.x+lr.y*tr.y;qr!==0&&lr._mult(1/qr),je.push(lr._mult(oe)._add(gt))}ke.push(je)}return ke})(z,Q*X)),(function(he,oe,ke){for(let ge of oe){if(he.length>=3){for(let Ue of ge)if(pn(he,Ue))return!0}if(wo(he,ge,ke))return!0}return!1})(Z,z,re)}isTileClipped(){return!0}}function $Q(M,h){return h>0?h+2*M:M}let Vnt=Re([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"},{name:\"a_pixeloffset\",components:4,type:\"Int16\"}],4),Gnt=Re([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4);Re([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4);let Hnt=Re([{name:\"a_placed\",components:2,type:\"Uint8\"},{name:\"a_shift\",components:2,type:\"Float32\"},{name:\"a_box_real\",components:2,type:\"Int16\"}]);Re([{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]);let KQ=Re([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4),jnt=Re([{name:\"a_pos\",components:2,type:\"Float32\"},{name:\"a_radius\",components:1,type:\"Float32\"},{name:\"a_flags\",components:2,type:\"Int16\"}],4);function Wnt(M,h,b){let D=h.layout.get(\"text-transform\").evaluate(b,{});return D===\"uppercase\"?M=M.toLocaleUpperCase():D===\"lowercase\"&&(M=M.toLocaleLowerCase()),Bi.applyArabicShaping&&(M=Bi.applyArabicShaping(M)),M}function Ynt(M,h,b){for(let D of M.sections)D.text=Wnt(D.text,h,b);return M}Re([{name:\"triangle\",components:3,type:\"Uint16\"}]),Re([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Uint16\",name:\"glyphStartIndex\"},{type:\"Uint16\",name:\"numGlyphs\"},{type:\"Uint32\",name:\"vertexStartIndex\"},{type:\"Uint32\",name:\"lineStartIndex\"},{type:\"Uint32\",name:\"lineLength\"},{type:\"Uint16\",name:\"segment\"},{type:\"Uint16\",name:\"lowerSize\"},{type:\"Uint16\",name:\"upperSize\"},{type:\"Float32\",name:\"lineOffsetX\"},{type:\"Float32\",name:\"lineOffsetY\"},{type:\"Uint8\",name:\"writingMode\"},{type:\"Uint8\",name:\"placedOrientation\"},{type:\"Uint8\",name:\"hidden\"},{type:\"Uint32\",name:\"crossTileID\"},{type:\"Int16\",name:\"associatedIconIndex\"}]),Re([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Int16\",name:\"rightJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"centerJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"leftJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"verticalPlacedTextSymbolIndex\"},{type:\"Int16\",name:\"placedIconSymbolIndex\"},{type:\"Int16\",name:\"verticalPlacedIconSymbolIndex\"},{type:\"Uint16\",name:\"key\"},{type:\"Uint16\",name:\"textBoxStartIndex\"},{type:\"Uint16\",name:\"textBoxEndIndex\"},{type:\"Uint16\",name:\"verticalTextBoxStartIndex\"},{type:\"Uint16\",name:\"verticalTextBoxEndIndex\"},{type:\"Uint16\",name:\"iconBoxStartIndex\"},{type:\"Uint16\",name:\"iconBoxEndIndex\"},{type:\"Uint16\",name:\"verticalIconBoxStartIndex\"},{type:\"Uint16\",name:\"verticalIconBoxEndIndex\"},{type:\"Uint16\",name:\"featureIndex\"},{type:\"Uint16\",name:\"numHorizontalGlyphVertices\"},{type:\"Uint16\",name:\"numVerticalGlyphVertices\"},{type:\"Uint16\",name:\"numIconVertices\"},{type:\"Uint16\",name:\"numVerticalIconVertices\"},{type:\"Uint16\",name:\"useRuntimeCollisionCircles\"},{type:\"Uint32\",name:\"crossTileID\"},{type:\"Float32\",name:\"textBoxScale\"},{type:\"Float32\",name:\"collisionCircleDiameter\"},{type:\"Uint16\",name:\"textAnchorOffsetStartIndex\"},{type:\"Uint16\",name:\"textAnchorOffsetEndIndex\"}]),Re([{type:\"Float32\",name:\"offsetX\"}]),Re([{type:\"Int16\",name:\"x\"},{type:\"Int16\",name:\"y\"},{type:\"Int16\",name:\"tileUnitDistanceFromAnchor\"}]),Re([{type:\"Uint16\",name:\"textAnchor\"},{type:\"Float32\",components:2,name:\"textOffset\"}]);var 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m4={\"!\":\"\\uFE15\",\"#\":\"\\uFF03\",$:\"\\uFF04\",\"%\":\"\\uFF05\",\"&\":\"\\uFF06\",\"(\":\"\\uFE35\",\")\":\"\\uFE36\",\"*\":\"\\uFF0A\",\"+\":\"\\uFF0B\",\",\":\"\\uFE10\",\"-\":\"\\uFE32\",\".\":\"\\u30FB\",\"/\":\"\\uFF0F\",\":\":\"\\uFE13\",\";\":\"\\uFE14\",\"<\":\"\\uFE3F\",\"=\":\"\\uFF1D\",\">\":\"\\uFE40\",\"?\":\"\\uFE16\",\"@\":\"\\uFF20\",\"[\":\"\\uFE47\",\"\\\\\":\"\\uFF3C\",\"]\":\"\\uFE48\",\"^\":\"\\uFF3E\",_:\"\\uFE33\",\"`\":\"\\uFF40\",\"{\":\"\\uFE37\",\"|\":\"\\u2015\",\"}\":\"\\uFE38\",\"~\":\"\\uFF5E\",\"\\xA2\":\"\\uFFE0\",\"\\xA3\":\"\\uFFE1\",\"\\xA5\":\"\\uFFE5\",\"\\xA6\":\"\\uFFE4\",\"\\xAC\":\"\\uFFE2\",\"\\xAF\":\"\\uFFE3\",\"\\u2013\":\"\\uFE32\",\"\\u2014\":\"\\uFE31\",\"\\u2018\":\"\\uFE43\",\"\\u2019\":\"\\uFE44\",\"\\u201C\":\"\\uFE41\",\"\\u201D\":\"\\uFE42\",\"\\u2026\":\"\\uFE19\",\"\\u22EF\":\"\\uFE19\",\"\\u2027\":\"\\u30FB\",\"\\u20A9\":\"\\uFFE6\",\"\\u3001\":\"\\uFE11\",\"\\u3002\":\"\\uFE12\",\"\\u3008\":\"\\uFE3F\",\"\\u3009\":\"\\uFE40\",\"\\u300A\":\"\\uFE3D\",\"\\u300B\":\"\\uFE3E\",\"\\u300C\":\"\\uFE41\",\"\\u300D\":\"\\uFE42\",\"\\u300E\":\"\\uFE43\",\"\\u300F\":\"\\uFE44\",\"\\u3010\":\"\\uFE3B\",\"\\u3011\":\"\\uFE3C\",\"\\u3014\":\"\\uFE39\",\"\\u3015\":\"\\uFE3A\",\"\\u3016\":\"\\uFE17\",\"\\u3017\":\"\\uFE18\",\"\\uFF01\":\"\\uFE15\",\"\\uFF08\":\"\\uFE35\",\"\\uFF09\":\"\\uFE36\",\"\\uFF0C\":\"\\uFE10\",\"\\uFF0D\":\"\\uFE32\",\"\\uFF0E\":\"\\u30FB\",\"\\uFF1A\":\"\\uFE13\",\"\\uFF1B\":\"\\uFE14\",\"\\uFF1C\":\"\\uFE3F\",\"\\uFF1E\":\"\\uFE40\",\"\\uFF1F\":\"\\uFE16\",\"\\uFF3B\":\"\\uFE47\",\"\\uFF3D\":\"\\uFE48\",\"\\uFF3F\":\"\\uFE33\",\"\\uFF5B\":\"\\uFE37\",\"\\uFF5C\":\"\\u2015\",\"\\uFF5D\":\"\\uFE38\",\"\\uFF5F\":\"\\uFE35\",\"\\uFF60\":\"\\uFE36\",\"\\uFF61\":\"\\uFE12\",\"\\uFF62\":\"\\uFE41\",\"\\uFF63\":\"\\uFE42\"},Xnt={10:!0,32:!0,38:!0,41:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0,8231:!0},Znt={40:!0};function 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re&&(z[X++]=239,z[X++]=191,z[X++]=189,re=null);Z<128?z[X++]=Z:(Z<2048?z[X++]=Z>>6|192:(Z<65536?z[X++]=Z>>12|224:(z[X++]=Z>>18|240,z[X++]=Z>>12&63|128),z[X++]=Z>>6&63|128),z[X++]=63&Z|128)}return X})(this.buf,h,this.pos);let D=this.pos-b;D>=128&&iee(b,D,this),this.pos=b-1,this.writeVarint(D),this.pos+=D}writeFloat(h){this.realloc(4),this.dataView.setFloat32(this.pos,h,!0),this.pos+=4}writeDouble(h){this.realloc(8),this.dataView.setFloat64(this.pos,h,!0),this.pos+=8}writeBytes(h){let b=h.length;this.writeVarint(b),this.realloc(b);for(let D=0;D<b;D++)this.buf[this.pos++]=h[D]}writeRawMessage(h,b){this.pos++;let D=this.pos;h(b,this);let z=this.pos-D;z>=128&&iee(D,z,this),this.pos=D-1,this.writeVarint(z),this.pos+=z}writeMessage(h,b,D){this.writeTag(h,2),this.writeRawMessage(b,D)}writePackedVarint(h,b){b.length&&this.writeMessage(h,Knt,b)}writePackedSVarint(h,b){b.length&&this.writeMessage(h,Jnt,b)}writePackedBoolean(h,b){b.length&&this.writeMessage(h,tit,b)}writePackedFloat(h,b){b.length&&this.writeMessage(h,Qnt,b)}writePackedDouble(h,b){b.length&&this.writeMessage(h,eit,b)}writePackedFixed32(h,b){b.length&&this.writeMessage(h,rit,b)}writePackedSFixed32(h,b){b.length&&this.writeMessage(h,nit,b)}writePackedFixed64(h,b){b.length&&this.writeMessage(h,iit,b)}writePackedSFixed64(h,b){b.length&&this.writeMessage(h,ait,b)}writeBytesField(h,b){this.writeTag(h,2),this.writeBytes(b)}writeFixed32Field(h,b){this.writeTag(h,5),this.writeFixed32(b)}writeSFixed32Field(h,b){this.writeTag(h,5),this.writeSFixed32(b)}writeFixed64Field(h,b){this.writeTag(h,1),this.writeFixed64(b)}writeSFixed64Field(h,b){this.writeTag(h,1),this.writeSFixed64(b)}writeVarintField(h,b){this.writeTag(h,0),this.writeVarint(b)}writeSVarintField(h,b){this.writeTag(h,0),this.writeSVarint(b)}writeStringField(h,b){this.writeTag(h,2),this.writeString(b)}writeFloatField(h,b){this.writeTag(h,5),this.writeFloat(b)}writeDoubleField(h,b){this.writeTag(h,1),this.writeDouble(b)}writeBooleanField(h,b){this.writeVarintField(h,+b)}}function Y_(M,h,b){return b?4294967296*h+(M>>>0):4294967296*(h>>>0)+(M>>>0)}function iee(M,h,b){let D=h<=16383?1:h<=2097151?2:h<=268435455?3:Math.floor(Math.log(h)/(7*Math.LN2));b.realloc(D);for(let z=b.pos-1;z>=M;z--)b.buf[z+D]=b.buf[z]}function Knt(M,h){for(let b=0;b<M.length;b++)h.writeVarint(M[b])}function Jnt(M,h){for(let b=0;b<M.length;b++)h.writeSVarint(M[b])}function Qnt(M,h){for(let b=0;b<M.length;b++)h.writeFloat(M[b])}function eit(M,h){for(let b=0;b<M.length;b++)h.writeDouble(M[b])}function tit(M,h){for(let b=0;b<M.length;b++)h.writeBoolean(M[b])}function rit(M,h){for(let b=0;b<M.length;b++)h.writeFixed32(M[b])}function nit(M,h){for(let b=0;b<M.length;b++)h.writeSFixed32(M[b])}function iit(M,h){for(let b=0;b<M.length;b++)h.writeFixed64(M[b])}function ait(M,h){for(let b=0;b<M.length;b++)h.writeSFixed64(M[b])}function oit(M,h,b){M===1&&b.readMessage(sit,h)}function sit(M,h,b){if(M===3){let{id:D,bitmap:z,width:G,height:X,left:Z,top:re,advance:Q}=b.readMessage(lit,{});h.push({id:D,bitmap:new Dc({width:G+6,height:X+6},z),metrics:{width:G,height:X,left:Z,top:re,advance:Q}})}}function lit(M,h,b){M===1?h.id=b.readVarint():M===2?h.bitmap=b.readBytes():M===3?h.width=b.readVarint():M===4?h.height=b.readVarint():M===5?h.left=b.readSVarint():M===6?h.top=b.readSVarint():M===7&&(h.advance=b.readVarint())}function aee(M){let h=0,b=0;for(let X of M)h+=X.w*X.h,b=Math.max(b,X.w);M.sort(((X,Z)=>Z.h-X.h));let D=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(h/.95)),b),h:1/0}],z=0,G=0;for(let X of M)for(let Z=D.length-1;Z>=0;Z--){let re=D[Z];if(!(X.w>re.w||X.h>re.h)){if(X.x=re.x,X.y=re.y,G=Math.max(G,X.y+X.h),z=Math.max(z,X.x+X.w),X.w===re.w&&X.h===re.h){let Q=D.pop();Q&&Z<D.length&&(D[Z]=Q)}else X.h===re.h?(re.x+=X.w,re.w-=X.w):X.w===re.w?(re.y+=X.h,re.h-=X.h):(D.push({x:re.x+X.w,y:re.y,w:re.w-X.w,h:X.h}),re.y+=X.h,re.h-=X.h);break}}return{w:z,h:G,fill:h/(z*G)||0}}class cI{constructor(h,{pixelRatio:b,version:D,stretchX:z,stretchY:G,content:X,textFitWidth:Z,textFitHeight:re}){this.paddedRect=h,this.pixelRatio=b,this.stretchX=z,this.stretchY=G,this.content=X,this.version=D,this.textFitWidth=Z,this.textFitHeight=re}get tl(){return[this.paddedRect.x+1,this.paddedRect.y+1]}get br(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]}get tlbr(){return this.tl.concat(this.br)}get displaySize(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]}}class oee{constructor(h,b){let D={},z={};this.haveRenderCallbacks=[];let G=[];this.addImages(h,D,G),this.addImages(b,z,G);let{w:X,h:Z}=aee(G),re=new Kl({width:X||1,height:Z||1});for(let Q in h){let he=h[Q],oe=D[Q].paddedRect;Kl.copy(he.data,re,{x:0,y:0},{x:oe.x+1,y:oe.y+1},he.data)}for(let Q in b){let he=b[Q],oe=z[Q].paddedRect,ke=oe.x+1,ge=oe.y+1,Ue=he.data.width,je=he.data.height;Kl.copy(he.data,re,{x:0,y:0},{x:ke,y:ge},he.data),Kl.copy(he.data,re,{x:0,y:je-1},{x:ke,y:ge-1},{width:Ue,height:1}),Kl.copy(he.data,re,{x:0,y:0},{x:ke,y:ge+je},{width:Ue,height:1}),Kl.copy(he.data,re,{x:Ue-1,y:0},{x:ke-1,y:ge},{width:1,height:je}),Kl.copy(he.data,re,{x:0,y:0},{x:ke+Ue,y:ge},{width:1,height:je})}this.image=re,this.iconPositions=D,this.patternPositions=z}addImages(h,b,D){for(let z in h){let G=h[z],X={x:0,y:0,w:G.data.width+2,h:G.data.height+2};D.push(X),b[z]=new cI(X,G),G.hasRenderCallback&&this.haveRenderCallbacks.push(z)}}patchUpdatedImages(h,b){h.dispatchRenderCallbacks(this.haveRenderCallbacks);for(let D in h.updatedImages)this.patchUpdatedImage(this.iconPositions[D],h.getImage(D),b),this.patchUpdatedImage(this.patternPositions[D],h.getImage(D),b)}patchUpdatedImage(h,b,D){if(!h||!b||h.version===b.version)return;h.version=b.version;let[z,G]=h.tl;D.update(b.data,void 0,{x:z,y:G})}}var Wg;function zS(M,h,b,D,z,G,X,Z,re,Q,he,oe,ke,ge,Ue){let je=W_.fromFeature(M,z),vt;oe===a.ax.vertical&&je.verticalizePunctuation();let gt=je.determineLineBreaks(Q,G,h,D,ge),{processBidirectionalText:It,processStyledBidirectionalText:_t}=Bi;if(It&&je.sections.length===1){vt=[],gt=gt.map((qr=>je.toCodeUnitIndex(qr)));let lr=It(je.toString(),gt);for(let qr of lr){let jr=[...qr].map((()=>0));vt.push(new W_(qr,je.sections,jr))}}else if(_t){vt=[],gt=gt.map((_n=>je.toCodeUnitIndex(_n)));let lr=0,qr=[];for(let _n of je.text)qr.push(...Array(_n.length).fill(je.sectionIndex[lr])),lr++;let jr=_t(je.text,qr,gt);for(let _n of jr){let fn=[],Dn=\"\";for(let Hn of _n[0])fn.push(_n[1][Dn.length]),Dn+=Hn;vt.push(new W_(_n[0],je.sections,fn))}}else vt=(function(lr,qr){let jr=[],_n=0;for(let fn of qr)jr.push(lr.substring(_n,fn)),_n=fn;return _n<lr.length()&&jr.push(lr.substring(_n,lr.length())),jr})(je,gt);let qt=[],tr={positionedLines:qt,text:je.toString(),top:he[1],bottom:he[1],left:he[0],right:he[0],writingMode:oe,iconsInText:!1,verticalizable:!1};return(function(lr,qr,jr,_n,fn,Dn,Hn,Ln,un,ei,ci,ti){let di=0,_a=0,Ao=0,us=0,ks=Ln===\"right\"?1:Ln===\"left\"?0:.5,Uf=Rc/ti,Jl=0;for(let Sl of fn){Sl.trim();let df=Sl.getMaxScale(),_h={positionedGlyphs:[],lineOffset:0};lr.positionedLines[Jl]=_h;let bh=_h.positionedGlyphs,Ad=0;if(!Sl.length()){_a+=Dn,++Jl;continue}let Vf=uit(_n,Sl,Uf),Md=0;for(let qc of Sl.text){let Bl=Sl.getSection(Md),ec=qc.codePointAt(0),Ql=cit(un,ci,ec),Bc={glyph:ec,imageName:null,x:di,y:_a+-17,vertical:Ql,scale:1,fontStack:\"\",sectionIndex:Sl.getSectionIndex(Md),metrics:null,rect:null},V1;if(\"fontStack\"in Bl){if(V1=fit(Bl,ec,Ql,Vf,qr,jr),!V1)continue;Bc.fontStack=Bl.fontStack}else{if(lr.iconsInText=!0,Bl.scale*=Uf,V1=hit(Bl,Ql,df,Vf,_n),!V1)continue;Ad=Math.max(Ad,V1.imageOffset),Bc.imageName=Bl.imageName}let{rect:Wv,metrics:w4,baselineOffset:Qg}=V1;Bc.y+=Qg,Bc.scale=Bl.scale,Bc.metrics=w4,Bc.rect=Wv,bh.push(Bc),Ql?(lr.verticalizable=!0,di+=(\"imageName\"in Bl?w4.advance:Rc)*Bl.scale+ei):di+=w4.advance*Bl.scale+ei,Md++}bh.length!==0&&(Ao=Math.max(di-ei,Ao),dit(bh,0,bh.length-1,ks)),di=0,_h.lineOffset=Math.max(Ad,(df-1)*Rc);let Z0=Dn*df+Ad;_a+=Z0,us=Math.max(Z0,us),++Jl}let{horizontalAlign:_c,verticalAlign:zc}=fI(Hn);(function(Sl,df,_h,bh,Ad,Vf,Md,Z0,qc){let Bl=(df-_h)*Ad,ec=0;ec=Vf!==Md?-Z0*bh- -17:-bh*qc*Md+.5*Md;for(let Ql of Sl)for(let Bc of Ql.positionedGlyphs)Bc.x+=Bl,Bc.y+=ec})(lr.positionedLines,ks,_c,zc,Ao,us,Dn,_a,fn.length),lr.top+=-zc*_a,lr.bottom=lr.top+_a,lr.left+=-_c*Ao,lr.right=lr.left+Ao})(tr,h,b,D,vt,X,Z,re,oe,Q,ke,Ue),!(function(lr){for(let qr of lr)if(qr.positionedGlyphs.length!==0)return!1;return!0})(qt)&&tr}function fI(M){let h=.5,b=.5;switch(M){case\"right\":case\"top-right\":case\"bottom-right\":h=1;break;case\"left\":case\"top-left\":case\"bottom-left\":h=0}switch(M){case\"bottom\":case\"bottom-right\":case\"bottom-left\":b=1;break;case\"top\":case\"top-right\":case\"top-left\":b=0}return{horizontalAlign:h,verticalAlign:b}}function uit(M,h,b){let D=h.getMaxScale()*Rc,{maxImageWidth:z,maxImageHeight:G}=h.getMaxImageSize(M),X=Math.max(D,G*b);return{verticalLineContentWidth:Math.max(D,z*b),horizontalLineContentHeight:X}}function see(M){switch(M){case\"top\":return 0;case\"center\":return .5;default:return 1}}function cit(M,h,b){return!(M===a.ax.horizontal||!h&&!lt(b)||h&&(Gt(b)||(D=b,new RegExp(\"\\\\p{sc=Arab}\",\"u\").test(String.fromCodePoint(D)))));var D}function fit(M,h,b,D,z,G){let X=G[M.fontStack],Z=(function(Q,he,oe,ke){if(Q!=null&&Q.rect)return Q;let ge=he[oe.fontStack],Ue=ge==null?void 0:ge[ke];return Ue?{rect:null,metrics:Ue.metrics}:null})(X==null?void 0:X[h],z,M,h);if(Z===null)return null;let re;if(b)re=D.verticalLineContentWidth-M.scale*Rc;else{let Q=see(M.verticalAlign);re=(D.horizontalLineContentHeight-M.scale*Rc)*Q}return{rect:Z.rect,metrics:Z.metrics,baselineOffset:re}}function hit(M,h,b,D,z){let G=z[M.imageName];if(!G)return null;let X=G.paddedRect,Z=G.displaySize,re={width:Z[0],height:Z[1],left:1,top:-3,advance:h?Z[1]:Z[0]},Q;if(h)Q=D.verticalLineContentWidth-Z[1]*M.scale;else{let he=see(M.verticalAlign);Q=(D.horizontalLineContentHeight-Z[1]*M.scale)*he}return{rect:X,metrics:re,baselineOffset:Q,imageOffset:(h?Z[0]:Z[1])*M.scale-Rc*b}}function dit(M,h,b,D){if(D===0)return;let z=M[b],G=(M[b].x+z.metrics.advance*z.scale)*D;for(let X=h;X<=b;X++)M[X].x-=G}function vit(M,h,b){let{horizontalAlign:D,verticalAlign:z}=fI(b),G=h[0]-M.displaySize[0]*D,X=h[1]-M.displaySize[1]*z;return{image:M,top:X,bottom:X+M.displaySize[1],left:G,right:G+M.displaySize[0]}}function lee(M){var h,b;let D=M.left,z=M.top,G=M.right-D,X=M.bottom-z,Z=(h=M.image.textFitWidth)!==null&&h!==void 0?h:\"stretchOrShrink\",re=(b=M.image.textFitHeight)!==null&&b!==void 0?b:\"stretchOrShrink\",Q=(M.image.content[2]-M.image.content[0])/(M.image.content[3]-M.image.content[1]);if(re===\"proportional\"){if(Z===\"stretchOnly\"&&G/X<Q||Z===\"proportional\"){let he=Math.ceil(X*Q);D*=he/G,G=he}}else if(Z===\"proportional\"&&re===\"stretchOnly\"&&Q!==0&&G/X>Q){let he=Math.ceil(G/Q);z*=he/X,X=he}return{x1:D,y1:z,x2:D+G,y2:z+X}}function uee(M,h,b,D,z,G){let X=M.image,Z;if(X.content){let vt=X.content,gt=X.pixelRatio||1;Z=[vt[0]/gt,vt[1]/gt,X.displaySize[0]-vt[2]/gt,X.displaySize[1]-vt[3]/gt]}let re=h.left*G,Q=h.right*G,he,oe,ke,ge;b===\"width\"||b===\"both\"?(ge=z[0]+re-D[3],oe=z[0]+Q+D[1]):(ge=z[0]+(re+Q-X.displaySize[0])/2,oe=ge+X.displaySize[0]);let Ue=h.top*G,je=h.bottom*G;return b===\"height\"||b===\"both\"?(he=z[1]+Ue-D[0],ke=z[1]+je+D[2]):(he=z[1]+(Ue+je-X.displaySize[1])/2,ke=he+X.displaySize[1]),{image:X,top:he,right:oe,bottom:ke,left:ge,collisionPadding:Z}}_i(\"ImagePosition\",cI),_i(\"ImageAtlas\",oee),a.ax=void 0,(Wg=a.ax||(a.ax={}))[Wg.none=0]=\"none\",Wg[Wg.horizontal=1]=\"horizontal\",Wg[Wg.vertical=2]=\"vertical\",Wg[Wg.horizontalOnly=3]=\"horizontalOnly\";let N1=128,Yg=32640;function cee(M,h){let{expression:b}=h;if(b.kind===\"constant\")return{kind:\"constant\",layoutSize:b.evaluate(new mi(M+1))};if(b.kind===\"source\")return{kind:\"source\"};{let{zoomStops:D,interpolationType:z}=b,G=0;for(;G<D.length&&D[G]<=M;)G++;G=Math.max(0,G-1);let X=G;for(;X<D.length&&D[X]<M+1;)X++;X=Math.min(D.length-1,X);let Z=D[G],re=D[X];return b.kind===\"composite\"?{kind:\"composite\",minZoom:Z,maxZoom:re,interpolationType:z}:{kind:\"camera\",minZoom:Z,maxZoom:re,minSize:b.evaluate(new mi(Z)),maxSize:b.evaluate(new mi(re)),interpolationType:z}}}function hI(M,h,b){let D=\"never\",z=M.get(h);return z?D=z:M.get(b)&&(D=\"always\"),D}let pit=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function qS(M,h,b,D,z,G,X,Z,re,Q,he,oe,ke){let ge=Z?Math.min(Yg,Math.round(Z[0])):0,Ue=Z?Math.min(Yg,Math.round(Z[1])):0;M.emplaceBack(h,b,Math.round(32*D),Math.round(32*z),G,X,(ge<<1)+(re?1:0),Ue,16*Q,16*he,256*oe,256*ke)}function dI(M,h,b){M.emplaceBack(h.x,h.y,b),M.emplaceBack(h.x,h.y,b),M.emplaceBack(h.x,h.y,b),M.emplaceBack(h.x,h.y,b)}function git(M){for(let h of M.sections)if(gi(h.text))return!0;return!1}class vI{constructor(h){this.layoutVertexArray=new Ht,this.indexArray=new Er,this.programConfigurations=h,this.segments=new an,this.dynamicLayoutVertexArray=new dr,this.opacityVertexArray=new Xr,this.hasVisibleVertices=!1,this.placedSymbolArray=new se}isEmpty(){return this.layoutVertexArray.length===0&&this.indexArray.length===0&&this.dynamicLayoutVertexArray.length===0&&this.opacityVertexArray.length===0}upload(h,b,D,z){this.isEmpty()||(D&&(this.layoutVertexBuffer=h.createVertexBuffer(this.layoutVertexArray,Vnt.members),this.indexBuffer=h.createIndexBuffer(this.indexArray,b),this.dynamicLayoutVertexBuffer=h.createVertexBuffer(this.dynamicLayoutVertexArray,Gnt.members,!0),this.opacityVertexBuffer=h.createVertexBuffer(this.opacityVertexArray,pit,!0),this.opacityVertexBuffer.itemSize=1),(D||z)&&this.programConfigurations.upload(h))}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())}}_i(\"SymbolBuffers\",vI);class pI{constructor(h,b,D){this.layoutVertexArray=new h,this.layoutAttributes=b,this.indexArray=new D,this.segments=new an,this.collisionVertexArray=new $r}upload(h){this.layoutVertexBuffer=h.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=h.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=h.createVertexBuffer(this.collisionVertexArray,Hnt.members,!0)}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())}}_i(\"CollisionBuffers\",pI);class X_{constructor(h){this.collisionBoxArray=h.collisionBoxArray,this.zoom=h.zoom,this.overscaling=h.overscaling,this.layers=h.layers,this.layerIds=this.layers.map((X=>X.id)),this.index=h.index,this.pixelRatio=h.pixelRatio,this.sourceLayerIndex=h.sourceLayerIndex,this.hasDependencies=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[];let b=this.layers[0]._unevaluatedLayout._values;this.textSizeData=cee(this.zoom,b[\"text-size\"]),this.iconSizeData=cee(this.zoom,b[\"icon-size\"]);let D=this.layers[0].layout,z=D.get(\"symbol-sort-key\"),G=D.get(\"symbol-z-order\");this.canOverlap=hI(D,\"text-overlap\",\"text-allow-overlap\")!==\"never\"||hI(D,\"icon-overlap\",\"icon-allow-overlap\")!==\"never\"||D.get(\"text-ignore-placement\")||D.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=G!==\"viewport-y\"&&!z.isConstant(),this.sortFeaturesByY=(G===\"viewport-y\"||G===\"auto\"&&!this.sortFeaturesByKey)&&this.canOverlap,D.get(\"symbol-placement\")===\"point\"&&(this.writingModes=D.get(\"text-writing-mode\").map((X=>a.ax[X]))),this.stateDependentLayerIds=this.layers.filter((X=>X.isStateDependent())).map((X=>X.id)),this.sourceID=h.sourceID}createArrays(){this.text=new vI(new Ta(this.layers,this.zoom,(h=>h.startsWith(\"text\")))),this.icon=new vI(new Ta(this.layers,this.zoom,(h=>h.startsWith(\"icon\")))),this.glyphOffsetArray=new Fe,this.lineVertexArray=new Ie,this.symbolInstances=new Ae,this.textAnchorOffsets=new st}calculateGlyphDependencies(h,b,D,z,G){for(let X of h)if(b[X.codePointAt(0)]=!0,(D||z)&&G){let Z=m4[X];Z&&(b[Z.codePointAt(0)]=!0)}}populate(h,b,D){var z;let G=this.layers[0],X=G.layout,Z=X.get(\"text-font\"),re=X.get(\"text-field\"),Q=X.get(\"icon-image\"),he=(re.value.kind!==\"constant\"||re.value.value instanceof $a&&!re.value.value.isEmpty()||re.value.value.toString().length>0)&&(Z.value.kind!==\"constant\"||Z.value.value.length>0),oe=Q.value.kind!==\"constant\"||!!Q.value.value||Object.keys(Q.parameters).length>0,ke=X.get(\"symbol-sort-key\");if(this.features=[],!he&&!oe)return;let ge=b.iconDependencies,Ue=b.glyphDependencies,je=b.availableImages,vt=new mi(this.zoom);for(let{feature:gt,id:It,index:_t,sourceLayerIndex:qt}of h){let tr=G._featureFilter.needGeometry,lr=xa(gt,tr);if(!G._featureFilter.filter(vt,lr,D))continue;let qr,jr;if(tr||(lr.geometry=$l(gt)),he){let fn=G.getValueAndResolveTokens(\"text-field\",lr,D,je),Dn=$a.factory(fn);this.hasRTLText||(this.hasRTLText=git(Dn)),(!this.hasRTLText||Bi.getRTLTextPluginStatus()===\"unavailable\"||this.hasRTLText&&Bi.isParsed())&&(qr=Ynt(Dn,G,lr))}if(oe){let fn=G.getValueAndResolveTokens(\"icon-image\",lr,D,je);jr=fn instanceof so?fn:so.fromString(fn)}if(!qr&&!jr)continue;let _n=this.sortFeaturesByKey?ke.evaluate(lr,{},D):void 0;if(this.features.push({id:It,text:qr,icon:jr,index:_t,sourceLayerIndex:qt,geometry:lr.geometry,properties:gt.properties,type:h4.types[gt.type],sortKey:_n}),jr&&(ge[jr.name]=!0),qr){let fn=Z.evaluate(lr,{},D).join(\",\"),Dn=X.get(\"text-rotation-alignment\")!==\"viewport\"&&X.get(\"symbol-placement\")!==\"point\";this.allowVerticalPlacement=(z=this.writingModes)===null||z===void 0?void 0:z.includes(a.ax.vertical);for(let Hn of qr.sections)if(Hn.image)ge[Hn.image.name]=!0;else{let Ln=Zt(qr.toString()),un=Hn.fontStack||fn;Ue[un]||(Ue[un]={}),this.calculateGlyphDependencies(Hn.text,Ue[un],Dn,this.allowVerticalPlacement,Ln)}}}X.get(\"symbol-placement\")===\"line\"&&(this.features=(function(gt){let It={},_t={},qt=[],tr=0;function lr(fn){qt.push(gt[fn]),tr++}function qr(fn,Dn,Hn){let Ln=_t[fn];return delete _t[fn],_t[Dn]=Ln,qt[Ln].geometry[0].pop(),qt[Ln].geometry[0]=qt[Ln].geometry[0].concat(Hn[0]),Ln}function jr(fn,Dn,Hn){let Ln=It[Dn];return delete It[Dn],It[fn]=Ln,qt[Ln].geometry[0].shift(),qt[Ln].geometry[0]=Hn[0].concat(qt[Ln].geometry[0]),Ln}function _n(fn,Dn,Hn){let Ln=Hn?Dn[0][Dn[0].length-1]:Dn[0][0];return`${fn}:${Ln.x}:${Ln.y}`}for(let fn=0;fn<gt.length;fn++){let Dn=gt[fn],Hn=Dn.geometry,Ln=Dn.text?Dn.text.toString():null;if(!Ln){lr(fn);continue}let un=_n(Ln,Hn),ei=_n(Ln,Hn,!0);if(un in _t&&ei in It&&_t[un]!==It[ei]){let ci=jr(un,ei,Hn),ti=qr(un,ei,qt[ci].geometry);delete It[un],delete _t[ei],_t[_n(Ln,qt[ti].geometry,!0)]=ti,qt[ci].geometry=null}else un in _t?qr(un,ei,Hn):ei in It?jr(un,ei,Hn):(lr(fn),It[un]=tr-1,_t[ei]=tr-1)}return qt.filter((fn=>fn.geometry))})(this.features)),this.sortFeaturesByKey&&this.features.sort(((gt,It)=>gt.sortKey-It.sortKey))}update(h,b,D){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(h,b,this.layers,{imagePositions:D}),this.icon.programConfigurations.updatePaintArrays(h,b,this.layers,{imagePositions:D}))}isEmpty(){return this.symbolInstances.length===0&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(h){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(h),this.iconCollisionBox.upload(h)),this.text.upload(h,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(h,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(h,b){let D=this.lineVertexArray.length;if(h.segment!==void 0){let z=h.dist(b[h.segment+1]),G=h.dist(b[h.segment]),X={};for(let Z=h.segment+1;Z<b.length;Z++)X[Z]={x:b[Z].x,y:b[Z].y,tileUnitDistanceFromAnchor:z},Z<b.length-1&&(z+=b[Z+1].dist(b[Z]));for(let Z=h.segment||0;Z>=0;Z--)X[Z]={x:b[Z].x,y:b[Z].y,tileUnitDistanceFromAnchor:G},Z>0&&(G+=b[Z-1].dist(b[Z]));for(let Z=0;Z<b.length;Z++){let re=X[Z];this.lineVertexArray.emplaceBack(re.x,re.y,re.tileUnitDistanceFromAnchor)}}return{lineStartIndex:D,lineLength:this.lineVertexArray.length-D}}addSymbols(h,b,D,z,G,X,Z,re,Q,he,oe,ke){let ge=h.indexArray,Ue=h.layoutVertexArray,je=h.segments.prepareSegment(4*b.length,Ue,ge,this.canOverlap?X.sortKey:void 0),vt=this.glyphOffsetArray.length,gt=je.vertexLength,It=this.allowVerticalPlacement&&Z===a.ax.vertical?Math.PI/2:0,_t=X.text&&X.text.sections;for(let qt=0;qt<b.length;qt++){let{tl:tr,tr:lr,bl:qr,br:jr,tex:_n,pixelOffsetTL:fn,pixelOffsetBR:Dn,minFontScaleX:Hn,minFontScaleY:Ln,glyphOffset:un,isSDF:ei,sectionIndex:ci}=b[qt],ti=je.vertexLength,di=un[1];qS(Ue,re.x,re.y,tr.x,di+tr.y,_n.x,_n.y,D,ei,fn.x,fn.y,Hn,Ln),qS(Ue,re.x,re.y,lr.x,di+lr.y,_n.x+_n.w,_n.y,D,ei,Dn.x,fn.y,Hn,Ln),qS(Ue,re.x,re.y,qr.x,di+qr.y,_n.x,_n.y+_n.h,D,ei,fn.x,Dn.y,Hn,Ln),qS(Ue,re.x,re.y,jr.x,di+jr.y,_n.x+_n.w,_n.y+_n.h,D,ei,Dn.x,Dn.y,Hn,Ln),dI(h.dynamicLayoutVertexArray,re,It),ge.emplaceBack(ti,ti+2,ti+1),ge.emplaceBack(ti+1,ti+2,ti+3),je.vertexLength+=4,je.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(un[0]),qt!==b.length-1&&ci===b[qt+1].sectionIndex||h.programConfigurations.populatePaintArrays(Ue.length,X,X.index,{imagePositions:{},canonical:ke,formattedSection:_t==null?void 0:_t[ci]})}h.placedSymbolArray.emplaceBack(re.x,re.y,vt,this.glyphOffsetArray.length-vt,gt,Q,he,re.segment,D?D[0]:0,D?D[1]:0,z[0],z[1],Z,0,!1,0,oe)}_addCollisionDebugVertex(h,b,D,z,G,X){return b.emplaceBack(0,0),h.emplaceBack(D.x,D.y,z,G,Math.round(X.x),Math.round(X.y))}addCollisionDebugVertices(h,b,D,z,G,X,Z){let re=G.segments.prepareSegment(4,G.layoutVertexArray,G.indexArray),Q=re.vertexLength,he=G.layoutVertexArray,oe=G.collisionVertexArray,ke=Z.anchorX,ge=Z.anchorY;this._addCollisionDebugVertex(he,oe,X,ke,ge,new o(h,b)),this._addCollisionDebugVertex(he,oe,X,ke,ge,new o(D,b)),this._addCollisionDebugVertex(he,oe,X,ke,ge,new o(D,z)),this._addCollisionDebugVertex(he,oe,X,ke,ge,new o(h,z)),re.vertexLength+=4;let Ue=G.indexArray;Ue.emplaceBack(Q,Q+1),Ue.emplaceBack(Q+1,Q+2),Ue.emplaceBack(Q+2,Q+3),Ue.emplaceBack(Q+3,Q),re.primitiveLength+=4}addDebugCollisionBoxes(h,b,D,z){for(let G=h;G<b;G++){let X=this.collisionBoxArray.get(G);this.addCollisionDebugVertices(X.x1,X.y1,X.x2,X.y2,z?this.textCollisionBox:this.iconCollisionBox,X.anchorPoint,D)}}generateCollisionDebugBuffers(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new pI(Or,KQ.members,Tr),this.iconCollisionBox=new pI(Or,KQ.members,Tr);for(let h=0;h<this.symbolInstances.length;h++){let b=this.symbolInstances.get(h);this.addDebugCollisionBoxes(b.textBoxStartIndex,b.textBoxEndIndex,b,!0),this.addDebugCollisionBoxes(b.verticalTextBoxStartIndex,b.verticalTextBoxEndIndex,b,!0),this.addDebugCollisionBoxes(b.iconBoxStartIndex,b.iconBoxEndIndex,b,!1),this.addDebugCollisionBoxes(b.verticalIconBoxStartIndex,b.verticalIconBoxEndIndex,b,!1)}}_deserializeCollisionBoxesForSymbol(h,b,D,z,G,X,Z,re,Q){let he={};for(let oe=b;oe<D;oe++){let ke=h.get(oe);he.textBox={x1:ke.x1,y1:ke.y1,x2:ke.x2,y2:ke.y2,anchorPointX:ke.anchorPointX,anchorPointY:ke.anchorPointY},he.textFeatureIndex=ke.featureIndex;break}for(let oe=z;oe<G;oe++){let ke=h.get(oe);he.verticalTextBox={x1:ke.x1,y1:ke.y1,x2:ke.x2,y2:ke.y2,anchorPointX:ke.anchorPointX,anchorPointY:ke.anchorPointY},he.verticalTextFeatureIndex=ke.featureIndex;break}for(let oe=X;oe<Z;oe++){let ke=h.get(oe);he.iconBox={x1:ke.x1,y1:ke.y1,x2:ke.x2,y2:ke.y2,anchorPointX:ke.anchorPointX,anchorPointY:ke.anchorPointY},he.iconFeatureIndex=ke.featureIndex;break}for(let oe=re;oe<Q;oe++){let ke=h.get(oe);he.verticalIconBox={x1:ke.x1,y1:ke.y1,x2:ke.x2,y2:ke.y2,anchorPointX:ke.anchorPointX,anchorPointY:ke.anchorPointY},he.verticalIconFeatureIndex=ke.featureIndex;break}return he}deserializeCollisionBoxes(h){this.collisionArrays=[];for(let b=0;b<this.symbolInstances.length;b++){let D=this.symbolInstances.get(b);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(h,D.textBoxStartIndex,D.textBoxEndIndex,D.verticalTextBoxStartIndex,D.verticalTextBoxEndIndex,D.iconBoxStartIndex,D.iconBoxEndIndex,D.verticalIconBoxStartIndex,D.verticalIconBoxEndIndex))}}hasTextData(){return this.text.segments.get().length>0}hasIconData(){return this.icon.segments.get().length>0}hasDebugData(){return this.textCollisionBox&&this.iconCollisionBox}hasTextCollisionBoxData(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0}hasIconCollisionBoxData(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0}addIndicesForPlacedSymbol(h,b){let D=h.placedSymbolArray.get(b),z=D.vertexStartIndex+4*D.numGlyphs;for(let G=D.vertexStartIndex;G<z;G+=4)h.indexArray.emplaceBack(G,G+2,G+1),h.indexArray.emplaceBack(G+1,G+2,G+3)}getSortedSymbolIndexes(h){if(this.sortedAngle===h&&this.symbolInstanceIndexes!==void 0)return this.symbolInstanceIndexes;let b=Math.sin(h),D=Math.cos(h),z=[],G=[],X=[];for(let Z=0;Z<this.symbolInstances.length;++Z){X.push(Z);let re=this.symbolInstances.get(Z);z.push(0|Math.round(b*re.anchorX+D*re.anchorY)),G.push(re.featureIndex)}return X.sort(((Z,re)=>z[Z]-z[re]||G[re]-G[Z])),X}addToSortKeyRanges(h,b){let D=this.sortKeyRanges[this.sortKeyRanges.length-1];(D==null?void 0:D.sortKey)===b?D.symbolInstanceEnd=h+1:this.sortKeyRanges.push({sortKey:b,symbolInstanceStart:h,symbolInstanceEnd:h+1})}sortFeatures(h){if(this.sortFeaturesByY&&this.sortedAngle!==h&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(h),this.sortedAngle=h,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(let b of this.symbolInstanceIndexes){let D=this.symbolInstances.get(b);this.featureSortOrder.push(D.featureIndex);let z=[D.rightJustifiedTextSymbolIndex,D.centerJustifiedTextSymbolIndex,D.leftJustifiedTextSymbolIndex];for(let G=0;G<z.length;G++){let X=z[G];X>=0&&z.indexOf(X)===G&&this.addIndicesForPlacedSymbol(this.text,X)}D.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,D.verticalPlacedTextSymbolIndex),D.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,D.placedIconSymbolIndex),D.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,D.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let fee,hee;_i(\"SymbolBucket\",X_,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),X_.MAX_GLYPHS=65535,X_.addDynamicAttributes=dI;var gI={get paint(){return hee=hee||new ms({\"icon-opacity\":new Di(Ct.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Di(Ct.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Di(Ct.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Di(Ct.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Di(Ct.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Ct.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Ct.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Di(Ct.paint_symbol[\"text-opacity\"]),\"text-color\":new Di(Ct.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:M=>M.textColor,hasOverride:M=>!!M.textColor}),\"text-halo-color\":new Di(Ct.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Di(Ct.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Di(Ct.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Ct.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Ct.paint_symbol[\"text-translate-anchor\"])})},get layout(){return fee=fee||new ms({\"symbol-placement\":new wi(Ct.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Ct.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Ct.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Di(Ct.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Ct.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Ct.layout_symbol[\"icon-allow-overlap\"]),\"icon-overlap\":new wi(Ct.layout_symbol[\"icon-overlap\"]),\"icon-ignore-placement\":new wi(Ct.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Ct.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Ct.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Di(Ct.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Ct.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Ct.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Di(Ct.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Di(Ct.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new Di(Ct.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Ct.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Di(Ct.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Di(Ct.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Ct.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Ct.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Ct.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Di(Ct.layout_symbol[\"text-field\"]),\"text-font\":new Di(Ct.layout_symbol[\"text-font\"]),\"text-size\":new Di(Ct.layout_symbol[\"text-size\"]),\"text-max-width\":new Di(Ct.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Ct.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Di(Ct.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Di(Ct.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Di(Ct.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Ct.layout_symbol[\"text-variable-anchor\"]),\"text-variable-anchor-offset\":new Di(Ct.layout_symbol[\"text-variable-anchor-offset\"]),\"text-anchor\":new Di(Ct.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Ct.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Ct.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Di(Ct.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Ct.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Ct.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Di(Ct.layout_symbol[\"text-transform\"]),\"text-offset\":new Di(Ct.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Ct.layout_symbol[\"text-allow-overlap\"]),\"text-overlap\":new wi(Ct.layout_symbol[\"text-overlap\"]),\"text-ignore-placement\":new wi(Ct.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Ct.layout_symbol[\"text-optional\"])})}};class dee{constructor(h){if(h.property.overrides===void 0)throw new Error(\"overrides must be provided to instantiate FormatSectionOverride class\");this.type=h.property.overrides?h.property.overrides.runtimeType:Dt,this.defaultValue=h}evaluate(h){if(h.formattedSection){let b=this.defaultValue.property.overrides;if(b!=null&&b.hasOverride(h.formattedSection))return b.getOverride(h.formattedSection)}return h.feature&&h.featureState?this.defaultValue.evaluate(h.feature,h.featureState):this.defaultValue.property.specification.default}eachChild(h){this.defaultValue.isConstant()||h(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}_i(\"FormatSectionOverride\",dee,{omit:[\"defaultValue\"]});class BS extends hl{constructor(h,b){super(h,gI,b)}recalculate(h,b){if(super.recalculate(h,b),this.layout.get(\"icon-rotation-alignment\")===\"auto\"&&(this.layout._values[\"icon-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-rotation-alignment\")===\"auto\"&&(this.layout._values[\"text-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-pitch-alignment\")===\"auto\"&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")===\"map\"?\"map\":\"viewport\"),this.layout.get(\"icon-pitch-alignment\")===\"auto\"&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),this.layout.get(\"symbol-placement\")===\"point\"){let D=this.layout.get(\"text-writing-mode\");if(D){let z=[];for(let G of D)z.includes(G)||z.push(G);this.layout._values[\"text-writing-mode\"]=z}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()}getValueAndResolveTokens(h,b,D,z){let G=this.layout.get(h).evaluate(b,{},D,z),X=this._unevaluatedLayout._values[h];return X.isDataDriven()||If(X.value)||!G?G:(function(Z,re){return re.replace(/{([^{}]+)}/g,((Q,he)=>Z&&he in Z?String(Z[he]):\"\"))})(b.properties,G)}createBucket(h){return new X_(h)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error(\"Should take a different path in FeatureIndex\")}_setPaintOverrides(){for(let h of gI.paint.overridableProperties){if(!BS.hasPaintOverride(this.layout,h))continue;let b=this.paint.get(h),D=new dee(b),z=new Wl(D,b.property.specification),G=null;G=b.value.kind===\"constant\"||b.value.kind===\"source\"?new zf(\"source\",z):new qf(\"composite\",z,b.value.zoomStops),this.paint._values[h]=new el(b.property,G,b.parameters)}}_handleOverridablePaintPropertyUpdate(h,b,D){return!(!this.layout||b.isDataDriven()||D.isDataDriven())&&BS.hasPaintOverride(this.layout,h)}static hasPaintOverride(h,b){let D=h.get(\"text-field\"),z=gI.paint.properties[b],G=!1,X=Z=>{var re;for(let Q of Z)if(!((re=z.overrides)===null||re===void 0)&&re.hasOverride(Q))return void(G=!0)};if(D.value.kind===\"constant\"&&D.value.value instanceof $a)X(D.value.value.sections);else if(D.value.kind===\"source\"||D.value.kind===\"composite\"){let Z=Q=>{G||(Q instanceof Ms&&la(Q.value)===ri?X(Q.value.sections):Q instanceof Yo?X(Q.sections):Q.eachChild(Z))},re=D.value;re._styleExpression&&Z(re._styleExpression.expression)}return G}}let vee;var mit={get paint(){return vee=vee||new ms({\"background-color\":new wi(Ct.paint_background[\"background-color\"]),\"background-pattern\":new Fc(Ct.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Ct.paint_background[\"background-opacity\"])})}};class yit extends hl{constructor(h,b){super(h,mit,b)}}class xit extends hl{constructor(h,b){super(h,{},b),this.onAdd=D=>{this.implementation.onAdd&&this.implementation.onAdd(D,D.painter.context.gl)},this.onRemove=D=>{this.implementation.onRemove&&this.implementation.onRemove(D,D.painter.context.gl)},this.implementation=h}is3D(){return this.implementation.renderingMode===\"3d\"}hasOffscreenPass(){return this.implementation.prerender!==void 0}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error(\"Custom layers cannot be serialized\")}}class _it{constructor(h){this._methodToThrottle=h,this._triggered=!1,this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._methodToThrottle()}}trigger(){var h;this._triggered||(this._triggered=!0,(h=this._channel)===null||h===void 0||h.port1.postMessage(!0))}remove(){delete this._channel,this._methodToThrottle=()=>{}}}let bit={once:!0},mI=63710088e-1;class Xg{constructor(h,b){if(isNaN(h)||isNaN(b))throw new Error(`Invalid LngLat object: (${h}, ${b})`);if(this.lng=+h,this.lat=+b,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}wrap(){return new Xg(K(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(h){let b=Math.PI/180,D=this.lat*b,z=h.lat*b,G=Math.sin(D)*Math.sin(z)+Math.cos(D)*Math.cos(z)*Math.cos((h.lng-this.lng)*b);return mI*Math.acos(Math.min(G,1))}static convert(h){if(h instanceof Xg)return h;if(Array.isArray(h)&&(h.length===2||h.length===3))return new Xg(Number(h[0]),Number(h[1]));if(!Array.isArray(h)&&typeof h==\"object\"&&h!==null)return new Xg(Number(\"lng\"in h?h.lng:h.lon),Number(h.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")}}let pee=2*Math.PI*mI;function gee(M){return pee*Math.cos(M*Math.PI/180)}function mee(M){return(180+M)/360}function yee(M){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+M*Math.PI/360)))/360}function xee(M,h){return M/gee(h)}function _ee(M){return 360*M-180}function OS(M){return 360/Math.PI*Math.atan(Math.exp((180-360*M)*Math.PI/180))-90}function bee(M,h){return M*gee(OS(h))}class y4{constructor(h,b,D=0){this.x=+h,this.y=+b,this.z=+D}static fromLngLat(h,b=0){let D=Xg.convert(h);return new y4(mee(D.lng),yee(D.lat),xee(b,D.lat))}toLngLat(){return new Xg(_ee(this.x),OS(this.y))}toAltitude(){return bee(this.z,this.y)}meterInMercatorCoordinateUnits(){return 1/pee*(h=OS(this.y),1/Math.cos(h*Math.PI/180));var h}}function wee(M,h,b){var D=2*Math.PI*6378137/256/Math.pow(2,b);return[M*D-2*Math.PI*6378137/2,h*D-2*Math.PI*6378137/2]}class yI{constructor(h,b,D){if(!(function(z,G,X){return!(z<0||z>25||X<0||X>=Math.pow(2,z)||G<0||G>=Math.pow(2,z))})(h,b,D))throw new Error(`x=${b}, y=${D}, z=${h} outside of bounds. 0<=x<${Math.pow(2,h)}, 0<=y<${Math.pow(2,h)} 0<=z<=25 `);this.z=h,this.x=b,this.y=D,this.key=Z_(0,h,h,b,D)}equals(h){return this.z===h.z&&this.x===h.x&&this.y===h.y}url(h,b,D){let z=(X=this.y,Z=this.z,re=wee(256*(G=this.x),256*(X=Math.pow(2,Z)-X-1),Z),Q=wee(256*(G+1),256*(X+1),Z),re[0]+\",\"+re[1]+\",\"+Q[0]+\",\"+Q[1]);var G,X,Z,re,Q;let he=(function(oe,ke,ge){let Ue=\"\";for(let je=oe;je>0;je--){let vt=1<<je-1;Ue+=(ke&vt?1:0)+(ge&vt?2:0)}return Ue})(this.z,this.x,this.y);return h[(this.x+this.y)%h.length].replace(/{prefix}/g,(this.x%16).toString(16)+(this.y%16).toString(16)).replace(/{z}/g,String(this.z)).replace(/{x}/g,String(this.x)).replace(/{y}/g,String(D===\"tms\"?Math.pow(2,this.z)-this.y-1:this.y)).replace(/{ratio}/g,b>1?\"@2x\":\"\").replace(/{quadkey}/g,he).replace(/{bbox-epsg-3857}/g,z)}isChildOf(h){let b=this.z-h.z;return b>0&&h.x===this.x>>b&&h.y===this.y>>b}getTilePoint(h){let b=Math.pow(2,this.z);return new o((h.x*b-this.x)*j,(h.y*b-this.y)*j)}toString(){return`${this.z}/${this.x}/${this.y}`}}class Tee{constructor(h,b){this.wrap=h,this.canonical=b,this.key=Z_(h,b.z,b.z,b.x,b.y)}}class Yd{constructor(h,b,D,z,G){if(this.terrainRttPosMatrix32f=null,h<D)throw new Error(`overscaledZ should be >= z; overscaledZ = ${h}; z = ${D}`);this.overscaledZ=h,this.wrap=b,this.canonical=new yI(D,+z,+G),this.key=Z_(b,h,D,z,G)}clone(){return new Yd(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(h){return this.overscaledZ===h.overscaledZ&&this.wrap===h.wrap&&this.canonical.equals(h.canonical)}scaledTo(h){if(h>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${h}; overscaledZ = ${this.overscaledZ}`);let b=this.canonical.z-h;return h>this.canonical.z?new Yd(h,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Yd(h,this.wrap,h,this.canonical.x>>b,this.canonical.y>>b)}isOverscaled(){return this.overscaledZ>this.canonical.z}calculateScaledKey(h,b){if(h>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${h}; overscaledZ = ${this.overscaledZ}`);let D=this.canonical.z-h;return h>this.canonical.z?Z_(this.wrap*+b,h,this.canonical.z,this.canonical.x,this.canonical.y):Z_(this.wrap*+b,h,h,this.canonical.x>>D,this.canonical.y>>D)}isChildOf(h){if(h.wrap!==this.wrap||this.overscaledZ-h.overscaledZ<=0)return!1;if(h.overscaledZ===0)return this.overscaledZ>0;let b=this.canonical.z-h.canonical.z;return!(b<0)&&h.canonical.x===this.canonical.x>>b&&h.canonical.y===this.canonical.y>>b}children(h){if(this.overscaledZ>=h)return[new Yd(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];let b=this.canonical.z+1,D=2*this.canonical.x,z=2*this.canonical.y;return[new Yd(b,this.wrap,b,D,z),new Yd(b,this.wrap,b,D+1,z),new Yd(b,this.wrap,b,D,z+1),new Yd(b,this.wrap,b,D+1,z+1)]}isLessThan(h){return this.wrap<h.wrap||!(this.wrap>h.wrap)&&(this.overscaledZ<h.overscaledZ||!(this.overscaledZ>h.overscaledZ)&&(this.canonical.x<h.canonical.x||!(this.canonical.x>h.canonical.x)&&this.canonical.y<h.canonical.y))}wrapped(){return new Yd(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)}unwrapTo(h){return new Yd(this.overscaledZ,h,this.canonical.z,this.canonical.x,this.canonical.y)}overscaleFactor(){return Math.pow(2,this.overscaledZ-this.canonical.z)}toUnwrapped(){return new Tee(this.wrap,this.canonical)}toString(){return`${this.overscaledZ}/${this.canonical.x}/${this.canonical.y}`}getTilePoint(h){return this.canonical.getTilePoint(new y4(h.x-this.wrap,h.y))}normalizeCoordinates(h,b,D=8192){if(h>=0&&h<D&&b>=0&&b<D)return{tileID:this,x:h,y:b};let z=Math.floor(h/D),G=Math.floor(b/D),X=h-z*D,Z=b-G*D,re=this.canonical.z,Q=1<<re,he=this.canonical.y+G;if(he<0||he>=Q)return null;let oe=this.canonical.x+z,ke=this.wrap;return oe<0?(ke-=Math.ceil(-oe/Q),oe=(oe%Q+Q)%Q):oe>=Q&&(ke+=Math.floor(oe/Q),oe%=Q),{tileID:new Yd(this.overscaledZ,ke,re,oe,he),x:X,y:Z}}}function Z_(M,h,b,D,z){(M*=2)<0&&(M=-1*M-1);let G=1<<b;return(G*G*M+G*z+D).toString(36)+b.toString(36)+h.toString(36)}_i(\"CanonicalTileID\",yI),_i(\"OverscaledTileID\",Yd,{omit:[\"terrainRttPosMatrix32f\"]});class ix{constructor(){this.minX=1/0,this.maxX=-1/0,this.minY=1/0,this.maxY=-1/0}extend(h){return this.minX=Math.min(this.minX,h.x),this.minY=Math.min(this.minY,h.y),this.maxX=Math.max(this.maxX,h.x),this.maxY=Math.max(this.maxY,h.y),this}expandBy(h){return this.minX-=h,this.minY-=h,this.maxX+=h,this.maxY+=h,(this.minX>this.maxX||this.minY>this.maxY)&&(this.minX=1/0,this.maxX=-1/0,this.minY=1/0,this.maxY=-1/0),this}shrinkBy(h){return this.expandBy(-h)}map(h){let b=new ix;return b.extend(h(new o(this.minX,this.minY))),b.extend(h(new o(this.maxX,this.minY))),b.extend(h(new o(this.minX,this.maxY))),b.extend(h(new o(this.maxX,this.maxY))),b}static fromPoints(h){let b=new ix;for(let D of h)b.extend(D);return b}contains(h){return h.x>=this.minX&&h.x<=this.maxX&&h.y>=this.minY&&h.y<=this.maxY}empty(){return this.minX>this.maxX}width(){return this.maxX-this.minX}height(){return this.maxY-this.minY}covers(h){return!this.empty()&&!h.empty()&&h.minX>=this.minX&&h.maxX<=this.maxX&&h.minY>=this.minY&&h.maxY<=this.maxY}intersects(h){return!this.empty()&&!h.empty()&&h.minX<=this.maxX&&h.maxX>=this.minX&&h.minY<=this.maxY&&h.maxY>=this.minY}}class Aee{constructor(h){this._stringToNumber={},this._numberToString=[];for(let b=0;b<h.length;b++){let D=h[b];this._stringToNumber[D]=b,this._numberToString[b]=D}}encode(h){return this._stringToNumber[h]}decode(h){if(h>=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${h} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[h]}}class Mee{constructor(h,b,D,z,G){this.type=\"Feature\",this._vectorTileFeature=h,this._x=D,this._y=z,this._z=b,this.properties=h.properties,this.id=G}projectPoint(h,b,D,z){return[360*(h.x+b)/z-180,360/Math.PI*Math.atan(Math.exp((1-2*(h.y+D)/z)*Math.PI))-90]}projectLine(h,b,D,z){return h.map((G=>this.projectPoint(G,b,D,z)))}get geometry(){if(this._geometry)return this._geometry;let h=this._vectorTileFeature,b=h.extent*Math.pow(2,this._z),D=h.extent*this._x,z=h.extent*this._y,G=h.loadGeometry();switch(h.type){case 1:{let X=[];for(let re of G)X.push(re[0]);let Z=this.projectLine(X,D,z,b);this._geometry=X.length===1?{type:\"Point\",coordinates:Z[0]}:{type:\"MultiPoint\",coordinates:Z};break}case 2:{let X=G.map((Z=>this.projectLine(Z,D,z,b)));this._geometry=X.length===1?{type:\"LineString\",coordinates:X[0]}:{type:\"MultiLineString\",coordinates:X};break}case 3:{let X=kQ(G),Z=[];for(let re of X)Z.push(re.map((Q=>this.projectLine(Q,D,z,b))));this._geometry=Z.length===1?{type:\"Polygon\",coordinates:Z[0]}:{type:\"MultiPolygon\",coordinates:Z};break}default:throw new Error(`unknown feature type: ${h.type}`)}return this._geometry}set geometry(h){this._geometry=h}toJSON(){let h={geometry:this.geometry};for(let b in this)b!==\"_geometry\"&&b!==\"_vectorTileFeature\"&&b!==\"_x\"&&b!==\"_y\"&&b!==\"_z\"&&(h[b]=this[b]);return h}}class $_{constructor(h,b,D){this._name=h,this.dataBuffer=b,typeof D==\"number\"?this._size=D:(this.nullabilityBuffer=D,this._size=D.size())}getValue(h){return this.nullabilityBuffer&&!this.nullabilityBuffer.get(h)?null:this.getValueFromBuffer(h)}has(h){var b;return((b=this.nullabilityBuffer)==null?void 0:b.get(h))||!this.nullabilityBuffer}get name(){return this._name}get size(){return this._size}}class NS extends $_{}class xI extends NS{getValueFromBuffer(h){return this.dataBuffer[h]}}class _I extends 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ke=G[he++]>>>0,ge=G[he++]>>>0,Ue=G[he++]>>>0,je=G[he++]>>>0;Z[Q++]=ke>>>0&15,Z[Q++]=ke>>>4&15,Z[Q++]=ke>>>8&15,Z[Q++]=ke>>>12&15,Z[Q++]=ke>>>16&15,Z[Q++]=ke>>>20&15,Z[Q++]=ke>>>24&15,Z[Q++]=ke>>>28&15,Z[Q++]=ge>>>0&15,Z[Q++]=ge>>>4&15,Z[Q++]=ge>>>8&15,Z[Q++]=ge>>>12&15,Z[Q++]=ge>>>16&15,Z[Q++]=ge>>>20&15,Z[Q++]=ge>>>24&15,Z[Q++]=ge>>>28&15,Z[Q++]=Ue>>>0&15,Z[Q++]=Ue>>>4&15,Z[Q++]=Ue>>>8&15,Z[Q++]=Ue>>>12&15,Z[Q++]=Ue>>>16&15,Z[Q++]=Ue>>>20&15,Z[Q++]=Ue>>>24&15,Z[Q++]=Ue>>>28&15,Z[Q++]=je>>>0&15,Z[Q++]=je>>>4&15,Z[Q++]=je>>>8&15,Z[Q++]=je>>>12&15,Z[Q++]=je>>>16&15,Z[Q++]=je>>>20&15,Z[Q++]=je>>>24&15,Z[Q++]=je>>>28&15}})(M,h,b,D);break;case 5:(function(G,X,Z,re){let Q=re,he=X;for(let oe=0;oe<8;oe++){let ke=G[he++]>>>0,ge=G[he++]>>>0,Ue=G[he++]>>>0,je=G[he++]>>>0,vt=G[he++]>>>0;Z[Q++]=ke>>>0&31,Z[Q++]=ke>>>5&31,Z[Q++]=ke>>>10&31,Z[Q++]=ke>>>15&31,Z[Q++]=ke>>>20&31,Z[Q++]=ke>>>25&31,Z[Q++]=31&(ke>>>30|(7&ge)<<2),Z[Q++]=ge>>>3&31,Z[Q++]=ge>>>8&31,Z[Q++]=ge>>>13&31,Z[Q++]=ge>>>18&31,Z[Q++]=ge>>>23&31,Z[Q++]=31&(ge>>>28|(1&Ue)<<4),Z[Q++]=Ue>>>1&31,Z[Q++]=Ue>>>6&31,Z[Q++]=Ue>>>11&31,Z[Q++]=Ue>>>16&31,Z[Q++]=Ue>>>21&31,Z[Q++]=Ue>>>26&31,Z[Q++]=31&(Ue>>>31|(15&je)<<1),Z[Q++]=je>>>4&31,Z[Q++]=je>>>9&31,Z[Q++]=je>>>14&31,Z[Q++]=je>>>19&31,Z[Q++]=je>>>24&31,Z[Q++]=31&(je>>>29|(3&vt)<<3),Z[Q++]=vt>>>2&31,Z[Q++]=vt>>>7&31,Z[Q++]=vt>>>12&31,Z[Q++]=vt>>>17&31,Z[Q++]=vt>>>22&31,Z[Q++]=vt>>>27&31}})(M,h,b,D);break;case 6:(function(G,X,Z,re){let Q=re,he=X;for(let oe=0;oe<8;oe++){let ke=G[he++]>>>0,ge=G[he++]>>>0,Ue=G[he++]>>>0,je=G[he++]>>>0,vt=G[he++]>>>0,gt=G[he++]>>>0;Z[Q++]=ke>>>0&63,Z[Q++]=ke>>>6&63,Z[Q++]=ke>>>12&63,Z[Q++]=ke>>>18&63,Z[Q++]=ke>>>24&63,Z[Q++]=63&(ke>>>30|(15&ge)<<2),Z[Q++]=ge>>>4&63,Z[Q++]=ge>>>10&63,Z[Q++]=ge>>>16&63,Z[Q++]=ge>>>22&63,Z[Q++]=63&(ge>>>28|(3&Ue)<<4),Z[Q++]=Ue>>>2&63,Z[Q++]=Ue>>>8&63,Z[Q++]=Ue>>>14&63,Z[Q++]=Ue>>>20&63,Z[Q++]=Ue>>>26&63,Z[Q++]=je>>>0&63,Z[Q++]=je>>>6&63,Z[Q++]=je>>>12&63,Z[Q++]=je>>>18&63,Z[Q++]=je>>>24&63,Z[Q++]=63&(je>>>30|(15&vt)<<2),Z[Q++]=vt>>>4&63,Z[Q++]=vt>>>10&63,Z[Q++]=vt>>>16&63,Z[Q++]=vt>>>22&63,Z[Q++]=63&(vt>>>28|(3&gt)<<4),Z[Q++]=gt>>>2&63,Z[Q++]=gt>>>8&63,Z[Q++]=gt>>>14&63,Z[Q++]=gt>>>20&63,Z[Q++]=gt>>>26&63}})(M,h,b,D);break;case 7:(function(G,X,Z,re){let Q=re,he=X;for(let oe=0;oe<8;oe++){let ke=G[he++]>>>0,ge=G[he++]>>>0,Ue=G[he++]>>>0,je=G[he++]>>>0,vt=G[he++]>>>0,gt=G[he++]>>>0,It=G[he++]>>>0;Z[Q++]=ke>>>0&127,Z[Q++]=ke>>>7&127,Z[Q++]=ke>>>14&127,Z[Q++]=ke>>>21&127,Z[Q++]=127&(ke>>>28|(7&ge)<<4),Z[Q++]=ge>>>3&127,Z[Q++]=ge>>>10&127,Z[Q++]=ge>>>17&127,Z[Q++]=ge>>>24&127,Z[Q++]=127&(ge>>>31|(63&Ue)<<1),Z[Q++]=Ue>>>6&127,Z[Q++]=Ue>>>13&127,Z[Q++]=Ue>>>20&127,Z[Q++]=127&(Ue>>>27|(3&je)<<5),Z[Q++]=je>>>2&127,Z[Q++]=je>>>9&127,Z[Q++]=je>>>16&127,Z[Q++]=je>>>23&127,Z[Q++]=127&(je>>>30|(31&vt)<<2),Z[Q++]=vt>>>5&127,Z[Q++]=vt>>>12&127,Z[Q++]=vt>>>19&127,Z[Q++]=127&(vt>>>26|(1&gt)<<6),Z[Q++]=gt>>>1&127,Z[Q++]=gt>>>8&127,Z[Q++]=gt>>>15&127,Z[Q++]=gt>>>22&127,Z[Q++]=127&(gt>>>29|(15&It)<<3),Z[Q++]=It>>>4&127,Z[Q++]=It>>>11&127,Z[Q++]=It>>>18&127,Z[Q++]=It>>>25&127}})(M,h,b,D);break;case 8:(function(G,X,Z,re){let Q=re,he=X;for(let oe=0;oe<8;oe++){let ke=G[he++]>>>0,ge=G[he++]>>>0,Ue=G[he++]>>>0,je=G[he++]>>>0,vt=G[he++]>>>0,gt=G[he++]>>>0,It=G[he++]>>>0,_t=G[he++]>>>0;Z[Q++]=ke>>>0&255,Z[Q++]=ke>>>8&255,Z[Q++]=ke>>>16&255,Z[Q++]=ke>>>24&255,Z[Q++]=ge>>>0&255,Z[Q++]=ge>>>8&255,Z[Q++]=ge>>>16&255,Z[Q++]=ge>>>24&255,Z[Q++]=Ue>>>0&255,Z[Q++]=Ue>>>8&255,Z[Q++]=Ue>>>16&255,Z[Q++]=Ue>>>24&255,Z[Q++]=je>>>0&255,Z[Q++]=je>>>8&255,Z[Q++]=je>>>16&255,Z[Q++]=je>>>24&255,Z[Q++]=vt>>>0&255,Z[Q++]=vt>>>8&255,Z[Q++]=vt>>>16&255,Z[Q++]=vt>>>24&255,Z[Q++]=gt>>>0&255,Z[Q++]=gt>>>8&255,Z[Q++]=gt>>>16&255,Z[Q++]=gt>>>24&255,Z[Q++]=It>>>0&255,Z[Q++]=It>>>8&255,Z[Q++]=It>>>16&255,Z[Q++]=It>>>24&255,Z[Q++]=_t>>>0&255,Z[Q++]=_t>>>8&255,Z[Q++]=_t>>>16&255,Z[Q++]=_t>>>24&255}})(M,h,b,D);break;case 16:(function(G,X,Z,re){let Q=re,he=X;for(let oe=0;oe<128;oe++){let ke=G[he++]>>>0;Z[Q++]=65535&ke,Z[Q++]=ke>>>16&65535}})(M,h,b,D);break;default:(function(G,X,Z,re,Q){let he=AI[Q]>>>0,oe=X,ke=0,ge=G[oe]>>>0,Ue=re;for(let je=0;je<8;je++){for(let vt=0;vt<32;vt++)if(ke+Q<=32)Z[Ue+vt]=ge>>>ke&he,ke+=Q,ke===32&&(ke=0,oe++,vt!==31&&(ge=G[oe]>>>0));else{let gt=32-ke,It=ge>>>ke;oe++,ge=G[oe]>>>0;let _t=Q-gt;Z[Ue+vt]=(It|(ge&-1>>>32-_t>>>0)<<gt)&he,ke=_t}Ue+=32,ke=0,je<7&&(ge=G[oe]>>>0)}})(M,h,b,D,z)}return h+(z<<3)|0}function Sit(M,h,b,D){if(b+2>h)throw new Error(`FastPFOR decode: byteContainer underflow at block=${D} (need 2 bytes for [bitWidth, exceptionCount], bytePos=${b}, byteSize=${h})`);let z=M[b++],G=M[b++];if(z>32)throw new Error(`FastPFOR decode: invalid bitWidth=${z} at block=${D} (expected 0..32). This likely indicates corrupted or truncated input.`);return{bitWidth:z,exceptionCount:G,bytePosIn:b}}function Eit(M,h,b,D,z,G,X,Z,re){let{maxBits:Q,exceptionBitWidth:he,bytePosIn:oe}=(function(vt,gt,It,_t,qt,tr){if(It+1>gt)throw new Error(`FastPFOR decode: exception header underflow at block=${tr} (need 1 byte for maxBits, bytePos=${It}, byteSize=${gt})`);let lr=vt[It++];if(lr<_t||lr>32)throw new Error(`FastPFOR decode: invalid maxBits=${lr} at block=${tr} (bitWidth=${_t}, expected ${_t}..32)`);let qr=lr-_t|0;if(qr<1||qr>32)throw new Error(`FastPFOR decode: invalid exceptionBitWidth=${qr} at block=${tr} (bitWidth=${_t}, maxBits=${lr})`);if(It+qt>gt)throw new Error(`FastPFOR decode: exception positions underflow at block=${tr} (need=${qt}, have=${gt-It})`);return{maxBits:lr,exceptionBitWidth:qr,bytePosIn:It}})(z,G,X,b,D,re);if(X=oe,he===1){let vt=1<<b;for(let gt=0;gt<D;gt=gt+1|0)M[z[X++]+h|0]|=vt;return X}let ke=Z.dataToBePacked[he];if(!ke)throw new Error(`FastPFOR decode: missing exception stream for exceptionBitWidth=${he} (bitWidth=${b}, maxBits=${Q}) at block ${re}`);let ge=Z.dataPointers,Ue=0|ge[he],je=0|Z.exceptionSizes[he];if(Ue+D>je)throw new Error(`FastPFOR decode: exception stream overflow for exceptionBitWidth=${he} (ptr=${Ue}, need ${D}, size=${je}) at block ${re}`);for(let vt=0;vt<D;vt=vt+1|0){let gt=z[X++],It=0|ke[Ue++];M[gt+h|0]|=It<<b}return ge[he]=Ue,X}function kit(M,h,b,D,z,G){let X=0|b,Z=0|M[X];if(Z<=0||X+Z>M.length-1)throw new Error(`FastPFOR decode: invalid whereMeta=${Z} at pageStart=${X} (expected > 0 and pageStart+whereMeta < encoded.length=${M.length})`);let re=X+1|0,Q=X+Z|0,he=M[Q]>>>0,oe=he+3>>>2,ke=Q+1,ge=ke+oe;if(ge>=M.length)throw new Error(`FastPFOR decode: invalid byteSize=${he} (metaInts=${oe}, pageStart=${X}, packedEnd=${Q}, byteContainerStart=${ke}) causes bitmapPos=${ge} out of bounds (encoded.length=${M.length})`);let Ue=(function(gt,It,_t,qt){qt.byteContainer.length<_t&&(qt.byteContainer=new Uint8Array(2*_t),qt.byteContainerI32=void 0);let tr=qt.byteContainer,lr=_t>>>2;if(3&tr.byteOffset)for(let jr=0;jr<lr;jr=jr+1|0){let _n=0|gt[It+jr|0],fn=jr<<2;tr[fn]=255&_n,tr[fn+1|0]=_n>>>8&255,tr[fn+2|0]=_n>>>16&255,tr[fn+3|0]=_n>>>24&255}else{let jr=qt.byteContainerI32;(!jr||jr.buffer!==tr.buffer||jr.byteOffset!==tr.byteOffset||jr.length<lr)&&(jr=qt.byteContainerI32=new Int32Array(tr.buffer,tr.byteOffset,tr.byteLength>>>2)),jr.set(gt.subarray(It,It+lr))}let qr=3&_t;if(qr>0){let jr=0|gt[It+lr|0],_n=lr<<2;for(let fn=0;fn<qr;fn=fn+1|0)tr[_n+fn|0]=jr>>>(fn<<3)&255}return tr})(M,ke,he,G),je=he,vt=(function(gt,It,_t){let qt=0|gt[It++],tr=_t.dataToBePacked;for(let lr=2;lr<=32;lr=lr+1|0){if(!(qt>>>lr-1&1))continue;if(It>=gt.length)throw new Error(`FastPFOR decode: truncated exception stream header (bitWidth=${lr}, streamWordIndex=${It}, needWords=1, availableWords=${gt.length-It}, encodedWords=${gt.length})`);let qr=gt[It++]>>>0,jr=MI(qr+31,32),_n=qr*lr+31>>>5;if(It+_n>gt.length)throw new Error(`FastPFOR decode: truncated exception stream (bitWidth=${lr}, size=${qr}, streamWordIndex=${It}, needWords=${_n}, availableWords=${gt.length-It}, encodedWords=${gt.length})`);let fn=tr[lr];(!fn||fn.length<jr)&&(fn=tr[lr]=new Uint32Array(jr));let Dn=0;for(;Dn<qr;Dn=Dn+32|0)Cit(gt,It,fn,Dn,lr),It=It+lr|0;It=It-((Dn-qr|0)*lr>>>5)|0,_t.exceptionSizes[lr]=qr}return It})(M,ge,G);return G.dataPointers.fill(0),(function(gt,It,_t,qt,tr,lr,qr,jr,_n,fn){let Dn=0|_t,Hn=0;for(let Ln=0;Ln<qr;Ln=Ln+1|0){let un=Sit(jr,_n,Hn,Ln);Hn=un.bytePosIn;let ei=un.bitWidth,ci=un.exceptionCount,ti=lr+Ln*U1|0;switch(ei){case 0:tr.fill(0,ti,ti+U1);break;case 32:for(let di=0;di<U1;di=di+1|0)tr[ti+di|0]=0|gt[Dn+di|0];Dn=Dn+U1|0;break;default:Dn=Mit(gt,Dn,tr,ti,ei)}ci>0&&(Hn=Eit(tr,ti,ei,ci,jr,_n,Hn,fn,Ln))}if(Dn!==qt)throw new Error(`FastPFOR decode: packed region mismatch (pageStart=${It}, packedStart=${_t}, consumedPackedEnd=${Dn}, expectedPackedEnd=${qt}, packedWords=${qt-_t}, encoded.length=${gt.length})`)})(M,X,re,Q,h,0|D,z/U1|0,Ue,je,G),vt}function Cit(M,h,b,D,z){switch(z){case 2:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0;oe[ge++]=Ue>>>0&3,oe[ge++]=Ue>>>2&3,oe[ge++]=Ue>>>4&3,oe[ge++]=Ue>>>6&3,oe[ge++]=Ue>>>8&3,oe[ge++]=Ue>>>10&3,oe[ge++]=Ue>>>12&3,oe[ge++]=Ue>>>14&3,oe[ge++]=Ue>>>16&3,oe[ge++]=Ue>>>18&3,oe[ge++]=Ue>>>20&3,oe[ge++]=Ue>>>22&3,oe[ge++]=Ue>>>24&3,oe[ge++]=Ue>>>26&3,oe[ge++]=Ue>>>28&3,oe[ge++]=Ue>>>30&3,oe[ge++]=je>>>0&3,oe[ge++]=je>>>2&3,oe[ge++]=je>>>4&3,oe[ge++]=je>>>6&3,oe[ge++]=je>>>8&3,oe[ge++]=je>>>10&3,oe[ge++]=je>>>12&3,oe[ge++]=je>>>14&3,oe[ge++]=je>>>16&3,oe[ge++]=je>>>18&3,oe[ge++]=je>>>20&3,oe[ge++]=je>>>22&3,oe[ge++]=je>>>24&3,oe[ge++]=je>>>26&3,oe[ge++]=je>>>28&3,oe[ge]=je>>>30&3})(M,h,b,D);case 3:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0;oe[ge++]=Ue>>>0&7,oe[ge++]=Ue>>>3&7,oe[ge++]=Ue>>>6&7,oe[ge++]=Ue>>>9&7,oe[ge++]=Ue>>>12&7,oe[ge++]=Ue>>>15&7,oe[ge++]=Ue>>>18&7,oe[ge++]=Ue>>>21&7,oe[ge++]=Ue>>>24&7,oe[ge++]=Ue>>>27&7,oe[ge++]=7&(Ue>>>30|(1&je)<<2),oe[ge++]=je>>>1&7,oe[ge++]=je>>>4&7,oe[ge++]=je>>>7&7,oe[ge++]=je>>>10&7,oe[ge++]=je>>>13&7,oe[ge++]=je>>>16&7,oe[ge++]=je>>>19&7,oe[ge++]=je>>>22&7,oe[ge++]=je>>>25&7,oe[ge++]=je>>>28&7,oe[ge++]=7&(je>>>31|(3&vt)<<1),oe[ge++]=vt>>>2&7,oe[ge++]=vt>>>5&7,oe[ge++]=vt>>>8&7,oe[ge++]=vt>>>11&7,oe[ge++]=vt>>>14&7,oe[ge++]=vt>>>17&7,oe[ge++]=vt>>>20&7,oe[ge++]=vt>>>23&7,oe[ge++]=vt>>>26&7,oe[ge]=vt>>>29&7})(M,h,b,D);case 4:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0;oe[ge++]=Ue>>>0&15,oe[ge++]=Ue>>>4&15,oe[ge++]=Ue>>>8&15,oe[ge++]=Ue>>>12&15,oe[ge++]=Ue>>>16&15,oe[ge++]=Ue>>>20&15,oe[ge++]=Ue>>>24&15,oe[ge++]=Ue>>>28&15,oe[ge++]=je>>>0&15,oe[ge++]=je>>>4&15,oe[ge++]=je>>>8&15,oe[ge++]=je>>>12&15,oe[ge++]=je>>>16&15,oe[ge++]=je>>>20&15,oe[ge++]=je>>>24&15,oe[ge++]=je>>>28&15,oe[ge++]=vt>>>0&15,oe[ge++]=vt>>>4&15,oe[ge++]=vt>>>8&15,oe[ge++]=vt>>>12&15,oe[ge++]=vt>>>16&15,oe[ge++]=vt>>>20&15,oe[ge++]=vt>>>24&15,oe[ge++]=vt>>>28&15,oe[ge++]=gt>>>0&15,oe[ge++]=gt>>>4&15,oe[ge++]=gt>>>8&15,oe[ge++]=gt>>>12&15,oe[ge++]=gt>>>16&15,oe[ge++]=gt>>>20&15,oe[ge++]=gt>>>24&15,oe[ge]=gt>>>28&15})(M,h,b,D);case 5:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0;oe[ge++]=Ue>>>0&31,oe[ge++]=Ue>>>5&31,oe[ge++]=Ue>>>10&31,oe[ge++]=Ue>>>15&31,oe[ge++]=Ue>>>20&31,oe[ge++]=Ue>>>25&31,oe[ge++]=31&(Ue>>>30|(7&je)<<2),oe[ge++]=je>>>3&31,oe[ge++]=je>>>8&31,oe[ge++]=je>>>13&31,oe[ge++]=je>>>18&31,oe[ge++]=je>>>23&31,oe[ge++]=31&(je>>>28|(1&vt)<<4),oe[ge++]=vt>>>1&31,oe[ge++]=vt>>>6&31,oe[ge++]=vt>>>11&31,oe[ge++]=vt>>>16&31,oe[ge++]=vt>>>21&31,oe[ge++]=vt>>>26&31,oe[ge++]=31&(vt>>>31|(15&gt)<<1),oe[ge++]=gt>>>4&31,oe[ge++]=gt>>>9&31,oe[ge++]=gt>>>14&31,oe[ge++]=gt>>>19&31,oe[ge++]=gt>>>24&31,oe[ge++]=31&(gt>>>29|(3&It)<<3),oe[ge++]=It>>>2&31,oe[ge++]=It>>>7&31,oe[ge++]=It>>>12&31,oe[ge++]=It>>>17&31,oe[ge++]=It>>>22&31,oe[ge]=It>>>27&31})(M,h,b,D);case 6:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0,_t=Q[he+5]>>>0;oe[ge++]=Ue>>>0&63,oe[ge++]=Ue>>>6&63,oe[ge++]=Ue>>>12&63,oe[ge++]=Ue>>>18&63,oe[ge++]=Ue>>>24&63,oe[ge++]=63&(Ue>>>30|(15&je)<<2),oe[ge++]=je>>>4&63,oe[ge++]=je>>>10&63,oe[ge++]=je>>>16&63,oe[ge++]=je>>>22&63,oe[ge++]=63&(je>>>28|(3&vt)<<4),oe[ge++]=vt>>>2&63,oe[ge++]=vt>>>8&63,oe[ge++]=vt>>>14&63,oe[ge++]=vt>>>20&63,oe[ge++]=vt>>>26&63,oe[ge++]=gt>>>0&63,oe[ge++]=gt>>>6&63,oe[ge++]=gt>>>12&63,oe[ge++]=gt>>>18&63,oe[ge++]=gt>>>24&63,oe[ge++]=63&(gt>>>30|(15&It)<<2),oe[ge++]=It>>>4&63,oe[ge++]=It>>>10&63,oe[ge++]=It>>>16&63,oe[ge++]=It>>>22&63,oe[ge++]=63&(It>>>28|(3&_t)<<4),oe[ge++]=_t>>>2&63,oe[ge++]=_t>>>8&63,oe[ge++]=_t>>>14&63,oe[ge++]=_t>>>20&63,oe[ge]=_t>>>26&63})(M,h,b,D);case 7:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0,_t=Q[he+5]>>>0,qt=Q[he+6]>>>0;oe[ge++]=Ue>>>0&127,oe[ge++]=Ue>>>7&127,oe[ge++]=Ue>>>14&127,oe[ge++]=Ue>>>21&127,oe[ge++]=127&(Ue>>>28|(7&je)<<4),oe[ge++]=je>>>3&127,oe[ge++]=je>>>10&127,oe[ge++]=je>>>17&127,oe[ge++]=je>>>24&127,oe[ge++]=127&(je>>>31|(63&vt)<<1),oe[ge++]=vt>>>6&127,oe[ge++]=vt>>>13&127,oe[ge++]=vt>>>20&127,oe[ge++]=127&(vt>>>27|(3&gt)<<5),oe[ge++]=gt>>>2&127,oe[ge++]=gt>>>9&127,oe[ge++]=gt>>>16&127,oe[ge++]=gt>>>23&127,oe[ge++]=127&(gt>>>30|(31&It)<<2),oe[ge++]=It>>>5&127,oe[ge++]=It>>>12&127,oe[ge++]=It>>>19&127,oe[ge++]=127&(It>>>26|(1&_t)<<6),oe[ge++]=_t>>>1&127,oe[ge++]=_t>>>8&127,oe[ge++]=_t>>>15&127,oe[ge++]=_t>>>22&127,oe[ge++]=127&(_t>>>29|(15&qt)<<3),oe[ge++]=qt>>>4&127,oe[ge++]=qt>>>11&127,oe[ge++]=qt>>>18&127,oe[ge]=qt>>>25&127})(M,h,b,D);case 8:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0,_t=Q[he+5]>>>0,qt=Q[he+6]>>>0,tr=Q[he+7]>>>0;oe[ge++]=Ue>>>0&255,oe[ge++]=Ue>>>8&255,oe[ge++]=Ue>>>16&255,oe[ge++]=Ue>>>24&255,oe[ge++]=je>>>0&255,oe[ge++]=je>>>8&255,oe[ge++]=je>>>16&255,oe[ge++]=je>>>24&255,oe[ge++]=vt>>>0&255,oe[ge++]=vt>>>8&255,oe[ge++]=vt>>>16&255,oe[ge++]=vt>>>24&255,oe[ge++]=gt>>>0&255,oe[ge++]=gt>>>8&255,oe[ge++]=gt>>>16&255,oe[ge++]=gt>>>24&255,oe[ge++]=It>>>0&255,oe[ge++]=It>>>8&255,oe[ge++]=It>>>16&255,oe[ge++]=It>>>24&255,oe[ge++]=_t>>>0&255,oe[ge++]=_t>>>8&255,oe[ge++]=_t>>>16&255,oe[ge++]=_t>>>24&255,oe[ge++]=qt>>>0&255,oe[ge++]=qt>>>8&255,oe[ge++]=qt>>>16&255,oe[ge++]=qt>>>24&255,oe[ge++]=tr>>>0&255,oe[ge++]=tr>>>8&255,oe[ge++]=tr>>>16&255,oe[ge]=tr>>>24&255})(M,h,b,D);case 9:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0,_t=Q[he+5]>>>0,qt=Q[he+6]>>>0,tr=Q[he+7]>>>0,lr=Q[he+8]>>>0;oe[ge++]=Ue>>>0&511,oe[ge++]=Ue>>>9&511,oe[ge++]=Ue>>>18&511,oe[ge++]=511&(Ue>>>27|(15&je)<<5),oe[ge++]=je>>>4&511,oe[ge++]=je>>>13&511,oe[ge++]=je>>>22&511,oe[ge++]=511&(je>>>31|(255&vt)<<1),oe[ge++]=vt>>>8&511,oe[ge++]=vt>>>17&511,oe[ge++]=511&(vt>>>26|(7&gt)<<6),oe[ge++]=gt>>>3&511,oe[ge++]=gt>>>12&511,oe[ge++]=gt>>>21&511,oe[ge++]=511&(gt>>>30|(127&It)<<2),oe[ge++]=It>>>7&511,oe[ge++]=It>>>16&511,oe[ge++]=511&(It>>>25|(3&_t)<<7),oe[ge++]=_t>>>2&511,oe[ge++]=_t>>>11&511,oe[ge++]=_t>>>20&511,oe[ge++]=511&(_t>>>29|(63&qt)<<3),oe[ge++]=qt>>>6&511,oe[ge++]=qt>>>15&511,oe[ge++]=511&(qt>>>24|(1&tr)<<8),oe[ge++]=tr>>>1&511,oe[ge++]=tr>>>10&511,oe[ge++]=tr>>>19&511,oe[ge++]=511&(tr>>>28|(31&lr)<<4),oe[ge++]=lr>>>5&511,oe[ge++]=lr>>>14&511,oe[ge]=lr>>>23&511})(M,h,b,D);case 10:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0,_t=Q[he+5]>>>0,qt=Q[he+6]>>>0,tr=Q[he+7]>>>0,lr=Q[he+8]>>>0,qr=Q[he+9]>>>0;oe[ge++]=Ue>>>0&1023,oe[ge++]=Ue>>>10&1023,oe[ge++]=Ue>>>20&1023,oe[ge++]=1023&(Ue>>>30|(255&je)<<2),oe[ge++]=je>>>8&1023,oe[ge++]=je>>>18&1023,oe[ge++]=1023&(je>>>28|(63&vt)<<4),oe[ge++]=vt>>>6&1023,oe[ge++]=vt>>>16&1023,oe[ge++]=1023&(vt>>>26|(15&gt)<<6),oe[ge++]=gt>>>4&1023,oe[ge++]=gt>>>14&1023,oe[ge++]=1023&(gt>>>24|(3&It)<<8),oe[ge++]=It>>>2&1023,oe[ge++]=It>>>12&1023,oe[ge++]=It>>>22&1023,oe[ge++]=_t>>>0&1023,oe[ge++]=_t>>>10&1023,oe[ge++]=_t>>>20&1023,oe[ge++]=1023&(_t>>>30|(255&qt)<<2),oe[ge++]=qt>>>8&1023,oe[ge++]=qt>>>18&1023,oe[ge++]=1023&(qt>>>28|(63&tr)<<4),oe[ge++]=tr>>>6&1023,oe[ge++]=tr>>>16&1023,oe[ge++]=1023&(tr>>>26|(15&lr)<<6),oe[ge++]=lr>>>4&1023,oe[ge++]=lr>>>14&1023,oe[ge++]=1023&(lr>>>24|(3&qr)<<8),oe[ge++]=qr>>>2&1023,oe[ge++]=qr>>>12&1023,oe[ge]=qr>>>22&1023})(M,h,b,D);case 11:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0,_t=Q[he+5]>>>0,qt=Q[he+6]>>>0,tr=Q[he+7]>>>0,lr=Q[he+8]>>>0,qr=Q[he+9]>>>0,jr=Q[he+10]>>>0;oe[ge++]=Ue>>>0&2047,oe[ge++]=Ue>>>11&2047,oe[ge++]=2047&(Ue>>>22|(1&je)<<10),oe[ge++]=je>>>1&2047,oe[ge++]=je>>>12&2047,oe[ge++]=2047&(je>>>23|(3&vt)<<9),oe[ge++]=vt>>>2&2047,oe[ge++]=vt>>>13&2047,oe[ge++]=2047&(vt>>>24|(7&gt)<<8),oe[ge++]=gt>>>3&2047,oe[ge++]=gt>>>14&2047,oe[ge++]=2047&(gt>>>25|(15&It)<<7),oe[ge++]=It>>>4&2047,oe[ge++]=It>>>15&2047,oe[ge++]=2047&(It>>>26|(31&_t)<<6),oe[ge++]=_t>>>5&2047,oe[ge++]=_t>>>16&2047,oe[ge++]=2047&(_t>>>27|(63&qt)<<5),oe[ge++]=qt>>>6&2047,oe[ge++]=qt>>>17&2047,oe[ge++]=2047&(qt>>>28|(127&tr)<<4),oe[ge++]=tr>>>7&2047,oe[ge++]=tr>>>18&2047,oe[ge++]=2047&(tr>>>29|(255&lr)<<3),oe[ge++]=lr>>>8&2047,oe[ge++]=lr>>>19&2047,oe[ge++]=2047&(lr>>>30|(511&qr)<<2),oe[ge++]=qr>>>9&2047,oe[ge++]=qr>>>20&2047,oe[ge++]=2047&(qr>>>31|(1023&jr)<<1),oe[ge++]=jr>>>10&2047,oe[ge]=jr>>>21&2047})(M,h,b,D);case 12:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0,_t=Q[he+5]>>>0,qt=Q[he+6]>>>0,tr=Q[he+7]>>>0,lr=Q[he+8]>>>0,qr=Q[he+9]>>>0,jr=Q[he+10]>>>0,_n=Q[he+11]>>>0;oe[ge++]=Ue>>>0&4095,oe[ge++]=Ue>>>12&4095,oe[ge++]=4095&(Ue>>>24|(15&je)<<8),oe[ge++]=je>>>4&4095,oe[ge++]=je>>>16&4095,oe[ge++]=4095&(je>>>28|(255&vt)<<4),oe[ge++]=vt>>>8&4095,oe[ge++]=vt>>>20&4095,oe[ge++]=gt>>>0&4095,oe[ge++]=gt>>>12&4095,oe[ge++]=4095&(gt>>>24|(15&It)<<8),oe[ge++]=It>>>4&4095,oe[ge++]=It>>>16&4095,oe[ge++]=4095&(It>>>28|(255&_t)<<4),oe[ge++]=_t>>>8&4095,oe[ge++]=_t>>>20&4095,oe[ge++]=qt>>>0&4095,oe[ge++]=qt>>>12&4095,oe[ge++]=4095&(qt>>>24|(15&tr)<<8),oe[ge++]=tr>>>4&4095,oe[ge++]=tr>>>16&4095,oe[ge++]=4095&(tr>>>28|(255&lr)<<4),oe[ge++]=lr>>>8&4095,oe[ge++]=lr>>>20&4095,oe[ge++]=qr>>>0&4095,oe[ge++]=qr>>>12&4095,oe[ge++]=4095&(qr>>>24|(15&jr)<<8),oe[ge++]=jr>>>4&4095,oe[ge++]=jr>>>16&4095,oe[ge++]=4095&(jr>>>28|(255&_n)<<4),oe[ge++]=_n>>>8&4095,oe[ge]=_n>>>20&4095})(M,h,b,D);case 16:return void(function(Q,he,oe,ke){let ge=ke,Ue=Q[he]>>>0,je=Q[he+1]>>>0,vt=Q[he+2]>>>0,gt=Q[he+3]>>>0,It=Q[he+4]>>>0,_t=Q[he+5]>>>0,qt=Q[he+6]>>>0,tr=Q[he+7]>>>0,lr=Q[he+8]>>>0,qr=Q[he+9]>>>0,jr=Q[he+10]>>>0,_n=Q[he+11]>>>0,fn=Q[he+12]>>>0,Dn=Q[he+13]>>>0,Hn=Q[he+14]>>>0,Ln=Q[he+15]>>>0;oe[ge++]=Ue>>>0&65535,oe[ge++]=Ue>>>16&65535,oe[ge++]=je>>>0&65535,oe[ge++]=je>>>16&65535,oe[ge++]=vt>>>0&65535,oe[ge++]=vt>>>16&65535,oe[ge++]=gt>>>0&65535,oe[ge++]=gt>>>16&65535,oe[ge++]=It>>>0&65535,oe[ge++]=It>>>16&65535,oe[ge++]=_t>>>0&65535,oe[ge++]=_t>>>16&65535,oe[ge++]=qt>>>0&65535,oe[ge++]=qt>>>16&65535,oe[ge++]=tr>>>0&65535,oe[ge++]=tr>>>16&65535,oe[ge++]=lr>>>0&65535,oe[ge++]=lr>>>16&65535,oe[ge++]=qr>>>0&65535,oe[ge++]=qr>>>16&65535,oe[ge++]=jr>>>0&65535,oe[ge++]=jr>>>16&65535,oe[ge++]=_n>>>0&65535,oe[ge++]=_n>>>16&65535,oe[ge++]=fn>>>0&65535,oe[ge++]=fn>>>16&65535,oe[ge++]=Dn>>>0&65535,oe[ge++]=Dn>>>16&65535,oe[ge++]=Hn>>>0&65535,oe[ge++]=Hn>>>16&65535,oe[ge++]=Ln>>>0&65535,oe[ge]=Ln>>>16&65535})(M,h,b,D);case 32:for(let Q=0;Q<32;Q=Q+1|0)b[D+Q|0]=0|M[h+Q|0];return}let G=AI[z]>>>0,X=h,Z=0,re=M[X]>>>0;for(let Q=0;Q<32;Q++)if(Z+z<=32)b[D+Q]=re>>>Z&G,Z+=z,Z===32&&(Z=0,X++,Q!==31&&(re=M[X]>>>0));else{let he=32-Z,oe=re>>>Z;X++,re=M[X]>>>0,b[D+Q]=(oe|(re&AI[z-he]>>>0)<<he)&G,Z=z-he}}function xh(M,h,b){let D=new Uint32Array(b),z=0,G=h.get();for(let X=0;X<D.length;X++){let Z=M[G++],re=127&Z;Z<128||(Z=M[G++],re|=(127&Z)<<7,Z<128||(Z=M[G++],re|=(127&Z)<<14,Z<128||(Z=M[G++],re|=(127&Z)<<21,Z<128||(Z=M[G++],re|=(15&Z)<<28)))),D[z++]=re}return h.set(G),D}function K_(M,h,b){let D=new BigUint64Array(b);for(let z=0;z<D.length;z++)D[z]=Lit(M,h);return D}function Lit(M,h){let b=BigInt(0),D=0,z=h.get();for(;z<M.length;){let G=M[z++];if(b|=BigInt(127&G)<<BigInt(D),!(128&G))break;if(D+=7,D>=64)throw new Error(\"Varint too long\")}return h.set(z),b}function Pit(M,h){let b,D;return D=M[h.get()],h.increment(),b=127&D,D<128?b:(D=M[h.get()],h.increment(),b|=(127&D)<<7,D<128?b:(D=M[h.get()],h.increment(),b|=(127&D)<<14,D<128?b:(D=M[h.get()],h.increment(),b|=(127&D)<<21,D<128?b:(D=M[h.get()],b|=(15&D)<<28,(function(z,G,X){let Z,re;if(re=G[X.get()],X.increment(),Z=(112&re)>>4,re<128||(re=G[X.get()],X.increment(),Z|=(127&re)<<3,re<128)||(re=G[X.get()],X.increment(),Z|=(127&re)<<10,re<128)||(re=G[X.get()],X.increment(),Z|=(127&re)<<17,re<128)||(re=G[X.get()],X.increment(),Z|=(127&re)<<24,re<128)||(re=G[X.get()],X.increment(),Z|=(1&re)<<31,re<128))return 4294967296*Z+(z>>>0);throw new Error(\"Expected varint not more than 10 bytes\")})(b,M,h)))))}function Fs(M){return M>>>1^-(1&M)}function Zh(M){return M>>BigInt(1)^-(BigInt(1)&M)}function J_(M){return M%2==1?(M+1)/-2:M/2}function EI(M,h,b){if(b===void 0){b=0;for(let G=0;G<h;G++)b+=M[G]}let D=new Uint32Array(b),z=0;for(let G=0;G<h;G++){let X=M[G];D.fill(M[G+h],z,z+X),z+=X}return D}function kI(M,h,b){if(b===void 0){b=0;for(let G=0;G<h;G++)b+=Number(M[G])}let D=new BigUint64Array(b),z=0;for(let G=0;G<h;G++){let X=Number(M[G]);D.fill(M[G+h],z,z+X),z+=X}return D}function Cee(M,h,b){let D=new Float64Array(b),z=0;for(let G=0;G<h;G++){let X=M[G];D.fill(M[G+h],z,z+X),z+=X}return D}function Lee(M){let h=new Int32Array(M.length);h[0]=Fs(M[0]);let b=M.length/4*4,D=1;if(b>=4)for(;D<b-4;D+=4){let z=M[D+1],G=M[D+2],X=M[D+3];h[D]=Fs(M[D])+h[D-1],h[D+1]=Fs(z)+h[D],h[D+2]=Fs(G)+h[D+1],h[D+3]=Fs(X)+h[D+2]}for(;D!==M.length;++D)h[D]=Fs(M[D])+h[D-1];return h}function Pee(M){let h=new BigInt64Array(M.length);h[0]=Zh(M[0]);let b=M.length/4*4,D=1;if(b>=4)for(;D<b-4;D+=4){let z=M[D+1],G=M[D+2],X=M[D+3];h[D]=Zh(M[D])+h[D-1],h[D+1]=Zh(z)+h[D],h[D+2]=Zh(G)+h[D+1],h[D+3]=Zh(X)+h[D+2]}for(;D!==h.length;++D)h[D]=Zh(M[D])+h[D-1];return h}function CI(M){let h=M.length/4*4,b=1;if(h>=4)for(let D=M[0];b<h-4;b+=4)D=M[b]+=D,D=M[b+1]+=D,D=M[b+2]+=D,D=M[b+3]+=D;for(;b!==M.length;)M[b]+=M[b-1],++b}var Of,Hv,ax,Xd,Ml,Ic,Fee,$g;function $h(M,h){let b=(function(D,z){let G=D[z.get()],X=Object.values(Of)[G>>4],Z=null;switch(X){case Of.DATA:Z={dictionaryType:Object.values(Hv)[15&G]};break;case Of.OFFSET:Z={offsetType:Object.values(ax)[15&G]};break;case Of.LENGTH:Z={lengthType:Object.values(Xd)[15&G]}}z.increment();let re=D[z.get()],Q=Object.values(To)[re>>5],he=Object.values(To)[re>>2&7],oe=Object.values(Zg)[3&re];z.increment();let ke=xh(D,z,2),ge=ke[0];return{physicalStreamType:X,logicalStreamType:Z,logicalLevelTechnique1:Q,logicalLevelTechnique2:he,physicalLevelTechnique:oe,numValues:ge,byteLength:ke[1],decompressedCount:ge}})(M,h);return b.logicalLevelTechnique1===To.MORTON?(function(D,z,G){let X=xh(z,G,2);return{physicalStreamType:D.physicalStreamType,logicalStreamType:D.logicalStreamType,logicalLevelTechnique1:D.logicalLevelTechnique1,logicalLevelTechnique2:D.logicalLevelTechnique2,physicalLevelTechnique:D.physicalLevelTechnique,numValues:D.numValues,byteLength:D.byteLength,decompressedCount:D.decompressedCount,numBits:X[0],coordinateShift:X[1]}})(b,M,h):To.RLE!==b.logicalLevelTechnique1&&To.RLE!==b.logicalLevelTechnique2||Zg.NONE===b.physicalLevelTechnique?b:(function(D,z,G){let X=xh(z,G,2);return{physicalStreamType:D.physicalStreamType,logicalStreamType:D.logicalStreamType,logicalLevelTechnique1:D.logicalLevelTechnique1,logicalLevelTechnique2:D.logicalLevelTechnique2,physicalLevelTechnique:D.physicalLevelTechnique,numValues:D.numValues,byteLength:D.byteLength,decompressedCount:X[1],runs:X[0],numRleValues:X[1]}})(b,M,h)}(function(M){M.PRESENT=\"PRESENT\",M.DATA=\"DATA\",M.OFFSET=\"OFFSET\",M.LENGTH=\"LENGTH\"})(Of||(Of={})),(function(M){M.NONE=\"NONE\",M.SINGLE=\"SINGLE\",M.SHARED=\"SHARED\",M.VERTEX=\"VERTEX\",M.MORTON=\"MORTON\",M.FSST=\"FSST\"})(Hv||(Hv={})),(function(M){M.VERTEX=\"VERTEX\",M.INDEX=\"INDEX\",M.STRING=\"STRING\",M.KEY=\"KEY\"})(ax||(ax={})),(function(M){M.VAR_BINARY=\"VAR_BINARY\",M.GEOMETRIES=\"GEOMETRIES\",M.PARTS=\"PARTS\",M.RINGS=\"RINGS\",M.TRIANGLES=\"TRIANGLES\",M.SYMBOL=\"SYMBOL\",M.DICTIONARY=\"DICTIONARY\"})(Xd||(Xd={})),(function(M){M[M.FLAT=0]=\"FLAT\",M[M.CONST=1]=\"CONST\",M[M.SEQUENCE=2]=\"SEQUENCE\",M[M.DICTIONARY=3]=\"DICTIONARY\",M[M.FSST_DICTIONARY=4]=\"FSST_DICTIONARY\"})(Ml||(Ml={}));class jv{constructor(h,b){this.values=h,this._size=b}get(h){let b=Math.floor(h/8);return(this.values[b]>>h%8&1)==1}set(h,b){let D=Math.floor(h/8);this.values[D]=this.values[D]|(b?1:0)<<h%8}getInt(h){let b=Math.floor(h/8);return this.values[b]>>h%8&1}size(){return this._size}getBuffer(){return this.values}}function Q_(M,h,b){if(!h)return M;let D=h.size(),z=new M.constructor(D),G=0;for(let X=0;X<D;X++)z[X]=h.get(X)?M[G++]:b;return z}function LI(M,h,b,D,z){return(function(G,X,Z,re){let Q;switch(X.logicalLevelTechnique1){case To.DELTA:if(X.logicalLevelTechnique2===To.RLE){let he=X;if(!re)return(function(oe,ke,ge){let Ue=new Int32Array(ge),je=0,vt=0;for(let gt=0;gt<ke;gt++){let It=oe[gt],_t=Fs(oe[gt+ke]);for(let qt=0;qt<It;qt++)vt+=_t,Ue[je++]=vt}return Ue})(G,he.runs,he.numRleValues);Q=Lee(G=EI(G,he.runs,he.numRleValues))}else Q=Lee(G);break;case To.RLE:Q=(function(he,oe,ke){if(ke===void 0){ke=0;for(let je=0;je<oe;je++)ke+=he[je]}let ge=new Int32Array(ke),Ue=0;for(let je=0;je<oe;je++){let vt=he[je],gt=he[je+oe];gt=Fs(gt),ge.fill(gt,Ue,Ue+vt),Ue+=vt}return ge})(G,X.runs,X.numRleValues);break;case To.MORTON:CI(G),Q=new Int32Array(G);break;case To.COMPONENTWISE_DELTA:Q=(function(he){if(he.length<2)return new Int32Array(he);let oe=new Int32Array(he.length);oe[0]=Fs(he[0]),oe[1]=Fs(he[1]);let ke=he.length/4*4,ge=2;if(ke>=4)for(;ge<ke-4;ge+=4){let Ue=he[ge+1],je=he[ge+2],vt=he[ge+3];oe[ge]=Fs(he[ge])+oe[ge-2],oe[ge+1]=Fs(Ue)+oe[ge-1],oe[ge+2]=Fs(je)+oe[ge],oe[ge+3]=Fs(vt)+oe[ge+1]}for(;ge!==he.length;ge+=2)oe[ge]=Fs(he[ge])+oe[ge-2],oe[ge+1]=Fs(he[ge+1])+oe[ge-1];return oe})(G);break;case To.NONE:Q=(function(he){let oe=new Int32Array(he.length);for(let ke=0;ke<he.length;ke++)oe[ke]=Fs(he[ke]);return oe})(G);break;default:throw new Error(`The specified Logical level technique is not supported: ${X.logicalLevelTechnique1}`)}return re?Q_(Q,re,0):Q})(eb(M,h,b),b,0,z)}function Zd(M,h,b,D,z){return(function(G,X,Z,re){let Q;switch(X.logicalLevelTechnique1){case To.DELTA:Q=(function(he){let oe=new Uint32Array(he.length);oe[0]=Fs(he[0])>>>0;for(let ke=1;ke<he.length;ke++)oe[ke]=oe[ke-1]+Fs(he[ke])>>>0;return oe})(X.logicalLevelTechnique2===To.RLE?EI(G,X.runs,X.numRleValues):G);break;case To.RLE:Q=EI(G,X.runs,X.numRleValues);break;case To.MORTON:CI(G),Q=G;break;case To.COMPONENTWISE_DELTA:Q=(function(he){if(he.length<2)return new Uint32Array(he);let oe=new Uint32Array(he.length);oe[0]=Fs(he[0])>>>0,oe[1]=Fs(he[1])>>>0;for(let ke=2;ke<he.length;ke+=2)oe[ke]=oe[ke-2]+Fs(he[ke])>>>0,oe[ke+1]=oe[ke-1]+Fs(he[ke+1])>>>0;return oe})(G);break;case To.NONE:Q=G;break;default:throw new Error(`The specified Logical level technique is not supported: ${X.logicalLevelTechnique1}`)}return re?Q_(Q,re,0):Q})(eb(M,h,b),b,0,z)}function Kg(M,h,b){return(function(D,z){if(z.logicalLevelTechnique1===To.DELTA&&z.logicalLevelTechnique2===To.NONE)return(function(G){let X=new Int32Array(G.length+1);X[0]=0,X[1]=Fs(G[0]);let Z=X[1];for(let re=2;re!==X.length;++re)Z+=Fs(G[re-1]),X[re]=X[re-1]+Z;return new Uint32Array(X)})(D);if(z.logicalLevelTechnique1===To.RLE&&z.logicalLevelTechnique2===To.NONE)return(function(G,X,Z){let re=new Uint32Array(Z+1);re[0]=0;let Q=1,he=re[0];for(let oe=0;oe<X;oe++){let ke=G[oe],ge=G[oe+X];for(let Ue=Q;Ue<Q+ke;Ue++)re[Ue]=ge+he,he=re[Ue];Q+=ke}return re})(D,z.runs,z.numRleValues);if(z.logicalLevelTechnique1===To.NONE&&z.logicalLevelTechnique2===To.NONE){(function(X){let Z=0;for(let re=0;re<X.length;re++)X[re]+=Z,Z=X[re]})(D);let G=new Uint32Array(z.numValues+1);return G[0]=0,G.set(D,1),G}if(z.logicalLevelTechnique1===To.DELTA&&z.logicalLevelTechnique2===To.RLE){let G=(function(X,Z,re){let Q=new Int32Array(re+1);Q[0]=0;let he=1,oe=Q[0];for(let ke=0;ke<Z;ke++){let ge=X[ke],Ue=X[ke+Z];Ue=Fs(Ue);for(let je=he;je<he+ge;je++)Q[je]=Ue+oe,oe=Q[je];he+=ge}return Q})(D,z.runs,z.numRleValues);return CI(G),new Uint32Array(G)}throw new Error(\"Only delta encoding is supported for transforming length to offset streams yet.\")})(eb(M,h,b),b)}function eb(M,h,b){let D=b.physicalLevelTechnique;switch(D){case Zg.FAST_PFOR:return(function(z,G,X,Z){return(function(re,Q,he,oe,ke){let ge=oe.get();if(3&he)throw new Error(`FastPFOR: invalid encodedByteLength=${he} at offset=${ge} (encodedBytes.length=${re.length}; expected a multiple of 4 bytes for an int32 big-endian word stream)`);let Ue=he>>>2,je=(function(gt,It){if(It<=gt.encodedWords.length)return gt.encodedWords;let _t=new Uint32Array(Math.max(16,2*It));return gt.encodedWords=_t,_t})(ke,Ue);(function(gt,It,_t,qt){if(It<0||_t<0||It+_t>gt.length)throw new RangeError(`decodeBigEndianInt32sInto: out of bounds (offset=${It}, byteLength=${_t}, bytes.length=${gt.length})`);let tr=Math.floor(_t/4),lr=_t%4!=0,qr=lr?tr+1:tr;if(qt.length<qr)throw new RangeError(`decodeBigEndianInt32sInto: out.length=${qt.length} < ${qr}`);if(tr>0){let jr=gt.byteOffset+It;if(3&jr)for(let _n=0;_n<tr;_n++){let fn=It+4*_n;qt[_n]=gt[fn]<<24|gt[fn+1]<<16|gt[fn+2]<<8|gt[fn+3]}else{let _n=new Uint32Array(gt.buffer,jr,tr);for(let fn=0;fn<tr;fn++)qt[fn]=0|Tit(_n[fn])}}if(lr){let jr=It+4*tr,_n=_t-4*tr,fn=0;for(let Dn=0;Dn<_n;Dn++)fn|=gt[jr+Dn]<<24-8*Dn;qt[tr]=0|fn}})(re,ge,he,je);let vt=(function(gt,It,_t){let qt=0,tr=0,lr=new Uint32Array(It),qr=_t!=null?_t:kee();if(gt.length>0){let jr=0|gt[qt];if(qt=qt+1|0,255&jr)throw new Error(`FastPFOR decode: invalid alignedLength=${jr} (expected multiple of 256)`);if(tr+jr>lr.length)throw new Error(`FastPFOR decode: output buffer too small (outPos=${tr}, alignedLength=${jr}, out.length=${lr.length})`);qt=(function(_n,fn,Dn,Hn,Ln,un){let ei=Hn+MI(Ln,U1),ci=Hn,ti=Dn;for(;ci!==ei;){let di=Math.min(SI,ei-ci);ti=kit(_n,fn,ti,ci,di,un),ci=ci+di|0}return ti})(gt,lr,qt,tr,jr,qr),tr=tr+jr|0}return(function(jr,_n,fn,Dn,Hn,Ln){if(Ln===0)return _n;let un=0,ei=_n,ci=_n+fn,ti=Hn,di=Hn,_a=Hn+Ln,Ao=0,us=0;for(;ei<ci&&di<_a;){let ks=jr[ei]>>>un&255;if(un+=8,ei+=un>>>5,un&=31,Ao|=(127&ks)<<us,128&ks)Dn[di++]=0|Ao,Ao=0,us=0;else if(us+=7,us>28)throw new Error(`FastPFOR VByte: unterminated value (expected MSB=1 terminator within 5 bytes; shift=${us}, partial=${Ao}, decoded=${di-ti}/${Ln}, inPos=${ei}, inEnd=${ci})`)}if(di!==_a)throw new Error(`FastPFOR VByte: truncated stream (decoded=${di-ti}, expected=${Ln}, consumedWords=${ei-_n}/${fn}, vbyteStart=${_n}, vbyteEnd=${ci})`)})(gt,qt,gt.length-qt|0,lr,tr,It-tr|0),lr})(je.subarray(0,Ue),Q,ke.decoderWorkspace);return oe.add(he),vt})(z,G,X,Z,(function(re=16){if(re<0)throw new RangeError(`initialEncodedWordCapacity must be >= 0, got ${re}`);let Q=Math.max(16,0|re);return{encodedWords:new Uint32Array(Q),decoderWorkspace:kee()}})(X>>>2))})(M,b.numValues,b.byteLength,h);case Zg.VARINT:return xh(M,h,b.numValues);case Zg.NONE:{let z=h.get();h.add(b.byteLength);let G=M.subarray(z,h.get());return new Uint32Array(G)}default:throw new Error(`Specified physicalLevelTechnique ${D} is not supported (yet).`)}}function PI(M,h,b){let D=eb(M,h,b);return D.length===1?D[0]:(function(z){return z[1]})(D)}function Dee(M,h,b){return(function(D){if(D.length===2){let z=Fs(D[1]);return[z,z]}return[Fs(D[2]),Fs(D[3])]})(eb(M,h,b))}function Ree(M,h,b){return(function(D){if(D.length===2){let z=Zh(D[1]);return[z,z]}return[Zh(D[2]),Zh(D[3])]})(K_(M,h,b.numValues))}function Iee(M,h,b,D){return(function(z,G,X){let Z;switch(G.logicalLevelTechnique1){case To.DELTA:Z=(function(re){let Q=new BigUint64Array(re.length);Q[0]=BigInt.asUintN(64,Zh(re[0]));for(let he=1;he<re.length;he++)Q[he]=BigInt.asUintN(64,Q[he-1]+Zh(re[he]));return Q})(G.logicalLevelTechnique2===To.RLE?kI(z,G.runs,G.numRleValues):z);break;case To.RLE:Z=kI(z,G.runs,G.numRleValues);break;case To.NONE:Z=z;break;default:throw new Error(`The specified Logical level technique is not supported: ${G.logicalLevelTechnique1}`)}return X?Q_(Z,X,BigInt(0)):Z})(K_(M,h,b.numValues),b,D)}function zee(M,h,b){let D=K_(M,h,b.numValues);return D.length===1?D[0]:(function(z){return z[1]})(D)}function US(M,h,b,D,z=\"int32\"){let G=M.logicalLevelTechnique1;if(G===To.RLE)return M.runs===1?Ml.CONST:Ml.FLAT;if(G!==To.DELTA||M.logicalLevelTechnique2!==To.RLE)return M.numValues===1?Ml.CONST:Ml.FLAT;let X=h instanceof jv?h.size():h;if(M.numRleValues!==X)return Ml.FLAT;if(M.runs===1)return Ml.SEQUENCE;if(M.runs!==2)return M.numValues===1?Ml.CONST:Ml.FLAT;let Z=D.get();if(M.physicalLevelTechnique===Zg.VARINT)return(function(he,oe,ke){let ge=new Eee(oe.get());if(ke===\"int64\"){let je=K_(he,ge,4);return je[2]===BigInt(2)&&je[3]===BigInt(2)}let Ue=xh(he,ge,4);return Ue[2]===2&&Ue[3]===2})(b,D,z)?Ml.SEQUENCE:M.numValues===1?Ml.CONST:Ml.FLAT;let re=D.get(),Q=new Int32Array(b.buffer,b.byteOffset+re,4);return D.set(Z),Q[2]===2&&Q[3]===2?Ml.SEQUENCE:M.numValues===1?Ml.CONST:Ml.FLAT}class qee extends NS{getValueFromBuffer(h){return this.dataBuffer[h]}}class Bee extends See{constructor(h,b,D,z){super(h,BigInt64Array.of(b),D,z)}getValueFromBuffer(h){return this.dataBuffer[0]+BigInt(h)*this.delta}}function FI(M,h,b){return{x:Oee(M,h)-b,y:Oee(M>>1,h)-b}}function Oee(M,h){let b=0;for(let D=0;D<h;D++)b|=(M&1<<2*D)>>D;return b}function tb(M,h,b,D,z,G,X){return M===$g.MORTON?(function(Z,re,Q,he,oe,ke){let ge=new Array(oe?he+1:he);for(let Ue=0;Ue<he;Ue++){let je=FI(Z[re[Q+Ue]],ke.numBits,ke.coordinateShift);ge[Ue]=new o(je.x,je.y)}return oe&&(ge[ge.length-1]=ge[0]),ge})(h,b,D,z,G,X):(function(Z,re,Q,he,oe){let ke=new Array(oe?he+1:he);for(let ge=0;ge<2*he;ge+=2){let Ue=2*re[Q+ge/2];ke[ge/2]=new o(Z[Ue],Z[Ue+1])}return oe&&(ke[ke.length-1]=ke[0]),ke})(h,b,D,z,G)}function rb(M,h,b,D){let z=new Array(D?b+1:b);for(let G=0;G<2*b;G+=2)z[G/2]=new o(M[h+G],M[h+G+1]);return D&&(z[z.length-1]=z[0]),z}(function(M){M[M.POINT=0]=\"POINT\",M[M.LINESTRING=1]=\"LINESTRING\",M[M.POLYGON=2]=\"POLYGON\",M[M.MULTIPOINT=3]=\"MULTIPOINT\",M[M.MULTILINESTRING=4]=\"MULTILINESTRING\",M[M.MULTIPOLYGON=5]=\"MULTIPOLYGON\"})(Ic||(Ic={})),(function(M){M[M.POINT=0]=\"POINT\",M[M.LINESTRING=1]=\"LINESTRING\",M[M.POLYGON=2]=\"POLYGON\"})(Fee||(Fee={})),(function(M){M[M.MORTON=0]=\"MORTON\",M[M.VEC_2=1]=\"VEC_2\",M[M.VEC_3=2]=\"VEC_3\"})($g||($g={}));class Nee{constructor(h,b,D,z,G){this._vertexBufferType=h,this._topologyVector=b,this._vertexOffsets=D,this._vertexBuffer=z,this._mortonSettings=G}get vertexBufferType(){return this._vertexBufferType}get topologyVector(){return this._topologyVector}get vertexOffsets(){return this._vertexOffsets}get vertexBuffer(){return this._vertexBuffer}getSimpleEncodedVertex(h){let b=this.vertexOffsets?2*this.vertexOffsets[h]:2*h;return[this.vertexBuffer[b],this.vertexBuffer[b+1]]}getVertex(h){if(this.vertexOffsets&&this.mortonSettings){let D=FI(this.vertexBuffer[this.vertexOffsets[h]],this.mortonSettings.numBits,this.mortonSettings.coordinateShift);return[D.x,D.y]}let b=this.vertexOffsets?2*this.vertexOffsets[h]:2*h;return[this.vertexBuffer[b],this.vertexBuffer[b+1]]}getGeometries(){return(function(h){let b=new Array(h.numGeometries),D=1,z=1,G=1,X=0,Z=0,re=0,Q=h.mortonSettings,he=h.topologyVector,oe=he.geometryOffsets,ke=he.partOffsets,ge=he.ringOffsets,Ue=h.vertexOffsets,je=!Ue||Ue.length===0,vt=h.containsPolygonGeometry(),gt=h.vertexBuffer;for(let It=0;It<h.numGeometries;It++)switch(h.geometryType(It)){case Ic.POINT:{let _t,qt;if(je)_t=gt[Z++],qt=gt[Z++];else if(h.vertexBufferType===$g.MORTON){let tr=FI(gt[Ue[re++]],Q.numBits,Q.coordinateShift);_t=tr.x,qt=tr.y}else{let tr=2*Ue[re++];_t=gt[tr],qt=gt[tr+1]}b[X++]=[[new o(_t,qt)]],oe&&G++,ke&&D++,ge&&z++}break;case Ic.MULTIPOINT:{let _t=oe[G]-oe[G-1];G++;let qt=new Array(_t);if(je)for(let tr=0;tr<_t;tr++){let lr=gt[Z++],qr=gt[Z++];qt[tr]=new o(lr,qr)}else for(let tr=0;tr<_t;tr++){let lr=2*Ue[re++];qt[tr]=new o(gt[lr],gt[lr+1])}b[X++]=qt.map((tr=>[tr])),D+=_t,z+=_t}break;case Ic.LINESTRING:{let _t,qt;vt?(_t=ge[z]-ge[z-1],z++):_t=ke[D]-ke[D-1],D++,je?(qt=rb(gt,Z,_t,!1),Z+=2*_t):(qt=tb(h.vertexBufferType,gt,Ue,re,_t,!1,Q),re+=_t),b[X++]=[qt],oe&&G++}break;case Ic.POLYGON:{let _t=ke[D]-ke[D-1];D++;let qt=new Array(_t-1),tr,lr=ge[z]-ge[z-1];if(z++,je){tr=rb(gt,Z,lr,!0),Z+=2*lr;for(let qr=0;qr<qt.length;qr++)lr=ge[z]-ge[z-1],z++,qt[qr]=rb(gt,Z,lr,!0),Z+=2*lr}else{tr=tb(h.vertexBufferType,gt,Ue,re,lr,!0,Q),re+=lr;for(let qr=0;qr<qt.length;qr++)lr=ge[z]-ge[z-1],z++,qt[qr]=tb(h.vertexBufferType,gt,Ue,re,lr,!0,Q),re+=lr}b[X++]=[tr].concat(qt),oe&&G++}break;case Ic.MULTILINESTRING:{let _t=oe[G]-oe[G-1];G++;let qt=new Array(_t);for(let tr=0;tr<_t;tr++){let lr;if(vt?(lr=ge[z]-ge[z-1],z++):lr=ke[D]-ke[D-1],D++,je)qt[tr]=rb(gt,Z,lr,!1),Z+=2*lr;else{let qr=tb(h.vertexBufferType,gt,Ue,re,lr,!1,Q);qt[tr]=qr,re+=lr}}b[X++]=qt}break;case Ic.MULTIPOLYGON:{let _t=oe[G]-oe[G-1];G++;let qt=new Array(_t);for(let tr=0;tr<_t;tr++){let lr=ke[D]-ke[D-1],qr;D++;let jr=new Array(lr-1),_n=ge[z]-ge[z-1];z++,je?(qr=rb(gt,Z,_n,!0),Z+=2*_n):(qr=tb(h.vertexBufferType,gt,Ue,re,_n,!0,Q),re+=_n);for(let fn=0;fn<jr.length;fn++){let Dn=ge[z]-ge[z-1];z++,je?(jr[fn]=rb(gt,Z,Dn,!0),Z+=2*Dn):(jr[fn]=tb(h.vertexBufferType,gt,Ue,re,Dn,!0,Q),re+=Dn)}qt[tr]=[qr].concat(jr)}b[X++]=qt.flat()}break;default:throw new Error(\"The specified geometry type is currently not supported.\")}return b})(this)}get mortonSettings(){return this._mortonSettings}}class Uee extends Nee{constructor(h,b,D,z,G,X,Z){super(D,z,G,X,Z),this._numGeometries=h,this._geometryType=b}geometryType(h){return this._geometryType}get numGeometries(){return this._numGeometries}containsPolygonGeometry(){return this._geometryType===Ic.POLYGON||this._geometryType===Ic.MULTIPOLYGON}containsSingleGeometryType(){return!0}}class Vee extends Nee{constructor(h,b,D,z,G,X){super(h,D,z,G,X),this._geometryTypes=b}geometryType(h){return this._geometryTypes[h]}get numGeometries(){return this._geometryTypes.length}containsPolygonGeometry(){for(let h=0;h<this.numGeometries;h++)if(this.geometryType(h)===Ic.POLYGON||this.geometryType(h)===Ic.MULTIPOLYGON)return!0;return!1}containsSingleGeometryType(){return!1}}class Gee{constructor(h,b,D,z){this._triangleOffsets=h,this._indexBuffer=b,this._vertexBuffer=D,this._topologyVector=z}get triangleOffsets(){return this._triangleOffsets}get indexBuffer(){return this._indexBuffer}get vertexBuffer(){return this._vertexBuffer}get topologyVector(){return this._topologyVector}getGeometries(){if(!this._topologyVector)throw new Error(\"Cannot convert GpuVector to coordinates without topology information\");let h=new Array(this.numGeometries),b=this._topologyVector,D=b.partOffsets,z=b.ringOffsets,G=b.geometryOffsets,X=0,Z=1,re=1,Q=1;for(let he=0;he<this.numGeometries;he++)switch(this.geometryType(he)){case Ic.POLYGON:{let oe=D[Z]-D[Z-1];Z++;let ke=[];for(let ge=0;ge<oe;ge++){let Ue=z[re]-z[re-1];re++;let je=[];for(let vt=0;vt<Ue;vt++){let gt=this._vertexBuffer[X++],It=this._vertexBuffer[X++];je.push(new o(gt,It))}je.length>0&&je.push(je[0]),ke.push(je)}h[he]=ke,G&&Q++}break;case Ic.MULTIPOLYGON:{let oe=G[Q]-G[Q-1];Q++;let ke=[];for(let ge=0;ge<oe;ge++){let Ue=D[Z]-D[Z-1];Z++;for(let je=0;je<Ue;je++){let vt=z[re]-z[re-1];re++;let gt=[];for(let It=0;It<vt;It++){let _t=this._vertexBuffer[X++],qt=this._vertexBuffer[X++];gt.push(new o(_t,qt))}gt.length>0&&gt.push(gt[0]),ke.push(gt)}}h[he]=ke}}return h}[Symbol.iterator](){return null}}function Hee(M,h,b,D,z,G){return new Fit(M,h,b,D,z,G)}class Fit extends Gee{constructor(h,b,D,z,G,X){super(D,z,G,X),this._numGeometries=h,this._geometryType=b}geometryType(h){return this._geometryType}get numGeometries(){return this._numGeometries}containsSingleGeometryType(){return!0}}function jee(M,h,b,D,z){return new Dit(M,h,b,D,z)}class Dit extends Gee{constructor(h,b,D,z,G){super(b,D,z,G),this._geometryTypes=h}geometryType(h){return this._geometryTypes[h]}get numGeometries(){return this._geometryTypes.length}containsSingleGeometryType(){return!1}}function Rit(M,h,b,D,z){let G=$h(M,b),X,Z,re,Q;if(US(G,D,M,b)===Ml.CONST){let It=PI(M,b,G),_t,qt,tr,lr;for(let qr=0;qr<h-1;qr++){let jr=$h(M,b);switch(jr.physicalStreamType){case Of.LENGTH:switch(jr.logicalStreamType.lengthType){case Xd.GEOMETRIES:_t=Kg(M,b,jr);break;case Xd.PARTS:qt=Kg(M,b,jr);break;case Xd.RINGS:tr=Kg(M,b,jr);break;case Xd.TRIANGLES:lr=Kg(M,b,jr)}break;case Of.OFFSET:switch(jr.logicalStreamType.offsetType){case ax.VERTEX:X=Zd(M,b,jr);break;case ax.INDEX:Q=Zd(M,b,jr)}break;case Of.DATA:Hv.VERTEX===jr.logicalStreamType.dictionaryType?Z=LI(M,b,jr):(re={numBits:jr.numBits,coordinateShift:jr.coordinateShift},Z=Zd(M,b,jr))}}return Q?_t!==void 0||qt!==void 0?Hee(D,It,lr,Q,Z,{geometryOffsets:_t,partOffsets:qt,ringOffsets:tr}):Hee(D,It,lr,Q,Z):re===void 0?(function(qr,jr,_n,fn,Dn){return new Uee(qr,jr,$g.VEC_2,_n,fn,Dn)})(D,It,{geometryOffsets:_t,partOffsets:qt,ringOffsets:tr},X,Z):(function(qr,jr,_n,fn,Dn,Hn){return new Uee(qr,jr,$g.MORTON,_n,fn,Dn,Hn)})(D,It,{geometryOffsets:_t,partOffsets:qt,ringOffsets:tr},X,Z,re)}let he=Zd(M,b,G),oe,ke,ge,Ue,je,vt,gt;for(let It=0;It<h-1;It++){let _t=$h(M,b);switch(_t.physicalStreamType){case Of.LENGTH:switch(_t.logicalStreamType.lengthType){case Xd.GEOMETRIES:oe=Zd(M,b,_t);break;case Xd.PARTS:ke=Zd(M,b,_t);break;case Xd.RINGS:ge=Zd(M,b,_t);break;case Xd.TRIANGLES:Ue=Kg(M,b,_t)}break;case Of.OFFSET:switch(_t.logicalStreamType.offsetType){case ax.VERTEX:X=Zd(M,b,_t);break;case ax.INDEX:Q=Zd(M,b,_t)}break;case Of.DATA:Hv.VERTEX===_t.logicalStreamType.dictionaryType?Z=LI(M,b,_t):(re={numBits:_t.numBits,coordinateShift:_t.coordinateShift},Z=Zd(M,b,_t))}}return oe?(je=DI(he,oe,2),ke&&ge?(vt=Wee(he,je,ke,!1),gt=(function(It,_t,qt,tr){let lr=new Uint32Array(qt[qt.length-1]+1),qr=0;lr[0]=qr;let jr=1,_n=1,fn=0;for(let Dn=0;Dn<It.length;Dn++){let Hn=It[Dn],Ln=_t[Dn+1]-_t[Dn];if(Hn!==0&&Hn!==3)for(let un=0;un<Ln;un++){let ei=qt[jr]-qt[jr-1];jr++;for(let ci=0;ci<ei;ci++)qr=lr[_n++]=qr+tr[fn++]}else for(let un=0;un<Ln;un++)lr[_n++]=++qr,jr++}return lr})(he,je,vt,ge)):ke&&(vt=(function(It,_t,qt){let tr=new Uint32Array(_t[_t.length-1]+1),lr=0;tr[0]=lr;let qr=1,jr=0;for(let _n=0;_n<It.length;_n++){let fn=It[_n],Dn=_t[_n+1]-_t[_n];if(fn===4||fn===1)for(let Hn=0;Hn<Dn;Hn++)lr=tr[qr++]=lr+qt[jr++];else for(let Hn=0;Hn<Dn;Hn++)tr[qr++]=++lr}return tr})(he,je,ke))):ke&&ge?(vt=DI(he,ke,1),gt=Wee(he,vt,ge,!0)):ke&&(vt=DI(he,ke,0)),Q&&!vt?jee(he,Ue,Q,Z):Q?jee(he,Ue,Q,Z,{geometryOffsets:je,partOffsets:vt,ringOffsets:gt}):re===void 0?(function(It,_t,qt,tr){return new Vee($g.VEC_2,It,_t,qt,tr)})(he,{geometryOffsets:je,partOffsets:vt,ringOffsets:gt},X,Z):(function(It,_t,qt,tr,lr){return new Vee($g.MORTON,It,_t,qt,tr,lr)})(he,{geometryOffsets:je,partOffsets:vt,ringOffsets:gt},X,Z,re)}function DI(M,h,b){let D=new Uint32Array(M.length+1),z=0;D[0]=z;let G=0;for(let X=0;X<M.length;X++)z=D[X+1]=z+(M[X]>b?h[G++]:1);return D}function Wee(M,h,b,D){let z=new Uint32Array(h[h.length-1]+1),G=0;z[0]=G;let X=1,Z=0;for(let re=0;re<M.length;re++){let Q=M[re],he=h[re+1]-h[re];if(Q===5||Q===2||D&&(Q===4||Q===1))for(let oe=0;oe<he;oe++)G=z[X++]=G+b[Z++];else for(let oe=0;oe<he;oe++)z[X++]=++G}return z}class Iit extends $_{constructor(h,b,D){super(h,b.getBuffer(),D),this.dataVector=b}getValueFromBuffer(h){return this.dataVector.get(h)}}class zit extends NS{getValueFromBuffer(h){return this.dataBuffer[h]}}class Yee extends $_{constructor(h,b,D,z){super(h,z?BigInt64Array.of(b):BigUint64Array.of(b),D)}getValueFromBuffer(h){return this.dataBuffer[0]}}function x4(M,h,b,D,z){let G=(function(X,Z,re,Q){let he=new Uint8Array(Z),oe=0,ke=Q.get()+re;for(;oe<Z&&!(Q.get()>=ke);){let ge=X[Q.increment()];if(ge<=127){let Ue=ge+3,je=X[Q.increment()],vt=Math.min(oe+Ue,Z);he.fill(je,oe,vt),oe=vt}else{let Ue=256-ge;for(let je=0;je<Ue&&oe<Z;je++)he[oe++]=X[Q.increment()]}}return Q.set(ke),he})(M,Math.ceil(h/8),b,D);return z?(function(X,Z,re){if(!re)return X;let Q=re.size(),he=new jv(X,Z),oe=new jv(new Uint8Array(Math.ceil(Q/8)),Q),ke=0;for(let ge=0;ge<Q;ge++){let Ue=!!re.get(ge)&&he.get(ke++);oe.set(ge,Ue)}return oe.getBuffer()})(G,h,z):G}let qit=new TextDecoder;function RI(M,h,b){return b-h>=12?qit.decode(M.subarray(h,b)):(function(D,z,G){let X=\"\",Z=z;for(;Z<G;){let re=D[Z],Q,he,oe,ke=null,ge=re>239?4:re>223?3:re>191?2:1;if(Z+ge>G)break;ge===1?re<128&&(ke=re):ge===2?(Q=D[Z+1],(192&Q)==128&&(ke=(31&re)<<6|63&Q,ke<=127&&(ke=null))):ge===3?(Q=D[Z+1],he=D[Z+2],(192&Q)==128&&(192&he)==128&&(ke=(15&re)<<12|(63&Q)<<6|63&he,(ke<=2047||ke>=55296&&ke<=57343)&&(ke=null))):ge===4&&(Q=D[Z+1],he=D[Z+2],oe=D[Z+3],(192&Q)==128&&(192&he)==128&&(192&oe)==128&&(ke=(15&re)<<18|(63&Q)<<12|(63&he)<<6|63&oe,(ke<=65535||ke>=1114112)&&(ke=null))),ke===null?(ke=65533,ge=1):ke>65535&&(ke-=65536,X+=String.fromCharCode(ke>>>10&1023|55296),ke=56320|1023&ke),X+=String.fromCharCode(ke),Z+=ge}return X})(M,h,b)}class II extends $_{constructor(h,b,D,z){super(h,D,z),this.offsetBuffer=b}}class Xee extends II{constructor(h,b,D,z){super(h,b,D,z!=null?z:b.length-1)}getValueFromBuffer(h){return RI(this.dataBuffer,this.offsetBuffer[h],this.offsetBuffer[h+1])}}class nb extends II{constructor(h,b,D,z,G){super(h,D,z,G!=null?G:b.length),this.indexBuffer=b,this.indexBuffer=b}getValueFromBuffer(h){let b=this.indexBuffer[h];return RI(this.dataBuffer,this.offsetBuffer[b],this.offsetBuffer[b+1])}}class Zee extends II{constructor(h,b,D,z,G,X,Z){super(h,D,z,Z),this.indexBuffer=b,this.symbolOffsetBuffer=G,this.symbolTableBuffer=X}getValueFromBuffer(h){this.decodedDictionary==null&&(this.symbolLengthBuffer==null&&(this.symbolLengthBuffer=this.offsetToLengthBuffer(this.symbolOffsetBuffer)),this.decodedDictionary=(function(D,z,G){let X=[],Z=new Array(z.length).fill(0);for(let re=1;re<z.length;re++)Z[re]=Z[re-1]+z[re-1];for(let re=0;re<G.length;re++)if(G[re]===255)X.push(G[++re]);else{let Q=z[G[re]],he=Z[G[re]];for(let oe=0;oe<Q;oe++)X.push(D[he+oe])}return new Uint8Array(X)})(this.symbolTableBuffer,this.symbolLengthBuffer,this.dataBuffer));let b=this.indexBuffer[h];return RI(this.decodedDictionary,this.offsetBuffer[b],this.offsetBuffer[b+1])}offsetToLengthBuffer(h){let b=new Uint32Array(h.length-1),D=h[0];for(let z=1;z<h.length;z++){let G=h[z];b[z-1]=G-D,D=G}return b}}function Bit(M,h,b,D,z,G){return b.type===\"scalarType\"?(function(X,Z,re,Q,he,oe){let ke=null;if(X===0)return null;if(oe.nullable){let Ue=$h(Z,re),je=Ue.numValues,vt=re.get(),gt=x4(Z,je,Ue.byteLength,re);re.set(vt+Ue.byteLength),ke=new jv(gt,Ue.numValues)}let ge=ke!=null?ke:Q;switch(he.physicalType){case 4:case 3:return(function(Ue,je,vt,gt,It){let _t=$h(Ue,je),qt=US(_t,It,Ue,je),tr=gt.physicalType===3;if(qt===Ml.FLAT){let qr=_4(It)?It:void 0,jr=tr?LI(Ue,je,_t,0,qr):Zd(Ue,je,_t,0,qr);return new xI(vt.name,jr,It)}if(qt===Ml.SEQUENCE){let qr=Dee(Ue,je,_t);return new bI(vt.name,qr[0],qr[1],_t.numRleValues)}let lr=tr?(function(qr,jr,_n){let fn=eb(qr,jr,_n);return fn.length===1?Fs(fn[0]):(function(Dn){return Fs(Dn[1])})(fn)})(Ue,je,_t):PI(Ue,je,_t);return new wI(vt.name,lr,It,tr)})(Z,re,oe,he,ge);case 9:return(function(Ue,je,vt,gt,It){var Dn,Hn;let _t=null,qt=null,tr=null,lr=null,qr=null,jr=It!=null?It:null,_n=null,fn=null;for(let Ln=0;Ln<gt;Ln++){let un=$h(je,vt);switch(un.physicalStreamType){case Of.PRESENT:{let ei=x4(je,un.numValues,un.byteLength,vt),ci=new jv(ei,un.numValues);jr=It!=null?It:ci;break}case Of.OFFSET:qt=Zd(je,vt,un,0,jr);break;case Of.LENGTH:{let ei=Kg(je,vt,un);Xd.DICTIONARY===un.logicalStreamType.lengthType?_t=ei:Xd.SYMBOL===un.logicalStreamType.lengthType?lr=ei:_n=ei;break}case Of.DATA:{let ei=je.subarray(vt.get(),vt.get()+un.byteLength);vt.add(un.byteLength);let ci=un.logicalStreamType.dictionaryType;Hv.FSST===ci?qr=ei:Hv.SINGLE===ci||Hv.SHARED===ci?tr=ei:Hv.NONE===ci&&(fn=ei);break}}}return(Hn=(Dn=(function(Ln,un,ei,ci,ti,di,_a){return un?new Zee(Ln,ei,ci,ti,di,un,_a):null})(Ue,qr,qt,_t,tr,lr,jr))!=null?Dn:(function(Ln,un,ei,ci,ti){return un?ti?new nb(Ln,ei,ci,un,ti):new nb(Ln,ei,ci,un):null})(Ue,tr,qt,_t,jr))!=null?Hn:(function(Ln,un,ei,ci,ti){if(!un||!ei)return null;if(ci)return ti?new nb(Ln,ci,un,ei,ti):new nb(Ln,ci,un,ei);if(ti&&ti.size()!==un.length-1){let di=new Uint32Array(ti.size()),_a=0;for(let Ao=0;Ao<ti.size();Ao++)di[Ao]=ti.get(Ao)?_a++:0;return new nb(Ln,di,un,ei,ti)}return ti?new Xee(Ln,un,ei,ti):new Xee(Ln,un,ei)})(Ue,_n,fn,qt,jr)})(oe.name,Z,re,oe.nullable?X-1:X,ke);case 0:return(function(Ue,je,vt,gt,It){let _t=$h(Ue,je),qt=_t.numValues,tr=je.get(),lr=_4(It)?It:void 0,qr=x4(Ue,qt,_t.byteLength,je,lr);je.set(tr+_t.byteLength);let jr=new jv(qr,qt);return new Iit(vt.name,jr,It)})(Z,re,oe,0,ge);case 6:case 5:return(function(Ue,je,vt,gt,It){let _t=$h(Ue,je),qt=US(_t,gt,Ue,je,\"int64\"),tr=It.physicalType===5;if(qt===Ml.FLAT){let qr=_4(gt)?gt:void 0,jr=tr?(function(_n,fn,Dn,Hn){return(function(Ln,un,ei){let ci;switch(un.logicalLevelTechnique1){case To.DELTA:if(un.logicalLevelTechnique2===To.RLE){let ti=un;if(!ei)return(function(di,_a,Ao){let us=new BigInt64Array(Ao),ks=0,Uf=BigInt(0);for(let Jl=0;Jl<_a;Jl++){let _c=Number(di[Jl]),zc=Zh(di[Jl+_a]);for(let Sl=0;Sl<_c;Sl++)Uf+=zc,us[ks++]=Uf}return us})(Ln,ti.runs,ti.numRleValues);ci=Pee(Ln=kI(Ln,ti.runs,ti.numRleValues))}else ci=Pee(Ln);break;case To.RLE:ci=(function(ti,di,_a){if(_a===void 0){_a=0;for(let ks=0;ks<di;ks++)_a+=Number(ti[ks])}let Ao=new BigInt64Array(_a),us=0;for(let ks=0;ks<di;ks++){let Uf=Number(ti[ks]),Jl=ti[ks+di];Jl=Zh(Jl),Ao.fill(Jl,us,us+Uf),us+=Uf}return Ao})(Ln,un.runs,un.numRleValues);break;case To.NONE:ci=(function(ti){let di=new BigInt64Array(ti.length);for(let _a=0;_a<ti.length;_a++)di[_a]=Zh(ti[_a]);return di})(Ln);break;default:throw new Error(`The specified Logical level technique is not supported: ${un.logicalLevelTechnique1}`)}return ei?Q_(ci,ei,BigInt(0)):ci})(K_(_n,fn,Dn.numValues),Dn,Hn)})(Ue,je,_t,qr):Iee(Ue,je,_t,qr);return new qee(vt.name,jr,gt)}if(qt===Ml.SEQUENCE){let qr=Ree(Ue,je,_t);return new Bee(vt.name,qr[0],qr[1],_t.numRleValues)}let lr=tr?(function(qr,jr,_n){let fn=K_(qr,jr,_n.numValues);return fn.length===1?Zh(fn[0]):(function(Dn){return Zh(Dn[1])})(fn)})(Ue,je,_t):zee(Ue,je,_t);return new Yee(vt.name,lr,gt,tr)})(Z,re,oe,ge,he);case 7:return(function(Ue,je,vt,gt){let It=$h(Ue,je),_t=_4(gt)?gt:void 0,qt=(function(tr,lr,qr,jr){let _n=lr.get(),fn=_n+qr*Float32Array.BYTES_PER_ELEMENT,Dn=new Uint8Array(tr.subarray(_n,fn)).buffer,Hn=new Float32Array(Dn);return lr.set(fn),jr?Q_(Hn,jr,0):Hn})(Ue,je,It.numValues,_t);return new zit(vt.name,qt,gt)})(Z,re,oe,ge);case 8:return(function(Ue,je,vt,gt){let It=$h(Ue,je),_t=_4(gt)?gt:void 0,qt=(function(tr,lr,qr,jr){let _n=lr.get(),fn=_n+qr*Float64Array.BYTES_PER_ELEMENT,Dn=new Uint8Array(tr.subarray(_n,fn)).buffer,Hn=new Float64Array(Dn);return lr.set(fn),jr?Q_(Hn,jr,0):Hn})(Ue,je,It.numValues,_t);return new _I(vt.name,qt,gt)})(Z,re,oe,ge);default:throw new Error(`The specified data type for the field is currently not supported: ${he}`)}})(D,M,h,z,b.scalarType,b):D===0?null:(function(X,Z,re,Q){let he=null,oe=null,ke=null,ge=null,Ue=!1;for(;!Ue;){let It=$h(X,Z);switch(It.physicalStreamType){case Of.LENGTH:Xd.DICTIONARY===It.logicalStreamType.lengthType?he=Kg(X,Z,It):ke=Kg(X,Z,It);break;case Of.DATA:Hv.SINGLE===It.logicalStreamType.dictionaryType||Hv.SHARED===It.logicalStreamType.dictionaryType?(oe=X.subarray(Z.get(),Z.get()+It.byteLength),Ue=!0):ge=X.subarray(Z.get(),Z.get()+It.byteLength),Z.add(It.byteLength)}}let je=re.complexType.children,vt=[],gt=0;for(let It of je){let _t=xh(X,Z,1)[0];if(_t===0)continue;let qt=It.name?`${re.name}${It.name}`:re.name;if(_t!==2||It.type!==\"scalarField\"||It.scalarField.physicalType!==9)throw new Error(\"Currently only optional string fields are implemented for a struct.\");let tr=$h(X,Z),lr=x4(X,tr.numValues,tr.byteLength,Z),qr=$h(X,Z),jr=Zd(X,Z,qr,0,qr.decompressedCount!==Q?new jv(lr,tr.numValues):void 0);vt[gt++]=ge?new Zee(qt,jr,he,oe,ke,ge,new jv(lr,tr.numValues)):new nb(qt,jr,he,oe,new jv(lr,tr.numValues))}return vt})(M,h,b,z)}function _4(M){return M instanceof jv}function $ee(M){switch(M){case 0:case 1:case 2:case 3:{let h={};h.nullable=!!(1&M),h.columnScope=0;let b={type:\"logicalType\",logicalType:0};return b.longID=!!(2&M),h.scalarType=b,h.type=\"scalarType\",h}case 4:{let h={nullable:!1,columnScope:0},b={type:\"physicalType\",physicalType:0};return h.type=\"complexType\",h.complexType=b,h}case 30:{let h={nullable:!1,columnScope:0},b={type:\"physicalType\",physicalType:1};return h.type=\"complexType\",h.complexType=b,h}default:return(function(h){let b;switch(h){case 10:case 11:b=0;break;case 12:case 13:b=1;break;case 14:case 15:b=2;break;case 16:case 17:b=3;break;case 18:case 19:b=4;break;case 20:case 21:b=5;break;case 22:case 23:b=6;break;case 24:case 25:b=7;break;case 26:case 27:b=8;break;case 28:case 29:b=9;break;default:return null}let D={};D.nullable=!!(1&h),D.columnScope=0;let z={type:\"physicalType\"};return z.physicalType=b,D.type=\"scalarType\",D.scalarType=z,D})(M)}}function Kee(M){return M>=10}function Jee(M){return M===30}function Oit(M){if(M.type===\"scalarType\"){let h=M.scalarType;if(h.type===\"physicalType\")switch(h.physicalType){case 0:case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:default:return!1;case 9:return!0}if(h.type===\"logicalType\")return!1}else if(M.type===\"complexType\"){let h=M.complexType;if(h.type===\"physicalType\")switch(h.physicalType){case 0:case 1:return!0;default:return!1}}return console.warn(\"Unexpected column type in hasStreamCount\",M),!1}function Nit(M){var h;return M.type===\"complexType\"&&((h=M.complexType)==null?void 0:h.type)===\"physicalType\"&&M.complexType.physicalType===0}let Uit=new TextDecoder;function zI(M,h){let b=xh(M,h,1)[0];if(b===0)return\"\";let D=h.get(),z=M.subarray(D,D+b);return h.add(b),Uit.decode(z)}function Qee(M,h){let b=xh(M,h,1)[0]>>>0;if(b<10||b>30)throw new Error(`Unsupported field type code ${b}. Supported: 10-29(scalars), 30(STRUCT)`);let D=$ee(b);if(Kee(b)&&(D.name=zI(M,h)),Jee(b)){let z=xh(M,h,1)[0]>>>0;D.complexType.children=new Array(z);for(let G=0;G<z;G++)D.complexType.children[G]=Qee(M,h)}return(function(z){return{name:z.name,nullable:z.nullable,scalarField:z.scalarType,complexField:z.complexType,type:z.type===\"scalarType\"?\"scalarField\":\"complexField\"}})(D)}function Vit(M,h){let b=xh(M,h,1)[0]>>>0,D=$ee(b);if(!D)throw new Error(`Unsupported column type code ${b}. Supported: 0-3(ID), 4(GEOMETRY), 10-29(scalars), 30(STRUCT)`);if(Kee(b)?D.name=zI(M,h):b>=0&&b<=3?D.name=\"id\":b===4&&(D.name=\"geometry\"),Jee(b)){let z=xh(M,h,1)[0]>>>0,G=D.complexType;G.children=new Array(z);for(let X=0;X<z;X++)G.children[X]=Qee(M,h)}return D}function Git(M,h){let b={featureTables:[]},D={};D.name=zI(M,h);let z=xh(M,h,1)[0]>>>0,G=xh(M,h,1)[0]>>>0;D.columns=new Array(G);for(let X=0;X<G;X++)D.columns[X]=Vit(M,h);return b.featureTables.push(D),[b,z]}function Hit(M,h,b,D,z,G,X=!1){let Z=h.scalarType;if(!Z||Z.type!==\"logicalType\"||Z.logicalType!==0)throw new Error(`ID column must be a logical ID scalar type: ${D}`);let re=Z.longID?6:4,Q=typeof G==\"number\"?void 0:G,he=US(z,G,M,b,re===6?\"int64\":\"int32\");if(re===4)switch(he){case Ml.FLAT:{let oe=Zd(M,b,z,0,Q);return new xI(D,oe,G)}case Ml.SEQUENCE:{let oe=Dee(M,b,z);return new bI(D,oe[0],oe[1],z.numRleValues)}case Ml.CONST:{let oe=PI(M,b,z);return new wI(D,oe,G,!1)}}switch(he){case Ml.FLAT:{if(X){let ke=(function(ge,Ue,je){let vt=(function(gt,It,_t){let qt=new Float64Array(_t);for(let tr=0;tr<_t;tr++)qt[tr]=Pit(gt,It);return qt})(ge,Ue,je.numValues);return(function(gt,It){switch(It.logicalLevelTechnique1){case To.DELTA:return It.logicalLevelTechnique2===To.RLE&&(gt=Cee(gt,It.runs,It.numRleValues)),(function(_t){_t[0]=J_(_t[0]);let qt=_t.length/4*4,tr=1;if(qt>=4)for(;tr<qt-4;tr+=4){let lr=_t[tr+1],qr=_t[tr+2],jr=_t[tr+3];_t[tr]=J_(_t[tr])+_t[tr-1],_t[tr+1]=J_(lr)+_t[tr],_t[tr+2]=J_(qr)+_t[tr+1],_t[tr+3]=J_(jr)+_t[tr+2]}for(;tr!==_t.length;++tr)_t[tr]=J_(_t[tr])+_t[tr-1]})(gt),gt;case To.RLE:return(function(_t,qt){return Cee(_t,qt.runs,qt.numRleValues)})(gt,It);case To.NONE:return gt;default:throw new Error(`The specified Logical level technique is not supported: ${It.logicalLevelTechnique1}`)}})(vt,je)})(M,b,z);return new _I(D,ke,G)}let oe=Iee(M,b,z,Q);return new qee(D,oe,G)}case Ml.SEQUENCE:{let oe=Ree(M,b,z);return new Bee(D,oe[0],oe[1],z.numRleValues)}case Ml.CONST:{let oe=zee(M,b,z);return new Yee(D,oe,G,!1)}}throw new Error(\"Vector type not supported for id column.\")}class jit{constructor(h,b){var D;switch(this._featureData=h,this.properties=this._featureData.properties||{},(D=this._featureData.geometry)===null||D===void 0?void 0:D.type){case Ic.POINT:case Ic.MULTIPOINT:this.type=1;break;case Ic.LINESTRING:case Ic.MULTILINESTRING:this.type=2;break;case Ic.POLYGON:case Ic.MULTIPOLYGON:this.type=3;break;default:this.type=0}this.extent=b,this.id=Number(this._featureData.id)}loadGeometry(){let h=[];for(let b of this._featureData.geometry.coordinates){let D=[];for(let z of b)D.push(new o(z.x,z.y));h.push(D)}return h}}class Wit{constructor(h){this.features=[],this.featureTable=h,this.name=h.name,this.extent=h.extent,this.version=2,this.features=h.getFeatures(),this.length=this.features.length}feature(h){return new jit(this.features[h],this.extent)}}class ete{constructor(h){this.layers={};let b=(function(D,z,G=!0){var Q;let X=new Eee(0),Z=[];for(;X.get()<D.length;){let he=xh(D,X,1)[0]>>>0,oe=X.get()+he;if(oe>D.length)throw new Error(`Block overruns tile: ${oe} > ${D.length}`);if(xh(D,X,1)[0]>>>0!=1){X.set(oe);continue}let[ke,ge]=Git(D,X),Ue=ke.featureTables[0],je=null,vt=null,gt=[],It=0;for(let qt of Ue.columns){let tr=qt.name;if((re=qt).type===\"scalarType\"&&((Q=re.scalarType)==null?void 0:Q.type)===\"logicalType\"&&re.scalarType.logicalType===0){let lr=null;if(qt.nullable){let jr=$h(D,X),_n=X.get(),fn=x4(D,jr.numValues,jr.byteLength,X);X.set(_n+jr.byteLength),lr=new jv(fn,jr.numValues)}let qr=$h(D,X);It=lr?lr.size():qr.decompressedCount,je=Hit(D,qt,X,tr,qr,lr!=null?lr:It,G)}else if(Nit(qt)){let lr=xh(D,X,1)[0];if(It===0){let qr=X.get();It=$h(D,X).decompressedCount,X.set(qr)}vt=Rit(D,lr,X,It)}else{let lr=Oit(qt)?xh(D,X,1)[0]:1;if(lr===0)continue;let qr=Bit(D,X,qt,lr,It);if(qr)if(Array.isArray(qr))for(let jr of qr)gt.push(jr);else gt.push(qr)}}let _t=new wit(Ue.name,vt,je,gt,ge);Z.push(_t),X.set(oe)}var re;return Z})(new Uint8Array(h));this.layers=b.reduce(((D,z)=>Object.assign(Object.assign({},D),{[z.name]:new Wit(z)})),{})}}class Yit{constructor(h,b){this.feature=h,this.type=h.type,this.properties=h.tags?h.tags:{},this.extent=b,\"id\"in h&&(typeof h.id==\"string\"?this.id=parseInt(h.id,10):typeof h.id!=\"number\"||isNaN(h.id)||(this.id=h.id))}loadGeometry(){let h=[],b=this.feature.type===1?[this.feature.geometry]:this.feature.geometry;for(let D of b){let z=[];for(let G of D)z.push(new o(G[0],G[1]));h.push(z)}return h}}let b4=\"_geojsonTileLayer\";function Xit(M,h,b=\"\"){h.writeVarintField(15,M.version||1),h.writeStringField(1,M.name||\"\"),h.writeVarintField(5,M.extent||4096);let D={jsonPrefix:b,keys:[],values:[],keycache:{},valuecache:{}};for(let X=0;X<M.length;X++)D.feature=M.feature(X),h.writeMessage(2,Zit,D);let z=D.keys;for(let X of z)h.writeStringField(3,X);let G=D.values;for(let X of G)h.writeMessage(4,Jit,X)}function Zit(M,h){if(!M.feature)return;let b=M.feature;b.id!==void 0&&h.writeVarintField(1,b.id),h.writeMessage(2,$it,M),h.writeVarintField(3,b.type),h.writeMessage(4,Kit,b)}function $it(M,h){var b;for(let D in(b=M.feature)==null?void 0:b.properties){let z=M.feature.properties[D],G=M.keycache[D];if(z==null)continue;G===void 0&&(M.keys.push(D),G=M.keys.length-1,M.keycache[D]=G),h.writeVarint(G),typeof z!=\"string\"&&typeof z!=\"boolean\"&&typeof z!=\"number\"&&(z=M.jsonPrefix+JSON.stringify(z));let X=typeof z+\":\"+z,Z=M.valuecache[X];Z===void 0&&(M.values.push(z),Z=M.values.length-1,M.valuecache[X]=Z),h.writeVarint(Z)}}function qI(M,h){return(h<<3)+(7&M)}function tte(M){return M<<1^M>>31}function Kit(M,h){let b=M.loadGeometry(),D=M.type,z=0,G=0;for(let X of b){let Z=1;D===1&&(Z=X.length),h.writeVarint(qI(1,Z));let re=D===3?X.length-1:X.length;for(let Q=0;Q<re;Q++){Q===1&&D!==1&&h.writeVarint(qI(2,re-1));let he=X[Q].x-z,oe=X[Q].y-G;h.writeVarint(tte(he)),h.writeVarint(tte(oe)),z+=he,G+=oe}M.type===3&&h.writeVarint(qI(7,1))}}function Jit(M,h){let b=typeof M;b===\"string\"?h.writeStringField(1,M):b===\"boolean\"?h.writeBooleanField(7,M):b===\"number\"&&(M%1!=0?h.writeDoubleField(3,M):M<0?h.writeSVarintField(6,M):h.writeVarintField(5,M))}class rte{constructor(h,b){this.tileID=h,this.x=h.canonical.x,this.y=h.canonical.y,this.z=h.canonical.z,this.grid=new hu(j,16,0),this.grid3D=new hu(j,16,0),this.featureIndexArray=new Pe,this.promoteId=b}insert(h,b,D,z,G,X){let Z=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(D,z,G);let re=X?this.grid3D:this.grid;for(let Q of b){let he=[1/0,1/0,-1/0,-1/0];for(let oe of Q)he[0]=Math.min(he[0],oe.x),he[1]=Math.min(he[1],oe.y),he[2]=Math.max(he[2],oe.x),he[3]=Math.max(he[3],oe.y);he[0]<j&&he[1]<j&&he[2]>=0&&he[3]>=0&&re.insert(Z,he[0],he[1],he[2],he[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=this.encoding!==\"mlt\"?new CQ(new IS(this.rawTileData)).layers:new ete(this.rawTileData).layers,this.sourceLayerCoder=new Aee(this.vtLayers?Object.keys(this.vtLayers).sort():[b4])),this.vtLayers}query(h,b,D,z){this.loadVTLayers();let G=h.params,X=j/h.tileSize/h.scale,Z=cn(G.filter,G.globalState),re=h.queryGeometry,Q=h.queryPadding*X,he=ix.fromPoints(re),oe=this.grid.query(he.minX-Q,he.minY-Q,he.maxX+Q,he.maxY+Q),ke=ix.fromPoints(h.cameraQueryGeometry).expandBy(Q),ge=this.grid3D.query(ke.minX,ke.minY,ke.maxX,ke.maxY,((vt,gt,It,_t)=>(function(qt,tr,lr,qr,jr){for(let fn of qt)if(tr<=fn.x&&lr<=fn.y&&qr>=fn.x&&jr>=fn.y)return!0;let _n=[new o(tr,lr),new o(tr,jr),new o(qr,jr),new o(qr,lr)];if(qt.length>2){for(let fn of _n)if(pn(qt,fn))return!0}for(let fn=0;fn<qt.length-1;fn++)if(Mn(qt[fn],qt[fn+1],_n))return!0;return!1})(h.cameraQueryGeometry,vt-Q,gt-Q,It+Q,_t+Q)));for(let vt of ge)oe.push(vt);oe.sort(Qit);let Ue={},je;for(let vt of oe){if(vt===je)continue;je=vt;let gt=this.featureIndexArray.get(vt),It=null;this.loadMatchingFeature(Ue,gt.bucketIndex,gt.sourceLayerIndex,gt.featureIndex,Z,G.layers,G.availableImages,b,D,z,((_t,qt,tr)=>(It||(It=$l(_t)),qt.queryIntersectsFeature({queryGeometry:re,feature:_t,featureState:tr,geometry:It,zoom:this.z,transform:h.transform,pixelsToTileUnits:X,pixelPosMatrix:h.pixelPosMatrix,unwrappedTileID:this.tileID.toUnwrapped(),getElevation:h.getElevation}))))}return Ue}loadMatchingFeature(h,b,D,z,G,X,Z,re,Q,he,oe){let ke=this.bucketLayerIDs[b];if(X&&!ke.some((vt=>X.has(vt))))return;let ge=this.sourceLayerCoder.decode(D),Ue=this.vtLayers[ge].feature(z);if(G.needGeometry){let vt=xa(Ue,!0);if(!G.filter(new mi(this.tileID.overscaledZ),vt,this.tileID.canonical))return}else if(!G.filter(new mi(this.tileID.overscaledZ),Ue))return;let je=this.getId(Ue,ge);for(let vt of ke){if(X&&!X.has(vt))continue;let gt=re[vt];if(!gt)continue;let It={};je&&he&&(It=he.getState(gt.sourceLayer||b4,je));let _t=_e({},Q[vt]);_t.paint=nte(_t.paint,gt.paint,Ue,It,Z),_t.layout=nte(_t.layout,gt.layout,Ue,It,Z);let qt=!oe||oe(Ue,gt,It);if(!qt)continue;let tr=new Mee(Ue,this.z,this.x,this.y,je);tr.layer=_t;let lr=h[vt];lr===void 0&&(lr=h[vt]=[]),lr.push({featureIndex:z,feature:tr,intersectionZ:qt})}}lookupSymbolFeatures(h,b,D,z,G,X,Z,re){let Q={};this.loadVTLayers();let he=cn(G.filterSpec,G.globalState);for(let oe of h)this.loadMatchingFeature(Q,D,z,oe,he,X,Z,re,b);return Q}hasLayer(h){for(let b of this.bucketLayerIDs)for(let D of b)if(h===D)return!0;return!1}getId(h,b){var D;let z=h.id;return this.promoteId&&(z=h.properties[typeof this.promoteId==\"string\"?this.promoteId:this.promoteId[b]],typeof z==\"boolean\"&&(z=Number(z)),z===void 0&&(!((D=h.properties)===null||D===void 0)&&D.cluster)&&this.promoteId&&(z=Number(h.properties.cluster_id))),z}}function nte(M,h,b,D,z){return ae(M,((G,X)=>{let Z=h instanceof yc?h.get(X):null;return Z!=null&&Z.evaluate?Z.evaluate(b,D,z):Z}))}function Qit(M,h){return h-M}function ite(M,h,b,D,z){let G=[];for(let X of M){let Z;for(let re=0;re<X.length-1;re++){let Q=X[re],he=X[re+1];Q.x<h&&he.x<h||(Q.x<h?Q=new o(h,Q.y+(h-Q.x)/(he.x-Q.x)*(he.y-Q.y))._round():he.x<h&&(he=new o(h,Q.y+(h-Q.x)/(he.x-Q.x)*(he.y-Q.y))._round()),Q.y<b&&he.y<b||(Q.y<b?Q=new o(Q.x+(b-Q.y)/(he.y-Q.y)*(he.x-Q.x),b)._round():he.y<b&&(he=new o(Q.x+(b-Q.y)/(he.y-Q.y)*(he.x-Q.x),b)._round()),Q.x>=D&&he.x>=D||(Q.x>=D?Q=new o(D,Q.y+(D-Q.x)/(he.x-Q.x)*(he.y-Q.y))._round():he.x>=D&&(he=new o(D,Q.y+(D-Q.x)/(he.x-Q.x)*(he.y-Q.y))._round()),Q.y>=z&&he.y>=z||(Q.y>=z?Q=new o(Q.x+(z-Q.y)/(he.y-Q.y)*(he.x-Q.x),z)._round():he.y>=z&&(he=new o(Q.x+(z-Q.y)/(he.y-Q.y)*(he.x-Q.x),z)._round()),Z&&Q.equals(Z[Z.length-1])||(Z=[Q],G.push(Z)),Z.push(he)))))}}return G}function ate(M,h,b,D,z){switch(h){case 1:return(function(G,X,Z,re){let Q=[];for(let he of G)for(let oe of he){let ke=re===0?oe.x:oe.y;ke>=X&&ke<=Z&&Q.push([oe])}return Q})(M,b,D,z);case 2:return ote(M,b,D,z,!1);case 3:return 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Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.`)}return N}getPixelSize(){let{width:d,height:P}=this.atlasImage;return{width:d,height:P}}getPattern(d){let P=this.patterns[d],N=this.getImage(d);if(!N)return null;if(P&&P.position.version===N.version)return P.position;if(P)P.position.version=N.version;else{let H={w:N.data.width+2,h:N.data.height+2,x:0,y:0},$=new i.I(H,N);this.patterns[d]={bin:H,position:$}}return this._updatePatternAtlas(),this.patterns[d].position}bind(d){let P=d.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new i.T(d,this.atlasImage,P.RGBA),this.atlasTexture.bind(P.LINEAR,P.CLAMP_TO_EDGE)}_updatePatternAtlas(){let d=[];for(let $ in this.patterns)d.push(this.patterns[$].bin);let{w:P,h:N}=i.p(d),H=this.atlasImage;H.resize({width:P||1,height:N||1});for(let $ in this.patterns){let{bin:se}=this.patterns[$],de=se.x+1,Ae=se.y+1,Fe=this.getImage($).data,Ie=Fe.width,Ye=Fe.height;i.R.copy(Fe,H,{x:0,y:0},{x:de,y:Ae},{width:Ie,height:Ye}),i.R.copy(Fe,H,{x:0,y:Ye-1},{x:de,y:Ae-1},{width:Ie,height:1}),i.R.copy(Fe,H,{x:0,y:0},{x:de,y:Ae+Ye},{width:Ie,height:1}),i.R.copy(Fe,H,{x:Ie-1,y:0},{x:de-1,y:Ae},{width:1,height:Ye}),i.R.copy(Fe,H,{x:0,y:0},{x:de+Ie,y:Ae},{width:1,height:Ye})}this.dirty=!0}beginFrame(){this.callbackDispatchedThisFrame={}}dispatchRenderCallbacks(d){for(let P of d){if(this.callbackDispatchedThisFrame[P])continue;this.callbackDispatchedThisFrame[P]=!0;let N=this.getImage(P);N||i.w(`Image with ID: \"${P}\" was not found`),S(N)&&this.updateImage(P,N)}}cloneImages(){let d={};for(let P in this.images){let N=this.images[P];d[P]=Object.assign(Object.assign({},N),{data:N.data?N.data.clone():null})}return d}}let w=1e20,A=new Float64Array(256);for(let Y=0;Y<256;Y++){let d=.5-Math.pow(Y/255,.45454545454545453);A[Y]=d*Math.abs(d)}function T(Y,d,P,N,H,$,se,de,Ae){for(let Fe=d;Fe<d+N;Fe++)m(Y,P*$+Fe,$,H,se,de,Ae);for(let Fe=P;Fe<P+H;Fe++)m(Y,Fe*$+d,1,N,se,de,Ae)}function m(Y,d,P,N,H,$,se){$[0]=0,se[0]=-w,se[1]=w,H[0]=Y[d];for(let de=1,Ae=0,Fe=0;de<N;de++){H[de]=Y[d+de*P];let Ie=de*de;do{let Ye=$[Ae];Fe=(H[de]-H[Ye]+Ie-Ye*Ye)/(de-Ye)/2}while(Fe<=se[Ae]&&--Ae>-1);Ae++,$[Ae]=de,se[Ae]=Fe,se[Ae+1]=w}for(let de=0,Ae=0;de<N;de++){for(;se[Ae+1]<de;)Ae++;let Fe=$[Ae],Ie=de-Fe;Y[d+de*P]=H[Fe]+Ie*Ie}}A[255]=-w;let F=i.v.layout_symbol[\"text-font\"].default.join(\",\");class E{constructor(d,P,N){this.requestManager=d,this.localIdeographFontFamily=P,this.entries={},this.lang=N}setURL(d){this.url=d}getGlyphs(d){return i._(this,void 0,void 0,(function*(){let P=[];for(let $ in d)for(let se of d[$])P.push(this._getAndCacheGlyphsPromise($,se));let N=yield Promise.all(P),H={};for(let{stack:$,id:se,glyph:de}of N)H[$]||(H[$]={}),H[$][se]=de&&{id:de.id,bitmap:de.bitmap.clone(),metrics:de.metrics};return H}))}_getAndCacheGlyphsPromise(d,P){return i._(this,void 0,void 0,(function*(){var N,H;(N=(H=this.entries)[d])!==null&&N!==void 0||(H[d]={glyphs:{},requests:{},ranges:{}});let $=this.entries[d],se=$.glyphs[P];return se!==void 0?{stack:d,id:P,glyph:se}:!this.url||this._charUsesLocalIdeographFontFamily(P)?(se=$.glyphs[P]=this._drawGlyph($,d,P),{stack:d,id:P,glyph:se}):yield this._downloadAndCacheRangePromise(d,P)}))}_downloadAndCacheRangePromise(d,P){return i._(this,void 0,void 0,(function*(){var N;let H=this.entries[d],$=Math.floor(P/256);if(H.ranges[$])return{stack:d,id:P,glyph:null};(N=H.requests)[$]||(N[$]=E.loadGlyphRange(d,$,this.url,this.requestManager));try{let se=yield H.requests[$];for(let de in se)H.glyphs[+de]=se[+de];return H.ranges[$]=!0,{stack:d,id:P,glyph:se[P]||null}}catch(se){let de=H.glyphs[P]=this._drawGlyph(H,d,P);return this._warnOnMissingGlyphRange(de,$,P,i.d(se)),{stack:d,id:P,glyph:de}}}))}_warnOnMissingGlyphRange(d,P,N,H){let $=256*P,se=$+255,de=N.toString(16).padStart(4,\"0\").toUpperCase();i.w(`Unable to load glyph range ${P}, ${$}-${se}. Rendering codepoint U+${de} locally instead. ${H}`)}_charUsesLocalIdeographFontFamily(d){return!!this.localIdeographFontFamily&&i.r(d)}_drawGlyph(d,P,N){let H=P===F&&this.localIdeographFontFamily!==\"\"&&this._charUsesLocalIdeographFontFamily(N),$=H?\"ideographTinySDF\":\"tinySDF\";d[$]||(d[$]=this._createTinySDF(H?this.localIdeographFontFamily:P));let se=d[$].draw(String.fromCodePoint(N)),de=new RegExp(\"^\\\\p{gc=Cf}+$\",\"u\").test(String.fromCodePoint(N));return{id:N,bitmap:new i.t({width:se.width||60,height:se.height||60},se.data),metrics:{width:de?0:se.glyphWidth/2||24,height:se.glyphHeight/2||24,left:se.glyphLeft/2+.5||0,top:se.glyphTop/2-27.5||-8,advance:de?0:se.glyphAdvance/2||24,isDoubleResolution:!0}}}_createTinySDF(d){let P=d?d.split(\",\"):[];P.push(\"sans-serif\");let N=P.map((H=>/[-\\w]+/.test(H)?H:`'${CSS.escape(H)}'`)).join(\",\");return new E.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:N,fontWeight:this._fontWeight(P[0]),fontStyle:this._fontStyle(P[0]),lang:this.lang})}_fontStyle(d){return/italic/i.test(d)?\"italic\":/oblique/i.test(d)?\"oblique\":\"normal\"}_fontWeight(d){let P={thin:100,hairline:100,\"extra light\":200,\"ultra light\":200,light:300,normal:400,regular:400,medium:500,semibold:600,demibold:600,bold:700,\"extra bold\":800,\"ultra bold\":800,black:900,heavy:900,\"extra black\":950,\"ultra black\":950},N;for(let[H,$]of Object.entries(P))new RegExp(`\\\\b${H}\\\\b`,\"i\").test(d)&&(N=`${$}`);return N}destroy(){for(let d in this.entries){let P=this.entries[d];P.tinySDF=null,P.ideographTinySDF=null,P.glyphs={},P.requests={},P.ranges={}}this.entries={}}}E.loadGlyphRange=function(Y,d,P,N){return i._(this,void 0,void 0,(function*(){let H=256*d,$=H+255,se=yield N.transformRequest(P.replace(\"{fontstack}\",Y).replace(\"{range}\",`${H}-${$}`),\"Glyphs\"),de=yield i.o(se,new AbortController);if(!(de!=null&&de.data))throw new Error(`Could not load glyph range. range: ${d}, ${H}-${$}`);let Ae={};for(let Fe of i.q(de.data))Ae[Fe.id]=Fe;return Ae}))},E.TinySDF=class{constructor({fontSize:Y=24,buffer:d=3,radius:P=8,cutoff:N=.25,fontFamily:H=\"sans-serif\",fontWeight:$=\"normal\",fontStyle:se=\"normal\",lang:de=null}={}){this.buffer=d,this.radius=P,this.cutoff=N,this.lang=de;let Ae=this.size=Y+4*d,Fe=this._createCanvas(Ae),Ie=this.ctx=Fe.getContext(\"2d\",{willReadFrequently:!0});Ie.font=`${se} ${$} ${Y}px ${H}`,Ie.textBaseline=\"alphabetic\",Ie.textAlign=\"left\",Ie.fillStyle=\"black\",this.gridOuter=new Float64Array(Ae*Ae),this.gridInner=new Float64Array(Ae*Ae),this.f=new Float64Array(Ae),this.z=new Float64Array(Ae+1),this.v=new Uint16Array(Ae)}_createCanvas(Y){if(typeof OffscreenCanvas!=\"undefined\")return new OffscreenCanvas(Y,Y);let d=document.createElement(\"canvas\");return d.width=d.height=Y,d}draw(Y){let{width:d,actualBoundingBoxAscent:P,actualBoundingBoxDescent:N,actualBoundingBoxLeft:H,actualBoundingBoxRight:$}=this.ctx.measureText(Y),se=Math.ceil(P),de=Math.floor(H),Ae=Math.max(0,Math.min(this.size-this.buffer,Math.ceil($)-de)),Fe=Math.max(0,Math.min(this.size-this.buffer,se+Math.ceil(N))),Ie=Ae+2*this.buffer,Ye=Fe+2*this.buffer,st=Math.max(Ie*Ye,0),dt=new Uint8ClampedArray(st),Pe={data:dt,width:Ie,height:Ye,glyphWidth:Ae,glyphHeight:Fe,glyphTop:se,glyphLeft:de,glyphAdvance:d};if(Ae===0||Fe===0)return Pe;let{ctx:Xe,buffer:at,gridInner:xt,gridOuter:Et}=this;this.lang&&(Xe.lang=this.lang),Xe.clearRect(at,at,Ae,Fe),Xe.fillText(Y,at-de,at+se);let Bt=Xe.getImageData(at,at,Ae,Fe);Et.fill(w,0,st),xt.fill(0,0,st);let St=3;for(let Ht=0;Ht<Fe;Ht++){let dr=(Ht+at)*Ie+at;for(let Xr=0;Xr<Ae;Xr++,St+=4,dr++){let Or=Bt.data[St];if(Or===0)continue;let $r=A[Or];Et[dr]=Math.max(0,$r),xt[dr]=Math.max(0,-$r)}}T(Et,0,0,Ie,Ye,Ie,this.f,this.v,this.z),T(xt,at,at,Ae,Fe,Ie,this.f,this.v,this.z);let Pt=255/this.radius,$t=255*(1-this.cutoff);for(let Ht=0;Ht<st;Ht++){let dr=Math.sqrt(Et[Ht])-Math.sqrt(xt[Ht]);dt[Ht]=Math.round($t-Pt*dr)}return Pe}};class I{constructor(){this.specification=i.x.light.position}possiblyEvaluate(d,P){return i.G(d.expression.evaluate(P))}interpolate(d,P,N){return{x:i.H.number(d.x,P.x,N),y:i.H.number(d.y,P.y,N),z:i.H.number(d.z,P.z,N)}}}let R;class B extends i.E{constructor(d){super(),R||(R=new i.u({anchor:new i.D(i.x.light.anchor),position:new I,color:new i.D(i.x.light.color),intensity:new i.D(i.x.light.intensity)})),this._transitionable=new i.y(R,void 0),this.setLight(d),this._transitioning=this._transitionable.untransitioned()}getLight(){return this._transitionable.serialize()}setLight(d,P={}){if(!this._validate(i.z,d,P))for(let N in d){let H=d[N];N.endsWith(i.B)?this._transitionable.setTransition(N.slice(0,-i.B.length),H):this._transitionable.setValue(N,H)}}updateTransitions(d){this._transitioning=this._transitionable.transitioned(d,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(d){this.properties=this._transitioning.possiblyEvaluate(d)}_validate(d,P,N){return(N==null?void 0:N.validate)!==!1&&i.C(this,d.call(i.F,{value:P,style:{glyphs:!0,sprite:!0},styleSpec:i.x}))}}let O=new i.u({\"sky-color\":new i.D(i.x.sky[\"sky-color\"]),\"horizon-color\":new i.D(i.x.sky[\"horizon-color\"]),\"fog-color\":new i.D(i.x.sky[\"fog-color\"]),\"fog-ground-blend\":new i.D(i.x.sky[\"fog-ground-blend\"]),\"horizon-fog-blend\":new i.D(i.x.sky[\"horizon-fog-blend\"]),\"sky-horizon-blend\":new i.D(i.x.sky[\"sky-horizon-blend\"]),\"atmosphere-blend\":new i.D(i.x.sky[\"atmosphere-blend\"])});class V extends i.E{constructor(d){super(),this._transitionable=new i.y(O,void 0),this.setSky(d),this._transitioning=this._transitionable.untransitioned(),this.recalculate(new i.J(0))}setSky(d,P={}){if(!this._validate(i.K,d,P)){d||(d={\"sky-color\":\"transparent\",\"horizon-color\":\"transparent\",\"fog-color\":\"transparent\",\"fog-ground-blend\":1,\"atmosphere-blend\":0});for(let N in d){let H=d[N];N.endsWith(i.B)?this._transitionable.setTransition(N.slice(0,-i.B.length),H):this._transitionable.setValue(N,H)}}}getSky(){return this._transitionable.serialize()}updateTransitions(d){this._transitioning=this._transitionable.transitioned(d,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(d){this.properties=this._transitioning.possiblyEvaluate(d)}_validate(d,P,N={}){return(N==null?void 0:N.validate)!==!1&&i.C(this,d.call(i.F,i.e({value:P,style:{glyphs:!0,sprite:!0},styleSpec:i.x})))}calculateFogBlendOpacity(d){return d<60?0:d<70?(d-60)/10:1}}class j{constructor(d,P){this.width=d,this.height=P,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}}getDash(d,P){var N;let H=d.join(\",\")+String(P);return(N=this.dashEntry)[H]||(N[H]=this.addDash(d,P)),this.dashEntry[H]}getDashRanges(d,P,N){let H=[],$=d.length%2==1?-d[d.length-1]*N:0,se=d[0]*N,de=!0;H.push({left:$,right:se,isDash:de,zeroLength:d[0]===0});let Ae=d[0];for(let Fe=1;Fe<d.length;Fe++){de=!de;let Ie=d[Fe];$=Ae*N,Ae+=Ie,se=Ae*N,H.push({left:$,right:se,isDash:de,zeroLength:Ie===0})}return H}addRoundDash(d,P,N){let H=P/2;for(let $=-N;$<=N;$++){let se=this.width*(this.nextRow+N+$),de=0,Ae=d[de];for(let Fe=0;Fe<this.width;Fe++){Fe/Ae.right>1&&(Ae=d[++de]);let Ie=Math.abs(Fe-Ae.left),Ye=Math.abs(Fe-Ae.right),st=Math.min(Ie,Ye),dt,Pe=$/N*(H+1);if(Ae.isDash){let Xe=H-Math.abs(Pe);dt=Math.sqrt(st*st+Xe*Xe)}else dt=H-Math.sqrt(st*st+Pe*Pe);this.data[se+Fe]=Math.max(0,Math.min(255,dt+128))}}}addRegularDash(d){for(let de=d.length-1;de>=0;--de){let Ae=d[de],Fe=d[de+1];Ae.zeroLength?d.splice(de,1):Fe&&Fe.isDash===Ae.isDash&&(Fe.left=Ae.left,d.splice(de,1))}let P=d[0],N=d[d.length-1];P.isDash===N.isDash&&(P.left=N.left-this.width,N.right=P.right+this.width);let H=this.width*this.nextRow,$=0,se=d[$];for(let de=0;de<this.width;de++){de/se.right>1&&(se=d[++$]);let Ae=Math.abs(de-se.left),Fe=Math.abs(de-se.right),Ie=Math.min(Ae,Fe);this.data[H+de]=Math.max(0,Math.min(255,(se.isDash?Ie:-Ie)+128))}}addDash(d,P){let N=P?7:0,H=2*N+1;if(this.nextRow+H>this.height)return i.w(\"LineAtlas out of space\"),null;let $=0;for(let de of d)$+=de;if($!==0){let de=this.width/$,Ae=this.getDashRanges(d,this.width,de);P?this.addRoundDash(Ae,de,N):this.addRegularDash(Ae)}let se={y:this.nextRow+N,height:2*N,width:$};return this.nextRow+=H,this.dirty=!0,se}bind(d){let P=d.gl;this.texture?(P.bindTexture(P.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,P.texSubImage2D(P.TEXTURE_2D,0,0,0,this.width,this.height,P.ALPHA,P.UNSIGNED_BYTE,this.data))):(this.texture=P.createTexture(),P.bindTexture(P.TEXTURE_2D,this.texture),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_WRAP_S,P.REPEAT),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_WRAP_T,P.REPEAT),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_MIN_FILTER,P.LINEAR),P.texParameteri(P.TEXTURE_2D,P.TEXTURE_MAG_FILTER,P.LINEAR),P.texImage2D(P.TEXTURE_2D,0,P.ALPHA,this.width,this.height,0,P.ALPHA,P.UNSIGNED_BYTE,this.data))}}let q=\"maplibre_preloaded_worker_pool\";class U{constructor(){this.active={}}acquire(d){if(!this.workers)for(this.workers=[];this.workers.length<U.workerCount;)this.workers.push(new Worker(i.c.WORKER_URL));return this.active[d]=!0,this.workers.slice()}release(d){if(delete this.active[d],this.numActive()===0){for(let P of this.workers)P.terminate();this.workers=null}}isPreloaded(){return!!this.active[q]}numActive(){return Object.keys(this.active).length}}let J=Math.floor(f.hardwareConcurrency/2),ee,ce;function ye(){return ee||(ee=new U),ee}U.workerCount=i.L(globalThis)?Math.max(Math.min(J,3),1):1;class xe{constructor(d,P){this.workerPool=d,this.actors=[],this.currentActor=0,this.id=P;let N=this.workerPool.acquire(P);for(let H=0;H<N.length;H++){let $=new i.N(N[H],P);$.name=`Worker ${H}`,this.actors.push($)}if(!this.actors.length)throw new Error(\"No actors found\")}broadcast(d,P){let N=[];for(let H of this.actors)N.push(H.sendAsync({type:d,data:P}));return Promise.all(N)}getActor(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]}remove(d=!0){for(let P of this.actors)P.remove();this.actors=[],d&&this.workerPool.release(this.id)}registerMessageHandler(d,P){for(let N of this.actors)N.registerMessageHandler(d,P)}unregisterMessageHandler(d){for(let P of this.actors)P.unregisterMessageHandler(d)}}function ue(){return ce||(ce=new xe(ye(),i.M),ce.registerMessageHandler(\"GR\",((Y,d,P)=>i.m(d,P)))),ce}function K(Y,d){let P=i.O();return i.Q(P,P,[1,1,0]),i.S(P,P,[.5*Y.width,.5*Y.height,1]),Y.calculatePosMatrix?i.U(P,P,Y.calculatePosMatrix(d.toUnwrapped())):P}function _e(Y,d,P,N,H,$,se){var de;let Ae=(function(st,dt,Pe){if(st)for(let Xe of st){let at=dt[Xe];if((at==null?void 0:at.source)===Pe&&at.type===\"fill-extrusion\")return!0}else for(let Xe in dt){let at=dt[Xe];if(at.source===Pe&&at.type===\"fill-extrusion\")return!0}return!1})((de=H==null?void 0:H.layers)!==null&&de!==void 0?de:null,d,Y.id),Fe=$.maxPitchScaleFactor(),Ie=Y.tilesIn(N,Fe,Ae);Ie.sort(ne);let Ye=[];for(let st of Ie)Ye.push({wrappedTileID:st.tileID.wrapped().key,queryResults:st.tile.queryRenderedFeatures(d,P,Y.getState(),st.queryGeometry,st.cameraQueryGeometry,st.scale,H,$,Fe,K($,st.tileID),se?(dt,Pe)=>se(st.tileID,dt,Pe):void 0)});return(function(st,dt){for(let Pe in st)for(let Xe of st[Pe])ae(Xe,dt);return st})((function(st){let dt={},Pe={};for(let{queryResults:Xe,wrappedTileID:at}of st){Pe[at]||(Pe[at]={});let xt=Pe[at];for(let Et in Xe){let Bt=Xe[Et];xt[Et]||(xt[Et]={});let St=xt[Et];dt[Et]||(dt[Et]=[]);for(let Pt of Bt)St[Pt.featureIndex]||(St[Pt.featureIndex]=!0,dt[Et].push(Pt))}}return dt})(Ye),Y)}function ne(Y,d){let P=Y.tileID,N=d.tileID;return P.overscaledZ-N.overscaledZ||P.canonical.y-N.canonical.y||P.wrap-N.wrap||P.canonical.x-N.canonical.x}function ae(Y,d){let P=Y.feature,N=d.getFeatureState(P.layer[\"source-layer\"],P.id);P.source=P.layer.source,P.layer[\"source-layer\"]&&(P.sourceLayer=P.layer[\"source-layer\"]),P.state=N}function pe(Y,d,P,N){return i._(this,void 0,void 0,(function*(){let H=Y;if(Y.url?H=(yield i.k(yield d.transformRequest(Y.url,\"Source\"),P)).data:yield f.frameAsync(P,N),!H)return null;let $=i.V(i.e(H,Y),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);return\"vector_layers\"in H&&H.vector_layers&&($.vectorLayerIds=H.vector_layers.map((se=>se.id))),$}))}class ie{constructor(d,P){d&&(P?this.setSouthWest(d).setNorthEast(P):Array.isArray(d)&&(d.length===4?this.setSouthWest([d[0],d[1]]).setNorthEast([d[2],d[3]]):this.setSouthWest(d[0]).setNorthEast(d[1])))}setNorthEast(d){return this._ne=d instanceof i.W?new i.W(d.lng,d.lat):i.W.convert(d),this}setSouthWest(d){return this._sw=d instanceof i.W?new i.W(d.lng,d.lat):i.W.convert(d),this}extend(d){let P=this._sw,N=this._ne,H,$;if(d instanceof i.W)H=d,$=d;else{if(!(d instanceof ie))return Array.isArray(d)?d.length===4||d.every(Array.isArray)?this.extend(ie.convert(d)):this.extend(i.W.convert(d)):d&&(\"lng\"in d||\"lon\"in d)&&\"lat\"in d?this.extend(i.W.convert(d)):this;if(H=d._sw,$=d._ne,!H||!$)return this}return P||N?(P.lng=Math.min(H.lng,P.lng),P.lat=Math.min(H.lat,P.lat),N.lng=Math.max($.lng,N.lng),N.lat=Math.max($.lat,N.lat)):(this._sw=new i.W(H.lng,H.lat),this._ne=new i.W($.lng,$.lat)),this}getCenter(){return new i.W((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new i.W(this.getWest(),this.getNorth())}getSouthEast(){return new i.W(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(d){let{lng:P,lat:N}=i.W.convert(d),H=this._sw.lng<=P&&P<=this._ne.lng;return this._sw.lng>this._ne.lng&&(H=this._sw.lng>=P&&P>=this._ne.lng),this._sw.lat<=N&&N<=this._ne.lat&&H}intersects(d){if(!((d=ie.convert(d)).getNorth()>=this.getSouth()&&d.getSouth()<=this.getNorth()))return!1;let P=Math.abs(this.getEast()-this.getWest()),N=Math.abs(d.getEast()-d.getWest());if(P>=360||N>=360)return!0;let H=i.X(this.getWest(),-180,180),$=i.X(this.getEast(),-180,180),se=i.X(d.getWest(),-180,180),de=i.X(d.getEast(),-180,180),Ae=H>$,Fe=se>de;return!(!Ae||!Fe)||(Ae?de>=H||se<=$:Fe?$>=se||H<=de:se<=$&&de>=H)}static convert(d){return d instanceof ie?d:d&&new ie(d)}static fromLngLat(d,P=0){let N=360*P/40075017,H=N/Math.cos(Math.PI/180*d.lat);return new ie(new i.W(d.lng-H,d.lat-N),new i.W(d.lng+H,d.lat+N))}adjustAntiMeridian(){let d=new i.W(this._sw.lng,this._sw.lat),P=new i.W(this._ne.lng,this._ne.lat);return new ie(d,d.lng>P.lng?new i.W(P.lng+360,P.lat):P)}}class le{constructor(d,P,N){this.bounds=ie.convert(this.validateBounds(d)),this.minzoom=P||0,this.maxzoom=N||24}validateBounds(d){return Array.isArray(d)&&d.length===4?[Math.max(-180,d[0]),Math.max(-90,d[1]),Math.min(180,d[2]),Math.min(90,d[3])]:[-180,-90,180,90]}contains(d){let P=Math.pow(2,d.z),N=Math.floor(i.Z(this.bounds.getWest())*P),H=Math.floor(i.Y(this.bounds.getNorth())*P),$=Math.ceil(i.Z(this.bounds.getEast())*P),se=Math.ceil(i.Y(this.bounds.getSouth())*P);return d.x>=N&&d.x<$&&d.y>=H&&d.y<se}}class me extends i.E{constructor(d,P,N,H){if(super(),this.id=d,this.dispatcher=N,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,i.e(this,i.V(P,[\"url\",\"scheme\",\"tileSize\",\"promoteId\",\"encoding\"])),this._options=i.e({type:\"vector\"},P),this._collectResourceTiming=P.collectResourceTiming,this.tileSize!==512)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(H)}load(){return i._(this,arguments,void 0,(function*(d=!1){this._loaded=!1,this.fire(new i.n(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=new AbortController;try{let P=yield pe(this._options,this.map._requestManager,this._tileJSONRequest,this.map._ownerWindow);this._tileJSONRequest=null,this._loaded=!0,P&&(i.e(this,P),P.bounds&&(this.tileBounds=new le(P.bounds,this.minzoom,this.maxzoom)),this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"content\",sourceDataChanged:d})))}catch(P){this._tileJSONRequest=null,this._loaded=!0,i.$(P)||this.fire(new i.l(i.d(P)))}}))}loaded(){return this._loaded}hasTile(d){return!this.tileBounds||this.tileBounds.contains(d.canonical)}onAdd(d){this.map=d,this.load()}setSourceProperty(d){this._tileJSONRequest&&this._tileJSONRequest.abort(),d(),this.load(!0)}setTiles(d){return this.setSourceProperty((()=>{this._options.tiles=d})),this}setUrl(d){return this.setSourceProperty((()=>{this.url=d,this._options.url=d})),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}serialize(){return i.e({},this._options)}loadTile(d){return i._(this,void 0,void 0,(function*(){let P=d.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),N={request:yield this.map._requestManager.transformRequest(P,\"Tile\"),uid:d.uid,tileID:d.tileID,zoom:d.tileID.overscaledZ,tileSize:this.tileSize*d.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId,subdivisionGranularity:this.map.style.projection.subdivisionGranularity,encoding:this.encoding,overzoomParameters:yield this._getOverzoomParameters(d),etag:d.etag};N.request.collectResourceTiming=this._collectResourceTiming;let H=\"RT\";if(d.actor&&d.state!==\"expired\"){if(d.state===\"loading\")return new Promise((($,se)=>{d.reloadPromise={resolve:$,reject:se}}))}else d.actor=this.dispatcher.getActor(),H=\"LT\";d.abortController=new AbortController;try{let $=yield d.actor.sendAsync({type:H,data:N},d.abortController);if(delete d.abortController,d.aborted)return;this._afterTileLoadWorkerResponse(d,$);let se={};return $!=null&&$.etagUnmodified&&(se.unmodified=!0),se}catch($){if(delete d.abortController,d.aborted)return;if($&&$.status!==404)throw $;this._afterTileLoadWorkerResponse(d,null)}}))}_getOverzoomParameters(d){return i._(this,void 0,void 0,(function*(){if(d.tileID.canonical.z<=this.maxzoom||this.map._zoomLevelsToOverscale===void 0)return;let P=d.tileID.scaledTo(this.maxzoom).canonical,N=P.url(this.tiles,this.map.getPixelRatio(),this.scheme);return{maxZoomTileID:P,overzoomRequest:yield this.map._requestManager.transformRequest(N,\"Tile\")}}))}_afterTileLoadWorkerResponse(d,P){if(P!=null&&P.resourceTiming&&(d.resourceTiming=P.resourceTiming),P&&this.map._refreshExpiredTiles&&d.setExpiryData(P),d.etag=P==null?void 0:P.etag,d.loadVectorData(P,this.map.painter),d.reloadPromise){let N=d.reloadPromise;d.reloadPromise=null,this.loadTile(d).then(N.resolve).catch(N.reject)}}abortTile(d){return i._(this,void 0,void 0,(function*(){d.abortController&&(d.abortController.abort(),delete d.abortController),d.actor&&(yield d.actor.sendAsync({type:\"AT\",data:{uid:d.uid,type:this.type,source:this.id}}))}))}unloadTile(d){return i._(this,void 0,void 0,(function*(){d.unloadVectorData(),d.actor&&(yield d.actor.sendAsync({type:\"RMT\",data:{uid:d.uid,type:this.type,source:this.id}}))}))}hasTransition(){return!1}}class be extends i.E{constructor(d,P,N,H){super(),this.id=d,this.dispatcher=N,this.setEventedParent(H),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=i.e({type:\"raster\"},P),i.e(this,i.V(P,[\"url\",\"scheme\",\"tileSize\"]))}load(){return i._(this,arguments,void 0,(function*(d=!1){this._loaded=!1,this.fire(new i.n(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=new AbortController;try{let P=yield pe(this._options,this.map._requestManager,this._tileJSONRequest,this.map._ownerWindow);this._tileJSONRequest=null,this._loaded=!0,P&&(i.e(this,P),P.bounds&&(this.tileBounds=new le(P.bounds,this.minzoom,this.maxzoom)),this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"content\",sourceDataChanged:d})))}catch(P){this._tileJSONRequest=null,this._loaded=!0,i.$(P)||this.fire(new i.l(i.d(P)))}}))}loaded(){return this._loaded}onAdd(d){this.map=d,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}setSourceProperty(d){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null),d(),this.load(!0)}setTiles(d){return this.setSourceProperty((()=>{this._options.tiles=d})),this}setUrl(d){return this.setSourceProperty((()=>{this.url=d,this._options.url=d})),this}serialize(){return i.e({},this._options)}hasTile(d){return!this.tileBounds||this.tileBounds.contains(d.canonical)}loadTile(d){return i._(this,void 0,void 0,(function*(){let P=d.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);d.abortController=new AbortController;try{let N=yield x.getImage(yield this.map._requestManager.transformRequest(P,\"Tile\"),d.abortController,this.map._refreshExpiredTiles);if(delete d.abortController,d.aborted)return void(d.state=\"unloaded\");if(N!=null&&N.data){this.map._refreshExpiredTiles&&(N.cacheControl||N.expires)&&d.setExpiryData({cacheControl:N.cacheControl,expires:N.expires});let H=this.map.painter.context,$=H.gl,se=N.data;d.texture=this.map.painter.getTileTexture(se.width),d.texture?d.texture.update(se,{useMipmap:!0}):(d.texture=new i.T(H,se,$.RGBA,{useMipmap:!0}),d.texture.bind($.LINEAR,$.CLAMP_TO_EDGE,$.LINEAR_MIPMAP_NEAREST)),d.state=\"loaded\"}}catch(N){if(delete d.abortController,d.aborted)d.state=\"unloaded\";else if(N)throw d.state=\"errored\",N}}))}abortTile(d){return i._(this,void 0,void 0,(function*(){d.abortController&&(d.abortController.abort(),delete d.abortController)}))}unloadTile(d){return i._(this,void 0,void 0,(function*(){d.texture&&this.map.painter.saveTileTexture(d.texture)}))}hasTransition(){return!1}}class De extends be{constructor(d,P,N,H){super(d,P,N,H),this.type=\"raster-dem\",this.maxzoom=22,this._options=i.e({type:\"raster-dem\"},P),this.encoding=P.encoding||\"mapbox\",this.redFactor=P.redFactor,this.greenFactor=P.greenFactor,this.blueFactor=P.blueFactor,this.baseShift=P.baseShift}loadTile(d){return i._(this,void 0,void 0,(function*(){let P=d.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),N=yield this.map._requestManager.transformRequest(P,\"Tile\");d.neighboringTiles=this._getNeighboringTiles(d.tileID),d.abortController=new AbortController;try{let H=yield x.getImage(N,d.abortController,this.map._refreshExpiredTiles);if(delete d.abortController,d.aborted)return void(d.state=\"unloaded\");if(H!=null&&H.data){let $=H.data;this.map._refreshExpiredTiles&&(H.cacheControl||H.expires)&&d.setExpiryData({cacheControl:H.cacheControl,expires:H.expires});let se=i.b($)&&i.a0()?$:yield this.readImageNow($),de={type:this.type,uid:d.uid,source:this.id,rawImageData:se,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};if(d.actor&&d.state!==\"expired\"&&d.state!==\"reloading\")return;d.actor&&d.state!==\"expired\"||(d.actor=this.dispatcher.getActor()),d.dem=yield d.actor.sendAsync({type:\"LDT\",data:de}),d.needsHillshadePrepare=!0,d.needsTerrainPrepare=!0,d.state=\"loaded\"}}catch(H){if(delete d.abortController,d.aborted)d.state=\"unloaded\";else if(H)throw d.state=\"errored\",H}}))}readImageNow(d){return i._(this,void 0,void 0,(function*(){if(typeof VideoFrame!=\"undefined\"&&i.a1()){let P=d.width+2,N=d.height+2;try{return new i.R({width:P,height:N},yield i.a2(d,-1,-1,P,N))}catch(H){}}return f.getImageData(d,1)}))}_getNeighboringTiles(d){let P=d.canonical,N=Math.pow(2,P.z),H=(P.x-1+N)%N,$=P.x===0?d.wrap-1:d.wrap,se=(P.x+1+N)%N,de=P.x+1===N?d.wrap+1:d.wrap,Ae={};return Ae[new i.a3(d.overscaledZ,$,P.z,H,P.y).key]={backfilled:!1},Ae[new i.a3(d.overscaledZ,de,P.z,se,P.y).key]={backfilled:!1},P.y>0&&(Ae[new i.a3(d.overscaledZ,$,P.z,H,P.y-1).key]={backfilled:!1},Ae[new i.a3(d.overscaledZ,d.wrap,P.z,P.x,P.y-1).key]={backfilled:!1},Ae[new i.a3(d.overscaledZ,de,P.z,se,P.y-1).key]={backfilled:!1}),P.y+1<N&&(Ae[new i.a3(d.overscaledZ,$,P.z,H,P.y+1).key]={backfilled:!1},Ae[new i.a3(d.overscaledZ,d.wrap,P.z,P.x,P.y+1).key]={backfilled:!1},Ae[new i.a3(d.overscaledZ,de,P.z,se,P.y+1).key]={backfilled:!1}),Ae}unloadTile(d){return i._(this,void 0,void 0,(function*(){d.demTexture&&this.map.painter.saveTileTexture(d.demTexture),d.fbo&&(d.fbo.destroy(),delete d.fbo),d.dem&&delete d.dem,delete d.neighboringTiles,d.state=\"unloaded\",d.actor&&(yield d.actor.sendAsync({type:\"RDT\",data:{type:this.type,uid:d.uid,source:this.id}}))}))}}function Ge(Y,d){return d?Y.properties[d]:Y.id}function ht(Y,d){let P={id:Y.id};if(d.removeAllProperties&&(delete Y.removeProperties,delete Y.addOrUpdateProperties,delete d.removeProperties),d.removeProperties)for(let N of d.removeProperties){let H=Y.addOrUpdateProperties.findIndex(($=>$.key===N));H>-1&&Y.addOrUpdateProperties.splice(H,1)}return(Y.removeAllProperties||d.removeAllProperties)&&(P.removeAllProperties=!0),(Y.removeProperties||d.removeProperties)&&(P.removeProperties=[...Y.removeProperties||[],...d.removeProperties||[]]),(Y.addOrUpdateProperties||d.addOrUpdateProperties)&&(P.addOrUpdateProperties=[...Y.addOrUpdateProperties||[],...d.addOrUpdateProperties||[]]),(Y.newGeometry||d.newGeometry)&&(P.newGeometry=d.newGeometry||Y.newGeometry),P}function it(Y){var d,P;if(!Y)return{};let N={};return N.removeAll=Y.removeAll,N.remove=new Set(Y.remove||[]),N.add=new Map((d=Y.add)===null||d===void 0?void 0:d.map((H=>[H.id,H]))),N.update=new Map((P=Y.update)===null||P===void 0?void 0:P.map((H=>[H.id,H]))),N}function Oe(Y){return Y&&Y.length!==0?typeof Y[0]==\"number\"?[Y]:Y.flatMap((d=>Oe(d))):[]}function ze(Y){return Y.type===\"GeometryCollection\"?Y.geometries.flatMap((d=>ze(d))):Oe(Y.coordinates)}function ct(Y){let d=new ie,P;switch(Y.type){case\"FeatureCollection\":P=Y.features.flatMap((N=>ze(N.geometry)));break;case\"Feature\":P=ze(Y.geometry);break;default:P=ze(Y)}if(P.length===0)return d;for(let N of P){let[H,$]=N;d.extend([H,$])}return d}class et extends i.E{constructor(d,P,N,H){super(),this.id=d,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._isUpdatingWorker=!1,this._pendingWorkerUpdate={data:P.data},this.actor=N.getActor(),this.setEventedParent(H),this._data=typeof P.data==\"string\"?{url:P.data}:{geojson:P.data},this._options=i.e({},P),this._collectResourceTiming=P.collectResourceTiming,P.maxzoom!==void 0&&(this.maxzoom=P.maxzoom),P.type&&(this.type=P.type),P.attribution&&(this.attribution=P.attribution),this.promoteId=P.promoteId,P.clusterMaxZoom!==void 0&&this.maxzoom<=P.clusterMaxZoom&&i.w(`The maxzoom value \"${this.maxzoom}\" is expected to be greater than the clusterMaxZoom value \"${P.clusterMaxZoom}\".`),this.workerOptions=i.e({source:this.id,geojsonVtOptions:{buffer:this._pixelsToTileUnits(P.buffer!==void 0?P.buffer:128),tolerance:this._pixelsToTileUnits(P.tolerance!==void 0?P.tolerance:.375),extent:i.a6,maxZoom:this.maxzoom,lineMetrics:P.lineMetrics||!1,generateId:P.generateId||!1,promoteId:typeof P.promoteId==\"string\"?P.promoteId:void 0,cluster:P.cluster||!1,clusterOptions:{maxZoom:this._getClusterMaxZoom(P.clusterMaxZoom),minPoints:Math.max(2,P.clusterMinPoints||2),extent:i.a6,radius:this._pixelsToTileUnits(P.clusterRadius||50),log:!1,generateId:P.generateId||!1}},clusterProperties:P.clusterProperties,filter:P.filter},P.workerOptions)}_hasPendingWorkerUpdate(){return this._pendingWorkerUpdate.data!==void 0||this._pendingWorkerUpdate.diff!==void 0||this._pendingWorkerUpdate.updateCluster}_pixelsToTileUnits(d){return d*(i.a6/this.tileSize)}_getClusterMaxZoom(d){let P=d?Math.round(d):this.maxzoom-1;return Number.isInteger(d)||d===void 0||i.w(`Integer expected for option 'clusterMaxZoom': provided value \"${d}\" rounded to \"${P}\"`),P}load(){return i._(this,void 0,void 0,(function*(){yield this._updateWorkerData()}))}onAdd(d){this.map=d,this.load()}setData(d,P){this._data=typeof d==\"string\"?{url:d}:{geojson:d},this._pendingWorkerUpdate={data:d};let N=this._updateWorkerData();return P?N:this}updateData(d,P){this._pendingWorkerUpdate.diff=(function(H,$){if(!H)return $||{};if(!$)return H||{};let se=it(H),de=it($);(function(Fe,Ie){Ie.removeAll&&(Fe.add.clear(),Fe.update.clear(),Fe.remove.clear(),Ie.remove.clear());for(let Ye of Ie.remove)Fe.add.delete(Ye),Fe.update.delete(Ye);for(let[Ye,st]of Ie.update){let dt=Fe.update.get(Ye);dt&&(Ie.update.set(Ye,ht(dt,st)),Fe.update.delete(Ye))}})(se,de);let Ae={};if((se.removeAll||de.removeAll)&&(Ae.removeAll=!0),Ae.remove=new Set([...se.remove,...de.remove]),Ae.add=new Map([...se.add,...de.add]),Ae.update=new Map([...se.update,...de.update]),Ae.remove.size&&Ae.add.size)for(let Fe of Ae.add.keys())Ae.remove.delete(Fe);return(function(Fe){let Ie={};return Fe.removeAll&&(Ie.removeAll=Fe.removeAll),Fe.remove&&(Ie.remove=Array.from(Fe.remove)),Fe.add&&(Ie.add=Array.from(Fe.add.values())),Fe.update&&(Ie.update=Array.from(Fe.update.values())),Ie})(Ae)})(this._pendingWorkerUpdate.diff,d);let N=this._updateWorkerData();return P?N:this}getData(){return i._(this,void 0,void 0,(function*(){return this._data.url&&(yield this.once(\"data\")),this._data.geojson?this._data.geojson:{type:\"FeatureCollection\",features:Array.from(this._data.updateable.values())}}))}getBounds(){return i._(this,void 0,void 0,(function*(){return ct(yield this.getData())}))}setClusterOptions(d){return this.workerOptions.geojsonVtOptions.cluster=d.cluster,d.clusterRadius!==void 0&&(this.workerOptions.geojsonVtOptions.clusterOptions.radius=this._pixelsToTileUnits(d.clusterRadius)),d.clusterMaxZoom!==void 0&&(this.workerOptions.geojsonVtOptions.clusterOptions.maxZoom=this._getClusterMaxZoom(d.clusterMaxZoom)),this._pendingWorkerUpdate.updateCluster=!0,this._updateWorkerData(),this}getClusterExpansionZoom(d){return this.actor.sendAsync({type:\"GCEZ\",data:{type:this.type,clusterId:d,source:this.id}})}getClusterChildren(d){return this.actor.sendAsync({type:\"GCC\",data:{type:this.type,clusterId:d,source:this.id}})}getClusterLeaves(d,P,N){return this.actor.sendAsync({type:\"GCL\",data:{type:this.type,source:this.id,clusterId:d,limit:P,offset:N}})}_updateWorkerData(){return i._(this,void 0,void 0,(function*(){if(this._isUpdatingWorker)return;if(!this._hasPendingWorkerUpdate())return void i.w(`No pending worker updates for GeoJSONSource ${this.id}.`);let{data:d,diff:P,updateCluster:N}=this._pendingWorkerUpdate,H=this._getLoadGeoJSONParameters(d,P,N);d!==void 0?this._pendingWorkerUpdate.data=void 0:P?this._pendingWorkerUpdate.diff=void 0:N&&(this._pendingWorkerUpdate.updateCluster=void 0),yield this._dispatchWorkerUpdate(H)}))}_getLoadGeoJSONParameters(d,P,N){return i._(this,void 0,void 0,(function*(){let H=i.e({type:this.type},this.workerOptions);return typeof d==\"string\"?(H.request=yield this.map._requestManager.transformRequest(f.resolveURL(d),\"Source\"),H.request.collectResourceTiming=this._collectResourceTiming,H):d!==void 0?(H.data=d,H):P?(H.dataDiff=P,H):N?(H.updateCluster=!0,H):void 0}))}_dispatchWorkerUpdate(d){return i._(this,void 0,void 0,(function*(){this._isUpdatingWorker=!0,this.fire(new i.n(\"dataloading\",{dataType:\"source\"}));try{let P=yield d,N=yield this.actor.sendAsync({type:\"LD\",data:P});if(this._isUpdatingWorker=!1,this._removed||N.abandoned)return void this.fire(new i.n(\"dataabort\",{dataType:\"source\"}));N.data&&(this._data={geojson:N.data});let H=this._applyDiffToSource(P.dataDiff),$=this._getShouldReloadTileOptions(H),se={dataType:\"source\"};this._applyResourceTiming(se,N),this.fire(new i.n(\"data\",Object.assign(Object.assign({},se),{sourceDataType:\"metadata\"}))),this.fire(new i.n(\"data\",Object.assign(Object.assign({},se),{sourceDataType:\"content\",shouldReloadTileOptions:$})))}catch(P){if(this._isUpdatingWorker=!1,this._removed)return void this.fire(new i.n(\"dataabort\",{dataType:\"source\"}));this.fire(new i.l(i.d(P)))}finally{this._hasPendingWorkerUpdate()&&this._updateWorkerData()}}))}_applyResourceTiming(d,P){var N;if(!this._collectResourceTiming)return;let H=(N=P.resourceTiming)===null||N===void 0?void 0:N[this.id];if(!H)return;let $=H.slice(0);$!=null&&$.length&&i.e(d,{resourceTiming:$})}_applyDiffToSource(d){if(!d)return;let P=typeof this.promoteId==\"string\"?this.promoteId:void 0;if(!this._data.url&&!this._data.updateable){let H=(function($,se){let de=new Map;if($==null||$.type==null)return de;if($.type===\"Feature\"){let Ae=Ge($,se);return Ae==null?void 0:(de.set(Ae,$),de)}if($.type===\"FeatureCollection\"){let Ae=new Set;for(let Fe of $.features){let Ie=Ge(Fe,se);if(Ie==null||Ae.has(Ie))return;Ae.add(Ie),de.set(Ie,Fe)}return de}})(this._data.geojson,P);if(!H)throw new Error(`GeoJSONSource \"${this.id}\": GeoJSON data is not compatible with updateData`);this._data={updateable:H}}if(!this._data.updateable)return;let N=(function(H,$,se){var de,Ae;let Fe=[];if($.removeAll)H.clear();else if($.remove)for(let Ie of $.remove){let Ye=H.get(Ie);Ye&&(Fe.push(Ye.geometry),H.delete(Ie))}if($.add)for(let Ie of $.add){let Ye=Ge(Ie,se);if(Ye==null)continue;let st=H.get(Ye);st&&Fe.push(st.geometry),Fe.push(Ie.geometry),H.set(Ye,Ie)}if($.update)for(let Ie of $.update){let Ye=H.get(Ie.id);if(!Ye)continue;let st=!!Ie.newGeometry,dt=Ie.removeAllProperties||((de=Ie.removeProperties)===null||de===void 0?void 0:de.length)>0||((Ae=Ie.addOrUpdateProperties)===null||Ae===void 0?void 0:Ae.length)>0;if(!st&&!dt)continue;Fe.push(Ye.geometry);let Pe=Object.assign({},Ye);if(H.set(Ie.id,Pe),st&&(Fe.push(Ie.newGeometry),Pe.geometry=Ie.newGeometry),dt){if(Pe.properties=Ie.removeAllProperties?{}:Object.assign({},Pe.properties||{}),Ie.removeProperties)for(let Xe of Ie.removeProperties)delete Pe.properties[Xe];if(Ie.addOrUpdateProperties)for(let{key:Xe,value:at}of Ie.addOrUpdateProperties)Pe.properties[Xe]=at}}return Fe})(this._data.updateable,d,P);return d.removeAll||this._options.cluster?void 0:N}_getShouldReloadTileOptions(d){if(d)return{affectedBounds:d.filter(Boolean).map((P=>ct(P)))}}shouldReloadTile(d,{affectedBounds:P}){if(d.state===\"loading\")return!0;if(d.state===\"unloaded\")return!1;let{buffer:N,extent:H}=this.workerOptions.geojsonVtOptions,$=(function({x:se,y:de,z:Ae},Fe=0){let Ie=i.a4((se-Fe)/Math.pow(2,Ae)),Ye=i.a5((de+1+Fe)/Math.pow(2,Ae)),st=i.a4((se+1+Fe)/Math.pow(2,Ae)),dt=i.a5((de-Fe)/Math.pow(2,Ae));return new ie([Ie,Ye],[st,dt])})(d.tileID.canonical,N/H);for(let se of P)if($.intersects(se))return!0;return!1}loaded(){return!this._isUpdatingWorker&&!this._hasPendingWorkerUpdate()}loadTile(d){return i._(this,void 0,void 0,(function*(){let P=d.actor?\"RT\":\"LT\";d.actor=this.actor;let N={type:this.type,uid:d.uid,tileID:d.tileID,zoom:d.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId,subdivisionGranularity:this.map.style.projection.subdivisionGranularity};d.abortController=new AbortController;let H=yield this.actor.sendAsync({type:P,data:N},d.abortController);delete d.abortController,d.unloadVectorData(),d.aborted||d.loadVectorData(H,this.map.painter,P===\"RT\")}))}abortTile(d){return i._(this,void 0,void 0,(function*(){d.abortController&&(d.abortController.abort(),delete d.abortController),d.aborted=!0}))}unloadTile(d){return i._(this,void 0,void 0,(function*(){d.unloadVectorData(),yield this.actor.sendAsync({type:\"RMT\",data:{uid:d.uid,type:this.type,source:this.id}})}))}onRemove(){this._removed=!0,this.actor.sendAsync({type:\"RS\",data:{type:this.type,source:this.id}})}serialize(){return i.e({},this._options,{type:this.type,data:this._data.updateable?{type:\"FeatureCollection\",features:Array.from(this._data.updateable.values())}:this._data.url||this._data.geojson})}hasTransition(){return!1}}class qe extends i.E{constructor(d,P,N,H){super(),this.flippedWindingOrder=!1,this.id=d,this.dispatcher=N,this.coordinates=P.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(H),this.options=P}load(d){return i._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new i.n(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,this._request=new AbortController;try{let P=yield x.getImage(yield this.map._requestManager.transformRequest(this.url,\"Image\"),this._request);this._request=null,this._loaded=!0,P!=null&&P.data&&(this.image=P.data,d&&(this.coordinates=d),this._finishLoading())}catch(P){this._request=null,this._loaded=!0,i.$(P)||this.fire(new i.l(i.d(P)))}}))}loaded(){return this._loaded}updateImage(d){return d.url?(this._request&&(this._request.abort(),this._request=null),this.options.url=d.url,this.load(d.coordinates).finally((()=>this.texture=null)),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))}onAdd(d){this.map=d,this.load()}onRemove(){this._request&&(this._request.abort(),this._request=null)}setCoordinates(d){this.coordinates=d;let P=d.map(i.a7.fromLngLat);var N;return this.tileID=(function(H){let $=i.a8.fromPoints(H),se=$.width(),de=$.height(),Ae=Math.max(se,de),Fe=Math.max(0,Math.floor(-Math.log(Ae)/Math.LN2)),Ie=Math.pow(2,Fe);return new i.aa(Fe,Math.floor(($.minX+$.maxX)/2*Ie),Math.floor(($.minY+$.maxY)/2*Ie))})(P),this.terrainTileRanges=this._getOverlappingTileRanges(P),this.minzoom=this.maxzoom=this.tileID.z,this.tileCoords=P.map((H=>this.tileID.getTilePoint(H)._round())),this.flippedWindingOrder=((N=this.tileCoords)[1].x-N[0].x)*(N[2].y-N[0].y)-(N[1].y-N[0].y)*(N[2].x-N[0].x)<0,this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this}prepare(){if(Object.keys(this.tiles).length===0||!this.image)return;let d=this.map.painter.context,P=d.gl;this.texture||(this.texture=new i.T(d,this.image,P.RGBA),this.texture.bind(P.LINEAR,P.CLAMP_TO_EDGE));let N=!1;for(let H in this.tiles){let $=this.tiles[H];$.state!==\"loaded\"&&($.state=\"loaded\",$.texture=this.texture,N=!0)}N&&this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}loadTile(d){return i._(this,void 0,void 0,(function*(){var P;!((P=this.tileID)===null||P===void 0)&&P.equals(d.tileID.canonical)?(this.tiles[String(d.tileID.wrap)]=d,d.buckets={}):d.state=\"errored\"}))}serialize(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}}hasTransition(){return!1}_getOverlappingTileRanges(d){let{minX:P,minY:N,maxX:H,maxY:$}=i.a8.fromPoints(d),se={};for(let de=0;de<=i.a9;de++){let Ae=Math.pow(2,de),Fe=Math.floor(P*Ae),Ie=Math.floor(N*Ae),Ye=Math.floor(H*Ae),st=Math.floor($*Ae),dt=(Fe%Ae+Ae)%Ae,Pe=Ye%Ae,Xe=Math.floor(Fe/Ae),at=Math.floor(Ye/Ae);se[de]={minWrap:Xe,maxWrap:at,minTileXWrapped:dt,maxTileXWrapped:Pe,minTileY:Ie,maxTileY:st}}return se}}class we extends qe{constructor(d,P,N,H){super(d,P,N,H),this._onPlayingHandler=()=>{var $;($=this.map)===null||$===void 0||$.triggerRepaint()},this.roundZoom=!0,this.type=\"video\",this.options=P}load(){return i._(this,void 0,void 0,(function*(){this._loaded=!1;let d=this.options;this.urls=[];for(let P of d.urls)this.urls.push((yield this.map._requestManager.transformRequest(P,\"Source\")).url);try{let P=yield i.ab(this.urls);if(this._loaded=!0,!P)return;this.video=P,this.video.loop=!0,this.video.addEventListener(\"playing\",this._onPlayingHandler),this.map&&this.video.play(),this._finishLoading()}catch(P){this.fire(new i.l(i.d(P)))}}))}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(d){if(this.video){let P=this.video.seekable;d<P.start(0)||d>P.end(0)?this.fire(new i.l(new i.ac(`sources.${this.id}`,null,`Playback for this video can be set only between the ${P.start(0)} and ${P.end(0)}-second mark.`))):this.video.currentTime=d}}getVideo(){return this.video}onAdd(d){this.map||(this.map=d,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}onRemove(){super.onRemove(),this.video&&(this.video.removeEventListener(\"playing\",this._onPlayingHandler),this.video.pause())}prepare(){if(Object.keys(this.tiles).length===0||this.video.readyState<2)return;let d=this.map.painter.context,P=d.gl;this.texture?this.video.paused||(this.texture.bind(P.LINEAR,P.CLAMP_TO_EDGE),P.texSubImage2D(P.TEXTURE_2D,0,0,0,P.RGBA,P.UNSIGNED_BYTE,this.video)):(this.texture=new i.T(d,this.video,P.RGBA),this.texture.bind(P.LINEAR,P.CLAMP_TO_EDGE));let N=!1;for(let H in this.tiles){let $=this.tiles[H];$.state!==\"loaded\"&&($.state=\"loaded\",$.texture=this.texture,N=!0)}N&&this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}hasTransition(){return this.video&&!this.video.paused}}class Le extends qe{constructor(d,P,N,H){super(d,P,N,H),P.coordinates?Array.isArray(P.coordinates)&&P.coordinates.length===4&&!P.coordinates.some(($=>!Array.isArray($)||$.length!==2||$.some((se=>typeof se!=\"number\"))))||this.fire(new i.l(new i.ac(`sources.${d}`,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new i.l(new i.ac(`sources.${d}`,null,'missing required property \"coordinates\"'))),P.animate&&typeof P.animate!=\"boolean\"&&this.fire(new i.l(new i.ac(`sources.${d}`,null,'optional \"animate\" property must be a boolean value'))),P.canvas?typeof P.canvas==\"string\"||P.canvas instanceof HTMLCanvasElement||this.fire(new i.l(new i.ac(`sources.${d}`,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new i.l(new i.ac(`sources.${d}`,null,'missing required property \"canvas\"'))),this.options=P,this.animate=P.animate===void 0||P.animate}load(){return i._(this,void 0,void 0,(function*(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new i.l(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())}))}getCanvas(){return this.canvas}onAdd(d){this.map=d,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}prepare(){let d=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,d=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,d=!0),this._hasInvalidDimensions()||Object.keys(this.tiles).length===0)return;let P=this.map.painter.context,N=P.gl;this.texture?(d||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):(this.texture=new i.T(P,this.canvas,N.RGBA,{premultiply:!0}),this.texture.bind(N.LINEAR,N.CLAMP_TO_EDGE));let H=!1;for(let $ in this.tiles){let se=this.tiles[$];se.state!==\"loaded\"&&(se.state=\"loaded\",se.texture=this.texture,H=!0)}H&&this.fire(new i.n(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"canvas\",animate:this.animate,canvas:this.options.canvas,coordinates:this.coordinates}}hasTransition(){return this._playing}_hasInvalidDimensions(){for(let d of[this.canvas.width,this.canvas.height])if(isNaN(d)||d<=0)return!0;return!1}}let Ke={},Te=Y=>{switch(Y){case\"geojson\":return et;case\"image\":return qe;case\"raster\":return be;case\"raster-dem\":return De;case\"vector\":return me;case\"video\":return we;case\"canvas\":return Le}return Ke[Y]},Ve=\"RTLPluginLoaded\";class $e extends i.E{constructor(){super(...arguments),this.status=\"unavailable\",this.url=null,this.dispatcher=ue()}_syncState(d){return this.status=d,this.dispatcher.broadcast(\"SRPS\",{pluginStatus:d,pluginURL:this.url}).catch((P=>{throw this.status=\"error\",P}))}getRTLTextPluginStatus(){return this.status}clearRTLTextPlugin(){this.status=\"unavailable\",this.url=null}setRTLTextPlugin(d){return i._(this,arguments,void 0,(function*(P,N=!1){if(this.url)throw new Error(\"setRTLTextPlugin cannot be called multiple times.\");if(this.url=f.resolveURL(P),!this.url)throw new Error(`requested url ${P} is invalid`);if(this.status===\"unavailable\"){if(!N)return this._requestImport();this.status=\"deferred\",this._syncState(this.status)}else if(this.status===\"requested\")return this._requestImport()}))}_requestImport(){return i._(this,void 0,void 0,(function*(){yield this._syncState(\"loading\"),this.status=\"loaded\",this.fire(new i.n(Ve))}))}lazyLoad(){this.status===\"unavailable\"?this.status=\"requested\":this.status===\"deferred\"&&this._requestImport()}}let ot=null;function tt(){return ot||(ot=new $e),ot}var nt,We;(function(Y){Y[Y.Base=0]=\"Base\",Y[Y.Parent=1]=\"Parent\"})(nt||(nt={})),(function(Y){Y[Y.Departing=0]=\"Departing\",Y[Y.Incoming=1]=\"Incoming\"})(We||(We={}));class mt{constructor(d,P){this.timeAdded=0,this.fadeEndTime=0,this.fadeOpacity=1,this.tileID=d,this.uid=i.ad(),this.uses=0,this.tileSize=P,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttFingerprint={},this.expiredRequestCount=0,this.state=\"loading\"}isRenderable(d){return this.hasData()&&(!this.fadeEndTime||this.fadeOpacity>0)&&(d||!this.holdingForSymbolFade())}setCrossFadeLogic({fadingRole:d,fadingDirection:P,fadingParentID:N,fadeEndTime:H}){this.resetFadeLogic(),this.fadingRole=d,this.fadingDirection=P,this.fadingParentID=N,this.fadeEndTime=H}setSelfFadeLogic(d){this.resetFadeLogic(),this.selfFading=!0,this.fadeEndTime=d}resetFadeLogic(){this.fadingRole=null,this.fadingDirection=null,this.fadingParentID=null,this.selfFading=!1,this.timeAdded=g(),this.fadeEndTime=0,this.fadeOpacity=1}wasRequested(){return this.state===\"errored\"||this.state===\"loaded\"||this.state===\"reloading\"}clearTextures(d){this.demTexture&&d.saveTileTexture(this.demTexture),this.demTexture=null}loadVectorData(d,P,N){if((d==null?void 0:d.etagUnmodified)!==!0)if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",d){d.featureIndex&&(this.latestFeatureIndex=d.featureIndex,d.rawTileData?(this.latestRawTileData=d.rawTileData,this.latestEncoding=d.encoding,this.latestFeatureIndex.rawTileData=d.rawTileData,this.latestFeatureIndex.encoding=d.encoding):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData,this.latestFeatureIndex.encoding=this.latestEncoding)),this.collisionBoxArray=d.collisionBoxArray,this.buckets=(function(H,$){let se={};if(!$)return se;for(let de of H){let Ae=de.layerIds.map((Fe=>$.getLayer(Fe))).filter(Boolean);if(Ae.length!==0){de.layers=Ae,de.stateDependentLayerIds&&(de.stateDependentLayers=de.stateDependentLayerIds.map((Fe=>Ae.filter((Ie=>Ie.id===Fe))[0])));for(let Fe of Ae)se[Fe.id]=de}}return se})(d.buckets,P==null?void 0:P.style),this.hasSymbolBuckets=!1;for(let H in this.buckets){let $=this.buckets[H];if($ instanceof i.af){if(this.hasSymbolBuckets=!0,!N)break;$.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(let H in this.buckets){let $=this.buckets[H];if($ instanceof i.af&&$.hasRTLText){this.hasRTLText=!0,tt().lazyLoad();break}}this.queryPadding=0;for(let H in this.buckets){let $=this.buckets[H];this.queryPadding=Math.max(this.queryPadding,P.style.getLayer(H).queryRadius($))}d.imageAtlas&&(this.imageAtlas=d.imageAtlas),d.glyphAtlasImage&&(this.glyphAtlasImage=d.glyphAtlasImage),this.dashPositions=d.dashPositions}else this.collisionBoxArray=new i.ae;else this.state=\"loaded\"}unloadVectorData(){for(let d in this.buckets)this.buckets[d].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.imageAtlas=null,this.dashPositions=null,this.latestFeatureIndex=null,this.state=\"unloaded\"}getBucket(d){return this.buckets[d.id]}upload(d){for(let N in this.buckets){let H=this.buckets[N];H.uploadPending()&&H.upload(d)}let P=d.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new i.T(d,this.imageAtlas.image,P.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new i.T(d,this.glyphAtlasImage,P.ALPHA),this.glyphAtlasImage=null)}prepare(d){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(d,this.imageAtlasTexture)}queryRenderedFeatures(d,P,N,H,$,se,de,Ae,Fe,Ie,Ye){var st;return!((st=this.latestFeatureIndex)===null||st===void 0)&&st.rawTileData?this.latestFeatureIndex.query({queryGeometry:H,cameraQueryGeometry:$,scale:se,tileSize:this.tileSize,pixelPosMatrix:Ie,transform:Ae,params:de,queryPadding:this.queryPadding*Fe,getElevation:Ye},d,P,N):{}}querySourceFeatures(d,P){let N=this.latestFeatureIndex;if(!(N!=null&&N.rawTileData))return;let H=N.loadVTLayers(),$=P!=null&&P.sourceLayer?P.sourceLayer:\"\",se=H[i.ag]||H[$];if(!se)return;let de=i.ah(P==null?void 0:P.filter,P==null?void 0:P.globalState),{z:Ae,x:Fe,y:Ie}=this.tileID.canonical,Ye={z:Ae,x:Fe,y:Ie};for(let st=0;st<se.length;st++){let dt=se.feature(st);if(de.needGeometry){let at=i.ai(dt,!0);if(!de.filter(new i.J(this.tileID.overscaledZ),at,this.tileID.canonical))continue}else if(!de.filter(new i.J(this.tileID.overscaledZ),dt))continue;let Pe=N.getId(dt,$),Xe=new i.aj(dt,Ae,Fe,Ie,Pe);Xe.tile=Ye,d.push(Xe)}}hasData(){return this.state===\"loaded\"||this.state===\"reloading\"||this.state===\"expired\"}patternsLoaded(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length}setExpiryData(d){let P=this.expirationTime;if(d.cacheControl){let N=i.ak(d.cacheControl);N[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*N[\"max-age\"])}else d.expires&&(this.expirationTime=new Date(d.expires).getTime());if(this.expirationTime){let N=Date.now(),H=!1;if(this.expirationTime>N)H=!1;else if(P)if(this.expirationTime<P)H=!0;else{let $=this.expirationTime-P;$?this.expirationTime=N+Math.max($,3e4):H=!0}else H=!0;H?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}}getExpiryTimeout(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-new Date().getTime(),Math.pow(2,31)-1)}setFeatureState(d,P){var N,H,$;if(!(!((N=this.latestFeatureIndex)===null||N===void 0)&&N.rawTileData)||Object.keys(d).length===0)return;let se=this.latestFeatureIndex.loadVTLayers();for(let de in this.buckets){if(!P.style.hasLayer(de))continue;let Ae=this.buckets[de],Fe=Ae.layers[0].sourceLayer||i.ag,Ie=se[Fe],Ye=d[Fe];if(!Ie||!Ye||Object.keys(Ye).length===0)continue;Ae.update(Ye,Ie,((H=this.imageAtlas)===null||H===void 0?void 0:H.patternPositions)||{},this.dashPositions||{});let st=($=P==null?void 0:P.style)===null||$===void 0?void 0:$.getLayer(de);st&&(this.queryPadding=Math.max(this.queryPadding,st.queryRadius(Ae)))}}holdingForSymbolFade(){return this.symbolFadeHoldUntil!==void 0}symbolFadeFinished(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<g()}clearSymbolFadeHold(){this.symbolFadeHoldUntil=void 0}setSymbolHoldDuration(d){this.symbolFadeHoldUntil=g()+d}setDependencies(d,P){let N={};for(let H of P)N[H]=!0;this.dependencies[d]=N}hasDependency(d,P){for(let N of d){let H=this.dependencies[N];if(H){for(let $ of P)if(H[$])return!0}}return!1}}class bt{constructor(){this.state={},this.stateChanges={},this.deletedStates={},this.revision=0}updateState(d,P,N){var H,$,se,de,Ae;let Fe=String(P);if((de=this.stateChanges)[d]||(de[d]={}),(Ae=this.stateChanges[d])[Fe]||(Ae[Fe]={}),i.e(this.stateChanges[d][Fe],N),this.deletedStates[d]===null){this.deletedStates[d]={};for(let Ie in this.state[d])Ie!==Fe&&(this.deletedStates[d][Ie]=null)}else if(((H=this.deletedStates[d])===null||H===void 0?void 0:H[Fe])===null){this.deletedStates[d][Fe]={};for(let Ie in this.state[d][Fe])N[Ie]||(this.deletedStates[d][Fe][Ie]=null)}else for(let Ie in N)((se=($=this.deletedStates[d])===null||$===void 0?void 0:$[Fe])===null||se===void 0?void 0:se[Ie])===null&&delete this.deletedStates[d][Fe][Ie]}removeFeatureState(d,P,N){var H,$,se;if(this.deletedStates[d]===null)return;let de=String(P);if(($=this.deletedStates)[d]||($[d]={}),N&&P!==void 0)this.deletedStates[d][de]!==null&&((se=this.deletedStates[d])[de]||(se[de]={}),this.deletedStates[d][de][N]=null);else if(P!==void 0)if(!((H=this.stateChanges[d])===null||H===void 0)&&H[de])for(N in this.deletedStates[d][de]={},this.stateChanges[d][de])this.deletedStates[d][de][N]=null;else this.deletedStates[d][de]=null;else this.deletedStates[d]=null}getState(d,P){let N=String(P),H=i.e({},(this.state[d]||{})[N],(this.stateChanges[d]||{})[N]);if(this.deletedStates[d]===null)return{};if(this.deletedStates[d]){let $=this.deletedStates[d][P];if($===null)return{};for(let se in $)delete H[se]}return H}initializeTileState(d,P){d.setFeatureState(this.state,P)}coalesceChanges(d,P){var N,H,$;let se={};for(let de in this.stateChanges){(N=this.state)[de]||(N[de]={});let Ae={};for(let Fe in this.stateChanges[de])(H=this.state[de])[Fe]||(H[Fe]={}),i.e(this.state[de][Fe],this.stateChanges[de][Fe]),Ae[Fe]=this.state[de][Fe];se[de]=Ae}for(let de in this.deletedStates){($=this.state)[de]||($[de]={});let Ae={};if(this.deletedStates[de]===null)for(let Fe in this.state[de])Ae[Fe]={},this.state[de][Fe]={};else for(let Fe in this.deletedStates[de]){if(this.deletedStates[de][Fe]===null)this.state[de][Fe]={};else for(let Ie of Object.keys(this.deletedStates[de][Fe]))delete this.state[de][Fe][Ie];Ae[Fe]=this.state[de][Fe]}se[de]||(se[de]={}),i.e(se[de],Ae)}this.stateChanges={},this.deletedStates={},Object.keys(se).length!==0&&(this.revision++,d.setFeatureState(se,P))}}let Yt=89.25;function jt(Y,d){let P=i.al(d.lat,-i.am,i.am);return new i.P(i.Z(d.lng)*Y,i.Y(P)*Y)}function mr(Y,d){return new i.a7(d.x/Y,d.y/Y).toLngLat()}function or(Y){return Y.cameraToCenterDistance*Math.min(.85*Math.tan(i.an(90-Y.pitch)),Math.tan(i.an(Yt-Y.pitch)))}function fr(Y,d){let P=Y.canonical,N=d/i.ao(P.z),H=P.x+Math.pow(2,P.z)*Y.wrap,$=i.ap(new Float64Array(16));return i.Q($,$,[H*N,P.y*N,0]),i.S($,$,[N/i.a6,N/i.a6,1]),$}function Ot(Y,d,P,N,H){let $=i.a7.fromLngLat(Y,d),se=H*i.aq(1,Y.lat),{x:de,y:Ae,z:Fe}=cr(P,N);return new i.a7($.x+se*-de,$.y+se*-Ae,$.z+se*-Fe)}function cr(Y,d){let P=i.an(Y),N=i.an(d),H=Math.cos(-P),$=Math.sin(P);return{x:$*Math.sin(N),y:-$*Math.cos(N),z:H}}function Ct(Y,d,P){let N=d.intersectsFrustum(Y);if(!P||N===0)return N;let H=d.intersectsPlane(P);return H===0?0:N===2&&H===2?2:1}function wt(Y,d,P){let N=0,H=(P-d)/10;for(let $=0;$<10;$++)N+=H*Math.pow(Math.cos(d+($+.5)/10*(P-d)),Y);return N}function Tt(Y,d){return function(P,N,H,$,se){let de=2*((Y-1)/i.ar(Math.cos(i.an(Yt-se))/Math.cos(i.an(Yt)))-1),Ae=Math.acos(H/$),Fe=2*wt(de-1,0,i.an(se/2)),Ie=Math.min(i.an(Yt),Ae+i.an(se/2)),Ye=wt(de-1,Math.min(Ie,Ae-i.an(se/2)),Ie),st=Math.atan(N/H),dt=Math.hypot(N,H),Pe=P;return Pe+=i.ar($/dt/Math.max(.5,Math.cos(i.an(se/2)))),Pe+=de*i.ar(Math.cos(st))/2,Pe-=i.ar(Math.max(1,Ye/Fe/d))/2,Pe}}let Mt=Tt(9.314,3);function Lt(Y,d){let P=(d.roundZoom?Math.round:Math.floor)(Y.zoom+i.ar(Y.tileSize/d.tileSize));return Math.max(0,P)}function ft(Y,d){let P=Y.getCameraFrustum(),N=Y.getClippingPlane(),H=Y.screenPointToMercatorCoordinate(Y.getCameraPoint()),$=i.a7.fromLngLat(Y.center,Y.elevation);H.z=$.z+Math.cos(Y.pitchInRadians)*Y.cameraToCenterDistance/Y.worldSize;let se=Y.getCoveringTilesDetailsProvider(),de=se.allowVariableZoom(Y,d),Ae=Lt(Y,d),Fe=d.minzoom||0,Ie=d.maxzoom!==void 0?d.maxzoom:Y.maxZoom,Ye=Math.min(Math.max(0,Ae),Ie),st=Math.pow(2,Ye),dt=[st*H.x,st*H.y,0],Pe=[st*$.x,st*$.y,0],Xe=Math.hypot($.x-H.x,$.y-H.y),at=Math.abs($.z-H.z),xt=Math.hypot(Xe,at),Et=Pt=>({zoom:0,x:0,y:0,wrap:Pt,fullyVisible:!1}),Bt=[],St=[];if(Y.renderWorldCopies&&se.allowWorldCopies())for(let Pt=1;Pt<=3;Pt++)Bt.push(Et(-Pt)),Bt.push(Et(Pt));for(Bt.push(Et(0));Bt.length>0;){let Pt=Bt.pop(),$t=Pt.x,Ht=Pt.y,dr=Pt.fullyVisible,Xr={x:$t,y:Ht,z:Pt.zoom},Or=se.getTileBoundingVolume(Xr,Pt.wrap,Y.elevation,d);if(!dr){let Rr=Ct(P,Or,N);if(Rr===0)continue;dr=Rr===2}let $r=se.distanceToTile2d(H.x,H.y,Xr,Or),Er=Ae;de&&(Er=(d.calculateTileZoom||Mt)(Y.zoom+i.ar(Y.tileSize/d.tileSize),$r,at,xt,Y.fov)),Er=(d.roundZoom?Math.round:Math.floor)(Er),Er=Math.max(0,Er);let Tr=Math.min(Er,Ie);if(Pt.wrap=se.getWrap($,Xr,Pt.wrap),Pt.zoom>=Tr){if(Pt.zoom<Fe)continue;let Rr=Ye-Pt.zoom,Lr=dt[0]-.5-($t<<Rr),an=dt[1]-.5-(Ht<<Rr),on=d.reparseOverscaled?Math.max(Pt.zoom,Er):Pt.zoom;St.push({tileID:new i.a3(Pt.zoom===Ie?on:Pt.zoom,Pt.wrap,Pt.zoom,$t,Ht),distanceSq:i.as([Pe[0]-.5-$t,Pe[1]-.5-Ht]),tileDistanceToCamera:Math.sqrt(Lr*Lr+an*an)})}else for(let Rr=0;Rr<4;Rr++)Bt.push({zoom:Pt.zoom+1,x:($t<<1)+Rr%2,y:(Ht<<1)+(Rr>>1),wrap:Pt.wrap,fullyVisible:dr})}return St.sort(((Pt,$t)=>Pt.distanceSq-$t.distanceSq)).map((Pt=>Pt.tileID))}let kt=i.a8.fromPoints([new i.P(0,0),new i.P(i.a6,i.a6)]);function rr(Y){return Y===\"raster\"||Y===\"image\"||Y===\"video\"}function pr(Y,d,P,N,H,$,se){if(!d.hasData())return!1;let{tileID:de,fadingRole:Ae,fadingDirection:Fe,fadingParentID:Ie}=d;if(Ae===nt.Base&&Fe===We.Incoming&&Ie)return P[Ie.key]=Ie,!0;let Ye=Math.max(de.overscaledZ-H,$);for(let st=de.overscaledZ-1;st>=Ye;st--){let dt=de.scaledTo(st),Pe=Y.getLoadedTile(dt);if(Pe)return d.setCrossFadeLogic({fadingRole:nt.Base,fadingDirection:We.Incoming,fadingParentID:Pe.tileID,fadeEndTime:N+se}),Pe.setCrossFadeLogic({fadingRole:nt.Parent,fadingDirection:We.Departing,fadeEndTime:N+se}),P[dt.key]=dt,!0}return!1}function Kr(Y,d,P,N,H,$){if(!d.hasData())return!1;let se=d.tileID.children(H),de=Nr(Y,d,se,P,N,H,$);if(de)return!0;for(let Ae of se)Nr(Y,d,Ae.children(H),P,N,H,$)&&(de=!0);return de}function Nr(Y,d,P,N,H,$,se){if(P[0].overscaledZ>=$)return!1;let de=!1;for(let Ae of P){let Fe=Y.getLoadedTile(Ae);if(!Fe)continue;let{fadingRole:Ie,fadingDirection:Ye,fadingParentID:st}=Fe;Ie===nt.Base&&Ye===We.Departing&&st||(Fe.setCrossFadeLogic({fadingRole:nt.Base,fadingDirection:We.Departing,fadingParentID:d.tileID,fadeEndTime:H+se}),d.setCrossFadeLogic({fadingRole:nt.Parent,fadingDirection:We.Incoming,fadeEndTime:H+se})),N[Ae.key]=Ae,de=!0}return de}function dn(Y,d,P,N){let H=Y.tileID;return!!Y.selfFading||!Y.hasData()&&!!d.has(H)&&(Y.setSelfFadeLogic(P+N),!0)}function xr(Y,d){var P;Y.needsHillshadePrepare=!0,Y.needsTerrainPrepare=!0;let N=d.tileID.canonical.x-Y.tileID.canonical.x,H=d.tileID.canonical.y-Y.tileID.canonical.y,$=Math.pow(2,Y.tileID.canonical.z),se=d.tileID.key;N===0&&H===0||Math.abs(H)>1||(Math.abs(N)>1&&(Math.abs(N+$)===1?N+=$:Math.abs(N-$)===1&&(N-=$)),d.dem&&Y.dem&&(Y.dem.backfillBorder(d.dem,N,H),!((P=Y.neighboringTiles)===null||P===void 0)&&P[se]&&(Y.neighboringTiles[se].backfilled=!0)))}class Fr{constructor(){this._tiles={}}handleWrapJump(d){let P={};for(let N in this._tiles){let H=this._tiles[N];H.tileID=H.tileID.unwrapTo(H.tileID.wrap+d),P[H.tileID.key]=H}this._tiles=P}setFeatureState(d,P){for(let N in this._tiles)this._tiles[N].setFeatureState(d,P)}getAllTiles(){return Object.values(this._tiles)}getAllIds(d=!1){return d?Object.values(this._tiles).map((P=>P.tileID)).sort(i.au).map((P=>P.key)):Object.keys(this._tiles)}getTileById(d){return this._tiles[d]}setTile(d,P){this._tiles[d]=P}deleteTileById(d){delete this._tiles[d]}getLoadedTile(d){let P=this.getTileById(d.key);return P!=null&&P.hasData()?P:null}isIdRenderable(d,P=!1){var N;return(N=this.getTileById(d))===null||N===void 0?void 0:N.isRenderable(P)}getRenderableIds(d=0,P){let N=[];for(let H of this.getAllIds())this.isIdRenderable(H,P)&&N.push(this.getTileById(H));return P?N.sort(((H,$)=>{let se=H.tileID,de=$.tileID,Ae=new i.P(se.canonical.x,se.canonical.y)._rotate(-d),Fe=new i.P(de.canonical.x,de.canonical.y)._rotate(-d);return se.overscaledZ-de.overscaledZ||Fe.y-Ae.y||Fe.x-Ae.x})).map((H=>H.tileID.key)):N.map((H=>H.tileID)).sort(i.au).map((H=>H.key))}}class Zr extends i.E{constructor(d,P,N){super(),this.id=d,this.dispatcher=N,this.on(\"data\",(H=>{this._dataHandler(H)})),this.on(\"dataloading\",(()=>{this._sourceErrored=!1})),this.on(\"error\",(()=>{this._sourceErrored=this._source.loaded()})),this._source=((H,$,se,de)=>{let Ae=new(Te($.type))(H,$,se,de);if(Ae.id!==H)throw new Error(`Expected Source id to be ${H} instead of ${Ae.id}`);return Ae})(d,P,N,this),this._inViewTiles=new Fr,this._outOfViewCache=new i.av(0,(H=>this._unloadTile(H))),this._timers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._rasterFadeDuration=0,this._maxFadingAncestorLevels=5,this._state=new bt,this._didEmitContent=!1,this._updated=!1}onAdd(d){var P;this.map=d,this._maxTileCacheSize=d?d._maxTileCacheSize:null,this._maxTileCacheZoomLevels=d?d._maxTileCacheZoomLevels:null,!((P=this._source)===null||P===void 0)&&P.onAdd&&this._source.onAdd(d)}onRemove(d){var P;for(let N of this._inViewTiles.getAllTiles())N.unloadVectorData();this.clearTiles(),!((P=this._source)===null||P===void 0)&&P.onRemove&&this._source.onRemove(d),this._inViewTiles=new Fr}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;if(!(this.used===void 0&&this.usedForTerrain===void 0||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(let d of this._inViewTiles.getAllTiles())if(d.state!==\"loaded\"&&d.state!==\"errored\")return!1;return!0}getSource(){return this._source}getState(){return this._state}pause(){this._paused=!0}resume(){if(!this._paused)return;let d=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,d&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(d,P,N){return i._(this,void 0,void 0,(function*(){try{let H=yield this._source.loadTile(d);this._tileLoaded(d,P,N,H)}catch(H){d.state=\"errored\",H.status!==404?this._source.fire(new i.l(i.d(H),{tile:d})):this.update(this.transform,this.terrain)}}))}_unloadTile(d){this._source.unloadTile&&this._source.unloadTile(d)}_abortTile(d){this._source.abortTile&&this._source.abortTile(d),this._source.fire(new i.n(\"dataabort\",{tile:d,coord:d.tileID,dataType:\"source\"}))}serialize(){return this._source.serialize()}prepare(d){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._inViewTiles,this.map?this.map.painter:null);for(let P of this._inViewTiles.getAllTiles())P.upload(d),P.prepare(this.map.style.imageManager)}getIds(){return this._inViewTiles.getAllIds(!0)}getRenderableIds(d){var P;return this._inViewTiles.getRenderableIds((P=this.transform)===null||P===void 0?void 0:P.bearingInRadians,d)}hasRenderableParent(d){let P=d.overscaledZ-1;if(P>=this._source.minzoom){let N=this.getLoadedTile(d.scaledTo(P));if(N)return this._inViewTiles.isIdRenderable(N.tileID.key)}return!1}reload(d,P=void 0){if(this._paused)this._shouldReloadOnResume=!0;else{this._outOfViewCache.reset();for(let N of this._inViewTiles.getAllIds()){let H=this._inViewTiles.getTileById(N);P&&!this._source.shouldReloadTile(H,P)||(d?this._reloadTile(N,\"expired\"):H.state!==\"errored\"&&this._reloadTile(N,\"reloading\"))}}}_reloadTile(d,P){return i._(this,void 0,void 0,(function*(){let N=this._inViewTiles.getTileById(d);N&&(N.state!==\"loading\"&&(N.state=P),yield this._loadTile(N,d,P))}))}_tileLoaded(d,P,N,H){d.timeAdded=g(),d.selfFading&&(d.fadeEndTime=d.timeAdded+this._rasterFadeDuration),N===\"expired\"&&(d.refreshedUponExpiration=!0),this._setTileReloadTimer(P,d),H!=null&&H.unmodified||(this.getSource().type===\"raster-dem\"&&d.dem&&(function($,se){var de,Ae,Fe;let Ie=se.getRenderableIds();for(let Ye of Ie){if(!(!((de=$.neighboringTiles)===null||de===void 0)&&de[Ye]))continue;let st=se.getTileById(Ye);$.neighboringTiles[Ye].backfilled||xr($,st),!((Fe=(Ae=st.neighboringTiles)===null||Ae===void 0?void 0:Ae[$.tileID.key])===null||Fe===void 0)&&Fe.backfilled||xr(st,$)}})(d,this._inViewTiles),this._state.initializeTileState(d,this.map?this.map.painter:null),d.aborted||this._source.fire(new i.n(\"data\",{dataType:\"source\",tile:d,coord:d.tileID})))}getTile(d){return this.getTileByID(d.key)}getTileByID(d){return this._inViewTiles.getTileById(d)}_retainLoadedChildren(d,P){let N=this._getLoadedDescendents(P),H=new Set;for(let $ of P){let se=N[$.key];if(!(se!=null&&se.length)){H.add($);continue}let de=$.overscaledZ+Zr.maxOverzooming,Ae=se.filter((Ye=>Ye.tileID.overscaledZ<=de));if(!Ae.length){H.add($);continue}let Fe=Math.min(...Ae.map((Ye=>Ye.tileID.overscaledZ))),Ie=Ae.filter((Ye=>Ye.tileID.overscaledZ===Fe)).map((Ye=>Ye.tileID));for(let Ye of Ie)d[Ye.key]=Ye;this._areDescendentsComplete(Ie,Fe,$.overscaledZ)||H.add($)}return H}_getLoadedDescendents(d){var P;let N={};for(let H of this._inViewTiles.getAllTiles().filter(($=>$.hasData())))for(let $ of d)H.tileID.isChildOf($)&&(N[P=$.key]||(N[P]=[]),N[$.key].push(H));return N}_areDescendentsComplete(d,P,N){return d.length===1&&d[0].isOverscaled()?d[0].overscaledZ===P:Math.pow(4,P-N)===d.length}getLoadedTile(d){return this._inViewTiles.getLoadedTile(d)}updateCacheSize(d){let P=Math.ceil(d.width/this._source.tileSize)+1,N=Math.ceil(d.height/this._source.tileSize)+1,H=Math.floor(P*N*(this._maxTileCacheZoomLevels===null?i.c.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels)),$=typeof this._maxTileCacheSize==\"number\"?Math.min(this._maxTileCacheSize,H):H;this._outOfViewCache.setMaxSize($)}handleWrapJump(d){let P=Math.round((d-(this._prevLng===void 0?d:this._prevLng))/360);this._prevLng=d,P&&(this._inViewTiles.handleWrapJump(P),this._resetTileReloadTimers())}update(d,P){if(!this._sourceLoaded||this._paused)return;let N;this.transform=d,this.terrain=P,this.updateCacheSize(d),this.handleWrapJump(this.transform.center.lng),this.used||this.usedForTerrain?this._source.tileID?N=d.getVisibleUnwrappedCoordinates(this._source.tileID).map((Ae=>new i.a3(Ae.canonical.z,Ae.wrap,Ae.canonical.z,Ae.canonical.x,Ae.canonical.y))):(N=ft(d,{tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.type===\"vector\"&&this.map._zoomLevelsToOverscale!==void 0?d.maxZoom-this.map._zoomLevelsToOverscale:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:P,calculateTileZoom:this._source.calculateTileZoom}),this._source.hasTile&&(N=N.filter((Ae=>this._source.hasTile(Ae))))):N=[],this.usedForTerrain&&(N=this._addTerrainIdealTiles(N));let H=N.length===0&&!this._updated&&this._didEmitContent;this._updated=!0,H&&this.fire(new i.n(\"data\",{sourceDataType:\"idle\",dataType:\"source\",sourceId:this.id}));let $=Lt(d,this._source),se=this._updateRetainedTiles(N,$),de=rr(this._source.type);de&&this._rasterFadeDuration>0&&!P&&(function(Ae,Fe,Ie,Ye,st,dt,Pe){let Xe=g(),at=i.at(Fe);for(let xt of Fe){let Et=Ae.getTileById(xt.key);Et.fadingDirection!==We.Departing&&Et.fadeOpacity!==0||Et.resetFadeLogic(),pr(Ae,Et,Ie,Xe,Ye,st,Pe)||Kr(Ae,Et,Ie,Xe,dt,Pe)||dn(Et,at,Xe,Pe)||Et.resetFadeLogic()}})(this._inViewTiles,N,se,this._maxFadingAncestorLevels,this._source.minzoom,this._source.maxzoom,this._rasterFadeDuration),de?this._cleanUpRasterTiles(se):this._cleanUpVectorTiles(se)}_cleanUpRasterTiles(d){for(let P of this._inViewTiles.getAllIds())d[P]||this._removeTile(P)}_cleanUpVectorTiles(d){for(let P of this._inViewTiles.getAllIds()){let N=this._inViewTiles.getTileById(P);d[P]?N.clearSymbolFadeHold():N.hasSymbolBuckets?N.holdingForSymbolFade()?N.symbolFadeFinished()&&this._removeTile(P):N.setSymbolHoldDuration(this.map._fadeDuration):this._removeTile(P)}}_addTerrainIdealTiles(d){let P=[];for(let N of d)if(N.canonical.z>this._source.minzoom){let H=N.scaledTo(N.canonical.z-1);P.push(H);let $=N.scaledTo(Math.max(this._source.minzoom,Math.min(N.canonical.z,5)));P.push($)}return d.concat(P)}releaseSymbolFadeTiles(){for(let d of this._inViewTiles.getAllIds())this._inViewTiles.getTileById(d).holdingForSymbolFade()&&this._removeTile(d)}_updateRetainedTiles(d,P){var N;let H=new Set;for(let Fe of d)this._addTile(Fe).hasData()||H.add(Fe);let $=d.reduce(((Fe,Ie)=>(Fe[Ie.key]=Ie,Fe)),{}),se=this._retainLoadedChildren($,H),de={},Ae=Math.max(P-Zr.maxUnderzooming,this._source.minzoom);for(let Fe of se){let Ie=this._inViewTiles.getTileById(Fe.key),Ye=Ie==null?void 0:Ie.wasRequested();for(let st=Fe.overscaledZ-1;st>=Ae;--st){let dt=Fe.scaledTo(st);if(de[dt.key])break;if(de[dt.key]=!0,Ie=this.getTile(dt),!Ie&&Ye&&(Ie=this._addTile(dt)),Ie){let Pe=Ie.hasData();if((Pe||!(!((N=this.map)===null||N===void 0)&&N.cancelPendingTileRequestsWhileZooming)||Ye)&&($[dt.key]=dt),Ye=Ie.wasRequested(),Pe)break}}}return $}_addTile(d){let P=this._inViewTiles.getTileById(d.key);if(P)return P;P=this._outOfViewCache.getAndRemove(d),P&&(P.resetFadeLogic(),this._setTileReloadTimer(d.key,P),P.tileID=d,this._state.initializeTileState(P,this.map?this.map.painter:null));let N=P;return P||(P=new mt(d,this._source.tileSize*d.overscaleFactor()),this._loadTile(P,d.key,P.state)),P.uses++,this._inViewTiles.setTile(d.key,P),N||this._source.fire(new i.n(\"dataloading\",{tile:P,coord:P.tileID,dataType:\"source\"})),P}_setTileReloadTimer(d,P){this._clearTileReloadTimer(d);let N=P.getExpiryTimeout();N&&(this._timers[d]=setTimeout((()=>{this._reloadTile(d,\"expired\"),delete this._timers[d]}),N))}_clearTileReloadTimer(d){let P=this._timers[d];P&&(clearTimeout(P),delete this._timers[d])}_resetTileReloadTimers(){for(let d in this._timers)clearTimeout(this._timers[d]),delete this._timers[d];for(let d of this._inViewTiles.getAllIds()){let P=this._inViewTiles.getTileById(d);this._setTileReloadTimer(d,P)}}refreshTiles(d){for(let P of this._inViewTiles.getAllIds()){let N=this._inViewTiles.getTileById(P);(this._inViewTiles.isIdRenderable(P)||N.state==\"errored\")&&d.some((H=>H.equals(N.tileID.canonical)))&&this._reloadTile(P,\"expired\")}}_removeTile(d){let P=this._inViewTiles.getTileById(d);P&&(P.uses--,this._inViewTiles.deleteTileById(d),this._clearTileReloadTimer(d),P.uses>0||(P.hasData()&&P.state!==\"reloading\"?this._outOfViewCache.add(P.tileID,P,P.getExpiryTimeout()):(P.aborted=!0,this._abortTile(P),this._unloadTile(P))))}_dataHandler(d){d.dataType===\"source\"&&(d.sourceDataType!==\"metadata\"?d.sourceDataType===\"content\"&&this._sourceLoaded&&!this._paused&&(this.reload(d.sourceDataChanged,d.shouldReloadTileOptions),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0):this._sourceLoaded=!0)}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(let d of this._inViewTiles.getAllIds())this._removeTile(d);this._outOfViewCache.reset()}tilesIn(d,P,N){let H=[],$=this.transform;if(!$)return H;let se=$.getCoveringTilesDetailsProvider().allowWorldCopies(),de=N?$.getCameraQueryGeometry(d):d,Ae=dt=>$.screenPointToMercatorCoordinate(dt,this.terrain),Fe=this.transformBbox(d,Ae,!se),Ie=this.transformBbox(de,Ae,!se),Ye=this.getIds(),st=i.a8.fromPoints(Ie);for(let dt of Ye){let Pe=this._inViewTiles.getTileById(dt);if(Pe.holdingForSymbolFade())continue;let Xe=se?[Pe.tileID]:[Pe.tileID.unwrapTo(-1),Pe.tileID.unwrapTo(0)],at=Math.pow(2,$.zoom-Pe.tileID.overscaledZ),xt=P*Pe.queryPadding*i.a6/Pe.tileSize/at;for(let Et of Xe){let Bt=st.map((St=>Et.getTilePoint(new i.a7(St.x,St.y))));if(Bt.expandBy(xt),Bt.intersects(kt)){let St=Fe.map(($t=>Et.getTilePoint($t))),Pt=Ie.map(($t=>Et.getTilePoint($t)));H.push({tile:Pe,tileID:se?Et:Et.unwrapTo(0),queryGeometry:St,cameraQueryGeometry:Pt,scale:at})}}}return H}transformBbox(d,P,N){let H=d.map(P);if(N){let $=i.a8.fromPoints(d);$.shrinkBy(.001*Math.min($.width(),$.height()));let se=$.map(P);i.a8.fromPoints(H).covers(se)||(H=H.map((de=>de.x>.5?new i.a7(de.x-1,de.y,de.z):de)))}return H}getVisibleCoordinates(d){let P=this.getRenderableIds(d).map((N=>this._inViewTiles.getTileById(N).tileID));return this.transform&&this.transform.populateCache(P),P}hasTransition(){return!!this._source.hasTransition()||rr(this._source.type)&&(function(d,P){if(P<=0)return!1;let N=g();for(let H of d.getAllTiles())if(H.fadeEndTime>=N)return!0;return!1})(this._inViewTiles,this._rasterFadeDuration)}setRasterFadeDuration(d){this._rasterFadeDuration=d}setFeatureState(d,P,N){d||(d=i.ag),this._state.updateState(d,P,N)}removeFeatureState(d,P,N){d||(d=i.ag),this._state.removeFeatureState(d,P,N)}getFeatureState(d,P){return d||(d=i.ag),this._state.getState(d,P)}setDependencies(d,P,N){let H=this._inViewTiles.getTileById(d);H&&H.setDependencies(P,N)}reloadTilesForDependencies(d,P){for(let N of this._inViewTiles.getAllIds())this._inViewTiles.getTileById(N).hasDependency(d,P)&&this._reloadTile(N,\"reloading\");this._outOfViewCache.filter((N=>!N.hasDependency(d,P)))}areTilesLoaded(){for(let d of this._inViewTiles.getAllTiles())if(d.state!==\"loaded\"&&d.state!==\"errored\")return!1;return!0}}Zr.maxUnderzooming=10,Zr.maxOverzooming=3;class Cn{constructor(d,P){this.reset(d,P)}reset(d,P){this.points=d||[],this._distances=[0];for(let N=1;N<this.points.length;N++)this._distances[N]=this._distances[N-1]+this.points[N].dist(this.points[N-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(P||0,.5*this.length),this.paddedLength=this.length-2*this.padding}lerp(d){if(this.points.length===1)return this.points[0];d=i.al(d,0,1);let P=1,N=this._distances[P],H=d*this.paddedLength+this.padding;for(;N<H&&P<this._distances.length;)N=this._distances[++P];let $=P-1,se=this._distances[$],de=N-se,Ae=de>0?(H-se)/de:0;return this.points[$].mult(1-Ae).add(this.points[P].mult(Ae))}}function Dt(Y,d){let P=!0;return Y===\"always\"||Y!==\"never\"&&d!==\"never\"||(P=!1),P}class At{constructor(d,P,N){let H=this.boxCells=[],$=this.circleCells=[];this.xCellCount=Math.ceil(d/N),this.yCellCount=Math.ceil(P/N);for(let se=0;se<this.xCellCount*this.yCellCount;se++)H.push([]),$.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=d,this.height=P,this.xScale=this.xCellCount/d,this.yScale=this.yCellCount/P,this.boxUid=0,this.circleUid=0}keysLength(){return this.boxKeys.length+this.circleKeys.length}insert(d,P,N,H,$){this._forEachCell(P,N,H,$,this._insertBoxCell,this.boxUid++),this.boxKeys.push(d),this.bboxes.push(P),this.bboxes.push(N),this.bboxes.push(H),this.bboxes.push($)}insertCircle(d,P,N,H){this._forEachCell(P-H,N-H,P+H,N+H,this._insertCircleCell,this.circleUid++),this.circleKeys.push(d),this.circles.push(P),this.circles.push(N),this.circles.push(H)}_insertBoxCell(d,P,N,H,$,se){this.boxCells[$].push(se)}_insertCircleCell(d,P,N,H,$,se){this.circleCells[$].push(se)}_query(d,P,N,H,$,se,de){if(N<0||d>this.width||H<0||P>this.height)return[];let Ae=[];if(d<=0&&P<=0&&this.width<=N&&this.height<=H){if($)return[{key:null,x1:d,y1:P,x2:N,y2:H}];for(let Fe=0;Fe<this.boxKeys.length;Fe++)Ae.push({key:this.boxKeys[Fe],x1:this.bboxes[4*Fe],y1:this.bboxes[4*Fe+1],x2:this.bboxes[4*Fe+2],y2:this.bboxes[4*Fe+3]});for(let Fe=0;Fe<this.circleKeys.length;Fe++){let Ie=this.circles[3*Fe],Ye=this.circles[3*Fe+1],st=this.circles[3*Fe+2];Ae.push({key:this.circleKeys[Fe],x1:Ie-st,y1:Ye-st,x2:Ie+st,y2:Ye+st})}}else this._forEachCell(d,P,N,H,this._queryCell,Ae,{hitTest:$,overlapMode:se,seenUids:{box:{},circle:{}}},de);return Ae}query(d,P,N,H){return this._query(d,P,N,H,!1,null)}hitTest(d,P,N,H,$,se){return this._query(d,P,N,H,!0,$,se).length>0}hitTestCircle(d,P,N,H,$){let se=d-N,de=d+N,Ae=P-N,Fe=P+N;if(de<0||se>this.width||Fe<0||Ae>this.height)return!1;let Ie=[];return this._forEachCell(se,Ae,de,Fe,this._queryCellCircle,Ie,{hitTest:!0,overlapMode:H,circle:{x:d,y:P,radius:N},seenUids:{box:{},circle:{}}},$),Ie.length>0}_queryCell(d,P,N,H,$,se,de,Ae){let{seenUids:Fe,hitTest:Ie,overlapMode:Ye}=de,st=this.boxCells[$],dt=1e-6;if(st!==null){let Xe=this.bboxes;for(let at of st)if(!Fe.box[at]){Fe.box[at]=!0;let xt=4*at,Et=this.boxKeys[at];if(d<=Xe[xt+2]+dt&&P<=Xe[xt+3]+dt&&N>=Xe[xt+0]-dt&&H>=Xe[xt+1]-dt&&(!Ae||Ae(Et))&&(!Ie||!Dt(Ye,Et.overlapMode))&&(se.push({key:Et,x1:Xe[xt],y1:Xe[xt+1],x2:Xe[xt+2],y2:Xe[xt+3]}),Ie))return!0}}let Pe=this.circleCells[$];if(Pe!==null){let Xe=this.circles;for(let at of Pe)if(!Fe.circle[at]){Fe.circle[at]=!0;let xt=3*at,Et=this.circleKeys[at];if(this._circleAndRectCollide(Xe[xt],Xe[xt+1],Xe[xt+2],d,P,N,H)&&(!Ae||Ae(Et))&&(!Ie||!Dt(Ye,Et.overlapMode))){let Bt=Xe[xt],St=Xe[xt+1],Pt=Xe[xt+2];if(se.push({key:Et,x1:Bt-Pt,y1:St-Pt,x2:Bt+Pt,y2:St+Pt}),Ie)return!0}}}return!1}_queryCellCircle(d,P,N,H,$,se,de,Ae){let{circle:Fe,seenUids:Ie,overlapMode:Ye}=de,st=this.boxCells[$];if(st!==null){let Pe=this.bboxes;for(let Xe of st)if(!Ie.box[Xe]){Ie.box[Xe]=!0;let at=4*Xe,xt=this.boxKeys[Xe];if(this._circleAndRectCollide(Fe.x,Fe.y,Fe.radius,Pe[at+0],Pe[at+1],Pe[at+2],Pe[at+3])&&(!Ae||Ae(xt))&&!Dt(Ye,xt.overlapMode))return se.push(!0),!0}}let dt=this.circleCells[$];if(dt!==null){let Pe=this.circles;for(let Xe of dt)if(!Ie.circle[Xe]){Ie.circle[Xe]=!0;let at=3*Xe,xt=this.circleKeys[Xe];if(this._circlesCollide(Pe[at],Pe[at+1],Pe[at+2],Fe.x,Fe.y,Fe.radius)&&(!Ae||Ae(xt))&&!Dt(Ye,xt.overlapMode))return se.push(!0),!0}}}_forEachCell(d,P,N,H,$,se,de,Ae){let Fe=this._convertToXCellCoord(d),Ie=this._convertToYCellCoord(P),Ye=this._convertToXCellCoord(N),st=this._convertToYCellCoord(H);for(let dt=Fe;dt<=Ye;dt++)for(let Pe=Ie;Pe<=st;Pe++)if($.call(this,d,P,N,H,this.xCellCount*Pe+dt,se,de,Ae))return}_convertToXCellCoord(d){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(d*this.xScale)))}_convertToYCellCoord(d){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(d*this.yScale)))}_circlesCollide(d,P,N,H,$,se){let de=H-d,Ae=$-P,Fe=N+se;return Fe*Fe>de*de+Ae*Ae}_circleAndRectCollide(d,P,N,H,$,se,de){let Ae=(se-H)/2,Fe=Math.abs(d-(H+Ae));if(Fe>Ae+N)return!1;let Ie=(de-$)/2,Ye=Math.abs(P-($+Ie));if(Ye>Ie+N)return!1;if(Fe<=Ae||Ye<=Ie)return!0;let st=Fe-Ae,dt=Ye-Ie;return st*st+dt*dt<=N*N}}function Vt(Y,d){let P=1/(d[0]*d[0]+d[1]*d[1]+d[2]*d[2]),N=1/(d[8]*d[8]+d[9]*d[9]+d[10]*d[10]),H=d[0]*P,$=d[4]*P,se=d[8]*N,de=d[1]*P,Ae=d[5]*P,Fe=d[9]*N,Ie=d[2]*P,Ye=d[6]*P,st=d[10]*N;Y[0]=H,Y[1]=$,Y[2]=se,Y[4]=de,Y[5]=Ae,Y[6]=Fe,Y[8]=Ie,Y[9]=Ye,Y[10]=st;let dt=d[12],Pe=d[13],Xe=d[14];return Y[12]=-H*dt-de*Pe-Ie*Xe,Y[13]=-$*dt-Ae*Pe-Ye*Xe,Y[14]=-se*dt-Fe*Pe-st*Xe,Y[3]=0,Y[7]=0,Y[11]=0,Y[15]=1,Y}let Qt=i.O();function Ut(Y,d,P){let N=i.O();if(!Y){let{vecSouth:Ye,vecEast:st}=rn(d),dt=s();dt[0]=st[0],dt[1]=st[1],dt[2]=Ye[0],dt[3]=Ye[1],H=dt,(Ie=(se=($=dt)[0])*(Fe=$[3])-(Ae=$[2])*(de=$[1]))&&(H[0]=Fe*(Ie=1/Ie),H[1]=-de*Ie,H[2]=-Ae*Ie,H[3]=se*Ie),N[0]=dt[0],N[1]=dt[1],N[4]=dt[2],N[5]=dt[3]}var H,$,se,de,Ae,Fe,Ie;return i.S(N,N,[1/P,1/P,1]),N}function _r(Y,d,P,N){if(Y){let H=i.O();if(!d){let{vecSouth:$,vecEast:se}=rn(P);H[0]=se[0],H[1]=se[1],H[4]=$[0],H[5]=$[1]}return i.S(H,H,[N,N,1]),H}return P.pixelsToClipSpaceMatrix}function rn(Y){let d=Math.cos(Y.rollInRadians),P=Math.sin(Y.rollInRadians),N=Math.cos(Y.pitchInRadians),H=Math.cos(Y.bearingInRadians),$=Math.sin(Y.bearingInRadians),se=i.az();se[0]=-H*N*P-$*d,se[1]=-$*N*P+H*d;let de=i.aA(se);de<1e-9?i.aB(se):i.aC(se,se,1/de);let Ae=i.az();Ae[0]=H*N*d-$*P,Ae[1]=$*N*d+H*P;let Fe=i.aA(Ae);return Fe<1e-9?i.aB(Ae):i.aC(Ae,Ae,1/Fe),{vecEast:Ae,vecSouth:se}}function Jr(Y,d,P,N){let H;N?(H=[Y,d,N(Y,d),1],i.aE(H,H,P)):(H=[Y,d,0,1],ao(H,H,P));let $=H[3];return{point:new i.P(H[0]/$,H[1]/$),signedDistanceFromCamera:$,isOccluded:!1}}function qn(Y,d){return .5+Y/d*.5}function ri(Y,d){return Y.x>=-d[0]&&Y.x<=d[0]&&Y.y>=-d[1]&&Y.y<=d[1]}function Jn(Y,d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st){let dt=P?Y.textSizeData:Y.iconSizeData,Pe=i.aw(dt,d.transform.zoom),Xe=[256/d.width*2+1,256/d.height*2+1],at=P?Y.text.dynamicLayoutVertexArray:Y.icon.dynamicLayoutVertexArray;at.clear();let xt=Y.lineVertexArray,Et=P?Y.text.placedSymbolArray:Y.icon.placedSymbolArray,Bt=d.transform.width/d.transform.height,St=!1;for(let Pt=0;Pt<Et.length;Pt++){let $t=Et.get(Pt);if($t.hidden||$t.writingMode===i.ax.vertical&&!St){Eo($t.numGlyphs,at);continue}St=!1;let Ht=new i.P($t.anchorX,$t.anchorY),dr={getElevation:st,pitchedLabelPlaneMatrix:N,lineVertexArray:xt,pitchWithMap:$,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},transform:d.transform,tileAnchorPoint:Ht,unwrappedTileID:Ae,width:Fe,height:Ie,translation:Ye},Xr=Ma($t.anchorX,$t.anchorY,dr);if(!ri(Xr.point,Xe)){Eo($t.numGlyphs,at);continue}let Or=qn(d.transform.cameraToCenterDistance,Xr.signedDistanceFromCamera),$r=i.ay(dt,Pe,$t),Er=$?$r*d.transform.getPitchedTextCorrection($t.anchorX,$t.anchorY,Ae)/Or:$r*Or,Tr=ra({projectionContext:dr,pitchedLabelPlaneMatrixInverse:H,symbol:$t,fontSize:Er,flip:!1,keepUpright:se,glyphOffsetArray:Y.glyphOffsetArray,dynamicLayoutVertexArray:at,aspectRatio:Bt,rotateToLine:de});St=Tr.useVertical,(Tr.notEnoughRoom||St||Tr.needsFlipping&&ra({projectionContext:dr,pitchedLabelPlaneMatrixInverse:H,symbol:$t,fontSize:Er,flip:!0,keepUpright:se,glyphOffsetArray:Y.glyphOffsetArray,dynamicLayoutVertexArray:at,aspectRatio:Bt,rotateToLine:de}).notEnoughRoom)&&Eo($t.numGlyphs,at)}P?Y.text.dynamicLayoutVertexBuffer.updateData(at):Y.icon.dynamicLayoutVertexBuffer.updateData(at)}function Ii(Y,d,P,N,H,$,se,de){let Ae=$.glyphStartIndex+$.numGlyphs,Fe=$.lineStartIndex,Ie=$.lineStartIndex+$.lineLength,Ye=d.getoffsetX($.glyphStartIndex),st=d.getoffsetX(Ae-1),dt=mo(Y*Ye,P,N,H,$.segment,Fe,Ie,de,se);if(!dt)return null;let Pe=mo(Y*st,P,N,H,$.segment,Fe,Ie,de,se);return Pe?de.projectionCache.anyProjectionOccluded?null:{first:dt,last:Pe}:null}function Vi(Y,d,P,N){return Y===i.ax.horizontal&&Math.abs(P.y-d.y)>Math.abs(P.x-d.x)*N?{useVertical:!0}:(Y===i.ax.vertical?d.y<P.y:d.x>P.x)?{needsFlipping:!0}:null}function ra(Y){let{projectionContext:d,pitchedLabelPlaneMatrixInverse:P,symbol:N,fontSize:H,flip:$,keepUpright:se,glyphOffsetArray:de,dynamicLayoutVertexArray:Ae,aspectRatio:Fe,rotateToLine:Ie}=Y,Ye=H/24,st=N.lineOffsetX*Ye,dt=N.lineOffsetY*Ye,Pe;if(N.numGlyphs>1){let Xe=N.glyphStartIndex+N.numGlyphs,at=N.lineStartIndex,xt=N.lineStartIndex+N.lineLength,Et=Ii(Ye,de,st,dt,$,N,Ie,d);if(!Et)return{notEnoughRoom:!0};let Bt=oa(Et.first.point.x,Et.first.point.y,d,P),St=oa(Et.last.point.x,Et.last.point.y,d,P);if(se&&!$){let Pt=Vi(N.writingMode,Bt,St,Fe);if(Pt)return Pt}Pe=[Et.first];for(let Pt=N.glyphStartIndex+1;Pt<Xe-1;Pt++){let $t=mo(Ye*de.getoffsetX(Pt),st,dt,$,N.segment,at,xt,d,Ie);if(!$t)return{notEnoughRoom:!0};Pe.push($t)}Pe.push(Et.last)}else{if(se&&!$){let at=Xi(d.tileAnchorPoint.x,d.tileAnchorPoint.y,d).point,xt=N.lineStartIndex+N.segment+1,Et=new i.P(d.lineVertexArray.getx(xt),d.lineVertexArray.gety(xt)),Bt=Xi(Et.x,Et.y,d),St=Bt.signedDistanceFromCamera>0?Bt.point:ha(d.tileAnchorPoint,Et,at,1,d),Pt=oa(at.x,at.y,d,P),$t=oa(St.x,St.y,d,P),Ht=Vi(N.writingMode,Pt,$t,Fe);if(Ht)return Ht}let Xe=mo(Ye*de.getoffsetX(N.glyphStartIndex),st,dt,$,N.segment,N.lineStartIndex,N.lineStartIndex+N.lineLength,d,Ie);if(!Xe||d.projectionCache.anyProjectionOccluded)return{notEnoughRoom:!0};Pe=[Xe]}for(let Xe of Pe)i.aD(Ae,Xe.point,Xe.angle);return{}}function ha(Y,d,P,N,H){let $=Y.add(Y.sub(d)._unit()),se=Xi($.x,$.y,H).point,de=P.sub(se);return P.add(de._mult(N/de.mag()))}function va(Y,d,P){let N=d.projectionCache;if(N.projections[Y])return N.projections[Y];let H=new i.P(d.lineVertexArray.getx(Y),d.lineVertexArray.gety(Y)),$=Xi(H.x,H.y,d);if($.signedDistanceFromCamera>0)return N.projections[Y]=$.point,N.anyProjectionOccluded||(N.anyProjectionOccluded=$.isOccluded),$.point;let se=Y-P.direction;return ha(P.distanceFromAnchor===0?d.tileAnchorPoint:new i.P(d.lineVertexArray.getx(se),d.lineVertexArray.gety(se)),H,P.previousVertex,P.absOffsetX-P.distanceFromAnchor+1,d)}function Xi(Y,d,P){let N=Y+P.translation[0],H=d+P.translation[1],$;return P.pitchWithMap?($=Jr(N,H,P.pitchedLabelPlaneMatrix,P.getElevation),$.isOccluded=!1):($=P.transform.projectTileCoordinates(N,H,P.unwrappedTileID,P.getElevation),$.point.x=(.5*$.point.x+.5)*P.width,$.point.y=(.5*-$.point.y+.5)*P.height),$}function oa(Y,d,P,N){if(P.pitchWithMap){let H=[Y,d,0,1];return i.aE(H,H,N),P.transform.projectTileCoordinates(H[0]/H[3],H[1]/H[3],P.unwrappedTileID,P.getElevation).point}return{x:Y/P.width*2-1,y:1-d/P.height*2}}function Ma(Y,d,P){return P.transform.projectTileCoordinates(Y,d,P.unwrappedTileID,P.getElevation)}function Ga(Y,d,P){return Y._unit()._perp()._mult(d*P)}function ss(Y,d,P,N,H,$,se,de,Ae){if(de.projectionCache.offsets[Y])return de.projectionCache.offsets[Y];let Fe=P.add(d);if(Y+Ae.direction<N||Y+Ae.direction>=H)return de.projectionCache.offsets[Y]=Fe,Fe;let Ie=va(Y+Ae.direction,de,Ae),Ye=Ga(Ie.sub(P),se,Ae.direction),st=P.add(Ye),dt=Ie.add(Ye);return de.projectionCache.offsets[Y]=i.aF($,Fe,st,dt)||Fe,de.projectionCache.offsets[Y]}function mo(Y,d,P,N,H,$,se,de,Ae){let Fe=N?Y-d:Y+d,Ie=Fe>0?1:-1,Ye=0;N&&(Ie*=-1,Ye=Math.PI),Ie<0&&(Ye+=Math.PI);let st,dt=Ie>0?$+H:$+H+1;de.projectionCache.cachedAnchorPoint?st=de.projectionCache.cachedAnchorPoint:(st=Xi(de.tileAnchorPoint.x,de.tileAnchorPoint.y,de).point,de.projectionCache.cachedAnchorPoint=st);let Pe,Xe,at=st,xt=st,Et=0,Bt=0,St=Math.abs(Fe),Pt=[],$t;for(;Et+Bt<=St;){if(dt+=Ie,dt<$||dt>=se)return null;Et+=Bt,xt=at,Xe=Pe;let Xr={absOffsetX:St,direction:Ie,distanceFromAnchor:Et,previousVertex:xt};if(at=va(dt,de,Xr),P===0)Pt.push(xt),$t=at.sub(xt);else{let Or,$r=at.sub(xt);Or=$r.mag()===0?Ga(va(dt+Ie,de,Xr).sub(at),P,Ie):Ga($r,P,Ie),Xe||(Xe=xt.add(Or)),Pe=ss(dt,Or,at,$,se,Xe,P,de,Xr),Pt.push(Xe),$t=Pe.sub(Xe)}Bt=$t.mag()}let Ht=$t._mult((St-Et)/Bt)._add(Xe||xt),dr=Ye+Math.atan2(at.y-xt.y,at.x-xt.x);return Pt.push(Ht),{point:Ht,angle:Ae?dr:0,path:Pt}}let sl=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function Eo(Y,d){for(let P=0;P<Y;P++){let N=d.length;d.resize(N+4),d.float32.set(sl,3*N)}}function ao(Y,d,P){let N=d[0],H=d[1];return Y[0]=P[0]*N+P[4]*H+P[12],Y[1]=P[1]*N+P[5]*H+P[13],Y[3]=P[3]*N+P[7]*H+P[15],Y}let Da=100;class bl{constructor(d,P=new At(d.width+200,d.height+200,25),N=new At(d.width+200,d.height+200,25)){this.transform=d,this.grid=P,this.ignoredGrid=N,this.pitchFactor=Math.cos(d.pitch*Math.PI/180)*d.cameraToCenterDistance,this.screenRightBoundary=d.width+Da,this.screenBottomBoundary=d.height+Da,this.gridRightBoundary=d.width+200,this.gridBottomBoundary=d.height+200,this.perspectiveRatioCutoff=.6}placeCollisionBox(d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st){let dt=this.projectAndGetPerspectiveRatio(d.anchorPointX+Ae[0],d.anchorPointY+Ae[1],$,Ie,st),Pe=N*dt.perspectiveRatio,Xe;if(se||de)Xe=this._projectCollisionBox(d,Pe,H,$,se,de,Ae,dt,Ie,Ye,st);else{let $t=dt.x+(Ye?Ye.x*Pe:0),Ht=dt.y+(Ye?Ye.y*Pe:0);Xe={allPointsOccluded:!1,box:[$t+d.x1*Pe,Ht+d.y1*Pe,$t+d.x2*Pe,Ht+d.y2*Pe]}}let[at,xt,Et,Bt]=Xe.box,St=se?Xe.allPointsOccluded:dt.isOccluded,Pt=St;return Pt||(Pt=dt.perspectiveRatio<this.perspectiveRatioCutoff),Pt||(Pt=!this.isInsideGrid(at,xt,Et,Bt)),Pt||P!==\"always\"&&this.grid.hitTest(at,xt,Et,Bt,P,Fe)?{box:[at,xt,Et,Bt],placeable:!1,offscreen:!1,occluded:St}:{box:[at,xt,Et,Bt],placeable:!0,offscreen:this.isOffscreen(at,xt,Et,Bt),occluded:St}}placeCollisionCircles(d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st,dt,Pe){let Xe=[],at=new i.P(P.anchorX,P.anchorY),xt=this.getPerspectiveRatio(at.x,at.y,se,Pe),Et=(Fe?$*this.transform.getPitchedTextCorrection(P.anchorX,P.anchorY,se)/xt:$*xt)/i.aJ,Bt={getElevation:Pe,pitchedLabelPlaneMatrix:de,lineVertexArray:N,pitchWithMap:Fe,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},transform:this.transform,tileAnchorPoint:at,unwrappedTileID:se,width:this.transform.width,height:this.transform.height,translation:dt},St=Ii(Et,H,P.lineOffsetX*Et,P.lineOffsetY*Et,!1,P,!1,Bt),Pt=!1,$t=!1,Ht=!0;if(St){let dr=.5*Ye*xt+st,Xr=new i.P(-100,-100),Or=new i.P(this.screenRightBoundary,this.screenBottomBoundary),$r=new Cn,Er=St.first,Tr=St.last,Rr=[];for(let on=Er.path.length-1;on>=1;on--)Rr.push(Er.path[on]);for(let on=1;on<Tr.path.length;on++)Rr.push(Tr.path[on]);let Lr=2.5*dr;if(Fe){let on=this.projectPathToScreenSpace(Rr,Bt);Rr=on.some((Nn=>Nn.signedDistanceFromCamera<=0))?[]:on.map((Nn=>Nn.point))}let an=[];if(Rr.length>0){let on=Rr[0].clone(),Nn=Rr[0].clone();for(let Yn=1;Yn<Rr.length;Yn++)on.x=Math.min(on.x,Rr[Yn].x),on.y=Math.min(on.y,Rr[Yn].y),Nn.x=Math.max(Nn.x,Rr[Yn].x),Nn.y=Math.max(Nn.y,Rr[Yn].y);an=on.x>=Xr.x&&Nn.x<=Or.x&&on.y>=Xr.y&&Nn.y<=Or.y?[Rr]:Nn.x<Xr.x||on.x>Or.x||Nn.y<Xr.y||on.y>Or.y?[]:i.aG([Rr],Xr.x,Xr.y,Or.x,Or.y)}for(let on of an){$r.reset(on,.25*dr);let Nn=0;Nn=$r.length<=.5*dr?1:Math.ceil($r.paddedLength/Lr)+1;for(let Yn=0;Yn<Nn;Yn++){let yi=Yn/Math.max(Nn-1,1),Sn=$r.lerp(yi),An=Sn.x+Da,li=Sn.y+Da;Xe.push(An,li,dr,0);let Vn=An-dr,jn=li-dr,Wn=An+dr,si=li+dr;if(Ht&&(Ht=this.isOffscreen(Vn,jn,Wn,si)),$t||($t=this.isInsideGrid(Vn,jn,Wn,si)),d!==\"always\"&&this.grid.hitTestCircle(An,li,dr,d,Ie)&&(Pt=!0,!Ae))return{circles:[],offscreen:!1,collisionDetected:Pt}}}}return{circles:!Ae&&Pt||!$t||xt<this.perspectiveRatioCutoff?[]:Xe,offscreen:Ht,collisionDetected:Pt}}projectPathToScreenSpace(d,P){let N=(function(H,$){let se=Qt;return(function(de,Ae){let Fe=1/(Ae[0]*Ae[5]-Ae[1]*Ae[4]);de[0]=Ae[5]*Fe,de[1]=-Ae[1]*Fe,de[2]=0,de[3]=0,de[4]=-Ae[4]*Fe,de[5]=Ae[0]*Fe,de[6]=0,de[7]=0,de[8]=0,de[9]=0,de[10]=1/Ae[10],de[11]=0,de[12]=0,de[13]=0,de[14]=0,de[15]=1/Ae[15]})(se,$.pitchedLabelPlaneMatrix),H.map((de=>{let Ae=Jr(de.x,de.y,se,$.getElevation),Fe=$.transform.projectTileCoordinates(Ae.point.x,Ae.point.y,$.unwrappedTileID,$.getElevation);return Fe.point.x=(.5*Fe.point.x+.5)*$.width,Fe.point.y=(.5*-Fe.point.y+.5)*$.height,Fe}))})(d,P);return(function(H){let $=0,se=0,de=0,Ae=0;for(let Fe=0;Fe<H.length;Fe++)H[Fe].isOccluded?(de=Fe+1,Ae=0):(Ae++,Ae>se&&(se=Ae,$=de));return H.slice($,$+se)})(N)}queryRenderedSymbols(d){if(d.length===0||this.grid.keysLength()===0&&this.ignoredGrid.keysLength()===0)return{};let P=[],N=new i.a8;for(let Ye of d){let st=new i.P(Ye.x+Da,Ye.y+Da);N.extend(st),P.push(st)}let{minX:H,minY:$,maxX:se,maxY:de}=N,Ae=this.grid.query(H,$,se,de).concat(this.ignoredGrid.query(H,$,se,de)),Fe={},Ie={};for(let Ye of Ae){let st=Ye.key;if(Fe[st.bucketInstanceId]===void 0&&(Fe[st.bucketInstanceId]={}),Fe[st.bucketInstanceId][st.featureIndex])continue;let dt=[new i.P(Ye.x1,Ye.y1),new i.P(Ye.x2,Ye.y1),new i.P(Ye.x2,Ye.y2),new i.P(Ye.x1,Ye.y2)];i.aH(P,dt)&&(Fe[st.bucketInstanceId][st.featureIndex]=!0,Ie[st.bucketInstanceId]===void 0&&(Ie[st.bucketInstanceId]=[]),Ie[st.bucketInstanceId].push(st.featureIndex))}return Ie}insertCollisionBox(d,P,N,H,$,se){(N?this.ignoredGrid:this.grid).insert({bucketInstanceId:H,featureIndex:$,collisionGroupID:se,overlapMode:P},d[0],d[1],d[2],d[3])}insertCollisionCircles(d,P,N,H,$,se){let de=N?this.ignoredGrid:this.grid,Ae={bucketInstanceId:H,featureIndex:$,collisionGroupID:se,overlapMode:P};for(let Fe=0;Fe<d.length;Fe+=4)de.insertCircle(Ae,d[Fe],d[Fe+1],d[Fe+2])}projectAndGetPerspectiveRatio(d,P,N,H,$){if($){let se;H?(se=[d,P,H(d,P),1],i.aE(se,se,$)):(se=[d,P,0,1],ao(se,se,$));let de=se[3];return{x:(se[0]/de+1)/2*this.transform.width+Da,y:(-se[1]/de+1)/2*this.transform.height+Da,perspectiveRatio:.5+this.transform.cameraToCenterDistance/de*.5,isOccluded:!1,signedDistanceFromCamera:de}}{let se=this.transform.projectTileCoordinates(d,P,N,H);return{x:(se.point.x+1)/2*this.transform.width+Da,y:(1-se.point.y)/2*this.transform.height+Da,perspectiveRatio:.5+this.transform.cameraToCenterDistance/se.signedDistanceFromCamera*.5,isOccluded:se.isOccluded,signedDistanceFromCamera:se.signedDistanceFromCamera}}}getPerspectiveRatio(d,P,N,H){let $=this.transform.projectTileCoordinates(d,P,N,H);return .5+this.transform.cameraToCenterDistance/$.signedDistanceFromCamera*.5}isOffscreen(d,P,N,H){return N<Da||d>=this.screenRightBoundary||H<Da||P>this.screenBottomBoundary}isInsideGrid(d,P,N,H){return N>=0&&d<this.gridRightBoundary&&H>=0&&P<this.gridBottomBoundary}getViewportMatrix(){let d=i.ap([]);return i.Q(d,d,[-100,-100,0]),d}_projectCollisionBox(d,P,N,H,$,se,de,Ae,Fe,Ie,Ye){let st=1,dt=0,Pe=0,Xe=1,at=d.anchorPointX+de[0],xt=d.anchorPointY+de[1];if(se&&!$){let Rr=this.projectAndGetPerspectiveRatio(at+1,xt,H,Fe,Ye),Lr=Rr.x-Ae.x,an=Math.atan((Rr.y-Ae.y)/Lr)+(Lr<0?Math.PI:0),on=Math.sin(an),Nn=Math.cos(an);st=Nn,dt=on,Pe=-on,Xe=Nn}else if(!se&&$){let Rr=rn(this.transform);st=Rr.vecEast[0],dt=Rr.vecEast[1],Pe=Rr.vecSouth[0],Xe=Rr.vecSouth[1]}let Et=Ae.x,Bt=Ae.y,St=P;$&&(Et=at,Bt=xt,St=Math.pow(2,-(this.transform.zoom-N.overscaledZ)),St*=this.transform.getPitchedTextCorrection(at,xt,H),Ie||(St*=i.al(.5+Ae.signedDistanceFromCamera/this.transform.cameraToCenterDistance*.5,0,4))),Ie&&(Et+=st*Ie.x*St+Pe*Ie.y*St,Bt+=dt*Ie.x*St+Xe*Ie.y*St);let Pt=d.x1*St,$t=d.x2*St,Ht=(Pt+$t)/2,dr=d.y1*St,Xr=d.y2*St,Or=(dr+Xr)/2,$r=[{offsetX:Pt,offsetY:dr},{offsetX:Ht,offsetY:dr},{offsetX:$t,offsetY:dr},{offsetX:$t,offsetY:Or},{offsetX:$t,offsetY:Xr},{offsetX:Ht,offsetY:Xr},{offsetX:Pt,offsetY:Xr},{offsetX:Pt,offsetY:Or}],Er=[];for(let{offsetX:Rr,offsetY:Lr}of $r)Er.push(new i.P(Et+st*Rr+Pe*Lr,Bt+dt*Rr+Xe*Lr));let Tr=!1;if($){let Rr=Er.map((Lr=>this.projectAndGetPerspectiveRatio(Lr.x,Lr.y,H,Fe,Ye)));Tr=Rr.some((Lr=>!Lr.isOccluded)),Er=Rr.map((Lr=>new i.P(Lr.x,Lr.y)))}else Tr=!0;return{box:i.aI(Er),allPointsOccluded:!Tr}}}class bo{constructor(d,P,N,H){this.opacity=d?Math.max(0,Math.min(1,d.opacity+(d.placed?P:-P))):H&&N?1:0,this.placed=N}isHidden(){return this.opacity===0&&!this.placed}}class Zs{constructor(d,P,N,H,$){this.text=new bo(d?d.text:null,P,N,$),this.icon=new bo(d?d.icon:null,P,H,$)}isHidden(){return this.text.isHidden()&&this.icon.isHidden()}}class su{constructor(d,P,N){this.text=d,this.icon=P,this.skipFade=N}}class fc{constructor(d,P,N,H,$){this.bucketInstanceId=d,this.featureIndex=P,this.sourceLayerIndex=N,this.bucketIndex=H,this.tileID=$}}class Af{constructor(d){this.crossSourceCollisions=d,this.maxGroupID=0,this.collisionGroups={}}get(d){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[d]){let P=++this.maxGroupID;this.collisionGroups[d]={ID:P,predicate:N=>N.collisionGroupID===P}}return this.collisionGroups[d]}}function ut(Y,d,P,N,H){let{horizontalAlign:$,verticalAlign:se}=i.aP(Y);return new i.P(-($-.5)*d+N[0]*H,-(se-.5)*P+N[1]*H)}class Jt{constructor(d,P,N,H,$){this.transform=d.clone(),this.terrain=P,this.collisionIndex=new bl(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=N,this.retainedQueryData={},this.collisionGroups=new Af(H),this.collisionCircleArrays={},this.collisionBoxArrays=new Map,this.prevPlacement=$,$&&($.prevPlacement=void 0),this.placedOrientations={}}_getTerrainElevationFunc(d){let P=this.terrain;return P?(N,H)=>P.getElevation(d,N,H):null}getBucketParts(d,P,N,H){let $=N.getBucket(P),se=N.latestFeatureIndex;if(!$||!se||P.id!==$.layerIds[0])return;let de=N.collisionBoxArray,Ae=$.layers[0].layout,Fe=$.layers[0].paint,Ie=Math.pow(2,this.transform.zoom-N.tileID.overscaledZ),Ye=N.tileSize/i.a6,st=N.tileID.toUnwrapped(),dt=Ae.get(\"text-rotation-alignment\")===\"map\",Pe=i.aK(N,1,this.transform.zoom),Xe=i.aL(this.collisionIndex.transform,N,Fe.get(\"text-translate\"),Fe.get(\"text-translate-anchor\")),at=i.aL(this.collisionIndex.transform,N,Fe.get(\"icon-translate\"),Fe.get(\"icon-translate-anchor\")),xt=Ut(dt,this.transform,Pe);this.retainedQueryData[$.bucketInstanceId]=new fc($.bucketInstanceId,se,$.sourceLayerIndex,$.index,N.tileID);let Et={bucket:$,layout:Ae,translationText:Xe,translationIcon:at,unwrappedTileID:st,pitchedLabelPlaneMatrix:xt,scale:Ie,textPixelRatio:Ye,holdingForFade:N.holdingForSymbolFade(),collisionBoxArray:de,partiallyEvaluatedTextSize:i.aw($.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get($.sourceID)};if(H)for(let Bt of $.sortKeyRanges){let{sortKey:St,symbolInstanceStart:Pt,symbolInstanceEnd:$t}=Bt;d.push({sortKey:St,symbolInstanceStart:Pt,symbolInstanceEnd:$t,parameters:Et})}else d.push({symbolInstanceStart:0,symbolInstanceEnd:$.symbolInstances.length,parameters:Et})}attemptAnchorPlacement(d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st,dt,Pe,Xe,at,xt,Et,Bt,St){var Pt,$t,Ht;let dr=i.aM[d.textAnchor],Xr=[d.textOffset0,d.textOffset1],Or=ut(dr,N,H,Xr,$),$r=this.collisionIndex.placeCollisionBox(P,st,Ae,Fe,Ie,de,se,at,Ye.predicate,Bt,Or,St);if((!Et||this.collisionIndex.placeCollisionBox(Et,st,Ae,Fe,Ie,de,se,xt,Ye.predicate,Bt,Or,St).placeable)&&$r.placeable){let Er;if(!((Pt=this.prevPlacement)===null||Pt===void 0)&&Pt.variableOffsets[dt.crossTileID]&&(!((Ht=($t=this.prevPlacement)===null||$t===void 0?void 0:$t.placements[dt.crossTileID])===null||Ht===void 0)&&Ht.text)&&(Er=this.prevPlacement.variableOffsets[dt.crossTileID].anchor),dt.crossTileID===0)throw new Error(\"symbolInstance.crossTileID can't be 0\");return this.variableOffsets[dt.crossTileID]={textOffset:Xr,width:N,height:H,anchor:dr,textBoxScale:$,prevAnchor:Er},this.markUsedJustification(Pe,dr,dt,Xe),Pe.allowVerticalPlacement&&(this.markUsedOrientation(Pe,Xe,dt),this.placedOrientations[dt.crossTileID]=Xe),{shift:Or,placedGlyphBoxes:$r}}}placeLayerBucketPart(d,P,N){let{bucket:H,layout:$,translationText:se,translationIcon:de,unwrappedTileID:Ae,pitchedLabelPlaneMatrix:Fe,textPixelRatio:Ie,holdingForFade:Ye,collisionBoxArray:st,partiallyEvaluatedTextSize:dt,collisionGroup:Pe}=d.parameters,Xe=$.get(\"text-optional\"),at=$.get(\"icon-optional\"),xt=i.aN($,\"text-overlap\",\"text-allow-overlap\"),Et=xt===\"always\",Bt=i.aN($,\"icon-overlap\",\"icon-allow-overlap\"),St=Bt===\"always\",Pt=$.get(\"text-rotation-alignment\")===\"map\",$t=$.get(\"text-pitch-alignment\")===\"map\",Ht=$.get(\"icon-text-fit\")!==\"none\",dr=$.get(\"symbol-z-order\")===\"viewport-y\",Xr=Et&&(St||!H.hasIconData()||at),Or=St&&(Et||!H.hasTextData()||Xe);!H.collisionArrays&&st&&H.deserializeCollisionBoxes(st);let $r=this.retainedQueryData[H.bucketInstanceId].tileID,Er=this._getTerrainElevationFunc($r),Tr=this.transform.getFastPathSimpleProjectionMatrix($r),Rr=(Lr,an,on)=>{var Nn,Yn;if(P[Lr.crossTileID])return;if(Ye)return void(this.placements[Lr.crossTileID]=new su(!1,!1,!1));let yi=!1,Sn=!1,An=!0,li=null,Vn={box:null,placeable:!1,offscreen:null,occluded:!1},jn={placeable:!1},Wn=null,si=null,zi=null,da=0,ui=0,Li=0;an.textFeatureIndex?da=an.textFeatureIndex:Lr.useRuntimeCollisionCircles&&(da=Lr.featureIndex),an.verticalTextFeatureIndex&&(ui=an.verticalTextFeatureIndex);let Ki=an.textBox;if(Ki){let Ss=_o=>{let po=i.ax.horizontal;if(H.allowVerticalPlacement&&!_o&&this.prevPlacement){let Xo=this.prevPlacement.placedOrientations[Lr.crossTileID];Xo&&(this.placedOrientations[Lr.crossTileID]=Xo,po=Xo,this.markUsedOrientation(H,po,Lr))}return po},Na=(_o,po)=>{if(H.allowVerticalPlacement&&Lr.numVerticalGlyphVertices>0&&an.verticalTextBox){for(let Xo of H.writingModes)if(Xo===i.ax.vertical?(Vn=po(),jn=Vn):Vn=_o(),Vn==null?void 0:Vn.placeable)break}else Vn=_o()},Ns=Lr.textAnchorOffsetStartIndex,vo=Lr.textAnchorOffsetEndIndex;if(vo===Ns){let _o=(po,Xo)=>{let no=this.collisionIndex.placeCollisionBox(po,xt,Ie,$r,Ae,$t,Pt,se,Pe.predicate,Er,void 0,Tr);return no!=null&&no.placeable&&(this.markUsedOrientation(H,Xo,Lr),this.placedOrientations[Lr.crossTileID]=Xo),no};Na((()=>_o(Ki,i.ax.horizontal)),(()=>{let po=an.verticalTextBox;return H.allowVerticalPlacement&&Lr.numVerticalGlyphVertices>0&&po?_o(po,i.ax.vertical):{box:null,offscreen:null}})),Ss(Vn==null?void 0:Vn.placeable)}else{let _o=i.aM[(Yn=(Nn=this.prevPlacement)===null||Nn===void 0?void 0:Nn.variableOffsets[Lr.crossTileID])===null||Yn===void 0?void 0:Yn.anchor],po=(no,Us,ql)=>{let Ta=no.x2-no.x1,vu=no.y2-no.y1,ys=Lr.textBoxScale,xc=Ht&&Bt===\"never\"?Us:null,Zl=null,$l=xt===\"never\"?1:2,xa=\"never\";_o&&$l++;for(let uo=0;uo<$l;uo++){for(let Es=Ns;Es<vo;Es++){let vr=H.textAnchorOffsets.get(Es);if(_o&&vr.textAnchor!==_o)continue;let gn=this.attemptAnchorPlacement(vr,no,Ta,vu,ys,Pt,$t,Ie,$r,Ae,Pe,xa,Lr,H,ql,se,de,xc,Er);if(gn&&(Zl=gn.placedGlyphBoxes,Zl==null?void 0:Zl.placeable))return yi=!0,li=gn.shift,Zl}_o?_o=null:xa=xt}return N&&!Zl&&(Zl={box:this.collisionIndex.placeCollisionBox(Ki,\"always\",Ie,$r,Ae,$t,Pt,se,Pe.predicate,Er,void 0,Tr).box,offscreen:!1,placeable:!1,occluded:!1}),Zl};Na((()=>po(Ki,an.iconBox,i.ax.horizontal)),(()=>{let no=an.verticalTextBox;return H.allowVerticalPlacement&&!(Vn!=null&&Vn.placeable)&&Lr.numVerticalGlyphVertices>0&&no?po(no,an.verticalIconBox,i.ax.vertical):{box:null,occluded:!0,offscreen:null}})),Vn&&(yi=Vn.placeable,An=Vn.offscreen);let Xo=Ss(Vn==null?void 0:Vn.placeable);if(!yi&&this.prevPlacement){let no=this.prevPlacement.variableOffsets[Lr.crossTileID];no&&(this.variableOffsets[Lr.crossTileID]=no,this.markUsedJustification(H,no.anchor,Lr,Xo))}}}if(Wn=Vn,yi=Wn==null?void 0:Wn.placeable,An=Wn==null?void 0:Wn.offscreen,Lr.useRuntimeCollisionCircles&&Lr.centerJustifiedTextSymbolIndex>=0){let Ss=H.text.placedSymbolArray.get(Lr.centerJustifiedTextSymbolIndex),Na=i.ay(H.textSizeData,dt,Ss),Ns=$.get(\"text-padding\");si=this.collisionIndex.placeCollisionCircles(xt,Ss,H.lineVertexArray,H.glyphOffsetArray,Na,Ae,Fe,N,$t,Pe.predicate,Lr.collisionCircleDiameter,Ns,se,Er),si.circles.length&&si.collisionDetected&&!N&&i.w(\"Collisions detected, but collision boxes are not shown\"),yi=Et||si.circles.length>0&&!si.collisionDetected,An&&(An=si.offscreen)}if(an.iconFeatureIndex&&(Li=an.iconFeatureIndex),an.iconBox){let Ss=Na=>this.collisionIndex.placeCollisionBox(Na,Bt,Ie,$r,Ae,$t,Pt,de,Pe.predicate,Er,Ht&&li?li:void 0,Tr);jn&&jn.placeable&&an.verticalIconBox?(zi=Ss(an.verticalIconBox),Sn=zi.placeable):(zi=Ss(an.iconBox),Sn=zi.placeable),An&&(An=zi.offscreen)}let ca=Xe||Lr.numHorizontalGlyphVertices===0&&Lr.numVerticalGlyphVertices===0,Ia=at||Lr.numIconVertices===0;ca||Ia?Ia?ca||Sn&&(Sn=yi):yi=Sn&&yi:Sn=yi=Sn&&yi;let Os=Sn&&zi.placeable;if(yi&&Wn.placeable&&this.collisionIndex.insertCollisionBox(Wn.box,xt,$.get(\"text-ignore-placement\"),H.bucketInstanceId,jn&&jn.placeable&&ui?ui:da,Pe.ID),Os&&this.collisionIndex.insertCollisionBox(zi.box,Bt,$.get(\"icon-ignore-placement\"),H.bucketInstanceId,Li,Pe.ID),si&&yi&&this.collisionIndex.insertCollisionCircles(si.circles,xt,$.get(\"text-ignore-placement\"),H.bucketInstanceId,da,Pe.ID),N&&this.storeCollisionData(H.bucketInstanceId,on,an,Wn,zi,si),Lr.crossTileID===0)throw new Error(\"symbolInstance.crossTileID can't be 0\");if(H.bucketInstanceId===0)throw new Error(\"bucket.bucketInstanceId can't be 0\");this.placements[Lr.crossTileID]=new su((yi||Xr)&&!(Wn!=null&&Wn.occluded),(Sn||Or)&&!(zi!=null&&zi.occluded),An||H.justReloaded),P[Lr.crossTileID]=!0};if(dr){if(d.symbolInstanceStart!==0)throw new Error(\"bucket.bucketInstanceId should be 0\");let Lr=H.getSortedSymbolIndexes(-this.transform.bearingInRadians);for(let an=Lr.length-1;an>=0;--an){let on=Lr[an];Rr(H.symbolInstances.get(on),H.collisionArrays[on],on)}}else for(let Lr=d.symbolInstanceStart;Lr<d.symbolInstanceEnd;Lr++)Rr(H.symbolInstances.get(Lr),H.collisionArrays[Lr],Lr);H.justReloaded=!1}storeCollisionData(d,P,N,H,$,se){if(N.textBox||N.iconBox){let de,Ae;this.collisionBoxArrays.has(d)?de=this.collisionBoxArrays.get(d):(de=new Map,this.collisionBoxArrays.set(d,de)),de.has(P)?Ae=de.get(P):(Ae={text:null,icon:null},de.set(P,Ae)),N.textBox&&(Ae.text=H.box),N.iconBox&&(Ae.icon=$.box)}if(se){let de=this.collisionCircleArrays[d];de===void 0&&(de=this.collisionCircleArrays[d]=[]);for(let Ae=0;Ae<se.circles.length;Ae+=4)de.push(se.circles[Ae+0]-Da),de.push(se.circles[Ae+1]-Da),de.push(se.circles[Ae+2]),de.push(se.collisionDetected?1:0)}}markUsedJustification(d,P,N,H){let $;$=H===i.ax.vertical?N.verticalPlacedTextSymbolIndex:{left:N.leftJustifiedTextSymbolIndex,center:N.centerJustifiedTextSymbolIndex,right:N.rightJustifiedTextSymbolIndex}[i.aO(P)];let se=[N.leftJustifiedTextSymbolIndex,N.centerJustifiedTextSymbolIndex,N.rightJustifiedTextSymbolIndex,N.verticalPlacedTextSymbolIndex];for(let de of se)de>=0&&(d.text.placedSymbolArray.get(de).crossTileID=$>=0&&de!==$?0:N.crossTileID)}markUsedOrientation(d,P,N){let H=P===i.ax.horizontal||P===i.ax.horizontalOnly?P:0,$=P===i.ax.vertical?P:0,se=[N.leftJustifiedTextSymbolIndex,N.centerJustifiedTextSymbolIndex,N.rightJustifiedTextSymbolIndex];for(let de of se)d.text.placedSymbolArray.get(de).placedOrientation=H;N.verticalPlacedTextSymbolIndex&&(d.text.placedSymbolArray.get(N.verticalPlacedTextSymbolIndex).placedOrientation=$)}commit(d){this.commitTime=d,this.zoomAtLastRecencyCheck=this.transform.zoom;let P=this.prevPlacement,N=!1;this.prevZoomAdjustment=P?P.zoomAdjustment(this.transform.zoom):0;let H=P?P.symbolFadeChange(d):1,$=P?P.opacities:{},se=P?P.variableOffsets:{},de=P?P.placedOrientations:{};for(let Ae in this.placements){let Fe=this.placements[Ae],Ie=$[Ae];Ie?(this.opacities[Ae]=new Zs(Ie,H,Fe.text,Fe.icon),N||(N=Fe.text!==Ie.text.placed),N||(N=Fe.icon!==Ie.icon.placed)):(this.opacities[Ae]=new Zs(null,H,Fe.text,Fe.icon,Fe.skipFade),N||(N=Fe.text||Fe.icon))}for(let Ae in $){let Fe=$[Ae];if(!this.opacities[Ae]){let Ie=new Zs(Fe,H,!1,!1);Ie.isHidden()||(this.opacities[Ae]=Ie,N||(N=Fe.text.placed),N||(N=Fe.icon.placed))}}for(let Ae in se)this.variableOffsets[Ae]||!this.opacities[Ae]||this.opacities[Ae].isHidden()||(this.variableOffsets[Ae]=se[Ae]);for(let Ae in de)this.placedOrientations[Ae]||!this.opacities[Ae]||this.opacities[Ae].isHidden()||(this.placedOrientations[Ae]=de[Ae]);if(P&&P.lastPlacementChangeTime===void 0)throw new Error(\"Last placement time for previous placement is not defined\");N?this.lastPlacementChangeTime=d:typeof this.lastPlacementChangeTime!=\"number\"&&(this.lastPlacementChangeTime=P?P.lastPlacementChangeTime:d)}updateLayerOpacities(d,P){let N={};for(let H of P){let $=H.getBucket(d);$&&H.latestFeatureIndex&&d.id===$.layerIds[0]&&this.updateBucketOpacities($,H.tileID,N,H.collisionBoxArray)}}updateBucketOpacities(d,P,N,H){d.hasTextData()&&(d.text.opacityVertexArray.clear(),d.text.hasVisibleVertices=!1),d.hasIconData()&&(d.icon.opacityVertexArray.clear(),d.icon.hasVisibleVertices=!1),d.hasIconCollisionBoxData()&&d.iconCollisionBox.collisionVertexArray.clear(),d.hasTextCollisionBoxData()&&d.textCollisionBox.collisionVertexArray.clear();let $=d.layers[0],se=$.layout,de=new Zs(null,0,!1,!1,!0),Ae=se.get(\"text-allow-overlap\"),Fe=se.get(\"icon-allow-overlap\"),Ie=$._unevaluatedLayout.hasValue(\"text-variable-anchor\")||$._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),Ye=se.get(\"text-rotation-alignment\")===\"map\",st=se.get(\"text-pitch-alignment\")===\"map\",dt=se.get(\"icon-text-fit\")!==\"none\",Pe=new Zs(null,0,Ae&&(Fe||!d.hasIconData()||se.get(\"icon-optional\")),Fe&&(Ae||!d.hasTextData()||se.get(\"text-optional\")),!0);!d.collisionArrays&&H&&(d.hasIconCollisionBoxData()||d.hasTextCollisionBoxData())&&d.deserializeCollisionBoxes(H);let Xe=(xt,Et,Bt)=>{for(let St=0;St<Et/4;St++)xt.opacityVertexArray.emplaceBack(Bt);xt.hasVisibleVertices||(xt.hasVisibleVertices=Bt!==Ai)},at=this.collisionBoxArrays.get(d.bucketInstanceId);for(let xt=0;xt<d.symbolInstances.length;xt++){let Et=d.symbolInstances.get(xt),{numHorizontalGlyphVertices:Bt,numVerticalGlyphVertices:St,crossTileID:Pt}=Et,$t=this.opacities[Pt];N[Pt]?$t=de:$t||($t=Pe,this.opacities[Pt]=$t),N[Pt]=!0;let Ht=Et.numIconVertices>0,dr=this.placedOrientations[Et.crossTileID],Xr=dr===i.ax.vertical,Or=dr===i.ax.horizontal||dr===i.ax.horizontalOnly;if(Bt>0||St>0){let Er=Qn($t.text);Xe(d.text,Bt,Xr?Ai:Er),Xe(d.text,St,Or?Ai:Er);let Tr=$t.text.isHidden(),Rr=[Et.rightJustifiedTextSymbolIndex,Et.centerJustifiedTextSymbolIndex,Et.leftJustifiedTextSymbolIndex];for(let on of Rr)on>=0&&(d.text.placedSymbolArray.get(on).hidden=Tr||Xr?1:0);Et.verticalPlacedTextSymbolIndex>=0&&(d.text.placedSymbolArray.get(Et.verticalPlacedTextSymbolIndex).hidden=Tr||Or?1:0);let Lr=this.variableOffsets[Et.crossTileID];Lr&&this.markUsedJustification(d,Lr.anchor,Et,dr);let an=this.placedOrientations[Et.crossTileID];an&&(this.markUsedJustification(d,\"left\",Et,an),this.markUsedOrientation(d,an,Et))}if(Ht){let Er=Qn($t.icon),Tr=!(dt&&Et.verticalPlacedIconSymbolIndex&&Xr);Et.placedIconSymbolIndex>=0&&(Xe(d.icon,Et.numIconVertices,Tr?Er:Ai),d.icon.placedSymbolArray.get(Et.placedIconSymbolIndex).hidden=$t.icon.isHidden()),Et.verticalPlacedIconSymbolIndex>=0&&(Xe(d.icon,Et.numVerticalIconVertices,Tr?Ai:Er),d.icon.placedSymbolArray.get(Et.verticalPlacedIconSymbolIndex).hidden=$t.icon.isHidden())}let $r=at!=null&&at.has(xt)?at.get(xt):{text:null,icon:null};if(d.hasIconCollisionBoxData()||d.hasTextCollisionBoxData()){let Er=d.collisionArrays[xt];if(Er){let Tr=new i.P(0,0);if(Er.textBox||Er.verticalTextBox){let Rr=!0;if(Ie){let Lr=this.variableOffsets[Pt];Lr?(Tr=ut(Lr.anchor,Lr.width,Lr.height,Lr.textOffset,Lr.textBoxScale),Ye&&Tr._rotate(st?-this.transform.bearingInRadians:this.transform.bearingInRadians)):Rr=!1}if(Er.textBox||Er.verticalTextBox){let Lr;Er.textBox&&(Lr=Xr),Er.verticalTextBox&&(Lr=Or),er(d.textCollisionBox.collisionVertexArray,$t.text.placed,!Rr||Lr,$r.text,Tr.x,Tr.y)}}if(Er.iconBox||Er.verticalIconBox){let Rr=!!(!Or&&Er.verticalIconBox),Lr;Er.iconBox&&(Lr=Rr),Er.verticalIconBox&&(Lr=!Rr),er(d.iconCollisionBox.collisionVertexArray,$t.icon.placed,Lr,$r.icon,dt?Tr.x:0,dt?Tr.y:0)}}}}if(d.sortFeatures(-this.transform.bearingInRadians),this.retainedQueryData[d.bucketInstanceId]&&(this.retainedQueryData[d.bucketInstanceId].featureSortOrder=d.featureSortOrder),d.hasTextData()&&d.text.opacityVertexBuffer&&d.text.opacityVertexBuffer.updateData(d.text.opacityVertexArray),d.hasIconData()&&d.icon.opacityVertexBuffer&&d.icon.opacityVertexBuffer.updateData(d.icon.opacityVertexArray),d.hasIconCollisionBoxData()&&d.iconCollisionBox.collisionVertexBuffer&&d.iconCollisionBox.collisionVertexBuffer.updateData(d.iconCollisionBox.collisionVertexArray),d.hasTextCollisionBoxData()&&d.textCollisionBox.collisionVertexBuffer&&d.textCollisionBox.collisionVertexBuffer.updateData(d.textCollisionBox.collisionVertexArray),d.text.opacityVertexArray.length!==d.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${d.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${d.text.layoutVertexArray.length}) / 4`);if(d.icon.opacityVertexArray.length!==d.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${d.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${d.icon.layoutVertexArray.length}) / 4`);d.bucketInstanceId in this.collisionCircleArrays&&(d.collisionCircleArray=this.collisionCircleArrays[d.bucketInstanceId],delete this.collisionCircleArrays[d.bucketInstanceId])}symbolFadeChange(d){return this.fadeDuration===0?1:(d-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(d){return Math.max(0,(this.transform.zoom-d)/1.5)}hasTransitions(d){return this.stale||d-this.lastPlacementChangeTime<this.fadeDuration}stillRecent(d,P){let N=this.zoomAtLastRecencyCheck===P?1-this.zoomAdjustment(P):1;return this.zoomAtLastRecencyCheck=P,this.commitTime+this.fadeDuration*N>d}setStale(){this.stale=!0}}function er(Y,d,P,N,H,$){N&&N.length!==0||(N=[0,0,0,0]);let se=N[0]-Da,de=N[1]-Da,Ae=N[2]-Da,Fe=N[3]-Da;Y.emplaceBack(d?1:0,P?1:0,H||0,$||0,se,de),Y.emplaceBack(d?1:0,P?1:0,H||0,$||0,Ae,de),Y.emplaceBack(d?1:0,P?1:0,H||0,$||0,Ae,Fe),Y.emplaceBack(d?1:0,P?1:0,H||0,$||0,se,Fe)}let wr=Math.pow(2,25),nn=Math.pow(2,24),Dr=Math.pow(2,17),bn=Math.pow(2,16),Yr=Math.pow(2,9),Tn=Math.pow(2,8),Kn=Math.pow(2,1);function Qn(Y){if(Y.opacity===0&&!Y.placed)return 0;if(Y.opacity===1&&Y.placed)return 4294967295;let d=Y.placed?1:0,P=Math.floor(127*Y.opacity);return P*wr+d*nn+P*Dr+d*bn+P*Yr+d*Tn+P*Kn+d}let Ai=0;class Ji{constructor(d){this._sortAcrossTiles=d.layout.get(\"symbol-z-order\")!==\"viewport-y\"&&!d.layout.get(\"symbol-sort-key\").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(d,P,N,H,$){let se=this._bucketParts;for(;this._currentTileIndex<d.length;)if(P.getBucketParts(se,H,d[this._currentTileIndex],this._sortAcrossTiles),this._currentTileIndex++,$())return!0;for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,se.sort(((de,Ae)=>de.sortKey-Ae.sortKey)));this._currentPartIndex<se.length;)if(P.placeLayerBucketPart(se[this._currentPartIndex],this._seenCrossTileIDs,N),this._currentPartIndex++,$())return!0;return!1}}class Ra{constructor(d,P,N,H,$,se,de,Ae){this.placement=new Jt(d,P,se,de,Ae),this._currentPlacementIndex=N.length-1,this._forceFullPlacement=H,this._showCollisionBoxes=$,this._done=!1}isDone(){return this._done}continuePlacement(d,P,N){let H=g(),$=()=>!this._forceFullPlacement&&g()-H>2;for(;this._currentPlacementIndex>=0;){let se=P[d[this._currentPlacementIndex]],de=this.placement.collisionIndex.transform.zoom;if(i.aQ(se)&&se.layout&&(!se.minzoom||se.minzoom<=de)&&(!se.maxzoom||se.maxzoom>de)){if(this._inProgressLayer||(this._inProgressLayer=new Ji(se)),this._inProgressLayer.continuePlacement(N[se.source],this.placement,this._showCollisionBoxes,se,$))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(d){return this.placement.commit(d),this.placement}}let ii=512/i.a6/2;class Za{constructor(d,P,N){this.tileID=d,this.bucketInstanceId=N,this._symbolsByKey={};let H=new Map;for(let $=0;$<P.length;$++){let se=P.get($),de=se.key,Ae=H.get(de);Ae?Ae.push(se):H.set(de,[se])}for(let[$,se]of H){let de={positions:se.map((Ae=>({x:Math.floor(Ae.anchorX*ii),y:Math.floor(Ae.anchorY*ii)}))),crossTileIDs:se.map((Ae=>Ae.crossTileID))};if(de.positions.length>128){let Ae=new i.aR(de.positions.length,16,Uint16Array);for(let{x:Fe,y:Ie}of de.positions)Ae.add(Fe,Ie);Ae.finish(),delete de.positions,de.index=Ae}this._symbolsByKey[$]=de}}getScaledCoordinates(d,P){let{x:N,y:H,z:$}=this.tileID.canonical,{x:se,y:de,z:Ae}=P.canonical,Fe=ii/Math.pow(2,Ae-$),Ie=(de*i.a6+d.anchorY)*Fe,Ye=H*i.a6*ii;return{x:Math.floor((se*i.a6+d.anchorX)*Fe-N*i.a6*ii),y:Math.floor(Ie-Ye)}}findMatches(d,P,N){let H=this.tileID.canonical.z<P.canonical.z?1:Math.pow(2,this.tileID.canonical.z-P.canonical.z);for(let $=0;$<d.length;$++){let se=d.get($);if(se.crossTileID)continue;let de=this._symbolsByKey[se.key];if(!de)continue;let Ae=this.getScaledCoordinates(se,P);if(de.index){let Fe=de.index.range(Ae.x-H,Ae.y-H,Ae.x+H,Ae.y+H).sort();for(let Ie of Fe){let Ye=de.crossTileIDs[Ie];if(!N[Ye]){N[Ye]=!0,se.crossTileID=Ye;break}}}else if(de.positions)for(let Fe=0;Fe<de.positions.length;Fe++){let Ie=de.positions[Fe],Ye=de.crossTileIDs[Fe];if(Math.abs(Ie.x-Ae.x)<=H&&Math.abs(Ie.y-Ae.y)<=H&&!N[Ye]){N[Ye]=!0,se.crossTileID=Ye;break}}}}getCrossTileIDsLists(){return Object.values(this._symbolsByKey).map((({crossTileIDs:d})=>d))}}class ja{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class qo{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(d){let P=Math.round((d-this.lng)/360);if(P!==0)for(let N in this.indexes){let H=this.indexes[N],$={};for(let se in H){let de=H[se];de.tileID=de.tileID.unwrapTo(de.tileID.wrap+P),$[de.tileID.key]=de}this.indexes[N]=$}this.lng=d}addBucket(d,P,N){var H,$,se;if(!((H=this.indexes[d.overscaledZ])===null||H===void 0)&&H[d.key]){if(this.indexes[d.overscaledZ][d.key].bucketInstanceId===P.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(d.overscaledZ,this.indexes[d.overscaledZ][d.key])}for(let Ae=0;Ae<P.symbolInstances.length;Ae++)P.symbolInstances.get(Ae).crossTileID=0;($=this.usedCrossTileIDs)[se=d.overscaledZ]||($[se]={});let de=this.usedCrossTileIDs[d.overscaledZ];for(let Ae in this.indexes){let Fe=this.indexes[Ae];if(Number(Ae)>d.overscaledZ)for(let Ie in Fe){let Ye=Fe[Ie];Ye.tileID.isChildOf(d)&&Ye.findMatches(P.symbolInstances,d,de)}else{let Ie=Fe[d.scaledTo(Number(Ae)).key];Ie&&Ie.findMatches(P.symbolInstances,d,de)}}for(let Ae=0;Ae<P.symbolInstances.length;Ae++){let Fe=P.symbolInstances.get(Ae);Fe.crossTileID||(Fe.crossTileID=N.generate(),de[Fe.crossTileID]=!0)}return this.indexes[d.overscaledZ]===void 0&&(this.indexes[d.overscaledZ]={}),this.indexes[d.overscaledZ][d.key]=new Za(d,P.symbolInstances,P.bucketInstanceId),!0}removeBucketCrossTileIDs(d,P){for(let N of P.getCrossTileIDsLists())for(let H of N)delete this.usedCrossTileIDs[d][H]}removeStaleBuckets(d){let P=!1;for(let N in this.indexes){let H=this.indexes[N];for(let $ in H)d[H[$].bucketInstanceId]||(this.removeBucketCrossTileIDs(N,H[$]),delete H[$],P=!0)}return P}}class $a{constructor(){this.layerIndexes={},this.crossTileIDs=new ja,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}}addLayer(d,P,N){let H=this.layerIndexes[d.id];H===void 0&&(H=this.layerIndexes[d.id]=new qo);let $=!1,se={};H.handleWrapJump(N);for(let de of P){let Ae=de.getBucket(d);d.id===(Ae==null?void 0:Ae.layerIds[0])&&(Ae.bucketInstanceId||(this.maxBucketInstanceId+=1,Ae.bucketInstanceId=this.maxBucketInstanceId),H.addBucket(de.tileID,Ae,this.crossTileIDs)&&($=!0),se[Ae.bucketInstanceId]=!0)}return H.removeStaleBuckets(se)&&($=!0),$}pruneUnusedLayers(d){let P={};for(let N of d)P[N]=!0;for(let N in this.layerIndexes)P[N]||delete this.layerIndexes[N]}}var Ka=\"void main() {fragColor=vec4(1.0);}\";let Oa={prelude:Ni(`#ifdef GL_ES\n",
        "precision mediump float;\n",
        "#else\n",
        "#if !defined(lowp)\n",
@@ -3284,7 +2781,7 @@
        "#define highp\n",
        "#endif\n",
        "#endif\n",
-       "`,`#ifdef GL_ES\n",
+       "out highp vec4 fragColor;`,`#ifdef GL_ES\n",
        "precision highp float;\n",
        "#else\n",
        "#if !defined(lowp)\n",
@@ -3298,13 +2795,14 @@
        "#endif\n",
        "#endif\n",
        "vec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n",
-       ");}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\n",
+       ");}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}mat3 rotationMatrixFromAxisAngle(vec3 u,float angle) {float c=cos(angle);float s=sin(angle);float c2=1.0-c;return mat3(u.x*u.x*c2+      c,u.x*u.y*c2-u.z*s,u.x*u.z*c2+u.y*s,u.y*u.x*c2+u.z*s,u.y*u.y*c2+    c,u.y*u.z*c2-u.x*s,u.z*u.x*c2-u.y*s,u.z*u.y*c2+u.x*s,u.z*u.z*c2+    c\n",
+       ");}\n",
        "#ifdef TERRAIN3D\n",
        "uniform sampler2D u_terrain;uniform float u_terrain_dim;uniform mat4 u_terrain_matrix;uniform vec4 u_terrain_unpack;uniform float u_terrain_exaggeration;uniform highp sampler2D u_depth;\n",
        "#endif\n",
        "const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitShifts=vec4(1.)/bitSh;highp float unpack(highp vec4 color) {return dot(color,bitShifts);}highp float depthOpacity(vec3 frag) {\n",
        "#ifdef TERRAIN3D\n",
-       "highp float d=unpack(texture2D(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));\n",
+       "highp float d=unpack(texture(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));\n",
        "#else\n",
        "return 1.0;\n",
        "#endif\n",
@@ -3316,25 +2814,32 @@
        "#endif\n",
        "}float ele(vec2 pos) {\n",
        "#ifdef TERRAIN3D\n",
-       "vec4 rgb=(texture2D(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;\n",
+       "vec4 rgb=(texture(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;\n",
        "#else\n",
        "return 0.0;\n",
        "#endif\n",
        "}float get_elevation(vec2 pos) {\n",
        "#ifdef TERRAIN3D\n",
+       "#ifdef GLOBE\n",
+       "if ((pos.y <-32767.5) || (pos.y > 32766.5)) {return 0.0;}\n",
+       "#endif\n",
        "vec2 coord=(u_terrain_matrix*vec4(pos,0.0,1.0)).xy*u_terrain_dim+1.0;vec2 f=fract(coord);vec2 c=(floor(coord)+0.5)/(u_terrain_dim+2.0);float d=1.0/(u_terrain_dim+2.0);float tl=ele(c);float tr=ele(c+vec2(d,0.0));float bl=ele(c+vec2(0.0,d));float br=ele(c+vec2(d,d));float elevation=mix(mix(tl,tr,f.x),mix(bl,br,f.x),f.y);return elevation*u_terrain_exaggeration;\n",
        "#else\n",
        "return 0.0;\n",
        "#endif\n",
-       "}`),background:Ct(`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n",
+       "}const float PI=3.141592653589793;uniform mat4 u_projection_matrix;`),projectionMercator:Ni(\"\",\"float projectLineThickness(float tileY) {return 1.0;}float projectCircleRadius(float tileY) {return 1.0;}vec4 projectTile(vec2 p) {vec4 result=u_projection_matrix*vec4(p,0.0,1.0);return result;}vec4 projectTile(vec2 p,vec2 rawPos) {vec4 result=u_projection_matrix*vec4(p,0.0,1.0);if (rawPos.y <-32767.5 || rawPos.y > 32766.5) {result.z=-10000000.0;}return result;}vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_projection_matrix*vec4(posInTile,elevation,1.0);}vec4 projectTileFor3D(vec2 posInTile,float elevation) {return projectTileWithElevation(posInTile,elevation);}\"),projectionGlobe:Ni(\"\",`#define GLOBE_RADIUS 6371008.8\n",
+       "uniform highp vec4 u_projection_tile_mercator_coords;uniform highp vec4 u_projection_clipping_plane;uniform highp float u_projection_transition;uniform mat4 u_projection_fallback_matrix;vec3 globeRotateVector(vec3 vec,vec2 angles) {vec3 axisRight=vec3(vec.z,0.0,-vec.x);vec3 axisUp=cross(axisRight,vec);axisRight=normalize(axisRight);axisUp=normalize(axisUp);vec2 t=tan(angles);return normalize(vec+axisRight*t.x+axisUp*t.y);}mat3 globeGetRotationMatrix(vec3 spherePos) {vec3 axisRight=vec3(spherePos.z,0.0,-spherePos.x);vec3 axisDown=cross(axisRight,spherePos);axisRight=normalize(axisRight);axisDown=normalize(axisDown);return mat3(axisRight,axisDown,spherePos\n",
+       ");}float circumferenceRatioAtTileY(float tileY) {float mercator_pos_y=u_projection_tile_mercator_coords.y+u_projection_tile_mercator_coords.w*tileY;float spherical_y=2.0*atan(exp(PI-(mercator_pos_y*PI*2.0)))-PI*0.5;return cos(spherical_y);}float projectLineThickness(float tileY) {float thickness=1.0/circumferenceRatioAtTileY(tileY); \n",
+       "if (u_projection_transition < 0.999) {return mix(1.0,thickness,u_projection_transition);} else {return thickness;}}vec3 projectToSphere(vec2 translatedPos,vec2 rawPos) {vec2 mercator_pos=u_projection_tile_mercator_coords.xy+u_projection_tile_mercator_coords.zw*translatedPos;vec2 spherical;spherical.x=mercator_pos.x*PI*2.0+PI;spherical.y=2.0*atan(exp(PI-(mercator_pos.y*PI*2.0)))-PI*0.5;float len=cos(spherical.y);vec3 pos=vec3(sin(spherical.x)*len,sin(spherical.y),cos(spherical.x)*len\n",
+       ");if (rawPos.y <-32767.5) {pos=vec3(0.0,1.0,0.0);}if (rawPos.y > 32766.5) {pos=vec3(0.0,-1.0,0.0);}return pos;}vec3 projectToSphere(vec2 posInTile) {return projectToSphere(posInTile,vec2(0.0,0.0));}float globeComputeClippingZ(vec3 spherePos) {return (1.0-(dot(spherePos,u_projection_clipping_plane.xyz)+u_projection_clipping_plane.w));}vec4 interpolateProjection(vec2 posInTile,vec3 spherePos,float elevation) {vec3 elevatedPos=spherePos*(1.0+elevation/GLOBE_RADIUS);vec4 globePosition=u_projection_matrix*vec4(elevatedPos,1.0);globePosition.z=globeComputeClippingZ(elevatedPos)*globePosition.w;if (u_projection_transition > 0.999) {return globePosition;}vec4 flatPosition=u_projection_fallback_matrix*vec4(posInTile,elevation,1.0);const float z_globeness_threshold=0.2;vec4 result=globePosition;result.z=mix(0.0,globePosition.z,clamp((u_projection_transition-z_globeness_threshold)/(1.0-z_globeness_threshold),0.0,1.0));result.xyw=mix(flatPosition.xyw,globePosition.xyw,u_projection_transition);if ((posInTile.y <-32767.5) || (posInTile.y > 32766.5)) {result=globePosition;const float poles_hidden_anim_percentage=0.02;result.z=mix(globePosition.z,100.0,pow(max((1.0-u_projection_transition)/poles_hidden_anim_percentage,0.0),8.0));}return result;}vec4 interpolateProjectionFor3D(vec2 posInTile,vec3 spherePos,float elevation) {vec3 elevatedPos=spherePos*(1.0+elevation/GLOBE_RADIUS);vec4 globePosition=u_projection_matrix*vec4(elevatedPos,1.0);if (u_projection_transition > 0.999) {return globePosition;}vec4 fallbackPosition=u_projection_fallback_matrix*vec4(posInTile,elevation,1.0);return mix(fallbackPosition,globePosition,u_projection_transition);}vec4 projectTile(vec2 posInTile) {return interpolateProjection(posInTile,projectToSphere(posInTile),0.0);}vec4 projectTile(vec2 posInTile,vec2 rawPos) {return interpolateProjection(posInTile,projectToSphere(posInTile,rawPos),0.0);}vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return interpolateProjection(posInTile,projectToSphere(posInTile),elevation);}vec4 projectTileFor3D(vec2 posInTile,float elevation) {vec3 spherePos=projectToSphere(posInTile,posInTile);return interpolateProjectionFor3D(posInTile,spherePos,elevation);}`),background:Ni(`uniform vec4 u_color;uniform float u_opacity;void main() {fragColor=u_color*u_opacity;\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),backgroundPattern:Ct(`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n",
+       "}`,\"in vec2 a_pos;void main() {gl_Position=projectTile(a_pos);}\"),backgroundPattern:Ni(`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;in vec2 v_pos_a;in vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture(u_image,pos2);fragColor=mix(color1,color2,u_mix)*u_opacity;\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),circle:Ct(`varying vec3 v_data;varying float v_visibility;\n",
+       "}`,\"uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;in vec2 a_pos;out vec2 v_pos_a;out vec2 v_pos_b;void main() {gl_Position=projectTile(a_pos);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),circle:Ni(`in vec3 v_data;in float v_visibility;\n",
        "#pragma mapbox: define highp vec4 color\n",
        "#pragma mapbox: define mediump float radius\n",
        "#pragma mapbox: define lowp float blur\n",
@@ -3350,11 +2855,11 @@
        "#pragma mapbox: initialize highp vec4 stroke_color\n",
        "#pragma mapbox: initialize mediump float stroke_width\n",
        "#pragma mapbox: initialize lowp float stroke_opacity\n",
-       "vec2 extrude=v_data.xy;float extrude_length=length(extrude);float antialiased_blur=v_data.z;float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n",
+       "vec2 extrude=v_data.xy;float extrude_length=length(extrude);float antialiased_blur=v_data.z;float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));fragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);const float epsilon=0.5/255.0;if (fragColor.r < epsilon && fragColor.g < epsilon && fragColor.b < epsilon && fragColor.a < epsilon) {discard;}\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;varying float v_visibility;\n",
+       "}`,`uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform highp float u_globe_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;uniform vec2 u_translate;in vec2 a_pos;out vec3 v_data;out float v_visibility;\n",
        "#pragma mapbox: define highp vec4 color\n",
        "#pragma mapbox: define mediump float radius\n",
        "#pragma mapbox: define lowp float blur\n",
@@ -3370,16 +2875,32 @@
        "#pragma mapbox: initialize highp vec4 stroke_color\n",
        "#pragma mapbox: initialize mediump float stroke_width\n",
        "#pragma mapbox: initialize lowp float stroke_opacity\n",
-       "vec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);float ele=get_elevation(circle_center);v_visibility=calculate_visibility(u_matrix*vec4(circle_center,ele,1.0));if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,ele,1);} else {gl_Position=u_matrix*vec4(circle_center,ele,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}float antialiasblur=-max(1.0/u_device_pixel_ratio/(radius+stroke_width),blur);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`),clippingMask:Ct(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),heatmap:Ct(`uniform highp float u_intensity;varying vec2 v_extrude;\n",
+       "vec2 pos_raw=a_pos+32768.0;vec2 extrude=vec2(mod(pos_raw,8.0)/7.0*2.0-1.0);vec2 circle_center=floor(pos_raw/8.0)+u_translate;float ele=get_elevation(circle_center);v_visibility=calculate_visibility(projectTileWithElevation(circle_center,ele));if (u_pitch_with_map) {\n",
+       "#ifdef GLOBE\n",
+       "vec3 center_vector=projectToSphere(circle_center);\n",
+       "#endif\n",
+       "float angle_scale=u_globe_extrude_scale;vec2 corner_position=circle_center;if (u_scale_with_map) {angle_scale*=(radius+stroke_width);corner_position+=extrude*u_extrude_scale*(radius+stroke_width);} else {\n",
+       "#ifdef GLOBE\n",
+       "vec4 projected_center=interpolateProjection(circle_center,center_vector,ele);\n",
+       "#else\n",
+       "vec4 projected_center=projectTileWithElevation(circle_center,ele);\n",
+       "#endif\n",
+       "corner_position+=extrude*u_extrude_scale*(radius+stroke_width)*(projected_center.w/u_camera_to_center_distance);angle_scale*=(radius+stroke_width)*(projected_center.w/u_camera_to_center_distance);}\n",
+       "#ifdef GLOBE\n",
+       "vec2 angles=extrude*angle_scale;vec3 corner_vector=globeRotateVector(center_vector,angles);gl_Position=interpolateProjection(corner_position,corner_vector,ele);\n",
+       "#else\n",
+       "gl_Position=projectTileWithElevation(corner_position,ele);\n",
+       "#endif\n",
+       "} else {gl_Position=projectTileWithElevation(circle_center,ele);if (gl_Position.z/gl_Position.w > 1.0) {gl_Position.xy=vec2(10000.0);}if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}float antialiasblur=-max(1.0/u_device_pixel_ratio/(radius+stroke_width),blur);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`),clippingMask:Ni(Ka,\"in vec2 a_pos;void main() {gl_Position=projectTile(a_pos);}\"),heatmap:Ni(`uniform highp float u_intensity;in vec2 v_extrude;\n",
        "#pragma mapbox: define highp float weight\n",
        "#define GAUSS_COEF 0.3989422804014327\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp float weight\n",
-       "float d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n",
+       "float d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);fragColor=vec4(val,1.0,1.0,1.0);\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n",
+       "}`,`uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;uniform highp float u_globe_extrude_scale;in vec2 a_pos;out vec2 v_extrude;\n",
        "#pragma mapbox: define highp float weight\n",
        "#pragma mapbox: define mediump float radius\n",
        "const highp float ZERO=1.0/255.0/16.0;\n",
@@ -3387,42 +2908,78 @@
        "void main(void) {\n",
        "#pragma mapbox: initialize highp float weight\n",
        "#pragma mapbox: initialize mediump float radius\n",
-       "vec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,get_elevation(floor(a_pos*0.5)),1);gl_Position=u_matrix*pos;}`),heatmapTexture:Ct(`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n",
+       "vec2 pos_raw=a_pos+32768.0;vec2 unscaled_extrude=vec2(mod(pos_raw,8.0)/7.0*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec2 circle_center=floor(pos_raw/8.0);\n",
+       "#ifdef GLOBE\n",
+       "vec2 angles=v_extrude*radius*u_globe_extrude_scale;vec3 center_vector=projectToSphere(circle_center);vec3 corner_vector=globeRotateVector(center_vector,angles);gl_Position=interpolateProjection(circle_center+extrude,corner_vector,0.0);\n",
+       "#else\n",
+       "gl_Position=projectTileFor3D(circle_center+extrude,get_elevation(circle_center));\n",
+       "#endif\n",
+       "}`),heatmapTexture:Ni(`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;in vec2 v_pos;void main() {float t=texture(u_image,v_pos).r;vec4 color=texture(u_color_ramp,vec2(t,0.5));fragColor=color*u_opacity;\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "fragColor=vec4(0.0);\n",
+       "#endif\n",
+       "}`,\"uniform mat4 u_matrix;uniform vec2 u_world;in vec2 a_pos;out vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),collisionBox:Ni(\"in float v_placed;in float v_notUsed;void main() {float alpha=0.5;fragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {fragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {fragColor*=.1;}}\",\"in vec2 a_anchor_pos;in vec2 a_placed;in vec2 a_box_real;uniform vec2 u_pixel_extrude_scale;out float v_placed;out float v_notUsed;void main() {gl_Position=projectTileWithElevation(a_anchor_pos,get_elevation(a_anchor_pos));gl_Position.xy=((a_box_real+0.5)*u_pixel_extrude_scale*2.0-1.0)*vec2(1.0,-1.0)*gl_Position.w;if (gl_Position.z/gl_Position.w < 1.1) {gl_Position.z=0.5;}v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),collisionCircle:Ni(\"in float v_radius;in vec2 v_extrude;in float v_collision;void main() {float alpha=0.5;float stroke_radius=0.9;float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);fragColor=color*alpha*opacity_t;}\",\"in vec2 a_pos;in float a_radius;in vec2 a_flags;uniform vec2 u_viewport_size;out float v_radius;out vec2 v_extrude;out float v_collision;void main() {float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_collision=collision;gl_Position=vec4((a_pos/u_viewport_size*2.0-1.0)*vec2(1.0,-1.0),0.0,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),colorRelief:Ni(`#ifdef GL_ES\n",
+       "precision highp float;\n",
+       "#endif\n",
+       "uniform sampler2D u_image;uniform vec4 u_unpack;uniform sampler2D u_elevation_stops;uniform sampler2D u_color_stops;uniform int u_color_ramp_size;uniform float u_opacity;in vec2 v_pos;float getElevation(vec2 coord) {vec4 data=texture(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack);}float getElevationStop(int stop) {float x=(float(stop)+0.5)/float(u_color_ramp_size);vec4 data=texture(u_elevation_stops,vec2(x,0))*255.0;data.a=-1.0;return dot(data,u_unpack);}void main() {float el=getElevation(v_pos);int r=(u_color_ramp_size-1);int l=0;float el_l=getElevationStop(l);float el_r=getElevationStop(r);while(r-l > 1){int m=(r+l)/2;float el_m=getElevationStop(m);if(el < el_m){r=m;el_r=el_m;}else\n",
+       "{l=m;el_l=el_m;}}float x=(float(l)+(el-el_l)/(el_r-el_l)+0.5)/float(u_color_ramp_size);fragColor=u_opacity*texture(u_color_stops,vec2(x,0));\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(0.0);\n",
+       "fragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,\"uniform vec2 u_dimension;in vec2 a_pos;out vec2 v_pos;void main() {gl_Position=projectTile(a_pos,a_pos);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_pos/8192.0)*scale+epsilon;if (a_pos.y <-32767.5) {v_pos.y=0.0;}if (a_pos.y > 32766.5) {v_pos.y=1.0;}}\"),debug:Ni(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;in vec2 v_uv;void main() {vec4 overlay_color=texture(u_overlay,v_uv);fragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"in vec2 a_pos;out vec2 v_uv;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=projectTileWithElevation(a_pos*u_overlay_scale,get_elevation(a_pos));}\"),depth:Ni(Ka,`in vec2 a_pos;void main() {\n",
+       "#ifdef GLOBE\n",
+       "gl_Position=projectTileFor3D(a_pos,0.0);\n",
+       "#else\n",
+       "gl_Position=u_projection_matrix*vec4(a_pos,0.0,1.0);\n",
        "#endif\n",
-       "}`,\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),collisionBox:Ct(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_anchor_pos;attribute vec2 a_placed;attribute vec2 a_box_real;uniform mat4 u_matrix;uniform vec2 u_pixel_extrude_scale;varying float v_placed;varying float v_notUsed;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}void main() {gl_Position=projectTileWithElevation(a_anchor_pos,get_elevation(a_anchor_pos));gl_Position.xy=((a_box_real+0.5)*u_pixel_extrude_scale*2.0-1.0)*vec2(1.0,-1.0)*gl_Position.w;if (gl_Position.z/gl_Position.w < 1.1) {gl_Position.z=0.5;}v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),collisionCircle:Ct(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),debug:Ct(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,get_elevation(a_pos),1);}\"),fill:Ct(`#pragma mapbox: define highp vec4 color\n",
+       "}`),fill:Ni(`#pragma mapbox: define highp vec4 color\n",
        "#pragma mapbox: define lowp float opacity\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp vec4 color\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "gl_FragColor=color*opacity;\n",
+       "fragColor=color*opacity;\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`attribute vec2 a_pos;uniform mat4 u_matrix;\n",
+       "}`,`uniform vec2 u_fill_translate;in vec2 a_pos;\n",
        "#pragma mapbox: define highp vec4 color\n",
        "#pragma mapbox: define lowp float opacity\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp vec4 color\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "gl_Position=u_matrix*vec4(a_pos,0,1);}`),fillOutline:Ct(`varying vec2 v_pos;\n",
+       "gl_Position=projectTile(a_pos+u_fill_translate,a_pos);}`),fillOutline:Ni(`in vec2 v_pos;\n",
+       "#ifdef GLOBE\n",
+       "in float v_depth;\n",
+       "#endif\n",
        "#pragma mapbox: define highp vec4 outline_color\n",
        "#pragma mapbox: define lowp float opacity\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp vec4 outline_color\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n",
+       "float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);fragColor=outline_color*(alpha*opacity);\n",
+       "#ifdef GLOBE\n",
+       "if (v_depth > 1.0) {discard;}\n",
+       "#endif\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform vec2 u_world;uniform vec2 u_fill_translate;in vec2 a_pos;out vec2 v_pos;\n",
+       "#ifdef GLOBE\n",
+       "out float v_depth;\n",
        "#endif\n",
-       "}`,`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n",
        "#pragma mapbox: define highp vec4 outline_color\n",
        "#pragma mapbox: define lowp float opacity\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp vec4 outline_color\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillOutlinePattern:Ct(`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n",
+       "gl_Position=projectTile(a_pos+u_fill_translate,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;\n",
+       "#ifdef GLOBE\n",
+       "v_depth=gl_Position.z/gl_Position.w;\n",
+       "#endif\n",
+       "}`),fillOutlinePattern:Ni(`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;in vec2 v_pos_a;in vec2 v_pos_b;in vec2 v_pos;\n",
+       "#ifdef GLOBE\n",
+       "in float v_depth;\n",
+       "#endif\n",
        "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define lowp vec4 pattern_from\n",
        "#pragma mapbox: define lowp vec4 pattern_to\n",
@@ -3430,11 +2987,17 @@
        "#pragma mapbox: initialize lowp float opacity\n",
        "#pragma mapbox: initialize mediump vec4 pattern_from\n",
        "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);fragColor=mix(color1,color2,u_fade)*alpha*opacity;\n",
+       "#ifdef GLOBE\n",
+       "if (v_depth > 1.0) {discard;}\n",
+       "#endif\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;uniform vec2 u_fill_translate;in vec2 a_pos;out vec2 v_pos_a;out vec2 v_pos_b;out vec2 v_pos;\n",
+       "#ifdef GLOBE\n",
+       "out float v_depth;\n",
        "#endif\n",
-       "}`,`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n",
        "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define lowp vec4 pattern_from\n",
        "#pragma mapbox: define lowp vec4 pattern_to\n",
@@ -3446,10 +3009,14 @@
        "#pragma mapbox: initialize mediump vec4 pattern_to\n",
        "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
        "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillPattern:Ct(`#ifdef GL_ES\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=projectTile(a_pos+u_fill_translate,a_pos);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;\n",
+       "#ifdef GLOBE\n",
+       "v_depth=gl_Position.z/gl_Position.w;\n",
+       "#endif\n",
+       "}`),fillPattern:Ni(`#ifdef GL_ES\n",
        "precision highp float;\n",
        "#endif\n",
-       "uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n",
+       "uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;in vec2 v_pos_a;in vec2 v_pos_b;\n",
        "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define lowp vec4 pattern_from\n",
        "#pragma mapbox: define lowp vec4 pattern_to\n",
@@ -3457,11 +3024,11 @@
        "#pragma mapbox: initialize lowp float opacity\n",
        "#pragma mapbox: initialize mediump vec4 pattern_from\n",
        "#pragma mapbox: initialize mediump vec4 pattern_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture(u_image,pos2);fragColor=mix(color1,color2,u_fade)*opacity;\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n",
+       "}`,`uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;uniform vec2 u_fill_translate;in vec2 a_pos;out vec2 v_pos_a;out vec2 v_pos_b;\n",
        "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define lowp vec4 pattern_from\n",
        "#pragma mapbox: define lowp vec4 pattern_to\n",
@@ -3473,15 +3040,15 @@
        "#pragma mapbox: initialize mediump vec4 pattern_to\n",
        "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
        "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`),fillExtrusion:Ct(`varying vec4 v_color;void main() {gl_FragColor=v_color;\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=projectTile(a_pos+u_fill_translate,a_pos);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`),fillExtrusion:Ni(`in vec4 v_color;void main() {fragColor=v_color;\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n",
+       "}`,`uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp vec3 u_lightpos_globe;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec2 u_fill_translate;in vec2 a_pos;in vec4 a_normal_ed;\n",
        "#ifdef TERRAIN3D\n",
-       "attribute vec2 a_centroid;\n",
+       "in vec2 a_centroid;\n",
        "#endif\n",
-       "varying vec4 v_color;\n",
+       "out vec4 v_color;\n",
        "#pragma mapbox: define highp float base\n",
        "#pragma mapbox: define highp float height\n",
        "#pragma mapbox: define highp vec4 color\n",
@@ -3495,7 +3062,17 @@
        "#else\n",
        "float height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n",
        "#endif\n",
-       "base=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`),fillExtrusionPattern:Ct(`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n",
+       "base=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float elevation=t > 0.0 ? height : base;vec2 posInTile=a_pos+u_fill_translate;\n",
+       "#ifdef GLOBE\n",
+       "vec3 spherePos=projectToSphere(posInTile,a_pos);gl_Position=interpolateProjectionFor3D(posInTile,spherePos,elevation);\n",
+       "#else\n",
+       "gl_Position=u_projection_matrix*vec4(posInTile,elevation,1.0);\n",
+       "#endif\n",
+       "float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;vec3 normalForLighting=normal/16384.0;float directional=clamp(dot(normalForLighting,u_lightpos),0.0,1.0);\n",
+       "#ifdef GLOBE\n",
+       "mat3 rotMatrix=globeGetRotationMatrix(spherePos);normalForLighting=rotMatrix*normalForLighting;directional=mix(directional,clamp(dot(normalForLighting,u_lightpos_globe),0.0,1.0),u_projection_transition);\n",
+       "#endif\n",
+       "directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`),fillExtrusionPattern:Ni(`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;in vec2 v_pos_a;in vec2 v_pos_b;in vec4 v_lighting;\n",
        "#pragma mapbox: define lowp float base\n",
        "#pragma mapbox: define lowp float height\n",
        "#pragma mapbox: define lowp vec4 pattern_from\n",
@@ -3509,15 +3086,18 @@
        "#pragma mapbox: initialize mediump vec4 pattern_to\n",
        "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
        "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);fragColor=mixedColor*v_lighting;\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n",
+       "}`,`uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec2 u_fill_translate;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp vec3 u_lightpos_globe;uniform lowp float u_lightintensity;in vec2 a_pos;in vec4 a_normal_ed;\n",
        "#ifdef TERRAIN3D\n",
-       "attribute vec2 a_centroid;\n",
+       "in vec2 a_centroid;\n",
+       "#endif\n",
+       "#ifdef GLOBE\n",
+       "out vec3 v_sphere_pos;\n",
        "#endif\n",
-       "varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n",
+       "out vec2 v_pos_a;out vec2 v_pos_b;out vec4 v_lighting;\n",
        "#pragma mapbox: define lowp float base\n",
        "#pragma mapbox: define lowp float height\n",
        "#pragma mapbox: define lowp vec4 pattern_from\n",
@@ -3537,22 +3117,38 @@
        "#else\n",
        "float height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n",
        "#endif\n",
-       "base=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n",
+       "base=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float elevation=t > 0.0 ? height : base;vec2 posInTile=a_pos+u_fill_translate;\n",
+       "#ifdef GLOBE\n",
+       "vec3 spherePos=projectToSphere(posInTile,a_pos);vec3 elevatedPos=spherePos*(1.0+elevation/GLOBE_RADIUS);v_sphere_pos=elevatedPos;gl_Position=interpolateProjectionFor3D(posInTile,spherePos,elevation);\n",
+       "#else\n",
+       "gl_Position=u_projection_matrix*vec4(posInTile,elevation,1.0);\n",
+       "#endif\n",
+       "vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n",
        "? a_pos\n",
-       ": vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`),hillshadePrepare:Ct(`#ifdef GL_ES\n",
+       ": vec2(edgedistance,elevation*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`),hillshadePrepare:Ni(`#ifdef GL_ES\n",
        "precision highp float;\n",
        "#endif\n",
-       "uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n",
+       "uniform sampler2D u_image;in vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack);}void main() {vec2 epsilon=1.0/u_dimension;float tileSize=u_dimension.x-2.0;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))*tileSize/pow(2.0,exaggeration+(28.2562-u_zoom));fragColor=clamp(vec4(deriv.x/8.0+0.5,deriv.y/8.0+0.5,1.0,1.0),0.0,1.0);\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hillshade:Ct(`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n",
+       "}`,\"uniform mat4 u_matrix;uniform vec2 u_dimension;in vec2 a_pos;in vec2 a_texture_pos;out vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hillshade:Ni(`uniform sampler2D u_image;in vec2 v_pos;uniform vec2 u_latrange;uniform float u_exaggeration;uniform vec4 u_accent;uniform int u_method;uniform float u_altitudes[NUM_ILLUMINATION_SOURCES];uniform float u_azimuths[NUM_ILLUMINATION_SOURCES];uniform vec4 u_shadows[NUM_ILLUMINATION_SOURCES];uniform vec4 u_highlights[NUM_ILLUMINATION_SOURCES];\n",
        "#define PI 3.141592653589793\n",
-       "void main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n",
+       "#define STANDARD 0\n",
+       "#define COMBINED 1\n",
+       "#define IGOR 2\n",
+       "#define MULTIDIRECTIONAL 3\n",
+       "#define BASIC 4\n",
+       "float get_aspect(vec2 deriv){return deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);}void igor_hillshade(vec2 deriv){deriv=deriv*u_exaggeration*2.0;float aspect=get_aspect(deriv);float azimuth=u_azimuths[0]+PI;float slope_stength=atan(length(deriv))*2.0/PI;float aspect_strength=1.0-abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);float shadow_strength=slope_stength*aspect_strength;float highlight_strength=slope_stength*(1.0-aspect_strength);fragColor=u_shadows[0]*shadow_strength+u_highlights[0]*highlight_strength;}void standard_hillshade(vec2 deriv){float azimuth=u_azimuths[0]+PI;float slope=atan(0.625*length(deriv));float aspect=get_aspect(deriv);float intensity=u_exaggeration;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadows[0],u_highlights[0],shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);fragColor=accent_color*(1.0-shade_color.a)+shade_color;}void basic_hillshade(vec2 deriv){deriv=deriv*u_exaggeration*2.0;float azimuth=u_azimuths[0]+PI;float cos_az=cos(azimuth);float sin_az=sin(azimuth);float cos_alt=cos(u_altitudes[0]);float sin_alt=sin(u_altitudes[0]);float cang=(sin_alt-(deriv.y*cos_az*cos_alt-deriv.x*sin_az*cos_alt))/sqrt(1.0+dot(deriv,deriv));float shade=clamp(cang,0.0,1.0);if(shade > 0.5){fragColor=u_highlights[0]*(2.0*shade-1.0);}else\n",
+       "{fragColor=u_shadows[0]*(1.0-2.0*shade);}}void multidirectional_hillshade(vec2 deriv){deriv=deriv*u_exaggeration*2.0;fragColor=vec4(0,0,0,0);for(int i=0; i < NUM_ILLUMINATION_SOURCES; i++){float cos_alt=cos(u_altitudes[i]);float sin_alt=sin(u_altitudes[i]);float cos_az=-cos(u_azimuths[i]);float sin_az=-sin(u_azimuths[i]);float cang=(sin_alt-(deriv.y*cos_az*cos_alt-deriv.x*sin_az*cos_alt))/sqrt(1.0+dot(deriv,deriv));float shade=clamp(cang,0.0,1.0);if(shade > 0.5){fragColor+=u_highlights[i]*(2.0*shade-1.0)/float(NUM_ILLUMINATION_SOURCES);}else\n",
+       "{fragColor+=u_shadows[i]*(1.0-2.0*shade)/float(NUM_ILLUMINATION_SOURCES);}}}void combined_hillshade(vec2 deriv){deriv=deriv*u_exaggeration*2.0;float azimuth=u_azimuths[0]+PI;float cos_az=cos(azimuth);float sin_az=sin(azimuth);float cos_alt=cos(u_altitudes[0]);float sin_alt=sin(u_altitudes[0]);float cang=acos((sin_alt-(deriv.y*cos_az*cos_alt-deriv.x*sin_az*cos_alt))/sqrt(1.0+dot(deriv,deriv)));cang=clamp(cang,0.0,PI/2.0);float shade=cang*atan(length(deriv))*4.0/PI/PI;float highlight=(PI/2.0-cang)*atan(length(deriv))*4.0/PI/PI;fragColor=u_shadows[0]*shade+u_highlights[0]*highlight;}void main() {vec4 pixel=texture(u_image,v_pos);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));vec2 deriv=((pixel.rg*8.0)-4.0)/scaleFactor;if (u_method==BASIC) {basic_hillshade(deriv);} else if (u_method==COMBINED) {combined_hillshade(deriv);} else if (u_method==IGOR) {igor_hillshade(deriv);} else if (u_method==MULTIDIRECTIONAL) {multidirectional_hillshade(deriv);} else if (u_method==STANDARD) {standard_hillshade(deriv);} else {standard_hillshade(deriv);}\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,\"uniform mat4 u_matrix;in vec2 a_pos;out vec2 v_pos;void main() {gl_Position=projectTile(a_pos,a_pos);v_pos=a_pos/8192.0;if (a_pos.y <-32767.5) {v_pos.y=0.0;}if (a_pos.y > 32766.5) {v_pos.y=1.0;}}\"),line:Ni(`uniform lowp float u_device_pixel_ratio;in vec2 v_width2;in vec2 v_normal;in float v_gamma_scale;\n",
+       "#ifdef GLOBE\n",
+       "in float v_depth;\n",
        "#endif\n",
-       "}`,\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),line:Ct(`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n",
        "#pragma mapbox: define highp vec4 color\n",
        "#pragma mapbox: define lowp float blur\n",
        "#pragma mapbox: define lowp float opacity\n",
@@ -3560,13 +3156,19 @@
        "#pragma mapbox: initialize highp vec4 color\n",
        "#pragma mapbox: initialize lowp float blur\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);fragColor=color*(alpha*opacity);\n",
+       "#ifdef GLOBE\n",
+       "if (v_depth > 1.0) {discard;}\n",
+       "#endif\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
        "}`,`\n",
        "#define scale 0.015873016\n",
-       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n",
+       "in vec2 a_pos_normal;in vec4 a_data;uniform vec2 u_translation;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;out vec2 v_normal;out vec2 v_width2;out float v_gamma_scale;out highp float v_linesofar;\n",
+       "#ifdef GLOBE\n",
+       "out float v_depth;\n",
+       "#endif\n",
        "#pragma mapbox: define highp vec4 color\n",
        "#pragma mapbox: define lowp float blur\n",
        "#pragma mapbox: define lowp float opacity\n",
@@ -3580,25 +3182,37 @@
        "#pragma mapbox: initialize mediump float gapwidth\n",
        "#pragma mapbox: initialize lowp float offset\n",
        "#pragma mapbox: initialize mediump float width\n",
-       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);float adjustedThickness=projectLineThickness(pos.y);vec4 projected_no_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation);vec4 projected_with_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation+dist/u_ratio*adjustedThickness);gl_Position=projected_with_extrude;\n",
+       "#ifdef GLOBE\n",
+       "v_depth=gl_Position.z/gl_Position.w;\n",
+       "#endif\n",
        "#ifdef TERRAIN3D\n",
        "v_gamma_scale=1.0;\n",
        "#else\n",
-       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length((projected_with_extrude.xy-projected_no_extrude.xy)/projected_with_extrude.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "#endif\n",
+       "v_width2=vec2(outset,inset);}`),lineGradient:Ni(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;in vec2 v_width2;in vec2 v_normal;in float v_gamma_scale;in highp vec2 v_uv;\n",
+       "#ifdef GLOBE\n",
+       "in float v_depth;\n",
        "#endif\n",
-       "v_width2=vec2(outset,inset);}`),lineGradient:Ct(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n",
        "#pragma mapbox: define lowp float blur\n",
        "#pragma mapbox: define lowp float opacity\n",
        "void main() {\n",
        "#pragma mapbox: initialize lowp float blur\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture(u_image,v_uv);fragColor=color*(alpha*opacity);\n",
+       "#ifdef GLOBE\n",
+       "if (v_depth > 1.0) {discard;}\n",
+       "#endif\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
        "}`,`\n",
        "#define scale 0.015873016\n",
-       "attribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n",
+       "in vec2 a_pos_normal;in vec4 a_data;in float a_uv_x;in float a_split_index;uniform vec2 u_translation;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;out vec2 v_normal;out vec2 v_width2;out float v_gamma_scale;out highp vec2 v_uv;\n",
+       "#ifdef GLOBE\n",
+       "out float v_depth;\n",
+       "#endif\n",
        "#pragma mapbox: define lowp float blur\n",
        "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define mediump float gapwidth\n",
@@ -3610,16 +3224,22 @@
        "#pragma mapbox: initialize mediump float gapwidth\n",
        "#pragma mapbox: initialize lowp float offset\n",
        "#pragma mapbox: initialize mediump float width\n",
-       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);float adjustedThickness=projectLineThickness(pos.y);vec4 projected_no_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation);vec4 projected_with_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation+dist/u_ratio*adjustedThickness);gl_Position=projected_with_extrude;\n",
+       "#ifdef GLOBE\n",
+       "v_depth=gl_Position.z/gl_Position.w;\n",
+       "#endif\n",
        "#ifdef TERRAIN3D\n",
        "v_gamma_scale=1.0;\n",
        "#else\n",
-       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length((projected_with_extrude.xy-projected_no_extrude.xy)/projected_with_extrude.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
        "#endif\n",
-       "v_width2=vec2(outset,inset);}`),linePattern:Ct(`#ifdef GL_ES\n",
+       "v_width2=vec2(outset,inset);}`),linePattern:Ni(`#ifdef GL_ES\n",
        "precision highp float;\n",
        "#endif\n",
-       "uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n",
+       "uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;in vec2 v_normal;in vec2 v_width2;in float v_linesofar;in float v_gamma_scale;in float v_width;\n",
+       "#ifdef GLOBE\n",
+       "in float v_depth;\n",
+       "#endif\n",
        "#pragma mapbox: define lowp vec4 pattern_from\n",
        "#pragma mapbox: define lowp vec4 pattern_to\n",
        "#pragma mapbox: define lowp float pixel_ratio_from\n",
@@ -3633,14 +3253,20 @@
        "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
        "#pragma mapbox: initialize lowp float blur\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n",
+       "vec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture(u_image,pos_a),texture(u_image,pos_b),u_fade);fragColor=color*alpha*opacity;\n",
+       "#ifdef GLOBE\n",
+       "if (v_depth > 1.0) {discard;}\n",
+       "#endif\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
        "}`,`\n",
        "#define scale 0.015873016\n",
        "#define LINE_DISTANCE_SCALE 2.0\n",
-       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n",
+       "in vec2 a_pos_normal;in vec4 a_data;uniform vec2 u_translation;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;out vec2 v_normal;out vec2 v_width2;out float v_linesofar;out float v_gamma_scale;out float v_width;\n",
+       "#ifdef GLOBE\n",
+       "out float v_depth;\n",
+       "#endif\n",
        "#pragma mapbox: define lowp float blur\n",
        "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define lowp float offset\n",
@@ -3662,32 +3288,48 @@
        "#pragma mapbox: initialize mediump vec4 pattern_to\n",
        "#pragma mapbox: initialize lowp float pixel_ratio_from\n",
        "#pragma mapbox: initialize lowp float pixel_ratio_to\n",
-       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);float adjustedThickness=projectLineThickness(pos.y);vec4 projected_no_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation);vec4 projected_with_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation+dist/u_ratio*adjustedThickness);gl_Position=projected_with_extrude;\n",
+       "#ifdef GLOBE\n",
+       "v_depth=gl_Position.z/gl_Position.w;\n",
+       "#endif\n",
        "#ifdef TERRAIN3D\n",
        "v_gamma_scale=1.0;\n",
        "#else\n",
-       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length((projected_with_extrude.xy-projected_no_extrude.xy)/projected_with_extrude.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "#endif\n",
+       "v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`),lineSDF:Ni(`uniform lowp float u_device_pixel_ratio;uniform lowp float u_lineatlas_width;uniform sampler2D u_image;uniform float u_mix;in vec2 v_normal;in vec2 v_width2;in vec2 v_tex_a;in vec2 v_tex_b;in float v_gamma_scale;\n",
+       "#ifdef GLOBE\n",
+       "in float v_depth;\n",
        "#endif\n",
-       "v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`),lineSDF:Ct(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n",
        "#pragma mapbox: define highp vec4 color\n",
        "#pragma mapbox: define lowp float blur\n",
        "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define mediump float width\n",
        "#pragma mapbox: define lowp float floorwidth\n",
+       "#pragma mapbox: define mediump vec4 dasharray_from\n",
+       "#pragma mapbox: define mediump vec4 dasharray_to\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp vec4 color\n",
        "#pragma mapbox: initialize lowp float blur\n",
        "#pragma mapbox: initialize lowp float opacity\n",
        "#pragma mapbox: initialize mediump float width\n",
        "#pragma mapbox: initialize lowp float floorwidth\n",
-       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n",
+       "#pragma mapbox: initialize mediump vec4 dasharray_from\n",
+       "#pragma mapbox: initialize mediump vec4 dasharray_to\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture(u_image,v_tex_a).a;float sdfdist_b=texture(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);float sdfgamma=(u_lineatlas_width/256.0/u_device_pixel_ratio)/min(dasharray_from.w,dasharray_to.w);alpha*=smoothstep(0.5-sdfgamma/floorwidth,0.5+sdfgamma/floorwidth,sdfdist);fragColor=color*(alpha*opacity);\n",
+       "#ifdef GLOBE\n",
+       "if (v_depth > 1.0) {discard;}\n",
+       "#endif\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
        "}`,`\n",
        "#define scale 0.015873016\n",
        "#define LINE_DISTANCE_SCALE 2.0\n",
-       "attribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n",
+       "in vec2 a_pos_normal;in vec4 a_data;uniform vec2 u_translation;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_tileratio;uniform float u_crossfade_from;uniform float u_crossfade_to;uniform float u_lineatlas_height;out vec2 v_normal;out vec2 v_width2;out vec2 v_tex_a;out vec2 v_tex_b;out float v_gamma_scale;\n",
+       "#ifdef GLOBE\n",
+       "out float v_depth;\n",
+       "#endif\n",
        "#pragma mapbox: define highp vec4 color\n",
        "#pragma mapbox: define lowp float blur\n",
        "#pragma mapbox: define lowp float opacity\n",
@@ -3695,6 +3337,8 @@
        "#pragma mapbox: define lowp float offset\n",
        "#pragma mapbox: define mediump float width\n",
        "#pragma mapbox: define lowp float floorwidth\n",
+       "#pragma mapbox: define mediump vec4 dasharray_from\n",
+       "#pragma mapbox: define mediump vec4 dasharray_to\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp vec4 color\n",
        "#pragma mapbox: initialize lowp float blur\n",
@@ -3703,48 +3347,112 @@
        "#pragma mapbox: initialize lowp float offset\n",
        "#pragma mapbox: initialize mediump float width\n",
        "#pragma mapbox: initialize lowp float floorwidth\n",
-       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n",
+       "#pragma mapbox: initialize mediump vec4 dasharray_from\n",
+       "#pragma mapbox: initialize mediump vec4 dasharray_to\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);float adjustedThickness=projectLineThickness(pos.y);vec4 projected_no_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation);vec4 projected_with_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation+dist/u_ratio*adjustedThickness);gl_Position=projected_with_extrude;\n",
+       "#ifdef GLOBE\n",
+       "v_depth=gl_Position.z/gl_Position.w;\n",
+       "#endif\n",
        "#ifdef TERRAIN3D\n",
        "v_gamma_scale=1.0;\n",
        "#else\n",
-       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length((projected_with_extrude.xy-projected_no_extrude.xy)/projected_with_extrude.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "#endif\n",
+       "float u_patternscale_a_x=u_tileratio/dasharray_from.w/u_crossfade_from;float u_patternscale_a_y=-dasharray_from.z/2.0/u_lineatlas_height;float u_patternscale_b_x=u_tileratio/dasharray_to.w/u_crossfade_to;float u_patternscale_b_y=-dasharray_to.z/2.0/u_lineatlas_height;v_tex_a=vec2(a_linesofar*u_patternscale_a_x/floorwidth,normal.y*u_patternscale_a_y+(float(dasharray_from.y)+0.5)/u_lineatlas_height);v_tex_b=vec2(a_linesofar*u_patternscale_b_x/floorwidth,normal.y*u_patternscale_b_y+(float(dasharray_to.y)+0.5)/u_lineatlas_height);v_width2=vec2(outset,inset);}`),lineGradientSDF:Ni(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform sampler2D u_image_dash;uniform float u_mix;uniform lowp float u_lineatlas_width;in vec2 v_normal;in vec2 v_width2;in vec2 v_tex_a;in vec2 v_tex_b;in float v_gamma_scale;in highp vec2 v_uv;\n",
+       "#ifdef GLOBE\n",
+       "in float v_depth;\n",
+       "#endif\n",
+       "#pragma mapbox: define lowp float blur\n",
+       "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float width\n",
+       "#pragma mapbox: define lowp float floorwidth\n",
+       "#pragma mapbox: define mediump vec4 dasharray_from\n",
+       "#pragma mapbox: define mediump vec4 dasharray_to\n",
+       "void main() {\n",
+       "#pragma mapbox: initialize lowp float blur\n",
+       "#pragma mapbox: initialize lowp float opacity\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "#pragma mapbox: initialize lowp float floorwidth\n",
+       "#pragma mapbox: initialize mediump vec4 dasharray_from\n",
+       "#pragma mapbox: initialize mediump vec4 dasharray_to\n",
+       "float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture(u_image,v_uv);float sdfdist_a=texture(u_image_dash,v_tex_a).a;float sdfdist_b=texture(u_image_dash,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);float sdfgamma=(u_lineatlas_width/256.0)/min(dasharray_from.w,dasharray_to.w);float dash_alpha=smoothstep(0.5-sdfgamma/floorwidth,0.5+sdfgamma/floorwidth,sdfdist);fragColor=color*(alpha*dash_alpha*opacity);\n",
+       "#ifdef GLOBE\n",
+       "if (v_depth > 1.0) {discard;}\n",
        "#endif\n",
-       "v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`),raster:Ct(`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`\n",
+       "#define scale 0.015873016\n",
+       "#define LINE_DISTANCE_SCALE 2.0\n",
+       "in vec2 a_pos_normal;in vec4 a_data;in float a_uv_x;in float a_split_index;uniform vec2 u_translation;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;uniform float u_tileratio;uniform float u_crossfade_from;uniform float u_crossfade_to;uniform float u_lineatlas_height;out vec2 v_normal;out vec2 v_width2;out float v_gamma_scale;out highp vec2 v_uv;out vec2 v_tex_a;out vec2 v_tex_b;\n",
+       "#ifdef GLOBE\n",
+       "out float v_depth;\n",
        "#endif\n",
-       "}`,\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),symbolIcon:Ct(`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n",
+       "#pragma mapbox: define lowp float blur\n",
        "#pragma mapbox: define lowp float opacity\n",
+       "#pragma mapbox: define mediump float gapwidth\n",
+       "#pragma mapbox: define lowp float offset\n",
+       "#pragma mapbox: define mediump float width\n",
+       "#pragma mapbox: define lowp float floorwidth\n",
+       "#pragma mapbox: define mediump vec4 dasharray_from\n",
+       "#pragma mapbox: define mediump vec4 dasharray_to\n",
        "void main() {\n",
+       "#pragma mapbox: initialize lowp float blur\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "lowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n",
+       "#pragma mapbox: initialize mediump float gapwidth\n",
+       "#pragma mapbox: initialize lowp float offset\n",
+       "#pragma mapbox: initialize mediump float width\n",
+       "#pragma mapbox: initialize lowp float floorwidth\n",
+       "#pragma mapbox: initialize mediump vec4 dasharray_from\n",
+       "#pragma mapbox: initialize mediump vec4 dasharray_to\n",
+       "float ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;float texel_height=1.0/u_image_height;float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);float adjustedThickness=projectLineThickness(pos.y);vec4 projected_no_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation);vec4 projected_with_extrude=projectTile(pos+offset2/u_ratio*adjustedThickness+u_translation+dist/u_ratio*adjustedThickness);gl_Position=projected_with_extrude;\n",
+       "#ifdef GLOBE\n",
+       "v_depth=gl_Position.z/gl_Position.w;\n",
+       "#endif\n",
+       "#ifdef TERRAIN3D\n",
+       "v_gamma_scale=1.0;\n",
+       "#else\n",
+       "float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length((projected_with_extrude.xy-projected_no_extrude.xy)/projected_with_extrude.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n",
+       "#endif\n",
+       "float u_patternscale_a_x=u_tileratio/dasharray_from.w/u_crossfade_from;float u_patternscale_a_y=-dasharray_from.z/2.0/u_lineatlas_height;float u_patternscale_b_x=u_tileratio/dasharray_to.w/u_crossfade_to;float u_patternscale_b_y=-dasharray_to.z/2.0/u_lineatlas_height;v_tex_a=vec2(a_linesofar*u_patternscale_a_x/floorwidth,normal.y*u_patternscale_a_y+(float(dasharray_from.y)+0.5)/u_lineatlas_height);v_tex_b=vec2(a_linesofar*u_patternscale_b_x/floorwidth,normal.y*u_patternscale_b_y+(float(dasharray_to.y)+0.5)/u_lineatlas_height);v_width2=vec2(outset,inset);}`),raster:Ni(`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;in vec2 v_pos0;in vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture(u_image0,v_pos0);vec4 color1=texture(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);fragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n",
+       "#ifdef OVERDRAW_INSPECTOR\n",
+       "fragColor=vec4(1.0);\n",
+       "#endif\n",
+       "}`,`uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;uniform vec4 u_coords_top;uniform vec4 u_coords_bottom;in vec2 a_pos;out vec2 v_pos0;out vec2 v_pos1;void main() {vec2 fractionalPos=a_pos/8192.0;vec2 position=mix(mix(u_coords_top.xy,u_coords_top.zw,fractionalPos.x),mix(u_coords_bottom.xy,u_coords_bottom.zw,fractionalPos.x),fractionalPos.y);gl_Position=projectTile(position,position);v_pos0=((fractionalPos-0.5)/u_buffer_scale)+0.5;\n",
+       "#ifdef GLOBE\n",
+       "if (a_pos.y <-32767.5) {v_pos0.y=0.0;}if (a_pos.y > 32766.5) {v_pos0.y=1.0;}\n",
+       "#endif\n",
+       "v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}`),symbolIcon:Ni(`uniform sampler2D u_texture;in vec2 v_tex;in float v_total_opacity;void main() {fragColor=texture(u_texture,v_tex)*v_total_opacity;\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_tex;varying float v_fade_opacity;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n",
+       "}`,`in vec4 a_pos_offset;in vec4 a_data;in vec4 a_pixeloffset;in vec3 a_projected_pos;in float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;out vec2 v_tex;out float v_total_opacity;\n",
        "#pragma mapbox: define lowp float opacity\n",
        "void main() {\n",
        "#pragma mapbox: initialize lowp float opacity\n",
-       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_total_opacity=opacity*max(0.0,min(visibility,fade_opacity[0]+fade_change));if (v_total_opacity < 0.1){gl_Position=vec4(-2.,-2.,-2.,1.);return;}highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
        "camera_to_anchor_distance/u_camera_to_center_distance :\n",
-       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}gl_Position=finalPos;v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));}`),symbolSDF:Ct(`#define SDF_PX 8.0\n",
-       "uniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;\n",
+       "#ifdef GLOBE\n",
+       "if(u_pitch_with_map) {float anchor_pos_tile_y=(u_coord_matrix*vec4(projected_pos.xy/projected_pos.w,z,1.0)).y;projectionScaling=mix(projectionScaling,1.0/circumferenceRatioAtTileY(anchor_pos_tile_y)*u_pitched_scale,u_projection_transition);}\n",
+       "#endif\n",
+       "vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}gl_Position=finalPos;v_tex=a_tex/u_texsize;}`),symbolSDF:Ni(`#define SDF_PX 8.0\n",
+       "uniform bool u_is_halo;uniform bool u_is_plain;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;in vec2 v_data0;in vec3 v_data1;\n",
        "#pragma mapbox: define highp vec4 fill_color\n",
        "#pragma mapbox: define highp vec4 halo_color\n",
-       "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define lowp float halo_width\n",
        "#pragma mapbox: define lowp float halo_blur\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp vec4 fill_color\n",
        "#pragma mapbox: initialize highp vec4 halo_color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
        "#pragma mapbox: initialize lowp float halo_width\n",
        "#pragma mapbox: initialize lowp float halo_blur\n",
-       "float EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);inner_edge=inner_edge+gamma*gamma_scale;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);if (u_is_halo) {lowp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha=min(smoothstep(halo_edge-gamma_scaled,halo_edge+gamma_scaled,dist),1.0-alpha);}gl_FragColor=color*(alpha*opacity*fade_opacity);\n",
+       "float EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float total_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;lowp float dist=texture(u_texture,tex).a;lowp vec4 color_alpha_out_text,color_alpha_out_halo;if (u_is_plain){highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);color_alpha_out_text=total_opacity*alpha*fill_color;}if (u_is_halo) {float gamma_halo=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);float inner_edge_halo=inner_edge+gamma_halo*gamma_scale;highp float gamma_scaled_halo=gamma_halo*gamma_scale;highp float alpha_halo=smoothstep(inner_edge_halo-gamma_scaled_halo,inner_edge_halo+gamma_scaled_halo,dist);highp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha_halo= min(smoothstep(halo_edge-gamma_scaled_halo,halo_edge+gamma_scaled_halo,dist),1.0-alpha_halo);color_alpha_out_halo=total_opacity*alpha_halo*halo_color;}if (u_is_plain && u_is_halo) {fragColor=color_alpha_out_text+(1.-color_alpha_out_text.a)*color_alpha_out_halo;} else if (u_is_halo){fragColor=color_alpha_out_halo;} else {fragColor=color_alpha_out_text;}\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_data0;varying vec3 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n",
+       "}`,`in vec4 a_pos_offset;in vec4 a_data;in vec4 a_pixeloffset;in vec3 a_projected_pos;in float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_translation;uniform float u_pitched_scale;out vec2 v_data0;out vec3 v_data1;\n",
        "#pragma mapbox: define highp vec4 fill_color\n",
        "#pragma mapbox: define highp vec4 halo_color\n",
        "#pragma mapbox: define lowp float opacity\n",
@@ -3756,32 +3464,34 @@
        "#pragma mapbox: initialize lowp float opacity\n",
        "#pragma mapbox: initialize lowp float halo_width\n",
        "#pragma mapbox: initialize lowp float halo_blur\n",
-       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy/16.0;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));float total_opacity=opacity*interpolated_fade_opacity;if (total_opacity < 0.1){gl_Position=vec4(-2.,-2.,-2.,1.);return;}highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
        "camera_to_anchor_distance/u_camera_to_center_distance :\n",
-       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`),symbolTextAndIcon:Ct(`#define SDF_PX 8.0\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;\n",
+       "#ifdef GLOBE\n",
+       "if(u_pitch_with_map) {float anchor_pos_tile_y=(u_coord_matrix*vec4(projected_pos.xy/projected_pos.w,z,1.0)).y;projectionScaling=mix(projectionScaling,1.0/circumferenceRatioAtTileY(anchor_pos_tile_y)*u_pitched_scale,u_projection_transition);}\n",
+       "#endif\n",
+       "vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,total_opacity);}`),symbolTextAndIcon:Ni(`#define SDF_PX 8.0\n",
        "#define SDF 1.0\n",
        "#define ICON 0.0\n",
-       "uniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n",
+       "uniform bool u_is_halo;uniform bool u_is_text;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;in vec4 v_data0;in vec4 v_data1;\n",
        "#pragma mapbox: define highp vec4 fill_color\n",
        "#pragma mapbox: define highp vec4 halo_color\n",
-       "#pragma mapbox: define lowp float opacity\n",
        "#pragma mapbox: define lowp float halo_width\n",
        "#pragma mapbox: define lowp float halo_blur\n",
        "void main() {\n",
        "#pragma mapbox: initialize highp vec4 fill_color\n",
        "#pragma mapbox: initialize highp vec4 halo_color\n",
-       "#pragma mapbox: initialize lowp float opacity\n",
        "#pragma mapbox: initialize lowp float halo_width\n",
        "#pragma mapbox: initialize lowp float halo_blur\n",
-       "float fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n",
+       "float total_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;fragColor=texture(u_texture_icon,tex_icon)*total_opacity;\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "return;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n",
+       "return;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;lowp float dist=texture(u_texture,tex).a;lowp vec4 color_alpha_out,color_alpha_out_halo;if (u_is_text) {highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);color_alpha_out=fill_color*(alpha*total_opacity);}if (u_is_halo) {highp float gamma_halo=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);lowp float buff_halo=(6.0-halo_width/fontScale)/SDF_PX;highp float gamma_scaled_halo=gamma_halo*gamma_scale;highp float alpha_halo=smoothstep(buff_halo-gamma_scaled_halo,buff_halo+gamma_scaled_halo,dist);color_alpha_out_halo=halo_color*(alpha_halo*total_opacity);}if (u_is_text && u_is_halo) {fragColor=color_alpha_out+(1.-color_alpha_out.a)*color_alpha_out_halo;} else if (u_is_halo) {fragColor=color_alpha_out_halo;} else {fragColor=color_alpha_out;}\n",
        "#ifdef OVERDRAW_INSPECTOR\n",
-       "gl_FragColor=vec4(1.0);\n",
+       "fragColor=vec4(1.0);\n",
        "#endif\n",
-       "}`,`attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec4 v_data0;varying vec4 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n",
+       "}`,`in vec4 a_pos_offset;in vec4 a_data;in vec3 a_projected_pos;in float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;out vec4 v_data0;out vec4 v_data1;\n",
        "#pragma mapbox: define highp vec4 fill_color\n",
        "#pragma mapbox: define highp vec4 halo_color\n",
        "#pragma mapbox: define lowp float opacity\n",
@@ -3793,60 +3503,70 @@
        "#pragma mapbox: initialize lowp float opacity\n",
        "#pragma mapbox: initialize lowp float halo_width\n",
        "#pragma mapbox: initialize lowp float halo_blur\n",
-       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
+       "vec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));float total_opacity=opacity*interpolated_fade_opacity;if (total_opacity < 0.1){gl_Position=vec4(-2.,-2.,-2.,1.);return;}highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\n",
        "camera_to_anchor_distance/u_camera_to_center_distance :\n",
-       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`),terrain:Ct(\"uniform sampler2D u_texture;uniform vec4 u_fog_color;uniform vec4 u_horizon_color;uniform float u_fog_ground_blend;uniform float u_fog_ground_blend_opacity;uniform float u_horizon_fog_blend;varying vec2 v_texture_pos;varying float v_fog_depth;const float gamma=2.2;vec4 gammaToLinear(vec4 color) {return pow(color,vec4(gamma));}vec4 linearToGamma(vec4 color) {return pow(color,vec4(1.0/gamma));}void main() {vec4 surface_color=texture2D(u_texture,v_texture_pos);if (v_fog_depth > u_fog_ground_blend) {vec4 surface_color_linear=gammaToLinear(surface_color);float blend_color=smoothstep(0.0,1.0,max((v_fog_depth-u_horizon_fog_blend)/(1.0-u_horizon_fog_blend),0.0));vec4 fog_horizon_color_linear=mix(gammaToLinear(u_fog_color),gammaToLinear(u_horizon_color),blend_color);float factor_fog=max(v_fog_depth-u_fog_ground_blend,0.0)/(1.0-u_fog_ground_blend);gl_FragColor=linearToGamma(mix(surface_color_linear,fog_horizon_color_linear,pow(factor_fog,2.0)*u_fog_ground_blend_opacity));} else {gl_FragColor=surface_color;}}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform mat4 u_fog_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;varying float v_fog_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);vec4 pos=u_fog_matrix*vec4(a_pos3d.xy,ele,1.0);v_fog_depth=pos.z/pos.w*0.5+0.5;}\"),terrainDepth:Ct(\"varying float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {gl_FragColor=pack(v_depth);}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying float v_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);v_depth=gl_Position.z/gl_Position.w;}\"),terrainCoords:Ct(\"precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;varying vec2 v_texture_pos;void main() {vec4 rgba=texture2D(u_texture,v_texture_pos);gl_FragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);}\"),sky:Ct(\"uniform vec4 u_sky_color;uniform vec4 u_horizon_color;uniform float u_horizon;uniform float u_sky_horizon_blend;void main() {float y=gl_FragCoord.y;if (y > u_horizon) {float blend=y-u_horizon;if (blend < u_sky_horizon_blend) {gl_FragColor=mix(u_sky_color,u_horizon_color,pow(1.0-blend/u_sky_horizon_blend,2.0));} else {gl_FragColor=u_sky_color;}}}\",\"attribute vec2 a_pos;void main() {gl_Position=vec4(a_pos,1.0,1.0);}\")};function Ct(ue,w){let B=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,Q=w.match(/attribute ([\\w]+) ([\\w]+)/g),ee=ue.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),le=w.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),Fe=le?le.concat(ee):ee,Ze={};return{fragmentSource:ue=ue.replace(B,((ut,Mt,Jt,$t,yr)=>(Ze[yr]=!0,Mt===\"define\"?`\n",
-       "#ifndef HAS_UNIFORM_u_${yr}\n",
-       "varying ${Jt} ${$t} ${yr};\n",
+       "u_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;\n",
+       "#ifdef GLOBE\n",
+       "if(u_pitch_with_map && !u_is_along_line) {float anchor_pos_tile_y=(u_coord_matrix*vec4(projected_pos.xy/projected_pos.w,z,1.0)).y;projectionScaling=mix(projectionScaling,1.0/circumferenceRatioAtTileY(anchor_pos_tile_y)*u_pitched_scale,u_projection_transition);}\n",
+       "#endif\n",
+       "vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,total_opacity,is_sdf);}`),terrain:Ni(\"uniform sampler2D u_texture;uniform vec4 u_fog_color;uniform vec4 u_horizon_color;uniform float u_fog_ground_blend;uniform float u_fog_ground_blend_opacity;uniform float u_horizon_fog_blend;uniform bool u_is_globe_mode;in vec2 v_texture_pos;in float v_fog_depth;const float gamma=2.2;vec4 gammaToLinear(vec4 color) {return pow(color,vec4(gamma));}vec4 linearToGamma(vec4 color) {return pow(color,vec4(1.0/gamma));}void main() {vec4 surface_color=texture(u_texture,vec2(v_texture_pos.x,1.0-v_texture_pos.y));if (!u_is_globe_mode && u_fog_ground_blend_opacity > 0.0 && v_fog_depth > u_fog_ground_blend) {vec4 surface_color_linear=gammaToLinear(surface_color);float blend_color=smoothstep(0.0,1.0,max((v_fog_depth-u_horizon_fog_blend)/(1.0-u_horizon_fog_blend),0.0));vec4 fog_horizon_color_linear=mix(gammaToLinear(u_fog_color),gammaToLinear(u_horizon_color),blend_color);float factor_fog=max(v_fog_depth-u_fog_ground_blend,0.0)/(1.0-u_fog_ground_blend);fragColor=linearToGamma(mix(surface_color_linear,fog_horizon_color_linear,pow(factor_fog,2.0)*u_fog_ground_blend_opacity));} else {fragColor=surface_color;}}\",\"in vec3 a_pos3d;uniform mat4 u_fog_matrix;uniform float u_ele_delta;out vec2 v_texture_pos;out float v_fog_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=projectTileFor3D(a_pos3d.xy,ele-ele_delta);vec4 pos=u_fog_matrix*vec4(a_pos3d.xy,ele,1.0);v_fog_depth=pos.z/pos.w*0.5+0.5;}\"),terrainDepth:Ni(\"in float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {fragColor=pack(v_depth);}\",\"in vec3 a_pos3d;uniform float u_ele_delta;out float v_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;gl_Position=projectTileFor3D(a_pos3d.xy,ele-ele_delta);v_depth=gl_Position.z/gl_Position.w;}\"),terrainCoords:Ni(\"precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;in vec2 v_texture_pos;void main() {vec4 rgba=texture(u_texture,v_texture_pos);fragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}\",\"in vec3 a_pos3d;uniform float u_ele_delta;out vec2 v_texture_pos;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=projectTileFor3D(a_pos3d.xy,ele-ele_delta);}\"),projectionErrorMeasurement:Ni(\"in vec4 v_output_error_encoded;void main() {fragColor=v_output_error_encoded;}\",\"in vec2 a_pos;uniform highp float u_input;uniform highp float u_output_expected;out vec4 v_output_error_encoded;void main() {float real_output=2.0*atan(exp(PI-(u_input*PI*2.0)))-PI*0.5;float error=real_output-u_output_expected;float abs_error=abs(error)*128.0;v_output_error_encoded.x=min(floor(abs_error*256.0),255.0)/255.0;abs_error-=v_output_error_encoded.x;v_output_error_encoded.y=min(floor(abs_error*65536.0),255.0)/255.0;abs_error-=v_output_error_encoded.x/255.0;v_output_error_encoded.z=min(floor(abs_error*16777216.0),255.0)/255.0;v_output_error_encoded.w=error >=0.0 ? 1.0 : 0.0;gl_Position=vec4(a_pos,0.0,1.0);}\"),atmosphere:Ni(`#ifdef GL_ES\n",
+       "precision highp float;\n",
+       "#endif\n",
+       "in vec3 view_direction;uniform vec3 u_sun_pos;uniform vec3 u_globe_position;uniform float u_globe_radius;uniform float u_atmosphere_blend;/**Shader use from https:*Made some change to adapt to MapLibre Globe geometry*/const float PI=3.141592653589793;const int iSteps=5;const int jSteps=3;/*radius of the planet*/const float EARTH_RADIUS=6371e3;/*radius of the atmosphere*/const float ATMOS_RADIUS=6471e3;vec2 rsi(vec3 r0,vec3 rd,float sr) {float a=dot(rd,rd);float b=2.0*dot(rd,r0);float c=dot(r0,r0)-(sr*sr);float d=(b*b)-4.0*a*c;if (d < 0.0) return vec2(1e5,-1e5);return vec2((-b-sqrt(d))/(2.0*a),(-b+sqrt(d))/(2.0*a));}vec4 atmosphere(vec3 r,vec3 r0,vec3 pSun,float iSun,float rPlanet,float rAtmos,vec3 kRlh,float kMie,float shRlh,float shMie,float g) {pSun=normalize(pSun);r=normalize(r);vec2 p=rsi(r0,r,rAtmos);if (p.x > p.y) {return vec4(0.0,0.0,0.0,1.0);}if (p.x < 0.0) {p.x=0.0;}vec3 pos=r0+r*p.x;vec2 p2=rsi(r0,r,rPlanet);if (p2.x <=p2.y && p2.x > 0.0) {p.y=min(p.y,p2.x);}float iStepSize=(p.y-p.x)/float(iSteps);float iTime=p.x+iStepSize*0.5;vec3 totalRlh=vec3(0,0,0);vec3 totalMie=vec3(0,0,0);float iOdRlh=0.0;float iOdMie=0.0;float mu=dot(r,pSun);float mumu=mu*mu;float gg=g*g;float pRlh=3.0/(16.0*PI)*(1.0+mumu);float pMie=3.0/(8.0*PI)*((1.0-gg)*(mumu+1.0))/(pow(1.0+gg-2.0*mu*g,1.5)*(2.0+gg));for (int i=0; i < iSteps; i++) {vec3 iPos=r0+r*iTime;float iHeight=length(iPos)-rPlanet;float odStepRlh=exp(-iHeight/shRlh)*iStepSize;float odStepMie=exp(-iHeight/shMie)*iStepSize;iOdRlh+=odStepRlh;iOdMie+=odStepMie;float jStepSize=rsi(iPos,pSun,rAtmos).y/float(jSteps);float jTime=jStepSize*0.5;float jOdRlh=0.0;float jOdMie=0.0;for (int j=0; j < jSteps; j++) {vec3 jPos=iPos+pSun*jTime;float jHeight=length(jPos)-rPlanet;jOdRlh+=exp(-jHeight/shRlh)*jStepSize;jOdMie+=exp(-jHeight/shMie)*jStepSize;jTime+=jStepSize;}vec3 attn=exp(-(kMie*(iOdMie+jOdMie)+kRlh*(iOdRlh+jOdRlh)));totalRlh+=odStepRlh*attn;totalMie+=odStepMie*attn;iTime+=iStepSize;}float opacity=exp(-(length(kRlh)*length(totalRlh)+kMie*length(totalMie)));vec3 color=iSun*(pRlh*kRlh*totalRlh+pMie*kMie*totalMie);return vec4(color,opacity);}void main() {vec3 scale_camera_pos=-u_globe_position*EARTH_RADIUS/u_globe_radius;vec4 color=atmosphere(normalize(view_direction),scale_camera_pos,u_sun_pos,22.0,EARTH_RADIUS,ATMOS_RADIUS,vec3(5.5e-6,13.0e-6,22.4e-6),21e-6,8e3,1.2e3,0.758\n",
+       ");color.rgb=1.0-exp(-1.0*color.rgb);color=pow(color,vec4(1.0/2.2));fragColor=vec4(color.rgb,1.0-color.a)*u_atmosphere_blend;}`,\"in vec2 a_pos;uniform mat4 u_inv_proj_matrix;out vec3 view_direction;void main() {view_direction=(u_inv_proj_matrix*vec4(a_pos,0.0,1.0)).xyz;gl_Position=vec4(a_pos,0.0,1.0);}\"),sky:Ni(\"uniform vec4 u_sky_color;uniform vec4 u_horizon_color;uniform vec2 u_horizon;uniform vec2 u_horizon_normal;uniform float u_sky_horizon_blend;uniform float u_sky_blend;void main() {float x=gl_FragCoord.x;float y=gl_FragCoord.y;float blend=(y-u_horizon.y)*u_horizon_normal.y+(x-u_horizon.x)*u_horizon_normal.x;if (blend > 0.0) {if (blend < u_sky_horizon_blend) {fragColor=mix(u_sky_color,u_horizon_color,pow(1.0-blend/u_sky_horizon_blend,2.0));} else {fragColor=u_sky_color;}}fragColor=mix(fragColor,vec4(vec3(0.0),0.0),u_sky_blend);}\",\"in vec2 a_pos;void main() {gl_Position=vec4(a_pos,1.0,1.0);}\")};function Ni(Y,d){let P=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,N=d.match(/in ([\\w]+) ([\\w]+)/g),H=Y.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),$=d.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),se=$?$.concat(H):H,de={};return{fragmentSource:Y=Y.replace(P,((Ae,Fe,Ie,Ye,st)=>(de[st]=!0,Fe===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${st}\n",
+       "in ${Ie} ${Ye} ${st};\n",
        "#else\n",
-       "uniform ${Jt} ${$t} u_${yr};\n",
+       "uniform ${Ie} ${Ye} u_${st};\n",
        "#endif\n",
        "`:`\n",
-       "#ifdef HAS_UNIFORM_u_${yr}\n",
-       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "#ifdef HAS_UNIFORM_u_${st}\n",
+       "    ${Ie} ${Ye} ${st} = u_${st};\n",
        "#endif\n",
-       "`))),vertexSource:w=w.replace(B,((ut,Mt,Jt,$t,yr)=>{let Cr=$t===\"float\"?\"vec2\":\"vec4\",ve=yr.match(/color/)?\"color\":Cr;return Ze[yr]?Mt===\"define\"?`\n",
-       "#ifndef HAS_UNIFORM_u_${yr}\n",
-       "uniform lowp float u_${yr}_t;\n",
-       "attribute ${Jt} ${Cr} a_${yr};\n",
-       "varying ${Jt} ${$t} ${yr};\n",
+       "`))),vertexSource:d=d.replace(P,((Ae,Fe,Ie,Ye,st)=>{let dt=Ye===\"float\"?\"vec2\":\"vec4\",Pe=st.match(/color/)?\"color\":dt;return de[st]?Fe===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${st}\n",
+       "uniform lowp float u_${st}_t;\n",
+       "in ${Ie} ${dt} a_${st};\n",
+       "out ${Ie} ${Ye} ${st};\n",
        "#else\n",
-       "uniform ${Jt} ${$t} u_${yr};\n",
+       "uniform ${Ie} ${Ye} u_${st};\n",
        "#endif\n",
-       "`:ve===\"vec4\"?`\n",
-       "#ifndef HAS_UNIFORM_u_${yr}\n",
-       "    ${yr} = a_${yr};\n",
+       "`:Pe===\"vec4\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${st}\n",
+       "    ${st} = a_${st};\n",
        "#else\n",
-       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "    ${Ie} ${Ye} ${st} = u_${st};\n",
        "#endif\n",
        "`:`\n",
-       "#ifndef HAS_UNIFORM_u_${yr}\n",
-       "    ${yr} = unpack_mix_${ve}(a_${yr}, u_${yr}_t);\n",
+       "#ifndef HAS_UNIFORM_u_${st}\n",
+       "    ${st} = unpack_mix_${Pe}(a_${st}, u_${st}_t);\n",
        "#else\n",
-       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "    ${Ie} ${Ye} ${st} = u_${st};\n",
        "#endif\n",
-       "`:Mt===\"define\"?`\n",
-       "#ifndef HAS_UNIFORM_u_${yr}\n",
-       "uniform lowp float u_${yr}_t;\n",
-       "attribute ${Jt} ${Cr} a_${yr};\n",
+       "`:Fe===\"define\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${st}\n",
+       "uniform lowp float u_${st}_t;\n",
+       "in ${Ie} ${dt} a_${st};\n",
        "#else\n",
-       "uniform ${Jt} ${$t} u_${yr};\n",
+       "uniform ${Ie} ${Ye} u_${st};\n",
        "#endif\n",
-       "`:ve===\"vec4\"?`\n",
-       "#ifndef HAS_UNIFORM_u_${yr}\n",
-       "    ${Jt} ${$t} ${yr} = a_${yr};\n",
+       "`:Pe===\"vec4\"?`\n",
+       "#ifndef HAS_UNIFORM_u_${st}\n",
+       "    ${Ie} ${Ye} ${st} = a_${st};\n",
        "#else\n",
-       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "    ${Ie} ${Ye} ${st} = u_${st};\n",
        "#endif\n",
        "`:`\n",
-       "#ifndef HAS_UNIFORM_u_${yr}\n",
-       "    ${Jt} ${$t} ${yr} = unpack_mix_${ve}(a_${yr}, u_${yr}_t);\n",
+       "#ifndef HAS_UNIFORM_u_${st}\n",
+       "    ${Ie} ${Ye} ${st} = unpack_mix_${Pe}(a_${st}, u_${st}_t);\n",
        "#else\n",
-       "    ${Jt} ${$t} ${yr} = u_${yr};\n",
+       "    ${Ie} ${Ye} ${st} = u_${st};\n",
        "#endif\n",
-       "`})),staticAttributes:Q,staticUniforms:Fe}}class Sr{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(w,B,Q,ee,le,Fe,Ze,ut,Mt){this.context=w;let Jt=this.boundPaintVertexBuffers.length!==ee.length;for(let $t=0;!Jt&&$t<ee.length;$t++)this.boundPaintVertexBuffers[$t]!==ee[$t]&&(Jt=!0);!this.vao||this.boundProgram!==B||this.boundLayoutVertexBuffer!==Q||Jt||this.boundIndexBuffer!==le||this.boundVertexOffset!==Fe||this.boundDynamicVertexBuffer!==Ze||this.boundDynamicVertexBuffer2!==ut||this.boundDynamicVertexBuffer3!==Mt?this.freshBind(B,Q,ee,le,Fe,Ze,ut,Mt):(w.bindVertexArray.set(this.vao),Ze&&Ze.bind(),le&&le.dynamicDraw&&le.bind(),ut&&ut.bind(),Mt&&Mt.bind())}freshBind(w,B,Q,ee,le,Fe,Ze,ut){let Mt=w.numAttributes,Jt=this.context,$t=Jt.gl;this.vao&&this.destroy(),this.vao=Jt.createVertexArray(),Jt.bindVertexArray.set(this.vao),this.boundProgram=w,this.boundLayoutVertexBuffer=B,this.boundPaintVertexBuffers=Q,this.boundIndexBuffer=ee,this.boundVertexOffset=le,this.boundDynamicVertexBuffer=Fe,this.boundDynamicVertexBuffer2=Ze,this.boundDynamicVertexBuffer3=ut,B.enableAttributes($t,w);for(let yr of Q)yr.enableAttributes($t,w);Fe&&Fe.enableAttributes($t,w),Ze&&Ze.enableAttributes($t,w),ut&&ut.enableAttributes($t,w),B.bind(),B.setVertexAttribPointers($t,w,le);for(let yr of Q)yr.bind(),yr.setVertexAttribPointers($t,w,le);Fe&&(Fe.bind(),Fe.setVertexAttribPointers($t,w,le)),ee&&ee.bind(),Ze&&(Ze.bind(),Ze.setVertexAttribPointers($t,w,le)),ut&&(ut.bind(),ut.setVertexAttribPointers($t,w,le)),Jt.currentNumAttributes=Mt}destroy(){this.vao&&(this.context.deleteVertexArray(this.vao),this.vao=null)}}let Jr=(ue,w,B,Q,ee)=>({u_matrix:ue,u_texture:0,u_ele_delta:w,u_fog_matrix:B,u_fog_color:Q?Q.properties.get(\"fog-color\"):a.aM.white,u_fog_ground_blend:Q?Q.properties.get(\"fog-ground-blend\"):1,u_fog_ground_blend_opacity:Q?Q.calculateFogBlendOpacity(ee):0,u_horizon_color:Q?Q.properties.get(\"horizon-color\"):a.aM.white,u_horizon_fog_blend:Q?Q.properties.get(\"horizon-fog-blend\"):1});function hi(ue){let w=[];for(let B=0;B<ue.length;B++){if(ue[B]===null)continue;let Q=ue[B].split(\" \");w.push(Q.pop())}return w}class hn{constructor(w,B,Q,ee,le,Fe){let Ze=w.gl;this.program=Ze.createProgram();let ut=hi(B.staticAttributes),Mt=Q?Q.getBinderAttributes():[],Jt=ut.concat(Mt),$t=Gn.prelude.staticUniforms?hi(Gn.prelude.staticUniforms):[],yr=B.staticUniforms?hi(B.staticUniforms):[],Cr=Q?Q.getBinderUniforms():[],ve=$t.concat(yr).concat(Cr),xe=[];for(let Wt of ve)xe.indexOf(Wt)<0&&xe.push(Wt);let Ie=Q?Q.defines():[];le&&Ie.push(\"#define OVERDRAW_INSPECTOR;\"),Fe&&Ie.push(\"#define TERRAIN3D;\");let ze=Ie.concat(Gn.prelude.fragmentSource,B.fragmentSource).join(`\n",
-       "`),Qe=Ie.concat(Gn.prelude.vertexSource,B.vertexSource).join(`\n",
-       "`),Xe=Ze.createShader(Ze.FRAGMENT_SHADER);if(Ze.isContextLost())return void(this.failedToCreate=!0);if(Ze.shaderSource(Xe,ze),Ze.compileShader(Xe),!Ze.getShaderParameter(Xe,Ze.COMPILE_STATUS))throw new Error(`Could not compile fragment shader: ${Ze.getShaderInfoLog(Xe)}`);Ze.attachShader(this.program,Xe);let nt=Ze.createShader(Ze.VERTEX_SHADER);if(Ze.isContextLost())return void(this.failedToCreate=!0);if(Ze.shaderSource(nt,Qe),Ze.compileShader(nt),!Ze.getShaderParameter(nt,Ze.COMPILE_STATUS))throw new Error(`Could not compile vertex shader: ${Ze.getShaderInfoLog(nt)}`);Ze.attachShader(this.program,nt),this.attributes={};let qt={};this.numAttributes=Jt.length;for(let Wt=0;Wt<this.numAttributes;Wt++)Jt[Wt]&&(Ze.bindAttribLocation(this.program,Wt,Jt[Wt]),this.attributes[Jt[Wt]]=Wt);if(Ze.linkProgram(this.program),!Ze.getProgramParameter(this.program,Ze.LINK_STATUS))throw new Error(`Program failed to link: ${Ze.getProgramInfoLog(this.program)}`);Ze.deleteShader(nt),Ze.deleteShader(Xe);for(let Wt=0;Wt<xe.length;Wt++){let nr=xe[Wt];if(nr&&!qt[nr]){let fr=Ze.getUniformLocation(this.program,nr);fr&&(qt[nr]=fr)}}this.fixedUniforms=ee(w,qt),this.terrainUniforms=((Wt,nr)=>({u_depth:new a.aH(Wt,nr.u_depth),u_terrain:new a.aH(Wt,nr.u_terrain),u_terrain_dim:new a.aI(Wt,nr.u_terrain_dim),u_terrain_matrix:new a.aJ(Wt,nr.u_terrain_matrix),u_terrain_unpack:new a.aK(Wt,nr.u_terrain_unpack),u_terrain_exaggeration:new a.aI(Wt,nr.u_terrain_exaggeration)}))(w,qt),this.binderUniforms=Q?Q.getUniforms(w,qt):[]}draw(w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve,xe,Ie,ze,Qe){let Xe=w.gl;if(this.failedToCreate)return;if(w.program.set(this.program),w.setDepthMode(Q),w.setStencilMode(ee),w.setColorMode(le),w.setCullFace(Fe),ut){w.activeTexture.set(Xe.TEXTURE2),Xe.bindTexture(Xe.TEXTURE_2D,ut.depthTexture),w.activeTexture.set(Xe.TEXTURE3),Xe.bindTexture(Xe.TEXTURE_2D,ut.texture);for(let qt in this.terrainUniforms)this.terrainUniforms[qt].set(ut[qt])}for(let qt in this.fixedUniforms)this.fixedUniforms[qt].set(Ze[qt]);xe&&xe.setUniforms(w,this.binderUniforms,Cr,{zoom:ve});let nt=0;switch(B){case Xe.LINES:nt=2;break;case Xe.TRIANGLES:nt=3;break;case Xe.LINE_STRIP:nt=1}for(let qt of yr.get()){let Wt=qt.vaos||(qt.vaos={});(Wt[Mt]||(Wt[Mt]=new Sr)).bind(w,this,Jt,xe?xe.getPaintVertexBuffers():[],$t,qt.vertexOffset,Ie,ze,Qe),Xe.drawElements(B,qt.primitiveLength*nt,Xe.UNSIGNED_SHORT,qt.primitiveOffset*nt*2)}}}function Sn(ue,w,B){let Q=1/Fn(B,1,w.transform.tileZoom),ee=Math.pow(2,B.tileID.overscaledZ),le=B.tileSize*Math.pow(2,w.transform.tileZoom)/ee,Fe=le*(B.tileID.canonical.x+B.tileID.wrap*ee),Ze=le*B.tileID.canonical.y;return{u_image:0,u_texsize:B.imageAtlasTexture.size,u_scale:[Q,ue.fromScale,ue.toScale],u_fade:ue.t,u_pixel_coord_upper:[Fe>>16,Ze>>16],u_pixel_coord_lower:[65535&Fe,65535&Ze]}}let En=(ue,w,B,Q)=>{let ee=w.style.light,le=ee.properties.get(\"position\"),Fe=[le.x,le.y,le.z],Ze=(function(){var Mt=new a.A(9);return a.A!=Float32Array&&(Mt[1]=0,Mt[2]=0,Mt[3]=0,Mt[5]=0,Mt[6]=0,Mt[7]=0),Mt[0]=1,Mt[4]=1,Mt[8]=1,Mt})();ee.properties.get(\"anchor\")===\"viewport\"&&(function(Mt,Jt){var $t=Math.sin(Jt),yr=Math.cos(Jt);Mt[0]=yr,Mt[1]=$t,Mt[2]=0,Mt[3]=-$t,Mt[4]=yr,Mt[5]=0,Mt[6]=0,Mt[7]=0,Mt[8]=1})(Ze,-w.transform.angle),(function(Mt,Jt,$t){var yr=Jt[0],Cr=Jt[1],ve=Jt[2];Mt[0]=yr*$t[0]+Cr*$t[3]+ve*$t[6],Mt[1]=yr*$t[1]+Cr*$t[4]+ve*$t[7],Mt[2]=yr*$t[2]+Cr*$t[5]+ve*$t[8]})(Fe,Fe,Ze);let ut=ee.properties.get(\"color\");return{u_matrix:ue,u_lightpos:Fe,u_lightintensity:ee.properties.get(\"intensity\"),u_lightcolor:[ut.r,ut.g,ut.b],u_vertical_gradient:+B,u_opacity:Q}},ki=(ue,w,B,Q,ee,le,Fe)=>a.e(En(ue,w,B,Q),Sn(le,w,Fe),{u_height_factor:-Math.pow(2,ee.overscaledZ)/Fe.tileSize/8}),_n=ue=>({u_matrix:ue}),ya=(ue,w,B,Q)=>a.e(_n(ue),Sn(B,w,Q)),ea=(ue,w)=>({u_matrix:ue,u_world:w}),Ma=(ue,w,B,Q,ee)=>a.e(ya(ue,w,B,Q),{u_world:ee}),_o=(ue,w,B,Q)=>{let ee=ue.transform,le,Fe;if(Q.paint.get(\"circle-pitch-alignment\")===\"map\"){let Ze=Fn(B,1,ee.zoom);le=!0,Fe=[Ze,Ze]}else le=!1,Fe=ee.pixelsToGLUnits;return{u_camera_to_center_distance:ee.cameraToCenterDistance,u_scale_with_map:+(Q.paint.get(\"circle-pitch-scale\")===\"map\"),u_matrix:ue.translatePosMatrix(w.posMatrix,B,Q.paint.get(\"circle-translate\"),Q.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+le,u_device_pixel_ratio:ue.pixelRatio,u_extrude_scale:Fe}},No=(ue,w,B)=>({u_matrix:ue,u_inv_matrix:w,u_camera_to_center_distance:B.cameraToCenterDistance,u_viewport_size:[B.width,B.height]}),po=(ue,w,B=1)=>({u_matrix:ue,u_color:w,u_overlay:0,u_overlay_scale:B}),Lo=ue=>({u_matrix:ue}),ko=(ue,w,B,Q)=>({u_matrix:ue,u_extrude_scale:Fn(w,1,B),u_intensity:Q}),Ds=(ue,w,B,Q)=>{let ee=a.H();a.aP(ee,0,ue.width,ue.height,0,0,1);let le=ue.context.gl;return{u_matrix:ee,u_world:[le.drawingBufferWidth,le.drawingBufferHeight],u_image:B,u_color_ramp:Q,u_opacity:w.paint.get(\"heatmap-opacity\")}};function Fs(ue,w){let B=Math.pow(2,w.canonical.z),Q=w.canonical.y;return[new a.Z(0,Q/B).toLngLat().lat,new 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a.e(ll(ue,w,B,le),{u_patternscale_a:[Ze/$t,-Mt.height/2],u_patternscale_b:[Ze/yr,-Jt.height/2],u_sdfgamma:Fe.width/(256*Math.min($t,yr)*ue.pixelRatio)/2,u_image:0,u_tex_y_a:Mt.y,u_tex_y_b:Jt.y,u_mix:ee.t})};function rl(ue,w){return 1/Fn(ue,1,w.tileZoom)}function As(ue,w,B,Q){return ue.translatePosMatrix(Q?Q.posMatrix:w.tileID.posMatrix,w,B.paint.get(\"line-translate\"),B.paint.get(\"line-translate-anchor\"))}let cl=(ue,w,B,Q,ee)=>{return{u_matrix:ue,u_tl_parent:w,u_scale_parent:B,u_buffer_scale:1,u_fade_t:Q.mix,u_opacity:Q.opacity*ee.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:ee.paint.get(\"raster-brightness-min\"),u_brightness_high:ee.paint.get(\"raster-brightness-max\"),u_saturation_factor:(Fe=ee.paint.get(\"raster-saturation\"),Fe>0?1-1/(1.001-Fe):-Fe),u_contrast_factor:(le=ee.paint.get(\"raster-contrast\"),le>0?1/(1-le):1+le),u_spin_weights:Ks(ee.paint.get(\"raster-hue-rotate\"))};var le,Fe};function Ks(ue){ue*=Math.PI/180;let w=Math.sin(ue),B=Math.cos(ue);return[(2*B+1)/3,(-Math.sqrt(3)*w-B+1)/3,(Math.sqrt(3)*w-B+1)/3]}let zs=(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr)=>{let ve=Fe.transform;return{u_is_size_zoom_constant:+(ue===\"constant\"||ue===\"source\"),u_is_size_feature_constant:+(ue===\"constant\"||ue===\"camera\"),u_size_t:w?w.uSizeT:0,u_size:w?w.uSize:0,u_camera_to_center_distance:ve.cameraToCenterDistance,u_pitch:ve.pitch/360*2*Math.PI,u_rotate_symbol:+B,u_aspect_ratio:ve.width/ve.height,u_fade_change:Fe.options.fadeDuration?Fe.symbolFadeChange:1,u_matrix:Ze,u_label_plane_matrix:ut,u_coord_matrix:Mt,u_is_text:+$t,u_pitch_with_map:+Q,u_is_along_line:ee,u_is_variable_anchor:le,u_texsize:yr,u_texture:0,u_translation:Jt,u_pitched_scale:Cr}},Io=(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr,ve)=>{let xe=Fe.transform;return a.e(zs(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,ve),{u_gamma_scale:Q?Math.cos(xe._pitch)*xe.cameraToCenterDistance:1,u_device_pixel_ratio:Fe.pixelRatio,u_is_halo:+Cr})},ls=(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr)=>a.e(Io(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,!0,$t,!0,Cr),{u_texsize_icon:yr,u_texture_icon:1}),Yl=(ue,w,B)=>({u_matrix:ue,u_opacity:w,u_color:B}),Su=(ue,w,B,Q,ee,le)=>a.e((function(Fe,Ze,ut,Mt){let Jt=ut.imageManager.getPattern(Fe.from.toString()),$t=ut.imageManager.getPattern(Fe.to.toString()),{width:yr,height:Cr}=ut.imageManager.getPixelSize(),ve=Math.pow(2,Mt.tileID.overscaledZ),xe=Mt.tileSize*Math.pow(2,ut.transform.tileZoom)/ve,Ie=xe*(Mt.tileID.canonical.x+Mt.tileID.wrap*ve),ze=xe*Mt.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:Jt.tl,u_pattern_br_a:Jt.br,u_pattern_tl_b:$t.tl,u_pattern_br_b:$t.br,u_texsize:[yr,Cr],u_mix:Ze.t,u_pattern_size_a:Jt.displaySize,u_pattern_size_b:$t.displaySize,u_scale_a:Ze.fromScale,u_scale_b:Ze.toScale,u_tile_units_to_pixels:1/Fn(Mt,1,ut.transform.tileZoom),u_pixel_coord_upper:[Ie>>16,ze>>16],u_pixel_coord_lower:[65535&Ie,65535&ze]}})(Q,le,B,ee),{u_matrix:ue,u_opacity:w}),nc={fillExtrusion:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_lightpos:new a.aN(ue,w.u_lightpos),u_lightintensity:new a.aI(ue,w.u_lightintensity),u_lightcolor:new a.aN(ue,w.u_lightcolor),u_vertical_gradient:new a.aI(ue,w.u_vertical_gradient),u_opacity:new 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a.aO(ue,w.u_viewport_size)}),debug:(ue,w)=>({u_color:new a.aL(ue,w.u_color),u_matrix:new a.aJ(ue,w.u_matrix),u_overlay:new a.aH(ue,w.u_overlay),u_overlay_scale:new a.aI(ue,w.u_overlay_scale)}),clippingMask:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix)}),heatmap:(ue,w)=>({u_extrude_scale:new a.aI(ue,w.u_extrude_scale),u_intensity:new a.aI(ue,w.u_intensity),u_matrix:new a.aJ(ue,w.u_matrix)}),heatmapTexture:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_world:new a.aO(ue,w.u_world),u_image:new a.aH(ue,w.u_image),u_color_ramp:new a.aH(ue,w.u_color_ramp),u_opacity:new a.aI(ue,w.u_opacity)}),hillshade:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_image:new a.aH(ue,w.u_image),u_latrange:new a.aO(ue,w.u_latrange),u_light:new a.aO(ue,w.u_light),u_shadow:new a.aL(ue,w.u_shadow),u_highlight:new a.aL(ue,w.u_highlight),u_accent:new a.aL(ue,w.u_accent)}),hillshadePrepare:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_image:new a.aH(ue,w.u_image),u_dimension:new a.aO(ue,w.u_dimension),u_zoom:new a.aI(ue,w.u_zoom),u_unpack:new a.aK(ue,w.u_unpack)}),line:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_ratio:new a.aI(ue,w.u_ratio),u_device_pixel_ratio:new a.aI(ue,w.u_device_pixel_ratio),u_units_to_pixels:new a.aO(ue,w.u_units_to_pixels)}),lineGradient:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_ratio:new a.aI(ue,w.u_ratio),u_device_pixel_ratio:new a.aI(ue,w.u_device_pixel_ratio),u_units_to_pixels:new a.aO(ue,w.u_units_to_pixels),u_image:new a.aH(ue,w.u_image),u_image_height:new a.aI(ue,w.u_image_height)}),linePattern:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_texsize:new a.aO(ue,w.u_texsize),u_ratio:new a.aI(ue,w.u_ratio),u_device_pixel_ratio:new a.aI(ue,w.u_device_pixel_ratio),u_image:new a.aH(ue,w.u_image),u_units_to_pixels:new a.aO(ue,w.u_units_to_pixels),u_scale:new a.aN(ue,w.u_scale),u_fade:new a.aI(ue,w.u_fade)}),lineSDF:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_ratio:new a.aI(ue,w.u_ratio),u_device_pixel_ratio:new a.aI(ue,w.u_device_pixel_ratio),u_units_to_pixels:new a.aO(ue,w.u_units_to_pixels),u_patternscale_a:new a.aO(ue,w.u_patternscale_a),u_patternscale_b:new a.aO(ue,w.u_patternscale_b),u_sdfgamma:new a.aI(ue,w.u_sdfgamma),u_image:new a.aH(ue,w.u_image),u_tex_y_a:new a.aI(ue,w.u_tex_y_a),u_tex_y_b:new a.aI(ue,w.u_tex_y_b),u_mix:new a.aI(ue,w.u_mix)}),raster:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_tl_parent:new a.aO(ue,w.u_tl_parent),u_scale_parent:new a.aI(ue,w.u_scale_parent),u_buffer_scale:new a.aI(ue,w.u_buffer_scale),u_fade_t:new a.aI(ue,w.u_fade_t),u_opacity:new a.aI(ue,w.u_opacity),u_image0:new a.aH(ue,w.u_image0),u_image1:new a.aH(ue,w.u_image1),u_brightness_low:new a.aI(ue,w.u_brightness_low),u_brightness_high:new a.aI(ue,w.u_brightness_high),u_saturation_factor:new a.aI(ue,w.u_saturation_factor),u_contrast_factor:new a.aI(ue,w.u_contrast_factor),u_spin_weights:new a.aN(ue,w.u_spin_weights)}),symbolIcon:(ue,w)=>({u_is_size_zoom_constant:new a.aH(ue,w.u_is_size_zoom_constant),u_is_size_feature_constant:new 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a.aI(ue,w.u_size_t),u_size:new a.aI(ue,w.u_size),u_camera_to_center_distance:new a.aI(ue,w.u_camera_to_center_distance),u_pitch:new a.aI(ue,w.u_pitch),u_rotate_symbol:new a.aH(ue,w.u_rotate_symbol),u_aspect_ratio:new a.aI(ue,w.u_aspect_ratio),u_fade_change:new a.aI(ue,w.u_fade_change),u_matrix:new a.aJ(ue,w.u_matrix),u_label_plane_matrix:new a.aJ(ue,w.u_label_plane_matrix),u_coord_matrix:new a.aJ(ue,w.u_coord_matrix),u_is_text:new a.aH(ue,w.u_is_text),u_pitch_with_map:new a.aH(ue,w.u_pitch_with_map),u_is_along_line:new a.aH(ue,w.u_is_along_line),u_is_variable_anchor:new a.aH(ue,w.u_is_variable_anchor),u_texsize:new a.aO(ue,w.u_texsize),u_texture:new a.aH(ue,w.u_texture),u_gamma_scale:new a.aI(ue,w.u_gamma_scale),u_device_pixel_ratio:new a.aI(ue,w.u_device_pixel_ratio),u_is_halo:new a.aH(ue,w.u_is_halo),u_translation:new a.aO(ue,w.u_translation),u_pitched_scale:new a.aI(ue,w.u_pitched_scale)}),symbolTextAndIcon:(ue,w)=>({u_is_size_zoom_constant:new 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a.aH(ue,w.u_is_halo),u_translation:new a.aO(ue,w.u_translation),u_pitched_scale:new a.aI(ue,w.u_pitched_scale)}),background:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_opacity:new a.aI(ue,w.u_opacity),u_color:new a.aL(ue,w.u_color)}),backgroundPattern:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_opacity:new a.aI(ue,w.u_opacity),u_image:new a.aH(ue,w.u_image),u_pattern_tl_a:new a.aO(ue,w.u_pattern_tl_a),u_pattern_br_a:new a.aO(ue,w.u_pattern_br_a),u_pattern_tl_b:new a.aO(ue,w.u_pattern_tl_b),u_pattern_br_b:new a.aO(ue,w.u_pattern_br_b),u_texsize:new a.aO(ue,w.u_texsize),u_mix:new a.aI(ue,w.u_mix),u_pattern_size_a:new a.aO(ue,w.u_pattern_size_a),u_pattern_size_b:new a.aO(ue,w.u_pattern_size_b),u_scale_a:new a.aI(ue,w.u_scale_a),u_scale_b:new a.aI(ue,w.u_scale_b),u_pixel_coord_upper:new a.aO(ue,w.u_pixel_coord_upper),u_pixel_coord_lower:new a.aO(ue,w.u_pixel_coord_lower),u_tile_units_to_pixels:new a.aI(ue,w.u_tile_units_to_pixels)}),terrain:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_texture:new a.aH(ue,w.u_texture),u_ele_delta:new a.aI(ue,w.u_ele_delta),u_fog_matrix:new a.aJ(ue,w.u_fog_matrix),u_fog_color:new a.aL(ue,w.u_fog_color),u_fog_ground_blend:new a.aI(ue,w.u_fog_ground_blend),u_fog_ground_blend_opacity:new a.aI(ue,w.u_fog_ground_blend_opacity),u_horizon_color:new a.aL(ue,w.u_horizon_color),u_horizon_fog_blend:new a.aI(ue,w.u_horizon_fog_blend)}),terrainDepth:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_ele_delta:new a.aI(ue,w.u_ele_delta)}),terrainCoords:(ue,w)=>({u_matrix:new a.aJ(ue,w.u_matrix),u_texture:new a.aH(ue,w.u_texture),u_terrain_coords_id:new a.aI(ue,w.u_terrain_coords_id),u_ele_delta:new a.aI(ue,w.u_ele_delta)}),sky:(ue,w)=>({u_sky_color:new a.aL(ue,w.u_sky_color),u_horizon_color:new a.aL(ue,w.u_horizon_color),u_horizon:new a.aI(ue,w.u_horizon),u_sky_horizon_blend:new a.aI(ue,w.u_sky_horizon_blend)})};class bs{constructor(w,B,Q){this.context=w;let ee=w.gl;this.buffer=ee.createBuffer(),this.dynamicDraw=!!Q,this.context.unbindVAO(),w.bindElementBuffer.set(this.buffer),ee.bufferData(ee.ELEMENT_ARRAY_BUFFER,B.arrayBuffer,this.dynamicDraw?ee.DYNAMIC_DRAW:ee.STATIC_DRAW),this.dynamicDraw||delete B.arrayBuffer}bind(){this.context.bindElementBuffer.set(this.buffer)}updateData(w){let B=this.context.gl;if(!this.dynamicDraw)throw new Error(\"Attempted to update data while not in dynamic mode.\");this.context.unbindVAO(),this.bind(),B.bufferSubData(B.ELEMENT_ARRAY_BUFFER,0,w.arrayBuffer)}destroy(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)}}let Rn={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"};class _a{constructor(w,B,Q,ee){this.length=B.length,this.attributes=Q,this.itemSize=B.bytesPerElement,this.dynamicDraw=ee,this.context=w;let le=w.gl;this.buffer=le.createBuffer(),w.bindVertexBuffer.set(this.buffer),le.bufferData(le.ARRAY_BUFFER,B.arrayBuffer,this.dynamicDraw?le.DYNAMIC_DRAW:le.STATIC_DRAW),this.dynamicDraw||delete B.arrayBuffer}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(w){if(w.length!==this.length)throw new Error(`Length of new data is ${w.length}, which doesn't match current length of ${this.length}`);let B=this.context.gl;this.bind(),B.bufferSubData(B.ARRAY_BUFFER,0,w.arrayBuffer)}enableAttributes(w,B){for(let Q=0;Q<this.attributes.length;Q++){let ee=B.attributes[this.attributes[Q].name];ee!==void 0&&w.enableVertexAttribArray(ee)}}setVertexAttribPointers(w,B,Q){for(let ee=0;ee<this.attributes.length;ee++){let le=this.attributes[ee],Fe=B.attributes[le.name];Fe!==void 0&&w.vertexAttribPointer(Fe,le.components,w[Rn[le.type]],!1,this.itemSize,le.offset+this.itemSize*(Q||0))}}destroy(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)}}let Vu=new WeakMap;function zl(ue){var w;if(Vu.has(ue))return Vu.get(ue);{let B=(w=ue.getParameter(ue.VERSION))===null||w===void 0?void 0:w.startsWith(\"WebGL 2.0\");return Vu.set(ue,B),B}}class xo{constructor(w){this.gl=w.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1}get(){return this.current}set(w){}getDefault(){return this.default}setDefault(){this.set(this.default)}}class Kl extends xo{getDefault(){return a.aM.transparent}set(w){let B=this.current;(w.r!==B.r||w.g!==B.g||w.b!==B.b||w.a!==B.a||this.dirty)&&(this.gl.clearColor(w.r,w.g,w.b,w.a),this.current=w,this.dirty=!1)}}class Ns extends xo{getDefault(){return 1}set(w){(w!==this.current||this.dirty)&&(this.gl.clearDepth(w),this.current=w,this.dirty=!1)}}class Hl extends xo{getDefault(){return 0}set(w){(w!==this.current||this.dirty)&&(this.gl.clearStencil(w),this.current=w,this.dirty=!1)}}class ac extends xo{getDefault(){return[!0,!0,!0,!0]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||w[3]!==B[3]||this.dirty)&&(this.gl.colorMask(w[0],w[1],w[2],w[3]),this.current=w,this.dirty=!1)}}class oa extends xo{getDefault(){return!0}set(w){(w!==this.current||this.dirty)&&(this.gl.depthMask(w),this.current=w,this.dirty=!1)}}class qo extends xo{getDefault(){return 255}set(w){(w!==this.current||this.dirty)&&(this.gl.stencilMask(w),this.current=w,this.dirty=!1)}}class Oo extends xo{getDefault(){return{func:this.gl.ALWAYS,ref:0,mask:255}}set(w){let B=this.current;(w.func!==B.func||w.ref!==B.ref||w.mask!==B.mask||this.dirty)&&(this.gl.stencilFunc(w.func,w.ref,w.mask),this.current=w,this.dirty=!1)}}class ql extends xo{getDefault(){let w=this.gl;return[w.KEEP,w.KEEP,w.KEEP]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||this.dirty)&&(this.gl.stencilOp(w[0],w[1],w[2]),this.current=w,this.dirty=!1)}}class Pc extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.STENCIL_TEST):B.disable(B.STENCIL_TEST),this.current=w,this.dirty=!1}}class Do extends xo{getDefault(){return[0,1]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||this.dirty)&&(this.gl.depthRange(w[0],w[1]),this.current=w,this.dirty=!1)}}class nf extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.DEPTH_TEST):B.disable(B.DEPTH_TEST),this.current=w,this.dirty=!1}}class Vf extends xo{getDefault(){return this.gl.LESS}set(w){(w!==this.current||this.dirty)&&(this.gl.depthFunc(w),this.current=w,this.dirty=!1)}}class pl extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.BLEND):B.disable(B.BLEND),this.current=w,this.dirty=!1}}class Zc extends xo{getDefault(){let w=this.gl;return[w.ONE,w.ZERO]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||this.dirty)&&(this.gl.blendFunc(w[0],w[1]),this.current=w,this.dirty=!1)}}class Jl extends xo{getDefault(){return a.aM.transparent}set(w){let B=this.current;(w.r!==B.r||w.g!==B.g||w.b!==B.b||w.a!==B.a||this.dirty)&&(this.gl.blendColor(w.r,w.g,w.b,w.a),this.current=w,this.dirty=!1)}}class qs extends xo{getDefault(){return this.gl.FUNC_ADD}set(w){(w!==this.current||this.dirty)&&(this.gl.blendEquation(w),this.current=w,this.dirty=!1)}}class yu extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;w?B.enable(B.CULL_FACE):B.disable(B.CULL_FACE),this.current=w,this.dirty=!1}}class oc extends xo{getDefault(){return this.gl.BACK}set(w){(w!==this.current||this.dirty)&&(this.gl.cullFace(w),this.current=w,this.dirty=!1)}}class Cf extends xo{getDefault(){return this.gl.CCW}set(w){(w!==this.current||this.dirty)&&(this.gl.frontFace(w),this.current=w,this.dirty=!1)}}class sc extends xo{getDefault(){return null}set(w){(w!==this.current||this.dirty)&&(this.gl.useProgram(w),this.current=w,this.dirty=!1)}}class jh extends xo{getDefault(){return this.gl.TEXTURE0}set(w){(w!==this.current||this.dirty)&&(this.gl.activeTexture(w),this.current=w,this.dirty=!1)}}class Lf extends xo{getDefault(){let w=this.gl;return[0,0,w.drawingBufferWidth,w.drawingBufferHeight]}set(w){let B=this.current;(w[0]!==B[0]||w[1]!==B[1]||w[2]!==B[2]||w[3]!==B[3]||this.dirty)&&(this.gl.viewport(w[0],w[1],w[2],w[3]),this.current=w,this.dirty=!1)}}class cs extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindFramebuffer(B.FRAMEBUFFER,w),this.current=w,this.dirty=!1}}class af extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindRenderbuffer(B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class Gf extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindTexture(B.TEXTURE_2D,w),this.current=w,this.dirty=!1}}class $l extends xo{getDefault(){return null}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.bindBuffer(B.ARRAY_BUFFER,w),this.current=w,this.dirty=!1}}class fl extends xo{getDefault(){return null}set(w){let B=this.gl;B.bindBuffer(B.ELEMENT_ARRAY_BUFFER,w),this.current=w,this.dirty=!1}}class lc extends xo{getDefault(){return null}set(w){var B;if(w===this.current&&!this.dirty)return;let Q=this.gl;zl(Q)?Q.bindVertexArray(w):(B=Q.getExtension(\"OES_vertex_array_object\"))===null||B===void 0||B.bindVertexArrayOES(w),this.current=w,this.dirty=!1}}class Fu extends xo{getDefault(){return 4}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_ALIGNMENT,w),this.current=w,this.dirty=!1}}class Es extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_PREMULTIPLY_ALPHA_WEBGL,w),this.current=w,this.dirty=!1}}class Hs extends xo{getDefault(){return!1}set(w){if(w===this.current&&!this.dirty)return;let B=this.gl;B.pixelStorei(B.UNPACK_FLIP_Y_WEBGL,w),this.current=w,this.dirty=!1}}class Go extends xo{constructor(w,B){super(w),this.context=w,this.parent=B}getDefault(){return null}}class ps extends Go{setDirty(){this.dirty=!0}set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferTexture2D(B.FRAMEBUFFER,B.COLOR_ATTACHMENT0,B.TEXTURE_2D,w,0),this.current=w,this.dirty=!1}}class uc extends Go{set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferRenderbuffer(B.FRAMEBUFFER,B.DEPTH_ATTACHMENT,B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class bl extends Go{set(w){if(w===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let B=this.gl;B.framebufferRenderbuffer(B.FRAMEBUFFER,B.DEPTH_STENCIL_ATTACHMENT,B.RENDERBUFFER,w),this.current=w,this.dirty=!1}}class Gu{constructor(w,B,Q,ee,le){this.context=w,this.width=B,this.height=Q;let Fe=w.gl,Ze=this.framebuffer=Fe.createFramebuffer();if(this.colorAttachment=new ps(w,Ze),ee)this.depthAttachment=le?new bl(w,Ze):new uc(w,Ze);else if(le)throw new Error(\"Stencil cannot be set without depth\");if(Fe.checkFramebufferStatus(Fe.FRAMEBUFFER)!==Fe.FRAMEBUFFER_COMPLETE)throw new Error(\"Framebuffer is not complete\")}destroy(){let w=this.context.gl,B=this.colorAttachment.get();if(B&&w.deleteTexture(B),this.depthAttachment){let Q=this.depthAttachment.get();Q&&w.deleteRenderbuffer(Q)}w.deleteFramebuffer(this.framebuffer)}}class Os{constructor(w,B,Q){this.blendFunction=w,this.blendColor=B,this.mask=Q}}Os.Replace=[1,0],Os.disabled=new Os(Os.Replace,a.aM.transparent,[!1,!1,!1,!1]),Os.unblended=new Os(Os.Replace,a.aM.transparent,[!0,!0,!0,!0]),Os.alphaBlended=new Os([1,771],a.aM.transparent,[!0,!0,!0,!0]);class od{constructor(w){var B,Q;if(this.gl=w,this.clearColor=new Kl(this),this.clearDepth=new Ns(this),this.clearStencil=new Hl(this),this.colorMask=new ac(this),this.depthMask=new oa(this),this.stencilMask=new qo(this),this.stencilFunc=new Oo(this),this.stencilOp=new ql(this),this.stencilTest=new Pc(this),this.depthRange=new Do(this),this.depthTest=new nf(this),this.depthFunc=new Vf(this),this.blend=new pl(this),this.blendFunc=new Zc(this),this.blendColor=new Jl(this),this.blendEquation=new qs(this),this.cullFace=new yu(this),this.cullFaceSide=new oc(this),this.frontFace=new Cf(this),this.program=new sc(this),this.activeTexture=new jh(this),this.viewport=new Lf(this),this.bindFramebuffer=new cs(this),this.bindRenderbuffer=new af(this),this.bindTexture=new Gf(this),this.bindVertexBuffer=new $l(this),this.bindElementBuffer=new fl(this),this.bindVertexArray=new lc(this),this.pixelStoreUnpack=new Fu(this),this.pixelStoreUnpackPremultiplyAlpha=new Es(this),this.pixelStoreUnpackFlipY=new Hs(this),this.extTextureFilterAnisotropic=w.getExtension(\"EXT_texture_filter_anisotropic\")||w.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||w.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=w.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.maxTextureSize=w.getParameter(w.MAX_TEXTURE_SIZE),zl(w)){this.HALF_FLOAT=w.HALF_FLOAT;let ee=w.getExtension(\"EXT_color_buffer_half_float\");this.RGBA16F=(B=w.RGBA16F)!==null&&B!==void 0?B:ee==null?void 0:ee.RGBA16F_EXT,this.RGB16F=(Q=w.RGB16F)!==null&&Q!==void 0?Q:ee==null?void 0:ee.RGB16F_EXT,w.getExtension(\"EXT_color_buffer_float\")}else{w.getExtension(\"EXT_color_buffer_half_float\"),w.getExtension(\"OES_texture_half_float_linear\");let ee=w.getExtension(\"OES_texture_half_float\");this.HALF_FLOAT=ee==null?void 0:ee.HALF_FLOAT_OES}}setDefault(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()}setDirty(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.bindVertexArray.dirty=!0,this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0}createIndexBuffer(w,B){return new bs(this,w,B)}createVertexBuffer(w,B,Q){return new _a(this,w,B,Q)}createRenderbuffer(w,B,Q){let ee=this.gl,le=ee.createRenderbuffer();return this.bindRenderbuffer.set(le),ee.renderbufferStorage(ee.RENDERBUFFER,w,B,Q),this.bindRenderbuffer.set(null),le}createFramebuffer(w,B,Q,ee){return new Gu(this,w,B,Q,ee)}clear({color:w,depth:B,stencil:Q}){let ee=this.gl,le=0;w&&(le|=ee.COLOR_BUFFER_BIT,this.clearColor.set(w),this.colorMask.set([!0,!0,!0,!0])),B!==void 0&&(le|=ee.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(B),this.depthMask.set(!0)),Q!==void 0&&(le|=ee.STENCIL_BUFFER_BIT,this.clearStencil.set(Q),this.stencilMask.set(255)),ee.clear(le)}setCullFace(w){w.enable===!1?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(w.mode),this.frontFace.set(w.frontFace))}setDepthMode(w){w.func!==this.gl.ALWAYS||w.mask?(this.depthTest.set(!0),this.depthFunc.set(w.func),this.depthMask.set(w.mask),this.depthRange.set(w.range)):this.depthTest.set(!1)}setStencilMode(w){w.test.func!==this.gl.ALWAYS||w.mask?(this.stencilTest.set(!0),this.stencilMask.set(w.mask),this.stencilOp.set([w.fail,w.depthFail,w.pass]),this.stencilFunc.set({func:w.test.func,ref:w.ref,mask:w.test.mask})):this.stencilTest.set(!1)}setColorMode(w){a.aE(w.blendFunction,Os.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(w.blendFunction),this.blendColor.set(w.blendColor)),this.colorMask.set(w.mask)}createVertexArray(){var w;return zl(this.gl)?this.gl.createVertexArray():(w=this.gl.getExtension(\"OES_vertex_array_object\"))===null||w===void 0?void 0:w.createVertexArrayOES()}deleteVertexArray(w){var B;return zl(this.gl)?this.gl.deleteVertexArray(w):(B=this.gl.getExtension(\"OES_vertex_array_object\"))===null||B===void 0?void 0:B.deleteVertexArrayOES(w)}unbindVAO(){this.bindVertexArray.set(null)}}class Po{constructor(w,B,Q){this.func=w,this.mask=B,this.range=Q}}Po.ReadOnly=!1,Po.ReadWrite=!0,Po.disabled=new Po(519,Po.ReadOnly,[0,1]);let sd=7680;class Ko{constructor(w,B,Q,ee,le,Fe){this.test=w,this.ref=B,this.mask=Q,this.fail=ee,this.depthFail=le,this.pass=Fe}}Ko.disabled=new Ko({func:519,mask:0},0,0,sd,sd,sd);class Pa{constructor(w,B,Q){this.enable=w,this.mode=B,this.frontFace=Q}}let of;function Hu(ue,w,B,Q,ee){let le=ue.context,Fe=le.gl,Ze=ue.useProgram(\"collisionBox\"),ut=[],Mt=0,Jt=0;for(let ze=0;ze<Q.length;ze++){let Qe=Q[ze],Xe=w.getTile(Qe).getBucket(B);if(!Xe)continue;let nt=ee?Xe.textCollisionBox:Xe.iconCollisionBox,qt=Xe.collisionCircleArray;if(qt.length>0){let Wt=a.H();a.aQ(Wt,Xe.placementInvProjMatrix,ue.transform.glCoordMatrix),a.aQ(Wt,Wt,Xe.placementViewportMatrix),ut.push({circleArray:qt,circleOffset:Jt,transform:Qe.posMatrix,invTransform:Wt,coord:Qe}),Mt+=qt.length/4,Jt=Mt}nt&&Ze.draw(le,Fe.LINES,Po.disabled,Ko.disabled,ue.colorModeForRenderPass(),Pa.disabled,{u_matrix:Qe.posMatrix,u_pixel_extrude_scale:[1/($t=ue.transform).width,1/$t.height]},ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(Qe),B.id,nt.layoutVertexBuffer,nt.indexBuffer,nt.segments,null,ue.transform.zoom,null,null,nt.collisionVertexBuffer)}var $t;if(!ee||!ut.length)return;let yr=ue.useProgram(\"collisionCircle\"),Cr=new a.aR;Cr.resize(4*Mt),Cr._trim();let ve=0;for(let ze of ut)for(let Qe=0;Qe<ze.circleArray.length/4;Qe++){let Xe=4*Qe,nt=ze.circleArray[Xe+0],qt=ze.circleArray[Xe+1],Wt=ze.circleArray[Xe+2],nr=ze.circleArray[Xe+3];Cr.emplace(ve++,nt,qt,Wt,nr,0),Cr.emplace(ve++,nt,qt,Wt,nr,1),Cr.emplace(ve++,nt,qt,Wt,nr,2),Cr.emplace(ve++,nt,qt,Wt,nr,3)}(!of||of.length<2*Mt)&&(of=(function(ze){let Qe=2*ze,Xe=new a.aT;Xe.resize(Qe),Xe._trim();for(let nt=0;nt<Qe;nt++){let qt=6*nt;Xe.uint16[qt+0]=4*nt+0,Xe.uint16[qt+1]=4*nt+1,Xe.uint16[qt+2]=4*nt+2,Xe.uint16[qt+3]=4*nt+2,Xe.uint16[qt+4]=4*nt+3,Xe.uint16[qt+5]=4*nt+0}return Xe})(Mt));let xe=le.createIndexBuffer(of,!0),Ie=le.createVertexBuffer(Cr,a.aS.members,!0);for(let ze of ut){let Qe=No(ze.transform,ze.invTransform,ue.transform);yr.draw(le,Fe.TRIANGLES,Po.disabled,Ko.disabled,ue.colorModeForRenderPass(),Pa.disabled,Qe,ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(ze.coord),B.id,Ie,xe,a.a0.simpleSegment(0,2*ze.circleOffset,ze.circleArray.length,ze.circleArray.length/2),null,ue.transform.zoom,null,null,null)}Ie.destroy(),xe.destroy()}Pa.disabled=new Pa(!1,1029,2305),Pa.backCCW=new Pa(!0,1029,2305);let wl=a.an(new Float32Array(16));function Hf(ue,w,B,Q,ee,le){let{horizontalAlign:Fe,verticalAlign:Ze}=a.au(ue);return new a.P((-(Fe-.5)*w/ee+Q[0])*le,(-(Ze-.5)*B/ee+Q[1])*le)}function Ic(ue,w,B,Q,ee,le){let Fe=w.tileAnchorPoint.add(new a.P(w.translation[0],w.translation[1]));if(w.pitchWithMap){let Ze=Q.mult(le);B||(Ze=Ze.rotate(-ee));let ut=Fe.add(Ze);return gt(ut.x,ut.y,w.labelPlaneMatrix,w.getElevation).point}if(B){let Ze=Mr(w.tileAnchorPoint.x+1,w.tileAnchorPoint.y,w).point.sub(ue),ut=Math.atan(Ze.y/Ze.x)+(Ze.x<0?Math.PI:0);return ue.add(Q.rotate(ut))}return ue.add(Q)}function yf(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t,yr,Cr){let ve=ue.text.placedSymbolArray,xe=ue.text.dynamicLayoutVertexArray,Ie=ue.icon.dynamicLayoutVertexArray,ze={};xe.clear();for(let Qe=0;Qe<ve.length;Qe++){let Xe=ve.get(Qe),nt=Xe.hidden||!Xe.crossTileID||ue.allowVerticalPlacement&&!Xe.placedOrientation?null:Q[Xe.crossTileID];if(nt){let qt=new a.P(Xe.anchorX,Xe.anchorY),Wt={getElevation:Cr,width:ee.width,height:ee.height,labelPlaneMatrix:le,lineVertexArray:null,pitchWithMap:B,projection:Jt,projectionCache:null,tileAnchorPoint:qt,translation:$t,unwrappedTileID:yr},nr=B?gt(qt.x,qt.y,Fe,Cr):Mr(qt.x,qt.y,Wt),fr=Ge(ee.cameraToCenterDistance,nr.signedDistanceFromCamera),br=a.ai(ue.textSizeData,ut,Xe)*fr/a.ap;B&&(br*=ue.tilePixelRatio/Ze);let{width:Fr,height:Hr,anchor:ri,textOffset:Ci,textBoxScale:cn}=nt,sn=Hf(ri,Fr,Hr,Ci,cn,br),Ln=Jt.getPitchedTextCorrection(ee,qt.add(new a.P($t[0],$t[1])),yr),Kn=Ic(nr.point,Wt,w,sn,ee.angle,Ln),Aa=ue.allowVerticalPlacement&&Xe.placedOrientation===a.ah.vertical?Math.PI/2:0;for(let fa=0;fa<Xe.numGlyphs;fa++)a.aj(xe,Kn,Aa);Mt&&Xe.associatedIconIndex>=0&&(ze[Xe.associatedIconIndex]={shiftedAnchor:Kn,angle:Aa})}else Pi(Xe.numGlyphs,xe)}if(Mt){Ie.clear();let Qe=ue.icon.placedSymbolArray;for(let Xe=0;Xe<Qe.length;Xe++){let nt=Qe.get(Xe);if(nt.hidden)Pi(nt.numGlyphs,Ie);else{let qt=ze[Xe];if(qt)for(let Wt=0;Wt<nt.numGlyphs;Wt++)a.aj(Ie,qt.shiftedAnchor,qt.angle);else Pi(nt.numGlyphs,Ie)}}ue.icon.dynamicLayoutVertexBuffer.updateData(Ie)}ue.text.dynamicLayoutVertexBuffer.updateData(xe)}function Ol(ue,w,B){return B.iconsInText&&w?\"symbolTextAndIcon\":ue?\"symbolSDF\":\"symbolIcon\"}function Ah(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt,Jt,$t){let yr=ue.context,Cr=yr.gl,ve=ue.transform,xe=yn(),Ie=Ze===\"map\",ze=ut===\"map\",Qe=Ze!==\"viewport\"&&B.layout.get(\"symbol-placement\")!==\"point\",Xe=Ie&&!ze&&!Qe,nt=!ze&&Qe,qt=!B.layout.get(\"symbol-sort-key\").isConstant(),Wt=!1,nr=ue.depthModeForSublayer(0,Po.ReadOnly),fr=B._unevaluatedLayout.hasValue(\"text-variable-anchor\")||B._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),br=[],Fr=xe.getCircleRadiusCorrection(ve);for(let Hr of Q){let ri=w.getTile(Hr),Ci=ri.getBucket(B);if(!Ci)continue;let cn=ee?Ci.text:Ci.icon;if(!cn||!cn.segments.get().length||!cn.hasVisibleVertices)continue;let sn=cn.programConfigurations.get(B.id),Ln=ee||Ci.sdfIcons,Kn=ee?Ci.textSizeData:Ci.iconSizeData,Aa=ze||ve.pitch!==0,fa=ue.useProgram(Ol(Ln,ee,Ci),sn),$a=a.ag(Kn,ve.zoom),Co=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(Hr),Qa,mo,Bo,Ps,Ts=[0,0],wo=null;if(ee)mo=ri.glyphAtlasTexture,Bo=Cr.LINEAR,Qa=ri.glyphAtlasTexture.size,Ci.iconsInText&&(Ts=ri.imageAtlasTexture.size,wo=ri.imageAtlasTexture,Ps=Aa||ue.options.rotating||ue.options.zooming||Kn.kind===\"composite\"||Kn.kind===\"camera\"?Cr.LINEAR:Cr.NEAREST);else{let Ye=B.layout.get(\"icon-size\").constantOr(0)!==1||Ci.iconsNeedLinear;mo=ri.imageAtlasTexture,Bo=Ln||ue.options.rotating||ue.options.zooming||Ye||Aa?Cr.LINEAR:Cr.NEAREST,Qa=ri.imageAtlasTexture.size}let To=Fn(ri,1,ue.transform.zoom),hl=nt?Hr.posMatrix:wl,Nl=qr(hl,ze,Ie,ue.transform,To),Lu=jr(hl,ze,Ie,ue.transform,To),au=jr(Hr.posMatrix,ze,Ie,ue.transform,To),Js=xe.translatePosition(ue.transform,ri,le,Fe),eu=fr&&Ci.hasTextData(),dc=B.layout.get(\"icon-text-fit\")!==\"none\"&&eu&&Ci.hasIconData();if(Qe){let Ye=ue.style.map.terrain?(ot,Xt)=>ue.style.map.terrain.getElevation(Hr,ot,Xt):null,Pt=B.layout.get(\"text-rotation-alignment\")===\"map\";We(Ci,Hr.posMatrix,ue,ee,Nl,au,ze,Mt,Pt,xe,Hr.toUnwrapped(),ve.width,ve.height,Js,Ye)}let Al=Hr.posMatrix,Sl=ee&&fr||dc,X=Qe||Sl?wl:Nl,se=Lu,Te=Ln&&B.paint.get(ee?\"text-halo-width\":\"icon-halo-width\").constantOr(1)!==0,qe;qe=Ln?Ci.iconsInText?ls(Kn.kind,$a,Xe,ze,Qe,Sl,ue,Al,X,se,Js,Qa,Ts,Fr):Io(Kn.kind,$a,Xe,ze,Qe,Sl,ue,Al,X,se,Js,ee,Qa,!0,Fr):zs(Kn.kind,$a,Xe,ze,Qe,Sl,ue,Al,X,se,Js,ee,Qa,Fr);let Ve={program:fa,buffers:cn,uniformValues:qe,atlasTexture:mo,atlasTextureIcon:wo,atlasInterpolation:Bo,atlasInterpolationIcon:Ps,isSDF:Ln,hasHalo:Te};if(qt&&Ci.canOverlap){Wt=!0;let Ye=cn.segments.get();for(let Pt of Ye)br.push({segments:new a.a0([Pt]),sortKey:Pt.sortKey,state:Ve,terrainData:Co})}else br.push({segments:cn.segments,sortKey:0,state:Ve,terrainData:Co})}Wt&&br.sort(((Hr,ri)=>Hr.sortKey-ri.sortKey));for(let Hr of br){let ri=Hr.state;if(yr.activeTexture.set(Cr.TEXTURE0),ri.atlasTexture.bind(ri.atlasInterpolation,Cr.CLAMP_TO_EDGE),ri.atlasTextureIcon&&(yr.activeTexture.set(Cr.TEXTURE1),ri.atlasTextureIcon&&ri.atlasTextureIcon.bind(ri.atlasInterpolationIcon,Cr.CLAMP_TO_EDGE)),ri.isSDF){let Ci=ri.uniformValues;ri.hasHalo&&(Ci.u_is_halo=1,Qf(ri.buffers,Hr.segments,B,ue,ri.program,nr,Jt,$t,Ci,Hr.terrainData)),Ci.u_is_halo=0}Qf(ri.buffers,Hr.segments,B,ue,ri.program,nr,Jt,$t,ri.uniformValues,Hr.terrainData)}}function Qf(ue,w,B,Q,ee,le,Fe,Ze,ut,Mt){let Jt=Q.context;ee.draw(Jt,Jt.gl.TRIANGLES,le,Fe,Ze,Pa.disabled,ut,Mt,B.id,ue.layoutVertexBuffer,ue.indexBuffer,w,B.paint,Q.transform.zoom,ue.programConfigurations.get(B.id),ue.dynamicLayoutVertexBuffer,ue.opacityVertexBuffer)}function _f(ue,w,B,Q){let ee=ue.context,le=ee.gl,Fe=Ko.disabled,Ze=new Os([le.ONE,le.ONE],a.aM.transparent,[!0,!0,!0,!0]),ut=w.getBucket(B);if(!ut)return;let Mt=Q.key,Jt=B.heatmapFbos.get(Mt);Jt||(Jt=eh(ee,w.tileSize,w.tileSize),B.heatmapFbos.set(Mt,Jt)),ee.bindFramebuffer.set(Jt.framebuffer),ee.viewport.set([0,0,w.tileSize,w.tileSize]),ee.clear({color:a.aM.transparent});let $t=ut.programConfigurations.get(B.id),yr=ue.useProgram(\"heatmap\",$t),Cr=ue.style.map.terrain.getTerrainData(Q);yr.draw(ee,le.TRIANGLES,Po.disabled,Fe,Ze,Pa.disabled,ko(Q.posMatrix,w,ue.transform.zoom,B.paint.get(\"heatmap-intensity\")),Cr,B.id,ut.layoutVertexBuffer,ut.indexBuffer,ut.segments,B.paint,ue.transform.zoom,$t)}function Yc(ue,w,B){let Q=ue.context,ee=Q.gl;Q.setColorMode(ue.colorModeForRenderPass());let le=th(Q,w),Fe=B.key,Ze=w.heatmapFbos.get(Fe);Ze&&(Q.activeTexture.set(ee.TEXTURE0),ee.bindTexture(ee.TEXTURE_2D,Ze.colorAttachment.get()),Q.activeTexture.set(ee.TEXTURE1),le.bind(ee.LINEAR,ee.CLAMP_TO_EDGE),ue.useProgram(\"heatmapTexture\").draw(Q,ee.TRIANGLES,Po.disabled,Ko.disabled,ue.colorModeForRenderPass(),Pa.disabled,Ds(ue,w,0,1),null,w.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments,w.paint,ue.transform.zoom),Ze.destroy(),w.heatmapFbos.delete(Fe))}function eh(ue,w,B){var Q,ee;let le=ue.gl,Fe=le.createTexture();le.bindTexture(le.TEXTURE_2D,Fe),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_S,le.CLAMP_TO_EDGE),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_WRAP_T,le.CLAMP_TO_EDGE),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MIN_FILTER,le.LINEAR),le.texParameteri(le.TEXTURE_2D,le.TEXTURE_MAG_FILTER,le.LINEAR);let Ze=(Q=ue.HALF_FLOAT)!==null&&Q!==void 0?Q:le.UNSIGNED_BYTE,ut=(ee=ue.RGBA16F)!==null&&ee!==void 0?ee:le.RGBA;le.texImage2D(le.TEXTURE_2D,0,ut,w,B,0,le.RGBA,Ze,null);let Mt=ue.createFramebuffer(w,B,!1,!1);return Mt.colorAttachment.set(Fe),Mt}function th(ue,w){return w.colorRampTexture||(w.colorRampTexture=new g(ue,w.colorRamp,ue.gl.RGBA)),w.colorRampTexture}function ju(ue,w,B,Q,ee){if(!B||!Q||!Q.imageAtlas)return;let le=Q.imageAtlas.patternPositions,Fe=le[B.to.toString()],Ze=le[B.from.toString()];if(!Fe&&Ze&&(Fe=Ze),!Ze&&Fe&&(Ze=Fe),!Fe||!Ze){let ut=ee.getPaintProperty(w);Fe=le[ut],Ze=le[ut]}Fe&&Ze&&ue.setConstantPatternPositions(Fe,Ze)}function jf(ue,w,B,Q,ee,le,Fe){let Ze=ue.context.gl,ut=\"fill-pattern\",Mt=B.paint.get(ut),Jt=Mt&&Mt.constantOr(1),$t=B.getCrossfadeParameters(),yr,Cr,ve,xe,Ie;Fe?(Cr=Jt&&!B.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",yr=Ze.LINES):(Cr=Jt?\"fillPattern\":\"fill\",yr=Ze.TRIANGLES);let ze=Mt.constantOr(null);for(let Qe of Q){let Xe=w.getTile(Qe);if(Jt&&!Xe.patternsLoaded())continue;let nt=Xe.getBucket(B);if(!nt)continue;let qt=nt.programConfigurations.get(B.id),Wt=ue.useProgram(Cr,qt),nr=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(Qe);Jt&&(ue.context.activeTexture.set(Ze.TEXTURE0),Xe.imageAtlasTexture.bind(Ze.LINEAR,Ze.CLAMP_TO_EDGE),qt.updatePaintBuffers($t)),ju(qt,ut,ze,Xe,B);let fr=nr?Qe:null,br=ue.translatePosMatrix(fr?fr.posMatrix:Qe.posMatrix,Xe,B.paint.get(\"fill-translate\"),B.paint.get(\"fill-translate-anchor\"));if(Fe){xe=nt.indexBuffer2,Ie=nt.segments2;let Fr=[Ze.drawingBufferWidth,Ze.drawingBufferHeight];ve=Cr===\"fillOutlinePattern\"&&Jt?Ma(br,ue,$t,Xe,Fr):ea(br,Fr)}else xe=nt.indexBuffer,Ie=nt.segments,ve=Jt?ya(br,ue,$t,Xe):_n(br);Wt.draw(ue.context,yr,ee,ue.stencilModeForClipping(Qe),le,Pa.disabled,ve,nr,B.id,nt.layoutVertexBuffer,xe,Ie,B.paint,ue.transform.zoom,qt)}}function cc(ue,w,B,Q,ee,le,Fe){let Ze=ue.context,ut=Ze.gl,Mt=\"fill-extrusion-pattern\",Jt=B.paint.get(Mt),$t=Jt.constantOr(1),yr=B.getCrossfadeParameters(),Cr=B.paint.get(\"fill-extrusion-opacity\"),ve=Jt.constantOr(null);for(let xe of Q){let Ie=w.getTile(xe),ze=Ie.getBucket(B);if(!ze)continue;let Qe=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(xe),Xe=ze.programConfigurations.get(B.id),nt=ue.useProgram($t?\"fillExtrusionPattern\":\"fillExtrusion\",Xe);$t&&(ue.context.activeTexture.set(ut.TEXTURE0),Ie.imageAtlasTexture.bind(ut.LINEAR,ut.CLAMP_TO_EDGE),Xe.updatePaintBuffers(yr)),ju(Xe,Mt,ve,Ie,B);let qt=ue.translatePosMatrix(xe.posMatrix,Ie,B.paint.get(\"fill-extrusion-translate\"),B.paint.get(\"fill-extrusion-translate-anchor\")),Wt=B.paint.get(\"fill-extrusion-vertical-gradient\"),nr=$t?ki(qt,ue,Wt,Cr,xe,yr,Ie):En(qt,ue,Wt,Cr);nt.draw(Ze,Ze.gl.TRIANGLES,ee,le,Fe,Pa.backCCW,nr,Qe,B.id,ze.layoutVertexBuffer,ze.indexBuffer,ze.segments,B.paint,ue.transform.zoom,Xe,ue.style.map.terrain&&ze.centroidVertexBuffer)}}function sf(ue,w,B,Q,ee,le,Fe){let Ze=ue.context,ut=Ze.gl,Mt=B.fbo;if(!Mt)return;let Jt=ue.useProgram(\"hillshade\"),$t=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(w);Ze.activeTexture.set(ut.TEXTURE0),ut.bindTexture(ut.TEXTURE_2D,Mt.colorAttachment.get()),Jt.draw(Ze,ut.TRIANGLES,ee,le,Fe,Pa.disabled,((yr,Cr,ve,xe)=>{let Ie=ve.paint.get(\"hillshade-shadow-color\"),ze=ve.paint.get(\"hillshade-highlight-color\"),Qe=ve.paint.get(\"hillshade-accent-color\"),Xe=ve.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);ve.paint.get(\"hillshade-illumination-anchor\")===\"viewport\"&&(Xe-=yr.transform.angle);let nt=!yr.options.moving;return{u_matrix:xe?xe.posMatrix:yr.transform.calculatePosMatrix(Cr.tileID.toUnwrapped(),nt),u_image:0,u_latrange:Fs(0,Cr.tileID),u_light:[ve.paint.get(\"hillshade-exaggeration\"),Xe],u_shadow:Ie,u_highlight:ze,u_accent:Qe}})(ue,B,Q,$t?w:null),$t,Q.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments)}function Bl(ue,w,B,Q,ee,le){let Fe=ue.context,Ze=Fe.gl,ut=w.dem;if(ut&&ut.data){let Mt=ut.dim,Jt=ut.stride,$t=ut.getPixels();if(Fe.activeTexture.set(Ze.TEXTURE1),Fe.pixelStoreUnpackPremultiplyAlpha.set(!1),w.demTexture=w.demTexture||ue.getTileTexture(Jt),w.demTexture){let Cr=w.demTexture;Cr.update($t,{premultiply:!1}),Cr.bind(Ze.NEAREST,Ze.CLAMP_TO_EDGE)}else w.demTexture=new g(Fe,$t,Ze.RGBA,{premultiply:!1}),w.demTexture.bind(Ze.NEAREST,Ze.CLAMP_TO_EDGE);Fe.activeTexture.set(Ze.TEXTURE0);let yr=w.fbo;if(!yr){let Cr=new g(Fe,{width:Mt,height:Mt,data:null},Ze.RGBA);Cr.bind(Ze.LINEAR,Ze.CLAMP_TO_EDGE),yr=w.fbo=Fe.createFramebuffer(Mt,Mt,!0,!1),yr.colorAttachment.set(Cr.texture)}Fe.bindFramebuffer.set(yr.framebuffer),Fe.viewport.set([0,0,Mt,Mt]),ue.useProgram(\"hillshadePrepare\").draw(Fe,Ze.TRIANGLES,Q,ee,le,Pa.disabled,((Cr,ve)=>{let xe=ve.stride,Ie=a.H();return a.aP(Ie,0,a.X,-a.X,0,0,1),a.J(Ie,Ie,[0,-a.X,0]),{u_matrix:Ie,u_image:1,u_dimension:[xe,xe],u_zoom:Cr.overscaledZ,u_unpack:ve.getUnpackVector()}})(w.tileID,ut),null,B.id,ue.rasterBoundsBuffer,ue.quadTriangleIndexBuffer,ue.rasterBoundsSegments),w.needsHillshadePrepare=!1}}function Kc(ue,w,B,Q,ee,le){let Fe=Q.paint.get(\"raster-fade-duration\");if(!le&&Fe>0){let Ze=u.now(),ut=(Ze-ue.timeAdded)/Fe,Mt=w?(Ze-w.timeAdded)/Fe:-1,Jt=B.getSource(),$t=ee.coveringZoomLevel({tileSize:Jt.tileSize,roundZoom:Jt.roundZoom}),yr=!w||Math.abs(w.tileID.overscaledZ-$t)>Math.abs(ue.tileID.overscaledZ-$t),Cr=yr&&ue.refreshedUponExpiration?1:a.ac(yr?ut:1-Mt,0,1);return ue.refreshedUponExpiration&&ut>=1&&(ue.refreshedUponExpiration=!1),w?{opacity:1,mix:1-Cr}:{opacity:Cr,mix:0}}return{opacity:1,mix:0}}let Rc=new a.aM(1,0,0,1),gs=new a.aM(0,1,0,1),Wf=new a.aM(0,0,1,1),Wh=new a.aM(1,0,1,1),rh=new a.aM(0,1,1,1);function lf(ue,w,B,Q){Mu(ue,0,w+B/2,ue.transform.width,B,Q)}function Sh(ue,w,B,Q){Mu(ue,w-B/2,0,B,ue.transform.height,Q)}function Mu(ue,w,B,Q,ee,le){let Fe=ue.context,Ze=Fe.gl;Ze.enable(Ze.SCISSOR_TEST),Ze.scissor(w*ue.pixelRatio,B*ue.pixelRatio,Q*ue.pixelRatio,ee*ue.pixelRatio),Fe.clear({color:le}),Ze.disable(Ze.SCISSOR_TEST)}function ih(ue,w,B){let Q=ue.context,ee=Q.gl,le=B.posMatrix,Fe=ue.useProgram(\"debug\"),Ze=Po.disabled,ut=Ko.disabled,Mt=ue.colorModeForRenderPass(),Jt=\"$debug\",$t=ue.style.map.terrain&&ue.style.map.terrain.getTerrainData(B);Q.activeTexture.set(ee.TEXTURE0);let yr=w.getTileByID(B.key).latestRawTileData,Cr=Math.floor((yr&&yr.byteLength||0)/1024),ve=w.getTile(B).tileSize,xe=512/Math.min(ve,512)*(B.overscaledZ/ue.transform.zoom)*.5,Ie=B.canonical.toString();B.overscaledZ!==B.canonical.z&&(Ie+=` => ${B.overscaledZ}`),(function(ze,Qe){ze.initDebugOverlayCanvas();let Xe=ze.debugOverlayCanvas,nt=ze.context.gl,qt=ze.debugOverlayCanvas.getContext(\"2d\");qt.clearRect(0,0,Xe.width,Xe.height),qt.shadowColor=\"white\",qt.shadowBlur=2,qt.lineWidth=1.5,qt.strokeStyle=\"white\",qt.textBaseline=\"top\",qt.font=\"bold 36px Open Sans, sans-serif\",qt.fillText(Qe,5,5),qt.strokeText(Qe,5,5),ze.debugOverlayTexture.update(Xe),ze.debugOverlayTexture.bind(nt.LINEAR,nt.CLAMP_TO_EDGE)})(ue,`${Ie} ${Cr}kB`),Fe.draw(Q,ee.TRIANGLES,Ze,ut,Os.alphaBlended,Pa.disabled,po(le,a.aM.transparent,xe),null,Jt,ue.debugBuffer,ue.quadTriangleIndexBuffer,ue.debugSegments),Fe.draw(Q,ee.LINE_STRIP,Ze,ut,Mt,Pa.disabled,po(le,a.aM.red),$t,Jt,ue.debugBuffer,ue.tileBorderIndexBuffer,ue.debugSegments)}function js(ue,w,B){let Q=ue.context,ee=Q.gl,le=ue.colorModeForRenderPass(),Fe=new Po(ee.LEQUAL,Po.ReadWrite,ue.depthRangeFor3D),Ze=ue.useProgram(\"terrain\"),ut=w.getTerrainMesh();Q.bindFramebuffer.set(null),Q.viewport.set([0,0,ue.width,ue.height]);for(let Mt of B){let Jt=ue.renderToTexture.getTexture(Mt),$t=w.getTerrainData(Mt.tileID);Q.activeTexture.set(ee.TEXTURE0),ee.bindTexture(ee.TEXTURE_2D,Jt.texture);let yr=ue.transform.calculatePosMatrix(Mt.tileID.toUnwrapped()),Cr=w.getMeshFrameDelta(ue.transform.zoom),ve=ue.transform.calculateFogMatrix(Mt.tileID.toUnwrapped()),xe=Jr(yr,Cr,ve,ue.style.sky,ue.transform.pitch);Ze.draw(Q,ee.TRIANGLES,Fe,Ko.disabled,le,Pa.backCCW,xe,$t,\"terrain\",ut.vertexBuffer,ut.indexBuffer,ut.segments)}}class Eu{constructor(w,B,Q){this.vertexBuffer=w,this.indexBuffer=B,this.segments=Q}destroy(){this.vertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.vertexBuffer=null,this.indexBuffer=null,this.segments=null}}class Dc{constructor(w,B){this.context=new od(w),this.transform=B,this._tileTextures={},this.terrainFacilitator={dirty:!0,matrix:a.an(new Float64Array(16)),renderTime:0},this.setup(),this.numSublayers=pt.maxUnderzooming+pt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Xo}resize(w,B,Q){if(this.width=Math.floor(w*Q),this.height=Math.floor(B*Q),this.pixelRatio=Q,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(let ee of this.style._order)this.style._layers[ee].resize()}setup(){let w=this.context,B=new a.aX;B.emplaceBack(0,0),B.emplaceBack(a.X,0),B.emplaceBack(0,a.X),B.emplaceBack(a.X,a.X),this.tileExtentBuffer=w.createVertexBuffer(B,vo.members),this.tileExtentSegments=a.a0.simpleSegment(0,0,4,2);let Q=new a.aX;Q.emplaceBack(0,0),Q.emplaceBack(a.X,0),Q.emplaceBack(0,a.X),Q.emplaceBack(a.X,a.X),this.debugBuffer=w.createVertexBuffer(Q,vo.members),this.debugSegments=a.a0.simpleSegment(0,0,4,5);let ee=new a.$;ee.emplaceBack(0,0,0,0),ee.emplaceBack(a.X,0,a.X,0),ee.emplaceBack(0,a.X,0,a.X),ee.emplaceBack(a.X,a.X,a.X,a.X),this.rasterBoundsBuffer=w.createVertexBuffer(ee,ct.members),this.rasterBoundsSegments=a.a0.simpleSegment(0,0,4,2);let le=new a.aX;le.emplaceBack(0,0),le.emplaceBack(1,0),le.emplaceBack(0,1),le.emplaceBack(1,1),this.viewportBuffer=w.createVertexBuffer(le,vo.members),this.viewportSegments=a.a0.simpleSegment(0,0,4,2);let Fe=new a.aZ;Fe.emplaceBack(0),Fe.emplaceBack(1),Fe.emplaceBack(3),Fe.emplaceBack(2),Fe.emplaceBack(0),this.tileBorderIndexBuffer=w.createIndexBuffer(Fe);let Ze=new a.aY;Ze.emplaceBack(0,1,2),Ze.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=w.createIndexBuffer(Ze);let ut=this.context.gl;this.stencilClearMode=new Ko({func:ut.ALWAYS,mask:0},0,255,ut.ZERO,ut.ZERO,ut.ZERO)}clearStencil(){let w=this.context,B=w.gl;this.nextStencilID=1,this.currentStencilSource=void 0;let Q=a.H();a.aP(Q,0,this.width,this.height,0,0,1),a.K(Q,Q,[B.drawingBufferWidth,B.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(w,B.TRIANGLES,Po.disabled,this.stencilClearMode,Os.disabled,Pa.disabled,Lo(Q),null,\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(w,B){if(this.currentStencilSource===w.source||!w.isTileClipped()||!B||!B.length)return;this.currentStencilSource=w.source;let Q=this.context,ee=Q.gl;this.nextStencilID+B.length>256&&this.clearStencil(),Q.setColorMode(Os.disabled),Q.setDepthMode(Po.disabled);let le=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(let Fe of B){let Ze=this._tileClippingMaskIDs[Fe.key]=this.nextStencilID++,ut=this.style.map.terrain&&this.style.map.terrain.getTerrainData(Fe);le.draw(Q,ee.TRIANGLES,Po.disabled,new Ko({func:ee.ALWAYS,mask:0},Ze,255,ee.KEEP,ee.KEEP,ee.REPLACE),Os.disabled,Pa.disabled,Lo(Fe.posMatrix),ut,\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();let w=this.nextStencilID++,B=this.context.gl;return new Ko({func:B.NOTEQUAL,mask:255},w,255,B.KEEP,B.KEEP,B.REPLACE)}stencilModeForClipping(w){let B=this.context.gl;return new Ko({func:B.EQUAL,mask:255},this._tileClippingMaskIDs[w.key],0,B.KEEP,B.KEEP,B.REPLACE)}stencilConfigForOverlap(w){let B=this.context.gl,Q=w.sort(((Fe,Ze)=>Ze.overscaledZ-Fe.overscaledZ)),ee=Q[Q.length-1].overscaledZ,le=Q[0].overscaledZ-ee+1;if(le>1){this.currentStencilSource=void 0,this.nextStencilID+le>256&&this.clearStencil();let Fe={};for(let Ze=0;Ze<le;Ze++)Fe[Ze+ee]=new Ko({func:B.GEQUAL,mask:255},Ze+this.nextStencilID,255,B.KEEP,B.KEEP,B.REPLACE);return this.nextStencilID+=le,[Fe,Q]}return[{[ee]:Ko.disabled},Q]}colorModeForRenderPass(){let w=this.context.gl;return this._showOverdrawInspector?new Os([w.CONSTANT_COLOR,w.ONE],new a.aM(.125,.125,.125,0),[!0,!0,!0,!0]):this.renderPass===\"opaque\"?Os.unblended:Os.alphaBlended}depthModeForSublayer(w,B,Q){if(!this.opaquePassEnabledForLayer())return Po.disabled;let ee=1-((1+this.currentLayer)*this.numSublayers+w)*this.depthEpsilon;return new Po(Q||this.context.gl.LEQUAL,B,[ee,ee])}opaquePassEnabledForLayer(){return this.currentLayer<this.opaquePassCutoff}render(w,B){this.style=w,this.options=B,this.lineAtlas=w.lineAtlas,this.imageManager=w.imageManager,this.glyphManager=w.glyphManager,this.symbolFadeChange=w.placement.symbolFadeChange(u.now()),this.imageManager.beginFrame();let Q=this.style._order,ee=this.style.sourceCaches,le={},Fe={},Ze={};for(let ut in ee){let Mt=ee[ut];Mt.used&&Mt.prepare(this.context),le[ut]=Mt.getVisibleCoordinates(),Fe[ut]=le[ut].slice().reverse(),Ze[ut]=Mt.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(let ut=0;ut<Q.length;ut++)if(this.style._layers[Q[ut]].is3D()){this.opaquePassCutoff=ut;break}this.maybeDrawDepthAndCoords(!1),this.renderToTexture&&(this.renderToTexture.prepareForRender(this.style,this.transform.zoom),this.opaquePassCutoff=0),this.renderPass=\"offscreen\";for(let ut of Q){let Mt=this.style._layers[ut];if(!Mt.hasOffscreenPass()||Mt.isHidden(this.transform.zoom))continue;let Jt=Fe[Mt.source];(Mt.type===\"custom\"||Jt.length)&&this.renderLayer(this,ee[Mt.source],Mt,Jt)}if(this.context.bindFramebuffer.set(null),this.context.clear({color:B.showOverdrawInspector?a.aM.black:a.aM.transparent,depth:1}),this.clearStencil(),this.style.sky&&(function(ut,Mt){let Jt=ut.context,$t=Jt.gl,yr=((ze,Qe,Xe)=>({u_sky_color:ze.properties.get(\"sky-color\"),u_horizon_color:ze.properties.get(\"horizon-color\"),u_horizon:(Qe.height/2+Qe.getHorizon())*Xe,u_sky_horizon_blend:ze.properties.get(\"sky-horizon-blend\")*Qe.height/2*Xe}))(Mt,ut.style.map.transform,ut.pixelRatio),Cr=new Po($t.LEQUAL,Po.ReadWrite,[0,1]),ve=Ko.disabled,xe=ut.colorModeForRenderPass(),Ie=ut.useProgram(\"sky\");if(!Mt.mesh){let ze=new a.aX;ze.emplaceBack(-1,-1),ze.emplaceBack(1,-1),ze.emplaceBack(1,1),ze.emplaceBack(-1,1);let Qe=new a.aY;Qe.emplaceBack(0,1,2),Qe.emplaceBack(0,2,3),Mt.mesh=new Eu(Jt.createVertexBuffer(ze,vo.members),Jt.createIndexBuffer(Qe),a.a0.simpleSegment(0,0,ze.length,Qe.length))}Ie.draw(Jt,$t.TRIANGLES,Cr,ve,xe,Pa.disabled,yr,void 0,\"sky\",Mt.mesh.vertexBuffer,Mt.mesh.indexBuffer,Mt.mesh.segments)})(this,this.style.sky),this._showOverdrawInspector=B.showOverdrawInspector,this.depthRangeFor3D=[0,1-(w._order.length+2)*this.numSublayers*this.depthEpsilon],!this.renderToTexture)for(this.renderPass=\"opaque\",this.currentLayer=Q.length-1;this.currentLayer>=0;this.currentLayer--){let ut=this.style._layers[Q[this.currentLayer]],Mt=ee[ut.source],Jt=le[ut.source];this._renderTileClippingMasks(ut,Jt),this.renderLayer(this,Mt,ut,Jt)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<Q.length;this.currentLayer++){let ut=this.style._layers[Q[this.currentLayer]],Mt=ee[ut.source];if(this.renderToTexture&&this.renderToTexture.renderLayer(ut))continue;let Jt=(ut.type===\"symbol\"?Ze:Fe)[ut.source];this._renderTileClippingMasks(ut,le[ut.source]),this.renderLayer(this,Mt,ut,Jt)}if(this.options.showTileBoundaries){let ut=(function(Mt,Jt){let $t=null,yr=Object.values(Mt._layers).flatMap((Ie=>Ie.source&&!Ie.isHidden(Jt)?[Mt.sourceCaches[Ie.source]]:[])),Cr=yr.filter((Ie=>Ie.getSource().type===\"vector\")),ve=yr.filter((Ie=>Ie.getSource().type!==\"vector\")),xe=Ie=>{(!$t||$t.getSource().maxzoom<Ie.getSource().maxzoom)&&($t=Ie)};return Cr.forEach((Ie=>xe(Ie))),$t||ve.forEach((Ie=>xe(Ie))),$t})(this.style,this.transform.zoom);ut&&(function(Mt,Jt,$t){for(let yr=0;yr<$t.length;yr++)ih(Mt,Jt,$t[yr])})(this,ut,ut.getVisibleCoordinates())}this.options.showPadding&&(function(ut){let Mt=ut.transform.padding;lf(ut,ut.transform.height-(Mt.top||0),3,Rc),lf(ut,Mt.bottom||0,3,gs),Sh(ut,Mt.left||0,3,Wf),Sh(ut,ut.transform.width-(Mt.right||0),3,Wh);let Jt=ut.transform.centerPoint;(function($t,yr,Cr,ve){Mu($t,yr-1,Cr-10,2,20,ve),Mu($t,yr-10,Cr-1,20,2,ve)})(ut,Jt.x,ut.transform.height-Jt.y,rh)})(this),this.context.setDefault()}maybeDrawDepthAndCoords(w){if(!this.style||!this.style.map||!this.style.map.terrain)return;let B=this.terrainFacilitator.matrix,Q=this.transform.modelViewProjectionMatrix,ee=this.terrainFacilitator.dirty;ee||(ee=w?!a.a_(B,Q):!a.a$(B,Q)),ee||(ee=this.style.map.terrain.sourceCache.tilesAfterTime(this.terrainFacilitator.renderTime).length>0),ee&&(a.b0(B,Q),this.terrainFacilitator.renderTime=Date.now(),this.terrainFacilitator.dirty=!1,(function(le,Fe){let Ze=le.context,ut=Ze.gl,Mt=Os.unblended,Jt=new Po(ut.LEQUAL,Po.ReadWrite,[0,1]),$t=Fe.getTerrainMesh(),yr=Fe.sourceCache.getRenderableTiles(),Cr=le.useProgram(\"terrainDepth\");Ze.bindFramebuffer.set(Fe.getFramebuffer(\"depth\").framebuffer),Ze.viewport.set([0,0,le.width/devicePixelRatio,le.height/devicePixelRatio]),Ze.clear({color:a.aM.transparent,depth:1});for(let ve of yr){let xe=Fe.getTerrainData(ve.tileID),Ie={u_matrix:le.transform.calculatePosMatrix(ve.tileID.toUnwrapped()),u_ele_delta:Fe.getMeshFrameDelta(le.transform.zoom)};Cr.draw(Ze,ut.TRIANGLES,Jt,Ko.disabled,Mt,Pa.backCCW,Ie,xe,\"terrain\",$t.vertexBuffer,$t.indexBuffer,$t.segments)}Ze.bindFramebuffer.set(null),Ze.viewport.set([0,0,le.width,le.height])})(this,this.style.map.terrain),(function(le,Fe){let Ze=le.context,ut=Ze.gl,Mt=Os.unblended,Jt=new Po(ut.LEQUAL,Po.ReadWrite,[0,1]),$t=Fe.getTerrainMesh(),yr=Fe.getCoordsTexture(),Cr=Fe.sourceCache.getRenderableTiles(),ve=le.useProgram(\"terrainCoords\");Ze.bindFramebuffer.set(Fe.getFramebuffer(\"coords\").framebuffer),Ze.viewport.set([0,0,le.width/devicePixelRatio,le.height/devicePixelRatio]),Ze.clear({color:a.aM.transparent,depth:1}),Fe.coordsIndex=[];for(let xe of Cr){let Ie=Fe.getTerrainData(xe.tileID);Ze.activeTexture.set(ut.TEXTURE0),ut.bindTexture(ut.TEXTURE_2D,yr.texture);let ze={u_matrix:le.transform.calculatePosMatrix(xe.tileID.toUnwrapped()),u_terrain_coords_id:(255-Fe.coordsIndex.length)/255,u_texture:0,u_ele_delta:Fe.getMeshFrameDelta(le.transform.zoom)};ve.draw(Ze,ut.TRIANGLES,Jt,Ko.disabled,Mt,Pa.backCCW,ze,Ie,\"terrain\",$t.vertexBuffer,$t.indexBuffer,$t.segments),Fe.coordsIndex.push(xe.tileID.key)}Ze.bindFramebuffer.set(null),Ze.viewport.set([0,0,le.width,le.height])})(this,this.style.map.terrain))}renderLayer(w,B,Q,ee){if(!Q.isHidden(this.transform.zoom)&&(Q.type===\"background\"||Q.type===\"custom\"||(ee||[]).length))switch(this.id=Q.id,Q.type){case\"symbol\":(function(le,Fe,Ze,ut,Mt){if(le.renderPass!==\"translucent\")return;let Jt=Ko.disabled,$t=le.colorModeForRenderPass();(Ze._unevaluatedLayout.hasValue(\"text-variable-anchor\")||Ze._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"))&&(function(yr,Cr,ve,xe,Ie,ze,Qe,Xe,nt){let qt=Cr.transform,Wt=yn(),nr=Ie===\"map\",fr=ze===\"map\";for(let br of yr){let Fr=xe.getTile(br),Hr=Fr.getBucket(ve);if(!Hr||!Hr.text||!Hr.text.segments.get().length)continue;let ri=a.ag(Hr.textSizeData,qt.zoom),Ci=Fn(Fr,1,Cr.transform.zoom),cn=qr(br.posMatrix,fr,nr,Cr.transform,Ci),sn=ve.layout.get(\"icon-text-fit\")!==\"none\"&&Hr.hasIconData();if(ri){let Ln=Math.pow(2,qt.zoom-Fr.tileID.overscaledZ),Kn=Cr.style.map.terrain?(fa,$a)=>Cr.style.map.terrain.getElevation(br,fa,$a):null,Aa=Wt.translatePosition(qt,Fr,Qe,Xe);yf(Hr,nr,fr,nt,qt,cn,br.posMatrix,Ln,ri,sn,Wt,Aa,br.toUnwrapped(),Kn)}}})(ut,le,Ze,Fe,Ze.layout.get(\"text-rotation-alignment\"),Ze.layout.get(\"text-pitch-alignment\"),Ze.paint.get(\"text-translate\"),Ze.paint.get(\"text-translate-anchor\"),Mt),Ze.paint.get(\"icon-opacity\").constantOr(1)!==0&&Ah(le,Fe,Ze,ut,!1,Ze.paint.get(\"icon-translate\"),Ze.paint.get(\"icon-translate-anchor\"),Ze.layout.get(\"icon-rotation-alignment\"),Ze.layout.get(\"icon-pitch-alignment\"),Ze.layout.get(\"icon-keep-upright\"),Jt,$t),Ze.paint.get(\"text-opacity\").constantOr(1)!==0&&Ah(le,Fe,Ze,ut,!0,Ze.paint.get(\"text-translate\"),Ze.paint.get(\"text-translate-anchor\"),Ze.layout.get(\"text-rotation-alignment\"),Ze.layout.get(\"text-pitch-alignment\"),Ze.layout.get(\"text-keep-upright\"),Jt,$t),Fe.map.showCollisionBoxes&&(Hu(le,Fe,Ze,ut,!0),Hu(le,Fe,Ze,ut,!1))})(w,B,Q,ee,this.style.placement.variableOffsets);break;case\"circle\":(function(le,Fe,Ze,ut){if(le.renderPass!==\"translucent\")return;let Mt=Ze.paint.get(\"circle-opacity\"),Jt=Ze.paint.get(\"circle-stroke-width\"),$t=Ze.paint.get(\"circle-stroke-opacity\"),yr=!Ze.layout.get(\"circle-sort-key\").isConstant();if(Mt.constantOr(1)===0&&(Jt.constantOr(1)===0||$t.constantOr(1)===0))return;let Cr=le.context,ve=Cr.gl,xe=le.depthModeForSublayer(0,Po.ReadOnly),Ie=Ko.disabled,ze=le.colorModeForRenderPass(),Qe=[];for(let Xe=0;Xe<ut.length;Xe++){let nt=ut[Xe],qt=Fe.getTile(nt),Wt=qt.getBucket(Ze);if(!Wt)continue;let nr=Wt.programConfigurations.get(Ze.id),fr=le.useProgram(\"circle\",nr),br=Wt.layoutVertexBuffer,Fr=Wt.indexBuffer,Hr=le.style.map.terrain&&le.style.map.terrain.getTerrainData(nt),ri={programConfiguration:nr,program:fr,layoutVertexBuffer:br,indexBuffer:Fr,uniformValues:_o(le,nt,qt,Ze),terrainData:Hr};if(yr){let Ci=Wt.segments.get();for(let cn of Ci)Qe.push({segments:new a.a0([cn]),sortKey:cn.sortKey,state:ri})}else Qe.push({segments:Wt.segments,sortKey:0,state:ri})}yr&&Qe.sort(((Xe,nt)=>Xe.sortKey-nt.sortKey));for(let Xe of Qe){let{programConfiguration:nt,program:qt,layoutVertexBuffer:Wt,indexBuffer:nr,uniformValues:fr,terrainData:br}=Xe.state;qt.draw(Cr,ve.TRIANGLES,xe,Ie,ze,Pa.disabled,fr,br,Ze.id,Wt,nr,Xe.segments,Ze.paint,le.transform.zoom,nt)}})(w,B,Q,ee);break;case\"heatmap\":(function(le,Fe,Ze,ut){if(Ze.paint.get(\"heatmap-opacity\")===0)return;let Mt=le.context;if(le.style.map.terrain){for(let Jt of ut){let $t=Fe.getTile(Jt);Fe.hasRenderableParent(Jt)||(le.renderPass===\"offscreen\"?_f(le,$t,Ze,Jt):le.renderPass===\"translucent\"&&Yc(le,Ze,Jt))}Mt.viewport.set([0,0,le.width,le.height])}else le.renderPass===\"offscreen\"?(function(Jt,$t,yr,Cr){let ve=Jt.context,xe=ve.gl,Ie=Ko.disabled,ze=new Os([xe.ONE,xe.ONE],a.aM.transparent,[!0,!0,!0,!0]);(function(Qe,Xe,nt){let qt=Qe.gl;Qe.activeTexture.set(qt.TEXTURE1),Qe.viewport.set([0,0,Xe.width/4,Xe.height/4]);let Wt=nt.heatmapFbos.get(a.aU);Wt?(qt.bindTexture(qt.TEXTURE_2D,Wt.colorAttachment.get()),Qe.bindFramebuffer.set(Wt.framebuffer)):(Wt=eh(Qe,Xe.width/4,Xe.height/4),nt.heatmapFbos.set(a.aU,Wt))})(ve,Jt,yr),ve.clear({color:a.aM.transparent});for(let Qe=0;Qe<Cr.length;Qe++){let Xe=Cr[Qe];if($t.hasRenderableParent(Xe))continue;let nt=$t.getTile(Xe),qt=nt.getBucket(yr);if(!qt)continue;let Wt=qt.programConfigurations.get(yr.id),nr=Jt.useProgram(\"heatmap\",Wt),{zoom:fr}=Jt.transform;nr.draw(ve,xe.TRIANGLES,Po.disabled,Ie,ze,Pa.disabled,ko(Xe.posMatrix,nt,fr,yr.paint.get(\"heatmap-intensity\")),null,yr.id,qt.layoutVertexBuffer,qt.indexBuffer,qt.segments,yr.paint,Jt.transform.zoom,Wt)}ve.viewport.set([0,0,Jt.width,Jt.height])})(le,Fe,Ze,ut):le.renderPass===\"translucent\"&&(function(Jt,$t){let yr=Jt.context,Cr=yr.gl;yr.setColorMode(Jt.colorModeForRenderPass());let ve=$t.heatmapFbos.get(a.aU);ve&&(yr.activeTexture.set(Cr.TEXTURE0),Cr.bindTexture(Cr.TEXTURE_2D,ve.colorAttachment.get()),yr.activeTexture.set(Cr.TEXTURE1),th(yr,$t).bind(Cr.LINEAR,Cr.CLAMP_TO_EDGE),Jt.useProgram(\"heatmapTexture\").draw(yr,Cr.TRIANGLES,Po.disabled,Ko.disabled,Jt.colorModeForRenderPass(),Pa.disabled,Ds(Jt,$t,0,1),null,$t.id,Jt.viewportBuffer,Jt.quadTriangleIndexBuffer,Jt.viewportSegments,$t.paint,Jt.transform.zoom))})(le,Ze)})(w,B,Q,ee);break;case\"line\":(function(le,Fe,Ze,ut){if(le.renderPass!==\"translucent\")return;let Mt=Ze.paint.get(\"line-opacity\"),Jt=Ze.paint.get(\"line-width\");if(Mt.constantOr(1)===0||Jt.constantOr(1)===0)return;let $t=le.depthModeForSublayer(0,Po.ReadOnly),yr=le.colorModeForRenderPass(),Cr=Ze.paint.get(\"line-dasharray\"),ve=Ze.paint.get(\"line-pattern\"),xe=ve.constantOr(1),Ie=Ze.paint.get(\"line-gradient\"),ze=Ze.getCrossfadeParameters(),Qe=xe?\"linePattern\":Cr?\"lineSDF\":Ie?\"lineGradient\":\"line\",Xe=le.context,nt=Xe.gl,qt=!0;for(let Wt of ut){let nr=Fe.getTile(Wt);if(xe&&!nr.patternsLoaded())continue;let fr=nr.getBucket(Ze);if(!fr)continue;let br=fr.programConfigurations.get(Ze.id),Fr=le.context.program.get(),Hr=le.useProgram(Qe,br),ri=qt||Hr.program!==Fr,Ci=le.style.map.terrain&&le.style.map.terrain.getTerrainData(Wt),cn=ve.constantOr(null);if(cn&&nr.imageAtlas){let Kn=nr.imageAtlas,Aa=Kn.patternPositions[cn.to.toString()],fa=Kn.patternPositions[cn.from.toString()];Aa&&fa&&br.setConstantPatternPositions(Aa,fa)}let sn=Ci?Wt:null,Ln=xe?Fl(le,nr,Ze,ze,sn):Cr?us(le,nr,Ze,Cr,ze,sn):Ie?ul(le,nr,Ze,fr.lineClipsArray.length,sn):ll(le,nr,Ze,sn);if(xe)Xe.activeTexture.set(nt.TEXTURE0),nr.imageAtlasTexture.bind(nt.LINEAR,nt.CLAMP_TO_EDGE),br.updatePaintBuffers(ze);else if(Cr&&(ri||le.lineAtlas.dirty))Xe.activeTexture.set(nt.TEXTURE0),le.lineAtlas.bind(Xe);else if(Ie){let Kn=fr.gradients[Ze.id],Aa=Kn.texture;if(Ze.gradientVersion!==Kn.version){let fa=256;if(Ze.stepInterpolant){let $a=Fe.getSource().maxzoom,Co=Wt.canonical.z===$a?Math.ceil(1<<le.transform.maxZoom-Wt.canonical.z):1;fa=a.ac(a.aV(fr.maxLineLength/a.X*1024*Co),256,Xe.maxTextureSize)}Kn.gradient=a.aW({expression:Ze.gradientExpression(),evaluationKey:\"lineProgress\",resolution:fa,image:Kn.gradient||void 0,clips:fr.lineClipsArray}),Kn.texture?Kn.texture.update(Kn.gradient):Kn.texture=new g(Xe,Kn.gradient,nt.RGBA),Kn.version=Ze.gradientVersion,Aa=Kn.texture}Xe.activeTexture.set(nt.TEXTURE0),Aa.bind(Ze.stepInterpolant?nt.NEAREST:nt.LINEAR,nt.CLAMP_TO_EDGE)}Hr.draw(Xe,nt.TRIANGLES,$t,le.stencilModeForClipping(Wt),yr,Pa.disabled,Ln,Ci,Ze.id,fr.layoutVertexBuffer,fr.indexBuffer,fr.segments,Ze.paint,le.transform.zoom,br,fr.layoutVertexBuffer2),qt=!1}})(w,B,Q,ee);break;case\"fill\":(function(le,Fe,Ze,ut){let Mt=Ze.paint.get(\"fill-color\"),Jt=Ze.paint.get(\"fill-opacity\");if(Jt.constantOr(1)===0)return;let $t=le.colorModeForRenderPass(),yr=Ze.paint.get(\"fill-pattern\"),Cr=le.opaquePassEnabledForLayer()&&!yr.constantOr(1)&&Mt.constantOr(a.aM.transparent).a===1&&Jt.constantOr(0)===1?\"opaque\":\"translucent\";if(le.renderPass===Cr){let ve=le.depthModeForSublayer(1,le.renderPass===\"opaque\"?Po.ReadWrite:Po.ReadOnly);jf(le,Fe,Ze,ut,ve,$t,!1)}if(le.renderPass===\"translucent\"&&Ze.paint.get(\"fill-antialias\")){let ve=le.depthModeForSublayer(Ze.getPaintProperty(\"fill-outline-color\")?2:0,Po.ReadOnly);jf(le,Fe,Ze,ut,ve,$t,!0)}})(w,B,Q,ee);break;case\"fill-extrusion\":(function(le,Fe,Ze,ut){let Mt=Ze.paint.get(\"fill-extrusion-opacity\");if(Mt!==0&&le.renderPass===\"translucent\"){let Jt=new Po(le.context.gl.LEQUAL,Po.ReadWrite,le.depthRangeFor3D);if(Mt!==1||Ze.paint.get(\"fill-extrusion-pattern\").constantOr(1))cc(le,Fe,Ze,ut,Jt,Ko.disabled,Os.disabled),cc(le,Fe,Ze,ut,Jt,le.stencilModeFor3D(),le.colorModeForRenderPass());else{let $t=le.colorModeForRenderPass();cc(le,Fe,Ze,ut,Jt,Ko.disabled,$t)}}})(w,B,Q,ee);break;case\"hillshade\":(function(le,Fe,Ze,ut){if(le.renderPass!==\"offscreen\"&&le.renderPass!==\"translucent\")return;let Mt=le.context,Jt=le.depthModeForSublayer(0,Po.ReadOnly),$t=le.colorModeForRenderPass(),[yr,Cr]=le.renderPass===\"translucent\"?le.stencilConfigForOverlap(ut):[{},ut];for(let ve of Cr){let xe=Fe.getTile(ve);xe.needsHillshadePrepare!==void 0&&xe.needsHillshadePrepare&&le.renderPass===\"offscreen\"?Bl(le,xe,Ze,Jt,Ko.disabled,$t):le.renderPass===\"translucent\"&&sf(le,ve,xe,Ze,Jt,yr[ve.overscaledZ],$t)}Mt.viewport.set([0,0,le.width,le.height])})(w,B,Q,ee);break;case\"raster\":(function(le,Fe,Ze,ut){if(le.renderPass!==\"translucent\"||Ze.paint.get(\"raster-opacity\")===0||!ut.length)return;let Mt=le.context,Jt=Mt.gl,$t=Fe.getSource(),yr=le.useProgram(\"raster\"),Cr=le.colorModeForRenderPass(),[ve,xe]=$t instanceof ar?[{},ut]:le.stencilConfigForOverlap(ut),Ie=xe[xe.length-1].overscaledZ,ze=!le.options.moving;for(let Qe of xe){let Xe=le.depthModeForSublayer(Qe.overscaledZ-Ie,Ze.paint.get(\"raster-opacity\")===1?Po.ReadWrite:Po.ReadOnly,Jt.LESS),nt=Fe.getTile(Qe);nt.registerFadeDuration(Ze.paint.get(\"raster-fade-duration\"));let qt=Fe.findLoadedParent(Qe,0),Wt=Fe.findLoadedSibling(Qe),nr=Kc(nt,qt||Wt||null,Fe,Ze,le.transform,le.style.map.terrain),fr,br,Fr=Ze.paint.get(\"raster-resampling\")===\"nearest\"?Jt.NEAREST:Jt.LINEAR;Mt.activeTexture.set(Jt.TEXTURE0),nt.texture.bind(Fr,Jt.CLAMP_TO_EDGE,Jt.LINEAR_MIPMAP_NEAREST),Mt.activeTexture.set(Jt.TEXTURE1),qt?(qt.texture.bind(Fr,Jt.CLAMP_TO_EDGE,Jt.LINEAR_MIPMAP_NEAREST),fr=Math.pow(2,qt.tileID.overscaledZ-nt.tileID.overscaledZ),br=[nt.tileID.canonical.x*fr%1,nt.tileID.canonical.y*fr%1]):nt.texture.bind(Fr,Jt.CLAMP_TO_EDGE,Jt.LINEAR_MIPMAP_NEAREST),nt.texture.useMipmap&&Mt.extTextureFilterAnisotropic&&le.transform.pitch>20&&Jt.texParameterf(Jt.TEXTURE_2D,Mt.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,Mt.extTextureFilterAnisotropicMax);let Hr=le.style.map.terrain&&le.style.map.terrain.getTerrainData(Qe),ri=Hr?Qe:null,Ci=ri?ri.posMatrix:le.transform.calculatePosMatrix(Qe.toUnwrapped(),ze),cn=cl(Ci,br||[0,0],fr||1,nr,Ze);$t instanceof ar?yr.draw(Mt,Jt.TRIANGLES,Xe,Ko.disabled,Cr,Pa.disabled,cn,Hr,Ze.id,$t.boundsBuffer,le.quadTriangleIndexBuffer,$t.boundsSegments):yr.draw(Mt,Jt.TRIANGLES,Xe,ve[Qe.overscaledZ],Cr,Pa.disabled,cn,Hr,Ze.id,le.rasterBoundsBuffer,le.quadTriangleIndexBuffer,le.rasterBoundsSegments)}})(w,B,Q,ee);break;case\"background\":(function(le,Fe,Ze,ut){let Mt=Ze.paint.get(\"background-color\"),Jt=Ze.paint.get(\"background-opacity\");if(Jt===0)return;let $t=le.context,yr=$t.gl,Cr=le.transform,ve=Cr.tileSize,xe=Ze.paint.get(\"background-pattern\");if(le.isPatternMissing(xe))return;let Ie=!xe&&Mt.a===1&&Jt===1&&le.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(le.renderPass!==Ie)return;let ze=Ko.disabled,Qe=le.depthModeForSublayer(0,Ie===\"opaque\"?Po.ReadWrite:Po.ReadOnly),Xe=le.colorModeForRenderPass(),nt=le.useProgram(xe?\"backgroundPattern\":\"background\"),qt=ut||Cr.coveringTiles({tileSize:ve,terrain:le.style.map.terrain});xe&&($t.activeTexture.set(yr.TEXTURE0),le.imageManager.bind(le.context));let Wt=Ze.getCrossfadeParameters();for(let nr of qt){let fr=ut?nr.posMatrix:le.transform.calculatePosMatrix(nr.toUnwrapped()),br=xe?Su(fr,Jt,le,xe,{tileID:nr,tileSize:ve},Wt):Yl(fr,Jt,Mt),Fr=le.style.map.terrain&&le.style.map.terrain.getTerrainData(nr);nt.draw($t,yr.TRIANGLES,Qe,ze,Xe,Pa.disabled,br,Fr,Ze.id,le.tileExtentBuffer,le.quadTriangleIndexBuffer,le.tileExtentSegments)}})(w,0,Q,ee);break;case\"custom\":(function(le,Fe,Ze){let ut=le.context,Mt=Ze.implementation;if(le.renderPass===\"offscreen\"){let Jt=Mt.prerender;Jt&&(le.setCustomLayerDefaults(),ut.setColorMode(le.colorModeForRenderPass()),Jt.call(Mt,ut.gl,le.transform.customLayerMatrix()),ut.setDirty(),le.setBaseState())}else if(le.renderPass===\"translucent\"){le.setCustomLayerDefaults(),ut.setColorMode(le.colorModeForRenderPass()),ut.setStencilMode(Ko.disabled);let Jt=Mt.renderingMode===\"3d\"?new Po(le.context.gl.LEQUAL,Po.ReadWrite,le.depthRangeFor3D):le.depthModeForSublayer(0,Po.ReadOnly);ut.setDepthMode(Jt),Mt.render(ut.gl,le.transform.customLayerMatrix(),{farZ:le.transform.farZ,nearZ:le.transform.nearZ,fov:le.transform._fov,modelViewProjectionMatrix:le.transform.modelViewProjectionMatrix,projectionMatrix:le.transform.projectionMatrix}),ut.setDirty(),le.setBaseState(),ut.bindFramebuffer.set(null)}})(w,0,Q)}}translatePosMatrix(w,B,Q,ee,le){if(!Q[0]&&!Q[1])return w;let Fe=le?ee===\"map\"?this.transform.angle:0:ee===\"viewport\"?-this.transform.angle:0;if(Fe){let Mt=Math.sin(Fe),Jt=Math.cos(Fe);Q=[Q[0]*Jt-Q[1]*Mt,Q[0]*Mt+Q[1]*Jt]}let Ze=[le?Q[0]:Fn(B,Q[0],this.transform.zoom),le?Q[1]:Fn(B,Q[1],this.transform.zoom),0],ut=new Float32Array(16);return a.J(ut,w,Ze),ut}saveTileTexture(w){let B=this._tileTextures[w.size[0]];B?B.push(w):this._tileTextures[w.size[0]]=[w]}getTileTexture(w){let B=this._tileTextures[w];return B&&B.length>0?B.pop():null}isPatternMissing(w){if(!w)return!1;if(!w.from||!w.to)return!0;let B=this.imageManager.getPattern(w.from.toString()),Q=this.imageManager.getPattern(w.to.toString());return!B||!Q}useProgram(w,B){this.cache=this.cache||{};let Q=w+(B?B.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\")+(this.style.map.terrain?\"/terrain\":\"\");return this.cache[Q]||(this.cache[Q]=new hn(this.context,Gn[w],B,nc[w],this._showOverdrawInspector,this.style.map.terrain)),this.cache[Q]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){let w=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(w.FUNC_ADD)}initDebugOverlayCanvas(){this.debugOverlayCanvas==null&&(this.debugOverlayCanvas=document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new g(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))}destroy(){this.debugOverlayTexture&&this.debugOverlayTexture.destroy()}overLimit(){let{drawingBufferWidth:w,drawingBufferHeight:B}=this.context.gl;return this.width!==w||this.height!==B}}class ks{constructor(w,B){this.points=w,this.planes=B}static fromInvProjectionMatrix(w,B,Q){let ee=Math.pow(2,Q),le=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((Ze=>{let ut=1/(Ze=a.af([],Ze,w))[3]/B*ee;return a.b1(Ze,Ze,[ut,ut,1/Ze[3],ut])})),Fe=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((Ze=>{let ut=(function(yr,Cr){var ve=Cr[0],xe=Cr[1],Ie=Cr[2],ze=ve*ve+xe*xe+Ie*Ie;return ze>0&&(ze=1/Math.sqrt(ze)),yr[0]=Cr[0]*ze,yr[1]=Cr[1]*ze,yr[2]=Cr[2]*ze,yr})([],(function(yr,Cr,ve){var xe=Cr[0],Ie=Cr[1],ze=Cr[2],Qe=ve[0],Xe=ve[1],nt=ve[2];return yr[0]=Ie*nt-ze*Xe,yr[1]=ze*Qe-xe*nt,yr[2]=xe*Xe-Ie*Qe,yr})([],L([],le[Ze[0]],le[Ze[1]]),L([],le[Ze[2]],le[Ze[1]]))),Mt=-((Jt=ut)[0]*($t=le[Ze[1]])[0]+Jt[1]*$t[1]+Jt[2]*$t[2]);var Jt,$t;return ut.concat(Mt)}));return new ks(le,Fe)}}class bc{constructor(w,B){this.min=w,this.max=B,this.center=(function(Q,ee,le){return Q[0]=.5*ee[0],Q[1]=.5*ee[1],Q[2]=.5*ee[2],Q})([],(function(Q,ee,le){return Q[0]=ee[0]+le[0],Q[1]=ee[1]+le[1],Q[2]=ee[2]+le[2],Q})([],this.min,this.max))}quadrant(w){let B=[w%2==0,w<2],Q=M(this.min),ee=M(this.max);for(let le=0;le<B.length;le++)Q[le]=B[le]?this.min[le]:this.center[le],ee[le]=B[le]?this.center[le]:this.max[le];return ee[2]=this.max[2],new bc(Q,ee)}distanceX(w){return Math.max(Math.min(this.max[0],w[0]),this.min[0])-w[0]}distanceY(w){return Math.max(Math.min(this.max[1],w[1]),this.min[1])-w[1]}intersects(w){let B=[[this.min[0],this.min[1],this.min[2],1],[this.max[0],this.min[1],this.min[2],1],[this.max[0],this.max[1],this.min[2],1],[this.min[0],this.max[1],this.min[2],1],[this.min[0],this.min[1],this.max[2],1],[this.max[0],this.min[1],this.max[2],1],[this.max[0],this.max[1],this.max[2],1],[this.min[0],this.max[1],this.max[2],1]],Q=!0;for(let ee=0;ee<w.planes.length;ee++){let le=w.planes[ee],Fe=0;for(let Ze=0;Ze<B.length;Ze++)a.b2(le,B[Ze])>=0&&Fe++;if(Fe===0)return 0;Fe!==B.length&&(Q=!1)}if(Q)return 2;for(let ee=0;ee<3;ee++){let le=Number.MAX_VALUE,Fe=-Number.MAX_VALUE;for(let Ze=0;Ze<w.points.length;Ze++){let ut=w.points[Ze][ee]-this.min[ee];le=Math.min(le,ut),Fe=Math.max(Fe,ut)}if(Fe<0||le>this.max[ee]-this.min[ee])return 0}return 1}}class hu{constructor(w=0,B=0,Q=0,ee=0){if(isNaN(w)||w<0||isNaN(B)||B<0||isNaN(Q)||Q<0||isNaN(ee)||ee<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=w,this.bottom=B,this.left=Q,this.right=ee}interpolate(w,B,Q){return B.top!=null&&w.top!=null&&(this.top=a.y.number(w.top,B.top,Q)),B.bottom!=null&&w.bottom!=null&&(this.bottom=a.y.number(w.bottom,B.bottom,Q)),B.left!=null&&w.left!=null&&(this.left=a.y.number(w.left,B.left,Q)),B.right!=null&&w.right!=null&&(this.right=a.y.number(w.right,B.right,Q)),this}getCenter(w,B){let Q=a.ac((this.left+w-this.right)/2,0,w),ee=a.ac((this.top+B-this.bottom)/2,0,B);return new a.P(Q,ee)}equals(w){return this.top===w.top&&this.bottom===w.bottom&&this.left===w.left&&this.right===w.right}clone(){return new hu(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}let _u=85.051129;class il{constructor(w,B,Q,ee,le){this.tileSize=512,this._renderWorldCopies=le===void 0||!!le,this._minZoom=w||0,this._maxZoom=B||22,this._minPitch=Q==null?0:Q,this._maxPitch=ee==null?60:ee,this.setMaxBounds(),this.width=0,this.height=0,this._center=new a.N(0,0),this._elevation=0,this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new hu,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={},this.minElevationForCurrentTile=0}clone(){let w=new il(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return w.apply(this),w}apply(w){this.tileSize=w.tileSize,this.latRange=w.latRange,this.lngRange=w.lngRange,this.width=w.width,this.height=w.height,this._center=w._center,this._elevation=w._elevation,this.minElevationForCurrentTile=w.minElevationForCurrentTile,this.zoom=w.zoom,this.angle=w.angle,this._fov=w._fov,this._pitch=w._pitch,this._unmodified=w._unmodified,this._edgeInsets=w._edgeInsets.clone(),this._calcMatrices()}get minZoom(){return this._minZoom}set minZoom(w){this._minZoom!==w&&(this._minZoom=w,this.zoom=Math.max(this.zoom,w))}get maxZoom(){return this._maxZoom}set maxZoom(w){this._maxZoom!==w&&(this._maxZoom=w,this.zoom=Math.min(this.zoom,w))}get minPitch(){return this._minPitch}set minPitch(w){this._minPitch!==w&&(this._minPitch=w,this.pitch=Math.max(this.pitch,w))}get maxPitch(){return this._maxPitch}set maxPitch(w){this._maxPitch!==w&&(this._maxPitch=w,this.pitch=Math.min(this.pitch,w))}get renderWorldCopies(){return this._renderWorldCopies}set renderWorldCopies(w){w===void 0?w=!0:w===null&&(w=!1),this._renderWorldCopies=w}get worldSize(){return this.tileSize*this.scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new a.P(this.width,this.height)}get bearing(){return-this.angle/Math.PI*180}set bearing(w){let B=-a.b3(w,-180,180)*Math.PI/180;this.angle!==B&&(this._unmodified=!1,this.angle=B,this._calcMatrices(),this.rotationMatrix=(function(){var Q=new a.A(4);return a.A!=Float32Array&&(Q[1]=0,Q[2]=0),Q[0]=1,Q[3]=1,Q})(),(function(Q,ee,le){var Fe=ee[0],Ze=ee[1],ut=ee[2],Mt=ee[3],Jt=Math.sin(le),$t=Math.cos(le);Q[0]=Fe*$t+ut*Jt,Q[1]=Ze*$t+Mt*Jt,Q[2]=Fe*-Jt+ut*$t,Q[3]=Ze*-Jt+Mt*$t})(this.rotationMatrix,this.rotationMatrix,this.angle))}get pitch(){return this._pitch/Math.PI*180}set pitch(w){let B=a.ac(w,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==B&&(this._unmodified=!1,this._pitch=B,this._calcMatrices())}get fov(){return this._fov/Math.PI*180}set fov(w){w=Math.max(.01,Math.min(60,w)),this._fov!==w&&(this._unmodified=!1,this._fov=w/180*Math.PI,this._calcMatrices())}get zoom(){return this._zoom}set zoom(w){let B=Math.min(Math.max(w,this.minZoom),this.maxZoom);this._zoom!==B&&(this._unmodified=!1,this._zoom=B,this.tileZoom=Math.max(0,Math.floor(B)),this.scale=this.zoomScale(B),this._constrain(),this._calcMatrices())}get center(){return this._center}set center(w){w.lat===this._center.lat&&w.lng===this._center.lng||(this._unmodified=!1,this._center=w,this._constrain(),this._calcMatrices())}get elevation(){return this._elevation}set elevation(w){w!==this._elevation&&(this._elevation=w,this._constrain(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}set padding(w){this._edgeInsets.equals(w)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,w,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this.width,this.height)}isPaddingEqual(w){return this._edgeInsets.equals(w)}interpolatePadding(w,B,Q){this._unmodified=!1,this._edgeInsets.interpolate(w,B,Q),this._constrain(),this._calcMatrices()}coveringZoomLevel(w){let B=(w.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/w.tileSize));return Math.max(0,B)}getVisibleUnwrappedCoordinates(w){let B=[new a.b4(0,w)];if(this._renderWorldCopies){let Q=this.pointCoordinate(new a.P(0,0)),ee=this.pointCoordinate(new a.P(this.width,0)),le=this.pointCoordinate(new a.P(this.width,this.height)),Fe=this.pointCoordinate(new a.P(0,this.height)),Ze=Math.floor(Math.min(Q.x,ee.x,le.x,Fe.x)),ut=Math.floor(Math.max(Q.x,ee.x,le.x,Fe.x)),Mt=1;for(let Jt=Ze-Mt;Jt<=ut+Mt;Jt++)Jt!==0&&B.push(new a.b4(Jt,w))}return B}coveringTiles(w){var B,Q;let ee=this.coveringZoomLevel(w),le=ee;if(w.minzoom!==void 0&&ee<w.minzoom)return[];w.maxzoom!==void 0&&ee>w.maxzoom&&(ee=w.maxzoom);let Fe=this.pointCoordinate(this.getCameraPoint()),Ze=a.Z.fromLngLat(this.center),ut=Math.pow(2,ee),Mt=[ut*Fe.x,ut*Fe.y,0],Jt=[ut*Ze.x,ut*Ze.y,0],$t=ks.fromInvProjectionMatrix(this.invModelViewProjectionMatrix,this.worldSize,ee),yr=w.minzoom||0;!w.terrain&&this.pitch<=60&&this._edgeInsets.top<.1&&(yr=ee);let Cr=w.terrain?2/Math.min(this.tileSize,w.tileSize)*this.tileSize:3,ve=Xe=>({aabb:new bc([Xe*ut,0,0],[(Xe+1)*ut,ut,0]),zoom:0,x:0,y:0,wrap:Xe,fullyVisible:!1}),xe=[],Ie=[],ze=ee,Qe=w.reparseOverscaled?le:ee;if(this._renderWorldCopies)for(let Xe=1;Xe<=3;Xe++)xe.push(ve(-Xe)),xe.push(ve(Xe));for(xe.push(ve(0));xe.length>0;){let Xe=xe.pop(),nt=Xe.x,qt=Xe.y,Wt=Xe.fullyVisible;if(!Wt){let Hr=Xe.aabb.intersects($t);if(Hr===0)continue;Wt=Hr===2}let nr=w.terrain?Mt:Jt,fr=Xe.aabb.distanceX(nr),br=Xe.aabb.distanceY(nr),Fr=Math.max(Math.abs(fr),Math.abs(br));if(Xe.zoom===ze||Fr>Cr+(1<<ze-Xe.zoom)-2&&Xe.zoom>=yr){let Hr=ze-Xe.zoom,ri=Mt[0]-.5-(nt<<Hr),Ci=Mt[1]-.5-(qt<<Hr);Ie.push({tileID:new a.S(Xe.zoom===ze?Qe:Xe.zoom,Xe.wrap,Xe.zoom,nt,qt),distanceSq:x([Jt[0]-.5-nt,Jt[1]-.5-qt]),tileDistanceToCamera:Math.sqrt(ri*ri+Ci*Ci)})}else for(let Hr=0;Hr<4;Hr++){let ri=(nt<<1)+Hr%2,Ci=(qt<<1)+(Hr>>1),cn=Xe.zoom+1,sn=Xe.aabb.quadrant(Hr);if(w.terrain){let Ln=new a.S(cn,Xe.wrap,cn,ri,Ci),Kn=w.terrain.getMinMaxElevation(Ln),Aa=(B=Kn.minElevation)!==null&&B!==void 0?B:this.elevation,fa=(Q=Kn.maxElevation)!==null&&Q!==void 0?Q:this.elevation;sn=new bc([sn.min[0],sn.min[1],Aa],[sn.max[0],sn.max[1],fa])}xe.push({aabb:sn,zoom:cn,x:ri,y:Ci,wrap:Xe.wrap,fullyVisible:Wt})}}return Ie.sort(((Xe,nt)=>Xe.distanceSq-nt.distanceSq)).map((Xe=>Xe.tileID))}resize(w,B){this.width=w,this.height=B,this.pixelsToGLUnits=[2/w,-2/B],this._constrain(),this._calcMatrices()}get unmodified(){return this._unmodified}zoomScale(w){return Math.pow(2,w)}scaleZoom(w){return Math.log(w)/Math.LN2}project(w){let B=a.ac(w.lat,-85.051129,_u);return new a.P(a.O(w.lng)*this.worldSize,a.Q(B)*this.worldSize)}unproject(w){return new a.Z(w.x/this.worldSize,w.y/this.worldSize).toLngLat()}get point(){return this.project(this.center)}getCameraPosition(){return{lngLat:this.pointLocation(this.getCameraPoint()),altitude:Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter+this.elevation}}recalculateZoom(w){let B=this.elevation,Q=Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter,ee=this.pointLocation(this.centerPoint,w),le=w.getElevationForLngLatZoom(ee,this.tileZoom);if(!(this.elevation-le))return;let Fe=Q+B-le,Ze=Math.cos(this._pitch)*this.cameraToCenterDistance/Fe/a.b5(1,ee.lat),ut=this.scaleZoom(Ze/this.tileSize);this._elevation=le,this._center=ee,this.zoom=ut}setLocationAtPoint(w,B){let Q=this.pointCoordinate(B),ee=this.pointCoordinate(this.centerPoint),le=this.locationCoordinate(w),Fe=new a.Z(le.x-(Q.x-ee.x),le.y-(Q.y-ee.y));this.center=this.coordinateLocation(Fe),this._renderWorldCopies&&(this.center=this.center.wrap())}locationPoint(w,B){return B?this.coordinatePoint(this.locationCoordinate(w),B.getElevationForLngLatZoom(w,this.tileZoom),this.pixelMatrix3D):this.coordinatePoint(this.locationCoordinate(w))}pointLocation(w,B){return this.coordinateLocation(this.pointCoordinate(w,B))}locationCoordinate(w){return a.Z.fromLngLat(w)}coordinateLocation(w){return w&&w.toLngLat()}pointCoordinate(w,B){if(B){let yr=B.pointCoordinate(w);if(yr!=null)return yr}let Q=[w.x,w.y,0,1],ee=[w.x,w.y,1,1];a.af(Q,Q,this.pixelMatrixInverse),a.af(ee,ee,this.pixelMatrixInverse);let le=Q[3],Fe=ee[3],Ze=Q[1]/le,ut=ee[1]/Fe,Mt=Q[2]/le,Jt=ee[2]/Fe,$t=Mt===Jt?0:(0-Mt)/(Jt-Mt);return new a.Z(a.y.number(Q[0]/le,ee[0]/Fe,$t)/this.worldSize,a.y.number(Ze,ut,$t)/this.worldSize)}coordinatePoint(w,B=0,Q=this.pixelMatrix){let ee=[w.x*this.worldSize,w.y*this.worldSize,B,1];return a.af(ee,ee,Q),new a.P(ee[0]/ee[3],ee[1]/ee[3])}getBounds(){let w=Math.max(0,this.height/2-this.getHorizon());return new ce().extend(this.pointLocation(new a.P(0,w))).extend(this.pointLocation(new a.P(this.width,w))).extend(this.pointLocation(new a.P(this.width,this.height))).extend(this.pointLocation(new a.P(0,this.height)))}getMaxBounds(){return this.latRange&&this.latRange.length===2&&this.lngRange&&this.lngRange.length===2?new ce([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null}getHorizon(){return Math.tan(Math.PI/2-this._pitch)*this.cameraToCenterDistance*.85}setMaxBounds(w){w?(this.lngRange=[w.getWest(),w.getEast()],this.latRange=[w.getSouth(),w.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-85.051129,_u])}calculateTileMatrix(w){let B=w.canonical,Q=this.worldSize/this.zoomScale(B.z),ee=B.x+Math.pow(2,B.z)*w.wrap,le=a.an(new Float64Array(16));return a.J(le,le,[ee*Q,B.y*Q,0]),a.K(le,le,[Q/a.X,Q/a.X,1]),le}calculatePosMatrix(w,B=!1){let Q=w.key,ee=B?this._alignedPosMatrixCache:this._posMatrixCache;if(ee[Q])return ee[Q];let le=this.calculateTileMatrix(w);return a.L(le,B?this.alignedModelViewProjectionMatrix:this.modelViewProjectionMatrix,le),ee[Q]=new Float32Array(le),ee[Q]}calculateFogMatrix(w){let B=w.key,Q=this._fogMatrixCache;if(Q[B])return Q[B];let ee=this.calculateTileMatrix(w);return a.L(ee,this.fogMatrix,ee),Q[B]=new Float32Array(ee),Q[B]}customLayerMatrix(){return this.mercatorMatrix.slice()}getConstrained(w,B){B=a.ac(+B,this.minZoom,this.maxZoom);let Q={center:new a.N(w.lng,w.lat),zoom:B},ee=this.lngRange;if(!this._renderWorldCopies&&ee===null){let Xe=179.9999999999;ee=[-Xe,Xe]}let le=this.tileSize*this.zoomScale(Q.zoom),Fe=0,Ze=le,ut=0,Mt=le,Jt=0,$t=0,{x:yr,y:Cr}=this.size;if(this.latRange){let Xe=this.latRange;Fe=a.Q(Xe[1])*le,Ze=a.Q(Xe[0])*le,Ze-Fe<Cr&&(Jt=Cr/(Ze-Fe))}ee&&(ut=a.b3(a.O(ee[0])*le,0,le),Mt=a.b3(a.O(ee[1])*le,0,le),Mt<ut&&(Mt+=le),Mt-ut<yr&&($t=yr/(Mt-ut)));let{x:ve,y:xe}=this.project.call({worldSize:le},w),Ie,ze,Qe=Math.max($t||0,Jt||0);if(Qe){let Xe=new a.P($t?(Mt+ut)/2:ve,Jt?(Ze+Fe)/2:xe);return Q.center=this.unproject.call({worldSize:le},Xe).wrap(),Q.zoom+=this.scaleZoom(Qe),Q}if(this.latRange){let Xe=Cr/2;xe-Xe<Fe&&(ze=Fe+Xe),xe+Xe>Ze&&(ze=Ze-Xe)}if(ee){let Xe=(ut+Mt)/2,nt=ve;this._renderWorldCopies&&(nt=a.b3(ve,Xe-le/2,Xe+le/2));let qt=yr/2;nt-qt<ut&&(Ie=ut+qt),nt+qt>Mt&&(Ie=Mt-qt)}if(Ie!==void 0||ze!==void 0){let Xe=new a.P(Ie!=null?Ie:ve,ze!=null?ze:xe);Q.center=this.unproject.call({worldSize:le},Xe).wrap()}return Q}_constrain(){if(!this.center||!this.width||!this.height||this._constraining)return;this._constraining=!0;let w=this._unmodified,{center:B,zoom:Q}=this.getConstrained(this.center,this.zoom);this.center=B,this.zoom=Q,this._unmodified=w,this._constraining=!1}_calcMatrices(){if(!this.height)return;let w=this.centerOffset,B=this.point.x,Q=this.point.y;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height,this._pixelPerMeter=a.b5(1,this.center.lat)*this.worldSize;let ee=a.an(new Float64Array(16));a.K(ee,ee,[this.width/2,-this.height/2,1]),a.J(ee,ee,[1,-1,0]),this.labelPlaneMatrix=ee,ee=a.an(new Float64Array(16)),a.K(ee,ee,[1,-1,1]),a.J(ee,ee,[-1,-1,0]),a.K(ee,ee,[2/this.width,2/this.height,1]),this.glCoordMatrix=ee;let le=this.cameraToCenterDistance+this._elevation*this._pixelPerMeter/Math.cos(this._pitch),Fe=Math.min(this.elevation,this.minElevationForCurrentTile),Ze=le-Fe*this._pixelPerMeter/Math.cos(this._pitch),ut=Fe<0?Ze:le,Mt=Math.PI/2+this._pitch,Jt=this._fov*(.5+w.y/this.height),$t=Math.sin(Jt)*ut/Math.sin(a.ac(Math.PI-Mt-Jt,.01,Math.PI-.01)),yr=this.getHorizon(),Cr=2*Math.atan(yr/this.cameraToCenterDistance)*(.5+w.y/(2*yr)),ve=Math.sin(Cr)*ut/Math.sin(a.ac(Math.PI-Mt-Cr,.01,Math.PI-.01)),xe=Math.min($t,ve);this.farZ=1.01*(Math.cos(Math.PI/2-this._pitch)*xe+ut),this.nearZ=this.height/50,ee=new Float64Array(16),a.b6(ee,this._fov,this.width/this.height,this.nearZ,this.farZ),ee[8]=2*-w.x/this.width,ee[9]=2*w.y/this.height,this.projectionMatrix=a.ae(ee),a.K(ee,ee,[1,-1,1]),a.J(ee,ee,[0,0,-this.cameraToCenterDistance]),a.b7(ee,ee,this._pitch),a.ad(ee,ee,this.angle),a.J(ee,ee,[-B,-Q,0]),this.mercatorMatrix=a.K([],ee,[this.worldSize,this.worldSize,this.worldSize]),a.K(ee,ee,[1,1,this._pixelPerMeter]),this.pixelMatrix=a.L(new Float64Array(16),this.labelPlaneMatrix,ee),a.J(ee,ee,[0,0,-this.elevation]),this.modelViewProjectionMatrix=ee,this.invModelViewProjectionMatrix=a.as([],ee),this.fogMatrix=new Float64Array(16),a.b6(this.fogMatrix,this._fov,this.width/this.height,le,this.farZ),this.fogMatrix[8]=2*-w.x/this.width,this.fogMatrix[9]=2*w.y/this.height,a.K(this.fogMatrix,this.fogMatrix,[1,-1,1]),a.J(this.fogMatrix,this.fogMatrix,[0,0,-this.cameraToCenterDistance]),a.b7(this.fogMatrix,this.fogMatrix,this._pitch),a.ad(this.fogMatrix,this.fogMatrix,this.angle),a.J(this.fogMatrix,this.fogMatrix,[-B,-Q,0]),a.K(this.fogMatrix,this.fogMatrix,[1,1,this._pixelPerMeter]),a.J(this.fogMatrix,this.fogMatrix,[0,0,-this.elevation]),this.pixelMatrix3D=a.L(new Float64Array(16),this.labelPlaneMatrix,ee);let Ie=this.width%2/2,ze=this.height%2/2,Qe=Math.cos(this.angle),Xe=Math.sin(this.angle),nt=B-Math.round(B)+Qe*Ie+Xe*ze,qt=Q-Math.round(Q)+Qe*ze+Xe*Ie,Wt=new Float64Array(ee);if(a.J(Wt,Wt,[nt>.5?nt-1:nt,qt>.5?qt-1:qt,0]),this.alignedModelViewProjectionMatrix=Wt,ee=a.as(new Float64Array(16),this.pixelMatrix),!ee)throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=ee,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={}}maxPitchScaleFactor(){if(!this.pixelMatrixInverse)return 1;let w=this.pointCoordinate(new a.P(0,0)),B=[w.x*this.worldSize,w.y*this.worldSize,0,1];return a.af(B,B,this.pixelMatrix)[3]/this.cameraToCenterDistance}getCameraPoint(){let w=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new a.P(0,w))}getCameraQueryGeometry(w){let B=this.getCameraPoint();if(w.length===1)return[w[0],B];{let Q=B.x,ee=B.y,le=B.x,Fe=B.y;for(let Ze of w)Q=Math.min(Q,Ze.x),ee=Math.min(ee,Ze.y),le=Math.max(le,Ze.x),Fe=Math.max(Fe,Ze.y);return[new a.P(Q,ee),new a.P(le,ee),new a.P(le,Fe),new a.P(Q,Fe),new a.P(Q,ee)]}}lngLatToCameraDepth(w,B){let Q=this.locationCoordinate(w),ee=[Q.x*this.worldSize,Q.y*this.worldSize,B,1];return a.af(ee,ee,this.modelViewProjectionMatrix),ee[2]/ee[3]}}function nh(ue,w){let B,Q=!1,ee=null,le=null,Fe=()=>{ee=null,Q&&(ue.apply(le,B),ee=setTimeout(Fe,w),Q=!1)};return(...Ze)=>(Q=!0,le=this,B=Ze,ee||Fe(),ee)}class Mh{constructor(w){this._getCurrentHash=()=>{let B=window.location.hash.replace(\"#\",\"\");if(this._hashName){let Q;return B.split(\"&\").map((ee=>ee.split(\"=\"))).forEach((ee=>{ee[0]===this._hashName&&(Q=ee)})),(Q&&Q[1]||\"\").split(\"/\")}return B.split(\"/\")},this._onHashChange=()=>{let B=this._getCurrentHash();if(B.length>=3&&!B.some((Q=>isNaN(Q)))){let Q=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(B[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+B[2],+B[1]],zoom:+B[0],bearing:Q,pitch:+(B[4]||0)}),!0}return!1},this._updateHashUnthrottled=()=>{let B=window.location.href.replace(/(#.*)?$/,this.getHashString());window.history.replaceState(window.history.state,null,B)},this._removeHash=()=>{let B=this._getCurrentHash();if(B.length===0)return;let Q=B.join(\"/\"),ee=Q;ee.split(\"&\").length>0&&(ee=ee.split(\"&\")[0]),this._hashName&&(ee=`${this._hashName}=${Q}`);let le=window.location.hash.replace(ee,\"\");le.startsWith(\"#&\")?le=le.slice(0,1)+le.slice(2):le===\"#\"&&(le=\"\");let Fe=window.location.href.replace(/(#.+)?$/,le);Fe=Fe.replace(\"&&\",\"&\"),window.history.replaceState(window.history.state,null,Fe)},this._updateHash=nh(this._updateHashUnthrottled,300),this._hashName=w&&encodeURIComponent(w)}addTo(w){return this._map=w,addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this}remove(){return removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),this._removeHash(),delete this._map,this}getHashString(w){let B=this._map.getCenter(),Q=Math.round(100*this._map.getZoom())/100,ee=Math.ceil((Q*Math.LN2+Math.log(512/360/.5))/Math.LN10),le=Math.pow(10,ee),Fe=Math.round(B.lng*le)/le,Ze=Math.round(B.lat*le)/le,ut=this._map.getBearing(),Mt=this._map.getPitch(),Jt=\"\";if(Jt+=w?`/${Fe}/${Ze}/${Q}`:`${Q}/${Ze}/${Fe}`,(ut||Mt)&&(Jt+=\"/\"+Math.round(10*ut)/10),Mt&&(Jt+=`/${Math.round(Mt)}`),this._hashName){let $t=this._hashName,yr=!1,Cr=window.location.hash.slice(1).split(\"&\").map((ve=>{let xe=ve.split(\"=\")[0];return xe===$t?(yr=!0,`${xe}=${Jt}`):ve})).filter((ve=>ve));return yr||Cr.push(`${$t}=${Jt}`),`#${Cr.join(\"&\")}`}return`#${Jt}`}}let zu={linearity:.3,easing:a.b8(0,0,.3,1)},Fc=a.e({deceleration:2500,maxSpeed:1400},zu),wc=a.e({deceleration:20,maxSpeed:1400},zu),bd=a.e({deceleration:1e3,maxSpeed:360},zu),xf=a.e({deceleration:1e3,maxSpeed:90},zu);class Pf{constructor(w){this._map=w,this.clear()}clear(){this._inertiaBuffer=[]}record(w){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:u.now(),settings:w})}_drainInertiaBuffer(){let w=this._inertiaBuffer,B=u.now();for(;w.length>0&&B-w[0].time>160;)w.shift()}_onMoveEnd(w){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;let B={zoom:0,bearing:0,pitch:0,pan:new a.P(0,0),pinchAround:void 0,around:void 0};for(let{settings:le}of this._inertiaBuffer)B.zoom+=le.zoomDelta||0,B.bearing+=le.bearingDelta||0,B.pitch+=le.pitchDelta||0,le.panDelta&&B.pan._add(le.panDelta),le.around&&(B.around=le.around),le.pinchAround&&(B.pinchAround=le.pinchAround);let Q=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,ee={};if(B.pan.mag()){let le=bf(B.pan.mag(),Q,a.e({},Fc,w||{}));ee.offset=B.pan.mult(le.amount/B.pan.mag()),ee.center=this._map.transform.center,qu(ee,le)}if(B.zoom){let le=bf(B.zoom,Q,wc);ee.zoom=this._map.transform.zoom+le.amount,qu(ee,le)}if(B.bearing){let le=bf(B.bearing,Q,bd);ee.bearing=this._map.transform.bearing+a.ac(le.amount,-179,179),qu(ee,le)}if(B.pitch){let le=bf(B.pitch,Q,xf);ee.pitch=this._map.transform.pitch+le.amount,qu(ee,le)}if(ee.zoom||ee.bearing){let le=B.pinchAround===void 0?B.around:B.pinchAround;ee.around=le?this._map.unproject(le):this._map.getCenter()}return this.clear(),a.e(ee,{noMoveStart:!0})}}function qu(ue,w){(!ue.duration||ue.duration<w.duration)&&(ue.duration=w.duration,ue.easing=w.easing)}function bf(ue,w,B){let{maxSpeed:Q,linearity:ee,deceleration:le}=B,Fe=a.ac(ue*ee/(w/1e3),-Q,Q),Ze=Math.abs(Fe)/(le*ee);return{easing:B.easing,duration:1e3*Ze,amount:Fe*(Ze/2)}}class jl extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q,ee={}){let le=c.mousePos(B.getCanvas(),Q),Fe=B.unproject(le);super(w,a.e({point:le,lngLat:Fe,originalEvent:Q},ee)),this._defaultPrevented=!1,this.target=B}}class uf extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q){let ee=w===\"touchend\"?Q.changedTouches:Q.touches,le=c.touchPos(B.getCanvasContainer(),ee),Fe=le.map((ut=>B.unproject(ut))),Ze=le.reduce(((ut,Mt,Jt,$t)=>ut.add(Mt.div($t.length))),new a.P(0,0));super(w,{points:le,point:Ze,lngLats:Fe,lngLat:B.unproject(Ze),originalEvent:Q}),this._defaultPrevented=!1}}class Xh extends a.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(w,B,Q){super(w,{originalEvent:Q}),this._defaultPrevented=!1}}class If{constructor(w,B){this._map=w,this._clickTolerance=B.clickTolerance}reset(){delete this._mousedownPos}wheel(w){return this._firePreventable(new Xh(w.type,this._map,w))}mousedown(w,B){return this._mousedownPos=B,this._firePreventable(new jl(w.type,this._map,w))}mouseup(w){this._map.fire(new jl(w.type,this._map,w))}click(w,B){this._mousedownPos&&this._mousedownPos.dist(B)>=this._clickTolerance||this._map.fire(new jl(w.type,this._map,w))}dblclick(w){return this._firePreventable(new jl(w.type,this._map,w))}mouseover(w){this._map.fire(new jl(w.type,this._map,w))}mouseout(w){this._map.fire(new jl(w.type,this._map,w))}touchstart(w){return this._firePreventable(new uf(w.type,this._map,w))}touchmove(w){this._map.fire(new uf(w.type,this._map,w))}touchend(w){this._map.fire(new uf(w.type,this._map,w))}touchcancel(w){this._map.fire(new uf(w.type,this._map,w))}_firePreventable(w){if(this._map.fire(w),w.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Cs{constructor(w){this._map=w}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(w){this._map.fire(new jl(w.type,this._map,w))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new jl(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(w){this._delayContextMenu?this._contextMenuEvent=w:this._ignoreContextMenu||this._map.fire(new jl(w.type,this._map,w)),this._map.listens(\"contextmenu\")&&w.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class du{constructor(w){this._map=w}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(w){return this.transform.pointLocation(a.P.convert(w),this._map.terrain)}}class ku{constructor(w,B){this._map=w,this._tr=new du(w),this._el=w.getCanvasContainer(),this._container=w.getContainer(),this._clickTolerance=B.clickTolerance||1}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(w,B){this.isEnabled()&&w.shiftKey&&w.button===0&&(c.disableDrag(),this._startPos=this._lastPos=B,this._active=!0)}mousemoveWindow(w,B){if(!this._active)return;let Q=B;if(this._lastPos.equals(Q)||!this._box&&Q.dist(this._startPos)<this._clickTolerance)return;let ee=this._startPos;this._lastPos=Q,this._box||(this._box=c.create(\"div\",\"maplibregl-boxzoom\",this._container),this._container.classList.add(\"maplibregl-crosshair\"),this._fireEvent(\"boxzoomstart\",w));let le=Math.min(ee.x,Q.x),Fe=Math.max(ee.x,Q.x),Ze=Math.min(ee.y,Q.y),ut=Math.max(ee.y,Q.y);c.setTransform(this._box,`translate(${le}px,${Ze}px)`),this._box.style.width=Fe-le+\"px\",this._box.style.height=ut-Ze+\"px\"}mouseupWindow(w,B){if(!this._active||w.button!==0)return;let Q=this._startPos,ee=B;if(this.reset(),c.suppressClick(),Q.x!==ee.x||Q.y!==ee.y)return this._map.fire(new a.k(\"boxzoomend\",{originalEvent:w})),{cameraAnimation:le=>le.fitScreenCoordinates(Q,ee,this._tr.bearing,{linear:!0})};this._fireEvent(\"boxzoomcancel\",w)}keydown(w){this._active&&w.keyCode===27&&(this.reset(),this._fireEvent(\"boxzoomcancel\",w))}reset(){this._active=!1,this._container.classList.remove(\"maplibregl-crosshair\"),this._box&&(c.remove(this._box),this._box=null),c.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(w,B){return this._map.fire(new a.k(w,{originalEvent:B}))}}function Xf(ue,w){if(ue.length!==w.length)throw new Error(`The number of touches and points are not equal - touches ${ue.length}, points ${w.length}`);let B={};for(let Q=0;Q<ue.length;Q++)B[ue[Q].identifier]=w[Q];return B}class Us{constructor(w){this.reset(),this.numTouches=w.numTouches}reset(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1}touchstart(w,B,Q){(this.centroid||Q.length>this.numTouches)&&(this.aborted=!0),this.aborted||(this.startTime===void 0&&(this.startTime=w.timeStamp),Q.length===this.numTouches&&(this.centroid=(function(ee){let le=new a.P(0,0);for(let Fe of ee)le._add(Fe);return le.div(ee.length)})(B),this.touches=Xf(Q,B)))}touchmove(w,B,Q){if(this.aborted||!this.centroid)return;let ee=Xf(Q,B);for(let le in this.touches){let Fe=ee[le];(!Fe||Fe.dist(this.touches[le])>30)&&(this.aborted=!0)}}touchend(w,B,Q){if((!this.centroid||w.timeStamp-this.startTime>500)&&(this.aborted=!0),Q.length===0){let ee=!this.aborted&&this.centroid;if(this.reset(),ee)return ee}}}class wf{constructor(w){this.singleTap=new Us(w),this.numTaps=w.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(w,B,Q){this.singleTap.touchstart(w,B,Q)}touchmove(w,B,Q){this.singleTap.touchmove(w,B,Q)}touchend(w,B,Q){let ee=this.singleTap.touchend(w,B,Q);if(ee){let le=w.timeStamp-this.lastTime<500,Fe=!this.lastTap||this.lastTap.dist(ee)<30;if(le&&Fe||this.reset(),this.count++,this.lastTime=w.timeStamp,this.lastTap=ee,this.count===this.numTaps)return this.reset(),ee}}}class zc{constructor(w){this._tr=new du(w),this._zoomIn=new wf({numTouches:1,numTaps:2}),this._zoomOut=new wf({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(w,B,Q){this._zoomIn.touchstart(w,B,Q),this._zoomOut.touchstart(w,B,Q)}touchmove(w,B,Q){this._zoomIn.touchmove(w,B,Q),this._zoomOut.touchmove(w,B,Q)}touchend(w,B,Q){let ee=this._zoomIn.touchend(w,B,Q),le=this._zoomOut.touchend(w,B,Q),Fe=this._tr;return ee?(this._active=!0,w.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:Ze=>Ze.easeTo({duration:300,zoom:Fe.zoom+1,around:Fe.unproject(ee)},{originalEvent:w})}):le?(this._active=!0,w.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:Ze=>Ze.easeTo({duration:300,zoom:Fe.zoom-1,around:Fe.unproject(le)},{originalEvent:w})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class Wu{constructor(w){this._enabled=!!w.enable,this._moveStateManager=w.moveStateManager,this._clickTolerance=w.clickTolerance||1,this._moveFunction=w.move,this._activateOnStart=!!w.activateOnStart,w.assignEvents(this),this.reset()}reset(w){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(w)}_move(...w){let B=this._moveFunction(...w);if(B.bearingDelta||B.pitchDelta||B.around||B.panDelta)return this._active=!0,B}dragStart(w,B){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(w)&&(this._moveStateManager.startMove(w),this._lastPoint=B.length?B[0]:B,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(w,B){if(!this.isEnabled())return;let Q=this._lastPoint;if(!Q)return;if(w.preventDefault(),!this._moveStateManager.isValidMoveEvent(w))return void this.reset(w);let ee=B.length?B[0]:B;return!this._moved&&ee.dist(Q)<this._clickTolerance?void 0:(this._moved=!0,this._lastPoint=ee,this._move(Q,ee))}dragEnd(w){this.isEnabled()&&this._lastPoint&&this._moveStateManager.isValidEndEvent(w)&&(this._moved&&c.suppressClick(),this.reset(w))}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}getClickTolerance(){return this._clickTolerance}}let Rf={0:1,2:2};class Xu{constructor(w){this._correctEvent=w.checkCorrectEvent}startMove(w){let B=c.mouseButton(w);this._eventButton=B}endMove(w){delete this._eventButton}isValidStartEvent(w){return this._correctEvent(w)}isValidMoveEvent(w){return!(function(B,Q){let ee=Rf[Q];return B.buttons===void 0||(B.buttons&ee)!==ee})(w,this._eventButton)}isValidEndEvent(w){return c.mouseButton(w)===this._eventButton}}class cf{constructor(){this._firstTouch=void 0}_isOneFingerTouch(w){return w.targetTouches.length===1}_isSameTouchEvent(w){return w.targetTouches[0].identifier===this._firstTouch}startMove(w){this._firstTouch=w.targetTouches[0].identifier}endMove(w){delete this._firstTouch}isValidStartEvent(w){return this._isOneFingerTouch(w)}isValidMoveEvent(w){return this._isOneFingerTouch(w)&&this._isSameTouchEvent(w)}isValidEndEvent(w){return this._isOneFingerTouch(w)&&this._isSameTouchEvent(w)}}let Zf=ue=>{ue.mousedown=ue.dragStart,ue.mousemoveWindow=ue.dragMove,ue.mouseup=ue.dragEnd,ue.contextmenu=w=>{w.preventDefault()}},Wl=({enable:ue,clickTolerance:w,bearingDegreesPerPixelMoved:B=.8})=>{let Q=new Xu({checkCorrectEvent:ee=>c.mouseButton(ee)===0&&ee.ctrlKey||c.mouseButton(ee)===2});return new Wu({clickTolerance:w,move:(ee,le)=>({bearingDelta:(le.x-ee.x)*B}),moveStateManager:Q,enable:ue,assignEvents:Zf})},ah=({enable:ue,clickTolerance:w,pitchDegreesPerPixelMoved:B=-.5})=>{let Q=new Xu({checkCorrectEvent:ee=>c.mouseButton(ee)===0&&ee.ctrlKey||c.mouseButton(ee)===2});return new Wu({clickTolerance:w,move:(ee,le)=>({pitchDelta:(le.y-ee.y)*B}),moveStateManager:Q,enable:ue,assignEvents:Zf})};class Zu{constructor(w,B){this._clickTolerance=w.clickTolerance||1,this._map=B,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new a.P(0,0)}_shouldBePrevented(w){return w<(this._map.cooperativeGestures.isEnabled()?2:1)}touchstart(w,B,Q){return this._calculateTransform(w,B,Q)}touchmove(w,B,Q){if(this._active){if(!this._shouldBePrevented(Q.length))return w.preventDefault(),this._calculateTransform(w,B,Q);this._map.cooperativeGestures.notifyGestureBlocked(\"touch_pan\",w)}}touchend(w,B,Q){this._calculateTransform(w,B,Q),this._active&&this._shouldBePrevented(Q.length)&&this.reset()}touchcancel(){this.reset()}_calculateTransform(w,B,Q){Q.length>0&&(this._active=!0);let ee=Xf(Q,B),le=new a.P(0,0),Fe=new a.P(0,0),Ze=0;for(let Mt in ee){let Jt=ee[Mt],$t=this._touches[Mt];$t&&(le._add(Jt),Fe._add(Jt.sub($t)),Ze++,ee[Mt]=Jt)}if(this._touches=ee,this._shouldBePrevented(Ze)||!Fe.mag())return;let ut=Fe.div(Ze);return this._sum._add(ut),this._sum.mag()<this._clickTolerance?void 0:{around:le.div(Ze),panDelta:ut}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class qc{constructor(){this.reset()}reset(){this._active=!1,delete this._firstTwoTouches}touchstart(w,B,Q){this._firstTwoTouches||Q.length<2||(this._firstTwoTouches=[Q[0].identifier,Q[1].identifier],this._start([B[0],B[1]]))}touchmove(w,B,Q){if(!this._firstTwoTouches)return;w.preventDefault();let[ee,le]=this._firstTwoTouches,Fe=Tc(Q,B,ee),Ze=Tc(Q,B,le);if(!Fe||!Ze)return;let ut=this._aroundCenter?null:Fe.add(Ze).div(2);return this._move([Fe,Ze],ut,w)}touchend(w,B,Q){if(!this._firstTwoTouches)return;let[ee,le]=this._firstTwoTouches,Fe=Tc(Q,B,ee),Ze=Tc(Q,B,le);Fe&&Ze||(this._active&&c.suppressClick(),this.reset())}touchcancel(){this.reset()}enable(w){this._enabled=!0,this._aroundCenter=!!w&&w.around===\"center\"}disable(){this._enabled=!1,this.reset()}isEnabled(){return!!this._enabled}isActive(){return!!this._active}}function Tc(ue,w,B){for(let Q=0;Q<ue.length;Q++)if(ue[Q].identifier===B)return w[Q]}function Tl(ue,w){return Math.log(ue/w)/Math.LN2}class vu extends qc{reset(){super.reset(),delete this._distance,delete this._startDistance}_start(w){this._startDistance=this._distance=w[0].dist(w[1])}_move(w,B){let Q=this._distance;if(this._distance=w[0].dist(w[1]),this._active||!(Math.abs(Tl(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:Tl(this._distance,Q),pinchAround:B}}}function Oc(ue,w){return 180*ue.angleWith(w)/Math.PI}class ff extends qc{reset(){super.reset(),delete this._minDiameter,delete this._startVector,delete this._vector}_start(w){this._startVector=this._vector=w[0].sub(w[1]),this._minDiameter=w[0].dist(w[1])}_move(w,B,Q){let ee=this._vector;if(this._vector=w[0].sub(w[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:Oc(this._vector,ee),pinchAround:B}}_isBelowThreshold(w){this._minDiameter=Math.min(this._minDiameter,w.mag());let B=25/(Math.PI*this._minDiameter)*360,Q=Oc(w,this._startVector);return Math.abs(Q)<B}}function fc(ue){return Math.abs(ue.y)>Math.abs(ue.x)}class Bc extends qc{constructor(w){super(),this._currentTouchCount=0,this._map=w}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(w,B,Q){super.touchstart(w,B,Q),this._currentTouchCount=Q.length}_start(w){this._lastPoints=w,fc(w[0].sub(w[1]))&&(this._valid=!1)}_move(w,B,Q){if(this._map.cooperativeGestures.isEnabled()&&this._currentTouchCount<3)return;let ee=w[0].sub(this._lastPoints[0]),le=w[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(ee,le,Q.timeStamp),this._valid?(this._lastPoints=w,this._active=!0,{pitchDelta:(ee.y+le.y)/2*-.5}):void 0}gestureBeginsVertically(w,B,Q){if(this._valid!==void 0)return this._valid;let ee=w.mag()>=2,le=B.mag()>=2;if(!ee&&!le)return;if(!ee||!le)return this._firstMove===void 0&&(this._firstMove=Q),Q-this._firstMove<100&&void 0;let Fe=w.y>0==B.y>0;return fc(w)&&fc(B)&&Fe}}let kt={panStep:100,bearingStep:15,pitchStep:10};class Zt{constructor(w){this._tr=new du(w);let B=kt;this._panStep=B.panStep,this._bearingStep=B.bearingStep,this._pitchStep=B.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(w){if(w.altKey||w.ctrlKey||w.metaKey)return;let B=0,Q=0,ee=0,le=0,Fe=0;switch(w.keyCode){case 61:case 107:case 171:case 187:B=1;break;case 189:case 109:case 173:B=-1;break;case 37:w.shiftKey?Q=-1:(w.preventDefault(),le=-1);break;case 39:w.shiftKey?Q=1:(w.preventDefault(),le=1);break;case 38:w.shiftKey?ee=1:(w.preventDefault(),Fe=-1);break;case 40:w.shiftKey?ee=-1:(w.preventDefault(),Fe=1);break;default:return}return this._rotationDisabled&&(Q=0,ee=0),{cameraAnimation:Ze=>{let ut=this._tr;Ze.easeTo({duration:300,easeId:\"keyboardHandler\",easing:Er,zoom:B?Math.round(ut.zoom)+B*(w.shiftKey?2:1):ut.zoom,bearing:ut.bearing+Q*this._bearingStep,pitch:ut.pitch+ee*this._pitchStep,offset:[-le*this._panStep,-Fe*this._panStep],center:ut.center},{originalEvent:w})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function Er(ue){return ue*(2-ue)}let xr=4.000244140625;class Kr{constructor(w,B){this._onTimeout=Q=>{this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(Q)},this._map=w,this._tr=new du(w),this._triggerRenderFrame=B,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222}setZoomRate(w){this._defaultZoomRate=w}setWheelZoomRate(w){this._wheelZoomRate=w}isEnabled(){return!!this._enabled}isActive(){return!!this._active||this._finishTimeout!==void 0}isZooming(){return!!this._zooming}enable(w){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!w&&w.around===\"center\")}disable(){this.isEnabled()&&(this._enabled=!1)}_shouldBePrevented(w){return!!this._map.cooperativeGestures.isEnabled()&&!(w.ctrlKey||this._map.cooperativeGestures.isBypassed(w))}wheel(w){if(!this.isEnabled())return;if(this._shouldBePrevented(w))return void this._map.cooperativeGestures.notifyGestureBlocked(\"wheel_zoom\",w);let B=w.deltaMode===WheelEvent.DOM_DELTA_LINE?40*w.deltaY:w.deltaY,Q=u.now(),ee=Q-(this._lastWheelEventTime||0);this._lastWheelEventTime=Q,B!==0&&B%xr==0?this._type=\"wheel\":B!==0&&Math.abs(B)<4?this._type=\"trackpad\":ee>400?(this._type=null,this._lastValue=B,this._timeout=setTimeout(this._onTimeout,40,w)):this._type||(this._type=Math.abs(ee*B)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,B+=this._lastValue)),w.shiftKey&&B&&(B/=4),this._type&&(this._lastWheelEvent=w,this._delta-=B,this._active||this._start(w)),w.preventDefault()}_start(w){if(!this._delta)return;this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);let B=c.mousePos(this._map.getCanvas(),w),Q=this._tr;this._around=B.y>Q.transform.height/2-Q.transform.getHorizon()?a.N.convert(this._aroundCenter?Q.center:Q.unproject(B)):a.N.convert(Q.center),this._aroundPoint=Q.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._triggerRenderFrame())}renderFrame(){if(!this._frameId||(this._frameId=null,!this.isActive()))return;let w=this._tr.transform;if(this._delta!==0){let ut=this._type===\"wheel\"&&Math.abs(this._delta)>xr?this._wheelZoomRate:this._defaultZoomRate,Mt=2/(1+Math.exp(-Math.abs(this._delta*ut)));this._delta<0&&Mt!==0&&(Mt=1/Mt);let Jt=typeof this._targetZoom==\"number\"?w.zoomScale(this._targetZoom):w.scale;this._targetZoom=Math.min(w.maxZoom,Math.max(w.minZoom,w.scaleZoom(Jt*Mt))),this._type===\"wheel\"&&(this._startZoom=w.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}let B=typeof this._targetZoom==\"number\"?this._targetZoom:w.zoom,Q=this._startZoom,ee=this._easing,le,Fe=!1,Ze=u.now()-this._lastWheelEventTime;if(this._type===\"wheel\"&&Q&&ee&&Ze){let ut=Math.min(Ze/200,1),Mt=ee(ut);le=a.y.number(Q,B,Mt),ut<1?this._frameId||(this._frameId=!0):Fe=!0}else le=B,Fe=!0;return this._active=!0,Fe&&(this._active=!1,this._finishTimeout=setTimeout((()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!Fe,zoomDelta:le-w.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(w){let B=a.b9;if(this._prevEase){let Q=this._prevEase,ee=(u.now()-Q.start)/Q.duration,le=Q.easing(ee+.01)-Q.easing(ee),Fe=.27/Math.sqrt(le*le+1e-4)*.01,Ze=Math.sqrt(.0729-Fe*Fe);B=a.b8(Fe,Ze,.25,1)}return this._prevEase={start:u.now(),duration:w,easing:B},B}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class Mi{constructor(w,B){this._clickZoom=w,this._tapZoom=B}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class Xi{constructor(w){this._tr=new du(w),this.reset()}reset(){this._active=!1}dblclick(w,B){return w.preventDefault(),{cameraAnimation:Q=>{Q.easeTo({duration:300,zoom:this._tr.zoom+(w.shiftKey?-1:1),around:this._tr.unproject(B)},{originalEvent:w})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class dn{constructor(){this._tap=new wf({numTouches:1,numTaps:1}),this.reset()}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(w,B,Q){if(!this._swipePoint)if(this._tapTime){let ee=B[0],le=w.timeStamp-this._tapTime<500,Fe=this._tapPoint.dist(ee)<30;le&&Fe?Q.length>0&&(this._swipePoint=ee,this._swipeTouch=Q[0].identifier):this.reset()}else this._tap.touchstart(w,B,Q)}touchmove(w,B,Q){if(this._tapTime){if(this._swipePoint){if(Q[0].identifier!==this._swipeTouch)return;let ee=B[0],le=ee.y-this._swipePoint.y;return this._swipePoint=ee,w.preventDefault(),this._active=!0,{zoomDelta:le/128}}}else this._tap.touchmove(w,B,Q)}touchend(w,B,Q){if(this._tapTime)this._swipePoint&&Q.length===0&&this.reset();else{let ee=this._tap.touchend(w,B,Q);ee&&(this._tapTime=w.timeStamp,this._tapPoint=ee)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class wn{constructor(w,B,Q){this._el=w,this._mousePan=B,this._touchPan=Q}enable(w){this._inertiaOptions=w||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"maplibregl-touch-drag-pan\")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"maplibregl-touch-drag-pan\")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class On{constructor(w,B,Q){this._pitchWithRotate=w.pitchWithRotate,this._mouseRotate=B,this._mousePitch=Q}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()}}class Yi{constructor(w,B,Q,ee){this._el=w,this._touchZoom=B,this._touchRotate=Q,this._tapDragZoom=ee,this._rotationDisabled=!1,this._enabled=!0}enable(w){this._touchZoom.enable(w),this._rotationDisabled||this._touchRotate.enable(w),this._tapDragZoom.enable(),this._el.classList.add(\"maplibregl-touch-zoom-rotate\")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"maplibregl-touch-zoom-rotate\")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}class $i{constructor(w,B){this._bypassKey=navigator.userAgent.indexOf(\"Mac\")!==-1?\"metaKey\":\"ctrlKey\",this._map=w,this._options=B,this._enabled=!1}isActive(){return!1}reset(){}_setupUI(){if(this._container)return;let w=this._map.getCanvasContainer();w.classList.add(\"maplibregl-cooperative-gestures\"),this._container=c.create(\"div\",\"maplibregl-cooperative-gesture-screen\",w);let B=this._map._getUIString(\"CooperativeGesturesHandler.WindowsHelpText\");this._bypassKey===\"metaKey\"&&(B=this._map._getUIString(\"CooperativeGesturesHandler.MacHelpText\"));let Q=this._map._getUIString(\"CooperativeGesturesHandler.MobileHelpText\"),ee=document.createElement(\"div\");ee.className=\"maplibregl-desktop-message\",ee.textContent=B,this._container.appendChild(ee);let le=document.createElement(\"div\");le.className=\"maplibregl-mobile-message\",le.textContent=Q,this._container.appendChild(le),this._container.setAttribute(\"aria-hidden\",\"true\")}_destroyUI(){this._container&&(c.remove(this._container),this._map.getCanvasContainer().classList.remove(\"maplibregl-cooperative-gestures\")),delete this._container}enable(){this._setupUI(),this._enabled=!0}disable(){this._enabled=!1,this._destroyUI()}isEnabled(){return this._enabled}isBypassed(w){return w[this._bypassKey]}notifyGestureBlocked(w,B){this._enabled&&(this._map.fire(new a.k(\"cooperativegestureprevented\",{gestureType:w,originalEvent:B})),this._container.classList.add(\"maplibregl-show\"),setTimeout((()=>{this._container.classList.remove(\"maplibregl-show\")}),100))}}let an=ue=>ue.zoom||ue.drag||ue.pitch||ue.rotate;class Fi extends a.k{}function ta(ue){return ue.panDelta&&ue.panDelta.mag()||ue.zoomDelta||ue.bearingDelta||ue.pitchDelta}class Ca{constructor(w,B){this.handleWindowEvent=ee=>{this.handleEvent(ee,`${ee.type}Window`)},this.handleEvent=(ee,le)=>{if(ee.type===\"blur\")return void this.stop(!0);this._updatingCamera=!0;let Fe=ee.type===\"renderFrame\"?void 0:ee,Ze={needsRenderFrame:!1},ut={},Mt={},Jt=ee.touches,$t=Jt?this._getMapTouches(Jt):void 0,yr=$t?c.touchPos(this._map.getCanvas(),$t):c.mousePos(this._map.getCanvas(),ee);for(let{handlerName:xe,handler:Ie,allowed:ze}of this._handlers){if(!Ie.isEnabled())continue;let Qe;this._blockedByActive(Mt,ze,xe)?Ie.reset():Ie[le||ee.type]&&(Qe=Ie[le||ee.type](ee,yr,$t),this.mergeHandlerResult(Ze,ut,Qe,xe,Fe),Qe&&Qe.needsRenderFrame&&this._triggerRenderFrame()),(Qe||Ie.isActive())&&(Mt[xe]=Ie)}let Cr={};for(let xe in this._previousActiveHandlers)Mt[xe]||(Cr[xe]=Fe);this._previousActiveHandlers=Mt,(Object.keys(Cr).length||ta(Ze))&&(this._changes.push([Ze,ut,Cr]),this._triggerRenderFrame()),(Object.keys(Mt).length||ta(Ze))&&this._map._stop(!0),this._updatingCamera=!1;let{cameraAnimation:ve}=Ze;ve&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],ve(this._map))},this._map=w,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Pf(w),this._bearingSnap=B.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(B);let Q=this._el;this._listeners=[[Q,\"touchstart\",{passive:!0}],[Q,\"touchmove\",{passive:!1}],[Q,\"touchend\",void 0],[Q,\"touchcancel\",void 0],[Q,\"mousedown\",void 0],[Q,\"mousemove\",void 0],[Q,\"mouseup\",void 0],[document,\"mousemove\",{capture:!0}],[document,\"mouseup\",void 0],[Q,\"mouseover\",void 0],[Q,\"mouseout\",void 0],[Q,\"dblclick\",void 0],[Q,\"click\",void 0],[Q,\"keydown\",{capture:!1}],[Q,\"keyup\",void 0],[Q,\"wheel\",{passive:!1}],[Q,\"contextmenu\",void 0],[window,\"blur\",void 0]];for(let[ee,le,Fe]of this._listeners)c.addEventListener(ee,le,ee===document?this.handleWindowEvent:this.handleEvent,Fe)}destroy(){for(let[w,B,Q]of this._listeners)c.removeEventListener(w,B,w===document?this.handleWindowEvent:this.handleEvent,Q)}_addDefaultHandlers(w){let B=this._map,Q=B.getCanvasContainer();this._add(\"mapEvent\",new If(B,w));let ee=B.boxZoom=new ku(B,w);this._add(\"boxZoom\",ee),w.interactive&&w.boxZoom&&ee.enable();let le=B.cooperativeGestures=new $i(B,w.cooperativeGestures);this._add(\"cooperativeGestures\",le),w.cooperativeGestures&&le.enable();let Fe=new zc(B),Ze=new Xi(B);B.doubleClickZoom=new Mi(Ze,Fe),this._add(\"tapZoom\",Fe),this._add(\"clickZoom\",Ze),w.interactive&&w.doubleClickZoom&&B.doubleClickZoom.enable();let ut=new dn;this._add(\"tapDragZoom\",ut);let Mt=B.touchPitch=new Bc(B);this._add(\"touchPitch\",Mt),w.interactive&&w.touchPitch&&B.touchPitch.enable(w.touchPitch);let Jt=Wl(w),$t=ah(w);B.dragRotate=new On(w,Jt,$t),this._add(\"mouseRotate\",Jt,[\"mousePitch\"]),this._add(\"mousePitch\",$t,[\"mouseRotate\"]),w.interactive&&w.dragRotate&&B.dragRotate.enable();let yr=(({enable:Qe,clickTolerance:Xe})=>{let nt=new Xu({checkCorrectEvent:qt=>c.mouseButton(qt)===0&&!qt.ctrlKey});return new Wu({clickTolerance:Xe,move:(qt,Wt)=>({around:Wt,panDelta:Wt.sub(qt)}),activateOnStart:!0,moveStateManager:nt,enable:Qe,assignEvents:Zf})})(w),Cr=new Zu(w,B);B.dragPan=new wn(Q,yr,Cr),this._add(\"mousePan\",yr),this._add(\"touchPan\",Cr,[\"touchZoom\",\"touchRotate\"]),w.interactive&&w.dragPan&&B.dragPan.enable(w.dragPan);let ve=new ff,xe=new vu;B.touchZoomRotate=new Yi(Q,xe,ve,ut),this._add(\"touchRotate\",ve,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",xe,[\"touchPan\",\"touchRotate\"]),w.interactive&&w.touchZoomRotate&&B.touchZoomRotate.enable(w.touchZoomRotate);let Ie=B.scrollZoom=new Kr(B,(()=>this._triggerRenderFrame()));this._add(\"scrollZoom\",Ie,[\"mousePan\"]),w.interactive&&w.scrollZoom&&B.scrollZoom.enable(w.scrollZoom);let ze=B.keyboard=new Zt(B);this._add(\"keyboard\",ze),w.interactive&&w.keyboard&&B.keyboard.enable(),this._add(\"blockableMapEvent\",new Cs(B))}_add(w,B,Q){this._handlers.push({handlerName:w,handler:B,allowed:Q}),this._handlersById[w]=B}stop(w){if(!this._updatingCamera){for(let{handler:B}of this._handlers)B.reset();this._inertia.clear(),this._fireEvents({},{},w),this._changes=[]}}isActive(){for(let{handler:w}of this._handlers)if(w.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return!!an(this._eventsInProgress)||this.isZooming()}_blockedByActive(w,B,Q){for(let ee in w)if(ee!==Q&&(!B||B.indexOf(ee)<0))return!0;return!1}_getMapTouches(w){let B=[];for(let Q of w)this._el.contains(Q.target)&&B.push(Q);return B}mergeHandlerResult(w,B,Q,ee,le){if(!Q)return;a.e(w,Q);let Fe={handlerName:ee,originalEvent:Q.originalEvent||le};Q.zoomDelta!==void 0&&(B.zoom=Fe),Q.panDelta!==void 0&&(B.drag=Fe),Q.pitchDelta!==void 0&&(B.pitch=Fe),Q.bearingDelta!==void 0&&(B.rotate=Fe)}_applyChanges(){let w={},B={},Q={};for(let[ee,le,Fe]of this._changes)ee.panDelta&&(w.panDelta=(w.panDelta||new a.P(0,0))._add(ee.panDelta)),ee.zoomDelta&&(w.zoomDelta=(w.zoomDelta||0)+ee.zoomDelta),ee.bearingDelta&&(w.bearingDelta=(w.bearingDelta||0)+ee.bearingDelta),ee.pitchDelta&&(w.pitchDelta=(w.pitchDelta||0)+ee.pitchDelta),ee.around!==void 0&&(w.around=ee.around),ee.pinchAround!==void 0&&(w.pinchAround=ee.pinchAround),ee.noInertia&&(w.noInertia=ee.noInertia),a.e(B,le),a.e(Q,Fe);this._updateMapTransform(w,B,Q),this._changes=[]}_updateMapTransform(w,B,Q){let ee=this._map,le=ee._getTransformForUpdate(),Fe=ee.terrain;if(!(ta(w)||Fe&&this._terrainMovement))return this._fireEvents(B,Q,!0);let{panDelta:Ze,zoomDelta:ut,bearingDelta:Mt,pitchDelta:Jt,around:$t,pinchAround:yr}=w;yr!==void 0&&($t=yr),ee._stop(!0),$t=$t||ee.transform.centerPoint;let Cr=le.pointLocation(Ze?$t.sub(Ze):$t);Mt&&(le.bearing+=Mt),Jt&&(le.pitch+=Jt),ut&&(le.zoom+=ut),Fe?this._terrainMovement||!B.drag&&!B.zoom?B.drag&&this._terrainMovement?le.center=le.pointLocation(le.centerPoint.sub(Ze)):le.setLocationAtPoint(Cr,$t):(this._terrainMovement=!0,this._map._elevationFreeze=!0,le.setLocationAtPoint(Cr,$t)):le.setLocationAtPoint(Cr,$t),ee._applyUpdatedTransform(le),this._map._update(),w.noInertia||this._inertia.record(w),this._fireEvents(B,Q,!0)}_fireEvents(w,B,Q){let ee=an(this._eventsInProgress),le=an(w),Fe={};for(let $t in w){let{originalEvent:yr}=w[$t];this._eventsInProgress[$t]||(Fe[`${$t}start`]=yr),this._eventsInProgress[$t]=w[$t]}!ee&&le&&this._fireEvent(\"movestart\",le.originalEvent);for(let $t in Fe)this._fireEvent($t,Fe[$t]);le&&this._fireEvent(\"move\",le.originalEvent);for(let $t in w){let{originalEvent:yr}=w[$t];this._fireEvent($t,yr)}let Ze={},ut;for(let $t in this._eventsInProgress){let{handlerName:yr,originalEvent:Cr}=this._eventsInProgress[$t];this._handlersById[yr].isActive()||(delete this._eventsInProgress[$t],ut=B[yr]||Cr,Ze[`${$t}end`]=ut)}for(let $t in Ze)this._fireEvent($t,Ze[$t]);let Mt=an(this._eventsInProgress),Jt=(ee||le)&&!Mt;if(Jt&&this._terrainMovement){this._map._elevationFreeze=!1,this._terrainMovement=!1;let $t=this._map._getTransformForUpdate();$t.recalculateZoom(this._map.terrain),this._map._applyUpdatedTransform($t)}if(Q&&Jt){this._updatingCamera=!0;let $t=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),yr=Cr=>Cr!==0&&-this._bearingSnap<Cr&&Cr<this._bearingSnap;!$t||!$t.essential&&u.prefersReducedMotion?(this._map.fire(new a.k(\"moveend\",{originalEvent:ut})),yr(this._map.getBearing())&&this._map.resetNorth()):(yr($t.bearing||this._map.getBearing())&&($t.bearing=0),$t.freezeElevation=!0,this._map.easeTo($t,{originalEvent:ut})),this._updatingCamera=!1}}_fireEvent(w,B){this._map.fire(new a.k(w,B?{originalEvent:B}:{}))}_requestFrame(){return this._map.triggerRepaint(),this._map._renderTaskQueue.add((w=>{delete this._frameId,this.handleEvent(new Fi(\"renderFrame\",{timeStamp:w})),this._applyChanges()}))}_triggerRenderFrame(){this._frameId===void 0&&(this._frameId=this._requestFrame())}}class Ra extends a.E{constructor(w,B){super(),this._renderFrameCallback=()=>{let Q=Math.min((u.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(Q)),Q<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=w,this._bearingSnap=B.bearingSnap,this.on(\"moveend\",(()=>{delete this._requestedCameraState}))}getCenter(){return new a.N(this.transform.center.lng,this.transform.center.lat)}setCenter(w,B){return this.jumpTo({center:w},B)}panBy(w,B,Q){return w=a.P.convert(w).mult(-1),this.panTo(this.transform.center,a.e({offset:w},B),Q)}panTo(w,B,Q){return this.easeTo(a.e({center:w},B),Q)}getZoom(){return this.transform.zoom}setZoom(w,B){return this.jumpTo({zoom:w},B),this}zoomTo(w,B,Q){return this.easeTo(a.e({zoom:w},B),Q)}zoomIn(w,B){return this.zoomTo(this.getZoom()+1,w,B),this}zoomOut(w,B){return this.zoomTo(this.getZoom()-1,w,B),this}getBearing(){return this.transform.bearing}setBearing(w,B){return this.jumpTo({bearing:w},B),this}getPadding(){return this.transform.padding}setPadding(w,B){return this.jumpTo({padding:w},B),this}rotateTo(w,B,Q){return this.easeTo(a.e({bearing:w},B),Q)}resetNorth(w,B){return this.rotateTo(0,a.e({duration:1e3},w),B),this}resetNorthPitch(w,B){return this.easeTo(a.e({bearing:0,pitch:0,duration:1e3},w),B),this}snapToNorth(w,B){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(w,B):this}getPitch(){return this.transform.pitch}setPitch(w,B){return this.jumpTo({pitch:w},B),this}cameraForBounds(w,B){w=ce.convert(w).adjustAntiMeridian();let Q=B&&B.bearing||0;return this._cameraForBoxAndBearing(w.getNorthWest(),w.getSouthEast(),Q,B)}_cameraForBoxAndBearing(w,B,Q,ee){let le={top:0,bottom:0,right:0,left:0};if(typeof(ee=a.e({padding:le,offset:[0,0],maxZoom:this.transform.maxZoom},ee)).padding==\"number\"){let Hr=ee.padding;ee.padding={top:Hr,bottom:Hr,right:Hr,left:Hr}}ee.padding=a.e(le,ee.padding);let Fe=this.transform,Ze=Fe.padding,ut=new ce(w,B),Mt=Fe.project(ut.getNorthWest()),Jt=Fe.project(ut.getNorthEast()),$t=Fe.project(ut.getSouthEast()),yr=Fe.project(ut.getSouthWest()),Cr=a.ba(-Q),ve=Mt.rotate(Cr),xe=Jt.rotate(Cr),Ie=$t.rotate(Cr),ze=yr.rotate(Cr),Qe=new a.P(Math.max(ve.x,xe.x,ze.x,Ie.x),Math.max(ve.y,xe.y,ze.y,Ie.y)),Xe=new a.P(Math.min(ve.x,xe.x,ze.x,Ie.x),Math.min(ve.y,xe.y,ze.y,Ie.y)),nt=Qe.sub(Xe),qt=(Fe.width-(Ze.left+Ze.right+ee.padding.left+ee.padding.right))/nt.x,Wt=(Fe.height-(Ze.top+Ze.bottom+ee.padding.top+ee.padding.bottom))/nt.y;if(Wt<0||qt<0)return void a.w(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\");let nr=Math.min(Fe.scaleZoom(Fe.scale*Math.min(qt,Wt)),ee.maxZoom),fr=a.P.convert(ee.offset),br=new a.P((ee.padding.left-ee.padding.right)/2,(ee.padding.top-ee.padding.bottom)/2).rotate(a.ba(Q)),Fr=fr.add(br).mult(Fe.scale/Fe.zoomScale(nr));return{center:Fe.unproject(Mt.add($t).div(2).sub(Fr)),zoom:nr,bearing:Q}}fitBounds(w,B,Q){return this._fitInternal(this.cameraForBounds(w,B),B,Q)}fitScreenCoordinates(w,B,Q,ee,le){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(a.P.convert(w)),this.transform.pointLocation(a.P.convert(B)),Q,ee),ee,le)}_fitInternal(w,B,Q){return w?(delete(B=a.e(w,B)).padding,B.linear?this.easeTo(B,Q):this.flyTo(B,Q)):this}jumpTo(w,B){this.stop();let Q=this._getTransformForUpdate(),ee=!1,le=!1,Fe=!1;return\"zoom\"in w&&Q.zoom!==+w.zoom&&(ee=!0,Q.zoom=+w.zoom),w.center!==void 0&&(Q.center=a.N.convert(w.center)),\"bearing\"in w&&Q.bearing!==+w.bearing&&(le=!0,Q.bearing=+w.bearing),\"pitch\"in w&&Q.pitch!==+w.pitch&&(Fe=!0,Q.pitch=+w.pitch),w.padding==null||Q.isPaddingEqual(w.padding)||(Q.padding=w.padding),this._applyUpdatedTransform(Q),this.fire(new a.k(\"movestart\",B)).fire(new a.k(\"move\",B)),ee&&this.fire(new a.k(\"zoomstart\",B)).fire(new a.k(\"zoom\",B)).fire(new a.k(\"zoomend\",B)),le&&this.fire(new a.k(\"rotatestart\",B)).fire(new a.k(\"rotate\",B)).fire(new a.k(\"rotateend\",B)),Fe&&this.fire(new a.k(\"pitchstart\",B)).fire(new a.k(\"pitch\",B)).fire(new a.k(\"pitchend\",B)),this.fire(new a.k(\"moveend\",B))}calculateCameraOptionsFromTo(w,B,Q,ee=0){let le=a.Z.fromLngLat(w,B),Fe=a.Z.fromLngLat(Q,ee),Ze=Fe.x-le.x,ut=Fe.y-le.y,Mt=Fe.z-le.z,Jt=Math.hypot(Ze,ut,Mt);if(Jt===0)throw new Error(\"Can't calculate camera options with same From and To\");let $t=Math.hypot(Ze,ut),yr=this.transform.scaleZoom(this.transform.cameraToCenterDistance/Jt/this.transform.tileSize),Cr=180*Math.atan2(Ze,-ut)/Math.PI,ve=180*Math.acos($t/Jt)/Math.PI;return ve=Mt<0?90-ve:90+ve,{center:Fe.toLngLat(),zoom:yr,pitch:ve,bearing:Cr}}easeTo(w,B){var Q;this._stop(!1,w.easeId),((w=a.e({offset:[0,0],duration:500,easing:a.b9},w)).animate===!1||!w.essential&&u.prefersReducedMotion)&&(w.duration=0);let ee=this._getTransformForUpdate(),le=ee.zoom,Fe=ee.bearing,Ze=ee.pitch,ut=ee.padding,Mt=\"bearing\"in w?this._normalizeBearing(w.bearing,Fe):Fe,Jt=\"pitch\"in w?+w.pitch:Ze,$t=\"padding\"in w?w.padding:ee.padding,yr=a.P.convert(w.offset),Cr=ee.centerPoint.add(yr),ve=ee.pointLocation(Cr),{center:xe,zoom:Ie}=ee.getConstrained(a.N.convert(w.center||ve),(Q=w.zoom)!==null&&Q!==void 0?Q:le);this._normalizeCenter(xe,ee);let ze=ee.project(ve),Qe=ee.project(xe).sub(ze),Xe=ee.zoomScale(Ie-le),nt,qt;w.around&&(nt=a.N.convert(w.around),qt=ee.locationPoint(nt));let Wt={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||Ie!==le,this._rotating=this._rotating||Fe!==Mt,this._pitching=this._pitching||Jt!==Ze,this._padding=!ee.isPaddingEqual($t),this._easeId=w.easeId,this._prepareEase(B,w.noMoveStart,Wt),this.terrain&&this._prepareElevation(xe),this._ease((nr=>{if(this._zooming&&(ee.zoom=a.y.number(le,Ie,nr)),this._rotating&&(ee.bearing=a.y.number(Fe,Mt,nr)),this._pitching&&(ee.pitch=a.y.number(Ze,Jt,nr)),this._padding&&(ee.interpolatePadding(ut,$t,nr),Cr=ee.centerPoint.add(yr)),this.terrain&&!w.freezeElevation&&this._updateElevation(nr),nt)ee.setLocationAtPoint(nt,qt);else{let fr=ee.zoomScale(ee.zoom-le),br=Ie>le?Math.min(2,Xe):Math.max(.5,Xe),Fr=Math.pow(br,1-nr),Hr=ee.unproject(ze.add(Qe.mult(nr*Fr)).mult(fr));ee.setLocationAtPoint(ee.renderWorldCopies?Hr.wrap():Hr,Cr)}this._applyUpdatedTransform(ee),this._fireMoveEvents(B)}),(nr=>{this.terrain&&w.freezeElevation&&this._finalizeElevation(),this._afterEase(B,nr)}),w),this}_prepareEase(w,B,Q={}){this._moving=!0,B||Q.moving||this.fire(new a.k(\"movestart\",w)),this._zooming&&!Q.zooming&&this.fire(new a.k(\"zoomstart\",w)),this._rotating&&!Q.rotating&&this.fire(new a.k(\"rotatestart\",w)),this._pitching&&!Q.pitching&&this.fire(new a.k(\"pitchstart\",w))}_prepareElevation(w){this._elevationCenter=w,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(w,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(w){this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);let B=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(w<1&&B!==this._elevationTarget){let Q=this._elevationTarget-this._elevationStart;this._elevationStart+=w*(Q-(B-(Q*w+this._elevationStart))/(1-w)),this._elevationTarget=B}this.transform.elevation=a.y.number(this._elevationStart,this._elevationTarget,w)}_finalizeElevation(){this._elevationFreeze=!1,this.transform.recalculateZoom(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate||this.terrain?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_elevateCameraIfInsideTerrain(w){let B=w.getCameraPosition(),Q=this.terrain.getElevationForLngLatZoom(B.lngLat,w.zoom);if(B.altitude<Q){let ee=this.calculateCameraOptionsFromTo(B.lngLat,Q,w.center,w.elevation);return{pitch:ee.pitch,zoom:ee.zoom}}return{}}_applyUpdatedTransform(w){let B=[];if(this.terrain&&B.push((ee=>this._elevateCameraIfInsideTerrain(ee))),this.transformCameraUpdate&&B.push((ee=>this.transformCameraUpdate(ee))),!B.length)return;let Q=w.clone();for(let ee of B){let le=Q.clone(),{center:Fe,zoom:Ze,pitch:ut,bearing:Mt,elevation:Jt}=ee(le);Fe&&(le.center=Fe),Ze!==void 0&&(le.zoom=Ze),ut!==void 0&&(le.pitch=ut),Mt!==void 0&&(le.bearing=Mt),Jt!==void 0&&(le.elevation=Jt),Q.apply(le)}this.transform.apply(Q)}_fireMoveEvents(w){this.fire(new a.k(\"move\",w)),this._zooming&&this.fire(new a.k(\"zoom\",w)),this._rotating&&this.fire(new a.k(\"rotate\",w)),this._pitching&&this.fire(new a.k(\"pitch\",w))}_afterEase(w,B){if(this._easeId&&B&&this._easeId===B)return;delete this._easeId;let Q=this._zooming,ee=this._rotating,le=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,Q&&this.fire(new a.k(\"zoomend\",w)),ee&&this.fire(new a.k(\"rotateend\",w)),le&&this.fire(new a.k(\"pitchend\",w)),this.fire(new a.k(\"moveend\",w))}flyTo(w,B){var Q;if(!w.essential&&u.prefersReducedMotion){let Ln=a.M(w,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(Ln,B)}this.stop(),w=a.e({offset:[0,0],speed:1.2,curve:1.42,easing:a.b9},w);let ee=this._getTransformForUpdate(),le=ee.zoom,Fe=ee.bearing,Ze=ee.pitch,ut=ee.padding,Mt=\"bearing\"in w?this._normalizeBearing(w.bearing,Fe):Fe,Jt=\"pitch\"in w?+w.pitch:Ze,$t=\"padding\"in w?w.padding:ee.padding,yr=a.P.convert(w.offset),Cr=ee.centerPoint.add(yr),ve=ee.pointLocation(Cr),{center:xe,zoom:Ie}=ee.getConstrained(a.N.convert(w.center||ve),(Q=w.zoom)!==null&&Q!==void 0?Q:le);this._normalizeCenter(xe,ee);let ze=ee.zoomScale(Ie-le),Qe=ee.project(ve),Xe=ee.project(xe).sub(Qe),nt=w.curve,qt=Math.max(ee.width,ee.height),Wt=qt/ze,nr=Xe.mag();if(\"minZoom\"in w){let Ln=a.ac(Math.min(w.minZoom,le,Ie),ee.minZoom,ee.maxZoom),Kn=qt/ee.zoomScale(Ln-le);nt=Math.sqrt(Kn/nr*2)}let fr=nt*nt;function br(Ln){let Kn=(Wt*Wt-qt*qt+(Ln?-1:1)*fr*fr*nr*nr)/(2*(Ln?Wt:qt)*fr*nr);return Math.log(Math.sqrt(Kn*Kn+1)-Kn)}function Fr(Ln){return(Math.exp(Ln)-Math.exp(-Ln))/2}function Hr(Ln){return(Math.exp(Ln)+Math.exp(-Ln))/2}let ri=br(!1),Ci=function(Ln){return Hr(ri)/Hr(ri+nt*Ln)},cn=function(Ln){return qt*((Hr(ri)*(Fr(Kn=ri+nt*Ln)/Hr(Kn))-Fr(ri))/fr)/nr;var Kn},sn=(br(!0)-ri)/nt;if(Math.abs(nr)<1e-6||!isFinite(sn)){if(Math.abs(qt-Wt)<1e-6)return this.easeTo(w,B);let Ln=Wt<qt?-1:1;sn=Math.abs(Math.log(Wt/qt))/nt,cn=()=>0,Ci=Kn=>Math.exp(Ln*nt*Kn)}return w.duration=\"duration\"in w?+w.duration:1e3*sn/(\"screenSpeed\"in w?+w.screenSpeed/nt:+w.speed),w.maxDuration&&w.duration>w.maxDuration&&(w.duration=0),this._zooming=!0,this._rotating=Fe!==Mt,this._pitching=Jt!==Ze,this._padding=!ee.isPaddingEqual($t),this._prepareEase(B,!1),this.terrain&&this._prepareElevation(xe),this._ease((Ln=>{let Kn=Ln*sn,Aa=1/Ci(Kn);ee.zoom=Ln===1?Ie:le+ee.scaleZoom(Aa),this._rotating&&(ee.bearing=a.y.number(Fe,Mt,Ln)),this._pitching&&(ee.pitch=a.y.number(Ze,Jt,Ln)),this._padding&&(ee.interpolatePadding(ut,$t,Ln),Cr=ee.centerPoint.add(yr)),this.terrain&&!w.freezeElevation&&this._updateElevation(Ln);let fa=Ln===1?xe:ee.unproject(Qe.add(Xe.mult(cn(Kn))).mult(Aa));ee.setLocationAtPoint(ee.renderWorldCopies?fa.wrap():fa,Cr),this._applyUpdatedTransform(ee),this._fireMoveEvents(B)}),(()=>{this.terrain&&w.freezeElevation&&this._finalizeElevation(),this._afterEase(B)}),w),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(w,B){var Q;if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){let ee=this._onEaseEnd;delete this._onEaseEnd,ee.call(this,B)}return w||(Q=this.handlers)===null||Q===void 0||Q.stop(!1),this}_ease(w,B,Q){Q.animate===!1||Q.duration===0?(w(1),B()):(this._easeStart=u.now(),this._easeOptions=Q,this._onEaseFrame=w,this._onEaseEnd=B,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(w,B){w=a.b3(w,-180,180);let Q=Math.abs(w-B);return Math.abs(w-360-B)<Q&&(w-=360),Math.abs(w+360-B)<Q&&(w+=360),w}_normalizeCenter(w,B){if(!B.renderWorldCopies||B.lngRange)return;let Q=w.lng-B.center.lng;w.lng+=Q>180?-360:Q<-180?360:0}queryTerrainElevation(w){return this.terrain?this.terrain.getElevationForLngLatZoom(a.N.convert(w),this.transform.tileZoom)-this.transform.elevation:null}}let La={compact:!0,customAttribution:'<a href=\"https://maplibre.org/\" target=\"_blank\">MapLibre</a>'};class Ua{constructor(w=La){this._toggleAttribution=()=>{this._container.classList.contains(\"maplibregl-compact\")&&(this._container.classList.contains(\"maplibregl-compact-show\")?(this._container.setAttribute(\"open\",\"\"),this._container.classList.remove(\"maplibregl-compact-show\")):(this._container.classList.add(\"maplibregl-compact-show\"),this._container.removeAttribute(\"open\")))},this._updateData=B=>{!B||B.sourceDataType!==\"metadata\"&&B.sourceDataType!==\"visibility\"&&B.dataType!==\"style\"&&B.type!==\"terrain\"||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact===!1?this._container.setAttribute(\"open\",\"\"):this._container.classList.contains(\"maplibregl-compact\")||this._container.classList.contains(\"maplibregl-attrib-empty\")||(this._container.setAttribute(\"open\",\"\"),this._container.classList.add(\"maplibregl-compact\",\"maplibregl-compact-show\")):(this._container.setAttribute(\"open\",\"\"),this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.remove(\"maplibregl-compact\",\"maplibregl-compact-show\"))},this._updateCompactMinimize=()=>{this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.contains(\"maplibregl-compact-show\")&&this._container.classList.remove(\"maplibregl-compact-show\")},this.options=w}getDefaultPosition(){return\"bottom-right\"}onAdd(w){return this._map=w,this._compact=this.options.compact,this._container=c.create(\"details\",\"maplibregl-ctrl maplibregl-ctrl-attrib\"),this._compactButton=c.create(\"summary\",\"maplibregl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=c.create(\"div\",\"maplibregl-ctrl-attrib-inner\",this._container),this._updateAttributions(),this._updateCompact(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"terrain\",this._updateData),this._map.on(\"resize\",this._updateCompact),this._map.on(\"drag\",this._updateCompactMinimize),this._container}onRemove(){c.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"terrain\",this._updateData),this._map.off(\"resize\",this._updateCompact),this._map.off(\"drag\",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(w,B){let Q=this._map._getUIString(`AttributionControl.${B}`);w.title=Q,w.setAttribute(\"aria-label\",Q)}_updateAttributions(){if(!this._map.style)return;let w=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?w=w.concat(this.options.customAttribution.map((ee=>typeof ee!=\"string\"?\"\":ee))):typeof this.options.customAttribution==\"string\"&&w.push(this.options.customAttribution)),this._map.style.stylesheet){let ee=this._map.style.stylesheet;this.styleOwner=ee.owner,this.styleId=ee.id}let B=this._map.style.sourceCaches;for(let ee in B){let le=B[ee];if(le.used||le.usedForTerrain){let Fe=le.getSource();Fe.attribution&&w.indexOf(Fe.attribution)<0&&w.push(Fe.attribution)}}w=w.filter((ee=>String(ee).trim())),w.sort(((ee,le)=>ee.length-le.length)),w=w.filter(((ee,le)=>{for(let Fe=le+1;Fe<w.length;Fe++)if(w[Fe].indexOf(ee)>=0)return!1;return!0}));let Q=w.join(\" | \");Q!==this._attribHTML&&(this._attribHTML=Q,w.length?(this._innerContainer.innerHTML=Q,this._container.classList.remove(\"maplibregl-attrib-empty\")):this._container.classList.add(\"maplibregl-attrib-empty\"),this._updateCompact(),this._editLink=null)}}class Zn{constructor(w={}){this._updateCompact=()=>{let B=this._container.children;if(B.length){let Q=B[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact!==!1&&Q.classList.add(\"maplibregl-compact\"):Q.classList.remove(\"maplibregl-compact\")}},this.options=w}getDefaultPosition(){return\"bottom-left\"}onAdd(w){this._map=w,this._compact=this.options&&this.options.compact,this._container=c.create(\"div\",\"maplibregl-ctrl\");let B=c.create(\"a\",\"maplibregl-ctrl-logo\");return B.target=\"_blank\",B.rel=\"noopener nofollow\",B.href=\"https://maplibre.org/\",B.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),B.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(B),this._container.style.display=\"block\",this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container}onRemove(){c.remove(this._container),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._compact=void 0}}class Dn{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(w){let B=++this._id;return this._queue.push({callback:w,id:B,cancelled:!1}),B}remove(w){let B=this._currentlyRunning,Q=B?this._queue.concat(B):this._queue;for(let ee of Q)if(ee.id===w)return void(ee.cancelled=!0)}run(w=0){if(this._currentlyRunning)throw new Error(\"Attempting to run(), but is already running.\");let B=this._currentlyRunning=this._queue;this._queue=[];for(let Q of B)if(!Q.cancelled&&(Q.callback(w),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}var Ka=a.Y([{name:\"a_pos3d\",type:\"Int16\",components:3}]);class bo extends a.E{constructor(w){super(),this.sourceCache=w,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.deltaZoom=1,w.usedForTerrain=!0,w.tileSize=this.tileSize*2**this.deltaZoom}destruct(){this.sourceCache.usedForTerrain=!1,this.sourceCache.tileSize=null}update(w,B){this.sourceCache.update(w,B),this._renderableTilesKeys=[];let Q={};for(let ee of w.coveringTiles({tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:B}))Q[ee.key]=!0,this._renderableTilesKeys.push(ee.key),this._tiles[ee.key]||(ee.posMatrix=new Float64Array(16),a.aP(ee.posMatrix,0,a.X,0,a.X,0,1),this._tiles[ee.key]=new Tt(ee,this.tileSize));for(let ee in this._tiles)Q[ee]||delete this._tiles[ee]}freeRtt(w){for(let B in this._tiles){let Q=this._tiles[B];(!w||Q.tileID.equals(w)||Q.tileID.isChildOf(w)||w.isChildOf(Q.tileID))&&(Q.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map((w=>this.getTileByID(w)))}getTileByID(w){return this._tiles[w]}getTerrainCoords(w){let B={};for(let Q of this._renderableTilesKeys){let ee=this._tiles[Q].tileID;if(ee.canonical.equals(w.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16),a.aP(le.posMatrix,0,a.X,0,a.X,0,1),B[Q]=le}else if(ee.canonical.isChildOf(w.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16);let Fe=ee.canonical.z-w.canonical.z,Ze=ee.canonical.x-(ee.canonical.x>>Fe<<Fe),ut=ee.canonical.y-(ee.canonical.y>>Fe<<Fe),Mt=a.X>>Fe;a.aP(le.posMatrix,0,Mt,0,Mt,0,1),a.J(le.posMatrix,le.posMatrix,[-Ze*Mt,-ut*Mt,0]),B[Q]=le}else if(w.canonical.isChildOf(ee.canonical)){let le=w.clone();le.posMatrix=new Float64Array(16);let Fe=w.canonical.z-ee.canonical.z,Ze=w.canonical.x-(w.canonical.x>>Fe<<Fe),ut=w.canonical.y-(w.canonical.y>>Fe<<Fe),Mt=a.X>>Fe;a.aP(le.posMatrix,0,a.X,0,a.X,0,1),a.J(le.posMatrix,le.posMatrix,[Ze*Mt,ut*Mt,0]),a.K(le.posMatrix,le.posMatrix,[1/2**Fe,1/2**Fe,0]),B[Q]=le}}return B}getSourceTile(w,B){let Q=this.sourceCache._source,ee=w.overscaledZ-this.deltaZoom;if(ee>Q.maxzoom&&(ee=Q.maxzoom),ee<Q.minzoom)return null;this._sourceTileCache[w.key]||(this._sourceTileCache[w.key]=w.scaledTo(ee).key);let le=this.sourceCache.getTileByID(this._sourceTileCache[w.key]);if((!le||!le.dem)&&B)for(;ee>=Q.minzoom&&(!le||!le.dem);)le=this.sourceCache.getTileByID(w.scaledTo(ee--).key);return le}tilesAfterTime(w=Date.now()){return Object.values(this._tiles).filter((B=>B.timeAdded>=w))}}class Zo{constructor(w,B,Q){this.painter=w,this.sourceCache=new bo(B),this.options=Q,this.exaggeration=typeof Q.exaggeration==\"number\"?Q.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}getDEMElevation(w,B,Q,ee=a.X){var le;if(!(B>=0&&B<ee&&Q>=0&&Q<ee))return 0;let Fe=this.getTerrainData(w),Ze=(le=Fe.tile)===null||le===void 0?void 0:le.dem;if(!Ze)return 0;let ut=(function(ve,xe,Ie){var ze=xe[0],Qe=xe[1];return ve[0]=Ie[0]*ze+Ie[4]*Qe+Ie[12],ve[1]=Ie[1]*ze+Ie[5]*Qe+Ie[13],ve})([],[B/ee*a.X,Q/ee*a.X],Fe.u_terrain_matrix),Mt=[ut[0]*Ze.dim,ut[1]*Ze.dim],Jt=Math.floor(Mt[0]),$t=Math.floor(Mt[1]),yr=Mt[0]-Jt,Cr=Mt[1]-$t;return Ze.get(Jt,$t)*(1-yr)*(1-Cr)+Ze.get(Jt+1,$t)*yr*(1-Cr)+Ze.get(Jt,$t+1)*(1-yr)*Cr+Ze.get(Jt+1,$t+1)*yr*Cr}getElevationForLngLatZoom(w,B){if(!a.bb(B,w.wrap()))return 0;let{tileID:Q,mercatorX:ee,mercatorY:le}=this._getOverscaledTileIDFromLngLatZoom(w,B);return this.getElevation(Q,ee%a.X,le%a.X,a.X)}getElevation(w,B,Q,ee=a.X){return this.getDEMElevation(w,B,Q,ee)*this.exaggeration}getTerrainData(w){if(!this._emptyDemTexture){let ee=this.painter.context,le=new a.R({width:1,height:1},new Uint8Array(4));this._emptyDepthTexture=new g(ee,le,ee.gl.RGBA,{premultiply:!1}),this._emptyDemUnpack=[0,0,0,0],this._emptyDemTexture=new g(ee,new a.R({width:1,height:1}),ee.gl.RGBA,{premultiply:!1}),this._emptyDemTexture.bind(ee.gl.NEAREST,ee.gl.CLAMP_TO_EDGE),this._emptyDemMatrix=a.an([])}let B=this.sourceCache.getSourceTile(w,!0);if(B&&B.dem&&(!B.demTexture||B.needsTerrainPrepare)){let ee=this.painter.context;B.demTexture=this.painter.getTileTexture(B.dem.stride),B.demTexture?B.demTexture.update(B.dem.getPixels(),{premultiply:!1}):B.demTexture=new g(ee,B.dem.getPixels(),ee.gl.RGBA,{premultiply:!1}),B.demTexture.bind(ee.gl.NEAREST,ee.gl.CLAMP_TO_EDGE),B.needsTerrainPrepare=!1}let Q=B&&B+B.tileID.key+w.key;if(Q&&!this._demMatrixCache[Q]){let ee=this.sourceCache.sourceCache._source.maxzoom,le=w.canonical.z-B.tileID.canonical.z;w.overscaledZ>w.canonical.z&&(w.canonical.z>=ee?le=w.canonical.z-ee:a.w(\"cannot calculate elevation if elevation maxzoom > source.maxzoom\"));let Fe=w.canonical.x-(w.canonical.x>>le<<le),Ze=w.canonical.y-(w.canonical.y>>le<<le),ut=a.bc(new Float64Array(16),[1/(a.X<<le),1/(a.X<<le),0]);a.J(ut,ut,[Fe*a.X,Ze*a.X,0]),this._demMatrixCache[w.key]={matrix:ut,coord:w}}return{u_depth:2,u_terrain:3,u_terrain_dim:B&&B.dem&&B.dem.dim||1,u_terrain_matrix:Q?this._demMatrixCache[w.key].matrix:this._emptyDemMatrix,u_terrain_unpack:B&&B.dem&&B.dem.getUnpackVector()||this._emptyDemUnpack,u_terrain_exaggeration:this.exaggeration,texture:(B&&B.demTexture||this._emptyDemTexture).texture,depthTexture:(this._fboDepthTexture||this._emptyDepthTexture).texture,tile:B}}getFramebuffer(w){let B=this.painter,Q=B.width/devicePixelRatio,ee=B.height/devicePixelRatio;return!this._fbo||this._fbo.width===Q&&this._fbo.height===ee||(this._fbo.destroy(),this._fboCoordsTexture.destroy(),this._fboDepthTexture.destroy(),delete this._fbo,delete this._fboDepthTexture,delete this._fboCoordsTexture),this._fboCoordsTexture||(this._fboCoordsTexture=new g(B.context,{width:Q,height:ee,data:null},B.context.gl.RGBA,{premultiply:!1}),this._fboCoordsTexture.bind(B.context.gl.NEAREST,B.context.gl.CLAMP_TO_EDGE)),this._fboDepthTexture||(this._fboDepthTexture=new g(B.context,{width:Q,height:ee,data:null},B.context.gl.RGBA,{premultiply:!1}),this._fboDepthTexture.bind(B.context.gl.NEAREST,B.context.gl.CLAMP_TO_EDGE)),this._fbo||(this._fbo=B.context.createFramebuffer(Q,ee,!0,!1),this._fbo.depthAttachment.set(B.context.createRenderbuffer(B.context.gl.DEPTH_COMPONENT16,Q,ee))),this._fbo.colorAttachment.set(w===\"coords\"?this._fboCoordsTexture.texture:this._fboDepthTexture.texture),this._fbo}getCoordsTexture(){let w=this.painter.context;if(this._coordsTexture)return this._coordsTexture;let B=new Uint8Array(this._coordsTextureSize*this._coordsTextureSize*4);for(let le=0,Fe=0;le<this._coordsTextureSize;le++)for(let Ze=0;Ze<this._coordsTextureSize;Ze++,Fe+=4)B[Fe+0]=255&Ze,B[Fe+1]=255&le,B[Fe+2]=Ze>>8<<4|le>>8,B[Fe+3]=0;let Q=new a.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(B.buffer)),ee=new g(w,Q,w.gl.RGBA,{premultiply:!1});return ee.bind(w.gl.NEAREST,w.gl.CLAMP_TO_EDGE),this._coordsTexture=ee,ee}pointCoordinate(w){this.painter.maybeDrawDepthAndCoords(!0);let B=new Uint8Array(4),Q=this.painter.context,ee=Q.gl,le=Math.round(w.x*this.painter.pixelRatio/devicePixelRatio),Fe=Math.round(w.y*this.painter.pixelRatio/devicePixelRatio),Ze=Math.round(this.painter.height/devicePixelRatio);Q.bindFramebuffer.set(this.getFramebuffer(\"coords\").framebuffer),ee.readPixels(le,Ze-Fe-1,1,1,ee.RGBA,ee.UNSIGNED_BYTE,B),Q.bindFramebuffer.set(null);let ut=B[0]+(B[2]>>4<<8),Mt=B[1]+((15&B[2])<<8),Jt=this.coordsIndex[255-B[3]],$t=Jt&&this.sourceCache.getTileByID(Jt);if(!$t)return null;let yr=this._coordsTextureSize,Cr=(1<<$t.tileID.canonical.z)*yr;return new a.Z(($t.tileID.canonical.x*yr+ut)/Cr+$t.tileID.wrap,($t.tileID.canonical.y*yr+Mt)/Cr,this.getElevation($t.tileID,ut,Mt,yr))}depthAtPoint(w){let B=new Uint8Array(4),Q=this.painter.context,ee=Q.gl;return Q.bindFramebuffer.set(this.getFramebuffer(\"depth\").framebuffer),ee.readPixels(w.x,this.painter.height/devicePixelRatio-w.y-1,1,1,ee.RGBA,ee.UNSIGNED_BYTE,B),Q.bindFramebuffer.set(null),(B[0]/16777216+B[1]/65536+B[2]/256+B[3])/256}getTerrainMesh(){if(this._mesh)return this._mesh;let w=this.painter.context,B=new a.bd,Q=new a.aY,ee=this.meshSize,le=a.X/ee,Fe=ee*ee;for(let $t=0;$t<=ee;$t++)for(let yr=0;yr<=ee;yr++)B.emplaceBack(yr*le,$t*le,0);for(let $t=0;$t<Fe;$t+=ee+1)for(let yr=0;yr<ee;yr++)Q.emplaceBack(yr+$t,ee+yr+$t+1,ee+yr+$t+2),Q.emplaceBack(yr+$t,ee+yr+$t+2,yr+$t+1);let Ze=B.length,ut=Ze+2*(ee+1);for(let $t of[0,1])for(let yr=0;yr<=ee;yr++)for(let Cr of[0,1])B.emplaceBack(yr*le,$t*a.X,Cr);for(let $t=0;$t<2*ee;$t+=2)Q.emplaceBack(ut+$t,ut+$t+1,ut+$t+3),Q.emplaceBack(ut+$t,ut+$t+3,ut+$t+2),Q.emplaceBack(Ze+$t,Ze+$t+3,Ze+$t+1),Q.emplaceBack(Ze+$t,Ze+$t+2,Ze+$t+3);let Mt=B.length,Jt=Mt+2*(ee+1);for(let $t of[0,1])for(let yr=0;yr<=ee;yr++)for(let Cr of[0,1])B.emplaceBack($t*a.X,yr*le,Cr);for(let $t=0;$t<2*ee;$t+=2)Q.emplaceBack(Mt+$t,Mt+$t+1,Mt+$t+3),Q.emplaceBack(Mt+$t,Mt+$t+3,Mt+$t+2),Q.emplaceBack(Jt+$t,Jt+$t+3,Jt+$t+1),Q.emplaceBack(Jt+$t,Jt+$t+2,Jt+$t+3);return this._mesh=new Eu(w.createVertexBuffer(B,Ka.members),w.createIndexBuffer(Q),a.a0.simpleSegment(0,0,B.length,Q.length)),this._mesh}getMeshFrameDelta(w){return 2*Math.PI*a.be/Math.pow(2,w)/5}getMinTileElevationForLngLatZoom(w,B){var Q;let{tileID:ee}=this._getOverscaledTileIDFromLngLatZoom(w,B);return(Q=this.getMinMaxElevation(ee).minElevation)!==null&&Q!==void 0?Q:0}getMinMaxElevation(w){let B=this.getTerrainData(w).tile,Q={minElevation:null,maxElevation:null};return B&&B.dem&&(Q.minElevation=B.dem.min*this.exaggeration,Q.maxElevation=B.dem.max*this.exaggeration),Q}_getOverscaledTileIDFromLngLatZoom(w,B){let Q=a.Z.fromLngLat(w.wrap()),ee=(1<<B)*a.X,le=Q.x*ee,Fe=Q.y*ee,Ze=Math.floor(le/a.X),ut=Math.floor(Fe/a.X);return{tileID:new a.S(B,0,B,Ze,ut),mercatorX:le,mercatorY:Fe}}}class Ss{constructor(w,B,Q){this._context=w,this._size=B,this._tileSize=Q,this._objects=[],this._recentlyUsed=[],this._stamp=0}destruct(){for(let w of this._objects)w.texture.destroy(),w.fbo.destroy()}_createObject(w){let B=this._context.createFramebuffer(this._tileSize,this._tileSize,!0,!0),Q=new g(this._context,{width:this._tileSize,height:this._tileSize,data:null},this._context.gl.RGBA);return Q.bind(this._context.gl.LINEAR,this._context.gl.CLAMP_TO_EDGE),B.depthAttachment.set(this._context.createRenderbuffer(this._context.gl.DEPTH_STENCIL,this._tileSize,this._tileSize)),B.colorAttachment.set(Q.texture),{id:w,fbo:B,texture:Q,stamp:-1,inUse:!1}}getObjectForId(w){return this._objects[w]}useObject(w){w.inUse=!0,this._recentlyUsed=this._recentlyUsed.filter((B=>w.id!==B)),this._recentlyUsed.push(w.id)}stampObject(w){w.stamp=++this._stamp}getOrCreateFreeObject(){for(let B of this._recentlyUsed)if(!this._objects[B].inUse)return this._objects[B];if(this._objects.length>=this._size)throw new Error(\"No free RenderPool available, call freeAllObjects() required!\");let w=this._createObject(this._objects.length);return this._objects.push(w),w}freeObject(w){w.inUse=!1}freeAllObjects(){for(let w of this._objects)this.freeObject(w)}isFull(){return!(this._objects.length<this._size)&&this._objects.some((w=>!w.inUse))===!1}}let as={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0};class ws{constructor(w,B){this.painter=w,this.terrain=B,this.pool=new Ss(w.context,30,B.sourceCache.tileSize*B.qualityFactor)}destruct(){this.pool.destruct()}getTexture(w){return this.pool.getObjectForId(w.rtt[this._stacks.length-1].id).texture}prepareForRender(w,B){this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.sourceCache.getRenderableTiles(),this._renderableLayerIds=w._order.filter((Q=>!w._layers[Q].isHidden(B))),this._coordsDescendingInv={};for(let Q in w.sourceCaches){this._coordsDescendingInv[Q]={};let ee=w.sourceCaches[Q].getVisibleCoordinates();for(let le of ee){let Fe=this.terrain.sourceCache.getTerrainCoords(le);for(let Ze in Fe)this._coordsDescendingInv[Q][Ze]||(this._coordsDescendingInv[Q][Ze]=[]),this._coordsDescendingInv[Q][Ze].push(Fe[Ze])}}this._coordsDescendingInvStr={};for(let Q of w._order){let ee=w._layers[Q],le=ee.source;if(as[ee.type]&&!this._coordsDescendingInvStr[le]){this._coordsDescendingInvStr[le]={};for(let Fe in this._coordsDescendingInv[le])this._coordsDescendingInvStr[le][Fe]=this._coordsDescendingInv[le][Fe].map((Ze=>Ze.key)).sort().join()}}for(let Q of this._renderableTiles)for(let ee in this._coordsDescendingInvStr){let le=this._coordsDescendingInvStr[ee][Q.tileID.key];le&&le!==Q.rttCoords[ee]&&(Q.rtt=[])}}renderLayer(w){if(w.isHidden(this.painter.transform.zoom))return!1;let B=w.type,Q=this.painter,ee=this._renderableLayerIds[this._renderableLayerIds.length-1]===w.id;if(as[B]&&(this._prevType&&as[this._prevType]||this._stacks.push([]),this._prevType=B,this._stacks[this._stacks.length-1].push(w.id),!ee))return!0;if(as[this._prevType]||as[B]&&ee){this._prevType=B;let le=this._stacks.length-1,Fe=this._stacks[le]||[];for(let Ze of this._renderableTiles){if(this.pool.isFull()&&(js(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(Ze),Ze.rtt[le]){let Mt=this.pool.getObjectForId(Ze.rtt[le].id);if(Mt.stamp===Ze.rtt[le].stamp){this.pool.useObject(Mt);continue}}let ut=this.pool.getOrCreateFreeObject();this.pool.useObject(ut),this.pool.stampObject(ut),Ze.rtt[le]={id:ut.id,stamp:ut.stamp},Q.context.bindFramebuffer.set(ut.fbo.framebuffer),Q.context.clear({color:a.aM.transparent,stencil:0}),Q.currentStencilSource=void 0;for(let Mt=0;Mt<Fe.length;Mt++){let Jt=Q.style._layers[Fe[Mt]],$t=Jt.source?this._coordsDescendingInv[Jt.source][Ze.tileID.key]:[Ze.tileID];Q.context.viewport.set([0,0,ut.fbo.width,ut.fbo.height]),Q._renderTileClippingMasks(Jt,$t),Q.renderLayer(Q,Q.style.sourceCaches[Jt.source],Jt,$t),Jt.source&&(Ze.rttCoords[Jt.source]=this._coordsDescendingInvStr[Jt.source][Ze.tileID.key])}}return js(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects(),as[B]}return!1}}let Ho={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"MapLibre logo\",\"Map.Title\":\"Map\",\"Marker.Title\":\"Map marker\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"Popup.Close\":\"Close popup\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\",\"TerrainControl.Enable\":\"Enable terrain\",\"TerrainControl.Disable\":\"Disable terrain\",\"CooperativeGesturesHandler.WindowsHelpText\":\"Use Ctrl + scroll to zoom the map\",\"CooperativeGesturesHandler.MacHelpText\":\"Use \\u2318 + scroll to zoom the map\",\"CooperativeGesturesHandler.MobileHelpText\":\"Use two fingers to move the map\"},ml=o,Ws={hash:!1,interactive:!0,bearingSnap:7,attributionControl:La,maplibreLogo:!1,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,refreshExpiredTiles:!0,scrollZoom:!0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,cooperativeGestures:!1,trackResize:!0,center:[0,0],zoom:0,bearing:0,pitch:0,renderWorldCopies:!0,maxTileCacheSize:null,maxTileCacheZoomLevels:a.a.MAX_TILE_CACHE_ZOOM_LEVELS,transformRequest:null,transformCameraUpdate:null,fadeDuration:300,crossSourceCollisions:!0,clickTolerance:3,localIdeographFontFamily:\"sans-serif\",pitchWithRotate:!0,validateStyle:!0,maxCanvasSize:[4096,4096],cancelPendingTileRequestsWhileZooming:!0},Ls=ue=>{ue.touchstart=ue.dragStart,ue.touchmoveWindow=ue.dragMove,ue.touchend=ue.dragEnd},va={showCompass:!0,showZoom:!0,visualizePitch:!1};class no{constructor(w,B,Q=!1){this.mousedown=Fe=>{this.startMouse(a.e({},Fe,{ctrlKey:!0,preventDefault:()=>Fe.preventDefault()}),c.mousePos(this.element,Fe)),c.addEventListener(window,\"mousemove\",this.mousemove),c.addEventListener(window,\"mouseup\",this.mouseup)},this.mousemove=Fe=>{this.moveMouse(Fe,c.mousePos(this.element,Fe))},this.mouseup=Fe=>{this.mouseRotate.dragEnd(Fe),this.mousePitch&&this.mousePitch.dragEnd(Fe),this.offTemp()},this.touchstart=Fe=>{Fe.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=c.touchPos(this.element,Fe.targetTouches)[0],this.startTouch(Fe,this._startPos),c.addEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.addEventListener(window,\"touchend\",this.touchend))},this.touchmove=Fe=>{Fe.targetTouches.length!==1?this.reset():(this._lastPos=c.touchPos(this.element,Fe.targetTouches)[0],this.moveTouch(Fe,this._lastPos))},this.touchend=Fe=>{Fe.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),delete this._startPos,delete this._lastPos,this.offTemp()},this.reset=()=>{this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),this.touchRotate.reset(),this.touchPitch&&this.touchPitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10;let ee=w.dragRotate._mouseRotate.getClickTolerance(),le=w.dragRotate._mousePitch.getClickTolerance();this.element=B,this.mouseRotate=Wl({clickTolerance:ee,enable:!0}),this.touchRotate=(({enable:Fe,clickTolerance:Ze,bearingDegreesPerPixelMoved:ut=.8})=>{let Mt=new cf;return new Wu({clickTolerance:Ze,move:(Jt,$t)=>({bearingDelta:($t.x-Jt.x)*ut}),moveStateManager:Mt,enable:Fe,assignEvents:Ls})})({clickTolerance:ee,enable:!0}),this.map=w,Q&&(this.mousePitch=ah({clickTolerance:le,enable:!0}),this.touchPitch=(({enable:Fe,clickTolerance:Ze,pitchDegreesPerPixelMoved:ut=-.5})=>{let Mt=new cf;return new Wu({clickTolerance:Ze,move:(Jt,$t)=>({pitchDelta:($t.y-Jt.y)*ut}),moveStateManager:Mt,enable:Fe,assignEvents:Ls})})({clickTolerance:le,enable:!0})),c.addEventListener(B,\"mousedown\",this.mousedown),c.addEventListener(B,\"touchstart\",this.touchstart,{passive:!1}),c.addEventListener(B,\"touchcancel\",this.reset)}startMouse(w,B){this.mouseRotate.dragStart(w,B),this.mousePitch&&this.mousePitch.dragStart(w,B),c.disableDrag()}startTouch(w,B){this.touchRotate.dragStart(w,B),this.touchPitch&&this.touchPitch.dragStart(w,B),c.disableDrag()}moveMouse(w,B){let Q=this.map,{bearingDelta:ee}=this.mouseRotate.dragMove(w,B)||{};if(ee&&Q.setBearing(Q.getBearing()+ee),this.mousePitch){let{pitchDelta:le}=this.mousePitch.dragMove(w,B)||{};le&&Q.setPitch(Q.getPitch()+le)}}moveTouch(w,B){let Q=this.map,{bearingDelta:ee}=this.touchRotate.dragMove(w,B)||{};if(ee&&Q.setBearing(Q.getBearing()+ee),this.touchPitch){let{pitchDelta:le}=this.touchPitch.dragMove(w,B)||{};le&&Q.setPitch(Q.getPitch()+le)}}off(){let w=this.element;c.removeEventListener(w,\"mousedown\",this.mousedown),c.removeEventListener(w,\"touchstart\",this.touchstart,{passive:!1}),c.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.removeEventListener(window,\"touchend\",this.touchend),c.removeEventListener(w,\"touchcancel\",this.reset),this.offTemp()}offTemp(){c.enableDrag(),c.removeEventListener(window,\"mousemove\",this.mousemove),c.removeEventListener(window,\"mouseup\",this.mouseup),c.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.removeEventListener(window,\"touchend\",this.touchend)}}let ys;function rs(ue,w,B){let Q=new a.N(ue.lng,ue.lat);if(ue=new a.N(ue.lng,ue.lat),w){let ee=new a.N(ue.lng-360,ue.lat),le=new a.N(ue.lng+360,ue.lat),Fe=B.locationPoint(ue).distSqr(w);B.locationPoint(ee).distSqr(w)<Fe?ue=ee:B.locationPoint(le).distSqr(w)<Fe&&(ue=le)}for(;Math.abs(ue.lng-B.center.lng)>180;){let ee=B.locationPoint(ue);if(ee.x>=0&&ee.y>=0&&ee.x<=B.width&&ee.y<=B.height)break;ue.lng>B.center.lng?ue.lng-=360:ue.lng+=360}return ue.lng!==Q.lng&&B.locationPoint(ue).y>B.height/2-B.getHorizon()?ue:Q}let Ql={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Cu(ue,w,B){let Q=ue.classList;for(let ee in Ql)Q.remove(`maplibregl-${B}-anchor-${ee}`);Q.add(`maplibregl-${B}-anchor-${w}`)}class Yu extends a.E{constructor(w){if(super(),this._onKeyPress=B=>{let Q=B.code,ee=B.charCode||B.keyCode;Q!==\"Space\"&&Q!==\"Enter\"&&ee!==32&&ee!==13||this.togglePopup()},this._onMapClick=B=>{let Q=B.originalEvent.target,ee=this._element;this._popup&&(Q===ee||ee.contains(Q))&&this.togglePopup()},this._update=B=>{var Q;if(!this._map)return;let ee=this._map.loaded()&&!this._map.isMoving();((B==null?void 0:B.type)===\"terrain\"||(B==null?void 0:B.type)===\"render\"&&!ee)&&this._map.once(\"render\",this._update),this._lngLat=this._map.transform.renderWorldCopies?rs(this._lngLat,this._flatPos,this._map.transform):(Q=this._lngLat)===null||Q===void 0?void 0:Q.wrap(),this._flatPos=this._pos=this._map.project(this._lngLat)._add(this._offset),this._map.terrain&&(this._flatPos=this._map.transform.locationPoint(this._lngLat)._add(this._offset));let le=\"\";this._rotationAlignment===\"viewport\"||this._rotationAlignment===\"auto\"?le=`rotateZ(${this._rotation}deg)`:this._rotationAlignment===\"map\"&&(le=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let Fe=\"\";this._pitchAlignment===\"viewport\"||this._pitchAlignment===\"auto\"?Fe=\"rotateX(0deg)\":this._pitchAlignment===\"map\"&&(Fe=`rotateX(${this._map.getPitch()}deg)`),this._subpixelPositioning||B&&B.type!==\"moveend\"||(this._pos=this._pos.round()),c.setTransform(this._element,`${Ql[this._anchor]} translate(${this._pos.x}px, ${this._pos.y}px) ${Fe} ${le}`),u.frameAsync(new AbortController).then((()=>{this._updateOpacity(B&&B.type===\"moveend\")})).catch((()=>{}))},this._onMove=B=>{if(!this._isDragging){let Q=this._clickTolerance||this._map._clickTolerance;this._isDragging=B.point.dist(this._pointerdownPos)>=Q}this._isDragging&&(this._pos=B.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",this._state===\"pending\"&&(this._state=\"active\",this.fire(new a.k(\"dragstart\"))),this.fire(new a.k(\"drag\")))},this._onUp=()=>{this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),this._state===\"active\"&&this.fire(new a.k(\"dragend\")),this._state=\"inactive\"},this._addDragHandler=B=>{this._element.contains(B.originalEvent.target)&&(B.preventDefault(),this._positionDelta=B.point.sub(this._pos).add(this._offset),this._pointerdownPos=B.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},this._anchor=w&&w.anchor||\"center\",this._color=w&&w.color||\"#3FB1CE\",this._scale=w&&w.scale||1,this._draggable=w&&w.draggable||!1,this._clickTolerance=w&&w.clickTolerance||0,this._subpixelPositioning=w&&w.subpixelPositioning||!1,this._isDragging=!1,this._state=\"inactive\",this._rotation=w&&w.rotation||0,this._rotationAlignment=w&&w.rotationAlignment||\"auto\",this._pitchAlignment=w&&w.pitchAlignment&&w.pitchAlignment!==\"auto\"?w.pitchAlignment:this._rotationAlignment,this.setOpacity(),this.setOpacity(w==null?void 0:w.opacity,w==null?void 0:w.opacityWhenCovered),w&&w.element)this._element=w.element,this._offset=a.P.convert(w&&w.offset||[0,0]);else{this._defaultMarker=!0,this._element=c.create(\"div\");let B=c.createNS(\"http://www.w3.org/2000/svg\",\"svg\"),Q=41,ee=27;B.setAttributeNS(null,\"display\",\"block\"),B.setAttributeNS(null,\"height\",`${Q}px`),B.setAttributeNS(null,\"width\",`${ee}px`),B.setAttributeNS(null,\"viewBox\",`0 0 ${ee} ${Q}`);let le=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");le.setAttributeNS(null,\"stroke\",\"none\"),le.setAttributeNS(null,\"stroke-width\",\"1\"),le.setAttributeNS(null,\"fill\",\"none\"),le.setAttributeNS(null,\"fill-rule\",\"evenodd\");let Fe=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");Fe.setAttributeNS(null,\"fill-rule\",\"nonzero\");let Ze=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");Ze.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),Ze.setAttributeNS(null,\"fill\",\"#000000\");let ut=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];for(let ze of ut){let Qe=c.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");Qe.setAttributeNS(null,\"opacity\",\"0.04\"),Qe.setAttributeNS(null,\"cx\",\"10.5\"),Qe.setAttributeNS(null,\"cy\",\"5.80029008\"),Qe.setAttributeNS(null,\"rx\",ze.rx),Qe.setAttributeNS(null,\"ry\",ze.ry),Ze.appendChild(Qe)}let Mt=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");Mt.setAttributeNS(null,\"fill\",this._color);let Jt=c.createNS(\"http://www.w3.org/2000/svg\",\"path\");Jt.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),Mt.appendChild(Jt);let 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this._lngLat}setLngLat(w){return this._lngLat=a.N.convert(w),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(w){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),w){if(!(\"offset\"in w.options)){let ee=Math.abs(13.5)/Math.SQRT2;w.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[ee,-1*(38.1-13.5+ee)],\"bottom-right\":[-ee,-1*(38.1-13.5+ee)],left:[13.5,-1*(38.1-13.5)],right:[-13.5,-1*(38.1-13.5)]}:this._offset}this._popup=w,this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this}setSubpixelPositioning(w){return this._subpixelPositioning=w,this}getPopup(){return this._popup}togglePopup(){let w=this._popup;return this._element.style.opacity===this._opacityWhenCovered?this:w?(w.isOpen()?w.remove():(w.setLngLat(this._lngLat),w.addTo(this._map)),this):this}_updateOpacity(w=!1){var B,Q;if(!(!((B=this._map)===null||B===void 0)&&B.terrain))return void(this._element.style.opacity!==this._opacity&&(this._element.style.opacity=this._opacity));if(w)this._opacityTimeout=null;else{if(this._opacityTimeout)return;this._opacityTimeout=setTimeout((()=>{this._opacityTimeout=null}),100)}let ee=this._map,le=ee.terrain.depthAtPoint(this._pos),Fe=ee.terrain.getElevationForLngLatZoom(this._lngLat,ee.transform.tileZoom);if(ee.transform.lngLatToCameraDepth(this._lngLat,Fe)-le<.006)return void(this._element.style.opacity=this._opacity);let Ze=-this._offset.y/ee.transform._pixelPerMeter,ut=Math.sin(ee.getPitch()*Math.PI/180)*Ze,Mt=ee.terrain.depthAtPoint(new a.P(this._pos.x,this._pos.y-this._offset.y)),Jt=ee.transform.lngLatToCameraDepth(this._lngLat,Fe+ut)-Mt>.006;!((Q=this._popup)===null||Q===void 0)&&Q.isOpen()&&Jt&&this._popup.remove(),this._element.style.opacity=Jt?this._opacityWhenCovered:this._opacity}getOffset(){return this._offset}setOffset(w){return this._offset=a.P.convert(w),this._update(),this}addClassName(w){this._element.classList.add(w)}removeClassName(w){this._element.classList.remove(w)}toggleClassName(w){return this._element.classList.toggle(w)}setDraggable(w){return this._draggable=!!w,this._map&&(w?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(w){return this._rotation=w||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(w){return this._rotationAlignment=w||\"auto\",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(w){return this._pitchAlignment=w&&w!==\"auto\"?w:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}setOpacity(w,B){return w===void 0&&B===void 0&&(this._opacity=\"1\",this._opacityWhenCovered=\"0.2\"),w!==void 0&&(this._opacity=w),B!==void 0&&(this._opacityWhenCovered=B),this._map&&this._updateOpacity(!0),this}}let Nc={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},pu=0,Uc=!1,xu={maxWidth:100,unit:\"metric\"};function Ac(ue,w,B){let Q=B&&B.maxWidth||100,ee=ue._container.clientHeight/2,le=ue.unproject([0,ee]),Fe=ue.unproject([Q,ee]),Ze=le.distanceTo(Fe);if(B&&B.unit===\"imperial\"){let ut=3.2808*Ze;ut>5280?Va(w,Q,ut/5280,ue._getUIString(\"ScaleControl.Miles\")):Va(w,Q,ut,ue._getUIString(\"ScaleControl.Feet\"))}else B&&B.unit===\"nautical\"?Va(w,Q,Ze/1852,ue._getUIString(\"ScaleControl.NauticalMiles\")):Ze>=1e3?Va(w,Q,Ze/1e3,ue._getUIString(\"ScaleControl.Kilometers\")):Va(w,Q,Ze,ue._getUIString(\"ScaleControl.Meters\"))}function Va(ue,w,B,Q){let ee=(function(le){let Fe=Math.pow(10,`${Math.floor(le)}`.length-1),Ze=le/Fe;return Ze=Ze>=10?10:Ze>=5?5:Ze>=3?3:Ze>=2?2:Ze>=1?1:(function(ut){let Mt=Math.pow(10,Math.ceil(-Math.log(ut)/Math.LN10));return Math.round(ut*Mt)/Mt})(Ze),Fe*Ze})(B);ue.style.width=w*(ee/B)+\"px\",ue.innerHTML=`${ee}&nbsp;${Q}`}let oo={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\",subpixelPositioning:!1},Vc=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \");function hc(ue){if(ue){if(typeof ue==\"number\"){let w=Math.round(Math.abs(ue)/Math.SQRT2);return{center:new a.P(0,0),top:new a.P(0,ue),\"top-left\":new a.P(w,w),\"top-right\":new a.P(-w,w),bottom:new a.P(0,-ue),\"bottom-left\":new a.P(w,-w),\"bottom-right\":new a.P(-w,-w),left:new a.P(ue,0),right:new a.P(-ue,0)}}if(ue instanceof a.P||Array.isArray(ue)){let w=a.P.convert(ue);return{center:w,top:w,\"top-left\":w,\"top-right\":w,bottom:w,\"bottom-left\":w,\"bottom-right\":w,left:w,right:w}}return{center:a.P.convert(ue.center||[0,0]),top:a.P.convert(ue.top||[0,0]),\"top-left\":a.P.convert(ue[\"top-left\"]||[0,0]),\"top-right\":a.P.convert(ue[\"top-right\"]||[0,0]),bottom:a.P.convert(ue.bottom||[0,0]),\"bottom-left\":a.P.convert(ue[\"bottom-left\"]||[0,0]),\"bottom-right\":a.P.convert(ue[\"bottom-right\"]||[0,0]),left:a.P.convert(ue.left||[0,0]),right:a.P.convert(ue.right||[0,0])}}return hc(new a.P(0,0))}let Ku=o;i.AJAXError=a.bh,i.Evented=a.E,i.LngLat=a.N,i.MercatorCoordinate=a.Z,i.Point=a.P,i.addProtocol=a.bi,i.config=a.a,i.removeProtocol=a.bj,i.AttributionControl=Ua,i.BoxZoomHandler=ku,i.CanvasSource=rr,i.CooperativeGesturesHandler=$i,i.DoubleClickZoomHandler=Mi,i.DragPanHandler=wn,i.DragRotateHandler=On,i.EdgeInsets=hu,i.FullscreenControl=class extends a.E{constructor(ue={}){super(),this._onFullscreenChange=()=>{var w;let B=window.document.fullscreenElement||window.document.mozFullScreenElement||window.document.webkitFullscreenElement||window.document.msFullscreenElement;for(;!((w=B==null?void 0:B.shadowRoot)===null||w===void 0)&&w.fullscreenElement;)B=B.shadowRoot.fullscreenElement;B===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,ue&&ue.container&&(ue.container instanceof HTMLElement?this._container=ue.container:a.w(\"Full screen control 'container' must be a DOM element.\")),\"onfullscreenchange\"in document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in document&&(this._fullscreenchange=\"MSFullscreenChange\")}onAdd(ue){return this._map=ue,this._container||(this._container=this._map.getContainer()),this._controlContainer=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),this._controlContainer}onRemove(){c.remove(this._controlContainer),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){let ue=this._fullscreenButton=c.create(\"button\",\"maplibregl-ctrl-fullscreen\",this._controlContainer);c.create(\"span\",\"maplibregl-ctrl-icon\",ue).setAttribute(\"aria-hidden\",\"true\"),ue.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){let ue=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",ue),this._fullscreenButton.title=ue}_getTitle(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")}_isFullscreen(){return this._fullscreen}_handleFullscreenChange(){this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-fullscreen\"),this._updateTitle(),this._fullscreen?(this.fire(new a.k(\"fullscreenstart\")),this._prevCooperativeGesturesEnabled=this._map.cooperativeGestures.isEnabled(),this._map.cooperativeGestures.disable()):(this.fire(new a.k(\"fullscreenend\")),this._prevCooperativeGesturesEnabled&&this._map.cooperativeGestures.enable())}_exitFullscreen(){window.document.exitFullscreen?window.document.exitFullscreen():window.document.mozCancelFullScreen?window.document.mozCancelFullScreen():window.document.msExitFullscreen?window.document.msExitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle(\"maplibregl-pseudo-fullscreen\"),this._handleFullscreenChange(),this._map.resize()}},i.GeoJSONSource=at,i.GeolocateControl=class extends a.E{constructor(ue){super(),this._onSuccess=w=>{if(this._map){if(this._isOutOfMapMaxBounds(w))return this._setErrorState(),this.fire(new a.k(\"outofmaxbounds\",w)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=w,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\");break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}this.options.showUserLocation&&this._watchState!==\"OFF\"&&this._updateMarker(w),this.options.trackUserLocation&&this._watchState!==\"ACTIVE_LOCK\"||this._updateCamera(w),this.options.showUserLocation&&this._dotElement.classList.remove(\"maplibregl-user-location-dot-stale\"),this.fire(new a.k(\"geolocate\",w)),this._finish()}},this._updateCamera=w=>{let B=new a.N(w.coords.longitude,w.coords.latitude),Q=w.coords.accuracy,ee=this._map.getBearing(),le=a.e({bearing:ee},this.options.fitBoundsOptions),Fe=ce.fromLngLat(B,Q);this._map.fitBounds(Fe,le,{geolocateSource:!0})},this._updateMarker=w=>{if(w){let B=new a.N(w.coords.longitude,w.coords.latitude);this._accuracyCircleMarker.setLngLat(B).addTo(this._map),this._userLocationDotMarker.setLngLat(B).addTo(this._map),this._accuracy=w.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onZoom=()=>{this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},this._onError=w=>{if(this._map){if(this.options.trackUserLocation)if(w.code===1){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;let B=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=B,this._geolocateButton.setAttribute(\"aria-label\",B),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(w.code===3&&Uc)return;this._setErrorState()}this._watchState!==\"OFF\"&&this.options.showUserLocation&&this._dotElement.classList.add(\"maplibregl-user-location-dot-stale\"),this.fire(new a.k(\"error\",w)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._setupUI=()=>{this._map&&(this._container.addEventListener(\"contextmenu\",(w=>w.preventDefault())),this._geolocateButton=c.create(\"button\",\"maplibregl-ctrl-geolocate\",this._container),c.create(\"span\",\"maplibregl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",\"true\"),this._geolocateButton.type=\"button\",this._geolocateButton.disabled=!0)},this._finishSetupUI=w=>{if(this._map){if(w===!1){a.w(\"Geolocation support is not available so the GeolocateControl will be disabled.\");let B=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=B,this._geolocateButton.setAttribute(\"aria-label\",B)}else{let B=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.disabled=!1,this._geolocateButton.title=B,this._geolocateButton.setAttribute(\"aria-label\",B)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=c.create(\"div\",\"maplibregl-user-location-dot\"),this._userLocationDotMarker=new Yu({element:this._dotElement}),this._circleElement=c.create(\"div\",\"maplibregl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Yu({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",(()=>this.trigger())),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(B=>{B.geolocateSource||this._watchState!==\"ACTIVE_LOCK\"||B.originalEvent&&B.originalEvent.type===\"resize\"||(this._watchState=\"BACKGROUND\",this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this.fire(new a.k(\"trackuserlocationend\")),this.fire(new a.k(\"userlocationlostfocus\")))}))}},this.options=a.e({},Nc,ue)}onAdd(ue){return this._map=ue,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),(function(){return a._(this,arguments,void 0,(function*(w=!1){if(ys!==void 0&&!w)return ys;if(window.navigator.permissions===void 0)return ys=!!window.navigator.geolocation,ys;try{ys=(yield window.navigator.permissions.query({name:\"geolocation\"})).state!==\"denied\"}catch(B){ys=!!window.navigator.geolocation}return ys}))})().then((w=>this._finishSetupUI(w))),this._container}onRemove(){this._geolocationWatchID!==void 0&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),c.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,pu=0,Uc=!1}_isOutOfMapMaxBounds(ue){let w=this._map.getMaxBounds(),B=ue.coords;return w&&(B.longitude<w.getWest()||B.longitude>w.getEast()||B.latitude<w.getSouth()||B.latitude>w.getNorth())}_setErrorState(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"ACTIVE_ERROR\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadius(){let ue=this._map.getBounds(),w=ue.getSouthEast(),B=ue.getNorthEast(),Q=w.distanceTo(B),ee=Math.ceil(this._accuracy/(Q/this._map._container.clientHeight)*2);this._circleElement.style.width=`${ee}px`,this._circleElement.style.height=`${ee}px`}trigger(){if(!this._setup)return a.w(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new a.k(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":pu--,Uc=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this.fire(new a.k(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new a.k(\"trackuserlocationstart\")),this.fire(new a.k(\"userlocationfocus\"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"OFF\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if(this._watchState===\"OFF\"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){let ue;this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),pu++,pu>1?(ue={maximumAge:6e5,timeout:0},Uc=!0):(ue=this.options.positionOptions,Uc=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,ue)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)}},i.Hash=Mh,i.ImageSource=ar,i.KeyboardHandler=Zt,i.LngLatBounds=ce,i.LogoControl=Zn,i.Map=class extends Ra{constructor(ue){a.bf.mark(a.bg.create);let w=Object.assign(Object.assign({},Ws),ue);if(w.minZoom!=null&&w.maxZoom!=null&&w.minZoom>w.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(w.minPitch!=null&&w.maxPitch!=null&&w.minPitch>w.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(w.minPitch!=null&&w.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(w.maxPitch!=null&&w.maxPitch>85)throw new Error(\"maxPitch must be less than or equal to 85\");if(super(new il(w.minZoom,w.maxZoom,w.minPitch,w.maxPitch,w.renderWorldCopies),{bearingSnap:w.bearingSnap}),this._idleTriggered=!1,this._crossFadingFactor=1,this._renderTaskQueue=new Dn,this._controls=[],this._mapId=a.a4(),this._contextLost=B=>{B.preventDefault(),this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this.fire(new a.k(\"webglcontextlost\",{originalEvent:B}))},this._contextRestored=B=>{this._setupPainter(),this.resize(),this._update(),this.fire(new a.k(\"webglcontextrestored\",{originalEvent:B}))},this._onMapScroll=B=>{if(B.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=w.interactive,this._maxTileCacheSize=w.maxTileCacheSize,this._maxTileCacheZoomLevels=w.maxTileCacheZoomLevels,this._failIfMajorPerformanceCaveat=w.failIfMajorPerformanceCaveat===!0,this._preserveDrawingBuffer=w.preserveDrawingBuffer===!0,this._antialias=w.antialias===!0,this._trackResize=w.trackResize===!0,this._bearingSnap=w.bearingSnap,this._refreshExpiredTiles=w.refreshExpiredTiles===!0,this._fadeDuration=w.fadeDuration,this._crossSourceCollisions=w.crossSourceCollisions===!0,this._collectResourceTiming=w.collectResourceTiming===!0,this._locale=Object.assign(Object.assign({},Ho),w.locale),this._clickTolerance=w.clickTolerance,this._overridePixelRatio=w.pixelRatio,this._maxCanvasSize=w.maxCanvasSize,this.transformCameraUpdate=w.transformCameraUpdate,this.cancelPendingTileRequestsWhileZooming=w.cancelPendingTileRequestsWhileZooming===!0,this._imageQueueHandle=p.addThrottleControl((()=>this.isMoving())),this._requestManager=new k(w.transformRequest),typeof w.container==\"string\"){if(this._container=document.getElementById(w.container),!this._container)throw new Error(`Container '${w.container}' not found.`)}else{if(!(w.container instanceof HTMLElement))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=w.container}if(w.maxBounds&&this.setMaxBounds(w.maxBounds),this._setupContainer(),this._setupPainter(),this.on(\"move\",(()=>this._update(!1))).on(\"moveend\",(()=>this._update(!1))).on(\"zoom\",(()=>this._update(!0))).on(\"terrain\",(()=>{this.painter.terrainFacilitator.dirty=!0,this._update(!0)})).once(\"idle\",(()=>{this._idleTriggered=!0})),typeof window!=\"undefined\"){addEventListener(\"online\",this._onWindowOnline,!1);let B=!1,Q=nh((ee=>{this._trackResize&&!this._removed&&(this.resize(ee),this.redraw())}),50);this._resizeObserver=new ResizeObserver((ee=>{B?Q(ee):B=!0})),this._resizeObserver.observe(this._container)}this.handlers=new Ca(this,w),this._hash=w.hash&&new Mh(typeof w.hash==\"string\"&&w.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:w.center,zoom:w.zoom,bearing:w.bearing,pitch:w.pitch}),w.bounds&&(this.resize(),this.fitBounds(w.bounds,a.e({},w.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=w.localIdeographFontFamily,this._validateStyle=w.validateStyle,w.style&&this.setStyle(w.style,{localIdeographFontFamily:w.localIdeographFontFamily}),w.attributionControl&&this.addControl(new Ua(typeof w.attributionControl==\"boolean\"?void 0:w.attributionControl)),w.maplibreLogo&&this.addControl(new Zn,w.logoPosition),this.on(\"style.load\",(()=>{this.transform.unmodified&&this.jumpTo(this.style.stylesheet)})),this.on(\"data\",(B=>{this._update(B.dataType===\"style\"),this.fire(new a.k(`${B.dataType}data`,B))})),this.on(\"dataloading\",(B=>{this.fire(new a.k(`${B.dataType}dataloading`,B))})),this.on(\"dataabort\",(B=>{this.fire(new a.k(\"sourcedataabort\",B))}))}_getMapId(){return this._mapId}addControl(ue,w){if(w===void 0&&(w=ue.getDefaultPosition?ue.getDefaultPosition():\"top-right\"),!ue||!ue.onAdd)return this.fire(new a.j(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));let B=ue.onAdd(this);this._controls.push(ue);let Q=this._controlPositions[w];return w.indexOf(\"bottom\")!==-1?Q.insertBefore(B,Q.firstChild):Q.appendChild(B),this}removeControl(ue){if(!ue||!ue.onRemove)return this.fire(new a.j(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));let w=this._controls.indexOf(ue);return w>-1&&this._controls.splice(w,1),ue.onRemove(this),this}hasControl(ue){return this._controls.indexOf(ue)>-1}calculateCameraOptionsFromTo(ue,w,B,Q){return Q==null&&this.terrain&&(Q=this.terrain.getElevationForLngLatZoom(B,this.transform.tileZoom)),super.calculateCameraOptionsFromTo(ue,w,B,Q)}resize(ue){var w;let B=this._containerDimensions(),Q=B[0],ee=B[1],le=this._getClampedPixelRatio(Q,ee);if(this._resizeCanvas(Q,ee,le),this.painter.resize(Q,ee,le),this.painter.overLimit()){let Ze=this.painter.context.gl;this._maxCanvasSize=[Ze.drawingBufferWidth,Ze.drawingBufferHeight];let ut=this._getClampedPixelRatio(Q,ee);this._resizeCanvas(Q,ee,ut),this.painter.resize(Q,ee,ut)}this.transform.resize(Q,ee),(w=this._requestedCameraState)===null||w===void 0||w.resize(Q,ee);let Fe=!this._moving;return Fe&&(this.stop(),this.fire(new a.k(\"movestart\",ue)).fire(new a.k(\"move\",ue))),this.fire(new a.k(\"resize\",ue)),Fe&&this.fire(new a.k(\"moveend\",ue)),this}_getClampedPixelRatio(ue,w){let{0:B,1:Q}=this._maxCanvasSize,ee=this.getPixelRatio(),le=ue*ee,Fe=w*ee;return Math.min(le>B?B/le:1,Fe>Q?Q/Fe:1)*ee}getPixelRatio(){var ue;return(ue=this._overridePixelRatio)!==null&&ue!==void 0?ue:devicePixelRatio}setPixelRatio(ue){this._overridePixelRatio=ue,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(ue){return this.transform.setMaxBounds(ce.convert(ue)),this._update()}setMinZoom(ue){if((ue=ue==null?-2:ue)>=-2&&ue<=this.transform.maxZoom)return this.transform.minZoom=ue,this._update(),this.getZoom()<ue&&this.setZoom(ue),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")}getMinZoom(){return this.transform.minZoom}setMaxZoom(ue){if((ue=ue==null?22:ue)>=this.transform.minZoom)return this.transform.maxZoom=ue,this._update(),this.getZoom()>ue&&this.setZoom(ue),this;throw new Error(\"maxZoom must be greater than the current minZoom\")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(ue){if((ue=ue==null?0:ue)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(ue>=0&&ue<=this.transform.maxPitch)return this.transform.minPitch=ue,this._update(),this.getPitch()<ue&&this.setPitch(ue),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")}getMinPitch(){return this.transform.minPitch}setMaxPitch(ue){if((ue=ue==null?60:ue)>85)throw new Error(\"maxPitch must be less than or equal to 85\");if(ue>=this.transform.minPitch)return this.transform.maxPitch=ue,this._update(),this.getPitch()>ue&&this.setPitch(ue),this;throw new Error(\"maxPitch must be greater than the current minPitch\")}getMaxPitch(){return this.transform.maxPitch}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(ue){return this.transform.renderWorldCopies=ue,this._update()}project(ue){return this.transform.locationPoint(a.N.convert(ue),this.style&&this.terrain)}unproject(ue){return this.transform.pointLocation(a.P.convert(ue),this.terrain)}isMoving(){var ue;return this._moving||((ue=this.handlers)===null||ue===void 0?void 0:ue.isMoving())}isZooming(){var ue;return this._zooming||((ue=this.handlers)===null||ue===void 0?void 0:ue.isZooming())}isRotating(){var ue;return this._rotating||((ue=this.handlers)===null||ue===void 0?void 0:ue.isRotating())}_createDelegatedListener(ue,w,B){if(ue===\"mouseenter\"||ue===\"mouseover\"){let Q=!1;return{layers:w,listener:B,delegates:{mousemove:le=>{let Fe=w.filter((ut=>this.getLayer(ut))),Ze=Fe.length!==0?this.queryRenderedFeatures(le.point,{layers:Fe}):[];Ze.length?Q||(Q=!0,B.call(this,new jl(ue,this,le.originalEvent,{features:Ze}))):Q=!1},mouseout:()=>{Q=!1}}}}if(ue===\"mouseleave\"||ue===\"mouseout\"){let Q=!1;return{layers:w,listener:B,delegates:{mousemove:Fe=>{let Ze=w.filter((ut=>this.getLayer(ut)));(Ze.length!==0?this.queryRenderedFeatures(Fe.point,{layers:Ze}):[]).length?Q=!0:Q&&(Q=!1,B.call(this,new jl(ue,this,Fe.originalEvent)))},mouseout:Fe=>{Q&&(Q=!1,B.call(this,new jl(ue,this,Fe.originalEvent)))}}}}{let Q=ee=>{let le=w.filter((Ze=>this.getLayer(Ze))),Fe=le.length!==0?this.queryRenderedFeatures(ee.point,{layers:le}):[];Fe.length&&(ee.features=Fe,B.call(this,ee),delete ee.features)};return{layers:w,listener:B,delegates:{[ue]:Q}}}}_saveDelegatedListener(ue,w){this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[ue]=this._delegatedListeners[ue]||[],this._delegatedListeners[ue].push(w)}_removeDelegatedListener(ue,w,B){if(!this._delegatedListeners||!this._delegatedListeners[ue])return;let Q=this._delegatedListeners[ue];for(let ee=0;ee<Q.length;ee++){let le=Q[ee];if(le.listener===B&&le.layers.length===w.length&&le.layers.every((Fe=>w.includes(Fe)))){for(let Fe in le.delegates)this.off(Fe,le.delegates[Fe]);return void Q.splice(ee,1)}}}on(ue,w,B){if(B===void 0)return super.on(ue,w);let Q=this._createDelegatedListener(ue,typeof w==\"string\"?[w]:w,B);this._saveDelegatedListener(ue,Q);for(let ee in Q.delegates)this.on(ee,Q.delegates[ee]);return this}once(ue,w,B){if(B===void 0)return super.once(ue,w);let Q=typeof w==\"string\"?[w]:w,ee=this._createDelegatedListener(ue,Q,B);for(let le in ee.delegates){let Fe=ee.delegates[le];ee.delegates[le]=(...Ze)=>{this._removeDelegatedListener(ue,Q,B),Fe(...Ze)}}this._saveDelegatedListener(ue,ee);for(let le in ee.delegates)this.once(le,ee.delegates[le]);return this}off(ue,w,B){return B===void 0?super.off(ue,w):(this._removeDelegatedListener(ue,typeof w==\"string\"?[w]:w,B),this)}queryRenderedFeatures(ue,w){if(!this.style)return[];let B,Q=ue instanceof a.P||Array.isArray(ue),ee=Q?ue:[[0,0],[this.transform.width,this.transform.height]];if(w=w||(Q?{}:ue)||{},ee instanceof a.P||typeof ee[0]==\"number\")B=[a.P.convert(ee)];else{let le=a.P.convert(ee[0]),Fe=a.P.convert(ee[1]);B=[le,new a.P(Fe.x,le.y),Fe,new a.P(le.x,Fe.y),le]}return this.style.queryRenderedFeatures(B,w,this.transform)}querySourceFeatures(ue,w){return this.style.querySourceFeatures(ue,w)}setStyle(ue,w){return(w=a.e({},{localIdeographFontFamily:this._localIdeographFontFamily,validate:this._validateStyle},w)).diff!==!1&&w.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&ue?(this._diffStyle(ue,w),this):(this._localIdeographFontFamily=w.localIdeographFontFamily,this._updateStyle(ue,w))}setTransformRequest(ue){return this._requestManager.setTransformRequest(ue),this}_getUIString(ue){let w=this._locale[ue];if(w==null)throw new Error(`Missing UI string '${ue}'`);return w}_updateStyle(ue,w){if(w.transformStyle&&this.style&&!this.style._loaded)return void this.style.once(\"style.load\",(()=>this._updateStyle(ue,w)));let B=this.style&&w.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!ue)),ue?(this.style=new Ha(this,w||{}),this.style.setEventedParent(this,{style:this.style}),typeof ue==\"string\"?this.style.loadURL(ue,w,B):this.style.loadJSON(ue,w,B),this):(delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new Ha(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(ue,w){if(typeof ue==\"string\"){let B=this._requestManager.transformRequest(ue,\"Style\");a.h(B,new AbortController).then((Q=>{this._updateDiff(Q.data,w)})).catch((Q=>{Q&&this.fire(new a.j(Q))}))}else typeof ue==\"object\"&&this._updateDiff(ue,w)}_updateDiff(ue,w){try{this.style.setState(ue,w)&&this._update(!0)}catch(B){a.w(`Unable to perform style diff: ${B.message||B.error||B}.  Rebuilding the style from scratch.`),this._updateStyle(ue,w)}}getStyle(){if(this.style)return this.style.serialize()}isStyleLoaded(){return this.style?this.style.loaded():a.w(\"There is no style added to the map.\")}addSource(ue,w){return this._lazyInitEmptyStyle(),this.style.addSource(ue,w),this._update(!0)}isSourceLoaded(ue){let w=this.style&&this.style.sourceCaches[ue];if(w!==void 0)return w.loaded();this.fire(new a.j(new Error(`There is no source with ID '${ue}'`)))}setTerrain(ue){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off(\"data\",this._terrainDataCallback),ue){let w=this.style.sourceCaches[ue.source];if(!w)throw new Error(`cannot load terrain, because there exists no source with ID: ${ue.source}`);this.terrain===null&&w.reload();for(let B in this.style._layers){let Q=this.style._layers[B];Q.type===\"hillshade\"&&Q.source===ue.source&&a.w(\"You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.\")}this.terrain=new Zo(this.painter,w,ue),this.painter.renderToTexture=new ws(this.painter,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._terrainDataCallback=B=>{B.dataType===\"style\"?this.terrain.sourceCache.freeRtt():B.dataType===\"source\"&&B.tile&&(B.sourceId!==ue.source||this._elevationFreeze||(this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.terrain.sourceCache.freeRtt(B.tile.tileID))},this.style.on(\"data\",this._terrainDataCallback)}else this.terrain&&this.terrain.sourceCache.destruct(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform.minElevationForCurrentTile=0,this.transform.elevation=0;return this.fire(new a.k(\"terrain\",{terrain:ue})),this}getTerrain(){var ue,w;return(w=(ue=this.terrain)===null||ue===void 0?void 0:ue.options)!==null&&w!==void 0?w:null}areTilesLoaded(){let ue=this.style&&this.style.sourceCaches;for(let w in ue){let B=ue[w]._tiles;for(let Q in B){let ee=B[Q];if(ee.state!==\"loaded\"&&ee.state!==\"errored\")return!1}}return!0}removeSource(ue){return this.style.removeSource(ue),this._update(!0)}getSource(ue){return this.style.getSource(ue)}addImage(ue,w,B={}){let{pixelRatio:Q=1,sdf:ee=!1,stretchX:le,stretchY:Fe,content:Ze,textFitWidth:ut,textFitHeight:Mt}=B;if(this._lazyInitEmptyStyle(),!(w instanceof HTMLImageElement||a.b(w))){if(w.width===void 0||w.height===void 0)return this.fire(new a.j(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));{let{width:Jt,height:$t,data:yr}=w,Cr=w;return this.style.addImage(ue,{data:new a.R({width:Jt,height:$t},new Uint8Array(yr)),pixelRatio:Q,stretchX:le,stretchY:Fe,content:Ze,textFitWidth:ut,textFitHeight:Mt,sdf:ee,version:0,userImage:Cr}),Cr.onAdd&&Cr.onAdd(this,ue),this}}{let{width:Jt,height:$t,data:yr}=u.getImageData(w);this.style.addImage(ue,{data:new a.R({width:Jt,height:$t},yr),pixelRatio:Q,stretchX:le,stretchY:Fe,content:Ze,textFitWidth:ut,textFitHeight:Mt,sdf:ee,version:0})}}updateImage(ue,w){let B=this.style.getImage(ue);if(!B)return this.fire(new a.j(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));let Q=w instanceof HTMLImageElement||a.b(w)?u.getImageData(w):w,{width:ee,height:le,data:Fe}=Q;if(ee===void 0||le===void 0)return this.fire(new a.j(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(ee!==B.data.width||le!==B.data.height)return this.fire(new a.j(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));let Ze=!(w instanceof HTMLImageElement||a.b(w));return B.data.replace(Fe,Ze),this.style.updateImage(ue,B),this}getImage(ue){return this.style.getImage(ue)}hasImage(ue){return ue?!!this.style.getImage(ue):(this.fire(new a.j(new Error(\"Missing required image id\"))),!1)}removeImage(ue){this.style.removeImage(ue)}loadImage(ue){return p.getImage(this._requestManager.transformRequest(ue,\"Image\"),new AbortController)}listImages(){return this.style.listImages()}addLayer(ue,w){return this._lazyInitEmptyStyle(),this.style.addLayer(ue,w),this._update(!0)}moveLayer(ue,w){return this.style.moveLayer(ue,w),this._update(!0)}removeLayer(ue){return this.style.removeLayer(ue),this._update(!0)}getLayer(ue){return this.style.getLayer(ue)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(ue,w,B){return this.style.setLayerZoomRange(ue,w,B),this._update(!0)}setFilter(ue,w,B={}){return this.style.setFilter(ue,w,B),this._update(!0)}getFilter(ue){return this.style.getFilter(ue)}setPaintProperty(ue,w,B,Q={}){return this.style.setPaintProperty(ue,w,B,Q),this._update(!0)}getPaintProperty(ue,w){return this.style.getPaintProperty(ue,w)}setLayoutProperty(ue,w,B,Q={}){return this.style.setLayoutProperty(ue,w,B,Q),this._update(!0)}getLayoutProperty(ue,w){return this.style.getLayoutProperty(ue,w)}setGlyphs(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(ue,w),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(ue,w,B={}){return this._lazyInitEmptyStyle(),this.style.addSprite(ue,w,B,(Q=>{Q||this._update(!0)})),this}removeSprite(ue){return this._lazyInitEmptyStyle(),this.style.removeSprite(ue),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setSprite(ue,w,(B=>{B||this._update(!0)})),this}setLight(ue,w={}){return this._lazyInitEmptyStyle(),this.style.setLight(ue,w),this._update(!0)}getLight(){return this.style.getLight()}setSky(ue){return this._lazyInitEmptyStyle(),this.style.setSky(ue),this._update(!0)}getSky(){return this.style.getSky()}setFeatureState(ue,w){return this.style.setFeatureState(ue,w),this._update()}removeFeatureState(ue,w){return this.style.removeFeatureState(ue,w),this._update()}getFeatureState(ue){return this.style.getFeatureState(ue)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let ue=0,w=0;return this._container&&(ue=this._container.clientWidth||400,w=this._container.clientHeight||300),[ue,w]}_setupContainer(){let ue=this._container;ue.classList.add(\"maplibregl-map\");let w=this._canvasContainer=c.create(\"div\",\"maplibregl-canvas-container\",ue);this._interactive&&w.classList.add(\"maplibregl-interactive\"),this._canvas=c.create(\"canvas\",\"maplibregl-canvas\",w),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",this._interactive?\"0\":\"-1\"),this._canvas.setAttribute(\"aria-label\",this._getUIString(\"Map.Title\")),this._canvas.setAttribute(\"role\",\"region\");let B=this._containerDimensions(),Q=this._getClampedPixelRatio(B[0],B[1]);this._resizeCanvas(B[0],B[1],Q);let ee=this._controlContainer=c.create(\"div\",\"maplibregl-control-container\",ue),le=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((Fe=>{le[Fe]=c.create(\"div\",`maplibregl-ctrl-${Fe} `,ee)})),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)}_resizeCanvas(ue,w,B){this._canvas.width=Math.floor(B*ue),this._canvas.height=Math.floor(B*w),this._canvas.style.width=`${ue}px`,this._canvas.style.height=`${w}px`}_setupPainter(){let ue={alpha:!0,stencil:!0,depth:!0,failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1},w=null;this._canvas.addEventListener(\"webglcontextcreationerror\",(Q=>{w={requestedAttributes:ue},Q&&(w.statusMessage=Q.statusMessage,w.type=Q.type)}),{once:!0});let B=this._canvas.getContext(\"webgl2\",ue)||this._canvas.getContext(\"webgl\",ue);if(!B){let Q=\"Failed to initialize WebGL\";throw w?(w.message=Q,new Error(JSON.stringify(w))):new Error(Q)}this.painter=new Dc(B,this.transform),f.testSupport(B)}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(ue){return this.style&&this.style._loaded?(this._styleDirty=this._styleDirty||ue,this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(ue){return this._update(),this._renderTaskQueue.add(ue)}_cancelRenderFrame(ue){this._renderTaskQueue.remove(ue)}_render(ue){let w=this._idleTriggered?this._fadeDuration:0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(ue),this._removed)return;let B=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;let ee=this.transform.zoom,le=u.now();this.style.zoomHistory.update(ee,le);let Fe=new a.z(ee,{now:le,fadeDuration:w,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),Ze=Fe.crossFadingFactor();Ze===1&&Ze===this._crossFadingFactor||(B=!0,this._crossFadingFactor=Ze),this.style.update(Fe)}this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.sourceCache.update(this.transform,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._elevationFreeze||(this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform.minElevationForCurrentTile=0,this.transform.elevation=0),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,w,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:w,showPadding:this.showPadding}),this.fire(new a.k(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,a.bf.mark(a.bg.load),this.fire(new a.k(\"load\"))),this.style&&(this.style.hasTransitions()||B)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();let Q=this._sourcesDirty||this._styleDirty||this._placementDirty;return Q||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new a.k(\"idle\")),!this._loaded||this._fullyLoaded||Q||(this._fullyLoaded=!0,a.bf.mark(a.bg.fullLoad)),this}redraw(){return this.style&&(this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._render(0)),this}remove(){var ue;this._hash&&this._hash.remove();for(let B of this._controls)B.onRemove(this);this._controls=[],this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof window!=\"undefined\"&&removeEventListener(\"online\",this._onWindowOnline,!1),p.removeThrottleControl(this._imageQueueHandle),(ue=this._resizeObserver)===null||ue===void 0||ue.disconnect();let w=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");w!=null&&w.loseContext&&w.loseContext(),this._canvas.removeEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.removeEventListener(\"webglcontextlost\",this._contextLost,!1),c.remove(this._canvasContainer),c.remove(this._controlContainer),this._container.classList.remove(\"maplibregl-map\"),a.bf.clearMetrics(),this._removed=!0,this.fire(new a.k(\"remove\"))}triggerRepaint(){this.style&&!this._frameRequest&&(this._frameRequest=new AbortController,u.frameAsync(this._frameRequest).then((ue=>{a.bf.frame(ue),this._frameRequest=null,this._render(ue)})).catch((()=>{})))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(ue){this._showTileBoundaries!==ue&&(this._showTileBoundaries=ue,this._update())}get showPadding(){return!!this._showPadding}set showPadding(ue){this._showPadding!==ue&&(this._showPadding=ue,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(ue){this._showCollisionBoxes!==ue&&(this._showCollisionBoxes=ue,ue?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(ue){this._showOverdrawInspector!==ue&&(this._showOverdrawInspector=ue,this._update())}get repaint(){return!!this._repaint}set repaint(ue){this._repaint!==ue&&(this._repaint=ue,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(ue){this._vertices=ue,this._update()}get version(){return ml}getCameraTargetElevation(){return this.transform.elevation}},i.MapMouseEvent=jl,i.MapTouchEvent=uf,i.MapWheelEvent=Xh,i.Marker=Yu,i.NavigationControl=class{constructor(ue){this._updateZoomButtons=()=>{let w=this._map.getZoom(),B=w===this._map.getMaxZoom(),Q=w===this._map.getMinZoom();this._zoomInButton.disabled=B,this._zoomOutButton.disabled=Q,this._zoomInButton.setAttribute(\"aria-disabled\",B.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",Q.toString())},this._rotateCompassArrow=()=>{let w=this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${this._map.transform.angle*(180/Math.PI)}deg)`:`rotate(${this._map.transform.angle*(180/Math.PI)}deg)`;this._compassIcon.style.transform=w},this._setButtonTitle=(w,B)=>{let Q=this._map._getUIString(`NavigationControl.${B}`);w.title=Q,w.setAttribute(\"aria-label\",Q)},this.options=a.e({},va,ue),this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(w=>w.preventDefault())),this.options.showZoom&&(this._zoomInButton=this._createButton(\"maplibregl-ctrl-zoom-in\",(w=>this._map.zoomIn({},{originalEvent:w}))),c.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",\"true\"),this._zoomOutButton=this._createButton(\"maplibregl-ctrl-zoom-out\",(w=>this._map.zoomOut({},{originalEvent:w}))),c.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",\"true\")),this.options.showCompass&&(this._compass=this._createButton(\"maplibregl-ctrl-compass\",(w=>{this.options.visualizePitch?this._map.resetNorthPitch({},{originalEvent:w}):this._map.resetNorth({},{originalEvent:w})})),this._compassIcon=c.create(\"span\",\"maplibregl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",\"true\"))}onAdd(ue){return this._map=ue,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new no(this._map,this._compass,this.options.visualizePitch)),this._container}onRemove(){c.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map}_createButton(ue,w){let B=c.create(\"button\",ue,this._container);return B.type=\"button\",B.addEventListener(\"click\",w),B}},i.Popup=class extends a.E{constructor(ue){super(),this.remove=()=>(this._content&&c.remove(this._content),this._container&&(c.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\"),delete this._map,this.fire(new a.k(\"close\"))),this),this._onMouseUp=w=>{this._update(w.point)},this._onMouseMove=w=>{this._update(w.point)},this._onDrag=w=>{this._update(w.point)},this._update=w=>{var B;if(!this._map||!this._lngLat&&!this._trackPointer||!this._content)return;if(!this._container){if(this._container=c.create(\"div\",\"maplibregl-popup\",this._map.getContainer()),this._tip=c.create(\"div\",\"maplibregl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className)for(let Ze of this.options.className.split(\" \"))this._container.classList.add(Ze);this._closeButton&&this._closeButton.setAttribute(\"aria-label\",this._map._getUIString(\"Popup.Close\")),this._trackPointer&&this._container.classList.add(\"maplibregl-popup-track-pointer\")}if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._lngLat=this._map.transform.renderWorldCopies&&!this._trackPointer?rs(this._lngLat,this._flatPos,this._map.transform):(B=this._lngLat)===null||B===void 0?void 0:B.wrap(),this._trackPointer&&!w)return;let Q=this._flatPos=this._pos=this._trackPointer&&w?w:this._map.project(this._lngLat);this._map.terrain&&(this._flatPos=this._trackPointer&&w?w:this._map.transform.locationPoint(this._lngLat));let ee=this.options.anchor,le=hc(this.options.offset);if(!ee){let Ze=this._container.offsetWidth,ut=this._container.offsetHeight,Mt;Mt=Q.y+le.bottom.y<ut?[\"top\"]:Q.y>this._map.transform.height-ut?[\"bottom\"]:[],Q.x<Ze/2?Mt.push(\"left\"):Q.x>this._map.transform.width-Ze/2&&Mt.push(\"right\"),ee=Mt.length===0?\"bottom\":Mt.join(\"-\")}let Fe=Q.add(le[ee]);this.options.subpixelPositioning||(Fe=Fe.round()),c.setTransform(this._container,`${Ql[ee]} translate(${Fe.x}px,${Fe.y}px)`),Cu(this._container,ee,\"popup\")},this._onClose=()=>{this.remove()},this.options=a.e(Object.create(oo),ue)}addTo(ue){return this._map&&this.remove(),this._map=ue,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new a.k(\"open\")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(ue){return this._lngLat=a.N.convert(ue),this._pos=null,this._flatPos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._flatPos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")),this}getElement(){return this._container}setText(ue){return this.setDOMContent(document.createTextNode(ue))}setHTML(ue){let w=document.createDocumentFragment(),B=document.createElement(\"body\"),Q;for(B.innerHTML=ue;Q=B.firstChild,Q;)w.appendChild(Q);return this.setDOMContent(w)}getMaxWidth(){var ue;return(ue=this._container)===null||ue===void 0?void 0:ue.style.maxWidth}setMaxWidth(ue){return this.options.maxWidth=ue,this._update(),this}setDOMContent(ue){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=c.create(\"div\",\"maplibregl-popup-content\",this._container);return this._content.appendChild(ue),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(ue){return this._container&&this._container.classList.add(ue),this}removeClassName(ue){return this._container&&this._container.classList.remove(ue),this}setOffset(ue){return this.options.offset=ue,this._update(),this}toggleClassName(ue){if(this._container)return this._container.classList.toggle(ue)}setSubpixelPositioning(ue){this.options.subpixelPositioning=ue}_createCloseButton(){this.options.closeButton&&(this._closeButton=c.create(\"button\",\"maplibregl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;let ue=this._container.querySelector(Vc);ue&&ue.focus()}},i.RasterDEMTileSource=Ht,i.RasterTileSource=mt,i.ScaleControl=class{constructor(ue){this._onMove=()=>{Ac(this._map,this._container,this.options)},this.setUnit=w=>{this.options.unit=w,Ac(this._map,this._container,this.options)},this.options=Object.assign(Object.assign({},xu),ue)}getDefaultPosition(){return\"bottom-left\"}onAdd(ue){return this._map=ue,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-scale\",ue.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container}onRemove(){c.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0}},i.ScrollZoomHandler=Kr,i.Style=Ha,i.TerrainControl=class{constructor(ue){this._toggleTerrain=()=>{this._map.getTerrain()?this._map.setTerrain(null):this._map.setTerrain(this.options),this._updateTerrainIcon()},this._updateTerrainIcon=()=>{this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain\"),this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain-enabled\"),this._map.terrain?(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain-enabled\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.Disable\")):(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.Enable\"))},this.options=ue}onAdd(ue){return this._map=ue,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._terrainButton=c.create(\"button\",\"maplibregl-ctrl-terrain\",this._container),c.create(\"span\",\"maplibregl-ctrl-icon\",this._terrainButton).setAttribute(\"aria-hidden\",\"true\"),this._terrainButton.type=\"button\",this._terrainButton.addEventListener(\"click\",this._toggleTerrain),this._updateTerrainIcon(),this._map.on(\"terrain\",this._updateTerrainIcon),this._container}onRemove(){c.remove(this._container),this._map.off(\"terrain\",this._updateTerrainIcon),this._map=void 0}},i.TwoFingersTouchPitchHandler=Bc,i.TwoFingersTouchRotateHandler=ff,i.TwoFingersTouchZoomHandler=vu,i.TwoFingersTouchZoomRotateHandler=Yi,i.VectorTileSource=lt,i.VideoSource=Vt,i.addSourceType=(ue,w)=>a._(void 0,void 0,void 0,(function*(){if(je(ue))throw new Error(`A source type called \"${ue}\" already exists.`);((B,Q)=>{tt[B]=Q})(ue,w)})),i.clearPrewarmedResources=function(){let ue=ge;ue&&(ue.isPreloaded()&&ue.numActive()===1?(ue.release(_e),ge=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},i.getMaxParallelImageRequests=function(){return a.a.MAX_PARALLEL_IMAGE_REQUESTS},i.getRTLTextPluginStatus=function(){return St().getRTLTextPluginStatus()},i.getVersion=function(){return Ku},i.getWorkerCount=function(){return Ee.workerCount},i.getWorkerUrl=function(){return a.a.WORKER_URL},i.importScriptInWorkers=function(ue){return be().broadcast(\"IS\",ue)},i.prewarm=function(){Se().acquire(_e)},i.setMaxParallelImageRequests=function(ue){a.a.MAX_PARALLEL_IMAGE_REQUESTS=ue},i.setRTLTextPlugin=function(ue,w){return St().setRTLTextPlugin(ue,w)},i.setWorkerCount=function(ue){Ee.workerCount=ue},i.setWorkerUrl=function(ue){a.a.WORKER_URL=ue}}));var n=e;return n}))});var Eje=ye((v2r,Mje)=>{\"use strict\";var yw=Pr(),lZt=Zl().sanitizeHTML,uZt=zJ(),Tje=Ix();function Aje(e,t){this.subplot=e,this.uid=e.uid+\"-\"+t,this.index=t,this.idSource=\"source-\"+this.uid,this.idLayer=Tje.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var sg=Aje.prototype;sg.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=y7(t)};sg.needsNewImage=function(e){var t=this.subplot.map;return t.getSource(this.idSource)&&this.sourceType===\"image\"&&e.sourcetype===\"image\"&&(this.source!==e.source||JSON.stringify(this.coordinates)!==JSON.stringify(e.coordinates))};sg.needsNewSource=function(e){return this.sourceType!==e.sourcetype||JSON.stringify(this.source)!==JSON.stringify(e.source)||this.layerType!==e.type};sg.needsNewLayer=function(e){return this.layerType!==e.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]};sg.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]};sg.updateImage=function(e){var t=this.subplot.map;t.getSource(this.idSource).updateImage({url:e.source,coordinates:e.coordinates});var r=this.findFollowingMapLayerId(this.lookupBelow());r!==null&&this.subplot.map.moveLayer(this.idLayer,r)};sg.updateSource=function(e){var t=this.subplot.map;if(t.getSource(this.idSource)&&t.removeSource(this.idSource),this.sourceType=e.sourcetype,this.source=e.source,!!y7(e)){var r=cZt(e);t.addSource(this.idSource,r)}};sg.findFollowingMapLayerId=function(e){if(e===\"traces\")for(var t=this.subplot.getMapLayers(),r=0;r<t.length;r++){var n=t[r].id;if(typeof n==\"string\"&&n.indexOf(Tje.traceLayerPrefix)===0){e=n;break}}return e};sg.updateLayer=function(e){var t=this.subplot,r=Sje(e),n=this.lookupBelow(),i=this.findFollowingMapLayerId(n);this.removeLayer(),y7(e)&&t.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":e.sourcelayer||\"\",type:e.type,minzoom:e.minzoom,maxzoom:e.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=e.type,this.below=n};sg.updateStyle=function(e){if(y7(e)){var t=Sje(e);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",t.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",t.paint)}};sg.removeLayer=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer)};sg.dispose=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),e.getSource(this.idSource)&&e.removeSource(this.idSource)};function y7(e){if(!e.visible)return!1;var t=e.source;if(Array.isArray(t)&&t.length>0){for(var r=0;r<t.length;r++)if(typeof t[r]!=\"string\"||t[r].length===0)return!1;return!0}return yw.isPlainObject(t)||typeof t==\"string\"&&t.length>0}function Sje(e){var t={},r={};switch(e.type){case\"circle\":yw.extendFlat(r,{\"circle-radius\":e.circle.radius,\"circle-color\":e.color,\"circle-opacity\":e.opacity});break;case\"line\":yw.extendFlat(r,{\"line-width\":e.line.width,\"line-color\":e.color,\"line-opacity\":e.opacity,\"line-dasharray\":e.line.dash});break;case\"fill\":yw.extendFlat(r,{\"fill-color\":e.color,\"fill-outline-color\":e.fill.outlinecolor,\"fill-opacity\":e.opacity});break;case\"symbol\":var n=e.symbol,i=uZt(n.textposition,n.iconsize);yw.extendFlat(t,{\"icon-image\":n.icon+\"-15\",\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":i.anchor,\"text-offset\":i.offset,\"symbol-placement\":n.placement}),yw.extendFlat(r,{\"icon-color\":e.color,\"text-color\":n.textfont.color,\"text-opacity\":e.opacity});break;case\"raster\":yw.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":e.opacity});break}return{layout:t,paint:r}}function cZt(e){var t=e.sourcetype,r=e.source,n={type:t},i;return t===\"geojson\"?i=\"data\":t===\"vector\"?i=typeof r==\"string\"?\"url\":\"tiles\":t===\"raster\"?(i=\"tiles\",n.tileSize=256):t===\"image\"&&(i=\"url\",n.coordinates=e.coordinates),n[i]=r,e.sourceattribution&&(n.attribution=lZt(e.sourceattribution)),n}Mje.exports=function(t,r,n){var i=new Aje(t,r);return i.update(n),i}});var Fje=ye((p2r,Dje)=>{\"use strict\";var VJ=wje(),GJ=Pr(),Lje=ey(),kje=Oa(),fZt=ho(),hZt=yv(),_7=ef(),Pje=Cg(),dZt=Pje.drawMode,vZt=Pje.selectMode,pZt=qf().prepSelect,gZt=qf().clearOutline,mZt=qf().clearSelectionsCache,yZt=qf().selectOnClick,_w=Ix(),_Zt=Eje();function Ije(e,t){this.id=t,this.gd=e;var r=e._fullLayout,n=e._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+\"-\"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var Hh=Ije.prototype;Hh.plot=function(e,t,r){var n=this,i;n.map?i=new Promise(function(a,o){n.updateMap(e,t,a,o)}):i=new Promise(function(a,o){n.createMap(e,t,a,o)}),r.push(i)};Hh.createMap=function(e,t,r,n){var i=this,a=t[i.id],o=i.styleObj=Rje(a.style),s=a.bounds,l=s?[[s.west,s.south],[s.east,s.north]]:null,u=i.map=new VJ.Map({container:i.div,style:o.style,center:HJ(a.center),zoom:a.zoom,bearing:a.bearing,pitch:a.pitch,maxBounds:l,interactive:!i.isStatic,preserveDrawingBuffer:i.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new VJ.AttributionControl({compact:!0})),c={};u.on(\"styleimagemissing\",function(h){var d=h.id;if(!c[d]&&d.includes(\"-15\")){c[d]=!0;var v=new Image(15,15);v.onload=function(){u.addImage(d,v)},v.crossOrigin=\"Anonymous\",v.src=\"https://unpkg.com/maki@2.1.0/icons/\"+d+\".svg\"}}),u.setTransformRequest(function(h){return h=h.replace(\"https://fonts.openmaptiles.org/Open Sans Extrabold\",\"https://fonts.openmaptiles.org/Open Sans Extra Bold\"),h=h.replace(\"https://tiles.basemaps.cartocdn.com/fonts/Open Sans Extrabold\",\"https://fonts.openmaptiles.org/Open Sans Extra Bold\"),h=h.replace(\"https://fonts.openmaptiles.org/Open Sans Regular,Arial Unicode MS Regular\",\"https://fonts.openmaptiles.org/Klokantech Noto Sans Regular\"),{url:h}}),u._canvas.style.left=\"0px\",u._canvas.style.top=\"0px\",i.rejectOnError(n),i.isStatic||i.initFx(e,t);var f=[];f.push(new Promise(function(h){u.once(\"load\",h)})),f=f.concat(Lje.fetchTraceGeoData(e)),Promise.all(f).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Hh.updateMap=function(e,t,r,n){var i=this,a=i.map,o=t[this.id];i.rejectOnError(n);var s=[],l=Rje(o.style);JSON.stringify(i.styleObj)!==JSON.stringify(l)&&(i.styleObj=l,a.setStyle(l.style),i.traceHash={},s.push(new Promise(function(u){a.once(\"styledata\",u)}))),s=s.concat(Lje.fetchTraceGeoData(e)),Promise.all(s).then(function(){i.fillBelowLookup(e,t),i.updateData(e),i.updateLayout(t),i.resolveOnRender(r)}).catch(n)};Hh.fillBelowLookup=function(e,t){var r=t[this.id],n=r.layers,i,a,o=this.belowLookup={},s=!1;for(i=0;i<e.length;i++){var l=e[i][0].trace,u=l._module;typeof l.below==\"string\"?a=l.below:u.getBelow&&(a=u.getBelow(l,this)),a===\"\"&&(s=!0),o[\"trace-\"+l.uid]=a||\"\"}for(i=0;i<n.length;i++){var c=n[i];typeof c.below==\"string\"?a=c.below:s?a=\"traces\":a=\"\",o[\"layout-\"+i]=a}var f={},h,d;for(h in o)a=o[h],f[a]?f[a].push(h):f[a]=[h];for(a in f){var v=f[a];if(v.length>1)for(i=0;i<v.length;i++)h=v[i],h.indexOf(\"trace-\")===0?(d=h.split(\"trace-\")[1],this.traceHash[d]&&(this.traceHash[d].below=null)):h.indexOf(\"layout-\")===0&&(d=h.split(\"layout-\")[1],this.layerList[d]&&(this.layerList[d].below=null))}};var Cje={choroplethmap:0,densitymap:1,scattermap:2};Hh.updateData=function(e){var t=this.traceHash,r,n,i,a,o=e.slice().sort(function(f,h){return Cje[f[0].trace.type]-Cje[h[0].trace.type]});for(i=0;i<o.length;i++){var s=o[i];n=s[0].trace,r=t[n.uid];var l=!1;r&&(r.type===n.type?(r.update(s),l=!0):r.dispose()),!l&&n._module&&(t[n.uid]=n._module.plot(this,s))}var u=Object.keys(t);e:for(i=0;i<u.length;i++){var c=u[i];for(a=0;a<e.length;a++)if(n=e[a][0].trace,c===n.uid)continue e;r=t[c],r.dispose(),delete t[c]}};Hh.updateLayout=function(e){var t=this.map,r=e[this.id];!this.dragging&&!this.wheeling&&(t.setCenter(HJ(r.center)),t.setZoom(r.zoom),t.setBearing(r.bearing),t.setPitch(r.pitch)),this.updateLayers(e),this.updateFramework(e),this.updateFx(e),this.map.resize(),this.gd._context._scrollZoom.map?t.scrollZoom.enable():t.scrollZoom.disable()};Hh.resolveOnRender=function(e){var t=this.map;t.on(\"render\",function r(){t.loaded()&&(t.off(\"render\",r),setTimeout(e,10))})};Hh.rejectOnError=function(e){var t=this.map;function r(){e(new Error(_w.mapOnErrorMsg))}t.once(\"error\",r),t.once(\"style.error\",r),t.once(\"source.error\",r),t.once(\"tile.error\",r),t.once(\"layer.error\",r)};Hh.createFramework=function(e){var t=this,r=t.div=document.createElement(\"div\");r.id=t.uid,r.style.position=\"absolute\",t.container.appendChild(r),t.xaxis={_id:\"x\",c2p:function(n){return t.project(n).x}},t.yaxis={_id:\"y\",c2p:function(n){return t.project(n).y}},t.updateFramework(e),t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},fZt.setConvert(t.mockAxis,e)};Hh.initFx=function(e,t){var r=this,n=r.gd,i=r.map;i.on(\"moveend\",function(s){if(r.map){var l=n._fullLayout;if(s.originalEvent||r.wheeling){var u=l[r.id];kje.call(\"_storeDirectGUIEdit\",n.layout,l._preGUI,r.getViewEdits(u));var c=r.getView();u._input.center=u.center=c.center,u._input.zoom=u.zoom=c.zoom,u._input.bearing=u.bearing=c.bearing,u._input.pitch=u.pitch=c.pitch,n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(c))}s.originalEvent&&s.originalEvent.type===\"mouseup\"?r.dragging=!1:r.wheeling&&(r.wheeling=!1),l&&l._rehover&&l._rehover()}}),i.on(\"wheel\",function(){r.wheeling=!0}),i.on(\"mousemove\",function(s){var l=r.div.getBoundingClientRect(),u=[s.originalEvent.offsetX,s.originalEvent.offsetY];s.target.getBoundingClientRect=function(){return l},r.xaxis.p2c=function(){return i.unproject(u).lng},r.yaxis.p2c=function(){return i.unproject(u).lat},n._fullLayout._rehover=function(){n._fullLayout._hoversubplot===r.id&&n._fullLayout[r.id]&&_7.hover(n,s,r.id)},_7.hover(n,s,r.id),n._fullLayout._hoversubplot=r.id});function a(){_7.loneUnhover(t._hoverlayer)}i.on(\"dragstart\",function(){r.dragging=!0,a()}),i.on(\"zoomstart\",a),i.on(\"mouseout\",function(){n._fullLayout._hoversubplot=null});function o(){var s=r.getView();n.emit(\"plotly_relayouting\",r.getViewEditsWithDerived(s))}i.on(\"drag\",o),i.on(\"zoom\",o),i.on(\"dblclick\",function(){var s=n._fullLayout[r.id];kje.call(\"_storeDirectGUIEdit\",n.layout,n._fullLayout._preGUI,r.getViewEdits(s));var l=r.viewInitial;i.setCenter(HJ(l.center)),i.setZoom(l.zoom),i.setBearing(l.bearing),i.setPitch(l.pitch);var u=r.getView();s._input.center=s.center=u.center,s._input.zoom=s.zoom=u.zoom,s._input.bearing=s.bearing=u.bearing,s._input.pitch=s.pitch=u.pitch,n.emit(\"plotly_doubleclick\",null),n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(u))}),r.clearOutline=function(){mZt(r.dragOptions),gZt(r.dragOptions.gd)},r.onClickInPanFn=function(s){return function(l){var u=n._fullLayout.clickmode;u.indexOf(\"select\")>-1&&yZt(l.originalEvent,n,[r.xaxis],[r.yaxis],r.id,s),u.indexOf(\"event\")>-1&&_7.click(n,l.originalEvent)}}};Hh.updateFx=function(e){var t=this,r=t.map,n=t.gd;if(t.isStatic)return;function i(l){var u=t.map.unproject(l);return[u.lng,u.lat]}var a=e.dragmode,o;o=function(l,u){if(u.isRect){var c=l.range={};c[t.id]=[i([u.xmin,u.ymin]),i([u.xmax,u.ymax])]}else{var f=l.lassoPoints={};f[t.id]=u.map(i)}};var s=t.dragOptions;t.dragOptions=GJ.extendDeep(s||{},{dragmode:e.dragmode,element:t.div,gd:n,plotinfo:{id:t.id,domain:e[t.id].domain,xaxis:t.xaxis,yaxis:t.yaxis,fillRangeItems:o},xaxes:[t.xaxis],yaxes:[t.yaxis],subplot:t.id}),r.off(\"click\",t.onClickInPanHandler),vZt(a)||dZt(a)?(r.dragPan.disable(),r.on(\"zoomstart\",t.clearOutline),t.dragOptions.prepFn=function(l,u,c){pZt(l,u,c,t.dragOptions,a)},hZt.init(t.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",t.clearOutline),t.div.onmousedown=null,t.div.ontouchstart=null,t.div.removeEventListener(\"touchstart\",t.div._ontouchstart),t.onClickInPanHandler=t.onClickInPanFn(t.dragOptions),r.on(\"click\",t.onClickInPanHandler))};Hh.updateFramework=function(e){var t=e[this.id].domain,r=e._size,n=this.div.style;n.width=r.w*(t.x[1]-t.x[0])+\"px\",n.height=r.h*(t.y[1]-t.y[0])+\"px\",n.left=r.l+t.x[0]*r.w+\"px\",n.top=r.t+(1-t.y[1])*r.h+\"px\",this.xaxis._offset=r.l+t.x[0]*r.w,this.xaxis._length=r.w*(t.x[1]-t.x[0]),this.yaxis._offset=r.t+(1-t.y[1])*r.h,this.yaxis._length=r.h*(t.y[1]-t.y[0])};Hh.updateLayers=function(e){var t=e[this.id],r=t.layers,n=this.layerList,i;if(r.length!==n.length){for(i=0;i<n.length;i++)n[i].dispose();for(n=this.layerList=[],i=0;i<r.length;i++)n.push(_Zt(this,i,r[i]))}else for(i=0;i<r.length;i++)n[i].update(r[i])};Hh.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))};Hh.toImage=function(){return this.map.stop(),this.map.getCanvas().toDataURL()};Hh.setOptions=function(e,t,r){for(var n in r)this.map[t](e,n,r[n])};Hh.getMapLayers=function(){return this.map.getStyle().layers};Hh.addLayer=function(e,t){var r=this.map;if(typeof t==\"string\"){if(t===\"\"){r.addLayer(e,t);return}for(var n=this.getMapLayers(),i=0;i<n.length;i++)if(t===n[i].id){r.addLayer(e,t);return}GJ.warn([\"Trying to add layer with *below* value\",t,\"referencing a layer that does not exist\",\"or that does not yet exist.\"].join(\" \"))}r.addLayer(e)};Hh.project=function(e){return this.map.project(new VJ.LngLat(e[0],e[1]))};Hh.getView=function(){var e=this.map,t=e.getCenter(),r=t.lng,n=t.lat,i={lon:r,lat:n},a=e.getCanvas(),o=parseInt(a.style.width),s=parseInt(a.style.height);return{center:i,zoom:e.getZoom(),bearing:e.getBearing(),pitch:e.getPitch(),_derived:{coordinates:[e.unproject([0,0]).toArray(),e.unproject([o,0]).toArray(),e.unproject([o,s]).toArray(),e.unproject([0,s]).toArray()]}}};Hh.getViewEdits=function(e){for(var t=this.id,r=[\"center\",\"zoom\",\"bearing\",\"pitch\"],n={},i=0;i<r.length;i++){var a=r[i];n[t+\".\"+a]=e[a]}return n};Hh.getViewEditsWithDerived=function(e){var t=this.id,r=this.getViewEdits(e);return r[t+\"._derived\"]=e._derived,r};function Rje(e){var t={};return GJ.isPlainObject(e)?(t.id=e.id,t.style=e):typeof e==\"string\"?(t.id=e,_w.stylesMap[e]?t.style=_w.stylesMap[e]:t.style=e):(t.id=_w.styleValueDflt,t.style=xZt(_w.styleValueDflt)),t.transition={duration:0,delay:0},t}function xZt(e){return _w.styleUrlPrefix+e+\"-\"+_w.styleUrlSuffix}function HJ(e){return[e.lon,e.lat]}Dje.exports=Ije});var Oje=ye((g2r,qje)=>{\"use strict\";var jJ=Pr(),bZt=O_(),wZt=Kd(),zje=rC();qje.exports=function(t,r,n){bZt(t,r,n,{type:\"map\",attributes:zje,handleDefaults:TZt,partition:\"y\"})};function TZt(e,t,r){r(\"style\"),r(\"center.lon\"),r(\"center.lat\"),r(\"zoom\"),r(\"bearing\"),r(\"pitch\");var n=r(\"bounds.west\"),i=r(\"bounds.east\"),a=r(\"bounds.south\"),o=r(\"bounds.north\");(n===void 0||i===void 0||a===void 0||o===void 0)&&delete t.bounds,wZt(e,t,{name:\"layers\",handleItemDefaults:AZt}),t._input=e}function AZt(e,t){function r(l,u){return jJ.coerce(e,t,zje.layers,l,u)}var n=r(\"visible\");if(n){var i=r(\"sourcetype\"),a=i===\"raster\"||i===\"image\";r(\"source\"),r(\"sourceattribution\"),i===\"vector\"&&r(\"sourcelayer\"),i===\"image\"&&r(\"coordinates\");var o;a&&(o=\"raster\");var s=r(\"type\",o);a&&s!==\"raster\"&&(s=t.type=\"raster\",jJ.log(\"Source types *raster* and *image* must drawn *raster* layer type.\")),r(\"below\"),r(\"color\"),r(\"opacity\"),r(\"minzoom\"),r(\"maxzoom\"),s===\"circle\"&&r(\"circle.radius\"),s===\"line\"&&(r(\"line.width\"),r(\"line.dash\")),s===\"fill\"&&r(\"fill.outlinecolor\"),s===\"symbol\"&&(r(\"symbol.icon\"),r(\"symbol.iconsize\"),r(\"symbol.text\"),jJ.coerceFont(r,\"symbol.textfont\",void 0,{noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}),r(\"symbol.textposition\"),r(\"symbol.placement\"))}}});var b7=ye(u0=>{\"use strict\";var x7=Pr(),Bje=x7.strTranslate,SZt=x7.strScale,MZt=Id().getSubplotCalcData,EZt=Wp(),kZt=Fa(),Nje=So(),CZt=Zl(),LZt=Fje(),Rx=\"map\";u0.name=Rx;u0.attr=\"subplot\";u0.idRoot=Rx;u0.idRegex=u0.attrRegex=x7.counterRegex(Rx);u0.attributes={subplot:{valType:\"subplotid\",dflt:\"map\",editType:\"calc\"}};u0.layoutAttributes=rC();u0.supplyLayoutDefaults=Oje();u0.plot=function(t){for(var r=t._fullLayout,n=t.calcdata,i=r._subplots[Rx],a=0;a<i.length;a++){var o=i[a],s=MZt(n,Rx,o),l=r[o],u=l._subplot;u||(u=new LZt(t,o),r[o]._subplot=u),u.viewInitial||(u.viewInitial={center:x7.extendFlat({},l.center),zoom:l.zoom,bearing:l.bearing,pitch:l.pitch}),u.plot(s,r,t._promises)}};u0.clean=function(e,t,r,n){for(var i=n._subplots[Rx]||[],a=0;a<i.length;a++){var o=i[a];!t[o]&&n[o]._subplot&&n[o]._subplot.destroy()}};u0.toSVG=function(e){for(var t=e._fullLayout,r=t._subplots[Rx],n=t._size,i=0;i<r.length;i++){var a=t[r[i]],o=a.domain,s=a._subplot,l=s.toImage(\"png\"),u=t._glimages.append(\"svg:image\");u.attr({xmlns:EZt.svg,\"xlink:href\":l,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"});var c=kZt.select(a._subplot.div),f=c.select(\".maplibregl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),h=t._glimages.append(\"g\"),d=h.append(\"text\");d.text(f).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":f});var v=Nje.bBox(d.node()),m=n.w*(o.x[1]-o.x[0]);if(v.width>m/2){var b=f.split(\"|\").join(\"<br>\");d.text(b).attr(\"data-unformatted\",b).call(CZt.convertToTspans,e),v=Nje.bBox(d.node())}d.attr(\"transform\",Bje(-3,-v.height+8)),h.insert(\"rect\",\".static-attribution\").attr({x:-v.width-6,y:-v.height-3,width:v.width+6,height:v.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var p=1;v.width+6>m&&(p=m/(v.width+6));var k=[n.l+n.w*o.x[1],n.t+n.h*(1-o.y[0])];h.attr(\"transform\",Bje(k[0],k[1])+SZt(p))}};u0.updateFx=function(e){for(var t=e._fullLayout,r=t._subplots[Rx],n=0;n<r.length;n++){var i=t[r[n]]._subplot;i.updateFx(t)}}});var Vje=ye((y2r,Uje)=>{\"use strict\";Uje.exports={attributes:h7(),supplyDefaults:eje(),colorbar:Qd(),formatLabels:FJ(),calc:TF(),plot:vje(),hoverPoints:m7().hoverPoints,eventData:yje(),selectPoints:xje(),styleOnSelect:function(e,t){if(t){var r=t[0].trace;r._glTrace.update(t)}},moduleType:\"trace\",name:\"scattermap\",basePlotModule:b7(),categories:[\"map\",\"gl\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}});var Hje=ye((_2r,Gje)=>{\"use strict\";Gje.exports=Vje()});var WJ=ye((x2r,jje)=>{\"use strict\";var b1=l5(),PZt=Tu(),{hovertemplateAttrs:IZt,templatefallbackAttrs:RZt}=Pl(),DZt=Vl(),Dx=Ao().extendFlat;jje.exports=Dx({locations:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},geojson:{valType:\"any\",editType:\"calc\"},featureidkey:Dx({},b1.featureidkey,{}),below:{valType:\"string\",editType:\"plot\"},text:b1.text,hovertext:b1.hovertext,marker:{line:{color:Dx({},b1.marker.line.color,{editType:\"plot\"}),width:Dx({},b1.marker.line.width,{editType:\"plot\"}),editType:\"calc\"},opacity:Dx({},b1.marker.opacity,{editType:\"plot\"}),editType:\"calc\"},selected:{marker:{opacity:Dx({},b1.selected.marker.opacity,{editType:\"plot\"}),editType:\"plot\"},editType:\"plot\"},unselected:{marker:{opacity:Dx({},b1.unselected.marker.opacity,{editType:\"plot\"}),editType:\"plot\"},editType:\"plot\"},hoverinfo:b1.hoverinfo,hovertemplate:IZt({},{keys:[\"properties\"]}),hovertemplatefallback:RZt(),showlegend:Dx({},DZt.showlegend,{dflt:!1})},PZt(\"\",{cLetter:\"z\",editTypeOverride:\"calc\"}))});var Xje=ye((b2r,Wje)=>{\"use strict\";var oC=Pr(),FZt=td(),zZt=WJ();Wje.exports=function(t,r,n,i){function a(c,f){return oC.coerce(t,r,zZt,c,f)}var o=a(\"locations\"),s=a(\"z\"),l=a(\"geojson\");if(!oC.isArrayOrTypedArray(o)||!o.length||!oC.isArrayOrTypedArray(s)||!s.length||!(typeof l==\"string\"&&l!==\"\"||oC.isPlainObject(l))){r.visible=!1;return}a(\"featureidkey\"),r._length=Math.min(o.length,s.length),a(\"below\"),a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\");var u=a(\"marker.line.width\");u&&a(\"marker.line.color\"),a(\"marker.opacity\"),FZt(t,r,i,a,{prefix:\"\",cLetter:\"z\"}),oC.coerceSelectionMarkerOpacity(r,a)}});var XJ=ye((w2r,Kje)=>{\"use strict\";var qZt=Eo(),w1=Pr(),OZt=tc(),BZt=So(),NZt=hx().makeBlank,Zje=ey();function UZt(e){var t=e[0].trace,r=t.visible===!0&&t._length!==0,n={layout:{visibility:\"none\"},paint:{}},i={layout:{visibility:\"none\"},paint:{}},a=t._opts={fill:n,line:i,geojson:NZt()};if(!r)return a;var 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t=e[0].trace,r=t._opts,n;if(t.selectedpoints){for(var i=BZt.makeSelectedPointStyleFns(t),a=0;a<e.length;a++){var o=e[a];o.fOut&&(o.fOut.properties.mo2=i.selectedOpacityFn(o))}n={type:\"identity\",property:\"mo2\"}}else n=w1.isArrayOrTypedArray(t.marker.opacity)?{type:\"identity\",property:\"mo\"}:t.marker.opacity;return w1.extendFlat(r.fill.paint,{\"fill-opacity\":n}),w1.extendFlat(r.line.paint,{\"line-opacity\":n}),r}Kje.exports={convert:UZt,convertOnSelect:Yje}});var tWe=ye((T2r,eWe)=>{\"use strict\";var $je=XJ().convert,VZt=XJ().convertOnSelect,Jje=Ix().traceLayerPrefix;function Qje(e,t){this.type=\"choroplethmap\",this.subplot=e,this.uid=t,this.sourceId=\"source-\"+t,this.layerList=[[\"fill\",Jje+t+\"-fill\"],[\"line\",Jje+t+\"-line\"]],this.below=null}var q5=Qje.prototype;q5.update=function(e){this._update($je(e)),e[0].trace._glTrace=this};q5.updateOnSelect=function(e){this._update(VZt(e))};q5._update=function(e){var 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GZt=Tu(),{hovertemplateAttrs:HZt,templatefallbackAttrs:jZt}=Pl(),oWe=Vl(),w7=h7(),ZJ=Ao().extendFlat;sWe.exports=ZJ({lon:w7.lon,lat:w7.lat,z:{valType:\"data_array\",editType:\"calc\"},radius:{valType:\"number\",editType:\"plot\",arrayOk:!0,min:1,dflt:30},below:{valType:\"string\",editType:\"plot\"},text:w7.text,hovertext:w7.hovertext,hoverinfo:ZJ({},oWe.hoverinfo,{flags:[\"lon\",\"lat\",\"z\",\"text\",\"name\"]}),hovertemplate:HZt(),hovertemplatefallback:jZt(),showlegend:ZJ({},oWe.showlegend,{dflt:!1})},GZt(\"\",{cLetter:\"z\",editTypeOverride:\"calc\"}))});var uWe=ye((E2r,lWe)=>{\"use strict\";var WZt=Pr(),XZt=td(),ZZt=YJ();lWe.exports=function(t,r,n,i){function a(u,c){return WZt.coerce(t,r,ZZt,u,c)}var o=a(\"lon\")||[],s=a(\"lat\")||[],l=Math.min(o.length,s.length);if(!l){r.visible=!1;return}r._length=l,a(\"z\"),a(\"radius\"),a(\"below\"),a(\"text\"),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),XZt(t,r,i,a,{prefix:\"\",cLetter:\"z\"})}});var hWe=ye((k2r,fWe)=>{\"use strict\";var KJ=Eo(),YZt=Pr().isArrayOrTypedArray,JJ=fs().BADNUM,KZt=gv(),cWe=Pr()._;fWe.exports=function(t,r){for(var n=r._length,i=new Array(n),a=r.z,o=YZt(a)&&a.length,s=0;s<n;s++){var l=i[s]={},u=r.lon[s],c=r.lat[s];if(l.lonlat=KJ(u)&&KJ(c)?[+u,+c]:[JJ,JJ],o){var f=a[s];l.z=KJ(f)?f:JJ}}return KZt(t,r,{vals:o?a:[0,1],containerStr:\"\",cLetter:\"z\"}),n&&(i[0].t={labels:{lat:cWe(t,\"lat:\")+\" \",lon:cWe(t,\"lon:\")+\" \"}}),i}});var mWe=ye((C2r,gWe)=>{\"use strict\";var JZt=Eo(),$J=Pr(),dWe=ka(),vWe=tc(),pWe=fs().BADNUM,$Zt=hx().makeBlank;gWe.exports=function(t){var r=t[0].trace,n=r.visible===!0&&r._length!==0,i={layout:{visibility:\"none\"},paint:{}},a=r._opts={heatmap:i,geojson:$Zt()};if(!n)return a;var o=[],s,l=r.z,u=r.radius,c=$J.isArrayOrTypedArray(l)&&l.length,f=$J.isArrayOrTypedArray(u);for(s=0;s<t.length;s++){var h=t[s],d=h.lonlat;if(d[0]!==pWe){var v={};if(c){var 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T7=_We.prototype;T7.update=function(e){var t=this.subplot,r=this.layerList,n=yWe(e),i=t.belowLookup[\"trace-\"+this.uid];t.map.getSource(this.sourceId).setData(n.geojson),i!==this.below&&(this._removeLayers(),this._addLayers(n,i),this.below=i);for(var a=0;a<r.length;a++){var o=r[a],s=o[0],l=o[1],u=n[s];t.setOptions(l,\"setLayoutProperty\",u.layout),u.layout.visibility===\"visible\"&&t.setOptions(l,\"setPaintProperty\",u.paint)}};T7._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},t)}};T7._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};T7.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};xWe.exports=function(t,r){var n=r[0].trace,i=new _We(t,n.uid),a=i.sourceId,o=yWe(r),s=i.below=t.belowLookup[\"trace-\"+n.uid];return 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3:this._point=4;default:or(this,Ct,Sr);break}this._x0=this._x1,this._x1=Ct,this._y0=this._y1,this._y1=Sr}};function gt(Ct){return new jr(Ct)}function Ge(Ct,Sr){this._basis=new mr(Ct),this._beta=Sr}Ge.prototype={lineStart:function(){this._x=[],this._y=[],this._basis.lineStart()},lineEnd:function(){var Ct=this._x,Sr=this._y,Jr=Ct.length-1;if(Jr>0)for(var hi=Ct[0],hn=Sr[0],Sn=Ct[Jr]-hi,En=Sr[Jr]-hn,ki=-1,_n;++ki<=Jr;)_n=ki/Jr,this._basis.point(this._beta*Ct[ki]+(1-this._beta)*(hi+_n*Sn),this._beta*Sr[ki]+(1-this._beta)*(hn+_n*En));this._x=this._y=null,this._basis.lineEnd()},point:function(Ct,Sr){this._x.push(+Ct),this._y.push(+Sr)}};var Je=(function Ct(Sr){function Jr(hi){return Sr===1?new mr(hi):new Ge(hi,Sr)}return Jr.beta=function(hi){return Ct(+hi)},Jr})(.85);function We(Ct,Sr,Jr){Ct._context.bezierCurveTo(Ct._x1+Ct._k*(Ct._x2-Ct._x0),Ct._y1+Ct._k*(Ct._y2-Ct._y0),Ct._x2+Ct._k*(Ct._x1-Sr),Ct._y2+Ct._k*(Ct._y1-Jr),Ct._x2,Ct._y2)}function et(Ct,Sr){this._context=Ct,this._k=(1-Sr)/6}et.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x2,this._y2);break;case 3:We(this,this._x1,this._y1);break}(this._line||this._line!==0&&this._point===1)&&this._context.closePath(),this._line=1-this._line},point:function(Ct,Sr){switch(Ct=+Ct,Sr=+Sr,this._point){case 0:this._point=1,this._line?this._context.lineTo(Ct,Sr):this._context.moveTo(Ct,Sr);break;case 1:this._point=2,this._x1=Ct,this._y1=Sr;break;case 2:this._point=3;default:We(this,Ct,Sr);break}this._x0=this._x1,this._x1=this._x2,this._x2=Ct,this._y0=this._y1,this._y1=this._y2,this._y2=Sr}};var xt=(function Ct(Sr){function Jr(hi){return new et(hi,Sr)}return Jr.tension=function(hi){return Ct(+hi)},Jr})(0);function At(Ct,Sr){this._context=Ct,this._k=(1-Sr)/6}At.prototype={areaStart:jt,areaEnd:jt,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._x5=this._y0=this._y1=this._y2=this._y3=this._y4=this._y5=NaN,this._point=0},lineEnd:function(){switch(this._point){case 1:{this._context.moveTo(this._x3,this._y3),this._context.closePath();break}case 2:{this._context.lineTo(this._x3,this._y3),this._context.closePath();break}case 3:{this.point(this._x3,this._y3),this.point(this._x4,this._y4),this.point(this._x5,this._y5);break}}},point:function(Ct,Sr){switch(Ct=+Ct,Sr=+Sr,this._point){case 0:this._point=1,this._x3=Ct,this._y3=Sr;break;case 1:this._point=2,this._context.moveTo(this._x4=Ct,this._y4=Sr);break;case 2:this._point=3,this._x5=Ct,this._y5=Sr;break;default:We(this,Ct,Sr);break}this._x0=this._x1,this._x1=this._x2,this._x2=Ct,this._y0=this._y1,this._y1=this._y2,this._y2=Sr}};var Kt=(function Ct(Sr){function Jr(hi){return new At(hi,Sr)}return Jr.tension=function(hi){return Ct(+hi)},Jr})(0);function Qt(Ct,Sr){this._context=Ct,this._k=(1-Sr)/6}Qt.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._point=0},lineEnd:function(){(this._line||this._line!==0&&this._point===3)&&this._context.closePath(),this._line=1-this._line},point:function(Ct,Sr){switch(Ct=+Ct,Sr=+Sr,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3,this._line?this._context.lineTo(this._x2,this._y2):this._context.moveTo(this._x2,this._y2);break;case 3:this._point=4;default:We(this,Ct,Sr);break}this._x0=this._x1,this._x1=this._x2,this._x2=Ct,this._y0=this._y1,this._y1=this._y2,this._y2=Sr}};var Mr=(function Ct(Sr){function Jr(hi){return new Qt(hi,Sr)}return Jr.tension=function(hi){return Ct(+hi)},Jr})(0);function Gr(Ct,Sr,Jr){var hi=Ct._x1,hn=Ct._y1,Sn=Ct._x2,En=Ct._y2;if(Ct._l01_a>c){var ki=2*Ct._l01_2a+3*Ct._l01_a*Ct._l12_a+Ct._l12_2a,_n=3*Ct._l01_a*(Ct._l01_a+Ct._l12_a);hi=(hi*ki-Ct._x0*Ct._l12_2a+Ct._x2*Ct._l01_2a)/_n,hn=(hn*ki-Ct._y0*Ct._l12_2a+Ct._y2*Ct._l01_2a)/_n}if(Ct._l23_a>c){var ya=2*Ct._l23_2a+3*Ct._l23_a*Ct._l12_a+Ct._l12_2a,ea=3*Ct._l23_a*(Ct._l23_a+Ct._l12_a);Sn=(Sn*ya+Ct._x1*Ct._l23_2a-Sr*Ct._l12_2a)/ea,En=(En*ya+Ct._y1*Ct._l23_2a-Jr*Ct._l12_2a)/ea}Ct._context.bezierCurveTo(hi,hn,Sn,En,Ct._x2,Ct._y2)}function Ir(Ct,Sr){this._context=Ct,this._alpha=Sr}Ir.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x2,this._y2);break;case 3:this.point(this._x2,this._y2);break}(this._line||this._line!==0&&this._point===1)&&this._context.closePath(),this._line=1-this._line},point:function(Ct,Sr){if(Ct=+Ct,Sr=+Sr,this._point){var Jr=this._x2-Ct,hi=this._y2-Sr;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(Jr*Jr+hi*hi,this._alpha))}switch(this._point){case 0:this._point=1,this._line?this._context.lineTo(Ct,Sr):this._context.moveTo(Ct,Sr);break;case 1:this._point=2;break;case 2:this._point=3;default:Gr(this,Ct,Sr);break}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=Ct,this._y0=this._y1,this._y1=this._y2,this._y2=Sr}};var Yr=(function Ct(Sr){function Jr(hi){return Sr?new Ir(hi,Sr):new et(hi,0)}return Jr.alpha=function(hi){return Ct(+hi)},Jr})(.5);function _i(Ct,Sr){this._context=Ct,this._alpha=Sr}_i.prototype={areaStart:jt,areaEnd:jt,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._x5=this._y0=this._y1=this._y2=this._y3=this._y4=this._y5=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){switch(this._point){case 1:{this._context.moveTo(this._x3,this._y3),this._context.closePath();break}case 2:{this._context.lineTo(this._x3,this._y3),this._context.closePath();break}case 3:{this.point(this._x3,this._y3),this.point(this._x4,this._y4),this.point(this._x5,this._y5);break}}},point:function(Ct,Sr){if(Ct=+Ct,Sr=+Sr,this._point){var Jr=this._x2-Ct,hi=this._y2-Sr;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(Jr*Jr+hi*hi,this._alpha))}switch(this._point){case 0:this._point=1,this._x3=Ct,this._y3=Sr;break;case 1:this._point=2,this._context.moveTo(this._x4=Ct,this._y4=Sr);break;case 2:this._point=3,this._x5=Ct,this._y5=Sr;break;default:Gr(this,Ct,Sr);break}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=Ct,this._y0=this._y1,this._y1=this._y2,this._y2=Sr}};var Pi=(function Ct(Sr){function Jr(hi){return Sr?new _i(hi,Sr):new At(hi,0)}return Jr.alpha=function(hi){return Ct(+hi)},Jr})(.5);function ai(Ct,Sr){this._context=Ct,this._alpha=Sr}ai.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){(this._line||this._line!==0&&this._point===3)&&this._context.closePath(),this._line=1-this._line},point:function(Ct,Sr){if(Ct=+Ct,Sr=+Sr,this._point){var Jr=this._x2-Ct,hi=this._y2-Sr;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(Jr*Jr+hi*hi,this._alpha))}switch(this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3,this._line?this._context.lineTo(this._x2,this._y2):this._context.moveTo(this._x2,this._y2);break;case 3:this._point=4;default:Gr(this,Ct,Sr);break}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=Ct,this._y0=this._y1,this._y1=this._y2,this._y2=Sr}};var mi=(function Ct(Sr){function Jr(hi){return Sr?new ai(hi,Sr):new Qt(hi,0)}return Jr.alpha=function(hi){return Ct(+hi)},Jr})(.5);function un(Ct){this._context=Ct}un.prototype={areaStart:jt,areaEnd:jt,lineStart:function(){this._point=0},lineEnd:function(){this._point&&this._context.closePath()},point:function(Ct,Sr){Ct=+Ct,Sr=+Sr,this._point?this._context.lineTo(Ct,Sr):(this._point=1,this._context.moveTo(Ct,Sr))}};function Fn(Ct){return new un(Ct)}function An(Ct){return Ct<0?-1:1}function Hn(Ct,Sr,Jr){var hi=Ct._x1-Ct._x0,hn=Sr-Ct._x1,Sn=(Ct._y1-Ct._y0)/(hi||hn<0&&-0),En=(Jr-Ct._y1)/(hn||hi<0&&-0),ki=(Sn*hn+En*hi)/(hi+hn);return(An(Sn)+An(En))*Math.min(Math.abs(Sn),Math.abs(En),.5*Math.abs(ki))||0}function Qn(Ct,Sr){var Jr=Ct._x1-Ct._x0;return Jr?(3*(Ct._y1-Ct._y0)/Jr-Sr)/2:Sr}function Vi(Ct,Sr,Jr){var hi=Ct._x0,hn=Ct._y0,Sn=Ct._x1,En=Ct._y1,ki=(Sn-hi)/3;Ct._context.bezierCurveTo(hi+ki,hn+ki*Sr,Sn-ki,En-ki*Jr,Sn,En)}function Jn(Ct){this._context=Ct}Jn.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=this._t0=NaN,this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x1,this._y1);break;case 3:Vi(this,this._t0,Qn(this,this._t0));break}(this._line||this._line!==0&&this._point===1)&&this._context.closePath(),this._line=1-this._line},point:function(Ct,Sr){var Jr=NaN;if(Ct=+Ct,Sr=+Sr,!(Ct===this._x1&&Sr===this._y1)){switch(this._point){case 0:this._point=1,this._line?this._context.lineTo(Ct,Sr):this._context.moveTo(Ct,Sr);break;case 1:this._point=2;break;case 2:this._point=3,Vi(this,Qn(this,Jr=Hn(this,Ct,Sr)),Jr);break;default:Vi(this,this._t0,Jr=Hn(this,Ct,Sr));break}this._x0=this._x1,this._x1=Ct,this._y0=this._y1,this._y1=Sr,this._t0=Jr}}};function Yn(Ct){this._context=new Gt(Ct)}(Yn.prototype=Object.create(Jn.prototype)).point=function(Ct,Sr){Jn.prototype.point.call(this,Sr,Ct)};function Gt(Ct){this._context=Ct}Gt.prototype={moveTo:function(Ct,Sr){this._context.moveTo(Sr,Ct)},closePath:function(){this._context.closePath()},lineTo:function(Ct,Sr){this._context.lineTo(Sr,Ct)},bezierCurveTo:function(Ct,Sr,Jr,hi,hn,Sn){this._context.bezierCurveTo(Sr,Ct,hi,Jr,Sn,hn)}};function wt(Ct){return new Jn(Ct)}function tr(Ct){return new Yn(Ct)}function ir(Ct){this._context=Ct}ir.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x=[],this._y=[]},lineEnd:function(){var Ct=this._x,Sr=this._y,Jr=Ct.length;if(Jr)if(this._line?this._context.lineTo(Ct[0],Sr[0]):this._context.moveTo(Ct[0],Sr[0]),Jr===2)this._context.lineTo(Ct[1],Sr[1]);else for(var hi=wr(Ct),hn=wr(Sr),Sn=0,En=1;En<Jr;++Sn,++En)this._context.bezierCurveTo(hi[0][Sn],hn[0][Sn],hi[1][Sn],hn[1][Sn],Ct[En],Sr[En]);(this._line||this._line!==0&&Jr===1)&&this._context.closePath(),this._line=1-this._line,this._x=this._y=null},point:function(Ct,Sr){this._x.push(+Ct),this._y.push(+Sr)}};function wr(Ct){var Sr,Jr=Ct.length-1,hi,hn=new Array(Jr),Sn=new Array(Jr),En=new Array(Jr);for(hn[0]=0,Sn[0]=2,En[0]=Ct[0]+2*Ct[1],Sr=1;Sr<Jr-1;++Sr)hn[Sr]=1,Sn[Sr]=4,En[Sr]=4*Ct[Sr]+2*Ct[Sr+1];for(hn[Jr-1]=2,Sn[Jr-1]=7,En[Jr-1]=8*Ct[Jr-1]+Ct[Jr],Sr=1;Sr<Jr;++Sr)hi=hn[Sr]/Sn[Sr-1],Sn[Sr]-=hi,En[Sr]-=hi*En[Sr-1];for(hn[Jr-1]=En[Jr-1]/Sn[Jr-1],Sr=Jr-2;Sr>=0;--Sr)hn[Sr]=(En[Sr]-hn[Sr+1])/Sn[Sr];for(Sn[Jr-1]=(Ct[Jr]+hn[Jr-1])/2,Sr=0;Sr<Jr-1;++Sr)Sn[Sr]=2*Ct[Sr+1]-hn[Sr+1];return[hn,Sn]}function Xr(Ct){return new ir(Ct)}function ti(Ct,Sr){this._context=Ct,this._t=Sr}ti.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x=this._y=NaN,this._point=0},lineEnd:function(){0<this._t&&this._t<1&&this._point===2&&this._context.lineTo(this._x,this._y),(this._line||this._line!==0&&this._point===1)&&this._context.closePath(),this._line>=0&&(this._t=1-this._t,this._line=1-this._line)},point:function(Ct,Sr){switch(Ct=+Ct,Sr=+Sr,this._point){case 0:this._point=1,this._line?this._context.lineTo(Ct,Sr):this._context.moveTo(Ct,Sr);break;case 1:this._point=2;default:{if(this._t<=0)this._context.lineTo(this._x,Sr),this._context.lineTo(Ct,Sr);else{var Jr=this._x*(1-this._t)+Ct*this._t;this._context.lineTo(Jr,this._y),this._context.lineTo(Jr,Sr)}break}}this._x=Ct,this._y=Sr}};function $r(Ct){return new ti(Ct,.5)}function Ri(Ct){return new ti(Ct,0)}function Hi(Ct){return new ti(Ct,1)}function en(Ct,Sr){if((En=Ct.length)>1)for(var Jr=1,hi,hn,Sn=Ct[Sr[0]],En,ki=Sn.length;Jr<En;++Jr)for(hn=Sn,Sn=Ct[Sr[Jr]],hi=0;hi<ki;++hi)Sn[hi][1]+=Sn[hi][0]=isNaN(hn[hi][1])?hn[hi][0]:hn[hi][1]}function fn(Ct){for(var Sr=Ct.length,Jr=new Array(Sr);--Sr>=0;)Jr[Sr]=Sr;return Jr}function yn(Ct,Sr){return Ct[Sr]}function Mn(){var Ct=r([]),Sr=fn,Jr=en,hi=yn;function hn(Sn){var En=Ct.apply(this,arguments),ki,_n=Sn.length,ya=En.length,ea=new Array(ya),Ma;for(ki=0;ki<ya;++ki){for(var _o=En[ki],No=ea[ki]=new Array(_n),po=0,Lo;po<_n;++po)No[po]=Lo=[0,+hi(Sn[po],_o,po,Sn)],Lo.data=Sn[po];No.key=_o}for(ki=0,Ma=Sr(ea);ki<ya;++ki)ea[Ma[ki]].index=ki;return Jr(ea,Ma),ea}return hn.keys=function(Sn){return arguments.length?(Ct=typeof Sn==\"function\"?Sn:r(Ce.call(Sn)),hn):Ct},hn.value=function(Sn){return arguments.length?(hi=typeof Sn==\"function\"?Sn:r(+Sn),hn):hi},hn.order=function(Sn){return arguments.length?(Sr=Sn==null?fn:typeof Sn==\"function\"?Sn:r(Ce.call(Sn)),hn):Sr},hn.offset=function(Sn){return arguments.length?(Jr=Sn==null?en:Sn,hn):Jr},hn}function Ba(Ct,Sr){if((hi=Ct.length)>0){for(var Jr,hi,hn=0,Sn=Ct[0].length,En;hn<Sn;++hn){for(En=Jr=0;Jr<hi;++Jr)En+=Ct[Jr][hn][1]||0;if(En)for(Jr=0;Jr<hi;++Jr)Ct[Jr][hn][1]/=En}en(Ct,Sr)}}function ua(Ct,Sr){if((_n=Ct.length)>0)for(var Jr,hi=0,hn,Sn,En,ki,_n,ya=Ct[Sr[0]].length;hi<ya;++hi)for(En=ki=0,Jr=0;Jr<_n;++Jr)(Sn=(hn=Ct[Sr[Jr]][hi])[1]-hn[0])>0?(hn[0]=En,hn[1]=En+=Sn):Sn<0?(hn[1]=ki,hn[0]=ki+=Sn):(hn[0]=0,hn[1]=Sn)}function 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at.targetLinks.forEach(function(rr){Vt=rr.circular&&!mt(rr,ct)?Vt+1:Vt}),ar+Vt}function j(at){var ct=at.source.sourceLinks,ar=0;ct.forEach(function(tt){ar=tt.circular?ar+1:ar});var Vt=at.target.targetLinks,rr=0;return Vt.forEach(function(tt){rr=tt.circular?rr+1:rr}),!(ar>1||rr>1)}function N(at,ct,ar){return at.sort(ae),at.forEach(function(Vt,rr){var tt=0;if(mt(Vt,ar)&&j(Vt))Vt.circularPathData.verticalBuffer=tt+Vt.width/2;else{var je=0;for(je;je<rr;je++)if(G(at[rr],at[je])){var Ue=at[je].circularPathData.verticalBuffer+at[je].width/2+ct;tt=Ue>tt?Ue:tt}Vt.circularPathData.verticalBuffer=tt+Vt.width/2}}),at}function H(at,ct,ar,Vt){var rr=5,tt=t.min(at.links,function(Ae){return Ae.source.y0});at.links.forEach(function(Ae){Ae.circular&&(Ae.circularPathData={})});var je=at.links.filter(function(Ae){return Ae.circularLinkType==\"top\"});N(je,ct,Vt);var Ue=at.links.filter(function(Ae){return 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rt=Ae.source.column,St=Ae.circularLinkType,Tt=at.links.filter(function(pt){return pt.source.column==rt&&pt.circularLinkType==St});Ae.circularLinkType==\"bottom\"?Tt.sort(Ee):Tt.sort(_e);var dt=0;Tt.forEach(function(pt,jt){pt.circularLinkID==Ae.circularLinkID&&(Ae.circularPathData.leftSmallArcRadius=g+Ae.width/2+dt,Ae.circularPathData.leftLargeArcRadius=g+Ae.width/2+jt*ct+dt),dt=dt+pt.width}),rt=Ae.target.column,Tt=at.links.filter(function(pt){return pt.target.column==rt&&pt.circularLinkType==St}),Ae.circularLinkType==\"bottom\"?Tt.sort(ge):Tt.sort(Ce),dt=0,Tt.forEach(function(pt,jt){pt.circularLinkID==Ae.circularLinkID&&(Ae.circularPathData.rightSmallArcRadius=g+Ae.width/2+dt,Ae.circularPathData.rightLargeArcRadius=g+Ae.width/2+jt*ct+dt),dt=dt+pt.width}),Ae.circularLinkType==\"bottom\"?(Ae.circularPathData.verticalFullExtent=Math.max(ar,Ae.source.y1,Ae.target.y1)+S+Ae.circularPathData.verticalBuffer,Ae.circularPathData.verticalLeftInnerExtent=Ae.circularPathData.verticalFullExtent-Ae.circularPathData.leftLargeArcRadius,Ae.circularPathData.verticalRightInnerExtent=Ae.circularPathData.verticalFullExtent-Ae.circularPathData.rightLargeArcRadius):(Ae.circularPathData.verticalFullExtent=tt-S-Ae.circularPathData.verticalBuffer,Ae.circularPathData.verticalLeftInnerExtent=Ae.circularPathData.verticalFullExtent+Ae.circularPathData.leftLargeArcRadius,Ae.circularPathData.verticalRightInnerExtent=Ae.circularPathData.verticalFullExtent+Ae.circularPathData.rightLargeArcRadius)}Ae.circularPathData.leftInnerExtent=Ae.circularPathData.sourceX+Ae.circularPathData.leftNodeBuffer,Ae.circularPathData.rightInnerExtent=Ae.circularPathData.targetX-Ae.circularPathData.rightNodeBuffer,Ae.circularPathData.leftFullExtent=Ae.circularPathData.sourceX+Ae.circularPathData.leftLargeArcRadius+Ae.circularPathData.leftNodeBuffer,Ae.circularPathData.rightFullExtent=Ae.circularPathData.targetX-Ae.circularPathData.rightLargeArcRadius-Ae.circularPathData.rightNodeBuffer}if(Ae.circular)Ae.path=re(Ae);else{var Et=n.linkHorizontal().source(function(pt){var jt=pt.source.x0+(pt.source.x1-pt.source.x0),or=pt.y0;return[jt,or]}).target(function(pt){var jt=pt.target.x0,or=pt.y1;return[jt,or]});Ae.path=Et(Ae)}})}function re(at){var ct=\"\";return at.circularLinkType==\"top\"?ct=\"M\"+at.circularPathData.sourceX+\" \"+at.circularPathData.sourceY+\" L\"+at.circularPathData.leftInnerExtent+\" \"+at.circularPathData.sourceY+\" A\"+at.circularPathData.leftLargeArcRadius+\" \"+at.circularPathData.leftSmallArcRadius+\" 0 0 0 \"+at.circularPathData.leftFullExtent+\" \"+(at.circularPathData.sourceY-at.circularPathData.leftSmallArcRadius)+\" L\"+at.circularPathData.leftFullExtent+\" \"+at.circularPathData.verticalLeftInnerExtent+\" A\"+at.circularPathData.leftLargeArcRadius+\" \"+at.circularPathData.leftLargeArcRadius+\" 0 0 0 \"+at.circularPathData.leftInnerExtent+\" \"+at.circularPathData.verticalFullExtent+\" L\"+at.circularPathData.rightInnerExtent+\" \"+at.circularPathData.verticalFullExtent+\" A\"+at.circularPathData.rightLargeArcRadius+\" \"+at.circularPathData.rightLargeArcRadius+\" 0 0 0 \"+at.circularPathData.rightFullExtent+\" \"+at.circularPathData.verticalRightInnerExtent+\" L\"+at.circularPathData.rightFullExtent+\" \"+(at.circularPathData.targetY-at.circularPathData.rightSmallArcRadius)+\" A\"+at.circularPathData.rightLargeArcRadius+\" \"+at.circularPathData.rightSmallArcRadius+\" 0 0 0 \"+at.circularPathData.rightInnerExtent+\" \"+at.circularPathData.targetY+\" L\"+at.circularPathData.targetX+\" \"+at.circularPathData.targetY:ct=\"M\"+at.circularPathData.sourceX+\" \"+at.circularPathData.sourceY+\" L\"+at.circularPathData.leftInnerExtent+\" \"+at.circularPathData.sourceY+\" A\"+at.circularPathData.leftLargeArcRadius+\" \"+at.circularPathData.leftSmallArcRadius+\" 0 0 1 \"+at.circularPathData.leftFullExtent+\" \"+(at.circularPathData.sourceY+at.circularPathData.leftSmallArcRadius)+\" L\"+at.circularPathData.leftFullExtent+\" \"+at.circularPathData.verticalLeftInnerExtent+\" A\"+at.circularPathData.leftLargeArcRadius+\" \"+at.circularPathData.leftLargeArcRadius+\" 0 0 1 \"+at.circularPathData.leftInnerExtent+\" \"+at.circularPathData.verticalFullExtent+\" L\"+at.circularPathData.rightInnerExtent+\" \"+at.circularPathData.verticalFullExtent+\" A\"+at.circularPathData.rightLargeArcRadius+\" \"+at.circularPathData.rightLargeArcRadius+\" 0 0 1 \"+at.circularPathData.rightFullExtent+\" \"+at.circularPathData.verticalRightInnerExtent+\" L\"+at.circularPathData.rightFullExtent+\" \"+(at.circularPathData.targetY+at.circularPathData.rightSmallArcRadius)+\" A\"+at.circularPathData.rightLargeArcRadius+\" \"+at.circularPathData.rightSmallArcRadius+\" 0 0 1 \"+at.circularPathData.rightInnerExtent+\" \"+at.circularPathData.targetY+\" L\"+at.circularPathData.targetX+\" \"+at.circularPathData.targetY,ct}function ae(at,ct){return ie(at)==ie(ct)?at.circularLinkType==\"bottom\"?Ee(at,ct):_e(at,ct):ie(ct)-ie(at)}function _e(at,ct){return at.y0-ct.y0}function Ee(at,ct){return ct.y0-at.y0}function Ce(at,ct){return at.y1-ct.y1}function ge(at,ct){return ct.y1-at.y1}function ie(at){return at.target.column-at.source.column}function Se(at){return at.target.x0-at.source.x1}function ke(at,ct){var ar=V(at),Vt=Se(ct)/Math.tan(ar),rr=lt(at)==\"up\"?at.y1+Vt:at.y1-Vt;return rr}function be(at,ct){var ar=V(at),Vt=Se(ct)/Math.tan(ar),rr=lt(at)==\"up\"?at.y1-Vt:at.y1+Vt;return rr}function Be(at,ct,ar,Vt){at.links.forEach(function(rr){if(!rr.circular&&rr.target.column-rr.source.column>1){var tt=rr.source.column+1,je=rr.target.column-1,Ue=1,Ae=je-tt+1;for(Ue=1;tt<=je;tt++,Ue++)at.nodes.forEach(function(rt){if(rt.column==tt){var St=Ue/(Ae+1),Tt=Math.pow(1-St,3),dt=3*St*Math.pow(1-St,2),Et=3*Math.pow(St,2)*(1-St),pt=Math.pow(St,3),jt=Tt*rr.y0+dt*rr.y0+Et*rr.y1+pt*rr.y1,or=jt-rr.width/2,mr=jt+rr.width/2,Ar;or>rt.y0&&or<rt.y1?(Ar=rt.y1-or+10,Ar=rt.circularLinkType==\"bottom\"?Ar:-Ar,rt=me(rt,Ar,ct,ar),at.nodes.forEach(function(ei){C(ei,Vt)==C(rt,Vt)||ei.column!=rt.column||Le(rt,ei)&&me(ei,Ar,ct,ar)})):mr>rt.y0&&mr<rt.y1?(Ar=mr-rt.y0+10,rt=me(rt,Ar,ct,ar),at.nodes.forEach(function(ei){C(ei,Vt)==C(rt,Vt)||ei.column!=rt.column||ei.y0<rt.y1&&ei.y1>rt.y1&&me(ei,Ar,ct,ar)})):or<rt.y0&&mr>rt.y1&&(Ar=mr-rt.y0+10,rt=me(rt,Ar,ct,ar),at.nodes.forEach(function(ei){C(ei,Vt)==C(rt,Vt)||ei.column!=rt.column||ei.y0<rt.y1&&ei.y1>rt.y1&&me(ei,Ar,ct,ar)}))}})}})}function Le(at,ct){return at.y0>ct.y0&&at.y0<ct.y1||at.y1>ct.y0&&at.y1<ct.y1?!0:at.y0<ct.y0&&at.y1>ct.y1}function me(at,ct,ar,Vt){return at.y0+ct>=ar&&at.y1+ct<=Vt&&(at.y0=at.y0+ct,at.y1=at.y1+ct,at.targetLinks.forEach(function(rr){rr.y1=rr.y1+ct}),at.sourceLinks.forEach(function(rr){rr.y0=rr.y0+ct})),at}function Pe(at,ct,ar,Vt){at.nodes.forEach(function(rr){Vt&&rr.y+(rr.y1-rr.y0)>ct&&(rr.y=rr.y-(rr.y+(rr.y1-rr.y0)-ct));var tt=at.links.filter(function(Ae){return C(Ae.source,ar)==C(rr,ar)}),je=tt.length;je>1&&tt.sort(function(Ae,rt){if(!Ae.circular&&!rt.circular){if(Ae.target.column==rt.target.column)return Ae.y1-rt.y1;if(He(Ae,rt)){if(Ae.target.column>rt.target.column){var St=be(rt,Ae);return Ae.y1-St}if(rt.target.column>Ae.target.column){var Tt=be(Ae,rt);return Tt-rt.y1}}else return Ae.y1-rt.y1}if(Ae.circular&&!rt.circular)return Ae.circularLinkType==\"top\"?-1:1;if(rt.circular&&!Ae.circular)return rt.circularLinkType==\"top\"?1:-1;if(Ae.circular&&rt.circular)return Ae.circularLinkType===rt.circularLinkType&&Ae.circularLinkType==\"top\"?Ae.target.column===rt.target.column?Ae.target.y1-rt.target.y1:rt.target.column-Ae.target.column:Ae.circularLinkType===rt.circularLinkType&&Ae.circularLinkType==\"bottom\"?Ae.target.column===rt.target.column?rt.target.y1-Ae.target.y1:Ae.target.column-rt.target.column:Ae.circularLinkType==\"top\"?-1:1});var Ue=rr.y0;tt.forEach(function(Ae){Ae.y0=Ue+Ae.width/2,Ue=Ue+Ae.width}),tt.forEach(function(Ae,rt){if(Ae.circularLinkType==\"bottom\"){var St=rt+1,Tt=0;for(St;St<je;St++)Tt=Tt+tt[St].width;Ae.y0=rr.y1-Tt-Ae.width/2}})})}function ce(at,ct,ar){at.nodes.forEach(function(Vt){var rr=at.links.filter(function(Ue){return C(Ue.target,ar)==C(Vt,ar)}),tt=rr.length;tt>1&&rr.sort(function(Ue,Ae){if(!Ue.circular&&!Ae.circular){if(Ue.source.column==Ae.source.column)return Ue.y0-Ae.y0;if(He(Ue,Ae)){if(Ae.source.column<Ue.source.column){var rt=ke(Ae,Ue);return Ue.y0-rt}if(Ue.source.column<Ae.source.column){var St=ke(Ue,Ae);return St-Ae.y0}}else return Ue.y0-Ae.y0}if(Ue.circular&&!Ae.circular)return Ue.circularLinkType==\"top\"?-1:1;if(Ae.circular&&!Ue.circular)return Ae.circularLinkType==\"top\"?1:-1;if(Ue.circular&&Ae.circular)return Ue.circularLinkType===Ae.circularLinkType&&Ue.circularLinkType==\"top\"?Ue.source.column===Ae.source.column?Ue.source.y1-Ae.source.y1:Ue.source.column-Ae.source.column:Ue.circularLinkType===Ae.circularLinkType&&Ue.circularLinkType==\"bottom\"?Ue.source.column===Ae.source.column?Ue.source.y1-Ae.source.y1:Ae.source.column-Ue.source.column:Ue.circularLinkType==\"top\"?-1:1});var je=Vt.y0;rr.forEach(function(Ue){Ue.y1=je+Ue.width/2,je=je+Ue.width}),rr.forEach(function(Ue,Ae){if(Ue.circularLinkType==\"bottom\"){var rt=Ae+1,St=0;for(rt;rt<tt;rt++)St=St+rr[rt].width;Ue.y1=Vt.y1-St-Ue.width/2}})})}function He(at,ct){return lt(at)==lt(ct)}function lt(at){return at.y0-at.y1>0?\"up\":\"down\"}function mt(at,ct){return C(at.source,ct)==C(at.target,ct)}function Ht(at,ct,ar){var Vt=at.nodes,rr=at.links,tt=!1,je=!1;if(rr.forEach(function(dt){dt.circularLinkType==\"top\"?tt=!0:dt.circularLinkType==\"bottom\"&&(je=!0)}),tt==!1||je==!1){var Ue=t.min(Vt,function(dt){return dt.y0}),Ae=t.max(Vt,function(dt){return dt.y1}),rt=Ae-Ue,St=ar-ct,Tt=St/rt;Vt.forEach(function(dt){var Et=(dt.y1-dt.y0)*Tt;dt.y0=(dt.y0-Ue)*Tt,dt.y1=dt.y0+Et}),rr.forEach(function(dt){dt.y0=(dt.y0-Ue)*Tt,dt.y1=(dt.y1-Ue)*Tt,dt.width=dt.width*Tt})}}e.sankeyCircular=E,e.sankeyCenter=u,e.sankeyLeft=o,e.sankeyRight=s,e.sankeyJustify=l,Object.defineProperty(e,\"__esModule\",{value:!0})}))});var i$=ye((U2r,uXe)=>{\"use strict\";uXe.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:\"linear\",cn:{sankey:\"sankey\",sankeyLinks:\"sankey-links\",sankeyLink:\"sankey-link\",sankeyNodeSet:\"sankey-node-set\",sankeyNode:\"sankey-node\",nodeRect:\"node-rect\",nodeLabel:\"node-label\"}}});var wXe=ye((V2r,bXe)=>{\"use strict\";var cXe=QWe(),xYt=(W2(),gb(j2)).interpolateNumber,N5=Fa(),lC=nXe(),bYt=lXe(),Nu=i$(),U5=fd(),xw=ka(),wYt=So(),T1=Pr(),o$=T1.strTranslate,TYt=T1.strRotate,s$=ay(),uC=s$.keyFun,F7=s$.repeat,gXe=s$.unwrap,fXe=Zl(),AYt=Oa(),mXe=Dh(),SYt=mXe.CAP_SHIFT,MYt=mXe.LINE_SPACING,EYt=3;function kYt(e,t,r){var n=gXe(t),i=n.trace,a=i.domain,o=i.orientation===\"h\",s=i.node.pad,l=i.node.thickness,u={justify:lC.sankeyJustify,left:lC.sankeyLeft,right:lC.sankeyRight,center:lC.sankeyCenter}[i.node.align],c=e.width*(a.x[1]-a.x[0]),f=e.height*(a.y[1]-a.y[0]),h=n._nodes,d=n._links,v=n.circular,m;v?m=bYt.sankeyCircular().circularLinkGap(0):m=lC.sankey(),m.iterations(Nu.sankeyIterations).size(o?[c,f]:[f,c]).nodeWidth(l).nodePadding(s).nodeId(function(V){return V.pointNumber}).nodeAlign(u).nodes(h).links(d);var b=m();m.nodePadding()<s&&T1.warn(\"node.pad was reduced to \",m.nodePadding(),\" to fit within the figure.\");var p,k,M;for(var T in n._groupLookup){var L=parseInt(n._groupLookup[T]),x;for(p=0;p<b.nodes.length;p++)if(b.nodes[p].pointNumber===L){x=b.nodes[p];break}if(x){var C={pointNumber:parseInt(T),x0:x.x0,x1:x.x1,y0:x.y0,y1:x.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};b.nodes.unshift(C),x.childrenNodes.unshift(C)}}function S(){for(p=0;p<b.nodes.length;p++){var V=b.nodes[p],G={},Z,j;for(k=0;k<V.targetLinks.length;k++)j=V.targetLinks[k],Z=j.source.pointNumber+\":\"+j.target.pointNumber,G.hasOwnProperty(Z)||(G[Z]=[]),G[Z].push(j);var N=Object.keys(G);for(k=0;k<N.length;k++){Z=N[k];var H=G[Z],re=0,ae={};for(M=0;M<H.length;M++)j=H[M],ae[j.label]||(ae[j.label]=0),ae[j.label]+=j.value,re+=j.value;for(M=0;M<H.length;M++)j=H[M],j.flow={value:re,labelConcentration:ae[j.label]/re,concentration:j.value/re,links:H},j.concentrationscale&&(j.color=U5(j.concentrationscale(j.flow.labelConcentration)))}var _e=0;for(k=0;k<V.sourceLinks.length;k++)_e+=V.sourceLinks[k].value;for(k=0;k<V.sourceLinks.length;k++)j=V.sourceLinks[k],j.concentrationOut=j.value/_e;var Ee=0;for(k=0;k<V.targetLinks.length;k++)Ee+=V.targetLinks[k].value;for(k=0;k<V.targetLinks.length;k++)j=V.targetLinks[k],j.concenrationIn=j.value/Ee}}S();function g(V){V.forEach(function(G){var Z,j,N=0,H=G.length,re;for(G.sort(function(ae,_e){return ae.y0-_e.y0}),re=0;re<H;++re)Z=G[re],Z.y0>=N||(j=N-Z.y0,j>1e-6&&(Z.y0+=j,Z.y1+=j)),N=Z.y1+s})}function P(V){var G=V.map(function(_e,Ee){return{x0:_e.x0,index:Ee}}).sort(function(_e,Ee){return _e.x0-Ee.x0}),Z=[],j=-1,N,H=-1/0,re;for(p=0;p<G.length;p++){var ae=V[G[p].index];ae.x0>H+l&&(j+=1,N=ae.x0),H=ae.x0,Z[j]||(Z[j]=[]),Z[j].push(ae),re=N-ae.x0,ae.x0+=re,ae.x1+=re}return Z}if(i.node.x.length&&i.node.y.length){for(p=0;p<Math.min(i.node.x.length,i.node.y.length,b.nodes.length);p++)if(i.node.x[p]&&i.node.y[p]){var E=[i.node.x[p]*c,i.node.y[p]*f];b.nodes[p].x0=E[0]-l/2,b.nodes[p].x1=E[0]+l/2;var z=b.nodes[p].y1-b.nodes[p].y0;b.nodes[p].y0=E[1]-z/2,b.nodes[p].y1=E[1]+z/2}if(i.arrangement===\"snap\"){h=b.nodes;var q=P(h);g(q)}m.update(b)}return{circular:v,key:r,trace:i,guid:T1.randstr(),horizontal:o,width:c,height:f,nodePad:i.node.pad,nodeLineColor:i.node.line.color,nodeLineWidth:i.node.line.width,linkLineColor:i.link.line.color,linkLineWidth:i.link.line.width,linkArrowLength:i.link.arrowlen,valueFormat:i.valueformat,valueSuffix:i.valuesuffix,textFont:i.textfont,translateX:a.x[0]*e.width+e.margin.l,translateY:e.height-a.y[1]*e.height+e.margin.t,dragParallel:o?f:c,dragPerpendicular:o?c:f,arrangement:i.arrangement,sankey:m,graph:b,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function CYt(e,t,r){var n=U5(t.color),i=U5(t.hovercolor),a=t.source.label+\"|\"+t.target.label,o=a+\"__\"+r;return t.trace=e.trace,t.curveNumber=e.trace.index,{circular:e.circular,key:o,traceId:e.key,pointNumber:t.pointNumber,link:t,tinyColorHue:xw.tinyRGB(n),tinyColorAlpha:n.getAlpha(),tinyColorHoverHue:xw.tinyRGB(i),tinyColorHoverAlpha:i.getAlpha(),linkPath:l$,linkLineColor:e.linkLineColor,linkLineWidth:e.linkLineWidth,linkArrowLength:e.linkArrowLength,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,parent:e,interactionState:e.interactionState,flow:t.flow}}function LYt(e,t){var r=\"\",n=e.width/2,i=e.circularPathData,a=i.sourceX+i.verticalBuffer<i.targetX,o=i.rightFullExtent-i.rightLargeArcRadius-t<=i.leftFullExtent-n,s=Math.abs(i.rightFullExtent-i.leftFullExtent-n)<n;return e.circularLinkType===\"top\"?(r=\"M \"+(i.targetX-t)+\" \"+(i.targetY+n)+\" L \"+(i.rightInnerExtent-t)+\" \"+(i.targetY+n)+\"A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightSmallArcRadius+n)+\" 0 0 1 \"+(i.rightFullExtent-n-t)+\" \"+(i.targetY-i.rightSmallArcRadius)+\"L \"+(i.rightFullExtent-n-t)+\" 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\"+(i.rightInnerExtent-t)+\" \"+(i.verticalFullExtent-n)+\" L \"+i.leftInnerExtent+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftLargeArcRadius+n)+\" 0 0 1 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent,r+=\" L \"+(i.leftFullExtent+n)+\" \"+(i.sourceY-i.leftSmallArcRadius)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 1 \"+i.leftInnerExtent+\" \"+(i.sourceY+n)+\" L \"+i.sourceX+\" \"+(i.sourceY+n)+\" L \"+i.sourceX+\" \"+(i.sourceY-n)+\" L \"+i.leftInnerExtent+\" \"+(i.sourceY-n)+\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftSmallArcRadius-n)+\" 0 0 0 \"+(i.leftFullExtent-n)+\" \"+(i.sourceY-i.leftSmallArcRadius)+\" L \"+(i.leftFullExtent-n)+\" \"+i.verticalLeftInnerExtent,a&&o?r+=\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 0 \"+(i.leftFullExtent-n)+\" \"+(i.verticalFullExtent+n)+\"L\"+(i.rightFullExtent+n-t)+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 0 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent:a?r+=\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 1 \"+(i.leftFullExtent+n)+\" \"+(i.verticalFullExtent-n)+\" L \"+(i.rightFullExtent-n-t)+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 1 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent:r+=\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftLargeArcRadius-n)+\" 0 0 0 \"+i.leftInnerExtent+\" \"+(i.verticalFullExtent+n)+\" L \"+(i.rightInnerExtent-t)+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightLargeArcRadius-n)+\" 0 0 0 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent,r+=\" L \"+(i.rightFullExtent+n-t)+\" \"+(i.targetY-i.rightSmallArcRadius)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 0 \"+(i.rightInnerExtent-t)+\" \"+(i.targetY-n)+\" L \"+(i.targetX-t)+\" \"+(i.targetY-n)+(t>0?\" L \"+i.targetX+\" \"+i.targetY:\"\")+\"Z\"):(r=\"M \"+(i.targetX-t)+\" \"+(i.targetY-n)+\"  L \"+(i.rightInnerExtent-t)+\" \"+(i.targetY-n)+\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightSmallArcRadius+n)+\" 0 0 0 \"+(i.rightFullExtent-n-t)+\" \"+(i.targetY+i.rightSmallArcRadius)+\" L \"+(i.rightFullExtent-n-t)+\" \"+i.verticalRightInnerExtent,a&&o?r+=\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.rightInnerExtent-n-t)+\" \"+(i.verticalFullExtent+n)+\" L \"+(i.rightFullExtent+n-t-(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent:a?r+=\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.rightFullExtent-t-n-(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent-n)+\" L \"+(i.leftFullExtent+n+(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent:r+=\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.rightInnerExtent-t)+\" \"+(i.verticalFullExtent+n)+\" L \"+i.leftInnerExtent+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftLargeArcRadius+n)+\" 0 0 0 \"+(i.leftFullExtent+n)+\" \"+i.verticalLeftInnerExtent,r+=\" L \"+(i.leftFullExtent+n)+\" \"+(i.sourceY+i.leftSmallArcRadius)+\" A \"+(i.leftLargeArcRadius+n)+\" \"+(i.leftSmallArcRadius+n)+\" 0 0 0 \"+i.leftInnerExtent+\" \"+(i.sourceY-n)+\" L \"+i.sourceX+\" \"+(i.sourceY-n)+\" L \"+i.sourceX+\" \"+(i.sourceY+n)+\" L \"+i.leftInnerExtent+\" \"+(i.sourceY+n)+\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftSmallArcRadius-n)+\" 0 0 1 \"+(i.leftFullExtent-n)+\" \"+(i.sourceY+i.leftSmallArcRadius)+\" L \"+(i.leftFullExtent-n)+\" \"+i.verticalLeftInnerExtent,a&&o?r+=\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.leftFullExtent-n-(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent-n)+\" L \"+(i.rightFullExtent+n-t+(i.rightLargeArcRadius-n))+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent:a?r+=\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.leftFullExtent+n)+\" \"+(i.verticalFullExtent+n)+\" L \"+(i.rightFullExtent-t-n)+\" \"+(i.verticalFullExtent+n)+\" A \"+(i.rightLargeArcRadius+n)+\" \"+(i.rightLargeArcRadius+n)+\" 0 0 0 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent:r+=\" A \"+(i.leftLargeArcRadius-n)+\" \"+(i.leftLargeArcRadius-n)+\" 0 0 1 \"+i.leftInnerExtent+\" \"+(i.verticalFullExtent-n)+\" L \"+(i.rightInnerExtent-t)+\" \"+(i.verticalFullExtent-n)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightLargeArcRadius-n)+\" 0 0 1 \"+(i.rightFullExtent+n-t)+\" \"+i.verticalRightInnerExtent,r+=\" L \"+(i.rightFullExtent+n-t)+\" \"+(i.targetY+i.rightSmallArcRadius)+\" A \"+(i.rightLargeArcRadius-n)+\" \"+(i.rightSmallArcRadius-n)+\" 0 0 1 \"+(i.rightInnerExtent-t)+\" \"+(i.targetY+n)+\" L \"+(i.targetX-t)+\" \"+(i.targetY+n)+(t>0?\" L \"+i.targetX+\" \"+i.targetY:\"\")+\"Z\"),r}function l$(){var e=.5;function t(r){var n=r.linkArrowLength;if(r.link.circular)return LYt(r.link,n);var i=Math.abs((r.link.target.x0-r.link.source.x1)/2);n>i&&(n=i);var a=r.link.source.x1,o=r.link.target.x0-n,s=xYt(a,o),l=s(e),u=s(1-e),c=r.link.y0-r.link.width/2,f=r.link.y0+r.link.width/2,h=r.link.y1-r.link.width/2,d=r.link.y1+r.link.width/2,v=\"M\"+a+\",\"+c,m=\"C\"+l+\",\"+c+\" \"+u+\",\"+h+\" \"+o+\",\"+h,b=\"C\"+u+\",\"+d+\" \"+l+\",\"+f+\" \"+a+\",\"+f,p=n>0?\"L\"+(o+n)+\",\"+(h+r.link.width/2):\"\";return p+=\"L\"+o+\",\"+d,v+m+p+b+\"Z\"}return t}function PYt(e,t){var r=U5(t.color),n=Nu.nodePadAcross,i=e.nodePad/2;t.dx=t.x1-t.x0,t.dy=t.y1-t.y0;var a=t.dx,o=Math.max(.5,t.dy),s=\"node_\"+t.pointNumber;return t.group&&(s=T1.randstr()),t.trace=e.trace,t.curveNumber=e.trace.index,{index:t.pointNumber,key:s,partOfGroup:t.partOfGroup||!1,group:t.group,traceId:e.key,trace:e.trace,node:t,nodePad:e.nodePad,nodeLineColor:e.nodeLineColor,nodeLineWidth:e.nodeLineWidth,textFont:e.textFont,size:e.horizontal?e.height:e.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:e.horizontal?t.dy/2+1:t.dx/2+1,left:t.originalLayer===1,sizeAcross:e.width,forceLayouts:e.forceLayouts,horizontal:e.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:xw.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,graph:e.graph,arrangement:e.arrangement,uniqueNodeLabelPathId:[e.guid,e.key,s].join(\"_\"),interactionState:e.interactionState,figure:e}}function a$(e){e.attr(\"transform\",function(t){return o$(t.node.x0.toFixed(3),t.node.y0.toFixed(3))})}function IYt(e){e.call(a$)}function yXe(e,t){e.call(IYt),t.attr(\"d\",l$())}function hXe(e){e.attr(\"width\",function(t){return t.node.x1-t.node.x0}).attr(\"height\",function(t){return t.visibleHeight})}function n$(e){return e.link.width>1||e.linkLineWidth>0}function dXe(e){var t=o$(e.translateX,e.translateY);return t+(e.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function vXe(e,t,r){e.on(\".basic\",null).on(\"mouseover.basic\",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.hover(this,n,t),n.interactionState.hovered=[this,n])}).on(\"mousemove.basic\",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.follow(this,n),n.interactionState.hovered=[this,n])}).on(\"mouseout.basic\",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.unhover(this,n,t),n.interactionState.hovered=!1)}).on(\"click.basic\",function(n){n.interactionState.hovered&&(r.unhover(this,n,t),n.interactionState.hovered=!1),!n.interactionState.dragInProgress&&!n.partOfGroup&&r.select(this,n,t)})}function RYt(e,t,r,n){var i=N5.behavior.drag().origin(function(a){return{x:a.node.x0+a.visibleWidth/2,y:a.node.y0+a.visibleHeight/2}}).on(\"dragstart\",function(a){if(a.arrangement!==\"fixed\"&&(T1.ensureSingle(n._fullLayout._infolayer,\"g\",\"dragcover\",function(s){n._fullLayout._dragCover=s}),T1.raiseToTop(this),a.interactionState.dragInProgress=a.node,pXe(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),a.arrangement===\"snap\")){var o=a.traceId+\"|\"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):DYt(e,o,a,n),FYt(e,t,a,o,n)}}).on(\"drag\",function(a){if(a.arrangement!==\"fixed\"){var o=N5.event.x,s=N5.event.y;a.arrangement===\"snap\"?(a.node.x0=o-a.visibleWidth/2,a.node.x1=o+a.visibleWidth/2,a.node.y0=s-a.visibleHeight/2,a.node.y1=s+a.visibleHeight/2):(a.arrangement===\"freeform\"&&(a.node.x0=o-a.visibleWidth/2,a.node.x1=o+a.visibleWidth/2),s=Math.max(0,Math.min(a.size-a.visibleHeight/2,s)),a.node.y0=s-a.visibleHeight/2,a.node.y1=s+a.visibleHeight/2),pXe(a.node),a.arrangement!==\"snap\"&&(a.sankey.update(a.graph),yXe(e.filter(xXe(a)),t))}}).on(\"dragend\",function(a){if(a.arrangement!==\"fixed\"){a.interactionState.dragInProgress=!1;for(var o=0;o<a.node.childrenNodes.length;o++)a.node.childrenNodes[o].x=a.node.x,a.node.childrenNodes[o].y=a.node.y;a.arrangement!==\"snap\"&&_Xe(a,n)}});e.on(\".drag\",null).call(i)}function DYt(e,t,r,n){OYt(r.graph.nodes);var i=r.graph.nodes.filter(function(a){return a.originalX===r.node.originalX}).filter(function(a){return!a.partOfGroup});r.forceLayouts[t]=cXe.forceSimulation(i).alphaDecay(0).force(\"collide\",cXe.forceCollide().radius(function(a){return a.dy/2+r.nodePad/2}).strength(1).iterations(Nu.forceIterations)).force(\"constrain\",zYt(e,t,i,r,n)).stop()}function FYt(e,t,r,n,i){window.requestAnimationFrame(function a(){var o;for(o=0;o<Nu.forceTicksPerFrame;o++)r.forceLayouts[n].tick();var s=r.graph.nodes;if(BYt(s),r.sankey.update(r.graph),yXe(e.filter(xXe(r)),t),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var l=r.node.originalX;r.node.x0=l-r.visibleWidth/2,r.node.x1=l+r.visibleWidth/2,_Xe(r,i)}})}function zYt(e,t,r,n){return function(){for(var a=0,o=0;o<r.length;o++){var s=r[o];s===n.interactionState.dragInProgress?(s.x=s.lastDraggedX,s.y=s.lastDraggedY):(s.vx=(s.originalX-s.x)/Nu.forceTicksPerFrame,s.y=Math.min(n.size-s.dy/2,Math.max(s.dy/2,s.y))),a=Math.max(a,Math.abs(s.vx),Math.abs(s.vy))}!n.interactionState.dragInProgress&&a<.1&&n.forceLayouts[t].alpha()>0&&n.forceLayouts[t].alpha(0)}}function _Xe(e,t){for(var r=[],n=[],i=0;i<e.graph.nodes.length;i++){var a=(e.graph.nodes[i].x0+e.graph.nodes[i].x1)/2,o=(e.graph.nodes[i].y0+e.graph.nodes[i].y1)/2;r.push(a/e.figure.width),n.push(o/e.figure.height)}AYt.call(\"_guiRestyle\",t,{\"node.x\":[r],\"node.y\":[n]},e.trace.index).then(function(){t._fullLayout._dragCover&&t._fullLayout._dragCover.remove()})}function qYt(e){var t=[],r;for(r=0;r<e.length;r++)e[r].originalX=(e[r].x0+e[r].x1)/2,e[r].originalY=(e[r].y0+e[r].y1)/2,t.indexOf(e[r].originalX)===-1&&t.push(e[r].originalX);for(t.sort(function(n,i){return n-i}),r=0;r<e.length;r++)e[r].originalLayerIndex=t.indexOf(e[r].originalX),e[r].originalLayer=e[r].originalLayerIndex/(t.length-1)}function pXe(e){e.lastDraggedX=e.x0+e.dx/2,e.lastDraggedY=e.y0+e.dy/2}function xXe(e){return function(t){return t.node.originalX===e.node.originalX}}function OYt(e){for(var t=0;t<e.length;t++)e[t].y=(e[t].y0+e[t].y1)/2,e[t].x=(e[t].x0+e[t].x1)/2}function BYt(e){for(var t=0;t<e.length;t++)e[t].y0=e[t].y-e[t].dy/2,e[t].y1=e[t].y0+e[t].dy,e[t].x0=e[t].x-e[t].dx/2,e[t].x1=e[t].x0+e[t].dx}bXe.exports=function(e,t,r,n,i){var a=e._context.staticPlot,o=!1;T1.ensureSingle(e._fullLayout._infolayer,\"g\",\"first-render\",function(){o=!0});var s=e._fullLayout._dragCover,l=r.filter(function(b){return gXe(b).trace.visible}).map(kYt.bind(null,n)),u=t.selectAll(\".\"+Nu.cn.sankey).data(l,uC);u.exit().remove(),u.enter().append(\"g\").classed(Nu.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",a?\"none\":\"auto\").attr(\"transform\",dXe),u.each(function(b,p){e._fullData[p]._sankey=b;var k=\"bgsankey-\"+b.trace.uid+\"-\"+p;T1.ensureSingle(e._fullLayout._draggers,\"rect\",k),e._fullData[p]._bgRect=N5.select(\".\"+k),e._fullData[p]._bgRect.style(\"pointer-events\",a?\"none\":\"all\").attr(\"width\",b.width).attr(\"height\",b.height).attr(\"x\",b.translateX).attr(\"y\",b.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})}),u.transition().ease(Nu.ease).duration(Nu.duration).attr(\"transform\",dXe);var c=u.selectAll(\".\"+Nu.cn.sankeyLinks).data(F7,uC);c.enter().append(\"g\").classed(Nu.cn.sankeyLinks,!0).style(\"fill\",\"none\");var f=c.selectAll(\".\"+Nu.cn.sankeyLink).data(function(b){var p=b.graph.links;return p.filter(function(k){return k.value}).map(CYt.bind(null,b))},uC);f.enter().append(\"path\").classed(Nu.cn.sankeyLink,!0).call(vXe,u,i.linkEvents),f.style(\"stroke\",function(b){return n$(b)?xw.tinyRGB(U5(b.linkLineColor)):b.tinyColorHue}).style(\"stroke-opacity\",function(b){return n$(b)?xw.opacity(b.linkLineColor):b.tinyColorAlpha}).style(\"fill\",function(b){return b.tinyColorHue}).style(\"fill-opacity\",function(b){return b.tinyColorAlpha}).style(\"stroke-width\",function(b){return n$(b)?b.linkLineWidth:1}).attr(\"d\",l$()),f.style(\"opacity\",function(){return e._context.staticPlot||o||s?1:0}).transition().ease(Nu.ease).duration(Nu.duration).style(\"opacity\",1),f.exit().transition().ease(Nu.ease).duration(Nu.duration).style(\"opacity\",0).remove();var h=u.selectAll(\".\"+Nu.cn.sankeyNodeSet).data(F7,uC);h.enter().append(\"g\").classed(Nu.cn.sankeyNodeSet,!0),h.style(\"cursor\",function(b){switch(b.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}});var d=h.selectAll(\".\"+Nu.cn.sankeyNode).data(function(b){var p=b.graph.nodes;return qYt(p),p.map(PYt.bind(null,b))},uC);d.enter().append(\"g\").classed(Nu.cn.sankeyNode,!0).call(a$).style(\"opacity\",function(b){return(e._context.staticPlot||o)&&!b.partOfGroup?1:0}),d.call(vXe,u,i.nodeEvents).call(RYt,f,i,e),d.transition().ease(Nu.ease).duration(Nu.duration).call(a$).style(\"opacity\",function(b){return b.partOfGroup?0:1}),d.exit().transition().ease(Nu.ease).duration(Nu.duration).style(\"opacity\",0).remove();var v=d.selectAll(\".\"+Nu.cn.nodeRect).data(F7);v.enter().append(\"rect\").classed(Nu.cn.nodeRect,!0).call(hXe),v.style(\"stroke-width\",function(b){return b.nodeLineWidth}).style(\"stroke\",function(b){return 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p$=ye((K2r,XXe)=>{\"use strict\";XXe.exports={defaultNumberFontSize:80,bulletNumberDomainSize:.25,bulletPadding:.025,innerRadius:.75,valueThickness:.5,titlePadding:5,horizontalPadding:10}});var KXe=ye((J2r,YXe)=>{\"use strict\";var uy=Pr(),N7=v$(),tKt=Cc().defaults,ZXe=vl(),rKt=Kd(),B7=p$(),iKt=Pb(),nKt=B3(),aKt=l_(),oKt=u_();function sKt(e,t,r,n){function i(x,C){return uy.coerce(e,t,N7,x,C)}tKt(t,n,i),i(\"mode\"),t._hasNumber=t.mode.indexOf(\"number\")!==-1,t._hasDelta=t.mode.indexOf(\"delta\")!==-1,t._hasGauge=t.mode.indexOf(\"gauge\")!==-1;var a=i(\"value\");t._range=[0,typeof a==\"number\"?1.5*a:1];var o=new Array(2),s;if(t._hasNumber){i(\"number.valueformat\");var l=uy.extendFlat({},n.font);l.size=void 0,uy.coerceFont(i,\"number.font\",l),t.number.font.size===void 0&&(t.number.font.size=B7.defaultNumberFontSize,o[0]=!0),i(\"number.prefix\"),i(\"number.suffix\"),s=t.number.font.size}var u;if(t._hasDelta){var c=uy.extendFlat({},n.font);c.size=void 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M=t._isAngular=t.gauge.shape===\"angular\";M||i(\"align\",\"center\"),b(\"bgcolor\",n.paper_bgcolor),b(\"borderwidth\"),b(\"bordercolor\"),b(\"bar.color\"),b(\"bar.line.color\"),b(\"bar.line.width\");var T=B7.valueThickness*(t.gauge.shape===\"bullet\"?.5:1);b(\"bar.thickness\",T),rKt(h,d,{name:\"steps\",handleItemDefaults:lKt}),b(\"threshold.value\"),b(\"threshold.thickness\"),b(\"threshold.line.width\"),b(\"threshold.line.color\"),v={},h&&(v=h.axis||{}),m=ZXe.newContainer(d,\"axis\"),p(\"visible\"),t._range=p(\"range\",t._range);var L={font:n.font,noAutotickangles:!0,outerTicks:!0,noTicklabelshift:!0,noTicklabelstandoff:!0};iKt(v,m,p,\"linear\"),oKt(v,m,p,\"linear\",L),aKt(v,m,p,\"linear\",L),nKt(v,m,p,L)}else i(\"title.align\",\"center\"),i(\"align\",\"center\"),t._isAngular=t._isBullet=!1;t._length=null}function lKt(e,t){function r(n,i){return uy.coerce(e,t,N7.gauge.steps,n,i)}r(\"color\"),r(\"line.color\"),r(\"line.width\"),r(\"range\"),r(\"thickness\")}YXe.exports={supplyDefaults:sKt}});var $Xe=ye(($2r,JXe)=>{\"use strict\";function uKt(e,t){var r=[],n=t.value;typeof t._lastValue!=\"number\"&&(t._lastValue=t.value);var i=t._lastValue,a=i;return t._hasDelta&&typeof t.delta.reference==\"number\"&&(a=t.delta.reference),r[0]={y:n,lastY:i,delta:n-a,relativeDelta:(n-a)/a},r}JXe.exports={calc:uKt}});var nZe=ye((Q2r,iZe)=>{\"use strict\";var Mw=Fa(),cKt=(W2(),gb(j2)).interpolate,QXe=(W2(),gb(j2)).interpolateNumber,qx=Pr(),fKt=qx.strScale,dC=qx.strTranslate,hKt=qx.rad2deg,dKt=Dh().MID_SHIFT,Sw=So(),ww=p$(),V7=Zl(),lv=ho(),vKt=v4(),pKt=gI(),gKt=Rd(),j5=ka(),g$={left:\"start\",center:\"middle\",right:\"end\"},Tw={left:0,center:.5,right:1},eZe=/[yzafpnµmkMGTPEZY]/;function vC(e){return e&&e.duration>0}iZe.exports=function(t,r,n,i){var a=t._fullLayout,o;vC(n)&&i&&(o=i()),qx.makeTraceGroups(a._indicatorlayer,r,\"trace\").each(function(s){var l=s[0],u=l.trace,c=Mw.select(this),f=u._hasGauge,h=u._isAngular,d=u._isBullet,v=u.domain,m={w:a._size.w*(v.x[1]-v.x[0]),h:a._size.h*(v.y[1]-v.y[0]),l:a._size.l+a._size.w*v.x[0],r:a._size.r+a._size.w*(1-v.x[1]),t:a._size.t+a._size.h*(1-v.y[1]),b:a._size.b+a._size.h*v.y[0]},b=m.l+m.w/2,p=m.t+m.h/2,k=Math.min(m.w/2,m.h),M=ww.innerRadius*k,T,L,x,C=u.align||\"center\";if(L=p,!f)T=m.l+Tw[C]*m.w,x=function(j){return tZe(j,m.w,m.h)};else if(h&&(T=b,L=p+k/2,x=function(j){return bKt(j,.9*M)}),d){var S=ww.bulletPadding,g=1-ww.bulletNumberDomainSize+S;T=m.l+(g+(1-g)*Tw[C])*m.w,x=function(j){return tZe(j,(ww.bulletNumberDomainSize-S)*m.w,m.h)}}_Kt(t,c,s,{numbersX:T,numbersY:L,numbersScaler:x,transitionOpts:n,onComplete:o});var P,E;f&&(P={range:u.gauge.axis.range,color:u.gauge.bgcolor,line:{color:u.gauge.bordercolor,width:0},thickness:1},E={range:u.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:u.gauge.bordercolor,width:u.gauge.borderwidth},thickness:1});var z=c.selectAll(\"g.angular\").data(h?s:[]);z.exit().remove();var q=c.selectAll(\"g.angularaxis\").data(h?s:[]);q.exit().remove(),h&&yKt(t,c,s,{radius:k,innerRadius:M,gauge:z,layer:q,size:m,gaugeBg:P,gaugeOutline:E,transitionOpts:n,onComplete:o});var V=c.selectAll(\"g.bullet\").data(d?s:[]);V.exit().remove();var G=c.selectAll(\"g.bulletaxis\").data(d?s:[]);G.exit().remove(),d&&mKt(t,c,s,{gauge:V,layer:G,size:m,gaugeBg:P,gaugeOutline:E,transitionOpts:n,onComplete:o});var Z=c.selectAll(\"text.title\").data(s);Z.exit().remove(),Z.enter().append(\"text\").classed(\"title\",!0),Z.attr(\"text-anchor\",function(){return d?g$.right:g$[u.title.align]}).text(u.title.text).call(Sw.font,u.title.font).call(V7.convertToTspans,t),Z.attr(\"transform\",function(){var j=m.l+m.w*Tw[u.title.align],N,H=ww.titlePadding,re=Sw.bBox(Z.node());if(f){if(h)if(u.gauge.axis.visible){var ae=Sw.bBox(q.node());N=ae.top-H-re.bottom}else N=m.t+m.h/2-k/2-re.bottom-H;d&&(N=L-(re.top+re.bottom)/2,j=m.l-ww.bulletPadding*m.w)}else N=u._numbersTop-H-re.bottom;return dC(j,N)})})};function mKt(e,t,r,n){var i=r[0].trace,a=n.gauge,o=n.layer,s=n.gaugeBg,l=n.gaugeOutline,u=n.size,c=i.domain,f=n.transitionOpts,h=n.onComplete,d,v,m,b,p;a.enter().append(\"g\").classed(\"bullet\",!0),a.attr(\"transform\",dC(u.l,u.t)),o.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),o.selectAll(\"g.xbulletaxistick,path,text\").remove();var k=u.h,M=i.gauge.bar.thickness*k,T=c.x[0],L=c.x[0]+(c.x[1]-c.x[0])*(i._hasNumber||i._hasDelta?1-ww.bulletNumberDomainSize:1);d=hC(e,i.gauge.axis),d._id=\"xbulletaxis\",d.domain=[T,L],d.setScale(),v=lv.calcTicks(d),m=lv.makeTransTickFn(d),b=lv.getTickSigns(d)[2],p=u.t+u.h,d.visible&&(lv.drawTicks(e,d,{vals:d.ticks===\"inside\"?lv.clipEnds(d,v):v,layer:o,path:lv.makeTickPath(d,p,b),transFn:m}),lv.drawLabels(e,d,{vals:v,layer:o,transFn:m,labelFns:lv.makeLabelFns(d,p)}));function x(q){q.attr(\"width\",function(V){return Math.max(0,d.c2p(V.range[1])-d.c2p(V.range[0]))}).attr(\"x\",function(V){return d.c2p(V.range[0])}).attr(\"y\",function(V){return .5*(1-V.thickness)*k}).attr(\"height\",function(V){return V.thickness*k})}var C=[s].concat(i.gauge.steps),S=a.selectAll(\"g.bg-bullet\").data(C);S.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),S.select(\"rect\").call(x).call(Aw),S.exit().remove();var g=a.selectAll(\"g.value-bullet\").data([i.gauge.bar]);g.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),g.select(\"rect\").attr(\"height\",M).attr(\"y\",(k-M)/2).call(Aw),vC(f)?g.select(\"rect\").transition().duration(f.duration).ease(f.easing).each(\"end\",function(){h&&h()}).each(\"interrupt\",function(){h&&h()}).attr(\"width\",Math.max(0,d.c2p(Math.min(i.gauge.axis.range[1],r[0].y)))):g.select(\"rect\").attr(\"width\",typeof r[0].y==\"number\"?Math.max(0,d.c2p(Math.min(i.gauge.axis.range[1],r[0].y))):0),g.exit().remove();var P=r.filter(function(){return i.gauge.threshold.value||i.gauge.threshold.value===0}),E=a.selectAll(\"g.threshold-bullet\").data(P);E.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),E.select(\"line\").attr(\"x1\",d.c2p(i.gauge.threshold.value)).attr(\"x2\",d.c2p(i.gauge.threshold.value)).attr(\"y1\",(1-i.gauge.threshold.thickness)/2*k).attr(\"y2\",(1-(1-i.gauge.threshold.thickness)/2)*k).call(j5.stroke,i.gauge.threshold.line.color).style(\"stroke-width\",i.gauge.threshold.line.width),E.exit().remove();var z=a.selectAll(\"g.gauge-outline\").data([l]);z.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),z.select(\"rect\").call(x).call(Aw),z.exit().remove()}function yKt(e,t,r,n){var i=r[0].trace,a=n.size,o=n.radius,s=n.innerRadius,l=n.gaugeBg,u=n.gaugeOutline,c=[a.l+a.w/2,a.t+a.h/2+o/2],f=n.gauge,h=n.layer,d=n.transitionOpts,v=n.onComplete,m=Math.PI/2;function b(_e){var Ee=i.gauge.axis.range[0],Ce=i.gauge.axis.range[1],ge=(_e-Ee)/(Ce-Ee)*Math.PI-m;return ge<-m?-m:ge>m?m:ge}function p(_e){return Mw.svg.arc().innerRadius((s+o)/2-_e/2*(o-s)).outerRadius((s+o)/2+_e/2*(o-s)).startAngle(-m)}function k(_e){_e.attr(\"d\",function(Ee){return p(Ee.thickness).startAngle(b(Ee.range[0])).endAngle(b(Ee.range[1]))()})}var M,T,L,x;f.enter().append(\"g\").classed(\"angular\",!0),f.attr(\"transform\",dC(c[0],c[1])),h.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),h.selectAll(\"g.xangularaxistick,path,text\").remove(),M=hC(e,i.gauge.axis),M.type=\"linear\",M.range=i.gauge.axis.range,M._id=\"xangularaxis\",M.ticklabeloverflow=\"allow\",M.setScale();var C=function(_e){return(M.range[0]-_e.x)/(M.range[1]-M.range[0])*Math.PI+Math.PI},S={},g=lv.makeLabelFns(M,0),P=g.labelStandoff;S.xFn=function(_e){var Ee=C(_e);return Math.cos(Ee)*P},S.yFn=function(_e){var Ee=C(_e),Ce=Math.sin(Ee)>0?.2:1;return-Math.sin(Ee)*(P+_e.fontSize*Ce)+Math.abs(Math.cos(Ee))*(_e.fontSize*dKt)},S.anchorFn=function(_e){var Ee=C(_e),Ce=Math.cos(Ee);return Math.abs(Ce)<.1?\"middle\":Ce>0?\"start\":\"end\"},S.heightFn=function(_e,Ee,Ce){var ge=C(_e);return-.5*(1+Math.sin(ge))*Ce};var E=function(_e){return dC(c[0]+o*Math.cos(_e),c[1]-o*Math.sin(_e))};L=function(_e){return E(C(_e))};var z=function(_e){var Ee=C(_e);return E(Ee)+\"rotate(\"+-hKt(Ee)+\")\"};if(T=lv.calcTicks(M),x=lv.getTickSigns(M)[2],M.visible){x=M.ticks===\"inside\"?-1:1;var q=(M.linewidth||1)/2;lv.drawTicks(e,M,{vals:T,layer:h,path:\"M\"+x*q+\",0h\"+x*M.ticklen,transFn:z}),lv.drawLabels(e,M,{vals:T,layer:h,transFn:L,labelFns:S})}var V=[l].concat(i.gauge.steps),G=f.selectAll(\"g.bg-arc\").data(V);G.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),G.select(\"path\").call(k).call(Aw),G.exit().remove();var Z=p(i.gauge.bar.thickness),j=f.selectAll(\"g.value-arc\").data([i.gauge.bar]);j.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var N=j.select(\"path\");vC(d)?(N.transition().duration(d.duration).ease(d.easing).each(\"end\",function(){v&&v()}).each(\"interrupt\",function(){v&&v()}).attrTween(\"d\",xKt(Z,b(r[0].lastY),b(r[0].y))),i._lastValue=r[0].y):N.attr(\"d\",typeof r[0].y==\"number\"?Z.endAngle(b(r[0].y)):\"M0,0Z\"),N.call(Aw),j.exit().remove(),V=[];var H=i.gauge.threshold.value;(H||H===0)&&V.push({range:[H,H],color:i.gauge.threshold.color,line:{color:i.gauge.threshold.line.color,width:i.gauge.threshold.line.width},thickness:i.gauge.threshold.thickness});var re=f.selectAll(\"g.threshold-arc\").data(V);re.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),re.select(\"path\").call(k).call(Aw),re.exit().remove();var ae=f.selectAll(\"g.gauge-outline\").data([u]);ae.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),ae.select(\"path\").call(k).call(Aw),ae.exit().remove()}function _Kt(e,t,r,n){var i=r[0].trace,a=n.numbersX,o=n.numbersY,s=i.align||\"center\",l=g$[s],u=n.transitionOpts,c=n.onComplete,f=qx.ensureSingle(t,\"g\",\"numbers\"),h,d,v,m=[];i._hasNumber&&m.push(\"number\"),i._hasDelta&&(m.push(\"delta\"),i.delta.position===\"left\"&&m.reverse());var b=f.selectAll(\"text\").data(m);b.enter().append(\"text\"),b.attr(\"text-anchor\",function(){return l}).attr(\"class\",function(E){return E}).attr(\"x\",null).attr(\"y\",null).attr(\"dx\",null).attr(\"dy\",null),b.exit().remove();function p(E,z,q,V){if(E.match(\"s\")&&q>=0!=V>=0&&!z(q).slice(-1).match(eZe)&&!z(V).slice(-1).match(eZe)){var G=E.slice().replace(\"s\",\"f\").replace(/\\d+/,function(j){return parseInt(j)-1}),Z=hC(e,{tickformat:G});return function(j){return Math.abs(j)<1?lv.tickText(Z,j).text:z(j)}}else return z}function k(){var E=hC(e,{tickformat:i.number.valueformat},i._range);E.setScale(),lv.prepTicks(E);var z=function(j){return lv.tickText(E,j).text},q=i.number.suffix,V=i.number.prefix,G=f.select(\"text.number\");function Z(){var j=typeof r[0].y==\"number\"?V+z(r[0].y)+q:\"-\";G.text(j).call(Sw.font,i.number.font).call(V7.convertToTspans,e)}return vC(u)?G.transition().duration(u.duration).ease(u.easing).each(\"end\",function(){Z(),c&&c()}).each(\"interrupt\",function(){Z(),c&&c()}).attrTween(\"text\",function(){var j=Mw.select(this),N=QXe(r[0].lastY,r[0].y);i._lastValue=r[0].y;var H=p(i.number.valueformat,z,r[0].lastY,r[0].y);return function(re){j.text(V+H(N(re))+q)}}):Z(),h=rZe(V+z(r[0].y)+q,i.number.font,l,e),G}function M(){var E=hC(e,{tickformat:i.delta.valueformat},i._range);E.setScale(),lv.prepTicks(E);var z=function(re){return lv.tickText(E,re).text},q=i.delta.suffix,V=i.delta.prefix,G=function(re){var ae=i.delta.relative?re.relativeDelta:re.delta;return ae},Z=function(re,ae){return re===0||typeof re!=\"number\"||isNaN(re)?\"-\":(re>0?i.delta.increasing.symbol:i.delta.decreasing.symbol)+V+ae(re)+q},j=function(re){return re.delta>=0?i.delta.increasing.color:i.delta.decreasing.color};i._deltaLastValue===void 0&&(i._deltaLastValue=G(r[0]));var N=f.select(\"text.delta\");N.call(Sw.font,i.delta.font).call(j5.fill,j({delta:i._deltaLastValue}));function H(){N.text(Z(G(r[0]),z)).call(j5.fill,j(r[0])).call(V7.convertToTspans,e)}return vC(u)?N.transition().duration(u.duration).ease(u.easing).tween(\"text\",function(){var re=Mw.select(this),ae=G(r[0]),_e=i._deltaLastValue,Ee=p(i.delta.valueformat,z,_e,ae),Ce=QXe(_e,ae);return i._deltaLastValue=ae,function(ge){re.text(Z(Ce(ge),Ee)),re.call(j5.fill,j({delta:Ce(ge)}))}}).each(\"end\",function(){H(),c&&c()}).each(\"interrupt\",function(){H(),c&&c()}):H(),d=rZe(Z(G(r[0]),z),i.delta.font,l,e),N}var T=i.mode+i.align,L;if(i._hasDelta&&(L=M(),T+=i.delta.position+i.delta.font.size+i.delta.font.family+i.delta.valueformat,T+=i.delta.increasing.symbol+i.delta.decreasing.symbol,v=d),i._hasNumber&&(k(),T+=i.number.font.size+i.number.font.family+i.number.valueformat+i.number.suffix+i.number.prefix,v=h),i._hasDelta&&i._hasNumber){var x=[(h.left+h.right)/2,(h.top+h.bottom)/2],C=[(d.left+d.right)/2,(d.top+d.bottom)/2],S,g,P=.75*i.delta.font.size;i.delta.position===\"left\"&&(S=U7(i,\"deltaPos\",0,-1*(h.width*Tw[i.align]+d.width*(1-Tw[i.align])+P),T,Math.min),g=x[1]-C[1],v={width:h.width+d.width+P,height:Math.max(h.height,d.height),left:d.left+S,right:h.right,top:Math.min(h.top,d.top+g),bottom:Math.max(h.bottom,d.bottom+g)}),i.delta.position===\"right\"&&(S=U7(i,\"deltaPos\",0,h.width*(1-Tw[i.align])+d.width*Tw[i.align]+P,T,Math.max),g=x[1]-C[1],v={width:h.width+d.width+P,height:Math.max(h.height,d.height),left:h.left,right:d.right+S,top:Math.min(h.top,d.top+g),bottom:Math.max(h.bottom,d.bottom+g)}),i.delta.position===\"bottom\"&&(S=null,g=d.height,v={width:Math.max(h.width,d.width),height:h.height+d.height,left:Math.min(h.left,d.left),right:Math.max(h.right,d.right),top:h.bottom-h.height,bottom:h.bottom+d.height}),i.delta.position===\"top\"&&(S=null,g=h.top,v={width:Math.max(h.width,d.width),height:h.height+d.height,left:Math.min(h.left,d.left),right:Math.max(h.right,d.right),top:h.bottom-h.height-d.height,bottom:h.bottom}),L.attr({dx:S,dy:g})}(i._hasNumber||i._hasDelta)&&f.attr(\"transform\",function(){var E=n.numbersScaler(v);T+=E[2];var z=U7(i,\"numbersScale\",1,E[0],T,Math.min),q;i._scaleNumbers||(z=1),i._isAngular?q=o-z*v.bottom:q=o-z*(v.top+v.bottom)/2,i._numbersTop=z*v.top+q;var V=v[s];s===\"center\"&&(V=(v.left+v.right)/2);var G=a-z*V;return G=U7(i,\"numbersTranslate\",0,G,T,Math.max),dC(G,q)+fKt(z)})}function Aw(e){e.each(function(t){j5.stroke(Mw.select(this),t.line.color)}).each(function(t){j5.fill(Mw.select(this),t.color)}).style(\"stroke-width\",function(t){return t.line.width})}function xKt(e,t,r){return function(){var n=cKt(t,r);return function(i){return e.endAngle(n(i))()}}}function hC(e,t,r){var n=e._fullLayout,i=qx.extendFlat({type:\"linear\",ticks:\"outside\",range:r,showline:!0},t),a={type:\"linear\",_id:\"x\"+t._id},o={letter:\"x\",font:n.font,noAutotickangles:!0,noHover:!0,noTickson:!0};function s(l,u){return qx.coerce(i,a,gKt,l,u)}return vKt(i,a,s,o,n),pKt(i,a,s,o),a}function tZe(e,t,r){var n=Math.min(t/e.width,r/e.height);return[n,e,t+\"x\"+r]}function bKt(e,t){var 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a$e(e,t,r){this.isSmith=r||!1,this.id=t,this.gd=e,this._hasClipOnAxisFalse=null,this.vangles=null,this.radialAxisAngle=null,this.traceHash={},this.layers={},this.clipPaths={},this.clipIds={},this.viewInitial={};var n=e._fullLayout,i=\"clip\"+n._uid+t;this.clipIds.forTraces=i+\"-for-traces\",this.clipPaths.forTraces=n._clips.append(\"clipPath\").attr(\"id\",this.clipIds.forTraces),this.clipPaths.forTraces.append(\"path\"),this.framework=n[\"_\"+(r?\"smith\":\"polar\")+\"layer\"].append(\"g\").attr(\"class\",t),this.getHole=function(a){return this.isSmith?0:a.hole},this.getSector=function(a){return this.isSmith?[0,360]:a.sector},this.getRadial=function(a){return this.isSmith?a.realaxis:a.radialaxis},this.getAngular=function(a){return this.isSmith?a.imaginaryaxis:a.angularaxis},r||(this.radialTickLayout=null,this.angularTickLayout=null)}var Nd=a$e.prototype;l$e.exports=function(t,r,n){return new a$e(t,r,n)};Nd.plot=function(e,t){for(var r=this,n=t[r.id],i=!1,a=0;a<e.length;a++){var o=e[a][0].trace;if(o.cliponaxis===!1){i=!0;break}}r._hasClipOnAxisFalse=i,r.updateLayers(t,n),r.updateLayout(t,n),bQt.generalUpdatePerTraceModule(r.gd,r,e,n),r.updateFx(t,n),r.isSmith&&(delete n.realaxis.range,delete n.imaginaryaxis.range)};Nd.updateLayers=function(e,t){var r=this,n=r.isSmith,i=r.layers,a=r.getRadial(t),o=r.getAngular(t),s=Nx.layerNames,l=s.indexOf(\"frontplot\"),u=s.slice(0,l),c=o.layer===\"below traces\",f=a.layer===\"below traces\";c&&u.push(\"angular-line\"),f&&u.push(\"radial-line\"),c&&u.push(\"angular-axis\"),f&&u.push(\"radial-axis\"),u.push(\"frontplot\"),c||u.push(\"angular-line\"),f||u.push(\"radial-line\"),c||u.push(\"angular-axis\"),f||u.push(\"radial-axis\");var h=(n?\"smith\":\"polar\")+\"sublayer\",d=r.framework.selectAll(\".\"+h).data(u,String);d.enter().append(\"g\").attr(\"class\",function(v){return h+\" \"+v}).each(function(v){var m=i[v]=Cw.select(this);switch(v){case\"frontplot\":n||m.append(\"g\").classed(\"barlayer\",!0),m.append(\"g\").classed(\"scatterlayer\",!0);break;case\"backplot\":m.append(\"g\").classed(\"maplayer\",!0);break;case\"plotbg\":i.bg=m.append(\"path\");break;case\"radial-grid\":m.style(\"fill\",\"none\");break;case\"angular-grid\":m.style(\"fill\",\"none\");break;case\"radial-line\":m.append(\"line\").style(\"fill\",\"none\");break;case\"angular-line\":m.append(\"path\").style(\"fill\",\"none\");break}}),d.order()};Nd.updateLayout=function(e,t){var r=this,n=r.layers,i=e._size,a=r.getRadial(t),o=r.getAngular(t),s=t.domain.x,l=t.domain.y;r.xOffset=i.l+i.w*s[0],r.yOffset=i.t+i.h*(1-l[1]);var u=r.xLength=i.w*(s[1]-s[0]),c=r.yLength=i.h*(l[1]-l[0]),f=r.getSector(t);r.sectorInRad=f.map(Ux);var h=r.sectorBBox=IQt(f),d=h[2]-h[0],v=h[3]-h[1],m=c/u,b=Math.abs(v/d),p,k,M,T,L;m>b?(p=u,k=u*b,L=(c-k)/i.h/2,M=[s[0],s[1]],T=[l[0]+L,l[1]-L]):(p=c/b,k=c,L=(u-p)/i.w/2,M=[s[0]+L,s[1]-L],T=[l[0],l[1]]),r.xLength2=p,r.yLength2=k,r.xDomain2=M,r.yDomain2=T;var x=r.xOffset2=i.l+i.w*M[0],C=r.yOffset2=i.t+i.h*(1-T[1]),S=r.radius=p/d,g=r.innerRadius=r.getHole(t)*S,P=r.cx=x-S*h[0],E=r.cy=C+S*h[3],z=r.cxx=P-x,q=r.cyy=E-C,V=a.side,G;V===\"counterclockwise\"?(G=V,V=\"top\"):V===\"clockwise\"&&(G=V,V=\"bottom\"),r.radialAxis=r.mockAxis(e,t,a,{_id:\"x\",side:V,_trueSide:G,domain:[g/i.w,S/i.w]}),r.angularAxis=r.mockAxis(e,t,o,{side:\"right\",domain:[0,Math.PI],autorange:!1}),r.doAutoRange(e,t),r.updateAngularAxis(e,t),r.updateRadialAxis(e,t),r.updateRadialAxisTitle(e,t),r.xaxis=r.mockCartesianAxis(e,t,{_id:\"x\",domain:M}),r.yaxis=r.mockCartesianAxis(e,t,{_id:\"y\",domain:T});var Z=r.pathSubplot();r.clipPaths.forTraces.select(\"path\").attr(\"d\",Z).attr(\"transform\",xd(z,q)),n.frontplot.attr(\"transform\",xd(x,C)).call(bC.setClipUrl,r._hasClipOnAxisFalse?null:r.clipIds.forTraces,r.gd),n.bg.attr(\"d\",Z).attr(\"transform\",xd(P,E)).call(iQ.fill,t.bgcolor)};Nd.mockAxis=function(e,t,r,n){var i=Xc.extendFlat({},r,n);return TQt(i,t,e),i};Nd.mockCartesianAxis=function(e,t,r){var n=this,i=n.isSmith,a=r._id,o=Xc.extendFlat({type:\"linear\"},r);wQt(o,e);var s={x:[0,2],y:[1,3]};return o.setRange=function(){var l=n.sectorBBox,u=s[a],c=n.radialAxis._rl,f=(c[1]-c[0])/(1-n.getHole(t));o.range=[l[u[0]]*f,l[u[1]]*f]},o.isPtWithinRange=a===\"x\"&&!i?function(l){return n.isPtInside(l)}:function(){return!0},o.setRange(),o.setScale(),o};Nd.doAutoRange=function(e,t){var r=this,n=r.gd,i=r.radialAxis,a=r.getRadial(t);AQt(n,i);var o=i.range;if(a.range=o.slice(),a._input.range=o.slice(),i._rl=[i.r2l(o[0],null,\"gregorian\"),i.r2l(o[1],null,\"gregorian\")],i.minallowed!==void 0){var s=i.r2l(i.minallowed);i._rl[0]>i._rl[1]?i._rl[1]=Math.max(i._rl[1],s):i._rl[0]=Math.max(i._rl[0],s)}if(i.maxallowed!==void 0){var l=i.r2l(i.maxallowed);i._rl[0]<i._rl[1]?i._rl[1]=Math.min(i._rl[1],l):i._rl[0]=Math.min(i._rl[0],l)}};Nd.updateRadialAxis=function(e,t){var r=this,n=r.gd,i=r.layers,a=r.radius,o=r.innerRadius,s=r.cx,l=r.cy,u=r.getRadial(t),c=n$e(r.getSector(t)[0],360),f=r.radialAxis,h=o<a,d=r.isSmith;d||(r.fillViewInitialKey(\"radialaxis.angle\",u.angle),r.fillViewInitialKey(\"radialaxis.range\",f.range.slice()),f.setGeometry()),f.tickangle===\"auto\"&&c>90&&c<=270&&(f.tickangle=180);var v=d?function(S){var g=b9(r,x9([S.x,0]));return xd(g[0]-s,g[1]-l)}:function(S){return xd(f.l2p(S.x)+o,0)},m=d?function(S){return LQt(r,S.x,-1/0,1/0)}:function(S){return r.pathArc(f.r2p(S.x)+o)},b=o$e(u);if(r.radialTickLayout!==b&&(i[\"radial-axis\"].selectAll(\".xtick\").remove(),r.radialTickLayout=b),h){f.setScale();var p=0,k=d?(f.tickvals||[]).filter(function(S){return S>=0}).map(function(S){return pp.tickText(f,S,!0,!1)}):pp.calcTicks(f),M=d?k:pp.clipEnds(f,k),T=pp.getTickSigns(f)[2];d&&((f.ticks===\"top\"&&f.side===\"bottom\"||f.ticks===\"bottom\"&&f.side===\"top\")&&(T=-T),f.ticks===\"top\"&&f.side===\"top\"&&(p=-f.ticklen),f.ticks===\"bottom\"&&f.side===\"bottom\"&&(p=f.ticklen)),pp.drawTicks(n,f,{vals:k,layer:i[\"radial-axis\"],path:pp.makeTickPath(f,0,T),transFn:v,crisp:!1}),pp.drawGrid(n,f,{vals:M,layer:i[\"radial-grid\"],path:m,transFn:Xc.noop,crisp:!1}),pp.drawLabels(n,f,{vals:k,layer:i[\"radial-axis\"],transFn:v,labelFns:pp.makeLabelFns(f,p)})}var L=r.radialAxisAngle=r.vangles?Lw(s$e(Ux(u.angle),r.vangles)):u.angle,x=xd(s,l),C=x+fy(-L);wC(i[\"radial-axis\"],h&&(u.showticklabels||u.ticks),{transform:C}),wC(i[\"radial-grid\"],h&&u.showgrid,{transform:d?\"\":x}),wC(i[\"radial-line\"].select(\"line\"),h&&u.showline,{x1:d?-a:o,y1:0,x2:a,y2:0,transform:C}).attr(\"stroke-width\",u.linewidth).call(iQ.stroke,u.linecolor)};Nd.updateRadialAxisTitle=function(e,t,r){if(!this.isSmith){var n=this,i=n.gd,a=n.radius,o=n.cx,s=n.cy,l=n.getRadial(t),u=n.id+\"title\",c=0;if(l.title){var f=bC.bBox(n.layers[\"radial-axis\"].node()).height,h=l.title.font.size,d=l.side;c=d===\"top\"?h:d===\"counterclockwise\"?-(f+h*.4):f+h*.8}var v=r!==void 0?r:n.radialAxisAngle,m=Ux(v),b=Math.cos(m),p=Math.sin(m),k=o+a/2*b+c*p,M=s-a/2*p+c*b;n.layers[\"radial-axis-title\"]=SQt.draw(i,u,{propContainer:l,propName:n.id+\".radialaxis.title.text\",placeholder:PQt(i,\"Click to enter radial axis title\"),attributes:{x:k,y:M,\"text-anchor\":\"middle\"},transform:{rotate:-v}})}};Nd.updateAngularAxis=function(e,t){var r=this,n=r.gd,i=r.layers,a=r.radius,o=r.innerRadius,s=r.cx,l=r.cy,u=r.getAngular(t),c=r.angularAxis,f=r.isSmith;f||(r.fillViewInitialKey(\"angularaxis.rotation\",u.rotation),c.setGeometry(),c.setScale());var h=f?function(g){var P=b9(r,x9([0,g.x]));return Math.atan2(P[0]-s,P[1]-l)-Math.PI/2}:function(g){return c.t2g(g.x)};c.type===\"linear\"&&c.thetaunit===\"radians\"&&(c.tick0=Lw(c.tick0),c.dtick=Lw(c.dtick));var d=function(g){return xd(s+a*Math.cos(g),l-a*Math.sin(g))},v=f?function(g){var P=b9(r,x9([0,g.x]));return xd(P[0],P[1])}:function(g){return d(h(g))},m=f?function(g){var P=b9(r,x9([0,g.x])),E=Math.atan2(P[0]-s,P[1]-l)-Math.PI/2;return xd(P[0],P[1])+fy(-Lw(E))}:function(g){var P=h(g);return d(P)+fy(-Lw(P))},b=f?function(g){return CQt(r,g.x,0,1/0)}:function(g){var P=h(g),E=Math.cos(P),z=Math.sin(P);return\"M\"+[s+o*E,l-o*z]+\"L\"+[s+a*E,l-a*z]},p=pp.makeLabelFns(c,0),k=p.labelStandoff,M={};M.xFn=function(g){var P=h(g);return Math.cos(P)*k},M.yFn=function(g){var P=h(g),E=Math.sin(P)>0?.2:1;return-Math.sin(P)*(k+g.fontSize*E)+Math.abs(Math.cos(P))*(g.fontSize*kQt)},M.anchorFn=function(g){var P=h(g),E=Math.cos(P);return Math.abs(E)<.1?\"middle\":E>0?\"start\":\"end\"},M.heightFn=function(g,P,E){var z=h(g);return-.5*(1+Math.sin(z))*E};var T=o$e(u);r.angularTickLayout!==T&&(i[\"angular-axis\"].selectAll(\".\"+c._id+\"tick\").remove(),r.angularTickLayout=T);var L=f?[1/0].concat(c.tickvals||[]).map(function(g){return pp.tickText(c,g,!0,!1)}):pp.calcTicks(c);f&&(L[0].text=\"\\u221E\",L[0].fontSize*=1.75);var x;if(t.gridshape===\"linear\"?(x=L.map(h),Xc.angleDelta(x[0],x[1])<0&&(x=x.slice().reverse())):x=null,r.vangles=x,c.type===\"category\"&&(L=L.filter(function(g){return Xc.isAngleInsideSector(h(g),r.sectorInRad)})),c.visible){var C=c.ticks===\"inside\"?-1:1,S=(c.linewidth||1)/2;pp.drawTicks(n,c,{vals:L,layer:i[\"angular-axis\"],path:\"M\"+C*S+\",0h\"+C*c.ticklen,transFn:m,crisp:!1}),pp.drawGrid(n,c,{vals:L,layer:i[\"angular-grid\"],path:b,transFn:Xc.noop,crisp:!1}),pp.drawLabels(n,c,{vals:L,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:v,labelFns:M})}wC(i[\"angular-line\"].select(\"path\"),u.showline,{d:r.pathSubplot(),transform:xd(s,l)}).attr(\"stroke-width\",u.linewidth).call(iQ.stroke,u.linecolor)};Nd.updateFx=function(e,t){if(!this.gd._context.staticPlot){var r=!this.isSmith;r&&(this.updateAngularDrag(e),this.updateRadialDrag(e,t,0),this.updateRadialDrag(e,t,1)),this.updateHoverAndMainDrag(e)}};Nd.updateHoverAndMainDrag=function(e){var t=this,r=t.isSmith,n=t.gd,i=t.layers,a=e._zoomlayer,o=Nx.MINZOOM,s=Nx.OFFEDGE,l=t.radius,u=t.innerRadius,c=t.cx,f=t.cy,h=t.cxx,d=t.cyy,v=t.sectorInRad,m=t.vangles,b=t.radialAxis,p=E1.clampTiny,k=E1.findXYatLength,M=E1.findEnclosingVertexAngles,T=Nx.cornerHalfWidth,L=Nx.cornerLen/2,x,C,S=M1.makeDragger(i,\"path\",\"maindrag\",e.dragmode===!1?\"none\":\"crosshair\");Cw.select(S).attr(\"d\",t.pathSubplot()).attr(\"transform\",xd(c,f)),S.onmousemove=function(ce){e$e.hover(n,ce,t.id),n._fullLayout._lasthover=S,n._fullLayout._hoversubplot=t.id},S.onmouseout=function(ce){n._dragging||w9.unhover(n,ce)};var g={element:S,gd:n,subplot:t.id,plotinfo:{id:t.id,xaxis:t.xaxis,yaxis:t.yaxis},xaxes:[t.xaxis],yaxes:[t.yaxis]},P,E,z,q,V,G,Z,j,N;function H(ce,He){return Math.sqrt(ce*ce+He*He)}function re(ce,He){return H(ce-h,He-d)}function ae(ce,He){return Math.atan2(d-He,ce-h)}function _e(ce,He){return[ce*Math.cos(He),ce*Math.sin(-He)]}function Ee(ce,He){if(ce===0)return t.pathSector(2*T);var lt=L/ce,mt=He-lt,Ht=He+lt,at=Math.max(0,Math.min(ce,l)),ct=at-T,ar=at+T;return\"M\"+_e(ct,mt)+\"A\"+[ct,ct]+\" 0,0,0 \"+_e(ct,Ht)+\"L\"+_e(ar,Ht)+\"A\"+[ar,ar]+\" 0,0,1 \"+_e(ar,mt)+\"Z\"}function Ce(ce,He,lt){if(ce===0)return t.pathSector(2*T);var mt=_e(ce,He),Ht=_e(ce,lt),at=p((mt[0]+Ht[0])/2),ct=p((mt[1]+Ht[1])/2),ar,Vt;if(at&&ct){var rr=ct/at,tt=-1/rr,je=k(T,rr,at,ct);ar=k(L,tt,je[0][0],je[0][1]),Vt=k(L,tt,je[1][0],je[1][1])}else{var Ue,Ae;ct?(Ue=L,Ae=T):(Ue=T,Ae=L),ar=[[at-Ue,ct-Ae],[at+Ue,ct-Ae]],Vt=[[at-Ue,ct+Ae],[at+Ue,ct+Ae]]}return\"M\"+ar.join(\"L\")+\"L\"+Vt.reverse().join(\"L\")+\"Z\"}function ge(){z=null,q=null,V=t.pathSubplot(),G=!1;var ce=n._fullLayout[t.id];Z=xQt(ce.bgcolor).getLuminance(),j=M1.makeZoombox(a,Z,c,f,V),j.attr(\"fill-rule\",\"evenodd\"),N=M1.makeCorners(a,c,f),nQ(n)}function ie(ce,He){return He=Math.max(Math.min(He,l),u),ce<s?ce=0:l-ce<s?ce=l:He<s?He=0:l-He<s&&(He=l),Math.abs(He-ce)>o?(ce<He?(z=ce,q=He):(z=He,q=ce),!0):(z=null,q=null,!1)}function Se(ce,He){ce=ce||V,He=He||\"M0,0Z\",j.attr(\"d\",ce),N.attr(\"d\",He),M1.transitionZoombox(j,N,G,Z),G=!0;var lt={};me(lt),n.emit(\"plotly_relayouting\",lt)}function ke(ce,He){ce=ce*x,He=He*C;var lt=P+ce,mt=E+He,Ht=re(P,E),at=Math.min(re(lt,mt),l),ct=ae(P,E),ar,Vt;ie(Ht,at)&&(ar=V+t.pathSector(q),z&&(ar+=t.pathSector(z)),Vt=Ee(z,ct)+Ee(q,ct)),Se(ar,Vt)}function be(ce,He,lt,mt){var Ht=E1.findIntersectionXY(lt,mt,lt,[ce-h,d-He]);return H(Ht[0],Ht[1])}function Be(ce,He){var lt=P+ce,mt=E+He,Ht=ae(P,E),at=ae(lt,mt),ct=M(Ht,m),ar=M(at,m),Vt=be(P,E,ct[0],ct[1]),rr=Math.min(be(lt,mt,ar[0],ar[1]),l),tt,je;ie(Vt,rr)&&(tt=V+t.pathSector(q),z&&(tt+=t.pathSector(z)),je=[Ce(z,ct[0],ct[1]),Ce(q,ct[0],ct[1])].join(\" \")),Se(tt,je)}function Le(){if(M1.removeZoombox(n),!(z===null||q===null)){var ce={};me(ce),M1.showDoubleClickNotifier(n),Pw.call(\"_guiRelayout\",n,ce)}}function me(ce){var He=b._rl,lt=(He[1]-He[0])/(1-u/l)/l,mt=[He[0]+(z-u)*lt,He[0]+(q-u)*lt];ce[t.id+\".radialaxis.range\"]=mt}function Pe(ce,He){var lt=n._fullLayout.clickmode;if(M1.removeZoombox(n),ce===2){var mt={};for(var Ht in t.viewInitial)mt[t.id+\".\"+Ht]=t.viewInitial[Ht];n.emit(\"plotly_doubleclick\",null),Pw.call(\"_guiRelayout\",n,mt)}lt.indexOf(\"select\")>-1&&ce===1&&EQt(He,n,[t.xaxis],[t.yaxis],t.id,g),lt.indexOf(\"event\")>-1&&e$e.click(n,He,t.id)}g.prepFn=function(ce,He,lt){var mt=n._fullLayout.dragmode,Ht=S.getBoundingClientRect();n._fullLayout._calcInverseTransform(n);var at=n._fullLayout._invTransform;x=n._fullLayout._invScaleX,C=n._fullLayout._invScaleY;var ct=Xc.apply3DTransform(at)(He-Ht.left,lt-Ht.top);if(P=ct[0],E=ct[1],m){var ar=E1.findPolygonOffset(l,v[0],v[1],m);P+=h+ar[0],E+=d+ar[1]}switch(mt){case\"zoom\":g.clickFn=Pe,r||(m?g.moveFn=Be:g.moveFn=ke,g.doneFn=Le,ge(ce,He,lt));break;case\"select\":case\"lasso\":MQt(ce,He,lt,g,mt);break}},w9.init(g)};Nd.updateRadialDrag=function(e,t,r){var n=this,i=n.gd,a=n.layers,o=n.radius,s=n.innerRadius,l=n.cx,u=n.cy,c=n.radialAxis,f=Nx.radialDragBoxSize,h=f/2;if(!c.visible)return;var d=Ux(n.radialAxisAngle),v=c._rl,m=v[0],b=v[1],p=v[r],k=.75*(v[1]-v[0])/(1-n.getHole(t))/o,M,T,L;r?(M=l+(o+h)*Math.cos(d),T=u-(o+h)*Math.sin(d),L=\"radialdrag\"):(M=l+(s-h)*Math.cos(d),T=u-(s-h)*Math.sin(d),L=\"radialdrag-inner\");var x=M1.makeRectDragger(a,L,\"crosshair\",-h,-h,f,f),C={element:x,gd:i};e.dragmode===!1&&(C.dragmode=!1),wC(Cw.select(x),c.visible&&s<o,{transform:xd(M,T)});var S,g,P;function E(Z,j){if(S)S(Z,j);else{var N=[Z,-j],H=[Math.cos(d),Math.sin(d)],re=Math.abs(Xc.dot(N,H)/Math.sqrt(Xc.dot(N,N)));isNaN(re)||(S=re<.5?V:G)}var ae={};z(ae),i.emit(\"plotly_relayouting\",ae)}function z(Z){g!==null?Z[n.id+\".radialaxis.angle\"]=g:P!==null&&(Z[n.id+\".radialaxis.range[\"+r+\"]\"]=P)}function q(){g!==null?Pw.call(\"_guiRelayout\",i,n.id+\".radialaxis.angle\",g):P!==null&&Pw.call(\"_guiRelayout\",i,n.id+\".radialaxis.range[\"+r+\"]\",P)}function V(Z,j){if(r!==0){var N=M+Z,H=T+j;g=Math.atan2(u-H,N-l),n.vangles&&(g=s$e(g,n.vangles)),g=Lw(g);var re=xd(l,u)+fy(-g);a[\"radial-axis\"].attr(\"transform\",re),a[\"radial-line\"].select(\"line\").attr(\"transform\",re);var ae=n.gd._fullLayout,_e=ae[n.id];n.updateRadialAxisTitle(ae,_e,g)}}function G(Z,j){var N=Xc.dot([Z,-j],[Math.cos(d),Math.sin(d)]);if(P=p-k*N,k>0!=(r?P>m:P<b)){P=null;return}var H=i._fullLayout,re=H[n.id];c.range[r]=P,c._rl[r]=P,n.updateRadialAxis(H,re),n.xaxis.setRange(),n.xaxis.setScale(),n.yaxis.setRange(),n.yaxis.setScale();var ae=!1;for(var _e in n.traceHash){var Ee=n.traceHash[_e],Ce=Xc.filterVisible(Ee),ge=Ee[0][0].trace._module;ge.plot(i,n,Ce,re),Pw.traceIs(_e,\"gl\")&&Ce.length&&(ae=!0)}ae&&(r$e(i),i$e(i))}C.prepFn=function(){S=null,g=null,P=null,C.moveFn=E,C.doneFn=q,nQ(i)},C.clampFn=function(Z,j){return Math.sqrt(Z*Z+j*j)<Nx.MINDRAG&&(Z=0,j=0),[Z,j]},w9.init(C)};Nd.updateAngularDrag=function(e){var t=this,r=t.gd,n=t.layers,i=t.radius,a=t.angularAxis,o=t.cx,s=t.cy,l=t.cxx,u=t.cyy,c=Nx.angularDragBoxSize,f=M1.makeDragger(n,\"path\",\"angulardrag\",e.dragmode===!1?\"none\":\"move\"),h={element:f,gd:r};e.dragmode===!1?h.dragmode=!1:Cw.select(f).attr(\"d\",t.pathAnnulus(i,i+c)).attr(\"transform\",xd(o,s)).call(t$e,\"move\");function d(P,E){return Math.atan2(u+c-E,P-l-c)}var v=n.frontplot.select(\".scatterlayer\").selectAll(\".trace\"),m=v.selectAll(\".point\"),b=v.selectAll(\".textpoint\"),p,k,M,T,L,x;function C(P,E){var z=t.gd._fullLayout,q=z[t.id],V=p+P*e._invScaleX,G=k+E*e._invScaleY,Z=d(V,G),j=Lw(Z-x);if(T=M+j,n.frontplot.attr(\"transform\",xd(t.xOffset2,t.yOffset2)+fy([-j,l,u])),t.vangles){L=t.radialAxisAngle+j;var N=xd(o,s)+fy(-j),H=xd(o,s)+fy(-L);n.bg.attr(\"transform\",N),n[\"radial-grid\"].attr(\"transform\",N),n[\"radial-axis\"].attr(\"transform\",H),n[\"radial-line\"].select(\"line\").attr(\"transform\",H),t.updateRadialAxisTitle(z,q,L)}else t.clipPaths.forTraces.select(\"path\").attr(\"transform\",xd(l,u)+fy(j));m.each(function(){var ie=Cw.select(this),Se=bC.getTranslate(ie);ie.attr(\"transform\",xd(Se.x,Se.y)+fy([j]))}),b.each(function(){var ie=Cw.select(this),Se=ie.select(\"text\"),ke=bC.getTranslate(ie);ie.attr(\"transform\",fy([j,Se.attr(\"x\"),Se.attr(\"y\")])+xd(ke.x,ke.y))}),a.rotation=Xc.modHalf(T,360),t.updateAngularAxis(z,q),t._hasClipOnAxisFalse&&!Xc.isFullCircle(t.sectorInRad)&&v.call(bC.hideOutsideRangePoints,t);var re=!1;for(var ae in t.traceHash)if(Pw.traceIs(ae,\"gl\")){var _e=t.traceHash[ae],Ee=Xc.filterVisible(_e),Ce=_e[0][0].trace._module;Ce.plot(r,t,Ee,q),Ee.length&&(re=!0)}re&&(r$e(r),i$e(r));var ge={};S(ge),r.emit(\"plotly_relayouting\",ge)}function S(P){P[t.id+\".angularaxis.rotation\"]=T,t.vangles&&(P[t.id+\".radialaxis.angle\"]=L)}function g(){b.select(\"text\").attr(\"transform\",null);var P={};S(P),Pw.call(\"_guiRelayout\",r,P)}h.prepFn=function(P,E,z){var q=e[t.id];M=q.angularaxis.rotation;var V=f.getBoundingClientRect();p=E-V.left,k=z-V.top,r._fullLayout._calcInverseTransform(r);var G=Xc.apply3DTransform(e._invTransform)(p,k);p=G[0],k=G[1],x=d(p,k),h.moveFn=C,h.doneFn=g,nQ(r)},t.vangles&&!Xc.isFullCircle(t.sectorInRad)&&(h.prepFn=Xc.noop,t$e(Cw.select(f),null)),w9.init(h)};Nd.isPtInside=function(e){if(this.isSmith)return!0;var t=this.sectorInRad,r=this.vangles,n=this.angularAxis.c2g(e.theta),i=this.radialAxis,a=i.c2l(e.r),o=i._rl,s=r?E1.isPtInsidePolygon:Xc.isPtInsideSector;return s(a,n,o,t,r)};Nd.pathArc=function(e){var t=this.sectorInRad,r=this.vangles,n=r?E1.pathPolygon:Xc.pathArc;return n(e,t[0],t[1],r)};Nd.pathSector=function(e){var t=this.sectorInRad,r=this.vangles,n=r?E1.pathPolygon:Xc.pathSector;return n(e,t[0],t[1],r)};Nd.pathAnnulus=function(e,t){var r=this.sectorInRad,n=this.vangles,i=n?E1.pathPolygonAnnulus:Xc.pathAnnulus;return i(e,t,r[0],r[1],n)};Nd.pathSubplot=function(){var e=this.innerRadius,t=this.radius;return e?this.pathAnnulus(e,t):this.pathSector(t)};Nd.fillViewInitialKey=function(e,t){e in this.viewInitial||(this.viewInitial[e]=t)};function o$e(e){var t=e.ticks+String(e.ticklen)+String(e.showticklabels);return\"side\"in e&&(t+=e.side),t}function IQt(e){var t=e[0],r=e[1],n=r-t,i=n$e(t,360),a=i+n,o=Math.cos(Ux(i)),s=Math.sin(Ux(i)),l=Math.cos(Ux(a)),u=Math.sin(Ux(a)),c,f,h,d;return i<=90&&a>=90||i>90&&a>=450?d=1:s<=0&&u<=0?d=0:d=Math.max(s,u),i<=180&&a>=180||i>180&&a>=540?c=-1:o>=0&&l>=0?c=0:c=Math.min(o,l),i<=270&&a>=270||i>270&&a>=630?f=-1:s>=0&&u>=0?f=0:f=Math.min(s,u),a>=360?h=1:o<=0&&l<=0?h=0:h=Math.max(o,l),[c,f,h,d]}function s$e(e,t){var r=function(i){return Xc.angleDist(e,i)},n=Xc.findIndexOfMin(t,r);return t[n]}function wC(e,t,r){return t?(e.attr(\"display\",null),e.attr(r)):e&&e.attr(\"display\",\"none\"),e}});var oQ=ye((w3r,v$e)=>{\"use strict\";var 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i=r.layers.frontplot.select(\"g.scatterlayer\"),a=r.xaxis,o=r.yaxis,s={xaxis:a,yaxis:o,plot:r.framework,layerClipId:r._hasClipOnAxisFalse?r.clipIds.forTraces:null},l=0;l<n.length;l++)for(var u=n[l],c=0;c<u.length;c++){c===0&&(u[0].trace._xA=a,u[0].trace._yA=o);var f=u[c],h=f.real;if(h===$Qe)f.x=f.y=$Qe;else{var d=srr([h,f.imag]);f.x=d[0],f.y=d[1]}}arr(t,s,n,i)}});var iet=ye((uTr,ret)=>{\"use strict\";var lrr=bT();function urr(e,t,r,n){var i=lrr(e,t,r,n);if(!(!i||i[0].index===!1)){var a=i[0];if(a.index===void 0)return i;var o=e.subplot,s=a.cd[a.index],l=a.trace;if(o.isPtInside(s))return a.xLabelVal=void 0,a.yLabelVal=void 0,tet(s,l,o,a),a.hovertemplate=l.hovertemplate,i}}function tet(e,t,r,n){var i=r.radialAxis,a=r.angularAxis;i._hovertitle=\"real\",a._hovertitle=\"imag\";var o={};o[t.subplot]={_subplot:r};var s=t._module.formatLabels(e,t,o);n.realLabel=s.realLabel,n.imagLabel=s.imagLabel;var l=e.hi||t.hoverinfo,u=[];function c(h,d){u.push(h._hovertitle+\": \"+d)}if(!t.hovertemplate){var f=l.split(\"+\");f.indexOf(\"all\")!==-1&&(f=[\"real\",\"imag\",\"text\"]),f.indexOf(\"real\")!==-1&&c(i,n.realLabel),f.indexOf(\"imag\")!==-1&&c(a,n.imagLabel),f.indexOf(\"text\")!==-1&&n.text&&(u.push(n.text),delete n.text),n.extraText=u.join(\"<br>\")}}ret.exports={hoverPoints:urr,makeHoverPointText:tet}});var aet=ye((cTr,net)=>{\"use strict\";net.exports={moduleType:\"trace\",name:\"scattersmith\",basePlotModule:NQe(),categories:[\"smith\",\"symbols\",\"showLegend\",\"scatter-like\"],attributes:yQ(),supplyDefaults:jQe(),colorbar:Qd(),formatLabels:ZQe(),calc:JQe(),plot:eet(),style:sp().style,styleOnSelect:sp().styleOnSelect,hoverPoints:iet().hoverPoints,selectPoints:wT(),meta:{}}});var set=ye((fTr,oet)=>{\"use strict\";oet.exports=aet()});var kv=ye((hTr,cet)=>{var O9=Nh();function uet(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}O9(uet.prototype,{instance:function(e,t){e=(e||\"gregorian\").toLowerCase(),t=t||\"\";var r=this._localCals[e+\"-\"+t];if(!r&&this.calendars[e]&&(r=new this.calendars[e](t),this._localCals[e+\"-\"+t]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,e);return r},newDate:function(e,t,r,n,i){return n=(e!=null&&e.year?e.calendar():typeof n==\"string\"?this.instance(n,i):n)||this.instance(),n.newDate(e,t,r)},substituteDigits:function(e){return function(t){return(t+\"\").replace(/[0-9]/g,function(r){return e[r]})}},substituteChineseDigits:function(e,t){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(a===0?\"\":e[a]+t[i])+n,i++,r=Math.floor(r/10)}return n.indexOf(e[1]+t[1])===0&&(n=n.substr(1)),n||e[0]}}});function _Q(e,t,r,n){if(this._calendar=e,this._year=t,this._month=r,this._day=n,this._calendar._validateLevel===0&&!this._calendar.isValid(this._year,this._month,this._day))throw(Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function q9(e,t){return e=\"\"+e,\"000000\".substring(0,t-e.length)+e}O9(_Q.prototype,{newDate:function(e,t,r){return this._calendar.newDate(e==null?this:e,t,r)},year:function(e){return arguments.length===0?this._year:this.set(e,\"y\")},month:function(e){return arguments.length===0?this._month:this.set(e,\"m\")},day:function(e){return arguments.length===0?this._day:this.set(e,\"d\")},date:function(e,t,r){if(!this._calendar.isValid(e,t,r))throw(Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=e,this._month=t,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(e,t){return this._calendar.add(this,e,t)},set:function(e,t){return this._calendar.set(this,e,t)},compareTo:function(e){if(this._calendar.name!==e._calendar.name)throw(Gs.local.differentCalendars||Gs.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,e._calendar.local.name);var t=this._year!==e._year?this._year-e._year:this._month!==e._month?this.monthOfYear()-e.monthOfYear():this._day-e._day;return t===0?0:t<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(e){return this._calendar.fromJD(e)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(e){return this._calendar.fromJSDate(e)},toString:function(){return(this.year()<0?\"-\":\"\")+q9(Math.abs(this.year()),4)+\"-\"+q9(this.month(),2)+\"-\"+q9(this.day(),2)}});function xQ(){this.shortYearCutoff=\"+10\"}O9(xQ.prototype,{_validateLevel:0,newDate:function(e,t,r){return e==null?this.today():(e.year&&(this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate),r=e.day(),t=e.month(),e=e.year()),new _Q(this,e,t,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(e){var t=this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[\"\"].invalidYear);return t.year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[\"\"].invalidYear);return(t.year()<0?\"-\":\"\")+q9(Math.abs(t.year()),4)},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(e,t){var r=this._validate(e,t,this.minDay,Gs.local.invalidMonth||Gs.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(e,t){var r=(t+this.firstMonth-2*this.minMonth)%this.monthsInYear(e)+this.minMonth;return this._validate(e,r,this.minDay,Gs.local.invalidMonth||Gs.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[\"\"].invalidYear);return this.leapYear(t)?366:365},dayOfYear:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(e,t,r){return this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate),{}},add:function(e,t,r){return this._validate(e,this.minMonth,this.minDay,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate),this._correctAdd(e,this._add(e,t,r),t,r)},_add:function(e,t,r){if(this._validateLevel++,r===\"d\"||r===\"w\"){var n=e.toJD()+t*(r===\"w\"?this.daysInWeek():1),i=e.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=e.year()+(r===\"y\"?t:0),o=e.monthOfYear()+(r===\"m\"?t:0),i=e.day(),s=function(c){for(;o<c.minMonth;)a--,o+=c.monthsInYear(a);for(var f=c.monthsInYear(a);o>f-1+c.minMonth;)a++,o-=f,f=c.monthsInYear(a)};r===\"y\"?(e.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,e.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):r===\"m\"&&(s(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var l=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,l}catch(u){throw this._validateLevel--,u}},_correctAdd:function(e,t,r,n){if(!this.hasYearZero&&(n===\"y\"||n===\"m\")&&(t[0]===0||e.year()>0!=t[0]>0)){var i={y:[1,1,\"y\"],m:[1,this.monthsInYear(-1),\"m\"],w:[this.daysInWeek(),this.daysInYear(-1),\"d\"],d:[1,this.daysInYear(-1),\"d\"]}[n],a=r<0?-1:1;t=this._add(e,r*i[0]+a*i[1],i[2])}return e.date(t[0],t[1],t[2])},set:function(e,t,r){this._validate(e,this.minMonth,this.minDay,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate);var n=r===\"y\"?t:e.year(),i=r===\"m\"?t:e.month(),a=r===\"d\"?t:e.day();return(r===\"y\"||r===\"m\")&&(a=Math.min(a,this.daysInMonth(n,i))),e.date(n,i,a)},isValid:function(e,t,r){this._validateLevel++;var n=this.hasYearZero||e!==0;if(n){var i=this.newDate(e,t,this.minDay);n=t>=this.minMonth&&t-this.minMonth<this.monthsInYear(i)&&r>=this.minDay&&r-this.minDay<this.daysInMonth(i)}return this._validateLevel--,n},toJSDate:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate);return Gs.instance().fromJD(this.toJD(n)).toJSDate()},fromJSDate:function(e){return this.fromJD(Gs.instance().fromJSDate(e).toJD())},_validate:function(e,t,r,n){if(e.year){if(this._validateLevel===0&&this.name!==e.calendar().name)throw(Gs.local.differentCalendars||Gs.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this.local.name).replace(/\\{1\\}/,e.calendar().local.name);return e}try{if(this._validateLevel++,this._validateLevel===1&&!this.isValid(e,t,r))throw n.replace(/\\{0\\}/,this.local.name);var i=this.newDate(e,t,r);return this._validateLevel--,i}catch(a){throw this._validateLevel--,a}}});function bQ(e){this.local=this.regionalOptions[e]||this.regionalOptions[\"\"]}bQ.prototype=new xQ;O9(bQ.prototype,{name:\"Gregorian\",jdEpoch:17214255e-1,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Gregorian\",epochs:[\"BCE\",\"CE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Gs.local.invalidYear||Gs.regionalOptions[\"\"].invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===0&&(r%100!==0||r%400===0)},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Gs.local.invalidMonth||Gs.regionalOptions[\"\"].invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate);e=n.year(),t=n.month(),r=n.day(),e<0&&e++,t<3&&(t+=12,e--);var i=Math.floor(e/100),a=2-i+Math.floor(i/4);return Math.floor(365.25*(e+4716))+Math.floor(30.6001*(t+1))+r+a-1524.5},fromJD:function(e){var t=Math.floor(e+.5),r=Math.floor((t-186721625e-2)/36524.25);r=t+1+r-Math.floor(r/4);var n=r+1524,i=Math.floor((n-122.1)/365.25),a=Math.floor(365.25*i),o=Math.floor((n-a)/30.6001),s=n-a-Math.floor(o*30.6001),l=o-(o>13.5?13:1),u=i-(l>2.5?4716:4715);return u<=0&&u--,this.newDate(u,l,s)},toJSDate:function(e,t,r){var n=this._validate(e,t,r,Gs.local.invalidDate||Gs.regionalOptions[\"\"].invalidDate),i=new Date(n.year(),n.month()-1,n.day());return i.setHours(0),i.setMinutes(0),i.setSeconds(0),i.setMilliseconds(0),i.setHours(i.getHours()>12?i.getHours()+2:0),i},fromJSDate:function(e){return this.newDate(e.getFullYear(),e.getMonth()+1,e.getDate())}});var Gs=cet.exports=new uet;Gs.cdate=_Q;Gs.baseCalendar=xQ;Gs.calendars.gregorian=bQ});var fet=ye(()=>{var wQ=Nh(),Ud=kv();wQ(Ud.regionalOptions[\"\"],{invalidArguments:\"Invalid arguments\",invalidFormat:\"Cannot format a date from another calendar\",missingNumberAt:\"Missing number at position {0}\",unknownNameAt:\"Unknown name at position {0}\",unexpectedLiteralAt:\"Unexpected literal at position {0}\",unexpectedText:\"Additional text found at end\"});Ud.local=Ud.regionalOptions[\"\"];wQ(Ud.cdate.prototype,{formatDate:function(e,t){return typeof e!=\"string\"&&(t=e,e=\"\"),this._calendar.formatDate(e||\"\",this,t)}});wQ(Ud.baseCalendar.prototype,{UNIX_EPOCH:Ud.instance().newDate(1970,1,1).toJD(),SECS_PER_DAY:1440*60,TICKS_EPOCH:Ud.instance().jdEpoch,TICKS_PER_DAY:1440*60*1e7,ATOM:\"yyyy-mm-dd\",COOKIE:\"D, dd M yyyy\",FULL:\"DD, MM d, yyyy\",ISO_8601:\"yyyy-mm-dd\",JULIAN:\"J\",RFC_822:\"D, d M yy\",RFC_850:\"DD, dd-M-yy\",RFC_1036:\"D, d M yy\",RFC_1123:\"D, d M yyyy\",RFC_2822:\"D, d M yyyy\",RSS:\"D, d M yy\",TICKS:\"!\",TIMESTAMP:\"@\",W3C:\"yyyy-mm-dd\",formatDate:function(e,t,r){if(typeof e!=\"string\"&&(r=t,t=e,e=\"\"),!t)return\"\";if(t.calendar()!==this)throw Ud.local.invalidFormat||Ud.regionalOptions[\"\"].invalidFormat;e=e||this.local.dateFormat,r=r||{};for(var n=r.dayNamesShort||this.local.dayNamesShort,i=r.dayNames||this.local.dayNames,a=r.monthNumbers||this.local.monthNumbers,o=r.monthNamesShort||this.local.monthNamesShort,s=r.monthNames||this.local.monthNames,l=r.calculateWeek||this.local.calculateWeek,u=function(T,L){for(var x=1;M+x<e.length&&e.charAt(M+x)===T;)x++;return M+=x-1,Math.floor(x/(L||1))>1},c=function(T,L,x,C){var S=\"\"+L;if(u(T,C))for(;S.length<x;)S=\"0\"+S;return S},f=function(T,L,x,C){return u(T)?C[L]:x[L]},h=this,d=function(T){return typeof a==\"function\"?a.call(h,T,u(\"m\")):b(c(\"m\",T.month(),2))},v=function(T,L){return L?typeof s==\"function\"?s.call(h,T):s[T.month()-h.minMonth]:typeof o==\"function\"?o.call(h,T):o[T.month()-h.minMonth]},m=this.local.digits,b=function(T){return r.localNumbers&&m?m(T):T},p=\"\",k=!1,M=0;M<e.length;M++)if(k)e.charAt(M)===\"'\"&&!u(\"'\")?k=!1:p+=e.charAt(M);else switch(e.charAt(M)){case\"d\":p+=b(c(\"d\",t.day(),2));break;case\"D\":p+=f(\"D\",t.dayOfWeek(),n,i);break;case\"o\":p+=c(\"o\",t.dayOfYear(),3);break;case\"w\":p+=c(\"w\",t.weekOfYear(),2);break;case\"m\":p+=d(t);break;case\"M\":p+=v(t,u(\"M\"));break;case\"y\":p+=u(\"y\",2)?t.year():(t.year()%100<10?\"0\":\"\")+t.year()%100;break;case\"Y\":u(\"Y\",2),p+=t.formatYear();break;case\"J\":p+=t.toJD();break;case\"@\":p+=(t.toJD()-this.UNIX_EPOCH)*this.SECS_PER_DAY;break;case\"!\":p+=(t.toJD()-this.TICKS_EPOCH)*this.TICKS_PER_DAY;break;case\"'\":u(\"'\")?p+=\"'\":k=!0;break;default:p+=e.charAt(M)}return p},parseDate:function(e,t,r){if(t==null)throw Ud.local.invalidArguments||Ud.regionalOptions[\"\"].invalidArguments;if(t=typeof t==\"object\"?t.toString():t+\"\",t===\"\")return null;e=e||this.local.dateFormat,r=r||{};var n=r.shortYearCutoff||this.shortYearCutoff;n=typeof n!=\"string\"?n:this.today().year()%100+parseInt(n,10);for(var i=r.dayNamesShort||this.local.dayNamesShort,a=r.dayNames||this.local.dayNames,o=r.parseMonth||this.local.parseMonth,s=r.monthNumbers||this.local.monthNumbers,l=r.monthNamesShort||this.local.monthNamesShort,u=r.monthNames||this.local.monthNames,c=-1,f=-1,h=-1,d=-1,v=-1,m=!1,b=!1,p=function(z,q){for(var V=1;g+V<e.length&&e.charAt(g+V)===z;)V++;return g+=V-1,Math.floor(V/(q||1))>1},k=function(z,q){var V=p(z,q),G=[2,3,V?4:2,V?4:2,10,11,20][\"oyYJ@!\".indexOf(z)+1],Z=new RegExp(\"^-?\\\\d{1,\"+G+\"}\"),j=t.substring(S).match(Z);if(!j)throw(Ud.local.missingNumberAt||Ud.regionalOptions[\"\"].missingNumberAt).replace(/\\{0\\}/,S);return S+=j[0].length,parseInt(j[0],10)},M=this,T=function(){if(typeof s==\"function\"){p(\"m\");var z=s.call(M,t.substring(S));return S+=z.length,z}return k(\"m\")},L=function(z,q,V,G){for(var Z=p(z,G)?V:q,j=0;j<Z.length;j++)if(t.substr(S,Z[j].length).toLowerCase()===Z[j].toLowerCase())return S+=Z[j].length,j+M.minMonth;throw(Ud.local.unknownNameAt||Ud.regionalOptions[\"\"].unknownNameAt).replace(/\\{0\\}/,S)},x=function(){if(typeof u==\"function\"){var z=p(\"M\")?u.call(M,t.substring(S)):l.call(M,t.substring(S));return S+=z.length,z}return L(\"M\",l,u)},C=function(){if(t.charAt(S)!==e.charAt(g))throw(Ud.local.unexpectedLiteralAt||Ud.regionalOptions[\"\"].unexpectedLiteralAt).replace(/\\{0\\}/,S);S++},S=0,g=0;g<e.length;g++)if(b)e.charAt(g)===\"'\"&&!p(\"'\")?b=!1:C();else switch(e.charAt(g)){case\"d\":d=k(\"d\");break;case\"D\":L(\"D\",i,a);break;case\"o\":v=k(\"o\");break;case\"w\":k(\"w\");break;case\"m\":h=T();break;case\"M\":h=x();break;case\"y\":var P=g;m=!p(\"y\",2),g=P,f=k(\"y\",2);break;case\"Y\":f=k(\"Y\",2);break;case\"J\":c=k(\"J\")+.5,t.charAt(S)===\".\"&&(S++,k(\"J\"));break;case\"@\":c=k(\"@\")/this.SECS_PER_DAY+this.UNIX_EPOCH;break;case\"!\":c=k(\"!\")/this.TICKS_PER_DAY+this.TICKS_EPOCH;break;case\"*\":S=t.length;break;case\"'\":p(\"'\")?C():b=!0;break;default:C()}if(S<t.length)throw Ud.local.unexpectedText||Ud.regionalOptions[\"\"].unexpectedText;if(f===-1?f=this.today().year():f<100&&m&&(f+=n===-1?1900:this.today().year()-this.today().year()%100-(f<=n?0:100)),typeof h==\"string\"&&(h=o.call(this,f,h)),v>-1){h=1,d=v;for(var E=this.daysInMonth(f,h);d>E;E=this.daysInMonth(f,h))h++,d-=E}return c>-1?this.fromJD(c):this.newDate(f,h,d)},determineDate:function(e,t,r,n,i){r&&typeof r!=\"object\"&&(i=n,n=r,r=null),typeof n!=\"string\"&&(i=n,n=\"\");var a=this,o=function(s){try{return a.parseDate(n,s,i)}catch(f){}s=s.toLowerCase();for(var l=(s.match(/^c/)&&r?r.newDate():null)||a.today(),u=/([+-]?[0-9]+)\\s*(d|w|m|y)?/g,c=u.exec(s);c;)l.add(parseInt(c[1],10),c[2]||\"d\"),c=u.exec(s);return l};return t=t?t.newDate():null,e=e==null?t:typeof e==\"string\"?o(e):typeof e==\"number\"?isNaN(e)||e===1/0||e===-1/0?t:a.today().add(e,\"d\"):a.newDate(e),e}})});var het=ye(()=>{var jx=kv(),crr=Nh(),TQ=jx.instance();function B9(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}B9.prototype=new jx.baseCalendar;crr(B9.prototype,{name:\"Chinese\",jdEpoch:17214255e-1,hasYearZero:!1,minMonth:0,firstMonth:0,minDay:1,regionalOptions:{\"\":{name:\"Chinese\",epochs:[\"BEC\",\"EC\"],monthNumbers:function(e,t){if(typeof e==\"string\"){var r=e.match(hrr);return r?r[0]:\"\"}var n=this._validateYear(e),i=e.month(),a=\"\"+this.toChineseMonth(n,i);return t&&a.length<2&&(a=\"0\"+a),this.isIntercalaryMonth(n,i)&&(a+=\"i\"),a},monthNames:function(e){if(typeof e==\"string\"){var t=e.match(drr);return t?t[0]:\"\"}var r=this._validateYear(e),n=e.month(),i=this.toChineseMonth(r,n),a=[\"\\u4E00\\u6708\",\"\\u4E8C\\u6708\",\"\\u4E09\\u6708\",\"\\u56DB\\u6708\",\"\\u4E94\\u6708\",\"\\u516D\\u6708\",\"\\u4E03\\u6708\",\"\\u516B\\u6708\",\"\\u4E5D\\u6708\",\"\\u5341\\u6708\",\"\\u5341\\u4E00\\u6708\",\"\\u5341\\u4E8C\\u6708\"][i-1];return this.isIntercalaryMonth(r,n)&&(a=\"\\u95F0\"+a),a},monthNamesShort:function(e){if(typeof e==\"string\"){var t=e.match(vrr);return t?t[0]:\"\"}var r=this._validateYear(e),n=e.month(),i=this.toChineseMonth(r,n),a=[\"\\u4E00\",\"\\u4E8C\",\"\\u4E09\",\"\\u56DB\",\"\\u4E94\",\"\\u516D\",\"\\u4E03\",\"\\u516B\",\"\\u4E5D\",\"\\u5341\",\"\\u5341\\u4E00\",\"\\u5341\\u4E8C\"][i-1];return this.isIntercalaryMonth(r,n)&&(a=\"\\u95F0\"+a),a},parseMonth:function(e,t){e=this._validateYear(e);var r=parseInt(t),n;if(isNaN(r))t[0]===\"\\u95F0\"&&(n=!0,t=t.substring(1)),t[t.length-1]===\"\\u6708\"&&(t=t.substring(0,t.length-1)),r=1+[\"\\u4E00\",\"\\u4E8C\",\"\\u4E09\",\"\\u56DB\",\"\\u4E94\",\"\\u516D\",\"\\u4E03\",\"\\u516B\",\"\\u4E5D\",\"\\u5341\",\"\\u5341\\u4E00\",\"\\u5341\\u4E8C\"].indexOf(t);else{var i=t[t.length-1];n=i===\"i\"||i===\"I\"}var a=this.toMonthIndex(e,r,n);return a},dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},_validateYear:function(e,t){if(e.year&&(e=e.year()),typeof e!=\"number\"||e<1888||e>2111)throw t.replace(/\\{0\\}/,this.local.name);return e},toMonthIndex:function(e,t,r){var n=this.intercalaryMonth(e),i=r&&t!==n;if(i||t<1||t>12)throw jx.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var a;return n?!r&&t<=n?a=t-1:a=t:a=t-1,a},toChineseMonth:function(e,t){e.year&&(e=e.year(),t=e.month());var r=this.intercalaryMonth(e),n=r?12:11;if(t<0||t>n)throw jx.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var i;return r?t<r?i=t+1:i=t:i=t+1,i},intercalaryMonth:function(e){e=this._validateYear(e);var t=Wx[e-Wx[0]],r=t>>13;return r},isIntercalaryMonth:function(e,t){e.year&&(e=e.year(),t=e.month());var r=this.intercalaryMonth(e);return!!r&&r===t},leapYear:function(e){return this.intercalaryMonth(e)!==0},weekOfYear:function(e,t,r){var n=this._validateYear(e,jx.local.invalidyear),i=Xx[n-Xx[0]],a=i>>9&4095,o=i>>5&15,s=i&31,l;l=TQ.newDate(a,o,s),l.add(4-(l.dayOfWeek()||7),\"d\");var u=this.toJD(e,t,r)-l.toJD();return 1+Math.floor(u/7)},monthsInYear:function(e){return this.leapYear(e)?13:12},daysInMonth:function(e,t){e.year&&(t=e.month(),e=e.year()),e=this._validateYear(e);var r=Wx[e-Wx[0]],n=r>>13,i=n?12:11;if(t>i)throw jx.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var a=r&1<<12-t?30:29;return a},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,a,r,jx.local.invalidDate);e=this._validateYear(n.year()),t=n.month(),r=n.day();var i=this.isIntercalaryMonth(e,t),a=this.toChineseMonth(e,t),o=grr(e,a,r,i);return TQ.toJD(o.year,o.month,o.day)},fromJD:function(e){var t=TQ.fromJD(e),r=prr(t.year(),t.month(),t.day()),n=this.toMonthIndex(r.year,r.month,r.isIntercalary);return this.newDate(r.year,n,r.day)},fromString:function(e){var t=e.match(frr),r=this._validateYear(+t[1]),n=+t[2],i=!!t[3],a=this.toMonthIndex(r,n,i),o=+t[4];return this.newDate(r,a,o)},add:function(e,t,r){var n=e.year(),i=e.month(),a=this.isIntercalaryMonth(n,i),o=this.toChineseMonth(n,i),s=Object.getPrototypeOf(B9.prototype).add.call(this,e,t,r);if(r===\"y\"){var l=s.year(),u=s.month(),c=this.isIntercalaryMonth(l,o),f=a&&c?this.toMonthIndex(l,o,!0):this.toMonthIndex(l,o,!1);f!==u&&s.month(f)}return s}});var frr=/^\\s*(-?\\d\\d\\d\\d|\\d\\d)[-/](\\d?\\d)([iI]?)[-/](\\d?\\d)/m,hrr=/^\\d?\\d[iI]?/m,drr=/^闰?十?[一二三四五六七八九]?月/m,vrr=/^闰?十?[一二三四五六七八九]?/m;jx.calendars.chinese=B9;var 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prr(e,t,r,n){var i,a;if(typeof e==\"object\")i=e,a=t||{};else{var o=typeof e==\"number\"&&e>=1888&&e<=2111;if(!o)throw new Error(\"Solar year outside range 1888-2111\");var s=typeof t==\"number\"&&t>=1&&t<=12;if(!s)throw new Error(\"Solar month outside range 1 - 12\");var l=typeof r==\"number\"&&r>=1&&r<=31;if(!l)throw new Error(\"Solar day outside range 1 - 31\");i={year:e,month:t,day:r},a=n||{}}var u=Xx[i.year-Xx[0]],c=i.year<<9|i.month<<5|i.day;a.year=c>=u?i.year:i.year-1,u=Xx[a.year-Xx[0]];var f=u>>9&4095,h=u>>5&15,d=u&31,v,m=new Date(f,h-1,d),b=new Date(i.year,i.month-1,i.day);v=Math.round((b-m)/(24*3600*1e3));var p=Wx[a.year-Wx[0]],k;for(k=0;k<13;k++){var M=p&1<<12-k?30:29;if(v<M)break;v-=M}var T=p>>13;return!T||k<T?(a.isIntercalary=!1,a.month=1+k):k===T?(a.isIntercalary=!0,a.month=k):(a.isIntercalary=!1,a.month=k),a.day=1+v,a}function grr(e,t,r,n,i){var a,o;if(typeof e==\"object\")o=e,a=t||{};else{var s=typeof e==\"number\"&&e>=1888&&e<=2111;if(!s)throw new Error(\"Lunar year 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Dw.baseCalendar;mrr(AQ.prototype,{name:\"Coptic\",jdEpoch:18250295e-1,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Coptic\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Thout\",\"Paopi\",\"Hathor\",\"Koiak\",\"Tobi\",\"Meshir\",\"Paremhat\",\"Paremoude\",\"Pashons\",\"Paoni\",\"Epip\",\"Mesori\",\"Pi Kogi Enavot\"],monthNamesShort:[\"Tho\",\"Pao\",\"Hath\",\"Koi\",\"Tob\",\"Mesh\",\"Pat\",\"Pad\",\"Pash\",\"Pao\",\"Epi\",\"Meso\",\"PiK\"],dayNames:[\"Tkyriaka\",\"Pesnau\",\"Pshoment\",\"Peftoou\",\"Ptiou\",\"Psoou\",\"Psabbaton\"],dayNamesShort:[\"Tky\",\"Pes\",\"Psh\",\"Pef\",\"Pti\",\"Pso\",\"Psa\"],dayNamesMin:[\"Tk\",\"Pes\",\"Psh\",\"Pef\",\"Pt\",\"Pso\",\"Psa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Dw.local.invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===3||r%4===-1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Dw.local.invalidYear||Dw.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Dw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===13&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,Dw.local.invalidDate);return e=n.year(),e<0&&e++,n.day()+(n.month()-1)*30+(e-1)*365+Math.floor(e/4)+this.jdEpoch-1},fromJD:function(e){var t=Math.floor(e)+.5-this.jdEpoch,r=Math.floor((t-Math.floor((t+366)/1461))/365)+1;r<=0&&r--,t=Math.floor(e)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(t/30)+1,i=t-(n-1)*30+1;return this.newDate(r,n,i)}});Dw.calendars.coptic=AQ});var vet=ye(()=>{var k1=kv(),yrr=Nh();function SQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}SQ.prototype=new k1.baseCalendar;yrr(SQ.prototype,{name:\"Discworld\",jdEpoch:17214255e-1,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(e){return this._validate(e,this.minMonth,this.minDay,k1.local.invalidYear),!1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,k1.local.invalidYear),13},daysInYear:function(e){return this._validate(e,this.minMonth,this.minDay,k1.local.invalidYear),400},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,k1.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,k1.local.invalidDate);return(n.day()+1)%8},weekDay:function(e,t,r){var n=this.dayOfWeek(e,t,r);return n>=2&&n<=6},extraInfo:function(e,t,r){var n=this._validate(e,t,r,k1.local.invalidDate);return{century:_rr[Math.floor((n.year()-1)/100)+1]||\"\"}},toJD:function(e,t,r){var n=this._validate(e,t,r,k1.local.invalidDate);return e=n.year()+(n.year()<0?1:0),t=n.month(),r=n.day(),r+(t>1?16:0)+(t>2?(t-2)*32:0)+(e-1)*400+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e+.5)-Math.floor(this.jdEpoch)-1;var t=Math.floor(e/400)+1;e-=(t-1)*400,e+=e>15?16:0;var r=Math.floor(e/32)+1,n=e-(r-1)*32+1;return this.newDate(t<=0?t-1:t,r,n)}});var _rr={20:\"Fruitbat\",21:\"Anchovy\"};k1.calendars.discworld=SQ});var pet=ye(()=>{var Fw=kv(),xrr=Nh();function MQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}MQ.prototype=new Fw.baseCalendar;xrr(MQ.prototype,{name:\"Ethiopian\",jdEpoch:17242205e-1,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Ethiopian\",epochs:[\"BEE\",\"EE\"],monthNames:[\"Meskerem\",\"Tikemet\",\"Hidar\",\"Tahesas\",\"Tir\",\"Yekatit\",\"Megabit\",\"Miazia\",\"Genbot\",\"Sene\",\"Hamle\",\"Nehase\",\"Pagume\"],monthNamesShort:[\"Mes\",\"Tik\",\"Hid\",\"Tah\",\"Tir\",\"Yek\",\"Meg\",\"Mia\",\"Gen\",\"Sen\",\"Ham\",\"Neh\",\"Pag\"],dayNames:[\"Ehud\",\"Segno\",\"Maksegno\",\"Irob\",\"Hamus\",\"Arb\",\"Kidame\"],dayNamesShort:[\"Ehu\",\"Seg\",\"Mak\",\"Iro\",\"Ham\",\"Arb\",\"Kid\"],dayNamesMin:[\"Eh\",\"Se\",\"Ma\",\"Ir\",\"Ha\",\"Ar\",\"Ki\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Fw.local.invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===3||r%4===-1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Fw.local.invalidYear||Fw.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Fw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===13&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,Fw.local.invalidDate);return e=n.year(),e<0&&e++,n.day()+(n.month()-1)*30+(e-1)*365+Math.floor(e/4)+this.jdEpoch-1},fromJD:function(e){var t=Math.floor(e)+.5-this.jdEpoch,r=Math.floor((t-Math.floor((t+366)/1461))/365)+1;r<=0&&r--,t=Math.floor(e)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(t/30)+1,i=t-(n-1)*30+1;return this.newDate(r,n,i)}});Fw.calendars.ethiopian=MQ});var get=ye(()=>{var Zx=kv(),brr=Nh();function EQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}EQ.prototype=new Zx.baseCalendar;brr(EQ.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Zx.local.invalidYear);return this._leapYear(t.year())},_leapYear:function(e){return e=e<0?e+1:e,N9(e*7+1,19)<7},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Zx.local.invalidYear),this._leapYear(e.year?e.year():e)?13:12},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Zx.local.invalidYear);return e=t.year(),this.toJD(e===-1?1:e+1,7,1)-this.toJD(e,7,1)},daysInMonth:function(e,t){return e.year&&(t=e.month(),e=e.year()),this._validate(e,t,this.minDay,Zx.local.invalidMonth),t===12&&this.leapYear(e)||t===8&&N9(this.daysInYear(e),10)===5?30:t===9&&N9(this.daysInYear(e),10)===3?29:this.daysPerMonth[t-1]},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==6},extraInfo:function(e,t,r){var n=this._validate(e,t,r,Zx.local.invalidDate);return{yearType:(this.leapYear(n)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(n)%10-3]}},toJD:function(e,t,r){var n=this._validate(e,t,r,Zx.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=e<=0?e+1:e,a=this.jdEpoch+this._delay1(i)+this._delay2(i)+r+1;if(t<7){for(var o=7;o<=this.monthsInYear(e);o++)a+=this.daysInMonth(e,o);for(var o=1;o<t;o++)a+=this.daysInMonth(e,o)}else for(var o=7;o<t;o++)a+=this.daysInMonth(e,o);return a},_delay1:function(e){var t=Math.floor((235*e-234)/19),r=12084+13753*t,n=t*29+Math.floor(r/25920);return N9(3*(n+1),7)<3&&n++,n},_delay2:function(e){var t=this._delay1(e-1),r=this._delay1(e),n=this._delay1(e+1);return n-r===356?2:r-t===382?1:0},fromJD:function(e){e=Math.floor(e)+.5;for(var t=Math.floor((e-this.jdEpoch)*98496/35975351)-1;e>=this.toJD(t===-1?1:t+1,7,1);)t++;for(var r=e<this.toJD(t,1,1)?7:1;e>this.toJD(t,r,this.daysInMonth(t,r));)r++;var n=e-this.toJD(t,r,1)+1;return this.newDate(t,r,n)}});function N9(e,t){return e-t*Math.floor(e/t)}Zx.calendars.hebrew=EQ});var met=ye(()=>{var AC=kv(),wrr=Nh();function kQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}kQ.prototype=new AC.baseCalendar;wrr(kQ.prototype,{name:\"Islamic\",jdEpoch:19484395e-1,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012Bs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,AC.local.invalidYear);return(t.year()*11+14)%30<11},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){return this.leapYear(e)?355:354},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,AC.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,AC.local.invalidDate);return e=n.year(),t=n.month(),r=n.day(),e=e<=0?e+1:e,r+Math.ceil(29.5*(t-1))+(e-1)*354+Math.floor((3+11*e)/30)+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e)+.5;var t=Math.floor((30*(e-this.jdEpoch)+10646)/10631);t=t<=0?t-1:t;var r=Math.min(12,Math.ceil((e-29-this.toJD(t,1,1))/29.5)+1),n=e-this.toJD(t,r,1)+1;return this.newDate(t,r,n)}});AC.calendars.islamic=kQ});var yet=ye(()=>{var SC=kv(),Trr=Nh();function CQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}CQ.prototype=new SC.baseCalendar;Trr(CQ.prototype,{name:\"Julian\",jdEpoch:17214235e-1,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,SC.local.invalidYear),r=t.year()<0?t.year()+1:t.year();return r%4===0},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,SC.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,SC.local.invalidDate);return e=n.year(),t=n.month(),r=n.day(),e<0&&e++,t<=2&&(e--,t+=12),Math.floor(365.25*(e+4716))+Math.floor(30.6001*(t+1))+r-1524.5},fromJD:function(e){var t=Math.floor(e+.5),r=t+1524,n=Math.floor((r-122.1)/365.25),i=Math.floor(365.25*n),a=Math.floor((r-i)/30.6001),o=a-Math.floor(a<14?1:13),s=n-Math.floor(o>2?4716:4715),l=r-i-Math.floor(30.6001*a);return s<=0&&s--,this.newDate(s,o,l)}});SC.calendars.julian=CQ});var xet=ye(()=>{var fg=kv(),Arr=Nh();function PQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}PQ.prototype=new fg.baseCalendar;Arr(PQ.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(e){return this._validate(e,this.minMonth,this.minDay,fg.local.invalidYear),!1},formatYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,fg.local.invalidYear);e=t.year();var r=Math.floor(e/400);e=e%400,e+=e<0?400:0;var n=Math.floor(e/20);return r+\".\"+n+\".\"+e%20},forYear:function(e){if(e=e.split(\".\"),e.length<3)throw\"Invalid Mayan year\";for(var t=0,r=0;r<e.length;r++){var n=parseInt(e[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";t=t*20+n}return t},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,fg.local.invalidYear),18},weekOfYear:function(e,t,r){return this._validate(e,t,r,fg.local.invalidDate),0},daysInYear:function(e){return this._validate(e,this.minMonth,this.minDay,fg.local.invalidYear),360},daysInMonth:function(e,t){return this._validate(e,t,this.minDay,fg.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,fg.local.invalidDate);return n.day()},weekDay:function(e,t,r){return this._validate(e,t,r,fg.local.invalidDate),!0},extraInfo:function(e,t,r){var n=this._validate(e,t,r,fg.local.invalidDate),i=n.toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(e){e-=this.jdEpoch;var t=LQ(e+8+340,365);return[Math.floor(t/20)+1,LQ(t,20)]},_toTzolkin:function(e){return e-=this.jdEpoch,[_et(e+20,20),_et(e+4,13)]},toJD:function(e,t,r){var n=this._validate(e,t,r,fg.local.invalidDate);return n.day()+n.month()*20+n.year()*360+this.jdEpoch},fromJD:function(e){e=Math.floor(e)+.5-this.jdEpoch;var t=Math.floor(e/360);e=e%360,e+=e<0?360:0;var r=Math.floor(e/20),n=e%20;return this.newDate(t,r,n)}});function LQ(e,t){return e-t*Math.floor(e/t)}function _et(e,t){return LQ(e-1,t)+1}fg.calendars.mayan=PQ});var wet=ye(()=>{var zw=kv(),Srr=Nh();function IQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}IQ.prototype=new zw.baseCalendar;var bet=zw.instance(\"gregorian\");Srr(IQ.prototype,{name:\"Nanakshahi\",jdEpoch:22576735e-1,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,zw.local.invalidYear||zw.regionalOptions[\"\"].invalidYear);return bet.leapYear(t.year()+(t.year()<1?1:0)+1469)},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,zw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,zw.local.invalidMonth),i=n.year();i<0&&i++;for(var a=n.day(),o=1;o<n.month();o++)a+=this.daysPerMonth[o-1];return a+bet.toJD(i+1468,3,13)},fromJD:function(e){e=Math.floor(e+.5);for(var t=Math.floor((e-(this.jdEpoch-1))/366);e>=this.toJD(t+1,1,1);)t++;for(var r=e-Math.floor(this.toJD(t,1,1)+.5)+1,n=1;r>this.daysInMonth(t,n);)r-=this.daysInMonth(t,n),n++;return this.newDate(t,n,r)}});zw.calendars.nanakshahi=IQ});var Tet=ye(()=>{var qw=kv(),Mrr=Nh();function RQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}RQ.prototype=new qw.baseCalendar;Mrr(RQ.prototype,{name:\"Nepali\",jdEpoch:17007095e-1,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(e){return this.daysInYear(e)!==this.daysPerYear},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,qw.local.invalidYear);if(e=t.year(),typeof this.NEPALI_CALENDAR_DATA[e]==\"undefined\")return this.daysPerYear;for(var r=0,n=this.minMonth;n<=12;n++)r+=this.NEPALI_CALENDAR_DATA[e][n];return r},daysInMonth:function(e,t){return e.year&&(t=e.month(),e=e.year()),this._validate(e,t,this.minDay,qw.local.invalidMonth),typeof this.NEPALI_CALENDAR_DATA[e]==\"undefined\"?this.daysPerMonth[t-1]:this.NEPALI_CALENDAR_DATA[e][t]},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==6},toJD:function(e,t,r){var n=this._validate(e,t,r,qw.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=qw.instance(),a=0,o=t,s=e;this._createMissingCalendarData(e);var l=e-(o>9||o===9&&r>=this.NEPALI_CALENDAR_DATA[s][0]?56:57);for(t!==9&&(a=r,o--);o!==9;)o<=0&&(o=12,s--),a+=this.NEPALI_CALENDAR_DATA[s][o],o--;return t===9?(a+=r-this.NEPALI_CALENDAR_DATA[s][0],a<0&&(a+=i.daysInYear(l))):a+=this.NEPALI_CALENDAR_DATA[s][9]-this.NEPALI_CALENDAR_DATA[s][0],i.newDate(l,1,1).add(a,\"d\").toJD()},fromJD:function(e){var t=qw.instance(),r=t.fromJD(e),n=r.year(),i=r.dayOfYear(),a=n+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)o++,o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var u=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,u)},_createMissingCalendarData:function(e){var t=this.daysPerMonth.slice(0);t.unshift(17);for(var r=e-1;r<e+2;r++)typeof this.NEPALI_CALENDAR_DATA[r]==\"undefined\"&&(this.NEPALI_CALENDAR_DATA[r]=t)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,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Aet=ye(()=>{var uS=kv(),Err=Nh();function V9(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}function U9(e){var t=e-475;e<0&&t++;var r=.242197,n=r*t,i=r*(t+1),a=n-Math.floor(n),o=i-Math.floor(i);return a>o}V9.prototype=new uS.baseCalendar;Err(V9.prototype,{name:\"Persian\",jdEpoch:19483205e-1,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Persian\",epochs:[\"BP\",\"AP\"],monthNames:[\"Farvardin\",\"Ordibehesht\",\"Khordad\",\"Tir\",\"Mordad\",\"Shahrivar\",\"Mehr\",\"Aban\",\"Azar\",\"Dey\",\"Bahman\",\"Esfand\"],monthNamesShort:[\"Far\",\"Ord\",\"Kho\",\"Tir\",\"Mor\",\"Sha\",\"Meh\",\"Aba\",\"Aza\",\"Dey\",\"Bah\",\"Esf\"],dayNames:[\"Yekshanbeh\",\"Doshanbeh\",\"Seshanbeh\",\"Chah\\u0101rshanbeh\",\"Panjshanbeh\",\"Jom'eh\",\"Shanbeh\"],dayNamesShort:[\"Yek\",\"Do\",\"Se\",\"Cha\",\"Panj\",\"Jom\",\"Sha\"],dayNamesMin:[\"Ye\",\"Do\",\"Se\",\"Ch\",\"Pa\",\"Jo\",\"Sh\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,uS.local.invalidYear);return U9(t.year())},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-((n.dayOfWeek()+1)%7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,uS.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,uS.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var i=0;if(e>0)for(var a=1;a<e;a++)U9(a)&&i++;else if(e<0)for(var a=e;a<0;a++)U9(a)&&i--;return r+(t<=7?(t-1)*31:(t-1)*30+6)+(e>0?e-1:e)*365+i+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e)+.5;var t=475+(e-this.toJD(475,1,1))/365.242197,r=Math.floor(t);r<=0&&r--,e>this.toJD(r,12,U9(r)?30:29)&&(r++,r===0&&r++);var n=e-this.toJD(r,1,1)+1,i=n<=186?Math.ceil(n/31):Math.ceil((n-6)/30),a=e-this.toJD(r,i,1)+1;return this.newDate(r,i,a)}});uS.calendars.persian=V9;uS.calendars.jalali=V9});var Met=ye(()=>{var Ow=kv(),krr=Nh(),G9=Ow.instance();function DQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}DQ.prototype=new Ow.baseCalendar;krr(DQ.prototype,{name:\"Taiwan\",jdEpoch:24194025e-1,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Ow.local.invalidYear),r=this._t2gYear(t.year());return G9.leapYear(r)},weekOfYear:function(i,t,r){var n=this._validate(i,this.minMonth,this.minDay,Ow.local.invalidYear),i=this._t2gYear(n.year());return G9.weekOfYear(i,n.month(),n.day())},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Ow.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,Ow.local.invalidDate),i=this._t2gYear(n.year());return G9.toJD(i,n.month(),n.day())},fromJD:function(e){var t=G9.fromJD(e),r=this._g2tYear(t.year());return this.newDate(r,t.month(),t.day())},_t2gYear:function(e){return e+this.yearsOffset+(e>=-this.yearsOffset&&e<=-1?1:0)},_g2tYear:function(e){return e-this.yearsOffset-(e>=1&&e<=this.yearsOffset?1:0)}});Ow.calendars.taiwan=DQ});var Eet=ye(()=>{var Bw=kv(),Crr=Nh(),H9=Bw.instance();function FQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}FQ.prototype=new Bw.baseCalendar;Crr(FQ.prototype,{name:\"Thai\",jdEpoch:15230985e-1,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,Bw.local.invalidYear),r=this._t2gYear(t.year());return H9.leapYear(r)},weekOfYear:function(i,t,r){var n=this._validate(i,this.minMonth,this.minDay,Bw.local.invalidYear),i=this._t2gYear(n.year());return H9.weekOfYear(i,n.month(),n.day())},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,Bw.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(i,t,r){var n=this._validate(i,t,r,Bw.local.invalidDate),i=this._t2gYear(n.year());return H9.toJD(i,n.month(),n.day())},fromJD:function(e){var t=H9.fromJD(e),r=this._g2tYear(t.year());return this.newDate(r,t.month(),t.day())},_t2gYear:function(e){return e-this.yearsOffset-(e>=1&&e<=this.yearsOffset?1:0)},_g2tYear:function(e){return e+this.yearsOffset+(e>=-this.yearsOffset&&e<=-1?1:0)}});Bw.calendars.thai=FQ});var ket=ye(()=>{var Nw=kv(),Lrr=Nh();function zQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}zQ.prototype=new Nw.baseCalendar;Lrr(zQ.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012Bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Nw.local.invalidYear);return this.daysInYear(t.year())===355},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){for(var t=0,r=1;r<=12;r++)t+=this.daysInMonth(e,r);return t},daysInMonth:function(e,t){for(var r=this._validate(e,t,this.minDay,Nw.local.invalidMonth),n=r.toJD()-24e5+.5,i=0,a=0;a<Yx.length;a++){if(Yx[a]>n)return Yx[i]-Yx[i-1];i++}return 30},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,Nw.local.invalidDate),i=12*(n.year()-1)+n.month()-15292,a=n.day()+Yx[i-1]-1;return a+24e5-.5},fromJD:function(e){for(var t=e-24e5+.5,r=0,n=0;n<Yx.length&&!(Yx[n]>t);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),o=a+1,s=i-12*a,l=t-Yx[r-1]+1;return this.newDate(o,s,l)},isValid:function(e,t,r){var n=Nw.baseCalendar.prototype.isValid.apply(this,arguments);return n&&(e=e.year!=null?e.year:e,n=e>=1276&&e<=1500),n},_validate:function(e,t,r,n){var i=Nw.baseCalendar.prototype._validate.apply(this,arguments);if(i.year<1276||i.year>1500)throw n.replace(/\\{0\\}/,this.local.name);return i}});Nw.calendars.ummalqura=zQ;var 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Let=ye((GTr,Cet)=>{\"use strict\";Cet.exports=kv();fet();het();det();vet();pet();get();met();yet();xet();wet();Tet();Aet();Met();Eet();ket()});var qet=ye((HTr,zet)=>{\"use strict\";var Iet=Let(),MC=Pr(),Ret=fs(),Prr=Ret.EPOCHJD,Irr=Ret.ONEDAY,BQ={valType:\"enumerated\",values:MC.sortObjectKeys(Iet.calendars),editType:\"calc\",dflt:\"gregorian\"},Det=function(e,t,r,n){var i={};return i[r]=BQ,MC.coerce(e,t,i,r,n)},Rrr=function(e,t,r,n){for(var i=0;i<r.length;i++)Det(e,t,r[i]+\"calendar\",n.calendar)},Drr={chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},Frr={chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},zrr={chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},j9=\"##\",qrr={d:{0:\"dd\",\"-\":\"d\"},e:{0:\"d\",\"-\":\"d\"},a:{0:\"D\",\"-\":\"D\"},A:{0:\"DD\",\"-\":\"DD\"},j:{0:\"oo\",\"-\":\"o\"},W:{0:\"ww\",\"-\":\"w\"},m:{0:\"mm\",\"-\":\"m\"},b:{0:\"M\",\"-\":\"M\"},B:{0:\"MM\",\"-\":\"MM\"},y:{0:\"yy\",\"-\":\"yy\"},Y:{0:\"yyyy\",\"-\":\"yyyy\"},U:j9,w:j9,c:{0:\"D M d %X yyyy\",\"-\":\"D M d %X yyyy\"},x:{0:\"mm/dd/yyyy\",\"-\":\"mm/dd/yyyy\"}};function Orr(e,t,r){for(var n=Math.floor((t+.05)/Irr)+Prr,i=Fet(r).fromJD(n),a=0,o,s,l,u,c;(a=e.indexOf(\"%\",a))!==-1;)o=e.charAt(a+1),o===\"0\"||o===\"-\"||o===\"_\"?(l=3,s=e.charAt(a+2),o===\"_\"&&(o=\"-\")):(s=o,o=\"0\",l=2),u=qrr[s],u?(u===j9?c=j9:c=i.formatDate(u[o]),e=e.slice(0,a)+c+e.slice(a+l),a+=c.length):a+=l;return e}var Pet={};function Fet(e){var t=Pet[e];return t||(t=Pet[e]=Iet.instance(e),t)}function EC(e){return MC.extendFlat({},BQ,{description:e})}function NQ(e){return\"Sets the calendar system to use with `\"+e+\"` date data.\"}var OQ={xcalendar:EC(NQ(\"x\"))},hy=MC.extendFlat({},OQ,{ycalendar:EC(NQ(\"y\"))}),qQ=MC.extendFlat({},hy,{zcalendar:EC(NQ(\"z\"))}),cS=EC([\"Sets the calendar system to use for `range` and `tick0`\",\"if this is a date axis. This does not set the calendar for\",\"interpreting data on this axis, that's specified in the trace\",\"or via the global `layout.calendar`\"].join(\" \"));zet.exports={moduleType:\"component\",name:\"calendars\",schema:{traces:{scatter:hy,bar:hy,box:hy,heatmap:hy,contour:hy,histogram:hy,histogram2d:hy,histogram2dcontour:hy,scatter3d:qQ,surface:qQ,mesh3d:qQ,scattergl:hy,ohlc:OQ,candlestick:OQ},layout:{calendar:EC([\"Sets the default calendar system to use for interpreting and\",\"displaying dates throughout the plot.\"].join(\" \"))},subplots:{xaxis:{calendar:cS},yaxis:{calendar:cS},scene:{xaxis:{calendar:cS},yaxis:{calendar:cS},zaxis:{calendar:cS}},polar:{radialaxis:{calendar:cS}}}},layoutAttributes:BQ,handleDefaults:Det,handleTraceDefaults:Rrr,CANONICAL_SUNDAY:Frr,CANONICAL_TICK:Drr,DFLTRANGE:zrr,getCal:Fet,worldCalFmt:Orr}});var Bet=ye((jTr,Oet)=>{\"use strict\";Oet.exports=qet()});var Brr=ye((WTr,Uet)=>{var Net=pye();Net.register([m1e(),n_e(),yxe(),Bxe(),Qxe(),Ybe(),l2e(),K2e(),Swe(),s3e(),Z3e(),oke(),Yke(),N6e(),kLe(),aPe(),CPe(),e8e(),_8e(),q8e(),Y8e(),uRe(),ARe(),NRe(),yFe(),OFe(),tNe(),tUe(),fVe(),qVe(),XGe(),lHe(),IHe(),Hje(),aWe(),CWe(),OXe(),lZe(),GZe(),fKe(),zKe(),iJe(),CJe(),GJe(),V$e(),sQe(),MQe(),set(),Bet()]);Uet.exports=Net});return Brr();})();\n",
+       "`})),staticAttributes:N,staticUniforms:se}}class ka{constructor(d,P,N){this.vertexBuffer=d,this.indexBuffer=P,this.segments=N}destroy(){this.vertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.vertexBuffer=null,this.indexBuffer=null,this.segments=null}}var vs=i.aS([{name:\"a_pos\",type:\"Int16\",components:2}]);let yo=\"#define PROJECTION_MERCATOR\",so=\"mercator\";class oo{constructor(){this._cachedMesh=null}get name(){return\"mercator\"}get useSubdivision(){return!1}get shaderVariantName(){return so}get shaderDefine(){return yo}get shaderPreludeCode(){return Oa.projectionMercator}get vertexShaderPreludeCode(){return Oa.projectionMercator.vertexSource}get subdivisionGranularity(){return i.aT.noSubdivision}get useGlobeControls(){return!1}get transitionState(){return 0}get latitudeErrorCorrectionRadians(){return 0}destroy(){}updateGPUdependent(d){}getMeshFromTileID(d,P,N,H,$){if(this._cachedMesh)return this._cachedMesh;let se=new i.aU;se.emplaceBack(0,0),se.emplaceBack(i.a6,0),se.emplaceBack(0,i.a6),se.emplaceBack(i.a6,i.a6);let de=d.createVertexBuffer(se,vs.members),Ae=i.aV.simpleSegment(0,0,4,2),Fe=new i.aW;Fe.emplaceBack(1,0,2),Fe.emplaceBack(1,2,3);let Ie=d.createIndexBuffer(Fe);return this._cachedMesh=new ka(de,Ie,Ae),this._cachedMesh}recalculate(){}hasTransition(){return!1}setErrorQueryLatitudeDegrees(d){}}class ko{constructor(d=0,P=0,N=0,H=0){if(isNaN(d)||d<0||isNaN(P)||P<0||isNaN(N)||N<0||isNaN(H)||H<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=d,this.bottom=P,this.left=N,this.right=H}interpolate(d,P,N){return P.top!=null&&d.top!=null&&(this.top=i.H.number(d.top,P.top,N)),P.bottom!=null&&d.bottom!=null&&(this.bottom=i.H.number(d.bottom,P.bottom,N)),P.left!=null&&d.left!=null&&(this.left=i.H.number(d.left,P.left,N)),P.right!=null&&d.right!=null&&(this.right=i.H.number(d.right,P.right,N)),this}getCenter(d,P){let N=i.al((this.left+d-this.right)/2,0,d),H=i.al((this.top+P-this.bottom)/2,0,P);return new i.P(N,H)}equals(d){return this.top===d.top&&this.bottom===d.bottom&&this.left===d.left&&this.right===d.right}clone(){return new ko(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}function pa(Y,d){if(!Y.renderWorldCopies||Y.lngRange)return;let P=d.lng-Y.center.lng;d.lng+=P>180?-360:P<-180?360:0}function la(Y){return Math.max(0,Math.floor(Y))}class As{constructor(d,P){var N;this.applyConstrain=(H,$)=>this._constrainOverride!==null?this._constrainOverride(H,$):this._callbacks.defaultConstrain(H,$),this._callbacks=d,this._tileSize=512,this._renderWorldCopies=(P==null?void 0:P.renderWorldCopies)===void 0||!!(P!=null&&P.renderWorldCopies),this._minZoom=(P==null?void 0:P.minZoom)||0,this._maxZoom=(P==null?void 0:P.maxZoom)||22,this._minPitch=(P==null?void 0:P.minPitch)==null?0:P==null?void 0:P.minPitch,this._maxPitch=(P==null?void 0:P.maxPitch)==null?60:P==null?void 0:P.maxPitch,this._constrainOverride=(N=P==null?void 0:P.constrainOverride)!==null&&N!==void 0?N:null,this.setMaxBounds(),this._width=0,this._height=0,this._center=new i.W(0,0),this._elevation=0,this._zoom=0,this._tileZoom=la(this._zoom),this._scale=i.ao(this._zoom),this._bearingInRadians=0,this._fovInRadians=.6435011087932844,this._pitchInRadians=0,this._rollInRadians=0,this._unmodified=!0,this._edgeInsets=new ko,this._minElevationForCurrentTile=0,this._autoCalculateNearFarZ=!0}apply(d,P,N){this._constrainOverride=d.constrainOverride,this._latRange=d.latRange,this._lngRange=d.lngRange,this._width=d.width,this._height=d.height,this._center=d.center,this._elevation=d.elevation,this._minElevationForCurrentTile=d.minElevationForCurrentTile,this._zoom=d.zoom,this._tileZoom=la(this._zoom),this._scale=i.ao(this._zoom),this._bearingInRadians=d.bearingInRadians,this._fovInRadians=d.fovInRadians,this._pitchInRadians=d.pitchInRadians,this._rollInRadians=d.rollInRadians,this._unmodified=d.unmodified,this._edgeInsets=new ko(d.padding.top,d.padding.bottom,d.padding.left,d.padding.right),this._minZoom=d.minZoom,this._maxZoom=d.maxZoom,this._minPitch=d.minPitch,this._maxPitch=d.maxPitch,this._renderWorldCopies=d.renderWorldCopies,this._cameraToCenterDistance=d.cameraToCenterDistance,this._nearZ=d.nearZ,this._farZ=d.farZ,this._autoCalculateNearFarZ=!N&&d.autoCalculateNearFarZ,P&&this.constrainInternal(),this._calcMatrices()}get pixelsToClipSpaceMatrix(){return this._pixelsToClipSpaceMatrix}get clipSpaceToPixelsMatrix(){return this._clipSpaceToPixelsMatrix}get minElevationForCurrentTile(){return this._minElevationForCurrentTile}setMinElevationForCurrentTile(d){this._minElevationForCurrentTile=d}get tileSize(){return this._tileSize}get tileZoom(){return this._tileZoom}get scale(){return this._scale}get width(){return this._width}get height(){return this._height}get bearingInRadians(){return this._bearingInRadians}get lngRange(){return this._lngRange}get latRange(){return this._latRange}get pixelsToGLUnits(){return this._pixelsToGLUnits}get minZoom(){return this._minZoom}setMinZoom(d){this._minZoom!==d&&(this._minZoom=d,this.setZoom(this.applyConstrain(this._center,this.zoom).zoom))}get maxZoom(){return this._maxZoom}setMaxZoom(d){this._maxZoom!==d&&(this._maxZoom=d,this.setZoom(this.applyConstrain(this._center,this.zoom).zoom))}get minPitch(){return this._minPitch}setMinPitch(d){this._minPitch!==d&&(this._minPitch=d,this.setPitch(Math.max(this.pitch,d)))}get maxPitch(){return this._maxPitch}setMaxPitch(d){this._maxPitch!==d&&(this._maxPitch=d,this.setPitch(Math.min(this.pitch,d)))}get renderWorldCopies(){return this._renderWorldCopies}setRenderWorldCopies(d){d===void 0?d=!0:d===null&&(d=!1),this._renderWorldCopies=d}get constrainOverride(){return this._constrainOverride}setConstrainOverride(d){d===void 0&&(d=null),this._constrainOverride!==d&&(this._constrainOverride=d,this.constrainInternal(),this._calcMatrices())}get worldSize(){return this._tileSize*this._scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new i.P(this._width,this._height)}get bearing(){return this._bearingInRadians/Math.PI*180}setBearing(d){let P=i.X(d,-180,180)*Math.PI/180;var N,H,$,se,de,Ae,Fe,Ie,Ye;this._bearingInRadians!==P&&(this._unmodified=!1,this._bearingInRadians=P,this._calcMatrices(),this._rotationMatrix=s(),N=this._rotationMatrix,$=-this._bearingInRadians,se=(H=this._rotationMatrix)[0],de=H[1],Ae=H[2],Fe=H[3],Ie=Math.sin($),Ye=Math.cos($),N[0]=se*Ye+Ae*Ie,N[1]=de*Ye+Fe*Ie,N[2]=se*-Ie+Ae*Ye,N[3]=de*-Ie+Fe*Ye)}get rotationMatrix(){return this._rotationMatrix}get pitchInRadians(){return this._pitchInRadians}get pitch(){return this._pitchInRadians/Math.PI*180}setPitch(d){let P=i.al(d,this.minPitch,this.maxPitch)/180*Math.PI;this._pitchInRadians!==P&&(this._unmodified=!1,this._pitchInRadians=P,this._calcMatrices())}get rollInRadians(){return this._rollInRadians}get roll(){return this._rollInRadians/Math.PI*180}setRoll(d){let P=d/180*Math.PI;this._rollInRadians!==P&&(this._unmodified=!1,this._rollInRadians=P,this._calcMatrices())}get fovInRadians(){return this._fovInRadians}get fov(){return i.aX(this._fovInRadians)}setFov(d){d=i.al(d,.1,150),this.fov!==d&&(this._unmodified=!1,this._fovInRadians=i.an(d),this._calcMatrices())}get zoom(){return this._zoom}setZoom(d){let P=this.applyConstrain(this._center,d).zoom;this._zoom!==P&&(this._unmodified=!1,this._zoom=P,this._tileZoom=Math.max(0,Math.floor(P)),this._scale=i.ao(P),this.constrainInternal(),this._calcMatrices())}get center(){return this._center}setCenter(d){d.lat===this._center.lat&&d.lng===this._center.lng||(this._unmodified=!1,this._center=d,this.constrainInternal(),this._calcMatrices())}get elevation(){return this._elevation}setElevation(d){d!==this._elevation&&(this._elevation=d,this.constrainInternal(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}setPadding(d){this._edgeInsets.equals(d)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,d,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this._width,this._height)}get pixelsPerMeter(){return this._pixelPerMeter}get unmodified(){return this._unmodified}get cameraToCenterDistance(){return this._cameraToCenterDistance}get nearZ(){return this._nearZ}get farZ(){return this._farZ}get autoCalculateNearFarZ(){return this._autoCalculateNearFarZ}overrideNearFarZ(d,P){this._autoCalculateNearFarZ=!1,this._nearZ=d,this._farZ=P,this._calcMatrices()}clearNearFarZOverride(){this._autoCalculateNearFarZ=!0,this._calcMatrices()}isPaddingEqual(d){return this._edgeInsets.equals(d)}interpolatePadding(d,P,N){this._unmodified=!1,this._edgeInsets.interpolate(d,P,N),this.constrainInternal(),this._calcMatrices()}resize(d,P,N=!0){this._width=d,this._height=P,N&&this.constrainInternal(),this._calcMatrices()}getMaxBounds(){var d,P;return((d=this._latRange)===null||d===void 0?void 0:d.length)!==2||((P=this._lngRange)===null||P===void 0?void 0:P.length)!==2?null:new ie([this._lngRange[0],this._latRange[0]],[this._lngRange[1],this._latRange[1]])}setMaxBounds(d){d?(this._lngRange=[d.getWest(),d.getEast()],this._latRange=[d.getSouth(),d.getNorth()],this.constrainInternal()):(this._lngRange=null,this._latRange=[-i.am,i.am])}getCameraQueryGeometry(d,P){if(P.length===1)return[P[0],d];{let{minX:N,minY:H,maxX:$,maxY:se}=i.a8.fromPoints(P).extend(d);return[new i.P(N,H),new i.P($,H),new i.P($,se),new i.P(N,se),new i.P(N,H)]}}constrainInternal(){if(!this.center||!this._width||!this._height||this._constraining)return;this._constraining=!0;let d=this._unmodified,{center:P,zoom:N}=this.applyConstrain(this.center,this.zoom);this.setCenter(P),this.setZoom(N),this._unmodified=d,this._constraining=!1}_calcMatrices(){if(this._width&&this._height){this._pixelsToGLUnits=[2/this._width,-2/this._height];let d=i.ap(new Float64Array(16));i.S(d,d,[this._width/2,-this._height/2,1]),i.Q(d,d,[1,-1,0]),this._clipSpaceToPixelsMatrix=d,d=i.ap(new Float64Array(16)),i.S(d,d,[1,-1,1]),i.Q(d,d,[-1,-1,0]),i.S(d,d,[2/this._width,2/this._height,1]),this._pixelsToClipSpaceMatrix=d,this._cameraToCenterDistance=.5/Math.tan(this.fovInRadians/2)*this._height}this._callbacks.calcMatrices()}calculateCenterFromCameraLngLatAlt(d,P,N,H){let $=N!==void 0?N:this.bearing,se=H=H!==void 0?H:this.pitch,{distanceToCenter:de,clampedElevation:Ae}=this._distanceToCenterFromAltElevationPitch(P,this.elevation,se),{x:Fe,y:Ie}=cr(se,$),Ye=i.a7.fromLngLat(d,P),st,dt,Pe=i.aY(1,Ye.y),Xe=0;do{if(Xe+=1,Xe>10)break;dt=de/Pe,st=new i.a7(Ye.x+Fe*dt,Ye.y+Ie*dt),Pe=1/st.meterInMercatorCoordinateUnits()}while(Math.abs(de-dt*Pe)>1e-12);return{center:st.toLngLat(),elevation:Ae,zoom:i.ar(this.height/2/Math.tan(this.fovInRadians/2)/dt/this.tileSize)}}recalculateZoomAndCenter(d){if(this.elevation-d==0)return;let P=1/this.worldSize,N=i.aq(1,this.center.lat)*this.worldSize,H=i.a7.fromLngLat(this.center,this.elevation),$=H.x/P,se=H.y/P,de=H.z/P,Ae=this.pitch,Fe=this.bearing,{x:Ie,y:Ye,z:st}=cr(Ae,Fe),dt=this.cameraToCenterDistance,Pe=$+dt*-Ie,Xe=se+dt*-Ye,at=de+dt*st,{distanceToCenter:xt,clampedElevation:Et}=this._distanceToCenterFromAltElevationPitch(at/N,d,Ae),Bt=xt*N,St=new i.a7((Pe+Ie*Bt)*P,(Xe+Ye*Bt)*P,0).toLngLat(),Pt=i.aq(1,St.lat),$t=i.ar(this.height/2/Math.tan(this.fovInRadians/2)/xt/Pt/this.tileSize);this._elevation=Et,this._center=St,this.setZoom($t)}_distanceToCenterFromAltElevationPitch(d,P,N){let H=-Math.cos(i.an(N)),$=d-P,se,de=P;return H*$>=0||Math.abs(H)<.1?(se=1e4,de=d+se*H):se=-$/H,{distanceToCenter:se,clampedElevation:de}}getCameraPoint(){let d=Math.tan(this.pitchInRadians)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new i.P(d*Math.sin(this.rollInRadians),d*Math.cos(this.rollInRadians)))}getCameraAltitude(){return Math.cos(this.pitchInRadians)*this._cameraToCenterDistance/this._pixelPerMeter+this.elevation}getCameraLngLat(){let d=i.aq(1,this.center.lat)*this.worldSize;return Ot(this.center,this.elevation,this.pitch,this.bearing,this.cameraToCenterDistance/d).toLngLat()}getMercatorTileCoordinates(d){if(!d)return[0,0,1,1];let P=d.canonical.z>=0?1<<d.canonical.z:Math.pow(2,d.canonical.z);return[d.canonical.x/P,d.canonical.y/P,1/P/i.a6,1/P/i.a6]}}class Ms{constructor(d,P){this.min=d,this.max=P,this.center=i.aZ([],i.a_([],this.min,this.max),.5)}quadrant(d){let P=[d%2==0,d<2],N=i.a$(this.min),H=i.a$(this.max);for(let $=0;$<P.length;$++)N[$]=P[$]?this.min[$]:this.center[$],H[$]=P[$]?this.center[$]:this.max[$];return H[2]=this.max[2],new Ms(N,H)}distanceX(d){return Math.max(Math.min(this.max[0],d[0]),this.min[0])-d[0]}distanceY(d){return Math.max(Math.min(this.max[1],d[1]),this.min[1])-d[1]}intersectsFrustum(d){let P=!0;for(let N of d.planes){let H=this.intersectsPlane(N);if(H===0)return 0;H===1&&(P=!1)}return P?2:d.aabb.min[0]>this.max[0]||d.aabb.min[1]>this.max[1]||d.aabb.min[2]>this.max[2]||d.aabb.max[0]<this.min[0]||d.aabb.max[1]<this.min[1]||d.aabb.max[2]<this.min[2]?0:1}intersectsPlane(d){let P=d[3],N=d[3];for(let H=0;H<3;H++)d[H]>0?(P+=d[H]*this.min[H],N+=d[H]*this.max[H]):(N+=d[H]*this.min[H],P+=d[H]*this.max[H]);return P>=0?2:N<0?0:1}}class xu{distanceToTile2d(d,P,N,H){let $=H,se=$.distanceX([d,P]),de=$.distanceY([d,P]);return Math.hypot(se,de)}getWrap(d,P,N){return N}getTileBoundingVolume(d,P,N,H){var $,se;let de=0,Ae=0;if(H!=null&&H.terrain){let Ie=new i.a3(d.z,P,d.z,d.x,d.y),Ye=H.terrain.getMinMaxElevation(Ie);de=($=Ye.minElevation)!==null&&$!==void 0?$:Math.min(0,N),Ae=(se=Ye.maxElevation)!==null&&se!==void 0?se:Math.max(0,N)}let Fe=1<<d.z;return new Ms([P+d.x/Fe,d.y/Fe,de],[P+(d.x+1)/Fe,(d.y+1)/Fe,Ae])}allowVariableZoom(d,P){let N=d.fov*(Math.abs(Math.cos(d.rollInRadians))*d.height+Math.abs(Math.sin(d.rollInRadians))*d.width)/d.height,H=i.al(78.5-N/2,0,60);return!!P.terrain||d.pitch>H}allowWorldCopies(){return!0}prepareNextFrame(){}}class rs{constructor(d,P,N){this.points=d,this.planes=P,this.aabb=N}static fromInvProjectionMatrix(d,P=1,N=0,H,$){let se=$?[[6,5,4],[0,1,2],[0,3,7],[2,1,5],[3,2,6],[0,4,5]]:[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]],de=Math.pow(2,N),Ae=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((st=>(function(dt,Pe,Xe,at){let xt=i.aE([],dt,Pe),Et=1/xt[3]/Xe*at;return i.b4(xt,xt,[Et,Et,1/xt[3],Et])})(st,d,P,de)));H&&(function(st,dt,Pe,Xe){let at=Xe?4:0,xt=Xe?0:4,Et=0,Bt=[],St=[];for(let Ht=0;Ht<4;Ht++){let dr=i.b0([],st[Ht+xt],st[Ht+at]),Xr=i.b5(dr);i.aZ(dr,dr,1/Xr),Bt.push(Xr),St.push(dr)}for(let Ht=0;Ht<4;Ht++){let dr=i.b6(st[Ht+at],St[Ht],Pe);Et=dr!==null&&dr>=0?Math.max(Et,dr):Math.max(Et,Bt[Ht])}let Pt=(function(Ht,dr){let Xr=i.b0([],Ht[dr[0]],Ht[dr[1]]),Or=i.b0([],Ht[dr[2]],Ht[dr[1]]),$r=[0,0,0,0];return i.b1($r,i.b2([],Xr,Or)),$r[3]=-i.b3($r,Ht[dr[0]]),$r})(st,dt),$t=(function(Ht,dr){let Xr=i.b7(Ht),Or=i.b8([],Ht,1/Xr),$r=i.b0([],dr,i.aZ([],Or,i.b3(dr,Or))),Er=i.b7($r);if(Er>0){let Tr=Math.sqrt(1-Or[3]*Or[3]),Rr=i.aZ([],Or,-Or[3]),Lr=i.a_([],Rr,i.aZ([],$r,Tr/Er));return i.b9(dr,Lr)}return null})(Pe,Pt);if($t!==null){let Ht=$t/i.b3(St[0],Pt);Et=Math.min(Et,Ht)}for(let Ht=0;Ht<4;Ht++){let dr=Math.min(Et,Bt[Ht]);st[Ht+xt]=[st[Ht+at][0]+St[Ht][0]*dr,st[Ht+at][1]+St[Ht][1]*dr,st[Ht+at][2]+St[Ht][2]*dr,1]}})(Ae,se[0],H,$);let Fe=se.map((st=>{let dt=i.b0([],Ae[st[0]],Ae[st[1]]),Pe=i.b0([],Ae[st[2]],Ae[st[1]]),Xe=i.b1([],i.b2([],dt,Pe)),at=-i.b3(Xe,Ae[st[1]]);return Xe.concat(at)})),Ie=[Number.POSITIVE_INFINITY,Number.POSITIVE_INFINITY,Number.POSITIVE_INFINITY],Ye=[Number.NEGATIVE_INFINITY,Number.NEGATIVE_INFINITY,Number.NEGATIVE_INFINITY];for(let st of Ae)for(let dt=0;dt<3;dt++)Ie[dt]=Math.min(Ie[dt],st[dt]),Ye[dt]=Math.max(Ye[dt],st[dt]);return new rs(Ae,Fe,new Ms(Ie,Ye))}}class Ls{get pixelsToClipSpaceMatrix(){return this._helper.pixelsToClipSpaceMatrix}get clipSpaceToPixelsMatrix(){return this._helper.clipSpaceToPixelsMatrix}get pixelsToGLUnits(){return this._helper.pixelsToGLUnits}get centerOffset(){return this._helper.centerOffset}get size(){return this._helper.size}get rotationMatrix(){return this._helper.rotationMatrix}get centerPoint(){return this._helper.centerPoint}get pixelsPerMeter(){return this._helper.pixelsPerMeter}setMinZoom(d){this._helper.setMinZoom(d)}setMaxZoom(d){this._helper.setMaxZoom(d)}setMinPitch(d){this._helper.setMinPitch(d)}setMaxPitch(d){this._helper.setMaxPitch(d)}setRenderWorldCopies(d){this._helper.setRenderWorldCopies(d)}setBearing(d){this._helper.setBearing(d)}setPitch(d){this._helper.setPitch(d)}setRoll(d){this._helper.setRoll(d)}setFov(d){this._helper.setFov(d)}setZoom(d){this._helper.setZoom(d)}setCenter(d){this._helper.setCenter(d)}setElevation(d){this._helper.setElevation(d)}setMinElevationForCurrentTile(d){this._helper.setMinElevationForCurrentTile(d)}setPadding(d){this._helper.setPadding(d)}interpolatePadding(d,P,N){this._helper.interpolatePadding(d,P,N)}isPaddingEqual(d){return this._helper.isPaddingEqual(d)}resize(d,P,N=!0){this._helper.resize(d,P,N)}getMaxBounds(){return this._helper.getMaxBounds()}setMaxBounds(d){this._helper.setMaxBounds(d)}setConstrainOverride(d){this._helper.setConstrainOverride(d)}overrideNearFarZ(d,P){this._helper.overrideNearFarZ(d,P)}clearNearFarZOverride(){this._helper.clearNearFarZOverride()}getCameraQueryGeometry(d){return this._helper.getCameraQueryGeometry(this.getCameraPoint(),d)}get tileSize(){return this._helper.tileSize}get tileZoom(){return this._helper.tileZoom}get scale(){return this._helper.scale}get worldSize(){return this._helper.worldSize}get width(){return this._helper.width}get height(){return this._helper.height}get lngRange(){return this._helper.lngRange}get latRange(){return this._helper.latRange}get minZoom(){return this._helper.minZoom}get maxZoom(){return this._helper.maxZoom}get zoom(){return this._helper.zoom}get center(){return this._helper.center}get minPitch(){return this._helper.minPitch}get maxPitch(){return this._helper.maxPitch}get pitch(){return this._helper.pitch}get pitchInRadians(){return this._helper.pitchInRadians}get roll(){return this._helper.roll}get rollInRadians(){return this._helper.rollInRadians}get bearing(){return this._helper.bearing}get bearingInRadians(){return this._helper.bearingInRadians}get fov(){return this._helper.fov}get fovInRadians(){return this._helper.fovInRadians}get elevation(){return this._helper.elevation}get minElevationForCurrentTile(){return this._helper.minElevationForCurrentTile}get padding(){return this._helper.padding}get unmodified(){return this._helper.unmodified}get renderWorldCopies(){return this._helper.renderWorldCopies}get cameraToCenterDistance(){return this._helper.cameraToCenterDistance}get constrainOverride(){return this._helper.constrainOverride}get nearZ(){return this._helper.nearZ}get farZ(){return this._helper.farZ}get autoCalculateNearFarZ(){return this._helper.autoCalculateNearFarZ}setTransitionState(d,P){}constructor(d){this._posMatrixCache=new Map,this._alignedPosMatrixCache=new Map,this._fogMatrixCacheF32=new Map,this.defaultConstrain=(P,N)=>{N=i.al(+N,this.minZoom,this.maxZoom);let H={center:new i.W(P.lng,P.lat),zoom:N},$=this._helper._lngRange;if(!this._helper._renderWorldCopies&&$===null){let St=179.9999999999;$=[-St,St]}let se=this.tileSize*i.ao(H.zoom),de=0,Ae=se,Fe=0,Ie=se,Ye=0,st=0,{x:dt,y:Pe}=this.size;if(this._helper._latRange){let St=this._helper._latRange;de=i.Y(St[1])*se,Ae=i.Y(St[0])*se,Ae-de<Pe&&(Ye=Pe/(Ae-de))}$&&(Fe=i.X(i.Z($[0])*se,0,se),Ie=i.X(i.Z($[1])*se,0,se),Ie<Fe&&(Ie+=se),Ie-Fe<dt&&(st=dt/(Ie-Fe)));let{x:Xe,y:at}=jt(se,P),xt,Et,Bt=Math.max(st||0,Ye||0);if(Bt){let St=new i.P(st?(Ie+Fe)/2:Xe,Ye?(Ae+de)/2:at);return H.center=mr(se,St).wrap(),H.zoom+=i.ar(Bt),H}if(this._helper._latRange){let St=Pe/2;at-St<de&&(Et=de+St),at+St>Ae&&(Et=Ae-St)}if($){let St=(Fe+Ie)/2,Pt=Xe;this._helper._renderWorldCopies&&(Pt=i.X(Xe,St-se/2,St+se/2));let $t=dt/2;Pt-$t<Fe&&(xt=Fe+$t),Pt+$t>Ie&&(xt=Ie-$t)}if(xt!==void 0||Et!==void 0){let St=new i.P(xt!=null?xt:Xe,Et!=null?Et:at);H.center=mr(se,St).wrap()}return H},this.applyConstrain=(P,N)=>this._helper.applyConstrain(P,N),this._helper=new As({calcMatrices:()=>this._calcMatrices(),defaultConstrain:(P,N)=>this.defaultConstrain(P,N)},d),this._coveringTilesDetailsProvider=new xu}clone(){let d=new Ls;return d.apply(this,!1),d}apply(d,P,N){this._helper.apply(d,P,N)}get cameraPosition(){return this._cameraPosition}get projectionMatrix(){return this._projectionMatrix}get modelViewProjectionMatrix(){return this._viewProjMatrix}get inverseProjectionMatrix(){return this._invProjMatrix}get mercatorMatrix(){return this._mercatorMatrix}getVisibleUnwrappedCoordinates(d){let P=[new i.ba(0,d)];if(this._helper._renderWorldCopies){let N=this.screenPointToMercatorCoordinate(new i.P(0,0)),H=this.screenPointToMercatorCoordinate(new i.P(this._helper._width,0)),$=this.screenPointToMercatorCoordinate(new i.P(this._helper._width,this._helper._height)),se=this.screenPointToMercatorCoordinate(new i.P(0,this._helper._height)),de=Math.floor(Math.min(N.x,H.x,$.x,se.x)),Ae=Math.floor(Math.max(N.x,H.x,$.x,se.x)),Fe=1;for(let Ie=de-Fe;Ie<=Ae+Fe;Ie++)Ie!==0&&P.push(new i.ba(Ie,d))}return P}getCameraFrustum(){return rs.fromInvProjectionMatrix(this._invViewProjMatrix,this.worldSize)}getClippingPlane(){return null}getCoveringTilesDetailsProvider(){return this._coveringTilesDetailsProvider}recalculateZoomAndCenter(d){let P=this.screenPointToLocation(this.centerPoint,d),N=d?d.getElevationForLngLatZoom(P,this._helper._tileZoom):0;this._helper.recalculateZoomAndCenter(N)}setLocationAtPoint(d,P){let N=i.aq(this.elevation,this.center.lat),H=this.screenPointToMercatorCoordinateAtZ(P,N),$=this.screenPointToMercatorCoordinateAtZ(this.centerPoint,N),se=i.a7.fromLngLat(d),de=new i.a7(se.x-(H.x-$.x),se.y-(H.y-$.y));this.setCenter(de==null?void 0:de.toLngLat()),this._helper._renderWorldCopies&&this.setCenter(this.center.wrap())}locationToScreenPoint(d,P){return P?this.coordinatePoint(i.a7.fromLngLat(d),P.getElevationForLngLat(d,this),this._pixelMatrix3D):this.coordinatePoint(i.a7.fromLngLat(d))}screenPointToLocation(d,P){var N;return(N=this.screenPointToMercatorCoordinate(d,P))===null||N===void 0?void 0:N.toLngLat()}screenPointToMercatorCoordinate(d,P){if(P){let N=P.pointCoordinate(d);if(N!=null)return N}return this.screenPointToMercatorCoordinateAtZ(d)}screenPointToMercatorCoordinateAtZ(d,P){let N=P||0,H=[d.x,d.y,0,1],$=[d.x,d.y,1,1];i.aE(H,H,this._pixelMatrixInverse),i.aE($,$,this._pixelMatrixInverse);let se=H[3],de=$[3],Ae=H[1]/se,Fe=$[1]/de,Ie=H[2]/se,Ye=$[2]/de,st=Ie===Ye?0:(N-Ie)/(Ye-Ie);return new i.a7(i.H.number(H[0]/se,$[0]/de,st)/this.worldSize,i.H.number(Ae,Fe,st)/this.worldSize,N)}coordinatePoint(d,P=0,N=this._pixelMatrix){let H=[d.x*this.worldSize,d.y*this.worldSize,P,1];return i.aE(H,H,N),new i.P(H[0]/H[3],H[1]/H[3])}getBounds(){let d=Math.max(0,this._helper._height/2-or(this));return new ie().extend(this.screenPointToLocation(new i.P(0,d))).extend(this.screenPointToLocation(new i.P(this._helper._width,d))).extend(this.screenPointToLocation(new i.P(this._helper._width,this._helper._height))).extend(this.screenPointToLocation(new i.P(0,this._helper._height)))}isPointOnMapSurface(d,P){return P?P.pointCoordinate(d)!=null:d.y>this.height/2-or(this)}calculatePosMatrix(d,P=!1,N){var H;let $=(H=d.key)!==null&&H!==void 0?H:i.bb(d.wrap,d.canonical.z,d.canonical.z,d.canonical.x,d.canonical.y),se=P?this._alignedPosMatrixCache:this._posMatrixCache;if(se.has($)){let Fe=se.get($);return N?Fe.f32:Fe.f64}let de=fr(d,this.worldSize);i.U(de,P?this._alignedProjMatrix:this._viewProjMatrix,de);let Ae={f64:de,f32:new Float32Array(de)};return se.set($,Ae),N?Ae.f32:Ae.f64}calculateFogMatrix(d){let P=d.key,N=this._fogMatrixCacheF32;if(N.has(P))return N.get(P);let H=fr(d,this.worldSize);return i.U(H,this._fogMatrix,H),N.set(P,new Float32Array(H)),N.get(P)}calculateCenterFromCameraLngLatAlt(d,P,N,H){return this._helper.calculateCenterFromCameraLngLatAlt(d,P,N,H)}_calculateNearFarZIfNeeded(d,P,N){if(!this._helper.autoCalculateNearFarZ)return;let H=Math.min(this.elevation,this.minElevationForCurrentTile,this.getCameraAltitude()-100),$=d-H*this._helper._pixelPerMeter/Math.cos(P),se=H<0?$:d,de=Math.PI/2+this.pitchInRadians,Ae=i.an(this.fov)*(Math.abs(Math.cos(i.an(this.roll)))*this.height+Math.abs(Math.sin(i.an(this.roll)))*this.width)/this.height*(.5+N.y/this.height),Fe=Math.sin(Ae)*se/Math.sin(i.al(Math.PI-de-Ae,.01,Math.PI-.01)),Ie=or(this),Ye=Math.atan(Ie/this._helper.cameraToCenterDistance),st=i.an(.75),dt=Ye>st?2*Ye*(.5+N.y/(2*Ie)):st,Pe=Math.sin(dt)*se/Math.sin(i.al(Math.PI-de-dt,.01,Math.PI-.01)),Xe=Math.min(Fe,Pe);this._helper._farZ=1.01*(Math.cos(Math.PI/2-P)*Xe+se),this._helper._nearZ=this._helper._height/50}_calcMatrices(){if(!this._helper._height)return;let d=this.centerOffset,P=jt(this.worldSize,this.center),N=P.x,H=P.y;this._helper._pixelPerMeter=i.aq(1,this.center.lat)*this.worldSize;let $=i.an(Math.min(this.pitch,Yt)),se=Math.max(this._helper.cameraToCenterDistance/2,this._helper.cameraToCenterDistance+this._helper._elevation*this._helper._pixelPerMeter/Math.cos($)),de;var Ae,Fe;this._calculateNearFarZIfNeeded(se,$,d),de=new Float64Array(16),i.bc(de,this.fovInRadians,this._helper._width/this._helper._height,this._helper._nearZ,this._helper._farZ),this._invProjMatrix=new Float64Array(16),(Ae=this._invProjMatrix)[0]=1/(Fe=de)[0],Ae[1]=0,Ae[2]=0,Ae[3]=0,Ae[4]=0,Ae[5]=1/Fe[5],Ae[6]=0,Ae[7]=0,Ae[8]=0,Ae[9]=0,Ae[10]=0,Ae[11]=1/Fe[14],Ae[12]=0,Ae[13]=0,Ae[14]=-1,Ae[15]=Fe[10]/Fe[14],de[8]=2*-d.x/this._helper._width,de[9]=2*d.y/this._helper._height,this._projectionMatrix=i.bd(de),i.S(de,de,[1,-1,1]),i.Q(de,de,[0,0,-this._helper.cameraToCenterDistance]),i.be(de,de,-this.rollInRadians),i.bf(de,de,this.pitchInRadians),i.be(de,de,-this.bearingInRadians),i.Q(de,de,[-N,-H,0]),this._mercatorMatrix=i.S([],de,[this.worldSize,this.worldSize,this.worldSize]),i.S(de,de,[1,1,this._helper._pixelPerMeter]),this._pixelMatrix=i.U(new Float64Array(16),this.clipSpaceToPixelsMatrix,de),i.Q(de,de,[0,0,-this.elevation]),this._viewProjMatrix=de,this._invViewProjMatrix=i.bg([],de);let Ie=[0,0,-1,1];i.aE(Ie,Ie,this._invViewProjMatrix),this._cameraPosition=[Ie[0]/Ie[3],Ie[1]/Ie[3],Ie[2]/Ie[3]],this._fogMatrix=new Float64Array(16),i.bc(this._fogMatrix,this.fovInRadians,this.width/this.height,se,this._helper._farZ),this._fogMatrix[8]=2*-d.x/this.width,this._fogMatrix[9]=2*d.y/this.height,i.S(this._fogMatrix,this._fogMatrix,[1,-1,1]),i.Q(this._fogMatrix,this._fogMatrix,[0,0,-this.cameraToCenterDistance]),i.be(this._fogMatrix,this._fogMatrix,-this.rollInRadians),i.bf(this._fogMatrix,this._fogMatrix,this.pitchInRadians),i.be(this._fogMatrix,this._fogMatrix,-this.bearingInRadians),i.Q(this._fogMatrix,this._fogMatrix,[-N,-H,0]),i.S(this._fogMatrix,this._fogMatrix,[1,1,this._helper._pixelPerMeter]),i.Q(this._fogMatrix,this._fogMatrix,[0,0,-this.elevation]),this._pixelMatrix3D=i.U(new Float64Array(16),this.clipSpaceToPixelsMatrix,de);let Ye=this._helper._width%2/2,st=this._helper._height%2/2,dt=Math.cos(this.bearingInRadians),Pe=Math.sin(-this.bearingInRadians),Xe=N-Math.round(N)+dt*Ye+Pe*st,at=H-Math.round(H)+dt*st+Pe*Ye,xt=new Float64Array(de);if(i.Q(xt,xt,[Xe>.5?Xe-1:Xe,at>.5?at-1:at,0]),this._alignedProjMatrix=xt,de=i.bg(new Float64Array(16),this._pixelMatrix),!de)throw new Error(\"failed to invert matrix\");this._pixelMatrixInverse=de,this._clearMatrixCaches()}_clearMatrixCaches(){this._posMatrixCache.clear(),this._alignedPosMatrixCache.clear(),this._fogMatrixCacheF32.clear()}maxPitchScaleFactor(){if(!this._pixelMatrixInverse)return 1;let d=this.screenPointToMercatorCoordinate(new i.P(0,0)),P=[d.x*this.worldSize,d.y*this.worldSize,0,1];return i.aE(P,P,this._pixelMatrix)[3]/this._helper.cameraToCenterDistance}getCameraPoint(){return this._helper.getCameraPoint()}getCameraAltitude(){return this._helper.getCameraAltitude()}getCameraLngLat(){let d=i.aq(1,this.center.lat)*this.worldSize;return Ot(this.center,this.elevation,this.pitch,this.bearing,this._helper.cameraToCenterDistance/d).toLngLat()}lngLatToCameraDepth(d,P){let N=i.a7.fromLngLat(d),H=[N.x*this.worldSize,N.y*this.worldSize,P,1];return i.aE(H,H,this._viewProjMatrix),H[2]/H[3]}getProjectionData(d){let{overscaledTileID:P,aligned:N,applyTerrainMatrix:H}=d,$=this._helper.getMercatorTileCoordinates(P),se=P?this.calculatePosMatrix(P,N,!0):null,de;return de=P!=null&&P.terrainRttPosMatrix32f&&H?P.terrainRttPosMatrix32f:se||i.bh(),{mainMatrix:de,tileMercatorCoords:$,clippingPlane:[0,0,0,0],projectionTransition:0,fallbackMatrix:de}}isLocationOccluded(d){return!1}getPixelScale(){return 1}getCircleRadiusCorrection(){return 1}getPitchedTextCorrection(d,P,N){return 1}transformLightDirection(d){return i.a$(d)}getRayDirectionFromPixel(d){throw new Error(\"Not implemented.\")}projectTileCoordinates(d,P,N,H){let $=this.calculatePosMatrix(N),se;H?(se=[d,P,H(d,P),1],i.aE(se,se,$)):(se=[d,P,0,1],ao(se,se,$));let de=se[3];return{point:new i.P(se[0]/de,se[1]/de),signedDistanceFromCamera:de,isOccluded:!1}}populateCache(d){for(let P of d)this.calculatePosMatrix(P)}getMatrixForModel(d,P){let N=i.a7.fromLngLat(d,P),H=N.meterInMercatorCoordinateUnits(),$=i.bi();return i.Q($,$,[N.x,N.y,N.z]),i.be($,$,Math.PI),i.bf($,$,Math.PI/2),i.S($,$,[-H,H,H]),$}getProjectionDataForCustomLayer(d=!0){let P=new i.a3(0,0,0,0,0),N=this.getProjectionData({overscaledTileID:P,applyGlobeMatrix:d}),H=fr(P,this.worldSize);i.U(H,this._viewProjMatrix,H),N.tileMercatorCoords=[0,0,1,1];let $=[i.a6,i.a6,this.worldSize/this._helper.pixelsPerMeter],se=i.bj();return i.S(se,H,$),N.fallbackMatrix=se,N.mainMatrix=se,N}getFastPathSimpleProjectionMatrix(d){return this.calculatePosMatrix(d)}}function $s(){i.w(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")}function Kc(Y){if(Y.useSlerp)if(Y.k<1){let d=i.bk(Y.startEulerAngles.roll,Y.startEulerAngles.pitch,Y.startEulerAngles.bearing),P=i.bk(Y.endEulerAngles.roll,Y.endEulerAngles.pitch,Y.endEulerAngles.bearing),N=new Float64Array(4);i.bl(N,d,P,Y.k);let H=i.bm(N);Y.tr.setRoll(H.roll),Y.tr.setPitch(H.pitch),Y.tr.setBearing(H.bearing)}else Y.tr.setRoll(Y.endEulerAngles.roll),Y.tr.setPitch(Y.endEulerAngles.pitch),Y.tr.setBearing(Y.endEulerAngles.bearing);else Y.tr.setRoll(i.H.number(Y.startEulerAngles.roll,Y.endEulerAngles.roll,Y.k)),Y.tr.setPitch(i.H.number(Y.startEulerAngles.pitch,Y.endEulerAngles.pitch,Y.k)),Y.tr.setBearing(i.H.number(Y.startEulerAngles.bearing,Y.endEulerAngles.bearing,Y.k))}function _u(Y,d,P,N,H){let $=H.padding,se=jt(H.worldSize,P.getNorthWest()),de=jt(H.worldSize,P.getNorthEast()),Ae=jt(H.worldSize,P.getSouthEast()),Fe=jt(H.worldSize,P.getSouthWest()),Ie=i.an(-N),Ye=se.rotate(Ie),st=de.rotate(Ie),dt=Ae.rotate(Ie),Pe=Fe.rotate(Ie),Xe=new i.P(Math.max(Ye.x,st.x,Pe.x,dt.x),Math.max(Ye.y,st.y,Pe.y,dt.y)),at=new i.P(Math.min(Ye.x,st.x,Pe.x,dt.x),Math.min(Ye.y,st.y,Pe.y,dt.y)),xt=Xe.sub(at),Et=(H.width-($.left+$.right+d.left+d.right))/xt.x,Bt=(H.height-($.top+$.bottom+d.top+d.bottom))/xt.y;if(Bt<0||Et<0)return void $s();let St=Math.min(i.ar(H.scale*Math.min(Et,Bt)),Y.maxZoom),Pt=i.P.convert(Y.offset),$t=new i.P((d.left-d.right)/2,(d.top-d.bottom)/2).rotate(i.an(N)),Ht=Pt.add($t).mult(H.scale/i.ao(St));return{center:mr(H.worldSize,se.add(Ae).div(2).sub(Ht)),zoom:St,bearing:N}}class Gu{get useGlobeControls(){return!1}handlePanInertia(d,P){let N=d.mag(),H=Math.abs(or(P));return{easingOffset:d.mult(Math.min(.75*H/N,1)),easingCenter:P.center}}handleMapControlsRollPitchBearingZoom(d,P){d.bearingDelta&&P.setBearing(P.bearing+d.bearingDelta),d.pitchDelta&&P.setPitch(P.pitch+d.pitchDelta),d.rollDelta&&P.setRoll(P.roll+d.rollDelta),d.zoomDelta&&P.setZoom(P.zoom+d.zoomDelta)}handleMapControlsPan(d,P,N){d.around.distSqr(P.centerPoint)<.01||P.setLocationAtPoint(N,d.around)}cameraForBoxAndBearing(d,P,N,H,$){return _u(d,P,N,H,$)}handleJumpToCenterZoom(d,P){d.zoom!==(P.zoom!==void 0?+P.zoom:d.zoom)&&d.setZoom(+P.zoom),P.center!==void 0&&d.setCenter(i.W.convert(P.center))}handleEaseTo(d,P){let N=d.zoom,H=d.padding,$={roll:d.roll,pitch:d.pitch,bearing:d.bearing},se={roll:P.roll===void 0?d.roll:P.roll,pitch:P.pitch===void 0?d.pitch:P.pitch,bearing:P.bearing===void 0?d.bearing:P.bearing},de=P.zoom!==void 0,Ae=!d.isPaddingEqual(P.padding),Fe=!1,Ie=de?+P.zoom:d.zoom,Ye=d.centerPoint.add(P.offsetAsPoint),st=d.screenPointToLocation(Ye),{center:dt,zoom:Pe}=d.applyConstrain(i.W.convert(P.center||st),Ie!=null?Ie:N);pa(d,dt);let Xe=jt(d.worldSize,st),at=jt(d.worldSize,dt).sub(Xe),xt=i.ao(Pe-N);return Fe=Pe!==N,{easeFunc:Et=>{if(Fe&&d.setZoom(i.H.number(N,Pe,Et)),i.bn($,se)||Kc({startEulerAngles:$,endEulerAngles:se,tr:d,k:Et,useSlerp:$.roll!=se.roll}),Ae&&(d.interpolatePadding(H,P.padding,Et),Ye=d.centerPoint.add(P.offsetAsPoint)),P.around)d.setLocationAtPoint(P.around,P.aroundPoint);else{let Bt=i.ao(d.zoom-N),St=Pe>N?Math.min(2,xt):Math.max(.5,xt),Pt=Math.pow(St,1-Et),$t=mr(d.worldSize,Xe.add(at.mult(Et*Pt)).mult(Bt));d.setLocationAtPoint(d.renderWorldCopies?$t.wrap():$t,Ye)}},isZooming:Fe,elevationCenter:dt}}handleFlyTo(d,P){let N=P.zoom!==void 0,H=d.zoom,$=d.applyConstrain(i.W.convert(P.center||P.locationAtOffset),N?+P.zoom:H),se=$.center,de=$.zoom;pa(d,se);let Ae=jt(d.worldSize,P.locationAtOffset),Fe=jt(d.worldSize,se).sub(Ae),Ie=Fe.mag(),Ye=i.ao(de-H),st;if(P.minZoom!==void 0){let dt=Math.min(+P.minZoom,H,de),Pe=d.applyConstrain(se,dt).zoom;st=i.ao(Pe-H)}return{easeFunc:(dt,Pe,Xe,at)=>{d.setZoom(dt===1?de:H+i.ar(Pe));let xt=dt===1?se:mr(d.worldSize,Ae.add(Fe.mult(Xe)).mult(Pe));d.setLocationAtPoint(d.renderWorldCopies?xt.wrap():xt,at)},scaleOfZoom:Ye,targetCenter:se,scaleOfMinZoom:st,pixelPathLength:Ie}}}class Ja{constructor(d,P,N){this.blendFunction=d,this.blendColor=P,this.mask=N}}Ja.Replace=[1,0],Ja.disabled=new Ja(Ja.Replace,i.bo.transparent,[!1,!1,!1,!1]),Ja.unblended=new Ja(Ja.Replace,i.bo.transparent,[!0,!0,!0,!0]),Ja.alphaBlended=new Ja([1,771],i.bo.transparent,[!0,!0,!0,!0]);let ps=2305;class Sa{constructor(d,P,N){this.enable=d,this.mode=P,this.frontFace=N}}Sa.disabled=new Sa(!1,1029,ps),Sa.backCCW=new Sa(!0,1029,ps),Sa.frontCCW=new Sa(!0,1028,ps);class ma{constructor(d,P,N){this.func=d,this.mask=P,this.range=N}}ma.ReadOnly=!1,ma.ReadWrite=!0,ma.disabled=new ma(519,ma.ReadOnly,[0,1]);let Ps=7680;class Ca{constructor(d,P,N,H,$,se){this.test=d,this.ref=P,this.mask=N,this.fail=H,this.depthFail=$,this.pass=se}}function Pl(Y){return typeof WebGL2RenderingContext!=\"undefined\"&&Y instanceof WebGL2RenderingContext}Ca.disabled=new Ca({func:519,mask:0},0,0,Ps,Ps,Ps);class ll{get awaitingQuery(){return!!this._readbackQueue}constructor(d){this._readbackWaitFrames=4,this._measureWaitFrames=6,this._texWidth=1,this._texHeight=1,this._measuredError=0,this._updateCount=0,this._lastReadbackFrame=-1e3,this._readbackQueue=null,this._cachedRenderContext=d;let P=d.context,N=P.gl;this._texFormat=N.RGBA,this._texType=N.UNSIGNED_BYTE;let H=new i.aU;H.emplaceBack(-1,-1),H.emplaceBack(2,-1),H.emplaceBack(-1,2);let $=new i.aW;$.emplaceBack(0,1,2),this._fullscreenTriangle=new ka(P.createVertexBuffer(H,vs.members),P.createIndexBuffer($),i.aV.simpleSegment(0,0,H.length,$.length)),this._resultBuffer=new Uint8Array(4),P.activeTexture.set(N.TEXTURE1);let se=N.createTexture();N.bindTexture(N.TEXTURE_2D,se),N.texParameteri(N.TEXTURE_2D,N.TEXTURE_WRAP_S,N.CLAMP_TO_EDGE),N.texParameteri(N.TEXTURE_2D,N.TEXTURE_WRAP_T,N.CLAMP_TO_EDGE),N.texParameteri(N.TEXTURE_2D,N.TEXTURE_MIN_FILTER,N.NEAREST),N.texParameteri(N.TEXTURE_2D,N.TEXTURE_MAG_FILTER,N.NEAREST),N.texImage2D(N.TEXTURE_2D,0,this._texFormat,this._texWidth,this._texHeight,0,this._texFormat,this._texType,null),this._fbo=P.createFramebuffer(this._texWidth,this._texHeight,!1,!1),this._fbo.colorAttachment.set(se),Pl(N)&&(this._pbo=N.createBuffer(),N.bindBuffer(N.PIXEL_PACK_BUFFER,this._pbo),N.bufferData(N.PIXEL_PACK_BUFFER,4,N.STREAM_READ),N.bindBuffer(N.PIXEL_PACK_BUFFER,null))}destroy(){let d=this._cachedRenderContext.context.gl;this._fullscreenTriangle.destroy(),this._fbo.destroy(),d.deleteBuffer(this._pbo),this._fullscreenTriangle=null,this._fbo=null,this._pbo=null,this._resultBuffer=null}updateErrorLoop(d,P){let N=this._updateCount;return this._readbackQueue?N>=this._readbackQueue.frameNumberIssued+this._readbackWaitFrames&&this._tryReadback():N>=this._lastReadbackFrame+this._measureWaitFrames&&this._renderErrorTexture(d,P),this._updateCount++,this._measuredError}_bindFramebuffer(){let d=this._cachedRenderContext.context,P=d.gl;d.activeTexture.set(P.TEXTURE1),P.bindTexture(P.TEXTURE_2D,this._fbo.colorAttachment.get()),d.bindFramebuffer.set(this._fbo.framebuffer)}_renderErrorTexture(d,P){let N=this._cachedRenderContext.context,H=N.gl;if(this._bindFramebuffer(),N.viewport.set([0,0,this._texWidth,this._texHeight]),N.clear({color:i.bo.transparent}),this._cachedRenderContext.useProgram(\"projectionErrorMeasurement\").draw(N,H.TRIANGLES,ma.disabled,Ca.disabled,Ja.unblended,Sa.disabled,(($,se)=>({u_input:$,u_output_expected:se}))(d,P),null,null,\"$clipping\",this._fullscreenTriangle.vertexBuffer,this._fullscreenTriangle.indexBuffer,this._fullscreenTriangle.segments),this._pbo&&Pl(H)){H.bindBuffer(H.PIXEL_PACK_BUFFER,this._pbo),H.readBuffer(H.COLOR_ATTACHMENT0),H.readPixels(0,0,this._texWidth,this._texHeight,this._texFormat,this._texType,0),H.bindBuffer(H.PIXEL_PACK_BUFFER,null);let $=H.fenceSync(H.SYNC_GPU_COMMANDS_COMPLETE,0);H.flush(),this._readbackQueue={frameNumberIssued:this._updateCount,sync:$}}else this._readbackQueue={frameNumberIssued:this._updateCount,sync:null}}_tryReadback(){let d=this._cachedRenderContext.context.gl;if(this._pbo&&this._readbackQueue&&Pl(d)){let P=d.clientWaitSync(this._readbackQueue.sync,0,0);if(P===d.WAIT_FAILED)return i.w(\"WebGL2 clientWaitSync failed.\"),this._readbackQueue=null,void(this._lastReadbackFrame=this._updateCount);if(P===d.TIMEOUT_EXPIRED)return;d.bindBuffer(d.PIXEL_PACK_BUFFER,this._pbo),d.getBufferSubData(d.PIXEL_PACK_BUFFER,0,this._resultBuffer,0,4),d.bindBuffer(d.PIXEL_PACK_BUFFER,null)}else this._bindFramebuffer(),d.readPixels(0,0,this._texWidth,this._texHeight,this._texFormat,this._texType,this._resultBuffer);this._readbackQueue=null,this._measuredError=ll._parseRGBA8float(this._resultBuffer),this._lastReadbackFrame=this._updateCount}static _parseRGBA8float(d){let P=0;return P+=d[0]/256,P+=d[1]/65536,P+=d[2]/16777216,d[3]<127&&(P=-P),P/128}}let Jc=i.a6/128;function lu(Y,d){let P=Y.granularity!==void 0?Math.max(Y.granularity,1):1,N=P+(Y.generateBorders?2:0),H=P+(Y.extendToNorthPole||Y.generateBorders?1:0)+(Y.extendToSouthPole||Y.generateBorders?1:0),$=N+1,se=H+1,de=Y.generateBorders?-1:0,Ae=Y.generateBorders||Y.extendToNorthPole?-1:0,Fe=P+(Y.generateBorders?1:0),Ie=P+(Y.generateBorders||Y.extendToSouthPole?1:0),Ye=$*se,st=N*H*6,dt=$*se>65536;if(dt&&d===\"16bit\")throw new Error(\"Granularity is too large and meshes would not fit inside 16 bit vertex indices.\");let Pe=dt||d===\"32bit\",Xe=new Int16Array(2*Ye),at=0;for(let Bt=Ae;Bt<=Ie;Bt++)for(let St=de;St<=Fe;St++){let Pt=St/P*i.a6;St===-1&&(Pt=-Jc),St===P+1&&(Pt=i.a6+Jc);let $t=Bt/P*i.a6;Bt===-1&&($t=Y.extendToNorthPole?i.bq:-Jc),Bt===P+1&&($t=Y.extendToSouthPole?i.br:i.a6+Jc),Xe[at++]=Pt,Xe[at++]=$t}let xt=Pe?new Uint32Array(st):new Uint16Array(st),Et=0;for(let Bt=0;Bt<H;Bt++)for(let St=0;St<N;St++){let Pt=St+1+Bt*$,$t=St+(Bt+1)*$,Ht=St+1+(Bt+1)*$;xt[Et++]=St+Bt*$,xt[Et++]=$t,xt[Et++]=Pt,xt[Et++]=Pt,xt[Et++]=$t,xt[Et++]=Ht}return{vertices:Xe.buffer.slice(0),indices:xt.buffer.slice(0),uses32bitIndices:Pe}}let Mf=new i.aT({fill:new i.bs(128,2),line:new i.bs(512,0),tile:new i.bs(128,32),stencil:new i.bs(128,1),circle:3});class Bd{constructor(){this._tileMeshCache={},this._errorCorrectionUsable=0,this._errorMeasurementLastValue=0,this._errorCorrectionPreviousValue=0,this._errorMeasurementLastChangeTime=-1e3}get name(){return\"vertical-perspective\"}get transitionState(){return 1}get useSubdivision(){return!0}get shaderVariantName(){return\"globe\"}get shaderDefine(){return\"#define GLOBE\"}get shaderPreludeCode(){return Oa.projectionGlobe}get vertexShaderPreludeCode(){return Oa.projectionMercator.vertexSource}get subdivisionGranularity(){return Mf}get useGlobeControls(){return!0}get latitudeErrorCorrectionRadians(){return this._errorCorrectionUsable}destroy(){this._errorMeasurement&&this._errorMeasurement.destroy()}updateGPUdependent(d){this._errorMeasurement||(this._errorMeasurement=new ll(d));let P=i.Y(this._errorQueryLatitudeDegrees),N=2*Math.atan(Math.exp(Math.PI-P*Math.PI*2))-.5*Math.PI,H=this._errorMeasurement.updateErrorLoop(P,N),$=g();H!==this._errorMeasurementLastValue&&(this._errorCorrectionPreviousValue=this._errorCorrectionUsable,this._errorMeasurementLastValue=H,this._errorMeasurementLastChangeTime=$);let se=Math.min(Math.max(($-this._errorMeasurementLastChangeTime)/1e3/.5,0),1);this._errorCorrectionUsable=i.bt(this._errorCorrectionPreviousValue,-this._errorMeasurementLastValue,i.bu(se))}_getMeshKey(d){return`${d.granularity.toString(36)}_${d.generateBorders?\"b\":\"\"}${d.extendToNorthPole?\"n\":\"\"}${d.extendToSouthPole?\"s\":\"\"}`}getMeshFromTileID(d,P,N,H,$){let se=($===\"stencil\"?Mf.stencil:Mf.tile).getGranularityForZoomLevel(P.z);return this._getMesh(d,{granularity:se,generateBorders:N,extendToNorthPole:P.y===0&&H,extendToSouthPole:P.y===(1<<P.z)-1&&H})}_getMesh(d,P){let N=this._getMeshKey(P);if(N in this._tileMeshCache)return this._tileMeshCache[N];let H=(function($,se){let de=lu(se,\"16bit\"),Ae=i.aU.deserialize({arrayBuffer:de.vertices,length:de.vertices.byteLength/2/2}),Fe=i.aW.deserialize({arrayBuffer:de.indices,length:de.indices.byteLength/2/3});return new ka($.createVertexBuffer(Ae,vs.members),$.createIndexBuffer(Fe),i.aV.simpleSegment(0,0,Ae.length,Fe.length))})(d,P);return this._tileMeshCache[N]=H,H}recalculate(d){}hasTransition(){var d;let P=g(),N=!1;return N||(N=(P-this._errorMeasurementLastChangeTime)/1e3<.7),N||(N=(d=this._errorMeasurement)===null||d===void 0?void 0:d.awaitingQuery),N}setErrorQueryLatitudeDegrees(d){this._errorQueryLatitudeDegrees=d}}let Hu=new i.u({type:new i.D(i.x.projection.type)});class Sf extends i.E{constructor(d){super(),this._transitionable=new i.y(Hu,void 0),this.setProjection(d),this._transitioning=this._transitionable.untransitioned(),this.recalculate(new i.J(0)),this._mercatorProjection=new oo,this._verticalPerspectiveProjection=new Bd}get transitionState(){let d=this.properties.get(\"type\");if(typeof d==\"string\"&&d===\"mercator\")return 0;if(typeof d==\"string\"&&d===\"vertical-perspective\")return 1;if(d instanceof i.bv){if(d.from===\"vertical-perspective\"&&d.to===\"mercator\")return 1-d.transition;if(d.from===\"mercator\"&&d.to===\"vertical-perspective\")return d.transition}return 1}get useGlobeRendering(){return this.transitionState>0}get latitudeErrorCorrectionRadians(){return this._verticalPerspectiveProjection.latitudeErrorCorrectionRadians}get currentProjection(){return this.useGlobeRendering?this._verticalPerspectiveProjection:this._mercatorProjection}get name(){return\"globe\"}get useSubdivision(){return this.currentProjection.useSubdivision}get shaderVariantName(){return this.currentProjection.shaderVariantName}get shaderDefine(){return this.currentProjection.shaderDefine}get shaderPreludeCode(){return this.currentProjection.shaderPreludeCode}get vertexShaderPreludeCode(){return this.currentProjection.vertexShaderPreludeCode}get subdivisionGranularity(){return this.currentProjection.subdivisionGranularity}get useGlobeControls(){return this.transitionState>0}destroy(){this._mercatorProjection.destroy(),this._verticalPerspectiveProjection.destroy()}updateGPUdependent(d){this._mercatorProjection.updateGPUdependent(d),this._verticalPerspectiveProjection.updateGPUdependent(d)}getMeshFromTileID(d,P,N,H,$){return this.currentProjection.getMeshFromTileID(d,P,N,H,$)}setProjection(d){this._transitionable.setValue(\"type\",(d==null?void 0:d.type)||\"mercator\")}updateTransitions(d){this._transitioning=this._transitionable.transitioned(d,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()||this.currentProjection.hasTransition()}recalculate(d){this.properties=this._transitioning.possiblyEvaluate(d)}setErrorQueryLatitudeDegrees(d){this._verticalPerspectiveProjection.setErrorQueryLatitudeDegrees(d),this._mercatorProjection.setErrorQueryLatitudeDegrees(d)}}function kc(Y){let d=Ks(Y.worldSize,Y.center.lat);return 2*Math.PI*d}function Uo(Y,d,P,N,H){let $=1/(1<<H),se=d/i.a6*$+N*$,de=i.by((Y/i.a6*$+P*$)*Math.PI*2+Math.PI,2*Math.PI),Ae=2*Math.atan(Math.exp(Math.PI-se*Math.PI*2))-.5*Math.PI,Fe=Math.cos(Ae),Ie=new Float64Array(3);return Ie[0]=Math.sin(de)*Fe,Ie[1]=Math.sin(Ae),Ie[2]=Math.cos(de)*Fe,Ie}function ns(Y){return(function(d,P){let N=Math.cos(P),H=new Float64Array(3);return H[0]=Math.sin(d)*N,H[1]=Math.sin(P),H[2]=Math.cos(d)*N,H})(Y.lng*Math.PI/180,Y.lat*Math.PI/180)}function Ks(Y,d){return Y/(2*Math.PI)/Math.cos(d*Math.PI/180)}function Ef(Y){let d=Math.asin(Y[1])/Math.PI*180,P=Math.sqrt(Y[0]*Y[0]+Y[2]*Y[2]);if(P>1e-6){let N=Y[0]/P,H=Math.acos(Y[2]/P),$=(N>0?H:-H)/Math.PI*180;return new i.W(i.X($,-180,180),d)}return new i.W(0,d)}function ju(Y){return Math.cos(Y*Math.PI/180)}function gs(Y,d){let P=ju(Y),N=ju(d);return i.ar(N/P)}function bu(Y,d){let P=Y.rotate(d.bearingInRadians),N=d.zoom+gs(d.center.lat,0),H=i.bt(1/ju(d.center.lat),1/ju(Math.min(Math.abs(d.center.lat),60)),i.bw(N,7,3,0,1)),$=360/kc({worldSize:d.worldSize,center:{lat:d.center.lat}});return new i.W(d.center.lng-P.x*$*H,i.al(d.center.lat+P.y*$,-i.am,i.am))}function kf(Y){let d=.5*Y,P=Math.sin(d),N=Math.cos(d);return Math.log(P+N)-Math.log(N-P)}function ah(Y,d,P,N){let H=Y.lat+P*N;if(Math.abs(P)>1){let $=(Math.sign(Y.lat+P)!==Math.sign(Y.lat)?-Math.abs(Y.lat):Math.abs(Y.lat))*Math.PI/180,se=Math.abs(Y.lat+P)*Math.PI/180,de=kf($+N*(se-$)),Ae=kf($),Fe=kf(se);return new i.W(Y.lng+d*((de-Ae)/(Fe-Ae)),H)}return new i.W(Y.lng+d*N,H)}class Od{constructor(d){this._cachePrevious=new Map,this._cache=new Map,this._hadAnyChanges=!1,this._boundingVolumeFactory=d}swapBuffers(){if(!this._hadAnyChanges)return;let d=this._cachePrevious;this._cachePrevious=this._cache,this._cache=d,this._cache.clear(),this._hadAnyChanges=!1}getTileBoundingVolume(d,P,N,H){let $=`${d.z}_${d.x}_${d.y}_${H!=null&&H.terrain?\"t\":\"\"}`,se=this._cache.get($);if(se)return se;let de=this._cachePrevious.get($);if(de)return this._cache.set($,de),de;let Ae=this._boundingVolumeFactory(d,P,N,H);return this._cache.set($,Ae),this._hadAnyChanges=!0,Ae}}class Qc{constructor(d,P,N,H){this.min=N,this.max=H,this.points=d,this.planes=P}static fromAabb(d,P){let N=[];for(let H=0;H<8;H++)N.push([1&~H?d[0]:P[0],(H>>1&1)==1?P[1]:d[1],(H>>2&1)==1?P[2]:d[2]]);return new Qc(N,[[-1,0,0,P[0]],[1,0,0,-d[0]],[0,-1,0,P[1]],[0,1,0,-d[1]],[0,0,-1,P[2]],[0,0,1,-d[2]]],d,P)}static fromCenterSizeAngles(d,P,N){let H=i.bA([],N[0],N[1],N[2]),$=i.bB([],[P[0],0,0],H),se=i.bB([],[0,P[1],0],H),de=i.bB([],[0,0,P[2]],H),Ae=[...d],Fe=[...d];for(let Ye=0;Ye<8;Ye++)for(let st=0;st<3;st++){let dt=d[st]+$[st]*(1&~Ye?-1:1)+se[st]*((Ye>>1&1)==1?1:-1)+de[st]*((Ye>>2&1)==1?1:-1);Ae[st]=Math.min(Ae[st],dt),Fe[st]=Math.max(Fe[st],dt)}let Ie=[];for(let Ye=0;Ye<8;Ye++){let st=[...d];i.a_(st,st,i.aZ([],$,1&~Ye?-1:1)),i.a_(st,st,i.aZ([],se,(Ye>>1&1)==1?1:-1)),i.a_(st,st,i.aZ([],de,(Ye>>2&1)==1?1:-1)),Ie.push(st)}return new Qc(Ie,[[...$,-i.b3($,Ie[0])],[...se,-i.b3(se,Ie[0])],[...de,-i.b3(de,Ie[0])],[-$[0],-$[1],-$[2],-i.b3($,Ie[7])],[-se[0],-se[1],-se[2],-i.b3(se,Ie[7])],[-de[0],-de[1],-de[2],-i.b3(de,Ie[7])]],Ae,Fe)}intersectsFrustum(d){let P=!0,N=this.points.length,H=this.planes.length,$=d.planes.length,se=d.points.length;for(let de=0;de<$;de++){let Ae=d.planes[de],Fe=0;for(let Ie=0;Ie<N;Ie++){let Ye=this.points[Ie];Ae[0]*Ye[0]+Ae[1]*Ye[1]+Ae[2]*Ye[2]+Ae[3]>=0&&Fe++}if(Fe===0)return 0;Fe<N&&(P=!1)}if(P)return 2;for(let de=0;de<H;de++){let Ae=this.planes[de],Fe=0;for(let Ie=0;Ie<se;Ie++){let Ye=d.points[Ie];Ae[0]*Ye[0]+Ae[1]*Ye[1]+Ae[2]*Ye[2]+Ae[3]>=0&&Fe++}if(Fe===0)return 0}return 1}intersectsPlane(d){let P=this.points.length,N=0;for(let H=0;H<P;H++){let $=this.points[H];d[0]*$[0]+d[1]*$[1]+d[2]*$[2]+d[3]>=0&&N++}return N===P?2:N===0?0:1}}function uu(Y,d,P){let N=Y-d;return N<0?-N:Math.max(0,N-P)}function oh(Y,d,P,N,H){let $=Y-P,se;return se=$<0?Math.min(-$,1+$-H):$>H?Math.min(Math.max($-H,0),1-$):0,Math.max(se,uu(d,N,H))}class hc{constructor(){this._boundingVolumeCache=new Od(this._computeTileBoundingVolume)}prepareNextFrame(){this._boundingVolumeCache.swapBuffers()}distanceToTile2d(d,P,N,H){let $=1<<N.z,se=1/$,de=N.x/$,Ae=N.y/$,Fe=2;return Fe=Math.min(Fe,oh(d,P,de,Ae,se)),Fe=Math.min(Fe,oh(d,P,de+.5,-Ae-se,se)),Fe=Math.min(Fe,oh(d,P,de+.5,2-Ae-se,se)),Fe}getWrap(d,P,N){let H=1<<P.z,$=1/H,se=P.x/H,de=uu(d.x,se,$),Ae=uu(d.x,se-1,$),Fe=uu(d.x,se+1,$),Ie=Math.min(de,Ae,Fe);return Ie===Fe?1:Ie===Ae?-1:0}allowVariableZoom(d,P){return Lt(d,P)>4}allowWorldCopies(){return!1}getTileBoundingVolume(d,P,N,H){return this._boundingVolumeCache.getTileBoundingVolume(d,P,N,H)}_computeTileBoundingVolume(d,P,N,H){var $,se;let de=0,Ae=0;if(H!=null&&H.terrain){let Fe=new i.a3(d.z,P,d.z,d.x,d.y),Ie=H.terrain.getMinMaxElevation(Fe);de=($=Ie.minElevation)!==null&&$!==void 0?$:Math.min(0,N),Ae=(se=Ie.maxElevation)!==null&&se!==void 0?se:Math.max(0,N)}if(de/=i.bD,Ae/=i.bD,de+=1,Ae+=1,d.z<=0)return Qc.fromAabb([-Ae,-Ae,-Ae],[Ae,Ae,Ae]);if(d.z===1)return Qc.fromAabb([d.x===0?-Ae:0,d.y===0?0:-Ae,-Ae],[d.x===0?0:Ae,d.y===0?Ae:0,Ae]);{let Fe=[Uo(0,0,d.x,d.y,d.z),Uo(i.a6,0,d.x,d.y,d.z),Uo(i.a6,i.a6,d.x,d.y,d.z),Uo(0,i.a6,d.x,d.y,d.z)],Ie=[];for(let $r of Fe)Ie.push(i.aZ([],$r,Ae));if(Ae!==de)for(let $r of Fe)Ie.push(i.aZ([],$r,de));d.y===0&&Ie.push([0,1,0]),d.y===(1<<d.z)-1&&Ie.push([0,-1,0]);let Ye=[1,1,1],st=[-1,-1,-1];for(let $r of Ie)for(let Er=0;Er<3;Er++)Ye[Er]=Math.min(Ye[Er],$r[Er]),st[Er]=Math.max(st[Er],$r[Er]);let dt=Uo(i.a6/2,i.a6/2,d.x,d.y,d.z),Pe=i.b2([],[0,1,0],dt);i.b1(Pe,Pe);let Xe=i.b2([],dt,Pe);i.b1(Xe,Xe);let at=i.b2([],Fe[2],Fe[1]);i.b1(at,at);let xt=i.b2([],Fe[0],Fe[3]);i.b1(xt,xt),Ie.push(i.aZ([],dt,Ae)),d.y>=(1<<d.z)/2&&Ie.push(i.aZ([],Uo(i.a6/2,0,d.x,d.y,d.z),Ae)),d.y<(1<<d.z)/2&&Ie.push(i.aZ([],Uo(i.a6/2,i.a6,d.x,d.y,d.z),Ae));let Et=Fl(dt,Ie),Bt=Fl(Xe,Ie),St=[-dt[0],-dt[1],-dt[2],Et.max],Pt=[dt[0],dt[1],dt[2],-Et.min],$t=[-Xe[0],-Xe[1],-Xe[2],Bt.max],Ht=[Xe[0],Xe[1],Xe[2],-Bt.min],dr=[...at,0],Xr=[...xt,0],Or=[];return d.y===0?Or.push(i.bC(Xr,dr,St),i.bC(Xr,dr,Pt)):Or.push(i.bC($t,dr,St),i.bC($t,dr,Pt),i.bC($t,Xr,St),i.bC($t,Xr,Pt)),d.y===(1<<d.z)-1?Or.push(i.bC(Xr,dr,St),i.bC(Xr,dr,Pt)):Or.push(i.bC(Ht,dr,St),i.bC(Ht,dr,Pt),i.bC(Ht,Xr,St),i.bC(Ht,Xr,Pt)),new Qc(Or,[St,Pt,$t,Ht,dr,Xr],Ye,st)}}}function Fl(Y,d){let P=1/0,N=-1/0;for(let H of d){let $=i.b3(Y,H);P=Math.min(P,$),N=Math.max(N,$)}return{min:P,max:N}}class Yo{get pixelsToClipSpaceMatrix(){return this._helper.pixelsToClipSpaceMatrix}get clipSpaceToPixelsMatrix(){return this._helper.clipSpaceToPixelsMatrix}get pixelsToGLUnits(){return this._helper.pixelsToGLUnits}get centerOffset(){return this._helper.centerOffset}get size(){return this._helper.size}get rotationMatrix(){return this._helper.rotationMatrix}get centerPoint(){return this._helper.centerPoint}get pixelsPerMeter(){return this._helper.pixelsPerMeter}setMinZoom(d){this._helper.setMinZoom(d)}setMaxZoom(d){this._helper.setMaxZoom(d)}setMinPitch(d){this._helper.setMinPitch(d)}setMaxPitch(d){this._helper.setMaxPitch(d)}setRenderWorldCopies(d){this._helper.setRenderWorldCopies(d)}setBearing(d){this._helper.setBearing(d)}setPitch(d){this._helper.setPitch(d)}setRoll(d){this._helper.setRoll(d)}setFov(d){this._helper.setFov(d)}setZoom(d){this._helper.setZoom(d)}setCenter(d){this._helper.setCenter(d)}setElevation(d){this._helper.setElevation(d)}setMinElevationForCurrentTile(d){this._helper.setMinElevationForCurrentTile(d)}setPadding(d){this._helper.setPadding(d)}interpolatePadding(d,P,N){this._helper.interpolatePadding(d,P,N)}isPaddingEqual(d){return this._helper.isPaddingEqual(d)}resize(d,P){this._helper.resize(d,P)}getMaxBounds(){return this._helper.getMaxBounds()}setMaxBounds(d){this._helper.setMaxBounds(d)}setConstrainOverride(d){this._helper.setConstrainOverride(d)}overrideNearFarZ(d,P){this._helper.overrideNearFarZ(d,P)}clearNearFarZOverride(){this._helper.clearNearFarZOverride()}getCameraQueryGeometry(d){return this._helper.getCameraQueryGeometry(this.getCameraPoint(),d)}get tileSize(){return this._helper.tileSize}get tileZoom(){return this._helper.tileZoom}get scale(){return this._helper.scale}get worldSize(){return this._helper.worldSize}get width(){return this._helper.width}get height(){return this._helper.height}get lngRange(){return this._helper.lngRange}get latRange(){return this._helper.latRange}get minZoom(){return this._helper.minZoom}get maxZoom(){return this._helper.maxZoom}get zoom(){return this._helper.zoom}get center(){return this._helper.center}get minPitch(){return this._helper.minPitch}get maxPitch(){return this._helper.maxPitch}get pitch(){return this._helper.pitch}get pitchInRadians(){return this._helper.pitchInRadians}get roll(){return this._helper.roll}get rollInRadians(){return this._helper.rollInRadians}get bearing(){return this._helper.bearing}get bearingInRadians(){return this._helper.bearingInRadians}get fov(){return this._helper.fov}get fovInRadians(){return this._helper.fovInRadians}get elevation(){return this._helper.elevation}get minElevationForCurrentTile(){return this._helper.minElevationForCurrentTile}get padding(){return this._helper.padding}get unmodified(){return this._helper.unmodified}get renderWorldCopies(){return this._helper.renderWorldCopies}get constrainOverride(){return this._helper.constrainOverride}get nearZ(){return this._helper.nearZ}get farZ(){return this._helper.farZ}get autoCalculateNearFarZ(){return this._helper.autoCalculateNearFarZ}setTransitionState(d){}constructor(d){this._cachedClippingPlane=i.bE(),this._projectionMatrix=i.bi(),this._globeViewProjMatrix32f=i.bh(),this._globeViewProjMatrixNoCorrection=i.bi(),this._globeViewProjMatrixNoCorrectionInverted=i.bi(),this._globeProjMatrixInverted=i.bi(),this._cameraPosition=i.bz(),this._globeLatitudeErrorCorrectionRadians=0,this.defaultConstrain=(P,N)=>{let H=i.al(P.lat,-i.am,i.am),$=i.al(+N,this.minZoom+gs(0,H),this.maxZoom);return{center:new i.W(P.lng,H),zoom:$}},this.applyConstrain=(P,N)=>this._helper.applyConstrain(P,N),this._helper=new As({calcMatrices:()=>this._calcMatrices(),defaultConstrain:(P,N)=>this.defaultConstrain(P,N)},d),this._coveringTilesDetailsProvider=new hc}clone(){let d=new Yo;return d.apply(this,!1),d}apply(d,P,N){this._globeLatitudeErrorCorrectionRadians=N||0,this._helper.apply(d,P)}get projectionMatrix(){return this._projectionMatrix}get modelViewProjectionMatrix(){return this._globeViewProjMatrixNoCorrection}get inverseProjectionMatrix(){return this._globeProjMatrixInverted}get cameraPosition(){let d=i.bz();return d[0]=this._cameraPosition[0],d[1]=this._cameraPosition[1],d[2]=this._cameraPosition[2],d}get cameraToCenterDistance(){return this._helper.cameraToCenterDistance}getProjectionData(d){let{overscaledTileID:P,applyGlobeMatrix:N}=d,H=this._helper.getMercatorTileCoordinates(P);return{mainMatrix:this._globeViewProjMatrix32f,tileMercatorCoords:H,clippingPlane:this._cachedClippingPlane,projectionTransition:N?1:0,fallbackMatrix:this._globeViewProjMatrix32f}}_computeClippingPlane(d){let P=this.pitchInRadians,N=this.cameraToCenterDistance/d,H=Math.sin(P)*N,$=Math.cos(P)*N+1,se=1/Math.sqrt(H*H+$*$)*1,de=-H,Ae=$,Fe=Math.sqrt(de*de+Ae*Ae);de/=Fe,Ae/=Fe;let Ie=[0,de,Ae];i.bF(Ie,Ie,[0,0,0],-this.bearingInRadians),i.bG(Ie,Ie,[0,0,0],-1*this.center.lat*Math.PI/180),i.bH(Ie,Ie,[0,0,0],this.center.lng*Math.PI/180);let Ye=1/i.b5(Ie);return i.aZ(Ie,Ie,Ye),[...Ie,-se*Ye]}isLocationOccluded(d){return!this.isSurfacePointVisible(ns(d))}transformLightDirection(d){let P=this._helper._center.lng*Math.PI/180,N=this._helper._center.lat*Math.PI/180,H=Math.cos(N),$=[Math.sin(P)*H,Math.sin(N),Math.cos(P)*H],se=[$[2],0,-$[0]],de=[0,0,0];i.b2(de,se,$),i.b1(se,se),i.b1(de,de);let Ae=[0,0,0];return i.b1(Ae,[se[0]*d[0]+de[0]*d[1]+$[0]*d[2],se[1]*d[0]+de[1]*d[1]+$[1]*d[2],se[2]*d[0]+de[2]*d[1]+$[2]*d[2]]),Ae}getPixelScale(){return 1/Math.cos(this._helper._center.lat*Math.PI/180)}getCircleRadiusCorrection(){return Math.cos(this._helper._center.lat*Math.PI/180)}getPitchedTextCorrection(d,P,N){let H=(function(de,Ae,Fe){let Ie=1/(1<<Fe.z);return new i.a7(de/i.a6*Ie+Fe.x*Ie,Ae/i.a6*Ie+Fe.y*Ie)})(d,P,N.canonical),$=(se=H.y,[i.by(H.x*Math.PI*2+Math.PI,2*Math.PI),2*Math.atan(Math.exp(Math.PI-se*Math.PI*2))-.5*Math.PI]);var se;return this.getCircleRadiusCorrection()/Math.cos($[1])}projectTileCoordinates(d,P,N,H){let $=N.canonical,se=Uo(d,P,$.x,$.y,$.z),de=1+(H?H(d,P):0)/i.bD,Ae=[se[0]*de,se[1]*de,se[2]*de,1];i.aE(Ae,Ae,this._globeViewProjMatrixNoCorrection);let Fe=this._cachedClippingPlane,Ie=Fe[0]*se[0]+Fe[1]*se[1]+Fe[2]*se[2]+Fe[3]<0;return{point:new i.P(Ae[0]/Ae[3],Ae[1]/Ae[3]),signedDistanceFromCamera:Ae[3],isOccluded:Ie}}_calcMatrices(){if(!this._helper._width||!this._helper._height)return;let d=Ks(this.worldSize,this.center.lat),P=i.bj(),N=i.bj();this._helper.autoCalculateNearFarZ&&(this._helper._nearZ=.5,this._helper._farZ=this.cameraToCenterDistance+2*d),i.bc(P,this.fovInRadians,this.width/this.height,this._helper._nearZ,this._helper._farZ);let H=this.centerOffset;P[8]=2*-H.x/this._helper._width,P[9]=2*H.y/this._helper._height,this._projectionMatrix=i.bd(P),this._globeProjMatrixInverted=i.bj(),i.bg(this._globeProjMatrixInverted,P),i.Q(P,P,[0,0,-this.cameraToCenterDistance]),i.be(P,P,this.rollInRadians),i.bf(P,P,-this.pitchInRadians),i.be(P,P,this.bearingInRadians),i.Q(P,P,[0,0,-d]);let $=i.bz();$[0]=d,$[1]=d,$[2]=d,i.bf(N,P,this.center.lat*Math.PI/180),i.bI(N,N,-this.center.lng*Math.PI/180),i.S(N,N,$),this._globeViewProjMatrixNoCorrection=N,i.bf(P,P,this.center.lat*Math.PI/180-this._globeLatitudeErrorCorrectionRadians),i.bI(P,P,-this.center.lng*Math.PI/180),i.S(P,P,$),this._globeViewProjMatrix32f=new Float32Array(P),this._globeViewProjMatrixNoCorrectionInverted=i.bj(),i.bg(this._globeViewProjMatrixNoCorrectionInverted,N);let se=i.bz();this._cameraPosition=i.bz(),this._cameraPosition[2]=this.cameraToCenterDistance/d,i.bF(this._cameraPosition,this._cameraPosition,se,-this.rollInRadians),i.bG(this._cameraPosition,this._cameraPosition,se,this.pitchInRadians),i.bF(this._cameraPosition,this._cameraPosition,se,-this.bearingInRadians),i.a_(this._cameraPosition,this._cameraPosition,[0,0,1]),i.bG(this._cameraPosition,this._cameraPosition,se,-this.center.lat*Math.PI/180),i.bH(this._cameraPosition,this._cameraPosition,se,this.center.lng*Math.PI/180),this._cachedClippingPlane=this._computeClippingPlane(d);let de=i.bd(this._globeViewProjMatrixNoCorrectionInverted);i.S(de,de,[1,1,-1]),this._cachedFrustum=rs.fromInvProjectionMatrix(de,1,0,this._cachedClippingPlane,!0)}calculateFogMatrix(d){i.w(\"calculateFogMatrix is not supported on globe projection.\");let P=i.bj();return i.ap(P),P}getVisibleUnwrappedCoordinates(d){return[new i.ba(0,d)]}getCameraFrustum(){return this._cachedFrustum}getClippingPlane(){return this._cachedClippingPlane}getCoveringTilesDetailsProvider(){return this._coveringTilesDetailsProvider}recalculateZoomAndCenter(d){d&&i.w(\"terrain is not fully supported on vertical perspective projection.\"),this._helper.recalculateZoomAndCenter(0)}maxPitchScaleFactor(){return 1}getCameraPoint(){return this._helper.getCameraPoint()}getCameraAltitude(){return this._helper.getCameraAltitude()}getCameraLngLat(){return this._helper.getCameraLngLat()}lngLatToCameraDepth(d,P){if(!this._globeViewProjMatrixNoCorrection)return 1;let N=ns(d);i.aZ(N,N,1+P/i.bD);let H=i.bE();return i.aE(H,[N[0],N[1],N[2],1],this._globeViewProjMatrixNoCorrection),H[2]/H[3]}populateCache(d){}getBounds(){let d=.5*this.width,P=.5*this.height,N=[new i.P(0,0),new i.P(d,0),new i.P(this.width,0),new i.P(this.width,P),new i.P(this.width,this.height),new i.P(d,this.height),new i.P(0,this.height),new i.P(0,P)],H=[];for(let Ye of N)H.push(this.unprojectScreenPoint(Ye));let $=0,se=0,de=0,Ae=0,Fe=this.center;for(let Ye of H){let st=i.bJ(Fe.lng,Ye.lng),dt=i.bJ(Fe.lat,Ye.lat);st<se&&(se=st),st>$&&($=st),dt<Ae&&(Ae=dt),dt>de&&(de=dt)}let Ie=[Fe.lng+se,Fe.lat+Ae,Fe.lng+$,Fe.lat+de];return this.isSurfacePointOnScreen([0,1,0])&&(Ie[3]=90,Ie[0]=-180,Ie[2]=180),this.isSurfacePointOnScreen([0,-1,0])&&(Ie[1]=-90,Ie[0]=-180,Ie[2]=180),new ie(Ie)}calculateCenterFromCameraLngLatAlt(d,P,N,H){return this._helper.calculateCenterFromCameraLngLatAlt(d,P,N,H)}setLocationAtPoint(d,P){let N=ns(this.unprojectScreenPoint(P)),H=ns(d),$=i.bz();i.bK($);let se=i.bz();i.bH(se,N,$,-this.center.lng*Math.PI/180),i.bG(se,se,$,this.center.lat*Math.PI/180);let de=H[0]*H[0]+H[2]*H[2],Ae=se[0]*se[0];if(de<Ae)return;let Fe=Math.sqrt(de-Ae),Ie=-Fe,Ye=i.bL(H[0],H[2],se[0],Fe),st=i.bL(H[0],H[2],se[0],Ie),dt=i.bz();i.bH(dt,H,$,-Ye);let Pe=i.bL(dt[1],dt[2],se[1],se[2]),Xe=i.bz();i.bH(Xe,H,$,-st);let at=i.bL(Xe[1],Xe[2],se[1],se[2]),xt=.5*Math.PI,Et=Pe>=-xt&&Pe<=xt,Bt=at>=-xt&&at<=xt,St,Pt;if(Et&&Bt){let Xr=this.center.lng*Math.PI/180,Or=this.center.lat*Math.PI/180;i.bM(Ye,Xr)+i.bM(Pe,Or)<i.bM(st,Xr)+i.bM(at,Or)?(St=Ye,Pt=Pe):(St=st,Pt=at)}else if(Et)St=Ye,Pt=Pe;else{if(!Bt)return;St=st,Pt=at}let $t=St/Math.PI*180,Ht=Pt/Math.PI*180,dr=this.center.lat;this.setCenter(new i.W($t,i.al(Ht,-90,90))),this.setZoom(this.zoom+gs(dr,this.center.lat))}locationToScreenPoint(d,P){let N=ns(d);if(P){let H=P.getElevationForLngLatZoom(d,this._helper._tileZoom);i.aZ(N,N,1+H/i.bD)}return this._projectSurfacePointToScreen(N)}_projectSurfacePointToScreen(d){let P=i.bE();return i.aE(P,[...d,1],this._globeViewProjMatrixNoCorrection),P[0]/=P[3],P[1]/=P[3],new i.P((.5*P[0]+.5)*this.width,(.5*-P[1]+.5)*this.height)}screenPointToMercatorCoordinate(d,P){if(P){let N=P.pointCoordinate(d);if(N)return N}return i.a7.fromLngLat(this.unprojectScreenPoint(d))}screenPointToLocation(d,P){var N;return(N=this.screenPointToMercatorCoordinate(d,P))===null||N===void 0?void 0:N.toLngLat()}isPointOnMapSurface(d,P){let N=this._cameraPosition,H=this.getRayDirectionFromPixel(d);return!!this.rayPlanetIntersection(N,H)}getRayDirectionFromPixel(d){let P=i.bE();P[0]=d.x/this.width*2-1,P[1]=-1*(d.y/this.height*2-1),P[2]=1,P[3]=1,i.aE(P,P,this._globeViewProjMatrixNoCorrectionInverted),P[0]/=P[3],P[1]/=P[3],P[2]/=P[3];let N=i.bz();N[0]=P[0]-this._cameraPosition[0],N[1]=P[1]-this._cameraPosition[1],N[2]=P[2]-this._cameraPosition[2];let H=i.bz();return i.b1(H,N),H}isSurfacePointVisible(d){let P=this._cachedClippingPlane;return P[0]*d[0]+P[1]*d[1]+P[2]*d[2]+P[3]>=0}isSurfacePointOnScreen(d){if(!this.isSurfacePointVisible(d))return!1;let P=i.bE();return i.aE(P,[...d,1],this._globeViewProjMatrixNoCorrection),P[0]/=P[3],P[1]/=P[3],P[2]/=P[3],P[0]>-1&&P[0]<1&&P[1]>-1&&P[1]<1&&P[2]>-1&&P[2]<1}rayPlanetIntersection(d,P){let N=i.b3(d,P),H=i.bz(),$=i.bz();i.aZ($,P,N),i.b0(H,d,$);let se=1-i.b3(H,H);if(se<0)return null;let de=i.b3(d,d)-1,Ae=-N+(N<0?1:-1)*Math.sqrt(se),Fe=de/Ae,Ie=Ae;return{tMin:Math.min(Fe,Ie),tMax:Math.max(Fe,Ie)}}unprojectScreenPoint(d){let P=this._cameraPosition,N=this.getRayDirectionFromPixel(d),H=this.rayPlanetIntersection(P,N);if(H){let Ie=i.bz();i.a_(Ie,P,[N[0]*H.tMin,N[1]*H.tMin,N[2]*H.tMin]);let Ye=i.bz();return i.b1(Ye,Ie),Ef(Ye)}let $=this._cachedClippingPlane,se=$[0]*N[0]+$[1]*N[1]+$[2]*N[2],de=-i.b9($,P)/se,Ae=i.bz();if(de>0)i.a_(Ae,P,[N[0]*de,N[1]*de,N[2]*de]);else{let Ie=i.bz();i.a_(Ie,P,[2*N[0],2*N[1],2*N[2]]);let Ye=i.b9(this._cachedClippingPlane,Ie);i.b0(Ae,Ie,[this._cachedClippingPlane[0]*Ye,this._cachedClippingPlane[1]*Ye,this._cachedClippingPlane[2]*Ye])}let Fe=(function(Ie){let Ye=i.bz();return Ye[0]=Ie[0]*-Ie[3],Ye[1]=Ie[1]*-Ie[3],Ye[2]=Ie[2]*-Ie[3],{center:Ye,radius:Math.sqrt(1-Ie[3]*Ie[3])}})($);return Ef((function(Ie,Ye,st){let dt=i.bz();i.b0(dt,st,Ie);let Pe=i.bz();return i.bx(Pe,Ie,dt,Ye/i.b7(dt)),Pe})(Fe.center,Fe.radius,Ae))}getMatrixForModel(d,P){let N=i.W.convert(d),H=1/i.bD,$=i.bi();return i.bI($,$,N.lng/180*Math.PI),i.bf($,$,-N.lat/180*Math.PI),i.Q($,$,[0,0,1+P/i.bD]),i.bf($,$,.5*Math.PI),i.S($,$,[H,H,H]),$}getProjectionDataForCustomLayer(d=!0){let P=this.getProjectionData({overscaledTileID:new i.a3(0,0,0,0,0),applyGlobeMatrix:d});return P.tileMercatorCoords=[0,0,1,1],P}getFastPathSimpleProjectionMatrix(d){}}class Ro{get pixelsToClipSpaceMatrix(){return this._helper.pixelsToClipSpaceMatrix}get clipSpaceToPixelsMatrix(){return this._helper.clipSpaceToPixelsMatrix}get pixelsToGLUnits(){return this._helper.pixelsToGLUnits}get centerOffset(){return this._helper.centerOffset}get size(){return this._helper.size}get rotationMatrix(){return this._helper.rotationMatrix}get centerPoint(){return this._helper.centerPoint}get pixelsPerMeter(){return this._helper.pixelsPerMeter}setMinZoom(d){this._helper.setMinZoom(d)}setMaxZoom(d){this._helper.setMaxZoom(d)}setMinPitch(d){this._helper.setMinPitch(d)}setMaxPitch(d){this._helper.setMaxPitch(d)}setRenderWorldCopies(d){this._helper.setRenderWorldCopies(d)}setBearing(d){this._helper.setBearing(d)}setPitch(d){this._helper.setPitch(d)}setRoll(d){this._helper.setRoll(d)}setFov(d){this._helper.setFov(d)}setZoom(d){this._helper.setZoom(d)}setCenter(d){this._helper.setCenter(d)}setElevation(d){this._helper.setElevation(d)}setMinElevationForCurrentTile(d){this._helper.setMinElevationForCurrentTile(d)}setPadding(d){this._helper.setPadding(d)}interpolatePadding(d,P,N){this._helper.interpolatePadding(d,P,N)}isPaddingEqual(d){return this._helper.isPaddingEqual(d)}resize(d,P,N=!0){this._helper.resize(d,P,N)}getMaxBounds(){return this._helper.getMaxBounds()}setMaxBounds(d){this._helper.setMaxBounds(d)}setConstrainOverride(d){this._helper.setConstrainOverride(d)}overrideNearFarZ(d,P){this._helper.overrideNearFarZ(d,P)}clearNearFarZOverride(){this._helper.clearNearFarZOverride()}getCameraQueryGeometry(d){return this._helper.getCameraQueryGeometry(this.getCameraPoint(),d)}get tileSize(){return this._helper.tileSize}get tileZoom(){return this._helper.tileZoom}get scale(){return this._helper.scale}get worldSize(){return this._helper.worldSize}get width(){return this._helper.width}get height(){return this._helper.height}get lngRange(){return this._helper.lngRange}get latRange(){return this._helper.latRange}get minZoom(){return this._helper.minZoom}get maxZoom(){return this._helper.maxZoom}get zoom(){return this._helper.zoom}get center(){return this._helper.center}get minPitch(){return this._helper.minPitch}get maxPitch(){return this._helper.maxPitch}get pitch(){return this._helper.pitch}get pitchInRadians(){return this._helper.pitchInRadians}get roll(){return this._helper.roll}get rollInRadians(){return this._helper.rollInRadians}get bearing(){return this._helper.bearing}get bearingInRadians(){return this._helper.bearingInRadians}get fov(){return this._helper.fov}get fovInRadians(){return this._helper.fovInRadians}get elevation(){return this._helper.elevation}get minElevationForCurrentTile(){return this._helper.minElevationForCurrentTile}get padding(){return this._helper.padding}get unmodified(){return this._helper.unmodified}get renderWorldCopies(){return this._helper.renderWorldCopies}get cameraToCenterDistance(){return this._helper.cameraToCenterDistance}get constrainOverride(){return this._helper.constrainOverride}get nearZ(){return this._helper.nearZ}get farZ(){return this._helper.farZ}get autoCalculateNearFarZ(){return this._helper.autoCalculateNearFarZ}get isGlobeRendering(){return this._globeness>0}setTransitionState(d,P){this._globeness=d,this._globeLatitudeErrorCorrectionRadians=P,this._calcMatrices(),this._verticalPerspectiveTransform.getCoveringTilesDetailsProvider().prepareNextFrame(),this._mercatorTransform.getCoveringTilesDetailsProvider().prepareNextFrame()}get currentTransform(){return this.isGlobeRendering?this._verticalPerspectiveTransform:this._mercatorTransform}constructor(d){this._globeLatitudeErrorCorrectionRadians=0,this._globeness=1,this.defaultConstrain=(P,N)=>this.currentTransform.defaultConstrain(P,N),this.applyConstrain=(P,N)=>this._helper.applyConstrain(P,N),this._helper=new As({calcMatrices:()=>this._calcMatrices(),defaultConstrain:(P,N)=>this.defaultConstrain(P,N)},d),this._globeness=1,this._mercatorTransform=new Ls,this._verticalPerspectiveTransform=new Yo}clone(){let d=new Ro;return d._globeness=this._globeness,d._globeLatitudeErrorCorrectionRadians=this._globeLatitudeErrorCorrectionRadians,d.apply(this,!1),d}apply(d,P){this._helper.apply(d,P),this._mercatorTransform.apply(this,!1),this._verticalPerspectiveTransform.apply(this,!1,this._globeLatitudeErrorCorrectionRadians)}get projectionMatrix(){return this.currentTransform.projectionMatrix}get modelViewProjectionMatrix(){return this.currentTransform.modelViewProjectionMatrix}get inverseProjectionMatrix(){return this.currentTransform.inverseProjectionMatrix}get cameraPosition(){return this.currentTransform.cameraPosition}getProjectionData(d){let P=this._mercatorTransform.getProjectionData(d),N=this._verticalPerspectiveTransform.getProjectionData(d);return{mainMatrix:this.isGlobeRendering?N.mainMatrix:P.mainMatrix,clippingPlane:N.clippingPlane,tileMercatorCoords:N.tileMercatorCoords,projectionTransition:d.applyGlobeMatrix?this._globeness:0,fallbackMatrix:P.fallbackMatrix}}isLocationOccluded(d){return this.currentTransform.isLocationOccluded(d)}transformLightDirection(d){return this.currentTransform.transformLightDirection(d)}getPixelScale(){return i.bt(this._mercatorTransform.getPixelScale(),this._verticalPerspectiveTransform.getPixelScale(),this._globeness)}getCircleRadiusCorrection(){return i.bt(this._mercatorTransform.getCircleRadiusCorrection(),this._verticalPerspectiveTransform.getCircleRadiusCorrection(),this._globeness)}getPitchedTextCorrection(d,P,N){let H=this._mercatorTransform.getPitchedTextCorrection(d,P,N),$=this._verticalPerspectiveTransform.getPitchedTextCorrection(d,P,N);return i.bt(H,$,this._globeness)}projectTileCoordinates(d,P,N,H){return this.currentTransform.projectTileCoordinates(d,P,N,H)}_calcMatrices(){this._helper._width&&this._helper._height&&(this._verticalPerspectiveTransform.apply(this,!1,this._globeLatitudeErrorCorrectionRadians),this._helper._nearZ=this._verticalPerspectiveTransform.nearZ,this._helper._farZ=this._verticalPerspectiveTransform.farZ,this._mercatorTransform.apply(this,!0,this.isGlobeRendering),this._helper._nearZ=this._mercatorTransform.nearZ,this._helper._farZ=this._mercatorTransform.farZ)}calculateFogMatrix(d){return this.currentTransform.calculateFogMatrix(d)}getVisibleUnwrappedCoordinates(d){return this.currentTransform.getVisibleUnwrappedCoordinates(d)}getCameraFrustum(){return this.currentTransform.getCameraFrustum()}getClippingPlane(){return this.currentTransform.getClippingPlane()}getCoveringTilesDetailsProvider(){return this.currentTransform.getCoveringTilesDetailsProvider()}recalculateZoomAndCenter(d){this._mercatorTransform.recalculateZoomAndCenter(d),this._verticalPerspectiveTransform.recalculateZoomAndCenter(d)}maxPitchScaleFactor(){return this._mercatorTransform.maxPitchScaleFactor()}getCameraPoint(){return this._helper.getCameraPoint()}getCameraAltitude(){return this._helper.getCameraAltitude()}getCameraLngLat(){return this._helper.getCameraLngLat()}lngLatToCameraDepth(d,P){return this.currentTransform.lngLatToCameraDepth(d,P)}populateCache(d){this._mercatorTransform.populateCache(d),this._verticalPerspectiveTransform.populateCache(d)}getBounds(){return this.currentTransform.getBounds()}calculateCenterFromCameraLngLatAlt(d,P,N,H){return this._helper.calculateCenterFromCameraLngLatAlt(d,P,N,H)}setLocationAtPoint(d,P){if(!this.isGlobeRendering)return this._mercatorTransform.setLocationAtPoint(d,P),void this.apply(this._mercatorTransform,!1);this._verticalPerspectiveTransform.setLocationAtPoint(d,P),this.apply(this._verticalPerspectiveTransform,!1)}locationToScreenPoint(d,P){return this.currentTransform.locationToScreenPoint(d,P)}screenPointToMercatorCoordinate(d,P){return this.currentTransform.screenPointToMercatorCoordinate(d,P)}screenPointToLocation(d,P){return this.currentTransform.screenPointToLocation(d,P)}isPointOnMapSurface(d,P){return this.currentTransform.isPointOnMapSurface(d,P)}getRayDirectionFromPixel(d){return this._verticalPerspectiveTransform.getRayDirectionFromPixel(d)}getMatrixForModel(d,P){return this.currentTransform.getMatrixForModel(d,P)}getProjectionDataForCustomLayer(d=!0){let P=this._mercatorTransform.getProjectionDataForCustomLayer(d);if(!this.isGlobeRendering)return P;let N=this._verticalPerspectiveTransform.getProjectionDataForCustomLayer(d);return N.fallbackMatrix=P.mainMatrix,N}getFastPathSimpleProjectionMatrix(d){return this.currentTransform.getFastPathSimpleProjectionMatrix(d)}}class is{get useGlobeControls(){return!0}handlePanInertia(d,P){let N=bu(d,P);return Math.abs(N.lng-P.center.lng)>180&&(N.lng=P.center.lng+179.5*Math.sign(N.lng-P.center.lng)),{easingCenter:N,easingOffset:new i.P(0,0)}}handleMapControlsRollPitchBearingZoom(d,P){let N=d.around,H=P.screenPointToLocation(N);d.bearingDelta&&P.setBearing(P.bearing+d.bearingDelta),d.pitchDelta&&P.setPitch(P.pitch+d.pitchDelta),d.rollDelta&&P.setRoll(P.roll+d.rollDelta);let $=P.zoom;d.zoomDelta&&P.setZoom(P.zoom+d.zoomDelta);let se=P.zoom-$;if(se===0)return;let de=i.bJ(P.center.lng,H.lng),Ae=de/(Math.abs(de/180)+1),Fe=i.bJ(P.center.lat,H.lat),Ie=P.getRayDirectionFromPixel(N),Ye=P.cameraPosition,st=-1*i.b3(Ye,Ie),dt=i.bz();i.a_(dt,Ye,[Ie[0]*st,Ie[1]*st,Ie[2]*st]);let Pe=i.b5(dt)-1,Xe=Math.exp(.5*-Math.max(Pe-.3,0)),at=Ks(P.worldSize,P.center.lat)/Math.min(P.width,P.height),xt=i.bw(at,.9,.5,1,.25),Et=(1-i.ao(-se))*Math.min(Xe,xt),Bt=P.center.lat,St=P.zoom,Pt=new i.W(P.center.lng+Ae*Et,i.al(P.center.lat+Fe*Et,-i.am,i.am));P.setLocationAtPoint(H,N);let $t=P.center,Ht=i.bw(Math.abs(de),45,85,0,1),dr=i.bw(at,.75,.35,0,1),Xr=Math.pow(Math.max(Ht,dr),.25),Or=i.bJ($t.lng,Pt.lng),$r=i.bJ($t.lat,Pt.lat);P.setCenter(new i.W($t.lng+Or*Xr,$t.lat+$r*Xr).wrap()),P.setZoom(St+gs(Bt,P.center.lat))}handleMapControlsPan(d,P,N){if(!d.panDelta)return;let H=P.center.lat,$=P.zoom;P.setCenter(bu(d.panDelta,P).wrap()),P.setZoom($+gs(H,P.center.lat))}cameraForBoxAndBearing(d,P,N,H,$){let se=_u(d,P,N,H,$),de=P.left/$.width*2-1,Ae=($.width-P.right)/$.width*2-1,Fe=P.top/$.height*-2+1,Ie=($.height-P.bottom)/$.height*-2+1,Ye=i.bJ(N.getWest(),N.getEast())<0,st=Ye?N.getEast():N.getWest(),dt=Ye?N.getWest():N.getEast(),Pe=Math.max(N.getNorth(),N.getSouth()),Xe=Math.min(N.getNorth(),N.getSouth()),at=st+.5*i.bJ(st,dt),xt=Pe+.5*i.bJ(Pe,Xe),Et=$.clone();Et.setCenter(se.center),Et.setBearing(se.bearing),Et.setPitch(0),Et.setRoll(0),Et.setZoom(se.zoom);let Bt=Et.modelViewProjectionMatrix,St=[ns(N.getNorthWest()),ns(N.getNorthEast()),ns(N.getSouthWest()),ns(N.getSouthEast()),ns(new i.W(dt,xt)),ns(new i.W(st,xt)),ns(new i.W(at,Pe)),ns(new i.W(at,Xe))],Pt=ns(se.center),$t=Number.POSITIVE_INFINITY;for(let Ht of St)de<0&&($t=is.getLesserNonNegativeNonNull($t,is.solveVectorScale(Ht,Pt,Bt,\"x\",de))),Ae>0&&($t=is.getLesserNonNegativeNonNull($t,is.solveVectorScale(Ht,Pt,Bt,\"x\",Ae))),Fe>0&&($t=is.getLesserNonNegativeNonNull($t,is.solveVectorScale(Ht,Pt,Bt,\"y\",Fe))),Ie<0&&($t=is.getLesserNonNegativeNonNull($t,is.solveVectorScale(Ht,Pt,Bt,\"y\",Ie)));if(Number.isFinite($t)&&$t!==0)return se.zoom=Math.min(Et.zoom+i.ar($t),d.maxZoom),se;$s()}handleJumpToCenterZoom(d,P){let N=d.center.lat,H=d.applyConstrain(P.center?i.W.convert(P.center):d.center,d.zoom).center;d.setCenter(H.wrap());let $=P.zoom!==void 0?+P.zoom:d.zoom+gs(N,H.lat);d.zoom!==$&&d.setZoom($)}handleEaseTo(d,P){let N=d.zoom,H=d.center,$=d.padding,se={roll:d.roll,pitch:d.pitch,bearing:d.bearing},de={roll:P.roll===void 0?d.roll:P.roll,pitch:P.pitch===void 0?d.pitch:P.pitch,bearing:P.bearing===void 0?d.bearing:P.bearing},Ae=P.zoom!==void 0,Fe=!d.isPaddingEqual(P.padding),Ie=!1,Ye=P.center?i.W.convert(P.center):H,st=d.applyConstrain(Ye,N).center;pa(d,st);let dt=d.clone();dt.setCenter(st),dt.setZoom(Ae?+P.zoom:N+gs(H.lat,Ye.lat)),dt.setBearing(P.bearing);let Pe=new i.P(i.al(d.centerPoint.x+P.offsetAsPoint.x,0,d.width),i.al(d.centerPoint.y+P.offsetAsPoint.y,0,d.height));dt.setLocationAtPoint(st,Pe);let Xe=(P.offset&&P.offsetAsPoint.mag())>0?dt.center:st,at=Ae?+P.zoom:N+gs(H.lat,Xe.lat),xt=N+gs(H.lat,0),Et=at+gs(Xe.lat,0),Bt=i.bJ(H.lng,Xe.lng),St=i.bJ(H.lat,Xe.lat),Pt=i.ao(Et-xt);return Ie=at!==N,{easeFunc:$t=>{if(i.bn(se,de)||Kc({startEulerAngles:se,endEulerAngles:de,tr:d,k:$t,useSlerp:se.roll!=de.roll}),Fe&&d.interpolatePadding($,P.padding,$t),P.around)i.w(\"Easing around a point is not supported under globe projection.\"),d.setLocationAtPoint(P.around,P.aroundPoint);else{let Ht=Et>xt?Math.min(2,Pt):Math.max(.5,Pt),dr=Math.pow(Ht,1-$t),Xr=ah(H,Bt,St,$t*dr);d.setCenter(Xr.wrap())}if(Ie){let Ht=i.H.number(xt,Et,$t)+gs(0,d.center.lat);d.setZoom(Ht)}},isZooming:Ie,elevationCenter:Xe}}handleFlyTo(d,P){let N=P.zoom!==void 0,H=d.center,$=d.zoom,se=d.padding,de=!d.isPaddingEqual(P.padding),Ae=d.applyConstrain(i.W.convert(P.center||P.locationAtOffset),$).center,Fe=N?+P.zoom:d.zoom+gs(d.center.lat,Ae.lat),Ie=d.clone();Ie.setCenter(Ae),Ie.setZoom(Fe),Ie.setBearing(P.bearing);let Ye=new i.P(i.al(d.centerPoint.x+P.offsetAsPoint.x,0,d.width),i.al(d.centerPoint.y+P.offsetAsPoint.y,0,d.height));Ie.setLocationAtPoint(Ae,Ye);let st=Ie.center;pa(d,st);let dt=(function(St,Pt,$t){let Ht=ns(Pt),dr=ns($t),Xr=i.b3(Ht,dr),Or=Math.acos(Xr),$r=kc(St);return Or/(2*Math.PI)*$r})(d,H,st),Pe=$+gs(H.lat,0),Xe=Fe+gs(st.lat,0),at=i.ao(Xe-Pe),xt;if(typeof P.minZoom==\"number\"){let St=+P.minZoom+gs(st.lat,0),Pt=Math.min(St,Pe,Xe)+gs(0,st.lat),$t=d.applyConstrain(st,Pt).zoom+gs(st.lat,0);xt=i.ao($t-Pe)}let Et=i.bJ(H.lng,st.lng),Bt=i.bJ(H.lat,st.lat);return{easeFunc:(St,Pt,$t,Ht)=>{let dr=ah(H,Et,Bt,$t);de&&d.interpolatePadding(se,P.padding,St);let Xr=St===1?st:dr;d.setCenter(Xr.wrap());let Or=Pe+i.ar(Pt);d.setZoom(St===1?Fe:Or+gs(0,Xr.lat))},scaleOfZoom:at,targetCenter:st,scaleOfMinZoom:xt,pixelPathLength:dt}}static solveVectorScale(d,P,N,H,$){let se=H===\"x\"?[N[0],N[4],N[8],N[12]]:[N[1],N[5],N[9],N[13]],de=[N[3],N[7],N[11],N[15]],Ae=d[0]*se[0]+d[1]*se[1]+d[2]*se[2],Fe=d[0]*de[0]+d[1]*de[1]+d[2]*de[2],Ie=P[0]*se[0]+P[1]*se[1]+P[2]*se[2],Ye=P[0]*de[0]+P[1]*de[1]+P[2]*de[2];return Ie+$*Fe===Ae+$*Ye||de[3]*(Ae-Ie)+se[3]*(Ye-Fe)+Ae*Ye==Ie*Fe?null:(Ie+se[3]-$*Ye-$*de[3])/(Ie-Ae-$*Ye+$*Fe)}static getLesserNonNegativeNonNull(d,P){return P!==null&&P>=0&&P<d?P:d}}class Dl{constructor(d){this._globe=d,this._mercatorCameraHelper=new Gu,this._verticalPerspectiveCameraHelper=new is}get useGlobeControls(){return this._globe.useGlobeRendering}get currentHelper(){return this.useGlobeControls?this._verticalPerspectiveCameraHelper:this._mercatorCameraHelper}handlePanInertia(d,P){return this.currentHelper.handlePanInertia(d,P)}handleMapControlsRollPitchBearingZoom(d,P){this.currentHelper.handleMapControlsRollPitchBearingZoom(d,P)}handleMapControlsPan(d,P,N){this.currentHelper.handleMapControlsPan(d,P,N)}cameraForBoxAndBearing(d,P,N,H,$){return this.currentHelper.cameraForBoxAndBearing(d,P,N,H,$)}handleJumpToCenterZoom(d,P){this.currentHelper.handleJumpToCenterZoom(d,P)}handleEaseTo(d,P){return this.currentHelper.handleEaseTo(d,P)}handleFlyTo(d,P){return this.currentHelper.handleFlyTo(d,P)}}let dc=(Y,d)=>i.C(Y,d==null?void 0:d.filter((P=>P.identifier!==\"source.canvas\"))),ef=i.bN();class Cc extends i.E{constructor(d,P={}){var N,H;super(),this._rtlPluginLoaded=()=>{for(let se in this.tileManagers){let de=this.tileManagers[se].getSource().type;de!==\"vector\"&&de!==\"geojson\"||this.tileManagers[se].reload()}},this.map=d,this.dispatcher=new xe(ye(),d._getMapId()),this.dispatcher.registerMessageHandler(\"GG\",((se,de)=>this.getGlyphs(se,de))),this.dispatcher.registerMessageHandler(\"GI\",((se,de)=>this.getImages(se,de))),this.dispatcher.registerMessageHandler(\"GDA\",((se,de)=>this.getDashes(se,de))),this.imageManager=new L,this.imageManager.setEventedParent(this);let $=((N=d._container)===null||N===void 0?void 0:N.lang)||typeof document!=\"undefined\"&&((H=document.documentElement)===null||H===void 0?void 0:H.lang)||void 0;this.glyphManager=new E(d._requestManager,P.localIdeographFontFamily,$),this.lineAtlas=new j(256,512),this.crossTileSymbolIndex=new $a,this._setInitialValues(),this._resetUpdates(),this.dispatcher.broadcast(\"SR\",i.bO()),tt().on(Ve,this._rtlPluginLoaded),this.on(\"data\",(se=>{if(se.dataType!==\"source\"||se.sourceDataType!==\"metadata\")return;let de=this.tileManagers[se.sourceId];if(!de)return;let Ae=de.getSource();if(Ae!=null&&Ae.vectorLayerIds)for(let Fe in this._layers){let Ie=this._layers[Fe];Ie.source===Ae.id&&this._validateLayer(Ie)}}))}_setInitialValues(){var d;this._spritesImagesIds={},this._layers={},this._order=[],this.tileManagers={},this.zoomHistory=new i.bP,this._availableImages=[],this._globalState={},this._serializedLayers={},this.stylesheet=null,this.light=null,this.sky=null,this.projection&&(this.projection.destroy(),delete this.projection),this._loaded=!1,this._changed=!1,this._updatedLayers={},this._updatedSources={},this._changedImages={},this._glyphsDidChange=!1,this._updatedPaintProps={},this._layerOrderChanged=!1,this.crossTileSymbolIndex=new(((d=this.crossTileSymbolIndex)===null||d===void 0?void 0:d.constructor)||Object),this.pauseablePlacement=void 0,this.placement=void 0,this.z=0}setGlobalStateProperty(d,P){var N,H,$;this._checkLoaded();let se=P===null?($=(H=(N=this.stylesheet.state)===null||N===void 0?void 0:N[d])===null||H===void 0?void 0:H.default)!==null&&$!==void 0?$:null:P;if(i.bQ(se,this._globalState[d]))return this;this._globalState[d]=se,this._applyGlobalStateChanges([d])}getGlobalState(){return this._globalState}setGlobalState(d){this._checkLoaded();let P=[];for(let N in d)!i.bQ(this._globalState[N],d[N].default)&&(P.push(N),this._globalState[N]=d[N].default);this._applyGlobalStateChanges(P)}_applyGlobalStateChanges(d){if(d.length===0)return;let P=new Set,N={};for(let H of d){N[H]=this._globalState[H];for(let $ in this._layers){let se=this._layers[$],de=se.getLayoutAffectingGlobalStateRefs(),Ae=se.getPaintAffectingGlobalStateRefs(),Fe=se.getVisibilityAffectingGlobalStateRefs();if(de.has(H)&&P.add(se.source),Ae.has(H))for(let{name:Ie,value:Ye}of Ae.get(H))this._updatePaintProperty(se,Ie,Ye);Fe!=null&&Fe.has(H)&&(se.recalculateVisibility(),this._updateLayer(se))}}this.dispatcher.broadcast(\"UGS\",N);for(let H in this.tileManagers)P.has(H)&&(this._reloadSource(H),this._changed=!0)}loadURL(d){return i._(this,arguments,void 0,(function*(P,N={},H){this.fire(new i.n(\"dataloading\",{dataType:\"style\"})),N.validate=typeof N.validate!=\"boolean\"||N.validate,this._loadStyleRequest=new AbortController;let $=this._loadStyleRequest;try{let se=yield this.map._requestManager.transformRequest(P,\"Style\");i.bR($.signal);let de=yield i.k(se,$);this._loadStyleRequest===$&&(this._loadStyleRequest=null),this._load(de.data,N,H)}catch(se){this._loadStyleRequest===$&&(this._loadStyleRequest=null),se&&!$.signal.aborted&&this.fire(new i.l(i.d(se)))}}))}loadJSON(d,P={},N){this.fire(new i.n(\"dataloading\",{dataType:\"style\"})),this._frameRequest=new AbortController,f.frameAsync(this._frameRequest,this.map._ownerWindow).then((()=>{this._frameRequest=null,P.validate=P.validate!==!1,this._load(d,P,N)})).catch((()=>{}))}loadEmpty(){this.fire(new i.n(\"dataloading\",{dataType:\"style\"})),this._load(ef,{validate:!1})}_load(d,P,N){var H,$;let se=P.transformStyle?P.transformStyle(N,d):d;if(!P.validate||!dc(this,i.F(se))){se=Object.assign({},se),this._loaded=!0,this.stylesheet=se;for(let de in se.sources)this.addSource(de,se.sources[de],{validate:!1});se.sprite?this._loadSprite(se.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(se.glyphs),this._createLayers(),this.light=new B(this.stylesheet.light),this._setProjectionInternal(((H=this.stylesheet.projection)===null||H===void 0?void 0:H.type)||\"mercator\"),this.sky=new V(this.stylesheet.sky),this.map.setTerrain(($=this.stylesheet.terrain)!==null&&$!==void 0?$:null),this.fire(new i.n(\"data\",{dataType:\"style\"})),this.fire(new i.n(\"style.load\"))}}_createLayers(){var d,P,N;let H=i.bS(this.stylesheet.layers);this.setGlobalState((d=this.stylesheet.state)!==null&&d!==void 0?d:null),this.dispatcher.broadcast(\"SL\",H),this._order=H.map(($=>$.id)),this._layers={},this._serializedLayers=null;for(let $ of H){let se=i.bT($,this._globalState);if(se.setEventedParent(this,{layer:{id:$.id}}),this._layers[$.id]=se,i.bU(se)&&this.tileManagers[se.source]){let de=(N=(P=$.paint)===null||P===void 0?void 0:P[\"raster-fade-duration\"])!==null&&N!==void 0?N:se.paint.get(\"raster-fade-duration\");this.tileManagers[se.source].setRasterFadeDuration(de)}}}_loadSprite(d,P=!1,N=void 0){this.imageManager.setLoaded(!1);let H=new AbortController,$;this._spriteRequest=H,(function(se,de,Ae,Fe){return i._(this,void 0,void 0,(function*(){let Ie=C(se),Ye=Ae>1?\"@2x\":\"\",st={},dt={};for(let{id:Pe,url:Xe}of Ie){let at=yield de.transformRequest(k(Xe,Ye,\".json\"),\"SpriteJSON\");st[Pe]=i.k(at,Fe);let xt=yield de.transformRequest(k(Xe,Ye,\".png\"),\"SpriteImage\");dt[Pe]=x.getImage(xt,Fe)}return yield Promise.all([...Object.values(st),...Object.values(dt)]),(function(Pe,Xe){return i._(this,void 0,void 0,(function*(){let at={};for(let xt in Pe){at[xt]={};let Et=f.getImageCanvasContext((yield Xe[xt]).data),Bt=(yield Pe[xt]).data;for(let St in Bt){let{width:Pt,height:$t,x:Ht,y:dr,sdf:Xr,pixelRatio:Or,stretchX:$r,stretchY:Er,content:Tr,textFitWidth:Rr,textFitHeight:Lr}=Bt[St];at[xt][St]={data:null,pixelRatio:Or,sdf:Xr,stretchX:$r,stretchY:Er,content:Tr,textFitWidth:Rr,textFitHeight:Lr,spriteData:{width:Pt,height:$t,x:Ht,y:dr,context:Et}}}}return at}))})(st,dt)}))})(d,this.map._requestManager,this.map.getPixelRatio(),this._spriteRequest).then((se=>{if(this._spriteRequest=null,se)for(let de in se){this._spritesImagesIds[de]=[];let Ae=this._spritesImagesIds[de]?this._spritesImagesIds[de].filter((Fe=>!(Fe in se))):[];for(let Fe of Ae)this.imageManager.removeImage(Fe),this._changedImages[Fe]=!0;for(let Fe in se[de]){let Ie=de===\"default\"?Fe:`${de}:${Fe}`;this._spritesImagesIds[de].push(Ie),Ie in this.imageManager.images?this.imageManager.updateImage(Ie,se[de][Fe],!1):this.imageManager.addImage(Ie,se[de][Fe]),P&&(this._changedImages[Ie]=!0)}}})).catch((se=>{this._spriteRequest=null,$=se,H.signal.aborted||this.fire(new i.l($))})).finally((()=>{this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),P&&(this._changed=!0),this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new i.n(\"data\",{dataType:\"style\"})),N&&N($)}))}_unloadSprite(){for(let d of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(d),this._changedImages[d]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new i.n(\"data\",{dataType:\"style\"}))}_validateLayer(d){let P=this.tileManagers[d.source];if(!P)return;let N=d.sourceLayer;if(!N)return;let H=P.getSource();(H.type===\"geojson\"||H.vectorLayerIds&&!H.vectorLayerIds.includes(N))&&this.fire(new i.l(new Error(`Source layer \"${N}\" does not exist on source \"${H.id}\" as specified by style layer \"${d.id}\".`)))}loaded(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(let d in this.tileManagers)if(!this.tileManagers[d].loaded())return!1;return this.imageManager.isLoaded()}_serializeByIds(d,P=!1){let N=this._serializedAllLayers();if(!d||d.length===0)return Object.values(P?i.bV(N):N);let H=[];for(let $ of d)if(N[$]){let se=P?i.bV(N[$]):N[$];H.push(se)}return H}_serializedAllLayers(){let d=this._serializedLayers;if(d)return d;d=this._serializedLayers={};let P=Object.keys(this._layers);for(let N of P){let H=this._layers[N];H.type!==\"custom\"&&(d[N]=H.serialize())}return d}hasTransitions(){var d,P,N;if(!((d=this.light)===null||d===void 0)&&d.hasTransition()||!((P=this.sky)===null||P===void 0)&&P.hasTransition()||!((N=this.projection)===null||N===void 0)&&N.hasTransition())return!0;for(let H in this.tileManagers)if(this.tileManagers[H].hasTransition())return!0;for(let H in this._layers)if(this._layers[H].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error(\"Style is not done loading.\")}update(d){if(!this._loaded)return;let P=this._changed;if(P){let H=Object.keys(this._updatedLayers),$=Object.keys(this._removedLayers);(H.length||$.length)&&this._updateWorkerLayers(H,$);for(let se in this._updatedSources){let de=this._updatedSources[se];if(de===\"reload\")this._reloadSource(se);else{if(de!==\"clear\")throw new Error(`Invalid action ${de}`);this._clearSource(se)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(let se in this._updatedPaintProps)this._layers[se].updateTransitions(d);this.light.updateTransitions(d),this.sky.updateTransitions(d),this._resetUpdates()}let N={};for(let H in this.tileManagers){let $=this.tileManagers[H];N[H]=$.used,$.used=!1}for(let H of this._order){let $=this._layers[H];$.recalculate(d,this._availableImages),!$.isHidden(d.zoom)&&$.source&&(this.tileManagers[$.source].used=!0)}for(let H in N){let $=this.tileManagers[H];!!N[H]!=!!$.used&&$.fire(new i.n(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:H}))}this.light.recalculate(d),this.sky.recalculate(d),this.projection.recalculate(d),this.z=d.zoom,P&&this.fire(new i.n(\"data\",{dataType:\"style\"}))}_updateTilesForChangedImages(){let d=Object.keys(this._changedImages);if(d.length){for(let P in this.tileManagers)this.tileManagers[P].reloadTilesForDependencies([\"icons\",\"patterns\"],d);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(let d in this.tileManagers)this.tileManagers[d].reloadTilesForDependencies([\"glyphs\"],[\"\"]);this._glyphsDidChange=!1}}_updateWorkerLayers(d,P){this.dispatcher.broadcast(\"UL\",{layers:this._serializeByIds(d,!1),removedIds:P})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(d,P={}){var N;this._checkLoaded();let H=this.serialize();if(d=P.transformStyle?P.transformStyle(H,d):d,((N=P.validate)===null||N===void 0||N)&&dc(this,i.F(d)))return!1;(d=i.bV(d)).layers=i.bS(d.layers);let $=i.bW(H,d),se=this._getOperationsToPerform($);if(se.unimplemented.length>0)throw new Error(`Unimplemented: ${se.unimplemented.join(\", \")}.`);if(se.operations.length===0)return!1;for(let de of se.operations)de();return this.stylesheet=d,this._serializedLayers=null,this.fire(new i.n(\"style.load\",{style:this})),!0}_getOperationsToPerform(d){let P=[],N=[];for(let H of d)switch(H.command){case\"setCenter\":case\"setZoom\":case\"setBearing\":case\"setPitch\":case\"setRoll\":continue;case\"addLayer\":P.push((()=>this.addLayer.apply(this,H.args)));break;case\"removeLayer\":P.push((()=>this.removeLayer.apply(this,H.args)));break;case\"setPaintProperty\":P.push((()=>this.setPaintProperty.apply(this,H.args)));break;case\"setLayoutProperty\":P.push((()=>this.setLayoutProperty.apply(this,H.args)));break;case\"setFilter\":P.push((()=>this.setFilter.apply(this,H.args)));break;case\"addSource\":P.push((()=>this.addSource.apply(this,H.args)));break;case\"removeSource\":P.push((()=>this.removeSource.apply(this,H.args)));break;case\"setLayerZoomRange\":P.push((()=>this.setLayerZoomRange.apply(this,H.args)));break;case\"setLight\":P.push((()=>this.setLight.apply(this,H.args)));break;case\"setGeoJSONSourceData\":P.push((()=>this.setGeoJSONSourceData.apply(this,H.args)));break;case\"setGlyphs\":P.push((()=>this.setGlyphs.apply(this,H.args)));break;case\"setSprite\":P.push((()=>this.setSprite.apply(this,H.args)));break;case\"setTerrain\":P.push((()=>this.map.setTerrain.apply(this,H.args)));break;case\"setSky\":P.push((()=>this.setSky.apply(this,H.args)));break;case\"setProjection\":this.setProjection.apply(this,H.args);break;case\"setGlobalState\":P.push((()=>this.setGlobalState.apply(this,H.args)));break;case\"setTransition\":P.push((()=>{}));break;default:N.push(H.command)}return{operations:P,unimplemented:N}}addImage(d,P){if(this.getImage(d))return this.fire(new i.l(new Error(`An image named \"${d}\" already exists.`)));this.imageManager.addImage(d,P),this._afterImageUpdated(d)}updateImage(d,P){this.imageManager.updateImage(d,P)}getImage(d){return this.imageManager.getImage(d)}removeImage(d){if(!this.getImage(d))return this.fire(new i.l(new Error(`An image named \"${d}\" does not exist.`)));this.imageManager.removeImage(d),this._afterImageUpdated(d)}_afterImageUpdated(d){this._availableImages=this.imageManager.listImages(),this._changedImages[d]=!0,this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new i.n(\"data\",{dataType:\"style\"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(d,P,N={}){var H;if(this._checkLoaded(),this.tileManagers[d]!==void 0)throw new Error(`Source \"${d}\" already exists.`);if(!P.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(P).join(\", \")}.`);if([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].includes(P.type)&&this._validate(i.F.source,`sources.${d}`,P,null,N))return;!((H=this.map)===null||H===void 0)&&H._collectResourceTiming&&(P.collectResourceTiming=!0);let $=this.tileManagers[d]=new Zr(d,P,this.dispatcher);$.style=this,$.setEventedParent(this,(()=>({isSourceLoaded:$.loaded(),source:$.serialize(),sourceId:d}))),$.onAdd(this.map),this._changed=!0}removeSource(d){if(this._checkLoaded(),this.tileManagers[d]===void 0)throw new Error(`There is no source with this ID=${d}`);for(let N in this._layers)if(this._layers[N].source===d)return this.fire(new i.l(new Error(`Source \"${d}\" cannot be removed while layer \"${N}\" is using it.`)));let P=this.tileManagers[d];delete this.tileManagers[d],delete this._updatedSources[d],P.fire(new i.n(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:d})),P.setEventedParent(null),P.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(d,P){if(this._checkLoaded(),this.tileManagers[d]===void 0)throw new Error(`There is no source with this ID=${d}`);let N=this.tileManagers[d].getSource();if(N.type!==\"geojson\")throw new Error(`geojsonSource.type is ${N.type}, which is !== 'geojson`);N.setData(P),this._changed=!0}getSource(d){var P;return(P=this.tileManagers[d])===null||P===void 0?void 0:P.getSource()}addLayer(d,P,N={}){this._checkLoaded();let H=d.id;if(this.getLayer(H))return void this.fire(new i.l(new Error(`Layer \"${H}\" already exists on this map.`)));let $;if(d.type===\"custom\"){if(dc(this,i.bX(d)))return;$=i.bT(d,this._globalState)}else{if(\"source\"in d&&typeof d.source==\"object\"&&(this.addSource(H,d.source),d=i.bV(d),d=i.e(d,{source:H})),this._validate(i.F.layer,`layers.${H}`,d,{arrayIndex:-1},N))return;$=i.bT(d,this._globalState),this._validateLayer($),$.setEventedParent(this,{layer:{id:H}})}let se=P?this._order.indexOf(P):this._order.length;if(P&&se===-1)this.fire(new i.l(new Error(`Cannot add layer \"${H}\" before non-existing layer \"${P}\".`)));else{if(this._order.splice(se,0,H),this._layerOrderChanged=!0,this._layers[H]=$,this._removedLayers[H]&&$.source&&$.type!==\"custom\"){let de=this._removedLayers[H];delete this._removedLayers[H],de.type!==$.type?this._updatedSources[$.source]=\"clear\":(this._updatedSources[$.source]=\"reload\",this.tileManagers[$.source].pause())}this._updateLayer($),$.onAdd&&$.onAdd(this.map)}}moveLayer(d,P){if(this._checkLoaded(),this._changed=!0,!this._layers[d])return void this.fire(new i.l(new Error(`The layer '${d}' does not exist in the map's style and cannot be moved.`)));if(d===P)return;let N=this._order.indexOf(d);this._order.splice(N,1);let H=P?this._order.indexOf(P):this._order.length;P&&H===-1?this.fire(new i.l(new Error(`Cannot move layer \"${d}\" before non-existing layer \"${P}\".`))):(this._order.splice(H,0,d),this._layerOrderChanged=!0)}removeLayer(d){this._checkLoaded();let P=this._layers[d];if(!P)return void this.fire(new i.l(new Error(`Cannot remove non-existing layer \"${d}\".`)));P.setEventedParent(null);let N=this._order.indexOf(d);this._order.splice(N,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[d]=P,delete this._layers[d],this._serializedLayers&&delete this._serializedLayers[d],delete this._updatedLayers[d],delete this._updatedPaintProps[d],P.onRemove&&P.onRemove(this.map)}getLayer(d){return this._layers[d]}getLayersOrder(){return[...this._order]}hasLayer(d){return d in this._layers}setLayerZoomRange(d,P,N){this._checkLoaded();let H=this.getLayer(d);H?H.minzoom===P&&H.maxzoom===N||(P!=null&&(H.minzoom=P),N!=null&&(H.maxzoom=N),this._updateLayer(H)):this.fire(new i.l(new Error(`Cannot set the zoom range of non-existing layer \"${d}\".`)))}setFilter(d,P,N={}){this._checkLoaded();let H=this.getLayer(d);if(H){if(!i.bQ(H.filter,P))return P==null?(H.setFilter(void 0),void this._updateLayer(H)):void(this._validate(i.F.filter,`layers.${H.id}.filter`,P,null,N)||(H.setFilter(i.bV(P)),this._updateLayer(H)))}else this.fire(new i.l(new Error(`Cannot filter non-existing layer \"${d}\".`)))}getFilter(d){return i.bV(this.getLayer(d).filter)}setLayoutProperty(d,P,N,H={}){this._checkLoaded();let $=this.getLayer(d);$?i.bQ($.getLayoutProperty(P),N)||($.setLayoutProperty(P,N,H),this._updateLayer($)):this.fire(new i.l(new Error(`Cannot style non-existing layer \"${d}\".`)))}getLayoutProperty(d,P){let N=this.getLayer(d);if(N)return N.getLayoutProperty(P);this.fire(new i.l(new Error(`Cannot get style of non-existing layer \"${d}\".`)))}setPaintProperty(d,P,N,H={}){this._checkLoaded();let $=this.getLayer(d);$?i.bQ($.getPaintProperty(P),N)||this._updatePaintProperty($,P,N,H):this.fire(new i.l(new Error(`Cannot style non-existing layer \"${d}\".`)))}_updatePaintProperty(d,P,N,H={}){d.setPaintProperty(P,N,H)&&this._updateLayer(d),i.bU(d)&&P===\"raster-fade-duration\"&&this.tileManagers[d.source].setRasterFadeDuration(N),this._changed=!0,this._updatedPaintProps[d.id]=!0,this._serializedLayers=null}getPaintProperty(d,P){return this.getLayer(d).getPaintProperty(P)}setFeatureState(d,P){this._checkLoaded();let N=d.source,H=d.sourceLayer,$=this.tileManagers[N];if($===void 0)return void this.fire(new i.l(new Error(`The source '${N}' does not exist in the map's style.`)));let se=$.getSource().type;se===\"geojson\"&&H?this.fire(new i.l(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):se!==\"vector\"||H?(d.id===void 0&&this.fire(new i.l(new Error(\"The feature id parameter must be provided.\"))),$.setFeatureState(H,d.id,P)):this.fire(new i.l(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}removeFeatureState(d,P){this._checkLoaded();let N=d.source,H=this.tileManagers[N];if(H===void 0)return void this.fire(new i.l(new Error(`The source '${N}' does not exist in the map's style.`)));let $=H.getSource().type,se=$===\"vector\"?d.sourceLayer:void 0;$!==\"vector\"||se?P&&typeof d.id!=\"string\"&&typeof d.id!=\"number\"?this.fire(new i.l(new Error(\"A feature id is required to remove its specific state property.\"))):H.removeFeatureState(se,d.id,P):this.fire(new i.l(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}getFeatureState(d){this._checkLoaded();let P=d.source,N=d.sourceLayer,H=this.tileManagers[P];if(H!==void 0)return H.getSource().type!==\"vector\"||N?(d.id===void 0&&this.fire(new i.l(new Error(\"The feature id parameter must be provided.\"))),H.getFeatureState(N,d.id)):void this.fire(new i.l(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));this.fire(new i.l(new Error(`The source '${P}' does not exist in the map's style.`)))}getTransition(){var d;return i.e({duration:300,delay:0},(d=this.stylesheet)===null||d===void 0?void 0:d.transition)}serialize(){if(!this._loaded)return;let d=i.bY(this.tileManagers,($=>$.serialize())),P=this._serializeByIds(this._order,!0),N=this.map.getTerrain()||void 0,H=this.stylesheet;return i.bZ({version:H.version,name:H.name,metadata:H.metadata,light:H.light,sky:H.sky,center:H.center,zoom:H.zoom,bearing:H.bearing,pitch:H.pitch,sprite:H.sprite,glyphs:H.glyphs,transition:H.transition,projection:H.projection,sources:d,layers:P,terrain:N},($=>$!==void 0))}_updateLayer(d){this._updatedLayers[d.id]=!0,d.source&&!this._updatedSources[d.source]&&this.tileManagers[d.source].getSource().type!==\"raster\"&&(this._updatedSources[d.source]=\"reload\",this.tileManagers[d.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(d){let P=se=>this._layers[se].type===\"fill-extrusion\",N={},H=[];for(let se=this._order.length-1;se>=0;se--){let de=this._order[se];if(P(de)){N[de]=se;for(let Ae of d){let Fe=Ae[de];if(Fe)for(let Ie of Fe)H.push(Ie)}}}H.sort(((se,de)=>de.intersectionZ-se.intersectionZ));let $=[];for(let se=this._order.length-1;se>=0;se--){let de=this._order[se];if(P(de))for(let Ae=H.length-1;Ae>=0;Ae--){let Fe=H[Ae].feature;if(N[Fe.layer.id]<se)break;$.push(Fe),H.pop()}else for(let Ae of d){let Fe=Ae[de];if(Fe)for(let Ie of Fe)$.push(Ie.feature)}}return $}queryRenderedFeatures(d,P,N){P!=null&&P.filter&&this._validate(i.F.filter,\"queryRenderedFeatures.filter\",P.filter,null,P);let H={};if(P!=null&&P.layers){if(!(Array.isArray(P.layers)||P.layers instanceof Set))return this.fire(new i.l(new Error(\"parameters.layers must be an Array or a Set of strings\"))),[];for(let Fe of P.layers){let Ie=this._layers[Fe];if(!Ie)return this.fire(new i.l(new Error(`The layer '${Fe}' does not exist in the map's style and cannot be queried for features.`))),[];H[Ie.source]=!0}}let $=[];P.availableImages=this._availableImages;let se=this._serializedAllLayers(),de=P.layers instanceof Set?P.layers:Array.isArray(P.layers)?new Set(P.layers):null,Ae=Object.assign(Object.assign({},P),{layers:de,globalState:this._globalState});for(let Fe in this.tileManagers)P.layers&&!H[Fe]||$.push(_e(this.tileManagers[Fe],this._layers,se,d,Ae,N,this.map.terrain?(Ie,Ye,st)=>this.map.terrain.getElevation(Ie,Ye,st):void 0));return this.placement&&$.push((function(Fe,Ie,Ye,st,dt,Pe,Xe){let at={},xt=Pe.queryRenderedSymbols(st),Et=[];for(let Bt of Object.keys(xt).map(Number))Et.push(Xe[Bt]);Et.sort(ne);for(let Bt of Et){let St=Bt.featureIndex.lookupSymbolFeatures(xt[Bt.bucketInstanceId],Ie,Bt.bucketIndex,Bt.sourceLayerIndex,{filterSpec:dt.filter,globalState:dt.globalState},dt.layers,dt.availableImages,Fe);for(let Pt in St){at[Pt]||(at[Pt]=[]);let $t=St[Pt];$t.sort(((Ht,dr)=>{let Xr=Bt.featureSortOrder;if(Xr){let Or=Xr.indexOf(Ht.featureIndex);return Xr.indexOf(dr.featureIndex)-Or}return dr.featureIndex-Ht.featureIndex}));for(let Ht of $t)at[Pt].push(Ht)}}return(function(Bt,St,Pt){for(let $t in Bt)for(let Ht of Bt[$t])ae(Ht,Pt[St[$t].source]);return Bt})(at,Fe,Ye)})(this._layers,se,this.tileManagers,d,Ae,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures($)}querySourceFeatures(d,P){P!=null&&P.filter&&this._validate(i.F.filter,\"querySourceFeatures.filter\",P.filter,null,P);let N=this.tileManagers[d];return N?(function(H,$){let se=H.getRenderableIds().map((Fe=>H.getTileByID(Fe))),de=[],Ae={};for(let Fe of se){let Ie=Fe.tileID.canonical.key;Ae[Ie]||(Ae[Ie]=!0,Fe.querySourceFeatures(de,$))}return de})(N,P?Object.assign(Object.assign({},P),{globalState:this._globalState}):{globalState:this._globalState}):[]}getLight(){return this.light.getLight()}setLight(d,P={}){this._checkLoaded();let N=this.light.getLight(),H=!1;for(let se in d)if(!i.bQ(d[se],N[se])){H=!0;break}if(!H)return;let $={now:g(),transition:i.e({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(d,P),this.light.updateTransitions($)}getProjection(){var d;return(d=this.stylesheet)===null||d===void 0?void 0:d.projection}setProjection(d){this._checkLoaded();let P=d!=null?d:{type:\"mercator\"};if(this.stylesheet.projection=d,this.projection){if(this.projection.name===P.type)return;this.projection.destroy(),delete this.projection}this._setProjectionInternal(P.type)}getSky(){var d;return(d=this.stylesheet)===null||d===void 0?void 0:d.sky}setSky(d,P={}){this._checkLoaded();let N=this.getSky(),H=!1;if(!d&&!N)return;if(d&&!N)H=!0;else if(!d&&N)H=!0;else for(let se in d)if(!i.bQ(d[se],N[se])){H=!0;break}if(!H)return;let $={now:g(),transition:i.e({duration:300,delay:0},this.stylesheet.transition)};this.stylesheet.sky=d,this.sky.setSky(d,P),this.sky.updateTransitions($)}_setProjectionInternal(d){let P=(function(N,H){let $={constrainOverride:H};if(Array.isArray(N)){let se=new Sf({type:N});return{projection:se,transform:new Ro($),cameraHelper:new Dl(se)}}switch(N){case\"mercator\":return{projection:new oo,transform:new Ls($),cameraHelper:new Gu};case\"globe\":{let se=new Sf({type:[\"interpolate\",[\"linear\"],[\"zoom\"],11,\"vertical-perspective\",12,\"mercator\"]});return{projection:se,transform:new Ro($),cameraHelper:new Dl(se)}}case\"vertical-perspective\":return{projection:new Bd,transform:new Yo($),cameraHelper:new is};default:return i.w(`Unknown projection name: ${N}. Falling back to mercator projection.`),{projection:new oo,transform:new Ls($),cameraHelper:new Gu}}})(d,this.map.transformConstrain);this.projection=P.projection,this.map.migrateProjection(P.transform,P.cameraHelper);for(let N in this.tileManagers)this.tileManagers[N].reload()}_validate(d,P,N,H,$={}){return($==null?void 0:$.validate)!==!1&&dc(this,d.call(i.F,i.e({key:P,style:this.serialize(),value:N,styleSpec:i.x},H)))}_remove(d=!0){this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._loadStyleRequest&&(this._loadStyleRequest.abort(),this._loadStyleRequest=null),this._spriteRequest&&(this._spriteRequest.abort(),this._spriteRequest=null),tt().off(Ve,this._rtlPluginLoaded);for(let P in this._layers)this._layers[P].setEventedParent(null);for(let P in this.tileManagers){let N=this.tileManagers[P];N.setEventedParent(null),N.onRemove(this.map)}this.imageManager.setEventedParent(null),this.setEventedParent(null),d&&this.dispatcher.broadcast(\"RM\",void 0),this.dispatcher.remove(d)}_clearSource(d){this.tileManagers[d].clearTiles()}_reloadSource(d){this.tileManagers[d].resume(),this.tileManagers[d].reload()}_updateSources(d){for(let P in this.tileManagers)this.tileManagers[P].update(d,this.map.terrain)}_generateCollisionBoxes(){for(let d in this.tileManagers)this._reloadSource(d)}_updatePlacement(d,P,N,H,$=!1){let se=!1,de=!1,Ae={};for(let Fe of this._order){let Ie=this._layers[Fe];if(Ie.type!==\"symbol\")continue;if(!Ae[Ie.source]){let st=this.tileManagers[Ie.source];Ae[Ie.source]=st.getRenderableIds(!0).map((dt=>st.getTileByID(dt))).sort(((dt,Pe)=>Pe.tileID.overscaledZ-dt.tileID.overscaledZ||(dt.tileID.isLessThan(Pe.tileID)?-1:1)))}let Ye=this.crossTileSymbolIndex.addLayer(Ie,Ae[Ie.source],d.center.lng);se||(se=Ye)}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),$||($=this._layerOrderChanged||N===0),($||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(g(),d.zoom))&&(this.pauseablePlacement=new Ra(d,this.map.terrain,this._order,$,P,N,H,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,Ae),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(g()),de=!0),se&&this.pauseablePlacement.placement.setStale()),de||se)for(let Fe of this._order){let Ie=this._layers[Fe];Ie.type===\"symbol\"&&this.placement.updateLayerOpacities(Ie,Ae[Ie.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(g())}_releaseSymbolFadeTiles(){for(let d in this.tileManagers)this.tileManagers[d].releaseSymbolFadeTiles()}getImages(d,P){return i._(this,void 0,void 0,(function*(){let N=yield this.imageManager.getImages(P.icons);this._updateTilesForChangedImages();let H=this.tileManagers[P.source];return H&&H.setDependencies(P.tileID.key,P.type,P.icons),N}))}getGlyphs(d,P){return i._(this,void 0,void 0,(function*(){let N=yield this.glyphManager.getGlyphs(P.stacks),H=this.tileManagers[P.source];return H&&H.setDependencies(P.tileID.key,P.type,[\"\"]),N}))}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(d,P={}){this._checkLoaded(),d&&this._validate(i.F.glyphs,\"glyphs\",d,null,P)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=d,this.glyphManager.entries={},this.glyphManager.setURL(d))}getDashes(d,P){return i._(this,void 0,void 0,(function*(){let N={};for(let[H,$]of Object.entries(P.dashes))N[H]=this.lineAtlas.getDash($.dasharray,$.round);return N}))}addSprite(d,P,N={},H){this._checkLoaded();let $=[{id:d,url:P}],se=[...C(this.stylesheet.sprite),...$];this._validate(i.F.sprite,\"sprite\",se,null,N)||(this.stylesheet.sprite=se,this._loadSprite($,!0,H))}removeSprite(d){this._checkLoaded();let P=C(this.stylesheet.sprite);if(P.find((N=>N.id===d))){if(this._spritesImagesIds[d])for(let N of this._spritesImagesIds[d])this.imageManager.removeImage(N),this._changedImages[N]=!0;P.splice(P.findIndex((N=>N.id===d)),1),this.stylesheet.sprite=P.length>0?P:void 0,delete this._spritesImagesIds[d],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new i.n(\"data\",{dataType:\"style\"}))}else this.fire(new i.l(new Error(`Sprite \"${d}\" doesn't exists on this map.`)))}getSprite(){return C(this.stylesheet.sprite)}setSprite(d,P={},N){this._checkLoaded(),d&&this._validate(i.F.sprite,\"sprite\",d,null,P)||(this.stylesheet.sprite=d,d?this._loadSprite(d,!0,N):(this._unloadSprite(),N&&N(null)))}destroy(){this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._loadStyleRequest&&(this._loadStyleRequest.abort(),this._loadStyleRequest=null),this._spriteRequest&&(this._spriteRequest.abort(),this._spriteRequest=null);for(let d in this.tileManagers){let P=this.tileManagers[d];P.setEventedParent(null),P.onRemove(this.map)}this.tileManagers={},this.imageManager&&(this.imageManager.setEventedParent(null),this.imageManager.destroy(),this._availableImages=[],this._spritesImagesIds={}),this.glyphManager&&this.glyphManager.destroy();for(let d in this._layers){let P=this._layers[d];P.setEventedParent(null),P.onRemove&&P.onRemove(this.map)}this._setInitialValues(),this.setEventedParent(null),this.dispatcher.unregisterMessageHandler(\"GG\"),this.dispatcher.unregisterMessageHandler(\"GI\"),this.dispatcher.unregisterMessageHandler(\"GDA\"),this.dispatcher.remove(!0),this._listeners={},this._oneTimeListeners={}}}var sh=i.aS([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]);class Y0{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(d,P,N,H,$,se,de,Ae,Fe){this.context=d;let Ie=this.boundPaintVertexBuffers.length!==H.length;for(let Ye=0;!Ie&&Ye<H.length;Ye++)this.boundPaintVertexBuffers[Ye]!==H[Ye]&&(Ie=!0);!this.vao||this.boundProgram!==P||this.boundLayoutVertexBuffer!==N||Ie||this.boundIndexBuffer!==$||this.boundVertexOffset!==se||this.boundDynamicVertexBuffer!==de||this.boundDynamicVertexBuffer2!==Ae||this.boundDynamicVertexBuffer3!==Fe?this.freshBind(P,N,H,$,se,de,Ae,Fe):(d.bindVertexArray.set(this.vao),de&&de.bind(),$!=null&&$.dynamicDraw&&$.bind(),Ae&&Ae.bind(),Fe&&Fe.bind())}freshBind(d,P,N,H,$,se,de,Ae){let Fe=d.numAttributes,Ie=this.context,Ye=Ie.gl;this.vao&&this.destroy(),this.vao=Ie.createVertexArray(),Ie.bindVertexArray.set(this.vao),this.boundProgram=d,this.boundLayoutVertexBuffer=P,this.boundPaintVertexBuffers=N,this.boundIndexBuffer=H,this.boundVertexOffset=$,this.boundDynamicVertexBuffer=se,this.boundDynamicVertexBuffer2=de,this.boundDynamicVertexBuffer3=Ae,P.enableAttributes(Ye,d);for(let st of N)st.enableAttributes(Ye,d);se&&se.enableAttributes(Ye,d),de&&de.enableAttributes(Ye,d),Ae&&Ae.enableAttributes(Ye,d),P.bind(),P.setVertexAttribPointers(Ye,d,$);for(let st of N)st.bind(),st.setVertexAttribPointers(Ye,d,$);se&&(se.bind(),se.setVertexAttribPointers(Ye,d,$)),H&&H.bind(),de&&(de.bind(),de.setVertexAttribPointers(Ye,d,$)),Ae&&(Ae.bind(),Ae.setVertexAttribPointers(Ye,d,$)),Ie.currentNumAttributes=Fe}destroy(){this.vao&&(this.context.deleteVertexArray(this.vao),this.vao=null)}}let Nd=(Y,d,P,N,H)=>({u_texture:0,u_ele_delta:Y,u_fog_matrix:d,u_fog_color:P?P.properties.get(\"fog-color\"):i.bo.white,u_fog_ground_blend:P?P.properties.get(\"fog-ground-blend\"):1,u_fog_ground_blend_opacity:H?0:P?P.calculateFogBlendOpacity(N):0,u_horizon_color:P?P.properties.get(\"horizon-color\"):i.bo.white,u_horizon_fog_blend:P?P.properties.get(\"horizon-fog-blend\"):1,u_is_globe_mode:H?1:0}),Cf={mainMatrix:\"u_projection_matrix\",tileMercatorCoords:\"u_projection_tile_mercator_coords\",clippingPlane:\"u_projection_clipping_plane\",projectionTransition:\"u_projection_transition\",fallbackMatrix:\"u_projection_fallback_matrix\"};function Vo(Y){let d=[];for(let P of Y){if(P===null)continue;let N=P.split(\" \");d.push(N.pop())}return d}class Ud{constructor(d,P,N,H,$,se,de,Ae,Fe=[]){let Ie=d.gl;this.program=Ie.createProgram();let Ye=Vo(P.staticAttributes),st=N?N.getBinderAttributes():[],dt=Ye.concat(st),Pe=Oa.prelude.staticUniforms?Vo(Oa.prelude.staticUniforms):[],Xe=de.staticUniforms?Vo(de.staticUniforms):[],at=P.staticUniforms?Vo(P.staticUniforms):[],xt=N?N.getBinderUniforms():[],Et=Pe.concat(Xe).concat(at).concat(xt),Bt=[];for(let Or of Et)Bt.includes(Or)||Bt.push(Or);let St=N?N.defines():[];Pl(Ie)&&St.unshift(\"#version 300 es\"),$&&St.push(\"#define OVERDRAW_INSPECTOR;\"),se&&St.push(\"#define TERRAIN3D;\"),Ae&&St.push(Ae),Fe&&St.push(...Fe);let Pt=St.concat(Oa.prelude.fragmentSource,de.fragmentSource,P.fragmentSource).join(`\n",
+       "`),$t=St.concat(Oa.prelude.vertexSource,de.vertexSource,P.vertexSource).join(`\n",
+       "`);Pl(Ie)||(Pt=(function(Or){return Or.replace(/\\bin\\s/g,\"varying \").replace(\"out highp vec4 fragColor;\",\"\").replace(/fragColor/g,\"gl_FragColor\").replace(/texture\\(/g,\"texture2D(\")})(Pt),$t=(function(Or){return Or.replace(/\\bin\\s/g,\"attribute \").replace(/\\bout\\s/g,\"varying \").replace(/texture\\(/g,\"texture2D(\")})($t));let Ht=Ie.createShader(Ie.FRAGMENT_SHADER);if(Ie.isContextLost())return void(this.failedToCreate=!0);if(Ie.shaderSource(Ht,Pt),Ie.compileShader(Ht),!Ie.getShaderParameter(Ht,Ie.COMPILE_STATUS))throw new Error(`Could not compile fragment shader: ${Ie.getShaderInfoLog(Ht)}`);Ie.attachShader(this.program,Ht);let dr=Ie.createShader(Ie.VERTEX_SHADER);if(Ie.isContextLost())return void(this.failedToCreate=!0);if(Ie.shaderSource(dr,$t),Ie.compileShader(dr),!Ie.getShaderParameter(dr,Ie.COMPILE_STATUS))throw new Error(`Could not compile vertex shader: ${Ie.getShaderInfoLog(dr)}`);Ie.attachShader(this.program,dr),this.attributes={};let Xr={};this.numAttributes=dt.length;for(let Or=0;Or<this.numAttributes;Or++)dt[Or]&&(Ie.bindAttribLocation(this.program,Or,dt[Or]),this.attributes[dt[Or]]=Or);if(Ie.linkProgram(this.program),!Ie.getProgramParameter(this.program,Ie.LINK_STATUS))throw new Error(`Program failed to link: ${Ie.getProgramInfoLog(this.program)}`);Ie.deleteShader(dr),Ie.deleteShader(Ht);for(let Or of Bt)if(Or&&!Xr[Or]){let $r=Ie.getUniformLocation(this.program,Or);$r&&(Xr[Or]=$r)}this.fixedUniforms=H(d,Xr),this.terrainUniforms=((Or,$r)=>({u_depth:new i.b_(Or,$r.u_depth),u_terrain:new i.b_(Or,$r.u_terrain),u_terrain_dim:new i.bp(Or,$r.u_terrain_dim),u_terrain_matrix:new i.c0(Or,$r.u_terrain_matrix),u_terrain_unpack:new i.c1(Or,$r.u_terrain_unpack),u_terrain_exaggeration:new i.bp(Or,$r.u_terrain_exaggeration)}))(d,Xr),this.projectionUniforms=((Or,$r)=>({u_projection_matrix:new i.c0(Or,$r.u_projection_matrix),u_projection_tile_mercator_coords:new i.c1(Or,$r.u_projection_tile_mercator_coords),u_projection_clipping_plane:new i.c1(Or,$r.u_projection_clipping_plane),u_projection_transition:new i.bp(Or,$r.u_projection_transition),u_projection_fallback_matrix:new i.c0(Or,$r.u_projection_fallback_matrix)}))(d,Xr),this.binderUniforms=N?N.getUniforms(d,Xr):[]}draw(d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st,dt,Pe,Xe,at,xt,Et,Bt){var St;let Pt=d.gl;if(this.failedToCreate)return;if(d.program.set(this.program),d.setDepthMode(N),d.setStencilMode(H),d.setColorMode($),d.setCullFace(se),Ae){d.activeTexture.set(Pt.TEXTURE2),Pt.bindTexture(Pt.TEXTURE_2D,Ae.depthTexture),d.activeTexture.set(Pt.TEXTURE3),Pt.bindTexture(Pt.TEXTURE_2D,Ae.texture);for(let Ht in this.terrainUniforms)this.terrainUniforms[Ht].set(Ae[Ht])}if(Fe)for(let Ht in Fe)this.projectionUniforms[Cf[Ht]].set(Fe[Ht]);if(de)for(let Ht in this.fixedUniforms)this.fixedUniforms[Ht].set(de[Ht]);at&&at.setUniforms(d,this.binderUniforms,Pe,{zoom:Xe});let $t=0;switch(P){case Pt.LINES:$t=2;break;case Pt.TRIANGLES:$t=3;break;case Pt.LINE_STRIP:$t=1}for(let Ht of dt.get())Ht.vaos||(Ht.vaos={}),(St=Ht.vaos)[Ie]||(St[Ie]=new Y0),Ht.vaos[Ie].bind(d,this,Ye,at?at.getPaintVertexBuffers():[],st,Ht.vertexOffset,xt,Et,Bt),Pt.drawElements(P,Ht.primitiveLength*$t,Pt.UNSIGNED_SHORT,Ht.primitiveOffset*$t*2)}}function zh(Y,d,P){let N=1/i.aK(P,1,d.transform.tileZoom),H=Math.pow(2,P.tileID.overscaledZ),$=P.tileSize*Math.pow(2,d.transform.tileZoom)/H,se=$*(P.tileID.canonical.x+P.tileID.wrap*H),de=$*P.tileID.canonical.y;return{u_image:0,u_texsize:P.imageAtlasTexture.size,u_scale:[N,Y.fromScale,Y.toScale],u_fade:Y.t,u_pixel_coord_upper:[se>>16,de>>16],u_pixel_coord_lower:[65535&se,65535&de]}}let tf=(Y,d,P,N)=>{let H=Y.style.light,$=H.properties.get(\"position\"),se=[$.x,$.y,$.z],de=i.c4();H.properties.get(\"anchor\")===\"viewport\"&&i.c5(de,Y.transform.bearingInRadians),i.c6(se,se,de);let Ae=Y.transform.transformLightDirection(se),Fe=H.properties.get(\"color\");return{u_lightpos:se,u_lightpos_globe:Ae,u_lightintensity:H.properties.get(\"intensity\"),u_lightcolor:[Fe.r,Fe.g,Fe.b],u_vertical_gradient:+d,u_opacity:P,u_fill_translate:N}},Vd=(Y,d,P,N,H,$,se)=>i.e(tf(Y,d,P,N),zh($,Y,se),{u_height_factor:-Math.pow(2,H.overscaledZ)/se.tileSize/8}),rf=(Y,d,P,N)=>i.e(zh(d,Y,P),{u_fill_translate:N}),qh=(Y,d)=>({u_world:Y,u_fill_translate:d}),vc=(Y,d,P,N,H)=>i.e(rf(Y,d,P,H),{u_world:N}),dd=(Y,d,P,N,H)=>{let $=Y.transform,se,de,Ae=0;if(P.paint.get(\"circle-pitch-alignment\")===\"map\"){let Fe=i.aK(d,1,$.zoom);se=!0,de=[Fe,Fe],Ae=Fe/(i.a6*Math.pow(2,d.tileID.overscaledZ))*2*Math.PI*H}else se=!1,de=$.pixelsToGLUnits;return{u_camera_to_center_distance:$.cameraToCenterDistance,u_scale_with_map:+(P.paint.get(\"circle-pitch-scale\")===\"map\"),u_pitch_with_map:+se,u_device_pixel_ratio:Y.pixelRatio,u_extrude_scale:de,u_globe_extrude_scale:Ae,u_translate:N}},Bh=Y=>({u_pixel_extrude_scale:[1/Y.width,1/Y.height]}),Wu=Y=>({u_viewport_size:[Y.width,Y.height]}),vd=(Y,d=1)=>({u_color:Y,u_overlay:0,u_overlay_scale:d}),pd=(Y,d,P,N)=>{let H=i.aK(Y,1,d)/(i.a6*Math.pow(2,Y.tileID.overscaledZ))*2*Math.PI*N;return{u_extrude_scale:i.aK(Y,1,d),u_intensity:P,u_globe_extrude_scale:H}},lh=(Y,d,P,N)=>{let H=i.O();i.c7(H,0,Y.width,Y.height,0,0,1);let $=Y.context.gl;return{u_matrix:H,u_world:[$.drawingBufferWidth,$.drawingBufferHeight],u_image:P,u_color_ramp:N,u_opacity:d.paint.get(\"heatmap-opacity\")}},nf=(Y,d,P)=>{let N=P.paint.get(\"hillshade-accent-color\"),H;switch(P.paint.get(\"hillshade-method\")){case\"basic\":H=4;break;case\"combined\":H=1;break;case\"igor\":H=2;break;case\"multidirectional\":H=3;break;default:H=0}let $=P.getIlluminationProperties();for(let se=0;se<$.directionRadians.length;se++)P.paint.get(\"hillshade-illumination-anchor\")===\"viewport\"&&($.directionRadians[se]+=Y.transform.bearingInRadians);return{u_image:0,u_latrange:af(0,d.tileID),u_exaggeration:P.paint.get(\"hillshade-exaggeration\"),u_altitudes:$.altitudeRadians,u_azimuths:$.directionRadians,u_accent:N,u_method:H,u_highlights:$.highlightColor,u_shadows:$.shadowColor}},uh=(Y,d)=>{let P=d.stride,N=i.O();return i.c7(N,0,i.a6,-i.a6,0,0,1),i.Q(N,N,[0,-i.a6,0]),{u_matrix:N,u_image:1,u_dimension:[P,P],u_zoom:Y.overscaledZ,u_unpack:d.getUnpackVector()}};function af(Y,d){let P=Math.pow(2,d.canonical.z),N=d.canonical.y;return[new i.a7(0,N/P).toLngLat().lat,new i.a7(0,(N+1)/P).toLngLat().lat]}let gd=(Y,d,P=0)=>({u_image:0,u_unpack:d.getUnpackVector(),u_dimension:[d.stride,d.stride],u_elevation_stops:1,u_color_stops:4,u_color_ramp_size:P,u_opacity:Y.paint.get(\"color-relief-opacity\")}),Rl=(Y,d,P,N)=>{let H=Y.transform;return{u_translation:wu(Y,d,P),u_ratio:N/i.aK(d,1,H.zoom),u_device_pixel_ratio:Y.pixelRatio,u_units_to_pixels:[1/H.pixelsToGLUnits[0],1/H.pixelsToGLUnits[1]]}},ch=(Y,d,P,N,H)=>i.e(Rl(Y,d,P,N),{u_image:0,u_image_height:H}),Lc=(Y,d,P,N,H)=>{let $=Y.transform,se=Lf(d,$);return{u_translation:wu(Y,d,P),u_texsize:d.imageAtlasTexture.size,u_ratio:N/i.aK(d,1,$.zoom),u_device_pixel_ratio:Y.pixelRatio,u_image:0,u_scale:[se,H.fromScale,H.toScale],u_fade:H.t,u_units_to_pixels:[1/$.pixelsToGLUnits[0],1/$.pixelsToGLUnits[1]]}},md=(Y,d,P,N,H)=>{let $=Lf(d,Y.transform);return i.e(Rl(Y,d,P,N),{u_tileratio:$,u_crossfade_from:H.fromScale,u_crossfade_to:H.toScale,u_image:0,u_mix:H.t,u_lineatlas_width:Y.lineAtlas.width,u_lineatlas_height:Y.lineAtlas.height})},yd=(Y,d,P,N,H,$)=>{let se=Lf(d,Y.transform);return i.e(Rl(Y,d,P,N),{u_image:0,u_image_height:$,u_tileratio:se,u_crossfade_from:H.fromScale,u_crossfade_to:H.toScale,u_image_dash:1,u_mix:H.t,u_lineatlas_width:Y.lineAtlas.width,u_lineatlas_height:Y.lineAtlas.height})};function Lf(Y,d){return 1/i.aK(Y,1,d.tileZoom)}function wu(Y,d,P){return i.aL(Y.transform,d,P.paint.get(\"line-translate\"),P.paint.get(\"line-translate-anchor\"))}let fh=(Y,d,P,N,H)=>{return{u_tl_parent:Y,u_scale_parent:d,u_buffer_scale:1,u_fade_t:P.mix,u_opacity:P.opacity*N.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:N.paint.get(\"raster-brightness-min\"),u_brightness_high:N.paint.get(\"raster-brightness-max\"),u_saturation_factor:(se=N.paint.get(\"raster-saturation\"),se>0?1-1/(1.001-se):-se),u_contrast_factor:($=N.paint.get(\"raster-contrast\"),$>0?1/(1-$):1+$),u_spin_weights:Oh(N.paint.get(\"raster-hue-rotate\")),u_coords_top:[H[0].x,H[0].y,H[1].x,H[1].y],u_coords_bottom:[H[3].x,H[3].y,H[2].x,H[2].y]};var $,se};function Oh(Y){Y*=Math.PI/180;let d=Math.sin(Y),P=Math.cos(Y);return[(2*P+1)/3,(-Math.sqrt(3)*d-P+1)/3,(Math.sqrt(3)*d-P+1)/3]}let hh=(Y,d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st)=>{let dt=se.transform;return{u_is_size_zoom_constant:+(Y===\"constant\"||Y===\"source\"),u_is_size_feature_constant:+(Y===\"constant\"||Y===\"camera\"),u_size_t:d?d.uSizeT:0,u_size:d?d.uSize:0,u_camera_to_center_distance:dt.cameraToCenterDistance,u_pitch:dt.pitch/360*2*Math.PI,u_rotate_symbol:+P,u_aspect_ratio:dt.width/dt.height,u_fade_change:se.options.fadeDuration?se.symbolFadeChange:1,u_label_plane_matrix:de,u_coord_matrix:Ae,u_is_text:+Ie,u_pitch_with_map:+N,u_is_along_line:H,u_is_variable_anchor:$,u_texsize:Ye,u_texture:0,u_translation:Fe,u_pitched_scale:st}},Gd=(Y,d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st,dt)=>{let Pe=se.transform;return i.e(hh(Y,d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,dt),{u_gamma_scale:N?Math.cos(Pe.pitch*Math.PI/180)*Pe.cameraToCenterDistance:1,u_device_pixel_ratio:se.pixelRatio,u_is_halo:st?1:0,u_is_plain:1})},Gl=(Y,d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st)=>i.e(Gd(Y,d,P,N,H,$,se,de,Ae,Fe,!0,Ie,!0,st),{u_texsize_icon:Ye,u_texture_icon:1}),wl=(Y,d)=>({u_opacity:Y,u_color:d}),xd=(Y,d,P,N,H)=>i.e((function($,se,de,Ae){let Fe=de.imageManager.getPattern($.from.toString()),Ie=de.imageManager.getPattern($.to.toString()),{width:Ye,height:st}=de.imageManager.getPixelSize(),dt=Math.pow(2,Ae.tileID.overscaledZ),Pe=Ae.tileSize*Math.pow(2,de.transform.tileZoom)/dt,Xe=Pe*(Ae.tileID.canonical.x+Ae.tileID.wrap*dt),at=Pe*Ae.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:Fe.tl,u_pattern_br_a:Fe.br,u_pattern_tl_b:Ie.tl,u_pattern_br_b:Ie.br,u_texsize:[Ye,st],u_mix:se.t,u_pattern_size_a:Fe.displaySize,u_pattern_size_b:Ie.displaySize,u_scale_a:se.fromScale,u_scale_b:se.toScale,u_tile_units_to_pixels:1/i.aK(Ae,1,de.transform.tileZoom),u_pixel_coord_upper:[Xe>>16,at>>16],u_pixel_coord_lower:[65535&Xe,65535&at]}})(P,H,d,N),{u_opacity:Y}),Hl=(Y,d)=>{},_d={fillExtrusion:(Y,d)=>({u_lightpos:new i.c2(Y,d.u_lightpos),u_lightpos_globe:new i.c2(Y,d.u_lightpos_globe),u_lightintensity:new i.bp(Y,d.u_lightintensity),u_lightcolor:new i.c2(Y,d.u_lightcolor),u_vertical_gradient:new i.bp(Y,d.u_vertical_gradient),u_opacity:new 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i.bp(Y,d.u_fade),u_fill_translate:new i.c3(Y,d.u_fill_translate)}),fillOutline:(Y,d)=>({u_world:new i.c3(Y,d.u_world),u_fill_translate:new i.c3(Y,d.u_fill_translate)}),fillOutlinePattern:(Y,d)=>({u_world:new i.c3(Y,d.u_world),u_image:new i.b_(Y,d.u_image),u_texsize:new i.c3(Y,d.u_texsize),u_pixel_coord_upper:new i.c3(Y,d.u_pixel_coord_upper),u_pixel_coord_lower:new i.c3(Y,d.u_pixel_coord_lower),u_scale:new i.c2(Y,d.u_scale),u_fade:new i.bp(Y,d.u_fade),u_fill_translate:new i.c3(Y,d.u_fill_translate)}),circle:(Y,d)=>({u_camera_to_center_distance:new i.bp(Y,d.u_camera_to_center_distance),u_scale_with_map:new i.b_(Y,d.u_scale_with_map),u_pitch_with_map:new i.b_(Y,d.u_pitch_with_map),u_extrude_scale:new i.c3(Y,d.u_extrude_scale),u_device_pixel_ratio:new i.bp(Y,d.u_device_pixel_ratio),u_globe_extrude_scale:new i.bp(Y,d.u_globe_extrude_scale),u_translate:new i.c3(Y,d.u_translate)}),collisionBox:(Y,d)=>({u_pixel_extrude_scale:new i.c3(Y,d.u_pixel_extrude_scale)}),collisionCircle:(Y,d)=>({u_viewport_size:new i.c3(Y,d.u_viewport_size)}),debug:(Y,d)=>({u_color:new i.b$(Y,d.u_color),u_overlay:new i.b_(Y,d.u_overlay),u_overlay_scale:new i.bp(Y,d.u_overlay_scale)}),depth:Hl,clippingMask:Hl,heatmap:(Y,d)=>({u_extrude_scale:new i.bp(Y,d.u_extrude_scale),u_intensity:new i.bp(Y,d.u_intensity),u_globe_extrude_scale:new i.bp(Y,d.u_globe_extrude_scale)}),heatmapTexture:(Y,d)=>({u_matrix:new i.c0(Y,d.u_matrix),u_world:new i.c3(Y,d.u_world),u_image:new i.b_(Y,d.u_image),u_color_ramp:new i.b_(Y,d.u_color_ramp),u_opacity:new i.bp(Y,d.u_opacity)}),hillshade:(Y,d)=>({u_image:new i.b_(Y,d.u_image),u_latrange:new i.c3(Y,d.u_latrange),u_exaggeration:new i.bp(Y,d.u_exaggeration),u_altitudes:new i.c9(Y,d.u_altitudes),u_azimuths:new i.c9(Y,d.u_azimuths),u_accent:new i.b$(Y,d.u_accent),u_method:new i.b_(Y,d.u_method),u_shadows:new i.c8(Y,d.u_shadows),u_highlights:new i.c8(Y,d.u_highlights)}),hillshadePrepare:(Y,d)=>({u_matrix:new i.c0(Y,d.u_matrix),u_image:new i.b_(Y,d.u_image),u_dimension:new i.c3(Y,d.u_dimension),u_zoom:new i.bp(Y,d.u_zoom),u_unpack:new i.c1(Y,d.u_unpack)}),colorRelief:(Y,d)=>({u_image:new i.b_(Y,d.u_image),u_unpack:new i.c1(Y,d.u_unpack),u_dimension:new i.c3(Y,d.u_dimension),u_elevation_stops:new i.b_(Y,d.u_elevation_stops),u_color_stops:new i.b_(Y,d.u_color_stops),u_color_ramp_size:new i.b_(Y,d.u_color_ramp_size),u_opacity:new i.bp(Y,d.u_opacity)}),line:(Y,d)=>({u_translation:new i.c3(Y,d.u_translation),u_ratio:new i.bp(Y,d.u_ratio),u_device_pixel_ratio:new i.bp(Y,d.u_device_pixel_ratio),u_units_to_pixels:new i.c3(Y,d.u_units_to_pixels)}),lineGradient:(Y,d)=>({u_translation:new i.c3(Y,d.u_translation),u_ratio:new i.bp(Y,d.u_ratio),u_device_pixel_ratio:new i.bp(Y,d.u_device_pixel_ratio),u_units_to_pixels:new i.c3(Y,d.u_units_to_pixels),u_image:new i.b_(Y,d.u_image),u_image_height:new i.bp(Y,d.u_image_height)}),linePattern:(Y,d)=>({u_translation:new i.c3(Y,d.u_translation),u_texsize:new i.c3(Y,d.u_texsize),u_ratio:new i.bp(Y,d.u_ratio),u_device_pixel_ratio:new i.bp(Y,d.u_device_pixel_ratio),u_image:new i.b_(Y,d.u_image),u_units_to_pixels:new i.c3(Y,d.u_units_to_pixels),u_scale:new i.c2(Y,d.u_scale),u_fade:new i.bp(Y,d.u_fade)}),lineSDF:(Y,d)=>({u_translation:new i.c3(Y,d.u_translation),u_ratio:new i.bp(Y,d.u_ratio),u_device_pixel_ratio:new i.bp(Y,d.u_device_pixel_ratio),u_units_to_pixels:new i.c3(Y,d.u_units_to_pixels),u_image:new i.b_(Y,d.u_image),u_mix:new i.bp(Y,d.u_mix),u_tileratio:new i.bp(Y,d.u_tileratio),u_crossfade_from:new i.bp(Y,d.u_crossfade_from),u_crossfade_to:new i.bp(Y,d.u_crossfade_to),u_lineatlas_width:new i.bp(Y,d.u_lineatlas_width),u_lineatlas_height:new i.bp(Y,d.u_lineatlas_height)}),lineGradientSDF:(Y,d)=>({u_translation:new i.c3(Y,d.u_translation),u_ratio:new i.bp(Y,d.u_ratio),u_device_pixel_ratio:new i.bp(Y,d.u_device_pixel_ratio),u_units_to_pixels:new i.c3(Y,d.u_units_to_pixels),u_image:new i.b_(Y,d.u_image),u_image_height:new i.bp(Y,d.u_image_height),u_tileratio:new i.bp(Y,d.u_tileratio),u_crossfade_from:new i.bp(Y,d.u_crossfade_from),u_crossfade_to:new i.bp(Y,d.u_crossfade_to),u_image_dash:new i.b_(Y,d.u_image_dash),u_mix:new i.bp(Y,d.u_mix),u_lineatlas_width:new i.bp(Y,d.u_lineatlas_width),u_lineatlas_height:new i.bp(Y,d.u_lineatlas_height)}),raster:(Y,d)=>({u_tl_parent:new i.c3(Y,d.u_tl_parent),u_scale_parent:new i.bp(Y,d.u_scale_parent),u_buffer_scale:new i.bp(Y,d.u_buffer_scale),u_fade_t:new i.bp(Y,d.u_fade_t),u_opacity:new i.bp(Y,d.u_opacity),u_image0:new i.b_(Y,d.u_image0),u_image1:new i.b_(Y,d.u_image1),u_brightness_low:new i.bp(Y,d.u_brightness_low),u_brightness_high:new i.bp(Y,d.u_brightness_high),u_saturation_factor:new i.bp(Y,d.u_saturation_factor),u_contrast_factor:new i.bp(Y,d.u_contrast_factor),u_spin_weights:new i.c2(Y,d.u_spin_weights),u_coords_top:new i.c1(Y,d.u_coords_top),u_coords_bottom:new i.c1(Y,d.u_coords_bottom)}),symbolIcon:(Y,d)=>({u_is_size_zoom_constant:new i.b_(Y,d.u_is_size_zoom_constant),u_is_size_feature_constant:new i.b_(Y,d.u_is_size_feature_constant),u_size_t:new i.bp(Y,d.u_size_t),u_size:new i.bp(Y,d.u_size),u_camera_to_center_distance:new i.bp(Y,d.u_camera_to_center_distance),u_pitch:new i.bp(Y,d.u_pitch),u_rotate_symbol:new i.b_(Y,d.u_rotate_symbol),u_aspect_ratio:new i.bp(Y,d.u_aspect_ratio),u_fade_change:new i.bp(Y,d.u_fade_change),u_label_plane_matrix:new i.c0(Y,d.u_label_plane_matrix),u_coord_matrix:new i.c0(Y,d.u_coord_matrix),u_is_text:new i.b_(Y,d.u_is_text),u_pitch_with_map:new i.b_(Y,d.u_pitch_with_map),u_is_along_line:new i.b_(Y,d.u_is_along_line),u_is_variable_anchor:new i.b_(Y,d.u_is_variable_anchor),u_texsize:new i.c3(Y,d.u_texsize),u_texture:new i.b_(Y,d.u_texture),u_translation:new i.c3(Y,d.u_translation),u_pitched_scale:new i.bp(Y,d.u_pitched_scale)}),symbolSDF:(Y,d)=>({u_is_size_zoom_constant:new i.b_(Y,d.u_is_size_zoom_constant),u_is_size_feature_constant:new i.b_(Y,d.u_is_size_feature_constant),u_size_t:new i.bp(Y,d.u_size_t),u_size:new i.bp(Y,d.u_size),u_camera_to_center_distance:new i.bp(Y,d.u_camera_to_center_distance),u_pitch:new i.bp(Y,d.u_pitch),u_rotate_symbol:new i.b_(Y,d.u_rotate_symbol),u_aspect_ratio:new i.bp(Y,d.u_aspect_ratio),u_fade_change:new i.bp(Y,d.u_fade_change),u_label_plane_matrix:new i.c0(Y,d.u_label_plane_matrix),u_coord_matrix:new i.c0(Y,d.u_coord_matrix),u_is_text:new i.b_(Y,d.u_is_text),u_pitch_with_map:new i.b_(Y,d.u_pitch_with_map),u_is_along_line:new i.b_(Y,d.u_is_along_line),u_is_variable_anchor:new i.b_(Y,d.u_is_variable_anchor),u_texsize:new i.c3(Y,d.u_texsize),u_texture:new i.b_(Y,d.u_texture),u_gamma_scale:new i.bp(Y,d.u_gamma_scale),u_device_pixel_ratio:new i.bp(Y,d.u_device_pixel_ratio),u_is_halo:new i.b_(Y,d.u_is_halo),u_is_plain:new i.b_(Y,d.u_is_plain),u_translation:new i.c3(Y,d.u_translation),u_pitched_scale:new i.bp(Y,d.u_pitched_scale)}),symbolTextAndIcon:(Y,d)=>({u_is_size_zoom_constant:new i.b_(Y,d.u_is_size_zoom_constant),u_is_size_feature_constant:new i.b_(Y,d.u_is_size_feature_constant),u_size_t:new i.bp(Y,d.u_size_t),u_size:new i.bp(Y,d.u_size),u_camera_to_center_distance:new i.bp(Y,d.u_camera_to_center_distance),u_pitch:new i.bp(Y,d.u_pitch),u_rotate_symbol:new i.b_(Y,d.u_rotate_symbol),u_aspect_ratio:new i.bp(Y,d.u_aspect_ratio),u_fade_change:new i.bp(Y,d.u_fade_change),u_label_plane_matrix:new i.c0(Y,d.u_label_plane_matrix),u_coord_matrix:new i.c0(Y,d.u_coord_matrix),u_is_text:new i.b_(Y,d.u_is_text),u_pitch_with_map:new i.b_(Y,d.u_pitch_with_map),u_is_along_line:new i.b_(Y,d.u_is_along_line),u_is_variable_anchor:new i.b_(Y,d.u_is_variable_anchor),u_texsize:new i.c3(Y,d.u_texsize),u_texsize_icon:new i.c3(Y,d.u_texsize_icon),u_texture:new i.b_(Y,d.u_texture),u_texture_icon:new i.b_(Y,d.u_texture_icon),u_gamma_scale:new i.bp(Y,d.u_gamma_scale),u_device_pixel_ratio:new i.bp(Y,d.u_device_pixel_ratio),u_is_halo:new i.b_(Y,d.u_is_halo),u_translation:new i.c3(Y,d.u_translation),u_pitched_scale:new i.bp(Y,d.u_pitched_scale)}),background:(Y,d)=>({u_opacity:new i.bp(Y,d.u_opacity),u_color:new i.b$(Y,d.u_color)}),backgroundPattern:(Y,d)=>({u_opacity:new i.bp(Y,d.u_opacity),u_image:new i.b_(Y,d.u_image),u_pattern_tl_a:new i.c3(Y,d.u_pattern_tl_a),u_pattern_br_a:new i.c3(Y,d.u_pattern_br_a),u_pattern_tl_b:new i.c3(Y,d.u_pattern_tl_b),u_pattern_br_b:new i.c3(Y,d.u_pattern_br_b),u_texsize:new i.c3(Y,d.u_texsize),u_mix:new i.bp(Y,d.u_mix),u_pattern_size_a:new i.c3(Y,d.u_pattern_size_a),u_pattern_size_b:new i.c3(Y,d.u_pattern_size_b),u_scale_a:new i.bp(Y,d.u_scale_a),u_scale_b:new i.bp(Y,d.u_scale_b),u_pixel_coord_upper:new i.c3(Y,d.u_pixel_coord_upper),u_pixel_coord_lower:new i.c3(Y,d.u_pixel_coord_lower),u_tile_units_to_pixels:new i.bp(Y,d.u_tile_units_to_pixels)}),terrain:(Y,d)=>({u_texture:new i.b_(Y,d.u_texture),u_ele_delta:new i.bp(Y,d.u_ele_delta),u_fog_matrix:new i.c0(Y,d.u_fog_matrix),u_fog_color:new i.b$(Y,d.u_fog_color),u_fog_ground_blend:new i.bp(Y,d.u_fog_ground_blend),u_fog_ground_blend_opacity:new i.bp(Y,d.u_fog_ground_blend_opacity),u_horizon_color:new i.b$(Y,d.u_horizon_color),u_horizon_fog_blend:new i.bp(Y,d.u_horizon_fog_blend),u_is_globe_mode:new i.bp(Y,d.u_is_globe_mode)}),terrainDepth:(Y,d)=>({u_ele_delta:new i.bp(Y,d.u_ele_delta)}),terrainCoords:(Y,d)=>({u_texture:new i.b_(Y,d.u_texture),u_terrain_coords_id:new i.bp(Y,d.u_terrain_coords_id),u_ele_delta:new i.bp(Y,d.u_ele_delta)}),projectionErrorMeasurement:(Y,d)=>({u_input:new i.bp(Y,d.u_input),u_output_expected:new i.bp(Y,d.u_output_expected)}),atmosphere:(Y,d)=>({u_sun_pos:new i.c2(Y,d.u_sun_pos),u_atmosphere_blend:new i.bp(Y,d.u_atmosphere_blend),u_globe_position:new i.c2(Y,d.u_globe_position),u_globe_radius:new i.bp(Y,d.u_globe_radius),u_inv_proj_matrix:new i.c0(Y,d.u_inv_proj_matrix)}),sky:(Y,d)=>({u_sky_color:new i.b$(Y,d.u_sky_color),u_horizon_color:new i.b$(Y,d.u_horizon_color),u_horizon:new i.c3(Y,d.u_horizon),u_horizon_normal:new i.c3(Y,d.u_horizon_normal),u_sky_horizon_blend:new i.bp(Y,d.u_sky_horizon_blend),u_sky_blend:new i.bp(Y,d.u_sky_blend)})};class dh{constructor(d,P,N){this.context=d;let H=d.gl;this.buffer=H.createBuffer(),this.dynamicDraw=!!N,this.context.unbindVAO(),d.bindElementBuffer.set(this.buffer),H.bufferData(H.ELEMENT_ARRAY_BUFFER,P.arrayBuffer,this.dynamicDraw?H.DYNAMIC_DRAW:H.STATIC_DRAW),this.dynamicDraw||P.freeBufferAfterUpload()}bind(){this.context.bindElementBuffer.set(this.buffer)}updateData(d){let P=this.context.gl;if(!this.dynamicDraw)throw new Error(\"Attempted to update data while not in dynamic mode.\");this.context.unbindVAO(),this.bind(),P.bufferSubData(P.ELEMENT_ARRAY_BUFFER,0,d.arrayBuffer)}destroy(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)}}let Pf={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"};class Hd{constructor(d,P,N,H){this.length=P.length,this.attributes=N,this.itemSize=P.bytesPerElement,this.dynamicDraw=H,this.context=d;let $=d.gl;this.buffer=$.createBuffer(),d.bindVertexBuffer.set(this.buffer),$.bufferData($.ARRAY_BUFFER,P.arrayBuffer,this.dynamicDraw?$.DYNAMIC_DRAW:$.STATIC_DRAW),this.dynamicDraw||P.freeBufferAfterUpload()}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(d){if(d.length!==this.length)throw new Error(`Length of new data is ${d.length}, which doesn't match current length of ${this.length}`);let P=this.context.gl;this.bind(),P.bufferSubData(P.ARRAY_BUFFER,0,d.arrayBuffer)}enableAttributes(d,P){for(let N of this.attributes){let H=P.attributes[N.name];H!==void 0&&d.enableVertexAttribArray(H)}}setVertexAttribPointers(d,P,N){for(let H of this.attributes){let $=P.attributes[H.name];$!==void 0&&d.vertexAttribPointer($,H.components,d[Pf[H.type]],!1,this.itemSize,H.offset+this.itemSize*(N||0))}}destroy(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)}}class xo{constructor(d){this.gl=d.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1}get(){return this.current}set(d){}getDefault(){return this.default}setDefault(){this.set(this.default)}}class p0 extends xo{getDefault(){return i.bo.transparent}set(d){let P=this.current;(d.r!==P.r||d.g!==P.g||d.b!==P.b||d.a!==P.a||this.dirty)&&(this.gl.clearColor(d.r,d.g,d.b,d.a),this.current=d,this.dirty=!1)}}class Tu extends xo{getDefault(){return 1}set(d){(d!==this.current||this.dirty)&&(this.gl.clearDepth(d),this.current=d,this.dirty=!1)}}class Au extends xo{getDefault(){return 0}set(d){(d!==this.current||this.dirty)&&(this.gl.clearStencil(d),this.current=d,this.dirty=!1)}}class Il extends xo{getDefault(){return[!0,!0,!0,!0]}set(d){let P=this.current;(d[0]!==P[0]||d[1]!==P[1]||d[2]!==P[2]||d[3]!==P[3]||this.dirty)&&(this.gl.colorMask(d[0],d[1],d[2],d[3]),this.current=d,this.dirty=!1)}}class g0 extends xo{getDefault(){return!0}set(d){(d!==this.current||this.dirty)&&(this.gl.depthMask(d),this.current=d,this.dirty=!1)}}class Yu extends xo{getDefault(){return 255}set(d){(d!==this.current||this.dirty)&&(this.gl.stencilMask(d),this.current=d,this.dirty=!1)}}class Nh extends xo{getDefault(){return{func:this.gl.ALWAYS,ref:0,mask:255}}set(d){let P=this.current;(d.func!==P.func||d.ref!==P.ref||d.mask!==P.mask||this.dirty)&&(this.gl.stencilFunc(d.func,d.ref,d.mask),this.current=d,this.dirty=!1)}}class Tl extends xo{getDefault(){let d=this.gl;return[d.KEEP,d.KEEP,d.KEEP]}set(d){let P=this.current;(d[0]!==P[0]||d[1]!==P[1]||d[2]!==P[2]||this.dirty)&&(this.gl.stencilOp(d[0],d[1],d[2]),this.current=d,this.dirty=!1)}}class jl extends xo{getDefault(){return!1}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;d?P.enable(P.STENCIL_TEST):P.disable(P.STENCIL_TEST),this.current=d,this.dirty=!1}}class Uh extends xo{getDefault(){return[0,1]}set(d){let P=this.current;(d[0]!==P[0]||d[1]!==P[1]||this.dirty)&&(this.gl.depthRange(d[0],d[1]),this.current=d,this.dirty=!1)}}class Vh extends xo{getDefault(){return!1}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;d?P.enable(P.DEPTH_TEST):P.disable(P.DEPTH_TEST),this.current=d,this.dirty=!1}}class jd extends xo{getDefault(){return this.gl.LESS}set(d){(d!==this.current||this.dirty)&&(this.gl.depthFunc(d),this.current=d,this.dirty=!1)}}class pc extends xo{getDefault(){return!1}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;d?P.enable(P.BLEND):P.disable(P.BLEND),this.current=d,this.dirty=!1}}class gc extends xo{getDefault(){let d=this.gl;return[d.ONE,d.ZERO]}set(d){let P=this.current;(d[0]!==P[0]||d[1]!==P[1]||this.dirty)&&(this.gl.blendFunc(d[0],d[1]),this.current=d,this.dirty=!1)}}class of extends xo{getDefault(){return i.bo.transparent}set(d){let P=this.current;(d.r!==P.r||d.g!==P.g||d.b!==P.b||d.a!==P.a||this.dirty)&&(this.gl.blendColor(d.r,d.g,d.b,d.a),this.current=d,this.dirty=!1)}}class sf extends xo{getDefault(){return this.gl.FUNC_ADD}set(d){(d!==this.current||this.dirty)&&(this.gl.blendEquation(d),this.current=d,this.dirty=!1)}}class bd extends xo{getDefault(){return!1}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;d?P.enable(P.CULL_FACE):P.disable(P.CULL_FACE),this.current=d,this.dirty=!1}}class Gh extends xo{getDefault(){return this.gl.BACK}set(d){(d!==this.current||this.dirty)&&(this.gl.cullFace(d),this.current=d,this.dirty=!1)}}class Xu extends xo{getDefault(){return this.gl.CCW}set(d){(d!==this.current||this.dirty)&&(this.gl.frontFace(d),this.current=d,this.dirty=!1)}}class Pc extends xo{getDefault(){return null}set(d){(d!==this.current||this.dirty)&&(this.gl.useProgram(d),this.current=d,this.dirty=!1)}}class Ff extends xo{getDefault(){return this.gl.TEXTURE0}set(d){(d!==this.current||this.dirty)&&(this.gl.activeTexture(d),this.current=d,this.dirty=!1)}}class Df extends xo{getDefault(){let d=this.gl;return[0,0,d.drawingBufferWidth,d.drawingBufferHeight]}set(d){let P=this.current;(d[0]!==P[0]||d[1]!==P[1]||d[2]!==P[2]||d[3]!==P[3]||this.dirty)&&(this.gl.viewport(d[0],d[1],d[2],d[3]),this.current=d,this.dirty=!1)}}class La extends xo{getDefault(){return null}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;P.bindFramebuffer(P.FRAMEBUFFER,d),this.current=d,this.dirty=!1}}class Rf extends xo{getDefault(){return null}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;P.bindRenderbuffer(P.RENDERBUFFER,d),this.current=d,this.dirty=!1}}class m0 extends xo{getDefault(){return null}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;P.bindTexture(P.TEXTURE_2D,d),this.current=d,this.dirty=!1}}class wd extends xo{getDefault(){return null}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;P.bindBuffer(P.ARRAY_BUFFER,d),this.current=d,this.dirty=!1}}class Hh extends xo{getDefault(){return null}set(d){let P=this.gl;P.bindBuffer(P.ELEMENT_ARRAY_BUFFER,d),this.current=d,this.dirty=!1}}class X0 extends xo{getDefault(){return null}set(d){var P;if(d===this.current&&!this.dirty)return;let N=this.gl;Pl(N)?N.bindVertexArray(d):(P=N.getExtension(\"OES_vertex_array_object\"))===null||P===void 0||P.bindVertexArrayOES(d),this.current=d,this.dirty=!1}}class y0 extends xo{getDefault(){return 4}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;P.pixelStorei(P.UNPACK_ALIGNMENT,d),this.current=d,this.dirty=!1}}class Td extends xo{getDefault(){return!1}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;P.pixelStorei(P.UNPACK_PREMULTIPLY_ALPHA_WEBGL,d),this.current=d,this.dirty=!1}}class ul extends xo{getDefault(){return!1}set(d){if(d===this.current&&!this.dirty)return;let P=this.gl;P.pixelStorei(P.UNPACK_FLIP_Y_WEBGL,d),this.current=d,this.dirty=!1}}class Wl extends xo{constructor(d,P){super(d),this.context=d,this.parent=P}getDefault(){return null}}class If extends Wl{setDirty(){this.dirty=!0}set(d){if(d===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let P=this.gl;P.framebufferTexture2D(P.FRAMEBUFFER,P.COLOR_ATTACHMENT0,P.TEXTURE_2D,d,0),this.current=d,this.dirty=!1}}class vh extends Wl{set(d){if(d===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let P=this.gl;P.framebufferRenderbuffer(P.FRAMEBUFFER,P.DEPTH_ATTACHMENT,P.RENDERBUFFER,d),this.current=d,this.dirty=!1}}class zf extends Wl{set(d){if(d===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);let P=this.gl;P.framebufferRenderbuffer(P.FRAMEBUFFER,P.DEPTH_STENCIL_ATTACHMENT,P.RENDERBUFFER,d),this.current=d,this.dirty=!1}}let qf=\"Framebuffer is not complete\";class Ze{constructor(d,P,N,H,$){this.context=d,this.width=P,this.height=N;let se=d.gl,de=this.framebuffer=se.createFramebuffer();if(this.colorAttachment=new If(d,de),H)this.depthAttachment=$?new zf(d,de):new vh(d,de);else if($)throw new Error(\"Stencil cannot be set without depth\");if(se.checkFramebufferStatus(se.FRAMEBUFFER)!==se.FRAMEBUFFER_COMPLETE)throw new Error(qf)}destroy(){let d=this.context.gl,P=this.colorAttachment.get();if(P&&d.deleteTexture(P),this.depthAttachment){let N=this.depthAttachment.get();N&&d.deleteRenderbuffer(N)}d.deleteFramebuffer(this.framebuffer)}}class yt{constructor(d){var P,N;if(this.gl=d,this.clearColor=new p0(this),this.clearDepth=new Tu(this),this.clearStencil=new Au(this),this.colorMask=new Il(this),this.depthMask=new g0(this),this.stencilMask=new Yu(this),this.stencilFunc=new Nh(this),this.stencilOp=new Tl(this),this.stencilTest=new jl(this),this.depthRange=new Uh(this),this.depthTest=new Vh(this),this.depthFunc=new jd(this),this.blend=new pc(this),this.blendFunc=new gc(this),this.blendColor=new of(this),this.blendEquation=new sf(this),this.cullFace=new bd(this),this.cullFaceSide=new Gh(this),this.frontFace=new Xu(this),this.program=new Pc(this),this.activeTexture=new Ff(this),this.viewport=new Df(this),this.bindFramebuffer=new La(this),this.bindRenderbuffer=new Rf(this),this.bindTexture=new m0(this),this.bindVertexBuffer=new wd(this),this.bindElementBuffer=new Hh(this),this.bindVertexArray=new X0(this),this.pixelStoreUnpack=new y0(this),this.pixelStoreUnpackPremultiplyAlpha=new Td(this),this.pixelStoreUnpackFlipY=new ul(this),this.extTextureFilterAnisotropic=d.getExtension(\"EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=d.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.maxTextureSize=d.getParameter(d.MAX_TEXTURE_SIZE),Pl(d)){this.HALF_FLOAT=d.HALF_FLOAT;let H=d.getExtension(\"EXT_color_buffer_half_float\");this.RGBA16F=(P=d.RGBA16F)!==null&&P!==void 0?P:H==null?void 0:H.RGBA16F_EXT,this.RGB16F=(N=d.RGB16F)!==null&&N!==void 0?N:H==null?void 0:H.RGB16F_EXT,d.getExtension(\"EXT_color_buffer_float\")}else{d.getExtension(\"EXT_color_buffer_half_float\"),d.getExtension(\"OES_texture_half_float_linear\");let H=d.getExtension(\"OES_texture_half_float\");this.HALF_FLOAT=H==null?void 0:H.HALF_FLOAT_OES}}setDefault(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()}setDirty(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.bindVertexArray.dirty=!0,this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0}createIndexBuffer(d,P){return new dh(this,d,P)}createVertexBuffer(d,P,N){return new Hd(this,d,P,N)}createRenderbuffer(d,P,N){let H=this.gl,$=H.createRenderbuffer();return this.bindRenderbuffer.set($),H.renderbufferStorage(H.RENDERBUFFER,d,P,N),this.bindRenderbuffer.set(null),$}createFramebuffer(d,P,N,H){return new Ze(this,d,P,N,H)}clear({color:d,depth:P,stencil:N}){let H=this.gl,$=0;d&&($|=H.COLOR_BUFFER_BIT,this.clearColor.set(d),this.colorMask.set([!0,!0,!0,!0])),P!==void 0&&($|=H.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(P),this.depthMask.set(!0)),N!==void 0&&($|=H.STENCIL_BUFFER_BIT,this.clearStencil.set(N),this.stencilMask.set(255)),H.clear($)}setCullFace(d){d.enable===!1?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(d.mode),this.frontFace.set(d.frontFace))}setDepthMode(d){d.func!==this.gl.ALWAYS||d.mask?(this.depthTest.set(!0),this.depthFunc.set(d.func),this.depthMask.set(d.mask),this.depthRange.set(d.range)):this.depthTest.set(!1)}setStencilMode(d){d.test.func!==this.gl.ALWAYS||d.mask?(this.stencilTest.set(!0),this.stencilMask.set(d.mask),this.stencilOp.set([d.fail,d.depthFail,d.pass]),this.stencilFunc.set({func:d.test.func,ref:d.ref,mask:d.test.mask})):this.stencilTest.set(!1)}setColorMode(d){i.bQ(d.blendFunction,Ja.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(d.blendFunction),this.blendColor.set(d.blendColor)),this.colorMask.set(d.mask)}createVertexArray(){var d;return Pl(this.gl)?this.gl.createVertexArray():(d=this.gl.getExtension(\"OES_vertex_array_object\"))===null||d===void 0?void 0:d.createVertexArrayOES()}deleteVertexArray(d){var P;Pl(this.gl)?this.gl.deleteVertexArray(d):(P=this.gl.getExtension(\"OES_vertex_array_object\"))===null||P===void 0||P.deleteVertexArrayOES(d)}unbindVAO(){this.bindVertexArray.set(null)}}let Wt;function zt(Y,d,P,N,H){var $,se;let de=Y.context,Ae=Y.transform,Fe=de.gl,Ie=Y.useProgram(\"collisionBox\"),Ye=[],st=0,dt=0;for(let Bt of N){let St=d.getTile(Bt).getBucket(P);if(!St)continue;let Pt=H?St.textCollisionBox:St.iconCollisionBox,$t=St.collisionCircleArray;$t.length>0&&(Ye.push({circleArray:$t,circleOffset:dt,coord:Bt}),st+=$t.length/4,dt=st),Pt&&Ie.draw(de,Fe.LINES,ma.disabled,Ca.disabled,Y.colorModeForRenderPass(),Sa.disabled,Bh(Y.transform),($=Y.style.map.terrain)===null||$===void 0?void 0:$.getTerrainData(Bt),Ae.getProjectionData({overscaledTileID:Bt,applyGlobeMatrix:!0,applyTerrainMatrix:!0}),P.id,Pt.layoutVertexBuffer,Pt.indexBuffer,Pt.segments,null,Y.transform.zoom,null,null,Pt.collisionVertexBuffer)}if(!H||!Ye.length)return;let Pe=Y.useProgram(\"collisionCircle\"),Xe=new i.ca;Xe.resize(4*st),Xe._trim();let at=0;for(let Bt of Ye)for(let St=0;St<Bt.circleArray.length/4;St++){let Pt=4*St,$t=Bt.circleArray[Pt+0],Ht=Bt.circleArray[Pt+1],dr=Bt.circleArray[Pt+2],Xr=Bt.circleArray[Pt+3];Xe.emplace(at++,$t,Ht,dr,Xr,0),Xe.emplace(at++,$t,Ht,dr,Xr,1),Xe.emplace(at++,$t,Ht,dr,Xr,2),Xe.emplace(at++,$t,Ht,dr,Xr,3)}(!Wt||Wt.length<2*st)&&(Wt=(function(Bt){let St=2*Bt,Pt=new i.cc;Pt.resize(St),Pt._trim();for(let $t=0;$t<St;$t++){let Ht=6*$t;Pt.uint16[Ht+0]=4*$t+0,Pt.uint16[Ht+1]=4*$t+1,Pt.uint16[Ht+2]=4*$t+2,Pt.uint16[Ht+3]=4*$t+2,Pt.uint16[Ht+4]=4*$t+3,Pt.uint16[Ht+5]=4*$t+0}return Pt})(st));let xt=de.createIndexBuffer(Wt,!0),Et=de.createVertexBuffer(Xe,i.cb.members,!0);for(let Bt of Ye){let St=Wu(Y.transform);Pe.draw(de,Fe.TRIANGLES,ma.disabled,Ca.disabled,Y.colorModeForRenderPass(),Sa.disabled,St,(se=Y.style.map.terrain)===null||se===void 0?void 0:se.getTerrainData(Bt.coord),null,P.id,Et,xt,i.aV.simpleSegment(0,2*Bt.circleOffset,Bt.circleArray.length,Bt.circleArray.length/2),null,Y.transform.zoom,null,null,null)}Et.destroy(),xt.destroy()}let hr=i.ap(new Float32Array(16));function Ur(Y,d,P,N,H,$){let{horizontalAlign:se,verticalAlign:de}=i.aP(Y);return new i.P((-(se-.5)*d/H+N[0])*$,(-(de-.5)*P/H+N[1])*$)}function Qr(Y,d,P,N,H,$){let se=d.tileAnchorPoint.add(new i.P(d.translation[0],d.translation[1]));if(d.pitchWithMap){let de=N.mult($);P||(de=de.rotate(-H));let Ae=se.add(de);return Jr(Ae.x,Ae.y,d.pitchedLabelPlaneMatrix,d.getElevation).point}if(P){let de=Xi(d.tileAnchorPoint.x+1,d.tileAnchorPoint.y,d).point.sub(Y),Ae=Math.atan(de.y/de.x)+(de.x<0?Math.PI:0);return Y.add(N.rotate(Ae))}return Y.add(N)}function cn(Y,d,P,N,H,$,se,de,Ae,Fe,Ie,Ye){let st=Y.text.placedSymbolArray,dt=Y.text.dynamicLayoutVertexArray,Pe=Y.icon.dynamicLayoutVertexArray,Xe={};dt.clear();for(let at=0;at<st.length;at++){let xt=st.get(at),Et=xt.hidden||!xt.crossTileID||Y.allowVerticalPlacement&&!xt.placedOrientation?null:N[xt.crossTileID];if(Et){let Bt=new i.P(xt.anchorX,xt.anchorY),St={getElevation:Ye,width:H.width,height:H.height,pitchedLabelPlaneMatrix:$,pitchWithMap:P,transform:H,tileAnchorPoint:Bt,translation:Fe,unwrappedTileID:Ie},Pt=P?Ma(Bt.x,Bt.y,St):Xi(Bt.x,Bt.y,St),$t=qn(H.cameraToCenterDistance,Pt.signedDistanceFromCamera),Ht=i.ay(Y.textSizeData,de,xt)*$t/i.aJ;P&&(Ht*=Y.tilePixelRatio/se);let{width:dr,height:Xr,anchor:Or,textOffset:$r,textBoxScale:Er}=Et,Tr=Ur(Or,dr,Xr,$r,Er,Ht),Rr=H.getPitchedTextCorrection(Bt.x+Fe[0],Bt.y+Fe[1],Ie),Lr=Qr(Pt.point,St,d,Tr,-H.bearingInRadians,Rr),an=Y.allowVerticalPlacement&&xt.placedOrientation===i.ax.vertical?Math.PI/2:0;for(let on=0;on<xt.numGlyphs;on++)i.aD(dt,Lr,an);Ae&&xt.associatedIconIndex>=0&&(Xe[xt.associatedIconIndex]={shiftedAnchor:Lr,angle:an})}else Eo(xt.numGlyphs,dt)}if(Ae){Pe.clear();let at=Y.icon.placedSymbolArray;for(let xt=0;xt<at.length;xt++){let Et=at.get(xt);if(Et.hidden)Eo(Et.numGlyphs,Pe);else{let Bt=Xe[xt];if(Bt)for(let St=0;St<Et.numGlyphs;St++)i.aD(Pe,Bt.shiftedAnchor,Bt.angle);else Eo(Et.numGlyphs,Pe)}}Y.icon.dynamicLayoutVertexBuffer.updateData(Pe)}Y.text.dynamicLayoutVertexBuffer.updateData(dt)}function yn(Y,d,P){return P.iconsInText&&d?\"symbolTextAndIcon\":Y?\"symbolSDF\":\"symbolIcon\"}function Fn(Y,d,P,N,H,$,se,de,Ae,Fe,Ie,Ye,st){var dt,Pe;let Xe=Y.context,at=Xe.gl,xt=Y.transform,Et=de===\"map\",Bt=Ae===\"map\",St=de!==\"viewport\"&&P.layout.get(\"symbol-placement\")!==\"point\",Pt=Et&&!Bt&&!St,$t=!P.layout.get(\"symbol-sort-key\").isConstant(),Ht=!1,dr=Y.getDepthModeForSublayer(0,ma.ReadOnly),Xr=P._unevaluatedLayout.hasValue(\"text-variable-anchor\")||P._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),Or=[],$r=xt.getCircleRadiusCorrection();for(let Rr of N){let Lr=d.getTile(Rr),an=Lr.getBucket(P);if(!an)continue;let on=H?an.text:an.icon;if(!(on!=null&&on.segments.get().length)||!on.hasVisibleVertices)continue;let Nn=on.programConfigurations.get(P.id),Yn=H||an.sdfIcons,yi=H?an.textSizeData:an.iconSizeData,Sn=Bt||xt.pitch!==0,An=Y.useProgram(yn(Yn,H,an),Nn),li=i.aw(yi,xt.zoom),Vn=(dt=Y.style.map.terrain)===null||dt===void 0?void 0:dt.getTerrainData(Rr),jn,Wn,si,zi,da=[0,0],ui=null;if(H)Wn=Lr.glyphAtlasTexture,si=at.LINEAR,jn=Lr.glyphAtlasTexture.size,an.iconsInText&&(da=Lr.imageAtlasTexture.size,ui=Lr.imageAtlasTexture,zi=Sn||Y.options.rotating||Y.options.zooming||yi.kind===\"composite\"||yi.kind===\"camera\"?at.LINEAR:at.NEAREST);else{let no=P.layout.get(\"icon-size\").constantOr(0)!==1||an.iconsNeedLinear;Wn=Lr.imageAtlasTexture,si=Yn||Y.options.rotating||Y.options.zooming||no||Sn?at.LINEAR:at.NEAREST,jn=Lr.imageAtlasTexture.size}let Li=i.aK(Lr,1,Y.transform.zoom),Ki=Ut(Et,Y.transform,Li),ca=_r(Bt,Et,Y.transform,Li),Ia=i.aL(xt,Lr,$,se),Os=xt.getProjectionData({overscaledTileID:Rr,applyGlobeMatrix:!st,applyTerrainMatrix:!0}),Ss=Xr&&an.hasTextData(),Na=P.layout.get(\"icon-text-fit\")!==\"none\"&&Ss&&an.hasIconData();if(St){let no=i.O();Vt(no,Ki);let Us=Y.style.map.terrain?(Ta,vu)=>Y.style.map.terrain.getElevation(Rr,Ta,vu):null,ql=P.layout.get(\"text-rotation-alignment\")===\"map\";Jn(an,Y,H,Ki,no,Bt,Fe,ql,Rr.toUnwrapped(),xt.width,xt.height,Ia,Us)}let Ns=H&&Xr||Na,vo=St||Ns?hr:Bt?Ki:Y.transform.clipSpaceToPixelsMatrix,_o=Yn&&P.paint.get(H?\"text-halo-width\":\"icon-halo-width\").constantOr(1)!==0,po;po=Yn?an.iconsInText?Gl(yi.kind,li,Pt,Bt,St,Ns,Y,vo,ca,Ia,jn,da,$r):Gd(yi.kind,li,Pt,Bt,St,Ns,Y,vo,ca,Ia,H,jn,_o,$r):hh(yi.kind,li,Pt,Bt,St,Ns,Y,vo,ca,Ia,H,jn,$r);let Xo={program:An,buffers:on,uniformValues:po,projectionData:Os,atlasTexture:Wn,atlasTextureIcon:ui,atlasInterpolation:si,atlasInterpolationIcon:zi,isSDF:Yn,hasHalo:_o};if($t&&an.canOverlap){Ht=!0;let no=on.segments.get();for(let Us of no)Or.push({segments:new i.aV([Us]),sortKey:Us.sortKey,state:Xo,terrainData:Vn})}else Or.push({segments:on.segments,sortKey:0,state:Xo,terrainData:Vn})}Ht&&Or.sort(((Rr,Lr)=>Rr.sortKey-Lr.sortKey));let Er=(Pe=P.paint.get(H?\"text-halo-width\":\"icon-halo-width\").constantOr(null))!==null&&Pe!==void 0?Pe:1/0,Tr=P.layout.get(\"text-letter-spacing\").constantOr(0)*i.aJ<0||Er>1;for(let Rr of Or){let Lr=Rr.state;Xe.activeTexture.set(at.TEXTURE0),Lr.atlasTexture.bind(Lr.atlasInterpolation,at.CLAMP_TO_EDGE),Lr.atlasTextureIcon&&(Xe.activeTexture.set(at.TEXTURE1),Lr.atlasTextureIcon&&Lr.atlasTextureIcon.bind(Lr.atlasInterpolationIcon,at.CLAMP_TO_EDGE));let an=Lr.isSDF&&Lr.hasHalo;if(an){let on=Lr.uniformValues;on.u_is_halo=1,Tr&&(on.u_is_plain=0,en(Lr.buffers,Rr.segments,P,Y,Lr.program,dr,Ie,Ye,on,Lr.projectionData,Rr.terrainData),on.u_is_halo=0,on.u_is_plain=1)}en(Lr.buffers,Rr.segments,P,Y,Lr.program,dr,Ie,Ye,Lr.uniformValues,Lr.projectionData,Rr.terrainData),an&&!Tr&&(Lr.uniformValues.u_is_halo=0)}}function en(Y,d,P,N,H,$,se,de,Ae,Fe,Ie){let Ye=N.context;H.draw(Ye,Ye.gl.TRIANGLES,$,se,de,Sa.backCCW,Ae,Ie,Fe,P.id,Y.layoutVertexBuffer,Y.indexBuffer,d,P.paint,N.transform.zoom,Y.programConfigurations.get(P.id),Y.dynamicLayoutVertexBuffer,Y.opacityVertexBuffer)}function vn(Y,d,P,N,H){let $=Y.context,se=$.gl,de=Ca.disabled,Ae=new Ja([se.ONE,se.ONE],i.bo.transparent,[!0,!0,!0,!0]),Fe=d.getBucket(P);if(!Fe)return;let Ie=N.key,Ye=P.heatmapFbos.get(Ie);Ye||(Ye=Pn($,d.tileSize,d.tileSize),P.heatmapFbos.set(Ie,Ye)),$.bindFramebuffer.set(Ye.framebuffer),$.viewport.set([0,0,d.tileSize,d.tileSize]),$.clear({color:i.bo.transparent});let st=Fe.programConfigurations.get(P.id),dt=Y.useProgram(\"heatmap\",st,!H),Pe=Y.transform.getProjectionData({overscaledTileID:d.tileID,applyGlobeMatrix:!0,applyTerrainMatrix:!0}),Xe=Y.style.map.terrain.getTerrainData(N);dt.draw($,se.TRIANGLES,ma.disabled,de,Ae,Sa.disabled,pd(d,Y.transform.zoom,P.paint.get(\"heatmap-intensity\"),1),Xe,Pe,P.id,Fe.layoutVertexBuffer,Fe.indexBuffer,Fe.segments,P.paint,Y.transform.zoom,st)}function mn(Y,d,P,N,H){let $=Y.context,se=$.gl,de=Y.transform;$.setColorMode(Y.colorModeForRenderPass());let Ae=On($,d),Fe=P.key,Ie=d.heatmapFbos.get(Fe);if(!Ie)return;$.activeTexture.set(se.TEXTURE0),se.bindTexture(se.TEXTURE_2D,Ie.colorAttachment.get()),$.activeTexture.set(se.TEXTURE1),Ae.bind(se.LINEAR,se.CLAMP_TO_EDGE);let Ye=de.getProjectionData({overscaledTileID:P,applyTerrainMatrix:H,applyGlobeMatrix:!N});Y.useProgram(\"heatmapTexture\").draw($,se.TRIANGLES,ma.disabled,Ca.disabled,Y.colorModeForRenderPass(),Sa.disabled,lh(Y,d,0,1),null,Ye,d.id,Y.rasterBoundsBuffer,Y.quadTriangleIndexBuffer,Y.rasterBoundsSegments,d.paint,de.zoom),Ie.destroy(),d.heatmapFbos.delete(Fe)}function Pn(Y,d,P){var N,H;let $=Y.gl,se=$.createTexture();$.bindTexture($.TEXTURE_2D,se),$.texParameteri($.TEXTURE_2D,$.TEXTURE_WRAP_S,$.CLAMP_TO_EDGE),$.texParameteri($.TEXTURE_2D,$.TEXTURE_WRAP_T,$.CLAMP_TO_EDGE),$.texParameteri($.TEXTURE_2D,$.TEXTURE_MIN_FILTER,$.LINEAR),$.texParameteri($.TEXTURE_2D,$.TEXTURE_MAG_FILTER,$.LINEAR);let de=(N=Y.HALF_FLOAT)!==null&&N!==void 0?N:$.UNSIGNED_BYTE,Ae=(H=Y.RGBA16F)!==null&&H!==void 0?H:$.RGBA;$.texImage2D($.TEXTURE_2D,0,Ae,d,P,0,$.RGBA,de,null);let Fe=Y.createFramebuffer(d,P,!1,!1);return Fe.colorAttachment.set(se),Fe}function On(Y,d){return d.colorRampTexture||(d.colorRampTexture=new 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de=$.gradients[H.id],Ae=de.texture;H.gradientVersion!==de.version&&(Ae=ki(Y,d,P,N,H,$,se,de)),P.activeTexture.set(N.TEXTURE0),Ae.bind(H.stepInterpolant?N.NEAREST:N.LINEAR,N.CLAMP_TO_EDGE)}function Un(Y,d,P,N,H,$,se,de,Ae){let Fe=$.gradients[H.id],Ie=Fe.texture;H.gradientVersion!==Fe.version&&(Ie=ki(Y,d,P,N,H,$,se,Fe)),P.activeTexture.set(N.TEXTURE0),Ie.bind(H.stepInterpolant?N.NEAREST:N.LINEAR,N.CLAMP_TO_EDGE),P.activeTexture.set(N.TEXTURE1),Y.lineAtlas.bind(P),de.updatePaintBuffers(Ae)}function Gn(Y,d,P,N,H){if(!P||!(N!=null&&N.imageAtlas))return;let $=N.imageAtlas.patternPositions,se=$[P.to.toString()],de=$[P.from.toString()];if(!se&&de&&(se=de),!de&&se&&(de=se),!se||!de){let Ae=H.getPaintProperty(d);se=$[Ae],de=$[Ae]}se&&de&&Y.setConstantPatternPositions(se,de)}function Zi(Y,d,P,N,H,$,se,de){var Ae;let Fe=Y.context.gl,Ie=\"fill-pattern\",Ye=P.paint.get(Ie),st=Ye==null?void 0:Ye.constantOr(1),dt=P.getCrossfadeParameters(),Pe,Xe,at,xt,Et,Bt=Y.transform,St=P.paint.get(\"fill-translate\"),Pt=P.paint.get(\"fill-translate-anchor\");se?(Xe=st&&!P.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",Pe=Fe.LINES):(Xe=st?\"fillPattern\":\"fill\",Pe=Fe.TRIANGLES);let $t=Ye.constantOr(null);for(let Ht of N){let dr=d.getTile(Ht);if(st&&!dr.patternsLoaded())continue;let Xr=dr.getBucket(P);if(!Xr)continue;let Or=Xr.programConfigurations.get(P.id),$r=Y.useProgram(Xe,Or),Er=(Ae=Y.style.map.terrain)===null||Ae===void 0?void 0:Ae.getTerrainData(Ht);st&&(Y.context.activeTexture.set(Fe.TEXTURE0),dr.imageAtlasTexture.bind(Fe.LINEAR,Fe.CLAMP_TO_EDGE),Or.updatePaintBuffers(dt)),Gn(Or,Ie,$t,dr,P);let Tr=Bt.getProjectionData({overscaledTileID:Ht,applyGlobeMatrix:!de,applyTerrainMatrix:!0}),Rr=i.aL(Bt,dr,St,Pt);if(se){xt=Xr.indexBuffer2,Et=Xr.segments2;let an=[Fe.drawingBufferWidth,Fe.drawingBufferHeight];at=Xe===\"fillOutlinePattern\"&&st?vc(Y,dt,dr,an,Rr):qh(an,Rr)}else xt=Xr.indexBuffer,Et=Xr.segments,at=st?rf(Y,dt,dr,Rr):{u_fill_translate:Rr};let Lr=Y.stencilModeForClipping(Ht);$r.draw(Y.context,Pe,H,Lr,$,Sa.backCCW,at,Er,Tr,P.id,Xr.layoutVertexBuffer,xt,Et,P.paint,Y.transform.zoom,Or)}}function na(Y,d,P,N,H,$,se,de){var Ae;let Fe=Y.context,Ie=Fe.gl,Ye=\"fill-extrusion-pattern\",st=P.paint.get(Ye),dt=st.constantOr(1),Pe=P.getCrossfadeParameters(),Xe=P.paint.get(\"fill-extrusion-opacity\"),at=st.constantOr(null),xt=Y.transform;for(let Et of N){let Bt=d.getTile(Et),St=Bt.getBucket(P);if(!St)continue;let Pt=(Ae=Y.style.map.terrain)===null||Ae===void 0?void 0:Ae.getTerrainData(Et),$t=St.programConfigurations.get(P.id),Ht=Y.useProgram(dt?\"fillExtrusionPattern\":\"fillExtrusion\",$t);dt&&(Y.context.activeTexture.set(Ie.TEXTURE0),Bt.imageAtlasTexture.bind(Ie.LINEAR,Ie.CLAMP_TO_EDGE),$t.updatePaintBuffers(Pe));let dr=xt.getProjectionData({overscaledTileID:Et,applyGlobeMatrix:!de,applyTerrainMatrix:!0});Gn($t,Ye,at,Bt,P);let Xr=i.aL(xt,Bt,P.paint.get(\"fill-extrusion-translate\"),P.paint.get(\"fill-extrusion-translate-anchor\")),Or=P.paint.get(\"fill-extrusion-vertical-gradient\"),$r=dt?Vd(Y,Or,Xe,Xr,Et,Pe,Bt):tf(Y,Or,Xe,Xr);Ht.draw(Fe,Fe.gl.TRIANGLES,H,$,se,Sa.backCCW,$r,Pt,dr,P.id,St.layoutVertexBuffer,St.indexBuffer,St.segments,P.paint,Y.transform.zoom,$t,Y.style.map.terrain&&St.centroidVertexBuffer)}}function Ha(Y,d,P,N,H,$,se,de,Ae){var Fe;let Ie=Y.style.projection,Ye=Y.context,st=Y.transform,dt=Ye.gl,Pe=[`#define NUM_ILLUMINATION_SOURCES ${P.paint.get(\"hillshade-highlight-color\").values.length}`],Xe=Y.useProgram(\"hillshade\",null,!1,Pe),at=!Y.options.moving;for(let xt of N){let Et=d.getTile(xt),Bt=Et.fbo;if(!Bt)continue;let St=Ie.getMeshFromTileID(Ye,xt.canonical,de,!0,\"raster\"),Pt=(Fe=Y.style.map.terrain)===null||Fe===void 0?void 0:Fe.getTerrainData(xt);Ye.activeTexture.set(dt.TEXTURE0),dt.bindTexture(dt.TEXTURE_2D,Bt.colorAttachment.get());let $t=st.getProjectionData({overscaledTileID:xt,aligned:at,applyGlobeMatrix:!Ae,applyTerrainMatrix:!0});Xe.draw(Ye,dt.TRIANGLES,$,H[xt.overscaledZ],se,Sa.backCCW,nf(Y,Et,P),Pt,$t,P.id,St.vertexBuffer,St.indexBuffer,St.segments)}}let Qa=0;function Ya(Y,d,P,N,H,$,se,de,Ae){var Fe;let Ie=Y.style.projection,Ye=Y.context,st=Y.transform,dt=Ye.gl,Pe=Y.useProgram(\"colorRelief\"),Xe=!Y.options.moving,at=P.paint.get(\"resampling\")===\"nearest\"?dt.NEAREST:dt.LINEAR,xt=!0,Et=0;for(let Bt of N){let St=d.getTile(Bt),Pt=St.dem;if(xt){Qa||(Qa=dt.getParameter(dt.MAX_TEXTURE_SIZE));let $r=Qa,{elevationTexture:Er,colorTexture:Tr}=P.getColorRampTextures(Ye,$r,Pt.getUnpackVector());Ye.activeTexture.set(dt.TEXTURE1),Er.bind(dt.NEAREST,dt.CLAMP_TO_EDGE),Ye.activeTexture.set(dt.TEXTURE4),Tr.bind(dt.LINEAR,dt.CLAMP_TO_EDGE),xt=!1,Et=Er.size[0]}if(!(Pt!=null&&Pt.data))continue;let $t=Pt.stride,Ht=Pt.getPixels();if(Ye.activeTexture.set(dt.TEXTURE0),Ye.pixelStoreUnpackPremultiplyAlpha.set(!1),St.demTexture||(St.demTexture=Y.getTileTexture($t)),St.demTexture){let $r=St.demTexture;$r.update(Ht,{premultiply:!1}),$r.bind(at,dt.CLAMP_TO_EDGE)}else St.demTexture=new i.T(Ye,Ht,dt.RGBA,{premultiply:!1}),St.demTexture.bind(at,dt.CLAMP_TO_EDGE);let dr=Ie.getMeshFromTileID(Ye,Bt.canonical,de,!0,\"raster\"),Xr=(Fe=Y.style.map.terrain)===null||Fe===void 0?void 0:Fe.getTerrainData(Bt),Or=st.getProjectionData({overscaledTileID:Bt,aligned:Xe,applyGlobeMatrix:!Ae,applyTerrainMatrix:!0});Pe.draw(Ye,dt.TRIANGLES,$,H[Bt.overscaledZ],se,Sa.backCCW,gd(P,St.dem,Et),Xr,Or,P.id,dr.vertexBuffer,dr.indexBuffer,dr.segments)}}let ho=[new i.P(0,0),new i.P(i.a6,0),new i.P(i.a6,i.a6),new i.P(0,i.a6)];function Pa(Y,d,P,N,H,$,se,de,Ae=!1,Fe=!1){var Ie;let Ye=N[N.length-1].overscaledZ,st=Y.context,dt=st.gl,Pe=Y.useProgram(\"raster\"),Xe=Y.transform,at=Y.style.projection,xt=Y.colorModeForRenderPass(),Et=!Y.options.moving,Bt=P.paint.get(\"raster-opacity\"),St=P.paint.get(\"resampling\")===\"nearest\"||P.paint.get(\"raster-resampling\")===\"nearest\"?dt.NEAREST:dt.LINEAR,Pt=P.paint.get(\"raster-fade-duration\"),$t=!!Y.style.map.terrain;for(let Ht of N){let dr=Y.getDepthModeForSublayer(Ht.overscaledZ-Ye,Bt===1?ma.ReadWrite:ma.ReadOnly,dt.LESS),Xr=d.getTile(Ht);st.activeTexture.set(dt.TEXTURE0),Xr.texture.bind(St,dt.CLAMP_TO_EDGE,dt.LINEAR_MIPMAP_NEAREST),st.activeTexture.set(dt.TEXTURE1);let{parentTile:Or,parentScaleBy:$r,parentTopLeft:Er,fadeValues:Tr}=Js(Xr,d,Pt,$t);Xr.fadeOpacity=Tr.tileOpacity,Or?(Or.fadeOpacity=Tr.parentTileOpacity,Or.texture.bind(St,dt.CLAMP_TO_EDGE,dt.LINEAR_MIPMAP_NEAREST)):Xr.texture.bind(St,dt.CLAMP_TO_EDGE,dt.LINEAR_MIPMAP_NEAREST),Xr.texture.useMipmap&&st.extTextureFilterAnisotropic&&Y.transform.pitch>Y.options.anisotropicFilterPitch&&dt.texParameterf(dt.TEXTURE_2D,st.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,st.extTextureFilterAnisotropicMax);let Rr=(Ie=Y.style.map.terrain)===null||Ie===void 0?void 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st=g(),dt=(st-Ie.timeAdded)/Ye,Pe=Fe.fadingDirection===We.Incoming,Xe=i.al((st-Fe.timeAdded)/Ye,0,1),at=i.al(1-dt,0,1),xt=Pe?Xe:at;return{tileOpacity:xt,parentTileOpacity:Pe?at:Xe,fadeMix:{opacity:1,mix:1-xt}}})(Y,$,P);return{parentTile:$,parentScaleBy:se,parentTopLeft:de,fadeValues:Ae}}if(Y.selfFading){let $=(function(se,de){let Ae=(g()-se.timeAdded)/de,Fe=i.al(Ae,0,1);return{tileOpacity:Fe,fadeMix:{opacity:Fe,mix:0}}})(Y,P);return{parentTile:null,parentScaleBy:1,parentTopLeft:[0,0],fadeValues:$}}return H}let fs=new i.bo(1,0,0,1),Io=new i.bo(0,1,0,1),oi=new i.bo(0,0,1,1),ro=new i.bo(1,0,1,1),Go=new i.bo(0,1,1,1);function Qs(Y,d,P,N){cu(Y,0,d+P/2,Y.transform.width,P,N)}function zl(Y,d,P,N){cu(Y,d-P/2,0,P,Y.transform.height,N)}function cu(Y,d,P,N,H,$){let se=Y.context,de=se.gl;de.enable(de.SCISSOR_TEST),de.scissor(d*Y.pixelRatio,P*Y.pixelRatio,N*Y.pixelRatio,H*Y.pixelRatio),se.clear({color:$}),de.disable(de.SCISSOR_TEST)}function Zu(Y,d,P){var N;let H=Y.context,$=H.gl,se=Y.useProgram(\"debug\"),de=ma.disabled,Ae=Ca.disabled,Fe=Y.colorModeForRenderPass(),Ie=\"$debug\",Ye=(N=Y.style.map.terrain)===null||N===void 0?void 0:N.getTerrainData(P);H.activeTexture.set($.TEXTURE0);let st=d.getTileByID(P.key).latestRawTileData,dt=Math.floor(((st==null?void 0:st.byteLength)||0)/1024),Pe=d.getTile(P).tileSize,Xe=512/Math.min(Pe,512)*(P.overscaledZ/Y.transform.zoom)*.5,at=P.canonical.toString();P.overscaledZ!==P.canonical.z&&(at+=` => ${P.overscaledZ}`),(function(Et,Bt){Et.initDebugOverlayCanvas();let St=Et.debugOverlayCanvas,Pt=Et.context.gl,$t=Et.debugOverlayCanvas.getContext(\"2d\");$t.clearRect(0,0,St.width,St.height),$t.shadowColor=\"white\",$t.shadowBlur=2,$t.lineWidth=1.5,$t.strokeStyle=\"white\",$t.textBaseline=\"top\",$t.font=\"bold 36px Open Sans, sans-serif\",$t.fillText(Bt,5,5),$t.strokeText(Bt,5,5),Et.debugOverlayTexture.update(St),Et.debugOverlayTexture.bind(Pt.LINEAR,Pt.CLAMP_TO_EDGE)})(Y,`${at} ${dt}kB`);let xt=Y.transform.getProjectionData({overscaledTileID:P,applyGlobeMatrix:!0,applyTerrainMatrix:!0});se.draw(H,$.TRIANGLES,de,Ae,Ja.alphaBlended,Sa.disabled,vd(i.bo.transparent,Xe),null,xt,Ie,Y.debugBuffer,Y.quadTriangleIndexBuffer,Y.debugSegments),se.draw(H,$.LINE_STRIP,de,Ae,Fe,Sa.disabled,vd(i.bo.red),Ye,xt,Ie,Y.debugBuffer,Y.tileBorderIndexBuffer,Y.debugSegments)}function mc(Y,d,P,N){let{isRenderingGlobe:H}=N,$=Y.context,se=$.gl,de=Y.transform,Ae=Y.colorModeForRenderPass(),Fe=Y.getDepthModeFor3D(),Ie=Y.useProgram(\"terrain\");$.bindFramebuffer.set(null),$.viewport.set([0,0,Y.width,Y.height]);for(let Ye of P){let st=d.getTerrainMesh(Ye.tileID),dt=Y.renderToTexture.getTexture(Ye),Pe=d.getTerrainData(Ye.tileID);$.activeTexture.set(se.TEXTURE0),se.bindTexture(se.TEXTURE_2D,dt.texture);let Xe=d.getMeshFrameDelta(de.zoom),at=de.calculateFogMatrix(Ye.tileID.toUnwrapped()),xt=Nd(Xe,at,Y.style.sky,de.pitch,H),Et=de.getProjectionData({overscaledTileID:Ye.tileID,applyTerrainMatrix:!1,applyGlobeMatrix:!0});Ie.draw($,se.TRIANGLES,Fe,Ca.disabled,Ae,Sa.backCCW,xt,Pe,Et,\"terrain\",st.vertexBuffer,st.indexBuffer,st.segments)}}function Yl(Y,d){if(!d.mesh){let P=new i.aU;P.emplaceBack(-1,-1),P.emplaceBack(1,-1),P.emplaceBack(1,1),P.emplaceBack(-1,1);let N=new i.aW;N.emplaceBack(0,1,2),N.emplaceBack(0,2,3),d.mesh=new ka(Y.createVertexBuffer(P,vs.members),Y.createIndexBuffer(N),i.aV.simpleSegment(0,0,P.length,N.length))}return d.mesh}let lf={symbol:function(Y,d,P,N,H,$){if(Y.renderPass!==\"translucent\")return;let{isRenderingToTexture:se}=$,de=Ca.disabled,Ae=Y.colorModeForRenderPass();(P._unevaluatedLayout.hasValue(\"text-variable-anchor\")||P._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"))&&(function(Fe,Ie,Ye,st,dt,Pe,Xe,at,xt){var Et;let Bt=Ie.transform,St=Ie.style.map.terrain,Pt=dt===\"map\",$t=Pe===\"map\";for(let Ht of Fe){let dr=st.getTile(Ht),Xr=dr.getBucket(Ye);if(!(!((Et=Xr==null?void 0:Xr.text)===null||Et===void 0)&&Et.segments.get().length))continue;let Or=i.aw(Xr.textSizeData,Bt.zoom),$r=i.aK(dr,1,Ie.transform.zoom),Er=Ut(Pt,Ie.transform,$r),Tr=Ye.layout.get(\"icon-text-fit\")!==\"none\"&&Xr.hasIconData();if(Or){let 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$;if(Y.renderPass!==\"translucent\")return;let{isRenderingToTexture:se}=H,de=P.paint.get(\"circle-opacity\"),Ae=P.paint.get(\"circle-stroke-width\"),Fe=P.paint.get(\"circle-stroke-opacity\"),Ie=!P.layout.get(\"circle-sort-key\").isConstant();if(de.constantOr(1)===0&&(Ae.constantOr(1)===0||Fe.constantOr(1)===0))return;let Ye=Y.context,st=Ye.gl,dt=Y.transform,Pe=Y.getDepthModeForSublayer(0,ma.ReadOnly),Xe=Ca.disabled,at=Y.colorModeForRenderPass(),xt=[],Et=dt.getCircleRadiusCorrection();for(let Bt of N){let St=d.getTile(Bt),Pt=St.getBucket(P);if(!Pt)continue;let $t=P.paint.get(\"circle-translate\"),Ht=P.paint.get(\"circle-translate-anchor\"),dr=i.aL(dt,St,$t,Ht),Xr=Pt.programConfigurations.get(P.id),Or=Y.useProgram(\"circle\",Xr),$r=Pt.layoutVertexBuffer,Er=Pt.indexBuffer,Tr=($=Y.style.map.terrain)===null||$===void 0?void 0:$.getTerrainData(Bt),Rr={programConfiguration:Xr,program:Or,layoutVertexBuffer:$r,indexBuffer:Er,uniformValues:dd(Y,St,P,dr,Et),terrainData:Tr,projectionData:dt.getProjectionData({overscaledTileID:Bt,applyGlobeMatrix:!se,applyTerrainMatrix:!0})};if(Ie){let Lr=Pt.segments.get();for(let an of Lr)xt.push({segments:new i.aV([an]),sortKey:an.sortKey,state:Rr})}else xt.push({segments:Pt.segments,sortKey:0,state:Rr})}Ie&&xt.sort(((Bt,St)=>Bt.sortKey-St.sortKey));for(let Bt of xt){let{programConfiguration:St,program:Pt,layoutVertexBuffer:$t,indexBuffer:Ht,uniformValues:dr,terrainData:Xr,projectionData:Or}=Bt.state;Pt.draw(Ye,st.TRIANGLES,Pe,Xe,at,Sa.backCCW,dr,Xr,Or,P.id,$t,Ht,Bt.segments,P.paint,Y.transform.zoom,St)}},heatmap:function(Y,d,P,N,H){if(P.paint.get(\"heatmap-opacity\")===0)return;let $=Y.context,{isRenderingToTexture:se,isRenderingGlobe:de}=H;if(Y.style.map.terrain){for(let Ae of N){let Fe=d.getTile(Ae);d.hasRenderableParent(Ae)||(Y.renderPass===\"offscreen\"?vn(Y,Fe,P,Ae,de):Y.renderPass===\"translucent\"&&mn(Y,P,Ae,se,de))}$.viewport.set([0,0,Y.width,Y.height])}else Y.renderPass===\"offscreen\"?(function(Ae,Fe,Ie,Ye){let st=Ae.context,dt=st.gl,Pe=Ae.transform,Xe=Ca.disabled,at=new Ja([dt.ONE,dt.ONE],i.bo.transparent,[!0,!0,!0,!0]);(function(xt,Et,Bt){let St=xt.gl;xt.activeTexture.set(St.TEXTURE1),xt.viewport.set([0,0,Et.width/4,Et.height/4]);let Pt=Bt.heatmapFbos.get(i.cd);Pt?(St.bindTexture(St.TEXTURE_2D,Pt.colorAttachment.get()),xt.bindFramebuffer.set(Pt.framebuffer)):(Pt=Pn(xt,Et.width/4,Et.height/4),Bt.heatmapFbos.set(i.cd,Pt))})(st,Ae,Ie),st.clear({color:i.bo.transparent});for(let xt of Ye){if(Fe.hasRenderableParent(xt))continue;let Et=Fe.getTile(xt),Bt=Et.getBucket(Ie);if(!Bt)continue;let St=Bt.programConfigurations.get(Ie.id),Pt=Ae.useProgram(\"heatmap\",St),$t=Pe.getProjectionData({overscaledTileID:xt,applyGlobeMatrix:!0,applyTerrainMatrix:!1}),Ht=Pe.getCircleRadiusCorrection();Pt.draw(st,dt.TRIANGLES,ma.disabled,Xe,at,Sa.backCCW,pd(Et,Pe.zoom,Ie.paint.get(\"heatmap-intensity\"),Ht),null,$t,Ie.id,Bt.layoutVertexBuffer,Bt.indexBuffer,Bt.segments,Ie.paint,Pe.zoom,St)}st.viewport.set([0,0,Ae.width,Ae.height])})(Y,d,P,N):Y.renderPass===\"translucent\"&&(function(Ae,Fe){let Ie=Ae.context,Ye=Ie.gl;Ie.setColorMode(Ae.colorModeForRenderPass());let st=Fe.heatmapFbos.get(i.cd);st&&(Ie.activeTexture.set(Ye.TEXTURE0),Ye.bindTexture(Ye.TEXTURE_2D,st.colorAttachment.get()),Ie.activeTexture.set(Ye.TEXTURE1),On(Ie,Fe).bind(Ye.LINEAR,Ye.CLAMP_TO_EDGE),Ae.useProgram(\"heatmapTexture\").draw(Ie,Ye.TRIANGLES,ma.disabled,Ca.disabled,Ae.colorModeForRenderPass(),Sa.disabled,lh(Ae,Fe,0,1),null,null,Fe.id,Ae.viewportBuffer,Ae.quadTriangleIndexBuffer,Ae.viewportSegments,Fe.paint,Ae.transform.zoom))})(Y,P)},line:function(Y,d,P,N,H){var $;if(Y.renderPass!==\"translucent\")return;let{isRenderingToTexture:se}=H,de=P.paint.get(\"line-opacity\"),Ae=P.paint.get(\"line-width\");if(de.constantOr(1)===0||Ae.constantOr(1)===0)return;let Fe=Y.getDepthModeForSublayer(0,ma.ReadOnly),Ie=Y.colorModeForRenderPass(),Ye=P.paint.get(\"line-dasharray\"),st=Ye.constantOr(1),dt=P.paint.get(\"line-pattern\"),Pe=dt.constantOr(1),Xe=P.paint.get(\"line-gradient\"),at=P.getCrossfadeParameters(),xt;xt=Pe?\"linePattern\":st&&Xe?\"lineGradientSDF\":st?\"lineSDF\":Xe?\"lineGradient\":\"line\";let Et=Y.context,Bt=Et.gl,St=Y.transform,Pt=!0;for(let $t of N){let Ht=d.getTile($t);if(Pe&&!Ht.patternsLoaded())continue;let dr=Ht.getBucket(P);if(!dr)continue;let Xr=dr.programConfigurations.get(P.id),Or=Y.context.program.get(),$r=Y.useProgram(xt,Xr),Er=Pt||$r.program!==Or,Tr=($=Y.style.map.terrain)===null||$===void 0?void 0:$.getTerrainData($t),Rr=dt.constantOr(null),Lr=Ye==null?void 0:Ye.constantOr(null);if(Rr&&Ht.imageAtlas){let yi=Ht.imageAtlas,Sn=yi.patternPositions[Rr.to.toString()],An=yi.patternPositions[Rr.from.toString()];Sn&&An&&Xr.setConstantPatternPositions(Sn,An)}else if(Lr){let yi=P.layout.get(\"line-cap\").constantOr(null)===\"round\",Sn=Y.lineAtlas.getDash(Lr.to,yi),An=Y.lineAtlas.getDash(Lr.from,yi);Xr.setConstantDashPositions(Sn,An)}let an=St.getProjectionData({overscaledTileID:$t,applyGlobeMatrix:!se,applyTerrainMatrix:!0}),on=St.getPixelScale(),Nn;Pe?(Nn=Lc(Y,Ht,P,on,at),Ci(Et,Bt,Ht,Xr,at)):st&&Xe?(Nn=yd(Y,Ht,P,on,at,dr.lineClipsArray.length),Un(Y,d,Et,Bt,P,dr,$t,Xr,at)):st?(Nn=md(Y,Ht,P,on,at),qi(Y,Et,Bt,Xr,Er,at)):Xe?(Nn=ch(Y,Ht,P,on,dr.lineClipsArray.length),pi(Y,d,Et,Bt,P,dr,$t)):Nn=Rl(Y,Ht,P,on);let Yn=Y.stencilModeForClipping($t);$r.draw(Et,Bt.TRIANGLES,Fe,Yn,Ie,Sa.disabled,Nn,Tr,an,P.id,dr.layoutVertexBuffer,dr.indexBuffer,dr.segments,P.paint,Y.transform.zoom,Xr,dr.layoutVertexBuffer2),Pt=!1}},fill:function(Y,d,P,N,H){let $=P.paint.get(\"fill-color\"),se=P.paint.get(\"fill-opacity\");if(se.constantOr(1)===0)return;let{isRenderingToTexture:de}=H,Ae=Y.colorModeForRenderPass(),Fe=P.paint.get(\"fill-pattern\"),Ie=Y.opaquePassEnabledForLayer()&&!Fe.constantOr(1)&&$.constantOr(i.bo.transparent).a===1&&se.constantOr(0)===1?\"opaque\":\"translucent\";if(Y.renderPass===Ie){let Ye=Y.getDepthModeForSublayer(1,Y.renderPass===\"opaque\"?ma.ReadWrite:ma.ReadOnly);Zi(Y,d,P,N,Ye,Ae,!1,de)}if(Y.renderPass===\"translucent\"&&P.paint.get(\"fill-antialias\")){let Ye=Y.getDepthModeForSublayer(P.getPaintProperty(\"fill-outline-color\")?2:0,ma.ReadOnly);Zi(Y,d,P,N,Ye,Ae,!0,de)}},fillExtrusion:function(Y,d,P,N,H){let $=P.paint.get(\"fill-extrusion-opacity\");if($===0)return;let{isRenderingToTexture:se}=H;if(Y.renderPass===\"translucent\"){let de=new ma(Y.context.gl.LEQUAL,ma.ReadWrite,Y.depthRangeFor3D);if($!==1||P.paint.get(\"fill-extrusion-pattern\").constantOr(1))na(Y,d,P,N,de,Ca.disabled,Ja.disabled,se),na(Y,d,P,N,de,Y.stencilModeFor3D(),Y.colorModeForRenderPass(),se);else{let Ae=Y.colorModeForRenderPass();na(Y,d,P,N,de,Ca.disabled,Ae,se)}}},hillshade:function(Y,d,P,N,H){if(Y.renderPass!==\"offscreen\"&&Y.renderPass!==\"translucent\")return;let{isRenderingToTexture:$}=H,se=Y.context,de=Y.style.projection.useSubdivision,Ae=Y.getDepthModeForSublayer(0,ma.ReadOnly),Fe=Y.colorModeForRenderPass();if(Y.renderPass===\"offscreen\")(function(Ie,Ye,st,dt,Pe,Xe,at){let xt=Ie.context,Et=xt.gl,Bt=dt.paint.get(\"resampling\")===\"nearest\"?Et.NEAREST:Et.LINEAR;for(let St of st){let Pt=Ye.getTile(St),$t=Pt.dem;if(!($t!=null&&$t.data)||!Pt.needsHillshadePrepare)continue;let Ht=$t.dim,dr=$t.stride,Xr=$t.getPixels();if(xt.activeTexture.set(Et.TEXTURE1),xt.pixelStoreUnpackPremultiplyAlpha.set(!1),Pt.demTexture||(Pt.demTexture=Ie.getTileTexture(dr)),Pt.demTexture){let $r=Pt.demTexture;$r.update(Xr,{premultiply:!1}),$r.bind(Et.NEAREST,Et.CLAMP_TO_EDGE)}else Pt.demTexture=new i.T(xt,Xr,Et.RGBA,{premultiply:!1}),Pt.demTexture.bind(Et.NEAREST,Et.CLAMP_TO_EDGE);xt.activeTexture.set(Et.TEXTURE0);let Or=Pt.fbo;if(!Or){let $r=new i.T(xt,{width:Ht,height:Ht,data:null},Et.RGBA);$r.bind(Bt,Et.CLAMP_TO_EDGE),Or=Pt.fbo=xt.createFramebuffer(Ht,Ht,!0,!1),Or.colorAttachment.set($r.texture)}xt.bindFramebuffer.set(Or.framebuffer),xt.viewport.set([0,0,Ht,Ht]),Ie.useProgram(\"hillshadePrepare\").draw(xt,Et.TRIANGLES,Pe,Xe,at,Sa.disabled,uh(Pt.tileID,$t),null,null,dt.id,Ie.rasterBoundsBuffer,Ie.quadTriangleIndexBuffer,Ie.rasterBoundsSegments),Pt.needsHillshadePrepare=!1}})(Y,d,N,P,Ae,Ca.disabled,Fe),se.viewport.set([0,0,Y.width,Y.height]);else if(Y.renderPass===\"translucent\")if(de){let[Ie,Ye,st]=Y.stencilConfigForOverlapTwoPass(N);Ha(Y,d,P,st,Ie,Ae,Fe,!1,$),Ha(Y,d,P,st,Ye,Ae,Fe,!0,$)}else{let[Ie,Ye]=Y.getStencilConfigForOverlapAndUpdateStencilID(N);Ha(Y,d,P,Ye,Ie,Ae,Fe,!1,$)}},colorRelief:function(Y,d,P,N,H){if(Y.renderPass!==\"translucent\"||!N.length)return;let{isRenderingToTexture:$}=H,se=Y.style.projection.useSubdivision,de=Y.getDepthModeForSublayer(0,ma.ReadOnly),Ae=Y.colorModeForRenderPass();if(se){let[Fe,Ie,Ye]=Y.stencilConfigForOverlapTwoPass(N);Ya(Y,d,P,Ye,Fe,de,Ae,!1,$),Ya(Y,d,P,Ye,Ie,de,Ae,!0,$)}else{let[Fe,Ie]=Y.getStencilConfigForOverlapAndUpdateStencilID(N);Ya(Y,d,P,Ie,Fe,de,Ae,!1,$)}},raster:function(Y,d,P,N,H){if(Y.renderPass!==\"translucent\"||P.paint.get(\"raster-opacity\")===0||!N.length)return;let{isRenderingToTexture:$}=H,se=d.getSource(),de=Y.style.projection.useSubdivision;if(se instanceof qe)Pa(Y,d,P,N,null,!1,!1,se.tileCoords,se.flippedWindingOrder,$);else if(de){let[Ae,Fe,Ie]=Y.stencilConfigForOverlapTwoPass(N);Pa(Y,d,P,Ie,Ae,!1,!0,ho,!1,$),Pa(Y,d,P,Ie,Fe,!0,!0,ho,!1,$)}else{let[Ae,Fe]=Y.getStencilConfigForOverlapAndUpdateStencilID(N);Pa(Y,d,P,Fe,Ae,!1,!0,ho,!1,$)}},background:function(Y,d,P,N,H){var $;let se=P.paint.get(\"background-color\"),de=P.paint.get(\"background-opacity\");if(de===0)return;let{isRenderingToTexture:Ae}=H,Fe=Y.context,Ie=Fe.gl,Ye=Y.style.projection,st=Y.transform,dt=st.tileSize,Pe=P.paint.get(\"background-pattern\");if(Y.isPatternMissing(Pe))return;let Xe=!Pe&&se.a===1&&de===1&&Y.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(Y.renderPass!==Xe)return;let at=Ca.disabled,xt=Y.getDepthModeForSublayer(0,Xe===\"opaque\"?ma.ReadWrite:ma.ReadOnly),Et=Y.colorModeForRenderPass(),Bt=Y.useProgram(Pe?\"backgroundPattern\":\"background\"),St=N||ft(st,{tileSize:dt,terrain:Y.style.map.terrain});Pe&&(Fe.activeTexture.set(Ie.TEXTURE0),Y.imageManager.bind(Y.context));let Pt=P.getCrossfadeParameters();for(let $t of St){let Ht=st.getProjectionData({overscaledTileID:$t,applyGlobeMatrix:!Ae,applyTerrainMatrix:!0}),dr=Pe?xd(de,Y,Pe,{tileID:$t,tileSize:dt},Pt):wl(de,se),Xr=($=Y.style.map.terrain)===null||$===void 0?void 0:$.getTerrainData($t),Or=Ye.getMeshFromTileID(Fe,$t.canonical,!1,!0,\"raster\");Bt.draw(Fe,Ie.TRIANGLES,xt,at,Et,Sa.backCCW,dr,Xr,Ht,P.id,Or.vertexBuffer,Or.indexBuffer,Or.segments)}},sky:function(Y,d){let P=Y.context,N=P.gl,H=((Ie,Ye,st)=>{let dt=Math.cos(Ye.rollInRadians),Pe=Math.sin(Ye.rollInRadians),Xe=or(Ye),at=Ye.getProjectionData({overscaledTileID:null,applyGlobeMatrix:!0,applyTerrainMatrix:!0}).projectionTransition;return{u_sky_color:Ie.properties.get(\"sky-color\"),u_horizon_color:Ie.properties.get(\"horizon-color\"),u_horizon:[(Ye.width/2-Xe*Pe)*st,(Ye.height/2+Xe*dt)*st],u_horizon_normal:[-Pe,dt],u_sky_horizon_blend:Ie.properties.get(\"sky-horizon-blend\")*Ye.height/2*st,u_sky_blend:at}})(d,Y.style.map.transform,Y.pixelRatio),$=new ma(N.LEQUAL,ma.ReadWrite,[0,1]),se=Ca.disabled,de=Y.colorModeForRenderPass(),Ae=Y.useProgram(\"sky\"),Fe=Yl(P,d);Ae.draw(P,N.TRIANGLES,$,se,de,Sa.disabled,H,null,void 0,\"sky\",Fe.vertexBuffer,Fe.indexBuffer,Fe.segments)},atmosphere:function(Y,d,P){let N=Y.context,H=N.gl,$=Y.useProgram(\"atmosphere\"),se=new ma(H.LEQUAL,ma.ReadOnly,[0,1]),de=Y.transform,Ae=(function(at,xt){let Et=at.properties.get(\"position\"),Bt=[-Et.x,-Et.y,-Et.z],St=i.ap(new Float64Array(16));return at.properties.get(\"anchor\")===\"map\"&&(i.be(St,St,xt.rollInRadians),i.bf(St,St,-xt.pitchInRadians),i.be(St,St,xt.bearingInRadians),i.bf(St,St,xt.center.lat*Math.PI/180),i.bI(St,St,-xt.center.lng*Math.PI/180)),i.cg(Bt,Bt,St),Bt})(P,Y.transform),Fe=de.getProjectionData({overscaledTileID:null,applyGlobeMatrix:!0,applyTerrainMatrix:!0}),Ie=d.properties.get(\"atmosphere-blend\")*Fe.projectionTransition;if(Ie===0)return;let Ye=Ks(de.worldSize,de.center.lat),st=de.inverseProjectionMatrix,dt=new Float64Array(4);dt[3]=1,i.aE(dt,dt,de.modelViewProjectionMatrix),dt[0]/=dt[3],dt[1]/=dt[3],dt[2]/=dt[3],dt[3]=1,i.aE(dt,dt,st),dt[0]/=dt[3],dt[1]/=dt[3],dt[2]/=dt[3],dt[3]=1;let Pe=((at,xt,Et,Bt,St)=>({u_sun_pos:at,u_atmosphere_blend:xt,u_globe_position:Et,u_globe_radius:Bt,u_inv_proj_matrix:St}))(Ae,Ie,[dt[0],dt[1],dt[2]],Ye,st),Xe=Yl(N,d);$.draw(N,H.TRIANGLES,se,Ca.disabled,Ja.alphaBlended,Sa.disabled,Pe,null,null,\"atmosphere\",Xe.vertexBuffer,Xe.indexBuffer,Xe.segments)},custom:function(Y,d,P,N){let{isRenderingGlobe:H}=N,$=Y.context,se=P.implementation,de=Y.style.projection,Ae=Y.transform,Fe=Ae.getProjectionDataForCustomLayer(H),Ie={farZ:Ae.farZ,nearZ:Ae.nearZ,fov:Ae.fov*Math.PI/180,modelViewProjectionMatrix:Ae.modelViewProjectionMatrix,projectionMatrix:Ae.projectionMatrix,shaderData:{variantName:de.shaderVariantName,vertexShaderPrelude:`const float PI = 3.141592653589793;\n",
+       "uniform mat4 u_projection_matrix;\n",
+       "${de.shaderPreludeCode.vertexSource}`,define:de.shaderDefine},defaultProjectionData:Fe},Ye=se.renderingMode?se.renderingMode:\"2d\";if(Y.renderPass===\"offscreen\"){let st=se.prerender;st&&(Y.setCustomLayerDefaults(),$.setColorMode(Y.colorModeForRenderPass()),st.call(se,$.gl,Ie),$.setDirty(),Y.setBaseState())}else if(Y.renderPass===\"translucent\"){Y.setCustomLayerDefaults(),$.setColorMode(Y.colorModeForRenderPass()),$.setStencilMode(Ca.disabled);let st=Ye===\"3d\"?Y.getDepthModeFor3D():Y.getDepthModeForSublayer(0,ma.ReadOnly);$.setDepthMode(st),se.render($.gl,Ie),$.setDirty(),Y.setBaseState(),$.bindFramebuffer.set(null)}},debug:function(Y,d,P){for(let N of P)Zu(Y,d,N)},debugPadding:function(Y){let d=Y.transform.padding;Qs(Y,Y.transform.height-(d.top||0),3,fs),Qs(Y,d.bottom||0,3,Io),zl(Y,d.left||0,3,oi),zl(Y,Y.transform.width-(d.right||0),3,ro);let P=Y.transform.centerPoint;(function(N,H,$,se){cu(N,H-1,$-10,2,20,se),cu(N,H-10,$-1,20,2,se)})(Y,P.x,Y.transform.height-P.y,Go)},terrainDepth:function(Y,d){let P=Y.context,N=P.gl,H=Y.transform,$=Ja.unblended,se=new ma(N.LEQUAL,ma.ReadWrite,[0,1]),de=d.tileManager.getRenderableTiles(),Ae=Y.useProgram(\"terrainDepth\");P.bindFramebuffer.set(d.getFramebuffer(\"depth\").framebuffer),P.viewport.set([0,0,Y.width/devicePixelRatio,Y.height/devicePixelRatio]),P.clear({color:i.bo.transparent,depth:1});for(let Fe of de){let Ie=d.getTerrainMesh(Fe.tileID),Ye=d.getTerrainData(Fe.tileID),st=H.getProjectionData({overscaledTileID:Fe.tileID,applyTerrainMatrix:!1,applyGlobeMatrix:!0}),dt={u_ele_delta:d.getMeshFrameDelta(H.zoom)};Ae.draw(P,N.TRIANGLES,se,Ca.disabled,$,Sa.backCCW,dt,Ye,st,\"terrain\",Ie.vertexBuffer,Ie.indexBuffer,Ie.segments)}P.bindFramebuffer.set(null),P.viewport.set([0,0,Y.width,Y.height])},terrainCoords:function(Y,d){let P=Y.context,N=P.gl,H=Y.transform,$=Ja.unblended,se=new ma(N.LEQUAL,ma.ReadWrite,[0,1]),de=d.getCoordsTexture(),Ae=d.tileManager.getRenderableTiles(),Fe=Y.useProgram(\"terrainCoords\");P.bindFramebuffer.set(d.getFramebuffer(\"coords\").framebuffer),P.viewport.set([0,0,Y.width/devicePixelRatio,Y.height/devicePixelRatio]),P.clear({color:i.bo.transparent,depth:1}),d.coordsIndex=[];for(let Ie of Ae){let Ye=d.getTerrainMesh(Ie.tileID),st=d.getTerrainData(Ie.tileID);P.activeTexture.set(N.TEXTURE0),N.bindTexture(N.TEXTURE_2D,de.texture);let dt={u_terrain_coords_id:(255-d.coordsIndex.length)/255,u_texture:0,u_ele_delta:d.getMeshFrameDelta(H.zoom)},Pe=H.getProjectionData({overscaledTileID:Ie.tileID,applyTerrainMatrix:!1,applyGlobeMatrix:!0});Fe.draw(P,N.TRIANGLES,se,Ca.disabled,$,Sa.backCCW,dt,st,Pe,\"terrain\",Ye.vertexBuffer,Ye.indexBuffer,Ye.segments),d.coordsIndex.push(Ie.tileID.key)}P.bindFramebuffer.set(null),P.viewport.set([0,0,Y.width,Y.height])}};class $u{constructor(d,P){this.drawFunctions=lf,this.context=new yt(d),this.transform=P,this._tileTextures={},this.terrainFacilitator={depthDirty:!0,coordsDirty:!1,matrix:i.ap(new Float64Array(16)),renderTime:0},this.setup(),this.numSublayers=Zr.maxOverzooming+Zr.maxUnderzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new $a}resize(d,P,N){if(this.width=Math.floor(d*N),this.height=Math.floor(P*N),this.pixelRatio=N,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(let H of this.style._order)this.style._layers[H].resize()}setup(){let d=this.context,P=new i.aU;P.emplaceBack(0,0),P.emplaceBack(i.a6,0),P.emplaceBack(0,i.a6),P.emplaceBack(i.a6,i.a6),this.tileExtentBuffer=d.createVertexBuffer(P,vs.members),this.tileExtentSegments=i.aV.simpleSegment(0,0,4,2);let N=new i.aU;N.emplaceBack(0,0),N.emplaceBack(i.a6,0),N.emplaceBack(0,i.a6),N.emplaceBack(i.a6,i.a6),this.debugBuffer=d.createVertexBuffer(N,vs.members),this.debugSegments=i.aV.simpleSegment(0,0,4,5);let H=new i.ch;H.emplaceBack(0,0,0,0),H.emplaceBack(i.a6,0,i.a6,0),H.emplaceBack(0,i.a6,0,i.a6),H.emplaceBack(i.a6,i.a6,i.a6,i.a6),this.rasterBoundsBuffer=d.createVertexBuffer(H,sh.members),this.rasterBoundsSegments=i.aV.simpleSegment(0,0,4,2);let $=new i.aU;$.emplaceBack(0,0),$.emplaceBack(i.a6,0),$.emplaceBack(0,i.a6),$.emplaceBack(i.a6,i.a6),this.rasterBoundsBufferPosOnly=d.createVertexBuffer($,vs.members),this.rasterBoundsSegmentsPosOnly=i.aV.simpleSegment(0,0,4,5);let se=new i.aU;se.emplaceBack(0,0),se.emplaceBack(1,0),se.emplaceBack(0,1),se.emplaceBack(1,1),this.viewportBuffer=d.createVertexBuffer(se,vs.members),this.viewportSegments=i.aV.simpleSegment(0,0,4,2);let de=new i.ci;de.emplaceBack(0),de.emplaceBack(1),de.emplaceBack(3),de.emplaceBack(2),de.emplaceBack(0),this.tileBorderIndexBuffer=d.createIndexBuffer(de);let Ae=new i.aW;Ae.emplaceBack(1,0,2),Ae.emplaceBack(1,2,3),this.quadTriangleIndexBuffer=d.createIndexBuffer(Ae);let Fe=this.context.gl;this.stencilClearMode=new Ca({func:Fe.ALWAYS,mask:0},0,255,Fe.ZERO,Fe.ZERO,Fe.ZERO),this.tileExtentMesh=new ka(this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}clearStencil(){let d=this.context,P=d.gl;this.nextStencilID=1,this.currentStencilSource=void 0;let N=i.O();i.c7(N,0,this.width,this.height,0,0,1),i.S(N,N,[P.drawingBufferWidth,P.drawingBufferHeight,0]);let H={mainMatrix:N,tileMercatorCoords:[0,0,1,1],clippingPlane:[0,0,0,0],projectionTransition:0,fallbackMatrix:N};this.useProgram(\"clippingMask\",null,!0).draw(d,P.TRIANGLES,ma.disabled,this.stencilClearMode,Ja.disabled,Sa.disabled,null,null,H,\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(d,P,N){if(this.currentStencilSource===d.source||!d.isTileClipped()||!(P!=null&&P.length))return;this.currentStencilSource=d.source,this.nextStencilID+P.length>256&&this.clearStencil();let H=this.context;H.setColorMode(Ja.disabled),H.setDepthMode(ma.disabled);let $={};for(let se of P)$[se.key]=this.nextStencilID++;this._renderTileMasks($,P,N,!0),this._renderTileMasks($,P,N,!1),this._tileClippingMaskIDs=$}_renderTileMasks(d,P,N,H){var $;let se=this.context,de=se.gl,Ae=this.style.projection,Fe=this.transform,Ie=this.useProgram(\"clippingMask\");for(let Ye of P){let st=d[Ye.key],dt=($=this.style.map.terrain)===null||$===void 0?void 0:$.getTerrainData(Ye),Pe=Ae.getMeshFromTileID(this.context,Ye.canonical,H,!0,\"stencil\"),Xe=Fe.getProjectionData({overscaledTileID:Ye,applyGlobeMatrix:!N,applyTerrainMatrix:!0});Ie.draw(se,de.TRIANGLES,ma.disabled,new Ca({func:de.ALWAYS,mask:0},st,255,de.KEEP,de.KEEP,de.REPLACE),Ja.disabled,N?Sa.disabled:Sa.backCCW,null,dt,Xe,\"$clipping\",Pe.vertexBuffer,Pe.indexBuffer,Pe.segments)}}_renderTilesDepthBuffer(){var d;let P=this.context,N=P.gl,H=this.style.projection,$=this.transform,se=this.useProgram(\"depth\"),de=this.getDepthModeFor3D(),Ae=ft($,{tileSize:$.tileSize});for(let Fe of Ae){let Ie=(d=this.style.map.terrain)===null||d===void 0?void 0:d.getTerrainData(Fe),Ye=H.getMeshFromTileID(this.context,Fe.canonical,!0,!0,\"raster\"),st=$.getProjectionData({overscaledTileID:Fe,applyGlobeMatrix:!0,applyTerrainMatrix:!0});se.draw(P,N.TRIANGLES,de,Ca.disabled,Ja.disabled,Sa.backCCW,null,Ie,st,\"$clipping\",Ye.vertexBuffer,Ye.indexBuffer,Ye.segments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();let d=this.nextStencilID++,P=this.context.gl;return new Ca({func:P.NOTEQUAL,mask:255},d,255,P.KEEP,P.KEEP,P.REPLACE)}stencilModeForClipping(d){let P=this.context.gl;return new Ca({func:P.EQUAL,mask:255},this._tileClippingMaskIDs[d.key],0,P.KEEP,P.KEEP,P.REPLACE)}getStencilConfigForOverlapAndUpdateStencilID(d){let P=this.context.gl,N=d.sort(((se,de)=>de.overscaledZ-se.overscaledZ)),H=N[N.length-1].overscaledZ,$=N[0].overscaledZ-H+1;if($>1){this.currentStencilSource=void 0,this.nextStencilID+$>256&&this.clearStencil();let se={};for(let de=0;de<$;de++)se[de+H]=new Ca({func:P.GEQUAL,mask:255},de+this.nextStencilID,255,P.KEEP,P.KEEP,P.REPLACE);return this.nextStencilID+=$,[se,N]}return[{[H]:Ca.disabled},N]}stencilConfigForOverlapTwoPass(d){let P=this.context.gl,N=d.sort(((se,de)=>de.overscaledZ-se.overscaledZ)),H=N[N.length-1].overscaledZ,$=N[0].overscaledZ-H+1;if(this.clearStencil(),$>1){let se={},de={};for(let Ae=0;Ae<$;Ae++)se[Ae+H]=new Ca({func:P.GREATER,mask:255},$+1+Ae,255,P.KEEP,P.KEEP,P.REPLACE),de[Ae+H]=new Ca({func:P.GREATER,mask:255},1+Ae,255,P.KEEP,P.KEEP,P.REPLACE);return this.nextStencilID=2*$+1,[se,de,N]}return this.nextStencilID=3,[{[H]:new Ca({func:P.GREATER,mask:255},2,255,P.KEEP,P.KEEP,P.REPLACE)},{[H]:new Ca({func:P.GREATER,mask:255},1,255,P.KEEP,P.KEEP,P.REPLACE)},N]}colorModeForRenderPass(){let d=this.context.gl;return this._showOverdrawInspector?new Ja([d.CONSTANT_COLOR,d.ONE],new i.bo(.125,.125,.125,0),[!0,!0,!0,!0]):this.renderPass===\"opaque\"?Ja.unblended:Ja.alphaBlended}getDepthModeForSublayer(d,P,N){if(!this.opaquePassEnabledForLayer())return ma.disabled;let H=1-((1+this.currentLayer)*this.numSublayers+d)*this.depthEpsilon;return new ma(N||this.context.gl.LEQUAL,P,[H,H])}getDepthModeFor3D(){return new ma(this.context.gl.LEQUAL,ma.ReadWrite,this.depthRangeFor3D)}opaquePassEnabledForLayer(){return this.currentLayer<this.opaquePassCutoff}render(d,P){var N,H,$;this.style=d,this.options=P,this.lineAtlas=d.lineAtlas,this.imageManager=d.imageManager,this.glyphManager=d.glyphManager,this.symbolFadeChange=d.placement.symbolFadeChange(g()),this.imageManager.beginFrame();let se=this.style._order,de=this.style.tileManagers,Ae={},Fe={},Ie={},Ye={isRenderingToTexture:!1,isRenderingGlobe:((N=d.projection)===null||N===void 0?void 0:N.transitionState)>0};for(let dt in de){let Pe=de[dt];Pe.used&&Pe.prepare(this.context),Ae[dt]=Pe.getVisibleCoordinates(!1),Fe[dt]=Ae[dt].slice().reverse(),Ie[dt]=Pe.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(let dt=0;dt<se.length;dt++)if(this.style._layers[se[dt]].is3D()){this.opaquePassCutoff=dt;break}this.maybeDrawDepth(!1),this.renderToTexture&&(this.renderToTexture.prepareForRender(this.style,this.transform.zoom),this.opaquePassCutoff=0),this.renderPass=\"offscreen\";for(let dt of se){let Pe=this.style._layers[dt];if(!Pe.hasOffscreenPass()||Pe.isHidden(this.transform.zoom))continue;let Xe=Fe[Pe.source];(Pe.type===\"custom\"||Xe.length)&&this.renderLayer(this,de[Pe.source],Pe,Xe,Ye)}if((H=this.style.projection)===null||H===void 0||H.updateGPUdependent({context:this.context,useProgram:dt=>this.useProgram(dt)}),this.context.viewport.set([0,0,this.width,this.height]),this.context.bindFramebuffer.set(null),this.context.clear({color:P.showOverdrawInspector?i.bo.black:i.bo.transparent,depth:1}),this.clearStencil(),this.style.sky&&this.drawFunctions.sky(this,this.style.sky),this._showOverdrawInspector=P.showOverdrawInspector,this.depthRangeFor3D=[0,1-(d._order.length+2)*this.numSublayers*this.depthEpsilon],!this.renderToTexture)for(this.renderPass=\"opaque\",this.currentLayer=se.length-1;this.currentLayer>=0;this.currentLayer--){let dt=this.style._layers[se[this.currentLayer]],Pe=de[dt.source],Xe=Ae[dt.source];this._renderTileClippingMasks(dt,Xe,!1),this.renderLayer(this,Pe,dt,Xe,Ye)}this.renderPass=\"translucent\";let st=!1;for(this.currentLayer=0;this.currentLayer<se.length;this.currentLayer++){let dt=this.style._layers[se[this.currentLayer]],Pe=de[dt.source];if(!(($=this.renderToTexture)===null||$===void 0)&&$.renderLayer(dt,Ye))continue;this.opaquePassEnabledForLayer()||st||(st=!0,Ye.isRenderingGlobe&&!this.style.map.terrain&&this._renderTilesDepthBuffer());let Xe=(dt.type===\"symbol\"?Ie:Fe)[dt.source];this._renderTileClippingMasks(dt,Ae[dt.source],!!this.renderToTexture),this.renderLayer(this,Pe,dt,Xe,Ye)}if(Ye.isRenderingGlobe&&this.drawFunctions.atmosphere(this,this.style.sky,this.style.light),this.options.showTileBoundaries){let dt=(function(Pe,Xe){let at=null,xt=Object.values(Pe._layers).flatMap((Pt=>Pt.source&&!Pt.isHidden(Xe)?[Pe.tileManagers[Pt.source]]:[])),Et=xt.filter((Pt=>Pt.getSource().type===\"vector\")),Bt=xt.filter((Pt=>Pt.getSource().type!==\"vector\")),St=Pt=>{(!at||at.getSource().maxzoom<Pt.getSource().maxzoom)&&(at=Pt)};for(let Pt of Et)St(Pt);if(!at)for(let Pt of Bt)St(Pt);return at})(this.style,this.transform.zoom);dt&&this.drawFunctions.debug(this,dt,dt.getVisibleCoordinates())}this.options.showPadding&&this.drawFunctions.debugPadding(this),this.context.setDefault()}maybeDrawDepth(d){var P,N;if(!(!((N=(P=this.style)===null||P===void 0?void 0:P.map)===null||N===void 0)&&N.terrain))return;let H=this.terrainFacilitator.matrix,$=this.transform.modelViewProjectionMatrix,se=this.terrainFacilitator.depthDirty;se||(se=d?!i.cj(H,$):!i.ck(H,$)),se||(se=this.style.map.terrain.tileManager.anyTilesAfterTime(this.terrainFacilitator.renderTime)),se&&(i.cl(H,$),this.terrainFacilitator.renderTime=Date.now(),this.terrainFacilitator.depthDirty=!1,this.terrainFacilitator.coordsDirty=!0,this.drawFunctions.terrainDepth(this,this.style.map.terrain))}maybeDrawCoords(){var d,P;!((P=(d=this.style)===null||d===void 0?void 0:d.map)===null||P===void 0)&&P.terrain&&this.terrainFacilitator.coordsDirty&&(this.terrainFacilitator.coordsDirty=!1,this.drawFunctions.terrainCoords(this,this.style.map.terrain))}renderLayer(d,P,N,H,$){if(N.isHidden(this.transform.zoom)||N.type!==\"background\"&&N.type!==\"custom\"&&!(H||[]).length)return;this.id=N.id;let se=this.drawFunctions;i.aQ(N)?se.symbol(d,P,N,H,this.style.placement.variableOffsets,$):i.cm(N)?se.circle(d,P,N,H,$):i.cn(N)?se.heatmap(d,P,N,H,$):i.co(N)?se.line(d,P,N,H,$):i.cp(N)?se.fill(d,P,N,H,$):i.cq(N)?se.fillExtrusion(d,P,N,H,$):i.cr(N)?se.hillshade(d,P,N,H,$):i.cs(N)?se.colorRelief(d,P,N,H,$):i.bU(N)?se.raster(d,P,N,H,$):i.ct(N)?se.background(d,P,N,H,$):i.cu(N)&&se.custom(d,P,N,$)}saveTileTexture(d){let P=this._tileTextures[d.size[0]];P?P.length<$u.MAX_TEXTURE_POOL_SIZE_PER_BUCKET?P.push(d):d.destroy():this._tileTextures[d.size[0]]=[d]}getTileTexture(d){let P=this._tileTextures[d];return P&&P.length>0?P.pop():null}isPatternMissing(d){if(!d)return!1;if(!d.from||!d.to)return!0;let P=this.imageManager.getPattern(d.from.toString()),N=this.imageManager.getPattern(d.to.toString());return!P||!N}useProgram(d,P,N=!1,H=[]){var $;this.cache||(this.cache={});let se=!!this.style.map.terrain,de=this.style.projection,Ae=N?Oa.projectionMercator:de.shaderPreludeCode,Fe=N?yo:de.shaderDefine,Ie=d+(P?P.cacheKey:\"\")+`/${N?so:de.shaderVariantName}`+(this._showOverdrawInspector?\"/overdraw\":\"\")+(se?\"/terrain\":\"\")+(H?`/${H.join(\"/\")}`:\"\");return($=this.cache)[Ie]||($[Ie]=new Ud(this.context,Oa[d],P,_d[d],this._showOverdrawInspector,se,Ae,Fe,H)),this.cache[Ie]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){let d=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(d.FUNC_ADD)}initDebugOverlayCanvas(){this.debugOverlayCanvas==null&&(this.debugOverlayCanvas=document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new i.T(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))}destroy(){var d,P;if(this._tileTextures){for(let N in this._tileTextures){let H=this._tileTextures[N];if(H)for(let $ of H)$.destroy()}this._tileTextures={}}if(this.tileExtentBuffer&&this.tileExtentBuffer.destroy(),this.debugBuffer&&this.debugBuffer.destroy(),this.rasterBoundsBuffer&&this.rasterBoundsBuffer.destroy(),this.rasterBoundsBufferPosOnly&&this.rasterBoundsBufferPosOnly.destroy(),this.viewportBuffer&&this.viewportBuffer.destroy(),this.tileBorderIndexBuffer&&this.tileBorderIndexBuffer.destroy(),this.quadTriangleIndexBuffer&&this.quadTriangleIndexBuffer.destroy(),this.tileExtentMesh&&((d=this.tileExtentMesh.vertexBuffer)===null||d===void 0||d.destroy()),this.tileExtentMesh&&((P=this.tileExtentMesh.indexBuffer)===null||P===void 0||P.destroy()),this.debugOverlayTexture&&this.debugOverlayTexture.destroy(),this.cache){for(let N in this.cache){let H=this.cache[N];H!=null&&H.program&&this.context.gl.deleteProgram(H.program)}this.cache={}}this.context&&this.context.setDefault()}overLimit(){let{drawingBufferWidth:d,drawingBufferHeight:P}=this.context.gl;return this.width!==d||this.height!==P}}function ph(Y,d){let P,N=!1,H=null,$=()=>{H=null,N&&(Y(...P),H=setTimeout($,d),N=!1)};return(...se)=>(N=!0,P=se,H||$(),H)}$u.MAX_TEXTURE_POOL_SIZE_PER_BUCKET=50;class gh{constructor(d){this._getCurrentHash=()=>{let P=window.location.hash.replace(\"#\",\"\");if(this._hashName){let N,H=P.split(\"&\").map(($=>$.split(\"=\")));for(let $ of H)$[0]===this._hashName&&(N=$);return(N&&N[1]||\"\").split(\"/\")}return P.split(\"/\")},this._onHashChange=()=>{let P=this._getCurrentHash();if(!this._isValidHash(P))return!1;let N=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(P[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+P[2],+P[1]],zoom:+P[0],bearing:N,pitch:+(P[4]||0)}),!0},this._updateHashUnthrottled=()=>{let P=window.location.href.replace(/(#.*)?$/,this.getHashString());window.history.replaceState(window.history.state,null,P)},this._removeHash=()=>{let P=this._getCurrentHash();if(P.length===0)return;let N=P.join(\"/\"),H=N;H.split(\"&\").length>0&&(H=H.split(\"&\")[0]),this._hashName&&(H=`${this._hashName}=${N}`);let $=window.location.hash.replace(H,\"\");$.startsWith(\"#&\")?$=$.slice(0,1)+$.slice(2):$===\"#\"&&($=\"\");let se=window.location.href.replace(/(#.+)?$/,$);se=se.replace(\"&&\",\"&\"),window.history.replaceState(window.history.state,null,se)},this._updateHash=ph(this._updateHashUnthrottled,300),this._hashName=d&&encodeURIComponent(d)}addTo(d){return this._map=d,addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this}remove(){return removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),this._removeHash(),delete this._map,this}getHashString(d){let P=this._map.getCenter(),N=Math.round(100*this._map.getZoom())/100,H=Math.ceil((N*Math.LN2+Math.log(512/360/.5))/Math.LN10),$=Math.pow(10,H),se=Math.round(P.lng*$)/$,de=Math.round(P.lat*$)/$,Ae=this._map.getBearing(),Fe=this._map.getPitch(),Ie=\"\";if(Ie+=d?`/${se}/${de}/${N}`:`${N}/${de}/${se}`,(Ae||Fe)&&(Ie+=\"/\"+Math.round(10*Ae)/10),Fe&&(Ie+=`/${Math.round(Fe)}`),this._hashName){let Ye=this._hashName,st=!1,dt=window.location.hash.slice(1).split(\"&\").map((Pe=>{let Xe=Pe.split(\"=\")[0];return Xe===Ye?(st=!0,`${Xe}=${Ie}`):Pe})).filter((Pe=>Pe));return st||dt.push(`${Ye}=${Ie}`),`#${dt.join(\"&\")}`}return`#${Ie}`}_isValidHash(d){if(d.length<3||d.some(isNaN))return!1;try{new i.W(+d[2],+d[1])}catch($){return!1}let P=+d[0],N=+(d[3]||0),H=+(d[4]||0);return P>=this._map.getMinZoom()&&P<=this._map.getMaxZoom()&&N>=-180&&N<=180&&H>=this._map.getMinPitch()&&H<=this._map.getMaxPitch()}}let uf={linearity:.3,easing:i.cv(0,0,.3,1)},Ku=i.e({deceleration:2500,maxSpeed:1400},uf),fu=i.e({deceleration:20,maxSpeed:1400},uf),Ju=i.e({deceleration:1e3,maxSpeed:360},uf),Qu=i.e({deceleration:1e3,maxSpeed:90},uf),Wd=i.e({deceleration:1e3,maxSpeed:360},uf);class x0{constructor(d){this._map=d,this.clear()}clear(){this._inertiaBuffer=[]}record(d){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:g(),settings:d})}_drainInertiaBuffer(){let d=this._inertiaBuffer,P=g();for(;d.length>0&&P-d[0].time>160;)d.shift()}_onMoveEnd(d){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;let P={zoom:0,bearing:0,pitch:0,roll:0,pan:new i.P(0,0),pinchAround:void 0,around:void 0};for(let{settings:$}of this._inertiaBuffer)P.zoom+=$.zoomDelta||0,P.bearing+=$.bearingDelta||0,P.pitch+=$.pitchDelta||0,P.roll+=$.rollDelta||0,$.panDelta&&P.pan._add($.panDelta),$.around&&(P.around=$.around),$.pinchAround&&(P.pinchAround=$.pinchAround);let N=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,H={};if(P.pan.mag()){let $=Al(P.pan.mag(),N,i.e({},Ku,d||{})),se=P.pan.mult($.amount/P.pan.mag()),de=this._map.cameraHelper.handlePanInertia(se,this._map.transform);H.center=de.easingCenter,H.offset=de.easingOffset,Bf(H,$)}if(P.zoom){let $=Al(P.zoom,N,fu);H.zoom=i.cw(this._map.transform.zoom+$.amount,this._map.getZoomSnap(),$.amount),Bf(H,$)}if(P.bearing){let $=Al(P.bearing,N,Ju);H.bearing=this._map.transform.bearing+i.al($.amount,-179,179),Bf(H,$)}if(P.pitch){let $=Al(P.pitch,N,Qu);H.pitch=this._map.transform.pitch+$.amount,Bf(H,$)}if(P.roll){let $=Al(P.roll,N,Wd);H.roll=this._map.transform.roll+i.al($.amount,-179,179),Bf(H,$)}if(H.zoom||H.bearing){let $=P.pinchAround===void 0?P.around:P.pinchAround;H.around=$?this._map.unproject($):this._map.getCenter()}return this.clear(),i.e(H,{noMoveStart:!0})}}function Bf(Y,d){(!Y.duration||Y.duration<d.duration)&&(Y.duration=d.duration,Y.easing=d.easing)}function Al(Y,d,P){let{maxSpeed:N,linearity:H,deceleration:$}=P,se=i.al(Y*H/(d/1e3),-N,N),de=Math.abs(se)/($*H);return{easing:P.easing,duration:1e3*de,amount:se*(de/2)}}class Xl extends i.n{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(d,P,N,H={}){N=N instanceof MouseEvent?N:new MouseEvent(d,N);let $=_.mousePos(P.getCanvas(),N),se=P.unproject($);super(d,i.e({point:$,lngLat:se,originalEvent:N},H)),this._defaultPrevented=!1,this.target=P}}class cf extends i.n{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(d,P,N){let H=d===\"touchend\"?N.changedTouches:N.touches,$=_.touchPos(P.getCanvasContainer(),H),se=$.map((Ae=>P.unproject(Ae))),de=$.reduce(((Ae,Fe,Ie,Ye)=>Ae.add(Fe.div(Ye.length))),new i.P(0,0));super(d,{points:$,point:de,lngLats:se,lngLat:P.unproject(de),originalEvent:N}),this._defaultPrevented=!1}}class jh extends i.n{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(d,P,N){super(d,{originalEvent:N}),this._defaultPrevented=!1}}class Wh{constructor(d,P){this._map=d,this._clickTolerance=P.clickTolerance}reset(){delete this._mousedownPos}wheel(d){return this._firePreventable(new jh(d.type,this._map,d))}mousedown(d,P){return this._mousedownPos=P,this._firePreventable(new Xl(d.type,this._map,d))}mouseup(d){this._map.fire(new Xl(d.type,this._map,d))}click(d,P){this._mousedownPos&&this._mousedownPos.dist(P)>=this._clickTolerance||this._map.fire(new Xl(d.type,this._map,d))}dblclick(d){return this._firePreventable(new Xl(d.type,this._map,d))}mouseover(d){this._map.fire(new Xl(d.type,this._map,d))}mouseout(d){this._map.fire(new Xl(d.type,this._map,d))}touchstart(d){return this._firePreventable(new cf(d.type,this._map,d))}touchmove(d){this._map.fire(new cf(d.type,this._map,d))}touchend(d){this._map.fire(new cf(d.type,this._map,d))}touchcancel(d){this._map.fire(new cf(d.type,this._map,d))}_firePreventable(d){if(this._map.fire(d),d.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Yh{constructor(d){this._map=d}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(d){this._map.fire(new Xl(d.type,this._map,d))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Xl(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(d){this._delayContextMenu?this._contextMenuEvent=d:this._ignoreContextMenu||this._map.fire(new Xl(d.type,this._map,d)),this._map.listens(\"contextmenu\")&&d.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class hu{constructor(d){this._map=d}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(d){return this.transform.screenPointToLocation(i.P.convert(d),this._map.terrain)}}class Mu{constructor(d,P){this._map=d,this._tr=new hu(d),this._el=d.getCanvasContainer(),this._container=d.getContainer(),this._clickTolerance=P.clickTolerance||1,P.boxZoom&&typeof P.boxZoom==\"object\"&&(this._boxZoomEnd=P.boxZoom.boxZoomEnd)}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(d,P){this.isEnabled()&&d.shiftKey&&d.button===0&&(_.disableDrag(),this._startPos=this._lastPos=P,this._active=!0)}mousemoveWindow(d,P){if(!this._active)return;let N=P;if(this._lastPos.equals(N)||!this._box&&N.dist(this._startPos)<this._clickTolerance)return;let H=this._startPos;this._lastPos=N,this._box||(this._box=_.create(\"div\",\"maplibregl-boxzoom\",this._container),this._container.classList.add(\"maplibregl-crosshair\"),this._fireEvent(\"boxzoomstart\",d));let $=Math.min(H.x,N.x),se=Math.max(H.x,N.x),de=Math.min(H.y,N.y),Ae=Math.max(H.y,N.y);this._box.style.transform=`translate(${$}px,${de}px)`,this._box.style.width=se-$+\"px\",this._box.style.height=Ae-de+\"px\"}mouseupWindow(d,P){if(!this._active||d.button!==0)return;let N=this._startPos,H=P;if(this.reset(),_.suppressClick(),N.x!==H.x||N.y!==H.y)return this._map.fire(new i.n(\"boxzoomend\",{originalEvent:d})),this._boxZoomEnd?void this._boxZoomEnd(this._map,N,H,d):{cameraAnimation:$=>$.fitScreenCoordinates(N,H,this._tr.bearing,{linear:!0})};this._fireEvent(\"boxzoomcancel\",d)}keydown(d){this._active&&d.keyCode===27&&(this.reset(),this._fireEvent(\"boxzoomcancel\",d))}reset(){this._active=!1,this._container.classList.remove(\"maplibregl-crosshair\"),this._box&&(this._box.remove(),this._box=null),_.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(d,P){return this._map.fire(new i.n(d,{originalEvent:P}))}}function _i(Y,d){if(Y.length!==d.length)throw new Error(`The number of touches and points are not equal - touches ${Y.length}, points ${d.length}`);let P={};for(let N=0;N<Y.length;N++)P[Y[N].identifier]=d[N];return P}class Su{constructor(d){this.reset(),this.numTouches=d.numTouches}reset(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1}touchstart(d,P,N){(this.centroid||N.length>this.numTouches)&&(this.aborted=!0),this.aborted||(this.startTime===void 0&&(this.startTime=d.timeStamp),N.length===this.numTouches&&(this.centroid=(function(H){let $=new i.P(0,0);for(let se of H)$._add(se);return $.div(H.length)})(P),this.touches=_i(N,P)))}touchmove(d,P,N){if(this.aborted||!this.centroid)return;let H=_i(N,P);for(let $ in this.touches){let se=H[$];(!se||se.dist(this.touches[$])>30)&&(this.aborted=!0)}}touchend(d,P,N){if((!this.centroid||d.timeStamp-this.startTime>500)&&(this.aborted=!0),N.length===0){let H=!this.aborted&&this.centroid;if(this.reset(),H)return H}}}class Me{constructor(d){this.singleTap=new Su(d),this.numTaps=d.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(d,P,N){this.singleTap.touchstart(d,P,N)}touchmove(d,P,N){this.singleTap.touchmove(d,P,N)}touchend(d,P,N){let H=this.singleTap.touchend(d,P,N);if(H){let $=d.timeStamp-this.lastTime<500,se=!this.lastTap||this.lastTap.dist(H)<30;if($&&se||this.reset(),this.count++,this.lastTime=d.timeStamp,this.lastTap=H,this.count===this.numTaps)return this.reset(),H}}}class Ce{constructor(d){this._tr=new hu(d),this._zoomIn=new Me({numTouches:1,numTaps:2}),this._zoomOut=new Me({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(d,P,N){this._zoomIn.touchstart(d,P,N),this._zoomOut.touchstart(d,P,N)}touchmove(d,P,N){this._zoomIn.touchmove(d,P,N),this._zoomOut.touchmove(d,P,N)}touchend(d,P,N){let H=this._zoomIn.touchend(d,P,N),$=this._zoomOut.touchend(d,P,N),se=this._tr;return H?(this._active=!0,d.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:de=>de.easeTo({duration:300,zoom:i.cw(se.zoom+1,de.getZoomSnap()),around:se.unproject(H)},{originalEvent:d})}):$?(this._active=!0,d.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:de=>de.easeTo({duration:300,zoom:i.cw(se.zoom-1,de.getZoomSnap()),around:se.unproject($)},{originalEvent:d})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class Be{constructor(d){this._enabled=!!d.enable,this._moveStateManager=d.moveStateManager,this._clickTolerance=d.clickTolerance||1,this._moveFunction=d.move,this._activateOnStart=!!d.activateOnStart,d.assignEvents(this),this.reset()}reset(d){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(d)}_move(...d){let P=this._moveFunction(...d);if(P.bearingDelta||P.pitchDelta||P.rollDelta||P.around||P.panDelta)return this._active=!0,P}dragStart(d,P){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(d)&&(this._moveStateManager.startMove(d),this._lastPoint=Array.isArray(P)?P[0]:P,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(d,P){if(!this.isEnabled())return;let N=this._lastPoint;if(!N)return;if(d.preventDefault(),!this._moveStateManager.isValidMoveEvent(d))return void this.reset(d);let H=Array.isArray(P)?P[0]:P;return!this._moved&&H.dist(N)<this._clickTolerance?void 0:(this._moved=!0,this._lastPoint=H,this._move(N,H))}dragEnd(d){this.isEnabled()&&this._lastPoint&&this._moveStateManager.isValidEndEvent(d)&&(this._moved&&_.suppressClick(),this.reset(d))}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}getClickTolerance(){return this._clickTolerance}}let Ne=0,Qe=2,Je={[Ne]:1,[Qe]:2};class lt{constructor(d){this._correctEvent=d.checkCorrectEvent}startMove(d){this._eventButton=d.button}endMove(d){delete this._eventButton}isValidStartEvent(d){return this._correctEvent(d)}isValidMoveEvent(d){return!(function(P,N){let H=Je[N];return P.buttons===void 0||(P.buttons&H)!==H})(d,this._eventButton)}isValidEndEvent(d){return d.button===this._eventButton}}class Gt{constructor(){this._firstTouch=void 0}_isOneFingerTouch(d){return d.targetTouches.length===1}_isSameTouchEvent(d){return d.targetTouches[0].identifier===this._firstTouch}startMove(d){this._firstTouch=d.targetTouches[0].identifier}endMove(d){delete this._firstTouch}isValidStartEvent(d){return this._isOneFingerTouch(d)}isValidMoveEvent(d){return this._isOneFingerTouch(d)&&this._isSameTouchEvent(d)}isValidEndEvent(d){return this._isOneFingerTouch(d)&&this._isSameTouchEvent(d)}}class Zt{constructor(d=new lt({checkCorrectEvent:()=>!0}),P=new Gt){this.mouseMoveStateManager=d,this.oneFingerTouchMoveStateManager=P}_executeRelevantHandler(d,P,N){return d instanceof MouseEvent?P(d):typeof TouchEvent!=\"undefined\"&&d instanceof TouchEvent?N(d):void 0}startMove(d){this._executeRelevantHandler(d,(P=>{this.mouseMoveStateManager.startMove(P)}),(P=>{this.oneFingerTouchMoveStateManager.startMove(P)}))}endMove(d){this._executeRelevantHandler(d,(P=>{this.mouseMoveStateManager.endMove(P)}),(P=>{this.oneFingerTouchMoveStateManager.endMove(P)}))}isValidStartEvent(d){return this._executeRelevantHandler(d,(P=>this.mouseMoveStateManager.isValidStartEvent(P)),(P=>this.oneFingerTouchMoveStateManager.isValidStartEvent(P)))}isValidMoveEvent(d){return this._executeRelevantHandler(d,(P=>this.mouseMoveStateManager.isValidMoveEvent(P)),(P=>this.oneFingerTouchMoveStateManager.isValidMoveEvent(P)))}isValidEndEvent(d){return this._executeRelevantHandler(d,(P=>this.mouseMoveStateManager.isValidEndEvent(P)),(P=>this.oneFingerTouchMoveStateManager.isValidEndEvent(P)))}}let Nt=Y=>{Y.mousedown=Y.dragStart,Y.mousemoveWindow=Y.dragMove,Y.mouseup=Y.dragEnd,Y.contextmenu=d=>{d.preventDefault()}};class Kt{constructor(d,P){this._clickTolerance=d.clickTolerance||1,this._map=P,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new i.P(0,0)}_shouldBePrevented(d){return d<(this._map.cooperativeGestures.isEnabled()?2:1)}touchstart(d,P,N){return this._calculateTransform(d,P,N)}touchmove(d,P,N){if(this._active){if(!this._shouldBePrevented(N.length))return d.preventDefault(),this._calculateTransform(d,P,N);this._map.cooperativeGestures.notifyGestureBlocked(\"touch_pan\",d)}}touchend(d,P,N){this._calculateTransform(d,P,N),this._active&&this._shouldBePrevented(N.length)&&this.reset()}touchcancel(){this.reset()}_calculateTransform(d,P,N){N.length>0&&(this._active=!0);let H=_i(N,P),$=new i.P(0,0),se=new i.P(0,0),de=0;for(let Fe in H){let Ie=H[Fe],Ye=this._touches[Fe];Ye&&($._add(Ie),se._add(Ie.sub(Ye)),de++,H[Fe]=Ie)}if(this._touches=H,this._shouldBePrevented(de)||!se.mag())return;let Ae=se.div(de);return this._sum._add(Ae),this._sum.mag()<this._clickTolerance?void 0:{around:$.div(de),panDelta:Ae}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class ur{constructor(){this.reset()}reset(){this._active=!1,delete this._firstTwoTouches}touchstart(d,P,N){this._firstTwoTouches||N.length<2||(this._firstTwoTouches=[N[0].identifier,N[1].identifier],this._start([P[0],P[1]]))}touchmove(d,P,N){if(!this._firstTwoTouches)return;d.preventDefault();let[H,$]=this._firstTwoTouches,se=Sr(N,P,H),de=Sr(N,P,$);if(!se||!de)return;let Ae=this._aroundCenter?null:se.add(de).div(2);return this._move([se,de],Ae,d)}touchend(d,P,N){if(!this._firstTwoTouches)return;let[H,$]=this._firstTwoTouches,se=Sr(N,P,H),de=Sr(N,P,$);se&&de||(this._active&&_.suppressClick(),this.reset())}touchcancel(){this.reset()}enable(d){this._enabled=!0,this._aroundCenter=!!d&&d.around===\"center\"}disable(){this._enabled=!1,this.reset()}isEnabled(){return!!this._enabled}isActive(){return!!this._active}}function Sr(Y,d,P){for(let N=0;N<Y.length;N++)if(Y[N].identifier===P)return d[N]}function Br(Y,d){return Math.log(Y/d)/Math.LN2}class ln extends ur{constructor(){super(),this._zoomRate=1,this._zoomThreshold=.1}setZoomRate(d){this._zoomRate=d!=null?d:1}setZoomThreshold(d){this._zoomThreshold=d!=null?d:.1}reset(){super.reset(),delete this._distance,delete this._startDistance}_start(d){this._startDistance=this._distance=d[0].dist(d[1])}_move(d,P){let N=this._distance;if(this._distance=d[0].dist(d[1]),this._active||!(Math.abs(Br(this._distance,this._startDistance))<this._zoomThreshold))return this._active=!0,{zoomDelta:Br(this._distance,N)*this._zoomRate,pinchAround:P}}}function wn(Y,d){return 180*Y.angleWith(d)/Math.PI}class In extends ur{reset(){super.reset(),delete this._minDiameter,delete this._startVector,delete this._vector}_start(d){this._startVector=this._vector=d[0].sub(d[1]),this._minDiameter=d[0].dist(d[1])}_move(d,P,N){let H=this._vector;if(this._vector=d[0].sub(d[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:wn(this._vector,H),pinchAround:P}}_isBelowThreshold(d){this._minDiameter=Math.min(this._minDiameter,d.mag());let P=25/(Math.PI*this._minDiameter)*360,N=wn(d,this._startVector);return Math.abs(N)<P}}function gi(Y){return Math.abs(Y.y)>Math.abs(Y.x)}class Bi extends ur{constructor(d){super(),this._currentTouchCount=0,this._map=d}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(d,P,N){super.touchstart(d,P,N),this._currentTouchCount=N.length}_start(d){this._lastPoints=d,gi(d[0].sub(d[1]))&&(this._valid=!1)}_move(d,P,N){if(this._map.cooperativeGestures.isEnabled()&&this._currentTouchCount<3)return;let H=d[0].sub(this._lastPoints[0]),$=d[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(H,$,N.timeStamp),this._valid?(this._lastPoints=d,this._active=!0,{pitchDelta:(H.y+$.y)/2*-.5}):void 0}gestureBeginsVertically(d,P,N){if(this._valid!==void 0)return this._valid;let H=d.mag()>=2,$=P.mag()>=2;if(!H&&!$)return;if(!H||!$)return this._firstMove===void 0&&(this._firstMove=N),N-this._firstMove<100&&void 0;let se=d.y>0==P.y>0;return gi(d)&&gi(P)&&se}}let mi={panStep:100,bearingStep:15,pitchStep:10};class ia{constructor(d){this._tr=new hu(d);let P=mi;this._panStep=P.panStep,this._bearingStep=P.bearingStep,this._pitchStep=P.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(d){if(d.altKey||d.ctrlKey||d.metaKey)return;let P=0,N=0,H=0,$=0,se=0;switch(d.keyCode){case 61:case 107:case 171:case 187:P=1;break;case 189:case 109:case 173:P=-1;break;case 37:d.shiftKey?N=-1:(d.preventDefault(),$=-1);break;case 39:d.shiftKey?N=1:(d.preventDefault(),$=1);break;case 38:d.shiftKey?H=1:(d.preventDefault(),se=-1);break;case 40:d.shiftKey?H=-1:(d.preventDefault(),se=1);break;default:return}return this._rotationDisabled&&(N=0,H=0),{cameraAnimation:de=>{let Ae=this._tr;de.easeTo({duration:300,easeId:\"keyboardHandler\",easing:ua,zoom:P?i.cw(Ae.zoom+P*(d.shiftKey?2:1),de.getZoomSnap()):Ae.zoom,bearing:Ae.bearing+N*this._bearingStep,pitch:Ae.pitch+H*this._pitchStep,offset:[-$*this._panStep,-se*this._panStep],center:Ae.center},{originalEvent:d})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function ua(Y){return Y*(2-Y)}let lo=4.000244140625,Bo=1/450;class Ua{constructor(d,P){this._onTimeout=N=>{this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(N)},this._map=d,this._tr=new hu(d),this._triggerRenderFrame=P,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=Bo}setZoomRate(d){this._defaultZoomRate=d}setWheelZoomRate(d){this._wheelZoomRate=d}isEnabled(){return!!this._enabled}isActive(){return!!this._active||this._finishTimeout!==void 0}isZooming(){return!!this._zooming}enable(d){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!d&&d.around===\"center\")}disable(){this.isEnabled()&&(this._enabled=!1)}_shouldBePrevented(d){return!!this._map.cooperativeGestures.isEnabled()&&!(d.ctrlKey||this._map.cooperativeGestures.isBypassed(d))}wheel(d){if(!this.isEnabled())return;if(this._shouldBePrevented(d))return void this._map.cooperativeGestures.notifyGestureBlocked(\"wheel_zoom\",d);let P=d.deltaMode===WheelEvent.DOM_DELTA_LINE?40*d.deltaY:d.deltaY,N=g(),H=N-(this._lastWheelEventTime||0);this._lastWheelEventTime=N,P!==0&&P%lo==0?this._type=\"wheel\":P!==0&&Math.abs(P)<4?this._type=\"trackpad\":H>400?(this._type=null,this._lastValue=P,this._timeout=setTimeout(this._onTimeout,40,d)):this._type||(this._type=Math.abs(H*P)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,P+=this._lastValue)),d.shiftKey&&P&&(P/=4),this._type&&(this._lastWheelEvent=d,this._delta-=P,this._active||this._start(d)),d.preventDefault()}_start(d){if(!this._delta)return;this._needsRerender=!1,this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);let P=_.mousePos(this._map.getCanvas(),d),N=this._tr;this._aroundPoint=this._aroundCenter?N.transform.locationToScreenPoint(i.W.convert(N.center)):P,this._needsRerender||(this._needsRerender=!0,this._triggerRenderFrame())}renderFrame(){if(!this._needsRerender||(this._needsRerender=!1,!this.isActive()))return;let d=this._tr.transform;if(typeof this._lastExpectedZoom==\"number\"){let de=d.zoom-this._lastExpectedZoom;typeof this._startZoom==\"number\"&&(this._startZoom+=de),typeof this._targetZoom==\"number\"&&(this._targetZoom+=de)}if(this._delta!==0){let de=this._type===\"wheel\"&&Math.abs(this._delta)>lo?this._wheelZoomRate:this._defaultZoomRate,Ae=2/(1+Math.exp(-Math.abs(this._delta*de)));this._delta<0&&Ae!==0&&(Ae=1/Ae);let Fe=typeof this._targetZoom!=\"number\"?d.scale:i.ao(this._targetZoom),Ie=d.applyConstrain(d.getCameraLngLat(),i.ar(Fe*Ae)).zoom,Ye=this._map.getZoomSnap();if(this._type===\"wheel\"&&Ye>0){let st=i.cw(d.zoom,Ye);this._targetZoom=i.cw(Ie,Ye,Ie-st)}else this._targetZoom=Ie;this._type===\"wheel\"&&(this._startZoom=d.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}let P=typeof this._targetZoom!=\"number\"?d.zoom:this._targetZoom,N=this._startZoom,H=this._easing,$,se=!1;if(this._type===\"wheel\"&&N&&H){let de=g()-this._lastWheelEventTime,Ae=Math.min((de+5)/200,1),Fe=H(Ae);$=i.H.number(N,P,Fe),Ae<1?this._needsRerender=!0:se=!0}else $=P,se=!0;return this._active=!0,se&&(this._active=!1,this._finishTimeout=setTimeout((()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._lastExpectedZoom,delete this._finishTimeout}),200)),this._lastExpectedZoom=$,{noInertia:!0,needsRenderFrame:!se,zoomDelta:$-d.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(d){let P=i.cy;if(this._prevEase){let N=this._prevEase,H=(g()-N.start)/N.duration,$=N.easing(H+.01)-N.easing(H),se=.27/Math.sqrt($*$+1e-4)*.01,de=Math.sqrt(.0729-se*se);P=i.cv(se,de,.25,1)}return this._prevEase={start:g(),duration:d,easing:P},P}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,delete this._lastExpectedZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class ls{constructor(d,P){this._clickZoom=d,this._tapZoom=P}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class cl{constructor(d){this._tr=new hu(d),this.reset()}reset(){this._active=!1}dblclick(d,P){return d.preventDefault(),{cameraAnimation:N=>{N.easeTo({duration:300,zoom:i.cw(this._tr.zoom+(d.shiftKey?-1:1),N.getZoomSnap()),around:this._tr.unproject(P)},{originalEvent:d})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class Eu{constructor(){this._tap=new Me({numTouches:1,numTaps:1}),this._zoomRate=1,this.reset()}setZoomRate(d){this._zoomRate=d!=null?d:1}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(d,P,N){if(!this._swipePoint)if(this._tapTime){let H=P[0],$=d.timeStamp-this._tapTime<500,se=this._tapPoint.dist(H)<30;$&&se?N.length>0&&(this._swipePoint=H,this._swipeTouch=N[0].identifier):this.reset()}else this._tap.touchstart(d,P,N)}touchmove(d,P,N){if(this._tapTime){if(this._swipePoint){if(N[0].identifier!==this._swipeTouch)return;let H=P[0],$=H.y-this._swipePoint.y;return this._swipePoint=H,d.preventDefault(),this._active=!0,{zoomDelta:$/128*this._zoomRate}}}else this._tap.touchmove(d,P,N)}touchend(d,P,N){if(this._tapTime)this._swipePoint&&N.length===0&&this.reset();else{let H=this._tap.touchend(d,P,N);H&&(this._tapTime=d.timeStamp,this._tapPoint=H)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class el{constructor(d,P,N){this._el=d,this._mousePan=P,this._touchPan=N}enable(d){this._inertiaOptions=d||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"maplibregl-touch-drag-pan\")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"maplibregl-touch-drag-pan\")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class yc{constructor(d,P,N,H){this._pitchWithRotate=d.pitchWithRotate,this._rollEnabled=d.rollEnabled,this._mouseRotate=P,this._mousePitch=N,this._mouseRoll=H}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable(),this._rollEnabled&&this._mouseRoll.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable(),this._mouseRoll.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())&&(!this._rollEnabled||this._mouseRoll.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()||this._mouseRoll.isActive()}}class wi{constructor(d,P,N,H){this._el=d,this._touchZoom=P,this._touchRotate=N,this._tapDragZoom=H,this._rotationDisabled=!1,this._enabled=!0}enable(d){this._touchZoom.enable(d),this._rotationDisabled||this._touchRotate.enable(d),this._tapDragZoom.enable(),this._el.classList.add(\"maplibregl-touch-zoom-rotate\")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"maplibregl-touch-zoom-rotate\")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}setZoomRate(d){this._touchZoom.setZoomRate(d),this._tapDragZoom.setZoomRate(d)}setZoomThreshold(d){this._touchZoom.setZoomThreshold(d)}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}class Di{constructor(d,P){this._bypassKey=navigator.userAgent.includes(\"Mac\")?\"metaKey\":\"ctrlKey\",this._map=d,this._options=P,this._enabled=!1}isActive(){return!1}reset(){}_setupUI(){if(this._container)return;let d=this._map.getCanvasContainer();d.classList.add(\"maplibregl-cooperative-gestures\"),this._container=_.create(\"div\",\"maplibregl-cooperative-gesture-screen\",d);let P=this._map._getUIString(\"CooperativeGesturesHandler.WindowsHelpText\");this._bypassKey===\"metaKey\"&&(P=this._map._getUIString(\"CooperativeGesturesHandler.MacHelpText\"));let N=this._map._getUIString(\"CooperativeGesturesHandler.MobileHelpText\"),H=document.createElement(\"div\");H.className=\"maplibregl-desktop-message\",H.textContent=P,this._container.appendChild(H);let $=document.createElement(\"div\");$.className=\"maplibregl-mobile-message\",$.textContent=N,this._container.appendChild($),this._container.setAttribute(\"aria-hidden\",\"true\")}_destroyUI(){this._container&&(this._container.remove(),this._map.getCanvasContainer().classList.remove(\"maplibregl-cooperative-gestures\")),delete this._container}enable(){this._setupUI(),this._enabled=!0}disable(){this._enabled=!1,this._destroyUI()}isEnabled(){return this._enabled}isBypassed(d){return d[this._bypassKey]}notifyGestureBlocked(d,P){this._enabled&&(this._map.fire(new i.n(\"cooperativegestureprevented\",{gestureType:d,originalEvent:P})),this._container.classList.add(\"maplibregl-show\"),setTimeout((()=>{this._container.classList.remove(\"maplibregl-show\")}),100))}}let fl=Y=>Y.zoom||Y.drag||Y.roll||Y.pitch||Y.rotate;class Fc extends i.n{}function du(Y){var d;return((d=Y.panDelta)===null||d===void 0?void 0:d.mag())||Y.zoomDelta||Y.bearingDelta||Y.pitchDelta||Y.rollDelta}class ms{get _ownerDocument(){var d;return((d=this._el)===null||d===void 0?void 0:d.ownerDocument)||document}get _ownerWindow(){var d,P;return((P=(d=this._el)===null||d===void 0?void 0:d.ownerDocument)===null||P===void 0?void 0:P.defaultView)||window}constructor(d,P){this.handleWindowEvent=H=>{this.handleEvent(H,`${H.type}Window`)},this.handleEvent=(H,$)=>{if(H.type===\"blur\")return void this.stop(!0);this._updatingCamera=!0;let se=H.type===\"renderFrame\"?void 0:H,de={needsRenderFrame:!1},Ae={},Fe={};for(let{handlerName:st,handler:dt,allowed:Pe}of this._handlers){if(!dt.isEnabled())continue;let Xe;if(this._blockedByActive(Fe,Pe,st))dt.reset();else if(dt[$||H.type]){if(i.cz(H,$||H.type)){let at=_.mousePos(this._map.getCanvas(),H);Xe=dt[$||H.type](H,at)}else if(i.cA(H,$||H.type)){let at=this._getMapTouches(H.touches),xt=_.touchPos(this._map.getCanvas(),at);Xe=dt[$||H.type](H,xt,at)}else i.cB($||H.type)||(Xe=dt[$||H.type](H));this.mergeHandlerResult(de,Ae,Xe,st,se),Xe!=null&&Xe.needsRenderFrame&&this._triggerRenderFrame()}(Xe||dt.isActive())&&(Fe[st]=dt)}let Ie={};for(let st in this._previousActiveHandlers)Fe[st]||(Ie[st]=se);this._previousActiveHandlers=Fe,(Object.keys(Ie).length||du(de))&&(this._changes.push([de,Ae,Ie]),this._triggerRenderFrame()),(Object.keys(Fe).length||du(de))&&this._map._stop(!0),this._updatingCamera=!1;let{cameraAnimation:Ye}=de;Ye&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],Ye(this._map))},this._map=d,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new x0(d),this._bearingSnap=P.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(P);let N=this._el;this._listeners=[[N,\"touchstart\",{passive:!0}],[N,\"touchmove\",{passive:!1}],[N,\"touchend\",void 0],[N,\"touchcancel\",void 0],[N,\"mousedown\",void 0],[N,\"mousemove\",void 0],[N,\"mouseup\",void 0],[this._ownerDocument,\"mousemove\",{capture:!0}],[this._ownerDocument,\"mouseup\",void 0],[N,\"mouseover\",void 0],[N,\"mouseout\",void 0],[N,\"dblclick\",void 0],[N,\"click\",void 0],[N,\"keydown\",{capture:!1}],[N,\"keyup\",void 0],[N,\"wheel\",{passive:!1}],[N,\"contextmenu\",void 0],[this._ownerWindow,\"blur\",void 0]];for(let[H,$,se]of this._listeners)H.addEventListener($,H===this._ownerDocument?this.handleWindowEvent:this.handleEvent,se)}destroy(){for(let[d,P,N]of this._listeners)d.removeEventListener(P,d===this._ownerDocument?this.handleWindowEvent:this.handleEvent,N)}_addDefaultHandlers(d){let P=this._map,N=P.getCanvasContainer();this._add(\"mapEvent\",new Wh(P,d));let H=P.boxZoom=new Mu(P,d);this._add(\"boxZoom\",H),d.interactive&&d.boxZoom&&H.enable();let $=P.cooperativeGestures=new Di(P,d.cooperativeGestures);this._add(\"cooperativeGestures\",$),d.cooperativeGestures&&$.enable();let se=new Ce(P),de=new cl(P);P.doubleClickZoom=new ls(de,se),this._add(\"tapZoom\",se),this._add(\"clickZoom\",de),d.interactive&&d.doubleClickZoom&&P.doubleClickZoom.enable();let Ae=new Eu;this._add(\"tapDragZoom\",Ae);let Fe=P.touchPitch=new Bi(P);this._add(\"touchPitch\",Fe),d.interactive&&d.touchPitch&&P.touchPitch.enable(d.touchPitch);let Ie=()=>P.project(P.getCenter()),Ye=(function({enable:St,clickTolerance:Pt,aroundCenter:$t=!0,minPixelCenterThreshold:Ht=100,rotateDegreesPerPixelMoved:dr=.8},Xr){let Or=new lt({checkCorrectEvent:$r=>$r.button===0&&$r.ctrlKey||$r.button===2&&!$r.ctrlKey});return new Be({clickTolerance:Pt,move:($r,Er)=>{let Tr=Xr();if($t&&Math.abs(Tr.y-$r.y)>Ht)return{bearingDelta:i.cx(new i.P($r.x,Er.y),Er,Tr)};let Rr=(Er.x-$r.x)*dr;return $t&&Er.y<Tr.y&&(Rr=-Rr),{bearingDelta:Rr}},moveStateManager:Or,enable:St,assignEvents:Nt})})(d,Ie),st=(function({enable:St,clickTolerance:Pt,pitchDegreesPerPixelMoved:$t=-.5}){let Ht=new lt({checkCorrectEvent:dr=>dr.button===0&&dr.ctrlKey||dr.button===2});return new Be({clickTolerance:Pt,move:(dr,Xr)=>({pitchDelta:(Xr.y-dr.y)*$t}),moveStateManager:Ht,enable:St,assignEvents:Nt})})(d),dt=(function({enable:St,clickTolerance:Pt,rollDegreesPerPixelMoved:$t=.3},Ht){let dr=new lt({checkCorrectEvent:Xr=>Xr.button===2&&Xr.ctrlKey});return new Be({clickTolerance:Pt,move:(Xr,Or)=>{let $r=Ht(),Er=(Or.x-Xr.x)*$t;return Or.y<$r.y&&(Er=-Er),{rollDelta:Er}},moveStateManager:dr,enable:St,assignEvents:Nt})})(d,Ie);P.dragRotate=new yc(d,Ye,st,dt),this._add(\"mouseRotate\",Ye,[\"mousePitch\"]),this._add(\"mousePitch\",st,[\"mouseRotate\",\"mouseRoll\"]),this._add(\"mouseRoll\",dt,[\"mousePitch\"]),d.interactive&&d.dragRotate&&P.dragRotate.enable();let Pe=(function({enable:St,clickTolerance:Pt}){let $t=new lt({checkCorrectEvent:Ht=>Ht.button===0&&!Ht.ctrlKey});return new Be({clickTolerance:Pt,move:(Ht,dr)=>({around:dr,panDelta:dr.sub(Ht)}),activateOnStart:!0,moveStateManager:$t,enable:St,assignEvents:Nt})})(d),Xe=new Kt(d,P);P.dragPan=new el(N,Pe,Xe),this._add(\"mousePan\",Pe),this._add(\"touchPan\",Xe,[\"touchZoom\",\"touchRotate\"]),d.interactive&&d.dragPan&&P.dragPan.enable(d.dragPan);let at=new In,xt=new ln;P.touchZoomRotate=new wi(N,xt,at,Ae),this._add(\"touchRotate\",at,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",xt,[\"touchPan\",\"touchRotate\"]),d.interactive&&d.touchZoomRotate&&P.touchZoomRotate.enable(d.touchZoomRotate),this._add(\"blockableMapEvent\",new Yh(P));let Et=P.scrollZoom=new Ua(P,(()=>this._triggerRenderFrame()));this._add(\"scrollZoom\",Et,[\"mousePan\"]),d.interactive&&d.scrollZoom&&P.scrollZoom.enable(d.scrollZoom);let Bt=P.keyboard=new ia(P);this._add(\"keyboard\",Bt),d.interactive&&d.keyboard&&P.keyboard.enable()}_add(d,P,N){this._handlers.push({handlerName:d,handler:P,allowed:N}),this._handlersById[d]=P}stop(d){if(!this._updatingCamera){for(let{handler:P}of this._handlers)P.reset();this._inertia.clear(),this._fireEvents({},{},d),this._changes=[]}}isActive(){for(let{handler:d}of this._handlers)if(d.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return!!fl(this._eventsInProgress)||this.isZooming()}_blockedByActive(d,P,N){for(let H in d)if(H!==N&&!(P!=null&&P.includes(H)))return!0;return!1}_getMapTouches(d){let P=[];for(let N of d)this._el.contains(N.target)&&P.push(N);return P}mergeHandlerResult(d,P,N,H,$){if(!N)return;i.e(d,N);let se={handlerName:H,originalEvent:N.originalEvent||$};N.zoomDelta!==void 0&&(P.zoom=se),N.panDelta!==void 0&&(P.drag=se),N.rollDelta!==void 0&&(P.roll=se),N.pitchDelta!==void 0&&(P.pitch=se),N.bearingDelta!==void 0&&(P.rotate=se)}_applyChanges(){let d={},P={},N={};for(let[H,$,se]of this._changes)H.panDelta&&(d.panDelta=(d.panDelta||new i.P(0,0))._add(H.panDelta)),H.zoomDelta&&(d.zoomDelta=(d.zoomDelta||0)+H.zoomDelta),H.bearingDelta&&(d.bearingDelta=(d.bearingDelta||0)+H.bearingDelta),H.pitchDelta&&(d.pitchDelta=(d.pitchDelta||0)+H.pitchDelta),H.rollDelta&&(d.rollDelta=(d.rollDelta||0)+H.rollDelta),H.around!==void 0&&(d.around=H.around),H.pinchAround!==void 0&&(d.pinchAround=H.pinchAround),H.noInertia&&(d.noInertia=H.noInertia),i.e(P,$),i.e(N,se);this._updateMapTransform(d,P,N),this._changes=[]}_updateMapTransform(d,P,N){let H=this._map,$=H._getTransformForUpdate(),se=H.terrain;if(!(du(d)||se&&this._terrainMovement))return void this._fireEvents(P,N,!0);H._stop(!0);let{panDelta:de,zoomDelta:Ae,bearingDelta:Fe,pitchDelta:Ie,rollDelta:Ye,around:st,pinchAround:dt}=d;dt!==void 0&&(st=dt),st||(st=H.transform.centerPoint),se&&!$.isPointOnMapSurface(st)&&(st=$.centerPoint);let Pe={panDelta:de,zoomDelta:Ae,rollDelta:Ye,pitchDelta:Ie,bearingDelta:Fe,around:st};this._map.cameraHelper.useGlobeControls&&!$.isPointOnMapSurface(st)&&(st=$.centerPoint);let Xe=st.distSqr($.centerPoint)<.01?$.center:$.screenPointToLocation(de?st.sub(de):st);this._handleMapControls({terrain:se,tr:$,deltasForHelper:Pe,preZoomAroundLoc:Xe,combinedEventsInProgress:P,panDelta:de}),H._applyUpdatedTransform($),this._map._update(),d.noInertia||this._inertia.record(d),this._fireEvents(P,N,!0)}_handleMapControls({terrain:d,tr:P,deltasForHelper:N,preZoomAroundLoc:H,combinedEventsInProgress:$,panDelta:se}){let de=this._map.cameraHelper;if(de.handleMapControlsRollPitchBearingZoom(N,P),d)return de.useGlobeControls?(this._terrainMovement||!$.drag&&!$.zoom||(this._terrainMovement=!0,this._map._elevationFreeze=!0),void de.handleMapControlsPan(N,P,H)):this._terrainMovement||!$.drag&&!$.zoom?void($.drag&&this._terrainMovement&&se?P.setCenter(P.screenPointToLocation(P.centerPoint.sub(se))):de.handleMapControlsPan(N,P,H)):(this._terrainMovement=!0,this._map._elevationFreeze=!0,void de.handleMapControlsPan(N,P,H));de.handleMapControlsPan(N,P,H)}_fireEvents(d,P,N){let H=fl(this._eventsInProgress),$=fl(d),se={};for(let Ye in d){let{originalEvent:st}=d[Ye];this._eventsInProgress[Ye]||(se[`${Ye}start`]=st),this._eventsInProgress[Ye]=d[Ye]}!H&&$&&this._fireEvent(\"movestart\",$.originalEvent);for(let Ye in se)this._fireEvent(Ye,se[Ye]);$&&this._fireEvent(\"move\",$.originalEvent);for(let Ye in d){let{originalEvent:st}=d[Ye];this._fireEvent(Ye,st)}let de={},Ae;for(let Ye in this._eventsInProgress){let{handlerName:st,originalEvent:dt}=this._eventsInProgress[Ye];this._handlersById[st].isActive()||(delete this._eventsInProgress[Ye],Ae=P[st]||dt,de[`${Ye}end`]=Ae)}for(let Ye in de)this._fireEvent(Ye,de[Ye]);let Fe=fl(this._eventsInProgress),Ie=(H||$)&&!Fe;if(Ie&&this._terrainMovement){this._map._elevationFreeze=!1,this._terrainMovement=!1;let Ye=this._map._getTransformForUpdate();this._map.getCenterClampedToGround()&&Ye.recalculateZoomAndCenter(this._map.terrain),this._map._applyUpdatedTransform(Ye)}if(N&&Ie){this._updatingCamera=!0;let Ye=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),st=dt=>dt!==0&&-this._bearingSnap<dt&&dt<this._bearingSnap;!Ye||!Ye.essential&&f.prefersReducedMotion?(this._map.fire(new i.n(\"moveend\",{originalEvent:Ae})),st(this._map.getBearing())&&this._map.resetNorth()):(st(Ye.bearing||this._map.getBearing())&&(Ye.bearing=0),Ye.freezeElevation=!0,this._map.easeTo(Ye,{originalEvent:Ae})),this._updatingCamera=!1}}_fireEvent(d,P){this._map.fire(new i.n(d,P?{originalEvent:P}:{}))}_requestFrame(){return this._map.triggerRepaint(),this._map._renderTaskQueue.add((d=>{delete this._frameId,this.handleEvent(new Fc(\"renderFrame\",{timeStamp:d})),this._applyChanges()}))}_triggerRenderFrame(){this._frameId===void 0&&(this._frameId=this._requestFrame())}}class ff extends i.E{constructor(d,P,N){super(),this._renderFrameCallback=()=>{let H=Math.min((g()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(H)),H<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=d,this._bearingSnap=N.bearingSnap,this._zoomSnap=N.zoomSnap,this.cameraHelper=P,this.on(\"moveend\",(()=>{delete this._requestedCameraState}))}migrateProjection(d,P){d.apply(this.transform,!0),this.transform=d,this.cameraHelper=P}getCenter(){return new i.W(this.transform.center.lng,this.transform.center.lat)}setCenter(d,P){return this.jumpTo({center:d},P)}getCenterElevation(){return this.transform.elevation}setCenterElevation(d,P){return this.jumpTo({elevation:d},P),this}getCenterClampedToGround(){return this._centerClampedToGround}setCenterClampedToGround(d){this._centerClampedToGround=d}panBy(d,P,N){return d=i.P.convert(d).mult(-1),this.panTo(this.transform.center,i.e({offset:d},P),N)}panTo(d,P,N){return this.easeTo(i.e({center:d},P),N)}getZoom(){return this.transform.zoom}setZoom(d,P){return this.jumpTo({zoom:d},P),this}zoomTo(d,P,N){return this.easeTo(i.e({zoom:d},P),N)}zoomIn(d,P){return this.zoomTo(i.cw(this.getZoom()+1,this._zoomSnap),d,P),this}zoomOut(d,P){return this.zoomTo(i.cw(this.getZoom()-1,this._zoomSnap),d,P),this}getVerticalFieldOfView(){return this.transform.fov}setVerticalFieldOfView(d,P){return d!=this.transform.fov&&(this.transform.setFov(d),this.fire(new i.n(\"movestart\",P)).fire(new i.n(\"move\",P)).fire(new i.n(\"moveend\",P))),this}getBearing(){return this.transform.bearing}setZoomSnap(d){return this._zoomSnap=d,this}getZoomSnap(){return this._zoomSnap}setBearing(d,P){return this.jumpTo({bearing:d},P),this}getPadding(){return this.transform.padding}setPadding(d,P){return this.jumpTo({padding:d},P),this}rotateTo(d,P,N){return this.easeTo(i.e({bearing:d},P),N)}resetNorth(d,P){return this.rotateTo(0,i.e({duration:1e3},d),P),this}resetNorthPitch(d,P){return this.easeTo(i.e({bearing:0,pitch:0,roll:0,duration:1e3},d),P),this}snapToNorth(d,P){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(d,P):this}getPitch(){return this.transform.pitch}setPitch(d,P){return this.jumpTo({pitch:d},P),this}getRoll(){return this.transform.roll}setRoll(d,P){return this.jumpTo({roll:d},P),this}cameraForBounds(d,P){d=ie.convert(d).adjustAntiMeridian();let N=(P==null?void 0:P.bearing)||0;return this._cameraForBoxAndBearing(d.getNorthWest(),d.getSouthEast(),N,P)}_cameraForBoxAndBearing(d,P,N,H){let $={top:0,bottom:0,right:0,left:0};if(typeof(H=i.e({padding:$,offset:[0,0],maxZoom:this.transform.maxZoom},H)).padding==\"number\"){let Ie=H.padding;H.padding={top:Ie,bottom:Ie,right:Ie,left:Ie}}let se=i.e($,H.padding);H.padding=se;let de=this.transform,Ae=new ie(d,P),Fe=this.cameraHelper.cameraForBoxAndBearing(H,se,Ae,N,de);return Fe&&this._zoomSnap&&(Fe.zoom=i.cw(Fe.zoom,this._zoomSnap,-1)),Fe}fitBounds(d,P,N){return this._fitInternal(this.cameraForBounds(d,P),P,N)}fitScreenCoordinates(d,P,N,H,$){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.screenPointToLocation(i.P.convert(d)),this.transform.screenPointToLocation(i.P.convert(P)),N,H),H,$)}_fitInternal(d,P,N){return d?(delete(P=i.e(d,P)).padding,P.linear?this.easeTo(P,N):this.flyTo(P,N)):this}jumpTo(d,P){this.stop(),\"zoom\"in d&&this._zoomSnap&&(d.zoom=i.cw(d.zoom,this._zoomSnap));let N=this._getTransformForUpdate(),H=!1,$=!1,se=!1,de=N.zoom;this.terrain&&N.setElevation(this.terrain.getElevationForLngLatZoom(d.center?i.W.convert(d.center):N.center,d.zoom||N.tileZoom)),this.cameraHelper.handleJumpToCenterZoom(N,d);let Ae=N.zoom!==de;return\"elevation\"in d&&N.elevation!==+d.elevation&&N.setElevation(+d.elevation),\"bearing\"in d&&N.bearing!==+d.bearing&&(H=!0,N.setBearing(+d.bearing)),\"pitch\"in d&&N.pitch!==+d.pitch&&($=!0,N.setPitch(+d.pitch)),\"roll\"in d&&N.roll!==+d.roll&&(se=!0,N.setRoll(+d.roll)),d.padding==null||N.isPaddingEqual(d.padding)||N.setPadding(d.padding),this._applyUpdatedTransform(N),this.fire(new i.n(\"movestart\",P)).fire(new i.n(\"move\",P)),Ae&&this.fire(new i.n(\"zoomstart\",P)).fire(new i.n(\"zoom\",P)).fire(new i.n(\"zoomend\",P)),H&&this.fire(new i.n(\"rotatestart\",P)).fire(new i.n(\"rotate\",P)).fire(new i.n(\"rotateend\",P)),$&&this.fire(new i.n(\"pitchstart\",P)).fire(new i.n(\"pitch\",P)).fire(new i.n(\"pitchend\",P)),se&&this.fire(new i.n(\"rollstart\",P)).fire(new i.n(\"roll\",P)).fire(new i.n(\"rollend\",P)),this.fire(new i.n(\"moveend\",P))}calculateCameraOptionsFromTo(d,P,N,H=0){let $=i.a7.fromLngLat(d,P),se=i.a7.fromLngLat(N,H),de=se.x-$.x,Ae=se.y-$.y,Fe=se.z-$.z,Ie=Math.hypot(de,Ae,Fe);if(Ie===0)throw new Error(\"Can't calculate camera options with same From and To\");let Ye=Math.hypot(de,Ae),st=i.ar(this.transform.cameraToCenterDistance/Ie/this.transform.tileSize),dt=180*Math.atan2(de,-Ae)/Math.PI,Pe=180*Math.acos(Ye/Ie)/Math.PI;return Pe=Fe<0?90-Pe:90+Pe,{center:se.toLngLat(),elevation:H,zoom:st,pitch:Pe,bearing:dt}}calculateCameraOptionsFromCameraLngLatAltRotation(d,P,N,H,$){let se=this.transform.calculateCenterFromCameraLngLatAlt(d,P,N,H);return{center:se.center,elevation:se.elevation,zoom:se.zoom,bearing:N,pitch:H,roll:$}}easeTo(d,P){this._stop(!1,d.easeId),\"zoom\"in(d=i.e({offset:[0,0],duration:500,easing:i.cy},d))&&this._zoomSnap&&(d.zoom=i.cw(d.zoom,this._zoomSnap)),(d.animate===!1||!d.essential&&f.prefersReducedMotion)&&(d.duration=0);let N=this._getTransformForUpdate(),H=this.getBearing(),$=N.pitch,se=N.roll,de=\"bearing\"in d?this._normalizeBearing(d.bearing,H):H,Ae=\"pitch\"in d?+d.pitch:$,Fe=\"roll\"in d?this._normalizeBearing(d.roll,se):se,Ie=\"padding\"in d?d.padding:N.padding,Ye=i.P.convert(d.offset),st,dt;d.around&&(st=i.W.convert(d.around),dt=N.locationToScreenPoint(st));let Pe={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching,rolling:this._rolling},Xe=this.cameraHelper.handleEaseTo(N,{bearing:de,pitch:Ae,roll:Fe,padding:Ie,around:st,aroundPoint:dt,offsetAsPoint:Ye,offset:d.offset,zoom:d.zoom,center:d.center});return this._rotating||(this._rotating=H!==de),this._pitching||(this._pitching=Ae!==$),this._rolling||(this._rolling=Fe!==se),this._padding=!N.isPaddingEqual(Ie),this._zooming||(this._zooming=Xe.isZooming),this._easeId=d.easeId,this._prepareEase(P,d.noMoveStart,Pe),this.terrain&&this._prepareElevation(Xe.elevationCenter),this._ease((at=>{Xe.easeFunc(at),this.terrain&&!d.freezeElevation&&this._updateElevation(at),this._applyUpdatedTransform(N),this._fireMoveEvents(P)}),(at=>{this.terrain&&d.freezeElevation&&this._finalizeElevation(),this._afterEase(P,at)}),d),this}_prepareEase(d,P,N={}){this._moving=!0,P||N.moving||this.fire(new i.n(\"movestart\",d)),this._zooming&&!N.zooming&&this.fire(new i.n(\"zoomstart\",d)),this._rotating&&!N.rotating&&this.fire(new i.n(\"rotatestart\",d)),this._pitching&&!N.pitching&&this.fire(new i.n(\"pitchstart\",d)),this._rolling&&!N.rolling&&this.fire(new i.n(\"rollstart\",d))}_prepareElevation(d){this._elevationCenter=d,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(d,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(d){this._elevationStart!==void 0&&this._elevationCenter!==void 0||this._prepareElevation(this.transform.center),this.transform.setMinElevationForCurrentTile(this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom));let P=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(d<1&&P!==this._elevationTarget){let N=this._elevationTarget-this._elevationStart;this._elevationStart+=d*(N-(P-(N*d+this._elevationStart))/(1-d)),this._elevationTarget=P}this.transform.setElevation(i.H.number(this._elevationStart,this._elevationTarget,d))}_finalizeElevation(){this._elevationFreeze=!1,this.getCenterClampedToGround()&&this.transform.recalculateZoomAndCenter(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate||this.terrain?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_elevateCameraIfInsideTerrain(d){if(!this.terrain&&d.elevation>=0&&d.pitch<=90)return{};let P=d.getCameraLngLat(),N=d.getCameraAltitude(),H=this.terrain?this.terrain.getElevationForLngLatZoom(P,d.zoom):0;if(N<H){let $=this.calculateCameraOptionsFromTo(P,H,d.center,d.elevation);return{pitch:$.pitch,zoom:$.zoom}}return{}}_applyUpdatedTransform(d){let P=[];if(P.push((H=>this._elevateCameraIfInsideTerrain(H))),this.transformCameraUpdate&&P.push((H=>this.transformCameraUpdate(H))),!P.length)return;let N=d.clone();for(let H of P){let $=N.clone(),{center:se,zoom:de,roll:Ae,pitch:Fe,bearing:Ie,elevation:Ye}=H($);se&&$.setCenter(se),Ye!==void 0&&$.setElevation(Ye),de!==void 0&&$.setZoom(de),Ae!==void 0&&$.setRoll(Ae),Fe!==void 0&&$.setPitch(Fe),Ie!==void 0&&$.setBearing(Ie),N.apply($,!1)}this.transform.apply(N,!1)}_fireMoveEvents(d){this.fire(new i.n(\"move\",d)),this._zooming&&this.fire(new i.n(\"zoom\",d)),this._rotating&&this.fire(new i.n(\"rotate\",d)),this._pitching&&this.fire(new i.n(\"pitch\",d)),this._rolling&&this.fire(new i.n(\"roll\",d))}_afterEase(d,P){if(this._easeId&&P&&this._easeId===P)return;delete this._easeId;let N=this._zooming,H=this._rotating,$=this._pitching,se=this._rolling;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._rolling=!1,this._padding=!1,N&&this.fire(new i.n(\"zoomend\",d)),H&&this.fire(new i.n(\"rotateend\",d)),$&&this.fire(new i.n(\"pitchend\",d)),se&&this.fire(new i.n(\"rollend\",d)),this.fire(new i.n(\"moveend\",d))}flyTo(d,P){if(!d.essential&&f.prefersReducedMotion){let Er=i.V(d,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"roll\",\"elevation\",\"padding\"]);return this.jumpTo(Er,P)}this.stop(),\"zoom\"in(d=i.e({offset:[0,0],speed:1.2,curve:1.42,easing:i.cy},d))&&this._zoomSnap&&(d.zoom=i.cw(d.zoom,this._zoomSnap));let N=this._getTransformForUpdate(),H=N.bearing,$=N.pitch,se=N.roll,de=N.padding,Ae=\"bearing\"in d?this._normalizeBearing(d.bearing,H):H,Fe=\"pitch\"in d?+d.pitch:$,Ie=\"roll\"in d?this._normalizeBearing(d.roll,se):se,Ye=\"padding\"in d?d.padding:N.padding,st=i.P.convert(d.offset),dt=N.centerPoint.add(st),Pe=N.screenPointToLocation(dt),Xe=this.cameraHelper.handleFlyTo(N,{bearing:Ae,pitch:Fe,roll:Ie,padding:Ye,locationAtOffset:Pe,offsetAsPoint:st,center:d.center,minZoom:d.minZoom,zoom:d.zoom}),at=d.curve,xt=Math.max(N.width,N.height),Et=xt/Xe.scaleOfZoom,Bt=Xe.pixelPathLength;typeof Xe.scaleOfMinZoom==\"number\"&&(at=Math.sqrt(xt/Xe.scaleOfMinZoom/Bt*2));let St=at*at;function Pt(Er){let Tr=(Et*Et-xt*xt+(Er?-1:1)*St*St*Bt*Bt)/(2*(Er?Et:xt)*St*Bt);return Math.log(Math.sqrt(Tr*Tr+1)-Tr)}function $t(Er){return(Math.exp(Er)-Math.exp(-Er))/2}function Ht(Er){return(Math.exp(Er)+Math.exp(-Er))/2}let dr=Pt(!1),Xr=function(Er){return Ht(dr)/Ht(dr+at*Er)},Or=function(Er){return xt*((Ht(dr)*($t(Tr=dr+at*Er)/Ht(Tr))-$t(dr))/St)/Bt;var Tr},$r=(Pt(!0)-dr)/at;if(Math.abs(Bt)<2e-6||!isFinite($r)){if(Math.abs(xt-Et)<1e-6)return this.easeTo(d,P);let Er=Et<xt?-1:1;$r=Math.abs(Math.log(Et/xt))/at,Or=()=>0,Xr=Tr=>Math.exp(Er*at*Tr)}return d.duration=\"duration\"in d?+d.duration:1e3*$r/(\"screenSpeed\"in d?+d.screenSpeed/at:+d.speed),d.maxDuration&&d.duration>d.maxDuration&&(d.duration=0),this._zooming=!0,this._rotating=H!==Ae,this._pitching=Fe!==$,this._rolling=Ie!==se,this._padding=!N.isPaddingEqual(Ye),this._prepareEase(P,!1),this.terrain&&this._prepareElevation(Xe.targetCenter),this._ease((Er=>{let Tr=Er*$r,Rr=1/Xr(Tr),Lr=Or(Tr);this._rotating&&N.setBearing(i.H.number(H,Ae,Er)),this._pitching&&N.setPitch(i.H.number($,Fe,Er)),this._rolling&&N.setRoll(i.H.number(se,Ie,Er)),this._padding&&(N.interpolatePadding(de,Ye,Er),dt=N.centerPoint.add(st)),Xe.easeFunc(Er,Rr,Lr,dt),this.terrain&&!d.freezeElevation&&this._updateElevation(Er),this._applyUpdatedTransform(N),this._fireMoveEvents(P)}),(()=>{this.terrain&&d.freezeElevation&&this._finalizeElevation(),this._afterEase(P)}),d),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(d,P){var N;if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){let H=this._onEaseEnd;delete this._onEaseEnd,H.call(this,P)}return d||(N=this.handlers)===null||N===void 0||N.stop(!1),this}_ease(d,P,N){N.animate===!1||N.duration===0?(d(1),P()):(this._easeStart=g(),this._easeOptions=N,this._onEaseFrame=d,this._onEaseEnd=P,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(d,P){d=i.X(d,-180,180);let N=Math.abs(d-P);return Math.abs(d-360-P)<N&&(d-=360),Math.abs(d+360-P)<N&&(d+=360),d}queryTerrainElevation(d){return this.terrain?this.terrain.getElevationForLngLat(i.W.convert(d),this.transform):null}}let ku={compact:!0,customAttribution:'<a href=\"https://maplibre.org/\" target=\"_blank\">MapLibre</a>'};class hl{constructor(d=ku){this._toggleAttribution=()=>{this._container.classList.contains(\"maplibregl-compact\")&&(this._container.classList.contains(\"maplibregl-compact-show\")?(this._container.setAttribute(\"open\",\"\"),this._container.classList.remove(\"maplibregl-compact-show\")):(this._container.classList.add(\"maplibregl-compact-show\"),this._container.removeAttribute(\"open\")))},this._updateData=P=>{!P||P.sourceDataType!==\"metadata\"&&P.sourceDataType!==\"visibility\"&&P.dataType!==\"style\"&&P.type!==\"terrain\"||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact===!1?this._container.setAttribute(\"open\",\"\"):this._container.classList.contains(\"maplibregl-compact\")||this._container.classList.contains(\"maplibregl-attrib-empty\")||(this._container.setAttribute(\"open\",\"\"),this._container.classList.add(\"maplibregl-compact\",\"maplibregl-compact-show\")):(this._container.setAttribute(\"open\",\"\"),this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.remove(\"maplibregl-compact\",\"maplibregl-compact-show\"))},this._updateCompactMinimize=()=>{this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.contains(\"maplibregl-compact-show\")&&this._container.classList.remove(\"maplibregl-compact-show\")},this.options=d}getDefaultPosition(){return\"bottom-right\"}onAdd(d){return this._map=d,this._compact=this.options.compact,this._container=_.create(\"details\",\"maplibregl-ctrl maplibregl-ctrl-attrib\"),this._compactButton=_.create(\"summary\",\"maplibregl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=_.create(\"div\",\"maplibregl-ctrl-attrib-inner\",this._container),this._updateAttributions(),this._updateCompact(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"terrain\",this._updateData),this._map.on(\"resize\",this._updateCompact),this._map.on(\"drag\",this._updateCompactMinimize),this._container}onRemove(){this._container.remove(),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"terrain\",this._updateData),this._map.off(\"resize\",this._updateCompact),this._map.off(\"drag\",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(d,P){let N=this._map._getUIString(`AttributionControl.${P}`);d.title=N,d.setAttribute(\"aria-label\",N)}_updateAttributions(){if(!this._map.style)return;let d=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?d=d.concat(this.options.customAttribution.map((H=>typeof H!=\"string\"?\"\":H))):typeof this.options.customAttribution==\"string\"&&d.push(this.options.customAttribution)),this._map.style.stylesheet){let H=this._map.style.stylesheet;this.styleOwner=H.owner,this.styleId=H.id}let P=this._map.style.tileManagers;for(let H in P){let $=P[H];if($.used||$.usedForTerrain){let se=$.getSource();se.attribution&&!d.includes(se.attribution)&&d.push(se.attribution)}}d=d.filter((H=>String(H).trim())),d.sort(((H,$)=>H.length-$.length)),d=d.filter(((H,$)=>{for(let se=$+1;se<d.length;se++)if(d[se].includes(H))return!1;return!0}));let N=d.join(\" | \");N!==this._attribHTML&&(this._attribHTML=N,d.length?(this._innerContainer.innerHTML=_.sanitize(N),this._container.classList.remove(\"maplibregl-attrib-empty\")):this._container.classList.add(\"maplibregl-attrib-empty\"),this._updateCompact(),this._editLink=null)}}class mh{constructor(d={}){this._updateCompact=()=>{let P=this._container.children;if(P.length){let N=P[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact!==!1&&N.classList.add(\"maplibregl-compact\"):N.classList.remove(\"maplibregl-compact\")}},this.options=d}getDefaultPosition(){return\"bottom-left\"}onAdd(d){var P;this._map=d,this._compact=(P=this.options)===null||P===void 0?void 0:P.compact,this._container=_.create(\"div\",\"maplibregl-ctrl\");let N=_.create(\"a\",\"maplibregl-ctrl-logo\");return N.target=\"_blank\",N.rel=\"noopener nofollow\",N.href=\"https://maplibre.org/\",N.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),N.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(N),this._container.style.display=\"block\",this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container}onRemove(){this._container.remove(),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._compact=void 0}}class W{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(d){let P=++this._id;return this._queue.push({callback:d,id:P,cancelled:!1}),P}remove(d){let P=this._currentlyRunning,N=P?this._queue.concat(P):this._queue;for(let H of N)if(H.id===d)return void(H.cancelled=!0)}run(d=0){if(this._currentlyRunning)throw new Error(\"Attempting to run(), but is already running.\");let P=this._currentlyRunning=this._queue;this._queue=[];for(let N of P)if(!N.cancelled&&(N.callback(d),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}var te=i.aS([{name:\"a_pos3d\",type:\"Int16\",components:3}]);class ve extends i.E{constructor(d){super(),this._lastTilesetChange=g(),this.tileManager=d,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.deltaZoom=1,this.tileSize=d._source.tileSize*2**this.deltaZoom,d.usedForTerrain=!0,d.tileSize=this.tileSize}destruct(){this.tileManager.usedForTerrain=!1,this.tileManager.tileSize=null}getSource(){return this.tileManager._source}update(d,P){this.tileManager.update(d,P),this._renderableTilesKeys=[];let N={};for(let H of ft(d,{tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:P,calculateTileZoom:this.tileManager._source.calculateTileZoom}))N[H.key]=!0,this._renderableTilesKeys.push(H.key),this._tiles[H.key]||(H.terrainRttPosMatrix32f=new Float64Array(16),i.c7(H.terrainRttPosMatrix32f,0,i.a6,i.a6,0,0,1),this._tiles[H.key]=new mt(H,this.tileSize),this._lastTilesetChange=g());for(let H in this._tiles)N[H]||delete this._tiles[H]}freeRtt(d){for(let P in this._tiles){let N=this._tiles[P];(!d||N.tileID.equals(d)||N.tileID.isChildOf(d)||d.isChildOf(N.tileID))&&(N.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map((d=>this.getTileByID(d)))}getTileByID(d){return this._tiles[d]}getTerrainCoords(d,P){return P?this._getTerrainCoordsForTileRanges(d,P):this._getTerrainCoordsForRegularTile(d)}_getTerrainCoordsForRegularTile(d){let P={};for(let N of this._renderableTilesKeys){let H=this._tiles[N].tileID,$=d.clone(),se=i.bj();if(H.canonical.equals(d.canonical))i.c7(se,0,i.a6,i.a6,0,0,1);else if(H.canonical.isChildOf(d.canonical)){let de=H.canonical.z-d.canonical.z,Ae=H.canonical.x-(H.canonical.x>>de<<de),Fe=H.canonical.y-(H.canonical.y>>de<<de),Ie=i.a6>>de;i.c7(se,0,Ie,Ie,0,0,1),i.Q(se,se,[-Ae*Ie,-Fe*Ie,0])}else{if(!d.canonical.isChildOf(H.canonical))continue;{let de=d.canonical.z-H.canonical.z,Ae=d.canonical.x-(d.canonical.x>>de<<de),Fe=d.canonical.y-(d.canonical.y>>de<<de),Ie=i.a6>>de;i.c7(se,0,i.a6,i.a6,0,0,1),i.Q(se,se,[Ae*Ie,Fe*Ie,0]),i.S(se,se,[1/2**de,1/2**de,0])}}$.terrainRttPosMatrix32f=new Float32Array(se),P[N]=$}return P}_getTerrainCoordsForTileRanges(d,P){let N={};for(let H of this._renderableTilesKeys){let $=this._tiles[H].tileID;if(!this._isWithinTileRanges($,P))continue;let se=d.clone(),de=i.bj();if($.canonical.z===d.canonical.z){let Ae=d.canonical.x-$.canonical.x+d.wrap*(1<<d.canonical.z),Fe=d.canonical.y-$.canonical.y;i.c7(de,0,i.a6,i.a6,0,0,1),i.Q(de,de,[Ae*i.a6,Fe*i.a6,0])}else if($.canonical.z>d.canonical.z){let Ae=$.canonical.z-d.canonical.z,Fe=$.canonical.x-($.canonical.x>>Ae<<Ae)+d.wrap*(1<<$.canonical.z),Ie=$.canonical.y-($.canonical.y>>Ae<<Ae),Ye=d.canonical.x-($.canonical.x>>Ae),st=d.canonical.y-($.canonical.y>>Ae),dt=i.a6>>Ae;i.c7(de,0,dt,dt,0,0,1),i.Q(de,de,[-Fe*dt+Ye*i.a6,-Ie*dt+st*i.a6,0])}else{let Ae=d.canonical.z-$.canonical.z,Fe=d.canonical.x-(d.canonical.x>>Ae<<Ae)+d.wrap*(1<<d.canonical.z),Ie=d.canonical.y-(d.canonical.y>>Ae<<Ae),Ye=(d.canonical.x>>Ae)-$.canonical.x,st=(d.canonical.y>>Ae)-$.canonical.y,dt=i.a6<<Ae;i.c7(de,0,dt,dt,0,0,1),i.Q(de,de,[Fe*i.a6+Ye*dt,Ie*i.a6+st*dt,0])}se.terrainRttPosMatrix32f=new Float32Array(de),N[H]=se}return N}getSourceTile(d,P){var N,H;let $=this.tileManager._source,se=d.overscaledZ-this.deltaZoom;if(se>$.maxzoom&&(se=$.maxzoom),se<$.minzoom)return;(N=this._sourceTileCache)[H=d.key]||(N[H]=d.scaledTo(se).key);let de=this.findTileInCaches(this._sourceTileCache[d.key]);if(!(de!=null&&de.dem)&&P)for(;se>=$.minzoom&&!(de!=null&&de.dem);)de=this.findTileInCaches(d.scaledTo(se--).key);return de}findTileInCaches(d){let P=this.tileManager.getTileByID(d);return P||(P=this.tileManager._outOfViewCache.getByKey(d),P)}anyTilesAfterTime(d=Date.now()){return this._lastTilesetChange>=d}_isWithinTileRanges(d,P){let N=P[d.canonical.z];return!!N&&(d.wrap>N.minWrap||d.wrap<N.maxWrap||d.canonical.x>=N.minTileXWrapped&&d.canonical.x<=N.maxTileXWrapped&&d.canonical.y>=N.minTileY&&d.canonical.y<=N.maxTileY)}}class Se{constructor(d,P,N){this._meshCache={},this.painter=d,this.tileManager=new ve(P),this.options=N,this.exaggeration=typeof N.exaggeration==\"number\"?N.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}destroy(){this._fbo&&(this._fbo.destroy(),this._fbo=null),this._fboCoordsTexture&&(this._fboCoordsTexture.destroy(),this._fboCoordsTexture=null),this._fboDepthTexture&&(this._fboDepthTexture.destroy(),this._fboDepthTexture=null),this._emptyDemTexture&&(this._emptyDemTexture.destroy(),this._emptyDemTexture=null),this._emptyDepthTexture&&(this._emptyDepthTexture.destroy(),this._emptyDepthTexture=null),this._coordsTexture&&(this._coordsTexture.destroy(),this._coordsTexture=null);for(let d in this._meshCache)this._meshCache[d].destroy();this._meshCache={},this.tileManager.destruct()}getDEMElevation(d,P,N,H=i.a6){var $;let se=d.normalizeCoordinates(P,N,H);if(!se)return 0;let de=this.getTerrainData(se.tileID),Ae=($=de.tile)===null||$===void 0?void 0:$.dem;if(!Ae)return 0;let Fe=i.cC([],[se.x/H*i.a6,se.y/H*i.a6],de.u_terrain_matrix),Ie=[Fe[0]*Ae.dim,Fe[1]*Ae.dim],Ye=Math.floor(Ie[0]),st=Math.floor(Ie[1]),dt=Ie[0]-Ye,Pe=Ie[1]-st;return Ae.get(Ye,st)*(1-dt)*(1-Pe)+Ae.get(Ye+1,st)*dt*(1-Pe)+Ae.get(Ye,st+1)*(1-dt)*Pe+Ae.get(Ye+1,st+1)*dt*Pe}getElevationForLngLatZoom(d,P){if(!i.cD(P,d.wrap()))return 0;let{tileID:N,mercatorX:H,mercatorY:$}=this._getOverscaledTileIDFromLngLatZoom(d,P);return this.getElevation(N,H%i.a6,$%i.a6,i.a6)}getElevationForLngLat(d,P){let N=ft(P,{maxzoom:this.tileManager.maxzoom,minzoom:this.tileManager.minzoom,tileSize:512,terrain:this}),H=0;for(let $ of N)$.canonical.z>H&&(H=Math.min($.canonical.z,this.tileManager.maxzoom));return this.getElevationForLngLatZoom(d,H)}getElevation(d,P,N,H=i.a6){return this.getDEMElevation(d,P,N,H)*this.exaggeration}getTerrainData(d){var P,N;if(!this._emptyDemTexture){let se=this.painter.context,de=new i.R({width:1,height:1},new Uint8Array(4));this._emptyDepthTexture=new i.T(se,de,se.gl.RGBA,{premultiply:!1}),this._emptyDemUnpack=[0,0,0,0],this._emptyDemTexture=new i.T(se,new i.R({width:1,height:1}),se.gl.RGBA,{premultiply:!1}),this._emptyDemTexture.bind(se.gl.NEAREST,se.gl.CLAMP_TO_EDGE),this._emptyDemMatrix=i.ap([])}let H=this.tileManager.getSourceTile(d,!0);if(H!=null&&H.dem&&(!H.demTexture||H.needsTerrainPrepare)){let se=this.painter.context;H.demTexture=this.painter.getTileTexture(H.dem.stride),H.demTexture?H.demTexture.update(H.dem.getPixels(),{premultiply:!1}):H.demTexture=new i.T(se,H.dem.getPixels(),se.gl.RGBA,{premultiply:!1}),H.demTexture.bind(se.gl.NEAREST,se.gl.CLAMP_TO_EDGE),H.needsTerrainPrepare=!1}let $=H&&H.toString()+H.tileID.key+d.key;if($&&!this._demMatrixCache[$]){let se=this.tileManager.getSource().maxzoom,de=d.canonical.z-H.tileID.canonical.z;d.overscaledZ>d.canonical.z&&(d.canonical.z>=se?de=d.canonical.z-se:i.w(\"cannot calculate elevation if elevation maxzoom > source.maxzoom\"));let Ae=d.canonical.x-(d.canonical.x>>de<<de),Fe=d.canonical.y-(d.canonical.y>>de<<de),Ie=i.cE(new Float64Array(16),[1/(i.a6<<de),1/(i.a6<<de),0]);i.Q(Ie,Ie,[Ae*i.a6,Fe*i.a6,0]),this._demMatrixCache[$]={matrix:Ie,coord:d}}return{u_depth:2,u_terrain:3,u_terrain_dim:((P=H==null?void 0:H.dem)===null||P===void 0?void 0:P.dim)||1,u_terrain_matrix:$?this._demMatrixCache[$].matrix:this._emptyDemMatrix,u_terrain_unpack:((N=H==null?void 0:H.dem)===null||N===void 0?void 0:N.getUnpackVector())||this._emptyDemUnpack,u_terrain_exaggeration:this.exaggeration,texture:((H==null?void 0:H.demTexture)||this._emptyDemTexture).texture,depthTexture:(this._fboDepthTexture||this._emptyDepthTexture).texture,tile:H}}getFramebuffer(d){let P=this.painter,N=P.width/devicePixelRatio,H=P.height/devicePixelRatio;return!this._fbo||this._fbo.width===N&&this._fbo.height===H||(this._fbo.destroy(),this._fboCoordsTexture.destroy(),this._fboDepthTexture.destroy(),delete this._fbo,delete this._fboDepthTexture,delete this._fboCoordsTexture),this._fboCoordsTexture||(this._fboCoordsTexture=new i.T(P.context,{width:N,height:H,data:null},P.context.gl.RGBA,{premultiply:!1}),this._fboCoordsTexture.bind(P.context.gl.NEAREST,P.context.gl.CLAMP_TO_EDGE)),this._fboDepthTexture||(this._fboDepthTexture=new i.T(P.context,{width:N,height:H,data:null},P.context.gl.RGBA,{premultiply:!1}),this._fboDepthTexture.bind(P.context.gl.NEAREST,P.context.gl.CLAMP_TO_EDGE)),this._fbo||(this._fbo=P.context.createFramebuffer(N,H,!0,!1),this._fbo.depthAttachment.set(P.context.createRenderbuffer(P.context.gl.DEPTH_COMPONENT16,N,H))),this._fbo.colorAttachment.set(d===\"coords\"?this._fboCoordsTexture.texture:this._fboDepthTexture.texture),this._fbo}getCoordsTexture(){let d=this.painter.context;if(this._coordsTexture)return this._coordsTexture;let P=new Uint8Array(this._coordsTextureSize*this._coordsTextureSize*4);for(let $=0,se=0;$<this._coordsTextureSize;$++)for(let de=0;de<this._coordsTextureSize;de++,se+=4)P[se+0]=255&de,P[se+1]=255&$,P[se+2]=de>>8<<4|$>>8,P[se+3]=0;let N=new i.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(P.buffer)),H=new i.T(d,N,d.gl.RGBA,{premultiply:!1});return H.bind(d.gl.NEAREST,d.gl.CLAMP_TO_EDGE),this._coordsTexture=H,H}pointCoordinate(d){this.painter.maybeDrawDepth(!0),this.painter.maybeDrawCoords();let P=new Uint8Array(4),N=this.painter.context,H=N.gl,$=Math.round(d.x*this.painter.pixelRatio/devicePixelRatio),se=Math.round(d.y*this.painter.pixelRatio/devicePixelRatio),de=Math.round(this.painter.height/devicePixelRatio);N.bindFramebuffer.set(this.getFramebuffer(\"coords\").framebuffer),H.readPixels($,de-se-1,1,1,H.RGBA,H.UNSIGNED_BYTE,P),N.bindFramebuffer.set(null);let Ae=P[0]+(P[2]>>4<<8),Fe=P[1]+((15&P[2])<<8),Ie=this.coordsIndex[255-P[3]],Ye=Ie&&this.tileManager.getTileByID(Ie);if(!Ye)return null;let st=this._coordsTextureSize,dt=(1<<Ye.tileID.canonical.z)*st;return new i.a7((Ye.tileID.canonical.x*st+Ae)/dt+Ye.tileID.wrap,(Ye.tileID.canonical.y*st+Fe)/dt,this.getElevation(Ye.tileID,Ae,Fe,st))}depthAtPoint(d){let P=new Uint8Array(4),N=this.painter.context,H=N.gl;return N.bindFramebuffer.set(this.getFramebuffer(\"depth\").framebuffer),H.readPixels(d.x,this.painter.height/devicePixelRatio-d.y-1,1,1,H.RGBA,H.UNSIGNED_BYTE,P),N.bindFramebuffer.set(null),(P[0]/16777216+P[1]/65536+P[2]/256+P[3])/256}getTerrainMesh(d){var P;let N=((P=this.painter.style.projection)===null||P===void 0?void 0:P.transitionState)>0,H=N&&d.canonical.y===0,$=N&&d.canonical.y===(1<<d.canonical.z)-1,se=`m_${H?\"n\":\"\"}_${$?\"s\":\"\"}`;if(this._meshCache[se])return this._meshCache[se];let de=this.painter.context,Ae=new i.cF,Fe=new i.aW,Ie=this.meshSize,Ye=i.a6/Ie,st=Ie*Ie;for(let Ht=0;Ht<=Ie;Ht++)for(let dr=0;dr<=Ie;dr++)Ae.emplaceBack(dr*Ye,Ht*Ye,0);for(let Ht=0;Ht<st;Ht+=Ie+1)for(let dr=0;dr<Ie;dr++)Fe.emplaceBack(dr+Ht,Ie+dr+Ht+1,Ie+dr+Ht+2),Fe.emplaceBack(dr+Ht,Ie+dr+Ht+2,dr+Ht+1);let dt=Ae.length,Pe=dt+(Ie+1),Xe=(Ie+1)*Ie,at=H?i.bq:0,xt=H?0:1,Et=$?i.br:i.a6,Bt=$?0:1;for(let Ht=0;Ht<=Ie;Ht++)Ae.emplaceBack(Ht*Ye,at,xt);for(let Ht=0;Ht<=Ie;Ht++)Ae.emplaceBack(Ht*Ye,Et,Bt);for(let Ht=0;Ht<Ie;Ht++)Fe.emplaceBack(Xe+Ht,Pe+Ht,Pe+Ht+1),Fe.emplaceBack(Xe+Ht,Pe+Ht+1,Xe+Ht+1),Fe.emplaceBack(0+Ht,dt+Ht+1,dt+Ht),Fe.emplaceBack(0+Ht,0+Ht+1,dt+Ht+1);let St=Ae.length,Pt=St+2*(Ie+1);for(let Ht of[0,1])for(let dr=0;dr<=Ie;dr++)for(let Xr of[0,1])Ae.emplaceBack(Ht*i.a6,dr*Ye,Xr);for(let Ht=0;Ht<2*Ie;Ht+=2)Fe.emplaceBack(St+Ht,St+Ht+1,St+Ht+3),Fe.emplaceBack(St+Ht,St+Ht+3,St+Ht+2),Fe.emplaceBack(Pt+Ht,Pt+Ht+3,Pt+Ht+1),Fe.emplaceBack(Pt+Ht,Pt+Ht+2,Pt+Ht+3);let $t=new ka(de.createVertexBuffer(Ae,te.members),de.createIndexBuffer(Fe),i.aV.simpleSegment(0,0,Ae.length,Fe.length));return this._meshCache[se]=$t,$t}getMeshFrameDelta(d){return 2*Math.PI*i.bD/Math.pow(2,Math.max(d,0))/5}getMinTileElevationForLngLatZoom(d,P){var N;if(!i.cD(P,d.wrap()))return 0;let{tileID:H}=this._getOverscaledTileIDFromLngLatZoom(d,P);return(N=this.getMinMaxElevation(H).minElevation)!==null&&N!==void 0?N:0}getMinMaxElevation(d){let P=this.getTerrainData(d).tile,N={minElevation:null,maxElevation:null};return P!=null&&P.dem&&(N.minElevation=P.dem.min*this.exaggeration,N.maxElevation=P.dem.max*this.exaggeration),N}_getOverscaledTileIDFromLngLatZoom(d,P){let N=i.a7.fromLngLat(d.wrap()),H=(1<<P)*i.a6,$=N.x*H,se=N.y*H,de=Math.floor($/i.a6),Ae=Math.floor(se/i.a6);return{tileID:new i.a3(P,0,P,de,Ae),mercatorX:$,mercatorY:se}}}class Ee{constructor(d,P,N){this._context=d,this._size=P,this._tileSize=N,this._objects=[],this._recentlyUsed=[],this._stamp=0}destruct(){for(let d of this._objects)d.texture.destroy(),d.fbo.destroy()}_createObject(d){let P=this._context.createFramebuffer(this._tileSize,this._tileSize,!0,!0),N=new i.T(this._context,{width:this._tileSize,height:this._tileSize,data:null},this._context.gl.RGBA);return N.bind(this._context.gl.LINEAR,this._context.gl.CLAMP_TO_EDGE),this._context.extTextureFilterAnisotropic&&this._context.gl.texParameterf(this._context.gl.TEXTURE_2D,this._context.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,this._context.extTextureFilterAnisotropicMax),P.depthAttachment.set(this._context.createRenderbuffer(this._context.gl.DEPTH_STENCIL,this._tileSize,this._tileSize)),P.colorAttachment.set(N.texture),{id:d,fbo:P,texture:N,stamp:-1,inUse:!1}}getObjectForId(d){return this._objects[d]}useObject(d){d.inUse=!0,this._recentlyUsed=this._recentlyUsed.filter((P=>d.id!==P)),this._recentlyUsed.push(d.id)}stampObject(d){d.stamp=++this._stamp}getOrCreateFreeObject(){for(let P of this._recentlyUsed)if(!this._objects[P].inUse)return this._objects[P];if(this._objects.length>=this._size)throw new Error(\"No free RenderPool available, call freeAllObjects() required!\");let d=this._createObject(this._objects.length);return this._objects.push(d),d}freeObject(d){d.inUse=!1}freeAllObjects(){for(let d of this._objects)this.freeObject(d)}isFull(){return!(this._objects.length<this._size)&&this._objects.some((d=>!d.inUse))===!1}}let Re={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0,\"color-relief\":!0};class rt{constructor(d,P){this.painter=d,this.terrain=P,this.pool=new Ee(d.context,30,P.tileManager.tileSize*P.qualityFactor)}destruct(){this.pool.destruct()}getTexture(d){return this.pool.getObjectForId(d.rtt[this._stacks.length-1].id).texture}prepareForRender(d,P){var N,H,$;this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.tileManager.getRenderableTiles(),this._renderableLayerIds=d._order.filter((se=>!d._layers[se].isHidden(P))),this._coordsAscending={};for(let se in d.tileManagers){this._coordsAscending[se]={};let de=d.tileManagers[se].getVisibleCoordinates(),Ae=d.tileManagers[se].getSource(),Fe=Ae instanceof qe?Ae.terrainTileRanges:null;for(let Ie of de){let Ye=this.terrain.tileManager.getTerrainCoords(Ie,Fe);for(let st in Ye)($=this._coordsAscending[se])[st]||($[st]=[]),this._coordsAscending[se][st].push(Ye[st])}}this._rttFingerprints={};for(let se of d._order){let de=d._layers[se],Ae=de.source;if(Re[de.type]&&!this._rttFingerprints[Ae]){this._rttFingerprints[Ae]={};let Fe=(H=(N=d.tileManagers[Ae])===null||N===void 0?void 0:N.getState().revision)!==null&&H!==void 0?H:0;for(let Ie in this._coordsAscending[Ae])this._rttFingerprints[Ae][Ie]=`${this._coordsAscending[Ae][Ie].map((Ye=>Ye.key)).sort().join()}#${Fe}`}}for(let se of this._renderableTiles)for(let de in this._rttFingerprints){let Ae=this._rttFingerprints[de][se.tileID.key];Ae&&Ae!==se.rttFingerprint[de]&&(se.rtt=[])}}renderLayer(d,P){if(d.isHidden(this.painter.transform.zoom))return!1;let N=Object.assign(Object.assign({},P),{isRenderingToTexture:!0}),H=d.type,$=this.painter,se=this._renderableLayerIds[this._renderableLayerIds.length-1]===d.id;if(Re[H]&&(this._prevType&&Re[this._prevType]||this._stacks.push([]),this._prevType=H,this._stacks[this._stacks.length-1].push(d.id),!se))return!0;if(Re[this._prevType]||Re[H]&&se){this._prevType=H;let de=this._stacks.length-1,Ae=this._stacks[de]||[];for(let Fe of this._renderableTiles){if(this.pool.isFull()&&(mc(this.painter,this.terrain,this._rttTiles,N),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(Fe),Fe.rtt[de]){let Ye=this.pool.getObjectForId(Fe.rtt[de].id);if(Ye.stamp===Fe.rtt[de].stamp){this.pool.useObject(Ye);continue}}let Ie=this.pool.getOrCreateFreeObject();this.pool.useObject(Ie),this.pool.stampObject(Ie),Fe.rtt[de]={id:Ie.id,stamp:Ie.stamp},$.context.bindFramebuffer.set(Ie.fbo.framebuffer),$.context.clear({color:i.bo.transparent,stencil:0}),$.currentStencilSource=void 0;for(let Ye of Ae){let st=$.style._layers[Ye],dt=st.source?this._coordsAscending[st.source][Fe.tileID.key]:[Fe.tileID];$.context.viewport.set([0,0,Ie.fbo.width,Ie.fbo.height]),$._renderTileClippingMasks(st,dt,!0),$.renderLayer($,$.style.tileManagers[st.source],st,dt,N),st.source&&(Fe.rttFingerprint[st.source]=this._rttFingerprints[st.source][Fe.tileID.key])}}return mc(this.painter,this.terrain,this._rttTiles,N),this._rttTiles=[],this.pool.freeAllObjects(),Re[H]}return!1}}let He={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"MapLibre logo\",\"Map.Title\":\"Map\",\"Marker.Title\":\"Map marker\",\"NavigationControl.ResetBearing\":\"Drag to rotate map, click to reset north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"Popup.Close\":\"Close popup\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\",\"GlobeControl.Enable\":\"Enable globe\",\"GlobeControl.Disable\":\"Disable globe\",\"TerrainControl.Enable\":\"Enable terrain\",\"TerrainControl.Disable\":\"Disable terrain\",\"CooperativeGesturesHandler.WindowsHelpText\":\"Use Ctrl + scroll to zoom the map\",\"CooperativeGesturesHandler.MacHelpText\":\"Use \\u2318 + scroll to zoom the map\",\"CooperativeGesturesHandler.MobileHelpText\":\"Use two fingers to move the map\"},pt=o,Ft={hash:!1,interactive:!0,bearingSnap:7,zoomSnap:0,attributionControl:ku,maplibreLogo:!1,refreshExpiredTiles:!0,canvasContextAttributes:{antialias:!1,preserveDrawingBuffer:!1,powerPreference:\"high-performance\",failIfMajorPerformanceCaveat:!1,desynchronized:!1,contextType:void 0},scrollZoom:!0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,cooperativeGestures:!1,trackResize:!0,center:[0,0],elevation:0,zoom:0,bearing:0,pitch:0,roll:0,renderWorldCopies:!0,maxTileCacheSize:null,maxTileCacheZoomLevels:i.c.MAX_TILE_CACHE_ZOOM_LEVELS,transformRequest:null,transformCameraUpdate:null,transformConstrain:null,fadeDuration:300,crossSourceCollisions:!0,clickTolerance:3,localIdeographFontFamily:\"sans-serif\",pitchWithRotate:!0,rollEnabled:!1,reduceMotion:void 0,validateStyle:!0,maxCanvasSize:[4096,4096],cancelPendingTileRequestsWhileZooming:!0,centerClampedToGround:!0,experimentalZoomLevelsToOverscale:void 0,anisotropicFilterPitch:20},Rt=class extends ff{get _ownerWindow(){var Y,d;return((d=(Y=this._container)===null||Y===void 0?void 0:Y.ownerDocument)===null||d===void 0?void 0:d.defaultView)||window}constructor(Y){var d,P,N;let H=Object.assign(Object.assign(Object.assign({},Ft),Y),{canvasContextAttributes:Object.assign(Object.assign({},Ft.canvasContextAttributes),Y.canvasContextAttributes)});if(H.minZoom!=null&&H.maxZoom!=null&&H.minZoom>H.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(H.minPitch!=null&&H.maxPitch!=null&&H.minPitch>H.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(H.minPitch!=null&&H.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(H.maxPitch!=null&&H.maxPitch>180)throw new Error(\"maxPitch must be less than or equal to 180\");let $=new Ls,se=new Gu;H.minZoom!==void 0&&$.setMinZoom(H.minZoom),H.maxZoom!==void 0&&$.setMaxZoom(H.maxZoom),H.minPitch!==void 0&&$.setMinPitch(H.minPitch),H.maxPitch!==void 0&&$.setMaxPitch(H.maxPitch),H.renderWorldCopies!==void 0&&$.setRenderWorldCopies(H.renderWorldCopies),H.transformConstrain!==null&&$.setConstrainOverride(H.transformConstrain),super($,se,{bearingSnap:H.bearingSnap,zoomSnap:H.zoomSnap}),this._idleTriggered=!1,this._crossFadingFactor=1,this._renderTaskQueue=new W,this._controls=[],this._mapId=i.ad(),this._lostContextStyle={style:null,images:null},this._contextLost=Ae=>{if(Ae.preventDefault(),this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this.painter.destroy(),this._lostContextStyle=this._getStyleAndImages(),this.style){for(let Fe of Object.values(this.style._layers))if(Fe.type===\"custom\"&&console.warn(`Custom layer with id '${Fe.id}' cannot be restored after WebGL context loss. You will need to re-add it manually after context restoration.`),Fe._listeners)for(let[Ie]of Object.entries(Fe._listeners))console.warn(`Custom layer with id '${Fe.id}' had event listeners for event '${Ie}' which cannot be restored after WebGL context loss. You will need to re-add them manually after context restoration.`);this.style.destroy(),this.style=null,this.fire(new i.n(\"webglcontextlost\",{originalEvent:Ae}))}else this.fire(new i.n(\"webglcontextlost\",{originalEvent:Ae}))},this._contextRestored=Ae=>{this._lostContextStyle.style&&this.setStyle(this._lostContextStyle.style,{diff:!1}),this._lostContextStyle.images&&this.style&&(this.style.imageManager.images=this._lostContextStyle.images),this._lostContextStyle={style:null,images:null},this._setupPainter(),this.resize(),this._update(),this._resizeInternal(),this.fire(new i.n(\"webglcontextrestored\",{originalEvent:Ae}))},this._onMapScroll=Ae=>{if(Ae.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=H.interactive,this._maxTileCacheSize=H.maxTileCacheSize,this._maxTileCacheZoomLevels=H.maxTileCacheZoomLevels,this._canvasContextAttributes=Object.assign({},H.canvasContextAttributes),this._trackResize=H.trackResize===!0,this._bearingSnap=H.bearingSnap,this._zoomSnap=H.zoomSnap,this._centerClampedToGround=H.centerClampedToGround,this._refreshExpiredTiles=H.refreshExpiredTiles===!0,this._fadeDuration=H.fadeDuration,this._crossSourceCollisions=H.crossSourceCollisions===!0,this._collectResourceTiming=H.collectResourceTiming===!0,this._locale=Object.assign(Object.assign({},He),H.locale),this._clickTolerance=H.clickTolerance,this._overridePixelRatio=H.pixelRatio,this._maxCanvasSize=H.maxCanvasSize,this._zoomLevelsToOverscale=H.experimentalZoomLevelsToOverscale,this.transformCameraUpdate=H.transformCameraUpdate,this.transformConstrain=H.transformConstrain,this.cancelPendingTileRequestsWhileZooming=H.cancelPendingTileRequestsWhileZooming===!0,this.setAnisotropicFilterPitch(H.anisotropicFilterPitch),H.reduceMotion!==void 0&&(f.prefersReducedMotion=H.reduceMotion),this._imageQueueHandle=x.addThrottleControl((()=>this.isMoving())),this._requestManager=new y(H.transformRequest),this._container=this._resolveContainer(H.container),H.maxBounds&&this.setMaxBounds(H.maxBounds),this._setupContainer(),this._setupPainter(),this.on(\"move\",(()=>this._update(!1))),this.on(\"moveend\",(()=>this._update(!1))),this.on(\"zoom\",(()=>this._update(!0))),this.on(\"terrain\",(()=>{this.painter.terrainFacilitator.depthDirty=!0,this._update(!0)})),this.once(\"idle\",(()=>this._idleTriggered=!0)),typeof window!=\"undefined\"&&(this._ownerWindow.addEventListener(\"online\",this._onWindowOnline,!1),this._setupResizeObserver()),this.handlers=new ms(this,H),this._hash=H.hash?new gh(typeof H.hash==\"string\"&&H.hash||void 0).addTo(this):void 0,!((d=this._hash)===null||d===void 0)&&d._onHashChange()||(this.jumpTo({center:H.center,elevation:H.elevation,zoom:H.zoom,bearing:H.bearing,pitch:H.pitch,roll:H.roll}),H.bounds&&(this.resize(),this.fitBounds(H.bounds,i.e({},H.fitBoundsOptions,{duration:0}))));let de=typeof H.style==\"string\"||((N=(P=H.style)===null||P===void 0?void 0:P.projection)===null||N===void 0?void 0:N.type)!==\"globe\";this.resize(null,de),this._localIdeographFontFamily=H.localIdeographFontFamily,this._validateStyle=H.validateStyle,H.style&&this.setStyle(H.style,{localIdeographFontFamily:H.localIdeographFontFamily}),H.attributionControl&&this.addControl(new hl(typeof H.attributionControl==\"boolean\"?void 0:H.attributionControl)),H.maplibreLogo&&this.addControl(new mh,H.logoPosition),this.on(\"style.load\",(()=>{if(de||this._resizeTransform(),this.transform.unmodified){let Ae=i.V(this.style.stylesheet,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"roll\"]);this.jumpTo(Ae)}})),this.on(\"data\",(Ae=>{this._update(Ae.dataType===\"style\"),this.fire(new i.n(`${Ae.dataType}data`,Ae))})),this.on(\"dataloading\",(Ae=>{this.fire(new i.n(`${Ae.dataType}dataloading`,Ae))})),this.on(\"dataabort\",(Ae=>{this.fire(new i.n(\"sourcedataabort\",Ae))}))}_getMapId(){return this._mapId}setGlobalStateProperty(Y,d){return this.style.setGlobalStateProperty(Y,d),this._update(!0)}getGlobalState(){return this.style.getGlobalState()}addControl(Y,d){if(d===void 0&&(d=Y.getDefaultPosition?Y.getDefaultPosition():\"top-right\"),!(Y!=null&&Y.onAdd))return this.fire(new i.l(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));let P=Y.onAdd(this);this._controls.push(Y);let N=this._controlPositions[d];return d.includes(\"bottom\")?N.insertBefore(P,N.firstChild):N.appendChild(P),this}removeControl(Y){if(!(Y!=null&&Y.onRemove))return this.fire(new i.l(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));let d=this._controls.indexOf(Y);return d>-1&&this._controls.splice(d,1),Y.onRemove(this),this}hasControl(Y){return this._controls.includes(Y)}coveringTiles(Y){return ft(this.transform,Y)}calculateCameraOptionsFromTo(Y,d,P,N){return N==null&&this.terrain&&(N=this.terrain.getElevationForLngLat(P,this.transform)),super.calculateCameraOptionsFromTo(Y,d,P,N)}resize(Y,d=!0){if(this._lostContextStyle.style!==null)return this;this._resizeInternal(d);let P=!this._moving;return P&&(this.stop(),this.fire(new i.n(\"movestart\",Y)).fire(new i.n(\"move\",Y))),this.fire(new i.n(\"resize\",Y)),P&&this.fire(new i.n(\"moveend\",Y)),this}_resizeInternal(Y=!0){let[d,P]=this._containerDimensions(),N=this._getClampedPixelRatio(d,P);if(this._resizeCanvas(d,P,N),this.painter.resize(d,P,N),this.painter.overLimit()){let H=this.painter.context.gl;this._maxCanvasSize=[H.drawingBufferWidth,H.drawingBufferHeight];let $=this._getClampedPixelRatio(d,P);this._resizeCanvas(d,P,$),this.painter.resize(d,P,$)}this._resizeTransform(Y)}_resizeTransform(Y=!0){var d;let[P,N]=this._containerDimensions();this.transform.resize(P,N,Y),(d=this._requestedCameraState)===null||d===void 0||d.resize(P,N,Y)}_getClampedPixelRatio(Y,d){let{0:P,1:N}=this._maxCanvasSize,H=this.getPixelRatio(),$=Y*H,se=d*H;return Math.min($>P?P/$:1,se>N?N/se:1)*H}getPixelRatio(){var Y;return(Y=this._overridePixelRatio)!==null&&Y!==void 0?Y:devicePixelRatio}setPixelRatio(Y){this._overridePixelRatio=Y,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(Y){return this.transform.setMaxBounds(ie.convert(Y)),this._update()}setMinZoom(Y){if((Y=Y==null?-2:Y)>=-2&&Y<=this.transform.maxZoom){let d=this.transform.zoom,P=this._getTransformForUpdate();return P.setMinZoom(Y),this._applyUpdatedTransform(P),this._update(),d!==this.transform.zoom&&this.fire(new i.n(\"zoomstart\")).fire(new i.n(\"zoom\")).fire(new i.n(\"zoomend\")).fire(new i.n(\"movestart\")).fire(new i.n(\"move\")).fire(new i.n(\"moveend\")),this}throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")}getMinZoom(){return this.transform.minZoom}setMaxZoom(Y){if((Y=Y==null?22:Y)>=this.transform.minZoom){let d=this.transform.zoom,P=this._getTransformForUpdate();return P.setMaxZoom(Y),this._applyUpdatedTransform(P),this._update(),d!==this.transform.zoom&&this.fire(new i.n(\"zoomstart\")).fire(new i.n(\"zoom\")).fire(new i.n(\"zoomend\")).fire(new i.n(\"movestart\")).fire(new i.n(\"move\")).fire(new i.n(\"moveend\")),this}throw new Error(\"maxZoom must be greater than the current minZoom\")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(Y){if((Y=Y==null?0:Y)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(Y>=0&&Y<=this.transform.maxPitch){let d=this.transform.pitch,P=this._getTransformForUpdate();return P.setMinPitch(Y),this._applyUpdatedTransform(P),this._update(),d!==this.transform.pitch&&this.fire(new i.n(\"pitchstart\")).fire(new i.n(\"pitch\")).fire(new i.n(\"pitchend\")).fire(new i.n(\"movestart\")).fire(new i.n(\"move\")).fire(new i.n(\"moveend\")),this}throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")}getMinPitch(){return this.transform.minPitch}setMaxPitch(Y){if((Y=Y==null?60:Y)>180)throw new Error(\"maxPitch must be less than or equal to 180\");if(Y>=this.transform.minPitch){let d=this.transform.pitch,P=this._getTransformForUpdate();return P.setMaxPitch(Y),this._applyUpdatedTransform(P),this._update(),d!==this.transform.pitch&&this.fire(new i.n(\"pitchstart\")).fire(new i.n(\"pitch\")).fire(new i.n(\"pitchend\")).fire(new i.n(\"movestart\")).fire(new i.n(\"move\")).fire(new i.n(\"moveend\")),this}throw new Error(\"maxPitch must be greater than the current minPitch\")}getMaxPitch(){return this.transform.maxPitch}getAnisotropicFilterPitch(){return this._anisotropicFilterPitch}setAnisotropicFilterPitch(Y){if((Y=Y==null?20:Y)>180)throw new Error(\"anisotropicFilterPitch must be less than or equal to 180\");if(Y<0)throw new Error(\"anisotropicFilterPitch must be greater than or equal to 0\");return this._anisotropicFilterPitch=Y,this._update()}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(Y){return this.transform.setRenderWorldCopies(Y),this._update()}setTransformConstrain(Y){return this.transform.setConstrainOverride(Y),this._update()}project(Y){return this.transform.locationToScreenPoint(i.W.convert(Y),this.style&&this.terrain)}unproject(Y){return this.transform.screenPointToLocation(i.P.convert(Y),this.terrain)}isMoving(){var Y;return this._moving||((Y=this.handlers)===null||Y===void 0?void 0:Y.isMoving())}isZooming(){var Y;return this._zooming||((Y=this.handlers)===null||Y===void 0?void 0:Y.isZooming())}isRotating(){var Y;return this._rotating||((Y=this.handlers)===null||Y===void 0?void 0:Y.isRotating())}_createDelegatedListener(Y,d,P){if(Y===\"mouseenter\"||Y===\"mouseover\"){let N=!1;return{layers:d,listener:P,delegates:{mousemove:$=>{let se=d.filter((Ae=>this.getLayer(Ae))),de=se.length!==0?this.queryRenderedFeatures($.point,{layers:se}):[];de.length?N||(N=!0,P.call(this,new Xl(Y,this,$.originalEvent,{features:de}))):N=!1},mouseout:()=>{N=!1}}}}if(Y===\"mouseleave\"||Y===\"mouseout\"){let N=!1;return{layers:d,listener:P,delegates:{mousemove:se=>{let de=d.filter((Ae=>this.getLayer(Ae)));(de.length!==0?this.queryRenderedFeatures(se.point,{layers:de}):[]).length?N=!0:N&&(N=!1,P.call(this,new Xl(Y,this,se.originalEvent)))},mouseout:se=>{N&&(N=!1,P.call(this,new Xl(Y,this,se.originalEvent)))}}}}{let N=H=>{let $=d.filter((de=>this.getLayer(de))),se=$.length!==0?this.queryRenderedFeatures(H.point,{layers:$}):[];se.length&&(H.features=se,P.call(this,H),delete H.features)};return{layers:d,listener:P,delegates:{[Y]:N}}}}_saveDelegatedListener(Y,d){var P;this._delegatedListeners||(this._delegatedListeners={}),(P=this._delegatedListeners)[Y]||(P[Y]=[]),this._delegatedListeners[Y].push(d)}_removeDelegatedListener(Y,d,P){var N;if(!(!((N=this._delegatedListeners)===null||N===void 0)&&N[Y]))return;let H=this._delegatedListeners[Y];for(let $=0;$<H.length;$++){let se=H[$];if(se.listener===P&&se.layers.length===d.length&&se.layers.every((de=>d.includes(de)))){for(let de in se.delegates)this.off(de,se.delegates[de]);return void H.splice($,1)}}}on(Y,d,P){if(P===void 0)return super.on(Y,d);let N=typeof d==\"string\"?[d]:d,H=this._createDelegatedListener(Y,N,P);this._saveDelegatedListener(Y,H);for(let $ in H.delegates)this.on($,H.delegates[$]);return{unsubscribe:()=>{this._removeDelegatedListener(Y,N,P)}}}once(Y,d,P){if(P===void 0)return super.once(Y,d);let N=typeof d==\"string\"?[d]:d,H=this._createDelegatedListener(Y,N,P);for(let $ in H.delegates){let se=H.delegates[$];H.delegates[$]=(...de)=>{this._removeDelegatedListener(Y,N,P),se(...de)}}this._saveDelegatedListener(Y,H);for(let $ in H.delegates)this.once($,H.delegates[$]);return this}off(Y,d,P){return P===void 0?super.off(Y,d):(this._removeDelegatedListener(Y,typeof d==\"string\"?[d]:d,P),this)}queryRenderedFeatures(Y,d){if(!this.style)return[];let P,N=Y instanceof i.P||Array.isArray(Y),H=N?Y:[[0,0],[this.transform.width,this.transform.height]];if(d||(d=(N?{}:Y)||{}),H instanceof i.P||typeof H[0]==\"number\")P=[i.P.convert(H)];else{let $=i.P.convert(H[0]),se=i.P.convert(H[1]);P=[$,new i.P(se.x,$.y),se,new i.P($.x,se.y),$]}return this.style.queryRenderedFeatures(P,d,this.transform)}querySourceFeatures(Y,d){return this.style.querySourceFeatures(Y,d)}setStyle(Y,d){return(d=i.e({},{localIdeographFontFamily:this._localIdeographFontFamily,validate:this._validateStyle},d)).diff!==!1&&d.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&Y?(this._diffStyle(Y,d),this):(this._localIdeographFontFamily=d.localIdeographFontFamily,this._updateStyle(Y,d))}setTransformRequest(Y){return this._requestManager.setTransformRequest(Y),this}_getUIString(Y){let d=this._locale[Y];if(d==null)throw new Error(`Missing UI string '${Y}'`);return d}_updateStyle(Y,d){var P,N,H;if((P=this._diffStyleRequest)===null||P===void 0||P.abort(),this._diffStyleRequest=null,d.transformStyle&&this.style&&!this.style._loaded)return void this.style.once(\"style.load\",(()=>this._updateStyle(Y,d)));let $=this.style&&d.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!Y)),Y?(this.style=new Cc(this,d||{}),this.style.setEventedParent(this,{style:this.style}),typeof Y==\"string\"?this.style.loadURL(Y,d,$):this.style.loadJSON(Y,d,$),this):(this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),(H=(N=this.style)===null||N===void 0?void 0:N.projection)===null||H===void 0||H.destroy(),delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new Cc(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(Y,d){return i._(this,void 0,void 0,(function*(){var P;if((P=this._diffStyleRequest)===null||P===void 0||P.abort(),typeof Y==\"string\"){let N=Y;this._diffStyleRequest=new AbortController;let H=this._diffStyleRequest;try{let $=yield this._requestManager.transformRequest(N,\"Style\");if(H.signal.aborted)return void(this._diffStyleRequest=null);let se=yield i.k($,H);this._diffStyleRequest=null,this._updateDiff(se.data,d)}catch($){this._diffStyleRequest=null,i.$($)||this.fire(new i.l(i.d($)))}}else typeof Y==\"object\"&&(this._diffStyleRequest=null,this._updateDiff(Y,d))}))}_updateDiff(Y,d){try{this.style.setState(Y,d)&&this._update(!0)}catch(P){i.w(`Unable to perform style diff: ${i.d(P).message}.  Rebuilding the style from scratch.`),this._updateStyle(Y,d)}}getStyle(){if(this.style)return this.style.serialize()}_getStyleAndImages(){return this.style?{style:this.style.serialize(),images:this.style.imageManager.cloneImages()}:{style:null,images:{}}}isStyleLoaded(){if(this.style)return this.style.loaded();i.w(\"There is no style added to the map.\")}addSource(Y,d){return this._lazyInitEmptyStyle(),this.style.addSource(Y,d),this._update(!0)}isSourceLoaded(Y){var d;let P=(d=this.style)===null||d===void 0?void 0:d.tileManagers[Y];if(P!==void 0)return P.loaded();this.fire(new i.l(new Error(`There is no tile manager with ID '${Y}'`)))}setTerrain(Y){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off(\"data\",this._terrainDataCallback),Y){let d=this.style.tileManagers[Y.source];if(!d)throw new Error(`cannot load terrain, because there exists no source with ID: ${Y.source}`);this.terrain===null&&d.reload();for(let P in this.style._layers){let N=this.style._layers[P];N.type===\"hillshade\"&&N.source===Y.source&&i.w(\"You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.\"),N.type===\"color-relief\"&&N.source===Y.source&&i.w(\"You are using the same source for a color-relief layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.\")}this.terrain=new Se(this.painter,d,Y),this.painter.renderToTexture=new rt(this.painter,this.terrain),this.transform.setMinElevationForCurrentTile(this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.transform.setElevation(this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this._terrainDataCallback=P=>{var N;P.dataType===\"style\"?this.terrain.tileManager.freeRtt():P.dataType===\"source\"&&P.tile&&(P.sourceId!==Y.source||this._elevationFreeze||(this.transform.setMinElevationForCurrentTile(this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this._centerClampedToGround&&this.transform.setElevation(this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))),((N=P.source)===null||N===void 0?void 0:N.type)===\"image\"?this.terrain.tileManager.freeRtt():this.terrain.tileManager.freeRtt(P.tile.tileID))},this.style.on(\"data\",this._terrainDataCallback)}else this.terrain&&this.terrain.destroy(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform.setMinElevationForCurrentTile(0),this._centerClampedToGround&&this.transform.setElevation(0);return this.fire(new i.n(\"terrain\",{terrain:Y})),this}getTerrain(){var Y,d;return(d=(Y=this.terrain)===null||Y===void 0?void 0:Y.options)!==null&&d!==void 0?d:null}areTilesLoaded(){var Y;let d=(Y=this.style)===null||Y===void 0?void 0:Y.tileManagers;for(let P of Object.values(d))if(!P.areTilesLoaded())return!1;return!0}removeSource(Y){return this.style.removeSource(Y),this._update(!0)}getSource(Y){return this.style.getSource(Y)}setSourceTileLodParams(Y,d,P){if(P){let N=this.getSource(P);if(!N)throw new Error(`There is no source with ID \"${P}\", cannot set LOD parameters`);N.calculateTileZoom=Tt(Math.max(1,Y),Math.max(1,d))}else for(let N in this.style.tileManagers)this.style.tileManagers[N].getSource().calculateTileZoom=Tt(Math.max(1,Y),Math.max(1,d));return this._update(!0),this}refreshTiles(Y,d){let P=this.style.tileManagers[Y];if(!P)throw new Error(`There is no tile manager with ID \"${Y}\", cannot refresh tile`);d===void 0?P.reload(!0):P.refreshTiles(d.map((N=>new i.aa(N.z,N.x,N.y))))}addImage(Y,d,P={}){let{pixelRatio:N=1,sdf:H=!1,stretchX:$,stretchY:se,content:de,textFitWidth:Ae,textFitHeight:Fe}=P;if(this._lazyInitEmptyStyle(),!(d instanceof HTMLImageElement||i.b(d))){if(d.width===void 0||d.height===void 0)return this.fire(new i.l(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));{let{width:Ie,height:Ye,data:st}=d,dt=d;return this.style.addImage(Y,{data:new i.R({width:Ie,height:Ye},new Uint8Array(st)),pixelRatio:N,stretchX:$,stretchY:se,content:de,textFitWidth:Ae,textFitHeight:Fe,sdf:H,version:0,userImage:dt}),dt.onAdd&&dt.onAdd(this,Y),this}}{let{width:Ie,height:Ye,data:st}=f.getImageData(d);this.style.addImage(Y,{data:new i.R({width:Ie,height:Ye},st),pixelRatio:N,stretchX:$,stretchY:se,content:de,textFitWidth:Ae,textFitHeight:Fe,sdf:H,version:0})}}updateImage(Y,d){let P=this.style.getImage(Y);if(!P)return this.fire(new i.l(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));let N=d instanceof HTMLImageElement||i.b(d)?f.getImageData(d):d,{width:H,height:$,data:se}=N;if(H===void 0||$===void 0)return this.fire(new i.l(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(H!==P.data.width||$!==P.data.height)return this.fire(new i.l(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));let de=!(d instanceof HTMLImageElement||i.b(d));return P.data.replace(se,de),this.style.updateImage(Y,P),this}getImage(Y){return this.style.getImage(Y)}hasImage(Y){return Y?!!this.style.getImage(Y):(this.fire(new i.l(new Error(\"Missing required image id\"))),!1)}removeImage(Y){this.style.removeImage(Y)}loadImage(Y){return i._(this,void 0,void 0,(function*(){return x.getImage(yield this._requestManager.transformRequest(Y,\"Image\"),new AbortController)}))}listImages(){return this.style.listImages()}addLayer(Y,d){return this._lazyInitEmptyStyle(),this.style.addLayer(Y,d),this._update(!0)}moveLayer(Y,d){return this.style.moveLayer(Y,d),this._update(!0)}removeLayer(Y){return this.style.removeLayer(Y),this._update(!0)}getLayer(Y){return this.style.getLayer(Y)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(Y,d,P){return this.style.setLayerZoomRange(Y,d,P),this._update(!0)}setFilter(Y,d,P={}){return this.style.setFilter(Y,d,P),this._update(!0)}getFilter(Y){return this.style.getFilter(Y)}setPaintProperty(Y,d,P,N={}){return this.style.setPaintProperty(Y,d,P,N),this._update(!0)}getPaintProperty(Y,d){return this.style.getPaintProperty(Y,d)}setLayoutProperty(Y,d,P,N={}){return this.style.setLayoutProperty(Y,d,P,N),this._update(!0)}getLayoutProperty(Y,d){return this.style.getLayoutProperty(Y,d)}setGlyphs(Y,d={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(Y,d),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(Y,d,P={}){return this._lazyInitEmptyStyle(),this.style.addSprite(Y,d,P,(N=>{N||this._update(!0)})),this}removeSprite(Y){return this._lazyInitEmptyStyle(),this.style.removeSprite(Y),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(Y,d={}){return this._lazyInitEmptyStyle(),this.style.setSprite(Y,d,(P=>{P||this._update(!0)})),this}setLight(Y,d={}){return this._lazyInitEmptyStyle(),this.style.setLight(Y,d),this._update(!0)}getLight(){return this.style.getLight()}setSky(Y,d={}){return this._lazyInitEmptyStyle(),this.style.setSky(Y,d),this._update(!0)}getSky(){return this.style.getSky()}setFeatureState(Y,d){return this.style.setFeatureState(Y,d),this._update()}removeFeatureState(Y,d){return this.style.removeFeatureState(Y,d),this._update()}getFeatureState(Y){return this.style.getFeatureState(Y)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let Y=0,d=0;return this._container&&(Y=this._container.clientWidth||400,d=this._container.clientHeight||300),[Y,d]}_setupResizeObserver(){var Y;let d=!1,P=ph((H=>{this._trackResize&&!this._removed&&(this.resize(H),this.redraw())}),50),N=(Y=this._ownerWindow.ResizeObserver)!==null&&Y!==void 0?Y:ResizeObserver;this._resizeObserver=new N((H=>{d?P(H):d=!0})),this._resizeObserver.observe(this._container)}_resolveContainer(Y){if(typeof Y==\"string\"){let d=document.getElementById(Y);if(!d)throw new Error(`Container '${Y}' not found.`);return d}if(Y instanceof HTMLElement||Y&&typeof Y==\"object\"&&Y.nodeType===1)return Y;throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\")}_setupContainer(){let Y=this._container;Y.classList.add(\"maplibregl-map\");let d=this._canvasContainer=_.create(\"div\",\"maplibregl-canvas-container\",Y);this._interactive&&d.classList.add(\"maplibregl-interactive\"),this._canvas=_.create(\"canvas\",\"maplibregl-canvas\",d),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",this._interactive?\"0\":\"-1\"),this._canvas.setAttribute(\"aria-label\",this._getUIString(\"Map.Title\")),this._canvas.setAttribute(\"role\",\"region\");let P=this._containerDimensions(),N=this._getClampedPixelRatio(P[0],P[1]);this._resizeCanvas(P[0],P[1],N);let H=this._controlContainer=_.create(\"div\",\"maplibregl-control-container\",Y),$=this._controlPositions={};for(let se of[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"])$[se]=_.create(\"div\",`maplibregl-ctrl-${se} `,H);this._container.addEventListener(\"scroll\",this._onMapScroll,!1)}_resizeCanvas(Y,d,P){this._canvas.width=Math.floor(P*Y),this._canvas.height=Math.floor(P*d),this._canvas.style.width=`${Y}px`,this._canvas.style.height=`${d}px`}_setupPainter(){let Y=Object.assign(Object.assign({},this._canvasContextAttributes),{alpha:!0,depth:!0,stencil:!0,premultipliedAlpha:!0}),d=null;this._canvas.addEventListener(\"webglcontextcreationerror\",(N=>{d={requestedAttributes:Y},N&&(d.statusMessage=N.statusMessage,d.type=N.type)}),{once:!0});let P=null;if(P=this._canvasContextAttributes.contextType?this._canvas.getContext(this._canvasContextAttributes.contextType,Y):this._canvas.getContext(\"webgl2\",Y)||this._canvas.getContext(\"webgl\",Y),!P){let N=\"Failed to initialize WebGL\";throw d?(d.message=N,new Error(JSON.stringify(d))):new Error(N)}this.painter=new $u(P,this.transform)}migrateProjection(Y,d){super.migrateProjection(Y,d),this.painter.transform=Y,this.fire(new i.n(\"projectiontransition\",{newProjection:this.style.projection.name}))}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(Y){var d;return!((d=this.style)===null||d===void 0)&&d._loaded?(this._styleDirty||(this._styleDirty=Y),this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(Y){return this._update(),this._renderTaskQueue.add(Y)}_cancelRenderFrame(Y){this._renderTaskQueue.remove(Y)}_render(Y){var d,P,N,H,$,se;let de=this._idleTriggered?this._fadeDuration:0,Ae=((d=this.style.projection)===null||d===void 0?void 0:d.transitionState)>0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(Y),this._removed)return;let Fe=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;let st=this.transform.zoom,dt=g();this.style.zoomHistory.update(st,dt);let Pe=new i.J(st,{now:dt,fadeDuration:de,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),Xe=Pe.crossFadingFactor();Xe===1&&Xe===this._crossFadingFactor||(Fe=!0,this._crossFadingFactor=Xe),this.style.update(Pe)}let Ie=((P=this.style.projection)===null||P===void 0?void 0:P.transitionState)>0!==Ae;(N=this.style.projection)===null||N===void 0||N.setErrorQueryLatitudeDegrees(this.transform.center.lat),this.transform.setTransitionState((H=this.style.projection)===null||H===void 0?void 0:H.transitionState,($=this.style.projection)===null||$===void 0?void 0:$.latitudeErrorCorrectionRadians),this.style&&(this._sourcesDirty||Ie)&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.tileManager.update(this.transform,this.terrain),this.transform.setMinElevationForCurrentTile(this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),!this._elevationFreeze&&this._centerClampedToGround&&this.transform.setElevation(this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform.setMinElevationForCurrentTile(0),this._centerClampedToGround&&this.transform.setElevation(0)),this._placementDirty=(se=this.style)===null||se===void 0?void 0:se._updatePlacement(this.transform,this.showCollisionBoxes,de,this._crossSourceCollisions,Ie),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:de,showPadding:this.showPadding,anisotropicFilterPitch:this.getAnisotropicFilterPitch()}),this.fire(new i.n(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new i.n(\"load\"))),this.style&&(this.style.hasTransitions()||Fe)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();let Ye=this._sourcesDirty||this._styleDirty||this._placementDirty;return Ye||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new i.n(\"idle\")),!this._loaded||this._fullyLoaded||Ye||(this._fullyLoaded=!0),this}redraw(){return this.style&&(this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._render(0)),this}remove(){var Y,d;this._hash&&this._hash.remove();for(let N of this._controls)N.onRemove(this);this._controls=[],this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._renderTaskQueue.clear(),(Y=this._diffStyleRequest)===null||Y===void 0||Y.abort(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof window!=\"undefined\"&&this._ownerWindow.removeEventListener(\"online\",this._onWindowOnline,!1),x.removeThrottleControl(this._imageQueueHandle),(d=this._resizeObserver)===null||d===void 0||d.disconnect();let P=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");P!=null&&P.loseContext&&P.loseContext(),this._canvas.removeEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.removeEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvasContainer.remove(),this._controlContainer.remove(),this._container.removeEventListener(\"scroll\",this._onMapScroll,!1),this._container.classList.remove(\"maplibregl-map\"),this._removed=!0,this.fire(new i.n(\"remove\"))}triggerRepaint(){this.style&&!this._frameRequest&&(this._frameRequest=new AbortController,f.frame(this._frameRequest,(Y=>{this._frameRequest=null;try{this._render(Y)}catch(d){if(!i.$(d)&&!(function(P){return P.message===qf})(d))throw d}}),(()=>{}),this._ownerWindow))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(Y){this._showTileBoundaries!==Y&&(this._showTileBoundaries=Y,this._update())}get showPadding(){return!!this._showPadding}set showPadding(Y){this._showPadding!==Y&&(this._showPadding=Y,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(Y){this._showCollisionBoxes!==Y&&(this._showCollisionBoxes=Y,Y?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(Y){this._showOverdrawInspector!==Y&&(this._showOverdrawInspector=Y,this._update())}get repaint(){return!!this._repaint}set repaint(Y){this._repaint!==Y&&(this._repaint=Y,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(Y){this._vertices=Y,this._update()}get version(){return pt}getCameraTargetElevation(){return this.transform.elevation}getProjection(){return this.style.getProjection()}setProjection(Y){return this._lazyInitEmptyStyle(),this.style.setProjection(Y),this._update(!0)}},ir={showCompass:!0,showZoom:!0,visualizePitch:!1,visualizeRoll:!0};class nr{constructor(d,P,N=!1){this.mousedown=$=>{this.startMove($,_.mousePos(this.element,$)),window.addEventListener(\"mousemove\",this.mousemove),window.addEventListener(\"mouseup\",this.mouseup)},this.mousemove=$=>{this.move($,_.mousePos(this.element,$))},this.mouseup=$=>{this._rotatePitchHandler.dragEnd($),this.offTemp()},this.touchstart=$=>{$.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=_.touchPos(this.element,$.targetTouches)[0],this.startMove($,this._startPos),window.addEventListener(\"touchmove\",this.touchmove,{passive:!1}),window.addEventListener(\"touchend\",this.touchend))},this.touchmove=$=>{$.targetTouches.length!==1?this.reset():(this._lastPos=_.touchPos(this.element,$.targetTouches)[0],this.move($,this._lastPos))},this.touchend=$=>{$.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),delete this._startPos,delete this._lastPos,this.offTemp()},this.reset=()=>{this._rotatePitchHandler.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10,this.element=P;let H=new Zt;this._rotatePitchHandler=new Be({clickTolerance:3,move:($,se)=>{let de=P.getBoundingClientRect(),Ae=new i.P((de.bottom-de.top)/2,(de.right-de.left)/2);return{bearingDelta:i.cx(new i.P($.x,se.y),se,Ae),pitchDelta:N?-.5*(se.y-$.y):void 0}},moveStateManager:H,enable:!0,assignEvents:()=>{}}),this.map=d,P.addEventListener(\"mousedown\",this.mousedown),P.addEventListener(\"touchstart\",this.touchstart,{passive:!1}),P.addEventListener(\"touchcancel\",this.reset)}startMove(d,P){this._rotatePitchHandler.dragStart(d,P),_.disableDrag()}move(d,P){let N=this.map,{bearingDelta:H,pitchDelta:$}=this._rotatePitchHandler.dragMove(d,P)||{};H&&N.setBearing(N.getBearing()+H),$&&N.setPitch(N.getPitch()+$)}off(){let d=this.element;d.removeEventListener(\"mousedown\",this.mousedown),d.removeEventListener(\"touchstart\",this.touchstart),window.removeEventListener(\"touchmove\",this.touchmove),window.removeEventListener(\"touchend\",this.touchend),d.removeEventListener(\"touchcancel\",this.reset),this.offTemp()}offTemp(){_.enableDrag(),window.removeEventListener(\"mousemove\",this.mousemove),window.removeEventListener(\"mouseup\",this.mouseup),window.removeEventListener(\"touchmove\",this.touchmove),window.removeEventListener(\"touchend\",this.touchend)}}let Cr;function Vr(Y,d,P,N=!1){if(N||!P.getCoveringTilesDetailsProvider().allowWorldCopies())return Y==null?void 0:Y.wrap();let H=new i.W(Y.lng,Y.lat);if(Y=new i.W(Y.lng,Y.lat),d){let $=new i.W(Y.lng-360,Y.lat),se=new i.W(Y.lng+360,Y.lat),de=P.locationToScreenPoint(Y).distSqr(d);P.locationToScreenPoint($).distSqr(d)<de?Y=$:P.locationToScreenPoint(se).distSqr(d)<de&&(Y=se)}for(;Math.abs(Y.lng-P.center.lng)>180;){let $=P.locationToScreenPoint(Y);if($.x>=0&&$.y>=0&&$.x<=P.width&&$.y<=P.height)break;Y.lng>P.center.lng?Y.lng-=360:Y.lng+=360}return Y.lng!==H.lng&&P.isPointOnMapSurface(P.locationToScreenPoint(Y))?Y:H}let sr={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function br(Y,d,P){let N=Y.classList;for(let H in sr)N.remove(`maplibregl-${P}-anchor-${H}`);N.add(`maplibregl-${P}-anchor-${d}`)}class kr extends i.E{constructor(d){if(super(),this._onClick=P=>{this.fire(new i.n(\"click\",{originalEvent:P}))},this._onKeyPress=P=>{P.code!==\"Space\"&&P.code!==\"Enter\"||this.togglePopup()},this._onMapClick=P=>{let N=P.originalEvent.target,H=this._element;this._popup&&(N===H||H.contains(N))&&this.togglePopup()},this._update=P=>{if(!this._map)return;let N=this._map.loaded()&&!this._map.isMoving();((P==null?void 0:P.type)===\"terrain\"||(P==null?void 0:P.type)===\"render\"&&!N)&&this._map.once(\"render\",this._update),this._lngLat=Vr(this._lngLat,this._flatPos,this._map.transform),this._flatPos=this._pos=this._map.project(this._lngLat)._add(this._offset),this._map.terrain&&(this._flatPos=this._map.transform.locationToScreenPoint(this._lngLat)._add(this._offset));let H=\"\";this._rotationAlignment===\"viewport\"||this._rotationAlignment===\"auto\"?H=`rotateZ(${this._rotation}deg)`:this._rotationAlignment===\"map\"&&(H=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let $=\"\";this._pitchAlignment===\"viewport\"||this._pitchAlignment===\"auto\"?$=\"rotateX(0deg)\":this._pitchAlignment===\"map\"&&($=`rotateX(${this._map.getPitch()}deg)`),this._subpixelPositioning||P&&P.type!==\"moveend\"||(this._pos=this._pos.round()),this._element.style.transform=`${sr[this._anchor]} translate(${this._pos.x}px, ${this._pos.y}px) ${$} ${H}`,f.frameAsync(new AbortController,this._map._ownerWindow).then((()=>{this._updateOpacity((P==null?void 0:P.type)===\"moveend\")})).catch((()=>{}))},this._onMove=P=>{if(!this._isDragging){let N=this._clickTolerance||this._map._clickTolerance;this._isDragging=P.point.dist(this._pointerdownPos)>=N}this._isDragging&&(this._pos=P.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",this._state===\"pending\"&&(this._state=\"active\",this.fire(new i.n(\"dragstart\"))),this.fire(new i.n(\"drag\")))},this._onUp=()=>{this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),this._state===\"active\"&&this.fire(new i.n(\"dragend\")),this._state=\"inactive\"},this._addDragHandler=P=>{this._element.contains(P.originalEvent.target)&&(P.preventDefault(),this._positionDelta=P.point.sub(this._pos).add(this._offset),this._pointerdownPos=P.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},this._anchor=(d==null?void 0:d.anchor)||\"center\",this._color=(d==null?void 0:d.color)||\"#3FB1CE\",this._scale=(d==null?void 0:d.scale)||1,this._draggable=(d==null?void 0:d.draggable)||!1,this._clickTolerance=(d==null?void 0:d.clickTolerance)||0,this._subpixelPositioning=(d==null?void 0:d.subpixelPositioning)||!1,this._isDragging=!1,this._state=\"inactive\",this._rotation=(d==null?void 0:d.rotation)||0,this._rotationAlignment=(d==null?void 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at=_.createNS(\"http://www.w3.org/2000/svg\",\"circle\");at.setAttributeNS(null,\"fill\",\"#FFFFFF\"),at.setAttributeNS(null,\"cx\",\"5.5\"),at.setAttributeNS(null,\"cy\",\"5.5\"),at.setAttributeNS(null,\"r\",\"5.4999962\"),Pe.appendChild(Xe),Pe.appendChild(at),se.appendChild(de),se.appendChild(Fe),se.appendChild(Ye),se.appendChild(dt),se.appendChild(Pe),P.appendChild(se),P.setAttributeNS(null,\"height\",N*this._scale+\"px\"),P.setAttributeNS(null,\"width\",H*this._scale+\"px\"),this._element.appendChild(P),this._offset=i.P.convert((d==null?void 0:d.offset)||[0,-14])}if(this._element.classList.add(\"maplibregl-marker\"),this._element.addEventListener(\"dragstart\",(P=>{P.preventDefault()})),this._element.addEventListener(\"mousedown\",(P=>{P.preventDefault()})),br(this._element,this._anchor,\"marker\"),d==null?void 0:d.className)for(let P of d.className.split(\" \"))this._element.classList.add(P);this._popup=null}addTo(d){return this.remove(),this._map=d,this._element.hasAttribute(\"aria-label\")||this._element.setAttribute(\"aria-label\",d._getUIString(\"Marker.Title\")),this._element.hasAttribute(\"role\")||this._element.setAttribute(\"role\",\"button\"),d.getCanvasContainer().appendChild(this._element),d.on(\"move\",this._update),d.on(\"moveend\",this._update),d.on(\"terrain\",this._update),d.on(\"projectiontransition\",this._update),this._element.addEventListener(\"click\",this._onClick),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this}remove(){return this._opacityTimeout&&(clearTimeout(this._opacityTimeout),delete this._opacityTimeout),this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"terrain\",this._update),this._map.off(\"projectiontransition\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),this._element.removeEventListener(\"click\",this._onClick),this._element.remove(),this._popup&&this._popup.remove(),this}getLngLat(){return this._lngLat}setLngLat(d){return this._lngLat=i.W.convert(d),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(d){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),d){if(!(\"offset\"in d.options)){let H=Math.abs(13.5)/Math.SQRT2;d.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[H,-1*(38.1-13.5+H)],\"bottom-right\":[-H,-1*(38.1-13.5+H)],left:[13.5,-1*(38.1-13.5)],right:[-13.5,-1*(38.1-13.5)]}:this._offset}this._popup=d,this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this}setSubpixelPositioning(d){return this._subpixelPositioning=d,this}getPopup(){return this._popup}togglePopup(){let d=this._popup;return this._element.style.opacity===this._opacityWhenCovered?this:d?(d.isOpen()?d.remove():(d.setLngLat(this._lngLat),d.addTo(this._map)),this):this}_updateOpacity(d=!1){var P,N;let H=(P=this._map)===null||P===void 0?void 0:P.terrain,$=this._map.transform.isLocationOccluded(this._lngLat);if(!H||$){let dt=$?this._opacityWhenCovered:this._opacity;return void(this._element.style.opacity!==dt&&(this._element.style.opacity=dt,this._element.classList.toggle(\"maplibregl-marker-covered\",$)))}if(d)this._opacityTimeout=null;else{if(this._opacityTimeout)return;this._opacityTimeout=setTimeout((()=>{this._opacityTimeout=null}),100)}let se=this._map,de=se.terrain.depthAtPoint(this._pos),Ae=se.terrain.getElevationForLngLat(this._lngLat,se.transform);if(se.transform.lngLatToCameraDepth(this._lngLat,Ae)-de<.006)return this._element.style.opacity=this._opacity,void this._element.classList.remove(\"maplibregl-marker-covered\");let Fe=-this._offset.y/se.transform.pixelsPerMeter,Ie=Math.sin(se.getPitch()*Math.PI/180)*Fe,Ye=se.terrain.depthAtPoint(new i.P(this._pos.x,this._pos.y-this._offset.y)),st=se.transform.lngLatToCameraDepth(this._lngLat,Ae+Ie)-Ye>.006;!((N=this._popup)===null||N===void 0)&&N.isOpen()&&st&&this._popup.remove(),this._element.style.opacity=st?this._opacityWhenCovered:this._opacity,this._element.classList.toggle(\"maplibregl-marker-covered\",st)}getOffset(){return this._offset}setOffset(d){return this._offset=i.P.convert(d),this._update(),this}addClassName(d){this._element.classList.add(d)}removeClassName(d){this._element.classList.remove(d)}toggleClassName(d){return this._element.classList.toggle(d)}setDraggable(d){return this._draggable=!!d,this._map&&(d?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(d){return this._rotation=d||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(d){return this._rotationAlignment=d||\"auto\",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(d){return this._pitchAlignment=d&&d!==\"auto\"?d:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}setOpacity(d,P){return(this._opacity===void 0||d===void 0&&P===void 0)&&(this._opacity=\"1\",this._opacityWhenCovered=\"0.2\"),d!==void 0&&(this._opacity=String(d)),P!==void 0&&(this._opacityWhenCovered=String(P)),this._map&&this._updateOpacity(!0),this}}let Gr={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},Wr=0,Ar=!1,En={maxWidth:100,unit:\"metric\"};function zn(Y,d,P){let N=(P==null?void 0:P.maxWidth)||100,H=Y._container.clientHeight/2,$=Y._container.clientWidth/2,se=Y.unproject([$-N/2,H]),de=Y.unproject([$+N/2,H]),Ae=Math.round(Y.project(de).x-Y.project(se).x),Fe=Math.min(N,Ae,Y._container.clientWidth),Ie=se.distanceTo(de);if((P==null?void 0:P.unit)===\"imperial\"){let Ye=3.2808*Ie;Ye>5280?Zn(d,Fe,Ye/5280,Y._getUIString(\"ScaleControl.Miles\")):Zn(d,Fe,Ye,Y._getUIString(\"ScaleControl.Feet\"))}else(P==null?void 0:P.unit)===\"nautical\"?Zn(d,Fe,Ie/1852,Y._getUIString(\"ScaleControl.NauticalMiles\")):Ie>=1e3?Zn(d,Fe,Ie/1e3,Y._getUIString(\"ScaleControl.Kilometers\")):Zn(d,Fe,Ie,Y._getUIString(\"ScaleControl.Meters\"))}function Zn(Y,d,P,N){let H=(function($){let se=Math.pow(10,`${Math.floor($)}`.length-1),de=$/se;return de=de>=10?10:de>=5?5:de>=3?3:de>=2?2:de>=1?1:(function(Ae){let Fe=Math.pow(10,Math.ceil(-Math.log(Ae)/Math.LN10));return Math.round(Ae*Fe)/Fe})(de),se*de})(P);Y.style.width=d*(H/P)+\"px\",Y.innerHTML=`${H}&nbsp;${N}`}let ai={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\",subpixelPositioning:!1,locationOccludedOpacity:void 0,padding:void 0},Mi=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \");function Si(Y){if(Y){if(typeof Y==\"number\"){let d=Math.round(Math.abs(Y)/Math.SQRT2);return{center:new i.P(0,0),top:new i.P(0,Y),\"top-left\":new i.P(d,d),\"top-right\":new i.P(-d,d),bottom:new i.P(0,-Y),\"bottom-left\":new i.P(d,-d),\"bottom-right\":new i.P(-d,-d),left:new i.P(Y,0),right:new i.P(-Y,0)}}if(Y instanceof i.P||Array.isArray(Y)){let d=i.P.convert(Y);return{center:d,top:d,\"top-left\":d,\"top-right\":d,bottom:d,\"bottom-left\":d,\"bottom-right\":d,left:d,right:d}}return{center:i.P.convert(Y.center||[0,0]),top:i.P.convert(Y.top||[0,0]),\"top-left\":i.P.convert(Y[\"top-left\"]||[0,0]),\"top-right\":i.P.convert(Y[\"top-right\"]||[0,0]),bottom:i.P.convert(Y.bottom||[0,0]),\"bottom-left\":i.P.convert(Y[\"bottom-left\"]||[0,0]),\"bottom-right\":i.P.convert(Y[\"bottom-right\"]||[0,0]),left:i.P.convert(Y.left||[0,0]),right:i.P.convert(Y.right||[0,0])}}return Si(new i.P(0,0))}let ya=o;a.AJAXError=i.cG,a.EXTENT=i.a6,a.Event=i.n,a.Evented=i.E,a.LngLat=i.W,a.MercatorCoordinate=i.a7,a.Point=i.P,a.addProtocol=i.cH,a.config=i.c,a.removeProtocol=i.cI,a.AttributionControl=hl,a.BoxZoomHandler=Mu,a.CanvasSource=Le,a.CooperativeGesturesHandler=Di,a.DoubleClickZoomHandler=ls,a.DragPanHandler=el,a.DragRotateHandler=yc,a.EdgeInsets=ko,a.FullscreenControl=class extends i.E{constructor(Y={}){var d;super(),this._onFullscreenChange=()=>{var P;let N=window.document.fullscreenElement||window.document.webkitFullscreenElement;for(;!((P=N==null?void 0:N.shadowRoot)===null||P===void 0)&&P.fullscreenElement;)N=N.shadowRoot.fullscreenElement;N===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,this._pseudo=(d=Y.pseudo)!==null&&d!==void 0&&d,Y!=null&&Y.container&&(Y.container instanceof HTMLElement?this._container=Y.container:i.w(\"Full screen control 'container' must be a DOM element.\")),\"onfullscreenchange\"in document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in document&&(this._fullscreenchange=\"MSFullscreenChange\")}onAdd(Y){return this._map=Y,this._container||(this._container=this._map.getContainer()),this._controlContainer=_.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),this._controlContainer}onRemove(){this._controlContainer.remove(),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){let Y=this._fullscreenButton=_.create(\"button\",\"maplibregl-ctrl-fullscreen\",this._controlContainer);_.create(\"span\",\"maplibregl-ctrl-icon\",Y).setAttribute(\"aria-hidden\",\"true\"),Y.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){let Y=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",Y),this._fullscreenButton.title=Y}_getTitle(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")}_isFullscreen(){return 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i.n(\"fullscreenend\")),this._prevCooperativeGesturesEnabled&&this._map.cooperativeGestures.enable())}_exitFullscreen(){this._pseudo?this._togglePseudoFullScreen():window.document.exitFullscreen?window.document.exitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._pseudo?this._togglePseudoFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle(\"maplibregl-pseudo-fullscreen\"),this._handleFullscreenChange(),this._map.resize()}},a.GeoJSONSource=et,a.GeolocateControl=class extends i.E{constructor(Y){super(),this._onSuccess=d=>{if(this._map){if(this._isOutOfMapMaxBounds(d))return this._setErrorState(),this.fire(new i.n(\"outofmaxbounds\",d)),this._updateMarker(),void 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${this._watchState}`)}this.options.showUserLocation&&this._watchState!==\"OFF\"&&this._updateMarker(d),this.options.trackUserLocation&&this._watchState!==\"ACTIVE_LOCK\"||this._updateCamera(d),this.options.showUserLocation&&this._dotElement.classList.remove(\"maplibregl-user-location-dot-stale\"),this.fire(new i.n(\"geolocate\",d)),this._finish()}},this._updateCamera=d=>{let P=new i.W(d.coords.longitude,d.coords.latitude),N=d.coords.accuracy,H=this._map.getBearing(),$=i.e({bearing:H},this.options.fitBoundsOptions),se=ie.fromLngLat(P,N);this._map.fitBounds(se,$,{geolocateSource:!0})},this._updateMarker=d=>{if(d){let P=new i.W(d.coords.longitude,d.coords.latitude);this._accuracyCircleMarker.setLngLat(P).addTo(this._map),this._userLocationDotMarker.setLngLat(P).addTo(this._map),this._accuracy=d.coords.accuracy,this._updateCircleRadiusIfNeeded()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onUpdate=()=>{this._updateCircleRadiusIfNeeded()},this._onError=d=>{if(this._map){if(d.code===1){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;let P=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=P,this._geolocateButton.setAttribute(\"aria-label\",P),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(d.code===3&&Ar)return;this._setErrorState()}this._watchState!==\"OFF\"&&this.options.showUserLocation&&this._dotElement.classList.add(\"maplibregl-user-location-dot-stale\"),this.fire(new i.n(\"error\",d)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._onMoveStart=d=>{if(!this._map)return;let P=(d==null?void 0:d[0])instanceof ResizeObserverEntry;d.geolocateSource||this._watchState!==\"ACTIVE_LOCK\"||P||this._map.isZooming()||(this._watchState=\"BACKGROUND\",this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this.fire(new i.n(\"trackuserlocationend\")),this.fire(new i.n(\"userlocationlostfocus\")))},this._setupUI=()=>{this._map&&(this._container.addEventListener(\"contextmenu\",(d=>{d.preventDefault()})),this._geolocateButton=_.create(\"button\",\"maplibregl-ctrl-geolocate\",this._container),_.create(\"span\",\"maplibregl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",\"true\"),this._geolocateButton.type=\"button\",this._geolocateButton.disabled=!0)},this._finishSetupUI=d=>{if(this._map){if(d===!1){i.w(\"Geolocation support is not available so the GeolocateControl will be disabled.\");let P=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=P,this._geolocateButton.setAttribute(\"aria-label\",P)}else{let P=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.disabled=!1,this._geolocateButton.title=P,this._geolocateButton.setAttribute(\"aria-label\",P)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=_.create(\"div\",\"maplibregl-user-location-dot\"),this._userLocationDotMarker=new kr({element:this._dotElement}),this._circleElement=_.create(\"div\",\"maplibregl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new kr({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onUpdate),this._map.on(\"move\",this._onUpdate),this._map.on(\"rotate\",this._onUpdate),this._map.on(\"pitch\",this._onUpdate)),this._geolocateButton.addEventListener(\"click\",(()=>this.trigger())),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",this._onMoveStart)}},this.options=i.e({},Gr,Y)}onAdd(Y){return this._map=Y,this._container=_.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),(function(){return i._(this,arguments,void 0,(function*(d=!1){if(Cr!==void 0&&!d)return Cr;if(window.navigator.permissions===void 0)return Cr=!!window.navigator.geolocation,Cr;try{Cr=(yield window.navigator.permissions.query({name:\"geolocation\"})).state!==\"denied\"}catch(P){Cr=!!window.navigator.geolocation}return Cr}))})().then((d=>this._finishSetupUI(d))),this._container}onRemove(){this._geolocationWatchID!==void 0&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),this._container.remove(),this._map.off(\"movestart\",this._onMoveStart),this._map.off(\"zoom\",this._onUpdate),this._map.off(\"move\",this._onUpdate),this._map.off(\"rotate\",this._onUpdate),this._map.off(\"pitch\",this._onUpdate),this._map=void 0,Wr=0,Ar=!1}_isOutOfMapMaxBounds(Y){let d=this._map.getMaxBounds(),P=Y.coords;return d&&(P.longitude<d.getWest()||P.longitude>d.getEast()||P.latitude<d.getSouth()||P.latitude>d.getNorth())}_setErrorState(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":case\"OFF\":case void 0:break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadiusIfNeeded(){let Y=this._userLocationDotMarker.getLngLat();if(!(this.options.showUserLocation&&this.options.showAccuracyCircle&&this._accuracy&&Y))return;let d=this._map.project(Y),P=this._map.unproject([d.x+100,d.y]),N=Y.distanceTo(P)/100,H=2*this._accuracy/N;this._circleElement.style.width=`${H.toFixed(2)}px`,this._circleElement.style.height=`${H.toFixed(2)}px`}trigger(){if(!this._setup)return i.w(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new i.n(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Wr--,Ar=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this.fire(new i.n(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new i.n(\"trackuserlocationstart\")),this.fire(new i.n(\"userlocationfocus\"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"OFF\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if(this._watchState===\"OFF\"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){let Y;this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),Wr++,Wr>1?(Y={maximumAge:6e5,timeout:0},Ar=!0):(Y=this.options.positionOptions,Ar=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,Y)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)}},a.GlobeControl=class{constructor(){this._toggleProjection=()=>{var Y;let d=(Y=this._map.getProjection())===null||Y===void 0?void 0:Y.type;this._map.setProjection(d!==\"mercator\"&&d?{type:\"mercator\"}:{type:\"globe\"}),this._updateGlobeIcon()},this._updateGlobeIcon=()=>{var Y;this._globeButton.classList.remove(\"maplibregl-ctrl-globe\"),this._globeButton.classList.remove(\"maplibregl-ctrl-globe-enabled\"),((Y=this._map.getProjection())===null||Y===void 0?void 0:Y.type)===\"globe\"?(this._globeButton.classList.add(\"maplibregl-ctrl-globe-enabled\"),this._globeButton.title=this._map._getUIString(\"GlobeControl.Disable\")):(this._globeButton.classList.add(\"maplibregl-ctrl-globe\"),this._globeButton.title=this._map._getUIString(\"GlobeControl.Enable\"))}}onAdd(Y){return this._map=Y,this._container=_.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._globeButton=_.create(\"button\",\"maplibregl-ctrl-globe\",this._container),_.create(\"span\",\"maplibregl-ctrl-icon\",this._globeButton).setAttribute(\"aria-hidden\",\"true\"),this._globeButton.type=\"button\",this._globeButton.addEventListener(\"click\",this._toggleProjection),this._updateGlobeIcon(),this._map.on(\"styledata\",this._updateGlobeIcon),this._map.on(\"projectiontransition\",this._updateGlobeIcon),this._container}onRemove(){this._container.remove(),this._map.off(\"styledata\",this._updateGlobeIcon),this._map.off(\"projectiontransition\",this._updateGlobeIcon),this._globeButton.removeEventListener(\"click\",this._toggleProjection),this._map=void 0}},a.Hash=gh,a.ImageSource=qe,a.KeyboardHandler=ia,a.LngLatBounds=ie,a.LogoControl=mh,a.Map=Rt,a.MapLibreMap=Rt,a.MapMouseEvent=Xl,a.MapTouchEvent=cf,a.MapWheelEvent=jh,a.Marker=kr,a.NavigationControl=class{constructor(Y){this._updateZoomButtons=()=>{let d=this._map.getZoom(),P=d===this._map.getMaxZoom(),N=d===this._map.getMinZoom();this._zoomInButton.disabled=P,this._zoomOutButton.disabled=N,this._zoomInButton.setAttribute(\"aria-disabled\",P.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",N.toString())},this._rotateCompassArrow=()=>{this._compassIcon.style.transform=this.options.visualizePitch&&this.options.visualizeRoll?`scale(${1/Math.pow(Math.cos(this._map.transform.pitchInRadians),.5)}) rotateZ(${-this._map.transform.roll}deg) rotateX(${this._map.transform.pitch}deg) rotateZ(${-this._map.transform.bearing}deg)`:this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitchInRadians),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${-this._map.transform.bearing}deg)`:this.options.visualizeRoll?`rotate(${-this._map.transform.bearing-this._map.transform.roll}deg)`:`rotate(${-this._map.transform.bearing}deg)`},this._setButtonTitle=(d,P)=>{let N=this._map._getUIString(`NavigationControl.${P}`);d.title=N,d.setAttribute(\"aria-label\",N)},this.options=i.e({},ir,Y),this._container=_.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(d=>d.preventDefault())),this.options.showZoom&&(this._zoomInButton=this._createButton(\"maplibregl-ctrl-zoom-in\",(d=>this._map.zoomIn({},{originalEvent:d}))),_.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",\"true\"),this._zoomOutButton=this._createButton(\"maplibregl-ctrl-zoom-out\",(d=>this._map.zoomOut({},{originalEvent:d}))),_.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",\"true\")),this.options.showCompass&&(this._compass=this._createButton(\"maplibregl-ctrl-compass\",(d=>{this.options.visualizePitch?this._map.resetNorthPitch({},{originalEvent:d}):this._map.resetNorth({},{originalEvent:d})})),this._compassIcon=_.create(\"span\",\"maplibregl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",\"true\"))}onAdd(Y){return this._map=Y,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this.options.visualizeRoll&&this._map.on(\"roll\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new nr(this._map,this._compass,this.options.visualizePitch)),this._container}onRemove(){this._container.remove(),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this.options.visualizeRoll&&this._map.off(\"roll\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map}_createButton(Y,d){let P=_.create(\"button\",Y,this._container);return P.type=\"button\",P.addEventListener(\"click\",d),P}},a.Popup=class extends i.E{constructor(Y){super(),this._updateOpacity=()=>{this.options.locationOccludedOpacity!==void 0&&(this._container.style.opacity=this._map.transform.isLocationOccluded(this.getLngLat())?`${this.options.locationOccludedOpacity}`:\"\")},this.remove=()=>(this._content&&this._content.remove(),this._container&&(this._container.remove(),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"terrain\",this._update),this._map.off(\"projectiontransition\",this._update),this._map.off(\"mousemove\",this._update),this._map.off(\"mouseup\",this._update),this._map.off(\"drag\",this._update),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\"),delete this._map,this.fire(new i.n(\"close\"))),this),this._update=d=>{if(!this._map||!this._lngLat&&!this._trackPointer||!this._content)return;if(!this._container){if(this._container=_.create(\"div\",\"maplibregl-popup\",this._map.getContainer()),this._tip=_.create(\"div\",\"maplibregl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className)for(let de of this.options.className.split(\" \"))this._container.classList.add(de);this._closeButton&&this._closeButton.setAttribute(\"aria-label\",this._map._getUIString(\"Popup.Close\")),this._trackPointer&&this._container.classList.add(\"maplibregl-popup-track-pointer\")}let P;if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._lngLat=Vr(this._lngLat,this._flatPos,this._map.transform,this._trackPointer),d&&\"point\"in d&&d.point&&(P=d.point),this._trackPointer&&!P)return;let N=this._flatPos=this._pos=this._trackPointer&&P?P:this._map.project(this._lngLat);this._map.terrain&&(this._flatPos=this._trackPointer&&P?P:this._map.transform.locationToScreenPoint(this._lngLat));let H=this.options.anchor,$=Si(this.options.offset);if(!H){let de=this._container.offsetWidth,Ae=this._container.offsetHeight,Fe=(function(Ye){var st,dt,Pe,Xe;return Ye?{top:(st=Ye.top)!==null&&st!==void 0?st:0,right:(dt=Ye.right)!==null&&dt!==void 0?dt:0,bottom:(Pe=Ye.bottom)!==null&&Pe!==void 0?Pe:0,left:(Xe=Ye.left)!==null&&Xe!==void 0?Xe:0}:{top:0,right:0,bottom:0,left:0}})(this.options.padding),Ie;Ie=N.y+$.bottom.y<Ae+Fe.top?[\"top\"]:N.y>this._map.transform.height-Ae-Fe.bottom?[\"bottom\"]:[],N.x<de/2+Fe.left?Ie.push(\"left\"):N.x>this._map.transform.width-de/2-Fe.right&&Ie.push(\"right\"),H=Ie.length===0?\"bottom\":Ie.join(\"-\")}let se=N.add($[H]);this.options.subpixelPositioning||(se=se.round()),this._container.style.transform=`${sr[H]} translate(${se.x}px,${se.y}px)`,br(this._container,H,\"popup\"),this._updateOpacity()},this._onClose=()=>{this.remove()},this.options=i.e(Object.create(ai),Y)}addTo(Y){return this._map&&this.remove(),this._map=Y,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._map.on(\"terrain\",this._update),this._map.on(\"projectiontransition\",this._update),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._update),this._map.on(\"mouseup\",this._update),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new i.n(\"open\")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(Y){return this._lngLat=i.W.convert(Y),this._pos=null,this._flatPos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._update),this._container&&this._container.classList.remove(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._flatPos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._update),this._map.on(\"drag\",this._update),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")),this}getElement(){return this._container}setText(Y){return this.setDOMContent(document.createTextNode(Y))}setHTML(Y){let d=document.createDocumentFragment(),P=document.createElement(\"body\"),N;for(P.innerHTML=Y;N=P.firstChild,N;)d.appendChild(N);return this.setDOMContent(d)}getMaxWidth(){var Y;return(Y=this._container)===null||Y===void 0?void 0:Y.style.maxWidth}setMaxWidth(Y){return this.options.maxWidth=Y,this._update(),this}setDOMContent(Y){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=_.create(\"div\",\"maplibregl-popup-content\",this._container);return this._content.appendChild(Y),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(Y){return this._container&&this._container.classList.add(Y),this}removeClassName(Y){return this._container&&this._container.classList.remove(Y),this}setOffset(Y){return this.options.offset=Y,this._update(),this}toggleClassName(Y){if(this._container)return this._container.classList.toggle(Y)}setSubpixelPositioning(Y){this.options.subpixelPositioning=Y}setPadding(Y){this.options.padding=Y,this._update()}_createCloseButton(){this.options.closeButton&&(this._closeButton=_.create(\"button\",\"maplibregl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;let Y=this._container.querySelector(Mi);Y&&Y.focus()}},a.RasterDEMTileSource=De,a.RasterTileSource=be,a.ScaleControl=class{constructor(Y){this._onMove=()=>{zn(this._map,this._container,this.options)},this.setUnit=d=>{this.options.unit=d,zn(this._map,this._container,this.options)},this.options=Object.assign(Object.assign({},En),Y)}getDefaultPosition(){return\"bottom-left\"}onAdd(Y){return this._map=Y,this._container=_.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-scale\",Y.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container}onRemove(){this._container.remove(),this._map.off(\"move\",this._onMove),this._map=void 0}},a.ScrollZoomHandler=Ua,a.Style=Cc,a.TerrainControl=class{constructor(Y){this._toggleTerrain=()=>{this._map.getTerrain()?this._map.setTerrain(null):this._map.setTerrain(this.options),this._updateTerrainIcon()},this._updateTerrainIcon=()=>{this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain\"),this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain-enabled\"),this._map.terrain?(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain-enabled\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.Disable\")):(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.Enable\"))},this.options=Y}onAdd(Y){return this._map=Y,this._container=_.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._terrainButton=_.create(\"button\",\"maplibregl-ctrl-terrain\",this._container),_.create(\"span\",\"maplibregl-ctrl-icon\",this._terrainButton).setAttribute(\"aria-hidden\",\"true\"),this._terrainButton.type=\"button\",this._terrainButton.addEventListener(\"click\",this._toggleTerrain),this._updateTerrainIcon(),this._map.on(\"terrain\",this._updateTerrainIcon),this._container}onRemove(){this._container.remove(),this._map.off(\"terrain\",this._updateTerrainIcon),this._map=void 0}},a.TwoFingersTouchPitchHandler=Bi,a.TwoFingersTouchRotateHandler=In,a.TwoFingersTouchZoomHandler=ln,a.TwoFingersTouchZoomRotateHandler=wi,a.VectorTileSource=me,a.VideoSource=we,a.addSourceType=(Y,d)=>i._(void 0,void 0,void 0,(function*(){if(Te(Y))throw new Error(`A source type called \"${Y}\" already exists.`);((P,N)=>{Ke[P]=N})(Y,d)})),a.clearPrewarmedResources=function(){let Y=ee;Y&&(Y.isPreloaded()&&Y.numActive()===1?(Y.release(q),ee=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},a.createTileMesh=lu,a.getGlobalDispatcher=ue,a.getMaxParallelImageRequests=function(){return i.c.MAX_PARALLEL_IMAGE_REQUESTS},a.getRTLTextPluginStatus=function(){return tt().getRTLTextPluginStatus()},a.getVersion=function(){return ya},a.getWorkerCount=function(){return U.workerCount},a.getWorkerUrl=function(){return i.c.WORKER_URL},a.importScriptInWorkers=function(Y){return ue().broadcast(\"IS\",Y)},a.isTimeFrozen=function(){return v.isFrozen()},a.now=g,a.prewarm=function(){ye().acquire(q)},a.restoreNow=function(){v.restoreNow()},a.setMaxParallelImageRequests=function(Y){i.c.MAX_PARALLEL_IMAGE_REQUESTS=Y},a.setNow=function(Y){v.setNow(Y)},a.setRTLTextPlugin=function(Y,d){return tt().setRTLTextPlugin(Y,d)},a.setWorkerCount=function(Y){U.workerCount=Y},a.setWorkerUrl=function(Y){i.c.WORKER_URL=Y}}));var n=e;return n}))});var GWe=fe((I6r,VWe)=>{\"use strict\";var g_=Xt(),Btr=as().sanitizeHTML,Otr=oK(),OWe=zy();function NWe(e,t){this.subplot=e,this.uid=e.uid+\"-\"+t,this.index=t,this.idSource=\"source-\"+this.uid,this.idLayer=OWe.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var qv=NWe.prototype;qv.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=oD(t)};qv.needsNewImage=function(e){var t=this.subplot.map;return t.getSource(this.idSource)&&this.sourceType===\"image\"&&e.sourcetype===\"image\"&&(this.source!==e.source||JSON.stringify(this.coordinates)!==JSON.stringify(e.coordinates))};qv.needsNewSource=function(e){return this.sourceType!==e.sourcetype||JSON.stringify(this.source)!==JSON.stringify(e.source)||this.layerType!==e.type};qv.needsNewLayer=function(e){return this.layerType!==e.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]};qv.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]};qv.updateImage=function(e){var t=this.subplot.map;t.getSource(this.idSource).updateImage({url:e.source,coordinates:e.coordinates});var r=this.findFollowingMapLayerId(this.lookupBelow());r!==null&&this.subplot.map.moveLayer(this.idLayer,r)};qv.updateSource=function(e){var t=this.subplot.map;if(t.getSource(this.idSource)&&t.removeSource(this.idSource),this.sourceType=e.sourcetype,this.source=e.source,!!oD(e)){var r=Ntr(e);t.addSource(this.idSource,r)}};qv.findFollowingMapLayerId=function(e){if(e===\"traces\")for(var t=this.subplot.getMapLayers(),r=0;r<t.length;r++){var n=t[r].id;if(typeof n==\"string\"&&n.indexOf(OWe.traceLayerPrefix)===0){e=n;break}}return e};qv.updateLayer=function(e){var t=this.subplot,r=UWe(e),n=this.lookupBelow(),a=this.findFollowingMapLayerId(n);this.removeLayer(),oD(e)&&t.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":e.sourcelayer||\"\",type:e.type,minzoom:e.minzoom,maxzoom:e.maxzoom,layout:r.layout,paint:r.paint},a),this.layerType=e.type,this.below=n};qv.updateStyle=function(e){if(oD(e)){var t=UWe(e);this.subplot.setOptions(this.idLayer,\"setLayoutProperty\",t.layout),this.subplot.setOptions(this.idLayer,\"setPaintProperty\",t.paint)}};qv.removeLayer=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer)};qv.dispose=function(){var e=this.subplot.map;e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),e.getSource(this.idSource)&&e.removeSource(this.idSource)};function oD(e){if(!e.visible)return!1;var t=e.source;if(Array.isArray(t)&&t.length>0){for(var r=0;r<t.length;r++)if(typeof t[r]!=\"string\"||t[r].length===0)return!1;return!0}return g_.isPlainObject(t)||typeof t==\"string\"&&t.length>0}function UWe(e){var t={},r={};switch(e.type){case\"circle\":g_.extendFlat(r,{\"circle-radius\":e.circle.radius,\"circle-color\":e.color,\"circle-opacity\":e.opacity});break;case\"line\":g_.extendFlat(r,{\"line-width\":e.line.width,\"line-color\":e.color,\"line-opacity\":e.opacity,\"line-dasharray\":e.line.dash});break;case\"fill\":g_.extendFlat(r,{\"fill-color\":e.color,\"fill-outline-color\":e.fill.outlinecolor,\"fill-opacity\":e.opacity});break;case\"symbol\":var n=e.symbol,a=Otr(n.textposition,n.iconsize);g_.extendFlat(t,{\"icon-image\":n.icon,\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":a.anchor,\"text-offset\":a.offset,\"symbol-placement\":n.placement}),g_.extendFlat(r,{\"icon-color\":e.color,\"text-color\":n.textfont.color,\"text-opacity\":e.opacity});break;case\"raster\":g_.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":e.opacity});break}return{layout:t,paint:r}}function Ntr(e){var t=e.sourcetype,r=e.source,n={type:t},a;return t===\"geojson\"?a=\"data\":t===\"vector\"?a=typeof r==\"string\"?\"url\":\"tiles\":t===\"raster\"?(a=\"tiles\",n.tileSize=256):t===\"image\"&&(a=\"url\",n.coordinates=e.coordinates),n[a]=r,e.sourceattribution&&(n.attribution=Btr(e.sourceattribution)),n}VWe.exports=function(t,r,n){var a=new NWe(t,r);return a.update(n),a}});var QWe=fe((z6r,JWe)=>{\"use strict\";var hK=BWe(),lD=Xt(),WWe=Cg(),HWe=bi(),Utr=Yi(),Vtr=ed(),sD=kl(),YWe=$p(),Gtr=YWe.drawMode,Htr=YWe.selectMode,jtr=Ac().prepSelect,Wtr=Ac().clearOutline,Ytr=Ac().clearSelectionsCache,Xtr=Ac().selectOnClick,qy=zy(),Ztr=GWe();function XWe(e,t){this.id=t,this.gd=e;var r=e._fullLayout,n=e._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+\"-\"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var cc=XWe.prototype;cc.plot=function(e,t,r){var n=this,a;n.map?a=new Promise(function(i,o){n.updateMap(e,t,i,o)}):a=new Promise(function(i,o){n.createMap(e,t,i,o)}),r.push(a)};cc.createMap=function(e,t,r,n){var a=this,i=t[a.id],o=a.styleObj=KWe(i.style),s=i.bounds,l=s?[[s.west,s.south],[s.east,s.north]]:null;let u={container:a.div,style:o.style,center:dK(i.center),zoom:i.zoom,bearing:i.bearing,pitch:i.pitch,maxBounds:l,interactive:!a.isStatic,canvasContextAttributes:{preserveDrawingBuffer:a.isStatic},doubleClickZoom:!1,boxZoom:!1,attributionControl:!1},c=i._fitBounds;c&&(u.bounds=[[c.west,c.south],[c.east,c.north]],u.fitBoundsOptions={padding:qy.fitBoundsPadding});let f=a.map=new hK.Map(u).addControl(new hK.AttributionControl({compact:!0}));var v={};f.on(\"styleimagemissing\",function(p){var x=p.id;if(!v[x]&&/^[a-zA-Z0-9-]+$/.test(x)){v[x]=!0;var _=new Image(15,15);_.onload=function(){f.addImage(x,_,{sdf:!0})},_.crossOrigin=\"Anonymous\",_.src=\"https://cdn.jsdelivr.net/npm/@mapbox/maki@8.2.0/icons/\"+x+\".svg\"}}),f.setTransformRequest(function(p){return p=p.replace(\"https://fonts.openmaptiles.org/Open Sans Extrabold\",\"https://fonts.openmaptiles.org/Open Sans Extra Bold\"),p=p.replace(\"https://tiles.basemaps.cartocdn.com/fonts/Open Sans Extrabold\",\"https://fonts.openmaptiles.org/Open Sans Extra Bold\"),p=p.replace(\"https://fonts.openmaptiles.org/Open Sans Regular,Arial Unicode MS Regular\",\"https://fonts.openmaptiles.org/Klokantech Noto Sans Regular\"),{url:p}}),f._canvas.style.left=\"0px\",f._canvas.style.top=\"0px\",a.rejectOnError(n),a.isStatic||a.initFx(e,t);var g=[];g.push(new Promise(function(p){f.once(\"load\",p)})),g=g.concat(WWe.fetchTraceGeoData(e)),Promise.all(g).then(function(){c&&ZWe(a,i),$We(a,i),a.fillBelowLookup(e,t),a.updateData(e),a.updateLayout(t),a.resolveOnRender(r)}).catch(n)};cc.updateMap=function(e,t,r,n){var a=this,i=a.map,o=t[this.id];a.rejectOnError(n);var s=[],l=KWe(o.style);JSON.stringify(a.styleObj)!==JSON.stringify(l)&&(a.styleObj=l,i.setStyle(l.style),a.traceHash={},s.push(new Promise(function(u){i.once(\"styledata\",u)}))),s=s.concat(WWe.fetchTraceGeoData(e)),Promise.all(s).then(function(){a.fillBelowLookup(e,t),a.updateData(e);let u=o._fitBounds;u&&(i.fitBounds([[u.west,u.south],[u.east,u.north]],{padding:qy.fitBoundsPadding,animate:!1}),ZWe(a,o),$We(a,o)),a.updateLayout(t),a.resolveOnRender(r)}).catch(n)};function ZWe(e,t){let{center:r,zoom:n}=e.getView();t._input.center=t.center=r,t._input.zoom=t.zoom=n,t._input._fitView={center:zo({},r),zoom:n}}function $We(e,t){let r=e.getView();e.viewInitial={center:lD.extendFlat({},r.center),bearing:t.bearing,pitch:t.pitch,zoom:r.zoom}}cc.fillBelowLookup=function(e,t){var r=t[this.id],n=r.layers,a,i,o=this.belowLookup={},s=!1;for(a=0;a<e.length;a++){var l=e[a][0].trace,u=l._module;typeof l.below==\"string\"?i=l.below:u.getBelow&&(i=u.getBelow(l,this)),i===\"\"&&(s=!0),o[\"trace-\"+l.uid]=i||\"\"}for(a=0;a<n.length;a++){var c=n[a];typeof c.below==\"string\"?i=c.below:s?i=\"traces\":i=\"\",o[\"layout-\"+a]=i}var f={},v,g;for(v in o)i=o[v],f[i]?f[i].push(v):f[i]=[v];for(i in f){var p=f[i];if(p.length>1)for(a=0;a<p.length;a++)v=p[a],v.indexOf(\"trace-\")===0?(g=v.split(\"trace-\")[1],this.traceHash[g]&&(this.traceHash[g].below=null)):v.indexOf(\"layout-\")===0&&(g=v.split(\"layout-\")[1],this.layerList[g]&&(this.layerList[g].below=null))}};var jWe={choroplethmap:0,densitymap:1,scattermap:2};cc.updateData=function(e){var t=this.traceHash,r,n,a,i,o=e.slice().sort(function(f,v){return jWe[f[0].trace.type]-jWe[v[0].trace.type]});for(a=0;a<o.length;a++){var s=o[a];n=s[0].trace,r=t[n.uid];var l=!1;r&&(r.type===n.type?(r.update(s),l=!0):r.dispose()),!l&&n._module&&(t[n.uid]=n._module.plot(this,s))}var u=Object.keys(t);e:for(a=0;a<u.length;a++){var c=u[a];for(i=0;i<e.length;i++)if(n=e[i][0].trace,c===n.uid)continue e;r=t[c],r.dispose(),delete t[c]}};cc.updateLayout=function(e){var t=this.map,r=e[this.id];!this.dragging&&!this.wheeling&&(t.setCenter(dK(r.center)),t.setZoom(r.zoom),t.setBearing(r.bearing),t.setPitch(r.pitch)),this.updateLayers(e),this.updateFramework(e),this.updateFx(e),this.map.resize(),this.gd._context._scrollZoom.map?t.scrollZoom.enable():t.scrollZoom.disable()};cc.resolveOnRender=function(e){var t=this.map;t.on(\"render\",function r(){t.loaded()&&(t.off(\"render\",r),setTimeout(e,10))})};cc.rejectOnError=function(e){var t=this.map;function r(){e(new Error(qy.mapOnErrorMsg))}t.once(\"error\",r),t.once(\"style.error\",r),t.once(\"source.error\",r),t.once(\"tile.error\",r),t.once(\"layer.error\",r)};cc.createFramework=function(e){var t=this,r=t.div=document.createElement(\"div\");r.id=t.uid,r.style.position=\"absolute\",t.container.appendChild(r),t.xaxis={_id:\"x\",c2p:n=>t.project(n).x,_subplot:t},t.yaxis={_id:\"y\",c2p:n=>t.project(n).y,_subplot:t},t.updateFramework(e),t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},Utr.setConvert(t.mockAxis,e)};cc.initFx=function(e,t){var r=this,n=r.gd,a=r.map;a.on(\"moveend\",function(s){if(r.map){var l=n._fullLayout;if(s.originalEvent||r.wheeling){var u=l[r.id];HWe.call(\"_storeDirectGUIEdit\",n.layout,l._preGUI,r.getViewEdits(u));var c=r.getView();u._input.center=u.center=c.center,u._input.zoom=u.zoom=c.zoom,u._input.bearing=u.bearing=c.bearing,u._input.pitch=u.pitch=c.pitch,n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(c))}s.originalEvent&&s.originalEvent.type===\"mouseup\"?r.dragging=!1:r.wheeling&&(r.wheeling=!1),l&&l._rehover&&l._rehover()}}),a.on(\"wheel\",function(){r.wheeling=!0}),a.on(\"mousemove\",function(s){var l=r.div.getBoundingClientRect(),u=[s.originalEvent.offsetX,s.originalEvent.offsetY];s.target.getBoundingClientRect=function(){return l},r.xaxis.p2c=function(){return a.unproject(u).lng},r.yaxis.p2c=function(){return a.unproject(u).lat},n._fullLayout._rehover=function(){n._fullLayout._hoversubplot===r.id&&n._fullLayout[r.id]&&sD.hover(n,s,r.id)},sD.hover(n,s,r.id),n._fullLayout._hoversubplot=r.id});function i(){sD.loneUnhover(t._hoverlayer)}a.on(\"dragstart\",function(){r.dragging=!0,i()}),a.on(\"zoomstart\",i),a.on(\"mouseout\",function(){n._fullLayout._hoversubplot=null});function o(){var s=r.getView();n.emit(\"plotly_relayouting\",r.getViewEditsWithDerived(s))}a.on(\"drag\",o),a.on(\"zoom\",o),a.on(\"dblclick\",function(){var s=n._fullLayout[r.id];HWe.call(\"_storeDirectGUIEdit\",n.layout,n._fullLayout._preGUI,r.getViewEdits(s));var l=r.viewInitial;a.setCenter(dK(l.center)),a.setZoom(l.zoom),a.setBearing(l.bearing),a.setPitch(l.pitch);var u=r.getView();s._input.center=s.center=u.center,s._input.zoom=s.zoom=u.zoom,s._input.bearing=s.bearing=u.bearing,s._input.pitch=s.pitch=u.pitch,n.emit(\"plotly_doubleclick\",null),n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(u))}),r.clearOutline=function(){Ytr(r.dragOptions),Wtr(r.dragOptions.gd)},r.onClickInPanFn=function(s){return function(l){var u=n._fullLayout.clickmode;u.indexOf(\"select\")>-1&&Xtr(l.originalEvent,n,[r.xaxis],[r.yaxis],r.id,s),u.indexOf(\"event\")>-1&&sD.click(n,l.originalEvent)}}};cc.updateFx=function(e){var t=this,r=t.map,n=t.gd;if(t.isStatic)return;function a(l){var u=t.map.unproject(l);return[u.lng,u.lat]}var i=e.dragmode,o;o=function(l,u){if(u.isRect){var c=l.range={};c[t.id]=[a([u.xmin,u.ymin]),a([u.xmax,u.ymax])]}else{var f=l.lassoPoints={};f[t.id]=u.map(a)}};var s=t.dragOptions;t.dragOptions=lD.extendDeep(s||{},{dragmode:e.dragmode,element:t.div,gd:n,plotinfo:{id:t.id,domain:e[t.id].domain,xaxis:t.xaxis,yaxis:t.yaxis,fillRangeItems:o},xaxes:[t.xaxis],yaxes:[t.yaxis],subplot:t.id}),r.off(\"click\",t.onClickInPanHandler),Htr(i)||Gtr(i)?(r.dragPan.disable(),r.on(\"zoomstart\",t.clearOutline),t.dragOptions.prepFn=function(l,u,c){jtr(l,u,c,t.dragOptions,i)},Vtr.init(t.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",t.clearOutline),t.div.onmousedown=null,t.div.ontouchstart=null,t.div.removeEventListener(\"touchstart\",t.div._ontouchstart),t.onClickInPanHandler=t.onClickInPanFn(t.dragOptions),r.on(\"click\",t.onClickInPanHandler))};cc.updateFramework=function(e){var t=e[this.id].domain,r=e._size,n=this.div.style;n.width=r.w*(t.x[1]-t.x[0])+\"px\",n.height=r.h*(t.y[1]-t.y[0])+\"px\",n.left=r.l+t.x[0]*r.w+\"px\",n.top=r.t+(1-t.y[1])*r.h+\"px\",this.xaxis._offset=r.l+t.x[0]*r.w,this.xaxis._length=r.w*(t.x[1]-t.x[0]),this.yaxis._offset=r.t+(1-t.y[1])*r.h,this.yaxis._length=r.h*(t.y[1]-t.y[0])};cc.updateLayers=function(e){var t=e[this.id],r=t.layers,n=this.layerList,a;if(r.length!==n.length){for(a=0;a<n.length;a++)n[a].dispose();for(n=this.layerList=[],a=0;a<r.length;a++)n.push(Ztr(this,a,r[a]))}else for(a=0;a<r.length;a++)n[a].update(r[a])};cc.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))};cc.toImage=function(){return this.map.stop(),this.map.getCanvas().toDataURL()};cc.setOptions=function(e,t,r){for(var n in r)this.map[t](e,n,r[n])};cc.getMapLayers=function(){return this.map.getStyle().layers};cc.addLayer=function(e,t){var r=this.map;if(typeof t==\"string\"){if(t===\"\"){r.addLayer(e,t);return}for(var n=this.getMapLayers(),a=0;a<n.length;a++)if(t===n[a].id){r.addLayer(e,t);return}lD.warn([\"Trying to add layer with *below* value\",t,\"referencing a layer that does not exist\",\"or that does not yet exist.\"].join(\" \"))}r.addLayer(e)};cc.project=function([e,t]){return this.map.project(new hK.LngLat(e,t))};cc.getView=function(){var e=this.map,t=e.getCenter(),r=t.lng,n=t.lat,a={lon:r,lat:n},i=e.getCanvas(),o=parseInt(i.style.width),s=parseInt(i.style.height);return{center:a,zoom:e.getZoom(),bearing:e.getBearing(),pitch:e.getPitch(),_derived:{coordinates:[e.unproject([0,0]).toArray(),e.unproject([o,0]).toArray(),e.unproject([o,s]).toArray(),e.unproject([0,s]).toArray()]}}};cc.getViewEdits=function(e){for(var t=this.id,r=[\"center\",\"zoom\",\"bearing\",\"pitch\"],n={},a=0;a<r.length;a++){var i=r[a];n[t+\".\"+i]=e[i]}return n};cc.getViewEditsWithDerived=function(e){var t=this.id,r=this.getViewEdits(e);return r[t+\"._derived\"]=e._derived,r};function KWe(e){var t={};return lD.isPlainObject(e)?(t.id=e.id,t.style=e):typeof e==\"string\"?(t.id=e,qy.stylesMap[e]?t.style=qy.stylesMap[e]:t.style=e):(t.id=qy.styleValueDflt,t.style=qy.stylesMap[qy.styleValueDflt]),t.transition={duration:0,delay:0},t}function dK(e){return[e.lon,e.lat]}JWe.exports=XWe});var tYe={};zp(tYe,{getMapFitBounds:()=>$tr});function $tr(e,t){let r=[];for(let l of e){if(l.subplot!==t||l.visible!==!0)continue;if(l.type===\"choroplethmap\")return null;let{lat:u,lon:c}=l;if(!c||!u)continue;let f=Math.min(c.length,u.length);for(let v=0;v<f;v++){let g=c[v],p=u[v];Number.isFinite(g)&&Number.isFinite(p)&&r.push([g,p])}}let n=(0,eYe.computeBbox)({type:\"MultiPoint\",coordinates:r});if(!n)return null;let[a,i,o,s]=n;return{west:a,east:o,south:i,north:s}}var eYe,rYe=Co(()=>{\"use strict\";eYe=$0(Cg())});var aYe=fe((B6r,iYe)=>{\"use strict\";var 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DYe=fe(($6r,FYe)=>{\"use strict\";var _K=aa(),Srr=Xt().isArrayOrTypedArray,bK=Wa().BADNUM,Err=Mh(),PYe=Xt()._;FYe.exports=function(t,r){for(var n=r._length,a=new Array(n),i=r.z,o=Srr(i)&&i.length,s=0;s<n;s++){var l=a[s]={},u=r.lon[s],c=r.lat[s];if(l.lonlat=_K(u)&&_K(c)?[+u,+c]:[bK,bK],o){var f=i[s];l.z=_K(f)?f:bK}}return Err(t,r,{vals:o?i:[0,1],containerStr:\"\",cLetter:\"z\"}),n&&(a[0].t={labels:{lat:PYe(t,\"lat:\")+\" \",lon:PYe(t,\"lon:\")+\" \"}}),a}});var BYe=fe((K6r,qYe)=>{\"use strict\";var krr=aa(),wK=Xt(),RYe=ni(),IYe=js(),zYe=Wa().BADNUM,Crr=cM().makeBlank;qYe.exports=function(t){var r=t[0].trace,n=r.visible===!0&&r._length!==0,a={layout:{visibility:\"none\"},paint:{}},i=r._opts={heatmap:a,geojson:Crr()};if(!n)return i;var o=[],s,l=r.z,u=r.radius,c=wK.isArrayOrTypedArray(l)&&l.length,f=wK.isArrayOrTypedArray(u);for(s=0;s<t.length;s++){var v=t[s],g=v.lonlat;if(g[0]!==zYe){var p={};if(c){var 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fD=NYe.prototype;fD.update=function(e){var t=this.subplot,r=this.layerList,n=OYe(e),a=t.belowLookup[\"trace-\"+this.uid];t.map.getSource(this.sourceId).setData(n.geojson),a!==this.below&&(this._removeLayers(),this._addLayers(n,a),this.below=a);for(var i=0;i<r.length;i++){var o=r[i],s=o[0],l=o[1],u=n[s];t.setOptions(l,\"setLayoutProperty\",u.layout),u.layout.visibility===\"visible\"&&t.setOptions(l,\"setPaintProperty\",u.paint)}};fD._addLayers=function(e,t){for(var r=this.subplot,n=this.layerList,a=this.sourceId,i=0;i<n.length;i++){var o=n[i],s=o[0],l=e[s];r.addLayer({type:s,id:o[1],source:a,layout:l.layout,paint:l.paint},t)}};fD._removeLayers=function(){for(var e=this.subplot.map,t=this.layerList,r=t.length-1;r>=0;r--)e.removeLayer(t[r][1])};fD.dispose=function(){var e=this.subplot.map;this._removeLayers(),e.removeSource(this.sourceId)};UYe.exports=function(t,r){var n=r[0].trace,a=new NYe(t,n.uid),i=a.sourceId,o=OYe(r),s=a.below=t.belowLookup[\"trace-\"+n.uid];return 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Te.circularLinkType==\"bottom\"});q(Ke,ze,et),Oe.links.forEach(function(Te){if(Te.circular){if(Te.circularPathData.arcRadius=Te.width+m,Te.circularPathData.leftNodeBuffer=qe,Te.circularPathData.rightNodeBuffer=qe,Te.circularPathData.sourceWidth=Te.source.x1-Te.source.x0,Te.circularPathData.sourceX=Te.source.x0+Te.circularPathData.sourceWidth,Te.circularPathData.targetX=Te.target.x0,Te.circularPathData.sourceY=Te.y0,Te.circularPathData.targetY=Te.y1,ht(Te,et)&&j(Te))Te.circularPathData.leftSmallArcRadius=m+Te.width/2,Te.circularPathData.leftLargeArcRadius=m+Te.width/2,Te.circularPathData.rightSmallArcRadius=m+Te.width/2,Te.circularPathData.rightLargeArcRadius=m+Te.width/2,Te.circularLinkType==\"bottom\"?(Te.circularPathData.verticalFullExtent=Te.source.y1+T+Te.circularPathData.verticalBuffer,Te.circularPathData.verticalLeftInnerExtent=Te.circularPathData.verticalFullExtent-Te.circularPathData.leftLargeArcRadius,Te.circularPathData.verticalRightInnerExtent=Te.circularPathData.verticalFullExtent-Te.circularPathData.rightLargeArcRadius):(Te.circularPathData.verticalFullExtent=Te.source.y0-T-Te.circularPathData.verticalBuffer,Te.circularPathData.verticalLeftInnerExtent=Te.circularPathData.verticalFullExtent+Te.circularPathData.leftLargeArcRadius,Te.circularPathData.verticalRightInnerExtent=Te.circularPathData.verticalFullExtent+Te.circularPathData.rightLargeArcRadius);else{var Ve=Te.source.column,$e=Te.circularLinkType,ot=Oe.links.filter(function(We){return We.source.column==Ve&&We.circularLinkType==$e});Te.circularLinkType==\"bottom\"?ot.sort(ye):ot.sort(ce);var tt=0;ot.forEach(function(We,mt){We.circularLinkID==Te.circularLinkID&&(Te.circularPathData.leftSmallArcRadius=m+Te.width/2+tt,Te.circularPathData.leftLargeArcRadius=m+Te.width/2+mt*ze+tt),tt=tt+We.width}),Ve=Te.target.column,ot=Oe.links.filter(function(We){return We.target.column==Ve&&We.circularLinkType==$e}),Te.circularLinkType==\"bottom\"?ot.sort(ue):ot.sort(xe),tt=0,ot.forEach(function(We,mt){We.circularLinkID==Te.circularLinkID&&(Te.circularPathData.rightSmallArcRadius=m+Te.width/2+tt,Te.circularPathData.rightLargeArcRadius=m+Te.width/2+mt*ze+tt),tt=tt+We.width}),Te.circularLinkType==\"bottom\"?(Te.circularPathData.verticalFullExtent=Math.max(ct,Te.source.y1,Te.target.y1)+T+Te.circularPathData.verticalBuffer,Te.circularPathData.verticalLeftInnerExtent=Te.circularPathData.verticalFullExtent-Te.circularPathData.leftLargeArcRadius,Te.circularPathData.verticalRightInnerExtent=Te.circularPathData.verticalFullExtent-Te.circularPathData.rightLargeArcRadius):(Te.circularPathData.verticalFullExtent=we-T-Te.circularPathData.verticalBuffer,Te.circularPathData.verticalLeftInnerExtent=Te.circularPathData.verticalFullExtent+Te.circularPathData.leftLargeArcRadius,Te.circularPathData.verticalRightInnerExtent=Te.circularPathData.verticalFullExtent+Te.circularPathData.rightLargeArcRadius)}Te.circularPathData.leftInnerExtent=Te.circularPathData.sourceX+Te.circularPathData.leftNodeBuffer,Te.circularPathData.rightInnerExtent=Te.circularPathData.targetX-Te.circularPathData.rightNodeBuffer,Te.circularPathData.leftFullExtent=Te.circularPathData.sourceX+Te.circularPathData.leftLargeArcRadius+Te.circularPathData.leftNodeBuffer,Te.circularPathData.rightFullExtent=Te.circularPathData.targetX-Te.circularPathData.rightLargeArcRadius-Te.circularPathData.rightNodeBuffer}if(Te.circular)Te.path=J(Te);else{var nt=n.linkHorizontal().source(function(We){var mt=We.source.x0+(We.source.x1-We.source.x0),bt=We.y0;return[mt,bt]}).target(function(We){var mt=We.target.x0,bt=We.y1;return[mt,bt]});Te.path=nt(Te)}})}function J(Oe){var ze=\"\";return Oe.circularLinkType==\"top\"?ze=\"M\"+Oe.circularPathData.sourceX+\" \"+Oe.circularPathData.sourceY+\" L\"+Oe.circularPathData.leftInnerExtent+\" \"+Oe.circularPathData.sourceY+\" A\"+Oe.circularPathData.leftLargeArcRadius+\" \"+Oe.circularPathData.leftSmallArcRadius+\" 0 0 0 \"+Oe.circularPathData.leftFullExtent+\" \"+(Oe.circularPathData.sourceY-Oe.circularPathData.leftSmallArcRadius)+\" L\"+Oe.circularPathData.leftFullExtent+\" \"+Oe.circularPathData.verticalLeftInnerExtent+\" A\"+Oe.circularPathData.leftLargeArcRadius+\" \"+Oe.circularPathData.leftLargeArcRadius+\" 0 0 0 \"+Oe.circularPathData.leftInnerExtent+\" \"+Oe.circularPathData.verticalFullExtent+\" L\"+Oe.circularPathData.rightInnerExtent+\" \"+Oe.circularPathData.verticalFullExtent+\" A\"+Oe.circularPathData.rightLargeArcRadius+\" \"+Oe.circularPathData.rightLargeArcRadius+\" 0 0 0 \"+Oe.circularPathData.rightFullExtent+\" \"+Oe.circularPathData.verticalRightInnerExtent+\" L\"+Oe.circularPathData.rightFullExtent+\" \"+(Oe.circularPathData.targetY-Oe.circularPathData.rightSmallArcRadius)+\" A\"+Oe.circularPathData.rightLargeArcRadius+\" \"+Oe.circularPathData.rightSmallArcRadius+\" 0 0 0 \"+Oe.circularPathData.rightInnerExtent+\" \"+Oe.circularPathData.targetY+\" L\"+Oe.circularPathData.targetX+\" \"+Oe.circularPathData.targetY:ze=\"M\"+Oe.circularPathData.sourceX+\" \"+Oe.circularPathData.sourceY+\" L\"+Oe.circularPathData.leftInnerExtent+\" \"+Oe.circularPathData.sourceY+\" A\"+Oe.circularPathData.leftLargeArcRadius+\" \"+Oe.circularPathData.leftSmallArcRadius+\" 0 0 1 \"+Oe.circularPathData.leftFullExtent+\" \"+(Oe.circularPathData.sourceY+Oe.circularPathData.leftSmallArcRadius)+\" L\"+Oe.circularPathData.leftFullExtent+\" \"+Oe.circularPathData.verticalLeftInnerExtent+\" A\"+Oe.circularPathData.leftLargeArcRadius+\" \"+Oe.circularPathData.leftLargeArcRadius+\" 0 0 1 \"+Oe.circularPathData.leftInnerExtent+\" \"+Oe.circularPathData.verticalFullExtent+\" L\"+Oe.circularPathData.rightInnerExtent+\" \"+Oe.circularPathData.verticalFullExtent+\" A\"+Oe.circularPathData.rightLargeArcRadius+\" \"+Oe.circularPathData.rightLargeArcRadius+\" 0 0 1 \"+Oe.circularPathData.rightFullExtent+\" \"+Oe.circularPathData.verticalRightInnerExtent+\" L\"+Oe.circularPathData.rightFullExtent+\" \"+(Oe.circularPathData.targetY+Oe.circularPathData.rightSmallArcRadius)+\" A\"+Oe.circularPathData.rightLargeArcRadius+\" \"+Oe.circularPathData.rightSmallArcRadius+\" 0 0 1 \"+Oe.circularPathData.rightInnerExtent+\" \"+Oe.circularPathData.targetY+\" L\"+Oe.circularPathData.targetX+\" \"+Oe.circularPathData.targetY,ze}function ee(Oe,ze){return K(Oe)==K(ze)?Oe.circularLinkType==\"bottom\"?ye(Oe,ze):ce(Oe,ze):K(ze)-K(Oe)}function ce(Oe,ze){return Oe.y0-ze.y0}function ye(Oe,ze){return ze.y0-Oe.y0}function xe(Oe,ze){return Oe.y1-ze.y1}function ue(Oe,ze){return ze.y1-Oe.y1}function K(Oe){return Oe.target.column-Oe.source.column}function _e(Oe){return Oe.target.x0-Oe.source.x1}function ne(Oe,ze){var ct=B(Oe),et=_e(ze)/Math.tan(ct),qe=Ge(Oe)==\"up\"?Oe.y1+et:Oe.y1-et;return qe}function ae(Oe,ze){var ct=B(Oe),et=_e(ze)/Math.tan(ct),qe=Ge(Oe)==\"up\"?Oe.y1-et:Oe.y1+et;return qe}function pe(Oe,ze,ct,et){Oe.links.forEach(function(qe){if(!qe.circular&&qe.target.column-qe.source.column>1){var we=qe.source.column+1,Le=qe.target.column-1,Ke=1,Te=Le-we+1;for(Ke=1;we<=Le;we++,Ke++)Oe.nodes.forEach(function(Ve){if(Ve.column==we){var $e=Ke/(Te+1),ot=Math.pow(1-$e,3),tt=3*$e*Math.pow(1-$e,2),nt=3*Math.pow($e,2)*(1-$e),We=Math.pow($e,3),mt=ot*qe.y0+tt*qe.y0+nt*qe.y1+We*qe.y1,bt=mt-qe.width/2,Yt=mt+qe.width/2,jt;bt>Ve.y0&&bt<Ve.y1?(jt=Ve.y1-bt+10,jt=Ve.circularLinkType==\"bottom\"?jt:-jt,Ve=le(Ve,jt,ze,ct),Oe.nodes.forEach(function(mr){A(mr,et)==A(Ve,et)||mr.column!=Ve.column||ie(Ve,mr)&&le(mr,jt,ze,ct)})):Yt>Ve.y0&&Yt<Ve.y1?(jt=Yt-Ve.y0+10,Ve=le(Ve,jt,ze,ct),Oe.nodes.forEach(function(mr){A(mr,et)==A(Ve,et)||mr.column!=Ve.column||mr.y0<Ve.y1&&mr.y1>Ve.y1&&le(mr,jt,ze,ct)})):bt<Ve.y0&&Yt>Ve.y1&&(jt=Yt-Ve.y0+10,Ve=le(Ve,jt,ze,ct),Oe.nodes.forEach(function(mr){A(mr,et)==A(Ve,et)||mr.column!=Ve.column||mr.y0<Ve.y1&&mr.y1>Ve.y1&&le(mr,jt,ze,ct)}))}})}})}function ie(Oe,ze){return Oe.y0>ze.y0&&Oe.y0<ze.y1||Oe.y1>ze.y0&&Oe.y1<ze.y1?!0:Oe.y0<ze.y0&&Oe.y1>ze.y1}function le(Oe,ze,ct,et){return Oe.y0+ze>=ct&&Oe.y1+ze<=et&&(Oe.y0=Oe.y0+ze,Oe.y1=Oe.y1+ze,Oe.targetLinks.forEach(function(qe){qe.y1=qe.y1+ze}),Oe.sourceLinks.forEach(function(qe){qe.y0=qe.y0+ze})),Oe}function me(Oe,ze,ct,et){Oe.nodes.forEach(function(qe){et&&qe.y+(qe.y1-qe.y0)>ze&&(qe.y=qe.y-(qe.y+(qe.y1-qe.y0)-ze));var we=Oe.links.filter(function(Te){return A(Te.source,ct)==A(qe,ct)}),Le=we.length;Le>1&&we.sort(function(Te,Ve){if(!Te.circular&&!Ve.circular){if(Te.target.column==Ve.target.column)return Te.y1-Ve.y1;if(De(Te,Ve)){if(Te.target.column>Ve.target.column){var $e=ae(Ve,Te);return Te.y1-$e}if(Ve.target.column>Te.target.column){var ot=ae(Te,Ve);return ot-Ve.y1}}else return Te.y1-Ve.y1}if(Te.circular&&!Ve.circular)return Te.circularLinkType==\"top\"?-1:1;if(Ve.circular&&!Te.circular)return Ve.circularLinkType==\"top\"?1:-1;if(Te.circular&&Ve.circular)return Te.circularLinkType===Ve.circularLinkType&&Te.circularLinkType==\"top\"?Te.target.column===Ve.target.column?Te.target.y1-Ve.target.y1:Ve.target.column-Te.target.column:Te.circularLinkType===Ve.circularLinkType&&Te.circularLinkType==\"bottom\"?Te.target.column===Ve.target.column?Ve.target.y1-Te.target.y1:Te.target.column-Ve.target.column:Te.circularLinkType==\"top\"?-1:1});var Ke=qe.y0;we.forEach(function(Te){Te.y0=Ke+Te.width/2,Ke=Ke+Te.width}),we.forEach(function(Te,Ve){if(Te.circularLinkType==\"bottom\"){var $e=Ve+1,ot=0;for($e;$e<Le;$e++)ot=ot+we[$e].width;Te.y0=qe.y1-ot-Te.width/2}})})}function be(Oe,ze,ct){Oe.nodes.forEach(function(et){var qe=Oe.links.filter(function(Ke){return A(Ke.target,ct)==A(et,ct)}),we=qe.length;we>1&&qe.sort(function(Ke,Te){if(!Ke.circular&&!Te.circular){if(Ke.source.column==Te.source.column)return Ke.y0-Te.y0;if(De(Ke,Te)){if(Te.source.column<Ke.source.column){var Ve=ne(Te,Ke);return Ke.y0-Ve}if(Ke.source.column<Te.source.column){var $e=ne(Ke,Te);return $e-Te.y0}}else return Ke.y0-Te.y0}if(Ke.circular&&!Te.circular)return Ke.circularLinkType==\"top\"?-1:1;if(Te.circular&&!Ke.circular)return Te.circularLinkType==\"top\"?1:-1;if(Ke.circular&&Te.circular)return Ke.circularLinkType===Te.circularLinkType&&Ke.circularLinkType==\"top\"?Ke.source.column===Te.source.column?Ke.source.y1-Te.source.y1:Ke.source.column-Te.source.column:Ke.circularLinkType===Te.circularLinkType&&Ke.circularLinkType==\"bottom\"?Ke.source.column===Te.source.column?Ke.source.y1-Te.source.y1:Te.source.column-Ke.source.column:Ke.circularLinkType==\"top\"?-1:1});var Le=et.y0;qe.forEach(function(Ke){Ke.y1=Le+Ke.width/2,Le=Le+Ke.width}),qe.forEach(function(Ke,Te){if(Ke.circularLinkType==\"bottom\"){var Ve=Te+1,$e=0;for(Ve;Ve<we;Ve++)$e=$e+qe[Ve].width;Ke.y1=et.y1-$e-Ke.width/2}})})}function De(Oe,ze){return Ge(Oe)==Ge(ze)}function Ge(Oe){return Oe.y0-Oe.y1>0?\"up\":\"down\"}function ht(Oe,ze){return A(Oe.source,ze)==A(Oe.target,ze)}function it(Oe,ze,ct){var et=Oe.nodes,qe=Oe.links,we=!1,Le=!1;if(qe.forEach(function(tt){tt.circularLinkType==\"top\"?we=!0:tt.circularLinkType==\"bottom\"&&(Le=!0)}),we==!1||Le==!1){var Ke=t.min(et,function(tt){return tt.y0}),Te=t.max(et,function(tt){return tt.y1}),Ve=Te-Ke,$e=ct-ze,ot=$e/Ve;et.forEach(function(tt){var nt=(tt.y1-tt.y0)*ot;tt.y0=(tt.y0-Ke)*ot,tt.y1=tt.y0+nt}),qe.forEach(function(tt){tt.y0=(tt.y0-Ke)*ot,tt.y1=(tt.y1-Ke)*ot,tt.width=tt.width*ot})}}e.sankeyCircular=E,e.sankeyCenter=u,e.sankeyLeft=o,e.sankeyRight=s,e.sankeyJustify=l,Object.defineProperty(e,\"__esModule\",{value:!0})}))});var kK=fe((cMr,DXe)=>{\"use strict\";DXe.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:\"linear\",cn:{sankey:\"sankey\",sankeyLinks:\"sankey-links\",sankeyLink:\"sankey-link\",sankeyNodeSet:\"sankey-node-set\",sankeyNode:\"sankey-node\",nodeRect:\"node-rect\",nodeLabel:\"node-label\"}}});var YXe=fe((fMr,WXe)=>{\"use strict\";var RXe=xXe(),$rr=($2(),Gs(Z2)).interpolateNumber,I5=xi(),tS=SXe(),Krr=FXe(),ws=kK(),D1=ni(),Jrr=ea(),R1=Xt(),y_=R1.strTranslate,IXe=R1.strRotate,PK=L1(),rS=PK.keyFun,AD=PK.repeat,UXe=PK.unwrap,zXe=as(),Qrr=bi(),VXe=nc(),enr=VXe.CAP_SHIFT,tnr=VXe.LINE_SPACING,rnr=3;function nnr(e,t,r){var n=UXe(t),a=n.trace,i=a.domain,o=a.orientation===\"h\",s=a.direction===\"reversed\",l=a.node.pad,u=a.node.thickness,c={justify:tS.sankeyJustify,left:tS.sankeyLeft,right:tS.sankeyRight,center:tS.sankeyCenter}[a.node.align],f=e.width*(i.x[1]-i.x[0]),v=e.height*(i.y[1]-i.y[0]),g=n._nodes,p=n._links,x=n.circular,_;x?_=Krr.sankeyCircular().circularLinkGap(0):_=tS.sankey(),_.iterations(ws.sankeyIterations).size(o?[f,v]:[v,f]).nodeWidth(u).nodePadding(l).nodeId(function(V){return V.pointNumber}).nodeAlign(c).nodes(g).links(p);function y(V){a[V].sort===\"input\"&&(x?R1.warn(`Circular Sankey diagrams do not support the \"input\" ${V}.sort mode; falling back to the default sort.`):_[`${V}Sort`](null))}y(\"link\"),y(\"node\");var C=_();_.nodePadding()<l&&R1.warn(\"node.pad was reduced to \",_.nodePadding(),\" to fit within the figure.\");var k,S,L;for(var w in n._groupLookup){var A=parseInt(n._groupLookup[w]),T;for(k=0;k<C.nodes.length;k++)if(C.nodes[k].pointNumber===A){T=C.nodes[k];break}if(T){var m={pointNumber:parseInt(w),x0:T.x0,x1:T.x1,y0:T.y0,y1:T.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};C.nodes.unshift(m),T.childrenNodes.unshift(m)}}function F(){for(k=0;k<C.nodes.length;k++){var V=C.nodes[k],j={},q,U;for(S=0;S<V.targetLinks.length;S++)U=V.targetLinks[S],q=U.source.pointNumber+\":\"+U.target.pointNumber,j.hasOwnProperty(q)||(j[q]=[]),j[q].push(U);var J=Object.keys(j);for(S=0;S<J.length;S++){q=J[S];var ee=j[q],ce=0,ye={};for(L=0;L<ee.length;L++)U=ee[L],ye[U.label]||(ye[U.label]=0),ye[U.label]+=U.value,ce+=U.value;for(L=0;L<ee.length;L++)U=ee[L],U.flow={value:ce,labelConcentration:ye[U.label]/ce,concentration:U.value/ce,links:ee},U.concentrationscale&&(U.color=U.concentrationscale(U.flow.labelConcentration))}var xe=0;for(S=0;S<V.sourceLinks.length;S++)xe+=V.sourceLinks[S].value;for(S=0;S<V.sourceLinks.length;S++)U=V.sourceLinks[S],U.concentrationOut=U.value/xe;var ue=0;for(S=0;S<V.targetLinks.length;S++)ue+=V.targetLinks[S].value;for(S=0;S<V.targetLinks.length;S++)U=V.targetLinks[S],U.concenrationIn=U.value/ue}}F();function E(V){V.forEach(function(j){var q,U,J=0,ee=j.length,ce;for(j.sort(function(ye,xe){return ye.y0-xe.y0}),ce=0;ce<ee;++ce)q=j[ce],q.y0>=J||(U=J-q.y0,U>1e-6&&(q.y0+=U,q.y1+=U)),J=q.y1+l})}function I(V){var j=V.map(function(xe,ue){return{x0:xe.x0,index:ue}}).sort(function(xe,ue){return xe.x0-ue.x0}),q=[],U=-1,J,ee=-1/0,ce;for(k=0;k<j.length;k++){var ye=V[j[k].index];ye.x0>ee+u&&(U+=1,J=ye.x0),ee=ye.x0,q[U]||(q[U]=[]),q[U].push(ye),ce=J-ye.x0,ye.x0+=ce,ye.x1+=ce}return q}if(a.node.x.length&&a.node.y.length){for(k=0;k<Math.min(a.node.x.length,a.node.y.length,C.nodes.length);k++)if(a.node.x[k]&&a.node.y[k]){var R=[a.node.x[k]*f,a.node.y[k]*v];C.nodes[k].x0=R[0]-u/2,C.nodes[k].x1=R[0]+u/2;var B=C.nodes[k].y1-C.nodes[k].y0;C.nodes[k].y0=R[1]-B/2,C.nodes[k].y1=R[1]+B/2}if(a.arrangement===\"snap\"){g=C.nodes;var O=I(g);E(O)}_.update(C)}return{circular:x,key:r,trace:a,guid:R1.randstr(),horizontal:o,reversed:s,width:f,height:v,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,linkArrowLength:a.link.arrowlen,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:i.x[0]*e.width+e.margin.l,translateY:e.height-i.y[1]*e.height+e.margin.t,dragParallel:o?v:f,dragPerpendicular:o?f:v,arrangement:a.arrangement,sankey:_,graph:C,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function inr(e,t,r){var n=t.source.label+\"|\"+t.target.label,a=n+\"__\"+r;return t.trace=e.trace,t.curveNumber=e.trace.index,{circular:e.circular,key:a,traceId:e.key,pointNumber:t.pointNumber,link:t,rgb:D1.rgb(t.color),alpha:D1.parse(t.color).alpha,hoverRgb:D1.rgb(t.hovercolor),hoverAlpha:D1.parse(t.hovercolor).alpha,linkPath:FK,linkLineColor:e.linkLineColor,linkLineWidth:e.linkLineWidth,linkArrowLength:e.linkArrowLength,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,parent:e,interactionState:e.interactionState,flow:t.flow}}function anr(e,t){var r=\"\",n=e.width/2,a=e.circularPathData,i=a.sourceX+a.verticalBuffer<a.targetX,o=a.rightFullExtent-a.rightLargeArcRadius-t<=a.leftFullExtent-n,s=Math.abs(a.rightFullExtent-a.leftFullExtent-n)<n;return e.circularLinkType===\"top\"?(r=\"M \"+(a.targetX-t)+\" \"+(a.targetY+n)+\" L \"+(a.rightInnerExtent-t)+\" \"+(a.targetY+n)+\"A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightSmallArcRadius+n)+\" 0 0 1 \"+(a.rightFullExtent-n-t)+\" \"+(a.targetY-a.rightSmallArcRadius)+\"L \"+(a.rightFullExtent-n-t)+\" \"+a.verticalRightInnerExtent,i&&o?r+=\" A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightLargeArcRadius+n)+\" 0 0 1 \"+(a.rightFullExtent+n-t-(a.rightLargeArcRadius-n))+\" \"+(a.verticalRightInnerExtent-(a.rightLargeArcRadius+n))+\" L \"+(a.rightFullExtent+n-(a.rightLargeArcRadius-n)-t)+\" \"+(a.verticalRightInnerExtent-(a.rightLargeArcRadius+n))+\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftLargeArcRadius+n)+\" 0 0 1 \"+(a.leftFullExtent+n)+\" \"+a.verticalRightInnerExtent:i?r+=\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightLargeArcRadius-n)+\" 0 0 0 \"+(a.rightFullExtent-n-t-(a.rightLargeArcRadius-n))+\" \"+(a.verticalRightInnerExtent-(a.rightLargeArcRadius-n))+\" L \"+(a.leftFullExtent+n+(a.rightLargeArcRadius-n))+\" \"+(a.verticalRightInnerExtent-(a.rightLargeArcRadius-n))+\" A \"+(a.leftLargeArcRadius-n)+\" \"+(a.leftLargeArcRadius-n)+\" 0 0 0 \"+(a.leftFullExtent+n)+\" \"+a.verticalLeftInnerExtent:r+=\" A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightLargeArcRadius+n)+\" 0 0 1 \"+(a.rightInnerExtent-t)+\" \"+(a.verticalFullExtent-n)+\" L \"+a.leftInnerExtent+\" \"+(a.verticalFullExtent-n)+\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftLargeArcRadius+n)+\" 0 0 1 \"+(a.leftFullExtent+n)+\" \"+a.verticalLeftInnerExtent,r+=\" L \"+(a.leftFullExtent+n)+\" \"+(a.sourceY-a.leftSmallArcRadius)+\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftSmallArcRadius+n)+\" 0 0 1 \"+a.leftInnerExtent+\" \"+(a.sourceY+n)+\" L \"+a.sourceX+\" \"+(a.sourceY+n)+\" L \"+a.sourceX+\" \"+(a.sourceY-n)+\" L \"+a.leftInnerExtent+\" \"+(a.sourceY-n)+\" A \"+(a.leftLargeArcRadius-n)+\" \"+(a.leftSmallArcRadius-n)+\" 0 0 0 \"+(a.leftFullExtent-n)+\" \"+(a.sourceY-a.leftSmallArcRadius)+\" L \"+(a.leftFullExtent-n)+\" \"+a.verticalLeftInnerExtent,i&&o?r+=\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftSmallArcRadius+n)+\" 0 0 0 \"+(a.leftFullExtent-n)+\" \"+(a.verticalFullExtent+n)+\"L\"+(a.rightFullExtent+n-t)+\" \"+(a.verticalFullExtent+n)+\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftSmallArcRadius+n)+\" 0 0 0 \"+(a.rightFullExtent+n-t)+\" \"+a.verticalRightInnerExtent:i?r+=\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftSmallArcRadius+n)+\" 0 0 1 \"+(a.leftFullExtent+n)+\" \"+(a.verticalFullExtent-n)+\" L \"+(a.rightFullExtent-n-t)+\" \"+(a.verticalFullExtent-n)+\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftSmallArcRadius+n)+\" 0 0 1 \"+(a.rightFullExtent+n-t)+\" \"+a.verticalRightInnerExtent:r+=\" A \"+(a.leftLargeArcRadius-n)+\" \"+(a.leftLargeArcRadius-n)+\" 0 0 0 \"+a.leftInnerExtent+\" \"+(a.verticalFullExtent+n)+\" L \"+(a.rightInnerExtent-t)+\" \"+(a.verticalFullExtent+n)+\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightLargeArcRadius-n)+\" 0 0 0 \"+(a.rightFullExtent+n-t)+\" \"+a.verticalRightInnerExtent,r+=\" L \"+(a.rightFullExtent+n-t)+\" \"+(a.targetY-a.rightSmallArcRadius)+\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightSmallArcRadius-n)+\" 0 0 0 \"+(a.rightInnerExtent-t)+\" \"+(a.targetY-n)+\" L \"+(a.targetX-t)+\" \"+(a.targetY-n)+(t>0?\" L \"+a.targetX+\" \"+a.targetY:\"\")+\"Z\"):(r=\"M \"+(a.targetX-t)+\" \"+(a.targetY-n)+\"  L \"+(a.rightInnerExtent-t)+\" \"+(a.targetY-n)+\" A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightSmallArcRadius+n)+\" 0 0 0 \"+(a.rightFullExtent-n-t)+\" \"+(a.targetY+a.rightSmallArcRadius)+\" L \"+(a.rightFullExtent-n-t)+\" \"+a.verticalRightInnerExtent,i&&o?r+=\" A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightLargeArcRadius+n)+\" 0 0 0 \"+(a.rightInnerExtent-n-t)+\" \"+(a.verticalFullExtent+n)+\" L \"+(a.rightFullExtent+n-t-(a.rightLargeArcRadius-n))+\" \"+(a.verticalFullExtent+n)+\" A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightLargeArcRadius+n)+\" 0 0 0 \"+(a.leftFullExtent+n)+\" \"+a.verticalLeftInnerExtent:i?r+=\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightSmallArcRadius-n)+\" 0 0 1 \"+(a.rightFullExtent-t-n-(a.rightLargeArcRadius-n))+\" \"+(a.verticalFullExtent-n)+\" L \"+(a.leftFullExtent+n+(a.rightLargeArcRadius-n))+\" \"+(a.verticalFullExtent-n)+\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightSmallArcRadius-n)+\" 0 0 1 \"+(a.leftFullExtent+n)+\" \"+a.verticalLeftInnerExtent:r+=\" A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightLargeArcRadius+n)+\" 0 0 0 \"+(a.rightInnerExtent-t)+\" \"+(a.verticalFullExtent+n)+\" L \"+a.leftInnerExtent+\" \"+(a.verticalFullExtent+n)+\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftLargeArcRadius+n)+\" 0 0 0 \"+(a.leftFullExtent+n)+\" \"+a.verticalLeftInnerExtent,r+=\" L \"+(a.leftFullExtent+n)+\" \"+(a.sourceY+a.leftSmallArcRadius)+\" A \"+(a.leftLargeArcRadius+n)+\" \"+(a.leftSmallArcRadius+n)+\" 0 0 0 \"+a.leftInnerExtent+\" \"+(a.sourceY-n)+\" L \"+a.sourceX+\" \"+(a.sourceY-n)+\" L \"+a.sourceX+\" \"+(a.sourceY+n)+\" L \"+a.leftInnerExtent+\" \"+(a.sourceY+n)+\" A \"+(a.leftLargeArcRadius-n)+\" \"+(a.leftSmallArcRadius-n)+\" 0 0 1 \"+(a.leftFullExtent-n)+\" \"+(a.sourceY+a.leftSmallArcRadius)+\" L \"+(a.leftFullExtent-n)+\" \"+a.verticalLeftInnerExtent,i&&o?r+=\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightSmallArcRadius-n)+\" 0 0 1 \"+(a.leftFullExtent-n-(a.rightLargeArcRadius-n))+\" \"+(a.verticalFullExtent-n)+\" L \"+(a.rightFullExtent+n-t+(a.rightLargeArcRadius-n))+\" \"+(a.verticalFullExtent-n)+\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightSmallArcRadius-n)+\" 0 0 1 \"+(a.rightFullExtent+n-t)+\" \"+a.verticalRightInnerExtent:i?r+=\" A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightLargeArcRadius+n)+\" 0 0 0 \"+(a.leftFullExtent+n)+\" \"+(a.verticalFullExtent+n)+\" L \"+(a.rightFullExtent-t-n)+\" \"+(a.verticalFullExtent+n)+\" A \"+(a.rightLargeArcRadius+n)+\" \"+(a.rightLargeArcRadius+n)+\" 0 0 0 \"+(a.rightFullExtent+n-t)+\" \"+a.verticalRightInnerExtent:r+=\" A \"+(a.leftLargeArcRadius-n)+\" \"+(a.leftLargeArcRadius-n)+\" 0 0 1 \"+a.leftInnerExtent+\" \"+(a.verticalFullExtent-n)+\" L \"+(a.rightInnerExtent-t)+\" \"+(a.verticalFullExtent-n)+\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightLargeArcRadius-n)+\" 0 0 1 \"+(a.rightFullExtent+n-t)+\" \"+a.verticalRightInnerExtent,r+=\" L \"+(a.rightFullExtent+n-t)+\" \"+(a.targetY+a.rightSmallArcRadius)+\" A \"+(a.rightLargeArcRadius-n)+\" \"+(a.rightSmallArcRadius-n)+\" 0 0 1 \"+(a.rightInnerExtent-t)+\" \"+(a.targetY+n)+\" L \"+(a.targetX-t)+\" \"+(a.targetY+n)+(t>0?\" L \"+a.targetX+\" \"+a.targetY:\"\")+\"Z\"),r}function FK(){var e=.5;function t(r){var n=r.linkArrowLength;if(r.link.circular)return anr(r.link,n);var a=Math.abs((r.link.target.x0-r.link.source.x1)/2);n>a&&(n=a);var i=r.link.source.x1,o=r.link.target.x0-n,s=$rr(i,o),l=s(e),u=s(1-e),c=r.link.y0-r.link.width/2,f=r.link.y0+r.link.width/2,v=r.link.y1-r.link.width/2,g=r.link.y1+r.link.width/2,p=\"M\"+i+\",\"+c,x=\"C\"+l+\",\"+c+\" \"+u+\",\"+v+\" \"+o+\",\"+v,_=\"C\"+u+\",\"+g+\" \"+l+\",\"+f+\" \"+i+\",\"+f,y=n>0?\"L\"+(o+n)+\",\"+(v+r.link.width/2):\"\";return y+=\"L\"+o+\",\"+g,p+x+y+_+\"Z\"}return t}function onr(e,t){var r=ws.nodePadAcross,n=e.nodePad/2;t.dx=t.x1-t.x0,t.dy=t.y1-t.y0;var a=t.dx,i=Math.max(.5,t.dy),o=\"node_\"+t.pointNumber;return t.group&&(o=R1.randstr()),t.trace=e.trace,t.curveNumber=e.trace.index,{index:t.pointNumber,key:o,partOfGroup:t.partOfGroup||!1,group:t.group,traceId:e.key,trace:e.trace,node:t,nodePad:e.nodePad,nodeLineColor:e.nodeLineColor,nodeLineWidth:e.nodeLineWidth,textFont:e.textFont,size:e.horizontal?e.height:e.width,visibleWidth:Math.ceil(a),visibleHeight:i,zoneX:-r,zoneY:-n,zoneWidth:a+2*r,zoneHeight:i+2*n,labelY:e.horizontal?t.dy/2+1:t.dx/2+1,left:t.originalLayer===1,sizeAcross:e.width,forceLayouts:e.forceLayouts,horizontal:e.horizontal,reversed:e.reversed,rgb:D1.rgb(t.color),alpha:D1.parse(t.color).alpha,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,graph:e.graph,arrangement:e.arrangement,uniqueNodeLabelPathId:[e.guid,e.key,o].join(\"_\"),interactionState:e.interactionState,figure:e}}function LK(e){e.attr(\"transform\",function(t){return y_(t.node.x0.toFixed(3),t.node.y0.toFixed(3))})}function snr(e){e.call(LK)}function GXe(e,t){e.call(snr),t.attr(\"d\",FK())}function qXe(e){e.attr(\"width\",function(t){return t.node.x1-t.node.x0}).attr(\"height\",function(t){return t.visibleHeight})}function CK(e){return e.link.width>1||e.linkLineWidth>0}function BXe(e){return e.horizontal?e.reversed?y_(e.translateX+e.width,e.translateY)+\"matrix(-1 0 0 1 0 0)\":y_(e.translateX,e.translateY)+\"matrix(1 0 0 1 0 0)\":e.reversed?y_(e.translateX,e.translateY+e.height)+\"matrix(0 -1 1 0 0 0)\":y_(e.translateX,e.translateY)+\"matrix(0 1 1 0 0 0)\"}function OXe(e,t,r){e.on(\".basic\",null).on(\"mouseover.basic\",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.hover(this,n,t),n.interactionState.hovered=[this,n])}).on(\"mousemove.basic\",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.follow(this,n),n.interactionState.hovered=[this,n])}).on(\"mouseout.basic\",function(n){!n.interactionState.dragInProgress&&!n.partOfGroup&&(r.unhover(this,n,t),n.interactionState.hovered=!1)}).on(\"click.basic\",function(n){n.interactionState.hovered&&(r.unhover(this,n,t),n.interactionState.hovered=!1),!n.interactionState.dragInProgress&&!n.partOfGroup&&r.select(this,n,t)})}function lnr(e,t,r,n){var a=I5.behavior.drag().origin(function(i){return{x:i.node.x0+i.visibleWidth/2,y:i.node.y0+i.visibleHeight/2}}).on(\"dragstart\",function(i){if(i.arrangement!==\"fixed\"&&(R1.ensureSingle(n._fullLayout._infolayer,\"g\",\"dragcover\",function(s){n._fullLayout._dragCover=s}),R1.raiseToTop(this),i.interactionState.dragInProgress=i.node,NXe(i.node),i.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,i.interactionState.hovered),i.interactionState.hovered=!1),i.arrangement===\"snap\")){var o=i.traceId+\"|\"+i.key;i.forceLayouts[o]?i.forceLayouts[o].alpha(1):unr(e,o,i,n),cnr(e,t,i,o,n)}}).on(\"drag\",function(i){if(i.arrangement!==\"fixed\"){var o=I5.event.x,s=I5.event.y;i.arrangement===\"snap\"?(i.node.x0=o-i.visibleWidth/2,i.node.x1=o+i.visibleWidth/2,i.node.y0=s-i.visibleHeight/2,i.node.y1=s+i.visibleHeight/2):(i.arrangement===\"freeform\"&&(i.node.x0=o-i.visibleWidth/2,i.node.x1=o+i.visibleWidth/2),s=Math.max(0,Math.min(i.size-i.visibleHeight/2,s)),i.node.y0=s-i.visibleHeight/2,i.node.y1=s+i.visibleHeight/2),NXe(i.node),i.arrangement!==\"snap\"&&(i.sankey.update(i.graph),GXe(e.filter(jXe(i)),t))}}).on(\"dragend\",function(i){if(i.arrangement!==\"fixed\"){i.interactionState.dragInProgress=!1;for(var o=0;o<i.node.childrenNodes.length;o++)i.node.childrenNodes[o].x=i.node.x,i.node.childrenNodes[o].y=i.node.y;i.arrangement!==\"snap\"&&HXe(i,n)}});e.on(\".drag\",null).call(a)}function unr(e,t,r,n){dnr(r.graph.nodes);var a=r.graph.nodes.filter(function(i){return i.originalX===r.node.originalX}).filter(function(i){return!i.partOfGroup});r.forceLayouts[t]=RXe.forceSimulation(a).alphaDecay(0).force(\"collide\",RXe.forceCollide().radius(function(i){return i.dy/2+r.nodePad/2}).strength(1).iterations(ws.forceIterations)).force(\"constrain\",fnr(e,t,a,r,n)).stop()}function cnr(e,t,r,n,a){window.requestAnimationFrame(function i(){var o;for(o=0;o<ws.forceTicksPerFrame;o++)r.forceLayouts[n].tick();var s=r.graph.nodes;if(vnr(s),r.sankey.update(r.graph),GXe(e.filter(jXe(r)),t),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(i);else{var l=r.node.originalX;r.node.x0=l-r.visibleWidth/2,r.node.x1=l+r.visibleWidth/2,HXe(r,a)}})}function fnr(e,t,r,n){return function(){for(var i=0,o=0;o<r.length;o++){var s=r[o];s===n.interactionState.dragInProgress?(s.x=s.lastDraggedX,s.y=s.lastDraggedY):(s.vx=(s.originalX-s.x)/ws.forceTicksPerFrame,s.y=Math.min(n.size-s.dy/2,Math.max(s.dy/2,s.y))),i=Math.max(i,Math.abs(s.vx),Math.abs(s.vy))}!n.interactionState.dragInProgress&&i<.1&&n.forceLayouts[t].alpha()>0&&n.forceLayouts[t].alpha(0)}}function HXe(e,t){for(var r=[],n=[],a=0;a<e.graph.nodes.length;a++){var i=(e.graph.nodes[a].x0+e.graph.nodes[a].x1)/2,o=(e.graph.nodes[a].y0+e.graph.nodes[a].y1)/2;r.push(i/e.figure.width),n.push(o/e.figure.height)}Qrr.call(\"_guiRestyle\",t,{\"node.x\":[r],\"node.y\":[n]},e.trace.index).then(function(){t._fullLayout._dragCover&&t._fullLayout._dragCover.remove()})}function hnr(e){var t=[],r;for(r=0;r<e.length;r++)e[r].originalX=(e[r].x0+e[r].x1)/2,e[r].originalY=(e[r].y0+e[r].y1)/2,t.indexOf(e[r].originalX)===-1&&t.push(e[r].originalX);for(t.sort(function(n,a){return n-a}),r=0;r<e.length;r++)e[r].originalLayerIndex=t.indexOf(e[r].originalX),e[r].originalLayer=e[r].originalLayerIndex/(t.length-1)}function NXe(e){e.lastDraggedX=e.x0+e.dx/2,e.lastDraggedY=e.y0+e.dy/2}function jXe(e){return function(t){return t.node.originalX===e.node.originalX}}function dnr(e){for(var t=0;t<e.length;t++)e[t].y=(e[t].y0+e[t].y1)/2,e[t].x=(e[t].x0+e[t].x1)/2}function vnr(e){for(var t=0;t<e.length;t++)e[t].y0=e[t].y-e[t].dy/2,e[t].y1=e[t].y0+e[t].dy,e[t].x0=e[t].x-e[t].dx/2,e[t].x1=e[t].x0+e[t].dx}WXe.exports=function(e,t,r,n,a){var i=e._context.staticPlot,o=!1;R1.ensureSingle(e._fullLayout._infolayer,\"g\",\"first-render\",function(){o=!0});var s=e._fullLayout._dragCover,l=r.filter(function(_){return UXe(_).trace.visible}).map(nnr.bind(null,n)),u=t.selectAll(\".\"+ws.cn.sankey).data(l,rS);u.exit().remove(),u.enter().append(\"g\").classed(ws.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",i?\"none\":\"auto\").attr(\"transform\",BXe),u.each(function(_,y){e._fullData[y]._sankey=_;var C=\"bgsankey-\"+_.trace.uid+\"-\"+y;R1.ensureSingle(e._fullLayout._draggers,\"rect\",C),e._fullData[y]._bgRect=I5.select(\".\"+C),e._fullData[y]._bgRect.style(\"pointer-events\",i?\"none\":\"all\").attr(\"width\",_.width).attr(\"height\",_.height).attr(\"x\",_.translateX).attr(\"y\",_.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})}),u.transition().ease(ws.ease).duration(ws.duration).attr(\"transform\",BXe);var c=u.selectAll(\".\"+ws.cn.sankeyLinks).data(AD,rS);c.enter().append(\"g\").classed(ws.cn.sankeyLinks,!0).style(\"fill\",\"none\");var f=c.selectAll(\".\"+ws.cn.sankeyLink).data(function(_){var y=_.graph.links;return y.filter(function(C){return C.value}).map(inr.bind(null,_))},rS);f.enter().append(\"path\").classed(ws.cn.sankeyLink,!0).call(OXe,u,a.linkEvents),f.style(\"stroke\",_=>CK(_)?D1.rgb(_.linkLineColor):_.rgb).style(\"stroke-opacity\",_=>CK(_)?D1.opacity(_.linkLineColor):_.alpha).style(\"fill\",_=>_.rgb).style(\"fill-opacity\",_=>_.alpha).style(\"stroke-width\",_=>CK(_)?_.linkLineWidth:1).attr(\"d\",FK()),f.style(\"opacity\",function(){return e._context.staticPlot||o||s?1:0}).transition().ease(ws.ease).duration(ws.duration).style(\"opacity\",1),f.exit().transition().ease(ws.ease).duration(ws.duration).style(\"opacity\",0).remove();var v=u.selectAll(\".\"+ws.cn.sankeyNodeSet).data(AD,rS);v.enter().append(\"g\").classed(ws.cn.sankeyNodeSet,!0),v.style(\"cursor\",function(_){switch(_.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}});var g=v.selectAll(\".\"+ws.cn.sankeyNode).data(function(_){var y=_.graph.nodes;return hnr(y),y.map(onr.bind(null,_))},rS);g.enter().append(\"g\").classed(ws.cn.sankeyNode,!0).call(LK).style(\"opacity\",function(_){return(e._context.staticPlot||o)&&!_.partOfGroup?1:0}),g.call(OXe,u,a.nodeEvents).call(lnr,f,a,e),g.transition().ease(ws.ease).duration(ws.duration).call(LK).style(\"opacity\",function(_){return _.partOfGroup?0:1}),g.exit().transition().ease(ws.ease).duration(ws.duration).style(\"opacity\",0).remove();var p=g.selectAll(\".\"+ws.cn.nodeRect).data(AD);p.enter().append(\"rect\").classed(ws.cn.nodeRect,!0).call(qXe),p.style(\"stroke-width\",function(_){return _.nodeLineWidth}).style(\"stroke\",function(_){return D1.rgb(_.nodeLineColor)}).style(\"stroke-opacity\",function(_){return D1.opacity(_.nodeLineColor)}).style(\"fill\",_=>_.rgb).style(\"fill-opacity\",_=>_.alpha),p.transition().ease(ws.ease).duration(ws.duration).call(qXe);var x=g.selectAll(\".\"+ws.cn.nodeLabel).data(AD);x.enter().append(\"text\").classed(ws.cn.nodeLabel,!0).style(\"cursor\",\"default\"),x.attr(\"data-notex\",1).text(function(_){return _.node.label}).each(function(_){var y=I5.select(this);Jrr.font(y,_.textFont),zXe.convertToTspans(y,e)}).attr(\"text-anchor\",function(_){return _.horizontal&&_.left!==_.reversed?\"end\":\"start\"}).attr(\"transform\",function(_){var y=I5.select(this),C=zXe.lineCount(y),k=_.textFont.size*((C-1)*tnr-enr),S=_.nodeLineWidth/2+rnr;if(!_.horizontal){var A=_.reversed?(_.visibleWidth+k)/2:(_.visibleWidth-k)/2,L=_.reversed?IXe(90):\"scale(-1,1)\"+IXe(90);return y_(A,S)+L}var w=S,A=(_.visibleHeight-k)/2;return 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nZe=fe(x_=>{\"use strict\";var mnr=rl().overrideAll,ynr=jf().getModuleCalcData,xnr=IK(),_nr=tm(),bnr=np(),wnr=ed(),Tnr=Ac().prepSelect,zK=Xt(),Anr=bi(),SD=\"sankey\";x_.name=SD;x_.baseLayoutAttrOverrides=mnr({hoverlabel:_nr.hoverlabel},\"plot\",\"nested\");x_.plot=function(e){var t=ynr(e.calcdata,SD)[0];xnr(e,t),x_.updateFx(e)};x_.clean=function(e,t,r,n){var a=n._has&&n._has(SD),i=t._has&&t._has(SD);a&&!i&&(n._paperdiv.selectAll(\".sankey\").remove(),n._paperdiv.selectAll(\".bgsankey\").remove())};x_.updateFx=function(e){for(var t=0;t<e._fullData.length;t++)Mnr(e,t)};function Mnr(e,t){var r=e._fullData[t],n=e._fullLayout,a=n.dragmode,i=n.dragmode===\"pan\"?\"move\":\"crosshair\",o=r._bgRect;if(o&&!(a===\"pan\"||a===\"zoom\")){bnr(o,i);var s={_id:\"x\",c2p:zK.identity,_offset:r._sankey.translateX,_length:r._sankey.width},l={_id:\"y\",c2p:zK.identity,_offset:r._sankey.translateY,_length:r._sankey.height},u={gd:e,element:o.node(),plotinfo:{id:t,xaxis:s,yaxis:l,fillRangeItems:zK.noop},subplot:t,xaxes:[s],yaxes:[l],doneFnCompleted:function(c){var f=e._fullData[t],v,g=f.node.groups.slice(),p=[];function x(k){for(var S=f._sankey.graph.nodes,L=0;L<S.length;L++)if(S[L].pointNumber===k)return S[L]}for(var _=0;_<c.length;_++){var y=x(c[_].pointNumber);if(y)if(y.group){for(var C=0;C<y.childrenNodes.length;C++)p.push(y.childrenNodes[C].pointNumber);g[y.pointNumber-f.node._count]=!1}else p.push(y.pointNumber)}v=g.filter(Boolean).concat([p]),Anr.call(\"_guiRestyle\",e,{\"node.groups\":[v]},t)}};u.prepFn=function(c,f,v){Tnr(c,f,v,u,a)},wnr.init(u)}}});var aZe=fe((vMr,iZe)=>{\"use strict\";iZe.exports=function(t,r){for(var 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OK=fe((xMr,yZe)=>{\"use strict\";yZe.exports={defaultNumberFontSize:80,bulletNumberDomainSize:.25,bulletPadding:.025,innerRadius:.75,valueThickness:.5,titlePadding:5,horizontalPadding:10}});var bZe=fe((_Mr,_Ze)=>{\"use strict\";var I1=Xt(),CD=BK(),Lnr=nl().defaults,xZe=Mo(),Pnr=Ah(),kD=OK(),Fnr=Fx(),Dnr=Ub(),Rnr=gm(),Inr=mm();function znr(e,t,r,n){function a(w,A){return I1.coerce(e,t,CD,w,A)}Lnr(t,n,a),a(\"mode\"),t._hasNumber=t.mode.indexOf(\"number\")!==-1,t._hasDelta=t.mode.indexOf(\"delta\")!==-1,t._hasGauge=t.mode.indexOf(\"gauge\")!==-1;var i=a(\"value\");t._range=[0,typeof i==\"number\"?1.5*i:1];var o=new Array(2),s;if(t._hasNumber){a(\"number.valueformat\");var l=I1.extendFlat({},n.font);l.size=void 0,I1.coerceFont(a,\"number.font\",l),t.number.font.size===void 0&&(t.number.font.size=kD.defaultNumberFontSize,o[0]=!0),a(\"number.prefix\"),a(\"number.suffix\"),s=t.number.font.size}var u;if(t._hasDelta){var c=I1.extendFlat({},n.font);c.size=void 0,I1.coerceFont(a,\"delta.font\",c),t.delta.font.size===void 0&&(t.delta.font.size=(t._hasNumber?.5:1)*(s||kD.defaultNumberFontSize),o[1]=!0),a(\"delta.reference\",t.value),a(\"delta.relative\"),a(\"delta.valueformat\",t.delta.relative?\"2%\":\"\"),a(\"delta.increasing.symbol\"),a(\"delta.increasing.color\"),a(\"delta.decreasing.symbol\"),a(\"delta.decreasing.color\"),a(\"delta.position\"),a(\"delta.prefix\"),a(\"delta.suffix\"),u=t.delta.font.size}t._scaleNumbers=(!t._hasNumber||o[0])&&(!t._hasDelta||o[1])||!1;var f=I1.extendFlat({},n.font);f.size=.25*(s||u||kD.defaultNumberFontSize),I1.coerceFont(a,\"title.font\",f),a(\"title.text\");var v,g,p,x;function _(w,A){return I1.coerce(v,g,CD.gauge,w,A)}function y(w,A){return I1.coerce(p,x,CD.gauge.axis,w,A)}if(t._hasGauge){v=e.gauge,v||(v={}),g=xZe.newContainer(t,\"gauge\"),_(\"shape\");var C=t._isBullet=t.gauge.shape===\"bullet\";C||a(\"title.align\",\"center\");var k=t._isAngular=t.gauge.shape===\"angular\";k||a(\"align\",\"center\"),_(\"bgcolor\",n.paper_bgcolor),_(\"borderwidth\"),_(\"bordercolor\"),_(\"bar.color\"),_(\"bar.line.color\"),_(\"bar.line.width\");var S=kD.valueThickness*(t.gauge.shape===\"bullet\"?.5:1);_(\"bar.thickness\",S),Pnr(v,g,{name:\"steps\",handleItemDefaults:qnr}),_(\"threshold.value\"),_(\"threshold.thickness\"),_(\"threshold.line.width\"),_(\"threshold.line.color\"),p={},v&&(p=v.axis||{}),x=xZe.newContainer(g,\"axis\"),y(\"visible\"),t._range=y(\"range\",t._range);var L={font:n.font,noAutotickangles:!0,outerTicks:!0,noTicklabelshift:!0,noTicklabelstandoff:!0};Fnr(p,x,y,\"linear\"),Inr(p,x,y,\"linear\",L),Rnr(p,x,y,\"linear\",L),Dnr(p,x,y,L)}else a(\"title.align\",\"center\"),a(\"align\",\"center\"),t._isAngular=t._isBullet=!1;t._length=null}function qnr(e,t){function r(n,a){return I1.coerce(e,t,CD.gauge.steps,n,a)}r(\"color\"),r(\"line.color\"),r(\"line.width\"),r(\"range\"),r(\"thickness\")}_Ze.exports={supplyDefaults:znr}});var TZe=fe((bMr,wZe)=>{\"use strict\";function Bnr(e,t){var r=[],n=t.value;typeof t._lastValue!=\"number\"&&(t._lastValue=t.value);var a=t._lastValue,i=a;return t._hasDelta&&typeof t.delta.reference==\"number\"&&(i=t.delta.reference),r[0]={y:n,lastY:a,delta:n-i,relativeDelta:(n-i)/i},r}wZe.exports={calc:Bnr}});var CZe=fe((wMr,kZe)=>{\"use strict\";var A_=xi(),Onr=($2(),Gs(Z2)).interpolate,AZe=($2(),Gs(Z2)).interpolateNumber,Vy=Xt(),Nnr=Vy.strScale,oS=Vy.strTranslate,Unr=Vy.rad2deg,Vnr=nc().MID_SHIFT,T_=ea(),__=OK(),PD=as(),ih=Yi(),Gnr=vA(),Hnr=gC(),jnr=pf(),O5=ni(),NK={left:\"start\",center:\"middle\",right:\"end\"},b_={left:0,center:.5,right:1},MZe=/[yzafpnµmkMGTPEZY]/;function sS(e){return e&&e.duration>0}kZe.exports=function(t,r,n,a){var i=t._fullLayout,o;sS(n)&&a&&(o=a()),Vy.makeTraceGroups(i._indicatorlayer,r,\"trace\").each(function(s){var l=s[0],u=l.trace,c=A_.select(this),f=u._hasGauge,v=u._isAngular,g=u._isBullet,p=u.domain,x={w:i._size.w*(p.x[1]-p.x[0]),h:i._size.h*(p.y[1]-p.y[0]),l:i._size.l+i._size.w*p.x[0],r:i._size.r+i._size.w*(1-p.x[1]),t:i._size.t+i._size.h*(1-p.y[1]),b:i._size.b+i._size.h*p.y[0]},_=x.l+x.w/2,y=x.t+x.h/2,C=Math.min(x.w/2,x.h),k=__.innerRadius*C,S,L,w,A=u.align||\"center\";if(L=y,!f)S=x.l+b_[A]*x.w,w=function(j){return SZe(j,x.w,x.h)};else if(v&&(S=_,L=y+C/2,w=function(j){return $nr(j,.9*k)}),g){var T=__.bulletPadding,m=1-__.bulletNumberDomainSize+T;S=x.l+(m+(1-m)*b_[A])*x.w,w=function(j){return SZe(j,(__.bulletNumberDomainSize-T)*x.w,x.h)}}Xnr(t,c,s,{numbersX:S,numbersY:L,numbersScaler:w,transitionOpts:n,onComplete:o});var F,E;f&&(F={range:u.gauge.axis.range,color:u.gauge.bgcolor,line:{color:u.gauge.bordercolor,width:0},thickness:1},E={range:u.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:u.gauge.bordercolor,width:u.gauge.borderwidth},thickness:1});var I=c.selectAll(\"g.angular\").data(v?s:[]);I.exit().remove();var R=c.selectAll(\"g.angularaxis\").data(v?s:[]);R.exit().remove(),v&&Ynr(t,c,s,{radius:C,innerRadius:k,gauge:I,layer:R,size:x,gaugeBg:F,gaugeOutline:E,transitionOpts:n,onComplete:o});var B=c.selectAll(\"g.bullet\").data(g?s:[]);B.exit().remove();var O=c.selectAll(\"g.bulletaxis\").data(g?s:[]);O.exit().remove(),g&&Wnr(t,c,s,{gauge:B,layer:O,size:x,gaugeBg:F,gaugeOutline:E,transitionOpts:n,onComplete:o});var V=c.selectAll(\"text.title\").data(s);V.exit().remove(),V.enter().append(\"text\").classed(\"title\",!0),V.attr(\"text-anchor\",function(){return g?NK.right:NK[u.title.align]}).text(u.title.text).call(T_.font,u.title.font).call(PD.convertToTspans,t),V.attr(\"transform\",function(){var j=x.l+x.w*b_[u.title.align],q,U=__.titlePadding,J=T_.bBox(V.node());if(f){if(v)if(u.gauge.axis.visible){var ee=T_.bBox(R.node());q=ee.top-U-J.bottom}else 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w(R){R.attr(\"width\",function(B){return Math.max(0,g.c2p(B.range[1])-g.c2p(B.range[0]))}).attr(\"x\",function(B){return g.c2p(B.range[0])}).attr(\"y\",function(B){return .5*(1-B.thickness)*C}).attr(\"height\",function(B){return B.thickness*C})}var A=[s].concat(a.gauge.steps),T=i.selectAll(\"g.bg-bullet\").data(A);T.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),T.select(\"rect\").call(w).call(w_),T.exit().remove();var m=i.selectAll(\"g.value-bullet\").data([a.gauge.bar]);m.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),m.select(\"rect\").attr(\"height\",k).attr(\"y\",(C-k)/2).call(w_),sS(f)?m.select(\"rect\").transition().duration(f.duration).ease(f.easing).each(\"end\",function(){v&&v()}).each(\"interrupt\",function(){v&&v()}).attr(\"width\",Math.max(0,g.c2p(Math.min(a.gauge.axis.range[1],r[0].y)))):m.select(\"rect\").attr(\"width\",typeof r[0].y==\"number\"?Math.max(0,g.c2p(Math.min(a.gauge.axis.range[1],r[0].y))):0),m.exit().remove();var F=r.filter(function(){return a.gauge.threshold.value||a.gauge.threshold.value===0}),E=i.selectAll(\"g.threshold-bullet\").data(F);E.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),E.select(\"line\").attr(\"x1\",g.c2p(a.gauge.threshold.value)).attr(\"x2\",g.c2p(a.gauge.threshold.value)).attr(\"y1\",(1-a.gauge.threshold.thickness)/2*C).attr(\"y2\",(1-(1-a.gauge.threshold.thickness)/2)*C).call(O5.stroke,a.gauge.threshold.line.color).style(\"stroke-width\",a.gauge.threshold.line.width),E.exit().remove();var I=i.selectAll(\"g.gauge-outline\").data([l]);I.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),I.select(\"rect\").call(w).call(w_),I.exit().remove()}function Ynr(e,t,r,n){var a=r[0].trace,i=n.size,o=n.radius,s=n.innerRadius,l=n.gaugeBg,u=n.gaugeOutline,c=[i.l+i.w/2,i.t+i.h/2+o/2],f=n.gauge,v=n.layer,g=n.transitionOpts,p=n.onComplete,x=Math.PI/2;function _(ce){var ye=a.gauge.axis.range[0],xe=a.gauge.axis.range[1],ue=(ce-ye)/(xe-ye)*Math.PI-x;return ue<-x?-x:ue>x?x:ue}function y(ce){return A_.svg.arc().innerRadius((s+o)/2-ce/2*(o-s)).outerRadius((s+o)/2+ce/2*(o-s)).startAngle(-x)}function C(ce){ce.attr(\"d\",function(ye){return y(ye.thickness).startAngle(_(ye.range[0])).endAngle(_(ye.range[1]))()})}var k,S,L,w;f.enter().append(\"g\").classed(\"angular\",!0),f.attr(\"transform\",oS(c[0],c[1])),v.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),v.selectAll(\"g.xangularaxistick,path,text\").remove(),k=aS(e,a.gauge.axis),k.type=\"linear\",k.range=a.gauge.axis.range,k._id=\"xangularaxis\",k.ticklabeloverflow=\"allow\",k.setScale();var A=function(ce){return(k.range[0]-ce.x)/(k.range[1]-k.range[0])*Math.PI+Math.PI},T={},m=ih.makeLabelFns(k,0),F=m.labelStandoff;T.xFn=function(ce){var ye=A(ce);return Math.cos(ye)*F},T.yFn=function(ce){var ye=A(ce),xe=Math.sin(ye)>0?.2:1;return-Math.sin(ye)*(F+ce.fontSize*xe)+Math.abs(Math.cos(ye))*(ce.fontSize*Vnr)},T.anchorFn=function(ce){var ye=A(ce),xe=Math.cos(ye);return Math.abs(xe)<.1?\"middle\":xe>0?\"start\":\"end\"},T.heightFn=function(ce,ye,xe){var ue=A(ce);return-.5*(1+Math.sin(ue))*xe};var E=function(ce){return oS(c[0]+o*Math.cos(ce),c[1]-o*Math.sin(ce))};L=function(ce){return E(A(ce))};var I=function(ce){var ye=A(ce);return E(ye)+\"rotate(\"+-Unr(ye)+\")\"};if(S=ih.calcTicks(k),w=ih.getTickSigns(k)[2],k.visible){w=k.ticks===\"inside\"?-1:1;var R=(k.linewidth||1)/2;ih.drawTicks(e,k,{vals:S,layer:v,path:\"M\"+w*R+\",0h\"+w*k.ticklen,transFn:I}),ih.drawLabels(e,k,{vals:S,layer:v,transFn:L,labelFns:T})}var B=[l].concat(a.gauge.steps),O=f.selectAll(\"g.bg-arc\").data(B);O.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),O.select(\"path\").call(C).call(w_),O.exit().remove();var V=y(a.gauge.bar.thickness),j=f.selectAll(\"g.value-arc\").data([a.gauge.bar]);j.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var q=j.select(\"path\");sS(g)?(q.transition().duration(g.duration).ease(g.easing).each(\"end\",function(){p&&p()}).each(\"interrupt\",function(){p&&p()}).attrTween(\"d\",Znr(V,_(r[0].lastY),_(r[0].y))),a._lastValue=r[0].y):q.attr(\"d\",typeof r[0].y==\"number\"?V.endAngle(_(r[0].y)):\"M0,0Z\"),q.call(w_),j.exit().remove(),B=[];var U=a.gauge.threshold.value;(U||U===0)&&B.push({range:[U,U],color:a.gauge.threshold.color,line:{color:a.gauge.threshold.line.color,width:a.gauge.threshold.line.width},thickness:a.gauge.threshold.thickness});var J=f.selectAll(\"g.threshold-arc\").data(B);J.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),J.select(\"path\").call(C).call(w_),J.exit().remove();var ee=f.selectAll(\"g.gauge-outline\").data([u]);ee.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),ee.select(\"path\").call(C).call(w_),ee.exit().remove()}function Xnr(e,t,r,n){var a=r[0].trace,i=n.numbersX,o=n.numbersY,s=a.align||\"center\",l=NK[s],u=n.transitionOpts,c=n.onComplete,f=Vy.ensureSingle(t,\"g\",\"numbers\"),v,g,p,x=[];a._hasNumber&&x.push(\"number\"),a._hasDelta&&(x.push(\"delta\"),a.delta.position===\"left\"&&x.reverse());var _=f.selectAll(\"text\").data(x);_.enter().append(\"text\"),_.attr(\"text-anchor\",function(){return l}).attr(\"class\",function(E){return E}).attr(\"x\",null).attr(\"y\",null).attr(\"dx\",null).attr(\"dy\",null),_.exit().remove();function y(E,I,R,B){if(E.match(\"s\")&&R>=0!=B>=0&&!I(R).slice(-1).match(MZe)&&!I(B).slice(-1).match(MZe)){var O=E.slice().replace(\"s\",\"f\").replace(/\\d+/,function(j){return parseInt(j)-1}),V=aS(e,{tickformat:O});return function(j){return Math.abs(j)<1?ih.tickText(V,j).text:I(j)}}else return 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J===0||typeof J!=\"number\"||isNaN(J)?\"-\":(J>0?a.delta.increasing.symbol:a.delta.decreasing.symbol)+B+ee(J)+R},j=function(J){return J.delta>=0?a.delta.increasing.color:a.delta.decreasing.color};a._deltaLastValue===void 0&&(a._deltaLastValue=O(r[0]));var q=f.select(\"text.delta\");q.call(T_.font,a.delta.font).call(O5.fill,j({delta:a._deltaLastValue}));function U(){q.text(V(O(r[0]),I)).call(O5.fill,j(r[0])).call(PD.convertToTspans,e)}return sS(u)?q.transition().duration(u.duration).ease(u.easing).tween(\"text\",function(){var J=A_.select(this),ee=O(r[0]),ce=a._deltaLastValue,ye=y(a.delta.valueformat,I,ce,ee),xe=AZe(ce,ee);return a._deltaLastValue=ee,function(ue){J.text(V(xe(ue),ye)),J.call(O5.fill,j({delta:xe(ue)}))}}).each(\"end\",function(){U(),c&&c()}).each(\"interrupt\",function(){U(),c&&c()}):U(),g=EZe(V(O(r[0]),I),a.delta.font,l,e),q}var S=a.mode+a.align,L;if(a._hasDelta&&(L=k(),S+=a.delta.position+a.delta.font.size+a.delta.font.family+a.delta.valueformat,S+=a.delta.increasing.symbol+a.delta.decreasing.symbol,p=g),a._hasNumber&&(C(),S+=a.number.font.size+a.number.font.family+a.number.valueformat+a.number.suffix+a.number.prefix,p=v),a._hasDelta&&a._hasNumber){var w=[(v.left+v.right)/2,(v.top+v.bottom)/2],A=[(g.left+g.right)/2,(g.top+g.bottom)/2],T,m,F=.75*a.delta.font.size;a.delta.position===\"left\"&&(T=LD(a,\"deltaPos\",0,-1*(v.width*b_[a.align]+g.width*(1-b_[a.align])+F),S,Math.min),m=w[1]-A[1],p={width:v.width+g.width+F,height:Math.max(v.height,g.height),left:g.left+T,right:v.right,top:Math.min(v.top,g.top+m),bottom:Math.max(v.bottom,g.bottom+m)}),a.delta.position===\"right\"&&(T=LD(a,\"deltaPos\",0,v.width*(1-b_[a.align])+g.width*b_[a.align]+F,S,Math.max),m=w[1]-A[1],p={width:v.width+g.width+F,height:Math.max(v.height,g.height),left:v.left,right:g.right+T,top:Math.min(v.top,g.top+m),bottom:Math.max(v.bottom,g.bottom+m)}),a.delta.position===\"bottom\"&&(T=null,m=g.height,p={width:Math.max(v.width,g.width),height:v.height+g.height,left:Math.min(v.left,g.left),right:Math.max(v.right,g.right),top:v.bottom-v.height,bottom:v.bottom+g.height}),a.delta.position===\"top\"&&(T=null,m=v.top,p={width:Math.max(v.width,g.width),height:v.height+g.height,left:Math.min(v.left,g.left),right:Math.max(v.right,g.right),top:v.bottom-v.height-g.height,bottom:v.bottom}),L.attr({dx:T,dy:m})}(a._hasNumber||a._hasDelta)&&f.attr(\"transform\",function(){var E=n.numbersScaler(p);S+=E[2];var I=LD(a,\"numbersScale\",1,E[0],S,Math.min),R;a._scaleNumbers||(I=1),a._isAngular?R=o-I*p.bottom:R=o-I*(p.top+p.bottom)/2,a._numbersTop=I*p.top+R;var B=p[s];s===\"center\"&&(B=(p.left+p.right)/2);var O=i-I*B;return O=LD(a,\"numbersTranslate\",0,O,S,Math.max),oS(O,R)+Nnr(I)})}function w_(e){e.each(function(t){O5.stroke(A_.select(this),t.line.color)}).each(function(t){O5.fill(A_.select(this),t.color)}).style(\"stroke-width\",function(t){return t.line.width})}function Znr(e,t,r){return function(){var n=Onr(t,r);return function(a){return e.endAngle(n(a))()}}}function aS(e,t,r){var n=e._fullLayout,a=Vy.extendFlat({type:\"linear\",ticks:\"outside\",range:r,showline:!0},t),i={type:\"linear\",_id:\"x\"+t._id},o={letter:\"x\",font:n.font,noAutotickangles:!0,noHover:!0,noTickson:!0};function s(l,u){return Vy.coerce(a,i,jnr,l,u)}return Gnr(a,i,s,o,n),Hnr(a,i,s,o),i}function SZe(e,t,r){var n=Math.min(t/e.width,r/e.height);return[n,e,t+\"x\"+r]}function $nr(e,t){var 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f(y)?yQe(y,t):1/0}),g=u(n[v],n[c(v-1)],t);for(i.push(g),o=v,s=0;s<a;o++,s++){var p=n[c(o)];if(!f(p))break;i.push(l(p))}var x=AJ(n,function(y){return f(y)?yQe(y,r):1/0}),_=u(n[x],n[c(x+1)],r);return i.push(_),i.push([0,0]),i.push(i[0].slice()),i}function W5(e,t,r,n){return S_.isFullCircle([t,r])?Gor(e,n):Hor(e,t,r,n)}function jor(e,t,r,n){for(var a=1/0,i=1/0,o=W5(e,t,r,n),s=0;s<o.length;s++){var l=o[s];a=Math.min(a,l[0]),i=Math.min(i,-l[1])}return[a,i]}function Wor(e,t){var r=function(i){var o=Nor(i,e);return o>0?o:1/0},n=AJ(t,r),a=S_.mod(n+1,t.length);return[t[n],t[a]]}function sR(e){return Math.abs(e)>1e-10?e:0}function MJ(e,t,r){t=t||0,r=r||0;for(var n=e.length,a=new Array(n),i=0;i<n;i++){var o=e[i];a[i]=[t+o[0],r-o[1]]}return a}function Yor(e,t,r,n,a,i){var o=W5(e,t,r,n);return\"M\"+MJ(o,a,i).join(\"L\")}function Xor(e,t,r,n,a,i,o){var s,l;e<t?(s=e,l=t):(s=t,l=e);var u=MJ(W5(s,r,n,a),i,o),c=MJ(W5(l,r,n,a),i,o);return\"M\"+c.reverse().join(\"L\")+\"M\"+u.join(\"L\")}bQe.exports={isPtInsidePolygon:Uor,findPolygonOffset:jor,findEnclosingVertexAngles:Wor,findIntersectionXY:_Qe,findXYatLength:Vor,clampTiny:sR,pathPolygon:Yor,pathPolygonAnnulus:Xor}});var SJ=fe((GSr,AQe)=>{\"use strict\";function wQe(e){return e<0?-1:e>0?1:0}function Y5(e){var t=e[0],r=e[1];if(!isFinite(t)||!isFinite(r))return[1,0];var n=(t+1)*(t+1)+r*r;return[(t*t+r*r-1)/n,2*r/n]}function X5(e,t){var r=t[0],n=t[1];return[r*e.radius+e.cx,-n*e.radius+e.cy]}function TQe(e,t){return t*e.radius}function Zor(e,t,r,n){var a=X5(e,Y5([r,t])),i=a[0],o=a[1],s=X5(e,Y5([n,t])),l=s[0],u=s[1];if(t===0)return[\"M\"+i+\",\"+o,\"L\"+l+\",\"+u].join(\" \");var c=TQe(e,1/Math.abs(t));return[\"M\"+i+\",\"+o,\"A\"+c+\",\"+c+\" 0 0,\"+(t<0?1:0)+\" \"+l+\",\"+u].join(\" \")}function $or(e,t,r,n){var a=TQe(e,1/(t+1)),i=X5(e,Y5([t,r])),o=i[0],s=i[1],l=X5(e,Y5([t,n])),u=l[0],c=l[1];if(wQe(r)!==wQe(n)){var f=X5(e,Y5([t,0])),v=f[0],g=f[1];return[\"M\"+o+\",\"+s,\"A\"+a+\",\"+a+\" 0 0,\"+(0<r?0:1)+\" \"+v+\",\"+g,\"A\"+a+\",\"+a+\" 0 0,\"+(n<0?0:1)+u+\",\"+c].join(\" \")}return[\"M\"+o+\",\"+s,\"A\"+a+\",\"+a+\" 0 0,\"+(n<r?0:1)+\" \"+u+\",\"+c].join(\" \")}AQe.exports={smith:Y5,reactanceArc:Zor,resistanceArc:$or,smithTransform:X5}});var kJ=fe((HSr,DQe)=>{\"use strict\";var E_=xi(),C_=bi(),ol=Xt(),q1=ol.strRotate,$c=ol.strTranslate,hR=ni(),vS=ea(),Kor=Hs(),qd=Yi(),Jor=Hp(),Qor=TJ(),esr=rp().doAutoRange,Vg=NN(),fR=ed(),MQe=kl(),tsr=qx(),rsr=Ac().prepSelect,nsr=Ac().selectOnClick,EJ=Ac().clearOutline,SQe=np(),EQe=ST(),kQe=RT().redrawReglTraces,isr=nc().MID_SHIFT,jy=oR(),Gg=lR(),dR=SJ(),uR=dR.smith,asr=dR.reactanceArc,osr=dR.resistanceArc,cR=dR.smithTransform,ssr=ol._,CQe=ol.mod,Wy=ol.deg2rad,k_=ol.rad2deg;function LQe(e,t,r){this.isSmith=r||!1,this.id=t,this.gd=e,this._hasClipOnAxisFalse=null,this.vangles=null,this.radialAxisAngle=null,this.traceHash={},this.layers={},this.clipPaths={},this.clipIds={},this.viewInitial={};var n=e._fullLayout,a=\"clip\"+n._uid+t;this.clipIds.forTraces=a+\"-for-traces\",this.clipPaths.forTraces=n._clips.append(\"clipPath\").attr(\"id\",this.clipIds.forTraces),this.clipPaths.forTraces.append(\"path\"),this.framework=n[\"_\"+(r?\"smith\":\"polar\")+\"layer\"].append(\"g\").attr(\"class\",t),this.getHole=function(i){return this.isSmith?0:i.hole},this.getSector=function(i){return this.isSmith?[0,360]:i.sector},this.getRadial=function(i){return this.isSmith?i.realaxis:i.radialaxis},this.getAngular=function(i){return this.isSmith?i.imaginaryaxis:i.angularaxis},r||(this.radialTickLayout=null,this.angularTickLayout=null)}var wf=LQe.prototype;DQe.exports=function(t,r,n){return new LQe(t,r,n)};wf.plot=function(e,t){for(var r=this,n=t[r.id],a=!1,i=0;i<e.length;i++){var o=e[i][0].trace;if(o.cliponaxis===!1){a=!0;break}}r._hasClipOnAxisFalse=a,r.updateLayers(t,n),r.updateLayout(t,n),Kor.generalUpdatePerTraceModule(r.gd,r,e,n),r.updateFx(t,n),r.isSmith&&(delete n.realaxis.range,delete n.imaginaryaxis.range)};wf.updateLayers=function(e,t){var r=this,n=r.isSmith,a=r.layers,i=r.getRadial(t),o=r.getAngular(t),s=jy.layerNames,l=s.indexOf(\"frontplot\"),u=s.slice(0,l),c=o.layer===\"below traces\",f=i.layer===\"below traces\";c&&u.push(\"angular-line\"),f&&u.push(\"radial-line\"),c&&u.push(\"angular-axis\"),f&&u.push(\"radial-axis\"),u.push(\"frontplot\"),c||u.push(\"angular-line\"),f||u.push(\"radial-line\"),c||u.push(\"angular-axis\"),f||u.push(\"radial-axis\");var v=(n?\"smith\":\"polar\")+\"sublayer\",g=r.framework.selectAll(\".\"+v).data(u,String);g.enter().append(\"g\").attr(\"class\",function(p){return v+\" \"+p}).each(function(p){var x=a[p]=E_.select(this);switch(p){case\"frontplot\":n||x.append(\"g\").classed(\"barlayer\",!0),x.append(\"g\").classed(\"scatterlayer\",!0);break;case\"backplot\":x.append(\"g\").classed(\"maplayer\",!0);break;case\"plotbg\":a.bg=x.append(\"path\");break;case\"radial-grid\":x.style(\"fill\",\"none\");break;case\"angular-grid\":x.style(\"fill\",\"none\");break;case\"radial-line\":x.append(\"line\").style(\"fill\",\"none\");break;case\"angular-line\":x.append(\"path\").style(\"fill\",\"none\");break}}),g.order()};wf.updateLayout=function(e,t){var r=this,n=r.layers,a=e._size,i=r.getRadial(t),o=r.getAngular(t),s=t.domain.x,l=t.domain.y;r.xOffset=a.l+a.w*s[0],r.yOffset=a.t+a.h*(1-l[1]);var u=r.xLength=a.w*(s[1]-s[0]),c=r.yLength=a.h*(l[1]-l[0]),f=r.getSector(t);r.sectorInRad=f.map(Wy);var v=r.sectorBBox=lsr(f),g=v[2]-v[0],p=v[3]-v[1],x=c/u,_=Math.abs(p/g),y,C,k,S,L;x>_?(y=u,C=u*_,L=(c-C)/a.h/2,k=[s[0],s[1]],S=[l[0]+L,l[1]-L]):(y=c/_,C=c,L=(u-y)/a.w/2,k=[s[0]+L,s[1]-L],S=[l[0],l[1]]),r.xLength2=y,r.yLength2=C,r.xDomain2=k,r.yDomain2=S;var w=r.xOffset2=a.l+a.w*k[0],A=r.yOffset2=a.t+a.h*(1-S[1]),T=r.radius=y/g,m=r.innerRadius=r.getHole(t)*T,F=r.cx=w-T*v[0],E=r.cy=A+T*v[3],I=r.cxx=F-w,R=r.cyy=E-A,B=i.side,O;B===\"counterclockwise\"?(O=B,B=\"top\"):B===\"clockwise\"&&(O=B,B=\"bottom\"),r.radialAxis=r.mockAxis(e,t,i,{_id:\"x\",side:B,_trueSide:O,domain:[m/a.w,T/a.w]}),r.angularAxis=r.mockAxis(e,t,o,{side:\"right\",domain:[0,Math.PI],autorange:!1}),r.doAutoRange(e,t),r.updateAngularAxis(e,t),r.updateRadialAxis(e,t),r.updateRadialAxisTitle(e,t),r.xaxis=r.mockCartesianAxis(e,t,{_id:\"x\",domain:k}),r.yaxis=r.mockCartesianAxis(e,t,{_id:\"y\",domain:S});var V=r.pathSubplot();r.clipPaths.forTraces.select(\"path\").attr(\"d\",V).attr(\"transform\",$c(I,R)),n.frontplot.attr(\"transform\",$c(w,A)).call(vS.setClipUrl,r._hasClipOnAxisFalse?null:r.clipIds.forTraces,r.gd),n.bg.attr(\"d\",V).attr(\"transform\",$c(F,E)).call(hR.fill,t.bgcolor)};wf.mockAxis=function(e,t,r,n){var a=ol.extendFlat({},r,n);return Qor(a,t,e),a};wf.mockCartesianAxis=function(e,t,r){var n=this,a=n.isSmith,i=r._id,o=ol.extendFlat({type:\"linear\"},r);Jor(o,e);var s={x:[0,2],y:[1,3]};return o.setRange=function(){var l=n.sectorBBox,u=s[i],c=n.radialAxis._rl,f=(c[1]-c[0])/(1-n.getHole(t));o.range=[l[u[0]]*f,l[u[1]]*f]},o.isPtWithinRange=i===\"x\"&&!a?function(l){return n.isPtInside(l)}:function(){return!0},o.setRange(),o.setScale(),o};wf.doAutoRange=function(e,t){var r=this,n=r.gd,a=r.radialAxis,i=r.getRadial(t);esr(n,a);var o=a.range;if(i.range=o.slice(),i._input.range=o.slice(),a._rl=[a.r2l(o[0],null,\"gregorian\"),a.r2l(o[1],null,\"gregorian\")],a.minallowed!==void 0){var s=a.r2l(a.minallowed);a._rl[0]>a._rl[1]?a._rl[1]=Math.max(a._rl[1],s):a._rl[0]=Math.max(a._rl[0],s)}if(a.maxallowed!==void 0){var l=a.r2l(a.maxallowed);a._rl[0]<a._rl[1]?a._rl[1]=Math.min(a._rl[1],l):a._rl[0]=Math.min(a._rl[0],l)}};wf.updateRadialAxis=function(e,t){var r=this,n=r.gd,a=r.layers,i=r.radius,o=r.innerRadius,s=r.cx,l=r.cy,u=r.getRadial(t),c=CQe(r.getSector(t)[0],360),f=r.radialAxis,v=o<i,g=r.isSmith;g||(r.fillViewInitialKey(\"radialaxis.angle\",u.angle),r.fillViewInitialKey(\"radialaxis.range\",f.range.slice()),f.setGeometry()),f.tickangle===\"auto\"&&c>90&&c<=270&&(f.tickangle=180);var p=g?function(T){var m=cR(r,uR([T.x,0]));return $c(m[0]-s,m[1]-l)}:function(T){return $c(f.l2p(T.x)+o,0)},x=g?function(T){return osr(r,T.x,-1/0,1/0)}:function(T){return r.pathArc(f.r2p(T.x)+o)},_=PQe(u);if(r.radialTickLayout!==_&&(a[\"radial-axis\"].selectAll(\".xtick\").remove(),r.radialTickLayout=_),v){f.setScale();var y=0,C=g?(f.tickvals||[]).filter(function(T){return T>=0}).map(function(T){return qd.tickText(f,T,!0,!1)}):qd.calcTicks(f),k=g?C:qd.clipEnds(f,C),S=qd.getTickSigns(f)[2];g&&((f.ticks===\"top\"&&f.side===\"bottom\"||f.ticks===\"bottom\"&&f.side===\"top\")&&(S=-S),f.ticks===\"top\"&&f.side===\"top\"&&(y=-f.ticklen),f.ticks===\"bottom\"&&f.side===\"bottom\"&&(y=f.ticklen)),qd.drawTicks(n,f,{vals:C,layer:a[\"radial-axis\"],path:qd.makeTickPath(f,0,S),transFn:p,crisp:!1}),qd.drawGrid(n,f,{vals:k,layer:a[\"radial-grid\"],path:x,transFn:ol.noop,crisp:!1}),qd.drawLabels(n,f,{vals:C,layer:a[\"radial-axis\"],transFn:p,labelFns:qd.makeLabelFns(f,y)})}var L=r.radialAxisAngle=r.vangles?k_(FQe(Wy(u.angle),r.vangles)):u.angle,w=$c(s,l),A=w+q1(-L);pS(a[\"radial-axis\"],v&&(u.showticklabels||u.ticks),{transform:A}),pS(a[\"radial-grid\"],v&&u.showgrid,{transform:g?\"\":w}),pS(a[\"radial-line\"].select(\"line\"),v&&u.showline,{x1:g?-i:o,y1:0,x2:i,y2:0,transform:A}).attr(\"stroke-width\",u.linewidth).call(hR.stroke,u.linecolor)};wf.updateRadialAxisTitle=function(e,t,r){if(!this.isSmith){var n=this,a=n.gd,i=n.radius,o=n.cx,s=n.cy,l=n.getRadial(t),u=n.id+\"title\",c=0;if(l.title){var f=vS.bBox(n.layers[\"radial-axis\"].node()).height,v=l.title.font.size,g=l.side;c=g===\"top\"?v:g===\"counterclockwise\"?-(f+v*.4):f+v*.8}var p=r!==void 0?r:n.radialAxisAngle,x=Wy(p),_=Math.cos(x),y=Math.sin(x),C=o+i/2*_+c*y,k=s-i/2*y+c*_;n.layers[\"radial-axis-title\"]=tsr.draw(a,u,{propContainer:l,propName:n.id+\".radialaxis.title.text\",placeholder:ssr(a,\"Click to enter radial axis title\"),attributes:{x:C,y:k,\"text-anchor\":\"middle\"},transform:{rotate:-p}})}};wf.updateAngularAxis=function(e,t){var r=this,n=r.gd,a=r.layers,i=r.radius,o=r.innerRadius,s=r.cx,l=r.cy,u=r.getAngular(t),c=r.angularAxis,f=r.isSmith;f||(r.fillViewInitialKey(\"angularaxis.rotation\",u.rotation),c.setGeometry(),c.setScale());var v=f?function(m){var F=cR(r,uR([0,m.x]));return Math.atan2(F[0]-s,F[1]-l)-Math.PI/2}:function(m){return c.t2g(m.x)};c.type===\"linear\"&&c.thetaunit===\"radians\"&&(c.tick0=k_(c.tick0),c.dtick=k_(c.dtick));var g=function(m){return $c(s+i*Math.cos(m),l-i*Math.sin(m))},p=f?function(m){var F=cR(r,uR([0,m.x]));return $c(F[0],F[1])}:function(m){return g(v(m))},x=f?function(m){var F=cR(r,uR([0,m.x])),E=Math.atan2(F[0]-s,F[1]-l)-Math.PI/2;return $c(F[0],F[1])+q1(-k_(E))}:function(m){var F=v(m);return g(F)+q1(-k_(F))},_=f?function(m){return asr(r,m.x,0,1/0)}:function(m){var F=v(m),E=Math.cos(F),I=Math.sin(F);return\"M\"+[s+o*E,l-o*I]+\"L\"+[s+i*E,l-i*I]},y=qd.makeLabelFns(c,0),C=y.labelStandoff,k={};k.xFn=function(m){var F=v(m);return Math.cos(F)*C},k.yFn=function(m){var F=v(m),E=Math.sin(F)>0?.2:1;return-Math.sin(F)*(C+m.fontSize*E)+Math.abs(Math.cos(F))*(m.fontSize*isr)},k.anchorFn=function(m){var F=v(m),E=Math.cos(F);return Math.abs(E)<.1?\"middle\":E>0?\"start\":\"end\"},k.heightFn=function(m,F,E){var I=v(m);return-.5*(1+Math.sin(I))*E};var S=PQe(u);r.angularTickLayout!==S&&(a[\"angular-axis\"].selectAll(\".\"+c._id+\"tick\").remove(),r.angularTickLayout=S);var L=f?[1/0].concat(c.tickvals||[]).map(function(m){return qd.tickText(c,m,!0,!1)}):qd.calcTicks(c);f&&(L[0].text=\"\\u221E\",L[0].fontSize*=1.75);var w;if(t.gridshape===\"linear\"?(w=L.map(v),ol.angleDelta(w[0],w[1])<0&&(w=w.slice().reverse())):w=null,r.vangles=w,c.type===\"category\"&&(L=L.filter(function(m){return ol.isAngleInsideSector(v(m),r.sectorInRad)})),c.visible){var A=c.ticks===\"inside\"?-1:1,T=(c.linewidth||1)/2;qd.drawTicks(n,c,{vals:L,layer:a[\"angular-axis\"],path:\"M\"+A*T+\",0h\"+A*c.ticklen,transFn:x,crisp:!1}),qd.drawGrid(n,c,{vals:L,layer:a[\"angular-grid\"],path:_,transFn:ol.noop,crisp:!1}),qd.drawLabels(n,c,{vals:L,layer:a[\"angular-axis\"],repositionOnUpdate:!0,transFn:p,labelFns:k})}pS(a[\"angular-line\"].select(\"path\"),u.showline,{d:r.pathSubplot(),transform:$c(s,l)}).attr(\"stroke-width\",u.linewidth).call(hR.stroke,u.linecolor)};wf.updateFx=function(e,t){if(!this.gd._context.staticPlot){var r=!this.isSmith;r&&(this.updateAngularDrag(e),this.updateRadialDrag(e,t,0),this.updateRadialDrag(e,t,1)),this.updateHoverAndMainDrag(e)}};wf.updateHoverAndMainDrag=function(e){var t=this,r=t.isSmith,n=t.gd,a=t.layers,i=e._zoomlayer,o=jy.MINZOOM,s=jy.OFFEDGE,l=t.radius,u=t.innerRadius,c=t.cx,f=t.cy,v=t.cxx,g=t.cyy,p=t.sectorInRad,x=t.vangles,_=t.radialAxis,y=Gg.clampTiny,C=Gg.findXYatLength,k=Gg.findEnclosingVertexAngles,S=jy.cornerHalfWidth,L=jy.cornerLen/2,w,A,T=Vg.makeDragger(a,\"path\",\"maindrag\",e.dragmode===!1?\"none\":\"crosshair\");E_.select(T).attr(\"d\",t.pathSubplot()).attr(\"transform\",$c(c,f)),T.onmousemove=function(be){MQe.hover(n,be,t.id),n._fullLayout._lasthover=T,n._fullLayout._hoversubplot=t.id},T.onmouseout=function(be){n._dragging||fR.unhover(n,be)};var m={element:T,gd:n,subplot:t.id,plotinfo:{id:t.id,xaxis:t.xaxis,yaxis:t.yaxis},xaxes:[t.xaxis],yaxes:[t.yaxis]},F,E,I,R,B,O,V,j,q;function U(be,De){return Math.sqrt(be*be+De*De)}function J(be,De){return U(be-v,De-g)}function ee(be,De){return Math.atan2(g-De,be-v)}function ce(be,De){return[be*Math.cos(De),be*Math.sin(-De)]}function ye(be,De){if(be===0)return t.pathSector(2*S);var 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n=this,a=n.gd,i=n.layers,o=n.radius,s=n.innerRadius,l=n.cx,u=n.cy,c=n.radialAxis,f=jy.radialDragBoxSize,v=f/2;if(!c.visible)return;var g=Wy(n.radialAxisAngle),p=c._rl,x=p[0],_=p[1],y=p[r],C=.75*(p[1]-p[0])/(1-n.getHole(t))/o,k,S,L;r?(k=l+(o+v)*Math.cos(g),S=u-(o+v)*Math.sin(g),L=\"radialdrag\"):(k=l+(s-v)*Math.cos(g),S=u-(s-v)*Math.sin(g),L=\"radialdrag-inner\");var w=Vg.makeRectDragger(i,L,\"crosshair\",-v,-v,f,f),A={element:w,gd:a};e.dragmode===!1&&(A.dragmode=!1),pS(E_.select(w),c.visible&&s<o,{transform:$c(k,S)});var T,m,F;function E(V,j){if(T)T(V,j);else{var q=[V,-j],U=[Math.cos(g),Math.sin(g)],J=Math.abs(ol.dot(q,U)/Math.sqrt(ol.dot(q,q)));isNaN(J)||(T=J<.5?B:O)}var ee={};I(ee),a.emit(\"plotly_relayouting\",ee)}function I(V){m!==null?V[n.id+\".radialaxis.angle\"]=m:F!==null&&(V[n.id+\".radialaxis.range[\"+r+\"]\"]=F)}function R(){m!==null?C_.call(\"_guiRelayout\",a,n.id+\".radialaxis.angle\",m):F!==null&&C_.call(\"_guiRelayout\",a,n.id+\".radialaxis.range[\"+r+\"]\",F)}function 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Fet=fe((hEr,Pet)=>{\"use strict\";var Let=ket();Let.plot=Cet();Pet.exports=Let});var Ret=fe((dEr,Det)=>{\"use strict\";Det.exports=Fet()});var DJ=fe((vEr,Iet)=>{\"use strict\";var{hovertemplateAttrs:Llr,templatefallbackAttrs:Plr}=Oo(),K5=ta().extendFlat,Yy=gS(),Xy=u1();Iet.exports={r:Yy.r,theta:Yy.theta,r0:Yy.r0,dr:Yy.dr,theta0:Yy.theta0,dtheta:Yy.dtheta,thetaunit:Yy.thetaunit,base:K5({},Xy.base,{}),offset:K5({},Xy.offset,{}),width:K5({},Xy.width,{}),text:K5({},Xy.text,{}),hovertext:K5({},Xy.hovertext,{}),marker:Flr(),hoverinfo:Yy.hoverinfo,hovertemplate:Llr(),hovertemplatefallback:Plr(),selected:Xy.selected,unselected:Xy.unselected};function Flr(){var e=K5({},Xy.marker);return delete e.cornerradius,e}});var RJ=fe((pEr,zet)=>{\"use strict\";zet.exports={barmode:{valType:\"enumerated\",values:[\"stack\",\"overlay\"],dflt:\"stack\",editType:\"calc\"},bargap:{valType:\"number\",dflt:.1,min:0,max:1,editType:\"calc\"}}});var Oet=fe((gEr,Bet)=>{\"use strict\";var qet=Xt(),Dlr=xR().handleRThetaDefaults,Rlr=ZC(),Ilr=DJ();Bet.exports=function(t,r,n,a){function i(s,l){return qet.coerce(t,r,Ilr,s,l)}var o=Dlr(t,r,a,i);if(!o){r.visible=!1;return}i(\"thetaunit\"),i(\"base\"),i(\"offset\"),i(\"width\"),i(\"text\"),i(\"hovertext\"),i(\"hovertemplate\"),i(\"hovertemplatefallback\"),Rlr(t,r,i,n,a),qet.coerceSelectionMarkerOpacity(r,i)}});var Uet=fe((mEr,Net)=>{\"use strict\";var zlr=Xt(),qlr=RJ();Net.exports=function(e,t,r){var n={},a;function i(l,u){return zlr.coerce(e[a]||{},t[a],qlr,l,u)}for(var o=0;o<r.length;o++){var s=r[o];s.type===\"barpolar\"&&s.visible===!0&&(a=s.subplot,n[a]||(i(\"barmode\"),i(\"bargap\"),n[a]=1))}}});var IJ=fe((yEr,Het)=>{\"use strict\";var Vet=Wf().hasColorscale,Get=Mh(),Blr=Xt().isArrayOrTypedArray,Olr=EA(),Nlr=i2().setGroupPositions,Ulr=P0(),Vlr=bi().traceIs,Glr=Xt().extendFlat;function Hlr(e,t){for(var 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stt=fe((MEr,ott)=>{\"use strict\";var Q5=Xt(),nur=ni(),iur=Mo(),aur=b2(),our=jf().getSubplotData,sur=mm(),lur=gm(),uur=hA(),cur=Hp(),e4=OJ(),NJ=BJ(),att=NJ.axisNames,fur=dur(function(e){return Q5.isTypedArray(e)&&(e=Array.from(e)),e.slice().reverse().map(function(t){return-t}).concat([0]).concat(e)},String);function hur(e,t,r,n){var a=r(\"bgcolor\");n.bgColor=nur.combine(a,n.paper_bgcolor);var i=our(n.fullData,NJ.name,n.id),o=n.layoutOut,s;function l(L,w){return r(s+\".\"+L,w)}for(var u=0;u<att.length;u++){s=att[u],Q5.isPlainObject(e[s])||(e[s]={});var c=e[s],f=iur.newContainer(t,s);f._id=f._name=s,f._attr=n.id+\".\"+s,f._traceIndices=i.map(function(L){return L.index});var v=l(\"visible\");if(f.type=\"linear\",cur(f,o),sur(c,f,l,f.type),v){var g=s===\"realaxis\";if(g&&l(\"side\"),g)l(\"tickvals\");else{var p=fur(t.realaxis.tickvals||e4.realaxis.tickvals.dflt);l(\"tickvals\",p)}Q5.isTypedArray(f.tickvals)&&(f.tickvals=Array.from(f.tickvals));var 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n=this._validate(e,t,r,io.local.invalidDate||io.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(e,t,r){return this._validate(e,t,r,io.local.invalidDate||io.regionalOptions[\"\"].invalidDate),{}},add:function(e,t,r){return this._validate(e,this.minMonth,this.minDay,io.local.invalidDate||io.regionalOptions[\"\"].invalidDate),this._correctAdd(e,this._add(e,t,r),t,r)},_add:function(e,t,r){if(this._validateLevel++,r===\"d\"||r===\"w\"){var n=e.toJD()+t*(r===\"w\"?this.daysInWeek():1),a=e.calendar().fromJD(n);return this._validateLevel--,[a.year(),a.month(),a.day()]}try{var i=e.year()+(r===\"y\"?t:0),o=e.monthOfYear()+(r===\"m\"?t:0),a=e.day(),s=function(c){for(;o<c.minMonth;)i--,o+=c.monthsInYear(i);for(var f=c.monthsInYear(i);o>f-1+c.minMonth;)i++,o-=f,f=c.monthsInYear(i)};r===\"y\"?(e.month()!==this.fromMonthOfYear(i,o)&&(o=this.newDate(i,e.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(i)),a=Math.min(a,this.daysInMonth(i,this.fromMonthOfYear(i,o)))):r===\"m\"&&(s(this),a=Math.min(a,this.daysInMonth(i,this.fromMonthOfYear(i,o))));var l=[i,this.fromMonthOfYear(i,o),a];return this._validateLevel--,l}catch(u){throw this._validateLevel--,u}},_correctAdd:function(e,t,r,n){if(!this.hasYearZero&&(n===\"y\"||n===\"m\")&&(t[0]===0||e.year()>0!=t[0]>0)){var a={y:[1,1,\"y\"],m:[1,this.monthsInYear(-1),\"m\"],w:[this.daysInWeek(),this.daysInYear(-1),\"d\"],d:[1,this.daysInYear(-1),\"d\"]}[n],i=r<0?-1:1;t=this._add(e,r*a[0]+i*a[1],a[2])}return e.date(t[0],t[1],t[2])},set:function(e,t,r){this._validate(e,this.minMonth,this.minDay,io.local.invalidDate||io.regionalOptions[\"\"].invalidDate);var n=r===\"y\"?t:e.year(),a=r===\"m\"?t:e.month(),i=r===\"d\"?t:e.day();return(r===\"y\"||r===\"m\")&&(i=Math.min(i,this.daysInMonth(n,a))),e.date(n,a,i)},isValid:function(e,t,r){this._validateLevel++;var n=this.hasYearZero||e!==0;if(n){var a=this.newDate(e,t,this.minDay);n=t>=this.minMonth&&t-this.minMonth<this.monthsInYear(a)&&r>=this.minDay&&r-this.minDay<this.daysInMonth(a)}return this._validateLevel--,n},toJSDate:function(e,t,r){var n=this._validate(e,t,r,io.local.invalidDate||io.regionalOptions[\"\"].invalidDate);return io.instance().fromJD(this.toJD(n)).toJSDate()},fromJSDate:function(e){return this.fromJD(io.instance().fromJSDate(e).toJD())},_validate:function(e,t,r,n){if(e.year){if(this._validateLevel===0&&this.name!==e.calendar().name)throw(io.local.differentCalendars||io.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this.local.name).replace(/\\{1\\}/,e.calendar().local.name);return e}try{if(this._validateLevel++,this._validateLevel===1&&!this.isValid(e,t,r))throw n.replace(/\\{0\\}/,this.local.name);var a=this.newDate(e,t,r);return this._validateLevel--,a}catch(i){throw this._validateLevel--,i}}});function XJ(e){this.local=this.regionalOptions[e]||this.regionalOptions[\"\"]}XJ.prototype=new YJ;FR(XJ.prototype,{name:\"Gregorian\",jdEpoch:17214255e-1,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Gregorian\",epochs:[\"BCE\",\"CE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,io.local.invalidYear||io.regionalOptions[\"\"].invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===0&&(r%100!==0||r%400===0)},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,io.local.invalidMonth||io.regionalOptions[\"\"].invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,io.local.invalidDate||io.regionalOptions[\"\"].invalidDate);e=n.year(),t=n.month(),r=n.day(),e<0&&e++,t<3&&(t+=12,e--);var a=Math.floor(e/100),i=2-a+Math.floor(a/4);return Math.floor(365.25*(e+4716))+Math.floor(30.6001*(t+1))+r+i-1524.5},fromJD:function(e){var t=Math.floor(e+.5),r=Math.floor((t-186721625e-2)/36524.25);r=t+1+r-Math.floor(r/4);var n=r+1524,a=Math.floor((n-122.1)/365.25),i=Math.floor(365.25*a),o=Math.floor((n-i)/30.6001),s=n-i-Math.floor(o*30.6001),l=o-(o>13.5?13:1),u=a-(l>2.5?4716:4715);return u<=0&&u--,this.newDate(u,l,s)},toJSDate:function(e,t,r){var n=this._validate(e,t,r,io.local.invalidDate||io.regionalOptions[\"\"].invalidDate),a=new Date(n.year(),n.month()-1,n.day());return a.setHours(0),a.setMinutes(0),a.setSeconds(0),a.setMilliseconds(0),a.setHours(a.getHours()>12?a.getHours()+2:0),a},fromJSDate:function(e){return this.newDate(e.getFullYear(),e.getMonth()+1,e.getDate())}});var io=lrt.exports=new srt;io.cdate=WJ;io.baseCalendar=YJ;io.calendars.gregorian=XJ});var urt=fe(()=>{var ZJ=Zc(),Tf=Ih();ZJ(Tf.regionalOptions[\"\"],{invalidArguments:\"Invalid arguments\",invalidFormat:\"Cannot format a date from another calendar\",missingNumberAt:\"Missing number at position {0}\",unknownNameAt:\"Unknown name at position {0}\",unexpectedLiteralAt:\"Unexpected literal at position {0}\",unexpectedText:\"Additional text found at end\"});Tf.local=Tf.regionalOptions[\"\"];ZJ(Tf.cdate.prototype,{formatDate:function(e,t){return typeof e!=\"string\"&&(t=e,e=\"\"),this._calendar.formatDate(e||\"\",this,t)}});ZJ(Tf.baseCalendar.prototype,{UNIX_EPOCH:Tf.instance().newDate(1970,1,1).toJD(),SECS_PER_DAY:1440*60,TICKS_EPOCH:Tf.instance().jdEpoch,TICKS_PER_DAY:1440*60*1e7,ATOM:\"yyyy-mm-dd\",COOKIE:\"D, dd M yyyy\",FULL:\"DD, MM d, yyyy\",ISO_8601:\"yyyy-mm-dd\",JULIAN:\"J\",RFC_822:\"D, d M yy\",RFC_850:\"DD, dd-M-yy\",RFC_1036:\"D, d M yy\",RFC_1123:\"D, d M yyyy\",RFC_2822:\"D, d M yyyy\",RSS:\"D, d M yy\",TICKS:\"!\",TIMESTAMP:\"@\",W3C:\"yyyy-mm-dd\",formatDate:function(e,t,r){if(typeof e!=\"string\"&&(r=t,t=e,e=\"\"),!t)return\"\";if(t.calendar()!==this)throw Tf.local.invalidFormat||Tf.regionalOptions[\"\"].invalidFormat;e=e||this.local.dateFormat,r=r||{};for(var n=r.dayNamesShort||this.local.dayNamesShort,a=r.dayNames||this.local.dayNames,i=r.monthNumbers||this.local.monthNumbers,o=r.monthNamesShort||this.local.monthNamesShort,s=r.monthNames||this.local.monthNames,l=r.calculateWeek||this.local.calculateWeek,u=function(S,L){for(var w=1;k+w<e.length&&e.charAt(k+w)===S;)w++;return k+=w-1,Math.floor(w/(L||1))>1},c=function(S,L,w,A){var T=\"\"+L;if(u(S,A))for(;T.length<w;)T=\"0\"+T;return T},f=function(S,L,w,A){return u(S)?A[L]:w[L]},v=this,g=function(S){return typeof i==\"function\"?i.call(v,S,u(\"m\")):_(c(\"m\",S.month(),2))},p=function(S,L){return L?typeof s==\"function\"?s.call(v,S):s[S.month()-v.minMonth]:typeof o==\"function\"?o.call(v,S):o[S.month()-v.minMonth]},x=this.local.digits,_=function(S){return r.localNumbers&&x?x(S):S},y=\"\",C=!1,k=0;k<e.length;k++)if(C)e.charAt(k)===\"'\"&&!u(\"'\")?C=!1:y+=e.charAt(k);else switch(e.charAt(k)){case\"d\":y+=_(c(\"d\",t.day(),2));break;case\"D\":y+=f(\"D\",t.dayOfWeek(),n,a);break;case\"o\":y+=c(\"o\",t.dayOfYear(),3);break;case\"w\":y+=c(\"w\",t.weekOfYear(),2);break;case\"m\":y+=g(t);break;case\"M\":y+=p(t,u(\"M\"));break;case\"y\":y+=u(\"y\",2)?t.year():(t.year()%100<10?\"0\":\"\")+t.year()%100;break;case\"Y\":u(\"Y\",2),y+=t.formatYear();break;case\"J\":y+=t.toJD();break;case\"@\":y+=(t.toJD()-this.UNIX_EPOCH)*this.SECS_PER_DAY;break;case\"!\":y+=(t.toJD()-this.TICKS_EPOCH)*this.TICKS_PER_DAY;break;case\"'\":u(\"'\")?y+=\"'\":C=!0;break;default:y+=e.charAt(k)}return y},parseDate:function(e,t,r){if(t==null)throw Tf.local.invalidArguments||Tf.regionalOptions[\"\"].invalidArguments;if(t=typeof t==\"object\"?t.toString():t+\"\",t===\"\")return null;e=e||this.local.dateFormat,r=r||{};var n=r.shortYearCutoff||this.shortYearCutoff;n=typeof n!=\"string\"?n:this.today().year()%100+parseInt(n,10);for(var a=r.dayNamesShort||this.local.dayNamesShort,i=r.dayNames||this.local.dayNames,o=r.parseMonth||this.local.parseMonth,s=r.monthNumbers||this.local.monthNumbers,l=r.monthNamesShort||this.local.monthNamesShort,u=r.monthNames||this.local.monthNames,c=-1,f=-1,v=-1,g=-1,p=-1,x=!1,_=!1,y=function(I,R){for(var B=1;m+B<e.length&&e.charAt(m+B)===I;)B++;return m+=B-1,Math.floor(B/(R||1))>1},C=function(I,R){var B=y(I,R),O=[2,3,B?4:2,B?4:2,10,11,20][\"oyYJ@!\".indexOf(I)+1],V=new RegExp(\"^-?\\\\d{1,\"+O+\"}\"),j=t.substring(T).match(V);if(!j)throw(Tf.local.missingNumberAt||Tf.regionalOptions[\"\"].missingNumberAt).replace(/\\{0\\}/,T);return T+=j[0].length,parseInt(j[0],10)},k=this,S=function(){if(typeof s==\"function\"){y(\"m\");var I=s.call(k,t.substring(T));return T+=I.length,I}return C(\"m\")},L=function(I,R,B,O){for(var V=y(I,O)?B:R,j=0;j<V.length;j++)if(t.substr(T,V[j].length).toLowerCase()===V[j].toLowerCase())return T+=V[j].length,j+k.minMonth;throw(Tf.local.unknownNameAt||Tf.regionalOptions[\"\"].unknownNameAt).replace(/\\{0\\}/,T)},w=function(){if(typeof u==\"function\"){var I=y(\"M\")?u.call(k,t.substring(T)):l.call(k,t.substring(T));return T+=I.length,I}return L(\"M\",l,u)},A=function(){if(t.charAt(T)!==e.charAt(m))throw(Tf.local.unexpectedLiteralAt||Tf.regionalOptions[\"\"].unexpectedLiteralAt).replace(/\\{0\\}/,T);T++},T=0,m=0;m<e.length;m++)if(_)e.charAt(m)===\"'\"&&!y(\"'\")?_=!1:A();else switch(e.charAt(m)){case\"d\":g=C(\"d\");break;case\"D\":L(\"D\",a,i);break;case\"o\":p=C(\"o\");break;case\"w\":C(\"w\");break;case\"m\":v=S();break;case\"M\":v=w();break;case\"y\":var F=m;x=!y(\"y\",2),m=F,f=C(\"y\",2);break;case\"Y\":f=C(\"Y\",2);break;case\"J\":c=C(\"J\")+.5,t.charAt(T)===\".\"&&(T++,C(\"J\"));break;case\"@\":c=C(\"@\")/this.SECS_PER_DAY+this.UNIX_EPOCH;break;case\"!\":c=C(\"!\")/this.TICKS_PER_DAY+this.TICKS_EPOCH;break;case\"*\":T=t.length;break;case\"'\":y(\"'\")?A():_=!0;break;default:A()}if(T<t.length)throw Tf.local.unexpectedText||Tf.regionalOptions[\"\"].unexpectedText;if(f===-1?f=this.today().year():f<100&&x&&(f+=n===-1?1900:this.today().year()-this.today().year()%100-(f<=n?0:100)),typeof v==\"string\"&&(v=o.call(this,f,v)),p>-1){v=1,g=p;for(var E=this.daysInMonth(f,v);g>E;E=this.daysInMonth(f,v))v++,g-=E}return c>-1?this.fromJD(c):this.newDate(f,v,g)},determineDate:function(e,t,r,n,a){r&&typeof r!=\"object\"&&(a=n,n=r,r=null),typeof n!=\"string\"&&(a=n,n=\"\");var i=this,o=function(s){try{return i.parseDate(n,s,a)}catch(f){}s=s.toLowerCase();for(var l=(s.match(/^c/)&&r?r.newDate():null)||i.today(),u=/([+-]?[0-9]+)\\s*(d|w|m|y)?/g,c=u.exec(s);c;)l.add(parseInt(c[1],10),c[2]||\"d\"),c=u.exec(s);return l};return t=t?t.newDate():null,e=e==null?t:typeof e==\"string\"?o(e):typeof e==\"number\"?isNaN(e)||e===1/0||e===-1/0?t:i.today().add(e,\"d\"):i.newDate(e),e}})});var crt=fe(()=>{var $y=Ih(),fcr=Zc(),$J=$y.instance();function DR(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}DR.prototype=new $y.baseCalendar;fcr(DR.prototype,{name:\"Chinese\",jdEpoch:17214255e-1,hasYearZero:!1,minMonth:0,firstMonth:0,minDay:1,regionalOptions:{\"\":{name:\"Chinese\",epochs:[\"BEC\",\"EC\"],monthNumbers:function(e,t){if(typeof e==\"string\"){var r=e.match(dcr);return r?r[0]:\"\"}var n=this._validateYear(e),a=e.month(),i=\"\"+this.toChineseMonth(n,a);return t&&i.length<2&&(i=\"0\"+i),this.isIntercalaryMonth(n,a)&&(i+=\"i\"),i},monthNames:function(e){if(typeof e==\"string\"){var t=e.match(vcr);return t?t[0]:\"\"}var r=this._validateYear(e),n=e.month(),a=this.toChineseMonth(r,n),i=[\"\\u4E00\\u6708\",\"\\u4E8C\\u6708\",\"\\u4E09\\u6708\",\"\\u56DB\\u6708\",\"\\u4E94\\u6708\",\"\\u516D\\u6708\",\"\\u4E03\\u6708\",\"\\u516B\\u6708\",\"\\u4E5D\\u6708\",\"\\u5341\\u6708\",\"\\u5341\\u4E00\\u6708\",\"\\u5341\\u4E8C\\u6708\"][a-1];return this.isIntercalaryMonth(r,n)&&(i=\"\\u95F0\"+i),i},monthNamesShort:function(e){if(typeof e==\"string\"){var t=e.match(pcr);return t?t[0]:\"\"}var r=this._validateYear(e),n=e.month(),a=this.toChineseMonth(r,n),i=[\"\\u4E00\",\"\\u4E8C\",\"\\u4E09\",\"\\u56DB\",\"\\u4E94\",\"\\u516D\",\"\\u4E03\",\"\\u516B\",\"\\u4E5D\",\"\\u5341\",\"\\u5341\\u4E00\",\"\\u5341\\u4E8C\"][a-1];return this.isIntercalaryMonth(r,n)&&(i=\"\\u95F0\"+i),i},parseMonth:function(e,t){e=this._validateYear(e);var r=parseInt(t),n;if(isNaN(r))t[0]===\"\\u95F0\"&&(n=!0,t=t.substring(1)),t[t.length-1]===\"\\u6708\"&&(t=t.substring(0,t.length-1)),r=1+[\"\\u4E00\",\"\\u4E8C\",\"\\u4E09\",\"\\u56DB\",\"\\u4E94\",\"\\u516D\",\"\\u4E03\",\"\\u516B\",\"\\u4E5D\",\"\\u5341\",\"\\u5341\\u4E00\",\"\\u5341\\u4E8C\"].indexOf(t);else{var a=t[t.length-1];n=a===\"i\"||a===\"I\"}var i=this.toMonthIndex(e,r,n);return i},dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},_validateYear:function(e,t){if(e.year&&(e=e.year()),typeof e!=\"number\"||e<1888||e>2111)throw t.replace(/\\{0\\}/,this.local.name);return e},toMonthIndex:function(e,t,r){var n=this.intercalaryMonth(e),a=r&&t!==n;if(a||t<1||t>12)throw $y.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var i;return n?!r&&t<=n?i=t-1:i=t:i=t-1,i},toChineseMonth:function(e,t){e.year&&(e=e.year(),t=e.month());var r=this.intercalaryMonth(e),n=r?12:11;if(t<0||t>n)throw $y.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var a;return r?t<r?a=t+1:a=t:a=t+1,a},intercalaryMonth:function(e){e=this._validateYear(e);var t=Ky[e-Ky[0]],r=t>>13;return r},isIntercalaryMonth:function(e,t){e.year&&(e=e.year(),t=e.month());var r=this.intercalaryMonth(e);return!!r&&r===t},leapYear:function(e){return this.intercalaryMonth(e)!==0},weekOfYear:function(e,t,r){var n=this._validateYear(e,$y.local.invalidyear),a=Jy[n-Jy[0]],i=a>>9&4095,o=a>>5&15,s=a&31,l;l=$J.newDate(i,o,s),l.add(4-(l.dayOfWeek()||7),\"d\");var u=this.toJD(e,t,r)-l.toJD();return 1+Math.floor(u/7)},monthsInYear:function(e){return this.leapYear(e)?13:12},daysInMonth:function(e,t){e.year&&(t=e.month(),e=e.year()),e=this._validateYear(e);var r=Ky[e-Ky[0]],n=r>>13,a=n?12:11;if(t>a)throw $y.local.invalidMonth.replace(/\\{0\\}/,this.local.name);var i=r&1<<12-t?30:29;return i},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,i,r,$y.local.invalidDate);e=this._validateYear(n.year()),t=n.month(),r=n.day();var a=this.isIntercalaryMonth(e,t),i=this.toChineseMonth(e,t),o=mcr(e,i,r,a);return $J.toJD(o.year,o.month,o.day)},fromJD:function(e){var t=$J.fromJD(e),r=gcr(t.year(),t.month(),t.day()),n=this.toMonthIndex(r.year,r.month,r.isIntercalary);return this.newDate(r.year,n,r.day)},fromString:function(e){var t=e.match(hcr),r=this._validateYear(+t[1]),n=+t[2],a=!!t[3],i=this.toMonthIndex(r,n,a),o=+t[4];return this.newDate(r,i,o)},add:function(e,t,r){var n=e.year(),a=e.month(),i=this.isIntercalaryMonth(n,a),o=this.toChineseMonth(n,a),s=Object.getPrototypeOf(DR.prototype).add.call(this,e,t,r);if(r===\"y\"){var l=s.year(),u=s.month(),c=this.isIntercalaryMonth(l,o),f=i&&c?this.toMonthIndex(l,o,!0):this.toMonthIndex(l,o,!1);f!==u&&s.month(f)}return s}});var hcr=/^\\s*(-?\\d\\d\\d\\d|\\d\\d)[-/](\\d?\\d)([iI]?)[-/](\\d?\\d)/m,dcr=/^\\d?\\d[iI]?/m,vcr=/^闰?十?[一二三四五六七八九]?月/m,pcr=/^闰?十?[一二三四五六七八九]?/m;$y.calendars.chinese=DR;var 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gcr(e,t,r,n){var a,i;if(typeof e==\"object\")a=e,i=t||{};else{var o=typeof e==\"number\"&&e>=1888&&e<=2111;if(!o)throw new Error(\"Solar year outside range 1888-2111\");var s=typeof t==\"number\"&&t>=1&&t<=12;if(!s)throw new Error(\"Solar month outside range 1 - 12\");var l=typeof r==\"number\"&&r>=1&&r<=31;if(!l)throw new Error(\"Solar day outside range 1 - 31\");a={year:e,month:t,day:r},i=n||{}}var u=Jy[a.year-Jy[0]],c=a.year<<9|a.month<<5|a.day;i.year=c>=u?a.year:a.year-1,u=Jy[i.year-Jy[0]];var f=u>>9&4095,v=u>>5&15,g=u&31,p,x=new Date(f,v-1,g),_=new Date(a.year,a.month-1,a.day);p=Math.round((_-x)/(24*3600*1e3));var y=Ky[i.year-Ky[0]],C;for(C=0;C<13;C++){var k=y&1<<12-C?30:29;if(p<k)break;p-=k}var S=y>>13;return!S||C<S?(i.isIntercalary=!1,i.month=1+C):C===S?(i.isIntercalary=!0,i.month=C):(i.isIntercalary=!1,i.month=C),i.day=1+p,i}function mcr(e,t,r,n,a){var i,o;if(typeof e==\"object\")o=e,i=t||{};else{var s=typeof e==\"number\"&&e>=1888&&e<=2111;if(!s)throw new Error(\"Lunar year 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this._validate(e,this.minMonth,this.minDay,D_.local.invalidYear||D_.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,D_.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===13&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,D_.local.invalidDate);return e=n.year(),e<0&&e++,n.day()+(n.month()-1)*30+(e-1)*365+Math.floor(e/4)+this.jdEpoch-1},fromJD:function(e){var t=Math.floor(e)+.5-this.jdEpoch,r=Math.floor((t-Math.floor((t+366)/1461))/365)+1;r<=0&&r--,t=Math.floor(e)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(t/30)+1,a=t-(n-1)*30+1;return this.newDate(r,n,a)}});D_.calendars.coptic=KJ});var hrt=fe(()=>{var jg=Ih(),xcr=Zc();function JJ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}JJ.prototype=new jg.baseCalendar;xcr(JJ.prototype,{name:\"Discworld\",jdEpoch:17214255e-1,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(e){return this._validate(e,this.minMonth,this.minDay,jg.local.invalidYear),!1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,jg.local.invalidYear),13},daysInYear:function(e){return this._validate(e,this.minMonth,this.minDay,jg.local.invalidYear),400},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,jg.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,jg.local.invalidDate);return(n.day()+1)%8},weekDay:function(e,t,r){var n=this.dayOfWeek(e,t,r);return n>=2&&n<=6},extraInfo:function(e,t,r){var n=this._validate(e,t,r,jg.local.invalidDate);return{century:_cr[Math.floor((n.year()-1)/100)+1]||\"\"}},toJD:function(e,t,r){var n=this._validate(e,t,r,jg.local.invalidDate);return e=n.year()+(n.year()<0?1:0),t=n.month(),r=n.day(),r+(t>1?16:0)+(t>2?(t-2)*32:0)+(e-1)*400+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e+.5)-Math.floor(this.jdEpoch)-1;var t=Math.floor(e/400)+1;e-=(t-1)*400,e+=e>15?16:0;var r=Math.floor(e/32)+1,n=e-(r-1)*32+1;return this.newDate(t<=0?t-1:t,r,n)}});var _cr={20:\"Fruitbat\",21:\"Anchovy\"};jg.calendars.discworld=JJ});var drt=fe(()=>{var R_=Ih(),bcr=Zc();function QJ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}QJ.prototype=new R_.baseCalendar;bcr(QJ.prototype,{name:\"Ethiopian\",jdEpoch:17242205e-1,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Ethiopian\",epochs:[\"BEE\",\"EE\"],monthNames:[\"Meskerem\",\"Tikemet\",\"Hidar\",\"Tahesas\",\"Tir\",\"Yekatit\",\"Megabit\",\"Miazia\",\"Genbot\",\"Sene\",\"Hamle\",\"Nehase\",\"Pagume\"],monthNamesShort:[\"Mes\",\"Tik\",\"Hid\",\"Tah\",\"Tir\",\"Yek\",\"Meg\",\"Mia\",\"Gen\",\"Sen\",\"Ham\",\"Neh\",\"Pag\"],dayNames:[\"Ehud\",\"Segno\",\"Maksegno\",\"Irob\",\"Hamus\",\"Arb\",\"Kidame\"],dayNamesShort:[\"Ehu\",\"Seg\",\"Mak\",\"Iro\",\"Ham\",\"Arb\",\"Kid\"],dayNamesMin:[\"Eh\",\"Se\",\"Ma\",\"Ir\",\"Ha\",\"Ar\",\"Ki\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,R_.local.invalidYear),r=t.year()+(t.year()<0?1:0);return r%4===3||r%4===-1},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,R_.local.invalidYear||R_.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,R_.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===13&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,R_.local.invalidDate);return e=n.year(),e<0&&e++,n.day()+(n.month()-1)*30+(e-1)*365+Math.floor(e/4)+this.jdEpoch-1},fromJD:function(e){var t=Math.floor(e)+.5-this.jdEpoch,r=Math.floor((t-Math.floor((t+366)/1461))/365)+1;r<=0&&r--,t=Math.floor(e)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(t/30)+1,a=t-(n-1)*30+1;return this.newDate(r,n,a)}});R_.calendars.ethiopian=QJ});var vrt=fe(()=>{var Qy=Ih(),wcr=Zc();function eQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}eQ.prototype=new Qy.baseCalendar;wcr(eQ.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Qy.local.invalidYear);return this._leapYear(t.year())},_leapYear:function(e){return e=e<0?e+1:e,RR(e*7+1,19)<7},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Qy.local.invalidYear),this._leapYear(e.year?e.year():e)?13:12},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Qy.local.invalidYear);return e=t.year(),this.toJD(e===-1?1:e+1,7,1)-this.toJD(e,7,1)},daysInMonth:function(e,t){return e.year&&(t=e.month(),e=e.year()),this._validate(e,t,this.minDay,Qy.local.invalidMonth),t===12&&this.leapYear(e)||t===8&&RR(this.daysInYear(e),10)===5?30:t===9&&RR(this.daysInYear(e),10)===3?29:this.daysPerMonth[t-1]},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==6},extraInfo:function(e,t,r){var n=this._validate(e,t,r,Qy.local.invalidDate);return{yearType:(this.leapYear(n)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(n)%10-3]}},toJD:function(e,t,r){var n=this._validate(e,t,r,Qy.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var a=e<=0?e+1:e,i=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(t<7){for(var o=7;o<=this.monthsInYear(e);o++)i+=this.daysInMonth(e,o);for(var o=1;o<t;o++)i+=this.daysInMonth(e,o)}else for(var o=7;o<t;o++)i+=this.daysInMonth(e,o);return i},_delay1:function(e){var t=Math.floor((235*e-234)/19),r=12084+13753*t,n=t*29+Math.floor(r/25920);return RR(3*(n+1),7)<3&&n++,n},_delay2:function(e){var t=this._delay1(e-1),r=this._delay1(e),n=this._delay1(e+1);return n-r===356?2:r-t===382?1:0},fromJD:function(e){e=Math.floor(e)+.5;for(var t=Math.floor((e-this.jdEpoch)*98496/35975351)-1;e>=this.toJD(t===-1?1:t+1,7,1);)t++;for(var r=e<this.toJD(t,1,1)?7:1;e>this.toJD(t,r,this.daysInMonth(t,r));)r++;var n=e-this.toJD(t,r,1)+1;return this.newDate(t,r,n)}});function RR(e,t){return e-t*Math.floor(e/t)}Qy.calendars.hebrew=eQ});var prt=fe(()=>{var bS=Ih(),Tcr=Zc();function tQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}tQ.prototype=new bS.baseCalendar;Tcr(tQ.prototype,{name:\"Islamic\",jdEpoch:19484395e-1,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012Bs\",\"Yawm al-jum'a\",\"Yawm 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t=Math.floor((30*(e-this.jdEpoch)+10646)/10631);t=t<=0?t-1:t;var r=Math.min(12,Math.ceil((e-29-this.toJD(t,1,1))/29.5)+1),n=e-this.toJD(t,r,1)+1;return this.newDate(t,r,n)}});bS.calendars.islamic=tQ});var grt=fe(()=>{var wS=Ih(),Acr=Zc();function rQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}rQ.prototype=new wS.baseCalendar;Acr(rQ.prototype,{name:\"Julian\",jdEpoch:17214235e-1,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,wS.local.invalidYear),r=t.year()<0?t.year()+1:t.year();return r%4===0},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,wS.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(e,t,r){var n=this._validate(e,t,r,wS.local.invalidDate);return e=n.year(),t=n.month(),r=n.day(),e<0&&e++,t<=2&&(e--,t+=12),Math.floor(365.25*(e+4716))+Math.floor(30.6001*(t+1))+r-1524.5},fromJD:function(e){var t=Math.floor(e+.5),r=t+1524,n=Math.floor((r-122.1)/365.25),a=Math.floor(365.25*n),i=Math.floor((r-a)/30.6001),o=i-Math.floor(i<14?1:13),s=n-Math.floor(o>2?4716:4715),l=r-a-Math.floor(30.6001*i);return s<=0&&s--,this.newDate(s,o,l)}});wS.calendars.julian=rQ});var yrt=fe(()=>{var Uv=Ih(),Mcr=Zc();function iQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}iQ.prototype=new Uv.baseCalendar;Mcr(iQ.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(e){return this._validate(e,this.minMonth,this.minDay,Uv.local.invalidYear),!1},formatYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,Uv.local.invalidYear);e=t.year();var r=Math.floor(e/400);e=e%400,e+=e<0?400:0;var n=Math.floor(e/20);return r+\".\"+n+\".\"+e%20},forYear:function(e){if(e=e.split(\".\"),e.length<3)throw\"Invalid Mayan year\";for(var t=0,r=0;r<e.length;r++){var n=parseInt(e[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";t=t*20+n}return t},monthsInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Uv.local.invalidYear),18},weekOfYear:function(e,t,r){return this._validate(e,t,r,Uv.local.invalidDate),0},daysInYear:function(e){return this._validate(e,this.minMonth,this.minDay,Uv.local.invalidYear),360},daysInMonth:function(e,t){return this._validate(e,t,this.minDay,Uv.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(e,t,r){var n=this._validate(e,t,r,Uv.local.invalidDate);return n.day()},weekDay:function(e,t,r){return this._validate(e,t,r,Uv.local.invalidDate),!0},extraInfo:function(e,t,r){var n=this._validate(e,t,r,Uv.local.invalidDate),a=n.toJD(),i=this._toHaab(a),o=this._toTzolkin(a);return{haabMonthName:this.local.haabMonths[i[0]-1],haabMonth:i[0],haabDay:i[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(e){e-=this.jdEpoch;var t=nQ(e+8+340,365);return[Math.floor(t/20)+1,nQ(t,20)]},_toTzolkin:function(e){return e-=this.jdEpoch,[mrt(e+20,20),mrt(e+4,13)]},toJD:function(e,t,r){var n=this._validate(e,t,r,Uv.local.invalidDate);return n.day()+n.month()*20+n.year()*360+this.jdEpoch},fromJD:function(e){e=Math.floor(e)+.5-this.jdEpoch;var t=Math.floor(e/360);e=e%360,e+=e<0?360:0;var r=Math.floor(e/20),n=e%20;return this.newDate(t,r,n)}});function nQ(e,t){return e-t*Math.floor(e/t)}function mrt(e,t){return nQ(e-1,t)+1}Uv.calendars.mayan=iQ});var _rt=fe(()=>{var I_=Ih(),Scr=Zc();function aQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}aQ.prototype=new I_.baseCalendar;var xrt=I_.instance(\"gregorian\");Scr(aQ.prototype,{name:\"Nanakshahi\",jdEpoch:22576735e-1,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,I_.local.invalidYear||I_.regionalOptions[\"\"].invalidYear);return xrt.leapYear(t.year()+(t.year()<1?1:0)+1469)},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,I_.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(a,t,r){var n=this._validate(a,t,r,I_.local.invalidMonth),a=n.year();a<0&&a++;for(var i=n.day(),o=1;o<n.month();o++)i+=this.daysPerMonth[o-1];return i+xrt.toJD(a+1468,3,13)},fromJD:function(e){e=Math.floor(e+.5);for(var t=Math.floor((e-(this.jdEpoch-1))/366);e>=this.toJD(t+1,1,1);)t++;for(var r=e-Math.floor(this.toJD(t,1,1)+.5)+1,n=1;r>this.daysInMonth(t,n);)r-=this.daysInMonth(t,n),n++;return this.newDate(t,n,r)}});I_.calendars.nanakshahi=aQ});var brt=fe(()=>{var z_=Ih(),Ecr=Zc();function oQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}oQ.prototype=new z_.baseCalendar;Ecr(oQ.prototype,{name:\"Nepali\",jdEpoch:17007095e-1,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(e){return 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l=e-(o>9||o===9&&r>=this.NEPALI_CALENDAR_DATA[s][0]?56:57);for(t!==9&&(i=r,o--);o!==9;)o<=0&&(o=12,s--),i+=this.NEPALI_CALENDAR_DATA[s][o],o--;return t===9?(i+=r-this.NEPALI_CALENDAR_DATA[s][0],i<0&&(i+=a.daysInYear(l))):i+=this.NEPALI_CALENDAR_DATA[s][9]-this.NEPALI_CALENDAR_DATA[s][0],a.newDate(l,1,1).add(i,\"d\").toJD()},fromJD:function(e){var t=z_.instance(),r=t.fromJD(e),n=r.year(),a=r.dayOfYear(),i=n+56;this._createMissingCalendarData(i);for(var o=9,s=this.NEPALI_CALENDAR_DATA[i][0],l=this.NEPALI_CALENDAR_DATA[i][o]-s+1;a>l;)o++,o>12&&(o=1,i++),l+=this.NEPALI_CALENDAR_DATA[i][o];var u=this.NEPALI_CALENDAR_DATA[i][o]-(l-a);return this.newDate(i,o,u)},_createMissingCalendarData:function(e){var t=this.daysPerMonth.slice(0);t.unshift(17);for(var r=e-1;r<e+2;r++)typeof 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n.add(-((n.dayOfWeek()+1)%7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,i4.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===12&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,i4.local.invalidDate);e=n.year(),t=n.month(),r=n.day();var a=0;if(e>0)for(var i=1;i<e;i++)IR(i)&&a++;else if(e<0)for(var i=e;i<0;i++)IR(i)&&a--;return r+(t<=7?(t-1)*31:(t-1)*30+6)+(e>0?e-1:e)*365+a+this.jdEpoch-1},fromJD:function(e){e=Math.floor(e)+.5;var t=475+(e-this.toJD(475,1,1))/365.242197,r=Math.floor(t);r<=0&&r--,e>this.toJD(r,12,IR(r)?30:29)&&(r++,r===0&&r++);var n=e-this.toJD(r,1,1)+1,a=n<=186?Math.ceil(n/31):Math.ceil((n-6)/30),i=e-this.toJD(r,a,1)+1;return this.newDate(r,a,i)}});i4.calendars.persian=zR;i4.calendars.jalali=zR});var Trt=fe(()=>{var q_=Ih(),Ccr=Zc(),qR=q_.instance();function 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B_=Ih(),Lcr=Zc(),BR=B_.instance();function lQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}lQ.prototype=new B_.baseCalendar;Lcr(lQ.prototype,{name:\"Thai\",jdEpoch:15230985e-1,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(r){var t=this._validate(r,this.minMonth,this.minDay,B_.local.invalidYear),r=this._t2gYear(t.year());return BR.leapYear(r)},weekOfYear:function(a,t,r){var n=this._validate(a,this.minMonth,this.minDay,B_.local.invalidYear),a=this._t2gYear(n.year());return BR.weekOfYear(a,n.month(),n.day())},daysInMonth:function(e,t){var r=this._validate(e,t,this.minDay,B_.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(r.month()===2&&this.leapYear(r.year())?1:0)},weekDay:function(e,t,r){return(this.dayOfWeek(e,t,r)||7)<6},toJD:function(a,t,r){var n=this._validate(a,t,r,B_.local.invalidDate),a=this._t2gYear(n.year());return BR.toJD(a,n.month(),n.day())},fromJD:function(e){var t=BR.fromJD(e),r=this._g2tYear(t.year());return this.newDate(r,t.month(),t.day())},_t2gYear:function(e){return e-this.yearsOffset-(e>=1&&e<=this.yearsOffset?1:0)},_g2tYear:function(e){return e+this.yearsOffset+(e>=-this.yearsOffset&&e<=-1?1:0)}});B_.calendars.thai=lQ});var Mrt=fe(()=>{var O_=Ih(),Pcr=Zc();function uQ(e){this.local=this.regionalOptions[e||\"\"]||this.regionalOptions[\"\"]}uQ.prototype=new O_.baseCalendar;Pcr(uQ.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012Bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(e){var t=this._validate(e,this.minMonth,this.minDay,O_.local.invalidYear);return this.daysInYear(t.year())===355},weekOfYear:function(e,t,r){var n=this.newDate(e,t,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(e){for(var t=0,r=1;r<=12;r++)t+=this.daysInMonth(e,r);return t},daysInMonth:function(e,t){for(var r=this._validate(e,t,this.minDay,O_.local.invalidMonth),n=r.toJD()-24e5+.5,a=0,i=0;i<ex.length;i++){if(ex[i]>n)return ex[a]-ex[a-1];a++}return 30},weekDay:function(e,t,r){return this.dayOfWeek(e,t,r)!==5},toJD:function(e,t,r){var n=this._validate(e,t,r,O_.local.invalidDate),a=12*(n.year()-1)+n.month()-15292,i=n.day()+ex[a-1]-1;return i+24e5-.5},fromJD:function(e){for(var t=e-24e5+.5,r=0,n=0;n<ex.length&&!(ex[n]>t);n++)r++;var a=r+15292,i=Math.floor((a-1)/12),o=i+1,s=a-12*i,l=t-ex[r-1]+1;return this.newDate(o,s,l)},isValid:function(e,t,r){var n=O_.baseCalendar.prototype.isValid.apply(this,arguments);return n&&(e=e.year!=null?e.year:e,n=e>=1276&&e<=1500),n},_validate:function(e,t,r,n){var a=O_.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw n.replace(/\\{0\\}/,this.local.name);return a}});O_.calendars.ummalqura=uQ;var 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Ert=fe((Tkr,Srt)=>{\"use strict\";Srt.exports=Ih();urt();crt();frt();hrt();drt();vrt();prt();grt();yrt();_rt();brt();wrt();Trt();Art();Mrt()});var Rrt=fe((Akr,Drt)=>{\"use strict\";var Crt=Ert(),TS=Xt(),Lrt=Wa(),Fcr=Lrt.EPOCHJD,Dcr=Lrt.ONEDAY,hQ={valType:\"enumerated\",values:TS.sortObjectKeys(Crt.calendars),editType:\"calc\",dflt:\"gregorian\"},Prt=function(e,t,r,n){var a={};return a[r]=hQ,TS.coerce(e,t,a,r,n)},Rcr=function(e,t,r,n){for(var a=0;a<r.length;a++)Prt(e,t,r[a]+\"calendar\",n.calendar)},Icr={chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},zcr={chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},qcr={chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},OR=\"##\",Bcr={d:{0:\"dd\",\"-\":\"d\"},e:{0:\"d\",\"-\":\"d\"},a:{0:\"D\",\"-\":\"D\"},A:{0:\"DD\",\"-\":\"DD\"},j:{0:\"oo\",\"-\":\"o\"},W:{0:\"ww\",\"-\":\"w\"},m:{0:\"mm\",\"-\":\"m\"},b:{0:\"M\",\"-\":\"M\"},B:{0:\"MM\",\"-\":\"MM\"},y:{0:\"yy\",\"-\":\"yy\"},Y:{0:\"yyyy\",\"-\":\"yyyy\"},U:OR,w:OR,c:{0:\"D M d %X yyyy\",\"-\":\"D M d %X yyyy\"},x:{0:\"mm/dd/yyyy\",\"-\":\"mm/dd/yyyy\"}};function Ocr(e,t,r){for(var n=Math.floor((t+.05)/Dcr)+Fcr,a=Frt(r).fromJD(n),i=0,o,s,l,u,c;(i=e.indexOf(\"%\",i))!==-1;)o=e.charAt(i+1),o===\"0\"||o===\"-\"||o===\"_\"?(l=3,s=e.charAt(i+2),o===\"_\"&&(o=\"-\")):(s=o,o=\"0\",l=2),u=Bcr[s],u?(u===OR?c=OR:c=a.formatDate(u[o]),e=e.slice(0,i)+c+e.slice(i+l),i+=c.length):i+=l;return e}var krt={};function Frt(e){var t=krt[e];return t||(t=krt[e]=Crt.instance(e),t)}function AS(e){return TS.extendFlat({},hQ,{description:e})}function dQ(e){return\"Sets the calendar system to use with `\"+e+\"` date data.\"}var fQ={xcalendar:AS(dQ(\"x\"))},B1=TS.extendFlat({},fQ,{ycalendar:AS(dQ(\"y\"))}),cQ=TS.extendFlat({},B1,{zcalendar:AS(dQ(\"z\"))}),a4=AS([\"Sets the calendar system to use for `range` and `tick0`\",\"if this is a date axis. This does not set the calendar for\",\"interpreting data on this axis, that's specified in the trace\",\"or via the global `layout.calendar`\"].join(\" \"));Drt.exports={moduleType:\"component\",name:\"calendars\",schema:{traces:{scatter:B1,bar:B1,box:B1,heatmap:B1,contour:B1,histogram:B1,histogram2d:B1,histogram2dcontour:B1,scatter3d:cQ,surface:cQ,mesh3d:cQ,scattergl:B1,ohlc:fQ,candlestick:fQ},layout:{calendar:AS([\"Sets the default calendar system to use for interpreting and\",\"displaying dates throughout the plot.\"].join(\" \"))},subplots:{xaxis:{calendar:a4},yaxis:{calendar:a4},scene:{xaxis:{calendar:a4},yaxis:{calendar:a4},zaxis:{calendar:a4}},polar:{radialaxis:{calendar:a4}}}},layoutAttributes:hQ,handleDefaults:Prt,handleTraceDefaults:Rcr,CANONICAL_SUNDAY:zcr,CANONICAL_TICK:Icr,DFLTRANGE:qcr,getCal:Frt,worldCalFmt:Ocr}});var zrt=fe((Mkr,Irt)=>{\"use strict\";Irt.exports=Rrt()});var Ncr=fe((Skr,Brt)=>{var qrt=_2e();qrt.register([w_e(),ube(),A3e(),j3e(),awe(),t5e(),v5e(),r4e(),P4e(),dTe(),eAe(),SCe(),p7e(),o9e(),X9e(),MPe(),ZPe(),uDe(),kDe(),WDe(),iRe(),yRe(),DRe(),ZRe(),Fze(),Jze(),xGe(),xHe(),wje(),Xje(),fYe(),SYe(),$Ye(),uZe(),DZe(),v$e(),zKe(),sJe(),kJe(),eQe(),vQe(),vet(),Ret(),Qet(),Rtt(),ort(),zrt()]);Brt.exports=qrt});return Ncr();})();\n",
        "/*!\n",
        " * The buffer module from node.js, for the browser.\n",
        " *\n",
@@ -3919,7 +3639,7 @@
        "maplibre-gl/dist/maplibre-gl.js:\n",
        "  (**\n",
        "   * MapLibre GL JS\n",
-       "   * @license 3-Clause BSD. Full text of license: https://github.com/maplibre/maplibre-gl-js/blob/v4.7.1/LICENSE.txt\n",
+       "   * @license 3-Clause BSD. Full text of license: https://github.com/maplibre/maplibre-gl-js/blob/v5.24.0/LICENSE.txt\n",
        "   *)\n",
        "*/\n",
        "\n",
@@ -3945,7 +3665,7 @@
     "import darts.metrics as metrics\n",
     "from darts import set_option\n",
     "from darts.datasets import AirPassengersDataset\n",
-    "from darts.models import LinearRegressionModel, RandomForestModel\n",
+    "from darts.models import ExponentialSmoothing, LinearRegressionModel\n",
     "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
     "from darts.utils.mlflow import autolog, load_model\n",
     "\n",
@@ -3978,19 +3698,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2026/09/03 17:38:23 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
-      "2026/09/03 17:38:23 INFO mlflow.store.db.utils: Updating database tables\n"
+      "2026/09/04 11:16:18 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+      "2026/09/04 11:16:18 INFO mlflow.store.db.utils: Updating database tables\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tracking URI: sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp35vve02p/mlflow.db\n",
+      "Tracking URI: sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp437y5jeb/mlflow.db\n",
       "Experiment: darts-mlflow-examples\n",
       "\n",
       "To explore runs in the UI:\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp35vve02p/mlflow.db\n"
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp437y5jeb/mlflow.db\n"
      ]
     }
    ],
@@ -4041,9 +3761,6 @@
     {
      "data": {
       "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
        "data": [
         {
          "hovertemplate": "<b>#Passengers</b>: %{y:.3g}<extra></extra>",
@@ -4319,9 +4036,9 @@
        }
       },
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    "source": [
-    "## 4. Compare model experiments\n",
+    "## 4. Compare different model runs\n",
     "\n",
     "We train three comparable sklearn-based models inside separate MLflow runs. Each run follows the same evaluation recipe:\n",
     "\n",
@@ -4434,25 +4151,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def run_experiment(run_name, model, *, plot_historical=False):\n",
+    "def run_experiment(name, model, pretrain, plot_historical=False):\n",
     "    \"\"\"Fit, evaluate, and autolog a single forecasting experiment.\"\"\"\n",
     "    hfc_kwargs = {\n",
     "        \"series\": series,\n",
     "        \"start\": val.start_time(),  # start time of the validation period\n",
     "        \"forecast_horizon\": FORECAST_HORIZON,  # forecast horizon\n",
     "        \"stride\": BACKTEST_STRIDE,  # step size between rolling forecasts\n",
-    "        \"retrain\": False,  # use pre-trained model\n",
+    "        \"retrain\": not pretrain,  # use pre-trained model\n",
     "        \"last_points_only\": False,  # use all available points for each forecast\n",
     "    }\n",
     "\n",
-    "    with mlflow.start_run(run_name=run_name):\n",
+    "    with mlflow.start_run(run_name=name):\n",
     "        # log pre-trained model artifact\n",
-    "        model.fit(train)\n",
+    "        if pretrain:\n",
+    "            model.fit(train)\n",
     "\n",
     "        hfcs = model.historical_forecasts(**hfc_kwargs)\n",
     "        bt_kwargs = {**hfc_kwargs, \"historical_forecasts\": hfcs, \"reduction\": None}\n",
     "\n",
     "        # windowed aggregate metric: stepped backtest_mae + scalar backtest_agg_mae\n",
+    "        # note: you can also pass multiple metrics to the `metric` argument (with dedicated `metric_kwargs`)\n",
     "        model.backtest(metric=metrics.mae, **bt_kwargs)\n",
     "\n",
     "        # time-dependent metric: per-horizon error `backtest_err` + scalar backtest_agg_err\n",
@@ -4462,9 +4181,7 @@
     "            fig = series.plotly(label=\"actual\")\n",
     "            for idx, hfc in enumerate(hfcs):\n",
     "                fig = hfc.plotly(label=f\"forecast {idx}\", fig=fig)\n",
-    "            fig.update_layout(\n",
-    "                title=f\"Historical forecasts — {run_name}\", **PLOTLY_KWARGS\n",
-    "            )\n",
+    "            fig.update_layout(title=f\"Historical forecasts — {name}\", **PLOTLY_KWARGS)\n",
     "            fig.show()\n",
     "\n",
     "    return model"
@@ -4480,15 +4197,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2026/09/03 17:38:25 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+      "2026/09/04 11:16:19 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
      ]
     },
     {
      "data": {
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-       },
        "data": [
         {
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@@ -4811,7 +4525,7 @@
          }
         },
         "title": {
-         "text": "Historical forecasts — linear_regression"
+         "text": "Historical forecasts — linear_regression_pretrained"
         },
         "xaxis": {
          "title": {
@@ -4821,9 +4535,9 @@
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        "                            \n",
-       "var gd = document.getElementById('1385b2ce-f5d4-497d-9661-ce1d497c1c3a');\n",
+       "var gd = document.getElementById('24eae8d4-1215-4ac0-af11-c9c4fe051b29');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -4851,53 +4565,60 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2026/09/03 17:38:25 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
-      "2026/09/03 17:38:25 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Finished run: linear_regression_pretrained\n",
       "Finished run: linear_regression\n",
-      "Finished run: random_forest\n",
-      "Finished run: linear_regression_lags24\n"
+      "Finished run: exponential_smoothing\n"
      ]
     }
    ],
    "source": [
-    "experiments = [\n",
-    "    (\n",
-    "        \"linear_regression\",\n",
-    "        LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),\n",
-    "        True,\n",
-    "    ),\n",
-    "    (\n",
-    "        \"random_forest\",\n",
-    "        RandomForestModel(\n",
-    "            lags=12,\n",
-    "            output_chunk_length=FORECAST_HORIZON,\n",
-    "            n_estimators=50,\n",
-    "            random_state=42,\n",
-    "        ),\n",
-    "        False,\n",
-    "    ),\n",
-    "    (\n",
-    "        \"linear_regression_lags24\",\n",
-    "        LinearRegressionModel(lags=24, output_chunk_length=FORECAST_HORIZON),\n",
-    "        False,\n",
-    "    ),\n",
+    "run_configs = [\n",
+    "    {\n",
+    "        \"name\": \"linear_regression_pretrained\",\n",
+    "        \"model\": LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),\n",
+    "        \"pretrain\": True,\n",
+    "        \"plot_historical\": True,\n",
+    "    },\n",
+    "    {\n",
+    "        \"name\": \"linear_regression\",\n",
+    "        \"model\": LinearRegressionModel(lags=12, output_chunk_length=FORECAST_HORIZON),\n",
+    "        \"pretrain\": False,\n",
+    "        \"plot_historical\": False,\n",
+    "    },\n",
+    "    {\n",
+    "        \"name\": \"exponential_smoothing\",\n",
+    "        \"model\": ExponentialSmoothing(),\n",
+    "        \"pretrain\": False,\n",
+    "        \"plot_historical\": False,\n",
+    "    },\n",
     "]\n",
     "\n",
-    "for run_name, model, plot_hist in experiments:\n",
-    "    run_experiment(run_name, model, plot_historical=plot_hist)\n",
-    "    print(f\"Finished run: {run_name}\")\n",
+    "for run_config in run_configs:\n",
+    "    run_experiment(**run_config)\n",
+    "    print(f\"Finished run: {run_config['name']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b04a702",
+   "metadata": {},
+   "source": [
+    "### Disable Autologging\n",
     "\n",
-    "# disable autologging\n",
+    "You can turn off autologging at any time with the following line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "893979c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "autolog(disable=True)"
    ]
   },
@@ -4913,7 +4634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "ed4e0c35",
    "metadata": {},
    "outputs": [
@@ -4923,7 +4644,7 @@
      "text": [
       "\n",
       "To explore runs in the UI:\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp35vve02p/mlflow.db\n"
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp437y5jeb/mlflow.db\n"
      ]
     }
    ],
@@ -4993,7 +4714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "id": "fa88b420",
    "metadata": {},
    "outputs": [
@@ -5026,36 +4747,36 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>b887a1aef1904bc69e4c39e9049c1420</td>\n",
-       "      <td>linear_regression</td>\n",
+       "      <td>7ca86dcdf1364029a8f2356f4bf4ca95</td>\n",
+       "      <td>linear_regression_pretrained</td>\n",
        "      <td>18.870433</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>40f92ba313d44527ad0b252ca68217ef</td>\n",
-       "      <td>linear_regression_lags24</td>\n",
-       "      <td>24.509846</td>\n",
+       "      <td>4ef1f73d74fd4327acbfb554ab579487</td>\n",
+       "      <td>exponential_smoothing</td>\n",
+       "      <td>19.609498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>ba40cdb96e5b40289919ca4c1b205dce</td>\n",
-       "      <td>random_forest</td>\n",
-       "      <td>77.316667</td>\n",
+       "      <td>597be7e9e15b4a3680f6a857e1a91483</td>\n",
+       "      <td>linear_regression</td>\n",
+       "      <td>22.533742</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                             run_id       tags.mlflow.runName  \\\n",
-       "0  b887a1aef1904bc69e4c39e9049c1420         linear_regression   \n",
-       "1  40f92ba313d44527ad0b252ca68217ef  linear_regression_lags24   \n",
-       "2  ba40cdb96e5b40289919ca4c1b205dce             random_forest   \n",
+       "                             run_id           tags.mlflow.runName  \\\n",
+       "0  7ca86dcdf1364029a8f2356f4bf4ca95  linear_regression_pretrained   \n",
+       "1  4ef1f73d74fd4327acbfb554ab579487         exponential_smoothing   \n",
+       "2  597be7e9e15b4a3680f6a857e1a91483             linear_regression   \n",
        "\n",
        "   metrics.backtest_agg_mae  \n",
        "0                 18.870433  \n",
-       "1                 24.509846  \n",
-       "2                 77.316667  "
+       "1                 19.609498  \n",
+       "2                 22.533742  "
       ]
      },
      "metadata": {},
@@ -5066,7 +4787,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Best run by backtest_agg_mae: linear_regression (b887a1aef1904bc69e4c39e9049c1420)\n"
+      "Best run by backtest_agg_mae: linear_regression_pretrained (7ca86dcdf1364029a8f2356f4bf4ca95)\n"
      ]
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    ],
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    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "id": "26ca96da",
    "metadata": {},
    "outputs": [
@@ -5105,7 +4826,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Best model URI: models:/m-4f23e6ce02f144cdb164b78ab5dc2dc6\n",
+      "Best model URI: models:/m-3df387ff9d254d68b1971c0c11b48359\n",
       "Aliases set: @champion\n"
      ]
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@@ -5113,8 +4834,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Successfully registered model 'darts-air-passengers-forecaster'.\n",
-      "Created version '1' of model 'darts-air-passengers-forecaster'.\n"
+      "Registered model 'darts-air-passengers-forecaster' already exists. Creating a new version of this model...\n",
+      "Created version '2' of model 'darts-air-passengers-forecaster'.\n"
      ]
     }
    ],
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   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "c12ba777",
    "metadata": {},
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        "                            \n",
-       "var gd = document.getElementById('3239abfb-57e1-4a23-9ac9-b7cad7addb02');\n",
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@@ -5523,6 +5241,12 @@
     "- [Darts MLflow API reference](https://unit8co.github.io/darts/generated_api/darts.utils.mlflow.html)\n",
     "- [MLflow API reference](https://mlflow.org/docs/latest/ml/tracking/tracking-api/)\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4d8cb09",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {

From 3f410024674b28812eb1bc16ce9844795833f85d Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 4 Sep 2026 14:04:53 +0200
Subject: [PATCH 152/154] also store untrained model with historical forecasts
 retrain=True

---
 darts/tests/optional_deps/test_mlflow.py      | 144 +++++++++++++++++-
 darts/utils/mlflow.py                         | 126 ++++++++++-----
 examples/29-MLflow-examples.ipynb             | 135 +++++++++-------
 .../images/mlflow_experiments_metrics.png     | Bin 335790 -> 340074 bytes
 .../images/mlflow_experiments_overview.png    | Bin 219488 -> 216793 bytes
 .../static/images/mlflow_model_registry.png   | Bin 148435 -> 142971 bytes
 .../static/images/mlflow_models_overview.png  | Bin 186819 -> 176656 bytes
 examples/static/images/mlflow_run_detail.png  | Bin 429667 -> 428474 bytes
 8 files changed, 309 insertions(+), 96 deletions(-)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index fd50c607c7..24229c0f7b 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -443,8 +443,8 @@ def test_autolog_series_info_static_covariates(
     def test_autolog_historical_forecasts_series_info_covariates(
         self, mlflow_tracking, autolog_context, method
     ):
-        """HF and backtest without a prior fit() still reports model params and series_info
-        (but not the model artifact)."""
+        """HF and backtest without a prior fit() still reports model params and series_info,
+        and logs an untrained model template when log_models=True."""
         target = TS_UNIVARIATE.with_static_covariates(
             pd.DataFrame({"static_feat": [1.0]})
         )
@@ -478,17 +478,19 @@ def test_autolog_historical_forecasts_series_info_covariates(
         assert tags["model_uses_future_covariates"] == "True"
         assert tags["model_uses_past_covariates"] == "True"
         assert tags["model_uses_static_covariates"] == "True"
+        assert tags["darts.model_is_pretrained"] == "False"
 
         params = mlflow.artifacts.load_dict(
             f"runs:/{run.info.run_id}/model_params.json"
         )
         assert params["lags"] == 5
 
-        # model artifact is not available
         logged_models = mlflow_tracking.search_logged_models(
             experiment_ids=[run.info.experiment_id]
         )
-        assert len(logged_models) == 0
+        assert len(logged_models) == 1
+        loaded_model = load_model(logged_models[0].model_uri)
+        assert loaded_model._fit_called is False
 
     def test_autolog_historical_forecasts_retrain_false_skips_model_setup(
         self, mlflow_tracking, autolog_context
@@ -531,6 +533,140 @@ def test_autolog_historical_forecasts_overwrites_fit_setup(
         )
         assert series_info["series"]["names"] == TS_MULTIVARIATE.components.tolist()
 
+    def test_autolog_historical_forecasts_retrain_logs_untrained_model(
+        self, mlflow_tracking, autolog_context
+    ):
+        """HF(retrain=True) without fit() logs an untrained model template."""
+        with autolog_context(log_models=True):
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5)
+                model.historical_forecasts(
+                    series=TS_UNIVARIATE,
+                    forecast_horizon=1,
+                    retrain=True,
+                    start=0.5,
+                )
+
+        logged_models = mlflow_tracking.search_logged_models(
+            experiment_ids=[run.info.experiment_id]
+        )
+        assert len(logged_models) == 1
+        assert logged_models[0].name == "LinearRegressionModel"
+
+        loaded_model = load_model(logged_models[0].model_uri)
+        assert loaded_model._fit_called is False
+
+        tags = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.tags
+        assert tags["darts.model_is_pretrained"] == "False"
+
+    def test_autolog_fit_then_historical_forecasts_logs_one_pretrained_model(
+        self, mlflow_tracking, autolog_context
+    ):
+        """fit() then HF(retrain=True) keeps the fit artifact and pretrained flag."""
+        with autolog_context(log_models=True):
+            with mlflow.start_run() as run:
+                model = LinearRegressionModel(lags=5)
+                model.fit(TS_UNIVARIATE[:30])
+                model.historical_forecasts(
+                    series=TS_UNIVARIATE,
+                    forecast_horizon=1,
+                    retrain=True,
+                    start=0.5,
+                )
+
+        logged_models = mlflow_tracking.search_logged_models(
+            experiment_ids=[run.info.experiment_id]
+        )
+        assert len(logged_models) == 1
+
+        loaded_model = load_model(logged_models[0].model_uri)
+        assert loaded_model._fit_called is True
+
+        tags = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.tags
+        assert tags["darts.model_is_pretrained"] == "True"
+
+    def test_autolog_manual_log_model_then_historical_forecasts(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Manual log_model() before HF keeps the user's artifact only."""
+        model = LinearRegressionModel(lags=5)
+        model.fit(TS_UNIVARIATE[:30])
+
+        with autolog_context(log_models=True):
+            with mlflow.start_run() as run:
+                log_model(model, name="manual_model")
+                model.historical_forecasts(
+                    series=TS_UNIVARIATE,
+                    forecast_horizon=1,
+                    retrain=True,
+                    start=0.5,
+                )
+
+        logged_models = mlflow_tracking.search_logged_models(
+            experiment_ids=[run.info.experiment_id]
+        )
+        assert len(logged_models) == 1
+        assert logged_models[0].name == "manual_model"
+
+        loaded_model = load_model(logged_models[0].model_uri)
+        assert loaded_model._fit_called is True
+
+        tags = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.tags
+        assert "darts.model_is_pretrained" not in tags
+
+    def test_autolog_historical_forecasts_retrain_false_no_logged_model(
+        self, mlflow_tracking, autolog_context
+    ):
+        """historical_forecasts(retrain=False) does not log a model artifact."""
+        model = LinearRegressionModel(lags=5)
+        model.fit(TS_UNIVARIATE[:40])
+        with autolog_context(log_models=True):
+            with mlflow.start_run() as run:
+                model.historical_forecasts(
+                    series=TS_UNIVARIATE,
+                    forecast_horizon=1,
+                    retrain=False,
+                    start=0.5,
+                )
+
+        logged_models = mlflow_tracking.search_logged_models(
+            experiment_ids=[run.info.experiment_id]
+        )
+        assert len(logged_models) == 0
+
+    def test_autolog_ensemble_historical_forecasts_retrain_logs_once(
+        self, mlflow_tracking, autolog_context
+    ):
+        """Ensemble HF(retrain=True) logs exactly one untrained template."""
+        model = RegressionEnsembleModel(
+            forecasting_models=[
+                NaiveSeasonal(K=12),
+                LinearRegressionModel(lags=12),
+            ],
+            regression_train_n_points=12,
+        )
+
+        with autolog_context(log_models=True):
+            with mlflow.start_run() as run:
+                model.historical_forecasts(
+                    series=TS_UNIVARIATE,
+                    forecast_horizon=1,
+                    retrain=True,
+                    start=0.5,
+                )
+
+        logged_models = mlflow_tracking.search_logged_models(
+            experiment_ids=[run.info.experiment_id]
+        )
+        assert len(logged_models) == 1
+        assert logged_models[0].name == "RegressionEnsembleModel"
+
+        loaded_model = load_model(logged_models[0].model_uri)
+        assert loaded_model._fit_called is False
+
+        tags = mlflow.tracking.MlflowClient().get_run(run.info.run_id).data.tags
+        assert tags["darts.model_is_pretrained"] == "False"
+
     def test_multivariate_with_all_covariate_types(self, mlflow_tracking):
         """Test saving/loading multivariate series with all covariate types"""
         # create multivariate target with static covariates
diff --git a/darts/utils/mlflow.py b/darts/utils/mlflow.py
index 7b7f1e6694..4fa712292a 100644
--- a/darts/utils/mlflow.py
+++ b/darts/utils/mlflow.py
@@ -86,14 +86,18 @@
   - Logs target series info and covariate usage information (past, future,
     and static covariates) as a ``series_info.json`` artifact.
   - Stores the trained model artifact when ``log_models=True`` (default:
-    ``False``).
+    ``False``), and sets ``darts.model_is_pretrained`` to ``True``.
   - Logs per-epoch training and validation metrics for PyTorch-based models.
 - Calling ``ForecastingModel.historical_forecasts(retrain=True)``:
 
   - Logs the same model creation parameters and ``series_info.json`` as
     ``fit()`` (overwriting any prior ``fit()`` artifacts in the same run).
-  - Does not log the trained model artifact; call ``fit()``
-    or ``log_model()`` manually if needed.
+  - Stores an untrained model template (via ``untrained_model()``) when
+    ``log_models=True`` and no model was logged yet in the run. Sets the run
+    tag and model metadata ``darts.model_is_pretrained`` to ``False`` so
+    downstream code knows the artifact must be refit before inference.
+    If a model was already logged (e.g. via ``fit()`` or manual
+    ``log_model()``), the existing artifact is kept unchanged.
 - Calling any Darts metric:
 
   - Logs the result of that metric call as an MLflow metric. More information
@@ -276,6 +280,7 @@
 
 _MODEL_FILE_STAT = "model.pkl"
 _MODEL_FILE_TORCH = "model.pt"
+_MODEL_IS_PRETRAINED_TAG = "darts.model_is_pretrained"
 
 # Thread-local flags used by _patched_fit to suppress nested/re-entrant
 # autologging: in_historical_forecasts covers historical_forecasts' internal
@@ -519,8 +524,11 @@ def autolog(
     Parameters
     ----------
     log_models
-        If ``True``, log the trained model artifact when calling ``fit()``.
-        Defaults to ``False``.
+        If ``True``, log a model artifact when calling ``fit()`` (trained
+        model) or ``historical_forecasts(retrain=True)`` (untrained template
+        via ``untrained_model()``). Defaults to ``False``. If a model was
+        already logged in the run (including via manual ``log_model()``),
+        autolog does not add another artifact.
     log_params
         If ``True`` (default), log model creation parameters when calling
         ``fit()`` or ``historical_forecasts()``.
@@ -706,35 +714,14 @@ def _patched_fit(original, self, *args, **kwargs):
             future_covariates=fit_kwargs.get("future_covariates"),
             log_params=log_params,
         )
-
-        param_logging_ops = autologging_client.flush(synchronous=False)
-
-        if log_models:
-            model_name = type(self).__name__
-            model: LoggedModel = _initialize_logged_model(
-                name=model_name, flavor=FLAVOR_NAME
-            )
-            try:
-                registered_model_name = get_autologging_config(
-                    flavor_name=FLAVOR_NAME,
-                    config_key="registered_model_name",
-                    default_value=None,
-                )
-                log_model(
-                    result,
-                    name=model_name,
-                    registered_model_name=registered_model_name,
-                    model_id=model.model_id,
-                )
-            # TODO dennis: maybe let it fail naturally?
-            except Exception:
-                raise_log(
-                    ValueError(
-                        f"Failed to autolog model artifact for {type(self).__name__}."
-                    )
-                )
-
-        param_logging_ops.await_completion()
+        _log_model_artifact(
+            model=result,
+            run_id=run_id,
+            is_pretrained=True,
+            log_models=log_models,
+            autologging_client=autologging_client,
+        )
+        autologging_client.flush(synchronous=False).await_completion()
 
         return result
 
@@ -743,10 +730,11 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
         ``retrain=True``.
 
         Sets a thread-local flag so ``_patched_fit`` skips autologging for the
-        internal ``fit()`` calls. When ``retrain is True`` and an MLflow run is
+        internal ``fit()`` calls. When retraining is enabled and an MLflow run is
         active, logs model tags, creation parameters, and series info once after
         the call (overwriting any prior ``fit()`` artifacts in the same run).
-        Does not start a run and does not log the trained model artifact.
+        When ``log_models=True`` and no model exists yet in the run, also logs
+        an untrained model template with ``darts.model_is_pretrained=False``.
         """
         _autolog_state.in_historical_forecasts = True
         try:
@@ -760,19 +748,29 @@ def _patched_historical_forecasts(original, self, *args, **kwargs):
 
         bound = inspect.signature(original).bind(self, *args, **kwargs)
         bound.apply_defaults()
-        if bound.arguments["retrain"] is False:
+
+        retrain = bound.arguments["retrain"]
+        if retrain is False or retrain == 0:
             return result
 
+        run_id = active_run.info.run_id
         autologging_client = MlflowAutologgingQueueingClient()
         _log_model_setup(
             model=self,
             autologging_client=autologging_client,
-            run_id=active_run.info.run_id,
+            run_id=run_id,
             series=bound.arguments["series"],
             past_covariates=bound.arguments.get("past_covariates"),
             future_covariates=bound.arguments.get("future_covariates"),
             log_params=log_params,
         )
+        _log_model_artifact(
+            model=self.untrained_model(),
+            run_id=run_id,
+            is_pretrained=False,
+            log_models=log_models,
+            autologging_client=autologging_client,
+        )
         autologging_client.flush(synchronous=False).await_completion()
         return result
 
@@ -1011,6 +1009,58 @@ def _get_model_info_tags(
     }
 
 
+def _run_has_logged_model(run_id: str) -> bool:
+    """Return whether the given run already has a logged model artifact."""
+    if getattr(_autolog_state, "model_logged_run_id", None) == run_id:
+        return True
+    try:
+        run = mlflow.get_run(run_id)
+        return bool(run.outputs and run.outputs.model_outputs)
+    except Exception:
+        return False
+
+
+def _log_model_artifact(
+    *,
+    model: ForecastingModel,
+    run_id: str,
+    is_pretrained: bool,
+    log_models: bool,
+    autologging_client: MlflowAutologgingQueueingClient,
+) -> None:
+    """Log a Darts model artifact to the active run unless one already exists.
+
+    When autolog skips because the user already logged a model manually, the
+    existing artifact and any user-provided metadata/tags are left unchanged.
+    """
+    if not log_models or _run_has_logged_model(run_id):
+        return
+
+    model_name = type(model).__name__
+    logged_model: LoggedModel = _initialize_logged_model(
+        name=model_name, flavor=FLAVOR_NAME
+    )
+    metadata = {_MODEL_IS_PRETRAINED_TAG: str(is_pretrained)}
+    registered_model_name = get_autologging_config(
+        flavor_name=FLAVOR_NAME,
+        config_key="registered_model_name",
+        default_value=None,
+    )
+    log_model(
+        model,
+        name=model_name,
+        registered_model_name=registered_model_name,
+        model_id=logged_model.model_id,
+        metadata=metadata,
+    )
+
+    autologging_client.set_tags(
+        run_id=run_id,
+        tags={_MODEL_IS_PRETRAINED_TAG: str(is_pretrained)},
+    )
+    _autolog_state.model_logged_run_id = run_id
+
+
 def _log_model_setup(
     model: ForecastingModel,
     autologging_client: MlflowAutologgingQueueingClient,
diff --git a/examples/29-MLflow-examples.ipynb b/examples/29-MLflow-examples.ipynb
index f8ba551244..71eac60a5f 100644
--- a/examples/29-MLflow-examples.ipynb
+++ b/examples/29-MLflow-examples.ipynb
@@ -3666,7 +3666,6 @@
     "from darts import set_option\n",
     "from darts.datasets import AirPassengersDataset\n",
     "from darts.models import ExponentialSmoothing, LinearRegressionModel\n",
-    "from darts.models.forecasting.forecasting_model import GlobalForecastingModel\n",
     "from darts.utils.mlflow import autolog, load_model\n",
     "\n",
     "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
@@ -3698,19 +3697,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2026/09/04 11:16:18 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
-      "2026/09/04 11:16:18 INFO mlflow.store.db.utils: Updating database tables\n"
+      "2026/09/04 14:03:54 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+      "2026/09/04 14:03:54 INFO mlflow.store.db.utils: Updating database tables\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tracking URI: sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp437y5jeb/mlflow.db\n",
+      "Tracking URI: sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmpjnang74m/mlflow.db\n",
       "Experiment: darts-mlflow-examples\n",
       "\n",
       "To explore runs in the UI:\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp437y5jeb/mlflow.db\n"
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmpjnang74m/mlflow.db\n"
      ]
     }
    ],
@@ -4036,9 +4035,9 @@
        }
       },
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     "\n",
     "| Trigger | Logged automatically |\n",
     "|---|---|\n",
-    "| `model.fit(...)` | Model tags, hyperparameters, input series info, and trained model artifact when `log_models=True` |\n",
+    "| `model.fit(...)` | Model tags, hyperparameters, input series info, and trained model artifact when `log_models=True` (`darts.model_is_pretrained=True`) |\n",
+    "| `model.historical_forecasts(..., retrain=True)` | Same tags, hyperparameters, and series info as `fit()`; when `log_models=True`, also logs an untrained model template (`darts.model_is_pretrained=False`) unless a model was already logged in the run |\n",
     "| Standalone metric calls | Time-aggregated scalar metrics (e.g. `mae()`) and time-dependent stepped metrics (e.g. `ae()`); detailed breakdowns go to `metrics_per_series.json` |\n",
     "| `model.backtest(...)` | Windowed-, per-horizon-, or aggregated metrics prefixed with `backtest_`; with `log_backtest_aggregate=True` also logs `backtest_agg_{metric}` |\n",
     "| PyTorch models (NBEATS, TFT, …) | Per-epoch `train_loss` / `val_loss` via MLflow's PyTorch autolog |\n",
@@ -4136,12 +4136,16 @@
    "source": [
     "## 4. Compare different model runs\n",
     "\n",
-    "We train three comparable sklearn-based models inside separate MLflow runs. Each run follows the same evaluation recipe:\n",
+    "We run three distinct modelling approaches inside separate MLflow runs and compare them over the same evaluation period (backtest on rolling forecasts):\n",
     "\n",
-    "1. **Fit** (pre-train) on the training series (params and model artifact logged automatically).\n",
-    "2. **Historical forecasts** over the same validation period (rolling forecasts).\n",
-    "3. **Backtest** with windowed `mae` (`reduction=None` → stepped `backtest_mae` chart + scalar aggregate `backtest_agg_mae`).\n",
-    "4. **Backtest** with time-dependent `err` (per-horizon error profile `backtest_ae` + scalar aggregate `backtest_agg_ae`)."
+    "1. A pre-trained global `LinearRegressionModel` that we pre-train only on the train set (`model.fit()`) and use it to produce all rolling forecasts (`retrain=False`)\n",
+    "2. A global `LinearRegressionModel` that we re-fit for every rolling forecast (`retrain=True`).\n",
+    "1. A local `ExponentialSmoothing` model that must be re-fit for every rolling forecast (`retrain=True`).\n",
+    "\n",
+    "We score the generated forecasts using `model.backtest()`:\n",
+    "\n",
+    "1. **Backtest** with windowed `mae` (`reduction=None` → stepped `backtest_mae` chart + scalar aggregate `backtest_agg_mae`).\n",
+    "2. **Backtest** with time-dependent `err` (per-horizon error profile `backtest_ae` + scalar aggregate `backtest_agg_ae`)."
    ]
   },
   {
@@ -4158,20 +4162,21 @@
     "        \"start\": val.start_time(),  # start time of the validation period\n",
     "        \"forecast_horizon\": FORECAST_HORIZON,  # forecast horizon\n",
     "        \"stride\": BACKTEST_STRIDE,  # step size between rolling forecasts\n",
-    "        \"retrain\": not pretrain,  # use pre-trained model\n",
+    "        \"retrain\": not pretrain,  # use pre-trained model or re-train for every forecast\n",
     "        \"last_points_only\": False,  # use all available points for each forecast\n",
     "    }\n",
     "\n",
     "    with mlflow.start_run(run_name=name):\n",
-    "        # log pre-trained model artifact\n",
+    "        # logs pre-trained model artifact\n",
     "        if pretrain:\n",
     "            model.fit(train)\n",
     "\n",
+    "        # logs model artifact if `retrain` is used\n",
     "        hfcs = model.historical_forecasts(**hfc_kwargs)\n",
-    "        bt_kwargs = {**hfc_kwargs, \"historical_forecasts\": hfcs, \"reduction\": None}\n",
     "\n",
     "        # windowed aggregate metric: stepped backtest_mae + scalar backtest_agg_mae\n",
     "        # note: you can also pass multiple metrics to the `metric` argument (with dedicated `metric_kwargs`)\n",
+    "        bt_kwargs = {**hfc_kwargs, \"historical_forecasts\": hfcs, \"reduction\": None}\n",
     "        model.backtest(metric=metrics.mae, **bt_kwargs)\n",
     "\n",
     "        # time-dependent metric: per-horizon error `backtest_err` + scalar backtest_agg_err\n",
@@ -4197,7 +4202,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2026/09/04 11:16:19 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+      "2026/09/04 14:03:56 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
      ]
     },
     {
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        "                            \n",
-       "var gd = document.getElementById('24eae8d4-1215-4ac0-af11-c9c4fe051b29');\n",
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@@ -4565,12 +4570,32 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/09/04 14:03:56 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Finished run: linear_regression_pretrained\n",
-      "Finished run: linear_regression\n",
+      "Finished run: linear_regression\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/09/04 14:03:56 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Finished run: exponential_smoothing\n"
      ]
     }
@@ -4644,7 +4669,7 @@
      "text": [
       "\n",
       "To explore runs in the UI:\n",
-      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmp437y5jeb/mlflow.db\n"
+      "  mlflow ui --backend-store-uri sqlite:////var/folders/4h/l09drklx06g8022q7khgyxyr0000gn/T/tmpjnang74m/mlflow.db\n"
      ]
     }
    ],
@@ -4664,7 +4689,10 @@
     "\n",
     "The MLflow Runs Table shows all runs under an experiment in tabular style. It allows you to filter, sort, compare anything that was logged. You can add any logged parameter (metrics, model parameters, ...) to the table via the `Columns` dropdown.\n",
     "\n",
-    "Here we see that `linear_regression` had the lowest aggregated MAE (`backtest_agg_mae`) and aggregated Horizon-Error (Bias) (`backtest_agg_err`) across all runs.\n",
+    "Here we see that:\n",
+    "\n",
+    "- `linear_regression_pretrained` had the lowest aggregated MAE (`backtest_agg_mae`).\n",
+    "- `exponential_smoothing` had the lowest aggregated horizon-based bias (`backtest_agg_err`).\n",
     "\n",
     "![MLflow runs table view](./static/images/mlflow_experiments_overview.png)\n",
     "\n",
@@ -4691,7 +4719,7 @@
     "<details>\n",
     "<summary><strong>MLflow Run Detail Page</strong> (click to expand)</summary>\n",
     "\n",
-    "Click on any run in the Runs Table to open the Run Detail Page. It shows an overview of the run, its recorded metrics, hyper-parameters, tags, and more. Play around with the UI to see the different views and features.\n",
+    "Click on any run in the Runs Table to open the Run Detail Page. It shows an overview of the run, its recorded metrics, hyper-parameters, input data information, tags, and more. Play around with the UI to see the different views and features.\n",
     "\n",
     "Scroll down to the \"Model\" section and you will see the model that was logged during training. Click on the model to view the details (inclduing the logged model artifacts).\n",
     "\n",
@@ -4707,14 +4735,14 @@
    "source": [
     "## 5. Find the best run\n",
     "\n",
-    "Above, we could already see that `linear_regression` was the best performing model overall.\n",
+    "Above, we could see that `linear_regression_pretrained` had the lowest MAE.\n",
     "\n",
-    "Let's also do it programatically by finding the model with the lowest MAE. With `log_backtest_aggregate=True` we get a convient sort key: one scalar that summarizes rolling backtest performance, regardless of how many windows or time series were evaluated. `backtest_agg_mae` is such a key."
+    "Let's do this once programmatically. We want to find the model with the lowest overall MAE. With `log_backtest_aggregate=True` we get a convient sort key: one scalar that summarizes rolling backtest performance, regardless of how many windows or time series were evaluated. `backtest_agg_mae` is such a key."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "fa88b420",
    "metadata": {},
    "outputs": [
@@ -4747,19 +4775,19 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>7ca86dcdf1364029a8f2356f4bf4ca95</td>\n",
+       "      <td>ee57c5714d6442ce93fbcf9899968842</td>\n",
        "      <td>linear_regression_pretrained</td>\n",
        "      <td>18.870433</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>4ef1f73d74fd4327acbfb554ab579487</td>\n",
+       "      <td>0baa4c6c0e4d4ad5b7b53f5ae887ba02</td>\n",
        "      <td>exponential_smoothing</td>\n",
        "      <td>19.609498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>597be7e9e15b4a3680f6a857e1a91483</td>\n",
+       "      <td>8869add4ede74eebae1570acae7d82c6</td>\n",
        "      <td>linear_regression</td>\n",
        "      <td>22.533742</td>\n",
        "    </tr>\n",
@@ -4769,9 +4797,9 @@
       ],
       "text/plain": [
        "                             run_id           tags.mlflow.runName  \\\n",
-       "0  7ca86dcdf1364029a8f2356f4bf4ca95  linear_regression_pretrained   \n",
-       "1  4ef1f73d74fd4327acbfb554ab579487         exponential_smoothing   \n",
-       "2  597be7e9e15b4a3680f6a857e1a91483             linear_regression   \n",
+       "0  ee57c5714d6442ce93fbcf9899968842  linear_regression_pretrained   \n",
+       "1  0baa4c6c0e4d4ad5b7b53f5ae887ba02         exponential_smoothing   \n",
+       "2  8869add4ede74eebae1570acae7d82c6             linear_regression   \n",
        "\n",
        "   metrics.backtest_agg_mae  \n",
        "0                 18.870433  \n",
@@ -4787,7 +4815,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Best run by backtest_agg_mae: linear_regression_pretrained (7ca86dcdf1364029a8f2356f4bf4ca95)\n"
+      "Best run by backtest_agg_mae: linear_regression_pretrained (ee57c5714d6442ce93fbcf9899968842)\n"
      ]
     }
    ],
@@ -4813,12 +4841,14 @@
     "\n",
     "We can promote the best run's model to the **MLflow Model Registry**, then assign a **champion** alias (or any other alias). Downstream code can then load the registered models via registered model name and alias.\n",
     "\n",
-    "> This assumes that model logging was enabled: `autolog(log_models=True)` and `fit()` was called within each MLflow run"
+    "> This assumes that model logging was enabled using `autolog(log_models=True)`:\n",
+    "> - runs that call `fit()` within the run store a pre-trained model\n",
+    "> - runs that only use `historical_forecasts(retrain=True)` store an untrained template (check `darts.model_is_pretrained` on the run or model metadata)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "26ca96da",
    "metadata": {},
    "outputs": [
@@ -4826,7 +4856,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Best model URI: models:/m-3df387ff9d254d68b1971c0c11b48359\n",
+      "Best model URI: models:/m-f25a0591479a43d085e31accbe45cc4a\n",
       "Aliases set: @champion\n"
      ]
     },
@@ -4834,8 +4864,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Registered model 'darts-air-passengers-forecaster' already exists. Creating a new version of this model...\n",
-      "Created version '2' of model 'darts-air-passengers-forecaster'.\n"
+      "Successfully registered model 'darts-air-passengers-forecaster'.\n",
+      "Created version '1' of model 'darts-air-passengers-forecaster'.\n"
      ]
     }
    ],
@@ -4881,10 +4911,9 @@
     "## 7. Load the champion and forecast\n",
     "\n",
     "Production inference loads the aliased registered model via `models:/<name>@champion` without hard-coding a version number.\n",
-    "The loaded model can then be used for prediction:\n",
+    "The loaded model can then be used for prediction.\n",
     "\n",
-    "- Global models (like the sklearn regressors used here) need `series=` at predict time because the model is saved without training data.\n",
-    "- Local models (like `ExponentialSmoothing` and others) must be re-fit before prediction.\n",
+    "We use `model.historical_forecasts()` with `start=\"end\"` (instead of `model.predict()`) to produce the final forecasts because it also handles potential re-training for local and non-pre-trained models.\n",
     "\n",
     "> Always load Darts models with `from darts.utils.mlflow import load_model` — not `mlflow.pyfunc.load_model`."
    ]
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unified\",\"legend\":{\"yanchor\":\"top\",\"y\":0.99,\"xanchor\":\"left\",\"x\":0.01}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('8cf58ce9-1b30-4666-8322-dc82b6851ea1');\n",
+       "var gd = document.getElementById('247caf7a-f08f-4de3-b3ea-62eff5eeab65');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
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@@ -5208,10 +5237,14 @@
    "source": [
     "champion = load_model(f\"models:/{REGISTERED_MODEL_NAME}@champion\")\n",
     "\n",
-    "if isinstance(champion, GlobalForecastingModel):\n",
-    "    forecast = champion.predict(n=FORECAST_HORIZON, series=series)\n",
-    "else:\n",
-    "    forecast = champion.fit(series).predict(n=FORECAST_HORIZON)\n",
+    "forecast = champion.historical_forecasts(\n",
+    "    start=\"end\",  # forward-looking forecast\n",
+    "    series=series,\n",
+    "    forecast_horizon=FORECAST_HORIZON,\n",
+    "    retrain=not champion._fit_called,\n",
+    "    last_points_only=False,\n",
+    "    overlap_end=True,\n",
+    ")[0]\n",
     "\n",
     "fig = series.plotly(label=\"full series\")\n",
     "fig = forecast.plotly(label=\"champion forecast\", fig=fig)\n",
@@ -5231,7 +5264,7 @@
     "\n",
     "1. **Setup** — tracking URI + experiment.\n",
     "2. **Autolog** — `autolog(log_models=True, log_backtest_aggregate=True)`.\n",
-    "3. **Experiment** — `with mlflow.start_run()`: fit, historical forecasts, backtest metrics, hold-out MAPE .\n",
+    "3. **Experiment** — `with mlflow.start_run()`: fit, historical forecasts, backtest metrics.\n",
     "4. **Compare** — `mlflow.search_runs(..., order_by=[\"metrics.backtest_agg_mae ASC\"])`.\n",
     "5. **Promote** — `register_model` + `set_registered_model_alias(..., \"champion\", ...)`.\n",
     "6. **Serve** — `load_model(\"models:/<name>@champion\").predict(...)`.\n",
@@ -5241,12 +5274,6 @@
     "- [Darts MLflow API reference](https://unit8co.github.io/darts/generated_api/darts.utils.mlflow.html)\n",
     "- [MLflow API reference](https://mlflow.org/docs/latest/ml/tracking/tracking-api/)\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4d8cb09",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/static/images/mlflow_experiments_metrics.png b/examples/static/images/mlflow_experiments_metrics.png
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R_z&>!!a2y<{4-W}{}10F7<>Q#


From 13c27f454a5f0d3b2a7b8053f62485dc50abc341 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 4 Sep 2026 14:48:14 +0200
Subject: [PATCH 153/154] update changelog

---
 CHANGELOG.md | 13 +++++--------
 INSTALL.md   |  1 +
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7836dfeed4..488f631866 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,12 +11,11 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Improved**
 
-- 🚀🚀 Added a custom MLflow model flavor for Darts, available under `darts.utils.mlflow`. It provides an MLflow integration for any Darts `ForecastingModel` (statistical, scikit-learn-like, and PyTorch-based). [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
-  - Added `save_model()`, `load_model()`, and `log_model()` to persist and reload Darts models as MLflow models, including model and covariate metadata.
-  - Added `autolog()` for model parameters, series/covariate metadata, optional model artifacts, PyTorch epoch metrics, and backtest metrics. Nested historical-forecast fits are suppressed.
-  - Darts metrics are logged with shape-aware keys; multi-series and window-level backtest details are available in `metrics_per_series.json`.
-  - Added a new notebook for [MLflow quickstart](https://unit8co.github.io/darts/examples/29-MLflow-examples.html) with detailed usage examples.
-  - Note: model serving and deployment (MLflow's `pyfunc` flavor, model signatures, and input examples) are not yet supported.
+- 🚀🚀 Added MLflow integration do Darts for automated tracking, comparing, and persisting of your Darts forecasting model experiments; including full support for local and global forecasting. [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao), [Michel Zeller](https://github.com/mizeller), and [Dennis Bader](https://github.com/dennisbader).
+  - `autolog()` automatically logs everything that is important for experimenting and comparing different model runs: model parameters, model artifacts, rich standalone and backtest metric results, target and covariate series info, as well as PyTorch-related training metrics.
+  - Metrics use shape-aware keys preserving time-, component-, and quantile / label dimensions based on the metric configuration. Depending on the metric and evaluation technique (e.g. `model.backtest()`) we either log aggregated scalar metrics or stepped metrics that track performance over time. Per-series and window-level backtest details are stored in a `metrics_per_series.json` artifact for multi-series / windowed evaluations.
+  - `save_model()`, `load_model()`, and `log_model()` allow to manually persist and load models as MLflow artifacts.
+  - See the [MLflow quickstart notebook](https://unit8co.github.io/darts/examples/29-MLflow-examples.html) for step-by-step examples of going from experimenting to registering and loading production models, and the [API reference](https://unit8co.github.io/darts/generated_api/darts.utils.mlflow.html) for configuration options.
 - Added support for per-timestep (non-aggregated) encoder and decoder variable importances in `TFTExplainer`, exposed as `TimeSeries` via `TFTExplainabilityResult.get_encoder_importance_over_time()` and `get_decoder_importance_over_time()`. [#3170](https://github.com/unit8co/darts/pull/3170) by [exactml](https://github.com/exactml).
 - Calling `TFTModel.fit_from_dataset()` on a dataset that does not have future covariates now raises an informative exception. [#3149](https://github.com/unit8co/darts/pull/3149) by [YOON KIWOONG](https://github.com/kiwoongyoon).
 - 🔴 Percentage and range-based metrics (`ape`, `mape`, `sape`, `smape`, `wmape`, `ope`, `arre`, `marre`, `coefficient_of_variation`) no longer raise a hard `ValueError` when the denominator is exactly zero. A new `zero_division` parameter controls the behavior: [#3122](https://github.com/unit8co/darts/pull/3122) by [Mahimn](https://github.com/mahimn01).
@@ -34,8 +33,6 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Dependencies**
 
-- Added `mlflow>=3.0` to the optional dependency group, enabling the MLflow integration. [#3022](https://github.com/unit8co/darts/pull/3022) by [Jakub Chłapek](https://github.com/jakubchlapek), [Zhihao Dai](https://github.com/daidahao) and [Michel Zeller](https://github.com/mizeller).
-
 ### For developers of the library:
 
 ## [0.46.1](https://github.com/unit8co/darts/tree/0.46.1) (2026-07-20)
diff --git a/INSTALL.md b/INSTALL.md
index 262aa692f2..ad775a2881 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -15,6 +15,7 @@ Darts offers a modular installation system with optional dependencies. Choose th
 If the PyTorch installation fails on your platform, please follow the official installation guide for [PyTorch](https://pytorch.org/get-started/locally/), then try installing Darts again.
 
 ### Optional dependencies for specific models
+
 Some models have additional dependencies that are not included in the `all` installation option. To use them, you need to install the following dependencies separately from PyPI:
 
 | Model                 | Dependencies          |

From 7fe67da3b3ff49a392ce5c9a8351c1cfa9b2a5a1 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 4 Sep 2026 15:10:21 +0200
Subject: [PATCH 154/154] improve coverage

---
 darts/tests/optional_deps/test_mlflow.py | 257 +++++++++++++++++++++++
 1 file changed, 257 insertions(+)

diff --git a/darts/tests/optional_deps/test_mlflow.py b/darts/tests/optional_deps/test_mlflow.py
index 24229c0f7b..37ca02645e 100644
--- a/darts/tests/optional_deps/test_mlflow.py
+++ b/darts/tests/optional_deps/test_mlflow.py
@@ -2,8 +2,10 @@
 import itertools
 import logging
 import os
+import sys
 import uuid
 from contextlib import contextmanager
+from unittest.mock import patch
 
 import numpy as np
 import pandas as pd
@@ -31,8 +33,12 @@
     )
 
 import mlflow
+import yaml
+from mlflow.models import ModelSignature
+from mlflow.types.schema import ColSpec, Schema
 from mlflow.utils.autologging_utils.client import MlflowAutologgingQueueingClient
 
+from darts.metrics.utils import unregister_metric_callback
 from darts.models import (
     ExponentialSmoothing,
     LinearRegressionModel,
@@ -44,7 +50,12 @@
     _flush_logged_metrics,
     _infer_metric_axes,
     _log_backtest_metrics,
+    _log_per_series_table,
+    _reshape_metric_result,
+    _resolve_backtest_layout,
+    _run_has_logged_model,
     autolog,
+    get_default_conda_env,
     load_model,
     log_model,
     save_model,
@@ -909,6 +920,143 @@ def test_save_load_with_special_series(self, tmp_path, series, series_name):
         pred_loaded = loaded_model.predict(n=3, series=series)
         assert pred_original.width == pred_loaded.width
 
+    def test_save_model_invalid_model_type(self, tmp_path):
+        """save_model rejects non-ForecastingModel instances."""
+        with pytest.raises(ValueError, match="ForecastingModel"):
+            save_model(object(), str(tmp_path / "bad_model"))
+
+    def test_save_model_with_signature_metadata_and_input_example(self, tmp_path):
+        """Optional MLflow Model fields are persisted when provided."""
+        model = LinearRegressionModel(lags=5)
+        model.fit(TS_UNIVARIATE)
+
+        model_path = tmp_path / "test_model"
+        signature = ModelSignature(
+            inputs=Schema([ColSpec("double", "input")]),
+            outputs=Schema([ColSpec("double", "output")]),
+        )
+        metadata = {"author": "darts-test"}
+        save_model(
+            model,
+            str(model_path),
+            signature=signature,
+            input_example={"n": 3},
+            metadata=metadata,
+        )
+
+        with open(model_path / "MLmodel") as f:
+            mlmodel_conf = yaml.safe_load(f)
+        assert mlmodel_conf["signature"] is not None
+        assert mlmodel_conf["metadata"] == metadata
+        assert os.path.exists(model_path / "input_example.json")
+
+    def test_save_model_custom_pip_requirements(self, tmp_path):
+        """Explicit pip_requirements override the default dependency list."""
+        model = LinearRegressionModel(lags=5)
+        model.fit(TS_UNIVARIATE)
+
+        model_path = tmp_path / "test_model"
+        custom_reqs = ["numpy>=1.0"]
+        save_model(model, str(model_path), pip_requirements=custom_reqs)
+
+        with open(model_path / "requirements.txt") as f:
+            requirements = f.read()
+        assert "numpy>=1.0" in requirements
+
+    def test_save_model_pip_constraints(self, tmp_path):
+        """Constraint files referenced in pip_requirements are written out."""
+        model = LinearRegressionModel(lags=5)
+        model.fit(TS_UNIVARIATE)
+
+        constraints_file = tmp_path / "constraints.txt"
+        constraints_file.write_text("numpy>=1.0")
+
+        model_path = tmp_path / "test_model"
+        save_model(
+            model,
+            str(model_path),
+            pip_requirements=[f"-c {constraints_file}"],
+        )
+        assert (model_path / "constraints.txt").exists()
+
+    def test_load_model_non_forecasting_model_class(self, tmp_path):
+        """load_model rejects flavor configs whose model_class is not a ForecastingModel."""
+        model = LinearRegressionModel(lags=5)
+        model.fit(TS_UNIVARIATE)
+
+        model_path = tmp_path / "test_model"
+        save_model(model, str(model_path))
+
+        mlmodel_path = model_path / "MLmodel"
+        with open(mlmodel_path) as f:
+            mlmodel_conf = yaml.safe_load(f)
+        mlmodel_conf["flavors"]["darts"]["model_class"] = "builtins.str"
+        with open(mlmodel_path, "w") as f:
+            yaml.safe_dump(mlmodel_conf, f)
+
+        with pytest.raises(ValueError, match="ForecastingModel"):
+            load_model(f"file://{model_path}")
+
+    def test_get_default_conda_env(self):
+        """Default conda env includes darts as a pip dependency."""
+        env = get_default_conda_env()
+        assert env["name"] == "mlflow-env"
+        pip_deps = env["dependencies"][-1]["pip"]
+        assert any("darts" in dep for dep in pip_deps)
+
+    def test_autolog_invalid_series_align(self):
+        """autolog rejects unsupported series_align values."""
+        with pytest.raises(ValueError, match="series_align"):
+            autolog(series_align="middle")
+
+    def test_autolog_pytorch_import_error_is_ignored(self, autolog_context):
+        """Missing mlflow.pytorch does not break autolog when log_torch_metrics=True."""
+        real_import = __import__
+
+        def mock_import(name, *args, **kwargs):
+            if name == "mlflow.pytorch":
+                raise ImportError("no pytorch flavor")
+            return real_import(name, *args, **kwargs)
+
+        with patch("builtins.__import__", side_effect=mock_import):
+            with autolog_context(log_torch_metrics=True):
+                with mlflow.start_run():
+                    LinearRegressionModel(lags=5).fit(TS_UNIVARIATE[:40])
+
+    def test_autolog_disable_pytorch_autolog_exception_is_ignored(self):
+        """Errors while disabling mlflow.pytorch autolog are swallowed."""
+        mock_pytorch = patch("mlflow.pytorch.autolog", side_effect=RuntimeError("boom"))
+        with mock_pytorch:
+            autolog(log_torch_metrics=True, disable=True)
+
+    def test_autolog_fit_no_active_run(self, autolog_context):
+        """fit() with autolog enabled but no active run completes without logging."""
+        with autolog_context(log_models=True):
+            model = LinearRegressionModel(lags=5)
+            model.fit(TS_UNIVARIATE[:40])
+
+        assert model._fit_called
+
+    def test_autolog_historical_forecasts_no_active_run(self, autolog_context):
+        """historical_forecasts(retrain=True) without a run does not raise."""
+        model = LinearRegressionModel(lags=5)
+        model.fit(TS_UNIVARIATE[:40])
+        with autolog_context(log_models=True):
+            model.historical_forecasts(
+                series=TS_UNIVARIATE,
+                forecast_horizon=1,
+                retrain=True,
+                start=0.5,
+            )
+
+    def test_autolog_backtest_no_active_run(self, autolog_context):
+        """backtest() with autolog enabled but no active run returns normally."""
+        with autolog_context(log_metrics=True):
+            ref = _fit_lr().backtest(
+                TS_UNIVARIATE, metric=dm.mae, retrain=False, start=-2
+            )
+        assert np.isfinite(float(np.asarray(ref).ravel()[0]))
+
 
 class TestAutoLogStandaloneMetrics:
     def test_autolog_metric_no_active_run(self, mlflow_tracking, autolog_context):
@@ -947,6 +1095,19 @@ def test_autolog_log_metrics_false(self, mlflow_tracking, autolog_context):
             "mape should NOT be logged when log_metrics=False"
         )
 
+    def test_autolog_metric_series_reduction(self, mlflow_tracking, autolog_context):
+        """series_reduction collapses multi-series input to one logged scalar."""
+        series = [TS_UNIVARIATE, TS_UNIVARIATE * 1.2]
+        pred = [TS_UNIVARIATE * 1.1, TS_UNIVARIATE * 1.3]
+        with autolog_context(log_metrics=True):
+            with mlflow.start_run() as run:
+                ref = dm.mae(series, pred, series_reduction=np.mean)
+
+        assert np.isscalar(ref)
+        run_data = mlflow.get_run(run.info.run_id).data
+        assert "mae" in run_data.metrics
+        assert run_data.metrics["mae"] == pytest.approx(ref)
+
     def test_autolog_metric_any_import_path_logs(
         self, mlflow_tracking, autolog_context
     ):
@@ -1464,6 +1625,17 @@ def test_autolog_backtest_scalar(self, mlflow_tracking, autolog_context):
         assert "backtest_mae" in run_data.metrics
         np.testing.assert_almost_equal(run_data.metrics["backtest_mae"], ref)
 
+    def test_autolog_backtest_log_metrics_false(self, mlflow_tracking, autolog_context):
+        """autolog(log_metrics=False) skips backtest metric logging."""
+        with autolog_context(log_metrics=False):
+            with mlflow.start_run() as run:
+                _fit_lr().backtest(
+                    TS_UNIVARIATE, metric=dm.mae, retrain=False, start=-2
+                )
+
+        run_data = mlflow.get_run(run.info.run_id).data
+        assert "backtest_mae" not in run_data.metrics
+
     def test_autolog_backtest_per_window_steps(self, mlflow_tracking, autolog_context):
         """reduction=None logs per-window values at steps."""
         with autolog_context(log_metrics=True):
@@ -2400,3 +2572,88 @@ def test_flush_logged_metrics_backtest_aggregate(self, mlflow_tracking):
         assert logged[0] == pytest.approx(2.0)
         assert logged[1] == pytest.approx(20.0)
         assert m["backtest_agg_mae"] == pytest.approx(np.mean([2.0, 20.0]))
+
+    def test_resolve_backtest_layout_series_reduction_disables_windows(self):
+        """series_reduction in metric_kwargs collapses the window axis."""
+        backtest_kwargs = {
+            "last_points_only": False,
+            "reduction": None,
+            "forecast_horizon": 3,
+            "historical_forecasts": None,
+        }
+        has_windows, _ = _resolve_backtest_layout(
+            backtest_kwargs=backtest_kwargs,
+            metric=dm.mae,
+            metric_kwargs={"series_reduction": np.mean},
+            is_single_series=True,
+        )
+        assert has_windows is False
+
+    def test_reshape_metric_result_invalid_scalar_shape_raises(self):
+        """Mis-sized reduced results raise a descriptive ValueError."""
+        metric_keys = _build_metric_keys(
+            ["mae"],
+            TS_UNIVARIATE.components.tolist(),
+            has_comp_axis=False,
+            metric_axes=[(False, False, [""])],
+        )
+        with pytest.raises(ValueError, match="expected a single scalar"):
+            _reshape_metric_result(
+                TS_UNIVARIATE,
+                [1.0, 2.0],
+                metric_keys=metric_keys,
+                has_time_axis=False,
+                has_windows=False,
+                forecast_horizon=None,
+                is_backtest=True,
+            )
+
+    def test_log_per_series_table_skips_empty_rows(self, mlflow_tracking):
+        """An empty row list is a no-op and does not create an artifact."""
+        with mlflow.start_run() as run:
+            _log_per_series_table([])
+
+        artifacts = mlflow_tracking.list_artifacts(run.info.run_id)
+        assert not any(a.path == "metrics_per_series.json" for a in artifacts)
+
+    def test_run_has_logged_model_handles_get_run_failure(self, monkeypatch):
+        """_run_has_logged_model returns False when run lookup fails."""
+
+        def _fail_get_run(run_id):
+            raise RuntimeError("connection failed")
+
+        monkeypatch.setattr(mlflow, "get_run", _fail_get_run)
+        assert _run_has_logged_model("fake_run_id") is False
+
+    def test_unregister_metric_callback_missing_is_noop(self):
+        """unregister_metric_callback ignores callbacks that were never registered."""
+
+        def _dummy_callback(func, result, args, kwargs):
+            pass
+
+        unregister_metric_callback(_dummy_callback)
+
+
+def test_z_mlflow_import_without_dependency(monkeypatch):
+    """Importing darts.utils.mlflow without mlflow installed raises ImportError."""
+    import builtins
+    import importlib
+
+    mod_name = "darts.utils.mlflow"
+    saved = sys.modules.pop(mod_name, None)
+    real_import = builtins.__import__
+
+    def mock_import(name, *args, **kwargs):
+        if name == "mlflow":
+            raise ImportError("no mlflow")
+        return real_import(name, *args, **kwargs)
+
+    monkeypatch.setattr(builtins, "__import__", mock_import)
+    with pytest.raises(ImportError, match="mlflow"):
+        importlib.import_module(mod_name)
+
+    sys.modules.pop(mod_name, None)
+    if saved is not None:
+        sys.modules[mod_name] = saved
+    else:
+        importlib.import_module(mod_name)